{"seq_id": "289285531", "text": "import sys\ndef install_pdb_handler():\n \"\"\"Signals to automatically start pdb:\n 1. CTRL+\\\\ breaks into pdb.\n 2. pdb gets launched on exception.\n \"\"\"\n\n import signal\n import pdb\n\n def handler(_signum, _frame):\n pdb.set_trace()\n\n signal.signal(signal.SIGQUIT, handler)\n # signal.signal(signal.SIGINT, handler)\n\n # Drop into PDB on exception\n # from https://stackoverflow.com/questions/13174412\n def info(type_, value, tb):\n if hasattr(sys, 'ps1') or not sys.stderr.isatty():\n # we are in interactive mode or we don't have a tty-like\n # device, so we call the default hook\n sys.__excepthook__(type_, value, tb)\n else:\n import traceback\n import pdb\n # we are NOT in interactive mode, print the exception...\n traceback.print_exception(type_, value, tb)\n print()\n # ...then start the debugger in post-mortem mode.\n pdb.pm()\n\n sys.excepthook = info", "sub_path": "tools/pdb_install_handle.py", "file_name": "pdb_install_handle.py", "file_ext": "py", "file_size_in_byte": 1014, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pdb.set_trace", "line_number": 12, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 14, "usage_type": "call"}, {"api_name": "signal.SIGQUIT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.stderr.isatty", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.__excepthook__", "line_number": 23, "usage_type": "call"}, {"api_name": "traceback.print_exception", "line_number": 28, "usage_type": "call"}, {"api_name": "pdb.pm", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.excepthook", "line_number": 33, "usage_type": "attribute"}]} {"seq_id": "171482037", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jan 10 14:58:10 2017\r\n@author: GARC7680\r\nScript to validate the Girona model\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n#from collections import OrderedDict\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport seaborn as sns\r\nos.chdir(\"C:\\\\Users\\\\GARC7680\\\\PhD_folder_local\\\\Third paper\\\\Resilience_modelling\\\\Validation_influent\\\\Model Validation\")\r\n\r\n#%%\r\n#Load data from excel\r\nexcel = pd.ExcelFile('Model_validation.xlsx')\r\nVal_data = excel.parse(\"validation\")\r\n\r\n#Prepare plot specs\r\nsns.set()\r\nsns.set_palette(\"Set1\", n_colors=8, desat=.9)\r\n#plt.rc('text', usetex=False)\r\nplt.rc('font', family='serif') # Use seri font for publication\r\nplt.rc('legend', fancybox=True, frameon=True) # Custom legend\r\n#plt.rc('mathtext', default='sf')\r\nplt.rc('mathtext', fontset='custom') \r\n#plt.rc('mathtext', it='STIXGeneral:italic') #(designed to blend well with Times)\r\nplt.rc('mathtext', it='serif:italic')\r\n\r\n# Figure size from the golden ratio and paper size\r\nfig_width_pt = 483.41216 # Get this from LaTeX using \\the\\columnwidth\r\ninches_per_pt = 1.0/72.27 # Convert pt to inch\r\ngolden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio\r\nfig_width = fig_width_pt*inches_per_pt # width in inches\r\nfig_height = fig_width*golden_mean # height in inches\r\nfig_size = pd.Series([fig_width,fig_height*golden_mean]) #Set figure size\r\n\r\n#Plot\r\nfig, ax = plt.subplots(figsize=fig_size*1)\r\nax.plot(Val_data.model_time, Val_data.model_flow/24, lw=1.5)\r\nax.plot(Val_data.Vmodel_time, Val_data.Vmodel_flow, lw=1.5)\r\nax.plot(Val_data.real_time, Val_data.real_flow)\r\n\r\n#Final adjustments and saving\r\nax.set_title('Model Validation', loc='Center', fontsize=11)\r\nax.legend([\"Model in Juan-Garcia \" + r'$\\mathit{et\\ al.,}$' + \" 2017b\", \"Model in present study\", \"Real measurements\"], loc=\"best\", fontsize=8)\r\nax.set_ylabel(r'airflow ($m^3/h$)')\r\nax.set_xlabel('Time (days)')\r\n\r\nplt.tight_layout()\r\nplt.savefig('Validation_NewModel.png', dpi=200)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "Validation.py", "file_name": "Validation.py", "file_ext": "py", "file_size_in_byte": 2047, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.ExcelFile", "line_number": 18, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 22, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} {"seq_id": "584609936", "text": "# %% [markdown]\n# # Project C116\n# %% [markdown]\n# ## Getting Data\n\n# %%\nimport pandas\n\ndata_frame = pandas.read_csv(\"https://raw.githubusercontent.com/whitehatjr/datasets/master/pro-c116/Admission_Predict.csv\")\n\ntoefl_score = data_frame[\"TOEFL Score\"].to_list()\ngre_score = data_frame[\"GRE Score\"].to_list()\ncoa = data_frame[\"Chance of admit\"].to_list()\n\n# %% [markdown]\n# ## Showing Data\n\n# %%\nimport plotly.express as px\n\nfigure = px.scatter(x=toefl_score, y=gre_score, color=coa, labels=dict(x=\"TOEFL Score\", y=\"GRE Score\", color=\"Chance of Admitting\"), title=\"TOEFL Score vs GRE Score\")\n\nfigure.update_traces(marker=dict(line=dict(color='DarkSlateGrey')))\n\nfigure.show()\n\n# %% [markdown]\n# ## Train Test Split\n\n# %%\nfrom sklearn.model_selection import train_test_split\n\nscores = data_frame[[\"TOEFL Score\", \"GRE Score\"]]\n\nscores_train, scores_test, coa_train, coa_test = train_test_split(scores, coa, test_size=0.25, random_state=0)\n\n# %% [markdown]\n# ## Logistic Regression\n\n# %%\nfrom sklearn.linear_model import LogisticRegression\n\nlog_reg = LogisticRegression(random_state=0)\nlog_reg.fit(scores_train, coa_train)\n\n# %% [markdown]\n# ## Prediction Accuracy\n\n# %%\nfrom sklearn.metrics import accuracy_score\n\nprediction = log_reg.predict(scores_test)\naccuracy = accuracy_score(coa_test, prediction)\n\nprint(f\"Accuracy of prediction: {accuracy}\")\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "plotly.express.scatter", "line_number": 21, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 21, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 53, "usage_type": "call"}]} {"seq_id": "92021222", "text": "\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport os\r\nimport logging\r\nimport functools\r\n\r\nimport numpy as np\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch._utils\r\nimport torch.nn.functional as F\r\n\r\nBN_MOMENTUM = 0.1\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass NNC(nn.Module):\r\n\r\n def __init__(self, istrain):\r\n super(NNC, self).__init__()\r\n\r\n self.istrain = istrain\r\n # 全局平均池化层\r\n self.pool = nn.AdaptiveAvgPool2d((1, 1))\r\n # 全连接层，分成两类，是否伪造\r\n self.linear = nn.Linear(1 * 1 * 1, 2)\r\n\r\n def forward(self, x):\r\n # 全局平均池化层 : [batch, 1, 1, 1]\r\n x = self.pool(x)\r\n # 为了进行FC，把原输出展成FC可接受的一维data [batch, 1*256*256]\r\n x = x.view(x.size(0), -1)\r\n x = self.linear(x)\r\n\r\n # torch.nn.CrossEntropyLoss()进行loss计算时不用经过softmax，否则计算不对\r\n if not self.istrain:\r\n # 按行计算\r\n x = torch.softmax(x, dim=1)\r\n\r\n return x\r\n\r\n\r\n def init_weights(self, pretrained='',):\r\n logger.info('=> init weights from normal distribution')\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n nn.init.kaiming_normal_(\r\n m.weight, mode='fan_out', nonlinearity='relu')\r\n elif isinstance(m, nn.BatchNorm2d):\r\n nn.init.constant_(m.weight, 1)\r\n nn.init.constant_(m.bias, 0)\r\n if os.path.isfile(pretrained):\r\n # 用cpu加载模型参数时\r\n pretrained_dict = torch.load(pretrained, map_location='cpu')\r\n logger.info('=> loading pretrained model {}'.format(pretrained))\r\n model_dict = self.state_dict()\r\n pretrained_dict = {k: v for k, v in pretrained_dict.items()\r\n if k in model_dict.keys()}\r\n for k, _ in pretrained_dict.items():\r\n logger.info(\r\n '=> loading {} pretrained model {}'.format(k, pretrained))\r\n model_dict.update(pretrained_dict)\r\n self.load_state_dict(model_dict)\r\n # 锁定原hrnet的参数\r\n for k, v in self.named_parameters():\r\n if 'upsample_modules' in k or 'output_modules' in k:\r\n continue\r\n else:\r\n v.requires_grad = False\r\n\r\n\r\ndef get_nnc(istrain):\r\n model = NNC(istrain)\r\n model.init_weights()\r\n return model\r\n", "sub_path": "Net/Net_cpuVer/models/nnc.py", "file_name": "nnc.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.softmax", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 57, "usage_type": "call"}]} {"seq_id": "233753323", "text": "import requests\nimport numpy as np\nimport re\n\n\ndef task_1(array_1, array_2):\n mat1 = np.matrix(array_1, dtype=np.float)\n mat2 = np.matrix(array_2, dtype=np.float)\n a = mat1 + mat2\n b = a.T\n c = np.rot90(mat2, k=3)\n d = b * c\n e = np.matrix([d.max(), d.min()]).mean()\n f = d[1:-1, 1:-1]\n if len(array_1) % 2 == 0:\n g = d[(len(array_1)//2)-1, (len(array_1)//2)-1]\n else:\n g = d[len(array_1)//2, len(array_1)//2]\n\n return a, b, c, d, e, f, g\n\n\ndef task_2(money):\n url = 'http://www.nbrb.by/API/ExRates/Rates/' + money + '?ParamMode=2'\n result = requests.get(url).json()\n return round(float(1/(result['Cur_OfficialRate']/result['Cur_Scale'])), 2)\n\n\ndef task_3():\n result = requests.get('https://yandex.by/pogoda/minsk')\n temp = re.findall(r'
'\n r'([+-]?\\d+)',\n result.text)\n return int('{}'.format(temp[0]))\n", "sub_path": "homework_old/homework9_4.py", "file_name": "homework9_4.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.matrix", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.rot90", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 31, "usage_type": "call"}]} {"seq_id": "435557131", "text": "from django.conf.urls.defaults import *\nfrom couchcms.article import views as bv\nfrom trumpet import views as tv\nfrom django.contrib import admin\nadmin.autodiscover()\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = patterns('',\n # Example:\n # (r'^couchcms/', include('couchcms.foo.urls')),\n\n # Uncomment the admin/doc line below and add 'django.contrib.admindocs'\n # to INSTALLED_APPS to enable admin documentation:\n # (r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n (r'^admin/(.*)', admin.site.root),\n #(r'^article/new/$', bv.new),\n url(r'^article/(?P.+)/(?P<id>[a-zA-Z0-9]+)/$', bv.content, name='article_content'),\n url(r'^$', bv.index, name='article_index'),\n #(r'^article/type/(?P<cat>\\w+)/$', bv.index),\n url(r'^tag/(?P<tag>[-\\w]+)/$', bv.tag, name='article_tag'),\n url(r'^trumpet/save/$', tv.new, name='trumpet_new'),\n url(r'^trumpet/map/$', tv.map, name='trumpet_map'),\n url(r'^trumpet/load/(?P<term>[,\\w]+)/(?P<type>\\d+)/$', tv.load, name='trumpet_load'),\n #url(r'^block/(?P<tag>\\w+)/list.html$', bv.block, name='article_block'),\n #(r'^article/author/(?P<author>\\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,4}\\b)/$', bv.index),\n #(r'^article/delete/(?P<id>\\w+)/$', bv.delete),\n #(r'^article/notice/(?P<msg>\\w+)/(?P<id>\\w+)/$', bv.notice),\n)\n", "sub_path": "couchcms/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 5, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "couchcms.article.views.content", "line_number": 22, "usage_type": "attribute"}, {"api_name": "couchcms.article.views", "line_number": 22, "usage_type": "name"}, {"api_name": "couchcms.article.views.index", "line_number": 23, "usage_type": "attribute"}, {"api_name": "couchcms.article.views", "line_number": 23, "usage_type": "name"}, {"api_name": "couchcms.article.views.tag", "line_number": 25, "usage_type": "attribute"}, {"api_name": "couchcms.article.views", "line_number": 25, "usage_type": "name"}, {"api_name": "trumpet.views.new", "line_number": 26, "usage_type": "attribute"}, {"api_name": "trumpet.views", "line_number": 26, "usage_type": "name"}, {"api_name": "trumpet.views.map", "line_number": 27, "usage_type": "attribute"}, {"api_name": "trumpet.views", "line_number": 27, "usage_type": "name"}, {"api_name": "trumpet.views.load", "line_number": 28, "usage_type": "attribute"}, {"api_name": "trumpet.views", "line_number": 28, "usage_type": "name"}]} {"seq_id": "48325515", "text": "import requests\nimport os\nimport datetime\n\nSITE = os.environ['site']\nEXPECTED = os.environ['expected']\n\n\ndef validate(res):\n return EXPECTED in res\n\n\ndef lambda_handler(event, context):\n print(f\"Test the site{SITE} at the time {event['time']}\")\n response = requests.get(url=\"https://www.amazon.in\", headers={'User-Agent': 'AWS Lambda'})\n try:\n if not validate(response.text):\n raise Exception(\"Validation failed\")\n except:\n print(\"Check failed\")\n else:\n print(\"okay\")\n finally:\n print(f\"Check complete at {str(datetime.datetime.now())}\")\n", "sub_path": "Lambda/canary.py", "file_name": "canary.py", "file_ext": "py", "file_size_in_byte": 598, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "275517519", "text": "from django.contrib import admin\nfrom django.utils.translation import ugettext_lazy as _\nfrom solo.admin import SingletonModelAdmin\n\nfrom dsmr_mqtt.models.settings import broker, day_totals, telegram, meter_statistics\n\n\n@admin.register(broker.MQTTBrokerSettings)\nclass MQTTBrokerSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['hostname', 'port', 'username', 'password', 'client_id'],\n 'description': _(\n 'These broker settings apply to all enabled MQTT configurations.'\n )\n }\n ),\n )\n\n\n@admin.register(telegram.RawTelegramMQTTSettings)\nclass RawTelegramMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'topic'],\n 'description': _(\n 'Triggered by the datalogger or any API calls using the v1 API. '\n 'Allows you to pass on any incoming raw telegrams to the MQTT broker.'\n )\n }\n ),\n )\n\n\n@admin.register(telegram.JSONTelegramMQTTSettings)\nclass JSONTelegramMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'topic', 'formatting'],\n 'description': _(\n 'Triggered by any method of reading insertion (datalogger or API). '\n 'Allows you to send newly created readings to the MQTT broker, as a JSON message. You can alter '\n 'the field names used in the JSON message. Removing lines will remove fields from the message as '\n 'well. '\n '''Default value:\n<pre>\n[mapping]\nid = id\ntimestamp = timestamp\nelectricity_delivered_1 = electricity_delivered_1\nelectricity_returned_1 = electricity_returned_1\nelectricity_delivered_2 = electricity_delivered_2\nelectricity_returned_2 = electricity_returned_2\nelectricity_currently_delivered = electricity_currently_delivered\nelectricity_currently_returned = electricity_currently_returned\nphase_currently_delivered_l1 = phase_currently_delivered_l1\nphase_currently_delivered_l2 = phase_currently_delivered_l2\nphase_currently_delivered_l3 = phase_currently_delivered_l3\nextra_device_timestamp = extra_device_timestamp\nextra_device_delivered = extra_device_delivered\n</pre>\n'''\n )\n }\n ),\n )\n\n\n@admin.register(telegram.SplitTopicTelegramMQTTSettings)\nclass SplitTopicTelegramMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'formatting'],\n 'description': _(\n 'Triggered by any method of reading insertion (datalogger or API). '\n 'Allows you to send newly created readings to the MQTT broker, splitted per field. You can '\n 'designate each field name to a different topic. Removing lines will prevent those fields from '\n 'being broadcast as well. '\n '''Default value:\n<pre>\n[mapping]\nid = dsmr/reading/id\ntimestamp = dsmr/reading/timestamp\nelectricity_delivered_1 = dsmr/reading/electricity_delivered_1\nelectricity_returned_1 = dsmr/reading/electricity_returned_1\nelectricity_delivered_2 = dsmr/reading/electricity_delivered_2\nelectricity_returned_2 = dsmr/reading/electricity_returned_2\nelectricity_currently_delivered = dsmr/reading/electricity_currently_delivered\nelectricity_currently_returned = dsmr/reading/electricity_currently_returned\nphase_currently_delivered_l1 = dsmr/reading/phase_currently_delivered_l1\nphase_currently_delivered_l2 = dsmr/reading/phase_currently_delivered_l2\nphase_currently_delivered_l3 = dsmr/reading/phase_currently_delivered_l3\nextra_device_timestamp = dsmr/reading/extra_device_timestamp\nextra_device_delivered = dsmr/reading/extra_device_delivered\n</pre>\n'''\n )\n }\n ),\n )\n\n\n@admin.register(day_totals.JSONDayTotalsMQTTSettings)\nclass JSONDayTotalsMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'topic', 'formatting'],\n 'description': _(\n 'Triggered by any method of reading insertion (datalogger or API). '\n 'Send the current day totals to the broker. You can alter the the field names used in the JSON '\n 'message. Removing lines will remove fields from the message as well. '\n '''Default value:\n<pre>\n[mapping]\n# DATA = JSON FIELD\nelectricity1 = electricity1\nelectricity2 = electricity2\nelectricity1_returned = electricity1_returned\nelectricity2_returned = electricity2_returned\nelectricity_merged = electricity_merged\nelectricity_returned_merged = electricity_returned_merged\nelectricity1_cost = electricity1_cost\nelectricity2_cost = electricity2_cost\nelectricity_cost_merged = electricity_cost_merged\n\n# Gas (if any)\ngas = gas\ngas_cost = gas_cost\ntotal_cost = total_cost\n\n# Your energy supplier prices (if set)\nenergy_supplier_price_electricity_delivered_1 = energy_supplier_price_electricity_delivered_1\nenergy_supplier_price_electricity_delivered_2 = energy_supplier_price_electricity_delivered_2\nenergy_supplier_price_electricity_returned_1 = energy_supplier_price_electricity_returned_1\nenergy_supplier_price_electricity_returned_2 = energy_supplier_price_electricity_returned_2\nenergy_supplier_price_gas = energy_supplier_price_gas\n</pre>\n'''\n )\n }\n ),\n )\n\n\n@admin.register(day_totals.SplitTopicDayTotalsMQTTSettings)\nclass SplitTopicDayTotalsMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'formatting'],\n 'description': _(\n 'Triggered by any method of reading insertion (datalogger or API). '\n 'Allows you to send day totals (dashboard) to the MQTT broker, splitted per field. You can '\n 'designate each field name to a different topic. Removing lines will prevent those fields from '\n 'being broadcast as well. '\n '''Default value:\n<pre>\n[mapping]\n# DATA = JSON FIELD\nelectricity1 = dsmr/day-totals/electricity1\nelectricity2 = dsmr/day-totals/electricity2\nelectricity1_returned = dsmr/day-totals/electricity1_returned\nelectricity2_returned = dsmr/day-totals/electricity2_returned\nelectricity_merged = dsmr/day-totals/electricity_merged\nelectricity_returned_merged = dsmr/day-totals/electricity_returned_merged\nelectricity1_cost = dsmr/day-totals/electricity1_cost\nelectricity2_cost = dsmr/day-totals/electricity2_cost\nelectricity_cost_merged = dsmr/day-totals/electricity_cost_merged\n\n# Gas (if any)\ngas = dsmr/day-totals/gas\ngas_cost = dsmr/day-totals/gas_cost\ntotal_cost = dsmr/day-totals/total_cost\n\n# Your energy supplier prices (if set)\nenergy_supplier_price_electricity_delivered_1 = dsmr/day-totals/energy_supplier_price_electricity_delivered_1\nenergy_supplier_price_electricity_delivered_2 = dsmr/day-totals/energy_supplier_price_electricity_delivered_2\nenergy_supplier_price_electricity_returned_1 = dsmr/day-totals/energy_supplier_price_electricity_returned_1\nenergy_supplier_price_electricity_returned_2 = dsmr/day-totals/energy_supplier_price_electricity_returned_2\nenergy_supplier_price_gas = dsmr/day-totals/energy_supplier_price_gas\n</pre>\n'''\n )\n }\n ),\n )\n\n\n@admin.register(meter_statistics.SplitTopicMeterStatisticsMQTTSettings)\nclass SplitTopicMeterStatisticsMQTTSettingsAdmin(SingletonModelAdmin):\n fieldsets = (\n (\n None, {\n 'fields': ['enabled', 'formatting'],\n 'description': _(\n 'Triggered by any method of reading insertion (datalogger or API). '\n 'Allows you to send meter statistics to the MQTT broker, splitted per field. You can '\n 'designate each field name to a different topic. Removing lines will prevent those fields from '\n 'being broadcast as well. '\n '''Default value:\n<pre>\n[mapping]\n# DATA = TOPIC PATH\ndsmr_version = dsmr/meter-stats/dsmr_version\nelectricity_tariff = dsmr/meter-stats/electricity_tariff\npower_failure_count = dsmr/meter-stats/power_failure_count\nlong_power_failure_count = dsmr/meter-stats/long_power_failure_count\nvoltage_sag_count_l1 = dsmr/meter-stats/voltage_sag_count_l1\nvoltage_sag_count_l2 = dsmr/meter-stats/voltage_sag_count_l2\nvoltage_sag_count_l3 = dsmr/meter-stats/voltage_sag_count_l3\nvoltage_swell_count_l1 = dsmr/meter-stats/voltage_swell_count_l1\nvoltage_swell_count_l2 = dsmr/meter-stats/voltage_swell_count_l2\nvoltage_swell_count_l3 = dsmr/meter-stats/voltage_swell_count_l3\nrejected_telegrams = dsmr/meter-stats/rejected_telegrams\n</pre>\n'''\n )\n }\n ),\n )\n", "sub_path": "dsmr_mqtt/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 8960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "solo.admin.SingletonModelAdmin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.broker.MQTTBrokerSettings", "line_number": 8, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.broker", "line_number": 8, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 23, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 22, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.telegram.RawTelegramMQTTSettings", "line_number": 22, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.telegram", "line_number": 22, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 37, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.telegram.JSONTelegramMQTTSettings", "line_number": 37, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.telegram", "line_number": 37, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 73, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 78, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 72, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.telegram.SplitTopicTelegramMQTTSettings", "line_number": 72, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.telegram", "line_number": 72, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 108, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 113, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 107, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.day_totals.JSONDayTotalsMQTTSettings", "line_number": 107, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.day_totals", "line_number": 107, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 151, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 150, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 150, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.day_totals.SplitTopicDayTotalsMQTTSettings", "line_number": 150, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.day_totals", "line_number": 150, "usage_type": "name"}, {"api_name": "solo.admin.SingletonModelAdmin", "line_number": 195, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 200, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 194, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 194, "usage_type": "name"}, {"api_name": "dsmr_mqtt.models.settings.meter_statistics.SplitTopicMeterStatisticsMQTTSettings", "line_number": 194, "usage_type": "attribute"}, {"api_name": "dsmr_mqtt.models.settings.meter_statistics", "line_number": 194, "usage_type": "name"}]} {"seq_id": "201782623", "text": "import json\nfrom flask import jsonify, request, Blueprint\nfrom config import TEAM_NAME\n\n#firebase implementation\nimport pyrebase\n\nfirebaseConfig = {\n \"apiKey\": \"AIzaSyC3R0WyJqIebFKIauw-BZ3ANd4mwTdEXvU\",\n \"authDomain\": \"deltahacks2021-78be0.firebaseapp.com\",\n \"databaseURL\": \"https://deltahacks2021-78be0-default-rtdb.firebaseio.com\",\n \"projectId\": \"deltahacks2021-78be0\",\n \"storageBucket\": \"deltahacks2021-78be0.appspot.com\",\n \"messagingSenderId\": \"974523835590\",\n \"appId\": \"1:974523835590:web:793aaee57e22dd4a40e3c7\",\n \"measurementId\": \"G-RDDBVYNMYG\"\n}\n\nfirebase = pyrebase.initialize_app(firebaseConfig)\nauth = firebase.auth()\n\nlogin_handler = Blueprint('login_handler', __name__)\n\n@login_handler.route('/login', methods=['GET', 'POST'])\ndef login():\n if request.method == 'POST':\n body = json.loads(request.get_data())\n \n user = auth.create_user_with_email_and_password(body['username'],body['password'])\n return jsonify({'response': \"{} is now part of the team\".format(body['teamName'])}), 200\n", "sub_path": "server/api/signup_handler.py", "file_name": "signup_handler.py", "file_ext": "py", "file_size_in_byte": 1053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pyrebase.initialize_app", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}]} {"seq_id": "197481078", "text": "import sys\nimport base64\nimport googleapiclient.discovery\nimport json\n\ndef recognize(speech_file,context):\n with open(speech_file, 'rb') as speech:\n speech_content = base64.b64encode(speech.read())\n\n service = googleapiclient.discovery.build('speech', 'v1')\n service_request = service.speech().recognize(\n body={\n \"config\": {\n \"encoding\": \"LINEAR16\",\n \"sampleRateHertz\": 44100,\n \"languageCode\": \"en-US\",\n \"speechContexts\": {\n \"phrases\":context\n }\n },\n \"audio\": {\n \"content\": speech_content\n }\n })\n result=service_request.execute()\n print(json.dumps(result,sort_keys=False,indent=4,separators=(',',':')))\n\nif __name__ == '__main__':\n if len(sys.argv)<2:\n print(\"Not Enough Parameter.\")\n sys.exit(0)\n filename=sys.argv[1]\n context=[\"anteversion\",\"abduction\",\"angle\",\"leg\",\"length\",\"leg length\",\"anteversion angle\",\n \"abduction angle\",\"select study\",\"scan\",\"complete study\",\"print\",\"send\",\"burn CD\",\"manage\",\n \"new patient\",\"new study\",\"select pacs\",\"worklist\",\"search\",\"reset\",\"view\",\"report\",\"delete\",\n \"grab frame\",\"pacs\"]\n recognize(filename,context)\n", "sub_path": "transcribe_with_context.py", "file_name": "transcribe_with_context.py", "file_ext": "py", "file_size_in_byte": 1296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "base64.b64encode", "line_number": 8, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery.build", "line_number": 10, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery", "line_number": 10, "usage_type": "attribute"}, {"api_name": "googleapiclient.discovery", "line_number": 10, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}]} {"seq_id": "165081867", "text": "from rest_framework import serializers\nfrom file.models import File\nimport views\nfrom django.contrib.auth.models import User\nfrom rest_framework.authtoken.models import Token\nimport os\n\nclass FileSerializer(serializers.ModelSerializer):\n class Meta:\n model = File\n fields = ('name', 'user', 'path', )\n\n\nclass UserSerializer(serializers.ModelSerializer):\n files = FileSerializer(many=True)\n class Meta:\n model = User\n fields = ('id','first_name','last_name','username', 'password', 'email', 'files')\n depth = 2\n\n def create(self, validated_data):\n files_data = validated_data.pop('files')\n user = User.objects.create_user(**validated_data)\n for file_data in files_data:\n name = file_data['name']\n path = file_data['path']\n File.objects.create(user=user, name = name, path=path)\n return user\n\n\n", "sub_path": "file/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "file.models.File", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 23, "usage_type": "name"}, {"api_name": "file.models.File.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "file.models.File.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "file.models.File", "line_number": 27, "usage_type": "name"}]} {"seq_id": "537907303", "text": "import os\nimport numpy as np\nimport pandas as pd\nfrom keras.preprocessing.image import array_to_img, img_to_array, load_img\nimport h5py\n\ndef createDataset(size, filename, yfilename, folderpath):\n \"\"\"\n create .hdf5 dataset file at given folder\n \"\"\"\n\n f = h5py.File(filename,\"w\")\n X=[]\n path = folderpath \n\n y = np.array(pd.read_table(path+yfilename, header = None))\n y = y[:,0]\n\n for _, _, file in os.walk(path):\n for filename in file:\n img = load_img(path+filename)\n x = img_to_array(img)\n x = x.reshape((1,) + x.shape)\n X.append(x)\n\n\n f.create_dataset(\"dataset_x\",data=X)\n f.create_dataset(\"dataset_y\", data=y)\n\n f.close()", "sub_path": "util/create_database.py", "file_name": "create_database.py", "file_ext": "py", "file_size_in_byte": 710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "h5py.File", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 16, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 22, "usage_type": "call"}]} {"seq_id": "221058962", "text": "#importing neccesary libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn import utils\nimport matplotlib.pyplot as plt\n\n#importing the dataset\ndf = pd.read_csv('/home/pritish/DS_ML_FinalTask/student-math.csv', sep=\";\")\n\n#Encoding all nominal and binary values\nfrom sklearn import preprocessing\nle = preprocessing.LabelEncoder()\n\ndf['school']= le.fit_transform(df['school'])\ndf['Pstatus']= le.fit_transform(df['Pstatus'])\ndf['sex']= le.fit_transform(df['sex'])\ndf['address']= le.fit_transform(df['address'])\ndf['famsize']= le.fit_transform(df['famsize'])\ndf['Mjob']= le.fit_transform(df['Mjob'])\ndf['Fjob']= le.fit_transform(df['Fjob'])\ndf['reason']= le.fit_transform(df['reason'])\ndf['guardian']= le.fit_transform(df['guardian'])\ndf['schoolsup']= le.fit_transform(df['schoolsup'])\ndf['famsup']= le.fit_transform(df['famsup'])\ndf['paid']= le.fit_transform(df['paid'])\ndf['activities']= le.fit_transform(df['activities'])\ndf['nursery']= le.fit_transform(df['nursery'])\ndf['higher']= le.fit_transform(df['higher'])\ndf['internet']= le.fit_transform(df['internet'])\ndf['romantic']= le.fit_transform(df['romantic'])\n\n#creating a new column final_grade in the dataframe from the mean of G1, G2 and G3\ncol = df.loc[: , \"G1\":\"G3\"]\ndf['final_grade'] = col.mean(axis=1)\n\n#new csv file after adding final_grade column\ndf.to_csv('/home/pritish/DS_ML_FinalTask/final_student-math.csv', sep = ';')\n\n#store final_grade column as an array in y\ny = df['final_grade'].to_numpy()\n\n#store all columns upto G2 in x as array\ncol = df.loc[:,\"school\":\"G2\"]\nx = col.to_numpy()\n\n#function to predict and test the accuracy of the predicted value with the true value\ndef predict(x):\n #splitting the dataset\n from sklearn.model_selection import train_test_split\n x_train,x_test,y_train,y_test = train_test_split(x,y)\n \n #fitting linear regression to the train set\n from sklearn.linear_model import LinearRegression\n reg = LinearRegression()\n reg.fit(x_train,y_train)\n \n #predicting the output from testing set of x\n prediction = reg.predict(x_test)\n \n #calculate Accuracy Score\n from sklearn.metrics import r2_score\n print(\"Prediction Accuracy = \",r2_score(y_test, prediction)*100, \"%\")\n print(\"Test Accuracy = \",reg.score(x_test, y_test)*100, \"%\") \n\n #visualisation of scatter plot between true value and predicted value\n plt.scatter(y_test, prediction, color = 'b')\n plt.xlabel('True Value --->')\n plt.ylabel('Predicted Value --->')\n plt.show()\n\n#Creating backward elimination model for optimisation of the dataset\nimport statsmodels.api as smt\ndef bkwdelm(x,sl):\n k = len(x[0])\n for i in range(0,k):\n reg_OLS = smt.OLS(y,x).fit()\n Max = max(reg_OLS.pvalues).astype(float)\n if Max > sl:\n for j in range(0,k-i):\n if (reg_OLS.pvalues[j].astype(float) == Max):\n x = np.delete(x,j,1)\n print(reg_OLS.summary())\n return x\n \nsl = 0.005\nx_opt = x[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]]\nx_modeled = bkwdelm(x_opt, sl)\n\n#Calling the function \"predict\" to predict the result and calculate the accuracy with the optimised dataset\npredict(x_modeled)\n\n", "sub_path": "final_code.py", "file_name": "final_code.py", "file_ext": "py", "file_size_in_byte": 3246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 12, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "statsmodels.api.OLS", "line_number": 76, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.delete", "line_number": 81, "usage_type": "call"}]} {"seq_id": "578214205", "text": "from PyQt5.QtWidgets import QStyle, QStyleOptionButton\nfrom PyQt5.QtCore import pyqtSignal, Qt, QRect\nfrom PyQt5.QtWidgets import QHeaderView\n\n\nclass CheckBoxHeader(QHeaderView):\n clicked = pyqtSignal(bool)\n\n _x_offset = 3\n _y_offset = 0\n _width = 20\n _height = 20\n\n def __init__(self, orientation=Qt.Horizontal, parent=None):\n super(CheckBoxHeader, self).__init__(orientation, parent)\n self.isOn = False\n\n def paintSection(self, painter, rect, logicalIndex):\n painter.save()\n super(CheckBoxHeader, self).paintSection(painter, rect, logicalIndex)\n painter.restore()\n\n self._y_offset = int((rect.height()-self._width)/2.)\n\n if logicalIndex == 0:\n option = QStyleOptionButton()\n option.rect = QRect(rect.x() + self._x_offset, rect.y() + self._y_offset, self._width, self._height)\n option.state = QStyle.State_Enabled | QStyle.State_Active\n if self.isOn:\n option.state |= QStyle.State_On\n else:\n option.state |= QStyle.State_Off\n self.style().drawControl(QStyle.CE_CheckBox, option, painter)\n\n def mousePressEvent(self, event):\n index = self.logicalIndexAt(event.pos())\n if 0 == index:\n x = self.sectionPosition(index)\n if x + self._x_offset < event.pos().x() < x + self._x_offset + self._width and self._y_offset < event.pos().y() < self._y_offset + self._height:\n if self.isOn:\n self.isOn = False\n else:\n self.isOn = True\n self.clicked.emit(self.isOn)\n self.update()\n super(CheckBoxHeader, self).mousePressEvent(event)\n", "sub_path": "Agreement/CS/skio/view/header.py", "file_name": "header.py", "file_ext": "py", "file_size_in_byte": 1728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 7, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 14, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyleOptionButton", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyle.State_Enabled", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.State_Active", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle.State_On", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.State_Off", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.CE_CheckBox", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 33, "usage_type": "name"}]} {"seq_id": "117986918", "text": "import asyncio\nimport time as ttime\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.backends.backend_qt5\nfrom matplotlib.backends.backend_qt5 import _create_qApp\n_create_qApp()\n\nqApp = matplotlib.backends.backend_qt5.qApp\n\nplt.close('all')\nfig, ax = plt.subplots()\nln, = ax.plot([], [], marker='o')\nax.set_xlim(0, 50)\nax.set_ylim(0, 50)\n\n\nloop = asyncio.get_event_loop()\n\n\ndef dummy(start_time, j, timeout):\n if loop.time() > start_time + timeout:\n print(\"skipping {}\".format(j))\n return\n print(\"running! {}\".format(j))\n\n\ndef plotter(j):\n N = 10000\n for _ in range(N):\n ttime.sleep(.3 / N)\n ln.set_xdata(np.r_[ln.get_xdata(), j])\n ln.set_ydata(np.r_[ln.get_ydata(), j])\n\n\ndef expiring_function(func, *args, **kwargs):\n def dummy(start_time, timeout):\n if loop.time() > start_time + timeout:\n print(\"skipping\")\n return\n print(\"running!\")\n return func(*args, **kwargs)\n\n return dummy\n\n\n@asyncio.coroutine\ndef manager(n):\n tasks = []\n for j in range(n):\n start_time = loop.time()\n dummy = expiring_function(plotter, j)\n t = loop.run_in_executor(None, dummy, start_time, 10)\n tasks.append(t)\n yield from asyncio.sleep(.1)\n\n yield from asyncio.wait(tasks)\n\n\ndef qt_kicker():\n plt.draw_all()\n qApp.processEvents()\n loop.call_later(.1, qt_kicker)\n\nloop.call_later(.1, qt_kicker)\n\n\nloop.run_until_complete(manager(50))\n", "sub_path": "examples/scratch.py", "file_name": "scratch.py", "file_ext": "py", "file_size_in_byte": 1474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "matplotlib.backends.backend_qt5._create_qApp", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.backends", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 33, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 57, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 47, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.draw_all", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} {"seq_id": "359850257", "text": "__author__ = 'Denis_Lebedev'\n\n\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2013 DV-LEBEDEV\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\"\"\"\n\n\n\"\"\"\n\n Calculation of risk for trade using Sharpe model\n\n\"\"\"\n\nfrom terminaltables import AsciiTable\nfrom dateutil import parser\nimport math\n\n\n\"\"\"\n-------------------- LINEAR REGRESSION ----------------------\n\"\"\"\n\n\nclass Regression(object):\n def __init__(self, x, y):\n x_average = sum(x) / float(len(x))\n y_average = sum(y) / float(len(y))\n\n s2x = (get_pow(x) / len(x)) - pow(x_average, 2)\n cov_xy = get_multi(x, y) / len(x) - x_average * y_average\n\n self.beta = cov_xy / s2x\n self.alpha = y_average - self.beta * x_average\n\n self.cor = self.beta * (get_sd(x) / get_sd(y))\n self.det = pow(self.cor, 2)\n\n\n\"\"\"\n---------------------- FUNCTIONS ----------------------\n\"\"\"\n\n\ndef get_sd(values):\n average = sum(values) / float(len(values))\n deviation = math.sqrt(sum([(i - average) ** 2 for i in values]) / float(len(values)))\n return deviation\n\n\ndef get_pow(values):\n return sum([pow(i, 2) for i in values])\n\n\ndef get_multi(x, y):\n return sum([x[i] * y[i] for i in range(len(x))])\n\n\ndef read_csv_file(path, date_index=0, time_index=1, price_index=5):\n with open(path, 'r') as f:\n data = []\n for line in f:\n items = line.split(',')\n dt = parser.parse(\"{0} {1}\".format(items[date_index], items[time_index]))\n price = float(items[price_index])\n data.append((dt, price))\n return data\n\n\ndef get_prices(path):\n return [float(i[1]) for i in read_csv_file(path)]\n\n\n\n\n\"\"\"\n---------------------- INIT. DATA ----------------------\n\"\"\"\n\n\n\n# 'TICKER': LOT\nstocks = {\n\n 'AFLT': 100,\n 'AVAZ': 100,\n 'DIXY': 10,\n 'GAZP': 10,\n 'MFON': 10,\n 'MGNT': 1,\n 'MTSS': 10,\n 'ROSN': 10,\n 'SBER': 10,\n 'VTBR': 10000\n}\n\nindex_path = 'data/RTSI.txt'\n\nvalues = {}\nregressions = {}\nweigths = {}\n\ndef set_weigths():\n summ = 0\n for i, j in regressions.items():\n weigths[i] = 1 / (1 + abs(j.beta))\n summ += weigths[i]\n\n for i, j in weigths.items():\n weigths[i] = weigths[i] / summ\n\n\n\"\"\"\n---------------------- __MAIN__ ----------------------\n\"\"\"\n\n\ntrade_volume = 1700000.00\ncommission = 0.0295\nstop_loss = 0.02\n\n\nif __name__ == \"__main__\":\n\n index_values = get_prices(index_path)\n\n for code in stocks:\n values[code] = [i * stocks[code] for i in get_prices('data/' + code + '.txt')]\n regressions[code] = Regression(index_values, values[code])\n\n for i, j in values.items():\n if len(j) is not len(index_values):\n raise Exception('Different lengths!')\n\n set_weigths()\n\n print('Trade Volume = {} \\n'.format(trade_volume))\n print('Commission = {} \\n'.format(commission))\n print('Stop Loss = {} \\n'.format(stop_loss))\n\n table_data = []\n\n table_data.append(['code', 'beta', 'r_value', 'weight', 'trade_vol', 'commission', 'stop_loss'])\n\n for code in stocks:\n \ttable_data.append([code,\n \t\tstr(round(regressions[code].beta, 4)), \n \t\tstr(round(regressions[code].cor, 4)),\n \t\tstr(round(weigths[code], 4)), \n \t\tstr(round(trade_volume * weigths[code], 2)),\n \t\tstr(round(trade_volume * weigths[code] * (commission / 100), 2)),\n \t\tstr(round(trade_volume * weigths[code] * stop_loss, 2))])\n\n table = AsciiTable(table_data, '')\n table.justify_columns = {\n \t0: 'center', \n \t1: 'right', \n \t2: 'right',\n \t3: 'right',\n \t4: 'right',\n \t5: 'center',\n \t6: 'left'}\n\n print()\n print(table.table)\n\n\n input()\n", "sub_path": "risk_calculation.py", "file_name": "risk_calculation.py", "file_ext": "py", "file_size_in_byte": 4597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "math.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 83, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 83, "usage_type": "name"}, {"api_name": "terminaltables.AsciiTable", "line_number": 173, "usage_type": "call"}]} {"seq_id": "22165811", "text": "import json\nimport time\nimport random\nimport requests\nimport re\n\n# 获取response.json dict\nwith open('./response.json', 'r', encoding='utf8') as fp:\n response_json = json.load(fp)\n college_all = response_json['data']['collegeAll']\n major_all = response_json['data']['majorAll']\n class_all = response_json['data']['classAll']\n\ncheck_url = \"https://reportedh5.17wanxiao.com/sass/api/epmpics\"\n\n# 输入\nstus = []\ntmp = input()\nwhile tmp != 'end':\n stus.append(tmp.split(','))\n tmp = input()\n\n# 存放网络错误时没有打卡成功的成员信息，用于重新打卡\nerror = []\n\n\ndef main():\n # 遍历所有需要打卡的成员\n for stu in stus:\n # 获取dept_text以及uid\n dept_text = stu[2]\n uid = stu[3]\n # 获取学院、专业和班级信息\n try:\n tmp = dept_text.split('-', 3)\n college_name = tmp[0]\n major_name = tmp[1]\n class_name = tmp[2]\n except IndexError:\n print_info_error()\n exit(1)\n\n # 获取deptId\n try:\n print('获取deptId中...')\n for college in college_all:\n if college['name'] == college_name:\n college_id = college['deptId']\n for major in major_all:\n if (major['name'] == major_name) & (major['parentId'] == college_id):\n major_id = major['deptId']\n for class_ in class_all:\n if (class_['name'] == class_name) & (class_['parentId'] == major_id):\n class_id = class_['deptId']\n stu.append(class_id)\n if class_id:\n print('获取deptId成功!')\n except NameError:\n print_info_error()\n exit(1)\n msg = check(stu)\n print(msg)\n wechat_push(uid, msg)\n # 当error list不为空时一直循环打卡 直到清空error\n while len(error) != 0:\n # 等待5min\n time.sleep(300)\n for i in range(len(error)-1, -1, -1):\n msg = check(error[i])\n print(msg)\n wechat_push(uid, msg)\n # 打卡成功后从error中删除对应成员\n if re.search('打卡成功', msg):\n del error[i]\n\n# 获取当前时间\ndef get_time():\n return[(time.localtime().tm_hour + 8) % 24,\n time.localtime().tm_min,\n time.localtime().tm_sec]\n\n# 获取随机温度\ndef random_temperature():\n a = random.uniform(36.2, 36.5)\n return round(a, 1)\n\n# 打印错误信息\ndef print_info_error():\n \"\"\"\n 打印 个人信息错误\n \"\"\"\n print('请检查你填写的学院、专业、班级信息！')\n print('见完美校园健康打卡页面')\n print('如 理学院-应用物理学-应物1901')\n\n# 微信推送\ndef wechat_push(uid, msg):\n json = {\n \"appToken\": \"AT_hHtOWzcFDw3nhEWfhLNJgnNDAO132pFK\",\n \"content\": msg,\n \"contentType\": 1,\n \"uids\": [uid]\n }\n response = requests.post(\n \"http://wxpusher.zjiecode.com/api/send/message\", json=json)\n if response.status_code == 200:\n print('微信推送成功!')\n else:\n print('微信推送失败!')\n\n\n# 打卡\ndef check(stu):\n stu_name = stu[0]\n stu_id = stu[1]\n dept_text = stu[2]\n class_id = stu[4]\n now = get_time()\n check_json = {\n \"businessType\": \"epmpics\",\n \"method\": \"submitUpInfoSchool\",\n \"jsonData\": {\n \"deptStr\": {\n \"deptid\": class_id,\n \"text\": dept_text\n },\n \"areaStr\": {\n \"streetNumber\": \"\", \"street\": \"长椿路辅路\", \"district\": \"中原区\", \"city\": \"郑州市\", \"province\": \"河南省\",\n \"town\": \"\", \"pois\": \"河南工业大学(莲花街校区)\", \"lng\": 113.544407 + random.random() / 10000,\n \"lat\": 34.831014 + random.random() / 10000, \"address\": \"中原区长椿路辅路河南工业大学(莲花街校区)\",\n \"text\": \"河南省-郑州市\", \"code\": \"\"\n },\n \"reportdate\": round(time.time() * 1000),\n \"customerid\": 43,\n \"deptid\": class_id,\n \"source\": \"app\",\n \"templateid\": \"clockSign2\",\n \"stuNo\": stu_id,\n \"username\": stu_name,\n \"userid\": round(time.time()),\n \"updatainfo\": [\n {\n \"propertyname\": \"temperature\",\n \"value\": random_temperature()\n },\n {\n \"propertyname\": \"symptom\",\n \"value\": \"无症状\"\n }\n ],\n \"customerAppTypeRuleId\": 147,\n \"clockState\": 0\n },\n }\n flag = 0\n for i in range(1, 2):\n print('{0}第{1}次尝试打卡中...'.format(stu_name, i))\n response = requests.post(check_url, json=check_json)\n if response.status_code == 200:\n flag = 1\n break\n else:\n print('{0}第{1}次打卡失败!30s后重新打卡'.format(stu_name, i))\n time.sleep(30)\n print(response.text)\n time_msg = str(now[0]) + '时' + str(now[1]) + '分' + str(now[2]) + '秒'\n if flag == 1:\n if response.json()[\"msg\"] == '成功':\n msg = time_msg + '时' + stu_name + \"打卡成功\"\n else:\n msg = time_msg + \"打卡异常\"\n else:\n msg = time_msg + \"网络错误打卡失败!5min后重新打卡!\"\n error.append(stu)\n return msg\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "re.search", "line_number": 73, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 78, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 79, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 80, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 104, "usage_type": "call"}, {"api_name": "random.random", "line_number": 129, "usage_type": "call"}, {"api_name": "random.random", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "time.time", "line_number": 140, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 158, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 164, "usage_type": "call"}]} {"seq_id": "245713329", "text": "import os\nimport sys\nimport re\nimport socket\nimport pytz\nfrom urlparse import urljoin\nfrom datetime import datetime, time\nfrom flask import Flask, render_template, request, url_for, Response\nfrom flask_assets import Environment\nfrom flask_frozen import Freezer\nfrom collections import defaultdict\nfrom flask_flatpages import FlatPages\nfrom werkzeug.contrib.atom import AtomFeed\n\nutc = pytz.utc\n\nHOSTNAME = socket.gethostname()\nDEBUG = False\nif '.local' in HOSTNAME:\n DEBUG = True\n\nSITE_ROOT = os.path.dirname(os.path.realpath(__file__))\nFLATPAGES_AUTO_RELOAD = DEBUG\nFLATPAGES_EXTENSION = '.md'\n# LESS_BIN = os.path.join(SCRIPT_ROOT, '..', 'node_modules', 'less', 'bin', 'lessc')\nLESS_LINE_NUMBERS = 'mediaquery'\nFREEZER_DESTINATION = os.path.join(SITE_ROOT, '..', '..', 'build')\nFLATPAGES_ROOT = os.path.join(SITE_ROOT, '..', '..', 'pages')\n\napp = Flask(\n import_name='codingnotes',\n static_folder=os.path.join(SITE_ROOT, '..', '..', 'static'),\n template_folder=os.path.join(SITE_ROOT, '..', '..', 'templates'),\n)\n\nassets = Environment(app)\nassets.manifest = False\nassets.cache = False\nif DEBUG:\n assets.manifest = False\n assets.cache = None\nfreezer = Freezer(app)\npages_on_disk = FlatPages(app)\n\napp.config.from_object(__name__)\n\n\ndef is_published(post_date):\n return utc.localize(datetime.utcnow()) >= utc.localize(datetime.combine(post_date, time(0, 0, 0)))\npublished_pages = sorted([p for p in pages_on_disk if is_published(p.meta.get('date', '2099-12-31'))], key=lambda p: p.meta['date'])\n\n\ndef get_latest_pages(limit=10):\n pages = published_pages if not request.args.get('preview') else pages_on_disk\n # Articles are pages with a publication date\n articles = (p for p in pages if 'date' in p.meta)\n # Show the 10 most recent articles, most recent first.\n latest = sorted(articles, reverse=True,\n key=lambda p: p.meta['date'])\n return latest[:limit]\n\n\ndef get_tags():\n pages = published_pages if not request.args.get('preview') else pages_on_disk\n\n tags = defaultdict(int)\n for page in pages:\n for tag in page.meta['tags']:\n tags[tag] += 1\n return tags\n\n\ndef make_external(url):\n return urljoin(request.url_root, url)\n\n\ndef feed(articles):\n articles = sorted(articles, reverse=True,\n key=lambda p: p.meta['date'])\n feed = AtomFeed(\n 'Coding notes by Riccardo Forina',\n feed_url=request.url,\n url=request.url_root,\n icon=make_external(url_for('static', filename=\"icons/favicon.ico\")),\n logo=make_external(url_for('static', filename=\"icons/favicon.ico\")),\n )\n for article in articles:\n feed.add(\n article.meta['title'],\n unicode(article.html),\n content_type='html',\n author='Riccardo Forina',\n url=make_external(url_for('page', path=article.path)),\n updated=article.meta.get('updated', article.meta['date']),\n published=article.meta['date'],\n )\n return feed.get_response()\n\n\n@app.errorhandler(404)\ndef page_not_found(e):\n latest = get_latest_pages()\n return render_template('404.html', pages=latest), 404\n\n\n@app.route('/')\ndef index():\n latest = get_latest_pages(limit=None)\n return render_template('index.html',\n pages=latest,\n tags=get_tags(),\n now=utc.localize(datetime.utcnow())\n )\n\n\n@app.route('/tag/<string:tag>/recent.atom')\ndef tag_feed(tag):\n pages = published_pages if not request.args.get('preview') else pages_on_disk\n tagged = [p for p in pages if tag in p.meta.get('tags', [])]\n return feed(tagged)\n\n\n@freezer.register_generator\ndef tag_feed_generator():\n for tag in get_tags():\n yield 'tag_feed', {'tag': tag}\n\n\n@app.route('/tag/<string:tag>/')\ndef tag(tag):\n pages = published_pages if not request.args.get('preview') else pages_on_disk\n\n tagged = [p for p in pages if tag in p.meta.get('tags', [])]\n return render_template('tag.html',\n pages=tagged,\n tag=tag,\n tags=get_tags(),\n )\n\n\n@app.route('/<path:path>/')\ndef page(path):\n # pages = published_pages if not request.args.get('preview') else pages_on_disk\n\n page = pages_on_disk.get_or_404(path)\n index = published_pages.index(page)\n previous_page = published_pages[index - 1] if index > 0 else None\n next_page = published_pages[index + 1] if index < (len(published_pages) - 1) else None\n # print previous_page, next_page\n # if not page in pages:\n # abort(404)\n return render_template('page.html',\n page=page,\n previous_page=previous_page,\n next_page=next_page,\n latest=get_latest_pages(5),\n tags=get_tags()\n )\n\n\n@app.route('/status/')\ndef status():\n return render_template('status.html')\n\n\n@app.route('/resume/')\ndef resume():\n return render_template('resume.html',\n latest=get_latest_pages(5)\n )\n\n\n@app.route('/recent.atom')\ndef site_feed():\n return feed(get_latest_pages())\n\n\n@app.route('/robots.txt')\ndef robots():\n return render_template('robots.txt', sitemap_url=make_external(url_for('sitemap')))\n\n\n@app.route('/sitemap.xml')\ndef sitemap():\n urls = [make_external(url_for('index')), make_external(url_for('status'))]\n urls += [make_external(url_for('page', path=article.path)) for article in published_pages]\n urls += [make_external(url_for('tag', tag=tag)) for tag in get_tags()]\n return Response(\n response=render_template('sitemap.xml', urls=urls), mimetype='app/xml')\n\n\ndef truncate_html_words(s, num):\n \"\"\"\n Truncates html to a certain number of words (not counting tags and comments).\n Closes opened tags if they were correctly closed in the given html.\n \"\"\"\n length = int(num)\n if length <= 0:\n return ''\n # Set up regular expressions\n re_whitespace = re.compile(r'\\s+')\n re_html_comment = re.compile(r'<!--.*?-->', re.DOTALL)\n re_tag_singlet = re.compile(r'<[^>]+/>')\n re_tag = re.compile(r'<([^>/\\s]+)[^>]*>')\n re_tag_close = re.compile(r'</([^>\\s]+)[^>]*>')\n re_non_alphanumeric = re.compile(r'[^\\w<]+')\n re_word = re.compile(r'[^<\\s]+')\n # Set up everything else\n tags = []\n words = 0\n pos = 0\n len_s = len(s)\n elipsis_pos = 0\n elipsis_required = 0\n while pos < len_s:\n # Skip white space, comment, or singlet\n m = re_whitespace.match(s, pos) or re_html_comment.match(s, pos) or re_tag_singlet.match(s, pos)\n if m:\n pos = m.end(0)\n continue\n # Check for tag\n m = re_tag.match(s, pos)\n if m:\n pos = m.end(0)\n if not elipsis_pos:\n tag = m.group(1).lower()\n tags.append(tag)\n continue\n # Check for close tag\n m = re_tag_close.match(s, pos)\n if m:\n pos = m.end(0)\n if not elipsis_pos:\n tag = m.group(1).lower()\n try:\n tags.remove(tag)\n except ValueError:\n pass\n continue\n # Skip non-alphanumeric\n m = re_non_alphanumeric.match(s, pos)\n if m:\n pos = m.end(0)\n continue\n # Check for word\n m = re_word.match(s, pos)\n if m:\n pos = m.end(0)\n words += 1\n if words == length:\n elipsis_pos = pos\n if words > length:\n elipsis_required = 1\n break\n continue\n # Shouldn't ever actually get here\n break\n if elipsis_required:\n out = s[:elipsis_pos]\n if not out.endswith('...'):\n out += ' ...'\n else:\n out = s[:pos]\n # Look for closing tags for any tags still open\n tags.reverse()\n temppos = pos\n for tag in tags:\n while 1:\n m = re_tag_close.search(s, temppos)\n if m:\n temppos = m.end(0)\n if m.group(1) == tag:\n out += m.group(0)\n pos = temppos\n break\n else:\n break\n return out\n\n\napp.jinja_env.filters['truncate_html_words'] = truncate_html_words\n\n\nif __name__ == '__main__':\n if len(sys.argv) > 1 and sys.argv[1] == \"build\":\n freezer.freeze()\n else:\n app.run('0.0.0.0')\n\n\nif __name__ != '__main__':\n import newrelic.agent\n newrelic.agent.initialize(os.path.join(SITE_ROOT, 'newrelic.ini'))\n", "sub_path": "src/codingnotes/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 8448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pytz.utc", "line_number": 15, "usage_type": "attribute"}, {"api_name": "socket.gethostname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask_assets.Environment", "line_number": 36, "usage_type": "call"}, {"api_name": "flask_frozen.Freezer", "line_number": 42, "usage_type": "call"}, {"api_name": "flask_flatpages.FlatPages", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 66, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.url_root", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "werkzeug.contrib.atom.AtomFeed", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.url_root", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 163, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 186, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 187, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 189, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 201, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 202, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 202, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 203, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 204, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 205, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 206, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 207, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 285, "usage_type": "attribute"}, {"api_name": "newrelic.agent.agent.initialize", "line_number": 293, "usage_type": "call"}, {"api_name": "newrelic.agent.agent", "line_number": 293, "usage_type": "attribute"}, {"api_name": "newrelic.agent", "line_number": 293, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}]} {"seq_id": "437401481", "text": "import os\nimport cv2\nimport time\n \n# This is a demo of running face recognition on a video file and saving the results to a new video file.\n#\n# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.\n# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this\n# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.\nlength = 10\n \n# Create an output movie file (make sure resolution/frame rate matches input video!)\n#fourcc = cv2.VideoWriter_fourcc(*'MP4V')\nfourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')\n# output_movie_1 = cv2.VideoWriter('output.mp4', fourcc, length, (320, 240))\n# output_movie_2 = cv2.VideoWriter('output.mp4', fourcc, length, (640, 480))\n\ncap = cv2.VideoCapture(0)\ncap.open(0)\n##########################\ncap.set(3,320) # width\ncap.set(4,240) # length\n##########################\n\nnT, oT = 0, 0\nvidNum = 0\nwhile(True):\n if vidNum > 20:\n break\n output_movie_1 = cv2.VideoWriter('1output'+str(vidNum)+'.mp4', fourcc, length, (320, 240))\n output_movie_2 = cv2.VideoWriter('0output'+str(vidNum)+'.mp4', fourcc, length, (640, 480))\n frame_number = 0\n # frameBuffer = []\n oT = nT\n nT = time.time()\n print(nT-oT)\n while (True):\n fT = time.time()\n ret, frame = cap.read()\n if ret == False:\n print('f')\n continue\n output_movie_1.write(frame)\n output_movie_2.write(frame)\n # Grab a single frame of video\n \n frame_number += 1\n if frame_number == 50:\n break\n dt = time.time()-fT\n if (dt<0.1):\n time.sleep(0.1-dt)\n # print(\"!!!<0.1\")\n\n\n # print(time.time() - T)\n # for frame in frameBuffer:\n # cv2.imshow('frame', frame)\n # # print(f, end = ' ')\n # if cv2.waitKey(1) & 0xFF == ord('q'):\n # break\n # # Write the resulting image to the output video file\n # output_movie.write(frame)\n print(\"end write of\"+ str(vidNum))\n # f = open(\"vidnum.txt\", 'w')\n # f.write(str(vidNum))\n vidNum+=1\n dt = time.time()-nT\n if (dt<5):\n time.sleep(5-dt-0.004)\n# All done!\n\ncv2.destroyAllWindows()\n", "sub_path": "vwritemp4.py", "file_name": "vwritemp4.py", "file_ext": "py", "file_size_in_byte": 2284, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.VideoWriter_fourcc", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 73, "usage_type": "call"}]} {"seq_id": "266914337", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\winstrument\\utils.py\n# Compiled at: 2020-02-05 20:03:04\n# Size of source mod 2**32: 3470 bytes\nfrom winstrument.data.module_message import ModuleMessage\nfrom tabulate import tabulate\nimport json\nfrom collections import namedtuple\nimport os\n\ndef format_table(messagelist, verbosity=0):\n if verbosity < 1:\n return tabulate([elipsize_message(message).flatten() for message in messagelist], headers='keys')\n return tabulate([message.flatten() for message in messagelist], headers='keys')\n\n\ndef format_json(messagelist, verbosity=0):\n return json.dumps([message.flatten() for message in messagelist])\n\n\ndef mask_to_str(mask, enum_map):\n \"\"\"\n Attempts to produce a string of set flags from the given mask and dict of enum value-to-name mappings\n mask: int - bitmask from e.g. Windows API\n enum_map: dict[int -> str]\n returns a string in the form: FLAG 1 | FLAG 2 ...\n \"\"\"\n flags_set = []\n for flag in enum_map.keys():\n if mask & flag == flag:\n flags_set.append(enum_map[flag])\n\n return ' | '.join(flags_set)\n\n\ndef format_grep(messagelist, verbosity=0):\n outlines = []\n sep = '|'\n for message in messagelist:\n outline = f\"{message.module}{sep}{message.time}{sep}{message.target}\"\n for k, v in message.data.items():\n outline += f\"{sep}{k}:{v}\"\n\n outlines.append(outline)\n\n return '\\n'.join(outlines)\n\n\ndef elipsize_path(path):\n \"\"\"\n Converts a full Windows path into a path like C:/.../filename.exe\n path - str\n Return - shortened path: str\n \"\"\"\n path_start, tail = os.path.splitdrive(path)\n last_part = os.path.split(tail)[(-1)]\n return f\"{path_start}/.../{last_part}\"\n\n\ndef elipsize_message(message):\n \"\"\"\n Creates a new message from the original with the target path shortend\n \"\"\"\n new_target = elipsize_path(message.target)\n return ModuleMessage((message.module), new_target, (message.data), time=(message.time))\n\n\ndef get_formatters():\n \"\"\"\n Returns namedtuple of all available formatters and human readable names\n Fields:\n name - human readable name for use in command arguments etc\n function - function object to the formatter\n \"\"\"\n Formatter = namedtuple('Formatter', 'name function')\n formatters = [Formatter(name='table', function=format_table),\n Formatter(name='json', function=format_json),\n Formatter(name='grep', function=format_grep)]\n return formatters\n\n\ndef get_formatter(name):\n \"\"\"\n Returns the formatter callback for the formatter with the speicfied name.\n Returns None if no such formatter exists\n \"\"\"\n formatter_list = get_formatters()\n for formatter in formatter_list:\n if name.lower() == formatter.name.lower():\n return formatter.function\n\n raise ValueError(f\"No formatter {name}\")", "sub_path": "pycfiles/winstrument-0.1.0-py3.7/utils.cpython-37.py", "file_name": "utils.cpython-37.py", "file_ext": "py", "file_size_in_byte": 2999, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tabulate.tabulate", "line_number": 16, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.splitdrive", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "winstrument.data.module_message.ModuleMessage", "line_number": 68, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 78, "usage_type": "call"}]} {"seq_id": "24096084", "text": "#-*- coding: UTF-8 -*-\n__author__ = 'mcxiaoke'\n\nimport requests\nimport json\n\ndef test():\n return \"test, hello!\"\n\ndef get_current():\n url = \"http://moment.douban.com/api/stream/current?format=lite\"\n req = requests.get(url)\n if req.status_code == 200:\n return req.text\n else:\n return \"\"\n", "sub_path": "data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}]} {"seq_id": "521204905", "text": "#!/usr/bin/env python3\n# implementation of a cubic hermite spline\n\n# load modules\nfrom math import pi\nimport matplotlib.pyplot as plt\nfrom pylab import *\n\nclass cubic_hermite_spline():\n\tdef __init__(self):\n\t\tpass\n\n\tdef v2d_scalar_mul (self, v, f):\n\t\treturn [v[0]*f, v[1]*f]\n\n\tdef v2d_add (self, v1, v2):\n\t\treturn [v1[0]+v2[0], v1[1]+v2[1]]\n\n\tdef goto_wpt (self, p1, t1, p2, t2, steps):\n\t\t# http://cubic.org/docs/hermite.htm\n\t\t# http://en.wikipedia.org/wiki/Cubic_Hermite_spline#Interpolation%20on%20a%20single%20interval\n\t\tp = []\n\t\tfor t in range(steps):\n\t\t\ts = t/(steps * 1.0) # scale s to go from 0 to 1\n\n\t\t\t# # calculate basis function\n\t\t\th1 = 2*s**3 - 3*s**2 + 1\n\t\t\th2 = -2*s**3 + 3*s**2\n\t\t\th3 = s**3 - 2*s**2 + s\n\t\t\th4 = s**3 - s**2\n\n\t\t\t# multiply and sum functions together to build the interpolated point along the curve\n\t\t\tv1 = self.v2d_scalar_mul(p1,h1)\n\t\t\tv2 = self.v2d_scalar_mul(p2,h2)\n\t\t\tv3 = self.v2d_scalar_mul(t1,h3)\n\t\t\tv4 = self.v2d_scalar_mul(t2,h4)\n\n\t\t\tp.append(self.v2d_add(self.v2d_add(self.v2d_add(v1,v2),v3),v4))\n\t\treturn p\n\nchs = cubic_hermite_spline()\n\np1 = [0.0, 0.0]\nt1 = [0.0, 5.0]\np2 = [2.0, 0.0]\nt2 = [0.0, 5.0]\nsteps = 50\n\nrte = chs.goto_wpt (p1,t1,p2,t2,steps)\n\nprint (rte)\n\n# plot route\nrteT = list(zip(*rte))\nrte_plt = plot(rteT[0],rteT[1],'blue')\n\ntitle ('Route')\naxis('equal')\nxlabel('Easting [m]')\nylabel('Northing [m]')\nplt.savefig ('route_plan.png')\nion() # turn interaction mode on\nshow()\nioff()\n\n", "sub_path": "UTMHandling/fixedWing/hermite.py", "file_name": "hermite.py", "file_ext": "py", "file_size_in_byte": 1437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "matplotlib.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} {"seq_id": "573625684", "text": "from model_configs.core import ModelConfigBase\nfrom utils.training_utils \\\n import disentanglement_loss\nfrom model_configs import thin_resnet_encode\nfrom model_configs import thin_resnet_decode\n\nfrom keras.layers import BatchNormalization\nfrom keras.layers import UpSampling2D\nfrom keras.layers import Concatenate\nfrom keras.layers import Activation\nfrom keras.layers import Flatten\nfrom keras.layers import Reshape\nfrom keras.layers import Dropout\nfrom keras.layers import Conv2D\nfrom keras.layers import Dense\nfrom keras.layers import Input\nfrom keras.models import Model\nfrom keras.regularizers import l2\nimport keras\n\nweight_decay = 1e-4\n\n\nclass ModelConfig(ModelConfigBase):\n\n def __init__(self):\n\n super(ModelConfig, self).__init__()\n self.nclasses = 200\n self.x_shape = (512, ) # log-mel FB energies of 4 sec segment\n self.embedding_dim_1 = 128 # Change this later\n self.embedding_dim_2 = 128 # Change this later\n self.nz = 'tanh'\n self.predictor_loss = 'sparse_categorical_crossentropy'\n self.decoder_loss = 'mean_squared_error'\n self.disentangler_loss = disentanglement_loss\n\n def encoder(self, name='encoder'):\n\n x_in = Input(self.x_shape, name='encoder_input')\n \n x = x_in\n x = Dense(256, name='enc_fc1', activation='relu',\n use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n bias_regularizer=keras.regularizers.l2(weight_decay))(x)\n x = BatchNormalization()(x)\n x = Dropout(0.4)(x)\n\n #x = Dense(256, name='enc_fc2', activation='relu',\n # use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n # bias_regularizer=keras.regularizers.l2(weight_decay))(x)\n #x = BatchNormalization()(x)\n #x = Dropout(0.2)(x)\n\n e1 = Dense(self.embedding_dim_1, name='embedding_1', activation=self.nz, kernel_initializer='orthogonal',\n use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n bias_regularizer=keras.regularizers.l2(weight_decay))(x)\n e2 = Dense(self.embedding_dim_2, name='embedding_2', activation=self.nz, kernel_initializer='orthogonal',\n use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n bias_regularizer=keras.regularizers.l2(weight_decay))(x)\n\n m = Model(inputs=[x_in], outputs=[e1, e2], name=name)\n m.summary()\n return m\n\n def noisy_transformer(self, params=[0.5], name='noisy_transformer'):\n dropout_rate = params[0]\n return Dropout(dropout_rate)\n\n def predictor(self, name='predictor'):\n e1 = Input((self.embedding_dim_1,), name='predictor_input')\n h = e1 #BatchNormalization(name='predictor_bn1')(e1)\n\n h = Dense(128, name='predictor_fc1')(h)\n h = BatchNormalization(name='predictor_bn1')(h)\n h = Activation('relu', name='predictor_relu1')(h)\n h = Dropout(0.4)(h)\n\n #h = Dense(512, name='predictor_fc3')(h)\n #h = BatchNormalization(name='predictor_bn3')(h)\n #h = Activation('relu', name='predictor_relu3')(h)\n #h = Dropout(0.2)(h)\n\n y = Dense(self.nclasses, activation='softmax', name='predictor_output')(h)\n\n return Model(e1, y, name=name)\n\n def decoder(self, name='decoder'):\n\n e1 = Input((self.embedding_dim_1,))\n e2 = Input((self.embedding_dim_2,))\n e = Concatenate()([e1, e2])\n \n h = e\n #h = Dense(512, name='dec_fc1',\n # use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n # bias_regularizer=keras.regularizers.l2(weight_decay))(h)\n #h = BatchNormalization(name='decoder_bn1')(h)\n #h = Activation('relu', name='decoder_relu1')(h)\n\n h = Dense(256, name='dec_fc2',\n use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n bias_regularizer=keras.regularizers.l2(weight_decay))(h)\n h = BatchNormalization(name='decoder_bn2')(h)\n h = Activation('relu', name='decoder_relu2')(h)\n\n x = Dense(self.x_shape[0], name='dec_fc3',\n use_bias=True, trainable=True, kernel_regularizer=keras.regularizers.l2(weight_decay),\n bias_regularizer=keras.regularizers.l2(weight_decay))(h)\n x = Activation('linear')(x)\n x_out = Reshape(self.x_shape,name='decoder_output')(x)\n\n m = Model(inputs=[e1, e2], outputs=[x_out], name=name)\n m.summary()\n return m\n\n def disentangler(self, input_dim=None, output_dim=None, name='disentangler'):\n if input_dim is None:\n input_dim = self.embedding_dim_2\n if output_dim is None:\n output_dim = self.embedding_dim_1\n\n ei = Input((input_dim,), name='disentangler_input')\n \n e = ei\n #e = Dense(self.embedding_dim_1, \n # activation='relu', \n # name='dis_hidden1')(ei)\n #e = Dropout(0.2)(e)\n\n #e = Dense(self.embedding_dim_1, \n # activation='relu', \n # name='dis_hidden2')(e)\n #e = Dropout(0.2)(e)\n \n ej = Dense(\n output_dim, activation=self.nz,\n name='disentangler_output'\n )(e)\n\n return Model(ei, ej, name=name)\n", "sub_path": "predict/unified_adversarial_invariance/model_configs/xvector_uai_voices.py", "file_name": "xvector_uai_voices.py", "file_ext": "py", "file_size_in_byte": 5490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "model_configs.core.ModelConfigBase", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.training_utils.disentanglement_loss", "line_number": 36, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 44, "usage_type": "attribute"}, {"api_name": "keras.regularizers.l2", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 45, "usage_type": "attribute"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 56, "usage_type": "attribute"}, {"api_name": "keras.regularizers.l2", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 57, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "keras.regularizers.l2", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 60, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 102, "usage_type": "attribute"}, {"api_name": "keras.regularizers.l2", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 103, "usage_type": "attribute"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 108, "usage_type": "attribute"}, {"api_name": "keras.regularizers.l2", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 109, "usage_type": "attribute"}, {"api_name": "keras.layers.Activation", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 123, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 141, "usage_type": "call"}]} {"seq_id": "231792117", "text": "#!/usr/bin/python3\n\nimport tweepy\nfrom textblob import TextBlob\nimport matplotlib.pyplot as plt\nimport nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nimport string\n\nConsumer_key='LY7g0NUTBbRqimm5XAFWIUUbE'\n\nConsumer_Secret= 'iXSSyImidCWIiclvwh9hC6bPkkF1YjYpNKNC93RMQmYUc6ECpg'\n\nAccess_Token ='774600536968527872-Ee5SrpNF29e4hBg5sDrN7Ft6R0XQIkM' \nAccess_Token_Secret= 'qkxiGFun50SVKEgcpyBE8aubkL0IAHlxy48nT7VLRdrse'\n\n#connecting for authentication\n\nauth=tweepy.OAuthHandler(Consumer_key,Consumer_Secret)\nauth.set_access_token(Access_Token , Access_Token_Secret)\n\n#connecting with the api\nconnect = tweepy.API(auth)\n\ntweets = connect.search('modi')\n\nsenti=[]\n#printing data\nfor tweet in tweets:\n\ttweet_text=tweet.text\n\n\t#removing the punctuation marks from tweet obtained\n\ttweet_without_punc=[i for i in tweet_text if i not in string.punctuation]\n\t# print(\"#------------------------------------------without punc--------------------------------------------------------\")\n\t# print(tweet_without_punc)\n\t# print(\"#------------------------------------------join--------------------------------------------------------\")\n\t\n\n\t#join the tweet after removing punctuation\n\ttweet_punc_clean=''.join(tweet_without_punc)\n\tprint(tweet_punc_clean)\n\t\n\t# tokenizing words\n\t# print(\"#------------------------------------------tokenize--------------------------------------------------------\")\n\ttweet_without_punc= word_tokenize(tweet_punc_clean)\n\t# print(tweet_without_punc)\n\t\n\t# removing the stopwords\n\ttweet_stopwords=[i for i in tweet_without_punc if i not in stopwords.words('english')]\n\ttweet_stopwords=' '.join(tweet_stopwords)\n\t#print(tweet_stopwords)\n\t\n\tanalysis=TextBlob(tweet_stopwords)\n\tsenti.append(analysis.sentiment.polarity)\n\tprint(senti)\n\n\nneut=[]\nhappy=[]\nsorrow=[]\n\n\n# distributing the emotions according to the polarity\nfor polarity in senti:\n\tif polarity==0:\n\t\tneut.append(\"netural\")\n\n\tif polarity>0:\n\t\thappy.append(\"positive\")\n\n\tif polarity<0:\n\t\tsorrow.append(\"negative\")\n\n#calculating the length of each emotion\nneutral=len(neut)\ndukhi=len(sorrow)\nkhush=len(happy)\n\n# plotting Bar graph\nplt.bar(['khush','neutral','dukhi'],[khush,neutral,dukhi],color=\"red\")\n\nplt.show()", "sub_path": "Nltk/tweet_real_data_graph.py", "file_name": "tweet_real_data_graph.py", "file_ext": "py", "file_size_in_byte": 2205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 24, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 50, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 50, "usage_type": "name"}, {"api_name": "textblob.TextBlob", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} {"seq_id": "463467513", "text": "import webapp2\nimport json\nfrom events import Event\nfrom users import User\nimport event_actions\nfrom firebaseManager import FirebaseManager\n\nclass MainPage(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n self.response.write('Whats up ?, remember we have a meeting on Sunday.\\n see you then :D')\n\nclass BhadelPage(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n self.response.write('Sho ya bhadel ?')\n\n\nclass Events(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n userId = body[\"userId\"]\n body.pop(\"userId\", None)\n Event.createEvent(userId, body)\n self.response.write('OK')\n\n\nclass Register(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n User.registerUser(body)\n self.response.write('OK')\n\n\nclass DB(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n event_actions.updateDB(body[\"eventType\"], body[\"shoppingList\"])\n self.response.write('OK')\n\nclass Test(webapp2.RequestHandler):\n def get(self):\n pokerArr = []\n pokerArr.append([\"xl\", \"beer\", \"snacks\"])\n # pokerArr.append([\"bagel\", \"cups\", \"drinks\", \"sunflowerSeeds\"])\n # pokerArr.append([\"drinks\", \"cups\", \"snacks\"])\n # pokerArr.append([\"pokerset\", \"chips\", \"beer\", \"coke\", \"sunflowerSeeds\"])\n # pokerArr.append([\"drinks\", \"sunflowerSeeds\", \"pizza\"])\n # pokerArr.append([\"pizza\", \"beer\"])\n # pokerArr.append([\"pizza\", \"beer\"])\n # pokerArr.append([\"pizza\", \"beer\"])\n # dinnerArr = []\n # dinnerArr.append([\"pizza\", \"cups\", \"drinks\"])\n # dinnerArr.append([\"coke\", \"cups\", \"napkins\"])\n # dinnerArr.append([\"icecream\", \"spoons\", \"cookies\"])\n # dinnerArr.append([\"salt\", \"meat\", \"beer\"])\n # dinnerArr.append([\"meat\", \"beer\", \"knives\", \"forks\"])\n # breakfastArr = []\n # breakfastArr.append([\"coffee\", \"eggs\", \"milk\"])\n # breakfastArr.append([\"coffee\", \"milk\", \"sugar\"])\n # breakfastArr.append([\"eggs\", \"salt\", \"tomatoes\"])\n # breakfastArr.append([\"tea\", \"milk\", \"sugar\"])\n # bbqArr =[]\n # bbqArr.append([\"meat\", \"beer\", \"coal\", \"lighter\"])\n # bbqArr.append([\"meat\", \"beer\", \"bottleOpenner\", \"grill\"])\n # bbqArr.append([\"meat\", \"grill\", \"coal\"])\n\n for arr in pokerArr:\n event_actions.updateDB(\"PokerNight\", arr)\n # for arr in dinnerArr:\n # event_actions.updateDB(\"Dinner\", arr)\n # for arr in breakfastArr:\n # event_actions.updateDB(\"Breakfast\", arr)\n # for arr in bbqArr:\n # event_actions.updateDB(\"Barbeque\", arr)\n\n self.response.write('OK')\n\n\nclass Suggested(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n eventType = \"\"\n shoppingList=[]\n if \"eventType\" in body:\n eventType = body[\"eventType\"]\n if \"shoppingList\" in body:\n shoppingList = body[\"shoppingList\"]\n res = event_actions.getSuggestedItems(eventType, shoppingList)\n self.response.write(json.dumps(res))\n\n\nclass AllUserEvents(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n userId = body[\"userId\"]\n self.response.write(json.dumps(Event.getUserEvents(userId)))\n\n\nclass GetEvent(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n eventId = body[\"eventId\"]\n self.response.write(json.dumps(Event.getEvent(eventId)))\n\n\nclass CleanEvent(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n eventId = body[\"eventId\"]\n Event.removeEvent(eventId)\n\n self.response.write(\"Clean\")\n\n\nclass Names(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n userIds = json.loads(self.request.body)\n names = []\n for i in range(0, len(userIds)):\n user = json.loads(FirebaseManager.getFromFB(\"users/\"+userIds[i]))\n if \"firstName\" in user:\n names.append(user[\"firstName\"])\n else:\n names.append(\"\")\n self.response.write(json.dumps(names))\n\nclass Items(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n eventId = body[\"eventId\"]\n shoppingList = body[\"shoppingList\"]\n FirebaseManager.saveToFB(shoppingList, \"events/upcoming/{}/shoppingList\".format(eventId))\n self.response.write(\"ok\")\n\n\nclass Dates(webapp2.RequestHandler):\n def post(self):\n self.response.headers.add_header(\"Access-Control-Allow-Origin\", \"*\")\n body = json.loads(self.request.body)\n eventId = body[\"eventId\"]\n goodDates = body[\"goodDates\"]\n userId = body[\"userId\"]\n Event.confirmDates(eventId, userId, goodDates)\n self.response.write(\"ok\")\n\napp = webapp2.WSGIApplication([\n ('/', MainPage),\n ('/bhadel', BhadelPage),\n ('/createevent', Events),\n ('/register', Register),\n ('/db', DB),\n ('/suggested', Suggested),\n ('/alluserevents', AllUserEvents),\n ('/cleanevent', CleanEvent),\n ('/names', Names),\n ('/event', GetEvent),\n ('/items', Items),\n ('/dates', Dates),\n\n # ('/test', Test),\n], debug=True)\n", "sub_path": "hackathon.py", "file_name": "hackathon.py", "file_ext": "py", "file_size_in_byte": 6114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "webapp2.RequestHandler", "line_number": 8, "usage_type": "attribute"}, {"api_name": "webapp2.RequestHandler", "line_number": 13, "usage_type": "attribute"}, {"api_name": "webapp2.RequestHandler", "line_number": 19, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "events.Event.createEvent", "line_number": 25, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 25, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "users.User.registerUser", "line_number": 33, "usage_type": "call"}, {"api_name": "users.User", "line_number": 33, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 37, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "event_actions.updateDB", "line_number": 41, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 44, "usage_type": "attribute"}, {"api_name": "event_actions.updateDB", "line_number": 72, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 83, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 86, "usage_type": "call"}, {"api_name": "event_actions.getSuggestedItems", "line_number": 93, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 94, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 97, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 102, "usage_type": "call"}, {"api_name": "events.Event.getUserEvents", "line_number": 102, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 102, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 105, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 108, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 110, "usage_type": "call"}, {"api_name": "events.Event.getEvent", "line_number": 110, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 110, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 113, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 116, "usage_type": "call"}, {"api_name": "events.Event.removeEvent", "line_number": 118, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 118, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 123, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 126, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 129, "usage_type": "call"}, {"api_name": "firebaseManager.FirebaseManager.getFromFB", "line_number": 129, "usage_type": "call"}, {"api_name": "firebaseManager.FirebaseManager", "line_number": 129, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 136, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 139, "usage_type": "call"}, {"api_name": "firebaseManager.FirebaseManager.saveToFB", "line_number": 142, "usage_type": "call"}, {"api_name": "firebaseManager.FirebaseManager", "line_number": 142, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 146, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 149, "usage_type": "call"}, {"api_name": "events.Event.confirmDates", "line_number": 153, "usage_type": "call"}, {"api_name": "events.Event", "line_number": 153, "usage_type": "name"}, {"api_name": "webapp2.WSGIApplication", "line_number": 156, "usage_type": "call"}]} {"seq_id": "314106005", "text": "from pretrainedmodels.models import bninception, inceptionresnetv2\nfrom torch import nn\nfrom config import config\n\n\ndef get_net():\n if config.model_name == 'bninception_bcelog':\n return get_bninception()\n elif config.model_name == 'inceptionresnetv2':\n return get_inception_resnet_v2()\n else:\n raise ValueError('Unknown Model Name %s' % config.model_name)\n\n\ndef get_bninception():\n model = bninception(pretrained=\"imagenet\")\n model.global_pool = nn.AdaptiveAvgPool2d(1)\n model.conv1_7x7_s2 = nn.Conv2d(config.in_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n model.last_linear = nn.Sequential(\n nn.BatchNorm1d(1024),\n nn.Dropout(0.5),\n nn.Linear(1024, config.num_classes),\n )\n if config.with_mse_loss:\n model.reconstruct_layer = nn.Sequential(\n nn.BatchNorm2d(1024),\n nn.UpsamplingBilinear2d([int(config.img_height/16), int(config.img_width/16)]),\n nn.Conv2d(1024, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/8), int(config.img_width/8)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/4), int(config.img_width/4)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/2), int(config.img_width/2)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([config.img_height, config.img_width]),\n nn.Conv2d(32, config.out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.Sigmoid(),\n )\n\n return model\n\n\ndef get_inception_resnet_v2():\n class BasicConv2d(nn.Module):\n\n def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):\n super(BasicConv2d, self).__init__()\n self.conv = nn.Conv2d(in_planes, out_planes,\n kernel_size=kernel_size, stride=stride,\n padding=padding, bias=False) # verify bias false\n self.bn = nn.BatchNorm2d(out_planes,\n eps=0.001, # value found in tensorflow\n momentum=0.1, # default pytorch value\n affine=True)\n self.relu = nn.ReLU(inplace=False)\n\n def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n x = self.relu(x)\n return x\n\n model = inceptionresnetv2(pretrained=\"imagenet\")\n model.conv2d_1a = BasicConv2d(config.in_channels, 32, kernel_size=3, stride=2)\n model.avgpool_1a = nn.AdaptiveAvgPool2d(1)\n model.last_linear = nn.Sequential(\n nn.BatchNorm1d(1536),\n nn.Dropout(0.5),\n nn.Linear(1536, config.num_classes),\n )\n\n if config.with_mse_loss:\n model.reconstruct_layer = nn.Sequential(\n nn.BatchNorm2d(1536),\n nn.UpsamplingBilinear2d([int(config.img_height/16), int(config.img_width/16)]),\n nn.Conv2d(1536, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/8), int(config.img_width/8)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/4), int(config.img_width/4)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([int(config.img_height/2), int(config.img_width/2)]),\n nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.BatchNorm2d(32, affine=True),\n nn.ReLU(),\n nn.UpsamplingBilinear2d([config.img_height, config.img_width]),\n nn.Conv2d(32, config.out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n nn.Sigmoid(),\n )\n return model\n", "sub_path": "models/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4550, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "config.config.model_name", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 7, "usage_type": "name"}, {"api_name": "config.config.model_name", "line_number": 9, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 9, "usage_type": "name"}, {"api_name": "config.config.model_name", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 12, "usage_type": "name"}, {"api_name": "pretrainedmodels.models.bninception", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "config.config.in_channels", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "config.config.num_classes", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 22, "usage_type": "name"}, {"api_name": "config.config.with_mse_loss", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 27, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 27, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 31, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 31, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 35, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 39, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 43, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 43, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "config.config.out_channels", "line_number": 44, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "pretrainedmodels.models.inceptionresnetv2", "line_number": 71, "usage_type": "call"}, {"api_name": "config.config.in_channels", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "config.config.num_classes", "line_number": 77, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 77, "usage_type": "name"}, {"api_name": "config.config.with_mse_loss", "line_number": 80, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 83, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 83, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 87, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 87, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 91, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 91, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 95, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 95, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "config.config.img_height", "line_number": 99, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 99, "usage_type": "name"}, {"api_name": "config.config.img_width", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "config.config.out_channels", "line_number": 100, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}]} {"seq_id": "145911411", "text": "import pandas as pd\nimport torch\nimport numpy as np\nimport os\nfrom torch.utils.data import Dataset\n\n_FILE_PATH = \"data\\\\processed\"\n_FILE_NAME = \"70man\"\n_DAY = 96\n\nclass ASPDataset(Dataset):\n \"\"\"\n This is our custom dataset. In this project, it will load data to train from csv files.\n If databases which have all of informations for training exist, it will load data to train from the database\n\n \"\"\"\n def __init__(self, file_path = _FILE_PATH, file_name = _FILE_NAME, mode = \"train\"):\n super().__init__()\n self.file_path = file_path\n self.file_name = file_name\n self.mode = mode\n self.dataframe = pd.read_csv(os.path.join(self.file_path, \"{}_{}.csv\".format(self.file_name, self.mode)), header = 0, names = [\"Time\", \"Glucose\"])\n self.predict_length = 3\n self.sequence_length = 3\n self.dataset_length = len(self.dataframe) - (self.predict_length*_DAY + self.sequence_length*_DAY) + 1\n \n def __len__(self):\n return self.dataset_length\n\n def __getitem__(self, idx):\n # Get data for training\n data = np.array(self.dataframe[\"Glucose\"][idx : idx + (self.predict_length + self.sequence_length)*_DAY])\n\n # Split data to input and label\n input_data = torch.tensor(data[:-(self.predict_length*_DAY)], dtype = torch.long).clone()\n label = torch.tensor(data[-(self.predict_length*_DAY):], dtype = torch.float32).clone()\n return input_data, label\n\n\n", "sub_path": "Capstone_Design2/src/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 1470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 36, "usage_type": "attribute"}]} {"seq_id": "513340679", "text": "from torch.nn import functional as F\nfrom torchvision.transforms import functional as Fv\nfrom PIL import Image\nimport pandas as pd\nimport torchvision\n\nfrom matplotlib import pyplot as plt\nimport time\n\nfrom kymatio.torch import Scattering2D\ndevice = \"cuda:0\"\n\nt0 = time.time()\n\n#img = Image.open('../trump.jpg')\nimg = Image.open('../chris.jpeg')\nimg_tensor = Fv.to_tensor(img)\nx = img_tensor[None]\nx = x.cuda()\n\nB,C,W,H = x.shape\nmodel = Scattering2D(J=2, shape=(W,H), L=8)\nmodel.to(device)\nmodel.to(device)\ny = model(x)\ny = y.view(y.size(0), -1, y.size(3), y.size(4))\n\nfor i in range(243):\n name = \"out/order2/scatter/chris_scatt_{}.png\".format(i)\n #torchvision.utils.save_image(y[0,i:i+1].detach().cpu(), name)\n plt.imshow(y[0,i].detach().cpu().numpy())\n plt.savefig(name)\n plt.show()\n\ntFinnish = time.time() - t0\nprint(\"time: \", tFinnish)\n\n# =============================================================================\n# R, G, B = torch.chunk(y, 3, dim=1) \n# for i in range(9):\n# name = \"out/s_color_{}.png\".format(i) \n# \n# out = torch.stack([R[0,i], G[0,i], B[0,i]], dim=0) \n# torchvision.utils.save_image(out.detach().cpu(), name)\n# \n# plt.imshow(Fv.to_pil_image(out.detach().cpu())) \n# plt.show()\n# =============================================================================\n", "sub_path": "test_result/f6_test_similar_scatter_L2.py", "file_name": "f6_test_similar_scatter_L2.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 17, "usage_type": "name"}, {"api_name": "kymatio.torch.Scattering2D", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}]} {"seq_id": "534797563", "text": "from rest_framework.authentication import get_authorization_header\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom pitter.acc_actions.auth import TokenAuthentication\nfrom pitter.models import Pitt, Follower\nfrom pitter.models.user_model import User\nfrom pitter.decorators import request_query_serializer\n\nfrom api_client.validation_serializers.user_serializers import FindRequest\n\n\nclass FindUser(APIView):\n @classmethod\n @request_query_serializer(FindRequest)\n def get(cls, request) -> Response:\n \"\"\"\n Finds user in the DB with the login from the query\n :param request:\n :return: Response dict with the user data\n \"\"\"\n access = TokenAuthentication.get(request)\n auth_token = get_authorization_header(request).decode('utf-8')\n\n data = request.query_params\n login = data['login']\n feed_pitts = []\n follow_link = f'http://localhost:8000/follownode/?login={login}&token={auth_token}'\n unfollow_link = f'http://localhost:8000/follownode/?login={login}&unfollow=True&token={auth_token}'\n\n try:\n user = User.objects.get(login=login)\n follower = User.objects.get(email_address=access['email'])\n\n except User.DoesNotExist:\n return Response('User is not found.', status=200)\n except KeyError:\n return Response('Yiu are logged out.', status=200)\n\n for pitt in Pitt.objects.all():\n if pitt.user_id == user.id:\n pitt_info = (pitt.audio_decoded, pitt.created_at)\n feed_pitts.append(pitt_info)\n\n returned_data = dict(\n id=user.id,\n login=user.login,\n email=user.email_address,\n pitts=feed_pitts,\n )\n\n try:\n follower_exists = Follower.objects.get(user_id=user.id, follower_id=follower.id)\n if follower_exists:\n returned_data['unfollow_link'] = unfollow_link\n return Response(returned_data, status=200)\n\n except Follower.DoesNotExist:\n returned_data['follow_link'] = follow_link\n return Response(returned_data, status=200)\n", "sub_path": "src/api_client/views/finduser_view.py", "file_name": "finduser_view.py", "file_ext": "py", "file_size_in_byte": 2199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 13, "usage_type": "name"}, {"api_name": "pitter.acc_actions.auth.TokenAuthentication.get", "line_number": 22, "usage_type": "call"}, {"api_name": "pitter.acc_actions.auth.TokenAuthentication", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.authentication.get_authorization_header", "line_number": 23, "usage_type": "call"}, {"api_name": "pitter.models.user_model.User.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "pitter.models.user_model.User.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pitter.models.user_model.User", "line_number": 32, "usage_type": "name"}, {"api_name": "pitter.models.user_model.User.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "pitter.models.user_model.User.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pitter.models.user_model.User", "line_number": 33, "usage_type": "name"}, {"api_name": "pitter.models.user_model.User.DoesNotExist", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pitter.models.user_model.User", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 38, "usage_type": "call"}, {"api_name": "pitter.models.Pitt.objects.all", "line_number": 40, "usage_type": "call"}, {"api_name": "pitter.models.Pitt.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pitter.models.Pitt", "line_number": 40, "usage_type": "name"}, {"api_name": "pitter.models.Follower.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "pitter.models.Follower.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pitter.models.Follower", "line_number": 53, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 56, "usage_type": "call"}, {"api_name": "pitter.models.Follower.DoesNotExist", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pitter.models.Follower", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "pitter.decorators.request_query_serializer", "line_number": 15, "usage_type": "call"}, {"api_name": "api_client.validation_serializers.user_serializers.FindRequest", "line_number": 15, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 16, "usage_type": "name"}]} {"seq_id": "181858669", "text": "from PIL import Image\nim = Image.open(\"bnn8.PNG\")\npix = im.load()\nh,w= im.size\n\nfor i in range(h):\n for j in range(w):\n r,g,b,h= pix[i,j]\n #av=(r+g+b)/3\n pix[i,j]=(r,b,g,h)\nim.save('nabaGray4.png')\n\n", "sub_path": "website_kenta/changeColorPil.py", "file_name": "changeColorPil.py", "file_ext": "py", "file_size_in_byte": 223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PIL.Image.open", "line_number": 2, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 2, "usage_type": "name"}]} {"seq_id": "540795304", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('product', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Business',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=50)),\n ],\n ),\n migrations.CreateModel(\n name='Link',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('url_link', models.CharField(max_length=250)),\n ('business', models.ForeignKey(to='business.Business')),\n ],\n ),\n migrations.CreateModel(\n name='Sucursal',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('lat', models.FloatField()),\n ('lon', models.FloatField()),\n ('business', models.ForeignKey(to='business.Business')),\n ('products', models.ManyToManyField(to='product.Product')),\n ],\n ),\n ]\n", "sub_path": "business/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}]} {"seq_id": "488249460", "text": "import torch.nn as nn\nfrom models.functions import ReverseLayerF\nimport torch\n\n\nclass Self_Attn(nn.Module):\n \"\"\" Self attention Layer\"\"\"\n\n def __init__(self, in_dim, activation):\n super(Self_Attn, self).__init__()\n self.chanel_in = in_dim\n self.activation = activation\n\n self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n self.gamma = nn.Parameter(torch.zeros(1))\n\n self.softmax = nn.Softmax(dim=-1) #\n\n def forward(self, x):\n \"\"\"\n inputs :\n x : input feature maps( B X C X W X H)\n returns :\n out : self attention value + input feature\n attention: B X N X N (N is Width*Height)\n \"\"\"\n m_batchsize, C, width, height = x.size()\n proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) # B X CX(N)\n proj_key = self.key_conv(x).view(m_batchsize, -1, width * height) # B X C x (*W*H)\n energy = torch.bmm(proj_query, proj_key) # transpose check\n attention = self.softmax(energy) # BX (N) X (N)\n proj_value = self.value_conv(x).view(m_batchsize, -1, width * height) # B X C X N\n\n out = torch.bmm(proj_value, attention.permute(0, 2, 1))\n out = out.view(m_batchsize, C, width, height)\n\n out = self.gamma * out + x\n return out, attention\n\n\nclass CNNModel(nn.Module):\n\n def __init__(self):\n super(CNNModel, self).__init__()\n self.feature = nn.Sequential()\n self.feature.add_module('f_conv1', nn.Conv2d(3, 64, kernel_size=5))\n self.feature.add_module('f_bn1', nn.BatchNorm2d(64))\n self.feature.add_module('f_pool1', nn.MaxPool2d(2))\n self.feature.add_module('f_relu1', nn.ReLU(True))\n self.feature.add_module('f_conv2', nn.Conv2d(64, 50, kernel_size=5))\n self.feature.add_module('f_bn2', nn.BatchNorm2d(50))\n self.feature.add_module('f_drop1', nn.Dropout2d())\n self.feature.add_module('f_pool2', nn.MaxPool2d(2))\n self.feature.add_module('f_relu2', nn.ReLU(True))\n\n self.class_classifier = nn.Sequential()\n self.class_classifier.add_module('c_fc1', nn.Linear(50 * 4 * 4, 100))\n self.class_classifier.add_module('c_bn1', nn.BatchNorm1d(100))\n self.class_classifier.add_module('c_relu1', nn.ReLU(True))\n self.class_classifier.add_module('c_drop1', nn.Dropout2d())\n self.class_classifier.add_module('c_fc2', nn.Linear(100, 100))\n self.class_classifier.add_module('c_bn2', nn.BatchNorm1d(100))\n self.class_classifier.add_module('c_relu2', nn.ReLU(True))\n self.class_classifier.add_module('c_fc3', nn.Linear(100, 10))\n self.class_classifier.add_module('c_softmax', nn.LogSoftmax())\n\n self.domain_classifier = nn.Sequential()\n self.domain_classifier.add_module('d_fc1', nn.Linear(50 * 4 * 4, 100))\n self.domain_classifier.add_module('d_bn1', nn.BatchNorm1d(100))\n self.domain_classifier.add_module('d_relu1', nn.ReLU(True))\n self.domain_classifier.add_module('d_fc2', nn.Linear(100, 2))\n self.domain_classifier.add_module('d_softmax', nn.LogSoftmax(dim=1))\n\n def forward(self, input_data, alpha):\n input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)\n feature = self.feature(input_data)\n feature = feature.view(-1, 50 * 4 * 4)\n reverse_feature = ReverseLayerF.apply(feature, alpha)\n class_output = self.class_classifier(feature)\n domain_output = self.domain_classifier(reverse_feature)\n\n return class_output, domain_output\n", "sub_path": "models/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 3828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.bmm", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "models.functions.ReverseLayerF.apply", "line_number": 80, "usage_type": "call"}, {"api_name": "models.functions.ReverseLayerF", "line_number": 80, "usage_type": "name"}]} {"seq_id": "337249758", "text": "import uuid\n\nimport face_recognition\n\nimport constants as const\nimport storage\n\n\n# -- FUNCTIONS -- #\n\n\ndef add_face(io_stream):\n try:\n encodings = get_encodings(io_stream)\n face_count = len(encodings)\n\n if face_count == 1:\n encoding = encodings[0].tolist()\n identifier = str(uuid.uuid4()).encode('utf-8')\n return storage.store_encoding(identifier, encoding)\n else:\n return check_face_count(face_count)\n except Exception as e:\n print('ERROR: ' + str(e))\n return {const.KEY_ERROR: str(e)}\n\n\ndef predict(io_stream):\n try:\n encodings = get_encodings(io_stream)\n face_count = len(encodings)\n\n if face_count == 1:\n unknown_face_encoding = encodings[0]\n known_encodings = storage.load_encodings() # load encodings from file (/ db?)\n\n known_face_encodings = list(known_encodings.values())\n known_face_labels = list(known_encodings.keys())\n print('checking ' + str(len(known_face_labels)) + ' known faces') # debug\n\n results = face_recognition.compare_faces(known_face_encodings, unknown_face_encoding)\n ids = []\n for idx in range(len(results)):\n if results[idx]:\n label = known_face_labels[idx].replace(const.ENC_FILE, '')\n ids.append({const.KEY_ID: label})\n\n return ids\n else:\n return check_face_count(face_count)\n except Exception as e:\n print('ERROR: ' + str(e))\n return {const.KEY_ERROR: str(e)}\n\n\ndef remove(identifier):\n try:\n return storage.remove_encoding(identifier)\n except Exception as e:\n print('ERROR: ' + str(e))\n return {const.KEY_ERROR: str(e)}\n\n\n# -- UTILITY METHODS -- #\n\ndef get_encodings(io_stream):\n img_arr = face_recognition.load_image_file(io_stream)\n return face_recognition.face_encodings(img_arr)\n\n\ndef check_face_count(face_count):\n if face_count < 1:\n return {const.KEY_ERROR: const.MSG_FNF}\n elif face_count > 1:\n return {const.KEY_ERROR: const.MSG_TOO_MANY}\n else:\n return {}\n", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 2167, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "uuid.uuid4", "line_number": 19, "usage_type": "call"}, {"api_name": "storage.store_encoding", "line_number": 20, "usage_type": "call"}, {"api_name": "constants.KEY_ERROR", "line_number": 25, "usage_type": "attribute"}, {"api_name": "storage.load_encodings", "line_number": 35, "usage_type": "call"}, {"api_name": "face_recognition.compare_faces", "line_number": 41, "usage_type": "call"}, {"api_name": "constants.ENC_FILE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "constants.KEY_ID", "line_number": 46, "usage_type": "attribute"}, {"api_name": "constants.KEY_ERROR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "storage.remove_encoding", "line_number": 58, "usage_type": "call"}, {"api_name": "constants.KEY_ERROR", "line_number": 61, "usage_type": "attribute"}, {"api_name": "face_recognition.load_image_file", "line_number": 67, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 68, "usage_type": "call"}, {"api_name": "constants.KEY_ERROR", "line_number": 73, "usage_type": "attribute"}, {"api_name": "constants.MSG_FNF", "line_number": 73, "usage_type": "attribute"}, {"api_name": "constants.KEY_ERROR", "line_number": 75, "usage_type": "attribute"}, {"api_name": "constants.MSG_TOO_MANY", "line_number": 75, "usage_type": "attribute"}]} {"seq_id": "254800766", "text": "import openpyxl\nfrom openpyxl import load_workbook\nfrom datetime import datetime\nimport json\n\nclass ExtractExcelInsert:\n FILE_NAME = '.\\excel_config.json'\n\n #Constructor\n def __init__(self):\n #Get data from JSON config file\n self._config = self.retrieveData()\n \n #Initialize variables\n self._wb_name = ''\n self._directory = ''\n self._rows = ''\n self._columns = ''\n self._row_start = ''\n self._col_start = ''\n\n if(str(self._config) != '{}' and input('Use recent config? (Enter = yes, Value = no) ') == ''):\n self._wb_name = self._config['workbook']\n self._directory = self._config['destination']\n self._rows = self._config['rows']\n self._columns = self._config['columns']\n self._row_start = self._config['row-start']\n self._col_start = self._config['col-start']\n else:\n self.userInput()\n self.saveConfig()\n \n self._wb = load_workbook(self._wb_name, data_only=True)\n self._ws = self._wb.active\n self.exportData()\n\n #Get data to export\n def retrieveData(self):\n with open(self.FILE_NAME) as f:\n return json.load(f)\n\n def userInput(self):\n self._wb_name = input('Enter absolute path for Workbook \"C:\\\\directory name\\\\<file name>.xlsx\" ')\n self._directory = input('Enter absolute path for Export Location \"C:\\\\directory name\\\\<file name>.sql\" ')\n self._rows = int(input('Rows: '))\n self._columns = int(input('Columns: '))\n self._row_start = int(input('Enter row start '))\n self._col_start = int(input('Enter column start '))\n\n def saveConfig(self):\n new_config = {\n \"workbook\" : self._wb_name,\n \"destination\" : self._directory,\n \"rows\" : self._rows,\n \"columns\" : self._columns,\n \"row-start\" : self._row_start,\n \"col-start\" : self._col_start\n }\n\n json_obj = json.dumps(new_config, indent = 6)\n with open(self.FILE_NAME, \"w\") as f:\n f.write(json_obj)\n\n #Export data from excel to sql file\n def exportData(self):\n with open (self._directory, 'w') as f:\n for i in range(self._row_start, self._rows + self._row_start):\n f.write('INSERT INTO table_name VALUES(')\n data_str = ''\n\n for j in range(self._col_start, self._columns + self._col_start):\n \n if(self._ws.cell(row=i,column=j).value is None or self._ws.cell(row=i,column=j).value == 'NULL'):\n data_str += 'NULL,'\n\n elif (isinstance(self._ws.cell(row=i,column=j).value, int)):\n data_str += str(self._ws.cell(row=i, column=j).value).strip() + \",\"\n\n else:\n data_str += \"'\"+ str(self._ws.cell(row=i, column=j).value).strip() + \"',\"\n\n else:\n f.write(data_str[:-1] + ')\\n')\n \n print('Complete')\n\n\n#Main Method\ndef main():\n ExtractExcelInsert()\n\nif __name__=='__main__': \n main()\n", "sub_path": "extract_excel_insert.py", "file_name": "extract_excel_insert.py", "file_ext": "py", "file_size_in_byte": 3182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 33, "usage_type": "call"}, {"api_name": "json.load", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}]} {"seq_id": "564351323", "text": "import pyautogui\r\nimport cv2\r\nfrom time import sleep\r\nimport keyboard\r\nimport numpy as np\r\n\r\n\r\ndef clippaperclip():\r\n sleep(5)\r\n pyautogui.screenshot('img/screenshot.png')\r\n img_rgb = cv2.imread('img/screenshot.png')\r\n img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)\r\n template = cv2.imread('img/paperclip.png', 0)\r\n res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)\r\n threshold = 0.8\r\n loc = np.where(res >= threshold)\r\n\r\n spacepress = False\r\n for pt in zip(*loc[::-1]):\r\n count = 0\r\n print(\"Clicking...\")\r\n if spacepress:\r\n quit()\r\n while True: # making a loop\r\n try: # used try so that if user pressed other than the given key error will not be shown\r\n if keyboard.is_pressed('q'): # if key 'q' is pressed\r\n spacepress = True\r\n break # finishing the loop\r\n else:\r\n pyautogui.click(pt[0] + 20, pt[1] + 5)\r\n except:\r\n pass\r\n\r\n\r\nif __name__ == '__main__':\r\n clippaperclip()\r\n", "sub_path": "paperclipignoreme.py", "file_name": "paperclipignoreme.py", "file_ext": "py", "file_size_in_byte": 1096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.sleep", "line_number": 9, "usage_type": "call"}, {"api_name": "pyautogui.screenshot", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 16, "usage_type": "call"}, {"api_name": "keyboard.is_pressed", "line_number": 26, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 30, "usage_type": "call"}]} {"seq_id": "98559672", "text": "import pandas as pd\nimport numpy as np\n#收集資料 https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases\n\n#讀取資料\ndata = pd.read_csv(\"time_series_covid19_confirmed_global.csv\")\n\n#觀察資料\n# print(\"資料數量\", data.shape) # 幾欄、幾列\n# print(\"資料欄位\", data.columns) \n# print(data[\"Country/Region\"][240])\n\n\ncondition = data[\"Country/Region\"] == \"Taiwan*\" # 布林值\ntwindex = data[condition].index # Taiwan* 的索引值為 240\ntwdata = np.array(data.iloc[twindex])\ntwdata = twdata[0][4:] # 此為每日累積確診人數\n# column=0~3 ：Province/State, Country/Region, Lat, Long\n\ntwdata2 = twdata.copy() # 另存一份，用以紀錄每日新增確診人數\nprint(twdata)\n\nfor i in range(1,len(twdata)):\n twdata2[i] = twdata[i] - twdata[i-1] # 紀錄每日新增確診人數\nprint(twdata2)\nprint(data.columns[4:])\n\n\n\nimport matplotlib.pyplot as plt\n\nindex = np.arange(len(twdata)) # 若直接印出日期 X 軸太多文字 #index = data.columns[4:]\n\n# plt.plot(index, twdata) # 累積人數\n# plt.xlabel(\"Date\") # X 軸\n# plt.ylabel(\"person\") # Y 軸\n# plt.title(\"COVID-19 accumulation confirmed of Taiwan\") # 圖表名稱\n# plt.show() # 印出圖表\n\n# plt.plot(index, twdata2)\n# plt.xlabel(\"Date\")\n# plt.ylabel(\"person\")\n# plt.title(\"COVID-19 confirmed of Taiwan\")\n# plt.show()\n\nplt.plot(index, twdata)\nplt.plot(index, twdata2*15) # 每日確診人數的數值放大 20 倍，僅為方便觀察\nplt.xlabel(\"Date\")\nplt.ylabel(\"person\")\nplt.title(\"COVID-19 confirmed and accumulation confirmed of Taiwan\")\nplt.show()\n", "sub_path": "pandas_covid19.py", "file_name": "pandas_covid19.py", "file_ext": "py", "file_size_in_byte": 1560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} {"seq_id": "506494515", "text": "#!/usr/bin/env python\n\nimport setuptools\n\n_depends = '''\nnumpy\n'''\n\nsetuptools.setup( \\\n name='automatamm', \\\n version='git', \\\n description='automata-- wrapper for python', \\\n author='Brandon Niemczyk', \\\n author_email='brandon.niemczyk@gmail.com', \\\n url='http://github.com/bniemczyk/automata--', \\\n py_modules=['automatamm', 'smbuilder'], \\\n test_suite='tests', \\\n license='BSD', \\\n install_requires=_depends, \\\n classifiers = [ \\\n 'Development Status :: 3 - Alpha', \\\n 'Intended Audience :: Developers', \\\n 'Intended Audience :: Science/Research', \\\n 'License :: OSI Approved :: BSD License', \\\n 'Topic :: Scientific/Engineering :: Mathematics' \\\n ]\n )\n", "sub_path": "python/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}]} {"seq_id": "291666931", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: D:\\BuildAgent\\work\\test/iobjectspy/ml\\vision\\_inference.py\n# Compiled at: 2019-12-31 04:09:00\n# Size of source mod 2**32: 5871 bytes\nimport os, tempfile\nfrom iobjectspy import Dataset, conversion\nfrom iobjectspy._jsuperpy._utils import check_lic\nfrom ._inference_collector import ObjectDetection, BinaryClassification, MultiClassification, SceneClassification\nfrom toolkit._toolkit import _is_image_file, del_dir, _get_dataset_readonly\n\nclass Inference:\n\n def __init__(self, input_data, model_path, out_data, out_dataset_name):\n \"\"\"\n 图像数据模型推理功能入口\n\n :param input_data: 待推理的数据\n :type input_data: str or Dataset\n :param model_path: 模型存储路径\n :type model_path: str\n :param out_data: 输出文件(或数据源)路径\n :type out_data: str or Datasource or DatasourceConnectionInfo\n :param out_dataset_name: 输出文件(或数据集)名称\n :type out_dataset_name: str\n \"\"\"\n if _is_image_file(input_data):\n self.is_del_tmp_file = False\n else:\n self.is_del_tmp_file = True\n input_data = _get_dataset_readonly(input_data)\n temp_tif_path = os.path.join(tempfile.mkdtemp(), 'temp') + '.tif'\n conversion.export_to_tif(input_data, temp_tif_path)\n input_data = temp_tif_path\n self.input_data = input_data\n self.model_path = model_path\n self.out_data = out_data\n self.out_dataset_name = out_dataset_name\n check_lic()\n\n def object_detect_infer(self, category_name, nms_thresh=0.3, score_thresh=0.5):\n \"\"\"\n 影像数据目标检测\n\n | 支持 tif、img(Erdas Image)等影像文件，以及 jpg、png等图像文件，检测结果为GeoJSON格式文件\n | 支持SuperMap SDX下的影像数据集，检测结果为矢量线数据集\n\n 需要注意:\n - 当 input_data 为待检测文件时，out_data 为输出文件路径，out_dataset_name 为.json后缀的文件名\n - 当 input_data 为待检测数据集时，out_data 为输出数据源路径(或数据源对象)，out_dataset_name 为数据集名\n\n :param category_name: 目标检测类别，支持 'plane', 'ship', 'tennis-court', 'vehicle'\n :type category_name: list[str] or str\n :param nms_thresh: nms的阈值\n :type nms_thresh: float\n :param score_thresh: 类别分数的阈值\n :type score_thresh: float\n :return: None\n \"\"\"\n result = ObjectDetection(self.input_data, self.model_path, category_name, self.out_data, self.out_dataset_name, nms_thresh, score_thresh).infer()\n del_dir(os.path.abspath(os.path.join(self.input_data, os.path.pardir)), self.is_del_tmp_file)\n return result\n\n def binary_classify_infer(self, offset, result_type, **kwargs):\n \"\"\"\n 遥感影像数据二元分类\n 支持 tif、img(Erdas Image)等影像文件，以及 jpg、png等图像文件，分类结果为二值栅格或矢量文件\n 支持SuperMap SDX下的影像数据集，分类结果为矢量或栅格数据集\n\n 可添加关键字参数：'dsm_dataset' 输入与影像相匹配的DSM数据，实现基于DOM和DSM提取建筑物面。\n 其中影像和DSM可以使用SuperMap iDesktop 桌面基于倾斜摄影数据提取：\n 打开三维场景，使用 三维分析->生成DOM 三维分析->生成DSM，分辨率建议选择0.1m\n\n :param offset: 图像分块偏移，大幅图像需分块预测，其值为分块间重叠部分大小，以提高图像块边缘预测结果\n :type offset: int\n :param result_type: 结果返回类型，支持矢量面和栅格: 'region' or 'grid'\n :type result_type: str\n :return: 数据集名字\n \"\"\"\n result = BinaryClassification((self.input_data), (self.model_path), (self.out_data), (self.out_dataset_name), offset, \n result_type, **kwargs).infer()\n del_dir(os.path.abspath(os.path.join(self.input_data, os.path.pardir)), self.is_del_tmp_file)\n return result\n\n def scene_classify_infer(self, result_type, **kwargs):\n \"\"\"\n 遥感影像数据场景分类\n 支持 tif、img(Erdas Image)等影像文件，以及 jpg、png等图像文件，分类结果为二值栅格或矢量文件\n 支持SuperMap SDX下的影像数据集，分类结果为矢量或栅格数据集\n\n :param result_type: 结果返回类型，支持矢量面和栅格: 'region' or 'grid'\n :type result_type: str\n :return: 数据集名字\n \"\"\"\n result = SceneClassification((self.input_data), (self.model_path), (self.out_data), (self.out_dataset_name), \n result_type, **kwargs).infer()\n del_dir(os.path.abspath(os.path.join(self.input_data, os.path.pardir)), self.is_del_tmp_file)\n return result\n\n def multi_classify_infer(self, offset, result_type, **kwargs):\n \"\"\"\n 遥感影像数据多分类，地物分类\n 支持 tif、img(Erdas Image)等影像文件，以及 jpg、png等图像文件，分类结果为多值栅格或矢量文件\n 支持SuperMap SDX下的影像数据集，分类结果为矢量或栅格数据集\n\n :param offset: 图像分块偏移，大幅图像需分块预测，其值为分块间重叠部分大小，以提高图像块边缘预测结果\n :type offset: int\n :param result_type: 结果返回类型，支持矢量面和栅格: 'region' or 'grid'\n :type result_type: str\n :return: 数据集名字\n \"\"\"\n result = MultiClassification((self.input_data), (self.model_path), (self.out_data), (self.out_dataset_name), offset, \n result_type, **kwargs).infer()\n del_dir(os.path.abspath(os.path.join(self.input_data, os.path.pardir)), self.is_del_tmp_file)\n return result", "sub_path": "pycfiles/iobjectspy-10.0.1.0.tar/_inference.py", "file_name": "_inference.py", "file_ext": "py", "file_size_in_byte": 6066, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "toolkit._toolkit._is_image_file", "line_number": 29, "usage_type": "call"}, {"api_name": "toolkit._toolkit._get_dataset_readonly", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 34, "usage_type": "call"}, {"api_name": "iobjectspy.conversion.export_to_tif", "line_number": 35, "usage_type": "call"}, {"api_name": "iobjectspy.conversion", "line_number": 35, "usage_type": "name"}, {"api_name": "iobjectspy._jsuperpy._utils.check_lic", "line_number": 41, "usage_type": "call"}, {"api_name": "_inference_collector.ObjectDetection", "line_number": 62, "usage_type": "call"}, {"api_name": "toolkit._toolkit.del_dir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "_inference_collector.BinaryClassification", "line_number": 82, "usage_type": "call"}, {"api_name": "toolkit._toolkit.del_dir", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "_inference_collector.SceneClassification", "line_number": 97, "usage_type": "call"}, {"api_name": "toolkit._toolkit.del_dir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "_inference_collector.MultiClassification", "line_number": 114, "usage_type": "call"}, {"api_name": "toolkit._toolkit.del_dir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}]} {"seq_id": "488778389", "text": "#!/usr/bin/env python3\n\n\"\"\"\nAlign sequences using a given substitution matrix\n\nUsage:\n homework3 align <substitution_matrix> <sequence_pairs> [options]\n homework3 gaps\n homework3 thresholds\n homework3 compare [options]\n homework3 optimize <matrix_to_optimize>\n\n\nArguments:\n <substitution_matrix>\n Name of the substitution matrix to use for the alignments\n\n <sequence_pairs>\n Name of file containing space delimited pairs of sequences to align\n\n align\n Run alignment with specified substitution matrix and sequence pairs\n\n thresholds\n Run routine to determine the 0.7 score threshold for each gap opening/\n extension penalty combination\n\n gaps\n Run routine to determine optimal gap penalties for the BLOSUM50 matrix\n using the previously determined threshold scores\n\n compare\n Compare substitution matrices in terms of false positive rate. Also\n generate ROC curves\n\n optimize\n Run algorithm to optimize scoring matrix...\n\n <matrix_to_optimize>\n Name of the matrix to run optimization on\n\n\nOptions:\n -n --normalize\n Normalize the raw scores from the alignment by the length of the\n shorter of the two sequences\n\n -o --output <path>\n Save alignment output to a file named <path>\n\n -c --compare_optimized <matrix>\n Compare 1) default matrix, 2) optimized scoring matrix against default\n matrix alignments, and 3) optimized scoring matrix against optimized\n alignments\n\n\"\"\"\n\nif __name__ == '__main__':\n from .align import align\n from .util import determine_thresholds, determine_gap_penalties, run_alignments, compare_matrices, compare_optimized, matrix_optimization\n\n import docopt\n import re\n import collections\n import os\n import sys\n import numpy as np\n import pandas as pd\n from Bio import SeqIO\n import seaborn as sns\n import matplotlib.pyplot as plt\n\n args = docopt.docopt(__doc__)\n seq_align = align()\n\n # Set substitution matrix\n if args['align']:\n # Initialize variables and stuff\n\n substitution_matrices = {'BLOSUM50': 6,\n 'BLOSUM62': 6,\n 'BLOSUM62-Optimized': 0,\n 'MATIO': 2,\n 'MATIO-Optimized': 0,\n 'PAM100': 9,\n 'PAM100-Optimized': 0,\n 'PAM250': 9\n }\n\n seq_align.substitution_matrix = pd.read_table(open('./{}'.format(args['<substitution_matrix>'])),\n delim_whitespace=True,\n header=substitution_matrices[args['<substitution_matrix>']]\n )\n\n seq_align.substitution_matrix = seq_align.substitution_matrix.set_index(seq_align.substitution_matrix.columns.values)\n\n seq_align.substitution_matrix.to_csv('{}.csv'.format(args['<substitution_matrix>']))\n\n seq_align.working_pairs = open(args['<sequence_pairs>'])\n run_alignments(seq_align, args['--output'])\n\n if args['thresholds'] == True:\n determine_thresholds()\n\n if args['gaps'] == True:\n determine_gap_penalties()\n\n if args['compare'] == True:\n if args['--normalize']:\n normalize = True\n else:\n normalize = False\n\n if args['--compare_optimized']:\n compare_optimized(args['--compare_optimized'])\n else:\n compare_matrices(normalize)\n\n if args['optimize'] == True:\n matrix_optimization(args['<matrix_to_optimize>'])", "sub_path": "homework3/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 3753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "docopt.docopt", "line_number": 73, "usage_type": "call"}, {"api_name": "align.align", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 90, "usage_type": "call"}, {"api_name": "util.run_alignments", "line_number": 100, "usage_type": "call"}, {"api_name": "util.determine_thresholds", "line_number": 103, "usage_type": "call"}, {"api_name": "util.determine_gap_penalties", "line_number": 106, "usage_type": "call"}, {"api_name": "util.compare_optimized", "line_number": 115, "usage_type": "call"}, {"api_name": "util.compare_matrices", "line_number": 117, "usage_type": "call"}, {"api_name": "util.matrix_optimization", "line_number": 120, "usage_type": "call"}]} {"seq_id": "410356304", "text": "'''\nThis set of tests performs tests on all parameterized endpoints generically, any\nmore specific tests should be broken out into their own files.\n'''\n\nimport random\nimport pytest\n\nfrom model_mommy import mommy\nfrom rest_framework import status\n\nfrom django.apps import apps\n\nfrom talentmap_api.user_profile.tests.mommy_recipes import owned_saved_search\nfrom talentmap_api.messaging.tests.mommy_recipes import owned_notification\n\nparameterized_fields = \"endpoint, model, recipe, retrievable\"\nparameterized_data = [\n # Permission Endpoints\n ('/api/v1/permission/group/', 'auth.Group', None, True),\n\n # Saved Searches\n ('/api/v1/searches/', 'user_profile.SavedSearch', owned_saved_search, True),\n\n # Messaging Endpoints\n ('/api/v1/notification/', 'messaging.Notification', owned_notification, True),\n\n # Glossary\n ('/api/v1/glossary/', 'glossary.GlossaryEntry', None, True),\n]\n\n\n@pytest.mark.django_db(transaction=True)\n@pytest.mark.parametrize(parameterized_fields, parameterized_data)\ndef test_endpoints_list(authorized_client, authorized_user, endpoint, model, recipe, retrievable):\n number = random.randint(5, 10)\n # Create a random amount of objects from the recipe, if it is given\n if recipe:\n if callable(recipe):\n for i in range(0, number):\n recipe()\n else:\n mommy.make_recipe(recipe, _quantity=number)\n elif model:\n mommy.make(model, _quantity=number)\n\n response = authorized_client.get(endpoint)\n\n assert response.status_code == status.HTTP_200_OK\n assert len(response.data[\"results\"]) == apps.get_model(model).objects.count()\n assert len(response.data[\"results\"]) == number\n\n\n@pytest.mark.django_db(transaction=True)\n@pytest.mark.parametrize(parameterized_fields, parameterized_data)\ndef test_endpoints_retrieve(authorized_client, authorized_user, endpoint, model, recipe, retrievable):\n # Skip any endpoints that don't support \"retrieve\" actions\n if not retrievable:\n return\n\n number = random.randint(5, 10)\n # Create a random amount of objects from the recipe, if it is given\n if recipe:\n if callable(recipe):\n for i in range(0, number):\n recipe()\n else:\n mommy.make_recipe(recipe, _quantity=number)\n elif model:\n mommy.make(model, _quantity=number)\n\n # Check that each item is retrievable\n for id in apps.get_model(model).objects.values_list('id', flat=True):\n response = authorized_client.get(f\"{endpoint}{id}/\")\n assert response.status_code == status.HTTP_200_OK\n", "sub_path": "talentmap_api/common/tests/test_endpoints.py", "file_name": "test_endpoints.py", "file_ext": "py", "file_size_in_byte": 2587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "talentmap_api.user_profile.tests.mommy_recipes.owned_saved_search", "line_number": 23, "usage_type": "name"}, {"api_name": "talentmap_api.messaging.tests.mommy_recipes.owned_notification", "line_number": 26, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "model_mommy.mommy.make_recipe", "line_number": 43, "usage_type": "call"}, {"api_name": "model_mommy.mommy", "line_number": 43, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 45, "usage_type": "call"}, {"api_name": "model_mommy.mommy", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 49, "usage_type": "name"}, {"api_name": "django.apps.apps.get_model", "line_number": 50, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 50, "usage_type": "name"}, {"api_name": "pytest.mark.django_db", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 34, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 61, "usage_type": "call"}, {"api_name": "model_mommy.mommy.make_recipe", "line_number": 68, "usage_type": "call"}, {"api_name": "model_mommy.mommy", "line_number": 68, "usage_type": "name"}, {"api_name": "model_mommy.mommy.make", "line_number": 70, "usage_type": "call"}, {"api_name": "model_mommy.mommy", "line_number": 70, "usage_type": "name"}, {"api_name": "django.apps.apps.get_model", "line_number": 73, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}, {"api_name": "pytest.mark.django_db", "line_number": 54, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 55, "usage_type": "attribute"}]} {"seq_id": "5360228", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jun 27 12:14:44 2015\r\n\r\n@author: gl391\r\n\"\"\"\r\n# %%\r\nimport comm.ScpiInstrumentWrapper as Wrap\r\nimport numpy as np\r\nimport time\r\nfrom wanglib.wanglib import pylab_extensions\r\n# import matplotlib\r\n\r\n# %%\r\n# Define variables: frequency list\r\nf_upper = 100 # MHz\r\nf_lower = 60 # MHz\r\nn_steps = 400\r\nf_list = np.linspace(f_lower, f_upper, num=n_steps, retstep=True)\r\n# Define variables: FM\r\nf_mod = 1E3 # Hz\r\nf_range = 5E3 # Hz\r\namp_a = 0.5 # V\r\nprint(\"step width: \", f_list[1])\r\n# %%\r\n# define instrument handles\r\nsig = Wrap.ScpiInstrumentWrapper('PROLOGIX::COM3::GPIB::13')\r\namp = Wrap.ScpiInstrumentWrapper('PROLOGIX::COM3::GPIB::8')\r\n\r\n# %%\r\n# test/print identity\r\nprint(sig.query('*IDN?'))\r\nprint(amp.query('*IDN?'))\r\n\r\n# %%\r\n# reset signal generator and amplifier\r\nsig.write('*RST')\r\ntime.sleep(0.1)\r\nsig.write('*CLS')\r\ntime.sleep(0.1)\r\namp.write('*RST')\r\ntime.sleep(0.1)\r\n\r\n# %%\r\n# set lock in amp\r\namp.write('SLVL %f' % amp_a)\r\ntime.sleep(0.1)\r\namp.write('FREQ %f' % f_mod)\r\ntime.sleep(0.1)\r\n\r\n# %%\r\n# set signal generator\r\nsig.write('FM1:STAT ON')\r\ntime.sleep(0.1)\r\nsig.write('FM1:SOUR EXT1')\r\ntime.sleep(0.1)\r\nsig.write('FM1:DEV %f HZ' % f_range)\r\ntime.sleep(0.1)\r\nsig.write('POW:AMPL 10dBm')\r\ntime.sleep(0.1)\r\nsig.write('FREQ %d MHz' % f_lower)\r\ntime.sleep(0.1)\r\nsig.write('OUTP:STAT ON')\r\ntime.sleep(0.1)\r\n\r\n\r\n# %%\r\ndef scan_fq(fqs):\r\n for fq in fqs:\r\n sig.write('FREQ %d MHz' % fq)\r\n time.sleep(0.3)\r\n val = amp.query('OUTP?1')\r\n yield fq, val\r\n\r\nfqs = np.arange(f_lower, f_upper, f_list[1])\r\nfig1 = pylab_extensions.plotgen(scan_fq(fqs))\r\n", "sub_path": "control/control_script.py", "file_name": "control_script.py", "file_ext": "py", "file_size_in_byte": 1629, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.linspace", "line_number": 19, "usage_type": "call"}, {"api_name": "comm.ScpiInstrumentWrapper.ScpiInstrumentWrapper", "line_number": 27, "usage_type": "call"}, {"api_name": "comm.ScpiInstrumentWrapper", "line_number": 27, "usage_type": "name"}, {"api_name": "comm.ScpiInstrumentWrapper.ScpiInstrumentWrapper", "line_number": 28, "usage_type": "call"}, {"api_name": "comm.ScpiInstrumentWrapper", "line_number": 28, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 75, "usage_type": "call"}, {"api_name": "wanglib.wanglib.pylab_extensions.plotgen", "line_number": 76, "usage_type": "call"}, {"api_name": "wanglib.wanglib.pylab_extensions", "line_number": 76, "usage_type": "name"}]} {"seq_id": "244202128", "text": "from affine import Affine\nfrom atbash import Atbash\nfrom keywords import Keyword\n\n\ndef run():\n \"\"\"\n Command line interface for encrypting / decrypting messages.\n :return: None\n \"\"\"\n # dictionary containing implemented ciphers\n cipher_dict = {'affine': Affine, 'atbash': Atbash, 'keyword': Keyword}\n while True:\n print('This is the Secret Messages project for the '\n 'Treehouse Techdegree \\n')\n print('The following ciphers are available:\\n')\n print('1. Affine \\n')\n print('2. Atbash \\n')\n print('3. Keyword \\n')\n print('Enter quit or q to end.')\n print()\n user_input = input(\"Which cipher would you like to use? \").lower()\n print()\n if user_input == 'q' or user_input == 'quit':\n print('Have a wonderful day!')\n break\n if user_input in cipher_dict:\n message = input(\"What's the message? \")\n method = input('Do you want to encrypt or decrypt? ')\n while True:\n if method == 'encrypt':\n cipher = cipher_dict[user_input]()\n print('Your message has been encrypted:\\n')\n print(cipher.encrypt(message))\n break\n elif method == 'decrypt':\n cipher = cipher_dict[user_input]()\n print('Your message has been decrypted:\\n')\n print(f'*'*4, cipher.decrypt(message), '*'*4)\n break\n else:\n print('[!] - Incorrect entry you can only encrypt '\n 'or decrypt')\n method = input('Please try again would you like to encrypt '\n 'or decrypt')\n\n print()\n another = input('Would you like to Encrypt/Decrypt'\n ' another Y/N: ').lower()\n if another == 'y' or another == 'yes':\n continue\n else:\n break\n else:\n print('[!] - You can only select from the ciphers listed')\n\n\nif __name__ == \"__main__\":\n run()\n", "sub_path": "secret_message.py", "file_name": "secret_message.py", "file_ext": "py", "file_size_in_byte": 2157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "affine.Affine", "line_number": 12, "usage_type": "name"}, {"api_name": "atbash.Atbash", "line_number": 12, "usage_type": "name"}, {"api_name": "keywords.Keyword", "line_number": 12, "usage_type": "name"}]} {"seq_id": "450336030", "text": "import math\nimport os\nimport pyparsing\nfrom termcolor import cprint, colored\n\n\ndef non_ansi_string(s):\n \"\"\" Take a string and return it stripped from ANSI characters.\n \"\"\"\n\n esc = pyparsing.Literal('\\x1b')\n integer = pyparsing.Word(pyparsing.nums)\n escape_seq = pyparsing.Combine(esc + '[' + pyparsing.Optional(pyparsing.delimitedList(integer, ';')) +\n pyparsing.oneOf(list(pyparsing.alphas)))\n return pyparsing.Suppress(escape_seq).transformString(s)\n\n\ndef puzzle_to_str(puzzle, puzzle_size, highlight=()):\n \"\"\" Convert a puzzle to a list of list of str.\n\n The if a tile's coordinates are in the `highlight` parameter, the tile will be colored.\n :param puzzle: (list (int)) Puzzle to convert\n :param puzzle_size: (int) Size of the puzzle's side\n :param highlight: (list (int, int)) Coordinates of the tile to color\n :return: (list (list (str))) Converted puzzle\n \"\"\"\n\n str_puzzle = []\n puzzle = list(map(str, puzzle))\n puzzle = [puzzle[x:x + puzzle_size] for x in range(0, len(puzzle), puzzle_size)]\n for y, x in highlight:\n puzzle[y][x] = colored(puzzle[y][x], \"green\", attrs=[\"bold\"])\n str_puzzle.append(\"┌{}──┐\".format(\"──┬\" * (puzzle_size - 1)))\n for index, line in enumerate(puzzle):\n if index != 0:\n str_puzzle.append(\"├{}──┤\".format(\"──┼\" * (puzzle_size - 1)))\n str_puzzle.append(\"│{}│\".format('│'.join(\" \" * (2 - len(non_ansi_string(tile)))\n + tile if tile != \"0\" else \" \" for tile in line)))\n str_puzzle.append(\"└{}──┘\".format(\"──┴\" * (puzzle_size - 1)))\n return str_puzzle\n\n\ndef print_puzzle(puzzle, puzzle_size):\n for line in puzzle_to_str(puzzle, puzzle_size):\n print(line)\n\n\ndef print_puzzle_line(line, puzzle_size):\n \"\"\" Take a list of puzzles and print them horizontally rather than vertically\n\n :param line: (list (puzzle_to_str) List of puzzles to print\n :param puzzle_size: (int) Size of the puzzle's side\n :return None\n \"\"\"\n if not line:\n return\n lines = {}\n for B in line:\n for i, l in enumerate(B):\n if i in lines:\n lines[i].append(l)\n else:\n lines[i] = [l]\n for x in range(puzzle_size * 2 + 1):\n if x == puzzle_size:\n print(' → '.join(lines[x]))\n else:\n print(' '.join(lines[x]))\n print()\n\n\ndef print_steps(solution, puzzle_size):\n try:\n rows, columns = map(int, os.popen('stty size', 'r').read().split())\n nbr_puzzle = columns // (puzzle_size * 4 + 1) - 1\n except ValueError:\n nbr_puzzle = 3\n line = []\n last = None\n highlight = []\n for index, puzzle in enumerate(solution):\n if last is not None:\n for i in range(puzzle_size * puzzle_size):\n if puzzle.puzzle[i] != last.puzzle[i] and puzzle.puzzle[i] != 0:\n highlight = [puzzle.get_coord(puzzle.puzzle[i])]\n break\n str_puzzle = puzzle_to_str(puzzle.puzzle, puzzle_size, highlight=highlight)\n line.append(str_puzzle)\n if index != 0 and index % nbr_puzzle == 0 or index == len(solution) - 1:\n print_puzzle_line(line, puzzle_size)\n line = []\n last = puzzle\n\n\ndef print_solution(puzzle, solution, complexity_in_time, complexity_in_size, verbosity=False):\n cprint(\"Solution found!\", \"white\", attrs=[\"bold\"])\n puzzle_size = int(math.sqrt(len(puzzle)))\n if verbosity:\n print_steps(solution, puzzle_size)\n print(\"- Steps: {}\".format(len(solution)))\n print(\"- Complexity in time: {}\".format(complexity_in_time))\n print(\"- Complexity in size: {}\".format(complexity_in_size))\n", "sub_path": "src/print_solution.py", "file_name": "print_solution.py", "file_ext": "py", "file_size_in_byte": 3812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pyparsing.Literal", "line_number": 11, "usage_type": "call"}, {"api_name": "pyparsing.Word", "line_number": 12, "usage_type": "call"}, {"api_name": "pyparsing.nums", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyparsing.Combine", "line_number": 13, "usage_type": "call"}, {"api_name": "pyparsing.Optional", "line_number": 13, "usage_type": "call"}, {"api_name": "pyparsing.delimitedList", "line_number": 13, "usage_type": "call"}, {"api_name": "pyparsing.oneOf", "line_number": 14, "usage_type": "call"}, {"api_name": "pyparsing.alphas", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyparsing.Suppress", "line_number": 15, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 32, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 74, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 96, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 97, "usage_type": "call"}]} {"seq_id": "473845181", "text": "# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\nfrom datetime import timedelta\nimport json\n\nclass DependingTasks(models.Model):\n _name = \"project.depending.tasks\"\n _description = \"The many2many table that has extra info (relation_type)\"\n\n task_id = fields.Many2one('project.task')\n project_id = fields.Many2one(related='task_id.project_id')\n depending_task_id = fields.Many2one('project.task')\n relation_type = fields.Selection([\n (\"0\", \"Finish to Start\"), \n (\"1\", \"Start to Start\"), \n (\"2\", \"Finish to Finish\"), \n (\"3\", \"Start to Finish\")\n ], default=\"0\", required=True)\n state = fields.Selection([('draft', 'Draft'), ('confirm', 'Confirm'), ('done', 'Done')], default='draft')\n\n\nclass Task(models.Model):\n _inherit = \"project.task\"\n\n planned_duration = fields.Integer(\"Planned Duration (in Days)\")\n date_start = fields.Datetime('Start Date')\n date_end = fields.Datetime('End Date')\n progress = fields.Float('Progress')\n is_open = fields.Boolean('Open')\n depending_task_ids = fields.One2many('project.depending.tasks', 'task_id')\n dependency_task_ids = fields.One2many('project.depending.tasks', 'depending_task_id')\n links_serialized_json = fields.Char('Serialized Links JSON', compute=\"compute_links_json\")\n\n @api.multi\n def compute_links_json(self):\n for r in self:\n links = []\n r.links_serialized_json = '['\n for link in r.dependency_task_ids:\n json_obj = {\n 'id': link.id,\n 'source': link.task_id.id,\n 'target': link.depending_task_id.id,\n 'type': link.relation_type\n }\n links.append(json_obj)\n r.links_serialized_json = json.dumps(links)\n\n def duration_between_dates(self, date_from, date_to):\n return (date_to - date_from).days\n\n def add_days(self, target_date, days):\n return target_date + timedelta(days=days)\n\n @api.multi\n def compute_critical_path(self):\n # evidently the critical path is the longest path on the network graph\n project = self.project_id\n tasks = project.task_ids.sorted('date_start')\n critical_path = []\n # last_end_date = False\n current_task = tasks and tasks[0] or False\n while current_task:\n critical_path.append(current_task)\n print(current_task.depending_task_ids)\n depending_tasks = current_task.depending_task_ids.mapped('depending_task_id')\n sorted_by_duration = depending_tasks.sorted('planned_duration', True)\n current_task = sorted_by_duration and sorted_by_duration[0] or False\n print('critical_path')\n txt = ''\n for path in critical_path:\n txt += str(path.date_start) + ' >> '\n print(txt)\n", "sub_path": "dhx_gantt/models/task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 2871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "odoo.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 7, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 11, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 13, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 14, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.models.Model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 23, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 26, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 29, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.fields.One2many", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.fields.One2many", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 33, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 35, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 54, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 56, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 56, "usage_type": "name"}]} {"seq_id": "197097622", "text": "\"\"\" The basic field attributes. \"\"\"\n\nfrom pymongo.dbref import DBRef\n\nclass EmptyRequiredField(Exception):\n \"\"\" Raised when a required field is not set on the model instance. \"\"\"\n pass\n\nclass Field(object):\n \"\"\"\n This class may eventually do type-checking, default values,\n etc. but for right now it's for subclassing and glorified\n documentation.\n \"\"\"\n\n default = None\n value_type = None\n\n def __init__(self, value_type=None, **kwargs):\n self.value_type = value_type\n self.required = kwargs.get(\"required\", False) is True\n self.default = kwargs.get(\"default\", None)\n self._set_callback = kwargs.get(\"set_callback\")\n self._get_callback = kwargs.get(\"get_callback\")\n self.id = id(self)\n\n def __get__(self, instance, klass=None):\n if instance is None:\n # Classes see the descriptor itself\n return self\n value = self._get_value(instance)\n return value\n\n def _get_field_name(self, instance):\n \"\"\" Try to retrieve field name from instance \"\"\"\n fields = getattr(instance, \"_fields\")\n return fields[self.id]\n\n def _get_value(self, instance):\n \"\"\" Retrieve the value from the instance \"\"\"\n field_name = self._get_field_name(instance)\n if not instance.has_key(field_name):\n if self.required:\n raise EmptyRequiredField(\"'%s' is required but is empty.\"\n % field_name)\n else:\n instance[field_name] = self._get_default()\n value = instance[field_name]\n if self._get_callback:\n value = self._get_callback(value)\n return value\n\n def _get_default(self):\n \"\"\" Retrieve the default value and return it \"\"\"\n if callable(self.default):\n return self.default()\n else:\n return self.default\n\n def _check_value_type(self, value):\n \"\"\" Verifies that a value is the proper type \"\"\"\n if value is not None and self.value_type is not None:\n valid = isinstance(value, self.value_type)\n if not valid:\n return False\n return True\n\n def __set__(self, instance, value):\n value_type = type(value)\n if not self._check_value_type(value):\n try:\n value = self.value_type(value)\n except:\n raise TypeError(\"Invalid type %s instead of %s\" %\n (value_type, self.value_type)\n )\n if self._set_callback:\n value = self._set_callback(value)\n field_name = self._get_field_name(instance)\n instance[field_name] = value\n\n\nclass ReferenceField(Field):\n \"\"\" Simply holds information about the reference model. \"\"\"\n\n def __init__(self, model, **kwargs):\n kwargs.setdefault(\"set_callback\", self._set_callback)\n kwargs.setdefault(\"get_callback\", self._get_callback)\n super(ReferenceField, self).__init__(model, **kwargs)\n self.model = model\n\n def _set_callback(self, value):\n \"\"\" Resolves a Model to a DBRef \"\"\"\n if value:\n value = DBRef(self.model._get_name(), value.id)\n return value\n\n def _get_callback(self, value):\n \"\"\" Retrieves the id, then retrieves the model from the db \"\"\"\n if value:\n # Should be a DBRef\n return self.model.find_one({\"_id\": value.id})\n return value\n", "sub_path": "mogo/field.py", "file_name": "field.py", "file_ext": "py", "file_size_in_byte": 3463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pymongo.dbref.DBRef", "line_number": 95, "usage_type": "call"}]} {"seq_id": "489855470", "text": "from app import db\nfrom .base import Base\nfrom sqlalchemy.orm import relationship, backref\nfrom .base import Association, Branch\nfrom datetime import datetime, date\n\n\nclass Exam(Base):\n __tablename__ = \"exam\"\n\n name = db.Column(db.String(100), nullable=False, unique=True)\n start_date = db.Column(db.Date(), nullable=False, default=datetime.utcnow)\n end_date = db.Column(db.Date(), nullable=True)\n state = db.Column(db.String(100), nullable=True)\n branch_id = db.Column(db.Integer, db.ForeignKey(\"branch.id\"), nullable=True)\n\n tests = relationship(\n \"Test\", back_populates=\"exam\", cascade=\"all, delete, delete-orphan\"\n )\n\n students = relationship(\n \"StudentTestAssociation\", cascade=\"all, delete, delete-orphan\"\n )\n\n def __init__(self, name, branch_id, start_date=None, end_date=None, state=None):\n self.name = name\n if start_date:\n self.start_date = datetime.strptime(start_date, \"%d/%m/%Y\")\n if end_date:\n self.end_date = datetime.strptime(end_date, \"%d/%m/%Y\")\n if state:\n self.state = state\n\n branch_id = int(branch_id)\n branch = Branch.query.get(branch_id)\n if not branch:\n raise ValueError('No Branch with id \"%s\" found' % branch_id)\n self.branch_id = branch.id\n\n def serialize(self):\n return dict(\n id=self.id,\n name=self.name,\n start_date=self.start_date,\n end_date=self.end_date,\n state=self.state,\n tests=[test.serialize() for test in self.tests],\n students=[a.student_id for a in self.students],\n )\n\n\nclass Test(Base):\n __tablename__ = \"test\"\n\n name = db.Column(db.String(100), nullable=False)\n exam_id = db.Column(db.Integer, db.ForeignKey(\"exam.id\"))\n state = db.Column(db.String(100), nullable=True)\n date = db.Column(db.Date(), nullable=False)\n max_marks = db.Column(db.Integer, nullable=False)\n cat_sub_id = db.Column(db.Integer, db.ForeignKey(\"association.id\"))\n evaluator_id = db.Column(db.Integer(), db.ForeignKey(\"faculty.id\"))\n\n cat_sub_association = relationship(\"Association\")\n exam = relationship(\"Exam\", back_populates=\"tests\")\n evaluator = relationship(\"Faculty\")\n\n students = relationship(\n \"StudentTestAssociation\", cascade=\"all, delete, delete-orphan\"\n )\n\n __table_args__ = (\n db.UniqueConstraint(\"name\", \"exam_id\", name=\"testcode_in_exam_uc\"),\n )\n\n def __init__(self, name, max_marks, exam_id, cat_sub_id, test_date, state=None):\n self.name = name\n self.exam_id = int(exam_id)\n self.max_marks = float(max_marks)\n cat_sub_association = Association.query.filter_by(id=int(cat_sub_id)).first()\n if cat_sub_association:\n self.cat_sub_association = cat_sub_association\n if isinstance(test_date, str):\n test_date = datetime.strptime(test_date, \"%d/%m/%Y\")\n if isinstance(test_date, datetime) or isinstance(test_date, date):\n self.date = test_date\n if state:\n self.state = state\n\n def serialize(self):\n return dict(\n id=self.id,\n name=self.name,\n max_marks=self.max_marks,\n exam_id=self.exam_id,\n date=self.date.strftime(\"%d/%m/%Y\"),\n state=self.state,\n subject=self.cat_sub_association.subject.id,\n category=self.cat_sub_association.category.id,\n evaluator=self.evaluator_id,\n students=[std.student_id for std in self.students],\n )\n\n\nclass StudentTestAssociation(Base):\n __tablename__ = \"std_test_association\"\n\n student_id = db.Column(db.Integer, db.ForeignKey(\"student.id\"), nullable=False)\n test_id = db.Column(db.Integer, db.ForeignKey(\"test.id\"), nullable=False)\n exam_id = db.Column(db.Integer, db.ForeignKey(\"exam.id\"), nullable=False)\n\n test = relationship(\"Test\")\n student = relationship(\"Student\")\n exam = relationship(\"Exam\")\n\n __table_args__ = (\n db.UniqueConstraint(\"test_id\", \"student_id\", \"exam_id\", name=\"std_test_uc\"),\n )\n\n\nclass Grade(Base):\n\n lower = db.Column(db.Integer, nullable=False)\n upper = db.Column(db.Integer, nullable=False)\n grade = db.Column(db.String(5), nullable=False)\n comment = db.Column(db.String(50), nullable=True)\n\n branch_id = db.Column(db.Integer, db.ForeignKey(\"branch.id\"), nullable=False)\n\n __table_args__ = (\n db.UniqueConstraint(\"lower\", \"upper\", name=\"lower_upper_uc\"),\n db.UniqueConstraint(\"branch_id\", \"grade\", name=\"branch_grade_uc\"),\n )\n\n def __init__(self, lower, upper, grade, branch_id, comment=None):\n if lower is not None:\n self.lower = int(lower)\n if upper is not None:\n self.upper = int(upper)\n if grade and isinstance(grade, str):\n self.grade = grade\n if comment and isinstance(comment, str):\n self.comment = comment\n\n branch_id = int(branch_id)\n branch = Branch.query.get(branch_id)\n if not branch:\n raise ValueError('No Branch with id \"%s\" found' % branch_id)\n self.branch_id = branch.id\n\n def serialize(self):\n return dict(\n id=self.id,\n min=self.lower,\n max=self.upper,\n grade=self.grade,\n branch_id=self.branch_id,\n comment=self.comment,\n )\n\n\nclass Marks(Base):\n\n marks = db.Column(db.Float, nullable=False)\n comments = db.Column(db.String(50), nullable=True)\n\n test_id = db.Column(db.Integer, db.ForeignKey(\"test.id\"), nullable=False)\n student_id = db.Column(db.Integer, db.ForeignKey(\"student.id\"), nullable=False)\n\n __table_args__ = (\n db.UniqueConstraint(\"test_id\", \"student_id\", name=\"student_test_uc\"),\n )\n\n def __init__(self, test_id, student_id, marks, comments=None):\n self.test_id = int(test_id)\n self.student_id = int(student_id)\n self.marks = float(marks)\n if comments:\n self.comments = str(comments)\n\n def serialize(self):\n return dict(\n id=self.id,\n test_id=self.test_id,\n student_id=self.student_id,\n marks=self.marks,\n comments=self.comments,\n )\n", "sub_path": "app/models/exam.py", "file_name": "exam.py", "file_ext": "py", "file_size_in_byte": 6286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "base.Base", "line_number": 8, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "app.db", "line_number": 11, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 11, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "app.db", "line_number": 12, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "app.db", "line_number": 13, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 13, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db", "line_number": 14, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "app.db", "line_number": 15, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "base.Branch.query.get", "line_number": 35, "usage_type": "call"}, {"api_name": "base.Branch.query", "line_number": 35, "usage_type": "attribute"}, {"api_name": "base.Branch", "line_number": 35, "usage_type": "name"}, {"api_name": "base.Base", "line_number": 52, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "app.db", "line_number": 55, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 55, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db", "line_number": 56, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "app.db", "line_number": 57, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 58, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 58, "usage_type": "call"}, {"api_name": "app.db", "line_number": 58, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 58, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "app.db", "line_number": 59, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 59, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "app.db", "line_number": 60, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "app.db", "line_number": 61, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 61, "usage_type": "call"}, {"api_name": "app.db.ForeignKey", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 67, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 72, "usage_type": "call"}, {"api_name": "app.db", "line_number": 72, "usage_type": "name"}, {"api_name": "base.Association.query.filter_by", "line_number": 79, "usage_type": "call"}, {"api_name": "base.Association.query", "line_number": 79, "usage_type": "attribute"}, {"api_name": "base.Association", "line_number": 79, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 84, "usage_type": "argument"}, {"api_name": "base.Base", "line_number": 104, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 107, "usage_type": "call"}, {"api_name": "app.db", "line_number": 107, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 107, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 107, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 108, "usage_type": "call"}, {"api_name": "app.db", "line_number": 108, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 108, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 108, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 109, "usage_type": "call"}, {"api_name": "app.db", "line_number": 109, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 109, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 109, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 111, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 112, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 113, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 116, "usage_type": "call"}, {"api_name": "app.db", "line_number": 116, "usage_type": "name"}, {"api_name": "base.Base", "line_number": 120, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 122, "usage_type": "call"}, {"api_name": "app.db", "line_number": 122, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 122, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 123, "usage_type": "call"}, {"api_name": "app.db", "line_number": 123, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 123, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 124, "usage_type": "call"}, {"api_name": "app.db", "line_number": 124, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 124, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 125, "usage_type": "call"}, {"api_name": "app.db", "line_number": 125, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 125, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 127, "usage_type": "call"}, {"api_name": "app.db", "line_number": 127, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 127, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 127, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 130, "usage_type": "call"}, {"api_name": "app.db", "line_number": 130, "usage_type": "name"}, {"api_name": "app.db.UniqueConstraint", "line_number": 131, "usage_type": "call"}, {"api_name": "app.db", "line_number": 131, "usage_type": "name"}, {"api_name": "base.Branch.query.get", "line_number": 145, "usage_type": "call"}, {"api_name": "base.Branch.query", "line_number": 145, "usage_type": "attribute"}, {"api_name": "base.Branch", "line_number": 145, "usage_type": "name"}, {"api_name": "base.Base", "line_number": 161, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 163, "usage_type": "call"}, {"api_name": "app.db", "line_number": 163, "usage_type": "name"}, {"api_name": "app.db.Float", "line_number": 163, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 164, "usage_type": "call"}, {"api_name": "app.db", "line_number": 164, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 164, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 166, "usage_type": "call"}, {"api_name": "app.db", "line_number": 166, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 166, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 166, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 167, "usage_type": "call"}, {"api_name": "app.db", "line_number": 167, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 167, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 167, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 170, "usage_type": "call"}, {"api_name": "app.db", "line_number": 170, "usage_type": "name"}]} {"seq_id": "1910238", "text": "import logging\nimport re\n\nfrom bs4 import BeautifulSoup\nfrom decimal import Decimal\n\nfrom storescraper.categories import CPU_COOLER\nfrom storescraper.product import Product\nfrom storescraper.store import Store\nfrom storescraper.utils import session_with_proxy, remove_words, \\\n html_to_markdown\n\n\nclass Zegucom(Store):\n @classmethod\n def categories(cls):\n return [\n # 'ExternalStorageDrive',\n 'StorageDrive',\n 'SolidStateDrive',\n 'Motherboard',\n 'Processor',\n CPU_COOLER,\n 'Ram',\n 'VideoCard',\n 'PowerSupply',\n 'ComputerCase',\n # 'Ups',\n 'Mouse',\n 'Keyboard',\n 'KeyboardMouseCombo',\n 'Monitor',\n # 'Headphones',\n 'Tablet',\n 'Notebook',\n # 'StereoSystem',\n # 'OpticalDiskPlayer',\n # 'Printer',\n # 'MemoryCard',\n 'Cell',\n # 'UsbFlashDrive',\n 'Television',\n # 'Camera',\n # 'Projector',\n # 'AllInOne',\n ]\n\n @classmethod\n def discover_urls_for_category(cls, category, extra_args=None):\n url_extensions = [\n ['sub=SAT', 'StorageDrive'],\n ['sub=DNS', 'StorageDrive'],\n ['sub=SSD', 'SolidStateDrive'],\n ['fam=TM', 'Motherboard'],\n ['fam=PR', 'Processor'],\n ['sub=DIS', CPU_COOLER],\n ['sub=ENF', CPU_COOLER],\n ['sub=DR3', 'Ram'],\n ['sub=DR4', 'Ram'],\n ['sub=LD3', 'Ram'],\n ['sub=LD4', 'Ram'],\n # ['sub=PPC', 'VideoCard'],\n ['sub=PCI', 'VideoCard'],\n ['sub=FP', 'PowerSupply'],\n ['sub=GC', 'ComputerCase'],\n ['sub=OP', 'Mouse'],\n ['sub=TE', 'Keyboard'],\n ['sub=TI', 'Keyboard'],\n ['sub=TCO', 'KeyboardMouseCombo'],\n ['sub=TCI', 'KeyboardMouseCombo'],\n ['sub=LED', 'Monitor'],\n ['sub=TAB', 'Tablet'],\n ['sub=NT', 'Notebook'],\n ['sub=IY', 'Printer'],\n ['sub=LS', 'Printer'],\n ['sub=MFI', 'Printer'],\n ['sub=MFL', 'Printer'],\n ['sub=SMP', 'Cell'],\n ['sub=TLED', 'Television'],\n ['sub=AIO', 'AllInOne'],\n ]\n\n base_url = 'https://www.zegucom.com.mx/index.php?' \\\n 'mod=search&{}&sp={}'\n\n product_urls = []\n session = session_with_proxy(extra_args)\n\n for url_extension, local_category in url_extensions:\n if local_category != category:\n continue\n\n page = 0\n\n while True:\n url = base_url.format(url_extension, page)\n print(url)\n\n if page >= 20:\n raise Exception('Page overflow: ' + url)\n\n soup = BeautifulSoup(session.get(url).text, 'html.parser')\n product_container = soup.find('div', 'search-results')\n if not product_container or not \\\n product_container.findAll('div', 'result-description'):\n if page == 0:\n logging.warning('Empty category: ' + url_extension)\n break\n products = product_container.findAll(\n 'div', 'result-description')\n\n for product in products:\n product_urls.append('https://www.zegucom.com.mx{}'.format(\n product.find('a')['href']))\n\n page += 1\n\n return product_urls\n\n @classmethod\n def products_for_url(cls, url, category=None, extra_args=None):\n print(url)\n session = session_with_proxy(extra_args)\n\n page_source = session.get(url).text\n soup = BeautifulSoup(page_source, 'html.parser')\n\n well = soup.find('div', 'well')\n if well and 'no disponible' in well.text:\n return []\n\n if not soup.find('div', 'item-description'):\n return []\n\n name = soup.find('div', 'item-description').text.strip()[:250]\n\n sku = None\n part_number = None\n stock = 0\n\n data_block = soup.findAll('div', 'item-tech-info')\n\n for data_row in data_block:\n data_row_contents = data_row.findAll('div', 'col-flex')\n for data_col in data_row_contents:\n data_pair = data_col.text.strip().split(':')\n if data_pair[0] == 'UPC':\n sku = data_pair[1].strip()\n if data_pair[0] == 'Núm. de parte':\n part_number = data_pair[1].strip()\n if data_pair[0] == 'Disponibilidad':\n stock = int(data_pair[1].strip().split(' ')[0])\n\n price_tags = soup.findAll('span', 'price-text')\n prices = []\n\n for price_tag in price_tags:\n price_text = re.search(r'\\$(\\d*,?\\d+\\.?\\d*)',\n price_tag.text).groups()[0]\n prices.append(Decimal(price_text.replace(',', '')))\n\n price = min(prices)\n\n if soup.find('img', 'larger2'):\n picture_urls = ['https://www.zegucom.com.mx/{}'.format(\n soup.find('img', 'larger2')['src'])]\n else:\n picture_urls = []\n\n description = html_to_markdown(\n str(soup.find('div', {'id': 'ficha'})))\n\n p = Product(\n name,\n cls.__name__,\n category,\n url,\n url,\n sku,\n stock,\n price,\n price,\n 'MXN',\n sku=sku,\n picture_urls=picture_urls,\n description=description,\n part_number=part_number\n )\n\n return [p]\n", "sub_path": "storescraper/stores/zegucom.py", "file_name": "zegucom.py", "file_ext": "py", "file_size_in_byte": 5833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "storescraper.store.Store", "line_number": 14, "usage_type": "name"}, {"api_name": "storescraper.categories.CPU_COOLER", "line_number": 23, "usage_type": "name"}, {"api_name": "storescraper.categories.CPU_COOLER", "line_number": 56, "usage_type": "name"}, {"api_name": "storescraper.categories.CPU_COOLER", "line_number": 57, "usage_type": "name"}, {"api_name": "storescraper.utils.session_with_proxy", "line_number": 87, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 102, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 107, "usage_type": "call"}, {"api_name": "storescraper.utils.session_with_proxy", "line_number": 123, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 126, "usage_type": "call"}, {"api_name": "re.search", "line_number": 158, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 160, "usage_type": "call"}, {"api_name": "storescraper.utils.html_to_markdown", "line_number": 170, "usage_type": "call"}, {"api_name": "storescraper.product.Product", "line_number": 173, "usage_type": "call"}]} {"seq_id": "586782156", "text": "import os\nimport sys\nimport json\n\nsys.path.append(os.getcwd()+'/../')\n\nfrom flask import Flask, request, render_template, session, jsonify\nfrom MrUirf import main\nfrom MrUirf.twitter import collector_by_web\nfrom MrUirf.facebook import collector\nfrom MrUirf.extraction.util import relextractor_map\n\napp = Flask(__name__)\napp.secret_key = \"really_secret_key\"\n\n@app.route('/')\ndef index():\n\n\treturn render_template(\"index.html\")\n\n@app.route('/uir', methods=['POST'])\ndef uir():\n\n\terror = \"\"\n\n\tif request.method == \"POST\":\n\n\t\tresults = main.start(request.form[\"github_username\"], \n\t\t\t\t\t\t\t request.form[\"twitter_username\"], \n\t\t\t\t\t\t\t int(request.form[\"depth\"]), \n\t\t\t\t\t\t\t int(request.form[\"iterations\"]))\n\n\t\treturn render_template(\"results.html\", results=results)\n\n@app.route('/uif')\ndef uif_index():\n return render_template(\"uif/index.html\")\n\n@app.route('/uif/text', methods=['GET', 'POST'])\ndef uif_text():\n if request.method == 'GET':session['method']='GET'\n data = {}\n data = get_texts(data)\n return render_template('uif/text.html', data=data)\n\n@app.route('/uif/text/change_page')\ndef uif_text_change_page():\n data = {}\n data = get_texts_page(data)\n return jsonify(texts=data['texts'])\n\n@app.route('/uif/token', methods=['GET', 'POST'])\ndef uif_token():\n if request.method == 'GET':session['method']='GET'\n data = {}\n data = get_texts(data)\n return render_template('uif/token.html', data=data)\n\n@app.route('/uif/token/change_page')\ndef uif_token_change_page():\n data = {}\n data = get_texts_page(data)\n return jsonify(texts=data['texts'])\n\n@app.route('/uif/pos', methods=['GET', 'POST'])\ndef uif_pos():\n if request.method == 'GET':session['method']='GET'\n data = {}\n if request.method == \"POST\":\n data = get_texts(data)\n texts= data['texts']\n for text in texts:\n pos = text['pos']\n pos = [(p[0], relextractor_map.convert_pos(p[1])) for p in pos]\n text['pos'] = pos\n data['texts'] = texts\n return render_template('uif/pos.html', data=data)\n\n@app.route('/uif/pos/change_page')\ndef uif_pos_change_page():\n data = {}\n data = get_texts_page(data)\n texts= data['texts']\n for text in texts:\n pos = text['pos']\n pos = [(p[0], relextractor_map.convert_pos(p[1])) for p in pos]\n text['pos'] = pos\n data['texts'] = texts\n return jsonify(texts=data['texts'])\n\n@app.route('/uif/extractor', methods=['GET', 'POST'])\ndef uif_extraction():\n data = {}\n if request.method == 'GET':\n data['method'] = \"GET\"\n return render_template(\"uif/extractor.html\", data=data)\n elif request.method == 'POST':\n data['method'] = 'POST'\n return render_template('uif/extractor.html', data=data)\n\ndef get_texts(data):\n session['host'] = request.url_root\n if request.method == 'GET':\n session['method'] = 'GET'\n return render_template(\"uif/text.html\", data=data)\n if request.method == 'POST':\n session['method'] = 'POST'\n try:session['user_id'] = request.form['user_id']\n except:pass\n try:session['tw_page_no'] = request.form['tw_page_no']\n except:pass\n try:session['fb_page_no'] = request.form['fb_page_no']\n except:pass\n try:session['source'] = request.form['source']\n except:pass\n\n with file('tw_fb.account', 'r') as f:\n account_data = json.load(f)\n if session['source'] == \"twitter\":\n tw_username = account_data[session['user_id']]['tw_username']\n tw_page_no = session['tw_page_no']\n data['texts']=collector_by_web.fetch_tweets(tw_username, tw_page_no)\n elif session['source'] == \"facebook\":\n fb_username = account_data[session['user_id']]['fb_username']\n fb_page_no = session['fb_page_no']\n data['texts']=collector.fetch_status(fb_username, fb_page_no)\n return data\n\ndef get_texts_page(data):\n with file('tw_fb.account', 'r') as f:\n account_data = json.load(f)\n source = session['source']\n turn_pg= request.args.get('page', 0, type=str)\n if source == \"twitter\":\n tw_username = account_data[session['user_id']]['tw_username']\n if turn_pg == \"next\":session['tw_page_no']=int(session['tw_page_no'])+1\n if turn_pg == \"prev\":session['tw_page_no']=int(session['tw_page_no'])-1\n tw_page_no = session['tw_page_no']\n data['texts']=collector_by_web.fetch_tweets(tw_username, tw_page_no)\n elif source == \"facebook\":\n fb_username = account_data[session['user_id']]['fb_username']\n if turn_pg == \"next\":session['fb_page_no']=int(session['fb_page_no'])+1\n if turn_pg == \"prev\":session['fb_page_no']=int(session['fb_page_no'])-1\n fb_page_no = session['fb_page_no']\n data['texts']=collector.fetch_status(fb_username, fb_page_no)\n return data\n\n", "sub_path": "visualization/mruirf.py", "file_name": "mruirf.py", "file_ext": "py", "file_size_in_byte": 4903, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "MrUirf.main.start", "line_number": 28, "usage_type": "call"}, {"api_name": "MrUirf.main", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "MrUirf.extraction.util.relextractor_map.convert_pos", "line_number": 74, "usage_type": "call"}, {"api_name": "MrUirf.extraction.util.relextractor_map", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "MrUirf.extraction.util.relextractor_map.convert_pos", "line_number": 86, "usage_type": "call"}, {"api_name": "MrUirf.extraction.util.relextractor_map", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.request.url_root", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 112, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "json.load", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 121, "usage_type": "name"}, {"api_name": "MrUirf.twitter.collector_by_web.fetch_tweets", "line_number": 122, "usage_type": "call"}, {"api_name": "MrUirf.twitter.collector_by_web", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 125, "usage_type": "name"}, {"api_name": "MrUirf.facebook.collector.fetch_status", "line_number": 126, "usage_type": "call"}, {"api_name": "MrUirf.facebook.collector", "line_number": 126, "usage_type": "name"}, {"api_name": "json.load", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 138, "usage_type": "name"}, {"api_name": "MrUirf.twitter.collector_by_web.fetch_tweets", "line_number": 139, "usage_type": "call"}, {"api_name": "MrUirf.twitter.collector_by_web", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 144, "usage_type": "name"}, {"api_name": "MrUirf.facebook.collector.fetch_status", "line_number": 145, "usage_type": "call"}, {"api_name": "MrUirf.facebook.collector", "line_number": 145, "usage_type": "name"}]} {"seq_id": "616309715", "text": "from __future__ import print_function\nfrom SimPEG import Problem, Mesh\nfrom SimPEG import Utils\nfrom SimPEG.Utils import mkvc\nfrom SimPEG import Props\nimport scipy as sp\nimport scipy.constants as constants\nimport os\nimport time\nimport numpy as np\n\nclass GravityIntegral(Problem.LinearProblem):\n\n rho, rhoMap, rhoDeriv = Props.Invertible(\n \"Specific density (g/cc)\",\n default=1.\n )\n\n # surveyPair = Survey.LinearSurvey\n forwardOnly = False # Is TRUE, forward matrix not stored to memory\n actInd = None #: Active cell indices provided\n rx_type = 'z'\n silent = False\n memory_saving_mode = False\n parallelized = False\n n_cpu = None\n progress_index = -1\n gtgdiag = None\n\n aa = []\n\n def __init__(self, mesh, **kwargs):\n Problem.BaseProblem.__init__(self, mesh, **kwargs)\n\n def fields(self, m):\n\n model = self.rhoMap*m\n\n if self.forwardOnly:\n\n # Compute the linear operation without forming the full dense G\n fields = self.Intrgl_Fwr_Op(m=m)\n\n return mkvc(fields)\n\n else:\n vec = np.dot(self.G, model.astype(np.float32))\n\n return vec.astype(np.float64)\n\n\n def getJtJdiag(self, m, W=None):\n \"\"\"\n Return the diagonal of JtJ\n \"\"\"\n\n if self.gtgdiag is None:\n\n if W is None:\n w = np.ones(self.G.shape[1])\n else:\n w = W.diagonal()\n\n dmudm = self.rhoMap.deriv(m)\n self.gtgdiag = np.zeros(dmudm.shape[1])\n\n for ii in range(self.G.shape[0]):\n\n self.gtgdiag += (w[ii]*self.G[ii, :]*dmudm)**2.\n\n return self.gtgdiag\n\n def getJ(self, m, f=None):\n \"\"\"\n Sensitivity matrix\n \"\"\"\n return self.G\n\n def Jvec(self, m, v, f=None):\n dmudm = self.rhoMap.deriv(m)\n return self.G.dot(dmudm*v)\n\n def Jtvec(self, m, v, f=None):\n dmudm = self.rhoMap.deriv(m)\n return dmudm.T * (self.G.T.dot(v))\n\n @property\n def G(self):\n if not self.ispaired:\n raise Exception('Need to pair!')\n\n if getattr(self, '_G', None) is None:\n print(\"Begin linear forward calculation: \" + self.rx_type)\n start = time.time()\n self._G = self.Intrgl_Fwr_Op()\n print(\"Linear forward calculation ended in: \" + str(time.time()-start) + \" sec\")\n return self._G\n\n def Intrgl_Fwr_Op(self, m=None, rx_type='z'):\n\n \"\"\"\n\n Gravity forward operator in integral form\n\n flag = 'z' | 'xyz'\n\n Return\n _G = Linear forward modeling operation\n\n Created on March, 15th 2016\n\n @author: dominiquef\n\n \"\"\"\n\n if m is not None:\n self.model = self.rhoMap*m\n\n if getattr(self, 'actInd', None) is not None:\n\n if self.actInd.dtype == 'bool':\n inds = np.where(self.actInd)[0]\n else:\n inds = self.actInd\n\n else:\n\n inds = np.asarray(range(self.mesh.nC))\n\n self.nC = len(inds)\n\n # Create active cell projector\n P = sp.sparse.csr_matrix(\n (np.ones(self.nC), (inds, range(self.nC))),\n shape=(self.mesh.nC, self.nC)\n )\n\n # Create vectors of nodal location\n # (lower and upper corners for each cell)\n if isinstance(self.mesh, Mesh.TreeMesh):\n # Get upper and lower corners of each cell\n bsw = (self.mesh.gridCC -\n np.kron(self.mesh.vol.T**(1/3)/2,\n np.ones(3)).reshape((self.mesh.nC, 3)))\n tne = (self.mesh.gridCC +\n np.kron(self.mesh.vol.T**(1/3)/2,\n np.ones(3)).reshape((self.mesh.nC, 3)))\n\n xn1, xn2 = bsw[:, 0], tne[:, 0]\n yn1, yn2 = bsw[:, 1], tne[:, 1]\n zn1, zn2 = bsw[:, 2], tne[:, 2]\n\n else:\n\n xn = self.mesh.vectorNx\n yn = self.mesh.vectorNy\n zn = self.mesh.vectorNz\n\n yn2, xn2, zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:])\n yn1, xn1, zn1 = np.meshgrid(yn[:-1], xn[:-1], zn[:-1])\n\n self.Yn = P.T*np.c_[Utils.mkvc(yn1), Utils.mkvc(yn2)]\n self.Xn = P.T*np.c_[Utils.mkvc(xn1), Utils.mkvc(xn2)]\n self.Zn = P.T*np.c_[Utils.mkvc(zn1), Utils.mkvc(zn2)]\n\n self.rxLoc = self.survey.srcField.rxList[0].locs\n self.nD = int(self.rxLoc.shape[0])\n\n # if self.n_cpu is None:\n # self.n_cpu = multiprocessing.cpu_count()\n\n # Switch to determine if the process has to be run in parallel\n job = Forward(\n rxLoc=self.rxLoc, Xn=self.Xn, Yn=self.Yn, Zn=self.Zn,\n n_cpu=self.n_cpu, forwardOnly=self.forwardOnly,\n model=self.model, rx_type=self.rx_type,\n parallelized=self.parallelized\n )\n\n G = job.calculate()\n\n return G\n\n @property\n def modelMap(self):\n \"\"\"\n Call for general mapping of the problem\n \"\"\"\n return self.rhoMap\n\n\nclass Forward(object):\n \"\"\"\n Add docstring once it works\n \"\"\"\n\n progress_index = -1\n parallelized = False\n rxLoc = None\n Xn, Yn, Zn = None, None, None\n n_cpu = None\n forwardOnly = False\n model = None\n rx_type = 'z'\n\n def __init__(self, **kwargs):\n super(Forward, self).__init__()\n Utils.setKwargs(self, **kwargs)\n\n def calculate(self):\n\n self.nD = self.rxLoc.shape[0]\n\n if self.parallelized:\n if self.n_cpu is None:\n\n # By default take half the cores, turns out be faster\n # than running full threads\n self.n_cpu = int(multiprocessing.cpu_count()/2)\n\n pool = multiprocessing.Pool(self.n_cpu)\n\n result = pool.map(self.calcTrow, [self.rxLoc[ii, :] for ii in range(self.nD)])\n pool.close()\n pool.join()\n\n else:\n\n result = []\n for ii in range(self.nD):\n result += [self.calcTrow(self.rxLoc[ii, :])]\n self.progress(ii, self.nD)\n\n if self.forwardOnly:\n return mkvc(np.vstack(result))\n\n else:\n return np.vstack(result)\n\n def calcTrow(self, xyzLoc):\n \"\"\"\n Load in the active nodes of a tensor mesh and computes the gravity tensor\n for a given observation location xyzLoc[obsx, obsy, obsz]\n\n INPUT:\n Xn, Yn, Zn: Node location matrix for the lower and upper most corners of\n all cells in the mesh shape[nC,2]\n M\n OUTPUT:\n Tx = [Txx Txy Txz]\n Ty = [Tyx Tyy Tyz]\n Tz = [Tzx Tzy Tzz]\n\n where each elements have dimension 1-by-nC.\n Only the upper half 5 elements have to be computed since symetric.\n Currently done as for-loops but will eventually be changed to vector\n indexing, once the topography has been figured out.\n\n \"\"\"\n\n NewtG = constants.G*1e+8 # Convertion from mGal (1e-5) and g/cc (1e-3)\n eps = 1e-8 # add a small value to the locations to avoid\n\n # Pre-allocate space for 1D array\n row = np.zeros((1, self.Xn.shape[0]))\n\n dz = xyzLoc[2] - self.Zn\n\n dy = self.Yn - xyzLoc[1]\n\n dx = self.Xn - xyzLoc[0]\n\n # Compute contribution from each corners\n for aa in range(2):\n for bb in range(2):\n for cc in range(2):\n\n r = (\n mkvc(dx[:, aa]) ** 2 +\n mkvc(dy[:, bb]) ** 2 +\n mkvc(dz[:, cc]) ** 2\n ) ** (0.50)\n\n if self.rx_type == 'x':\n row -= NewtG * (-1) ** aa * (-1) ** bb * (-1) ** cc * (\n dy[:, bb] * np.log(dz[:, cc] + r + eps) +\n dz[:, cc] * np.log(dy[:, bb] + r + eps) -\n dx[:, aa] * np.arctan(dy[:, bb] * dz[:, cc] /\n (dx[:, aa] * r + eps)))\n\n elif self.rx_type == 'y':\n row -= NewtG * (-1) ** aa * (-1) ** bb * (-1) ** cc * (\n dx[:, aa] * np.log(dz[:, cc] + r + eps) +\n dz[:, cc] * np.log(dx[:, aa] + r + eps) -\n dy[:, bb] * np.arctan(dx[:, aa] * dz[:, cc] /\n (dy[:, bb] * r + eps)))\n\n else:\n row -= NewtG * (-1) ** aa * (-1) ** bb * (-1) ** cc * (\n dx[:, aa] * np.log(dy[:, bb] + r + eps) +\n dy[:, bb] * np.log(dx[:, aa] + r + eps) -\n dz[:, cc] * np.arctan(dx[:, aa] * dy[:, bb] /\n (dz[:, cc] * r + eps)))\n\n if self.forwardOnly:\n return np.dot(row, self.model)\n else:\n return row\n\n def progress(self, ind, total):\n \"\"\"\n progress(ind,prog,final)\n\n Function measuring the progress of a process and print to screen the %.\n Useful to estimate the remaining runtime of a large problem.\n\n Created on Dec, 20th 2015\n\n @author: dominiquef\n \"\"\"\n arg = np.floor(ind/total*10.)\n if arg > self.progress_index:\n print(\"Done \" + str(arg*10) + \" %\")\n self.progress_index = arg\n\n\nclass Problem3D_Diff(Problem.BaseProblem):\n \"\"\"\n Gravity in differential equations!\n \"\"\"\n\n _depreciate_main_map = 'rhoMap'\n\n rho, rhoMap, rhoDeriv = Props.Invertible(\n \"Specific density (g/cc)\",\n default=1.\n )\n\n solver = None\n\n def __init__(self, mesh, **kwargs):\n Problem.BaseProblem.__init__(self, mesh, **kwargs)\n\n self.mesh.setCellGradBC('dirichlet')\n\n self._Div = self.mesh.cellGrad\n\n @property\n def MfI(self): return self._MfI\n\n @property\n def Mfi(self): return self._Mfi\n\n def makeMassMatrices(self, m):\n self.model = m\n self._Mfi = self.mesh.getFaceInnerProduct()\n self._MfI = Utils.sdiag(1. / self._Mfi.diagonal())\n\n def getRHS(self, m):\n \"\"\"\n\n\n \"\"\"\n\n Mc = Utils.sdiag(self.mesh.vol)\n\n self.model = m\n rho = self.rho\n\n return Mc * rho\n\n def getA(self, m):\n \"\"\"\n GetA creates and returns the A matrix for the Gravity nodal problem\n\n The A matrix has the form:\n\n .. math ::\n\n \\mathbf{A} = \\Div(\\MfMui)^{-1}\\Div^{T}\n\n \"\"\"\n return -self._Div.T * self.Mfi * self._Div\n\n def fields(self, m):\n \"\"\"\n Return gravity potential (u) and field (g)\n u: defined on the cell nodes [nC x 1]\n gField: defined on the cell faces [nF x 1]\n\n \"\"\"\n from scipy.constants import G as NewtG\n\n self.makeMassMatrices(m)\n A = self.getA(m)\n RHS = self.getRHS(m)\n\n if self.solver is None:\n m1 = sp.linalg.interface.aslinearoperator(\n Utils.sdiag(1 / A.diagonal())\n )\n u, info = sp.linalg.bicgstab(A, RHS, tol=1e-6, maxiter=1000, M=m1)\n\n else:\n print(\"Solving with Paradiso\")\n Ainv = self.solver(A)\n u = Ainv * RHS\n\n gField = 4. * np.pi * NewtG * 1e+8 * self._Div * u\n\n return {'G': gField, 'u': u}\n", "sub_path": "SimPEG/PF/Gravity.py", "file_name": "Gravity.py", "file_ext": "py", "file_size_in_byte": 11470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "SimPEG.Problem.LinearProblem", "line_number": 12, "usage_type": "attribute"}, {"api_name": "SimPEG.Problem", "line_number": 12, "usage_type": "name"}, {"api_name": "SimPEG.Props.Invertible", "line_number": 14, "usage_type": "call"}, {"api_name": "SimPEG.Props", "line_number": 14, "usage_type": "name"}, {"api_name": "SimPEG.Problem.BaseProblem.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "SimPEG.Problem.BaseProblem", "line_number": 33, "usage_type": "attribute"}, {"api_name": "SimPEG.Problem", "line_number": 33, "usage_type": "name"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 128, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 133, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 134, "usage_type": "call"}, {"api_name": "SimPEG.Mesh.TreeMesh", "line_number": 140, "usage_type": "attribute"}, {"api_name": "SimPEG.Mesh", "line_number": 140, "usage_type": "name"}, {"api_name": "numpy.kron", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 162, "usage_type": "attribute"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 162, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.c_", "line_number": 163, "usage_type": "attribute"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 163, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.c_", "line_number": 164, "usage_type": "attribute"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 164, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 164, "usage_type": "name"}, {"api_name": "SimPEG.Utils.setKwargs", "line_number": 208, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 208, "usage_type": "name"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 238, "usage_type": "call"}, {"api_name": "scipy.constants.G", "line_number": 261, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 261, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 265, "usage_type": "call"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 279, "usage_type": "call"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 280, "usage_type": "call"}, {"api_name": "SimPEG.Utils.mkvc", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 321, "usage_type": "call"}, {"api_name": "SimPEG.Problem.BaseProblem", "line_number": 327, "usage_type": "attribute"}, {"api_name": "SimPEG.Problem", "line_number": 327, "usage_type": "name"}, {"api_name": "SimPEG.Props.Invertible", "line_number": 334, "usage_type": "call"}, {"api_name": "SimPEG.Props", "line_number": 334, "usage_type": "name"}, {"api_name": "SimPEG.Problem.BaseProblem.__init__", "line_number": 342, "usage_type": "call"}, {"api_name": "SimPEG.Problem.BaseProblem", "line_number": 342, "usage_type": "attribute"}, {"api_name": "SimPEG.Problem", "line_number": 342, "usage_type": "name"}, {"api_name": "SimPEG.Utils.sdiag", "line_number": 357, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 357, "usage_type": "name"}, {"api_name": "SimPEG.Utils.sdiag", "line_number": 365, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 365, "usage_type": "name"}, {"api_name": "scipy.linalg.interface.aslinearoperator", "line_number": 399, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 399, "usage_type": "attribute"}, {"api_name": "SimPEG.Utils.sdiag", "line_number": 400, "usage_type": "call"}, {"api_name": "SimPEG.Utils", "line_number": 400, "usage_type": "name"}, {"api_name": "scipy.linalg.bicgstab", "line_number": 402, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 402, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 409, "usage_type": "attribute"}, {"api_name": "scipy.constants.G", "line_number": 409, "usage_type": "name"}]} {"seq_id": "643608803", "text": "import torch\nimport numpy as np\nimport timeit\nfrom random import sample\nfrom sklearn.metrics import pairwise_distances\n\n\ndef spearman(a, b):\n\n order_penalty = (a - b) ** 2\n weight = a * b\n\n distance = 1e4 * np.sum(order_penalty * weight)\n return distance\n\n\ndef spearman_pairwise_distance(X, centers=None):\n\n if (centers is not None):\n return torch_spearman_pairwise_distance(X, centers)\n\n else:\n return torch_spearman_pairwise_distance(X, X)\n\n\ndef torch_spearman_pairwise_distance(X, centers):\n\n max_calculation_size = 250000000\n calculation_size = X.shape[0] * X.shape[1] * centers.shape[0]\n size_ratio = max_calculation_size / calculation_size\n\n if (size_ratio >= 1):\n return torch_spearman_pairwise_distance_core(X, centers)\n\n else:\n\n nb_center_per_step = int(size_ratio * len(centers))\n if (nb_center_per_step <= 0):\n nb_center_per_step = 1\n\n all_column_distance = []\n for i in range(0, len(centers), nb_center_per_step):\n all_column_distance += [torch_spearman_pairwise_distance_core(X, centers[i:i+nb_center_per_step])]\n\n return np.concatenate(all_column_distance, axis=1)\n\n\ndef torch_spearman_pairwise_distance_core(X, centers):\n\n nb_data = X.shape[0]\n nb_cluster = centers.shape[0]\n vectSize = centers.shape[1]\n\n torch_x = torch.from_numpy(X).cuda()\n torch_x = torch_x.unsqueeze(1)\n torch_x = torch_x.expand(nb_data, nb_cluster, vectSize)\n\n torch_centers = torch.from_numpy(centers).cuda()\n torch_centers = torch_centers.expand(nb_data, nb_cluster, vectSize)\n\n order_penalty = (torch_x - torch_centers) ** 2\n weight = torch_x * torch_centers\n\n distance = 1e4 * torch.sum(order_penalty * weight, dim=2)\n\n return distance.data.cpu().numpy()\n\n\nif __name__ == '__main__':\n\n nb_row = 500\n nb_feature = 20\n nb_cluster = 7\n nb_run = 5\n\n a = np.random.uniform(0, 10, (nb_row,nb_feature))\n\n cluster_index = sample(list(range(nb_row)), nb_cluster)\n b = a[cluster_index, :]\n\n\n print(\"\\n\\nSK Dist\")\n\n sk_dist = None\n all_time = []\n for i in range(nb_run):\n\n start = timeit.default_timer()\n\n sk_dist = pairwise_distances(a, b, metric=spearman, n_jobs=12)\n\n stop = timeit.default_timer()\n time_in_second = stop - start\n all_time += [time_in_second]\n\n print(sk_dist)\n print(sk_dist.shape)\n print(np.mean(all_time))\n print(np.sum(all_time))\n \n\n\n print(\"\\nTorch Dist\")\n\n torch_dist = None\n all_time = []\n for i in range(nb_run):\n start = timeit.default_timer()\n\n torch_dist = spearman_pairwise_distance(a, b)\n\n stop = timeit.default_timer()\n time_in_second = stop - start\n all_time += [time_in_second]\n\n print(torch_dist)\n print(torch_dist.shape)\n print(np.mean(all_time))\n print(np.sum(all_time))\n\n print(np.allclose(sk_dist, torch_dist, atol=0.1))\n\n\n print(\"\\nTorch Dist 2\")\n\n torch_dist = None\n all_time = []\n for i in range(nb_run):\n start = timeit.default_timer()\n\n torch_dist = spearman_pairwise_distance(a)\n\n stop = timeit.default_timer()\n time_in_second = stop - start\n all_time += [time_in_second]\n\n print(torch_dist)\n print(torch_dist.shape)\n print(np.mean(all_time))\n print(np.sum(all_time))\n\n\n print(\"\\n\\nSK Dist 2\")\n\n sk_dist = None\n all_time = []\n for i in range(nb_run):\n start = timeit.default_timer()\n\n sk_dist = pairwise_distances(a, metric=spearman, n_jobs=12)\n\n stop = timeit.default_timer()\n time_in_second = stop - start\n all_time += [time_in_second]\n\n print(sk_dist)\n print(sk_dist.shape)\n print(np.mean(all_time))\n print(np.sum(all_time))\n\n print(np.allclose(sk_dist, torch_dist, atol=0.1))\n\n\n", "sub_path": "gam/pairwise_distances.py", "file_name": "pairwise_distances.py", "file_ext": "py", "file_size_in_byte": 3821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.sum", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 78, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise_distances", "line_number": 90, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 99, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 108, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 121, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 129, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 140, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 148, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise_distances", "line_number": 150, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 161, "usage_type": "call"}]} {"seq_id": "5459095", "text": "from time import sleep\n\nfrom appium import webdriver\n\n\nclass TestWebViewXueQiu():\n def setup(self):\n desired_caps = {\n \"platformName\": \"android\",\n \"platformVersion\": \"6.0\",\n \"deviceName\": \"127.0.0.1:7555\",\n \"appPackage\": \"com.xueqiu.android\",\n \"appActivity\": \"com.xueqiu.android.common.MainActivity\",\n \"noReset\": True, # 保留上次操作痕迹（比如登录状态）\n \"dontStopAppOnReset\": True,\n \"unicodeKeyBoard\": True,\n \"resetKeyBoard\": True,\n \"chromedriverExecutable\": \"C:/Users/Administrator/Desktop/chromedriver52.exe\"\n }\n self.driver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)\n self.driver.implicitly_wait(5)\n\n def teardown(self):\n self.driver.quit()\n\n def test_xueqiuweb(self):\n self.driver.find_element_by_xpath(\n \"//android.widget.TextView[@resource-id='com.xueqiu.android:id/tab_name' and @text='交易']\").click()\n print(self.driver.contexts)\n\n self.driver.find_element_by_xpath(\"//android.view.View[@content-desc='A股开户']\").click()\n sleep(3)\n print(self.driver.contexts)\n\n self.driver.switch_to.context(self.driver.contexts[-1])\n self.driver.find_element_by_id('phone-number').send_keys('15986612345')\n self.driver.find_element_by_id('code').send_keys('123456')\n self.driver.find_element_by_xpath('//*[@class=\"btn-submit\"]').click()\n", "sub_path": "Python_Appium/test_webview_xueqiu.py", "file_name": "test_webview_xueqiu.py", "file_ext": "py", "file_size_in_byte": 1505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "appium.webdriver.Remote", "line_number": 20, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 20, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}]} {"seq_id": "302574486", "text": "import os\nimport logging\nfrom telegram import ReplyKeyboardMarkup, KeyboardButton, ReplyKeyboardRemove, \\\n InlineKeyboardButton, InlineKeyboardMarkup\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters, ConversationHandler, \\\n CallbackQueryHandler\nfrom services.service_data import store_bot_data, upload_image_to_dbx, get_telegram_upload_image_paths\nfrom bots.bot_services import get_station_by_location\nimport bots.constants as const\n# TODO delete before production!:\n\nlogging.basicConfig(level=logging.DEBUG, format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\nlogger = logging.getLogger(__name__)\n\nPHOTO, CHOICE, SELECT_STATION, LOCATION, SET_DATA, GET_DATA, DATALOC, IN_DEV = range(8)\n\n\ndef start(bot, update):\n\n bot.send_message(chat_id=update.effective_message.chat_id,\n text=const.start_text,\n reply_markup=InlineKeyboardMarkup(const.start_keyboard))\n return CHOICE\n\n\ndef start_button(bot, update):\n query = update.callback_query\n if query.data == 'send':\n return send_location(bot, update)\n elif query.data == 'get':\n return getdata(bot, update)\n\n\ndef getdata(bot, update):\n query = update.callback_query\n location_button = KeyboardButton('Sent location', request_location=True)\n no_matter = KeyboardButton('Doesn\\'t matter')\n bot.send_message(chat_id=query.message.chat_id,\n text='Please share your location to get data depending your place '\n 'or press \"Skip\" if location doesn\\'t matter',\n reply_markup=ReplyKeyboardMarkup([[location_button], [no_matter]],\n one_time_keyboard=True, resize_keyboard=True)\n )\n return GET_DATA\n\n\ndef setdata(bot, update, location, user_data):\n stations = get_station_by_location(lat=location['latitude'], lng=location['longitude'])\n if not stations:\n bot.send_message(text=\"There are no gas stations in your location, please try again.\",\n chat_id=update.message.chat_id)\n return send_location(bot, update)\n user_data['stations'] = stations\n buttons = [[InlineKeyboardButton(text=st[\"name\"]+f'\\n{st[\"adress\"]}',\n callback_data=stations.index(st))] for st in stations]\n bot.send_message(text=\"Please choose fuel company from the list: \",\n chat_id=update.message.chat_id,\n reply_markup=InlineKeyboardMarkup(buttons))\n return SELECT_STATION\n\n\ndef select_station(bot, update, user_data):\n query = update.callback_query\n user_data['gas_st'] = user_data['stations'][int(query.data)]\n bot.send_message(text='You\\'ve selected \"{}\".\\n Please send us your photo:'\n .format(user_data['stations'][int(query.data)]['name']),\n chat_id=query.message.chat_id)\n return PHOTO\n\n\ndef send_location(bot, update):\n location_button = KeyboardButton('Send current location', request_location=True)\n bot.send_message(chat_id=update.effective_message.chat_id,\n text='Please, share you location so we can find nearest gas stations.\\n'\n 'Tap the button if you are near gas station now, or choose location manually',\n reply_markup=ReplyKeyboardMarkup([[location_button]],\n one_time_keyboard=True, resize_keyboard=True))\n return LOCATION\n\n\ndef got_location(bot, update, user_data):\n chat_id = update.message.chat_id\n bot.send_message(chat_id=chat_id, text=\"Thanks!\", reply_markup=ReplyKeyboardRemove())\n loc = update.message.location\n return setdata(bot, update, loc, user_data)\n\n\ndef get_data_by_location(bot, update):\n\n bot.send_message(chat_id=update.message.chat_id, text=\"ok!\",\n reply_markup=ReplyKeyboardRemove())\n bot.send_message(chat_id=update.message.chat_id, text=\"Please choose:\",\n reply_markup=InlineKeyboardMarkup(const.data_by_loc_keyboard))\n return DATALOC\n\n\ndef dataloc(bot, update):\n bot.send_message(chat_id=update.effective_message.chat_id,\n text=\"Select types of fuel:\\n\\nIN DEVELOPMENT\",\n reply_markup=ReplyKeyboardRemove())\n return start(bot, update)\n\n\ndef helpme(bot, update):\n update.message.reply_text(\"Still in development. /start\")\n\n\ndef error(bot, update, err):\n logger.warning(\"Update {} caused error {}\".format(update, err))\n\n\ndef cancel(bot, update):\n return ConversationHandler.END\n\n\ndef send_file_dbx(bot, update, user_data):\n # TODO move this to services, realize else\n if update.message.document:\n file_id = update.message.document.file_id\n elif update.message.photo:\n file_id = update.message.photo[-1].file_id\n else:\n file_id = None\n pass\n user_id = update.message.from_user.id\n station_name = user_data['gas_st']['name']\n adress = user_data['gas_st']['adress']\n lat, lng = user_data['gas_st']['lat'], user_data['gas_st']['lng']\n tg_down_path, dbx_path = get_telegram_upload_image_paths(file_id)\n\n dbx_link = upload_image_to_dbx(tg_down_path, dbx_path)\n bot.send_message(chat_id=update.message.chat_id, text=\"download success! \"+dbx_path)\n response = store_bot_data(telegram_id=user_id, image_link=dbx_link, company_name=station_name,\n address=adress, lat=lat, lng=lng)\n bot.send_message(chat_id=update.message.chat_id, text=response)\n \"\"\"\n is_recognized, fuel_type, price = digit_to_price(dbx_path)\n if is_recognized:\n bot.send_message(chat_id=update.message.chat_id, text=f\"Recognized!\\n\"\n f\"A{fuel_type}: {price}грн\")\n else:\n bot.send_message(chat_id=update.message.chat_id, text=\"Failed to recognize\")\n \"\"\"\n return start(bot, update)\n\n\ndef main(poll=True):\n telegram_token = os.environ['TELEGRAM_TOKEN']\n updater = Updater(telegram_token)\n disp = updater.dispatcher\n disp.add_error_handler(error)\n\n conv_handler = ConversationHandler(\n entry_points=[CommandHandler('start', start), ],\n\n states={\n CHOICE: [CallbackQueryHandler(start_button)],\n LOCATION: [MessageHandler(Filters.location, got_location, pass_user_data=True)],\n GET_DATA: [MessageHandler(Filters.location, get_data_by_location), MessageHandler(Filters.text, dataloc)],\n DATALOC: [CallbackQueryHandler(dataloc)],\n SELECT_STATION: [CallbackQueryHandler(select_station, pass_user_data=True)],\n PHOTO: [(MessageHandler(Filters.document, send_file_dbx, pass_user_data=True)),\n MessageHandler(Filters.photo, send_file_dbx, pass_user_data=True)]\n },\n\n fallbacks=[CommandHandler('cancel', cancel)]\n )\n disp.add_handler(conv_handler)\n disp.add_handler(CommandHandler(\"help\", helpme))\n disp.add_handler(CommandHandler(\"start\", start))\n disp.add_error_handler(error)\n\n if poll:\n updater.start_polling()\n updater.idle()\n else:\n updater.start_webhook(listen=\"0.0.0.0\",\n port=int(os.environ['PORT']),\n url_path=telegram_token)\n updater.bot.setWebhook(f'{os.environ[\"URL_PATH\"]}/{telegram_token}')\n updater.idle()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "bots/telegram_bot.py", "file_name": "telegram_bot.py", "file_ext": "py", "file_size_in_byte": 7479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "bots.constants.start_text", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bots.constants", "line_number": 21, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 22, "usage_type": "call"}, {"api_name": "bots.constants.start_keyboard", "line_number": 22, "usage_type": "attribute"}, {"api_name": "bots.constants", "line_number": 22, "usage_type": "name"}, {"api_name": "telegram.KeyboardButton", "line_number": 36, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 37, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 41, "usage_type": "call"}, {"api_name": "bots.bot_services.get_station_by_location", "line_number": 48, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 54, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 58, "usage_type": "call"}, {"api_name": "telegram.KeyboardButton", "line_number": 72, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardMarkup", "line_number": 76, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardRemove", "line_number": 83, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardRemove", "line_number": 91, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 93, "usage_type": "call"}, {"api_name": "bots.constants.data_by_loc_keyboard", "line_number": 93, "usage_type": "attribute"}, {"api_name": "bots.constants", "line_number": 93, "usage_type": "name"}, {"api_name": "telegram.ReplyKeyboardRemove", "line_number": 100, "usage_type": "call"}, {"api_name": "telegram.ext.ConversationHandler.END", "line_number": 113, "usage_type": "attribute"}, {"api_name": "telegram.ext.ConversationHandler", "line_number": 113, "usage_type": "name"}, {"api_name": "services.service_data.get_telegram_upload_image_paths", "line_number": 129, "usage_type": "call"}, {"api_name": "services.service_data.upload_image_to_dbx", "line_number": 131, "usage_type": "call"}, {"api_name": "services.service_data.store_bot_data", "line_number": 133, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 148, "usage_type": "attribute"}, {"api_name": "telegram.ext.Updater", "line_number": 149, "usage_type": "call"}, {"api_name": "telegram.ext.ConversationHandler", "line_number": 153, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 154, "usage_type": "call"}, {"api_name": "telegram.ext.CallbackQueryHandler", "line_number": 157, "usage_type": "call"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 158, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.location", "line_number": 158, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 158, "usage_type": "name"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 159, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.location", "line_number": 159, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 159, "usage_type": "name"}, {"api_name": "telegram.ext.Filters.text", "line_number": 159, "usage_type": "attribute"}, {"api_name": "telegram.ext.CallbackQueryHandler", "line_number": 160, "usage_type": "call"}, {"api_name": "telegram.ext.CallbackQueryHandler", "line_number": 161, "usage_type": "call"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 162, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.document", "line_number": 162, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 162, "usage_type": "name"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 163, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.photo", "line_number": 163, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 163, "usage_type": "name"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 166, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 169, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 170, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 180, "usage_type": "attribute"}]} {"seq_id": "13364631", "text": "from flask import current_app, render_template, Flask, redirect, request, url_for\nimport firestore\nimport storage\n\n\napp = Flask(__name__)\napp.config.update(\n SECRET_KEY='secret',\n MAX_CONTENT_LENGTH=8 * 1024 * 1024,\n ALLOWED_EXTENSIONS=set(['png', 'jpg', 'jpeg', 'gif']),\n)\n\napp.debug = False\napp.testing = False\n\n\n\n@app.route(\"/\")\ndef list_items():\n # start_after = request.args.get('start_after', None)\n # items, last_item_id = firestore.next_page(start_after=start_after)\n # return render_template(\"item_list.html\", items=items, last_item_id=last_item_id)\n\n return \"Hello Microservice Orders,PlaceHolder, Yeeehaaa!!!! ;>\"\n\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=8080, debug=True)\n", "sub_path": "microservices/src/orders/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}]} {"seq_id": "452435075", "text": "from collections import defaultdict\nfrom pathlib import Path\nfrom typing import Union\n\nimport numpy as np\nimport csv\n\nimport matplotlib\nimport math\n\nmatplotlib.use(\"Agg\")\n\nimport matplotlib.pyplot as plt\n\n# header for 'weights.txt'\nWEIGHT_NAME = 1\nWEIGHT_NUMBER = 2\nWEIGHT_VALUE = 3\n\n\nclass Plotter(object):\n \"\"\"\n Plots training parameters (loss, f-score, and accuracy) and training weights over time.\n Input files are the output files 'loss.tsv' and 'weights.txt' from training either a sequence tagger or text\n classification model.\n \"\"\"\n\n @staticmethod\n def _extract_evaluation_data(file_name: Path) -> dict:\n training_curves = {\n \"train\": {\"loss\": [], \"f_score\": [], \"acc\": []},\n \"test\": {\"loss\": [], \"f_score\": [], \"acc\": []},\n \"dev\": {\"loss\": [], \"f_score\": [], \"acc\": []},\n }\n\n with open(file_name, \"r\") as tsvin:\n tsvin = csv.reader(tsvin, delimiter=\"\\t\")\n\n # determine the column index of loss, f-score and accuracy for train, dev and test split\n row = next(tsvin, None)\n TRAIN_LOSS = row.index(\"TRAIN_LOSS\")\n TRAIN_F_SCORE = row.index(\"TRAIN_F-SCORE\")\n TRAIN_ACCURACY = row.index(\"TRAIN_ACCURACY\")\n DEV_LOSS = row.index(\"DEV_LOSS\")\n DEV_F_SCORE = row.index(\"DEV_F-SCORE\")\n DEV_ACCURACY = row.index(\"DEV_ACCURACY\")\n TEST_LOSS = row.index(\"TEST_LOSS\")\n TEST_F_SCORE = row.index(\"TEST_F-SCORE\")\n TEST_ACCURACY = row.index(\"TEST_ACCURACY\")\n\n # then get all relevant values from the tsv\n for row in tsvin:\n if row[TRAIN_LOSS] != \"_\":\n training_curves[\"train\"][\"loss\"].append(float(row[TRAIN_LOSS]))\n if row[TRAIN_F_SCORE] != \"_\":\n training_curves[\"train\"][\"f_score\"].append(\n float(row[TRAIN_F_SCORE])\n )\n if row[TRAIN_ACCURACY] != \"_\":\n training_curves[\"train\"][\"acc\"].append(float(row[TRAIN_ACCURACY]))\n if row[DEV_LOSS] != \"_\":\n training_curves[\"dev\"][\"loss\"].append(float(row[DEV_LOSS]))\n if row[DEV_F_SCORE] != \"_\":\n training_curves[\"dev\"][\"f_score\"].append(float(row[DEV_F_SCORE]))\n if row[DEV_ACCURACY] != \"_\":\n training_curves[\"dev\"][\"acc\"].append(float(row[DEV_ACCURACY]))\n if row[TEST_LOSS] != \"_\":\n training_curves[\"test\"][\"loss\"].append(float(row[TEST_LOSS]))\n if row[TEST_F_SCORE] != \"_\":\n training_curves[\"test\"][\"f_score\"].append(float(row[TEST_F_SCORE]))\n if row[TEST_ACCURACY] != \"_\":\n training_curves[\"test\"][\"acc\"].append(float(row[TEST_ACCURACY]))\n\n return training_curves\n\n @staticmethod\n def _extract_weight_data(file_name: Path) -> dict:\n weights = defaultdict(lambda: defaultdict(lambda: list()))\n\n with open(file_name, \"r\") as tsvin:\n tsvin = csv.reader(tsvin, delimiter=\"\\t\")\n\n for row in tsvin:\n name = row[WEIGHT_NAME]\n param = row[WEIGHT_NUMBER]\n value = float(row[WEIGHT_VALUE])\n\n weights[name][param].append(value)\n\n return weights\n\n @staticmethod\n def _extract_learning_rate(file_name: Path):\n lrs = []\n losses = []\n\n with open(file_name, \"r\") as tsvin:\n tsvin = csv.reader(tsvin, delimiter=\"\\t\")\n row = next(tsvin, None)\n LEARNING_RATE = row.index(\"LEARNING_RATE\")\n TRAIN_LOSS = row.index(\"TRAIN_LOSS\")\n\n # then get all relevant values from the tsv\n for row in tsvin:\n if row[TRAIN_LOSS] != \"_\":\n losses.append(float(row[TRAIN_LOSS]))\n if row[LEARNING_RATE] != \"_\":\n lrs.append(float(row[LEARNING_RATE]))\n\n return lrs, losses\n\n def plot_weights(self, file_name: Union[str, Path]):\n if type(file_name) is str:\n file_name = Path(file_name)\n\n weights = self._extract_weight_data(file_name)\n\n total = len(weights)\n columns = 2\n rows = max(2, int(math.ceil(total / columns)))\n\n figsize = (5, 5)\n if rows != columns:\n figsize = (5, rows + 5)\n\n fig = plt.figure()\n f, axarr = plt.subplots(rows, columns, figsize=figsize)\n\n c = 0\n r = 0\n for name, values in weights.items():\n # plot i\n axarr[r, c].set_title(name, fontsize=6)\n for _, v in values.items():\n axarr[r, c].plot(np.arange(0, len(v)), v, linewidth=0.35)\n axarr[r, c].set_yticks([])\n axarr[r, c].set_xticks([])\n c += 1\n if c == columns:\n c = 0\n r += 1\n\n while r != rows and c != columns:\n axarr[r, c].set_yticks([])\n axarr[r, c].set_xticks([])\n c += 1\n if c == columns:\n c = 0\n r += 1\n\n # save plots\n f.subplots_adjust(hspace=0.5)\n plt.tight_layout(pad=1.0)\n path = file_name.parent / \"weights.png\"\n plt.savefig(path, dpi=300)\n\n plt.close(fig)\n\n def plot_training_curves(self, file_name: Union[str, Path]):\n if type(file_name) is str:\n file_name = Path(file_name)\n\n fig = plt.figure(figsize=(15, 10))\n\n training_curves = self._extract_evaluation_data(file_name)\n\n # plot 1\n plt.subplot(3, 1, 1)\n if training_curves[\"train\"][\"loss\"]:\n x = np.arange(0, len(training_curves[\"train\"][\"loss\"]))\n plt.plot(x, training_curves[\"train\"][\"loss\"], label=\"training loss\")\n if training_curves[\"dev\"][\"loss\"]:\n x = np.arange(0, len(training_curves[\"dev\"][\"loss\"]))\n plt.plot(x, training_curves[\"dev\"][\"loss\"], label=\"validation loss\")\n if training_curves[\"test\"][\"loss\"]:\n x = np.arange(0, len(training_curves[\"test\"][\"loss\"]))\n plt.plot(x, training_curves[\"test\"][\"loss\"], label=\"test loss\")\n plt.legend(bbox_to_anchor=(1.04, 0), loc=\"lower left\", borderaxespad=0)\n plt.ylabel(\"loss\")\n plt.xlabel(\"epochs\")\n\n # plot 2\n plt.subplot(3, 1, 2)\n if training_curves[\"train\"][\"acc\"]:\n x = np.arange(0, len(training_curves[\"train\"][\"acc\"]))\n plt.plot(x, training_curves[\"train\"][\"acc\"], label=\"training accuracy\")\n if training_curves[\"dev\"][\"acc\"]:\n x = np.arange(0, len(training_curves[\"dev\"][\"acc\"]))\n plt.plot(x, training_curves[\"dev\"][\"acc\"], label=\"validation accuracy\")\n if training_curves[\"test\"][\"acc\"]:\n x = np.arange(0, len(training_curves[\"test\"][\"acc\"]))\n plt.plot(x, training_curves[\"test\"][\"acc\"], label=\"test accuracy\")\n plt.legend(bbox_to_anchor=(1.04, 0), loc=\"lower left\", borderaxespad=0)\n plt.ylabel(\"accuracy\")\n plt.xlabel(\"epochs\")\n\n # plot 3\n plt.subplot(3, 1, 3)\n if training_curves[\"train\"][\"f_score\"]:\n x = np.arange(0, len(training_curves[\"train\"][\"f_score\"]))\n plt.plot(x, training_curves[\"train\"][\"f_score\"], label=\"training f1-score\")\n if training_curves[\"dev\"][\"f_score\"]:\n x = np.arange(0, len(training_curves[\"dev\"][\"f_score\"]))\n plt.plot(x, training_curves[\"dev\"][\"f_score\"], label=\"validation f1-score\")\n if training_curves[\"test\"][\"f_score\"]:\n x = np.arange(0, len(training_curves[\"test\"][\"f_score\"]))\n plt.plot(x, training_curves[\"test\"][\"f_score\"], label=\"test f1-score\")\n plt.legend(bbox_to_anchor=(1.04, 0), loc=\"lower left\", borderaxespad=0)\n plt.ylabel(\"f1-score\")\n plt.xlabel(\"epochs\")\n\n # save plots\n plt.tight_layout(pad=1.0)\n path = file_name.parent / \"training.png\"\n plt.savefig(path, dpi=300)\n\n plt.close(fig)\n\n def plot_learning_rate(\n self, file_name: Union[str, Path], skip_first: int = 10, skip_last: int = 5\n ):\n if type(file_name) is str:\n file_name = Path(file_name)\n\n lrs, losses = self._extract_learning_rate(file_name)\n lrs = lrs[skip_first:-skip_last] if skip_last > 0 else lrs[skip_first:]\n losses = losses[skip_first:-skip_last] if skip_last > 0 else losses[skip_first:]\n\n fig, ax = plt.subplots(1, 1)\n ax.plot(lrs, losses)\n ax.set_ylabel(\"Loss\")\n ax.set_xlabel(\"Learning Rate\")\n ax.set_xscale(\"log\")\n ax.xaxis.set_major_formatter(plt.FormatStrFormatter(\"%.0e\"))\n\n # save plot\n plt.tight_layout(pad=1.0)\n path = file_name.parent / \"learning_rate.png\"\n plt.savefig(path, dpi=300)\n\n plt.close(fig)\n", "sub_path": "flair/visual/training_curves.py", "file_name": "training_curves.py", "file_ext": "py", "file_size_in_byte": 8905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "matplotlib.use", "line_number": 11, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 37, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 81, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 93, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 98, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 112, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 112, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 114, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 159, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 159, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 220, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 220, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.FormatStrFormatter", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}]} {"seq_id": "599055852", "text": "import numpy as np\r\nimport random\r\nfrom PIL import Image\r\nimport os\r\n\r\n\r\ndef get_random_image_name():\r\n face_path = 'train/face/'\r\n non_face_path = 'train/non-face/'\r\n face = random.randint(0,1)\r\n file_name = random.choice(os.listdir(face_path if face else non_face_path))\r\n return (face_path if face else non_face_path) + file_name, face\r\n\r\n\r\ndef generate_validation_image():\r\n images = map(Image.open, [get_random_image_name()[0] for _ in range(169)])\r\n new_img = Image.new('RGB', (256, 256))\r\n tlx = 0\r\n tly = 0\r\n for img in images:\r\n new_img.paste(img, (tlx, tly))\r\n if tlx < 12*19:\r\n tlx += 19\r\n else:\r\n tlx = 0\r\n tly += 19\r\n new_img.save('test.png')\r\n\r\n\r\ndef main():\r\n generate_validation_image()\r\n\r\n\r\nif __name__ == '__main__':\r\n random.seed(109993439)\r\n main()", "sub_path": "generate_scamp_test_img.py", "file_name": "generate_scamp_test_img.py", "file_ext": "py", "file_size_in_byte": 864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random.randint", "line_number": 10, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 35, "usage_type": "call"}]} {"seq_id": "386392164", "text": "import pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\nimport streamlit as st\n\n\ndef read_sql(sql_string):\n with open('/Users/martindanek/Documents/programovani/autent.txt',\n encoding='utf-8') as file:\n autent_list = eval(file.read())['raspberry']['mariadb']\n\n user = autent_list[0]\n psw = autent_list[2]\n\n conn_string = f\"mysql+pymysql://{user}:{psw}@192.168.0.199/engeto\"\n pi_conn = create_engine(conn_string, echo=False)\n\n return pd.read_sql_query(sql_string, pi_conn)\n\n\ndef recommend_book(option, df_rated_books):\n\n selection = df_rated_books['title'] == option\n readers = np.unique(df_rated_books.loc[selection, 'id'].to_list())\n\n selection = df_rated_books['id'].isin(readers)\n df_suitable_books = df_rated_books.loc[selection, :]\n\n df_suit_books_freq = df_suitable_books \\\n .groupby(['title']) \\\n .agg({'rating': 'mean', 'id': 'count'}) \\\n .reset_index()\n\n selection = df_suit_books_freq['id'] >= 3\n books_to_compare = df_suit_books_freq.loc[\n selection, 'title'].to_list()\n\n selection = df_suitable_books['title'].isin(books_to_compare)\n df_ratings_data_raw = df_suitable_books.loc[\n selection, ['id', 'rating', 'title']]\n\n df_mean_rate = df_ratings_data_raw \\\n .groupby(['id', 'title']) \\\n .agg({'rating': 'mean'}) \\\n .reset_index()\n\n dataset_for_corr = df_mean_rate.pivot(index='id',\n columns='title',\n values='rating')\n\n dataset_of_other_books = dataset_for_corr.copy(deep=False)\n try:\n dataset_of_other_books.drop([option], axis=1, inplace=True)\n except:\n return pd.DataFrame(columns=['tile', 'rating'])\n\n book_titles = []\n correlations = []\n avgrating = []\n\n for book_title in list(dataset_of_other_books.columns.values):\n book_titles.append(book_title)\n\n correlations\\\n .append(dataset_for_corr[option]\n .corr(dataset_of_other_books[book_title], method='pearson')\n )\n\n selection = df_ratings_data_raw['title'] == book_title\n df_tab = df_ratings_data_raw\\\n .loc[selection, ['title', 'rating']]\\\n .groupby('title')\\\n .agg({'rating': 'mean'})\n avgrating.append(df_tab['rating'].min())\n\n corr_fellowship = pd.DataFrame(\n list(zip(book_titles, correlations, avgrating)),\n columns=['book', 'corr', 'avg_rating'])\n\n return corr_fellowship.sort_values('corr', ascending=False).iloc[:3, :]\n\n\nif __name__ == '__main__':\n\n basic_sql = \"\"\"\n WITH b_rate AS (\n SELECT *\n FROM `BX-Book-Ratings`\n WHERE `Book-Rating` <> 0\n ), b_book AS (\n SELECT *\n FROM `BX-Books`\n )\n SELECT b_rate.`User-ID` AS id,\n b_rate.ISBN AS isbn,\n b_rate.`Book-Rating` AS rating,\n b_book.`Book-Title` AS title,\n b_book.`Book-Author` AS author,\n b_book.Publisher AS publisher,\n b_book.`Year-Of-Publication` as year,\n b_book.`Image-URL-M` AS url_image\n FROM b_rate\n JOIN b_book ON b_rate.ISBN = b_book.ISBN\n ;\"\"\"\n\n st.title('BOOK RECOMMENDATION TASK')\n st.balloons()\n st.markdown('__SELECTED BOOK__')\n\n df_rated_books = read_sql(basic_sql)\n\n option = st.selectbox(\n 'Choose book:', pd.Series(df_rated_books['title']).unique().tolist()\n )\n\n sql_string = f\"\"\"\n SELECT *\n FROM `BX-Books`\n WHERE `Book-Title` = '{option}'\n AND `Year-Of-Publication` = (\n SELECT MAX(`Year-Of-Publication`) as max_year\n FROM `BX-Books`\n WHERE `Book-Title` = '{option}'\n );\"\"\"\n\n df = read_sql(sql_string)\n\n st.write(df.iloc[0, :5])\n st.sidebar.image(f\"{df['Image-URL-M'][0]}\")\n\n isbn = df['ISBN'][0]\n sql_string = f\"\"\"\n SELECT AVG(`Book-Rating`) AS average_rating\n FROM `BX-Book-Ratings`\n WHERE `BX-Book-Ratings`.ISBN = '{isbn}'\n \"\"\"\n df = read_sql(sql_string)\n\n st.sidebar.write(f\"RATING - {df['average_rating'][0]}\")\n\n st.markdown('__RECOMMENDED BOOKS__')\n st.sidebar.markdown('__RECOMMENDED BOOKS__')\n\n df_rec = recommend_book(option, df_rated_books)\n st.write(df_rec)\n\n try:\n for book in df_rec['book'].to_list():\n sql_string = f\"\"\"\n SELECT `BX-Books`.`Image-URL-M`\n FROM `BX-Books`\n WHERE `BX-Books`.`Book-Title` = '{book}'\n \"\"\"\n df = read_sql(sql_string)\n st.sidebar.image(f\"{df['Image-URL-M'].to_list()[0]}\")\n except:\n st.sidebar.write('No recommended book!')\n\n# streamlit run task_stream.py\n", "sub_path": "task_stream.py", "file_name": "task_stream.py", "file_ext": "py", "file_size_in_byte": 4818, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 106, "usage_type": "call"}, {"api_name": "streamlit.balloons", "line_number": 107, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 108, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.sidebar.image", "line_number": 129, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 129, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 139, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 139, "usage_type": "attribute"}, {"api_name": "streamlit.markdown", "line_number": 141, "usage_type": "call"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 142, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 142, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 145, "usage_type": "call"}, {"api_name": "streamlit.sidebar.image", "line_number": 155, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 155, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 157, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 157, "usage_type": "attribute"}]} {"seq_id": "225963368", "text": "import sys\nimport psycopg2\nimport pytz\nimport re\nfrom datetime import datetime\nimport numpy as np\n#import logging\n\nconn = psycopg2.connect(host='localhost', user='postgres', password='postgres',dbname='eyes4d')\n\nstore_contacts = conn.cursor()\nfetch_uuid = conn.cursor()\nrefresh_subscribers = conn.cursor()\nstore_contact_groups = conn.cursor()\nstore_fetchLog = conn.cursor()\nload_fetchLog = conn.cursor()\nload_contacts = conn.cursor()\n\nfrom temba_client.v2 import TembaClient\n\nclient = TembaClient('rapidpro.ilhasoft.mobi', '')\ngroup_uuid_eyes4d_december = \"7332c53c-0cb8-48c0-9512-76e93d4dd590\"\ngroup_uuid_eyes4d_december_mbarali = \"4afadd2f-c071-451c-8d3a-9fd6b533ac02\"\ngroup_uuid_eyes4d_december_mbeya = \"688781d1-4b0b-4454-8e6a-d996365573aa\"\nfetched_on = datetime.now()\n\n\ngroup_uuid_arr = [group_uuid_eyes4d_december]\n\n#logging.basicConfig(filename='output.log', filemode='w', level=logging.DEBUG)\n#logger = logging.getLogger(__name__)\ndef fetchContacts():\n for contact_batch in client.get_contacts(group=group_uuid_eyes4d_december,after=last_date_of_fetch).iterfetches():\n for contact in contact_batch:\n # fetch values from rapid pro for each contact in the group\n contact_uuid = str(contact.uuid)\n contact_urn = contact.urns if contact.urns else \"\"\n contact_groups = contact.groups\n contact_name = contact.fields['contact_name'] if contact.fields['contact_name'] != None else ''\n contact_region = contact.fields['region'] if contact.fields['region'] != None else ''\n contact_district = contact.fields['district'] if contact.fields['district'] != None else ''\n contact_village_assigned = contact.fields['villageassigned'] if contact.fields['villageassigned'] != None else ''\n contact_households_visited = contact.fields['households_visited_total'] if contact.fields['households_visited_total'] != None else 0\n contact_gender = contact.fields['gender'] if contact.fields['gender'] != None else ''\n \n birth_year = contact.fields['born'] if contact.fields['born'] != None else None\n # birth_year = birth_year if birth_year != None and birth_year.isdigit() else '0'\n last_household_visit_date = np.datetime64(contact.fields['last_household_visit_date']).astype(datetime).date() if contact.fields['last_household_visit_date'] != None else None\n created_on = np.datetime64(contact.created_on).astype(datetime)\n \n if True:\n #inserting values in the contacts table\n\n subscriber = checkuuid(contact_uuid)\n if subscriber != None:\n refresh_subscription(contact_uuid)\n conn.commit() \n\n print(contact_name,contact_uuid,contact_urn if contact_urn else \"no urn details\" )\n\n #last_successful_fetch_on = datetime.now()\n\n contact_sql = \"\"\"INSERT INTO contacts (uuid,urn,name,birth_year,gender,region,district,village_assigned,households_visited,last_household_visit,created_on)\n VALUES (%s, %s, %s,%s, %s , %s, %s, %s, %s, %s, %s ) \"\"\"\n \n try:\n #if True: \n store_contacts.execute(contact_sql,(contact_uuid,contact_urn,contact_name,birth_year,contact_gender,contact_region,contact_district,contact_village_assigned,contact_households_visited,last_household_visit_date,created_on))\n #break\n except:\n print(\"Unexpected error: \", sys.exc_info()[0], sys.exc_info()[1] )\n raise\n #commiting changes to the database\n\n\n conn.commit()\n putFetchLog(group_uuid_eyes4d_december)\n #print(\"Row id \" , store_contacts.lastrowid)\n\n print (\"success\")\n\n\ndef refresh_subscription(uuid):\n refresh_query = \"\"\" DELETE FROM contacts WHERE contacts.uuid = %s \"\"\"\n refresh_subscribers.execute(refresh_query,(uuid,))\n\ndef checkuuid(uuid):\n uuid_query = \"\"\" SELECT contacts.uuid FROM contacts WHERE contacts.uuid = %s \"\"\"\n fetch_uuid.execute(uuid_query,(uuid,))\n subscriber = fetch_uuid.fetchone()\n return subscriber\n\n\ndef putFetchLog(uuid):\n last_successful_fetch_on = datetime.now()\n fetchLog_sql = \"\"\" INSERT INTO fetchlog (uuid,run_or_contact,last_successful_fetch_on,created_on)\n VALUES (%s,%s,%s,%s) \"\"\"\n\n store_fetchLog.execute(fetchLog_sql,(uuid,'contact',last_successful_fetch_on,last_successful_fetch_on))\n\n conn.commit()\n print (\"success fetchlog\")\n\ndef checkFetchLog():\n\n load_fetchLog.execute(\"\"\"SELECT last_successful_fetch_on\n FROM fetchlog\n WHERE run_or_contact=%s\n AND uuid = %s\n ORDER BY last_successful_fetch_on DESC\n LIMIT 1\n \"\"\",\n ('contact', group_uuid_eyes4d_december))\n\n row = load_fetchLog.fetchone()\n\n\n last_fetch_time = datetime.strptime('1970-01-01 00:00:00','%Y-%m-%d %H:%M:%S')\n if (row is not None):\n last_fetch_time = row[0]\n\n utc = pytz.timezone('GMT')\n aware_last_fetch_time = utc.localize(last_fetch_time)\n aware_last_fetch_time.tzinfo # <UTC>\n #aware_last_fetch_time.strftime(\"%a %b %d %H:%M:%S %Y\") # Wed Nov 11 13:00:00 2015\n\n return aware_last_fetch_time\n\ndef hasSevenDigitNumber(str):\n return bool(re.search(r'\\d{7}', str))\n \nlast_date_of_fetch = checkFetchLog()\n\nfetchContacts()\n", "sub_path": "eyes4d_get_contacts.py", "file_name": "eyes4d_get_contacts.py", "file_ext": "py", "file_size_in_byte": 5476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "psycopg2.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "temba_client.v2.TembaClient", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "argument"}, {"api_name": "numpy.datetime64", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "argument"}, {"api_name": "numpy.datetime64", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 122, "usage_type": "call"}, {"api_name": "re.search", "line_number": 130, "usage_type": "call"}]} {"seq_id": "238430686", "text": "#!/usr/bin/env python\n\"\"\"\nScript Header\n\n$Id: $ US24359\n\nCopyright (c) 2016-2017 Cisco Systems, Inc.\n\nName:\n cmCC24359_3pcc_BS_Functional_107_OutboundCallfromAnonymous.py\n\nPurpose:\n This test case verifies the DUT completes an anonymous outbound call\n\nAuthor:\n Rajakumara M R (rajmr@cisco.com)\nModified By:\n Sinja Satheesh P(spoothat@cisco.com)\n\nDescription:\n Originate a call from a Phone 1 to Phone 2, dialling the caller ID\n blocking feature code \"*67\" followed by Phone 2's number. Answer\n the call. Disconnect the call from DUT1.\n\nTest bed requirement:\n 1: Two 3pcc phones\n 2: Both phones should be registered successfully before\n running script\n 3. Phone 2 should be in different group than DUT1.\n\nTest Steps:\n 1. Phone 1 dials (*67) followed by the Phone 2 number\n 2. Phone 2 answers the call\n 3. Phone 1 puts the call on hold\n 4. Phone 1 hangs up\n\nVerify:\n 1. Phone 2 phone rings and displays anonymous caller id,\n Phone 1 hears ring back\n 2. Two way voice path is established\n 3. Call is released between phones.\n\nKnown Bugs:\n\nEnd of Header\n\"\"\"\n\nimport tng\nimport logging\nfrom tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended\\\n import wait_for_ccapi_call_states, register_lines\nfrom tng.frontend.timing import wait\nfrom tng_sl.contrib.mpp.phone_line_reg_helper import PhoneLineRegHelper\nfrom tng_sl.contrib.mpp.phone_line_reg_helper import PhoneConfigHelper\nfrom tng_sl.contrib.setup_helper import SetupHelpersTestCase\nfrom tng_sl.contrib.mpp.tshark_helper import TsharkHelper\nfrom tng_sl.contrib.mpp.broadsoft.broadsoft_config import BroadsoftConfig\n\nlog = logging.getLogger('OutboundCallfromAnonymous')\n\n\nclass OutboundCallfromAnonymous(SetupHelpersTestCase, tng.api.TestCase):\n\n helpers = (\n PhoneConfigHelper, TsharkHelper, PhoneLineRegHelper)\n helper_num_devices = 2\n\n def setUp(self):\n log.info(\"Start of setUp\")\n self.xsi_user_id1 = self.toolkit.get_test_env_info(\n section='bsoft', parameter_name=\"xsi_user_id1\")\n\n self.broadsoft = BroadsoftConfig()\n phone_data1 = self.toolkit.get_test_env_info(\n section='phone_gp1')\n\n self.user_id2 = phone_data1['userID1']\n\n self.p1_fname, self.p1_lname = self.broadsoft.get_first_and_last_name(\n user_id_proxy=self.xsi_user_id1, user_id=self.user_id1)\n\n log.info(\"Configure Phone2 to register\")\n register_lines(phone_data1, [self.oPhone2], lines=[1])\n\n log.info(\"End of setUp\")\n\n def test_OutboundCallfromAnonymous(self):\n\n log.info(\"Start of test_OutboundCallfromAnonymous\")\n self.oPhone2.register_call_event('VOIP_MSG_CALL_EVENT_INCOMING')\n self.oPhone2.ccapi.feedback_subscribe(self.oPhone2.subscribed_callback)\n log.info('Set *67 as Block CID star code in phone1 webpage')\n self.oPhone1.ui.set_web_parameter_http(\n Block_CID_Act_Code_=['Regional', 'Block CID Act Code', '*67'])\n\n block_cid_code = self.oPhone1.ui.get_web_parameter_http(\n 'Regional', 'Block CID Act Code')\n self.assertEqual('*67', block_cid_code)\n\n log.info(\n \"Phone1 dial's *67 followed by Phone2's number: {}\".format(\n self.user_id2))\n userid2_hash = \"{}#\".format(self.user_id2)\n self.oPhone1.ccapi.dial('null', '*67', '', 1, 0, 1)\n wait(3, 'wait before dialing digits')\n for digit in list(userid2_hash):\n self.oPhone1.ccapi.sendDialDigit('0000', digit)\n ph1_identity_string = '{} {}'.format(self.p1_fname, self.p1_lname)\n # Check phone1 in proceeding and phone2 in ringing status\n wait_for_ccapi_call_states(\n self.devices, (\"PROCEEDING\", \"RINGING\"), timeout=20)\n wait(3, \"Check for anonymous caller id on Phone2\")\n # check for anonymous caller id on phone2\n self.oPhone2.check_caller_id('', '')\n self.oPhone2.ccapi.feedback_unsubscribe(\n self.oPhone2.subscribed_callback)\n self.oPhone2.unregister_call_event('VOIP_MSG_CALL_EVENT_INCOMING')\n\n log.info(\"Phone2 accepts the call\")\n self.oPhone2.ccapi.accept('0000')\n # Check phone1 and phone2 are in connected status\n wait_for_ccapi_call_states(\n self.devices, (\"CONNECTED\", \"CONNECTED\"), timeout=20)\n\n log.info(\"Phone1 disconnects the call\")\n self.oPhone1.ccapi.hangUp('0000')\n self.oPhone1.ccapi.hangUp('0001')\n # Check phone1 and phone2 in idle status\n wait_for_ccapi_call_states(\n self.devices, (\"IDLE\", \"IDLE\"), timeout=20)\n\n log.info(\"End of test_OutboundCallfromAnonymous\")\n\n\n# this is called by 'tng run'\ndef main():\n tng.api.runner()\n\nif __name__ == '__main__':\n tng.run(main)\n", "sub_path": "common/IOT/Broadsoft_Functional/cmCC24359_3pcc_BS_Functional_107_OutboundCallfromAnonymous.py", "file_name": "cmCC24359_3pcc_BS_Functional_107_OutboundCallfromAnonymous.py", "file_ext": "py", "file_size_in_byte": 4778, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 59, "usage_type": "call"}, {"api_name": "tng_sl.contrib.setup_helper.SetupHelpersTestCase", "line_number": 62, "usage_type": "name"}, {"api_name": "tng.api", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tng_sl.contrib.mpp.phone_line_reg_helper.PhoneConfigHelper", "line_number": 65, "usage_type": "name"}, {"api_name": "tng_sl.contrib.mpp.tshark_helper.TsharkHelper", "line_number": 65, "usage_type": "name"}, {"api_name": "tng_sl.contrib.mpp.phone_line_reg_helper.PhoneLineRegHelper", "line_number": 65, "usage_type": "name"}, {"api_name": "tng_sl.contrib.mpp.broadsoft.broadsoft_config.BroadsoftConfig", "line_number": 73, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.register_lines", "line_number": 83, "usage_type": "call"}, {"api_name": "tng.frontend.timing.wait", "line_number": 105, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.wait_for_ccapi_call_states", "line_number": 110, "usage_type": "call"}, {"api_name": "tng.frontend.timing.wait", "line_number": 112, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.wait_for_ccapi_call_states", "line_number": 122, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.wait_for_ccapi_call_states", "line_number": 129, "usage_type": "call"}, {"api_name": "tng.api.runner", "line_number": 137, "usage_type": "call"}, {"api_name": "tng.api", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tng.run", "line_number": 140, "usage_type": "call"}]} {"seq_id": "209683218", "text": "#!/usr/bin/env python3\nfrom __future__ import print_function\n\nimport random\nimport string\nimport subprocess\nfrom random import shuffle\nfrom flask import Flask, render_template, request, jsonify, abort, session\nfrom mongo_db import store_valid_in_mongo, replete_valid_db, store_anno_in_mongo, replete_anno_db\nfrom nmtkeras.sample_ensemble import predict\nfrom proces_and_convert_to_char import process, convert_char, restore\nfrom byte_pair_loading import bpe, bpe_nl, graph\nfrom nmtkeras.sample_ensemble import load_in\n\n# from loading_models import *\n# initialize the Flask application and the Keras model\napp = Flask(__name__)\n\napp.secret_key = ''.join(random.choice(string.printable) for _ in range(20))\n\nchar_args, char_params, char_models, char_dataset = load_in(\"char\", \"NL_GRO\")\nchar_args_nl, char_params_nl, char_models_nl, char_dataset_nl = load_in(\"char\", \"GRO_NL\")\n\nbpe_args, bpe_params, bpe_models, bpe_dataset = load_in(\"BPE\", \"NL_GRO\")\nbpe_args_nl, bpe_params_nl, bpe_models_nl, bpe_dataset_nl = load_in(\"BPE\", \"GRO_NL\")\n\n\n# to use flask.session, a secret key must be passed to the app instance\n\n\ndef bpe_encode(input):\n return \"\".join(bpe.process_line(input))\n\n\ndef bpe_encode_nl(input):\n return \"\".join(bpe_nl.process_line(input))\n\n\ndef get_predictions(text, args, params, models, dataset):\n pred = predict(text, args, params, models, dataset)\n return pred\n\n\n\"\"\"Flask env \"\"\"\n\n\n@app.route('/')\ndef home():\n return render_template('vertaal.html')\n\n\ndef update_anno(read_items=None):\n if read_items is None:\n read_items = []\n files = replete_anno_db(read_items)\n all = [(str(instance._id), instance.orginal_gronings) for instance in files if\n str(instance._id) not in read_items]\n return all\n\n\ndef update_valid(read_items=None):\n if read_items is None:\n read_items = []\n files = replete_valid_db(read_items)\n all = [(str(instance._id), instance.annotated_gronings, instance.orginal_gronings) for\n instance in files if\n str(instance._id) not in read_items]\n return all\n\n\n@app.route('/help', methods=['GET'])\ndef display_sent():\n \"\"\"function to return the HTML page to display the sentence\"\"\"\n session['count'] = 0\n\n if \"read_items\" in session:\n read_items = session.get('read_items', None)\n session['read_items'] = read_items\n all = update_anno(read_items)\n\n else:\n all = update_anno()\n shuffle(all)\n\n return render_template('help.html', all=all, count=session['count'])\n\n\n@app.route('/get_anno', methods=['GET'])\ndef get_anno():\n _direction = request.args.get('direction')\n count = session.get('count', None)\n session['count'] = count\n session['count'] = session['count'] + (1 if _direction == 'f' else - 1)\n if \"read_items\" in session:\n read_items = session.get('read_items', None)\n session['read_items'] = read_items\n all = update_anno(read_items)\n\n else:\n all = update_anno()\n\n return jsonify(\n {'forward': str(session['count'] + 1 < len(all)),\n 'back': str(bool(session['count'])), \"count\": session['count'], \"all\": all})\n\n\n@app.route('/store_in_mongo', methods=['POST'])\ndef store_in_mongo():\n if request.method == 'POST':\n anno = request.form['annotation']\n original_id = request.form['original_id']\n store_anno_in_mongo(anno, original_id)\n if 'read_items' in session:\n read_items = session.get('read_items', None)\n read_items.append(str(original_id))\n session['read_items'] = read_items\n else:\n session['read_items'] = [str(original_id)]\n\n if \"read_items\" in session:\n read_items = session.get('read_items', None)\n session['read_items'] = read_items\n all = update_anno(read_items)\n\n else:\n all = update_anno()\n count = session.get('count', None)\n\n if int(count) != 0:\n session['count'] = count - 1\n\n return jsonify({\"count\": session['count'], \"all\": all})\n\n\n@app.route('/validation', methods=['GET'])\ndef display_validation():\n \"\"\"function to return the HTML page to display the sentence\"\"\"\n session['validation_count'] = 0\n\n if \"read_validations\" in session:\n read_items = session.get('read_validations', None)\n session['read_validations'] = read_items\n all = update_valid(read_items)\n\n else:\n all = update_valid()\n\n shuffle(all)\n\n return render_template('validation.html', all=all, count=session['validation_count'])\n\n\n@app.route('/get_validations', methods=['GET'])\ndef get_validations():\n _direction = request.args.get('direction')\n val_count = session.get('validation_count', None)\n session['validation_count'] = val_count\n session['validation_count'] = session['validation_count'] + (1 if _direction == 'f' else - 1)\n\n if \"read_validations\" in session:\n read_items = session.get('read_validations', None)\n session['read_validations'] = read_items\n all = update_valid(read_items)\n\n else:\n all = update_valid()\n\n return jsonify(\n {'forward': str(session['validation_count'] + 1 < len(session['all_validations'])),\n 'back': str(bool(session['validation_count'])), \"count\": session['validation_count'],\n 'all': all})\n\n\n@app.route('/store_validation_in_mongo', methods=['POST'])\ndef store_validation_in_mongo():\n if request.method == 'POST':\n original_id = request.form['original_id']\n best = request.form['best_pick']\n store_valid_in_mongo(best, original_id)\n if 'read_validations' in session:\n read_items = session.get('read_validations', None)\n read_items.append(str(original_id))\n session['read_validations'] = read_items\n else:\n session['read_validations'] = [str(original_id)]\n\n if \"read_validations\" in session:\n read_items = session.get('read_validations', None)\n session['read_validations'] = read_items\n all = update_valid(read_items)\n\n else:\n all = update_valid()\n\n count = session.get('validation_count', None)\n if int(count) != 0:\n session['validation_count'] = count - 1\n\n return jsonify({'count': session['validation_count'],\n 'all_validations': all,\n 'data': render_template('response.html', count=session['validation_count'],\n all_validations=all)})\n\n\n\"\"\"Char NL\"\"\"\n\n\n@app.route('/predict_CHAR_nl-gro', methods=['POST'])\ndef predict_predict_nl_gro():\n # Validation\n if request.method == 'POST':\n translation_query = request.form['translation']\n translation_query = translation_query.strip()\n\n if translation_query[-1] not in string.punctuation:\n translation_query = translation_query + \".\"\n punctuation_alert = True\n else:\n punctuation_alert = False\n # Preprocess\n processed = process(translation_query)\n # Tokenize to Char\n char_encoding = convert_char(processed)\n # Translate\n with graph.as_default():\n output = get_predictions(char_encoding, char_args, char_params, char_models, char_dataset)\n # Detokenize and restore\n output_sen = restore(output)\n if punctuation_alert:\n output_sen = output_sen[0:-1]\n return jsonify({'translation': output_sen})\n else:\n return abort(404)\n\n\n\"\"\"Char gro nl\"\"\"\n\n\n@app.route('/predict_CHAR_gro-nl', methods=['POST'])\ndef predict_gro_nl():\n # Validation\n if request.method == 'POST':\n translation_query = request.form['translation']\n translation_query = translation_query.strip()\n\n if translation_query[-1] not in string.punctuation:\n translation_query = translation_query + \".\"\n punctuation_alert = True\n else:\n punctuation_alert = False\n # Preprocess\n processed = process(translation_query)\n # Tokenize to Char\n char_encoding = convert_char(processed)\n # Translate\n with graph.as_default():\n output = get_predictions(char_encoding, char_args_nl, char_params_nl, char_models_nl, char_dataset_nl)\n # Detokenize and restore\n output_sen = restore(output)\n if punctuation_alert:\n output_sen = output_sen[0:-1]\n\n return jsonify({'translation': output_sen})\n\n else:\n return abort(404)\n\n\n\"\"\"BPE NL GRO\"\"\"\n\n\n@app.route('/predict_BPE_nl-gro', methods=['POST'])\ndef predict_nl_gro_bpe():\n # Validation\n if request.method == 'POST':\n translation_query = request.form['translation']\n translation_query = translation_query.strip()\n\n if translation_query[-1] not in string.punctuation:\n translation_query = translation_query + \".\"\n punctuation_alert = True\n else:\n punctuation_alert = False\n # Preprocess\n encoded_text = bpe_encode(translation_query)\n\n # Translate\n with graph.as_default():\n output = get_predictions(encoded_text, bpe_args, bpe_params, bpe_models, bpe_dataset)\n # Detokenize and restore\n output_string = \"\".join(output)\n\n decode_string = subprocess.run(['bash', 'restore.sh', output_string],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False,\n check=True,\n text=True)\n output_sen = decode_string.stdout\n if punctuation_alert:\n output_sen = output_sen[0:-1]\n\n return jsonify({'translation': output_sen})\n\n else:\n return abort(404)\n\n\n\"\"\"BPE GRO NL\"\"\"\n\n\n@app.route('/predict_BPE_gro-nl', methods=['POST'])\ndef predict_gro_nl_bpe():\n # Validation\n if request.method == 'POST':\n translation_query = request.form['translation']\n translation_query = translation_query.strip()\n\n if translation_query[-1] not in string.punctuation:\n translation_query = translation_query + \".\"\n punctuation_alert = True\n else:\n punctuation_alert = False\n # Preprocess\n encoded_text = bpe_encode_nl(translation_query)\n\n with graph.as_default():\n\n output = get_predictions(encoded_text, bpe_args_nl, bpe_params_nl, bpe_models_nl, bpe_dataset_nl)\n # Detokenize and restore\n output_string = \"\".join(output)\n\n decode_string = subprocess.run(['bash', 'restore.sh', output_string],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False,\n check=True,\n text=True)\n output_sen = decode_string.stdout\n if punctuation_alert:\n output_sen = output_sen[0:-1]\n\n return jsonify({'translation': output_sen})\n\n else:\n return abort(404)\n\n\nif __name__ == '__main__':\n print((\"* Loading Keras model and Flask starting server...\"\"please wait until server has fully started\"))\n\n app.run(debug=False, use_reloader=False)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 11213, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "string.printable", "line_number": 19, "usage_type": "attribute"}, {"api_name": "nmtkeras.sample_ensemble.load_in", "line_number": 21, "usage_type": "call"}, {"api_name": "nmtkeras.sample_ensemble.load_in", "line_number": 22, "usage_type": "call"}, {"api_name": "nmtkeras.sample_ensemble.load_in", "line_number": 24, "usage_type": "call"}, {"api_name": "nmtkeras.sample_ensemble.load_in", "line_number": 25, "usage_type": "call"}, {"api_name": "byte_pair_loading.bpe.process_line", "line_number": 32, "usage_type": "call"}, {"api_name": "byte_pair_loading.bpe", "line_number": 32, "usage_type": "name"}, {"api_name": "byte_pair_loading.bpe_nl.process_line", "line_number": 36, "usage_type": "call"}, {"api_name": "byte_pair_loading.bpe_nl", "line_number": 36, "usage_type": "name"}, {"api_name": "nmtkeras.sample_ensemble.predict", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "mongo_db.replete_anno_db", "line_number": 55, "usage_type": "call"}, {"api_name": "mongo_db.replete_valid_db", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 78, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "mongo_db.store_anno_in_mongo", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 127, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 142, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 160, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 169, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 170, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 178, "usage_type": "name"}, {"api_name": "mongo_db.store_valid_in_mongo", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 180, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 181, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 181, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 183, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 185, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 187, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 188, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 189, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 197, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 199, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 201, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 211, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 212, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 212, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 215, "usage_type": "attribute"}, {"api_name": "proces_and_convert_to_char.process", "line_number": 221, "usage_type": "call"}, {"api_name": "proces_and_convert_to_char.convert_char", "line_number": 223, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph.as_default", "line_number": 225, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph", "line_number": 225, "usage_type": "name"}, {"api_name": "proces_and_convert_to_char.restore", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 233, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 242, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 242, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 243, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 243, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 246, "usage_type": "attribute"}, {"api_name": "proces_and_convert_to_char.process", "line_number": 252, "usage_type": "call"}, {"api_name": "proces_and_convert_to_char.convert_char", "line_number": 254, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph.as_default", "line_number": 256, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph", "line_number": 256, "usage_type": "name"}, {"api_name": "proces_and_convert_to_char.restore", "line_number": 259, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 266, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 275, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 275, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 276, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 276, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 279, "usage_type": "attribute"}, {"api_name": "byte_pair_loading.graph.as_default", "line_number": 288, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph", "line_number": 288, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 293, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 294, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 301, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 304, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 313, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 313, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 314, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 314, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 317, "usage_type": "attribute"}, {"api_name": "byte_pair_loading.graph.as_default", "line_number": 325, "usage_type": "call"}, {"api_name": "byte_pair_loading.graph", "line_number": 325, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 331, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 332, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 339, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 342, "usage_type": "call"}]} {"seq_id": "311628670", "text": "#coding: utf-8\n\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.shortcuts import get_object_or_404\nfrom django.core.urlresolvers import reverse as r\nfrom django.contrib.auth.decorators import login_required\n\nfrom grupos.forms import ApartamentoForm\nfrom grupos.models import Grupo, Apartamento\nfrom clientes.models import Cliente\n\nfrom core.views import group_required\n\n@group_required(['Administrador'])\ndef apartamento_lista(request,grupo_id):\n grupo = get_object_or_404(Grupo,id=grupo_id)\n return render(request,\"apartamento_lista.html\",{'grupo':grupo,'apartamentos':Apartamento.objects.filter(grupo=grupo).order_by('descricao')})\n\n@group_required(['Administrador'])\ndef apartamento_create(request,grupo_id):\n '''\n @apartamento_create: View para verificar se é post ou get na criacao de um apartamento\n '''\n grupo = get_object_or_404(Grupo,id=grupo_id)\n if request.method == 'POST':\n return apartamento_create_post(request,grupo)\n else:\n return render(request,\"apartamento_novo.html\",{'grupo':grupo,'form':ApartamentoForm()})\n\n@group_required(['Administrador'])\ndef apartamento_create_post(request,grupo):\n '''\n @apartamento_create_post:\n '''\n form = ApartamentoForm(request.POST)\n if form.is_valid():\n obj = form.save(commit=False)\n obj.grupo = grupo\n obj.save()\n \n return HttpResponseRedirect(grupo.apartamentos())\n else:\n return render(request,\"apartamento_novo.html\",{'grupo':grupo,'form':form})\n\n@group_required(['Administrador'])\ndef apartamento_edit(request,grupo_id,apartamento_id):\n '''\n @apartamento_edit: View para editar as informações de um quarto\n '''\n grupo = get_object_or_404(Grupo,id=grupo_id)\n apartamento = get_object_or_404(Apartamento,id=apartamento_id)\n if request.method == 'POST':\n return apartamento_edit_post(request,grupo,apartamento)\n else:\n return render(request,\"apartamento_edit.html\",\n {'grupo':grupo,'apartamento':apartamento, 'form':ApartamentoForm(instance=apartamento)})\n\n@group_required(['Administrador'])\ndef remover_apartamento(request,grupo_id,apartamento_id):\n '''\n @remover_receita: View para deletar uma receita\n '''\n grupo = get_object_or_404(Grupo,id=grupo_id)\n Apartamento.objects.get(id=apartamento_id,grupo=grupo).delete()\n return HttpResponseRedirect(grupo.apartamentos())\n\n@group_required(['Administrador'])\ndef apartamento_edit_post(request,grupo,apartamento):\n '''\n @apartamento_edit_post: View para salvar a edição de um apartamento\n '''\n form = ApartamentoForm(request.POST,instance=apartamento)\n if form.is_valid():\n obj = form.save(commit=False)\n obj.grupo = grupo\n obj.save()\n return HttpResponseRedirect(grupo.apartamentos())\n else:\n return render(request,\"apartamento_edit.html\",{'grupo':grupo,'apartamento':apartamento,'form':form})\n\n@group_required(['Administrador'])\ndef clientes_fora_apartamento(request,grupo_id,apartamento_id):\n '''\n @clientes_fora_apartamento: View para buscar os clientes que não estão neste apartamento\n '''\n grupo = get_object_or_404(Grupo,id=grupo_id)\n apartamento = get_object_or_404(Apartamento,id=apartamento_id)\n \n\n apts = Apartamento.objects.filter(grupo=grupo)#.exclude(id=apartamento_id)\n\n #clientes de todos os apartamentos\n lista1 = []\n for ap in apts:\n for c in ap.clientes.all():\n lista1.append(c)\n \n clientes = grupo.clientes.all()\n \n\n lista2 = []\n for c in clientes:\n if c not in lista1:\n lista2.append(c)\n\n return render(request,\"apartamento_clientes_fora.html\",{'grupo':grupo,'apartamento':apartamento,'clientes_fora':lista2})\n\n@group_required(['Administrador'])\ndef clientes_do_apartamento(request,grupo_id,apartamento_id):\n '''\n @clientes_do_apartamento: View para exibir os clientes do apartamento\n '''\n grupo = get_object_or_404(Grupo,id=grupo_id)\n apartamento = get_object_or_404(Apartamento,id=apartamento_id)\n return render(request,\"apartamento_clientes_dentro.html\",{'grupo':grupo,'apartamento':apartamento,'clientes':apartamento.clientes.all()})\n\n@group_required(['Administrador'])\ndef add_cliente_apartamento(request,grupo_id,apartamento_id,cliente_id):\n grupo = get_object_or_404(Grupo,id=grupo_id)\n apartamento = get_object_or_404(Apartamento,id=apartamento_id,grupo=grupo)\n cliente = get_object_or_404(Cliente,id=cliente_id)\n\n apartamento.clientes.add(cliente)\n\n return HttpResponseRedirect(r('grupos:clientes_fora_apartamento',kwargs={'grupo_id':grupo.id,'apartamento_id':apartamento.id}))\n\n@group_required(['Administrador'])\ndef rm_cliente_apartamento(request,grupo_id,apartamento_id,cliente_id):\n grupo = get_object_or_404(Grupo,id=grupo_id)\n apartamento = get_object_or_404(Apartamento,id=apartamento_id,grupo=grupo)\n cliente = get_object_or_404(Cliente,id=cliente_id)\n\n apartamento.clientes.remove(cliente)\n\n return HttpResponseRedirect(r('grupos:clientes_do_apartamento',kwargs={'grupo_id':grupo.id,'apartamento_id':apartamento.id}))", "sub_path": "grupos/views/apartamento.py", "file_name": "apartamento.py", "file_ext": "py", "file_size_in_byte": 5199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.shortcuts.get_object_or_404", "line_number": 17, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "grupos.models.Apartamento", "line_number": 18, "usage_type": "name"}, {"api_name": "core.views.group_required", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 25, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "grupos.forms.ApartamentoForm", "line_number": 29, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 20, "usage_type": "call"}, {"api_name": "grupos.forms.ApartamentoForm", "line_number": 36, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 51, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 51, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 52, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento", "line_number": 52, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "grupos.forms.ApartamentoForm", "line_number": 57, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 64, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 64, "usage_type": "argument"}, {"api_name": "grupos.models.Apartamento.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "grupos.models.Apartamento", "line_number": 65, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 66, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 59, "usage_type": "call"}, {"api_name": "grupos.forms.ApartamentoForm", "line_number": 73, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 87, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 87, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 88, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento", "line_number": 88, "usage_type": "argument"}, {"api_name": "grupos.models.Apartamento.objects.filter", "line_number": 91, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "grupos.models.Apartamento", "line_number": 91, "usage_type": "name"}, {"api_name": "clientes.models", "line_number": 99, "usage_type": "name"}, {"api_name": "clientes.models", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 114, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 114, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 115, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento", "line_number": 115, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 116, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 120, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 120, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 121, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento", "line_number": 121, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 122, "usage_type": "call"}, {"api_name": "clientes.models.Cliente", "line_number": 122, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 126, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 126, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 130, "usage_type": "call"}, {"api_name": "grupos.models.Grupo", "line_number": 130, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 131, "usage_type": "call"}, {"api_name": "grupos.models.Apartamento", "line_number": 131, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 132, "usage_type": "call"}, {"api_name": "clientes.models.Cliente", "line_number": 132, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 136, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 136, "usage_type": "call"}, {"api_name": "core.views.group_required", "line_number": 128, "usage_type": "call"}]} {"seq_id": "587177330", "text": "# Room definition\nfrom lib import reader\n\n\nclass Choices():\n PATH = './txt/rooms.txt'\n\n def __init__(self, room_id):\n\n self.room_id = room_id\n self.choices = [\n {\n 'id': 'A',\n 'descriptor': self.reader_lookup('A'),\n 'description': self.reader_lookup('X'),\n 'exhausted': 'false',\n },\n {\n 'id': 'B',\n 'descriptor': self.reader_lookup('B'),\n 'description': self.reader_lookup('Y'),\n 'exhausted': 'false',\n },\n {\n 'id': 'C',\n 'descriptor': self.reader_lookup('C'),\n 'description': self.reader_lookup('Z'),\n 'exhausted': 'false',\n\n }\n ]\n\n def reader_lookup(self, id):\n return reader.parse(self.PATH, ''.join((self.room_id, id)))\n\n\nclass Room():\n PATH = './txt/rooms.txt'\n\n def __init__(self, room_id):\n self.name = reader.parse(self.PATH, ''.join((room_id, 'N')))\n self.description = reader.parse(self.PATH, ''.join((room_id, 'D')))\n self.visited = 'false'\n self.room_id = room_id\n self.connectors = [] # Connectors(room_id)\n self.choices = Choices(room_id).choices\n\n def init_room(self):\n print(self.name)\n print(self.description)\n\n if len(self.choices) > 0:\n self.get_choices()\n\n def set_visited(self):\n self.visited = 'true'\n\n def exhaust_choice(self, id):\n for choice in self.choices:\n if choice['id'] == id:\n choice['exhausted'] = 'true'\n\n def get_choices(self):\n gen = (choice for choice in self.choices if choice['exhausted'] != 'true')\n selection = ''\n\n for choice in gen:\n print(choice['id'] + ' ' + choice['descriptor'])\n# needs to exclude exhausted items of gen instead static list\n while selection.upper() not in ['A', 'B', 'C']:\n selection = input('Select: ')\n self.exhaust_choice(selection)\n", "sub_path": "rooms/room.py", "file_name": "room.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "lib.reader.parse", "line_number": 34, "usage_type": "call"}, {"api_name": "lib.reader", "line_number": 34, "usage_type": "name"}, {"api_name": "lib.reader.parse", "line_number": 41, "usage_type": "call"}, {"api_name": "lib.reader", "line_number": 41, "usage_type": "name"}, {"api_name": "lib.reader.parse", "line_number": 42, "usage_type": "call"}, {"api_name": "lib.reader", "line_number": 42, "usage_type": "name"}]} {"seq_id": "80872313", "text": "#!/usr/bin/python\n\n# read input file\n# calculate size of matrix from input\n# fill matrix with added weigths\n# store where the maximum weight came from (=path)\n# store maximum weight\n# if both equally good prefer going south\n# output: max weight\n# option -t : output max weight \\n path (e.g ESES E...East, S...South)\n\nimport sys\nimport argparse\nimport re\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"filename\")\nparser.add_argument(\"-t\", action=\"store_true\")\nargs = parser.parse_args()\n\n## list for the read in data\ninput_data = []\nwith open(args.filename, \"r\") as infile:\n for line in infile:\n if line.startswith(\"#\"):\n continue\n elif not line.strip():\n continue\n else:\n line = line.strip()\n line = re.sub(\"[^\\w]\", \" \", line).split()\n input_data.append(list(line))\n\n\n## matrix for weights:\nrows = int(input_data[0][0])\ncolumns = int(input_data[0][1])\ninput_data.pop(0) # remove the dimensions from list\nweight_matrix = [[0 for x in range(columns)] for y in range(rows)]\n\n## make individual lists for east-west and north-south\neast_west_streets = []\nnorth_south_streets = []\nfor i in range(0, rows):\n east_west_streets.append(input_data[i])\nfor i in range(rows, len(input_data)):\n north_south_streets.append(input_data[i])\n\n## make ints out of all weigths:\nfor i in range(0, len(east_west_streets)):\n east_west_streets[i] = [int(x) for x in east_west_streets[i]]\nfor i in range(0, len(north_south_streets)):\n north_south_streets[i] = [int(x) for x in north_south_streets[i]]\n\nmax_weight = 0\n## fill weight-matrix:\nfor i in range(0, rows):\n for j in range(0, columns):\n ## first line and column\n if i == 0 and j > 0:\n weight_matrix[i][j] = weight_matrix[i][j-1] + east_west_streets[i][j-1]\n elif j == 0 and i > 0:\n weight_matrix[i][j] = weight_matrix[i-1][j] + north_south_streets[i-1][j]\n elif i > 0 and j > 0:\n weight_matrix[i][j] = max(weight_matrix[i-1][j] + north_south_streets[i-1][j], weight_matrix[i][j-1] + east_west_streets[i][j-1])\n if i == rows-1 and j == columns-1:\n max_weight = weight_matrix[i][j]\n#print(weight_matrix)\nprint(max_weight)\n\n## backtracking\nif args.t:\n path = \"\"\n i = rows-1\n j = columns-1\n while i > 0 or j > 0:\n if i > 0 and weight_matrix[i][j] == weight_matrix[i-1][j] + north_south_streets[i-1][j]:\n path = \"S\" + path\n i -= 1\n elif j > 0 and weight_matrix[i][j] == weight_matrix[i][j-1] + east_west_streets[i][j-1]:\n path = \"E\" + path\n j -= 1\n print(path)\n", "sub_path": "A3/chrisuWa-Manhattan.py", "file_name": "chrisuWa-Manhattan.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}]} {"seq_id": "378025829", "text": "#!/usr/bin/python3\n\nfrom getpass import getpass\nfrom hexbytes import HexBytes\nimport os\nfrom pathlib import Path\nimport json\n\nfrom eth_hash.auto import keccak\nimport eth_keys\nfrom eth_utils import to_checksum_address\n\nfrom brownie.cli.utils import color\nfrom brownie.exceptions import VirtualMachineError\nfrom brownie.network.transaction import TransactionReceipt\nfrom brownie.network.web3 import web3\nfrom brownie.types.convert import wei\n\nimport brownie._registry as _registry\nimport brownie._config\nCONFIG = brownie._config.CONFIG\n\n\nclass Accounts:\n\n '''List-like container that holds all of the available Account instances.'''\n\n def __init__(self):\n self._accounts = []\n # prevent private keys from being stored in read history\n self.add.__dict__['_private'] = True\n _registry.add(self)\n\n def _notify_reset(self):\n self._accounts.clear()\n try:\n self._accounts = [Account(i) for i in web3.eth.accounts]\n except Exception:\n pass\n\n def _notify_revert(self):\n for i in self._accounts:\n i.nonce = web3.eth.getTransactionCount(str(i))\n\n def __contains__(self, address):\n try:\n address = to_checksum_address(address)\n return address in self._accounts\n except ValueError:\n return False\n\n def _console_repr(self):\n return str(self._accounts)\n\n def __iter__(self):\n return iter(self._accounts)\n\n def __getitem__(self, key):\n return self._accounts[key]\n\n def __delitem__(self, key):\n del self._accounts[key]\n\n def __len__(self):\n return len(self._accounts)\n\n def add(self, priv_key=None):\n '''Creates a new ``LocalAccount`` instance and appends it to the container.\n\n Args:\n priv_key: Private key of the account. If none is given, one is\n randomly generated.\n\n Returns:\n Account instance.\n '''\n if not priv_key:\n priv_key = \"0x\"+keccak(os.urandom(8192)).hex()\n w3account = web3.eth.account.privateKeyToAccount(priv_key)\n if w3account.address in self._accounts:\n return self.at(w3account.address)\n account = LocalAccount(w3account.address, w3account, priv_key)\n self._accounts.append(account)\n return account\n\n def load(self, identifier):\n json_file = Path(CONFIG['folders']['brownie']).joinpath(\"data/accounts/{}.json\".format(identifier))\n if not json_file.exists():\n raise FileNotFoundError(\"Account with this identifier does not exist\")\n priv_key = web3.eth.account.decrypt(\n json.load(json_file.open()),\n getpass(\"Enter the password for this account: \")\n )\n return self.add(priv_key)\n\n def at(self, address):\n '''Retrieves an Account instance from the address string. Raises\n ValueError if the account cannot be found.\n\n Args:\n address: string of the account address.\n\n Returns:\n Account instance.\n '''\n try:\n address = to_checksum_address(address)\n except ValueError:\n raise ValueError(\"{} is not a valid address\".format(address))\n try:\n return next(i for i in self._accounts if i == address)\n except StopIteration:\n raise ValueError(\"No account exists for {}\".format(address))\n\n def remove(self, address):\n '''Removes an account instance from the container.\n\n Args:\n address: Account instance or address string of account to remove.'''\n self._accounts.remove(address)\n\n def clear(self):\n '''Empties the container.'''\n self._accounts.clear()\n\n\nclass _AccountBase:\n\n '''Base class for Account and LocalAccount'''\n\n def __init__(self, addr):\n self.address = addr\n self.nonce = web3.eth.getTransactionCount(self.address)\n\n def __hash__(self):\n return hash(self.address)\n\n def __repr__(self):\n return \"'{0[string]}{1}{0}'\".format(color, self.address)\n\n def __str__(self):\n return self.address\n\n def __eq__(self, other):\n if type(other) is str:\n try:\n address = to_checksum_address(other)\n return address == self.address\n except ValueError:\n return False\n return super().__eq__(other)\n\n def balance(self):\n '''Returns the current balance at the address, in wei.'''\n return web3.eth.getBalance(self.address)\n\n def deploy(self, contract, *args):\n '''Deploys a contract.\n\n Args:\n contract: ContractContainer instance.\n *args: Constructor arguments. The last argument may optionally be\n a dictionary of transaction values.\n\n Returns:\n * Contract instance if the transaction confirms\n * TransactionReceipt if the transaction is pending or reverts'''\n return contract.deploy(self, *args)\n\n def estimate_gas(self, to, amount, data=\"\"):\n '''Estimates the gas cost for a transaction. Raises VirtualMachineError\n if the transaction would revert.\n\n Args:\n to: Account instance or address string of transaction recipient.\n amount: Amount of ether to send in wei.\n data: Transaction data hexstring.\n\n Returns:\n Estimated gas value in wei.'''\n return web3.eth.estimateGas({\n 'from': self.address,\n 'to': str(to),\n 'data': HexBytes(data),\n 'value': wei(amount)\n })\n\n def _gas_limit(self, to, amount, data=\"\"):\n if type(CONFIG['active_network']['gas_limit']) is int:\n return CONFIG['active_network']['gas_limit']\n return self.estimate_gas(to, amount, data)\n\n def _gas_price(self):\n return CONFIG['active_network']['gas_price'] or web3.eth.gasPrice\n\n\nclass Account(_AccountBase):\n\n '''Class for interacting with an Ethereum account.\n\n Attributes:\n address: Public address of the account.\n nonce: Current nonce of the account.'''\n\n def _console_repr(self):\n return \"<Account object '{0[string]}{1}{0}'>\".format(color, self.address)\n\n def transfer(self, to, amount, gas_limit=None, gas_price=None, data=''):\n '''Transfers ether from this account.\n\n Args:\n to: Account instance or address string to transfer to.\n amount: Amount of ether to send, in wei.\n gas_limit: Gas limit of the transaction.\n gas_price: Gas price of the transaction.\n\n Returns:\n TransactionReceipt instance'''\n try:\n txid = web3.eth.sendTransaction({\n 'from': self.address,\n 'to': str(to),\n 'value': wei(amount),\n 'gasPrice': wei(gas_price) or self._gas_price(),\n 'gas': wei(gas_limit) or self._gas_limit(to, amount, data),\n 'data': HexBytes(data)\n })\n except ValueError as e:\n txid = raise_or_return_tx(e)\n self.nonce += 1\n return TransactionReceipt(txid, self)\n\n def _contract_tx(self, fn, args, tx, name, callback=None):\n tx['from'] = self.address\n if type(CONFIG['active_network']['gas_price']) is int:\n tx['gasPrice'] = CONFIG['active_network']['gas_price']\n if type(CONFIG['active_network']['gas_limit']) is int:\n tx['gas'] = CONFIG['active_network']['gas_limit']\n try:\n txid = fn(*args).transact(tx)\n except ValueError as e:\n txid = raise_or_return_tx(e)\n self.nonce += 1\n return TransactionReceipt(txid, self, name=name, callback=callback)\n\n\nclass LocalAccount(_AccountBase):\n\n '''Class for interacting with an Ethereum account.\n\n Attributes:\n address: Public address of the account.\n nonce: Current nonce of the account.\n private_key: Account private key.\n public_key: Account public key.'''\n\n def __init__(self, address, account, priv_key):\n self._acct = account\n self.private_key = priv_key\n self.public_key = eth_keys.keys.PrivateKey(HexBytes(priv_key)).public_key\n super().__init__(address)\n\n def _console_repr(self):\n return \"<LocalAccount object '{0[string]}{1}{0}'>\".format(color, self.address)\n\n def save(self, identifier, overwrite=False):\n path = Path(CONFIG['folders']['brownie']).joinpath('data/accounts')\n path.mkdir(exist_ok=True)\n json_file = path.joinpath(\"{}.json\".format(identifier))\n if not overwrite and json_file.exists():\n raise FileExistsError(\"Account with this identifier already exists\")\n encrypted = web3.eth.account.encrypt(\n self.private_key,\n getpass(\"Enter the password to encrypt this account with: \")\n )\n json.dump(encrypted, json_file.open('w'))\n print(\"Saved to {}\".format(json_file))\n\n def transfer(self, to, amount, gas_limit=None, gas_price=None, data=''):\n '''Transfers ether from this account.\n\n Args:\n to: Account instance or address string to transfer to.\n amount: Amount of ether to send, in wei.\n gas_limit: Gas limit of the transaction.\n gas_price: Gas price of the transaction.\n\n Returns:\n TransactionReceipt instance'''\n try:\n signed_tx = self._acct.signTransaction({\n 'from': self.address,\n 'nonce': self.nonce,\n 'gasPrice': wei(gas_price) or self._gas_price(),\n 'gas': wei(gas_limit) or self._gas_limit(to, amount, data),\n 'to': str(to),\n 'value': wei(amount),\n 'data': HexBytes(data)\n }).rawTransaction\n txid = web3.eth.sendRawTransaction(signed_tx)\n except ValueError as e:\n txid = raise_or_return_tx(e)\n self.nonce += 1\n return TransactionReceipt(txid, self)\n\n def _contract_tx(self, fn, args, tx, name, callback=None):\n try:\n tx.update({\n 'from': self.address,\n 'nonce': self.nonce,\n 'gasPrice': self._gas_price(),\n 'gas': (\n CONFIG['active_network']['gas_limit'] or\n fn(*args).estimateGas({'from': self.address})\n )\n })\n raw = fn(*args).buildTransaction(tx)\n txid = web3.eth.sendRawTransaction(\n self._acct.signTransaction(raw).rawTransaction\n )\n except ValueError as e:\n txid = raise_or_return_tx(e)\n self.nonce += 1\n return TransactionReceipt(txid, self, name=name, callback=callback)\n\n\ndef raise_or_return_tx(exc):\n data = eval(str(exc))\n try:\n return next(i for i in data['data'].keys() if i[:2] == \"0x\")\n except Exception:\n raise VirtualMachineError(exc)\n", "sub_path": "brownie/network/account.py", "file_name": "account.py", "file_ext": "py", "file_size_in_byte": 11002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "brownie.cli.utils._config", "line_number": 21, "usage_type": "attribute"}, {"api_name": "brownie.cli.utils", "line_number": 21, "usage_type": "name"}, {"api_name": "brownie._registry.add", "line_number": 32, "usage_type": "call"}, {"api_name": "brownie._registry", "line_number": 32, "usage_type": "name"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 37, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 37, "usage_type": "name"}, {"api_name": "brownie.network.web3.web3.eth.getTransactionCount", "line_number": 43, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 43, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 43, "usage_type": "name"}, {"api_name": "eth_utils.to_checksum_address", "line_number": 47, "usage_type": "call"}, {"api_name": "eth_hash.auto.keccak", "line_number": 78, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 78, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.account.privateKeyToAccount", "line_number": 79, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 79, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 79, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 87, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.account.decrypt", "line_number": 90, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 90, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 90, "usage_type": "name"}, {"api_name": "json.load", "line_number": 91, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 92, "usage_type": "call"}, {"api_name": "eth_utils.to_checksum_address", "line_number": 107, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.getTransactionCount", "line_number": 133, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 133, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 133, "usage_type": "name"}, {"api_name": "brownie.cli.utils.color", "line_number": 139, "usage_type": "argument"}, {"api_name": "eth_utils.to_checksum_address", "line_number": 147, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.getBalance", "line_number": 155, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 155, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 155, "usage_type": "name"}, {"api_name": "brownie.network.web3.web3.eth.estimateGas", "line_number": 181, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 181, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 181, "usage_type": "name"}, {"api_name": "hexbytes.HexBytes", "line_number": 184, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 185, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 194, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 194, "usage_type": "name"}, {"api_name": "brownie.cli.utils.color", "line_number": 206, "usage_type": "argument"}, {"api_name": "brownie.network.web3.web3.eth.sendTransaction", "line_number": 220, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 220, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 220, "usage_type": "name"}, {"api_name": "brownie.types.convert.wei", "line_number": 223, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 224, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 225, "usage_type": "call"}, {"api_name": "hexbytes.HexBytes", "line_number": 226, "usage_type": "call"}, {"api_name": "brownie.network.transaction.TransactionReceipt", "line_number": 231, "usage_type": "call"}, {"api_name": "brownie.network.transaction.TransactionReceipt", "line_number": 244, "usage_type": "call"}, {"api_name": "eth_keys.keys.PrivateKey", "line_number": 260, "usage_type": "call"}, {"api_name": "eth_keys.keys", "line_number": 260, "usage_type": "attribute"}, {"api_name": "hexbytes.HexBytes", "line_number": 260, "usage_type": "call"}, {"api_name": "brownie.cli.utils.color", "line_number": 264, "usage_type": "argument"}, {"api_name": "pathlib.Path", "line_number": 267, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.account.encrypt", "line_number": 272, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 272, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 272, "usage_type": "name"}, {"api_name": "getpass.getpass", "line_number": 274, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 276, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 294, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 295, "usage_type": "call"}, {"api_name": "brownie.types.convert.wei", "line_number": 297, "usage_type": "call"}, {"api_name": "hexbytes.HexBytes", "line_number": 298, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.sendRawTransaction", "line_number": 300, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 300, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 300, "usage_type": "name"}, {"api_name": "brownie.network.transaction.TransactionReceipt", "line_number": 304, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth.sendRawTransaction", "line_number": 318, "usage_type": "call"}, {"api_name": "brownie.network.web3.web3.eth", "line_number": 318, "usage_type": "attribute"}, {"api_name": "brownie.network.web3.web3", "line_number": 318, "usage_type": "name"}, {"api_name": "brownie.network.transaction.TransactionReceipt", "line_number": 324, "usage_type": "call"}, {"api_name": "brownie.exceptions.VirtualMachineError", "line_number": 332, "usage_type": "call"}]} {"seq_id": "156942665", "text": "import meshio\n\n\ndef convert_med_to_xdmf(\n medfilename,\n cell_file=\"mesh_domains.xdmf\",\n facet_file=\"mesh_boundaries.xdmf\",\n cell_type=\"tetra\",\n facet_type=\"triangle\",\n):\n \"\"\"_summary_\n Args:\n medfilename (_type_): _description_\n cell_file (str, optional): _description_. Defaults to \"mesh_domains.xdmf\".\n facet_file (str, optional): _description_. Defaults to \"mesh_boundaries.xdmf\".\n cell_type (str, optional): _description_. Defaults to \"tetra\".\n facet_type (str, optional): _description_. Defaults to \"triangle\".\n Returns:\n dict, dict: the correspondance dict, the cell types\n \"\"\"\n msh = meshio.read(medfilename)\n\n correspondance_dict = msh.cell_tags\n\n cell_data_types = msh.cell_data_dict[\"cell_tags\"].keys()\n\n for mesh_block in msh.cells:\n if mesh_block.type == cell_type:\n\n meshio.write_points_cells(\n cell_file,\n msh.points,\n [mesh_block],\n cell_data={\"f\": [-1 * msh.cell_data_dict[\"cell_tags\"][cell_type]]},\n )\n elif mesh_block.type == facet_type:\n meshio.write_points_cells(\n facet_file,\n msh.points,\n [mesh_block],\n cell_data={\"f\": [-1 * msh.cell_data_dict[\"cell_tags\"][facet_type]]},\n )\n\n return correspondance_dict, cell_data_types\n\n\nif __name__ == \"__main__\":\n for thickness in [4, 5, 6, 7, 8, 9, 10, 14]:\n folder = \"{}mm_thickness\".format(thickness)\n correspondance_dict, cell_data_types = convert_med_to_xdmf(\n medfilename=\"{}/mesh_3D_{}mm.med\".format(folder, thickness),\n cell_file=\"{}/mesh_cells.xdmf\".format(folder),\n facet_file=\"{}/mesh_facets.xdmf\".format(folder),\n cell_type=\"tetra\",\n facet_type=\"triangle\",\n )\n\n print(correspondance_dict)\n", "sub_path": "meshes/convert_mesh.py", "file_name": "convert_mesh.py", "file_ext": "py", "file_size_in_byte": 1905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "meshio.read", "line_number": 21, "usage_type": "call"}, {"api_name": "meshio.write_points_cells", "line_number": 30, "usage_type": "call"}, {"api_name": "meshio.write_points_cells", "line_number": 37, "usage_type": "call"}]} {"seq_id": "44407316", "text": "import random\nimport math\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom DiscreteAnt import DiscreteAnt\n\ncity_count = 10 #都市の数\nmap_size = 100 #地図の大きさ\nmax_stagnant = 10 #滞りの最大値\nmax_iteration = 100 #繰り返しの最大値\nis_draw_graph = True #グラフを描画するか\n\nant_count = 30 #アリの数\nalpha = 1 #フェロモンの影響度\nbeta = 5 #距離の影響度\nevaporation = 0.5 #フェロモンの蒸発率\nq = 500 #1匹あたりのフェロモンの総量\ninitial_pheromone = 1.0 #フェロモンの初期値\npr = 0.01 #アリがランダムにルートを決める確率\n\ncities = [] #都市の座標の保存用\npheromone = [] #フェロモンの保存用\nants = [] #アリの保存用\nbest_route = [] #最短ルートの保存用\nbest_cost = float(\"inf\") #最短ルートの移動距離\n\n\ndef initialize_pheromone():\n for i in range(0, city_count):\n temp = []\n for j in range(0, city_count):\n if i == j:\n temp.append(0)\n else:\n temp.append(initial_pheromone)\n pheromone.append(temp)\n\ndef create_city():\n for i in range(0, city_count):\n while True:\n city = (random.randint(0, map_size-1), random.randint(0, map_size-1))\n if city not in cities:\n break\n cities.append(city)\n\ndef create_ants():\n for i in range(0, ant_count):\n ants.append(DiscreteAnt(city_count))\n\ndef graph_score(a, b):\n distance = math.sqrt( (cities[a][0]-cities[b][0])**2 + (cities[a][1]-cities[b][1])**2 )\n return distance\n\ndef calculate_prpbability(current_index, ant):\n result = [0] * city_count\n\n i = ant.path[current_index - 1]\n\n d = 0.0\n for l in range(0, city_count):\n if not ant.visited[l]:\n #?\n d = d + (pheromone[i][l]**alpha) * (1.0/graph_score(i, l)**beta)\n\n for j in range(0, city_count):\n if ant.visited[j]:\n result[j] = 0.0\n else:\n #?\n n = (pheromone[i][j]**alpha) * ((1.0/graph_score(i, j))**beta)\n result[j] = n/d\n\n return result\n\n\ndef choose_next_step(current_index, ant):\n if current_index == 0 or random.uniform(0, 1) < pr:\n index = -1\n while index == -1 or ant.visited[index]:\n index = random.randint(0, city_count-1)\n return index\n else:\n prob = calculate_prpbability(current_index, ant)\n\n r = random.uniform(0,1)\n\n sum = 0\n for i in range(0, city_count):\n sum = sum + prob[i]\n if sum > r:\n return i\n\ndef march():\n for current_index in range(0, city_count):\n for ant in ants:\n next = choose_next_step(current_index, ant)\n ant.visit(current_index, next)\n\ndef update_pherpmpn():\n # pheromone evaporation\n for row in range(0, len(pheromone)):\n for col in range(0, len(pheromone[row])):\n pheromone[row][col] = pheromone[row][col] * evaporation\n\n # pheromone update\n for ant in ants:\n d = q / ant.calculate_cost(city_count, graph_score)\n\n for i in range(0, city_count-1):\n pheromone[ant.path[i]][ant.path[i+1]] = pheromone[ant.path[i]][ant.path[i+1]] + d\n\n pheromone[ant.path[city_count-1]][ant.path[0]] = pheromone[ant.path[city_count-1]][ant.path[i + 0]] + d\n\ndef find_route():\n global best_cost\n global best_route\n for ant in ants:\n cost = ant.calculate_cost(city_count, graph_score)\n if best_cost > cost:\n best_cost = cost\n best_route = ant.path\n\ndef reset_ants():\n for ant in ants:\n ant.clear()\n\ndef iterate():\n reset_ants()\n march()\n update_pherpmpn()\n find_route()\n\n\n\nif __name__ == \"__main__\":\n\n # create_city()\n # print(\"cities\")\n # for i in range(0, len(cities)):\n # print(cities[i])\n #\n # initialize_pheromone()\n # print(\"pheromone\")\n # for row in range(0, len(pheromone)):\n # for col in range(0, len(pheromone[row])):\n # print(\"{0:6.3f}\".format(pheromone[row][col]), end=\" \")\n # print(\"\")\n #\n # create_ants()\n # print(\"ants\")\n # for i in range(0, len(ants)):\n # print(ants[i])\n #\n # march()\n # print(\"march\")\n # for i in range(0, len(ants)):\n # print(ants[i], i)\n # for j in range(0, city_count):\n # print(ants[i].path[j], end=\" \")\n # print(\"\")\n #\n # update_pherpmpn()\n # print(\"pheromone\")\n # for row in range(0, len(pheromone)):\n # for col in range(0, len(pheromone[row])):\n # print(\"{0:6.3f}\".format(pheromone[row][col]), end=\" \")\n # print(\"\")\n #\n # find_route()\n # print(\"best route\")\n # for i in range(0, city_count):\n # print(best_route[i], end=\" \")\n # print(\"\")\n # print(\"best cost\")\n # print(best_cost)\n #\n # reset_ants()\n # print(\"reset ants\")\n # for i in range(0, len(ants)):\n # print(ants[i], i)\n # for j in range(0, city_count):\n # print(ants[i].path[j], end=\" \")\n # print(\"\")\n\n create_city()\n create_ants()\n initialize_pheromone()\n\n iteration = 0 #繰り返し回数\n stagnant = 0 #滞り回数\n while iteration < max_iteration and stagnant < max_stagnant:\n last_cost = best_cost\n iterate()\n iteration += 1\n if last_cost == best_cost: #最短ルートが更新されなかった場合\n stagnant += 1\n else:\n stagnant = 0\n print(\"Iteration #\" + str(\"{0:2d}\".format(iteration)) + \\\n \" Score=\" + str(best_cost) + \\\n \" stagnant=\" + str(stagnant))\n\n print(\"Best Score : \" + str(best_cost))\n print(\"Best Route : \", end=\"\")\n for i in range(0, city_count):\n print(best_route[i], end=\" \")\n print(\"\")\n print(\"pheromone\")\n for row in range(0, len(pheromone)):\n for col in range(0, len(pheromone[row])):\n print(\"{0:8.5f}\".format(pheromone[row][col]), end=\" \")\n print(\"\")\n\n if is_draw_graph:\n print(\"Draw Graph!!\")\n graph = nx.DiGraph()\n graph.add_nodes_from(range(0, city_count))\n for j in range(0, city_count-1):\n graph.add_edge(best_route[j], best_route[j+1])\n graph.add_edge(best_route[city_count-1], best_route[0])\n nx.draw(graph, cities, with_labels=True)\n plt.show()\n", "sub_path": "aco_tsp.py", "file_name": "aco_tsp.py", "file_ext": "py", "file_size_in_byte": 6388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "DiscreteAnt.DiscreteAnt", "line_number": 48, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 51, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 77, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 85, "usage_type": "call"}, {"api_name": "networkx.DiGraph", "line_number": 216, "usage_type": "call"}, {"api_name": "networkx.draw", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}]} {"seq_id": "644899914", "text": "import parsley, itertools, math\nfrom utils import *\nfrom Naub import Naub\nfrom NaubJoint import NaubJoint\n\n\n\ngrammar = '''\nname = letter:c (letterOrDigit*):cs -> \"\".join([c] + cs)\nref = ref_pos | ref_name | ref_desc\nref_pos = '{}' -> interpreter.ref_pos()\nref_name = '{' name:n '}' -> interpreter.ref_name(n)\nref_desc = name:n -> interpreter.ref_desc(n)\nchain = ref:r ('-' ref)*:rs -> RefChain([r] + rs)\nspec = chain:c (ws chain)*:cs -> RefGraph([c] + cs)\n'''\n\nclass Interpreter(object):\n\n def __init__(self, args, kwargs):\n self.args = args\n self.kwargs = kwargs\n self.ref_pos_i = 0\n self.refs = {}\n\n def ref_pos(self):\n ref = Ref(\n val = self.args[self.ref_pos_i])\n self.ref_pos_i += 1\n return ref\n\n def ref_name(self, name):\n try: return self.refs[name]\n except:\n ref = Ref(\n name = name,\n val = self.kwargs[name])\n self.refs[name] = ref\n return ref\n\n def ref_desc(self, desc):\n return Ref(desc = desc)\n\n\n\nclass Ref(object):\n\n def __init__(self, name = None, desc = None, val = None):\n self.name = name\n self.desc = desc\n self.val = val\n\nclass RefJoin(object):\n\n def __init__(self, a, b, val = None):\n assert a != b\n self.a = a\n self.b = b\n self.val = val\n\nclass RefChain(object):\n\n def __init__(self, refs):\n self.refs = refs\n self.joins = self.__join()\n\n @gather()\n def __join(self):\n refs = self.refs\n for a, b in zip(refs, refs[1:]):\n yield RefJoin(a, b)\n\nclass RefGraph(object):\n\n def __init__(self, chains):\n self.chains = chains\n refs = self.refs\n refs = sorted(refs, key = lambda ref: ref.desc)\n groups = itertools.groupby(refs, lambda ref: ref.desc)\n groups = {group: list(refs) for group, refs in groups}\n self.groups = groups\n\n @property\n def joins(self):\n joins = (chain.joins for chain in self.chains)\n return itertools.chain(*joins)\n\n @property\n def refs(self):\n refs = (chain.refs for chain in self.chains)\n return itertools.chain(*refs)\n\n def by_name(self, name):\n return filter(self.refs, lambda ref: ref.name == name)[0]\n\n\n\nclass NaubSpecBase(object):\n\n def __init__(self, _ref):\n self._ref = _ref\n self.fill_with_default_naubs()\n self.fill_with_default_naub_joins()\n\n @property\n def naubs(self):\n return (ref.val for ref in self._ref.refs)\n\n @property\n def joins(self):\n return (join.val for join in self._ref.joins)\n\n @property\n def groups(self):\n return {group: [ref.val for ref in refs] for group, refs in self._ref.groups.items()}\n\n def fill_with_default_naubs(self):\n refs = (ref for ref in self._ref.refs if ref.val is None)\n for ref in refs:\n ref.val = Naub()\n\n def fill_with_default_naub_joins(self):\n joins = (join for join in self._ref.joins if join.val is None)\n for join in joins:\n join.val = NaubJoint(join.a.val, join.b.val)\n join.a.val.join_naub(join.b.val, join.val)\n\n def colorize(self, palette):\n groups = self.groups\n colors = itertools.cycle(palette)\n for color, naubs in zip(colors, groups.values()):\n for naub in naubs:\n naub.color = color\n\n def position_on_circle(self, radius):\n naubs = list(self.naubs)\n circle = math.pi * 2 / len(naubs)\n for i, naub in enumerate(naubs):\n x = i * circle\n naub.pos = radius * math.cos(x), radius * math.sin(x)\n\nclass NaubSpecChain(NaubSpecBase):\n\n def __repr__(self):\n cls = type(self)\n return \"<{name} {list}>\".format(\n #module = cls.__module__,\n name = cls.__name__,\n list = list(self.naubs))\n\nclass NaubSpecGraph(NaubSpecBase):\n\n @property\n def chains(self):\n return (NaubSpecChain(chain) for chain in self._ref.chains)\n\n\n\ndef parse_spec(spec, *args, **kwargs):\n interpreter = Interpreter(args, kwargs)\n bindings = dict(\n interpreter = interpreter,\n RefChain = RefChain,\n RefGraph = RefGraph)\n parser = parsley.makeGrammar(grammar, bindings)\n return parser(spec).spec()\n\ndef spec(spec, *args, **kwargs):\n ref_graph = parse_spec(spec, *args, **kwargs)\n return NaubSpecGraph(ref_graph)\n", "sub_path": "src/naubino/naub_spec.py", "file_name": "naub_spec.py", "file_ext": "py", "file_size_in_byte": 4815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "itertools.groupby", "line_number": 79, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 86, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 91, "usage_type": "call"}, {"api_name": "Naub.Naub", "line_number": 120, "usage_type": "call"}, {"api_name": "NaubJoint.NaubJoint", "line_number": 125, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 130, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 137, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 140, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 140, "usage_type": "call"}, {"api_name": "parsley.makeGrammar", "line_number": 165, "usage_type": "call"}]} {"seq_id": "354801453", "text": "#!/usr/bin/env python\r\nimport pygame, sys, time\r\nfrom pygame.locals import *\r\n\r\nSoundsMaterialPath = 'material\\\\sounds\\\\'\r\n\r\npygame.init()\r\n\r\nsoundObj = pygame.mixer.Sound(SoundsMaterialPath+'beeps.wav')\r\nsoundObj.play()\r\n\r\ntime.sleep(1)\r\nsoundObj.stop()\r\n\r\nsoundObj = pygame.mixer.Sound(SoundsMaterialPath+'beepingsound.wav')\r\nsoundObj.play()\r\n\r\n#pygame.mixer.music.load(SoundsMaterialPath+'backgroundmusic.mp3')\r\n#pygame.mixer.music.play(-1, 0, 0)\r\ntime.sleep(10)\r\npygame.mixer.music.stop()\r\n", "sub_path": "pygame/sound_test.py", "file_name": "sound_test.py", "file_ext": "py", "file_size_in_byte": 494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.mixer.Sound", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.mixer.Sound", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mixer.music.stop", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 21, "usage_type": "attribute"}]} {"seq_id": "380724460", "text": "import json\n\nfn = \"A17000000J-030096-o7m.json\"\nwith open(fn, encoding=\"utf-8\") as fnObj:\n datas = json.load(fnObj)\n\nfor data in datas:\n if data[\"職類別\"] == \"軟體開發及程式設計師\":\n year = data[\"年度\"]\n industry = data[\"行業別\"]\n salary = data[\"經常性薪資\"]\n print(\"年度:\", year, \"職類別\", industry, \"經常性薪資\", salary)\n print(\"----------------------------------------------------\")", "sub_path": "JSON/ch24_11_2.py", "file_name": "ch24_11_2.py", "file_ext": "py", "file_size_in_byte": 458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}]} {"seq_id": "295070932", "text": "#!/usr/bin/env python\n'''\n*********************************************************\nCopyright @ 2015 EMC Corporation All Rights Reserved\n*********************************************************\n'''\n# -*- coding: utf-8 -*-\n\"\"\"\nTest InfraSIM core component:\n - QEMU\n - ipmi\n - ...\nCheck:\n - binary exist\n - corresponding process can be started by start service\n - corresponding process can be ended by stop service\n\"\"\"\n\nfrom infrasim import qemu\nfrom infrasim import ipmi\nfrom infrasim import socat\nfrom infrasim import model\nfrom infrasim import VM_DEFAULT_CONFIG\nimport time\nimport yaml\nimport os\n\n\ndef setUp():\n workspace = \"{}/.infrasim/node-0\".format(os.environ[\"HOME\"])\n if os.path.exists(workspace):\n os.system(\"rm -rf {}\".format(workspace))\n with open(VM_DEFAULT_CONFIG, 'r') as f_yml:\n conf = yaml.load(f_yml)\n node = model.CNode(conf)\n node.init_workspace()\n\n\ndef tearDown():\n workspace = \"{}/.infrasim/node-0\".format(os.environ[\"HOME\"])\n if os.path.exists(workspace):\n os.system(\"rm -rf {}\".format(workspace))\n\n\ndef test_qemu_exist():\n try:\n qemu.get_qemu()\n assert True\n except:\n assert False\n\n\ndef test_ipmi_exist():\n try:\n ipmi.get_ipmi()\n assert True\n except:\n assert False\n\n\ndef test_socat_exist():\n try:\n socat.get_socat()\n assert True\n except:\n assert False\n\n\ndef test_socat_process_start():\n try:\n socat.start_socat()\n time.sleep(2)\n socat.status_socat()\n assert True\n except:\n assert False\n\n\ndef test_ipmi_process_start():\n try:\n ipmi.start_ipmi()\n time.sleep(2)\n ipmi.status_ipmi()\n assert True\n except:\n assert False\n\n\ndef test_qemu_process_start():\n try:\n qemu.status_qemu()\n assert True\n except:\n assert False\n\n\ndef test_qemu_process_status_running():\n try:\n qemu.status_qemu()\n assert True\n except:\n assert False\n\n\ndef test_ipmi_process_status_running():\n try:\n ipmi.status_ipmi()\n assert True\n except:\n assert False\n\n\ndef test_socat_process_status_running():\n try:\n socat.status_socat()\n assert True\n except:\n assert False\n\n\ndef test_qemu_prcess_stop():\n try:\n qemu.stop_qemu()\n qemu.status_qemu()\n assert False\n except:\n assert True\n\n\ndef test_ipmi_process_stop():\n try:\n ipmi.stop_ipmi()\n ipmi.status_ipmi()\n assert False\n except:\n assert True\n\n\ndef test_socat_process_stop():\n try:\n socat.stop_socat()\n socat.status_socat()\n assert False\n except:\n assert True\n", "sub_path": "test/unit/test_core_process.py", "file_name": "test_core_process.py", "file_ext": "py", "file_size_in_byte": 2727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 32, "usage_type": "call"}, {"api_name": "infrasim.VM_DEFAULT_CONFIG", "line_number": 33, "usage_type": "argument"}, {"api_name": "yaml.load", "line_number": 34, "usage_type": "call"}, {"api_name": "infrasim.model.CNode", "line_number": 35, "usage_type": "call"}, {"api_name": "infrasim.model", "line_number": 35, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 42, "usage_type": "call"}, {"api_name": "infrasim.qemu.get_qemu", "line_number": 47, "usage_type": "call"}, {"api_name": "infrasim.qemu", "line_number": 47, "usage_type": "name"}, {"api_name": "infrasim.ipmi.get_ipmi", "line_number": 55, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 55, "usage_type": "name"}, {"api_name": "infrasim.socat.get_socat", "line_number": 63, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 63, "usage_type": "name"}, {"api_name": "infrasim.socat.start_socat", "line_number": 71, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 71, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "infrasim.socat.status_socat", "line_number": 73, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 73, "usage_type": "name"}, {"api_name": "infrasim.ipmi.start_ipmi", "line_number": 81, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 81, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "infrasim.ipmi.status_ipmi", "line_number": 83, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 83, "usage_type": "name"}, {"api_name": "infrasim.qemu.status_qemu", "line_number": 91, "usage_type": "call"}, {"api_name": "infrasim.qemu", "line_number": 91, "usage_type": "name"}, {"api_name": "infrasim.qemu.status_qemu", "line_number": 99, "usage_type": "call"}, {"api_name": "infrasim.qemu", "line_number": 99, "usage_type": "name"}, {"api_name": "infrasim.ipmi.status_ipmi", "line_number": 107, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 107, "usage_type": "name"}, {"api_name": "infrasim.socat.status_socat", "line_number": 115, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 115, "usage_type": "name"}, {"api_name": "infrasim.qemu.stop_qemu", "line_number": 123, "usage_type": "call"}, {"api_name": "infrasim.qemu", "line_number": 123, "usage_type": "name"}, {"api_name": "infrasim.qemu.status_qemu", "line_number": 124, "usage_type": "call"}, {"api_name": "infrasim.qemu", "line_number": 124, "usage_type": "name"}, {"api_name": "infrasim.ipmi.stop_ipmi", "line_number": 132, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 132, "usage_type": "name"}, {"api_name": "infrasim.ipmi.status_ipmi", "line_number": 133, "usage_type": "call"}, {"api_name": "infrasim.ipmi", "line_number": 133, "usage_type": "name"}, {"api_name": "infrasim.socat.stop_socat", "line_number": 141, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 141, "usage_type": "name"}, {"api_name": "infrasim.socat.status_socat", "line_number": 142, "usage_type": "call"}, {"api_name": "infrasim.socat", "line_number": 142, "usage_type": "name"}]} {"seq_id": "237089653", "text": "import torch.nn as nn\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport random as rd\r\nimport numpy as np\r\nfrom torch.autograd import Variable\r\nimport time\r\n\r\n\r\n\r\nclass LSTMNet(nn.Module):\r\n \r\n \r\n def __init__(self, input_size, output_size, hidden_dim, n_layers, dim_linear, device):\r\n super(LSTMNet, self).__init__()\r\n \r\n # Defining some parameters\r\n self.hidden_dim = hidden_dim\r\n self.n_layers = n_layers\r\n self.device = device\r\n \r\n # Defining the layers\r\n # RNN Layer\r\n self.lstm = nn.LSTM(input_size, hidden_dim, n_layers, batch_first=True)\r\n # stack of fully connected layers\r\n self.seq = nn.Sequential(nn.Linear(hidden_dim, dim_linear), nn.CELU(), nn.Linear(dim_linear, output_size), nn.CELU())\r\n \r\n \r\n def forward(self, x):\r\n \r\n batch_size = x.size(0)\r\n \r\n # Initializing hidden state for first input using method defined below\r\n hidden, cell = self.init_hidden_and_cell(batch_size)\r\n hidden, cell = hidden.double(), cell.double()\r\n \r\n # Passing in the input and hidden state into the model and obtaining outputs\r\n out, (_, _) = self.lstm(x, (hidden, cell))\r\n #print('1 : ', out.shape, hidden.shape)\r\n \r\n # Reshaping the outputs such that it can be fit into the fully connected layers\r\n out = out[ : , -1]\r\n out = self.seq(out)\r\n \r\n return out, hidden\r\n \r\n \r\n def init_hidden_and_cell(self, batch_size):\r\n # This method generates the first hidden state of zeros which we'll use in the forward pass\r\n # We'll send the tensor holding the hidden state to the device we specified earlier as well\r\n hidden = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(self.device)\r\n cell = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(self.device)\r\n return hidden, cell\r\n\r\n\r\ndef train(model, x_train, y_train, x_validation, y_validation, nb_epochs, learning_rate, max_batch_size, criterion=nn.MSELoss()):\r\n \r\n optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\r\n index = np.arange(x_train.shape[0])\r\n criterion = criterion.double()\r\n \r\n errors_train, errors_validation = [], []\r\n nb_batchs = int(x_train.shape[0] // max_batch_size) + 1 * (x_train.shape[0] % max_batch_size != 0)\r\n \r\n t = time.time()\r\n \r\n for n in range(1, nb_epochs + 1):\r\n \r\n rd.shuffle(index)\r\n x_train, y_train = x_train[index], y_train[index]\r\n epoch_loss = 0\r\n \r\n for k in range(nb_batchs):\r\n \r\n # Clearing existing gradients from previous epoch\r\n optimizer.zero_grad()\r\n input, target = x_train[k * max_batch_size : (k + 1) * max_batch_size], y_train[k * max_batch_size : (k + 1) * max_batch_size]\r\n #print('0 : ', input.shape, target.shape)\r\n model = model.double()\r\n output, _ = model(input)\r\n #print('2 : ', output.shape, target.shape)\r\n loss = criterion(output, target).double()\r\n # Performing backprop\r\n loss.backward()\r\n # Updating weights\r\n optimizer.step()\r\n # Adding the weighted loss to epoch_loss\r\n epoch_loss += loss.item() * input.shape[0]\r\n \r\n errors_train.append(epoch_loss / x_train.shape[0])\r\n errors_validation.append(compute_loss(model, x_validation, y_validation, max_batch_size, criterion))\r\n \r\n if n % 10 == 0:\r\n \r\n print(f\"Epoch: {n}/{nb_epochs}.............\", end=' ')\r\n print(f\"Train loss: {round(errors_train[-1], 6)}\")\r\n print(f\" ............. Test loss: {round(errors_validation[-1], 6)}\")\r\n t = time.time() - t\r\n print(\"Time elapsed for the last 10 epochs : \", round(t / 60, 2), 'min\\n')\r\n t = time.time()\r\n \r\n return errors_train, errors_validation\r\n\r\n\r\ndef compute_loss(model, x, y, max_batch_size, criterion=nn.MSELoss()):\r\n \r\n nb_batchs = int(x.shape[0] // max_batch_size) + 1 * (x.shape[0] % max_batch_size != 0) \r\n total_loss = 0\r\n \r\n with torch.no_grad():\r\n \r\n for k in range(nb_batchs):\r\n \r\n input, target = x[k * max_batch_size : (k + 1) * max_batch_size], y[k * max_batch_size : (k + 1) * max_batch_size]\r\n #print('0 : ', input.shape, target.shape)\r\n model = model.double()\r\n output, _ = model(input)\r\n #print('2 : ', output.shape, target.shape)\r\n loss = criterion(output, target).double()\r\n # Adding the weighted loss to total_loss\r\n total_loss += loss.item() * input.shape[0]\r\n \r\n return total_loss / x.shape[0]", "sub_path": "LSTM.py", "file_name": "LSTM.py", "file_ext": "py", "file_size_in_byte": 4839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.CELU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 110, "usage_type": "call"}]} {"seq_id": "362888581", "text": "import numpy as np\nfrom sklearn import linear_model\nfrom numba import jit\n#from sklearn.linear_model import LinearRegression\n\n@jit\ndef polynomial_this(x,y,n):\n \"\"\"\n Lager designmatrise for for en funksjon av to variabler\n for alle polynomer opp til og med grad n\n \"\"\"\n X = np.c_[np.ones(len(x))]\n for i in range(1,n+1):\n X = np.c_[X,x**(i)]\n for j in range(i-1,0,-1):\n X = np.c_[X,(x**(j))*(y**(i-j))] \n X = np.c_[X,y**(i)]\n return X\n\ndef bias(true, pred):\n bias = np.mean((true - np.mean(pred))**2)\n return bias\n\n \ndef MSE(true, pred):\n MSE = sum((true-pred)**2)/(len(true))\n return MSE\n \ndef R2(true, pred):\n R2 = 1-(np.sum((true - pred)**2)/np.sum((true-np.mean(pred))**2))\n return R2\n\ndef KfoldCrossVal(dataset, dataset2, Numbfold):\n \"\"\"\n Takes in two coupled datasets and returns them splitted into k-matching \n \"folds\" by produsing randomindices. \n \n KfoldCrosVal([1,4,5,6],[7,6,8,5],2) may return\n \n [[1,5],[4,6]], [[7,8],[6,2]]\n \n by produsing the indices [[0,2],[1,3]] from input \"dataset\"\n \"\"\"\n indices = np.arange(len(dataset[:, 0]))\n random_indices = np.random.choice(indices, size = len(dataset[:, 0]), replace = False)\n interval = int(len(dataset[:, 0])/Numbfold)\n datasetsplit = []\n dataset2split = []\n for k in range(Numbfold):\n datasetsplit.append(dataset[random_indices[interval*k : interval*(k + 1)]]) \n dataset2split.append(dataset2[random_indices[interval*k : interval*(k + 1)]])\n\n return np.asarray(datasetsplit), np.asarray(dataset2split) \n\n\nclass regression:\n def __init__(self,X,z):\n self.z = z\n self.X = X\n \n @jit \n def ridge(self,lambd):\n X = self.X\n beta = np.linalg.inv(X.T.dot(X)+lambd*np.identity(X.shape[1])).dot(X.T.dot(self.z))\n self.znew = X.dot(beta)\n\n self.beta = beta\n return beta#plutt\n \n @jit\n def OLS(self):\n X = self.X\n beta = np.linalg.pinv(X.T.dot(X)).dot(X.T.dot(self.z))\n self.znew = X.dot(beta)\n return beta\n \n @jit\n def lasso(self, lambd):\n lasso = linear_model.Lasso(alpha = lambd,fit_intercept = False)\n lasso.fit(self.X, self.z)\n beta = lasso.coef_#[:,np.newaxis]\n self.znew = self.X.dot(beta)\n return beta\n\n \n def beata_variance(self):\n sigma2 = (1./(len(self.z)-self.X.shape[1]-1))*sum((self.z-self.znew)**2)\n covar = np.linalg.inv(self.X.T.dot(self.X))*sigma2\n var = np.diagonal(covar)\n return beta_var\n\n \n def MSE(self):\n MSE = np.mean((self.z-self.znew)**2)\n return MSE\n \n def R2(self):\n self.R2 = 1-(np.sum((self.z - self.znew)**2)/np.sum((self.z-np.mean(self.z))**2))\n return self.R2\n\n\ndef FRANK(x, y):\n \"\"\"\n Frankie function for testing the class\n \"\"\"\n term1 = 0.75*np.exp(-(0.25*(9*x - 2)**2) - 0.25*((9*y-2)**2))\n term2 = 0.75*np.exp(-((9*x + 1)**2)/49.0 - 0.1*(9*y+1))\n term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n return term1 + term2 + term3 + term4 \n \n\n\nif __name__== \"__main__\" :\n def test_reg():\n a = 3\n \n ", "sub_path": "project2/reg_class.py", "file_name": "reg_class.py", "file_ext": "py", "file_size_in_byte": 3253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.c_", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 64, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.linalg.pinv", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 70, "usage_type": "name"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 79, "usage_type": "name"}, {"api_name": "numba.jit", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.linalg.inv", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.diagonal", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 109, "usage_type": "call"}]} {"seq_id": "566710978", "text": "'''\nAuthor: Swarfte\nDate: 2021-08-09 21:36:18\nLastEditors: Chau Lap Tou\nLastEditTime: 2021-08-30 21:57:56\nFilePath: \\calculate\\local_function.py\nFileOutput: pyinstaller -F -w file_name.py -p C:/python/lib/site-packages \nGithubName: Swarfte\n'''\nimport random as rd\nimport math\nimport re\nimport sympy as SP\n\nLastnumber = \"\"#*用作記憶功能,即紀錄結果\nDecimalPlaces = 2 #*預設保留的小數位\n\ndef CalcLog(sentence):#*計算對數\n get_num = sentence\n log_base = 10\n ans = 0\n if \",\" in get_num:#*輸入底數的情況\n number = get_num.split(\",\")\n log_base = float(number[0])#*用前面那個數為底\n logarithm = float(number[1])#*逗號後的為對數\n else:#*沒有輸入底數的情況\n logarithm = float(sentence)\n ans = math.log(logarithm,log_base)#*log() 函式接受兩個引數。第一個引數是數字，第二個引數是基值\n return ans\n\ndef CalcTrigonometric(sentence,mode):#*計算三角函數\n get_num = sentence\n ans = 0\n if \",\" in get_num:\n get_num = get_num[:len(get_num)-1]\n num = float(get_num)\n if mode == \"sin\" :\n ans = SP.cos(math.radians(num))#*化為弧度制\n elif mode == \"tan\":\n ans = SP.cot(math.radians(num))\n elif mode == \"sec\":\n ans = SP.csc(math.radians(num))\n else:\n num = float(get_num)\n if mode == \"sin\" :\n ans = SP.sin(math.radians(num))\n elif mode == \"tan\":\n ans = SP.tan(math.radians(num))\n if mode == \"sec\" :\n ans = SP.sec(math.radians(num))\n return ans#*預設保留小數位後2位\n\ndef ReplaceExpression(sentence):\n ans = sentence\n if \"..\" in ans: #*在運算時改變式子保留的小數點,且只允許式子出現一次\n choose = re.compile(r\"\\.\\.[0-9]+\")\n num_native = re.search(choose,ans).group()#*帶有..\n num_get = num_native[2:]\n global DecimalPlaces\n DecimalPlaces = int(num_get)\n ans = ans.replace(num_native,\"\")#*用空白替換\n \n while \"log\" in ans:#*處理式子中全部Log數\n choose = re.compile(r\"log[0-9]+\\.?[0-9]*\\,[0-9]+\\.?[0-9]*|log[0-9]+\\.?[0-9]*\")#*找出log數的位置\n log_native = re.search(choose,ans).group()#*帶有Log字串\n log_get = log_native[3:]#*獲取關建的數字(log的數字)\n log_ans = str(CalcLog(log_get)) #*獲取答案\n ans = ans.replace(log_native,log_ans)#*把答案取代輸入中的log,同時覆蓋原來的輸入端\n \n while \"sin\" in ans:#*處理式子中全迎的sin/cos\n choose = re.compile(r\"sin[0-9]+\\.?[0-9]*\\,?\")#*找出sin的位置\n num_native = re.search(choose,ans).group()#*帶有sin\n num_get = num_native[3:]\n num_ans= str(CalcTrigonometric(num_get,\"sin\"))\n ans = ans.replace(num_native,num_ans)\n \n while \"tan\" in ans:#*處理式子中全迎的tan/cos\n choose = re.compile(r\"tan[0-9]+\\.?[0-9]*\\,?\")#*找出tan的位置\n num_native = re.search(choose,ans).group()#*帶有tan\n num_get = num_native[3:]\n num_ans= str(CalcTrigonometric(num_get,\"tan\"))\n ans = ans.replace(num_native,num_ans)\n \n while\"sec\" in ans:#*處理式子中全迎的sec/csc\n choose = re.compile(r\"sec[0-9]+\\.?[0-9]*\\,?\")#*找出sec的位置\n num_native = re.search(choose,ans).group()#*帶有sce\n num_get = num_native[3:]\n num_ans= str(CalcTrigonometric(num_get,\"sec\"))\n ans = ans.replace(num_native,num_ans)\n \n return ans\n\nclass function(object):\n def __init__(self,gui):\n super().__init__()\n self.function = gui#*獲取傳入的gui\n self.input = self.function.InputLine.text()#*獲取當前輸入端的資料\n self.output = self.function.OutputLine.text()#*獲取當前輸出端的資料\n \n def Test(self):\n print(\"hello\")\n \n def Printf(self,push,string):#*輸出新增的文字\n self.input = push.text()#*獲取傳入的元件的資料(針對Line)\n self.input += string\n push.setText(self.input)\n \n def Clean(self,inputline,outputline) :#*清除line的資料\n self.input = inputline.text()\n self.input = \"\"#*重置輸入欄\n inputline.setText(self.input)\n outputline.setText(self.input)\n \n def Delete(self,push):#*用作清除不需要的字元\n self.input = push.text()\n self.input = self.input[:-1]#*刪除最後的字元\n push.setText(self.input)\n \n def RandomMath(self,inputline,outputline):#*獲取隨機數字\n try:\n global Lastnumber\n num = 0\n self.input = inputline.text()\n if len(self.input) == 0 :#*沒有輸入數字時,則像普體骰子一樣輸出1~6\n num = rd.randint(1,6)\n else:\n if \",\" in self.input :#*輸入區間的話則骰出區間內的數字\n numlist = self.input.split(\",\")\n num = rd.randint(int(numlist[0]),int(numlist[1]))\n else:#*只輸入一個數字的話則為1~該數字隨機骰出一個\n num = rd.randint(1,int(self.input))\n Lastnumber = str(num)\n outputline.setText(str(num))\n except :\n outputline.setText(\"error\")\n \n def RandomEnglish(self,inputline,outputline):#*隨機生成英文\n try:\n global Lastnumber\n english =[\"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 eng = \"\"\n self.input = inputline.text()\n if len(self.input) == 0 :#*沒有輸入數字時,則像普體選擇題一樣輸出A~D\n eng = english[rd.randint(0,3)]\n else:#*輸入區間的話則骰出區間內的字母\n if \",\" in self.input :\n englist = self.input.split(\",\")\n eng = english[rd.randint(int(englist[0])-1,int(englist[1])-1)]\n else:#*只輸入一個數字的話則為1~該數字隨機骰出一個字母\n eng = english[rd.randint(1,int(self.input))]\n Lastnumber = eng\n outputline.setText(eng)\n except :\n outputline.setText(\"error\")\n \n def RememberNumber(self,push):#*記憶功能\n global Lastnumber\n self.input = push.text()\n self.input += Lastnumber #*加左原本的句子上\n push.setText(self.input)\n \n \n def Basic (self,inputline,outputline):#*用作計算結果\n try:\n global Lastnumber\n self.input = inputline.text()\n self.input = ReplaceExpression(self.input)#*處理log數\n \n global DecimalPlaces\n try :\n ans = round(eval(self.input),DecimalPlaces)#*計算結果\n except:\n ans = str(eval(self.input))\n ans += \"it is a error\"\n outputline.setText(str(ans))#*在輸出端顯示結果\n Lastnumber = str(ans)\n except :\n outputline.setText(\"error\")#*有問題時則報錯", "sub_path": "local_function.py", "file_name": "local_function.py", "file_ext": "py", "file_size_in_byte": 7176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "math.log", "line_number": 28, "usage_type": "call"}, {"api_name": "sympy.cos", "line_number": 38, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 38, "usage_type": "call"}, {"api_name": "sympy.cot", "line_number": 40, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 40, "usage_type": "call"}, {"api_name": "sympy.csc", "line_number": 42, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 42, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 46, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 46, "usage_type": "call"}, {"api_name": "sympy.tan", "line_number": 48, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.sec", "line_number": 50, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 50, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 56, "usage_type": "call"}, {"api_name": "re.search", "line_number": 57, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 64, "usage_type": "call"}, {"api_name": "re.search", "line_number": 65, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 71, "usage_type": "call"}, {"api_name": "re.search", "line_number": 72, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 78, "usage_type": "call"}, {"api_name": "re.search", "line_number": 79, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 85, "usage_type": "call"}, {"api_name": "re.search", "line_number": 86, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 125, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 129, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 144, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 148, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 150, "usage_type": "call"}]} {"seq_id": "484880680", "text": "import read_doc as rd\nimport matplotlib.pyplot as plt\nfrom math import *\n\nglobalInfo = rd.collectDataFromTab(rd.readFile())\n\nfor x in ['Arithmancy', 'Astronomy', 'Herbology', 'Defense Against the Dark Arts', 'Divination', 'Muggle Studies', 'Ancient Runes', 'History of Magic', 'Transfiguration', 'Potions', 'Care of Magical Creatures', 'Charms', 'Flying']:\n\txTab = []\n\tfor feature in globalInfo:\n\t\tif feature[0] == x:\n\t\t\tfor grade in feature[1:]:\n\t\t\t\ttry:\n\t\t\t\t\tfloat(grade)\n\t\t\t\texcept:\n\t\t\t\t\txTab.append('NaN')\n\t\t\t\telse:\n\t\t\t\t\txTab.append(float(grade))\n\tfor y in ['Arithmancy', 'Astronomy', 'Herbology', 'Defense Against the Dark Arts', 'Divination', 'Muggle Studies', 'Ancient Runes', 'History of Magic', 'Transfiguration', 'Potions', 'Care of Magical Creatures', 'Charms', 'Flying']:\n\t\tif y == x:\n\t\t\tpass\n\t\telse:\n\t\t\tyTab = []\n\t\t\tfor feature in globalInfo:\n\t\t\t\tif feature[0] == y:\n\t\t\t\t\tfor grade in feature[1:]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tfloat(grade)\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tyTab.append('NaN')\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tyTab.append(float(grade))\n\n\t\t\tif len(xTab) < len(yTab):\n\t\t\t\tyTab = yTab[:len(xTab)]\n\t\t\telif len(xTab) > len(yTab):\n\t\t\t\txTab = xTab[:len(yTab)]\n\n\t\t\tastronomy = xTab\n\t\t\tdefense = yTab\n\n\t\t\tplt.scatter(astronomy, defense, s=0.5)\n\n\t\t\tplt.suptitle(\"Quelles sont les deux features qui sont semblables ?\")\n\t\t\tplt.title(x + \" et \" + y)\n\t\t\tplt.xlabel(x)\n\t\t\tplt.ylabel(y)\n\t\t\tplt.show()", "sub_path": "scatter_plot.py", "file_name": "scatter_plot.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "read_doc.collectDataFromTab", "line_number": 5, "usage_type": "call"}, {"api_name": "read_doc.readFile", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} {"seq_id": "165523225", "text": "from django.contrib.auth.models import User\nfrom django.views.generic import ListView\nfrom django.views.generic.base import TemplateView\nfrom django_tables2 import SingleTableView\nimport django_tables2 as tables\nfrom django_tables2.config import RequestConfig\nfrom django.shortcuts import render\nimport csv\nfrom django.http import HttpResponse\n\nfrom django.shortcuts import render, get_object_or_404\n\nfrom .models import DailyRecord, Participant\nfrom .tables import DailyRecordTable, DailyTable \n# Create your views here.\n\ndef index(request):\n\treturn render(request, 'dailyrecord/index.html')\n\n\n\ndef export_daily_record_csv(request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"nja_daily_record.csv\"'\n\n writer = csv.writer(response)\n writer.writerow(['S.n.', 'Ogransied Date', 'Program Name',\t'Venue', 'Organiser'])\n\n dailyrecords = DailyRecord.objects.all().values_list('id', 'date_organised', 'program_name', 'venue', 'organiser')\n for dailyrecord in dailyrecords:\n writer.writerow([u''+ str(row)+' ' for row in dailyrecord])\n\n return response\n\ndef dailyrecord_list(request):\n\tdailyrecords = DailyRecord.public.all()\n\tcontext = {'dailyrecords': dailyrecords}\n\treturn render(request, 'dailyrecord/programs.html', context)\n\n\n#test method\n# def daily_home(request):\n# \t return render(request, 'dailyrecord/daily-home.html', {'dailyrecords': DailyRecord.public.all()})\n\ndef daily_home(request):\n\ttable = DailyTable(DailyRecord.objects.all())\n\tRequestConfig(request).configure(table)\n\treturn render(request, 'dailyrecord/daily-home.html', {'dailyrecords': table})\n\n\nclass ProgramListAll(ListView):\n\tmodel = DailyRecord\n\ttemplate_name = \"dailyrecord/programs.html\"\n\nclass HomePageView(TemplateView):\n\ttemplate_name = \"dailyrecord/index.html\"\n\nclass AboutPageView(TemplateView):\n\ttemplate_name = \"dailyrecord/about.html\"\n\nqs = DailyRecord.public.all()\n\nclass DailyActivityPageView(SingleTableView):\n\ttable_class = DailyRecordTable\n\tqueryset = DailyRecord.public.all()\t\n\t# queryset = Participant(DailyRecord.objects.all())\n\t# RequestConfig(request).configure(queryset)\t\n\ttemplate_name = 'dailyrecord/daily-activity-record.html'\n\n\tdef get_context_data(self, **kwargs):\n\t\tcontext = super(DailyActivityPageView, self).get_context_data(**kwargs)\n\t\tcontext['table_participant'] = Participant(DailyRecord.public.all()),\n\t\treturn context\n\n\nclass ExtraCuricularActivityPageView(TemplateView):\n\ttemplate_name = \"dailyrecord/extra-curicular-activity.html\" ", "sub_path": "dailyrecord/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2534, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 23, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 26, "usage_type": "call"}, {"api_name": "models.DailyRecord.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "models.DailyRecord.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 29, "usage_type": "name"}, {"api_name": "models.DailyRecord.public.all", "line_number": 36, "usage_type": "call"}, {"api_name": "models.DailyRecord.public", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 36, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "tables.DailyTable", "line_number": 46, "usage_type": "call"}, {"api_name": "models.DailyRecord.objects.all", "line_number": 46, "usage_type": "call"}, {"api_name": "models.DailyRecord.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 46, "usage_type": "name"}, {"api_name": "django_tables2.config.RequestConfig", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 51, "usage_type": "name"}, {"api_name": "models.DailyRecord", "line_number": 52, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateView", "line_number": 55, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateView", "line_number": 58, "usage_type": "name"}, {"api_name": "models.DailyRecord.public.all", "line_number": 61, "usage_type": "call"}, {"api_name": "models.DailyRecord.public", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 61, "usage_type": "name"}, {"api_name": "django_tables2.SingleTableView", "line_number": 63, "usage_type": "name"}, {"api_name": "tables.DailyRecordTable", "line_number": 64, "usage_type": "name"}, {"api_name": "models.DailyRecord.public.all", "line_number": 65, "usage_type": "call"}, {"api_name": "models.DailyRecord.public", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Participant", "line_number": 72, "usage_type": "call"}, {"api_name": "models.DailyRecord.public.all", "line_number": 72, "usage_type": "call"}, {"api_name": "models.DailyRecord.public", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.DailyRecord", "line_number": 72, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateView", "line_number": 76, "usage_type": "name"}]} {"seq_id": "249297866", "text": "# date: 2019-12-22\n# author: jannik wiessler\n# e-mail: jannik.wiessler@googlemail.com\n# GUI for invoice feature extraction \n\nimport wx\nfrom invoiceFrame import invoiceFrame\nimport os\nimport sys\nimport logging\n\n\n# get current directory: for logger\nif getattr(sys, 'frozen', False): \n application_path = os.path.dirname(sys.executable)\nelif __file__:\n application_path = os.path.abspath(__file__)[0:-(len(__file__)+1)]\n\n\nclass MyApp(wx.App):\n def __init__(self):\n super().__init__()\n\n self.__frame = invoiceFrame(parent=None, title=\"Menu Bar App\")\n self.__frame.Show()\n \n \nif __name__ == '__main__':\n \n logging.basicConfig(filename=application_path+'/GuiWxInfo.log',\n level=logging.DEBUG,\n format='%(asctime)-15s %(name)s %(message)s')\n app = MyApp()\n app.MainLoop()", "sub_path": "Gui_wx.py", "file_name": "Gui_wx.py", "file_ext": "py", "file_size_in_byte": 853, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wx.App", "line_number": 20, "usage_type": "attribute"}, {"api_name": "invoiceFrame.invoiceFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 31, "usage_type": "attribute"}]} {"seq_id": "403255300", "text": "import pytest\nfrom pupa.scrape import Bill\nfrom pupa.utils.generic import get_pseudo_id\nfrom pupa.exceptions import ScrapeValueError\n\n\ndef toy_bill():\n b = Bill(\n identifier=\"HB 2017\",\n legislative_session=\"2012A\",\n title=\"A bill for an act to raise the cookie budget by 200%\",\n from_organization=\"Foo Senate\",\n classification=\"bill\",\n )\n b.add_source(\"http://uri.example.com/\", note=\"foo\")\n return b\n\n\ndef test_basic_valid_bill():\n b = toy_bill()\n b.validate()\n assert \"we got here\"\n\n\ndef test_bill_type_setting():\n # default\n b = Bill(identifier=\"some bill\", legislative_session=\"session\", title=\"the title\")\n assert b.classification == [\"bill\"]\n\n # string -> list\n b = Bill(\n identifier=\"some bill\",\n legislative_session=\"session\",\n title=\"the title\",\n classification=\"string\",\n )\n assert b.classification == [\"string\"]\n\n # list unmodified\n b = Bill(\n identifier=\"some bill\",\n legislative_session=\"session\",\n title=\"the title\",\n classification=[\"two\", \"items\"],\n )\n assert b.classification == [\"two\", \"items\"]\n\n # tuple -> list\n b = Bill(\n identifier=\"some bill\",\n legislative_session=\"session\",\n title=\"the title\",\n classification=(\"two\", \"items\"),\n )\n assert b.classification == [\"two\", \"items\"]\n\n\ndef test_basic_invalid_bill():\n \"\"\"Test that we can create an invalid bill, and validation will fail\"\"\"\n b = toy_bill()\n b.identifier = None\n with pytest.raises(ValueError):\n b.validate()\n\n\ndef test_from_organization():\n # none set\n assert get_pseudo_id(Bill(\"HB 1\", \"2014\", \"Some Bill\").from_organization) == {\n \"classification\": \"legislature\"\n }\n\n # chamber set\n assert get_pseudo_id(\n Bill(\"SB 1\", \"2014\", \"Some Bill\", chamber=\"upper\").from_organization\n ) == {\"classification\": \"upper\"}\n # org direct set\n assert (\n Bill(\"HB 1\", \"2014\", \"Some Bill\", from_organization=\"test\").from_organization\n == \"test\"\n )\n\n # can't set both\n with pytest.raises(ValueError):\n Bill(\"HB 1\", \"2014\", \"Some Bill\", from_organization=\"upper\", chamber=\"upper\")\n\n\ndef test_add_action():\n \"\"\"Make sure actions work\"\"\"\n b = toy_bill()\n b.add_action(\"Some dude liked it.\", \"2013-04-29T20:00Z\", chamber=\"lower\")\n assert len(b.actions) == 1\n assert b.actions[0][\"description\"] == \"Some dude liked it.\"\n assert get_pseudo_id(b.actions[0][\"organization_id\"]) == {\"classification\": \"lower\"}\n assert b.actions[0][\"date\"] == \"2013-04-29T20:00Z\"\n b.validate()\n\n\ndef test_action_extra():\n b = toy_bill()\n b.add_action(\n \"an action with some extra information\",\n \"2017-01-01\",\n extras=dict(sitting_chair=\"Adams\"),\n )\n assert b.actions[0][\"extras\"] == {\"sitting_chair\": \"Adams\"}\n\n\ndef test_add_related_bill():\n \"\"\"Make sure related bills work\"\"\"\n b = toy_bill()\n b.add_related_bill(\n identifier=\"HB 2020\", legislative_session=\"2011A\", relation_type=\"companion\"\n )\n assert len(b.related_bills) == 1\n assert b.related_bills[0] == {\n \"identifier\": \"HB 2020\",\n \"legislative_session\": \"2011A\",\n \"relation_type\": \"companion\",\n }\n b.validate()\n\n\ndef test_add_sponsor():\n b = toy_bill()\n b.add_sponsorship(\n name=\"Joe Bleu\",\n classification=\"Author\",\n entity_type=\"person\",\n primary=True,\n chamber=\"upper\",\n )\n assert len(b.sponsorships) == 1\n assert b.sponsorships[0] == {\n \"person_id\": '~{\"name\": \"Joe Bleu\"}',\n \"name\": \"Joe Bleu\",\n \"classification\": \"Author\",\n \"entity_type\": \"person\",\n \"primary\": True,\n \"organization_id\": None,\n }\n b.validate()\n\n\ndef test_subjects():\n b = toy_bill()\n b.add_subject(\"Foo\")\n b.add_subject(\"Bar\")\n assert b.subject == [\"Foo\", \"Bar\"]\n b.validate()\n\n\ndef test_abstract():\n b = toy_bill()\n b.add_abstract(\"this bill is stupid\", \"K-5\", \"1969-10-20\")\n b.add_abstract(\"this legislative document is ignorant\", \"6-12\", \"2010-10-10\")\n assert b.abstracts == [\n {\"note\": \"K-5\", \"abstract\": \"this bill is stupid\", \"date\": \"1969-10-20\"},\n {\n \"note\": \"6-12\",\n \"abstract\": \"this legislative document is ignorant\",\n \"date\": \"2010-10-10\",\n },\n ]\n\n\ndef test_add_documents():\n b = toy_bill()\n\n # should only add one document since they all have same note\n b.add_document_link(\n note=\"Fiscal Impact\",\n date=\"2013-04\",\n url=\"http://hi.example.com/foo#bar\",\n media_type=\"text/html\",\n )\n b.add_document_link(note=\"Fiscal Impact\", date=\"2013-04\", url=\"http://foobar.baz\")\n assert len(b.documents) == 1\n\n # should now be two documents\n b.add_document_link(\n note=\"Other Document\", date=\"2013-04\", url=\"http://foobar.baz/other\"\n )\n assert len(b.documents) == 2\n\n # valid documents so far\n b.validate()\n\n # an invalid document\n b.add_document_link(\n note=\"Fiscal Impact\", date=\"2013-04\", url=None, media_type=\"foo\"\n )\n with pytest.raises(ScrapeValueError):\n b.validate()\n\n\ndef test_versions():\n b = toy_bill()\n\n # only one document, multiple links\n b.add_version_link(url=\"http://pault.ag/\", note=\"Final Version\", date=\"2013-04\")\n b.add_version_link(url=\"http://pault.ag/foo\", note=\"Final Version\", date=\"2013-04\")\n b.validate()\n assert len(b.versions) == 1\n assert len(b.versions[0][\"links\"]) == 2\n\n # duplicate!\n with pytest.raises(ValueError):\n b.add_version_link(\n url=\"http://pault.ag/foo\", note=\"Final Version\", date=\"2013-04\"\n )\n\n # ignore duplicate - nothing should change\n b.add_version_link(\n url=\"http://pault.ag/foo\",\n note=\"Final Version\",\n date=\"2013-04\",\n on_duplicate=\"ignore\",\n )\n assert len(b.versions) == 1\n assert len(b.versions[0][\"links\"]) == 2\n\n # duplicate URL\n with pytest.raises(ValueError):\n b.add_version_link(\n url=\"http://pault.ag/foo\", note=\"Finals Versions\", date=\"2013-04\"\n )\n assert len(b.versions) == 1\n assert len(b.versions[0][\"links\"]) == 2\n\n # a new doc, numbers go up\n b.add_version_link(\n url=\"http://pault.ag/foovbar\", note=\"Finals Versions\", date=\"2013-04\"\n )\n assert len(b.versions) == 2\n assert len(b.versions[1][\"links\"]) == 1\n\n # still validates\n b.validate()\n\n\ndef test_str():\n b = toy_bill()\n assert b.identifier in str(b)\n\n\ndef test_no_whitespace_in_uri():\n b = Bill(\n identifier=\"HB 2017\",\n legislative_session=\"2012A\",\n title=\"A bill for an act to raise the cookie budget by 200%\",\n from_organization=\"Foo Senate\",\n classification=\"bill\",\n )\n b.add_source(\"http://uri.example.com/fail here\", note=\"foo\")\n with pytest.raises(ScrapeValueError):\n b.validate()\n", "sub_path": "pupa/tests/scrape/test_bill_scrape.py", "file_name": "test_bill_scrape.py", "file_ext": "py", "file_size_in_byte": 6960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pupa.scrape.Bill", "line_number": 8, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 27, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 31, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 40, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 62, "usage_type": "call"}, {"api_name": "pupa.utils.generic.get_pseudo_id", "line_number": 68, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 68, "usage_type": "call"}, {"api_name": "pupa.utils.generic.get_pseudo_id", "line_number": 73, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 74, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 83, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 84, "usage_type": "call"}, {"api_name": "pupa.utils.generic.get_pseudo_id", "line_number": 93, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 192, "usage_type": "call"}, {"api_name": "pupa.exceptions.ScrapeValueError", "line_number": 192, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 207, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 223, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 247, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 255, "usage_type": "call"}, {"api_name": "pupa.exceptions.ScrapeValueError", "line_number": 255, "usage_type": "argument"}]} {"seq_id": "633851586", "text": "from lh3.api import *\nfrom dashboard.utils.utils import (\n soft_anonimyzation,\n operatorview_helper,\n Chats,\n retrieve_transcript,\n search_chats,\n)\nfrom datetime import datetime, timedelta, timezone\nfrom django.views.generic import TemplateView\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import Http404\nfrom django.http import HttpResponse, HttpResponseNotFound\nfrom pprint import pprint as print\nfrom django.contrib import messages\nimport random\n\n\nfrom datetime import datetime\nfrom datetime import timezone\nimport json\n\nfrom dateutil.parser import parse\n\nfrom django.http import JsonResponse\n\nfrom dashboard.utils.ask_schools import (\n find_school_by_operator_suffix,\n find_queues_from_a_school_name,\n find_school_by_queue_or_profile_name,\n)\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\n\"\"\"\nList of VIEW FUNCTIONS/CLASSES\nSearchProfileResultsView\nget_this_profile\nget_transcript\nget_chats_for_this_school_using_an_username\nget_chats_for_this_school_using_this_queue_name\nget_chats_from_this_queue_using_only_the_queue_name\nget_chats_for_this_user\nget_chats_for_this_queue\nget_chats_from_yesterday\nget_chats_from_yesterday_from_mentees\n\"\"\"\n\n\n\n\ndef get_chats_for_this_school_using_an_username(request, *args, **kwargs):\n \"\"\"[summary]\n\n Args:\n request ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\"\n client = Client()\n username = kwargs.get(\"username\", None)\n school_name = find_school_by_operator_suffix(username).lower()\n queues_from_school = find_queues_from_a_school_name(school_name)\n print(\"username: {0}\".format(username))\n print(\"school_name: {0}\".format(school_name))\n query = {\n \"query\": {\n \"queue\": queues_from_school,\n \"from\": \"2021-01-01\",\n \"to\": \"2021-12-31\",\n },\n \"sort\": [{\"started\": \"descending\"}],\n }\n queue_chats = client.api().post(\"v4\", \"/chat/_search\", json=query)\n chats = soft_anonimyzation(queue_chats)\n today = datetime.today()\n current_year = today.year\n total_chats = len(chats)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n # print(chats)\n chats = [Chats(chat) for chat in chats]\n if request.is_ajax():\n return JsonResponse(\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": school_name,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n safe=False,\n )\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": school_name,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n )\n\n\ndef get_chats_for_this_school_using_this_queue_name(request, *args, **kwargs):\n \"\"\"[summary]\n\n Args:\n request ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\"\n client = Client()\n queue_name = kwargs.get(\"queue_name\", None)\n school_name = find_school_by_queue_or_profile_name(queue_name)\n queues_from_school = find_queues_from_a_school_name(school_name)\n\n print(\"queue_name: {0}\".format(queue_name))\n print(\"school_name: {0}\".format(school_name))\n query = {\n \"query\": {\n \"queue\": queues_from_school,\n \"from\": \"2021-01-01\",\n \"to\": \"2021-12-31\",\n },\n \"sort\": [{\"started\": \"descending\"}],\n }\n # breakpoint()\n queue_chats = client.api().post(\"v4\", \"/chat/_search\", json=query)\n chats = soft_anonimyzation(queue_chats)\n today = datetime.today()\n current_year = today.year\n total_chats = len(chats)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n # print(chats)\n chats = [Chats(chat) for chat in chats]\n if request.is_ajax():\n return JsonResponse(\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": school_name,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n safe=False,\n )\n return render(\n request,\n \"results/chats_from_queues.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": school_name,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n )\n\n\n\ndef get_chats_from_this_queue_for_this_year_using_only_the_queue_name(\n request, *args, **kwargs\n):\n client = Client()\n queue_name = kwargs.get(\"queue_name\", None)\n today = datetime.today()\n\n\n print(\"queue_name: {0}\".format(queue_name))\n query = {\n \"query\": {\"queue\": [queue_name], \"from\": str(today.year)+\"-01-01\", \"to\": str(today.year)+\"-12-31\"},\n \"sort\": [{\"started\": \"descending\"}],\n }\n queue_chats, content_range = search_chats(\n client, query, chat_range=(0, 500)\n )\n # breakpoint()\n chats = soft_anonimyzation(queue_chats)\n today = datetime.today()\n current_year = today.year\n total_chats = len(chats)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n # print(chats)\n chats = [Chats(chat) for chat in chats]\n if request.is_ajax():\n return JsonResponse(\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": queue_name,\n \"get_chat_for_this_year\": True,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n safe=False,\n )\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"get_chat_for_this_year\": True,\n \"heatmap_chats\": heatmap_chats,\n \"username\": queue_name,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n )\n\n\ndef get_chats_for_this_user(request, username):\n \"\"\"[summary]\n\n Args:\n request ([type]): [description]\n username ([type]): [description]\n information ([type], optional): [description]. Defaults to None.\n\n Returns:\n [type]: [description]\n \"\"\"\n client = Client()\n today = datetime.today()\n query = {\n \"query\": {\"operator\": [username], \"from\": str(today.year)+\"-01-01\", \"to\": str(today.year)+\"-12-31\"},\n \"sort\": [{\"started\": \"descending\"}],\n }\n chats_from_users, content_range = search_chats(\n client, query, chat_range=(0, 500)\n )\n chats = soft_anonimyzation(chats_from_users)\n\n today = datetime.today()\n current_year = today.year\n total_chats = len(chats_from_users)\n\n assignments = operatorview_helper(username)\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n if request.is_ajax():\n return JsonResponse(\n {\n \"chats\": chats,\n \"assignments\": assignments,\n \"get_chat_for_this_year\": True,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n safe=False,\n )\n chats = [Chats(chat) for chat in chats]\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"assignments\": assignments,\n \"get_chat_for_this_year\": True,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n )\n\n\ndef get_chats_for_this_user_for_this_year(request, username, information=None):\n client = Client()\n today = datetime.today()\n query = {\n \"query\": {\n \"operator\": [username],\n \"from\": str(today.year) + \"-01-01\",\n \"to\": str(today.year) + \"-12-31\",\n },\n \"sort\": [{\"started\": \"descending\"}],\n }\n chats_from_users, content_range = search_chats(client, query, chat_range=(0, 500))\n chats = soft_anonimyzation(chats_from_users)\n\n today = datetime.today()\n current_year = today.year\n total_chats = len(chats_from_users)\n\n assignments = operatorview_helper(username)\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n if request.is_ajax():\n return JsonResponse(\n {\n \"chats\": chats,\n \"assignments\": assignments,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n safe=False,\n )\n chats = [Chats(chat) for chat in chats]\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"assignments\": assignments,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": current_year,\n \"total_chats\": total_chats,\n },\n )\n\n\ndef get_chats_for_this_queue(request, *args, **kwargs):\n client = Client()\n queue = kwargs.get(\"queue_name\", None)\n query = {\n \"query\": {\"queue\": [queue], \"from\": \"2021-01-01\", \"to\": \"2021-01-19\"},\n \"sort\": [{\"started\": \"descending\"}],\n }\n queue_chats = client.api().post(\"v4\", \"/chat/_search\", json=query)\n chats = soft_anonimyzation(queue_chats)\n return JsonResponse(chats, safe=False)\n\n\ndef get_chats_of_today(request, *args, **kwargs):\n client = Client()\n today = datetime.today()\n\n query = {\n \"query\": {\"from\": today.strftime(\"%Y-%m-%d\")},\n \"sort\": [{\"started\": \"ascending\"}],\n }\n chats_from_users = client.api().post(\"v4\", \"/chat/_search\", json=query)\n chats = soft_anonimyzation(chats_from_users)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n username = \"Yesterday\"\n # filter chats from yesteday only\n chats = [Chats(chat) for chat in chats]\n\n # return JsonResponse(chats, safe=False)\n if request.is_ajax():\n return JsonResponse(chats, safe=False)\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": \"Yesterday\",\n \"total_chats\": len(chats),\n },\n )\n\n\ndef get_chats_from_yesterday(request, *args, **kwargs):\n client = Client()\n today = datetime.today()\n yesterday = today - timedelta(days=1)\n\n query = {\n \"query\": {\"from\": yesterday.strftime(\"%Y-%m-%d\")},\n \"sort\": [{\"started\": \"ascending\"}],\n }\n # chats_from_users = client.api().post('v4', '/chat/_search', json = query)\n chats_from_users, content_range = search_chats(client, query, chat_range=(0, 350))\n chats = soft_anonimyzation(chats_from_users)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n username = \"Yesterday\"\n # filter chats from yesteday only\n chats = [\n chat\n for chat in chats\n if yesterday.strftime(\"%Y-%m-%d\")\n == parse(chat.get(\"started\")).strftime(\"%Y-%m-%d\")\n ]\n chats = [Chats(chat) for chat in chats]\n\n # return JsonResponse(chats, safe=False)\n if request.is_ajax():\n return JsonResponse(chats, safe=False)\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": \"Yesterday\",\n \"total_chats\": len(chats),\n },\n )\n\n\ndef get_chats_from_yesterday_from_mentees(request, *args, **kwargs):\n client = Client()\n today = datetime.today()\n yesterday = today - timedelta(days=1)\n\n query = {\n \"query\": {\"from\": yesterday.strftime(\"%Y-%m-%d\")},\n \"sort\": [{\"started\": \"descending\"}],\n }\n # chats_from_users = client.api().post('v4', '/chat/_search', json = query)\n chats_from_users, content_range = search_chats(client, query, chat_range=(0, 100))\n chats = soft_anonimyzation(chats_from_users)\n chat_picked_up_by_mentees = list()\n for chat in chats:\n if chat.get(\"accepted\"):\n if \"_int\" in chat.get(\"operator\"):\n chat_picked_up_by_mentees.append(chat)\n chats = chat_picked_up_by_mentees\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n username = \"Mentees\"\n # filter chats from yesteday only\n chats = [\n chat\n for chat in chats\n if yesterday.strftime(\"%Y-%m-%d\")\n == parse(chat.get(\"started\")).strftime(\"%Y-%m-%d\")\n ]\n chats = [Chats(chat) for chat in chats]\n\n # return JsonResponse(chats, safe=False)\n if request.is_ajax():\n return JsonResponse(chats, safe=False)\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": \"Yesterday\",\n \"total_chats\": len(chats),\n },\n )\n\n\ndef get_chats_from_yesterday_sample_size(request, *args, **kwargs):\n client = Client()\n today = datetime.today()\n yesterday = today - timedelta(days=1)\n\n query = {\n \"query\": {\"from\": yesterday.strftime(\"%Y-%m-%d\")},\n \"sort\": [{\"started\": \"ascending\"}],\n }\n chats_from_users, content_range = search_chats(client, query, chat_range=(0, 350))\n chats_from_users = random.sample(chats_from_users, int(50 * 0.20))\n chats = soft_anonimyzation(chats_from_users)\n\n heatmap = [\n parse(chat.get(\"started\")).replace(tzinfo=timezone.utc).timestamp()\n for chat in chats\n ]\n counter = {x: heatmap.count(x) for x in heatmap}\n heatmap_chats = json.dumps(counter)\n username = \"Yesterday\"\n # filter chats from yesteday only\n chats = [\n chat\n for chat in chats\n if yesterday.strftime(\"%Y-%m-%d\")\n == parse(chat.get(\"started\")).strftime(\"%Y-%m-%d\")\n ]\n chats = [Chats(chat) for chat in chats]\n\n # return JsonResponse(chats, safe=False)\n if request.is_ajax():\n return JsonResponse(chats, safe=False)\n return render(\n request,\n \"results/chats.html\",\n {\n \"object_list\": chats,\n \"heatmap_chats\": heatmap_chats,\n \"username\": username,\n \"current_year\": \"Yesterday\",\n \"total_chats\": len(chats),\n },\n )\n\nimport os\nfrom tempfile import gettempdir\nfrom pathlib import Path\nimport pandas as pd\nimport pathlib\nimport random\nfrom django.http import FileResponse\nfrom shutil import rmtree\n\ndef download_get_chat_for_date_range(request, *args, **kwargs):\n filename = kwargs.get(\"filename\", \"\")\n tmp_folder_name = kwargs.get(\"tmp_folder_name\", \"\")\n if filename:\n tmp = os.path.join(gettempdir(), '.{}'.format(hash(int(tmp_folder_name))))\n filepath = str(pathlib.PurePath(tmp, filename))\n return FileResponse(open(filepath, \"rb\"), as_attachment=True, filename=filename)\n else:\n raise Http404()\n\n\n\ndef get_chat_for_date_range(request, *args, **kwargs):\n start_date = request.GET.get(\"start_date\", \"\")\n end_date = request.GET.get(\"end_date\", \"\")\n if start_date and end_date:\n if start_date:\n start_date_with_time = parse(start_date)\n\n if end_date:\n end_date_with_time = parse(end_date)\n\n if end_date < start_date:\n messages.warning(\n request, \"The End_date should be greater than the Start Date\"\n )\n return render(\n request, \"results/search_between_date.html\", {\"object_list\": None}\n )\n client = Client()\n chats = client.chats()\n to_date = (\n str(end_date_with_time.year)\n + \"-\"\n + \"{:02d}\".format(end_date_with_time.month)\n + \"-\"\n + str(end_date_with_time.day)\n )\n all_chats = chats.list_day(\n year=start_date_with_time.year,\n month=start_date_with_time.month,\n day=start_date_with_time.day,\n to=to_date,\n )\n\n #Tmp save the chats list \n df = pd.DataFrame(all_chats)\n del df['tags']\n del df['referrer']\n del df['id']\n del df['profile']\n del df['desktracker_id']\n del df['reftracker_url']\n del df['ip']\n del df['reftracker_id']\n del df['desktracker_url']\n df['school_from_operator_username'] = df['operator'].apply(lambda x: find_school_by_operator_suffix(x))\n df['school_from_queue_name'] = df['queue'].apply(lambda x: find_school_by_queue_or_profile_name(x))\n df['guest'] = df['guest'].apply(lambda x: x.split('@')[0][0:8])\n \n today = datetime.today().strftime(\"%Y-%m-%d-%H:%M\")\n\n tmp_folder_name ='4564565464321'\n rmtree(tmp_folder_name, ignore_errors=True)\n tmp = os.path.join(gettempdir(), '.{}'.format(hash(4564565464321)))\n try:\n os.makedirs(tmp)\n except:\n pass\n\n filename = \"list_of_chats_from_date_range_results_\" + str(random.randint(1,7000))+\".xlsx\"\n filepath = str(pathlib.PurePath(tmp, filename))\n\n writer = pd.ExcelWriter(filepath, engine=\"xlsxwriter\")\n df.to_excel(writer, index=False)\n writer.save()\n\n # Continue\n\n chats = [Chats(chat) for chat in all_chats]\n selected_chats = list()\n for chat in chats:\n if (parse(chat.started) >= start_date_with_time):\n try:\n if (parse(chat.ended) <= end_date_with_time):\n selected_chats.append(chat)\n except:\n selected_chats.append(chat)\n return render(\n request, \"results/search_between_date.html\", {\n \"object_list\": selected_chats, \n \"filename\":filename,\n \"tmp_folder_name\":tmp_folder_name}\n )\n else:\n messages.warning(\n request, \"There should be a valid Start_Date and End_date\"\n )\n return render(request, \"results/search_between_date.html\", {\"object_list\": None})\n\n\ndef search_chats_within_2_hours(request, *args, **kwargs):\n client = Client()\n chat_id = int(kwargs.get(\"chat_id\", None))\n chat = client.one(\"chats\", chat_id).get()\n\n if chat:\n start_date = parse(chat.get(\"started\"))\n chats = client.chats()\n chats = chats.list_day(start_date.year, start_date.month, start_date.day)\n\n chat_within_2_hours = list()\n for chat in chats:\n started = parse(chat.get(\"started\"))\n # print(\"{0} > {1} < {2}\".format(started-timedelta(minutes=60), start_date , started+timedelta(minutes=60)))\n if started - timedelta(60) > start_date < started + timedelta(60):\n chat_within_2_hours.append(chats)\n\n # print(chat_within_2_hours)\n chats = None\n if chat_within_2_hours:\n chats = soft_anonimyzation(chat_within_2_hours)\n\n return JsonResponse(chats, safe=False)\n return JsonResponse(None, safe=False)\n\n\n@csrf_exempt\ndef search_chats_with_this_guestID(request, *args, **kwargs):\n guest_id = request.POST.get(\"guest_id\", None)\n chats = None\n if guest_id:\n if \"@\" in guest_id:\n pass\n else:\n guest_id = guest_id + \"*\"\n query = {\n \"query\": {\n \"guest\": [guest_id],\n },\n \"sort\": [{\"started\": \"descending\"}],\n }\n client = Client()\n chats = client.api().post(\"v4\", \"/chat/_search\", json=query)\n chats = soft_anonimyzation(chats)\n chats = [Chats(chat) for chat in chats]\n if request.is_ajax():\n return JsonResponse(\n {\"object_list\": chats, \"guest_id\": guest_id}\n )\n return render(\n request,\n \"results/search_guest.html\",\n {\"object_list\": chats, \"guest_id\": guest_id},\n )\n if request.is_ajax():\n return JsonResponse(\n {\"object_list\": None}\n )\n return render(request, \"results/search_guest.html\", {\"object_list\": None})\n\nclass SearchGuestResultsView(TemplateView):\n template_name = \"results/search_guest.html\"\n\n", "sub_path": "dashboard/views/views_search.py", "file_name": "views_search.py", "file_ext": "py", "file_size_in_byte": 21849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "warnings.filterwarnings", "line_number": 36, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_school_by_operator_suffix", "line_number": 66, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_queues_from_a_school_name", "line_number": 67, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 68, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 69, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 85, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 91, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_school_by_queue_or_profile_name", "line_number": 127, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_queues_from_a_school_name", "line_number": 128, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 130, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 131, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 148, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 148, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 152, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 154, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 156, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 185, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 185, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 188, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 193, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 198, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 198, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 203, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 203, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 207, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 209, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 211, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 248, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 248, "usage_type": "name"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 253, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 256, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "name"}, {"api_name": "dashboard.utils.utils.operatorview_helper", "line_number": 262, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 264, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 264, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 268, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 270, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 282, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 300, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 300, "usage_type": "name"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 309, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 310, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 312, "usage_type": "name"}, {"api_name": "dashboard.utils.utils.operatorview_helper", "line_number": 316, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 318, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 318, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 318, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 322, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 324, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 335, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 336, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 358, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 359, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 364, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 364, "usage_type": "name"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 371, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 374, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 374, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 374, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 378, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 381, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 385, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 386, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 401, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 401, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 402, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 409, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 410, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 413, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 413, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 413, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 417, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 424, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 426, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 430, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 431, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 446, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 446, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 447, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 454, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 455, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 464, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 464, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 464, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 468, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 475, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 477, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 481, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 482, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 497, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 497, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 498, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.search_chats", "line_number": 504, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 505, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 506, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 509, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 509, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 509, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 513, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 520, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 522, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 526, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 527, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 552, "usage_type": "call"}, {"api_name": "os.path", "line_number": 552, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 552, "usage_type": "call"}, {"api_name": "pathlib.PurePath", "line_number": 553, "usage_type": "call"}, {"api_name": "django.http.FileResponse", "line_number": 554, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 556, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 565, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 568, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 571, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 571, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 574, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 594, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_school_by_operator_suffix", "line_number": 604, "usage_type": "call"}, {"api_name": "dashboard.utils.ask_schools.find_school_by_queue_or_profile_name", "line_number": 605, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 608, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 608, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 611, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 612, "usage_type": "call"}, {"api_name": "os.path", "line_number": 612, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 612, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 614, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 618, "usage_type": "call"}, {"api_name": "pathlib.PurePath", "line_number": 619, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 621, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 627, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 630, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 632, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 636, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 643, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 643, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 646, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 655, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 661, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 663, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 669, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 671, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 672, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.soft_anonimyzation", "line_number": 692, "usage_type": "call"}, {"api_name": "dashboard.utils.utils.Chats", "line_number": 693, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 695, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 698, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 704, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 707, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 675, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 709, "usage_type": "name"}]} {"seq_id": "354917721", "text": "import numba\nimport numpy as np\nfrom math import exp\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.datasets.samples_generator import make_blobs\nimport time\n\nnp.random.seed(1111)\n\n(X, y) = make_blobs(n_samples=500, n_features=2, centers=2, cluster_std=2.5)\nX = np.c_[np.ones((X.shape[0])), X]\nXl = X.tolist()\nyl = y.tolist()\nw = np.random.uniform(size=(X.shape[1],))\nwl = w.tolist()\n\n\ndef sgd_sklearn(X, y):\n clf = SGDClassifier(loss=\"log\", fit_intercept=False, n_iter=15,\n shuffle=False, learning_rate='constant', eta0=0.3,\n penalty='None')\n clf.fit(X, y)\n return clf.coef_\n\n\n@numba.jit\ndef sigmoid(r, w):\n\tx = 0\n\tfor i in range(len(r)):\n\t\tx += w[i] * r[i]\n\treturn 1.0 / (1.0 + exp(-x))\n\n\n@numba.jit\ndef sgd_numba(X, y, w, epochs=15, eta=0.3):\n for epoch in range(epochs):\n \tfor i, r in enumerate(X):\n yhat = sigmoid(r, w)\n error = yhat - y[i]\n for j in range(len(r)):\n w[j] -= eta * r[j] * error\n return w\n\ncc = sgd_numba(Xl, yl, wl)\n\n\n@numba.jit\ndef sgd_nbnp(X, y, w, epochs=15, eta=0.3):\n for epoch in range(epochs):\n \tfor i in range(X.shape[0]):\n \t\tw -= eta * X[i].T.dot((1.0 / (1.0 + np.exp(-X[i].dot(w)))) - y[i])\n return w\n\ncc = sgd_nbnp(X, y, w)\n\n\ndef sgd(X, y, w, epochs=15, eta=0.3):\n for epoch in range(epochs):\n \tfor i in range(X.shape[0]):\n \t\tw -= eta * X[i].T.dot((1.0 / (1.0 + np.exp(-X[i].dot(w)))) - y[i])\n return w\n\n\ndef test_python(benchmark):\n benchmark(sgd, X, y, w)\n\ndef test_sklearn(benchmark):\n benchmark(sgd_sklearn, X, y)\n\ndef test_numba(benchmark):\n benchmark(sgd_numba, Xl, yl, wl)\n\ndef test_nbnp(benchmark):\n benchmark(sgd_nbnp, X, y, w)\n", "sub_path": "sgd.py", "file_name": "sgd.py", "file_ext": "py", "file_size_in_byte": 1729, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.random.seed", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.samples_generator.make_blobs", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sklearn.linear_model.SGDClassifier", "line_number": 19, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 31, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 51, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 60, "usage_type": "call"}]} {"seq_id": "618309618", "text": "import json\nimport re\nimport pprint\nimport os\nimport argparse\nimport pandas as pd\nimport random\n\nimport numpy as np\nfrom tqdm import tqdm\n\nSQUAD_TEST_HEADS = ['where were', 'what political', 'what religion', 'why did', 'what type', 'what language', 'who had', 'what percentage', 'what can', 'how much']\n\ndef strip(sent):\n return sent.strip(\" \").rstrip('.').rstrip('?').rstrip('!').rstrip('\"')\n\nblacklist = [\"of the\", \"is a\", \"is the\", \"did the\"]\n\nwh = {\n \"(what|what's)\": 0,\n \"(who|who's)\": 0,\n \"where\": 0,\n \"when\": 0,\n \"which\": 0,\n \"whose\": 0,\n \"whom\": 0,\n \"how\": 0,\n \"why\": 0,\n \"(can|could|may|might|should)\": 0,\n \"(is|are|were|was)\": 0,\n \"(will|would)\": 0,\n \"(do|does|did)\": 0,\n \"(has|have|had)\": 0,\n \"(name|identify|describe|define)\": 0\n}\n\nwh2 = {}\nwh3 = {}\n\nkeys = wh.keys()\nisascii = lambda s: len(s) == len(s.encode())\n\n\ndef find_match(query, keys):\n for key in keys:\n if re.search('^' + key + '$', query):\n return key\n return None\n\n\ndef dict_add(entry, example, dict):\n if entry in blacklist:\n return\n if entry in dict:\n dict[entry].append(example)\n else:\n dict[entry] = [example]\n\ndef find_top_q_head(examples, topn):\n for example in examples:\n question_text = strip(example[\"question\"])\n\n # simple tokenization\n t = question_text.split(\" \")\n t = [strip(item.lower()) for item in t]\n\n # search if the any key is in the first three words\n flag = False\n for i in range(3):\n if i >= len(t):\n break\n key = find_match(t[i], keys)\n if key:\n wh[key] += 1\n try:\n if key == \"which\" and \"in which\" in question_text:\n st2 = \" \".join(t[i - 1:i + 1])\n st3 = \" \".join(t[i - 1:i + 2])\n elif key == \"whom\" and \"by whom\" in question_text:\n st2 = \" \".join(t[i - 1:i + 1])\n st3 = \" \".join(t[i - 1:i + 2])\n else:\n st2 = \" \".join(t[i:i + 2])\n st3 = \" \".join(t[i:i + 3])\n dict_add(st2, example, wh2)\n # dict_add(st3, example, wh3)\n except Exception as e:\n print(e.args)\n flag = True\n break\n\n if not flag:\n for i in range(len(t)):\n key = find_match(t[len(t) - i - 1], keys)\n if key:\n wh[key] += 1\n flag = True\n idx = len(t) - i - 1\n try:\n if key == \"which\" and \"in which\" in question_text:\n st2 = \" \".join(t[i - 1:i + 1])\n st3 = \" \".join(t[i - 1:i + 2])\n elif key == \"whom\" and \"by whom\" in question_text:\n st2 = \" \".join(t[i - 1:i + 1])\n st3 = \" \".join(t[i - 1:i + 2])\n else:\n st2 = \" \".join(t[i:i + 2])\n st3 = \" \".join(t[i:i + 3])\n dict_add(st2, example, wh2)\n # dict_add(st3, wh3)\n except Exception as e:\n print(e.args)\n break\n # if not flag:\n # print(\"No question word found: \", question_text)\n\n sorted_x = sorted(wh2.items(), key=lambda kv: len(kv[1]), reverse=True)\n print('#Question Head:', len(sorted_x))\n # for i in range(topn):\n # print(sorted_x[i][0], len(sorted_x[i][1]))\n # pp = pprint.PrettyPrinter(indent=4)\n # print(sorted_x[:topn])\n # print('#Hits in Top {}:'.format(topn), sum(item[1] for item in sorted_x[:40]))\n # print('#Examples', len(examples))\n # return [kv[0] for kv in sorted_x[:topn]]\n return sorted_x[:topn]\n\n\ndef get_questions(examples, head, num):\n random.shuffle(examples)\n ret = []\n count = 0\n for example in examples:\n if head in example.question_text.lower() and len(example.orig_answer_text) > 0 \\\n and isascii(example.orig_answer_text) and isascii(\" \".join(example.doc_tokens)):\n ret.append(example)\n count += 1\n if count == num:\n break\n if count != num:\n print(head)\n print(ret)\n return ret\n\ndef read_nq_examples(input_file):\n with open(input_file, 'r') as fin:\n lines = fin.readlines()\n lines = lines[1:] # exclude the header\n source = [json.loads(line.strip()) for line in lines]\n\n total = 0\n examples = []\n for para in tqdm(source):\n context = para[\"context\"]\n for qa in para[\"qas\"]:\n total += 1\n ques = qa[\"question\"]\n ans = qa[\"answers\"]\n examples.append({'context': context, 'question': ques, 'answer': [{'text': ans[0]}]})\n\n print(examples[:5])\n return examples\n\ndef down_sample_and_split(heads, n_per_head):\n random.shuffle(heads)\n new_heads = {}\n\n for head in heads:\n if len(head[1]) < n_per_head:\n continue\n new_heads[head[0]] = random.sample(head[1], n_per_head)\n \n print(new_heads.keys())\n\n test = {head: new_heads[head] for head in SQUAD_TEST_HEADS if head in new_heads}\n\n return test\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--in_file\", default='../data/newsqa/NewsQA.jsonl', type=str, required=False)\n parser.add_argument(\"--out_dir\", default='../data/newsqa/', type=str, required=False,\n help=\"Output directory\")\n parser.add_argument(\"--out_train\", default='zs_train.json', type=str, required=False)\n parser.add_argument(\"--out_dev\", default='zs_dev.json', type=str, required=False)\n parser.add_argument(\"--out_test\", default='zs_test.json', type=str, required=False)\n parser.add_argument('--seed', type=int, default=55, help=\"random seed\")\n\n args = parser.parse_args()\n opt = vars(args)\n\n random.seed(opt['seed'])\n\n examples = read_nq_examples(opt['in_file'])\n top_heads = find_top_q_head(examples, topn=300)\n test = down_sample_and_split(top_heads, n_per_head=64)\n\n print('Test heads: {}'.format(test.keys()))\n\n # if not os.path.exists(opt['out_dir']):\n # os.makedirs(opt['out_dir'])\n # with open(os.path.join(opt['out_dir'], opt['out_train']), 'w') as fout:\n # json.dump(train, fout)\n # with open(os.path.join(opt['out_dir'], opt['out_dev']), 'w') as fout:\n # json.dump(dev, fout)\n with open(os.path.join(opt['out_dir'], opt['out_test']), 'w') as fout:\n json.dump(test, fout)\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "playground/get_nq_test.py", "file_name": "get_nq_test.py", "file_ext": "py", "file_size_in_byte": 6780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "re.search", "line_number": 46, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 130, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 149, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 153, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 165, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 171, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 180, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 207, "usage_type": "call"}]} {"seq_id": "4600694", "text": "# Finds in-degree distribution, out-degree distribution, \n# and reciprocal-degree distribution.\n# SNAP folds reciprocal degrees into their in/out degree calculations\n#\t\twhich is less than ideal for this project \n# first argument is input file name\n# second argument is output file base name\n# ONLY TESTED FOR PYTHON 3.3\n\nfrom scipy import sparse\nimport numpy\nimport sys\n\ndef convertListToDistro(degreeList):\n\t#takes row sum list and converts it to degree distribution\n\tdistro = {}\n\n\tfor value in degreeList:\n\t\ttry:\n\t\t\tdistro[value] += 1\n\t\texcept:\n\t\t\tdistro[value] = 1\n\n\treturn distro\n\ndef outputToFile(distroDict, outputFile):\n\tfor key in sorted(distroDict):\n\t\ttemp = str(int(key))+' '+str(distroDict[key])+'\\n'\n\t\toutputFile.write(temp)\n\n#Read the edge list passed as first parameter created for networks\nFile = open(sys.argv[1],\"r\")\n\n#fix nodes and edges for graph being read in \nNODES = 81306\nEDGES = 1768149\n\n#declare sparse matrices\ninDeg = sparse.lil_matrix((NODES+1,NODES+1))\noutDeg = sparse.lil_matrix((NODES+1,NODES+1))\nrecipDeg = sparse.lil_matrix((NODES+1,NODES+1))\n\nfor line in File:\n\t#split line\n\ttemp = line.split(' ')\n\tedge1 = int(temp[0]) #follower\n\tedge2 = int(temp[1]) #followed\n\n\t#update in degree matrix\n\tinDeg[edge2,edge1] = 1\n\n\t#update out degree matrix\n\toutDeg[edge1, edge2] = 1\n\n\t#update reciprocal degree matrix if reciprocal degree exists\n\tif (inDeg[edge1,edge2] == 1):\n\t\trecipDeg[edge1,edge2] = 1\n\t\trecipDeg[edge2,edge1] = 1\n\n\t#ensure we are not double counting for all reciprocal edges\n\tif (recipDeg[edge1,edge2] == 1): \n\t\tinDeg[edge1,edge2] = 0\n\t\toutDeg[edge2,edge1] = 0\n\t\tinDeg[edge2,edge1] = 0\n\t\toutDeg[edge1, edge2] = 0\n\n#close file\nFile.close()\n\n#get row sums of each matrix into lists\ninDegList = numpy.array(inDeg.sum(1)).reshape(-1,).tolist()\noutDegList = numpy.array(outDeg.sum(1)).reshape(-1,).tolist()\nrecipDegList = numpy.array(recipDeg.sum(1)).reshape(-1,).tolist()\n\n#convert lists to proper degree distributions\ninDegDistro = convertListToDistro(inDegList)\noutDegDistro = convertListToDistro(outDegList)\nrecipDegDistro = convertListToDistro(recipDegList)\n\n\n#output degree distributions to three files\ninDegFile = open('indeg_'+sys.argv[2], 'w')\noutDegFile = open('outdeg_'+sys.argv[2], 'w')\nrecipDegFile = open('recipdeg_'+sys.argv[2], 'w')\n\noutputToFile(inDegDistro,inDegFile)\ninDegFile.close()\n\noutputToFile(outDegDistro,outDegFile)\noutDegFile.close()\n\noutputToFile(recipDegDistro,recipDegFile)\nrecipDegFile.close()\n\n", "sub_path": "proj-degreeDistros.py", "file_name": "proj-degreeDistros.py", "file_ext": "py", "file_size_in_byte": 2466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 38, "usage_type": "name"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 39, "usage_type": "name"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 83, "usage_type": "attribute"}]} {"seq_id": "196031380", "text": "from __future__ import print_function\nimport datetime\nimport pickle\nimport os.path\nimport Utils.dateFunctions\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\nSCOPES = ['https://www.googleapis.com/auth/calendar']\n\n\nclass GoogleCalAPI():\n def __init__(self):\n creds = None\n self.calendarId = None\n if os.path.exists('../token.pickle'):\n with open('../token.pickle', 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file('Credentials/credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('../token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n self.service = build('calendar', 'v3', credentials=creds)\n\n self.calendar_list_entry = self.service.calendarList().get(calendarId='primary').execute()\n\n if not self.calendar_list_entry['accessRole']:\n print('API can\\' connect!')\n\n def get_calendar_list_entry(self):\n return self.calendar_list_entry\n\n def get_my_calendars(self):\n result = self.service.calendarList().list().execute()['items'][0]\n self.calendarId = result['id']\n return result\n\n def get_my_events(self, date=None):\n if date is None:\n date = datetime.datetime.utcnow().isoformat() + 'Z' # 'Z' indicates UTC time\n else:\n date = Utils.dateFunctions.getDateFromString(date)\n\n events_result = self.service.events().list(calendarId='primary', timeMin=date, maxResults=10, singleEvents=True,\n orderBy='startTime').execute()\n\n events = events_result.get('items', [])\n if not events:\n return 'No upcoming events found.'\n for event in events:\n start = event['start'].get('dateTime', event['start'].get('date'))\n return {start, event['summary']}\n\n def create_event(self, start_time_str, summary, duration=1, unit='days', attendees=None,\n description=None, location=None):\n\n interval = Utils.dateFunctions.getDateInterval(start_time_str, duration, unit)\n\n emails = []\n if attendees is not None & isinstance(attendees, list):\n for email in attendees:\n emails.append({'email': email})\n\n event = {\n 'summary': summary,\n 'location': location,\n 'description': description,\n 'start': {\n 'dateTime': interval['start_time'],\n 'timeZone': Utils.dateFunctions.getSystemTimeZone(),\n },\n 'end': {\n 'dateTime': interval['end_time'],\n 'timeZone': Utils.dateFunctions.getSystemTimeZone(),\n },\n 'recurrence': [\n 'RRULE:FREQ=DAILY;COUNT=2'\n ],\n 'attendees': emails,\n 'reminders': {\n 'useDefault': False,\n 'overrides': [\n {'method': 'email', 'minutes': 24 * 60},\n {'method': 'popup', 'minutes': 10},\n ],\n },\n }\n\n event = self.service.events().insert(calendarId='primary', body=event).execute()\n print('Event created: %s' % (event.get('htmlLink')))\n", "sub_path": "API/GoogleCalAPI.py", "file_name": "GoogleCalAPI.py", "file_ext": "py", "file_size_in_byte": 3704, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "google.auth.transport.requests.Request", "line_number": 23, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file", "line_number": 25, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow", "line_number": 25, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 29, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.build", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "Utils.dateFunctions.dateFunctions.getDateFromString", "line_number": 50, "usage_type": "call"}, {"api_name": "Utils.dateFunctions.dateFunctions", "line_number": 50, "usage_type": "attribute"}, {"api_name": "Utils.dateFunctions", "line_number": 50, "usage_type": "name"}, {"api_name": "Utils.dateFunctions.dateFunctions.getDateInterval", "line_number": 65, "usage_type": "call"}, {"api_name": "Utils.dateFunctions.dateFunctions", "line_number": 65, "usage_type": "attribute"}, {"api_name": "Utils.dateFunctions", "line_number": 65, "usage_type": "name"}, {"api_name": "Utils.dateFunctions.dateFunctions.getSystemTimeZone", "line_number": 78, "usage_type": "call"}, {"api_name": "Utils.dateFunctions.dateFunctions", "line_number": 78, "usage_type": "attribute"}, {"api_name": "Utils.dateFunctions", "line_number": 78, "usage_type": "name"}, {"api_name": "Utils.dateFunctions.dateFunctions.getSystemTimeZone", "line_number": 82, "usage_type": "call"}, {"api_name": "Utils.dateFunctions.dateFunctions", "line_number": 82, "usage_type": "attribute"}, {"api_name": "Utils.dateFunctions", "line_number": 82, "usage_type": "name"}]} {"seq_id": "106723869", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 13 12:31:50 2021\n\n@author: vazqu\n\"\"\"\nimport nltk\nimport pandas as pd\nimport string \nfrom unidecode import unidecode\nimport re\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.stem.snowball import SnowballStemmer\nimport spacy\n\nclass Procesamiento():\n def __init__(self, comentarios):\n self.comentarios= comentarios\n \n def tokenizar(self, texto):\n texto = texto.lower()\n tokenizer = RegexpTokenizer(r'\\w+')\n #token= nltk.tokenize.word_tokenize(texto)\n return tokenizer.tokenize(texto)\n \n def remover_url(self,text):\n url = re.compile(r'https?://\\S+|www\\.\\S+')\n return url.sub(r'',text) \n \n def remover_caracteres(self,texto):\n #llega formato [['texto']]\n #print(texto)\n \n texto = re.sub(\"[#$?,:;'¡!@#$%^&*()\\[\\]\\{\\}/<´>|`+=_-]1234567890\", '', texto)\n texto = re.sub(\"\\d+\", '', texto)\n texto = re.sub(\"[àáâãäå]\", 'a', texto)\n texto = re.sub(\"[èéêë]\", 'e', texto)\n texto = re.sub(\"[ìíîï]\", 'i', texto)\n texto = re.sub(\"[òóôõö]\", 'o', texto)\n texto = re.sub(\"[ùúûü]\", 'u', texto)\n texto=unidecode(texto)\n \n texto= re.sub(r'\\^[a-zA-Z]\\s+', ' ', texto) #eliminar caracteres simples\n return texto\n \n def remover_stop_words(self,textoTokenizado):\n stop_words = set(stopwords.words('spanish')) \n return [word for word in textoTokenizado if word not in stop_words]\n \n def stemming(self, token):\n stemmer=SnowballStemmer(\"spanish\",ignore_stopwords=False)\n datos=[]\n for i in token:\n d=[]\n for word in i:\n d.append(stemmer.stem(word))\n datos.append(d)\n return datos\n \n def lemantizar(self,datos):\n #python -m spacy download en_core_web_sm\n sp = spacy.load('es_core_news_md')\n lemantizado=[]\n for i in datos:\n doc=[]\n for j in i:\n for word in sp(j):\n doc.append(word.lemma_) \n lemantizado.append(doc)\n return lemantizado\n \n def limpieza(self):\n self.comentarios=[self.remover_url(texto) for texto in self.comentarios]\n self.comentarios = [self.remover_caracteres(texto)for texto in self.comentarios]\n token=[self.tokenizar(i)for i in self.comentarios]\n stop=[self.remover_stop_words(i) for i in token]\n stem=self.stemming(stop)\n leman=self.lemantizar(stop)\n \n return token,stem, leman", "sub_path": "django/api/desarrollo/LimpiezaPLN.py", "file_name": "LimpiezaPLN.py", "file_ext": "py", "file_size_in_byte": 2677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 37, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 38, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 40, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 45, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 49, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 49, "usage_type": "name"}, {"api_name": "nltk.stem.snowball.SnowballStemmer", "line_number": 53, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 64, "usage_type": "call"}]} {"seq_id": "386496666", "text": "from django.shortcuts import render\nfrom django.views.generic import TemplateView\nfrom .forms import ImageForm\nfrom .main import detect\n\nclass MyDetectorView(TemplateView):\n # 생성자\n def __init__(self):\n self.params = {'result_list':[],\n 'result_name':\"\",\n 'result_img':\"\",\n 'form': ImageForm()}\n\n # GET request (index.html 파일 초기 표시)\n def get(self, req):\n return render(req, 'mydetector/index.html', self.params)\n\n # POST request (index.html 파일에 결과 표시)\n def post(self, req):\n # POST 메소드에 의해서 전달되는 FORM DATA \n print(\"post 시작\")\n form = ImageForm(req.POST, req.FILES)\n # FORM DATA 에러 체크 \n print(\"Valueform은 실행되는가\")\n if not form.is_valid():\n raise ValueForm('invalid form')\n print(\"valueform 후\")\n # FORM DATA에서 이미지 파일 얻기 \n image = form.cleaned_data['image']\n # 이미지 파일을 지정해서 얼굴 인식\n result = detect(image)\n\n # 얼굴 분류된 결과 저장\n self.params['result_list'], self.params['result_name'], self.params['result_img'] = result\n\n # 페이지에 화면 표시\n return render(req, 'mydetector/index.html', self.params)", "sub_path": "django/mysite/mydetector/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.views.generic.TemplateView", "line_number": 6, "usage_type": "name"}, {"api_name": "forms.ImageForm", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "forms.ImageForm", "line_number": 22, "usage_type": "call"}, {"api_name": "main.detect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}]} {"seq_id": "269912851", "text": "# python3\r\nimport sys\r\nfrom collections import defaultdict\r\n\r\n\r\nclass Node:\r\n def __init__(self, node_id, depth, start, end, parent):\r\n self.id = node_id\r\n self.start = start\r\n self.end = end\r\n self.depth = depth\r\n self.parent = parent\r\n # self.children = children\r\n\r\n\r\nclass SuffixTree:\r\n def __init__(self, sa, lcp, text):\r\n self.text = text\r\n self.node_counter = 0\r\n self.nodes = {}\r\n self.tree = defaultdict(dict)\r\n self.build_tree(sa, lcp)\r\n\r\n def build_tree(self, sa, lcp):\r\n root = Node(node_id=self.node_counter, depth=0, start=-1, end=-1, parent=None)\r\n self.nodes[self.node_counter] = root\r\n self.tree[self.node_counter] = {}\r\n lcp_prev = 0\r\n current_node = root\r\n for i in range(0, len(self.text)):\r\n suffix = sa[i]\r\n while current_node.depth > lcp_prev:\r\n current_node = current_node.parent\r\n if current_node.depth == lcp_prev:\r\n current_node = self.create_new_node(current_node, suffix)\r\n else:\r\n start = sa[i-1] + current_node.depth\r\n offset = lcp_prev - current_node.depth\r\n mid_node = self.break_edge(current_node, start, offset)\r\n current_node = self.create_new_node(mid_node, suffix)\r\n if i < len(self.text) - 1:\r\n lcp_prev = lcp[i]\r\n\r\n def create_new_node(self, node, suffix):\r\n self.node_counter += 1\r\n leaf = Node(node_id=self.node_counter, depth=len(self.text) - suffix, start=suffix + node.depth, end=len(self.text)-1, parent=node)\r\n self.nodes[self.node_counter] = leaf\r\n self.tree[node.id][self.text[leaf.start]] = (self.node_counter, leaf.start, leaf.end+1)\r\n return leaf\r\n\r\n def break_edge(self, node, start, offset):\r\n start_char = self.text[start]\r\n mid_char = self.text[start+offset]\r\n self.node_counter += 1\r\n mid_node = Node(node_id=self.node_counter, depth=node.depth+offset, start=start, end=start+offset-1, parent=node)\r\n self.nodes[self.node_counter] = mid_node\r\n self.nodes[self.tree[node.id][start_char][0]].parent = mid_node\r\n self.nodes[self.tree[node.id][start_char][0]].start += offset\r\n self.tree[mid_node.id][mid_char] = (self.tree[node.id][start_char][0], mid_node.end+1, self.tree[node.id][start_char][2])\r\n self.tree[node.id][start_char] = (self.node_counter, mid_node.start, mid_node.end+1)\r\n return mid_node\r\n\r\n\r\ndef suffix_array_to_suffix_tree(sa, lcp, text):\r\n \"\"\"\r\n Build suffix tree of the string text given its suffix array suffix_array\r\n and LCP array lcp_array. Return the tree as a mapping from a node ID\r\n to the list of all outgoing edges of the corresponding node. The edges in the\r\n list must be sorted in the ascending order by the first character of the edge label.\r\n Root must have node ID = 0, and all other node IDs must be different\r\n nonnegative integers. Each edge must be represented by a tuple (node, start, end), where\r\n * node is the node ID of the ending node of the edge\r\n * start is the starting position (0-based) of the substring of text corresponding to the edge label\r\n * end is the first position (0-based) after the end of the substring corresponding to the edge label\r\n\r\n For example, if text = \"ACACAA$\", an edge with label \"$\" from root to a node with ID 1\r\n must be represented by a tuple (1, 6, 7). This edge must be present in the list tree[0]\r\n (corresponding to the root node), and it should be the first edge in the list (because\r\n it has the smallest first character of all edges outgoing from the root).\r\n \"\"\"\r\n st = SuffixTree(sa, lcp, text)\r\n\r\n return st.tree\r\n\r\n\r\nif __name__ == '__main__':\r\n text = sys.stdin.readline().strip()\r\n sa = list(map(int, sys.stdin.readline().strip().split()))\r\n lcp = list(map(int, sys.stdin.readline().strip().split()))\r\n print(text)\r\n # Build the suffix tree and get a mapping from \r\n # suffix tree node ID to the list of outgoing Edges.\r\n tree = suffix_array_to_suffix_tree(sa, lcp, text)\r\n \"\"\"\r\n Output the edges of the suffix tree in the required order.\r\n Note that we use here the contract that the root of the tree\r\n will have node ID = 0 and that each vector of outgoing edges\r\n will be sorted by the first character of the corresponding edge label.\r\n \r\n The following code avoids recursion to avoid stack overflow issues.\r\n It uses two stacks to convert recursive function to a while loop.\r\n This code is an equivalent of \r\n \r\n OutputEdges(tree, 0);\r\n \r\n for the following _recursive_ function OutputEdges:\r\n \r\n def OutputEdges(tree, node_id):\r\n edges = tree[node_id]\r\n for edge in edges:\r\n print(\"%d %d\" % (edge[1], edge[2]))\r\n OutputEdges(tree, edge[0]);\r\n \r\n \"\"\"\r\n stack = [(0, 0)]\r\n result_edges = []\r\n while len(stack) > 0:\r\n (node, edge_index) = stack[-1]\r\n stack.pop()\r\n if not node in tree:\r\n continue\r\n edges = list(tree[node].values())\r\n if edge_index + 1 < len(edges):\r\n stack.append((node, edge_index + 1))\r\n print(\"%d %d\" % (edges[edge_index][1], edges[edge_index][2]))\r\n stack.append((edges[edge_index][0], 0))\r\n", "sub_path": "c4/week4/suffix_tree_from_array/suffix_tree_from_array.py", "file_name": "suffix_tree_from_array.py", "file_ext": "py", "file_size_in_byte": 5414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "collections.defaultdict", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 87, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 88, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 89, "usage_type": "attribute"}]} {"seq_id": "280393278", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport datetime as dt\n\ncurrent_time = dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\ntemporary_filename = \"sendgrid_events_append.json\"\nfilename_for_s3 = \"sendgrid_events_ready_{}.json\".format(current_time)\n\n\ndef main():\n os.chdir(\"/tmp/\")\n os.rename(temporary_filename, filename_for_s3)\n os.system(\"aws s3 mv {0} s3://<BUCKET NAME>/{0}\".format(filename_for_s3))\n\nif __name__ == '__main__':\n main()\n", "sub_path": "send_to_s3.py", "file_name": "send_to_s3.py", "file_ext": "py", "file_size_in_byte": 464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 14, "usage_type": "call"}, {"api_name": "os.system", "line_number": 15, "usage_type": "call"}]} {"seq_id": "357306964", "text": "import sys, os\nfrom pathlib import Path\nfrom os import listdir\nfrom os.path import isdir,isfile, join\nimport json\n\nsys.path.append(join(sys.path[0],'..','interface'))\nsys.path.append(join(sys.path[0],'..'))\n\nimport fileinfo\nimport datetime\nimport path_function as pf \nimport json_function as jf\npath = os.path.join(os.path.abspath(os.path.dirname(__file__)), \"../config.json\")\nwith open(path) as cf:\n config = json.load(cf)\n\nbase = config['base-directory']\n\n\ndef find_json_file(l,path):\n for file in listdir(path):\n if (isdir(join(path,file))):\n find_json_file(l,join(path,file))\n elif (join(path,file).endswith(\".json\")):\n l.append(join(path,file))\n return l\n \ndef group_json_file(path):\n json_group = dict()\n for p in path:\n # omit the MEL+MER_output\n if not 'MEL+NER_output' in p:\n key_dict = '{}--{}--{}'.format(str(p).split('--')[0],str(p).split('--')[1],str(p).split('--')[-1])\n json_group.setdefault(key_dict,[]).append(p)\n return json_group\n\n# l is a dictionary which is form like \n# {'basedir\\\\Use_case_1\\\\Dataset\\\\2017-18 PBS - Finance.pdf--NER--8e8cca60facbdf.json': \n# ['basedir\\\\Use_case_1\\\\Dataset\\\\2017-18 PBS - Finance.pdf--NER--(spacy_lg_model)--8e8cca60facbdf.json',\n# 'basedir\\\\Use_case_1\\\\Dataset\\\\2017-18 PBS - Finance.pdf--NER--(spacy_md_model)--8e8cca60facbdf.json', \n# 'basedir\\\\Use_case_1\\\\Dataset\\\\2017-18 PBS - Finance.pdf--NER--(spacy_sm_model)--8e8cca60facbdf.json']}\n# the key is path to json without model and the key is existing model\n# it merges every model and provide the results\ndef aggregate_jsonfile_summary(l):\n \n # @sergio(2021-03-15): Handling different versions of the JSON structure for the \"Summary\" object.\n def getSummaryNER_JSONobj(_json):\n if ('NLP-NER-Summary' in jsonfile):\n return jsonfile['NLP-NER-Summary']\n elif ('NLP-NER-Summary-(From-Last-Run)' in jsonfile):\n return jsonfile['NLP-NER-Summary-(From-Last-Run)']\n return None\n \n filename_summary = dict()\n\n for key in l.keys():\n\n # if file exists then do nothing\n tailpath = str(Path(key).parts[-1])\n jsonpath = pf.removeTailPath(key)\n jsonfilename = '{}--{}--summary--{}'.format(tailpath.split('--')[0],tailpath.split('--')[1],tailpath.split('--')[-1])\n jsonfilepath = Path(jsonpath).joinpath(jsonfilename)\n\n if (jsonfilepath).exists():\n continue\n \n for value in l[key]:\n\n with open(value) as file:\n jsonfile = json.load(file)\n \n if str(jsonfilepath) not in filename_summary:\n filename_summary.update( { str(jsonfilepath) : getSummaryNER_JSONobj(jsonfile) } )\n else:\n filename_summary[str(jsonfilepath)] = update(filename_summary[str(jsonfilepath)], getSummaryNER_JSONobj(jsonfile))\n\n _gm = jsonfile['General-Metadata']\n filename_summary[str(jsonfilepath)] = jf.Json_dict('NLP-NER-Aggregated-Summary',filename_summary[str(jsonfilepath)])\n filename_summary[str(jsonfilepath)].update({'General-Metadata':_gm})\n filename_summary[str(jsonfilepath)]['NLP-NER-Aggregated-Summary'] = filename_summary[str(jsonfilepath)].pop('NLP-NER-Aggregated-Summary')\n # filename_summary[str(jsonfilepath)]['NLP-NER-Summary-(From-Last-Run)'] = filename_summary[str(jsonfilepath)].pop('NLP-NER-Summary-(From-Last-Run)')\n for filepath in filename_summary.keys():\n with open(filepath,'w') as outfile:\n json.dump(filename_summary[filepath],outfile)\n \n return filename_summary\n\n\ndef update(summary, data):\n # no need to update\n\n if summary == data:\n return summary\n \n for data_key in data.keys():\n for entity in data[data_key].keys():\n # if the model does not exist in current summary\n if entity not in summary[data_key].keys():\n summary[data_key].update({entity:data[data_key][entity]})\n else:\n # \n for index in range(len(data[data_key][entity][:-1])):\n if data[data_key][entity][index]['model'] not in summary[data_key][entity][index].values():\n # append the model\n summary[data_key][entity].insert(-2,updatedict(\n data[data_key][entity][index]['model'],\n data[data_key][entity][index]['category'],\n data[data_key][entity][index]['count'],\n ))\n \n summary[data_key][entity][-1]['total'] += data[data_key][entity][index]['count']\n\n return summary\n\n\ndef updatedict(model,category,count):\n return {\n \"model\" : model,\n \"category\" : category,\n \"count\" : count\n }\n", "sub_path": "code/NLP_NER_API/analysis/functionforfile.py", "file_name": "functionforfile.py", "file_ext": "py", "file_size_in_byte": 4869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 60, "usage_type": "call"}, {"api_name": "path_function.removeTailPath", "line_number": 61, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "call"}, {"api_name": "json.load", "line_number": 71, "usage_type": "call"}, {"api_name": "json_function.Json_dict", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 85, "usage_type": "call"}]} {"seq_id": "398208112", "text": "from django import db\nfrom django.db import models\nfrom numpy import mod\n\n\nclass Film(models.Model):\n id = models.IntegerField(\n db_column='id',\n primary_key=True\n )\n\n title = models.CharField(\n db_column='tx_title',\n default='',\n max_length=256,\n null=True,\n blank=True\n )\n\n genres = models.TextField(\n db_column='tx_genres',\n default='',\n max_length=256,\n null=True,\n blank=True\n )\n\n class Meta:\n managed = True\n db_table = 'film'\n ordering = ['-id']\n\n def __str__(self):\n return str(self.id)\n\n\nclass FilmLink(models.Model):\n\n film_id = models.OneToOneField(\n Film,\n on_delete=models.CASCADE,\n db_column='film_id',\n )\n\n imdb_id = models.IntegerField(\n db_column='imdb_id',\n default=0\n )\n\n tmdb_id = models.IntegerField(\n db_column='tmdb_id',\n default=0\n )\n\n class Meta:\n managed = True\n db_table = 'film_link'\n ordering = ['-id']\n \n def __str__(self):\n return self.film_id\n\n\nclass Rating(models.Model):\n\n film_id = models.ForeignKey(\n Film,\n on_delete=models.CASCADE,\n db_column='movie_id',\n )\n\n rating = models.FloatField(\n db_column=\"rating\",\n default=0.0\n )\n\n class Meta:\n managed = True\n db_table = 'rating'\n ordering = ['-id']\n\n def __str__(self):\n return self.film_id\n", "sub_path": "backend/movies/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}]} {"seq_id": "404261584", "text": "\nfrom flask import Flask, render_template, session, request, flash, url_for, get_flashed_messages, redirect, make_response, g\nimport json, os\n\nfrom utils.counter import increment\n\nwing_name=''\nwing_name2=''\nhall=''\nc_residence=''\n\n\ndef president_view():\n with open(os.path.join('./seed/data.json')) as file:\n data = json.load(file)\n role = data['President']['role']\n name = data['President']['name']\n img = data['President']['images']\n\n if request.method == 'POST':\n vote = request.form['like'] #do a check on the when the user selects none\n if vote == '':\n #print('User never choose anyone to vote for')\n pass\n increment('president',vote)\n #print('Voted for', vote)\n return redirect(url_for('sec')) #this is where the logic for solving the hall and wings problem will be about. Here we can use a switch case or a simple if-else\n return render_template('pres.html', role=role, img=img, name=name)\n\n # return render_template(url_for('logout'))\n", "sub_path": "views/president.py", "file_name": "president.py", "file_ext": "py", "file_size_in_byte": 1044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.counter.increment", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}]} {"seq_id": "250838606", "text": "# coding=utf-8\n# Copyright 2020 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Data loaders.\"\"\"\n\nfrom typing import Generator, Tuple\n\nimport jax\nimport numpy as onp\nfrom jax import numpy as jnp\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\nfrom tensorflow.keras.datasets import imdb\nfrom tensorflow.keras.preprocessing import sequence\nfrom enum import Enum\n\n# SupervisedDataset = Tuple[onp.ndarray, onp.ndarray]\n# SupervisedDatasetGen = Generator[SupervisedDataset, None, None]\n\n_CHECKPOINT_FORMAT_STRING = \"model_step_{}.pt\"\n\n\nclass ImgDatasets(Enum):\n CIFAR10 = \"cifar10\"\n CIFAR100 = \"cifar100\"\n MNIST = \"mnist\"\n\n\n# Format: (img_mean, img_std)\n_ALL_IMG_DS_STATS = {\n ImgDatasets.CIFAR10: ((0.49, 0.48, 0.44), (0.2, 0.2, 0.2)),\n ImgDatasets.CIFAR100: ((0.49, 0.48, 0.44), (0.2, 0.2, 0.2)),\n ImgDatasets.MNIST: ((0.1307,), (0.3081,))\n}\n\n_IMDB_CONFIG = {\n \"max_features\": 20000,\n \"max_len\": 100,\n \"num_train\": 20000\n}\n\n\ndef load_imdb_dataset():\n \"\"\"\n Load the IMDB reviews dataset.\n \n Code adapted from the code for\n _How Good is the Bayes Posterior in Deep Neural Networks Really?_:\n https://github.com/google-research/google-research/blob/master/cold_posterior_bnn/imdb/imdb_data.py\n \"\"\"\n (x_train, y_train), (x_test, y_test) = imdb.load_data(\n path=\"./datasets\", num_words=_IMDB_CONFIG[\"max_features\"])\n num_train = _IMDB_CONFIG[\"num_train\"]\n x_train, x_val = x_train[:num_train], x_train[num_train:]\n y_train, y_val = y_train[:num_train], y_train[num_train:]\n\n def preprocess(x, y, max_length):\n x = sequence.pad_sequences(x, maxlen=max_length)\n y = onp.array(y)\n x = onp.array(x)\n return x, y\n\n max_length = _IMDB_CONFIG[\"max_len\"]\n x_train, y_train = preprocess(x_train, y_train, max_length=max_length)\n x_val, y_val = preprocess(x_val, y_val, max_length=max_length)\n x_test, y_test = preprocess(x_test, y_test, max_length=max_length)\n return (x_train, y_train), (x_test, y_test), (x_val, y_val), 2\n\n\ndef load_image_dataset(\n split, batch_size, name=\"cifar10\", repeat=False, shuffle=False,\n shuffle_seed=None\n):\n \"\"\"Loads the dataset as a generator of batches.\"\"\"\n # Do no data augmentation.\n ds, dataset_info = tfds.load(name, split=split, as_supervised=True,\n with_info=True)\n num_classes = dataset_info.features[\"label\"].num_classes\n num_examples = dataset_info.splits[split].num_examples\n num_channels = dataset_info.features['image'].shape[-1]\n \n def img_to_float32(image, label):\n return tf.image.convert_image_dtype(image, tf.float32), label\n\n ds = ds.map(img_to_float32).cache()\n ds_stats = _ALL_IMG_DS_STATS[ImgDatasets(name)]\n def img_normalize(image, label):\n \"\"\"Normalize the image to zero mean and unit variance.\"\"\"\n mean, std = ds_stats\n image -= tf.constant(mean, shape=[1, 1, num_channels], dtype=image.dtype)\n image /= tf.constant(std, shape=[1, 1, num_channels], dtype=image.dtype)\n return image, label\n\n ds = ds.map(img_normalize)\n if batch_size == -1:\n batch_size = num_examples\n if repeat:\n ds = ds.repeat()\n if shuffle:\n ds = ds.shuffle(buffer_size=10 * batch_size, seed=shuffle_seed)\n ds = ds.batch(batch_size)\n return tfds.as_numpy(ds), num_classes, num_examples\n\n\ndef get_image_dataset(name):\n train_set, n_classes, _ = load_image_dataset(\"train\", -1, name)\n train_set = next(iter(train_set))\n \n test_set, _, _ = load_image_dataset(\"test\", -1, name)\n test_set = next(iter(test_set))\n return train_set, test_set, None, n_classes\n\n\ndef batch_split_axis(batch, n_split):\n \"\"\"Reshapes batch to have first axes size equal n_split.\"\"\"\n x, y = batch\n n = x.shape[0]\n n_new = n / n_split\n assert n_new == int(n_new), (\n \"First axis cannot be split: batch dimension was {} when \"\n \"n_split was {}.\".format(x.shape[0], n_split))\n n_new = int(n_new)\n return tuple(arr.reshape([n_split, n_new, *arr.shape[1:]]) for arr in (x, y))\n\n\ndef pmap_dataset(ds, n_devices=None):\n \"\"\"Shard the dataset to devices.\"\"\"\n n_devices = n_devices or len(jax.local_devices())\n return jax.pmap(lambda x: x)(batch_split_axis(ds, n_devices))\n \n\ndef make_ds_pmap_fullbatch(name, dtype, n_devices=None):\n \"\"\"Make train and test sets sharded over batch dim.\"\"\"\n name = name.lower()\n if name in ImgDatasets._value2member_map_:\n train_set, test_set, _, n_classes = get_image_dataset(name)\n elif name == \"imdb\":\n train_set, test_set, _, n_classes = load_imdb_dataset()\n dtype = jnp.int32\n else:\n raise ValueError(\"Unknown dataset name: {}\".format(name))\n \n train_set, test_set = tuple(pmap_dataset(ds, n_devices)\n for ds in (train_set, test_set))\n\n train_set, test_set = map(\n lambda ds: (ds[0].astype(dtype), ds[1]), (train_set, test_set))\n \n return train_set, test_set, n_classes\n", "sub_path": "bnn_hmc/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 5324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "enum.Enum", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.keras.datasets.imdb.load_data", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.imdb", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.image.convert_image_dtype", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow_datasets.as_numpy", "line_number": 114, "usage_type": "call"}, {"api_name": "jax.local_devices", "line_number": 140, "usage_type": "call"}, {"api_name": "jax.pmap", "line_number": 141, "usage_type": "call"}, {"api_name": "jax.numpy.int32", "line_number": 151, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 151, "usage_type": "name"}]} {"seq_id": "45583672", "text": "import serial #import serial module\n#import RPi.GPIO as GPIO\n#GPIO.setmode(11)\n\ndef read_rfid ():\n ser = serial.Serial (\"/dev/ttyAMA0\") #Open named port \n ser.baudrate = 9600 #Set baud rate to 9600\n ser.write('h') #Read 12 characters from serial port to data\n ser.close () #Close port\n #return data #Return data\n\n#while(1):\n# id = read_rfid () #Function call\n# print(id) #Print RFID\n\n\n", "sub_path": "New/codes/abhi/rf.py", "file_name": "rf.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "serial.Serial", "line_number": 6, "usage_type": "call"}]} {"seq_id": "106837849", "text": "import psycopg2\nimport csv\nimport os\nfrom pathlib import Path\n\npath = Path(os.path.abspath(os.curdir)).parent.absolute()\nepidemologistFile = str(path) + \"/data_csv_files/hospitals.csv\"\n\nlist_of_epidemiologist = []\nlist_temp = []\n\n\ndef open_folder_csv(file, list, delim):\n with open(file, \"r\") as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=delim)\n for line in csv_reader:\n list.append(line)\n\n\ndef isolate_date(date):\n name_of_table = \"\"\n b = 0\n for i in range(len(date)):\n if date[i] == ' ':\n b = i\n name_of_table = date[0:b]\n return name_of_table\n\n\ndef convert_to_int(number):\n a = None\n if number != '':\n a = int(float(number))\n return a\n\n\nopen_folder_csv(epidemologistFile, list_of_epidemiologist, ',')\n\n# Retire le premier élément de la liste (titre des données)\nlist_of_epidemiologist.pop(0)\n\nfor i in list_of_epidemiologist:\n if [i[-1], i[-1]] not in list_temp:\n list_temp.append([i[-1], i[-1]])\n\nprint(list_temp)\ntuples = tuple(tuple(x) for x in list_temp)\nprint(tuples)\n\nDB_NAME = \"INFO-H303\"\nDB_USER = \"postgres\"\nDB_PASS = \"root\"\nDB_HOST = \"localhost\"\nDB_PORT = \"5432\"\n\ncon = psycopg2.connect(database=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST, port=DB_PORT)\n\nprint(\"Database connected successfully\")\n\nwith con:\n cur = con.cursor()\n query = \"INSERT INTO Epidemiologist (user_source, epidemiologist_source) VALUES (%s, %s)\"\n cur.executemany(query, tuples)\n con.commit()\n\nprint(\"Data inserted Successfully\")\ncon.close()\n", "sub_path": "Project-INFOH303/localhost/dmlepidemiologistseb.py", "file_name": "dmlepidemiologistseb.py", "file_ext": "py", "file_size_in_byte": 1552, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 6, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 15, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 56, "usage_type": "call"}]} {"seq_id": "359004911", "text": "from packet import Packet\nfrom data import location\n\nclass Shoot2Packet(Packet):\n \n def __init__(self, bulletId=None, ownerId=None, containerType=None, startingPos=None, angle=None, damage=None):\n self.bulletId = bulletId\n self.ownerId = ownerId\n self.containerType = containerType\n if not startingPos:\n startingPos = location.Location()\n self.startingPos = startingPos\n self.angle = angle\n self.damage = damage\n \n def parseFromInput(self, stream):\n self.bulletId = stream.readUnsignedByte()\n self.ownerId = stream.readInt()\n self.containerType = stream.readUnsignedByte()\n self.startingPos.parseFromInput(stream)\n self.angle = stream.readFloat()\n self.damage = stream.readShort()\n", "sub_path": "packets/shoot2.py", "file_name": "shoot2.py", "file_ext": "py", "file_size_in_byte": 795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "packet.Packet", "line_number": 4, "usage_type": "name"}, {"api_name": "data.location.Location", "line_number": 11, "usage_type": "call"}, {"api_name": "data.location", "line_number": 11, "usage_type": "name"}]} {"seq_id": "440822819", "text": "from django.conf.urls import url\nfrom financiera.views import TransportesView, TransportesEstadoView, TransportesEliminarView\nfrom financiera.views import TransportesCreateView, TransportesUpdateView\nfrom financiera.views import TransportesAprobadasFinancieraView, TransportesAprobadasLideresView, TransportesRechazadasView, TransportesPendientesView\nfrom financiera.views import TransportesConsignadasFinancieraView\nfrom financiera.views import SemanasListView, FormadoresCronogramaListView, CronogramaFormadorView\nfrom financiera.views import CronogramaFormadorNuevoView, CronogramaFormadorUpdateView, CronogramaFormadorDeleteView\nfrom financiera.views import ContratosListView, ContratosCreateView, ContratosUpdateView\nfrom financiera.views import EntregablesListView\nfrom financiera.views import CortesListView, NuevoCorteView\n\nurlpatterns = [\n url(r'^transportes/$', TransportesView.as_view()),\n\n url(r'^transportes/consignadas/(?P<id_formador>\\w+)/$', TransportesConsignadasFinancieraView.as_view()),\n url(r'^transportes/consignadas/(?P<id_formador>\\w+)/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/consignadas/(?P<id_formador>\\w+)/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n url(r'^transportes/consignadas/(?P<id_formador>\\w+)/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n\n\n url(r'^transportes/aprobadasfinanciera/(?P<id_formador>\\w+)/$', TransportesAprobadasFinancieraView.as_view()),\n url(r'^transportes/aprobadasfinanciera/(?P<id_formador>\\w+)/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/aprobadasfinanciera/(?P<id_formador>\\w+)/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n url(r'^transportes/aprobadasfinanciera/(?P<id_formador>\\w+)/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n\n\n url(r'^transportes/aprobadaslideres/(?P<id_formador>\\w+)/$', TransportesAprobadasLideresView.as_view()),\n url(r'^transportes/aprobadaslideres/(?P<id_formador>\\w+)/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/aprobadaslideres/(?P<id_formador>\\w+)/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n url(r'^transportes/aprobadaslideres/(?P<id_formador>\\w+)/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n\n\n url(r'^transportes/rechazadas/(?P<id_formador>\\w+)/$', TransportesRechazadasView.as_view()),\n url(r'^transportes/rechazadas/(?P<id_formador>\\w+)/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/rechazadas/(?P<id_formador>\\w+)/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n url(r'^transportes/rechazadas/(?P<id_formador>\\w+)/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n\n\n url(r'^transportes/pendientes/(?P<id_formador>\\w+)/$', TransportesPendientesView.as_view()),\n url(r'^transportes/pendientes/(?P<id_formador>\\w+)/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/pendientes/(?P<id_formador>\\w+)/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n url(r'^transportes/pendientes/(?P<id_formador>\\w+)/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n\n\n\n url(r'^transportes/estado/(?P<pk>\\w+)/$', TransportesEstadoView.as_view()),\n url(r'^transportes/nuevo/$', TransportesCreateView.as_view()),\n url(r'^transportes/eliminar/(?P<pk>\\w+)/$', TransportesEliminarView.as_view()),\n url(r'^transportes/editar/(?P<pk>\\w+)/$', TransportesUpdateView.as_view()),\n\n\n url(r'^cronograma/$', SemanasListView.as_view()),\n url(r'^cronograma/semana/(?P<semana_id>\\w+)/$', FormadoresCronogramaListView.as_view()),\n url(r'^cronograma/semana/(?P<semana_id>[0-9]+)/editar/(?P<id>[0-9]+)/$', CronogramaFormadorView.as_view()),\n url(r'^cronograma/semana/(?P<semana_id>[0-9]+)/editar/(?P<id>[0-9]+)/nuevo/$', CronogramaFormadorNuevoView.as_view()),\n url(r'^cronograma/semana/(?P<semana_id>[0-9]+)/editar/(?P<id>[0-9]+)/entrada/(?P<id_entrada>[0-9]+)/$', CronogramaFormadorUpdateView.as_view()),\n url(r'^cronograma/semana/(?P<semana_id>[0-9]+)/editar/(?P<id>[0-9]+)/eliminar/(?P<id_entrada>[0-9]+)/$', CronogramaFormadorDeleteView.as_view()),\n\n\n url(r'^contratos/$', ContratosListView.as_view()),\n url(r'^contratos/nuevo/$', ContratosCreateView.as_view()),\n url(r'^contratos/editar/(?P<id_contrato>[0-9]+)/$', ContratosUpdateView.as_view()),\n\n url(r'^contratos/entregables/(?P<id_contrato>[0-9]+)/$', EntregablesListView.as_view()),\n\n\n\n\n url(r'^cortes/$', CortesListView.as_view()),\n url(r'^cortes/nuevo/$', NuevoCorteView.as_view()),\n]", "sub_path": "financiera/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 4595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "financiera.views.TransportesView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "financiera.views.TransportesView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "financiera.views.TransportesConsignadasFinancieraView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "financiera.views.TransportesConsignadasFinancieraView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "financiera.views.TransportesAprobadasFinancieraView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "financiera.views.TransportesAprobadasFinancieraView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "financiera.views.TransportesAprobadasLideresView.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "financiera.views.TransportesAprobadasLideresView", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 30, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "financiera.views.TransportesRechazadasView.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "financiera.views.TransportesRechazadasView", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 35, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}, {"api_name": "financiera.views.TransportesPendientesView.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "financiera.views.TransportesPendientesView", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 40, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 40, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 41, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 41, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEstadoView", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "financiera.views.TransportesCreateView.as_view", "line_number": 47, "usage_type": "call"}, {"api_name": "financiera.views.TransportesCreateView", "line_number": 47, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "financiera.views.TransportesEliminarView", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 49, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView.as_view", "line_number": 49, "usage_type": "call"}, {"api_name": "financiera.views.TransportesUpdateView", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "financiera.views.SemanasListView.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "financiera.views.SemanasListView", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "financiera.views.FormadoresCronogramaListView.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "financiera.views.FormadoresCronogramaListView", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 54, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorView.as_view", "line_number": 54, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorView", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 55, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorNuevoView.as_view", "line_number": 55, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorNuevoView", "line_number": 55, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 56, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorUpdateView.as_view", "line_number": 56, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorUpdateView", "line_number": 56, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 57, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorDeleteView.as_view", "line_number": 57, "usage_type": "call"}, {"api_name": "financiera.views.CronogramaFormadorDeleteView", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 60, "usage_type": "call"}, {"api_name": "financiera.views.ContratosListView.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "financiera.views.ContratosListView", "line_number": 60, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "financiera.views.ContratosCreateView.as_view", "line_number": 61, "usage_type": "call"}, {"api_name": "financiera.views.ContratosCreateView", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 62, "usage_type": "call"}, {"api_name": "financiera.views.ContratosUpdateView.as_view", "line_number": 62, "usage_type": "call"}, {"api_name": "financiera.views.ContratosUpdateView", "line_number": 62, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "financiera.views.EntregablesListView.as_view", "line_number": 64, "usage_type": "call"}, {"api_name": "financiera.views.EntregablesListView", "line_number": 64, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 69, "usage_type": "call"}, {"api_name": "financiera.views.CortesListView.as_view", "line_number": 69, "usage_type": "call"}, {"api_name": "financiera.views.CortesListView", "line_number": 69, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 70, "usage_type": "call"}, {"api_name": "financiera.views.NuevoCorteView.as_view", "line_number": 70, "usage_type": "call"}, {"api_name": "financiera.views.NuevoCorteView", "line_number": 70, "usage_type": "name"}]} {"seq_id": "74970926", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport warnings\n\nfrom typing import List\nfrom typing import Union\nfrom typing import Optional\nfrom typing import Tuple\nfrom typing import TypeVar\n\nimport cv2\nimport ezdxf\nimport numpy as np\nfrom ezdxf.drawing import Drawing\nfrom ezdxf.legacy.graphics import GraphicEntity\nfrom ezdxf.legacy.tableentries import Layer\n\nfrom dxfvis.draw_funcs import draw_arc\nfrom dxfvis.draw_funcs import draw_circle\nfrom dxfvis.draw_funcs import draw_line\nfrom dxfvis.draw_funcs import draw_polyline\nfrom dxfvis.draw_funcs import draw_lwpolyline\nfrom dxfvis.draw_funcs import draw_text\nfrom dxfvis.draw_funcs import draw_point\nfrom dxfvis.draw_funcs import draw_insert\nfrom dxfvis.draw_funcs import draw_mtext\nfrom dxfvis.draw_funcs import draw_ellipse\n\nfrom dxfvis.types import OpenCVOp\nfrom dxfvis.types import DXFPoint\nfrom dxfvis.types import NPPoint\nfrom dxfvis.types import Size\nfrom dxfvis.types import Scalar\nfrom dxfvis.types import BoundingBox\n\n\nACCEPTED_DXFTYPES = ['LINE', 'CIRCLE', 'ARC', 'POLYLINE', 'LWPOLYLINE']\n\n\ndef render_dxf(\n drawing: Union[str, Drawing],\n image_size: int,\n is_plain: bool = False) -> np.ndarray:\n \"\"\"render a dxf file and return as numpy array\n\n :param drawing: path or object for a DXF file\n :param image_size: maximum edge length of the image to return. original scales are applied in default.\n :param is_plain: limit the use of advansed properties. better speed & less possibility to encounter unknown errors, instead of less quality.\n \"\"\"\n\n if isinstance(drawing, str):\n drawing = ezdxf.readfile(drawing)\n\n ops: List[OpenCVOp] = []\n drawing_xmin = np.inf\n drawing_xmax = -np.inf\n drawing_ymin = np.inf\n drawing_ymax = -np.inf\n msp = drawing.modelspace()\n for entity in msp:\n if entity.dxftype() == 'DIMENSION':\n if 'geometry' not in entity.dxfattribs():\n warnings.warn('No block for DIMENSION ENTITY')\n continue\n\n block = drawing.blocks.get(entity.dxf.geometry)\n for entity_ in block:\n entity_rep = draw_entity(entity_, drawing)\n if entity_rep is None:\n continue\n\n op, bb = entity_rep\n ops.append(op)\n drawing_xmin = min(drawing_xmin, bb[0][0])\n drawing_xmax = max(drawing_xmax, bb[1][0])\n drawing_ymin = min(drawing_ymin, bb[0][1])\n drawing_ymax = max(drawing_ymax, bb[1][1])\n\n elif entity.dxftype() == 'INSERT':\n if 'name' not in entity.dxfattribs():\n warnings.warn('No block for INSERT ENTITY')\n continue\n\n block = drawing.blocks.get(entity.dxf.name)\n for entity_ in block:\n entity_rep = draw_entity(entity_, drawing)\n if entity_rep is None:\n continue\n\n op, bb = entity_rep\n ops.append(op)\n drawing_xmin = min(drawing_xmin, bb[0][0])\n drawing_xmax = max(drawing_xmax, bb[1][0])\n drawing_ymin = min(drawing_ymin, bb[0][1])\n drawing_ymax = max(drawing_ymax, bb[1][1])\n\n else:\n entity_rep = draw_entity(entity, drawing)\n if entity_rep is None:\n continue\n\n op, bb = entity_rep\n ops.append(op)\n drawing_xmin = min(drawing_xmin, bb[0][0])\n drawing_xmax = max(drawing_xmax, bb[1][0])\n drawing_ymin = min(drawing_ymin, bb[0][1])\n drawing_ymax = max(drawing_ymax, bb[1][1])\n\n # render dxf as an image\n dxf_space = ((drawing_xmin, drawing_ymin), (drawing_xmax, drawing_ymax))\n aspect_ratio = (drawing_ymax - drawing_ymin) / (drawing_xmax - drawing_xmin)\n scale = image_size / max((drawing_ymax - drawing_ymin), (drawing_xmax - drawing_xmin))\n if aspect_ratio > 1:\n image_shape = (image_size, int(image_size / aspect_ratio), 3)\n else:\n image_shape = (int(image_size * aspect_ratio), image_size, 3)\n\n canvas = np.zeros(image_shape)\n # actual drawing\n for op in ops:\n if op is None:\n continue\n\n op(canvas, dxf_space)\n\n return canvas\n\n\ndef draw_entity(\n obj: GraphicEntity,\n drawing: Drawing) -> Optional[Tuple[OpenCVOp, BoundingBox]]:\n \"\"\"DXFファイル上のEntityをキャンバスに描画します\n\n :param obj: 描画対象のエンティティ\n :param drawing: DXFファイル\n\n :returns エンティティの描画メソッド、エンティティのbb\n\n .. Note::\n * 対応していないdxftypeが存在します。\n * DXFファイル上でエンティティが非表示の場合・フォーマットが非対応の場合には描画を行いません。\n \"\"\"\n\n if drawing.layers.has_entry(obj.dxf.layer):\n layer: Layer = drawing.layers.get(obj.dxf.layer)\n if not layer.is_on():\n return None\n\n layer_params = layer.dxfattribs()\n else:\n layer_params = {}\n\n dxftype = obj.dxftype()\n if dxftype not in ACCEPTED_DXFTYPES:\n return None\n\n if dxftype == 'ARC':\n return draw_arc(obj, drawing)\n elif dxftype == 'CIRCLE':\n return draw_circle(obj, drawing)\n elif dxftype == 'LINE':\n return draw_line(obj, drawing)\n elif dxftype == 'POLYLINE':\n return draw_polyline(obj, drawing)\n elif dxftype == 'LWPOLYLINE':\n return draw_lwpolyline(obj, drawing)\n elif dxftype == 'TEXT':\n return draw_text(obj, drawing)\n elif dxftype == 'POINT':\n return draw_point(obj, drawing)\n elif dxftype == 'MTEXT':\n return draw_mtext(obj, drawing)\n elif dxftype == 'ELLIPSE':\n return draw_ellipse(obj, drawing)\n else:\n return None\n", "sub_path": "dxfvis/render.py", "file_name": "render.py", "file_ext": "py", "file_size_in_byte": 5834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.Union", "line_number": 42, "usage_type": "name"}, {"api_name": "ezdxf.drawing.Drawing", "line_number": 42, "usage_type": "name"}, {"api_name": "ezdxf.readfile", "line_number": 53, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "dxfvis.types.OpenCVOp", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 59, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 64, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ezdxf.legacy.graphics.GraphicEntity", "line_number": 131, "usage_type": "name"}, {"api_name": "ezdxf.drawing.Drawing", "line_number": 132, "usage_type": "name"}, {"api_name": "ezdxf.legacy.tableentries.Layer", "line_number": 146, "usage_type": "name"}, {"api_name": "dxfvis.draw_funcs.draw_arc", "line_number": 159, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_circle", "line_number": 161, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_line", "line_number": 163, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_polyline", "line_number": 165, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_lwpolyline", "line_number": 167, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_text", "line_number": 169, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_point", "line_number": 171, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_mtext", "line_number": 173, "usage_type": "call"}, {"api_name": "dxfvis.draw_funcs.draw_ellipse", "line_number": 175, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 132, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 132, "usage_type": "name"}, {"api_name": "dxfvis.types.OpenCVOp", "line_number": 132, "usage_type": "name"}, {"api_name": "dxfvis.types.BoundingBox", "line_number": 132, "usage_type": "name"}]} {"seq_id": "216880543", "text": "#\n# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n# --------------------------------------------------------------------------\nfrom typing import TYPE_CHECKING\nfrom azure.core.pipeline.policies import AsyncBearerTokenCredentialPolicy\n\nif TYPE_CHECKING:\n from azure.core.credentials import TokenCredential\n\ndef get_authentication_policy(\n credential: 'TokenCredential'\n) -> AsyncBearerTokenCredentialPolicy:\n \"\"\"Returns the correct authentication policy\n \"\"\"\n\n if credential is None:\n raise ValueError(\"Parameter 'credential' must not be None.\")\n if hasattr(credential, \"get_token\"):\n return AsyncBearerTokenCredentialPolicy(credential, \"https://api.loganalytics.io/.default\")\n\n raise TypeError(\"Unsupported credential\")\n\ndef get_metrics_authentication_policy(\n credential: 'TokenCredential'\n) -> AsyncBearerTokenCredentialPolicy:\n \"\"\"Returns the correct authentication policy\n \"\"\"\n\n if credential is None:\n raise ValueError(\"Parameter 'credential' must not be None.\")\n if hasattr(credential, \"get_token\"):\n return AsyncBearerTokenCredentialPolicy(credential, \"https://management.azure.com/.default\")\n\n raise TypeError(\"Unsupported credential\")\n", "sub_path": "sdk/monitor/azure-monitor-query/azure/monitor/query/aio/_helpers_asyc.py", "file_name": "_helpers_asyc.py", "file_ext": "py", "file_size_in_byte": 1404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 10, "usage_type": "name"}, {"api_name": "azure.core.pipeline.policies.AsyncBearerTokenCredentialPolicy", "line_number": 22, "usage_type": "call"}, {"api_name": "azure.core.pipeline.policies.AsyncBearerTokenCredentialPolicy", "line_number": 15, "usage_type": "name"}, {"api_name": "azure.core.pipeline.policies.AsyncBearerTokenCredentialPolicy", "line_number": 35, "usage_type": "call"}, {"api_name": "azure.core.pipeline.policies.AsyncBearerTokenCredentialPolicy", "line_number": 28, "usage_type": "name"}]} {"seq_id": "433828263", "text": "import json\nimport requests\nimport time\nfrom requests.exceptions import ConnectionError\n\n\ndata = json.load(open('file.json'))\ntmp = 0\n\nfor row in data[\"content\"]:\n tmp += 1\n\nwork_endpoints = []\nwork_endpoints.append(\"172.21.0.2:5000\")\nwork_endpoints.append(\"172.21.0.3:5000\")\nwork_endpoints.append(\"172.21.0.4:5000\")\nilosc = len(work_endpoints)\ntmp2 = tmp / ilosc\ntmp = 0\ntmp3 = 0\n\n\ndef checkContainersAvailability():\n areNotAvailable = True\n\n while areNotAvailable:\n try:\n requests.get(\"http://\" + work_endpoints[0] + \"/ping\")\n requests.get(\"http://\" + work_endpoints[1] + \"/ping\")\n requests.get(\"http://\" + work_endpoints[2] + \"/ping\")\n except ConnectionError as e:\n print(\"Some workers are still unavailable\")\n time.sleep(1)\n continue\n areNotAvailable = False\n\n print(\"Workers are available\")\n\n\ncheckContainersAvailability()\n\n\nfor row in data[\"content\"]:\n r = requests.post('http://' + work_endpoints[tmp3] + '/add', json.dumps(row))\n tmp3 += 1\n\n if tmp3 == ilosc:\n tmp3 = 0\n pliktmp = {}\n\nresponse = requests.get('http://' + work_endpoints[0] + '/test1')\nprint(\"Workers are currently learned\")\n", "sub_path": "master/bayes_master.py", "file_name": "bayes_master.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 31, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}]} {"seq_id": "644578875", "text": "\"\"\"\nreadData.py\n\"\"\"\nimport sys\nimport math\nimport numpy as np\nfrom numpy import genfromtxt\nimport matplotlib.pyplot as plt\nimport datetime\n\ndef main():\n table = genfromtxt('data.csv', delimiter=',')\n Lmax = int(max(table[:,0]))\n colormap = plt.cm.gist_ncar\n plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0,0.9,Lmax)])\n n = np.empty(0)\n totalTime = np.empty(0)\n for L in xrange(3,Lmax+1):\n x = []\n y = []\n N = []\n t = []\n for row in xrange(table.shape[0]):\n if int(table[row,0]) == L and 0 < table[row,1] and table[row,1] <= 0.15:\n x.append(table[row,1])\n y.append(table[row,2])\n N.append(int(table[row,3]))\n t.append(table[row,4])\n if len(x) > 0:\n # Merge duplicate x-values\n index = 0\n while index < len(x)-1:\n for i in xrange(index+1,len(x)-2):\n if index < i and i < len(x):\n if x[index] == x[i]:\n x.pop(i)\n y[index] = (y[index]*N[index]+y.pop(i)*N[i])/(N[index]+N[i])\n t[index] = (t[index]/N[i]+t.pop(i)/N[index])*(N[index]+N[i])\n N[index] += N.pop(i)\n index += 1\n # Convert to NumPy arrays\n x = np.array(x)\n y = np.array(y)\n N = np.array(N)\n t = np.array(t)\n # Calculate polynomial fit\n pmax = max(x)\n pstep = pmax/len(x)\n z = np.polyfit(x,y,10)\n print(z)\n p = np.poly1d(z)\n xp = np.linspace(min(x)-.001,pmax+0.001,100)\n # Plot fit and points\n plt.plot(xp,p(xp))\n plt.errorbar(x,y,xerr=np.divide(pstep/2,np.sqrt(N)),yerr=np.sqrt(np.divide(np.multiply(y,(1-y)),N)+np.power(N,-2)),fmt='.',label='$L = %i$'%L)\n n = np.append(n,L*L)\n totalTime = np.append(totalTime,sum(t))\n #plt.ylim(-0.05,1.05)\n plt.xlabel(r'Bit Flip Error Rate $p_{flip}$')\n plt.ylabel(r'Error Correction Failure rate $p_{fail}$')\n plt.title(r'Plot of Failure Rate $p_{fail}$ vs. Error Rate $p_{flip}$')\n plt.legend(loc='upper left')\n fileName = 'Figure%s.pdf'%datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n plt.savefig(fileName)\n plt.show()\n print(totalTime)\n\n## z = np.polyfit(n,totalTime,10)\n## print(z)\n## p = np.poly1d(z)\n## x = np.linspace(min(n),max(n),100)\n##\n## plt.plot(x,p(x),c='red')\n## plt.errorbar(n,totalTime,fmt='+',c='blue')\n## plt.xlabel('Number of Qubits $n = L^2$')\n## plt.ylabel('Computation Time $t_C$ (s)')\n## plt.title('Plot of $t_C$ vs. $n$')\n## fileName = 'CompTime%s.pdf'%datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n## plt.savefig(fileName)\n## plt.show()\n##\n## lnn = np.log(n)\n## lntt = np.log(totalTime)\n## z = np.polyfit(lnn[15:],lntt[15:],1)\n## print(z)\n## p = np.poly1d(z)\n## x = np.linspace(min(lnn),max(lnn),100)\n## plt.plot(x,p(x),c='red')\n## plt.errorbar(lnn,lntt,c='red',fmt='+')\n## plt.show()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Simulation/Large Range/readData3.py", "file_name": "readData3.py", "file_ext": "py", "file_size_in_byte": 3185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.genfromtxt", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.divide", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}]} {"seq_id": "353056793", "text": "from collections import defaultdict\n\n\ndef get_total_num_orbits(orbits):\n orbiters = defaultdict(lambda: set())\n for satellite, obj in orbits.items():\n orbiters[obj].add(satellite)\n depths = dict()\n\n def fill_depths(base_obj, depth):\n depths[base_obj] = depth\n for sat in orbiters[base_obj]:\n fill_depths(sat, depth+1)\n\n fill_depths('COM', 0)\n\n num_orbits = 0\n for depth in depths.values():\n num_orbits += depth\n\n return num_orbits\n\n\ndef get_path_to_com(object, orbits):\n path = [object]\n next_object = object\n while next_object != 'COM':\n next_object = orbits[next_object]\n path.append(next_object)\n return path\n\n\ndef get_shortest_path_length(obj_1, obj_2, orbits):\n obj_1_to_com = get_path_to_com(obj_1, orbits)\n obj_2_to_com = get_path_to_com(obj_2, orbits)\n\n split_idx = 0\n for iter1, iter2 in zip(reversed(obj_1_to_com), reversed(obj_2_to_com)):\n if iter1 != iter2:\n break\n split_idx += 1\n\n return len(obj_2_to_com) + len(obj_1_to_com) - 2*split_idx\n\n\nif __name__ == \"__main__\":\n orbits = dict()\n\n with open('input/dec6.txt', 'r') as file:\n for line in file:\n objs = line.split(')')\n orbits[objs[1].rstrip('\\n')] = objs[0]\n\n print(get_total_num_orbits(orbits=orbits))\n print(get_shortest_path_length('YOU', 'SAN', orbits) - 2)\n", "sub_path": "2019/dec6.py", "file_name": "dec6.py", "file_ext": "py", "file_size_in_byte": 1401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "collections.defaultdict", "line_number": 5, "usage_type": "call"}]} {"seq_id": "596044674", "text": "# -*- coding: utf-8 -*-\nimport os\nimport redis\nimport logging\nfrom tornado import ioloop, web, httpserver\nfrom tornado.options import options, define\nfrom conf import load_app_options\nfrom controllers import handlers, MenuModule\n\n\nif __name__ == '__main__':\n define('app_path', os.path.dirname(os.path.abspath(__file__))) # app.py所在目录\n define('app_port', 8501)\n\n load_app_options() # 加载配置\n\n settings = {\n 'template_path': os.path.join(options.app_path, 'templates'),\n 'static_path': os.path.join(options.app_path, 'static'),\n 'cookie_secret': options.cookie_secret,\n 'debug': options.app_debug,\n 'login_url': '/intro',\n 'xsrf_cookies': True,\n 'ui_modules': {\n 'menu': MenuModule,\n },\n }\n\n application = web.Application(handlers, **settings)\n application.redis = redis.StrictRedis(host=options.redis_host, port=options.redis_port, db=options.redis_db)\n\n server = httpserver.HTTPServer(application)\n server.listen(options.app_port)\n\n logging.info('application started on port:%s', options.app_port)\n ioloop.IOLoop.instance().start()\n", "sub_path": "apps/seewi/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tornado.options.define", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 13, "usage_type": "call"}, {"api_name": "conf.load_app_options", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tornado.options.options.app_path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.options.options.app_path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.options.options.cookie_secret", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 20, "usage_type": "name"}, {"api_name": "tornado.options.options.app_debug", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 21, "usage_type": "name"}, {"api_name": "controllers.MenuModule", "line_number": 25, "usage_type": "name"}, {"api_name": "tornado.web.Application", "line_number": 29, "usage_type": "call"}, {"api_name": "controllers.handlers", "line_number": 29, "usage_type": "argument"}, {"api_name": "tornado.web", "line_number": 29, "usage_type": "name"}, {"api_name": "redis.StrictRedis", "line_number": 30, "usage_type": "call"}, {"api_name": "tornado.options.options.redis_host", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 30, "usage_type": "name"}, {"api_name": "tornado.options.options.redis_port", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tornado.options.options.redis_db", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tornado.httpserver.HTTPServer", "line_number": 32, "usage_type": "call"}, {"api_name": "tornado.httpserver", "line_number": 32, "usage_type": "name"}, {"api_name": "tornado.options.options.app_port", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 33, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "tornado.options.options.app_port", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 35, "usage_type": "name"}, {"api_name": "tornado.ioloop.IOLoop.instance", "line_number": 36, "usage_type": "call"}, {"api_name": "tornado.ioloop.IOLoop", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 36, "usage_type": "name"}]} {"seq_id": "605621892", "text": "\"\"\"\nExample of using reduce to analyze car trends\n- calculate average profits of cars by mileage as well as fuel efficiency\n\"\"\"\nfrom functools import reduce\n\nxs = {}\n\n\ndef get_profit(d):\n return d.get('price-sell', 0) - d.get('price-buy', 0)\n\n\ndef low_med_hi(d, key, low, high):\n if d[key] < low:\n return 'low'\n elif d[key] < high:\n return 'med'\n return 'high'\n\n\ndef clean_entry(d):\n r = dict()\n r['profit'] = get_profit(d)\n r['mpg'] = low_med_hi(d, 'mpg', (18, 35))\n r['odo'] = low_med_hi(d, 'odo', (60000, 105000))\n return r\n\n\ndef acc_average(acc, profit):\n acc['total'] = acc.get('total', 0) + profit\n acc['count'] = acc.get('count', 0) + 1\n acc['average'] = acc['total'] / acc['count']\n return acc\n\n\ndef sort_and_add(acc, nxt):\n profit = nxt('profit')\n acc['mpg'][nxt['mpg']] = acc_average(acc['mpg'].get(nxt['mpg'], {}), profit)\n acc['odo'][nxt['odo']] = acc_average(acc['odo'].get(nxt['odo'], {}), profit)\n return acc\n\n\nreduce(sort_and_add, map(clean_entry, xs), {})\n", "sub_path": "reduce_cartrends.py", "file_name": "reduce_cartrends.py", "file_ext": "py", "file_size_in_byte": 1038, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "functools.reduce", "line_number": 44, "usage_type": "call"}]} {"seq_id": "176111557", "text": "from django.db import models\n\nfrom django.core.validators import MaxLengthValidator\nfrom django.core.validators import MinValueValidator\n\n\nclass Tag(models.Model):\n name = models.CharField(\n max_length=50,\n unique=True,\n blank=False,\n validators=[MaxLengthValidator(50)]\n )\n\n def __str__(self):\n return self.name\n\n\nclass Event(models.Model):\n title = models.CharField(\n max_length=100,\n unique=True,\n blank=False,\n validators=[MaxLengthValidator(100)]\n )\n\n description = models.TextField(\n max_length=500,\n blank=True,\n validators=[MaxLengthValidator(500)]\n )\n\n price = models.DecimalField(\n max_digits=6,\n decimal_places=2,\n validators=[MinValueValidator(0.00)]\n )\n\n tags = models.ManyToManyField(Tag)\n\n def __str__(self):\n return ('%(title)s (%(price)s €)' %\n {'title': self.title, 'price': self.price})\n", "sub_path": "src/events/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.core.validators.MaxLengthValidator", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.validators.MaxLengthValidator", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.core.validators.MaxLengthValidator", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}]} {"seq_id": "152659456", "text": "from queue import Empty\nimport wx\n\nfrom platypush.plugins.camera.model.writer.preview.wx import WxPreviewWriter\n\n\nclass Panel(wx.Panel):\n def __init__(self, parent, process, width: int, height: int):\n import wx\n super().__init__(parent, -1)\n\n self.process: WxPreviewWriter = process\n self.SetBackgroundStyle(wx.BG_STYLE_CUSTOM)\n self.SetSize(width, height)\n self.Bind(wx.EVT_PAINT, self.on_paint)\n self.update()\n\n @staticmethod\n def img_to_bitmap(image) -> wx.Bitmap:\n import wx\n return wx.Bitmap.FromBuffer(image.width, image.height, image.tobytes())\n\n def get_bitmap(self):\n try:\n return self.process.bitmap_queue.get(block=True, timeout=1.0)\n except Empty:\n return None\n\n def update(self):\n import wx\n self.Refresh()\n self.Update()\n wx.CallLater(15, self.update)\n\n def create_bitmap(self):\n image = self.get_bitmap()\n if image is None:\n return\n\n return self.img_to_bitmap(image)\n\n def on_paint(self, *_, **__):\n import wx\n bitmap = self.create_bitmap()\n if not bitmap:\n return\n\n dc = wx.AutoBufferedPaintDC(self)\n dc.DrawBitmap(bitmap, 0, 0)\n\n\nclass Frame(wx.Frame):\n def __init__(self, process):\n import wx\n style = wx.DEFAULT_FRAME_STYLE & ~wx.RESIZE_BORDER & ~wx.MAXIMIZE_BOX\n self.process = process\n image = self.process.bitmap_queue.get()\n\n super().__init__(None, -1, process.camera.info.device or 'Camera Preview', style=style)\n self.Bind(wx.EVT_WINDOW_DESTROY, self.on_close)\n self.panel = Panel(self, process, width=image.width, height=image.height)\n self.Fit()\n\n def on_close(self, *_, **__):\n self.process.close()\n\n\n# vim:sw=4:ts=4:et:\n", "sub_path": "platypush/plugins/camera/model/writer/preview/wx/ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 1844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "wx.Panel", "line_number": 7, "usage_type": "attribute"}, {"api_name": "platypush.plugins.camera.model.writer.preview.wx.WxPreviewWriter", "line_number": 12, "usage_type": "name"}, {"api_name": "wx.BG_STYLE_CUSTOM", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wx.EVT_PAINT", "line_number": 15, "usage_type": "attribute"}, {"api_name": "wx.Bitmap.FromBuffer", "line_number": 21, "usage_type": "call"}, {"api_name": "wx.Bitmap", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.Bitmap", "line_number": 19, "usage_type": "attribute"}, {"api_name": "queue.Empty", "line_number": 26, "usage_type": "name"}, {"api_name": "wx.CallLater", "line_number": 33, "usage_type": "call"}, {"api_name": "wx.AutoBufferedPaintDC", "line_number": 48, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_FRAME_STYLE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.RESIZE_BORDER", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.MAXIMIZE_BOX", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.EVT_WINDOW_DESTROY", "line_number": 60, "usage_type": "attribute"}]} {"seq_id": "157815249", "text": "import sys\nimport time\nfrom argparse import ArgumentParser\n\nimport networkx as nx\nimport numpy as np\n\nimport MEP as mep\nimport tools\n\n\ndef main():\n # Argument Passer\n infinity = 10000000000000 # 10 Trillion\n threshold = 0.0000001\n p = ArgumentParser()\n p.add_argument('-f', '--folder', type=str, default='')\n p.add_argument('-i', '--input', type=str, default='SocialMedia.txt')\n p.add_argument('-aff', '--aff_file', type=str, default='aff.txt')\n p.add_argument('-l', '--L', type=int, default=4)\n p.add_argument('-k', '--K', type=int, default=5)\n p.add_argument('-r', '--num_realisation', type=int, default=1)\n p.add_argument('-t', '--tolerance', type=float, default=0.1)\n p.add_argument('-e', '--err', type=float, default=0.1)\n p.add_argument('-u', '--undirected', type=int, default=0)\n p.add_argument('-s', '--rseed', type=int, default=0)\n args = p.parse_args()\n folder = \"./data/\" + args.folder\n \n if (args.undirected == True):\n A = [nx.MultiGraph() for l in range(args.L)] # For graphs\n else:\n A = [nx.MultiDiGraph() for l in range(args.L)] # For Directed Graphs\n\n tools.readGraph(folder, args.input, A)\n print(\"Undirected: \", bool(args.undirected))\n tools.printGraphStat(A, args.undirected)\n\n if (args.undirected == True):\n out_list = inc_list = tools.removeZeroEntriesUndirected(A) # list of nodes with zero in and out degree\n else:\n out_list = tools.removeZeroEntriesOut(A) # list of nodes with zero out degree\n inc_list = tools.removeZeroEntriesIn(A) # list of nodes with zero in degree\n\n # Call to the EM function\n MEP = mep.MEP(\n N=A[0].number_of_nodes(),\n L=args.L,\n K=args.K,\n num_realisation=args.num_realisation,\n tolerance=args.tolerance,\n rseed=args.rseed,\n infinity=infinity,\n threshold=threshold,\n err=args.err,\n undirected=bool(args.undirected),\n folder=folder,\n input=args.input,\n aff_file=args.aff_file)\n\n # Start the clock\n startTimer = time.clock()\n N = A[0].number_of_nodes() # Actual graph\n # print(\"@@@\",A[0].nodes())\n # print(\"%%%\",A[0].edges())\n B = np.empty(shape=[args.L, N, N]) # L*N*N matrix represntation of the graph\n # Populate the matrix B\n for l in range(args.L):\n B[l, :, :] = nx.to_numpy_matrix(A[l], weight='weight')\n\n MEP.cycleRealizations(A, B, out_list, inc_list)\n\n # Stop the clock\n stopTimer = time.clock()\n print(stopTimer - startTimer, \" seconds.\")\n\nif __name__ == '__main__':\n main()\n", "sub_path": "MEP/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "networkx.MultiGraph", "line_number": 31, "usage_type": "call"}, {"api_name": "networkx.MultiDiGraph", "line_number": 33, "usage_type": "call"}, {"api_name": "tools.readGraph", "line_number": 35, "usage_type": "call"}, {"api_name": "tools.printGraphStat", "line_number": 37, "usage_type": "call"}, {"api_name": "tools.removeZeroEntriesUndirected", "line_number": 40, "usage_type": "call"}, {"api_name": "tools.removeZeroEntriesOut", "line_number": 42, "usage_type": "call"}, {"api_name": "tools.removeZeroEntriesIn", "line_number": 43, "usage_type": "call"}, {"api_name": "MEP.MEP", "line_number": 46, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 66, "usage_type": "call"}, {"api_name": "networkx.to_numpy_matrix", "line_number": 69, "usage_type": "call"}, {"api_name": "MEP.cycleRealizations", "line_number": 71, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 74, "usage_type": "call"}]} {"seq_id": "63464724", "text": "# Import Flask modules\nfrom flask import Flask, request, render_template\nfrom flaskext.mysql import MySQL\nimport os\n\n# Create an object named app\napp = Flask(__name__)\n\n# Configure mysql database\napp.config['MYSQL_DATABASE_HOST'] = os.getenv('MYSQL_DATABASE_HOST')\napp.config['MYSQL_DATABASE_PASSWORD'] = os.getenv('MYSQL_PASSWORD')\napp.config['MYSQL_DATABASE_DB'] = os.getenv('MYSQL_DATABASE')\napp.config['MYSQL_DATABASE_USER'] = os.getenv('MYSQL_USER')\n#app.config['MYSQL_DATABASE_PORT'] = 3306\nmysql = MySQL()\nmysql.init_app(app) \nconnection = mysql.connect()\nconnection.autocommit(True)\ncursor = connection.cursor()\n\n# Write a function named `find_persons` which finds persons' record using the keyword from the phonebook table in the db,\n# and returns result as list of dictionary \n# `[{'id': 1, 'name':'XXXX', 'number': 'XXXXXX'}]`.\ndef find_persons(keyword):\n query = f\"\"\"\n SELECT * FROM phonebook WHERE name like '%{keyword.strip().lower()}%';\n \"\"\"\n cursor.execute(query)\n result = cursor.fetchall()\n persons =[{'id':row[0], 'name':row[1].strip().title(), 'number':row[2]} for row in result]\n if len(persons) == 0:\n persons = [{'name':'No Result', 'number':'No Result'}]\n return persons\n\n\n# Write a function named `find_records` which finds phone records by keyword using `GET` and `POST` methods,\n# using template files named `index.html` given under `templates` folder\n# and assign to the static route of ('/')\n@app.route('/', methods=['GET', 'POST'])\ndef find_records():\n if request.method == 'POST':\n keyword = request.form['username']\n persons = find_persons(keyword)\n return render_template('index.html', persons=persons, keyword=keyword, show_result=True, developer_name='Hasan Kaval')\n else:\n return render_template('index.html', show_result=False, developer_name='Hasan Kaval')\n\n\n# Add a statement to run the Flask application which can be reached from any host on port 80.\nif __name__== '__main__':\n #app.run(debug=True)\n app.run(host='0.0.0.0', port=80) \n", "sub_path": "devops/projects/203-Kubernetes-Microservice-Phonebook/Students_files/image_for_result_server/phonebook_search.py", "file_name": "phonebook_search.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "flaskext.mysql.MySQL", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}]} {"seq_id": "152187715", "text": "#Date: 20th July, 2020\n#Dang Thai Hai Vu _ Author\n#Email: vudang2414@gmail.com\n#Odontology project with DDS. Nguyen Dac Bao Chinh (DDS at HT Smile)\nfrom tkinter import * \nfrom tkinter import messagebox\nimport glob\nimport os\nimport PIL\nfrom PIL import Image\n\n# Notification GUI\ndef notibox():\n window = Tk()\n window.eval('tk::PlaceWindow %s center' % window.winfo_toplevel())\n window.withdraw()\n\n messagebox.showinfo('HT_Smile','Task has been done !')\n\n window.deiconify()\n window.destroy()\n window.quit()\n\n# Lower Jaw: Flip and Rotate 180 deg\ndef Lower_Jaw(foto):\n im = Image.open(foto)\n img = im.transpose(PIL.Image.FLIP_LEFT_RIGHT).rotate(180)\n dst = os.path.splitext(foto)[0] + \"-Flipped\" + \".jpg\"\n img.save(foto)\n os.rename(foto,dst)\n\n# Upper Jaw: Flip\ndef Upper_Jaw(foto):\n im = Image.open(foto).convert('RGB')\n img = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n dst = os.path.splitext(foto)[0] + \"-Flipped\" + \".jpg\"\n img.save(foto)\n os.rename(foto,dst)\n\n# Left Jaw: Flip\ndef Left_Jaw(foto):\n im = Image.open(foto).convert('RGB')\n img = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n dst = os.path.splitext(foto)[0] + \"-Flipped\" + \".jpg\"\n img.save(foto)\n os.rename(foto,dst)\n\n# Right Jaw: Flip\ndef Right_Jaw(foto):\n im = Image.open(foto).convert('RGB')\n img = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n dst = os.path.splitext(foto)[0] + \"-Flipped\" + \".jpg\"\n img.save(foto)\n os.rename(foto,dst)\n\n\nif __name__ == \"__main__\":\n dirName = 'C:\\\\Users\\\\vudan\\\\OneDrive\\\\Desktop\\\\Automation Project\\\\Odontology\\\\Flip-image\\\\images'\n #Create list that keeps the foto\n allFiles = list()\n listofFile = os.listdir(dirName)\n for files in listofFile:\n fullPath = os.path.join(dirName,files)\n allFiles.append(fullPath)\n \n #Start to rotate foto, this loop will run through each case resprectively \n # and repeat until it reach the last foto\n i=0\n while i < (len(allFiles)):\n #No3_Lower_Jaw\n i += 2\n Lower_Jaw(allFiles[i]) \n #No4_Upper_Jaw\n i += 1\n Upper_Jaw(allFiles[i])\n #No5_Left_Jaw\n i += 1\n Left_Jaw(allFiles[i])\n #No6_Right_Jaw\n i += 1\n Right_Jaw(allFiles[i])\n if i == len(allFiles):\n break\n i += 1\n #Notification box\n notibox()\n \n", "sub_path": "Final/Cach 1/App.py", "file_name": "App.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tkinter.messagebox.showinfo", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 34, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 34, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 42, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 42, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 54, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}]} {"seq_id": "522408942", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom django.contrib.admin import AdminSite\nfrom django.utils.translation import ugettext_lazy\nfrom import_export import resources, fields\nfrom import_export.admin import ImportExportModelAdmin\nfrom import_export.widgets import ForeignKeyWidget\n\nfrom fluxo.core.models import ContasPagar, Categoria, SubCategoria, Banco, Fornecedor, Status\n\nAdminSite.site_header = ugettext_lazy('BioLife')\nadmin.site.site_title = \"BioLife\"\nadmin.site.index_title = \"BioLife\"\n\n\nclass ContasPagarResource(resources.ModelResource):\n data_pagamento = fields.Field(\n column_name='Data Pagamento',\n attribute='data_pagamento',\n widget=ForeignKeyWidget(ContasPagar, 'data_pagamento')\n )\n subcategoria = fields.Field(\n column_name='Nomeclatura',\n attribute='nomeclatura',\n widget=ForeignKeyWidget(SubCategoria.sub_categoria, 'sub_categoria')\n )\n fornecedor = fields.Field(\n column_name='Fornecedor',\n attribute='fornecedor',\n widget=ForeignKeyWidget(Fornecedor, 'nome')\n )\n\n status = fields.Field(\n column_name='Status',\n attribute='status',\n widget=ForeignKeyWidget(Status, 'status')\n )\n\n banco = fields.Field(\n column_name='Banco',\n attribute='banco',\n widget=ForeignKeyWidget(Banco.banco, 'banco')\n )\n\n class Meta:\n model = ContasPagar\n export_order = ('data_pagamento', 'data_vencimento', 'notafiscal', 'fornecedor', 'historico', 'valor', 'n_documento', 'banco', 'conciliado', 'status')\n exclude = ('id', )\n fields = ('data_pagamento', 'data_vencimento', 'notafiscal', 'fornecedor', 'historico', 'valor', 'n_documento', 'banco', 'conciliado', 'status', 'nomeclatura__categoria__nome', 'nomeclatura__codigo')\n\n\n widgets = {\n 'data_vencimento': {'format': '%d/%m/%Y'},\n 'data_pagamento': {'format': '%d/%m/%Y'},\n }\n\n\n\nclass ContasPagarAdmin(ImportExportModelAdmin):\n list_display = ['data_vencimento', 'data_pagamento','notafiscal', 'nomeclatura', 'fornecedor', 'historico', 'valor', 'n_documento', 'banco', 'conciliado', 'status']\n fields = ['data_pagamento', 'data_vencimento', 'notafiscal', 'nomeclatura', 'fornecedor', 'historico',\n 'valor', 'n_documento', 'banco', 'conciliado', 'status']\n list_filter = ['data_pagamento', 'data_vencimento', 'nomeclatura', 'banco']\n resource_class = ContasPagarResource\n\n actions = ['conciliar', 'pago']\n\n\n def valor(self, obj):\n return 'R$ {:.2f}'.format(obj.total_net_value).replace('.', ',')\n\n def conciliar(self, request, queryset):\n count = queryset.update(conciliado=True)\n\n if count == 1:\n msg = '{} um lançamento foi concilado.'\n else:\n msg = '{} lançamentos foram conciliados.'\n\n self.message_user(request, msg.format(count))\n\n conciliar.short_description = 'Marcar como conciliado'\n\n def pago(self, request, queryset):\n count = queryset.update(status=2)\n\n if count == 1:\n msg = '{} um lançamento foi pago.'\n else:\n msg = '{} lançamentos foram pagos.'\n\n self.message_user(request, msg.format(count))\n\n pago.short_description = 'Pago'\n\n list_editable = ['banco', 'data_pagamento']\n\nclass SubCategoriaAdmin(admin.ModelAdmin):\n list_display = ['codigo', 'categoria', 'sub_categoria']\n\n\nadmin.site.register(ContasPagar, ContasPagarAdmin)\nadmin.site.register(Categoria)\nadmin.site.register(SubCategoria, SubCategoriaAdmin)\nadmin.site.register(Banco)\nadmin.site.register(Fornecedor)\nadmin.site.register(Status)\n\n\n", "sub_path": "fluxo/core/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 3663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.contrib.admin.AdminSite.site_header", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin.AdminSite", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 14, "usage_type": "name"}, {"api_name": "import_export.resources.ModelResource", "line_number": 17, "usage_type": "attribute"}, {"api_name": "import_export.resources", "line_number": 17, "usage_type": "name"}, {"api_name": "import_export.fields.Field", "line_number": 18, "usage_type": "call"}, {"api_name": "import_export.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "import_export.widgets.ForeignKeyWidget", "line_number": 21, "usage_type": "call"}, {"api_name": "fluxo.core.models.ContasPagar", "line_number": 21, "usage_type": "argument"}, {"api_name": "import_export.fields.Field", "line_number": 23, "usage_type": "call"}, {"api_name": "import_export.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "import_export.widgets.ForeignKeyWidget", "line_number": 26, "usage_type": "call"}, {"api_name": "fluxo.core.models.SubCategoria.sub_categoria", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fluxo.core.models.SubCategoria", "line_number": 26, "usage_type": "name"}, {"api_name": "import_export.fields.Field", "line_number": 28, "usage_type": "call"}, {"api_name": "import_export.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "import_export.widgets.ForeignKeyWidget", "line_number": 31, "usage_type": "call"}, {"api_name": "fluxo.core.models.Fornecedor", "line_number": 31, "usage_type": "argument"}, {"api_name": "import_export.fields.Field", "line_number": 34, "usage_type": "call"}, {"api_name": "import_export.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "import_export.widgets.ForeignKeyWidget", "line_number": 37, "usage_type": "call"}, {"api_name": "fluxo.core.models.Status", "line_number": 37, "usage_type": "argument"}, {"api_name": "import_export.fields.Field", "line_number": 40, "usage_type": "call"}, {"api_name": "import_export.fields", "line_number": 40, "usage_type": "name"}, {"api_name": "import_export.widgets.ForeignKeyWidget", "line_number": 43, "usage_type": "call"}, {"api_name": "fluxo.core.models.Banco.banco", "line_number": 43, "usage_type": "attribute"}, {"api_name": "fluxo.core.models.Banco", "line_number": 43, "usage_type": "name"}, {"api_name": "fluxo.core.models.ContasPagar", "line_number": 47, "usage_type": "name"}, {"api_name": "import_export.fields", "line_number": 50, "usage_type": "name"}, {"api_name": "import_export.admin.ImportExportModelAdmin", "line_number": 60, "usage_type": "name"}, {"api_name": "import_export.fields", "line_number": 62, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 99, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 103, "usage_type": "call"}, {"api_name": "fluxo.core.models.ContasPagar", "line_number": 103, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 103, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 103, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 104, "usage_type": "call"}, {"api_name": "fluxo.core.models.Categoria", "line_number": 104, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 104, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 104, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 105, "usage_type": "call"}, {"api_name": "fluxo.core.models.SubCategoria", "line_number": 105, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 105, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 106, "usage_type": "call"}, {"api_name": "fluxo.core.models.Banco", "line_number": 106, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 106, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 107, "usage_type": "call"}, {"api_name": "fluxo.core.models.Fornecedor", "line_number": 107, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 107, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 108, "usage_type": "call"}, {"api_name": "fluxo.core.models.Status", "line_number": 108, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 108, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 108, "usage_type": "name"}]} {"seq_id": "115982064", "text": "import time\nfrom typing import List, Dict\n\nfrom app.Redis.utils import encode_ok_response\n\n\ndef ping(rest: List[str], *args, **kwargs) -> bytes:\n if not rest:\n return encode_ok_response(\"PONG\")\n else:\n return encode_ok_response(rest[0])\n\n\ndef echo(rest: List[str], *args, **kwargs) -> bytes:\n if not rest:\n raise ValueError(\"ECHO needs a parameter\")\n else:\n return encode_ok_response(rest[0], bulk_str=True)\n\n\nkv_map: Dict[str, str] = dict()\nexpiry_time: Dict[str, float] = dict()\n\n\ndef set(rest: List[str], *args, **kwargs) -> bytes:\n if len(rest) < 2:\n raise ValueError(\"SET needs 2 parameters\")\n\n key, val = rest[0], rest[1]\n\n if len(rest) == 4 and rest[2].lower() == 'px':\n # We need to set expiry as well\n offset = int(rest[3])\n expiry_time[key] = time.time() + offset / 1000\n else:\n expiry_time[key] = -1\n\n kv_map[key] = val\n\n return encode_ok_response(\"OK\")\n\n\ndef get(rest: List[str], *args, **kwargs) -> bytes:\n if not rest:\n raise ValueError(\"GET needs 1 parameters\")\n\n key = rest[0]\n\n exp_time = expiry_time.get(key, -1)\n\n # Check if this key was expired.\n if exp_time != -1 and exp_time < time.time():\n del expiry_time[key]\n del kv_map[key]\n\n return encode_ok_response(kv_map.get(key, None), bulk_str=True)\n", "sub_path": "app/Redis/commands.py", "file_name": "commands.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "app.Redis.utils.encode_ok_response", "line_number": 9, "usage_type": "call"}, {"api_name": "app.Redis.utils.encode_ok_response", "line_number": 11, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "app.Redis.utils.encode_ok_response", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "app.Redis.utils.encode_ok_response", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "app.Redis.utils.encode_ok_response", "line_number": 56, "usage_type": "call"}]} {"seq_id": "459242525", "text": "# import cv2\r\n# img = cv2.imread(\"face.jpg\",1)\r\n\r\n# # resize the image to smaller image\r\n# resized = cv2.resize(img, (int(img.shape[1]/2),int(img.shape[0]/2)))\r\n# cv2.imshow(\"legend\", resized)\r\n# # window will stay open for 2000 mil/sec\r\n# cv2.waitKey(2000)\r\n\r\n# cv2.destroyAllWindows()\r\n\r\nimport cv2\r\n\r\n# create a cascadeclassifier\r\nface_cascade = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\r\n\r\n# read our image\r\nimg = cv2.imread(\"face.jpg\")\r\n# read the image as a gray scale \r\ngray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n\r\n# search for the cordinates of the image, set scale factor = 1.05 and minnighbors = 5\r\nfaces = face_cascade.detectMultiScale(gray_img, 1.05, 5)\r\n\r\nprint(type(faces))\r\nprint(faces)\r\n\r\ncolor = (0,255,0)\r\n# for loop to display a box around the face\r\n\r\nfor x,y,w,h in faces:\r\n img = cv2.rectangle(img, (x,y), (x+w, y+h), color , 3 )\r\ncv2.imshow(\"gray\", img)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()", "sub_path": "face and motion detector/face detection/face_detection.py", "file_name": "face_detection.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 35, "usage_type": "call"}]} {"seq_id": "303922979", "text": "import logging\nfrom dataclasses import asdict\nfrom flask import Blueprint, request\nfrom app.models.signup import SignupFormRequest\nfrom app.models.responses.signup import SignupResponse, SignupErrorResponse\nfrom exceptions.validation import ValidationError\n\nLOG = logging.getLogger(__name__)\n\n__all__ = [\"signup\"]\n\nsignup = Blueprint('signup', __name__, url_prefix=\"/v1/signup\")\n\ndef _error_response(sfr: SignupFormRequest, exc: ValidationError) -> SignupResponse:\n \"\"\"Returns correct error response.\n\n If the validation error is on the honeypot field then we return a successfull message\n with an error to tell the front-end to show a successful message but the signup\n did not actually go through.\n\n sfr (SignupFormRequest): Form data instance.\n\n returns: SignupResponse\n \"\"\"\n error = SignupErrorResponse(\n field_name=exc.field_name, message=exc.message\n )\n\n response = SignupResponse(\n success=False,\n error=error,\n message=\"Signup form error.\",\n request=sfr\n )\n return response\n\ndef _success_response(sfr: SignupFormRequest) -> SignupResponse:\n \"\"\"Returns success response.\n\n sfr (SignupFormRequest): Form data istance.\n\n returns: SignupResponse\n \"\"\"\n return SignupResponse(\n success=True,\n error=SignupErrorResponse(),\n message=\"Successfully signed up\",\n request=sfr\n )\n\n\n@signup.route(\"\", methods=[\"POST\"])\ndef signup_form():\n \"\"\"Handler for signup form.\"\"\"\n sfr = SignupFormRequest(**request.get_json())\n try :\n sfr.validate()\n except ValidationError as exc:\n LOG.exception(exc)\n return asdict(_error_response(sfr, exc)), 400\n\n return asdict(_success_response(sfr)), 200\n", "sub_path": "api/app/views/signup.py", "file_name": "signup.py", "file_ext": "py", "file_size_in_byte": 1731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "app.models.signup.SignupFormRequest", "line_number": 14, "usage_type": "name"}, {"api_name": "exceptions.validation.ValidationError", "line_number": 14, "usage_type": "name"}, {"api_name": "app.models.responses.signup.SignupErrorResponse", "line_number": 25, "usage_type": "call"}, {"api_name": "app.models.responses.signup.SignupResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "app.models.responses.signup.SignupResponse", "line_number": 14, "usage_type": "name"}, {"api_name": "app.models.signup.SignupFormRequest", "line_number": 37, "usage_type": "name"}, {"api_name": "app.models.responses.signup.SignupResponse", "line_number": 44, "usage_type": "call"}, {"api_name": "app.models.responses.signup.SignupErrorResponse", "line_number": 46, "usage_type": "call"}, {"api_name": "app.models.responses.signup.SignupResponse", "line_number": 37, "usage_type": "name"}, {"api_name": "app.models.signup.SignupFormRequest", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "exceptions.validation.ValidationError", "line_number": 58, "usage_type": "name"}, {"api_name": "dataclasses.asdict", "line_number": 60, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 62, "usage_type": "call"}]} {"seq_id": "177976200", "text": "import tornado.web\nimport tornado.ioloop\nimport tornado.httpserver\nimport tornado.options\nimport os\nimport datetime\nimport persudoDatabase\nfrom tornado import gen\nfrom tornado.web import RequestHandler\nfrom tornado.options import define, options\nfrom tornado.websocket import WebSocketHandler\n\ndefine(\"port\", default=8000, type=int)\n\nclass IndexHandler(RequestHandler):\n def get(self):\n self.render(\"index.html\")\n\nclass AdminHandler(RequestHandler):\n def get(self):\n self.render(\"register.html\")\n\nclass RegisterHandler(RequestHandler):\n def get(self,sensorId):\n print(sensorId)\n if sensorId!=None and not persudoDatabase.database.sensors.__contains__(sensorId):\n persudoDatabase.database.sensors[sensorId]=self\n msg='sensor %s is Registered'%(sensorId)\n print(msg)\n self.write(msg)\n else:\n self.write_error(400,'fail!')\n\nclass CancellationHandler(RequestHandler):\n def get(self,sensorId):\n if sensorId!=None and persudoDatabase.database.sensors.__contains__(sensorId):\n print('sensor Cancellation!')\n persudoDatabase.database.sensors.pop(sensorId)\n\nclass SensorHandler(WebSocketHandler):\n def open(self,sensorId):\n if sensorId!=None and persudoDatabase.database.sensors.__contains__(sensorId):\n self.write_message('sensor, welcome to publish.')\n else:\n persudoDatabase.database.users.add(self)\n self.write_message('user, welcome to subscribe.')\n\n def on_message(self, message):\n for u in persudoDatabase.database.users:\n u.write_message(message)\n \n def check_origin(self, origin):\n return True # 允许WebSocket的跨域请求\n\nif __name__ == '__main__':\n tornado.options.parse_command_line()\n app = tornado.web.Application([\n (r\"/\", IndexHandler),\n (r\"/sensor/(.*)\", SensorHandler),\n (r\"/register/(.*)\", RegisterHandler),\n (r\"/admin\", AdminHandler),\n ],\n static_path = os.path.join(os.path.dirname(__file__), \"static\"),\n template_path = os.path.join(os.path.dirname(__file__), \"template\"),\n debug = True\n )\n http_server = tornado.httpserver.HTTPServer(app)\n http_server.listen(options.port)\n tornado.ioloop.IOLoop.current().start()", "sub_path": "src/backEnd/potatoServer.py", "file_name": "potatoServer.py", "file_ext": "py", "file_size_in_byte": 2342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tornado.options.define", "line_number": 13, "usage_type": "call"}, {"api_name": "tornado.web.RequestHandler", "line_number": 15, "usage_type": "name"}, {"api_name": "tornado.web.RequestHandler", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.web.RequestHandler", "line_number": 23, "usage_type": "name"}, {"api_name": "persudoDatabase.database.sensors.__contains__", "line_number": 26, "usage_type": "call"}, {"api_name": "persudoDatabase.database", "line_number": 26, "usage_type": "attribute"}, {"api_name": "persudoDatabase.database", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tornado.web.RequestHandler", "line_number": 34, "usage_type": "name"}, {"api_name": "persudoDatabase.database.sensors.__contains__", "line_number": 36, "usage_type": "call"}, {"api_name": "persudoDatabase.database", "line_number": 36, "usage_type": "attribute"}, {"api_name": "persudoDatabase.database.sensors.pop", "line_number": 38, "usage_type": "call"}, {"api_name": "persudoDatabase.database", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tornado.websocket.WebSocketHandler", "line_number": 40, "usage_type": "name"}, {"api_name": "persudoDatabase.database.sensors.__contains__", "line_number": 42, "usage_type": "call"}, {"api_name": "persudoDatabase.database", "line_number": 42, "usage_type": "attribute"}, {"api_name": "persudoDatabase.database.users.add", "line_number": 45, "usage_type": "call"}, {"api_name": "persudoDatabase.database", "line_number": 45, "usage_type": "attribute"}, {"api_name": "persudoDatabase.database", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tornado.web.options.parse_command_line", "line_number": 56, "usage_type": "call"}, {"api_name": "tornado.web.options", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 56, "usage_type": "name"}, {"api_name": "tornado.web.web.Application", "line_number": 57, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 64, "usage_type": "call"}, {"api_name": "tornado.web.httpserver.HTTPServer", "line_number": 67, "usage_type": "call"}, {"api_name": "tornado.web.httpserver", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 67, "usage_type": "name"}, {"api_name": "tornado.options.options.port", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 68, "usage_type": "name"}, {"api_name": "tornado.web.ioloop.IOLoop.current", "line_number": 69, "usage_type": "call"}, {"api_name": "tornado.web.ioloop", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 69, "usage_type": "name"}]} {"seq_id": "301930159", "text": "\"\"\"OpenAPI core contrib falcon handlers module\"\"\"\nfrom json import dumps\nfrom typing import Any\nfrom typing import Dict\nfrom typing import Iterable\nfrom typing import Type\n\nfrom falcon import status_codes\nfrom falcon.constants import MEDIA_JSON\nfrom falcon.request import Request\nfrom falcon.response import Response\n\nfrom openapi_core.templating.media_types.exceptions import MediaTypeNotFound\nfrom openapi_core.templating.paths.exceptions import OperationNotFound\nfrom openapi_core.templating.paths.exceptions import PathNotFound\nfrom openapi_core.templating.paths.exceptions import ServerNotFound\nfrom openapi_core.templating.security.exceptions import SecurityNotFound\n\n\nclass FalconOpenAPIErrorsHandler:\n OPENAPI_ERROR_STATUS: Dict[Type[BaseException], int] = {\n ServerNotFound: 400,\n SecurityNotFound: 403,\n OperationNotFound: 405,\n PathNotFound: 404,\n MediaTypeNotFound: 415,\n }\n\n @classmethod\n def handle(\n cls, req: Request, resp: Response, errors: Iterable[Exception]\n ) -> None:\n data_errors = [cls.format_openapi_error(err) for err in errors]\n data = {\n \"errors\": data_errors,\n }\n data_str = dumps(data)\n data_error_max = max(data_errors, key=cls.get_error_status)\n resp.content_type = MEDIA_JSON\n resp.status = getattr(\n status_codes,\n f\"HTTP_{data_error_max['status']}\",\n status_codes.HTTP_400,\n )\n resp.text = data_str\n resp.complete = True\n\n @classmethod\n def format_openapi_error(cls, error: BaseException) -> Dict[str, Any]:\n if error.__cause__ is not None:\n error = error.__cause__\n return {\n \"title\": str(error),\n \"status\": cls.OPENAPI_ERROR_STATUS.get(error.__class__, 400),\n \"type\": str(type(error)),\n }\n\n @classmethod\n def get_error_status(cls, error: Dict[str, Any]) -> int:\n return int(error[\"status\"])\n", "sub_path": "openapi_core/contrib/falcon/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 1983, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 21, "usage_type": "name"}, {"api_name": "openapi_core.templating.paths.exceptions.ServerNotFound", "line_number": 22, "usage_type": "name"}, {"api_name": "openapi_core.templating.security.exceptions.SecurityNotFound", "line_number": 23, "usage_type": "name"}, {"api_name": "openapi_core.templating.paths.exceptions.OperationNotFound", "line_number": 24, "usage_type": "name"}, {"api_name": "openapi_core.templating.paths.exceptions.PathNotFound", "line_number": 25, "usage_type": "name"}, {"api_name": "openapi_core.templating.media_types.exceptions.MediaTypeNotFound", "line_number": 26, "usage_type": "name"}, {"api_name": "falcon.request.Request", "line_number": 31, "usage_type": "name"}, {"api_name": "falcon.response.Response", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 31, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}, {"api_name": "falcon.constants.MEDIA_JSON", "line_number": 39, "usage_type": "name"}, {"api_name": "falcon.status_codes", "line_number": 41, "usage_type": "argument"}, {"api_name": "falcon.status_codes.HTTP_400", "line_number": 43, "usage_type": "attribute"}, {"api_name": "falcon.status_codes", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 59, "usage_type": "name"}]} {"seq_id": "297146804", "text": "#! -*- coding:utf-8 -*-\nimport tornado.ioloop\nimport os\nfrom handlers import *\nimport config\nfrom tornado.options import options, parse_command_line, define\n\ndefine(\"port\", default=8080, help=\"run on the given port\", type=int)\n\nsettings = {\n \"static_path\": os.path.join(os.path.dirname(__file__), \"static\"),\n \"cookie_secret\": config.COOKIE_SECRET,\n \"template_path\":os.path.join(os.path.dirname(__file__), \"template\"),\n \"login_url\": \"/login\",\n \"xsrf_cookies\": config.XSRF_COOKIES,\n \"debug\":config.DEBUG\n}\n\n\napplication = tornado.web.Application([\n (r\"/\", IndexHandler),\n (r\"/login\", LoginHandler),\n (r\"/logout\", LogoutHandler),\n], **settings)\n\n\nif __name__ == \"__main__\":\n parse_command_line()\n application.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tornado.options.define", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "config.COOKIE_SECRET", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "config.XSRF_COOKIES", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.web.Application", "line_number": 20, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 20, "usage_type": "name"}, {"api_name": "tornado.options.parse_command_line", "line_number": 28, "usage_type": "call"}, {"api_name": "tornado.options.options.port", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 29, "usage_type": "name"}, {"api_name": "tornado.ioloop.ioloop.IOLoop.instance", "line_number": 30, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 30, "usage_type": "name"}]} {"seq_id": "513779750", "text": "from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nimport pandas as pd\nimport os\n\nfrom src.etl_script import User, Transaction\n\nDATABASE_STRING = 'postgresql://luke:password@localhost:5432/revolut_data'\n\n\ndef features_for_user(session, user_id):\n \"\"\"\n This function assumes the database is populated as described in task 1\n :param db_string:\n :return:\n \"\"\"\n # this is an O(n^2) solution which is particularly slow\n user = session.query(User).filter(User.id == user_id).one()\n user_transaction_history = session.query(Transaction).filter(Transaction.user_id == user_id).all()\n\n if len(user_transaction_history) > 0:\n number_of_card_payments = len([x for x in user_transaction_history if x.type == \"CARD_PAYMENT\"])\n number_of_atm_uses = len([x for x in user_transaction_history if x.type == \"ATM\"])\n number_of_bank_transfers = len([x for x in user_transaction_history if x.type == \"BANK_TRANSFER\"])\n else:\n number_of_card_payments = 0\n number_of_atm_uses = 0\n number_of_bank_transfers = 0\n\n # fraudsters = session.query(User).filter(User.is_fraudster).all()\n\n return pd.DataFrame({\"birth_year\": [user.birth_year],\n \"has_email\": [user.has_email],\n \"kyc_passed\": [user.kyc == \"PASSED\"],\n \"failed_sign_in_attempts\": [user.failed_sign_in_attempts],\n \"user_signed_terms\": [user.terms_version != None],\n \"user_is_from_uk\": [user.country == \"GB\"],\n \"number_of_transactions\": [len(user_transaction_history)],\n \"number_of_card_payments\": [number_of_card_payments],\n \"number_of_atm_uses\": [number_of_atm_uses],\n \"number_of_bank_transfers\": [number_of_bank_transfers]},\n index=[user_id])\n\n\ndef get_all_features(db_string):\n \"\"\"\n\n :param db_string:\n :return:\n \"\"\"\n db = create_engine(db_string)\n Session = sessionmaker(db)\n session = Session()\n users = session.query(User)\n\n feature_vec = pd.DataFrame()\n for i, user in enumerate(users):\n feature_vec = feature_vec.append(features_for_user(session, user.id))\n if i % 100 == 0:\n print(i)\n\n feature_vec = feature_vec.sort_index()\n feature_vec.to_csv(os.path.join(\"results\", \"training_features.csv\"))\n\n\nif __name__ == \"__main__\":\n get_all_features(DATABASE_STRING)\n", "sub_path": "src/generate_features_from_db.py", "file_name": "generate_features_from_db.py", "file_ext": "py", "file_size_in_byte": 2506, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "src.etl_script.User", "line_number": 19, "usage_type": "argument"}, {"api_name": "src.etl_script.User.id", "line_number": 19, "usage_type": "attribute"}, {"api_name": "src.etl_script.Transaction", "line_number": 20, "usage_type": "argument"}, {"api_name": "src.etl_script.Transaction.user_id", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 53, "usage_type": "call"}, {"api_name": "src.etl_script.User", "line_number": 55, "usage_type": "argument"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}]} {"seq_id": "339903891", "text": "#!/usr/bin/python3\n\"\"\"\nCity\n\"\"\"\nfrom flask import jsonify, abort, request\nfrom api.v1.views import app_views\nfrom models import storage\nfrom models.place import Place\nfrom models.city import City\n\n\n@app_views.route('/cities/<city_id>/places',\n methods=['GET'], strict_slashes=False)\ndef show_places(city_id):\n \"\"\"This functions lists all the users\"\"\"\n list_t = []\n cities = storage.all(\"City\")\n c_id = \"City.\" + city_id\n if cities.get(c_id) is None:\n abort(404)\n else:\n places = storage.all(\"Place\")\n for place in places.values():\n if place.city_id == city_id:\n list_t.append(place.to_dict())\n return jsonify(list_t)\n\n\n@app_views.route('places/<place_id>', methods=['GET'], strict_slashes=False)\ndef get_place(place_id):\n \"\"\"This functions get a specific state by id\"\"\"\n places = storage.all(\"Place\")\n p_id = \"Place.\" + place_id\n if places.get(p_id) is None:\n abort(404)\n place = places.get(p_id).to_dict()\n return place\n\n\n@app_views.route('/places/<place_id>',\n methods=['DELETE'], strict_slashes=False)\ndef delete_place(place_id):\n \"\"\"This function delete a state by id\"\"\"\n places = storage.all(Place)\n p_id = \"Place.\" + place_id\n to_del = places.get(p_id)\n if to_del is None:\n abort(404)\n storage.delete(to_del)\n storage.save()\n return jsonify({}), 200\n\n\n@app_views.route('/cities/<city_id>/places',\n methods=['POST'], strict_slashes=False)\ndef create_place(city_id):\n \"\"\"This function creates a new place\"\"\"\n data = request.get_json()\n c_id = storage.get(\"City\", city_id)\n if c_id is None:\n abort(404)\n if not data:\n abort(400, 'Not a JSON')\n if data.get('user_id') is None:\n abort(400, 'Missing user_id')\n u_id = data['user_id']\n look_user = storage.get(\"User\", u_id)\n if look_user is None:\n abort(404)\n if data.get('name') is None:\n abort(400, 'Missing name')\n data['city_id'] = city_id\n place = Place(**data)\n storage.new(place)\n storage.save()\n return jsonify(place.to_dict()), 201\n\n\n@app_views.route('/places/<place_id>', methods=['PUT'], strict_slashes=False)\ndef update_place(place_id):\n \"\"\"This function update a state by id\"\"\"\n data = request.get_json()\n places = storage.all(\"Place\")\n p_id = \"Place.\" + place_id\n if places.get(p_id) is None:\n abort(404)\n if not data:\n abort(400, 'Not a JSON')\n else:\n place = places.get(p_id)\n for key, value in data.items():\n if key != \"id\" and key != \"created_at\" \\\n and key != \"updated_at\" and key != 'city_id'\\\n and key != \"user_id\":\n setattr(place, key, value)\n place.save()\n place = place.to_dict()\n return jsonify(place), 200\n", "sub_path": "api/v1/views/places.py", "file_name": "places.py", "file_ext": "py", "file_size_in_byte": 2863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "models.storage.all", "line_number": 17, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 20, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 12, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 12, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 32, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 35, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 29, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 29, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 44, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 44, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 48, "usage_type": "call"}, {"api_name": "models.storage.delete", "line_number": 49, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 49, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 50, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 40, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 59, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 65, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 67, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 71, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 73, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 74, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 74, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 75, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 54, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 83, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 98, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 79, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 79, "usage_type": "name"}]} {"seq_id": "444396525", "text": "# -*- coding: utf-8 -*-\r\n# @Author: YangZhou\r\n# @Date: 2017-06-14 22:16:16\r\n# @Last Modified by: YangZhou\r\n# @Last Modified time: 2018-01-11 15:18:39\r\nfrom aces.tools import *\r\nfrom aces.graph import fig, setLegend, pl, fit\r\nimport numpy as np\r\n\r\nfrom ase import io\r\n\r\nkas = []\r\nwith fig('chi.eps', figsize=(7, 8)):\r\n markers = ['s', '8', '^', 'D', '>']\r\n colors = \"firebrick,r,b,k,g,hotpink,orange,r\".split(\r\n ',')\r\n text_style = dict(horizontalalignment='left', verticalalignment='center',\r\n fontsize=14, fontdict={'family': 'serif'})\r\n fi, axes = pl.subplots(1, 1, sharex=True, figsize=(7, 4))\r\n ax1 = axes\r\n ks = []\r\n vs = '2l1,2l2,2lhex,3l1,4l1,5l1,6l1,8l1'.split(',')\r\n for i, v in enumerate(vs):\r\n\r\n dir = \"%s/0/SHENG\" % v\r\n if v == \"2l1\":\r\n dir = \"2l1/0/sheng.1\"\r\n kappa = np.loadtxt(\r\n dir + '/T300K/BTE.cumulative_kappa_tensor') # [::3]\r\n # m = markers[i]\r\n c = colors[i]\r\n l = kappa[:, 0]\r\n atoms = io.read(v + \"/0/secondorder/POSCAR\")\r\n nlayer = int(v.split('l')[0])\r\n ss = np.linalg.norm(atoms.cell[2]) / (\r\n 3.34 * nlayer)\r\n\r\n z = kappa[:, 1] * ss\r\n a = kappa[:, 5] * ss\r\n opts = dict(\r\n # markeredgecolor='w',\r\n # marker=m,\r\n color=c,\r\n # markersize=1 + 2,\r\n lw=2,\r\n ls='-')\r\n o1 = \"-\"\r\n ka = np.abs(z - a) / np.max(np.c_[z, a], axis=1) * 100\r\n if v == '2lhex':\r\n v = '2l3'\r\n label = v\r\n ax1.semilogx(l, ka, label=label.replace('l', 'L'), **opts)\r\n ax1.set_xlabel(\"Cutoff Mean Free Path for Phonons (${ \\\\AA }$)\")\r\n # ax1.set_ylim([0, 15])\r\n ax1.set_xlim([10, 1e5])\r\n ax1.set_ylim([0, 100])\r\n ax1.set_ylabel(\"Thermal Conductivity Anisotropy (%)\")\r\n # ax1.text(-.15, 1, 'b)', transform=ax1.transAxes, **text_style)\r\n setLegend(ax1, ncol=4, loc=0, fontsize=12)\r\n fi.subplots_adjust(\r\n left=None,\r\n bottom=None,\r\n right=None,\r\n top=None,\r\n wspace=0,\r\n hspace=0)\r\n", "sub_path": "src/chi.py", "file_name": "chi.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "aces.graph.fig", "line_number": 13, "usage_type": "call"}, {"api_name": "aces.graph.pl.subplots", "line_number": 19, "usage_type": "call"}, {"api_name": "aces.graph.pl", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "ase.io.read", "line_number": 33, "usage_type": "call"}, {"api_name": "ase.io", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 48, "usage_type": "attribute"}, {"api_name": "aces.graph.setLegend", "line_number": 59, "usage_type": "call"}]} {"seq_id": "68455557", "text": "# -*- encoding:utf-8 -*-\n# Copyright (c) Alibaba, Inc. and its affiliates.\nimport logging\n\nimport tensorflow as tf\n\nfrom easy_rec.python.utils.load_class import load_by_path\n\nif tf.__version__ >= '2.0':\n tf = tf.compat.v1\n\n\nclass DNN:\n\n def __init__(self, dnn_config, l2_reg, name='dnn', is_training=False):\n \"\"\"Initializes a `DNN` Layer.\n\n Args:\n dnn_config: instance of easy_rec.python.protos.dnn_pb2.DNN\n l2_reg: l2 regularizer\n name: scope of the DNN, so that the parameters could be separated from other dnns\n is_training: train phase or not, impact batchnorm and dropout\n \"\"\"\n self._config = dnn_config\n self._l2_reg = l2_reg\n self._name = name\n self._is_training = is_training\n logging.info('dnn activation function = %s' % self._config.activation)\n self.activation = load_by_path(self._config.activation)\n\n @property\n def hidden_units(self):\n return self._config.hidden_units\n\n @property\n def dropout_ratio(self):\n return self._config.dropout_ratio\n\n def __call__(self, deep_fea, hidden_layer_feature_output=False):\n hidden_units_len = len(self.hidden_units)\n hidden_feature_dict = {}\n for i, unit in enumerate(self.hidden_units):\n deep_fea = tf.layers.dense(\n inputs=deep_fea,\n units=unit,\n kernel_regularizer=self._l2_reg,\n activation=None,\n name='%s/dnn_%d' % (self._name, i))\n if self._config.use_bn:\n deep_fea = tf.layers.batch_normalization(\n deep_fea,\n training=self._is_training,\n trainable=True,\n name='%s/dnn_%d/bn' % (self._name, i))\n deep_fea = self.activation(\n deep_fea, name='%s/dnn_%d/act' % (self._name, i))\n if len(self.dropout_ratio) > 0 and self._is_training:\n assert self.dropout_ratio[\n i] < 1, 'invalid dropout_ratio: %.3f' % self.dropout_ratio[i]\n deep_fea = tf.nn.dropout(\n deep_fea,\n keep_prob=1 - self.dropout_ratio[i],\n name='%s/%d/dropout' % (self._name, i))\n\n if hidden_layer_feature_output:\n hidden_feature_dict['hidden_layer' + str(i)] = deep_fea\n if (i + 1 == hidden_units_len):\n hidden_feature_dict['hidden_layer_end'] = deep_fea\n return hidden_feature_dict\n else:\n return deep_fea\n", "sub_path": "easy_rec/python/layers/dnn.py", "file_name": "dnn.py", "file_ext": "py", "file_size_in_byte": 2321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tensorflow.__version__", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.compat", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 28, "usage_type": "call"}, {"api_name": "easy_rec.python.utils.load_class.load_by_path", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.layers.dense", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 60, "usage_type": "attribute"}]} {"seq_id": "545620073", "text": "import discord\nfrom discord.ext import commands\nimport re\nimport json\nfrom pyNyaav2 import SearchTorrent\n\ndef setup(bot):\n\tbot.add_cog(Nyaav2(bot))\n\nwith open('conf.json') as fp:\n\tconf = json.load(fp)\nprefix = conf['prefix']\nNyaaUser = conf['nyaausername']\nNyaaPass = conf['nyaapassword']\n\ndef checkCategory(b):\n\tcategorylist = ['all', 'amv', 'anime_eng', 'anime_non-eng', 'anime_raw', 'audio_lossless', 'audio_lossy', 'books_eng', 'books_non-eng', 'books_raw', 'la_eng', 'la_idolpv', 'la_non-eng', 'la_raw', 'pics_graphics', 'pics_photos', 'sw_apps', 'sw_games']\n\tif b not in categorylist:\n\t\treturn False\n\treturn True\n\ndef searchTor(keyword, category, number):\n\tnumber = number - 1\n\tparsedQuery = []\n\tsearch = SearchTorrent(NyaaUser, NyaaPass, keyword, category, 1)\n\tfor query in search:\n\t\tNAME = query['name']\n\t\tID = query['id']\n\t\tSUBMIT = query['submitter']\n\t\tif SUBMIT is None:\n\t\t\tSUBMIT = 'Anonymous'\n\t\telse:\n\t\t\tpass\n\t\tCREADATE = query['creation']\n\t\tSIZE = query['filesize']\n\t\tCATEG = query['category'] + ' (' + query['category_id'] + ')'\n\t\tSTATS = '**Seeders**: {} || **Leechers**: {} || **Completed**: {}'.format(query['seeders'], query['leechers'], query['completed'])\n\t\tDLLINK = 'https://nyaa.si{}'.format(query['download_link'])\n\t\tMAGNET = query['magnet_link']\n\t\tTORURL = query['url']\n\t\tTORHASH = query['hash']\n\n\t\tTRUST = query['is_trusted']\n\t\tREMAKE = query['is_remake']\n\n\t\tif TRUST:\n\t\t\tcolor = '0x72ee6f'\n\t\telif REMAKE:\n\t\t\tcolor = '0xf16b6f'\n\t\telse:\n\t\t\tcolor = '0x73e1ee'\n\n\t\ttemptext = {'name': NAME, 'id': ID, 'size': SIZE, 'submitter': SUBMIT, 'category': CATEG, 'stats': STATS, 'DL': DLLINK, 'magnet': MAGNET, 'URL': TORURL, 'hash': TORHASH, 'date': CREADATE, 'tortype': color, 'dataLength': str(len(search))}\n\t\tparsedQuery.append(temptext)\n\n\treturn parsedQuery[number]\n\nclass Nyaav2:\n\tdef __init__(self, bot):\n\t\tself.bot = bot\n\t\n\tdef __local_check(self, ctx):\n\t\tif not ctx.guild:\n\t\t\traise commands.NoPrivateMessage('This command cannot be used in a private message.')\n\t\treturn True\n\t\t\n\tasync def __error(self, ctx, error):\n\t\tif not isinstance(error, commands.UserInputError):\n\t\t\traise error\n\t\t\n\t\ttry:\n\t\t\tawait ctx.send(error)\n\t\texcept discord.Forbidden:\n\t\t\tpass\n\n\t@commands.command(pass_context=True)\n\tasync def nyaacategory(self, ctx):\n\t\tlistcategory = {'all': 'All category', 'amv': 'Anime music video', 'anime_eng': 'Anime - English-translated', 'anime_non-eng': 'Anime Non-english-translated', 'anime_raw': 'Anime - Raw', 'audio_lossless': 'Audio - Lossless', 'audio_lossy': 'Audio - Lossy', 'books_eng': 'Literature - English', 'books_non-eng': 'Literature - Non-english', 'books_raw': 'Literature - Raw', 'la_eng': 'Live action - English', 'la_idolpv': 'Live action - Idol/PV', 'la_non-eng': 'Live action - Non-english', 'la_raw': 'Live action - Raw', 'pics_graphics': 'Pictures - Graphics', 'pics_photos': 'Pictures - Photos', 'sw_apps': 'Software - Apps', 'sw_games': 'Software - Games'}\n\t\ttext = '**Nyaa v2 Category List**\\n'\n\t\tfor kc, vc in listcategory:\n\t\t\ttemptext = '- **{}** == {}'.format(kc, vc)\n\t\t\ttext += temptext\n\t\t\n\t\tm = await self.bot.say(text)\n\n\t\tdef checkReaction(reaction, user):\n\t\t\te = str(reaction.emoji)\n\t\t\treturn e.startswith('✅')\n\t\tawait self.bot.add_reaction(m, '✅')\n\t\t\n\t\tmm = await self.bot.wait_for_reaction(message=m, user=ctx.message.author, timeout=10, check=checkReaction)\n\t\tif mm is None:\n\t\t\tawait self.bot.delete_message(ctx.message)\n\t\t\tawait self.bot.delete_message(m)\n\t\telif '✅' in str(mm.reaction.emoji):\n\t\t\tawait self.bot.delete_message(ctx.message)\n\t\t\tawait self.bot.delete_message(m)\n\n\t@commands.command(pass_context=True, aliases=['nyaav2'])\n\tasync def nyaasearch(self, ctx, category, *, keyword):\n\t\t\"\"\"Search nyaa torrent\"\"\"\n\t\tcheck = checkCategory(category)\n\t\tif not check:\n\t\t\tawait self.bot.say('Category doesn\\'t exist, please refer to {}nyaacategory'.format(prefix))\n\t\t\traise ValueError('nyaav2: category are not exist')\n\t\telse:\n\t\t\tpass\n\t\t\n\t\tinit = searchTor(keyword, category, 1)\n\n\t\tmaxPage = int(init['dataLength'])\n\t\tfirstRun = True\n\t\twhile True:\n\t\t\tif firstRun:\n\t\t\t\tfirstRun = False\n\t\t\t\tnum = 1\n\t\t\t\t#temptext = {'name': NAME, 'id': ID, 'size': SIZE, 'submitter': SUBMIT, 'category': CATEG, 'stats': STATS, 'DL': DLLINK, 'magnet': MAGNET, 'URL': TORURL, 'hash': TORHASH, 'date': CREADATE, 'tortype': color, 'dataLength': str(len(search))}\n\t\t\t\tfind = searchTor(keyword, category, 1)\n\t\t\t\tconstrctInfo = '**ID**: {}\\n**Name**: {}\\n**Size**: {}\\n**Category**: {}\\n**Statistics**: {}\\n**Submitted by: {}'.format(find['id'], find['name'], find['size'], find['category'], find['stats'], find['submitter'])\n\t\t\t\tconstrctDownload = '📥 || [URL]({}) || [Download]({}) || [Magnet]({})'.format(find['URL'], find['DL'], find['magnet'])\n\t\t\t\tconstrctFoot = '⏲️ || Posted at {} || {}'.format(find['date'], find['hash'])\n\t\t\t\tembed=discord.Embed(title=\"Nyaa v2 Search\", url='https://nyaa.si', color=int(find['tortype']))\n\t\t\t\tembed.set_thumbnail(url=\"https://i2.wp.com/nyaa.si/static/img/avatar/default.png?ssl=1\")\n\t\t\t\tembed.add_field(name='Information', value=constrctInfo, inline=False)\n\t\t\t\tembed.add_field(name='Link', value=constrctDownload, inline=False)\n\t\t\t\tembed.set_footer(text=constrctFoot)\n\t\t\t\tmsg = await self.bot.say(embed=embed)\n\n\t\t\tif maxPage == 1 and num == 1:\n\t\t\t\ttoReact = ['✅']\n\t\t\telif num == 1:\n\t\t\t\ttoReact = ['⏩', '✅']\n\t\t\telif num == maxPage:\n\t\t\t\ttoReact = ['⏪', '✅']\n\t\t\telif num > 1 and num < maxPage:\n\t\t\t\ttoReact = ['⏪', '⏩', '✅']\n\t\t\tfor reaction in toReact:\n\t\t\t\tawait self.bot.add_reaction(msg, reaction)\n\t\t\t#feel free to change ✅ to 🆗 or the opposite\n\t\t\tdef checkReaction(reaction, user):\n\t\t\t\te = str(reaction.emoji)\n\t\t\t\treturn e.startswith(('⏪', '⏩', '✅'))\n\n\t\t\tres = await self.bot.wait_for_reaction(message=msg, user=ctx.message.author, timeout=10, check=checkReaction)\n\t\t\tif res is None:\n\t\t\t\tawait self.bot.delete_message(ctx.message)\n\t\t\t\tawait self.bot.delete_message(msg)\n\t\t\t\tbreak\n\t\t\telif '⏪' in str(res.reaction.emoji):\n\t\t\t\tnum = num - 1\n\t\t\t\tfind = searchTor(keyword, category, 1)\n\t\t\t\tconstrctInfo = '**ID**: {}\\n**Name**: {}\\n**Size**: {}\\n**Category**: {}\\n**Statistics**: {}\\n**Submitted by: {}'.format(find['id'], find['name'], find['size'], find['category'], find['stats'], find['submitter'])\n\t\t\t\tconstrctDownload = '📥 || [URL]({}) || [Download]({}) || [Magnet]({})'.format(find['URL'], find['DL'], find['magnet'])\n\t\t\t\tconstrctFoot = '⏲️ || Posted at {} || {}'.format(find['date'], find['hash'])\n\t\t\t\tembed=discord.Embed(title=\"Nyaa v2 Search\", url='https://nyaa.si', color=int(find['tortype']))\n\t\t\t\tembed.set_thumbnail(url=\"https://i2.wp.com/nyaa.si/static/img/avatar/default.png?ssl=1\")\n\t\t\t\tembed.add_field(name='Information', value=constrctInfo, inline=False)\n\t\t\t\tembed.add_field(name='Link', value=constrctDownload, inline=False)\n\t\t\t\tembed.set_footer(text=constrctFoot)\n\t\t\t\tawait self.bot.clear_reactions(msg)\n\t\t\t\tmsg = await self.bot.edit_message(msg, embed=embed)\n\t\t\telif '⏩' in str(res.reaction.emoji):\n\t\t\t\tnum = num + 1\n\t\t\t\tfind = searchTor(keyword, category, 1)\n\t\t\t\tconstrctInfo = '**ID**: {}\\n**Name**: {}\\n**Size**: {}\\n**Category**: {}\\n**Statistics**: {}\\n**Submitted by: {}'.format(find['id'], find['name'], find['size'], find['category'], find['stats'], find['submitter'])\n\t\t\t\tconstrctDownload = '📥 || [URL]({}) || [Download]({}) || [Magnet]({})'.format(find['URL'], find['DL'], find['magnet'])\n\t\t\t\tconstrctFoot = '⏲️ || Posted at {} || {}'.format(find['date'], find['hash'])\n\t\t\t\tembed=discord.Embed(title=\"Nyaa v2 Search\", url='https://nyaa.si', color=int(find['tortype']))\n\t\t\t\tembed.set_thumbnail(url=\"https://i2.wp.com/nyaa.si/static/img/avatar/default.png?ssl=1\")\n\t\t\t\tembed.add_field(name='Information', value=constrctInfo, inline=False)\n\t\t\t\tembed.add_field(name='Link', value=constrctDownload, inline=False)\n\t\t\t\tembed.set_footer(text=constrctFoot)\n\t\t\t\tawait self.bot.clear_reactions(msg)\n\t\t\t\tmsg = await self.bot.edit_message(msg, embed=embed)\n\t\t\telif '✅' in str(res.reaction.emoji):\n\t\t\t\tawait self.bot.delete_message(ctx.message)\n\t\t\t\tawait self.bot.delete_message(msg)\n\t\t\t\tbreak\n\t\n\t@nyaasearch.error\n\tasync def nyaasearch_handler(self, error, ctx):\n\t\tif isinstance(error, commands.MissingRequiredArgument):\n\t\t\tawait self.bot.say(\"Argumen yang benar adalah:\\n**{}nyaasearch <category> <keyword>**\\nUntuk melihat kategori: {}nyaacategory\".format(prefix, prefix))\n\t\tif isinstance(error, Exception):\n\t\t\tawait self.bot.say(\"Terjadi kesalahan di bagian bot, tolong perbaiki dan restart bot\")\n\t\tprint(error)", "sub_path": "nyaav2.py", "file_name": "nyaav2.py", "file_ext": "py", "file_size_in_byte": 8466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pyNyaav2.SearchTorrent", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.ext.commands.NoPrivateMessage", "line_number": 64, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 64, "usage_type": "name"}, {"api_name": "discord.ext.commands.UserInputError", "line_number": 68, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 68, "usage_type": "name"}, {"api_name": "discord.Forbidden", "line_number": 73, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 76, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 122, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 155, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 168, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 99, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 99, "usage_type": "name"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 182, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 182, "usage_type": "name"}]} {"seq_id": "367551994", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='WorkUnit',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('user', models.CharField(max_length=50)),\n ('project', models.CharField(max_length=50)),\n ('name', models.CharField(max_length=50)),\n ('start', models.DateTimeField(verbose_name=b'start time')),\n ('end', models.DateTimeField(verbose_name=b'end time')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AlterUniqueTogether(\n name='workunit',\n unique_together=set([('user', 'start', 'end')]),\n ),\n ]\n", "sub_path": "timesheet/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}]} {"seq_id": "469696833", "text": "import json\nimport subprocess\n\nfrom flask_restful import Resource, reqparse\n\n\nclass Info(Resource):\n\n def post(self):\n parser = reqparse.RequestParser()\n parser.add_argument('url', type(str))\n data = parser.parse_args()\n\n url = data['url']\n\n info = subprocess.check_output([\"you-get\", url, \"--json\"])\n\n return {\n 'info': json.loads(info)\n }, 200\n", "sub_path": "api/resources/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask_restful.Resource", "line_number": 7, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 10, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 16, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}]} {"seq_id": "135012154", "text": "import io\nimport datetime\nfrom decimal import Decimal\n\nfrom django.core import management\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom cinnabar.test_helper import TestHelper\n\nfrom .models import Connection\nfrom .models import Project\nfrom .models import DataAcquisition\nfrom .models import RedmineUser\nfrom .models import ProjectVersion\nfrom .models import CustomField\nfrom .models import IssueTracker\nfrom .models import IssueStatus\nfrom .models import IssuePriority\nfrom .models import Issue\nfrom .models import IssueCustomField\nfrom .models import TimeEntryActivity\nfrom .models import TimeEntry\n\nfrom .forms import ConnectionFormSet\nfrom .forms import ProjectFormSet\n\n\nclass TestsCmdRedmineAcquireData(TestCase):\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n TestHelper.acquire_projects()\n TestHelper.enable_all_projects()\n TestHelper.acquire_data()\n\n def setUp(self):\n self.TEST_REDMINE_USER = {\n 'redmine_id': 1,\n 'login_name': 'testuser',\n 'first_name': 'UserName',\n 'last_name': 'LastName',\n }\n self.TEST_CUSTOM_FIELDS = [\n {\n 'redmine_id': 1,\n 'name': '備考',\n 'customized_type': 'issue',\n 'field_format': 'string',\n 'multiple': False,\n },\n {\n 'redmine_id': 2,\n 'name': '費用',\n 'customized_type': 'issue',\n 'field_format': 'int',\n 'multiple': False,\n },\n {\n 'redmine_id': 3,\n 'name': 'ポイント',\n 'customized_type': 'issue',\n 'field_format': 'float',\n 'multiple': False,\n },\n {\n 'redmine_id': 4,\n 'name': '関連バージョン',\n 'customized_type': 'issue',\n 'field_format': 'version',\n 'multiple': True,\n },\n {\n 'redmine_id': 5,\n 'name': '備考',\n 'customized_type': 'project',\n 'field_format': 'string',\n 'multiple': False,\n },\n ]\n self.TEST_ISSUE_TRACKER = {\n 'redmine_id': 2,\n 'name': 'Feature',\n }\n self.TEST_ISSUE_STATUS = {\n 'redmine_id': 5,\n 'name': 'Closed',\n 'is_closed': True,\n }\n self.TEST_ISSUE_PRIORITY = {\n 'redmine_id': 3,\n 'name': 'High',\n }\n self.TEST_PROJECT_VERSION = {\n 'redmine_id': 1,\n 'name': '2017年3Q',\n }\n self.TEST_ISSUES = [\n {\n 'redmine_id': 1,\n 'project__redmine_id': 1,\n 'tracker__redmine_id': 2,\n 'status__redmine_id': 1,\n 'priority__redmine_id': 2,\n 'parent_id': None,\n 'assigned_user__redmine_id': 1,\n 'version__redmine_id': 1,\n 'name': '材料を用意する',\n 'due_date': None,\n 'done_ratio': 17,\n 'estimated_hours': None,\n },\n {\n 'redmine_id': 2,\n 'project__redmine_id': 1,\n 'tracker__redmine_id': 2,\n 'status__redmine_id': 5,\n 'priority__redmine_id': 2,\n 'parent_id': 1,\n 'assigned_user__redmine_id': None,\n 'version__redmine_id': None,\n 'name': '冷蔵庫の中身を確認する',\n 'due_date': None,\n 'done_ratio': 100,\n 'estimated_hours': Decimal('0.11'),\n },\n {\n 'redmine_id': 3,\n 'project__redmine_id': 1,\n 'tracker__redmine_id': 2,\n 'status__redmine_id': 2,\n 'priority__redmine_id': 2,\n 'parent_id': 1,\n 'assigned_user__redmine_id': None,\n 'version__redmine_id': None,\n 'name': 'レシピを確認する',\n 'due_date': None,\n 'done_ratio': 50,\n 'estimated_hours': Decimal('0.25'),\n },\n ]\n self.TEST_ISSUE_CUSTOM_FIELDS = [\n {\n 'issue__redmine_id': 4,\n 'custom_field__redmine_id': 1,\n 'value_text': 'ついでに牛乳も買ってくる',\n },\n {\n 'issue__redmine_id': 4,\n 'custom_field__redmine_id': 2,\n 'value_text': '1000',\n },\n {\n 'issue__redmine_id': 4,\n 'custom_field__redmine_id': 3,\n 'value_text': '0.123456789',\n },\n ]\n self.TEST_TIME_ENTRY_ACTIVITIES = [\n {\n 'redmine_id': 8,\n 'name': 'Design',\n },\n {\n 'redmine_id': 9,\n 'name': 'Development',\n },\n ]\n self.TEST_TIME_ENTRIES = [\n {\n 'redmine_id': 1,\n 'project__name': '肉じゃが',\n 'issue_is_none': False,\n 'issue__redmine_id': 2,\n 'activity__redmine_id': 9,\n 'user__redmine_id': 1,\n 'hours': Decimal('0.05'),\n },\n {\n 'redmine_id': 2,\n 'project__name': '肉じゃが',\n 'issue_is_none': True,\n 'issue__redmine_id': None,\n 'activity__redmine_id': 9,\n 'user__redmine_id': 1,\n 'hours': Decimal('1.23'),\n },\n\n ]\n\n self.today = datetime.date.today()\n\n def test_acquisition(self):\n # Check DataAcquisition\n today_da = DataAcquisition.objects.filter(\n date=datetime.date.today())\n self.assertEqual(today_da.count(), 1)\n\n def test_acquisition_of_redmine_user(self):\n # Check RedmineUser\n for test_cnct in TestHelper.CONNECTIONS:\n testuser = RedmineUser.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=self.TEST_REDMINE_USER['redmine_id'])\n self.assertEqual(\n testuser.login_name,\n self.TEST_REDMINE_USER['login_name'])\n self.assertEqual(\n testuser.first_name,\n self.TEST_REDMINE_USER['first_name'])\n self.assertEqual(\n testuser.last_name,\n self.TEST_REDMINE_USER['last_name'])\n\n def test_acquisition_of_custom_field(self):\n # Check CustomField\n for test_cnct in TestHelper.CONNECTIONS:\n for test_cf in self.TEST_CUSTOM_FIELDS:\n cf = CustomField.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=test_cf['redmine_id'])\n self.assertEqual(\n cf.name,\n test_cf['name'])\n self.assertEqual(\n cf.customized_type,\n test_cf['customized_type'])\n self.assertEqual(\n cf.field_format,\n test_cf['field_format'])\n self.assertEqual(\n cf.multiple,\n test_cf['multiple'])\n\n def test_acquisition_of_issue_tracker(self):\n # Check IssueTracker\n for test_cnct in TestHelper.CONNECTIONS:\n issue_tracker = IssueTracker.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=self.TEST_ISSUE_TRACKER['redmine_id'])\n self.assertEqual(\n issue_tracker.name,\n self.TEST_ISSUE_TRACKER['name'])\n\n def test_acquisition_of_issue_status(self):\n # Check IssueStatus\n for test_cnct in TestHelper.CONNECTIONS:\n issue_status = IssueStatus.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=self.TEST_ISSUE_STATUS['redmine_id'])\n self.assertEqual(\n issue_status.name,\n self.TEST_ISSUE_STATUS['name'])\n\n def test_acquisition_of_issue_priority(self):\n # Check IssuePriority\n for test_cnct in TestHelper.CONNECTIONS:\n issue_priority = IssuePriority.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=self.TEST_ISSUE_PRIORITY['redmine_id'])\n self.assertEqual(\n issue_priority.name,\n self.TEST_ISSUE_PRIORITY['name'])\n\n def test_acquisition_of_project_version(self):\n # Check Project Version\n for test_cnct in TestHelper.CONNECTIONS:\n project_version = ProjectVersion.objects.get(\n project__connection__name=test_cnct['name'],\n project__redmine_id=TestHelper.SUB_PROJECT['redmine_id'],\n redmine_id=self.TEST_PROJECT_VERSION['redmine_id'])\n self.assertEqual(\n project_version.name,\n self.TEST_PROJECT_VERSION['name'])\n\n def test_acquisition_of_issue(self):\n # Check Issue\n for test_cnct in TestHelper.CONNECTIONS:\n for test_issue in self.TEST_ISSUES:\n issue = Issue.objects.get(\n project__connection__name=test_cnct['name'],\n project__redmine_id=test_issue['project__redmine_id'],\n redmine_id=test_issue['redmine_id'])\n self.assertEqual(\n issue.tracker.redmine_id,\n test_issue['tracker__redmine_id'])\n self.assertEqual(\n issue.status.redmine_id,\n test_issue['status__redmine_id'])\n self.assertEqual(\n issue.priority.redmine_id,\n test_issue['priority__redmine_id'])\n self.assertEqual(\n issue.parent_id,\n test_issue['parent_id'])\n\n if issue.assigned_user is None:\n assigned_user__redmine_id = None\n else:\n assigned_user__redmine_id = issue.assigned_user.redmine_id\n self.assertEqual(\n assigned_user__redmine_id,\n test_issue['assigned_user__redmine_id'])\n\n if issue.version is None:\n issue_version__redmine_id = None\n else:\n issue_version__redmine_id = issue.version.redmine_id\n self.assertEqual(\n issue_version__redmine_id,\n test_issue['version__redmine_id'])\n\n self.assertEqual(\n issue.name,\n test_issue['name'])\n self.assertEqual(\n issue.due_date,\n test_issue['due_date'])\n self.assertEqual(\n issue.done_ratio,\n test_issue['done_ratio'])\n self.assertEqual(\n issue.estimated_hours,\n test_issue['estimated_hours'])\n\n def test_acquisition_of_issue_custom_field(self):\n # Check IssueCustomField\n for test_cnct in TestHelper.CONNECTIONS:\n for test_icf in self.TEST_ISSUE_CUSTOM_FIELDS:\n icf = IssueCustomField.objects.get(\n custom_field__connection__name=test_cnct['name'],\n issue__redmine_id=test_icf['issue__redmine_id'],\n custom_field__redmine_id=test_icf['custom_field__redmine_id'])\n self.assertEqual(icf.value_text, test_icf['value_text'])\n\n def test_acquisition_of_time_entry_activities(self):\n # Check TimeEntryActivity\n for test_cnct in TestHelper.CONNECTIONS:\n for test_tea in self.TEST_TIME_ENTRY_ACTIVITIES:\n tea = TimeEntryActivity.objects.get(\n connection__name=test_cnct['name'],\n redmine_id=test_tea['redmine_id'])\n self.assertEqual(tea.name, test_tea['name'])\n\n def test_acquisition_of_time_entries(self):\n # Check TimeEntry\n for test_cnct in TestHelper.CONNECTIONS:\n for test_te in self.TEST_TIME_ENTRIES:\n te = TimeEntry.objects.get(\n project__connection__name=test_cnct['name'],\n redmine_id=test_te['redmine_id'])\n self.assertEqual(te.project.name, test_te['project__name'])\n if test_te['issue_is_none']:\n self.assertEqual(te.issue, None)\n else:\n self.assertEqual(te.issue.redmine_id, test_te['issue__redmine_id'])\n self.assertEqual(te.user.redmine_id, test_te['user__redmine_id'])\n self.assertEqual(te.activity.redmine_id, test_te['activity__redmine_id'])\n self.assertEqual(te.hours, test_te['hours'])\n\n def test_call_twice(self):\n sio = io.StringIO()\n management.call_command('redmine_acquire_data', stdout=sio)\n self.assertIn(\n 'Succeess:redmine_acquire_data', sio.getvalue())\n sio = io.StringIO()\n management.call_command('redmine_acquire_data', stdout=sio)\n self.assertIn(\n 'Succeess:redmine_acquire_data', sio.getvalue())\n # Check DataAcquisition\n today_da = DataAcquisition.objects.filter(\n date=datetime.date.today())\n self.assertEqual(today_da.count(), 1)\n", "sub_path": "src/django/redmine/tests_cmd_redmine_acquire_data.py", "file_name": "tests_cmd_redmine_acquire_data.py", "file_ext": "py", "file_size_in_byte": 14669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.test.TestCase", "line_number": 29, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.acquire_projects", "line_number": 34, "usage_type": "call"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 34, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.enable_all_projects", "line_number": 35, "usage_type": "call"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 35, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.acquire_data", "line_number": 36, "usage_type": "call"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 36, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 126, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 140, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 178, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 187, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 192, "usage_type": "attribute"}, {"api_name": "models.DataAcquisition.objects.filter", "line_number": 196, "usage_type": "call"}, {"api_name": "models.DataAcquisition.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.DataAcquisition", "line_number": 196, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 197, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 202, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 202, "usage_type": "name"}, {"api_name": "models.RedmineUser.objects.get", "line_number": 203, "usage_type": "call"}, {"api_name": "models.RedmineUser.objects", "line_number": 203, "usage_type": "attribute"}, {"api_name": "models.RedmineUser", "line_number": 203, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 218, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 218, "usage_type": "name"}, {"api_name": "models.CustomField.objects.get", "line_number": 220, "usage_type": "call"}, {"api_name": "models.CustomField.objects", "line_number": 220, "usage_type": "attribute"}, {"api_name": "models.CustomField", "line_number": 220, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 238, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 238, "usage_type": "name"}, {"api_name": "models.IssueTracker.objects.get", "line_number": 239, "usage_type": "call"}, {"api_name": "models.IssueTracker.objects", "line_number": 239, "usage_type": "attribute"}, {"api_name": "models.IssueTracker", "line_number": 239, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 248, "usage_type": "name"}, {"api_name": "models.IssueStatus.objects.get", "line_number": 249, "usage_type": "call"}, {"api_name": "models.IssueStatus.objects", "line_number": 249, "usage_type": "attribute"}, {"api_name": "models.IssueStatus", "line_number": 249, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 258, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 258, "usage_type": "name"}, {"api_name": "models.IssuePriority.objects.get", "line_number": 259, "usage_type": "call"}, {"api_name": "models.IssuePriority.objects", "line_number": 259, "usage_type": "attribute"}, {"api_name": "models.IssuePriority", "line_number": 259, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 268, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 268, "usage_type": "name"}, {"api_name": "models.ProjectVersion.objects.get", "line_number": 269, "usage_type": "call"}, {"api_name": "models.ProjectVersion.objects", "line_number": 269, "usage_type": "attribute"}, {"api_name": "models.ProjectVersion", "line_number": 269, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.SUB_PROJECT", "line_number": 271, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 271, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 279, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 279, "usage_type": "name"}, {"api_name": "models.Issue.objects.get", "line_number": 281, "usage_type": "call"}, {"api_name": "models.Issue.objects", "line_number": 281, "usage_type": "attribute"}, {"api_name": "models.Issue", "line_number": 281, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 329, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 329, "usage_type": "name"}, {"api_name": "models.IssueCustomField.objects.get", "line_number": 331, "usage_type": "call"}, {"api_name": "models.IssueCustomField.objects", "line_number": 331, "usage_type": "attribute"}, {"api_name": "models.IssueCustomField", "line_number": 331, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 339, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 339, "usage_type": "name"}, {"api_name": "models.TimeEntryActivity.objects.get", "line_number": 341, "usage_type": "call"}, {"api_name": "models.TimeEntryActivity.objects", "line_number": 341, "usage_type": "attribute"}, {"api_name": "models.TimeEntryActivity", "line_number": 341, "usage_type": "name"}, {"api_name": "cinnabar.test_helper.TestHelper.CONNECTIONS", "line_number": 348, "usage_type": "attribute"}, {"api_name": "cinnabar.test_helper.TestHelper", "line_number": 348, "usage_type": "name"}, {"api_name": "models.TimeEntry.objects.get", "line_number": 350, "usage_type": "call"}, {"api_name": "models.TimeEntry.objects", "line_number": 350, "usage_type": "attribute"}, {"api_name": "models.TimeEntry", "line_number": 350, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 363, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 364, "usage_type": "call"}, {"api_name": "django.core.management", "line_number": 364, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 367, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 368, "usage_type": "call"}, {"api_name": "django.core.management", "line_number": 368, "usage_type": "name"}, {"api_name": "models.DataAcquisition.objects.filter", "line_number": 372, "usage_type": "call"}, {"api_name": "models.DataAcquisition.objects", "line_number": 372, "usage_type": "attribute"}, {"api_name": "models.DataAcquisition", "line_number": 372, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 373, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 373, "usage_type": "attribute"}]} {"seq_id": "425277627", "text": "from django.urls import path\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n\tpath('',views.index,name='index'),\r\n\tpath('list/',views.blog_list,name='blog_list'),\r\n\tpath('item/<int:blog_id>',views.blog_item,name='blog_item'),\r\n\tpath('post/',views.blog_post,name='blog_post'),\r\n\tpath('post/submit/',views.blog_post_submit,name='blog_post_submit'),\r\n\tpath('edit/<int:blog_id>',views.blog_edit,name='blog_edit'),\r\n\tpath('update/<int:blog_id>',views.blog_update,name='blog_update'),\r\n\tpath('delete/<int:blog_id>',views.blog_delete,name='blog_delete'),\r\n]", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} {"seq_id": "257792100", "text": "import json,requests\nimport sys\n\nfrom flask import Flask, request, render_template\nsys.path.append(sys.argv[0] + '../')\nfrom flask_run import WXBizDataCrypt\n\napp = Flask(__name__)\n\n@app.route('/login', mothod=[\"GET\",'POST'])\ndef login():\n if request.method == 'GET':\n return render_template('login/login.html') # 模板名，关键字传参\n elif request.method == 'POST':\n pass\n \n\n@app.route('/wxlogin', method=['POST'])\ndef wxlogin():\n '''微信登录'''\n data = json.loads(request.get_data().decode('utf-8')) # 将前端Json数据转为字典\n appID = 'appID' # 开发者关于微信小程序的appID\n appSecret = 'appSecret' # 开发者关于微信小程序的appSecret\n code = data['platCode'] # 前端POST过来的微信临时登录凭证code\n encryptedData = data['platUserInfoMap']['encryptedData']\n iv = data['platUserInfoMap']['iv']\n req_params = {\n 'appid': appID,\n 'secret': appSecret,\n 'js_code': code,\n 'grant_type': 'authorization_code'\n }\n wx_login_api = 'https://api.weixin.qq.com/sns/jscode2session'\n response_data = requests.get(wx_login_api, params=req_params) # 向API发起GET请求\n resData = response_data.json()\n openid = resData ['openid'] # 得到用户关于当前小程序的OpenID\n session_key = resData ['session_key'] # 得到用户关于当前小程序的会话密钥session_key\n\n pc = WXBizDataCrypt(appID, session_key) #对用户信息进行解密\n userinfo = pc.decrypt(encryptedData, iv) #获得用户信息\n print(userinfo)\n\n '''下面部分是通过判断数据库中用户是否存在来确定添加或返回自定义登录态（若用户不存在则添加；若用户存在，返回用户信息）'''\n\n return json.dumps({\"code\": 200, \"msg\": \"登录成功\", \"userinfo\": userinfo}, indent=4, sort_keys=True, default=str, ensure_ascii=False)\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=8888, debug=True)\n", "sub_path": "ServerFlask/flask_run.py", "file_name": "flask_run.py", "file_ext": "py", "file_size_in_byte": 1972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "flask_run.WXBizDataCrypt", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 45, "usage_type": "call"}]} {"seq_id": "487220103", "text": "import torch\nimport torch_geometric\nimport networkx as nx\nimport random\n\nTorchNodes = torch.LongTensor\nTorchEdges = torch.LongTensor\nTorchMappings = torch.LongTensor\nTorchEdgeMask = torch.BoolTensor\n\n\ndef augment(s_1: TorchEdges, s_1_nodes: TorchNodes, s_2: TorchEdges, s_2_nodes: TorchNodes, G_noisy: nx.Graph, t: set((\"src\", \"tar\"))) -> (TorchEdges, TorchEdges, list((\"src\", \"tar\")), list((\"src\", \"tar\"))):\n d1 = torch_geometric.data.Data(edge_index=s_1)\n d1.num_nodes = 0\n d2 = torch_geometric.data.Data(edge_index=s_2)\n d2.num_nodes = 0\n s_1_nx = torch_geometric.utils.to_networkx(d1, to_undirected=False)\n s_2_nx = torch_geometric.utils.to_networkx(d2, to_undirected=False)\n s_12_nx = G_noisy.subgraph(list(s_1_nx.nodes) + list(s_2_nx.nodes)).copy()\n\n y_s_12 = list(filter(lambda x: s_12_nx.has_edge(x[0], x[1]), t))\n\n y_s_12_sample = []\n if len(y_s_12) != 0:\n y_s_12_sample = random.sample(y_s_12, random.randint(\n 0, max(1, min(len(y_s_12) - 1, min((s_1[0].size(0) // 2) - 1, (s_2[0].size(0) // 2) - 1)))))\n\n if (len(s_1_nx.edges)) > 2:\n s_1_nx.remove_edges_from(y_s_12_sample)\n s_1_nx.remove_edges_from(list(map(swap, y_s_12_sample)))\n if (len(s_2_nx.edges)) > 2:\n s_2_nx.remove_edges_from(y_s_12_sample)\n s_2_nx.remove_edges_from(list(map(swap, y_s_12_sample)))\n\n y_neg = [(random.choice(list(s_1_nx.nodes) + list(s_2_nx.nodes)), random.randint(\n len(G_noisy.nodes) + 1, len(G_noisy.nodes) * 2) - 1) for _ in range(len(y_s_12_sample))]\n\n y_neg_s_1 = list(filter(lambda x: x[0] in s_1_nx.nodes, y_neg))\n y_neg_s_2 = list(filter(lambda x: x[0] in s_2_nx.nodes, y_neg))\n s_1_nx.add_edges_from(y_neg_s_1)\n s_1_nx.add_edges_from(list(map(swap, y_neg_s_1)))\n s_2_nx.add_edges_from(y_neg_s_2)\n s_2_nx.add_edges_from(list(map(swap, y_neg_s_2)))\n\n return torch.LongTensor(list(s_1_nx.edges)).t().contiguous(), torch.LongTensor(list(s_2_nx.edges)).t().contiguous(), y_s_12, y_s_12_sample, y_neg\n\n\ndef swap(x):\n return (x[1], x[0])\n", "sub_path": "utils/augment.py", "file_name": "augment.py", "file_ext": "py", "file_size_in_byte": 2048, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torch.LongTensor", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.BoolTensor", "line_number": 9, "usage_type": "attribute"}, {"api_name": "networkx.Graph", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch_geometric.data.Data", "line_number": 13, "usage_type": "call"}, {"api_name": "torch_geometric.data", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch_geometric.data.Data", "line_number": 15, "usage_type": "call"}, {"api_name": "torch_geometric.data", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch_geometric.utils.to_networkx", "line_number": 17, "usage_type": "call"}, {"api_name": "torch_geometric.utils", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch_geometric.utils.to_networkx", "line_number": 18, "usage_type": "call"}, {"api_name": "torch_geometric.utils", "line_number": 18, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 25, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 45, "usage_type": "call"}]} {"seq_id": "142679335", "text": "import pygame as pg\nimport random\nfrom utils import LIST_DICTIONARY, COLOR_LINES, ORANGE, PURPLE, FONT\n\nclass GuessWord:\n def __init__(self):\n self.guess_word = \"\"\n self.x = 150\n self.w = 190\n self.new_x = 40\n self.new_y = 50\n self.space = 10\n self.y, self.h, = 130, 130\n self.color = COLOR_LINES\n self.rect = []\n self.txt_surface = FONT.render('', True, self.color)\n self.txt_surf_win = FONT.render('You won!', True, ORANGE)\n self.rect_win = pg.Rect(self.x + 150, self.y + 60, self.w - 50, self.h - 80)\n\n def random_word(self):\n rand_num = random.randint(0, len(LIST_DICTIONARY) - 1)\n self.guess_word = LIST_DICTIONARY[rand_num]\n return self.guess_word\n\n def check_in(self, letter, print_word):\n for i in range(len(self.guess_word)):\n if self.guess_word[i] == letter:\n print_word[i] = letter\n return print_word\n\n def draw_lines(self, screen, print_word):\n x = self.x\n w = self.w\n new_x = self.new_x\n space = self.space\n y, h = self.y, self.h\n txt = [\" \" if not i else i for i in print_word]\n for i in range(len(self.guess_word)):\n self.rect.append(pg.Rect(x, y-self.new_y, new_x, new_x))\n self.txt_surface = FONT.render(txt[i], True, self.color)\n screen.blit(self.txt_surface, (self.rect[i].x + 5, self.rect[i].y-5))\n pg.draw.line(\n screen, COLOR_LINES, (x, y), (w, h), 5)\n pg.draw.rect(screen, PURPLE, self.rect[i], 2)\n x = x + new_x + space\n w = w + new_x + space\n if all([' ' != i for i in txt]):\n screen.blit(self.txt_surf_win, (self.rect_win.x + 5, self.rect_win.y - 5))\n pg.draw.rect(screen, PURPLE, self.rect_win, 2)\n\n\n", "sub_path": "project2/guess_word.py", "file_name": "guess_word.py", "file_ext": "py", "file_size_in_byte": 1854, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "utils.COLOR_LINES", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.FONT.render", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.FONT", "line_number": 16, "usage_type": "name"}, {"api_name": "utils.FONT.render", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.ORANGE", "line_number": 17, "usage_type": "argument"}, {"api_name": "utils.FONT", "line_number": 17, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 18, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.LIST_DICTIONARY", "line_number": 21, "usage_type": "argument"}, {"api_name": "utils.LIST_DICTIONARY", "line_number": 22, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.FONT.render", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.FONT", "line_number": 40, "usage_type": "name"}, {"api_name": "pygame.draw.line", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.COLOR_LINES", "line_number": 43, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.PURPLE", "line_number": 44, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.PURPLE", "line_number": 49, "usage_type": "argument"}, {"api_name": "pygame.draw", "line_number": 49, "usage_type": "attribute"}]} {"seq_id": "275233712", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'index', views.index, name='index'),\n url(r'create', views.create_role, name='create'),\n url(r'update', views.update_role, name='update'),\n url(r'delete', views.delete_role, name='delete'),\n url(r'assign', views.assign_role, name='assign'),\n url(r'deassign', views.deassign_role, name='deassign'),\n]\n", "sub_path": "roles/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}]} {"seq_id": "340983426", "text": "# -*- coding: utf-8 -*-\nimport os, sys, cgi, time, types, exif\n\n\nPATH = u\"/var/www/public/fotos\"\nURL = u\"https://example.com/fotos\"\nTEMPLATE = open(os.path.join(os.path.dirname(__file__), \"template.html\")).read().decode(\"utf8\")\nNPERPAGE = 10\n\nprint >> sys.stderr, \"booted\"\n\n\nlisting, ts = None, 0\n\ndef application(env, start_resp):\n global listing, ts\n\n\n def ret_badrequest():\n text = \"400 Bad Request\"\n start_resp(\"400 Bad Request\",\n [(\"Content-Type\", \"text/plain\"),\n (\"Content-Length\", str(len(text)))])\n return [text]\n\n def ret_response(ctype, data):\n ctype = str(ctype)\n if not type(data) == types.StringType:\n data = data.encode(\"utf8\")\n start_resp(\"200 OK\", [\n (\"Cache-Control\", \"max-age=0, no-store\"),\n (\"Content-Type\", ctype),\n (\"Content-Length\", str(len(data)))])\n return [data]\n\n\n cts = os.path.getmtime(PATH) #int(time.time())\n #if cts - ts > 1200:\n if not cts == ts:\n ts = cts\n print >> sys.stderr, \"reload listing\"\n listing = []\n for root, dirs, files in os.walk(PATH):\n for f in files:\n if not f.startswith(\".\") and os.path.splitext(f)[1].lower() in [\".jpg\", \".jpeg\"]:\n path = os.path.join(root, f)\n created = exif.parse(path).get(\"DateTimeOriginal\")\n url = u\"%s%s\" % (URL, path[len(PATH):])\n listing.append((created, url))\n listing = sorted(listing)\n\n\n env_script = env[\"SCRIPT_URI\"]\n\n params = env[\"QUERY_STRING\"]\n params = cgi.parse_qs(params)\n params = dict([(key, params[key][0].strip()) for key in params])\n env_offs = params.get(\"offs\", len(listing) - 1)\n env_count = params.get(\"count\", NPERPAGE)\n del params\n try:\n env_offs = int(env_offs)\n env_count = int(env_count)\n except ValueError:\n return ret_badrequest()\n if env_offs < 0:\n #env_offs = len(listing) - 1\n return ret_badrequest()\n if env_count <= 0:\n #env_count = NPERPAGE\n return ret_badrequest()\n\n\n count = env_count\n if env_offs - count + 1 < 0:\n count = env_offs + 1\n\n page = listing[env_offs - count + 1: env_offs + 1]\n page.reverse()\n page = [u'<div class=\"entry\">\\n<a class=\"imglink\" href=\"%s\"><img class=\"img\" src=\"%s\"/></a><br>\\n<span class=\"date\">%s</span>\\n</div>' % (url, url, created) for created, url in page]\n page = \"\\n\".join(page)\n\n linkprev, linknext = u\"prev\", u\"next\"\n\n if env_offs - count >= 0:\n linkprev = u'<a class=\"link\" href=\"%s?offs=%i&count=%i\">prev</a>' % (\n env_script, env_offs - env_count, env_count)\n\n if env_offs < len(listing) - 1:\n linknext = u'<a class=\"link\" href=\"%s?offs=%i&count=%i\">next</a>' % (\n env_script, env_offs + env_count, env_count)\n\n navi = u'<span class=\"navi\">\\n<span class=\"prev\">%s</span>\\n<span class=\"next\">%s</span>\\n</span>' % (\n linkprev, linknext)\n\n content = u'<div class=\"content\">\\n%s<br>\\n%s<br>\\n%s</div>' % (navi, page, navi)\n content = TEMPLATE % content\n\n return ret_response(\"text/html\", content)\n\n", "sub_path": "snip/wsgi/fotos/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 10, "usage_type": "attribute"}, {"api_name": "types.StringType", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "exif.parse", "line_number": 47, "usage_type": "call"}, {"api_name": "cgi.parse_qs", "line_number": 56, "usage_type": "call"}]} {"seq_id": "406366036", "text": "from config import Configuration\nfrom embedder import train_embedder\nimport os\nimport pandas as pd\n\n\n\ndata_name = 'CarEvaluation'\nprint(data_name)\n\n# data = pd.read_csv('data/{}.csv'.format(data_name)).drop('class',axis=1)\ndata = pd.read_csv('data/{}.csv'.format(data_name))\n\nembedding_size = 4\ncorruption_ratio_train = 0.0\ncorruption_ratio_validation = 0.05\nconfig_data = Configuration(train_batch_size=128, val_batch_size= int(len(data) * 0.2),\n embedding_size=embedding_size, max_iteration=20000,\n print_loss_every=100,\n LOG_DIR = './{}/embedding_{}/corruption_{}/validation_{}/class_var/log/'.format(\n data_name, embedding_size, corruption_ratio_train,\n corruption_ratio_validation),\n corruption_ratio_train=corruption_ratio_train,\n corruption_ratio_validation = corruption_ratio_validation,\n model_save_filename = 'model.ckpt',\n metadata_filename = 'metadata.tsv',\n )\n\nif not os.path.exists(config_data.LOG_DIR):\n os.makedirs(config_data.LOG_DIR)\n\nwith open(os.path.join(config_data.LOG_DIR, 'config.txt'), 'w') as f:\n f.write(repr(config_data))\n\nfor col in data.columns:\n if data[col].dtype == int or data[col].dtype == float:\n data[col] = data[col].astype('str')\n\ntrain_embedder(data, config_data)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "config.Configuration", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "embedder.train_embedder", "line_number": 39, "usage_type": "call"}]} {"seq_id": "71657128", "text": "import sys\nfrom flask import Flask, jsonify, make_response, request, abort\n\napp = Flask(__name__)\n\n@app.after_request\ndef after_request(data):\n\tresponse = make_response(data)\n\tresponse.headers['Content-Type'] = 'application/json'\n\tresponse.headers['Access-Control-Allow-Origin'] = '*'\n\tresponse.headers['Access-Control-Allow-Headers'] = \"Origin, X-Requested-With, Content-Type, Accept\"\n\tresponse.headers['Access-Control-Allow-Methods'] = 'GET, POST, PUT, DELETE'\n\treturn response\n\ntasks = []\n\n@app.errorhandler(400)\ndef bad_request(error):\n return make_response(jsonify( { 'error': 'Bad Request' } ), 400)\n\n@app.errorhandler(404)\ndef not_found(error):\n return make_response(jsonify( { 'error': 'Not Found' } ), 404)\n\n@app.route('/tasks', methods = ['POST'])\ndef create_task():\n if not request.json or not 'task' in request.json:\n abort(400)\n task = {\n 'id': len(tasks),\n 'task': request.json['task']\n }\n tasks.append(task)\n return jsonify( { 'task': task } ), 201\n\n@app.route('/tasks', methods = ['GET'])\ndef get_tasks():\n return jsonify( { 'tasks': tasks } )\n\t\n@app.route('/tasks/<int:task_id>', methods = ['GET'])\ndef get_task(task_id):\n task = filter(lambda t: t['id'] == task_id, tasks)\n if len(task) == 0:\n abort(404)\n return jsonify( { 'task': task[0] } )\n\t\n@app.route('/tasks/<int:task_id>', methods = ['PUT'])\ndef update_task(task_id):\n task = filter(lambda t: t['id'] == task_id, tasks)\n if len(task) == 0 or not request.json:\n abort(404)\n if 'task' in request.json and type(request.json['task']) is not unicode:\n abort(400)\n task[0]['task'] = request.json.get('task', task[0]['task'])\n return jsonify( { 'task': task[0] } )\n\n@app.route('/tasks/<int:task_id>', methods = ['DELETE'])\ndef delete_task(task_id):\n task = filter(lambda t: t['id'] == task_id, tasks)\n if len(task) == 0:\n abort(404)\n tasks.remove(task[0])\n return jsonify( { 'result': True } )\n\nif __name__ == '__main__':\n app.run(debug = True)", "sub_path": "labo5/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.json.get", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}]} {"seq_id": "342471086", "text": "import copy\nimport os, time\nimport augment\nimport pretrained_model\n\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport cv2\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import Dataset, DataLoader\nimport torchvision.models as models\nfrom tqdm import tqdm\nfrom time import sleep\n\ncudnn.benchmark = True\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n since = time.time() # get the starting time of training\n\n best_loss = 100000.0\n tmax_factor = 1.5 # with warm restart, multiply the max factor by this amount\n tmax = 10 # after tmax iterations, the learning rate is reset\n\n for epoch in range(num_epochs):\n print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n model.train() # Set model to training mode\n else:\n model.eval() # Set model to evaluate mode\n\n running_loss = 0.0\n iterations = len(dataloaders[phase])\n\n # Iterate over data.\n pbar = tqdm(total=iterations,desc=phase,ncols=70)\n for inputs, labels in dataloaders[phase]:\n inputs = inputs.to(device)\n output_tensor = labels.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # Run the forward pass and track history if only in training\n with torch.set_grad_enabled(phase == 'train'):\n outputs = model(inputs)\n preds = outputs\n loss = criterion(outputs, output_tensor)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.item() * inputs.size(0)\n pbar.update(1)\n sleep(0.01) #delay to print stats\n pbar.close()\n\n if phase == 'train': #adjust the learning rate if training\n scheduler.step()\n\n # With the cosine annealing scheduler, the learning rate is\n # gradually reduced. If the learning rate is very small, then\n # reset the scheduler to do a warm restart\n if optimizer.param_groups[0]['lr'] < .0000001:\n tmax = int(tmax * tmax_factor)\n scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=tmax, verbose=True)\n print (\"New T_max: \" + str(tmax))\n\n epoch_loss = running_loss / dataset_sizes[phase]\n\n print('{} Loss: {:.4f}'.format(phase, epoch_loss))\n\n # deep copy the model\n if phase == 'val' and epoch_loss < best_loss:\n best_loss = epoch_loss\n best_model_wts = copy.deepcopy(model.state_dict())\n print (\"Saving new best model...\")\n torch.save(model, 'test.pt')\n\n print()\n\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(\n time_elapsed // 60, time_elapsed % 60))\n print('Best val Acc: {:4f}'.format(best_loss))\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model\n\nif __name__ == '__main__':\n train_fp, val_fp = augment.setup_dir()\n train_d, val_d = augment.create_datasets(train_fp, val_fp)\n\n image_datasets = {}\n image_datasets['train'] = train_d\n image_datasets['val'] = val_d\n\n dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], \\\n batch_size=32, shuffle=True, num_workers=12) for x in ['train', 'val']}\n\n dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n print (dataset_sizes)\n\n #create the model\n m = pretrained_model.Pretrained_Model(shape=(360,640,3), num_outputs=80)\n model = m.build()\n\n if torch.cuda.is_available(): #send the model to the GPU if available\n model.cuda()\n\n #configure the training\n criterion = nn.L1Loss()\n optimizer = optim.AdamW(model.parameters(), lr=0.005)\n exp_lr_scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, verbose=True)\n\n #train the model\n #model = train_model(model, criterion, optimizer, exp_lr_scheduler, num_epochs=480)\n model = train_model(model, criterion, optimizer, exp_lr_scheduler, num_epochs=203)\n\n", "sub_path": "pytorch_networks/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 4690, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.backends.cudnn.benchmark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 21, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.set_grad_enabled", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 78, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 90, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "augment.setup_dir", "line_number": 104, "usage_type": "call"}, {"api_name": "augment.create_datasets", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pretrained_model.Pretrained_Model", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.nn.L1Loss", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.optim.AdamW", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 127, "usage_type": "name"}]} {"seq_id": "268109963", "text": "import datetime\nimport redis\n\nfrom django.db import models\nfrom django.db.models import Q\nfrom django.utils import timezone\nfrom django.utils.translation import to_locale, get_language\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom django.contrib.gis.db.models import PointField\nfrom django_extensions.db.models import (\n TitleSlugDescriptionModel, TimeStampedModel)\nfrom django.utils.translation import gettext_lazy as _\n\n\ndef user_directory_path(instance, filename):\n # file will be uploaded to MEDIA_ROOT / user_<id>/<filename>\n return f'media/{instance.__class__.__name__}/{instance.owner.id}_{filename}'\n\n\nclass AdvertsManager(models.Manager):\n def search(self, query=None):\n qs = self.get_queryset()\n if query is not None:\n or_lookup = (Q(title__icontains=query) |\n Q(description__icontains=query))\n qs = qs.filter(or_lookup).distinct() # distinct() is often necessary with Q lookups\n return qs\n\n\nclass Advert(TitleSlugDescriptionModel, TimeStampedModel):\n owner = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, null=True, verbose_name=_('owner'))\n expires = models.DateTimeField(blank=True, null=True, help_text=_('Format mm.dd.yyyy'),\n verbose_name=_('expires'))\n\n LOCALES = (('en', 'en_US'),\n ('uk', 'uk_UA'),\n ('ru', 'ru_RU'),\n ('pl', 'pl_PL'),\n )\n\n local = models.CharField(\n max_length=2,\n choices=LOCALES,\n default='en'\n )\n\n objects = AdvertsManager()\n\n class Meta:\n ordering = ['modified']\n abstract = True\n\n def was_published_recently(self):\n return self.created >= timezone.now() - datetime.timedelta(days=1)\n\n def save(self, *args, **kwargs):\n if not self.local:\n lang = get_language()\n if lang:\n self.local = lang[:2]\n super(Advert, self).save(*args, **kwargs)\n\n r = redis.StrictRedis(host=settings.REDIS_HOST,\n port=settings.REDIS_PORT,\n db=settings.REDIS_DB)\n if (self.modified - self.created).seconds == 0:\n r.incr(f'Total:saved')\n r.incr(f'{self.__class__.__name__}:{self.id}:saved')\n data = self.created.timestamp()\n score = f'{self.__class__.__name__}:{self.created.timestamp()}'\n r.zadd(\"adverts\", {score: data})\n\n def delete(self, using=None, keep_parents=False):\n r = redis.StrictRedis(host=settings.REDIS_HOST,\n port=settings.REDIS_PORT,\n db=settings.REDIS_DB)\n super(Advert, self).delete()\n r.decr(f'Total:saved')\n r.decr(f'{self.__class__.__name__}:{self.id}:saved')\n data = self.created.timestamp()\n score = f'{self.__class__.__name__}:{self.created.timestamp()}'\n r.zadd(\"adverts\", {score: data})\n\n def __str__(self):\n return self.title\n\n\nclass Location(models.Model):\n city = models.CharField(max_length=50, default='Chicago', verbose_name=_('city'))\n address = models.CharField(max_length=100, blank=True, verbose_name=_('address'))\n point = PointField(blank=True, verbose_name=_('map'), null=True)\n\n # zipcode =\n\n @property\n def lat_lng(self):\n return list(getattr(self.point, 'coords', [])[::-1])\n\n class Meta:\n ordering = ['city']\n abstract = True\n\n def __unicode__(self):\n return self.city\n\n def __str__(self):\n return self.city\n", "sub_path": "adverts/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3624, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.models.Manager", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 26, "usage_type": "call"}, {"api_name": "django_extensions.db.models.TitleSlugDescriptionModel", "line_number": 31, "usage_type": "name"}, {"api_name": "django_extensions.db.models.TimeStampedModel", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 55, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}, {"api_name": "django.utils.translation.get_language", "line_number": 59, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 64, "usage_type": "call"}, {"api_name": "django.conf.settings.REDIS_HOST", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 64, "usage_type": "name"}, {"api_name": "django.conf.settings.REDIS_PORT", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 65, "usage_type": "name"}, {"api_name": "django.conf.settings.REDIS_DB", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 66, "usage_type": "name"}, {"api_name": "redis.StrictRedis", "line_number": 75, "usage_type": "call"}, {"api_name": "django.conf.settings.REDIS_HOST", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 75, "usage_type": "name"}, {"api_name": "django.conf.settings.REDIS_PORT", "line_number": 76, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 76, "usage_type": "name"}, {"api_name": "django.conf.settings.REDIS_DB", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 77, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 89, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 89, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 90, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 90, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 91, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 91, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.gis.db.models.PointField", "line_number": 92, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 92, "usage_type": "call"}]} {"seq_id": "357318350", "text": "# -*- coding: utf-8 -*-\nfrom distutils.core import setup\nfrom setuptools import find_packages\nimport re\nfrom os import path\n\nv_file = open(path.join(path.dirname(__file__), \n 'ceda_markup', '__init__.py'))\nVERSION = re.compile(r\".*__version__ = '(.*?)'\",\n re.S).match(v_file.read()).group(1)\n\nsetup(\n name='ceda-markup',\n version=VERSION,\n author=u'Maurizio Nagni',\n author_email='maurizio.nagni',\n packages=find_packages(),\n url='http://proj.badc.rl.ac.uk/svn/ndg/mauRepo/CedaMarkup',\n license='BSD licence, see LICENCE',\n description='Collection of markup classes as geosrss, gml, atom, rss...' + \\\n ' Contains an OpenSearch server (just the core not the server)',\n long_description=open('README').read(),\n zip_safe=False,\n)\n\n'''\nzip_safe=False option. It prevents the package manager to install a \n python egg, instead you'll get a real directory with files in it.\n'''", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "re.S", "line_number": 10, "usage_type": "attribute"}, {"api_name": "distutils.core.setup", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 17, "usage_type": "call"}]} {"seq_id": "79772065", "text": "import wx\nimport time\n\ndef LongSimulation(self,input_):\n # Simulate the behavior of the simulation code that I have\n # Actually it returns more than just the progress\n # Occasionally it also returns some partial summary result for diagnostic\n for i in range(0, 10):\n self.log.write('Progress: %3.0f\\r' % float(i))\n time.sleep(1)\n wx.GetApp().Yield()\n return 'answer'\n\nclass MyForm(wx.Frame):\n\n def __init__(self):\n wx.Frame.__init__(self, None)\n\n # Add a panel so it looks the correct on all platforms\n panel = wx.Panel(self, wx.ID_ANY)\n style = wx.TE_MULTILINE|wx.TE_READONLY|wx.HSCROLL\n\n sizer = wx.BoxSizer(wx.VERTICAL)\n\n self.log = wx.TextCtrl(panel, wx.ID_ANY, size=(300,100), style=style)\n sizer.Add(self.log, 1, wx.ALL|wx.EXPAND, 5)\n\n btn = wx.Button(panel, wx.ID_ANY, 'Start')\n self.Bind(wx.EVT_BUTTON, self.onButton, btn)\n\n sizer.Add(btn, 0, wx.ALL|wx.CENTER, 5)\n panel.SetSizer(sizer)\n\n def onButton(self, event):\n input_ = 0\n res = LongSimulation(self,input_)\n self.log.write(res)\n self.log.write('\\n2222')\n\n# Run the program\nif __name__ == \"__main__\":\n app = wx.App(False)\n frame = MyForm()\n frame.Show()\n app.MainLoop()", "sub_path": "_misc/lsim.py", "file_name": "lsim.py", "file_ext": "py", "file_size_in_byte": 1291, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "wx.GetApp", "line_number": 11, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 14, "usage_type": "attribute"}, {"api_name": "wx.Frame.__init__", "line_number": 17, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 20, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "wx.TE_MULTILINE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.TE_READONLY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.HSCROLL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 23, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 25, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 26, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 28, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.CENTER", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.App", "line_number": 42, "usage_type": "call"}]} {"seq_id": "423960867", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport math\nimport torch.utils.model_zoo as model_zoo\n\nfrom .blocks import *\n\nBN_MOMENTUM = 0.1\n\n# __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n# 'resnet152']\n\n\nmodel_urls = {\n 'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',\n 'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',\n 'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',\n 'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',\n 'resnet152': 'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth',\n}\n\n\n\nclass ResNet(nn.Module):\n def __init__(self, block, layers, num_classes=1000):\n self.inplanes = 64\n super(ResNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])\n self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n def _make_layer(self, block, planes, num_blocks, stride=1):\n layers = []\n layers.append(block(self.inplanes, planes, stride))\n self.inplanes = planes * block.expansion\n for i in range(1, num_blocks):\n layers.append(block(self.inplanes, planes))\n\n return nn.Sequential(*layers)\n\ndef resnet18(pretrained=False):\n \"\"\"Constructs a ResNet-18 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(BasicBlock, [2, 2, 2, 2])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))\n return model\n\n\ndef resnet34(pretrained=False):\n \"\"\"Constructs a ResNet-34 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(BasicBlock, [3, 4, 6, 3])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))\n return model\n\n\ndef resnet50(pretrained=False):\n \"\"\"Constructs a ResNet-50 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(Bottleneck, [3, 4, 6, 3])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))\n return model\n\n\ndef resnet101(pretrained=False):\n \"\"\"Constructs a ResNet-101 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(Bottleneck, [3, 4, 23, 3])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))\n return model\n\n\ndef resnet152(pretrained=False):\n \"\"\"Constructs a ResNet-152 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(Bottleneck, [3, 8, 36, 3])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))\n return model\n\nclass deconv_resnet(nn.Module):\n def __init__(self, num_layers=50, num_classes=17, pretrained=False):\n super(deconv_resnet, self).__init__()\n num_feats = 256\n bias = False\n\n if num_layers == 50:\n self.resnet = resnet50()\n # self.resnet = resnet50(pretrained)\n model_path = 'data/pretrained/resnet50.pth'\n elif num_layers == 101:\n self.resnet = resnet101()\n # self.resnet = resnet101(pretrained)\n model_path = 'data/pretrained/resnet101.pth'\n elif num_layers == 152:\n self.resnet = resnet152()\n # self.resnet = resnet152(pretrained)\n model_path = 'data/pretrained/resnet152.pth'\n\n if pretrained:\n print(\"Loading pretrained weights from %s\" %(model_path))\n state_dict = torch.load(model_path)\n self.resnet.load_state_dict({k:v for k,v in state_dict.items() if k in self.resnet.state_dict()})\n\n self.backbone = nn.Sequential(self.resnet.conv1, self.resnet.bn1, self.resnet.relu, self.resnet.maxpool,\n self.resnet.layer1, self.resnet.layer2, self.resnet.layer3, self.resnet.layer4)\n\n self.deconv = nn.Sequential(\n nn.ConvTranspose2d(2048, num_feats, kernel_size=4, stride=2, padding=1, bias = bias),\n nn.BatchNorm2d(num_feats, momentum=BN_MOMENTUM),\n nn.ReLU(inplace=True),\n nn.ConvTranspose2d(num_feats, num_feats, kernel_size=4, stride=2, padding=1, bias=bias),\n nn.BatchNorm2d(num_feats, momentum=BN_MOMENTUM),\n nn.ReLU(inplace=True),\n nn.ConvTranspose2d(num_feats, num_feats, kernel_size=4, stride=2, padding=1, bias=bias),\n nn.BatchNorm2d(num_feats, momentum=BN_MOMENTUM),\n nn.ReLU(inplace=True) )\n\n self.heatmap = nn.Conv2d(num_feats, num_classes, kernel_size=1)\n\n for m in self.deconv.modules():\n if isinstance(m, nn.ConvTranspose2d):\n m.weight.data.normal_(0, 0.001)\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n # bound = math.sqrt(6. / n)\n # m.weight.data.uniform_(-bound, bound)\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n for m in self.heatmap.modules():\n if isinstance(m, nn.Conv2d):\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n m.weight.data.normal_(0, 0.001)\n m.bias.data.zero_()\n\n\n def set_bn_fix(m):\n classname = m.__class__.__name__\n if classname.find('BatchNorm') != -1:\n for p in m.parameters(): p.requires_grad=False\n\n\n # self.resnet.apply(set_bn_fix)\n\n def forward(self, x):\n x = self.backbone(x)\n x = self.deconv(x)\n out = self.heatmap(x)\n\n return out\n\n # def train(self, mode=True):\n # # Override train so that the training mode is set as we want\n # nn.Module.train(self, mode)\n # if mode:\n # # Set fixed blocks to be in eval mode (not really doing anything)\n # self.resnet.eval()\n # if self.FIXED_BLOCKS <= 3:\n # self.resnet.layer4.train()\n # if self.FIXED_BLOCKS <= 2:\n # self.resnet.layer3.train()\n # if self.FIXED_BLOCKS <= 1:\n # self.resnet.layer2.train()\n # if self.FIXED_BLOCKS == 0:\n # self.resnet.layer1.train()\n\n # # Set batchnorm always in eval mode during training\n # def set_bn_eval(m):\n # classname = m.__class__.__name__\n # if classname.find('BatchNorm') != -1:\n # m.eval()\n\n # self.resnet.apply(set_bn_eval)\n\n def load_pretrained_resnet(self, state_dict):\n self.resnet.load_state_dict({k: state_dict[k] for k in list(self.resnet.state_dict())})\n", "sub_path": "lib/pose/models/deconv_resnet.py", "file_name": "deconv_resnet.py", "file_ext": "py", "file_size_in_byte": 7975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 167, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}]} {"seq_id": "486758452", "text": "from __future__ import unicode_literals\r\nimport sys\r\nimport os\r\nimport json\r\nimport collections\r\n\r\nfrom pip import cmdoptions\r\n\r\nfrom pip.basecommand import Command\r\ntry:\r\n from pip.log import logger\r\nexcept ImportError:\r\n from pip import logger # 6.0\r\nfrom pip.index import PackageFinder\r\nfrom pip.req import RequirementSet, InstallRequirement, parse_requirements\r\nfrom pip.locations import build_prefix, src_prefix\r\n\r\nfrom .dependency import trace_dependencies\r\nfrom .version import __version__\r\n\r\n\r\ndef pretty_project_name(req):\r\n \"\"\"Get project name in a pretty form:\r\n\r\n name-version\r\n\r\n \"\"\"\r\n try:\r\n print(req.name, req.installed_version)\r\n except Exception:\r\n print(\"dep %s has a problem sire.\" % req.name)\r\n return req.name\r\n return '%s-%s' % (req.name, req.installed_version)\r\n\r\n\r\nclass DependencyChecker(Command):\r\n bundle = False\r\n name = 'dependency'\r\n\r\n def __init__(self, *args, **kw):\r\n super(DependencyChecker, self).__init__(*args, **kw)\r\n\r\n # fix prog name to be gluttony instead of pip dependancy\r\n self.parser.prog = 'gluttony'\r\n\r\n self.cmd_opts.add_option(cmdoptions.requirements.make())\r\n self.cmd_opts.add_option(cmdoptions.build_dir.make())\r\n self.cmd_opts.add_option(cmdoptions.download_cache.make())\r\n\r\n # cmdoptions.editable exist in pip's git\r\n self.parser.add_option(\r\n '-e', '--editable',\r\n dest='editables',\r\n action='append',\r\n default=[],\r\n metavar='VCS+REPOS_URL[@REV]#egg=PACKAGE',\r\n help='Install a package directly from a checkout. Source will be checked '\r\n 'out into src/PACKAGE (lower-case) and installed in-place (using '\r\n 'setup.py develop). You can run this on an existing directory/checkout (like '\r\n 'pip install -e src/mycheckout). This option may be provided multiple times. '\r\n 'Possible values for VCS are: svn, git, hg and bzr.')\r\n\r\n self.parser.add_option(\r\n '-d', '--download', '--download-dir', '--download-directory',\r\n dest='download_dir',\r\n metavar='DIR',\r\n default=None,\r\n help='Download packages into DIR instead of installing them')\r\n self.parser.add_option(\r\n '--src', '--source', '--source-dir', '--source-directory',\r\n dest='src_dir',\r\n metavar='DIR',\r\n default=None,\r\n help='Check out --editable packages into DIR (default %s)' % src_prefix)\r\n self.parser.add_option(\r\n '-U', '--upgrade',\r\n dest='upgrade',\r\n action='store_true',\r\n help='Upgrade all packages to the newest available version')\r\n self.parser.add_option(\r\n '-I', '--ignore-installed',\r\n dest='ignore_installed',\r\n action='store_true',\r\n help='Ignore the installed packages (reinstalling instead)')\r\n\r\n # options for output\r\n self.parser.add_option(\r\n '--dump',\r\n dest='dump',\r\n metavar='FILE',\r\n help='dump dependancy by level')\r\n self.parser.add_option(\r\n '-j', '--json',\r\n dest='json_file',\r\n metavar='FILE',\r\n help='JSON filename for result output')\r\n self.parser.add_option(\r\n '--pydot',\r\n dest='py_dot',\r\n metavar='FILE',\r\n help='Output dot file with pydot')\r\n self.parser.add_option(\r\n '--pygraphviz',\r\n dest='py_graphviz',\r\n metavar='FILE',\r\n help='Output dot file with PyGraphviz')\r\n self.parser.add_option(\r\n '--display', '--display-graph',\r\n dest='display_graph',\r\n action='store_true',\r\n help='Display graph with Networkx and matplotlib')\r\n self.parser.add_option(\r\n '-R', '--reverse',\r\n dest='reverse',\r\n action='store_true',\r\n help='Reverse the direction of edge')\r\n\r\n index_opts = cmdoptions.make_option_group(\r\n cmdoptions.index_group,\r\n self.parser,\r\n )\r\n\r\n self.parser.insert_option_group(0, index_opts)\r\n\r\n def _build_package_finder(self, options, index_urls, session):\r\n \"\"\"\r\n Create a package finder appropriate to this install command.\r\n This method is meant to be overridden by subclasses, not\r\n called directly.\r\n \"\"\"\r\n return PackageFinder(\r\n use_wheel=False,\r\n find_links=options.find_links,\r\n index_urls=index_urls,\r\n allow_external=options.allow_external,\r\n allow_unverified=options.allow_unverified,\r\n allow_all_external=options.allow_all_external,\r\n session=session,\r\n )\r\n\r\n def run(self, options, args):\r\n if not options.build_dir:\r\n options.build_dir = build_prefix\r\n if not options.src_dir:\r\n options.src_dir = src_prefix\r\n if options.download_dir:\r\n options.no_install = True\r\n options.ignore_installed = True\r\n else:\r\n options.build_dir = os.path.abspath(options.build_dir)\r\n options.src_dir = os.path.abspath(options.src_dir)\r\n session = self._build_session(options)\r\n index_urls = [options.index_url] + options.extra_index_urls\r\n if options.no_index:\r\n logger.notify('Ignoring indexes: %s' % ','.join(index_urls))\r\n index_urls = []\r\n finder = self._build_package_finder(options, index_urls, session)\r\n requirement_set = RequirementSet(\r\n build_dir=options.build_dir,\r\n src_dir=options.src_dir,\r\n download_dir=options.download_dir,\r\n download_cache=options.download_cache,\r\n upgrade=options.upgrade,\r\n ignore_installed=options.ignore_installed,\r\n ignore_dependencies=False,\r\n session=session,\r\n )\r\n\r\n for name in args:\r\n requirement_set.add_requirement(\r\n InstallRequirement.from_line(name, None))\r\n for name in options.editables:\r\n requirement_set.add_requirement(\r\n InstallRequirement.from_editable(name, default_vcs=options.default_vcs))\r\n for filename in options.requirements:\r\n for req in parse_requirements(filename, finder=finder, options=options):\r\n requirement_set.add_requirement(req)\r\n\r\n requirement_set.prepare_files(\r\n finder,\r\n force_root_egg_info=self.bundle,\r\n bundle=self.bundle,\r\n )\r\n\r\n return requirement_set\r\n\r\n def _output_json(self, json_file, dependencies):\r\n packages = set()\r\n json_deps = []\r\n for src, dest in dependencies:\r\n packages.add(src)\r\n packages.add(dest)\r\n json_deps.append([\r\n pretty_project_name(src),\r\n pretty_project_name(dest),\r\n ])\r\n\r\n json_packages = []\r\n for package in packages:\r\n json_packages.append(dict(\r\n name=package.name,\r\n installed_version=package.installed_version,\r\n ))\r\n\r\n with open(json_file, 'wt') as jfile:\r\n json.dump(dict(\r\n packages=json_packages,\r\n dependencies=json_deps,\r\n ), jfile, sort_keys=True, indent=4, separators=(',', ': '))\r\n\r\n def check_conflicts(self, dependencies):\r\n dependancies_flattened = collections.defaultdict(set)\r\n for dep1, dep2 in dependencies:\r\n try:\r\n if dep1.installed_version is not None:\r\n dependancies_flattened[dep1.name].add(dep1.installed_version)\r\n except Exception:\r\n print(\"%s has an unknown version\" % dep1.name)\r\n\r\n try:\r\n if dep2.installed_version is not None:\r\n dependancies_flattened[dep2.name].add(dep2.installed_version)\r\n except Exception:\r\n print(\"%s has an unknown version\" % dep2.name)\r\n\r\n for dependency_name, dependency_versions in dependancies_flattened.items():\r\n if dependency_versions and len(dependency_versions) > 1:\r\n print(\"Warning: This project requires %s in multiple versions:\" % dependency_name, \",\".join(dependency_versions))\r\n\r\n def output(self, options, args, dependencies):\r\n \"\"\"Output result\r\n\r\n \"\"\"\r\n\r\n if options.reverse:\r\n dependencies = map(lambda x: x[::-1], dependencies)\r\n\r\n if options.json_file:\r\n self._output_json(options.json_file, dependencies)\r\n logger.notify(\"Dependencies relationships result is in %s now\",\r\n options.json_file)\r\n\r\n self.check_conflicts(dependencies)\r\n\r\n if options.display_graph or options.py_dot or options.py_graphviz or options.dump:\r\n import networkx as nx\r\n\r\n # extract name and version\r\n def convert(pair):\r\n return (\r\n pretty_project_name(pair[0]),\r\n pretty_project_name(pair[1]),\r\n )\r\n plain_dependencies = map(convert, dependencies)\r\n dg = nx.DiGraph()\r\n dg.add_edges_from(plain_dependencies)\r\n\r\n if options.dump:\r\n dependancies_ordered = []\r\n for n, nbrs in dg.adjacency_iter():\r\n for nbr, eattr in nbrs.items():\r\n dependancies_ordered.append(nbr)\r\n\r\n dependancies_ordered = set(dependancies_ordered)\r\n with open(options.dump, mode='wt') as myfile:\r\n myfile.write('\\n'.join(dependancies_ordered))\r\n\r\n if options.py_dot:\r\n logger.notify(\"Writing dot to %s with Pydot ...\",\r\n options.py_dot)\r\n from networkx.drawing.nx_pydot import write_dot\r\n write_dot(dg, options.py_dot)\r\n if options.py_graphviz:\r\n logger.notify(\"Writing dot to %s with PyGraphviz ...\",\r\n options.py_graphviz)\r\n from networkx.drawing.nx_agraph import write_dot\r\n write_dot(dg, options.py_graphviz)\r\n if options.display_graph:\r\n import matplotlib.pyplot as plt\r\n logger.notify(\"Drawing graph ...\")\r\n\r\n if not plain_dependencies:\r\n logger.notify(\"There is no dependency to draw.\")\r\n else:\r\n pydot_graph = nx.drawing.to_pydot(dg)\r\n pydot_graph.write_png(\"dependency.png\")\r\n\r\n def main(self, args):\r\n options, args = self.parser.parse_args(args)\r\n if not args:\r\n self.parser.print_help()\r\n return\r\n\r\n level = 1 # Notify\r\n logger.level_for_integer(level)\r\n logger.consumers.extend([(level, sys.stdout)])\r\n # get all files\r\n requirement_set = self.run(options, args)\r\n # trace dependencies\r\n logger.notify(\"Tracing dependencies ...\")\r\n dependencies = []\r\n values = None\r\n if hasattr(requirement_set.requirements, 'itervalues'):\r\n values = list(requirement_set.requirements.itervalues())\r\n elif hasattr(requirement_set.requirements, 'values'):\r\n values = list(requirement_set.requirements.values())\r\n for req in values:\r\n trace_dependencies(req, requirement_set, dependencies)\r\n # output the result\r\n logger.notify(\"Output result ...\")\r\n self.output(options, args, dependencies)\r\n requirement_set.cleanup_files()\r\n\r\n\r\ndef main():\r\n command = DependencyChecker()\r\n command.main(sys.argv[1:])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "gluttony/commands.py", "file_name": "commands.py", "file_ext": "py", "file_size_in_byte": 11939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pip.basecommand.Command", "line_number": 36, "usage_type": "name"}, {"api_name": "pip.cmdoptions.requirements.make", "line_number": 46, "usage_type": "call"}, {"api_name": "pip.cmdoptions.requirements", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pip.cmdoptions", "line_number": 46, "usage_type": "name"}, {"api_name": "pip.cmdoptions.build_dir.make", "line_number": 47, "usage_type": "call"}, {"api_name": "pip.cmdoptions.build_dir", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pip.cmdoptions", "line_number": 47, "usage_type": "name"}, {"api_name": "pip.cmdoptions.download_cache.make", "line_number": 48, "usage_type": "call"}, {"api_name": "pip.cmdoptions.download_cache", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pip.cmdoptions", "line_number": 48, "usage_type": "name"}, {"api_name": "pip.locations.src_prefix", "line_number": 74, "usage_type": "name"}, {"api_name": "pip.cmdoptions.make_option_group", "line_number": 118, "usage_type": "call"}, {"api_name": "pip.cmdoptions", "line_number": 118, "usage_type": "name"}, {"api_name": "pip.cmdoptions.index_group", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pip.cmdoptions", "line_number": 119, "usage_type": "name"}, {"api_name": "pip.index.PackageFinder", "line_number": 131, "usage_type": "call"}, {"api_name": "pip.locations.build_prefix", "line_number": 143, "usage_type": "name"}, {"api_name": "pip.locations.src_prefix", "line_number": 145, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pip.logger.notify", "line_number": 155, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 155, "usage_type": "name"}, {"api_name": "pip.req.RequirementSet", "line_number": 158, "usage_type": "call"}, {"api_name": "pip.req.InstallRequirement.from_line", "line_number": 171, "usage_type": "call"}, {"api_name": "pip.req.InstallRequirement", "line_number": 171, "usage_type": "name"}, {"api_name": "pip.req.InstallRequirement.from_editable", "line_number": 174, "usage_type": "call"}, {"api_name": "pip.req.InstallRequirement", "line_number": 174, "usage_type": "name"}, {"api_name": "pip.req.parse_requirements", "line_number": 176, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 206, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 212, "usage_type": "call"}, {"api_name": "pip.logger.notify", "line_number": 240, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 240, "usage_type": "name"}, {"api_name": "networkx.DiGraph", "line_number": 255, "usage_type": "call"}, {"api_name": "pip.logger.notify", "line_number": 269, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 269, "usage_type": "name"}, {"api_name": "networkx.drawing.nx_pydot.write_dot", "line_number": 272, "usage_type": "call"}, {"api_name": "pip.logger.notify", "line_number": 274, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 274, "usage_type": "name"}, {"api_name": "networkx.drawing.nx_agraph.write_dot", "line_number": 277, "usage_type": "call"}, {"api_name": "pip.logger.notify", "line_number": 280, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 280, "usage_type": "name"}, {"api_name": "pip.logger.notify", "line_number": 283, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 283, "usage_type": "name"}, {"api_name": "networkx.drawing.to_pydot", "line_number": 285, "usage_type": "call"}, {"api_name": "networkx.drawing", "line_number": 285, "usage_type": "attribute"}, {"api_name": "pip.logger.level_for_integer", "line_number": 295, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 295, "usage_type": "name"}, {"api_name": "pip.logger.consumers.extend", "line_number": 296, "usage_type": "call"}, {"api_name": "pip.logger.consumers", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pip.logger", "line_number": 296, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pip.logger.notify", "line_number": 300, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 300, "usage_type": "name"}, {"api_name": "dependency.trace_dependencies", "line_number": 308, "usage_type": "call"}, {"api_name": "pip.logger.notify", "line_number": 310, "usage_type": "call"}, {"api_name": "pip.logger", "line_number": 310, "usage_type": "name"}, {"api_name": "{'nx': 'networkx', 'write_dot': 'networkx.drawing.nx_agraph.write_dot', 'plt': 'matplotlib.pyplot'}", "line_number": 316, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 317, "usage_type": "attribute"}]} {"seq_id": "268872152", "text": "\n# coding: utf-8\n\n# ### Notebook to configure model\n\n# In[1]:\n\nimport time\nimport math\nimport copy\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nsns.set_context(context=\"talk\")\n\nimport torch\nimport torch.nn as nn\nimport tensorflow as tf\nimport torch.nn.functional as F\nfrom torchvision import datasets\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nfrom torch.utils import data\n\n\n# In[2]:\n\n\nisRetrain = False\n\n\n# Hyperparameter\n\nQUERY_SIZE = 10\nEMBEDDING_SIZE = 50\n\n# HIDDEN_SIZE = 512\n# ATTENTION_SIZE = 64\n\nparameter_name = 'lr5em2_lm1em4_batch100_q1_e50'\n\nLEARNING_RATE = 5*1e-2\nBATCH_SIZE = 100\nLAMBDA = 1e-4\n\nnum_epochs = 100\n\nVOCAB_SIZE = 5\nNUM_CLASSES = 2\n\n# Data-specific\n\nREAD_LENGTH = 100\n\n# # KRAS\n# GENOME_START = 25204789\n# GENOME_END = 25250936\n\n# # NOTCH1\n# GENOME_START = 136494433 \n# GENOME_END = 136545786\n\n# NRAS\nfile_name = 'NRAS-germline'\nGENOME_START = 114704464 \nGENOME_END = 114716894\n\nGENOME_LENGTH = GENOME_END - GENOME_START + 1 \nCONTEXT_SIZE = GENOME_LENGTH\n\nref_names = [\"class\", \"ref\", \"sequence\"]\nVOCAB = ['N','A','T','C','G']\n\n\n# In[3]:\n\n\ndef load_ref_data(file_name, sample_ratio= 1, n_class=2, names=ref_names):\n \n csv_file = pd.read_csv(file_name, names=ref_names)\n shuffle_csv = csv_file.sample(frac=sample_ratio).reset_index()\n# x = pd.Series(shuffle_csv[\"sequence\"])\n x = list(shuffle_csv[\"sequence\"])\n# ref = pd.Series(shuffle_csv[\"ref\"])\n ref = list(shuffle_csv[\"ref\"])\n y = pd.Series(shuffle_csv[\"class\"])\n y = to_one_hot(y, n_class)\n print(y.shape)\n# print(type(x))\n# print(type(y))\n# print(type(ref))\n\n return x, ref, y\n\n\n# In[4]:\n\n\ndef create_synthetic_data(file_name, cancer_genes, benign_genes, num_patients=10, num_reads_per_patients=3, read_length=READ_LENGTH, genome_length=GENOME_LENGTH, vocab=VOCAB, isSomatic=True, print_seq=False):\n \n seq_list = np.random.choice(vocab, [num_patients, genome_length], replace=True)\n backup_seq_list = seq_list\n \n for loc, mutation in cancer_genes.items():\n seq_list[np.random.choice(num_patients, int(num_patients*mutation[1]), replace=False), loc] = mutation[0]\n \n genomes = []\n for r in range(seq_list.shape[0]):\n seq = ''.join(seq_list[r,:])\n if print_seq:\n print(seq)\n genomes.append(seq)\n\n locs = np.random.choice(genome_length-read_length, num_patients*num_reads_per_patients)\n\n file = open('./tumor-genome-'+file_name+'.txt','w')\n count = 0\n reads = []\n for genome in genomes:\n for t in range(num_reads_per_patients):\n index = count*num_reads_per_patients+t\n reads.append(genome[locs[index]:locs[index]+read_length])\n file.write(\"%s\\n\" % genome)\n count = count + 1\n file.close() \n\n file = open('./tumor-syn-'+file_name+'.csv','w')\n for r in range(num_patients*num_reads_per_patients):\n file.write(\"1, %d, %s\\n\" % (locs[r], reads[r]))\n file.close()\n \n tumor_locs = locs\n tumor_reads = reads\n\n if isSomatic:\n seq_list = backup_seq_list\n else:\n seq_list = np.random.choice(vocab, [num_patients, genome_length], replace=True)\n \n for loc, mutation in benign_genes.items():\n seq_list[np.random.choice(num_patients, int(num_patients*mutation[1]), replace=False), loc] = mutation[0]\n \n genomes = []\n for r in range(seq_list.shape[0]):\n seq = ''.join(seq_list[r,:])\n if print_seq:\n print(seq)\n genomes.append(seq)\n\n locs = np.random.choice(genome_length-read_length, num_patients*num_reads_per_patients)\n \n file = open('./normal-genome-'+file_name+'.txt','w')\n count = 0\n reads = []\n for genome in genomes:\n for t in range(num_reads_per_patients):\n index = count*num_reads_per_patients+t\n reads.append(genome[locs[index]:locs[index]+read_length])\n file.write(\"%s\\n\" % genome)\n count = count + 1\n file.close() \n\n file = open('./normal-syn-'+file_name+'.csv','w')\n for r in range(num_patients*num_reads_per_patients):\n file.write(\"0, %d, %s\\n\" % (locs[r], reads[r]))\n file.close() \n \n normal_locs = locs\n normal_reads = reads\n \n file = open('./syn-'+file_name+'.csv','w')\n for r in range(num_patients*num_reads_per_patients):\n file.write(\"1,%d,%s\\n\" % (tumor_locs[r], tumor_reads[r]))\n file.write(\"0,%d,%s\\n\" % (normal_locs[r], normal_reads[r]))\n file.close() \n \n return './syn-'+file_name+'.csv'\n \n \n \n\n\n# In[5]:\n\n\ndef to_one_hot(y, n_class):\n \n return np.eye(n_class)[y.astype(int)]\n\n\n# In[6]:\n\n\ndef split_ref_dataset(x_test, y_test, ref_test, dev_ratio):\n\n test_size = len(x_test)\n print(test_size)\n dev_size = (int)(test_size * dev_ratio)\n print(dev_size)\n\n x_dev = x_test[:dev_size]\n x_test = x_test[dev_size:]\n y_dev = y_test[:dev_size]\n y_test = y_test[dev_size:]\n ref_dev = ref_test[:dev_size]\n ref_test = ref_test[dev_size:]\n\n return x_test, x_dev, y_test, y_dev, ref_test, ref_dev, dev_size, test_size - dev_size\n\n\n# In[7]:\n\n\nclass TensorizedReadDataset(torch.utils.data.DataLoader):\n 'Characterizes a Tensorized dataset for genome reads in PyTorch'\n \n def __init__(self, reads, ref_locs, labels, read_length=100, genome_start=0, genome_end=0):\n# super(TensorizedReadDataset, self).__init__()\n \n # self.read_length = read_length\n self.labels = labels\n self.reads = reads\n self.ref_locs = ref_locs\n self.genome_start = genome_start\n self.genome_end = genome_end\n\n def __len__(self):\n return len(self.reads)\n\n def __getitem__(self, index):\n \n vals = list(self.reads[index])\n read_length = len(vals)\n locs = list(np.arange(self.ref_locs[index]-self.genome_start,self.ref_locs[index]+read_length-self.genome_start))\n\n# print(len(vals))\n# print(len(locs))\n \n vals2idx = {'N': 0, 'A': 1, 'C': 2, 'T': 3, 'G': 4}\n# read = torch.LongTensor(np.array([vals2idx[val]+loc*len(vals2idx) for val, loc in zip(vals, locs)], dtype=int), requires_grad=False)\n\n read = torch.autograd.Variable(torch.LongTensor(np.array([vals2idx[val]+loc*len(vals2idx) for val, loc in zip(vals, locs)], dtype=int)), requires_grad=False)\n \n X = read\n Y = self.labels[index,:]\n\n# torch.LongTensor(self.labels[index,:])\n\n return X, Y\n \n\n\n# In[8]:\n\n\nclass SequenceAttentionClassifier(nn.Module):\n \n def __init__(self, genome_length, vocab_size=5, query_size=10, embedding_size=128, num_classes=2):\n \n super(SequenceAttentionClassifier, self).__init__()\n \n self.genome_length = genome_length\n self.vocab_size = vocab_size\n self.query_size = query_size\n self.embedding_size = embedding_size\n self.num_classes = num_classes\n self.K = nn.Embedding(vocab_size*genome_length, embedding_size)\n self.V = nn.Embedding(vocab_size*genome_length, query_size)\n self.W = nn.Linear(query_size, num_classes)\n self.Q = nn.Linear(embedding_size, query_size)\n \n def forward(self, read):\n \n # 'read' here should be mapped to a flattened form where X_ij = 1 maps to i*vocab_size + j\n K_lookup = self.K(read) # Get the relevant keys\n V_lookup = self.V(read) # Get the relevant values\n\n # Get the attention weights\n logits = self.Q(K_lookup) / math.sqrt(self.embedding_size)\n probs = F.softmax(logits, dim = 0)\n \n # Calculate the covariates for the logistic regression\n# X = torch.matmul(probs.transpose(1,2), V_lookup)\n# X = probs * V_lookup\n X = (probs * V_lookup).sum(dim=1)\n\n # Right now we can just ignore the fact that we're doing a linear-transform.\n # In the future we'll add nonlinearities\n\n # Return the logits for the classifier\n return self.W(X), K_lookup, V_lookup\n \n\n\n# In[9]:\n\n\ndef GetTensorBatch(reads, ref_locs, labels, batch_size=100, genome_start=0, genome_end=0):\n \n batches = {}\n set_size = len(ref_locs)\n for batch in range(set_size // batch_size):\n \n x_batch = []\n y_batch = []\n \n for index in range(batch_size):\n \n vals = list(reads[index])\n read_length = len(vals)\n locs = list(np.arange(ref_locs[index]-genome_start,ref_locs[index]+read_length-genome_start))\n \n vals2idx = {'N': 0, 'A': 1, 'C': 2, 'T': 3, 'G': 4}\n read = torch.autograd.Variable(torch.LongTensor(np.array([vals2idx[val]+loc*len(vals2idx) for val, loc in zip(vals, locs)], dtype=int)), requires_grad=False)\n \n X = read\n Y = labels[index,:]\n x_batch.append(X)\n y_batch.append(Y)\n \n batches[batch] = [x_batch, y_batch]\n\n return batches\n \n\n\n# In[14]:\n\n\n# load data\n\nx_train, refs_train, y_train = load_ref_data(\"../data/ref-germline-NRAS-train39000.csv\", sample_ratio=1)\nx_hardtest, refs_hardtest, y_hardtest = load_ref_data(\"../data/ref-germline-NRAS-test1000.csv\", sample_ratio=1)\n\n# split dataset to train and validation\nx_train, x_test, y_train, y_test, refs_train, refs_test, test_size, train_size = split_ref_dataset(x_train, y_train, refs_train, 0.2)\n \n# split dataset to test and dev\nx_softtest, x_val, y_softtest, y_val, refs_softtest, refs_val, val_size, softtest_size = split_ref_dataset(x_test, y_test, refs_test, 0.5)\n\nprint(\"Training size: \", train_size)\nprint(\"Soft Test size: \", softtest_size)\nprint(\"Hard Test size: \", len(y_hardtest))\nprint(\"Validation size: \", val_size)\n\n# In[15]:\n\n\n# Generators\n# train_dataset = TensorizedReadDataset(reads=x_train, \n# ref_locs=refs_train, \n# labels=y_train, \n# read_length=READ_LENGTH, \n# genome_start=GENOME_START, \n# genome_end=GENOME_END)\n\n# hardval_dataset = TensorizedReadDataset(reads=x_test, \n# ref_locs=refs_test, \n# labels=y_test, \n# read_length=READ_LENGTH, \n# genome_start=GENOME_START, \n# genome_end=GENOME_END)\n\n# softval_dataset = TensorizedReadDataset(reads=x_softval, \n# ref_locs=refs_softval, \n# labels=y_softval, \n# read_length=READ_LENGTH, \n# genome_start=GENOME_START, \n# genome_end=GENOME_END)\n\n# # Input pipeline\n# train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n# batch_size=BATCH_SIZE,\n# shuffle=True)\n\n# hardtest_loader = torch.utils.data.DataLoader(dataset=hardval_dataset,\n# batch_size=BATCH_SIZE,\n# shuffle=True)\n\n# softval_loader = torch.utils.data.DataLoader(dataset=softval_dataset,\n# batch_size=BATCH_SIZE,\n# shuffle=True)\n\ntrain_loader = GetTensorBatch(reads=x_train, \n ref_locs=refs_train, \n labels=y_train, \n batch_size=BATCH_SIZE,\n genome_start=GENOME_START, \n genome_end=GENOME_END)\n\nval_loader = GetTensorBatch(reads=x_val, \n ref_locs=refs_val, \n labels=y_val, \n batch_size=BATCH_SIZE,\n genome_start=GENOME_START,\n genome_end=GENOME_END)\n\nhardtest_loader = GetTensorBatch(reads=x_hardtest, \n ref_locs=refs_hardtest, \n labels=y_hardtest, \n batch_size=BATCH_SIZE, \n genome_start=GENOME_START, \n genome_end=GENOME_END)\n\nsofttest_loader = GetTensorBatch(reads=x_softtest, \n ref_locs=refs_softtest, \n labels=y_softtest, \n batch_size=BATCH_SIZE,\n genome_start=GENOME_START, \n genome_end=GENOME_END)\n\n\n\n# In[16]:\n\n\n# isRetrain = True\n\n\n# In[17]:\n\n\nmodel = SequenceAttentionClassifier(genome_length=GENOME_LENGTH,\n\n vocab_size=VOCAB_SIZE,\n query_size=QUERY_SIZE,\n embedding_size=EMBEDDING_SIZE,\n num_classes=NUM_CLASSES)\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)\n\nif isRetrain:\n model.load_state_dict(torch.load('./'+file_name+'_model.pth'))\n\n\n# In[18]:\n\nf1 = open('cv_acc_each_epoch_'+parameter_name+'.txt', 'a+')\nf2 = open('cv_loss_each_batch_'+parameter_name+'.txt', 'a+')\nf3 = open('cv_loss_val_each_batch_'+parameter_name+'.txt', 'a+')\nf4 = open('cv_acc_each_batch_'+parameter_name+'.txt', 'a+')\n\nlam = LAMBDA\n\n# Training process\nfor epoch in range(num_epochs):\n# b = 0 # count batch\n for b, [x_batch, y_batch] in train_loader.items():\n \n full_loss = 0\n full_data_loss = 0\n optimizer.zero_grad()\n for c in range(BATCH_SIZE):\n \n x_input = x_batch[c].view(1, x_batch[c].shape[0])\n y_input = torch.Tensor(y_batch[c]).type(torch.float64).view(1, y_batch[c].shape[0])\n \n outputs, K, V = model(x_input)\n data_loss = criterion(outputs, torch.max(y_input, 1)[1])\n \n reg_loss = (torch.norm(K,2,1).mean() + torch.norm(V,2,1).mean())*lam\n loss = data_loss + reg_loss\n full_loss = full_loss + loss\n full_data_loss = full_data_loss + data_loss\n\n loss.backward()\n optimizer.step()\n f2.write(repr(data_loss)+\"\\n\")\n \n# full_loss = full_loss / 2\n # full_loss.backward()\n # optimizer.step()\n \n # if (b + 1) % 1 == 0:\n print(\"Epoch {}, Batch {}, loss :{}\".format(epoch + 1, b + 1, full_loss))\n# b = b + 1\n \n # loss_each_batch.append(full_loss)\n # f2.write(repr(full_data_loss)+\"\\n\")\n\n full_loss = 0\n full_data_loss = 0\n \n correct = 0\n total = 0\n for b, [x_batch, y_batch] in val_loader.items():\n full_vloss = 0\n for c in range(BATCH_SIZE):\n \n x_input = x_batch[c].view(1, x_batch[c].shape[0])\n y_input = torch.Tensor(y_batch[c]).type(torch.float64).view(1, y_batch[c].shape[0])\n \n outputs, K, V = model(x_input)\n _, predicted = torch.max(outputs.data, 1)\n \n data_vloss = criterion(outputs, torch.max(y_input, 1)[1])\n full_vloss = full_vloss + data_vloss\n\n total += len(y_input)\n correct += (predicted == torch.max(y_input.type(torch.LongTensor), 1)[1]).sum()\n\n f3.write(repr(data_vloss)+\"\\n\")\n # f3.write(repr(full_vloss)+\"\\n\")\n \n acc = int(correct) / total\n print('Validation Accuracy: {}%'.format(100 * acc)) \n # acc_each_epoch.append(acc)\n f4.write(repr(acc)+\"\\n\")\n \n correct = 0\n total = 0\n for b, [x_batch, y_batch] in val_loader.items():\n for c in range(BATCH_SIZE):\n \n x_input = x_batch[c].view(1, x_batch[c].shape[0])\n y_input = torch.Tensor(y_batch[c]).type(torch.float64).view(1, y_batch[c].shape[0])\n \n outputs, K, V = model(x_input)\n _, predicted = torch.max(outputs.data, 1)\n total += len(y_input)\n correct += (predicted == torch.max(y_input.type(torch.LongTensor), 1)[1]).sum()\n acc = int(correct) / total\n print('Validation Accuracy: {}%'.format(100 * acc)) \n # acc_each_epoch.append(acc)\n f1.write(repr(acc)+\"\\n\")\n\nf1.close() \nf2.close() \nf3.close() \nf4.close() \n\n# In[202]:\n\ntorch.save(model, './full_' + file_name + '_' + parameter_name + '_model.pt')\ntorch.save(model.state_dict(), './'+file_name + '_' + parameter_name + '_model.pth')\n\n\n\n# In[ ]:\n\n\n# plt.rcParams['figure.figsize'] = [30, 5]\n\n# plt.plot(np.arange(len(loss_each_batch))+1, loss_each_batch)\n# plt.xlim(1, len(loss_each_batch))\n# plt.title('loss vs. batchs')\n# plt.show()\n\n# plt.plot(np.arange(len(acc_each_batch))+1, acc_each_batch)\n# plt.xlim(1, len(acc_each_batch))\n# plt.title('accuracy vs. batchs')\n# plt.show()\n\n# plt.plot(np.arange(len(acc_each_epoch))+1, acc_each_epoch)\n# plt.xlim(1, len(acc_each_epoch))\n# plt.title('accuracy vs. epochs')\n# plt.show()\n\n\n# In[10]:\n\n\n# model = torch.load('./full_'+file_name+'_model.pt')\n# model.load_state_dict(torch.load('./'+file_name+'_model.pth'))\n\n\n# In[16]:\n\n\n\ncorrect = 0\ntotal = 0\nfor b, [x_batch, y_batch] in softtest_loader.items():\n for c in range(BATCH_SIZE):\n \n x_input = x_batch[c].view(1, x_batch[c].shape[0])\n y_input = torch.Tensor(y_batch[c]).type(torch.float64).view(1, y_batch[c].shape[0])\n \n outputs, K, V = model(x_input)\n _, predicted = torch.max(outputs.data, 1)\n total += len(y_input)\n correct += (predicted == torch.max(y_input.type(torch.LongTensor), 1)[1]).sum()\nacc = int(correct) / total\nprint('Soft Test Accuracy: {}%'.format(100 * acc)) \n\n\ncorrect = 0\ntotal = 0\nfor b, [x_batch, y_batch] in hardtest_loader.items():\n for c in range(BATCH_SIZE):\n \n x_input = x_batch[c].view(1, x_batch[c].shape[0])\n y_input = torch.Tensor(y_batch[c]).type(torch.float64).view(1, y_batch[c].shape[0])\n \n outputs, K, V = model(x_input)\n _, predicted = torch.max(outputs.data, 1)\n total += len(y_input)\n correct += (predicted == torch.max(y_input.type(torch.LongTensor), 1)[1]).sum()\nacc = int(correct) / total\nprint('Hard Test Accuracy: {}%'.format(100 * acc))\n\n", "sub_path": "Attention-Classification/NRAS-germline-lr5em2_lm1em4_batch100_q10_e50.py", "file_name": "NRAS-germline-lr5em2_lm1em4_batch100_q10_e50.py", "file_ext": "py", "file_size_in_byte": 18789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "seaborn.set_context", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 247, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 261, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 272, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 273, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 274, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 275, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 285, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 285, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 319, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 319, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 319, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 435, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 435, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 436, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 436, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 439, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 462, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 462, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 465, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 467, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 497, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 497, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 500, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 502, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 506, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 506, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 522, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 522, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 525, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 527, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 527, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 540, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 541, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 583, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 586, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 588, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 588, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 599, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 599, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 602, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 604, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 604, "usage_type": "attribute"}]} {"seq_id": "421789991", "text": "import logging, fastaResFun\nfrom Bio import SeqIO\nimport json, sys\n\ndef isoformAln(aln, o):\n \"\"\"Function to cluster isoforms according to the alignment. Return the\n overall coverage of these isoforms.\n\n Isoforms are from the same species (recognized through keyword\n xxxXxx at the beginning of their name) and same letters or\n indels at same positions in alignment.\n\n @param1 aln: Path to alignment\n @param2 o: output directory\n @return outAln: Path to file of resulting alignment\n\n \"\"\"\n\n logger = logging.getLogger(\"main.alignment\")\n print(\"Clustering isoforms.\")\n\n dRem={} #for remaining sequences\n dId2Seq={} #for remaining sequences\n laln=0 #alignement length\n for fasta in SeqIO.parse(open(aln),'fasta'):\n post=fasta.id.find(\"_\")\n if post!=-1: #regular format\n sp=fasta.id[:post]\n tag=fasta.id[post+1:]\n if not sp in dId2Seq:\n dId2Seq[sp]={}\n dId2Seq[sp][tag]=str(fasta.seq)\n if laln==0:\n laln=len(fasta.seq)\n else:\n dRem[fasta.id]=str(fasta.seq)\n\n \n outCov = o\n clustok=False #flag to check if a cluster has occured\n for sp,dtagseq in dId2Seq.items():\n lclust=[list(dtagseq)] #list of clusters of tags to be split\n for pos in range(laln):\n lclust2=[]\n for clust in lclust:\n dlet={tag:dtagseq[tag][pos] for tag in clust}\n llet=set([x for x in dlet.values() if x!=\"-\"])\n if len(llet)<=1: #one letter at most, keep all\n lclust2.append(clust)\n continue\n else:\n for x in llet:\n lclust2.append([tag for tag in clust if dlet[tag]==x])\n lind=[tag for tag in clust if dlet[tag]==\"-\"] #conservative, do not know wether to merge, may be improved\n if len(lind)!=0:\n lclust2.append(lind)\n lclust=lclust2\n \n #now merge sequences in each cluster\n for clust in lclust:\n if len(clust)==1:\n dRem[sp+\"_\"+clust[0]]=dtagseq[clust[0]]\n else:\n clustok=True\n ntag=clust[-1]+\"_clust\"\n print(\"Clustered sequences \" + sp+\"_\" + (\", %s_\"%(sp)).join(clust) + \" into %s_\"%(sp)+ntag)\n nseq=\"\".join([max([dtagseq[tag][pos] for tag in clust]) for pos in range(laln)])\n dRem[sp+\"_\"+ntag]=nseq\n\n if clustok:\n with open(outCov, \"w\") as outC:\n outC.write(fastaResFun.dict2fasta(dRem))\n outC.close()\n \n return(outCov)\n else:\n return(aln)\n", "sub_path": "lib/clusterIsoFun.py", "file_name": "clusterIsoFun.py", "file_ext": "py", "file_size_in_byte": 3396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 25, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 25, "usage_type": "name"}, {"api_name": "fastaResFun.dict2fasta", "line_number": 72, "usage_type": "call"}]} {"seq_id": "407579298", "text": "#!/usr/bin/env python3\n\nimport sys\nimport collections\nimport itertools\nimport numpy\n\nimport aoc\n\ninfilename = 'input.txt'\nif len(sys.argv) > 2 and sys.argv[1] == '-i':\n infilename = sys.argv[2]\n\nprint('Using input file: %s' % infilename)\n\nf = open(infilename, 'r')\ndata = f.readlines()\nf.close()\n\ndata = [line.strip() for line in data]\n\nscanners = {}\nfor ii in range(len(data)):\n line = data[ii]\n if not line:\n continue\n\n if line.startswith('--'):\n curr = int(line.split()[2])\n scanners[curr] = []\n continue\n\n scanners[curr].append(list(map(int, line.split(','))))\n\n# compute manhattan distances from each sensor node to all other nodes, compare to\n# each sensor and find sets that have 12 of the same distances which are then the\n# shared nodes between two sensors\nmatches=[]\nfor mm in range(len(scanners)):\n for nn in range(mm+1, len(scanners)):\n if mm == nn:\n continue\n for xx in range(len(scanners[mm])):\n for yy in range(len(scanners[nn])):\n dists0 = [aoc.mandist(scanners[mm][xx], pos) for pos in scanners[mm]]\n dists1 = [aoc.mandist(scanners[nn][yy], pos) for pos in scanners[nn]]\n s = set(dists0).intersection(set(dists1))\n if len(s)>=12:\n print(mm, nn,xx,yy, s)\n matches.append((mm, nn, xx, yy))\n matches.append((nn, mm, yy, xx))\n\ndef translate_all(scanners, matches):\n open_set = list(range(len(scanners)))\n open_set.remove(0)\n closed_set = [0]\n\n beacons = [tuple(t) for t in scanners[0]]\n\n match_pairs = list(set([(el[0], el[1]) for el in matches]))\n\n translated = {}\n offsets = {0:0}\n\n sensor_pos = [(0,0,0)]\n\n iters = 0\n while open_set:\n\n # get the next mapping to do - any pair that matches a closed sensor\n # to an open sensor\n pair = None\n for p in match_pairs:\n if p[0] in closed_set and p[1] in open_set:\n pair = p\n break\n assert(pair)\n\n left = pair[0]\n right = pair[1]\n curr_batch = [el for el in matches if el[0] == left and el[1] == right]\n\n done = False\n # figure out which orientation results in all of the shared nodes point to the same\n # sensor pos\n for inds in itertools.permutations(range(3),3):\n for signs in itertools.product([-1,1], repeat=3):\n inds = numpy.array(inds)\n signs = numpy.array(signs)\n\n # found when all shared nodes rotate and offset to the same position relative to sensor 0\n pos = [aoc.tup_add(beacons[offsets[left]+el[2]], numpy.array(scanners[right][el[3]])[inds] * numpy.array(signs)) for el in curr_batch]\n if len(set(pos))==1:\n #print(pos)\n sensor_pos.append(pos[0])\n done = True\n break\n if done:\n break\n\n assert(done)\n\n # translate all of the beacons to the sensor 0 space (shared plus nonshared) and\n # track the offsets so our original mapping from above still works\n offsets[right] = len(beacons)\n trans = [aoc.tup_sub(pos[0], numpy.array(el)[inds] * numpy.array(signs)) for el in scanners[right]]\n beacons.extend(trans)\n translated[right] = trans\n\n iters += 1\n\n open_set.remove(right)\n closed_set.append(right)\n\n return beacons, sensor_pos\n\nall_beacons, sensors = translate_all(scanners, matches)\nprint(len(sensors), sensors)\nprint(len(set(all_beacons)))\n\nmaxd = 0\nfor xx, yy in itertools.combinations(range(len(sensors)),2):\n d = aoc.mandist(sensors[xx], sensors[yy])\n if d > maxd:\n maxd = d\nprint(maxd)", "sub_path": "2021/19/puzzle19.py", "file_name": "puzzle19.py", "file_ext": "py", "file_size_in_byte": 3789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "aoc.mandist", "line_number": 45, "usage_type": "call"}, {"api_name": "aoc.mandist", "line_number": 46, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 86, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "aoc.tup_add", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "aoc.tup_sub", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 122, "usage_type": "call"}, {"api_name": "aoc.mandist", "line_number": 123, "usage_type": "call"}]} {"seq_id": "25576708", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 29 11:30:46 2017\n\n@author: Deepak\n\nPart A - Network A\n\"\"\"\n\nimport sys\nsys.path.append('../../')\nsys.path.append('../')\nimport tensorflow as tf\nfrom fdl_examples.datatools import input_data\nimport scipy\n#import numpy as np\n#import time, shutil, os\n#import matplotlib.pyplot as plt\n#from scipy import misc\n\n# read in MNIST data --------------------------------------------------\nmnist = input_data.read_data_sets(\"../../data/\", one_hot=True)\n\n# run network ----------------------------------------------------------\n \n# Parameters\nlearning_rate = 0.01\ntraining_epochs = 50 # NOTE: you'll want to eventually change this \nbatch_size = 100\ndisplay_step = 1\n\ndef inference(x,W,b):\n output = tf.nn.softmax(tf.matmul(x, W) + b)\n w_hist = tf.summary.histogram(\"weights\", W)\n b_hist = tf.summary.histogram(\"biases\", b)\n y_hist = tf.summary.histogram(\"output\", output)\n return output\n\ndef loss(output, y):\n dot_product = y * tf.log(output)\n # Reduction along axis 0 collapses each column into a single\n # value, whereas reduction along axis 1 collapses each row \n # into a single value. In general, reduction along axis i \n # collapses the ith dimension of a tensor to size 1.\n xentropy = -tf.reduce_sum(dot_product, axis=1)\n loss = tf.reduce_mean(xentropy)\n return loss\n\ndef training(cost, global_step):\n tf.summary.scalar(\"cost\", cost)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n train_op = optimizer.minimize(cost, global_step=global_step)\n return train_op\n\ndef evaluate(output, y):\n correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y, 1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.summary.scalar(\"validation error\", (1.0 - accuracy))\n return accuracy\n\nif __name__ == '__main__':\n# if os.path.exists(\"logistic_logs/\"):\n# shutil.rmtree(\"logistic_logs/\")\n\n with tf.Graph().as_default():\n x = tf.placeholder(\"float\", [None, 784]) # mnist data image of shape 28*28=784\n y = tf.placeholder(\"float\", [None, 10]) # 0-9 digits recognition => 10 classes\n init = tf.constant_initializer(value=0)\n W = tf.get_variable(\"W\", [784, 10],\n initializer=init) \n b = tf.get_variable(\"b\", [10],\n initializer=init)\n output = inference(x,W,b)\n cost = loss(output, y)\n global_step = tf.Variable(0, name='global_step', trainable=False) \n train_op = training(cost, global_step)\n eval_op = evaluate(output, y)\n summary_op = tf.summary.merge_all()\n saver = tf.train.Saver()\n sess = tf.Session()\n\n # summary_writer = tf.summary.FileWriter(\"logistic_logs/\",\n # graph_def=sess.graph_def)\n \n init_op = tf.global_variables_initializer()\n sess.run(init_op)\n\n # PLOTTING EACH DIGIT\n #x[7]=0, x[6]=1, x[13]=2, x[1]=3, x[2]=4, x[27]=5, x[26]=6, x[25]=7, x[9]=8, x[8]=9 \n mini_x, mini_y = mnist.train.next_batch(30)\n\n num=mini_x[7]\n num0=num.reshape(28,28)\n\n num=mini_x[6]\n num1=num.reshape(28,28)\n \n num=mini_x[13]\n num2=num.reshape(28,28)\n \n num=mini_x[1]\n num3=num.reshape(28,28)\n \n num=mini_x[2]\n num4=num.reshape(28,28)\n \n num=mini_x[27]\n num5=num.reshape(28,28)\n \n num=mini_x[26]\n num6=num.reshape(28,28)\n \n num=mini_x[25]\n num7=num.reshape(28,28)\n \n num=mini_x[9]\n num8=num.reshape(28,28)\n \n num=mini_x[8]\n num9=num.reshape(28,28)\n \n# plt.imshow(num1)\n# \n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/0.jpg', num0)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/1.jpg', num1)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/2.jpg', num2)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/3.jpg', num3)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/4.jpg', num4)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/5.jpg', num5)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/6.jpg', num6)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/7.jpg', num7)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/8.jpg', num8)\n scipy.misc.imsave('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/9.jpg', num9)\n\n# scipy.misc.imsave('outfile.jpg', image_array)\n\n # Training cycle\n for epoch in range(training_epochs):\n avg_cost = 0.\n total_batch = int(mnist.train.num_examples/batch_size)\n # Loop over all batches\n for i in range(batch_size):\n minibatch_x, minibatch_y = mnist.train.next_batch(batch_size) \n # Fit training using batch data\n sess.run(train_op, feed_dict={x: minibatch_x, y: minibatch_y})\n # Compute average loss\n avg_cost += sess.run(cost, feed_dict={x: minibatch_x, y: minibatch_y})/total_batch\n # Display logs per epoch step\n if epoch % display_step == 0:\n print(\"Epoch:\", '%04d' % (epoch+1), \"cost =\", \"{:.9f}\".format(avg_cost))\n accuracy = sess.run(eval_op, feed_dict={x: mnist.validation.images, y: mnist.validation.labels})\n print(\"Validation Error:\", (1 - accuracy))\n summary_str = sess.run(summary_op, feed_dict={x: minibatch_x, y: minibatch_y})\n #summary_writer.add_summary(summary_str, sess.run(global_step))\n #saver.save(sess, \"logistic_logs/model-checkpoint\", global_step=global_step)\n print(\"Optimization Finished!\") \n accuracy = sess.run(eval_op, feed_dict={x: mnist.test.images, y: mnist.test.labels})\n \n #PLOTTING FIRST 10 WEIGHTS\n for wi in range (9):\n weights=sess.run(W)\n im=weights[:,wi]\n wim=im.reshape(28,28)\n filename = \"C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/W_%d.jpg\"%wi\n scipy.misc.imsave(filename, wim)\n print(\"Test Accuracy:\", accuracy)\n \n with open('C:/Users/Deepak/Dropbox/Deep Learning/Project 2/Part A1 Output/Accuracy.txt', 'w') as f:\n print('Accuracy = ',accuracy, file=f)\n f.close() ", "sub_path": "mnist_insights/Codes/P2_A1_Canvas.py", "file_name": "P2_A1_Canvas.py", "file_ext": "py", "file_size_in_byte": 6757, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "fdl_examples.datatools.input_data.read_data_sets", "line_number": 22, "usage_type": "call"}, {"api_name": "fdl_examples.datatools.input_data", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.nn.softmax", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.log", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.train.GradientDescentOptimizer", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.Graph", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.misc.imsave", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 124, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 125, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 126, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 126, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 127, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 128, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 128, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 129, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 129, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 130, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 131, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 131, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 132, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 132, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 133, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 133, "usage_type": "attribute"}, {"api_name": "scipy.misc.imsave", "line_number": 165, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 165, "usage_type": "attribute"}]} {"seq_id": "310473988", "text": "import sys\nimport os\nimport time\nimport tempfile\n\nimport arcade\nimport pyglet\n\nfrom arcade.color import RED\n\nfrom ev3dev2simulator.state import WorldState\nfrom ev3dev2simulator.visualisation.Sidebar import Sidebar\nfrom ev3dev2simulator.config.config import get_simulation_settings, debug\n\n\ndef start():\n arcade.run()\n\n\ndef get_screens():\n display = pyglet.canvas.get_display()\n screens = display.get_screens()\n return screens\n\n\nclass Visualiser(arcade.Window):\n \"\"\"\n Main simulator class.\n This class extends from arcade.Window and manages the updates and rendering of the simulator window.\n \"\"\"\n\n def __init__(self, update_world_cb, world_state: WorldState, show_fullscreen: bool,\n show_maximized: bool, use_second_screen_to_show_simulator: bool):\n\n self.pid_file = None\n self.pid = None\n self.check_for_unique_instance()\n self.update_callback = update_world_cb\n\n self.sidebar = None\n self.world_state = world_state\n\n self.current_screen_index = None\n self.set_screen_to_display_simulator_at_startup(use_second_screen_to_show_simulator)\n\n sim_settings = get_simulation_settings()\n\n self.scale = None\n self.screen_width = int(sim_settings['screen_settings']['screen_width'])\n self.screen_height = int(sim_settings['screen_settings']['screen_height'])\n self.side_bar_width = int(sim_settings['screen_settings']['side_bar_width'])\n\n from ev3dev2.version import __version__ as api_version\n from ev3dev2simulator.version import __version__ as sim_version\n screen_title = sim_settings['screen_settings'][\n 'screen_title'] + f' version: {sim_version} ev3dev2 api: {api_version}'\n\n self.frames_per_second = sim_settings['exec_settings']['frames_per_second']\n self.falling_msg = sim_settings['screen_settings']['falling_message']\n self.restart_msg = sim_settings['screen_settings']['restart_message']\n\n self.change_scale(self.screen_width, self.screen_height)\n if debug:\n print('starting simulation with scaling', self.scale)\n print('arcade version: ', arcade.version.VERSION)\n\n super(Visualiser, self).__init__(self.screen_width, self.screen_height, screen_title, update_rate=1 / 30,\n resizable=True)\n\n icon1 = pyglet.image.load(r'assets/images/body.png')\n self.set_icon(icon1)\n arcade.set_background_color(eval(sim_settings['screen_settings']['background_color']))\n\n self.msg_counter = 0\n\n self.setup_sidebar()\n self.world_state.setup_pymunk_shapes(self.scale)\n self.world_state.setup_visuals(self.scale)\n\n if show_fullscreen:\n self.toggleFullScreenOnCurrentScreen()\n\n if show_maximized:\n self.maximize()\n\n self.check_for_activation()\n\n @property\n def msg_x(self):\n return (self.screen_width - self.side_bar_width) / 2\n\n def change_scale(self, new_screen_width, new_screen_height):\n x_scale = (new_screen_width - self.side_bar_width) / self.world_state.board_width\n y_scale = new_screen_height / self.world_state.board_height\n if x_scale <= y_scale:\n scale = x_scale\n else:\n scale = y_scale\n self.screen_height = int(scale * self.world_state.board_height)\n self.screen_width = self.side_bar_width + int(scale * self.world_state.board_width)\n self.scale = scale\n\n def setup_sidebar(self):\n self.sidebar = Sidebar(self.screen_width - self.side_bar_width, self.screen_height - 70,\n self.side_bar_width, self.screen_height)\n for robot in self.world_state.get_robots():\n self.sidebar.init_robot(robot.name, robot.sensors, robot.bricks, robot.side_bar_sprites)\n\n def set_screen_to_display_simulator_at_startup(self, use_second_screen_to_show_simulator):\n \"\"\" Set screen to use to display the simulator at startup. For windows this works only in fullscreen mode.\n\n By default set current screen to show simulator, but if use_second_screen_to_show_simulator==True\n then change screen to other screen.\n\n On MacOS this works for both fullscreen and none-fullscreen mode.\n On Windows this only works for fullscreen mode. For none-fullscreen always the first screen is used.\n \"\"\"\n\n # get current_screen_index\n current_screen_index = 1 if use_second_screen_to_show_simulator else 0\n screens = get_screens()\n # for screen in screens: print(screen)\n num_screens = len(screens)\n if num_screens == 1:\n current_screen_index = 0\n self.current_screen_index = current_screen_index\n\n # change screen to show simulator\n # HACK override default screen function to change it.\n # Note: arcade window class doesn't has the screen parameter which pyglet has, so by overriding\n # the get_default_screen method we can still change the screen parameter.\n def get_default_screen():\n \"\"\"Get the default screen as specified by the user's operating system preferences.\"\"\"\n return screens[self.current_screen_index]\n\n display = pyglet.canvas.get_display()\n display.get_default_screen = get_default_screen\n\n # note:\n # for macOS get_default_screen() is also used to as the screen to draw the window initially\n # for windows the current screen is used to to draw the window initially,\n # however the value set by get_default_screen() is used as the screen\n # where to display the window fullscreen!\n\n # note: BUG: dragging window to other screen in macOS messes up view size\n # for macOS the screen of the mac can have higher pixel ratio (self.get_pixel_ratio())\n # then the second screen connected. If you drag the window from the mac screen to the\n # second screen then the windows may be the same size, but the simulator is drawn in only\n # in the lower left quart of the window.\n # => we need somehow make drawing of the simulator larger\n\n # how to view simulator window on second screen when dragging not working?\n # SOLUTION: just when starting up the simulator set it to open on the second screen,\n # then it goes well, and you can also open it fullscreen on the second screen\n # see also : https://stackoverflow.com/questions/49302201/highdpi-retina-windows-in-pyglet\n\n def check_for_unique_instance(self):\n \"\"\" Detect whether an other instance is already running. If so then trigger the\n activation for the other instance and terminate this instance.\n \"\"\"\n\n tmpdir = tempfile.gettempdir()\n self.pid_file = os.path.join(tmpdir, \"ev3dev2simulator.pid\")\n\n self.pid = str(os.getpid())\n f = open(self.pid_file, 'w')\n f.write(self.pid)\n f.flush()\n f.close()\n\n time.sleep(2)\n\n file = open(self.pid_file, 'r')\n line = file.readline()\n file.close()\n read_pid = line.rstrip()\n if read_pid != self.pid:\n # other process already running\n sys.exit()\n\n def check_for_activation(self):\n \"\"\" checks each interval whether the simulator windows must be activated (bring to front)\n\n note: activation can happen when one tries to start another instance of the simulator,\n and that instance detects an instance is already running. It then triggers the\n activation for the other instance and terminates itself.\n \"\"\"\n from pyglet import clock\n\n def callback(_):\n file = open(self.pid_file, 'r')\n line = file.readline()\n file.close()\n read_pid = line.rstrip()\n if read_pid != self.pid:\n\n # other simulator tries to start running\n # write pid to pid_file to notify this simulator is already running\n f = open(self.pid_file, 'w')\n f.write(self.pid)\n f.close()\n\n import platform\n if platform.system().lower().startswith('win'):\n self.windows_activate()\n else:\n self.activate()\n\n clock.schedule_interval(callback, 1)\n\n def windows_activate(self):\n # noinspection PyProtectedMember\n from pyglet.libs.win32 import _user32\n from pyglet.libs.win32.constants import SW_SHOWMINIMIZED, SW_SHOWNORMAL\n _user32.ShowWindow(self._hwnd, SW_SHOWMINIMIZED)\n _user32.ShowWindow(self._hwnd, SW_SHOWNORMAL)\n\n def on_close(self):\n sys.exit(0)\n\n def on_key_press(self, key, modifiers):\n \"\"\"Called whenever a key is pressed. \"\"\"\n\n # Quit the simulator\n if key == arcade.key.Q:\n self.on_close()\n\n # Toggle fullscreen between screens (only works at fullscreen mode)\n elif key == arcade.key.T:\n # User hits T. When at fullscreen, then switch screen used for fullscreen.\n if len(get_screens()) == 0:\n return\n if self.fullscreen:\n # to switch screen when in fullscreen we first have to back to normal window, and do fullscreen again\n self._set_full_screen(False)\n # Toggle between first and second screen (other screens are ignored)\n self.toggle_screen_used_for_fullscreen()\n self._set_full_screen(True)\n\n # Maximize window\n # note: is toggle on macOS, but not on windows\n elif key == arcade.key.M:\n self.maximize()\n\n # Toggle between Fullscreen and window\n # keeps viewport coordinates the same STRETCHED (FULLSCREEN)\n # Instead of a one-to-one mapping to screen size, we use stretch/squash window to match the constants.\n # src: http://arcade.academy/examples/full_screen_example.html\n elif key == arcade.key.F:\n self.update_current_screen()\n self._set_full_screen(not self.fullscreen)\n\n # toggle screen for fullscreen\n # BUG: doesn't work on macOS => see explanation in set_screen_to_display_simulator_at_startup() method\n def toggle_screen_used_for_fullscreen(self):\n\n # toggle only between screen 0 and 1 (other screens are ignored)\n self.current_screen_index = (self.current_screen_index + 1) % 2\n\n # override hidden screen parameter in window\n screens = get_screens()\n self._screen = screens[self.current_screen_index]\n\n def update_current_screen(self):\n \"\"\" using the windows position and size we determine on which screen it is currently displayed and make that\n current screen for displaying in fullscreen!!\n \"\"\"\n\n screens = get_screens()\n if len(screens) == 1:\n return\n\n location = self.get_location()\n top_left_x = location[0]\n top_left_y = location[1]\n size = self.get_size()\n win_width = size[0]\n win_height = size[1]\n\n done = False\n locations = [location, (top_left_x + win_width, top_left_y), (top_left_x, top_left_y + win_height),\n (top_left_x + win_width, top_left_y + win_height)]\n for location in locations:\n if done:\n break\n loc_x = location[0]\n loc_y = location[1]\n num = 0\n for screen in screens:\n within_screen_width = screen.x <= loc_x < (screen.x + screen.width)\n within_screen_height = screen.y <= (loc_y < (screen.y + screen.height))\n if within_screen_width and within_screen_height:\n self.current_screen_index = num\n done = True\n break\n num += 1\n\n # override hidden screen parameter in window\n self._screen = screens[self.current_screen_index]\n\n def _set_full_screen(self, is_full_screen: bool = True):\n self.set_fullscreen(is_full_screen)\n\n # Instead of a one-to-one mapping, stretch/squash window to match the\n # constants. This does NOT respect aspect ratio. You'd need to\n # do a bit of math for that.\n self.set_viewport(0, self.screen_width, 0, self.screen_height)\n\n # HACK for macOS: without this hack fullscreen on the second screen is shifted downwards in the y direction\n # By also calling the maximize function te position the fullscreen in second screen is corrected!)\n import platform\n if platform.system().lower() == \"darwin\":\n self.maximize()\n\n def on_resize(self, width, height):\n \"\"\" This method is automatically called when the window is resized. \"\"\"\n\n # Call the parent. Failing to do this will mess up the coordinates, and default to 0,0 at the center and the\n # edges being -1 to 1.\n super().on_resize(self.screen_width, self.screen_height)\n\n def on_draw(self):\n \"\"\"\n Render the simulation.\n \"\"\"\n\n arcade.start_render()\n\n for obstacleList in self.world_state.static_obstacles:\n for shape in obstacleList.get_shapes():\n if shape.line_width == 1:\n shape.draw()\n else:\n print(shape)\n self.world_state.sprite_list.draw()\n\n for robot in self.world_state.get_robots():\n robot.get_sprites().draw()\n\n if debug:\n for sprite in robot.get_sprites():\n sprite.draw_hit_box(color=RED, line_thickness=5)\n if robot.debug_shapes is not None:\n for shape in robot.debug_shapes:\n shape.draw()\n robot.debug_shapes.clear()\n\n if robot.is_stuck and self.msg_counter <= 0:\n self.msg_counter = self.frames_per_second * 3\n\n for robot in self.world_state.get_robots():\n self.sidebar.add_robot_info(robot.name, robot.values, robot.sounds)\n\n self.sidebar.draw()\n if self.msg_counter > 0:\n self.msg_counter -= 1\n\n arcade.draw_text(self.falling_msg, self.msg_x, self.screen_height - 100, arcade.color.RADICAL_RED,\n 14,\n anchor_x=\"center\")\n arcade.draw_text(self.restart_msg, self.msg_x, self.screen_height - 130, arcade.color.RADICAL_RED,\n 14,\n anchor_x=\"center\")\n\n def update(self, delta_time):\n \"\"\"\n All the logic to move the robot. Collision detection is also performed.\n Callback to WorldSimulator.update is called\n \"\"\"\n self.update_callback()\n\n def on_mouse_press(self, x: float, y: float, button: int, modifiers: int):\n self.world_state.set_object_at_position_as_selected((x, y))\n\n def on_mouse_release(self, x: float, y: float, button: int,\n modifiers: int):\n self.world_state.unselect_object()\n\n def on_mouse_drag(self, x: float, y: float, dx: float, dy: float, buttons: int, modifiers: int):\n if buttons == arcade.MOUSE_BUTTON_LEFT:\n self.world_state.move_selected_object(dx, dy)\n if buttons == arcade.MOUSE_BUTTON_RIGHT:\n self.world_state.rotate_selected_object(dy)\n", "sub_path": "ev3dev2simulator/visualisation/Visualiser.py", "file_name": "Visualiser.py", "file_ext": "py", "file_size_in_byte": 15545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "arcade.run", "line_number": 17, "usage_type": "call"}, {"api_name": "pyglet.canvas.get_display", "line_number": 21, "usage_type": "call"}, {"api_name": "pyglet.canvas", "line_number": 21, "usage_type": "attribute"}, {"api_name": "arcade.Window", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ev3dev2simulator.state.WorldState", "line_number": 32, "usage_type": "name"}, {"api_name": "ev3dev2simulator.config.config.get_simulation_settings", "line_number": 46, "usage_type": "call"}, {"api_name": "ev3dev2simulator.version.__version__", "line_number": 56, "usage_type": "name"}, {"api_name": "ev3dev2.version.__version__", "line_number": 56, "usage_type": "name"}, {"api_name": "ev3dev2simulator.config.config.debug", "line_number": 63, "usage_type": "name"}, {"api_name": "arcade.version", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pyglet.image.load", "line_number": 70, "usage_type": "call"}, {"api_name": "pyglet.image", "line_number": 70, "usage_type": "attribute"}, {"api_name": "arcade.set_background_color", "line_number": 72, "usage_type": "call"}, {"api_name": "ev3dev2simulator.visualisation.Sidebar.Sidebar", "line_number": 104, "usage_type": "call"}, {"api_name": "pyglet.canvas.get_display", "line_number": 136, "usage_type": "call"}, {"api_name": "pyglet.canvas", "line_number": 136, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 165, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 171, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 179, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 204, "usage_type": "call"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 209, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 209, "usage_type": "name"}, {"api_name": "pyglet.libs.win32._user32.ShowWindow", "line_number": 215, "usage_type": "call"}, {"api_name": "pyglet.libs.win32._user32", "line_number": 215, "usage_type": "name"}, {"api_name": "pyglet.libs.win32.constants.SW_SHOWMINIMIZED", "line_number": 215, "usage_type": "name"}, {"api_name": "pyglet.libs.win32._user32.ShowWindow", "line_number": 216, "usage_type": "call"}, {"api_name": "pyglet.libs.win32._user32", "line_number": 216, "usage_type": "name"}, {"api_name": "pyglet.libs.win32.constants.SW_SHOWNORMAL", "line_number": 216, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 219, "usage_type": "call"}, {"api_name": "arcade.key", "line_number": 225, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 229, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 242, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 249, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 312, "usage_type": "call"}, {"api_name": "arcade.start_render", "line_number": 327, "usage_type": "call"}, {"api_name": "ev3dev2simulator.config.config.debug", "line_number": 340, "usage_type": "name"}, {"api_name": "arcade.color.RED", "line_number": 342, "usage_type": "name"}, {"api_name": "arcade.draw_text", "line_number": 358, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 358, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 361, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 361, "usage_type": "attribute"}, {"api_name": "arcade.MOUSE_BUTTON_LEFT", "line_number": 380, "usage_type": "attribute"}, {"api_name": "arcade.MOUSE_BUTTON_RIGHT", "line_number": 382, "usage_type": "attribute"}]} {"seq_id": "640088811", "text": "from tqdm import tqdm\nimport pandas as pd\nimport numpy as np\nimport sys\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nimport random as rnd\nfrom imblearn.over_sampling import SMOTE\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import confusion_matrix, precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score, classification_report\nimport sor_wc_wk_joint_noredundant as sor\nimport time\nimport cv_split\nimport pickle\n\nscaler = StandardScaler()\n\npca = PCA(0.99)\n\npfile = 'test_data/test_' + sys.argv[1] + '.npz'\n\nparams = np.load(pfile)\n\nprint(params.files)\n\ntrain_classes = params['train_classes']\ntest_classes = params['test_classes']\ntrain_index = params['train_index']\ntest_index = params['test_index']\n\nrisk_class_files = ['Drought.csv', 'Earthquakes.csv', 'Explosions.csv', 'Floods.csv', 'Forest_and_Brush_Fire.csv', 'Hazardous_and_Toxic_Substance.csv', 'Landslides.csv', 'Lighting.csv', 'Snowstorms.csv', 'Tornado.csv', 'Tropical Storms.csv', 'Volcanoes.csv', 'Water_Pollution.csv']\n\nrisk_class_dict = {}\nfor i in range(len(risk_class_files)):\n risk_class_dict[i+1] = risk_class_files[i]\n\ndef remove_label(docs):\n for i in range(len(docs)):\n docs[i] = docs[i].replace('\"1, ','').replace('\"0, ','').replace(\"'0, \",'').replace(\"'0, \",'')\n return docs\n\nrisk_classes = {}\nfor risk_file in risk_class_files:\n risk_classes[risk_file] = pd.read_csv('../data/NYTimes_data/'+risk_file, header = None)[0].tolist()\n\nnon_risk_file = 'non_risk_docs.csv'\n\nnon_risk_class = pd.read_csv('../data/NYTimes_data/'+non_risk_file, header = None)[0].tolist()\n\nX = []\nY = []\n\nclass_id = 1\n\nfor risk_file in risk_class_files:\n X += risk_classes[risk_file]\n Y += [class_id] * len(risk_classes[risk_file])\n class_id += 1\n\nX += non_risk_class\nY += [0] * len(non_risk_class) \n\nX = remove_label(X)\n\ntfidf = TfidfVectorizer(ngram_range=(1,1), stop_words='english', token_pattern=u'(?ui)\\\\b\\\\w*[a-z]+\\\\w*\\\\b')\n\nfeatures = tfidf.fit_transform(X).toarray()\nlabels = Y\n\ndef run_test(features, labels, train_classes, test_classes, train_index, test_index):\n features = np.array(features)\n labels = np.array(labels)\n xtrain = features[np.isin(labels,train_classes),:]\n ytrain = labels[np.isin(labels,train_classes)]\n\n xtest = features[np.isin(labels,test_classes),:]\n ytest = labels[np.isin(labels,test_classes)]\n\n RR = 0\n R2Ri = 0\n RNR = 0\n NRNR = 0\n NRR = 0\n RiR = np.zeros(len(test_classes))\n RiNR = np.zeros(len(test_classes))\n\n X_train, X_test = xtrain[train_index], xtrain[test_index]\n y_train, y_test = ytrain[train_index], ytrain[test_index]\n\n scaler.fit(X_train)\n pca.fit(scaler.transform(X_train))\n X_train = pca.transform(scaler.transform(X_train))\n X_test = pca.transform(scaler.transform(X_test))\n xtest = pca.transform(scaler.transform(xtest))\n\n y_test_l = y_test.tolist()\n\n model = sor.HierarchicalClassifierModel(input_size = X_train[0].size, num_classes = len(risk_class_files), learning_rate = 1e-3, num_epochs = 1000, batch_size = 100, l1 = 0, l2 = 0, train_classes = train_classes)\n\n parameters = {'l1':np.logspace(-2,2,5), 'l2':np.append(np.logspace(-3,1,5),0)}\n splitter = cv_split.UnseenTestSplit()\n cmodel = GridSearchCV(model, parameters, cv = splitter, verbose=5, n_jobs = 10, error_score='raise')\n cmodel.fit(X_train, y_train.ravel())\n\n print('best params: l1=', cmodel.best_params_['l1'], 'l2=', cmodel.best_params_['l2'])\n\n model = sor.HierarchicalClassifierModel(input_size = X_train[0].size, num_classes = len(risk_class_files), learning_rate = 1e-3, num_epochs = 1000, batch_size = 100, l1 = cmodel.best_params_['l1'], l2 = cmodel.best_params_['l2'], train_classes = train_classes)\n\n model.fit(X_train, y_train)\n\n y_pred = model.predict(X_test, 0)\n y_pred_score = model.predict_score(X_test, 0)\n np.savetxt('test_results/' + sys.argv[1] + '_pca_joint_seen.out', y_pred_score)\n\n for j in range(len(y_test)):\n if y_test_l[j] >= 1:\n if y_pred[j] == 1:\n RR += 1\n y_pred_class = model.predict(X_test[j,:].reshape(1,-1), int(y_test[j]))\n if y_pred_class == 1:\n R2Ri += 1\n else:\n RNR += 1\n else:\n if y_pred[j] == -1:\n NRNR += 1\n else:\n NRR += 1\n\n for classk in range(len(test_classes)):\n xtest_ri = xtest[ytest == test_classes[classk]] \n y_pred_ri = model.predict(xtest_ri, 0)\n y_pred_ri_score = model.predict_score(xtest_ri, 0)\n np.savetxt('test_results/' + sys.argv[1] + '_pca_joint_unseen_' + str(classk)+ '.out', y_pred_ri_score)\n\n for j in range(len(y_pred_ri)):\n if y_pred_ri[j] == 1:\n RiR[classk] += 1\n else:\n RiNR[classk] += 1\n\n print(RR, RNR, NRR, NRNR, RiR, RiNR, R2Ri)\n pickle.dump(model, open('test_results/trained_model_' + sys.argv[1] + '_pca_joint' + '.m', 'wb'))\n\nprint('Train classes:', train_classes)\nprint('Test classes:', test_classes)\n\nrun_test(features, labels, train_classes, test_classes, train_index, test_index)\n", "sub_path": "code/RaRecognize_pca.py", "file_name": "RaRecognize_pca.py", "file_ext": "py", "file_size_in_byte": 5775, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "sor_wc_wk_joint_noredundant.HierarchicalClassifierModel", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 110, "usage_type": "call"}, {"api_name": "cv_split.UnseenTestSplit", "line_number": 111, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 112, "usage_type": "call"}, {"api_name": "sor_wc_wk_joint_noredundant.HierarchicalClassifierModel", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 144, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 153, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 153, "usage_type": "attribute"}]} {"seq_id": "518561237", "text": "# -*- coding: utf-8 -*-\n# @Time : 2018/5/21 9:07\n# @Author : lanxin yang\n# @FileName: augmentation.py\n\nimport cv2\nimport numpy as np\nfrom utils.config import *\n\n\ndef gamma_transform(img,gamma_vari):\n \"\"\"\n Gamma变换： y=x^gamma;\n 通过非线性变换让图像从对曝光强度的线性响应变得更接近人眼感受到的响应\n gamma>1, 拉伸图像中灰度级较低的区域，同时压缩灰度级较高的部分，图像整体变暗；\n gamma<1, 拉伸图像中灰度级较高的区域，同时压缩灰度级较低的部分，图像整体变亮；\n \"\"\"\n log_gamma_vari = np.log(gamma_vari)\n alpha = np.random.uniform(-log_gamma_vari, log_gamma_vari)\n gamma = np.exp(alpha)\n\n gamma_table=[np.power(x/255.0,gamma)*255.0 for x in range(256)]\n gamma_table=np.round(np.array(gamma_table)).astype(np.uint8)\n\n return cv2.LUT(img,gamma_table)\n\n\ndef rotate(img,lab,angle):\n \"\"\"\n 旋转操作\n \"\"\"\n M_rotate = cv2.getRotationMatrix2D((IMG_W/2, IMG_H/2), angle, 1)\n img = cv2.warpAffine(img, M_rotate, (IMG_W, IMG_H))\n lab = cv2.warpAffine(lab, M_rotate, (IMG_W, IMG_H))\n return img,lab\n\n\ndef blur(img):\n \"\"\"\n 用低通滤波来平滑图像\n \"\"\"\n img=cv2.blur(img,(3,3))\n return img\n\n\ndef add_noise(img):\n \"\"\"\n 加噪操作\n \"\"\"\n for i in range(200):\n temp_x = np.random.randint(0,img.shape[0])\n temp_y = np.random.randint(0,img.shape[1])\n img[temp_x][temp_y] = 255\n return img\n\n\ndef data_augment(img, lab):\n \"\"\"\n 增强操作\n \"\"\"\n if np.random.random() < 0.25:\n img, yb = rotate(img, lab, 90)\n if np.random.random() < 0.25:\n img, yb = rotate(img, lab, 180)\n if np.random.random() < 0.25:\n img, yb = rotate(img, lab, 270)\n if np.random.random() < 0.25:\n img = cv2.flip(img, 1) # flipcode > 0：沿y轴翻转\n lab = cv2.flip(lab, 1)\n\n if np.random.random() < 0.25:\n img = gamma_transform(img, 1.0)\n\n if np.random.random() < 0.25:\n img = blur(img)\n\n if np.random.random() < 0.2:\n img = add_noise(img)\n\n return img, lab\n", "sub_path": "semantic-segmentation/utils/augmentation.py", "file_name": "augmentation.py", "file_ext": "py", "file_size_in_byte": 2122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.log", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.LUT", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}]} {"seq_id": "306118240", "text": "#!/usr/bin/python3\nimport matplotlib\nmatplotlib.use('Agg')\nimport os\nimport sys\nimport pylab\n\nimport haitai.graph\nimport haitai.common\nimport haitai\n\nif __name__ == '__main__':\n ndays=3000\n bk='全部'\n bk='中小'\n bk='主板'\n\n # dates\n dates=haitai.common.recent_n_days(ndays)\n\n # stocks\n th = set(haitai.get_symbol_list(bk))\n\n d = 'data/163_daily'\n fs = os.listdir(d)\n fs = [f for f in fs if f != '000300.ss']\n print(fs)\n #th = set(list(th)[:100]) # for debug only\n\n # get data\n pss, vols = haitai.common.load_stock_set(fs,ndays,dates)\n\n # compute\n p=sum(pss)\n print(p)\n p=p/p[0]\n v=haitai.common.mean_with_nan(vols)\n\n # fig\n pylab.figure(figsize=(12,6))\n both_ticks=haitai.graph.gen_ticks(dates)\n\n ax=pylab.subplot(2,1,1)\n haitai.graph.draw_lines(ax,[[p]],log=True)\n haitai.graph.draw_grid(ax,both_ticks)\n\n ax=pylab.subplot(2,1,2)\n haitai.graph.draw_lines(ax,[[v,'k']],log=True)\n haitai.graph.draw_grid(ax,both_ticks)\n\n filename=haitai.output_dir+'/'+bk+'_mean.svg'\n pylab.savefig(filename)\n pylab.clf()\n\n # save mean curve\n file=open(bk+'_mean.txt','w')\n for dd,pp,vv in zip(dates,p,v):\n print(dd,pp,vv,file=file)\n file.close()\n", "sub_path": "haitai/scripts/mean.py", "file_name": "mean.py", "file_ext": "py", "file_size_in_byte": 1249, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "matplotlib.use", "line_number": 3, "usage_type": "call"}, {"api_name": "haitai.common.recent_n_days", "line_number": 19, "usage_type": "call"}, {"api_name": "haitai.common", "line_number": 19, "usage_type": "attribute"}, {"api_name": "haitai.get_symbol_list", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "haitai.common.load_stock_set", "line_number": 31, "usage_type": "call"}, {"api_name": "haitai.common", "line_number": 31, "usage_type": "attribute"}, {"api_name": "haitai.common.mean_with_nan", "line_number": 37, "usage_type": "call"}, {"api_name": "haitai.common", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pylab.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "haitai.graph.gen_ticks", "line_number": 41, "usage_type": "call"}, {"api_name": "haitai.graph", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pylab.subplot", "line_number": 43, "usage_type": "call"}, {"api_name": "haitai.graph.draw_lines", "line_number": 44, "usage_type": "call"}, {"api_name": "haitai.graph", "line_number": 44, "usage_type": "attribute"}, {"api_name": "haitai.graph.draw_grid", "line_number": 45, "usage_type": "call"}, {"api_name": "haitai.graph", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pylab.subplot", "line_number": 47, "usage_type": "call"}, {"api_name": "haitai.graph.draw_lines", "line_number": 48, "usage_type": "call"}, {"api_name": "haitai.graph", "line_number": 48, "usage_type": "attribute"}, {"api_name": "haitai.graph.draw_grid", "line_number": 49, "usage_type": "call"}, {"api_name": "haitai.graph", "line_number": 49, "usage_type": "attribute"}, {"api_name": "haitai.output_dir", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pylab.savefig", "line_number": 52, "usage_type": "call"}, {"api_name": "pylab.clf", "line_number": 53, "usage_type": "call"}]} {"seq_id": "214857535", "text": "from tracer import logger\nimport json\n\nclass display:\n\n def __init__(self, printAll, jsonFormat):\n '''\n Initializes the attributes of the packetDict object\n\n Starts the packet dict with an empty dictionary\n\n The constructor for the packetDict class\n takes a packet object and a fieldName \n The field name is the first line of the header\n which details the HTTP method and URL\n\n Parameters:\n\n boolean printAll - If True, print all intercepted HTTP packets\n boolean jsonFormat - If True, print to stdout in newline-separated JSON objects \n If False, print to stdout in pretty-printed format\n Pretty-printed format uses newline-separated key-value pairs\n whose keys are separated from their values by a colon and whitespace.\n\n Returns: packetDict\n '''\n self.printAll = printAll\n self.json = jsonFormat\n\n access_token = 'access_token'\n access_token_expiry = 'expires_in'\n refresh_token = 'refresh_token'\n refresh_token_expiry = 'refresh_expires_in'\n token_type = 'token_type'\n id_token = 'id_token'\n not_before_policy = 'not-before-policy'\n session_state = 'session_state'\n\n packetSize = 'packetSize'\n sourceIP = 'sourceIP'\n destIP = 'destIP'\n destPort = 'destPort'\n sourcePort = 'sourcePort'\n clientSecret = 'clientSecret'\n grantType = 'grantType'\n clientId = 'clientId'\n httpHeader = 'httpHeader'\n refreshToken = 'refreshToken'\n authorizationCode = 'authorizationCode'\n redirectUri = 'redirectUri'\n scope = 'scope'\n accessToken = 'accessToken'\n accessTokenExpiry = 'accessTokenExpiry'\n refreshToken = 'refreshToken'\n refreshTokenExpiry = 'refreshTokenExpiry'\n tokenType = 'tokenType'\n idToken = 'idToken'\n timestamp = 'timestamp'\n\n # printList defines the default sequence to print\n # printList is a list of doubles\n # the first entry of a double is the pretty-printed name\n # and the second entry is the key to access the pDict for the value\n # keys that dont have entries are ignored in the pDict\n self.printList = [(\"Timestamp\", timestamp), \n (\"Transmission Time\", \"http_time\"), \n (\"Packet Size\", packetSize), \n (\"Date\", \"http_date\"),\n (\"Cookie\", \"http_cookie\"),\n (\"User Agent\", \"http_user_agent\"),\n (\"Connection\", \"http_connection\"),\n (\"Request Full URI\", \"http_request_full_uri\"),\n (\"Request Method\", \"http_request_method\"),\n (\"Accept Language\", \"http_accept_language\"),\n (\"Chat\", \"http_chat\"),\n (\"Content Type\", \"content_type\"), \n (\"Response Code\", \"response_code\"), \n (\"Response Phrase\", \"response_phrase\"), \n (\"Source IP\", sourceIP),\n (\"Source Port\", sourcePort), \n (\"Destination IP\", destIP), \n (\"Destination Port\", destPort), \n (\"Server\", \"server\"),\n (\"Client Secret\", clientSecret), \n (\"Client Id\", clientId), \n (\"Grant Type\", grantType), \n (\"Authorization Code\", authorizationCode), \n (\"Redirect Uri\", redirectUri), \n (\"Scope\", scope), \n (\"Access Token\", accessToken), \n (\"Access Token Expiry\", accessTokenExpiry), \n (\"Refresh Token\", refreshToken), \n (\"Refresh Token Expiry\", refreshTokenExpiry), \n (\"Token Type\", tokenType),\n (\"Id Token\", idToken)]\n # (\"File Data\", \"file_data\")\n\n # a filter is a lambda\n # that takes parameter x which is a pDict \n # that when evaluated to True will cause\n # the display class to output the packet \n\n # filterToken is a predicate lambda that evaluates to True\n # only for packets that are responses containing tokens\n # or are requests for tokens\n self.filterToken = lambda x : self.index(x, refreshToken) or self.index(x, grantType) or self.index(x, accessToken) or self.index(x, idToken)\n\n # filterNone performs no filtering\n # and prints all (http) packets\n self.filterNone = lambda x : True\n\n # sets the filter to use\n # printAll switches the filter to print all (http) packets\n # the default is the token filter\n if self.printAll:\n self.filterCurrent = self.filterNone\n else:\n self.filterCurrent = self.filterToken\n\n\n def index(self, iDict, iKey):\n \"\"\"\n Determines if iDict contains an entry for key iKey\n\n Parameters:\n\n dict iDict - a python dictionary\n str iKey - the key to index into iDict\n\n Returns True if dict iDict contains a value for key iKey\n Returns False on KeyError\n \"\"\"\n try:\n iDict[iKey]\n except KeyError:\n return False\n return True\n\n\n def outPrint(self, pDict, fieldName, key):\n \"\"\"\n Output function that prints the entry in pDict\n with key \"key\" with pretty-printed name \"fieldName\"\n\n Parameters:\n\n dict pDict\n str fieldName\n str key\n\n Returns:\n\n data\n \"\"\"\n try:\n data = pDict[key]\n except KeyError:\n #logger.debug(\"KeyError on key {0}\".format(key))\n return None\n print(\"{0:21}{1}\".format(fieldName + \":\", data))\n return data\n\n def prettyPrint(self, pDict):\n '''\n Pretty-prints the packet data to stdout\n\n Tightly coupled to the packetDict object\n\n Parameters:\n\n dict pDict - the dictionary representing the flattened packet data\n\n Returns: None\n ''' \n logger.debug('Pretty-printing...')\n # printList is the sequence to print\n for item in self.printList:\n self.outPrint(pDict, item[0], item[1])\n print('')\n \n def jsonWrite(self, pDict):\n \"\"\"\n Outputs JSON format to stdout\n\n Parameters: \n\n dict pDict - the dictionary representing the flattened packet data\n\n Returns: None\n \"\"\"\n logger.debug('Outputting JSON')\n jsonString = json.JSONEncoder().encode(pDict)\n print(jsonString)\n\n def output(self, pDict):\n '''\n Main logging function\n\n Decides which format to print to stdout\n\n Parameters: None\n\n Returns: None\n '''\n logger.debug('Preparing to output...')\n packetTest = self.filterCurrent\n\n # if packetTest evaluates to True over the pDict\n # then output the packet\n if packetTest(pDict):\n if self.json:\n self.jsonWrite(pDict)\n else:\n self.prettyPrint(pDict)\n", "sub_path": "tokenTracer/display.py", "file_name": "display.py", "file_ext": "py", "file_size_in_byte": 7649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tracer.logger.debug", "line_number": 175, "usage_type": "call"}, {"api_name": "tracer.logger", "line_number": 175, "usage_type": "name"}, {"api_name": "tracer.logger.debug", "line_number": 191, "usage_type": "call"}, {"api_name": "tracer.logger", "line_number": 191, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 192, "usage_type": "call"}, {"api_name": "tracer.logger.debug", "line_number": 205, "usage_type": "call"}, {"api_name": "tracer.logger", "line_number": 205, "usage_type": "name"}]} {"seq_id": "45801763", "text": "# coding:utf-8\n\"\"\"\"\nProgram: coucou\nDescription:\nAuthor: XY - mailyanxin@gmail.com\nDate: 2018-05-31 06:29:05\nLast modified: 2018-05-31 06:29:05\nPython release: 3.5.2\n\"\"\"\n\nfrom decimal import Decimal\n\nfrom flask import current_app\nfrom flask import jsonify\nfrom flask import request\nfrom flask.views import View\nfrom flask.wrappers import Response\nfrom flask_login import current_user\n\nfrom coucou.extensions import db\nfrom coucou.utils.helpers import decimal_2_float\nfrom coucou.utils.helpers import set_float_prec\n\n\nclass MyPostView(View):\n '''\n 输入参数预处理\n 结果自动处理\n '''\n methods = ['POST', ]\n\n def __init__(self):\n data = request.get_json()\n self.params = self.preprocess_data(data)\n\n self.logger = current_app.logger\n self.debug = True\n\n if self.debug:\n self.logger.debug(self.params)\n\n self.token_name_id = self.params['token_name_id'] if 'token_name_id' in self.params else None\n if self.token_name_id == 'dacc_token':\n self.token_name_id = 'dacc'\n\n self.token_name = self.params['token_name'] if 'token_name' in self.params else None\n if self.token_name == 'dacc_token':\n self.token_name = 'dacc'\n self.params['token_name'] = 'dacc'\n\n self.phone_num = current_user.phone_num if not current_user.is_anonymous else None\n\n for k, v in self.params.items():\n setattr(self, k, v)\n\n super().__init__()\n\n def dispatch_request(self):\n\n self.ret_data = self.logic_view()\n\n # customize errcode\n if isinstance(self.ret_data, dict) and self.ret_data.get('errcode', 0) != 0:\n return jsonify(self.ret_data)\n # remark\n if isinstance(self.ret_data, dict) and\\\n self.ret_data.get('errcode',1) == 0 and\\\n self.ret_data.get('remark',None):\n return jsonify(self.ret_data)\n # None\n if self.ret_data is None:\n ret = dict(errcode=0, errmsg='ok')\n if self.debug:\n self.logger.debug(ret)\n return jsonify(ret)\n # 0 errcode\n if isinstance(self.ret_data, dict) or \\\n isinstance(self.ret_data, list):\n ret = dict(errcode=0, errmsg='ok')\n self.after_process_data()\n ret['data'] = self.ret_data\n if self.debug:\n self.logger.debug(ret)\n return jsonify(ret)\n # login required ..\n if isinstance(self.ret_data, Response):\n return self.ret_data\n\n else:\n self.logger.info(self.ret_data)\n self.logger.info(type(self.ret_data))\n return self.ret_data\n\n def preprocess_data(self, data):\n\n column_type_info = dict()\n for table_name, table_obj in db.metadata.tables.items():\n colums = table_obj.columns.items()\n for i in colums:\n column_type_info[i[0]] = i[1].type\n\n new_data = dict()\n for k, v in data.items():\n k_type = column_type_info.get(k, None)\n if k_type:\n if isinstance(k_type, db.String):\n v = str(v)\n if isinstance(k_type, db.Integer):\n v = int(v)\n if isinstance(k_type, db.Float):\n v = float(v)\n if isinstance(k_type, db.DECIMAL):\n if isinstance(v, float):\n v = Decimal(str(v))\n else:\n v = Decimal(v)\n\n new_data[k] = v\n\n return new_data\n\n def after_process_data(self):\n\n if isinstance(self.ret_data, dict):\n new_data = dict()\n for k, v in self.ret_data.items():\n if isinstance(v, Decimal):\n new_data[k] = decimal_2_float(v)\n if isinstance(v, float):\n if 'star' in k:\n new_data[k] = set_float_prec(v)\n else:\n new_data[k] = v\n else:\n new_data[k] = v\n\n self.ret_data = new_data\n else:\n pass\n", "sub_path": "coucou/MyView.py", "file_name": "MyView.py", "file_ext": "py", "file_size_in_byte": 4186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask.views.View", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.current_app.logger", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 36, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_anonymous", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 51, "usage_type": "name"}, {"api_name": "flask_login.current_user.phone_num", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.wrappers.Response", "line_number": 86, "usage_type": "argument"}, {"api_name": "coucou.extensions.db.metadata.tables.items", "line_number": 97, "usage_type": "call"}, {"api_name": "coucou.extensions.db.metadata", "line_number": 97, "usage_type": "attribute"}, {"api_name": "coucou.extensions.db", "line_number": 97, "usage_type": "name"}, {"api_name": "coucou.extensions.db.String", "line_number": 106, "usage_type": "attribute"}, {"api_name": "coucou.extensions.db", "line_number": 106, "usage_type": "name"}, {"api_name": "coucou.extensions.db.Integer", "line_number": 108, "usage_type": "attribute"}, {"api_name": "coucou.extensions.db", "line_number": 108, "usage_type": "name"}, {"api_name": "coucou.extensions.db.Float", "line_number": 110, "usage_type": "attribute"}, {"api_name": "coucou.extensions.db", "line_number": 110, "usage_type": "name"}, {"api_name": "coucou.extensions.db.DECIMAL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "coucou.extensions.db", "line_number": 112, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 114, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 116, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 127, "usage_type": "argument"}, {"api_name": "coucou.utils.helpers.decimal_2_float", "line_number": 128, "usage_type": "call"}, {"api_name": "coucou.utils.helpers.set_float_prec", "line_number": 131, "usage_type": "call"}]} {"seq_id": "602746725", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /tmp/pip-build-ed191__6/requests-toolbelt/requests_toolbelt/auth/http_proxy_digest.py\n# Compiled at: 2020-01-10 16:25:32\n# Size of source mod 2**32: 3705 bytes\n\"\"\"The module containing HTTPProxyDigestAuth.\"\"\"\nimport re\nfrom requests import cookies, utils\nfrom . import _digest_auth_compat as auth\n\nclass HTTPProxyDigestAuth(auth.HTTPDigestAuth):\n __doc__ = 'HTTP digest authentication between proxy\\n\\n :param stale_rejects: The number of rejects indicate that:\\n the client may wish to simply retry the request\\n with a new encrypted response, without reprompting the user for a\\n new username and password. i.e., retry build_digest_header\\n :type stale_rejects: int\\n '\n _pat = re.compile('digest ', flags=(re.IGNORECASE))\n\n def __init__(self, *args, **kwargs):\n (super(HTTPProxyDigestAuth, self).__init__)(*args, **kwargs)\n self.stale_rejects = 0\n self.init_per_thread_state()\n\n @property\n def stale_rejects(self):\n thread_local = getattr(self, '_thread_local', None)\n if thread_local is None:\n return self._stale_rejects\n else:\n return thread_local.stale_rejects\n\n @stale_rejects.setter\n def stale_rejects(self, value):\n thread_local = getattr(self, '_thread_local', None)\n if thread_local is None:\n self._stale_rejects = value\n else:\n thread_local.stale_rejects = value\n\n def init_per_thread_state(self):\n try:\n super(HTTPProxyDigestAuth, self).init_per_thread_state()\n except AttributeError:\n pass\n\n def handle_407(self, r, **kwargs):\n \"\"\"Handle HTTP 407 only once, otherwise give up\n\n :param r: current response\n :returns: responses, along with the new response\n \"\"\"\n if r.status_code == 407 and self.stale_rejects < 2:\n s_auth = r.headers.get('proxy-authenticate')\n if s_auth is None:\n raise IOError('proxy server violated RFC 7235:407 response MUST contain header proxy-authenticate')\n else:\n if not self._pat.match(s_auth):\n return r\n self.chal = utils.parse_dict_header(self._pat.sub('', s_auth, count=1))\n if 'Proxy-Authorization' in r.request.headers:\n if 'stale' in self.chal:\n if self.chal['stale'].lower() == 'true':\n self.stale_rejects += 1\n elif self.chal['stale'].lower() == 'false':\n raise IOError('User or password is invalid')\n r.content\n r.close()\n prep = r.request.copy()\n cookies.extract_cookies_to_jar(prep._cookies, r.request, r.raw)\n prep.prepare_cookies(prep._cookies)\n prep.headers['Proxy-Authorization'] = self.build_digest_header(prep.method, prep.url)\n _r = (r.connection.send)(prep, **kwargs)\n _r.history.append(r)\n _r.request = prep\n return _r\n else:\n return r\n\n def __call__(self, r):\n self.init_per_thread_state()\n if self.last_nonce:\n r.headers['Proxy-Authorization'] = self.build_digest_header(r.method, r.url)\n r.register_hook('response', self.handle_407)\n return r", "sub_path": "pycfiles/libopenstorage_openstorage-0.42.24.1-py3-none-any/http_proxy_digest.cpython-36.py", "file_name": "http_proxy_digest.cpython-36.py", "file_ext": "py", "file_size_in_byte": 3471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "requests.utils.parse_dict_header", "line_number": 57, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 57, "usage_type": "name"}, {"api_name": "requests.cookies.extract_cookies_to_jar", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.cookies", "line_number": 67, "usage_type": "name"}]} {"seq_id": "163154417", "text": "import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import SGDRegressor\nfrom sklearn.metrics import roc_auc_score, r2_score\n\n# import the data file\ndf = pd.read_csv('train_features.csv')\ndy = pd.read_csv('train_labels.csv')\ndt = pd.read_csv('test_features.csv')\n\n#---Compare the number of missing values in each column---\n#missing = df.isnull().sum(0).reset_index()\n#missing.columns = ['column', 'count']\n#missing = missing.sort_values(by = 'count', ascending = False).loc[missing['count'] > 0]\n#missing['percentage'] = missing['count'] / float(df.shape[0]) * 100\n#ind = np.arange(missing.shape[0])\n#width = 1\n#fig, ax = plt.subplots(figsize=(8,10))\n#rects = ax.barh(ind, missing.percentage.values, color='b')\n#ax.set_yticks(ind)\n#ax.set_yticklabels(missing.column.values, rotation='horizontal')\n#ax.set_xlabel(\"Precentage of missing values %\", fontsize = 10)\n#ax.set_title(\"Number of missing values in each column\", fontsize = 12)\n#plt.show()\n#--------------------------------------------------------\n\n#-----------------Correlation diagram--------------------\n#colormap = plt.cm.RdBu\n#plt.figure(figsize=(24,22))\n#plt.title('Pearson Correlation of Features', y=1.05, size=15)\n#sns.heatmap(X_trains.astype(float).corr(),linewidths=0.1, vmax=1.0, \\\n# square=True, cmap=colormap, linecolor='white', annot=True)\n#--------------------------------------------------------\n\n# assign X and y from the train dataset\nall_pids = df.groupby('pid')\nap = list(all_pids.groups.keys())\ntrain_pids, val_pids = train_test_split(ap, test_size = 0.2, \\\n random_state = 42)\n\nvital = ['Heartrate', 'SpO2', 'ABPs', 'ABPm', 'RRate','ABPd']\ntest = ['Temp', 'EtCO2', 'PTT', 'BUN', 'Lactate', 'Hgb', 'HCO3', 'BaseExcess', \\\n 'Fibrinogen', 'Phosphate', 'WBC', 'Creatinine', 'PaCO2', 'AST', \\\n 'FiO2', 'Platelets', 'SaO2', 'Glucose', 'Magnesium', 'Potassium', \\\n 'Calcium', 'Alkalinephos', 'Bilirubin_direct', 'Chloride', \\\n 'Hct', 'Bilirubin_total', 'TroponinI', 'pH']\ntl = ['LABEL_BaseExcess', 'LABEL_Fibrinogen', 'LABEL_AST', 'LABEL_Alkalinephos', \\\n 'LABEL_Bilirubin_total', 'LABEL_Lactate', 'LABEL_TroponinI', 'LABEL_SaO2', \\\n 'LABEL_Bilirubin_direct', 'LABEL_EtCO2', 'LABEL_Sepsis']\nvl = ['LABEL_RRate', 'LABEL_ABPm', 'LABEL_SpO2', 'LABEL_Heartrate']\n\n#X_train = pd.DataFrame()\n#X_val = pd.DataFrame()\n#Y_train = pd.DataFrame()\n#Y_val = pd.DataFrame()\n#itt = IterativeImputer(max_iter=20, tol=0.01, random_state = 42)\n#preprocessor = ColumnTransformer(transformers = [ \\\n# ('vital', itt, vital)])\n#sc = StandardScaler()\n#\n##Imputing missing values\n#X_i = df.copy()\n#X_i.loc[:,test] = X_i.loc[:,test].notnull().astype('float')\n#imp = preprocessor.fit(X_i)\n#X_i.loc[:,vital] = imp.transform(X_i)\n#X_i = X_i.drop(['Bilirubin_total', 'PaCO2', 'BaseExcess', 'Alkalinephos', \\\n# 'HCO3', 'PTT', 'Phosphate', 'Magnesium', 'Creatinine', \\\n# 'Calcium', 'Platelets', 'WBC'], axis = 1)\n#\n#for pid in train_pids:\n# X_pid = X_i.groupby('pid').get_group(pid)\n# X_pid = X_pid.drop(['pid', 'Time'], axis=1)\n# xp = X_pid.iloc[11].values\n# xi = X_pid.iloc[:,:].drop(['Age'], axis=1).values.flatten()\n# xj = pd.DataFrame([[*xi, *xp]])\n# X_train = X_train.append(xj)\n#X_trains = pd.DataFrame(sc.fit_transform(X_train))\n#\n#for pid in val_pids:\n# X_pid = X_i.groupby('pid').get_group(pid)\n# X_pid = X_pid.drop(['pid', 'Time'], axis=1)\n# xp = X_pid.iloc[11].values\n# xi = X_pid.iloc[:,:].drop(['Age'], axis=1).values.flatten()\n# xj = pd.DataFrame([[*xi, *xp]])\n# X_val = X_val.append(xj)\n#X_vals = pd.DataFrame(sc.fit_transform(X_val))\n#\n#\n##Split y values into train and validation set\n#ypids = dy.groupby('pid')\n#for pid in train_pids:\n# Y_pid = ypids.get_group(pid)\n# Y_pid = Y_pid.drop(['pid'], axis=1)\n# Y_train = Y_train.append(Y_pid)\n#\n#for pid in val_pids:\n# Y_pid = ypids.get_group(pid)\n# Y_pid = Y_pid.drop(['pid'], axis=1)\n# Y_val = Y_val.append(Y_pid)\n# \n##Test set\n#X_t = dt.copy()\n#X_t.loc[:,test] = X_t.loc[:,test].notnull().astype('float')\n#X_t.loc[:,vital] = imp.transform(X_t)\n#X_t = X_t.drop(['Bilirubin_total', 'PaCO2', 'BaseExcess', 'Alkalinephos', \\\n# 'HCO3', 'PTT', 'Phosphate', 'Magnesium', 'Creatinine', \\\n# 'Calcium', 'Platelets', 'WBC'], axis = 1)\n#\n#test_pid = list(dt.groupby('pid').groups.keys())\n#X_test = pd.DataFrame()\n#for pid in test_pid:\n# X_pid = X_t.groupby('pid').get_group(pid)\n# X_pid = X_pid.drop(['pid', 'Time'], axis=1)\n# xp = X_pid.iloc[11].values\n# xi = X_pid.iloc[:,:].drop(['Age'], axis=1).values.flatten()\n# xj = pd.DataFrame([[*xi, *xp]])\n# X_test = X_test.append(xj) \n#X_tests = pd.DataFrame(sc.fit_transform(X_test))\n\n\nsvc = SVC(kernel='rbf', C=0.1, class_weight='balanced', gamma='scale', \\\n probability=True, random_state=0)\nsvr = SGDRegressor(penalty='elasticnet', alpha=0.05, l1_ratio=0.1)\nY_predd = pd.DataFrame()\nY_predd = Y_predd.append([pd.DataFrame(test_pid, columns=['pid'])])\n\n#for i in ['LABEL_Sepsis']:\n# svc.fit(X_trains, Y_train[i])\n# y_pred = svc.predict(X_vals)\n# print('roc_auc_score:', roc_auc_score(Y_val[i], y_pred))\n \nfor i in tl:\n svc.fit(X_trains, Y_train[i])\n y_pred = svc.predict_proba(X_tests)\n Y_predd[i] = pd.DataFrame(y_pred)[1]\n\n#for i in vl:\n# svr.fit(X_trains, Y_train[i])\n# y_pred = svr.predict(X_vals)\n# print('r2 score', r2_score(Y_val[i], y_pred))\n\nfor i in vl:\n svr.fit(X_trains, Y_train[i])\n y_pred = svr.predict(X_tests)\n Y_predd[i] = pd.DataFrame(y_pred)\n\nY_predd.to_csv('prediction.zip', index=False, float_format='%.3f', compression='zip')\n \n", "sub_path": "Don/Task2.py", "file_name": "Task2.py", "file_ext": "py", "file_size_in_byte": 6043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDRegressor", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}]} {"seq_id": "598139925", "text": "\"\"\"empty message\n\nRevision ID: a0fbd4d0f7c3\nRevises: \nCreate Date: 2020-11-20 15:39:08.444169\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a0fbd4d0f7c3'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('player',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.String(length=80), nullable=False),\n sa.Column('club', sa.String(length=120), nullable=False),\n sa.Column('age', sa.Integer(), nullable=False),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('player')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/a0fbd4d0f7c3_.py", "file_name": "a0fbd4d0f7c3_.py", "file_ext": "py", "file_size_in_byte": 837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} {"seq_id": "224483654", "text": "from serial import Serial\nfrom config import (\n SINGLE_PRESS_DURATION,\n MIN,\n CENTER,\n MAX,\n Y,\n B,\n A,\n X,\n L,\n R,\n ZL,\n ZR,\n MINUS,\n PLUS,\n LCLICK,\n RCLICK,\n HOME,\n HAT_CENTER,\n)\nimport time\n\n\ndef SplitHex(num):\n return [(num >> 8), (num & 0xFF)]\n\n\nclass Command:\n def __init__(\n self, lx=CENTER, ly=CENTER, rx=CENTER, ry=CENTER, hat=HAT_CENTER, button=0\n ):\n self.lx = lx\n self.ly = ly\n self.rx = rx\n self.ry = ry\n self.hat = hat\n self.button = button\n\n def __eq__(self, other):\n if isinstance(other, Command):\n return (\n self.lx == other.lx\n and self.ly == other.ly\n and self.rx == other.rx\n and self.ry == other.ry\n and self.hat == other.hat\n and self.button == other.button\n )\n return False\n\n def as_byte(self):\n button = SplitHex(self.button)\n return bytearray(\n [self.lx, self.ly, self.rx, self.ry, self.hat, button[0], button[1]]\n )\n\n\nclass Serial(Serial):\n def __init__(self, *args, **kwargs):\n self.p_command = None\n self.start_timer = 0\n self.stop_timer = 0\n self.csv_writer = None\n return super().__init__(*args, **kwargs)\n\n def attach_csv(self, csv_writer):\n self.csv_writer = csv_writer\n\n def my_write(self, command: Command):\n if self.p_command == None:\n self.p_command = command\n self.start_timer = time.time_ns()\n\n if self.p_command == command:\n self.counter = time.time()\n self.stop_timer = time.time_ns()\n else:\n self.csv_writer.writerow(\n [\n self.p_command.lx,\n self.p_command.ly,\n self.p_command.rx,\n self.p_command.ry,\n self.p_command.hat,\n self.p_command.button,\n (self.stop_timer - self.start_timer),\n ]\n )\n self.start_timer = time.time_ns()\n self.stop_timer = self.start_timer\n self.p_command = command\n\n self.write(command.as_byte())\n", "sub_path": "python/classes.py", "file_name": "classes.py", "file_ext": "py", "file_size_in_byte": 2278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "config.CENTER", "line_number": 31, "usage_type": "name"}, {"api_name": "config.HAT_CENTER", "line_number": 31, "usage_type": "name"}, {"api_name": "time.time_ns", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time_ns", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time_ns", "line_number": 90, "usage_type": "call"}]} {"seq_id": "379827972", "text": "from __future__ import absolute_import\n\nfrom ..vtask import VTask\n\nfrom thrift.server.TNonblockingServer import TNonblockingServer\nfrom thrift.transport.TSocket import TServerSocket\n\nimport time\n\n\nclass ThriftProcessorTask(VTask):\n LOOPLESS = True\n PROCESSOR = None \n\n def __init__(self, service):\n super(ThriftProcessorTask, self).__init__(service)\n assert self.PROCESSOR is not None\n self.processor = self.PROCESSOR(self.service)\n\n\nclass NBServerTask(VTask):\n DEFAULT_HOST = '0.0.0.0'\n DEFAULT_PORT = 0\n OPT_PREFIX = 'thrift'\n\n bound_host = bound_port = None\n\n def getProcessor(self):\n \"\"\"Automatically find the ThriftProcessorTask subclass\"\"\"\n found = None\n for task in self.service.tasks:\n if isinstance(task, ThriftProcessorTask):\n assert found is None, \"Multiple processor tasks! (%s, %s)\" % \\\n (found.name, task.name)\n found = task\n assert found is not None, \"No ThriftProcessorTask's found!\"\n return found.processor\n\n def initTask(self):\n super(NBServerTask, self).initTask()\n\n self._stopped = False\n self.socket = TServerSocket(\n self.getTaskOption('host'), self.getTaskOption('port'))\n self.server = TNonblockingServer(\n self.getProcessor(), self.socket,\n threads=self.getTaskOption('threads'))\n self.server.prepare()\n self.bound_host, self.bound_port = \\\n self.server.socket.handle.getsockname()\n self.logger.info(\"%s Server Started on %s:%s\",\n self.name, self.bound_host, self.bound_port)\n\n def stop(self):\n self.server.stop()\n self.server.close()\n self._stopped = True\n\n def _runloop(self):\n while not self.server._stop:\n self.server.serve()\n while not self._stopped:\n time.sleep(0.1)\n\n @classmethod\n def _addArguments(cls, ap):\n super(NBServerTask, cls)._addArguments(ap)\n ap.add_argument(cls._loptName('host'), default=cls.DEFAULT_HOST,\n metavar='HOST',\n help='Address to bind server to [%(default)s]')\n ap.add_argument(cls._loptName('port'), type=int, metavar='PORT',\n default=cls.DEFAULT_PORT,\n help='Port to run server on [%(default)s]')\n ap.add_argument(cls._loptName('threads'), type=int, default=10,\n metavar='N',\n help='Server Worker Threads [%(default)s]')\n", "sub_path": "sparts/tasks/thrift.py", "file_name": "thrift.py", "file_ext": "py", "file_size_in_byte": 2575, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "vtask.VTask", "line_number": 11, "usage_type": "name"}, {"api_name": "vtask.VTask", "line_number": 21, "usage_type": "name"}, {"api_name": "thrift.transport.TSocket.TServerSocket", "line_number": 43, "usage_type": "call"}, {"api_name": "thrift.server.TNonblockingServer.TNonblockingServer", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}]} {"seq_id": "231448694", "text": "import mock\nimport os\nimport unittest\n\nfrom ngi_pipeline.engines.sarek.exceptions import ParserException\nfrom ngi_pipeline.engines.sarek.models.sample import SarekAnalysisSample\nfrom ngi_pipeline.engines.sarek.models.workflow import SarekWorkflowStep, SarekMainStep\nfrom ngi_pipeline.tests.engines.sarek.models.test_sarek import TestSarekGermlineAnalysis\n\n\nclass TestSarekWorkflowStep(unittest.TestCase):\n\n CONFIG = TestSarekGermlineAnalysis.CONFIG\n\n def setUp(self):\n self.sarek_args = self.CONFIG[\"sarek\"].copy()\n self.sarek_workflow_step = SarekWorkflowStep(\n self.sarek_args[\"command\"],\n **{k: v for k, v in self.sarek_args.items() if k != \"command\"})\n\n def test__append_argument(self):\n base_string = \"this-is-the-base-string\"\n\n # test a non-existing attribute\n attribute = \"this-attribute-does-not-exist\"\n self.assertEqual(base_string, self.sarek_workflow_step._append_argument(base_string, attribute))\n\n # test a None attribute\n attribute = \"none_attribute\"\n setattr(self.sarek_workflow_step, attribute, None)\n self.assertEqual(base_string, self.sarek_workflow_step._append_argument(base_string, attribute))\n\n # test a list attribute\n attribute = \"list_attribute\"\n value = [\"this\", \"is\", \"a\", \"list\"]\n self.sarek_workflow_step.parameters[attribute] = value\n expected_result = \"{0} --{1} ${{{1}}}\".format(base_string, attribute)\n self.assertEqual(expected_result, self.sarek_workflow_step._append_argument(base_string, attribute))\n\n # test a string attribute\n attribute = \"string_attribute\"\n value = \"this-is-a-string\"\n self.sarek_workflow_step.parameters[attribute] = value\n expected_result = \"{0} --{1} ${{{1}}}\".format(base_string, attribute)\n self.assertEqual(expected_result, self.sarek_workflow_step._append_argument(base_string, attribute))\n\n # test a custom hyphen string\n hyphen = \"xyz\"\n expected_result = \"{0} {2}{1} ${{{1}}}\".format(base_string, attribute, hyphen)\n self.assertEqual(\n expected_result, self.sarek_workflow_step._append_argument(\n base_string, attribute, hyphen=hyphen))\n\n def test_sarek_step(self):\n with self.assertRaises(NotImplementedError):\n self.sarek_workflow_step.sarek_step()\n\n def test_command_line(self):\n sarek_workflow_step = SarekWorkflowStep(self.sarek_args[\"command\"])\n self.assertEqual(self.sarek_args[\"command\"], sarek_workflow_step.command_line())\n\n def test_command_line_args(self):\n valid_tools = self.sarek_args[\"tools\"]\n observed_command_line = self.sarek_workflow_step.command_line()\n self.assertIn(\"--tools\", observed_command_line)\n self.assertIn(\",\".join(valid_tools), observed_command_line)\n for key in [x for x in list(self.sarek_args.keys()) if x not in [\"tools\", \"command\"]]:\n self.assertIn(\"-{} {}\".format(key, self.sarek_args[key]), observed_command_line)\n\n\nclass TestSarekMainStep(unittest.TestCase):\n\n def test_report_files(self):\n analysis_sample = mock.Mock(spec=SarekAnalysisSample)\n analysis_sample.sampleid = \"this-is-a-sample-id\"\n analysis_sample.sample_analysis_path.return_value = \"this-is-a-path\"\n analysis_sample.sample_analysis_results_dir.return_value = \"this-is-the-results-path\"\n with mock.patch(\"os.listdir\") as list_mock:\n file_list = [\"file1\", \"file2.extension\", \"file3.metrics\", \"file4.metrics\"]\n for listed_files in [file_list[0:2], file_list]:\n list_mock.return_value = listed_files\n with self.assertRaises(ParserException):\n SarekMainStep.report_files(analysis_sample)\n list_mock.return_value = file_list[0:3]\n self.assertEqual(\n file_list[2], os.path.basename(SarekMainStep.report_files(analysis_sample)[1][1]))\n", "sub_path": "ngi_pipeline/tests/engines/sarek/models/test_workflow.py", "file_name": "test_workflow.py", "file_ext": "py", "file_size_in_byte": 3972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.tests.engines.sarek.models.test_sarek.TestSarekGermlineAnalysis.CONFIG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.tests.engines.sarek.models.test_sarek.TestSarekGermlineAnalysis", "line_number": 13, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekWorkflowStep", "line_number": 17, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekWorkflowStep", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 71, "usage_type": "attribute"}, {"api_name": "mock.Mock", "line_number": 74, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sample.SarekAnalysisSample", "line_number": 74, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 78, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.exceptions.ParserException", "line_number": 82, "usage_type": "argument"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekMainStep.report_files", "line_number": 83, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekMainStep", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekMainStep.report_files", "line_number": 86, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.workflow.SarekMainStep", "line_number": 86, "usage_type": "name"}]} {"seq_id": "90826283", "text": "from time import time\nfrom humanize import naturaldelta\nfrom ensmallen_graph import EnsmallenGraph # pylint: disable=no-name-in-module\nimport compress_json\nimport json\nimport numpy as np\n\nstart = time()\ngraph = EnsmallenGraph(\n edge_path=\"../graph/cooccurrence/edges.tsv\",\n sources_column=\"subject\",\n destinations_column=\"object\",\n directed=False,\n validate_input_data=True\n)\ncompleted_graph = time() - start\nstart_walk = time()\n\nwalks = graph.walk(\n iterations=1,\n length=80,\n return_weight=1,\n explore_weight=1,\n change_node_type_weight=1,\n change_edge_type_weight=1\n)\ndelta = time() - start\ntotal_walk_time = time() - start_walk\n\nmean_walks_length = np.mean([\n len(walk) for walk in walks\n])\n\nmedian_walks_length = np.median([\n len(walk) for walk in walks\n])\n\ndegrees = [\n graph.degree(node)\n for node in range(graph.get_nodes_number())\n]\n\nresponse = {\n \"directory\": \"cooccurrence\",\n \"total_required_time\": delta,\n \"building_graph_required_time\": completed_graph,\n \"random_walk_time\": total_walk_time,\n \"mean_walks_length\": mean_walks_length,\n \"median_walks_length\": median_walks_length,\n \"traps_rate\": graph.traps_rate(),\n \"mean_outbound_edges\": np.mean(degrees),\n \"median_outbound_edges\": np.median(degrees),\n \"nodes\": graph.get_nodes_number(),\n \"edges\": graph.get_edges_number()\n}\n\nprint(json.dumps(response, indent=4))\n\ncompress_json.dump(response, \"time_required.json\", json_kwargs={\"indent\": 4})\n", "sub_path": "notebooks_and_scripts/speed_test_cooccurrence.py", "file_name": "speed_test_cooccurrence.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.time", "line_number": 8, "usage_type": "call"}, {"api_name": "ensmallen_graph.EnsmallenGraph", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 52, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "compress_json.dump", "line_number": 59, "usage_type": "call"}]} {"seq_id": "618756846", "text": "# 我采用requests库\nimport requests\nimport time\n\n# 用来获取 时间戳\ndef gettime():\n return int(round(time.time() * 1000))\n\nif __name__ == '__main__':\n # 用来自定义头部的\n headers = {}\n # 用来传递参数的\n keyvalue = {}\n # 目标网址(问号前面的东西)\n url = 'http://data.stats.gov.cn/easyquery.htm'\n\n # 头部的填充\n headers['User-Agent'] = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14) ' \\\n 'AppleWebKit/605.1.15 (KHTML, like Gecko) ' \\\n 'Version/12.0 Safari/605.1.15'\n\n # 下面是参数的填充，参考图10\n keyvalue['m'] = 'QueryData'\n keyvalue['dbcode'] = 'hgnd'\n keyvalue['rowcode'] = 'zb'\n keyvalue['colcode'] = 'sj'\n keyvalue['wds'] = '[]'\n keyvalue['dfwds'] = '[{\"wdcode\":\"zb\",\"valuecode\":\"A0301\"}]'\n keyvalue['k1'] = str(gettime())\n\n # 发出请求，使用get方法，这里使用我们自定义的头部和参数\n # r = requests.get(url, headers=headers, params=keyvalue)\n # 建立一个Session\n s = requests.session()\n # 在Session基础上进行一次请求\n r = s.get(url, params=keyvalue, headers=headers)\n # 打印返回过来的状态码\n print (r.status_code)\n # 修改dfwds字段内容\n keyvalue['dfwds'] = '[{\"wdcode\":\"sj\",\"valuecode\":\"2000\"}]'\n # 再次进行请求\n r = s.get(url, params=keyvalue, headers=headers)\n # 此时我们就能获取到我们搜索到的数据了\n print (r.text)", "sub_path": "crawl.py", "file_name": "crawl.py", "file_ext": "py", "file_size_in_byte": 1494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.time", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 34, "usage_type": "call"}]} {"seq_id": "26339791", "text": "'''\n@author: etekinalp\n@date: Aug 26, 2014\n@mail: e.tekinalp@icloud.com\n@brief: This module setups a plugin\n'''\n\n\nfrom maya import cmds, mel\nfrom goe_plugins import plugin_master\nreload(plugin_master)\n\n\nclass Plug(plugin_master.PluginSetup):\n \"\"\"\n Subclass PluginSetup and setup the plugin test environment.\n \"\"\"\n\n def __init__(self, plugin, name):\n \"\"\"\n @param plugin(string): Plugin name without .so or .py suffix\n @param name(string): Name of plugin call\n \"\"\"\n\n super(Plug, self).__init__(plugin, 'so', True, True)\n\n # args\n self.plugin = plugin\n self.name = name\n\n # methods\n self._setup_plugin()\n # END __init__()\n\n def _setup_plugin(self):\n cmds.createNode(self.name)\n # END _setup_plugin()\n# END Plug()\n\nPlug(\"curveramp\", \"curveRamp\")\n", "sub_path": "src/python/tool/autorigger_v02_obsolete/__obsolete_rig_system/goe_plugins/goe_curveramp.py", "file_name": "goe_curveramp.py", "file_ext": "py", "file_size_in_byte": 849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "goe_plugins.plugin_master", "line_number": 11, "usage_type": "argument"}, {"api_name": "goe_plugins.plugin_master.PluginSetup", "line_number": 14, "usage_type": "attribute"}, {"api_name": "goe_plugins.plugin_master", "line_number": 14, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 36, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 36, "usage_type": "name"}]} {"seq_id": "256380413", "text": "import sys\nsys.path.append('..')\n\nfrom models.base_class import Model\nfrom activations import relu\nfrom layers import conv2d, batch_normalization, fully_connected, max_pooling, avg_pooling, global_avg_pooling\nimport tensorflow.compat.v1 as tf\n\nclass SKResNeXt(Model):\n STRUCTURES = {\n 29: {\n 'block_nums': [3, 3, 3],\n 'cardinality': 16\n }\n }\n\n FIRST_CONV_OUT_CHANNEL = 64\n BOTTLENECK_OUT_CHANNEL = 4\n BOTTLENECK_CHANNEL_EXPANSION = 4\n CONV_KERNEL = 3\n REDUCTION = 16\n L = 32\n M = [3, 1]\n\n def __init__(self, depth, class_num, is_small):\n self.depth = depth\n self.class_num = class_num\n self.is_small = is_small\n\n self.structure = self.STRUCTURES[self.depth]['block_nums']\n self.cardinality = self.STRUCTURES[self.depth]['cardinality']\n\n\n def sk_module(self, x, out_channel, stride, name):\n with tf.variable_scope(name):\n u_list = list()\n with tf.variable_scope('split'):\n # 对同一输入使用不同大小的卷积核完成卷积\n for idx, k in enumerate(self.M):\n u_list.append(\n conv2d(x, \n out_channel, \n kernel=k, \n stride=stride, \n padding='SAME', \n name='conv_{}'.format(idx))\n )\n # 将所有不同尺度的特征连接成一个张量\n # u_list形状为[num_fea, batch_size, H', W', C_out]\n u_list = tf.stack(u_list, axis=0)\n\n with tf.variable_scope('fuse'):\n # 把所有不同尺度的特征按位加得到融合特征\n # u的形状为[batch_size, H', W', C_out]\n u = tf.reduce_sum(u_list, axis=0, name='sum')\n\n # 将融合的特征进行压缩\n s = global_avg_pooling(u, name='global_avg_pool')\n \n # 计算全连接层输出的特征数\n fc_out = max(out_channel // self.REDUCTION, self.L)\n z = fully_connected(s, \n fc_out, \n name='fully_connected')\n z = relu(z, name='relu')\n\n attention_list = list()\n with tf.variable_scope('select'):\n # 对每一个不同尺度的特征计算相应的权重\n for idx in range(len(self.M)):\n attention_list.append(\n fully_connected(z, \n out_channel, \n name='fully_connected_{}'.format(idx))\n )\n # 将所有不同尺度特征的权重连接成一个张量\n # attention_list形状为[num_fea, batch_size, C_out]\n attention_list = tf.stack(attention_list, axis=0)\n\n # 不同尺度特征的权重之间使用Softmax互相抑制\n attention_list = tf.math.softmax(attention_list, \n axis=0, \n name='softmax')\n\n # 为了方便按位乘，将其形状变为[num_fea, batch_size, 1, 1, C_out]\n attention_list = tf.expand_dims(attention_list, axis=2)\n attention_list = tf.expand_dims(attention_list, axis=2)\n\n # 使用特征与其对应的权重按位乘\n # output形状为[num_fea, batch_size, H', W', C_out]\n output = u_list * attention_list\n\n # 将按位乘的结果按位加进行融合\n # output形状为[batch_size, H', W', C_out]\n output = tf.reduce_sum(output, axis=0, name='merge')\n\n return output\n\n\n def bottleneck(self, x, out_channel, stride, name):\n def transform(x, out_channel, stride):\n x_list = tf.split(x, \n num_or_size_splits=self.cardinality, \n axis=-1, name='split')\n out_channel = out_channel // self.cardinality\n\n # 将ResNeXt中的Bottleneck的分组卷积操作全部替换为SK模块\n for idx, x in enumerate(x_list):\n with tf.variable_scope('group_conv_{}'.format(idx)):\n x = self.sk_module(x, \n out_channel, \n stride=stride, \n name='sk_module_{}'.format(idx))\n x = batch_normalization(x, \n name='bn', \n is_training=self.is_training)\n x = relu(x, name='relu')\n out_x = x if idx == 0 else tf.concat([out_x, x], axis=-1)\n \n return out_x\n\n with tf.variable_scope(name):\n # 第1个1*1卷积\n output = conv2d(x, \n out_channel, \n kernel=1, \n stride=1, name='conv1')\n output = batch_normalization(output, \n name='bn1', \n is_training=self.is_training)\n output = relu(output, name='relu')\n\n # SK模块\n with tf.variable_scope('transform'):\n output = transform(output, \n out_channel=out_channel, \n stride=stride)\n\n # 第3个1*1卷积\n output = conv2d(output,\n out_channel * self.BOTTLENECK_CHANNEL_EXPANSION, \n kernel=1, \n stride=1, \n padding='SAME', name='conv2')\n output = batch_normalization(output, \n name='bn2', \n is_training=self.is_training)\n\n c_in = x.get_shape().as_list()[-1]\n c_out = out_channel * self.BOTTLENECK_CHANNEL_EXPANSION\n\n with tf.variable_scope('shortcut'):\n if stride != 1 or c_in != c_out:\n shortcut = conv2d(x, \n out_channel * self.BOTTLENECK_CHANNEL_EXPANSION, \n kernel=1, \n stride=stride, \n padding='SAME', name='conv')\n shortcut = batch_normalization(shortcut, \n name='bn', \n is_training=self.is_training)\n else:\n shortcut = x\n \n output = output + shortcut\n output = relu(output, name='relu')\n\n return output\n\n def build(self, x, is_training):\n self.is_training = is_training\n\n with tf.variable_scope('skresnext_{}_{}x{}d'.format(self.depth, self.cardinality, self.BOTTLENECK_OUT_CHANNEL)):\n with tf.variable_scope('preprocess_layers'):\n x = conv2d(x, self.FIRST_CONV_OUT_CHANNEL, kernel=3, stride=1, padding='SAME', name='conv')\n x = batch_normalization(x, name='bn', is_training=self.is_training)\n x = relu(x, name='relu')\n \n for idx, st in enumerate(self.structure):\n out_channel = self.cardinality * self.BOTTLENECK_OUT_CHANNEL * 2 ** idx\n\n if idx == 0:\n first_stride = 1\n else:\n first_stride = 2\n\n strides = [first_stride, *([1] * (st - 1))]\n\n for i, stride in zip(range(st), strides):\n x = self.bottleneck(x, out_channel=out_channel, stride=stride, name='block_{}_{}'.format(idx, i))\n\n with tf.variable_scope('postprocess_layers'):\n if self.is_small:\n x = global_avg_pooling(x, name='global_avg_pool')\n else:\n x = avg_pooling(x, kernel=4, stride=4, name='avg_pool')\n \n with tf.variable_scope('classifier'):\n x = fully_connected(x, self.class_num, name='fully_connected')\n\n return x\n\nif __name__ == \"__main__\":\n class_num = 10\n batch_size = None\n image = tf.placeholder(dtype=tf.float32, shape=[batch_size, 32, 32, 3])\n \n # model = SKResNeXt(29, class_num=class_num, is_small=False)\n \n # sk_out = model.sk_module(image, 4096, name='test_sk')\n # print(sk_out)\n # exit(0)\n \n for d in (29, ):\n model = SKResNeXt(d, class_num=class_num, is_small=False)\n output = model.build(image, is_training=False)\n\n print((batch_size, class_num), output.shape)\n\n from tools import print_net_info\n print_net_info()\n", "sub_path": "章节7/conv_nets/models/skresnext.py", "file_name": "skresnext.py", "file_ext": "py", "file_size_in_byte": 9035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "models.base_class.Model", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 37, "usage_type": "name"}, {"api_name": "layers.conv2d", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.stack", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 55, "usage_type": "name"}, {"api_name": "layers.global_avg_pooling", "line_number": 58, "usage_type": "call"}, {"api_name": "layers.fully_connected", "line_number": 62, "usage_type": "call"}, {"api_name": "activations.relu", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 68, "usage_type": "name"}, {"api_name": "layers.fully_connected", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.stack", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.math.softmax", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.math", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.expand_dims", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.expand_dims", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 87, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.split", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 102, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 109, "usage_type": "name"}, {"api_name": "layers.batch_normalization", "line_number": 114, "usage_type": "call"}, {"api_name": "activations.relu", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.concat", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 118, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 122, "usage_type": "name"}, {"api_name": "layers.conv2d", "line_number": 124, "usage_type": "call"}, {"api_name": "layers.batch_normalization", "line_number": 128, "usage_type": "call"}, {"api_name": "activations.relu", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 134, "usage_type": "name"}, {"api_name": "layers.conv2d", "line_number": 140, "usage_type": "call"}, {"api_name": "layers.batch_normalization", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 152, "usage_type": "name"}, {"api_name": "layers.conv2d", "line_number": 154, "usage_type": "call"}, {"api_name": "layers.batch_normalization", "line_number": 159, "usage_type": "call"}, {"api_name": "activations.relu", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 173, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 174, "usage_type": "name"}, {"api_name": "layers.conv2d", "line_number": 175, "usage_type": "call"}, {"api_name": "layers.batch_normalization", "line_number": 176, "usage_type": "call"}, {"api_name": "activations.relu", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 192, "usage_type": "name"}, {"api_name": "layers.global_avg_pooling", "line_number": 194, "usage_type": "call"}, {"api_name": "layers.avg_pooling", "line_number": 196, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 198, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 198, "usage_type": "name"}, {"api_name": "layers.fully_connected", "line_number": 199, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 206, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.float32", "line_number": 206, "usage_type": "attribute"}, {"api_name": "tools.print_net_info", "line_number": 221, "usage_type": "call"}]} {"seq_id": "604071223", "text": "#\n# Dimensional and dimensionless parameter values, and scales\n#\nimport pybamm\nimport pandas as pd\nimport os\nimport numbers\nimport warnings\nfrom pprint import pformat\nfrom collections import defaultdict\n\n\nclass ParameterValues:\n \"\"\"\n The parameter values for a simulation.\n\n Note that this class does not inherit directly from the python dictionary class as\n this causes issues with saving and loading simulations.\n\n Parameters\n ----------\n values : dict or string\n Explicit set of parameters, or reference to a file of parameters\n If string, gets passed to read_parameters_csv to read a file.\n chemistry : dict\n Dict of strings for default chemistries. Must be of the form:\n {\"base chemistry\": base_chemistry,\n \"cell\": cell_properties_authorYear,\n \"negative electrode\": negative_electrode_chemistry_authorYear,\n \"separator\": separator_chemistry_authorYear,\n \"positive electrode\": positive_electrode_chemistry_authorYear,\n \"electrolyte\": electrolyte_chemistry_authorYear,\n \"experiment\": experimental_conditions_authorYear}.\n Then the negative electrode chemistry is loaded from the file\n inputs/parameters/base_chemistry/negative electrodes/\n negative_electrode_chemistry_authorYear, etc.\n Parameters in \"cell\" should include geometry and current collector properties.\n Parameters in \"experiment\" should include parameters relating to experimental\n conditions, such as initial conditions and currents.\n\n Examples\n --------\n >>> import pybamm\n >>> values = {\"some parameter\": 1, \"another parameter\": 2}\n >>> param = pybamm.ParameterValues(values)\n >>> param[\"some parameter\"]\n 1\n >>> file = \"input/parameters/lithium-ion/cells/kokam_Marquis2019/parameters.csv\"\n >>> values_path = pybamm.get_parameters_filepath(file)\n >>> param = pybamm.ParameterValues(values=values_path)\n >>> param[\"Negative current collector thickness [m]\"]\n 2.5e-05\n >>> param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019)\n >>> param[\"Reference temperature [K]\"]\n 298.15\n\n \"\"\"\n\n def __init__(self, values=None, chemistry=None):\n self._dict_items = pybamm.FuzzyDict()\n # Must provide either values or chemistry, not both (nor neither)\n if values is not None and chemistry is not None:\n raise ValueError(\n \"Only one of values and chemistry can be provided. To change parameters\"\n \" slightly from a chemistry, first load parameters with the chemistry\"\n \" (param = pybamm.ParameterValues(chemistry=...)) and then update with\"\n \" param.update({dict of values}).\"\n )\n if values is None and chemistry is None:\n raise ValueError(\"values and chemistry cannot both be None\")\n # First load chemistry\n if chemistry is not None:\n self.update_from_chemistry(chemistry)\n # Then update with values dictionary or file\n if values is not None:\n # If base_parameters is a filename, load from that filename\n if isinstance(values, str):\n file_path = self.find_parameter(values)\n path = os.path.split(file_path)[0]\n values = self.read_parameters_csv(file_path)\n else:\n path = \"\"\n # Don't check parameter already exists when first creating it\n self.update(values, check_already_exists=False, path=path)\n\n # Initialise empty _processed_symbols dict (for caching)\n self._processed_symbols = {}\n self.parameter_events = []\n\n def __getitem__(self, key):\n return self._dict_items[key]\n\n def get(self, key, default=None):\n \"\"\"Return item correspoonding to key if it exists, otherwise return default\"\"\"\n try:\n return self._dict_items[key]\n except KeyError:\n return default\n\n def __setitem__(self, key, value):\n \"\"\"Call the update functionality when doing a setitem\"\"\"\n self.update({key: value})\n\n def __delitem__(self, key):\n del self._dict_items[key]\n\n def __repr__(self):\n return pformat(self._dict_items, width=1)\n\n def keys(self):\n \"\"\"Get the keys of the dictionary\"\"\"\n return self._dict_items.keys()\n\n def values(self):\n \"\"\"Get the values of the dictionary\"\"\"\n return self._dict_items.values()\n\n def items(self):\n \"\"\"Get the items of the dictionary\"\"\"\n return self._dict_items.items()\n\n def copy(self):\n \"\"\"Returns a copy of the parameter values. Makes sure to copy the internal\n dictionary.\"\"\"\n return ParameterValues(values=self._dict_items.copy())\n\n def search(self, key, print_values=True):\n \"\"\"\n Search dictionary for keys containing 'key'.\n\n See :meth:`pybamm.FuzzyDict.search()`.\n \"\"\"\n return self._dict_items.search(key, print_values)\n\n def update_from_chemistry(self, chemistry):\n \"\"\"\n Load standard set of components from a 'chemistry' dictionary\n \"\"\"\n base_chemistry = chemistry[\"chemistry\"]\n\n # Load each component name\n\n component_groups = [\n \"cell\",\n \"negative electrode\",\n \"positive electrode\",\n \"separator\",\n \"electrolyte\",\n \"experiment\",\n ]\n\n # add SEI parameters if provided\n if \"sei\" in chemistry:\n component_groups += [\"sei\"]\n\n if \"anode\" in chemistry.keys():\n if \"negative electrode\" in chemistry.keys():\n raise KeyError(\n \"both 'anode' and 'negative electrode' keys provided in the \"\n \"chemistry. The 'anode' notation will be deprecated in the next \"\n \"release so 'negative electrode' should be used instead.\"\n )\n else:\n chemistry[\"negative electrode\"] = chemistry[\"anode\"]\n warnings.warn(\n \"the 'anode' component notation will be deprecated in the next \"\n \"release, as it has now been renamed to 'negative electrode'. \"\n \"Simulation will continue passing the 'anode' component as \"\n \"'negative electrode' (it might overwrite any existing definition \"\n \"of the component).\",\n DeprecationWarning,\n )\n\n if \"cathode\" in chemistry.keys():\n if \"positive electrode\" in chemistry.keys():\n raise KeyError(\n \"both 'cathode' and 'positive electrode' keys provided in the \"\n \"chemistry. The 'cathode' notation will be deprecated in the next \"\n \"release so 'positive electrode' should be used instead.\"\n )\n else:\n chemistry[\"positive electrode\"] = chemistry[\"cathode\"]\n warnings.warn(\n \"the 'cathode' component notation will be deprecated in the next \"\n \"release, as it has now been renamed to 'positive electrode'. \"\n \"Simulation will continue passing the 'cathode' component as \"\n \"'positive electrode' (it might overwrite any existing definition \"\n \"of the component).\",\n DeprecationWarning,\n )\n\n for component_group in component_groups:\n # Make sure component is provided\n try:\n component = chemistry[component_group]\n except KeyError:\n raise KeyError(\n \"must provide '{}' parameters for {} chemistry\".format(\n component_group, base_chemistry\n )\n )\n # Create path to component and load values\n component_path = os.path.join(\n base_chemistry, component_group.replace(\" \", \"_\") + \"s\", component\n )\n file_path = self.find_parameter(\n os.path.join(component_path, \"parameters.csv\")\n )\n component_params = self.read_parameters_csv(file_path)\n\n # Update parameters, making sure to check any conflicts\n self.update(\n component_params,\n check_conflict=True,\n check_already_exists=False,\n path=os.path.dirname(file_path),\n )\n\n # register (list of) citations\n if \"citation\" in chemistry:\n citations = chemistry[\"citation\"]\n if not isinstance(citations, list):\n citations = [citations]\n for citation in citations:\n pybamm.citations.register(citation)\n\n def read_parameters_csv(self, filename):\n \"\"\"Reads parameters from csv file into dict.\n\n Parameters\n ----------\n filename : str\n The name of the csv file containing the parameters.\n\n Returns\n -------\n dict\n {name: value} pairs for the parameters.\n\n \"\"\"\n df = pd.read_csv(filename, comment=\"#\", skip_blank_lines=True)\n # Drop rows that are all NaN (seems to not work with skip_blank_lines)\n df.dropna(how=\"all\", inplace=True)\n return {k: v for (k, v) in zip(df[\"Name [units]\"], df[\"Value\"])}\n\n def update(self, values, check_conflict=False, check_already_exists=True, path=\"\"):\n \"\"\"\n Update parameter dictionary, while also performing some basic checks.\n\n Parameters\n ----------\n values : dict\n Dictionary of parameter values to update parameter dictionary with\n check_conflict : bool, optional\n Whether to check that a parameter in `values` has not already been defined\n in the parameter class when updating it, and if so that its value does not\n change. This is set to True during initialisation, when parameters are\n combined from different sources, and is False by default otherwise\n check_already_exists : bool, optional\n Whether to check that a parameter in `values` already exists when trying to\n update it. This is to avoid cases where an intended change in the parameters\n is ignored due a typo in the parameter name, and is True by default but can\n be manually overridden.\n path : string, optional\n Path from which to load functions\n \"\"\"\n # check parameter values\n self.check_parameter_values(values)\n # update\n for name, value in values.items():\n # check for conflicts\n if (\n check_conflict is True\n and name in self.keys()\n and not (self[name] == float(value) or self[name] == value)\n ):\n raise ValueError(\n \"parameter '{}' already defined with value '{}'\".format(\n name, self[name]\n )\n )\n # check parameter already exists (for updating parameters)\n if check_already_exists is True:\n try:\n self._dict_items[name]\n except KeyError as err:\n raise KeyError(\n \"Cannot update parameter '{}' as it does not \".format(name)\n + \"have a default value. ({}). If you are \".format(err.args[0])\n + \"sure you want to update this parameter, use \"\n + \"param.update({{name: value}}, check_already_exists=False)\"\n )\n # if no conflicts, update, loading functions and data if they are specified\n # Functions are flagged with the string \"[function]\"\n if isinstance(value, str):\n if value.startswith(\"[function]\"):\n loaded_value = pybamm.load_function(\n os.path.join(path, value[10:] + \".py\")\n )\n self._dict_items[name] = loaded_value\n values[name] = loaded_value\n # Data is flagged with the string \"[data]\" or \"[current data]\"\n elif value.startswith(\"[current data]\") or value.startswith(\"[data]\"):\n if value.startswith(\"[current data]\"):\n data_path = os.path.join(\n pybamm.root_dir(), \"pybamm\", \"input\", \"drive_cycles\"\n )\n filename = os.path.join(data_path, value[14:] + \".csv\")\n function_name = value[14:]\n else:\n filename = os.path.join(path, value[6:] + \".csv\")\n function_name = value[6:]\n filename = pybamm.get_parameters_filepath(filename)\n data = pd.read_csv(\n filename, comment=\"#\", skip_blank_lines=True, header=None\n ).to_numpy()\n # Save name and data\n self._dict_items[name] = (function_name, data)\n values[name] = (function_name, data)\n elif value == \"[input]\":\n self._dict_items[name] = pybamm.InputParameter(name)\n # Anything else should be a converted to a float\n else:\n self._dict_items[name] = float(value)\n values[name] = float(value)\n else:\n self._dict_items[name] = value\n # reset processed symbols\n self._processed_symbols = {}\n\n def check_parameter_values(self, values):\n # Make sure typical current is non-zero\n if \"Typical current [A]\" in values and values[\"Typical current [A]\"] == 0:\n raise ValueError(\n \"'Typical current [A]' cannot be zero. A possible alternative is to \"\n \"set 'Current function [A]' to `0` instead.\"\n )\n if \"C-rate\" in values:\n raise ValueError(\n \"The 'C-rate' parameter has been deprecated, \"\n \"use 'Current function [A]' instead. The Nominal \"\n \"cell capacity can be accessed as 'Nominal cell \"\n \"capacity [A.h]', and used to calculate current from C-rate.\"\n )\n if \"Cell capacity [A.h]\" in values:\n if \"Nominal cell capacity [A.h]\" in values:\n raise ValueError(\n \"both 'Cell capacity [A.h]' and 'Nominal cell capacity [A.h]' \"\n \"provided in values. The 'Cell capacity [A.h]' notation will be \"\n \"deprecated in the next release so 'Nominal cell capacity [A.h]' \"\n \"should be used instead.\"\n )\n else:\n values[\"Nominal cell capacity [A.h]\"] = values[\"Cell capacity [A.h]\"]\n warnings.warn(\n \"the 'Cell capacity [A.h]' notation will be \"\n \"deprecated in the next release, as it has now been renamed \"\n \"to 'Nominal cell capacity [A.h]'. Simulation will continue \"\n \"passing the 'Cell capacity [A.h]' as 'Nominal cell \"\n \"capacity [A.h]' (it might overwrite any existing definition \"\n \"of the component)\",\n DeprecationWarning,\n )\n for param in values:\n if \"surface area density\" in param:\n raise ValueError(\n \"Parameters involving 'surface area density' have been renamed to \"\n \"'surface area to volume ratio' ('{}' found)\".format(param)\n )\n if \"reaction rate\" in param:\n raise ValueError(\n \"Parameters involving 'reaction rate' have been replaced with \"\n \"'exchange-current density' ('{}' found)\".format(param)\n )\n for param in values:\n if \"particle distribution in x\" in param:\n raise ValueError(\n \"The parameter '{}' has been deprecated\".format(param)\n + \"The particle radius is now set as a function of x directly \"\n \"instead of providing a reference value and a distribution.\"\n )\n for param in values:\n if \"surface area to volume ratio distribution in x\" in param:\n raise ValueError(\n \"The parameter '{}' has been deprecated\".format(param)\n + \"The surface area to volume ratio is now set as a function \"\n \"of x directly instead of providing a reference value and a \"\n \"distribution.\"\n )\n\n def process_model(self, unprocessed_model, inplace=True):\n \"\"\"Assign parameter values to a model.\n Currently inplace, could be changed to return a new model.\n\n Parameters\n ----------\n unprocessed_model : :class:`pybamm.BaseModel`\n Model to assign parameter values for\n inplace: bool, optional\n If True, replace the parameters in the model in place. Otherwise, return a\n new model with parameter values set. Default is True.\n\n Raises\n ------\n :class:`pybamm.ModelError`\n If an empty model is passed (`model.rhs = {}` and `model.algebraic = {}` and\n `model.variables = {}`)\n\n \"\"\"\n pybamm.logger.info(\n \"Start setting parameters for {}\".format(unprocessed_model.name)\n )\n\n # set up inplace vs not inplace\n if inplace:\n # any changes to unprocessed_model attributes will change model attributes\n # since they point to the same object\n model = unprocessed_model\n else:\n # create a blank model of the same class\n model = unprocessed_model.new_empty_copy()\n\n if (\n len(unprocessed_model.rhs) == 0\n and len(unprocessed_model.algebraic) == 0\n and len(unprocessed_model.variables) == 0\n ):\n raise pybamm.ModelError(\"Cannot process parameters for empty model\")\n\n new_rhs = {}\n for variable, equation in unprocessed_model.rhs.items():\n pybamm.logger.verbose(\n \"Processing parameters for {!r} (rhs)\".format(variable)\n )\n new_rhs[variable] = self.process_symbol(equation)\n model.rhs = new_rhs\n\n new_algebraic = {}\n for variable, equation in unprocessed_model.algebraic.items():\n pybamm.logger.verbose(\n \"Processing parameters for {!r} (algebraic)\".format(variable)\n )\n new_algebraic[variable] = self.process_symbol(equation)\n model.algebraic = new_algebraic\n\n new_initial_conditions = {}\n for variable, equation in unprocessed_model.initial_conditions.items():\n pybamm.logger.verbose(\n \"Processing parameters for {!r} (initial conditions)\".format(variable)\n )\n new_initial_conditions[variable] = self.process_symbol(equation)\n model.initial_conditions = new_initial_conditions\n\n model.boundary_conditions = self.process_boundary_conditions(unprocessed_model)\n\n new_variables = {}\n for variable, equation in unprocessed_model.variables.items():\n pybamm.logger.verbose(\n \"Processing parameters for {!r} (variables)\".format(variable)\n )\n new_variables[variable] = self.process_symbol(equation)\n model.variables = new_variables\n\n new_events = []\n for event in unprocessed_model.events:\n pybamm.logger.verbose(\n \"Processing parameters for event '{}''\".format(event.name)\n )\n new_events.append(\n pybamm.Event(\n event.name, self.process_symbol(event.expression), event.event_type\n )\n )\n\n for event in self.parameter_events:\n pybamm.logger.verbose(\n \"Processing parameters for event '{}''\".format(event.name)\n )\n new_events.append(\n pybamm.Event(\n event.name, self.process_symbol(event.expression), event.event_type\n )\n )\n\n model.events = new_events\n\n # Set external variables\n model.external_variables = [\n self.process_symbol(var) for var in unprocessed_model.external_variables\n ]\n\n # Process timescale\n model.timescale = self.process_symbol(unprocessed_model.timescale)\n\n # Process length scales\n new_length_scales = {}\n for domain, scale in unprocessed_model.length_scales.items():\n new_length_scales[domain] = self.process_symbol(scale)\n model.length_scales = new_length_scales\n\n pybamm.logger.info(\"Finish setting parameters for {}\".format(model.name))\n\n return model\n\n def process_boundary_conditions(self, model):\n \"\"\"\n Process boundary conditions for a model\n Boundary conditions are dictionaries {\"left\": left bc, \"right\": right bc}\n in general, but may be imposed on the tabs (or *not* on the tab) for a\n small number of variables, e.g. {\"negative tab\": neg. tab bc,\n \"positive tab\": pos. tab bc \"no tab\": no tab bc}.\n \"\"\"\n new_boundary_conditions = {}\n sides = [\"left\", \"right\", \"negative tab\", \"positive tab\", \"no tab\"]\n for variable, bcs in model.boundary_conditions.items():\n processed_variable = self.process_symbol(variable)\n new_boundary_conditions[processed_variable] = {}\n for side in sides:\n try:\n bc, typ = bcs[side]\n pybamm.logger.verbose(\n \"Processing parameters for {!r} ({} bc)\".format(variable, side)\n )\n processed_bc = (self.process_symbol(bc), typ)\n new_boundary_conditions[processed_variable][side] = processed_bc\n except KeyError as err:\n # don't raise error if the key error comes from the side not being\n # found\n if err.args[0] in side:\n pass\n # do raise error otherwise (e.g. can't process symbol)\n else:\n raise KeyError(err)\n\n return new_boundary_conditions\n\n def update_model(self, model, disc):\n raise NotImplementedError(\n \"\"\"\n update_model functionality has been deprecated.\n Use pybamm.InputParameter to quickly change a parameter value instead\n \"\"\"\n )\n\n def process_geometry(self, geometry):\n \"\"\"\n Assign parameter values to a geometry (inplace).\n\n Parameters\n ----------\n geometry : dict\n Geometry specs to assign parameter values to\n \"\"\"\n for domain in geometry:\n for spatial_variable, spatial_limits in geometry[domain].items():\n # process tab information if using 1 or 2D current collectors\n if spatial_variable == \"tabs\":\n for tab, position_size in spatial_limits.items():\n for position_size, sym in position_size.items():\n geometry[domain][\"tabs\"][tab][\n position_size\n ] = self.process_symbol(sym)\n else:\n for lim, sym in spatial_limits.items():\n if isinstance(sym, pybamm.Symbol):\n geometry[domain][spatial_variable][\n lim\n ] = self.process_symbol(sym)\n\n def process_symbol(self, symbol):\n \"\"\"Walk through the symbol and replace any Parameter with a Value.\n If a symbol has already been processed, the stored value is returned.\n\n Parameters\n ----------\n symbol : :class:`pybamm.Symbol`\n Symbol or Expression tree to set parameters for\n\n Returns\n -------\n symbol : :class:`pybamm.Symbol`\n Symbol with Parameter instances replaced by Value\n\n \"\"\"\n\n try:\n return self._processed_symbols[symbol.id]\n except KeyError:\n processed_symbol = self._process_symbol(symbol)\n self._processed_symbols[symbol.id] = processed_symbol\n\n return processed_symbol\n\n def _process_symbol(self, symbol):\n \"\"\" See :meth:`ParameterValues.process_symbol()`. \"\"\"\n\n if isinstance(symbol, pybamm.Parameter):\n value = self[symbol.name]\n if isinstance(value, numbers.Number):\n # Scalar inherits name (for updating parameters) and domain (for\n # Broadcast)\n return pybamm.Scalar(value, name=symbol.name, domain=symbol.domain)\n elif isinstance(value, pybamm.Symbol):\n new_value = self.process_symbol(value)\n new_value.domain = symbol.domain\n return new_value\n else:\n raise TypeError(\"Cannot process parameter '{}'\".format(value))\n\n elif isinstance(symbol, pybamm.FunctionParameter):\n new_children = []\n for child in symbol.children:\n if symbol.diff_variable is not None and any(\n x.id == symbol.diff_variable.id for x in child.pre_order()\n ):\n # Wrap with NotConstant to avoid simplification,\n # which would stop symbolic diff from working properly\n new_child = pybamm.NotConstant(child.new_copy())\n new_children.append(self.process_symbol(new_child))\n else:\n new_children.append(self.process_symbol(child))\n function_name = self[symbol.name]\n\n # Create Function or Interpolant or Scalar object\n if isinstance(function_name, tuple):\n # If function_name is a tuple then it should be (name, data) and we need\n # to create an Interpolant\n name, data = function_name\n function = pybamm.Interpolant(\n data[:, 0], data[:, 1], *new_children, name=name\n )\n # Define event to catch extrapolation. In these events the sign is\n # important: it should be positive inside of the range and negative\n # outside of it\n self.parameter_events.append(\n pybamm.Event(\n \"Interpolant {} lower bound\".format(name),\n pybamm.min(new_children[0] - min(data[:, 0])),\n pybamm.EventType.INTERPOLANT_EXTRAPOLATION,\n )\n )\n self.parameter_events.append(\n pybamm.Event(\n \"Interpolant {} upper bound\".format(name),\n pybamm.min(max(data[:, 0]) - new_children[0]),\n pybamm.EventType.INTERPOLANT_EXTRAPOLATION,\n )\n )\n elif isinstance(function_name, numbers.Number):\n # If the \"function\" is provided is actually a scalar, return a Scalar\n # object instead of throwing an error.\n # Also use ones_like so that we get the right shapes\n function = pybamm.Scalar(\n function_name, name=symbol.name\n ) * pybamm.ones_like(*new_children)\n elif (\n isinstance(function_name, pybamm.Symbol)\n and function_name.evaluates_to_number()\n ):\n # If the \"function\" provided is a pybamm scalar-like, use ones_like to\n # get the right shape\n # This also catches input parameters\n function = function_name * pybamm.ones_like(*new_children)\n elif callable(function_name):\n # otherwise evaluate the function to create a new PyBaMM object\n function = function_name(*new_children)\n elif isinstance(function_name, pybamm.Interpolant):\n function = function_name\n else:\n raise TypeError(\n \"Parameter provided for '{}' \".format(symbol.name)\n + \"is of the wrong type (should either be scalar-like or callable)\"\n )\n # Differentiate if necessary\n if symbol.diff_variable is None:\n function_out = function\n else:\n # return differentiated function\n new_diff_variable = self.process_symbol(symbol.diff_variable)\n function_out = function.diff(new_diff_variable)\n # Convert possible float output to a pybamm scalar\n if isinstance(function_out, numbers.Number):\n return pybamm.Scalar(function_out)\n # Process again just to be sure\n return self.process_symbol(function_out)\n\n elif isinstance(symbol, pybamm.BinaryOperator):\n # process children\n new_left = self.process_symbol(symbol.left)\n new_right = self.process_symbol(symbol.right)\n # Special case for averages, which can appear as \"integral of a broadcast\"\n # divided by \"integral of a broadcast\"\n # this construction seems very specific but can appear often when averaging\n if (\n isinstance(symbol, pybamm.Division)\n # right is integral(Broadcast(1))\n and (\n isinstance(new_right, pybamm.Integral)\n and isinstance(new_right.child, pybamm.Broadcast)\n and new_right.child.child.id == pybamm.Scalar(1).id\n )\n # left is integral\n and isinstance(new_left, pybamm.Integral)\n ):\n # left is integral(Broadcast)\n if (\n isinstance(new_left.child, pybamm.Broadcast)\n and new_left.child.child.domain == []\n ):\n integrand = new_left.child\n if integrand.auxiliary_domains == {}:\n return integrand.orphans[0]\n else:\n domain = integrand.auxiliary_domains[\"secondary\"]\n if \"tertiary\" not in integrand.auxiliary_domains:\n return pybamm.PrimaryBroadcast(integrand.orphans[0], domain)\n else:\n auxiliary_domains = {\n \"secondary\": integrand.auxiliary_domains[\"tertiary\"]\n }\n return pybamm.FullBroadcast(\n integrand.orphans[0], domain, auxiliary_domains\n )\n # left is \"integral of concatenation of broadcasts\"\n elif isinstance(new_left.child, pybamm.Concatenation) and all(\n isinstance(child, pybamm.Broadcast)\n for child in new_left.child.children\n ):\n return self.process_symbol(pybamm.x_average(new_left.child))\n # make new symbol, ensure domain remains the same\n new_symbol = symbol._binary_new_copy(new_left, new_right)\n new_symbol.domain = symbol.domain\n return new_symbol\n\n # Unary operators\n elif isinstance(symbol, pybamm.UnaryOperator):\n new_child = self.process_symbol(symbol.child)\n new_symbol = symbol._unary_new_copy(new_child)\n # ensure domain remains the same\n new_symbol.domain = symbol.domain\n return new_symbol\n\n # Functions\n elif isinstance(symbol, pybamm.Function):\n new_children = [self.process_symbol(child) for child in symbol.children]\n return symbol._function_new_copy(new_children)\n\n # Concatenations\n elif isinstance(symbol, pybamm.Concatenation):\n new_children = [self.process_symbol(child) for child in symbol.children]\n return symbol._concatenation_new_copy(new_children)\n\n else:\n # Backup option: return new copy of the object\n try:\n return symbol.new_copy()\n except NotImplementedError:\n raise NotImplementedError(\n \"Cannot process parameters for symbol of type '{}'\".format(\n type(symbol)\n )\n )\n\n def evaluate(self, symbol):\n \"\"\"\n Process and evaluate a symbol.\n\n Parameters\n ----------\n symbol : :class:`pybamm.Symbol`\n Symbol or Expression tree to evaluate\n\n Returns\n -------\n number of array\n The evaluated symbol\n \"\"\"\n processed_symbol = self.process_symbol(symbol)\n if processed_symbol.evaluates_to_constant_number():\n return processed_symbol.evaluate()\n else:\n raise ValueError(\"symbol must evaluate to a constant scalar\")\n\n def _ipython_key_completions_(self):\n return list(self._dict_items.keys())\n\n def export_csv(self, filename):\n\n # process functions and data to output\n # like they appear in inputs csv files\n parameter_output = {}\n for key, val in self.items():\n if callable(val):\n val = \"[function]\" + val.__name__\n elif isinstance(val, tuple):\n val = \"[data]\" + val[0]\n parameter_output[key] = [val]\n\n df = pd.DataFrame(parameter_output)\n df = df.transpose()\n df.to_csv(filename, header=None)\n\n def print_parameters(self, parameters, output_file=None):\n \"\"\"\n Return dictionary of evaluated parameters, and optionally print these evaluated\n parameters to an output file.\n For dimensionless parameters that depend on the C-rate, the value is given as a\n function of the C-rate (either x * Crate or x / Crate depending on the\n dependence)\n\n Parameters\n ----------\n parameters : class or dict containing :class:`pybamm.Parameter` objects\n Class or dictionary containing all the parameters to be evaluated\n output_file : string, optional\n The file to print parameters to. If None, the parameters are not printed,\n and this function simply acts as a test that all the parameters can be\n evaluated, and returns the dictionary of evaluated parameters.\n\n Returns\n -------\n evaluated_parameters : defaultdict\n The evaluated parameters, for further processing if needed\n\n Notes\n -----\n A C-rate of 1 C is the current required to fully discharge the battery in 1\n hour, 2 C is current to discharge the battery in 0.5 hours, etc\n \"\"\"\n # Set list of attributes to ignore, for when we are evaluating parameters from\n # a class of parameters\n ignore = [\n \"__name__\",\n \"__doc__\",\n \"__package__\",\n \"__loader__\",\n \"__spec__\",\n \"__file__\",\n \"__cached__\",\n \"__builtins__\",\n \"absolute_import\",\n \"division\",\n \"print_function\",\n \"unicode_literals\",\n \"pybamm\",\n \"constants\",\n \"np\",\n \"geo\",\n \"elec\",\n \"therm\",\n ]\n\n # If 'parameters' is a class, extract the dict\n if not isinstance(parameters, dict):\n parameters = {\n k: v for k, v in parameters.__dict__.items() if k not in ignore\n }\n\n evaluated_parameters = defaultdict(list)\n # Calculate parameters for each C-rate\n for Crate in [1, 10]:\n # Update Crate\n capacity = self.get(\"Nominal cell capacity [A.h]\")\n if capacity is not None:\n self.update(\n {\"Current function [A]\": Crate * capacity},\n check_already_exists=False,\n )\n for name, symbol in parameters.items():\n if not callable(symbol):\n proc_symbol = self.process_symbol(symbol)\n if not (\n callable(proc_symbol)\n or proc_symbol.has_symbol_of_classes(\n (pybamm.Concatenation, pybamm.Broadcast)\n )\n ):\n evaluated_parameters[name].append(proc_symbol.evaluate(t=0))\n\n # Calculate C-dependence of the parameters based on the difference between the\n # value at 1C and the value at C / 10\n for name, values in evaluated_parameters.items():\n if values[1] == 0 or abs(values[0] / values[1] - 1) < 1e-10:\n C_dependence = \"\"\n elif abs(values[0] / values[1] - 10) < 1e-10:\n C_dependence = \" * Crate\"\n elif abs(values[0] / values[1] - 0.1) < 1e-10:\n C_dependence = \" / Crate\"\n evaluated_parameters[name] = (values[0], C_dependence)\n # Print the evaluated_parameters dict to output_file\n if output_file:\n self.print_evaluated_parameters(evaluated_parameters, output_file)\n\n return evaluated_parameters\n\n def print_evaluated_parameters(self, evaluated_parameters, output_file):\n \"\"\"\n Print a dictionary of evaluated parameters to an output file\n\n Parameters\n ----------\n evaluated_parameters : defaultdict\n The evaluated parameters, for further processing if needed\n output_file : string, optional\n The file to print parameters to. If None, the parameters are not printed,\n and this function simply acts as a test that all the parameters can be\n evaluated\n\n \"\"\"\n # Get column width for pretty printing\n column_width = max(len(name) for name in evaluated_parameters.keys())\n s = \"{{:>{}}}\".format(column_width)\n with open(output_file, \"w\") as file:\n for name, (value, C_dependence) in sorted(evaluated_parameters.items()):\n if 0.001 < abs(value) < 1000:\n file.write(\n (s + \" : {:10.4g}{!s}\\n\").format(name, value, C_dependence)\n )\n else:\n file.write(\n (s + \" : {:10.3E}{!s}\\n\").format(name, value, C_dependence)\n )\n\n @staticmethod\n def find_parameter(path):\n \"\"\"Look for parameter file in the different locations\n in PARAMETER_PATH\n \"\"\"\n for location in pybamm.PARAMETER_PATH:\n trial_path = os.path.join(location, path)\n if os.path.isfile(trial_path):\n return trial_path\n raise FileNotFoundError(\"Could not find parameter {}\".format(path))\n", "sub_path": "pybamm/parameters/parameter_values.py", "file_name": "parameter_values.py", "file_ext": "py", "file_size_in_byte": 39413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pybamm.FuzzyDict", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pprint.pformat", "line_number": 108, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 165, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "pybamm.citations.register", "line_number": 225, "usage_type": "call"}, {"api_name": "pybamm.citations", "line_number": 225, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 241, "usage_type": "call"}, {"api_name": "pybamm.load_function", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "pybamm.root_dir", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 308, "usage_type": "call"}, {"api_name": "os.path", "line_number": 308, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pybamm.get_parameters_filepath", "line_number": 313, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 314, "usage_type": "call"}, {"api_name": "pybamm.InputParameter", "line_number": 321, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 355, "usage_type": "call"}, {"api_name": "pybamm.logger.info", "line_number": 410, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 410, "usage_type": "attribute"}, {"api_name": "pybamm.ModelError", "line_number": 428, "usage_type": "call"}, {"api_name": "pybamm.logger.verbose", "line_number": 432, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 432, "usage_type": "attribute"}, {"api_name": "pybamm.logger.verbose", "line_number": 440, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 440, "usage_type": "attribute"}, {"api_name": "pybamm.logger.verbose", "line_number": 448, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 448, "usage_type": "attribute"}, {"api_name": "pybamm.logger.verbose", "line_number": 458, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 458, "usage_type": "attribute"}, {"api_name": "pybamm.logger.verbose", "line_number": 466, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 466, "usage_type": "attribute"}, {"api_name": "pybamm.Event", "line_number": 470, "usage_type": "call"}, {"api_name": "pybamm.logger.verbose", "line_number": 476, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 476, "usage_type": "attribute"}, {"api_name": "pybamm.Event", "line_number": 480, "usage_type": "call"}, {"api_name": "pybamm.logger.info", "line_number": 501, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 501, "usage_type": "attribute"}, {"api_name": "pybamm.logger.verbose", "line_number": 521, "usage_type": "call"}, {"api_name": "pybamm.logger", "line_number": 521, "usage_type": "attribute"}, {"api_name": "pybamm.Symbol", "line_number": 565, "usage_type": "attribute"}, {"api_name": "pybamm.Parameter", "line_number": 597, "usage_type": "attribute"}, {"api_name": "numbers.Number", "line_number": 599, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 602, "usage_type": "call"}, {"api_name": "pybamm.Symbol", "line_number": 603, "usage_type": "attribute"}, {"api_name": "pybamm.FunctionParameter", "line_number": 610, "usage_type": "attribute"}, {"api_name": "pybamm.NotConstant", "line_number": 618, "usage_type": "call"}, {"api_name": "pybamm.Interpolant", "line_number": 629, "usage_type": "call"}, {"api_name": "pybamm.Event", "line_number": 636, "usage_type": "call"}, {"api_name": "pybamm.min", "line_number": 638, "usage_type": "call"}, {"api_name": "pybamm.EventType", "line_number": 639, "usage_type": "attribute"}, {"api_name": "pybamm.Event", "line_number": 643, "usage_type": "call"}, {"api_name": "pybamm.min", "line_number": 645, "usage_type": "call"}, {"api_name": "pybamm.EventType", "line_number": 646, "usage_type": "attribute"}, {"api_name": "numbers.Number", "line_number": 649, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 653, "usage_type": "call"}, {"api_name": "pybamm.ones_like", "line_number": 655, "usage_type": "call"}, {"api_name": "pybamm.Symbol", "line_number": 657, "usage_type": "attribute"}, {"api_name": "pybamm.ones_like", "line_number": 663, "usage_type": "call"}, {"api_name": "pybamm.Interpolant", "line_number": 667, "usage_type": "attribute"}, {"api_name": "numbers.Number", "line_number": 682, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 683, "usage_type": "call"}, {"api_name": "pybamm.BinaryOperator", "line_number": 687, "usage_type": "attribute"}, {"api_name": "pybamm.Division", "line_number": 695, "usage_type": "attribute"}, {"api_name": "pybamm.Integral", "line_number": 698, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 699, "usage_type": "attribute"}, {"api_name": "pybamm.Scalar", "line_number": 700, "usage_type": "call"}, {"api_name": "pybamm.Integral", "line_number": 703, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 707, "usage_type": "attribute"}, {"api_name": "pybamm.PrimaryBroadcast", "line_number": 716, "usage_type": "call"}, {"api_name": "pybamm.FullBroadcast", "line_number": 721, "usage_type": "call"}, {"api_name": "pybamm.Concatenation", "line_number": 725, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 726, "usage_type": "attribute"}, {"api_name": "pybamm.x_average", "line_number": 729, "usage_type": "call"}, {"api_name": "pybamm.UnaryOperator", "line_number": 736, "usage_type": "attribute"}, {"api_name": "pybamm.Function", "line_number": 744, "usage_type": "attribute"}, {"api_name": "pybamm.Concatenation", "line_number": 749, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 799, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 859, "usage_type": "call"}, {"api_name": "pybamm.Concatenation", "line_number": 875, "usage_type": "attribute"}, {"api_name": "pybamm.Broadcast", "line_number": 875, "usage_type": "attribute"}, {"api_name": "pybamm.PARAMETER_PATH", "line_number": 929, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 930, "usage_type": "call"}, {"api_name": "os.path", "line_number": 930, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 931, "usage_type": "call"}, {"api_name": "os.path", "line_number": 931, "usage_type": "attribute"}]} {"seq_id": "637546848", "text": "import json\nimport traceback\nimport io\nimport pickle\nimport copy\nfrom git import Repo\nfrom . import app_logger\nfrom web_app.auxiliary import exceptions\n\n\ndef load_model(model_spec):\n \"\"\"Given a model and its version, load it\"\"\"\n\n #models_repo = r\"/Users/davidlaredorazo/Documents/Projects/Rappi Challenge/models_and_data\"\n models_repo = r\"/models_and_data\"\n\n model = None\n\n #Try to open repository\n try:\n repo = Repo(models_repo)\n except Exception as e:\n app_logger.error('Could not open repository')\n app_logger.error(traceback.format_exc())\n raise exceptions.FileError('Could not open repository')\n\n #Attempt to load models\n try:\n if not model_spec['model_tag']:\n raise exceptions.UnspecifiedModel('Model not specified')\n\n if model_spec['model_version']:\n commit = repo.commit(model_spec['model_version'])\n\n target_file = commit.tree / ('models/deploy/' + model_spec['model_tag'] + '.pkl')\n\n print(target_file)\n\n with io.BytesIO(target_file.data_stream.read()) as f:\n model = pickle.load(f)\n else:\n model = pickle.load(open(models_repo + '/models/deploy/' + model_spec['model_tag'] + '.pkl', 'rb'))\n except Exception as e:\n app_logger.error('Could not load model')\n app_logger.error(traceback.format_exc())\n raise exceptions.UnspecifiedModel('Could not load model')\n\n return model\n\n\ndef load_ml_models(app_root):\n \"\"\"Load all the models specified in the models_list\"\"\"\n\n models_list = None\n models = {}\n model = None\n print(app_root)\n\n try:\n\n app_logger.error(\"Loading models from\")\n app_logger.error(app_root / 'models_list.json')\n\n with open(app_root / 'models_list.json', 'r') as fp:\n models_list = json.load(fp)\n\n app_logger.error(models_list)\n\n except Exception as e:\n\n app_logger.error(\"Could not open models list file.\")\n app_logger.error(traceback.format_exc())\n print(\"Could not open models list file. Check app_log file\")\n\n for key in models_list:\n\n model_spec = models_list[key]\n model = load_model(model_spec)\n models[model_spec['model_tag'] + '/' +\n (model_spec['model_version'] if model_spec['model_version'] else 'latest')] = copy.deepcopy(model)\n\n return models\n\n", "sub_path": "web_app/ml/load_models.py", "file_name": "load_models.py", "file_ext": "py", "file_size_in_byte": 2387, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "git.Repo", "line_number": 21, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 24, "usage_type": "call"}, {"api_name": "web_app.auxiliary.exceptions.FileError", "line_number": 25, "usage_type": "call"}, {"api_name": "web_app.auxiliary.exceptions", "line_number": 25, "usage_type": "name"}, {"api_name": "web_app.auxiliary.exceptions.UnspecifiedModel", "line_number": 30, "usage_type": "call"}, {"api_name": "web_app.auxiliary.exceptions", "line_number": 30, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 42, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 45, "usage_type": "call"}, {"api_name": "web_app.auxiliary.exceptions.UnspecifiedModel", "line_number": 46, "usage_type": "call"}, {"api_name": "web_app.auxiliary.exceptions", "line_number": 46, "usage_type": "name"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 72, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 80, "usage_type": "call"}]} {"seq_id": "211222675", "text": "from django.test import TestCase,Client\nfrom .models import Airport \nfrom .models import Flight \n\n# Create your tests here.\n\nclass ModelsTestCase(TestCase):\n def setUp(self):\n\n a1 = Airport.objects.create(code=\"AAA\",city=\"City A\")\n a2 = Airport.objects.create(code= \"BBB\", city = \"City B\")\n\n Flight.objects.create(origin=a1,destination=a2,duration=100)\n Flight.objects.create(origin=a1,destination=a1,duration=200)\n Flight.objects.create(origin=a1,destination=a2,duration=-100)\n\n def test_departure_count(self):\n a = Airport.objects.get(code =\"AAA\")\n self.assertEqual(a.departures.count(),3)\n\n def test_arrival_count(self):\n a = Airport.objects.get(code =\"AAA\")\n self.assertEqual(a.arrivals.count(),1)\n\n def test_valid_flight(self):\n a1 = Airport.objects.get(code =\"AAA\")\n a2 = Airport.objects.get(code =\"BBB\")\n f = Flight.objects.get(origin=a1,destination= a2,duration=100)\n self.assertTrue(f.is_valid_flight())\n\n def test_invalid_flight_destination(self):\n a1 = Airport.objects.get(code =\"AAA\")\n f = Flight.objects.get(origin=a1,destination= a1) \n self.assertFalse(f.is_valid_flight())\n\n \n def test_invalid_flight_duration(self):\n a1 = Airport.objects.get(code =\"AAA\")\n a2 = Airport.objects.get(code =\"BBB\")\n f = Flight.objects.get(origin=a1,destination= a2,duration=-100)\n self.assertFalse(f.is_valid_flight())\n\n def test_index(self):\n c = Client()\n response = c.get(\"/\")\n self.assertEqual(response.status_code,200)\n self.assertEqual(response.context[\"flights\"].count(),3)\n\n def test_valid_flight_page(self):\n a1 = Airport.objects.get(code = \"AAA\")\n f = Flight.objects.get(origin = a1, destination = a1)\n c = Client()\n response = c.get(f\"/{f.id}\")\n self.assertEqual(response.status_code,200)\n\n #def test_invalid_flight(self):\n # max_id = Flight.objects.all().aggregate(Max(\"id\")[\"id_max\"])\n # c = Client()\n # response = c.get(f\"/{max_id +1}\")\n # self.assertEqual(response.status_code,404)\n\n", "sub_path": "flights/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Airport.objects.create", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Airport.objects.create", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Flight.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Flight.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Flight.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Flight.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Flight.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 33, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Airport.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Flight.objects.get", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 40, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Airport.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Airport.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Airport", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Flight.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Flight.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Flight", "line_number": 51, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 52, "usage_type": "call"}]} {"seq_id": "545710109", "text": "\n# coding: utf-8\n\n# In[ ]:\n\n\nimport datetime\nfrom sklearn.feature_selection import chi2, SelectPercentile\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom scipy import sparse\nimport lightgbm as lgb\nimport warnings\nimport time\nimport pandas as pd\nimport numpy as np\nimport os \n\n\n# In[ ]:\n\n\ntrain = pd.read_hdf('../data/train_data2.hdf', key='xunfei')\n\ntest = pd.read_hdf('../data/test_data2.hdf', key='xunfei')\ndata = pd.concat([train, test], axis=0, ignore_index=True)\n\n\n# In[ ]:\n\n\npd.set_option('display.max_columns',100)\ndata['time']=(data['timee']-data['timee'].min())/(data['timee'].max()-data['timee'].min())\n\n\n# In[ ]:\n\n\nad_cate_feature=['adid','advert_id','orderid','campaign_id','creative_id','creative_tp_dnf','creative_type',\n 'creative_is_jump','creative_is_download','creative_has_deeplink','advert_industry_inner0','advert_industry_inner1']\nmedia_cate_feature=['app_cate_id','f_channel','app_id','inner_slot_id']\ncontent_cate_feature=['city', 'carrier', 'province', 'nnt', 'devtype', 'osv', 'make', 'model']\n\nall_data=data.copy()\n\n\nneed=list(all_data.index)\n\n\n# 循环读取\nimport gc\nfrom tqdm import tqdm#'unsparse','timesparse',,'expsparse1','expsparse2','dupsparse','innersparse1','dupsparse1'\nfilelist=['expsparsex','expsparse1','innersparse1','appearsparses']#'svdsparsenew',\nstart=1\ntail=0\n##all_df=sparse.csr_matrix((len(all_data),0))\nmaxrec={}\nfor f in filelist:\n \n ttf=pd.read_hdf( '../data/'+f+'.hdf').astype(np.float32).iloc[need,:]\n orgshape=ttf.shape[1]\n \n \n '''use=pd.read_hdf('../data/'+f+'_catvip'+'.hdf',key='xunfei')['catvip'].values\n \n ttf=ttf.iloc[:,use]\n maxrec[f]=use#ttf.shape[1]'''\n if (start==1):\n all_df=ttf.copy()\n start=0\n else:\n all_df=pd.concat([all_df,ttf],axis=1)\n print(f,orgshape,(ttf.shape[1])/orgshape) \n del ttf\n gc.collect()\n\n\n# In[ ]:\n\n\nfrom tqdm import tqdm\nnum_feature = ['creative_width', 'creative_height', 'hour','user_tags_len']#,'time'\nttmp=data[num_feature].values.astype(np.int32)[need,:]\nfor col in tqdm(range(ttmp.shape[1])):\n all_df['num'+str(col)]=ttmp[:,col]\n\ndel ttmp\ngc.collect()\n\n\n# In[ ]:\n\n\n# 默认加载 如果 增加了cate类别特征 请改成false重新生成\nimport os\nif os.path.exists('../feature/base_train_csr.npz') and True:\n print('load_csr---------')\n base_train_csr = sparse.load_npz( '../feature/base_train_csr.npz').tocsr().astype('bool')\n base_predict_csr = sparse.load_npz('../feature/base_predict_csr.npz').tocsr().astype('bool')\nelse:\n base_train_csr = sparse.csr_matrix((len(train), 0))\n base_predict_csr = sparse.csr_matrix((len(predict_x), 0))\n\n enc = OneHotEncoder()\n for feature in cate_feature:\n enc.fit(data[feature].values.reshape(-1, 1))\n base_train_csr = sparse.hstack((base_train_csr, enc.transform(train_x[feature].values.reshape(-1, 1))), 'csr',\n 'bool')\n base_predict_csr = sparse.hstack((base_predict_csr, enc.transform(predict[feature].values.reshape(-1, 1))),\n 'csr',\n 'bool')\n print('one-hot prepared !')\n\n cv = CountVectorizer(min_df=10)\n for feature in ['user_tagss']:\n data[feature] = data[feature].astype(str)\n cv.fit(data[feature])\n base_train_csr = sparse.hstack((base_train_csr, cv.transform(train_x[feature].astype(str))), 'csr', 'bool')\n base_predict_csr = sparse.hstack((base_predict_csr, cv.transform(predict_x[feature].astype(str))), 'csr',\n 'bool')\n print('cv prepared !')\n\n sparse.save_npz( '../feature/base_train_csr.npz', base_train_csr)\n sparse.save_npz('../feature/base_predict_csr.npz', base_predict_csr)\n\n\n# In[ ]:\n\n\n#catvip=pd.read_hdf('../data/catvip.hdf', key='xunfei')['catvip'].values\nallbase=sparse.vstack((base_train_csr,base_predict_csr),'csr')\n#allbase=allbase[need,:][:,catvip]\n#allbase=allbase.toarray()\n\n\n# In[ ]:\n\n\nall_df=sparse.hstack((allbase,all_df),'csr')\n\n\n# In[ ]:\n\n\ntrain_x =all_df[:-len(test)]\ntrain_y = data[data.target != -1].target.values\n\n\n# In[ ]:\n\n\ntest_x=all_df[-len(test):]\n\n\n# In[ ]:\n\n\n\nimport gc\ndel allbase\ngc.collect()\ndel all_df\ngc.collect()\ndel data\ngc.collect()\n\n\n# In[ ]:\n\n\ndel all_data\ngc.collect()\n\n\n# In[ ]:\n\n\n\nimport datetime\nimport lightgbm as lgb\nimport gc\nfrom scipy.sparse import csr_matrix\nfrom sklearn.model_selection import KFold, StratifiedKFold\n# sklearn风格lightgbm\nimport random\nfea_rec=[]\nfea_vip=[]\nsub_preds = pd.DataFrame()\n\noof_predss = np.zeros(train_x.shape[0])\n#argsDict={'bagging_fraction': 0.7441379310344828, 'bagging_freq': 2, 'feature_fraction': 0.8489655172413793, 'learning_rate': 0.07241379310344827, 'max_bin': 268, 'max_depth': -1, 'min_child_weight': 1.3684210526315788, 'min_data_in_bin': 10, 'min_split_gain': 0.21052631578947367, 'num_boost_round': 5000, 'num_leaves': 50, 'rands': 396, 'reg_alpha': 1.8421052631578947, 'reg_lambda': 7.894736842105263, 'scale_pos_weight': 1.0}\n\nargsDict={'bagging_fraction': 0.98, 'bagging_freq': 8, 'feature_fraction': 0.9027586206896552, 'learning_rate': 0.052631578947368425, 'max_bin': 260, 'max_depth': -1, 'min_child_weight': 0.1724137931034483, 'min_data_in_bin': 7, 'min_split_gain': 0.1, 'num_boost_round': 5000, 'num_leaves': 56, 'rands': 152, 'reg_alpha': 6.842105263157895, 'reg_lambda': 13.974358974358974, 'scale_pos_weight': 1.0}\n\nLGB=lgb.LGBMClassifier(\n num_leaves=argsDict[\"num_leaves\"],\n max_depth=argsDict[\"max_depth\"],\n learning_rate=argsDict[\"learning_rate\"],\n n_estimators=argsDict['num_boost_round'],\n min_split_gain=argsDict[\"min_split_gain\"],\n #min_child_samples=argsDict[\"min_data_in_leaf\"],\n min_child_weight=argsDict[\"min_child_weight\"],\n subsample=argsDict[\"bagging_fraction\"],\n subsample_freq=argsDict[\"bagging_freq\"],\n colsample_bytree=argsDict[\"feature_fraction\"],\n reg_alpha=argsDict[\"reg_alpha\"],\n reg_lambda=argsDict[\"reg_lambda\"],\n scale_pos_weight=argsDict[\"scale_pos_weight\"],\n \n \n is_training_metric= True,\n boosting_type='gbdt',\n metric='binary_logloss',\n n_jobs=9,\n #n_threads=10,\n seed=argsDict[\"rands\"],\n drop_seed=argsDict[\"rands\"],\n bagging_seed=argsDict[\"rands\"],\n feature_fraction_seed=argsDict[\"rands\"],\n random_state=argsDict[\"rands\"],\n \n max_bin=argsDict[\"max_bin\"],\n min_data_in_bin=argsDict[\"min_data_in_bin\"],\n )\nrands=random.randint(0,100) \nskf = StratifiedKFold(n_splits=20, shuffle=True, random_state=argsDict[\"rands\"])\ntmprmselist=[]\ntreerec=[]\nfor ii,(train_index, test_index) in enumerate(skf.split(train_x, train_y)):\n \n x_train, x_val = train_x[train_index], train_x[test_index]\n y_train, y_val = train_y[train_index], train_y[test_index]\n print('fold:',ii)\n LGB.fit(x_train,y_train.reshape(-1,),eval_set=(x_val,y_val),eval_metric='logloss',early_stopping_rounds=100,verbose=100)\n tmplist=list(LGB.predict_proba(x_val)[:,1])\n oof_predss[test_index] =tmplist\n #print(LGB.evals_result_)\n tmprmse =np.mean(LGB.evals_result_['valid_0']['binary_logloss'])#np.sqrt(mean_squared_error(y_val, tmplist))\n print('best_itor:',LGB.best_iteration_,' logloss:',tmprmse) \n treerec.append(LGB.best_iteration_)\n tmprmselist.append(tmprmse)\n\n sub_preds['estimators'+str(ii)]=(LGB.predict_proba(test_x,num_iteration=LGB.best_iteration_))[:,1]\n \n importance_dict={}\n for col,val in zip(range(train_x.shape[1]),LGB.feature_importances_):\n importance_dict[col]=val\n ser=pd.Series(importance_dict).sort_values(ascending=False)\n fea_vip+=list(ser[ser>3].index)\n del x_train\n gc.collect()\n del x_val\n gc.collect()\n\n\n# In[ ]:\n\n\nsub=test[['instance_id']]\nsub['predicted_score']=(sub_preds.mean(axis=1)).values\norg_test=pd.read_table('../data/round2_iflyad_test_feature.txt')\nsub=org_test[['instance_id']].merge(sub,on='instance_id',how='left')\nsub.to_csv('../subs/10151.csv',index=None)\n\n\n# In[ ]:\n\n\noof_df=pd.DataFrame()\noof_df['instance_id']=train['instance_id'].values\noof_df['oof']=oof_predss.reshape(-1,)\noof_df.to_hdf('../oof/10151.csv',key='xunfei')\n\n", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 8370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_hdf", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_hdf", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_hdf", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 73, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 85, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "scipy.sparse.load_npz", "line_number": 99, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 99, "usage_type": "name"}, {"api_name": "scipy.sparse.load_npz", "line_number": 100, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 100, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 102, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 103, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 103, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 105, "usage_type": "call"}, {"api_name": "scipy.sparse.hstack", "line_number": 108, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 108, "usage_type": "name"}, {"api_name": "scipy.sparse.hstack", "line_number": 110, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 110, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.sparse.hstack", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 119, "usage_type": "name"}, {"api_name": "scipy.sparse.hstack", "line_number": 120, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 120, "usage_type": "name"}, {"api_name": "scipy.sparse.save_npz", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 124, "usage_type": "name"}, {"api_name": "scipy.sparse.save_npz", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 125, "usage_type": "name"}, {"api_name": "scipy.sparse.vstack", "line_number": 132, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 132, "usage_type": "name"}, {"api_name": "scipy.sparse.hstack", "line_number": 140, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 140, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 162, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 164, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 166, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 191, "usage_type": "call"}, {"api_name": "lightgbm.LGBMClassifier", "line_number": 196, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 226, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 239, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 249, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 252, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 254, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 262, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 270, "usage_type": "call"}]} {"seq_id": "535252901", "text": "import numpy as np\r\nimport random as rnd\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\nxx=np.arange(0,100)\r\nfor i in range(99):\r\n xx=np.append(xx,np.arange(0,100))\r\nyy=np.repeat(np.arange(0,100),100)\r\n\r\nfood=[]\r\nfor i in range(50):\r\n food=np.append(food,np.random.choice(np.arange(1,11),size=10))\r\n food=np.append(food,np.zeros(10))\r\n food=food.astype(int)\r\nfood=np.append(food,np.zeros(1000))\r\nfood=food.astype(int)\r\nfor i in range(50):\r\n food=np.append(food,np.zeros(10))\r\n food=food.astype(int)\r\n food=np.append(food,np.random.choice(np.arange(1,11),size=10))\r\nfood=np.append(food,np.zeros(1000))\r\nfood=food.astype(int)\r\nfor i in range(50):\r\n food=np.append(food,np.random.choice(np.arange(1,11),size=10))\r\n food=np.append(food,np.zeros(10))\r\n food=food.astype(int)\r\nfood=np.append(food,np.zeros(1000))\r\nfood=food.astype(int)\r\nfor i in range(50):\r\n food=np.append(food,np.zeros(10))\r\n food=food.astype(int)\r\n food=np.append(food,np.random.choice(np.arange(1,11),size=10))\r\nfood=np.append(food,np.zeros(1000))\r\nfood=food.astype(int)\r\nfor i in range(50):\r\n food=np.append(food,np.random.choice(np.arange(1,11),size=10))\r\n food=np.append(food,np.zeros(10))\r\n food=food.astype(int)\r\nfood=np.append(food,np.zeros(1000))\r\nfood=food.astype(int)\r\n\r\nmales=[[],[]]\r\nfor i in range(9998):\r\n males.append([])\r\nfemales=[[],[]]\r\nfor i in range(9998):\r\n females.append([])\r\n\r\ngrid=np.zeros((10000,4))\r\ngrid[0:10000,0]=np.ravel(xx)\r\ngrid[0:10000,1]=np.ravel(yy)\r\ngrid[0:10000,2]=np.ravel(food)\r\n\r\ndef grow_food():\r\n for i in range(0,10000):\r\n N=np.round((100*grid[i,2]*math.exp(0.8))/(100+(grid[i,2]*math.expm1(0.8))))\r\n grid[i,2]=N\r\n\r\ndef decay_pheromones():\r\n for i in range(0,10000):\r\n A=grid[i,3]*math.exp(-1)\r\n grid[i,3]=A\r\n\r\ndef list_sperm():\r\n for i in range(0,10000):\r\n if len(males[i])>0 and len(females[i])>0:\r\n for j in females[i]:\r\n for k in males[i]:\r\n P1.pop[j].sperm.append(P1.pop[k].dauer_1)\r\n P1.pop[j].sperm.append(P1.pop[k].dauer_2)\r\n\r\nclass Worm:\r\n species=\"C. nigoni\"\r\n def __init__(self,name):\r\n self.name=name\r\n self.gender=rnd.choice([\"hermaphrodite\",\"male\",\"female\"])\r\n self.age=0\r\n self.stage=\"egg\"\r\n self.location=[rnd.choice(xx),rnd.choice(yy)]\r\n self.energy=10\r\n self.L1_time=0\r\n self.L2d_time=0\r\n self.dauer_1=rnd.choice(np.arange(15,25))\r\n self.dauer_2=rnd.choice(np.arange(15,25))\r\n self.sperm=[]\r\n @property\r\n def position(self):\r\n return((100*self.location[1])+self.location[0])\r\n @property\r\n def dauer(self):\r\n return((self.dauer_1+self.dauer_2)/2)\r\n def move(self):\r\n if self.energy>0 or self.stage==\"dauer\":\r\n if self.stage!=\"dauer\":\r\n self.energy=self.energy-0.5\r\n neighbors=[[(self.location[0]+1)%100,self.location[1]],[(self.location[0]-1)%100,self.location[1]],[self.location[0],(self.location[1]+1)%100],[self.location[0],(self.location[1]-1)%100]]\r\n list=[]\r\n for i in range(4):\r\n list.append(grid[100*neighbors[i][1]+neighbors[i][0],3])\r\n Sum=(1/(1+list[0]))+(1/(1+list[1]))+(1/(1+list[2]))+(1/(1+list[3]))\r\n P_0=(1/(1+list[0]))/Sum\r\n P_1=(1/(1+list[1]))/Sum\r\n P_2=(1/(1+list[2]))/Sum\r\n P_3=(1/(1+list[3]))/Sum\r\n if self.stage==\"adult\" and self.gender==\"male\":\r\n males[self.position].remove(self.name)\r\n if self.stage==\"adult\" and self.gender==\"female\":\r\n females[self.position].remove(self.name)\r\n index=np.random.choice([0,1,2,3],p=[P_0,P_1,P_2,P_3])\r\n self.location=neighbors[index]\r\n grid[self.position,3]=grid[self.position,3]+1\r\n if self.stage==\"adult\" and self.gender==\"male\":\r\n males[self.position].append(self.name)\r\n if self.stage==\"adult\" and self.gender==\"female\":\r\n females[self.position].append(self.name)\r\n print(\"The location of\",self.name,\"is\",self.location)\r\n print(\"The amount of food is\",grid[self.position,2].astype(int))\r\n print(\"The amount of pheromones is\",grid[self.position,3])\r\n else:\r\n if self.stage==\"adult\" and self.gender==\"male\":\r\n males[self.position].remove(self.name)\r\n if self.stage==\"adult\" and self.gender==\"female\":\r\n females[self.position].remove(self.name)\r\n print(\"Worm\",self.name,\"has died of starvation\")\r\n P1.pop[self.name]=\"dead starving worm\"\r\n def eat(self):\r\n print(\"The location of\",self.name,\"is the same\")\r\n if self.stage==\"L1\":\r\n portion=1\r\n if self.stage==\"L2\" or self.stage==\"L2d\":\r\n portion=2\r\n if self.stage==\"L3\":\r\n portion=4\r\n if self.stage==\"dauer\":\r\n portion=min(4,grid[self.position,2])\r\n if self.stage==\"L4\":\r\n portion=8\r\n if self.stage==\"adult\":\r\n portion=16\r\n if self.stage==\"old\":\r\n portion=8\r\n number=0\r\n while number<portion:\r\n if grid[self.position,2]==0:\r\n break\r\n if self.age==400:\r\n break\r\n if self.stage!=\"dauer\":\r\n grid[self.position,2]=grid[self.position,2]-1\r\n self.energy=self.energy+1\r\n self.age=self.age+1\r\n self.grow()\r\n number=number+1\r\n if self.age<400:\r\n grid[self.position,3]=grid[self.position,3]+1\r\n print(\"The amount of food is\",grid[self.position,2].astype(int))\r\n print(\"The amount of pheromones is\",grid[self.position,3])\r\n def grow(self):\r\n if self.stage!=\"dauer\":\r\n self.energy=self.energy-0.5\r\n if self.age==10:\r\n self.stage=\"L1\"\r\n grid[self.position,3]=grid[self.position,3]+1\r\n print(\"Worm\",self.name,\"has reached L1\")\r\n if self.age==20:\r\n prob=1/(1+math.exp(self.dauer-self.L1_time))\r\n self.stage=np.random.choice([\"L2d\",\"L2\"],p=[prob,1-prob])\r\n print(\"Worm\",self.name,\"has reached\",self.stage)\r\n if self.age==40 and self.stage==\"L2\":\r\n self.stage=\"L3\"\r\n print(\"Worm\",self.name,\"has reached L3\")\r\n if self.age==45 and self.stage==\"L2d\":\r\n prob=1/(1+math.exp(self.dauer-self.L2d_time))\r\n self.stage=np.random.choice([\"dauer\",\"L3\"],p=[prob,1-prob])\r\n print(\"Worm\",self.name,\"has reached\",self.stage)\r\n self.age=40\r\n if self.age==80:\r\n self.stage=\"L4\"\r\n print(\"Worm\",self.name,\"has reached L4\")\r\n if self.age==160:\r\n self.stage=\"adult\"\r\n print(\"Worm\",self.name,\"has reached adulthood\")\r\n if self.gender==\"male\":\r\n males[self.position].append(self.name)\r\n if self.gender==\"female\":\r\n females[self.position].append(self.name)\r\n if self.age==320:\r\n self.stage=\"old\"\r\n print(\"Worm\",self.name,\"is old\")\r\n if self.gender==\"male\":\r\n males[self.position].remove(self.name)\r\n if self.gender==\"female\":\r\n females[self.position].remove(self.name)\r\n if self.age==400:\r\n print(\"Worm\",self.name,\"has died of old age\")\r\n P1.pop[self.name]=\"dead old worm\"\r\n def reproduce(self):\r\n if self.stage==\"adult\" and self.gender==\"hermaphrodite\":\r\n count=0\r\n for i in range(len(P1.pop),len(P1.pop)+10):\r\n if self.energy>0:\r\n self.energy=self.energy-0.5\r\n P1.pop.append(Worm(i))\r\n P1.pop[i].gender=np.random.choice([\"hermaphrodite\",\"male\"],p=[0.99,0.01])\r\n P1.pop[i].location=[self.location[0],self.location[1]]\r\n P1.pop[i].dauer_1=np.random.choice([self.dauer_1,self.dauer_2,np.random.normal(self.dauer_1,0.5),np.random.normal(self.dauer_2,0.5)],p=[0.4995,0.4995,0.0005,0.0005])\r\n P1.pop[i].dauer_2=np.random.choice([self.dauer_1,self.dauer_2,np.random.normal(self.dauer_1,0.5),np.random.normal(self.dauer_2,0.5)],p=[0.4995,0.4995,0.0005,0.0005])\r\n count=count+1\r\n print(count,\"eggs have been laid at\",self.location)\r\n if self.stage==\"adult\" and self.gender==\"female\" and len(self.sperm)>0:\r\n count=0\r\n for i in range(len(P1.pop),len(P1.pop)+10):\r\n if self.energy>0:\r\n self.energy=self.energy-0.5\r\n P1.pop.append(Worm(i))\r\n P1.pop[i].gender=np.random.choice([\"male\",\"female\"])\r\n P1.pop[i].location=[self.location[0],self.location[1]]\r\n P1.pop[i].dauer_1=np.random.choice([self.dauer_1,self.dauer_2,np.random.normal(self.dauer_1,0.5),np.random.normal(self.dauer_2,0.5)],p=[0.4995,0.4995,0.0005,0.0005])\r\n sperm=[]\r\n prob=[]\r\n for i in self.sperm:\r\n sperm.append(i)\r\n sperm.append(np.random.normal(i,0.5))\r\n prob.append(0.999/len(self.sperm))\r\n prob.append(0.001/len(self.sperm))\r\n P1.pop[i].dauer_2=np.random.choice(sperm,p=prob)\r\n count=count+1\r\n print(count,\"eggs have been laid at\",self.location)\r\n def decide(self):\r\n if self.stage==\"L1\":\r\n self.L1_time=self.L1_time+1\r\n if self.stage==\"L2d\":\r\n self.L2d_time=self.L2d_time+1\r\n if self.stage!=\"egg\":\r\n food_weights=np.round(np.arange(1,-0.0001,-(1/100)),8)\r\n neighbors_weights=np.arange(0,1.0001,(1/100))\r\n F=grid[self.position,2]\r\n F=F.astype(int)\r\n P=grid[self.position,3]+grid[100*self.location[1]+((self.location[0]+1)%100),3]+grid[100*self.location[1]+((self.location[0]-1)%100),3]+grid[100*((self.location[1]+1)%100)+self.location[0],3]+grid[100*((self.location[1]-1)%100)+self.location[0],3]\r\n P=np.round(P)\r\n P=P.astype(int)\r\n if F==0:\r\n probability=1\r\n elif self.energy==0:\r\n probability=0\r\n elif P>99:\r\n probability=(food_weights[F]+1)/2\r\n else:\r\n probability=(food_weights[F]+neighbors_weights[P])/2\r\n if np.random.choice([True,False],p=[probability,1-probability])==True:\r\n self.move()\r\n else:\r\n self.eat()\r\n self.reproduce()\r\n if self.stage==\"egg\":\r\n print(\"Worm\",self.name,\"is an egg\")\r\n self.age=self.age+1\r\n self.grow()\r\n if self.stage==\"L2d\" and (1/(1+math.exp(self.dauer-self.L2d_time)))>0.99:\r\n self.age=40\r\n self.stage=\"dauer\"\r\n print(\"Worm\",self.name,\"has reached dauer\")\r\n\r\nclass Population:\r\n def __init__(self,size):\r\n self.size=size\r\n self.pop=[]\r\n def populate(self):\r\n for i in range(0,self.size):\r\n self.pop.append(Worm(i))\r\n print(\"The location of\",self.pop[i].name,\"is\",self.pop[i].location)\r\n def decide(self,worm_lower,worm_upper,decision):\r\n self.worm_lower=worm_lower\r\n self.worm_upper=worm_upper\r\n self.decision=decision\r\n number=0\r\n while number<self.decision:\r\n bounds=np.arange(self.worm_lower,self.worm_upper)\r\n rnd.shuffle(bounds)\r\n for i in bounds:\r\n try:\r\n self.pop[i].decide()\r\n except AttributeError:\r\n print(\"Worm\",i,\"is dead\")\r\n continue\r\n number=number+1\r\n list_sperm()\r\n grow_food()\r\n decay_pheromones()\r\n\r\ndef worm_map():\r\n x,y=np.mgrid[slice(0,100),slice(0,100)]\r\n z=grid[100*y+x,3]\r\n plt.pcolormesh(x,y,z,cmap='Blues')\r\n #plt.pcolormesh(x,y,z,cmap='Blues',vmin=0,vmax=100) to normalize\r\n plt.title('Pheromone Density')\r\n plt.xlabel('X Coordinate')\r\n plt.ylabel('Y Coordinate')\r\n plt.axis([0,100,0,100])\r\n plt.colorbar()\r\n \r\ndef food_map():\r\n x,y=np.mgrid[slice(0,100),slice(0,100)]\r\n z=grid[100*y+x,2]\r\n plt.pcolormesh(x,y,z,cmap='Blues')\r\n #plt.pcolormesh(x,y,z,cmap='Blues',vmin=0,vmax=100) to normalize\r\n plt.title('Food Density')\r\n plt.xlabel('X Coordinate')\r\n plt.ylabel('Y Coordinate')\r\n plt.axis([0,100,0,100])\r\n plt.colorbar()\r\n\r\ndef prob_move():\r\n x,y=np.mgrid[slice(0,101),slice(0,101)]\r\n z=np.zeros((101,101))\r\n for i in range(0,101):\r\n for j in range(0,101):\r\n food_weights=np.round(np.arange(1,-0.0001,-(1/100)),8)\r\n neighbors_weights=np.arange(0,1.0001,(1/100))\r\n if i==0:\r\n z[i,j]=1\r\n elif j>99:\r\n z[i,j]=(food_weights[i]+1)/2\r\n else:\r\n z[i,j]=(food_weights[i]+neighbors_weights[j])/2\r\n plt.pcolormesh(x,y,z,cmap='Blues')\r\n plt.title('Probability of Moving')\r\n plt.xlabel('Amount of Food')\r\n plt.ylabel('Amount of Pheromones')\r\n plt.axis([0,100,0,100])\r\n plt.colorbar(ticks=[np.arange(0,1.1,0.1)])\r\n\r\ndef how_many():\r\n egg,L1,L2,L2d,L3,dauer,L4,adult,old,dsw,dow=0,0,0,0,0,0,0,0,0,0,0\r\n for i in range(len(P1.pop)):\r\n if P1.pop[i]==\"dead starving worm\":\r\n dsw=dsw+1\r\n if P1.pop[i]==\"dead old worm\":\r\n dow=dow+1\r\n try:\r\n if P1.pop[i].stage==\"egg\":\r\n egg=egg+1\r\n if P1.pop[i].stage==\"L1\":\r\n L1=L1+1\r\n if P1.pop[i].stage==\"L2\":\r\n L2=L2+1\r\n if P1.pop[i].stage==\"L2d\":\r\n L2d=L2d+1\r\n if P1.pop[i].stage==\"L3\":\r\n L3=L3+1\r\n if P1.pop[i].stage==\"dauer\":\r\n dauer=dauer+1\r\n if P1.pop[i].stage==\"L4\":\r\n L4=L4+1\r\n if P1.pop[i].stage==\"adult\":\r\n adult=adult+1\r\n if P1.pop[i].stage==\"old\":\r\n old=old+1\r\n except AttributeError:\r\n continue\r\n print(egg,\"egg\")\r\n print(L1,\"L1\")\r\n print(L2,\"L2\")\r\n print(L2d,\"L2d\")\r\n print(L3,\"L3\")\r\n print(dauer,\"dauer\")\r\n print(L4,\"L4\")\r\n print(adult,\"adult\")\r\n print(old,\"old\")\r\n print(dsw,\"dead starving worm\")\r\n print(dow,\"dead old worm\") \r\n \r\ndef stats():\r\n gene_1=[]\r\n gene_2=[]\r\n for i in range(len(P1.pop)):\r\n try:\r\n gene_1.append(P1.pop[i].dauer_1)\r\n gene_2.append(P1.pop[i].dauer_2)\r\n except AttributeError:\r\n continue\r\n print(\"Gene 1 mean\",np.mean(gene_1))\r\n print(\"Gene 2 mean\",np.mean(gene_2))\r\n print(\"Gene 1 std\",np.std(gene_1))\r\n print(\"Gene 2 std\",np.std(gene_2))\r\n \r\nP1=Population(1000)\r\nP1.populate()", "sub_path": "Worms Simulation.py", "file_name": "Worms Simulation.py", "file_ext": "py", "file_size_in_byte": 15077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.arange", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 57, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 57, "usage_type": "call"}, {"api_name": "math.expm1", "line_number": 57, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 62, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 77, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 80, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 84, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 168, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 205, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 208, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 227, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 251, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 279, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.mgrid", "line_number": 293, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "numpy.mgrid", "line_number": 304, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 306, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 306, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 312, "usage_type": "name"}, {"api_name": "numpy.mgrid", "line_number": 315, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 386, "usage_type": "call"}]} {"seq_id": "323268837", "text": "# -*- coding: utf-8 -*-\n\nfrom zhihu_scrapy import db\n\nzhihu = db.ZhihuMongo(\"zhihu\")\n\ndef main():\n while zhihu[\"waitting_urls\"].find_one():\n from scrapy.cmdline import execute\n execute(\"scrapy crawl zhihu_crawl\".split())\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "loop_spider.py", "file_name": "loop_spider.py", "file_ext": "py", "file_size_in_byte": 278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "zhihu_scrapy.db.ZhihuMongo", "line_number": 5, "usage_type": "call"}, {"api_name": "zhihu_scrapy.db", "line_number": 5, "usage_type": "name"}, {"api_name": "scrapy.cmdline.execute", "line_number": 10, "usage_type": "call"}]} {"seq_id": "453994065", "text": "from selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.by import By\r\nfrom random_user_agent.user_agent import UserAgent\r\n\r\n\r\nfrom random_user_agent.params import SoftwareName, OperatingSystem\r\nimport openpyxl\r\nimport random\r\nimport time\r\n\r\nsearch_char = ['L','S','T']\r\n\r\nsoftware_names = [SoftwareName.CHROME.value]\r\noperating_systems = [OperatingSystem.WINDOWS.value,\r\n OperatingSystem.LINUX.value]\r\nuser_agent_rotator = UserAgent(software_names = software_names,\r\n operating_systems = operating_systems,\r\n limit=100)\r\nuser_agent = user_agent_rotator.get_random_user_agent()\r\n\r\n\r\n\r\n\r\ndef click_element(elem_type=0, elem_selector=0):\r\n if(elem_type == 'css'):\r\n elem = browser.find_element_by_css_selector(elem_selector)\r\n elem.click\r\n\r\ndef sleep_fuc():\r\n time.sleep(random.randrange(2,4))\r\n \r\ndef get_search_window(srch_address,srch_prim_plan,srch_sub_plan,row_num,col_num,active_sheet):\r\n global browser\r\n global software_names, operating_systems, user_agent_rotator, user_agent\r\n\r\n chrome_options = Options()\r\n chrome_options.add_argument(\"user-agent ={}\".format(user_agent))\r\n\r\n browser = webdriver.Chrome(options=chrome_options)\r\n browser.get('https://www.blueshieldca.com/fap/app/find-a-doctor.html')\r\n \r\n \r\n\r\n browser.switch_to.window(browser.window_handles[1])\r\n\r\n doctor_button=browser.find_element_by_id('Doctor')\r\n\r\n doctor_button.click()\r\n\r\n #guest_button=find_element_by_id('nonMember')\r\n WebDriverWait(browser,2).until(EC.element_to_be_clickable((By.ID,'nonMember'))).click()\r\n\r\n #guest_button.click()\r\n\r\n location_input=browser.find_element_by_id('autocomplete')\r\n\r\n #location_input.click()\r\n #location_input.send_keys('Los Angeles, CA 90002, USA')\r\n\r\n\r\n\r\n sleep_fuc()\r\n\r\n while(browser.current_url != 'https://www.blueshieldca.com/fad/plans/chooseplan'):\r\n location_input.clear()\r\n location_input.send_keys(srch_address)\r\n \r\n location_input.send_keys(Keys.LEFT)\r\n location_input.send_keys(Keys.RIGHT)\r\n \r\n action = webdriver.common.action_chains.ActionChains(browser)\r\n action.move_to_element_with_offset(location_input, 100,60)\r\n action.click()\r\n action.perform()\r\n\r\n sleep_fuc()\r\n\r\n #CLICK CONTINUE\r\n #click_element('css','#main > div > app-location > div.location-container > div > div.wrapper-location.col-sm-12.col-md-10.col-lg-7 > button')\r\n continue_button = browser.find_element_by_css_selector('#main > div > app-location > div.location-container > div > div.wrapper-location.col-sm-12.col-md-10.col-lg-7 > button')\r\n sleep_fuc()\r\n\r\n continue_button.click()\r\n\r\n\r\n sleep_fuc()\r\n for tries in range(0,5):\r\n try:\r\n browser.refresh()\r\n allHTML = browser.find_element_by_tag_name('html')\r\n allHTML.send_keys(Keys.PAGE_DOWN)\r\n time.sleep(1)\r\n primary_plan_button = browser.find_element_by_css_selector('#main > div > app-choose-plans > div > div > div > div > div > button.btn.btn-primary.planButtonLeft')\r\n primary_plan_button.click()\r\n\r\n #CLICK PRIMARY PLAN DROPDOWN\r\n #click_element('css','#primaryPlanDropdown > div')\r\n #htmlElem = browser.find_element_by_tag_name('html')\r\n #htmlElem.send_keys(Keys.PAGE_DOWN)\r\n primary_plan_dropdown = browser.find_element_by_xpath('//*[@id=\"primaryPlanDropdown\"]')\r\n browser.execute_script(\"arguments[0].click();\", primary_plan_dropdown)\r\n time.sleep(2)\r\n\r\n\r\n primary_plan_ul = browser.find_element_by_xpath('//*[@id=\"main\"]/div/app-plans/div[1]/div/div/div/form/div/div/div/div[3]/div[2]/ul')\r\n primary_plans = primary_plan_ul.find_elements_by_tag_name('li')\r\n\r\n srch_prim_plan = srch_prim_plan.replace(\" \",\"\").lower()\r\n for plan in primary_plans:\r\n plan_text = plan.text\r\n plan_text = plan_text.replace(\" \",\"\").lower()\r\n if (plan_text == srch_prim_plan):\r\n plan.click()\r\n break\r\n\r\n try:\r\n sub_plan_dropdown = browser.find_element_by_xpath('//*[@id=\"subPlanDropdown\"]')\r\n browser.execute_script(\"arguments[0].click();\", sub_plan_dropdown)\r\n\r\n allHTML = browser.find_element_by_tag_name('html')\r\n allHTML.send_keys(Keys.PAGE_DOWN)\r\n time.sleep(1)\r\n\r\n sub_plan_ul = browser.find_element_by_xpath('//*[@id=\"main\"]/div/app-plans/div[1]/div/div/div/form/div/div/div/div[3]/div[3]/ul')\r\n sub_plans = sub_plan_ul.find_elements_by_tag_name('li')\r\n\r\n #Create search pattern\r\n \r\n srch_sub_plan = srch_sub_plan.replace(\" \",\"\")\r\n \r\n for sub in sub_plans:\r\n sub_text = sub.text\r\n sub_text = sub_text.replace(\" \",\"\")\r\n \r\n if (sub_text == srch_sub_plan):\r\n sub.click()\r\n break\r\n \r\n except:\r\n print('No Subplan')\r\n \r\n\r\n \r\n \r\n sleep_fuc()\r\n plan_continue = browser.find_element_by_xpath('//*[@id=\"continuePlan\"]')\r\n browser.execute_script(\"arguments[0].click();\", plan_continue)\r\n sleep_fuc()\r\n return True\r\n except Exception as e:\r\n #print(e)\r\n print('Request Denied, try# '+str(tries))\r\n #browser.quit()\r\n #software_names = [SoftwareName.CHROME.value]\r\n #operating_systems = [OperatingSystem.WINDOWS.value,\r\n #OperatingSystem.LINUX.value]\r\n #user_agent_rotator = UserAgent(software_names = software_names,\r\n #operating_systems = operating_systems,\r\n #limit=100)\r\n #user_agent = user_agent_rotator.get_random_user_agent()\r\n browser.back()\r\n time.sleep(2*tries)\r\n #get_search_window(srch_address,srch_prim_plan,srch_sub_plan,row_num,col_num,active_sheet)\r\n #active_sheet.cell(row = row_num, column = col_num).value = \"0 (Manually Check)\"\r\n return False\r\n#####################\r\n\r\ndef get_doctor_count(row_num,col_num,active_sheet):\r\n global search_char\r\n #doctor_name = browser.find_element_by_xpath('//*[@id=\"Enter last name\"]/label')\r\n #doctor_name.click()\r\n\r\n WebDriverWait(browser,2).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"Enter last name\"]/label'))).click()\r\n time.sleep(0.3)\r\n\r\n enter_name = WebDriverWait(browser,2).until(EC.presence_of_element_located((By.XPATH,'//*[@id=\"provider_name\"]')))\r\n enter_name.clear()\r\n enter_name.send_keys(search_char[0])\r\n\r\n search_btn = browser.find_element_by_xpath('//*[@id=\"searchBtn\"]')\r\n search_btn.click()\r\n\r\n sleep_fuc()\r\n\r\n try:\r\n miles = browser.find_element_by_xpath('//*[@id=\"searchResultMsg\"]/a')\r\n browser.execute_script(\"arguments[0].click();\", miles)\r\n except:\r\n total_doctors = \"0 (Failed at miles)\"\r\n active_sheet.cell(row = row_num, column = col_num).value = total_doctors\r\n print('Failed at miles')\r\n return\r\n\r\n miles_dropdown = browser.find_element_by_xpath('//*[@id=\"dropdownRadiusFilter\"]')\r\n browser.execute_script(\"arguments[0].click();\", miles_dropdown)\r\n\r\n time.sleep(1.5)\r\n\r\n #miles_ul = browser.find_element_by_xpath('//*[@id=\"affix_el\"]/app-filtersort/div/div[2]/form/fieldset/div[1]/div[1]/div/ul')\r\n miles_100 = browser.find_element_by_xpath('//*[@id=\"select_lo7\"]')\r\n miles_100.click()\r\n\r\n apply_btn = browser.find_element_by_xpath('//*[@id=\"filter-button-apply\"]')\r\n apply_btn.click()\r\n time.sleep(4)\r\n \r\n try:\r\n total_doctors = browser.find_element_by_xpath('//*[@id=\"searchResultCount\"]/span[1]')\r\n except:\r\n total_doctors = \"0\"\r\n\r\n active_sheet.cell(row = row_num, column = col_num).value = total_doctors.text\r\n print('Success!: '+ str(row_num)+', '+str(col_num))\r\n\r\n for index in range(1,3):\r\n col_num = col_num + 1\r\n enter_name = WebDriverWait(browser,2).until(EC.presence_of_element_located((By.XPATH,'//*[@id=\"provider_name\"]')))\r\n enter_name.clear()\r\n enter_name.send_keys(search_char[index])\r\n\r\n\r\n WebDriverWait(browser,2).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"searchBtn\"]'))).click()\r\n time.sleep(5)\r\n try:\r\n total_doctors = browser.find_element_by_xpath('//*[@id=\"searchResultCount\"]/span[1]')\r\n except:\r\n total_doctors = \"0\"\r\n \r\n active_sheet.cell(row = row_num, column = col_num).value = total_doctors.text\r\n print('Success!: '+ str(row_num)+', '+str(col_num))\r\n\r\n####################\r\n\r\n\r\n\r\ndef main():\r\n #OPEN EXCEL FILE\r\n #print('Please input the whole file name. (must be .xlsx file)')\r\n file = 'Site 5 Product Research Jun21.xlsx'\r\n wb = openpyxl.load_workbook(file)\r\n\r\n\r\n print('Start on which row?')\r\n start_row = input()\r\n print('Start on which column?')\r\n start_column = input()\r\n \r\n sheet = wb['Sheet1']\r\n\r\n for row in range(start_row, sheet.max_row +1):\r\n sub_plan = sheet['D' + str(row)].value\r\n prim_plan = sheet['E' + str(row)].value\r\n\r\n \r\n \r\n\r\n for col in range(start_column, sheet.max_column + 1, 3):\r\n search_value = sheet.cell(row = 1, column = col).value\r\n zip_code = search_value[0:5]\r\n #search_char = search_value[-1]\r\n\r\n sheet = wb['Sheet2']\r\n for zip_row in range(1,sheet.max_row+1):\r\n if (str(zip_code) == str(sheet['A'+str(zip_row)].value)):\r\n address = sheet['B'+str(zip_row)].value\r\n break\r\n \r\n sheet = wb['Sheet1']\r\n proceed = get_search_window(address,prim_plan,sub_plan,row,col,sheet)\r\n if (proceed):\r\n get_doctor_count(row,col,sheet)\r\n wb.save(file)\r\n browser.quit()\r\n\r\n start_column = 7\r\n\r\n\r\n \r\n print('Done!')\r\n\r\nmain()\r\n", "sub_path": "Site 5_automation.py", "file_name": "Site 5_automation.py", "file_ext": "py", "file_size_in_byte": 10611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random_user_agent.params.SoftwareName.CHROME", "line_number": 18, "usage_type": "attribute"}, {"api_name": "random_user_agent.params.SoftwareName", "line_number": 18, "usage_type": "name"}, {"api_name": "random_user_agent.params.OperatingSystem.WINDOWS", "line_number": 19, "usage_type": "attribute"}, {"api_name": "random_user_agent.params.OperatingSystem", "line_number": 19, "usage_type": "name"}, {"api_name": "random_user_agent.params.OperatingSystem.LINUX", "line_number": 20, "usage_type": "attribute"}, {"api_name": "random_user_agent.params.OperatingSystem", "line_number": 20, "usage_type": "name"}, {"api_name": "random_user_agent.user_agent.UserAgent", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 44, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 56, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 56, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 56, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.LEFT", "line_number": 73, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 73, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RIGHT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 74, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.action_chains.ActionChains", "line_number": 76, "usage_type": "call"}, {"api_name": "selenium.webdriver.common", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 76, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.PAGE_DOWN", "line_number": 96, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 96, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.PAGE_DOWN", "line_number": 126, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 126, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 127, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 167, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 178, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 178, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 178, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 178, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 178, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 179, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 181, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 181, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 181, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 181, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 181, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 202, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 210, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 222, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 222, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 222, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 222, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 222, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 227, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 227, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 227, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 227, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 227, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 228, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 245, "usage_type": "call"}]} {"seq_id": "347761178", "text": "# Information: https://clover.coex.tech/en/snippets.html#navigate_wait\n\nimport math\nimport rospy\nfrom clover import srv\nfrom std_srvs.srv import Trigger\nimport tf2_geometry_msgs \nfrom tf.transformations import euler_from_quaternion\n\n#rospy.init_node('flight')\n\nget_telemetry = rospy.ServiceProxy('get_telemetry', srv.GetTelemetry)\nnavigate = rospy.ServiceProxy('navigate', srv.Navigate)\nnavigate_global = rospy.ServiceProxy('navigate_global', srv.NavigateGlobal)\nset_position = rospy.ServiceProxy('set_position', srv.SetPosition)\nset_velocity = rospy.ServiceProxy('set_velocity', srv.SetVelocity)\nset_attitude = rospy.ServiceProxy('set_attitude', srv.SetAttitude)\nset_rates = rospy.ServiceProxy('set_rates', srv.SetRates)\nland = rospy.ServiceProxy('land', Trigger)\n\ndef navigate_wait(x=0, y=0, z=0, yaw=float('nan'), yaw_rate=0, speed=1, \\\n frame_id='body', tolerance=0.2, auto_arm=False):\n\n res = navigate(x=x, y=y, z=z, yaw=yaw, yaw_rate=yaw_rate, speed=speed, \\\n frame_id=frame_id, auto_arm=auto_arm)\n\n if not res.success:\n return res\n\n \n\nprint (\"Take off 1 meter\")\nnavigate_wait(z=2, frame_id='body', auto_arm=True)\n# Wait for 3 seconds\nprint(\"Done\")\nrospy.sleep(3)\n\n\n# Fly forward 1 m\n#navigate_wait(x=1, frame_id='body')\n\n# Land\n#land()\n\ndef goal_republish(posedata):\n\n print(\"Received Simple\")\n #do the transformation\n euler = euler_from_quaternion([posedata.pose.orientation.x, posedata.pose.orientation.y, posedata.pose.orientation.z, posedata.pose.orientation.w])\n print(\"Received Simple Goal: x=\",posedata.pose.position.x,\" y=\",posedata.pose.position.y,\" z=\",posedata.pose.position.z,\" yaw =\",euler[2],\" frameID=\",posedata.header.frame_id)\n navigate_wait( x=posedata.pose.position.x, y=posedata.pose.position.y, z=2, yaw=euler[2], frame_id=posedata.header.frame_id)\n print(\"Done\")\n #rospy.sleep(3)\n \n\nif __name__ == '__main__':\n\n rospy.init_node('republisher', anonymous=False)\n rospy.Subscriber(\"/move_base_simple/goal\", tf2_geometry_msgs.PoseStamped, goal_republish)\n rospy.spin()\n", "sub_path": "dronecore/src/navigate_wait_simplegoal_SIMULATION.py", "file_name": "navigate_wait_simplegoal_SIMULATION.py", "file_ext": "py", "file_size_in_byte": 2060, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "rospy.ServiceProxy", "line_number": 12, "usage_type": "call"}, {"api_name": "clover.srv.GetTelemetry", "line_number": 12, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 12, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 13, "usage_type": "call"}, {"api_name": "clover.srv.Navigate", "line_number": 13, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 13, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 14, "usage_type": "call"}, {"api_name": "clover.srv.NavigateGlobal", "line_number": 14, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 14, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 15, "usage_type": "call"}, {"api_name": "clover.srv.SetPosition", "line_number": 15, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 15, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 16, "usage_type": "call"}, {"api_name": "clover.srv.SetVelocity", "line_number": 16, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 16, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 17, "usage_type": "call"}, {"api_name": "clover.srv.SetAttitude", "line_number": 17, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 17, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 18, "usage_type": "call"}, {"api_name": "clover.srv.SetRates", "line_number": 18, "usage_type": "attribute"}, {"api_name": "clover.srv", "line_number": 18, "usage_type": "name"}, {"api_name": "rospy.ServiceProxy", "line_number": 19, "usage_type": "call"}, {"api_name": "std_srvs.srv.Trigger", "line_number": 19, "usage_type": "argument"}, {"api_name": "rospy.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "tf.transformations.euler_from_quaternion", "line_number": 49, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 58, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 59, "usage_type": "call"}, {"api_name": "tf2_geometry_msgs.PoseStamped", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rospy.spin", "line_number": 60, "usage_type": "call"}]} {"seq_id": "508608805", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 10 10:49:28 2021\n\n@author: t1\n\"\"\"\n\nimport time\nimport numpy as np\nfrom collections import Counter\n\ndef time_it(func):\n def wrapper(*args,**kwargs):\n start = time.clock()\n result = func(*args,**kwargs)\n end = time.clock()\n print(' {} executed in time : {:.6f} sec'.format(func.__name__,end - start))\n return result\n return wrapper\n\ndef read_file(file_path):\n M = None\n N = None\n spectrum = None\n with open(file_path) as f:\n lines = f.readlines()\n M = int(lines[0].strip())\n N = int(lines[1].strip())\n spectrum = [int(x) for x in lines[2].strip().split(' ')]\n return M,N,sorted(spectrum)\n\ndef get_convolution2(spectrum):\n '''\n spectrum -> list of ints\n \n returns :\n conv_list -> list of ints; convolution of spectrum\n '''\n spec_arr = np.array(spectrum)\n conv_mat = spec_arr.reshape(-1,1) - spec_arr.reshape(1,-1)\n conv_list = list(conv_mat[conv_mat > 0].ravel())\n return conv_list\n\ndef get_top_M_weights(M,conv_list):\n '''\n M -> int\n conv_list -> list of ints ; \n \n returns -> top M most common values in convolution \n '''\n conv_list = [el for el in conv_list if (el >= 57) and (el <= 200)]\n counts_ = Counter(conv_list)\n mc_list = counts_.most_common(M)\n last_count_val = mc_list[-1][1]\n for (key,val) in counts_.items():\n if (val == last_count_val) and ((key,val) not in mc_list):\n mc_list.append((key,val))\n return [w for (w,c) in mc_list]\n \ndef expand_cands(cand_list,mass_list):\n '''\n cand_list -> list of strs\n \n returns new collection containing all possible extensions of \n peptides in Peptides by a single amino acid mass\n '''\n new_cand_list = []\n for cand in cand_list:\n for mass in mass_list:\n if cand == \"\":\n new_cand = str(mass)\n else: \n new_cand = cand + '-' + str(mass)\n new_cand_list.append(new_cand)\n return new_cand_list\n\ndef get_cand_mass(cand):\n '''\n cand -> str\n returns total mass for candidate sequence\n '''\n mass_list = [int(m) for m in cand.split('-')]\n return sum(mass_list)\n\ndef get_cand_spectrum(cand):\n \n '''\n cand -> str of masses eg : '113-129-128-114'\n returns cyclo spectrum of peptides\n '''\n mass_list = [int(m) for m in cand.split('-')]\n spectrum = []\n for mass in mass_list:\n spectrum.append(mass)\n for k in range(2,len(mass_list)):\n for i in range(len(mass_list)):\n j = i+k\n w_ = None\n if j > len(mass_list):\n w_ = sum(mass_list[i:]) + sum(mass_list[:(j - len(mass_list))])\n else:\n w_ = sum(mass_list[i:j])\n spectrum.append(w_)\n if len(mass_list) > 1:\n spectrum.append(sum(mass_list))\n spectrum = [0] + spectrum\n return sorted(spectrum)\n \ndef get_cand_lin_spectrum(cand):\n mass_list = [int(m) for m in cand.split('-')]\n spectrum = []\n for mass in mass_list:\n spectrum.append(mass)\n for k in range(2,len(mass_list)):\n for i in range(len(mass_list) -k +1):\n j = i+k\n w_ = sum(mass_list[i:j])\n spectrum.append(w_)\n if len(mass_list) > 1:\n spectrum.append(sum(mass_list))\n spectrum = [0] + spectrum\n return sorted(spectrum)\n\ndef get_cand_lin_score(peptide,spectrum):\n '''\n peptide -> str\n spectrum -> list of ints\n score is defined as the number of masses shared between \n Cyclospectrum(Peptide) and Spectrum\n '''\n peptide_spectrum = get_cand_lin_spectrum(peptide)\n pep_spectrum_set = list(set(peptide_spectrum))\n score = 0\n for mass in pep_spectrum_set:\n count_1 = peptide_spectrum.count(mass)\n count_2 = spectrum.count(mass)\n score += min(count_1,count_2)\n return score\n\ndef get_cand_cyclic_score(peptide,spectrum):\n '''\n peptide -> str\n spectrum -> list of ints\n score is defined as the number of masses shared between \n Cyclospectrum(Peptide) and Spectrum\n '''\n peptide_spectrum = get_cand_spectrum(peptide)\n pep_spectrum_set = list(set(peptide_spectrum))\n score = 0\n for mass in pep_spectrum_set:\n count_1 = peptide_spectrum.count(mass)\n count_2 = spectrum.count(mass)\n score += min(count_1,count_2)\n return score \n\ndef trim(N,lead_board,spectrum):\n if len(lead_board) == 0:\n return []\n score_list = [(cand,get_cand_lin_score(cand, spectrum)) for cand in lead_board]\n sorted_score_list = sorted(score_list,key = lambda x : x[1],reverse = True)\n # print(sorted_score_list)\n trimmed_list = sorted_score_list[:N]\n nt_score = trimmed_list[-1][1]\n for cand,score in sorted_score_list[N:]:\n if score == nt_score:\n trimmed_list.append((cand,score))\n trimmed_cand_list = [x[0] for x in trimmed_list]\n # print(len(trimmed_cand_list))\n return trimmed_cand_list\n\ndef get_cand_peptide(N,spectrum,mass_list):\n '''\n N -> leaderboard size\n spectrum -> experimental spectrum\n returns peptide cancdidate with the highest score\n '''\n \n lead_board = [\"\"]\n lead_board_trim = []\n lead_cand = ''\n lead_score = 0\n cand_score_dict = {}\n PARENT_MASS = max(spectrum)\n while len(lead_board) > 0:\n lead_board = expand_cands(lead_board,mass_list)\n lead_board_trim = []\n \n for cand in lead_board:\n cand_mass = get_cand_mass(cand)\n # cand_score = get_cand_cyclic_score(cand, spectrum)\n # cand_score_dict[cand] = cand_score\n if cand_mass == PARENT_MASS:\n cand_score = get_cand_cyclic_score(cand, spectrum)\n cand_score_dict[cand] = cand_score\n \n if cand_score > lead_score:\n lead_cand = cand\n lead_score = cand_score\n if cand_mass <= PARENT_MASS:\n lead_board_trim.append(cand)\n lead_board = trim(N,lead_board_trim,spectrum)\n \n return lead_cand\n\n@time_it\ndef get_conv_cyclo_pep_seq(M,N,spectrum):\n '''\n M -> int ; top M most frequent weight values are taken from convolution of spectrum\n N -> int ; trim top N values in leaderboard cyclopeptide sequencing \n spectrum -> list of ints ; experimental spectrum\n \n returns : most probable sequence of peptide\n '''\n # get spectrum convolution\n conv_list = get_convolution2(spectrum)\n mass_list = get_top_M_weights(M, conv_list)\n # print('masses : ',len(mass_list),mass_list)\n \n lead_cand = get_cand_peptide(N, spectrum, mass_list)\n return lead_cand\n\n\n# file_path = 'sample_data/convol_cyclo_pep_seq.txt'\nfile_path = 'test_data/dataset_104_7.txt'\nM,N,spectrum = read_file(file_path)\nlead_cand = get_conv_cyclo_pep_seq(M, N, spectrum)\nprint(lead_cand)", "sub_path": "textbook/convol_cyclo_pep_seq.py", "file_name": "convol_cyclo_pep_seq.py", "file_ext": "py", "file_size_in_byte": 6950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.clock", "line_number": 15, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 53, "usage_type": "call"}]} {"seq_id": "434538793", "text": "import github_repo as gh\nimport json\n\nOWNER = 'glass-bead-labs'\nREPO = 'sensor-group'\n\nsensor_group = gh.GitHubRepo(OWNER, REPO)\n\ndata = {}\ndataset = []\n\nfor label in sensor_group.get_all_labels():\n issues = []\n for issue in sensor_group.get_issues_with_label(label):\n index = sensor_group.get_index(issue)\n size = sensor_group.get_num_comments(index) + 1\n issues.append({\"name\": issue, \"size\": size})\n dataset.append({\"name\": label, \"children\": issues})\n\nissues = []\nfor issue in sensor_group.get_issues_without_label():\n index = sensor_group.get_index(issue)\n size = sensor_group.get_num_comments(index) + 1\n issues.append({\"name\": issue, \"size\": size})\n\ndataset.append({\"name\": \"no label\", \"children\": issues})\ndata[\"name\"] = OWNER + '/' + REPO\ndata[\"children\"] = dataset\n\n# Creates another JSON file. \nwith open ('bubble.json', 'w') as outfile:\n json.dump(data, outfile, indent=4)", "sub_path": "bubble_chart/log_issues.py", "file_name": "log_issues.py", "file_ext": "py", "file_size_in_byte": 926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "github_repo.GitHubRepo", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 32, "usage_type": "call"}]} {"seq_id": "65215034", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 10 10:30:09 2019\n\n@author: koch\n\"\"\"\n\n# Import libraries\n\n# Misc\nfrom pathlib import Path\nfrom os import listdir\nfrom os import system\nfrom copy import deepcopy\nfrom sys import argv\n\n# Math\nimport numpy as np\nimport scipy.io\nimport scipy.stats\nfrom random import sample\nfrom collections import Counter\n\n# Image processing\nimport nibabel\nfrom nilearn import signal\nfrom nilearn import image\nimport pandas as pd\n\n# Machine learning\nimport sklearn.model_selection\nimport sklearn.svm\n\n# Plotting\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec\n\n\n''' # Define functions used in this script '''\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# 1. Create_raw_matrix\n# Function to create matrix for classifier test on raw data (each TR one row)\n# - Define path to raw data\n# - Load smoothed or unsmoothed raw_data\n# - Transform into test matrix (2D array: 1st D voxel array, 2nd D TR)\n# - Return test matrix of raw data for each run and mask in list of lists\n# - 1st list: Run 1, Run 2\n# - 2nd lst: Mask 1, ..., Mask n\n# - Example: output[0][4] = raw_mat of Run 1, 5th Mask\n# - Example: output[1][0] = raw_mat of Run 2, 1st Mask\n\n# Parameters:\n# 1. bids_dir: Path to BIDS structured directory\n# 2. sub_id: participant code\n# 3. masks: 3D array of mask values in 1s and 0s, 4D array in case of\n# multiple masks\n# 4. preproc: Logical to pick preprocessed or unprocessedraw data\n# - preproc already corrected, smoothed, and z-scored the data and\n# eliminated TRs which are not in the behavioral data\n# - 0: Use unprocessed data\n# - 1: Use preprocessed data\n# 5. smooth: In case of unprocessed data. Boolean to determine if data should\n# be smoothed\n# - 0: Data will not be smoothed\n# - 1: Data will be smoothed\n# 6. smooth_kernel: Number to determine kernel size of smoothing (in case\n# data will be smoothed)\n# 7. concat: Boolean to determine if separated runs should be returned\n# concatenated or as separated runs (in list format)\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef Create_raw_matrix(bids_dir, sub_id, masks, preproc, smooth, smooth_kernel,\n concat):\n\n # Pose input of masks in appropriate format in case only one mask is\n # given\n if len(masks.shape) == 3:\n # Add 4th dimension and fill first 3 with provided mask\n pattern = np.zeros([masks.shape[0], masks.shape[1], masks.shape[2], 1])\n pattern[:,:,:,0] = masks\n # Make new 4D array to masks\n masks = pattern\n\n # Provide path to raw data dependent on taking preprocessed or unprocessed\n # data\n if bool(preproc):\n raw_dir_r1 = (bids_dir + '/derivatives/' + sub_id +\n '/preproc/classification_raw/zscsar' + sub_id +\n '_task-feedback1_bold.nii')\n raw_dir_r2 = (bids_dir + '/derivatives/' + sub_id +\n '/preproc/classification_raw/zscsar' + sub_id +\n '_task-feedback2_bold.nii')\n else:\n raw_dir_r1 = (bids_dir + '/derivatives/' + sub_id +\n '/preproc/smooth/sar' + sub_id +\n '_task-feedback1_bold.nii')\n raw_dir_r2 = (bids_dir + '/derivatives/' + sub_id +\n '/preproc/smooth/sar' + sub_id +\n '_task-feedback2_bold.nii')\n\n # Load selected raw data into matrix\n nii_raw_r1 = nibabel.load(raw_dir_r1)\n nii_raw_r2 = nibabel.load(raw_dir_r2)\n\n # Smooth data with provided kernel size in case unprocessed data is loaded\n if bool(smooth):\n nii_raw_r1 = image.smooth_img(nii_raw_r1, smooth_kernel)\n nii_raw_r2 = image.smooth_img(nii_raw_r2, smooth_kernel)\n\n # Cast nii data into matrix\n volume_raw_r1 = nii_raw_r1.get_data()\n volume_raw_r2 = nii_raw_r2.get_data()\n\n # Ger number of TRs in each run\n n_tr_r1 = volume_raw_r1.shape[3]\n n_tr_r2 = volume_raw_r2.shape[3]\n\n # Allocate list to store raw matrices for different masks\n masked_raw_mat_r1 = [0] * masks.shape[3]\n masked_raw_mat_r2 = deepcopy(masked_raw_mat_r1)\n # Allocate list for concatenated runs\n masked_raw_mat_concat = deepcopy(masked_raw_mat_r1)\n\n # Create different raw matrix for each mask\n for mask_count in np.arange(masks.shape[3]):\n\n # Get current mask\n current_mask = masks[:,:,:,mask_count]\n\n # Allocate 2D array: 1st D holding all non-zero masked voxels, 2nd D\n # holding different TRs\n current_raw_mat_r1 = np.zeros([np.flatnonzero(current_mask).size,\n n_tr_r1])\n current_raw_mat_r2 = np.zeros([np.flatnonzero(current_mask).size,\n n_tr_r2])\n\n # Fill allocated raw matrices with masked TRs (Separated for runs\n # since number of TRs differs)\n # Run 1\n for tr_count in np.arange(volume_raw_r1.shape[3]):\n\n # Isolate current TR\n current_tr = volume_raw_r1[:,:,:,tr_count]\n\n # Fill raw matrix with masked values for current TR\n current_raw_mat_r1[:,tr_count] = (\n current_tr[np.nonzero(current_mask)]\n )\n\n # Run 2\n for tr_count in np.arange(volume_raw_r2.shape[3]):\n current_tr = volume_raw_r2[:,:,:,tr_count]\n current_raw_mat_r2[:,tr_count] = (\n current_tr[np.nonzero(current_mask)]\n )\n\n # Transpose to make different TRs the rows of the matrix\n current_raw_mat_r1 = np.transpose(current_raw_mat_r1, (1,0))\n current_raw_mat_r2 = np.transpose(current_raw_mat_r2, (1,0))\n\n # Fill list of raw matrices with raw matrix for current mask\n masked_raw_mat_r1[mask_count] = current_raw_mat_r1\n masked_raw_mat_r2[mask_count] = current_raw_mat_r2\n\n # Create concatenated runs to return\n masked_raw_mat_concat[mask_count] = np.concatenate(\n [masked_raw_mat_r1[mask_count],\n masked_raw_mat_r2[mask_count]])\n\n # Check for concatenate variable\n # Do not concatenate runs\n if bool(not concat):\n # Return complete list of masked raw matrices in list form\n return(masked_raw_mat_r1, masked_raw_mat_r2)\n elif bool(concat):\n # Return concatenated runs\n return(masked_raw_mat_concat)\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# 2. Create_beta_matrix\n# Function to create input matrix for classifier training on betas\n# - Define path to betas\n# - Load preprocessed betas\n# - Transform into input matrix (1D voxel array)\n# - Return input matrix for both buffers in list of lists\n# - 1st list: buffer 1, buffer 2\n# - 2nd lst: Mask 1, ..., Mask n\n# - Example: output[0][4] = beta_mat of Buffer1, 5th Mask\n# - Example: output[1][0] = beta_mat of Buffer2, 1st Mask\n\n# Parameters:\n# 1. bids_dir: Path to BIDS structured directory\n# 2. sub_id: participant code\n# 3. masks: 3D array of mask values in 1s and 0s, 4D array in case of\n# multiple masks\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef Create_beta_matrix(bids_dir, sub_id, masks):\n\n # Pose input of masks in appropriate format in case only one mask is\n # given\n if len(masks.shape) == 3:\n # Add 4th dimension and fill first 3 with provided mask\n pattern = np.zeros([masks.shape[0], masks.shape[1], masks.shape[2], 1])\n pattern[:,:,:,0] = masks\n # Make new 4D array to masks\n masks = pattern\n\n # Define path which contains all corrected, smoothed, and z-scored betas:\n corr_dir_b1 = (bids_dir + '/derivatives/' + sub_id +\n '/glm/buffer1' + '/estimate/corr')\n corr_dir_b2 = (bids_dir + '/derivatives/' + sub_id +\n '/glm/buffer2' + '/estimate/corr')\n\n # Give number of betas to load (skipping motion and mean related betas)\n req_betas = np.concatenate([np.arange(1,13),\n np.arange(19,31)])\n\n # Allocate 4D array to store betas, dim 1-3: Voxel values, dim 4:\n # different betas\n beta_mat_b1 = np.zeros([masks.shape[0],\n masks.shape[1],\n masks.shape[2],\n len(req_betas)])\n beta_mat_b2 = deepcopy(beta_mat_b1)\n\n # Load unmasked betas from both buffers\n for beta_count, beta_id in enumerate(req_betas):\n\n # Path to load specific beta\n path_b1 = corr_dir_b1 + '/zscbeta_' + str(beta_id).zfill(4) + '.nii'\n path_b2 = corr_dir_b2 + '/zscbeta_' + str(beta_id).zfill(4) + '.nii'\n\n # Load beta .nii with nibabel function\n nii_beta_b1 = nibabel.load(path_b1)\n nii_beta_b2 = nibabel.load(path_b2)\n\n # Transform nii file into matrix:\n volume_b1 = nii_beta_b1.get_data()\n volume_b2 = nii_beta_b2.get_data()\n\n # Concatenate beta into 4D array\n beta_mat_b1[:,:,:,beta_count] = volume_b1\n beta_mat_b2[:,:,:,beta_count] = volume_b2\n\n # Mask betas and pose into list with one entry for each mask\n # Create list of beta matrices with length of number of provided masks\n masked_beta_mat_b1 = [0] * masks.shape[3]\n masked_beta_mat_b2 = deepcopy(masked_beta_mat_b1)\n\n for mask_count in np.arange(masks.shape[3]):\n\n # Get current mask\n current_mask = masks[:,:,:,mask_count]\n\n # Allocate 2D array: 1st D holding all non-zero masked voxels, 2nd D\n # holding different betas\n current_beta_mat_b1 = np.zeros([np.flatnonzero(current_mask).size,\n len(req_betas)])\n current_beta_mat_b2 = deepcopy(current_beta_mat_b1)\n\n # Fill current beta mat with masked voxels for each beta\n for beta_count, beta_id in enumerate(req_betas):\n\n # Use mask to eliminate all values outside of the mask and\n # bring values into 1-D array for buffer 1 and 2\n masked_beta_b1 = beta_mat_b1[:,:,:,beta_count]\n masked_beta_b1 = masked_beta_b1[np.nonzero(current_mask)]\n\n masked_beta_b2 = beta_mat_b2[:,:,:,beta_count]\n masked_beta_b2 = masked_beta_b2[np.nonzero(current_mask)]\n\n # Fill current beta_mat\n current_beta_mat_b1[:,beta_count] = masked_beta_b1\n current_beta_mat_b2[:,beta_count] = masked_beta_b2\n\n # Transpose to make different betas the rows of the matrix\n current_beta_mat_b1 = np.transpose(current_beta_mat_b1, (1,0))\n current_beta_mat_b2 = np.transpose(current_beta_mat_b2, (1,0))\n\n # Fill list of different finished beta_mats for each mask\n masked_beta_mat_b1[mask_count] = current_beta_mat_b1\n masked_beta_mat_b2[mask_count] = current_beta_mat_b2\n\n # Return complete list of masked beta matrices in list form\n return(masked_beta_mat_b1, masked_beta_mat_b2)\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# Function to create 4D matrix of masks\n# - Define path to mask directory\n# - Load masks as nii files\n# - Transform into 3D arrays\n# - Append 3D arrays of masks to 4D array\n# - Return 4D matrix of masks\n\n\n# Parameters:\n# 1. bids_dir: Path to BIDS structured directory\n# 2. sub_id: participant code\n# 3. mask_names: Array of strings with mask names\n# 4. buffering: Logical indicating if to return one mask for each buffer or\n# across buffer masks (shared mask)\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef Load_masks(bids_dir, sub_id, mask_names, buffering):\n\n # Allocate list to safe mask matrixes in\n all_masks_b1 = [0] * len(mask_names)\n all_masks_b2 = deepcopy(all_masks_b1)\n\n # Loop over each mask\n for mask_count, mask_id in enumerate(mask_names):\n\n # Get path to mask\n mask_dir = bids_dir + '/derivatives/' + sub_id + '/preproc/seg'\n\n # Create mask path for both buffers\n path_mask_b1 = (mask_dir + '/' + sub_id + '_mask_' + mask_id +\n '_buffer1.nii')\n path_mask_b2 = (mask_dir + '/' + sub_id + '_mask_' + mask_id +\n '_buffer2.nii')\n\n # Load mask to .nii object\n mask_b1 = nibabel.load(path_mask_b1)\n mask_b2 = nibabel.load(path_mask_b2)\n\n # Extract matrix from .nii\n mask_b1 = mask_b1.get_data()\n mask_b2 = mask_b2.get_data()\n\n # Post matrices into lists\n all_masks_b1[mask_count] = mask_b1\n all_masks_b2[mask_count] = mask_b2\n\n # Post list into array format with 4th D being different masks\n # Create empty 4D array with correct dimensions (given by first mask)\n all_masks_mat_b1 = np.zeros([all_masks_b1[0].shape[0],\n all_masks_b1[0].shape[1],\n all_masks_b1[0].shape[2],\n len(mask_names)])\n all_masks_mat_b2 = deepcopy(all_masks_mat_b1)\n\n # Fill 4D array with masks from lists\n for mask_count, mask_id in enumerate(mask_names):\n all_masks_mat_b1[:,:,:,mask_count] = all_masks_b1[mask_count]\n all_masks_mat_b2[:,:,:,mask_count] = all_masks_b2[mask_count]\n\n # Return buffer specific masks if indicated by 'buffering' parameter\n if bool(buffering):\n return(all_masks_mat_b1, all_masks_mat_b2)\n\n # In case combined mask is required create combined mask\n else:\n # Allocate empty 4d array holding combined masks\n all_masks_mat = np.zeros([all_masks_b1[0].shape[0],\n all_masks_b1[0].shape[1],\n all_masks_b1[0].shape[2],\n len(mask_names)])\n\n # Fill empty 4D array with combined masks\n for mask_count, mask_id in enumerate(mask_names):\n # Allocate empty mask array same shape as other masks\n mask = np.zeros([all_masks_b1[0].shape[0],\n all_masks_b1[0].shape[1],\n all_masks_b1[0].shape[2]])\n\n # Fill empty mask with 1 at every coordinate where both bufferd\n # masks are 1 (shared mask)\n shared_voxels = np.where(all_masks_mat_b1[:,:,:,mask_count] +\n all_masks_mat_b2[:,:,:,mask_count] == 2)\n mask[shared_voxels] = 1\n\n # Fill empty array holding all shared masks with current mask\n all_masks_mat[:,:,:,mask_count] = mask\n\n # Return shared masks in 4D array\n return(all_masks_mat)\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# Function to load behavioral data about runs\n# - Load behavioral data about runs\n# - Option to relate run1 to run2 (e.g. if last TR run 1 = 100, First TR of\n# run 2 = 101)\n# - Option to concatenate behavioral data\n# - Add overall event counter\n\n\n# Parameters:\n# 1. bids_dir: Path to BIDS structured directory\n# 2. sub_id: participant code\n# 3. relate: Boolean if both runs should be ralted to each other (e.g. )\n# 4. concat: Boolean to determine if separated runs should be returned\n# concatenated or as separated runs (in list format)\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef Load_behavioral(bids_dir, sub_id, relate, concat):\n\n # Paths to both behavioral runs\n path_r1 = (bids_dir + '/derivatives/' + sub_id + '/behavior/' + sub_id +\n '_3d1_backwards.tsv')\n path_r2 = (bids_dir + '/derivatives/' + sub_id + '/behavior/' + sub_id +\n '_3d2_backwards.tsv')\n\n # Load behavioral .tsv files\n behav_r1 = pd.read_csv(path_r1, sep='\\t')\n behav_r2 = pd.read_csv(path_r2, sep='\\t')\n\n # If both runs should be related\n if bool(relate):\n\n # Make timing relative to previous timing\n behav_r2.loc[:, 'Time'] += behav_r1['Time'].iloc[-1]\n\n # Make TRs relative to first run\n behav_r2.loc[:, 'TR number corrected'] += (\n behav_r1['TR number uncorrected'].iloc[-1])\n behav_r2.loc[:, 'TR number uncorrected'] += (\n behav_r1['TR number uncorrected'].iloc[-1])\n\n # If both runs should be concatenated\n if bool(concat):\n\n # Concatenate both runs\n frames = [behav_r1, behav_r2]\n behav = pd.concat(frames)\n\n # Return concatenated runs\n return(behav)\n\n # If runs should be returned separately\n elif bool(not concat):\n return(behav_r1, behav_r2)\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# Function to load motion data of subject\n# - Load motion data about runs stemming from realignment\n# - Add overall event counter\n\n# Parameters:\n# 1. bids_dir: Path to BIDS structured directory\n# 2. sub_id: participant code\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef Load_motion(bids_dir, sub_id):\n\n # Paths to both behavioral runs\n path_r1 = (bids_dir + '/derivatives/' + sub_id + '/preproc/realign/' +\n sub_id + '_mov_reg1.txt')\n path_r2 = (bids_dir + '/derivatives/' + sub_id + '/preproc/realign/' +\n sub_id + '_mov_reg2.txt')\n\n # Load behavioral .tsv files\n motion_r1 = pd.read_csv(path_r1, delimiter=r\"\\s+\", header=None)\n motion_r2 = pd.read_csv(path_r2, delimiter=r\"\\s+\", header=None)\n\n return(motion_r1, motion_r2)\n\n\n\n''' # Start decoding script '''\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n# Function to decode and predict direction\n# - Load all data required (motion, raw, betas, behavioral)\n# - Train and test classifier for each mask and save accuracy and predictions\n# - Train classifier on permuted labels and save accuracy and predictions\n\n# Parameters:\n# 1. bids_dir: BIDS structured directory\n# 2. sub_id: Participant code (as string)\n# 3. mask_names: List of stings with mask codes\n# 4. n_permutation: Number of permutations\n# 5. out_dir: Output directory to save results\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n\ndef main(bids_dir, sub_id, mask_names, n_permutation, out_dir):\n\n #- - - - - - - - - - - - - - - - -\n # To select certain subjects:\n #sub_list = sub_list[0:1]\n #- - - - - - - - - - - - - - - - -\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Construct loop to let the classification run for separate masks\n #mask_names = ['beta',\n # 'entorhinal',\n # 'hc',\n # 'm1',\n # 'rsc',\n # 'subiculum',\n # 'thal',\n # 'v1',\n # 'ent_hc']\n # Alternative mask_names for only specific masks:\n #mask_names = ['v1', 'rsc', 'm1']\n\n # Df to store predictions in (1st C: Subjects, 2nd C: Mask, ...) for real\n # classification and permutation\n clf_prediction = pd.DataFrame()\n clf_prediction_permut = pd.DataFrame()\n # Df to store acuracy in\n clf_accuracy = pd.DataFrame()\n clf_accuracy_permut = pd.DataFrame()\n\n\n\n # Give message to user which subject is investigated\n print('------------------------------------------------------------------')\n print('Working on subject:\\t\\t\\t--|', sub_id, '|--')\n print('------------------------------------------------------------------')\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Load required data\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Give message to user\n print('\\tLoading required data...')\n\n # Unbuffered (shared mask array)\n mask_mat = Load_masks(bids_dir, sub_id, mask_names, buffering=0)\n # Buffered (two seperate mask arrays)\n mask_mat_b1, mask_mat_b2 = Load_masks(bids_dir, sub_id, mask_names,\n buffering=1)\n\n # Load betas for each buffer into 2D matrix (rows: betas, cols: voxels)\n beta_mat_b1, beta_mat_b2 = Create_beta_matrix(bids_dir, sub_id, mask_mat)\n\n # Relate betas to directions\n labels_beta = pd.DataFrame()\n labels_beta['angle_bin'] = np.tile(np.arange(1,7), 4)\n labels_beta['fold'] = np.repeat(np.arange(1,5), 6)\n\n # Load raw data into same format as beta matrix, masked by shared mask\n raw_mat_r1, raw_mat_r2 = Create_raw_matrix(bids_dir, sub_id, mask_mat,\n preproc=0,\n smooth=1, smooth_kernel=3,\n concat=0)\n raw_mat = Create_raw_matrix(bids_dir, sub_id, mask_mat,\n preproc=0,\n smooth=1, smooth_kernel=3,\n concat=1)\n\n\n # Load subject motion from realignment for signal.claen\n motion_r1, motion_r2 = Load_motion(bids_dir, sub_id)\n\n # Signal clean raw mat runs for each mask\n raw_mat_r1 = list(\n signal.clean(x,\n confounds=motion_r1.values,\n high_pass = 1/128,\n t_r = 2.4)\n for x in raw_mat_r1\n )\n raw_mat_r2 = list(\n signal.clean(x,\n confounds=motion_r2.values,\n high_pass = 1/128,\n t_r = 2.4)\n for x in raw_mat_r2\n )\n\n # Voxel-internal z-scoring\n raw_mat_r1 = list(scipy.stats.zscore(x, axis=0) for x in raw_mat_r1)\n raw_mat_r2 = list(scipy.stats.zscore(x, axis=0) for x in raw_mat_r2)\n\n # TR wise z-scoring\n raw_mat_r1 = list(scipy.stats.zscore(x, axis=1) for x in raw_mat_r1)\n raw_mat_r2 = list(scipy.stats.zscore(x, axis=1) for x in raw_mat_r2)\n\n # Load behavioral runs as concatenated file\n behav = Load_behavioral(bids_dir, sub_id, relate=1, concat=1)\n\n # Relate TRs to behavioral\n # Data frame holding TRs and associated direction\n labels_raw = pd.DataFrame()\n # trs of functional data (+1 since TRs in behav file are counted\n # from starting 0)\n labels_raw['tr'] = np.arange(raw_mat[0].shape[0]) + 1\n # Add column holding directional values for each TR\n labels_raw['angle_mode'] = float('nan')\n labels_raw['angle_mean'] = float('nan')\n\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Get directional value for each TR\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Loop over each TR with directional event (mode and mean)\n for tr_count, tr_id in enumerate(\n np.unique(behav['TR number corrected'])\n ):\n # Index of behavioral data for a certain TR\n index = behav.loc[:,'TR number corrected'] == tr_id\n # Get distribution of raw angles during TR\n angle_dist = np.round(\n behav.loc[index,'direction angle by location'].values,\n 2)\n # Get mode and mean of angles during one TR\n mode_angle = scipy.stats.mode(angle_dist).mode[0]\n mean_angle = np.mean(angle_dist)\n\n # Fill label array with directional events (fold not needed since\n # betas don't contain backwards events we are predicting)\n # Eliminate cases in which corrected TR is beyond the collected TRs\n # (since corrected TRs is +2 TRs that might not have been measured)\n if tr_id <= raw_mat[0].shape[0]:\n index = labels_raw.loc[:,'tr'] == tr_id\n labels_raw.loc[index,'angle_mode'] = mode_angle\n labels_raw.loc[index,'angle_mean'] = np.round(mean_angle, 2)\n\n # Get label vector that only contains events (eliminate NaN entries)\n index = np.where(~np.isnan(labels_raw.loc[:,'angle_mean']))[0]\n predict_labels = labels_raw.loc[index,:]\n\n # Add bin and prediction column to label vector\n predict_labels['bin_mode'] = float('nan')\n predict_labels['bin_mean'] = float('nan')\n predict_labels['prediction'] = float('nan')\n\n # Fill bin columns with correct bin\n predict_labels['bin_mode'] = np.floor(\n predict_labels['angle_mode'] / 60\n ) + 1\n predict_labels['bin_mean'] = np.floor(\n predict_labels['angle_mean'] / 60\n ) + 1\n\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Mask specific analysis\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Loop over masks\n for mask_count, mask_id in enumerate(mask_names):\n\n # Give message to user: Processed mask\n print('\\t', mask_id, '\\t:', end='')\n\n # Get current mask (unbuffered and buffered)\n c_mask = mask_mat[:,:,:,mask_count]\n c_mask_b1 = mask_mat_b1[:,:,:,mask_count]\n c_mask_b2 = mask_mat_b2[:,:,:,mask_count]\n\n # Get current beta_mat for both buffers\n c_beta_mat_b1 = beta_mat_b1[mask_count]\n c_beta_mat_b2 = beta_mat_b2[mask_count]\n\n # z-score beta mat (whole single beta) for both buffers\n c_beta_mat_b1 = scipy.stats.zscore(c_beta_mat_b1, axis=1)\n c_beta_mat_b2 = scipy.stats.zscore(c_beta_mat_b2, axis=1)\n\n # Get current concatenated raw_mat (from shared mask)\n c_raw_mat_r1 = raw_mat_r1[mask_count]\n c_raw_mat_r2 = raw_mat_r2[mask_count]\n\n # Concatenate cleaned runs\n c_raw_mat = np.concatenate([c_raw_mat_r1, c_raw_mat_r2])\n\n # Eliminate volumes from raw_mat which can't be predicted (since TRs\n # which where in no events)\n index = predict_labels.loc[:,'tr'] - 1\n predict_raw_mat = c_raw_mat[index,:]\n\n # Allocate df holding accuracy of classification for each fold\n results_accuracy = pd.DataFrame()\n results_accuracy['accuracy_mean'] = float('nan')\n results_accuracy['accuracy_mode'] = float('nan')\n results_accuracy['correlation_mean'] = float('nan')\n results_accuracy['correlation_mode'] = float('nan')\n\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Start classification\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Give message to user: Classification starts\n print('\\tClassification...', end='')\n\n\n # Classification of backwards events (no corss valitation needed since\n # backwards events are no in buffers)\n\n # Get training set and trinaing labels for fold (over concatenated\n # buffers of betas)\n train_set = np.concatenate([c_beta_mat_b1, c_beta_mat_b2])\n train_labels = np.concatenate([labels_beta.loc[:,'angle_bin'].values,\n labels_beta.loc[:,'angle_bin'].values])\n\n # Get testing set and testing labels (over concatenated and cleaned\n # raw_data)\n test_set = predict_raw_mat\n\n # Labels and bins for mode\n test_labels_mode = predict_labels.loc[:, 'angle_mode'].values\n test_bins_mode = predict_labels.loc[:, 'bin_mode'].values\n # Labels and bins for mean\n test_labels_mean = predict_labels.loc[:, 'angle_mean'].values\n test_bins_mean = predict_labels.loc[:, 'bin_mean'].values\n\n # Create classifier object\n clf = sklearn.svm.LinearSVC(C=1,\n max_iter=10000)\n\n # Train classifier\n clf.fit(train_set, train_labels)\n\n # Predict angle_bins\n prediction = clf.predict(test_set)\n\n # Put prediction into label vector\n predict_labels.loc[:,'prediction'] = prediction\n\n\n # Save accuracy for mean and mode by comparing number of correct\n # predictions of bin to overall number of predictions\n # Mean\n results_accuracy.loc[0,'accuracy_mean'] = (\n np.sum(np.equal(prediction, test_bins_mean)) /\n len(prediction)\n )\n # Mode\n results_accuracy.loc[0,'accuracy_mode'] = (\n np.sum(np.equal(prediction, test_bins_mode)) /\n len(prediction)\n )\n\n # Save correlation between predicted bin and continuous label\n # Mean\n results_accuracy.loc[0,'correlation_mean'] = (\n scipy.stats.pearsonr(prediction, test_labels_mean)[0]\n )\n # Mode\n results_accuracy.loc[0,'correlation_mode'] = (\n scipy.stats.pearsonr(prediction, test_labels_mode)[0]\n )\n\n # Save label vector with predictions into data frame\n results_prediction = deepcopy(predict_labels)\n results_prediction['sub_id'] = sub_id\n results_prediction['mask'] = mask_id\n\n # Add subject id and mask to data frame of accuracy\n results_accuracy['sub_id']= sub_id\n results_accuracy['mask']= mask_id\n\n\n # Append to results data frame\n clf_prediction = clf_prediction.append(results_prediction)\n clf_accuracy = clf_accuracy.append(results_accuracy)\n\n # Give message to user: Classification done\n print('Done | ', end='')\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Start permutation\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Give message to user: Permutation starts\n print('Permutation...', end='')\n\n # Same classification process with permuted labels\n for perm_count in np.arange(n_permutation):\n\n\n # Get training set and training labels for fold (over\n # concatenated buffers of betas)\n train_set = np.concatenate([c_beta_mat_b1, c_beta_mat_b2])\n\n # Shuffle labels inside each fold per buffer\n # Loop over cross validation folds (iterated fold is testing fold)\n train_labels = []\n for fold_count in np.arange(8):\n shuffled_labels = sample(list(np.arange(1,7)), k=6)\n train_labels = np.concatenate([train_labels, shuffled_labels])\n\n\n # Get testing set and testing labels (over concatenated and\n # cleaned raw_data) (test labels stay unshuffled)\n test_set = predict_raw_mat\n # Labels and bins for mode\n test_labels_mode = predict_labels.loc[:, 'angle_mode'].values\n test_bins_mode = predict_labels.loc[:, 'bin_mode'].values\n # Labels and bins for mean\n test_labels_mean = predict_labels.loc[:, 'angle_mean'].values\n test_bins_mean = predict_labels.loc[:, 'bin_mean'].values\n\n # Create classifier object\n clf = sklearn.svm.LinearSVC(C=1,\n max_iter=10000)\n\n # Train classifier\n clf.fit(train_set, train_labels)\n\n # Predict angle_bins\n prediction = clf.predict(test_set)\n\n # Put prediction into label vector\n predict_labels.loc[:,'prediction'] = prediction\n\n\n # Save accuracy for mean and mode by comparing number of\n # correct predictions of bin to overall number of predictions\n # Mean\n results_accuracy.loc[:,'accuracy_mean'] = (\n np.sum(np.equal(prediction, test_bins_mean)) /\n len(prediction)\n )\n # Mode\n results_accuracy.loc[:,'accuracy_mode'] = (\n np.sum(np.equal(prediction, test_bins_mode)) /\n len(prediction)\n )\n\n # Save correlation between predicted bin and continuous label\n # Mean\n results_accuracy.loc[:,'correlation_mean'] = (\n scipy.stats.pearsonr(prediction, test_labels_mean)[0]\n )\n # Mode\n results_accuracy.loc[:,'correlation_mode'] = (\n scipy.stats.pearsonr(prediction, test_labels_mode)[0]\n )\n\n # Save label vector with predictions into data frame\n results_prediction = deepcopy(predict_labels)\n results_prediction['sub_id'] = sub_id\n results_prediction['mask'] = mask_id\n results_prediction['permutation'] = perm_count\n\n\n # Add subject id and mask to data frame of accuracy\n results_accuracy['sub_id']= sub_id\n results_accuracy['mask']= mask_id\n results_accuracy['permutation']= perm_count\n\n # Append to results data frame\n clf_prediction_permut = clf_prediction_permut.append(\n results_prediction)\n clf_accuracy_permut = clf_accuracy_permut.append(results_accuracy)\n\n # Give message to user: Permutation done\n print('Done')\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Save results for subject\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n # Sort results DFs and save to .tsv\n # Prediction\n clf_prediction = clf_prediction[['sub_id', 'mask',\n 'tr',\n 'angle_mode', 'angle_mean',\n 'bin_mode', 'bin_mean',\n 'prediction']]\n path = out_dir + '/' + sub_id + '_clf_prediction.tsv'\n clf_prediction.to_csv(path, sep='\\t', index=False)\n # Accuracy\n clf_accuracy = clf_accuracy[['sub_id', 'mask',\n 'accuracy_mode', 'accuracy_mean',\n 'correlation_mode', 'correlation_mean']]\n path = out_dir + '/' + sub_id + '_clf_accuracy.tsv'\n clf_accuracy.to_csv(path, sep='\\t', index=False)\n\n\n # Permutation files\n # Prediction\n clf_prediction_permut = clf_prediction_permut[['sub_id', 'mask',\n 'tr',\n 'angle_mode',\n 'angle_mean',\n 'bin_mode', 'bin_mean',\n 'prediction',\n 'permutation']]\n path = out_dir + '/' + sub_id + '_clf_prediction_permut.tsv'\n clf_prediction_permut.to_csv(path, sep='\\t', index=False)\n # Accuracy\n clf_accuracy_permut = clf_accuracy_permut[['sub_id', 'mask',\n 'accuracy_mode',\n 'accuracy_mean',\n 'correlation_mode',\n 'correlation_mean',\n 'permutation']]\n path = out_dir + '/' + sub_id + '_clf_accuracy_permut.tsv'\n clf_accuracy_permut.to_csv(path, sep='\\t', index=False)\n\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Basic plotting\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n# for mask_count, mask_id in enumerate(mask_names):\n#\n# fig = plt.figure(figsize=(8,8))\n# gs = gridspec.GridSpec(2,3, wspace=0.3, hspace=0.2)\n#\n# mask_results = clf_prediction.loc[clf_prediction['mask'] == mask_id,:]\n#\n# for bin_count, bin_id in enumerate(np.arange(1,7)):\n#\n# pred_dist = mask_results.loc[mask_results['bin_mode'] == bin_id,\n# 'prediction']\n#\n# ax = fig.add_subplot(gs[bin_count])\n# ax.hist(pred_dist, bins=np.arange(1,8), density=True, rwidth=0.8)\n# ax.set_xticks(np.arange(1,7)+0.5)\n# ax.set_xticklabels(np.arange(1,7))\n# ax.set_ylim(bottom=0, top=1)\n# ax.set_title('Direction ' + str(bin_id), weight='bold')\n# fig.add_subplot(ax)\n# plt.show()\n\n# -----------------------------------------------------------------------------\n# -----------------------------------------------------------------------------\n# -----------------------------------------------------------------------------\n\n''' Decoding for subject depending on input'''\n\n# Set up paths\n# BIDS directory (containing betas and raw data)\nbids_dir = str(Path.home()) + '/direction_decoding_BIDS'\n# Path of repository\nrepo_dir = str(Path.home() + '/direction_decoding')\n# Path to save results to (relative to repo)\nsave_dir = (repo_dir + '/decoding/backwards_decoding/train_beta_test_raw' +\n '/results')\n\n# Define key variables based on command line input\nbids_dir = argv[1]\nsub_id = argv[2]\nmask_names = argv[3]\n# Convert input to list\nmask_names = mask_names.strip('[]').split(',')\nn_permutation = int(argv[4])\nout_dir = argv[5]\n\n\n# Call decoding function with user inputs\nmain(bids_dir, sub_id, mask_names, n_permutation, out_dir)\n", "sub_path": "decoding/backwards_decoding/train_beta_test_raw/train_beta_test_raw.py", "file_name": "train_beta_test_raw.py", "file_ext": "py", "file_size_in_byte": 36863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 103, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 104, "usage_type": "call"}, {"api_name": "nilearn.image.smooth_img", "line_number": 108, "usage_type": "call"}, {"api_name": "nilearn.image", "line_number": 108, "usage_type": "name"}, {"api_name": "nilearn.image.smooth_img", "line_number": 109, "usage_type": "call"}, {"api_name": "nilearn.image", "line_number": 109, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 121, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.flatnonzero", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.flatnonzero", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 222, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 226, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 236, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 237, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.flatnonzero", "line_number": 259, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 280, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 310, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 325, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 370, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 407, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 408, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 427, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 456, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 457, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 504, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 505, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 507, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 508, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 536, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 536, "usage_type": "call"}, {"api_name": "nilearn.signal.clean", "line_number": 554, "usage_type": "call"}, {"api_name": "nilearn.signal", "line_number": 554, "usage_type": "name"}, {"api_name": "nilearn.signal.clean", "line_number": 561, "usage_type": "call"}, {"api_name": "nilearn.signal", "line_number": 561, "usage_type": "name"}, {"api_name": "scipy.io.stats.zscore", "line_number": 569, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 569, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 569, "usage_type": "name"}, {"api_name": "scipy.io.stats.zscore", "line_number": 570, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 570, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 570, "usage_type": "name"}, {"api_name": "scipy.io.stats.zscore", "line_number": 573, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 573, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 573, "usage_type": "name"}, {"api_name": "scipy.io.stats.zscore", "line_number": 574, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 574, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 574, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 581, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 584, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 597, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 602, "usage_type": "call"}, {"api_name": "scipy.io.stats.mode", "line_number": 606, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 606, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 606, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 607, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 616, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 619, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 619, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 628, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 631, "usage_type": "call"}, {"api_name": "scipy.io.stats.zscore", "line_number": 657, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 657, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 657, "usage_type": "name"}, {"api_name": "scipy.io.stats.zscore", "line_number": 658, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 658, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 658, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 665, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 673, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 694, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 695, "usage_type": "call"}, {"api_name": "sklearn.model_selection.svm.LinearSVC", "line_number": 710, "usage_type": "call"}, {"api_name": "sklearn.model_selection.svm", "line_number": 710, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection", "line_number": 710, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 727, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 727, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 732, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 732, "usage_type": "call"}, {"api_name": "scipy.io.stats.pearsonr", "line_number": 739, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 739, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 739, "usage_type": "name"}, {"api_name": "scipy.io.stats.pearsonr", "line_number": 743, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 743, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 743, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 747, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 772, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 777, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 782, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 783, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 783, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 784, "usage_type": "call"}, {"api_name": "sklearn.model_selection.svm.LinearSVC", "line_number": 798, "usage_type": "call"}, {"api_name": "sklearn.model_selection.svm", "line_number": 798, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection", "line_number": 798, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 815, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 815, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 820, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 820, "usage_type": "call"}, {"api_name": "scipy.io.stats.pearsonr", "line_number": 827, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 827, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 827, "usage_type": "name"}, {"api_name": "scipy.io.stats.pearsonr", "line_number": 831, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 831, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 831, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 835, "usage_type": "call"}, {"api_name": "pathlib.Path.home", "line_number": 931, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 931, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 933, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 933, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 939, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 940, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 941, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 944, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 945, "usage_type": "name"}]} {"seq_id": "272864263", "text": "import os\nfrom pathlib import Path\nfrom collections import defaultdict\nimport torch\nimport torch.nn as nn\nimport torchaudio\nfrom torch.utils.data import Dataset as TorchDataset\nimport numpy as np\n\nfrom utils.audio import process_wav\n\n# inherits Dataset from PyTorch\nclass Dataset(TorchDataset):\n\n def __init__(self, name, split, sample_rate, audio_length, tracks_list, num_segments, audio_proc_dir, mean, std):\n self.name = name\n self.split = split\n self.audio_proc_dir\n self.sample_rate = sample_rate\n self.audio_length = audio_length\n self.tracks_list = tracks_list\n self.num_segments = num_segments \n self.mean = mean\n self.std = std\n\n print(f\"[{self.name} {self.split}]: Loaded {len(self.tracks_list)} audio segments\")\n\n\n def indexer(self, ids, id2audio_path, id2gt, dataset, onepos=False):\n index = []\n tmp = []\n track_index = defaultdict(list)\n track_idx = 0\n clip_idx = 0\n for clip_id in ids:\n fp = id2audio_path[clip_id]\n label = id2gt[clip_id]\n\n if dataset == \"magnatagatune\":\n track_id = \"\".join(Path(fp).stem.split(\"-\")[:-2])\n if track_id not in tmp:\n tmp.append(track_id)\n track_idx += 1\n fp = f\"{track_idx}-{clip_id}-{self.sample_rate}.wav\"\n fp = os.path.join(self.audio_proc_dir, self.split, fp)\n else:\n track_idx = clip_id\n clip_id = clip_idx\n fp = os.path.join(self.audio_proc_dir, fp)\n clip_idx += 1\n\n for s in range(self.num_segments): \n index.append([track_idx, clip_id, s, fp, label])\n track_index[track_idx].append([clip_id, s, fp, label])\n return index, track_index\n\n def loader(self, path):\n audio, sr = process_wav(self.sample_rate, path, False)\n return audio, sr\n\n def get_audio(self, fp):\n audio, sr = self.loader(fp)\n max_samples = audio.shape[0]\n # if sr != self.sample_rate:\n # raise Exception(\"Sample rate is not consistent throughout the dataset\")\n\n if max_samples - self.audio_length <= 0:\n raise Exception(\"Max samples exceeds number of samples in crop\")\n\n # if np.isnan(audio).any():\n # raise Exception(\"Audio contains NaN values\")\n\n return audio\n\n def __len__(self):\n return len(self.tracks_list)\n\n def get_full_size_audio(self, fp):\n audio = self.get_audio(fp)\n\n # split into equally sized tensors of self.audio_length\n audio = torch.from_numpy(audio).reshape(1, -1)\n batch = torch.split(audio, self.audio_length, dim=1)\n\n # remove last, since it is not a full self.audio_length, and stack\n batch = torch.cat(batch[:-1])\n\n # reshape to B x 1 x N\n batch = batch.reshape(batch.shape[0], 1, -1)\n return batch\n\n def normalise_audio(self, audio):\n return (audio - self.mean) / self.std\n\n def denormalise_audio(self, norm_audio):\n return (norm_audio * self.std) + self.mean\n\n def sample_audio_by_track_id(self, track_id, batch_size=20):\n \"\"\"\n Get audio samples based on the track_id (batch_size = num_samples)\n used for plotting the latent representations of different tracks\n \"\"\"\n batch = torch.zeros(batch_size, 1, self.audio_length)\n for idx in range(batch_size):\n _, _, fp, _ = self.track_index[track_id][0]\n\n audio = self.get_audio(fp)\n\n start_idx = idx * self.audio_length\n\n # too large\n if (start_idx + self.audio_length) > audio.shape[0]:\n return None\n\n batch[idx, 0, :] = torch.from_numpy(audio[start_idx : start_idx + self.audio_length])\n return batch\n", "sub_path": "data/audio/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 3884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 13, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 32, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "utils.audio.process_wav", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.split", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 115, "usage_type": "call"}]} {"seq_id": "45679762", "text": "from matplotlib import pylab, pyplot as plt\r\nimport networkx as nx\r\nimport json\r\nimport os\r\nimport numpy as np\r\nfrom adjacency import *\r\nfrom scipy import signal\r\n\r\n# SET THESE THREE PARAMS\r\n'''\r\nnum: [0-8] See \"sets\" variable. \r\n\r\n Set 2 has 3 agents, so you have to give options '20', '21', and '22' \r\n which correspond to the 1st, 2nd, and 3rd agents respectively. \r\n \r\n Set 7 has 2 agents, so you have to set'70' or '71' accordingly.\r\n \r\ncen_num: 0:closeness (num == 0,20,21,22, 9), 1:degree (num == 1 and 6), 2: eigenvector (num == 3,4)\r\n\r\ncolor: Turn on region coloring\r\n'''\r\nnum = 6\r\ncen_num = 0\r\ncolor = False\r\n\r\n# ================================================================================================\r\n\r\nagent_num = 0\r\nif 20 <= num <= 30:\r\n agent_num = num % 20\r\n num = int(num / 10)\r\n\r\nif 40 <= num <= 80:\r\n agent_num = num % 70\r\n num = int(num / 10)\r\n\r\nsets = ['4', # 0\r\n '12_55_47', # 1\r\n '11_30_18', # 2\r\n '7_30_18_2', # 3\r\n '10_30_18', # 4\r\n '13-2019-08-27-22-30-18', # 5\r\n '16_30_18', # 6\r\n '9_47_10', # 7\r\n '5_55_47', # 8\r\n '10_55_47'] # 9\r\nframes = [[0, 60], [47, 80], [[55, 95], [70, 98],[70, 98]],\r\n [52, 98], [14, 50], [59, 85], [65, 72], [ [9, 35], [50, 98]],\r\n [58, 90], [10, 98]]\r\nagent_IDs = [983, 2677, [2810, 2958, 2959], 1336, 3494, 1295, 1786, [[2562], [2564]], 1750, 868]\r\nradius = [10, 20, 10, 20, 20, 10, 20, 10, 10, 10]\r\nagent_labels = ['Black Car', 'White Car', 'White Car', 'White Bus',\r\n 'White Truck', 'White Lorry', 'Motorbike', 'Scooter', 'Scooter', 'Motorbike']\r\ncentrality_labels = ['Closeness Centrality Value', 'Degree Centrality Value', 'Eigenvector Centrality Value']\r\nthresholds = [[0, 35, 60], [47, 61, 80], [[55, 80, 95], [70, 80, 98], [70, 80, 98]], [52, 73, 98], [14, 40, 50],\r\n [59, 75, 85], [65, 68, 72], [ [9, 18, 35], [50, 75, 98]], [58, 80, 90], [24, 36]]\r\n\r\nvideo_set = sets[num]\r\nframe = frames[num] if (num != 2 and num != 7) else frames[num][agent_num]\r\nagent_ID = agent_IDs[num]\r\nrad = radius[num]\r\nagent_label = agent_labels[num]\r\ncentrality_label = centrality_labels[cen_num]\r\nx_lims = thresholds[num] if (num != 2 and num != 7) else thresholds[num][agent_num]\r\n\r\n# filenames = os.listdir('data/')\r\n\r\n# READ CSV FILE PANDAS\r\nfilepath = 'data/set' + str(video_set) + '_annotations_utm.json'\r\n\r\nwith open(filepath) as json_file:\r\n sample_annotation = json.load(json_file)\r\n\r\ndataset_id = 1\r\n\r\nto_list = []\r\n\r\nfor each_frame in sample_annotation:\r\n\r\n fr_id = each_frame['id']\r\n each_annotation = each_frame['annotations']\r\n for each_object in each_annotation:\r\n obj_id = each_object['classId']\r\n x = each_object['geometry']['position']['x']\r\n y = each_object['geometry']['position']['y']\r\n to_list.append([fr_id, obj_id, x, y, dataset_id])\r\n\r\nto_array = np.asarray(to_list)\r\nobj_IDs = np.unique(to_array[:, 1]).astype(int)\r\nagent_idx = list(obj_IDs).index(agent_ID[agent_num]) if (num == 2 or num == 7) else list(obj_IDs).index(agent_ID)\r\ndegree_mat, adj_mats = generate_adjacency(to_array, rad)\r\nweave_list = []\r\nweave_list2 = []\r\nweave_list3 = []\r\nfor fr, item in enumerate(adj_mats):\r\n G = nx.from_numpy_array(item['adj_matrix'])\r\n if cen_num == 0:\r\n g = nx.closeness_centrality(G)\r\n elif cen_num == 1:\r\n g = nx.degree_centrality(G)\r\n else:\r\n g = nx.eigenvector_centrality(G, max_iter=10000)\r\n # eg = nx.eigenvector_centrality(G)\r\n # print(fr,g[agent_idx])\r\n # if fr < 60:\r\n if frame[0] <= fr <= frame[1]:\r\n weave_list.append(g[0]) # num/id- 0,0, 1/1, 2/0, 3/0, 4/0\r\n weave_list2.append(g[agent_idx])\r\n # weave_list2.append(cg[7])\r\n # weave_list3.append(cg[8])\r\n\r\nbuffer2 = 7 if len(weave_list2) % 2 == 0 else 8\r\nbuffer = 7 if len(weave_list) % 2 == 0 else 8\r\nif num != 6:\r\n weave_list2 = signal.savgol_filter(weave_list2, len(weave_list2)-buffer2, 3)\r\n # weave_list = signal.savgol_filter(weave_list, len(weave_list)-buffer, 3)\r\n\r\n\r\ncmaps = ['#0038ff', '#00a4b2', '#4c6fb8', '#2c5167', '#9fd9ea']\r\nLineThick = 5\r\nFontSize = 40\r\n\r\nfig, ax = plt.subplots(figsize=(11.0, 8.0))\r\nx = np.arange(frame[0], frame[1] + 1)\r\ny = weave_list2\r\n# x = np.arange(frame[0], frame[1])\r\n# y = weave_list2[2:] - 2 * weave_list2[:-1]\r\nif len(agent_label.split()) > 1:\r\n agent_color = agent_label.split()[0]\r\nelse:\r\n agent_color = 'Gold'\r\nax.plot(x, y, linewidth=LineThick, label=agent_label, color='k')\r\nif num == 0:\r\n ax.plot(x, weave_list, linewidth=LineThick, color='k')\r\n # ax.plot(range(frame[0], frame[1]+1), weave_list, linewidth=LineThick, label='Red Car', color='Tomato')\r\nif color:\r\n for i in range(0, len(x_lims) - 1):\r\n ax.fill_between([x_lims[i], x_lims[i + 1]],\r\n np.max(y[x_lims[i]-frame[0]:x_lims[i + 1]-frame[0]+1]) + 0.002,\r\n np.min(y[x_lims[i]-frame[0]:x_lims[i + 1]-frame[0]+1]) - 0.002,\r\n facecolor=cmaps[i], alpha=0.6, interpolate=True)\r\n # ax.fill_between([25, 35], np.max(y),np.min(y), facecolor='red', alpha=0.2, interpolate=True)\r\n # ax.fill_between([75, 85], np.max(y),np.min(y), facecolor='red', alpha=0.2, interpolate=True)\r\nplt.grid(True)\r\nplt.xlabel('Frame Number', fontsize=FontSize)\r\nplt.ylabel(centrality_label.split()[0], fontsize=FontSize)\r\nfor tick in ax.xaxis.get_major_ticks():\r\n tick.label.set_fontsize(FontSize - 7)\r\nfor tick in ax.yaxis.get_major_ticks():\r\n tick.label.set_fontsize(FontSize - 7)\r\n# Turn off tick labels\r\n# ax.set_yticklabels([])\r\n# legend = plt.legend(loc='lower left', bbox_to_anchor=(0, 1.01), ncol=2,\r\n# borderaxespad=0, fontsize=FontSize - 5, fancybox=True,\r\n# facecolor='green', framealpha=0.4)\r\n# frame = legend.get_frame()\r\n# frame.set_facecolor('green')\r\n# frame.set_edgecolor('red')\r\n# plt.savefig('images/' + video_set + '_' + agent_label + '.png', bbox_inches='tight')\r\n# fig2, ax2 = plt.subplots(figsize=(15.0, 12.0))\r\n# line, = ax2.plot(x, y,linewidth=LineThick, color='k')\r\n# plt.grid(True)\r\n# # plt.xlabel('Frame Number', fontsize=FontSize)\r\n# # plt.ylabel(centrality_label.split()[0], fontsize=FontSize)\r\n# for tick in ax2.xaxis.get_major_ticks():\r\n# tick.label.set_fontsize(FontSize - 7)\r\n# for tick in ax2.yaxis.get_major_ticks():\r\n# tick.label.set_fontsize(FontSize - 7)\r\n# y_init = list([0]*len(y))\r\n\r\n\r\n\r\n# for n in range(len(x)):\r\n# line.set_data(x[:n], y[:n])\r\n# # ax2.axis([0, 100, 0, ])\r\n# fig.canvas.draw()\r\n# plt.savefig('video_materials/' + video_set + '_' + '{}.png'.format(n), bbox_inches='tight')\r\n\r\n\r\n# plt.plot(range(frame[0], frame[1]+1), weave_list2, linewidth= LineThick, label=agent_label )\r\n# if num==0:\r\n# plt.plot(range(frame[0], frame[1]+1), weave_list, linewidth= LineThick,label=\"Scooter\" )\r\n# plt.grid(True)\r\n# plt.xlabel('Time (Frame Number)', fontsize=FontSize)\r\n# plt.ylabel(centrality_label, fontsize=FontSize)\r\n# plt.legend(loc='upper left', fontsize=FontSize)\r\nplt.show()\r\n", "sub_path": "closeness_demo.py", "file_name": "closeness_demo.py", "file_ext": "py", "file_size_in_byte": 7248, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 89, "usage_type": "call"}, {"api_name": "networkx.from_numpy_array", "line_number": 96, "usage_type": "call"}, {"api_name": "networkx.closeness_centrality", "line_number": 98, "usage_type": "call"}, {"api_name": "networkx.degree_centrality", "line_number": 100, "usage_type": "call"}, {"api_name": "networkx.eigenvector_centrality", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}]} {"seq_id": "627706310", "text": "# -*- coding: utf-8 -*-\nimport os\nimport re\nimport tempfile\nfrom test.data import (\n bob,\n cheese,\n context0,\n context1,\n context2,\n hates,\n likes,\n michel,\n pizza,\n tarek,\n)\n\nimport pytest\nfrom rdflib import RDF, RDFS, BNode, Literal, URIRef, Variable\nfrom rdflib.graph import Dataset, QuotedGraph\n\nstorename = \"SQLiteStore\"\npath = tempfile.mktemp(prefix=\"test\", dir=\"/tmp\")\n\n\ngraphuri = URIRef(\"urn:example:graph\")\nothergraphuri = URIRef(\"urn:example:othergraph\")\n\nimplies = URIRef(\"http://www.w3.org/2000/10/swap/log#implies\")\n\ntest_n3 = \"\"\"\\\n@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n@prefix : <http://test/> .\n{:a :b :c;a :foo} => {:a :d :c,?y} .\n_:foo a rdfs:Class .\n:a :d :c .\n\"\"\"\n\n\ndef expunge_path():\n if path is not None:\n if os.path.exists(path):\n if os.path.isdir(path):\n for f in os.listdir(path):\n os.unlink(path + \"/\" + f)\n os.rmdir(path)\n elif len(path.split(\":\")) == 1:\n os.unlink(path)\n else:\n os.remove(path)\n\n\n@pytest.fixture(scope=\"function\")\ndef dataset():\n\n expunge_path()\n graph = Dataset(store=storename, identifier=context0)\n graph.open(path, create=True)\n\n yield graph\n\n graph.close()\n\n expunge_path()\n\n\ndef test_simple_graph(dataset):\n graph = dataset.get_context(graphuri)\n graph.add((tarek, likes, pizza))\n graph.add((bob, likes, pizza))\n graph.add((bob, likes, cheese))\n\n g2 = dataset.get_context(othergraphuri)\n g2.add((michel, likes, pizza))\n\n assert len(graph) == 3, \"graph contains 3 triples\"\n assert len(g2) == 1, \"other graph contains 1 triple\"\n\n r = graph.query(\"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\")\n assert len(list(r)) == 2, \"two people like pizza\"\n\n r = graph.triples((None, likes, pizza))\n assert len(list(r)) == 2, \"two people like pizza\"\n\n # Test initBindings\n r = graph.query(\n \"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\",\n initBindings={\"s\": tarek},\n )\n assert len(list(r)) == 1, \"i was asking only about tarek\"\n\n r = graph.triples((tarek, likes, pizza))\n assert len(list(r)) == 1, \"i was asking only about tarek\"\n\n r = graph.triples((tarek, likes, cheese))\n assert len(list(r)) == 0, \"tarek doesn't like cheese\"\n\n g2.add((tarek, likes, pizza))\n graph.remove((tarek, likes, pizza))\n r = graph.query(\"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\")\n\n\ndef test_dataset_default(dataset):\n dataset.default_union = True\n\n subgraph1 = dataset.get_context(context1)\n\n subgraph1.add((tarek, likes, pizza))\n\n subgraph2 = dataset.get_context(context2)\n subgraph2.add((bob, likes, pizza))\n\n subgraph1.add((tarek, hates, cheese))\n\n assert len(subgraph1) == 2, \"graph contains 2 triples\"\n\n # the following are actually bad tests as they depend on your endpoint,\n # as pointed out in the sparqlstore.py code:\n #\n # # For Datasets, reading is done from the \"default graph\" Exactly\n # # what this means depends on your endpoint, because SPARQL does not offer a\n # # simple way to query the union of all graphs as it would be expected for a\n # # ConjuntiveGraph.\n # #\n # # Fuseki/TDB has a flag for specifying that the default graph\n # # is the union of all graphs (tdb:unionDefaultGraph in the Fuseki config).\n assert (\n len(dataset) == 3\n ), f\"default union graph should contain three triples but contains {list(dataset)}:\\n\"\n\n r = dataset.query(\"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\")\n\n assert len(list(r)) == 2, f\"two people should like pizza, not {len(list(r))}\"\n\n r = dataset.query(\n \"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\",\n initBindings={\"s\": tarek},\n )\n assert len(list(r)) == 1, \"i was asking only about tarek\"\n\n r = dataset.triples((tarek, likes, pizza))\n assert len(list(r)) == 1, \"i was asking only about tarek\"\n\n r = dataset.triples((tarek, likes, cheese))\n assert len(list(r)) == 0, \"tarek doesn't like cheese\"\n\n subgraph2.remove((bob, likes, pizza))\n\n r = dataset.query(\"SELECT * WHERE { ?s <urn:example:likes> <urn:example:pizza> . }\")\n assert len(list(r)) == 1, \"only tarek likes pizza\"\n\n\ndef test_update(dataset):\n dataset.update(\n \"INSERT DATA { GRAPH <urn:example:graph> { <urn:example:michel> <urn:example:likes> <urn:example:pizza> . } }\"\n )\n\n graph = dataset.get_context(graphuri)\n assert len(graph) == 1, \"graph contains 1 triple\"\n\n\ndef test_update_with_initns(dataset):\n dataset.update(\n \"INSERT DATA { GRAPH ns:graph { ns:michel ns:likes ns:pizza . } }\",\n initNs={\"ns\": URIRef(\"urn:example:\")},\n )\n\n graph = dataset.get_context(graphuri)\n assert set(graph.triples((None, None, None))) == set(\n [(michel, likes, pizza)]\n ), \"only michel likes pizza\"\n\n\ndef test_update_with_initbindings(dataset):\n dataset.update(\n \"INSERT { GRAPH <urn:example:graph> { ?a ?b ?c . } } WherE { }\",\n initBindings={\n \"a\": michel,\n \"b\": likes,\n \"c\": pizza,\n },\n )\n\n graph = dataset.get_context(graphuri)\n assert set(graph.triples((None, None, None))) == set(\n [(michel, likes, pizza)]\n ), \"only michel likes pizza\"\n\n\ndef test_multiple_update_with_initbindings(dataset):\n dataset.update(\n \"INSERT { GRAPH <urn:example:graph> { ?a ?b ?c . } } WHERE { };\"\n \"INSERT { GRAPH <urn:example:graph> { ?d ?b ?c . } } WHERE { }\",\n initBindings={\n \"a\": michel,\n \"b\": likes,\n \"c\": pizza,\n \"d\": bob,\n },\n )\n\n graph = dataset.get_context(graphuri)\n assert set(graph.triples((None, None, None))) == set(\n [(michel, likes, pizza), (bob, likes, pizza)]\n ), \"michel and bob like pizza\"\n\n\ndef test_named_graph_r3_update(dataset):\n graph = dataset.get_context(graphuri)\n\n # Strings with unbalanced curly braces\n tricky_strs = [\"With an unbalanced curly brace %s \" % brace for brace in [\"{\", \"}\"]]\n for tricky_str in tricky_strs:\n r3 = (\n \"\"\"INSERT { ?b <urn:example:says> \"%s\" }\n WHERE { ?b <urn:example:likes> <urn:example:pizza>} \"\"\"\n % tricky_str\n )\n graph.update(r3)\n\n\ndef test_named_graph_update(dataset):\n graph = dataset.get_context(graphuri)\n r1 = \"INSERT DATA { <urn:example:michel> <urn:example:likes> <urn:example:pizza> }\"\n graph.update(r1)\n assert set(graph.triples((None, None, None))) == set(\n [(michel, likes, pizza)]\n ), \"only michel likes pizza\"\n\n r2 = (\n \"DELETE { <urn:example:michel> <urn:example:likes> <urn:example:pizza> } \"\n + \"INSERT { <urn:example:bob> <urn:example:likes> <urn:example:pizza> } WHERE {}\"\n )\n graph.update(r2)\n assert set(graph.triples((None, None, None))) == set(\n [(bob, likes, pizza)]\n ), \"only bob likes pizza\"\n says = URIRef(\"urn:example:says\")\n\n # Strings with unbalanced curly braces\n tricky_strs = [\"With an unbalanced curly brace %s \" % brace for brace in [\"{\", \"}\"]]\n for tricky_str in tricky_strs:\n r3 = (\n \"\"\"INSERT { ?b <urn:example:says> \"%s\" }\n WHERE { ?b <urn:example:likes> <urn:example:pizza>} \"\"\"\n % tricky_str\n )\n graph.update(r3)\n\n values = set()\n for v in graph.objects(bob, says):\n values.add(str(v))\n assert values == set(tricky_strs)\n\n\n# @pytest.mark.xfail(reason=\"Failure in SQL query string handling\")\ndef test_named_graph_update_complicated_strings(dataset):\n graph = dataset.get_context(graphuri)\n says = URIRef(\"urn:example:says\")\n # Complicated Strings\n r4strings = []\n r4strings.append(r'''\"1: adfk { ' \\\\\\\" \\\" { \"''')\n r4strings.append(r'''\"2: adfk } <foo> #éï \\\\\"''')\n\n r4strings.append(r\"\"\"'3: adfk { \" \\\\\\' \\' { '\"\"\")\n r4strings.append(r\"\"\"'4: adfk } <foo> #éï \\\\'\"\"\")\n\n r4strings.append(r'''\"\"\"5: adfk { ' \\\\\\\" \\\" { \"\"\"''')\n r4strings.append(r'''\"\"\"6: adfk } <foo> #éï \\\\\"\"\"''')\n r4strings.append('\"\"\"7: ad adsfj \\n { \\n sadfj\"\"\"')\n\n r4strings.append(r\"\"\"'''8: adfk { \" \\\\\\' \\' { '''\"\"\")\n r4strings.append(r\"\"\"'''9: adfk } <foo> #éï \\\\'''\"\"\")\n r4strings.append(\"'''10: ad adsfj \\n { \\n sadfj'''\")\n\n r4 = \"\\n\".join(\n [\n \"INSERT DATA { <urn:example:michel> <urn:example:says> %s } ;\" % s\n for s in r4strings\n ]\n )\n graph.update(r4)\n values = set()\n for v in sorted(graph.objects(michel, says)):\n values.add(str(v))\n assert values == set(\n [\n re.sub(\n r\"\\\\(.)\",\n r\"\\1\",\n re.sub(r\"^'''|'''$|^'|'$|\" + r'^\"\"\"|\"\"\"$|^\"|\"$', r\"\", s),\n )\n for s in r4strings\n ]\n )\n\n\n# @pytest.mark.xfail(reason=\"Failure in SQL query string handling\")\ndef test_named_graph_update_iri_containing_octothorpe(dataset):\n graph = dataset.get_context(graphuri)\n\n # IRI Containing ' or #\n # The fragment identifier must not be misinterpreted as a comment\n # (commenting out the end of the block).\n # The ' must not be interpreted as the start of a string, causing the }\n # in the literal to be identified as the end of the block.\n r5 = \"\"\"INSERT DATA { <urn:example:michel> <urn:example:hates> <urn:example:foo'bar?baz;a=1&b=2#fragment>, \"'}\" }\"\"\"\n\n graph.update(r5)\n values = set()\n for v in graph.objects(michel, hates):\n values.add(str(v))\n assert values == set([\"urn:example:foo'bar?baz;a=1&b=2#fragment\", \"'}\"])\n\n # Comments\n r6 = \"\"\"\n INSERT DATA {\n <urn:example:bob> <urn:example:hates> <urn:example:bob> . # No closing brace: }\n <urn:example:bob> <urn:example:hates> <urn:example:michel>.\n }\n #Final { } comment\"\"\"\n\n graph.update(r6)\n values = set()\n for v in graph.objects(bob, hates):\n values.add(v)\n assert values == set([bob, michel])\n\n\ndef test_named_graph_update_with_initbindings(dataset):\n graph = dataset.get_context(graphuri)\n r = \"INSERT { ?a ?b ?c } WHERE {}\"\n graph.update(r, initBindings={\"a\": michel, \"b\": likes, \"c\": pizza})\n assert set(graph.triples((None, None, None))) == set(\n [(michel, likes, pizza)]\n ), \"only michel likes pizza\"\n\n\ndef test_empty_literal(dataset):\n # test for https://github.com/RDFLib/rdflib/issues/457\n # also see test_issue457.py which is sparql store independent!\n graph = dataset.get_context(graphuri)\n graph.add(\n (\n URIRef(\"http://example.com/s\"),\n URIRef(\"http://example.com/p\"),\n Literal(\"\"),\n )\n )\n\n o = tuple(graph)[0][2]\n assert Literal(\"\") == o, repr(o)\n\n\ndef test_n3_store(dataset):\n dataset.default_union = True\n dataset.parse(data=test_n3, format=\"n3\")\n formula_a = BNode()\n formula_b = BNode()\n for s, p, o in dataset.triples((None, implies, None)):\n formula_a = s\n formula_b = o\n\n assert type(formula_a) == QuotedGraph and type(formula_b) == QuotedGraph\n a = URIRef(\"http://test/a\")\n b = URIRef(\"http://test/b\")\n c = URIRef(\"http://test/c\")\n d = URIRef(\"http://test/d\")\n v = Variable(\"y\")\n\n universe = Dataset(dataset.store)\n\n # test formula as terms\n assert len(list(universe.triples((formula_a, implies, formula_b)))) == 1\n\n # test variable as term and variable roundtrip\n assert len(list(formula_b.triples((None, None, v)))) == 1\n for s, p, o in formula_b.triples((None, d, None)):\n if o != c:\n assert isinstance(o, Variable)\n assert o == v\n s = list(universe.subjects(RDF.type, RDFS.Class))[0]\n assert isinstance(s, BNode)\n assert len(list(universe.triples((None, implies, None)))) == 1\n assert len(list(universe.triples((None, RDF.type, None)))) == 1\n assert len(list(formula_a.triples((None, RDF.type, None)))) == 1\n assert len(list(formula_a.triples((None, None, None)))) == 2\n assert len(list(formula_b.triples((None, None, None)))) == 2\n assert len(list(universe.triples((None, None, None)))) == 3\n assert len(list(formula_b.triples((None, URIRef(\"http://test/d\"), None)))) == 2\n assert len(list(universe.triples((None, URIRef(\"http://test/d\"), None)))) == 1\n\n # context tests\n # test contexts with triple argument\n assert len(list(universe.contexts((a, d, c)))) == 1\n\n # Remove test cases\n universe.remove((None, implies, None))\n assert len(list(universe.triples((None, implies, None)))) == 0\n assert len(list(formula_a.triples((None, None, None)))) == 2\n assert len(list(formula_b.triples((None, None, None)))) == 2\n\n formula_a.remove((None, b, None))\n assert len(list(formula_a.triples((None, None, None)))) == 1\n formula_a.remove((None, RDF.type, None))\n assert len(list(formula_a.triples((None, None, None)))) == 0\n\n universe.remove((None, RDF.type, RDFS.Class))\n\n # remove_context tests\n universe.remove_graph(formula_b)\n assert len(list(universe.triples((None, RDF.type, None)))) == 0\n assert len(universe) == 1\n assert len(formula_b) == 0\n\n universe.remove((None, None, None))\n assert len(universe) == 0\n\n dataset.close()\n dataset.store.destroy(path)\n\n\nxmltestdoc = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<rdf:RDF\n xmlns=\"http://example.org/\"\n xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"\n>\n <rdf:Description rdf:about=\"http://example.org/a\">\n <b rdf:resource=\"http://example.org/c\"/>\n </rdf:Description>¬\n</rdf:RDF>\n\"\"\"\n\nn3testdoc = \"\"\"@prefix : <http://example.org/> .\n\n:a :b :c .\n\"\"\"\n\nnttestdoc = \"<http://example.org/a> <http://example.org/b> <http://example.org/c> .\\n\"\n", "sub_path": "test/test_sqlitestore/test_sqlitestore_functionality.py", "file_name": "test_sqlitestore_functionality.py", "file_ext": "py", "file_size_in_byte": 13817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tempfile.mktemp", "line_number": 23, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 26, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 27, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 46, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 51, "usage_type": "call"}, {"api_name": "rdflib.graph.Dataset", "line_number": 58, "usage_type": "call"}, {"api_name": "test.data.context0", "line_number": 58, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 54, "usage_type": "call"}, {"api_name": "test.data.tarek", "line_number": 70, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 70, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 70, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 71, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 71, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 71, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 72, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 72, "usage_type": "name"}, {"api_name": "test.data.cheese", "line_number": 72, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 75, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 75, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 75, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 83, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 83, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 89, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 93, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 93, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 93, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 96, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 96, "usage_type": "name"}, {"api_name": "test.data.cheese", "line_number": 96, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 99, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 99, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 99, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 100, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 100, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 100, "usage_type": "name"}, {"api_name": "test.data.context1", "line_number": 107, "usage_type": "argument"}, {"api_name": "test.data.tarek", "line_number": 109, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 109, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 109, "usage_type": "name"}, {"api_name": "test.data.context2", "line_number": 111, "usage_type": "argument"}, {"api_name": "test.data.bob", "line_number": 112, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 112, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 112, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 114, "usage_type": "name"}, {"api_name": "test.data.hates", "line_number": 114, "usage_type": "name"}, {"api_name": "test.data.cheese", "line_number": 114, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 138, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 142, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 142, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 142, "usage_type": "name"}, {"api_name": "test.data.tarek", "line_number": 145, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 145, "usage_type": "name"}, {"api_name": "test.data.cheese", "line_number": 145, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 148, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 148, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 148, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 166, "usage_type": "call"}, {"api_name": "test.data.michel", "line_number": 171, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 171, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 171, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 179, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 180, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 181, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 187, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 187, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 187, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 196, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 197, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 198, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 199, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 205, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 205, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 205, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 205, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 228, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 228, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 228, "usage_type": "name"}, {"api_name": "test.data.bob", "line_number": 237, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 237, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 237, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 239, "usage_type": "call"}, {"api_name": "test.data.bob", "line_number": 252, "usage_type": "argument"}, {"api_name": "rdflib.URIRef", "line_number": 260, "usage_type": "call"}, {"api_name": "test.data.michel", "line_number": 285, "usage_type": "argument"}, {"api_name": "re.sub", "line_number": 289, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 292, "usage_type": "call"}, {"api_name": "test.data.michel", "line_number": 312, "usage_type": "argument"}, {"api_name": "test.data.hates", "line_number": 312, "usage_type": "argument"}, {"api_name": "test.data.bob", "line_number": 326, "usage_type": "argument"}, {"api_name": "test.data.hates", "line_number": 326, "usage_type": "argument"}, {"api_name": "test.data.bob", "line_number": 328, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 328, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 334, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 334, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 334, "usage_type": "name"}, {"api_name": "test.data.michel", "line_number": 336, "usage_type": "name"}, {"api_name": "test.data.likes", "line_number": 336, "usage_type": "name"}, {"api_name": "test.data.pizza", "line_number": 336, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 346, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 347, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 348, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 353, "usage_type": "call"}, {"api_name": "rdflib.BNode", "line_number": 359, "usage_type": "call"}, {"api_name": "rdflib.BNode", "line_number": 360, "usage_type": "call"}, {"api_name": "rdflib.graph.QuotedGraph", "line_number": 365, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 366, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 367, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 368, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 369, "usage_type": "call"}, {"api_name": "rdflib.Variable", "line_number": 370, "usage_type": "call"}, {"api_name": "rdflib.graph.Dataset", "line_number": 372, "usage_type": "call"}, {"api_name": "rdflib.Variable", "line_number": 381, "usage_type": "argument"}, {"api_name": "rdflib.RDF.type", "line_number": 383, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 383, "usage_type": "name"}, {"api_name": "rdflib.RDFS.Class", "line_number": 383, "usage_type": "attribute"}, {"api_name": "rdflib.RDFS", "line_number": 383, "usage_type": "name"}, {"api_name": "rdflib.BNode", "line_number": 384, "usage_type": "argument"}, {"api_name": "rdflib.RDF.type", "line_number": 386, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 386, "usage_type": "name"}, {"api_name": "rdflib.RDF.type", "line_number": 387, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 387, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 391, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 392, "usage_type": "call"}, {"api_name": "rdflib.RDF.type", "line_number": 406, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 406, "usage_type": "name"}, {"api_name": "rdflib.RDF.type", "line_number": 409, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 409, "usage_type": "name"}, {"api_name": "rdflib.RDFS.Class", "line_number": 409, "usage_type": "attribute"}, {"api_name": "rdflib.RDFS", "line_number": 409, "usage_type": "name"}, {"api_name": "rdflib.RDF.type", "line_number": 413, "usage_type": "attribute"}, {"api_name": "rdflib.RDF", "line_number": 413, "usage_type": "name"}]} {"seq_id": "249520983", "text": "#!/usr/bin/env python\nimport datetime\nimport datetime\nimport numpy as np\nimport os\nfrom PIL import Image, ImageFilter, ImageDraw, ImageFont\n# import time\nfrom bpassW import *\n# from matplotlib import pyplot as plt\n# from math import pi\nfrom decimal import Decimal\nfrom pkfndW2 import *\nfrom skimage.draw import ellipse\n\n\n# camera libraries\n#from gpiozero import LED \n#from picamera import PiCamera \n#import io \n\ndef format_e(n):\n\ta = '%E' % n\n\treturn a.split('E')[0].rstrip('0').rstrip('.') + 'E' + a.split('E')[1]\n\ndef kukicounter(imagefile):\n\tnp.set_printoptions(precision=5,suppress=True)\n\t# ## take photo\n\t# led = LED(17)\n\t# led.on()\n\t# print (\"LED on\")\n\t# body = camera.get_frame()\n\t# now = datetime.datetime.now()\n\t# dstfilename = \"Kyu\" + now.strftime(\"%Y-%m-%d %Hh%Mm\")\n\t# with open(dstfilename,'wb') as f:\n\t# \tf.write(body)\n\t# \tf.close()\n\t# print(\"LED off\")\n\t# led.off()\n\t#imagefile = 'C:/Users/kuki/Downloads/071910am/1b01.jpeg'\n\t## pre-processing\n\ta = Image.open(imagefile) # open image\n\tag = a.convert(mode=\"L\") # mode=L (8bit, black and white) aka greyscale\n\tscf=0.5\n\tresize = tuple(int(i*scf) for i in ag.size)\n\tag = ag.resize(resize, Image.ANTIALIAS)\n\ta = a.resize(resize, Image.ANTIALIAS)\n\tag = np.asarray(ag)\n\n\tbg1 = np.mean(ag)\n\t# mean filter: subtract mean of all pixels from each pixel \n\t# noise-reduction\n\timbr0 = ag-bg1 \n\timbr = np.abs(imbr0)\n\t#ag=Image.fromarray(ag)\n\t#median = ag.filter(ImageFilter.MedianFilter)\n\t#ag=np.asarray(ag)\n\t## cell dectection \n\tb0 = bpassW(imbr,lnoise=1,lobject=3) #put median for median filter\n\t\n\tvar1=13\n\tibg = np.mean(b0)+var1*np.std(b0)\n\n\tvar2=20\n\tpk = pkfndW2(b0,ibg,var2)\n\n\tpk5=pk\n\n\tcc=np.zeros(len(pk5))\n\tcc[pk5.T[0]>0]=True\n\tcc[pk5.T[1]>0]=True\n\tpks=pk5\n\n\tno_cells = len(pks)*2\n\n\tprint ('detected {:3.0f} object inbound out of total {:3.0f}'.format(np.sum(cc),len(cc)))\n\t\n\n\t# draw dot on top of cells\n\ta_draw = ImageDraw.Draw(a)\n\tfor centroid in pks:\n\t\ty = centroid[0]\n\t\tx = centroid[1]\n\t\te=[x-1,y-1,x+1,y+1]\n\t\ta_draw.ellipse(e,fill='rgb(100%,0%,0%)')\n\tdel a_draw\n\n\t# area_px = 'imagesize' \n\t# px_size = 7.45 #um/px\n\t# area_mm = area_px*(px_size*0.001)**2\n\t# chamber_height = 0.1 #100nm\n\t# chamber_vol = (area_mm*chamber_height)/1000\n\t# density = format_e(Decimal(str(no_cells/chamber_vol)))\n\t# densitye = Decimal(str(no_cells/chamber_vol))\n\t# print ('cell density-- {:.2E}cells/ml'.format(densitye)) \n\n\t# get a font\n\t#fnt = ImageFont.load_default()\n\t# get a drawing context\n\t#d = ImageDraw.Draw(a)\n\t# draw text\n\t#d.text((10,10), 'cell density-- {:.2E}cells/ml'.format(densitye), font=fnt, fill=(255,255,255))\n\t# draw text, full opacity\n\t#d.text((10,60), \"World\", font=fnt, fill=(255,255,255))\n\tfile,ext=os.path.splitext(os.path.basename(imagefile))\n\n\t# create destination folder\n\tdesktop = os.path.join(os.environ[\"USERPROFILE\"], \"Desktop\")\n\tdstfolder = os.path.join('C:/Users/kuki/Downloads/071910am','count_results')\n\tif not os.path.exists(dstfolder): os.mkdir(dstfolder)\n\t# prevent overwriting counted image\n\tif not os.path.exists(file+'_counted.jpg'):\n\t\ta.save(os.path.join(dstfolder,file+'_counted.jpg'),format=\"JPEG\")\n\treturn no_cells\n\n\t\nfol='C:/Users/kuki/Downloads/071910am'\nims = [os.path.join(fol,_) for _ in os.listdir(fol) if _.endswith('jpeg')]\nfor im in ims:\n\tno_cells = kukicounter(im)\nprint(no_cells)", "sub_path": "3Dmicroscope/cellcounter/newkukicounter.py", "file_name": "newkukicounter.py", "file_ext": "py", "file_size_in_byte": 3275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.set_printoptions", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 75, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 117, "usage_type": "call"}]} {"seq_id": "590419894", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 5 15:23:01 2013\n\nFinite Difference Approximation and Convergence\n\n@author: jspineda\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom array_op import rotate #use np.roll\n\n\n#interval [-2,6]\n\ndef f(xin):\n \"Function f = x3 - 5x2 + x\"\n x = np.array(xin)\n return np.power(x,3) - 5*np.power(x,2) + x\n \ndef df_sol(xin):\n \"Analytic Derivative of f, see above\"\n x = np.array(xin)\n return 3*np.power(x,2) - 10*x + 1\n\ndef df_for(x):\n \"Forward derivative at x, using input descritization, ouput array has n-1\"\n h = (rotate(x,-1) - x)[1]\n return ( f(rotate(x,-1)) - f(x))[:-1] / h #does not include the latter edge point\n \n\ndef df_cent(x):\n \"Central derivative at x, in step size h\"\n h = (rotate(x,-1) - x)[1]\n return (f(rotate(x,-1)) - f(rotate(x,1)))[1:-1] / (2*h) #does not include edge points\n \n \ndef absicc(h,interval):\n \"produce absicca on interval [-2,6] with given stepsize\"\n n = int((interval[1] - interval[0])/ h)\n return h*np.arange(0,n+1) - 2\n \n \ndef quest(h):\n \"Command for executing code for problem 1\"\n interval = [-2,6.]\n x1 = absicc(h,interval) # define x axis on the interval with resolution h\n xf1 = x1[:-1]\n xc1 = x1[1:-1]\n x2 = absicc(h/2.,interval) # define x axis on the interval with resolution h/2\n xf2 = x2[:-1]\n xc2 = x2[1:-1]\n\n plt.clf()\n fig1 = plt.figure(1) \n plt.plot(xf1,df_for(x1) - df_sol(xf1),color='orange',label='Step Size: {0}'.format(h))\n plt.plot(xf2,df_for(x2) - df_sol(xf2),color='red',label='Step Size: {0}'.format(h/2.))\n plt.legend(loc='lower right') \n plt.title('Forward Differencing')\n pp1 = PdfPages('ws2_prob1_fig1.pdf')\n pp1.savefig(fig1)\n pp1.close()\n \n plt.clf()\n fig2 = plt.figure(2)\n plt.plot(xc1,df_cent(x1) - df_sol(xc1),color='darkgreen',label='Step Size: {0}'.format(h))\n plt.plot(xc2,df_cent(x2) - df_sol(xc2),color='blue',label='Step Size: {0}'.format(h/2))\n plt.legend(loc='center right')\n plt.title('Central Differencing')\n pp2 = PdfPages('ws2_prob1_fig2.pdf')\n pp2.savefig(fig2)\n pp2.close()\n \n \n\n\n \n return 'Done, see plots'", "sub_path": "ws2_hw2/ws2_prob1.py", "file_name": "ws2_prob1.py", "file_ext": "py", "file_size_in_byte": 2257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 25, "usage_type": "call"}, {"api_name": "array_op.rotate", "line_number": 29, "usage_type": "call"}, {"api_name": "array_op.rotate", "line_number": 30, "usage_type": "call"}, {"api_name": "array_op.rotate", "line_number": 35, "usage_type": "call"}, {"api_name": "array_op.rotate", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_pdf.PdfPages", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_pdf.PdfPages", "line_number": 71, "usage_type": "call"}]} {"seq_id": "408615406", "text": "\"\"\"Read analytics from local SQL files.\"\"\"\nfrom os import listdir\nfrom os.path import isfile, join\nfrom typing import List\n\nfrom sqlalchemy.engine.result import ResultProxy\n\nfrom config import basedir\nfrom database import rdbms\n\n\ndef collect_sql_queries(subdirectory: str) -> dict:\n \"\"\"\n Create dict of SQL queries to be run where `keys` are filenames and `values` are queries.\n\n :param subdirectory: Directory containing .sql queries to run in bulk.\n :type subdirectory: str\n :returns: dict\n \"\"\"\n sql_file_paths = fetch_sql_files(subdirectory)\n sql_queries = parse_sql_batch(sql_file_paths)\n sql_file_names = [file.split(\"/\")[-1] for file in sql_file_paths]\n query_dict = dict(zip(sql_file_names, sql_queries))\n return query_dict\n\n\ndef fetch_sql_files(subdirectory: str) -> List[str]:\n \"\"\"\n Fetch all SQL query files in folder.\n\n :param subdirectory: Subdirectory containing SQL files to fetch.\n :type subdirectory: str\n :returns: List[str]\n \"\"\"\n folder = f\"{basedir}/database/queries/{subdirectory}\"\n directory = listdir(folder)\n files = [\n folder + \"/\" + f for f in directory if isfile(join(folder, f)) if \".sql\" in f\n ]\n return files\n\n\ndef parse_sql_batch(sql_file_paths: List[str]) -> List[str]:\n \"\"\"\n Read SQL analytics from .sql files.\n\n :param sql_file_paths: List of paths to SQL files to read and parse.\n :type sql_file_paths: List[str]\n :returns: List[str]\n \"\"\"\n queries = []\n for file in sql_file_paths:\n sql_file = open(file, \"r\")\n query = sql_file.read()\n queries.append(query)\n sql_file.close()\n return queries\n\n\ndef fetch_raw_lynx_posts() -> ResultProxy:\n \"\"\"\n Find all Lynx posts lacking embedded link previews.\n\n :returns: ResultProxy\n \"\"\"\n sql_file = open(f\"{basedir}/database/queries/posts/selects/lynx_bookmarks.sql\", \"r\")\n query = sql_file.read()\n posts = rdbms.execute_query(query, \"hackers_prod\").fetchall()\n return posts\n", "sub_path": "database/read_sql.py", "file_name": "read_sql.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "config.basedir", "line_number": 35, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "config.basedir", "line_number": 66, "usage_type": "name"}, {"api_name": "database.rdbms.execute_query", "line_number": 68, "usage_type": "call"}, {"api_name": "database.rdbms", "line_number": 68, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.result.ResultProxy", "line_number": 60, "usage_type": "name"}]} {"seq_id": "59460808", "text": "from numpy.lib.arraysetops import isin\nfrom numpy.lib.function_base import average\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, f1_score, r2_score\nfrom sklearn.metrics import precision_score, recall_score\nimport numpy as np\nimport pandas as pd\n\ndef mae(app_gt,app_pred):\n return mean_absolute_error(app_gt,app_pred)\n\ndef rmse(app_gt, app_pred):\n return mean_squared_error(app_gt,app_pred)**(.5)\n\ndef f1score(app_gt, app_pred):\n threshold = 10\n gt_temp = np.array(app_gt)\n gt_temp = np.where(gt_temp<threshold,0,1)\n pred_temp = np.array(app_pred)\n pred_temp = np.where(pred_temp<threshold,0,1)\n\n return f1_score(gt_temp, pred_temp)\n\ndef f1score_(app_gt, app_pred):\n threshold = 10\n gt_temp = np.array(app_gt)\n gt_temp = np.where(gt_temp<threshold,0,1)\n pred_temp = np.array(app_pred)\n pred_temp = np.where(pred_temp<threshold,0,1)\n\n return f1_score(gt_temp, pred_temp, average='macro')\n\ndef recall(app_gt, app_pred):\n threshold = 10\n gt_temp = np.array(app_gt)\n gt_temp = np.where(gt_temp<threshold,0,1)\n pred_temp = np.array(app_pred)\n pred_temp = np.where(pred_temp<threshold,0,1)\n\n return recall_score(gt_temp, pred_temp, )\n\ndef precision(app_gt, app_pred):\n threshold = 10\n gt_temp = np.array(app_gt)\n gt_temp = np.where(gt_temp<threshold,0,1)\n pred_temp = np.array(app_pred)\n pred_temp = np.where(pred_temp<threshold,0,1)\n\n return precision_score(gt_temp, pred_temp, )\n\ndef relative_error(app_gt,app_pred):\n constant = 1\n numerator = np.abs(app_gt - app_pred)\n denominator = constant + app_pred\n return np.mean(numerator/denominator)\n\ndef r2score(app_gt,app_pred):\n # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score\n return r2_score(app_gt, app_pred)\n\ndef nde(app_gt,app_pred):\n # Normalized Disaggregation Error (NDE)\n # Inspired by http://proceedings.mlr.press/v22/zico12/zico12.pdf\n numerator = np.sum((app_gt-app_pred)**2)\n denominator = np.sum(app_gt**2)\n\n # if denominator < 1e-5:\n # denominator = 1e-5\n\n frac = numerator/denominator\n if isinstance(frac, pd.Series):\n return np.sqrt(frac[0])\n else:\n return np.sqrt(frac)\n\ndef nep(app_gt,app_pred):\n # Normalized Error in Assigned Power (NEP)\n # Inspired by https://www.springer.com/gp/book/9783030307813\n numerator = np.sum(np.abs(app_gt-app_pred))\n denominator = np.sum(app_gt)\n\n # if denominator < 1e-5:\n # denominator = 1e-5\n\n frac = numerator/denominator\n if isinstance(frac, pd.Series):\n return np.sqrt(frac[0])\n else:\n return np.sqrt(frac)\n", "sub_path": "nilmtk/losses.py", "file_name": "losses.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 88, "usage_type": "call"}]} {"seq_id": "295141467", "text": "from scrapy.spider import Spider\nfrom scrapy.log import logger\nfrom scrapy import Request\nfrom ..items import VideoItem\n\n'''\n电影 电视剧 动漫 综艺\n'''\n\n\nclass VideoSpider(Spider):\n name = \"videoSpider\"\n # 电影\n start_urls = [\"http://m.77kp.com/vod-type-id-1-pg-1.html\"]\n\n def parse(self, response):\n logger.info(\"---------------视频页当前数据的抓取 Begin------------------------------\")\n title_divs = response.xpath(\"//div[@class='son_nav']\")\n movice_divs = response.xpath(\"//div[@class='list mb']\")\n\n for i, div in enumerate(title_divs):\n title = div.xpath(\"//li[@class='n1']/a/text()\").extract()[i]\n logger.info(title)\n movices_lis = movice_divs[i].xpath(\".//ul/li\")\n\n for item_li in movices_lis:\n name = item_li.xpath(\".//a/@title\").extract()\n link = \"http://m.77kp.com\" + item_li.xpath(\".//a/@href\").extract_first()\n img = item_li.xpath(\".//a/img/@src\").extract()\n logger.info(\"视频名--->{}|视频详细链接--->{}|视频图片--->{}\".format(name[0], link, img))\n logger.info(\"---------------视频页当前数据的抓取 End------------------------------\")\n logger.info(\"---------------类型的抓取，同时构建类型页面的request---------------\")\n # 所有类型的as\n type_as = response.xpath(\"//ul[@class='ui-tab fn-clear']/li/a\")\n typeitem = {'bigtype': '', 'smailtype': '','name':name[0]}\n for a in type_as[:len(type_as) - 1]:\n\n type = a.xpath(\"text()\").extract()\n type_link = \"http://m.77kp.com\" + a.xpath(\"@href\").extract_first()\n if \"片\" in type[0]:\n typeitem['bigtype'] = '电影'\n typeitem['smailtype'] = type[0]\n else:\n typeitem['bigtype'] = '电视剧'\n typeitem['smailtype'] = type[0]\n yield Request(response.urljoin(type_link), callback=self.parse_video, meta={'typeitem': typeitem})\n logger.info(\"小的类型名--->{}|链接地址---->{}\".format(type[0], type_link))\n logger.info(\"---------------类型的抓取 End------------------------------\")\n # 测试\n # yield Request(response.urljoin(\"http://m.77kp.com/vod-type-id-5-pg-1.html\"), callback=self.parse_video,\n # meta={'typeitem': typeitem})\n\n def parse_video(self, response):\n links = []\n video_as = response.xpath(\"//div[@class='list mb bt']/ul/li/a\")\n for a in video_as:\n name = a.xpath(\"@title\").extract()\n link = \"http://m.77kp.com\" + a.xpath(\"@href\").extract_first()\n img = a.xpath(\".//img/@src\").extract()\n links.append(link)\n logger.info(\"视频名--->{}|视频详细链接--->{}|视频图片--->{}\".format(name[0], link, img[0]))\n\n next_page = \"http://m.77kp.com\" + response.xpath(\"//div[@class='ui-pages']/a/@href\")[-2].extract()\n if next_page:\n logger.info(\"下一页网址----->{}\".format(next_page))\n yield Request(response.urljoin(next_page), callback=self.parse_video)\n\n for link in links:\n yield Request(response.urljoin(link), callback=self.parse_videodetail, meta=response.meta)\n\n def parse_videodetail(self, response):\n typeitem = response.meta['typeitem']\n title = response.xpath(\"//div[@class='title']/text()\").extract_first()\n performer = response.xpath(\"//div[@class='ui-detail fn-clear']/p/text()\").extract_first()\n img = response.xpath(\"//div[@class='ui-img']/img/@src\").extract_first()\n introduction = response.xpath(\"//div[@class='ui-detail-info']/text()\").extract()[1]\n\n logger.info(\"大类型--->{}|小类型--->{}|视频名--->{}|演员--->{}|图片--->{}|简介--->{}\".format(typeitem['bigtype'],\n typeitem['smailtype'], title,\n performer, img, introduction))\n video_urls = response.xpath(\"//div[@class='detail-list fn-clear']/a/@href\").extract()\n\n online_urls = []\n thunder_urls = []\n magnet_urls = []\n\n for url in video_urls:\n if \".html\" in url:\n online_urls.append(\"http://m.77kp.com\" + url)\n elif \"thunder://\" in url:\n thunder_urls.append(url)\n elif \"magnet:?xt\" in url:\n magnet_urls.append(url)\n logger.info(\"在线播放地址--->{}|迅雷下载地址--->{}|磁力链接地址---->{}\".format(online_urls, thunder_urls, magnet_urls))\n\n videoitem = VideoItem()\n videoitem['bigtype'] = typeitem['bigtype']\n videoitem['smailtype'] = typeitem['smailtype']\n videoitem['name'] = typeitem['name']\n videoitem['pic'] = img\n videoitem['performer'] = performer\n videoitem['introduction'] = introduction\n videoitem['videohtml']=response.url\n videoitem['videourls'] = online_urls\n videoitem['thunderurls'] = thunder_urls\n videoitem['magneturls'] = magnet_urls\n\n yield videoitem\n", "sub_path": "QiQi/spiders/videospider.py", "file_name": "videospider.py", "file_ext": "py", "file_size_in_byte": 5221, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "scrapy.spider.Spider", "line_number": 11, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 17, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 23, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 23, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 30, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 31, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 31, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 32, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 32, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 46, "usage_type": "call"}, {"api_name": "scrapy.log.logger.info", "line_number": 47, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 47, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 48, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 48, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 61, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 61, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 65, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 65, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 66, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 69, "usage_type": "call"}, {"api_name": "scrapy.log.logger.info", "line_number": 78, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 78, "usage_type": "name"}, {"api_name": "scrapy.log.logger.info", "line_number": 94, "usage_type": "call"}, {"api_name": "scrapy.log.logger", "line_number": 94, "usage_type": "name"}, {"api_name": "items.VideoItem", "line_number": 96, "usage_type": "call"}]} {"seq_id": "158934833", "text": "import os\nimport sys\nimport argparse\nfrom tqdm import tqdm\n\nimport numpy as np\nfrom PIL import Image\n\nimport pkgutil\nfrom pathlib import Path\nfrom importlib import import_module\n\nfrom robosat_pink.config import load_config\nfrom robosat_pink.tiles import tiles_from_slippy_map\n\n\ndef add_parser(subparser):\n parser = subparser.add_parser(\n \"features\",\n help=\"extracts simplified GeoJSON features from segmentation masks\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n parser.add_argument(\"--type\", type=str, required=True, help=\"type of feature to extract\")\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to configuration file\")\n parser.add_argument(\"--path\", type=str, help=\"path to user's extension modules dir\")\n parser.add_argument(\"masks\", type=str, help=\"slippy map directory with segmentation masks\")\n parser.add_argument(\"out\", type=str, help=\"path to GeoJSON file to store features in\")\n\n parser.set_defaults(func=main)\n\n\ndef main(args):\n\n module_search_path = [args.path] if args.path else []\n module_search_path.append(os.path.join(Path(__file__).parent.parent, \"features\"))\n modules = [(path, name) for path, name, _ in pkgutil.iter_modules(module_search_path) if name != \"core\"]\n if args.type not in [name for _, name in modules]:\n sys.exit(\"Unknown type, thoses available are {}\".format([name for _, name in modules]))\n\n config = load_config(args.config)\n labels = config[\"classes\"][\"titles\"]\n if args.type not in labels:\n sys.exit(\"The type you asked is not consistent with yours classes in the config file provided.\")\n index = labels.index(args.type)\n\n if args.path:\n sys.path.append(args.path)\n module = import_module(args.type)\n else:\n module = import_module(\"robosat_pink.features.{}\".format(args.type))\n\n handler = getattr(module, \"{}Handler\".format(args.type.title()))()\n\n for tile, path in tqdm(list(tiles_from_slippy_map(args.masks)), ascii=True, unit=\"mask\"):\n image = np.array(Image.open(path).convert(\"P\"), dtype=np.uint8)\n mask = (image == index).astype(np.uint8)\n handler.apply(tile, mask)\n\n handler.save(args.out)\n", "sub_path": "robosat_pink/tools/features.py", "file_name": "features.py", "file_ext": "py", "file_size_in_byte": 2219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "pkgutil.iter_modules", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "robosat_pink.config.load_config", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 49, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 51, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 55, "usage_type": "call"}, {"api_name": "robosat_pink.tiles.tiles_from_slippy_map", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 57, "usage_type": "attribute"}]} {"seq_id": "469186214", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 1 10:06:23 2017\n\n@author: samuel\n\"\"\"\n#加载所需模块，pandas提供excel支持，matplotlib.pyplot提供plt支持\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n#从excel中加载excel文件,目录自行修改\ndf = pd.read_excel(r'G:\\Seafile\\临时\\Biodegradation of sulfur-rich oil\\负离子excel\\2.xlsx')\n#将intensity转换为float类型\ndf['intensity']=df['intensity'].astype(float)\n#按ppm筛选所需数据\ndf = df[(df.ppm>-2.0) & (df.ppm<2.0)]\n#读取数据的所有化合物类，先剔除掉重复项，再将剩下的列举出来\ny=df['class']\ny=y.drop_duplicates()\ny=y.reset_index()\nm=len(y)\ni=0\nspecie=0\n#创建文件保存位置\nscript_dir = os.path.dirname(__file__)\nresults_dir = os.path.join(script_dir, '负2/')\nif not os.path.isdir(results_dir):\n os.makedirs(results_dir)\n#遍历上述操作找到的所有化合物类，分别绘制图谱\nwhile i<m:\n specie=y.loc[i,'class']\n x=df[df['class']==specie]\n x['normalized']=x['intensity']/x['intensity'].sum()\n while x['normalized'].max()<100:\n x['normalized']=2*x['normalized']\n #分别绘图\n plt.figure(i)\n #设置图片格式\n font = {'family' : 'serif', \n 'color' : 'black', \n 'weight' : 'normal', \n 'size' : 14, \n } \n plt.axis([0,60,0,16])\n plt.xlabel(\"Carbon Number\",fontdict=font)\n plt.ylabel(\"DBE\",fontdict=font)\n plt.text(1,14,s=specie,fontdict=font)\n plt.scatter(x['C'],x['DBE'],s=x['normalized'],alpha=0.8,edgecolors='white')\n sample_file_name = specie\n #保存图片\n plt.savefig(results_dir + sample_file_name,dpi=600)\n i=i+1\n", "sub_path": "气泡图/bubble.py", "file_name": "bubble.py", "file_ext": "py", "file_size_in_byte": 1667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_excel", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} {"seq_id": "627666649", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.datasets import mnist\nimport tensorflow as tf\ngpus = tf.config.experimental.list_physical_devixe('GPU')\nif gpus :\n try:\n tf.config.experimental.set_visible_devices(gpus[1],'GPU')\n except RuntimeError as e :\n print(e)\n\n(x_train, y_train),(x_test, y_test) = mnist.load_data()\n\nprint(x_train.shape, y_train.shape)#(60000, 28, 28) (60000,)\nprint(x_test.shape, y_test.shape)#(10000, 28, 28) (10000,)\n\nprint(x_train[0])\nprint('y_train[0] : ', y_train[0])\nprint(x_train[0].shape)#(28, 28)\n\ny_test1=y_test\nx_train = x_train.reshape(60000,28,28,1)/255.\nx_test = x_test.reshape(10000,28,28,1)/255.\n\n\n#OnehotEncoding\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nencoder = OneHotEncoder()\ny_train = encoder.fit_transform(y_train.reshape(-1,1)).toarray()\ny_test = encoder.fit_transform(y_test.reshape(-1,1)).toarray()\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\nfrom tensorflow.keras.layers import BatchNormalization, Activation\nfrom tensorflow.keras.regularizers import l1, l2, l1_l2\nmodel = Sequential()\nmodel.add(Conv2D(filters=64, kernel_size=(2,2), padding='same', strides=1, input_shape=(28,28,1)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\n\nmodel.add(Conv2D(32,(2,2), kernel_initializer='he_normal'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu')) # relu 계열 - he_normal sigmoid,softmax계열 - xavier\n\nmodel.add(Conv2D(32,(2,2), kernel_regularizer=l1(l1=0.01)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(32,(2,2), strides=2))\nmodel.add(MaxPool2D(pool_size=2))\n\nmodel.add(Flatten())\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\n\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nes = EarlyStopping(monitor='val loss', patience=5)\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\nhist = model.fit(x_train, y_train, batch_size=128, epochs=5, validation_split=0.2, callbacks=[es])\n\nloss, acc = model.evaluate(x_test, y_test, batch_size=200)\nprint('loss : ', loss)\nprint('accuracy : ', acc)\n\n# loss : 0.09585337340831757\n# accuracy : 0.9824000000953674", "sub_path": "keras2/keras68_mnist2_gpu_device.py", "file_name": "keras68_mnist2_gpu_device.py", "file_ext": "py", "file_size_in_byte": 2272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tensorflow.config.experimental.list_physical_devixe", "line_number": 5, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.set_visible_devices", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist", "line_number": 12, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l1", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPool2D", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 56, "usage_type": "call"}]} {"seq_id": "612562639", "text": "import addressify\nimport unittest\n\nfrom parameterized import parameterized\n\n\nclass AddressifyTest(unittest.TestCase):\n\n @parameterized.expand([\n (\"none argument\", None, AssertionError),\n (\"empty argument\", \"\", AssertionError),\n (\"single word address\", \"MyAddress1\", AssertionError),\n (\"address without number\", \"Street street\", Exception),\n (\"address without street\", \"18 19\", Exception),\n (\"address with more than 5 components\", \"abyss street devoid of end no. 555\", AssertionError)\n ])\n def test_invalid_address(self, name, input_data, expected_result):\n with self.assertRaises(expected_result):\n addressify.parse(input_data)\n\n @parameterized.expand([\n (\"common german address\", \"Winterallee 3\", {\n \"street\": \"Winterallee\",\n \"housenumber\": \"3\"\n }),\n (\"german address with letter on number\", \"Blaufeldweg 123B\", {\n \"street\": \"Blaufeldweg\",\n \"housenumber\": \"123B\"\n }),\n (\"german adress with multiple words\", \"Am Bächle 23\", {\n \"street\": \"Am Bächle\",\n \"housenumber\": \"23\"\n }),\n (\"german address with multiple words and number with letter already spaced\", \"Auf der Vogelwiese 23 b\", {\n \"street\": \"Auf der Vogelwiese\",\n \"housenumber\": \"23 b\"\n }),\n (\"french address\", \"4, rue de la revolution\", {\n \"street\": \"rue de la revolution\",\n \"housenumber\": \"4\"\n }),\n (\"north american address\", \"200 Broadway Av\", {\n \"street\": \"Broadway Av\",\n \"housenumber\": \"200\"\n }),\n (\"spanish address\", \"Calle Aduana, 29\", {\n \"street\": \"Calle Aduana\",\n \"housenumber\": \"29\"\n }),\n (\"spanish address name has number on it\", \"Calle 39 No 1540\", {\n \"street\": \"Calle 39\",\n \"housenumber\": \"No 1540\"\n }),\n (\"made up address starting with number using letter\", \"574C, obliteration street\", {\n \"street\": \"obliteration street\",\n \"housenumber\": \"574C\"\n }),\n (\"chinese address using number abbreviation with period\", \"NO. 36 BAOSHAN JIUCUN\", {\n \"street\": \"BAOSHAN JIUCUN\",\n \"housenumber\": \"NO. 36\"\n }),\n (\"made up address with exactly 5 components\", \"forsaken abyss street no. 555\", {\n \"street\": \"forsaken abyss street\",\n \"housenumber\": \"no. 555\"\n })\n ])\n def test_parse(self, name, input_data, expected_result):\n result = addressify.parse(input_data)\n self.assertDictEqual(expected_result, result)\n\n @parameterized.expand([\n (\"north american addresss\", \"200 doomsday street\", True),\n (\"spanish address\", \"Calle del L'via L'Viaquez 3\", False),\n (\"german address\", \"Am Bächle 23\", False),\n (\"chinese address\", \" NO. 36 BAOSHAN JIUCUN\", True)\n ])\n def test_guess_if_address_starts_with_number(self, name, input_data, expected_result):\n test_input = input_data.split()\n result = addressify.does_it_starts_with_number(test_input)\n self.assertEqual(expected_result, result)\n\n @parameterized.expand([\n (\"abbreviation with period\", \"nO.\", True),\n (\"abbreviation without period\", \"no\", True),\n (\"no abbreviation\", \"on\", False),\n (\"unclear abbreviation\", \"-No.\", False)\n ])\n def test_is_number_abbreviation(self, name, input_data, expected_result):\n result = addressify.is_number_prefix(input_data)\n self.assertEqual(expected_result, result)\n\n @parameterized.expand([\n (\"only digits\", \"555\", True),\n (\"digits with letter\", \"1548B\", True),\n (\"digits with comma\", \"1548B,\", True),\n (\"no digits\", \"true\", False)\n ])\n def test_internal_has_digit(self, name, input_data, expected_result):\n result = addressify.has_digit(input_data)\n self.assertEqual(expected_result, result)\n", "sub_path": "test_addressify.py", "file_name": "test_addressify.py", "file_ext": "py", "file_size_in_byte": 3972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "attribute"}, {"api_name": "addressify.parse", "line_number": 19, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 9, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 9, "usage_type": "name"}, {"api_name": "addressify.parse", "line_number": 68, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 21, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 21, "usage_type": "name"}, {"api_name": "addressify.does_it_starts_with_number", "line_number": 79, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 71, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 71, "usage_type": "name"}, {"api_name": "addressify.is_number_prefix", "line_number": 89, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 82, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 82, "usage_type": "name"}, {"api_name": "addressify.has_digit", "line_number": 99, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 92, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 92, "usage_type": "name"}]} {"seq_id": "4713688", "text": "import lxml.html\nimport datetime\nimport dateutil.relativedelta\nimport logging\nimport requests\nimport os\nimport tarfile\nfrom io import BytesIO\n\n\nclass WikipediaLogDownloader:\n\n def __init__(self, folder=\"\"):\n self.folder = folder\n if not os.path.exists(folder):\n os.makedirs(folder)\n self.log = logging.getLogger(__name__)\n logging.getLogger(\"requests\").setLevel(logging.WARNING)\n\n def download_file(self, file_url, file_date, hour, tgz=None):\n r = requests.get(file_url)\n if r.status_code != 200:\n self.log.error(\"Can't get file %s\", file_url)\n return\n\n if self.folder != \"\":\n folder = self.folder + \"/\"\n else:\n folder = self.folder\n\n\n if tgz is None:\n try:\n with open(folder + str(file_date) +\n \"_\" + hour + \".log\", \"w\") as f:\n f.write(r.text)\n except:\n self.log.error(\"Error for file \" + file_url)\n with open(folder + \"error_log.txt\", \"a\") as ef:\n ef.write(\"Error for file \" + file_url)\n return\n else:\n try:\n info = tarfile.TarInfo(str(file_date) + \"_\" + hour + \".log\")\n info.size = len(r.content)\n tgz.addfile(info, BytesIO(bytes(r.content)))\n except:\n self.log.error(\"Error for file \" + file_url)\n with open(folder + \"error_log.txt\", \"a\") as ef:\n ef.write(\"Error for file \" + file_url)\n return\n\n def obsolete_download_month(self, current_date, start_date, end_date):\n '''Downloads all the files for the month of current_date as long as they are\n before end_date. Writes all of them in a single .tgz file.\n All dates are datetime.date objects.\n '''\n base_url = 'http://dumps.wikimedia.org/other/pagecounts-raw'\n\n year = str(current_date.year)\n month = \"{:02d}\".format(current_date.month)\n\n index_url = base_url + '/' + year + '/' + year + \"-\" + month\n\n dom = lxml.html.fromstring(requests.get(index_url).text)\n\n tgz = tarfile.open(self.folder + \"/\" +\n datetime.date(int(year), int(month)) +\n \".tgz\", mode=\"w:bz2\")\n for link in dom.xpath('//a/@href'):\n # select the url in href for all a tags\n if (link.startswith('projectcounts')):\n hour = link[-6:-4]\n day = link[-9:-7]\n file_url = index_url + '/' + link\n file_date = datetime.date(int(year), int(month), int(day))\n if start_date <= file_date < end_date:\n self.download_file(file_url, file_date, hour, tgz=tgz)\n tgz.close()\n\n def download_month(self, year, month):\n '''Downloads all the files for the month of current_date as long as they are\n before end_date. Writes all of them in a single .tgz file.\n All dates are datetime.date objects.\n '''\n base_url = 'http://dumps.wikimedia.org/other/pagecounts-raw'\n\n start_date = datetime.date(year, month, 1)\n end_date = start_date + dateutil.relativedelta.relativedelta(months=+1)\n year = str(year)\n month = \"{:02d}\".format(month)\n\n index_url = base_url + '/' + year + '/' + year + \"-\" + month\n\n dom = lxml.html.fromstring(requests.get(index_url).text)\n\n tgz = tarfile.open(\"{}/{}-{}.tbz\".format(self.folder, year, month),\n mode=\"w:bz2\")\n for link in dom.xpath('//a/@href'):\n # select the url in href for all a tags\n if (link.startswith('projectcounts')):\n hour = link[-6:-4]\n day = link[-9:-7]\n file_url = index_url + '/' + link\n file_date = datetime.date(int(year), int(month), int(day))\n if start_date <= file_date < end_date:\n self.download_file(file_url, file_date, hour, tgz=tgz)\n tgz.close()\n\n def download(self, start_year, start_month, start_day,\n number_of_days, folder=\"\"):\n start_date = datetime.date(start_year, start_month, start_day)\n end_date = start_date + datetime.timedelta(days=number_of_days)\n\n self.folder = folder\n\n # Iterate over the months, as there is one index page per month\n current_date = start_date\n while current_date < end_date:\n self.download_month(current_date, start_date, end_date)\n current_date = (current_date +\n dateutil.relativedelta.relativedelta(months=+1))\n", "sub_path": "src/data/wikipedia_download.py", "file_name": "wikipedia_download.py", "file_ext": "py", "file_size_in_byte": 4719, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 18, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "tarfile.TarInfo", "line_number": 44, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 46, "usage_type": "call"}, {"api_name": "lxml.html.html.fromstring", "line_number": 65, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 65, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 65, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 65, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 88, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta.relativedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 89, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta", "line_number": 89, "usage_type": "name"}, {"api_name": "lxml.html.html.fromstring", "line_number": 95, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 95, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 95, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 113, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta.relativedelta", "line_number": 122, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 122, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta", "line_number": 122, "usage_type": "name"}]} {"seq_id": "21723198", "text": "from angular_flask import app\nimport influxdb\nfrom influxdb import InfluxDBClient\n\nclass ConnectInflux():\n\tdef __init__(self):\n\t\tself.client = InfluxDBClient('influxdb-prod.juspay.in',8086,'realtime-user', 'MgqEK,nWx2bV','godel-realtime')\n\t\n\t\n\tdef acsMetrics(self):\n\t\tquery1 = \"select count(distinct(session_id)) as sess_count from \\\n\t\t\t\tpayment_details_data where time > now() - 5m and session_id<>'null' and \\\n\t\t\t\tbank<>'null' and app_name<>'null' group by \\\n\t\t\t\tacs_hash\"\n\t\tquery2 = \"select count(distinct(session_id)) from \\\n\t\t\t\tpayment_details_data where time > now() - 5m and session_id<>'null' and \\\n\t\t\t\tbank<>'null' and app_name<>'null' and payment_status='SUCCESS' group by \\\n\t\t\t\tacs_hash\"\n\t\tquery3 = \"select count(distinct(session_id)) ,sum(potential_payment_flow_error) \\\n\t\t\t\t as pay_error,sum(user_error) as user_error ,sum(godel_exception) as exp,\\\n\t\t\t\t sum(acs_error) as acs_error from session_aggregates where time> now() - 5m \\\n\t\t\t\t and session_id<>'null' and bank<>'null' and app_name<>'null' group by acs_hash\"\n\t\tresult1 = self.client.query(query1)\n\t\tresult2 = self.client.query(query2)\t\n\t\tresult3 = self.client.query(query3)\t\n\t\t\n\t\tjoin_list = {}\n\t\tfor i in result1[0]['points']:\n\t\t\tjoin_list[i[2]] = [i[1],0,0,0,0,0] #l-4 :u'pay_error', u'user_error', u'exp', u'acs_error', u'acs_hash'\n\n\t\tfor i in result2[0]['points']:\n\t\t\tif i[2] in join_list:\n\t\t\t\tjoin_list[i[2]][1] = i[1]\n\t\t\telse:\n\t\t\t\tjoin_list[i[2]] = [0,i[1],0,0,0,0]\n\n\t\tfor i in result3[0]['points']:\n\t\t\tif i[6] in join_list:\n\t\t\t\tjoin_list[i[6]][2] = i[2]\n\t\t\t\tjoin_list[i[6]][3] = i[3]\n\t\t\t\tjoin_list[i[6]][4] = i[4]\n\t\t\t\tjoin_list[i[6]][5] = i[5]\n\t\t\telse:\n\t\t\t\tjoin_list[i[6]] = [0,0,i[2],i[3],i[4],i[5]]\n\n\t\tres = {'columns':['acs_hash','tot_sess_count','success_count','success_rate','potential_error', 'user_error', 'godel_exception', 'acs_error'],'rows':[]}\n\t\tmap_column = {}\n\t\tfor i in join_list:\n\t\t\trow = []\n\t\t\trow.append(i)\n\t\t\trow.append(join_list[i][0])\n\t\t\trow.append(join_list[i][1])\n\t\t\tif join_list[i][0]==0:\n\t\t\t\trow.append('not-def')\n\t\t\telse:\n\t\t\t\trow.append(round(float(join_list[i][1])/float(join_list[i][0])*100,2))\n\t\t\trow.append(join_list[i][2])\n\t\t\trow.append(join_list[i][3])\n\t\t\trow.append(join_list[i][4])\n\t\t\trow.append(join_list[i][5])\n\t\t\tres['rows'].append(row)\n\n\t\treturn res\n\n\tdef liveSessionStream(self):\n\t\tquery = \"select session_id, acs_hash, bank, name, numscreens, numevents, acs_error,\\\n\t\t\t\t potential_payment_flow_error,user_error,godel_exception from \\\n\t\t\t\t internal_session_aggregates where time > now() - 5m\"\n\t\t\n\t\tres = {'columns':[],'rows':[]}\n\t\tresult = self.client.query(query)\n\t\tcolumns=['session_id','acs_hash','bank','name' ,'numscreens','acs_error', 'potential_payment_flow_error','user_error','godel_exception']\n\t\tmap_list = {}\n\t\t\n\t\tfor i,j in enumerate(result[0]['columns']):\n\t\t\tmap_list[j] =i \n\n\t\tfor i in result[0]['points']:\n\t\t\tarr = []\n\t\t\tfor j in columns:\n\t\t\t\tarr.append(i[map_list[j]])\n\t\t\tres['rows'].append(arr)\n\n\t\tres['columns'] = columns\n\t\treturn res\n", "sub_path": "angular_flask/client/influx/connect.py", "file_name": "connect.py", "file_ext": "py", "file_size_in_byte": 2974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "influxdb.InfluxDBClient", "line_number": 7, "usage_type": "call"}]} {"seq_id": "411885137", "text": "import requests\nimport re\nfrom requests.exceptions import RequestException\nimport json\nfrom multiprocessing import Pool\nimport pymongo\n\nclient = pymongo.MongoClient('localhost')\ndb = client['maoyan']\n\ndef get_html(url):\n headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 \\\n (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36'}\n try:\n response = requests.get(url, headers=headers)\n response.raise_for_status()\n return response.text\n except RequestException:\n print('Failed to connect')\n\ndef parse_html(html):\n pattern = re.compile('<dd>.*?board-index.*?>(\\d+)</i>.*?title=\"(.*?)\".*?data-src=\"(.*?)\".*?star\">'\n +'(.*?)</p>.*?releasetime\">(.*?)</p>.*?integer\">(.*?)</i>.*?fraction\">(.*?)</i></p>', re.S)\n items = re.findall(pattern, html)\n for item in items:\n yield {\n 'index': item[0],\n 'title': item[1],\n 'image': item[2],\n 'actor': item[3].strip()[3:],\n 'time': item[4].strip()[5:],\n 'score': item[5]+item[6]\n }\n\ndef save_to_file(content):\n with open('movie100.txt', 'a', encoding='utf-8') as f:\n f.write(json.dumps(content, ensure_ascii=False) + '\\n')\n f.close()\n\ndef main(page):\n url = 'http://maoyan.com/board/4?offset=' + str(page)\n html = get_html(url)\n items = parse_html(html)\n n = 1\n for item in items:\n #save_to_file(item)\n if db[\"maoyan\"].insert(item):\n print(\"第 %d 条保存到数据库成功\" % n)\n n += 1\n \n\nif __name__ == '__main__':\n #pool = Pool()\n #pool.map(main, [i*10 for i in range(10)])\n for i in range(10):\n main(i * 10)\n \n ", "sub_path": "Python/spider/maoyan.py", "file_name": "maoyan.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pymongo.MongoClient", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.exceptions.RequestException", "line_number": 18, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.S", "line_number": 23, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}]} {"seq_id": "357438877", "text": "from django.forms import Form, ModelForm\nfrom django import forms\nfrom todo_maker.models import Task\nfrom django.utils.safestring import mark_safe\n\nclass TaskCreateForm(ModelForm):\n id = forms.CharField(required=False)\n task_accomplished = forms.ChoiceField(\n widget = forms.Select(attrs={'class': \"form-control\"}),\n choices=[(0, \"Task Pending\"), (1, \"Task Accomplished\")],\n )\n class Meta:\n model = Task\n fields = (\"id\", \"title\", \"description\", \"task_accomplished\",)\n labels = {\n \"title\" : mark_safe(\"Task Title {}\".format(\"<span class='required' >*</span>\")),\n \"task_accomplished\" : mark_safe(\"Task accomplished {}\".format(\"<span class='required' >*</span>\")),\n }\n widgets = {\n \"title\" : forms.TextInput(attrs={'class' : 'form-control'}),\n \"description\" : forms.Textarea(attrs={'class' : 'form-control', 'rows' : 2}),\n }\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n", "sub_path": "todo_maker/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "todo_maker.models.Task", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}]} {"seq_id": "112868857", "text": "import calendar\nfrom datetime import date\n\n\n_weekday_to_index = {\n 'Monday': calendar.MONDAY,\n 'Tuesday': calendar.TUESDAY,\n 'Wednesday': calendar.WEDNESDAY,\n 'Thursday': calendar.THURSDAY,\n 'Friday': calendar.FRIDAY,\n 'Saturday': calendar.SATURDAY,\n 'Sunday': calendar.SUNDAY,\n}\n\n\n_nth_map = {\n '1st': lambda days: days[0][0],\n 'first': lambda days: days[0][0],\n '2nd': lambda days: days[1][0],\n 'second': lambda days: days[1][0],\n '3rd': lambda days: days[2][0],\n 'third': lambda days: days[2][0],\n '4th': lambda days: days[3][0],\n 'fourth': lambda days: days[3][0],\n 'last': lambda days: days[-1:][0][0],\n 'teenth': lambda days: list(\n filter(lambda d: d[0] >= 13 and d[0] <= 19,\n days))[0][0]\n}\n\n\ndef nth_to_index(nth: str, days: list) -> int:\n get_index = _nth_map.get(nth)\n\n if get_index:\n return get_index(days)\n else:\n raise ValueError(\"%s is not an index in %s\" %\n (nth, sorted(_nth_map.keys())))\n\n\ndef meetup_day(year: int, month: int, day: str, nth: str) -> date:\n cal = calendar.Calendar()\n\n days = [(md, wd) for md, wd in cal.itermonthdays2(year, month)\n if _weekday_to_index[day] == wd and md != 0]\n\n target_day = nth_to_index(nth, days)\n\n return date(year, month, target_day)\n", "sub_path": "all_data/exercism_data/python/meetup/ab3c019b55b74ee6a79019065acce6ff.py", "file_name": "ab3c019b55b74ee6a79019065acce6ff.py", "file_ext": "py", "file_size_in_byte": 1334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "calendar.MONDAY", "line_number": 6, "usage_type": "attribute"}, {"api_name": "calendar.TUESDAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "calendar.WEDNESDAY", "line_number": 8, "usage_type": "attribute"}, {"api_name": "calendar.THURSDAY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "calendar.FRIDAY", "line_number": 10, "usage_type": "attribute"}, {"api_name": "calendar.SATURDAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "calendar.SUNDAY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "calendar.Calendar", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 42, "usage_type": "name"}]} {"seq_id": "460541598", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.4 (3310)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/lib64/python3.4/site-packages/PyIRC/extensions/altnick.py\n# Compiled at: 2015-10-08 05:15:23\n# Size of source mod 2**32: 3758 bytes\n\"\"\"Some alternate nick handlers.\n\nThis contains an underscore-appending handler and a number-substituting\n(leetifying) handler.\n\n\"\"\"\nfrom logging import getLogger\nfrom taillight.signal import SignalStop\nfrom PyIRC.signal import event\nfrom PyIRC.numerics import Numerics\nfrom PyIRC.extensions import BaseExtension\n_logger = getLogger(__name__)\n\nclass UnderscoreAlt(BaseExtension):\n __doc__ = 'This class attempts to append underscores to the nickname.\\n\\n If :py:class:`~PyIRC.extensions.ISupport` is present, it will try until\\n the maximum nick length is reached; otherwise, it will try 5 times.\\n\\n '\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.attempt_nick = self.nick\n self.attempts = 0\n\n @event('commands', Numerics.ERR_NICKNAMEINUSE, priority=-1000)\n @event('commands', Numerics.ERR_ERRONEOUSNICKNAME, priority=-1000)\n @event('commands', Numerics.ERR_NONICKNAMEGIVEN, priority=-1000)\n def change_nick(self, _, line):\n \"\"\"Try to complete registration with a long _.\"\"\"\n if self.registered:\n raise SignalStop()\n isupport = self.get_extension('ISupport')\n if not isupport:\n if self.attempts_count >= 5:\n return\n elif len(self.attempt_nick) == isupport.get('NICKLEN'):\n return\n self.attempt_nick += '_'\n self.attempts += 1\n self.send('NICK', [self.attempt_nick])\n raise SignalStop()\n\n\nclass NumberSubstitueAlt(BaseExtension):\n __doc__ = 'This class attempts to substitute letters for numbers and vis versa.\\n\\n This extension will try until all opportunities for leetifying have been\\n exhausted.\\n\\n '\n leetmap = {'A': '4', \n 'a': '4', \n 'B': '8', \n 'E': '3', \n 'e': '3', \n 'G': '6', \n 'g': '9', \n 'I': '1', \n 'i': '1', \n 'O': '0', \n 'o': '0', \n 'S': '5', \n 's': '5', \n 'T': '7', \n 't': '7', \n '`': '\\\\'}\n unleetmap = {v:k for k, v in leetmap.items()}\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.attempt_nick = self.nick\n self.index = 0\n\n @event('commands', Numerics.ERR_NICKNAMEINUSE, priority=-1000)\n @event('commands', Numerics.ERR_ERRONEOUSNICKNAME, priority=-1000)\n @event('commands', Numerics.ERR_NONICKNAMEGIVEN, priority=-1000)\n def change_nick(self, _, line):\n \"\"\"Try to complete registration by being a 1337 h4x0r.\"\"\"\n if self.registered:\n raise SignalStop()\n while self.index < len(self.attempt_nick):\n char = self.attempt_nick[self.index]\n if self.index > 0 and char in self.leetmap:\n char = self.leetmap[char]\n else:\n if char in self.unleetmap:\n char = self.unleetmap[char]\n else:\n self.index += 1\n continue\n self.attempt_nick = self.attempt_nick[:self.index] + char + self.attempt_nick[self.index + 1:]\n self.send('NICK', [self.attempt_nick])\n self.index += 1\n raise SignalStop()", "sub_path": "pycfiles/PyIRC-3.0b1.linux-x86_64.tar/altnick.cpython-34.py", "file_name": "altnick.cpython-34.py", "file_ext": "py", "file_size_in_byte": 3447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "PyIRC.extensions.BaseExtension", "line_number": 21, "usage_type": "name"}, {"api_name": "taillight.signal.SignalStop", "line_number": 35, "usage_type": "call"}, {"api_name": "taillight.signal.SignalStop", "line_number": 45, "usage_type": "call"}, {"api_name": "PyIRC.signal.event", "line_number": 29, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_NICKNAMEINUSE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 29, "usage_type": "name"}, {"api_name": "PyIRC.signal.event", "line_number": 30, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_ERRONEOUSNICKNAME", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 30, "usage_type": "name"}, {"api_name": "PyIRC.signal.event", "line_number": 31, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_NONICKNAMEGIVEN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 31, "usage_type": "name"}, {"api_name": "PyIRC.extensions.BaseExtension", "line_number": 48, "usage_type": "name"}, {"api_name": "taillight.signal.SignalStop", "line_number": 79, "usage_type": "call"}, {"api_name": "taillight.signal.SignalStop", "line_number": 93, "usage_type": "call"}, {"api_name": "PyIRC.signal.event", "line_number": 73, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_NICKNAMEINUSE", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 73, "usage_type": "name"}, {"api_name": "PyIRC.signal.event", "line_number": 74, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_ERRONEOUSNICKNAME", "line_number": 74, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 74, "usage_type": "name"}, {"api_name": "PyIRC.signal.event", "line_number": 75, "usage_type": "call"}, {"api_name": "PyIRC.numerics.Numerics.ERR_NONICKNAMEGIVEN", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PyIRC.numerics.Numerics", "line_number": 75, "usage_type": "name"}]} {"seq_id": "61961281", "text": "# !/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 31 16:23:13 2018\n\n@author: samschott\n\"\"\"\n# system imports\nimport os.path as osp\nimport time\nfrom distutils.version import LooseVersion\n\n# external packages\nfrom PyQt5 import QtGui, QtCore, QtWidgets, uic\n\n# maestral modules\nfrom maestral import __version__, __author__, __url__\nfrom maestral.utils.appdirs import get_home_dir\nfrom maestral.utils.notify import FILECHANGE, SYNCISSUE\nfrom maestral.utils.autostart import AutoStart\n\n# local imports\nfrom .selective_sync_dialog import SelectiveSyncDialog\nfrom .resources import (get_native_item_icon, UNLINK_DIALOG_PATH,\n SETTINGS_WINDOW_PATH, APP_ICON_PATH, FACEHOLDER_PATH)\nfrom .utils import (\n UserDialog,\n get_scaled_font, isDarkWindow, center_window,\n LINE_COLOR_DARK, LINE_COLOR_LIGHT,\n icon_to_pixmap, get_masked_image, MaestralBackgroundTask\n)\n\n\nNEW_QT = LooseVersion(QtCore.QT_VERSION_STR) >= LooseVersion('5.11')\n\n\nclass UnlinkDialog(QtWidgets.QDialog):\n\n # noinspection PyArgumentList\n def __init__(self, mdbx, restart_func, parent=None):\n super().__init__(parent=parent)\n # load user interface layout from .ui file\n uic.loadUi(UNLINK_DIALOG_PATH, self)\n\n self.setWindowFlags(QtCore.Qt.Sheet)\n self.setModal(True)\n\n self.restart_func = restart_func\n self.mdbx = mdbx\n\n self.buttonBox.buttons()[0].setText('Unlink')\n self.titleLabel.setFont(get_scaled_font(bold=True))\n self.infoLabel.setFont(get_scaled_font(scaling=0.9))\n\n icon = QtGui.QIcon(APP_ICON_PATH)\n pixmap = icon_to_pixmap(icon, self.iconLabel.width(), self.iconLabel.height())\n self.iconLabel.setPixmap(pixmap)\n\n def accept(self):\n\n self.buttonBox.setEnabled(False)\n self.progressIndicator.startAnimation()\n self.unlink_thread = MaestralBackgroundTask(self, self.mdbx.config_name, 'unlink')\n self.unlink_thread.sig_done.connect(self.restart_func)\n\n\n# noinspection PyArgumentList\nclass SettingsWindow(QtWidgets.QWidget):\n \"\"\"A widget showing all of Maestral's settings.\"\"\"\n\n _update_interval_mapping = {0: 60*60*24, 1: 60*60*24*7, 2: 60*60*24*30, 3: 0}\n\n def __init__(self, parent, mdbx):\n super().__init__()\n uic.loadUi(SETTINGS_WINDOW_PATH, self)\n self.setWindowFlags(self.windowFlags() | QtCore.Qt.WindowStaysOnTopHint)\n self._parent = parent\n self.update_dark_mode()\n\n self.adjustSize()\n\n self.mdbx = mdbx\n self.selective_sync_dialog = SelectiveSyncDialog(self.mdbx, parent=self)\n self.unlink_dialog = UnlinkDialog(self.mdbx, self._parent.restart, parent=self)\n self.autostart = AutoStart(self.mdbx.config_name, gui=True)\n\n self.labelAccountName.setFont(get_scaled_font(1.5))\n self.labelAccountInfo.setFont(get_scaled_font(0.9))\n self.labelSpaceUsage.setFont(get_scaled_font(0.9))\n\n # fixes sizes of label and profile pic\n self.labelAccountName.setFixedHeight(self.labelAccountName.height())\n self._profile_pic_height = round(self.labelUserProfilePic.height() * 0.65)\n\n self.refresh_gui()\n\n # update profile pic and account info periodically\n self.update_timer = QtCore.QTimer()\n self.update_timer.timeout.connect(self.refresh_gui)\n self.update_timer.start(5000) # every 5 sec\n\n # connect callbacks\n self.pushButtonUnlink.clicked.connect(self.unlink_dialog.exec_)\n self.pushButtonExcludedFolders.clicked.connect(self.selective_sync_dialog.populate_folders_list)\n self.pushButtonExcludedFolders.clicked.connect(self.selective_sync_dialog.open)\n self.checkBoxStartup.stateChanged.connect(self.on_start_on_login_clicked)\n self.checkBoxNotifications.stateChanged.connect(self.on_notifications_clicked)\n self.checkBoxAnalytics.stateChanged.connect(self.on_analytics_clicked)\n self.comboBoxUpdateInterval.currentIndexChanged.connect(\n self.on_combobox_update_interval)\n self.comboBoxDropboxPath.currentIndexChanged.connect(self.on_combobox_path)\n msg = ('Choose a location for your Dropbox. A folder named \"{0}\" will be ' +\n 'created inside the folder you select.'.format(\n self.mdbx.get_conf('main', 'default_dir_name')))\n self.dropbox_folder_dialog = QtWidgets.QFileDialog(self, caption=msg)\n self.dropbox_folder_dialog.setModal(True)\n self.dropbox_folder_dialog.setAcceptMode(QtWidgets.QFileDialog.AcceptOpen)\n self.dropbox_folder_dialog.setFileMode(QtWidgets.QFileDialog.Directory)\n self.dropbox_folder_dialog.setOption(QtWidgets.QFileDialog.ShowDirsOnly, True)\n self.dropbox_folder_dialog.fileSelected.connect(self.on_new_dbx_folder)\n self.dropbox_folder_dialog.rejected.connect(\n lambda: self.comboBoxDropboxPath.setCurrentIndex(0))\n\n center_window(self)\n\n @QtCore.pyqtSlot()\n def refresh_gui(self):\n\n # populate account info\n self.set_profile_pic_from_cache()\n self.set_account_info_from_cache()\n\n # populate sync section\n parent_dir = osp.split(self.mdbx.dropbox_path)[0]\n relative_path = self.rel_path(parent_dir)\n folder_icon = get_native_item_icon(parent_dir)\n\n self.comboBoxDropboxPath.clear()\n self.comboBoxDropboxPath.addItem(folder_icon, relative_path)\n self.comboBoxDropboxPath.insertSeparator(1)\n self.comboBoxDropboxPath.addItem(QtGui.QIcon(), 'Other...')\n\n # populate app section\n self.checkBoxStartup.setChecked(self.autostart.enabled)\n self.checkBoxNotifications.setChecked(self.mdbx.notification_level == FILECHANGE)\n self.checkBoxAnalytics.setChecked(self.mdbx.analytics)\n update_interval = self.mdbx.get_conf('app', 'update_notification_interval')\n closest_key = min(\n self._update_interval_mapping,\n key=lambda x: abs(self._update_interval_mapping[x] - update_interval)\n )\n self.comboBoxUpdateInterval.setCurrentIndex(closest_key)\n\n # populate about section\n year = time.localtime().tm_year\n self.labelVersion.setText(self.labelVersion.text().format(__version__))\n self.labelUrl.setText(self.labelUrl.text().format(__url__))\n self.labelCopyright.setText(self.labelCopyright.text().format(year, __author__))\n\n def set_profile_pic_from_cache(self):\n\n try:\n pixmap = get_masked_image(self.mdbx.account_profile_pic_path, size=self._profile_pic_height)\n except OSError:\n pixmap = get_masked_image(FACEHOLDER_PATH, size=self._profile_pic_height)\n\n self.labelUserProfilePic.setPixmap(pixmap)\n\n def set_account_info_from_cache(self):\n\n acc_display_name = self.mdbx.get_state('account', 'display_name')\n acc_mail = self.mdbx.get_state('account', 'email')\n acc_type = self.mdbx.get_state('account', 'type')\n acc_space_usage = self.mdbx.get_state('account', 'usage')\n acc_space_usage_type = self.mdbx.get_state('account', 'usage_type')\n\n if acc_space_usage_type == 'team':\n acc_space_usage += ' (Team)'\n\n # if the display name is longer than 230 pixels, reduce font-size\n default_font = get_scaled_font(1.5)\n if NEW_QT:\n account_display_name_length = QtGui.QFontMetrics(default_font).horizontalAdvance(acc_display_name)\n else:\n account_display_name_length = QtGui.QFontMetrics(default_font).width(acc_display_name)\n if account_display_name_length > 240:\n font = get_scaled_font(scaling=1.5*240/account_display_name_length)\n self.labelAccountName.setFont(font)\n self.labelAccountName.setText(acc_display_name)\n\n if acc_type != '':\n acc_type_text = ', Dropbox {0}'.format(acc_type.capitalize())\n else:\n acc_type_text = ''\n self.labelAccountInfo.setText(acc_mail + acc_type_text)\n self.labelSpaceUsage.setText(acc_space_usage)\n\n @QtCore.pyqtSlot(int)\n def on_combobox_path(self, idx):\n if idx == 2:\n self.dropbox_folder_dialog.open()\n\n @QtCore.pyqtSlot(int)\n def on_combobox_update_interval(self, idx):\n self.mdbx.set_conf('app', 'update_notification_interval',\n self._update_interval_mapping[idx])\n\n @QtCore.pyqtSlot(str)\n def on_new_dbx_folder(self, new_location):\n\n self.comboBoxDropboxPath.setCurrentIndex(0)\n if not new_location == '':\n\n new_path = osp.join(new_location,\n self.mdbx.get_conf('main', 'default_dir_name'))\n\n try:\n self.mdbx.move_dropbox_directory(new_path)\n except OSError:\n msg = ('Please check if you have permissions to write to the '\n 'selected location.')\n msg_box = UserDialog('Could not create directory', msg, parent=self)\n msg_box.open() # no need to block with exec\n self.mdbx.resume_sync()\n else:\n self.comboBoxDropboxPath.setItemText(0, self.rel_path(new_location))\n self.comboBoxDropboxPath.setItemIcon(0, get_native_item_icon(new_location))\n\n @QtCore.pyqtSlot(int)\n def on_start_on_login_clicked(self, state):\n self.autostart.enabled = state == 2\n\n @QtCore.pyqtSlot(int)\n def on_notifications_clicked(self, state):\n self.mdbx.notification_level = FILECHANGE if state == 2 else SYNCISSUE\n\n @QtCore.pyqtSlot(int)\n def on_analytics_clicked(self, state):\n self.mdbx.analytics = state == 2\n\n @staticmethod\n def rel_path(path):\n \"\"\"\n Returns the path relative to the users directory, or the absolute\n path if not in a user directory.\n \"\"\"\n usr = osp.abspath(osp.join(get_home_dir(), osp.pardir))\n if osp.commonprefix([path, usr]) == usr:\n return osp.relpath(path, usr)\n else:\n return path\n\n def changeEvent(self, QEvent):\n\n if QEvent.type() == QtCore.QEvent.PaletteChange:\n self.update_dark_mode()\n\n def update_dark_mode(self):\n rgb = LINE_COLOR_DARK if isDarkWindow() else LINE_COLOR_LIGHT\n line_style = 'color: rgb({0}, {1}, {2})'.format(*rgb)\n\n self.line0.setStyleSheet(line_style)\n self.line1.setStyleSheet(line_style)\n self.line2.setStyleSheet(line_style)\n", "sub_path": "maestral_qt/settings_window.py", "file_name": "settings_window.py", "file_ext": "py", "file_size_in_byte": 10486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "distutils.version.LooseVersion", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QT_VERSION_STR", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 43, "usage_type": "call"}, {"api_name": "resources.UNLINK_DIALOG_PATH", "line_number": 43, "usage_type": "argument"}, {"api_name": "PyQt5.uic", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 45, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.get_scaled_font", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.get_scaled_font", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 55, "usage_type": "call"}, {"api_name": "resources.APP_ICON_PATH", "line_number": 55, "usage_type": "argument"}, {"api_name": "PyQt5.QtGui", "line_number": 55, "usage_type": "name"}, {"api_name": "utils.icon_to_pixmap", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.MaestralBackgroundTask", "line_number": 63, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 68, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 68, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 75, "usage_type": "call"}, {"api_name": "resources.SETTINGS_WINDOW_PATH", "line_number": 75, "usage_type": "argument"}, {"api_name": "PyQt5.uic", "line_number": 75, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 76, "usage_type": "name"}, {"api_name": "selective_sync_dialog.SelectiveSyncDialog", "line_number": 83, "usage_type": "call"}, {"api_name": "maestral.utils.autostart.AutoStart", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.get_scaled_font", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.get_scaled_font", "line_number": 88, "usage_type": "call"}, {"api_name": "utils.get_scaled_font", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 98, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 115, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 115, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 117, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 117, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 118, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 119, "usage_type": "name"}, {"api_name": "utils.center_window", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "name"}, {"api_name": "resources.get_native_item_icon", "line_number": 136, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 141, "usage_type": "name"}, {"api_name": "maestral.utils.notify.FILECHANGE", "line_number": 145, "usage_type": "name"}, {"api_name": "time.localtime", "line_number": 155, "usage_type": "call"}, {"api_name": "maestral.__version__", "line_number": 156, "usage_type": "argument"}, {"api_name": "maestral.__url__", "line_number": 157, "usage_type": "argument"}, {"api_name": "maestral.__author__", "line_number": 158, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 126, "usage_type": "name"}, {"api_name": "utils.get_masked_image", "line_number": 163, "usage_type": "call"}, {"api_name": "utils.get_masked_image", "line_number": 165, "usage_type": "call"}, {"api_name": "resources.FACEHOLDER_PATH", "line_number": 165, "usage_type": "argument"}, {"api_name": "utils.get_scaled_font", "line_number": 181, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFontMetrics", "line_number": 183, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 183, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFontMetrics", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 185, "usage_type": "name"}, {"api_name": "utils.get_scaled_font", "line_number": 187, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 198, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 198, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 203, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "name"}, {"api_name": "utils.UserDialog", "line_number": 222, "usage_type": "call"}, {"api_name": "resources.get_native_item_icon", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 208, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 208, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 229, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 229, "usage_type": "name"}, {"api_name": "maestral.utils.notify.FILECHANGE", "line_number": 235, "usage_type": "name"}, {"api_name": "maestral.utils.notify.SYNCISSUE", "line_number": 235, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 233, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 233, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 237, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 237, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 247, "usage_type": "call"}, {"api_name": "maestral.utils.appdirs.get_home_dir", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.pardir", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.path.commonprefix", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "name"}, {"api_name": "os.path.relpath", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path", "line_number": 249, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QEvent", "line_number": 255, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 255, "usage_type": "name"}, {"api_name": "utils.isDarkWindow", "line_number": 259, "usage_type": "call"}, {"api_name": "utils.LINE_COLOR_DARK", "line_number": 259, "usage_type": "name"}, {"api_name": "utils.LINE_COLOR_LIGHT", "line_number": 259, "usage_type": "name"}]} {"seq_id": "530814551", "text": "from os import path\n\nimport pytest\nimport datashader as ds\nimport rasterio as rio\n\nBASE_PATH = path.split(__file__)[0]\nDATA_PATH = path.abspath(path.join(BASE_PATH, 'data'))\nTEST_RASTER_PATH = path.join(DATA_PATH, 'world.rgb.tif')\n\nwith rio.open(TEST_RASTER_PATH) as src:\n x_range = (src.bounds.left, src.bounds.right)\n y_range = (src.bounds.bottom, src.bounds.top)\n cvs = ds.Canvas(plot_width=2,\n plot_height=2,\n x_range=x_range,\n y_range=y_range)\n\n\ndef test_raster_aggregate_default():\n with rio.open(TEST_RASTER_PATH) as src:\n agg = cvs.raster(src)\n assert agg is not None\n\n\ndef test_raster_aggregate_nearest():\n with rio.open(TEST_RASTER_PATH) as src:\n agg = cvs.raster(src, resample_method='nearest')\n assert agg is not None\n\n\ndef test_raster_aggregate_with_overviews():\n with rio.open(TEST_RASTER_PATH) as src:\n agg = cvs.raster(src, use_overviews=True)\n assert agg is not None\n\n\ndef test_raster_aggregate_without_overviews():\n with rio.open(TEST_RASTER_PATH) as src:\n agg = cvs.raster(src, use_overviews=False)\n assert agg is not None\n\n\ndef test_out_of_bounds_return_correct_size():\n with rio.open(TEST_RASTER_PATH) as src:\n cvs = ds.Canvas(plot_width=2,\n plot_height=2,\n x_range=[1e10, 1e20],\n y_range=[1e10, 1e20])\n agg = cvs.raster(src)\n assert agg.shape == (2, 2)\n assert agg is not None\n\n\ndef test_partial_extent_returns_correct_size():\n with rio.open(TEST_RASTER_PATH) as src:\n half_width = (src.bounds.right - src.bounds.left) / 2\n half_height = (src.bounds.top - src.bounds.bottom) / 2\n cvs = ds.Canvas(plot_width=512,\n plot_height=256,\n x_range=[src.bounds.left-half_width, src.bounds.left+half_width],\n y_range=[src.bounds.bottom-half_height, src.bounds.bottom+half_height])\n agg = cvs.raster(src)\n assert agg.shape == (256, 512)\n assert agg is not None\n", "sub_path": "datashader/tests/test_raster.py", "file_name": "test_raster.py", "file_ext": "py", "file_size_in_byte": 2119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.split", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "name"}, {"api_name": "rasterio.open", "line_number": 11, "usage_type": "call"}, {"api_name": "datashader.Canvas", "line_number": 14, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 21, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 27, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 33, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 39, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 45, "usage_type": "call"}, {"api_name": "datashader.Canvas", "line_number": 46, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 56, "usage_type": "call"}, {"api_name": "datashader.Canvas", "line_number": 59, "usage_type": "call"}]} {"seq_id": "442667742", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 16 09:24:58 2015\n\"\"\"\n\n# from __future__ import division\nimport matplotlib\nimport pylab as pl\nimport seaborn as sbn\nimport h5py\nimport numpy as np\nfrom collections import deque\nfrom scipy.integrate import cumtrapz\n\nf = h5py.File('/home/nicolas/datasave_validation_papa.h5', 'r')\nlist_grp = list(f.keys())\ncases = []\nfor i, grp in enumerate(list_grp):\n cases.append(grp)\n\n\nfig, axs = pl.subplots(3, 2)\naxs_flat = [item for sublist in axs for item in sublist]\nfor n, case in enumerate(cases):\n x = f[case]['x'].value\n s = f[case]['s'].value\n y = np.linspace(np.min(s), np.max(f[case]['h'] + s), 2000)\n\n shape = y.shape[0], f[case]['x'].shape[0]\n\n h = f[case]['h'].value\n t = f[case]['t'].value\n q = f[case]['q'].value\n L = np.max(x)\n # beta = np.arctan(1/f[case].attrs['Ct'])\n # Re = f[case].attrs['Re']\n # nu = 1E-6\n # g = 9.81\n # hN = (Re * 3. * nu**2 / (g * np.sin(beta)))**(1./3.)\n\n # print(hN)\n\n ax = axs_flat[n]\n index = -1\n h_ = h[index]\n q_ = q[index]\n t_ = t[index]\n u = np.zeros(shape)\n v = np.zeros(shape)\n w = np.zeros(shape)\n\n flag = np.zeros(shape)\n\n for i, x_ in enumerate(x):\n flagsup = np.where(y <= h_[i] + s[i],\n 1, np.nan)\n flaginf = np.where(y >= s[i],\n 1, np.nan)\n for j, y_ in enumerate(y):\n u_loc = (3. * (q_[i] / h_[i]) * ((y_ - s[i]) / h_[i] -\n (1./2) * ((y_ - s[i]) / h_[i])**2))\n u[j, i] = u_loc\n u[:, i]\n flag[:, i] = (flagsup * flaginf)\n v = -cumtrapz(np.gradient(np.nan_to_num(u))[1], y, axis=0, initial=0)\n w = cumtrapz(v, x, initial=0, axis=1)\n\n v *= flag\n u *= flag\n w *= flag\n\n magn = np.sqrt(u**2 + v**2)\n ax.plot(x, (h_ + s), color='black')\n ax.plot(x, s, color='black')\n\n# CS3 = ax.contour(w, 10,\n# colors='black',\n# extent=[x.min(), x.max(), y.min(), y.max()],\n# aspect='auto',\n# linewidths=.8)\n CS = ax.imshow(magn[::-1, ::1],\n cmap='RdGy',\n extent=[x.min(), x.max(), y.min(), y.max()],\n aspect='auto')\n ax.legend(['t = %.0f' % t_])\n ax.set_xlim(0, x[-1])\n ax.set_ylim(-2.5, 3)\n #ax.streamplot(x, y, u, v, density=[1, 1], color='black')\npl.tight_layout\npl.show()\n", "sub_path": "toolscripts/analysis_validation2.py", "file_name": "analysis_validation2.py", "file_ext": "py", "file_size_in_byte": 2427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "h5py.File", "line_number": 15, "usage_type": "call"}, {"api_name": "pylab.subplots", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 58, "usage_type": "attribute"}, {"api_name": "scipy.integrate.cumtrapz", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.integrate.cumtrapz", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "pylab.tight_layout", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pylab.show", "line_number": 90, "usage_type": "call"}]} {"seq_id": "537438872", "text": "#!/usr/bin/env python3\n\n\nimport nltk\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import StratifiedKFold\n\nimport numpy as np\nimport pickle\n\n\n######################################################################\nCLASSIFIERS = ['maxent', 'naive_bayes', 'linear_svm', 'rbf01_svm', 'rbf02_svm']\n\ndef getROC(predictions, target_classes):\n tp, fp, tn, fn = [0] * 4\n for prediction,target_class in zip(predictions, target_classes):\n if (prediction == 1):\n if (target_class == 1):\n tp += 1\n else:\n fp += 1\n else:\n if (target_class < 1):\n tn += 1\n else:\n fn += 1\n \n tpr = tp/(tp + fn)\n fpr = fp/(fp + tn)\n acc = (tp + tn)/(tp + tn + fp + fn)\n return tpr, fpr, acc\n \n \ndef main():\n with open('nltk_featuresets.pickle', 'rb') as f:\n nltk_featuresets = pickle.load(f)\n with open('sklearn_featuresets.pickle', 'rb') as f:\n sklearn_featuresets = pickle.load(f)\n with open('target_classes.pickle', 'rb') as f:\n target_classes = pickle.load(f)\n \n \n trump_tweets_classifiers = []\n trump_tweets_classifiers.append((nltk.MaxentClassifier, \"maxent\"))\n trump_tweets_classifiers.append((nltk.NaiveBayesClassifier, \"naive_bayes\"))\n trump_tweets_classifiers.append((SVC(kernel='linear', probability=True), \"linear_svm\"))\n trump_tweets_classifiers.append((SVC(gamma=0.1), \"rbf01_svm\"))\n trump_tweets_classifiers.append((SVC(gamma=0.05), \"rbf005_svm\"))\n trump_tweets_classifiers.append((SVC(gamma=0.2), \"rbf02_svm\")) \n\n classifier_tpr = []\n classifier_fpr = []\n classifier_acc = []\n num_splits = 5\n cv = StratifiedKFold(n_splits=num_splits)\n for train, test in cv.split(nltk_featuresets, target_classes):\n training_set = [nltk_featuresets[i] for i in list(train)]\n testing_set = [nltk_featuresets[i] for i in list(test)]\n split_tpr = []\n split_fpr = []\n split_acc = []\n for classifier, name in trump_tweets_classifiers:\n if name == \"maxent\":\n trained_classifier = classifier.train(training_set, nltk.classify.MaxentClassifier.ALGORITHMS[0], max_iter=10)\n elif name == \"naive_bayes\":\n trained_classifier = nltk.NaiveBayesClassifier.train(training_set)\n else:\n trained_classifier = classifier.fit(sklearn_featuresets[train], target_classes[train])\n \n if name == \"maxent\" or name == \"naive_bayes\":\n predictions = [trained_classifier.classify(nltk_featuresets[i][0]) for i in list(test)]\n else:\n predictions = trained_classifier.predict(sklearn_featuresets[test])\n \n tpr, fpr, acc = getROC(target_classes[test], predictions[:])\n split_tpr.append(tpr)\n split_fpr.append(fpr)\n split_acc.append(acc)\n classifier_tpr.append(split_tpr)\n classifier_fpr.append(split_fpr)\n classifier_acc.append(split_acc)\n\n\n for i,name in enumerate(CLASSIFIERS):\n print(name + \" tpr: = {}\".format(np.mean([row[i] for row in classifier_tpr])))\n print(name + \" fpr: = {}\".format(np.mean([row[i] for row in classifier_fpr])))\n print(name + \" acc: = {}\".format(np.mean([row[i] for row in classifier_acc])))\n \n \nif __name__ == '__main__':\n main()\n\n ", "sub_path": "test_classifiers.py", "file_name": "test_classifiers.py", "file_ext": "py", "file_size_in_byte": 3440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pickle.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 41, "usage_type": "call"}, {"api_name": "nltk.MaxentClassifier", "line_number": 45, "usage_type": "attribute"}, {"api_name": "nltk.NaiveBayesClassifier", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sklearn.svm.SVC", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 56, "usage_type": "call"}, {"api_name": "nltk.classify", "line_number": 65, "usage_type": "attribute"}, {"api_name": "nltk.NaiveBayesClassifier.train", "line_number": 67, "usage_type": "call"}, {"api_name": "nltk.NaiveBayesClassifier", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 88, "usage_type": "call"}]} {"seq_id": "274787546", "text": "# Opmerkingen:\n# Dit script kan enkel werken als je webcam nog niet in gebruik is door een andere applicatie\n\n\nimport cv2\nimport numpy as np\n\n# Yolo model inladen en klaarzetten voor gebruik\nnet = cv2.dnn.readNet(\"weights/TinyWeightsV4.weights\", \"configs/TinyConfig.cfg\") # weight en configuration file ophalen\nlayer_names = net.getLayerNames()\noutput_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n# Namen van de classes definiëren (volgorde is zeer belangrijk)\nclasses = [\"Jam\", \"Knife\", \"Bread\", \"Choco\"]\ncolors = np.random.uniform(0, 255, size=(len(classes), 3))\n\n# Webcam properties instellen\ncap = cv2.VideoCapture(0) # 0 is je standaard webcam\ncap.set(3, 640) # x breedte van webcam instellen\ncap.set(4, 480) # y hoogte van webcam instellen\n# cap.set(10, 150) #brightness van webcam instellen\n\n\ndef drawboxes(img):\n height, width, channels = img.shape\n\n # Detecting objects\n blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n net.setInput(blob)\n outs = net.forward(output_layers)\n\n class_ids, confidences, boxes = [], [], []\n for out in outs:\n for detection in out:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n if confidence > 0.3:\n center_x = int(detection[0] * width)\n center_y = int(detection[1] * height)\n w = int(detection[2] * width)\n h = int(detection[3] * height)\n\n # Rectangle coordinates\n x = int(center_x - w / 2)\n y = int(center_y - h / 2)\n\n boxes.append([x, y, w, h])\n confidences.append(float(confidence))\n class_ids.append(class_id)\n\n indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n # boxes tekenen\n for i in range(len(boxes)):\n if i in indexes:\n x, y, w, h = boxes[i]\n label = str(classes[int(class_ids[i])])\n color = colors[class_ids[i]]\n cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n cv2.putText(img, label, (x, y + 30), cv2.FONT_HERSHEY_PLAIN, 2, color, 2)\n return img\n\n\nwhile True:\n # Image van webcam inlezen\n success, img = cap.read()\n if not success:\n break\n\n # object detection uitvoeren\n img = drawboxes(img)\n\n # image weergeven\n cv2.imshow(\"frame\", img)\n\n # zorgen dat de image zichtbaar blijft\n # druk op de 'q' knop om de script te stoppen\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\n", "sub_path": "04_YOLO/webcam_object_detection.py", "file_name": "webcam_object_detection.py", "file_ext": "py", "file_size_in_byte": 2624, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.dnn.readNet", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.dnn.NMSBoxes", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 81, "usage_type": "call"}]} {"seq_id": "69744732", "text": "# -*- coding: utf-8 -*-\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nT = []\r\nwith open(\"T_1.csv\",\"rb\") as file:\r\n for line in file:\r\n t = float(line[:-1])\r\n T.append(t)\r\n \r\n#----------------------------------------------------------\r\n\r\n#Ahora, armamos una lista L = [l1,l2,l3,...]\r\n#donde 'li' es una lista li = [ti1,ti2,ti3,...]\r\n#donde 'tij' es el tiempo, en el i-esimo periodo,\r\n#en el que llega el j-esimo foton gamma\r\n\r\ndt = 0.003 #dwell time de 10ms\r\nt = dt\r\nL = []\r\nfor x in T:\r\n if x < t:\r\n try:\r\n l.append(x)\r\n except:\r\n l = [x]\r\n else:\r\n L.append(l)\r\n l = [x]\r\n t += dt\r\n\r\ncounts = [len(l) for l in L]\r\nm = max(counts)+1\r\nH = [counts.count(i) for i in range(1,m)]\r\n\r\nplt.plot(range(1,m),H,marker='s',mfc='r',mec='k',linestyle='none')\r\nplt.grid()\r\nplt.show()\r\n\r\n", "sub_path": "Labo 5/estadistica.py", "file_name": "estadistica.py", "file_ext": "py", "file_size_in_byte": 851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]} {"seq_id": "102272997", "text": "\"\"\"Initial migration\n\nRevision ID: dddf4c7e2d8f\nRevises: \nCreate Date: 2016-11-18 20:41:13.784921\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'dddf4c7e2d8f'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('urls',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('key', sa.String(length=20), nullable=True),\n sa.Column('original_url', sa.Text(), nullable=True),\n sa.Column('created', sa.DateTime(), nullable=True),\n sa.Column('last_visited', sa.DateTime(), nullable=True),\n sa.Column('visits', sa.Integer(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_index(op.f('ix_urls_key'), 'urls', ['key'], unique=True)\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_index(op.f('ix_urls_key'), table_name='urls')\n op.drop_table('urls')\n ### end Alembic commands ###\n", "sub_path": "migrations/versions/dddf4c7e2d8f_initial_migration.py", "file_name": "dddf4c7e2d8f_initial_migration.py", "file_ext": "py", "file_size_in_byte": 1070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.create_index", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.f", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op.drop_index", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 36, "usage_type": "name"}, {"api_name": "alembic.op.f", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 37, "usage_type": "name"}]} {"seq_id": "280433732", "text": "import torch\r\nimport torch.nn as nn\r\nimport random\r\n\r\nfrom Networks import *\r\n\r\nimport sys\r\nsys.path.insert(0, './Tasks')\r\nfrom Tasks.t1 import *\r\nfrom Tasks.t2 import *\r\nfrom Tasks.t3 import *\r\n\r\n\r\n# method that copies the parameters of the value network to the target value network\r\ndef hard_update(target, source):\r\n for target_param, param in zip(target.parameters(), source.parameters()):\r\n target_param.data.copy_(param.data)\r\n\r\n\r\n# main training method which contains the training loop\r\ndef train(idx, value_network, target_value_network, optimizer, lock, counter, max_steps, epsilon_min, task,\r\n num_action, path, alg, discounted, beta, discount_factor, r_seed):\r\n\r\n DF = discount_factor\r\n I_update = 5\r\n I_target = 40000\r\n BETA = beta\r\n SAVE_EVERY = 10**5\r\n crit = nn.MSELoss()\r\n\r\n if task == 1:\r\n Env = task1(r_seed)\r\n if task == 2:\r\n Env = task2(r_seed)\r\n if task == 3:\r\n Env = task3(r_seed)\r\n\r\n f = open(path+'/log'+str(idx), 'w+')\r\n f.write(\"reward, avg\\n\")\r\n f.flush()\r\n\r\n t = 0\r\n steps = 0\r\n avg_reward = 0\r\n state = torch.Tensor([Env.state])\r\n\r\n # training loop\r\n while steps < max_steps:\r\n steps += 1\r\n t += 1\r\n\r\n epsilon = set_epsilon(counter.value, epsilon_min)\r\n # Take action a with ε-greedy policy based on Q(s, a; θ)\r\n action = get_egreedy_action(state, value_network, epsilon, num_action)\r\n\r\n # Receive new state s′ and reward r\r\n newObservation, reward = Env.step(action)\r\n next_state= torch.Tensor([newObservation])\r\n\r\n if (alg == 'SARSA' and steps > 1) or (alg == 'Q') or (alg == 'doubleQ'):\r\n\r\n if alg == 'SARSA':\r\n Q = computePrediction(past_state, past_action, value_network)\r\n Ut = computeTargets_SARSA(past_reward, state, action, DF, target_value_network, discounted,avg_reward)\r\n if alg == 'Q':\r\n Q = computePrediction(state, action, value_network)\r\n Ut = computeTargets_Q(reward, next_state, DF, target_value_network, discounted,avg_reward)\r\n if alg == 'doubleQ':\r\n Q = computePrediction(state, action, value_network)\r\n Ut = computeTargets_doubleQ(reward, next_state, DF, target_value_network, value_network, discounted,\r\n avg_reward)\r\n\r\n # Accumulate gradients wrt θ\r\n loss = crit(Q, Ut)\r\n loss.backward()\r\n avg_reward += float(BETA*(Ut-Q))\r\n\r\n # in this mutex lock we perform hard updates to the target value network, save the value network's\r\n # parameters and also log process. The lock is necessary as for the asynchronous agents to not\r\n # overwrite each other updates\r\n with lock:\r\n counter.value += 1\r\n\r\n # Update the target network θ− ← θ\r\n if counter.value % I_target == 0:\r\n hard_update(target_value_network, value_network)\r\n\r\n if counter.value % SAVE_EVERY == 0:\r\n saveModelNetwork(target_value_network, path+'/params_'+str(int(counter.value/SAVE_EVERY)))\r\n\r\n if counter.value % 100 == 0:\r\n logProgress(counter, SAVE_EVERY, 50)\r\n\r\n # Perform asynchronous update of θ using dθ. Clear gradients dθ ← 0.\r\n if t % I_update == 0:\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n\r\n f.write(\"%.3f, %.2f\\n\" % (reward,avg_reward))\r\n f.flush()\r\n\r\n past_action = action\r\n past_reward = reward\r\n past_state = state\r\n state = next_state\r\n\r\n saveModelNetwork(value_network, path+'/params_last')\r\n return\r\n\r\n\r\n# method which sets and returns epsilon parameter given the specific time-step\r\ndef set_epsilon(frames, min_e):\r\n e = 1 - frames/(4000000/(1-min_e))\r\n return max(min_e, e)\r\n\r\n\r\n# method which performs epsilon-greedy exploration: epsilon probability of following a random action\r\n# and 1-epsilon probability of following the best-rewarding action at this state.\r\ndef get_egreedy_action(observation, q_model, epsilon,num_actions):\r\n random_action= random.random()<epsilon\r\n\r\n if random_action:\r\n action= random.randint(0,num_actions-1)\r\n else:\r\n Qpreds=q_model.forward(observation).detach()\r\n action = Qpreds.argmax()\r\n return action\r\n\r\n\r\n# method which computes and returns target update for the SARSA learning algorithm\r\ndef computeTargets_SARSA(reward, observation, action, discount_factor, target_network, discounted, avg_reward):\r\n q_pred = target_network.forward(observation).detach()\r\n if discounted:\r\n target = reward + discount_factor * q_pred[0][action]\r\n else:\r\n target = reward - avg_reward + q_pred[0][action]\r\n\r\n return target\r\n\r\n\r\n# method which computes and returns the target update for the Q-learning algorithm\r\ndef computeTargets_Q(reward, next_observation, discount_factor, target_network, discounted, avg_reward):\r\n q_pred = target_network.forward(next_observation).detach()\r\n if discounted:\r\n target = reward + discount_factor * q_pred.max()\r\n else:\r\n target = reward - avg_reward + q_pred.max()\r\n return target\r\n\r\n\r\n# method which computes and returns the target update for the double Q-learning algorithm\r\ndef computeTargets_doubleQ(reward, next_observation, discount_factor, target_network, value_network,\r\n discounted, avg_reward):\r\n target_q_pred = target_network.forward(next_observation).detach()\r\n q_pred = value_network.forward(next_observation)[:, torch.argmax(target_q_pred)].detach()\r\n\r\n if discounted:\r\n target = reward + discount_factor * q_pred\r\n else:\r\n target = reward - avg_reward + q_pred\r\n return target\r\n\r\n\r\n# method that returns the state-action value given a state, a value and a value network\r\ndef computePrediction(state, action, value_network):\r\n out = value_network.forward(state)\r\n return out[0][action]\r\n\r\n\r\n# method saving the given model's parameters in the given directory\r\ndef saveModelNetwork(model, str_directory):\r\n torch.save(model.state_dict(), str_directory)\r\n\r\n\r\n# method used to display a progress bar on the terminal\r\ndef logProgress(counter, saveIternval, barLength):\r\n save_iteration = int(counter.value/saveIternval)\r\n percent = counter.value%saveIternval * 100 / saveIternval\r\n makers = '|' * int(percent/100 * barLength - 1)\r\n spaces = ' ' * (barLength - len(makers))\r\n string = 'Parameter %d progress:\\t |%s%s| %d %%' % (save_iteration, makers, spaces, percent)\r\n print(string.ljust(100), end='\\r')\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "Code/Worker.py", "file_name": "Worker.py", "file_ext": "py", "file_size_in_byte": 6710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.insert", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 58, "usage_type": "call"}, {"api_name": "random.random", "line_number": 120, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 172, "usage_type": "call"}]} {"seq_id": "319471336", "text": "#!/usr/bin/python3\n\nimport serial, os, time\n\nnotes = [\"Do\", \"Re\", \"Mi\", \"Fa\", \"Sol\", \"La\"]\n\ntry:\n sequence = [str(notes.index(note)).encode('ascii') for note in os.sys.argv[1:]]\nexcept ValueError:\n print(\"Invalid input\")\n os.sys.exit(1)\n\ntry:\n connection = serial.Serial(\"/dev/ttyS0\", baudrate=38400)\n\n for note in sequence:\n connection.write(note)\n answer = connection.read() # blocking call, waiting for confirmation\n time.sleep(0.3)\n\n print(\"Sequence finished.\") # What gets printed last is seen on the web interface\nexcept:\n print(\"Failed during communication with the device.\")\n", "sub_path": "www/play_sequence.py", "file_name": "play_sequence.py", "file_ext": "py", "file_size_in_byte": 625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.sys", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.sys.exit", "line_number": 11, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 11, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}]} {"seq_id": "419578078", "text": "from django.contrib.gis.geos import fromstr\nfrom rest_framework import filters\n\n\nclass LocationFilterBackend(filters.BaseFilterBackend):\n def filter_queryset(self, request, queryset, view):\n \"\"\"\n Check the query params for a location query. If present,\n filter the queryset based on the values provided, with an\n optional radius given in metres. Otherwise, just return\n the queryset.\n \"\"\"\n location_query = request.query_params.get(\n 'location',\n None\n )\n # Default radius of 2km\n radius_query = request.query_params.get(\n 'radius',\n 2000\n )\n if location_query:\n lat, lng = location_query.split(',')\n reference_location = fromstr(\n 'POINT(%s %s)' % (\n lat,\n lng\n )\n )\n return queryset.filter(\n location__coordinates__distance_lte=(\n reference_location,\n radius_query\n )\n )\n return queryset\n", "sub_path": "meadows/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 1119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "rest_framework.filters.BaseFilterBackend", "line_number": 5, "usage_type": "attribute"}, {"api_name": "rest_framework.filters", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.fromstr", "line_number": 24, "usage_type": "call"}]} {"seq_id": "510824610", "text": "from celery.decorators import task\nfrom testproj.clr import app as celery_app\nfrom PumpManager.models import Song, Chart, Mix\nfrom PumpManager.pumpdb import getSong\nimport logging\n\nlogging.basicConfig(filename='history.log', format='%(levelname)s:%(message)s', level=logging.DEBUG)\n\n@celery_app.task(name = \"upgr_db\")\ndef upgr_db():\n print(\"START UPDATING SONG DB\")\n song = \"default\"\n chart = \"default\"\n for i in range(1, 800):\n song = getSong(i)\n if song:\n try:\n print(song)\n #print(song['name'], song['author'], song['bpm'], song['type'], song['cathegory'])\n s = Song( name=song['name'],\n author = song['author'],\n bpm = int(float(song['bpm'])),\n type = song['type'],\n cathegory = song['cathegory'],\n )\n try:\n s.save()\n except Exception as e:\n logging.error(e)\n logging.error(song)\n\n for mix in song['mixes']:\n print(mix)\n s.mix.add(Mix.objects.get(name = mix))\n s.save()\n\n for chart in song['charts']:\n c = Chart( lvl=chart['lvl'],\n type=chart['type'],\n song=s,\n )\n try:\n c.save()\n except Exception as e:\n logging.error(e)\n logging.error(chart)\n\n except Exception as e:\n logging.warning(\"Cycle\")\n logging.error(song)\n logging.error(chart)\n logging.error(e)\n\n print(\"END UPDATING SONG DB\")\n\nif __name__ == \"__main__\":\n upgr_db()\n", "sub_path": "testproj/PumpManager/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PumpManager.pumpdb.getSong", "line_number": 15, "usage_type": "call"}, {"api_name": "PumpManager.models.Song", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 30, "usage_type": "call"}, {"api_name": "PumpManager.models.Mix.objects.get", "line_number": 34, "usage_type": "call"}, {"api_name": "PumpManager.models.Mix.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PumpManager.models.Mix", "line_number": 34, "usage_type": "name"}, {"api_name": "PumpManager.models.Chart", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 52, "usage_type": "call"}, {"api_name": "testproj.clr.app.task", "line_number": 9, "usage_type": "call"}, {"api_name": "testproj.clr.app", "line_number": 9, "usage_type": "name"}]} {"seq_id": "155723471", "text": "import os\nos.chdir('E:\\\\Master_E\\\\Workspace\\\\bidding_learning') \nimport sys\n#sys.path.append('../src/')\n#import gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom src.agent_ddpg import agent_ddpg\nfrom src.utils import OUNoise, Memory, GaussianNoise\nfrom src.environment_bid_market import EnvironmentBidMarket\n\n\n\n'''\n High-Level Interface that calls learning algorithm and Energy-Market Environment\n subject to user-specified inputs\n\nEnvironment Parameters\n\ncapacities: np.array 1x(number of Agents)\ncosts: np.array 1x(number of Agents)\n\nAttention these np.arrays have to correspond to the number of Agents\n\nDemand: np.array 1x2\nChooses demand from arang between [min,max-1]\nFor fixed Demand, write: the preferred [Number, Number +1] (e.g. Demand = 99 -> [99,100])\n\nAgents: scalar\nNumber of learning agents\n\nRewards: scalar\nType of Reward function. Usually 0.\n\nSplit: binary\nAllow Split Bids\n\npast_action: binary\nAllow agents to learn from all agents past actions\n\nlr_actor: float\nLearning Rate Actor\n\n\nlr_critic: float\nLearning Rate Critic\n\nDiscrete: binary\nEnables Discrete Spaces (Not yet functional)\n'''\n\n\nPOWER_CAPACITIES = [5]\nPRODUCTION_COSTS = [0]\nDEMAND = [15,16]\nLEARNING_RATE_ACTOR = 1e-4\nLEARNING_RATE_CRITIC = 1e-3\nNUMBER_OF_AGENTS = 1\n\nenv = EnvironmentBidMarket(capacities = POWER_CAPACITIES, costs = PRODUCTION_COSTS, demand = DEMAND, agents = NUMBER_OF_AGENTS, \n fringe_player = 1, rewards = 0, split = 0, past_action= 0, \n lr_actor = LEARNING_RATE_ACTOR, lr_critic = LEARNING_RATE_CRITIC, discrete = [0, 10, 0])\n\nagents = env.create_agents(env)\nrewards = []\navg_rewards = []\n# 2 different noise models\n\n# Ohrenstein Ullenbck Noise\n# This is a popular noise in machine learning. \n# It starts with one distribution and then converges to another.\n# Frequently, this is used to explore more in the beginning than in the end of the algorithm.\nnoise = OUNoise(env.action_space, mu=0.0, theta=0.15, max_sigma=0.3, min_sigma=0.3, decay_period=100000)\n\n# Gaussian Noise \n# The standard normal distributed noise with variance sigma scaled to the action spaces size\n#(default: (mean = 0, sigma = 0.1) * action_space_distance)\n#noise = GaussianNoise(env.action_space, mu= 0, sigma = 0.1, regulation_coef= 100, decay_rate = 0.1)\n\n\n\n# Learning continues for a number of episodes, \n# divided into batches consisting of rounds\n# Each episode resets the environment, it consits of rounds\n# After a number of rounds equal to the batch size, the neural networks are updated\ntotal_episodes = 150\nrounds_per_episode = 500\nbatch_size = 128\n\n# Start Learning\n\nfor episode in range(total_episodes):\n state = env.reset()\n noise.reset()\n episode_reward = 0\n \n for step in range(rounds_per_episode):\n actions = []\n for n in range(len(agents)):\n #Neural Network Chooses Action and Adds Noise\n action_temp = agents[n].get_action(state)\n action_temp = noise.get_action(action_temp, episode) \n actions.append(action_temp[:])\n \n actions = np.asarray(actions)\n # Environment delivers output\n new_state, reward, done, _ = env.step(actions) \n \n # Add new experience to memory\n for n in range(len(agents)):\n agents[n].memory.push(state, actions[n], np.array([reward[n]]), new_state, done)\n \n #Update Neural Network\n if len(agents[0].memory) > batch_size:\n for n in range(len(agents)):\n agents[n].update(batch_size)\n \n \n state = new_state\n episode_reward += reward\n\n if done:\n sys.stdout.write(\"***episode: {}, reward: {}, average _reward: {} \\n\".format(episode, np.round(episode_reward, decimals=2), np.mean(rewards[-10:])))\n env.render()\n break\n\n rewards.append(episode_reward)\n avg_rewards.append(np.mean(rewards[-10:]))\n\n\nplt.plot(rewards)\nplt.plot(avg_rewards)\nplt.plot()\nplt.xlabel('Episode')\nplt.ylabel('Reward')\nplt.show()\n", "sub_path": "bin/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4047, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.chdir", "line_number": 2, "usage_type": "call"}, {"api_name": "src.environment_bid_market.EnvironmentBidMarket", "line_number": 61, "usage_type": "call"}, {"api_name": "src.utils.OUNoise", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}]} {"seq_id": "378223353", "text": "from keras.models import Sequential\nfrom keras.layers import Conv2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Flatten\nfrom keras.layers import Dense\n\nclassifier = Sequential()\nclassifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size = (2, 2)))\nclassifier.add(Flatten())\nclassifier.add(Dense(units = 128, activation = 'relu'))\nclassifier.add(Dense(units = 1, activation = 'sigmoid'))\nclassifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n\nfrom keras.preprocessing.image import ImageDataGenerator\ntrain_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True)\ntest_datagen = ImageDataGenerator(rescale = 1./255)\nprint(train_datagen.__doc__)\ntraining_set = train_datagen.flow_from_directory('train',target_size = (64, 64),batch_size = 32,class_mode = 'binary')\ntest_set = test_datagen.flow_from_directory('train',target_size = (64, 64),batch_size = 32,class_mode = 'binary')\nclassifier.fit_generator(training_set,steps_per_epoch = 8,epochs = 1000,validation_data = test_set,validation_steps = 2000)\n\nimport numpy as np\nfrom keras.preprocessing import image\ntest_image = image.load_img('/Users/saranshmittal/Development/Data-Science/Cat classification/train/cat/cat.100.jpg', target_size = (64, 64))\ntest_image = image.img_to_array(test_image)\ntest_image = np.expand_dims(test_image, axis = 0)\nresult = classifier.predict(test_image)\nif result[0][0] == 1:\n prediction = 'dog'\n print(prediction)\nelse:\n prediction = 'cat'\n print(prediction)", "sub_path": "Cat classification/testing.py", "file_name": "testing.py", "file_ext": "py", "file_size_in_byte": 1619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "keras.models.Sequential", "line_number": 7, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 8, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 9, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 10, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 27, "usage_type": "call"}]} {"seq_id": "652804468", "text": "def model_to_excel(model,name=\"model.xlsx\"):\n import openpyxl \n wb = openpyxl.Workbook()\n sheet = wb.get_active_sheet()\n sheet.title = 'Metabolites'\n sheet['A1']=\"Metabolite id\"\n sheet['B1']=\"Metabolite name\"\n sheet['C1']=\"Metabolite compartment\"\n sheet['D1']=\"Metabolite formula\"\n for n, metabolite in enumerate(model.metabolites):\n nrow=n+2\n sheet['A'+str(nrow)]=metabolite.id\n sheet['B'+str(nrow)]=metabolite.name\n sheet['C'+str(nrow)]=metabolite.compartment\n #sheet['D'+str(nrow)]=metabolite.formula.id \n \n sheet=wb.create_sheet(title=\"Reactions\")\n sheet['A1']=\"Reaction id\"\n sheet['B1']=\"Reaction name\"\n sheet['C1']=\"Reaction subsystem\"\n sheet['D1']=\"Reaction\"\n for n, reaction in enumerate(model.reactions):\n nrow=n+2\n sheet['A'+str(nrow)]=reaction.id\n sheet['B'+str(nrow)]=reaction.name\n sheet['C'+str(nrow)]=reaction.subsystem\n sheet['D'+str(nrow)]=reaction.reaction\n \n wb.save(name)\n", "sub_path": "iso2flux/output_functions/model_to_excel.py", "file_name": "model_to_excel.py", "file_ext": "py", "file_size_in_byte": 1019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "openpyxl.Workbook", "line_number": 3, "usage_type": "call"}]} {"seq_id": "302560529", "text": "# _*_ coding : UTF-8 _*_\n# 开发团队 : 柴俊峰\n# 开发人员 ：柴俊峰\n# 开发时间 ：2020/12/7 23:49\n# 文件名称 : 爬虫-快代理ip.py\n# 开发工具 ：PyCharm\n\nimport requests\nimport time\nfrom fake_useragent import UserAgent\nimport json\n# from lxml import etree\nfrom lxml import etree\n\n\nclass SpiderKuaidaili(object):\n def __init__(self):\n ua = UserAgent()\n self.base_url = \"https://www.kuaidaili.com/free/inha/{}\"\n self.headers = {\n 'User-Agent': ua.random\n }\n\n #获取url列表\n def get_url_list(self,n):\n print(\"获取url列表\")\n url_list = []\n for i in range(1,n):\n url = self.base_url.format(i)\n url_list.append(url)\n return url_list\n\n #请求数据\n def getPage(self,url):\n print(\"请求数据\")\n try:\n responses = requests.get(url,headers=self.headers,timeout=5)\n if responses.status_code == 200:\n responses = responses.content.decode('utf8')\n return responses\n except:\n return False\n\n #解析数据\n def parseHTML(self,responses):\n print(\"解析数据\")\n response= etree.HTML(responses)\n resips = response.xpath('//table[@class=\"table table-bordered table-striped\"]//tr/td[1]/text()')\n resports = response.xpath('//table[@class=\"table table-bordered table-striped\"]//tr/td[2]/text()')\n resip = list(zip(resips,resports))\n resip = [{i[0]:i[1]} for i in resip]\n print(resip)\n return resip\n\n\n # resips = []\n # resports = []\n # for resdata in resdatas:\n # resip = resdata.xpath('./td[1]/text()')\n # resips.append(resip)\n # resport = resdata.xpath('./td[2]/text()')\n # resports.append(resport)\n # # okip = list(zip(resip,resport))\n # # return okip\n # print(resips[1:])\n # print(resports[1:])\n # resip = list(zip(resips[1:],resports[1:]))\n # print(resip)\n\n #验证数据\n\n #保存数据\n def save_data(self,data):\n print(\"保存数据\")\n with open('ip文件.json','a+') as f:\n for i in data:\n # print(json.dumps(i))\n f.write(json.dumps(i))\n f.write(\"\\n\")\n f.close()\n\n #统筹数据\n def run(self):\n n = int(input(\"请输入你想要爬取的页数：\"))\n url_list = self.get_url_list(n)\n for url in url_list:\n # print(f\"正在爬取第{i}页\")\n time.sleep(2)\n responses = self.getPage(url)\n okip = self.parseHTML(responses)\n self.save_data(okip)\n\n\nif __name__ == '__main__':\n SpiderKuaidaili().run()", "sub_path": "03python-spider/爬虫-快代理ip.py", "file_name": "爬虫-快代理ip.py", "file_ext": "py", "file_size_in_byte": 2750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "fake_useragent.UserAgent", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 47, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 47, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}]} {"seq_id": "427934433", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport datetime\nimport re\nimport csv\nfrom sortedcontainers import SortedDict\n\nclass Constants:\n\n @staticmethod\n def general_ranking_category():\n return \"general\"\n\n @staticmethod\n def default_ranking_category():\n return Constants.general_ranking_category()\n\n @staticmethod\n def ranking_categories():\n return [\"economia\", \"gobernador\", \"gobierno\", \\\n \"judicial\", \"legislativo\", \"obra.publica\", \\\n \"pavimentacion\", \"presidente\", \"recoleccion.basura\", \\\n \"salud\", \"seguridad\", \"servicio.agua\", \\\n \"servicios\", \"transporte.publico\"]\n\n @staticmethod\n def ranking_categories_in_general_rank():\n return [\"gobernador\", \"legislativo\", \"obra.publica\", \"servicios\", \"economia\", \"seguridad\"]\n\n @staticmethod\n def ranking_combinado_table_name():\n return \"tbl_rank_general\"\n\n @staticmethod\n def ranking_noticias_table_name():\n return \"tbl_rank_news\"\n\n @staticmethod\n def ranking_social_table_name():\n return \"tbl_rank_tw\"\n\n @staticmethod\n def categories_dict():\n cd = [('economia', \"Economía\"),\n ('gobernador', \"Gobernador\"),\n ('gobierno', \"Gobierno\"),\n ('judicial', \"Jueces\"),\n ('legislativo', \"Diputados y Senadores\"),\n ('obra.publica', \"Obra pública\"),\n ('pavimentacion', \"Pavimentación\"),\n ('presidente', \"Presidente\"),\n ('recoleccion.basura', \"Recolección de basura\"),\n ('salud', \"Salud\"),\n ('seguridad', \"Seguridad\"),\n ('servicio.agua', \"Servicio de agua\"),\n ('servicios', \"Servicios\"),\n ('transporte.publico', \"Transporte público\")]\n return cd\n\n @staticmethod\n def states_dict():\n reader = csv.reader(open(\"./utils/formato_estados.csv\"))\n _ = reader.next()\n\n estados = [(row[0], row[1]) for row in reader]\n\n return estados\n\n @staticmethod\n def state_performance_categories_dict():\n cd = [('gobernador', \"Gobernador\"),\n ('obra.publica', \"Obra pública\"),\n ('pavimentacion', \"Pavimentación\"),\n ('recoleccion.basura', \"Recolección de basura\"),\n ('salud', \"Salud\"),\n ('seguridad', \"Seguridad\"),\n ('servicio.agua', \"Servicio de agua\"),\n ('transporte.publico', \"Transporte público\")]\n return cd\n\n @staticmethod\n def s_and_h_categories_dict():\n cd = [('presidente', 'Presidente de la república'),\n ('gobernador', 'Gobernadores'),\n ('gobierno', 'Gobierno'),\n ('legislativo', 'Diputados y Senadores'),\n ('seguridad', 'Seguridad'),\n ('servicios', 'Servicios'),\n ('economia', 'Economía')]\n\n return cd\n\n @staticmethod\n def grouping_options_dict():\n od = [('week', 'Semanal'), ('month', 'Mensual')]\n\n return od\n\n\nclass Utilities:\n\n @staticmethod\n def array_to_csv(array):\n result = \"'\" + \"','\".join(array) + \"'\"\n\n return result\n\n @staticmethod\n def last_week_start_date():\n today = datetime.date.today()\n\n start_date = today - datetime.timedelta(days=today.weekday(), weeks=1)\n\n return start_date\n\n @staticmethod\n def last_week_end_date():\n end_date = Utilities.last_week_start_date() + datetime.timedelta(days=6)\n\n return end_date\n\n @staticmethod\n def last_monday_date(my_date = None):\n if my_date:\n seed_date = my_date\n else:\n seed_date = datetime.datetime.today()\n\n last_monday_date = seed_date - datetime.timedelta(days = seed_date.weekday())\n\n return last_monday_date\n\n @staticmethod\n def next_sunday_date(my_date = None):\n if my_date:\n seed_date = my_date\n else:\n seed_date = datetime.datetime.now()\n\n next_sunday_date = seed_date + datetime.timedelta(days = 6 - seed_date.weekday())\n\n return next_sunday_date\n\n @staticmethod\n def to_utf8_html_encoding(tagged_text):\n converted_text = tagged_text\n\n reg_exp = re.compile(r\"<[a-fA-F0-9]{2}>\", re.IGNORECASE)\n tags = re.findall(reg_exp, tagged_text)\n\n if(len(tags) > 0):\n for replaceable_tag in tags:\n html_encoded_char = str(replaceable_tag).upper().replace(\"<\", \"&#x\").replace(\">\", \";\")\n converted_text = converted_text.replace(str(replaceable_tag), html_encoded_char)\n\n return converted_text\n\n @staticmethod\n def get_category_label(category_key):\n categries_dict = dict(Constants.categories_dict())\n category_label = \"General\"\n\n for item in categries_dict.keys():\n if item == category_key:\n category_label = categries_dict[category_key]\n continue\n\n return category_label\n\n @staticmethod\n def get_state_label(state_key):\n states_dict = dict(Constants.states_dict())\n state_label = \"\"\n\n if state_key == 'pais':\n return 'País'\n\n for item in states_dict.keys():\n if item == state_key:\n state_label = states_dict[state_key]\n continue\n\n return state_label\n\n @staticmethod\n def get_exportpp_csv_columns_header(category):\n score_header_label = \"Score\"\n rank_header_label = \"Rank\"\n\n if category:\n cat_label = Utilities.get_category_label(category)\n score_header_label = \" \".join([score_header_label, cat_label])\n rank_header_label = \" \".join([rank_header_label, cat_label])\n\n headers = (\"Estado\", \"Fecha\", rank_header_label, score_header_label)\n\n return headers\n\n @staticmethod\n def get_export_states_performance_csv_column_header(category):\n score_header_label = \"Score\"\n rank_header_label = \"Rank\"\n\n if category:\n cat_label = Utilities.get_category_label(category)\n score_header_label = \" \".join([score_header_label, cat_label])\n rank_header_label = \" \".join([rank_header_label, cat_label])\n\n headers = (\"Estado\", \"Inicio Periodo\", \"Fin Periodo\", rank_header_label, score_header_label)\n\n return headers\n\n @staticmethod\n def full_state_performance_categories_dict():\n full_dict = Constants.state_performance_categories_dict()\n full_dict.insert(0, ('general', \"General\"))\n return full_dict\n\n @staticmethod\n def datepickerstring_to_date(datepickerstring):\n converted = datetime.datetime.strptime(datepickerstring, \"%m/%d/%Y\").date()\n return converted\n\n @staticmethod\n def is_number(value):\n try:\n float(value) # for int, long and float\n except ValueError:\n return False\n\n return True\n", "sub_path": "utils/Utilities.py", "file_name": "Utilities.py", "file_ext": "py", "file_size_in_byte": 6940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "csv.reader", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 110, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 140, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 148, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 148, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 221, "usage_type": "attribute"}]} {"seq_id": "361533073", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras import models\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.callbacks import EarlyStopping, LambdaCallback\nfrom keras import regularizers\n\n\n\n\n\n# Hyper parameters\nbatch_size = 32\nnum_classes = 10\nepochs = 100\ndata_augmentation = True\nnum_predictions = 20\nmodel_name = 'cnn_cifar10.h5'\n\n# The data, split between train and test sets:\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\nlabels = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n## Check the data\ndef close_on_key(event):\n plt.close()\n\nfig = plt.figure(figsize=(12,6))\nfig.canvas.mpl_connect('key_press_event', close_on_key)\nax = fig.subplots(3,6)\nax = np.reshape(ax, ax.size)\nfig.suptitle(\"Labels: \" + str(np.squeeze(y_train[:ax.size])))\n\nfor i, a in enumerate(ax):\n a.set_axis_off()\n a.imshow(np.squeeze(x_train[i]), cmap='Greys')\n\nprint(\"Close the window to continue!\")\n#plt.tight_layout()\nplt.show()\n\n# Convert class vectors to binary class matrices.\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n# 24bit colors to 32bit float colors\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\nx_train /= 255\nx_test /= 255\nx_tensor = (1, x_train.shape[1], x_train.shape[2], x_train.shape[3])\n\n\n\n# Load or Configure model\nload_model = input(\"Load a pretrained model? (filename or keep blank):\")\nif load_model:\n # Load the model\n model = models.load_model(load_model)\n model.summary()\nelse:\n # Configure the model\n model = Sequential()\n model.add(\n Conv2D(32, (3, 3),\n padding='same',\n input_shape=x_train.shape[1:]\n ))\n model.add(Activation('relu'))\n model.add(Conv2D(32, (3, 3)\n ))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Dropout(0.25))\n\n model.add(Conv2D(64, (3, 3), padding='same'))\n model.add(Activation('relu'))\n model.add(Conv2D(64, (3, 3)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Dropout(0.25))\n\n model.add(Flatten())\n model.add(Dense(512))\n model.add(Activation('relu'))\n model.add(Dropout(0.5))\n model.add(Dense(num_classes,\n kernel_regularizer=regularizers.l2(0.001)\n ))\n model.add(Activation('softmax'))\n \n # Compile the model with Adam optimizer\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n model.summary()\n\n def run_eval(epoch, logs):\n print(\"Test:\")\n eval = model.evaluate(x_test, y_test)\n logs['test_loss'] = eval[0]\n logs['test_acc'] = eval[1]\n print('Test loss:', eval[0], 'Test accuracy:', eval[1])\n\n # Train the model\n history = model.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n validation_split=0.1,\n shuffle=True,\n callbacks=[\n EarlyStopping(),\n LambdaCallback(\n on_epoch_end = run_eval\n )\n ]\n )\n # Save model and weights\n model.save(model_name)\n \n ## Plot the Losses\n fig, ax_l = plt.subplots(1)\n fig.canvas.mpl_connect('key_press_event', close_on_key)\n ax_l.plot(history.history['loss'], label='train_loss')\n ax_l.plot(history.history['val_loss'], label='val_loss')\n ax_l.plot(history.history['test_loss'], label='test_loss')\n ax_l.set_ylabel(\"Loss\")\n ax_l.legend(loc=2)\n\n ## Plot the Accuracy\n ax_r = ax_l.twinx()\n ax_r.plot(history.history['acc'], '--', label='train_acc')\n ax_r.plot(history.history['val_acc'], '--', label='val_acc')\n ax_r.plot(history.history['test_acc'], '--', label='test_acc')\n ax_r.set_ylabel(\"Accuracy\")\n ax_r.legend()\n\n plt.title(\"Learning Curve / Training Accuracy\")\n plt.xlabel(\"Epoch\")\n\n print(\"Close the window to continue!\")\n plt.show()\n\n\n## Plot Layer Weights\nfig, ax = plt.subplots(2,2)\nax = ax.flatten()\ntitles = [\"Conv2D_1\", \"Conv2D_2\", \"Conv2D_3\", \"Conv2D_4\"]\nids = [0, 2, 6, 8]\nfig.canvas.mpl_connect('key_press_event', close_on_key)\nfor i, a in enumerate(ax):\n weights, biases = model.layers[ids[i]].get_weights()\n a.set_title(titles[i])\n a.hist(weights.flatten())\n a.set_xlabel(\"Value Bins\")\n a.set_ylabel(\"Occurrence\")\nplt.tight_layout()\nplt.show()\n\n\n\n## Qualitative examples:\nclass Event:\n def __init__(self, key):\n self.key = key\n pass\n\ndef sample_handler(event):\n if event.key is 'escape':\n plt.close()\n elif event.key is 'enter':\n sample = np.random.randint(x_test.shape[0])\n gt = y_test[sample].argmax()\n pred = model.predict(x_test[sample].reshape(x_tensor)).argmax()\n plt.imshow(x_test[sample].reshape(x_tensor[1:]))\n plt.title(\"Prediction: {}, Label: {}\".format(labels[pred], labels[gt]))\n print(\"Prediction:\", labels[pred], \"Label:\", labels[gt])\n plt.draw()\n\n\nfig = plt.figure(figsize=(6,4))\nfig.canvas.mpl_connect('key_press_event', sample_handler)\nsample_handler(Event('enter'))\n\nprint(\"-----------------Controls-----------------\")\nprint(\"Press 'Enter' for next layer.\")\nprint(\"Press 'Escape' to close the plot window.\")\nprint(\"Close the window to continue!\")\nplt.show()\n\n\n\n## Find and Viz Mismatch\nmispred = model.predict(x_test.reshape(-1, x_tensor[1], x_tensor[2], x_tensor[3])).argmax(axis=1)\nmismatch = mispred != y_test.argmax(axis=1)\nprint(\"Num of Mismatches: \", mismatch.sum())\nmismatch = np.where(mismatch)[0]\nprint(mismatch)\nmmiter = iter(mismatch)\n\ndef mismatch_handler(event):\n if event.key is 'escape':\n plt.close()\n elif event.key is 'enter':\n try:\n sample = next(mmiter)\n print(sample)\n except:\n print(\"No more samples!\")\n plt.close()\n return\n gt = y_test[sample].argmax()\n plt.imshow(x_test[sample].reshape(x_tensor[1:]),cmap='Greys')\n plt.title(\"Mis-Prediction: {}, Label: {}\".format(labels[mispred[sample]], labels[gt]))\n print(\"Mis-Prediction:\", labels[mispred[sample]], \"Label:\", labels[gt])\n plt.draw()\n\n\nfig = plt.figure(figsize=(6,4))\nfig.canvas.mpl_connect('key_press_event', mismatch_handler)\nmismatch_handler(Event('enter'))\n\nprint(\"-----------------Controls-----------------\")\nprint(\"Press 'Enter' for next layer.\")\nprint(\"Press 'Escape' to close the plot window.\")\nprint(\"Close the window to continue!\")\nplt.show()\n\n\n\n\n## Viz the Activation Potentials\nlayer_names = [model.layers[id].name for id in ids]\nprint(layer_names)\nlayer_iter = iter(layer_names)\nsample = 0\nname = None\n\ndef act_pot_handler(event):\n global name\n global sample\n if event.key is 'escape':\n plt.close()\n return\n elif event.key is 'enter':\n try:\n name = next(layer_iter)\n except:\n print(\"No more layers!\")\n plt.close()\n return\n elif event.key is 'n':\n sample += 1\n \n layer_output = model.get_layer(name).output\n act_model = models.Model(inputs=model.input, outputs=layer_output)\n act = act_model.predict(x_test[sample].reshape(x_tensor))\n _, V, U, C = act.shape\n cells = np.ceil(np.sqrt(C)).astype(int)\n fmap = np.zeros((V*cells,U*cells))\n imn = 0\n for v in range(cells):\n for u in range(cells):\n if imn >= act.shape[-1]:\n break\n vc, uc = v*V, u*U\n fmap[vc:vc+V, uc:uc+U] = act[:,:,:,imn]\n imn += 1\n ax.set_title(name)\n ax.imshow(fmap)\n plt.draw()\n \nfig, ax = plt.subplots(1)\nfig.canvas.mpl_connect('key_press_event', act_pot_handler)\nact_pot_handler(Event('enter'))\n\nprint(\"-----------------Controls-----------------\")\nprint(\"Press 'Enter' for next layer.\")\nprint(\"Press 'N' for random sample.\")\nprint(\"Press 'Escape' to close the plot window.\")\nprint(\"Close the window to continue!\")\nplt.show()", "sub_path": "cnn/cnn_cifar10_L2.py", "file_name": "cnn_cifar10_L2.py", "file_ext": "py", "file_size_in_byte": 8287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "keras.datasets.cifar10.load_data", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.utils.to_categorical", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 51, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 52, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 66, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 95, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.callbacks.LambdaCallback", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 180, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 264, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 264, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}]} {"seq_id": "319852719", "text": "import cv2 \n\n#We need two classifiers one to detect frontal face and one to detect eye\nface_clf=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\nsmile=cv2.CascadeClassifier('haarcascade_smile.xml')\n# we define a detect function which takes a gray scale image to processs and a normal \n# image to return after processing\n\ndef facedetect(gray,frame):\n #for face we'll use the face classifier and a function which takes three\n # arguments - image,scaling factor (by how much it scales image) and min neighbours to check\n face=face_clf.detectMultiScale(gray,1.3,5)\n # face will return a tuple of 4 things- x coordinate ,y coord\n # width ,length of detected face\n \n #we will iterate through the faces to draw rectange over detected images\n \n for (x,y,w,h) in face:\n #we use rectangle function to draw rectangle on detected faces \n cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n \n # to detect smiles we will scan inside the faces and to do that we need\n # two regions of interest(roi) 1-for grascale 2- for original image\n \n roi_gray=gray[y:y+h,x:x+w]\n roi_color=frame[y:y+h,x:x+w]\n \n # just like we did for face we do for smiles\n smiles=smile.detectMultiScale(roi_gray,1.1,100)\n \n for (x,y,w,h) in smiles:\n cv2.rectangle(roi_color,(x,y),(x+w,y+h),(0,255,0),2)\n \n return frame\n\n#Now we need to initialize webcam to record video\nvideo_cap=cv2.VideoCapture(0)# 0 for internal webcam,1 for external webcam\n\nwhile True:# We repeat infinitely (until break):\n _,frame=video_cap.read() # We get the last frame.\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # We do some colour transformations.\n \n canvas = facedetect(gray, frame) # We get the output of our detect function.\n \n cv2.imshow('Video', canvas) # We display the outputs.\n \n if cv2.waitKey(1) & 0xFF == ord('q'): # If we type on the keyboard:\n break \n \n \nvideo_cap.release() # We turn the webcam off.\ncv2.destroyAllWindows() # We destroy all the windows inside which the images were displayed.\n \n \n \n \n", "sub_path": "smiledetector.py", "file_name": "smiledetector.py", "file_ext": "py", "file_size_in_byte": 2176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 52, "usage_type": "call"}]} {"seq_id": "99026275", "text": "#!/usr/bin/python\n\n'''\ndataLogger for Raspberry Pi.\nreads from GPS tracker + data sensors,\nbuilds a csv packet, writes packet to file,\nand transmits packet through Iridium modem.\nSBD packet format:\n\t$GPGGA,timestamp,lat,N,lon,W,fixQuality,numSats,hDilution,altitude,M,geoid,M,*,*,checksum,temp1,intTemp,extTemp,pressure,cutdown\n'''\n\nimport MPL\nimport serial\nimport RPi.GPIO as GPIO\nimport time\nimport smbus\n\nnetAVpin = 16\ncutdownPin = 12\ntempString = \"\"\npacket = \"\"\nincomingPacket = \"\"\ngpsString = \"\"\nsensor = smbus.SMBus(1)\ntempAddress = 0x04\ncutdown = False\n\ncutdownThreshold = 90000 #feet\ncutdownCount = 0\n\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(netAVpin,GPIO.IN)\nGPIO.setup(cutdownPin,GPIO.OUT)\nGPIO.output(cutdownPin,GPIO.LOW)\nGPIO.setwarnings(False)\n\n# open usb ports for iridium and gps io\niridium = serial.Serial(\"/dev/ttyUSB0\",baudrate = 19200,timeout = 4)\nvenusGPS = serial.Serial(\"/dev/ttyS0\",baudrate = 9600)\n\n\n#transmit packet:\ndef irTransmit(packet):\n iridium.write(\"AT+SBDWT\" + '\\r')\n print(\"SBDWT\")\n time.sleep(2)\n\n mt = iridium.readline()\n while(mt != \"READY\\r\\n\"):\n print(mt)\n mt = iridium.readline()\n\n iridium.write(packet + '\\r')\n print(\"writing packet\")\n time.sleep(2)\n\n mt = iridium.readline()\n while(mt != \"0\\r\\n\"):\n print(mt)\n mt = iridium.readline()\n iridium.write(\"AT+SBDI\\r\")\n print(\"SBDI\")\n\n time.sleep(2)\n mt = iridium.readline()\n while(\"+SBDI: \" not in mt):\n print(mt)\n mt = iridium.readline()\n print(mt)\n\n if(mt[7] == '1'):\n print(\"packet successfully transmitted.\")\n else:\n print(\"error transmitting.\")\n time.sleep(2)\n\n\n#check that iridium has satellite signal.\ndef checkSignal():\n state = GPIO.input(netAVpin)\n while not state:\n state = GPIO.input(netAVpin)\n if state:\n print(\"signal found.\")\n else:\n print(\"no Iridium signal.\")\n time.sleep(1)\n\n\ndef checkForIncomingPacket():\n iridium.write(\"AT+SDBRB\" + '\\r')\n time.sleep(4)\n incomingPacket = iridium.readline()\n if(incomingPacket == \"cutdown\"):\n cutdown = True\n cd = GPIO.PWM(cutdownPin,10)\n cd.start(0)\n time.sleep(0.1)\n cd.ChangeDutyCycle(1)\n\n\npacketFile = \"/home/pi/data/packets.csv\"\npacketFileWriter = open(packetFile,\"w\")\n\n#column headers\npacketFileWriter.write(\"$GPGGA,timestamp,lat,N,lon,W,fixQuality,numSats,hDilution,altitude,M,geoid,M,*,*,checksum,temp1,intTemp,extTemp,pressure,cutdown\\n\")\npacketFileWriter.close()\n\n\nwhile 1:\n checkSignal()\n time.sleep(3)\n #GPS:\n print(\"reading GPS data....\")\n venusGPS.reset_input_buffer()\n NMEAString = venusGPS.readline()\n while(NMEAString[0:6] != \"$GPGGA\"):\n NMEAString = venusGPS.readline()\n print(NMEAString)\n gpsString = NMEAString.strip()\n venusGPS.flush()\n time.sleep(0.5)\n\n\n #Temperature:\n try:\n tempString = \"\"\n tempData = sensor.read_i2c_block_data(tempAddress,0)\n print(\"reading temperature data....\")\n for c in tempData:\n if c > 31 and c < 58:\n tempString += chr(c)\n\n print(tempString)\n except:\n print(\"can't read temperature data.\")\n tempString = \"0.00,0.00\"\n time.sleep(0.5)\n\n #Pressure + altitude:\n try:\n altitude = \"\"\n temp = \"\"\n pres = \"\"\n print(\"reading pressure data....\")\n altitude = MPL.getAltitudeFt()\n time.sleep(0.5)\n temp = MPL.getTemperatureF()\n time.sleep(0.5)\n pres = MPL.getPressure()\n print(pres)\n print(altitude)\n print(temp)\n\n if(altitude >= cutdownThreshold):\n cutdownCount += 1\n else:\n cutdownCount = 0\n if(cutdownCount == 5):\n cutdown = True\n cd = GPIO.PWM(cutdownPin,10)\n cd.start(0)\n time.sleep(0.1)\n cd.ChangeDutyCycle(1)\n\n except:\n print(\"can't read pressure.\")\n pres = 0.0\n altitude = 0.0\n temp = 0.0\n time.sleep(0.5)\n\n packet = gpsString + ',' + str(temp) + ',' + tempString + ',' + str(pres) + ','\n if(cutdown): packet += 'cutdown'\n else: packet += 'notCutdown'\n \n with open(packetFile,\"a\") as packetWriter:\n packetWriter.write(packet + '\\n')\n with open(packetFile,\"r\") as packetReader:\n packet = list(packetReader)[-1]\n\n print(\"packet:\" + packet)\n\n print(\"transmitting packet....\")\n irTransmit(packet)\n time.sleep(60)\n", "sub_path": "dataLogger.py", "file_name": "dataLogger.py", "file_ext": "py", "file_size_in_byte": 4518, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "smbus.SMBus", "line_number": 24, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 31, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 31, "usage_type": "name"}, {"api_name": "RPi.GPIO.BOARD", "line_number": 31, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 32, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 32, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 32, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 33, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 33, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 34, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 34, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 34, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setwarnings", "line_number": 35, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 35, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 38, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 80, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 80, "usage_type": "name"}, {"api_name": "RPi.GPIO.input", "line_number": 82, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 82, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "RPi.GPIO.PWM", "line_number": 96, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 96, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "MPL.getAltitudeFt", "line_number": 146, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 147, "usage_type": "call"}, {"api_name": "MPL.getTemperatureF", "line_number": 148, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "MPL.getPressure", "line_number": 150, "usage_type": "call"}, {"api_name": "RPi.GPIO.PWM", "line_number": 161, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 161, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 163, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 171, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 186, "usage_type": "call"}]} {"seq_id": "199235597", "text": "import sqlite3\n\nclass Datasource():\n\t\"\"\"docstring for DB\"\"\"\n\tdb = \"DB.db\"\n\tcon = 'dbconnection'\n\tcur = 'cursor'\n\tdef __init__(self):\n\t\tself.con = sqlite3.connect(str(self.db))\n\t\tself.cur = self.con.cursor()\n\t\t\n\tdef create_table(self):\n\t\ttry:\n\t\t\t# Create table\n\t\t\tself.cur.execute('''CREATE TABLE property\n (id INTEGER PRIMARY KEY, date TEXT, price NUMBER, currency TEXT, rooms NUMBER, bath NUMBER, m2 NUMBER, name TEXT, description TEXT, location TEXT )''')\n\t\t\tprint(\"Table created successfully\")\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\n\tdef insert(self, table, data):\n\t\ttry:\n\t\t\t# Insert a row of data\n\t\t\tself.cur.executemany(\"INSERT INTO {str(self.table)} VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?), {str(self.data)}\")\n\t\t\t# Save (commit) the changes\n\t\t\tself.con.commit()\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\n\tdef close_conn(self):\n\t\tself.con.close()\n\n\n\n\n\n\n", "sub_path": "datasources.py", "file_name": "datasources.py", "file_ext": "py", "file_size_in_byte": 866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sqlite3.connect", "line_number": 9, "usage_type": "call"}]} {"seq_id": "162319525", "text": "import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport csv\r\nfile = open('alexnet_500M.csv',\"r\")\r\n# partition=[1.4e13,1.402e14]\r\nreader = csv.reader(file)\r\n# def index(address):\r\n# j=0\r\n# found=0\r\n# for i in range(0,len(partition)):\r\n# if(address>partition[i]):\r\n# j=i+1\r\n# return j\r\n# access=[]\r\n# time=[]\r\n# mem_add=[]\r\n# cycle=[]\r\n# for i in range(0,len(partition)+1):\r\n# mem_add.append([])\r\n# cycle.append([])\r\n# i=0\r\n# for line in reader:\r\n# if(i<20000):\r\n# w=[float(line[0]),float(line[1])]\r\n# j=index(w[1])\r\n# access.append(w[1])\r\n# time.append(w[0])\r\n# mem_add[j].append(w[1])\r\n# cycle[j].append(w[0])\r\n# i=i+1\r\n# for i in range(0,len(partition)+1):\r\n\r\ni=0\r\ncycle=[]\r\nmem_add=[]\r\nfor line in reader:\r\n if(i<6000000):\r\n cycle.append(float(line[0]))\r\n mem_add.append(float(line[1]))\r\n else:\r\n break\r\n i=i+1\r\n\r\n\r\nplt.plot(cycle,mem_add,'o')\r\nplt.grid('True','both','both')\r\nplt.title('alexnet_memory_access')\r\nplt.savefig('alexnet_memory_access'+'.png')\r\nplt.show()\r\n", "sub_path": "pl.py", "file_name": "pl.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "csv.reader", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]} {"seq_id": "625930746", "text": "import pandas as pd\nfrom pandas.api.types import CategoricalDtype\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef main():\n\n dtype_dict = {\n \"key\": \"string\",\n \"utility\": \"category\",\n \"usability\": \"float\",\n \"interface\": \"float\",\n \"relevance\": CategoricalDtype(\n categories=[\"Low\", \"Medium\", \"High\"], ordered=True),\n \"response_time\": CategoricalDtype(\n categories=[\"Slow\", \"Neutral\", \"Fast\"], ordered=True),\n \"quality\": CategoricalDtype(categories=[\"Bad\", \"Average\", \"Good\"], ordered=True),\n \"use?\": CategoricalDtype(categories=[\"No\", \"Yes\"], ordered=True),\n \"recommend?\": CategoricalDtype(categories=[\"No\", \"Yes\"], ordered=True),\n \"overall\": \"float\",\n \"num_liked\": \"float\"\n }\n\n df = pd.read_csv(\"./feedback.csv\", dtype=dtype_dict)\n cat_cols = df.select_dtypes([\"category\"]).columns\n df[cat_cols] = df[cat_cols].apply(lambda x: x.cat.codes)\n print(df)\n\n for column in df.columns:\n if column in [\"key\", \"utility\"]:\n continue\n x = [\"MovieRecommender\", \"PickAMovie\", \"SuggestMeMovie\"]\n means = []\n for site in df[\"utility\"].unique():\n sitedf = df.loc[df[\"utility\"] == site]\n means.append(sitedf[column].mean())\n x_pos = [i for i, _ in enumerate(x)]\n\n plt.clf()\n plt.bar(x_pos, means)\n plt.xlabel(\"website\")\n plt.ylabel(\"Mean {}\".format(column))\n plt.title(\"Website vs mean {}\".format(column))\n plt.xticks(x_pos, x)\n\n plt.savefig(\"./plots/{}.png\".format(column))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "Project3/barplots.py", "file_name": "barplots.py", "file_ext": "py", "file_size_in_byte": 1642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.api.types.CategoricalDtype", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.api.types.CategoricalDtype", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.api.types.CategoricalDtype", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.api.types.CategoricalDtype", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.api.types.CategoricalDtype", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} {"seq_id": "475102823", "text": "from socket import *\nimport yaml\n\n# read config file\nconfig_file_path = \"./config.yaml\"\nwith open(config_file_path, 'r', encoding = 'utf-8') as fp:\n config_data = yaml.load(fp, yaml.FullLoader)\n\naddress = config_data['address']\nport = config_data['port']\nbufferSize = config_data['buffersize']\nmaxConnection = config_data['max_connection']\n\n# create socket\ns = socket(AF_INET, SOCK_STREAM)\ns.bind((address, port))\ns.listen(maxConnection)", "sub_path": "TcpServer.py", "file_name": "TcpServer.py", "file_ext": "py", "file_size_in_byte": 440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "yaml.load", "line_number": 7, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 7, "usage_type": "attribute"}]} {"seq_id": "604841229", "text": "# Jayson Valderrama\r\n# ID: 001081738\r\n# Data Structures and Algorithms II C950\r\n\r\nfrom datetime import time, datetime\r\n\r\n\r\nclass Package:\r\n # Package statuses\r\n AT_FACILITY = \"At Facility\"\r\n OUT_FOR_DELIVERY = \"Out for Delivery\"\r\n DELIVERED = \"Delivered\"\r\n DELIVERED_LATE = \"Delivered Late\"\r\n\r\n # Manually defined End of Day as 5pm\r\n EOD_HOUR = 17\r\n EOD_MIN = 00\r\n\r\n # These packages are marked as a bundle via the project constraints\r\n BUNDLED_PACKAGES = [13, 14, 15, 16, 19, 20]\r\n\r\n # Package initialization\r\n def __init__(self, id_, address, city, state, zip_, deadline, kilos, note):\r\n self.id_ = int(id_)\r\n self.address = address\r\n self.city = city\r\n self.state = state\r\n self.zip_ = zip_\r\n self.deadline = deadline\r\n self.kilos = kilos\r\n self.note = note\r\n self.status = self.AT_FACILITY\r\n self.bundled = False\r\n self.truck_id = None\r\n\r\n # Updates package's delivery deadline to \"time\" object\r\n if self.deadline == \"EOD\":\r\n self.deadline = time(self.EOD_HOUR, self.EOD_MIN)\r\n else:\r\n self.deadline = datetime.strptime(self.deadline[0:5].strip(), \"%H:%M\").time()\r\n\r\n if note != '' or None:\r\n # Updates as True if a package is bundled\r\n if self.id_ in self.BUNDLED_PACKAGES:\r\n self.bundled = True\r\n\r\n # Assigns the Truck ID 2 if package requires it\r\n elif self.note[-7:] == \"truck 2\":\r\n self.truck_id = 2\r\n\r\n # Updates status of delayed packages\r\n elif self.note[0:7] == \"Delayed\":\r\n self.arrive_time = self.note[-7:-3]\r\n\r\n # Updates incorrect address\r\n elif self.note == \"Wrong address listed\":\r\n self.address = \"410 S State St\"\r\n self.zip_ = \"84111\"\r\n\r\n # Formatted Package display when called in Main\r\n def __str__(self):\r\n return '{:<5}'.format(str(self.id_)) + \\\r\n '{:<41}'.format(self.address) + \\\r\n '{:<19}{:<8}{:<8}'.format(self.city, self.state, self.zip_) + \\\r\n '{}'.format( self.deadline) + \\\r\n '{:>8}{:<3}'.format(self.kilos, '') + \\\r\n '{:<19}'.format(self.status) + \\\r\n '{}'.format(self.note)\r\n", "sub_path": "Package.py", "file_name": "Package.py", "file_ext": "py", "file_size_in_byte": 2334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "datetime.time", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}]} {"seq_id": "71469634", "text": "import datetime\nimport unittest\n\nimport transaction\nfrom pyramid import testing, httpexceptions\n\nfrom ccvpn.models import User, APIAccessToken, Profile\nfrom ccvpn import views, setup_routes\nfrom ccvpn.tests import setup_database, DummyRequest\n\n\nclass TestAPIViews(unittest.TestCase):\n def setUp(self):\n self.config = testing.setUp()\n setup_routes(self.config)\n self.session = setup_database()\n\n with transaction.manager:\n user = User(username='test', password='testpw')\n user.add_paid_time(datetime.timedelta(days=30))\n baduser = User(username='badtest', password='testpw')\n self.session.add(user)\n self.session.add(baduser)\n with transaction.manager:\n token = APIAccessToken(token='apitoken')\n self.session.add(token)\n\n restricted_token = APIAccessToken(token='restricted_apitoken')\n restricted_token.remote_addr = '127.0.0.1'\n self.session.add(restricted_token)\n with transaction.manager:\n profile = Profile(uid=user.id, name='testprofile')\n self.session.add(profile)\n\n def tearDown(self):\n self.session.remove()\n testing.tearDown()\n\n def test_disconnect(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n })\n resp = views.api.api_server_disconnect(req)\n self.assertEqual(resp.code, 200)\n self.assertEqual(resp.body, b'')\n\n def test_server_auth(self):\n function = views.api.require_api_token(None)(lambda req: True)\n\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n })\n self.assertEqual(function(req), True)\n\n req = DummyRequest(headers={\n 'X-API-Token': 'notapitoken'\n })\n self.assertIsInstance(function(req), httpexceptions.HTTPForbidden)\n\n req = DummyRequest(headers={\n 'X-API-Token': 'restricted_apitoken'\n }, remote_addr='1.2.3.4')\n self.assertIsInstance(function(req), httpexceptions.HTTPUnauthorized)\n\n req = DummyRequest(headers={\n 'X-API-Token': 'restricted_apitoken'\n }, remote_addr='127.0.0.1')\n self.assertEqual(function(req), True)\n\n req = DummyRequest()\n self.assertIsInstance(function(req), httpexceptions.HTTPBadRequest)\n\n def test_config(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, params={\n 'username': 'test',\n })\n resp = views.api.api_server_config(req)\n self.assertEqual(resp.code, 200)\n self.assertEqual(resp.body, b'')\n\n def test_config_with_profile(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, params={\n 'username': 'test/testprofile',\n })\n resp = views.api.api_server_config(req)\n self.assertEqual(resp.code, 200)\n self.assertEqual(resp.body, b'')\n\n def test_config_no_post(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n })\n resp = views.api.api_server_config(req)\n self.assertEqual(resp.code, 400)\n\n def test_config_unknown_user(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, params={\n 'username': 'nottest',\n })\n resp = views.api.api_server_config(req)\n self.assertEqual(resp.code, 404)\n\n def test_config_unknown_profile(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, params={\n 'username': 'test/nottesttprofile',\n })\n resp = views.api.api_server_config(req)\n self.assertEqual(resp.code, 404)\n\n def test_user_auth(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, post={\n 'username': 'test',\n 'password': 'testpw'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 200)\n\n def test_user_auth_profile(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, post={\n 'username': 'test/testprofile',\n 'password': 'testpw'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 200)\n\n def test_user_auth_no_post(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 400)\n\n def test_user_auth_unknown_user(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, post={\n 'username': 'nottest',\n 'password': 'testpw'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 403)\n\n def test_user_auth_unknown_profile(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, post={\n 'username': 'test/nottestprofile',\n 'password': 'testpw'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 403)\n\n def test_user_auth_expired(self):\n req = DummyRequest(headers={\n 'X-API-Token': 'apitoken'\n }, post={\n 'username': 'badtest',\n 'password': 'testpw'\n })\n resp = views.api.api_server_auth(req)\n self.assertEqual(resp.code, 401)\n\n", "sub_path": "ccvpn/tests/views_api.py", "file_name": "views_api.py", "file_ext": "py", "file_size_in_byte": 5469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyramid.testing.setUp", "line_number": 14, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 14, "usage_type": "name"}, {"api_name": "ccvpn.setup_routes", "line_number": 15, "usage_type": "call"}, {"api_name": "ccvpn.tests.setup_database", "line_number": 16, "usage_type": "call"}, {"api_name": "transaction.manager", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ccvpn.models.User", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 20, "usage_type": "call"}, {"api_name": "ccvpn.models.User", "line_number": 21, "usage_type": "call"}, {"api_name": "transaction.manager", "line_number": 24, "usage_type": "attribute"}, {"api_name": "ccvpn.models.APIAccessToken", "line_number": 25, "usage_type": "call"}, {"api_name": "ccvpn.models.APIAccessToken", "line_number": 28, "usage_type": "call"}, {"api_name": "transaction.manager", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ccvpn.models.Profile", "line_number": 32, "usage_type": "call"}, {"api_name": "pyramid.testing.tearDown", "line_number": 37, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 37, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 40, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_disconnect", "line_number": 43, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 43, "usage_type": "name"}, {"api_name": "ccvpn.views.api.require_api_token", "line_number": 48, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 48, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 48, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 50, "usage_type": "call"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 55, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPForbidden", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pyramid.httpexceptions", "line_number": 58, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 60, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPUnauthorized", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pyramid.httpexceptions", "line_number": 63, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 65, "usage_type": "call"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 70, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPBadRequest", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pyramid.httpexceptions", "line_number": 71, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 74, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_config", "line_number": 79, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 79, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 84, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_config", "line_number": 89, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 89, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 89, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 94, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_config", "line_number": 97, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 97, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 97, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 101, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_config", "line_number": 106, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 106, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 106, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 110, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_config", "line_number": 115, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 115, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 115, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 119, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 125, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 125, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 125, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 129, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 135, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 135, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 135, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 139, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 142, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 142, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 142, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 146, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 152, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 152, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 152, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 156, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 162, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 162, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 162, "usage_type": "name"}, {"api_name": "ccvpn.tests.DummyRequest", "line_number": 166, "usage_type": "call"}, {"api_name": "ccvpn.views.api.api_server_auth", "line_number": 172, "usage_type": "call"}, {"api_name": "ccvpn.views.api", "line_number": 172, "usage_type": "attribute"}, {"api_name": "ccvpn.views", "line_number": 172, "usage_type": "name"}]} {"seq_id": "288800108", "text": "import unittest\nfrom app import app as webapp\nimport json\n\n\nclass TestApp(unittest.TestCase):\n\n def test_invalid_url(self):\n self.app = webapp.test_client()\n response = self.app.post('/shorten_url',\n headers={'Content-Type': 'application/json'},\n data=json.dumps(dict(url='ww.w.google.com')))\n self.assertEqual(response._status_code, 400, \"check bad request format code returned\")\n\n def test_valid_url(self):\n self.app = webapp.test_client()\n response = self.app.post('/shorten_url',\n headers={'Content-Type': 'application/json'},\n data=json.dumps(dict(url='www.google.com')))\n self.assertEqual(response._status_code, 201, \"check created error code returned\")\n\n def test_redirect(self):\n self.app = webapp.test_client()\n response = self.app.get('/www.google.com')\n self.assertEqual(response._status_code, 302, \"check identified as valid url and forwarded\")\n", "sub_path": "tests/test_app.py", "file_name": "test_app.py", "file_ext": "py", "file_size_in_byte": 1057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "unittest.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "app.app.test_client", "line_number": 9, "usage_type": "call"}, {"api_name": "app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "app.app.test_client", "line_number": 16, "usage_type": "call"}, {"api_name": "app.app", "line_number": 16, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 19, "usage_type": "call"}, {"api_name": "app.app.test_client", "line_number": 23, "usage_type": "call"}, {"api_name": "app.app", "line_number": 23, "usage_type": "name"}]} {"seq_id": "275583839", "text": "from schematics.types import ModelType, StringType, PolyModelType\n\nfrom spaceone.inventory.model.snapshot.data import Snapshot\nfrom spaceone.inventory.libs.schema.metadata.dynamic_field import TextDyField, DateTimeDyField, EnumDyField, ListDyField, SizeField\nfrom spaceone.inventory.libs.schema.metadata.dynamic_layout import ItemDynamicLayout, TableDynamicLayout, \\\n ListDynamicLayout\nfrom spaceone.inventory.libs.schema.cloud_service import CloudServiceResource, CloudServiceResponse, CloudServiceMeta\n\n'''\nSNAPSHOT\n'''\n# TAB - Default\nsnapshot_info_meta = ItemDynamicLayout.set_fields('Snapshot', fields=[\n\n TextDyField.data_source('Name', 'data.name'),\n TextDyField.data_source('Storage Type', 'data.sku.name'),\n SizeField.data_source('Size', 'data.size'),\n TextDyField.data_source('Source Disk', 'data.source_disk_name'),\n TextDyField.data_source('Location', 'data.location'),\n TextDyField.data_source('Resource ID', 'data.id'),\n TextDyField.data_source('Resource Group', 'data.resource_group'),\n EnumDyField.data_source('Snapshot state', 'data.disk_state', default_state={\n 'safe': ['ActiveSAS', 'ActiveUpload', 'Attached', 'Reserved'],\n 'warning': ['ReadyToUpload'],\n 'available': ['Unattached']\n }),\n TextDyField.data_source('Snapshot Type', 'data.incremental_display'),\n TextDyField.data_source('Subscription ID', 'data.subscription_id'),\n TextDyField.data_source('Subscription Name', 'data.subscription_name'),\n TextDyField.data_source('Encryption Type', 'data.encryption.type_display'),\n TextDyField.data_source('Network Access Policy', 'data.network_access_policy_display'),\n DateTimeDyField.data_source('Created Time', 'data.time_created')\n])\n\n# TAB - tags\nsnapshot_info_tags = TableDynamicLayout.set_fields('Tags', 'data.tags', fields=[\n TextDyField.data_source('Key', 'key'),\n TextDyField.data_source('Value', 'value')\n])\n\nsnapshot_meta = CloudServiceMeta.set_layouts([snapshot_info_meta, snapshot_info_tags])\n\n\nclass ComputeResource(CloudServiceResource):\n cloud_service_group = StringType(default='Compute')\n\n\nclass SnapshotResource(ComputeResource):\n cloud_service_type = StringType(default='Snapshot')\n data = ModelType(Snapshot)\n _metadata = ModelType(CloudServiceMeta, default=snapshot_meta, serialized_name='metadata')\n\n\nclass SnapshotResponse(CloudServiceResponse):\n resource = PolyModelType(SnapshotResource)\n", "sub_path": "src/spaceone/inventory/model/snapshot/cloud_service.py", "file_name": "cloud_service.py", "file_ext": "py", "file_size_in_byte": 2423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_layout.ItemDynamicLayout.set_fields", "line_number": 13, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_layout.ItemDynamicLayout", "line_number": 13, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 15, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 15, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 16, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 16, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.SizeField.data_source", "line_number": 17, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.SizeField", "line_number": 17, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 18, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 18, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 19, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 19, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 20, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 20, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 21, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 21, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.EnumDyField.data_source", "line_number": 22, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.EnumDyField", "line_number": 22, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 27, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 27, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 28, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 28, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 29, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 29, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 30, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 30, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 31, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 31, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.DateTimeDyField.data_source", "line_number": 32, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.DateTimeDyField", "line_number": 32, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_layout.TableDynamicLayout.set_fields", "line_number": 36, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_layout.TableDynamicLayout", "line_number": 36, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 37, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 37, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField.data_source", "line_number": 38, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.metadata.dynamic_field.TextDyField", "line_number": 38, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.cloud_service.CloudServiceMeta.set_layouts", "line_number": 41, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.cloud_service.CloudServiceMeta", "line_number": 41, "usage_type": "name"}, {"api_name": "spaceone.inventory.libs.schema.cloud_service.CloudServiceResource", "line_number": 44, "usage_type": "name"}, {"api_name": "schematics.types.StringType", "line_number": 45, "usage_type": "call"}, {"api_name": "schematics.types.StringType", "line_number": 49, "usage_type": "call"}, {"api_name": "schematics.types.ModelType", "line_number": 50, "usage_type": "call"}, {"api_name": "spaceone.inventory.model.snapshot.data.Snapshot", "line_number": 50, "usage_type": "argument"}, {"api_name": "schematics.types.ModelType", "line_number": 51, "usage_type": "call"}, {"api_name": "spaceone.inventory.libs.schema.cloud_service.CloudServiceMeta", "line_number": 51, "usage_type": "argument"}, {"api_name": "spaceone.inventory.libs.schema.cloud_service.CloudServiceResponse", "line_number": 54, "usage_type": "name"}, {"api_name": "schematics.types.PolyModelType", "line_number": 55, "usage_type": "call"}]} {"seq_id": "137508161", "text": "from sklearn.metrics.pairwise import cosine_similarity\nfrom scipy import sparse as sp\nimport numpy as np\nfrom collections import defaultdict\n\n\npath=\"./data/citeseer/\"\ndataset=\"citeseer\"\nthresh=0.4\nidx_features_labels = np.genfromtxt(\"{}{}.content\".format(path, dataset), dtype=np.dtype(str))\nfeatures = sp.csr_matrix(idx_features_labels[:, 1:-2], dtype=np.float32)\nsimilarities = cosine_similarity(features)\n\nidx = np.array(idx_features_labels[:, 0], dtype=np.dtype(str))\nidx_map = {j: i for i, j in enumerate(idx)}\nrev_idx_map={i:j for i,j in enumerate(idx)}\n\n\nsimilarities2 = similarities>thresh\nrow, col, data=sp.find(similarities2)\n\nindices=[]\nfor k,v in zip(row, col):\n indices.append([rev_idx_map[k],rev_idx_map[v]])\nnp.savetxt('{}{}_similarity_{}.csv'.format(path, dataset,str(thresh)), indices, fmt='%s')\n\n", "sub_path": "citeseer_similarity.py", "file_name": "citeseer_similarity.py", "file_ext": "py", "file_size_in_byte": 817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.genfromtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.sparse.find", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 25, "usage_type": "call"}]} {"seq_id": "416654334", "text": "from django.conf.urls import url\nfrom . import views\n\napp_name = 'market'\n\nurlpatterns = [\n url(r'^$',views.main,name=\"main\"),\n url(r'^new$', views.item_new, name='item_new'),\n url(r'^item/(?P<pk>\\d+)/$',views.item_detail,name=\"item_detail\"),\n url(r'^item/(?P<pk>\\d+)/edit$',views.item_edit,name=\"item_edit\"),\n url(r'404',views.render_404,name=\"render_404\"),\n]\n", "sub_path": "market/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 376, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}]} {"seq_id": "114078026", "text": "#converting the Twitter json dump in MongoDB to CSV using PyMongo\nfrom pymongo import MongoClient\nfrom operator import itemgetter\nimport csv\nimport os\n\nclient = MongoClient('localhost', 27017)\ndb = client['usa_db']\ncollection = db['usa_tweets_collection']\n\nif os.path.exists('usa_tweets.csv'):\n os.remove('usa_tweets.csv')\nwith open('usa_tweets.csv', 'w') as outfile:\n field_names = ['text', 'user', 'created_at', 'geo','location']\n writer = csv.DictWriter(outfile, delimiter=',', fieldnames=field_names)\n writer.writeheader()\n\n # tweets_iterator = collection.find().limit(1)\n # for data in tweets_iterator:\n # print(data['user']['location'])\n\n for data in db.usa_tweets_collection.find():\n writer.writerow({\n 'text': data['text'],\n 'user': data['user'],\n 'created_at': data['created_at'],\n 'geo': data['geo']['coordinates'],\n 'location' : data['user']['location']\n })\n\n outfile.close()", "sub_path": "mongo_python_tweet/json_to_csv.py", "file_name": "json_to_csv.py", "file_ext": "py", "file_size_in_byte": 930, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 12, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 15, "usage_type": "call"}]} {"seq_id": "646166571", "text": "import torch\r\nimport torch.nn as nn\r\nfrom collections import OrderedDict\r\n\r\nif torch.cuda.is_available():\r\n print(\"Using GPU\")\r\n\r\n\r\nclass DNNclassifier(nn.Module):\r\n def __init__(self, params):\r\n super(DNNclassifier, self).__init__()\r\n self.layer_stack = OrderedDict()\r\n self.inputDim = params['inputDim']\r\n ## hidden layers\r\n self.hidden = params['hiddenDim']\r\n self.outputDim = params['outputDim']\r\n self.device = params['device']\r\n\r\n self.layer_stack['hidden_{}'.format(0)] = nn.Linear(in_features=self.inputDim, out_features=self.hidden[0])\r\n self.layer_stack['hidden_bn_{}'.format(0)] = nn.BatchNorm1d(num_features = self.hidden[0])\r\n self.layer_stack['hidden_activation_{}'.format(0)] = nn.RReLU()\r\n\r\n for idx in range(len(self.hidden[1:])):\r\n self.layer_stack['hidden_{}'.format(idx+1)] = nn.Linear(in_features=self.hidden[idx], out_features=self.hidden[idx+1])\r\n self.layer_stack['hidden_bn_{}'.format(idx+1)] = nn.BatchNorm1d(self.hidden[idx+1])\r\n self.layer_stack['hidden_activation_{}'.format(idx+1)] = nn.RReLU()\r\n\r\n self.layer_stack['linear_output'] = nn.Linear(in_features = self.hidden[idx+1],out_features=self.outputDim)\r\n self.layer_stack['output'] = nn.Sigmoid()\r\n \r\n\r\n self.model = nn.Sequential(self.layer_stack)\r\n\r\n\r\n def forward(self, input):\r\n return self.model(input.to(self.device).squeeze(0))\r\n\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n params = {\r\n 'inputDim' : 64,\r\n 'hiddenDim' : [64,64,64,64],\r\n 'outputDim' : 2\r\n }\r\n dnn = DNNclassifier(params)\r\n print(dnn)\r\n pass", "sub_path": "src/DNN_classifier.py", "file_name": "DNN_classifier.py", "file_ext": "py", "file_size_in_byte": 1690, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.cuda.is_available", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.RReLU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.RReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}]} {"seq_id": "593980957", "text": "import os\nfrom PIL import Image\nimport mido\n\nbpm = 80\n\ndef get_color_diff(color1, color2):\n return abs(color1[0] - color2[0]) + abs(color1[1] - color2[1]) + abs(color1[2] - color2[2])\n\n# Get note mapping\nwith open('./note_mapping.txt') as file:\n lines = [x.strip().split(' ') for x in file.readlines()]\n\nnote_map = {}\nfor line in lines:\n note = line[0]\n x = line[1]\n y = line[2]\n if note.isdigit() and x.isdigit and y.isdigit():\n note_map[int(note)] = (int(x), int(y))\n\nfirst_frame_note_to_color_map = {}\nnote_on_map = { x: False for x in note_map.keys() }\nevents = []\n\n# Loop over frames\nfor i in range(len(os.listdir('./images'))):\n img = Image.open(f'./images/out-{i+1}.jpg')\n\n for note, coords in note_map.items():\n color = img.getpixel(coords)\n if i == 0:\n first_frame_note_to_color_map[note] = color\n else:\n first_color = first_frame_note_to_color_map[note]\n colorDiff = get_color_diff(first_color, color)\n if (colorDiff > 50):\n if not note_on_map[note]:\n note_on_map[note] = True\n events.append({ 'note': note, 'on': True, 'time': i })\n else:\n if note_on_map[note]:\n note_on_map[note] = False\n events.append({ 'note': note, 'on': False, 'time': i })\n\nmid = mido.MidiFile()\ntrack = mido.MidiTrack()\nmid.tracks.append(track)\n\n# Meta\ntrack.append(mido.MetaMessage('set_tempo', tempo=int(mido.bpm2tempo(bpm))))\nmid.ticks_per_beat = int((bpm / 60) / (1 / 29.97))\n\nlast_time = 0\nfor event in events:\n delta_time = event['time'] - last_time\n message_type = 'note_on' if event['on'] else 'note_off'\n track.append(mido.Message(message_type, note=event['note'], velocity=64, time=delta_time))\n last_time = event['time']\n\nmid.save('output.mid')", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 28, "usage_type": "name"}, {"api_name": "mido.MidiFile", "line_number": 46, "usage_type": "call"}, {"api_name": "mido.MidiTrack", "line_number": 47, "usage_type": "call"}, {"api_name": "mido.MetaMessage", "line_number": 51, "usage_type": "call"}, {"api_name": "mido.bpm2tempo", "line_number": 51, "usage_type": "call"}, {"api_name": "mido.Message", "line_number": 58, "usage_type": "call"}]} {"seq_id": "139033320", "text": "import sys\nimport constants\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nif __name__ == \"__main__\":\n\n\targc = len(sys.argv)\n\tquery = []\n\n\tif argc >= 2:\n\t\tfor x in range(1, argc):\n\t\t\tquery.append(sys.argv[x])\n\n\t\tquery_str = \" \".join(query)\n\t\t\n\t\tchrome_options = webdriver.ChromeOptions()\n\t\tchrome_options.add_argument('--headless')\n\t\tdriver = webdriver.Chrome(options=chrome_options)\n\t\tdriver.maximize_window()\n\n\t\tdriver.get('https://www.semanticscholar.org/')\n\t\tsearch_bar = driver.find_element_by_xpath('//*[@id=\"search-form\"]/div/div/input')\n\t\tsearch_bar.send_keys(query_str)\n\t\tsearch_bar.submit()\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tmain_div = driver.find_element(By.CLASS_NAME, 'result-page')\n\t\t\t\tbreak\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"DOM loading...\\n\")\n\n\t\tsplits = main_div.find_elements_by_css_selector('.cl-paper-row.serp-papers__paper-row.paper-row-normal')\n\n\t\tabstract_getable = []; abstract_expand_btns = []; abstract_spans = []\n\t\tabstract_div = None\n\n\t\tfor x in range(len(splits)): \n\t\t try:\n\t\t \t#print(\"Let's pick abstracts!!!\")\n\t\t \ttry:\n\t\t \t\tabstract_div = splits[x].find_element_by_css_selector('div.tldr-abstract-replacement')\n\t\t \texcept:\t# try second option\n\t\t \t\tabstract_div = splits[x].find_element_by_css_selector('div.cl-paper-abstract')\n\t\t \t\n\t\t \tabstract_spans.append(abstract_div)\n\t \t\tabstract_expand_btns.append(abstract_div.find_element_by_css_selector('span.more.mod-clickable'))\n\t \t\tabstract_getable.append(True)\n\t\t \n\t\t except:\n\t\t abstract_getable.append(False)\n\n\t\tprint(abstract_getable); print()\n\n\t\tfor x in range(len(abstract_expand_btns)):\n\t\t WebDriverWait(driver, constants.TIMEOUT).until(EC.visibility_of(abstract_expand_btns[x]))\n\t\t driver.execute_script(\"arguments[0].click();\", abstract_expand_btns[x])\n\n\t\turl_hrefs = main_div.find_elements_by_css_selector('div.cl-paper-row.serp-papers__paper-row > a')\n\t\ttitle_spans = main_div.find_elements_by_css_selector('div > a > div > span')\n\n\t\turls = [a.get_attribute('href') for a in url_hrefs]\n\t\ttitles = [title.text.encode('utf8') for title in title_spans]\n\t\tabstracts = [abstract.text.encode('utf8') for abstract in abstract_spans]\n\n\t\tlist_ = []\n\t\ti = 0\n\n\t\tfor x in range(len(splits)):\n\t\t if abstract_getable[x] == True:\n\t\t list_.append(\n\t\t {\n\t\t \"url\" : urls[x],\n\t\t \"title\" : titles[x],\n\t\t \"abstract\" : abstracts[i]\n\t\t }\n\t\t );\n\t\t i = i + 1\n\t\t else:\n\t\t list_.append(\n\t\t {\n\t\t \"url\" : urls[x],\n\t\t \"title\" : titles[x],\n\t\t \"abstract\" : \"\"\n\t\t }\n\t\t );\n\n\t\tprint(*list_, sep = \"\\n\\n\")\n\n\t\twith open('listfile.txt', 'w') as filehandle:\n\t\t for listitem in list_:\n\t\t filehandle.write('%s\\n\\n' % listitem)\n\n\t\tdriver.close()\n\n\telse:\n\t\tprint(\"Please enter the search query!\")\n\t\texit(1)\n", "sub_path": "bot_.py", "file_name": "bot_.py", "file_ext": "py", "file_size_in_byte": 3068, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 22, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 32, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 32, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 60, "usage_type": "call"}, {"api_name": "constants.TIMEOUT", "line_number": 60, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of", "line_number": 60, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 60, "usage_type": "name"}]} {"seq_id": "612434306", "text": "#! Python3\n\nimport random\nimport logging\nfrom train import train_and_accuracy\n\nlogging.debug('In network file.')\n\nclass Network():\n \n logging.debug('In network calss.')\n \n def __init__(self, para_choice = None):\n self.accuracy = 0\n self.mac_op = 0\n self.para_choice = para_choice\n self.network = {}\n self.kernel = []\n self.layers = []\n self.mac = 0\n self.mem = 0\n \n def create_random(self):\n \"Create a random network.\" \n for key in self.para_choice:\n self.network[key] = random.choice(self.para_choice[key])\n \n def create_set(self, network):\n '''\n Set network properties.\n \n Args:\n network_no (int): in each generation there are fixed number of network which are created.\n \n '''\n \n logging.debug('In create set in network.')\n self.network = network\n \n def train(self,generation_no: int, network_no: int, time: str) -> None:\n '''Train the network and record the accuracy.\n \n Args:\n generation_no (int): number of generation (iteration for evolutionary algorithm).\n network_no (int): in each generation there are fixed number of network which are created.\n time: current time (\"current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\")\n '''\n logging.debug('In Network, train function.') \n \n if self.accuracy == 0 and self.mac_op == 0 and self.kernel == [] and self.layers == [] and self.mac == 0 and self.mem == 0:\n self.accuracy, self.mac_op, self.kernel, self.layers, self.mac, self.mem = train_and_accuracy(self.network, generation_no, network_no, time)\n\n def get_network(self):\n return self.network\n \n def print_network(self):\n \"Print a network.\"\n logging.info('This is with Max_pool as first two layers.')\n logging.info(f'Network Info: {self.network}, Accuracy: {self.accuracy}, Mac and Memory: {self.mac_op}, Kernel_list: {self.kernel}, Layers: {self.layers}, Mac: {self.mac}, Mem: {self.mem}')\n #logging.info(f\"Network fitness: {(self.accuracy + self.mac_op)*100:.2f}\")\n\n\n\n", "sub_path": "network.py", "file_name": "network.py", "file_ext": "py", "file_size_in_byte": 2240, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.debug", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 11, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 48, "usage_type": "call"}, {"api_name": "train.train_and_accuracy", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 59, "usage_type": "call"}]} {"seq_id": "125324373", "text": "import logging\nimport requests\nimport re\nimport csv\nimport sys\nfrom html import unescape\nsys.stdout = open(\"output.txt\",\"w\")\n\ndef get_category_list(content):\n\t\"\"\"get_category_list() takes content of home page and returns a list of categories and their urls\"\"\"\n\t\n\treturn category_pat.findall(content)\n\n\ndef get_book_list(content):\n\t\"\"\"get_book_list(content) takes a content of a book list page and returns a list of books(name ans url)\"\"\"\n\t\n\tcontent = content.replace(\"\\n\", \" \")\n\tresult = book_list_pat.findall(content)\n\treturn result\n\n\ndef get_product_details(content):\n\t\"\"\"get_product_details(content) takes content of a product page, parses the page and returns details about a product\"\"\"\n\t\n\timage_base = \"http://books.toscrape.com/\"\n\tresult = img_pat.findall(content)\n\tif(len(result) == 0):\n\t\tlogging.warning(\"Image url not found!\")\n\t\timg_url = \"\"\n\telse:\n\t\timg_url = result[0].replace(\"../../\",\"\")\n\t\timg_url = image_base + img_url\n\n\tresult = desc_pat.findall(content)\n\tif(len(result) ==0):\n\t\tlogging.warning(\"Description not found!\")\n\t\tdescription = \"\"\n\telse:\n\t\tdescription = result[0]\n\n\tresult = upc_pat.findall(content)\n\tif(len(result) ==0):\n\t\tlogging.warning(\"UPC not found!\")\n\t\tupc = \"\"\n\telse:\n\t\tupc = result[0]\n\n\tresult = price_pat.findall(content)\n\tif(len(result) ==0):\n\t\tlogging.warning(\"Price not found!\")\n\t\tprice = \"\"\n\telse:\n\t\tprice = result[0]\n\n\tresult = avail_pat.findall(content)\n\tif(len(result) ==0):\n\t\tlogging.warning(\"UPC not found!\")\n\t\tavailability = \"\"\n\telse:\n\t\tavailability = result[0]\n\n\treturn upc, price, img_url, availability, description\n\n\ndef get_page_content(url):\n\t\"\"\"get_page_content() takes a url and returns the content of the page\"\"\"\n\t\n\ttry:\n\t\tresponse = requests.get(url)\n\t\t#print(response.ok)\n\texcept requests.exceptions.RequestException as e:\n\t\tlogging.error(e)\n\n\n\tif(response.ok):\n\t\treturn response.text\n\n\tlogging.error(\"Cannot get content from URL: \" + url)\n\n\treturn None\n\n\ndef get_next_page(url, content):\n\t\"\"\"Checks the content of a book list page and returns link of the next page\"\"\"\n\t\n\tresult = next_page_pat.findall(content)\n\tif(len(result) == 0):\n\t\treturn None\n\n\ti = url.rfind(\"/\")\n\treturn url[0:i+1] + result[0]\n\n\ndef scrape_book_info(book_info, category_name):\n\t\"\"\"gets the content of abook details page, and parsws different components and stores the info\"\"\"\n\t\n\tbook_url, book_name = book_info\n\tbook_dict = {\"Name\": unescape(book_name), \"Category\": category_name}\n\t\n\tbook_url = book_url.replace(\"../../../\",\"\")\n\tbook_url = \"http://books.toscrape.com/catalogue/\" + book_url\n\tbook_dict[\"URL\"] = book_url\n\n\t#print(\"Scraping book\", book_name)\n\tlogging.info(\"Scraping : \" + book_url)\n\n\tcontent = get_page_content(book_url)\n\tcontent = content.replace(\"\\n\", \" \")\n\n\tupc, price, img_url, availability, description = get_product_details(content)\n\n\tbook_dict[\"UPC\"] = upc\n\tbook_dict[\"Price\"] = price\n\tbook_dict[\"ImageURL\"] = img_url\n\tbook_dict[\"Availability\"] = availability\n\tbook_dict[\"Description\"] = unescape(description)\n\n\tcsv_writer.writerow(book_dict)\n\n\ndef crawl_category(category_name, category_url):\n\t\"\"\"crawls a particular category of book\"\"\"\n\n\twhile True:\n\t\tcontent = get_page_content(category_url)\n\t\tbook_list = get_book_list(content)\n\n\t\tfor book in book_list:\n\t\t\tscrape_book_info(book, category_name)\n\n\t\tnext_page = get_next_page(category_url, content)\n\t\tif next_page is None:\n\t\t\tbreak\n\n\t\tcategory_url = next_page\n\n\ndef crawl_website():\n\t\"\"\"crawl_website() is the main function that coordinates the whole crawling task\"\"\"\n\n\turl = \"http://books.toscrape.com/index.html\"\n\thost_name = \"books.toscrape.com\"\n\n\tcontent = get_page_content(url)\n\tif content is None:\n\t\tlogging.critical(\"Failed to get content from \" + url)\n\t\tsys.exit(1)\n\n\tcategory_list = get_category_list(content)\n\n\tfor category in category_list:\n\t\tcategory_url, category_name = category\n\t\tcategory_url = \"http://\" + host_name + \"/\" + category_url\n\t\t#print(category_url)\n\t\t#sys.exit(1)\n\t\tcrawl_category(category_name, category_url)\n\n\nif __name__ == \"__main__\":\n\t#Compile different regular expression patterns \n\tcategory_pat = re.compile(r'<li>\\s*<a href=\"(catalogue/category/books/.*?)\">\\s*(\\w+[\\s\\w]+\\w)\\s*?<', re.M | re.DOTALL)\n\t\n\tnext_page_pat = re.compile(r'<li class=\"next\"><a href=\"(.*?)\">next</a></li>')\n\n\tbook_list_pat = re.compile(r'<h3><a href=\"(.*?)\" title=\"(.*?)\">')\n\n\timg_pat = re.compile(r'<div class=\"item active\">\\s*<img src=\"(.*?)\"')\n\n\tdesc_pat = re.compile(r'<div id=\"product_description\" class=\"sub-header\">.*?<p>(.*?)</p>')\n\n\tupc_pat = re.compile(r'<th>UPC</th>\\s*<td>(.*?)</td>')\n\n\tprice_pat = re.compile(r'<th>Price \\(incl. tax\\)</th>\\s*<td>(.*?)</td>')\n\n\tprice_pat = re.compile(r'<th>Price \\(incl. tax\\)</th>\\s*<td>\\D+([\\d.]+?)</td>')\n\n\tavail_pat = re.compile(r'<th>Availability</th>\\s*<td>(.*?)</td>')\n\n\tlogging.basicConfig(format=\"%(asctime)s %(message)s\", datefmt=\"%d/%m/%y %I:%M:%S %p\", filename=\"bookstore_crawler.log\", level=logging.DEBUG)\n\n\tfield_names = [\"Name\", \"Category\", \"UPC\", \"URL\", \"ImageURL\", \"Price\", \"Availability\", \"Description\"]\n\n\twith open(\"book_list.csv\", \"w\",encoding=\"ISO-8859-1\") as csvf:\n\t\tcsv_writer = csv.DictWriter(csvf, fieldnames=field_names)\n\t\tcsv_writer.writeheader()\n\t\tcrawl_website()", "sub_path": "Web Crawling/Full Web Crawling Code.py", "file_name": "Full Web Crawling Code.py", "file_ext": "py", "file_size_in_byte": 5148, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.stdout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 72, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 79, "usage_type": "call"}, {"api_name": "html.unescape", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 106, "usage_type": "call"}, {"api_name": "html.unescape", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 148, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 162, "usage_type": "call"}, {"api_name": "re.M", "line_number": 162, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 162, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 164, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 166, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 168, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 170, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 172, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 174, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 176, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 178, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 180, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 180, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 185, "usage_type": "call"}]} {"seq_id": "142773184", "text": "# -*- coding: utf-8 -*-\nimport logging\nimport urllib\nimport webapp2 as webapp\n\nfrom google.appengine.api import memcache\nfrom google.appengine.ext import db\nfrom google.appengine.runtime import apiproxy_errors\n\nimport ditto.config as c\nimport ditto.util\nfrom ditto.content import MirroredContent\n\n\nclass BaseHandler(webapp.RequestHandler):\n def get_relative_url(self):\n slash = self.request.url.find(\"/\", len(self.request.scheme + \"://\"))\n if slash == -1:\n return \"/\"\n return self.request.url[slash:]\n\n\nclass MirrorHandler(BaseHandler):\n def get(self, base_url):\n assert base_url\n\n # Log the user-agent and referrer, to see who is linking to us.\n logging.debug('User-Agent = \"%s\", Referrer = \"%s\"',\n self.request.user_agent, self.request.referer)\n logging.debug('Base_url = \"%s\", url = \"%s\"',\n base_url, self.request.url)\n\n translated_address = self.get_relative_url()[1:] # remove leading /\n mirrored_url = c.HTTP_PREFIX + translated_address\n\n # Use sha256 hash instead of mirrored url for the key name; key\n # names can only be 500 bytes in length; URLs may be up to 2KB.\n key_name = ditto.util.get_url_key_name(mirrored_url)\n logging.info(\"Handling request for '%s' = '%s'\", mirrored_url, key_name)\n\n content = MirroredContent.get_by_key_name(key_name)\n cache_miss = False\n if content is None:\n logging.debug(\"Cache miss\")\n cache_miss = True\n content = MirroredContent.fetch_and_store(\n key_name, base_url, translated_address, mirrored_url)\n\n if content is None:\n return self.error(404)\n\n for key, value in content.headers.iteritems():\n self.response.headers[key] = value\n\n if not c.DEBUG:\n self.response.headers['cache-control'] = \\\n 'max-age=%d' % c.EXPIRATION_DELTA_SECONDS\n\n self.response.out.write(content.data)\n\n def post(self):\n # Handle the input form to redirect the user to a relative url\n form_url = self.request.get(\"url\")\n\n if form_url:\n inputted_url = urllib.unquote(form_url)\n\n # Accept URLs that still have a leading 'http://'\n if inputted_url.startswith(c.HTTP_PREFIX):\n inputted_url = inputted_url[len(c.HTTP_PREFIX):]\n\n return self.redirect(\"/\" + inputted_url)\n", "sub_path": "ditto/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 2470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "webapp2.RequestHandler", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 30, "usage_type": "call"}, {"api_name": "ditto.config.HTTP_PREFIX", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 34, "usage_type": "name"}, {"api_name": "ditto.config.util.get_url_key_name", "line_number": 38, "usage_type": "call"}, {"api_name": "ditto.config.util", "line_number": 38, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 38, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "ditto.content.MirroredContent.get_by_key_name", "line_number": 41, "usage_type": "call"}, {"api_name": "ditto.content.MirroredContent", "line_number": 41, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 44, "usage_type": "call"}, {"api_name": "ditto.content.MirroredContent.fetch_and_store", "line_number": 46, "usage_type": "call"}, {"api_name": "ditto.content.MirroredContent", "line_number": 46, "usage_type": "name"}, {"api_name": "ditto.config.DEBUG", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 55, "usage_type": "name"}, {"api_name": "ditto.config.EXPIRATION_DELTA_SECONDS", "line_number": 57, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 57, "usage_type": "name"}, {"api_name": "urllib.unquote", "line_number": 66, "usage_type": "call"}, {"api_name": "ditto.config.HTTP_PREFIX", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 69, "usage_type": "name"}, {"api_name": "ditto.config.HTTP_PREFIX", "line_number": 70, "usage_type": "attribute"}, {"api_name": "ditto.config", "line_number": 70, "usage_type": "name"}]} {"seq_id": "95220644", "text": "from django.contrib import auth\nfrom django.core import exceptions, management\nfrom django.db import connection\nfrom django.test import TestCase\n\nimport models\n\nfrom django_postgres.function import (create_function, create_functions,\n _function_exists)\n\n\nclass FunctionModelTestCase(TestCase):\n \"\"\"Test the Function API.\n \"\"\"\n def setUp(self):\n management.call_command('sync_pgfunctions', *[], **{})\n\n def test_get_counter(self):\n \"\"\"Must run call on the manager before querying the result.\n \"\"\"\n foo_user = auth.models.User.objects.create(\n username='foo', is_superuser=True)\n foo_user.set_password('blah')\n foo_user.save()\n\n foo_superuser = models.UserTypeCounter.objects.call(\n (True, ))\n\n self.assertEqual(foo_superuser.get().my_count, 1)\n\n def test_uncalled(self):\n \"\"\"Cannot execute the statement unless you explicitly call it first\n \"\"\"\n foo_user = auth.models.User.objects.create(\n username='foo', is_superuser=True)\n foo_user.set_password('blah')\n foo_user.save()\n\n self.assertRaises(\n exceptions.ObjectDoesNotExist,\n models.UserTypeCounter.objects.filter,\n pk=1)\n\n\nclass LowLeveFunctionTestCase(TestCase):\n \"\"\"Low level tests for function creation.\n \"\"\"\n def test_create_function(self):\n \"\"\"Create a function with the low-level create_function API.\n \"\"\"\n field = ('a_field integer', )\n definition = 'SELECT 1 from auth_user WHERE id = $1'\n name = 'my_function (integer)'\n created = create_function(connection, name, field, definition)\n\n self.assertEqual(created, 'CREATED')\n\n def test_update_function(self):\n \"\"\"Update a function with create_function. Functions can only be\n updated if their signature matches the existing function.\n \"\"\"\n field = ('a_field integer', )\n definition = 'SELECT 1 from auth_user WHERE id = $1'\n name = 'my_function (integer)'\n create_function(connection, name, field, definition)\n\n definition = 'SELECT 2 from auth_user WHERE id = $1'\n\n updated = create_function(connection, name, field, definition)\n\n self.assertEqual(updated, 'UPDATED')\n\n def test_error_function(self):\n \"\"\"Error out if the user tried to update a function with an\n incompatible signature.\n \"\"\"\n field = ('a_field integer', )\n definition = 'SELECT 1 from auth_user WHERE id = $1'\n name = 'my_function (integer)'\n create_function(connection, name, field, definition)\n\n name = 'my_function (integer, integer)'\n definition = 'SELECT 1 from auth_user WHERE id > $1 and id < $2'\n\n updated = create_function(connection, name, field, definition)\n\n self.assertEqual(updated, 'ERROR: Manually Drop This Function')\n\n def test_create_functions_from_models(self):\n \"\"\"Create functions using the create_functions and passing the models\n module.\n \"\"\"\n create_result = create_functions(models)\n\n for status, _, _ in create_result:\n self.assertEqual(status, 'CREATED')\n\n # Now check it was created\n cursor_wrapper = connection.cursor()\n cursor = cursor_wrapper.cursor\n self.assertEqual(_function_exists(cursor, 'user_type'), True)\n\n def test_create_command(self):\n \"\"\"Test the sync_pgfunctions command.\n \"\"\"\n management.call_command('sync_pgfunctions', *[], **{})\n\n # Check it was created\n cursor_wrapper = connection.cursor()\n cursor = cursor_wrapper.cursor\n self.assertEqual(_function_exists(cursor, 'user_type'), True)\n", "sub_path": "tests/test_project/functiontest/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3735, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 16, "usage_type": "call"}, {"api_name": "django.core.management", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.models", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.auth", "line_number": 21, "usage_type": "name"}, {"api_name": "models.UserTypeCounter.objects.call", "line_number": 26, "usage_type": "call"}, {"api_name": "models.UserTypeCounter", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.models", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.contrib.auth", "line_number": 34, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.core.exceptions", "line_number": 40, "usage_type": "name"}, {"api_name": "models.UserTypeCounter", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 45, "usage_type": "name"}, {"api_name": "django_postgres.function.create_function", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 54, "usage_type": "argument"}, {"api_name": "django_postgres.function.create_function", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 65, "usage_type": "argument"}, {"api_name": "django_postgres.function.create_function", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 69, "usage_type": "argument"}, {"api_name": "django_postgres.function.create_function", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 80, "usage_type": "argument"}, {"api_name": "django_postgres.function.create_function", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 85, "usage_type": "argument"}, {"api_name": "django_postgres.function.create_functions", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.connection.cursor", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 99, "usage_type": "name"}, {"api_name": "django_postgres.function._function_exists", "line_number": 101, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 106, "usage_type": "call"}, {"api_name": "django.core.management", "line_number": 106, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 109, "usage_type": "name"}, {"api_name": "django_postgres.function._function_exists", "line_number": 111, "usage_type": "call"}]} {"seq_id": "251166112", "text": "# -*- coding:utf-8 -*-\nfrom django.shortcuts import render\nfrom django.views.generic import ListView\nfrom django.core.cache import caches\nfrom .models import Article, Nav, Carousel\nfrom dnglin_comments.models import Comment\nfrom dnglin_system.models import Link\nfrom django.conf import settings\nimport logging\n# Create your views here.\n\n# 缓存\ntry:\n cache=caches['memcache']\nexcept ImportError as e:\n cache = caches['default']\n\n# logger\nlogger = logging.getLogger(__name__)\n\n\nclass BaseMixin(object):\n def get_context_data(self, *args, **kwargs):\n context = super(BaseMixin, self).get_context_data(*args, **kwargs)\n try:\n # 网站标题等内容\n context['website_title'] = settings.WEBSITE_TITLE\n context['website_welcome'] = settings.WEBSITE_WELCOME\n # 热门文章\n context['hot_article_list']=Article.objects.order_by('-view_times')[0:10]\n # 导航条\n context['nav_list']=Nav.objects.filter(status=0)\n # 最新评论\n context['latest_comment_list']=Comment.objects.order_by('-create_time')[0:10]\n # 友情链接\n context['links']=Link.objects.order_by('create_time').all()\n colors=['primary','success', 'info', 'warning', 'danger']\n for index,link in enumerate(context['links']):\n link.color=colors[index % len(colors)]\n # 用户未读信息数\n user=self.request.user\n if user.is_authenticated():\n context['notification_count']=user.to_user_notification_set.filter(is_read=0).count()\n except Exception as e:\n logger.error(u'[BaseMixin]加载基本信息出错')\n return context\n\n\nclass IndexView(BaseMixin,ListView):\n template_name = 'blog/index.html'\n context_object_name = 'article_list'\n paginate_by = settings.PAGE_NUM # 分页--每页的数目\n\n def get_context_data(self, *args, **kwargs):\n # 轮播\n kwargs['carousel_page_list']=Carousel.objects.all()\n return super(IndexView,self).get_context_data(*args,**kwargs)\n\n def get_queryset(self):\n article_list=Article.objects.filter(status=0)\n return article_list\n\n", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.core.cache.caches", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.cache.caches", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.settings.WEBSITE_TITLE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.settings.WEBSITE_WELCOME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Article.objects.order_by", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Nav.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Nav.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Nav", "line_number": 32, "usage_type": "name"}, {"api_name": "dnglin_comments.models.Comment.objects.order_by", "line_number": 34, "usage_type": "call"}, {"api_name": "dnglin_comments.models.Comment.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "dnglin_comments.models.Comment", "line_number": 34, "usage_type": "name"}, {"api_name": "dnglin_system.models.Link.objects.order_by", "line_number": 36, "usage_type": "call"}, {"api_name": "dnglin_system.models.Link.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "dnglin_system.models.Link", "line_number": 36, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.settings.PAGE_NUM", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 52, "usage_type": "name"}, {"api_name": "models.Carousel.objects.all", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Carousel.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.Carousel", "line_number": 56, "usage_type": "name"}, {"api_name": "models.Article.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Article.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 60, "usage_type": "name"}]} {"seq_id": "445101629", "text": "#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport xlrd\nclass Read_File:\n def read_xlsx(self,path):\n excel = xlrd.open_workbook(path)\n # count = len(excel.sheets())\n excel = excel.sheets()[0]\n # print(sheet.nrows)\n # print(sheet.row_values(0))\n # print(sheet)\n # print(count)\n # for sheet in excel.sheets():\n # print(sheet.text())\n # return sheet\n # table = read_excel('3.xlsx',0)\n list1 = []\n for rownum in range(1, excel.nrows): # 从第2行读取\n list = excel.row_values(rownum) # 获取行数据，为列表形式\n # for i in list:\n # print(i)", "sub_path": "BOSS/method/public/read_file.py", "file_name": "read_file.py", "file_ext": "py", "file_size_in_byte": 686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "xlrd.open_workbook", "line_number": 7, "usage_type": "call"}]} {"seq_id": "82086117", "text": "import argparse\nimport numpy as np\nfrom glob import glob\nfrom os import makedirs\nfrom scipy.misc import imread, imresize\nfrom os.path import exists, join, split, realpath, dirname\n\nimport torch\nimport torch.nn.functional as F\n\nfrom utils.swap import Swap\nfrom model.vgg import VGG19\nfrom model.srntt import SRNTT\n\n\nparser = argparse.ArgumentParser('offline_patchMatch_textureSwap')\nparser.add_argument('--data_folder', type=str, default='/home/zwj/Data/RefSR/DIV2K_small', help='The dir of dataset: CUFED or DIV2K')\nargs = parser.parse_args()\n\ndata_folder = args.data_folder\nif 'CUFED' in data_folder:\n input_size = 40\nelif 'DIV2K' in data_folder:\n input_size = 80\nelse:\n raise Exception('Unrecognized dataset!')\n\ninput_path = join(data_folder, 'input')\nref_path = join(data_folder, 'ref')\nmatching_layer = ['relu3_1', 'relu2_1', 'relu1_1']\nsave_path = join(data_folder, 'map_321')\nif not exists(save_path):\n makedirs(save_path)\n\ninput_files = sorted(glob(join(input_path, '*.png')))\nref_files = sorted(glob(join(ref_path, '*.png')))\nn_files = len(input_files)\nassert n_files == len(ref_files)\n\nsrntt = SRNTT(16).cuda()\nprint('Loading SRNTT ...')\nckpt = torch.load('/home/zwj/Projects/Python/SRNTT_Pytorch/log/srntt_vgg19_div2k/2019-09-20-10:06:34/' +\n 'checkpoint/best.pth')\nsrntt.load_state_dict(ckpt['srntt'])\nprint('Done.')\nprint('Loading VGG19 ...')\nnet_vgg19 = VGG19('relu_5-1', ['relu_1-1', 'relu_2-1', 'relu_3-1'], True).cuda()\nprint('Done.')\nswaper = Swap(3, 1)\n\nprint_format = '%%0%dd/%%0%dd' % (len(str(n_files)), len(str(n_files)))\nfor i in range(n_files):\n file_name = join(save_path, split(input_files[i])[-1].replace('.png', '.npz'))\n if exists(file_name):\n continue\n print(print_format % (i + 1, n_files))\n img_in_lr = imresize(imread(input_files[i], mode='RGB'), (input_size, input_size), interp='bicubic')\n img_in_lr = img_in_lr.astype(np.float32) / 127.5 - 1\n img_ref = imresize(imread(ref_files[i], mode='RGB'), (input_size * 4, input_size * 4), interp='bicubic')\n img_ref = img_ref.astype(np.float32) / 127.5 - 1\n img_ref_lr = imresize(img_ref, (input_size, input_size), interp='bicubic')\n img_ref_lr = img_ref_lr.astype(np.float32) / 127.5 - 1\n\n img_in_lr = torch.from_numpy(img_in_lr.transpose((2, 0, 1))).unsqueeze(0).cuda()\n img_ref = torch.from_numpy(img_ref.transpose((2, 0, 1))).unsqueeze(0).cuda()\n img_ref_lr = torch.from_numpy(img_ref_lr.transpose((2, 0, 1))).unsqueeze(0).cuda()\n\n with torch.no_grad():\n img_in_sr = (srntt(img_in_lr, None, None)[0] + 1) * 127.5\n img_ref_sr = (srntt(img_ref_lr, None, None)[0] + 1) * 127.5\n\n # get feature maps via VGG19\n map_in_sr = net_vgg19(img_in_sr)[0][-1]\n map_ref = net_vgg19(img_ref)[0]\n map_ref_sr = net_vgg19(img_ref_sr)[0][-1]\n\n # patch matching and swapping\n other_style = []\n for idx in range(len(map_ref)):\n map_ref[idx] = map_ref[idx].cpu().squeeze().numpy().transpose((1, 2, 0))\n other_style.append([map_ref[idx]])\n other_style = other_style[:-1]\n\n map_in_sr = map_in_sr.cpu().squeeze().numpy().transpose((1, 2, 0))\n map_ref_sr = map_ref_sr.cpu().squeeze().numpy().transpose((1, 2, 0))\n\n maps, weights, correspondence = swaper.conditional_swap_multi_layer(\n content=map_in_sr,\n style=[map_ref[-1]],\n condition=[map_ref_sr],\n other_styles=other_style,\n is_weight=True\n )\n\n # save maps\n np.savez(file_name, target_map=maps, weights=weights, correspondence=correspondence)\n", "sub_path": "offline_patchMatch_textureSwap.py", "file_name": "offline_patchMatch_textureSwap.py", "file_ext": "py", "file_size_in_byte": 3547, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 33, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "model.srntt.SRNTT", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 42, "usage_type": "call"}, {"api_name": "model.vgg.VGG19", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.swap.Swap", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.misc.imread", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "scipy.misc.imresize", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.misc.imread", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "scipy.misc.imresize", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 96, "usage_type": "call"}]} {"seq_id": "605009169", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 15 21:12:14 2019\n\n@author: karthikchowdary\n\"\"\"\nimport pandas\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation\n\n# load dataset\nfrom sklearn.model_selection import train_test_split\n\nimport pandas as pd\n\ndata = pd.read_csv(\"winequalityN.csv\", sep = \",\") \ndata.wine[data.wine == 'white'] = 1\ndata.wine[data.wine == 'red'] = 2\nwith open(\"winequalityN.csv\",'r') as f:\n with open(\"updated_test.csv\",'w') as f1:\n next(f) # skip header line\n for line in f:\n f1.write(line)\n\ndataset = pd.read_csv(\"updated_test.csv\", sep = \",\").values\n\nprint(dataset)\n\nimport numpy as np\nX_train, X_test, Y_train, Y_test = train_test_split(dataset[:,1:12], dataset[:,0],\n test_size=0.25, random_state=87)\n\n\n\nnp.random.seed(155)\n\nfrom tensorflow.python.framework import ops\nops.reset_default_graph()\n\nmy_first_nn = Sequential()\nmy_first_nn.add(Dense(105, input_dim=12, activation='relu'))\n\n\nmy_first_nn.add(Dense(125, input_dim=105, activation='relu'))\nmy_first_nn.add(Dense(1, activation='sigmoid')) \nmy_first_nn.compile(loss='binary_crossentropy', optimizer='adam')\nmy_first_nn_fitted = my_first_nn.fit(X_train, Y_train, epochs=100)\nprint(my_first_nn.summary())", "sub_path": "course/Deep Learning/project/wine.py", "file_name": "wine.py", "file_ext": "py", "file_size_in_byte": 1319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.python.framework.ops.reset_default_graph", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 39, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 46, "usage_type": "call"}]} {"seq_id": "212543419", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 08 09:26:21 2017\r\n\r\n@author: Harrison Ball\r\n\r\n2017-09-18, RW\r\n - ISEG EHQ102M serial number 480996 back from repair, implement again, seems to work\r\n\"\"\"\r\nfrom collections import OrderedDict\r\n\r\n#=================================================================================================\r\n\r\n# ------------------------ DICTIONARY OF ISEG IDENTIFIERS ------------------------\r\n# maps USB IDs assigned by computer to serial numbers of iseg modules. \r\n# user to ensure dictionary is current, with each re-installation any hardware interface\r\n\r\n#=================================================================================================\r\n\r\n# ------------------------# ------------------------# ------------------------\r\n# This self-contained code queries all ISEG modules an lists the USB IDs of all\r\n# modules currently connected, then gener*ates a list of corresponding serial\r\n# numbers\r\n# ------------------------# ------------------------# ------------------------ \r\n''' \r\nimport visa\r\nimport numpy as np \r\n\r\nrm = visa.ResourceManager()\r\nVISA_IDs= rm.list_resources() \r\nprint VISA_IDs\r\nDEVICES = [rm.open_resource(VISA_IDs[i]) for i in range(len(VISA_IDs))] \r\n\r\ndef DEVICES_ID(device):\r\n ID=device.query('*IDN?')\r\n return ID\r\n \r\nDEVICE_ID_INFO = np.zeros((1, 2)) \r\nfor i in range(len(VISA_IDs)): \r\n DEVICE_ID_INFO = np.concatenate((DEVICE_ID_INFO, np.array([[VISA_IDs[i], \r\n DEVICES_ID(DEVICES[i])]]))) \r\n#print DEVICE_ID_INFO\r\n\r\nfor i in range(len(DEVICE_ID_INFO)):\r\n print DEVICE_ID_INFO[i]\r\n\r\n \r\nCURRENT CONEXTIONS\r\n\r\n[u'ASRL4::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480996,3.14\\r\\n']\r\n[u'ASRL5::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480997,3.14\\r\\n']\r\n[u'ASRL17::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480936,3.14\\r\\n']\r\n[u'ASRL18::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480935,3.14\\r\\n']\r\n[u'ASRL19::INSTR' u'THURLBY THANDAR, MX100TP, 436129, 1.03-1.00-1.02\\r\\n']\r\n[u'ASRL20::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480931,3.14\\r\\n']\r\n[u'ASRL21::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480934,3.14\\r\\n']\r\n[u'ASRL22::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480932,3.14\\r\\n']\r\n[u'ASRL23::INSTR' u'iseg Spezialelektronik GmbH,EHQ 102,480933,3.14\\r\\n']\r\n \r\n''' \r\n \r\n#____________ BIJECTIVE DICTIONARY: return ID/USB given USB/ID ______________\r\n\r\nInstID_2_VisaID={}\r\n\r\nInstID_2_VisaID[u'ASRL4::INSTR'] = 'iseg:480996'\r\nInstID_2_VisaID[u'ASRL5::INSTR'] = 'iseg:480997'\r\n#InstID_2_VisaID[u'ASRL6::INSTR'] = 'TEMP:iseg:480499'#TEMPORARY UNIT ON LOAN WHILE UNIT 480996 IS BEING REPAIRED\r\nInstID_2_VisaID[u'ASRL17::INSTR'] = 'iseg:480936'\r\nInstID_2_VisaID[u'ASRL18::INSTR'] = 'iseg:480935'\r\nInstID_2_VisaID[u'ASRL19::INSTR'] = 'MX100TP:436129'\r\nInstID_2_VisaID[u'ASRL20::INSTR'] = 'iseg:480931'\r\nInstID_2_VisaID[u'ASRL21::INSTR'] = 'iseg:480934'\r\nInstID_2_VisaID[u'ASRL22::INSTR'] = 'iseg:480932'\r\nInstID_2_VisaID[u'ASRL23::INSTR'] = 'iseg:480933' \r\n\r\nInstID_2_VisaID['iseg:480996'] = u'ASRL4::INSTR' \r\nInstID_2_VisaID['iseg:480997'] = u'ASRL5::INSTR'\r\n#InstID_2_VisaID['TEMP:iseg:480499'] = u'ASRL6::INSTR' \r\nInstID_2_VisaID['iseg:480936'] = u'ASRL17::INSTR'\r\nInstID_2_VisaID['iseg:480935'] = u'ASRL18::INSTR'\r\nInstID_2_VisaID['MX100TP:436129'] = u'ASRL19::INSTR' \r\nInstID_2_VisaID['iseg:480931'] = u'ASRL20::INSTR' \r\nInstID_2_VisaID['iseg:480934'] = u'ASRL21::INSTR' \r\nInstID_2_VisaID['iseg:480932'] = u'ASRL22::INSTR' \r\nInstID_2_VisaID['iseg:480933'] = u'ASRL23::INSTR' #TEMPORARY UNIT ON LOAN WHILE UNIT 480996 IS BEING REPAIRED\r\n\r\nInstID_2_VisaID['ISEG GND'] = 'N/A: ISEG GND'\r\n\r\n#=================================================================================================\r\n\r\n# -------- CREATE LIST OF COLOURS FOR ASSOCIATING PERMENANTLY TO TRAP SURFACES -------\r\n\r\n#=================================================================================================\r\n\r\n\r\ncolours = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f']\r\n\r\n\r\n\r\n#=================================================================================================\r\n\r\n# -------- DEFINE DICTIONARIES FOR CNX ATTRIBUTES FOR ALL UHV SYSTEM COMPONENTS --------\r\n\r\n#=================================================================================================\r\n#\r\n#\r\n#\r\n# ________________________________________________________________________________________________\r\n\r\n# ----------------------# FT#1 (D-SUB) # ------------------------\r\n# ________________________________________________________________________________________________\r\n\r\n\r\n# ----------------------# ----------------------# ----------------------# ------------------------\r\n# ----------------------# ELECTRON GUN #1 # ------------------------\r\n# ----------------------# ----------------------# ----------------------# ------------------------\r\n#\r\n#____________________ 'Electron Gun #1: emission-bias' _____________________\r\n\r\negun1_emission_bias = {}\r\negun1_emission_bias['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: emission-bias'\r\negun1_emission_bias['TRAP_SURF_NAME_SHORT'] = 'egun1:emission-bias'\r\negun1_emission_bias['ID_TAG'] = '1.1 & 1.2'\r\negun1_emission_bias['FT_PIN'] = '1.1 & 1.2'\r\negun1_emission_bias['INST_ID'] = 'iseg:480935' #previously UNIT 480996, but exchanged with STring (now controlled by TEMP:480449 during repair of faulty 480996 unit)\r\negun1_emission_bias['INST_CHANNEL'] = 'N/A'\r\negun1_emission_bias['VISA_ID'] = InstID_2_VisaID[egun1_emission_bias['INST_ID']]\r\negun1_emission_bias['NIDAQ_AI'] = 'to be filled'\r\negun1_emission_bias['NIDAQ_2_HV_CONVERSION'] = 'N/A'\r\n\r\n#____________________ 'Electron Gun #1: emission-source' _____________________\r\n\r\negun1_emission_source = {}\r\negun1_emission_source['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: emission-source'\r\negun1_emission_source['TRAP_SURF_NAME_SHORT'] = 'egun1:emission-source'\r\negun1_emission_source['ID_TAG'] = '1.1/1.2'\r\negun1_emission_source['FT_PIN'] = '1.1/1.2'\r\negun1_emission_source['INST_ID'] = 'MX100TP:436129'\r\negun1_emission_source['INST_CHANNEL'] = 1\r\negun1_emission_source['VISA_ID'] = InstID_2_VisaID[egun1_emission_bias['INST_ID']]\r\negun1_emission_source['NIDAQ_AI'] = 'to be filled'\r\negun1_emission_source['COLOUR'] = colours[0]\r\n\r\n#____________________ 'Electron Gun #1: cathode plate' _____________________\r\n\r\negun1_cathode = {}\r\negun1_cathode['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: cathode plate'\r\negun1_cathode['TRAP_SURF_NAME_SHORT'] = 'egun1:cathode'\r\negun1_cathode['ID_TAG'] = '1.5'\r\negun1_cathode['FT_PIN'] = '1.5'\r\negun1_cathode['INST_ID'] = 'MX100TP:436129'\r\negun1_cathode['INST_CHANNEL'] = 3\r\negun1_cathode['VISA_ID'] = InstID_2_VisaID[egun1_cathode['INST_ID']]\r\negun1_cathode['NIDAQ_AI'] = 'to be filled'\r\negun1_cathode['COLOUR'] = colours[0]\r\n\r\n#____________________ 'Electron Gun #1: annode plate' _____________________\r\n\r\negun1_anode = {}\r\negun1_anode['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: anode plate'\r\negun1_anode['TRAP_SURF_NAME_SHORT'] = 'egun1:anode'\r\negun1_anode['ID_TAG'] = '1.6'\r\negun1_anode['FT_PIN'] = '1.6'\r\negun1_anode['INST_ID'] = 'MX100TP:436129'\r\negun1_anode['INST_CHANNEL'] = 2\r\negun1_anode['VISA_ID'] = InstID_2_VisaID[egun1_anode['INST_ID']]\r\negun1_anode['NIDAQ_AI'] = 'to be filled'\r\negun1_anode['COLOUR'] = colours[0]\r\n\r\n\r\n# ----------------------# ----------------------# ----------------------# ------------------------\r\n# ----------------------# ELECTRON GUN #2 # ------------------------\r\n# ----------------------# ----------------------# ----------------------# ------------------------\r\n\r\n\r\n#____________________ 'Electron Gun #2: emission-bias' _____________________\r\n\r\negun2_emission_bias = {}\r\negun2_emission_bias['TRAP_SURF_NAME_LONG'] = 'Electron Gun #2: emission-bias'\r\negun2_emission_bias['TRAP_SURF_NAME_SHORT'] = 'egun2:emission-bias'\r\negun2_emission_bias['ID_TAG'] = '1.7/1.8 OR 1.9/1.10'\r\negun2_emission_bias['FT_PIN'] = '1.7/1.8 OR 1.9/1.10'\r\negun2_emission_bias['INST_ID'] = 'MX100TP:436129'\r\negun2_emission_bias['INST_CHANNEL'] = 1\r\negun2_emission_bias['VISA_ID'] = InstID_2_VisaID[egun2_emission_bias['INST_ID']]\r\negun2_emission_bias['NIDAQ_AI'] = 'to be filled'\r\negun2_emission_bias['COLOUR'] = colours[0]\r\n\r\n#____________________ 'Electron Gun #2: cathode plate' _____________________\r\n\r\negun2_cathode = {}\r\negun2_cathode['TRAP_SURF_NAME_LONG'] = 'Electron Gun #2: cathode plate'\r\negun2_cathode['TRAP_SURF_NAME_SHORT'] = 'egun2:cathode'\r\negun2_cathode['ID_TAG'] = '1.11'\r\negun2_cathode['FT_PIN'] = '1.11'\r\negun2_cathode['INST_ID'] = 'MX100TP:436129'\r\negun2_cathode['INST_CHANNEL'] = 2\r\negun2_cathode['VISA_ID'] = InstID_2_VisaID[egun2_cathode['INST_ID']]\r\negun2_cathode['NIDAQ_AI'] = 'to be filled'\r\negun2_cathode['COLOUR'] = colours[0]\r\n\r\n#____________________ 'Electron Gun #2: annode plate' _____________________\r\n\r\negun2_anode = {}\r\negun2_anode['TRAP_SURF_NAME_LONG'] = 'Electron Gun #2: anode plate'\r\negun2_anode['TRAP_SURF_NAME_SHORT'] = 'egun2:anode'\r\negun2_anode['ID_TAG'] = '1.12'\r\negun2_anode['FT_PIN'] = '1.12'\r\negun2_anode['INST_ID'] = 'MX100TP:436129'\r\negun2_anode['INST_CHANNEL'] = 3\r\negun2_anode['VISA_ID'] = InstID_2_VisaID[egun2_anode['INST_ID']]\r\negun2_anode['NIDAQ_AI'] = 'to be filled'\r\negun2_anode['COLOUR'] = colours[0]\r\n\r\n\r\n# ________________________________________________________________________________________________\r\n\r\n# ----------------------# FT#2 # ------------------------\r\n# ________________________________________________________________________________________________\r\n##\r\n#\r\n#____________________ 'Electron Gun #1: ExB plate (LHS)' _____________________\r\n\r\negun1ExB_LHS = {}\r\negun1ExB_LHS['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: ExB plate (LHS)'\r\negun1ExB_LHS['TRAP_SURF_NAME_SHORT'] = 'egun1:ExB:LHS'\r\negun1ExB_LHS['ID_TAG'] = '2.1'\r\negun1ExB_LHS['FT_PIN'] = '2.1'\r\negun1ExB_LHS['INST_ID'] = 'iseg:480936'#(previously: 'iseg:480996', exchanged with STE2)\r\negun1ExB_LHS['INST_CHANNEL'] = 'N/A'\r\negun1ExB_LHS['VISA_ID'] = InstID_2_VisaID[egun1ExB_LHS['INST_ID']]\r\negun1ExB_LHS['NIDAQ_AI'] = '6'\r\negun1ExB_LHS['NIDAQ_2_HV_CONVERSION'] = 'N/A'\r\n\r\n#____________________ 'Electron Gun #1: ExB plate (RHS)' _____________________\r\n\r\negun1ExB_RHS = {}\r\negun1ExB_RHS['TRAP_SURF_NAME_LONG'] = 'Electron Gun #1: ExB plate (RHS)'\r\negun1ExB_RHS['TRAP_SURF_NAME_SHORT'] = 'egun1:ExB:RHS'\r\negun1ExB_RHS['ID_TAG'] = '2.2'\r\negun1ExB_RHS['FT_PIN'] = '2.2'\r\negun1ExB_RHS['INST_ID'] = 'iseg:480997'\r\negun1ExB_RHS['INST_CHANNEL'] = 'N/A'\r\negun1ExB_RHS['VISA_ID'] = InstID_2_VisaID[egun1ExB_RHS['INST_ID']]\r\negun1ExB_RHS['NIDAQ_AI'] = '7'\r\negun1ExB_RHS['NIDAQ_2_HV_CONVERSION'] = 'N/A'\r\n\r\n#____________________ 'Electron Gun #2: ExB plate (LHS)' _____________________\r\n\r\negun2ExB_LHS = {}\r\negun2ExB_LHS['TRAP_SURF_NAME_LONG'] = 'Electron Gun #2: ExB plate (LHS)'\r\negun2ExB_LHS['TRAP_SURF_NAME_SHORT'] = 'egun2:ExB:LHS'\r\negun2ExB_LHS['ID_TAG'] = '2.3'\r\negun2ExB_LHS['FT_PIN'] = '2.3'\r\negun2ExB_LHS['INST_ID'] = 'iseg:480996'\r\negun2ExB_LHS['INST_CHANNEL'] = 'N/A'\r\n#egun2ExB_LHS['VISA_ID'] = InstID_2_VisaID[egun2ExB_LHS['INST_ID']]\r\negun2ExB_LHS['NIDAQ_AI'] = '8'\r\negun2ExB_LHS['NIDAQ_2_HV_CONVERSION'] = 'N/A'\r\n\r\n#____________________ 'Electron Gun #2: ExB plate (RHS)' _____________________\r\n\r\negun2ExB_RHS = {}\r\negun2ExB_RHS['TRAP_SURF_NAME_LONG'] = 'Electron Gun #2: ExB plate (RHS)'\r\negun2ExB_RHS['TRAP_SURF_NAME_SHORT'] = 'egun2:ExB:RHS'\r\negun2ExB_RHS['ID_TAG'] = '2.4'\r\negun2ExB_RHS['FT_PIN'] = '2.4'\r\negun2ExB_RHS['INST_ID'] = 'iseg:480997'\r\negun2ExB_RHS['INST_CHANNEL'] = 'N/A'\r\n#egun2ExB_RHS['VISA_ID'] = InstID_2_VisaID[egun2ExB_RHS['INST_ID']]\r\negun2ExB_RHS['NIDAQ_AI'] = '9'\r\negun2ExB_RHS['NIDAQ_2_HV_CONVERSION'] = 'N/A'\r\n\r\n\r\n#____________________ 'Loading Trap: end-cap #1' _____________________\r\n\r\nLoading_EndCap1 = {}\r\nLoading_EndCap1['TRAP_SURF_NAME_LONG'] = 'Loading Trap: end-cap #1'\r\nLoading_EndCap1['TRAP_SURF_NAME_SHORT'] = 'LoadTrp:EC1'\r\nLoading_EndCap1['ID_TAG'] = '2.5'\r\nLoading_EndCap1['FT_PIN'] = '2.5'\r\nLoading_EndCap1['INST_ID'] = 'ISEG GND'\r\nLoading_EndCap1['VISA_ID'] = 'ISEG GND'\r\nLoading_EndCap1['NIDAQ_AI'] = 'to be filled'\r\n\r\n#____________________ 'Loading Trap: centre ring' _____________________\r\n\r\nLoading_CentreRing = {}\r\nLoading_CentreRing['TRAP_SURF_NAME_LONG'] = 'Loading Trap: centre ring'\r\nLoading_CentreRing['TRAP_SURF_NAME_SHORT'] = 'LoadTrp:CR'\r\nLoading_CentreRing['ID_TAG'] = '2.6'\r\nLoading_CentreRing['FT_PIN'] = '2.6'\r\nLoading_CentreRing['INST_ID'] = 'iseg:480931'\r\nLoading_CentreRing['VISA_ID'] = InstID_2_VisaID[Loading_CentreRing['INST_ID']]\r\nLoading_CentreRing['NIDAQ_AI'] = '0'\r\n#Loading_CentreRing['NIDAQ_2_HV_CONVERSION'] = 1000/4.250\r\nLoading_CentreRing['NIDAQ_2_HV_CONVERSION'] = 220.1218463\r\nLoading_CentreRing['COLOUR'] = colours[0]\r\n\r\n#____________________ 'Loading Trap: end-cap #2' _____________________\r\n\r\nLoading_EndCap2 = {}\r\nLoading_EndCap2['TRAP_SURF_NAME_LONG'] = 'Loading Trap: end-cap #2'\r\nLoading_EndCap2['TRAP_SURF_NAME_SHORT'] = 'LoadTrp:EC2'\r\nLoading_EndCap2['ID_TAG'] = '2.7'\r\nLoading_EndCap2['FT_PIN'] = '2.7'\r\nLoading_EndCap2['INST_ID'] = 'iseg:480932'\r\nLoading_EndCap2['VISA_ID'] = InstID_2_VisaID[Loading_EndCap2['INST_ID']]\r\nLoading_EndCap2['NIDAQ_AI'] = '1'\r\n#Loading_EndCap2['NIDAQ_2_HV_CONVERSION'] = 1000/4.160\r\nLoading_EndCap2['NIDAQ_2_HV_CONVERSION'] = 220.7230592\r\nLoading_EndCap2['COLOUR'] = colours[1]\r\n\r\n#\r\n#\r\n# ________________________________________________________________________________________________\r\n\r\n# ----------------------# FT#3 # ------------------------\r\n# ________________________________________________________________________________________________\r\n\r\n#____________________ 'Rotating Wall: Quadruplet #1' _____________________\r\n\r\nRW1 = {}\r\nRW1['TRAP_SURF_NAME_LONG'] = 'Rotating Wall: Quadruplet #1'\r\nRW1['TRAP_SURF_NAME_SHORT'] = 'RW1'\r\nRW1['ID_TAG'] = '3.4'\r\nRW1['FT_PIN'] = '3.1'\r\nRW1['INST_ID'] = 'iseg:480933'\r\nRW1['VISA_ID'] = InstID_2_VisaID[RW1['INST_ID']]\r\nRW1['NIDAQ_AI'] = '2'\r\n#RW1['NIDAQ_2_HV_CONVERSION'] = 1000/4.260\r\nRW1['NIDAQ_2_HV_CONVERSION'] = 220.6153391\r\nRW1['COLOUR'] = colours[2]\r\n\r\n#____________________ 'Rotating Wall: Quadruplet #2' _____________________\r\n\r\nRW2 = {}\r\nRW2['TRAP_SURF_NAME_LONG'] = 'Rotating Wall: Quadruplet #2'\r\nRW2['TRAP_SURF_NAME_SHORT'] = 'RW2'\r\nRW2['ID_TAG'] = '3.5'\r\nRW2['FT_PIN'] = '3.2'\r\nRW2['INST_ID'] = 'iseg:480933'\r\nRW2['VISA_ID'] = InstID_2_VisaID[RW2['INST_ID']]\r\nRW2['NIDAQ_AI'] = '2'\r\n#RW2['NIDAQ_2_HV_CONVERSION'] = 1000/4.260\r\nRW2['NIDAQ_2_HV_CONVERSION'] = RW1['NIDAQ_2_HV_CONVERSION']\r\nRW2['COLOUR'] = colours[2]\r\n\r\n#____________________ 'Rotating Wall: Quadruplet #3' _____________________\r\n\r\nRW3 = {}\r\nRW3['TRAP_SURF_NAME_LONG'] = 'Rotating Wall: Quadruplet #3'\r\nRW3['TRAP_SURF_NAME_SHORT'] = 'RW3'\r\nRW3['ID_TAG'] = '3.6'\r\nRW3['FT_PIN'] = '3.3'\r\nRW3['INST_ID'] = 'iseg:480933'\r\nRW3['VISA_ID'] = InstID_2_VisaID[RW3['INST_ID']]\r\nRW3['NIDAQ_AI'] = '2'\r\n#RW3['NIDAQ_2_HV_CONVERSION'] = 1000/4.260\r\nRW3['NIDAQ_2_HV_CONVERSION'] = RW1['NIDAQ_2_HV_CONVERSION']\r\nRW3['COLOUR'] = colours[2]\r\n\r\n#____________________ 'Rotating Wall: Quadruplet #4' _____________________\r\n\r\nRW4 = {}\r\nRW4['TRAP_SURF_NAME_LONG'] = 'Rotating Wall: Quadruplet #4'\r\nRW4['TRAP_SURF_NAME_SHORT'] = 'RW4'\r\nRW4['ID_TAG'] = '3.7'\r\nRW4['FT_PIN'] = '3.4'\r\nRW4['INST_ID'] = 'iseg:480933'\r\nRW4['VISA_ID'] = InstID_2_VisaID[RW4['INST_ID']]\r\nRW4['NIDAQ_AI'] = '2'\r\n#RW4['NIDAQ_2_HV_CONVERSION'] = 1000/4.260\r\nRW4['NIDAQ_2_HV_CONVERSION'] = RW1['NIDAQ_2_HV_CONVERSION']\r\nRW4['COLOUR'] = colours[2]\r\n#\r\n#\r\n# ________________________________________________________________________________________________\r\n\r\n# ----------------------# FT#4 # ------------------------\r\n# ________________________________________________________________________________________________\r\n\r\n#\r\n#____________________ 'Science Trap: end-cap #1' _____________________\r\n\r\nScience_EndCap1 = {}\r\nScience_EndCap1['TRAP_SURF_NAME_LONG'] = 'Science Trap: end-cap #1'\r\nScience_EndCap1['TRAP_SURF_NAME_SHORT'] = 'SciTrp:EC1'\r\nScience_EndCap1['ID_TAG'] = '3.1'\r\nScience_EndCap1['FT_PIN'] = '4.1'\r\nScience_EndCap1['INST_ID'] = 'iseg:480934'\r\nScience_EndCap1['VISA_ID'] = InstID_2_VisaID[Science_EndCap1['INST_ID']]\r\nScience_EndCap1['NIDAQ_AI'] = '3'\r\n#Science_EndCap1['NIDAQ_2_HV_CONVERSION'] = 1000/4.260\r\nScience_EndCap1['NIDAQ_2_HV_CONVERSION'] = 221.1746219\r\nScience_EndCap1['COLOUR'] = colours[3]\r\n\r\n#____________________ 'Science Trap: centre ring' _____________________\r\n\r\nScience_CentreRing = {}\r\nScience_CentreRing['TRAP_SURF_NAME_LONG'] = 'Science Trap: centre ring'\r\nScience_CentreRing['TRAP_SURF_NAME_SHORT'] = 'SciTrp:CR'\r\nScience_CentreRing['ID_TAG'] = '3.2'\r\nScience_CentreRing['FT_PIN'] = '4.2'\r\nScience_CentreRing['INST_ID'] = 'iseg:480996'# previously 'iseg:480935', before switching this unit to egun bias\r\nScience_CentreRing['VISA_ID'] = InstID_2_VisaID[Science_CentreRing['INST_ID']]\r\nScience_CentreRing['NIDAQ_AI'] = '4'\r\n#Science_CentreRing['NIDAQ_2_HV_CONVERSION'] = 1000/4.185\r\nScience_CentreRing['NIDAQ_2_HV_CONVERSION'] = 221.1201484\r\nScience_CentreRing['COLOUR'] = colours[4]\r\n\r\n#____________________ 'Science Trap: end-cap #2' _____________________\r\n\r\nScience_EndCap2 = {}\r\nScience_EndCap2['TRAP_SURF_NAME_LONG'] = 'Science Trap: end-cap #2'\r\nScience_EndCap2['TRAP_SURF_NAME_SHORT'] = 'SciTrp:EC2'\r\nScience_EndCap2['ID_TAG'] = '3.3'\r\nScience_EndCap2['FT_PIN'] = '4.3'\r\nScience_EndCap2['INST_ID'] = 'ISEG GND' #previously: iseg:480996' #previously: 'iseg:480936', exchanged with egun1 ExB LHS\r\nScience_EndCap2['VISA_ID'] = InstID_2_VisaID[Science_EndCap2['INST_ID']]\r\nScience_EndCap2['NIDAQ_AI'] = '5'\r\n#Science_EndCap2['NIDAQ_2_HV_CONVERSION'] = 1000/4.195\r\nScience_EndCap2['NIDAQ_2_HV_CONVERSION'] = 219.4036463\r\nScience_EndCap2['COLOUR'] = colours[5]\r\n\r\n#____________________ 'Wire Mesh' _____________________\r\n\r\nWireMesh = {}\r\nWireMesh['TRAP_SURF_NAME_LONG'] = 'Wire Mesh'\r\nWireMesh['TRAP_SURF_NAME_SHORT'] = 'Wire Mesh'\r\nWireMesh['ID_TAG'] = '3.4'\r\nWireMesh['FT_PIN'] = '4.4'\r\nWireMesh['INST_ID'] = 'ISEG GND'\r\nWireMesh['VISA_ID'] = 'ISEG GND'\r\nWireMesh['NIDAQ_AI'] = 'to be filled'\r\n\r\n#=================================================================================================\r\n\r\n# TOP-LEVEL DICTIONARY CALLING E-GUN SURFACES\r\n\r\n#=================================================================================================\r\n\r\negun1_surf_dict = OrderedDict()\r\n\r\n\r\negun1_surf_dict['Electron Gun #1: emission-bias']= egun1_emission_bias \r\negun1_surf_dict['Electron Gun #1: emission-source']= egun1_emission_source \r\n\r\negun1_surf_dict['Electron Gun #1: cathode plate']= egun1_cathode \r\negun1_surf_dict['Electron Gun #1: anode plate']= egun1_anode \r\n \r\negun1_surf_dict['Electron Gun #1: ExB plate (LHS)']= egun1ExB_LHS \r\negun1_surf_dict['Electron Gun #1: ExB plate (RHS)']= egun1ExB_RHS\r\n\r\n \r\negun2_surf_dict = OrderedDict() \r\n \r\negun2_surf_dict['Electron Gun #2: emission-bias']= egun2_emission_bias \r\negun2_surf_dict['Electron Gun #2: cathode plate']= egun2_cathode \r\negun2_surf_dict['Electron Gun #2: anode plate']= egun2_anode \r\n \r\negun2_surf_dict['Electron Gun #2: ExB plate (LHS)']= egun2ExB_LHS \r\negun2_surf_dict['Electron Gun #2: ExB plate (RHS)']= egun2ExB_RHS\r\n\r\n \r\n#=================================================================================================\r\n\r\n# TOP-LEVEL DICTIONARY CALLING TRAP-SUFRACES \r\n\r\n#=================================================================================================\r\n\r\n\r\ntrap_surf_dict = OrderedDict()\r\n\r\ntrap_surf_dict['Loading Trap: end-cap #1']= Loading_EndCap1\r\ntrap_surf_dict['Loading Trap: centre ring']= Loading_CentreRing\r\ntrap_surf_dict['Loading Trap: end-cap #2']= Loading_EndCap2\r\n\r\ntrap_surf_dict['Rotating Wall: Quadruplet #1']= RW1\r\ntrap_surf_dict['Rotating Wall: Quadruplet #2']= RW2\r\ntrap_surf_dict['Rotating Wall: Quadruplet #3']= RW3\r\ntrap_surf_dict['Rotating Wall: Quadruplet #4']= RW4\r\n\r\ntrap_surf_dict['Science Trap: end-cap #1']= Science_EndCap1\r\ntrap_surf_dict['Science Trap: centre ring']= Science_CentreRing\r\ntrap_surf_dict['Science Trap: end-cap #2']= Science_EndCap2\r\n\r\ntrap_surf_dict['Wire Mesh']= WireMesh\r\n\r\n#=================================================================================================\r\n\r\n# TOP-LEVEL DICTIONARY CALLING TRAP-SUFRACES TO BE CONTROLLED BY ISEG HV POWER SUPPLY \r\n\r\n#=================================================================================================\r\n\r\n\r\niseg_controlled_trap_surfaces = OrderedDict()\r\n\r\n #iseg_controlled_trap_surfaces['Loading Trap: end-cap #1']= Loading_EndCap1\r\n\r\niseg_controlled_trap_surfaces['Loading Trap: centre ring']= Loading_CentreRing\r\niseg_controlled_trap_surfaces['Loading Trap: end-cap #2']= Loading_EndCap2\r\niseg_controlled_trap_surfaces['Science Trap: end-cap #1']= Science_EndCap1\r\niseg_controlled_trap_surfaces['Rotating Wall: Quadruplet #1']= RW1\r\niseg_controlled_trap_surfaces['Science Trap: centre ring']= Science_CentreRing\r\n#iseg_controlled_trap_surfaces['Science Trap: end-cap #2']= Science_EndCap2\r\n\r\n #iseg_controlled_trap_surfaces['Wire Mesh']= WireMesh\r\n\r\n#=================================================================================================\r\n\r\n# TOP-LEVEL DICTIONARY CALLING EGUN-SUFRACES TO BE CONTROLLED BY ISEG HV POWER SUPPLY \r\n\r\n#=================================================================================================\r\n\r\nMX100TP_controlled_egun_surfaces = OrderedDict() \r\niseg_controlled_egun_surfaces = OrderedDict() \r\n\r\nMX100TP_controlled_egun_surfaces['Electron Gun #1: cathode plate']= egun1_cathode \r\nMX100TP_controlled_egun_surfaces['Electron Gun #1: anode plate']= egun1_anode \r\n\r\nMX100TP_controlled_egun_surfaces['Electron Gun #1: emission-source']= egun1_emission_source \r\niseg_controlled_egun_surfaces['Electron Gun #1: emission-bias']= egun1_emission_bias \r\n\r\niseg_controlled_egun_surfaces['Electron Gun #1: ExB plate (LHS)']= egun1ExB_LHS \r\niseg_controlled_egun_surfaces['Electron Gun #1: ExB plate (RHS)']= egun1ExB_RHS \r\n \r\n \r\n \r\n \r\n\r\n\r\n", "sub_path": "Penning_trap_cxn_dictionaries_v1.py", "file_name": "Penning_trap_cxn_dictionaries_v1.py", "file_ext": "py", "file_size_in_byte": 22605, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "collections.OrderedDict", "line_number": 444, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 457, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 474, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 498, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 517, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 518, "usage_type": "call"}]} {"seq_id": "69390624", "text": "# This file is part of Tryton. The COPYRIGHT file at the top level of\n# this repository contains the full copyright notices and license terms.\nimport warnings\n\nfrom sql import Cast, Literal, Query, Expression\nfrom sql.functions import Substring, Position\n\nfrom .field import (Field, search_order_validate, context_validate,\n with_inactive_records)\nfrom .selection import SelectionMixin\nfrom ...transaction import Transaction\nfrom ...pool import Pool\nfrom ...rpc import RPC\n\n\nclass Reference(Field, SelectionMixin):\n '''\n Define a reference field (``str``).\n '''\n _type = 'reference'\n _sql_type = 'VARCHAR'\n\n def __init__(self, string='', selection=None, selection_change_with=None,\n search_order=None, search_context=None, help='', required=False,\n readonly=False, domain=None, states=None, select=False,\n on_change=None, on_change_with=None, depends=None, context=None,\n loading='lazy', datetime_field=None):\n '''\n :param selection: A list or a function name that returns a list.\n The list must be a list of tuples. First member is an internal name\n of model and the second is the user name of model.\n :param datetime_field: The name of the field that contains the datetime\n value to read the target records.\n :param search_order: The order to use when searching for a record\n :param search_context: The context to use when searching for a record\n '''\n if datetime_field:\n if depends:\n depends.append(datetime_field)\n else:\n depends = [datetime_field]\n super(Reference, self).__init__(string=string, help=help,\n required=required, readonly=readonly, domain=domain, states=states,\n select=select, on_change=on_change, on_change_with=on_change_with,\n depends=depends, context=context, loading=loading)\n self.datetime_field = datetime_field\n self.selection = selection or None\n self.selection_change_with = set()\n if selection_change_with:\n warnings.warn('selection_change_with argument is deprecated, '\n 'use the depends decorator',\n DeprecationWarning, stacklevel=2)\n self.selection_change_with |= set(selection_change_with)\n self.__search_order = None\n self.search_order = search_order\n self.__search_context = None\n self.search_context = search_context or {}\n\n __init__.__doc__ += Field.__init__.__doc__\n\n @property\n def search_order(self):\n return self.__search_order\n\n @search_order.setter\n def search_order(self, value):\n search_order_validate(value)\n self.__search_order = value\n\n @property\n def search_context(self):\n return self.__search_context\n\n @search_context.setter\n def search_context(self, value):\n context_validate(value)\n self.__search_context = value\n\n def set_rpc(self, model):\n super(Reference, self).set_rpc(model)\n if not isinstance(self.selection, (list, tuple)):\n assert hasattr(model, self.selection), \\\n 'Missing %s on model %s' % (self.selection, model.__name__)\n instantiate = 0 if self.selection_change_with else None\n model.__rpc__.setdefault(\n self.selection, RPC(instantiate=instantiate))\n\n def get(self, ids, model, name, values=None):\n '''\n Replace removed reference id by None.\n '''\n pool = Pool()\n if values is None:\n values = {}\n res = {}\n for i in values:\n res[i['id']] = i[name]\n ref_to_check = {}\n for i in ids:\n if not (i in res):\n res[i] = None\n continue\n if not res[i]:\n continue\n ref_model, ref_id = res[i].split(',', 1)\n if not ref_model:\n continue\n try:\n ref_id = int(ref_id)\n except Exception:\n continue\n if ref_id < 0:\n continue\n res[i] = ref_model + ',' + str(ref_id)\n ref_to_check.setdefault(ref_model, (set(), []))\n ref_to_check[ref_model][0].add(ref_id)\n ref_to_check[ref_model][1].append(i)\n\n # Check if reference ids still exist\n with Transaction().set_context(active_test=False), \\\n Transaction().set_context(_check_access=False):\n for ref_model, (ref_ids, ids) in ref_to_check.items():\n try:\n pool.get(ref_model)\n except KeyError:\n res.update(dict((i, None) for i in ids))\n continue\n Ref = pool.get(ref_model)\n refs = Ref.search([\n ('id', 'in', list(ref_ids)),\n ], order=[])\n refs = list(map(str, refs))\n for i in ids:\n if res[i] not in refs:\n res[i] = None\n return res\n\n def __set__(self, inst, value):\n from ..model import Model\n if not isinstance(value, (Model, type(None))):\n if isinstance(value, str):\n target, value = value.split(',')\n else:\n target, value = value\n Target = Pool().get(target)\n if isinstance(value, dict):\n value = Target(**value)\n else:\n value = Target(value)\n super(Reference, self).__set__(inst, value)\n\n def sql_format(self, value):\n if not isinstance(value, (str, Query, Expression)):\n try:\n value = '%s,%s' % tuple(value)\n except TypeError:\n pass\n return super(Reference, self).sql_format(value)\n\n @with_inactive_records\n def convert_domain(self, domain, tables, Model):\n if '.' not in domain[0]:\n return super(Reference, self).convert_domain(domain, tables, Model)\n pool = Pool()\n name, operator, value, target = domain[:4]\n Target = pool.get(target)\n table, _ = tables[None]\n name, target_name = name.split('.', 1)\n assert name == self.name\n column = self.sql_column(table)\n target_domain = [(target_name,) + tuple(domain[1:3])\n + tuple(domain[4:])]\n if 'active' in Target._fields:\n target_domain.append(('active', 'in', [True, False]))\n query = Target.search(target_domain, order=[], query=True)\n return (Cast(Substring(column,\n Position(',', column) + Literal(1)),\n Model.id.sql_type().base).in_(query)\n & column.ilike(target + ',%'))\n", "sub_path": "lib/python3.8/site-packages/trytond/model/fields/reference.py", "file_name": "reference.py", "file_ext": "py", "file_size_in_byte": 6785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "field.Field", "line_number": 16, "usage_type": "name"}, {"api_name": "selection.SelectionMixin", "line_number": 16, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 50, "usage_type": "call"}, {"api_name": "field.Field.__init__", "line_number": 59, "usage_type": "attribute"}, {"api_name": "field.Field", "line_number": 59, "usage_type": "name"}, {"api_name": "field.search_order_validate", "line_number": 67, "usage_type": "call"}, {"api_name": "field.context_validate", "line_number": 76, "usage_type": "call"}, {"api_name": "rpc.RPC", "line_number": 86, "usage_type": "call"}, {"api_name": "pool.Pool", "line_number": 92, "usage_type": "call"}, {"api_name": "transaction.Transaction", "line_number": 120, "usage_type": "call"}, {"api_name": "transaction.Transaction", "line_number": 121, "usage_type": "call"}, {"api_name": "pool.get", "line_number": 124, "usage_type": "call"}, {"api_name": "pool.get", "line_number": 128, "usage_type": "call"}, {"api_name": "model.Model", "line_number": 140, "usage_type": "name"}, {"api_name": "pool.Pool", "line_number": 145, "usage_type": "call"}, {"api_name": "sql.Query", "line_number": 153, "usage_type": "name"}, {"api_name": "sql.Expression", "line_number": 153, "usage_type": "name"}, {"api_name": "model.Model", "line_number": 163, "usage_type": "name"}, {"api_name": "pool.Pool", "line_number": 164, "usage_type": "call"}, {"api_name": "pool.get", "line_number": 166, "usage_type": "call"}, {"api_name": "sql.Cast", "line_number": 176, "usage_type": "call"}, {"api_name": "sql.functions.Substring", "line_number": 176, "usage_type": "call"}, {"api_name": "sql.functions.Position", "line_number": 177, "usage_type": "call"}, {"api_name": "sql.Literal", "line_number": 177, "usage_type": "call"}, {"api_name": "model.Model.id.sql_type", "line_number": 178, "usage_type": "call"}, {"api_name": "model.Model.id", "line_number": 178, "usage_type": "attribute"}, {"api_name": "model.Model", "line_number": 178, "usage_type": "name"}, {"api_name": "field.with_inactive_records", "line_number": 160, "usage_type": "name"}]} {"seq_id": "426584858", "text": "#!/usr/bin/env python3\nimport time\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt\n\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\ndef importMNIST(numout):\n digit_mnist = keras.datasets.mnist\n (train_images, train_labels), (test_images, test_labels) = digit_mnist.load_data()\n image_size = train_images[0].shape[1] * train_images[0].shape[1]\n \n numin = image_size\n \n a = np.array([np.zeros(numin) for i in range(numout)])\n i = 0\n j = 0\n while(i < numout and j < numin):\n for x in range(numin // numout):\n a[i][j+x] = 1\n i = i + 1\n j = j + numin // numout\n a = np.matrix(a)\n image_size = train_images[0].shape[1] * train_images[0].shape[1]\n train_images = np.array([np.reshape(x, (image_size, 1)) for x in train_images])\n test_images = np.array([np.reshape(x, (image_size, 1)) for x in test_images])\n \n train_images = np.array([a * i for i in train_images]) #/ 255.0\n \n print(train_images) \n test_images = np.array([a * i for i in test_images]) #/ 255.0\n print(train_images.shape)\n print(test_images.shape)\n return train_images, train_labels, test_images, test_labels\n\n\ndef importMNIST():\n fashion_mnist = keras.datasets.fashion_mnist\n (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n plt.figure(figsize = (10, 10))\n for i in range(25):\n plt.subplot(5, 5, i+ 1)\n plt.xticks([])\n plt.yticks([])\n plt.grid(False)\n plt.imshow(train_images[i], cmap=plt.cm.binary)\n plt.xlabel(class_names[train_labels[i]])\n plt.show() \n image_size = train_images[0].shape[1] * train_images[0].shape[1]\n train_images = np.array([np.reshape(x, (image_size, 1)) for x in train_images])\n test_images = np.array([np.reshape(x, (image_size, 1)) for x in test_images])\n train_images = train_images / 1000.0 #PRODUCES SAME OUTPUT NO MATTER WHAT NUMBER\n test_images = test_images / 1000.0\n return train_images, train_labels, test_images, test_labels\n\ndef createAB(train_images, train_labels):\n image_size = train_images.shape[1]\n samples = train_images.shape[0]\n maxLab = max(train_labels)\n minLab = min(train_labels)\n outputSize = maxLab - minLab\n \n labels = np.array([np.zeros(outputSize + 1) for i in range(samples)])\n\n for i in range(samples):\n labels[i][train_labels[i] - minLab] = 1\n \n B = np.matrix(np.stack([x for x in labels]))\n A = np.hstack((np.matrix(np.array([1 for i in range(samples)])).T, np.matrix(train_images)))\n print(A[0])\n print(B[0])\n return A, B\n\ndef solveAB(A, B, startIndex):\n start = time.time()\n print(A.shape)\n A = A[:startIndex]\n B = B[:startIndex]\n print(A.shape)\n aTransp = A.T * A\n bTransp = A.T * B\n print(aTransp[0])\n print(bTransp[0])\n omega = np.linalg.solve(aTransp, bTransp)\n alpha = np.matrix(omega[0]).T\n beta = np.matrix(omega[1:]).T\n end = time.time()\n print(\"Time Taken to train model: %f seconds\" % (end - start))\n print(alpha, beta)\n return alpha, beta\n\ndef testAccuracy(test_images, test_labels, alpha, beta):\n image_size = test_images.shape[1]\n dif = 0\n errorsa = np.array([], np.int32)\n errorsV = np.array([], np.int32)\n print(test_images.shape)\n for i in range(test_images.shape[0]):\n value = test_labels[i]\n a = np.reshape(test_images[i], (1, image_size))\n a = np.matrix(a[0]).T\n result = np.array(((beta * a) + alpha).T)[0]\n a = result.argmax()\n if(a != value ):\n errorsa = np.append(errorsa, int(a))\n errorsV = np.append(errorsV, int(value))\n dif = dif+1\n \n n, bins, patches = plt.hist(errorsa, bins = 10)\n plt.show()\n\n\n\n print(\"Accuracy: out of %d samples, %d where incorrect -- %f%% accuracy\" % (test_images.shape[0], dif, (test_images.shape[0] - dif) / test_images.shape[0]))\n return (test_images.shape[0] - dif) / test_images.shape[0]\n \n\nif __name__ == \"__main__\": \n train_images, train_labels, test_images, test_labels = importMNIST()\n A, B = createAB(train_images, train_labels)\n alpha, beta = solveAB(A, B, 20000)\n testAccuracy(test_images, test_labels, alpha, beta)\n", "sub_path": "Leastsquares.py", "file_name": "Leastsquares.py", "file_ext": "py", "file_size_in_byte": 4399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tensorflow.keras.datasets", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 90, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}]} {"seq_id": "106951276", "text": "import pygame\r\nfrom sys import exit\r\nimport random\r\n\r\n\r\nclass Bullet:\r\n def __init__(self):\r\n self.x=0\r\n self.y=-1\r\n self.image= pygame.image.load('bullet.png').convert_alpha()\r\n self.active=False\r\n\r\n def move(self):\r\n if self.active:\r\n self.y-=3\r\n if self.y<0:\r\n self.active=False\r\n \r\n def restart(self):\r\n mousex,mousey=pygame.mouse.get_pos()\r\n self.x=mousex-self.image.get_width()/2\r\n self.y=mousey-self.image.get_height()/2\r\n self.active=True\r\n \r\n \r\nclass Air:\r\n def __init__(self):\r\n self.x=225\r\n self.y=400\r\n self.image=pygame.image.load('air.png').convert_alpha()\r\n\r\n def Move(self):\r\n mousex,mousey=pygame.mouse.get_pos()\r\n self.x=mousex-self.image.get_width()/2\r\n self.y=mousey-self.image.get_height()/2\r\n\r\n def restart(self):\r\n self.x=225\r\n self.y=400\r\n self.image=pygame.image.load('air.png').convert_alpha()\r\n \r\n\r\n\r\nclass Enemy:\r\n def restart(self):\r\n self.x=random.uniform(30,420)\r\n self.y=random.uniform(-200,-50)\r\n \r\n def __init__(self):\r\n self.restart()\r\n self.image=pygame.image.load('enemy.png')\r\n self.speed=0.08\r\n\r\n def move(self):\r\n if self.y>608:\r\n self.speed+=0.01\r\n self.restart()\r\n \r\n else:\r\n self.y=self.y+self.speed\r\n \r\n \r\n\r\npygame.init()\r\nscreen=pygame.display.set_mode((450,608))\r\npygame.display.set_caption('Star War')\r\nbackground=pygame.image.load('background.png').convert()\r\ninterval_b=0\r\nindex_b=0\r\nbullets=[]\r\n\r\n\r\nfor i in range(100):\r\n bullets.append(Bullet())\r\n\r\nair=Air()\r\nenemies=[]\r\nfor i in range(6):\r\n enemies.append(Enemy())\r\n\r\ndef checkHit(enemy, bullet):\r\n if (bullet.x > enemy.x and bullet.x < enemy.x + enemy.image.get_width())\\\r\n and (bullet.y > enemy.y and bullet.y < enemy.y + enemy.image.get_height()):\r\n enemy.restart()\r\n bullet.active = False\r\n return True\r\n return False\r\n\r\ndef checkCrash(enemy, air):\r\n if (enemy.x + enemy.image.get_width() > 1.2*air.x and\\\r\n enemy.x < air.x + 0.6*air.image.get_width())and\\\r\n (enemy.y<air.y+air.image.get_height()and\\\r\n enemy.y+enemy.image.get_height()>air.y):\r\n return True\r\n return False\r\n\r\n\r\n\r\n\r\n\r\nscore=0\r\nfont=pygame.font.Font(None,32)\r\ngameover=False\r\nstart=False\r\n\r\nwhile True:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n if gameover and event.type==pygame.MOUSEBUTTONUP:\r\n air.restart()\r\n for e in enemies:\r\n e.restart()\r\n for b in bullets:\r\n b.restart()\r\n score=0\r\n gameover=False\r\n \r\n if event.type==pygame.MOUSEBUTTONDOWN:\r\n start=True\r\n screen.blit(background,(0,0))\r\n interval_b-=10\r\n if interval_b<0:\r\n bullets[index_b].restart()\r\n interval_b=100\r\n index_b=(1+index_b)%100\r\n\r\n if not gameover and start:\r\n for b in bullets:\r\n if b.active:\r\n for e in enemies:\r\n if checkHit(e,b):\r\n score+=100\r\n b.move()\r\n screen.blit(b.image,(b.x,b.y))\r\n\r\n for e in enemies:\r\n if checkCrash(e,air):\r\n \r\n gameover=True\r\n start=False\r\n e.move()\r\n screen.blit(e.image,(e.x,e.y)) \r\n air.Move()\r\n screen.blit(air.image,(air.x,air.y))\r\n text=font.render('Score:%d' % score,1,(0,0,0))\r\n screen.blit(text,(0,0))\r\n \r\n\r\n if gameover:\r\n text=font.render('Score:%d' % score,1,(0,0,0))\r\n screen.blit(text,(160,150))\r\n if gameover==False and start==False:\r\n text=font.render('Click to Start Game!',1,(0,0,0))\r\n screen.blit(text,(100,150))\r\n \r\n pygame.display.update()\r\n \r\n \r\n \r\n \r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n \r\n", "sub_path": "star.py", "file_name": "star.py", "file_ext": "py", "file_size_in_byte": 4149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 40, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 46, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 109, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONUP", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 158, "usage_type": "attribute"}]} {"seq_id": "374638137", "text": "\"\"\"The main warehose pipeline definition.\n\"\"\"\n\nimport hashlib\nimport json\nimport logging\nimport os\nimport re\nimport struct\nfrom io import BytesIO\nfrom pathlib import Path, posixpath\n\nimport bonobo\nimport boto3\nimport mondrian\nimport pydicom\nfrom bonobo.config import use\nfrom botocore.exceptions import ClientError\n\nfrom warehouse.components import constants, services\n\n# set up logging\nmondrian.setup(excepthook=True)\nlogger = logging.getLogger()\n\ns3_resource = boto3.resource(\"s3\")\ns3_client = boto3.client(\"s3\")\n\nBUCKET_NAME = os.getenv(\"WAREHOUSE_BUCKET\", default=\"chest-data-warehouse\")\nbucket = s3_resource.Bucket(BUCKET_NAME)\n\nDRY_RUN = bool(os.getenv(\"DRY_RUN\", default=False))\n\nKB = 1024\n\n###\n# Helpers\n###\ndef object_exists(key):\n \"\"\" Checking whether a given object exists in our work bucket\n\n :param key: the object key in question\n :type key: string\n :raises botocore.exceptions.ClientError: if there's any transfer error\n :return: True if object exists in the work bucket\n :rtype: boolean\n \"\"\"\n try:\n bucket.Object(key).load()\n except ClientError as e:\n if e.response[\"Error\"][\"Code\"] == \"404\":\n return False\n else:\n raise ClientError\n else:\n return True\n\n\ndef get_date_from_key(key):\n \"\"\" Extract date from an object key from the bucket's directory pattern,\n for a given prefix\n\n :param key: the object key in question\n :type key: string\n :return: the extracted date if found\n :rtype: string or None\n \"\"\"\n date_match = re.match(r\"^.+/(?P<date>\\d{4}-\\d{2}-\\d{2})/.+\", key)\n if date_match:\n return date_match.group(\"date\")\n\n\ndef get_submitting_centre_from_object(obj):\n \"\"\"Extract the SubmittingCentre value from an S3 object that is\n a JSON file in the expected format.\n\n :param obj: the S3 object of the JSON file to process\n :type obj: boto3.resource('s3').ObjectSummary\n :return: the value defined for SubmittingCentre in the file\n :rtype: string or None\n \"\"\"\n file_content = obj.Object().get()[\"Body\"].read().decode(\"utf-8\")\n try:\n json_content = json.loads(file_content)\n except json.decoder.JSONDecodeError:\n logger.error(\"Couldn't decode contents of {obj.key} as JSON.\")\n raise\n return json_content.get(\"SubmittingCentre\")\n\n\ndef patient_in_training_set(\n patient_id, training_percent=constants.TRAINING_PERCENTAGE\n):\n \"\"\" Separating patient ID's into training and validation sets, check\n which one this ID should fall into.\n\n It uses a hashing (sha512) to get pseudo-randomisation based on ID,\n and do the cut-off with a set percentage.\n\n :param patient_id: the candidate patient ID\n :type patient_id: string\n :param training_percent: the percentage of patience to assign to the training set (defaults to the global TRAINING_PERCENTAGE)\n :type training_percent: int\n :return: True if the patient ID should fall into the training set\n :rtype: boolean\n \"\"\"\n return (\n int(\n hashlib.sha512(\n patient_id.strip().upper().encode(\"utf-8\")\n ).hexdigest(),\n 16,\n )\n % 100\n < training_percent\n )\n\n\ndef inplace_nullify(d, key):\n \"\"\"\n Recurse through a dictionary and set the value `key` to `None`\n\n Extracted from https://bitbucket.org/scicomcore/dcm2slimjson/src/master/dcm2slimjson/main.py\n\n :param d: dict to modify\n :type d: dict\n :param key: specific key to modify\n :type key: anything that can be a dict key\n \"\"\"\n if isinstance(d, list):\n [inplace_nullify(_, key) for _ in d]\n\n if isinstance(d, dict):\n for k, v in d.items():\n\n if k == key:\n d[k] = None\n\n if isinstance(v, (dict, list)):\n inplace_nullify(v, key)\n\n\ndef scrub_dicom(fd):\n \"\"\"Remove binary data and other unusuaed sections from a DICOM image.\n\n Extracted from https://bitbucket.org/scicomcore/dcm2slimjson/src/master/dcm2slimjson/main.py\n\n :param fd: image data to scrub\n :type fd: pydicom.FileDataset\n :return: the scrubbed image data\n :rtype: dict\n \"\"\"\n\n # Use a large value to bypass binary data handler\n out = fd.to_json_dict(bulk_data_threshold=1e20)\n\n # Drop binary data\n inplace_nullify(out, \"InlineBinary\")\n\n # Remove Value of Interest (VOI) transform data\n inplace_nullify(out, \"00283010\")\n\n return out\n\n\nclass PartialDicom:\n \"\"\"Download partial DICOM files iteratively, to save\n on traffic.\n \"\"\"\n\n def __init__(self, obj, initial_range_kb=20):\n # Default value of 20Kb initial range is based on\n # tests run on representative data\n self._found_image_tag = False\n self.obj = obj\n self.range_kb = initial_range_kb\n\n def _stop_when(self, tag, VR, length):\n \"\"\"Custom stopper for the DICOM reader, to stop\n at the pixel data, but also note whether that\n tag was actually reached.\n \"\"\"\n self._found_image_tag = tag == (0x7FE0, 0x0010)\n return self._found_image_tag\n\n def download(self):\n \"\"\"Download file iteratively, and return the image data\n \"\"\"\n with BytesIO() as tmp:\n while True:\n tmp.seek(0)\n toprange = (self.range_kb * KB) - 1\n stream = self.obj.get(Range=f\"bytes=0-{toprange}\")[\"Body\"]\n tmp.write(stream.read())\n tmp.seek(0)\n try:\n image_data = pydicom.filereader.read_partial(\n tmp, stop_when=self._stop_when\n )\n if self._found_image_tag or tmp.tell() < toprange:\n # We've found the image tag, or there was not image tag\n # to be found in this image\n break\n except (OSError, struct.error):\n # Can happen when file got truncated in the middle of a data field\n pass\n except Exception:\n raise\n self.range_kb *= 2\n return image_data\n\n\n###\n# Transformation steps\n###\n@use(\"config\")\ndef load_config(config):\n \"\"\"Load configuration from the bucket\n \"\"\"\n try:\n obj = bucket.Object(constants.CONFIG_KEY).get()\n contents = json.loads(obj[\"Body\"].read().decode(\"utf-8\"))\n config.set_config(contents)\n yield\n except ClientError as ex:\n if ex.response[\"Error\"][\"Code\"] == \"NoSuchKey\":\n logger.warning(\n \"No configuration found in the bucket! (not going to do any loading)\"\n )\n else:\n raise\n\n\n@use(\"keycache\")\n@use(\"patientcache\")\n@use(\"inventory\")\ndef load_existing_files(keycache, patientcache, inventory):\n \"\"\" Loading existing files from the training and\n validation sets into the keycache.\n\n :param keycache: the key cache service (provided by bonobo)\n :type keycache: Keycache\n \"\"\"\n # Set up our listing function.\n if inventory.enabled:\n listing = inventory.filter_keys\n else:\n # When using the original listing without inventory, we need to\n # transform the objects returned by the filter\n def listing(Prefix):\n return map(\n lambda obj: obj.key, bucket.objects.filter(Prefix=Prefix)\n )\n\n patient_file_name = re.compile(\n r\"^.+/data/(?P<patient_id>.*)/(?:data|status)_\\d{4}-\\d{2}-\\d{2}.json$\"\n )\n for group, prefix in [\n (\"validation\", constants.VALIDATION_PREFIX),\n (\"training\", constants.TRAINING_PREFIX),\n ]:\n for key in listing(Prefix=prefix):\n m = patient_file_name.match(key)\n if m:\n # It is a patient file\n patient_id = m.group(\"patient_id\")\n patientcache.add(patient_id, group)\n else:\n # It is an image file\n try:\n keycache.add(key)\n except services.DuplicateKeyError:\n logger.exception(f\"{key} is duplicate in cache.\")\n continue\n return bonobo.constants.NOT_MODIFIED\n\n\n@use(\"config\")\n@use(\"inventory\")\n@use(\"rawsubfolderlist\")\ndef extract_raw_folders(config, inventory, rawsubfolderlist):\n \"\"\" Extractor: get all date folders within the `raw/` data drop\n\n :return: subfolders within the `raw/` prefix (yield)\n :rtype: string\n \"\"\"\n for site_raw_prefix in config.get_raw_prefixes():\n if not site_raw_prefix.endswith(\"/\"):\n site_raw_prefix += \"/\"\n\n if inventory.enabled:\n prefixes = inventory.list_folders(site_raw_prefix)\n else:\n result = s3_client.list_objects(\n Bucket=BUCKET_NAME, Prefix=site_raw_prefix, Delimiter=\"/\"\n )\n prefixes = [p.get(\"Prefix\") for p in result.get(\"CommonPrefixes\")]\n # list folders in date order\n for folder in sorted(prefixes, reverse=False):\n for subfolder in rawsubfolderlist.get():\n yield folder + subfolder\n\n\n@use(\"inventory\")\ndef extract_raw_files_from_folder(folder, inventory):\n \"\"\" Extract files from a given date folder in the data dump\n\n :param folder: the folder to process\n :type key: string\n :return: each object (yield)\n :rtype: boto3.resource('s3').ObjectSummary\n \"\"\"\n listing = inventory.filter if inventory.enabled else bucket.objects.filter\n for obj in listing(Prefix=folder):\n yield \"process\", obj, None\n\n\n@use(\"keycache\")\n@use(\"config\")\n@use(\"patientcache\")\ndef process_image(*args, keycache, config, patientcache):\n \"\"\" Processing images from the raw dump\n\n Takes a single image, downloads it into temporary storage\n and extracts its metadata.\n\n The metadata is then uploaded here, except if the file already exists.\n\n If the image file already exists at the correct location, it's not passed\n on to the next step.\n\n :param obj: the object in question\n :type obj: boto3.resource('s3').ObjectSummary\n :param keycache: the key cache service (provided by bonobo)\n :type keycache: Keycache\n :return: a task name, the original object, and a new key where it should be copied within the bucket\n :rtype: (string, boto3.resource('s3').ObjectSummary, string)\n \"\"\"\n # check file type\n task, obj, _ = args\n if task != \"process\" or Path(obj.key).suffix.lower() != \".dcm\":\n # not an image, don't do anything with it\n return bonobo.constants.NOT_MODIFIED\n\n # check if work is already done\n image_in_cache = keycache.exists(obj.key)\n image_uuid = Path(obj.key).stem\n metadata_in_cache = keycache.exists(f\"{image_uuid}.json\")\n if metadata_in_cache and image_in_cache:\n # files exist, nothing to do here\n return\n\n # download the image\n image_data = PartialDicom(obj.Object()).download()\n if image_data is None:\n # we couldn't read the image data correctly\n logger.warning(\n f\"Object '{obj.key}' couldn't be loaded as a DICOM file, skipping!\"\n )\n return\n\n # extract the required data from the image\n patient_id = image_data.PatientID\n study_id = image_data.StudyInstanceUID\n series_id = image_data.SeriesInstanceUID\n group = patientcache.get_group(patient_id)\n if group is not None:\n training_set = group == \"training\"\n else:\n logger.error(\n f\"Image without patient data: {obj.key}; \"\n + f\"included patient ID: {patient_id}; \"\n + \"skipping!\"\n )\n return\n prefix = (\n constants.TRAINING_PREFIX\n if training_set\n else constants.VALIDATION_PREFIX\n )\n image_type = constants.MODALITY.get(\n image_data[\"Modality\"].value, \"unknown\"\n )\n\n date = get_date_from_key(obj.key)\n if date:\n # the location of the new files\n new_key = posixpath.join(\n prefix,\n image_type,\n patient_id,\n study_id,\n series_id,\n Path(obj.key).name,\n )\n metadata_key = posixpath.join(\n prefix,\n f\"{image_type}-metadata\",\n patient_id,\n study_id,\n series_id,\n f\"{image_uuid}.json\",\n )\n # send off to copy or upload steps\n if not object_exists(new_key):\n yield \"copy\", obj, new_key\n if not object_exists(metadata_key):\n yield \"metadata\", metadata_key, image_data\n\n\ndef process_dicom_data(*args):\n \"\"\"Process DICOM images, by scrubbing the image data\n\n :param task: task informatiomn, needs to be equal to \"metadata\" to be processed here\n :type task: string\n :param metadata_key: location to upload the extracted metadata later\n :type metadata_key: string\n :param image_data: DICOM image data\n :type image_data: pydicom.FileDataset\n :return: metadata key and scrubbed image data, if processed\n :rtype: tuple\n \"\"\"\n task, metadata_key, image_data, = args\n if task == \"metadata\":\n scrubbed_image_data = scrub_dicom(image_data)\n yield \"upload\", metadata_key, json.dumps(scrubbed_image_data)\n\n\ndef upload_text_data(*args):\n \"\"\"Upload the text data to the correct bucket location.\n\n :param task: selector to run this task or not, needs to be \"upload\" to process a file\n :type task: string\n :param outgoing_key: location to upload the data\n :type outgoing_key: string\n :param outgoing_data: text to file content to upload\n :type outgoing_data: string\n \"\"\"\n task, outgoing_key, outgoing_data, = args\n if (\n task == \"upload\"\n and outgoing_key is not None\n and outgoing_data is not None\n ):\n if DRY_RUN:\n logger.info(f\"Would upload to key: {outgoing_key}\")\n else:\n bucket.put_object(Body=outgoing_data, Key=outgoing_key)\n\n return bonobo.constants.NOT_MODIFIED\n\n\n@use(\"config\")\n@use(\"patientcache\")\ndef process_patient_data(*args, config, patientcache):\n \"\"\"Processing patient data from the raw dump\n\n Get the patient ID from the filename, do a training/validation\n test split, and create the key for the new location for the\n next processing step to copy things to.\n\n :param obj: the object in question\n :type obj: boto3.resource('s3').ObjectSummary\n :return: a task name, the original object, and a new key where it should be copied within the bucket\n :rtype: (string, boto3.resource('s3').ObjectSummary, string)\n \"\"\"\n task, obj, _ = args\n if task != \"process\" or Path(obj.key).suffix.lower() != \".json\":\n # Not a data file, don't do anything with it\n yield bonobo.constants.NOT_MODIFIED\n\n m = re.match(\n r\"^(?P<patient_id>.*)_(?P<outcome>data|status)$\", Path(obj.key).stem\n )\n if m is None:\n # Can't interpret this file based on name, skip\n return\n\n patient_id = m.group(\"patient_id\")\n outcome = m.group(\"outcome\")\n\n group = patientcache.get_group(patient_id)\n if group is not None:\n training_set = group == \"training\"\n else:\n # patient group is not cached\n submitting_centre = get_submitting_centre_from_object(obj)\n if submitting_centre is None:\n logger.error(\n f\"{obj.key} does not have 'SubmittingCentre' entry, skipping!\"\n )\n return\n\n config_group = config.get_site_group(submitting_centre)\n if config_group is None:\n logger.warning(\n f\"Site '{submitting_centre}' is not in configuration, skipping!\"\n )\n return\n if config_group == \"split\":\n training_set = patient_in_training_set(\n patient_id, config.get_training_percentage()\n )\n else:\n # deciding between \"training\" and \"validation\" groups.\n training_set = config_group == \"training\"\n patientcache.add(\n patient_id, \"training\" if training_set else \"validation\"\n )\n\n prefix = (\n constants.TRAINING_PREFIX\n if training_set\n else constants.VALIDATION_PREFIX\n )\n date = get_date_from_key(obj.key)\n if date is not None:\n new_key = f\"{prefix}data/{patient_id}/{outcome}_{date}.json\"\n if not object_exists(new_key):\n yield \"copy\", obj, new_key\n\n\ndef data_copy(*args):\n \"\"\"Copy objects within the bucket\n\n Only if both original object and new key is provided.\n\n :param task: selector to run this task or not, needs to be \"copy\" to process a file\n :type task: string\n :param obj: the object key in question\n :type obj: boto3.resource('s3').ObjectSummary\n :param obj: the new key to copy data to\n :type obj: string\n :return: standard constant for bonobo \"load\" steps, so they can be chained\n :rtype: bonobo.constants.NOT_MODIFIED\n \"\"\"\n task, obj, new_key, = args\n if task == \"copy\" and obj is not None and new_key is not None:\n if DRY_RUN:\n logger.info(f\"Would copy: {obj.key} -> {new_key}\")\n else:\n bucket.copy({\"Bucket\": obj.bucket_name, \"Key\": obj.key}, new_key)\n\n return bonobo.constants.NOT_MODIFIED\n\n\n###\n# Graph setup\n###\ndef get_graph(**options):\n \"\"\"\n This function builds the graph that needs to be executed.\n\n :return: bonobo.Graph\n \"\"\"\n graph = bonobo.Graph()\n\n graph.add_chain(\n load_config,\n load_existing_files,\n extract_raw_folders,\n extract_raw_files_from_folder,\n )\n\n graph.add_chain(data_copy, _input=None, _name=\"copy\")\n\n graph.add_chain(\n # bonobo.Limit(30),\n process_patient_data,\n _input=extract_raw_files_from_folder,\n _output=\"copy\",\n )\n\n graph.add_chain(\n # bonobo.Limit(30),\n process_image,\n _input=process_patient_data,\n _output=\"copy\",\n )\n\n graph.add_chain(process_dicom_data, upload_text_data, _input=process_image)\n\n return graph\n\n\ndef get_services(**options):\n \"\"\"\n This function builds the services dictionary, which is a simple dict of names-to-implementation used by bonobo\n for runtime injection.\n\n It will be used on top of the defaults provided by bonobo (fs, http, ...). You can override those defaults, or just\n let the framework define them. You can also define your own services and naming is up to you.\n\n :return: dict\n \"\"\"\n config = services.PipelineConfig()\n keycache = services.KeyCache()\n patientcache = services.PatientCache()\n rawsubfolderlist = services.SubFolderList()\n\n if bool(os.getenv(\"SKIP_INVENTORY\", default=False)):\n inventory = services.Inventory()\n else:\n inventory = services.Inventory(main_bucket=BUCKET_NAME)\n\n return {\n \"config\": config,\n \"keycache\": keycache,\n \"patientcache\": patientcache,\n \"rawsubfolderlist\": rawsubfolderlist,\n \"inventory\": inventory,\n }\n\n\ndef main():\n \"\"\"Execute the pipeline graph\n \"\"\"\n parser = bonobo.get_argument_parser()\n with bonobo.parse_args(parser) as options:\n bonobo.run(get_graph(**options), services=get_services(**options))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "warehouse-loader/warehouse/warehouseloader.py", "file_name": "warehouseloader.py", "file_ext": "py", "file_size_in_byte": 19210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "mondrian.setup", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 26, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 27, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 29, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 32, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 50, "usage_type": "name"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 54, "usage_type": "name"}, {"api_name": "re.match", "line_number": 68, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 85, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants.TRAINING_PERCENTAGE", "line_number": 92, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 92, "usage_type": "name"}, {"api_name": "hashlib.sha512", "line_number": 109, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 189, "usage_type": "call"}, {"api_name": "pydicom.filereader.read_partial", "line_number": 197, "usage_type": "call"}, {"api_name": "pydicom.filereader", "line_number": 197, "usage_type": "attribute"}, {"api_name": "struct.error", "line_number": 204, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants.CONFIG_KEY", "line_number": 221, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 221, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 222, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 225, "usage_type": "name"}, {"api_name": "bonobo.config.use", "line_number": 216, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 255, "usage_type": "call"}, {"api_name": "warehouse.components.constants.VALIDATION_PREFIX", "line_number": 259, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 259, "usage_type": "name"}, {"api_name": "warehouse.components.constants.TRAINING_PREFIX", "line_number": 260, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 260, "usage_type": "name"}, {"api_name": "warehouse.components.services.DuplicateKeyError", "line_number": 272, "usage_type": "attribute"}, {"api_name": "warehouse.components.services", "line_number": 272, "usage_type": "name"}, {"api_name": "bonobo.constants", "line_number": 275, "usage_type": "attribute"}, {"api_name": "bonobo.config.use", "line_number": 234, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 235, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 236, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 278, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 279, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 280, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 304, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 341, "usage_type": "call"}, {"api_name": "bonobo.constants", "line_number": 343, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 347, "usage_type": "call"}, {"api_name": "warehouse.components.constants.TRAINING_PREFIX", "line_number": 377, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 377, "usage_type": "name"}, {"api_name": "warehouse.components.constants.VALIDATION_PREFIX", "line_number": 379, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 379, "usage_type": "name"}, {"api_name": "warehouse.components.constants.MODALITY.get", "line_number": 381, "usage_type": "call"}, {"api_name": "warehouse.components.constants.MODALITY", "line_number": 381, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 381, "usage_type": "name"}, {"api_name": "pathlib.posixpath.join", "line_number": 388, "usage_type": "call"}, {"api_name": "pathlib.posixpath", "line_number": 388, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 394, "usage_type": "call"}, {"api_name": "pathlib.posixpath.join", "line_number": 396, "usage_type": "call"}, {"api_name": "pathlib.posixpath", "line_number": 396, "usage_type": "name"}, {"api_name": "bonobo.config.use", "line_number": 318, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 319, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 320, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 426, "usage_type": "call"}, {"api_name": "bonobo.constants", "line_number": 450, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 468, "usage_type": "call"}, {"api_name": "bonobo.constants", "line_number": 470, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 472, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 473, "usage_type": "call"}, {"api_name": "warehouse.components.constants.TRAINING_PREFIX", "line_number": 512, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 512, "usage_type": "name"}, {"api_name": "warehouse.components.constants.VALIDATION_PREFIX", "line_number": 514, "usage_type": "attribute"}, {"api_name": "warehouse.components.constants", "line_number": 514, "usage_type": "name"}, {"api_name": "bonobo.config.use", "line_number": 453, "usage_type": "call"}, {"api_name": "bonobo.config.use", "line_number": 454, "usage_type": "call"}, {"api_name": "bonobo.constants", "line_number": 544, "usage_type": "attribute"}, {"api_name": "bonobo.Graph", "line_number": 556, "usage_type": "call"}, {"api_name": "warehouse.components.services.PipelineConfig", "line_number": 596, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 596, "usage_type": "name"}, {"api_name": "warehouse.components.services.KeyCache", "line_number": 597, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 597, "usage_type": "name"}, {"api_name": "warehouse.components.services.PatientCache", "line_number": 598, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 598, "usage_type": "name"}, {"api_name": "warehouse.components.services.SubFolderList", "line_number": 599, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 599, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 601, "usage_type": "call"}, {"api_name": "warehouse.components.services.Inventory", "line_number": 602, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 602, "usage_type": "name"}, {"api_name": "warehouse.components.services.Inventory", "line_number": 604, "usage_type": "call"}, {"api_name": "warehouse.components.services", "line_number": 604, "usage_type": "name"}, {"api_name": "bonobo.get_argument_parser", "line_number": 618, "usage_type": "call"}, {"api_name": "bonobo.parse_args", "line_number": 619, "usage_type": "call"}, {"api_name": "bonobo.run", "line_number": 620, "usage_type": "call"}]} {"seq_id": "553616106", "text": "# Copyright (c) 2019 The Boule Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n#\n# This code is part of the Fatiando a Terra project (https://www.fatiando.org)\n#\n\"\"\"\nModule for defining and setting the reference ellipsoid.\n\"\"\"\nfrom warnings import warn\nimport attr\nimport numpy as np\n\n\n# Don't let ellipsoid parameters be changed to avoid messing up calculations\n# accidentally.\n@attr.s(frozen=True)\nclass Ellipsoid:\n \"\"\"\n Reference oblate ellipsoid.\n\n The ellipsoid is oblate and spins around it's minor axis. It is defined by\n four parameters (semi-major axis, flattening, geocentric gravitational\n constant, and angular velocity) and offers other derived quantities.\n\n **All attributes of this class are read-only and cannot be changed after\n instantiation.**\n\n All parameters are in SI units.\n\n .. note::\n\n Use :class:`boule.Sphere` if you desire zero flattening because there\n are singularities for this particular case in the normal gravity\n calculations.\n\n Parameters\n ----------\n name : str\n A short name for the ellipsoid, for example ``'WGS84'``.\n semimajor_axis : float\n The semi-major axis of the ellipsoid (equatorial radius), usually\n represented by \"a\" [meters].\n flattening : float\n The flattening of the ellipsoid (f) [adimensional].\n geocentric_grav_const : float\n The geocentric gravitational constant (GM) [m^3 s^-2].\n angular_velocity : float\n The angular velocity of the rotating ellipsoid (omega) [rad s^-1].\n long_name : str or None\n A long name for the ellipsoid, for example ``\"World Geodetic System\n 1984\"`` (optional).\n reference : str or None\n Citation for the ellipsoid parameter values (optional).\n\n Examples\n --------\n\n We can define an ellipsoid by setting the 4 key numerical parameters:\n\n >>> ellipsoid = Ellipsoid(\n ... name=\"oblate-ellipsoid\",\n ... long_name=\"Oblate Ellipsoid\",\n ... semimajor_axis=1,\n ... flattening=0.5,\n ... geocentric_grav_const=1,\n ... angular_velocity=0,\n ... )\n >>> print(ellipsoid) # doctest: +ELLIPSIS\n Ellipsoid(name='oblate-ellipsoid', ...)\n >>> print(ellipsoid.long_name)\n Oblate Ellipsoid\n\n The class defines several derived attributes based on the input parameters:\n\n >>> print(\"{:.2f}\".format(ellipsoid.semiminor_axis))\n 0.50\n >>> print(\"{:.2f}\".format(ellipsoid.mean_radius))\n 0.83\n >>> print(\"{:.2f}\".format(ellipsoid.linear_eccentricity))\n 0.87\n >>> print(\"{:.2f}\".format(ellipsoid.first_eccentricity))\n 0.87\n >>> print(\"{:.2f}\".format(ellipsoid.second_eccentricity))\n 1.73\n\n \"\"\"\n\n name = attr.ib()\n semimajor_axis = attr.ib()\n flattening = attr.ib()\n geocentric_grav_const = attr.ib()\n angular_velocity = attr.ib()\n long_name = attr.ib(default=None)\n reference = attr.ib(default=None)\n\n @flattening.validator\n def _check_flattening(\n self, flattening, value\n ): # pylint: disable=no-self-use,unused-argument\n \"\"\"\n Check if flattening is valid\n \"\"\"\n if value < 0 or value >= 1:\n raise ValueError(\n f\"Invalid flattening '{value}'. \"\n \"Should be greater than zero and lower than 1.\"\n )\n if value == 0:\n raise ValueError(\n \"Flattening equal to zero will lead to errors in normal gravity. \"\n \"Use boule.Sphere for representing ellipsoids with zero flattening.\"\n )\n if value < 1e-7:\n warn(\n f\"Flattening is too close to zero ('{value}'). \"\n \"This may lead to inaccurate results and division by zero errors. \"\n \"Use boule.Sphere for representing ellipsoids with zero flattening.\"\n )\n\n @semimajor_axis.validator\n def _check_semimajor_axis(\n self, semimajor_axis, value\n ): # pylint: disable=no-self-use,unused-argument\n \"\"\"\n Check if semimajor_axis is positive\n \"\"\"\n if not value > 0:\n raise ValueError(\n f\"Invalid semi-major axis '{value}'. Should be greater than zero.\"\n )\n\n @geocentric_grav_const.validator\n def _check_geocentric_grav_const(\n self, geocentric_grav_const, value\n ): # pylint: disable=no-self-use,unused-argument\n \"\"\"\n Warn if geocentric_grav_const is negative\n \"\"\"\n if value < 0:\n warn(f\"The geocentric gravitational constant is negative: '{value}'\")\n\n @property\n def semiminor_axis(self):\n \"The small (polar) axis of the ellipsoid [meters]\"\n return self.semimajor_axis * (1 - self.flattening)\n\n @property\n def linear_eccentricity(self):\n \"The linear eccentricity [meters]\"\n return np.sqrt(self.semimajor_axis ** 2 - self.semiminor_axis ** 2)\n\n @property\n def first_eccentricity(self):\n \"The first eccentricity [adimensional]\"\n return self.linear_eccentricity / self.semimajor_axis\n\n @property\n def second_eccentricity(self):\n \"The second eccentricity [adimensional]\"\n return self.linear_eccentricity / self.semiminor_axis\n\n @property\n def mean_radius(self):\n \"\"\"\n The arithmetic mean radius :math:`R_1=(2a+b)/3` [Moritz1988]_ [meters]\n \"\"\"\n return 1 / 3 * (2 * self.semimajor_axis + self.semiminor_axis)\n\n @property\n def emm(self):\n r\"Auxiliary quantity :math:`m = \\omega^2 a^2 b / (GM)`\"\n return (\n self.angular_velocity ** 2\n * self.semimajor_axis ** 2\n * self.semiminor_axis\n / self.geocentric_grav_const\n )\n\n @property\n def gravity_equator(self):\n \"\"\"\n The norm of the gravity vector on the ellipsoid at the equator [m/s²]\n \"\"\"\n ratio = self.semiminor_axis / self.linear_eccentricity\n arctan = np.arctan2(self.linear_eccentricity, self.semiminor_axis)\n aux = (\n self.second_eccentricity\n * (3 * (1 + ratio ** 2) * (1 - ratio * arctan) - 1)\n / (3 * ((1 + 3 * ratio ** 2) * arctan - 3 * ratio))\n )\n axis_mul = self.semimajor_axis * self.semiminor_axis\n result = self.geocentric_grav_const * (1 - self.emm - self.emm * aux) / axis_mul\n return result\n\n @property\n def gravity_pole(self):\n \"The norm of the gravity vector on the ellipsoid at the poles [m/s²]\"\n ratio = self.semiminor_axis / self.linear_eccentricity\n arctan = np.arctan2(self.linear_eccentricity, self.semiminor_axis)\n aux = (\n self.second_eccentricity\n * (3 * (1 + ratio ** 2) * (1 - ratio * arctan) - 1)\n / (1.5 * ((1 + 3 * ratio ** 2) * arctan - 3 * ratio))\n )\n result = (\n self.geocentric_grav_const * (1 + self.emm * aux) / self.semimajor_axis ** 2\n )\n return result\n\n def geocentric_radius(self, latitude, geodetic=True):\n r\"\"\"\n Distance from the center of the ellipsoid to its surface.\n\n The geocentric radius and is a function of the geodetic latitude\n :math:`\\phi` and the semi-major and semi-minor axis, a and b:\n\n .. math::\n\n R(\\phi) = \\sqrt{\\dfrac{\n (a^2\\cos\\phi)^2 + (b^2\\sin\\phi)^2}{\n (a\\cos\\phi)^2 + (b\\sin\\phi)^2 }\n }\n\n See https://en.wikipedia.org/wiki/Earth_radius#Geocentric_radius\n\n The same could be achieved with\n :meth:`boule.Ellipsoid.geodetic_to_spherical` by passing any value for\n the longitudes and heights equal to zero. This method provides a\n simpler and possibly faster alternative.\n\n Alternatively, the geocentric radius can also be expressed in terms of\n the geocentric (spherical) latitude :math:`\\theta`:\n\n .. math::\n\n R(\\theta) = \\sqrt{\\dfrac{1}{\n (\\frac{\\cos\\theta}{a})^2 + (\\frac{\\sin\\theta}{b})^2 }\n }\n\n This can be useful if you already have the geocentric latitudes and\n need the geocentric radius of the ellipsoid (for example, in spherical\n harmonic analysis). In these cases, the coordinate conversion route is\n not possible since we need the radial coordinates to do that in the\n first place.\n\n .. note::\n\n No elevation is taken into account (the height is zero). If you\n need the geocentric radius at a height other than zero, use\n :meth:`boule.Ellipsoid.geodetic_to_spherical` instead.\n\n Parameters\n ----------\n latitude : float or array\n Latitude coordinates on geodetic coordinate system in degrees.\n geodetic : bool\n If True (default), will assume that latitudes are geodetic\n latitudes. Otherwise, will that they are geocentric spherical\n latitudes.\n\n Returns\n -------\n geocentric_radius : float or array\n The geocentric radius for the given latitude(s) in the same units\n as the ellipsoid axis.\n\n \"\"\"\n latitude_rad = np.radians(latitude)\n coslat, sinlat = np.cos(latitude_rad), np.sin(latitude_rad)\n # Avoid doing this in favour of having the user do the conversions when\n # possible. It's not the case here, so we made an exception.\n if geodetic:\n radius = np.sqrt(\n (\n (self.semimajor_axis ** 2 * coslat) ** 2\n + (self.semiminor_axis ** 2 * sinlat) ** 2\n )\n / (\n (self.semimajor_axis * coslat) ** 2\n + (self.semiminor_axis * sinlat) ** 2\n )\n )\n else:\n radius = np.sqrt(\n 1\n / (\n (coslat / self.semimajor_axis) ** 2\n + (sinlat / self.semiminor_axis) ** 2\n )\n )\n return radius\n\n def prime_vertical_radius(self, sinlat):\n r\"\"\"\n Calculate the prime vertical radius for a given geodetic latitude\n\n The prime vertical radius is defined as:\n\n .. math::\n\n N(\\phi) = \\frac{a}{\\sqrt{1 - e^2 \\sin^2(\\phi)}}\n\n Where :math:`a` is the semi-major axis and :math:`e` is the first\n eccentricity.\n\n This function receives the sine of the latitude as input to avoid\n repeated computations of trigonometric functions.\n\n Parameters\n ----------\n sinlat : float or array-like\n Sine of the latitude angle.\n\n Returns\n -------\n prime_vertical_radius : float or array-like\n Prime vertical radius given in the same units as the semi-major\n axis\n\n \"\"\"\n return self.semimajor_axis / np.sqrt(\n 1 - self.first_eccentricity ** 2 * sinlat ** 2\n )\n\n def geodetic_to_spherical(self, longitude, latitude, height):\n \"\"\"\n Convert from geodetic to geocentric spherical coordinates.\n\n The geodetic datum is defined by this ellipsoid. The coordinates are\n converted following [Vermeille2002]_.\n\n Parameters\n ----------\n longitude : array\n Longitude coordinates on geodetic coordinate system in degrees.\n latitude : array\n Latitude coordinates on geodetic coordinate system in degrees.\n height : array\n Ellipsoidal heights in meters.\n\n Returns\n -------\n longitude : array\n Longitude coordinates on geocentric spherical coordinate system in\n degrees.\n The longitude coordinates are not modified during this conversion.\n spherical_latitude : array\n Converted latitude coordinates on geocentric spherical coordinate\n system in degrees.\n radius : array\n Converted spherical radius coordinates in meters.\n\n \"\"\"\n latitude_rad = np.radians(latitude)\n coslat, sinlat = np.cos(latitude_rad), np.sin(latitude_rad)\n prime_vertical_radius = self.prime_vertical_radius(sinlat)\n # Instead of computing X and Y, we only compute the projection on the\n # XY plane: xy_projection = sqrt( X**2 + Y**2 )\n xy_projection = (height + prime_vertical_radius) * coslat\n z_cartesian = (\n height + (1 - self.first_eccentricity ** 2) * prime_vertical_radius\n ) * sinlat\n radius = np.sqrt(xy_projection ** 2 + z_cartesian ** 2)\n spherical_latitude = np.degrees(np.arcsin(z_cartesian / radius))\n return longitude, spherical_latitude, radius\n\n def spherical_to_geodetic(self, longitude, spherical_latitude, radius):\n \"\"\"\n Convert from geocentric spherical to geodetic coordinates.\n\n The geodetic datum is defined by this ellipsoid. The coordinates are\n converted following [Vermeille2002]_.\n\n Parameters\n ----------\n longitude : array\n Longitude coordinates on geocentric spherical coordinate system in\n degrees.\n spherical_latitude : array\n Latitude coordinates on geocentric spherical coordinate system in\n degrees.\n radius : array\n Spherical radius coordinates in meters.\n\n Returns\n -------\n longitude : array\n Longitude coordinates on geodetic coordinate system in degrees.\n The longitude coordinates are not modified during this conversion.\n latitude : array\n Converted latitude coordinates on geodetic coordinate system in\n degrees.\n height : array\n Converted ellipsoidal height coordinates in meters.\n\n \"\"\"\n spherical_latitude = np.radians(spherical_latitude)\n k, big_z, big_d = self._spherical_to_geodetic_terms(spherical_latitude, radius)\n latitude = np.degrees(\n 2 * np.arctan(big_z / (big_d + np.sqrt(big_d ** 2 + big_z ** 2)))\n )\n height = (\n (k + self.first_eccentricity ** 2 - 1)\n / k\n * np.sqrt(big_d ** 2 + big_z ** 2)\n )\n return longitude, latitude, height\n\n def _spherical_to_geodetic_terms(self, spherical_latitude, radius):\n \"Calculate intermediate terms needed for the conversion.\"\n # Offload computation of these intermediate variables here to clean up\n # the main function body\n cos_latitude = np.cos(spherical_latitude)\n big_z = radius * np.sin(spherical_latitude)\n p_0 = radius ** 2 * cos_latitude ** 2 / self.semimajor_axis ** 2\n q_0 = (1 - self.first_eccentricity ** 2) / self.semimajor_axis ** 2 * big_z ** 2\n r_0 = (p_0 + q_0 - self.first_eccentricity ** 4) / 6\n s_0 = self.first_eccentricity ** 4 * p_0 * q_0 / 4 / r_0 ** 3\n t_0 = np.cbrt(1 + s_0 + np.sqrt(2 * s_0 + s_0 ** 2))\n u_0 = r_0 * (1 + t_0 + 1 / t_0)\n v_0 = np.sqrt(u_0 ** 2 + q_0 * self.first_eccentricity ** 4)\n w_0 = self.first_eccentricity ** 2 * (u_0 + v_0 - q_0) / 2 / v_0\n k = np.sqrt(u_0 + v_0 + w_0 ** 2) - w_0\n big_d = k * radius * cos_latitude / (k + self.first_eccentricity ** 2)\n return k, big_z, big_d\n\n def normal_gravity(\n self, latitude, height, si_units=False\n ): # pylint: disable=too-many-locals\n \"\"\"\n Calculate normal gravity at any latitude and height.\n\n Computes the magnitude of the gradient of the gravity potential\n (gravitational + centrifugal) generated by the ellipsoid at the given\n latitude and (geometric) height. Uses of a closed form expression of\n [LiGotze2001]_.\n\n Parameters\n ----------\n latitude : float or array\n The (geodetic) latitude where the normal gravity will be computed\n (in degrees).\n height : float or array\n The ellipsoidal (geometric) height of computation the point (in\n meters).\n si_units : bool\n Return the value in mGal (False, default) or SI units (True)\n\n Returns\n -------\n gamma : float or array\n The normal gravity in mGal.\n\n \"\"\"\n # Warn if height is negative\n if np.any(height < 0):\n warn(\n \"Formulas used are valid for points outside the ellipsoid.\"\n \"Height must be greater than or equal to zero.\"\n )\n\n sinlat = np.sin(np.deg2rad(latitude))\n coslat = np.sqrt(1 - sinlat ** 2)\n # The terms below follow the variable names from Li and Goetze (2001)\n cosbeta_l2, sinbeta_l2, b_l, q_0, q_l, big_w = self._normal_gravity_terms(\n sinlat, coslat, height\n )\n # Put together gamma using 3 terms\n term1 = self.geocentric_grav_const / (b_l ** 2 + self.linear_eccentricity ** 2)\n term2 = (0.5 * sinbeta_l2 - 1 / 6) * (\n self.semimajor_axis ** 2\n * self.linear_eccentricity\n * q_l\n * self.angular_velocity ** 2\n / ((b_l ** 2 + self.linear_eccentricity ** 2) * q_0)\n )\n term3 = -cosbeta_l2 * b_l * self.angular_velocity ** 2\n gamma = (term1 + term2 + term3) / big_w\n if si_units:\n return gamma\n # Convert gamma from SI to mGal\n return gamma * 1e5\n\n def _normal_gravity_terms(self, sinlat, coslat, height):\n \"Calculate intermediate terms needed for the calculations.\"\n # Offload computation of these intermediate variables here to clean up\n # the main function body\n beta = np.arctan2(self.semiminor_axis * sinlat, self.semimajor_axis * coslat)\n zl2 = (self.semiminor_axis * np.sin(beta) + height * sinlat) ** 2\n rl2 = (self.semimajor_axis * np.cos(beta) + height * coslat) ** 2\n big_d = (rl2 - zl2) / self.linear_eccentricity ** 2\n big_r = (rl2 + zl2) / self.linear_eccentricity ** 2\n cosbeta_l2 = 0.5 * (1 + big_r) - np.sqrt(0.25 * (1 + big_r ** 2) - 0.5 * big_d)\n sinbeta_l2 = 1 - cosbeta_l2\n b_l = np.sqrt(rl2 + zl2 - self.linear_eccentricity ** 2 * cosbeta_l2)\n q_0 = 0.5 * (\n (1 + 3 * (self.semiminor_axis / self.linear_eccentricity) ** 2)\n * np.arctan2(self.linear_eccentricity, self.semiminor_axis)\n - 3 * self.semiminor_axis / self.linear_eccentricity\n )\n q_l = (\n 3\n * (1 + (b_l / self.linear_eccentricity) ** 2)\n * (\n 1\n - b_l\n / self.linear_eccentricity\n * np.arctan2(self.linear_eccentricity, b_l)\n )\n - 1\n )\n big_w = np.sqrt(\n (b_l ** 2 + self.linear_eccentricity ** 2 * sinbeta_l2)\n / (b_l ** 2 + self.linear_eccentricity ** 2)\n )\n return cosbeta_l2, sinbeta_l2, b_l, q_0, q_l, big_w\n", "sub_path": "boule/ellipsoid.py", "file_name": "ellipsoid.py", "file_ext": "py", "file_size_in_byte": 18979, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "attr.ib", "line_number": 89, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 90, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 91, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 92, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 93, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 94, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 95, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 115, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.arcsin", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.cbrt", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 457, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 489, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 491, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 494, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 513, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 17, "usage_type": "call"}]} {"seq_id": "594216970", "text": "\n# Default configuration for Cherry Py REST server\n\nimport cherrypy\n\nconfig = {\n 'global': {\n 'server.thread_pool' : 30, # Thread pool size (sys def = 10)\n 'server.socket_host' : '0.0.0.0', # Current host\n 'server.socket_port' : 8000,\n 'server.max_request_body_size' : 400 * (1024 ** 2), # N megabytes\n 'engine.autoreload_on' : False, # Restart when source files changed?\n 'tools.gzip.on' : True, # Support gzip compression\n 'log.screen' : True,\n },\n '/': {\n 'request.dispatch' : cherrypy.dispatch.MethodDispatcher(),\n # Let user see tracebacks. Very useful for debugging, but \n # not typically considered good form as it exposes information\n # to users that could be exploited \n 'request.show_tracebacks' : True,\n #change timeout duration\n 'response.timeout' : 1080 #18 minutes\n }\n }\n", "sub_path": "src/REST/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cherrypy.dispatch.MethodDispatcher", "line_number": 17, "usage_type": "call"}, {"api_name": "cherrypy.dispatch", "line_number": 17, "usage_type": "attribute"}]} {"seq_id": "407871105", "text": "import gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnum_images = 100\nenv = gym.make('Pong-v0')\nenv.reset()\ndone = False\npath = 'env_images_test/'\nfor i in range(1000):\n # env.render()\n state, reward, done, _ = env.step(env.action_space.sample())\n state = state[35:195:2, ::2, 0]\n state[np.logical_or(state == 144, state == 109)] = 0\n state[state != 0] = 1\n state = state.astype(np.float)\n # state = np.expand_dims(state, axis=-1)\n if i>=500 and i<=500+num_images:\n plt.imsave(path+'{}.png'.format(i), state, cmap='gray')\n # print(state.shape)", "sub_path": "generate_frames_test.py", "file_name": "generate_frames_test.py", "file_ext": "py", "file_size_in_byte": 587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "gym.make", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} {"seq_id": "605890090", "text": "# -*- coding: utf-8 -*-\n'''Run a prediction for a comment through the reddit May 2015 hate speech model'''\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom string import punctuation\nfrom nltk import word_tokenize\nfrom nltk.stem import snowball\nimport xgboost as xgb\nimport cPickle as pickle\nimport numpy as np\nimport pandas as pd\n\n\nstemmer = snowball.SnowballStemmer(\"english\")\n\n\ndef stem_tokens(tokens, stemmer):\n '''Stem the tokens.'''\n stemmed = []\n for item in tokens:\n stemmed.append(stemmer.stem(item))\n return stemmed\n\n\ndef tokenize(text):\n '''Tokenize & stem. Stems automatically for now.\n Leaving \"stemmer\" out of function call, so it works with TfidfVectorizer'''\n tokens = word_tokenize(text)\n stems = stem_tokens(tokens, stemmer)\n return stems\n\ndef predict_comment(comment, classes, bst, vect):\n '''\n Where \"comment\" is the comment by the user, to be passed in.\n classes =\n '''\n comment_tfidf = vect.transform([comment])\n comment_xgb = xgb.DMatrix(comment_tfidf)\n yprob = bst.predict(comment_xgb).reshape(1, 5) # hard coding -- only one comment at a time in this case.\n ylabel = classes[np.argmax(yprob, axis=1)]\n\n # print('The class is: {0} with probability {1}%'.format(ylabel, round(100 * np.max(yprob), 1)))\n\n return ylabel, round(100*np.max(yprob), 1), comment\n\n\ndef main():\n classes = ['Not Hate', 'Size Hate', 'Gender Hate', 'Race Hate', 'Religion Hate']\n\n # load saved xgboost model\n bst = xgb.Booster()\n bst.load_model('../FinalModel/modelv1/BuildModel/hatespeech.model')\n # load tf-idf matrix\n # tfidf_X = pickle.load(open('../FinalModel/BuildModel/tfidf_X.p', 'rb'))\n vect = pickle.load(open('../FinalModel/modelv1/BuildModel/vect.p', 'rb'))\n\n # get comment from user\n comment = raw_input('Enter comment: ')\n # predict class of comment\n predict_comment(comment, classes, bst, vect)\n\n predict = raw_input(\"Enter 'y' to get another prediction.\")\n\n while predict == 'y':\n # get comment from user\n comment = raw_input('Enter comment: ')\n # predict class of comment\n predict_comment(comment, classes, bst, vect)\n predict = raw_input(\"Enter 'y' to get another prediction.\")\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "WebApp/webapp_v1/runfinalmodelpreds_v1.py", "file_name": "runfinalmodelpreds_v1.py", "file_ext": "py", "file_size_in_byte": 2287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "nltk.stem.snowball.SnowballStemmer", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.stem.snowball", "line_number": 14, "usage_type": "name"}, {"api_name": "nltk.word_tokenize", "line_number": 28, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 44, "usage_type": "call"}, {"api_name": "xgboost.Booster", "line_number": 51, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 55, "usage_type": "call"}]} {"seq_id": "488112766", "text": "'''\nCreated on Sep 18, 2016\n\n@author: Julia Yu\n'''\n\nimport os\nimport json\n\nclass compareAPIResult():\n def compareAPIResult(self, dellID, apiName, actualResult):\n baselineFilePath = os.path.join(os.path.dirname(os.path.realpath('__file__')),'testdata/ptagentTestResult_%s_baseline.json'%dellID)\n with open(baselineFilePath) as f:\n baseResults=json.load(f)\n for baseline in baseResults:\n if baseline['api'] == apiName:\n expectedResult = baseline['result']\n break\n if cmp(actualResult, expectedResult) == 0:\n return True\n else:\n return False", "sub_path": "VxRailManager/tasks/ptagent/compareAPIResult.py", "file_name": "compareAPIResult.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}]} {"seq_id": "527334055", "text": "# -*- coding: utf-8 -*-\nfrom gtts import gTTS\nfrom playsound import playsound\nfrom threading import Thread\nfrom datetime import date\nfrom fuzzywuzzy import process\nimport sys\nimport os\nimport time\nimport sched\nimport requests\nimport random\nimport json\nimport threading\n\n# this is a full text version of omega, for debugging\nval = 1\n\n\ndef omegaSpeak(text):\n print(text)\n\n\ndef omegaTaskCompleted(user):\n # possible responses\n val = ''\n # priant 1 random number in the range of 1 and 2\n for x in range(1):\n val = random.randint(1, 2)\n if val == 1:\n omegaSpeak('ok, done')\n if val == 2:\n omegaSpeak('That has finished %s' % user)\n\n\ndef takingCommands(request):\n if request == False:\n try:\n # command = r.recognize_google(audio)\n print(' 🧑 You said: {}'.format(command))\n omegaCommands(command.lower())\n except SystemExit:\n sys.exit()\n except:\n print('')\n else:\n omegaCommands(request)\n\n\ndef omegaCommands(command):\n # get the current user before every command\n name = readFromData()\n if command == 'omega':\n omegaSpeak('Yes %s' % name)\n if 'shutdown' in command:\n omegaSpeak(\"ok, closing\")\n\n if 'what is the date' in command:\n omegaSpeak('One second %s ' % name)\n today = str(date.today())\n omegaSpeak(today)\n if 'new user' in command:\n print('new user')\n if 'hello' in command:\n omegaSpeak('Hello %s' % name)\n omegaTaskCompleted(name)\n if 'what is my name' in command:\n if name == '':\n omegaSpeak('I cannot see you')\n elif name == 'no user':\n omegaSpeak('I cannot see you')\n else:\n omegaSpeak('You are %s' % name)\n if 'turn off' in command:\n lights(command, 'off', user)\n if 'turn on' in command:\n lights(command, 'on', user)\n\n# need an alwasys listening function and then it calls the commands function\n\n# hue light control\n# command is the command given\n# state is off or on\n# user is omega tries to get the user that it sees\n\n\ndef lights(command, state, user):\n hueLights = []\n # get lights\n resp = requests.get('https://discovery.meethue.com/')\n ipAddress = ''\n for ip in resp.json():\n ipAddress = ip['internalipaddress']\n lights = requests.get(\n 'http://%s/api/pf3enyrZJz4uvwgYf90t9E2FzLM4tW2GeGSnO-ut/lights' % ipAddress)\n # split the command at the '\n if 'turn off the' in command:\n command = command.split('turn off the')\n else:\n command = command.split('turn on the')\n lightName = command[1]\n # put all the light name from hue in array\n for i in lights.json():\n hueLights.append(\n {'index': i, 'name': '{}' .format(lights.json()[i]['name'])})\n # fuzzy search for best matching light name\n finalLight = process.extractOne(lightName, hueLights)\n print(finalLight[0]['index'])\n # set state of light\n if state == 'on':\n payload = \" {\\\"on\\\":true}\"\n else:\n payload = \" {\\\"on\\\":false}\"\n requests.put(\n 'http://%s/api/pf3enyrZJz4uvwgYf90t9E2FzLM4tW2GeGSnO-ut/lights/%s/state' % (ipAddress, finalLight[0]['index']), data=payload)\n # else:\n omegaTaskCompleted(user)\n\n\ndef typeToAssist():\n command = input('What is the command?')\n omegaCommands(command)\n\ndef readFromData():\n with open('../person.txt') as json_file:\n data = json.load(json_file)\n return data[\"person\"]\n\n\nwhile val == 1:\n typeToAssist()\n", "sub_path": "omega/textAssistant.py", "file_name": "textAssistant.py", "file_ext": "py", "file_size_in_byte": 3546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 90, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 94, "usage_type": "call"}, {"api_name": "fuzzywuzzy.process.extractOne", "line_number": 107, "usage_type": "call"}, {"api_name": "fuzzywuzzy.process", "line_number": 107, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 114, "usage_type": "call"}, {"api_name": "json.load", "line_number": 126, "usage_type": "call"}]} {"seq_id": "222183887", "text": "from graphviz import Digraph\n\n#We arrange the graph using Networkx and then paint it using Cytoscape.\nG = Digraph(comment=\"Lung Cancer Experiment\")\nG.node('1', \"Tobacco Use\")\nG.node('2', \"Lung Cancer\")\nG.node('3', \"Secondhand Smoke\")\nG.node('4', \"Heavy Smoking\")\nG.node('5', \"Complications\")\nG.node('6', \"Death\")\nG.node('7', \"Symptoms\")\nG.node('8', \"Bone Pain\")\nG.node('9', \"Treatable\")\nG.node('10',\"Radiation Therapy\")\nG.node('11',\"Bleeding\")\nG.node('12',\"Chemical Imbalances\")\nG.node('13',\"Pain\")\nG.node('14',\"Speech Difficulties\")\nG.node('15',\"Breathing Difficulties\")\nG.node('16',\"Metastasize\")\nG.node('17',\"Collect Fluid\")\nG.node('19',\"Fatigue\")\nG.node('20',\"Loss of appetite\")\nG.node('21',\"Workplace\")\nG.node('22',\"Radon Gas\")\nG.edge('1','2')\nG.edge('3','2')\nG.edge('4','2')\nG.edge('2','5')\nG.edge('2','5')\nG.edge('2','6')\nG.edge('2','7')\nG.edge('2','8')\nG.edge('2','9')\nG.edge('2','10')\nG.edge('2','11')\nG.edge('2','12')\nG.edge('2','13')\nG.edge('2','14')\nG.edge('2','15')\nG.edge('2','16')\nG.edge('2','17')\nG.edge('9','19')\nG.edge('9','20')\nG.edge('21','2')\nG.edge('22','2')\nG.render(\"causal_graph\")\n", "sub_path": "graphviz_drawer.py", "file_name": "graphviz_drawer.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "graphviz.Digraph", "line_number": 4, "usage_type": "call"}]} {"seq_id": "14677515", "text": "\nfrom sklearn.neighbors import KNeighborsClassifier, NearestNeighbors\nimport numpy as np\nfrom lmnn import LargeMarginNearestNeighbor as LMNN\n\n\nclass imls():\n def __init__(self, k=3, mu=0.5, coef=5, randomState=np.random):\n self.coef = coef\n self.k = k\n self.mu = mu\n self.randomState = randomState\n\n def fitPredict(self, Xtrain, ytrain, Xtest):\n while self.coef*self.k > Xtrain.shape[0]:\n self.coef -= 1\n nn = NearestNeighbors(n_neighbors=self.coef*self.k)\n nn.fit(Xtrain)\n nearestNeighbors = nn.kneighbors(Xtest, return_distance=False)\n lmnn = LMNN(k=self.k,randomState=self.randomState, mu=self.mu)\n lmnn.fit(Xtrain, ytrain)\n Xtrain = lmnn.transform(Xtrain)\n Xtest = lmnn.transform(Xtest)\n nn = NearestNeighbors(n_neighbors=self.coef*self.k)\n nn.fit(Xtrain)\n newNearestNeighbors = nn.kneighbors(Xtest, return_distance=False)\n matching = np.array([len(np.intersect1d(\n nearestNeighbors[i],newNearestNeighbors[i]))>=int(self.coef*0.8)\n for i in range(len(nearestNeighbors))])\n while matching.all() == False:\n nearestNeighbors = newNearestNeighbors.copy()\n lmnn = LMNN(k=self.k,randomState=self.randomState, mu=self.mu)\n lmnn.fit(Xtrain, ytrain)\n Xtrain = lmnn.transform(Xtrain)\n Xtest = lmnn.transform(Xtest)\n nn = NearestNeighbors(n_neighbors=self.coef*self.k)\n nn.fit(Xtrain)\n newNearestNeighbors = nn.kneighbors(Xtest, return_distance=False)\n matching = np.array([len(np.intersect1d(\n nearestNeighbors[i], newNearestNeighbors[i]))>=int(self.coef*0.8)\n for i in range(len(nearestNeighbors))])\n knc = KNeighborsClassifier(n_neighbors=self.k)\n knc.fit(Xtrain, ytrain)\n return knc.predict(Xtest)\n", "sub_path": "experiments_a_b-PRL/imls.py", "file_name": "imls.py", "file_ext": "py", "file_size_in_byte": 1938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.random", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 17, "usage_type": "call"}, {"api_name": "lmnn.LargeMarginNearestNeighbor", "line_number": 20, "usage_type": "call"}, {"api_name": "lmnn.fit", "line_number": 21, "usage_type": "call"}, {"api_name": "lmnn.transform", "line_number": 22, "usage_type": "call"}, {"api_name": "lmnn.transform", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 27, "usage_type": "call"}, {"api_name": "lmnn.LargeMarginNearestNeighbor", "line_number": 32, "usage_type": "call"}, {"api_name": "lmnn.fit", "line_number": 33, "usage_type": "call"}, {"api_name": "lmnn.transform", "line_number": 34, "usage_type": "call"}, {"api_name": "lmnn.transform", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 42, "usage_type": "call"}]} {"seq_id": "595003893", "text": "\"\"\"\nGive the Host and Gene name to print the express gene names\n\"\"\"\nimport argparse\nimport re\nimport sys\nfrom assignment5 import my_io\nfrom assignment5 import config\n\n\ndef main():\n \"\"\"\n Main function\n :return: Prints out Expressed genes in Mammal in STD.OUT format\n \"\"\"\n args = get_cli_args()\n # function to check for passed arguments\n temp_host, gene = check_passed_args(args)\n # getting scientific name\n host = modify_host_name(temp_host)\n # file name and absolute path\n file = \"/\".join((config.get_unigene_directory(),\n host, gene + \".\" + config.get_unigene_extension()))\n # checking if file exist in directory or not\n _check_file_exist(file, host, gene)\n # List of expressed genes sorted alphabetically\n tissue_strig = get_gene_data(file)\n # Print output on STD.OUT\n print_ouput(temp_host, gene, tissue_strig)\n\n\ndef print_ouput(host, gene, tissue_string):\n \"\"\"\n Print Expressed gene name on STD OUT\n :param host: Name of the host\n :param gene: Name of the gene passed\n :param tissue_string: sorted list of expressed genes\n :return: None\n \"\"\"\n print(f\"In {host}, There are {len(tissue_string)} \"\n f\"tissues that {gene} is expressed in:\\n\")\n\n for i, value in enumerate(tissue_string, start=1):\n print(f\"{i}. {value.title()}\")\n\n\ndef get_gene_data(gene_file):\n \"\"\"\n Get Epressed gene names from host file.\n :param gene_file: Absolute path to gene file of host\n :return: Sorted list of expresssed genes in host\n \"\"\"\n fh_in = my_io.get_fh(gene_file, \"r\")\n\n tissue_strig = []\n\n for line in fh_in:\n if re.search(\"EXPRESS\", line):\n line = line.replace(\"\\n\", \"\")\n line = re.sub('[A-Z]', \"\", line)\n tissue_strig = line.split(\"|\")\n tissue_strig = [x.strip(' ') for x in tissue_strig]\n\n my_io.get_fh(fh_in, \"close\")\n\n return sorted(tissue_strig)\n\n\ndef _check_file_exist(file, temp_host, gene):\n # check for the existence of file\n if my_io.is_valid_gene_file_name(file):\n # using f-strings\n print(f\"\\nFound Gene {gene} for {temp_host}\")\n else:\n print(\"Not found\")\n print(f\"Gene {gene} does not exist for {temp_host}. \"\n f\"exiting now...\", file=sys.stderr)\n sys.exit()\n\n\ndef modify_host_name(host_name):\n \"\"\"\n Get Scientific name from dictionary exist in config file\n if name with \"_\" is passed it can be treated as scientifc name\n :param host_name: Argument passed in CLI otions\n :return: Scientific name for Host\n \"\"\"\n scientific_name = \"\"\n\n if \"_\" in host_name:\n scientific_name = host_name\n else:\n if host_name.lower() in list(config.get_host_keywords().keys()):\n scientific_name = config.get_host_keywords()[host_name.lower()]\n else:\n _print_host_directories()\n scientific_name = host_name\n\n return scientific_name\n\n\ndef _print_host_directories():\n \"\"\"\n Internal function to print the name of valid Hosts data available\n in directory scientific and non-scientific both (case-insensitive)\n :return: NONE exits the program\n \"\"\"\n\n print(\"\\nEither the Host Name you are searching for is not in the database\"\n \"\\nor If you are trying to use the scientific name please \"\n \"put the name in double quotes:\\n\"\n \"\\n\\\"Scientific name\\\"\\n\"\n \"\\nHere is a (non-case sensitive) list of available Hosts by scientific name\\n\")\n\n for i, value in enumerate(set(list(config.get_host_keywords().values())), start=1):\n print(f\"{i}. {value}\")\n\n print(\"\\nHere is a (non-case sensitive) list of available Hosts by common name\\n\")\n\n for i, key in enumerate(list(config.get_host_keywords().keys()), start=1):\n print(f\"{i}. {key.title()}\")\n\n\ndef check_passed_args(args):\n \"\"\"\n Check how many arguments passed, if NONE: return\n default file options\n :param args: Argparse file arguments, passed in CLI\n :return: Names of the files to open\n \"\"\"\n\n host = \"Homo_sapiens\"\n gene = \"TGM1\"\n args_to_return1 = \"\"\n args_to_return2 = \"\"\n\n if len(sys.argv) > 2:\n args_to_return1 = args.HOST\n args_to_return2 = args.GENE\n else:\n args_to_return1 = host\n args_to_return2 = gene\n\n return args_to_return1, args_to_return2\n\n\ndef get_cli_args():\n \"\"\"\n Get Command Line Argument function to read arguments from command\n line using argparse\n :return: Argument Parser object with all the required options\n \"\"\"\n parser = argparse.ArgumentParser(description=\"Give the Host and Gene name\")\n\n parser.add_argument(\"-host\",\n dest=\"HOST\",\n type=str,\n help=\"Name of Host\",\n required=False)\n\n parser.add_argument(\"-gene\",\n dest=\"GENE\",\n type=str,\n help=\"Name of Gene\",\n required=False)\n\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "gene_information_query.py", "file_name": "gene_information_query.py", "file_ext": "py", "file_size_in_byte": 5081, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "assignment5.config.get_unigene_directory", "line_number": 22, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 22, "usage_type": "name"}, {"api_name": "assignment5.config.get_unigene_extension", "line_number": 23, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 23, "usage_type": "name"}, {"api_name": "assignment5.my_io.get_fh", "line_number": 53, "usage_type": "call"}, {"api_name": "assignment5.my_io", "line_number": 53, "usage_type": "name"}, {"api_name": "re.search", "line_number": 58, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 60, "usage_type": "call"}, {"api_name": "assignment5.my_io.get_fh", "line_number": 64, "usage_type": "call"}, {"api_name": "assignment5.my_io", "line_number": 64, "usage_type": "name"}, {"api_name": "assignment5.my_io.is_valid_gene_file_name", "line_number": 71, "usage_type": "call"}, {"api_name": "assignment5.my_io", "line_number": 71, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 77, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 78, "usage_type": "call"}, {"api_name": "assignment5.config.get_host_keywords", "line_number": 93, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 93, "usage_type": "name"}, {"api_name": "assignment5.config.get_host_keywords", "line_number": 94, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 94, "usage_type": "name"}, {"api_name": "assignment5.config.get_host_keywords", "line_number": 115, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 115, "usage_type": "name"}, {"api_name": "assignment5.config.get_host_keywords", "line_number": 120, "usage_type": "call"}, {"api_name": "assignment5.config", "line_number": 120, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 137, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 153, "usage_type": "call"}]} {"seq_id": "467937732", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"Utilities for building paths.\"\"\"\n\nimport logging\nimport os\nimport tarfile\nfrom typing import Optional\nfrom urllib.parse import urlparse\nfrom urllib.request import urlretrieve\n\nimport pandas as pd\n\nfrom .constants import PYOBO_HOME\n\n__all__ = [\n 'get_prefix_directory',\n 'prefix_directory_join',\n 'get_prefix_obo_path',\n 'get_url_filename',\n 'ensure_path',\n 'ensure_df',\n 'ensure_excel',\n 'ensure_tar_df',\n]\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_prefix_directory(prefix: str, *, version: Optional[str] = None) -> str:\n \"\"\"Get the directory.\"\"\"\n if version:\n directory = os.path.abspath(os.path.join(PYOBO_HOME, prefix, version))\n else:\n directory = os.path.abspath(os.path.join(PYOBO_HOME, prefix))\n os.makedirs(directory, exist_ok=True)\n return directory\n\n\ndef prefix_directory_join(prefix: str, *parts: str, version: Optional[str] = None) -> str:\n \"\"\"Join the parts onto the prefix directory.\"\"\"\n rv = os.path.join(get_prefix_directory(prefix, version=version), *parts)\n os.makedirs(os.path.dirname(rv), exist_ok=True)\n return rv\n\n\ndef get_prefix_obo_path(prefix: str) -> str:\n \"\"\"Get the canonical path to the OBO file.\"\"\"\n return prefix_directory_join(prefix, f\"{prefix}.obo\")\n\n\ndef get_url_filename(url: str) -> str:\n \"\"\"Get the filename from the end of the URL.\"\"\"\n parse_result = urlparse(url)\n return os.path.basename(parse_result.path)\n\n\ndef ensure_path(\n prefix: str,\n url: str,\n *,\n version: Optional[str] = None,\n path: Optional[str] = None,\n) -> str:\n \"\"\"Download a file if it doesn't exist.\"\"\"\n if path is None:\n path = get_url_filename(url)\n\n if version:\n path = prefix_directory_join(prefix, path, version=version)\n else:\n path = prefix_directory_join(prefix, path)\n\n if not os.path.exists(path):\n logger.info('[%s] downloading OBO from %s', prefix, url)\n urlretrieve(url, path)\n\n return path\n\n\ndef ensure_df(\n prefix: str,\n url: str,\n *,\n version: Optional[str] = None,\n path: Optional[str] = None,\n sep: str = '\\t',\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"Download a file and open as a dataframe.\"\"\"\n path = ensure_path(prefix, url, version=version, path=path)\n return pd.read_csv(path, sep=sep, **kwargs)\n\n\ndef ensure_excel(\n prefix: str,\n url: str,\n *,\n version: Optional[str] = None,\n path: Optional[str] = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"Download an excel file and open as a dataframe.\"\"\"\n path = ensure_path(prefix, url, version=version, path=path)\n return pd.read_excel(path, **kwargs)\n\n\ndef ensure_tar_df(\n prefix: str,\n url: str,\n inner_path: str,\n *,\n version: Optional[str] = None,\n path: Optional[str] = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"Download a tar file and open as a dataframe.\"\"\"\n path = ensure_path(prefix, url, version=version, path=path)\n with tarfile.open(path) as tar_file:\n with tar_file.extractfile(inner_path) as file:\n return pd.read_csv(file, **kwargs)\n", "sub_path": "src/pyobo/path_utils.py", "file_name": "path_utils.py", "file_ext": "py", "file_size_in_byte": 3096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "constants.PYOBO_HOME", "line_number": 33, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "constants.PYOBO_HOME", "line_number": 35, "usage_type": "argument"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlparse", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "urllib.request.urlretrieve", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 100, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 102, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 114, "usage_type": "name"}, {"api_name": "tarfile.open", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "attribute"}]} {"seq_id": "50551269", "text": "#!/usr/bin/python3\n\"\"\" states.py objects that handle all default API actions\n using GET, POST, DELETE\"\"\"\nfrom models import storage\nfrom models.state import State\nfrom flask import jsonify, abort, request\nfrom api.v1.views import app_views\n\n\n@app_views.route('/states', methods=['GET'], strict_slashes=False)\n@app_views.route('/states/<state_id>', methods=['GET'], strict_slashes=False)\ndef get_state_id(state_id=None):\n \"\"\" returns the state dictionary \"\"\"\n if state_id is None:\n obj_list = []\n objs = storage.all(\"State\")\n for obj in objs.values():\n obj_list.append(obj.to_dict())\n return jsonify(obj_list)\n obj = storage.get(\"State\", state_id)\n if obj is None:\n abort(404)\n return jsonify(obj.to_dict())\n\n\n@app_views.route('/states/<state_id>', methods=['DELETE'],\n strict_slashes=False)\ndef delete_state(state_id=None):\n \"\"\" returns the state id \"\"\"\n obj = storage.get(\"State\", state_id)\n if obj is None:\n abort(404)\n storage.delete(obj)\n storage.save()\n return jsonify({}), 200\n\n\n@app_views.route('/states', methods=['POST'], strict_slashes=False)\ndef new_state():\n \"\"\" creates a new state \"\"\"\n if request.get_json() is None:\n abort(400, \"Not a JSON\")\n dic = request.get_json()\n if 'name' not in dic:\n abort(400, \"Missing name\")\n obj = State(**dic)\n storage.new(obj)\n storage.save()\n return jsonify(obj.to_dict()), 201\n\n\n@app_views.route('/states/<state_id>', methods=['PUT'], strict_slashes=False)\ndef update_state(state_id=None):\n \"\"\" Updates the states \"\"\"\n if request.get_json() is None:\n abort(400, \"Not a JSON\")\n obj = storage.get(\"State\", state_id)\n if obj is None:\n abort(404)\n dic = request.get_json()\n dic.pop('created_at', None)\n dic.pop('updated_at', None)\n dic.pop('id', None)\n for key, value in dic.items():\n setattr(obj, key, value)\n obj.save()\n return jsonify(obj.to_dict()), 200\n", "sub_path": "api/v1/views/states.py", "file_name": "states.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "models.storage.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 20, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 10, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 10, "usage_type": "name"}, {"api_name": "api.v1.views.app_views.route", "line_number": 11, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 11, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 32, "usage_type": "call"}, {"api_name": "models.storage.delete", "line_number": 33, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 33, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 34, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 26, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 45, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 46, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 47, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 47, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 48, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 38, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 56, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 57, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 52, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 52, "usage_type": "name"}]} {"seq_id": "480606221", "text": "#!/usr/bin/python3\n\nfrom random import random, choice, randint\nfrom locale import getlocale\nfrom os.path import exists, join\nfrom datetime import datetime, timedelta\nfrom math import floor\n\nimport sqlite3\n\nMALE_FIRST_NAMES = 'maleFirstNames'\n\nclass DataSource:\n \n MALE_NAMES = 'maleNames'\n FEMALE_NAMES = 'femaleNames'\n LAST_NAMES = 'lastNames'\n \n def __init__(self, dataSource):\n self.dataSource = dataSource\n\n \n def loadDataItems(self, localesDir, dataItems=None, locales=None):\n pass\n # if localesDir == None or len(localesDir.strip()) == 0:\n # raise Exception('localesDir not specified')\n # \n # if not exists(localesDir):\n # raise Exception(\"%s not found\" % localesDir)\n # \n # self.localesDir = localesDir\n # \n # if dataItems == None or len(dataItems) == 0:\n # dataItems = DataSource.ALL_DATA_ITEMS\n # \n # if locales == None or len(locales) == 0:\n # locales = [getlocale()[0]]\n # \n # for dataItem in dataItems:\n # if dataItem not in DataSource.ALL_DATA_ITEMS:\n # raise Exception('unrecognized data item %s' % dataItem)\n # for locale in locales:\n # self.loadDataItem(dataItem, locale)\n \n def loadDataItem(self, dataItem, locale, *posArgs, **keywords):\n raise Exception('implement this method in subclass')\n \n def randomMaleName(self):\n return self.randomChoice(DataSource.MALE_NAMES)\n \n def randomFemaleName(self):\n return self.randomChoice(DataSource.FEMALE_NAMES)\n \n def randomLastName(self):\n return self.randomChoice(DataSource.LAST_NAMES)\n \nclass InMemoryDataSource(DataSource):\n \n def __init__(self):\n self.dataItems = {}\n \n def loadDataItem(self, dataItem, locale, *posArgs, **keywords):\n if 'values' not in keywords.keys():\n raise Exception('values not specified')\n \n self.dataItems[dataItem] = keywords['values']\n \n def randomChoice(self, dataItemName):\n if dataItemName not in self.dataItems.keys():\n raise Exception(dataItemName + \" not present in data items\")\n return choice(self.dataItems[dataItemName])\n \n \nclass SqliteDataSource(DataSource):\n \n def __init__(self):\n self.conn = None\n \n \n def open(self, dbFile):\n '''Opens an existing sqllite file if file exists. Will create one if \n it does not exist'''\n \n self.conn = sqlite3.connect(dbFile)\n \n def loadDataItem(self, dataItem, locale):\n # Call base class method to validate that files exist\n DataSource.loadDataItem(self, dataItem, locale)\n print('loadDataItem')\n \n cursor = self.conn.cursor()\n \n if not self.hasTable('nameControlTable'):\n cursor.execute(\n \"\"\"create table if not exists maleFirstNames (\n tableName text)\"\"\")\n \n if self.hasTable(dataItem):\n if self.hasRecords(dataItem):\n cursor.execute('delete maleFirstNames')\n else:\n cursor.execute(\n \"\"\"create table if not exists maleFirstNames (\n name text, \n randSort integer)\"\"\")\n \n sourceFile = open(self.sourceFilePath, 'r')\n \n for line in sourceFile:\n line = line.strip()\n print(line)\n cursor.execute(\n \"insert into maleFirstNames (name) values (?)\", \n (line,))\n \n \n sourceFile.close()\n \n \n def hasTable(self, tableName):\n cursor = self.conn.cursor()\n cursor.execute(\n \"select * from sqlite_master where tbl_name = ?\", \n (tableName,))\n for row in cursor:\n return True\n return False\n \n \n def hasRecords(self, tableName):\n cursor = self.conn.cursor()\n cursor.execute(\n \"select count(*) from %s\" % tableName)\n for row in cursor:\n if row[0] == 0:\n return False\n return True\n \n \n def randomMaleName(self):\n pass\n \n def randomFemaleName(self):\n pass\n \n def close(self):\n if self.conn != None:\n self.conn.close()\n \n \n # def __init__(self):\n # self.conn = None\n # \n # def reLoad(self):\n # \"\"\"opens an existing sqlite database\"\"\"\n # pass\n # \n # def load(self, localesDir, databaseFile, locales=None, dataItems=None):\n # \"\"\"clears database and loads data into sqlite database from file.\"\"\"\n # # check if localesDirectory exists\n # \n # \n # \n # if locales == None or len(locales) == 0:\n # locales = [getlocale()[0]]\n # \n # for locale in locales:\n # # do this in a cross platform way\n # localeDir = localesDir + '/' + locale\n # print(localeDir)\n # \n # def close(self):\n # self.conn.close()\n \nclass DataGenerationRule:\n def __init__(self):\n pass\n \nclass PersonAgeRule(DataGenerationRule):\n def getAge(self):\n return 90 * random()\n \nclass PersonSexRule(DataGenerationRule):\n def getSex(self):\n x = random()\n if x <= 0.495:\n return 'male'\n if x > 0.495 and x < 0.99:\n return 'female'\n return 'unknown'\n\n\nclass PersonContext:\n\n def __init__(self):\n self.currentDateTime = datetime.now()\n \n self.ageRule = PersonAgeRule()\n self.sexRule = PersonSexRule()\n\nclass Person:\n \n SEXES = ['male', 'female', 'unknown']\n \n def __init__(self, dataSource, context=None, **keywords):\n \n if context == None:\n context = PersonContext()\n \n if 'sex' in keywords.keys():\n self.sex = keywords['sex']\n if self.sex not in Person.SEXES:\n raise(ValueError())\n else:\n self.sex = context.sexRule.getSex()\n \n self.age = context.ageRule.getAge()\n \n self.dateOfBirth = datetime.today() - timedelta(self.age * 365.25)\n self.age = floor(self.age)\n \n self.firstName, tempSex = self.firstOrMiddleName(\n dataSource,\n self.sex, \n 'firstName', \n **keywords)\n \n self.middleName, tempSex = self.firstOrMiddleName(\n dataSource,\n tempSex,\n 'middleName',\n **keywords)\n \n self.lastName = dataSource.randomLastName()\n \n def firstOrMiddleName(self, dataSource, sex, nameType, **keywords):\n if nameType in keywords.keys():\n return keywords[nameType]\n if sex == 'male' or (sex == 'unknown' and random() < 0.5):\n return dataSource.randomMaleName(), 'male'\n return dataSource.randomFemaleName(), 'female'\n \n def __str__(self):\n array = {\n 'sex': self.sex,\n 'firstName': self.firstName,\n 'middleName': self.middleName,\n 'lastName': self.lastName,\n 'dateOfBirth':self.dateOfBirth,\n 'age':self.age}\n return str(array)\n \n \nclass Household:\n \n def __init__(self, dataSource, context=None, **keywords):\n numberOfAdults = randint(1,5)\n print(\"there are %d adults in the household\")\n \n self.members = []\n \n if numberOfAdults > 1:\n if self.hasMarriedCouple():\n self.generateMarriedCouple(dataSource)\n \n def __str__(self):\n return str([str(x) for x in self.members])\n \n def hasMarriedCouple(self):\n return choice([True, False])\n \n def generateMarriedCouple(self, dataSource):\n spouse1 = Person(dataSource)\n self.members.append(spouse1)\n if self.isHomosexualCouple():\n pass\n \n def isHomosexualCouple(self):\n return randint(1,50) == 1\n # if 'middleName' in keywords.keys():\n # self.middleName = keywords['middleName']\n # else:\n # self.middleName = randomMiddleName(self.sex)\n # \n # if 'lastName' in keywords.keys():\n # self.lastName = keywords['lastName']\n # self.lastName = randomLastName(self.sex)\n # \n # if 'minAge' in keywords.keys():\n # minAge = keywords['minAge']\n # # add check to ensure minAge is numeric\n # else:\n # minAge = 0\n # \n # if 'maxAge' in keywords.keys():\n # maxAge = keywords['maxAge']\n # # add check to ensure maxAge is numeric\n # else:\n # maxAge = 110\n # \n # self.age = randint(minAge*100, maxAge*100)/100.0\n \n\n\n# def randomCouple(**keywords):\n # \"\"\"Will generate two random people who may or may not have the last name.\n # This function will override values for the sex and lastName keywords\"\"\"\n # if random() < 0.05:\n # # same - sex couple\n # keywords['sex'] = randomSex()\n # if random() < 0.33:\n # # share last_name\n # keywords['lastName'] = randomLastName()\n # return RandomPerson(**keywords), RandomPerson(**keywords)\n # return RandomPerson(**keywords), RandomPerson(**keywords)\n# \n # if random() < 0.70 and 'lastName' not in keywords.keys():\n # keywords['lastName'] = randomLastName()\n # \n # keywords['sex'] = 'M'\n # person1 = RandomPerson(**keywords)\n # \n # keywords['sex'] = 'F'\n # person2 = RandomPerson(**keywords)\n # return person1, person2\n \n\n \n\nif __name__ == '__main__':\n pass\n\n", "sub_path": "DataGenerator.py", "file_name": "DataGenerator.py", "file_ext": "py", "file_size_in_byte": 9932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random.choice", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 84, "usage_type": "call"}, {"api_name": "random.random", "line_number": 181, "usage_type": "call"}, {"api_name": "random.random", "line_number": 185, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 196, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 196, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 219, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 219, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 220, "usage_type": "call"}, {"api_name": "random.random", "line_number": 239, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 257, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 270, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 279, "usage_type": "call"}]} {"seq_id": "506804920", "text": "import sys, os\n\nsys.path.insert(0, os.path.abspath('./'))\n\nfrom tools.deep_learning.cnn_utils import evaluate_prediction\n\npossible_selection_metrics = [\"best_loss\", \"best_balanced_accuracy\", \"last_checkpoint\"]\n\ndef get_test_prediction(test_diagnosis_df, merged_df):\n import pandas as pd\n\n test_diagnosis_df=merged_df.merge(test_diagnosis_df, on=[\"participant_id\"], how=\"right\")\n test_diagnosis_df=dict(list(test_diagnosis_df.groupby(\"T1w_mri_field\")))\n res_dict={}\n for key in [1.5, 3]:\n res_dict[\"test_%sT\"%str(key)]=test_diagnosis_df[key]\n return res_dict\n\ndef load_test_and_separate(results_dict, args, cnn_classification_dir, selection_metric, mode, fold, merged_df=None, stat_dict=None):\n import pandas as pd\n\n if args.bayesian:\n for test_mode, values_df in stat_dict[fold][selection_metric].groupby(\"mode\"):\n if \"test\" in test_mode:\n prediction_column = \"predicted_label_from_%s\" % args.ba_inference_mode\n test_diagnosis_eval = evaluate_prediction(values_df[[\"true_label\"]].values.astype(int),\n values_df[[prediction_column]].values.astype(int))\n test_diagnosis_df = pd.DataFrame(test_diagnosis_eval, index=[0])\n test_diagnosis_df = test_diagnosis_df.assign(fold=fold,\n mode=test_mode)\n\n results_dict[selection_metric] = pd.concat(\n [results_dict[selection_metric], test_diagnosis_df],\n axis=0)\n else:\n test_diagnosis_path = os.path.join(cnn_classification_dir, selection_metric,\n '%s_image_level_prediction.tsv' % (mode))\n test_diagnosis_df = pd.read_csv(test_diagnosis_path, sep='\\t')\n prediction_column = \"predicted_label\"\n\n test_diagnosis_dict = get_test_prediction(test_diagnosis_df, merged_df)\n for test_mode in test_diagnosis_dict:\n test_diagnosis_eval = evaluate_prediction(test_diagnosis_dict[test_mode][[\"true_label\"]].values.astype(int),\n test_diagnosis_dict[test_mode][[prediction_column]].values.astype(int))\n test_diagnosis_df = pd.DataFrame(test_diagnosis_eval, index=[0])\n test_diagnosis_df = test_diagnosis_df.assign(fold=fold,\n mode=test_mode)\n results_dict[selection_metric] = pd.concat(\n [results_dict[selection_metric], test_diagnosis_df],\n axis=0)\n return results_dict\n\ndef load_mode_results(results_dict, args, cnn_classification_dir, selection_metric, mode, fold, load_from_ba_predictions=False,stat_dict=None):\n import pandas as pd\n if args.bayesian and load_from_ba_predictions:\n values_df = stat_dict[fold][selection_metric].groupby(\"mode\").get_group(mode)\n prediction_column = \"predicted_label_from_%s\" % args.ba_inference_mode\n test_diagnosis_eval = evaluate_prediction(values_df[[\"true_label\"]].values.astype(int),\n values_df[[prediction_column]].values.astype(int))\n test_diagnosis_df = pd.DataFrame(test_diagnosis_eval, index=[0])\n test_diagnosis_df = test_diagnosis_df.assign(fold=fold,\n mode=mode)\n\n results_dict[selection_metric] = pd.concat(\n [results_dict[selection_metric], test_diagnosis_df],\n axis=0)\n else:\n test_diagnosis_path = os.path.join(cnn_classification_dir, selection_metric,\n '%s_image_level_metrics.tsv' % (mode))\n if os.path.exists(test_diagnosis_path):\n test_diagnosis_df = pd.read_csv(test_diagnosis_path, sep='\\t')\n test_diagnosis_df = test_diagnosis_df.assign(fold=fold,\n mode=mode)\n test_diagnosis_df = test_diagnosis_df.drop([\"total_loss\", \"image_id\", ], axis=1)\n\n results_dict[selection_metric] = pd.concat([results_dict[selection_metric], test_diagnosis_df],\n axis=0)\n return results_dict\n\n\ndef get_results(args, aggregation_type=\"average\"):\n # aggregation_type=[average, separate, together]\n import pandas as pd\n import os\n import pathlib\n import numpy as np\n\n merged_df = None\n stat_dict = None\n if args.bayesian:\n stat_dict = get_uncertainty_distribution(args, aggregation_type=\"separate\")\n else:\n if args.separate_by_MS:\n merged_df = pd.read_csv(args.merged_file, sep=\"\\t\")\n merged_df=merged_df[[\"participant_id\", \"T1w_mri_field\"]]\n\n\n\n\n results_dict = {}\n currentDirectory = pathlib.Path(args.model_path)\n currentPattern = \"fold-*\"\n\n for fold_dir in currentDirectory.glob(currentPattern):\n fold = int(str(fold_dir).split(\"-\")[-1])\n cnn_classification_dir = os.path.join(args.model_path, 'fold-%i' % fold, 'cnn_classification')\n if args.selection_metrics is None:\n selection_metrics = []\n for f in os.scandir(cnn_classification_dir):\n metric=os.path.basename(os.path.normpath(f.path))\n if metric in possible_selection_metrics:\n selection_metrics.append(metric)\n else:\n selection_metrics=args.selection_metrics\n\n for selection_metric in selection_metrics:\n if not selection_metric in results_dict.keys():\n results_dict[selection_metric] = pd.DataFrame()\n modes = ['train', 'validation']\n for ms_el in args.MS_list:\n modes.append('test_' + ms_el)\n\n for mode in modes:\n if \"test\" in mode:\n if args.separate_by_MS:\n results_dict=load_test_and_separate(results_dict, args, cnn_classification_dir, selection_metric, mode,\n fold, merged_df, stat_dict=stat_dict)\n else:\n results_dict=load_mode_results(results_dict, args, cnn_classification_dir, selection_metric, mode, fold, load_from_ba_predictions=True, stat_dict=stat_dict)\n\n else:\n results_dict=load_mode_results(results_dict, args, cnn_classification_dir, selection_metric, mode, fold,\n load_from_ba_predictions=False, stat_dict=None)\n\n resulting_metrics_dict = {}\n if aggregation_type==\"average\":\n for key in results_dict.keys():\n res_df = results_dict[key].drop([\"fold\"], axis=1)\n resulting_metrics_dict[key] = res_df.groupby([\"mode\"], as_index=False, sort=False).agg(np.mean)\n resulting_metrics_dict = {aggregation_type: resulting_metrics_dict}\n\n elif aggregation_type==\"separate\":\n for key in results_dict.keys():\n metric_dict = dict(list(results_dict[key].groupby(\"fold\")))\n for fold in metric_dict.keys():\n if fold not in resulting_metrics_dict.keys():\n resulting_metrics_dict[fold] = {}\n resulting_metrics_dict[fold][key] = metric_dict[fold]\n else:\n resulting_metrics_dict={\"all\":results_dict}\n return resulting_metrics_dict\n\n\ndef get_uncertainty_distribution(args, aggregation_type=\"average\"):\n import pandas as pd\n import os\n import pathlib\n import numpy as np\n\n if args.separate_by_MS:\n merged_df = pd.read_csv(args.merged_file, sep=\"\\t\")\n merged_df=merged_df[[\"participant_id\", \"T1w_mri_field\"]]\n currentDirectory = pathlib.Path(args.model_path)\n currentPattern = \"fold-*\"\n stat_dict = {}\n for fold_dir in currentDirectory.glob(currentPattern):\n fold = int(str(fold_dir).split(\"-\")[-1])\n cnn_classification_dir = os.path.join(args.model_path, 'fold-%i' % fold, 'cnn_classification')\n\n if args.selection_metrics is None:\n selection_metrics = []\n for f in os.scandir(cnn_classification_dir):\n metric=os.path.basename(os.path.normpath(f.path))\n if metric in possible_selection_metrics:\n selection_metrics.append(metric)\n else:\n selection_metrics = args.selection_metrics\n\n for selection_metric in selection_metrics:\n if not selection_metric in stat_dict.keys():\n stat_dict[selection_metric] = pd.DataFrame()\n modes = ['test_' + ms_el for ms_el in args.MS_list]\n\n for mode in modes:\n test_diagnosis_path = os.path.join(cnn_classification_dir, selection_metric,\n \"bayesian_statistics\", '%s_image_level_stats.tsv' % (mode))\n test_diagnosis_df = pd.read_csv(test_diagnosis_path, sep='\\t')\n test_diagnosis_df[\"class_variance\"] = test_diagnosis_df[\"class_variance\"].apply(\n lambda x: x[1:-1].split()).apply(lambda x: [float(i) for i in x])\n\n if \"test\" in mode and args.separate_by_MS:\n test_diagnosis_dict = get_test_prediction(test_diagnosis_df, merged_df)\n for key in test_diagnosis_dict:\n test_diagnosis_dict[key] = test_diagnosis_dict[key].assign(fold=fold, mode=key)\n stat_dict[selection_metric] = pd.concat([stat_dict[selection_metric], test_diagnosis_dict[key]],\n axis=0)\n else:\n test_diagnosis_df = test_diagnosis_df.assign(fold=fold, mode=mode)\n stat_dict[selection_metric] = pd.concat([stat_dict[selection_metric], test_diagnosis_df], axis=0)\n # stat_dict[selection_metric].reset_index(inplace=True, drop=True)\n\n resulting_stat_dict = {}\n if aggregation_type==\"average\" or aggregation_type==\"all\":\n for key in stat_dict.keys():\n stat_df = stat_dict[key]\n additional_colums_df = stat_df[\n [\"true_label\", \"predicted_label_from_mean\", \"predicted_label_from_mode\", \"mode\", \"participant_id\"]]\n additional_colums_df = additional_colums_df.groupby([\"mode\", \"participant_id\"], as_index=False,\n sort=False).agg(pd.Series.mode)\n stat_df = stat_df.drop(\n [\"true_label\", \"predicted_label_from_mean\", \"predicted_label_from_mode\", \"fold\"], axis=1)\n resulting_stat_dict[key] = stat_df.groupby([\"mode\", \"participant_id\"], as_index=False, sort=False).agg(np.mean)\n resulting_stat_dict[key]=resulting_stat_dict[key].merge(additional_colums_df, on=[\"mode\", \"participant_id\"], how=\"right\")\n resulting_stat_dict = {aggregation_type: resulting_stat_dict}\n\n elif aggregation_type == \"separate\":\n for key in stat_dict.keys():\n metric_dict = dict(list(stat_dict[key].groupby(\"fold\")))\n for fold in metric_dict.keys():\n if fold not in resulting_stat_dict.keys():\n resulting_stat_dict[fold] = {}\n resulting_stat_dict[fold][key] = metric_dict[fold]\n\n return resulting_stat_dict\n\n\ndef get_history(args, aggregation_type=\"average\"):\n import pandas as pd\n import os\n import pathlib\n import numpy as np\n\n currentDirectory = pathlib.Path(args.model_path)\n currentPattern = \"fold-*\"\n history_df = pd.DataFrame()\n for fold_dir in currentDirectory.glob(currentPattern):\n fold = int(str(fold_dir).split(\"-\")[-1])\n history = pd.read_csv(os.path.join(args.model_path, 'fold-%i' % fold, 'training.tsv'), sep='\\t')\n history = history.assign(fold=fold)\n history_df = pd.concat([history_df, history], axis=0)\n if aggregation_type == \"average\":\n history_df = history_df[\n [\"epoch\", \"balanced_accuracy_train\", \"loss_train\", \"balanced_accuracy_valid\", \"loss_valid\"]]\n history_df = {aggregation_type: history_df.groupby(\"epoch\", as_index=False).agg(np.mean)}\n\n elif aggregation_type == \"all\":\n history_df={aggregation_type:history_df}\n else:\n history_df = dict(list(history_df.groupby(\"fold\")))\n return history_df\n\ndef reshape_dictionary(dict_sample):\n res = dict()\n for key, val in dict_sample.items():\n for key_in, val_in in val.items():\n if key_in not in res:\n temp = dict()\n else:\n temp = res[key_in]\n temp[key] = val_in\n res[key_in] = temp\n return res\n\n\n\ndef get_data_generic(args, reshape_dict=True):\n data = {}\n for data_type in args.data_types:\n data[data_type] = eval(\"get_%s\" % data_type)(args, args.aggregation_type)\n #data is now in format {data_type: {fold_0:, ...fold_n etc}}\n\n #toDo: turn off this function?\n if reshape_dict:\n # reshape data to format {fold_0: {data_type_1:, ...data_type_i etc}}\n data = reshape_dictionary(data)\n return data\n", "sub_path": "clinicaaddl/clinicaaddl/visualize/data_utils.py", "file_name": "data_utils.py", "file_ext": "py", "file_size_in_byte": 13140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.insert", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "tools.deep_learning.cnn_utils.evaluate_prediction", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "tools.deep_learning.cnn_utils.evaluate_prediction", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 48, "usage_type": "call"}, {"api_name": "tools.deep_learning.cnn_utils.evaluate_prediction", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 94, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.scandir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 161, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.scandir", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 187, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 233, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 244, "usage_type": "attribute"}]} {"seq_id": "37441195", "text": "\nimport sys\n\nimport cv2 as cv\n\n\ndef inside(r, q):\n rx, ry, rw, rh = r\n qx, qy, qw, qh = q\n return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\n\ndef draw_detections(img, rects, thickness=1):\n for x, y, w, h in rects:\n # the HOG detector returns slightly larger rectangles than the real objects.\n # so we slightly shrink the rectangles to get a nicer output.\n pad_w, pad_h = int(0.15*w), int(0.05*h)\n cv.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)\n\n\ndef find_faces_haar(frame, face_cascade):\n gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n\n faces = face_cascade.detectMultiScale(\n gray,\n scaleFactor=1.1,\n minNeighbors=5,\n minSize=(30, 30),\n flags=cv.CASCADE_SCALE_IMAGE\n )\n\n # Draw a rectangle around the faces\n for (x, y, w, h) in faces:\n cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n return frame\n\n\ndef find_faces_hog(img, hog):\n found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)\n found_filtered = []\n for ri, r in enumerate(found):\n for qi, q in enumerate(found):\n if ri != qi and inside(r, q):\n break\n else:\n found_filtered.append(r)\n draw_detections(img, found)\n draw_detections(img, found_filtered, 3)\n return img\n\n\nif __name__ == '__main__':\n hog = cv.HOGDescriptor()\n hog.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetector())\n\n face_cascade = cv.CascadeClassifier(sys.argv[1])\n\n cap = cv.VideoCapture(1)\n while True:\n ret, img = cap.read()\n\n # face detection using HOG\n # img = find_faces_hog(img, hog=hog)\n\n # HAAR\n img = find_faces_haar(img, face_cascade)\n\n # show the result.\n cv.imshow('capture', img)\n ch = cv.waitKey(1)\n if ch == 27:\n break\n cv.destroyAllWindows()\n", "sub_path": "projects/democv/test_video.py", "file_name": "test_video.py", "file_ext": "py", "file_size_in_byte": 1948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.rectangle", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.CASCADE_SCALE_IMAGE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor_getDefaultPeopleDetector", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 74, "usage_type": "call"}]} {"seq_id": "492884313", "text": "import logging\nfrom unittest.mock import patch\n\nfrom django.test import override_settings\n\nimport pytest\n\nfrom thenewboston_node.business_logic.blockchain.base import BlockchainBase\nfrom thenewboston_node.business_logic.blockchain.memory_blockchain import MemoryBlockchain\nfrom thenewboston_node.business_logic.blockchain.mock_blockchain import MockBlockchain\nfrom thenewboston_node.business_logic.tests.factories import add_blocks_to_blockchain\nfrom thenewboston_node.business_logic.tests.mocks.storage_mock import StorageMock\nfrom thenewboston_node.business_logic.utils.iter import get_generator\n\nlogger = logging.getLogger(__name__)\n\nLARGE_MEMORY_BLOCKCHAIN_SIZE = 100\n\nMEMORY_BLOCKCHAIN_CLASS = 'thenewboston_node.business_logic.blockchain.memory_blockchain.MemoryBlockchain'\nFILE_BLOCKCHAIN_CLASS = 'thenewboston_node.business_logic.blockchain.file_blockchain.FileBlockchain'\nMOCK_BLOCKCHAIN_CLASS = 'thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain'\n\n\n@pytest.fixture\ndef get_head_block_mock():\n with patch.object(MockBlockchain, 'get_head_block', return_value=None) as mock:\n yield mock\n\n\n@pytest.fixture\ndef get_next_block_number_mock():\n with patch.object(MockBlockchain, 'get_next_block_number', return_value=0) as mock:\n yield mock\n\n\n@pytest.fixture\ndef get_next_block_identifier_mock():\n with patch.object(MockBlockchain, 'get_next_block_identifier', return_value='next-block-identifier') as mock:\n yield mock\n\n\n@pytest.fixture\ndef get_account_state_mock():\n with patch.object(MockBlockchain, 'get_account_balance', return_value=430) as mock:\n yield mock\n\n\n@pytest.fixture\ndef get_account_lock_mock():\n with patch.object(MockBlockchain, 'get_account_balance_lock', return_value='fake-balance-lock') as mock:\n yield mock\n\n\ndef yield_forced_blockchain(class_, class_kwargs=None):\n blockchain_settings = {'class': class_, 'kwargs': class_kwargs or {}}\n\n BlockchainBase.clear_instance_cache()\n with override_settings(BLOCKCHAIN=blockchain_settings):\n blockchain = BlockchainBase.get_instance()\n yield blockchain\n BlockchainBase.clear_instance_cache()\n\n\ndef yield_and_init_forced_blockchain(class_, blockchain_genesis_state, class_kwargs=None):\n blockchain = next(yield_forced_blockchain(class_, class_kwargs))\n blockchain.add_blockchain_state(blockchain_genesis_state)\n blockchain.validate()\n yield blockchain\n\n\n@pytest.fixture\ndef memory_blockchain(blockchain_genesis_state):\n blockchain = MemoryBlockchain()\n blockchain.add_blockchain_state(blockchain_genesis_state)\n blockchain.validate()\n yield blockchain\n\n\n@pytest.fixture\ndef forced_memory_blockchain(blockchain_genesis_state):\n yield from yield_and_init_forced_blockchain(MEMORY_BLOCKCHAIN_CLASS, blockchain_genesis_state)\n\n\n@pytest.fixture\ndef forced_file_blockchain(blockchain_genesis_state, blockchain_directory):\n yield from yield_and_init_forced_blockchain(\n FILE_BLOCKCHAIN_CLASS, blockchain_genesis_state, class_kwargs={'base_directory': blockchain_directory}\n )\n\n\n@pytest.fixture(autouse=True) # Autouse for safety reasons\ndef forced_mock_blockchain(blockchain_genesis_state):\n yield from yield_and_init_forced_blockchain(MOCK_BLOCKCHAIN_CLASS, blockchain_genesis_state)\n\n\n@pytest.fixture\ndef large_blockchain(treasury_account_key_pair):\n blockchain = BlockchainBase.get_instance()\n\n accounts = blockchain.get_first_blockchain_state().account_states\n assert len(accounts) == 1\n treasury_account, account_state = list(accounts.items())[0]\n assert treasury_account_key_pair.public == treasury_account\n assert account_state.balance > 10000000000 # tons of money present\n\n add_blocks_to_blockchain(blockchain, 100, treasury_account_key_pair.private)\n yield blockchain\n\n\n@pytest.fixture\ndef file_blockchain_w_memory_storage(\n forced_file_blockchain, blockchain_genesis_state, forced_mock_network, get_primary_validator_mock,\n get_preferred_node_mock\n):\n block_storage_mock = patch.object(forced_file_blockchain, 'block_storage', StorageMock())\n arf_storage_mock = patch.object(forced_file_blockchain, 'account_root_files_storage', StorageMock())\n\n with block_storage_mock, arf_storage_mock:\n forced_file_blockchain.add_blockchain_state(blockchain_genesis_state)\n forced_file_blockchain.validate()\n yield forced_file_blockchain\n\n\n@pytest.fixture\ndef blockchain_base(blockchain_genesis_state):\n blockchain = BlockchainBase()\n with patch.object(blockchain, 'yield_blockchain_states', get_generator([blockchain_genesis_state])):\n yield blockchain\n", "sub_path": "thenewboston_node/business_logic/tests/fixtures/blockchain.py", "file_name": "blockchain.py", "file_ext": "py", "file_size_in_byte": 4631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 26, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain", "line_number": 26, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 26, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 32, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain", "line_number": 32, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 32, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 30, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 38, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain", "line_number": 38, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 38, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 36, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 44, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain", "line_number": 44, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 44, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 42, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 50, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.mock_blockchain.MockBlockchain", "line_number": 50, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 50, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 48, "usage_type": "attribute"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase.clear_instance_cache", "line_number": 57, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase", "line_number": 57, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 58, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase.get_instance", "line_number": 59, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase", "line_number": 59, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase.clear_instance_cache", "line_number": 61, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase", "line_number": 61, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.blockchain.memory_blockchain.MemoryBlockchain", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 91, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase.get_instance", "line_number": 98, "usage_type": "call"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase", "line_number": 98, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.tests.factories.add_blocks_to_blockchain", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 96, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 115, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 115, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.tests.mocks.storage_mock.StorageMock", "line_number": 115, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 116, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 116, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.tests.mocks.storage_mock.StorageMock", "line_number": 116, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 110, "usage_type": "attribute"}, {"api_name": "thenewboston_node.business_logic.blockchain.base.BlockchainBase", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 127, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 127, "usage_type": "name"}, {"api_name": "thenewboston_node.business_logic.utils.iter.get_generator", "line_number": 127, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 124, "usage_type": "attribute"}]} {"seq_id": "471080374", "text": "import asyncio\nimport random\n\nimport discord\n\n\n#Token that connects with the bot\nTOKEN = 'SECRET KEY'\n\nclient = discord.Client()\n\nalive = True\nchannels = []\n\nreply = ['Mijn gemeente heeft rond de 41.000 inwoners',\n 'Je kan campussen van de UA vinden in de gemeente waar ik woon',\n 'Ik woon niet ver van een Fort',\n 'Als ge zat zijt, kunt ge op minder dan een half uur van de vizit tot bij mij thuis wandelen']\n\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n print(message)\n command = message.content.split(' ')\n if command[0] == 'bot':\n channel = client.get_channel(int(command[1]))\n if command[2] == 'conv_biokot':\n await asyncio.sleep(12)\n await channel.send(\n 'Awel das goe, dan babbel ik rechts van u wel met toffere mensen.')\n if command[2] == 'conv_hagar':\n await asyncio.sleep(16)\n await channel.send(\n 'Ik hoor het al Thomas, gij vindt mij genen toffe. Ik zal me wel aan de rechterkant van de tafel '\n 'zetten, zet gij u maar bij die middelste drie aan tafel. ‘T is al goe.')\n if command[2] == 'conv_all':\n await asyncio.sleep(2)\n await channel.send(\n 'Awel das goe, dan babbel ik rechts van u wel met toffere mensen.')\n await channel.send(\n 'Ik hoor het al Thomas, gij vindt mij genen toffe. Ik zal me wel aan de rechterkant van de tafel '\n 'zetten, zet gij u maar bij die middelste drie aan tafel. ‘T is al goe.')\n print(message.channel.type)\n if not message.guild:\n if 'waar' in message.content or 'Waar' in message.content:\n await message.channel.send(reply[random.randint(0, len(reply)-1)])\n\n\n\n\n\n@client.event\nasync def on_ready():\n print('Logged in as')\n print(client.user.name)\n print(client.user.id)\n print('------')\n\n\nif __name__ == '__main__':\n client.run(TOKEN)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2005, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "discord.Client", "line_number": 10, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 48, "usage_type": "call"}]} {"seq_id": "98097305", "text": "import requests\nimport re\n\ntolurl = 'https://www.ximalaya.com/revision/album/v1/getTracksList?albumId=18943952&pageNum=3'\nmyHeader = {\n 'User-Agent':\n\t'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:81.0) Gecko/20100101 Firefox/81.0'\n}\ncontent = requests.get(tolurl,headers = myHeader)\njsonContent = content.json()\nmyDatab = jsonContent['data']['tracks']\n#print(myDatab)\nurlList = []\nfor i in myDatab:\n urlList.append('https://www.ximalaya.com'+i['url'])\n\nprint(urlList)\n\n\n\n\n\n", "sub_path": "seleniumWebdriver/unittest01.py", "file_name": "unittest01.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}]} {"seq_id": "591597673", "text": "#encoding:utf-8\r\n#By:Eastmount CSDN 2021-08-20\r\nimport cv2 \r\nimport numpy as np \r\nimport matplotlib.pyplot as plt\r\n \r\n#读取图片\r\nimg = cv2.imread('lena-hd.png')\r\n\r\n#灰度转换\r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n \r\n#直方图均衡化处理\r\nresult = cv2.equalizeHist(gray)\r\n\r\n#显示图像\r\nplt.subplot(221)\r\nplt.imshow(gray, cmap=plt.cm.gray), plt.axis(\"off\"), plt.title('(a)') \r\nplt.subplot(222)\r\nplt.imshow(result, cmap=plt.cm.gray), plt.axis(\"off\"), plt.title('(b)') \r\nplt.subplot(223)\r\nplt.hist(img.ravel(), 256), plt.title('(c)') \r\nplt.subplot(224)\r\nplt.hist(result.ravel(), 256), plt.title('(d)') \r\nplt.show()\r\n", "sub_path": "blog44-ImageProcessingSummary/ImageProcessing_17_enhancement.py", "file_name": "ImageProcessing_17_enhancement.py", "file_ext": "py", "file_size_in_byte": 638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.equalizeHist", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} {"seq_id": "620280327", "text": "import json\nimport logging\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import (\n Http404,\n HttpResponse,\n HttpResponseForbidden\n)\nfrom papermerge.core.models import (\n BaseTreeNode,\n Page,\n Access\n)\nfrom papermerge.core.models.kvstore import (get_currency_formats,\n get_date_formats, get_kv_types,\n get_numeric_formats)\n\nlogger = logging.getLogger(__name__)\n\n\n@login_required\ndef metadata(request, model, id):\n \"\"\"\n model can be either node or page. Respectively\n id will be the 'id' of either node or page.\n E.g.\n POST /metadata/page/55 # will update metadata for page id=55\n POST /metadata/node/40 # will update metadata for node id=40\n \"\"\"\n if model == 'node':\n _Klass = BaseTreeNode\n else:\n _Klass = Page\n try:\n item = _Klass.objects.get(id=id)\n except _Klass.DoesNotExist:\n raise Http404(\"Node does not exists\")\n\n kvstore = []\n\n if request.method == 'GET':\n for kv in item.kv.all():\n kvstore.append(kv.to_dict())\n else:\n if isinstance(item, BaseTreeNode):\n node = item\n else:\n node = item.document\n\n if request.user.has_perm(Access.PERM_WRITE, node):\n kv_data = json.loads(request.body)\n if 'kvstore' in kv_data:\n if isinstance(kv_data['kvstore'], list):\n item.kv.update(\n _sanitize_kvstore_list(kv_data['kvstore'])\n )\n else:\n return HttpResponseForbidden()\n\n return HttpResponse(\n json.dumps(\n {\n 'kvstore': kvstore,\n 'currency_formats': get_currency_formats(),\n 'date_formats': get_date_formats(),\n 'numeric_formats': get_numeric_formats(),\n 'kv_types': get_kv_types()\n\n }\n ),\n content_type=\"application/json\"\n )\n\n\ndef _sanitize_kvstore_list(kvstore_list):\n \"\"\"\n Creates a new dictionay only with allowed keys.\n \"\"\"\n new_kvstore_list = []\n allowed_keys = [\n 'id',\n 'key',\n 'value',\n 'kv_type',\n 'kv_format',\n 'kv_inherited',\n ]\n\n for item in kvstore_list:\n sanitized_kvstore_item = {\n allowed_key: item.get(allowed_key, None)\n for allowed_key in allowed_keys\n }\n new_kvstore_list.append(sanitized_kvstore_item)\n\n return new_kvstore_list\n", "sub_path": "papermerge/core/views/metadata.py", "file_name": "metadata.py", "file_ext": "py", "file_size_in_byte": 2560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "papermerge.core.models.BaseTreeNode", "line_number": 32, "usage_type": "name"}, {"api_name": "papermerge.core.models.Page", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 38, "usage_type": "call"}, {"api_name": "papermerge.core.models.BaseTreeNode", "line_number": 46, "usage_type": "argument"}, {"api_name": "papermerge.core.models.Access.PERM_WRITE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "papermerge.core.models.Access", "line_number": 51, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 59, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "papermerge.core.models.kvstore.get_currency_formats", "line_number": 65, "usage_type": "call"}, {"api_name": "papermerge.core.models.kvstore.get_date_formats", "line_number": 66, "usage_type": "call"}, {"api_name": "papermerge.core.models.kvstore.get_numeric_formats", "line_number": 67, "usage_type": "call"}, {"api_name": "papermerge.core.models.kvstore.get_kv_types", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "name"}]} {"seq_id": "164646459", "text": "import numpy, scipy, sklearn.feature_selection, sys, pandas, scipy.stats\nsys.path.append(\"/Users/pwangel/Gene_Analysis\")\nfrom ga_utils import *\nfrom scipy import optimize\n\ndef transform_distribution(dataframe, scale_parameter):\n\n for i_col in dataframe.columns:\n\n col_range = dataframe[i_col].max()-dataframe[i_col].min()\n n_bins = int(col_range/dataframe[i_col].std()/scale_parameter)+2\n starting_point = dataframe[i_col].mean() + 0.5*dataframe[i_col].std()*scale_parameter\n minimum = dataframe[i_col].min()\n maximum = dataframe[i_col].max()\n\n\n upper_bins = numpy.array([dataframe[i_col].std()*scale_parameter*pos+starting_point for pos in range(n_bins//2+1)])\n lower_bins = numpy.array([-dataframe[i_col].std()*scale_parameter*pos+starting_point for pos in range(n_bins//2+1)])\n all_bins = numpy.concatenate((lower_bins[::-1], upper_bins[1:]))\n sel = (all_bins >= minimum) & (all_bins <= maximum)\n all_bins = all_bins[sel]\n labels = numpy.concatenate((numpy.arange(start =-len(lower_bins[lower_bins>=minimum])+2, stop=0), numpy.arange(start=0, stop=len(upper_bins[upper_bins <= maximum]))))\n dataframe[i_col] = pandas.cut(dataframe[i_col], bins=all_bins, labels=labels)\n\n return dataframe\n\n'''Finds common genes between a group of RNASeq and Microarray datasets.'''\n'''Then plots 2d Histogram comparing measure correlation coefficients between Microarray and RNASeq'''\n\nsample_threshold = 20\n\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nfrom plotly.graph_objs import *\nimport plotly.figure_factory as ff\n\n# Read in data \n\nRNASeq_data, labels = read_file_lists.smash_into_matrix('/Users/pwangel/Data/ensembl_hg19_v69_mm9_v67/', 'RNASeq', sample_threshold, 'TMM RPKM (Log2)', 'ENSG', False)\nMicroarray_data, labels = read_file_lists.smash_into_matrix('/Users/pwangel/Data/ensembl_hg19_v69_mm9_v67/', 'Microarray', sample_threshold, None, 'ENSG', False)\n\nprint(\"Comparing RNASeq and Microarray gene content\")\n\nRNASeq_data = RNASeq_data.loc[(RNASeq_data.count(axis=1).values > len(RNASeq_data.columns)/2)]\nMicroarray_data = Microarray_data.loc[(Microarray_data.count(axis=1).values > len(Microarray_data.columns)/2)]\n\ncommon_genes = numpy.intersect1d(RNASeq_data.index.values, Microarray_data.index.values) \n\nprint(\"Kept %d common genes\" %len(common_genes))\niu = numpy.triu_indices(len(common_genes),1)\n\ndf_gene_list = pandas.DataFrame(index=common_genes)\n\nRNASeq_data = df_gene_list.merge(RNASeq_data, how='left', left_index=True, right_index=True, validate='1:1')\nMicroarray_data = df_gene_list.merge(Microarray_data, how='left', left_index=True, right_index=True, validate='1:1').sample(frac=0.5, replace=False, axis=1) \n\nprint(\"Have dataframes of size:\")\n\nprint(RNASeq_data.shape)\nprint(Microarray_data.shape)\n\n# Let's do an experimental transform\n#bins = 100\n#labels = numpy.linspace(start=0.0, stop=1.0, num=bins+1)\n#labels = labels[:-1]+0.5*(labels[1]-labels[0])\n#labels = scipy.stats.lognorm.ppf(labels, s=0.75)\n\n#for i_col in RNASeq_data.columns:\n#\tRNASeq_data[i_col] = pandas.qcut(RNASeq_data[i_col], q=bins, labels =labels , retbins=False).astype(float)\n\n#for i_col in Microarray_data.columns:\n#\tMicroarray_data[i_col] = pandas.qcut(Microarray_data[i_col], q=bins, labels =labels , retbins=False).astype(float)\n\n#RNASeq_correlation_array = run_parallel.parallelized_correlation(RNASeq_data.sort_index()).as_matrix()[iu].astype(float)\n#non_parRNASeq_correlation_array = RNASeq_data.sort_index().transpose().corr(method='pearson', min_periods=10).as_matrix()[iu]\n#print(len(RNASeq_correlation_array[RNASeq_correlation_array!= non_parRNASeq_correlation_array]))\n\nprint(\"Completed RNASeq correlations\")\n\n#Microarray_correlation_array = Microarray_data.sample(n=1000, axis=1, replace =False).sort_index().transpose().corr(method='pearson', min_periods=10).as_matrix()[iu]\n#Microarray_correlation_array = run_parallel.parallelized_correlation(Microarray_data.sort_index()).as_matrix()[iu].astype(float)\nprint(\"Completed Microarray correlations\")\n\nsel = (~numpy.isnan(Microarray_correlation_array)) & (~numpy.isnan(RNASeq_correlation_array))\nheat, x_bins, y_bins = numpy.histogram2d(x=Microarray_correlation_array[sel], y=RNASeq_correlation_array[sel], bins=200)\nheat = numpy.power(heat, 1.0) #shows the trend more clearly, i hope\n\nfitfunc = lambda p, x: p[0]+ p[1]*x # target function\nerrfunc = lambda p, x, y, weight: (fitfunc(p, x) - y)/weight # distance to the target function\np0 = [0.1, 0.5] # initial guess for the parameters\n\nweights = []\ny_plot = []\nfor i_col in range(len(x_bins)-1):\n weights.append(1.0/scipy.stats.norm.fit(heat[:,i_col])[1]) \n y_plot.append(y_bins[numpy.argmax(heat[:,i_col])]) \n\np1, success = optimize.leastsq(errfunc, p0[:], args=(x_bins[:-1], y_plot, weights))\n\ndata_to_plot = [Heatmap(z=heat, x=x_bins, y=y_bins)]\ndata_to_plot.append(Scatter(x=[-0.9, 0.9], y=[-0.9, 0.9], mode='lines', name='1:1', \\\n line=dict(color = 'black', width=2, dash='dash')))\ndata_to_plot.append(Scatter(x=x_bins, y=fitfunc(p1, x_bins[:-1]), mode='lines', name='y=%.2f x +%.2f' %(p1[1], p1[0]), \\\n line=dict(color = 'k', width=2, dash='dash')))\ndata_to_plot.append(Scatter(x=x_bins, y=y_plot, mode='lines', name='Max density', \\\n line=dict(color = 'white', width=2, dash='dash')))\n\nfig = Figure(data=data_to_plot, \n layout=Layout(title='p(pearson)', xaxis=dict(title='micorarray correlation coefficient'), yaxis=dict(title='rnaseq correlation coefficient')) )\nplot(fig, auto_open=False, filename='/users/pwangel/plotlyworkspace/transformed_Microarray_vs_RNASeq_pearson.html')", "sub_path": "find_global_trends/transform_dist_Microarray_vs_RNASeq_correlation.py", "file_name": "transform_dist_Microarray_vs_RNASeq_correlation.py", "file_ext": "py", "file_size_in_byte": 5796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.triu_indices", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.histogram2d", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.stats.norm.fit", "line_number": 94, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.optimize.leastsq", "line_number": 97, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 97, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 109, "usage_type": "call"}]} {"seq_id": "534290940", "text": "import numpy as np\nimport sympy\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nfrom pylab import figure, axes, pie, title, show\n\n\nprint('Graphik1')\nprint('Steigung','Y-Achsenabschnitt')\nx, y = np.loadtxt('reichweite20.txt', unpack=True,delimiter=',')\nT=np.sqrt(y)\n\nplt.plot(x, y, \"kx\", label=\"Messwerte\")\nplt.errorbar(x, y, yerr=T, fmt=\"none\", capsize=3, capthick=1, ms=9, markerfacecolor=\"red\")\n\ny=42750+0*x\nplt.plot(y,label=r'$\\frac{N_{0}}{2}$')\n\n\nprint('Erste Grade')\nc, v = np.loadtxt('fit20.txt', unpack=True,delimiter=',')\ndef f(c,a,b):\n return a*c+b\npopt, pcov = curve_fit(f, c, v)\nprint(popt)\nprint(np.diag(pcov))\nc_new = np.linspace(x[10], x[-1], 500)\n#\n#\n#\n#print('Zweite Grade')\n#q, w = np.loadtxt('beta3.txt', unpack=True,delimiter=',')\n#def g(q,r,s):\n# return r*q+s\n#pqpt, pcqv = curve_fit(g, q, w)\n#print(pqpt)\n#print(np.diag(pcqv))\n#q_new = np.linspace(x[4], x[-1], 500)\n\nplt.figure(1)\n#plt.plot(x,y,'x')\nplt.plot(c_new,f(c_new,*popt),'-', label='Lineare Regression')\n#plt.plot(q_new,g(q_new,*pqpt),'-', label='Lineare Regression Hintergrundstrahlung')\nplt.xlabel('Effektive Länge $x/ 10^{-3}m$')\nplt.ylabel('Zählrate $N$')\nplt.grid()\nplt.legend()\n\n\n\nplt.savefig('reichweite20.pdf')\nprint ('Fertig')\n", "sub_path": "AP2/V701/reichweite20.py", "file_name": "reichweite20.py", "file_ext": "py", "file_size_in_byte": 1245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.loadtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} {"seq_id": "4138806", "text": "\"\"\"\nToluwanimi Akindele\nCMPUT 366 FALL 18\n1440804\n\nstate s: gambler's capital {1,2,...,99}\naction a: stakes {0,1,...,min(s, 100-s)}\nph: probability of the coin coming up heads\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef value_iteration(ph, goal_reward, states, theta, dph):\n value_func = np.zeros(states + 1)\n policy_func = np.zeros(states + 1)\n\n sweep = 0\n delta = 1.0\n while delta > theta:\n sweep += 1\n delta = 0.0\n\n # for each state s: gambler's capital {1,2,...,99} - s will range from 0 to 98\n for s in range(states):\n prev_value = value_func[s]\n max_val = 0.0\n pol = 0.0\n actions = list(range(min(s, 100-s) + 1))\n\n # each action leads to 2 possible states, win OR loss\n for a in actions:\n heads_state = s+a # heads\n tails_state = s-a\n # if it reaches 100, reward = 1, otherwise 0\n if s+a >= 100:\n heads_val = ph * (goal_reward + value_func[heads_state])\n else:\n heads_val = ph * value_func[heads_state]\n tails_val = (1-ph) * value_func[tails_state]\n\n temp_val = heads_val + tails_val\n if temp_val >= max_val:\n max_val = temp_val\n pol = a\n\n value_func[s] = max_val\n policy_func[s] = pol\n\n # update delta to show how much error\n delta = max(delta, abs(prev_value - value_func[s]))\n\n if sweep in [1,2,3,32]:\n plt.plot(value_func[:99])\n plt.ylim(top=1)\n plt.ylim(bottom=0)\n plt.xlabel(\"Capital\")\n plt.ylabel(\"Value Estimates\")\n plt.suptitle(\"Value Function. Ph =\" + str(dph))\n plt.show()\n plt.xlabel(\"Capital\")\n plt.ylabel(\"Final Policy (Stake)\")\n plt.plot(policy_func[:99])\n plt.suptitle(\"Final Policy. Ph =\" + str(dph))\n plt.show()\n print(\"This is sweep:\\t\"+str(sweep))\n\n\ndef main():\n theta = 1e-5\n states = 100\n goal_reward = 1\n ph_list = [0.25, 0.55]\n\n for ph in ph_list:\n value_iteration(ph, goal_reward, states, theta,ph)\n\n\nmain()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}]} {"seq_id": "554025797", "text": "from sikuli import *\nimport logging\nimport myTools\n\n#---------------------------------------------------#\ndef fCreate_OneDiscount(pClient,pMonth,pAmount):\n#---------------------------------------------------#\n\n logging.debug('- Create_OneDiscount: ' + str(pMonth) + \"-\" + pClient + \" = \" + str(pAmount))\n\n # new transaction\n type(\"n\",KeyModifier.CTRL)\n myTools.waitForTransEntry()\n\n # switch to Discount\n\n type(Key.UP) # this is to get by a UI defect\n time.sleep(1)\n \n type(\"d\")\n time.sleep(1) \n type(Key.TAB)\n \n # client\n myTools.enterClient(pClient)\n \n # date\n tranDate = str(pMonth) + \"/28/\" + Settings.dataYear\n type(tranDate)\n time.sleep(1)\n type(Key.TAB) \n \n # Amount\n type(str(pAmount))\n type(Key.TAB)\n \n # Description\n type(\"a\",KeyModifier.CTRL)\n type(\"Discount: \" + pClient + \" - \" + tranDate)\n type(Key.ENTER)\n time.sleep(1)\n\n # move to invoice list\n if (int(Settings.tsVersion) > 2016) and (Settings.tsDB == \"PREM\"):\n myTools.pressTAB(2)\n else:\n myTools.pressTAB(1)\n\n # move to last entry\n myTools.moveToLastTrans()\n myTools.clickApplyOne()\n\n # save\n type(\"s\",KeyModifier.CTRL)\n myTools.waitForTransSave() \n\n#---------------------------------------------------#\ndef fCreate_Discounts(pMonth):\n#---------------------------------------------------#\n\n myTools.sectionStartTimeStamp(\"discounts\" + str(pMonth))\n logging.debug('fCreate_Discounts: ' + str(pMonth))\n\n # list the client that will get a refund each month\n discountClients = [\"Natick\",\"Orange\",\"Oakham\",\"Oak Bluffs\",\"Southampton\",\"Otis\",\"Oxford\",\"Leyden\",\"Monroe\",\"Monson\",\"Methuen\",\"Uxbridge\"]\n oneClient = discountClients[(pMonth - 1)]\n\n myTools.getFocus()\n\n # open a/r tran list\n type(\"t\",KeyModifier.CTRL)\n myTools.waitForTransList()\n\n discountAmount = 49 + pMonth/float(100)\n fCreate_OneDiscount(oneClient,pMonth,discountAmount)\n\n type(Key.F4,KeyModifier.CTRL)\n time.sleep(1) \n type(Key.F4,KeyModifier.CTRL)\n \n myTools.sectionEndTimeStamp()\n myTools.checkProcesses()", "sub_path": "trans_Discounts.sikuli/trans_Discounts.py", "file_name": "trans_Discounts.py", "file_ext": "py", "file_size_in_byte": 2153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.debug", "line_number": 9, "usage_type": "call"}, {"api_name": "myTools.waitForTransEntry", "line_number": 13, "usage_type": "call"}, {"api_name": "myTools.enterClient", "line_number": 25, "usage_type": "call"}, {"api_name": "myTools.pressTAB", "line_number": 45, "usage_type": "call"}, {"api_name": "myTools.pressTAB", "line_number": 47, "usage_type": "call"}, {"api_name": "myTools.moveToLastTrans", "line_number": 50, "usage_type": "call"}, {"api_name": "myTools.clickApplyOne", "line_number": 51, "usage_type": "call"}, {"api_name": "myTools.waitForTransSave", "line_number": 55, "usage_type": "call"}, {"api_name": "myTools.sectionStartTimeStamp", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 62, "usage_type": "call"}, {"api_name": "myTools.getFocus", "line_number": 68, "usage_type": "call"}, {"api_name": "myTools.waitForTransList", "line_number": 72, "usage_type": "call"}, {"api_name": "myTools.sectionEndTimeStamp", "line_number": 81, "usage_type": "call"}, {"api_name": "myTools.checkProcesses", "line_number": 82, "usage_type": "call"}]} {"seq_id": "99817269", "text": "# coding: utf-8\n\nfrom urllib.parse import urlencode\n\nimport qiniu\nimport aiohttp\n\nfrom aioqiniu.services import StorageServiceMixin\nfrom aioqiniu.exceptions import HTTPError # noqa: F401\n\n__version__ = \"1.2.0\"\n\n\nclass QiniuClient(StorageServiceMixin):\n \"\"\"七牛云存储异步客户端\"\"\"\n\n def __init__(self, access_key: str, secret_key: str, httpclient=None):\n \"\"\"初始化七牛云异步客户端\n\n :param access_key: 七牛云的AccessKey\n :param secret_key: 七牛云的SecretKey\n :param httpclient: 自定义`aiohttp.ClientSession`对象，默认为空，自动创建\n \"\"\"\n self.__access_key = access_key\n self.__secret_key = secret_key\n self._auth = qiniu.Auth(access_key, secret_key)\n self._auto_close_httpclient = False\n if httpclient is None:\n httpclient = aiohttp.ClientSession()\n self._auto_close_httpclient = True\n self._httpclient = httpclient\n\n def get_token(self, data: str):\n \"\"\"从原始数据中生成的token\n\n 该方法等同于`qiniu.Auth.token`\n\n :param data: 待签名数据\n\n :return: 数据签名\n \"\"\"\n return self._auth.token(data)\n\n def get_token_with_data(self, data: str):\n \"\"\"生成带原始数据的token\n\n 该方法等同于`qiniu.Auth.token_with_data`\n\n :param data: 待签名数据\n\n :return: 数据签名，含已编码的原数据\n \"\"\"\n return self._auth.token_with_data(data)\n\n def get_access_token(self, path: str, query=\"\", body=\"\") -> str:\n \"\"\"生成七牛云的管理凭证(access token)\n\n :param path: URL路径\n :param query: URL查询字符串，可以是str或dict类型，默认为空\n :param body: 请求body，默认为空\n\n :return: 七牛云的管理凭证(access token)\n\n 详见：https://developer.qiniu.com/kodo/manual/1201/access-token\n \"\"\"\n if not query:\n return self._auth.token(\"{}\\n{}\".format(path, body))\n if isinstance(query, dict):\n query = urlencode(query)\n return self._auth.token(\"{}?{}\\n{}\".format(path, query, body))\n\n def get_upload_token(self, bucket: str, key=None, expires=3600,\n policy=None, strict_policy=True) -> str:\n \"\"\"生成七牛云的上传凭证(upload token)\n\n :param bucket: 空间名\n :param key: 上传的文件命名，默认为空\n :param expires: 上传凭证过期时间，单位为秒，默认为3600\n :param policy: 上传策略，默认为空\n\n :return: 七牛云的上传凭证(upload token)\n\n 详见：https://developer.qiniu.com/kodo/manual/1208/upload-token\n \"\"\"\n return self._auth.upload_token(bucket, key, expires, policy,\n strict_policy)\n\n def get_private_download_url(self, url, expires=3600) -> str:\n \"\"\"生成私有资源的下载url\n\n :param url: 私有资源的url\n :param expires: 下载url的过期时间，单位为秒，默认为3600\n\n :return: 私有资源的下载url\n\n 详见：https://developer.qiniu.com/kodo/manual/1202/download-token\n \"\"\"\n return self._auth.private_download_url(url, expires)\n\n def __del__(self):\n if self._auto_close_httpclient:\n self._httpclient.close()\n\n pass\n", "sub_path": "aioqiniu/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "aioqiniu.services.StorageServiceMixin", "line_number": 14, "usage_type": "name"}, {"api_name": "qiniu.Auth", "line_number": 26, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 69, "usage_type": "call"}]} {"seq_id": "428265518", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse #命令行参数选项解析模块\nimport tensorflow as tf\n\n\nimport lalala\n\nparser= argparse.ArgumentParser() #这是模块获取命令行的方法。得到一整个命令行\nparser.add_argument('--batch_size',default=100,type=int,help='batch size') #在获得的命令行上加 这一段参数\nparser.add_argument('--train_steps',default=1000,type=int,help='number of training steps') #在获得的命令行上加这一段参数\n\n\ndef main(argv):\n args=parser.parse_args(argv[1:]) #因为启动时输入python do.py,然后又在命令行上加了两个参数，然后启动main函数时\n #一整个命令行都是argv。从argv拿到第二个参数到结尾，也就是batch_size和train_steps\n #此时 args就有属性了。args.batch_size是一个batch大小。args.train_steps是训练的步数。\n \n (train_x,train_y),(test_x,text_y)=lalala.load_data()\n \n #从train_x中获取特征列以供dnn分类器参数使用\n my_feature_columns=[]\n for key in train_x.keys():\n my_feature_columns.append(tf.feature_column.numeric_column(key=key))\n \n classifier=tf.estimator.DNNClassifier(\n feature_columns=my_feature_columns,\n hidden_units=[10,10],\n n_classes=3) #定义分类器，输入特征列，隐藏层数，结果数\n \n classifier.train(input_fn=lambda:lalala.train_input_fn(train_x,train_y,args.batch_size),\n steps=args.train_steps)\n \n eval_result=classifier.evaluate(input_fn=lambda:lalala.eval_input_fn(test_x,test_y,args.batch_size))\n \n print('\\nTest set accuracy: {accuracy:0.3f}\\n'.format(**eval_result))\n \n expected = ['Setosa', 'Versicolor', 'Virginica']\n predict_x = {\n 'SepalLength': [5.1, 5.9, 6.9],\n 'SepalWidth': [3.3, 3.0, 3.1],\n 'PetalLength': [1.7, 4.2, 5.4],\n 'PetalWidth': [0.5, 1.5, 2.1],\n }\n\n predictions = classifier.predict(\n input_fn=lambda:iris_data.eval_input_fn(predict_x,\n labels=None,\n batch_size=args.batch_size))\n\n template = ('\\nPrediction is \"{}\" ({:.1f}%), expected \"{}\"')\n\n for pred_dict, expec in zip(predictions, expected):\n class_id = pred_dict['class_ids'][0]\n probability = pred_dict['probabilities'][class_id]\n\n print(template.format(iris_data.SPECIES[class_id],\n 100 * probability, expec))\n\n\nif __name__ == '__main__':\n tf.logging.set_verbosity(tf.logging.INFO)\n tf.app.run(main)\n \n", "sub_path": "do.py", "file_name": "do.py", "file_ext": "py", "file_size_in_byte": 2711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "lalala.load_data", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.feature_column.numeric_column", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator.DNNClassifier", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 28, "usage_type": "attribute"}, {"api_name": "lalala.train_input_fn", "line_number": 33, "usage_type": "call"}, {"api_name": "lalala.eval_input_fn", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.logging.set_verbosity", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.app.run", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 65, "usage_type": "attribute"}]} {"seq_id": "42744240", "text": "#!/usr/bin/env python3\nfrom itertools import islice, tee\nfrom operator import add\n\n\ndef fib_recursive(num):\n\t\"\"\"\n\tRecursive function for finding a Fibonnaci sequence given a \n\tpositive integer.\n\t\n\tF(n) = F(n-2) + F(n-1)\n\t\"\"\"\t\n\tif num < 2:\n\t\treturn num\t\n\telse:\n\t\treturn fib_recursive(num - 1) + fib_recursive(num - 2)\n\n\ndef fib_rec_memo(num, fib_memo={0: 0, 1: 1}):\n\t\"\"\"\n\tRecursive function for finding a Fibonnaci sequence given a \n\tpositive integer and store the key/value pair in a dictionary. \n\tIf the value is is present, simply return the value \n\t\n\tF(n) = F(n-2) + F(n-1)\n\t\"\"\"\n\tif num not in fib_memo:\n\t\tfib_memo[num] = fib_rec_memo(num - 1, fib_memo=fib_memo) + \\\n\t\t\t\t\t\tfib_rec_memo(num - 2, fib_memo=fib_memo)\n\n\treturn fib_memo[num]\n\n\ndef fib_iterative(num):\n\t\"\"\"\n\tIterative function for finding a Fibonnaci sequence given a \n\tpositive integer.\n\n\tF(n) = F(n-2) + F(n-1)\n\t\"\"\"\n\ta, b = 0, 1\n\tfor x in range(0, num):\n\t\ta, b = b, a + b\n\n\treturn a\n\n\ndef fib_iter_memo(num, fib_memo={0: 0, 1: 1}):\n\t\"\"\"\n\tIterative function for finding a Fibonnaci sequence given a \n\tpositive integer and store the key/value pair in a dictionary. \n\tIf the value is is present, simply return the value \n\t\n\tF(n) = F(n-2) + F(n-1)\n\t\"\"\"\n\tif num not in fib_memo:\n\t\tfor x in range(2, num + 1):\n\t\t\tif x not in fib_memo:\n\t\t\t\tfib_memo[x] = fib_memo[x - 1] + fib_memo[x - 2]\n\n\treturn fib_memo\n\n\n\ndef take(n, iterable):\n\t\t\"\"\"Return elements from 0 to n in a list.\"\"\"\n\t\treturn list(islice(iterable, 0, n))\n\n\ndef fib_lazy_haskell_python_3():\n\t\"\"\"A Haskell-style recursive function for finding the N-th \n\tfibonacci number as laid out by Joel Grus' blog post: \n\thttp://joelgrus.com/2015/07/07/haskell-style-fibonacci-in-python/\n\n\tThe function is 'lazy' in respect that it only calculates values\n\twhen needed.\n\n\tIn Haskell, this function would look like:\n\n\tfibs :: [Int]\n\tfibs = 1 : 1 : zipWith (+) fibs (tail fibs)\n\n\tPrelude> take 10 fibs\n\t[1,1,2,3,5,8,13,21,34,55]\n\n\tNote: the 'take' function must be written to create/access\n\tthe values created by this function. In python 3, it's pretty\n\tsimple; create a function that passes in 'n' and returns \n\t'list(islice(iterable, 0, n)'.\n\t\"\"\"\n\n\tdef tail(iterable):\n\t\t\"\"\"Return elements from 1 to forever.\"\"\"\n\t\treturn islice(iterable, 1, None)\n\n\tyield 1\n\tyield 1\n\t# essentially adds memoization to function, much more efficient\n\tfibs1, fibs2 = tee(fib_lazy_haskell_python_3())\n\t\n\ttry:\n\t\t# 'yield from' was added in Python 3.3 (PEP 308)\n\t\teval('yield from map(add, fibs1, tail(fibs2))')\n\texcept SyntaxError:\n\t\t# To make this work in Python versions < 3.3\n\t\t# Note: 'map' evaluates lazily in Python 3, in Python 2 use 'imap'\t\n\t\tfor value in map(add, fibs1, tail(fibs2)):\n\t\t\tyield value\n\t\n\t# Uncomment below if you want a much slower non-tee version \n\t#yield from map(add, fib_lazy_haskell_python_3(), tail(fib_lazy_haskell_python_3()))\n\n\t", "sub_path": "algorithms_math/fibonacci.py", "file_name": "fibonacci.py", "file_ext": "py", "file_size_in_byte": 2845, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "itertools.islice", "line_number": 67, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 94, "usage_type": "call"}, {"api_name": "itertools.tee", "line_number": 99, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 107, "usage_type": "argument"}]} {"seq_id": "435372383", "text": "from uuid import uuid4\n\nimport factory\nfrom django.db.models.signals import post_save\nfrom django.utils import timezone\n\nfrom questions.models import FileUpload, ImageUpload, Submission\n\nfrom .utils import get_dummy_file\n\n\nclass TimestampedModelFactory(factory.django.DjangoModelFactory):\n\n modified_at = factory.Faker(\n \"date_time_between\", tzinfo=timezone.utc, start_date=\"-1m\", end_date=\"now\"\n )\n created_at = factory.Faker(\n \"date_time_between\", tzinfo=timezone.utc, start_date=\"-2m\", end_date=\"-1m\"\n )\n\n\n@factory.django.mute_signals(post_save)\nclass SubmissionFactory(TimestampedModelFactory):\n class Meta:\n model = Submission\n\n id = factory.LazyAttribute(lambda x: uuid4())\n questions = {}\n answers = {}\n\n\n@factory.django.mute_signals(post_save)\nclass ImageUploadFactory(TimestampedModelFactory):\n class Meta:\n model = ImageUpload\n\n id = factory.LazyAttribute(lambda x: uuid4())\n\n @factory.post_generation\n def image(self, create, extracted, **kwargs):\n if extracted:\n file_name, file = extracted\n else:\n file_name = \"image.png\"\n file = get_dummy_file(file_name)\n\n self.image.save(file_name, file)\n\n\n@factory.django.mute_signals(post_save)\nclass FileUploadFactory(TimestampedModelFactory):\n class Meta:\n model = FileUpload\n\n id = factory.LazyAttribute(lambda x: uuid4())\n\n @factory.post_generation\n def file(self, create, extracted, **kwargs):\n if extracted:\n file_name, file = extracted\n else:\n file_name = \"doc.pdf\"\n file = get_dummy_file(file_name)\n\n self.file.save(file_name, file)\n", "sub_path": "app/questions/tests/factories.py", "file_name": "factories.py", "file_ext": "py", "file_size_in_byte": 1683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "factory.django", "line_number": 12, "usage_type": "attribute"}, {"api_name": "factory.Faker", "line_number": 14, "usage_type": "call"}, {"api_name": "django.utils.timezone.utc", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 15, "usage_type": "name"}, {"api_name": "factory.Faker", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.timezone.utc", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 18, "usage_type": "name"}, {"api_name": "questions.models.Submission", "line_number": 25, "usage_type": "name"}, {"api_name": "factory.LazyAttribute", "line_number": 27, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 27, "usage_type": "call"}, {"api_name": "questions.models", "line_number": 28, "usage_type": "name"}, {"api_name": "factory.django.mute_signals", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 22, "usage_type": "argument"}, {"api_name": "factory.django", "line_number": 22, "usage_type": "attribute"}, {"api_name": "questions.models.ImageUpload", "line_number": 35, "usage_type": "name"}, {"api_name": "factory.LazyAttribute", "line_number": 37, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.get_dummy_file", "line_number": 45, "usage_type": "call"}, {"api_name": "factory.post_generation", "line_number": 39, "usage_type": "attribute"}, {"api_name": "factory.django.mute_signals", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 32, "usage_type": "argument"}, {"api_name": "factory.django", "line_number": 32, "usage_type": "attribute"}, {"api_name": "questions.models.FileUpload", "line_number": 53, "usage_type": "name"}, {"api_name": "factory.LazyAttribute", "line_number": 55, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.get_dummy_file", "line_number": 63, "usage_type": "call"}, {"api_name": "factory.post_generation", "line_number": 57, "usage_type": "attribute"}, {"api_name": "factory.django.mute_signals", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 50, "usage_type": "argument"}, {"api_name": "factory.django", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "195927800", "text": "from JumpScale9 import j\n\nimport logging\nfrom .Handlers import *\n\nfrom .JSLogger import JSLogger\nfrom .JSLoggerDefault import JSLoggerDefault\nimport sys\n\n\nclass LoggerFactory():\n\n def __init__(self):\n self.__jslocation__ = \"j.core.logger\"\n self.logger_name = 'j'\n self.handlers = Handlers()\n self.loggers = {}\n self.exclude = []\n\n self._default = JSLoggerDefault(\"default\")\n\n self.logger = JSLogger(\"logger\")\n self.logger.addHandler(self.handlers.consoleHandler)\n\n self.enabled = True\n self.filter = [\"*\"] # default filter to see which loggers will be attached needs to have * or j.sal... inside\n\n # self.logger.debug(\"started logger factory\")\n\n def _getName(self, name):\n\n name = name.strip().lower()\n\n if name == \"\":\n path, ln, name, info = logging.root.findCaller()\n if path.startswith(j.dirs.LIBDIR):\n path = path.lstrip(j.dirs.LIBDIR)\n name = path.replace(os.sep, '.')\n\n if not name.startswith(self.logger_name):\n name = \"%s.%s\" % (self.logger_name, name)\n\n if len(name) > 22:\n name = name[-22:]\n\n return name\n\n def get(self, name=\"\", force=False): # -> JSLogger:\n \"\"\"\n Return a logger with the given name. Name will be prepend with 'j.' so\n every logger return by this function is a child of the jumpscale root logger 'j'\n\n \"\"\"\n name = self._getName(name)\n\n def check_(name):\n # print(\"check %s\"%name)\n for item in self.exclude:\n # print(\"check exclude:%s\"%item)\n if item == \"*\":\n # print(\"exclude %s:%s\" % (item, name))\n return False\n if name.find(item) != -1:\n # print(\"exclude %s:%s\" % (item, name))\n return False\n for item in self.filter:\n # print(\"check include:%s\"%item)\n if item == \"*\":\n # print(\"include: %s:%s\" % (item, name))\n return True\n if name.find(item) != -1:\n # print(\"include: %s:%s\" % (item, name))\n return True\n return False\n\n if force == False and self.enabled is False:\n self.loggers[name] = self._default\n # print(\"DEFAULT LOGGER (disabledlogger):%s\" % name)\n else:\n if force or check_(name):\n # print(\"JSLOGGER:%s\" % name)\n # logger = logging.getLogger(name)\n logger = JSLogger(name)\n logger.level = j.core.state.configGetFromDict(\"logging\", \"level\", 'DEBUG')\n\n for handler in self.handlers._all:\n logger.handlers = []\n logger.addHandler(handler)\n\n self.loggers[name] = logger\n else:\n # print(\"DEFAULT LOGGER:%s\" % name)\n self.loggers[name] = self._default\n\n return self.loggers[name]\n\n def disable(self):\n \"\"\"\n will transform all loggers to empty loggers which only act on errors, but ignore logs\n \"\"\"\n if self.enabled:\n self.enabled = False\n self.filter = []\n\n # for key, logger in self.loggers.items():\n # # print(\"disable logger: %s\"%key)\n # logger.setLevel(20)\n j.application.debug = False\n\n self.logger_filters_add()\n\n def enable(self):\n \"\"\"\n \"\"\"\n if self.enabled is False:\n self.enabled = True\n self.filter = []\n self.init()\n\n # def set_quiet(self, quiet):\n # self._quiet = quiet\n\n # def set_mode(self, mode):\n # if isinstance(mode, str):\n # if mode in _name_to_mode:\n # mode = _name_to_mode[mode]\n # else:\n # raise j.exceptions.Input(\"mode %s doesn't exist\" % mode)\n\n # if mode == self.PRODUCTION:\n # self._enable_production_mode()\n # elif mode == self.DEV:\n # self._enable_dev_mode()\n\n # def set_level(self, level=10):\n # \"\"\"\n # Set logging levels on all loggers and handlers\n # Added to support backward compatability\n # \"\"\"\n # self.loggers_level_set(level=level)\n\n def handlers_level_set(self, level=10):\n \"\"\"\n\n sets level in all handlers\n\n 10=debug\n 20=info\n\n info see:\n https://docs.python.org/3/library/logging.html#levels\n\n \"\"\"\n for handler in self.handlers._all:\n handler.setLevel(level)\n\n def loggers_level_set(self, level='DEBUG'):\n \"\"\"\n\n sets level in all handlers & loggers\n\n 10=debug\n 20=info\n\n info see:\n https://docs.python.org/3/library/logging.html#levels\n\n \"\"\"\n for key, logger in self.loggers.items():\n logger.setLevel(level)\n self.handlers_level_set(level)\n\n def handlers_attach(self):\n \"\"\"\n walk over all loggers, attach the handlers\n \"\"\"\n for key, logger in self.loggers.items():\n for handler in self.handlers._all:\n logger.handlers = []\n logger.addHandler(handler)\n\n def memhandler_enable(self):\n # self.logger.propagate = True\n self.logger.addHandler(self.handlers.memoryHandler)\n\n def consolehandler_enable(self):\n # self.logger.propagate = True\n self.logger.addHandler(self.handlers.consoleHandler)\n\n def telegramhandler_enable(self, client, chat_id):\n \"\"\"\n Enable a telegram handler to forward logs to a telegram group.\n @param client: A jumpscale telegram_bot client \n @param chat_id: Telegram chat id to which logs need to be forwarded\n \"\"\"\n self.logger.addHandler(self.handlers.telegramHandler(client, chat_id))\n\n def handlers_reset(self):\n self.logger.handlers = []\n\n def logger_filters_get(self):\n return j.core.state.config_js[\"logging\"][\"filter\"]\n\n def logger_filters_add(self, items=[], exclude=[], level=10, save=False):\n \"\"\"\n items is list or string e.g. prefab, exec\n will add the filters to the logger and save it in the config file\n\n \"\"\"\n items = j.data.types.list.fromString(items)\n exclude = j.data.types.list.fromString(exclude)\n if save:\n new = False\n for item in items:\n if item not in j.core.state.config_js[\"logging\"][\"filter\"]:\n j.core.state.config_js[\"logging\"][\"filter\"].append(item)\n new = True\n for item in exclude:\n if item not in j.core.state.config_js[\"logging\"][\"exclude\"]:\n j.core.state.config_js[\"logging\"][\"exclude\"].append(item)\n new = True\n if new:\n j.core.state.configSave()\n self.init()\n\n for item in items:\n item = item.strip().lower()\n if item not in self.filter:\n self.filter.append(item)\n\n for item in exclude:\n item = item.strip().lower()\n if item not in self.exclude:\n self.exclude.append(item)\n\n self.logger.debug(\"start re-init for logging\")\n\n self.handlers_level_set(level)\n\n # make sure all loggers are empty again\n j.dirs._logger = None\n j.core.platformtype._logger = None\n j.core.state._logger = None\n j.core.dirs._logger = None\n j.core.application._logger = None\n for cat in [j.data, j.clients, j.tools, j.sal]:\n for key, item in cat.__dict__.items():\n if item is not None:\n # if hasattr(item, '__jslocation__'):\n # print (item.__jslocation__)\n if 'logger' in item.__dict__:\n item.__dict__[\"logger\"] = self.get(item.__jslocation__)\n item._logger = None\n self.loggers = {}\n\n # print(j.tools.jsloader._logger)\n # print(j.tools.jsloader.logger)\n\n def init(self):\n \"\"\"\n get info from config file & make sure all logging is done properly\n \"\"\"\n self.enabled = j.core.state.configGetFromDict(\"logging\", \"enabled\", True)\n level = j.core.state.configGetFromDict(\"logging\", \"level\", 'DEBUG')\n self.loggers_level_set(level)\n self.handlers_level_set(level)\n self.filter = []\n self.loggers = {}\n items = j.core.state.configGetFromDict(\"logging\", \"filter\", [])\n exclude = j.core.state.configGetFromDict(\"logging\", \"exclude\", [])\n self.logger_filters_add(items=items, exclude=exclude, save=False)\n\n # def enableConsoleMemHandler(self):\n # self.logger.handlers = []\n # # self.logger.propagate = True\n # self.logger.addHandler(self.handlers.memoryHandler)\n # self.logger.addHandler(self.handlers.consoleHandler)\n\n # def _enable_production_mode(self):\n # self.logger.handlers = []\n # self.logger.addHandler(logging.NullHandler())\n # # self.logger.propagate = True\n\n # def _enable_dev_mode(self):\n # logging.setLoggerClass(JSLogger)\n # self.logger.setLevel(logging.DEBUG)\n # self.logger.propagate = False\n # logging.lastResort = None\n # self.enableConsoleHandler()\n # self.logger.addHandler(self.handlers.fileRotateHandler)\n\n def test(self):\n\n logger = self.get(\"loggerTest\")\n\n self.enableConsoleMemHandler()\n\n logger.info(\"a test\")\n\n self.enableMemHandler()\n\n def perftest(logger):\n print(\"start perftest logger\")\n start = time.time()\n nr = 30000\n for i in range(nr):\n logger.info(\"this is an info message\")\n # self.getActionObjFromMethod(test)\n stop = time.time()\n print(\"nr of logs per sec:%s\" % int(nr / (stop - start)))\n\n perftest(logger)\n\n # FOLLOWING PROVES THAT THE LOOKING FOR FILE & PATH INFO IS THE SLOWING DOWN FACTOR\n # j.tools.performancetrace.profile(\"perftest(logger)\", globals=locals()) # {\"perftest\": perftest}\n", "sub_path": "JumpScale9/logging/LoggerFactory.py", "file_name": "LoggerFactory.py", "file_ext": "py", "file_size_in_byte": 10285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "JSLoggerDefault.JSLoggerDefault", "line_number": 20, "usage_type": "call"}, {"api_name": "JSLogger.JSLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.root.findCaller", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 35, "usage_type": "attribute"}, {"api_name": "JumpScale9.j.dirs", "line_number": 36, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 36, "usage_type": "name"}, {"api_name": "JumpScale9.j.dirs", "line_number": 37, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 37, "usage_type": "name"}, {"api_name": "JSLogger.JSLogger", "line_number": 83, "usage_type": "call"}, {"api_name": "JumpScale9.j.core.state.configGetFromDict", "line_number": 84, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 84, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 84, "usage_type": "name"}, {"api_name": "JumpScale9.j.application", "line_number": 108, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 108, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 202, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 202, "usage_type": "name"}, {"api_name": "JumpScale9.j.data.types.list.fromString", "line_number": 210, "usage_type": "call"}, {"api_name": "JumpScale9.j.data", "line_number": 210, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 210, "usage_type": "name"}, {"api_name": "JumpScale9.j.data.types.list.fromString", "line_number": 211, "usage_type": "call"}, {"api_name": "JumpScale9.j.data", "line_number": 211, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 211, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 215, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 215, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 216, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 216, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 219, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 219, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 220, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 220, "usage_type": "name"}, {"api_name": "JumpScale9.j.core.state.configSave", "line_number": 223, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 223, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 223, "usage_type": "name"}, {"api_name": "JumpScale9.j.dirs", "line_number": 241, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 241, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 242, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 242, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 243, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 243, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 244, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 244, "usage_type": "name"}, {"api_name": "JumpScale9.j.core", "line_number": 245, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 245, "usage_type": "name"}, {"api_name": "JumpScale9.j.data", "line_number": 246, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 246, "usage_type": "name"}, {"api_name": "JumpScale9.j.clients", "line_number": 246, "usage_type": "attribute"}, {"api_name": "JumpScale9.j.tools", "line_number": 246, "usage_type": "attribute"}, {"api_name": "JumpScale9.j.sal", "line_number": 246, "usage_type": "attribute"}, {"api_name": "JumpScale9.j.core.state.configGetFromDict", "line_number": 263, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 263, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 263, "usage_type": "name"}, {"api_name": "JumpScale9.j.core.state.configGetFromDict", "line_number": 264, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 264, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 264, "usage_type": "name"}, {"api_name": "JumpScale9.j.core.state.configGetFromDict", "line_number": 269, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 269, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 269, "usage_type": "name"}, {"api_name": "JumpScale9.j.core.state.configGetFromDict", "line_number": 270, "usage_type": "call"}, {"api_name": "JumpScale9.j.core", "line_number": 270, "usage_type": "attribute"}, {"api_name": "JumpScale9.j", "line_number": 270, "usage_type": "name"}]} {"seq_id": "87534272", "text": "from django.core.mail import EmailMessage\nfrom django.shortcuts import render\n\nfrom cloud.forms import ContactForm\n\n\ndef contacts(request):\n if request.method == \"POST\":\n form = ContactForm(request.POST)\n if form.is_valid():\n name = form.cleaned_data['contact_name']\n email = form.cleaned_data['contact_email']\n subject = form.cleaned_data['subject']\n content = form.cleaned_data['content']\n mail_msg = content + \" <<< \" + email + \" >>>\" + \"(((\" + name + \")))\"\n email = EmailMessage(subject, mail_msg, email, to=[\"itmo.cloud@gmail.com\"])\n email.send()\n msg = \"Спасибо! Письмо отправлено. Ваше обращение будет рассмотрено в ближайшее время.\"\n return render(request, 'message.html', {'message': msg})\n else:\n msg = \"Форма заполнена неправильно. Письмо не было отправлено.\"\n return render(request, 'message.html', {'message': msg})\n else:\n form = ContactForm()\n return render(request, 'contacts.html', {'form': form})\n", "sub_path": "cloud/views/contacts.py", "file_name": "contacts.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cloud.forms.ContactForm", "line_number": 9, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "cloud.forms.ContactForm", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}]} {"seq_id": "608321967", "text": "# -*- coding: utf-8 -*-\n# ganben: this class defines the scheduler\n\nimport binascii\nimport os\nimport itertools\nimport copy\nimport random\n\n# use serializable \nimport rlp\n\n# use local\nimport classalloc\n\n# auxillary funcs\n\n# serializable data structs\n\nclass SchecStud(rlp.Serializable):\n #\n fields = [\n ('id', rlp.sedes.big_endian_int),\n # ('cmplan', rlp.sedes.big_endian_int),\n ('cmclass', rlp.sedes.big_endian_int),\n ('subjcomb', rlp.sedes.big_endian_int),\n ]\n\nclass ClassStud(rlp.Serializable):\n # in class with single sub stud\n fields = [\n ('id', rlp.sedes.big_endian_int),\n ('cmclass', rlp.sedes.big_endian_int),\n ('sub', rlp.sedes.big_endian_int),\n ]\n\nclass Teachers(rlp.Serializable):\n #\n fields = [\n ('tid', rlp.sedes.big_endian_int),\n ('subj', rlp.sedes.big_endian_int),\n ('cmclass', rlp.sedes.big_endian_int),\n ]\n# mapping class between sub and subcomb\n# \nclass MapCombSub:\n # use itertools to generate dict and inversed dict data structre\n\n def __init__(self, n_subs, m_picked):\n # n: a list of subs to pick\n # m: number of choices;\n #assert(m_picked>len(n_subs))\n #\n self.subs = n_subs\n self.comb_list = list(itertools.combinations(n_subs, m_picked))\n \n \n def if_contains(self, combs, sub):\n if self.comb_list[combs]:\n for el in self.comb_list[combs]:\n if el == sub :#they are all int(id, pk of original model)\n return True\n \n return False\n\n def get_subs(self, comb):\n # return a tuples\n # (sub1, sub2, sub3)\n return self.comb_list[comb]\n\n def get_combs(self, sub):\n # input: subs: 1 - 3 or more\n # unordered list of sub in integer\n # return a list of combs in integer\n # if len(subs)>1:# fail to find out a way, skip\n # r = []\n # for e in subs:\n # r.append()\n # else:\n # l = copy.deepcopy(self.comb_list) #useless\n for it in self.comb_list:\n res = []\n for el in it:\n if el == sub:\n res.append(it)\n\n return res\n \n @property\n def values(self):\n return range(len(self.comb_list))\n\n\nclass Croom(rlp.Serializable):\n fields = [\n ('rid', rlp.sedes.big_endian_int),\n ('cap', rlp.sedes.big_endian_int),\n ]\n\nclass TeachingSubs(rlp.Serializable):\n fields = [\n ('sid', rlp.sedes.big_endian_int),\n # ('nums', rlp.sedes.big_endian_int),\n ('is_bounded', rlp.sedes.boolean),\n ]\n\nclass SchecSetups(rlp.Serializable):\n #\n fields = [\n ('sum_weekly', rlp.sedes.big_endian_int),\n ('m_perday', rlp.sedes.big_endian_int),\n ('mx_rooms', rlp.sedes.big_endian_int),\n ('fix_subs', rlp.sedes.List(rlp.sedes.big_endian_int)),\n ('fix_weekly', rlp.sedes.big_endian_int), # simplified\n ('dyn_weekly', rlp.sedes.big_endian_int), # simplified\n ('min_members', rlp.sedes.big_endian_int),\n ('max_members', rlp.sedes.big_endian_int),\n ]\n\nclass BeforeSched(rlp.Serializable):\n #\n fields = [\n ('subs', rlp.sedes.CountableList(TeachingSubs)),\n ('teachers', rlp.sedes.CountableList(Teachers)),\n ('m_pick', rlp.sedes.big_endian_int),\n ('fix_subs', rlp.sedes.CountableList(TeachingSubs)),\n ('n_subs', rlp.sedes.big_endian_int),\n ('cmclasses', rlp.sedes.CountableList(classalloc.Cmclass)),\n ]\n\nclass UnitMember(rlp.Serializable):\n # mini unit\n fields = [\n ('stud', rlp.sedes.big_endian_int),\n # ('room', rlp.sedes.big_endian_int),\n ('sub', rlp.sedes.big_endian_int),\n # ('teacher', rlp.sedes.big_endian_int),\n ]\n\nclass SchedClass(rlp.Serializable):\n # sorted class unit\n fields = [\n ('sub', rlp.sedes.big_endian_int),\n ('room', rlp.sedes.big_endian_int),\n ('teacher', rlp.sedes.big_endian_int),\n ('studs', rlp.sedes.CountableList(UnitMember)),\n ]\n\nclass UnitClass():\n # methods to determine a complete or in-complete\n # mutable, to a immutable serializable sub class\n def __init__(self, pos = (0,0), room = 0):\n # pos1 pos2 is position of table matrix\n self.room = room\n # self.valid = False\n self.pos = pos\n self.cmcls = False # default is not a fix sub class\n self.studs = []\n self.sub = None\n self.teacher = None\n self.unused = [] # a swap stack of studs\n self.settled = 0\n # output a fixed result\n \n def setup(self, m_max, m_min, is_bound):\n # m_max = class member\n # m_min class member\n # is bound = selectable sub or forceable\n self.m_max = m_max\n self.m_min = m_min\n self.is_bound = is_bound\n\n @property\n def binary(self):\n if self.valid:\n uc = SchedClass(\n sub = self.sub,\n room = self.room,\n teacher = self.teacher,\n studs = self.studs\n )\n return rlp.encode(uc)\n else:\n raise Exception\n \n @property\n def valid(self):\n if not self.sub or not self.teacher:\n return False\n elif self.settled > self.m_min and self.settled == len(self.studs):\n return True\n else:\n return False\n \n @property\n def unused(self):\n return self.unused\n\n def add_stud(self, stud):\n self.unused.append(stud)\n \n def fill_fixed_stud(self, class_stud):\n # class_stud: the cmclass together stud list, without sub input\n if self.cmcls:\n #only work for fix cmcls class\n for s in class_stud: #copy these stud to class\n ss = {\n 'id':s.id,\n 'cmclass':self.cmcls,\n 'sub': self.sub\n }\n self.studs.append(ss)\n #TODO: maybe will fill with sub?\n\n def sched_stud(self, total_stud):\n #check if status ok\n # total_stud: un scheduded stud*sub list\n # use: the main thread: unitclass.sched_stud(current stud)\n if len(total_stud) > 0:\n for i in range(len(total_stud)):\n head = total_stud[i]\n if head.sub == self.sub and len(self.studs) < self.m_max:\n self.studs.append(head)\n self.settled += 1\n total_stud.pop(i)\n\n def assign_teacher(self, teacher_id):\n self.teacher = teacher_id\n\n def assign_room(self, room_id):\n self.room = room_id\n \n def assign_fix_cmcls(self, cmcls):\n self.cmcls = cmcls\n\n def assign_sub(self, sub_id):\n self.sub = sub_id\n\nclass ClassTable:\n # this is a multi dimension array\n # can be use to swap between \n def __init__(self, setup):\n self.setup = setup # {.sum_weekly, .m_perday, mx_rooms, fix_sub list, fix_weekly int, dyn_weekly}\n # generate empty slots\n self.slots = [[] for i in range(5)]\n self.units = []\n @classmethod\n def gen_slots(n):\n # 5 day per week, fixed\n lists = [[] for i in range(5)]\n return lists\n \n def gen_fix_unitclass(self, subs, cmclass_list):\n # xx it with unit classes with position x,y, and room id for conflict resolving\n # generate the forceable first\n for i in subs:#range(self.setup['fix_sub']):\n for j in cmclass_list:\n u = UnitClass()\n u.sub = i.sid\n u.assign_fix_cmcls(j.cmid)\n u.studs = j.studs[:]\n li = [u] * self.setup['fix_weekly']\n self.units += li\n # not position/room conflict resolved\n \n def gen_dyn_unitclass(self, subs):\n # generate free picked classes\n # mapCS = MapCombSubs objects\n for i in subs:\n # should times n for each subs ?\n u = UnitClass()\n u.sub = i.sid\n li = [u] * self.setup['dyn_weekly']\n self.units += li\n \n def slot_fill_fix(self):\n # each slot, fill with a list or enum kv store:?\n pass\n\n\n def class_fill(self, total_studs):\n for it in self.slots:\n for item in it:\n item.sched_stud(total_studs)\n\ndef gen_default_subs(n_fix=3, n_dyn=6):\n # generate default subs list1 fixed subs\n # subs list2: dyn subs list\n fix_list = []\n for i in range(n_fix):\n t = TeachingSubs(\n sid = i,\n is_bounded = True\n )\n fix_list.append(t)\n \n dyn_list = []\n k = random.randrange(10, 20)\n for i in range(n_dyn):\n t = TeachingSubs(\n sid = i + k,\n is_bounded = False\n )\n dyn_list.append(t)\n return fix_list, dyn_list\n\nDEFAULT_SETUP = SchecSetups(\n sum_weekly = 5*8,\n m_perday = 8,\n mx_rooms = 10,\n fix_subs = 3,\n fix_weekly = 5,\n dyn_weekly = 5,\n min_members = 30,\n max_members = 45\n)\n# main class for perform multi level iterator\nclass Scheduler:\n # in mem , serializable input/output class\n def __init__(self, fn):\n # deserialize BeforeSched\n with open(fn, 'rb') as f:\n b = f.read()\n self.substuds = []\n self.before = rlp.decode(b, BeforeSched)\n self.setup = DEFAULT_SETUP\n # generate mapcombs\n self.mapsub = MapCombSub(self.before.subs, self.before.m_picked)\n # generate stud with subs from mapcombs\n for ic in self.before.cmclasses:\n # loop each class\n # cid = ic.id\n for it in ic:\n # iter three tuples\n sublist = list(self.mapsub.get_subs(it.subjcomb))\n st = []\n for sub in sublist:\n cs = ClassStud(\n id = it.id,\n cmclass = ic.cmid,\n sub = sub\n )\n st.append(cs)\n self.substuds += st * self.setup.dyn_weekly\n self.table = ClassTable(self.setup)\n # self.fix_list, self.dyn_list = gen_default_subs()\n self.fix_list = self.before.fix_subs\n self.dyn_list = self.before.subs\n\n def start_schec(self):\n #\n\n for item in self.before.cmclasses:\n \n self.table.gen_fix_unitclass(self.fix_list, item) # the sub should be named;\n # need rewrite a lot...\n # for sub in self.mapsub.values:\n self.table.gen_dyn_unitclass(self.dyn_list) #\n # need rewrite a lot...\n\n \n def print_table(self, l = 1):\n # print 1st element or each slot;\n pass", "sub_path": "schecsite/scheduler/scheduler.py", "file_name": "scheduler.py", "file_ext": "py", "file_size_in_byte": 10744, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "rlp.Serializable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 42, "usage_type": "attribute"}, {"api_name": "itertools.combinations", "line_number": 55, "usage_type": "call"}, {"api_name": "rlp.Serializable", "line_number": 94, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 96, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 97, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 100, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 102, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 104, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 107, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 111, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 112, "usage_type": "attribute"}, {"api_name": "rlp.sedes.List", "line_number": 113, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 115, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 116, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 117, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 120, "usage_type": "attribute"}, {"api_name": "rlp.sedes.CountableList", "line_number": 123, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rlp.sedes.CountableList", "line_number": 124, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 125, "usage_type": "attribute"}, {"api_name": "rlp.sedes.CountableList", "line_number": 126, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 126, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 127, "usage_type": "attribute"}, {"api_name": "rlp.sedes.CountableList", "line_number": 128, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 128, "usage_type": "attribute"}, {"api_name": "classalloc.Cmclass", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 131, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 134, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 136, "usage_type": "attribute"}, {"api_name": "rlp.Serializable", "line_number": 140, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 143, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 144, "usage_type": "attribute"}, {"api_name": "rlp.sedes", "line_number": 145, "usage_type": "attribute"}, {"api_name": "rlp.sedes.CountableList", "line_number": 146, "usage_type": "call"}, {"api_name": "rlp.sedes", "line_number": 146, "usage_type": "attribute"}, {"api_name": "rlp.encode", "line_number": 182, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 298, "usage_type": "call"}, {"api_name": "rlp.decode", "line_number": 325, "usage_type": "call"}]} {"seq_id": "644705953", "text": "\n# Logging\nimport importlib\nfrom homeassistant.components.notify import (\n ATTR_DATA,\n ATTR_TARGET,\n # PLATFORM_SCHEMA,\n BaseNotificationService,\n)\n\nimport logging\n_LOGGER = logging.getLogger(__name__)\n\n# import voluptuous as vol\n# import homeassistant.helpers.config_validation as cv\n# PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(\n# {\n# # vol.Required(CONF_HOST): cv.string,\n# # vol.Optional(CONF_FILENAME, default=WEBOSTV_CONFIG_FILE): cv.string,\n# # vol.Optional(CONF_ICON): cv.string,\n# }\n# )\n\n\ndef get_service(hass, config, discovery_info=None):\n \"\"\"Return the notify service.\"\"\"\n return ZhiPlusNotificationService(config['targets'])\n\n\nclass ZhiPlusNotificationService(BaseNotificationService):\n \"\"\"Implement the notification service.\"\"\"\n\n def __init__(self, targets):\n \"\"\"Initialize the service.\"\"\"\n self._targets = targets\n\n @property\n def targets(self):\n return self._targets\n\n async def async_send_message(self, message=\"\", **kwargs):\n \"\"\"Send a message.\"\"\"\n try:\n conf = kwargs.get(ATTR_TARGET)[0]\n data = kwargs.get(ATTR_DATA)\n target = conf['target']\n mod = importlib.import_module('.' + target + 'tify', __package__)\n async_send = getattr(mod, 'async_send')\n await async_send(conf, message, data)\n except:\n import traceback\n _LOGGER.error(traceback.format_exc())\n", "sub_path": "extra/custom_components/zhiplus/notify.py", "file_name": "notify.py", "file_ext": "py", "file_size_in_byte": 1468, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "homeassistant.components.notify.BaseNotificationService", "line_number": 30, "usage_type": "name"}, {"api_name": "homeassistant.components.notify.ATTR_TARGET", "line_number": 44, "usage_type": "argument"}, {"api_name": "homeassistant.components.notify.ATTR_DATA", "line_number": 45, "usage_type": "argument"}, {"api_name": "importlib.import_module", "line_number": 47, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 52, "usage_type": "call"}]} {"seq_id": "361394904", "text": "#!/usr/bin/python\n'''\n Send openshift and docker versions with miq_metric tag to metric_sender\n\n Example:\n ./cron-send-docker-oc-versions.py -u oc_username -p oc_password\n'''\n# Disabling invalid-name because pylint doesn't like the naming conention we have.\n# pylint: disable=invalid-name,import-error\n\nfrom docker import AutoVersionClient\nimport subprocess\nimport argparse\nfrom openshift_tools.monitoring.metric_sender import MetricSender\n\n\ndef parse_args():\n '''Parse the arguments for this script'''\n parser = argparse.ArgumentParser(description=\"Tool to send docker and openshift versions\")\n parser.add_argument('-d', '--debug', default=False, action=\"store_true\", help=\"debug mode\")\n parser.add_argument('-v', '--verbose', default=False, action=\"store_true\", help=\"Verbose?\")\n\n args = parser.parse_args()\n return args\n\n\ndef main():\n '''get docker and openshift versions and send to metric sender\n '''\n\n args = parse_args()\n mts = MetricSender(verbose=args.verbose, debug=args.debug)\n\n # Get docker version\n cli = AutoVersionClient(base_url='unix://var/run/docker.sock', timeout=120)\n docker_version = cli.version()[\"Version\"]\n mts.add_metric({\"docker.version\": docker_version}, key_tags={'miq_metric': 'true'})\n\n # Get openshift version\n try:\n return_value = subprocess.check_output(\"oc version\", stderr=subprocess.STDOUT, shell=True)\n oc_version = return_value.split('\\n')[0].split(' ')[1]\n mts.add_metric({\"oc.version\": oc_version}, key_tags={'miq_metric': 'true'})\n\n except subprocess.CalledProcessError as error:\n print (\"Failed to get openshift version: \", error.output)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/monitoring/cron-send-docker-oc-versions.py", "file_name": "cron-send-docker-oc-versions.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "openshift_tools.monitoring.metric_sender.MetricSender", "line_number": 32, "usage_type": "call"}, {"api_name": "docker.AutoVersionClient", "line_number": 35, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 41, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "433496602", "text": "\"\"\"\nCreated on Aug 24, 2016\n\nMongoDB connector for water level receiver\n\n@author: Levan Tsinadze\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport datetime\n\nfrom org.maxin.mongo.abstract_mongo_connector import CONTAINER_KEY, INERT_ERROR, INERT_OK, LEVEL_KEY, RECORD_TIME, INERT_NONEED\nfrom org.maxin.mongo.abstract_mongo_connector import abstract_mongo_receiver\n\n\nclass mongo_receiver(abstract_mongo_receiver):\n \"\"\"MongoDB client for water level database\"\"\"\n \n def __init__(self, host='localhost', port=27017):\n super(mongo_receiver, self).__init__(host, port)\n \n def init_collection(self):\n \"\"\"Initializes water level collection \n from database\n Return:\n level_collection - water level collection\n \"\"\"\n \n db = self.init_database()\n level_collection = db.level_collection\n \n return level_collection\n \n def create_record(self, level_info, container_id):\n \"\"\"Creates water level record for database\n Args:\n level_info - water level\n container_id - identifier of water container\n Return:\n mongo_record - water level record\n \"\"\"\n return {\n CONTAINER_KEY: container_id,\n LEVEL_KEY: level_info,\n RECORD_TIME: datetime.datetime.utcnow()\n }\n \n def init_last_record_query(self, container_id):\n \"\"\"Initializes last record query\n Args:\n container_id - container identifier\n Return:\n last record query\n \"\"\"\n return {'$query': {CONTAINER_KEY:container_id},\n '$orderby': {RECORD_TIME:-1}}\n # Inserts record to database\n def insert_data(self, level_info, container_id):\n \"\"\"Inserts water level info\n Args:\n level_info - water level\n container_id - identifier of water container\n Return:\n level_id - assigned unique identifier of record\n \"\"\"\n \n mongo_record = self.create_record(level_info, container_id)\n level_collection = self.init_collection()\n level_id = level_collection.insert_one(mongo_record).inserted_id\n print(level_id)\n \n if level_id is not None:\n result_value = INERT_OK\n else:\n result_value = INERT_ERROR\n \n return result_value\n\n def read_last_record(self, container_id):\n \"\"\"Reads last record from database by container identifier\n Args:\n container_id - container identifier\n Return:\n level_record - water level info\n \"\"\"\n \n level_collection = self.init_collection()\n lr_query = self.init_last_record_query(container_id)\n level_record = level_collection.find_one(lr_query)\n \n return level_record\n \n def validate_level(self, ext_info, level_info):\n \"\"\"Validates where level info should be inserted\n Args:\n ext_info - existed level info\n level_info new level info\n \"\"\"\n ext_number = float(ext_info)\n return ext_number - 1 > level_info or ext_number + 1 < level_info\n \n def validate_and_insert(self, level_info, container_id):\n \"\"\"Validates and adds water level info to database\n Args:\n level_info - water level\n container_id - identifier of water container\n Return:\n level_id - assigned unique identifier of record\n \"\"\"\n \n if level_info is None:\n level_number = -1\n else:\n level_number = float(level_info)\n level_record = self.read_last_record(container_id)\n if level_record is None or self.validate_level(level_record[LEVEL_KEY], level_number) :\n result_value = self.insert_data(level_number, container_id)\n else:\n result_value = INERT_NONEED\n \n return result_value\n", "sub_path": "org/maxin/mongo/mongo_connector.py", "file_name": "mongo_connector.py", "file_ext": "py", "file_size_in_byte": 3669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "org.maxin.mongo.abstract_mongo_connector.abstract_mongo_receiver", "line_number": 18, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.CONTAINER_KEY", "line_number": 45, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.LEVEL_KEY", "line_number": 46, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.RECORD_TIME", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.CONTAINER_KEY", "line_number": 57, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.RECORD_TIME", "line_number": 58, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.INERT_OK", "line_number": 75, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.INERT_ERROR", "line_number": 77, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.LEVEL_KEY", "line_number": 118, "usage_type": "name"}, {"api_name": "org.maxin.mongo.abstract_mongo_connector.INERT_NONEED", "line_number": 121, "usage_type": "name"}]} {"seq_id": "79265729", "text": "import numpy as np\nimport pandas as pd\n\nfrom bokeh.plotting import figure, show\nfrom bokeh.models import ColumnDataSource, Panel\n\ndef boxplot_tab(data_frame_nasa):\n cats = [\"duringTime\"]\n df = pd.DataFrame(dict(score=data_frame_nasa['duringTime'], group='duringTime'))\n\n # find the quartiles and IQR for each category\n groups = df.groupby('group')\n q1 = groups.quantile(q=0.25)\n q2 = groups.quantile(q=0.5)\n q3 = groups.quantile(q=0.75)\n iqr = q3 - q1\n upper = q3 + 1.5 * iqr\n lower = q1 - 1.5 * iqr\n\n # find the outliers for each category\n def outliers(group):\n cat = group.name\n return group[(group.score > upper.loc[cat]['score']) | (group.score < lower.loc[cat]['score'])]['score']\n\n out = groups.apply(outliers).dropna()\n\n # prepare outlier data for plotting, we need coordinates for every outlier.\n if not out.empty:\n outx = list(out.index.get_level_values(0))\n outy = list(out.values)\n\n p = figure(tools=\"\", background_fill_color=\"#efefef\", x_range=cats, toolbar_location=None)\n\n # if no outliers, shrink lengths of stems to be no longer than the minimums or maximums\n qmin = groups.quantile(q=0.00)\n qmax = groups.quantile(q=1.00)\n upper.score = [min([x, y]) for (x, y) in zip(list(qmax.loc[:, 'score']), upper.score)]\n lower.score = [max([x, y]) for (x, y) in zip(list(qmin.loc[:, 'score']), lower.score)]\n\n # stems\n p.segment(cats, upper.score, cats, q3.score, line_color=\"black\")\n p.segment(cats, lower.score, cats, q1.score, line_color=\"black\")\n\n # boxes\n p.vbar(cats, 0.7, q2.score, q3.score, fill_color=\"#E08E79\", line_color=\"black\")\n p.vbar(cats, 0.7, q1.score, q2.score, fill_color=\"#3B8686\", line_color=\"black\")\n\n # whiskers (almost-0 height rects simpler than segments)\n p.rect(cats, lower.score, 0.2, 0.01, line_color=\"black\")\n p.rect(cats, upper.score, 0.2, 0.01, line_color=\"black\")\n\n # outliers\n if not out.empty:\n p.circle(outx, outy, size=6, color=\"#F38630\", fill_alpha=0.6)\n\n p.xgrid.grid_line_color = None\n p.ygrid.grid_line_color = \"white\"\n p.grid.grid_line_width = 2\n p.xaxis.major_label_text_font_size = \"16px\"\n\n tab = Panel(child=p, title='Boxplot')\n\n return tab\n", "sub_path": "scripts/boxplot.py", "file_name": "boxplot.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "call"}, {"api_name": "bokeh.plotting.figure", "line_number": 32, "usage_type": "call"}, {"api_name": "bokeh.models.Panel", "line_number": 61, "usage_type": "call"}]} {"seq_id": "230660741", "text": "# -*- coding: utf-8 -*-\n\nfrom cleo.commands.command import Command, CommandError\nfrom cleo.application import Application\nfrom cleo.inputs.input_definition import InputDefinition\nfrom cleo.inputs.input_argument import InputArgument\nfrom cleo.inputs.input_option import InputOption\nfrom cleo.inputs import ListInput\nfrom cleo.outputs import NullOutput\nfrom cleo.helpers import FormatterHelper\nfrom cleo.testers import CommandTester\nfrom cleo.validators import Integer, Boolean\nfrom .. import CleoTestCase\nfrom ..fixtures.some_command import SomeCommand\nfrom ..fixtures.no_configure_command import NoConfigureCommand\nfrom ..fixtures.signature_command import SignatureCommand\nfrom ..fixtures.inherited_command import ChildCommand\n\n\nclass CommandTest(CleoTestCase):\n\n NON_CALLABLE = None\n\n def test_init(self):\n \"\"\"\n Command.__init__() behaves properly\n \"\"\"\n self.assertRaises(Exception, Command)\n\n command = Command('foo:bar')\n self.assertEqual(\n 'foo:bar',\n command.get_name(),\n msg='__init__() takes the command name as its first argument'\n )\n\n def test_command_name_cannot_be_empty(self):\n \"\"\"\n A command name cannot be empty.\n \"\"\"\n self.assertRaises(\n Exception,\n Command\n )\n\n def test_set_application(self):\n \"\"\"\n Command.set_application() sets the current application\n \"\"\"\n application = Application()\n command = SomeCommand()\n command.set_application(application)\n self.assertEqual(application, command.get_application(), msg='.set_application() sets the current application')\n\n def test_set_get_definition(self):\n \"\"\"\n Command.get/set_definition properly sets and gets definition\n \"\"\"\n command = SomeCommand()\n definition = InputDefinition()\n ret = command.set_definition(definition)\n self.assertEqual(command, ret, msg='.set_definition() implements a fluent interface')\n self.assertEqual(definition, command.get_definition(),\n msg='.set_definition() sets the current InputDefinition instance')\n command.set_definition([InputArgument('foo'), InputOption('bar')])\n self.assertTrue(command.get_definition().has_argument('foo'),\n msg='.set_definition() also takes an array of InputArguments and InputOptions as an argument')\n self.assertTrue(command.get_definition().has_option('bar'),\n msg='.set_definition() also takes an array of InputArguments and InputOptions as an argument')\n command.set_definition(InputDefinition())\n\n def test_add_argument(self):\n \"\"\"\n Command.add_argument() adds an argument to command.\n \"\"\"\n command = SomeCommand()\n ret = command.add_argument('foo')\n\n self.assertEqual(ret, command)\n self.assertTrue(command.get_definition().has_argument('foo'))\n\n def test_add_option(self):\n \"\"\"\n Command.add_option() adds an option to command.\n \"\"\"\n command = SomeCommand()\n ret = command.add_option('foo')\n\n self.assertEqual(ret, command)\n self.assertTrue(command.get_definition().has_option('foo'))\n\n def test_get_namespace_get_name_set_name(self):\n command = SomeCommand()\n self.assertEqual('namespace:name', command.get_name())\n\n command.set_name('foo')\n self.assertEqual('foo', command.get_name())\n\n ret = command.set_name('foobar:bar')\n self.assertEqual(ret, command)\n self.assertEqual('foobar:bar', command.get_name())\n\n def test_invalid_command_names(self):\n data = ['', 'foo:']\n\n command = SomeCommand()\n\n for d in data:\n self.assertRaisesRegexp(\n CommandError,\n 'Command name \"%s\" is invalid.' % d,\n command.set_name,\n d\n )\n\n def test_set_get_description(self):\n command = SomeCommand()\n\n self.assertEqual('description', command.get_description())\n\n ret = command.set_description('description1')\n self.assertEqual(ret, command)\n self.assertEqual('description1', command.get_description())\n\n def test_set_get_help(self):\n command = SomeCommand()\n\n self.assertEqual('help', command.get_help())\n\n ret = command.set_description('help1')\n self.assertEqual(ret, command)\n self.assertEqual('help1', command.get_description())\n\n def test_get_processed_help(self):\n command = SomeCommand()\n\n command.set_help('The %command.name% command does... Example: python %command.full_name%.')\n self.assertRegex(\n command.get_processed_help(),\n 'The namespace:name command does...'\n )\n self.assertNotRegex(\n command.get_processed_help(),\n '%command.full_name%'\n )\n\n def test_set_get_aliases(self):\n command = SomeCommand()\n\n self.assertEqual(['name'], command.get_aliases())\n\n ret = command.set_aliases(['name1'])\n self.assertEqual(ret, command)\n self.assertEqual(['name1'], command.get_aliases())\n\n def test_get_synposis(self):\n command = SomeCommand()\n command.add_argument('bar')\n command.add_option('foo')\n\n self.assertEqual(\n 'namespace:name [--foo] [--] [<bar>]',\n command.get_synopsis()\n )\n\n def test_get_helper(self):\n application = Application()\n command = SomeCommand()\n command.set_application(application)\n formatter_helper = FormatterHelper()\n\n self.assertEqual(\n formatter_helper.get_name(),\n command.get_helper('formatter').get_name()\n )\n\n def test_merge_application_definition(self):\n \"\"\"\n Command.merge_application_definition() merges command and application.\n \"\"\"\n application1 = Application()\n application1.get_definition().add_arguments([InputArgument('foo')])\n application1.get_definition().add_options([InputOption('bar')])\n command = SomeCommand()\n command.set_application(application1)\n command.set_definition(\n InputDefinition([\n InputArgument('bar'),\n InputOption('foo')\n ])\n )\n\n command.merge_application_definition()\n self.assertTrue(command.get_definition().has_argument('foo'))\n self.assertTrue(command.get_definition().has_option('foo'))\n self.assertTrue(command.get_definition().has_argument('bar'))\n self.assertTrue(command.get_definition().has_option('bar'))\n\n # It should not merge the definitions twice\n command.merge_application_definition()\n self.assertEqual(3, command.get_definition().get_argument_count())\n\n def test_merge_application_definition_without_args_then_with_args_adds_args(self):\n application1 = Application()\n application1.get_definition().add_arguments([InputArgument('foo')])\n application1.get_definition().add_options([InputOption('bar')])\n command = SomeCommand()\n command.set_application(application1)\n command.set_definition(InputDefinition())\n\n command.merge_application_definition(False)\n self.assertFalse(command.get_definition().has_argument('foo'))\n self.assertTrue(command.get_definition().has_option('bar'))\n\n command.merge_application_definition(True)\n self.assertTrue(command.get_definition().has_argument('foo'))\n\n # It should not merge the definitions twice\n command.merge_application_definition()\n self.assertEqual(2, command.get_definition().get_argument_count())\n\n def test_run_interactive(self):\n tester = CommandTester(SomeCommand())\n\n tester.execute([], {'interactive': True})\n\n self.assertEqual(\n 'interact called\\nexecute called\\n',\n tester.get_display()\n )\n\n def test_run_non_interactive(self):\n tester = CommandTester(SomeCommand())\n\n tester.execute([], {'interactive': False})\n\n self.assertEqual(\n 'execute called\\n',\n tester.get_display()\n )\n\n def test_execute_method_needs_to_be_overwridden(self):\n command = Command('foo')\n self.assertRaises(\n NotImplementedError,\n command.run,\n ListInput([]),\n NullOutput()\n )\n\n def test_run_with_invalid_option(self):\n command = SomeCommand()\n tester = CommandTester(command)\n\n self.assertRaises(\n Exception,\n 'The \"--bar\" option does not exist.',\n tester.execute,\n [('--bar', True)]\n )\n\n def test_run_returns_integer_exit_code(self):\n command = SomeCommand()\n exit_code = command.run(ListInput([]), NullOutput())\n self.assertEqual(0, exit_code)\n\n command = SomeCommand()\n command.execute = self.mock().MagicMock(return_value=2.3)\n exit_code = command.run(ListInput([]), NullOutput())\n self.assertEqual(2, exit_code)\n\n def test_set_code(self):\n command = SomeCommand()\n ret = command.set_code(lambda c: c.line('from the code...'))\n self.assertEqual(ret, command)\n\n tester = CommandTester(command)\n tester.execute([])\n self.assertEqual(\n 'interact called\\nfrom the code...\\n',\n tester.get_display()\n )\n\n command = SomeCommand()\n command.set_code(self.callable_method)\n tester = CommandTester(command)\n tester.execute([])\n self.assertEqual(\n 'interact called\\nfrom the code...\\n',\n tester.get_display()\n )\n\n def test_set_code_with_non_callable(self):\n command = SomeCommand()\n\n self.assertRaisesRegexp(\n Exception,\n 'Invalid callable provided to Command.setCode().',\n command.set_code,\n self.NON_CALLABLE\n )\n\n def test_without_configure(self):\n command = NoConfigureCommand()\n\n self.assertEqual('no:configure', command.get_name())\n self.assertEqual('description', command.get_description())\n self.assertEqual('help', command.get_help())\n self.assertEqual(2, command.get_definition().get_argument_count())\n self.assertEqual(2, len(command.get_definition().get_options()))\n\n def test_with_signature(self):\n command = SignatureCommand()\n\n self.assertEqual('signature:command', command.name)\n self.assertEqual('description', command.description)\n self.assertEqual('help', command.help)\n self.assertEqual(2, command.get_definition().get_argument_count())\n self.assertEqual(2, len(command.get_definition().get_options()))\n self.assertIsInstance(command.get_definition().get_argument('foo').get_validator(), Integer)\n self.assertIsInstance(command.get_definition().get_option('baz').get_validator(), Boolean)\n\n def callable_method(self, c):\n c.line('from the code...')\n\n def test_signature_inheritance(self):\n command = ChildCommand()\n\n assert 'parent' == command.name\n assert 'Parent Command.' == command.description\n", "sub_path": "tests/commands/test_command.py", "file_name": "test_command.py", "file_ext": "py", "file_size_in_byte": 11312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "cleo.commands.command.Command", "line_number": 28, "usage_type": "argument"}, {"api_name": "cleo.commands.command.Command", "line_number": 30, "usage_type": "call"}, {"api_name": "cleo.commands.command.Command", "line_number": 43, "usage_type": "argument"}, {"api_name": "cleo.application.Application", "line_number": 50, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 51, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 59, "usage_type": "call"}, {"api_name": "cleo.inputs.input_definition.InputDefinition", "line_number": 60, "usage_type": "call"}, {"api_name": "cleo.inputs.input_argument.InputArgument", "line_number": 65, "usage_type": "call"}, {"api_name": "cleo.inputs.input_option.InputOption", "line_number": 65, "usage_type": "call"}, {"api_name": "cleo.inputs.input_definition.InputDefinition", "line_number": 70, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 76, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 86, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 93, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 106, "usage_type": "call"}, {"api_name": "cleo.commands.command.CommandError", "line_number": 110, "usage_type": "argument"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 117, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 126, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 135, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 148, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 157, "usage_type": "call"}, {"api_name": "cleo.application.Application", "line_number": 167, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 168, "usage_type": "call"}, {"api_name": "cleo.helpers.FormatterHelper", "line_number": 170, "usage_type": "call"}, {"api_name": "cleo.application.Application", "line_number": 181, "usage_type": "call"}, {"api_name": "cleo.inputs.input_argument.InputArgument", "line_number": 182, "usage_type": "call"}, {"api_name": "cleo.inputs.input_option.InputOption", "line_number": 183, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 184, "usage_type": "call"}, {"api_name": "cleo.inputs.input_definition.InputDefinition", "line_number": 187, "usage_type": "call"}, {"api_name": "cleo.inputs.input_argument.InputArgument", "line_number": 188, "usage_type": "call"}, {"api_name": "cleo.inputs.input_option.InputOption", "line_number": 189, "usage_type": "call"}, {"api_name": "cleo.application.Application", "line_number": 204, "usage_type": "call"}, {"api_name": "cleo.inputs.input_argument.InputArgument", "line_number": 205, "usage_type": "call"}, {"api_name": "cleo.inputs.input_option.InputOption", "line_number": 206, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 207, "usage_type": "call"}, {"api_name": "cleo.inputs.input_definition.InputDefinition", "line_number": 209, "usage_type": "call"}, {"api_name": "cleo.testers.CommandTester", "line_number": 223, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 223, "usage_type": "call"}, {"api_name": "cleo.testers.CommandTester", "line_number": 233, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 233, "usage_type": "call"}, {"api_name": "cleo.commands.command.Command", "line_number": 243, "usage_type": "call"}, {"api_name": "cleo.inputs.ListInput", "line_number": 247, "usage_type": "call"}, {"api_name": "cleo.outputs.NullOutput", "line_number": 248, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 252, "usage_type": "call"}, {"api_name": "cleo.testers.CommandTester", "line_number": 253, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 263, "usage_type": "call"}, {"api_name": "cleo.inputs.ListInput", "line_number": 264, "usage_type": "call"}, {"api_name": "cleo.outputs.NullOutput", "line_number": 264, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 267, "usage_type": "call"}, {"api_name": "cleo.inputs.ListInput", "line_number": 269, "usage_type": "call"}, {"api_name": "cleo.outputs.NullOutput", "line_number": 269, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 273, "usage_type": "call"}, {"api_name": "cleo.testers.CommandTester", "line_number": 277, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 284, "usage_type": "call"}, {"api_name": "cleo.testers.CommandTester", "line_number": 286, "usage_type": "call"}, {"api_name": "fixtures.some_command.SomeCommand", "line_number": 294, "usage_type": "call"}, {"api_name": "fixtures.no_configure_command.NoConfigureCommand", "line_number": 304, "usage_type": "call"}, {"api_name": "fixtures.signature_command.SignatureCommand", "line_number": 313, "usage_type": "call"}, {"api_name": "cleo.validators.Integer", "line_number": 320, "usage_type": "argument"}, {"api_name": "cleo.validators.Boolean", "line_number": 321, "usage_type": "argument"}, {"api_name": "fixtures.inherited_command.ChildCommand", "line_number": 327, "usage_type": "call"}]} {"seq_id": "587993002", "text": "from collections import defaultdict\n\ndef read_file(name):\n with open(f\"input.txt\") as f:\n content = f.readlines()\n return [x.strip() for x in content]\n\n\nclass Painter:\n def __init__(self, prog):\n self.prog = prog\n self.ip = 0\n self.output = 0\n self.rel_base = 0\n self.halt = False\n\n\ndef split_instruction(instruction):\n instruction = f\"{instruction:05}\"\n return instruction[3:], instruction[0:3]\n\n\ndef get_values(input, pos, op, modes, painter):\n mode_a, mode_b, mode_c = modes\n values = []\n offset = 0\n\n if op in [\"01\", \"02\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\"]:\n if mode_c == \"0\":\n values.append(input[input[pos + 1]])\n elif mode_c == \"1\":\n values.append(input[pos + 1])\n elif mode_c == \"2\":\n values.append(input[input[pos + 1] + painter.rel_base])\n\n if op in [\"01\", \"02\", \"05\", \"06\", \"07\", \"08\"]:\n if mode_b == \"0\":\n values.append(input[input[pos + 2]])\n elif mode_b == \"1\":\n values.append(input[pos + 2])\n elif mode_b == \"2\":\n values.append(input[input[pos + 2] + painter.rel_base])\n\n if op in []:\n if mode_a == \"0\":\n values.append(input[input[pos + 3]])\n elif mode_a == \"1\":\n values.append(input[pos + 3])\n elif mode_a == \"2\":\n values.append(input[input[pos + 3] + painter.rel_base])\n\n if op in [\"01\", \"02\", \"07\", \"08\"]:\n if mode_a == \"2\":\n offset = painter.rel_base\n\n if op in [\"03\"]:\n if mode_c == \"2\":\n offset = painter.rel_base\n\n return values, offset\n\n\ndef run_booster(input, painter):\n while painter.prog[painter.ip] != 99:\n op, modes = split_instruction(painter.prog[painter.ip])\n values, offset = get_values(painter.prog, painter.ip, op, modes, painter)\n\n if op == \"01\": # Addition\n painter.prog[painter.prog[painter.ip + 3] + offset] = values[0] + values[1]\n painter.ip += 4\n\n if op == \"02\": # Multiplication\n painter.prog[painter.prog[painter.ip + 3] + offset] = values[0] * values[1]\n painter.ip += 4\n\n if op == \"03\": # Read and Store input\n painter.prog[painter.prog[painter.ip + 1] + offset] = input\n painter.ip += 2\n\n if op == \"04\": # Print Output\n painter.output = values[0]\n # print(painter.output)\n painter.ip += 2\n return painter\n\n if op == \"05\": # Jump-if-True\n if values[0]:\n painter.ip = values[1]\n else:\n painter.ip += 3\n\n if op == \"06\": # Jump-if-False\n if not values[0]:\n painter.ip = values[1]\n else:\n painter.ip += 3\n\n if op == \"07\": # Less than\n if values[0] < values[1]:\n painter.prog[painter.prog[painter.ip + 3] + offset] = 1\n else:\n painter.prog[painter.prog[painter.ip + 3] + offset] = 0\n painter.ip += 4\n\n if op == \"08\": # Equals\n if values[0] == values[1]:\n painter.prog[painter.prog[painter.ip + 3] + offset] = 1\n else:\n painter.prog[painter.prog[painter.ip + 3] + offset] = 0\n painter.ip += 4\n\n if op == \"09\": # Adjust Relative Base\n painter.rel_base += values[0]\n painter.ip += 2\n\n painter.halt = True\n return painter\n\n\ndef create_program(input):\n prog = defaultdict(int)\n\n for i in range(len(input)):\n prog[i] = int(input[i])\n\n return prog\n\n\ndef turn_and_move(pos, dir, turn):\n if turn == 0:\n dir = (dir - 1) % 4\n else:\n dir = (dir + 1) % 4\n\n if dir == 0: # up\n pos = (pos[0], pos[1] + 1)\n elif dir == 1: # right\n pos = (pos[0] + 1, pos[1])\n elif dir == 2: # down\n pos = (pos[0], pos[1] - 1)\n elif dir == 3: # left\n pos = (pos[0] - 1, pos[1])\n\n return pos, dir\n\n\ndef solve():\n input = read_file(\"11\")[0].split(\",\")\n prog = create_program(input)\n\n panel = defaultdict(int)\n painted = defaultdict(int)\n painter = Painter(prog)\n\n dir = 0\n pos = (0, 0)\n\n while not painter.halt:\n painter = run_booster(panel[pos], painter)\n color = painter.output\n painter = run_booster(panel[pos], painter)\n turn = painter.output\n\n painted[pos] = 1\n panel[pos] = color\n\n pos, dir = turn_and_move(pos, dir, turn)\n\n return len(painted)\n\n\nprint(solve())", "sub_path": "Day11/question1.py", "file_name": "question1.py", "file_ext": "py", "file_size_in_byte": 4655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "collections.defaultdict", "line_number": 121, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 151, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 152, "usage_type": "call"}]} {"seq_id": "416678121", "text": "import cherrypy\nfrom splunk.appserver.mrsparkle.controllers import BaseController\nfrom splunk.appserver.mrsparkle.lib.decorators import expose_page\n\nimport formencode\nfrom formencode import validators\nimport logging\n\n\nlogger = logging.getLogger('splunk.appserver.controllers.prototype')\n\nclass YearBornValidator(validators.FancyValidator):\n \"\"\"\n An example of a custom form validator\n you can use this as\n userage = YearBornValidator()\n or\n yearborn = YearBornValidator(min_age=21, max_age=110)\n \"\"\"\n min_age = 12\n max_age = 100\n messages = {\n 'invalid': 'Please enter a valid year between %(minYear)i and %(maxYear)i',\n }\n def _to_python(self, value, state):\n import time\n thisyear = time.localtime()[0] \n minyear = thisyear - self.max_age\n maxyear = thisyear - self.min_age\n try:\n year = int(value)\n except (ValueError, TypeError):\n raise formencode.api.Invalid(self.message('invalid', state, minYear=minyear, maxYear=maxyear), value, state)\n if year < minyear or year > maxyear: \n raise formencode.api.Invalid(self.message('invalid', state, minYear=minyear, maxYear=maxyear), value, state)\n return year\n\n _from_python = _to_python\n\n\nclass TestForm(formencode.Schema):\n \"\"\"\n Example form used with PrototypeController.form1\n Have a look at validators.py to see all the other available validators\n \"\"\"\n allow_extra_fields = False\n email = validators.Email() # match an email address, could also add resolve_domain=True for additional checks\n name = formencode.All( # require all enclosed validators to pass, could also use formencode.Any\n validators.String(not_empty=True, min=2, max=50),\n validators.PlainText()\n )\n yearborn = YearBornValidator()\n\n\n\nclass PrototypeController(BaseController):\n \"\"\"\n Handle experimental ideas and code\n \"\"\"\n\n @expose_page(False, methods=['GET', 'POST'])\n def form1(self, **kw):\n \"\"\"A simple example of using form validation\"\"\"\n form = TestForm()\n form_errors = {}\n form_defaults = {}\n error = None\n if cherrypy.request.method == 'POST':\n try:\n form_data = form.to_python(kw)\n return \"\"\"Form Parsed OK\"\"\"\n except formencode.api.Invalid as e:\n form_defaults = kw\n if e.error_dict:\n form_errors = e.error_dict\n else:\n error = e.msg\n\n return self.render_template('prototype/form1.html', { \n 'error' : error,\n 'form_defaults' : form_defaults,\n 'form_errors' : form_errors\n })\n\n @expose_page(False)\n def sparklines(self):\n \"\"\"Example jquery.sparkline.js usage\"\"\"\n return self.render_template('prototype/sparklines.html')\n\n @expose_page(False)\n def scroll_performance(self):\n \"\"\"Test page for scroll bar performance testing\"\"\"\n return self.render_template('prototype/scroll_performance.html')\n\n @expose_page(False)\n def new_layout(self):\n return self.render_template('prototype/new_layout.html')\n", "sub_path": "appserver/mrsparkle/controllers/prototype.py", "file_name": "prototype.py", "file_ext": "py", "file_size_in_byte": 3207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "formencode.validators.FancyValidator", "line_number": 12, "usage_type": "attribute"}, {"api_name": "formencode.validators", "line_number": 12, "usage_type": "name"}, {"api_name": "time.localtime", "line_number": 27, "usage_type": "call"}, {"api_name": "formencode.api.Invalid", "line_number": 33, "usage_type": "call"}, {"api_name": "formencode.api", "line_number": 33, "usage_type": "attribute"}, {"api_name": "formencode.api.Invalid", "line_number": 35, "usage_type": "call"}, {"api_name": "formencode.api", "line_number": 35, "usage_type": "attribute"}, {"api_name": "formencode.Schema", "line_number": 41, "usage_type": "attribute"}, {"api_name": "formencode.validators.Email", "line_number": 47, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 47, "usage_type": "name"}, {"api_name": "formencode.All", "line_number": 48, "usage_type": "call"}, {"api_name": "formencode.validators.String", "line_number": 49, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 49, "usage_type": "name"}, {"api_name": "formencode.validators.PlainText", "line_number": 50, "usage_type": "call"}, {"api_name": "formencode.validators", "line_number": 50, "usage_type": "name"}, {"api_name": "splunk.appserver.mrsparkle.controllers.BaseController", "line_number": 56, "usage_type": "name"}, {"api_name": "cherrypy.request", "line_number": 68, "usage_type": "attribute"}, {"api_name": "formencode.api", "line_number": 72, "usage_type": "attribute"}, {"api_name": "splunk.appserver.mrsparkle.lib.decorators.expose_page", "line_number": 61, "usage_type": "call"}, {"api_name": "splunk.appserver.mrsparkle.lib.decorators.expose_page", "line_number": 85, "usage_type": "call"}, {"api_name": "splunk.appserver.mrsparkle.lib.decorators.expose_page", "line_number": 90, "usage_type": "call"}, {"api_name": "splunk.appserver.mrsparkle.lib.decorators.expose_page", "line_number": 95, "usage_type": "call"}]} {"seq_id": "295772445", "text": "#引用模块\r\nimport pymysql\r\nimport xlrd\r\n\r\ncon = pymysql.connect(host=\"localhost\",user=\"root\",password=\"123456\",database=\"a\")\r\n\r\n#创建控制台\r\ncursor = con.cursor()\r\n\r\n\r\n#excel文件提取\r\nwb = xlrd.open_workbook(filename=r\"E:\\python自动化测试\\专属项目\\python\\day07\\2020年每个月的销售情况.xlsx\",encoding_override=True)\r\nfor i in ('1月','2月','3月','4月','5月','6月','7月','8月','9月','10月','11月','12月'):#循环创建12个表\r\n sql = \"\"\"\r\n CREATE TABLE `%s` (\r\n `日期` varchar(20) DEFAULT NULL,\r\n `服装名称` varchar(20) DEFAULT NULL,\r\n `价格/件` decimal(20,2) DEFAULT NULL,\r\n `本月库存数量` int(11) DEFAULT NULL,\r\n `销售量/每日` int(11) DEFAULT NULL\r\n ) ENGINE=InnoDB DEFAULT CHARSET=utf8;\r\n \"\"\" %i\r\n#%s占位符，%i：把i赋给%s\r\n\r\n\r\n#用控制台执行sql语句，提交到缓冲区\r\ncursor.execute(sql)\r\n\r\n#提交到数据库\r\ncon.commit()\r\n\r\n#关闭资源，先开的后关，后开的前关。\r\ncursor.close()\r\ncon.close()\r\n\r\nfor k in range(0,12):\r\n # 打开excel表选项卡\r\n table = wb.sheet_by_index(k)#循环12个选项卡\r\n #获取列\r\n lie = table.nrows\r\n for i in range(1,lie):\r\n #table.cell(i,0) 获取当前Excel表中第i行，第0列，并赋值给。。。\r\n riqi = table.cell(i,0).value\r\n mingcheng = table.cell(i,1).value\r\n jiage = table.cell(i,2).value\r\n kucun = table.cell(i,3).value\r\n shouliang = table.cell(i,4).value\r\n for j in ('1月','2月','3月','4月','5月','6月','7月','8月','9月','10月','11月','12月'):#循环写入到数据库12个表中\r\n sql = \"insert into \"+j+\" values (%s,%s,%s,%s,%s)\"#写入数据，+j+：在sql语句中，只有这样写才能把j表名循环\r\n param = [riqi,mingcheng,jiage,kucun,shouliang]\r\n cursor.execute(sql,param) # 执行sql语句\r\n con.commit() # 提交数据\r\n cursor.close() # 关闭资源\r\n con.close()\r\n\r\n", "sub_path": "about Excel.py", "file_name": "about Excel.py", "file_ext": "py", "file_size_in_byte": 2015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pymysql.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 12, "usage_type": "call"}]} {"seq_id": "383388503", "text": "from apps.roles.models import Rol\nfrom django.contrib.auth.models import User, Permission\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django import forms\nclass Registrorol(forms.ModelForm):\n class Meta:\n model= Rol\n fields = [\n 'Nombre',\n 'privilegios',\n\n ]\n widgets = {\n 'Nombre': forms.TextInput(attrs={'class': 'form-control', 'required':'true'}),\n 'privilegios': forms.CheckboxSelectMultiple(),\n\n }\n", "sub_path": "apps/roles/form.py", "file_name": "form.py", "file_ext": "py", "file_size_in_byte": 501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "apps.roles.models.Rol", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}]} {"seq_id": "177657155", "text": "import requests, re, zipfile, shutil, os\n\nserver_dir = './journey\\'s-end'\n\n# Get current version from file.\ncurrent_version = int(open('./bin/current_version.txt', 'r').read())\n\n# Download wiki page that contains the downloads, then use regex to get all download links for the server files.\nopen('./bin/html_page','wb').write(requests.get(\"https://terraria.gamepedia.com/Server\", allow_redirects=True).content)\nterraria_links = re.findall('https:\\/\\/www.terraria.org\\/system\\/dedicated_servers.+.zip\\?\\d+', open('./bin/html_page','r').read())\n\n#print(terraria_links)\n\n# Get available versions from list of links gathered above\navailable_versions = []\nfor link in terraria_links:\n available_versions.append(int(re.search('-\\d+', link).group(0).strip('-')))\n\n# Determine if server is out of date by comparing the version number of the file link to the current version\n# stored in the 'current_version.txt' file.\nif max(available_versions) <= current_version : print(\"Server is up to date.\")\nelse :\n print(\"Updating server to Terraria \"+str(max(available_versions))+'.\\nDownloading server files from '+terraria_links[len(terraria_links)-1]+'...')\n open('./bin/terraria-server-'+str(max(available_versions))+'.zip', 'wb').write(requests.get(terraria_links[len(terraria_links)-1]).content)\n\n # Remove old server files.\n print('Removing old server files from '+server_dir+'...')\n folder = './journey\\'s-end'\n for filename in os.listdir(folder):\n file_path = os.path.join(folder, filename)\n try:\n if os.path.isfile(file_path) or os.path.islink(file_path):\n os.unlink(file_path)\n elif os.path.isdir(file_path):\n shutil.rmtree(file_path)\n except Exception as e:\n print('Failed to delete %s. Reason: %s' % (file_path, e))\n\n # Unzip file for copying.\n print('Unziping server files...')\n with zipfile.ZipFile('./bin/terraria-server-'+str(max(available_versions))+'.zip','r') as zip_ref:\n zip_ref.extractall('./bin')\n\n # Copy files into server dir.\n print('Copying Linux server files to '+server_dir+'...')\n files = os.listdir('./bin/1404/Linux')\n for file in files :\n name = os.path.join('./bin/1404/Linux', file)\n if os.path.isfile(name) :\n print('\\tcopying '+name)\n shutil.copy(name, './journey\\'s-end')\n elif os.path.isdir(name) :\n print('\\tcopying '+name+'/*')\n shutil.copytree(name, './journey\\'s-end/'+name.split('/')[len(name.split('/'))-1])\n\n print('Cleaning up:')\n print('\\tremoving ./bin/'+str(max(available_versions)))\n shutil.rmtree('./bin/'+str(max(available_versions)))\n print('\\tremoving ./bin/terraria-server-'+str(max(available_versions)))\n os.unlink('./bin/terraria-server-'+str(max(available_versions))+'.zip')\n print('\\tremoving ./bin/html_page')\n os.unlink('./bin/html_page')\n\n # Update the 'current_version.txt' listed version to the newly installed version.\n open('current_version.txt', 'w').write(str(max(available_versions)))\n\n print('Update Completed. Please remember to update the server file permissions.')\n ", "sub_path": "random-projects/python/terraria-server-update/tsu.py", "file_name": "tsu.py", "file_ext": "py", "file_size_in_byte": 3155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 10, "usage_type": "call"}, {"api_name": "re.search", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 32, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 35, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 54, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 58, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 60, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 62, "usage_type": "call"}]} {"seq_id": "115401935", "text": "from prefect import Flow, task\nfrom prefect.tasks.cloud import FlowRunTask\n\nflow_run_task = FlowRunTask(flow_name=\"ETL-s3\", project_name=\"Demo\")\n\nwith Flow(\n \"FlowRunTask\",\n) as flow:\n flow_run_task()\n\nflow.register(project_name=\"Demo\")\n", "sub_path": "flowruntask.py", "file_name": "flowruntask.py", "file_ext": "py", "file_size_in_byte": 243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "prefect.tasks.cloud.FlowRunTask", "line_number": 4, "usage_type": "call"}, {"api_name": "prefect.Flow", "line_number": 6, "usage_type": "call"}]} {"seq_id": "530418793", "text": "import cv2\nimport numpy as np\nimport os\n\ndef resize_padding_oneline(image):\n h, w, _ = image.shape\n ratio = 64.0/h\n new_w = int(w*ratio)\n \n if new_w < 256:\n image = cv2.resize(image, (new_w, 64), interpolation=cv2.INTER_CUBIC)\n pad_img = np.ones((64, 256-new_w, 3), dtype=np.uint8)*127\n image = np.concatenate((image, pad_img), axis=1)\n else:\n image = cv2.resize(image, (256, 64), interpolation=cv2.INTER_CUBIC)\n return image\n \ndef resize_padding_twoline(image):\n h, w, _ = image.shape\n ratio = 128.0/h\n new_w = int(w*ratio)\n \n if new_w < 256:\n image = cv2.resize(image, (new_w, 128), interpolation=cv2.INTER_CUBIC)\n else:\n image = cv2.resize(image, (256, 128), interpolation=cv2.INTER_CUBIC)\n return image\n\ndef preprocess(img, plate_shape):\n if plate_shape == 1:\n img = resize_padding_oneline(img)\n pad_img = np.ones((64, 256, 3), dtype=np.uint8)*127\n img = np.concatenate((img, pad_img), axis=0)\n # pad = (128-64)//2\n # img = np.pad(img, [(pad,), (0,)], mode='constant', constant_values=127)\n else:\n img = resize_padding_twoline(img)\n h, w, _ = img.shape\n pad = (256-w)//2\n img = np.pad(img, [(0,), (pad,), (0,)], mode='constant', constant_values=127)\n if (256 - w) % 2 == 1:\n pad_img = np.ones((h, 1, 3), dtype=np.uint8)*127\n img = np.concatenate((img, pad_img), axis=1)\n #img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n return img\n\n\n ", "sub_path": "ocr_plate_model/attention_ocr/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 1527, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.resize", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 43, "usage_type": "call"}]} {"seq_id": "514657121", "text": "import os\nimport sys\nimport fnmatch\nimport shlex\nimport difflib\nimport time\nimport shutil\nfrom optparse import OptionParser\n\n\ndef cmdsplit(args):\n if os.sep == '\\\\':\n args = args.replace('\\\\', '\\\\\\\\')\n return shlex.split(args)\n \ndef cleanDirs(path):\n if not os.path.isdir(path):\n return\n \n files = os.listdir(path)\n if len(files):\n for f in files:\n fullpath = os.path.join(path, f)\n if os.path.isdir(fullpath):\n cleanDirs(fullpath)\n \n files = os.listdir(path)\n if len(files) == 0:\n os.rmdir(path)\n \ndef main():\n print(\"applying patches\")\n parser = OptionParser()\n parser.add_option('-m', '--mcp-dir', action='store', dest='mcp_dir', help='Path to MCP', default=None)\n options, _ = parser.parse_args()\n \n forge_dir = os.path.dirname(os.path.abspath(__file__))\n fml_dir = os.path.join(forge_dir, \"fml\")\n sys.path.append(os.path.join(fml_dir, \"install\"))\n from forge import apply_forge_patches\n from fml import apply_fml_patches\n\n\n mcp = os.path.join(forge_dir, 'mcp')\n if not options.mcp_dir is None:\n mcp = os.path.abspath(options.mcp_dir)\n elif os.path.isfile(os.path.join('..', 'runtime', 'commands.py')):\n mcp = os.path.abspath('..')\n \n patchd = os.path.normpath(os.path.join(forge_dir, 'patches'))\n base = os.path.normpath(os.path.join(mcp, 'src_base'))\n work = os.path.normpath(os.path.join(mcp, 'src_work'))\n shutil.rmtree(work)\n shutil.copytree(base, work)\n\t#apply patches\n print(\"applying fml patches\")\n #apply_fml_patches(fml_dir, mcp, work)\n print(\"applying forge patches\")\n apply_forge_patches(fml_dir, mcp, forge_dir, work, False)\n\t\n \n cleanDirs(patchd)\n \nif __name__ == '__main__':\n main()", "sub_path": "rebuild_src_wrk_from_patches.py", "file_name": "rebuild_src_wrk_from_patches.py", "file_ext": "py", "file_size_in_byte": 1808, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.sep", "line_number": 12, "usage_type": "attribute"}, {"api_name": "shlex.split", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 29, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 53, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 54, "usage_type": "call"}, {"api_name": "forge.apply_forge_patches", "line_number": 59, "usage_type": "call"}]} {"seq_id": "420730662", "text": "cont = 0\nimport json\nimport oauth2\nfrom time import sleep\nimport urllib.parse\n\nconsumer_Key = 'Your_Consumer_Key'\nconsumer_Secret = 'Your_Secret_Consumer_Key'\naccess_Token = 'Your_Acess_Token'\naccess_Token_Secret = 'Your_Secret_Acess_Token'\n\nconsumer = oauth2.Consumer(consumer_Key, consumer_Secret)\ntoken = oauth2.Token(access_Token, access_Token_Secret)\ncliente = oauth2.Client(consumer, token)\nprint()\n\nwhile True:\n try:\n alfabeto = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',\n 'w', 'v', 'x', 'y', 'z']\n for letra in alfabeto:\n pesquisa = letra\n pesquisa_code = urllib.parse.quote(pesquisa)\n print()\n requisicao1 = cliente.request(\n 'https://api.twitter.com/1.1/search/tweets.json?q=' + pesquisa_code + '&lang=pt')\n decode = requisicao1[1].decode()\n resultado = json.loads(decode)\n tweets = resultado['statuses']\n for tweet in tweets:\n id = str(tweet['id'])\n requisicao2 = cliente.request('https://api.twitter.com/1.1/favorites/create.json?id=' + id,\n method='POST')\n cont += 1\n print()\n print(cont)\n print('-' * 43)\n sleep(15)\n print()\n except:\n quit()\n print('An error has ocurred, trying again in 30 seconds!')\n print()\n sleep(30)\n", "sub_path": "fav_bot.py", "file_name": "fav_bot.py", "file_ext": "py", "file_size_in_byte": 1530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "oauth2.Consumer", "line_number": 12, "usage_type": "call"}, {"api_name": "oauth2.Token", "line_number": 13, "usage_type": "call"}, {"api_name": "oauth2.Client", "line_number": 14, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 23, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 23, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}]} {"seq_id": "33031428", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport io\nfrom github import Github\nfrom datetime import datetime, timedelta\n\ng = Github()\n\none_month_ago = datetime.now() - timedelta(days=32)\n\ndef filter_date(issue):\n return issue.closed_at > one_month_ago\n\ndef format_number(number):\n if number > 1000:\n return u\"{:.1f}k\".format(float(number) / 1000)\n else:\n return u\"{}\".format(number)\n\nwith io.open(\"templates/auto/recent-updates.html\", 'w', encoding='utf8') as recent_updates:\n recent_updates.truncate()\n\n relnotes_issues = g.search_issues(\"is:merged\", repo=\"phil-opp/blog_os\", type=\"pr\", label=\"relnotes\")[:100]\n recent_relnotes_issues = filter(filter_date, relnotes_issues)\n\n if len(recent_relnotes_issues) == 0:\n recent_updates.write(u\"No notable updates recently.\")\n else:\n recent_updates.write(u\"<ul>\\n\")\n\n for pr in sorted(recent_relnotes_issues, key=lambda issue: issue.closed_at, reverse=True):\n link = '<a href=\"' + pr.html_url + '\">' + pr.title + \"</a> \"\n iso_date = pr.closed_at.isoformat()\n readable_date = pr.closed_at.strftime(\"%b %d\")\n datetime = '<time datetime=\"' + iso_date + '\">' + readable_date + '</time>'\n recent_updates.write(u\" <li>\" + link + datetime + \"</li>\\n\")\n\n recent_updates.write(u\"</ul>\")\n\nrepo = g.get_repo(\"phil-opp/blog_os\")\n\nwith io.open(\"templates/auto/stars.html\", 'w', encoding='utf8') as stars:\n stars.truncate()\n stars.write(format_number(repo.stargazers_count))\n\nwith io.open(\"templates/auto/forks.html\", 'w', encoding='utf8') as forks:\n forks.truncate()\n forks.write(format_number(repo.forks_count))\n", "sub_path": "blog/before_build.py", "file_name": "before_build.py", "file_ext": "py", "file_size_in_byte": 1686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "github.Github", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 10, "usage_type": "call"}, {"api_name": "io.open", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "io.open", "line_number": 43, "usage_type": "call"}, {"api_name": "io.open", "line_number": 47, "usage_type": "call"}]} {"seq_id": "328314880", "text": "#!/usr/bin/env python\n\n# IMPORTS\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport nibabel as nib\nimport sys\nfrom scipy.ndimage.filters import gaussian_filter\nfrom scipy.ndimage.filters import sobel\nfrom itertools import product\nfrom scipy.interpolate import RegularGridInterpolator\n#from scipy.ndimage.filters import convolve\nfrom scipy.signal import fftconvolve\nfrom time import time\nimport os\nfrom math import ceil\nfrom copy import deepcopy\nfrom pathos.pools import ProcessPool\n\n\n# DESCRIPTION\n\nusermanual = \\\n \"\"\"This utility tries to segment a 3D TOF (time-of-flight) MR image to \ncreate a binary MR Angiogram (MRA).\"\"\"\n\n# This module implements the algorithm originally described in:\n# Jiaxin Wang, Shifeng Zhao, Zifeng Liu, Yun Tian, Fuqing Duan, and Yutong Pan,\n# \"An Active Contour Model Based on Adaptive Threshold for Extraction of\n# Cerebral Vascular Structures\", Computational and Mathematical Methods in\n# Medicine, vol. 2016, Article ID 6472397, 9 pages, 2016.\n# doi:10.1155/2016/6472397\n\n\n# DEFINITIONS AND CODE\n\n# Width of the regularised Dirac-delta function\nEPSILON = 0.8\n# Vesselness shape descriptor coefficients\nV_ALPHA = None\nV_BETA = None\nV_GAMMA = None\n# Intensity weighting for the vesselness function\nTOFTRANS_MEAN = 90.0 # Mean of the TOF transition intensity range\nTOFTRANS_WIDTH = 10.0 # Width of the TOF transition intensity range\n# Vesselness lower threshold for Canny edge detector\nV_THRESHOLD = 0.5\n# Coefficient in [0.5, 1] for locally-specified dynamic threshold computation\nK = 0.9\n# Gaussian convolution kernel parameters\nKERNEL_SIGMA = 0.5\nKERNEL_RADIUS = int(ceil(3 * KERNEL_SIGMA))\n# Energy function coefficients\nALPHA1 = 0.002\nALPHA2 = 0.004\nBETA = 0.008\nGAMMA = 0.04\nMU_0 = 80\n# Time increment\nDT = 5\n# Convergence threshold\nPERCENT_CONVERGENCE = 1.0\n# Regularisation constant\nETA = 1e-8\n# Padding width along all axes to bypass boundary error\nPAD_WIDTH = 5\n# Maximum number of CPU cores allowed for use\nMAX_CPU = 2\n\n\ndef _h(x, epsilon=EPSILON):\n \"\"\"Quasi-smooth Heaviside function.\"\"\"\n res = 0.5 + np.arctan(x / epsilon) / np.pi\n #res[x < -EPSILON] = 0.0\n #res[x > EPSILON] = 1.0\n return res\n #return 0.5 + np.arctan(x / epsilon) / np.pi\n\n\ndef _delta(x, epsilon=EPSILON):\n \"\"\"Quasi-smooth Dirac delta function\"\"\"\n #return np.where(np.abs(x) > EPSILON, 0,\n # epsilon / (epsilon ** 2 + x ** 2) / np.pi)\n return (epsilon / (epsilon ** 2 + x ** 2)) / np.pi\n\n\ndef _div(vfield):\n \"\"\"Calculates the divergence of a vector field.\"\"\"\n return np.sum(np.stack([np.gradient(vfield[..., i], axis=i)\n for i in range(vfield.shape[-1])],\n axis=vfield.ndim-1), axis=-1)\n\n\n# Modified from source: https://stackoverflow.com/questions/31206443/\n# numpy-second-derivative-of-a-ndimensional-array\ndef _hessian(x):\n \"\"\"\n Calculate the hessian matrix with finite differences\n Parameters:\n - x : ndarray\n Returns:\n an array of shape x.shape + (x.ndim, x.ndim)\n where the array[... i, j] corresponds to the second derivative x_ij\n \"\"\"\n x_grad = np.gradient(x)\n hessian = np.empty(x.shape + (x.ndim, x.ndim), dtype=x.dtype)\n for k, grad_k in enumerate(x_grad):\n # iterate over dimensions\n # apply gradient again to every component of the first derivative.\n tmp_grad = np.gradient(grad_k)\n for l, grad_kl in enumerate(tmp_grad):\n hessian[..., k, l] = grad_kl\n return hessian\n\n\ndef _laplacian(sfield):\n \"\"\"Calculates the Laplacian of an n-dimensional scalar field.\"\"\"\n return _div(_grad(sfield))\n\n\ndef _vesselness(Ra, Rb, S, eigvals, alpha=None, beta=None, gamma=None,\n img=None):\n \"\"\"Calculates the vesselness score based on indicators of structuredness\n derived from the eigenanalysis of the local Hessians.\"\"\"\n\n # These parameter settings looked intuitive to me, albeit they have not been\n # mentioned in the literature\n if alpha is None:\n alpha = np.std(Ra[np.nonzero(Ra)])\n if beta is None:\n beta = np.std(Rb[np.nonzero(Rb)])\n if gamma is None:\n gamma = np.std(S[np.nonzero(S)])\n\n res = np.zeros_like(Rb)\n roi = np.where(np.logical_and(eigvals[..., 1] <= 0,\n eigvals[..., 2] <= 0))\n if img is None:\n # Frangi's definition\n res[roi] = (1.0 - np.exp(-(Ra[roi] ** 2) / (2 * alpha ** 2))) * \\\n np.exp(-(Rb[roi] ** 2) / (2 * beta ** 2)) \\\n * (1.0 - np.exp(-(S[roi] ** 2) / (2 * gamma ** 2)))\n else:\n # With intensity weighting (only if the TOF is homogeneous)\n res[roi] = (1.0 - np.exp(-(Ra[roi] ** 2) / (2 * alpha ** 2))) * \\\n np.exp(-(Rb[roi] ** 2) / (2 * beta ** 2)) \\\n * (1.0 - np.exp(-(S[roi] ** 2) / (2 * gamma ** 2))) \\\n * (0.5 + 1.0/np.pi * np.arctan((img[roi] - TOFTRANS_MEAN) /\n TOFTRANS_WIDTH))\n return res\n\n\ndef _shift(img, dirs, fill_value=0):\n \"\"\"Shifts an N-D image with the specified extent along each dimension.\n Linear interpolation is used to translate the image. The output has the same\n size and shape as the input. Pixels outside the original image domain are\n filled with a constant value.\"\"\"\n\n _dirs = np.asarray(dirs)\n assert img.ndim == _dirs.size, \\\n \"The inputs must have identical dimensionality.\"\n\n # Set up interpolator\n axes = tuple(np.arange(0, i) for i in img.shape)\n ipol = RegularGridInterpolator(axes, img, bounds_error=False,\n fill_value=fill_value, method='linear')\n\n # Calculate new coordinates\n new_axes = []\n for k in range(_dirs.size):\n new_axes.append(np.asarray(axes[k]) - dirs[k])\n\n # Return shifted image\n return ipol(np.stack(np.meshgrid(*tuple(new_axes), indexing='ij'))\n .reshape(_dirs.size, -1).T).reshape(img.shape)\n\n\n# The implementation of the N-D Canny edge detector was based on the following\n# description:\n# http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/\n# py_imgproc/py_canny/py_canny.html\n\ndef cannyND(img, sigma=1, minval=None, maxval=None, eta=1e-8):\n \"\"\"Canny edge detection for N dimensional images.\"\"\"\n\n dim = img.ndim\n\n # Gaussian filtering\n _img = gaussian_filter(img, sigma=sigma)\n\n # Sobel filtering in all N directions\n sfimg = np.stack([sobel(_img, axis=i) for i in range(dim)], axis=dim)\n magnitudes = np.linalg.norm(sfimg, axis=-1)\n\n # Find local maxima of the gradient magnitude\n # pdirs: principal directions (neighbourhood in N dimensions)\n pdirs = np.stack(product(*((-1, 0, 1),) * dim))\n pdirs = pdirs[np.any(pdirs != 0, axis=-1)]\n pdirs = pdirs[:pdirs.shape[0]/2, :]\n nbix = np.argmax(np.abs(np.sum(sfimg[..., np.newaxis, :] * pdirs, axis=-1)\n / np.linalg.norm(pdirs + eta, axis=-1)\n / np.repeat(magnitudes[..., np.newaxis] + eta,\n pdirs.shape[0], axis=-1)), axis=dim)\n edges = np.zeros_like(magnitudes)\n for k, direction in enumerate(pdirs):\n current_voxels = magnitudes[np.where(nbix == k)]\n ref1 = _shift(magnitudes, direction)[np.where(nbix == k)]\n ref2 = _shift(magnitudes, -direction)[np.where(nbix == k)]\n edges[np.where(nbix == k)] = \\\n np.logical_and(current_voxels > ref1, current_voxels > ref2)\\\n .astype(np.int8)\n # Release memory\n del current_voxels\n del ref1\n del ref2\n magnitudes *= edges\n\n # Set default values of minval and maxval\n if minval is None:\n minval = np.percentile(magnitudes[np.nonzero(magnitudes)], 50)\n print (\"Canny lower threshold value (minval): {0:0.03f}\".format(minval))\n if maxval is None:\n maxval = np.percentile(magnitudes[np.nonzero(magnitudes)], 95)\n print (\"Canny upper threshold value (maxval): {0:0.03f}\".format(maxval))\n\n # Handle user error\n if maxval < minval:\n print (\"WARNING: minval < maxval. Automatic correction: \"\n \"minval = maxval.\")\n minval = maxval\n\n # Histeresis thresholding\n edges = np.where(magnitudes > minval, 1, 0)\n edges_certain = np.where(magnitudes > maxval, 1, 0)\n nb_exploration = np.zeros(edges.shape + (pdirs.shape[0],))\n for k, direction in enumerate(pdirs):\n nb_exploration[..., k] = _shift(edges_certain, direction)\n edges *= np.any(nb_exploration, axis=-1)\n\n return edges\n\n\ndef _kernel(img, sigma=KERNEL_SIGMA, radius=KERNEL_RADIUS):\n \"\"\"Localised Gaussian convolution kernel.\"\"\"\n\n dim = img.ndim\n if radius is not None:\n kernel = np.stack(np.meshgrid(\n *(np.linspace(-radius, radius, 2 * radius + 1),) * dim,\n indexing='ij'), axis=dim).astype(np.float64)\n else:\n kernel = np.stack(\n np.meshgrid(*tuple(np.linspace(-i/2, i/2, i) for i in img.shape),\n indexing='ij'), axis=dim).astype(np.float64)\n kernel = np.linalg.norm(kernel, axis=-1)\n kernel = np.exp(-(kernel ** 2) / (2 * sigma ** 2)) \\\n / (np.sqrt(2 * np.pi * sigma ** 2) ** dim)\n\n return kernel\n\n\ndef _g(x):\n \"\"\"Regularised gradient map for geodesic active contour and nonlinear\n diffusion.\"\"\"\n return np.divide(1.0, 1.0 + x ** 2)\n\n\ndef _grad(sfield):\n \"\"\"Gradient of a scalar field\"\"\"\n return np.stack(np.gradient(sfield), axis=sfield.ndim)\n\n\ndef acm(tofimg, eta=1e-8):\n \"\"\"\n :param ndarray tofimg: 3D bias-corrected TOF (time-of-flight) image.\n \"\"\"\n\n # 1. Initialise vessel locations and their approximate boundaries\n # (Frangi's multi-scale vessel enhancement algorithm)\n\n R_multiscale = []\n scales = np.linspace(0, 1, 2, endpoint=True)\n for i, scale in enumerate(scales):\n\n # Update status\n print (\"Scale {0:0.02f} px ({1:d}/{2:d}):\"\n .format(scale, i+1, scales.size))\n\n # 1.1 Obtain the Hessian matrix for all voxels, perform eigenvalue\n # decomposition and order the eigenpairs by the magnitude of the\n # eigenvalues (the order is ascending)\n print (\"Calculating Hessians...\")\n smimg = gaussian_filter(tofimg, scale)\n eigvals, eigvects = np.linalg.eig(_hessian(smimg))\n eigval_order = np.argsort(np.abs(eigvals), axis=-1)\n grids = np.ogrid[[slice(0, i) for i in eigvals.shape]]\n eigvals = eigvals[tuple(grids)[:-1] + (eigval_order,)]\n grids = np.ogrid[[slice(0, i) for i in eigvects.shape]]\n eigvects = eigvects[tuple(grids)[:-1]\n + (np.expand_dims(eigval_order,\n axis=smimg.ndim),)]\n\n # 1.2 Define shape descriptors\n Ra = np.abs(eigvals[..., 1].astype(np.float64)) \\\n / np.abs(eigvals[..., 2].astype(np.float64) + eta)\n Ra[~np.isfinite(Ra)] = 0\n Rb = np.abs(eigvals[..., 0].astype(np.float64)) \\\n / np.sqrt(np.abs(eigvals[..., 1].astype(np.float64))\n * np.abs(eigvals[..., 2].astype(np.float64)) + eta)\n Rb[~np.isfinite(Rb)] = 0\n S = np.linalg.norm(eigvals.astype(np.float64), axis=-1)\n\n # 1.3 Calculate vesselness score\n print (\"Calculating vesselness...\")\n R_multiscale.append(\n _vesselness(Ra, Rb, S, eigvals, alpha=V_ALPHA, beta=V_BETA,\n gamma=V_GAMMA, img=smimg))\n\n # Select maximum vesselness value from all scales\n R = np.max(np.stack(R_multiscale, axis=smimg.ndim), axis=smimg.ndim)\n\n # 1.4 Run Canny edge detection to initialise the contour\n #print (\"Running N-dimensional Canny algorithm...\")\n #R_th = np.copy(R)\n #R_th[R < V_THRESHOLD] = 0\n #contour = cannyND(R_th, sigma=0.5, eta=ETA)\n\n # 1.5 Initialise the level-set function using both vesselness and contour\n print (\"Initialising level-set function...\")\n phi = np.where(R < V_THRESHOLD, -3 * EPSILON, 3 * EPSILON)\n #phi[contour != 0] = 0\n\n # 2. Run active contour segmentation\n # 2.1 Calculate kernel function\n kernel = _kernel(tofimg, sigma=KERNEL_SIGMA, radius=KERNEL_RADIUS)\n\n # 2.2 Calculate edge function\n smimg = gaussian_filter(tofimg, sigma=3)\n edge = _g(np.linalg.norm(_grad(smimg), axis=-1))\n\n iteration = 0\n e_change = -1\n while e_change < 0:\n\n # Update status\n start_t = time()\n iteration += 1\n print (\"Starting iteration No. {}...\".format(iteration))\n\n # 2.2 Calculate locally-specified dynamic threshold\n phi_h = _h(phi, epsilon=EPSILON)\n placement = (-KERNEL_RADIUS,) * kernel.ndim\n mu = K * fftconvolve(phi_h * R, kernel, mode='same') \\\n / (fftconvolve(phi_h, kernel, mode='same') + eta)\n\n # 2.3 Update phi to phi_k\n grad_phi = _grad(phi)\n delta_phi = _delta(phi, EPSILON)\n M1 = tofimg - MU_0\n M2 = R - mu\n M3 = _laplacian(phi) - \\\n _div(np.divide(grad_phi,\n np.repeat(\n np.linalg.norm(grad_phi + ETA, axis=-1)\n [..., np.newaxis],\n axis=-1, repeats=grad_phi.shape[-1])))\n\n phi_k = phi + DT * (ALPHA1 * M1 * delta_phi + ALPHA2 * M2 * delta_phi\n + GAMMA * M3)\n\n # 2.4 Update phi using phi_k\n grad_phi = _grad(phi_k)\n phi = phi_k + DT * delta_phi * BETA * _div(\n np.repeat(edge[..., np.newaxis],\n repeats=grad_phi.shape[-1], axis=-1)\n * np.divide(grad_phi,\n np.repeat(np.linalg.norm(ETA + grad_phi, axis=-1)\n [..., np.newaxis], axis=-1,\n repeats=grad_phi.shape[-1])))\n\n # 2.5 Calculate system total energy\n integral_1 = np.sum(M1 * phi_h)\n integral_2 = np.sum(M2 * phi_h)\n integral_3 = np.sum(edge * np.linalg.norm(_grad(phi_h), axis=-1))\n P = np.sum(0.5 * (np.linalg.norm(_grad(phi), axis=-1) - 1) ** 2)\n if iteration > 1:\n energy_old = energy\n energy = - ALPHA1 * integral_1 - ALPHA2 * integral_2 \\\n + BETA * integral_3 + GAMMA * P\n e_change = (energy - energy_old) / energy_old * 100.0\n print (\"Total energy: {0:0.04f}, change: {1:0.03f} %. \"\n \"Elapsed time: {2:0.01f} s.\".format(energy, e_change,\n time()-start_t))\n if np.abs(e_change) <= PERCENT_CONVERGENCE:\n break\n else:\n energy = -ALPHA1 * integral_1 - ALPHA2 * integral_2 \\\n + BETA * integral_3 + GAMMA * P\n print (\"Total energy: {0:0.04f}. Elapsed time: {1:0.01f} s.\"\n .format(energy, time()-start_t))\n return phi\n\n\ndef _filter_solitary(segmentation):\n \"\"\"Removes solitary points from the segmentation. The input should be a\n boolean mask (with values 1 for vessel, 0 for non-vessel) derived from the\n level-set function.\"\"\"\n\n dim = segmentation.ndim\n pdirs = np.stack(product(*((-1, 0, 1),) * dim))\n pdirs = pdirs[np.any(pdirs != 0, axis=-1)]\n pdirs = pdirs[:pdirs.shape[0] / 2, :]\n filter = np.where(segmentation > 0, 1, 0)\n nb_exploration = np.zeros(filter.shape + (pdirs.shape[0],))\n for k, direction in enumerate(pdirs):\n nb_exploration[..., k] = _shift(segmentation > 0, direction)\n filter *= np.any(nb_exploration, axis=-1).astype(np.int8)\n\n return filter\n\n\ndef parallel_job(imfile):\n \"\"\"A sub-routine that is called by each parallel workers.\"\"\"\n\n # Update status\n print (\"Processing {}...\".format(imfile))\n\n try:\n # Load image\n mri = nib.load(imfile)\n hdr = mri.header\n img = mri.get_data()\n # Pad image temporarily with emtpy slices\n print (\"Addig temporary padding to the image...\")\n img = np.pad(img, pad_width=PAD_WIDTH, mode='constant',\n constant_values=0)\n # Run segmentation\n print (\"Running the segmentation...\")\n phi = acm(img, eta=ETA)\n # Remove padding\n phi = phi[tuple(slice(PAD_WIDTH, phi.shape[i]-PAD_WIDTH)\n for i in range(phi.ndim))]\n # Filter out any individual points from the segmentation\n print (\"Filtering segmentation...\")\n segm = _filter_solitary(np.where(phi > 0, 1, 0))\n\n # Save output\n outputdir, outputname = os.path.split(os.path.abspath(imfile))\n if outputdir == \"\":\n outputdir = os.getcwd()\n try:\n fname = os.path.join(outputdir,\n outputname.replace(\".nii.gz\", \"_phi.nii.gz\"))\n nib.save(nib.Nifti1Image(phi, hdr.get_best_affine(), hdr), fname)\n print (\"SAVED: {}\".format(fname))\n except:\n print(\"ERROR while saving {}.\".format(fname))\n\n try:\n fname = os.path.join(outputdir,\n outputname.replace(\".nii.gz\", \"_segm.nii.gz\"))\n nib.save(nib.Nifti1Image(segm, hdr.get_best_affine(), hdr), fname)\n print (\"SAVED: {}\".format(fname))\n except:\n print(\"ERROR while saving {}.\".format(fname))\n return 0\n\n except:\n return 1\n\n\ndef main(args):\n \"\"\"Main program code.\"\"\"\n\n # Validate file paths in image list\n imfiles = []\n for imfile in args:\n try:\n _ = nib.load(imfile).header # low-cost load operation\n imfiles.append(imfile)\n except:\n print (\"SKIPPED: {} could not be opened.\".format(imfile))\n continue\n\n # Process the images\n n_cpu = min([len(imfiles), MAX_CPU])\n if n_cpu > 1:\n parpool = ProcessPool(nodes=n_cpu)\n err = parpool.map(parallel_job, imfiles)\n else:\n err = parallel_job(imfiles[0])\n\n if err == 0:\n print (\"All tasks were successfully completed.\")\n else:\n print (\"There were {} error(s).\".format(err))\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) > 1:\n main(sys.argv[1:])\n else:\n print (usermanual)\n print (\"\\nPlease specify an image in the command-line arguments.\")\n exit(0)\n", "sub_path": "nhls.py", "file_name": "nhls.py", "file_ext": "py", "file_size_in_byte": 18314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "math.ceil", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "scipy.interpolate.RegularGridInterpolator", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 175, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 193, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.sobel", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 198, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 203, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 212, "usage_type": "attribute"}, {"api_name": "numpy.percentile", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 251, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 256, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 258, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 283, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 295, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.ogrid", "line_number": 297, "usage_type": "attribute"}, {"api_name": "numpy.ogrid", "line_number": 299, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 305, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 306, "usage_type": "attribute"}, {"api_name": "numpy.isfinite", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 308, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 310, "usage_type": "attribute"}, {"api_name": "numpy.isfinite", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 312, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 312, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 331, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 340, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 347, "usage_type": "call"}, {"api_name": "scipy.signal.fftconvolve", "line_number": 354, "usage_type": "call"}, {"api_name": "scipy.signal.fftconvolve", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 365, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 366, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 375, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 378, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 379, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 385, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 386, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 395, "usage_type": "call"}, {"api_name": "time.time", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 411, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 418, "usage_type": "attribute"}, {"api_name": "nibabel.load", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 436, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 446, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 449, "usage_type": "call"}, {"api_name": "os.path", "line_number": 449, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 449, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 453, "usage_type": "call"}, {"api_name": "os.path", "line_number": 453, "usage_type": "attribute"}, {"api_name": "nibabel.save", "line_number": 455, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 455, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "attribute"}, {"api_name": "nibabel.save", "line_number": 463, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 463, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 480, "usage_type": "call"}, {"api_name": "pathos.pools.ProcessPool", "line_number": 489, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 501, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 502, "usage_type": "attribute"}]} {"seq_id": "30866479", "text": "import socket\nimport re\nimport multiprocessing\n\n\ndef service_client(new_socket):\n request = new_socket.recv(1024)\n request_lines = request.decode('utf-8').splitlines()\n print(request_lines)\n file_name = re.match(r\"[^/]+(/[^ ]*)\", request_lines[0]).group(1)\n\n if file_name == \"/\":\n file_name = \"./html\" + \"/index.html\"\n else:\n file_name = \"./html\" + file_name\n\n try:\n file = open(file_name, \"rb\")\n except Exception as ret:\n response = \"HTTP/1.1 404 NOT FOUND\\r\\n\"\n response += \"\\r\\n\"\n response += \"can not find the page\"\n new_socket.send(response.encode('utf-8'))\n else:\n response = \"HTTP/1.1 200 OK\\r\\n\"\n response += \"\\r\\n\"\n new_socket.send(response.encode('utf-8'))\n new_socket.send(file.read())\n finally:\n new_socket.close()\n\n\ndef main():\n \"\"\"用来完成整体的控制\"\"\"\n tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # 设置当服务器先close 即服务器端4次挥手之后资源能够立即释放，这样就保证了，下次运行程序时 可以立即绑定7788端口\n tcp_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n tcp_socket.bind((\"\", 7890))\n tcp_socket.listen(128)\n while True:\n new_socket, client_addr = tcp_socket.accept()\n process = multiprocessing.Process(target=service_client, args=(new_socket,))\n process.start()\n new_socket.close() # 关闭父进程的文件资源描述符\n tcp_socket.close()\n\n\nif __name__ == '__main__':\n main()\n ", "sub_path": "07web/multiprocess_webserver.py", "file_name": "multiprocess_webserver.py", "file_ext": "py", "file_size_in_byte": 1564, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "re.match", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 35, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 37, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 42, "usage_type": "call"}]} {"seq_id": "593617237", "text": "import pandas as pd\r\nimport numpy as np\r\nimport math\r\nfrom operator import itemgetter\r\nfrom collections import defaultdict,deque,namedtuple\r\n\r\n\r\nCustomer = pd.read_excel (r'C:\\Users\\ASUS\\Downloads\\Dataaa.xlsx' , sheet_name='Customer')\r\nHub = pd.read_excel (r'C:\\Users\\ASUS\\Downloads\\Dataaa.xlsx' , sheet_name='Hubs')\r\nWarehouse = pd.read_excel (r'C:\\Users\\ASUS\\Downloads\\Dataaa.xlsx' , sheet_name='Warehouse')\r\n\r\ncustomerID = np.array(Customer.id.values)\r\ncustomerLatitude = np.array(Customer.Latitude.values)\r\ncustomerLongitude = np.array(Customer.Longitude.values)\r\nhubID = np.array(Hub.Hub.values)\r\nhubLatitude = np.array(Hub.Latitude.values)\r\nhubLongitude = np.array(Hub.Longitude.values)\r\nwarehouseID = np.array(Warehouse.Warehouse.values)\r\nwarehouseLatitude = np.array(Warehouse.Latitude.values)\r\nwarehouseLongitude = np.array(Warehouse.Longitude.values)\r\n\r\ndef computeDist(x1,y1,x2,y2):\r\n return math.sqrt( ((float(x2)-float(x1))**2)+((float(y2)-float(y1))**2) )\r\n\r\ninf = float('inf')\r\nEdge = namedtuple('Edge', 'start, end, cost')\r\n\r\n\r\ndef make_edge(start, end, cost=1):\r\n return Edge(start, end, cost)\r\n\r\nclass Graph:\r\n def __init__(self, edges):\r\n wrong_edges = [i for i in edges if len(i) not in [2, 3]]\r\n if wrong_edges:\r\n raise ValueError('Wrong edges data: {}'.format(wrong_edges))\r\n\r\n self.edges = [make_edge(*edge) for edge in edges]\r\n\r\n @property\r\n def vertices(self):\r\n return set(\r\n sum(\r\n ([edge.start, edge.end] for edge in self.edges), []\r\n )\r\n )\r\n\r\n def get_node_pairs(self, n1, n2, both_ends=True):\r\n if both_ends:\r\n node_pairs = [[n1, n2], [n2, n1]]\r\n else:\r\n node_pairs = [[n1, n2]]\r\n return node_pairs\r\n\r\n def remove_edge(self, n1, n2, both_ends=True):\r\n node_pairs = self.get_node_pairs(n1, n2, both_ends)\r\n edges = self.edges[:]\r\n for edge in edges:\r\n if [edge.start, edge.end] in node_pairs:\r\n self.edges.remove(edge)\r\n\r\n def add_edge(self, n1, n2, cost=1, both_ends=True):\r\n node_pairs = self.get_node_pairs(n1, n2, both_ends)\r\n for edge in self.edges:\r\n if [edge.start, edge.end] in node_pairs:\r\n return ValueError('Edge {} {} already exists'.format(n1, n2))\r\n\r\n self.edges.append(Edge(start=n1, end=n2, cost=cost))\r\n if both_ends:\r\n self.edges.append(Edge(start=n2, end=n1, cost=cost))\r\n\r\n @property\r\n def neighbours(self):\r\n neighbours = {vertex: set() for vertex in self.vertices}\r\n for edge in self.edges:\r\n neighbours[edge.start].add((edge.end, edge.cost))\r\n\r\n return neighbours\r\n\r\n def dijkstra(self, source, dest):\r\n assert source in self.vertices, 'Such source node doesn\\'t exist'\r\n distances = {vertex: inf for vertex in self.vertices}\r\n previous_vertices = {\r\n vertex: None for vertex in self.vertices\r\n }\r\n distances[source] = 0\r\n vertices = self.vertices.copy()\r\n \r\n\r\n while vertices:\r\n current_vertex = min(\r\n vertices, key=lambda vertex: distances[vertex])\r\n vertices.remove(current_vertex)\r\n if distances[current_vertex] == inf:\r\n break\r\n for neighbour, cost in self.neighbours[current_vertex]:\r\n alternative_route = distances[current_vertex] + cost\r\n if alternative_route < distances[neighbour]:\r\n distances[neighbour] = alternative_route\r\n previous_vertices[neighbour] = current_vertex\r\n\r\n path, current_vertex = deque(), dest\r\n while previous_vertices[current_vertex] is not None:\r\n path.appendleft(current_vertex)\r\n current_vertex = previous_vertices[current_vertex]\r\n if path:\r\n path.appendleft(current_vertex)\r\n \r\n distance_between_nodes = 0\r\n for index in range(1, len(path)):\r\n for thing in self.edges:\r\n if thing.start == path[index - 1] and thing.end == path[index]:\r\n distance_between_nodes += thing.cost\r\n path2 = list(path)\r\n return path2, distance_between_nodes\r\n\r\nclass customer:\r\n def __init__(self, id, custLong, custLat, number):\r\n self.id = id\r\n self.custLong = custLong\r\n self.custLat = custLat\r\n self.nearestHub = self.nearestHub\r\n self.number = number\r\n def nearestHub(self):\r\n self.custToHub = list([computeDist(self.custLong, self.custLat, hubLongitude[i], hubLatitude[i]), hubID[i]] for i in range(0,len(hubID)))\r\n self.cTHSorted = sorted(self.custToHub, key = itemgetter(0))\r\n return self.cTHSorted[0]\r\n \r\ncustomersList = [customer(customerID[i], customerLongitude[i], customerLatitude[i], i) for i in range(0,len(customerID))]\r\n\r\nclass hub:\r\n def __init__(self, id, hubLong, hubLat, number):\r\n self.id = id\r\n self.hubLong = hubLong\r\n self.hubLat = hubLat\r\n self.nearestHub = self.nearestHub\r\n self.nearestWarehouse = self.nearestWarehouse\r\n self.number = number\r\n def nearestHub(self):\r\n self.hubToHub = list([computeDist(self.hubLong, self.hubLat, hubLongitude[i], hubLatitude[i]), hubID[i]] for i in range(0,len(hubID)))\r\n self.hTHSorted = sorted(self.hubToHub, key = itemgetter(0))\r\n return self.hTHSorted[1]\r\n def nearestWarehouse(self):\r\n self.hubToWarehouse = list([computeDist(self.hubLong, self.hubLat, warehouseLongitude[i], warehouseLatitude[i]), warehouseID[i]] for i in range(0,len(warehouseID)))\r\n self.hTWSorted = sorted(self.hubToWarehouse, key = itemgetter(0))\r\n return self.hTWSorted[0]\r\nhubsList = [hub(hubID[i], hubLongitude[i], hubLatitude[i], (i+len(customerID))) for i in range(0, len(hubID))]\r\n\r\nclass warehouse:\r\n def __init__(self, id, wareLong, wareLat,number):\r\n self.id = id\r\n self.wareLong = wareLong\r\n self.wareLat = wareLat\r\n self.number = number\r\nwarehouseList = [warehouse(warehouseID[i], warehouseLongitude[i], warehouseLatitude[i], (i+len(customerID) +len(hubID))) for i in range(0, len(warehouseID))]\r\n\r\ngrapharray = [] \r\n\r\nfor i in range(0,len(hubID)):\r\n for j in range(0,len(hubID)):\r\n if(hubsList[i].id != hubsList[j].id):\r\n grapharray.append((hubsList[i].id, hubsList[j].id, computeDist(hubsList[i].hubLong,hubsList[i].hubLat,hubsList[j].hubLong,hubsList[j].hubLat)))\r\n else:\r\n pass\r\n \r\n \r\nfor j in range(0,len(hubID)):\r\n for k in range(0,len(warehouseID)):\r\n grapharray.append((hubsList[j].id, warehouseList[k].id, computeDist(hubsList[j].hubLong,hubsList[j].hubLat,warehouseList[k].wareLong,warehouseList[k].wareLat)))\r\n\r\ngraph = Graph(grapharray)\r\ngrapharr = []\r\nfor i in range(0,len(hubID)):\r\n for j in range(0,len(warehouseID)):\r\n defGraph = graph.dijkstra(hubsList[i].id,warehouseList[j].id)\r\n grapharr.append(defGraph)\r\n \r\ngraphArrSorted = sorted(grapharr, key = itemgetter(1))\r\ndist = []\r\nthisprint = ()\r\ntotDist = 0\r\n#print(graphArrSorted)\r\nfor i in range (0, len(customerID)):\r\n print(customersList[i].id, \" -> \", customersList[i].nearestHub()[1], \" -> \", end = ' ')\r\n dist.append(customersList[i].nearestHub()[0])\r\n totDist += customersList[i].nearestHub()[0]\r\n for j in range (0, len(graphArrSorted)):\r\n if(customersList[i].nearestHub()[1].lower == graphArrSorted[j][0][0].lower):\r\n dist[i] += graphArrSorted[j][1]\r\n totDist += graphArrSorted[j][1]\r\n print(graphArrSorted[j][0][1], \" with distance \", dist[i])\r\n break\r\n else:\r\n pass\r\n \r\nprint (totDist, \" or \" , totDist*111.699, \" km.\")\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n # for j in range (0,len(hubID)):\r\n # kappa = False\r\n # if(str.strip(customersList[i].nearestHub()[1]) == str.strip(hubsList[j].nearestWarehouse()[1])):\r\n # dist[i] += hubsList[j].nearestWarehouse()[0]\r\n # kappa = True\r\n # thisprint1 = (hubsList[j].nearestWarehouse()[1], \" with distance = \", dist[i])\r\n # elif(customersList[i].nearestHub != hubsList[j].nearestWarehouse()[1]):\r\n # for k in range(1, len(warehouseID)):\r\n # if(hubsList[j].nearestHub()[1][0] < hubsList[j].nearestWarehouse()[0]):\r\n # if(hubsList[j].nearestHub()[k][1] == hubsList[j].nearestWarehouse()[1]):\r\n # thisprint = hubsList[j].nearestHub()[k][1]\r\n # elif(hubsList[j].nearestHub()[1][0] >= hubsList[j].nearestWarehouse()[0]):\r\n # thisprint = hubsList[k].nearestWarehouse()[1]\r\n # if(kappa):\r\n # print(thisprint1)\r\n # elif(kappa == False):\r\n # print(thisprint)\r\n \r\n \r\n", "sub_path": "Team 5 - Andreas Kevin Ghinaya Zahra/Final Project Science Management.py", "file_name": "Final Project Science Management.py", "file_ext": "py", "file_size_in_byte": 9045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_excel", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 102, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 126, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 141, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 145, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 178, "usage_type": "call"}]} {"seq_id": "126287618", "text": "from __future__ import absolute_import, print_function\n\nimport cv2\nimport numpy as np\nfrom torch.multiprocessing import Pool\n\nimport Detection.cfgs.config as cfg\nimport Detection.utils.network as net_utils\nfrom Detection.darknet import Darknet19\n# import .utils.yolo as yolo_utils\nfrom .utils import yolo as yolo_utils\n\n\ndef preprocess(fname):\n # return fname\n image = cv2.imread(fname)\n\n # print(\"fname:{}| size:{} \".format(fname, image.shape))\n\n im_data = np.expand_dims(yolo_utils.preprocess_test((image, None, cfg.inp_size))[0], 0)\n return image, im_data\n\ndef print_and_exit(str):\n print(str)\n exit()\n\nclass Singleton(object):\n _instance = None\n def __new__(cls, *args, **kwargs):\n if not cls._instance:\n cls._instance = super(Singleton, cls).__new__(cls)\n return cls._instance\n\n# hyper-parameters\n# npz_fname = 'models/yolo-voc.weights.npz'\n# h5_fname = 'models/yolo-voc.weights.h5'\ntrained_model = cfg.trained_model\n# trained_model = os.path.join(cfg.train_output_dir, 'darknet19_voc07trainval_exp3_158.h5')\nthresh = 0.5\n# im_path = 'demo'\n# im_path = '/share/shared/Dataset/CatDog/LinYi'\n# ---\nacceptable_format = set(['.jpg', '.JPG'])\n# im_fnames = sorted((fname for fname in os.listdir(im_path) if os.path.splitext(fname)[-1] in acceptable_format))\n# im_fnames_cp = im_fnames\n# im_fnames = (os.path.join(im_path, fname) for fname in im_fnames)\n\n# net.load_from_npz(npz_fname)\n# net_utils.save_net(h5_fname, net)\n\nclass Detector(Singleton):\n _net = Darknet19()\n # net_utils.load_net(trained_model, _net)\n _net.eval()\n _pool = Pool(processes=1)\n _status = 'ready'\n\n _label_names = ('aeroplane', 'bicycle', 'bird', 'boat',\n 'bottle', 'bus', 'car', 'cat', 'chair',\n 'cow', 'diningtable', 'dog', 'horse',\n 'motorbike', 'person', 'pottedplant',\n 'sheep', 'sofa', 'train', 'tvmonitor')\n\n def __init__(self, fname):\n super(Singleton, self).__init__()\n net_utils.load_net(fname, Detector._net)\n\n def from_names(self, im_fnames, **kwargs):\n if not isinstance(im_fnames, list) and not isinstance(im_fnames, tuple):\n im_fnames = [im_fnames]\n Detector._status = \"running\"\n result = []\n misc_result = []\n for i, (image, im_data) in enumerate(Detector._pool.imap(preprocess, im_fnames, chunksize=1)):\n misc = {\"scores\":None, \"classes\":None}\n im_data = net_utils.np_to_variable(im_data, is_cuda=False, volatile=True).permute(0, 3, 1, 2)\n\n bbox_pred, iou_pred, prob_pred = Detector._net(im_data)\n # to numpy\n bbox_pred = bbox_pred.data.cpu().numpy()\n iou_pred = iou_pred.data.cpu().numpy()\n prob_pred = prob_pred.data.cpu().numpy()\n\n # print bbox_pred.shape, iou_pred.shape, prob_pred.shape\n\n bboxes, scores, cls_inds = yolo_utils.postprocess(bbox_pred, iou_pred, prob_pred, image.shape, cfg, thresh)\n result.append(bboxes)\n\n misc['scores'] = scores\n misc['classes'] = [ Detector._label_names[cls_i] for cls_i in cls_inds]\n misc_result.append(misc)\n # print(bboxes)\n Detector._status = \"ready\"\n return result, misc_result\n\n def from_nparry(self, image_np, **kwargs):\n if len(image_np.shape) == 3:\n image_np = np.resize(image_np, (1, *image_np.shape))\n\n assert image_np.shape[3] == 3 # rgb image\n assert len(image_np.shape) == 4\n assert 0 <= np.min(image_np) <= np.max(image_np) <= 255\n\n # image_np = image_np.astype(np.float32)\n # for i in range(image_np.shape[0]):\n # image_np[i,:,:,:] = (image_np[i]/255 - self.mean) / self.std\n # image_np = image_np.transpose((0, 3, 1, 2))\n image_list, img_data_list = [], []\n for i in range(image_np.shape[0]):\n image_list.append(image_np[i])\n img_data_list.append(np.expand_dims(yolo_utils.preprocess_test((image_np[i], None, cfg.inp_size))[0], 0))\n\n Detector._status = \"running\"\n result = []\n misc_result = []\n for i, (image, im_data) in enumerate(zip(image_list, img_data_list)):\n misc = {\"scores\": None, \"classes\": None}\n im_data = net_utils.np_to_variable(im_data, is_cuda=False, volatile=True).permute(0, 3, 1, 2)\n\n bbox_pred, iou_pred, prob_pred = Detector._net(im_data)\n # to numpy\n bbox_pred = bbox_pred.data.cpu().numpy()\n iou_pred = iou_pred.data.cpu().numpy()\n prob_pred = prob_pred.data.cpu().numpy()\n\n # print bbox_pred.shape, iou_pred.shape, prob_pred.shape\n\n bboxes, scores, cls_inds = yolo_utils.postprocess(bbox_pred, iou_pred, prob_pred, image.shape, cfg, thresh)\n result.append(bboxes)\n\n misc['scores'] = scores\n misc['classes'] = [Detector._label_names[cls_i] for cls_i in cls_inds]\n misc_result.append(misc)\n # print(bboxes)\n Detector._status = \"ready\"\n return result, misc_result\n\n def __call__(self, im_fnames, **kwargs):\n if not isinstance(im_fnames, list) and not isinstance(im_fnames, tuple):\n im_fnames = [im_fnames]\n Detector._status = \"running\"\n result = []\n for i, (image, im_data) in enumerate(Detector._pool.imap(preprocess, im_fnames, chunksize=1)):\n im_data = net_utils.np_to_variable(im_data, volatile=True).permute(0, 3, 1, 2)\n if Detector._net.is_cuda:\n im_data = im_data.cuda()\n bbox_pred, iou_pred, prob_pred = Detector._net(im_data)\n # to numpy\n bbox_pred = bbox_pred.data.cpu().numpy()\n iou_pred = iou_pred.data.cpu().numpy()\n prob_pred = prob_pred.data.cpu().numpy()\n\n # print bbox_pred.shape, iou_pred.shape, prob_pred.shape\n\n bboxes, scores, cls_inds = yolo_utils.postprocess(bbox_pred, iou_pred, prob_pred, image.shape, cfg, thresh)\n result.append(bboxes)\n print(bboxes)\n Detector._status = \"ready\"\n return result\n\n def cuda(self):\n Detector._net.cuda()\n Detector._net.is_cuda = True\n\n# test_img_root = \"/share/shared/Dataset/CatDog/LinYi\"\n# test_img_name = [\"IMG_2778.JPG\", \"IMG_2779.JPG\"]\n# image_to_test = [ os.path.join(test_img_root, img_name) for img_name in test_img_name]\n# net_test = Detector()\n# net_test(image_to_test)\n\n# print_and_exit(\"so far done!\")\n\n#\n# t_det = Timer()\n# t_total = Timer()\n# # im_fnames = ['person.jpg']\n# pool = Pool(processes=1)\n# for i, (image, im_data) in enumerate(pool.imap(preprocess, im_fnames, chunksize=1)):\n# t_total.tic()\n# im_data = net_utils.np_to_variable(im_data, is_cuda=True, volatile=True).permute(0, 3, 1, 2)\n# t_det.tic()\n#\n# bbox_pred, iou_pred, prob_pred = net(im_data)\n# det_time = t_det.toc()\n# # to numpy\n# bbox_pred = bbox_pred.data.cpu().numpy()\n# iou_pred = iou_pred.data.cpu().numpy()\n# prob_pred = prob_pred.data.cpu().numpy()\n#\n# # print bbox_pred.shape, iou_pred.shape, prob_pred.shape\n#\n# bboxes, scores, cls_inds = yolo_utils.postprocess(bbox_pred, iou_pred, prob_pred, image.shape, cfg, thresh)\n#\n# im2show = yolo_utils.draw_detection(image, bboxes, scores, cls_inds, cfg)\n#\n# if im2show.shape[0] > 1100:\n# im2show = cv2.resize(im2show, (int(1000. * float(im2show.shape[1]) / im2show.shape[0]), 1000))\n# cv2.imshow('test', im2show)\n# cv2.imwrite('./output/{0}.jpg'.format(str(i)), im2show)\n# total_time = t_total.toc()\n# # wait_time = max(int(60 - total_time * 1000), 1)\n# cv2.waitKey(0)\n#\n# if i % 1 == 0:\n# format_str = 'frame: %d, (detection: %.1f Hz, %.1f ms) (total: %.1f Hz, %.1f ms)'\n# print(format_str % (\n# i, 1. / det_time, det_time * 1000, 1. / total_time, total_time * 1000))\n#\n# t_total.clear()\n# t_det.clear()\n#\n", "sub_path": "Detection/detection.py", "file_name": "detection.py", "file_ext": "py", "file_size_in_byte": 7993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.yolo.preprocess_test", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.yolo", "line_number": 20, "usage_type": "name"}, {"api_name": "Detection.cfgs.config.inp_size", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Detection.cfgs.config", "line_number": 20, "usage_type": "name"}, {"api_name": "Detection.cfgs.config.trained_model", "line_number": 37, "usage_type": "attribute"}, {"api_name": "Detection.cfgs.config", "line_number": 37, "usage_type": "name"}, {"api_name": "Detection.darknet.Darknet19", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.multiprocessing.Pool", "line_number": 55, "usage_type": "call"}, {"api_name": "Detection.utils.network.load_net", "line_number": 66, "usage_type": "call"}, {"api_name": "Detection.utils.network", "line_number": 66, "usage_type": "name"}, {"api_name": "Detection.utils.network.np_to_variable", "line_number": 76, "usage_type": "call"}, {"api_name": "Detection.utils.network", "line_number": 76, "usage_type": "name"}, {"api_name": "utils.yolo.postprocess", "line_number": 86, "usage_type": "call"}, {"api_name": "Detection.cfgs.config", "line_number": 86, "usage_type": "argument"}, {"api_name": "utils.yolo", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.resize", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.yolo.preprocess_test", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.yolo", "line_number": 111, "usage_type": "name"}, {"api_name": "Detection.cfgs.config.inp_size", "line_number": 111, "usage_type": "attribute"}, {"api_name": "Detection.cfgs.config", "line_number": 111, "usage_type": "name"}, {"api_name": "Detection.utils.network.np_to_variable", "line_number": 118, "usage_type": "call"}, {"api_name": "Detection.utils.network", "line_number": 118, "usage_type": "name"}, {"api_name": "utils.yolo.postprocess", "line_number": 128, "usage_type": "call"}, {"api_name": "Detection.cfgs.config", "line_number": 128, "usage_type": "argument"}, {"api_name": "utils.yolo", "line_number": 128, "usage_type": "name"}, {"api_name": "Detection.utils.network.np_to_variable", "line_number": 144, "usage_type": "call"}, {"api_name": "Detection.utils.network", "line_number": 144, "usage_type": "name"}, {"api_name": "utils.yolo.postprocess", "line_number": 155, "usage_type": "call"}, {"api_name": "Detection.cfgs.config", "line_number": 155, "usage_type": "argument"}, {"api_name": "utils.yolo", "line_number": 155, "usage_type": "name"}]} {"seq_id": "555010185", "text": "from django.urls import path\nfrom posts.views import post_list, post_create, post_detail, post_edit, post_delete, post_list_category\n\napp_name='posts'\nurlpatterns = [\n path('', post_list, name='post_list'),\n path('create/', post_create, name='post_create'),\n path('<slug>/', post_detail, name='post_detail'),\n path('<slug>/edit/', post_edit, name='post_edit'),\n path('<slug>/delete/', post_delete, name='post_delete'),\n path('categories/<category_slug>/', post_list_category, name='post_list_category')\n]\n", "sub_path": "src/posts/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 523, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "posts.views.post_list", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "posts.views.post_create", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "posts.views.post_detail", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "posts.views.post_edit", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "posts.views.post_delete", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "posts.views.post_list_category", "line_number": 11, "usage_type": "argument"}]} {"seq_id": "62123806", "text": "import pandas as pd\r\n\r\ndf = pd.read_csv(\"iris.csv\") #csv dosyamiza girmek icin kullanilir. bunu df ye atadik.\r\n\r\n#print(df.Species.unique()) # kac tur Species var onu verir. Unique, Kisacasi: o sutunda hangi cesit urunler var\r\n\r\n#df.info() # df dosyasinda kac kolon urun ortalama max min gibi degerler hakkinda bilgiler verir.\r\n\r\n#setosa = df[df.Species == \"Iris-setosa\"] #Species sutunundaki tum \"Iris-setosa\" olanlari suz ve setosa adli degiskene atadik\r\n\r\n#versicolor = df[df.Species == \"Iris-versicolor\"] #usttekinin aynisi :)\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndf1 = df.drop([\"Id\"], axis=1)\r\n\r\nsetosa = df[df.Species == \"Iris-setosa\"]\r\nversicolor = df[df.Species == \"Iris-versicolor\"]\r\nvirginica = df[df.Species == \"Iris-virginica\"]\r\n\r\nplt.plot(setosa.Id, setosa.PetalLengthCm, color=\"red\", label= \"setosa\")\r\nplt.plot(versicolor.Id, versicolor.PetalLengthCm, color=\"green\", label= \"versicolor\")\r\nplt.plot(virginica.Id, virginica.PetalLengthCm, color=\"blue\", label= \"virginica\")\r\nplt.legend()# x y cizgisi olusturur\r\nplt.xlabel(\"Id\")\r\nplt.ylabel(\"PetalLengthCm\")\r\nplt.show()", "sub_path": "pandas-w2.py", "file_name": "pandas-w2.py", "file_ext": "py", "file_size_in_byte": 1085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_csv", "line_number": 3, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} {"seq_id": "96977582", "text": "import json\nimport math\n\n\nclass CodeClassifier:\n with open('code_freq.json', 'r') as f:\n code_freq = json.load(f)\n with open('text_freq.json', 'r') as f:\n text_freq = json.load(f)\n\n def classify(self, str):\n text_sum = 0\n code_sum = 0\n for c in str:\n text_sum += math.log(CodeClassifier.text_freq[c])\n code_sum += math.log(CodeClassifier.code_freq[c])\n return 1 / (1 + math.exp(text_sum - code_sum))\n\n\ndef gen_next_split(src, n):\n m = len(src)\n tag = False\n for i in reversed(range(m)):\n if src[i] != n - (m - i - 1):\n tag = True\n break\n if not tag:\n return None\n new = src[i] + 1\n return src[:i] + list(range(new, new + m - i))\n\n\ndef otsu_n(arr, m):\n n = len(arr)\n splits = list(range(m))\n max_splits = splits.copy()\n max_val = 0\n mean = sum(arr) / n\n while splits is not None:\n val = 0\n extend = [0] + splits + [n]\n for i in list(range(m + 1)):\n start = extend[i]\n end = extend[i + 1]\n if start == end:\n continue\n miu = sum(arr[start:end]) / (end - start)\n val += (end - start) / n * (miu - mean) ** 2\n # print(\"splits=\" + str(splits))\n # print(\"\\tval=\" + str(val))\n if val > max_val:\n max_val = val\n max_splits = splits.copy()\n splits = gen_next_split(splits, n)\n\n return max_splits\n\n\ndef test_otsu():\n classifier = CodeClassifier()\n with open('test.txt', 'r') as f:\n lines = f.read().splitlines()\n probs = []\n for line in lines:\n probs.append(classifier.classify(line))\n\n print(\"splits:\")\n splits = otsu_n(probs, 5)\n j = 0\n for (i, prob) in enumerate(probs):\n if j < len(splits) and splits[j] == i:\n print('-------------------------------------------------------------------')\n j += 1\n print(\"%d: %.3f %s\" % (i, prob, lines[i]))\n\n\ndef test_next_split():\n splits = list(range(3))\n while splits is not None:\n print(splits)\n splits = gen_next_split(splits, 5)\n\n\ndef test_prob():\n classifier = CodeClassifier()\n with open('test.txt', 'r') as f:\n lines = f.read().splitlines()\n for line in lines:\n prob = classifier.classify(line)\n print('%.5f %s' % (prob, line))\n\n\nif __name__ == '__main__':\n # test_next_split()\n # test_otsu()\n test_prob()\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2487, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "math.log", "line_number": 15, "usage_type": "call"}, {"api_name": "math.log", "line_number": 16, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 17, "usage_type": "call"}]} {"seq_id": "53879326", "text": "import json\nimport bot_data\nimport dataset\nfrom os.path import isfile\nimport time\n\n# Checks if user statistics file exists\ndef check(user_id):\n if not isfile(\"rpg-data/social/\"+str(user_id)+\".json\"):\n with open(\"rpg-data/social/\"+str(user_id)+\".json\", \"w\") as social_file:\n social_file.write(json.dumps(bot_data.social_dict))\n\n# increments a stat\ndef increment(user_id, key, count=1):\n db = dataset.connect(\"sqlite:///pb.db\")\n user = db[\"users\"].find_one(user_id=user_id)\n nvalue = count\n if user:\n if key in user and user[key] != None:\n nvalue = user[key] + count\n db[\"users\"].update({\"user_id\": user_id, key: nvalue}, [\"user_id\"])\n\ndef genuser(user_id, username, first_name, last_name):\n db = dataset.connect(\"sqlite:///pb.db\")\n if db[\"users\"].find_one(user_id=user_id):\n db[\"users\"].update(dict(user_id=user_id,\n username=username,\n first_name=first_name,\n last_name=last_name), [\"user_id\"])\n else:\n db[\"users\"].insert(dict(user_id=user_id,\n username=username,\n first_name=first_name,\n last_name=last_name))\n# Check command timeout\ndef check_next_use(user_id, key, timeout=60):\n db = dataset.connect(\"sqlite:///pb.db\")\n data = db[\"users\"].find_one(user_id=user_id)\n\n if \"last_used_\"+key not in data or data[\"last_used_\"+key] == None:\n db[\"users\"].update({\"user_id\": user_id, \"last_used_\"+key: time.time()}, [\"user_id\"])\n return 0\n else:\n last_used = data[\"last_used_\"+key]\n if last_used < time.time()-timeout:\n db[\"users\"].update({\"user_id\": user_id, \"last_used_\"+key: time.time()}, [\"user_id\"])\n return 0\n else:\n return abs(int(last_used-time.time()+timeout))", "sub_path": "stats.py", "file_name": "stats.py", "file_ext": "py", "file_size_in_byte": 1906, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 11, "usage_type": "call"}, {"api_name": "bot_data.social_dict", "line_number": 11, "usage_type": "attribute"}, {"api_name": "dataset.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]} {"seq_id": "548533225", "text": "import logging\nimport urllib.parse\n\nimport environ\n\n\n_logger = logging.getLogger(__name__)\n\n\ndef _odbc_connect_params(s):\n return tuple(f'ODBC_{ss}' for ss in s.split(','))\n\n\n@environ.config(prefix='BIGRAYS')\nclass Config:\n\n # default all supported values to None\n AWS_REQUIRE_SECRETS = environ.bool_var(\n True,\n help=('Are AWS credentials required?'\n ' Set to False if using AWS roles or ~/.aws/credentials.'))\n AWS_ACCESS_KEY_ID = environ.var(None)\n AWS_SECRET_ACCESS_KEY = environ.var(None)\n AWS_REGION = environ.var(None)\n\n # we could do\n # @environ.config\n # class DB\n # here, but from the user perspective it doesn't matter\n # and not having a nested class makes requirement checking\n # simpler in resources.py\n ODBC_UID = environ.var(None, help='UID value for odbc_connect query parameter.')\n ODBC_PWD = environ.var(None, help='PWD value for odbc_connect query parameter.')\n ODBC_DSN = environ.var(None, help='DSN value for odbc_connect query parameter.')\n ODBC_SERVER = environ.var(None, help='Server value for odbc_connect query parameter.')\n ODBC_PORT = environ.var(None, help='Port value for odbc_connect query parameter.')\n ODBC_DRIVER = environ.var(None, help='The ODBC connection driver, e.g. \"{ODBC Driver 17 for SQL Server}\"')\n ODBC_FLAVOR = environ.var('mssql', help='The SQL flavor, or dialect.')\n\n ODBC_CONNECT_PARAMS = environ.var('SERVER,PORT,DRIVER,UID,PWD', converter=_odbc_connect_params)\n _connect_string = '{flavor}+pyodbc:///?odbc_connect={odbc_connect}'\n\n @property\n def ODBC_CONNECT_URL(self):\n odbc_connect = ';'.join(\n '%s=%s' % (k.replace('ODBC_', ''), getattr(self, k))\n for k in self.ODBC_CONNECT_PARAMS)\n connect_url = self._connect_string.format(\n flavor=self.ODBC_FLAVOR,\n odbc_connect=urllib.parse.quote_plus(odbc_connect)\n )\n return connect_url\n\nBigRaysConfig = Config.from_environ()\n\n\nif __name__ == '__main__':\n print('bigrays configurations. Set the following environment variables or '\n 'assign to bigrays.config.BigRaysConfig directly.')\n print(BigRaysConfig.generate_help())\n", "sub_path": "bigrays/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 2207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "environ.bool_var", "line_number": 18, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 22, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 23, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 24, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 32, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 33, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 34, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 35, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 36, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 37, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 38, "usage_type": "call"}, {"api_name": "environ.var", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote_plus", "line_number": 50, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 50, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 50, "usage_type": "name"}, {"api_name": "environ.config", "line_number": 14, "usage_type": "call"}]} {"seq_id": "258026565", "text": "# Kamil Pastwa\n# kpastew@gmail.com\n\nimport matplotlib.pyplot as plt\n\n# Problem fizyczny\nk = 1.5\nm = 2.1\n\n# Warunki rozwiazania numerycznego\ndelta_t_list = [0.001, 0.1, 0.5]\nx_0 = 1.75\nv_0 = 0.0\n\nt_0 = 0.0\nt_end = 15.0\n\n# tablica przechowujaca kolejne kroki czasowe - potrzebna do narysowania wykresu\ntime_lists = {}\n\n# slowniki z tablicami polozen i predkosci wyliczonymi zmodyfikowanym algorytmem Eulera dla kolejnych krokow czasowych\nmod_euler_x_lists = {}\nmod_euler_v_lists = {}\n\n# ... oraz algorytmem Verlet\nverlet_x_lists = {}\nverlet_v_lists = {}\n\nfor delta_t in delta_t_list:\n t = t_0\n x_mod_euler = x_0\n v_mod_euler = v_0\n x_verlet = x_0\n v_verlet = v_0\n\n time_list = [t_0]\n # tablice z polozeniami i predkosciami\n mod_euler_x_list = [x_0]\n mod_euler_v_list = [v_0]\n\n verlet_x_list = [x_0]\n verlet_v_list = [v_0]\n\n while t < t_end:\n\n # Zmodyfikowana metoda Eulera\n v_mod_euler += -k / m * mod_euler_x_list[-1] ** 3 * delta_t\n x_mod_euler += v_mod_euler * delta_t\n\n mod_euler_x_list.append(x_mod_euler)\n mod_euler_v_list.append(v_mod_euler)\n\n # Algorytm Verlet\n if t == t_0:\n x_verlet = x_mod_euler\n else:\n x_verlet = -verlet_x_list[-2] + 2 * verlet_x_list[-1] - (k / m * verlet_x_list[-1] ** 3) * delta_t ** 2\n\n v_verlet = (x_verlet - verlet_x_list[-1]) / delta_t\n\n verlet_x_list.append(x_verlet)\n verlet_v_list.append(v_verlet)\n\n t += delta_t\n time_list.append(t)\n\n\n time_lists[str(delta_t)] = time_list\n mod_euler_x_lists[str(delta_t)] = mod_euler_x_list\n mod_euler_v_lists[str(delta_t)] = mod_euler_v_list\n verlet_x_lists[str(delta_t)] = verlet_x_list\n verlet_v_lists[str(delta_t)] = verlet_v_list\n\n\ndef draw_chart(result_lists, title, ylabel):\n for delta_t in delta_t_list:\n plt.plot(time_lists[str(delta_t)], result_lists[str(delta_t)], label='dt = ' + str(delta_t))\n\n plt.title(title)\n plt.xlabel('czas [s]')\n plt.ylabel(ylabel)\n plt.legend()\n plt.show()\n\n\ndraw_chart(mod_euler_x_lists, title=\"Zmodyfikowany algorytm Eulera - wykres polozenia\", ylabel=\"polozenie[m]\")\ndraw_chart(mod_euler_v_lists, title=\"Zmodyfikowany algorytm Eulera - wykres predkosci\", ylabel=\"predosc[m/s]\")\ndraw_chart(verlet_x_lists, title=\"Algorytm Verlet - wykres polozenia\", ylabel=\"polozenie[m]\")\ndraw_chart(verlet_v_lists, title=\"Algorytm Verlet - wykres predkosci\", ylabel=\"predosc[m/s]\")\n", "sub_path": "zestaw3/Kamil_Pastwa_3_1.py", "file_name": "Kamil_Pastwa_3_1.py", "file_ext": "py", "file_size_in_byte": 2469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} {"seq_id": "199178293", "text": "# coding=utf-8\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render, redirect, resolve_url\nfrom complaints.forms import ComplainsForm\nfrom complaints.models import Complaint\n\n\ndef index(request):\n if request.method == \"POST\":\n form = ComplainsForm(request.POST, request.FILES)\n if form.is_valid():\n form.save()\n return redirect('complaints_index')\n\n else:\n form = ComplainsForm()\n\n complaints = Complaint.objects.published()\n return render(request, 'complaints/index.html',\n {\n 'complaints': complaints,\n 'form': form,\n })", "sub_path": "_step_08/complaints/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "complaints.forms.ComplainsForm", "line_number": 10, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 13, "usage_type": "call"}, {"api_name": "complaints.forms.ComplainsForm", "line_number": 16, "usage_type": "call"}, {"api_name": "complaints.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "complaints.models.Complaint.objects.published", "line_number": 18, "usage_type": "call"}, {"api_name": "complaints.models.Complaint.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "complaints.models.Complaint", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "complaints.forms", "line_number": 21, "usage_type": "name"}]} {"seq_id": "139243144", "text": "import time\nimport cv2\nimport sys\nimport numpy\nfrom commandesPython import Arduino\nfrom datetime import datetime\nfrom time import strftime\n\nport = 'COM3'\nard = Arduino(port)\n\nprint('access port available')\n\nxValue = 90\nyValue = 70\nard.servoAttach(1, 6)\nard.servoAttach(2, 7)\nard.servoWrite(1, xValue)\nard.servoWrite(2, yValue)\n\nvideo = cv2.VideoCapture(0)\n\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\n\ndateNow = datetime.now()\ndate = str(dateNow.day) + \"-\" + str(dateNow.hour) + \"-\" + str(dateNow.minute)\nnumber = dateNow.hour\nwriter = cv2.VideoWriter(\"videos/projet-\"+str(date)+\".avi\", fourcc, 25.0, (640, 480))\n\nface_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt2.xml')\n\n# Define an initial bounding box\nbbox = (287, 23, 86, 320)\n\nwhile True:\n\n ok, frame = video.read()\n if not ok:\n print('Cannot read video file')\n sys.exit()\n \n frame = cv2.flip(frame, 1)\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n\n height, width = frame.shape[:2]\n yScrCen = int(height/2)\n xScrCen = int(width/2)\n yScrLim = int(yScrCen/2)\n xScrLim = int(xScrCen/2)\n yScrSaf = int(height/15)\n xScrSaf = int(width/10)\n ec = 20\n\n for (x,y,w,h) in faces:\n cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n \n cv2.imshow('Detection',frame)\n\n #if cv2.waitKey(1) & 0xFF == ord('t'):\n if(faces != ()):\n\n bbox = tuple(faces[0])\n continuer = True\n\n while continuer:\n\n if(continuer):\n\n continuer = False\n \n # Initialize tracker with first frame and bounding box\n # change size : MedianFow, TDL\n # not : KCF, MIL\n\n # ATTENTION !\n \"\"\"\n SI TRACKER CHANGE :\n - MODIFIER CONDITION \"facesDet !=\" ? \n - ? par \"()\" si TLD\n - ? par \"0\" et \"facesDet\" par \"facesDet[0][0]\" si KCF\n\n \"\"\"\n tracker = cv2.TrackerKCF_create()\n ok = tracker.init(frame, bbox)\n\n end = False\n\n nbImages = 0\n\n while True:\n nbImages += 1\n \n # Read a new frame\n ok, frame = video.read()\n frame = cv2.flip(frame, 1)\n\n if not ok:\n print('we can not read the video')\n break\n \n # Update tracker\n ok, bbox = tracker.update(frame)\n \n if(not ok):\n if(not end):\n print(\"end of tracking\")\n end = True\n facesDet = face_cascade.detectMultiScale(frame, 1.2, 5)\n print(facesDet)\n if(facesDet != ()):\n if(facesDet[0][0] != 0):\n bbox = tuple(facesDet[0])\n print(\"new detection : \" + str(bbox))\n continuer = True\n break\n\n if(nbImages % 10 == 0 and ok):\n print(\"images = 10\")\n print(frame.shape)\n print(bbox)\n print(ok)\n print(int(bbox[0]+bbox[2])-int(bbox[0]))\n print(int(bbox[1]+bbox[3])-int(bbox[1]))\n frameROI = frame[int(bbox[1]-ec):int(bbox[1]+bbox[3]+ec), int(bbox[0]-ec):int(bbox[0]+bbox[2]+ec)]\n print(frameROI.shape)\n facesDet = face_cascade.detectMultiScale(frameROI, 1.2, 5)\n print(facesDet)\n if(facesDet == ()):\n facesDet = [[0,0,0,0]]\n print(\"test faces Det : '\" + str(facesDet[0][0]) + \"'\")\n if(facesDet[0][0] == 0):\n facesDet = face_cascade.detectMultiScale(frame, 1.2, 5)\n if(facesDet != ()):\n if(facesDet[0][0] != 0):\n new = bbox\n bbox = tuple(facesDet[0])\n continuer = True\n print(\"10 secondes worked : \" + str(new))\n print(\"10 secondes is now : \" + str(bbox))\n break\n \n\n # Draw bounding box\n if ok:\n x, y, w, h = (int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]))\n\n cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)\n \n xFaceCen = x + int(w/2)\n yFaceCen = y + int(h/2)\n\n xEcart = abs(xScrCen - xFaceCen) - xScrSaf\n yEcart = abs(yScrCen - yFaceCen) - yScrSaf\n\n xDep = int(xEcart / 100) + 1\n yDep = int(yEcart /100) + 1 \n\n\n if(xScrCen - xScrSaf > xFaceCen):\n xValue -= xDep\n if(xValue < 0):\n xValue = 0\n if(xScrSaf + xScrCen < xFaceCen):\n xValue += xDep\n if(xValue > 180):\n xValue = 180\n if(yScrCen - yScrSaf > yFaceCen):\n yValue -= yDep\n if(yValue < 0):\n yValue = 0\n if(yScrSaf + yScrCen < yFaceCen):\n yValue += yDep\n if(yValue > 180):\n yValue = 180\n\n ard.servoWrite(1, xValue)\n ard.servoWrite(2, yValue)\n\n # Display result\n cv2.imshow(\"Detection\", frame)\n writer.write(frame)\n print(x)\n print(y)\n \n # Exit if ESC pressed\n k = cv2.waitKey(1) & 0xff\n if k == ord('a') :\n break\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n\nvideo.release()\ncv2.destroyAllWindows()\n", "sub_path": "anciennes versions programmes/trackingComandesCameraSansTraitsAmelioré.py", "file_name": "trackingComandesCameraSansTraitsAmelioré.py", "file_ext": "py", "file_size_in_byte": 6623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "commandesPython.Arduino", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "cv2.VideoWriter", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.TrackerKCF_create", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 182, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 192, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 197, "usage_type": "call"}]} {"seq_id": "211872807", "text": "import requests, time, json\nimport pandas as pd\nimport numpy as np\nfrom collections import *\nimport matplotlib.pyplot as pl\nfrom hmmlearn import hmm\n# from SimpleHOHMM import HiddenMarkovModelBuilder as Builder\nimport random\nimport multiprocessing\nfrom multiprocessing import Pool\nimport multiprocessing.pool\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.utils import check_random_state\nfrom sklearn.model_selection import train_test_split\nimport joblib\nimport time\nfrom tqdm import tqdm\n#import cupy as np\n#import numba as nb\n\n\n\"\"\"\n * This program will be a little different from the last.\n * Instead of sticking with normal timeseries data, I will\n * use volume based data. What I mean by this is instead of\n * having periods be completed after certain periods of time,\n * periods will end after a certian amount of volume has been\n * accumulated.\n * \n * The idealistic benefit of making this change is that\n * the data will better represent 'true' price movements.\n * Price movement isn't driven by time, price movement is\n * driven by volume. To see this, time can move forward with\n * no volume and the price will never change because of\n * the lack of voulme.\n *\n * Using volume will add challenges from a programming standpoints.\n * Where in timeseries data, periods end regularly and are determined\n * external from the market, with volume based data, trades aren't\n * and depending on the market being conscious of these periods sizes \n * will be of much greater importance. \n * Along with this, visualizing will be very important for me to make\n * sense of the data I'm seeing. I've been finding it difficult to\n * locate anything on this form of analysis.\n Number of bullish/bearish periods and their ratio\n Body size of bullish/bearish periods\n Number of consecutive periods\n TODO:\n See what actual returns will be if you open a potistion at the beginning of a period (when theres positive movement) then close at the end\n compare this method to closing after with certain \"high\" percentages are reaches.\n then compare this to adding a stop loss\n GPU computations \n\"\"\"\n\n\n\n#Load credentials from json\n#cred = json.load(open(\"credentials.json\"))\nfiles = json.load(open(\"files.json\"))\n\ndef readFiles():\n #da = pd.read_csv(''+files['BTC']+'/2013/merged.csv')\n #da.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n #dat = pd.read_csv(''+files['BTC']+'/2014/merged.csv')\n #dat.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n #data = pd.read_csv(''+files['BTC']+'/2015/merged.csv')\n #data.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n\n #sticking to more recent data\n data0 = pd.read_csv(''+files['BTC']+'/2016/merged.csv')\n data0.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n data1 = pd.read_csv(''+files['BTC']+'/2017/merged.csv')\n data1.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n data2 = pd.read_csv(''+files['BTC']+'/2018/merged.csv')\n data2.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n #print(sum(pd.concat([da, dat, data, data0, data1, data2], ignore_index=True)['close']))\n return pd.concat([data0, data1, data2], ignore_index=True)\n\ndef readTestFiles():\n data = pd.read_csv(''+files['BTC']+'/2019/merged.csv')\n data.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n return pd.concat([data], ignore_index=True)\n\ndef readTestFiles():\n data = pd.read_csv(''+files['BTC']+'/2019/merged.csv')\n data.columns = ['time', 'open', 'close', 'high', 'min', 'volume']\n return pd.concat([data], ignore_index=True)\n\ndef readTestModelFiles():\n dat = pd.read_csv(''+files['AMD']+'/AMD_2000_2009.csv')\n dat.columns = ['time', 'open', 'high', 'min', 'close', 'volume']\n #dat['time'] = pd.to_datetime(dat['time'], infer_datetime_format=True)\n #dat = dat.set_index('time')\n \n \n \n \n data = pd.read_csv(''+files['AMD']+'/AMD_2010_2019.csv')\n data.columns = ['time', 'open', 'high', 'min', 'close', 'volume']\n #data['time'] = pd.to_datetime(data['time'], infer_datetime_format=True)\n #data = data.set_index('time')\n\n #print(data)\n return pd.concat([dat, data])\n\n\ndef readRecentTestModelFiles():\n data = pd.read_csv(''+files['AMD']+'/AMD_2010_2019.csv')\n data.columns = ['time', 'open', 'high', 'min', 'close', 'volume']\n #data['time'] = pd.to_datetime(data['time'], infer_datetime_format=True)\n #data = data.set_index('time')\n\n #print(data)\n return pd.concat([data])\n\ndef readSecondData(startY, startM, startD):\n dataPoints = []\n for i in range(startY, 2021):\n for j in range(startM, 13):\n for k in range(startD, 32):\n #print(''+files['AMD-S'] + str(i) + str(j).zfill(2) + str(k).zfill(2)+'/amd.csv')\n #dataPoints.append(pd.read_csv(''+files['AMD-S'] + str(i) + str(j).zfill(2) + str(k).zfill(2)+'/amd.csv'))\n #print(i)\n try:\n dataPoints.append(pd.read_csv(''+files['AMD-S'] + str(i) + str(j).zfill(2) + str(k).zfill(2)+'/amd.csv'))\n dataPoints[-1].columns = ['time', 'open', 'high', 'min', 'close', 'volume', 'susp']\n dataPoints[-1]['time'] = dataPoints[-1]['time'].apply(lambda x: pd.to_datetime(''+str(i)+\"-\"+ str(j).zfill(2) + \"-\" + str(k).zfill(2)) + np.timedelta64(x, 'ms'))\n dataPoints[-1] = dataPoints[-1].set_index('time')\n dataPoints[-1] = dataPoints[-1].between_time('8:29:59', '15:00:00')\n dataPoints[-1]['volume'] = np.log(dataPoints[-1]['volume'])\n #print(dataPoints[-1])\n except Exception:\n continue\n \n return dataPoints\ndef readModel():\n data0 = pd.read_csv(''+ files['local']+'/Model-V1.csv')\n data0.columns = ['index', 'fracC', 'fracH', 'fracL']\n return np.column_stack((data0['fracC'], data0['fracH'], data0['fracL']))\n\n\n# FLAW: If there is a minute tiemframe with enough volume to fill multiple periods, it will carry over into other periods.\n# Test fix 1: s = period % dif. This will accept the large volitility, but not have it carry over into other periods\n# True fix: get smaller timeframes... second based data maybe?\ndef getHistorical(period, data):\n hist = []\n conv = []\n o = -1\n vals = [o, -1, float('inf'), -1]\n s = 0\n dupeCount = 0\n for ind, p in data.iterrows():\n # get the open, close, min, and max for each volume period given the minute based data.\n if(vals[0] == -1 or vals[0] == 0):\n vals[0] = p['open']\n vals = [vals[0], -1, min(vals[2], p['min']), max(vals[3], p['high'])]\n s += p['volume']\n if(s > 2*period):\n dupeCount += 1\n if (s >= period):\n \n dif = s - period \n vals[1] = p['close']\n hist.append(vals)\n if(dif!=0):\n o = p['close']\n s = period % dif\n else:\n o = -1\n s = 0\n vals = [o, -1, float('inf'), -1]\n \n \n # Make sure to catch the last data point, even if it isn't full.\n if(not (vals[1] == -1)):\n hist.append(vals)\n\n #print(str(dupeCount) + \" condensed pointes for period \" + str(period))\n #print(str(len(hist)) + \" number of points for period \" + str(period))\n hist = pd.DataFrame(hist, columns = ['open', 'close', 'min', 'max'])\n return (hist, period)\n\n\n\"\"\"\nCalcuate the EMA of a given dataset\n\"\"\"\ndef ema(length, data):\n return data.ewm(span=length, adjust=False, min_periods=length-1).mean()\n \n #return (hist, period)\n\n\"\"\"\nTag data as either bullish or bearish\nLength - Length of EMA to lag\nData - Dataframe\n\"\"\"\ndef tagData(length, data):\n EMA = ema(length, data['close'])\n slope = EMA.diff().copy() # rough estimate\n #tagged = data.copy()\n data = data.copy()\n data['tag'] = pd.Series([0 for x in range(data['close'].__len__())], index=data.index)\n\n condition = slope.loc[slope > 0].index\n data.loc[condition, ['tag']] = 1\n \n return data\n\n\n\"\"\"\nTag data as either bullish or bearish\nminSize - Min size of success for dataframe to accept\nData - Dataframe\nVers - String to identify in parallelism.\n\"\"\"\ndef splitData(minSize, data, vers):\n split = []\n indP = data.index.tolist()[0]\n s = False\n for ind, row in tqdm(data.iterrows(), desc = vers + \" Splitting\"):\n d = (ind - indP)/np.timedelta64(1, 'm')\n if(d < .03):\n if(not s):\n split.append(pd.DataFrame())\n split[-1] = split[-1].append(row)\n s = True\n else:\n split[-1] = split[-1].append(row)\n \n else:\n s = False\n if(len(split)>0 and split[-1].__len__() < minSize):\n del split[-1]\n indP = ind\n return split\n\n\"\"\"\n converts from open, close, min, max\n to period change, max/min, fractional high, and fractional low\n TODO: DEBUGGGGGGGGG (DATA PRESENTATION IS THE MOST IMPORTANT PART OF THIS PROCESS)\n\"\"\"\ndef convert(hist):\n #print(\"Converting data\")\n conv = []\n\n o = np.array(hist['open'])\n c = np.array(hist['close'])\n h = np.array(hist['max'])\n l = np.array(hist['min'])\n \n fracC = []\n fracH = []\n fracL = []\n\n\n for i in range(len(o.tolist())):\n if(c[i]-o[i] < 0):\n if((o[i]-c[i])/o[i] >= 1 and (o[i]-c[i])/o[i] <=1.5):\n fracC.append(-.75)\n elif((o[i]-c[i])/o[i] > 1.5):\n fracC.append(-1)\n else:\n fracC.append(1/np.log((o[i]-c[i])/o[i]))\n elif(c[i]-o[i] > 0):\n if((c[i]-o[i])/o[i] >= 1 and (c[i]-o[i])/o[i] <= 1.5):\n fracC.append(.75)\n elif((c[i]-o[i])/o[i] > 1.5):\n fracC.append(1)\n else:\n fracC.append(-1/np.log((c[i]-o[i])/o[i]))\n else:\n fracC.append(0)\n\n #upward movements are unbound. should consider a way to account for this.\n if((h[i]-o[i]) <= 0):\n fracH.append(0)\n elif(np.log((h[i]-o[i])/o[i]) >= 0):\n fracH.append(10)\n else:\n fracH.append(-1/np.log((h[i]-o[i])/o[i]))\n \n #l is bound by zero\n if((o[i]-l[i]) <= 0):\n fracL.append(0)\n elif(np.log((o[i]-l[i])/o[i]) == 0):\n fracL.append(10)\n else:\n fracL.append(-1/np.log((o[i]-l[i])/o[i]))\n\n \n \n scalar = MinMaxScaler()\n scalar.fit(np.array(fracC).reshape(-1, 1))\n \n\n # Standardize data to a normal distribution centered around 0\n # Make price movements more priminent AND more consistent\n # Hopefully will increase the effectiveness of the model and its training.\n\n # works better with smaller non-volumetric periods (theory. will test tonight) \n #fracC = (scalar.transform(np.array(fracC).reshape(-1, 1)).flatten()-.5)\n #fracH = (scalar.transform(np.array(fracH).reshape(-1, 1)).flatten()-.5)\n #fracL = (scalar.transform(np.array(fracL).reshape(-1, 1)).flatten()-.5)\n \n #print(fracC)\n\n return np.column_stack((fracC, fracH, fracL))\n \ndef scale(array):\n scalarH = MinMaxScaler()\n scalarH.fit(np.array(array[:,1]).reshape(-1, 1))\n fracH = (scalarH.transform(np.array(array[:,1]).reshape(-1, 1)).flatten()-.5)\n scalarL = MinMaxScaler()\n scalarL.fit(np.array(array[:,2]).reshape(-1, 1))\n fracL = (scalarL.transform(np.array(array[:,2]).reshape(-1, 1)).flatten()-.5)\n scalarC = MinMaxScaler()\n scalarC.fit(np.array(array[:,0]).reshape(-1, 1))\n fracC = (scalarC.transform(np.array(array[:,0]).reshape(-1, 1)).flatten()-.5)\n return (np.column_stack((fracC, fracH, fracL)), (scalarC, scalarH, scalarL))\n\ndef run(period):\n #print('getting historical')\n #hist = getHistorical(period, readFiles())[0]\n #print('getting historical test')\n \n\n testFiles = readTestModelFiles()\n testFiles['time'] = pd.to_datetime(testFiles['time'], infer_datetime_format=True)\n testFiles = testFiles.set_index('time').loc['1/1/2018':'1/1/2019']\n print(testFiles)\n\n vol = int(testFiles['volume'].sum())\n\n print(vol)\n\n histT = getHistorical(period, testFiles)[0]\n\n #conv = convert(hist)\n\n #hist.to_csv('models/Hist-V1.csv')\n #histT.to_csv('models/Test-V1.csv')\n #pd.DataFrame(conv).to_csv('models/Model-V1.csv')\n \n\n #for i in conv:\n # print(i)\n \n #-------------------------------------------------------------------------------------------------------------------\n\n print('make hmm')\n \n HMM = hmm.GaussianHMM(n_components = 11 , covariance_type=\"full\", random_state=7, n_iter = 1000)\n\n HMM.fit(readModel())\n print(HMM.sample(10))\n print(HMM.transmat_)\n print('complete')\n \n #-------------------------------------------------------------------------------------------------------------------\n scores = defaultdict(list)\n pSize = random.randint(10, 75)\n strt = random.randint(8, histT.__len__()-pSize)\n for j in range(15):\n pSize = random.randint(10, 75)\n \n \n for i in range(75):\n #if(i == 0 and not scores[pSize] == None):\n # break\n strt = random.randint(6, histT.__len__()-pSize)\n pred, sc, ret = predict(HMM, histT, strt, strt+pSize, 5, 25000, False)\n scores[pSize].append((pred, sc, ret))\n \n\n #-------------------------------------------------------------------------------------------------------------------\n\n predictedCloseForTest, _, _ = predict(HMM, histT, strt, strt+pSize, 3, 25000, True)\n trueOpenForTest = histT.iloc[strt:strt+pSize]['open'].values\n trueCloseForTest = histT.iloc[strt:strt+pSize]['close'].values\n\n print(\"50 random periods w/50 different random tests resuts::\")\n\n for i in scores.keys():\n s = str(sum(n for _, n, _ in scores[i])/len(scores[i]))[0:5]\n ret = str(sum(n for _, _, n in scores[i])/len(scores[i]))[0:5]\n print(\"For the 75 random tests over \" + str(i) + \" periods, the HMM determined the direction correctly: \" + s + \"% of the time. Ret: \" + ret)\n #plotter(trueCloseForTest, predictedCloseForTest, trueOpenForTest, )\n\n\nclass NoDaemonProcess(multiprocessing.Process):\n # make 'daemon' attribute always return False\n def _get_daemon(self):\n return False\n def _set_daemon(self, value):\n pass\n daemon = property(_get_daemon, _set_daemon)\n\n# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool\n# because the latter is only a wrapper function, not a proper class.\nclass MyPool(multiprocessing.pool.Pool):\n Process = NoDaemonProcess\n\n\ndef optimizeGen():\n # Optimse the volumetric period for a given stock on the given model.\n # This will be a test to see if we can use 'generic' models as I theorize we can.\n\n scores = []\n testFiles = readTestModelFiles()\n testFiles['time'] = pd.to_datetime(testFiles['time'], infer_datetime_format=True)\n testFiles = testFiles.set_index('time').last('1Y')\n print(testFiles)\n vol = int(testFiles['volume'].sum())\n model = readModel()\n HMM = hmm.GaussianHMM(n_components = 11, covariance_type=\"full\", random_state=7, n_iter = 1000)\n HMM.fit(model)\n for i in tqdm(range(vol//365//6, vol//92, vol//365//4)):\n his = []\n res = []\n\n with Pool() as p:\n his = p.starmap(getHistorical, [(x, testFiles) for x in range(i, i + vol//365//4 - 10, ( vol//365//4)//4)])\n\n with Pool() as p:\n res = p.starmap(runTests, [(HMM, j[0], 15, 75, 5, j[1], -1) for j in his])\n\n\n for j in res:\n s = 0\n for k in j[0].keys():\n s+=sum(j[0][k])/len(j[0][k])\n t = k\n\n s = s/len(j[0].keys())\n scores.append((j[1], s))\n \n scores.sort(key = lambda x: x[1], reverse = True)\n print(scores[0:5])\n return scores\n\n\n\"\"\"\nData - Trainging Dataframe\nDataT - Testing Datafram \nMult - Multiplier from smallest accepted timeframe (will change if smaller data sizes can be acquired) (SECOND DATA ON THE COMEUP??)\nvers - String to identify in parallelism\n\"\"\"\ndef optimize(data, dataT, mult, vers):\n # Dictionary from Period to dict from HMM components to dict from HMM lookBack size to list of tuples of test length and score\n optimizer = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(list))))\n \n v = sum([x['volume'].sum() for x in data])//sum([len(x.index) for x in data]) # Average vol/min across all training periods.\n #v = data[0]['volume'].sum() // data[0].index.normalize().unique().__len__() # Average vol/min on most recent (and representative) dataset\n vtr = [x['volume'].sum()//len(x.index) for x in data] # Average Vol/Min in each given train period\n vT = [x['volume'].sum()//len(x.index) for x in dataT] # Average Vol/min in each given test period\n \n volAdjust = [x/v for x in vtr] # Attempt to standardize the avg vol per min throughout training data.\n volAdjustT = [x/v for x in vT] # same thing for test data.\n \n vol = sum([x['volume'].sum() for x in data]) # Total Volume across all periods\n\n dataPoints = sum([len(x.index) for x in data]) # Total number of datapoints in training\n dataPointsT = sum([len(x.index) for x in dataT]) # Total number of datapoints in testing\n\n # Document all acquired info above for potential debugging purposes\n with open(vers+'.txt', 'w') as f:\n f.write(vers+'\\n')\n f.write('Volume: ' + str(vol)+ '\\nTrain Data points: ' + str(dataPoints) + '\\nTest Data Points: '+str(dataPointsT) + '\\n')\n f.write('Vol/Min: ' + str(vol/dataPoints) + '\\n')\n #f.write(\"Train AdjVol: \" + str(volAdjust) + '\\n')\n #f.write(\"Test AdjVol: \" + str(volAdjustT) + '\\n')\n \n averageBest50 = []\n for i in tqdm(range(int(vol/(dataPoints/mult)), int(vol/(dataPoints/8)), int(vol/(dataPoints/mult*2))), desc=vers+\" volume Progress\"):\n print() #add some spacing and stuff...\n hist = [convert(getHistorical(i*volAdjust[x], data[x])[0]) for x in range(len(data))]\n \n # keep things spicy ;)\n random.shuffle(hist)\n \n # Keep all datapoints seperate@@@@@@@\n histT = [getHistorical(i*volAdjustT[j], dataT[j])[0] for j in range(len(dataT))]\n converted, scalar = scale(np.concatenate(hist))\n joblib.dump(scalar, \"scales/\"+vers+'-'+str(i)+\".pkl\")\n for j in tqdm(range(2), desc=vers+\" Components Progress\"):\n\n \n \n res = []\n with Pool() as p:\n res = p.starmap(makeModel, [(j*3 + x, converted, scalar, hist, histT, vol, i, dataPoints, vers) for x in range(3, 6)])\n\n optimizer[i][j*3][1] = res[0]\n optimizer[i][j*3+1][1] = res[1]\n optimizer[i][j*3+2][1] = res[2]\n \n for j in optimizer[i].keys():\n for k in optimizer[i][j].keys():\n s = 0\n for l in optimizer[i][j][k][0].keys():\n s += sum(optimizer[i][j][k][0][l])/len(optimizer[i][j][k][0][l])\n\n sc = s/len(optimizer[i][j][k][0].keys())\n\n if len(averageBest50) == 0 or averageBest50[-1][3] < sc:\n averageBest50.append((i, j+3, k, sc))\n averageBest50.sort(key = lambda x: x[3], reverse=True)\n if len(averageBest50) > 50:\n averageBest50.pop()\n with open(vers+'.txt', 'a') as f:\n f.write(vers + \" ::: \" + str(averageBest50[0:5])+\"\\n\")\n\n # Log all results\n for i in optimizer.keys():\n for j in optimizer[i].keys():\n for k in optimizer[i][j].keys():\n s = 0\n for l in optimizer[i][j][k][0].keys():\n s += sum(optimizer[i][j][k][0][l])/len(optimizer[i][j][k][0][l])\n sc = s/len(optimizer[i][j][k][0].keys())\n\n with open(vers+'.txt', 'a') as f:\n f.write(vers+\" : \" + str(i) + \" : \" + str(j+3) + \" : \" + str(k) + \" : \" + str(sc) + \" : \" + str(optimizer[i][j][k][3]) + \" : \" + str(optimizer[i][j][k][2])+\"\\n\")\n\n return averageBest50\ndef makeModel(components, converted, scalar, hist, histT, vol, i, dataPoints, vers):\n HMM = hmm.GaussianHMM(n_components = components, covariance_type=\"full\", n_iter = 750, verbose = False)\n HMM.fit(converted, lengths = [x.__len__() for x in hist])\n joblib.dump(HMM, \"models/\"+vers+str(i) + \"-\" + str(components) + \"-\" + str(1)+\".pkl\")\n return runTests(HMM, histT, 250, 1, vol, i, dataPoints, vers + str(i) + \"-\" + str(components) + \"-\" + str(1), scalar)\n\ndef runTests(HMM, histT, iter1, lookBack, v, p, days, vers, scalar):\n scores = defaultdict(list)\n strt = 0\n f = 0\n avgRet = 0\n for j in range(iter1):\n # Pick random histT period\n randInd = random.randint(0, len(histT)-1)\n \n if(len(histT[randInd]) > lookBack):\n pred, sc, ret = predict(HMM, histT[randInd], lookBack, 25000, False, vers, scalar)\n scores[randInd].append(sc)\n avgRet += ret\n else:\n f += 1\n return (scores, p, f, avgRet/(iter1-f))\n\n\n# Need to smooth data out before hand so i can get all datapoints in a given period. But if this is accurate, might just need to have the model make\n# less decisions that are more accurate.\ndef predict(hmm, histT, lookBack, ret, plot, vers, scalar):\n pred = []\n\n for i in range(lookBack, len(histT.index)):\n oPrice = histT.iloc[i]['open']\n cPrice = histT.iloc[i]['close']\n\n prevD = histT.iloc[i-lookBack:i]\n \n conv = convert(prevD)\n conv = np.column_stack(((scalar[0].transform(np.array(conv[:,0]).reshape(-1, 1)).flatten()-.5), (scalar[1].transform(np.array(conv[:,1]).reshape(-1, 1)).flatten()-.5), (scalar[2].transform(np.array(conv[:,2]).reshape(-1, 1)).flatten()-.5)))\n stateSeq = hmm.predict(conv)\n # print(vers + \" - \" + str(stateSeq))\n\n randstate = check_random_state(hmm.random_state)\n #print(vers + \" - \" + str(randstate.get_state()))\n nextState = (np.cumsum(hmm.transmat_, axis=1)[stateSeq[-1]] > randstate.rand())\n # print(np.cumsum(hmm.transmat_, axis=1)[stateSeq[-1]])\n # #print(vers + \" - \" + str(randstate.rand()))\n # print(vers + \" - \" + str(nextState))\n # print(vers + \" - \" + str(nextState.argmax()))\n nextObs = hmm._generate_sample_from_state(nextState.argmax(),randstate)\n # print(vers + \"----------------------------------\")\n #print(str(nextObs[0]) + \" - \" + vers)\n # if(nextObs[0] > 0):\n # pred.append(oPrice / (np.exp(1.0/nextObs[0])) + oPrice)\n # elif(nextObs[0] < 0):\n # pred.append(-oPrice / (np.exp(-1.0/nextObs[0])) + oPrice)\n # else:\n # pred.append(oPrice)\n pred.append(oPrice * (1+nextObs[0]*.005))\n\n # Score model/results (Compare predictions to actual results)\n c = 0\n s = 0\n v = 0\n for i in histT.iloc[lookBack:]['open'].values:\n if not ret == -1:\n if (vers[:4]==\"BULL\"):\n if(pred[s]-i > 0):\n temp = ret*.1\n ret -= temp\n ret += (temp) * histT.iloc[s+lookBack]['close']/i\n else:\n if(pred[s]-i < 0):\n temp = ret*.1\n ret -= temp\n ret += (temp) * i/histT.iloc[s+lookBack]['close']\n if (vers[:4]==\"BULL\"):\n if((pred[s]-i)>0 and (histT.iloc[s+lookBack]['close']-i) > 0):\n c+=1\n if(pred[s]-i > 0):\n v+=1\n else:\n if((pred[s]-i)<0 and (histT.iloc[s+lookBack]['close']-i) < 0):\n c+=1\n if(pred[s]-i < 0):\n v+=1\n s+=1\n\n \n #print(\"for this sample, the HMM predicted the correct direction \" + str(100*(c/s)) + \"% of the time. P = \" + str(endInd-startInd) + \".\")\n \n if(plot):\n # only log 10% of plots to save time and memory\n rand = random.random()\n if(rand < .1):\n plotter(histT.iloc[lookBack:]['close'].values, pred,histT.iloc[lookBack:]['open'].values, vers+\"-\"+str(ret)[0: 5])\n if(v == 0):\n c = 1\n v = 2\n return pred, (100*(c/v)), ret\n\n\ndef plotter(data, dataP, dataPO, name):\n #print(data)\n #print(dataP)\n pl.style.use('ggplot')\n plot = pl.figure()\n axis = plot.add_subplot(111)\n axis.plot([x for x in range(data.__len__())], data, 'bo-', label='real close')\n axis.plot([x for x in range(data.__len__())], dataP, 'r+-', label='predicted close (based on realO)')\n axis.plot([x for x in range(data.__len__())], dataPO, 'b+-', label='real open')\n pl.legend()\n pl.savefig(\"D:/plots/\"+name+\".png\")\n pl.close(plot)\n\ndef start():\n #t = api.get_clock()\n\n # PERIODS::: (average 12 ticks a day)\n # XRP: 1934152\n # XRP: ‭23209824‬ (average of a day)\n # ETH: 248185 (works really well.) 11 - 3 - 60%\n # BTC: 27040 (should mirror ETH) 11 - 5 - 65% - Basis for general model\n # AMD 129376990 11 - 5 - 59.64% - First result from general model\n \n # Stuff for minute based data\n #-------------------------------------------------------------------------------\n #data = readTestModelFiles()\n #data['time'] = pd.to_datetime(data['time'], infer_datetime_format=True)\n #data = data.set_index('time')\n #volcond = data.loc[data['volume'] == 0].index\n #data.loc[volcond, 'volume'] = 1\n #data['volume'] = np.log(data['volume'])\n \n #print(data['volume'])\n #print(np.log(0))\n \n # data = data.iloc[len(data['volume'].index)//2:]\n #data = data.iloc[5000:10000]\n # testtag = tagData(50, data.iloc[5000:10000])\n # testcond = testtag.loc[testtag['tag'] == 0] .index\n # testtag = testtag.loc[testcond]\n # testd = splitData(45, testtag, 'test')\n # test = getHistorical(80000, testd[0])[0]\n # convert(test)\n # print(test.iloc[0:50])\n # tagged = tagData(50, data)\n #-------------------------------------------------------------------------------\n\n # Automatically tag data as bearish or bullish with a simple EMA\n # (keeping the tagging simple will provide proof of concept.\n # more technical indicators can be used in the future)\n # This will maintain consistency in training data.\n\n # data = readSecondData(2018, 1, 1)\n # print(data)\n # tagged = [tagData(40, x) for x in data]\n # BearTag = []\n # BullTag = []\n\n # # get each conditional for each dataset\n # conditionBear = [tag.loc[tag['tag'] == 0].index for tag in tagged]\n # conditionBull = [tag.loc[tag['tag'] == 1].index for tag in tagged]\n \n # BearTag = pd.concat([data[i].loc[conditionBear[i]] for i in range(len(data))])\n # BullTag = pd.concat([data[i].loc[conditionBull[i]] for i in range(len(data))])\n \n # ############################################################################\n # # conditionBear = tagged.loc[tagged['tag'] == 0].index\n # # conditionBull = tagged.loc[tagged['tag'] == 1].index\n # # BearTag = pd.concat([d.loc[conditionBear] for d in data], copy = False)\n # # BullTag = pd.concat([d.loc[conditionBear] for d in data], copy = False)\n # ############################################################################\n \n # print(BearTag)\n # joblib.dump(BearTag, \"data/BearTag40.pkl\")\n # joblib.dump(BullTag, \"data/BullTag40.pkl\")\n res = []\n # Split data into continous sections\n # with MyPool(2) as p:\n # res = p.starmap(splitData, [(40, BullTag, 'Bull'), (40, BearTag, 'Bear')])\n \n # joblib.dump(res, \"data/split40.pkl\")\n res = joblib.load('data/split20.pkl')\n # Log lost datapoints\n #print(\"Split:: \" + str(sum([len(x.index) for x in res[0]])))\n #print(\"Original:: \" + str(len(BearTag.index)))\n\n random.shuffle(res[0])\n random.shuffle(res[1])\n Bull, BullT = train_test_split(res[0], train_size = .75, shuffle = False)\n Bear, BearT = train_test_split(res[1], train_size = .75, shuffle = False)\n \n # TRY LOG SHIFTING THE VOLUME BECAUSE IT IS SO RIGHTLY SKEWED...\n\n with MyPool(2) as p:\n res = p.starmap(optimize, [(Bull, BullT, 2, \"BULL\"),\n (Bear, BearT, 2, \"BEAR\")])\n\n # with MyPool(2) as p:\n # res = p.starmap(optimize, [([data.loc['10/8/2019':'12/31/2019'], data.loc['5/1/2018':'9/17/2018'], data.loc['1/1/2019':'8/1/2019'], data.loc['1/1/2009':'1/1/2010'], data.loc['5/5/2005':'2/8/2006']],\n # [data.loc['2/2/2016':'5/1/2017'], data.loc['3/1/2009':'1/1/2010']], .005, \"BULL\"),\n # ([data.loc['8/12/19':'10/4/19'], data.loc['9/2/18':'12/26/18'], data.loc['2/2/18':'4/20/18'], data.loc['4/11/12':'12/13/12'], data.loc['3/3/06':'8/16/06'], data.loc['10/4/06':'5/24/07'], data.loc['11/1/07':'5/2/08']],\n # [data.loc['2/27/18':'4/17/18'], data.loc['5/19/11':'12/21/11'], data.loc['5/4/10':'9/1/10'], data.loc['9/10/08':'3/4/09'], data.loc['9/4/14':'10/5/15']], .005, \"BEAR\")])\n \n\"\"\"\n ToDo: Run tests again and choose random periods to plot for the highest scoring tests. Make sure the program isnt just a fancy EMA trader (or one that doesnt use Machine learning to its true potential)...\n If it does just continously buy until the ema flips, try re-weighing what the program consideres success. (For example, Extra points for correctly determining when the next period wont be positive)\n\"\"\"\n\n\nif __name__ == \"__main__\":\n\n #print(optimize())\n #print(optimizeGen())\n start()\n \n\n\n\n\"\"\"\nMoved to seperate file\n\"\"\"", "sub_path": "PAT.py", "file_name": "PAT.py", "file_ext": "py", "file_size_in_byte": 29958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.timedelta64", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 139, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 202, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.timedelta64", "line_number": 221, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 288, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 307, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 312, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 315, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 319, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 328, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.GaussianHMM", "line_number": 352, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 352, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 361, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 362, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 364, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 370, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 390, "usage_type": "attribute"}, {"api_name": "multiprocessing.pool", "line_number": 400, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 410, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.GaussianHMM", "line_number": 415, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 415, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 417, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 421, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 424, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 474, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 479, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 483, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 484, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 485, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 490, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.GaussianHMM", "line_number": 527, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 527, "usage_type": "name"}, {"api_name": "joblib.dump", "line_number": 529, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 539, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 562, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 562, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.predict", "line_number": 563, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 563, "usage_type": "name"}, {"api_name": "sklearn.utils.check_random_state", "line_number": 566, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.random_state", "line_number": 566, "usage_type": "attribute"}, {"api_name": "hmmlearn.hmm", "line_number": 566, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 568, "usage_type": "call"}, {"api_name": "hmmlearn.hmm.transmat_", "line_number": 568, "usage_type": "attribute"}, {"api_name": "hmmlearn.hmm", "line_number": 568, "usage_type": "name"}, {"api_name": "hmmlearn.hmm._generate_sample_from_state", "line_number": 573, "usage_type": "call"}, {"api_name": "hmmlearn.hmm", "line_number": 573, "usage_type": "name"}, {"api_name": "random.random", "line_number": 617, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 629, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 629, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 629, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 630, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 630, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 635, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 635, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 636, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 636, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 637, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 637, "usage_type": "name"}, {"api_name": "joblib.load", "line_number": 707, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 712, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 713, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 714, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 715, "usage_type": "call"}]} {"seq_id": "388829334", "text": "from django.shortcuts import render, get_object_or_404, redirect\r\nfrom django.contrib import messages\r\nfrom bs4 import BeautifulSoup\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.views.decorators.csrf import csrf_exempt\r\n\r\nimport razorpay\r\nimport os\r\nfrom django.conf import settings\r\nfrom lxml import etree\r\nfrom englisttohindi.englisttohindi import EngtoHindi\r\n\r\nimport code128\r\nfrom datetime import date\r\nfrom main.models import Voter, Payment, PANCard\r\nfrom .const import PAY_AMOUNTS, POINTS_AMOUNTS\r\n\r\ndef protect_access(request):\r\n return request.user.points <= 0\r\n\r\ndef home(request):\r\n return render(request, 'home.html')\r\n\r\ndef contact(request):\r\n return render(request, 'contact.html')\r\n\r\n@login_required\r\ndef dashboard(request):\r\n context = {\r\n \"points\": request.user.points,\r\n \"voters\": Voter.objects.filter(user=request.user).count(),\r\n \"pans\": PANCard.objects.filter(user=request.user).count()\r\n }\r\n if request.method == 'POST':\r\n points = request.POST.get('points')\r\n amount = PAY_AMOUNTS[points] * 100\r\n key = settings.RAZORPAY_KEY_ID\r\n secret = settings.RAZORPAY_KEY_SECRET\r\n\r\n client = razorpay.Client(auth=(key, secret))\r\n order_currency = 'INR'\r\n\r\n payment = client.order.create(data={\"amount\": amount, \"currency\": order_currency})\r\n\r\n new_payment = Payment(\r\n razorpay_order_id=payment['id'],\r\n amount=float(PAY_AMOUNTS[points]),\r\n user=request.user\r\n )\r\n new_payment.save()\r\n context['payment'] = payment\r\n context['key'] = key\r\n return render(request, 'main/dashboard.html', context)\r\n return render(request, 'main/dashboard.html', context)\r\n\r\n@login_required\r\n@csrf_exempt\r\ndef success(request):\r\n if request.method == 'GET':\r\n messages.warning(request, 'You can not access.')\r\n return redirect('dashboard')\r\n razorpay_order_id = request.POST.get(\"razorpay_order_id\")\r\n payment = Payment.objects.filter(razorpay_order_id=razorpay_order_id).first()\r\n if payment.paid:\r\n messages.success(request, \"Payment completed\")\r\n return redirect(\"dashboard\")\r\n payment.paid = True\r\n payment.save()\r\n user = payment.user\r\n user.points = user.points + POINTS_AMOUNTS[str(int(payment.amount))]\r\n user.save()\r\n context = {\r\n \"oreder_id\": payment.razorpay_order_id,\r\n \"user\": payment.user,\r\n \"amount\": payment.amount,\r\n \"status\": 'success',\r\n \"timestamp\": payment.created\r\n }\r\n return render(request, 'main/success.html', context)\r\n\r\n@login_required\r\ndef upload_voter(request):\r\n if protect_access(request):\r\n messages.warning(request, \"You have no points to take any print.\")\r\n return redirect(\"dashboard\")\r\n\r\n if request.method == 'POST':\r\n data = request.FILES.get('voter', None)\r\n if not data:\r\n return render(request, 'main/upload-voter.html')\r\n soup = BeautifulSoup(data, 'lxml')\r\n voter = etree.HTML(str(soup))\r\n state = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[2]/td[2]')[0].text\r\n block = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[3]/td[2]')[0].text\r\n subblock = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[4]/td[2]')[0].text\r\n name1 = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[6]/td')[0].text\r\n name2 = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[5]/td[2]')[0].text\r\n gender = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[7]/td[2]')[0].text\r\n epic = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[8]/td[2]')[0].text\r\n gname1 = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[10]/td')[0].text\r\n gname2 = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[9]/td[2]')[0].text\r\n partno = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[11]/td[2]')[0].text\r\n partname = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[12]/td[2]')[0].text\r\n serialno = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[13]/td[2]')[0].text\r\n polling_station = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[14]/td[2]')[0].text\r\n guardian_title = voter.xpath(\r\n '//*[@id=\"ng-app\"]/body/div[4]/div/div[1]/form/table/tbody/tr[9]/td[1]')[0].text\r\n \r\n if Voter.objects.filter(epic=epic).exists():\r\n messages.warning(request, \"This voter card is already downloaded.\")\r\n return redirect('voters-list')\r\n\r\n path = settings.BASE_DIR / \"media/barcodes/\"\r\n code128.image(epic).save(os.path.join(path, f\"{name1+gname1}.png\"))\r\n sp = block.split(\" \")\r\n spblock = f'{sp[2]} {sp[1]} {sp[0]}'\r\n blck2 = f\"{sp[2]} {sp[1]} {EngtoHindi(sp[0]).convert}\"\r\n partname2 = EngtoHindi(partname).convert\r\n voter_data = Voter(\r\n epic=epic,\r\n name1=name1,\r\n name2=name2,\r\n state=state,\r\n blck1=spblock,\r\n blck2=blck2,\r\n subblock=subblock,\r\n gender=gender,\r\n gname1=gname1,\r\n gname2=gname2,\r\n partname1=partname,\r\n partname2=partname2,\r\n partno=partno,\r\n serialno=serialno,\r\n barcode=f\"barcodes/{name1+gname1}.png\",\r\n guardian_title=guardian_title.split('/')[1].strip(),\r\n user=request.user,\r\n )\r\n voter_data.save()\r\n user = request.user\r\n user.points = user.points - 1\r\n user.save()\r\n messages.success(\r\n request, \"Voter card added successfully. Please update it before print.\")\r\n return redirect(\"voters-list\")\r\n return render(request, 'main/upload-voter.html')\r\n\r\n@login_required\r\ndef fill_voter(request, id):\r\n if protect_access(request):\r\n messages.warning(request, \"You have no points to take any print.\")\r\n return redirect(\"dashboard\")\r\n voter = get_object_or_404(Voter, id=id)\r\n if request.method == \"POST\":\r\n add1 = request.POST.get(\"add1\", voter.address1)\r\n add2 = request.POST.get(\"add2\", voter.address2)\r\n birth = request.POST.get(\"birth\", voter.birth)\r\n blck2 = request.POST.get(\"blck2\", voter.blck2)\r\n partname2 = request.POST.get(\"partname2\", voter.partname2)\r\n photo = request.FILES.get(\"photo\", voter.photo)\r\n \r\n if add1 == 'None' or add1 == '' or photo == '' or birth == '':\r\n messages.warning(\r\n request, \"Please update address1, address2, date of birth and photo\")\r\n else:\r\n voter.address1 = add1\r\n voter.address2 = add2\r\n voter.photo = photo\r\n voter.birth = birth\r\n voter.blck2 = blck2\r\n voter.partname2 = partname2\r\n voter.partname2 = partname2\r\n voter.save()\r\n messages.success(request, \"Voter updated. Please check and return to voters list\")\r\n context = {\r\n \"voter\": voter\r\n }\r\n if voter.address2 == \"None\" or voter.address2 == \"\":\r\n res = EngtoHindi(voter.address1).convert\r\n context['address2'] = res\r\n return render(request, 'main/fill-voter.html', context)\r\n\r\n@login_required\r\ndef delete_voter(request, id):\r\n if protect_access(request):\r\n messages.warning(request, \"You have no points to take any print.\")\r\n return redirect(\"dashboard\")\r\n\r\n voter = get_object_or_404(Voter, id=id)\r\n voter.delete()\r\n messages.success(request, \"Voter deleted successfully.\")\r\n return redirect(\"voters-list\")\r\n\r\n@login_required\r\ndef voters_list(request):\r\n voters = Voter.objects.filter(user=request.user)\r\n return render(request, 'main/voters.html', {'voters': voters})\r\n\r\n@login_required\r\ndef generate_pdf(request, id):\r\n voter = get_object_or_404(Voter, id=id)\r\n if voter.address1 == '' or voter.address2 == '' or voter.birth == '' or voter.photo == '':\r\n messages.warning(request, \"Please update Address, Birth and Image.\")\r\n return redirect(f\"/fill-voter/{voter.id}\")\r\n context = {\r\n \"voter\": voter,\r\n \"date\": date.today().strftime(\"%d/%m/%Y\")\r\n }\r\n return render(request, \"voter.html\", context)\r\n\r\n\r\n@login_required\r\ndef pan_list(request):\r\n pans = PANCard.objects.filter(user=request.user)\r\n return render(request, \"main/pan-list.html\", {'pans': pans})\r\n\r\n@login_required\r\ndef new_pan(request):\r\n if protect_access(request):\r\n messages.warning(request, \"You have no points to take any print.\")\r\n return redirect(\"dashboard\")\r\n if request.method == 'POST':\r\n name = request.POST.get('name')\r\n fname = request.POST.get('fname')\r\n birth = request.POST.get('birth')\r\n pan = request.POST.get('pan')\r\n photo = request.FILES.get('photo')\r\n sign = request.FILES.get('sign')\r\n new_pan = PANCard(\r\n pan=pan,\r\n name=name,\r\n fname=fname,\r\n birth=birth,\r\n photo=photo,\r\n sign=sign,\r\n user=request.user\r\n )\r\n new_pan.save()\r\n user = request.user\r\n user.points = user.points - 1\r\n user.save()\r\n messages.success(request, \"PAN card created successfully.\")\r\n return redirect('pan-list')\r\n return render(request, \"main/new-pan.html\")\r\n\r\n@login_required\r\ndef pan_pdf(request, pk):\r\n pan = get_object_or_404(PANCard, pk=pk)\r\n return render(request, \"pan.html\", {'pan': pan})", "sub_path": "main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 10008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "main.models.Voter.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "main.models.Voter.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "main.models.Voter", "line_number": 31, "usage_type": "name"}, {"api_name": "main.models.PANCard.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "main.models.PANCard.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "main.models.PANCard", "line_number": 32, "usage_type": "name"}, {"api_name": "const.PAY_AMOUNTS", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.settings.RAZORPAY_KEY_ID", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.settings.RAZORPAY_KEY_SECRET", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 38, "usage_type": "name"}, {"api_name": "razorpay.Client", "line_number": 40, "usage_type": "call"}, {"api_name": "main.models.Payment", "line_number": 45, "usage_type": "call"}, {"api_name": "const.PAY_AMOUNTS", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "main.models.Payment.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "main.models.Payment.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "main.models.Payment", "line_number": 63, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 65, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 65, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 66, "usage_type": "call"}, {"api_name": "const.POINTS_AMOUNTS", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 56, "usage_type": "name"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 57, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 91, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 92, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 92, "usage_type": "name"}, {"api_name": "main.models.Voter.objects.filter", "line_number": 122, "usage_type": "call"}, {"api_name": "main.models.Voter.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "main.models.Voter", "line_number": 122, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 123, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 126, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 126, "usage_type": "name"}, {"api_name": "code128.image", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "englisttohindi.englisttohindi.EngtoHindi", "line_number": 130, "usage_type": "call"}, {"api_name": "englisttohindi.englisttohindi.EngtoHindi", "line_number": 131, "usage_type": "call"}, {"api_name": "main.models.Voter", "line_number": 132, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 155, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 155, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 157, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 158, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 81, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 163, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 163, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 165, "usage_type": "call"}, {"api_name": "main.models.Voter", "line_number": 165, "usage_type": "argument"}, {"api_name": "django.contrib.messages.warning", "line_number": 175, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 175, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 186, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 186, "usage_type": "name"}, {"api_name": "englisttohindi.englisttohindi.EngtoHindi", "line_number": 191, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 193, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 160, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 198, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 198, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 199, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 201, "usage_type": "call"}, {"api_name": "main.models.Voter", "line_number": 201, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 203, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 203, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 204, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 195, "usage_type": "name"}, {"api_name": "main.models.Voter.objects.filter", "line_number": 208, "usage_type": "call"}, {"api_name": "main.models.Voter.objects", "line_number": 208, "usage_type": "attribute"}, {"api_name": "main.models.Voter", "line_number": 208, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 209, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 206, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 213, "usage_type": "call"}, {"api_name": "main.models.Voter", "line_number": 213, "usage_type": "argument"}, {"api_name": "django.contrib.messages.warning", "line_number": 215, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 215, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 216, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 219, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 221, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 211, "usage_type": "name"}, {"api_name": "main.models.PANCard.objects.filter", "line_number": 226, "usage_type": "call"}, {"api_name": "main.models.PANCard.objects", "line_number": 226, "usage_type": "attribute"}, {"api_name": "main.models.PANCard", "line_number": 226, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 227, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 224, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 232, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 232, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 233, "usage_type": "call"}, {"api_name": "main.models.PANCard", "line_number": 241, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 254, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 254, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 255, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 256, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 229, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 260, "usage_type": "call"}, {"api_name": "main.models.PANCard", "line_number": 260, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 261, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 258, "usage_type": "name"}]} {"seq_id": "297598567", "text": "from maddpg.MADDPG import MADDPG\nimport numpy as np\nimport torch as th\nfrom maddpg.params import scale_reward\nimport gym\nimport ma_gym\n\n# do not render the scene\n\nenv_name = 'PredatorPrey5x5-v0'\n#random_seed = 543\n#torch.manual_seed(random_seed)\nenv = gym.make(env_name)\n\nreward_record = []\n\nnp.random.seed(1234)\nth.manual_seed(1234)\n\nn_agents = env.n_agents\nn_actions = env.action_space[0].n\nn_states = env.observation_space[0].shape[0]\n\ncapacity = 1000000\nbatch_size = 1000\n\nn_episode = 2000\nmax_steps = 100\nepisodes_before_train = 100\n\nwin = None\nparam = None\n\nmaddpg = MADDPG(n_agents, n_states, n_actions, batch_size, capacity,\n episodes_before_train)\n\nFloatTensor = th.cuda.FloatTensor if maddpg.use_cuda else th.FloatTensor\nfor i_episode in range(n_episode):\n obs = env.reset()\n obs = np.stack(obs)\n if isinstance(obs, np.ndarray):\n obs = th.from_numpy(obs).float()\n total_reward = 0.0\n rr = np.zeros((n_agents,))\n for t in range(max_steps):\n # render every 100 episodes to speed up training\n obs = obs.type(FloatTensor)\n action = maddpg.select_action(obs).data.cpu()\n obs_, reward, done, _ = env.step(action.numpy())\n\n reward = th.FloatTensor(reward).type(FloatTensor)\n obs_ = np.stack(obs_)\n obs_ = th.from_numpy(obs_).float()\n if t != max_steps - 1:\n next_obs = obs_\n else:\n next_obs = None\n\n total_reward += reward.sum()\n rr += reward.cpu().numpy()\n maddpg.memory.push(obs.data, action, next_obs, reward)\n obs = next_obs\n c_loss, a_loss = maddpg.update_policy()\n maddpg.episode_done += 1\n print('Episode: %d, reward = %f' % (i_episode, total_reward))\n reward_record.append(total_reward)\n\nnp.save('rewards_predator', reward_record)", "sub_path": "maddpg/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "gym.make", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 18, "usage_type": "call"}, {"api_name": "maddpg.MADDPG", "line_number": 34, "usage_type": "name"}, {"api_name": "maddpg.MADDPG.MADDPG", "line_number": 34, "usage_type": "call"}, {"api_name": "maddpg.MADDPG.use_cuda", "line_number": 37, "usage_type": "attribute"}, {"api_name": "maddpg.MADDPG", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.cuda", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "maddpg.MADDPG.select_action", "line_number": 48, "usage_type": "call"}, {"api_name": "maddpg.MADDPG", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}, {"api_name": "maddpg.MADDPG.memory.push", "line_number": 61, "usage_type": "call"}, {"api_name": "maddpg.MADDPG.memory", "line_number": 61, "usage_type": "attribute"}, {"api_name": "maddpg.MADDPG", "line_number": 61, "usage_type": "name"}, {"api_name": "maddpg.MADDPG.update_policy", "line_number": 63, "usage_type": "call"}, {"api_name": "maddpg.MADDPG", "line_number": 63, "usage_type": "name"}, {"api_name": "maddpg.MADDPG.episode_done", "line_number": 64, "usage_type": "attribute"}, {"api_name": "maddpg.MADDPG", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 68, "usage_type": "call"}]} {"seq_id": "468417625", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed May 18 15:19:00 2016\r\n\r\n@author: Ge\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport scipy.optimize as sco\r\n\r\ndata = pd.read_excel(\"C:\\\\Users\\\\Administrator.PC-20150227RCLX\\\\Desktop\\\\fund database\\\\trading funds.xlsx\")\r\ndataC = data[['HKJF12','HKJUP003','HKPREEF','HKS035','HKUL00005']]\r\ndataC = dataC.tail(8 * 365)\r\ndataC = dataC.fillna(method = 'backfill')\r\nrets = np.log(dataC / dataC.shift(1))\r\nCov = rets.cov() * 250\r\nMean = rets.mean() * 250\r\nprets = []\r\npvols = []\r\nfor p in range(2500):\r\n weights = np.random.random(5)\r\n weights /= np.sum(weights)\r\n prets.append(np.sum(Mean * weights))\r\n pvols.append(np.sqrt(np.dot(weights.T,np.dot(Cov,weights)))) \r\nprets = np.array(prets)\r\npvols = np.array(pvols)\r\nplt.figure(figsize=(8,4))\r\nplt.scatter(pvols,prets, c=prets/pvols, marker='o')\r\nplt.grid(True)\r\nplt.xlabel('expected volatility')\r\nplt.ylabel('expected return')\r\nplt.colorbar(label='Sharpe ratio')\r\ndef statf(weights):\r\n weights = np.array(weights)\r\n pret = np.sum(Mean * weights)\r\n pvol = np.sqrt(np.dot(weights.T,np.dot(Cov,weights)))\r\n return np.array([pret,pvol,pret/pvol])\r\ndef min_sharpef(weights):\r\n return -statf(weights)[-1]\r\ncons = ({'type':'eq','fun':lambda x: np.sum(x) - 1})\r\nbnds = tuple((0,1) for x in range(5))\r\np = [0.2,0.2,0.2,0.2,0.2]\r\nopts = sco.minimize(min_sharpef,p,method='SLSQP',bounds=bnds,constraints=cons)\r\nopts['x'].round(3)\r\nstatf(opts['x'].round(3)) \r\n ", "sub_path": "MPT.py", "file_name": "MPT.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_excel", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 44, "usage_type": "name"}]} {"seq_id": "576171018", "text": "from conftest import QL_URL, crate_translator as translator\nfrom datetime import datetime\nfrom reporter.tests.utils import insert_test_data\nfrom utils.common import assert_equal_time_index_arrays\nimport pytest\nimport requests\nimport pdb\n\nentity_type = 'Room'\nattr_name_1 = 'temperature'\nattr_name_2 = 'pressure'\nn_days = 6\n\n\ndef query_url(values=False):\n url = \"{qlUrl}/types/{entityType}\"\n if values:\n url += '/value'\n return url.format(\n qlUrl=QL_URL,\n entityType=entity_type\n )\n\n@pytest.fixture()\ndef reporter_dataset(translator):\n insert_test_data(translator, [entity_type], n_entities=3, index_size=n_days)\n yield\n\n\ndef assert_1TNENA_response(obtained, expected, values_only=False):\n \"\"\"\n Check API responses for 1TNENA\n \"\"\"\n assert isinstance(obtained, dict)\n if not values_only:\n assert obtained['entityType'] == entity_type\n obt_entities_index = obtained['entities'][0]['index']\n exp_entities_index = expected['entities'][0]['index']\n else:\n obt_entities_index = obtained['values'][0]['index']\n exp_entities_index = expected['values'][0]['index']\n\n assert_equal_time_index_arrays(obt_entities_index, exp_entities_index)\n\n assert obtained == expected\n\ndef test_1TNENA_defaults(reporter_dataset):\n r = requests.get(query_url())\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_temperatures = list(range(n_days))\n expected_pressures = [t*10 for t in expected_temperatures]\n expected_index = [\n '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures\n ]\n expected_attributes = [\n {\n 'attrName': attr_name_2,\n 'values' : expected_pressures\n },\n {\n 'attrName': attr_name_1,\n 'values' : expected_temperatures\n }\n ]\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': expected_index\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': expected_index\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room2',\n 'index': expected_index\n }\n ]\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert_1TNENA_response(obtained, expected)\n\ndef test_1TNENA_one_entity(reporter_dataset):\n # Query\n entity_id = 'Room1'\n query_params = {\n 'id': entity_id\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n obtained_data = r.json()\n assert isinstance(obtained_data, dict)\n\n expected_temperatures = list(range(n_days))\n expected_pressures = [t*10 for t in expected_temperatures]\n expected_index = [\n '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures\n ]\n\n expected_attributes = [\n {\n 'attrName': attr_name_2,\n 'values' : expected_pressures\n },\n {\n 'attrName': attr_name_1,\n 'values' : expected_temperatures\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': expected_index\n }\n ]\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n obtained = r.json()\n assert_1TNENA_response(obtained, expected)\n\ndef test_1TNENA_some_entities(reporter_dataset):\n # Query\n entity_id = 'Room0,Room2'\n query_params = {\n 'id': entity_id\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_temperatures = list(range(n_days))\n expected_pressures = [t*10 for t in expected_temperatures]\n expected_index = [\n '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures\n ]\n\n expected_attributes = [\n {\n 'attrName': attr_name_2,\n 'values' : expected_pressures\n },\n {\n 'attrName': attr_name_1,\n 'values' : expected_temperatures\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': expected_index\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room2',\n 'index': expected_index\n },\n ]\n\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert_1TNENA_response(obtained, expected)\n\ndef test_1TNENA_values_defaults(reporter_dataset):\n # Query\n query_params = {\n 'id': 'Room0,,Room1,RoomNotValid', # -> validates to Room0,Room1.\n }\n r = requests.get(query_url(values=True), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_temperatures = list(range(n_days))\n expected_pressures = [t*10 for t in expected_temperatures]\n expected_index = [\n '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures\n ]\n\n expected_attributes = [\n {\n 'attrName': attr_name_2,\n 'values' : expected_pressures\n },\n {\n 'attrName': attr_name_1,\n 'values' : expected_temperatures\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': expected_index\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': expected_index\n },\n ]\n\n expected = {\n 'values': expected_entities\n }\n\n obtained = r.json()\n assert_1TNENA_response(obtained, expected, values_only=True)\n\ndef test_not_found():\n query_params = {\n 'id': 'RoomNotValid'\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 404, r.text\n assert r.json() == {\n \"error\": \"Not Found\",\n \"description\": \"No records were found for such query.\"\n }\n\ndef test_weird_ids(reporter_dataset):\n \"\"\"\n Invalid ids are ignored (provided at least one is valid to avoid 404).\n Empty values are ignored.\n Order of ids is preserved in response (e.g., Room1 first, Room0 later)\n \"\"\"\n query_params = {\n 'id': 'Room1,RoomNotValid,,Room0,', # -> validates to Room0,Room1.\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_temperatures = list(range(n_days))\n expected_pressures = [t*10 for t in expected_temperatures]\n expected_index = [\n '1970-01-{:02}T00:00:00.000'.format(i+1) for i in expected_temperatures\n ]\n\n expected_attributes = [\n {\n 'attrName': attr_name_2,\n 'values' : expected_pressures\n },\n {\n 'attrName': attr_name_1,\n 'values' : expected_temperatures\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': expected_index\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': expected_index\n },\n ]\n\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert_1TNENA_response(obtained, expected)\n\ndef test_aggregation_is_per_instance(translator):\n \"\"\"\n Attribute Aggregation works by default on a per-instance basis.\n Cross-instance aggregation not yet supported.\n It would change the shape of the response.\n \"\"\"\n t = 'Room'\n insert_test_data(translator, [t], entity_id='Room0', index_size=3)\n insert_test_data(translator, [t], entity_id='Room1', index_size=3)\n\n query_params = {\n 'attrs': 'temperature',\n 'id': 'Room0,Room1',\n 'aggrMethod': 'sum'\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_attributes = [\n {\n 'attrName': attr_name_1,\n 'values' : [sum(range(3))]\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': ['', '']\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': ['', '']\n }\n ]\n\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert isinstance(obtained, dict)\n assert obtained == expected\n\n\n # Index array in the response is the used fromDate and toDate\n query_params = {\n 'attrs': 'temperature',\n 'id': 'Room0,Room1',\n 'aggrMethod': 'max',\n 'fromDate': datetime(1970, 1, 1).isoformat(),\n 'toDate': datetime(1970, 1, 6).isoformat(),\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n expected_attributes = [\n {\n 'attrName': attr_name_1,\n 'values' : [2]\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': ['1970-01-01T00:00:00', '1970-01-06T00:00:00']\n },\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room1',\n 'index': ['1970-01-01T00:00:00', '1970-01-06T00:00:00']\n }\n ]\n\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert isinstance(obtained, dict)\n assert obtained == expected\n\n@pytest.mark.parametrize(\"aggr_period, exp_index, ins_period\", [\n (\"day\", ['1970-01-01T00:00:00.000',\n '1970-01-02T00:00:00.000',\n '1970-01-03T00:00:00.000'], \"hour\"),\n (\"hour\", ['1970-01-01T00:00:00.000',\n '1970-01-01T01:00:00.000',\n '1970-01-01T02:00:00.000'], \"minute\"),\n (\"minute\", ['1970-01-01T00:00:00.000',\n '1970-01-01T00:01:00.000',\n '1970-01-01T00:02:00.000'], \"second\"),\n])\ndef test_1TNENA_aggrPeriod(translator, aggr_period, exp_index, ins_period):\n # Custom index to test aggrPeriod\n for i in exp_index:\n base = datetime.strptime(i, \"%Y-%m-%dT%H:%M:%S.%f\")\n insert_test_data(translator,\n [entity_type],\n index_size=5,\n index_base=base,\n index_period=ins_period)\n\n # aggrPeriod needs aggrMethod\n query_params = {\n 'aggrPeriod': aggr_period,\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 400, r.text\n\n # Check aggregation with aggrPeriod\n query_params = {\n 'attrs': 'temperature',\n 'aggrMethod': 'sum',\n 'aggrPeriod': aggr_period,\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 200, r.text\n\n # Assert Results\n exp_sum = 0 + 1 + 2 + 3 + 4\n\n expected_attributes = [\n {\n 'attrName': attr_name_1,\n 'values' : [exp_sum, exp_sum, exp_sum]\n }\n ]\n\n expected_entities = [\n {\n 'attributes': expected_attributes,\n 'entityId': 'Room0',\n 'index': exp_index\n }\n ]\n\n expected = {\n 'entities': expected_entities,\n 'entityType': entity_type\n }\n\n obtained = r.json()\n assert isinstance(obtained, dict)\n assert_1TNENA_response(obtained, expected)\n\ndef test_1TNENA_aggrScope(reporter_dataset):\n # Notify users when not yet implemented\n query_params = {\n 'aggrMethod': 'avg',\n 'aggrScope': 'global',\n }\n r = requests.get(query_url(), params=query_params)\n assert r.status_code == 501, r.text\n\n", "sub_path": "src/reporter/tests/test_1TNENA.py", "file_name": "test_1TNENA.py", "file_ext": "py", "file_size_in_byte": 12161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "conftest.QL_URL", "line_number": 20, "usage_type": "name"}, {"api_name": "reporter.tests.utils.insert_test_data", "line_number": 26, "usage_type": "call"}, {"api_name": "conftest.crate_translator", "line_number": 26, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.common.assert_equal_time_index_arrays", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 98, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 141, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 188, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 233, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 249, "usage_type": "call"}, {"api_name": "reporter.tests.utils.insert_test_data", "line_number": 298, "usage_type": "call"}, {"api_name": "conftest.crate_translator", "line_number": 298, "usage_type": "argument"}, {"api_name": "reporter.tests.utils.insert_test_data", "line_number": 299, "usage_type": "call"}, {"api_name": "conftest.crate_translator", "line_number": 299, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 306, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 345, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 346, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 348, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 395, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 395, "usage_type": "name"}, {"api_name": "reporter.tests.utils.insert_test_data", "line_number": 396, "usage_type": "call"}, {"api_name": "conftest.crate_translator", "line_number": 396, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 406, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 415, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 381, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 381, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 451, "usage_type": "call"}]} {"seq_id": "539576883", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]\n# Embedded file name: T:\\InGame\\Gameplay\\Scripts\\Server\\situations\\complex\\mother_plant_battle.py\n# Compiled at: 2020-09-17 02:52:35\n# Size of source mod 2**32: 26813 bytes\nimport itertools, random\nfrom buffs.tunable import TunableBuffReference\nfrom date_and_time import create_time_span\nfrom distributor.rollback import ProtocolBufferRollback\nfrom distributor.shared_messages import build_icon_info_msg, IconInfoData\nfrom event_testing.test_events import TestEvent\nfrom interactions.aop import AffordanceObjectPair\nfrom interactions.context import InteractionContext, QueueInsertStrategy, InteractionSource\nfrom interactions.priority import Priority\nfrom objects.components.state import TunableStateValueReference\nfrom sims4.localization import TunableLocalizedString\nfrom sims4.tuning.instances import lock_instance_tunables\nfrom sims4.tuning.tunable import TunableReference, TunableSimMinute, TunableResourceKey, TunableList\nfrom sims4.tuning.tunable_base import GroupNames\nfrom situations.base_situation import SituationDisplayPriority, _RequestUserData\nfrom situations.bouncer.bouncer_request import SelectableSimRequestFactory\nfrom situations.bouncer.bouncer_types import BouncerRequestPriority\nfrom situations.complex.mother_plant_battle_ops import MotherplantBattleStates\nfrom situations.situation_complex import SituationComplexCommon, SituationState, CommonSituationState, SituationStateData, TunableInteractionOfInterest\nfrom situations.situation_meter import StatBasedSituationMeterData\nfrom situations.situation_types import SituationDisplayType, SituationUserFacingType\nimport alarms, interactions, services, sims4.resources\nlogger = sims4.log.Logger('Situations', default_owner='jjacobson')\n\nclass PrepareForBattleSituationState(SituationState):\n\n def _on_set_sim_role_state(self, sim, *args, **kwargs):\n (super()._on_set_sim_role_state)(sim, *args, **kwargs)\n if self.owner.num_of_sims >= len(self.owner._guest_list):\n self.owner._change_state(self.owner.base_battle_situation_state())\n\n @property\n def zombie_attack_valid(self):\n return False\n\n def _get_role_state_overrides(self, sim, job_type, role_state_type, role_affordance_target):\n motherplant = self.owner._get_motherplant()\n return (role_state_type, motherplant)\n\n\nclass BattleThePlantSituationState(CommonSituationState):\n\n @property\n def zombie_attack_valid(self):\n return True\n\n\nclass AttackBattleThePlantSituationState(CommonSituationState):\n\n @property\n def zombie_attack_valid(self):\n return True\n\n def timer_expired(self):\n self.owner._change_state(self.owner.base_battle_situation_state())\n\n\nclass InspireBattleThePlantSituationState(CommonSituationState):\n\n @property\n def zombie_attack_valid(self):\n return True\n\n def timer_expired(self):\n self.owner._change_state(self.owner.base_battle_situation_state())\n\n\nclass RallyBattleThePlantSituationState(CommonSituationState):\n\n @property\n def zombie_attack_valid(self):\n return True\n\n def timer_expired(self):\n self.owner._change_state(self.owner.base_battle_situation_state())\n\n\nclass WarblingWarcryBattleThePlantSituationState(CommonSituationState):\n\n @property\n def zombie_attack_valid(self):\n return False\n\n def timer_expired(self):\n self.owner._change_state(self.owner.base_battle_situation_state())\n\n\nclass MotherPlantBattleSituation(SituationComplexCommon):\n MOTHER_PLANT_METER_ID = 1\n PLAYER_HEALTH_METER_ID = 2\n INSTANCE_TUNABLES = {'player_job':TunableReference(description='\\n Job for the main player sim that fights the plant.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'player_sim_role_state':TunableReference(description='\\n Role state for the main player sim Role.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'other_player_jobs':TunableReference(description='\\n Job for the other player Sims that are not the main Sim and are not\\n participating as helpers.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'other_player_sims_role_state':TunableReference(description='\\n Role state for the other player Sims.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'helper_1_job':TunableReference(description='\\n Job for one of the helper Sims for the fight.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'helper_2_job':TunableReference(description='\\n Job for one of the helper Sims for the fight.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'helper_3_job':TunableReference(description='\\n Job for one of the helper Sims for the fight.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'helper_sim_prepare_role_state_1':TunableReference(description='\\n Role state for helper Sim 1 when preparing for battle.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'helper_sim_prepare_role_state_2':TunableReference(description='\\n Role state for helper Sim 2 when preparing for battle.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'helper_sim_prepare_role_state_3':TunableReference(description='\\n Role state for helper Sim 3 when preparing for battle.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'zombie_job':TunableReference(description='\\n Job for the Zombies for the fight.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.SITUATION_JOB)), \n 'zombie_prepare_role_state':TunableReference(description='\\n Role state for the zombie Sims when preparing for battle.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.ROLE_STATE)), \n 'zombie_fight_interaction':TunableReference(description='\\n Interaction pushed on zombies to get them to fight a Sim.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.INTERACTION)), \n 'zombie_fight_interaction_timer':TunableSimMinute(description='\\n Timer for the amount of time between zombie attacks.\\n ',\n minimum=1,\n default=30), \n 'player_health_statistic':TunableReference(description=\"\\n The statistic that we will use in order to determine the Sim's\\n health for the motherplant.\\n \",\n manager=services.get_instance_manager(sims4.resources.Types.STATISTIC)), \n 'motherplant_health_statisic':TunableReference(description=\"\\n The statistic that we will use in order to determine the Sim's\\n health for the motherplant.\\n \",\n manager=services.get_instance_manager(sims4.resources.Types.STATISTIC)), \n 'victory_interaction_of_interest':TunableInteractionOfInterest(description='\\n The interaction of interest that we are looking for to determine\\n victory.\\n '), \n 'retreat_interaction_of_interest':TunableInteractionOfInterest(description='\\n The interaction of interest that we are looking for to determine\\n retreat.\\n '), \n 'loss_interaction_mixer':TunableReference(description='\\n The affordance that will be pushed on the primary Sims if they\\n lose.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.INTERACTION)), \n 'fight_affordance':TunableReference(description='\\n The primary fight interaction that we will use to run the defeat\\n mixer the player Sim.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.INTERACTION)), \n 'helper_victory_affordance':TunableReference(description='\\n The affordance that will be pushed on the helper Sims if they\\n achieve victory.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.INTERACTION)), \n 'helper_lose_affordance':TunableReference(description='\\n The affordance that will be pushed on the helper Sims if they\\n lose.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.INTERACTION)), \n 'mother_plant_definition':TunableReference(description='\\n The actual mother plant itself.\\n ',\n manager=services.definition_manager()), \n 'base_battle_situation_state':BattleThePlantSituationState.TunableFactory(locked_args={'allow_join_situation':True, \n 'time_out':None},\n tuning_group=GroupNames.STATE), \n 'attack_battle_situation_state':AttackBattleThePlantSituationState.TunableFactory(locked_args={'allow_join_situation': True},\n tuning_group=GroupNames.STATE), \n 'inspire_battle_situation_state':InspireBattleThePlantSituationState.TunableFactory(locked_args={'allow_join_situation': True},\n tuning_group=GroupNames.STATE), \n 'rally_battle_sitaution_state':RallyBattleThePlantSituationState.TunableFactory(locked_args={'allow_join_situation': True},\n tuning_group=GroupNames.STATE), \n 'warbling_warcry_battle_situation_state':WarblingWarcryBattleThePlantSituationState.TunableFactory(locked_args={'allow_join_situation': True},\n tuning_group=GroupNames.STATE), \n 'save_lock_tooltip':TunableLocalizedString(description='\\n The tooltip/message to show when the player tries to save the game\\n while this situation is running. Save is locked when situation starts.\\n ',\n tuning_group=GroupNames.UI), \n 'mother_plant_meter_settings':StatBasedSituationMeterData.TunableFactory(description='\\n The meter used to track the health of the mother plant.\\n ',\n tuning_group=GroupNames.SITUATION,\n locked_args={'_meter_id': MOTHER_PLANT_METER_ID}), \n 'player_health_meter_settings':StatBasedSituationMeterData.TunableFactory(description='\\n The meter used to track the health of the player team.\\n ',\n tuning_group=GroupNames.SITUATION,\n locked_args={'_meter_id': PLAYER_HEALTH_METER_ID}), \n 'mother_plant_icon':TunableResourceKey(description='\\n Icon to be displayed in the situation UI beside the mother plant\\n health bar.\\n ',\n resource_types=sims4.resources.CompoundTypes.IMAGE,\n default=None,\n allow_none=True,\n tuning_group=GroupNames.SITUATION), \n 'states_to_set_on_start':TunableList(description='\\n A list of states to set on the motherplant on start.\\n ',\n tunable=TunableStateValueReference(description='\\n The state to set.\\n ')), \n 'states_to_set_on_end':TunableList(description='\\n A list of states to set on the motherplant on end.\\n ',\n tunable=TunableStateValueReference(description='\\n The state to set.\\n ')), \n 'victory_reward':TunableReference(description='\\n The Reward received when the Sim wins the situation.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.REWARD)), \n 'victory_audio_sting':TunableResourceKey(description='\\n The sound to play when the Sim wins the battle.\\n ',\n resource_types=(\n sims4.resources.Types.PROPX,),\n default=None,\n tuning_group=GroupNames.AUDIO), \n 'defeat_audio_sting':TunableResourceKey(description='\\n The sound to play when the Sim loses the battle.\\n ',\n resource_types=(\n sims4.resources.Types.PROPX,),\n default=None,\n tuning_group=GroupNames.AUDIO), \n 'possessed_buff':TunableBuffReference(description='\\n Possessed Buff for zombie Sims. \\n ')}\n\n @property\n def user_facing_type(self):\n return SituationUserFacingType.MOTHER_PLANT_EVENT\n\n @property\n def situation_display_type(self):\n return SituationDisplayType.VET\n\n @property\n def situation_display_priority(self):\n return SituationDisplayPriority.HIGH\n\n @classmethod\n def _states(cls):\n return (SituationStateData(1, PrepareForBattleSituationState),\n SituationStateData.from_auto_factory(2, cls.base_battle_situation_state),\n SituationStateData.from_auto_factory(3, cls.attack_battle_situation_state),\n SituationStateData.from_auto_factory(4, cls.inspire_battle_situation_state),\n SituationStateData.from_auto_factory(5, cls.rally_battle_sitaution_state),\n SituationStateData.from_auto_factory(6, cls.warbling_warcry_battle_situation_state))\n\n @classmethod\n def default_job(cls):\n pass\n\n @classmethod\n def _get_tuned_job_and_default_role_state_tuples(cls):\n return ((cls.player_job, cls.player_sim_role_state),\n (\n cls.other_player_jobs, cls.other_player_sims_role_state),\n (\n cls.helper_1_job, cls.helper_sim_prepare_role_state_1),\n (\n cls.helper_2_job, cls.helper_sim_prepare_role_state_2),\n (\n cls.helper_3_job, cls.helper_sim_prepare_role_state_3),\n (\n cls.zombie_job, cls.zombie_prepare_role_state))\n\n def __init__(self, *args, **kwargs):\n (super().__init__)(*args, **kwargs)\n self._zombie_attack_alarm_handle = None\n self._registered_test_events = set()\n self._player_health_tracking_situation_goal = None\n self._statistic_watcher_handle = None\n self._victory = False\n\n @property\n def end_audio_sting(self):\n if self._victory:\n return self.victory_audio_sting\n return self.defeat_audio_sting\n\n def _get_reward(self):\n if self._victory:\n return self.victory_reward\n\n def _get_motherplant(self):\n return next(iter(services.object_manager().get_objects_of_type_gen(self.mother_plant_definition)))\n\n def _push_loss_on_player(self):\n motherplant = self._get_motherplant()\n for sim, situation_sim in self._situation_sims.items():\n if situation_sim.current_job_type is self.player_job:\n parent_si = sim.si_state.get_si_by_affordance(self.fight_affordance)\n if parent_si is not None:\n interaction_context = InteractionContext(sim, InteractionSource.PIE_MENU, Priority.Critical)\n aop = AffordanceObjectPair(self.loss_interaction_mixer, motherplant, self.fight_affordance, parent_si)\n aop.test_and_execute(interaction_context) or logger.error('Attempting to push Motherplant Battle Ending Interaction, but failed.')\n\n self._push_interaction_on_all_helpers(self.helper_lose_affordance)\n\n def on_goal_completed(self, goal):\n super().on_goal_completed(goal)\n self._push_loss_on_player()\n self._self_destruct()\n\n def _on_set_sim_job(self, sim, job_type):\n super()._on_set_sim_job(sim, job_type)\n if job_type is self.zombie_job:\n sim.add_buff_from_op((self.possessed_buff.buff_type), buff_reason=(self.possessed_buff.buff_reason))\n\n def _on_statistic_updated(self, stat_type, old_value, new_value):\n if stat_type is self.player_health_statistic:\n self._player_health_tracking_situation_goal.set_count(new_value)\n self._player_health_meter.send_update_if_dirty()\n else:\n if stat_type is self.motherplant_health_statisic:\n self._mother_plant_meter.send_update_if_dirty()\n\n def _zombie_attack(self, _):\n if not self._cur_state.zombie_attack_valid:\n return\n zombies = []\n for sim, situation_sim in self._situation_sims.items():\n if situation_sim.current_job_type is self.zombie_job:\n zombies.append(sim)\n\n zombie_to_attack = random.choice(zombies)\n context = InteractionContext(sim, (InteractionContext.SOURCE_SCRIPT),\n (interactions.priority.Priority.High),\n insert_strategy=(QueueInsertStrategy.NEXT),\n bucket=(interactions.context.InteractionBucketType.DEFAULT))\n zombie_to_attack.push_super_affordance(self.zombie_fight_interaction, None, context)\n\n def _push_interaction_on_all_helpers(self, interaction_to_push):\n for sim, situation_sim in self._situation_sims.items():\n if situation_sim.current_job_type is self.helper_1_job or situation_sim.current_job_type is self.helper_2_job or situation_sim.current_job_type is self.helper_3_job:\n context = InteractionContext(sim, (InteractionContext.SOURCE_SCRIPT),\n (interactions.priority.Priority.High),\n insert_strategy=(QueueInsertStrategy.NEXT),\n bucket=(interactions.context.InteractionBucketType.DEFAULT))\n sim.push_super_affordance(interaction_to_push, None, context)\n\n def handle_event(self, sim_info, event, resolver):\n super().handle_event(sim_info, event, resolver)\n if event != TestEvent.InteractionComplete:\n return\n elif resolver(self.victory_interaction_of_interest):\n self._push_interaction_on_all_helpers(self.helper_victory_affordance)\n self._victory = True\n self._self_destruct()\n else:\n if resolver(self.retreat_interaction_of_interest):\n self._push_loss_on_player()\n self._self_destruct()\n\n def start_situation(self):\n services.get_persistence_service().lock_save(self)\n super().start_situation()\n self._change_state(PrepareForBattleSituationState())\n motherplant = self._get_motherplant()\n motherplant.set_stat_value((self.player_health_statistic), 0, add=True)\n motherplant.set_stat_value((self.motherplant_health_statisic), (self.motherplant_health_statisic.max_value), add=True)\n for state_value in self.states_to_set_on_start:\n motherplant.set_state(state_value.state, state_value)\n\n statistic_tracker = motherplant.statistic_tracker\n self._statistic_watcher_handle = statistic_tracker.add_watcher(self._on_statistic_updated)\n self._setup_situation_meters()\n self._zombie_attack_alarm_handle = alarms.add_alarm(self, create_time_span(minutes=(self.zombie_fight_interaction_timer)),\n (self._zombie_attack),\n repeating=True)\n for custom_key in itertools.chain(self.victory_interaction_of_interest.custom_keys_gen(), self.retreat_interaction_of_interest.custom_keys_gen()):\n custom_key_tuple = (\n TestEvent.InteractionComplete, custom_key)\n self._registered_test_events.add(custom_key_tuple)\n services.get_event_manager().register_with_custom_key(self, TestEvent.InteractionComplete, custom_key)\n\n def _setup_situation_meters(self):\n motherplant = self._get_motherplant()\n self._mother_plant_meter = self.mother_plant_meter_settings.create_meter_with_sim_info(self, motherplant)\n self._player_health_meter = self.player_health_meter_settings.create_meter_with_sim_info(self, motherplant)\n\n def build_situation_start_message(self):\n msg = super().build_situation_start_message()\n with ProtocolBufferRollback(msg.meter_data) as (meter_data_msg):\n self.mother_plant_meter_settings.build_data_message(meter_data_msg)\n with ProtocolBufferRollback(msg.meter_data) as (meter_data_msg):\n self.player_health_meter_settings.build_data_message(meter_data_msg)\n build_icon_info_msg(IconInfoData(icon_resource=(self.mother_plant_icon)), None, msg.icon_info)\n return msg\n\n def _destroy(self):\n super()._destroy()\n services.get_persistence_service().unlock_save(self)\n for event_type, custom_key in self._registered_test_events:\n services.get_event_manager().unregister_with_custom_key(self, event_type, custom_key)\n\n motherplant = self._get_motherplant()\n statistic_tracker = motherplant.statistic_tracker\n statistic_tracker.remove_watcher(self._statistic_watcher_handle)\n for state_value in self.states_to_set_on_end:\n motherplant.set_state(state_value.state, state_value)\n\n self._registered_test_events.clear()\n if self._mother_plant_meter is not None:\n self._mother_plant_meter.destroy()\n if self._player_health_meter is not None:\n self._player_health_meter.destroy()\n\n def get_lock_save_reason(self):\n return self.save_lock_tooltip\n\n def set_motherplant_situation_state(self, motherplant_battle_state):\n if motherplant_battle_state == MotherplantBattleStates.ATTACK:\n self._change_state(self.attack_battle_situation_state())\n else:\n if motherplant_battle_state == MotherplantBattleStates.INSPIRE:\n self._change_state(self.inspire_battle_situation_state())\n else:\n if motherplant_battle_state == MotherplantBattleStates.RALLY:\n self._change_state(self.rally_battle_sitaution_state())\n else:\n if motherplant_battle_state == MotherplantBattleStates.WARBLING_WARCRY:\n self._change_state(self.warbling_warcry_battle_situation_state())\n\n def _on_proxy_situation_goal_setup(self, goal):\n self._player_health_tracking_situation_goal = goal\n\n def _issue_requests(self):\n super()._issue_requests()\n request = SelectableSimRequestFactory(self, (_RequestUserData()),\n (self.other_player_jobs),\n (self.exclusivity),\n request_priority=(BouncerRequestPriority.EVENT_DEFAULT_JOB))\n self.manager.bouncer.submit_request(request)\n\n\nlock_instance_tunables(MotherPlantBattleSituation, audio_sting_on_start=None,\n main_goal_audio_sting=None)", "sub_path": "Scripts/simulation/situations/complex/mother_plant_battle.py", "file_name": "mother_plant_battle.py", "file_ext": "py", "file_size_in_byte": 22432, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sims4.localization.log.Logger", "line_number": 29, "usage_type": "call"}, {"api_name": "sims4.localization.log", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 29, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationState", "line_number": 31, "usage_type": "name"}, {"api_name": "situations.situation_complex.CommonSituationState", "line_number": 47, "usage_type": "name"}, {"api_name": "situations.situation_complex.CommonSituationState", "line_number": 54, "usage_type": "name"}, {"api_name": "situations.situation_complex.CommonSituationState", "line_number": 64, "usage_type": "name"}, {"api_name": "situations.situation_complex.CommonSituationState", "line_number": 74, "usage_type": "name"}, {"api_name": "situations.situation_complex.CommonSituationState", "line_number": 84, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationComplexCommon", "line_number": 94, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 97, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 98, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 98, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 99, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 100, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 100, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 100, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 101, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 102, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 102, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 103, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 104, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 104, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 104, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 105, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 106, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 106, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 107, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 108, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 108, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 109, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 110, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 110, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 110, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 111, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 112, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 112, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 113, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 114, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 114, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 114, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 115, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 116, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 116, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 117, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 118, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 118, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 119, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 120, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 120, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 121, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 122, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 122, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableSimMinute", "line_number": 123, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 126, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 127, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 127, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 128, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 129, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 129, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 129, "usage_type": "name"}, {"api_name": "situations.situation_complex.TunableInteractionOfInterest", "line_number": 130, "usage_type": "call"}, {"api_name": "situations.situation_complex.TunableInteractionOfInterest", "line_number": 131, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 132, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 133, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 133, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 133, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 134, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 135, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 135, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 136, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 137, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 137, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 137, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 138, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 139, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 139, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 140, "usage_type": "call"}, {"api_name": "services.definition_manager", "line_number": 141, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.STATE", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 144, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.STATE", "line_number": 146, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 146, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.STATE", "line_number": 148, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 148, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.STATE", "line_number": 150, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 150, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.STATE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 152, "usage_type": "name"}, {"api_name": "sims4.localization.TunableLocalizedString", "line_number": 153, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.UI", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 154, "usage_type": "name"}, {"api_name": "situations.situation_meter.StatBasedSituationMeterData.TunableFactory", "line_number": 155, "usage_type": "call"}, {"api_name": "situations.situation_meter.StatBasedSituationMeterData", "line_number": 155, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.SITUATION", "line_number": 156, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 156, "usage_type": "name"}, {"api_name": "situations.situation_meter.StatBasedSituationMeterData.TunableFactory", "line_number": 158, "usage_type": "call"}, {"api_name": "situations.situation_meter.StatBasedSituationMeterData", "line_number": 158, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.SITUATION", "line_number": 159, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 159, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableResourceKey", "line_number": 161, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 162, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 162, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.SITUATION", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 165, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableList", "line_number": 166, "usage_type": "call"}, {"api_name": "objects.components.state.TunableStateValueReference", "line_number": 167, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable.TunableList", "line_number": 168, "usage_type": "call"}, {"api_name": "objects.components.state.TunableStateValueReference", "line_number": 169, "usage_type": "call"}, {"api_name": "sims4.tuning.tunable.TunableReference", "line_number": 170, "usage_type": "call"}, {"api_name": "services.get_instance_manager", "line_number": 171, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 171, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableResourceKey", "line_number": 172, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 174, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 174, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.AUDIO", "line_number": 176, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 176, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable.TunableResourceKey", "line_number": 177, "usage_type": "call"}, {"api_name": "sims4.localization.resources", "line_number": 179, "usage_type": "attribute"}, {"api_name": "sims4.localization", "line_number": 179, "usage_type": "name"}, {"api_name": "sims4.tuning.tunable_base.GroupNames.AUDIO", "line_number": 181, "usage_type": "attribute"}, {"api_name": "sims4.tuning.tunable_base.GroupNames", "line_number": 181, "usage_type": "name"}, {"api_name": "buffs.tunable.TunableBuffReference", "line_number": 182, "usage_type": "call"}, {"api_name": "situations.situation_types.SituationUserFacingType.MOTHER_PLANT_EVENT", "line_number": 186, "usage_type": "attribute"}, {"api_name": "situations.situation_types.SituationUserFacingType", "line_number": 186, "usage_type": "name"}, {"api_name": "situations.situation_types.SituationDisplayType.VET", "line_number": 190, "usage_type": "attribute"}, {"api_name": "situations.situation_types.SituationDisplayType", "line_number": 190, "usage_type": "name"}, {"api_name": "situations.base_situation.SituationDisplayPriority.HIGH", "line_number": 194, "usage_type": "attribute"}, {"api_name": "situations.base_situation.SituationDisplayPriority", "line_number": 194, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 198, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData.from_auto_factory", "line_number": 199, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 199, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationStateData.from_auto_factory", "line_number": 200, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 200, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationStateData.from_auto_factory", "line_number": 201, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 201, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationStateData.from_auto_factory", "line_number": 202, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 202, "usage_type": "name"}, {"api_name": "situations.situation_complex.SituationStateData.from_auto_factory", "line_number": 203, "usage_type": "call"}, {"api_name": "situations.situation_complex.SituationStateData", "line_number": 203, "usage_type": "name"}, {"api_name": "services.object_manager", "line_number": 242, "usage_type": "call"}, {"api_name": "interactions.context.InteractionContext", "line_number": 250, "usage_type": "call"}, {"api_name": "interactions.context.InteractionSource.PIE_MENU", "line_number": 250, "usage_type": "attribute"}, {"api_name": "interactions.context.InteractionSource", "line_number": 250, "usage_type": "name"}, {"api_name": "interactions.priority.Priority.Critical", "line_number": 250, "usage_type": "attribute"}, {"api_name": "interactions.priority.Priority", "line_number": 250, "usage_type": "name"}, {"api_name": "interactions.aop.AffordanceObjectPair", "line_number": 251, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 282, "usage_type": "call"}, {"api_name": "interactions.context.InteractionContext", "line_number": 283, "usage_type": "call"}, {"api_name": "interactions.context.InteractionContext.SOURCE_SCRIPT", "line_number": 283, "usage_type": "attribute"}, {"api_name": "interactions.priority", "line_number": 284, "usage_type": "attribute"}, {"api_name": "interactions.context.QueueInsertStrategy.NEXT", "line_number": 285, "usage_type": "attribute"}, {"api_name": "interactions.context.QueueInsertStrategy", "line_number": 285, "usage_type": "name"}, {"api_name": "interactions.context", "line_number": 286, "usage_type": "attribute"}, {"api_name": "interactions.context.InteractionContext", "line_number": 292, "usage_type": "call"}, {"api_name": "interactions.context.InteractionContext.SOURCE_SCRIPT", "line_number": 292, "usage_type": "attribute"}, {"api_name": "interactions.priority", "line_number": 293, "usage_type": "attribute"}, {"api_name": "interactions.context.QueueInsertStrategy.NEXT", "line_number": 294, "usage_type": "attribute"}, {"api_name": "interactions.context.QueueInsertStrategy", "line_number": 294, "usage_type": "name"}, {"api_name": "interactions.context", "line_number": 295, "usage_type": "attribute"}, {"api_name": "event_testing.test_events.TestEvent.InteractionComplete", "line_number": 300, "usage_type": "attribute"}, {"api_name": "event_testing.test_events.TestEvent", "line_number": 300, "usage_type": "name"}, {"api_name": "services.get_persistence_service", "line_number": 312, "usage_type": "call"}, {"api_name": "alarms.add_alarm", "line_number": 324, "usage_type": "call"}, {"api_name": "date_and_time.create_time_span", "line_number": 324, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 327, "usage_type": "call"}, {"api_name": "event_testing.test_events.TestEvent.InteractionComplete", "line_number": 329, "usage_type": "attribute"}, {"api_name": "event_testing.test_events.TestEvent", "line_number": 329, "usage_type": "name"}, {"api_name": "services.get_event_manager", "line_number": 331, "usage_type": "call"}, {"api_name": "event_testing.test_events.TestEvent.InteractionComplete", "line_number": 331, "usage_type": "attribute"}, {"api_name": "event_testing.test_events.TestEvent", "line_number": 331, "usage_type": "name"}, {"api_name": "distributor.rollback.ProtocolBufferRollback", "line_number": 340, "usage_type": "call"}, {"api_name": "distributor.rollback.ProtocolBufferRollback", "line_number": 342, "usage_type": "call"}, {"api_name": "distributor.shared_messages.build_icon_info_msg", "line_number": 344, "usage_type": "call"}, {"api_name": "distributor.shared_messages.IconInfoData", "line_number": 344, "usage_type": "call"}, {"api_name": "services.get_persistence_service", "line_number": 349, "usage_type": "call"}, {"api_name": "services.get_event_manager", "line_number": 351, "usage_type": "call"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates.ATTACK", "line_number": 369, "usage_type": "attribute"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates", "line_number": 369, "usage_type": "name"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates.INSPIRE", "line_number": 372, "usage_type": "attribute"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates", "line_number": 372, "usage_type": "name"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates.RALLY", "line_number": 375, "usage_type": "attribute"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates", "line_number": 375, "usage_type": "name"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates.WARBLING_WARCRY", "line_number": 378, "usage_type": "attribute"}, {"api_name": "situations.complex.mother_plant_battle_ops.MotherplantBattleStates", "line_number": 378, "usage_type": "name"}, {"api_name": "situations.bouncer.bouncer_request.SelectableSimRequestFactory", "line_number": 386, "usage_type": "call"}, {"api_name": "situations.base_situation._RequestUserData", "line_number": 386, "usage_type": "call"}, {"api_name": "situations.bouncer.bouncer_types.BouncerRequestPriority.EVENT_DEFAULT_JOB", "line_number": 389, "usage_type": "attribute"}, {"api_name": "situations.bouncer.bouncer_types.BouncerRequestPriority", "line_number": 389, "usage_type": "name"}, {"api_name": "sims4.tuning.instances.lock_instance_tunables", "line_number": 393, "usage_type": "call"}]} {"seq_id": "422445790", "text": "from dataclasses import dataclass, field\nfrom typing import List, Optional\nfrom ..core.datatypes_base import (\n AdExplicit,\n Ce,\n Cs,\n Ii,\n PnExplicit,\n TelExplicit,\n)\nfrom ..core.voc import (\n EntityClass,\n EntityDeterminer,\n NullFlavor,\n RoleClassAssignedEntity,\n)\nfrom .coct_mt150007_uv import CoctMt150007UvOrganization\n\n__NAMESPACE__ = \"urn:hl7-org:v3\"\n\n\n@dataclass\nclass CoctMt090108UvPerson:\n class Meta:\n name = \"COCT_MT090108UV.Person\"\n\n realm_code: List[Cs] = field(\n default_factory=list,\n metadata={\n \"name\": \"realmCode\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n type_id: Optional[Ii] = field(\n default=None,\n metadata={\n \"name\": \"typeId\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n template_id: List[Ii] = field(\n default_factory=list,\n metadata={\n \"name\": \"templateId\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n name: List[PnExplicit] = field(\n default_factory=list,\n metadata={\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n \"min_occurs\": 1,\n }\n )\n null_flavor: Optional[NullFlavor] = field(\n default=None,\n metadata={\n \"name\": \"nullFlavor\",\n \"type\": \"Attribute\",\n }\n )\n class_code: EntityClass = field(\n init=False,\n default=EntityClass.PSN,\n metadata={\n \"name\": \"classCode\",\n \"type\": \"Attribute\",\n \"required\": True,\n }\n )\n determiner_code: EntityDeterminer = field(\n init=False,\n default=EntityDeterminer.INSTANCE,\n metadata={\n \"name\": \"determinerCode\",\n \"type\": \"Attribute\",\n \"required\": True,\n }\n )\n\n\n@dataclass\nclass CoctMt090108UvAssignedPerson:\n class Meta:\n name = \"COCT_MT090108UV.AssignedPerson\"\n\n realm_code: List[Cs] = field(\n default_factory=list,\n metadata={\n \"name\": \"realmCode\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n type_id: Optional[Ii] = field(\n default=None,\n metadata={\n \"name\": \"typeId\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n template_id: List[Ii] = field(\n default_factory=list,\n metadata={\n \"name\": \"templateId\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n id: List[Ii] = field(\n default_factory=list,\n metadata={\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n \"min_occurs\": 1,\n }\n )\n code: Optional[Ce] = field(\n default=None,\n metadata={\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n addr: List[AdExplicit] = field(\n default_factory=list,\n metadata={\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n telecom: List[TelExplicit] = field(\n default_factory=list,\n metadata={\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n }\n )\n assigned_person: Optional[CoctMt090108UvPerson] = field(\n default=None,\n metadata={\n \"name\": \"assignedPerson\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n \"nillable\": True,\n }\n )\n represented_organization: Optional[CoctMt150007UvOrganization] = field(\n default=None,\n metadata={\n \"name\": \"representedOrganization\",\n \"type\": \"Element\",\n \"namespace\": \"urn:hl7-org:v3\",\n \"nillable\": True,\n }\n )\n null_flavor: Optional[NullFlavor] = field(\n default=None,\n metadata={\n \"name\": \"nullFlavor\",\n \"type\": \"Attribute\",\n }\n )\n class_code: RoleClassAssignedEntity = field(\n default=RoleClassAssignedEntity.ASSIGNED,\n metadata={\n \"name\": \"classCode\",\n \"type\": \"Attribute\",\n }\n )\n", "sub_path": "common_types/models/hl7_v3/ne2008/multi_cache/coct_mt090108_uv.py", "file_name": "coct_mt090108_uv.py", "file_ext": "py", "file_size_in_byte": 4288, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "core.datatypes_base.Cs", "line_number": 27, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 27, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 35, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ii", "line_number": 35, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ii", "line_number": 43, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 51, "usage_type": "name"}, {"api_name": "core.datatypes_base.PnExplicit", "line_number": 51, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "core.voc.NullFlavor", "line_number": 59, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 59, "usage_type": "call"}, {"api_name": "core.voc.EntityClass", "line_number": 66, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 66, "usage_type": "call"}, {"api_name": "core.voc.EntityClass.PSN", "line_number": 68, "usage_type": "attribute"}, {"api_name": "core.voc.EntityClass", "line_number": 68, "usage_type": "name"}, {"api_name": "core.voc.EntityDeterminer", "line_number": 75, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 75, "usage_type": "call"}, {"api_name": "core.voc.EntityDeterminer.INSTANCE", "line_number": 77, "usage_type": "attribute"}, {"api_name": "core.voc.EntityDeterminer", "line_number": 77, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 91, "usage_type": "name"}, {"api_name": "core.datatypes_base.Cs", "line_number": 91, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 91, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 99, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ii", "line_number": 99, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 99, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ii", "line_number": 107, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 107, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ii", "line_number": 115, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 115, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 123, "usage_type": "name"}, {"api_name": "core.datatypes_base.Ce", "line_number": 123, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 123, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 130, "usage_type": "name"}, {"api_name": "core.datatypes_base.AdExplicit", "line_number": 130, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 130, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 137, "usage_type": "name"}, {"api_name": "core.datatypes_base.TelExplicit", "line_number": 137, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 137, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 144, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 144, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 153, "usage_type": "name"}, {"api_name": "coct_mt150007_uv.CoctMt150007UvOrganization", "line_number": 153, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 153, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 162, "usage_type": "name"}, {"api_name": "core.voc.NullFlavor", "line_number": 162, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 162, "usage_type": "call"}, {"api_name": "core.voc.RoleClassAssignedEntity", "line_number": 169, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 169, "usage_type": "call"}, {"api_name": "core.voc.RoleClassAssignedEntity.ASSIGNED", "line_number": 170, "usage_type": "attribute"}, {"api_name": "core.voc.RoleClassAssignedEntity", "line_number": 170, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 86, "usage_type": "name"}]} {"seq_id": "140994811", "text": "from django.shortcuts import render\nfrom django.urls import reverse\nfrom django.http import HttpResponseRedirect\n\nfrom .forms import FrmSetting\nfrom .models import Setting, Usr, setting_upload_to\nfrom routines.utils import move_uploaded_file, hipernormalize\nfrom routines.mkitsafe import valida_acceso\n\n\n@valida_acceso()\ndef index(request):\n usuario = Usr.objects.filter(id=request.user.pk)[0]\n search_value = \"\"\n data = Setting.objects.filter(es_multiple=False)\n if \"POST\" == request.method:\n if \"singles\" == request.POST.get('action'):\n parametros = Setting.objects.filter(es_multiple=False)\n for parametro in parametros:\n if(\"INTEGER\" == parametro.tipo\n or \"STRING\" == parametro.tipo\n or \"TEXT\" == parametro.tipo):\n valor = request.POST.get(parametro.nombre)\n if valor is not None:\n parametro.valor = valor\n parametro.save()\n elif (\"PICTURE\" == parametro.tipo\n or \"FILE\" == parametro.tipo):\n file = request.FILES.get(parametro.nombre)\n if file is not None:\n parametro.valor = move_uploaded_file(\n file, setting_upload_to)\n parametro.save()\n data = Setting.objects.filter(es_multiple=False)\n elif \"search\" == request.POST.get('action'):\n search_value = hipernormalize(request.POST.get('valor'))\n data = [reg\n for reg in data if (\n search_value in hipernormalize(reg.seccion)\n or search_value in hipernormalize(reg.nombre)\n or search_value in hipernormalize(\n reg.nombre_para_mostrar)\n or search_value in hipernormalize(reg.tipo))\n ]\n toolbar = []\n toolbar.append({'type': 'search'})\n return render(\n request,\n 'initsys/setting/values.html', {\n 'menu_main': usuario.main_menu_struct(),\n 'titulo': 'Parámetros del Sistema',\n 'singles': data,\n 'multiples': Setting.objects.filter(es_multiple=True),\n 'toolbar': toolbar,\n 'search_value': search_value,\n })\n\n\n@valida_acceso(['setting.administrar_settings_setting'])\ndef index_adm(request):\n usuario = Usr.objects.filter(id=request.user.pk)[0]\n search_value = \"\"\n data = Setting.objects.all()\n if \"POST\" == request.method:\n if \"search\" == request.POST.get('action'):\n search_value = hipernormalize(request.POST.get('valor'))\n data = [reg\n for reg in data if (\n search_value in hipernormalize(reg.seccion)\n or search_value in hipernormalize(reg.nombre)\n or search_value in hipernormalize(\n reg.nombre_para_mostrar)\n or search_value in hipernormalize(reg.tipo))\n ]\n toolbar = []\n if usuario.has_perm_or_has_perm_child(\n 'setting.agregar_settings_setting'):\n toolbar.append({\n 'type': 'link',\n 'view': 'setting_new',\n 'label': '<i class=\"far fa-file\"></i> Nuevo'})\n toolbar.append({'type': 'search'})\n return render(\n request,\n 'initsys/setting/index.html', {\n 'menu_main': usuario.main_menu_struct(),\n 'titulo': 'Administración de Parámetros',\n 'data': data,\n 'toolbar': toolbar,\n 'search_value': search_value,\n })\n\n\n@valida_acceso(['setting.agregar_settings_setting'])\ndef new_adm(request):\n usuario = Usr.objects.filter(id=request.user.pk)[0]\n frm = FrmSetting(request.POST or None)\n if 'POST' == request.method:\n if frm.is_valid():\n obj = frm.save()\n return HttpResponseRedirect(reverse(\n 'setting_see', kwargs={'pk': obj.pk}))\n return render(request, 'global/form.html', {\n 'menu_main': usuario.main_menu_struct(),\n 'titulo': 'Parámetro',\n 'titulo_descripcion': 'Nuevo',\n 'frm': frm\n })\n\n\n@valida_acceso(['setting.agregar_settings_setting'])\ndef see_adm(request, pk):\n if not Setting.objects.filter(pk=pk).exists():\n return HttpResponseRedirect(reverse(\n 'item_no_encontrado'))\n usuario = Usr.objects.filter(id=request.user.pk)[0]\n obj = Setting.objects.get(pk=pk)\n frm = FrmSetting(instance=obj)\n toolbar = []\n if usuario.has_perm_or_has_perm_child(\n 'setting.administrar_settings_setting'):\n toolbar.append({\n 'type': 'link',\n 'view': 'setting_index',\n 'label': '<i class=\"fas fa-list-ul\"></i> Ver todos'})\n if usuario.has_perm_or_has_perm_child(\n 'setting.actualizar_settings_setting'):\n toolbar.append({\n 'type': 'link_pk',\n 'view': 'setting_update',\n 'label': '<i class=\"far fa-edit\"></i> Actualizar', 'pk': pk})\n if usuario.has_perm_or_has_perm_child(\n 'setting.eliminar_settings_setting'):\n toolbar.append({\n 'type': 'link_pk_del',\n 'view': 'setting_delete',\n 'label': '<i class=\"far fa-trash-alt\"></i> Eliminar',\n 'pk': pk})\n return render(request, 'global/form.html', {\n 'menu_main': usuario.main_menu_struct(),\n 'titulo': 'Parámetro',\n 'titulo_descripcion': obj,\n 'read_only': True,\n 'frm': frm,\n 'toolbar': toolbar\n })\n\n\n@valida_acceso(['setting.actualizar_settings_setting'])\ndef update_adm(request, pk):\n if not Setting.objects.filter(pk=pk).exists():\n return HttpResponseRedirect(reverse(\n 'item_no_encontrado'))\n usuario = Usr.objects.filter(id=request.user.pk)[0]\n obj = Setting.objects.get(pk=pk)\n frm = FrmSetting(instance=obj, data=request.POST or None)\n if 'POST' == request.method:\n if frm.is_valid():\n obj = frm.save()\n return HttpResponseRedirect(reverse(\n 'setting_see', kwargs={'pk': obj.pk}))\n return render(request, 'global/form.html', {\n 'menu_main': usuario.main_menu_struct(),\n 'titulo': 'Parámetro',\n 'titulo_descripcion': obj,\n 'frm': frm\n })\n\n\n@valida_acceso(['setting.eliminar_settings_setting'])\ndef delete_adm(request, pk):\n if not Setting.objects.filter(pk=pk).exists():\n return HttpResponseRedirect(reverse(\n 'item_no_encontrado'))\n Setting.objects.get(pk=pk).delete()\n return HttpResponseRedirect(reverse('setting_index'))\n", "sub_path": "initsys/vw_settings.py", "file_name": "vw_settings.py", "file_ext": "py", "file_size_in_byte": 6760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "models.Usr.objects.filter", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Usr.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Usr", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Setting.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Setting.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 18, "usage_type": "name"}, {"api_name": "routines.utils.move_uploaded_file", "line_number": 31, "usage_type": "call"}, {"api_name": "models.setting_upload_to", "line_number": 32, "usage_type": "argument"}, {"api_name": "models.Setting.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 34, "usage_type": "name"}, {"api_name": "routines.utils.hipernormalize", "line_number": 36, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 39, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 40, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 41, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Setting.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 53, "usage_type": "name"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Usr.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Usr.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.Usr", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Setting.objects.all", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 63, "usage_type": "name"}, {"api_name": "routines.utils.hipernormalize", "line_number": 66, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 69, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 70, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 71, "usage_type": "call"}, {"api_name": "routines.utils.hipernormalize", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Usr.objects.filter", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Usr.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.Usr", "line_number": 96, "usage_type": "name"}, {"api_name": "forms.FrmSetting", "line_number": 97, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 101, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 101, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Setting.objects.filter", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 113, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 114, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 114, "usage_type": "call"}, {"api_name": "models.Usr.objects.filter", "line_number": 116, "usage_type": "call"}, {"api_name": "models.Usr.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "models.Usr", "line_number": 116, "usage_type": "name"}, {"api_name": "models.Setting.objects.get", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 117, "usage_type": "name"}, {"api_name": "forms.FrmSetting", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 139, "usage_type": "call"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 111, "usage_type": "call"}, {"api_name": "models.Setting.objects.filter", "line_number": 151, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 151, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 152, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Usr.objects.filter", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Usr.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Usr", "line_number": 154, "usage_type": "name"}, {"api_name": "models.Setting.objects.get", "line_number": 155, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 155, "usage_type": "name"}, {"api_name": "forms.FrmSetting", "line_number": 156, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 160, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 160, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 162, "usage_type": "call"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 149, "usage_type": "call"}, {"api_name": "models.Setting.objects.filter", "line_number": 172, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 172, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 173, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 173, "usage_type": "call"}, {"api_name": "models.Setting.objects.get", "line_number": 175, "usage_type": "call"}, {"api_name": "models.Setting.objects", "line_number": 175, "usage_type": "attribute"}, {"api_name": "models.Setting", "line_number": 175, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 176, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 176, "usage_type": "call"}, {"api_name": "routines.mkitsafe.valida_acceso", "line_number": 170, "usage_type": "call"}]} {"seq_id": "371247319", "text": "def corrector_test():\n import nltk\n import nltk.tokenize as tok\n from corrector.corrector import find_error\n\n def process(sent, target):\n tokens = tok.word_tokenize(sent)\n print(tokens)\n result = tok.word_tokenize(target)\n alt, err = find_error(tokens, result)\n print(' Input:', sent)\n print('Output:', target)\n print(alt)\n for r, e in zip(alt, err):\n x = r[0][0] if r[0] else None\n y = r[1][0] if r[1] else None\n if not x:\n print('{:>13} ERROR, Add: {} '.format(e, y))\n elif not y:\n print('{:>13} ERROR, Remove: {} '.format(e, x))\n else:\n print('{:>13} ERROR, Replace: {} -> {}'.format(e, x, y))\n print('')\n\n sent = \"what you fuck the doing\"\n target = \"what are you fucking doing?\"\n\n sent = 'too young too simple'\n target = 'too young, too simple.'\n\n sent = 'what fuck'\n target = 'what the fuck!'\n\n sent = \"i'm teacher\"\n target = \"i am a teacher\"\n\n# process(sent, target)\n\ndef insert(a, i, ws):\n for w in reversed(ws):\n a.insert(i, w)\n\n\na = [1,2,3,4,5,6,67]\na[2:2] = [0]\nprint(a)", "sub_path": "t.py", "file_name": "t.py", "file_ext": "py", "file_size_in_byte": 1195, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "nltk.tokenize.word_tokenize", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 7, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 9, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 9, "usage_type": "name"}, {"api_name": "corrector.corrector.find_error", "line_number": 10, "usage_type": "call"}]} {"seq_id": "162423789", "text": "from PyQt5.QtWidgets import QLabel, QPushButton\nfrom PyQt5.QtWidgets import QGridLayout\nfrom PyQt5.QtGui import QPixmap, QPalette, QColor, QFont\nfrom PyQt5.QtWidgets import QWidget\n\nclass HowToPlay(QWidget):\n\n def __init__(self):\n super().__init__()\n\n pal = QPalette()\n pal.setColor(QPalette.Background, QColor(0, 0, 0))\n self.setPalette(pal)\n\n self.initUI()\n\n def initUI(self):\n self.grid = QGridLayout()\n\n self.playLabel = QLabel()\n self.setMinimumHeight(404)\n self.setMinimumWidth(650)\n pixmap = QPixmap('gamerule.png')\n pixmap = pixmap.scaledToHeight(404)\n self.playLabel.setPixmap(pixmap)\n\n self.close_Btn = QPushButton(\"CLOSE\")\n\n self.close_Btn.clicked.connect(self.end_window)\n\n self.grid.addWidget(self.playLabel, 0, 0, 8, 2)\n self.grid.addWidget(self.close_Btn, 8, 0, 1, 2)\n\n self.setLayout(self.grid)\n self.setWindowTitle('how To play Game?')\n self.setGeometry(620, 170, 680, 650)\n self.show()\n\n def end_window(self):\n self.deleteLater()", "sub_path": "Last/howtoplay.py", "file_name": "howtoplay.py", "file_ext": "py", "file_size_in_byte": 1103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPalette.Background", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 27, "usage_type": "call"}]} {"seq_id": "108828914", "text": "# -*- coding: utf-8 -*-\n\nimport autoroutes\nfrom collections import namedtuple, defaultdict\nfrom functools import lru_cache\n\n\n\nROUTER_CACHE_SIZE = 1024\nHTTP_METHODS = frozenset((\n 'GET', 'POST', 'PUT', 'HEAD', 'OPTIONS', 'PATCH', 'DELETE', 'ANY'))\n\nFound = namedtuple('Found', ['method', 'handler', 'params', 'consumed'])\nNotSupported = object()\nNotFound = object()\n\n\nclass RouteHandlerUndefined(autoroutes.InvalidRoute):\n pass\n\n\nclass RouteMethodAlreadyImplemented(autoroutes.InvalidRoute):\n pass\n\n\nclass Router:\n\n def __init__(self, prefix=\"\"):\n self.prefix = prefix\n self.routes = autoroutes.Routes()\n self._seen = defaultdict(set)\n\n def add(self, path, prefix=\"\", **methods):\n if not methods:\n raise RouteHandlerUndefined(\n \"No handlers specified for {}\".format(path))\n adding = frozenset(methods.keys())\n unknown = adding - HTTP_METHODS\n if unknown:\n raise KeyError(\n 'Route defines an unknown HTTP method(s): {}.'.format(unknown))\n\n pattern = (prefix or self.prefix) + path\n seen = self._seen[pattern]\n\n if not seen:\n seen.update(adding)\n else:\n existing = seen & adding\n if existing:\n raise RouteMethodAlreadyImplemented(\n 'Route {} already has a handler for {}.'.format(\n path, existing))\n seen.update(adding)\n self.routes.add(pattern, **methods)\n\n\n def lookup(self, path, method):\n payload, params = self.routes.match(path)\n if payload:\n if method in payload:\n return Found(method, payload[method], params, path)\n if 'ANY' in payload:\n return Found(method, payload['ANY'], params, path)\n return NotSupported\n return NotFound\n\n\nif __name__ == '__main__':\n router = Router()\n router.add('/path/to/view', POST='post_handler')\n router.add('/path/to/view', ANY='other_handler')\n router.add('/path/to/view', POST='faulty_handler')\n print(router.lookup('/path/to/view', 'POST'))\n", "sub_path": "src/shunting/shuntbox.py", "file_name": "shuntbox.py", "file_ext": "py", "file_size_in_byte": 2134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "collections.namedtuple", "line_number": 13, "usage_type": "call"}, {"api_name": "autoroutes.InvalidRoute", "line_number": 18, "usage_type": "attribute"}, {"api_name": "autoroutes.InvalidRoute", "line_number": 22, "usage_type": "attribute"}, {"api_name": "autoroutes.Routes", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "call"}]} {"seq_id": "23207817", "text": "'''\nCreated on Feb 26, 2015\n\n@author: grimel\n'''\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom bs4 import BeautifulSoup\nimport urllib.request\nimport re\n#url = \"http://www.sports.ru/liga-europa/calendar/?s=4395&m=2\"\nurl = \"http://terrikon.com/europa-league\"\nCNT = 16\ndef main():\n s = urllib.request.urlopen(url)\n soup = BeautifulSoup(s)\n pattern = \"(\\d{2}\\.\\d{2}\\.\\d{2}) (\\d{2}:\\d{2})\"\n regex = re.compile(pattern)\n time_a = []\n date_a = []\n try:\n for i in soup.findAll('td', {'class': \"date\"}):\n date = regex.match(i.text).group(1)\n time = regex.match(i.text).group(2)\n time_a.append(time)\n date_a.append(date)\n except:\n print(\"Done\")\n a = []\n for i in soup.findAll('td', {'class' : 'team'}):\n a.append(i.text)\n a = a[:CNT]\n home = [x for x in a if a.index(x)%2 == 0]\n away = [x for x in a if a.index(x)%2 != 0]\n prev_t = 0\n for t, c in zip(time_a, zip(home, away)):\n if prev_t != t:\n prev_t = t\n print(t)\n print(c[0], \"-\", c[1])\n \nif __name__ == \"__main__\":\n main()", "sub_path": "fast_LE.py", "file_name": "fast_LE.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 15, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 15, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}]} {"seq_id": "156894603", "text": "from flask import Blueprint, render_template, abort\nfrom flask import jsonify\n\nfrom lwpcms.mongo import db\nfrom bson.objectid import ObjectId\nimport pymongo\n\nfrom lwpcms.api.files import file_thumbnail\n\nimport os\n\n\nbp = Blueprint(\n __name__, __name__,\n template_folder='templates',\n url_prefix='/api'\n)\n\n@bp.route('/delete_file/<id>', methods=['POST', 'GET'])\ndef delete_file(id):\n file = db.collections.find_one({\"_id\": ObjectId(id)})\n print(file['content'])\n os.remove(\n os.path.dirname(os.path.realpath(__file__))\\\n +'/../../static/upload/{}'.format(file[\"content\"])\n )\n\n for size in [64, 32, 128]:\n os.remove(\n os.path.dirname(os.path.realpath(__file__))\\\n +'/../../static/upload/{}'.format(\n file_thumbnail(file[\"content\"], size)\n )\n )\n\n db.collections.delete_many({\"_id\": ObjectId(id)})\n return 'ok', 200\n\n\n@bp.route('/delete_post/<id>', methods=['POST', 'GET'])\ndef delete_post(id):\n db.collections.delete_many({\"_id\": ObjectId(id)})\n return 'ok', 200\n\n\n@bp.route('/query_files/<query>', defaults={'page': 0, 'limit': 100})\n@bp.route('/query_files/<query>/<page>/<limit>', methods=['POST', 'GET'])\ndef query_files(query, page, limit):\n\n page = int(page)\n limit = int(limit)\n\n if query != '*':\n obj = db.collections.find(\n {\n \"classes\": [\"post\", \"file\"],\n \"title\": {\"$regex\": u\"[a-zA-Z]*{}[a-zA-Z]*\".format(query)}\n }\n ).sort('created', pymongo.DESCENDING)\n if page != -1 and limit != -1:\n obj.skip(page * limit).limit(limit)\n\n files = list(\n obj\n )\n else:\n obj = db.collections.find(\n {\n \"classes\": [\"post\", \"file\"]\n }\n ).sort('created', pymongo.DESCENDING)\n if page != -1 and limit != -1:\n obj.skip(page * limit).limit(limit)\n\n files = list(\n obj\n )\n\n return jsonify(\n {\n 'meta':{\n 'length': len(files)\n },\n 'files':[\n {\n 'id': str(file[\"_id\"]),\n 'title': file[\"title\"],\n 'content': file[\"content\"],\n 'original': file['meta']['original_filename']\n }\n for file in files]\n } \n )\n\n\n@bp.route('/remove_attachment/<post_id>/<attach_id>', methods=['POST', 'GET'])\ndef remove_attachment(post_id, attach_id):\n db.collections.update_one(\n {\n '_id': ObjectId(post_id)\n },\n {\n '$pull': {\n 'attachments': {\n '_id': ObjectId(attach_id)\n }\n }\n }\n )\n return jsonify({\n 'status': 200\n }), 200\n", "sub_path": "lwpcms/views/api/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 3203, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask.Blueprint", "line_number": 13, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections.find_one", "line_number": 21, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 21, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 21, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 21, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 24, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 30, "usage_type": "call"}, {"api_name": "lwpcms.api.files.file_thumbnail", "line_number": 32, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections.delete_many", "line_number": 36, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 36, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 36, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 36, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections.delete_many", "line_number": 42, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 42, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 42, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 42, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections.find", "line_number": 54, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 54, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 54, "usage_type": "name"}, {"api_name": "pymongo.DESCENDING", "line_number": 59, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db.collections.find", "line_number": 67, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 67, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 67, "usage_type": "name"}, {"api_name": "pymongo.DESCENDING", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 79, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections.update_one", "line_number": 98, "usage_type": "call"}, {"api_name": "lwpcms.mongo.db.collections", "line_number": 98, "usage_type": "attribute"}, {"api_name": "lwpcms.mongo.db", "line_number": 98, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 100, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}]} {"seq_id": "575661004", "text": "import requests\nimport sys\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog\nfrom PyQt5.QtGui import QPixmap\n\n#from ui_file import Ui_MainWindow\n\nfrom os import access, F_OK, mkdir\nfrom shutil import move, rmtree\n\nCLIENT_ID = \"ZcGFMBG6uwJuWvAbdadqQxjy3Md2RhV1\"\n\n\nclass Ui_MainWindow(object):\n def setupUi(self, MainWindow):\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.resize(1080, 540)\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n self.enter = QtWidgets.QPushButton(self.centralwidget)\n self.enter.setGeometry(QtCore.QRect(150, 100, 180, 40))\n self.enter.setObjectName(\"enter\")\n self.link = QtWidgets.QLineEdit(self.centralwidget)\n self.link.setGeometry(QtCore.QRect(20, 40, 440, 25))\n self.link.setText(\"\")\n self.link.setObjectName(\"link\")\n self.name = QtWidgets.QLabel(self.centralwidget)\n self.name.setGeometry(QtCore.QRect(20, 320, 500, 40))\n font = QtGui.QFont()\n font.setFamily(\"Noto Sans Mono CJK JP\")\n font.setPointSize(15)\n self.name.setFont(font)\n self.name.setObjectName(\"name\")\n self.music = QtWidgets.QLabel(self.centralwidget)\n self.music.setGeometry(QtCore.QRect(20, 360, 500, 40))\n font = QtGui.QFont()\n font.setFamily(\"Noto Sans Mono CJK JP\")\n font.setPointSize(15)\n self.music.setFont(font)\n self.music.setObjectName(\"music\")\n self.plays = QtWidgets.QLabel(self.centralwidget)\n self.plays.setGeometry(QtCore.QRect(20, 400, 500, 40))\n font = QtGui.QFont()\n font.setFamily(\"Noto Sans Mono CJK JP\")\n font.setPointSize(15)\n self.plays.setFont(font)\n self.plays.setObjectName(\"plays\")\n self.likes = QtWidgets.QLabel(self.centralwidget)\n self.likes.setGeometry(QtCore.QRect(20, 440, 500, 40))\n font = QtGui.QFont()\n font.setFamily(\"Noto Sans Mono CJK JP\")\n font.setPointSize(15)\n self.likes.setFont(font)\n self.likes.setObjectName(\"likes\")\n self.followers = QtWidgets.QLabel(self.centralwidget)\n self.followers.setGeometry(QtCore.QRect(20, 480, 500, 40))\n font = QtGui.QFont()\n font.setFamily(\"Noto Sans Mono CJK JP\")\n font.setPointSize(15)\n self.followers.setFont(font)\n self.followers.setObjectName(\"followers\")\n self.save = QtWidgets.QToolButton(self.centralwidget)\n self.save.setGeometry(QtCore.QRect(150, 180, 180, 25))\n self.save.setObjectName(\"save\")\n self.avatar = QtWidgets.QLabel(self.centralwidget)\n self.avatar.setGeometry(QtCore.QRect(560, 20, 500, 500))\n self.avatar.setObjectName(\"avatar\")\n MainWindow.setCentralWidget(self.centralwidget)\n\n self.retranslateUi(MainWindow)\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"Soundcloud downloader\"))\n self.enter.setText(_translate(\"MainWindow\", \"Download\"))\n self.link.setWhatsThis(_translate(\"MainWindow\", \"<html><head/><body><p><br/></p></body></html>\"))\n self.name.setText(_translate(\"MainWindow\", \"Avtor: \"))\n self.music.setText(_translate(\"MainWindow\", \"Music name: \"))\n self.plays.setText(_translate(\"MainWindow\", \"Plays count: \"))\n self.likes.setText(_translate(\"MainWindow\", \"Likes count: \"))\n self.followers.setText(_translate(\"MainWindow\", \"Followers count: \"))\n self.save.setText(_translate(\"MainWindow\", \"Save as\"))\n self.avatar.setText(_translate(\"MainWindow\", \"TextLabel\"))\n\n\nclass MyWidget(QMainWindow, Ui_MainWindow):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.setFixedSize(500, 180)\n self.enter.clicked.connect(self.press_enter)\n self.save.clicked.connect(self.save_music)\n\n def press_enter(self):\n check = check_link(self.link.text())\n\n if not check:\n try:\n get = requests.get(self.link.text()).text\n\n name = get[get.find('\"username\"'):].split('\"')[3]\n self.name.setText(\"Author: \" + name)\n # print(name)\n\n music = get[get.find('alt='):].split('\"')[1]\n self.music.setText(\"Music name: \" + music)\n # print(music)\n\n plays = get[get.find('play_count\"'):].split('\"')[2]\n self.plays.setText(\"Plays count: \" + plays)\n # print(plays)\n\n likes = get[get.find('like_count\"'):].split('\"')[2]\n self.likes.setText(\"Likes count: \" + likes)\n # print(likes)\n\n followers = get[get.find('\"followers_count\"'):].split('\"')[2][1:-1]\n self.followers.setText(\"Followers count: \" + followers)\n # print(followers)\n\n download = get[get.find('\"download_url\"'):].split('\"')[3][:-8]\n\n img = get[get.find('<img'):].split('\"')[1]\n # print(img)\n\n if access(\".data\", F_OK):\n rmtree(\".data\")\n mkdir(\".data\")\n\n download_img(img)\n\n download_music(download)\n\n self.song = name + \" - \" + music\n\n pixmap = QPixmap(\".data/avatar.jpg\")\n self.avatar.setPixmap(pixmap)\n\n self.setFixedSize(1080, 540)\n\n except requests.exceptions.RequestException:\n self.link.setText(\"Sorry, but we can't help you. Try again latter.\")\n else:\n self.link.setText(check)\n\n def save_music(self):\n aim = QFileDialog.getExistingDirectory(self, \"Введите свою судьбу\")\n move(\".data/song.mp3\", f\"{aim}/{self.song}.mp3\")\n\n def closeEvent(self, event):\n if access(\".data\", F_OK):\n rmtree(\".data\")\n\n #if access(\"song.mp3\", F_OK):\n # remove(\"avatar.jpg\")\n\n # if access(\"song.mp3\", F_OK):\n # remove(\"song.mp3\")\n\n\ndef check_link(link):\n answer = \"\"\n try:\n if requests.get(link).status_code != 200:\n answer = f\"Error {requests.get(link).status_code}\"\n\n elif not (len(link) > 23 and link[:23] == \"https://soundcloud.com/\" and not \"/sets/\" in link):\n answer = \"It's not SoundCloud's song, check pls\"\n\n except requests.exceptions.ConnectionError:\n answer = \"Your network is very poor. Try again later\"\n\n except requests.exceptions.RequestException:\n answer = \"This place for url, check pls\"\n\n return answer\n\n\ndef download_img(img):\n with open('.data/avatar.jpg', 'wb') as handle:\n response = requests.get(img, stream=True)\n\n if not response.ok:\n print(response)\n\n for block in response.iter_content(1024):\n if not block:\n break\n\n handle.write(block)\n\n\ndef download_music(link):\n with open(\".data/song.mp3\", \"wb\") as handle, open(\"standart.mp3\", \"rb\") as standart:\n #response = requests.get(\"https://cf-media.sndcdn.com/s966iKYqfdff.128.mp3?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiKjovL2NmLW1lZGlhLnNuZGNkbi5jb20vczk2NmlLWXFmZGZmLjEyOC5tcDMiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE1NTAxMzYwNzV9fX1dfQ__&Signature=hoX70pR4zZEia7nMPm9AjIIoA3EpziGH6oJNZe8zRQkN9ul4zVnwwHvmP8oQAzt9DnFPoVsTyyO9E7zYciuJSN2nJeHYIeRC2N5s~RGMOGAXWyLlgGVZDc~Vn~YrHzNgihr9Nk4jKjKl75Xmed88bK4exQ~i-b4efz6eteu3RdyrfuSGARpWiknsAZ5NThXSkNY7M-ezajTRQ0s46YspQcnrwMG~CVkPOwGqOmLcEI65XHkR6asPR9H4pei-4BbkBimHun~ZArQQckX36YUUpu38EFcFoYT4nXI5AGtthZTGZoA-Nk9fFkVhSgLiAeDmQG~AF0ixF8eSLDY19RCFAg__&Key-Pair-Id=APKAJAGZ7VMH2PFPW6UQ\")\n #\n # standart.write(response.content)\n #if not response.ok:\n # print(response)\n#\n #for block in response.iter_content(1024):\n # if not block:\n # break\n#\n # handle.write(block)\n\n # handle.write(response.content)\n\n handle.write(standart.read())\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = MyWidget()\n ex.show()\n sys.exit(app.exec_())\n", "sub_path": "soundcloud.py", "file_name": "soundcloud.py", "file_ext": "py", "file_size_in_byte": 8313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 45, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 50, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QToolButton", "line_number": 64, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 65, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 67, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 68, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 89, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 102, "usage_type": "call"}, {"api_name": "os.access", "line_number": 129, "usage_type": "call"}, {"api_name": "os.F_OK", "line_number": 129, "usage_type": "argument"}, {"api_name": "shutil.rmtree", "line_number": 130, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 139, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 144, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 150, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 150, "usage_type": "name"}, {"api_name": "shutil.move", "line_number": 151, "usage_type": "call"}, {"api_name": "os.access", "line_number": 154, "usage_type": "call"}, {"api_name": "os.F_OK", "line_number": 154, "usage_type": "argument"}, {"api_name": "shutil.rmtree", "line_number": 155, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 167, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 168, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 173, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 176, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 216, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 216, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 219, "usage_type": "call"}]} {"seq_id": "631398350", "text": "from selenium import webdriver\n\ndriver = webdriver.Chrome(r'D:\\chromedriver.exe')\ndriver.implicitly_wait(5)\n\ndriver.get('http://www.baidu.com')\n\n# 获取窗口大小\nsize = driver.get_window_size()\nprint(size)\n\n# 改变窗口大小\ndriver.set_window_size(1000, 600)\n\n# 获取title\ntitle = driver.title\nprint(title)\n\n# 获取当前网页url\nurl = driver.current_url\nprint(url)", "sub_path": "6ActionChains/6.其他技巧.py", "file_name": "6.其他技巧.py", "file_ext": "py", "file_size_in_byte": 375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 3, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 3, "usage_type": "name"}]} {"seq_id": "640452825", "text": "import logging\nfrom functools import partial\n\nfrom django.db import connections\n\nfrom couchforms.models import XFormInstance\nfrom dimagi.utils.couch.database import iter_docs\n\nfrom corehq.apps.domain.dbaccessors import get_doc_count_in_domain_by_type\nfrom corehq.form_processor.models import XFormInstanceSQL\nfrom corehq.sql_db.util import split_list_by_db_partition\nfrom corehq.util.couch_helpers import NoSkipArgsProvider\nfrom corehq.util.log import with_progress_bar\nfrom corehq.util.pagination import ResumableFunctionIterator\n\nlog = logging.getLogger(__name__)\n\n\ndef iter_unmigrated_docs(domain, doc_types, migration_id, counter):\n if doc_types != [\"XFormInstance\"]:\n raise NotImplementedError(doc_types)\n [doc_type] = doc_types\n couch_db = XFormInstance.get_db()\n doc_count = counter.pop(doc_type)\n if doc_count:\n log.info(\"saved count of %s was %s\", doc_type, doc_count)\n doc_count = get_doc_count_in_domain_by_type(domain, doc_type, couch_db)\n add_docs = partial(counter.add, None, doc_type)\n batches = doc_count // iter_id_chunks.chunk_size\n iterable = iter_id_chunks(domain, doc_type, migration_id, couch_db)\n doc_ids = []\n for doc_ids in with_progress_bar(iterable, batches, prefix=doc_type, oneline=False):\n yield from iter_docs_not_in_sql(doc_ids, couch_db)\n add_docs(len(doc_ids))\n\n\ndef iter_id_chunks(domain, doc_type, migration_id, couch_db):\n def data_function(**view_kwargs):\n return couch_db.view('by_domain_doc_type_date/view', **view_kwargs)\n endkey, docid = get_endkey_docid(domain, doc_type, migration_id)\n args_provider = NoSkipArgsProvider({\n 'startkey': [domain, doc_type],\n 'endkey': endkey,\n 'endkey_docid': docid,\n 'inclusive_end': False,\n 'limit': iter_id_chunks.chunk_size,\n 'include_docs': False,\n 'reduce': False,\n })\n args, kwargs = args_provider.get_initial_args()\n while True:\n results = list(data_function(*args, **kwargs))\n results = args_provider.adjust_results(results, args, kwargs)\n if not results:\n break\n yield [r[\"id\"] for r in results]\n try:\n args, kwargs = args_provider.get_next_args(results[-1], *args, **kwargs)\n except StopIteration:\n break\n\n\niter_id_chunks.chunk_size = 5000\n\n\ndef get_endkey_docid(domain, doc_type, migration_id):\n resume_key = \"%s.%s.%s\" % (domain, doc_type, migration_id)\n state = ResumableFunctionIterator(resume_key, None, None, None).state\n assert getattr(state, '_rev', None), \"rebuild not necessary (no resume state)\"\n assert not state.complete, \"iteration is complete\"\n state_json = state.to_json()\n assert not state_json['args']\n kwargs = state_json['kwargs']\n return kwargs['startkey'], kwargs['startkey_docid']\n\n\ndef iter_docs_not_in_sql(form_ids, couch_db):\n def get_missing_form_ids(db, db_form_ids):\n with XFormInstanceSQL.get_cursor_for_partition_db(db, readonly=True) as cursor:\n cursor.execute(sql, [db_form_ids])\n return [r[0] for r in cursor.fetchall()]\n\n sql = f\"\"\"\n SELECT maybe_missing.id\n FROM (SELECT UNNEST(%s) AS id) maybe_missing\n LEFT JOIN {XFormInstanceSQL._meta.db_table} migrated_form\n ON migrated_form.form_id = maybe_missing.id\n WHERE migrated_form.id IS NULL\n \"\"\"\n\n for db_name, db_form_ids in split_list_by_db_partition(form_ids):\n missing_ids = get_missing_form_ids(db_name, db_form_ids)\n if missing_ids:\n log.debug(\"missing ids: %s\", missing_ids)\n yield from iter_docs(couch_db, missing_ids)\n", "sub_path": "corehq/apps/couch_sql_migration/staterebuilder.py", "file_name": "staterebuilder.py", "file_ext": "py", "file_size_in_byte": 3648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "couchforms.models.XFormInstance.get_db", "line_number": 23, "usage_type": "call"}, {"api_name": "couchforms.models.XFormInstance", "line_number": 23, "usage_type": "name"}, {"api_name": "corehq.apps.domain.dbaccessors.get_doc_count_in_domain_by_type", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 28, "usage_type": "call"}, {"api_name": "corehq.util.log.with_progress_bar", "line_number": 32, "usage_type": "call"}, {"api_name": "corehq.util.couch_helpers.NoSkipArgsProvider", "line_number": 41, "usage_type": "call"}, {"api_name": "corehq.util.pagination.ResumableFunctionIterator", "line_number": 68, "usage_type": "call"}, {"api_name": "corehq.form_processor.models.XFormInstanceSQL.get_cursor_for_partition_db", "line_number": 79, "usage_type": "call"}, {"api_name": "corehq.form_processor.models.XFormInstanceSQL", "line_number": 79, "usage_type": "name"}, {"api_name": "corehq.form_processor.models.XFormInstanceSQL._meta", "line_number": 86, "usage_type": "attribute"}, {"api_name": "corehq.form_processor.models.XFormInstanceSQL", "line_number": 86, "usage_type": "name"}, {"api_name": "corehq.sql_db.util.split_list_by_db_partition", "line_number": 91, "usage_type": "call"}, {"api_name": "dimagi.utils.couch.database.iter_docs", "line_number": 95, "usage_type": "call"}]} {"seq_id": "211773719", "text": "\"\"\"\r\n\r\n\"\"\"\r\nimport time\r\n\r\nfrom selenium.webdriver.common.by import By\r\nfrom common.base_page import BasePage\r\nfrom data import landName\r\n\r\n\r\nclass LandPage(BasePage):\r\n # url = '/login'\r\n #项目主数据\r\n xm_locator = (By.XPATH,\"//span[@title='项目主数据']\")\r\n #项目管理\r\n xmgl_locator = (By.XPATH, \"//span[@title='项目管理']\")\r\n #地块列表\r\n dk_locator = (By.XPATH,\"//span[@title='地块列表']\")\r\n #新增列表\r\n create_land_locator = (By.XPATH, \"//span[contains(text(),'新增列表')]\")\r\n #变更列表\r\n change_land_locator = (By.XPATH, \"//span[contains(text(),'变更列表')]\")\r\n #已审核列表\r\n land_locator = (By.XPATH, \"//span[contains(text(),'已审核列表')]\")\r\n #城市公司\r\n city_locator = (By.XPATH,\"//span[contains(text(),'苏州城市公司')]\")\r\n #新增按钮\r\n btn_create_locator = (By.XPATH,\"//button[@type='button']/span[contains(text(),'新增')]\")\r\n #保存按钮\r\n btn_save_locator = (By.XPATH,\"//span[contains(text(),'保存')]\")\r\n\r\n #选择项\r\n project_gain_value = (By.XPATH, \"//span[contains(text(),'勾地')]\")\r\n land_usage_value = (By.XPATH, \"//span[contains(text(),'产业用地')]\")\r\n province_value = (By.XPATH, \"//span[contains(text(),'江苏省')]\")\r\n city_value = (By.XPATH, \"//span[contains(text(),'苏州市')]\")\r\n region_value = (By.XPATH, \"//span[contains(text(),'苏州市本级')]\")\r\n\r\n #基本信息\r\n land_name = (By.XPATH,\"//input[@placeholder='地块名称']\")\r\n land_certificate_no = (By.XPATH,\"//input[@placeholder='土地证编号']\")\r\n project_gain_type = (By.XPATH,\"//span[@title='土地获取方式']/..//following-sibling::div//input\")\r\n land_gain_date = (By.XPATH,\"//span[@title='获取时间']/..//following-sibling::div//input\")\r\n land_usage_type = (By.XPATH,\"//span[@title='用地性质']/..//following-sibling::div//input\")\r\n land_use_period_type = (By.XPATH,\"//input[@placeholder='土地法定使用年限(年)']\")\r\n land_remainder_period = (By.XPATH,\"//input[@placeholder='土地剩余使用年限(年)']\")\r\n address = (By.XPATH,\"//textarea[@placeholder='地块所在四至范围']\")\r\n province_code = (By.XPATH,\"//span[@title='地块所在省']/..//following-sibling::div//input\")\r\n city_code = (By.XPATH,\"//span[@title='地块所在市']/..//following-sibling::div//input\")\r\n region_code = (By.XPATH,\"//span[@title='地块所在区县']/..//following-sibling::div//input\")\r\n land_address = (By.XPATH,\"//textarea[@placeholder='地块地址']\")\r\n delisting_unit = (By.XPATH,\"//input[@placeholder='摘牌单位']\")\r\n percent = (By.XPATH,\"//input[@placeholder='我方权益比例(%)']\")\r\n parcel_summary = (By.XPATH,\"//input[@placeholder='地块汇总测试字段']\")\r\n\r\n # 规划指标信息\r\n total_use_area = (By.XPATH,\"//input[@placeholder='总用地面积(㎡)']\")\r\n building_area = (By.XPATH,\"//input[@placeholder='净用地面积(㎡)']\")\r\n collection_of_land_area = (By.XPATH,\"//input[@placeholder='代征用地面积(㎡)']\")\r\n plot_ratio = (By.XPATH,\"//input[@placeholder='容积率']\")\r\n building_density = (By.XPATH,\"//input[@placeholder='建筑密度(%)']\")\r\n green_ratio = (By.XPATH,\"//input[@placeholder='绿地率(%)']\")\r\n limit_height = (By.XPATH,\"//input[@placeholder='限高(m)']\")\r\n\r\n #弹窗\r\n save_success = (By.XPATH,\"//p[contains(text(),'保存成功')]\")\r\n update_success = (By.XPATH,\"//p[contains(text(),'更新成功')]\")\r\n\r\n #获取发起审核按钮\r\n launch_btn = (By.XPATH,\"//span[text()='{}']/../../parent::tr//button[@title='详情']//following-sibling::span//button[@title='发起审核']\".format(landName))\r\n #获取发起审核的内容\r\n launch_locator =(By.XPATH,\"//div[@class='el-message-box']//span[contains(text(),'发起审核')]\")\r\n #点击确定\r\n determine_locator = (By.XPATH,\"//div[@class='el-message-box']//span[contains(text(),'确定')]\")\r\n\r\n wait_time = 20\r\n # def get(self):\r\n # \"\"\"访问登录页面\"\"\"\r\n # login_url = Setting.host + self.url\r\n # self.driver.get(login_url)\r\n\r\n def create_land(self, land_name, land_certificate_name, land_gain_date,\r\n land_use_period_name, land_remainder_name, address_name,\r\n land_address_name, delisting_unit_name, percent_name, parcel_summary_name,\r\n total_use_area_name, building_area_name, collection_of_land_area_name, plot_ratio_name,\r\n building_density_name, green_ratio_name, limit_height_name):\r\n\r\n #点击项目主数据\r\n self.js_to_bottom(self.xm_locator)\r\n\r\n #点击项目管理\r\n self.js_to_bottom(self.xmgl_locator)\r\n\r\n #点击地块\r\n self.js_to_bottom(self.dk_locator)\r\n\r\n #定位城市公司\r\n self.js_to_bottom(self.city_locator)\r\n\r\n #定位新增列表\r\n self.js_to_bottom(self.create_land_locator)\r\n\r\n #定位新增按钮\r\n self.js_to_bottom(self.btn_create_locator)\r\n\r\n #输入基本信息\r\n #1、用户输入地块名称\r\n self.user_input(self.land_name,land_name)\r\n\r\n # 2、用户输入土地证编号\r\n self.user_input(self.land_certificate_no, land_certificate_name)\r\n\r\n # 3、用户选择土地获取方式\r\n time.sleep(1)\r\n self.js_to_content(self.project_gain_type)\r\n self.js_to_bottom(self.project_gain_value)\r\n\r\n # 4、用户选择获取时间\r\n self.js_to_send_content(self.land_gain_date, land_gain_date)\r\n\r\n # 5、用户选择用地性质\r\n time.sleep(1)\r\n self.js_to_content(self.land_usage_type)\r\n self.js_to_bottom(self.land_usage_value)\r\n\r\n # 6、土地法定使用年限(年)\r\n self.user_input(self.land_use_period_type, land_use_period_name)\r\n\r\n # 7、土地剩余使用年限(年)\r\n self.user_input(self.land_remainder_period, land_remainder_name)\r\n\r\n # 8、地块所在四至范围\r\n self.user_input(self.address, address_name)\r\n\r\n # 9、地块所在省\r\n time.sleep(1)\r\n self.js_to_content(self.province_code)\r\n self.js_to_bottom(self.province_value)\r\n\r\n # 10、地块所在市\r\n time.sleep(1)\r\n self.js_to_content(self.city_code)\r\n self.js_to_bottom(self.city_value)\r\n\r\n # 11、地块所在区县\r\n time.sleep(1)\r\n self.js_to_content(self.region_code)\r\n self.js_to_bottom(self.region_value)\r\n\r\n # 12、地块地址\r\n self.user_input(self.land_address, land_address_name)\r\n\r\n # 13、摘牌单位\r\n self.user_input(self.delisting_unit, delisting_unit_name)\r\n\r\n # 13、我方权益比例(%)\r\n self.user_input(self.percent, percent_name)\r\n\r\n # 14、地块汇总测试字段\r\n self.user_input(self.parcel_summary, parcel_summary_name)\r\n\r\n # 规划指标信息\r\n # 1、总用地面积(㎡)\r\n self.user_input(self.total_use_area, total_use_area_name)\r\n\r\n # 2、净用地面积\r\n self.user_input(self.building_area, building_area_name)\r\n\r\n # 3、代征用地面积\r\n self.user_input(self.collection_of_land_area, collection_of_land_area_name)\r\n\r\n # 4、容积率\r\n self.user_input(self.plot_ratio, plot_ratio_name)\r\n\r\n # 5、建筑密度(%)\r\n self.user_input(self.building_density, building_density_name)\r\n\r\n # 6、绿地率(%)\r\n self.user_input(self.green_ratio, green_ratio_name)\r\n\r\n # 7、限高(m)\r\n self.user_input(self.limit_height, limit_height_name)\r\n\r\n\r\n #点击保存按钮\r\n self.js_to_bottom(self.btn_save_locator)\r\n time.sleep(3)\r\n\r\n\r\n\r\n\r\n\r\n def get_launch_btn(self):\r\n \"\"\"发起审核\"\"\"\r\n time.sleep(3)\r\n self.js_to_bottom(self.launch_btn)\r\n\r\n def determine_btn(self):\r\n \"\"\"点击确定\"\"\"\r\n time.sleep(3)\r\n self.js_to_bottom(self.determine_locator)\r\n time.sleep(3)\r\n\r\n\r\n def get_save_success_msg(self):\r\n \"\"\"获取正确信息\"\"\"\r\n save_success_elem = self.wait_presence_element(self.save_success)\r\n return save_success_elem.text\r\n\r\n def get_launch_msg(self):\r\n \"\"\"获取发起审核信息\"\"\"\r\n launch_elem = self.wait_presence_element(self.launch_locator)\r\n return launch_elem.text\r\n\r\n def get_update_success_msg(self):\r\n \"\"\"获取更新成功信息\"\"\"\r\n update_success_elem = self.wait_presence_element(self.update_success)\r\n return update_success_elem.text\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "pages/land_page.py", "file_name": "land_page.py", "file_ext": "py", "file_size_in_byte": 8610, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "common.base_page.BasePage", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 14, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 14, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 16, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 16, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 20, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 33, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 35, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 40, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 43, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 45, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 48, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 48, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 50, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 50, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 51, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 52, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 52, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 53, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 54, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 54, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 57, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 57, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 58, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 58, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 59, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 59, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 60, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 60, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 61, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 61, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 63, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 63, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 66, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 67, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 67, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 70, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 70, "usage_type": "name"}, {"api_name": "data.landName", "line_number": 70, "usage_type": "argument"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 72, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 72, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 74, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 74, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 136, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 141, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 187, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 195, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 200, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 202, "usage_type": "call"}]} {"seq_id": "482991947", "text": "# -*- coding: utf-8 -*-\n\nimport urllib\nfrom bs4 import BeautifulSoup\n\nimport sys\n\nif sys.version_info[0] < 3:\n reload(sys)\n sys.setdefaultencoding('utf8')\n\nkw = u'印度'\nkys = (u'台湾',)\n\nurl = 'http://tieba.baidu.com/f'\nvals = {'ie':'utf-8', 'kw':kw, 'pn':'0'}\n\noutf = open(kw + u'吧分析结果.txt', 'w')\nprint(u'开始分析...')\n\npages = 10\npn = 0\n\nfor i in range(pages):\n print(u'第' + str(i) +u'页')\n\n vals['pn'] = str(pn)\n param = urllib.urlencode(vals)\n full_url = url +'?'+ param\n\n response = urllib.urlopen(full_url)\n html = response.read()\n soup =BeautifulSoup(html)\n\n tiezi_ul = soup.find(id='thread_list')\n if tiezi_ul is None: break\n\n tiezi_li = tiezi_ul.find_all('li', class_='j_thread_list', recursive=False)\n for tiezi in tiezi_li:\n caption = tiezi.find('a', class_='j_th_tit')\n author = tiezi.find('a', class_='j_user_card')\n if caption is None or author is None: continue\n\n pn += 1\n\n for ky in kys:\n if ky in caption.string:\n print(caption.string + '- - - - - - - -' + author.string)\n\n tiezi_url = 'http://tieba.baidu.com' + caption['href']\n print(tiezi_url)\n\n outf.write(caption.string + '- - - - - - - -' + author.string)\n outf.write('\\n')\n outf.write(tiezi_url)\n outf.write('\\n')\n outf.write('\\n')\n\n break\n\noutf.close()\n", "sub_path": "tieba2.py", "file_name": "tieba2.py", "file_ext": "py", "file_size_in_byte": 1469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.version_info", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.setdefaultencoding", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}]} {"seq_id": "95966031", "text": "# 特征工程：\n\n# 第三步：特征降维 （提取重要特征，减少特征个数，得到一组不相关的特征，去除冗余特征），有两种方法\n# 1. 特征选择\n # Filter过滤式\n # 1. 方差选择法：将低方差的特征过滤掉\n # 2. 相关系数法：将相关系数较大的特征过滤掉\n # Embedded式\n\n# 2. 主成分分析(PCA)（可以理解为一种特征提取的方式）\n\n# sklearn.feature_selection\n\nimport pandas as pd\nfrom sklearn.feature_selection import VarianceThreshold\nfrom scipy.stats import pearsonr\n\n\ndef variance_demo():\n '''\n 1. 特征选择\n 过滤低方差特征\n '''\n # 1、获取数据\n data = pd.read_csv(\"factor_returns.csv\")\n data = data.iloc[:, 1:-2]\n print(\"data:\\n\", data, data.shape)\n\n # 2、实例化一个转换器类\n transfer = VarianceThreshold(threshold=10)\n\n # 3、调用fit_transform\n data_new = transfer.fit_transform(data)\n print(\"data_new:\\n\", data_new, data_new.shape)\n\n # 计算某两个变量之间的相关系数\n r1 = pearsonr(data[\"pe_ratio\"], data[\"pb_ratio\"])\n print(\"相关系数：\\n\", r1)\n r2 = pearsonr(data['revenue'], data['total_expense'])\n print(\"revenue与total_expense之间的相关性：\\n\", r2)\n\n\nvariance_demo()\n\n\nfrom sklearn.decomposition import PCA\n\ndef pca_demo():\n '''\n PCA 降维 （PCA 主成分分析）\n '''\n data = [[2,8,4,5], [6,3,0,8], [5,4,9,1]]\n\n # 1、实例化一个转换器类\n transfer = PCA(n_components=0.95)\n\n # 2、调用fit_transform\n data_new = transfer.fit_transform(data)\n print(\"data_new:\\n\", data_new)\n\n\npca_demo()\n\n\n", "sub_path": "ml-basic/day1/day1-4.py", "file_name": "day1-4.py", "file_ext": "py", "file_size_in_byte": 1639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.VarianceThreshold", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 55, "usage_type": "call"}]} {"seq_id": "583130455", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import norm\nimport math\n\ndef calc_arr_f(s0 = 50, r = 0.1, q = 0.00, vol = 0.3, k = 45, step = 0.01, maxtime = 5):\n\ttspace = np.arange(step, maxtime+step, step)\n\tcall = []\n\n\tfor t in tspace:\n\t\td1 = (math.log(s0/k) + (r-q+(vol**2)/2)*t)/(vol*math.sqrt(t))\n\t\td2 = d1 - vol*math.sqrt(t)\n\t\tcall1 = s0*math.exp(-1*q*t)*norm.cdf(d1) - k*math.exp(-1*r*t)*norm.cdf(d2)\n\t\tcall2 = min(call1, call1 - (s0 - k))\n\n\t\tcall.append(call2)\n\n\tdf = pd.DataFrame(index = tspace)\n\tdf['Call_'+str(k)] = call\n\n\treturn df\n\n\ndef plotgreek(k1, k2, k3, df, ylimit = None, type1 = \"Delta\"):\n\tstyles = ['dashdot', 'solid', 'dotted']\n\tlinewidths = [3, 3, 3]\n\tfig, ax = plt.subplots()\n\tfor col, style, lw in zip(df.columns, styles, linewidths):\n\t\tax.set_ylim(ylimit)\n\t\tdf[col].plot(linestyle=style, lw=lw, ax=ax, color = \"black\")\n\n\n\tplt.legend([\"In-the-money\", \"At-the-money\", \"Out-of-the-money\"], prop={'size':15})\n\ttitle = \"Variation of Extrinsic Value with Time to Maturity\"\n\tfig.suptitle(title, fontsize=16)\n\tplt.xlabel('Time to Maturity (Years)', fontsize=13)\n\tplt.ylabel('Extrinsic Value ($)', fontsize=13)\n\tfig.savefig('./Q1_extrinsic' + '.jpg')\n\nk1 = 40\nk2 = 50\nk3 = 60\ndf_out = calc_arr_f(k = k1)\ndf_at = calc_arr_f(k = k2)\ndf_in = calc_arr_f(k = k3)\ndf_c = pd.concat([df_out, df_at, df_in], axis=1)\n\n\n\ndf_call = df_c.filter(like='Call', axis=1)\nprint(df_call)\n\nplotgreek(k1, k2, k3, ylimit = [0,23.1], df = df_call, type1 = \"Delta\")\n# plotgreek(k1, k2, k3, ylimit = [0,0.06], df = df_gamma, type1 = \"Gamma\")\n# plotgreek(k1, k2, k3, ylimit = [-9,0], df = df_theta, type1 = \"Theta\")\n", "sub_path": "q1_extrinsic.py", "file_name": "q1_extrinsic.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.arange", "line_number": 8, "usage_type": "call"}, {"api_name": "math.log", "line_number": 12, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 13, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 47, "usage_type": "call"}]} {"seq_id": "509913346", "text": "from enum import Enum\nfrom Coordinates import Coordinates\nfrom Gui.Logger import Logger\nfrom Species.Animal import Animal\n\nSPECIAL_COUNTDOWN = 5\nSPECIAL_STRENGTH = 10\n\n\nclass HumanTasks(Enum):\n\tDO_NOTHING = 0\n\tGO_UP = 1\n\tGO_DOWN = 2\n\tGO_LEFT = 3\n\tGO_RIGHT = 4\n\tDO_SPECIAL = 5\n\n\nclass Human(Animal):\n\n\tdef __init__(self, fromworld, x=None, y=None, age=0, strength=0, initiative=0, name=\"\", specialcountdown=0):\n\t\tsuper().__init__(fromworld, x, y)\n\t\tself._age = age\n\t\tself._strength = strength if strength != 0 else 5\n\t\tself._initiative = initiative if initiative != 0 else 4\n\t\tif name != \"\":\n\t\t\tself._name = name\n\t\tself._type = 'H'\n\t\tself.__specialcountdown = specialcountdown\n\t\tself.__nexttask = HumanTasks.DO_NOTHING\n\t\tself._fromworld.humanalive = True\n\n\tdef clone(self, fromworld, position):\n\t\treturn Human(fromworld, position.x, position.y)\n\n\tdef action(self):\n\t\tif self.__specialcountdown > 0:\n\t\t\tself.__specialcountdown -= 1\n\t\t\tself._strength -= 1\n\t\t\tLogger.log(\"%s's strength is dropping! %i turns till normal\" % (self.introduce(), self.__specialcountdown))\n\t\tif self.__nexttask == HumanTasks.GO_UP:\n\t\t\tself.move(Coordinates(self._position.x, self._position.y - 1))\n\t\telif self.__nexttask == HumanTasks.GO_DOWN:\n\t\t\tself.move(Coordinates(self._position.x, self._position.y + 1))\n\t\telif self.__nexttask == HumanTasks.GO_LEFT:\n\t\t\tself.move(Coordinates(self._position.x - 1, self._position.y))\n\t\telif self.__nexttask == HumanTasks.GO_RIGHT:\n\t\t\tself.move(Coordinates(self._position.x + 1, self._position.y))\n\t\telif self.__nexttask == HumanTasks.DO_SPECIAL:\n\t\t\tself.__specialcountdown = SPECIAL_COUNTDOWN\n\t\t\tself._strength = SPECIAL_STRENGTH\n\t\t\tLogger.log(\"%s used their special ability!\" % (self.introduce()))\n\t\telse:\n\t\t\tLogger.log(\"%s had nothing to do this turn\" % (self.introduce()))\n\t\tself.__nexttask = HumanTasks.DO_NOTHING\n\n\tdef istasklegal(self, task):\n\t\tif task == HumanTasks.GO_UP:\n\t\t\treturn self._position.y - 1 >= 0\n\t\telif task == HumanTasks.GO_DOWN:\n\t\t\treturn self._position.y + 1 < self._fromworld.getmaxxy().y\n\t\telif task == HumanTasks.GO_LEFT:\n\t\t\treturn self._position.x - 1 >= 0\n\t\telif task == HumanTasks.GO_RIGHT:\n\t\t\treturn self._position.x + 1 < self._fromworld.getmaxxy().x\n\t\telif task == HumanTasks.DO_SPECIAL:\n\t\t\treturn self.__specialcountdown <= 0\n\t\telse:\n\t\t\treturn False\n\n\tdef setnexttask(self, task):\n\t\tself.__nexttask = task\n\n\tdef die(self):\n\t\tsuper().die()\n\t\tself._fromworld.humanalive = False\n\n\tdef tostring(self):\n\t\treturn \"%s%d\" % (super().tostring(), self.__specialcountdown)\n", "sub_path": "Species/Animals/Human.py", "file_name": "Human.py", "file_ext": "py", "file_size_in_byte": 2507, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "enum.Enum", "line_number": 10, "usage_type": "name"}, {"api_name": "Species.Animal.Animal", "line_number": 19, "usage_type": "name"}, {"api_name": "Gui.Logger.Logger.log", "line_number": 40, "usage_type": "call"}, {"api_name": "Gui.Logger.Logger", "line_number": 40, "usage_type": "name"}, {"api_name": "Coordinates.Coordinates", "line_number": 42, "usage_type": "call"}, {"api_name": "Coordinates.Coordinates", "line_number": 44, "usage_type": "call"}, {"api_name": "Coordinates.Coordinates", "line_number": 46, "usage_type": "call"}, {"api_name": "Coordinates.Coordinates", "line_number": 48, "usage_type": "call"}, {"api_name": "Gui.Logger.Logger.log", "line_number": 52, "usage_type": "call"}, {"api_name": "Gui.Logger.Logger", "line_number": 52, "usage_type": "name"}, {"api_name": "Gui.Logger.Logger.log", "line_number": 54, "usage_type": "call"}, {"api_name": "Gui.Logger.Logger", "line_number": 54, "usage_type": "name"}]} {"seq_id": "42255834", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 20 19:32:05 2020\n\n@author: davideferri\n\"\"\"\n\nimport logging \nimport numpy as np \nimport pandas as pd\nimport scipy.stats as ss \nimport pymc3 as pm \nimport arviz as az\n\n# initialize the logger\nlog = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.INFO,format='%(name)s - %(levelname)s - %(message)s')\n\n# --------------------- generate synthetic data -------------------------- # \n\n# get the number of observations by group\nN = 20 \n# get the number of groups \nM = 8\n# define the index array; all group have N observations but the last (only 1)\nidx = np.repeat(range(M-1),N)\nidx = np.append(idx,7)\nlog.info(\"The index list is: %s\",idx)\n# set a random seed \nnp.random.seed(314)\n\n# define the real coefficients\nalpha_real = ss.norm.rvs(loc=2.5,scale=0.5,size=M)\nlog.info(\"The alpha real is: %s\", alpha_real)\nbeta_real = np.random.beta(6,1,size=M)\nlog.info(\"The beta real is: %s\", beta_real)\neps_real = np.random.normal(0,0.5,size=len(idx))\n\n# set the independent variable\nx_m = np.random.normal(10,1,len(idx))\n# set the dependent variable\ny_m = alpha_real[idx] + beta_real[idx] * x_m + eps_real\n# plot the true data\nfig,ax = plt.subplots(2,4, figsize = (10,5), sharex = True, sharey = True)\nax = np.ravel(ax)\n# initialize j and k\nj, k = 0, N\nfor i in range(M):\n # scatter the data\n ax[i].scatter(x_m[j:k],y_m[j:k])\n # set the x label\n ax[i].set_xlabel(f\"x_{i}\")\n # set the y label\n ax[i].set_ylabel(f\"y_{i}\",rotation = 0, labelpad = 15)\n # set the x axis limit\n ax[i].set_xlim(6,15)\n # set the y axis limit\n ax[i].set_ylim(7,17)\n # update j,k \n j += N\n k += N\nplt.tight_layout()\nplt.show()\n# let us center the x data \nx_centered = x_m - x_m.mean()\n \n# --------------- specify a non-hierarchical (unpooled) probabilistic model -------------------------- #\n\nwith pm.Model() as unpooled_model:\n # set the priors on parameters\n alpha_temp = pm.Normal(\"alpha_temp\", mu = 0, sd = 10, shape = M)\n beta = pm.Normal(\"beta\",mu = 0, sd = 10, shape = M)\n # get the alpha for the uncentered data\n alpha = pm.Deterministic(\"alpha\", alpha_temp - beta * x_m.mean())\n # set the priors on scale and df\n sigma = pm.HalfCauchy(\"sigma\",5)\n df = pm.Exponential(\"df\",1/30)\n # specify the likelihood of the data\n y_obs = pm.StudentT(\"y_obs\", mu = alpha_temp[idx] + beta[idx] * x_centered, sd = sigma, nu = df, observed = y_m)\n # inference step \n trace_unp = pm.sample(2000)\n\n# -------------- analyse the posterior -------------------------------------- # \n \nwith unpooled_model:\n az.plot_forest(trace_unp, var_names = [\"alpha\",\"beta\"], combined = True)\n \n# ---------------- specify a hierarchical probabilistic model ----------------------------- #\n \nwith pm.Model() as hierarchical_model:\n # specify a set of hyper-priors\n alpha_m_temp = pm.Normal(\"alpha_m_temp\", mu = 0, sd = 10)\n alpha_s_temp = pm.HalfNormal(\"alpha_s_temp\",sd = 10)\n beta_m = pm.Normal(\"beta_m\", mu = 0, sd = 10)\n beta_s = pm.HalfNormal(\"beta_s\",sd = 10)\n # set the priors on parameters\n alpha_temp = pm.Normal(\"alpha_temp\", mu = alpha_m_temp, sd = alpha_s_temp, shape = M)\n beta = pm.Normal(\"beta\",mu = beta_m, sd = beta_s, shape = M)\n # get the alpha for the uncentered data\n alpha = pm.Deterministic(\"alpha\", alpha_temp - beta * x_m.mean())\n alpha_m = pm.Deterministic(\"alpha_m\", alpha_m_temp - beta_m * x_m.mean())\n # set the priors on scale and df\n sigma = pm.HalfCauchy(\"sigma\",5)\n df = pm.Exponential(\"df\",1/30)\n # set the likelihood \n y_obs = pm.StudentT(\"y_obs\", mu = alpha_temp[idx] + beta[idx] * x_centered, sd = sigma, nu = df, observed = y_m)\n # inference step \n trace_hm = pm.sample(2000,tune = 2000)\n \n# -------------- analyse the posterior ------------------------------ #\n \nwith hierarchical_model:\n az.plot_forest(trace_hm, var_names = [\"alpha\",\"beta\"], combined = True)\n az.plot_trace(trace_hm, var_names = [\"beta_m\",\"alpha_m\"])\n \n# # ----------------- plot the regression results for each one of the models ------------------------ #\n \nfig,ax = plt.subplots(2,4, figsize = (10,5), sharex = True, sharey = True)\nax = np.ravel(ax)\n# initialize j and k\nj, k = 0, N\nfor i in range(M):\n # scatter the data\n ax[i].scatter(x_m[j:k],y_m[j:k])\n # set the x label\n ax[i].set_xlabel(f\"x_{i}\")\n # set the y label\n ax[i].set_ylabel(f\"y_{i}\",rotation = 0, labelpad = 15)\n # set the x axis limit\n ax[i].set_xlim(6,15)\n # set the y axis limit\n ax[i].set_ylim(7,17)\n # get the alpha of the group (mean of the posterior)\n alpha = trace_hm[\"alpha\"][:,i].mean()\n # get the beta of the group (mean of the posterior)\n beta = trace_hm[\"beta\"][:,i].mean()\n # get the xrange for which to plot the line\n x_range = np.linspace(x_m.min(), x_m.max(), 10)\n # plot the regression line\n ax[i].plot(x_range, alpha + beta * x_range, c='k',label=f'y = {alpha:.2f} + {beta:.2f} * x')\n # update j,k \n j += N\n k += N\nplt.tight_layout()\nplt.show()\n \n \n \n ", "sub_path": "HierLinReg.py", "file_name": "HierLinReg.py", "file_ext": "py", "file_size_in_byte": 5115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "scipy.stats.norm.rvs", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 34, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.random.beta", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.ravel", "line_number": 46, "usage_type": "call"}, {"api_name": "pymc3.Model", "line_number": 70, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 72, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 73, "usage_type": "call"}, {"api_name": "pymc3.Deterministic", "line_number": 75, "usage_type": "call"}, {"api_name": "pymc3.HalfCauchy", "line_number": 77, "usage_type": "call"}, {"api_name": "pymc3.Exponential", "line_number": 78, "usage_type": "call"}, {"api_name": "pymc3.StudentT", "line_number": 80, "usage_type": "call"}, {"api_name": "pymc3.sample", "line_number": 82, "usage_type": "call"}, {"api_name": "arviz.plot_forest", "line_number": 87, "usage_type": "call"}, {"api_name": "pymc3.Model", "line_number": 91, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 93, "usage_type": "call"}, {"api_name": "pymc3.HalfNormal", "line_number": 94, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 95, "usage_type": "call"}, {"api_name": "pymc3.HalfNormal", "line_number": 96, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 98, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 99, "usage_type": "call"}, {"api_name": "pymc3.Deterministic", "line_number": 101, "usage_type": "call"}, {"api_name": "pymc3.Deterministic", "line_number": 102, "usage_type": "call"}, {"api_name": "pymc3.HalfCauchy", "line_number": 104, "usage_type": "call"}, {"api_name": "pymc3.Exponential", "line_number": 105, "usage_type": "call"}, {"api_name": "pymc3.StudentT", "line_number": 107, "usage_type": "call"}, {"api_name": "pymc3.sample", "line_number": 109, "usage_type": "call"}, {"api_name": "arviz.plot_forest", "line_number": 114, "usage_type": "call"}, {"api_name": "arviz.plot_trace", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 139, "usage_type": "call"}]} {"seq_id": "446851507", "text": "#\n# avg_pooling\n#\n\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nimport numpy as np\nfrom datetime import datetime\nimport os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '1'\n\nnow = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n# Training Parameters\nlearning_rate = 0.005\n# momentum = 0.1\nn_epochs = 10\nbatch_size = 1024\ndisplay_step = 200\npatience = 3\nmin_delta_acc = 0.001\n# Network Parameters\nnum_input = 3\ntimes_steps = 200\nnum_hidden = 512\n# num_hidden_2 = 128\nnum_classes = 107\nroot_logdir = './submit_model/logs/'\nlogdir = \"{}/run-{}/\".format(root_logdir, now)\nmodeldir = './submit_model/model/biLSTM_'\n\nTrain = np.load(\"../WriterID/Update_RHS_Data/Train107_200_3.npy\", allow_pickle=True)\n# np.random.shuffle(Train)\nTrain_X = Train[:, 0:-1].reshape((-1, times_steps, num_input))\nTrain_y = np.eye(num_classes)[Train[:, -1].astype(int)]\nTest = np.load(\"../WriterID/Update_RHS_Data/Test107_200_3.npy\", allow_pickle=True)\n# np.random.shuffle(Test)\nTest_X = Test[:, 0:-1].reshape((-1, times_steps, num_input))\nTest_y = np.eye(num_classes)[Test[:, -1].astype(int)]\n\nn_batches = int(np.ceil(Train.shape[0] / batch_size))\n\ntf_config = tf.ConfigProto()\n# tf_config.log_device_placement = True\ntf_config.gpu_options.allow_growth = True\n\n# tf Graph input\nX = tf.placeholder(\"float\", [None, times_steps, num_input], name='X')\nY = tf.placeholder(\"float\", [None, num_classes], name='Y')\n\n# X_input = tf.placeholder(dtype=tf.float32, shape=(None, times_steps, num_input))\n# Y_input = tf.placeholder(dtype=tf.float32, shape=(None, num_classes))\n#\n# X_assign = tf.assign(X, X_input)\n# Y_assign = tf.assign(Y, Y_input)\n\n# Define weights\nweights = {\n # Hidden layer weights => 2*n_hidden because of forward + backward cells\n 'out': tf.Variable(tf.random_normal([num_hidden, num_classes]), name='w_out')\n}\nbiases = {\n 'out': tf.Variable(tf.random_normal([num_classes]), name='b_out')\n}\n\ndef BiLstm(x, weights, biases):\n # Prepare data shape to match `rnn` function requirements\n # Current data input shape: (batch_size, timesteps, n_input)\n # Required shape: 'timesteps' tensors list of shape (batch_size, num_input)\n\n # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)\n x = tf.unstack(x, times_steps, 1)\n\n # Define lstm cells with tensorflow\n # Forward direction cell\n lstm_fw_cell_1 = tf.nn.rnn_cell.LSTMCell(num_hidden, forget_bias=1.0, name=\"fw_1\")\n # Backward direction cell\n lstm_bw_cell_1 = tf.nn.rnn_cell.LSTMCell(num_hidden, forget_bias=1.0, name=\"bw_1\")\n\n # lstm_fw_cell_2 = tf.nn.rnn_cell.LSTMCell(num_hidden_2, forget_bias=1.0, name=\"fw_2\")\n # # Backward direction cell\n # lstm_bw_cell_2 = tf.nn.rnn_cell.LSTMCell(num_hidden_2, forget_bias=1.0, name=\"bw_2\")\n\n # Get lstm cell output\n try:\n outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell_1, lstm_bw_cell_1, x,\n dtype=tf.float32)\n # o, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell_2, lstm_bw_cell_2, outputs,\n # dtype=tf.float32)\n except Exception: # Old TensorFlow version only returns outputs not states\n outputs = rnn.static_bidirectional_rnn(lstm_fw_cell_1, lstm_bw_cell_1, x,\n dtype=tf.float32)\n # o = rnn.static_bidirectional_rnn(lstm_fw_cell_2, lstm_bw_cell_2, outputs,\n # dtype=tf.float32)\n o1 = tf.expand_dims(outputs[-1], 1)\n o2 = tf.expand_dims(o1, 3)\n v = tf.reshape(tf.nn.avg_pool(o2, ksize=[1, 1, 2, 1], strides=[1, 1, 2, 1], padding='VALID'), [-1, num_hidden])\n return tf.matmul(v, weights['out']) + biases['out']\n\n\nlogits = BiLstm(X, weights, biases)\n\nprediction = tf.nn.softmax(logits, name=\"prediction\")\n\n# Define loss and optimizer\nloss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n logits=logits, labels=Y), name=\"loss_op\")\n# loss_l1 = tf.contrib.layers.l1_regularizer(scale)(tf.get_default_graph().get_tensor_by_name(\"w_out:0\"))\n# loss_op = tf.add(loss_base, loss_l1, name=\"loss\")\n# optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate,momentum=momentum)\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, name=\"Adam_Op\")\ntrain_op = optimizer.minimize(loss_op, name='train_op')\n\n# Evaluate model (with test logits, for dropout to be disabled)\ncorrect_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name=\"accuracy\")\n\ninit = tf.global_variables_initializer()\nloss = tf.summary.scalar(\"loss\",loss_op)\nacc = tf.summary.scalar(\"acc\", accuracy)\nmerged_summary_op = tf.summary.merge([loss, acc])\n\n\n# Run the initializer\n\ndef fetch_batch(epoch, batch_index, batch_size, train=True):\n np.random.seed(epoch * n_batches + batch_index) # not shown in the book\n if train:\n indices = np.random.randint(Train.shape[0], size=batch_size) # not shown\n X_batch = Train_X[indices] # not shown\n y_batch = Train_y[indices] # not shown\n else:\n indices = np.random.randint(Test.shape[0], size=batch_size) # not shown\n X_batch = Test_X[indices] # not shown\n y_batch = Test_y[indices] # not shown\n return X_batch, y_batch\n\n\nwith tf.Session(config=tf_config) as sess:\n saver = tf.train.Saver()\n sess.run(init)\n train_summary_writer = tf.summary.FileWriter(logdir+'train/', graph=tf.get_default_graph())\n test_summary_writer = tf.summary.FileWriter(logdir+'test/')\n acc_l =[]\n stop = False\n for epoch in range(n_epochs):\n if stop:\n break\n for batch_index in range(n_batches):\n batch_x, batch_y = fetch_batch(epoch, batch_index, batch_size)\n sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n step = epoch * n_batches + batch_index\n\n if step % display_step == 0:\n # Calculate batch loss and accuracy\n loss, acc, summary = sess.run([loss_op, accuracy, merged_summary_op], \\\n feed_dict={X: batch_x, Y: batch_y})\n train_summary_writer.add_summary(summary, int(step))\n\n batch_x_test, batch_y_test = fetch_batch(epoch, batch_index, 5120, False)\n loss_test, acc_test, summary_test = sess.run([loss_op, accuracy, merged_summary_op], \\\n feed_dict={X: batch_x_test, Y: batch_y_test})\n test_summary_writer.add_summary(summary_test, int(step))\n acc_l.append(acc_test)\n print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n \"{:.3f}\".format(acc) + \", Testing Accuracy= \" + \\\n \"{:.3f}\".format(acc_test))\n if step == 0 or acc_l[-1] - acc_l[-2] >= min_delta_acc:\n p_n = 0\n else:\n p_n += 1\n if p_n == patience:\n stop = True\n break\n print(\"Optimization Finished!\")\n print(\"Run the command line:\\n\"\n \"--> tensorboard --logdir=./logs/\"\n \"\\nThen open http://0.0.0.0:6006/ into your web browser\")\n\n saver.save(sess, modeldir + now + '/biLSTM')\nprint(now)\nprint(\"Finished!\")\n", "sub_path": "Code/biLSTM.py", "file_name": "biLSTM.py", "file_ext": "py", "file_size_in_byte": 7389, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.random_normal", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.random_normal", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.unstack", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.LSTMCell", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.rnn.static_bidirectional_rnn", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.contrib.rnn", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.rnn.static_bidirectional_rnn", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.contrib.rnn", "line_number": 91, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.nn.avg_pool", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.merge", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.get_default_graph", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 143, "usage_type": "attribute"}]} {"seq_id": "318141773", "text": "# -*- coding: UTF-8 -*-\n\"\"\"\nAll examples runner\n\"\"\"\n\nfrom importlib import import_module\nimport logging\nimport os\nimport sys\n\n# services dict: key is a name of CAPTCHA solving service, value is an env variable containing\n# the API key\nSERVICES = {\n '2captcha.com': '2CAPTCHA_API_KEY',\n 'anti-captcha.com': 'ANTICAPTCHA_API_KEY',\n 'azcaptcha.com': 'AZCAPTCHA_API_KEY',\n 'cptch.net': 'CPTCH_NET_API_KEY'\n}\n\n# list of modules containing CAPTCHA solving examples\nEXAMPLES = [\n 'image',\n 'recaptcha_v2',\n 'recaptcha_v2_invisible',\n 'recaptcha_v2_proxy',\n 'recaptcha_v3',\n 'hcaptcha'\n]\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\nif __name__ == '__main__':\n for service_name in SERVICES:\n api_key = os.getenv(SERVICES[service_name])\n print(f'######### Service: {service_name} #########')\n\n for example in EXAMPLES:\n module = import_module(example)\n module.solve(service_name, api_key)\n", "sub_path": "examples/run_all.py", "file_name": "run_all.py", "file_ext": "py", "file_size_in_byte": 955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 35, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 39, "usage_type": "call"}]} {"seq_id": "424292967", "text": "from boto.s3.connection import S3Connection\n\n# Import keys into a dict from txt document\namazon_keys = {}\nwith open('../keys.txt', 'r') as f:\n for line in f:\n line = line.strip()\n splitLine = line.split(',')\n amazon_keys[splitLine[0]] = splitLine[-1]\n\n\n# Create a connection to S3 using the keys from above\nconn = S3Connection(\n aws_access_key_id = amazon_keys['access_key'],\n aws_secret_access_key = amazon_keys['secret_key'],\n is_secure=False\n )\n\n# Access bucket called colin-greene\nbucket = conn.get_bucket('colin-greene') \n\n# Store path to the desired file\nunigram_summary = 'ngramcounts/all_unigrams.csv' \n\n# Generate a key for the file \nkey = bucket.get_key(unigram_summary) \n\n# URL that makes link available for 3 weeks\nurl = key.generate_url(86400*21) \n\nf = open('protected_url.txt', 'w')\n\nf.write(url)\nf.close()", "sub_path": "ps2/query_string_authentication.py", "file_name": "query_string_authentication.py", "file_ext": "py", "file_size_in_byte": 888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "boto.s3.connection.S3Connection", "line_number": 13, "usage_type": "call"}]} {"seq_id": "435716984", "text": "#!/usr/bin/env python\n\nimport os\nfrom setuptools import setup\n\n\ndef read(readme_file):\n return open(os.path.join(os.path.dirname(__file__), readme_file)).read()\n\n\nsetup(\n name=\"dragoman\",\n version=\"0.0.0\",\n author='Ross Fenning',\n author_email='ross.fenning@gmail.com',\n description='Translates RDF data between vocabularies.',\n url='http://github.com/AvengerPenguin/dragoman',\n install_requires=['docopt', 'FuXi', 'rdflib'],\n packages=['dragoman'],\n entry_points={\n 'console_scripts': [\n 'dragoman = dragoman:main',\n ],\n },\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}]} {"seq_id": "126397904", "text": "from collections import OrderedDict\n\nimport torch\n\n\ndef main():\n objs = torch.load('./models/mask_rcnn_X_101_32x8d_FPN_deform_mixup_0060000_without_box_pred.pth', map_location='cpu')\n model = objs['model']\n print(sum((v.numel() for _, v in model.items()), ))\n\n new_model = OrderedDict()\n for key in model.keys():\n if not key.startswith('roi_heads.box.feature_extractor.pooler'):\n new_model[key] = model[key]\n\n torch.save({\n 'model': new_model,\n }, './models/mask_rcnn_X_101_32x8d_FPN_deform_mixup_0060000_without_box_pred_and_pooler.pth')\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "tools/tianchi_xray/convert.py", "file_name": "convert.py", "file_ext": "py", "file_size_in_byte": 625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.load", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 16, "usage_type": "call"}]} {"seq_id": "566986500", "text": "import collections\n\n\nclass Solution:\n def intersection(self, nums1, nums2):\n counts = collections.Counter(nums1)\n ans = []\n for x in nums2:\n if counts[x] > 0: # 很巧, 这种以另一个数组中的元素为另一个数组中的索引的方法很巧\n ans.append(x)\n counts[x] -= 1\n return ans\n\nif __name__ == '__main__':\n temp = Solution()\n List1 = [1, 2, 3, 4, 5, 6]\n List2 = [2, 4, 6, 8, 10]\n print((\"输入：\" + str(List1) + \" \" + str(List2)))\n print((\"输出：\" + str(temp.intersection(List1, List2))))\n\n# import collections\n# class Solution:\n# def intersection(self, nums1, nums2):\n# counts = collections.Counter(nums1)\n# result = []\n# for num in nums2:\n# if counts[num] > 0:\n# result.append(num)\n# counts[num] -= 1\n# return result\n", "sub_path": "Python算法指南/65_两数组的交集II_Collection.Couter的巧妙应用.py", "file_name": "65_两数组的交集II_Collection.Couter的巧妙应用.py", "file_ext": "py", "file_size_in_byte": 909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "collections.Counter", "line_number": 6, "usage_type": "call"}]} {"seq_id": "54702528", "text": "from analyzer.recordset.recordset_factory import recordset_factory\nfrom django.urls import reverse\n\nfrom ..models import Dataset, DatasetMetadataConstraint, PostgresConnection\n\nfrom django.forms.models import inlineformset_factory, modelform_factory\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\n\n\n\n@login_required()\ndef manage_constraint(request, dataset_id):\n ConstraintForm = modelform_factory(DatasetMetadataConstraint, fields=(\n 'dataset', 'constraint_name', 'columns', 'constraint_type', 'constraint_definition'))\n ConstraintFormSet = inlineformset_factory(Dataset, DatasetMetadataConstraint,\n form=ConstraintForm, extra=0, can_delete=True)\n ConstraintFormSetAdd = inlineformset_factory(Dataset, DatasetMetadataConstraint,\n form=ConstraintForm, extra=1, can_delete=False)\n dataset = get_object_or_404(Dataset, id=dataset_id)\n if request.method == \"POST\":\n if request.POST[\"Operation\"] == \"Save\":\n if request.POST.get('Extraction') == 'true':\n formset = ConstraintFormSet(instance=dataset)\n for form in formset:\n obj = form.save(commit=False)\n obj.delete()\n\n formset = ConstraintFormSet(request.POST, instance=dataset)\n\n if formset.is_valid():\n formset.save()\n messages.success(request, 'Data was saved successfully.')\n return render(request, \"manage_constraint/manage_constraint.html\", {\"formset\": formset, \"dataset\": dataset})\n\n if request.POST[\"Operation\"] == 'Add':\n formset = ConstraintFormSetAdd(instance=dataset)\n return render(request, 'manage_constraint/manage_constraint.html', {'formset': formset, 'dataset': dataset})\n\n if request.POST[\"Operation\"] == 'Return':\n return redirect(\"dataset_detail\", dataset_id=dataset_id)\n\n if request.POST[\"Operation\"] == \"Undo\":\n return redirect(reverse(\"manage_constraint\", kwargs={\"dataset_id\": dataset_id}))\n\n if request.POST[\"Operation\"] == \"Extract\":\n recordset = recordset_factory(dataset)\n access_method = dataset.dataset_access_method\n\n if access_method == 'TABLE':\n table = dataset.dataset_extraction_table.split(\".\")[1]\n\n pk_and_unique_constraints = recordset.get_pk_and_unique_constraints(table)\n check_constraints = recordset.get_check_constraints(table)\n is_nullable_constraints = recordset.get_is_nullable_constraints(table)\n\n form = []\n form = fill_constraint_form(pk_and_unique_constraints, form, dataset, kind=\"PRIMARY KEY\")\n form = fill_constraint_form(check_constraints, form, dataset, kind=\"CHECK\")\n form = fill_constraint_form(is_nullable_constraints, form, dataset, kind=\"IS_NULLABLE\")\n\n ConstraintFormSetExtract = inlineformset_factory(Dataset, DatasetMetadataConstraint,\n form=ConstraintForm, extra=len(form),\n can_delete=False)\n\n formset = ConstraintFormSetExtract(queryset=DatasetMetadataConstraint.objects.none(), initial=form)\n return render(request, 'manage_constraint/manage_constraint.html', {'formset': formset, 'dataset': dataset, \"extraction\": True})\n\n formset = ConstraintFormSet(instance=dataset)\n return render(request, 'manage_constraint/manage_constraint.html', {'formset': formset, 'dataset': dataset})\n\n\ndef fill_constraint_form(constraint, form, dataset, **kwargs):\n if kwargs[\"kind\"] == \"PRIMARY KEY\":\n for i in range(len(constraint)):\n form.append({\n \"dataset\": dataset.pk,\n \"constraint_name\": constraint[i][0],\n \"columns\": constraint[i][1],\n \"constraint_type\": constraint[i][2],\n \"constraint_definition\": \"{0} is primary key\".format(constraint[i][1])\n })\n return form\n elif kwargs[\"kind\"] == \"CHECK\":\n for i in range(len(constraint)):\n form.append({\n \"dataset\": dataset.pk,\n \"constraint_name\": constraint[i][0],\n \"columns\": constraint[i][1],\n \"constraint_type\": \"CHECK\",\n \"constraint_definition\": constraint[i][2]\n })\n return form\n elif kwargs[\"kind\"] == \"IS_NULLABLE\":\n for i in range(len(constraint)):\n if constraint[i][1] == \"NO\":\n form.append({\n \"dataset\": dataset.pk,\n \"constraint_name\": \"{0}_not_null\".format(constraint[i][0]),\n \"columns\": constraint[i][0],\n \"constraint_type\": \"IS_NULLABLE\",\n \"constraint_definition\": \"{0} is not null\".format(constraint[i][0])\n })\n return form\n", "sub_path": "saefportal/saef/views/manage_constraint_view.py", "file_name": "manage_constraint_view.py", "file_ext": "py", "file_size_in_byte": 5146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.forms.models.modelform_factory", "line_number": 15, "usage_type": "call"}, {"api_name": "models.DatasetMetadataConstraint", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.forms.models.inlineformset_factory", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Dataset", "line_number": 17, "usage_type": "argument"}, {"api_name": "models.DatasetMetadataConstraint", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.forms.models.inlineformset_factory", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Dataset", "line_number": 19, "usage_type": "argument"}, {"api_name": "models.DatasetMetadataConstraint", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Dataset", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 45, "usage_type": "call"}, {"api_name": "analyzer.recordset.recordset_factory.recordset_factory", "line_number": 48, "usage_type": "call"}, {"api_name": "django.forms.models.inlineformset_factory", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Dataset", "line_number": 63, "usage_type": "argument"}, {"api_name": "models.DatasetMetadataConstraint", "line_number": 63, "usage_type": "argument"}, {"api_name": "models.DatasetMetadataConstraint.objects.none", "line_number": 67, "usage_type": "call"}, {"api_name": "models.DatasetMetadataConstraint.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.DatasetMetadataConstraint", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "call"}]} {"seq_id": "212763883", "text": "# ===========================================================================\n#\n# file : glyph.py\n# part of : godafoss micropython library\n# url : https://www.github.com/wovo/godafoss\n# author : Wouter van Ooijen (wouter@voti.nl) 2022\n# license : MIT license, see license variable in the code\n#\n# This file is part of the Godafoss perhiperal interface library.\n#\n# ===========================================================================\n\nimport sys\nfrom PIL import Image\n\n\n# ===========================================================================\n\ndef make_glyph( input_file, output_name, x_size, y_size ):\n\n im = Image.open( input_file )\n \n if x_size != 0:\n if y_size == 0:\n y_size = im.size[ 1 ] * x_size // im.size[ 0 ]\n \n if y_size != 0:\n if x_size == 0:\n x_size = im.size[ 0 ] * y_size // im.size[ 1 ]\n \n if x_size == 0:\n x_size, y_size = im.size\n\n im = im.resize( ( x_size, y_size ) ) \n if not isinstance( im.getpixel( ( 0, 0 ) ), int ):\n print( \"The input must be a b/w file.\" )\n \n b = 0\n n = 0\n data = []\n for y in range( y_size ):\n s = \"\"\n for x in range( x_size ):\n n += 1\n c = im.getpixel( ( x, y ) )\n b = b >> 1\n if c:\n b |= 0x80\n s += \" \"\n else:\n s += \"O\" \n if ( n % 8 == 7 ) or ( n + 1 == x * y ):\n data.append( b )\n b = 0\n if 1: print( \"%2d|\" % y + s + \"|\" )\n \n f = open( output_name + \".py\", \"w\" )\n \n f.write( \"from godafoss import xy, glyph\\n\" )\n f.write( \"\\n\" )\n f.write( \"class %s( glyph ):\\n\" % output_name )\n f.write( \" \\\"\\\"\\\"\\n\" );\n f.write( \" image generated from %s\\n\" % input_file )\n f.write( \" size %d * %d\\n\" % ( x_size, y_size ) ) \n f.write( \" \\\"\\\"\\\"\\n\" );\n f.write( \"\\n\" )\n f.write( \" def __init__( self ) -> None:\\n\" )\n f.write( \" glyph.__init__( self, xy( %d, %d ) )\\n\" \n % ( x_size, y_size ) )\n f.write( \" self.data = bytes( [\\n\" )\n s = \"\"\n for i in range( len( data ) ):\n s += \"%d,\" % data[ i ]\n if ( len( s ) > 50 ) or ( i + 1 == len( data )):\n f.write( \" %s\\n\" % s )\n s = \"\"\n f.write( \" ] )\\n\" ) \n f.write( \"\\n\" ) \n f.write( \" def read( self, location: xy ) -> color:\\n\" ) \n f.write( \" n = location.x + location.y * self.size.x\\n\" )\n f.write( \" b = self.data[ n // 8 ] & ( 0x1 << ( n % 8 ))\\n\" )\n f.write( \" return b != 0\\n\" ) \n \n f.close()\n im.close()\n\n \n# ===========================================================================\n\ndef run( args ):\n if len( args ) < 3:\n print( \"usage:\" )\n print( \" glyph input_file output [x_size] [y_size]\" )\n print( \"\" )\n print( \"input_file: image file, must be a b/w acceptable to PIL.Image.open()\" )\n print( \"output: output file name (.py will be appended) and python image class name\" )\n print( \"x_size: x_size of the written image. default: taken from input.\" )\n print( \"y_size: y_size of the written image. default: taken from input.\" )\n print( \" \" )\n print( \"When either the x_size is specified but the y_size is not or is 0,\" )\n print( \"or the y_size is omitted, the aspect ratio is maintained.\" )\n return\n \n make_glyph( \n args[ 1 ], \n args[ 2 ], \n int( args[ 4 ] ) if len( args ) > 4 else 0,\n int( args[ 5 ] ) if len( args ) > 5 else 0\n ) \n\n \n# ===========================================================================\n\nif __name__ == \"__main__\":\n run( sys.argv )\n\n \n# ===========================================================================\n ", "sub_path": "make/glyph.py", "file_name": "glyph.py", "file_ext": "py", "file_size_in_byte": 3930, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PIL.Image.open", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "584061483", "text": "import logging\nimport time\nimport json\nimport unittest\nfrom secrets import token_bytes\nfrom typing import Any, Callable, Dict, List, Optional, Set, Tuple\n\nimport asyncio\nimport aiosqlite\nimport sqlite3\n\nfrom blspy import AugSchemeMPL, G1Element, G2Element\nfrom hashlib import sha256\n\nfrom chives.consensus.constants import ConsensusConstants\nfrom chives.consensus.coinbase import create_puzzlehash_for_pk\nfrom chives.util.bech32m import decode_puzzle_hash, encode_puzzle_hash\nfrom chives.util.config import load_config\nfrom chives.util.default_root import DEFAULT_ROOT_PATH\nfrom chives.util.ints import uint32, uint64\nfrom chives.util.hash import std_hash\nfrom chives.util.db_wrapper import DBWrapper\nfrom chives.util.keychain import Keychain, bytes_from_mnemonic, bytes_to_mnemonic, generate_mnemonic, mnemonic_to_seed\nfrom chives.wallet.derive_keys import master_sk_to_farmer_sk, master_sk_to_pool_sk, master_sk_to_wallet_sk\nfrom chives.wallet.wallet_coin_store import WalletCoinStore\nfrom chives.types.blockchain_format.coin import Coin\nfrom chives.types.blockchain_format.program import Program, SerializedProgram\nfrom chives.types.blockchain_format.sized_bytes import bytes32\n\nfrom chives.wallet.util.wallet_types import WalletType\nfrom chives.wallet.wallet_coin_record import WalletCoinRecord\n\nfrom chives.wallet.puzzles.p2_delegated_puzzle_or_hidden_puzzle import (\n DEFAULT_HIDDEN_PUZZLE_HASH,\n calculate_synthetic_secret_key,\n puzzle_for_pk,\n solution_for_conditions,\n)\nfrom chives.wallet.puzzles.puzzle_utils import (\n make_assert_coin_announcement,\n make_assert_puzzle_announcement,\n make_assert_my_coin_id_condition,\n make_assert_absolute_seconds_exceeds_condition,\n make_create_coin_announcement,\n make_create_puzzle_announcement,\n make_create_coin_condition,\n make_reserve_fee_condition,\n)\n\nimport redis\nr = redis.Redis(host='localhost', port=6379, decode_responses=True)\n\nclass TesKeychain():\n \n async def puzzle_for_puzzle_hash(puzzle_hash: bytes32) -> Program:\n public_key = await TesKeychain.hack_populate_secret_key_for_puzzle_hash(puzzle_hash)\n return puzzle_for_pk(bytes(public_key))\n \n def make_solution(\n primaries: Optional[List[Dict[str, Any]]] = None,\n min_time=0,\n me=None,\n coin_announcements: Optional[List[bytes32]] = None,\n coin_announcements_to_assert: Optional[List[bytes32]] = None,\n puzzle_announcements=None,\n puzzle_announcements_to_assert=None,\n fee=0,\n ) -> Program:\n assert fee >= 0\n condition_list = []\n if primaries:\n for primary in primaries:\n condition_list.append(make_create_coin_condition(primary[\"puzzlehash\"], primary[\"amount\"]))\n if min_time > 0:\n condition_list.append(make_assert_absolute_seconds_exceeds_condition(min_time))\n if me:\n condition_list.append(make_assert_my_coin_id_condition(me[\"id\"]))\n if fee:\n condition_list.append(make_reserve_fee_condition(fee))\n if coin_announcements:\n for announcement in coin_announcements:\n condition_list.append(make_create_coin_announcement(announcement))\n if coin_announcements_to_assert:\n for announcement_hash in coin_announcements_to_assert:\n condition_list.append(make_assert_coin_announcement(announcement_hash))\n if puzzle_announcements:\n for announcement in puzzle_announcements:\n condition_list.append(make_create_puzzle_announcement(announcement))\n if puzzle_announcements_to_assert:\n for announcement_hash in puzzle_announcements_to_assert:\n condition_list.append(make_assert_puzzle_announcement(announcement_hash))\n return solution_for_conditions(condition_list)\n \n async def TestTransaction():\n root_path = DEFAULT_ROOT_PATH\n config = load_config(root_path, \"config.yaml\")\n selected = config[\"selected_network\"]\n prefix = config[\"network_overrides\"][\"config\"][selected][\"address_prefix\"]\n log = logging.Logger\n db_connection = await aiosqlite.connect(\"/home/wang/.chives/mainnet/db/blockchain_v1_mainnet.sqlite\")\n mnemonic = generate_mnemonic()\n mnemonic = \"hen battle gauge crouch dose weasel blind noble ugly pull cruel mutual slight tragic bean rule once garage valley ritual still couple charge rich\"\n entropy = bytes_from_mnemonic(mnemonic)\n seed = mnemonic_to_seed(mnemonic, \"\")\n seed_key = AugSchemeMPL.key_gen(seed)\n masterPublicKey = seed_key.get_g1()\n fingerprint = masterPublicKey.get_fingerprint()\n \n MapKeys = {}\n for i in range(10):\n primary_key = master_sk_to_wallet_sk(seed_key, uint32(i))\n public_key = primary_key.get_g1()\n puzzle_hash = create_puzzlehash_for_pk(public_key)\n address = encode_puzzle_hash(puzzle_hash, prefix)\n MapKeys[puzzle_hash] = public_key\n MapKeys[i] = puzzle_hash\n print(puzzle_hash)\n print(MapKeys) \n \n # Get coin infor\n coin_name = \"9d1cbc9cf8a5ad3883933fd05367562bb771ab5ef4cb6200b6b9acdb4b2c8117\";\n newpuzzlehash = MapKeys[2]\n SendAmount = 0.01*100000000\n fee = 0\n cursor = await db_connection.execute(\"SELECT * from coin_record WHERE coin_name=?\", (coin_name,))\n row = await cursor.fetchone()\n await cursor.close()\n if row is None:\n return None\n # parent_coin_info puzzle_hash amount\n coin = Coin(bytes32(bytes.fromhex(row[6])), bytes32(bytes.fromhex(row[5])), uint64.from_bytes(row[7]))\n # print(coin)\n WallTypeValue = 0\n WallTypeId = 1\n WalletCoinRecord(\n coin, uint32(row[1]), uint32(row[2]), bool(row[3]), bool(row[4]), WalletType(WallTypeValue), WallTypeId\n )\n # select_coins\n select_coins: Set = set()\n select_coins.add(coin)\n \n spends: List[CoinSolution] = []\n primary_announcement_hash: Optional[bytes32] = None\n \n origin_id = None\n primaries: Optional[List[Dict]] = None\n for coin in select_coins:\n # log.info(f\"coin from coins {coin}\")\n # print(coin)\n print(coin)\n #puzzle: Program = await TesKeychain.puzzle_for_puzzle_hash(coin.puzzle_hash)\n public_key = MapKeys[puzzle_hash]\n assert public_key is not None\n puzzle: Program = puzzle_for_pk(bytes(public_key))\n #print(public_key)\n #print(puzzle)\n \n change = coin.amount - SendAmount\n # Only one coin creates outputs\n if primary_announcement_hash is None and origin_id in (None, coin.name()):\n if primaries is None:\n primaries = [{\"puzzlehash\": newpuzzlehash, \"amount\": SendAmount}]\n else:\n primaries.append({\"puzzlehash\": newpuzzlehash, \"amount\": SendAmount})\n if change > 0:\n # CHANGE 地址为第二个地址\n change_puzzle_hash: bytes32 = MapKeys[1]\n primaries.append({\"puzzlehash\": change_puzzle_hash, \"amount\": change})\n message_list: List[bytes32] = [c.name() for c in select_coins]\n print(message_list)\n print('#############################')\n for primary in primaries:\n print(coin.name())\n coinNew = Coin(coin.name(), primary[\"puzzlehash\"], uint32(primary[\"amount\"])).name()\n message_list.append(coinNew)\n print('#############################')\n \n message: bytes32 = std_hash(b\"\".join(message_list))\n solution: Program = TesKeychain.make_solution(primaries=primaries, fee=fee, coin_announcements=[message])\n primary_announcement_hash = Announcement(coin.name(), message).name()\n else:\n solution = TesKeychain.make_solution(coin_announcements_to_assert=[primary_announcement_hash])\n\n spends.append(\n CoinSolution(\n coin, SerializedProgram.from_bytes(bytes(puzzle)), SerializedProgram.from_bytes(bytes(solution))\n )\n )\n \n #coin_record: WalletCoinRecord = WalletCoinRecord(\n # coin, height, uint32(0), False, farm_reward, wallet_type, wallet_id\n #)\n \n \n \n# xcc1dr0leqc48k0k3ul7386ulxppf8ru5rmqx6gjffdsdff0tgxj4wqssewhcj\n# 68dffc83153d9f68f3fe89f5cf982149c7ca0f60369124a5b06a52f5a0d2ab81\n# COIN_NAME 7541233a21d81a443c5809680aca026029547108c091869ee8fb1ad3b09850e5\n# COIN_NAME 6a5d959896271bbf01cb29c255cc9dfd33125a940676ec97b2da7decd56f5374\n# COIN_NAME 7badb9975ec2b4634093a4e74ecd840c527b0fdc81a42d5758b48c770f428cd9\nif __name__ == \"__main__\": \n loop = asyncio.get_event_loop()\n loop.run_until_complete(TesKeychain.TestTransaction())\n", "sub_path": "tests/wallet/wallet.py", "file_name": "wallet.py", "file_ext": "py", "file_size_in_byte": 9078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "redis.Redis", "line_number": 51, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 55, "usage_type": "name"}, {"api_name": "chives.wallet.puzzles.p2_delegated_puzzle_or_hidden_puzzle.puzzle_for_pk", "line_number": 57, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.program.Program", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 63, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 64, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 64, "usage_type": "name"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_create_coin_condition", "line_number": 73, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_assert_absolute_seconds_exceeds_condition", "line_number": 75, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_assert_my_coin_id_condition", "line_number": 77, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_reserve_fee_condition", "line_number": 79, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_create_coin_announcement", "line_number": 82, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_assert_coin_announcement", "line_number": 85, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_create_puzzle_announcement", "line_number": 88, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.puzzle_utils.make_assert_puzzle_announcement", "line_number": 91, "usage_type": "call"}, {"api_name": "chives.wallet.puzzles.p2_delegated_puzzle_or_hidden_puzzle.solution_for_conditions", "line_number": 92, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.program.Program", "line_number": 68, "usage_type": "name"}, {"api_name": "chives.util.default_root.DEFAULT_ROOT_PATH", "line_number": 95, "usage_type": "name"}, {"api_name": "chives.util.config.load_config", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.Logger", "line_number": 99, "usage_type": "attribute"}, {"api_name": "aiosqlite.connect", "line_number": 100, "usage_type": "call"}, {"api_name": "chives.util.keychain.generate_mnemonic", "line_number": 101, "usage_type": "call"}, {"api_name": "chives.util.keychain.bytes_from_mnemonic", "line_number": 103, "usage_type": "call"}, {"api_name": "chives.util.keychain.mnemonic_to_seed", "line_number": 104, "usage_type": "call"}, {"api_name": "blspy.AugSchemeMPL.key_gen", "line_number": 105, "usage_type": "call"}, {"api_name": "blspy.AugSchemeMPL", "line_number": 105, "usage_type": "name"}, {"api_name": "chives.wallet.derive_keys.master_sk_to_wallet_sk", "line_number": 111, "usage_type": "call"}, {"api_name": "chives.util.ints.uint32", "line_number": 111, "usage_type": "call"}, {"api_name": "chives.consensus.coinbase.create_puzzlehash_for_pk", "line_number": 113, "usage_type": "call"}, {"api_name": "chives.util.bech32m.encode_puzzle_hash", "line_number": 114, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.coin.Coin", "line_number": 131, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 131, "usage_type": "call"}, {"api_name": "chives.util.ints.uint64.from_bytes", "line_number": 131, "usage_type": "call"}, {"api_name": "chives.util.ints.uint64", "line_number": 131, "usage_type": "name"}, {"api_name": "chives.wallet.wallet_coin_record.WalletCoinRecord", "line_number": 135, "usage_type": "call"}, {"api_name": "chives.util.ints.uint32", "line_number": 136, "usage_type": "call"}, {"api_name": "chives.wallet.util.wallet_types.WalletType", "line_number": 136, "usage_type": "call"}, {"api_name": "typing.Set", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 143, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 146, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.program.Program", "line_number": 154, "usage_type": "name"}, {"api_name": "chives.wallet.puzzles.p2_delegated_puzzle_or_hidden_puzzle.puzzle_for_pk", "line_number": 154, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 169, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 169, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.coin.Coin", "line_number": 174, "usage_type": "call"}, {"api_name": "chives.util.ints.uint32", "line_number": 174, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.sized_bytes.bytes32", "line_number": 178, "usage_type": "name"}, {"api_name": "chives.util.hash.std_hash", "line_number": 178, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.program.Program", "line_number": 179, "usage_type": "name"}, {"api_name": "chives.types.blockchain_format.program.SerializedProgram.from_bytes", "line_number": 186, "usage_type": "call"}, {"api_name": "chives.types.blockchain_format.program.SerializedProgram", "line_number": 186, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 202, "usage_type": "call"}]} {"seq_id": "155851035", "text": "import numpy as np\nfrom sklearn import preprocessing\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom pandas import read_hdf, concat\nfrom sklearn.metrics import f1_score, accuracy_score\nfrom time import time\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom Utils.utils import type2idx\n\n# Load data\nTrainServices = read_hdf('D:\\python_projects\\ServeNet_others\\data\\\\ramdom_categorg_percent\\RandomSplittedByCatagories9.h5', key='Train')\nTestServices = read_hdf('D:\\python_projects\\ServeNet_others\\data\\\\ramdom_categorg_percent\\RandomSplittedByCatagories9.h5', key='Test')\nAllData = concat([TrainServices, TestServices])\n\ndata_train=list(TrainServices['Service Desciption'])\ntarget_train=list(TrainServices['Service Classification'])\ndata_test=list(TestServices['Service Desciption'])\ntarget_test=list(TestServices['Service Classification'])\n\nX_train=data_train\nY_train=target_train\nX_test=data_test\nY_test=target_test\n\nType_c = (list(np.unique(target_train)))\n\nencoder = preprocessing.LabelEncoder()\nY_train = encoder.fit_transform(Y_train)\nY_test = encoder.fit_transform(Y_test)\n\nmax_features = 2000\n\ntfidf_vectorizer=TfidfVectorizer(sublinear_tf=True, stop_words='english', max_features=max_features)\ntfidf_vectorizer.fit(list(AllData['Service Desciption']))\n\nX_train = tfidf_vectorizer.transform(X_train)\nX_test = tfidf_vectorizer.transform(X_test)\n\n# Train processing\nclf = RandomForestClassifier(n_estimators=2000, max_depth=40)\n\nt0 = time()\nclf.fit(X_train, Y_train)\nt1 = time()\nprint(\"Train time: \", t1 - t0)\n\ntrain_top5 = clf.predict_proba(X_train)\ntrain_top1 = clf.predict(X_train)\n\ntest_pre_top5 = clf.predict_proba(X_test)\ntest_pre_top1 = clf.predict(X_test)\n\ntest_ret = np.empty((len(Y_test),), dtype=np.int)\ntrain_ret = np.empty((len(Y_train),), dtype=np.int)\nfor i in range(len(Y_test)):\n Top5_test = sorted(zip(clf.classes_, test_pre_top5[i]), key=lambda x: x[1])[-5:]\n Top5_test=list(map(lambda x: x[0], Top5_test))\n\n if Y_test[i] in Top5_test:\n test_ret[i] = Y_test[i]\n else:\n test_ret[i] = Top5_test[-1]\n\nfor i in range(len(Y_train)):\n Top5_train = sorted(zip(clf.classes_, train_top5[i]), key=lambda x: x[1])[-5:]\n Top5_train = list(map(lambda x: x[0], Top5_train))\n\n if Y_train[i] in Top5_train:\n train_ret[i] = Y_train[i]\n else:\n train_ret[i] = Top5_train[-1]\n\nf1_s = f1_score(Y_test, test_ret, average='micro')\n\nprint(\"=\" * 60)\nprint(\"Test top5 acc:%.4f,train top5 acc:%.4f\" % (accuracy_score(Y_test, test_ret), accuracy_score(Y_train, train_ret)))\nprint(\"Test top1 acc:%.4f,train top1 acc:%.4f\" % (\naccuracy_score(Y_test, test_pre_top1), accuracy_score(Y_train, train_top1)))\nprint(\"F1_score:%.4f\" % float(f1_s))\nprint(\"=\" * 60)\n####################################################################\n# calculate accuracy of each category.\n# type_c_index = type2idx(Type_c, Type_c)\n#\n# result_dict = {}\n# total_dict = {}\n# for idx in type_c_index:\n# category = Type_c[idx]\n# total_count = 0\n# account = 0\n# for i in range(len(Y_test)):\n# if Y_test[i] == idx:\n# total_count += 1\n# if Y_test[i] == test_ret[i]:\n# account += 1\n#\n# result_dict[category] = account / total_count * 1.\n# total_dict[category] = total_count\n#\n# for cate in result_dict.keys():\n# total_account = total_dict[cate]\n# acc = result_dict[cate]\n# print(\"%s (%d): %.4f\" % (cate, total_account, acc))\n\n\n############################################\n# top-1 categories\nprint(\"=\" * 60)\ntype_c_index = type2idx(Type_c, Type_c)\n\nresult_dict = {}\ntotal_dict = {}\navg = 0.0\ncorrect_num = 0\nprint(Y_test.shape)\nprint(test_pre_top1.shape)\nfor idx in type_c_index:\n category = Type_c[idx]\n total_count = 0\n account = 0\n for i in range(len(Y_test)):\n if Y_test[i] == idx:\n total_count += 1\n if Y_test[i] == test_pre_top1[i]:\n account += 1\n correct_num += 1\n\n result_dict[category] = format(account / total_count * 100., '.2f')\n total_dict[category] = total_count\n\nlabels = [\"Tools\",\"Financial\",\"Messaging\",\"eCommerce\",\"Payments\",\"Social\",\"Enterprise\",\"Mapping\",\"Telephony\",\"Science\",\n \"Government\",\"Email\",\"Security\",\"Reference\",\"Video\",\"Travel\",\"Sports\",\"Search\",\"Advertising\",\"Transportation\",\n \"Education\",\"Games\",\"Music\",\"Photos\",\"Cloud\",\"Bitcoin\",\"Project Management\",\"Data\",\"Backend\",\"Database\",\n \"Shipping\",\"Weather\",\"Application Development\",\"Analytics\",\"Internet of Things\",\"Medical\",\"Real Estate\",\n \"Events\",\"Banking\",\"Stocks\",\"Entertainment\",\"Storage\",\"Marketing\",\"File Sharing\",\"News Services\",\"Domains\",\n \"Chat\",\"Media\",\"Images\",\"Other\"]\n\nfor label in labels:\n acc = result_dict[label]\n print(acc)", "sub_path": "Random_Forest_Net/random_forest_net.py", "file_name": "random_forest_net.py", "file_ext": "py", "file_size_in_byte": 4773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_hdf", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_hdf", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 28, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 79, "usage_type": "call"}, {"api_name": "Utils.utils.type2idx", "line_number": 110, "usage_type": "call"}]} {"seq_id": "212609100", "text": "import itertools\nimport numpy as np\nfrom lib_naloga1 import sample_distance\nfrom lib_naloga1 import nesttools\n\n########################\n# Author: Jernej Vivod #\n########################\n\n# average_linkage: return average distance between samples in group c1 and samples in group c2.\ndef average_linkage(c1, c2, data):\n\tc1_elements = list(nesttools.un_nest(c1)) # Get elements in groups c1 and c2.\n\tc2_elements = list(nesttools.un_nest(c2))\n\tprod = itertools.product(c1_elements, c2_elements) \t# Get cartesian product of elements from the groups.\n\n\t# Create accumulator for measuring the sum of distances of pairs in cartesian product.\n\ttotal_dist = 0\n\tfor pair in prod:\n\t\tpair_fst_data = data[pair[0]] # Get data for countries in pair.\n\t\tpair_snd_data = data[pair[1]]\n\t\tdist = sample_distance.euclidean_dist(pair_fst_data, pair_snd_data) # Compute distance and add to total.\n\t\ttotal_dist += dist\n\n\t# Return average distance between elements of groups.\n\treturn total_dist / (len(c1_elements) * len(c2_elements))\n\t\t\n# complete_linkage: return maximal distance between two samples where first sample is in group c1 and second sample in group c2.\ndef complete_linkage(c1, c2, data):\n\tc1_elements = list(nesttools.un_nest(c1))\t# Get elements in groups c1 and c2.\n\tc2_elements = list(nesttools.un_nest(c2))\n\n\t# Get list of of data for each country in each group.\n\tc1_data = list(map(lambda x: data[x], c1_elements))\n\tc2_data = list(map(lambda x: data[x], c2_elements))\n\n\t# Initialize max distance to 0.\n\tmax_dist = 0\n\n\t# Find max distance between samples in different groups.\n\tfor c1_sample in c1_data:\n\t\tfor c2_sample in c2_data:\n\t\t\tdist = sample_distance.euclidean_dist(c1_sample, c2_sample)\n\t\t\tif dist > max_dist: \t\t\t# If distance is new maximal distance...\n\t\t\t\tmax_dist = dist\n\n\t# Return found maximal distance\n\treturn max_dist\n\n# single_linkage: return minimal distance between two samples where first sample is in group c1 and second sample in group c2.\ndef single_linkage(c1, c2, data):\n\tc1_elements = list(nesttools.un_nest(c1)) # Get elements in groups c1 and c2.\n\tc2_elements = list(nesttools.un_nest(c2))\n\n\t# Get list of of data for each country in each group.\n\tc1_data = list(map(lambda x: data[x], c1_elements))\n\tc2_data = list(map(lambda x: data[x], c2_elements))\n\n\t# Initialize min distance to a very large value.\n\tmin_dist = int(1e20)\n\n\t# Find max distance between samples in different groups.\n\tfor c1_sample in c1_data:\n\t\tfor c2_sample in c2_data:\n\t\t\tdist = sample_distance.euclidean_dist(c1_sample, c2_sample)\n\t\t\tif dist < min_dist: \t# If distance is new minimal distance...\n\t\t\t\tmin_dist = dist\n\n\t# Return found maximal distance\n\treturn min_dist\n\n# ward_distance: compute ward distance between clusters c1 and c2.\ndef ward_distance(c1, c2, data):\n\tc1_elements = list(nesttools.un_nest(c1))\t# Get elements in groups c1 and c2.\n\tc2_elements = list(nesttools.un_nest(c2))\n\n\t# Get list of of data for each country in each group.\n\tc1_data = list(map(lambda x: data[x], c1_elements))\n\tc2_data = list(map(lambda x: data[x], c2_elements))\n\n\t# Find centroids of c1 and c2 (average of samples in groups).\n\tRc1 = np.zeros(47, dtype = int)\n\tfor el in c1_data:\n\t\tRc1 = np.add(Rc1, el)\n\tRc1 = np.true_divide(Rc1, len(c1_data))\n\n\tRc2 = np.zeros(47, dtype = int)\n\tfor el in c2_data:\n\t\tRc2 = np.add(Rc2, el)\n\tRc2 = np.true_divide(Rc2, len(c2_data))\n\n\t# Find centroid of union(c1 c2) (average of samples in union).\n\tRc1c2 = np.zeros(47, dtype = int)\n\tfor el in np.concatenate([c1_data, c2_data]):\n\t\tRc1c2 = np.add(Rc1c2, el)\n\tRc1c2 = np.true_divide(Rc1c2, len(np.concatenate([c1_data, c2_data])))\n\n\n\t# Compute and return ward distance using formula. \n\tsum_1 = 0\n\tfor el in np.concatenate([c1_data, c2_data]):\n\t\tsum_1 += sample_distance.manhattan_dist(el, Rc1c2)**2\n\n\tsum_2 = 0\n\tfor el in c1_data:\n\t\tsum_2 += sample_distance.manhattan_dist(el, Rc1)**2\n\t\n\tsum_3 = 0\n\tfor el in c2_data:\n\t\tsum_3 += sample_distance.manhattan_dist(el, Rc2)**2\n\n\treturn sum_1 - (sum_2 + sum_3)", "sub_path": "lib_naloga1/group_distance.py", "file_name": "group_distance.py", "file_ext": "py", "file_size_in_byte": 3964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 12, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 12, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 13, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 13, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 14, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance.euclidean_dist", "line_number": 21, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 21, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 29, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 29, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 30, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 30, "usage_type": "name"}, {"api_name": "lib_naloga1.sample_distance.euclidean_dist", "line_number": 42, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 42, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 51, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 51, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 52, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 52, "usage_type": "name"}, {"api_name": "lib_naloga1.sample_distance.euclidean_dist", "line_number": 64, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 64, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 73, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 73, "usage_type": "name"}, {"api_name": "lib_naloga1.nesttools.un_nest", "line_number": 74, "usage_type": "call"}, {"api_name": "lib_naloga1.nesttools", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 100, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance.manhattan_dist", "line_number": 101, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 101, "usage_type": "name"}, {"api_name": "lib_naloga1.sample_distance.manhattan_dist", "line_number": 105, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 105, "usage_type": "name"}, {"api_name": "lib_naloga1.sample_distance.manhattan_dist", "line_number": 109, "usage_type": "call"}, {"api_name": "lib_naloga1.sample_distance", "line_number": 109, "usage_type": "name"}]} {"seq_id": "411879139", "text": "import os\n\nfrom kivy.lang import Builder\nfrom kivy.metrics import dp\nfrom kivymd.uix.bottomnavigation import MDBottomNavigationItem\nfrom kivymd.uix.imagelist import SmartTile\nfrom kivy.uix.scrollview import ScrollView\nfrom kivymd.uix.gridlayout import MDGridLayout\nfrom kivymd.uix.button import MDFloatingActionButton\nfrom kivymd.uix.filemanager import MDFileManager\nfrom config import Config\n\n\nBuilder.load_file(f\"{Config.TEMPLATES_DIR}/imagecollectiontab.kv\")\n\n\nclass ImageCell(SmartTile):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.box_color = (0, 0, 0, 0)\n\n\nclass ImageGrid(MDGridLayout):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.padding = (dp(0), dp(0))\n self.spacing = dp(4)\n\n def get_free_cell(self):\n for image in self.images:\n if not image.source:\n return image\n return\n\n def add_image_cells(self):\n for image in self.images:\n self.add_widget(image)\n\n\nclass ThreeVerticalImagesGrid(ImageGrid):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.cols = 1\n self.rows = 3\n self.size_hint = (0.2, 0.67)\n self.images = (ImageCell(), ImageCell(), ImageCell())\n self.add_image_cells()\n\n\nclass BigImageGrid(ImageGrid):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.cols = 1\n self.rows = 1\n self.size_hint = (0.6, 0.67)\n self.images = (ImageCell(),)\n self.add_image_cells()\n\n\nclass BlockOfImages(ImageGrid):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._current_grid = None\n\n self._first_col_grid = None\n self._middle_block_grid = None\n self._last_col_grid = None\n\n self.rows = 1\n self.size_hint = (1, 0.5)\n\n self.images = []\n self.padding = (dp(2), dp(2))\n self._make_new_grid()\n\n def _to_next_grid(self):\n if self._current_grid == self._first_col_grid:\n self._current_grid = self._middle_block_grid\n elif self._current_grid == self._middle_block_grid:\n self._current_grid = self._last_col_grid\n elif self._current_grid == self._last_col_grid:\n self._make_new_grid()\n\n def get_free_cell(self):\n if self._last_col_grid.children[0].source:\n return\n image = self._current_grid.get_free_cell()\n if not image:\n self._to_next_grid()\n image = self._current_grid.get_free_cell()\n return image\n\n def _make_new_grid(self):\n self._first_col_grid = ThreeVerticalImagesGrid()\n self._middle_block_grid = BigImageGrid()\n self._last_col_grid = ThreeVerticalImagesGrid()\n\n self.add_widget(self._first_col_grid)\n self.add_widget(self._middle_block_grid)\n self.add_widget(self._last_col_grid)\n\n self._current_grid = self._first_col_grid\n\n\nclass ImageGridBuilder(MDGridLayout):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.blocks = [BlockOfImages(), BlockOfImages(), BlockOfImages()]\n self._idx = 0\n self._current_block = self.blocks[self._idx]\n self.cols = 1\n self.size_hint = (1, 1.5)\n\n for block in self.blocks:\n self.add_widget(block)\n\n def _to_next_block(self):\n self._idx += 1\n self._current_block = self.blocks[self._idx]\n\n def add_image(self, source):\n image = self._current_block.get_free_cell()\n if not image:\n self._to_next_block()\n image = self._current_block.get_free_cell()\n image.source = source\n\n\nclass ImageChooser(MDFileManager):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n self.exit_manager = self.exit\n self.preview = False\n self.external_storage = os.getenv('EXTERNAL_STORAGE')\n self.images_folder = f\"{self.external_storage}/Pictures\"\n\n def select_path(self, path):\n ImageCollectionTab.image_collection.builder.add_image(path)\n self.exit()\n\n def exit(self, *args):\n self.close()\n\n def open(self):\n self.show(self.images_folder)\n\n\nclass ImageCollection(MDGridLayout):\n def __init__(self, **kwargs):\n super().__init__(cols=1, **kwargs)\n\n self.__next_image_index = 0\n\n self.add_image_button = MDFloatingActionButton(\n icon=\"plus\",\n on_release=self.open_image_chooser\n )\n\n self.scroll_view = ScrollView(size_hint=(1, 1))\n\n self.builder = ImageGridBuilder()\n\n self.scroll_view.add_widget(self.builder)\n self.add_widget(self.scroll_view)\n self.add_widget(self.add_image_button)\n\n def open_image_chooser(self, touch):\n ImageCollectionTab.image_chooser.open()\n\n\nclass ImageCollectionTab(MDBottomNavigationItem):\n \"\"\"Tab that contains personal information.\"\"\"\n\n image_chooser = None\n image_collection = None\n x_size = None\n\n def __init__(self, **kwargs):\n super().__init__(name=\"img_collection\", text=\"Images\",\n icon=\"image-frame\", **kwargs)\n\n ImageCollectionTab.x_size = self.size[0]\n ImageCollectionTab.image_collection = ImageCollection()\n ImageCollectionTab.image_chooser = ImageChooser()\n\n self.add_widget(ImageCollectionTab.image_collection)\n", "sub_path": "lab5/src/ui/imagecollectiontab.py", "file_name": "imagecollectiontab.py", "file_ext": "py", "file_size_in_byte": 5393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "kivy.lang.Builder.load_file", "line_number": 14, "usage_type": "call"}, {"api_name": "kivy.lang.Builder", "line_number": 14, "usage_type": "name"}, {"api_name": "config.Config.TEMPLATES_DIR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 14, "usage_type": "name"}, {"api_name": "kivymd.uix.imagelist.SmartTile", "line_number": 17, "usage_type": "name"}, {"api_name": "kivymd.uix.gridlayout.MDGridLayout", "line_number": 24, "usage_type": "name"}, {"api_name": "kivy.metrics.dp", "line_number": 27, "usage_type": "call"}, {"api_name": "kivy.metrics.dp", "line_number": 28, "usage_type": "call"}, {"api_name": "kivy.metrics.dp", "line_number": 74, "usage_type": "call"}, {"api_name": "kivymd.uix.gridlayout.MDGridLayout", "line_number": 106, "usage_type": "name"}, {"api_name": "kivymd.uix.filemanager.MDFileManager", "line_number": 130, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 136, "usage_type": "call"}, {"api_name": "kivymd.uix.gridlayout.MDGridLayout", "line_number": 150, "usage_type": "name"}, {"api_name": "kivymd.uix.button.MDFloatingActionButton", "line_number": 156, "usage_type": "call"}, {"api_name": "kivy.uix.scrollview.ScrollView", "line_number": 161, "usage_type": "call"}, {"api_name": "kivymd.uix.bottomnavigation.MDBottomNavigationItem", "line_number": 173, "usage_type": "name"}]} {"seq_id": "419916864", "text": "import json\nimport os\nimport pickle as pkl\nimport sys\nfrom time import time, strftime, gmtime\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport xarray as xr\nfrom torch.utils.data import DataLoader\n\nimport model.Baseline as Baseline\nfrom utils import ModelUtils\nfrom utils.Logger import Logger\nfrom utils.data import DataUtils, DataLoaders\n\n# main method for training a neural network to directly predict the 2m-temperature prediction\ndef runModel(config, data_dictionary, data_statistics, train_test_folds):\n program_start_time = time()\n\n # assign all program arguments to local variables\n with open(config['model']['path']) as handle:\n ModelDict = json.loads(handle.read())\n\n # check if station and grid time invariant features should be used and set the list of desired parameters\n if not ('grid_time_invariant' in ModelDict and ModelDict['grid_time_invariant']): config['grid_time_invariant_parameters'] =[]\n if not ('station_time_invariant' in ModelDict and ModelDict['station_time_invariant']): config['station_parameters'] = []\n\n # update general static model information\n experiment_info = config\n experiment_info['model'] = ModelDict\n experiment_info['code_commit'] = ModelUtils.get_git_revision_short_hash()\n\n\n # if needed, load time invariant features\n with open(\"%s/%s/grid_size_%s/time_invariant_data_per_station.pkl\" % (config['input_source'], config['preprocessing'], config['original_grid_size']), \"rb\") as input_file:\n time_invarian_data = pkl.load(input_file)\n\n\n # initialize feature scaling function for each feature\n featureScaleFunctions = DataUtils.getFeatureScaleFunctions(ModelUtils.ParamNormalizationDict, data_statistics)\n\n # get optimizer config\n optimizer_config = config['optimizer']\n\n # generate output path for experiment information\n setting_string = '%s_grid_%s_bs_%s_tf_%s_optim_%s_lr_%s_sl_%s' % (\n config['model']['name'], config['grid_size'], config['batch_size'], config['test_fraction'], optimizer_config['algorithm'], optimizer_config['learning_rate'], config['slice_size'])\n output_path = '%s/%s' % (config['experiment_path'], setting_string)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n # time for the set up until first run\n experiment_info['set_up_time'] = time() - program_start_time\n print('[Time]: Set-up %s' % strftime(\"%H:%M:%S\", gmtime(experiment_info['set_up_time'])))\n sys.stdout.flush()\n\n # initialize statistics\n error_statistics = None\n run_times = None\n skip_statistics = None\n if 'per_station_rmse' in config:\n error_per_station_statistics = None\n\n # keep used learning rates\n experiment_info['scheduled_learning_rates'] = []\n\n # cross validation\n for run in range(config['runs']):\n # logger for tensorboardX\n train_logger = Logger(output_path + '/logs/run_%s/train' % run)\n test_logger = Logger(output_path + '/logs/run_%s/test' % run)\n\n print('[Run %s] Cross-validation test fold %s' % (str(run + 1), str(run + 1)))\n\n # take the right preprocessed train/test data set for the current run\n train_fold, test_fold = train_test_folds[run]\n\n # initialize best epoch test error\n best_epoch_test_rmse = float(\"inf\")\n\n # use different data loader if we want to train a 3nn model approach\n if \"knn\" in ModelDict:\n # initialize train and test dataloaders\n trainset = DataLoaders.CosmoData3NNData(\n config=config,\n station_data_dict=data_dictionary,\n files=train_fold,\n featureScaling=featureScaleFunctions,\n time_invariant_data=time_invarian_data)\n trainloader = DataLoader(trainset, batch_size=config['batch_size'], shuffle=True,\n num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)\n\n testset = DataLoaders.CosmoData3NNData(\n config=config,\n station_data_dict=data_dictionary,\n files=test_fold,\n featureScaling=featureScaleFunctions,\n time_invariant_data=time_invarian_data)\n testloader = DataLoader(testset, batch_size=config['batch_size'], shuffle=True,\n num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)\n else:\n # initialize train and test dataloaders\n trainset = DataLoaders.CosmoDataGridData(\n config=config,\n station_data_dict=data_dictionary,\n files=train_fold,\n featureScaling=featureScaleFunctions,\n time_invariant_data=time_invarian_data)\n trainloader = DataLoader(trainset, batch_size=config['batch_size'], shuffle=True,\n num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)\n\n testset = DataLoaders.CosmoDataGridData(\n config=config,\n station_data_dict=data_dictionary,\n files=test_fold,\n featureScaling=featureScaleFunctions,\n time_invariant_data=time_invarian_data)\n testloader = DataLoader(testset, batch_size=config['batch_size'], shuffle=True,\n num_workers=config['n_loaders'], collate_fn=DataLoaders.collate_fn)\n\n # initialize network, optimizer and loss function\n net = Baseline.model_factory(ModelDict, trainset.n_parameters, trainset.n_grid_time_invariant_parameters,\n config['grid_size'], config['prediction_times'])\n # store class name\n experiment_info['model_class'] = net.__class__.__name__\n\n if torch.cuda.device_count() > 1:\n net = nn.DataParallel(net)\n\n if torch.cuda.is_available():\n net.cuda()\n\n # load number of train and test samples\n n_train_samples, n_test_samples = len(train_fold), len(test_fold)\n\n optimizer, scheduler = ModelUtils.initializeOptimizer(optimizer_config, net)\n criterion = nn.MSELoss()\n\n # keep number of processed smaples over all epochs for tensorboard\n processed_train_samples_global = 0\n processed_test_samples_global = 0\n\n # start learning\n for epoch in range(config['epochs']):\n epoch_train_time = np.zeros((5,))\n epoch_start_time = time()\n print('Epoch: ' + str(epoch + 1) + '\\n------------------------------------------------------------')\n\n # adapt learning rate and store information in experiment attributes\n if scheduler is not None:\n scheduler.step()\n if run == 0: experiment_info['scheduled_learning_rates'] += scheduler.get_lr()\n print('Using learning rate %s' % str(scheduler.get_lr()))\n\n # TRAINING\n # initialize variables for epoch statistics\n LABELS, MODELoutputs, COSMOoutputs = None, None, None\n processed_train_samples = 0\n net.train(True)\n\n train_start_time = time()\n # loop over complete train set\n for i, data in enumerate(trainloader, 0):\n time_start = time()\n try:\n # get training batch, e.g. label, cosmo-1 output and time inv. features for station\n DATA = data\n # DATA has only length 4 if we do not use the station time invariant features\n if len(DATA) == 4:\n Blabel, Bip2d, BTimeData, init_station_temp = DATA\n station_time_inv_input = None\n elif len(DATA) == 5:\n Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA\n station_time_inv_input = ModelUtils.getVariable(StationTimeInv).float()\n else:\n raise Exception('Unknown data format for training...')\n input = ModelUtils.getVariable(Bip2d).float()\n time_data = ModelUtils.getVariable(BTimeData).float()\n target = ModelUtils.getVariable(Blabel).float()\n\n except TypeError:\n # when the batch size is small, it could happen, that all labels have been corrupted and therefore\n # collate_fn would return an empty list\n print('Value error...')\n continue\n time_after_data_preparation = time()\n\n processed_train_samples += len(Blabel)\n\n optimizer.zero_grad()\n out = net(input, time_data, station_time_inv_input)\n time_after_forward_pass = time()\n loss = criterion(out, target)\n loss.backward()\n optimizer.step()\n time_after_backward_pass = time()\n\n if LABELS is None:\n LABELS = Blabel.data\n MODELoutputs = out.data\n COSMOoutputs = init_station_temp[2].data\n else:\n LABELS = np.vstack((LABELS, Blabel.data))\n MODELoutputs = np.vstack((MODELoutputs, out.data))\n COSMOoutputs = np.vstack((COSMOoutputs, init_station_temp[2].data))\n\n time_after_label_stack = time()\n\n if (i + 1) % 64 == 0:\n\n print('Sample: %s \\t Loss: %s' % (processed_train_samples, float(np.sqrt(loss.data))))\n\n # ============ TensorBoard logging ============#\n # (1) Log the scalar values\n info = {\n setting_string: np.sqrt(loss.item()),\n }\n\n for tag, value in info.items():\n train_logger.scalar_summary(tag, value, processed_train_samples_global + processed_train_samples)\n\n # (2) Log values and gradients of the parameters (histogram)\n for tag, value in net.named_parameters():\n tag = tag.replace('.', '/')\n train_logger.histo_summary(tag, ModelUtils.to_np(value), i + 1)\n train_logger.histo_summary(tag + '/grad', ModelUtils.to_np(value.grad), i + 1)\n\n epoch_train_time += np.array((time_start - time_end,\n time_after_data_preparation - time_start,\n time_after_forward_pass - time_after_data_preparation,\n time_after_backward_pass - time_after_forward_pass,\n time_after_label_stack - time_after_backward_pass))\n\n time_end = time()\n\n # calculate error statistic of current epoch\n diff_model = MODELoutputs - LABELS\n diff_cosmo = COSMOoutputs - LABELS\n epoch_train_rmse_model = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_model, axis=0)\n epoch_train_rmse_cosmo = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_cosmo, axis=0)\n\n\n # update global processed samples\n processed_train_samples_global += processed_train_samples\n\n if np.isnan(epoch_train_rmse_model).any():\n print(\"Learning rate too large resulted in NaN-error while training. Stopped training...\")\n return\n # print epoch training times\n print('Timing: Waiting on data=%s, Data Preparation=%s,'\n 'Forward Pass=%s, Backward Pass=%s, Data Stacking=%s' % tuple(list(epoch_train_time / len(epoch_train_time))))\n\n # RMSE of epoch\n print('Train/test statistic for epoch: %s' % str(epoch + 1))\n print('Train RMSE COSMO: ' , \", \".join([\"T=%s: %s\" % (idx, epoch_train_rmse_cosmo[idx]) for idx in range(len(epoch_train_rmse_cosmo))]))\n print('Train RMSE Model: ' , \", \".join([\"T=%s: %s\" % (idx, epoch_train_rmse_model[idx]) for idx in range(len(epoch_train_rmse_model))]))\n sys.stdout.flush()\n\n train_time = time() - train_start_time\n\n # TESTING\n test_start_time = time()\n\n LABELS, MODELoutputs, COSMOoutputs, STATION = None, None, None, None\n processed_test_samples = 0\n net.eval()\n for i, data in enumerate(testloader, 0):\n try:\n # get training batch, e.g. label, cosmo-1 output and time inv. features for station\n DATA = data\n # DATA has only length 4 if we do not use the station time invariant features\n if len(DATA) == 4:\n Blabel, Bip2d, BTimeData, init_station_temp = DATA\n station_time_inv_input = None\n elif len(DATA) == 5:\n Blabel, Bip2d, BTimeData, StationTimeInv, init_station_temp = DATA\n station_time_inv_input = ModelUtils.getVariable(StationTimeInv).float()\n else:\n raise Exception('Unknown data format for training...')\n input = ModelUtils.getVariable(Bip2d).float()\n time_data = ModelUtils.getVariable(BTimeData).float()\n target = ModelUtils.getVariable(Blabel).float()\n\n except TypeError:\n # when the batch size is small, it could happen, that all labels have been corrupted and therefore\n # collate_fn would return an empty list\n print('Value error...')\n continue\n\n processed_test_samples += len(Blabel)\n\n out = net(input, time_data, station_time_inv_input)\n loss = criterion(out, target)\n\n if LABELS is None:\n LABELS = Blabel.data\n MODELoutputs = out.data\n COSMOoutputs = init_station_temp[2].data\n STATION = init_station_temp[1].data\n else:\n LABELS = np.vstack((LABELS, Blabel.data))\n MODELoutputs = np.vstack((MODELoutputs, out.data))\n COSMOoutputs = np.vstack((COSMOoutputs, init_station_temp[2].data))\n STATION = np.hstack((STATION, init_station_temp[1].data))\n\n if i % 16:\n # ============ TensorBoard logging ============#\n # (1) Log the scalar values\n info = {\n setting_string: np.sqrt(loss.item()),\n }\n\n for tag, value in info.items():\n test_logger.scalar_summary(tag, value, processed_test_samples_global + processed_test_samples)\n\n # calculate error statistic of current epoch\n diff_model = MODELoutputs - LABELS\n diff_cosmo = COSMOoutputs - LABELS\n\n # rmse\n epoch_test_rmse_model = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_model, axis=0)\n epoch_test_rmse_cosmo = np.apply_along_axis(func1d=ModelUtils.rmse, arr=diff_cosmo, axis=0)\n overall_test_rmse_model = ModelUtils.rmse(diff_model)\n overall_test_rmse_cosmo = ModelUtils.rmse(diff_cosmo)\n\n # mae\n epoch_test_mae_model = np.apply_along_axis(func1d=ModelUtils.mae, arr=diff_model, axis=0)\n epoch_test_mae_cosmo = np.apply_along_axis(func1d=ModelUtils.mae, arr=diff_cosmo, axis=0)\n overall_test_mae_model = ModelUtils.mae(diff_model)\n overall_test_mae_cosmo = ModelUtils.mae(diff_cosmo)\n\n # calculate per station rmse if desired (especially for K-fold station generalization experiment\n if \"per_station_rmse\" in config:\n max_station_id = 1435\n\n squared_errors_per_epoch = np.array((np.square(diff_model), np.square(diff_cosmo))).squeeze()\n\n # the highest index of data is 1435, thus we expect at least 1435 entries, which we can access by\n # station id\n test_samples_per_station = np.bincount(STATION, minlength=max_station_id+1)\n model_squared_error_per_station = np.bincount(STATION, weights=squared_errors_per_epoch[0], minlength=max_station_id+1)\n cosmo_squared_error_per_station = np.bincount(STATION, weights=squared_errors_per_epoch[1], minlength=max_station_id+1)\n\n # set division by zero/NaN warning to 'ignore'\n np.seterr(divide='ignore', invalid='ignore')\n\n # calculate rmse per station\n rmse_per_station = np.vstack((np.sqrt(np.divide(model_squared_error_per_station, test_samples_per_station)),\n np.sqrt(np.divide(cosmo_squared_error_per_station, test_samples_per_station)))).T\n\n # set division by zero/NaN warning to 'warn'\n np.seterr(divide='warn', invalid='warn')\n\n\n\n\n\n\n # update global processed samples\n processed_test_samples_global += processed_test_samples\n\n # RMSE of epoch\n print('Test RMSE COSMO: ', \", \".join(\n [\"T=%s: %s\" % (idx, epoch_test_rmse_cosmo[idx]) for idx in range(len(epoch_test_rmse_cosmo))]),\n \" (Overall: %s\" % overall_test_rmse_cosmo)\n print('Test RMSE Model: ' , \", \".join([\"T=%s: %s\" % (idx, epoch_test_rmse_model[idx]) for idx in range(len(epoch_test_rmse_model))]),\n \" (Overall: %s\" % overall_test_rmse_model)\n # mae of epoch\n print('Test MAE COSMO: ', \", \".join(\n [\"T=%s: %s\" % (idx, epoch_test_mae_cosmo[idx]) for idx in range(len(epoch_test_mae_cosmo))]),\n \" (Overall: %s\" % overall_test_mae_cosmo)\n print('Test MAE Model: ' , \", \".join([\"T=%s: %s\" % (idx, epoch_test_mae_model[idx]) for idx in range(len(epoch_test_mae_model))]),\n \" (Overall: %s\" % overall_test_mae_model)\n\n sys.stdout.flush()\n\n test_time = time() - test_start_time\n\n # time for epoch\n epoch_time = time() - epoch_start_time\n\n # update error statistics\n error_statistics = ModelUtils.updateErrorStatistic(error_statistics,\n np.array([epoch_train_rmse_model, epoch_test_rmse_model])[None, None, ...],\n run, epoch, config['prediction_times'])\n # update run times statistic\n run_times = ModelUtils.updateRuntimeStatistic(run_times, np.array([epoch_time, train_time, test_time])[None, None, ...],\n run, epoch)\n # update skip statistic\n skip_statistics = ModelUtils.updateSkipStatistic(skip_statistics,\n np.array([n_train_samples, processed_train_samples,\n n_test_samples, processed_test_samples])[None, None, ...],\n run, epoch)\n\n # update per station rmse data array over runs if desired (especially for K-fold station generalization experiment\n if \"per_station_rmse\" in config:\n error_per_station_statistics = ModelUtils.updatePerStationErrorStatistic(error_per_station_statistics, rmse_per_station, run, epoch, np.arange(max_station_id+1))\n\n # store model if it was the best yes\n is_best = overall_test_rmse_model <= best_epoch_test_rmse\n best_epoch_test_rmse = min(overall_test_rmse_model, best_epoch_test_rmse)\n ModelUtils.save_checkpoint({\n 'epoch': epoch,\n 'run': run,\n 'arch': net.__class__.__name__,\n 'state_dict': net.state_dict(),\n 'overall_test_rmse': overall_test_rmse_model,\n 'lead_test_rmse' : overall_test_rmse_model,\n 'best_epoch_test_rmse': best_epoch_test_rmse,\n 'optimizer': optimizer.state_dict(),\n }, is_best, output_path + '/stored_models/run_%s' % run)\n\n # flush output to see progress\n sys.stdout.flush()\n\n # update statistics dict\n ModelUtils.get_model_details(experiment_info, net, optimizer, criterion)\n\n # complete program runtime\n experiment_info['program_runtime'] = time() - program_start_time\n\n # generate data set of all experiment statistics and additional information\n experiment_statistic = xr.Dataset({\n 'error_statistic' : error_statistics,\n 'run_time_statistic': run_times,\n 'samples_statistic' : skip_statistics}).assign_attrs(experiment_info)\n\n # dump experiment statistic\n with open(output_path + '/experiment_statistic.pkl', 'wb') as handle:\n pkl.dump(experiment_statistic, handle, protocol=pkl.HIGHEST_PROTOCOL)\n\n if 'per_station_rmse' in config:\n # dump experiment statistic\n with open(output_path + '/rmse_per_station.pkl', 'wb') as handle:\n pkl.dump(error_per_station_statistics, handle, protocol=pkl.HIGHEST_PROTOCOL)\n\n # print program execution time\n m, s = divmod(experiment_info['program_runtime'], 60)\n h, m = divmod(m, 60)\n print('Experiment has successfully finished in %dh %02dmin %02ds' % (h, m, s))", "sub_path": "ModelRun.py", "file_name": "ModelRun.py", "file_ext": "py", "file_size_in_byte": 21650, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.ModelUtils.get_git_revision_short_hash", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 32, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.data.DataUtils.getFeatureScaleFunctions", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.data.DataUtils", "line_number": 41, "usage_type": "name"}, {"api_name": "utils.ModelUtils.ParamNormalizationDict", "line_number": 41, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 55, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 56, "usage_type": "attribute"}, {"api_name": "utils.Logger.Logger", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.Logger.Logger", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders.CosmoData3NNData", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders.collate_fn", "line_number": 92, "usage_type": "attribute"}, {"api_name": "utils.data.DataLoaders", "line_number": 92, "usage_type": "name"}, {"api_name": "utils.data.DataLoaders.CosmoData3NNData", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders.collate_fn", "line_number": 101, "usage_type": "attribute"}, {"api_name": "utils.data.DataLoaders", "line_number": 101, "usage_type": "name"}, {"api_name": "utils.data.DataLoaders.CosmoDataGridData", "line_number": 104, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 110, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders.collate_fn", "line_number": 111, "usage_type": "attribute"}, {"api_name": "utils.data.DataLoaders", "line_number": 111, "usage_type": "name"}, {"api_name": "utils.data.DataLoaders.CosmoDataGridData", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 119, "usage_type": "call"}, {"api_name": "utils.data.DataLoaders.collate_fn", "line_number": 120, "usage_type": "attribute"}, {"api_name": "utils.data.DataLoaders", "line_number": 120, "usage_type": "name"}, {"api_name": "model.Baseline.model_factory", "line_number": 123, "usage_type": "call"}, {"api_name": "model.Baseline", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.cuda.device_count", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 131, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils.initializeOptimizer", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}, {"api_name": "time.time", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 175, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 175, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 178, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 178, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 179, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 179, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 180, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 180, "usage_type": "name"}, {"api_name": "time.time", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "time.time", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 206, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 217, "usage_type": "call"}, {"api_name": "utils.ModelUtils.to_np", "line_number": 226, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 226, "usage_type": "name"}, {"api_name": "utils.ModelUtils.to_np", "line_number": 227, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 227, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "time.time", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 240, "usage_type": "call"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 240, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.apply_along_axis", "line_number": 241, "usage_type": "call"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 241, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 241, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 247, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 258, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 258, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 260, "usage_type": "call"}, {"api_name": "time.time", "line_number": 263, "usage_type": "call"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 278, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 278, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 281, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 281, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 282, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 282, "usage_type": "name"}, {"api_name": "utils.ModelUtils.getVariable", "line_number": 283, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 283, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 322, "usage_type": "call"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 322, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 322, "usage_type": "name"}, {"api_name": "numpy.apply_along_axis", "line_number": 323, "usage_type": "call"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 323, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 323, "usage_type": "name"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 324, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 324, "usage_type": "name"}, {"api_name": "utils.ModelUtils.rmse", "line_number": 325, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 325, "usage_type": "name"}, {"api_name": "numpy.apply_along_axis", "line_number": 328, "usage_type": "call"}, {"api_name": "utils.ModelUtils.mae", "line_number": 328, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 328, "usage_type": "name"}, {"api_name": "numpy.apply_along_axis", "line_number": 329, "usage_type": "call"}, {"api_name": "utils.ModelUtils.mae", "line_number": 329, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils", "line_number": 329, "usage_type": "name"}, {"api_name": "utils.ModelUtils.mae", "line_number": 330, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 330, "usage_type": "name"}, {"api_name": "utils.ModelUtils.mae", "line_number": 331, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 331, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.seterr", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.seterr", "line_number": 353, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 376, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 376, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 378, "usage_type": "call"}, {"api_name": "time.time", "line_number": 381, "usage_type": "call"}, {"api_name": "utils.ModelUtils.updateErrorStatistic", "line_number": 384, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 384, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 385, "usage_type": "call"}, {"api_name": "utils.ModelUtils.updateRuntimeStatistic", "line_number": 388, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 388, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 388, "usage_type": "call"}, {"api_name": "utils.ModelUtils.updateSkipStatistic", "line_number": 391, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 391, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 392, "usage_type": "call"}, {"api_name": "utils.ModelUtils.updatePerStationErrorStatistic", "line_number": 398, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 398, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 398, "usage_type": "call"}, {"api_name": "utils.ModelUtils.save_checkpoint", "line_number": 403, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 403, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 415, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 415, "usage_type": "attribute"}, {"api_name": "utils.ModelUtils.get_model_details", "line_number": 418, "usage_type": "call"}, {"api_name": "utils.ModelUtils", "line_number": 418, "usage_type": "name"}, {"api_name": "time.time", "line_number": 421, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 424, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 431, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 431, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 436, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 436, "usage_type": "attribute"}]} {"seq_id": "326555262", "text": "from datetime import datetime\nimport os.path\nimport time\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport importlib\nimport argparse\nimport facenet\nimport random\nimport itertools\nfrom sklearn.metrics.pairwise import euclidean_distances\nfrom sklearn.metrics.pairwise import pairwise_distances\nimport recordRetriever as rr\nfrom copy import deepcopy\nfrom collections import Counter\n\ndef main(args):\n tf.reset_default_graph()\n network = importlib.import_module(args.model_def)\n\n# subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')\n# log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)\n# if not os.path.isdir(log_dir): # Create the log directory if it doesn't exist\n# os.makedirs(log_dir)\n# model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)\n# if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist\n# os.makedirs(model_dir)\n\n # Write arguments to a text file\n# facenet.write_arguments_to_file(args, os.path.join(log_dir, 'arguments.txt'))\n\n # Store some git revision info in a text file in the log directory\n# src_path = ''\n# facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))\n\n np.random.seed(int(time.time()))\n\n DB_TF_RECORD_PATH_LIST = rr.selectedDiseasePath(args.select_disease, args.select_anatomy,\n args.with_biopsy, args.with_mask, args.root)\n \"\"\"Get shuffled train and validation tf record path\"\"\"\n# TRAIN_TF_RECORD_PATH_LIST, VALID_TF_RECORD_PATH_LIST, overall_dict, training_dict, test_dict = \\\n# rr.split_training_validation_sets(TF_RECORD_PATH_LIST, args.valid_fraction)\n\n \"\"\"ensure that the dataset split is correct\"\"\"\n# for key, value in overall_dict.items():\n# assert overall_dict[key] == training_dict[key] + test_dict[key], 'Train data + Test data != Overall data'\n\n\n \"\"\" CHECKING VALIDITY OF FILE PATH \"\"\"\n# rr.check_files(TRAIN_TF_RECORD_PATH_LIST, VALID_TF_RECORD_PATH_LIST)\n\n \"\"\"print out the stats of my dataset\"\"\"\n# TOTAL_TRAIN_DATA = len(TRAIN_TF_RECORD_PATH_LIST)\n# TOTAL_VALID_DATA = len(VALID_TF_RECORD_PATH_LIST)\n# TOTAL_DATA = len(TF_RECORD_PATH_LIST)\n\n# print ('total train data: ', TOTAL_TRAIN_DATA)\n# print ('total valid data: ', TOTAL_VALID_DATA)\n# print ('total data available: ', TOTAL_DATA)\n\n \"\"\"organize into list of disease classes for sample_disease\"\"\"\n# train_set_dict = {}\n# for path in TRAIN_TF_RECORD_PATH_LIST:\n# disease_name = path.split('/')[-1].split('_')[0]\n# if disease_name not in train_set_dict:\n# train_set_dict[disease_name] = [path]\n# else:\n# train_set_dict[disease_name].append(path)\n# train_set = facenet.get_dataset(train_set_dict)\n\n \"\"\"to get the number of brains in the most common disease for augmentation\"\"\"\n# max_brains_in_single_disease = max([value for key, value in training_dict.items()])\n# print('max brains in single disease :', max_brains_in_single_disease)\n\n \"\"\"to multiply more cases for the rare cases\"\"\"\n# for imageclass in train_set:\n# nbr_brains_in_imageclass = len(imageclass.image_paths)\n# multiplier = int(min(4, max_brains_in_single_disease/nbr_brains_in_imageclass))\n# imageclass.image_paths *= multiplier\n#\n\n# curr_brain_records = imageclass.image_paths.copy()\n# for augmentation_step in range(multiplier-1):\n# imageclass.image_paths += list(map(lambda x: x+'aug'+str(augmentation_step+1), curr_brain_records))\n# print('mutliplier: ', multiplier)\n# print('number of brains in {} changed from {} to {}'.format(imageclass.name, nbr_brains_in_imageclass,\n# nbr_brains_in_imageclass * multiplier))\n# print('disease in train set after augmenting, if there is')\n# for disease in train_set:\n# print(disease.name,': ',len(disease.image_paths))\n\n if args.pretrained_model:\n print('Pre-trained model: %s' % os.path.expanduser(args.pretrained_model))\n\n with tf.Graph().as_default():\n\n# last_run_step = 0\n\n# if args.pretrained_model:\n# last_run_step += int(args.pretrained_model.split(\"-\")[-1])\n\n# global_step = tf.Variable(max(0, last_run_step), trainable=False)\n\n# Placeholder for TL_scalar\n# TL_scalar = tf.constant(args.TL_scalar, tf.float32, name = 'TL_scalar')\n\n # Placeholder for the learning rate\n# with tf.name_scope('learning_rate'):\n# learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')\n\n# with tf.name_scope('labels'):\n# labels_placeholder = tf.placeholder(tf.int32, shape=[None], name = 'labels')\n\n with tf.name_scope('to_train_BN'):\n phase_train_BN_placeholder = tf.placeholder(tf.bool, name='to_train_BN')\n\n with tf.name_scope('to_train_DO'):\n phase_train_DO_placeholder = tf.placeholder(tf.bool, name='to_train_DO')\n\n with tf.name_scope('to_train'):\n phase_train_placeholder = tf.placeholder(tf.bool, name='to_train')\n\n with tf.name_scope('filenames'):\n filenames_placeholder = tf.placeholder(tf.string, shape=[None], name = 'filenames')\n\n\n iterator, next_batch = rr.dataPreparation(filenames_placeholder, batch_size = args.batch_size,\n image_shape=[args.slices, args.image_size, args.image_size, 1],\n new_image_shape = [args.slices, args.new_image_size, args.new_image_size, 1],\n training = phase_train_placeholder)\n\n\n t1_op, t2_op, _, _, _, _, _, mask_op, _, _, _, \\\n _, label_op, _, filename_op, _, _, _, _, _ = next_batch\n\n t1t2mask_images = tf.concat([t1_op, t2_op, mask_op], axis = 4)\n\n\n # Build the inference graph\n prelogits, end_points = network.inference(t1t2mask_images, args.keep_probability, args,\n phase_train_BN = phase_train_BN_placeholder,\n phase_train_DO = phase_train_DO_placeholder,\n bottleneck_layer_size=args.embedding_size, weight_decay=args.weight_decay)\n\n tensor_lst = []\n for key, value in end_points.items():\n tensor_lst.append(value)\n\n print('1')\n\n embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')\n\n# triplet_loss, fraction_positive_triplets = facenet.batch_all_triplet_loss(labels_placeholder, embeddings, args, squared=True)\n\n# if args.lossless == True:\n# TL_scalar = 1.0\n\n# triplet_loss = tf.scalar_mul(TL_scalar, triplet_loss)\n\n# learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,\n# args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)\n\n # Calculate the total losses\n# regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n# total_loss = tf.add_n([triplet_loss] + regularization_losses, name='total_loss')\n\n print('2')\n\n\n# if args.optimizer =='ADAM':\n# opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1).minimize(total_loss, global_step = global_step)\n# else:\n# raise ValueError('Invalid optimization algorithm')\n\n # Create a saver\n saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=1)\n\n # Build the summary operation based on the TF collection of Summaries.\n summary_op = tf.summary.merge_all()\n\n print('3')\n\n # Start running operations on the Graph.\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)\n sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n\n print('4')\n\n # Initialize variables\n sess.run(tf.global_variables_initializer())\n\n sess.run(tf.local_variables_initializer())\n\n print('5')\n\n# summary_writer = tf.summary.FileWriter(log_dir, sess.graph)\n print('6')\n\n best_top1_accu = 0\n\n\n\n with sess.as_default():\n\n if args.pretrained_model:\n print('Restoring pretrained model: %s' % args.pretrained_model)\n saver = tf.train.import_meta_graph(os.path.expanduser(args.metadata))\n saver.restore(sess, os.path.expanduser(args.pretrained_model))\n\n print('7')\n\n # Training and validation loop\n epoch = 0\n# while epoch < args.max_nrof_epochs:\n# step = sess.run(global_step, feed_dict=None)\n# epoch = step // args.epoch_size\n # Train for one epoch\n generate_emb(args, DB_TF_RECORD_PATH_LIST, sess, phase_train_BN_placeholder, phase_train_DO_placeholder, embeddings, summary_op,\n iterator, t1_op, t2_op, mask_op, label_op, filename_op, filenames_placeholder, t1t2mask_images, prelogits, tensor_lst, phase_train_placeholder)\n\n# #select data from train set to compute training accuracy\n# selected_set = train_data_for_accuracy(train_set, len(VALID_TF_RECORD_PATH_LIST))\n# print('selected train eval set...')\n# print(Counter(list(map(lambda x: get_disease_from_path(x), selected_set))))\n#\n# # Evaluate on test set\n# top1_accu = evaluate(args, sess, selected_set, VALID_TF_RECORD_PATH_LIST, embeddings, learning_rate_placeholder,\n# phase_train_BN_placeholder, phase_train_DO_placeholder, log_dir, step, summary_writer, iterator,\n# t1_op, t2_op, mask_op, label_op, filename_op, filenames_placeholder, t1t2mask_images, prelogits, tensor_lst, phase_train_placeholder)\n#\n# # if accuracy is best so far, save variables and the metagraph if it doesn't exist already\n# if top1_accu > best_top1_accu:\n# best_top1_accu = top1_accu\n# save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, step)\n#\n# print('best top1 accuracy: {}'.format(best_top1_accu))\n\n return None\n\n\ndef generate_emb(args, DB_TF_RECORD_PATH_LIST, sess, phase_train_BN_placeholder, phase_train_DO_placeholder,\n embeddings, summary_op,\n iterator, t1_op, t2_op, mask_op, label_op, filename_op, filenames_placeholder, t1t2mask_images, prelogits, tensor_lst, phase_train_placeholder):\n\n batch_size = args.batch_size\n sess.run(iterator.initializer, feed_dict={filenames_placeholder: DB_TF_RECORD_PATH_LIST,\n phase_train_placeholder: False})\n nrof_images = len(DB_TF_RECORD_PATH_LIST)\n emb_array = np.zeros((nrof_images, args.embedding_size))\n nrof_batches = int(np.ceil(nrof_images / batch_size))\n label_check_array = np.zeros((nrof_images,))\n\n for i in range(nrof_batches):\n batch_size = min(nrof_images-i*batch_size, batch_size)\n labels = list(range(i * args.batch_size, i * args.batch_size + batch_size))\n print('labels: ', labels)\n emb = sess.run([embeddings], feed_dict={phase_train_BN_placeholder: False,\n phase_train_DO_placeholder: False})\n emb_array[labels,:] = emb\n label_check_array[labels] = 1\n\n print('asserting')\n assert(np.all(label_check_array==1))\n print('preparing data')\n database_disease = list(map(lambda x: get_disease_from_path(x), DB_TF_RECORD_PATH_LIST))\n database_disease_labels = list(map(lambda x: rr.ACRONYM_LABEL_MAPPER[x], database_disease))\n database_disease_labels_np = np.asarray(database_disease_labels)\n print('saving...')\n np.savez('20180817-231301-'+'5400-'+'database.npz', disease_emb=emb_array, disease_labels=database_disease_labels_np)\n print('saved')\n\n return None\n\ndef get_disease_from_path(path):\n \"\"\"pass in tfrecord path to get the disease\n args: tfrecord path\n Returns: disease name\"\"\"\n disease_name = path.split('/')[-1].split('_')[0]\n\n return disease_name\n\ndef parse_arguments(argv):\n parser = argparse.ArgumentParser()\n parser.add_argument('--logs_base_dir', type=str,\n help='Directory where to write event logs.', default='logs/facenet')\n parser.add_argument('--models_base_dir', type=str,\n help='Directory where to write trained models and checkpoints.', default='models/facenet')\n parser.add_argument('--gpu_memory_fraction', type=float,\n help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)\n parser.add_argument('--pretrained_model', type=str,\n# help='Load a pretrained model before training starts.')\n help='Load a pretrained model before training starts.', default = 'models/facenet/20180817-231301/model-20180817-231301.ckpt-5400')\n parser.add_argument('--metadata', type=str,\n# help ='Load pretrained model metadata before training starts.')\n help ='Load pretrained model metadata before training starts.', default = 'models/facenet/20180817-231301/model-20180817-231301.meta')\n parser.add_argument('--model_def', type=str,\n help='Model definition. Points to a module containing the definition of the inference graph.', default='nn3')\n parser.add_argument('--max_nrof_epochs', type=int,\n help='Number of epochs to run.', default=2000)\n parser.add_argument('--batch_size', type=int,\n help='Number of images to process in a batch.', default=9)\n parser.add_argument('--image_size', type=int,\n help='Image size (height, width) in pixels.', default=320)\n parser.add_argument('--slices', type=int,\n help='number of slices in patients brain.', default=24)\n parser.add_argument('--brains_per_disease', type=int,\n help='max number of brains per disease.', default=3)\n parser.add_argument('--epoch_size', type=int,\n help='Number of batches per epoch.', default=200)\n parser.add_argument('--alpha', type=float,\n help='Positive to negative triplet distance margin.', default=0.3)\n parser.add_argument('--embedding_size', type=int,\n help='Dimensionality of the embedding.', default=1024)\n parser.add_argument('--keep_probability', type=float,\n help='Keep probability of dropout for the fully connected layer(s).', default=1.0)\n parser.add_argument('--weight_decay', type=float,\n help='L2 weight regularization.', default=1e-4)\n parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],\n help='The optimization algorithm to use', default='ADAM')\n parser.add_argument('--learning_rate', type=float,\n help='Initial learning rate.', default=0.01)\n parser.add_argument('--learning_rate_decay_epochs', type=int,\n help='Number of epochs between learning rate decay.', default=100)\n parser.add_argument('--learning_rate_decay_factor', type=float,\n help='Learning rate decay factor.', default= 0.8)\n parser.add_argument('--evaluation_metric', type=str,\n help ='The type of evaluation metric (cosine, euclidean or etc)', default = 'euclidean')\n parser.add_argument('--TL_scalar', type=float,\n help ='The amount to scale the triplet loss to match regularization loss', default = 10.0)\n parser.add_argument('--select_disease', type=None,\n help ='Which disease to select? Empty list will select all disease', default = [])\n parser.add_argument('--select_anatomy', type=None,\n help ='Which anatomy to select? Empty list will select all anatomies', default = [])\n parser.add_argument('--with_biopsy', type=bool,\n help ='select only patients with biopsy?', default = False)\n parser.add_argument('--with_mask', type=bool,\n help ='select only patients with mask?', default = True)\n parser.add_argument('--valid_fraction', type=float,\n help ='percentage of validation data', default = 0.05)\n parser.add_argument('--root', type=str,\n help ='The root directory where all TFrecords are stored',\n default = '/data/tumor/dicoms/TFRECORD_GRAND_5')\n parser.add_argument('--new_image_size', type=int,\n help ='resize t1, t2, mask to this size', default = 160)\n parser.add_argument('--lossless', type=bool,\n help ='use lossless triplet loss?', default = False)\n return parser.parse_args(argv)\n\n\n\nif __name__ == '__main__':\n main(parse_arguments(sys.argv[1:]))\n# print(emb_array)\n# assert(np.all(label_check_array==1))\n# database_disease = list(map(lambda x: get_disease_from_path(x), DB_TF_RECORD_PATH_LIST))\n# database_disease_labels = list(map(lambda x: rr.ACRONYM_LABEL_MAPPER[x], database_disease))\n# database_disease_labels_np = np.asarray(database_disease_labels)\n# np.savez('database.npz', disease_emb=emb_array, disease_labels=database_disease_labels_np)\n", "sub_path": "generate_DB_emb.py", "file_name": "generate_DB_emb.py", "file_ext": "py", "file_size_in_byte": 17084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tensorflow.reset_default_graph", "line_number": 19, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "recordRetriever.selectedDiseasePath", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.path.expanduser", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 94, "usage_type": "name"}, {"api_name": "tensorflow.Graph", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 125, "usage_type": "attribute"}, {"api_name": "recordRetriever.dataPreparation", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tensorflow.GPUOptions", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 191, "usage_type": "call"}, {"api_name": "tensorflow.local_variables_initializer", "line_number": 193, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 208, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path.path.expanduser", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 208, "usage_type": "name"}, {"api_name": "os.path.path.expanduser", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 209, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 264, "usage_type": "call"}, {"api_name": "recordRetriever.ACRONYM_LABEL_MAPPER", "line_number": 267, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 270, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 284, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 353, "usage_type": "attribute"}]} {"seq_id": "216508405", "text": "from models import *\nfrom database import db_session\nfrom pathlib import Path\n\nclass GroupInfo:\n def __init__(self, read_source):\n self.read_source = read_source\n\n def run(self):\n print(\"Agrupando objetos\")\n devices = db_session.query(Device).all()\n for device in devices:\n path = Path(device.folder)\n device.group = path.parent\n db_session.add(device)\n db_session.commit()", "sub_path": "processors/group_info.py", "file_name": "group_info.py", "file_ext": "py", "file_size_in_byte": 447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "database.db_session.query", "line_number": 11, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 11, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "database.db_session.add", "line_number": 15, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 15, "usage_type": "name"}, {"api_name": "database.db_session.commit", "line_number": 16, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 16, "usage_type": "name"}]} {"seq_id": "448274640", "text": "# -*- coding: utf-8 -*-\nimport logging\nimport sys\n\nfrom PyQt5.QtCore import (Qt, QModelIndex)\nfrom PyQt5.QtGui import QKeySequence\nfrom PyQt5.QtWidgets import (QMainWindow, QAction, QApplication,\n QSplitter, QGroupBox, QFormLayout,\n QLabel, QBoxLayout, QWidget, QGridLayout,\n QStyle, QTreeView, QFileDialog,\n QMessageBox, QTabWidget)\n\nfrom mhw_armor_edit import AmDat\nfrom mhw_armor_edit.assets import Definitions\nfrom mhw_armor_edit.tree import ArmorSetTreeModel, ArmorSetNode, ArmorListModel\nfrom mhw_armor_edit.view_ctrl import (ComboBoxWidgetCtrl, SpinBoxWidgetCtrl,\n LabelWidgetCtrl,\n PieceViewCtrl)\n\nlog = logging.getLogger()\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef groupbox(layout, title=None):\n box = QGroupBox()\n box.setStyleSheet(\"QGroupBox {font-weight:bold}\")\n if title:\n box.setTitle(title)\n box.setFlat(True)\n box.setLayout(layout)\n return box, layout\n\n\ndef tree_index_is_root(index: QModelIndex):\n return not index.isValid()\n\n\ndef create_action(icon, title, handler, shortcut=None):\n action = QAction(icon, title)\n if shortcut is not None:\n action.setShortcut(shortcut)\n action.triggered.connect(handler)\n return action\n\n\nclass FileModel:\n def __init__(self, path, data):\n self.path = path\n self.data = data\n\n def save(self):\n with open(self.path, \"wb\") as fp:\n fp.write(self.data.data)\n\n @classmethod\n def load(cls, path):\n with open(path, \"rb\") as fp:\n data = AmDat.make(fp)\n return cls(path, data)\n\n\nclass ArmorPieceWidget(QWidget):\n def __init__(self, view, *args, **kwargs):\n super().__init__(*args, *kwargs)\n self._init(view)\n\n def _init(self, view):\n layout = QBoxLayout(QBoxLayout.TopToBottom)\n self.setLayout(layout)\n self._init_basic(layout, view)\n self._init_resistance(layout, view)\n self._init_gem_slots(layout, view)\n self._init_set_skills(layout, view)\n self._init_piece_skills(layout, view)\n\n def _init_piece_skills(self, layout, view):\n box, box_layout = groupbox(QFormLayout(), \"Piece Skills\")\n layout.addWidget(box, 0)\n view.skill1.ctrl = ComboBoxWidgetCtrl(Definitions.skill, completer=True)\n view.skill1_lvl.ctrl = SpinBoxWidgetCtrl(0, 10)\n view.skill2.ctrl = ComboBoxWidgetCtrl(Definitions.skill, completer=True)\n view.skill2_lvl.ctrl = SpinBoxWidgetCtrl(0, 10)\n view.skill3.ctrl = ComboBoxWidgetCtrl(Definitions.skill, completer=True)\n view.skill3_lvl.ctrl = SpinBoxWidgetCtrl(0, 10)\n box_layout.addRow(QLabel(\"Skill 1\"), view.skill1.ctrl.widget)\n box_layout.addRow(QLabel(\"Level\"), view.skill1_lvl.ctrl.widget)\n box_layout.addRow(QLabel(\"Skill 2\"), view.skill2.ctrl.widget)\n box_layout.addRow(QLabel(\"Level\"), view.skill2_lvl.ctrl.widget)\n box_layout.addRow(QLabel(\"Skill 3\"), view.skill3.ctrl.widget)\n box_layout.addRow(QLabel(\"Level\"), view.skill3_lvl.ctrl.widget)\n\n def _init_set_skills(self, layout, view):\n box, box_layout = groupbox(QFormLayout(), \"Set Skills\")\n layout.addWidget(box, 0)\n view.set_skill1.ctrl = ComboBoxWidgetCtrl(Definitions.skill, completer=True)\n view.set_skill1_lvl.ctrl = SpinBoxWidgetCtrl(0, 10)\n view.set_skill2.ctrl = ComboBoxWidgetCtrl(Definitions.skill, completer=True)\n view.set_skill2_lvl.ctrl = SpinBoxWidgetCtrl(0, 10)\n box_layout.addRow(QLabel(\"Skill 1\"), view.set_skill1.ctrl.widget)\n box_layout.addRow(QLabel(\"Level\"), view.set_skill1_lvl.ctrl.widget)\n box_layout.addRow(QLabel(\"Skill 2\"), view.set_skill2.ctrl.widget)\n box_layout.addRow(QLabel(\"Level\"), view.set_skill2_lvl.ctrl.widget)\n\n def _init_gem_slots(self, layout, view):\n box, box_layout = groupbox(QFormLayout(), \"Gem Slots\")\n layout.addWidget(box, 0)\n view.num_gem_slots.ctrl = ComboBoxWidgetCtrl(Definitions.gem_slot)\n view.gem_slot1_lvl.ctrl = ComboBoxWidgetCtrl(Definitions.gem_slot)\n view.gem_slot2_lvl.ctrl = ComboBoxWidgetCtrl(Definitions.gem_slot)\n view.gem_slot3_lvl.ctrl = ComboBoxWidgetCtrl(Definitions.gem_slot)\n box_layout.addRow(QLabel(\"Active slots\"), view.num_gem_slots.ctrl.widget)\n box_layout.addRow(QLabel(\"Slot 1 Level\"), view.gem_slot1_lvl.ctrl.widget)\n box_layout.addRow(QLabel(\"Slot 2 Level\"), view.gem_slot2_lvl.ctrl.widget)\n box_layout.addRow(QLabel(\"Slot 3 Level\"), view.gem_slot3_lvl.ctrl.widget)\n\n def _init_resistance(self, layout, view):\n box, box_layout = groupbox(QFormLayout(), \"Resistance\")\n layout.addWidget(box, 0)\n view.fire_res.ctrl = SpinBoxWidgetCtrl(-127, 127)\n view.water_res.ctrl = SpinBoxWidgetCtrl(-127, 127)\n view.thunder_res.ctrl = SpinBoxWidgetCtrl(-127, 127)\n view.ice_res.ctrl = SpinBoxWidgetCtrl(-127, 127)\n view.dragon_res.ctrl = SpinBoxWidgetCtrl(-127, 127)\n box_layout.addRow(QLabel(\"Fire\"), view.fire_res.ctrl.widget)\n box_layout.addRow(QLabel(\"Water\"), view.water_res.ctrl.widget)\n box_layout.addRow(QLabel(\"Thunder\"), view.thunder_res.ctrl.widget)\n box_layout.addRow(QLabel(\"Ice\"), view.ice_res.ctrl.widget)\n box_layout.addRow(QLabel(\"Dragon\"), view.dragon_res.ctrl.widget)\n\n def _init_basic(self, layout, view):\n section_box, section_layout = groupbox(QGridLayout())\n layout.addWidget(section_box)\n section_layout.setColumnStretch(0, 0)\n section_layout.setColumnStretch(1, 1)\n section_layout.setColumnStretch(2, 0)\n section_layout.setColumnStretch(3, 1)\n\n view.set_name.ctrl = LabelWidgetCtrl(Definitions.set)\n section_layout.addWidget(QLabel(\"Set:\"), 0, 0, Qt.AlignLeft)\n section_layout.addWidget(view.set_name.ctrl.widget, 0, 1, Qt.AlignLeft)\n\n section_layout.addWidget(QLabel(\"Index:\"), 0, 2, Qt.AlignLeft)\n view.index.ctrl = LabelWidgetCtrl([])\n section_layout.addWidget(view.index.ctrl.widget, 0, 3, Qt.AlignLeft)\n\n section_layout.addWidget(QLabel(\"Variant:\"), 2, 0, Qt.AlignLeft)\n view.variant.ctrl = LabelWidgetCtrl(Definitions.variant)\n section_layout.addWidget(view.variant.ctrl.widget, 2, 1, Qt.AlignLeft)\n section_layout.addWidget(QLabel(\"Equip Slot:\"), 2, 2, Qt.AlignLeft)\n view.equip_slot.ctrl = LabelWidgetCtrl(Definitions.equip_slot)\n section_layout.addWidget(view.equip_slot.ctrl.widget, 2, 3, Qt.AlignLeft)\n\n section_box, section_layout = groupbox(QFormLayout(), \"Basic\")\n layout.addWidget(section_box, 0)\n view.defense.ctrl = SpinBoxWidgetCtrl(0, 0xffff)\n section_layout.addRow(QLabel(\"Defense\"), view.defense.ctrl.widget)\n view.rarity.ctrl = ComboBoxWidgetCtrl(Definitions.rarity)\n section_layout.addRow(QLabel(\"Rarity\"), view.rarity.ctrl.widget)\n view.cost.ctrl = SpinBoxWidgetCtrl(0, 0xffff)\n section_layout.addRow(QLabel(\"Cost\"), view.cost.ctrl.widget)\n\n\nclass StructuredEditorWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n self.file_model = None\n self.current_piece_view_ctrl = PieceViewCtrl()\n self.init_actions()\n self.init_toolbar()\n self.init_menubar()\n self.init_ui()\n self.current_piece_view_ctrl.update(None)\n\n def get_icon(self, name):\n return self.style().standardIcon(name)\n\n def init_actions(self):\n self.open_file_action = create_action(\n self.get_icon(QStyle.SP_DialogOpenButton),\n \"Open file ...\",\n self.handle_open_file_action,\n QKeySequence.Open)\n self.save_file_action = create_action(\n self.get_icon(QStyle.SP_DialogSaveButton),\n \"Save ...\",\n self.handle_save_file_action,\n QKeySequence.Save)\n self.save_file_as_action = create_action(\n self.get_icon(QStyle.SP_DialogSaveButton),\n \"Save as ...\",\n self.handle_save_file_as_action,\n QKeySequence.SaveAs)\n self.close_file_action = create_action(\n self.get_icon(QStyle.SP_DialogCloseButton),\n \"Close file\",\n self.handle_close_file_action,\n QKeySequence(Qt.CTRL + Qt.Key_W))\n\n def init_menubar(self):\n menubar = self.menuBar()\n file_menu = menubar.addMenu(\"File\")\n file_menu.insertAction(None, self.open_file_action)\n file_menu.insertAction(None, self.save_file_action)\n file_menu.insertAction(None, self.save_file_as_action)\n file_menu.insertAction(None, self.close_file_action)\n\n def init_ui(self):\n split = QSplitter(Qt.Horizontal, self)\n split.setChildrenCollapsible(False)\n tab_widget = QTabWidget(split)\n tab_widget.addTab(self.init_parts_tree(), \"Sets\")\n tab_widget.addTab(self.init_parts_list(), \"List\")\n split.addWidget(tab_widget)\n split.addWidget(ArmorPieceWidget(self.current_piece_view_ctrl))\n self.setCentralWidget(split)\n self.setGeometry(300, 300, 600, 400)\n self.setWindowTitle('Armor Editor')\n self.show()\n\n def init_toolbar(self):\n toolbar = self.addToolBar(\"Main\")\n toolbar.insertAction(None, self.open_file_action)\n toolbar.insertAction(None, self.save_file_action)\n toolbar.insertAction(None, self.close_file_action)\n\n def init_parts_list(self):\n self.parts_list_view = QTreeView()\n self.parts_list_view.activated.connect(self.handle_parts_list_activated)\n return self.parts_list_view\n\n def init_parts_tree(self):\n self.parts_tree_view = QTreeView()\n self.parts_tree_view.activated.connect(self.handle_parts_tree_activated)\n return self.parts_tree_view\n\n def handle_open_file_action(self):\n file_path, _ = QFileDialog.getOpenFileName(parent=self)\n if file_path:\n self.handle_file_selected(file_path)\n\n def handle_save_file_action(self):\n if self.file_model is None:\n return\n try:\n self.file_model.save()\n except Exception as e:\n QMessageBox.warning(self,\n \"Error writing file\", str(e),\n QMessageBox.Ok, QMessageBox.Ok)\n\n def handle_save_file_as_action(self):\n if self.file_model is None:\n return\n file_path, _ = QFileDialog.getSaveFileName(self)\n if file_path:\n self.file_model.path = file_path\n self.handle_save_file_action()\n\n def handle_close_file_action(self):\n self.file_model = None\n self.parts_tree_view.setModel(None)\n self.parts_list_view.setModel(None)\n self.current_piece_view_ctrl.update(None)\n\n def handle_file_selected(self, file_path):\n try:\n self.file_model = FileModel.load(file_path)\n except Exception as e:\n self.file_model = None\n QMessageBox.warning(self,\n \"Error opening file\", str(e),\n QMessageBox.Ok, QMessageBox.Ok)\n return\n self.parts_tree_view.setModel(\n ArmorSetTreeModel(self.file_model.data.entries))\n self.parts_list_view.setModel(\n ArmorListModel(self.file_model.data.entries))\n\n def handle_parts_tree_activated(self, qindex):\n if isinstance(qindex.internalPointer(), ArmorSetNode):\n return\n index = qindex.internalPointer().ref.index\n model = self.file_model.data.find_first(index=index)\n self.current_piece_view_ctrl.update(model)\n\n def handle_parts_list_activated(self, qindex):\n index = qindex.row()\n model = self.file_model.data.find_first(index=index)\n self.current_piece_view_ctrl.update(model)\n\n\nif __name__ == '__main__':\n Definitions.load()\n app = QApplication(sys.argv)\n ex = StructuredEditorWindow()\n sys.exit(app.exec_())\n", "sub_path": "src/mhw_armor_edit/armor_editor.py", "file_name": "armor_editor.py", "file_ext": "py", "file_size_in_byte": 12142, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QModelIndex", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 39, "usage_type": "call"}, {"api_name": "mhw_armor_edit.AmDat.make", "line_number": 58, "usage_type": "call"}, {"api_name": "mhw_armor_edit.AmDat", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 62, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QBoxLayout", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QBoxLayout.TopToBottom", "line_number": 68, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 77, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 79, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.skill", "line_number": 79, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 79, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 80, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 81, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.skill", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 81, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 82, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 83, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.skill", "line_number": 83, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 83, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 88, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 93, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 95, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.skill", "line_number": 95, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 95, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 96, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 97, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.skill", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 97, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 101, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 102, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 105, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 107, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.gem_slot", "line_number": 107, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 107, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 108, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.gem_slot", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 108, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 109, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.gem_slot", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 109, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 110, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.gem_slot", "line_number": 110, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 110, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 113, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 117, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 119, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 120, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 121, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 122, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 123, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 124, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 125, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 127, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 128, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 131, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.LabelWidgetCtrl", "line_number": 138, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.set", "line_number": 138, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 138, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 139, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 139, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 140, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 142, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 142, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 142, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.LabelWidgetCtrl", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 144, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 144, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 146, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 146, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.LabelWidgetCtrl", "line_number": 147, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.variant", "line_number": 147, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 147, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 148, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 148, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 149, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 149, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 149, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.LabelWidgetCtrl", "line_number": 150, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.equip_slot", "line_number": 150, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 150, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 151, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 151, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 153, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 155, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 156, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.ComboBoxWidgetCtrl", "line_number": 157, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions.rarity", "line_number": 157, "usage_type": "attribute"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 157, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 158, "usage_type": "call"}, {"api_name": "mhw_armor_edit.view_ctrl.SpinBoxWidgetCtrl", "line_number": 159, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 160, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 163, "usage_type": "name"}, {"api_name": "mhw_armor_edit.view_ctrl.PieceViewCtrl", "line_number": 167, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_DialogOpenButton", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QKeySequence.Open", "line_number": 182, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 182, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_DialogSaveButton", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QKeySequence.Save", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 187, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_DialogSaveButton", "line_number": 189, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 189, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QKeySequence.SaveAs", "line_number": 192, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 192, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.SP_DialogCloseButton", "line_number": 194, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 197, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.CTRL", "line_number": 197, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 197, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Key_W", "line_number": 197, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 208, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 208, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 208, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 210, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTreeView", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTreeView", "line_number": 232, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 237, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 237, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 247, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 247, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 249, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 249, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 254, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 254, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 270, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 270, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 272, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 272, "usage_type": "name"}, {"api_name": "mhw_armor_edit.tree.ArmorSetTreeModel", "line_number": 275, "usage_type": "call"}, {"api_name": "mhw_armor_edit.tree.ArmorListModel", "line_number": 277, "usage_type": "call"}, {"api_name": "mhw_armor_edit.tree.ArmorSetNode", "line_number": 280, "usage_type": "argument"}, {"api_name": "mhw_armor_edit.assets.Definitions.load", "line_number": 293, "usage_type": "call"}, {"api_name": "mhw_armor_edit.assets.Definitions", "line_number": 293, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 294, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 294, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 296, "usage_type": "call"}]} {"seq_id": "139951850", "text": "import json\nimport logging\nimport os\nimport re\nfrom dataclasses import dataclass, field\nfrom datetime import datetime\nfrom functools import lru_cache\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Union\n\nfrom azure.core.exceptions import HttpResponseError\nfrom azure.identity import DefaultAzureCredential # type: ignore\nfrom azure.mgmt.compute import ComputeManagementClient # type: ignore\nfrom azure.mgmt.compute.models import ResourceSku, VirtualMachine # type: ignore\nfrom azure.mgmt.network import NetworkManagementClient # type: ignore\nfrom azure.mgmt.network.models import InboundNatRule, NetworkInterface # type: ignore\nfrom azure.mgmt.resource import ( # type: ignore\n ResourceManagementClient,\n SubscriptionClient,\n)\nfrom azure.mgmt.resource.resources.models import ( # type: ignore\n Deployment,\n DeploymentMode,\n DeploymentProperties,\n)\nfrom dataclasses_json import LetterCase, dataclass_json # type: ignore\nfrom marshmallow import fields, validate\nfrom retry import retry # type: ignore\n\nfrom lisa import schema, search_space\nfrom lisa.environment import Environment\nfrom lisa.node import Node\nfrom lisa.platform_ import Platform\nfrom lisa.secret import PATTERN_GUID, PATTERN_HEADTAIL, add_secret\nfrom lisa.util import LisaException, constants, get_public_key_data\nfrom lisa.util.logger import Logger\n\nAZURE = \"azure\"\n\n# used by azure\nAZURE_DEPLOYMENT_NAME = \"lisa_default_deployment_script\"\nAZURE_RG_NAME_KEY = \"resource_group_name\"\n\nVM_SIZE_FALLBACK_LEVELS = [\n re.compile(r\"Standard_DS(\\d)+_v2\"),\n re.compile(r\"Standard_A(\\d)+\"),\n]\nLOCATIONS = [\"westus2\", \"eastus2\"]\nRESOURCE_GROUP_LOCATION = \"westus2\"\n\n# names in arm template, they should be changed with template together.\nRESOURCE_ID_LB = \"lisa-loadBalancer\"\nRESOURCE_ID_PUBLIC_IP = \"lisa-publicIPv4Address\"\nRESOURCE_ID_PORT_POSTFIX = \"-ssh\"\nRESOURCE_ID_NIC_PATTERN = re.compile(r\"([\\w]+-[\\d]+)-nic-0\")\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzureCapability:\n location: str\n vm_size: str\n capability: schema.NodeSpace\n estimated_cost: int\n resource_sku: Dict[str, Any]\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzureLocation:\n updated_time: datetime = field(\n default_factory=datetime.now,\n metadata=schema.metadata(\n fields.DateTime,\n encoder=datetime.isoformat,\n decoder=datetime.fromisoformat,\n data_key=\"updatedTime\",\n format=\"iso\",\n ),\n )\n location: str = \"\"\n capabilities: List[AzureCapability] = field(default_factory=list)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzureVmGallerySchema:\n publisher: str = \"Canonical\"\n offer: str = \"UbuntuServer\"\n sku: str = \"18.04-LTS\"\n version: str = \"Latest\"\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzureNodeSchema:\n name: str = \"\"\n vm_size: str = \"\"\n location: str = \"\"\n gallery: Optional[AzureVmGallerySchema] = None\n vhd: str = \"\"\n nic_count: int = 1\n\n def __post_init__(self, *args: Any, **kwargs: Any) -> None:\n add_secret(self.vhd)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzureArmParameter:\n location: str = \"westus2\"\n admin_username: str = \"\"\n admin_password: str = \"\"\n admin_key_data: str = \"\"\n nodes: List[AzureNodeSchema] = field(default_factory=list)\n\n def __post_init__(self, *args: Any, **kwargs: Any) -> None:\n add_secret(self.admin_username, PATTERN_HEADTAIL)\n add_secret(self.admin_password)\n add_secret(self.admin_key_data)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AzurePlatformSchema:\n service_principal_tenant_id: str = field(\n default=\"\",\n metadata=schema.metadata(\n data_key=\"servicePrincipalTenantId\",\n validate=validate.Regexp(constants.GUID_REGEXP),\n ),\n )\n service_principal_client_id: str = field(\n default=\"\",\n metadata=schema.metadata(\n data_key=\"servicePrincipalClientId\",\n validate=validate.Regexp(constants.GUID_REGEXP),\n ),\n )\n service_principal_key: str = field(default=\"\")\n subscription_id: str = field(\n default=\"\",\n metadata=schema.metadata(\n data_key=\"subscriptionId\",\n validate=validate.Regexp(constants.GUID_REGEXP),\n ),\n )\n\n resource_group_name: str = field(default=\"\")\n locations: Optional[Union[str, List[str]]] = field(default=None)\n\n log_level: str = field(\n default=logging.getLevelName(logging.WARN),\n metadata=schema.metadata(\n data_key=\"logLevel\",\n validate=validate.OneOf(\n [\n logging.getLevelName(logging.ERROR),\n logging.getLevelName(logging.WARN),\n logging.getLevelName(logging.INFO),\n logging.getLevelName(logging.DEBUG),\n ]\n ),\n ),\n )\n\n # do actual deployment, or pass through for troubleshooting\n dry_run: bool = False\n # do actual deployment, or try to retrieve existing vms\n deploy: bool = True\n # wait resource deleted or not\n wait_delete: bool = False\n\n def __post_init__(self, *args: Any, **kwargs: Any) -> None:\n add_secret(self.service_principal_tenant_id, mask=PATTERN_GUID)\n add_secret(self.service_principal_client_id, mask=PATTERN_GUID)\n add_secret(self.service_principal_key)\n add_secret(self.subscription_id, mask=PATTERN_GUID)\n\n if not self.locations:\n self.locations = LOCATIONS\n\n\n@dataclass\nclass EnvironmentContext:\n resource_group_name: str = \"\"\n resource_group_is_created: bool = False\n\n\n@dataclass\nclass NodeContext:\n vm_name: str = \"\"\n username: str = \"\"\n password: str = \"\"\n private_key_file: str = \"\"\n\n\nclass AzurePlatform(Platform):\n def __init__(self) -> None:\n super().__init__()\n self._credential: DefaultAzureCredential = None\n self._enviornment_counter = 0\n self._eligible_capabilities: Optional[Dict[str, List[AzureCapability]]] = None\n self._locations_data_cache: Optional[Dict[str, AzureLocation]] = None\n\n @classmethod\n def platform_type(cls) -> str:\n return AZURE\n\n def _prepare_environment( # noqa: C901\n self, environment: Environment, log: Logger\n ) -> bool:\n # TODO: Reduce this function's complexity and remove the disabled warning.\n \"\"\"\n Main flow\n\n _initialize_eligible_vm_sizes for all environments.\n 1. load location, vm size patterns firstly.\n 2. load avaiablbe vm sizes for each location.\n 3. match vm sizes by pattern.\n\n for each environment\n 1. If predefined location exists on node level, check conflict and use it.\n 2. If predefined vm size exists on node level, check exists and use it.\n 3. check capability for each node by order of pattern.\n 4. get min capability for each match\n \"\"\"\n\n is_success: bool = True\n\n if environment.runbook.nodes_requirement:\n is_success = False\n nodes_requirement = environment.runbook.nodes_requirement\n node_count = len(nodes_requirement)\n # fills predefined locations here.\n predefined_caps: List[Any] = [None] * node_count\n # make sure all vms are in same location.\n existing_location: str = \"\"\n predefined_cost: int = 0\n\n assert self._eligible_capabilities\n\n # check locations\n for req in nodes_requirement:\n # apply azure specified values\n # they will pass into arm template\n node_runbook: AzureNodeSchema = req.get_extended_runbook(\n AzureNodeSchema, AZURE\n )\n if node_runbook.location:\n if existing_location:\n # if any one has different location, calculate again\n if existing_location != node_runbook.location:\n raise LisaException(\n f\"predefined node must be in same location, \"\n f\"previous: {existing_location}, \"\n f\"found: {node_runbook.location}\"\n )\n else:\n existing_location = node_runbook.location\n\n if existing_location:\n locations = [existing_location]\n else:\n locations = LOCATIONS\n\n # check eligible locations\n found_or_skipped = False\n for location_name in locations:\n predefined_cost = 0\n predefined_caps = [None] * node_count\n for req_index, req in enumerate(nodes_requirement):\n found_or_skipped = False\n node_runbook = req.get_extended_runbook(AzureNodeSchema, AZURE)\n if not node_runbook.vm_size:\n # not to check, if no vm_size set\n found_or_skipped = True\n continue\n\n # find predefined vm size on all avaiable's.\n location_info: AzureLocation = self._get_location_info(\n location_name, log\n )\n for azure_cap in location_info.capabilities:\n if azure_cap.vm_size == node_runbook.vm_size:\n predefined_cost += azure_cap.estimated_cost\n\n min_cap: schema.NodeSpace = req.generate_min_capability(\n azure_cap.capability\n )\n # apply azure specified values\n # they will pass into arm template\n min_runbook = min_cap.get_extended_runbook(\n AzureNodeSchema, AZURE\n )\n # the location may not be set\n min_runbook.location = location_name\n min_runbook.vm_size = azure_cap.vm_size\n assert isinstance(min_cap.nic_count, int)\n min_runbook.nic_count = min_cap.nic_count\n if not existing_location:\n existing_location = location_name\n predefined_caps[req_index] = min_cap\n found_or_skipped = True\n break\n if not found_or_skipped:\n # if not found any, skip and try next location\n break\n if found_or_skipped:\n # if found all, skip other locations\n break\n if not found_or_skipped:\n # no location meet requirement\n raise LisaException(\n f\"cannot find predefined vm size [{node_runbook.vm_size}] \"\n f\"in location [{locations}]\"\n )\n for location_name, location_caps in self._eligible_capabilities.items():\n # in each location, all node must be found\n # fill them as None and check after meeted capability\n found_capabilities: List[Any] = list(predefined_caps)\n\n # skip unmatched location\n if existing_location and existing_location != location_name:\n continue\n\n estimated_cost: int = 0\n for req_index, req in enumerate(nodes_requirement):\n for azure_cap in location_caps:\n if found_capabilities[req_index]:\n # found, so skipped\n continue\n\n check_result = req.check(azure_cap.capability)\n if check_result.result:\n min_cap = req.generate_min_capability(azure_cap.capability)\n\n # apply azure specified values\n # they will pass into arm template\n node_runbook = min_cap.get_extended_runbook(\n AzureNodeSchema, AZURE\n )\n if node_runbook.location:\n assert node_runbook.location == azure_cap.location, (\n f\"predefined location [{node_runbook.location}] \"\n f\"must be same as \"\n f\"cap location [{azure_cap.location}]\"\n )\n\n # will pass into arm template\n node_runbook.location = azure_cap.location\n if not node_runbook.vm_size:\n node_runbook.vm_size = azure_cap.vm_size\n assert isinstance(\n min_cap.nic_count, int\n ), f\"actual: {min_cap.nic_count}\"\n node_runbook.nic_count = min_cap.nic_count\n\n estimated_cost += azure_cap.estimated_cost\n\n found_capabilities[req_index] = min_cap\n if all(x for x in found_capabilities):\n break\n\n if all(x for x in found_capabilities):\n # all found and replace current requirement\n environment.runbook.nodes_requirement = found_capabilities\n environment.cost = estimated_cost + predefined_cost\n is_success = True\n log.debug(\n f\"requirement meet, \"\n f\"cost: {environment.cost}, \"\n f\"cap: {environment.runbook.nodes_requirement}\"\n )\n break\n return is_success\n\n def _deploy_environment(self, environment: Environment, log: Logger) -> None:\n assert self._rm_client\n assert self._azure_runbook\n\n environment_context = environment.get_context(EnvironmentContext)\n if self._azure_runbook.resource_group_name:\n resource_group_name = self._azure_runbook.resource_group_name\n else:\n normalized_run_name = constants.NORMALIZE_PATTERN.sub(\n \"_\", constants.RUN_NAME\n )\n resource_group_name = f\"{normalized_run_name}_e{self._enviornment_counter}\"\n self._enviornment_counter += 1\n environment_context.resource_group_is_created = True\n\n environment_context.resource_group_name = resource_group_name\n if self._azure_runbook.dry_run:\n log.info(f\"dry_run: {self._azure_runbook.dry_run}\")\n else:\n try:\n if self._azure_runbook.deploy:\n log.info(\n f\"creating or updating resource group: {resource_group_name}\"\n )\n self._rm_client.resource_groups.create_or_update(\n resource_group_name, {\"location\": RESOURCE_GROUP_LOCATION}\n )\n else:\n log.info(f\"reusing resource group: {resource_group_name}\")\n\n deployment_parameters = self._create_deployment_parameters(\n resource_group_name, environment, log\n )\n\n if self._azure_runbook.deploy:\n self._validate_template(deployment_parameters, log)\n self._deploy(deployment_parameters, log)\n\n # Even skipped deploy, try best to initialize nodes\n self._initialize_nodes(environment)\n\n except Exception as identifier:\n self._delete_environment(environment, log)\n raise identifier\n environment.is_ready = True\n\n def _delete_environment(self, environment: Environment, log: Logger) -> None:\n environment_context = environment.get_context(EnvironmentContext)\n resource_group_name = environment_context.resource_group_name\n assert resource_group_name\n assert self._azure_runbook\n\n if not environment_context.resource_group_is_created:\n log.info(\n f\"skipped to delete resource group: {resource_group_name}, \"\n f\"as it's not created by this run.\"\n )\n elif self._runbook.reserve_environment:\n log.info(\n f\"skipped to delete resource group: {resource_group_name}, \"\n f\"as runbook set to reserve environment.\"\n )\n elif self._azure_runbook.dry_run:\n log.info(\n f\"skipped to delete resource group: {resource_group_name}, \"\n f\"as it's a dry run.\"\n )\n else:\n assert self._rm_client\n log.info(\n f\"deleting resource group: {resource_group_name}, \"\n f\"wait: {self._azure_runbook.wait_delete}\"\n )\n delete_operation = self._rm_client.resource_groups.begin_delete(\n resource_group_name\n )\n if self._azure_runbook.wait_delete:\n result = delete_operation.wait()\n if result:\n raise LisaException(f\"error on deleting resource group: {result}\")\n else:\n log.debug(\"not wait deleting\")\n\n def _initialize(self) -> None:\n # set needed environment variables for authentication\n azure_runbook = self._runbook.get_extended_runbook(AzurePlatformSchema)\n assert azure_runbook, \"platform runbook cannot be empty\"\n self._azure_runbook = azure_runbook\n\n # set azure log to warn level only\n logging.getLogger(\"azure\").setLevel(azure_runbook.log_level)\n\n os.environ[\"AZURE_TENANT_ID\"] = azure_runbook.service_principal_tenant_id\n os.environ[\"AZURE_CLIENT_ID\"] = azure_runbook.service_principal_client_id\n os.environ[\"AZURE_CLIENT_SECRET\"] = azure_runbook.service_principal_key\n\n self._credential = DefaultAzureCredential()\n\n self._sub_client = SubscriptionClient(self._credential)\n\n self._subscription_id = azure_runbook.subscription_id\n subscription = self._sub_client.subscriptions.get(self._subscription_id)\n if not subscription:\n raise LisaException(\n f\"cannot find subscription id: '{self._subscription_id}'\"\n )\n self._log.info(f\"connected to subscription: '{subscription.display_name}'\")\n\n self._rm_client = ResourceManagementClient(\n credential=self._credential, subscription_id=self._subscription_id\n )\n self._initialize_eligible_vm_sizes(self._log)\n\n @lru_cache\n def _load_template(self) -> Any:\n template_file_path = Path(__file__).parent / \"arm_template.json\"\n with open(template_file_path, \"r\") as f:\n template = json.load(f)\n return template\n\n @retry(tries=2) # type: ignore\n def _load_location_info_from_file(\n self, cached_file_name: Path, log: Logger\n ) -> Dict[str, AzureLocation]:\n if cached_file_name.exists():\n try:\n with open(cached_file_name, \"r\") as f:\n loaded_data: Dict[str, Any] = json.load(f)\n locations_data: Dict[str, AzureLocation] = dict()\n for loc_name, loc_data in loaded_data.items():\n loc_obj: AzureLocation = AzureLocation.schema().load( # type:ignore\n loc_data\n )\n locations_data[loc_name] = loc_obj\n except Exception as identifier:\n # if schema changed, There may be exception, remove cache and retry\n # Note: retry on this method depends on decorator\n log.debug(\"error on loading cache, delete cache and retry.\")\n cached_file_name.unlink()\n raise identifier\n else:\n locations_data = dict()\n return locations_data\n\n def _get_location_info(self, location: str, log: Logger) -> AzureLocation:\n cached_file_name = constants.CACHE_PATH.joinpath(\"azure_locations.json\")\n should_refresh: bool = True\n if not self._locations_data_cache:\n self._locations_data_cache = self._load_location_info_from_file(\n cached_file_name=cached_file_name, log=log\n )\n assert self._locations_data_cache\n location_data: Optional[AzureLocation] = self._locations_data_cache.get(\n location\n )\n\n if location_data:\n delta = datetime.now() - location_data.updated_time\n # refresh cached locations every 5 days.\n if delta.days < 5:\n should_refresh = False\n log.debug(\n f\"{location}: cache used: {location_data.updated_time}, \"\n f\"sku count: {len(location_data.capabilities)}\"\n )\n else:\n log.debug(\n f\"{location}: cache timeout: {location_data.updated_time},\"\n f\"sku count: {len(location_data.capabilities)}\"\n )\n else:\n log.debug(f\"{location}: no cache found\")\n if should_refresh:\n compute_client = ComputeManagementClient(\n credential=self._credential, subscription_id=self._subscription_id\n )\n\n log.debug(f\"{location}: querying\")\n all_skus: List[AzureCapability] = []\n paged_skus = compute_client.resource_skus.list(\n f\"location eq '{location}'\"\n ).by_page()\n for skus in paged_skus:\n for sku_obj in skus:\n try:\n if sku_obj.resource_type == \"virtualMachines\":\n if sku_obj.restrictions and any(\n restriction.type == \"Location\"\n for restriction in sku_obj.restrictions\n ):\n # restricted on this location\n continue\n resource_sku = sku_obj.as_dict()\n capability = self._resource_sku_to_capability(\n location, sku_obj\n )\n\n # estimate vm cost for priority\n assert isinstance(capability.core_count, int)\n assert isinstance(capability.gpu_count, int)\n estimated_cost = (\n capability.core_count + capability.gpu_count * 100\n )\n azure_capability = AzureCapability(\n location=location,\n vm_size=sku_obj.name,\n capability=capability,\n resource_sku=resource_sku,\n estimated_cost=estimated_cost,\n )\n all_skus.append(azure_capability)\n except Exception as identifier:\n log.error(f\"unknown sku: {sku_obj}\")\n raise identifier\n location_data = AzureLocation(location=location, capabilities=all_skus)\n self._locations_data_cache[location_data.location] = location_data\n log.debug(f\"{location}: saving to disk\")\n with open(cached_file_name, \"w\") as f:\n saved_data: Dict[str, Any] = dict()\n for name, value in self._locations_data_cache.items():\n saved_data[name] = value.to_dict() # type: ignore\n json.dump(saved_data, f)\n log.debug(\n f\"{location_data.location}: new data, \"\n f\"sku: {len(location_data.capabilities)}\"\n )\n\n assert location_data\n return location_data\n\n def _create_deployment_parameters(\n self, resource_group_name: str, environment: Environment, log: Logger\n ) -> Dict[str, Any]:\n assert environment.runbook, \"env data cannot be None\"\n assert environment.runbook.nodes_requirement, \"node requirement cannot be None\"\n\n log.debug(\"creating deployment\")\n # construct parameters\n arm_parameters = AzureArmParameter()\n arm_parameters.admin_username = self._runbook.admin_username\n if self._runbook.admin_private_key_file:\n arm_parameters.admin_key_data = get_public_key_data(\n self._runbook.admin_private_key_file\n )\n else:\n arm_parameters.admin_password = self._runbook.admin_password\n assert self._azure_runbook\n\n nodes_parameters: List[AzureNodeSchema] = []\n for node_space in environment.runbook.nodes_requirement:\n assert isinstance(\n node_space, schema.NodeSpace\n ), f\"actual: {type(node_space)}\"\n azure_node_runbook: AzureNodeSchema = node_space.get_extended_runbook(\n AzureNodeSchema, field_name=AZURE\n )\n\n # init node\n node = environment.nodes.from_requirement(node_space)\n if not azure_node_runbook.name:\n azure_node_runbook.name = f\"node-{len(nodes_parameters)}\"\n if not azure_node_runbook.vm_size:\n raise LisaException(\"vm_size is not detected before deploy\")\n if not azure_node_runbook.location:\n raise LisaException(\"location is not detected before deploy\")\n if azure_node_runbook.nic_count <= 0:\n raise LisaException(\n f\"nic_count need at least 1, but {azure_node_runbook.nic_count}\"\n )\n if azure_node_runbook.vhd:\n # vhd is higher priority\n azure_node_runbook.gallery = None\n elif not azure_node_runbook.gallery:\n # set to default gallery, if nothing secified\n azure_node_runbook.gallery = AzureVmGallerySchema()\n nodes_parameters.append(azure_node_runbook)\n\n # save vm's information into node\n node_context = node.get_context(NodeContext)\n # vm's name, use to find it from azure\n node_context.vm_name = azure_node_runbook.name\n # ssh related information will be filled back once vm is created\n node_context.username = arm_parameters.admin_username\n node_context.password = arm_parameters.admin_password\n node_context.private_key_file = self._runbook.admin_private_key_file\n\n arm_parameters.nodes = nodes_parameters\n\n # load template\n template = self._load_template()\n parameters = arm_parameters.to_dict() # type:ignore\n parameters = {k: {\"value\": v} for k, v in parameters.items()}\n log.debug(f\"parameters: {parameters}\")\n deployment_properties = DeploymentProperties(\n mode=DeploymentMode.incremental,\n template=template,\n parameters=parameters,\n )\n\n return {\n AZURE_RG_NAME_KEY: resource_group_name,\n \"deployment_name\": AZURE_DEPLOYMENT_NAME,\n \"parameters\": Deployment(properties=deployment_properties),\n }\n\n def _validate_template(\n self, deployment_parameters: Dict[str, Any], log: Logger\n ) -> None:\n resource_group_name = deployment_parameters[AZURE_RG_NAME_KEY]\n log.debug(\"validating deployment\")\n\n validate_operation: Any = None\n deployments = self._rm_client.deployments\n try:\n validate_operation = self._rm_client.deployments.begin_validate(\n **deployment_parameters\n )\n result = validate_operation.wait()\n if result:\n raise LisaException(f\"deploy failed: {result}\")\n except Exception as identifier:\n error_messages: List[str] = [str(identifier)]\n\n # default error message is too general in most case,\n # so check for more details.\n if validate_operation:\n # validate_operation returned, it means deployments created\n # successfuly. so check errors from deployments by name.\n deployment = deployments.get(resource_group_name, AZURE_DEPLOYMENT_NAME)\n # log more details for troubleshooting\n if deployment.properties.provisioning_state == \"Failed\":\n if deployment.properties.error.details:\n error_messages = [\n f\"{x.code}, {x.message}\"\n for x in deployment.properties.error.details\n ]\n elif isinstance(identifier, HttpResponseError) and identifier.error:\n # no validate_operation returned, the message may include\n # some errors, so check details\n if identifier.error.details:\n error_messages = [\n f\"{x.code}, {x.message}\" for x in identifier.error.details\n ]\n\n raise LisaException(\"\\n\".join(error_messages))\n\n assert result is None, f\"validate error: {result}\"\n\n def _deploy(self, deployment_parameters: Dict[str, Any], log: Logger) -> None:\n resource_group_name = deployment_parameters[AZURE_RG_NAME_KEY]\n log.info(f\"deploying {resource_group_name}\")\n\n deployment_operation: Any = None\n deployments = self._rm_client.deployments\n try:\n deployment_operation = deployments.begin_create_or_update(\n **deployment_parameters\n )\n result = deployment_operation.wait()\n if result:\n raise LisaException(f\"deploy failed: {result}\")\n except HttpResponseError as identifier:\n assert identifier.error\n error_messages = [\n f\"{x.code}, {x.message}\" for x in identifier.error.details\n ]\n # original message may not be friendly, refine it.\n raise LisaException(\"\\n\".join(error_messages))\n\n def _initialize_nodes(self, environment: Environment) -> None:\n\n node_context_map: Dict[str, Node] = dict()\n for node in environment.nodes.list():\n node_context = node.get_context(NodeContext)\n node_context_map[node_context.vm_name] = node\n\n compute_client = ComputeManagementClient(\n credential=self._credential, subscription_id=self._subscription_id\n )\n environment_context = environment.get_context(EnvironmentContext)\n vms_map: Dict[str, VirtualMachine] = dict()\n vms = compute_client.virtual_machines.list(\n environment_context.resource_group_name\n )\n for vm in vms:\n vms_map[vm.name] = vm\n\n network_client = NetworkManagementClient(\n credential=self._credential, subscription_id=self._subscription_id\n )\n\n # load port mappings\n nat_rules_map: Dict[str, InboundNatRule] = dict()\n load_balancing = network_client.load_balancers.get(\n environment_context.resource_group_name, RESOURCE_ID_LB\n )\n for rule in load_balancing.inbound_nat_rules:\n name = rule.name[: -len(RESOURCE_ID_PORT_POSTFIX)]\n nat_rules_map[name] = rule\n\n # load nics\n nic_map: Dict[str, NetworkInterface] = dict()\n network_interfaces = network_client.network_interfaces.list(\n environment_context.resource_group_name\n )\n for nic in network_interfaces:\n # nic name is like node-0-nic-2, get vm name part for later pick\n # only find primary nic, which is ended by -nic-0\n node_name_from_nic = RESOURCE_ID_NIC_PATTERN.findall(nic.name)\n if node_name_from_nic:\n name = node_name_from_nic[0]\n nic_map[name] = nic\n\n # get public IP\n public_ip_address = network_client.public_ip_addresses.get(\n environment_context.resource_group_name, RESOURCE_ID_PUBLIC_IP\n ).ip_address\n\n for vm_name, node in node_context_map.items():\n node_context = node.get_context(NodeContext)\n vm = vms_map.get(vm_name, None)\n if not vm:\n raise LisaException(\n f\"cannot find vm: '{vm_name}', make sure deployment is correct.\"\n )\n nic = nic_map[vm_name]\n nat_rule = nat_rules_map[vm_name]\n\n address = nic.ip_configurations[0].private_ip_address\n port = nat_rule.backend_port\n public_port = nat_rule.frontend_port\n node.set_connection_info(\n address=address,\n port=port,\n public_address=public_ip_address,\n public_port=public_port,\n username=node_context.username,\n password=node_context.password,\n private_key_file=node_context.private_key_file,\n )\n\n def _resource_sku_to_capability(\n self, location: str, resource_sku: ResourceSku\n ) -> schema.NodeSpace:\n # fill in default values, in case no capability meet.\n node_space = schema.NodeSpace(\n node_count=1,\n core_count=0,\n disk_count=0,\n memory_mb=0,\n nic_count=0,\n gpu_count=0,\n features=search_space.SetSpace[schema.Feature](is_allow_set=True),\n excluded_features=search_space.SetSpace[schema.Feature](is_allow_set=False),\n )\n node_space.name = f\"{location}_{resource_sku.name}\"\n for sku_capability in resource_sku.capabilities:\n name = sku_capability.name\n if name == \"vCPUs\":\n node_space.core_count = int(sku_capability.value)\n elif name == \"MaxDataDiskCount\":\n node_space.disk_count = search_space.IntRange(\n max=int(sku_capability.value)\n )\n elif name == \"MemoryGB\":\n node_space.memory_mb = int(float(sku_capability.value) * 1024)\n elif name == \"MaxNetworkInterfaces\":\n node_space.nic_count = search_space.IntRange(\n max=int(sku_capability.value)\n )\n elif name == \"GPUs\":\n node_space.gpu_count = int(sku_capability.value)\n\n return node_space\n\n def _initialize_eligible_vm_sizes(self, log: Logger) -> None:\n # load eligible vm sizes\n # 1. location is selected\n # 2. vm size supported in current location\n # 3. vm size match predefined pattern\n if self._eligible_capabilities is None:\n assert self._azure_runbook\n if isinstance(self._azure_runbook.locations, str):\n location_names = [self._azure_runbook.locations]\n else:\n assert isinstance(\n self._azure_runbook.locations, list\n ), f\"actual: {type(self._azure_runbook.locations)}\"\n location_names = self._azure_runbook.locations\n\n available_capabilities: Dict[str, List[AzureCapability]] = dict()\n\n # loop all locations\n for location_name in location_names:\n location_capabilities: List[AzureCapability] = []\n location_info: AzureLocation = self._get_location_info(\n location_name, log\n )\n\n # loop all fall back levels\n for fallback_pattern in VM_SIZE_FALLBACK_LEVELS:\n level_capabilities: List[AzureCapability] = []\n\n # loop all capabilities\n for capability in location_info.capabilities:\n if fallback_pattern.match(capability.vm_size):\n level_capabilities.append(capability)\n\n # sort by rough cost\n level_capabilities.sort(key=lambda x: (x.estimated_cost))\n log.debug(\n f\"{location_name}, pattern '{fallback_pattern.pattern}'\"\n f\" {len(level_capabilities)} candidates: \"\n f\"{[x.vm_size for x in level_capabilities]}\"\n )\n location_capabilities.extend(level_capabilities)\n available_capabilities[location_name] = location_capabilities\n self._eligible_capabilities = available_capabilities\n", "sub_path": "lisa/sut_orchestrator/azure/azure.py", "file_name": "azure.py", "file_ext": "py", "file_size_in_byte": 36978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 46, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 55, "usage_type": "call"}, {"api_name": "lisa.schema.NodeSpace", "line_number": 63, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 65, "usage_type": "name"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 58, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 58, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 58, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "lisa.schema.metadata", "line_number": 73, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 73, "usage_type": "name"}, {"api_name": "marshmallow.fields.DateTime", "line_number": 74, "usage_type": "attribute"}, {"api_name": "marshmallow.fields", "line_number": 74, "usage_type": "name"}, {"api_name": "datetime.datetime.isoformat", "line_number": 75, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "name"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 76, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 82, "usage_type": "call"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 68, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 68, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 68, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 69, "usage_type": "name"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 85, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 85, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 85, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 104, "usage_type": "name"}, {"api_name": "lisa.secret.add_secret", "line_number": 105, "usage_type": "call"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 94, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 94, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 94, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 115, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 117, "usage_type": "name"}, {"api_name": "lisa.secret.add_secret", "line_number": 118, "usage_type": "call"}, {"api_name": "lisa.secret.PATTERN_HEADTAIL", "line_number": 118, "usage_type": "argument"}, {"api_name": "lisa.secret.add_secret", "line_number": 119, "usage_type": "call"}, {"api_name": "lisa.secret.add_secret", "line_number": 120, "usage_type": "call"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 108, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 108, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 108, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 109, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 126, "usage_type": "call"}, {"api_name": "lisa.schema.metadata", "line_number": 128, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 128, "usage_type": "name"}, {"api_name": "marshmallow.validate.Regexp", "line_number": 130, "usage_type": "call"}, {"api_name": "marshmallow.validate", "line_number": 130, "usage_type": "name"}, {"api_name": "lisa.util.constants.GUID_REGEXP", "line_number": 130, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 130, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 133, "usage_type": "call"}, {"api_name": "lisa.schema.metadata", "line_number": 135, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 135, "usage_type": "name"}, {"api_name": "marshmallow.validate.Regexp", "line_number": 137, "usage_type": "call"}, {"api_name": "marshmallow.validate", "line_number": 137, "usage_type": "name"}, {"api_name": "lisa.util.constants.GUID_REGEXP", "line_number": 137, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 137, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 140, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 141, "usage_type": "call"}, {"api_name": "lisa.schema.metadata", "line_number": 143, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 143, "usage_type": "name"}, {"api_name": "marshmallow.validate.Regexp", "line_number": 145, "usage_type": "call"}, {"api_name": "marshmallow.validate", "line_number": 145, "usage_type": "name"}, {"api_name": "lisa.util.constants.GUID_REGEXP", "line_number": 145, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 145, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 149, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 150, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 150, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 152, "usage_type": "call"}, {"api_name": "logging.getLevelName", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 153, "usage_type": "attribute"}, {"api_name": "lisa.schema.metadata", "line_number": 154, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 154, "usage_type": "name"}, {"api_name": "marshmallow.validate.OneOf", "line_number": 156, "usage_type": "call"}, {"api_name": "marshmallow.validate", "line_number": 156, "usage_type": "name"}, {"api_name": "logging.getLevelName", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 158, "usage_type": "attribute"}, {"api_name": "logging.getLevelName", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 159, "usage_type": "attribute"}, {"api_name": "logging.getLevelName", "line_number": 160, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 160, "usage_type": "attribute"}, {"api_name": "logging.getLevelName", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 161, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 174, "usage_type": "name"}, {"api_name": "lisa.secret.add_secret", "line_number": 175, "usage_type": "call"}, {"api_name": "lisa.secret.PATTERN_GUID", "line_number": 175, "usage_type": "name"}, {"api_name": "lisa.secret.add_secret", "line_number": 176, "usage_type": "call"}, {"api_name": "lisa.secret.PATTERN_GUID", "line_number": 176, "usage_type": "name"}, {"api_name": "lisa.secret.add_secret", "line_number": 177, "usage_type": "call"}, {"api_name": "lisa.secret.add_secret", "line_number": 178, "usage_type": "call"}, {"api_name": "lisa.secret.PATTERN_GUID", "line_number": 178, "usage_type": "name"}, {"api_name": "dataclasses_json.dataclass_json", "line_number": 123, "usage_type": "call"}, {"api_name": "dataclasses_json.LetterCase.CAMEL", "line_number": 123, "usage_type": "attribute"}, {"api_name": "dataclasses_json.LetterCase", "line_number": 123, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 124, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 184, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 190, "usage_type": "name"}, {"api_name": "lisa.platform_.Platform", "line_number": 198, "usage_type": "name"}, {"api_name": "azure.identity.DefaultAzureCredential", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 204, "usage_type": "name"}, {"api_name": "lisa.environment.Environment", "line_number": 211, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 211, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 236, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 236, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 254, "usage_type": "call"}, {"api_name": "lisa.schema.NodeSpace", "line_number": 288, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 288, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 314, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 321, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 321, "usage_type": "name"}, {"api_name": "lisa.environment.Environment", "line_number": 378, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 378, "usage_type": "name"}, {"api_name": "lisa.util.constants.NORMALIZE_PATTERN.sub", "line_number": 386, "usage_type": "call"}, {"api_name": "lisa.util.constants.NORMALIZE_PATTERN", "line_number": 386, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 386, "usage_type": "name"}, {"api_name": "lisa.util.constants.RUN_NAME", "line_number": 387, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 387, "usage_type": "name"}, {"api_name": "lisa.environment.Environment", "line_number": 424, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 424, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 457, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 468, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 470, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 471, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 472, "usage_type": "attribute"}, {"api_name": "azure.identity.DefaultAzureCredential", "line_number": 474, "usage_type": "call"}, {"api_name": "azure.mgmt.resource.SubscriptionClient", "line_number": 476, "usage_type": "call"}, {"api_name": "lisa.util.LisaException", "line_number": 481, "usage_type": "call"}, {"api_name": "azure.mgmt.resource.ResourceManagementClient", "line_number": 486, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 493, "usage_type": "call"}, {"api_name": "json.load", "line_number": 495, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 491, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 492, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 500, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 500, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 505, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 505, "usage_type": "name"}, {"api_name": "json.load", "line_number": 505, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 506, "usage_type": "name"}, {"api_name": "retry.retry", "line_number": 498, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 501, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 522, "usage_type": "name"}, {"api_name": "lisa.util.constants.CACHE_PATH.joinpath", "line_number": 523, "usage_type": "call"}, {"api_name": "lisa.util.constants.CACHE_PATH", "line_number": 523, "usage_type": "attribute"}, {"api_name": "lisa.util.constants", "line_number": 523, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 530, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 535, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 535, "usage_type": "name"}, {"api_name": "azure.mgmt.compute.ComputeManagementClient", "line_number": 551, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 556, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 596, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 596, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 599, "usage_type": "call"}, {"api_name": "lisa.environment.Environment", "line_number": 609, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 609, "usage_type": "name"}, {"api_name": "lisa.util.get_public_key_data", "line_number": 619, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 626, "usage_type": "name"}, {"api_name": "lisa.schema.NodeSpace", "line_number": 629, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 629, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 640, "usage_type": "call"}, {"api_name": "lisa.util.LisaException", "line_number": 642, "usage_type": "call"}, {"api_name": "lisa.util.LisaException", "line_number": 644, "usage_type": "call"}, {"api_name": "azure.mgmt.resource.resources.models.DeploymentProperties", "line_number": 671, "usage_type": "call"}, {"api_name": "azure.mgmt.resource.resources.models.DeploymentMode.incremental", "line_number": 672, "usage_type": "attribute"}, {"api_name": "azure.mgmt.resource.resources.models.DeploymentMode", "line_number": 672, "usage_type": "name"}, {"api_name": "azure.mgmt.resource.resources.models.Deployment", "line_number": 680, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 610, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 610, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 684, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 684, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 684, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 689, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 697, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 699, "usage_type": "name"}, {"api_name": "azure.core.exceptions.HttpResponseError", "line_number": 714, "usage_type": "argument"}, {"api_name": "lisa.util.LisaException", "line_number": 722, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 726, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 726, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 726, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 730, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 738, "usage_type": "call"}, {"api_name": "azure.core.exceptions.HttpResponseError", "line_number": 739, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 745, "usage_type": "call"}, {"api_name": "lisa.environment.Environment", "line_number": 747, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 749, "usage_type": "name"}, {"api_name": "lisa.node.Node", "line_number": 749, "usage_type": "name"}, {"api_name": "azure.mgmt.compute.ComputeManagementClient", "line_number": 754, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 758, "usage_type": "name"}, {"api_name": "azure.mgmt.compute.models.VirtualMachine", "line_number": 758, "usage_type": "name"}, {"api_name": "azure.mgmt.network.NetworkManagementClient", "line_number": 765, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 770, "usage_type": "name"}, {"api_name": "azure.mgmt.network.models.InboundNatRule", "line_number": 770, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 779, "usage_type": "name"}, {"api_name": "azure.mgmt.network.models.NetworkInterface", "line_number": 779, "usage_type": "name"}, {"api_name": "lisa.util.LisaException", "line_number": 800, "usage_type": "call"}, {"api_name": "azure.mgmt.compute.models.ResourceSku", "line_number": 820, "usage_type": "name"}, {"api_name": "lisa.schema.NodeSpace", "line_number": 823, "usage_type": "call"}, {"api_name": "lisa.schema", "line_number": 823, "usage_type": "name"}, {"api_name": "lisa.search_space.SetSpace", "line_number": 830, "usage_type": "attribute"}, {"api_name": "lisa.search_space", "line_number": 830, "usage_type": "name"}, {"api_name": "lisa.schema.Feature", "line_number": 830, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 830, "usage_type": "name"}, {"api_name": "lisa.search_space.SetSpace", "line_number": 831, "usage_type": "attribute"}, {"api_name": "lisa.search_space", "line_number": 831, "usage_type": "name"}, {"api_name": "lisa.schema.Feature", "line_number": 831, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 831, "usage_type": "name"}, {"api_name": "lisa.search_space.IntRange", "line_number": 839, "usage_type": "call"}, {"api_name": "lisa.search_space", "line_number": 839, "usage_type": "name"}, {"api_name": "lisa.search_space.IntRange", "line_number": 845, "usage_type": "call"}, {"api_name": "lisa.search_space", "line_number": 845, "usage_type": "name"}, {"api_name": "lisa.schema.NodeSpace", "line_number": 821, "usage_type": "attribute"}, {"api_name": "lisa.schema", "line_number": 821, "usage_type": "name"}, {"api_name": "lisa.util.logger.Logger", "line_number": 853, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 868, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 868, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 872, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 879, "usage_type": "name"}]} {"seq_id": "80614777", "text": "import getpass\nimport exchangelib \nfrom exchangelib.account import Account\nimport email, email.policy, email.header, email.utils\nimport re\nimport os\nimport datetime\nimport logging\nimport configparser \n\nimport event\n\nwith open(os.path.join('texts', 'error_header.txt')) as fh:\n ERROR_HEADER = fh.readlines()\nwith open(os.path.join('texts', 'help_text.txt')) as fh:\n HELP_TEXT = fh.readlines()\n\nclass Email():\n def __init__(self,\n smtp_host,\n mailbox,\n username,\n password=None,\n poll_time=60):\n '''Class to handle connections with a Exchange Server'''\n self.username = username\n self.set_password(password)\n self.mailbox = mailbox\n self.ews_url = None\n self.ews_auth_type = None\n self.smtp_host = smtp_host\n self.poll_time = poll_time\n self.last_update_time = None\n logging.getLogger(__name__).debug('Email initiated')\n\n def is_time_to_update(self):\n '''Check if its time to update'''\n if not self.last_update_time:\n return True\n logging.getLogger(__name__).debug('Time to update')\n return self.last_update_time + datetime.timedelta(seconds=self.poll_time) < datetime.datetime.now() \n \n\n def set_password(self, password=None):\n '''If password is not set, use getpass to get it in a protected way\n\n WARNING, IDLE does not hide password.'''\n self._password = password if password else getpass.getpass()\n\n def fetch(self, server, nr_of_mails, what='', max_amount=50):\n '''Fetch the last max_amount(50) mails from server\n\n server is a folder instance, nr_of_mails is the number of mails in mailbox\n what is not used'''\n amount = min(nr_of_mails, max_amount)\n logging.getLogger(__name__).debug('Fetching: {} of mails'.format(amount))\n mails = server.all().order_by('-datetime_received')[:amount]\n return mails\n\n def select_mailbox(self, server, mailbox='Inbox'):\n '''Returns folder instance'''\n account = self.login(server)\n #folder = account.root.get_folder_by_name(mailbox)\n folder = account.inbox\n return folder, folder.total_count\n\n def get_events(self, max_amount=50):\n '''Gets the last 50 events'''\n new_messages = False\n try:\n l = logging.getLogger(__name__)\n l.debug('Get events')\n mailbox, nr_of_mails = self.select_mailbox(self.smtp_host, self.mailbox)\n if not nr_of_mails:\n l.debug('No mails to get')\n return []\n events = []\n commands = []\n \n if mailbox:\n for message in self.fetch(mailbox, nr_of_mails, '', max_amount):\n #Only certain mail addresses is OK\n if not self.valid_email_address(message) or self.erica(message):\n try:\n l.info('Invalid mail address: {}'.format(message.sender.email_address))\n except Exception:\n pass\n #Adding a event that only contains the mail message\n #will trigger removal of it.\n \n e = event.Event()\n e.email = message\n events.append(e)\n continue\n #Check if its a command and if its valid\n result = self.parse_command(message)\n if result:\n #Its a command, process it\n l.debug('Proccessing: {}'.format(result))\n if result[0]:\n commands.append(result)\n\n else:\n print(result[1], result[2])\n else:\n #parse message\n e = event.Event(message)\n events.append(e)\n \n if e.valid() and not (message.is_read or self.isreply(message)):\n new_messages = True\n l.debug('Sending confirmation email')\n to = message.sender.email_address\n subject = 'Message added to notice board'\n msg = ['Message with subject: ', message.subject,\n ' has been added to NoticeBoard', '\\n\\n',\n 'Send a delete-mail to remove the message from notice board. ',\n 'Press the following link to generate the correct subject format for the delete-mail: mailto:SE-LIN-TAVLAN@semcon.com?subject=%3Cdelete%3E{}'.format(message.item_id[:-1]),\n '%3D' ,'\\n']\n message.is_read = True\n message.save()\n self.send(to, subject, ''.join(msg))\n self.send_subscriptions(events, new_messages)\n except exchangelib.errors.ErrorInternalServerTransientError:\n l.warning('Get events failed', exc_info=True)\n return None,None\n\n self.last_update_time = datetime.datetime.now()\n #self.send_subscriptions(events, new_messages)\n return events, commands\n\n def parse_command(self, message):\n '''Parse a email message to see if it is a valid command'''\n valid_commands = {'list':self.list,\n 'help':self.help,\n 'delete':self.delete,\n 'subscribe':self.subscribe}\n command_string = '.?<(.+)>'\n l = logging.getLogger(__name__)\n if not isinstance(message, exchangelib.items.Message):\n l.warning('Message not a correct message {}'.format(message))\n return \n\n if not message.subject:\n l.warning('Message does not contain a subject')\n return\n match = re.match(command_string, message.subject)\n if not match:\n return\n command = match.group(1)\n command = command.lower().split(',')\n command[0] = command[0].strip()\n if command[0] in valid_commands:\n result = valid_commands[command[0]](message, *command[1:])\n return result\n else:\n return self.help(message)\n\n def valid_email_address(self, message):\n '''Check if message has a valid address'''\n try:\n return message.sender.email_address.lower().endswith('@semcon.com')\n except Exception:\n logging.getLogger(__name__).error('Error in checking for valid mail address', exc_info=True)\n\n def isreply(self, message):\n return (('SE-GOT-EX02.semcon.se' in message.message_id) or ('Message added to notice board' in message.subject))\n\n def create_mailbox(self, server, name):\n '''Create a new folder on server\n\n Not implemented in module yet'''\n pass\n \n def remove_events(self, events, send_error=True):\n '''Removes event from mailbox\n Call with events that are to be removed'''\n try:\n account = self.login(self.smtp_host)\n except exchangelib.errors.ErrorInternalServerTransientError:\n return\n items = []\n for event in events:\n items.append(event.email)\n if send_error and event.fail_reasons:\n subject = 'Något gick tyvärr fel'\n text = ['FEL: ']\n text.extend(event.fail_reasons)\n text.extend(['\\n','Ditt meddelande: ', str(event.email.subject), ' '])\n text.extend(ERROR_HEADER)\n text.extend(HELP_TEXT)\n text = ''.join(text)\n self.send(event.user_address, subject, text)\n account.bulk_move(items, account.trash)\n \n def send(self, to, subject, msg):\n '''send function\n\n Sends an email <to> <subject> <msg>'''\n account = self.login(self.smtp_host)\n email = exchangelib.Message(account=account,\n subject=subject,\n body=msg,\n to_recipients=[to])\n email.send()\n print('Email sent to:', to)\n\n def send_subscriptions(self, events, new_events):\n subscriptions = configparser.ConfigParser()\n result = subscriptions.read('subscriptions/user_subscriptions.ini')\n if not result:\n logging.getLogger(__name__).warn('No subscription file found')\n to_send = []\n #Order maters\n for section in ('each', 'daily', 'weekley'):\n if subscriptions.has_section(section):\n for option in subscriptions.options(section):\n #Special handling of each section so not to send if there are no new\n if section == 'each' and not new_events:\n break\n if option in to_send:\n #If we already have decided to send because of another section, just update last send time\n subscriptions.set(section, option, datetime.datetime.now().strftime('%Y%m%d%H%M'))\n else:\n date = subscriptions.get(section, option)\n if date == 'None':\n diff = None\n else:\n date = datetime.datetime(date, '%Y%m%d%H%M')\n now = datetime.datetime.now()\n diff = now - date\n if section == 'daily':\n if not diff or diff > datetime.datetime.timedelta(hours=24):\n to_send.append(option)\n subscription.set(section, option, date)\n elif section == 'weekley':\n if not diff or diff > datetime.datetime.timedelta(days=7):\n to_send.append(option)\n subscription.set(section, option, date)\n else:\n to_send.append(option)\n subscriptions.set(section, option, date)\n\n subject = 'Current Events on NoticeBoard'\n msg = []\n for event in events:\n msg.append(str(event))\n msg = '\\n'.join(msg) \n for address in to_send:\n self.send(address, subject, msg)\n \n def login(self, server):\n '''Login to server, return account instance'''\n \n credentials = exchangelib.ServiceAccount(username=self.username,\n password=self._password)\n if self.ews_url and self.ews_auth_type and self.smtp_host:\n config = exchangelib.Configuration(service_endpoint=self.ews_url,\n credentials=credentials,\n auth_type=self.ews_auth_type)\n\n account = exchangelib.Account(primary_smtp_address = server,\n config=config, autodiscover=False,\n access_type=exchangelib.DELEGATE)\n else:\n account = exchangelib.Account(primary_smtp_address=server,\n credentials=credentials,\n autodiscover=True,\n access_type=exchangelib.DELEGATE)\n \n self.ews_url = account.protocol.service_endpoint\n self.ews_auth_type = account.protocol.auth_type\n self.smtp_host = account.primary_smtp_address\n\n return account\n\n #Usercommands\n def delete(self, message, *args):\n '''Removes a message from mailbox'''\n try:\n account = self.login(self.smtp_host)\n account.bulk_move([message], account.trash)\n except Exception as err:\n return (False, 'delete', [err, message])\n else:\n return (True, 'delete', [message])\n \n \n def help(self, message, *args):\n '''Sends help text'''\n try:\n self.send(message.sender.email_address, 'Instruktion till Tavlan i Linköping', ''.join(HELP_TEXT))\n account = self.login(self.smtp_host)\n account.bulk_move([message], account.trash)\n except Exception as err:\n return (False, 'help', [err, message])\n else:\n return (True, 'help', [])\n\n def list(self, message, *args):\n '''Sends current messages to user'''\n try:\n account = self.login(self.smtp_host)\n account.bulk_move([message], account.trash)\n except Exception as err:\n return (False, 'list', [err, message])\n else:\n return (True, 'list', [message.sender.email_address])\n\n def subscribe(self, message, *args):\n def handle_subscription(address, subscription, subscription_type):\n if subscription_type == 'unsubscribe':\n for section in subscription.sections():\n subscription.remove_option(section, address)\n \n else:\n try:\n subscription.add_section(subscription_type)\n except configparser.DuplicateSectionError:\n pass\n subscription.set(subscription_type, address, 'None')\n self.delete(message)\n os.makedirs('subscriptions', exist_ok=True) \n subscriptions = configparser.ConfigParser()\n subscriptions.read('subscriptions/user_subscriptions.ini')\n if not args:\n handle_subscription(message.sender.email_address, subscriptions, 'each')\n else:\n args = [x.strip().lower() for x in args if x.strip() in ('daily', 'weekley', 'each', 'unsubscribe')]\n for arg in args:\n handle_subscription(message.sender.email_address, subscriptions, arg)\n \n with open('subscriptions/user_subscriptions.ini', 'w') as fh:\n subscriptions.write(fh)\n\n return (True, 'subscribe', [])\n \n #Taking care of childish behavior\n def erica(self, message):\n try:\n subject = message.subject.strip()\n match = re.match('(\\[.*\\])', subject)\n if match and 'bajskorv' in match.group(1).lower():\n self.send('erica.nilsbacken@semcon.com', 'Erica step away from the computer', 'Sluta larva dig och använd <help> istället')\n return True\n except Exception:\n pass\n", "sub_path": "exchange_email.py", "file_name": "exchange_email.py", "file_ext": "py", "file_size_in_byte": 14897, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}, {"api_name": "getpass.getpass", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 71, "usage_type": "call"}, {"api_name": "event.Event", "line_number": 91, "usage_type": "call"}, {"api_name": "event.Event", "line_number": 107, "usage_type": "call"}, {"api_name": "exchangelib.errors", "line_number": 124, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 139, "usage_type": "call"}, {"api_name": "exchangelib.items", "line_number": 140, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 147, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 164, "usage_type": "call"}, {"api_name": "exchangelib.errors", "line_number": 180, "usage_type": "attribute"}, {"api_name": "event.email", "line_number": 184, "usage_type": "attribute"}, {"api_name": "event.fail_reasons", "line_number": 185, "usage_type": "attribute"}, {"api_name": "event.fail_reasons", "line_number": 188, "usage_type": "attribute"}, {"api_name": "event.email", "line_number": 189, "usage_type": "attribute"}, {"api_name": "event.user_address", "line_number": 193, "usage_type": "attribute"}, {"api_name": "exchangelib.Message", "line_number": 201, "usage_type": "call"}, {"api_name": "email.send", "line_number": 205, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 209, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 223, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 223, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 229, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "attribute"}, {"api_name": "datetime.datetime.timedelta", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 233, "usage_type": "attribute"}, {"api_name": "datetime.datetime.timedelta", "line_number": 237, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 237, "usage_type": "attribute"}, {"api_name": "exchangelib.ServiceAccount", "line_number": 255, "usage_type": "call"}, {"api_name": "exchangelib.Configuration", "line_number": 258, "usage_type": "call"}, {"api_name": "exchangelib.Account", "line_number": 262, "usage_type": "call"}, {"api_name": "exchangelib.DELEGATE", "line_number": 264, "usage_type": "attribute"}, {"api_name": "exchangelib.Account", "line_number": 266, "usage_type": "call"}, {"api_name": "exchangelib.DELEGATE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "configparser.DuplicateSectionError", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 323, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 324, "usage_type": "call"}, {"api_name": "re.match", "line_number": 342, "usage_type": "call"}]} {"seq_id": "283076187", "text": "# Copyright 2012 Yelp\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import with_statement\n\nfrom sys import argv\nfrom sys import stderr\n\n\ncommands = {}\n\ndescriptions = {}\n\nusage = \"\"\"usage: mrjob {subcommand|--help}\"\n\nsubcommands:\"\"\"\n\n\ndef error(msg=None):\n if msg:\n print >> stderr, msg\n\n longest_command = max(len(name) for name in commands)\n\n def subcommand_line(name):\n spaces = ' ' * (longest_command - len(name))\n return ' %s: %s%s' % (\n name, spaces, descriptions[name])\n print >> stderr, usage\n print >> stderr, '\\n'.join(\n subcommand_line(name) for name in sorted(commands))\n\n\ndef command(name, description):\n def decorator(f):\n commands[name] = f\n descriptions[name] = description\n return f\n return decorator\n\n\ndef main(args=None):\n args = args or argv\n if not args[1:] or args[1] in ('-h', '--help'):\n error()\n elif args[1] not in commands:\n error('\"%s\" is not a command' % args[1])\n else:\n commands[args[1]](args[2:])\n\n\n@command('run', 'Run a job')\ndef run(args):\n from mrjob.launch import MRJobLauncher\n MRJobLauncher(args=args, from_cl=True).run_job()\n\n\n@command('audit-emr-usage', 'Audit EMR usage')\ndef audit_usage(args):\n from mrjob.tools.emr.audit_usage import main\n main(args)\n\n\n@command('collect-emr-active-stats', 'Collect EMR stats from active jobflows')\ndef collect_emr_stats(args):\n from mrjob.tools.emr.collect_emr_stats import main\n main(args)\n\n\n@command('create-job-flow', 'Create an EMR job flow')\ndef create_jf(args):\n from mrjob.tools.emr.create_job_flow import main\n main(args)\n\n\n@command('fetch-logs', 'Fetch and parse EMR logs for errors and counters')\ndef fetch_logs(args):\n from mrjob.tools.emr.fetch_logs import main\n main(args)\n\n\n@command('report-long-jobs', 'Report EMR jobs which have been running for a'\n ' long time')\ndef report_long_jobs(args):\n from mrjob.tools.emr.report_long_jobs import main\n main(args)\n\n\n@command('s3-tmpwatch', 'Delete S3 keys older than a specified time')\ndef s3_tmpwatch(args):\n from mrjob.tools.emr.s3_tmpwatch import main\n main(args)\n\n\n@command('terminate-idle-job-flows', 'Terminate idle EMR job flows')\ndef terminate_idle_jfs(args):\n from mrjob.tools.emr.terminate_idle_job_flows import main\n main(args)\n\n\n@command('terminate-job-flow', 'Terminate a single EMR job flow')\ndef terminate_jf(args):\n from mrjob.tools.emr.terminate_job_flow import main\n main(args)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "mrjob/cmd.py", "file_name": "cmd.py", "file_ext": "py", "file_size_in_byte": 3061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sys.stderr", "line_number": 32, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 41, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "name"}, {"api_name": "mrjob.launch.MRJobLauncher", "line_number": 66, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.audit_usage.main", "line_number": 72, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.collect_emr_stats.main", "line_number": 78, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.create_job_flow.main", "line_number": 84, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.fetch_logs.main", "line_number": 90, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.report_long_jobs.main", "line_number": 97, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.s3_tmpwatch.main", "line_number": 103, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.terminate_idle_job_flows.main", "line_number": 109, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.terminate_job_flow.main", "line_number": 115, "usage_type": "call"}, {"api_name": "mrjob.tools.emr.terminate_job_flow.main", "line_number": 119, "usage_type": "call"}]} {"seq_id": "253835423", "text": "import os\nfrom app.models import User\nfrom app import create_app, db\nfrom flask_script import Manager, Shell\nfrom werkzeug.exceptions import InternalServerError\n\n\napp = create_app()\nmanager = Manager(app)\n\n# 处理 500 内部错误，用于调试\n@app.errorhandler(InternalServerError)\ndef internal_server_error(e):\n print(e.code)\n print(e.name)\n print(e.description)\n return \"Internal Server Error\"\n\n\ndef make_shell_context():\n return dict(app=app, db=db, User=User)\n\n\n@manager.command\ndef run():\n app.run(port=80)\n\n\nmanager.add_command(\"shell\", Shell(make_context=make_shell_context))\n\n\nif __name__ == '__main__':\n manager.run()", "sub_path": "manage.py", "file_name": "manage.py", "file_ext": "py", "file_size_in_byte": 651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "app.models", "line_number": 8, "usage_type": "name"}, {"api_name": "app.create_app", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_script.Manager", "line_number": 9, "usage_type": "call"}, {"api_name": "app.models", "line_number": 9, "usage_type": "argument"}, {"api_name": "app.models.errorhandler", "line_number": 12, "usage_type": "call"}, {"api_name": "werkzeug.exceptions.InternalServerError", "line_number": 12, "usage_type": "argument"}, {"api_name": "app.models", "line_number": 12, "usage_type": "name"}, {"api_name": "app.models", "line_number": 21, "usage_type": "name"}, {"api_name": "app.db", "line_number": 21, "usage_type": "name"}, {"api_name": "app.models.User", "line_number": 21, "usage_type": "name"}, {"api_name": "app.models.run", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models", "line_number": 26, "usage_type": "name"}, {"api_name": "flask_script.Shell", "line_number": 29, "usage_type": "call"}]} {"seq_id": "231092459", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django_iban.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('motius_payment', '0004_auto_20141123_2358'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='ibanbankaccount',\n name='bic',\n field=django_iban.fields.SWIFTBICField(null=True, max_length=11, verbose_name='BIC', blank=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='ibanbankaccount',\n name='iban',\n field=django_iban.fields.IBANField(max_length=34, verbose_name='IBAN Number'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='ibanbankaccount',\n name='tax_id',\n field=models.CharField('Tax ID', null=True, max_length=50, blank=True),\n preserve_default=True,\n ),\n ]\n", "sub_path": "motius_payment/migrations/0005_auto_20141124_0009.py", "file_name": "0005_auto_20141124_0009.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django_iban.fields.fields.SWIFTBICField", "line_number": 18, "usage_type": "call"}, {"api_name": "django_iban.fields.fields", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django_iban.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django_iban.fields.fields.IBANField", "line_number": 24, "usage_type": "call"}, {"api_name": "django_iban.fields.fields", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django_iban.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}]} {"seq_id": "163678117", "text": "from write import write\n\nimport os\nimport sys\n\nsys.setrecursionlimit(26194)\n\nimport numpy as np\nimport random\nimport math\n\nimport time\n\nimport pygame\npyg = pygame\ndisp = pygame.display\n\npyg.init()\ndisp.init()\n\n\"\"\"Colors\"\"\"\nWHITE = pyg.Color('white')\nBLACK = pyg.Color('black')\nGREEN = pyg.Color('green')\nRED = pyg.Color('red')\nBLUE = pyg.Color('blue')\nYELLOW = pyg.Color('yellow')\nLIGHTBLUE = pyg.Color('lightblue')\nDARKGRAY = pyg.Color('gray10')\nGRAY = pyg.Color('gray')\nLIGHTGRAY = pyg.Color('gray40')\n\nbg_col = WHITE\nline_col = BLACK\ntext_col = BLACK\n\n\"\"\"Settings\"\"\"\n\n\n\"\"\"Screen\"\"\"\n# Resolutions: 960x720, 1080x720, 1540x840\nscreen = disp.set_mode((960, 720))\nscreen_w, screen_h = screen.get_size()\nscreen.fill(bg_col)\ndisp.flip()\n\ndisp.set_caption(\"Geometry Dash\")\n\n\"\"\"Functions\"\"\"\ndef update():\n disp.flip()\n\ndef clear():\n screen.fill(bg_col)\n\ndef load_img(name):\n \"\"\"Loads image\"\"\"\n image_name = os.path.join('img', 'Geometry Dash', name)\n image = pyg.image.load(image_name)\n image = image.convert()\n image_rect = image.get_rect()\n return image, image_rect\n\ndef resize(img, img_rect, scale):\n img_w, img_h = img.get_size()\n img_scaled = pyg.transform.scale(img, (img_w*scale, img_h*scale))\n img_rect_scaled = img_scaled.get_rect()\n return img_scaled, img_rect_scaled\n\n\"\"\"Classes\"\"\"\nclass Player():\n def __init__(self, x, y):\n # Load image\n self.img, self.rect = load_img('Player.bmp')\n # Set scale\n scale = screen_w//960\n # Resize image\n self.img, self.rect = resize(self.img, self.rect, scale)\n\n # (x, y) = top-left corner\n self.x = x\n self.y = y\n\n self.vel = 0\n\n def update(self):\n self.vel \n \n def draw(self):\n screen.blit(self.img, (self.x, self.y))\n\n def jump(self):\n self.vel = 5\n\nclass Spike():\n def __init__(self, x, y):\n # Load image\n self.img, self.rect = load_img('Spike.bmp')\n # Set scale\n scale = screen_w//960\n # Resize image\n self.img, self.rect = resize(self.img, self.rect, scale)\n\n # (x, y) = top-left corner\n self.x = x\n self.y = y\n\n def draw(self):\n screen.blit(self.img, (self.x, self.y))\n\nclass Platform():\n def __init__(self, x, y):\n # Load image\n self.img, self.rect = load_img('Platform.bmp')\n # Set scale\n scale = screen_w//960\n # Resize image\n self.img, self.rect = resize(self.img, self.rect, scale)\n\n # (x, y) = top-left corner\n self.x = x\n self.y = y\n\n def draw(self):\n screen.blit(self.img, (round(self.x), round(self.y)))\n\n\"\"\"Main\"\"\"\ndef main():\n global ground_floor, img_w\n ground_floor = []\n img, img_rect = load_img('Platform.bmp')\n img_w = (screen_w//960)*img.get_width()\n img_h = img_w\n img2, img_rect2 = load_img('Spike.bmp')\n img_h2 = (screen_w//960)*img2.get_height()\n x = 0\n y = screen_h-4*img_h\n while x <= screen_w+2*img_w:\n ground_floor.append(Platform(x, y))\n x += img_w\n del x, y\n player = Player(50, screen_h-5*img_h)\n next_spike = 25\n cur_spikes = []\n running = True\n while running:\n event = pyg.event.get()\n if event:\n event = event[0]\n else:\n event = pyg.event.Event(-1)\n\n if event.type == pyg.QUIT:\n running = False\n\n clear()\n for p in ground_floor:\n if p.x <= -2*img_w:\n ground_floor.remove(p)\n p.x = ground_floor[-1].x+img_w\n ground_floor.append(p)\n if next_spike > 0:\n next_spike -= 1\n p.draw()\n p.x -= 1\n\n if next_spike <= 0:\n cur_spikes.append(Spike(ground_floor[-1].x, screen_h-4*img_h-img_h2))\n k = random.random()\n if 0 < k < 0.1:\n next_spike = random.randint(5, 10)\n elif 0.1 < k < 0.3:\n next_spike = random.randint(3, 15)\n elif 0.3 < k < 1:\n next_spike = random.randint(10, 20)\n\n for s in cur_spikes:\n if s.x <= -2*img_w:\n cur_spikes.remove(s)\n s.draw()\n s.x -= 1\n\n player.draw()\n update()\n\n pyg.quit()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Geometry Dash.py", "file_name": "Geometry Dash.py", "file_ext": "py", "file_size_in_byte": 4331, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 169, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 171, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 173, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 175, "usage_type": "call"}]} {"seq_id": "645876288", "text": "\nfrom __future__ import absolute_import\nimport os\nfrom celery import Celery\n\n# 把置默认的django settings模块配置给celery\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'movieAnalysis.settings')\n\napp = Celery('movieAnalysis')\n\n# 这里使用字符串以使celery的worker不用为子进程序列化配置对象。\n# 命名空间 namespace='CELERY'定义所有与celery相关的配置的键名要以'CELERY_'为前缀。\napp.config_from_object('django.conf:settings', namespace='CELERY')\n\n# 从所有django app configs中加载task模块，\n# 如果你把所有的task都定义在单独的tasks.py模块中，\n# 加上这句话celery会自动发现这些模块中的task，实际上这句话可以省略。\napp.autodiscover_tasks()\n@app.task(bind=True)\ndef debug_task(self):\n print('Request: {0!r}'.format(self.request))", "sub_path": "movieAnalysis/movieAnalysis/celery.py", "file_name": "celery.py", "file_ext": "py", "file_size_in_byte": 832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.environ.setdefault", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 9, "usage_type": "call"}]} {"seq_id": "289871925", "text": "# Copyright (c) 2017-2020 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n\n\"\"\"\nAll of the snippets for the Postman tutorial. Executing this file as a module will test as many of\nthe snippets as possible in the way that they are intended to be used.\n\nThis file should not be modified without also carefully looking at ``tutorials_post_office.rst``\nin the documentation folder.\n\"\"\"\n\n\nimport unittest\nfrom dazl import setup_default_logger\nfrom dazl.client import ExitCode # noqa\nfrom dazl.client.config import LedgerNodeConfiguration\n\n# These imports are included in the documentation EXACTLY AS IS.\n# Only add imports here if they are used as part of the tutorial's documentation.\n# DOC_BEGIN: IMPORTS_CONSTANTS\nfrom os import path\n\nfrom dazl import create, sandbox\nfrom dazl.client import create_client\n\nDAML_FILE = path.realpath(path.join(path.dirname(__file__), './Main.daml'))\n\nPOSTMAN_PARTY = 'Postman'\nMEMBER_PARTY_COUNT = 10\n# DOC_END: IMPORTS_CONSTANTS\n\n\nsetup_default_logger()\nLedgerNodeConfiguration._defaults['poll_interval'] = 1.0\n\n\ndef create_postman():\n # DOC_BEGIN: CREATE_POSTMAN\n def run_test(url):\n all_parties = [POSTMAN_PARTY]\n\n with create_client(parties=all_parties, participant_url=url) as client_mgr:\n postman_client = client_mgr.new_client(POSTMAN_PARTY)\n postman_client.on_ready(\n lambda _, __: create('Main:PostmanRole', dict(postman=POSTMAN_PARTY)))\n\n ledger_run = client_mgr.run_until_complete()\n return ledger_run.exit_code\n # DOC_END: CREATE_POSTMAN\n return run_test\n\n\ndef inspect_ledger():\n # DOC_BEGIN: INSPECT_LEDGER\n from dazl.plugins import LedgerCapturePlugin\n\n def run_test(url):\n all_parties = [POSTMAN_PARTY]\n\n with create_client(parties=all_parties, participant_url=url) as client_mgr:\n inspector = LedgerCapturePlugin.stdout()\n try:\n postman_client = client_mgr.new_client(POSTMAN_PARTY)\n postman_client.on_ready(\n lambda _, __: create('Main:PostmanRole', dict(postman=POSTMAN_PARTY)))\n\n client_mgr.register(inspector)\n\n ledger_run = client_mgr.run_until_complete()\n return ledger_run.exit_code\n finally:\n inspector.dump_all()\n # DOC_END: INSPECT_LEDGER\n return run_test\n\n\ndef invite_participants():\n from dazl.plugins import LedgerCapturePlugin\n\n # DOC_BEGIN: INVITE_PARTICIPANTS\n def run_test(url):\n members = [dict(party=f'Member {i}', address=address(i)) for i in\n range(0, MEMBER_PARTY_COUNT)]\n all_parties = [POSTMAN_PARTY] + [member['party'] for member in members]\n\n with create_client(parties=all_parties, participant_url=url) as client_mgr:\n inspector = LedgerCapturePlugin.stdout()\n try:\n set_up(client_mgr, members)\n client_mgr.register(inspector)\n\n ledger_run = client_mgr.run_until_complete()\n return ledger_run.exit_code\n finally:\n inspector.dump_all()\n\n def set_up(client_mgr, members):\n postman_client = client_mgr.new_client(POSTMAN_PARTY)\n postman_client.on_ready(\n lambda _, __: create('Main:PostmanRole', dict(postman=POSTMAN_PARTY)))\n postman_client.on_created(\n 'Main:PostmanRole',\n lambda cid, cdata: [cid.exercise('InviteParticipant', m) for m in members])\n\n def address(index):\n return '{} Member Lane'.format(index)\n # DOC_END: INVITE_PARTICIPANTS\n return run_test\n\n\ndef final_run_test(set_up):\n from dazl.plugins import LedgerCapturePlugin\n\n def address(index):\n return f'{index} Member Lane'\n\n def run_test(url):\n members = [dict(party=f'Member {i}', address=address(i))\n for i in range(0, MEMBER_PARTY_COUNT)]\n all_parties = [POSTMAN_PARTY] + [member['party'] for member in members]\n\n with create_client(parties=all_parties, participant_url=url) as client_mgr:\n inspector = LedgerCapturePlugin.stdout()\n try:\n set_up(client_mgr, members)\n client_mgr.register(inspector)\n\n ledger_run = client_mgr.run_until_complete()\n return ledger_run.exit_code\n finally:\n inspector.dump_all()\n\n return run_test\n\n\ndef accept_invites():\n # DOC_BEGIN: ACCEPT_INVITES\n def set_up(client_mgr, members):\n postman_client = client_mgr.new_client(POSTMAN_PARTY)\n postman_client.on_ready(\n lambda _, __: create('Main:PostmanRole', dict(postman=POSTMAN_PARTY)))\n postman_client.on_created(\n 'Main:PostmanRole',\n lambda cid, cdata: [cid.exercise('InviteParticipant', m) for m in members])\n\n member_clients = [client_mgr.new_client(m['party']) for m in members]\n for member_client in member_clients:\n # every member automatically accepts\n member_client.on_created(\n 'Main:InviteAuthorRole', lambda cid, cdata: cid.exercise('AcceptInviteAuthorRole'))\n member_client.on_created(\n 'Main:InviteReceiverRole', lambda cid, cdata: cid.exercise('AcceptInviteReceiverRole'))\n # DOC_END: ACCEPT_INVITES\n return final_run_test(set_up)\n\n\ndef send_letters():\n def address(index):\n return f'{index} Member Lane'\n\n # DOC_BEGIN: SEND_LETTERS\n from functools import partial\n\n def set_up(client_mgr, members):\n postman_client = client_mgr.new_client(POSTMAN_PARTY)\n postman_client.on_ready(\n lambda _, __: create('Main:PostmanRole', dict(postman=POSTMAN_PARTY)))\n postman_client.on_created(\n 'Main:PostmanRole',\n lambda cid, cdata: [cid.exercise('InviteParticipant', m) for m in members])\n\n member_clients = [client_mgr.new_client(m['party']) for m in members]\n for member_client in member_clients:\n # every member automatically accepts\n member_client.on_created(\n 'Main:InviteAuthorRole', lambda cid, cdata: cid.exercise('AcceptInviteAuthorRole'))\n member_client.on_created(\n 'Main:InviteReceiverRole', lambda cid, cdata: cid.exercise('AcceptInviteReceiverRole'))\n member_client.on_created(\n 'Main:AuthorRole', partial(send_five_letters, member_client.party_name))\n\n def send_five_letters(party_name, cid, cdata):\n if party_name == cdata['author']:\n party_index = int(party_name.split(' ')[1])\n addresses = map(lambda i: address(i % MEMBER_PARTY_COUNT), range(party_index + 1, party_index + 6))\n\n # exercise the same non-consuming choice repeatedly\n return [cid.exercise(\n 'CreateLetter',\n dict(address=address, content=f'I am a letter from {party_name} to {address}'))\n for address in addresses]\n\n # DOC_END: SEND_LETTERS\n return final_run_test(set_up)\n\n\ndef main_boilerplate(globals_, run_test):\n __name__ = globals_['__name__']\n\n # DOC_BEGIN: MAIN-BOILERPLATE\n if __name__ == '__main__':\n import sys\n\n with sandbox(DAML_FILE) as server:\n exit_code = run_test(server.url)\n sys.exit(int(exit_code))\n # DOC_END: MAIN-BOILERPLATE\n\n\nclass TutorialTest(unittest.TestCase):\n def test_create_postman(self):\n run_test = create_postman()\n with sandbox(DAML_FILE) as server:\n self.assertEqual(run_test(server.url), ExitCode.SUCCESS)\n\n def test_inspect_ledger(self):\n run_test = inspect_ledger()\n with sandbox(DAML_FILE) as server:\n self.assertEqual(run_test(server.url), ExitCode.SUCCESS)\n\n def test_invite_participants(self):\n run_test = invite_participants()\n with sandbox(DAML_FILE) as server:\n self.assertEqual(run_test(server.url), ExitCode.SUCCESS)\n\n def test_accept_invites(self):\n run_test = accept_invites()\n with sandbox(DAML_FILE) as server:\n self.assertEqual(run_test(server.url), ExitCode.SUCCESS)\n\n def test_send_letters(self):\n run_test = send_letters()\n with sandbox(DAML_FILE) as server:\n self.assertEqual(run_test(server.url), ExitCode.SUCCESS)\n", "sub_path": "python/tests/tutorials/post_office/tutorial.py", "file_name": "tutorial.py", "file_ext": "py", "file_size_in_byte": 8410, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.realpath", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "dazl.setup_default_logger", "line_number": 33, "usage_type": "call"}, {"api_name": "dazl.client.config.LedgerNodeConfiguration._defaults", "line_number": 34, "usage_type": "attribute"}, {"api_name": "dazl.client.config.LedgerNodeConfiguration", "line_number": 34, "usage_type": "name"}, {"api_name": "dazl.client.create_client", "line_number": 42, "usage_type": "call"}, {"api_name": "dazl.create", "line_number": 45, "usage_type": "call"}, {"api_name": "dazl.client.create_client", "line_number": 60, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin.stdout", "line_number": 61, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin", "line_number": 61, "usage_type": "name"}, {"api_name": "dazl.create", "line_number": 65, "usage_type": "call"}, {"api_name": "dazl.client.create_client", "line_number": 86, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin.stdout", "line_number": 87, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin", "line_number": 87, "usage_type": "name"}, {"api_name": "dazl.create", "line_number": 100, "usage_type": "call"}, {"api_name": "dazl.client.create_client", "line_number": 122, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin.stdout", "line_number": 123, "usage_type": "call"}, {"api_name": "dazl.plugins.LedgerCapturePlugin", "line_number": 123, "usage_type": "name"}, {"api_name": "dazl.create", "line_number": 141, "usage_type": "call"}, {"api_name": "dazl.create", "line_number": 167, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 180, "usage_type": "call"}, {"api_name": "dazl.sandbox", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 206, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 210, "usage_type": "attribute"}, {"api_name": "dazl.sandbox", "line_number": 213, "usage_type": "call"}, {"api_name": "dazl.client.ExitCode.SUCCESS", "line_number": 214, "usage_type": "attribute"}, {"api_name": "dazl.client.ExitCode", "line_number": 214, "usage_type": "name"}, {"api_name": "dazl.sandbox", "line_number": 218, "usage_type": "call"}, {"api_name": "dazl.client.ExitCode.SUCCESS", "line_number": 219, "usage_type": "attribute"}, {"api_name": "dazl.client.ExitCode", "line_number": 219, "usage_type": "name"}, {"api_name": "dazl.sandbox", "line_number": 223, "usage_type": "call"}, {"api_name": "dazl.client.ExitCode.SUCCESS", "line_number": 224, "usage_type": "attribute"}, {"api_name": "dazl.client.ExitCode", "line_number": 224, "usage_type": "name"}, {"api_name": "dazl.sandbox", "line_number": 228, "usage_type": "call"}, {"api_name": "dazl.client.ExitCode.SUCCESS", "line_number": 229, "usage_type": "attribute"}, {"api_name": "dazl.client.ExitCode", "line_number": 229, "usage_type": "name"}, {"api_name": "dazl.sandbox", "line_number": 233, "usage_type": "call"}, {"api_name": "dazl.client.ExitCode.SUCCESS", "line_number": 234, "usage_type": "attribute"}, {"api_name": "dazl.client.ExitCode", "line_number": 234, "usage_type": "name"}]} {"seq_id": "43256422", "text": "from django.conf.urls import url\nfrom . import views\nfrom django.views.generic.base import RedirectView\n\napp_name = 'Book'\nurlpatterns = [\n url(r'^book/$', views.book, name='book'),\n url(r'^borrow/(\\d+)/$', views.borrow, name='borrow'),\n url(r'^user_info/$', views.user_info, name='user_info'),\n url(r'^borrow_record/$', views.borrow_record, name='borrow_record'),\n url(r'^search/$', views.search, name='search'),\n url(r'^upload/$', views.upload, name='upload'),\n url(r'^ajax/$', views.ajax, name='ajax'),\n url(r'^test/$', views.test, name='test'),\n url(r'^favicon\\.ico$', RedirectView.as_view(url=r'static/img/favicon.ico')),\n]\n", "sub_path": "Book/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 656, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.views.generic.base.RedirectView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "django.views.generic.base.RedirectView", "line_number": 15, "usage_type": "name"}]} {"seq_id": "219452996", "text": "from rest_framework import serializers\n\nfrom api.models import Company, Vacancy\n\n\nclass CompanySerilizer(serializers.Serializer):\n id = serializers.IntegerField(read_only=True);\n name = serializers.CharField(max_length=300)\n description = serializers.CharField(max_length=300)\n\n def create(self, validated_data):\n company = Company()\n company.name = validated_data.get('name', 'default name');\n company.description = validated_data.get('description', 'default description')\n company.save()\n return company\n\n\nclass VacancySerializer(serializers.ModelSerializer):\n class Meta:\n model = Vacancy\n fields = ('id', 'name', 'description', 'salary', 'company_id')\n", "sub_path": "Week13/hh_back/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "api.models.Company", "line_number": 12, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "api.models.Vacancy", "line_number": 21, "usage_type": "name"}]} {"seq_id": "505920786", "text": "# @Time : 2019/5/13 16:24\n# @Author : Xu Huipeng\n# @Blog : https://brycexxx.github.io/\n\nfrom typing import List\n\n\n# 参考题解：https://leetcode-cn.com/problems/trapping-rain-water/solution/zuo-you-liang-bian-de-zui-da-zhi-by-powcai/\n\nclass Solution:\n def trap(self, height: List[int]) -> int:\n if not height: return 0\n n = len(height)\n max_left = [0] * n\n max_right = [0] * n\n max_left[0], max_right[-1] = height[0], height[-1]\n for i in range(1, n):\n max_left[i] = max(height[i], max_left[i - 1])\n for j in range(n - 2, -1, -1):\n max_right[j] = max(height[j], max_right[j + 1])\n ret = 0\n for m in range(n):\n ret += min(max_left[m], max_right[m]) - height[m]\n return ret\n\n def trap1(self, height: List[int]) -> int:\n if not height: return 0\n n = len(height)\n max_left = height[0]\n max_right = height[n - 1]\n left, right = 0, n - 1\n ret = 0\n while left < right:\n if height[left] < height[right]:\n if max_left > height[left]:\n ret += max_left - height[left]\n else:\n max_left = height[left]\n left += 1\n else:\n if max_right > height[right]:\n ret += max_right - height[right]\n else:\n max_right = height[right]\n right -= 1\n return ret\n\n # 最不好想\n def trap2(self, height: List[int]) -> int:\n if not height: return 0\n n = len(height)\n stack = []\n res = 0\n for i in range(n):\n while stack and height[stack[-1]] < height[i]:\n tmp = stack.pop()\n if not stack: break\n res += (min(height[i], height[stack[-1]]) - height[tmp]) * (i - stack[-1] - 1)\n stack.append(i)\n return res\n\n\nif __name__ == '__main__':\n s = Solution()\n height = [0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1]\n print(s.trap(height))\n print(s.trap1(height))\n print(s.trap2(height))\n", "sub_path": "trap.py", "file_name": "trap.py", "file_ext": "py", "file_size_in_byte": 2127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}]} {"seq_id": "362445511", "text": "\"\"\"\nBase class for all Xena package tests.\n\n@author yoram@ignissoft.com\n\"\"\"\n\nfrom os import path\nimport pytest\n\nfrom trafficgenerator.tgn_utils import ApiType\nfrom trafficgenerator.test.test_tgn import TestTgnBase\nfrom xenavalkyrie.xena_app import init_xena\nfrom xenavalkyrie.xena_stream import XenaStream\n\n\nclass TestXenaBase(TestTgnBase):\n\n TestTgnBase.config_file = path.join(path.dirname(__file__), 'XenaValkyrie.ini')\n\n def setup(self):\n super(TestXenaBase, self).setup()\n\n self._get_config()\n\n self.xm = init_xena(self.api, self.logger, self.config.get('Xena', 'owner'), self.server_ip, self.server_port)\n self.temp_dir = self.config.get('General', 'temp_dir')\n self.xm.session.add_chassis(self.chassis)\n if self.chassis2:\n self.xm.session.add_chassis(self.chassis2)\n XenaStream.next_tpld_id = 0\n\n def teardown(self):\n self.xm.session.disconnect()\n\n def test_hello_world(self):\n pass\n\n def _get_config(self):\n\n self.api = ApiType[pytest.config.getoption('--api')] # @UndefinedVariable\n self.server_ip = pytest.config.getoption('--server') # @UndefinedVariable\n self.chassis = pytest.config.getoption('--chassis') # @UndefinedVariable\n self.port1 = '{}/{}'.format(self.chassis, pytest.config.getoption('--port1')) # @UndefinedVariable\n self.port2 = '{}/{}'.format(self.chassis, pytest.config.getoption('--port2')) # @UndefinedVariable\n self.port3 = pytest.config.getoption('--port3') # @UndefinedVariable\n self.chassis2 = self.port3.split('/')[0] if self.port3 else ''\n if self.server_ip:\n self.server_port = int(self.server_ip.split(':')[1]) if len(self.server_ip.split(':')) == 2 else 57911\n self.server_ip = self.server_ip.split(':')[0]\n else:\n self.server_ip = self.chassis\n self.server_port = 57911\n", "sub_path": "xenavalkyrie/tests/test_base.py", "file_name": "test_base.py", "file_ext": "py", "file_size_in_byte": 1911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "trafficgenerator.test.test_tgn.TestTgnBase", "line_number": 16, "usage_type": "name"}, {"api_name": "trafficgenerator.test.test_tgn.TestTgnBase.config_file", "line_number": 18, "usage_type": "attribute"}, {"api_name": "trafficgenerator.test.test_tgn.TestTgnBase", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "xenavalkyrie.xena_app.init_xena", "line_number": 25, "usage_type": "call"}, {"api_name": "xenavalkyrie.xena_stream.XenaStream.next_tpld_id", "line_number": 30, "usage_type": "attribute"}, {"api_name": "xenavalkyrie.xena_stream.XenaStream", "line_number": 30, "usage_type": "name"}, {"api_name": "trafficgenerator.tgn_utils.ApiType", "line_number": 40, "usage_type": "name"}, {"api_name": "pytest.config.getoption", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pytest.config.getoption", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pytest.config.getoption", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pytest.config.getoption", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pytest.config.getoption", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pytest.config.getoption", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.config", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "132168806", "text": "from adorym.ptychography import reconstruct_ptychography\nimport numpy as np\nimport dxchange\nimport datetime\nimport argparse\nimport os\n\ntimestr = str(datetime.datetime.today())\ntimestr = timestr[:timestr.find('.')]\nfor i in [':', '-', ' ']:\n if i == ' ':\n timestr = timestr.replace(i, '_')\n else:\n timestr = timestr.replace(i, '')\n\n\nparams_cameraman = {'fname': 'data_cameraman_err_10.h5',\n 'theta_st': 0,\n 'theta_end': 0,\n 'theta_downsample': 1,\n 'n_epochs': 1000,\n 'obj_size': (256, 256, 1),\n 'alpha_d': 0,\n 'alpha_b': 0,\n 'gamma': 0,\n 'probe_size': (72, 72),\n 'learning_rate': 4e-3,\n 'center': 512,\n 'energy_ev': 5000,\n 'psize_cm': 1.e-7,\n 'minibatch_size': 2704,\n 'n_batch_per_update': 1,\n 'output_folder': 'recon',\n 'cpu_only': False,\n 'save_path': 'cameraman_pos_error',\n 'multiscale_level': 1,\n 'n_epoch_final_pass': None,\n 'save_intermediate': True,\n 'full_intermediate': True,\n 'initial_guess': None,\n 'n_dp_batch': 20,\n 'probe_type': 'ifft',\n 'probe_initial': None,\n 'optimize_probe': True,\n 'forward_algorithm': 'fresnel',\n 'object_type': 'phase_only',\n 'probe_pos': np.array([(y, x) for y in np.arange(-10, 246, 5) for x in np.arange(-10, 246, 5)]),\n 'finite_support_mask': None,\n 'free_prop_cm': 'inf',\n 'optimizer': 'adam',\n 'two_d_mode': True,\n 'distribution_mode': None,\n 'use_checkpoint': False,\n 'optimize_all_probe_pos': True,\n 'save_history': True,\n 'backend': 'pytorch'\n }\n\nparams = params_cameraman\n\nreconstruct_ptychography(**params)\n", "sub_path": "demos/2d_ptychography_w_probe_optimization.py", "file_name": "2d_ptychography_w_probe_optimization.py", "file_ext": "py", "file_size_in_byte": 2236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "datetime.datetime.today", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "adorym.ptychography.reconstruct_ptychography", "line_number": 61, "usage_type": "call"}]} {"seq_id": "162440031", "text": "\nfrom contextlib import contextmanager\nfrom pyspark import SparkContext, SparkConf\nimport random\n\nSPARK_APP_NAME='pi'\n\ndef random_point(x):\n return (random.random(), random.random())\n\ndef inside(p):\n x, y = p\n return x*x + y*y < 1\n\ndef pi_approximation(spark_context, num_samples):\n \"\"\" Approximate pi via Monte Carlo method\"\"\"\n count = spark_context.range(num_samples).map(random_point).filter(inside).count()\n pi = 4 * count / num_samples\n return pi\n\n@contextmanager\ndef use_spark_context(sparkAppName):\n conf = SparkConf().setAppName(sparkAppName) \n spark_context = SparkContext(conf=conf)\n\n try:\n yield spark_context\n finally:\n spark_context.stop()\n\nwith use_spark_context(SPARK_APP_NAME) as spark_context:\n num_samples = 1000000000\n pi = pi_approximation(spark_context, num_samples)\n print()\n print(\"RESULT: pi is approximately \", pi)\n print()", "sub_path": "spark/scripts/.ipynb_checkpoints/pi_approximation_job_solution-checkpoint.py", "file_name": "pi_approximation_job_solution-checkpoint.py", "file_ext": "py", "file_size_in_byte": 910, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "random.random", "line_number": 9, "usage_type": "call"}, {"api_name": "pyspark.SparkConf", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 24, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 21, "usage_type": "name"}]} {"seq_id": "629948951", "text": "from django import forms\nfrom captcha.fields import ReCaptchaField\n#from captcha.widgets import ReCaptchaV2Checkbox\n\nclass CustomSignupForm(forms.Form):\n \"\"\"Defines 'extra' fields to extend the allauth signup form\"\"\"\n first_name = forms.CharField(max_length=30)\n last_name = forms.CharField(max_length=30)\n\n def signup(self, request, user):\n user.first_name = self.cleaned_data['first_name']\n user.last_name = self.cleaned_data['last_name']\n user.save()\n\nclass AllAuthSignupForm(forms.Form):\n\n captcha = ReCaptchaField()\n\n def signup(self, request, user):\n user.save()\n return user", "sub_path": "source/users/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.forms.Form", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "captcha.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "captcha.fields.ReCaptchaField", "line_number": 17, "usage_type": "call"}]} {"seq_id": "61286984", "text": "import datetime\nimport operator\nimport os\nfrom functools import reduce\n\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth import login as auth_login\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views.decorators.csrf import csrf_protect\n\nfrom DatShiroShop import services\nfrom DatShiroShop.forms import UploadFileForm, SignUpForm, GetSignatureForm\nfrom DatShiroShop.models import Song, Profile\nfrom api import drive_api\nfrom api.drive_api import list_files, get_file, load_files_to_sqlite, downloadFile, uploadFile, createFolder, deleteFile\n\n\ndef home(request):\n user_id = request.session.get('user_id', None)\n user = User.objects.get(pk=user_id) if user_id else None\n user_name_songs = [song['name'] for song in user.profile.songs.values()] if user else []\n\n list_songs = list_files()\n songs = []\n for song in list_songs:\n s = Song.objects.get(pk=song['id'])\n if s.name in user_name_songs: # Update song id if that song user archived\n s = user.profile.songs.values().get(name=s.name)\n songs.append(s)\n\n return render(request, 'index.html', {'songs': songs, 'user': user, 'user_name_songs': user_name_songs})\n\n\ndef download(request, song_id):\n song = Song.objects.get(pk=song_id)\n print(\"Start download file name: \" + song.name)\n downloadFile(song_id, song.name + \" - \" + song.author + \".\" + song.extension)\n print(\"Downloaded\")\n return HttpResponseRedirect(request.GET.get('return_url'))\n\n\n@login_required()\ndef upload(request):\n # if this is a POST request we need to process the form data\n if request.method == 'POST':\n # create a form instance and populate it with data from the request:\n form = UploadFileForm(request.POST, request.FILES)\n\n if form.is_valid(): # check whether it's valid\n name = form.cleaned_data['name']\n author = form.cleaned_data['author']\n price = form.cleaned_data['price']\n my_file = request.FILES['myFile']\n print(my_file.content_type)\n extension = my_file.name.rsplit('.', 1)[1]\n user = User.objects.get(pk=request.session['user_id'])\n if not user.is_superuser: # if normal user, upload to their own directory\n if user.profile.drive_folder_id:\n folder_id = user.profile.drive_folder_id\n else:\n folder_id = createFolder(user.username)\n user.profile.drive_folder_id = folder_id\n user.profile.save()\n else: # if superuser upload to shiro store directory\n folder_id = drive_api.shiro_store_folder_id\n file_id = uploadFile(name + \" - \" + author + \".\" + extension, my_file.temporary_file_path(), my_file.content_type, folder_id=folder_id)\n\n new_song = Song(id=file_id, name=name, author=author, extension=extension, price=price)\n if not user.is_superuser:\n new_song.owner = user\n user.profile.songs.add(new_song)\n user.profile.save()\n new_song.save()\n\n return redirect('homepage')\n\n # if a GET (or any other method) we'll create a blank form\n else:\n form = UploadFileForm()\n return render(request, 'upload.html', {'form': form})\n\n\ndef signup(request):\n if request.method == 'POST':\n form = SignUpForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n raw_password = form.cleaned_data.get('password1')\n user = authenticate(username=username, password=raw_password)\n auth_login(request, user)\n messages.success(request, 'Register new account succeeded!')\n return redirect('homepage')\n else:\n form = SignUpForm()\n return render(request, 'sites/signup.html', {'form':form})\n\n\n@login_required()\ndef buy_song(request, song_id):\n print(\"-------------Buy Song---------------\")\n # Get user info\n user = User.objects.get(pk=request.session['user_id'])\n origin_song = Song.objects.get(pk=song_id)\n\n #Get Song From Drive\n print(\"Start buy music\")\n file_path = os.path.expanduser(os.sep.join([\"~\", \"Downloads\"]))\n downloaded_file_name = \"{0} - {1}.{2}\".format(song_id, str(user.id), origin_song.extension)\n downloaded_file_path = downloadFile(file_id=song_id, file_name=downloaded_file_name, file_path=services.downloads_path)\n\n #Sign Signature To Song\n signature_message = \"|Song [{2}] - Signed by user: \\\"{0}\\\" - {1}\".format(request.session['username'], str(datetime.datetime.now()), origin_song.name)\n encoder = services.EncodeWAV()\n encoded_file_path = encoder.encode_file(file_path=downloaded_file_path, msg=signature_message, file_name=downloaded_file_name)\n\n\n #Upload Song to User Folder\n # decoder = services.DecodeWAV()\n # msg = decoder.decode_file(encoded_file_path)\n new_song_id = services.upload_new_song(user=user, song_id=song_id, file_path=encoded_file_path, signature=signature_message)\n\n #Delete on local\n os.remove(downloaded_file_path)\n print(\"Removed file: \", downloaded_file_path)\n # return signed_song\n # Save message to database\n messages.success(request, \"Succeeded buy song {0}\".format(origin_song.name))\n return redirect('info', username=user.username)\n\n\n@login_required()\ndef info(request, username):\n if username != request.session['username']:\n return redirect('info', username=request.session['username'])\n print(\"User info: \")\n user = User.objects.get(username=username)\n print(user.profile)\n list_songs_id = [song['id'] for song in user.profile.songs.values()]\n print(list_songs_id)\n # songs = Song.objects.get(id__contains=[list_songs_id])\n # query = reduce(operator.and_, (Q(id__contains=item) for item in list_songs_id))\n # songs = Song.objects.filter(query)\n songs = user.profile.songs.all\n\n return render(request, 'sites/info.html', {'user': user, 'songs': songs})\n\n\ndef ajax_signature(request, song_id):\n song = Song.objects.get(pk=song_id)\n if song.signature: # in case query from info page\n return HttpResponse(song.signature)\n else: # in case query from index\n current_user = User.objects.get(pk=request.session['user_id'])\n song = current_user.profile.songs.get(name=song.name)\n return HttpResponse(song.signature)\n\n\n@csrf_protect\ndef signature(request):\n \"\"\"\n Get Signature from uploaded file\n :param request:\n :return:\n \"\"\"\n if request.POST:\n form = GetSignatureForm(request.POST, request.FILES)\n if form.is_valid():\n f = form.cleaned_data['myFile']\n decoder = services.DecodeWAV()\n msg = decoder.decode_file(file_path=f.temporary_file_path())\n file_name = f.name\n print(\"file: \", f, \"| Temporary path: \", f.temporary_file_path(), \"| Msg: \", msg)\n return render(request, 'signature.html', {'form': form, 'msg': msg, 'file_name': file_name})\n else:\n form = GetSignatureForm()\n return render(request, 'signature.html', {'form': form})", "sub_path": "DatShiroShop/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 27, "usage_type": "name"}, {"api_name": "api.drive_api.list_files", "line_number": 30, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "DatShiroShop.models.Song", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "DatShiroShop.models.Song", "line_number": 42, "usage_type": "name"}, {"api_name": "api.drive_api.downloadFile", "line_number": 44, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 46, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.UploadFileForm", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 63, "usage_type": "name"}, {"api_name": "api.drive_api.createFolder", "line_number": 68, "usage_type": "call"}, {"api_name": "api.drive_api.shiro_store_folder_id", "line_number": 72, "usage_type": "attribute"}, {"api_name": "api.drive_api", "line_number": 72, "usage_type": "name"}, {"api_name": "api.drive_api.uploadFile", "line_number": 73, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.UploadFileForm", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 49, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.SignUpForm", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 98, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 99, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 100, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.SignUpForm", "line_number": 102, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 110, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 110, "usage_type": "name"}, {"api_name": "DatShiroShop.models.Song.objects.get", "line_number": 111, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "DatShiroShop.models.Song", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 115, "usage_type": "attribute"}, {"api_name": "api.drive_api.downloadFile", "line_number": 117, "usage_type": "call"}, {"api_name": "DatShiroShop.services.downloads_path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "DatShiroShop.services", "line_number": 117, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "DatShiroShop.services.EncodeWAV", "line_number": 121, "usage_type": "call"}, {"api_name": "DatShiroShop.services", "line_number": 121, "usage_type": "name"}, {"api_name": "DatShiroShop.services.upload_new_song", "line_number": 128, "usage_type": "call"}, {"api_name": "DatShiroShop.services", "line_number": 128, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 131, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 135, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 135, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 136, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 106, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 144, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 144, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 139, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects.get", "line_number": 157, "usage_type": "call"}, {"api_name": "DatShiroShop.models.Song.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "DatShiroShop.models.Song", "line_number": 157, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 159, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 161, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 161, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 161, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 163, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.GetSignatureForm", "line_number": 174, "usage_type": "call"}, {"api_name": "DatShiroShop.services.DecodeWAV", "line_number": 177, "usage_type": "call"}, {"api_name": "DatShiroShop.services", "line_number": 177, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 181, "usage_type": "call"}, {"api_name": "DatShiroShop.forms.GetSignatureForm", "line_number": 183, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 184, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_protect", "line_number": 166, "usage_type": "name"}]} {"seq_id": "635951380", "text": "from django.shortcuts import render, redirect\r\nfrom .models import Disciplina\r\nfrom .forms import disciplinaform\r\n\r\n# Create your views here.\r\ndef list_disciplinas(request):\r\n\tdisciplina = Disciplina.objects.all()\r\n\treturn render(request, 'Disciplina.html', {'curriculo': disciplina})\r\n\t\r\ndef create_disciplina(request):\r\n\tform = disciplinaform(request.POST or None)\r\n\t\r\n\tif form.is_valid():\r\n\t\tform.save()\r\n\t\treturn redirect('list_disciplinas')\r\n\treturn render(request, 'Disciplina-Form.html', {'form': form})\r\n\t\r\ndef update_disciplina(request, id):\r\n\tdisciplina = Disciplina.objects.get(id=id)\r\n\tform = disciplinaform(request.POST or None, instance=disciplina)\r\n\t\r\n\tif form.is_valid():\r\n\t\tform.save()\r\n\t\treturn redirect('list_disciplinas')\r\n\t\r\n\treturn render(request, 'Disciplina-Form.html', {'form':form,'disciplina':disciplina})\r\ndef delete_disciplina(request, id):\r\n\tdisciplina = Disciplina.objects.get(id=id)\r\n\t\r\n\tif request.method == 'POST':\r\n\t\tdisciplina.delete()\r\n\t\treturn redirect('list_disciplinas')\r\n\treturn render(request, 'Disciplina-Delete-Confirma.html', {'disciplina': disciplina})\r\n", "sub_path": "Atividade de Compensação de falta/atividade24042018/curriculo/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1100, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "models.Disciplina.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Disciplina.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Disciplina", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 8, "usage_type": "call"}, {"api_name": "forms.disciplinaform", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Disciplina.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Disciplina.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Disciplina", "line_number": 19, "usage_type": "name"}, {"api_name": "forms.disciplinaform", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Disciplina.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Disciplina.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Disciplina", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}]} {"seq_id": "220446596", "text": "import os\nimport sys\nimport django\nimport datetime\nimport json\n\n\nsys.path.append(os.path.abspath(__file__))\nos.environ['DJANGO_SETTINGS_MODULE'] = 'PrdDeployer.settings'\ndjango.setup()\n\nfrom awscredentialmgr.models import AWSProfile, AWSRegion\nfrom updateplanmgr.models import Module\nfrom ec2mgr.models import EC2Instance\n#from checktask import EC2CheckTask\nfrom schtasks.ec2stopper import EC2Stopper, StopperRunner\nfrom django.conf import settings\n#from django.db.models import Q\n\nKEY_FILEPATH = settings.PEM_DIR\n\n\"\"\"\nmodule = Module.objects.get(pk=7)\ninstance = EC2Instance.objects.get(pk=8)\n\"\"\"\n\ndef main():\n \"\"\"Multi-thread (parallel) stopping.\"\"\"\n for module in Module.objects.all():\n ec2instances = module.instances.filter(service_status__in=('to_stop', 'stopped'))\n #ec2instances = module.instances.all()\n runners = []\n for ec2instance in ec2instances:\n print(ec2instance.name)\n stopper = EC2Stopper(module,\n ec2instance,\n settings.PEM_DIR,\n settings.SERVICE_TYPES,\n settings.TIME_ZONE,\n settings.STOP_TIMEOUT)\n runners.append(StopperRunner(stopper))\n for runner in runners:\n runner.start()\n for runner in runners:\n runner.join()\n\n\ndef main1():\n \"\"\"Serial stopping.\"\"\"\n for module in Module.objects.all():\n ec2instances = module.instances.filter(service_status__in=('to_stop', 'stopped'))\n stoppers = []\n for ec2instance in ec2instances:\n stopper = EC2Stopper(module,\n ec2instance,\n settings.PEM_DIR,\n settings.SERVICE_TYPES,\n settings.TIME_ZONE,\n settings.STOP_TIMEOUT)\n stoppers.append(stopper)\n\n for stopper in stoppers:\n #actions, cmds = stopper.assemble_stop_cmd()\n #print(cmds)\n results = stopper.run_stop_commands()\n print(results)\n\nif __name__ == \"__main__\":\n main1()\n", "sub_path": "PrdDeployer/runstops.py", "file_name": "runstops.py", "file_ext": "py", "file_size_in_byte": 2209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.settings.PEM_DIR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "updateplanmgr.models.Module.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "updateplanmgr.models.Module.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "updateplanmgr.models.Module", "line_number": 29, "usage_type": "name"}, {"api_name": "schtasks.ec2stopper.EC2Stopper", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.settings.PEM_DIR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.settings.SERVICE_TYPES", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.settings.TIME_ZONE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.STOP_TIMEOUT", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 40, "usage_type": "name"}, {"api_name": "schtasks.ec2stopper.StopperRunner", "line_number": 41, "usage_type": "call"}, {"api_name": "updateplanmgr.models.Module.objects.all", "line_number": 50, "usage_type": "call"}, {"api_name": "updateplanmgr.models.Module.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "updateplanmgr.models.Module", "line_number": 50, "usage_type": "name"}, {"api_name": "schtasks.ec2stopper.EC2Stopper", "line_number": 54, "usage_type": "call"}, {"api_name": "django.conf.settings.PEM_DIR", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "django.conf.settings.SERVICE_TYPES", "line_number": 57, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.settings.TIME_ZONE", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.settings.STOP_TIMEOUT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 59, "usage_type": "name"}]} {"seq_id": "493722433", "text": "#--coding:utf-8\n\n__author__ = 'peic'\n\n#------------enum class demo-----------------\n\nfrom enum import Enum\n\n#继承定义枚举类\nclass Week(Enum):\n Sunday = 0\n Monday = 1\n Tuesday = 2\n Wednesday = 3\n Thursday = 4\n Friday = 5\n Saturday = 6\n\n#定义枚举类的另一种形式\nMonth = Enum('Month', ('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'))\n\nif __name__ == '__main__':\n # Week操作\n day1 = Week.Monday\n print(day1) #Week.Monday\n\n print(Week.Tuesday)\n print(Week['Tuesday']) #Week.Tuesday\n\n print(Week(1)) #Week.Monday\n\n for name, member in Week.__members__.items():\n print('name:%s --> member:%s' %(name, member))\n\n # Month操作\n print(Month(1)) #Month.Jan\n\n for name, member in Month.__members__.items():\n print('name:%s --> member:%s' %(name, member))\n\n", "sub_path": "python-toys/learn-python/EnumDemo.py", "file_name": "EnumDemo.py", "file_ext": "py", "file_size_in_byte": 912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "enum.Enum", "line_number": 10, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 20, "usage_type": "call"}]} {"seq_id": "117966579", "text": "#%%\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport random\nimport json\nimport os; \nimport copy\nimport sys; sys.path.append('/workspace/once-for-all')\nfrom ofa.model_zoo import ofa_net\nfrom ofa.imagenet_codebase.run_manager import ImagenetRunConfig\nfrom ofa.imagenet_codebase.run_manager import RunManager\n# from ofa.utils import download_url\n\nfrom ofa.tutorial import AccuracyPredictor\nfrom ofa.tutorial.evolution_finder import ArchManager\n\nSTAGE = 0\n\n\n# set random seed\nrandom_seed = 1028\nrandom.seed(random_seed)\nnp.random.seed(random_seed)\ntorch.manual_seed(random_seed)\nprint('Successfully imported all packages and configured random seed to %d!'%random_seed)\n\nofa_network = ofa_net('ofa_mbv3_d234_e346_k357_w1.2', pretrained=True)\nCONF_DIR = './assets/accuracy_data/ofa_mbv3_d234_e346_k357_w1.2/'\n\n#%%\n# accuracy_predictor = AccuracyPredictor(pretrained=True\n# ,device='cpu'\n# ,fname='./assets/accuracy_data/ofa_mbv3_d234_e346_k357_w1.2.pth'\n# ,dropout=0.0)\n# with open('./assets/accuracy_data/ofa_mbv3_d234_e346_k357_w1.2/9.json', 'r') as rf:\n# netconf = json.load(rf)\n# # with open('./assets/searched.json', 'r') as rf:\n# # netconf = json.load(rf)\n# ks_list = copy.deepcopy(netconf['ks'])\n# ex_list = copy.deepcopy(netconf['e'])\n# d_list = copy.deepcopy(netconf['d'])\n# r = copy.deepcopy(netconf['r'])[0]\n# print(r,d_list,ks_list,ex_list)\n# print(netconf['acc'])\n# feats = AccuracyPredictor.spec2feats(ks_list, ex_list, d_list, r).reshape(1, -1).to('cpu')\n# all_feats = [feats]\n# all_feats = torch.cat(all_feats, 0)\n# preds = accuracy_predictor.model(all_feats).to('cpu')\n# print(preds)\n#%%\nif STAGE == 1:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n # Stage1: collect data\n arch_manager = ArchManager()\n csum = 2700\n while True:\n net_config = arch_manager.random_sample()\n ofa_network.set_active_subnet(ks=net_config['ks']\n , d=net_config['d']\n , e=net_config['e']\n )\n subnet = ofa_network.get_active_subnet(preserve_weight=True)\n run_config = ImagenetRunConfig(test_batch_size=128, n_worker=4)\n run_manager = RunManager('.tmp/eval_subnet', subnet, run_config, init=False)\n run_config.data_provider.assign_active_img_size(net_config['r'][0])\n run_manager.reset_running_statistics(net=subnet)\n\n # print('=========> net_config is:', net_config)\n # print('=========> Random subnet is:', subnet.module_str)\n\n _, top1, _ = run_manager.validate(net=subnet)\n # print('==========> Results: top1=%.1f' % (top1))\n net_config['acc'] = top1\n with open('{}/{}.json'.format(CONF_DIR, csum), 'w') as wf:\n json.dump(net_config, wf)\n csum+=1\nelse:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = ''\n # Stage2: training\n accuracy_predictor = AccuracyPredictor(pretrained=False,device='cpu',dropout=0.0)\n # accuracy_predictor = AccuracyPredictor(pretrained=True,device='cpu',fname='./assets/accuracy_data/ofa_mbv3_d234_e346_k357_w1.2.pth')\n batch_size = 64\n net_confs = [os.path.join(CONF_DIR, each) for each in os.listdir(CONF_DIR)]\n optimizer = torch.optim.SGD(accuracy_predictor.model.parameters(), 1e-6, momentum=0.1, nesterov=True)\n # optimizer = torch.optim.Adam(accuracy_predictor.model.parameters(), 1e-6)\n try:\n while True:\n all_feats = []\n gts = []\n for i in range(batch_size):\n with open(random.choice(net_confs), 'r') as rf:\n netconf = json.load(rf)\n ks_list = copy.deepcopy(netconf['ks'])\n ex_list = copy.deepcopy(netconf['e'])\n d_list = copy.deepcopy(netconf['d'])\n r = copy.deepcopy(netconf['r'])[0]\n gts.append(netconf['acc'])\n feats = AccuracyPredictor.spec2feats(ks_list, ex_list, d_list, r).reshape(1, -1).to('cpu')\n all_feats.append(feats)\n all_feats = torch.cat(all_feats, 0)\n preds = accuracy_predictor.model(all_feats).to('cpu')\n gts = torch.Tensor(gts).to('cpu')\n gts = gts / 100.0\n loss = F.mse_loss(preds, gts, reduction='sum')\n # loss = loss * 100.0\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n print(loss, gts.mean(), preds.mean(), gts[0], preds[0])\n except (KeyboardInterrupt, SystemExit):\n print('saving trained model')\n torch.save(accuracy_predictor.model.state_dict(), './assets/accuracy_data/ofa_mbv3_d234_e346_k357_w1.2.pth')\n exit()\n", "sub_path": "jiangrong/train-accuracy-predictor.py", "file_name": "train-accuracy-predictor.py", "file_ext": "py", "file_size_in_byte": 4768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 25, "usage_type": "call"}, {"api_name": "ofa.model_zoo.ofa_net", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "ofa.tutorial.evolution_finder.ArchManager", "line_number": 55, "usage_type": "call"}, {"api_name": "ofa.imagenet_codebase.run_manager.ImagenetRunConfig", "line_number": 64, "usage_type": "call"}, {"api_name": "ofa.imagenet_codebase.run_manager.RunManager", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 79, "usage_type": "attribute"}, {"api_name": "ofa.tutorial.AccuracyPredictor", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 85, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 92, "usage_type": "call"}, {"api_name": "json.load", "line_number": 93, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 94, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 95, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 96, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 97, "usage_type": "call"}, {"api_name": "ofa.tutorial.AccuracyPredictor.spec2feats", "line_number": 99, "usage_type": "call"}, {"api_name": "ofa.tutorial.AccuracyPredictor", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 113, "usage_type": "call"}]} {"seq_id": "487704516", "text": "\"\"\"\nDistributed Learning using Pytorch's torch.distributed.launcher and\ntorch.nn.parallel.distributed_c10d on FfDL.\n\"\"\"\n\nimport time\nimport argparse\nimport sys\nimport os\nimport threading\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.utils.data\nimport torch.distributed.c10d\n\nfrom math import ceil\nfrom random import Random\nfrom torch.multiprocessing import Process\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\n\nclass Net(nn.Module):\n \"\"\" Network architecture. \"\"\"\n\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n self.conv2_drop = nn.Dropout2d()\n self.fc1 = nn.Linear(320, 50)\n self.fc2 = nn.Linear(50, 10)\n\n def forward(self, x):\n x = F.relu(F.max_pool2d(self.conv1(x), 2))\n x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n x = x.view(-1, 320)\n x = F.relu(self.fc1(x))\n x = F.dropout(x, training=self.training)\n x = self.fc2(x)\n return F.log_softmax(x, dim=1)\n\n\ndef partition_dataset(batch_size, world_size):\n \"\"\" Partitioning MNIST \"\"\"\n vision_data = os.environ.get(\"DATA_DIR\") + \"/data\"\n dataset = datasets.MNIST(\n vision_data,\n train=True,\n download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307, ), (0.3081, ))\n ]))\n\n bsz = int(batch_size / float(world_size))\n dataloader = torch.utils.data.DataLoader(dataset, batch_size=bsz, shuffle=True)\n\n return dataloader, bsz\n\ndef average_gradients(model, world_size, pg):\n \"\"\" Gradient averaging. \"\"\"\n for param in model.parameters():\n torch.distributed.c10d.all_reduce(param.grad.data, pg)\n param.grad.data /= world_size\n\n\ndef run(rank, world_rank, world_size, group, batch_size, is_gpu):\n \"\"\" Distributed Synchronous SGD Example \"\"\"\n torch.manual_seed(1234)\n size = os.environ.get(\"WORLD_SIZE\")\n result_dir = os.environ.get(\"RESULT_DIR\") + \"/saved_model\"\n train_set, bsz = partition_dataset(batch_size, world_size)\n # For GPU use\n if is_gpu:\n # device = torch.device(\"cuda:{}\".format(0))\n # model = Net().to(device)\n model = Net().cuda()\n else:\n model = Net()\n model = model\n model = torch.nn.parallel._DistributedDataParallelC10d(model, group)\n# model = model.cuda(rank)\n optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n\n num_batches = ceil(len(train_set.dataset) / float(bsz))\n for epoch in range(10):\n epoch_loss = 0.0\n for data, target in train_set:\n # For GPU use\n if is_gpu:\n data, target = data.cuda(), target.cuda()\n else:\n data, target = Variable(data), Variable(target)\n# data, target = Variable(data.cuda(rank)), Variable(target.cuda(rank))\n optimizer.zero_grad()\n output = model(data)\n loss = F.nll_loss(output, target)\n epoch_loss += loss.item()\n loss.backward()\n if not (size == 1):\n average_gradients(model, world_size, group)\n optimizer.step()\n print('Process ', world_rank,\n ', epoch ', epoch, ': ',\n epoch_loss / num_batches)\n torch.save(model.state_dict(), result_dir)\n\n# Change 'backend' to appropriate backend identifier\ndef init_processes(local_rank, world_rank, world_size, fn, batch_size, shared_file, is_gpu, backend):\n \"\"\" Initialize the distributed environment. \"\"\"\n print(\"World Rank: \" + str(world_rank) + \" Local Rank: \" + str(local_rank) + \" connected\")\n pg = torch.distributed.c10d.ProcessGroupGloo(shared_file, world_rank, world_size)\n pg.Options.timeout = 300.0 * world_size\n print(\"GROUP CREATED\")\n fn(local_rank, world_rank, world_size, pg, batch_size, is_gpu)\n\ndef local_process(target, args):\n return Process(target=target, args=args)\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--batch_size', help='Specify the batch size to be used in training')\n args = parser.parse_args()\n\n batch_size = args.batch_size\n # Default batch size is set to 1024. When using a large numbers of learners, a larger batch\n # size is sometimes necessary to see speed improvements.\n if batch_size is None:\n batch_size = 1024\n else:\n batch_size = int(batch_size)\n\n start_time = time.time()\n num_gpus = int(float(os.environ.get(\"GPU_COUNT\")))\n if num_gpus == 0:\n world_size = int(os.environ.get(\"NUM_LEARNERS\"))\n else:\n world_size = num_gpus * int(os.environ.get(\"NUM_LEARNERS\"))\n data_dir = \"/job/\" + os.environ.get(\"TRAINING_ID\")\n processes = []\n\n start_time = time.time()\n shared_file = torch.distributed.c10d.FileStore(data_dir)\n processes = []\n\n print(\"SHARED FILE PATH: \" + data_dir, \" WORLD_SIZE: \" + str(world_size))\n world_rank = int(os.environ.get(\"LEARNER_ID\")) - 1\n\n if num_gpus == 0:\n args = (0, world_rank, world_size, run, batch_size, shared_file, True, 'gloo')\n p = local_process(init_processes, args)\n p.start()\n processes.append(p)\n else:\n print(\"Opening processes\")\n for local_rank in range(0, num_gpus):\n args = (local_rank, world_rank, world_size, run, batch_size, shared_file, True, 'gloo')\n p = local_process(init_processes, args)\n print(\"Process Created\")\n p.start()\n processes.append(p)\n print(\"Process Added\")\n\n for p in processes:\n print(\"Waiting on Process\")\n p.join()\n\n print(\"COMPLETION TIME: \" + str(time.time() - start_time))\n\n if int(os.environ.get(\"LEARNER_ID\")) != 1:\n while True:\n time.sleep(1000000)\n", "sub_path": "train_dist_c10d.py", "file_name": "train_dist_c10d.py", "file_ext": "py", "file_size_in_byte": 5990, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 43, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 48, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.distributed.c10d.all_reduce", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 73, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 74, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.nn.parallel._DistributedDataParallelC10d", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 86, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.distributed.c10d.ProcessGroupGloo", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.multiprocessing.Process", "line_number": 121, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 125, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 138, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 140, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 142, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 143, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 143, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.distributed.c10d.FileStore", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 151, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 151, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 174, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 174, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 176, "usage_type": "call"}]} {"seq_id": "640619682", "text": "# coding:utf-8\n__author__ = 'Albert'\n\nfrom django.conf.urls import patterns, url\n\nfrom materials import views\n\nmaterials_views = patterns(\n '',\n url(r'^mis/update/student/materials$', views.update_student_material),\n url(r'^mis/material/packages$', views.list_material_packages),\n url(r'^mis/set/student/material$', views.set_student_material),\n url(r'^mis/material/upload$', views.material_upload),\n url(r'^mis/material/preview$', views.material_preview),\n url(r'^mis/unfinished/classes$', views.unfinished_classes),\n)\n", "sub_path": "mis/materials/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "materials.views.update_student_material", "line_number": 10, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "materials.views.list_material_packages", "line_number": 11, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "materials.views.set_student_material", "line_number": 12, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "materials.views.material_upload", "line_number": 13, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "materials.views.material_preview", "line_number": 14, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "materials.views.unfinished_classes", "line_number": 15, "usage_type": "attribute"}, {"api_name": "materials.views", "line_number": 15, "usage_type": "name"}]} {"seq_id": "163161391", "text": "# This code is modified from https://github.com/jakesnell/prototypical-networks \n\nimport backbone\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport torch.nn.functional as F\nfrom methods.meta_template import MetaTemplate\n\nimport utils\n\n\n####\nimport torch\nimport torchvision\n\nfrom torch import optim, nn\nfrom torch.nn import *\nfrom torchvision import transforms\nimport torch.utils.data as data\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n# from PIL import Image\n# from pathlib import Path\n\n# for ROC curve\n# from sklearn import metrics\n# from scipy import interp\n\nimport pandas as pd\nimport numpy as np\n\n# import matplotlib.pyplot as plt #, mpld3\n# import matplotlib\n\nimport math\nfrom typing import *\nimport time\nimport datetime\n\n# from IPython.display import display, clear_output\n# from IPython.display import HTML\n\n\n###\nimport warnings\n\n# from torch.nn.module import Module\nfrom torch.nn import functional as F\nfrom torch.nn import _reduction as _Reduction\n\nfrom torch import Tensor\nfrom typing import Optional\n\n\nclass _Loss(nn.Module):\n reduction: str\n\n def __init__(self, size_average=None, reduce=None, reduction: str = 'mean') -> None:\n super(_Loss, self).__init__()\n if size_average is not None or reduce is not None:\n self.reduction = _Reduction.legacy_get_string(size_average, reduce)\n else:\n self.reduction = reduction\n \n\nclass _WeightedLoss(_Loss):\n def __init__(self, weight: Optional[Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean') -> None:\n super(_WeightedLoss, self).__init__(size_average, reduce, reduction)\n self.register_buffer('weight', weight)\n\nclass CrossEntropyLoss(_WeightedLoss):\n __constants__ = ['ignore_index', 'reduction']\n ignore_index: int\n\n def __init__(self, weight: Optional[Tensor] = None, size_average=None, ignore_index: int = -100,\n reduce=None, reduction: str = 'mean') -> None:\n super(CrossEntropyLoss, self).__init__(weight, size_average, reduce, reduction)\n self.ignore_index = ignore_index\n\n def forward(self, input: Tensor, target: Tensor) -> Tensor:\n# print('input',input)\n# print('target',target)\n return F.cross_entropy(input, target, weight=self.weight,\n ignore_index=self.ignore_index, reduction=self.reduction)\n\n\n\n\nclass LargeMarginCosineLoss(nn.Module):\n \"\"\"\n Reference: \n H. Wang et al. CosFace: Large Margin Cosine Loss for Deep Face Recognition\n https://arxiv.org/pdf/1801.09414.pdf\n \n Also referenced cvqluu's implementation of Angular Penalty Loss:\n https://paperswithcode.com/paper/cosface-large-margin-cosine-loss-for-deep\n \"\"\"\n \n def __init__(self, in_features=5, out_features=2, s=64.0, m=0.35):\n super(LargeMarginCosineLoss, self).__init__()\n self.in_features = in_features\n self.out_features = out_features\n self.s = s\n self.m = m\n \n # cos(θ_j,i) = W_j^T * x_i\n self.linear = Linear(in_features, out_features, bias=False)\n \n def forward(self, x, targets):\n \n # normalize\n x = F.normalize(x, p=2, dim=1)\n for W in self.linear.parameters():\n W = F.normalize(W, p=2, dim=1)\n \n cos_θ = self.linear(x)\n s_cos_θ = self.s*torch.diagonal(cos_θ.transpose(0,1)[targets]-self.m)\n# print(s_cos_θ)\n try:\n cos_θj = [torch.cat((cos_θ[j,:y], cos_θ[j,(y+1):])).unsqueeze(0) for j, y in zip(len(targets), targets)] # <<<-- issue\n sum_j = torch.sum(torch.exp(self.s*torch.cat(cos_θj, dim=0)), dim=1)\n except:\n raise ValueError(cos_θ)\n \n result = torch.mean(torch.log(torch.exp(s_cos_θ) + sum_j) - torch.log(torch.exp(s_cos_θ)))\n \n return result\n\n\n\nclass ProtoNet(MetaTemplate):\n def __init__(self, model_func, n_way, n_support):\n super(ProtoNet, self).__init__( model_func, n_way, n_support)\n# self.loss_fn = nn.CrossEntropyLoss()\n# self.loss_fn = LargeMarginCosineLoss()\n self.loss_fn = CrossEntropyLoss()\n\n\n def set_forward(self,x,is_feature = False):\n z_support, z_query = self.parse_feature(x,is_feature)\n\n z_support = z_support.contiguous()\n z_proto = z_support.view(self.n_way, self.n_support, -1 ).mean(1) #the shape of z is [n_data, n_dim]\n z_query = z_query.contiguous().view(self.n_way* self.n_query, -1 )\n\n\n dists = euclidean_dist(z_query, z_proto)\n scores = -dists\n\n return scores\n\n\n def set_forward_loss(self, x):\n y_query = torch.from_numpy(np.repeat(range( self.n_way ), self.n_query ))\n y_query = Variable(y_query.cuda())\n\n scores = self.set_forward(x)\n# print('scores', scores.size())\n loss = self.loss_fn(scores, y_query)\n if isinstance(loss, float):\n pass\n# print('loss',loss)\n else:\n pass\n# print('> loss', loss)\n return loss\n\ndef euclidean_dist( x, y):\n # x: N x D\n # y: M x D\n n = x.size(0)\n m = y.size(0)\n d = x.size(1)\n assert d == y.size(1)\n\n x = x.unsqueeze(1).expand(n, m, d)\n y = y.unsqueeze(0).expand(n, m, d)\n\n return torch.pow(x - y, 2).sum(2)\n", "sub_path": "methods/protonet.py", "file_name": "protonet.py", "file_ext": "py", "file_size_in_byte": 5372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn._reduction.legacy_get_string", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn._reduction", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.functional.normalize", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.diagonal", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 128, "usage_type": "call"}, {"api_name": "methods.meta_template.MetaTemplate", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 182, "usage_type": "call"}]} {"seq_id": "254667756", "text": "#!/usr/bin/env python\nimport bz2\nfrom contextlib import contextmanager\nimport datetime\nfrom deepdiff import DeepDiff\nfrom distutils.version import LooseVersion as Version\nimport fire\nimport pathlib\nfrom pprint import pprint\nimport questionary\nimport re\nimport secrets\nimport string\nimport subprocess\nimport sys\nimport time\nfrom typing import Optional, Sequence\nimport yaml\n\n\ndependencies = {\n \"python\": (\n # Command to get version\n [\"python\", \"--version\"],\n # Extract *only* the version number\n lambda v: v.split()[1],\n # It must be >= 3.7\n \"3.7\",\n ),\n \"docker\": (\n # Command to get version\n [\"docker\", \"--version\"],\n # Extract *only* the version number\n lambda v: v.split()[2][:-1],\n # It must be >= 18.06\n \"18.06\",\n ),\n \"docker-compose\": (\n # Command to get version\n [\"docker-compose\", \"--version\"],\n # Extract *only* the version number\n lambda v: re.search(r\"\\s*([\\d.]+)\", v).group(0).strip(),\n # It must be >= 1.22.0\n \"1.22.0\",\n ),\n}\n\n\n@contextmanager\ndef status(message):\n \"\"\"\n Borrowed from https://github.com/cesium-ml/baselayer/\n\n :param message: message to print\n :return:\n \"\"\"\n print(f\"[·] {message}\", end=\"\")\n sys.stdout.flush()\n try:\n yield\n except Exception:\n print(f\"\\r[✗] {message}\")\n raise\n else:\n print(f\"\\r[✓] {message}\")\n\n\ndef deps_ok() -> bool:\n \"\"\"\n Check system dependencies\n\n Borrowed from https://github.com/cesium-ml/baselayer/\n :return:\n \"\"\"\n print(\"Checking system dependencies:\")\n\n fail = []\n\n for dep, (cmd, get_version, min_version) in dependencies.items():\n try:\n query = f\"{dep} >= {min_version}\"\n with status(query):\n p = subprocess.Popen(\n cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n )\n out, err = p.communicate()\n try:\n version = get_version(out.decode(\"utf-8\").strip())\n print(f\"[{version.rjust(8)}]\".rjust(40 - len(query)), end=\"\")\n except Exception:\n raise ValueError(\"Could not parse version\")\n\n if not (Version(version) >= Version(min_version)):\n raise RuntimeError(f\"Required {min_version}, found {version}\")\n except Exception as e:\n fail.append((dep, e))\n\n if fail:\n print()\n print(\"[!] Some system dependencies seem to be unsatisfied\")\n print()\n print(\" The failed checks were:\")\n print()\n for (pkg, exc) in fail:\n cmd, get_version, min_version = dependencies[pkg]\n print(f' - {pkg}: `{\" \".join(cmd)}`')\n print(\" \", exc)\n print()\n print(\n \" Please refer to https://github.com/dmitryduev/tails \"\n \"for installation instructions.\"\n )\n print()\n return False\n\n print(\"-\" * 20)\n return True\n\n\ndef check_configs(\n config_wildcards: Sequence = (\"config.*yaml\", \"docker-compose.*yaml\")\n):\n \"\"\"\n - Check if config files exist\n - Offer to use the config files that match the wildcards\n - For config.yaml, check its contents against the defaults to make sure nothing is missing/wrong\n\n :param config_wildcards:\n :return:\n \"\"\"\n path = pathlib.Path(__file__).parent.absolute()\n\n for config_wildcard in config_wildcards:\n config = config_wildcard.replace(\"*\", \"\")\n # use config defaults if configs do not exist?\n if not (path / config).exists():\n answer = questionary.select(\n f\"{config} does not exist, do you want to use one of the following\"\n \" (not recommended without inspection)?\",\n choices=[p.name for p in path.glob(config_wildcard)],\n ).ask()\n subprocess.run([\"cp\", f\"{path / answer}\", f\"{path / config}\"])\n\n # check contents of config.yaml WRT config.defaults.yaml\n if config == \"config.yaml\":\n with open(path / config.replace(\".yaml\", \".defaults.yaml\")) as config_yaml:\n config_defaults = yaml.load(config_yaml, Loader=yaml.FullLoader)\n with open(path / config) as config_yaml:\n config_wildcard = yaml.load(config_yaml, Loader=yaml.FullLoader)\n deep_diff = DeepDiff(config_wildcard, config_defaults, ignore_order=True)\n difference = {\n k: v\n for k, v in deep_diff.items()\n if k in (\"dictionary_item_added\", \"dictionary_item_removed\")\n }\n if len(difference) > 0:\n print(\"config.yaml structure differs from config.defaults.yaml\")\n pprint(difference)\n raise KeyError(\"Fix config.yaml before proceeding\")\n\n\ndef get_git_hash_date():\n \"\"\"Get git date and hash\n\n Borrowed from SkyPortal https://skyportal.io\n\n :return:\n \"\"\"\n hash_date = dict()\n try:\n p = subprocess.Popen(\n [\"git\", \"log\", \"-1\", '--format=\"%h %aI\"'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=pathlib.Path(__file__).parent.absolute(),\n )\n except FileNotFoundError:\n pass\n else:\n out, err = p.communicate()\n if p.returncode == 0:\n git_hash, git_date = (\n out.decode(\"utf-8\")\n .strip()\n .replace('\"', \"\")\n .split(\"T\")[0]\n .replace(\"-\", \"\")\n .split()\n )\n hash_date[\"hash\"] = git_hash\n hash_date[\"date\"] = git_date\n\n return hash_date\n\n\nclass Kowalski:\n def __init__(self, yes=False):\n \"\"\"\n\n :param yes: answer yes to all possible requests?\n \"\"\"\n self.yes = yes\n\n @staticmethod\n def check_containers_up(\n containers: Sequence,\n num_retries: int = 10,\n sleep_for_seconds: int = 10,\n ):\n \"\"\"Check if containers in question are up and running\n\n :param containers: container name sequence, e.g. (\"kowalski_api_1\", \"kowalski_mongo_1\")\n :param num_retries:\n :param sleep_for_seconds: number of seconds to sleep for before retrying\n :return:\n \"\"\"\n for i in range(num_retries):\n if i == num_retries - 1:\n raise RuntimeError(f\"{containers} containers failed to spin up\")\n\n command = [\"docker\", \"ps\", \"-a\"]\n container_list = (\n subprocess.check_output(command, universal_newlines=True)\n .strip()\n .split(\"\\n\")\n )\n print(container_list)\n if len(container_list) == 1:\n print(\"No containers are running, waiting...\")\n time.sleep(sleep_for_seconds)\n continue\n\n containers_up = (\n len(\n [\n container\n for container in container_list\n if (\n (container_name in container)\n and (\" Up \" in container)\n and (\"unhealthy\" not in container)\n and (\"health: starting\" not in container)\n )\n ]\n )\n > 0\n for container_name in containers\n )\n\n if not all(containers_up):\n print(f\"{containers} containers are not up, waiting...\")\n time.sleep(sleep_for_seconds)\n continue\n\n break\n\n @staticmethod\n def check_keyfile():\n \"\"\"Check if MongoDB keyfile for replica set authorization exists; generate one if not\"\"\"\n mongodb_keyfile = pathlib.Path(__file__).parent.absolute() / \"mongo_key.yaml\"\n if not mongodb_keyfile.exists():\n print(\"Generating MongoDB keyfile\")\n # generate a random key that is required to be able to use authorization with replica set\n key = \"\".join(\n secrets.choice(string.ascii_lowercase + string.digits)\n for _ in range(32)\n )\n with open(mongodb_keyfile, \"w\") as f:\n f.write(key)\n command = [\"chmod\", \"400\", \"mongo_key.yaml\"]\n subprocess.run(command)\n\n @classmethod\n def up(cls, build: bool = False):\n \"\"\"\n 🐧🚀 Launch Kowalski\n\n :param build: build the containers first?\n :return:\n \"\"\"\n print(\"Spinning up Kowalski 🐧🚀\")\n\n config_wildcards = [\"config.*yaml\", \"docker-compose.*yaml\"]\n\n # check configuration\n with status(\"Checking configuration\"):\n check_configs(config_wildcards=config_wildcards)\n\n cls.check_keyfile()\n\n if build:\n cls.build()\n\n command = [\"docker-compose\", \"-f\", \"docker-compose.yaml\", \"up\", \"-d\"]\n\n # start up Kowalski\n print(\"Starting up\")\n subprocess.run(command)\n\n @staticmethod\n def down():\n \"\"\"\n ✋ Shut down Kowalski\n\n :return:\n \"\"\"\n print(\"Shutting down Kowalski\")\n command = [\"docker-compose\", \"-f\", \"docker-compose.yaml\", \"down\"]\n\n subprocess.run(command)\n\n @classmethod\n def build(cls):\n \"\"\"\n Build Kowalski's containers\n\n :return:\n \"\"\"\n print(\"Building Kowalski\")\n\n config_wildcards = [\"config.*yaml\", \"docker-compose.*yaml\"]\n\n # always use docker-compose.yaml\n command = [\"docker-compose\", \"-f\", \"docker-compose.yaml\", \"build\"]\n\n # check configuration\n with status(\"Checking configuration\"):\n check_configs(config_wildcards=config_wildcards)\n\n # load config\n with open(\n pathlib.Path(__file__).parent.absolute() / \"config.yaml\"\n ) as config_yaml:\n config = yaml.load(config_yaml, Loader=yaml.FullLoader)[\"kowalski\"]\n\n # get git version:\n git_hash_date = get_git_hash_date()\n version = (\n f\"v{config['server']['version']}\"\n f\"+git{git_hash_date.get('date', datetime.datetime.utcnow().strftime('%Y%m%d'))}\"\n f\".{git_hash_date.get('hash', 'unknown')}\"\n )\n with open(\n pathlib.Path(__file__).parent.absolute() / \"version.txt\", \"w\"\n ) as version_file:\n version_file.write(f\"{version}\\n\")\n\n # check MongoDB keyfile\n cls.check_keyfile()\n\n subprocess.run(command)\n\n @staticmethod\n def seed(source: str = \"./\", drop: Optional[bool] = False):\n \"\"\"\n Ingest catalog dumps into Kowalski\n\n :param source: where to look for the dumps;\n can be a local path or a Google Cloud Storage bucket address, e.g. gs://kowalski-catalogs\n :param drop: drop existing collections with same names before ingesting?\n :return:\n \"\"\"\n print(\"Ingesting catalog dumps into a running Kowalski instance\")\n\n # check configuration\n with status(\"Checking configuration\"):\n check_configs(config_wildcards=[\"config.*yaml\"])\n\n with open(\n pathlib.Path(__file__).parent.absolute() / \"config.yaml\"\n ) as config_yaml:\n config = yaml.load(config_yaml, Loader=yaml.FullLoader)[\"kowalski\"]\n\n command = [\n \"docker\",\n \"exec\",\n \"-i\",\n \"kowalski_mongo_1\",\n \"mongorestore\",\n f\"-u={config['database']['admin_username']}\",\n f\"-p={config['database']['admin_password']}\",\n \"--authenticationDatabase=admin\",\n \"--archive\",\n ]\n\n if drop:\n command.append(\"--drop\")\n\n if \"gs://\" not in source:\n # ingesting from a local path\n path = pathlib.Path(source).absolute()\n\n dumps = [p.name for p in path.glob(\"*.dump\")]\n\n if len(dumps) == 0:\n print(f\"No catalog dumps found under {path}\")\n return False\n\n answer = questionary.checkbox(\n \"Found the following collection dumps. Which ones would you like to ingest?\",\n choices=dumps,\n ).ask()\n\n for dump in answer:\n with open(f\"{path / dump}\") as f:\n subprocess.call(command, stdin=f)\n\n else:\n # ingesting from Google Cloud\n path_tmp = pathlib.Path(__file__).parent / \".catalog_dumps\"\n if not path_tmp.exists():\n path_tmp.mkdir(parents=True, exist_ok=True)\n\n ls_command = [\"gsutil\", \"ls\", source]\n catalog_list = (\n subprocess.check_output(ls_command, universal_newlines=True)\n .strip()\n .split(\"\\n\")\n )\n dumps = [dump for dump in catalog_list if \"dump\" in dump]\n\n answer = questionary.checkbox(\n \"Found the following collection dumps. Which ones would you like to ingest?\",\n choices=dumps,\n ).ask()\n\n for dump in answer:\n cp_command = [\n \"gsutil\",\n \"-m\",\n \"cp\",\n \"-n\",\n dump,\n str(path_tmp),\n ]\n p = subprocess.run(cp_command, check=True)\n if p.returncode != 0:\n raise RuntimeError(f\"Failed to fetch {dump}\")\n\n path_dump = f\"{path_tmp / pathlib.Path(dump).name}\"\n if dump.endswith(\".bz2\"):\n with bz2.BZ2File(path_dump) as f:\n subprocess.call(command, stdin=f)\n elif dump.endswith(\".gz\"):\n with open(path_dump) as f:\n subprocess.call(command + [\"--gzip\"], stdin=f)\n else:\n with open(path_dump) as f:\n subprocess.call(command, stdin=f)\n\n rm_fetched = questionary.confirm(f\"Remove {path_dump}?\").ask()\n if rm_fetched:\n pathlib.Path(path_dump).unlink()\n\n @classmethod\n def test(cls):\n \"\"\"\n Run the test suite\n\n :return:\n \"\"\"\n print(\"Running the test suite\")\n\n # make sure the containers are up and running\n cls.check_containers_up(\n containers=(\"kowalski_ingester_1\", \"kowalski_api_1\", \"kowalski_mongo_1\"),\n sleep_for_seconds=10,\n )\n\n test_setups = [\n {\n \"part\": \"PGIR alert broker components\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_alert_broker_pgir.py\",\n \"flaky\": False,\n },\n {\n \"part\": \"ZTF alert broker components\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_alert_broker_ztf.py\",\n \"flaky\": False,\n },\n {\n \"part\": \"PGIR alert ingestion\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_ingester_pgir.py\",\n \"flaky\": False,\n },\n {\n \"part\": \"ZTF alert ingestion\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_ingester.py\",\n \"flaky\": False,\n },\n {\n \"part\": \"API\",\n \"container\": \"kowalski_api_1\",\n \"test_script\": \"test_api.py\",\n \"flaky\": False,\n },\n {\n \"part\": \"TNS monitoring\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_tns_watcher.py\",\n \"flaky\": True,\n },\n {\n \"part\": \"Tools\",\n \"container\": \"kowalski_ingester_1\",\n \"test_script\": \"test_tools.py\",\n \"flaky\": False,\n },\n ]\n\n failed_tests = []\n\n for setup in test_setups:\n print(f\"Testing {setup['part']}\")\n command = [\n \"docker\",\n \"exec\",\n \"-i\",\n setup[\"container\"],\n \"python\",\n \"-m\",\n \"pytest\",\n \"-s\",\n setup[\"test_script\"],\n ]\n try:\n subprocess.run(command, check=True)\n except subprocess.CalledProcessError:\n if not setup.get(\"flaky\", False):\n failed_tests.append(setup[\"part\"])\n else:\n print(f\"{setup['part']} test, marked as flaky, failed.\")\n continue\n\n if failed_tests:\n print(f\"Failed tests: {failed_tests}\")\n sys.exit(1)\n\n @staticmethod\n def develop():\n \"\"\"\n Install developer tools\n \"\"\"\n subprocess.run([\"pip\", \"install\", \"-U\", \"pre-commit\"], check=True)\n subprocess.run([\"pre-commit\", \"install\"], check=True)\n\n @classmethod\n def lint(cls):\n \"\"\"\n Lint the full code base\n\n :return:\n \"\"\"\n try:\n import pre_commit # noqa: F401\n except ImportError:\n cls.develop()\n\n try:\n subprocess.run([\"pre-commit\", \"run\", \"--all-files\"], check=True)\n except subprocess.CalledProcessError:\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n # check environment\n env_ok = deps_ok()\n if not env_ok:\n raise RuntimeError(\"Halting because of unsatisfied system dependencies\")\n\n fire.Fire(Kowalski)\n", "sub_path": "kowalski.py", "file_name": "kowalski.py", "file_ext": "py", "file_size_in_byte": 17814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 58, "usage_type": "attribute"}, {"api_name": "contextlib.contextmanager", "line_number": 49, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 83, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 84, "usage_type": "attribute"}, {"api_name": "distutils.version.LooseVersion", "line_number": 93, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 121, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 131, "usage_type": "call"}, {"api_name": "questionary.select", "line_number": 137, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 142, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 147, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 147, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 149, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 149, "usage_type": "attribute"}, {"api_name": "deepdiff.DeepDiff", "line_number": 150, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 158, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 171, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 173, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 175, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 206, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 223, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 230, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 252, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 260, "usage_type": "call"}, {"api_name": "secrets.choice", "line_number": 265, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 265, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 265, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 271, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 298, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 310, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 332, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 334, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 334, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 340, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 340, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 344, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 351, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 354, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 370, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 372, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 372, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 391, "usage_type": "call"}, {"api_name": "questionary.checkbox", "line_number": 399, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 406, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 410, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 416, "usage_type": "call"}, {"api_name": "questionary.checkbox", "line_number": 422, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 436, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 440, "usage_type": "call"}, {"api_name": "bz2.BZ2File", "line_number": 442, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 443, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 446, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 449, "usage_type": "call"}, {"api_name": "questionary.confirm", "line_number": 451, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 453, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 531, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 532, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 541, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 548, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 549, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 564, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 565, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 566, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 575, "usage_type": "call"}]} {"seq_id": "588183982", "text": "import os\nimport sys\nimport shutil\nimport subprocess\nfrom flask import current_app as app\nfrom threading import Thread\nfrom . import socketio\nfrom src.exceptions import InvalidSongSegmentException, ChordParseException\n\n\n'''\n从完整的歌曲对象分离出歌曲存储目录。\n\n参数:\n d_song: 数据库中的歌曲所映射的字典。\n返回:\n 该歌曲的存储目录。\n'''\ndef generate_song_directory(d_song):\n song_name = d_song['song_name']\n singer = d_song['singer']\n folder_name = song_name + '-' + singer\n return os.path.join(app.config['FILE_UPLOAD_DIR'], folder_name)\n\n\n'''\n由歌曲原唱生成歌曲打分文件。\n\n参数:\n real_app: Flask的app对象，需要手动在调用函数中传入。\n original_file_path: 歌曲原唱文件路径。\n song_info: 格式为“<歌曲名称>-<歌手>”的歌曲信息，用于向客户端发送socket。\n'''\ndef rate_by_original(real_app, original_file_path, song_info):\n\n with real_app.app_context():\n \n vocal_file_path = generate_vocal_file(original_file_path)\n single_track_file_path = generate_single_track_file(vocal_file_path)\n generate_rate_file(single_track_file_path)\n\n if song_info:\n socketio.emit('rate', song_info, namespace='/karaoke')\n\n\n'''\n解释和弦文件。\n\n参数:\n real_app: Flask的app对象，需要手动在调用函数中传入。\n org_chord_path: 原始和弦文件路径。\n song_info: 格式为“<歌曲名称>-<歌手>”的歌曲信息，用于向客户端发送socket。\n'''\ndef trans_chord(real_app, org_chord_path, song_info):\n\n with real_app.app_context():\n\n directory, _ = os.path.split(org_chord_path)\n chord_trans_path = os.path.join(directory, app.config['CHORD_TRANS_FILENAME'])\n\n shell_args = [app.config['CHORD_TRANSLATOR_PATH'], org_chord_path, chord_trans_path]\n process = subprocess.Popen(shell_args, cwd=app.config['CHORD_TRANS_WORKING_DIR'], \n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n # 删除原始和弦文件\n try:\n os.remove(org_chord_path)\n except OSError:\n pass\n\n if song_info:\n socketio.emit('chord', song_info, namespace='/karaoke')\n\n\n'''\n生成片段歌词文件、音轨文件等自弹自唱相关文件。\n\n参数:\n real_app: Flask的app对象，需要手动在调用函数中传入。\n chord_path: 解释完毕后的和弦文件路径。\n lyric_path: 完整歌词文件路径。\n original_path: 歌曲原唱文件路径。\n song_info: 格式为“<歌曲名称>-<歌手>”的歌曲信息，用于向客户端发送socket。\n'''\ndef generate_instrument_sing_files(real_app, chord_path, lyric_path, original_path, song_info):\n\n start_time, end_time = read_chord(chord_path)\n\n lrc_thread = Thread(target=trim_lrc, args=(real_app, lyric_path, start_time, end_time,))\n track_thread = Thread(target=separate_audio_track, args=(real_app, original_path, start_time, end_time,))\n\n lrc_thread.start()\n track_thread.start()\n\n lrc_thread.join()\n track_thread.join()\n\n if song_info:\n socketio.emit('instrument', song_info, namespace='/karaoke')\n\n\n'''\n对歌曲特定片段进行音轨分离。\n\n参数:\n real_app: Flask的app对象，需要手动在调用函数中传入。\n original_path: 歌曲原唱文件路径。\n start_time: 歌曲特定片段的开始时间。\n end_time: 歌曲特定片段的结束时间。\n'''\ndef separate_audio_track(real_app, original_path, start_time, end_time):\n\n with real_app.app_context():\n\n start_time = start_time - app.config['BUTTON_ANI_SEC']\n if start_time < 0:\n raise InvalidSongSegmentException\n\n trimmed_wav_path = trim_wav(original_path, start_time, end_time)\n generate_inst_wav(trimmed_wav_path)\n\n # 删除切分出的歌曲特定片段文件\n try:\n os.remove(trimmed_wav_path)\n except OSError:\n pass\n\n\n'''\n从歌曲原唱中分离出人声。\n\n参数:\n original_file_path: 歌曲原唱文件路径。\n\n返回:\n 分离出的无伴奏人声文件路径。\n'''\ndef generate_vocal_file(original_file_path):\n\n directory, original_filename = os.path.split(original_file_path)\n filename_without_suffix = original_filename.rsplit('.', 1)[0]\n\n shell_args = ['spleeter', 'separate', '-p', 'spleeter:2stems', '-o', \n directory, original_file_path]\n if sys.platform == 'win32':\n shell_args.insert(0, 'python')\n shell_args.insert(1, '-m')\n\n process = subprocess.Popen(shell_args, cwd=app.config['WORKING_DIR'],\n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n return os.path.join(directory, filename_without_suffix, 'vocals.wav')\n\n\n'''\n将无伴奏人声文件转换为单音轨。\n\n参数:\n vocal_file_path: 人声文件路径。\n\n返回:\n 单音轨无伴奏人声文件路径。\n'''\ndef generate_single_track_file(vocal_file_path):\n\n directory, _ = os.path.split(vocal_file_path)\n single_track_file_path = os.path.join(directory, 'vocal_single.wav')\n\n shell_args = ['ffmpeg', '-y', '-i', vocal_file_path, \n '-ar', '44100', '-ac', '1', single_track_file_path]\n process = subprocess.Popen(shell_args, cwd=app.config['WORKING_DIR'],\n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n return single_track_file_path\n\n\n'''\n由单音轨无伴奏人声文件生成歌曲打分文件。\n\n参数:\n single_track_file_path: 单音轨无伴奏人声文件路径。\n'''\ndef generate_rate_file(single_track_file_path):\n\n rm_dir, _ = os.path.split(single_track_file_path)\n\n if sys.platform == 'win32': # Windows\n directory = rm_dir.rsplit('\\\\', 1)[0]\n else: # Linux 或 MacOS\n directory = rm_dir.rsplit('/', 1)[0]\n\n rate_file_path = os.path.join(directory, app.config['RATE_FILENAME'])\n shell_args = [app.config['RATING_PATH'], single_track_file_path, \n '-t', '-s', '50', '-o', rate_file_path]\n process = subprocess.Popen(shell_args, cwd=app.config['WORKING_DIR'],\n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n try:\n shutil.rmtree(rm_dir)\n except OSError:\n pass\n\n\n'''\n从完整的歌词文件中提取出歌曲特定片段的歌词。\n\n参数:\n real_app: Flask的app对象，需要手动在调用函数中传入。\n org_lrc_path: 完整歌词文件路径。\n start_time: 歌曲特定片段的开始时间。\n end_time: 歌曲特定片段的结束时间。\n'''\ndef trim_lrc(real_app, org_lrc_path, start_time, end_time):\n\n with real_app.app_context():\n\n directory, _ = os.path.split(org_lrc_path)\n new_lrc_path = os.path.join(directory, app.config['LYRIC_INSTRUMENT_FILENAME'])\n \n shell_args = [app.config['TRIMMER_PATH'], str(start_time), str(end_time), org_lrc_path, new_lrc_path]\n process = subprocess.Popen(shell_args, cwd=app.config['WORKING_DIR'],\n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n\n'''\n从解释完毕的和弦文件中获取歌曲片段的开始时间与结束时间。\n\n参数:\n chord_trans_path: 解释完毕的和弦文件路径。\n\n返回:\n 歌曲特定片段的开始时间与结束时间。\n'''\ndef read_chord(chord_trans_path):\n\n with open(chord_trans_path, 'r') as chord:\n line = chord.readline()\n args = line.split()\n\n if len(args) != 5:\n raise ChordParseException\n \n start_time = float(args[3]) / 1000\n end_time = float(args[4]) / 1000\n return start_time, end_time\n\n\n'''\n切分特定片段的歌曲原唱。\n\n参数:\n org_wav_path: 歌曲原唱文件路径。\n start_time: 歌曲特定片段的开始时间。\n end_time: 歌曲特定片段的结束时间。\n\n返回:\n 切分出的歌曲原唱片段文件路径。\n'''\ndef trim_wav(org_wav_path, start_time, end_time):\n\n directory, _ = os.path.split(org_wav_path)\n trimmed_wav_path = os.path.join(directory, app.config['TRIMMED_WAV_FILENAME'])\n\n duration = end_time - start_time\n shell_args = ['ffmpeg', '-ss', str(start_time), '-t', str(duration), \n '-i', org_wav_path, trimmed_wav_path]\n\n process = subprocess.Popen(shell_args, shell=app.config['REQUIRE_SHELL'])\n process.wait()\n\n return trimmed_wav_path\n\n\n'''\n对歌曲原唱片段文件进行音轨分离，产生自弹自唱所需文件。\n\n参数:\n trimmed_wav_path: 歌曲原唱片段文件路径。\n'''\ndef generate_inst_wav(trimmed_wav_path):\n\n directory, _ = os.path.split(trimmed_wav_path)\n\n shell_args = ['spleeter', 'separate', '-p', 'spleeter:5stems', \n '-o', directory, trimmed_wav_path]\n if sys.platform == 'win32':\n shell_args.insert(0, 'python')\n shell_args.insert(1, '-m')\n\n process = subprocess.Popen(shell_args, cwd=app.config['WORKING_DIR'],\n shell=app.config['REQUIRE_SHELL'])\n process.wait()\n", "sub_path": "FinalRelease/code/WebUpload/server/src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 9155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 61, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 63, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 68, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 90, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 116, "usage_type": "name"}, {"api_name": "src.exceptions.InvalidSongSegmentException", "line_number": 118, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 146, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 151, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 173, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 173, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 174, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 174, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 196, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 196, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 198, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 198, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 198, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 199, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 199, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 222, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 224, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 224, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 225, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 225, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 225, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 226, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 226, "usage_type": "name"}, {"api_name": "src.exceptions.ChordParseException", "line_number": 246, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 267, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 267, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 273, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 273, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 291, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 295, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 295, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 295, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 296, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 296, "usage_type": "name"}]} {"seq_id": "137089097", "text": "import json\nfrom django.db import transaction\nfrom django.db.models import F\nfrom django.db.models.signals import pre_save, post_save, post_delete\nfrom django.dispatch import receiver\nfrom django.utils import timezone\nfrom django_celery_beat.models import PeriodicTask, IntervalSchedule\n\nfrom .models import Book, BookChapter\nfrom .tasks import book_scraper_info, book_scraper_chaps\n\n\n@receiver(post_save, sender=Book)\ndef book_scraper_initial_signal(sender, instance, created=False, **kwargs):\n if not instance.visited and instance.visit_id:\n schedule, created = IntervalSchedule.objects.get_or_create(\n every=15,\n period=IntervalSchedule.SECONDS,\n )\n PeriodicTask.objects.create(\n one_off=True,\n interval=schedule,\n name=f'Update book: {instance.title}',\n task='novel2read.apps.books.tasks.book_scraper_info',\n args=json.dumps([instance.pk]),\n )\n\n if not instance.chapters_count:\n schedule, created = IntervalSchedule.objects.get_or_create(\n every=50,\n period=IntervalSchedule.SECONDS,\n )\n PeriodicTask.objects.create(\n one_off=True,\n interval=schedule,\n name=f'Update book chapters init: {instance.title}',\n task='novel2read.apps.books.tasks.book_scraper_chaps',\n args=json.dumps([instance.pk]),\n )\n\n\n@receiver(post_save, sender=BookChapter)\ndef create_update_chapter_cid(sender, instance, created=False, **kwargs):\n if created:\n instance.book.update_chapters_count()\n instance.c_id = instance.book.chapters_count\n instance.save(update_fields=['c_id'])\n\n\n@receiver(post_delete, sender=BookChapter)\ndef delete_update_chapter_cid(sender, instance, **kwargs):\n instance.book.update_chapters_count()\n c_id_del = instance.c_id\n book_chaps = BookChapter.objects.filter(book__slug=instance.book.slug).filter(c_id__gt=c_id_del)\n book_chaps.update(c_id=F('c_id') - 1)\n", "sub_path": "novel2read/apps/books/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 2062, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django_celery_beat.models.IntervalSchedule.objects.get_or_create", "line_number": 16, "usage_type": "call"}, {"api_name": "django_celery_beat.models.IntervalSchedule.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.IntervalSchedule", "line_number": 16, "usage_type": "name"}, {"api_name": "django_celery_beat.models.IntervalSchedule.SECONDS", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.IntervalSchedule", "line_number": 18, "usage_type": "name"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.PeriodicTask", "line_number": 20, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "django_celery_beat.models.IntervalSchedule.objects.get_or_create", "line_number": 29, "usage_type": "call"}, {"api_name": "django_celery_beat.models.IntervalSchedule.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.IntervalSchedule", "line_number": 29, "usage_type": "name"}, {"api_name": "django_celery_beat.models.IntervalSchedule.SECONDS", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.IntervalSchedule", "line_number": 31, "usage_type": "name"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.PeriodicTask", "line_number": 33, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 13, "usage_type": "argument"}, {"api_name": "models.Book", "line_number": 13, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 42, "usage_type": "argument"}, {"api_name": "models.BookChapter", "line_number": 42, "usage_type": "name"}, {"api_name": "models.BookChapter.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "models.BookChapter.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.BookChapter", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 55, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_delete", "line_number": 50, "usage_type": "argument"}, {"api_name": "models.BookChapter", "line_number": 50, "usage_type": "name"}]} {"seq_id": "381310125", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom pastes import views as paste_views\n\nurlpatterns = [\n path('', include('home.urls', 'home')),\n path('admin/', admin.site.urls),\n path('pastes/', paste_views.ShowQueryResults.as_view(), name=\"query_results\"),\n path('pastes/<str:char_id>', paste_views.ShowPaste.as_view(), name=\"show_paste\"),\n path('pastes/<str:char_id>/confirm_delete', paste_views.ConfirmDelete.as_view(), name=\"confirm_delete\"),\n path('pastes/<str:char_id>/delete', paste_views.DeletePaste.as_view(), name='delete_paste'),\n]\n", "sub_path": "pastebin/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "pastes.views.ShowQueryResults.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "pastes.views.ShowQueryResults", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pastes.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "pastes.views.ShowPaste.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "pastes.views.ShowPaste", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pastes.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "pastes.views.ConfirmDelete.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "pastes.views.ConfirmDelete", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pastes.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "pastes.views.DeletePaste.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "pastes.views.DeletePaste", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pastes.views", "line_number": 11, "usage_type": "name"}]} {"seq_id": "150186888", "text": "# -*- coding: utf-8 -*- \r\nfrom zabbix_api import ZabbixAPI\r\nfrom datetime import datetime\r\nimport time\r\n\r\n\r\nzapi = ZabbixAPI(\"https://endereco.com.br/zabbix\")\r\n# Disable SSL certificate verification\r\nzapi.session.verify = False\r\n# Specify a timeout (in seconds)\r\nzapi.timeout = 10.1\r\n# informacoes de acesso\r\nzapi.login(\"usuario.zabbix\", \"senhaUsuario\")\r\n# mostra versao do zabbix\r\nprint(\"Connected to Zabbix API Version %s\" % zapi.api_version())\r\n\r\nfor hosts in zapi.host.get({'output': ['host','interface']}):\r\n print(hosts)\r\n\r\n#item_id = 1879051\r\n\r\n# Create a time range\r\n#time_till = time.mktime(datetime.now().timetuple())\r\n#time_from = time_till - 60 * 60 * 4 # 4 hours\r\n\r\n#time_from = time.mktime(datetime.now().timetuple()) - 60 * 5 # 5 min\r\n\r\n\r\n#historico = zapi.history.get({ 'itemids': [ item_id ], \r\n#'history': 0, \r\n#'output': 'extend', \r\n#'time_from': time_from, \r\n#'time_till': “1439250959” \r\n# }) \r\n\r\n# Print out each datapoint\r\n#for point in historico:\r\n# print(\"{0}: {1}\".format(datetime.fromtimestamp(int(point['clock']))\r\n# .strftime(\"%x %X\"), point['value']))\r\n\r\n# 0 pertence a data \r\n# 1 pertence a valor \r\n\r\n\r\n", "sub_path": "Outros scripts/extrair_zab.py", "file_name": "extrair_zab.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "zabbix_api.ZabbixAPI", "line_number": 7, "usage_type": "call"}]} {"seq_id": "593562435", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'ipetrash'\n\n\nimport time\nfrom PyQt5.QtWidgets import QApplication, QMessageBox\n\nfrom print__hprof_or_big_size_file import find_files_by_dirs, DIRS\n\n\nif __name__ == '__main__':\n app = QApplication([])\n\n while True:\n result = find_files_by_dirs(DIRS)\n if result:\n QMessageBox.warning(None, 'Warn', '\\n'.join(result))\n\n time.sleep(5 * 60 * 60)\n", "sub_path": "print__hprof_or_big_size_file__notify_with_MessageBox.py", "file_name": "print__hprof_or_big_size_file__notify_with_MessageBox.py", "file_ext": "py", "file_size_in_byte": 440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 14, "usage_type": "call"}, {"api_name": "print__hprof_or_big_size_file.find_files_by_dirs", "line_number": 17, "usage_type": "call"}, {"api_name": "print__hprof_or_big_size_file.DIRS", "line_number": 17, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 19, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}]} {"seq_id": "267407046", "text": "import argparse\nimport json\nimport os\nimport time\n\nimport requests\n\nimport dataclasses\nimport logging\nfrom typing import *\n\n\nL = logging.getLogger(__name__)\n\n\nclass RequestFailed(RuntimeError):\n def __init__(self, reason: str):\n super(\"request failed: \" + reason)\n\n\nclass Client:\n def __init__(self, token):\n self.baseurl = \"https://slack.com/api\"\n self.default_header = {\"content-type\": \"application/x-www-form-urlencoded\"}\n self.token = token\n\n def _get(self, url, params) -> requests.Response:\n headers = self.default_header\n params[\"token\"] = self.token\n res = requests.get(url, headers=headers, params=params)\n return self._decode_response(res)\n\n def _post(self, url, data) -> Any:\n headers = self.default_header\n data[\"token\"] = self.token\n res = requests.post(url, headers=headers, data=data)\n return self._decode_response(res)\n\n def _decode_response(self, res: requests.Response) -> Any:\n if res.status_code != 200:\n raise RequestFailed(f\"status_code isn't 200 ({res.status_code})\")\n return res.json()\n\n def auth_test(self):\n return self._get(self.baseurl + \"/auth.test\", {})\n\n def conversations_list(self, cursor: str = None):\n params = {\"types\": \"public_channel,private_channel,mpim\"}\n if cursor is not None:\n params[\"cursor\"] = cursor\n return self._get(self.baseurl + \"/conversations.list\", params)\n\n def conversations_members(self, channel: str, cursor: str = None):\n params = {\"channel\": channel}\n if cursor is not None:\n params[\"cursor\"] = cursor\n return self._get(self.baseurl + \"/conversations.members\", params)\n\n def conversations_history(self, channel: str, cursor: str = None):\n params = {\"channel\": channel}\n if cursor is not None:\n params[\"cursor\"] = cursor\n return self._get(self.baseurl + \"/conversations.history\", params)\n\n def conversations_replies(self, channel: str, ts: str, cursor: str = None):\n params = {\"channel\": channel, \"ts\": ts}\n if cursor is not None:\n params[\"cursor\"] = cursor\n return self._get(self.baseurl + \"/conversations.replies\", params)\n\n def conversations_join(self, channel: str):\n params = {\"channel\": channel}\n return self._post(self.baseurl + \"/conversations.join\", params)\n\n def users_list(self, cursor: str = None):\n params = {}\n if cursor is not None:\n params[\"cursor\"] = cursor\n return self._get(self.baseurl + \"/users.list\", params)\n\n def users_profile_set(self, user: str, key: str, value: str):\n params = {\"user\": user, \"name\": key, \"value\": value}\n return self._post(self.baseurl + \"/users.profile.set\", params)\n\n\ndef is_thread_parent(msg: Any) -> bool:\n return \"thread_ts\" in msg and msg[\"ts\"] == msg[\"thread_ts\"]\n\n\ndef get_channels(cli: Client) -> List[Any]:\n L.info(\"fetching channel metadata...\")\n channels: List[Any] = []\n next_cursor = None\n while next_cursor != \"\":\n data = cli.conversations_list(next_cursor)\n if not data[\"ok\"]:\n raise RuntimeError(f\"request failed: (data={data})\")\n channels += data[\"channels\"]\n next_cursor = data[\"response_metadata\"][\"next_cursor\"]\n\n L.info(\"fetching channel members...\")\n for c in channels:\n L.info(f\"fetching channel members for channel {c['name']}...\")\n\n c[\"members\"] = []\n if c[\"is_archived\"]:\n L.info(f\"channel {c['name']} is archived, skipped\")\n continue\n\n next_cursor = None\n try:\n while next_cursor != \"\":\n data = cli.conversations_members(c[\"id\"])\n if not data[\"ok\"]:\n raise RuntimeError(f\"request failed: (channel={c}, data={data})\")\n c[\"members\"] += data[\"members\"]\n next_cursor = data[\"response_metadata\"][\"next_cursor\"]\n except Exception as e:\n pass\n\n return channels\n\n\ndef get_users(cli: Client) -> List[Any]:\n L.info(\"fetching user metadata...\")\n users: List[Any] = []\n next_cursor = None\n while next_cursor != \"\":\n data = cli.users_list(next_cursor)\n if not data[\"ok\"]:\n raise RuntimeError(f\"request failed: (data={data})\")\n users += data[\"members\"]\n next_cursor = data[\"response_metadata\"][\"next_cursor\"]\n\n return users\n\n\ndef get_replies(cli: Client, channel: Any, ts: str) -> List[Any]:\n messages: List[Any] = []\n next_cursor = None\n while next_cursor != \"\":\n data = cli.conversations_replies(channel[\"id\"], ts, next_cursor)\n if not data[\"ok\"]:\n raise RuntimeError(f\"request failed: (data={data})\")\n messages += data[\"messages\"]\n next_cursor = data[\"response_metadata\"][\"next_cursor\"] if data[\"has_more\"] else \"\"\n\n return messages\n\n\ndef get_messages(cli: Client, channel: Any) -> List[Any]:\n L.info(f\"fetching messages for channel {channel['name']}...\")\n messages: List[Any] = []\n next_cursor = None\n while next_cursor != \"\":\n data = cli.conversations_history(channel[\"id\"], next_cursor)\n if not data[\"ok\"]:\n raise RuntimeError(f\"request failed: (data={data})\")\n messages += data[\"messages\"]\n next_cursor = data[\"response_metadata\"][\"next_cursor\"] if data[\"has_more\"] else \"\"\n\n thread_broadcast_set = set()\n for msg in messages:\n if \"subtype\" in msg and msg[\"subtype\"] == \"thread_broadcast\":\n thread_broadcast_set.add(msg[\"ts\"])\n\n for msg in messages:\n if is_thread_parent(msg):\n replies = get_replies(cli, channel, msg[\"thread_ts\"])\n msg[\"replies\"] = []\n for reply in replies:\n if msg[\"ts\"] == reply[\"ts\"]:\n continue\n msg[\"replies\"].append({\"user\": reply[\"user\"], \"ts\": reply[\"ts\"]})\n if not reply[\"ts\"] in thread_broadcast_set:\n messages.append(reply)\n\n return messages\n\n\ndef append_download_token(msg: Any, download_token: str):\n if not \"files\" in msg:\n return\n\n for f in msg[\"files\"]:\n if f[\"mimetype\"].startswith(\"image\"):\n for s in [64, 80, 360, 480, 160, 720, 800, 960, 1024]:\n try:\n f[f\"thumb_{s}\"] += f\"?t={download_token}\"\n except Exception as e:\n L.debug(\"exception occured in append_download_token, ignored...\")\n\n\ndef output(dest: str, channels: List[Any], users: List[Any], messages: Dict[str, List[Any]], download_token: Optional[str] = None):\n os.makedirs(dest, exist_ok=True)\n\n with open(f\"{dest}/channels.json\", \"w\") as f:\n f.write(json.dumps(channels))\n\n with open(f\"{dest}/users.json\", \"w\") as f:\n f.write(json.dumps(users))\n\n for channel in channels:\n channel_dir = f\"{dest}/{channel['name']}\"\n os.makedirs(channel_dir, exist_ok=True)\n\n if not channel[\"name\"] in messages:\n continue\n\n msgs = {}\n for msg in messages[channel[\"name\"]]:\n if download_token is not None:\n append_download_token(msg, download_token)\n\n t = time.gmtime(float(msg[\"ts\"]))\n key = f\"{t.tm_year:04}-{t.tm_mon:02}-{t.tm_mday:02}\"\n if not key in msgs:\n msgs[key] = []\n msgs[key].append(msg)\n\n for key in msgs.keys():\n msgs[key] = sorted(msgs[key], key=lambda m: float(m[\"ts\"]))\n with open(f\"{channel_dir}/{key}.json\", \"w\") as f:\n f.write(json.dumps(msgs[key]))\n\n\ndef main(args: argparse.Namespace):\n logging.basicConfig(level=logging.INFO)\n\n cli = Client(args.bot_token)\n\n L.info(\"checking validity of token...\")\n user = cli.auth_test()\n if not user[\"ok\"]:\n raise RuntimeError(\"token isn't valid\")\n\n L.info(\"fetching channels...\")\n channels = get_channels(cli)\n\n L.info(\"fetching users...\")\n users = get_users(cli)\n\n L.info(\"fetching messages...\")\n messages: Dict[str, List[Any]] = {}\n for channel in channels:\n if user[\"user_id\"] in channel[\"members\"]:\n messages[channel[\"name\"]] = get_messages(cli, channel)\n\n output(args.destination, channels, users, messages, args.download_token)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--bot-token\", help=\"token for accesssing Slack\")\n parser.add_argument(\"--download-token\", help=\"token for fetching assets from Slack\")\n parser.add_argument(\"--destination\", help=\"the output directory\")\n\n args: argparse.Namespace = parser.parse_args()\n\n main(args)\n\n", "sub_path": "scripts/export-history.py", "file_name": "export-history.py", "file_ext": "py", "file_size_in_byte": 8711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 27, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 36, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 195, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 198, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 205, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 215, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 224, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 227, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 228, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 228, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 253, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 259, "usage_type": "attribute"}]} {"seq_id": "219945413", "text": "import json\nimport logging\nimport os\n\nimport requests\nfrom django.core.management.base import BaseCommand\nfrom django.utils.http import urlencode\n\nfrom public_data_collector.models import Area, Sigungu, SmallArea\n\n\nclass Collector:\n def __init__(self):\n super(Collector, self).__init__()\n self.base_url = 'http://api.visitkorea.or.kr/openapi/service/rest/KorService'\n self.endpoint = self.base_url\n with open('/etc/secrets/culturedata_proto/service_key.txt') as f:\n self.service_key = f.read().strip()\n self.base_query_params = {\n 'pageNo': 1,\n 'MobileOS': 'ETC',\n 'MobileApp': 'culterdata_proto',\n }\n\n def send_request(self):\n return requests.get(self.endpoint)\n\n\nclass Command(Collector, BaseCommand):\n help = 'Collect public data'\n\n def __init__(self):\n super(Command, self).__init__()\n\n def update_setting(self, query_params):\n self.query_params = self.base_query_params.copy()\n self.query_params.update(query_params)\n self.endpoint = self.base_url \\\n + '/areaCode?ServiceKey={}&{}'.format(self.service_key, urlencode(self.query_params))\n\n def handle(self, *args, **options):\n self.update_setting({\n 'numOfRows': 1000,\n '_type': 'json',\n })\n response = self.send_request()\n areas = json.loads(response.text)['response']['body']['items']['item']\n for area in areas:\n area_instance = Area.objects.get_or_create(\n code=int(area['code']),\n name=area['name']\n )[0]\n self.update_setting({\n 'numOfRows': 1000,\n '_type': 'json',\n 'areaCode': area['code'],\n })\n response = self.send_request()\n sigungus = json.loads(response.text)['response']['body']['items']['item']\n if type(sigungus) is not list:\n sigungus = [sigungus]\n for sigungu in sigungus:\n sigungu_instance = Sigungu.objects.get_or_create(\n area=area_instance,\n code=int(sigungu['code']),\n name=sigungu['name']\n )[0]\n self.update_setting({\n 'numOfRows': 1000,\n '_type': 'json',\n 'areaCode': area['code'],\n 'sigunguCode': sigungu['code']\n })\n response = self.send_request()\n if json.loads(response.text)['response']['body']['totalCount'] == 0:\n continue\n small_areas = json.loads(response.text)['response']['body']['items']['item']\n for small_area in small_areas:\n SmallArea.objects.get_or_create(\n sigungu=sigungu_instance,\n code=int(small_area['code']),\n name=small_area['name']\n )\n\n return 'collect data complete.'\n", "sub_path": "public_data_collector/management/commands/collectdata.py", "file_name": "collectdata.py", "file_ext": "py", "file_size_in_byte": 3055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 29, "usage_type": "name"}, {"api_name": "django.utils.http.urlencode", "line_number": 39, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "public_data_collector.models.Area.objects.get_or_create", "line_number": 49, "usage_type": "call"}, {"api_name": "public_data_collector.models.Area.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "public_data_collector.models.Area", "line_number": 49, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "public_data_collector.models.Sigungu.objects.get_or_create", "line_number": 63, "usage_type": "call"}, {"api_name": "public_data_collector.models.Sigungu.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "public_data_collector.models.Sigungu", "line_number": 63, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "public_data_collector.models.SmallArea.objects.get_or_create", "line_number": 79, "usage_type": "call"}, {"api_name": "public_data_collector.models.SmallArea.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "public_data_collector.models.SmallArea", "line_number": 79, "usage_type": "name"}]} {"seq_id": "652280428", "text": "from django.shortcuts import (\n render, redirect, reverse, HttpResponse, get_object_or_404\n)\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom django.http import JsonResponse\nfrom django.template.loader import render_to_string\nfrom .forms import WishlistForm\nfrom products.models import Product\nfrom profiles.models import UserProfile\nfrom .models import Wishlist\n\n@login_required\ndef view_wishlist(request):\n \"\"\" A view that renders the wishlist contents page \"\"\"\n\n # basic view for displaying User wishlist page\n \n user = UserProfile.objects.get(user=request.user)\n\n # wishlist = get_object_or_404(Wishlist, user=user)\n\n try:\n wishlist = Wishlist.objects.get(user=user)\n except Wishlist.DoesNotExist:\n wishlist = None\n messages.warning(request, \"No items in the Wishlist yet!\")\n if wishlist:\n # user exist\n pass\n else:\n # user does not exist\n pass\n \n context={\n 'wishlist': wishlist,\n }\n\n return render(request, 'wishlist/wishlist.html', context)\n\n\n@login_required\ndef add_to_wishlist(request, product_id):\n\n product_wish = get_object_or_404(Product, pk=product_id) # get product\n \n wishlist, created = Wishlist.objects.get_or_create(\n user=request.user.userprofile,\n name='rick'\n )\n\n if wishlist.products.filter(name=product_wish).exists():\n messages.warning(request, \"Item already added to wishlist\")\n return redirect(reverse('view_wishlist'))\n else:\n wishlist.products.add(product_wish)\n messages.success(request, \"Item added to Wishlist!\")\n return redirect(reverse('view_wishlist'))\n\n\n@login_required\ndef remove_from_wishlist(request, product_id):\n\n product_wish = get_object_or_404(Product, pk=product_id) # get product\n user = UserProfile.objects.get(user=request.user) # get user\n\n wishlist = get_object_or_404(Wishlist, user=user) # filter wishlist item from with user\n\n wishlist.products.remove(product_wish) # remove item\n messages.success(request, 'Product Removed From Wishlist')\n\n return redirect(reverse('view_wishlist'))\n\n", "sub_path": "wishlist/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "profiles.models.UserProfile.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "profiles.models.UserProfile.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "profiles.models.UserProfile", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Wishlist.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Wishlist.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Wishlist", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Wishlist.DoesNotExist", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Wishlist", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 45, "usage_type": "call"}, {"api_name": "products.models.Product", "line_number": 45, "usage_type": "argument"}, {"api_name": "models.Wishlist.objects.get_or_create", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Wishlist.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Wishlist", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 42, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 64, "usage_type": "call"}, {"api_name": "products.models.Product", "line_number": 64, "usage_type": "argument"}, {"api_name": "profiles.models.UserProfile.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "profiles.models.UserProfile.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "profiles.models.UserProfile", "line_number": 65, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Wishlist", "line_number": 67, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 61, "usage_type": "name"}]} {"seq_id": "337877703", "text": "import os\n\nfrom torch.utils import data\nfrom torchvision import transforms\nfrom PIL import Image\n\n\ndef get_transform():\n \"\"\"Module for image pre-processing definition.\n\n You can customize this module.\n \"\"\"\n normalize = transforms.Normalize(\n mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],\n std=[x / 255.0 for x in [63.0, 62.1, 66.7]])\n transform = [\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n normalize\n ]\n return transforms.Compose(transform)\n\n\nclass CustomDataset(data.Dataset):\n \"\"\"Dataset class.\n\n This class is used for internal NSML inference system.\n You can modify this module for improving your data loading efficiency.\n \"\"\"\n def __init__(self, root, transform):\n self.data_root = os.path.join(root, 'test_data')\n self.transform = transform\n self.image_ids = [img for img in os.listdir(self.data_root)]\n\n def __getitem__(self, idx):\n image_id = self.image_ids[idx]\n image = Image.open(os.path.join(self.data_root, image_id))\n image = image.convert('RGB')\n image = self.transform(image)\n return image, image_id\n\n def __len__(self):\n return len(self.image_ids)\n\n\ndef data_loader(root, batch_size=64):\n \"\"\"Test data loading module.\n\n Args:\n root: string. dataset path.\n batch_size: int.\n\n Returns:\n DataLoader instance\n \"\"\"\n input_transform = get_transform()\n dataset = CustomDataset(root, input_transform)\n return data.DataLoader(dataset=dataset,\n batch_size=batch_size,\n shuffle=False)\n", "sub_path": "iitp_trash/data_local_loader.py", "file_name": "data_local_loader.py", "file_ext": "py", "file_size_in_byte": 1644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torchvision.transforms.Normalize", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 13, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 58, "usage_type": "name"}]} {"seq_id": "362034994", "text": "# -*- coding: utf-8 -*-\n# pylint: disable=W0621\nimport pytest\nfrom unittestzero import Assert\nfrom fixtures.server_roles import default_roles, server_roles\n\n@pytest.mark.nondestructive\n@pytest.mark.fixtureconf(server_roles=default_roles+('automate',))\n@pytest.mark.usefixtures(\n \"maximized\",\n \"setup_infrastructure_providers\",\n \"setup_pxe_provision\",\n \"mgmt_sys_api_clients\")\nclass TestTemplateProvisioning:\n def test_linux_template_cancel(\n self,\n provisioning_start_page,\n provisioning_data_basic_only):\n '''Test Cancel button'''\n provisioning_start_page.click_on_template_item(\n provisioning_data_basic_only[\"template\"])\n provision_pg = provisioning_start_page.click_on_continue()\n vm_pg = provision_pg.click_on_cancel()\n Assert.true(vm_pg.is_the_current_page,\n \"not returned to the correct page\")\n\n def test_linux_template_workflow(\n self,\n server_roles,\n provisioning_start_page,\n provisioning_data,\n mgmt_sys_api_clients,\n random_name):\n '''Test Basic Provisioning Workflow'''\n assert len(server_roles) == len(default_roles) + 1\n provisioning_start_page.click_on_template_item(\n provisioning_data[\"template\"])\n provision_pg = provisioning_start_page.click_on_continue()\n self.complete_provision_pages_info(provisioning_data, provision_pg, \\\n random_name)\n vm_pg = assert_vm_state(provisioning_data, provision_pg, \"on\", \\\n random_name)\n remove_vm(provisioning_data, vm_pg, mgmt_sys_api_clients, \\\n random_name)\n\n def complete_provision_pages_info(self,\n provisioning_data, provision_pg, random_name):\n ''' Fills in data for Provisioning tabs'''\n tab_buttons = provision_pg.tabbutton_region\n request_pg = tab_buttons.tabbutton_by_name(\"Request\").click()\n request_pg = request_pg.fill_fields(\n \"admin@example.com\",\n \"admin\",\n \"admin\",\n \"Adding a test note\",\n \"Manager Name\")\n purpose_pg = tab_buttons.tabbutton_by_name(\"Purpose\").click()\n # tree = purpose_pg.click_on_nodes(provisioning_data[\"node\"],\n # provisioning_data[\"child_node\")\n catalog_pg = tab_buttons.tabbutton_by_name(\"Catalog\").click()\n catalog_pg.fill_fields(\n provisioning_data[\"provision_type\"],\n provisioning_data[\"pxe_server\"],\n provisioning_data[\"server_image\"],\n str(provisioning_data[\"count\"]),\n '%s%s' % (provisioning_data[\"vm_name\"], random_name),\n provisioning_data[\"vm_description\"])\n environment_pg = tab_buttons.tabbutton_by_name(\"Environment\").click()\n environment_pg.fill_fields(\n unicode(provisioning_data[\"host\"]),\n unicode(provisioning_data[\"datastore\"]))\n hardware_pg = tab_buttons.tabbutton_by_name(\"Hardware\").click()\n network_pg = tab_buttons.tabbutton_by_name(\"Network\").click()\n if (\"PXE\" in provisioning_data[\"provision_type\"]) or \\\n (\"ISO\" in provisioning_data[\"provision_type\"]):\n customize_pg = tab_buttons.tabbutton_by_name(\"Customize\").click()\n customize_pg.fill_fields(\n provisioning_data[\"root_password\"],\n provisioning_data[\"address_node_value\"],\n provisioning_data[\"customization_template\"])\n schedule_pg = tab_buttons.tabbutton_by_name(\"Schedule\").click()\n schedule_pg.fill_fields(\n provisioning_data[\"when_to_provision\"],\n provisioning_data[\"power_on\"],\n str(provisioning_data[\"time_until_retirement\"]))\n\n services_requests_pg = schedule_pg.click_on_submit()\n Assert.true(services_requests_pg.is_the_current_page,\n \"not returned to the correct page\")\n Assert.equal(services_requests_pg.flash_message,\n \"VM Provision Request was Submitted, \"\\\n \"you will be notified when your VMs are ready\")\n services_requests_pg.approve_request(1)\n services_requests_pg.wait_for_request_status(\"Last 24 Hours\", \\\n \"Finished\", 12)\n\ndef assert_vm_state(provisioning_data, current_page, \\\n current_state, random_name):\n ''' Asserts that the VM is created in the expected state '''\n vm_pg = current_page.header.site_navigation_menu(\n 'Infrastructure').sub_navigation_menu('Virtual Machines').click()\n vm_pg.refresh()\n vm_pg.wait_for_vm_state_change( '%s%s' % (provisioning_data[\"vm_name\"],\n random_name), 'on', 12)\n Assert.equal(vm_pg.quadicon_region.get_quadicon_by_title(\n '%s%s' % (provisioning_data[\"vm_name\"], random_name))\\\n .current_state, current_state,\n \"vm not in correct state: \" + current_state)\n return vm_pg\n\ndef remove_vm(provisioning_data,\n current_page, provider_api_clients, random_name):\n '''Powers off and removes the VM'''\n vm_pg = current_page.header.site_navigation_menu(\n 'Infrastructure').sub_navigation_menu('Virtual Machines').click()\n vm_pg.power_off(['%s%s' % (provisioning_data[\"vm_name\"], random_name)])\n Assert.true(vm_pg.flash.message.startswith(\"Stop initiated\"))\n vm_pg.wait_for_vm_state_change(\n '%s%s' % (provisioning_data[\"vm_name\"], random_name), 'off', 12)\n Assert.equal(vm_pg.quadicon_region.get_quadicon_by_title(\n '%s%s' % (provisioning_data[\"vm_name\"], random_name))\\\n .current_state, 'off', \"vm running\")\n for provider in provider_api_clients.values():\n if ('%s%s' % (provisioning_data[\"vm_name\"], random_name) + \"/\" +\n '%s%s' % (provisioning_data[\"vm_name\"], random_name) + \".vmx\"\n ) in provider.list_vm() or \\\n '%s%s' % (provisioning_data[\"vm_name\"], random_name) \\\n in provider.list_vm():\n provider.delete_vm('%s%s' % (provisioning_data[\"vm_name\"], \\\n random_name))\n\n", "sub_path": "tests/ui/provisioning/test_template_provisioning.py", "file_name": "test_template_provisioning.py", "file_ext": "py", "file_size_in_byte": 6155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "unittestzero.Assert.true", "line_number": 24, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 24, "usage_type": "name"}, {"api_name": "fixtures.server_roles.server_roles", "line_number": 35, "usage_type": "argument"}, {"api_name": "fixtures.server_roles.default_roles", "line_number": 35, "usage_type": "argument"}, {"api_name": "unittestzero.Assert.true", "line_number": 88, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 88, "usage_type": "name"}, {"api_name": "unittestzero.Assert.equal", "line_number": 90, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 90, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pytest.mark.fixtureconf", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "fixtures.server_roles.default_roles", "line_number": 8, "usage_type": "name"}, {"api_name": "pytest.mark.usefixtures", "line_number": 9, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute"}, {"api_name": "unittestzero.Assert.equal", "line_number": 105, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 105, "usage_type": "name"}, {"api_name": "unittestzero.Assert.true", "line_number": 117, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 117, "usage_type": "name"}, {"api_name": "unittestzero.Assert.equal", "line_number": 120, "usage_type": "call"}, {"api_name": "unittestzero.Assert", "line_number": 120, "usage_type": "name"}]} {"seq_id": "626559559", "text": "import torch.nn as nn\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.utils import spectral_norm\nimport mit_resnet\nfrom torchvision import models\nfrom non_local_embedded_gaussian import NONLocalBlock2D\n\nMAX_LENGTH = 40\n\nPAD_token = 0\nSOS_token = 1\nEOS_token = 2\n\nclass ResidualBlock(nn.Module):\n def __init__(self, input_channels, output_channels, stride=1):\n super(ResidualBlock, self).__init__()\n self.input_channels = input_channels\n self.output_channels = output_channels\n self.stride = stride\n self.bn1 = nn.BatchNorm2d(input_channels)\n self.relu = nn.ReLU(inplace=True)\n self.conv1 = nn.Conv2d(input_channels, int(output_channels / 4), 1, 1, bias=False)\n self.bn2 = nn.BatchNorm2d(int(output_channels / 4))\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(int(output_channels / 4), int(output_channels / 4), 3, stride, padding=1, bias=False)\n self.bn3 = nn.BatchNorm2d(int(output_channels / 4))\n self.relu = nn.ReLU(inplace=True)\n self.conv3 = nn.Conv2d(int(output_channels / 4), output_channels, 1, 1, bias=False)\n self.conv4 = nn.Conv2d(input_channels, output_channels, 1, stride, bias=False)\n\n def forward(self, x):\n residual = x\n out = self.bn1(x)\n out1 = self.relu(out)\n out = self.conv1(out1)\n out = self.bn2(out)\n out = self.relu(out)\n out = self.conv2(out)\n out = self.bn3(out)\n out = self.relu(out)\n out = self.conv3(out)\n if (self.input_channels != self.output_channels) or (self.stride != 1):\n residual = self.conv4(out1)\n out += residual\n return out\n\nclass cnn_att(nn.Module):\n # input size is 8*8\n def __init__(self, in_channels, out_channels):\n super(cnn_att, self).__init__()\n self.first_residual_blocks = ResidualBlock(in_channels, out_channels)\n\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n self.bn = self.bn4 = nn.BatchNorm2d(in_channels, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\n self.trunk_branches = nn.Sequential(\n ResidualBlock(in_channels, out_channels),\n ResidualBlock(in_channels, out_channels)\n )\n\n self.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # 4*4\n\n self.middle_2r_blocks = nn.Sequential(\n ResidualBlock(in_channels, out_channels),\n ResidualBlock(in_channels, out_channels)\n )\n\n # self.interpolation1 = nn.UpsamplingBilinear2d(size=size) # 8*8\n\n self.conv1_1_blocks = nn.Sequential(\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, bias=False),\n # nn.BatchNorm2d(out_channels),\n # nn.ReLU(inplace=True),\n # nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, bias = False),\n nn.Sigmoid()\n )\n\n\n self.last_blocks = ResidualBlock(in_channels, out_channels)\n\n def forward(self, x, size):\n x = self.bn(self.conv(x))\n out_trunk = self.bn(self.conv(x))\n out_mpool1 = self.mpool1(x)\n out_middle_2r_blocks = self.bn(self.conv(out_mpool1))\n #\n # out_interp = self.interpolation1(out_middle_2r_blocks) + out_trunk\n out_interp = F.upsample(out_middle_2r_blocks, size=size, mode='bilinear', align_corners=True) + out_trunk\n # print(out_skip2_connection.data)\n # print(out_interp3.data)\n out_conv1_1_blocks = self.conv1_1_blocks(out_interp)\n out = (1 + out_conv1_1_blocks) * out_trunk\n out_last = self.bn(self.conv(out))\n\n return out_last\n\nclass ResnetDilated(nn.Module):\n def __init__(self, orig_resnet, dilate_scale=8):\n super(ResnetDilated, self).__init__()\n from functools import partial\n\n if dilate_scale == 8:\n orig_resnet.layer3.apply(\n partial(self._nostride_dilate, dilate=2))\n orig_resnet.layer4.apply(\n partial(self._nostride_dilate, dilate=4))\n elif dilate_scale == 4:\n orig_resnet.layer2.apply(\n partial(self._nostride_dilate, dilate=2))\n orig_resnet.layer3.apply(\n partial(self._nostride_dilate, dilate=2))\n orig_resnet.layer4.apply(\n partial(self._nostride_dilate, dilate=4))\n\n # take pretrained resnet, except AvgPool and FC\n self.conv1 = orig_resnet.conv1\n self.bn1 = orig_resnet.bn1\n self.relu1 = orig_resnet.relu1\n self.conv2 = orig_resnet.conv2\n self.bn2 = orig_resnet.bn2\n self.relu2 = orig_resnet.relu2\n self.conv3 = orig_resnet.conv3\n self.bn3 = orig_resnet.bn3\n self.relu3 = orig_resnet.relu3\n self.maxpool = orig_resnet.maxpool\n self.layer1 = orig_resnet.layer1\n self.layer2 = orig_resnet.layer2\n self.layer3 = orig_resnet.layer3\n self.layer4 = orig_resnet.layer4\n self.non_local1 = NONLocalBlock2D(64, sub_sample=True, bn_layer=True)\n self.non_local2 = NONLocalBlock2D(128, sub_sample=True, bn_layer=True)\n\n def _nostride_dilate(self, m, dilate):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n # the convolution with stride\n if m.stride == (2, 2):\n m.stride = (1, 1)\n if m.kernel_size == (3, 3):\n m.dilation = (dilate//2, dilate//2)\n m.padding = (dilate//2, dilate//2)\n # other convoluions\n else:\n if m.kernel_size == (3, 3):\n m.dilation = (dilate, dilate)\n m.padding = (dilate, dilate)\n\n def forward(self, x, return_feature_maps=True):\n conv_out = []\n\n x = self.relu1(self.bn1(self.conv1(x)))\n x = self.relu2(self.bn2(self.conv2(x)))\n x = self.relu3(self.bn3(self.conv3(x)))\n x = self.maxpool(x)\n\n x = self.non_local1(self.layer1(x))\n conv_out.append(x)\n x = self.non_local2(self.layer2(x))\n conv_out.append(x)\n x = self.layer3(x)\n conv_out.append(x)\n x = self.layer4(x)\n conv_out.append(x)\n\n if return_feature_maps:\n return conv_out\n return [x]\n\nclass CNN(nn.Module):\n def __init__(self, imgH, nc, leakyRelu=False):\n super(CNN, self).__init__()\n assert imgH % 16 == 0, 'imgH has to be a multiple of 16'\n\n ks = [3, 3, 3, 3, 3, 3, 2]\n ps = [1, 1, 1, 1, 1, 1, 0]\n ss = [1, 1, 1, 1, 1, 1, 1]\n nm = [64, 128, 256, 256, 512, 512, 512]\n\n cnn = nn.Sequential()\n self.orig_resnet = mit_resnet.__dict__['resnet18'](pretrained=False)\n self.net_encoder = ResnetDilated(self.orig_resnet,\n dilate_scale=8)\n\n self.att = cnn_att(512, 512)\n self.conv4 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n self.bn4 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n self.relu4 = nn.ReLU(512)\n self.conv5 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n self.relu5 = nn.ReLU(512)\n self.bn5 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)\n\n self.conv6 = nn.Conv2d(512, 1024, kernel_size=(2, 2), stride=(2, 1), padding=(0, 0))\n self.pool4 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)\n self.bn6 = nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n self.relu6 = nn.ReLU(1024)\n\n def convRelu(i, batchNormalization=False):\n nIn = nc if i == 0 else nm[i - 1]\n nOut = nm[i]\n cnn.add_module('conv{0}'.format(i),\n nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))\n if batchNormalization:\n cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut))\n if leakyRelu:\n cnn.add_module('relu{0}'.format(i),\n nn.LeakyReLU(0.2, inplace=True))\n else:\n cnn.add_module('relu{0}'.format(i), nn.ReLU(True))\n\n convRelu(0)\n cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2)) # 64x16x64\n convRelu(1)\n cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2)) # 128x8x32\n convRelu(2, True)\n convRelu(3)\n cnn.add_module('pooling{0}'.format(2),\n nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 256x4x16\n convRelu(4, True)\n convRelu(5)\n cnn.add_module('pooling{0}'.format(3),\n nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 512x2x16\n convRelu(6, True) # 512x1x16\n\n self.cnn = cnn\n self.attention = SelfAttention(1024)\n\n def forward(self, input):\n conv_all = self.net_encoder(input)\n\n conv = self.conv4(conv_all[3])\n conv = self.bn4(conv)\n conv = self.relu4(conv)\n conv = self.att(conv, conv.size()[2:])\n conv = self.conv5(conv)\n conv = self.bn5(conv)\n conv = self.relu5(conv)\n conv = self.pool3(conv)\n conv = self.conv6(conv)\n conv = self.pool4(conv)\n conv = self.bn6(conv)\n conv = self.relu6(conv)\n conv = self.attention(conv)\n\n b, c, h, w = conv.size()\n assert h == 1, \"the height of conv must be 1\"\n conv = conv.squeeze(2) # b *512 * width\n conv = conv.permute(0, 2, 1) # [b, w, c]\n output = conv\n return output\n\nclass SelfAttention(nn.Module):\n\n def __init__(self, d):\n super(SelfAttention, self).__init__()\n\n assert d % 8 == 0\n self.projections = nn.ModuleList([\n spectral_norm(nn.Conv2d(d, d // 8, 1)),\n spectral_norm(nn.Conv2d(d, d // 8, 1)),\n spectral_norm(nn.Conv2d(d, d, 1))\n ])\n self.gamma = nn.Parameter(torch.zeros(1)) # shape [1]\n\n def forward(self, x):\n \"\"\"\n Arguments:\n x: a float tensor with shape [b, d, h, w].\n Returns:\n a float tensor with shape [b, d, h, w].\n \"\"\"\n b, d, h, w = x.size()\n\n q = self.projections[0](x)\n k = self.projections[1](x)\n v = self.projections[2](x)\n\n q = q.view(b, d // 8, h * w).permute(0, 2, 1)\n k = k.view(b, d // 8, h * w)\n v = v.view(b, d, h * w).permute(0, 2, 1)\n\n attention = torch.bmm(q, k) # shape [b, h * w, h * w]\n attention = F.softmax(attention, dim=2)\n\n out = torch.bmm(attention, v) # shape [b, h * w, d]\n out = out.permute(0, 2, 1).view(b, d, h, w)\n return x + self.gamma * out\n\nclass EncoderRNN(nn.Module):\n def __init__(self, input_size, hidden_size, n_layers=1, dropout=0.1):\n super(EncoderRNN, self).__init__()\n self.input_size = input_size\n self.hidden_size = hidden_size\n self.n_layers = n_layers\n self.gru = nn.GRU(self.input_size, self.hidden_size, num_layers=self.n_layers, bidirectional=True, dropout=(0 if n_layers == 1 else dropout))\n\n def forward(self, input, hidden=None):\n self.gru.flatten_parameters()\n outputs, hidden = self.gru(input, hidden)\n\n outputs = outputs[:,:,:self.hidden_size] + outputs[:,:,self.hidden_size:]\n return outputs, hidden\n\n\n\n# Luong attention layer\nclass Attn(torch.nn.Module):\n def __init__(self, method, hidden_size):\n super(Attn, self).__init__()\n self.method = method\n if self.method not in ['dot', 'general', 'concat']:\n raise ValueError(self.method, \"is not an appropriate attention method.\")\n self.hidden_size = hidden_size\n if self.method == 'general':\n self.attn = torch.nn.Linear(self.hidden_size, hidden_size)\n elif self.method == 'concat':\n self.attn = torch.nn.Linear(self.hidden_size * 2, hidden_size)\n self.v = torch.nn.Parameter(torch.FloatTensor(hidden_size))\n\n def dot_score(self, hidden, encoder_output):\n return torch.sum(hidden * encoder_output, dim=2)\n\n def general_score(self, hidden, encoder_output):\n energy = self.attn(encoder_output)\n return torch.sum(hidden * energy, dim=2)\n\n def concat_score(self, hidden, encoder_output):\n energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh()\n return torch.sum(self.v * energy, dim=2)\n\n def forward(self, hidden, encoder_outputs):\n # Calculate the attention weights (energies) based on the given method\n if self.method == 'general':\n attn_energies = self.general_score(hidden, encoder_outputs)\n elif self.method == 'concat':\n attn_energies = self.concat_score(hidden, encoder_outputs)\n elif self.method == 'dot':\n attn_energies = self.dot_score(hidden, encoder_outputs)\n\n # Transpose max_length and batch_size dimensions\n attn_energies = attn_energies.t()\n\n # Return the softmax normalized probability scores (with added dimension)\n return F.softmax(attn_energies, dim=1).unsqueeze(1)\n\nclass LuongAttnDecoderRNN(nn.Module):\n def __init__(self, attn_model, embedding, hidden_size, output_size, n_layers=1, dropout=0.1):\n super(LuongAttnDecoderRNN, self).__init__()\n\n # Keep for reference\n self.attn_model = attn_model\n self.hidden_size = hidden_size\n self.output_size = output_size\n self.n_layers = n_layers\n self.dropout = dropout\n\n # Define layers\n self.embedding = embedding\n self.embedding_dropout = nn.Dropout(dropout)\n self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=(0 if n_layers == 1 else dropout))\n self.concat = nn.Linear(hidden_size * 2, hidden_size)\n self.out = nn.Linear(hidden_size, output_size)\n\n self.attn = Attn(attn_model, hidden_size)\n\n def forward(self, input_step, last_hidden, encoder_outputs):\n # Note: we run this one step (word) at a time\n # Get embedding of current input word\n embedded = self.embedding(input_step).view(1,-1,self.hidden_size)\n embedded = self.embedding_dropout(embedded)\n # Forward through unidirectional GRU\n self.gru.flatten_parameters()\n rnn_output, hidden = self.gru(embedded, last_hidden)\n # Calculate attention weights from the current GRU output\n attn_weights = self.attn(rnn_output, encoder_outputs)\n # Multiply attention weights to encoder outputs to get new \"weighted sum\" context vector\n context = attn_weights.bmm(encoder_outputs.transpose(0, 1))\n # Concatenate weighted context vector and GRU output using Luong eq. 5\n rnn_output = rnn_output.squeeze(0)\n context = context.squeeze(1)\n concat_input = torch.cat((rnn_output, context), 1)\n concat_output = torch.tanh(self.concat(concat_input))\n # Predict next word using Luong eq. 6\n output = self.out(concat_output)\n output = F.log_softmax(output, dim=1)\n # Return output and final hidden state\n return output, hidden\n\n\nclass model(nn.Module):\n def __init__(self, cnn, encoder, decoder):\n super(model, self).__init__()\n\n self.cnn = cnn\n self.encoder = encoder\n self.decoder = decoder\n\n def forward(self, image, max_length):\n\n batch_size = image.size()[0]\n\n input_tensor = self.cnn(image)\n input_tensor = input_tensor.permute(1, 0, 2)\n\n encoder_outputs, encoder_hidden = self.encoder(\n input_tensor)\n\n decoder_input = torch.tensor([[SOS_token] * batch_size]).cuda()\n decoder_hidden = encoder_hidden[:self.decoder.n_layers]\n\n decoder_outputs = []\n for di in range(max_length):\n decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)\n decoder_scores, decoder_input = torch.max(decoder_output, dim=1)\n decoder_outputs.append(decoder_output)\n # loss += self.criterion(decoder_output, target_tensor[di].squeeze(1))\n decoder_outputs = torch.stack(decoder_outputs, 0)\n return decoder_outputs.permute(1, 0, 2)\n\n\n\n\n", "sub_path": "model_resnet_3.py", "file_name": "model_resnet_3.py", "file_ext": "py", "file_size_in_byte": 16568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.functional.upsample", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 107, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 109, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 112, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 114, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 116, "usage_type": "call"}, {"api_name": "non_local_embedded_gaussian.NONLocalBlock2D", "line_number": 133, "usage_type": "call"}, {"api_name": "non_local_embedded_gaussian.NONLocalBlock2D", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 172, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 182, "usage_type": "name"}, {"api_name": "mit_resnet.__dict__", "line_number": 183, "usage_type": "attribute"}, {"api_name": "{'partial': 'functools.partial'}", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 188, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 189, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 192, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 193, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 197, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 221, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 255, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "name"}, {"api_name": "torch.nn.utils.spectral_norm", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.nn.utils.spectral_norm", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.nn.utils.spectral_norm", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 266, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 285, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 286, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 286, "usage_type": "name"}, {"api_name": "torch.bmm", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 292, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 292, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 298, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 310, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 318, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 324, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 328, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 347, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 349, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 349, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 362, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 364, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 365, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 365, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 385, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 388, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 388, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 393, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 417, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 420, "usage_type": "call"}]} {"seq_id": "422472836", "text": "#from __future__ import print_function\nimport cv2\nimport argparse\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torchnet.meter as meter\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport torchvision.utils as vutils\nfrom torch.autograd import Variable\nfrom dataset import multiPIE\nfrom siamese_model_2nd import Siamese\nfrom contrastive import ContrastiveLoss\nimport numpy as np\n# import cv2\n#from pycrayon import CrayonClient\n \n#for plotting loss\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport time,math\nfrom logger import Logger\n# from models_Parsing import ParseNet\nsaveFile = open('/home/shumao/wyw_files/siamese_output_M_3/record.txt', 'w')\nsaveFile.write(\"niter:\" + str(50000) + \"\\n\")\nsaveFile.write(\"---lr:\" + str(0.0001) + \"\\n\")\nsaveFile.write(\"beta1:\" + str(0.7) + \"\\n\")\nsaveFile.write(\"W:-1-x-x-x-x-x-\" + \"\\n\")\nsaveFile.write(\"L1 Loss\" + \"\\n\")\nsaveFile.write(\"after load model from: train-3-28000pth\")\nlogger = Logger('./log_1');\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--batchSize', type=int, default=64, help='input batch size')\nparser.add_argument('--loadSize', type=int, default=100, help='the height / width of the input image to network')\nparser.add_argument('--fineSize', type=int, default=96, help='the height / width of the input image to network')\nparser.add_argument('--id_num', type=int, default=200, help='Total training identity.')\nparser.add_argument('--pose_num', type=int, default=9, help='Total training pose.')\nparser.add_argument('--light_num', type=int, default=20, help='Total training light.')\nparser.add_argument('--niter', type=int, default=50000, help='number of iterations to train for')\nparser.add_argument('--lr', type=float, default=0.0001, help='learning rate, default=0.0002')\nparser.add_argument('--beta1', type=float, default=0.7, help='beta1 for adam. default=0.7')\nparser.add_argument('--cuda', action='store_true', help='enables cuda')\nparser.add_argument('--outf', default='/home/shumao/wyw_files/siamese_output_M_3', help='folder to output images and model checkpoints')\nparser.add_argument('--manualSeed', type=int, help='manual seed')\nparser.add_argument('--dataPath', default='/home/shumao/dr-gan/Data_new_realigned2/setting2/train/', help='which dataset to train on')\nparser.add_argument('--modelPath', default='/home/shumao/wyw_files/siamese_output_3/netS_28000.pth', help='which dataset to train on')\nparser.add_argument('--save_step', type=int, default=400, help='save weights every 400 iterations ')\nparser.add_argument('--labelPath', default='/home/shumao/dr-gan/Data_new_realigned2/setting2/Facedata/', help='which dataset to train on')\n\n\nopt = parser.parse_args()\nprint(opt) # print every parser arguments\n# print(opt.niter)\n\n\ntry:\n os.makedirs(opt.outf)\nexcept OSError:\n pass\n\nw_r = 1\n# w_cL = 0.02\n# w_cP = 0.02\n# w_cI = 0.02\n# w_P = 0.02\n# w_L = 0.02\n\nif opt.manualSeed is None:\n opt.manualSeed = random.randint(1, 10000)\nprint(\"Random Seed: \", opt.manualSeed)\nrandom.seed(opt.manualSeed)\ntorch.manual_seed(opt.manualSeed)\nif opt.cuda:\n torch.cuda.manual_seed_all(opt.manualSeed)\n\ncudnn.benchmark = True\n#---------------------Load Mask-------------------\nmask = np.load('mask_20.npy')\nmask = mask.astype(np.float32)\nM = torch.from_numpy(mask.transpose((2, 0, 1)))\nFinalMask = M.expand(opt.batchSize,3,96,96)\n# print m.size()\n# 3x96x96\n\n\n#---------------------Load DATA-------------------------\ndataset_1 = multiPIE(opt.dataPath,opt.loadSize,opt.fineSize,labelPath = opt.labelPath)\n# dataset_2 = multiPIE(opt.dataPath,opt.loadSize,opt.fineSize,opt.labelPath)\ndataset_test = multiPIE('/home/shumao/dr-gan/comparison/',opt.loadSize,opt.fineSize,labelPath = opt.labelPath)\nloader_train_1 = torch.utils.data.DataLoader(dataset=dataset_1,\n batch_size = opt.batchSize,\n shuffle=True,\n num_workers=4,\n drop_last = True)\n# loader_train_2 = torch.utils.data.Dataloader(dataset=dataset_1,\n# batch_size = opt.batchSize,\n# shuffle=True,\n# num_workers=4)\n\n\nloader_test = torch.utils.data.DataLoader(dataset=dataset_test,\n batch_size = 9,\n shuffle=False,\n num_workers=4)\ndata_train_1 = iter(loader_train_1)\n# data_train_2 = iter(loader_train_2)\ndata_test = iter(loader_test)\n\n\n#----------------------Parameters-----------------------\nnum_pose = opt.pose_num\nnum_light = opt.light_num\nnum_iden = opt.id_num\n\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') !=-1:\n m.weight.data.normal_(0.0, 0.02)\n elif classname.find('BatchNorm') !=-1:\n m.weight.data.normal_(1.0, 0.02)\n m.bias.data.fill_(0)\n\n\n\n\nnetS = Siamese()\nnetS.load_state_dict(torch.load(opt.modelPath))\n\n#-----------------params freeze-----------------\nfor param in netS.conv11.parameters():\n param.requires_grad = False\nfor param in netS.conv1r.parameters():\n param.requires_grad = False\nfor param in netS.conv12.parameters():\n param.requires_grad = False\nfor param in netS.conv21.parameters():\n param.requires_grad = False\nfor param in netS.conv22.parameters():\n param.requires_grad = False\nfor param in netS.conv23.parameters():\n param.requires_grad = False\nfor param in netS.conv31.parameters():\n param.requires_grad = False\nfor param in netS.conv32.parameters():\n param.requires_grad = False\nfor param in netS.conv33.parameters():\n param.requires_grad = False\nfor param in netS.conv41.parameters():\n param.requires_grad = False\nfor param in netS.conv42.parameters():\n param.requires_grad = False\nfor param in netS.conv43.parameters():\n param.requires_grad = False\nfor param in netS.conv51.parameters():\n param.requires_grad = False\nfor param in netS.conv52.parameters():\n param.requires_grad = False\nfor param in netS.conv53.parameters():\n param.requires_grad = False\nfor param in netS.convfc.parameters():\n param.requires_grad = False\n\n\n#-----------------params freeze-----------------\nif(opt.cuda):\n netS.cuda()\n\n#-------------------Loss & Optimization\n\noptimizerS = torch.optim.Adam(filter(lambda p: p.requires_grad, netS.parameters()),lr=opt.lr, betas=(opt.beta1, 0.999))\n\nposs_contrastive_loss = ContrastiveLoss() # load from the begining\nlight_contrastive_loss = ContrastiveLoss()\nidentity_contrastive_loss = ContrastiveLoss()\nreconstructe_loss = nn.L1Loss()\npose_class_loss = nn.CrossEntropyLoss()\nlight_class_loss = nn.CrossEntropyLoss()\n\n#------------------ Global Variables------------------\ninput_pose_1 = torch.LongTensor(opt.batchSize)\ninput_light_1 = torch.LongTensor(opt.batchSize)\n# input_pose_2 = torch.LongTensor(opt.batchSize)\n# input_light_2 = torch.LongTensor(opt.batchSize)\n\ninputImg_1 = torch.FloatTensor(opt.batchSize, 3, opt.fineSize, opt.fineSize)\ninputImg_2 = torch.FloatTensor(opt.batchSize, 3, opt.fineSize, opt.fineSize)\nGT = torch.FloatTensor(opt.batchSize, 3,opt.fineSize, opt.fineSize)\nsame_pose = torch.FloatTensor(opt.batchSize)\nsame_iden = torch.FloatTensor(opt.batchSize)\nsame_light = torch.FloatTensor(opt.batchSize)\n\n# w_1 = torch.FloatTensor(1)\n# w_2 = torch.FloatTensor(20)\n# w_3 = torch.FloatTensor(10)\n# w_4 = torch.FloatTensor(10)\n# w_5 = torch.FloatTensor(10)\n# w_6 = torch.FloatTensor(20)\n# output_pose_1_label = torch.LongTensor(opt.batchSize)\n# output_pose_2_label = torch.LongTensor(opt.batchSize)\n# output_light_1_label = torch.LongTensor(opt.batchSize)\n# output_light_2_label = torch.LongTensor(opt.batchSize)\n\ninput_pose_1 = Variable(input_pose_1)\n# input_pose_2 = Variable(input_pose_2)\ninput_light_1 = Variable(input_light_1)\n# input_light_2 = Variable(input_light_2)\n\ninputImg_1 = Variable(inputImg_1)\ninputImg_2 = Variable(inputImg_2)\nGT = Variable(GT)\nsame_pose = Variable(same_pose)\nsame_iden = Variable(same_iden)\nsame_light = Variable(same_light)\n\nFinalMask = Variable(FinalMask)\n\n# w_1 = Variable(w_1, requires_grad = False)\n# w_2 = Variable(w_2, requires_grad = False)\n# w_3 = Variable(w_3, requires_grad = False)\n# w_4 = Variable(w_4, requires_grad = False)\n# w_5 = Variable(w_5, requires_grad = False)\n# w_6 = Variable(w_6, requires_grad = False)\n\n\npose_mtr = meter.ConfusionMeter(k=opt.pose_num)\nlight_mtr = meter.ConfusionMeter(k=opt.light_num)\n\n\nif(opt.cuda):\n\n input_pose_1 = input_pose_1.cuda()\n # input_pose_2 = input_pose_2.cuda()\n input_light_1 = input_light_1.cuda()\n # input_light_2 = input_light_2.cuda()\n inputImg_1 = inputImg_1.cuda()\n inputImg_2 = inputImg_2.cuda()\n GT = GT.cuda()\n same_pose = same_pose.cuda()\n same_light = same_light.cuda()\n same_iden = same_iden.cuda()\n\n FinalMask = FinalMask.cuda()\n\n # w_1 = w_1.cuda()\n # w_2 = w_1.cuda()\n # w_3 = w_1.cuda()\n # w_4 = w_1.cuda()\n # w_5 = w_1.cuda()\n # w_6 = w_1.cuda()\n # poss_contrastive_loss.cuda()\n # light_contrastive_loss.cuda()\n # identity_contrastive_loss.cuda()\n pose_class_loss.cuda()\n light_class_loss.cuda()\n reconstructe_loss.cuda()\n\n\n#------------------test---------\n\n# k = 0 # for meter\n\nerr_total = 0\nerr_recon = 0\nerr_contraL = 0\nerr_contraP = 0\nerr_contraI = 0\nerr_classP = 0\nerr_classL = 0\n\ndef test(iteration, data_test, loader_test):\n try:\n images_1,po_1,li_1,GT_1,by_image,same_po,same_li,same_id = data_test.next()\n except StopIteration:\n data_test = iter(loader_test)\n images_1,po_1,li_1,GT_1,by_image,same_po,same_li,same_id = data_test.next()\n\n GT.data.resize_(GT_1.size()).copy_(GT_1)\n inputImg_1.data.resize_(images_1.size()).copy_(images_1)\n inputImg_2.data.resize_(by_image.size()).copy_(by_image)\n input_pose_1.data.resize_(po_1.size()).copy_(po_1)\n input_light_1.data.resize_(li_1.size()).copy_(li_1)\n\n\n output_pose_1, output_pose_2, output_light_1, output_light_2, out_f_1, out_f_2, out = netS(inputImg_1, inputImg_2)\n vutils.save_image(out.data,\n '%s/fake_samples_iteration_%03d.png' % (opt.outf, iteration), normalize=True)\n vutils.save_image(inputImg_1.data,\n '%s/input_samples_iteration_%03d.png' % (opt.outf, iteration), normalize=True)\n\n\n\n#-------------------train----------------------\nfor iteration in range(1,opt.niter+1):\n running_corrects = 0\n running_corrects_light = 0\n try:\n images_1,po_1,li_1,GT_1,by_image,same_po,same_li,same_id= data_train_1.next()\n except StopIteration:\n data_train_1 = iter(loader_train_1)\n images_1,po_1,li_1,GT_1,by_image,same_po,same_li,same_id = data_train_1.next()\n\n GT.data.resize_(GT_1.size()).copy_(GT_1)\n\n\n\n inputImg_1.data.resize_(images_1.size()).copy_(images_1)\n inputImg_2.data.resize_(by_image.size()).copy_(by_image)\n\n input_pose_1.data.resize_(po_1.size()).copy_(po_1)\n input_light_1.data.resize_(li_1.size()).copy_(li_1)\n\n same_pose.data.resize_(same_po.size()).copy_(same_po)\n same_light.data.resize_(same_li.size()).copy_(same_li)\n same_iden.data.resize_(same_id.size()).copy_(same_id)\n netS.zero_grad()\n\n output_pose_1, output_pose_2, output_light_1, output_light_2, out_f_1, out_f_2, out = netS(inputImg_1, inputImg_2)\n #-----------------mask test area-----------------------------\n # print out.data.type()\n # print GT.data.type()\n # print FinalMask.data.type() same\n # print FinalMask.data.size() 64x3x96x96\n # print FinalMask.data.size()\n # print out.data.size()\n\n Final_out = FinalMask * out\n\n Final_GT = FinalMask * GT\n\n\n #-----------------mask test area-----------------------------\n # f_1 & f_2 variable\n # same_iden variable\n err_recon = reconstructe_loss(Final_out, Final_GT)\n err_contraI = identity_contrastive_loss(out_f_1, out_f_2, same_iden)\n err_contraP = poss_contrastive_loss(output_pose_1, output_pose_2, same_pose)\n err_contraL = light_contrastive_loss(output_light_1,output_light_2, same_light)\n err_classL = light_class_loss(output_light_1, input_light_1)\n err_classP = pose_class_loss(output_pose_1, input_pose_1)\n # print(err_recon.data.size())\n # print(err_contraL.data.size())\n # print(err_classP.data.size())\n # modify the contrastive loss function to make contrastive loss be 1Lx1L \n # contrastive loss and Softmax and Loss1 are all requires_grad\n # err_total = 1 * err_recon + 10 * err_contraP + 10 * err_contraI + 10 * err_classP + 20 * err_classL\n # err_total = err_recon + err_contraI + err_contraP + err_contraL + err_classL + err_classP\n err_total = w_r * err_recon\n err_total.backward()\n optimizerS.step()\n\n #----------------------Visualize-----------\n if(iteration % 200 == 0):\n\n pose_mtr.add(output_pose_1.data, input_pose_1.data)\n pose_trainacc = pose_mtr.value().diagonal().sum()*1.0/opt.batchSize\n pose_mtr.reset()\n\n light_mtr.add(output_light_1.data, input_light_1.data)\n light_trainacc = light_mtr.value().diagonal().sum()*1.0/opt.batchSize\n light_mtr.reset()\n #-----------------------------------------\n test(iteration, data_test, loader_test)\n # #pose prediction\n\n # preds_pose = torch.max(output_pose_1.data, 1)\n # running_corrects += torch.sum(preds == input_pose_1)\n # print('pose_accuracy: %.2f' \n # % (running_corrects * 1.0/images.size(0)))\n \n # #light prediction\n # preds_light = torch.max(output_light_1.data, 1)\n # running_corrects_light += torch.sum(preds_light == input_light_1)\n # print('light_accuracy: %.2f' \n # % (running_corrects_light * 1.0/images.size(0)))\n\n print('----------------------------------------')\n print('[%d/%d] Loss_S: %.4f ' %(iteration, opt.niter, err_total.data[0]))\n print(' Reco_S: %.4f ' %(err_recon.data[0]))\n print(' conL_S: %.4f ' %(err_contraL.data[0]))\n print(' conP_S: %.4f ' %(err_contraP.data[0]))\n print(' conI_S: %.4f ' %(err_contraI.data[0]))\n print(' Clas_P: %.4f ' %(err_classP.data[0]))\n print(' Clas_L: %.4f ' %(err_classL.data[0]))\n\n\n if(iteration % opt.save_step == 0):\n torch.save(netS.state_dict(), '%s/netS_%d.pth' % (opt.outf,iteration))\n\n\n", "sub_path": "siamese_train_M_3.py", "file_name": "siamese_train_M_3.py", "file_ext": "py", "file_size_in_byte": 14463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "matplotlib.use", "line_number": 24, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 37, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 64, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 76, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 87, "usage_type": "call"}, {"api_name": "dataset.multiPIE", "line_number": 94, "usage_type": "call"}, {"api_name": "dataset.multiPIE", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 108, "usage_type": "attribute"}, {"api_name": "siamese_model_2nd.Siamese", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 178, "usage_type": "attribute"}, {"api_name": "contrastive.ContrastiveLoss", "line_number": 180, "usage_type": "call"}, {"api_name": "contrastive.ContrastiveLoss", "line_number": 181, "usage_type": "call"}, {"api_name": "contrastive.ContrastiveLoss", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 184, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 223, "usage_type": "call"}, {"api_name": "torchnet.meter.ConfusionMeter", "line_number": 233, "usage_type": "call"}, {"api_name": "torchnet.meter", "line_number": 233, "usage_type": "name"}, {"api_name": "torchnet.meter.ConfusionMeter", "line_number": 234, "usage_type": "call"}, {"api_name": "torchnet.meter", "line_number": 234, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 293, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 293, "usage_type": "name"}, {"api_name": "torchvision.utils.save_image", "line_number": 295, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 295, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 395, "usage_type": "call"}]} {"seq_id": "265139169", "text": "from bot.models import User, Schedule, Spec, Group\nfrom .__utils import bot, MAIN_ACTIVITY_FLAG, SCHEDULE_ACTIVITY_FLAG, REGISTRATION_YEAR, REGISTRATION_ACTIVITY_FLAG\nfrom .__utils import MY_SCHEDULE, OTHER_SCHEDULES, CHANGE_MY_GROUP\nfrom .__utils import OTHER_SCHEDULE_SPEC, OTHER_SCHEDULE_GROUP, OTHER_SCHEDULE_SUBGROUP, OTHER_SCHEDULE_DISPLAY\nfrom .__fillers import ScheduleMessageFiller, ScheduleKeyboardFiller\nfrom telegram import Bot, InlineKeyboardMarkup, InlineKeyboardButton\nfrom datetime import datetime\n\n\n\n\nmessage_filler = ScheduleMessageFiller()\nkeyboard_filler = ScheduleKeyboardFiller()\n\n\n\n\ndef start_schedule_activity(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. start shcedule activity\\n')\n\n chat_id = context.chat_data.get('chat_id')\n\n start_schedule_activity_message = message_filler.start_schedule_activity_message\n\n my_schedule_button = InlineKeyboardButton(keyboard_filler.my_schedule_button_text, \n callback_data=MY_SCHEDULE)\n\n other_schedules_button = InlineKeyboardButton(keyboard_filler.other_schedules_button_text, \n callback_data=OTHER_SCHEDULES)\n\n keyboard = [[my_schedule_button],\n [other_schedules_button]]\n\n inline_markup = InlineKeyboardMarkup(keyboard)\n\n context.chat_data['message_id'] = bot.send_message(chat_id=chat_id, text=start_schedule_activity_message, \n reply_markup=inline_markup).message_id\n\n return SCHEDULE_ACTIVITY_FLAG\n\n\n\n\ndef start_my_schedule_activity(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. start my schedule activity\\n')\n\n chat_id = context.chat_data.get('chat_id')\n message_id = context.chat_data.get('message_id')\n\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=message_id)\n\n user_id = context.chat_data.get('user_id')\n\n user = User.objects.get(telegram_id=user_id)\n\n if user.group is None:\n print('---unregistered user---')\n\n unregistered_user_message = message_filler.unregistered_user_message\n\n bot.send_message(chat_id=chat_id, text=unregistered_user_message)\n\n return MAIN_ACTIVITY_FLAG\n\n schedule = Schedule.objects.filter(group=user.group, subgroup=user.subgroup).first()\n\n if schedule == None:\n print('---no schedule by this parameters---')\n\n no_schedule_message = message_filler.schedule_not_found_message\n\n bot.send_message(chat_id=chat_id, text=no_schedule_message)\n else:\n bot_before_schedule_message = message_filler.before_display_schedule_message\n\n bot.send_message(chat_id=chat_id, text=bot_before_schedule_message)\n\n schedule_message = schedule.schedule\n\n bot.send_message(chat_id=chat_id, text=schedule_message)\n\n return MAIN_ACTIVITY_FLAG\n\n\n\n\ndef start_other_schedules_activity(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. start other schedules activity. choise course\\n')\n\n chat_id = context.chat_data.get('chat_id')\n last_message_id = context.chat_data.get('message_id')\n\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=last_message_id, reply_markup=None)\n\n years = keyboard_filler.courses_buttons_texts\n current_date = datetime.today()\n\n SEPTEMBER = 9\n education_year = 0\n\n if current_date.month < SEPTEMBER:\n education_year = current_date.year - 1\n else:\n education_year = current_date.year\n\n keyboard = []\n\n COURSES_AMOUNT = 6\n\n for index in range(0, COURSES_AMOUNT):\n course_button = InlineKeyboardButton(years[index],\n callback_data=OTHER_SCHEDULE_SPEC + ' ' + str(education_year - index))\n keyboard.append([course_button])\n\n inline_markup = InlineKeyboardMarkup(keyboard)\n\n context.chat_data['message_id'] = bot.send_message(chat_id=chat_id, text=message_filler.choise_course_message,\n reply_markup=inline_markup).message_id\n\n print(f'Sent message: {context.chat_data.get(\"message_id\")}')\n\n return SCHEDULE_ACTIVITY_FLAG\n\n\ndef choise_spec(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. other schedules. choise spec\\n')\n\n chat_id = context.chat_data.get('chat_id')\n last_message_id = context.chat_data.get('message_id')\n\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=last_message_id, reply_markup=None)\n\n context.chat_data['year'] = int(update.callback_query.data.split(' ')[1])\n\n specs = Spec.objects.all()\n print(f'specs: {specs}')\n\n keyboard = []\n\n for spec in specs:\n keyboard.append([InlineKeyboardButton(str(spec), \n callback_data=str(OTHER_SCHEDULE_GROUP) + ' ' + str(spec.spec_id))])\n\n inline_markup = InlineKeyboardMarkup(keyboard)\n\n context.chat_data['message_id'] = bot.send_message(chat_id=chat_id, text=message_filler.choise_spec_message,\n reply_markup=inline_markup).message_id\n\n print(f'sent message: {context.chat_data.get(\"message_id\")}')\n\n return SCHEDULE_ACTIVITY_FLAG\n\n\ndef choise_group(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. other schedules. choise group\\n')\n\n chat_id = context.chat_data.get('chat_id')\n last_message_id = context.chat_data.get('message_id')\n\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=last_message_id, reply_markup=None)\n\n year = context.chat_data.get('year')\n spec_id = int(update.callback_query.data.split(' ')[1])\n context.chat_data['spec_id'] = spec_id\n\n groups = Group.objects.all().filter(year=year, spec__spec_id=spec_id)\n\n if len(groups) == 0:\n print('---No groups for this year---')\n\n group_not_found_message = message_filler.group_not_found_message\n\n bot.send_message(chat_id=chat_id, text=group_not_found_message)\n\n return MAIN_ACTIVITY_FLAG\n\n print(groups)\n\n keyboard = []\n\n for group in groups:\n keyboard.append([InlineKeyboardButton(str(group), \n callback_data=str(OTHER_SCHEDULE_SUBGROUP) + ' ' + str(group.pk))])\n\n inline_markup = InlineKeyboardMarkup(keyboard)\n print(inline_markup)\n\n context.chat_data['message_id'] = bot.send_message(chat_id=chat_id, text=message_filler.choise_group_message,\n reply_markup=inline_markup).message_id\n\n print(f'sent message: {context.chat_data.get(\"message_id\")}')\n\n return SCHEDULE_ACTIVITY_FLAG\n\n\ndef choise_subgroup(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. other schedules. choise subgroup\\n')\n\n chat_id = context.chat_data.get('chat_id')\n last_message_id = context.chat_data.get('message_id')\n\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=last_message_id, reply_markup=None)\n\n group_pk = int(update.callback_query.data.split(' ')[1])\n context.chat_data['group_pk'] = group_pk\n\n group = Group.objects.get(pk=group_pk)\n print(f'subgroup: {group.subgroup_amount}')\n\n if group.subgroup_amount == 0:\n context.chat_data['is_subgroups_zero'] = True\n return display_schedule(update, context)\n\n context.chat_data['is_subgroups_zero'] = False\n keyboard = []\n\n for subgroup_number in range(1, group.subgroup_amount + 1):\n print(subgroup_number)\n keyboard.append([InlineKeyboardButton(str(subgroup_number),\n callback_data=str(OTHER_SCHEDULE_DISPLAY) + ' ' + str(subgroup_number))])\n\n inline_markup = InlineKeyboardMarkup(keyboard)\n print(inline_markup)\n\n context.chat_data['message_id'] = bot.send_message(chat_id=chat_id, text=message_filler.choise_subgroup_message,\n reply_markup=inline_markup).message_id\n\n print(f'sent message: {context.chat_data.get(\"message_id\")}')\n\n return SCHEDULE_ACTIVITY_FLAG\n\n\ndef display_schedule(update, context):\n print('---------------------------------------------------------------------')\n print('schedule. other schedules. display schedule\\n')\n\n chat_id = context.chat_data.get('chat_id')\n\n group_pk = context.chat_data.get('group_pk')\n\n group = Group.objects.get(pk=group_pk)\n subgroup = 0\n\n is_subgroups_zero = context.chat_data.get('is_subgroups_zero')\n if not is_subgroups_zero:\n last_message_id = context.chat_data.get('message_id')\n bot.edit_message_reply_markup(chat_id=chat_id, message_id=last_message_id, reply_markup=None)\n\n subgroup = int(update.callback_query.data.split(' ')[1])\n\n schedule = Schedule.objects.filter(group=group, subgroup=subgroup).first()\n\n if schedule == None:\n bot.send_message(chat_id=chat_id, text=message_filler.schedule_not_found_message)\n else:\n bot.send_message(chat_id=chat_id, text=message_filler.before_display_schedule_message)\n bot.send_message(chat_id=chat_id, text=schedule.schedule)\n\n return MAIN_ACTIVITY_FLAG", "sub_path": "bot/management/commands/__schedule.py", "file_name": "__schedule.py", "file_ext": "py", "file_size_in_byte": 9350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "__fillers.ScheduleMessageFiller", "line_number": 12, "usage_type": "call"}, {"api_name": "__fillers.ScheduleKeyboardFiller", "line_number": 13, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 26, "usage_type": "call"}, {"api_name": "__utils.MY_SCHEDULE", "line_number": 27, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 29, "usage_type": "call"}, {"api_name": "__utils.OTHER_SCHEDULES", "line_number": 30, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 35, "usage_type": "call"}, {"api_name": "__utils.bot.send_message", "line_number": 37, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 37, "usage_type": "name"}, {"api_name": "__utils.SCHEDULE_ACTIVITY_FLAG", "line_number": 40, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 52, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 52, "usage_type": "name"}, {"api_name": "bot.models.User.objects.get", "line_number": 56, "usage_type": "call"}, {"api_name": "bot.models.User.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "bot.models.User", "line_number": 56, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 63, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 63, "usage_type": "name"}, {"api_name": "__utils.MAIN_ACTIVITY_FLAG", "line_number": 65, "usage_type": "name"}, {"api_name": "bot.models.Schedule.objects.filter", "line_number": 67, "usage_type": "call"}, {"api_name": "bot.models.Schedule.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "bot.models.Schedule", "line_number": 67, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 74, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 74, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 78, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 78, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 82, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 82, "usage_type": "name"}, {"api_name": "__utils.MAIN_ACTIVITY_FLAG", "line_number": 84, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 96, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 96, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 114, "usage_type": "call"}, {"api_name": "__utils.OTHER_SCHEDULE_SPEC", "line_number": 115, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 118, "usage_type": "call"}, {"api_name": "__utils.bot.send_message", "line_number": 120, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 120, "usage_type": "name"}, {"api_name": "__utils.SCHEDULE_ACTIVITY_FLAG", "line_number": 125, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 135, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 135, "usage_type": "name"}, {"api_name": "bot.models.Spec.objects.all", "line_number": 139, "usage_type": "call"}, {"api_name": "bot.models.Spec.objects", "line_number": 139, "usage_type": "attribute"}, {"api_name": "bot.models.Spec", "line_number": 139, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 145, "usage_type": "call"}, {"api_name": "__utils.OTHER_SCHEDULE_GROUP", "line_number": 146, "usage_type": "argument"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 148, "usage_type": "call"}, {"api_name": "__utils.bot.send_message", "line_number": 150, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 150, "usage_type": "name"}, {"api_name": "__utils.SCHEDULE_ACTIVITY_FLAG", "line_number": 155, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 165, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 165, "usage_type": "name"}, {"api_name": "bot.models.Group.objects.all", "line_number": 171, "usage_type": "call"}, {"api_name": "bot.models.Group.objects", "line_number": 171, "usage_type": "attribute"}, {"api_name": "bot.models.Group", "line_number": 171, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 178, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 178, "usage_type": "name"}, {"api_name": "__utils.MAIN_ACTIVITY_FLAG", "line_number": 180, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 187, "usage_type": "call"}, {"api_name": "__utils.OTHER_SCHEDULE_SUBGROUP", "line_number": 188, "usage_type": "argument"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 190, "usage_type": "call"}, {"api_name": "__utils.bot.send_message", "line_number": 193, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 193, "usage_type": "name"}, {"api_name": "__utils.SCHEDULE_ACTIVITY_FLAG", "line_number": 198, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 208, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 208, "usage_type": "name"}, {"api_name": "bot.models.Group.objects.get", "line_number": 213, "usage_type": "call"}, {"api_name": "bot.models.Group.objects", "line_number": 213, "usage_type": "attribute"}, {"api_name": "bot.models.Group", "line_number": 213, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 225, "usage_type": "call"}, {"api_name": "__utils.OTHER_SCHEDULE_DISPLAY", "line_number": 226, "usage_type": "argument"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 228, "usage_type": "call"}, {"api_name": "__utils.bot.send_message", "line_number": 231, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 231, "usage_type": "name"}, {"api_name": "__utils.SCHEDULE_ACTIVITY_FLAG", "line_number": 236, "usage_type": "name"}, {"api_name": "bot.models.Group.objects.get", "line_number": 247, "usage_type": "call"}, {"api_name": "bot.models.Group.objects", "line_number": 247, "usage_type": "attribute"}, {"api_name": "bot.models.Group", "line_number": 247, "usage_type": "name"}, {"api_name": "__utils.bot.edit_message_reply_markup", "line_number": 253, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 253, "usage_type": "name"}, {"api_name": "bot.models.Schedule.objects.filter", "line_number": 257, "usage_type": "call"}, {"api_name": "bot.models.Schedule.objects", "line_number": 257, "usage_type": "attribute"}, {"api_name": "bot.models.Schedule", "line_number": 257, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 260, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 260, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 262, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 262, "usage_type": "name"}, {"api_name": "__utils.bot.send_message", "line_number": 263, "usage_type": "call"}, {"api_name": "__utils.bot", "line_number": 263, "usage_type": "name"}, {"api_name": "__utils.MAIN_ACTIVITY_FLAG", "line_number": 265, "usage_type": "name"}]} {"seq_id": "59444402", "text": "\nimport numpy as np\n\n\ndef export_obj(vertices, triangles, filename):\n \"\"\"\n Exports a mesh in the (.obj) format.\n \"\"\"\n \n with open(filename, 'w') as fh:\n \n for v in vertices:\n fh.write(\"v {} {} {}\\n\".format(*v))\n \n for f in triangles:\n fh.write(\"f {} {} {}\\n\".format(*(f + 1)))\n\n\ndef export_off(vertices, triangles, filename):\n \"\"\"\n Exports a mesh in the (.off) format.\n \"\"\"\n \n with open(filename, 'w') as fh:\n fh.write('OFF\\n')\n fh.write('{} {} 0\\n'.format(len(vertices), len(triangles)))\n\n for v in vertices:\n fh.write(\"{} {} {}\\n\".format(*v))\n \n for f in triangles:\n fh.write(\"3 {} {} {}\\n\".format(*f))\n\n\ndef export_mesh(vertices, triangles, filename, mesh_name=\"mcubes_mesh\"):\n \"\"\"\n Exports a mesh in the COLLADA (.dae) format.\n \n Needs PyCollada (https://github.com/pycollada/pycollada).\n \"\"\"\n \n import collada\n \n mesh = collada.Collada()\n \n vert_src = collada.source.FloatSource(\"verts-array\", vertices, ('X','Y','Z'))\n geom = collada.geometry.Geometry(mesh, \"geometry0\", mesh_name, [vert_src])\n \n input_list = collada.source.InputList()\n input_list.addInput(0, 'VERTEX', \"#verts-array\")\n \n triset = geom.createTriangleSet(np.copy(triangles), input_list, \"\")\n geom.primitives.append(triset)\n mesh.geometries.append(geom)\n \n geomnode = collada.scene.GeometryNode(geom, [])\n node = collada.scene.Node(mesh_name, children=[geomnode])\n \n myscene = collada.scene.Scene(\"mcubes_scene\", [node])\n mesh.scenes.append(myscene)\n mesh.scene = myscene\n \n mesh.write(filename)\n", "sub_path": "src/utils/libmcubes/exporter.py", "file_name": "exporter.py", "file_ext": "py", "file_size_in_byte": 1697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "collada.Collada", "line_number": 44, "usage_type": "call"}, {"api_name": "collada.source.FloatSource", "line_number": 46, "usage_type": "call"}, {"api_name": "collada.source", "line_number": 46, "usage_type": "attribute"}, {"api_name": "collada.geometry.Geometry", "line_number": 47, "usage_type": "call"}, {"api_name": "collada.geometry", "line_number": 47, "usage_type": "attribute"}, {"api_name": "collada.source.InputList", "line_number": 49, "usage_type": "call"}, {"api_name": "collada.source", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 52, "usage_type": "call"}, {"api_name": "collada.scene.GeometryNode", "line_number": 56, "usage_type": "call"}, {"api_name": "collada.scene", "line_number": 56, "usage_type": "attribute"}, {"api_name": "collada.scene.Node", "line_number": 57, "usage_type": "call"}, {"api_name": "collada.scene", "line_number": 57, "usage_type": "attribute"}, {"api_name": "collada.scene.Scene", "line_number": 59, "usage_type": "call"}, {"api_name": "collada.scene", "line_number": 59, "usage_type": "attribute"}]} {"seq_id": "311216766", "text": "# -*- coding: utf-8 -*-\n\nimport rusloModis_mainmenu, os, sys, qgis.utils\nfrom qgis import core, gui, utils\nfrom qgis.core import *\nfrom PyQt4 import QtCore, QtGui, uic\nfrom PyQt4.QtCore import QFile, QFileInfo, QDir\nfrom qgis.core import *\nfrom qgis.utils import iface\nfrom qgis.core import QgsVectorLayer, QgsMapLayerRegistry\nfrom qgis.analysis import QgsRasterCalculator, QgsRasterCalculatorEntry\nfrom subprocess import call\nimport commonLibrary\nimport psycopg2\nimport codecs\nimport re\nimport time\nimport rusloModisLibrary\nimport math\n\n\nclass RusloModis_dlg(QtGui.QWidget):\n\n DBConnectionStatus = False\n\n def __init__(self, parent=None):\n QtGui.QWidget.__init__(self, parent)\n self.ui = rusloModis_mainmenu.Ui_Dialog()\n self.ui.setupUi(self)\n\n self.connect(self.ui.EditDBStructureButton, QtCore.SIGNAL(\"clicked()\"), self.openConfigurationFile)\n self.connect(self.ui.loadTabAddHVButton, QtCore.SIGNAL(\"clicked()\"), self.loadTabAddCurrentHVToList)\n self.connect(self.ui.loadTabDeleteHVButton, QtCore.SIGNAL(\"clicked()\"), self.loadTabDeleteSelectedHVFromList)\n self.connect(self.ui.ConnectToDBButton, QtCore.SIGNAL(\"clicked()\"), self.checkDatabaseConnection)\n self.connect(self.ui.loadTabAquaObjectButton, QtCore.SIGNAL(\"clicked()\"), self.loadTabGetHVFromFeatureExtent)\n self.connect(self.ui.loadTabLoadButton, QtCore.SIGNAL(\"clicked()\"), self.loadTabLoadData)\n self.connect(self.ui.statsTabRefreshButton, QtCore.SIGNAL(\"clicked()\"), self.statsTabRefreshTable)\n self.connect(self.ui.statsTabAddImageryToProjectButton, QtCore.SIGNAL(\"clicked()\"), self.statsTabAddImageryToProject)\n self.connect(self.ui.vodosborTabRefreshButton, QtCore.SIGNAL(\"clicked()\"), self.vodosborTabRefreshData)\n self.connect(self.ui.vodosborTabRunButton, QtCore.SIGNAL(\"clicked()\"), self.vodosborTabRunProcessing)\n self.connect(self.ui.statsTabAddIceLayersToProjectButton, QtCore.SIGNAL(\"clicked()\"), self.statsTabAddIceLayersToProject)\n self.connect(self.ui.monitorTabRefreshDatesButton, QtCore.SIGNAL(\"clicked()\"), self.monitorTabRefreshDates)\n self.connect(self.ui.statsTabWaterObjectCombobox, QtCore.SIGNAL(\"currentIndexChanged(const QString&)\"), self.statsTabWaterObjectComboboxItemChanged)\n self.connect(self.ui.monitorTabAddDateButton, QtCore.SIGNAL(\"clicked()\"), self.monitorTabAddDateToList)\n self.connect(self.ui.monitorTabDeleteDateButton, QtCore.SIGNAL(\"clicked()\"), self.monitorTabDeleteDateFromList)\n self.connect(self.ui.monitorTabRunButton, QtCore.SIGNAL(\"clicked()\"), self.monitorTabRunProcessing)\n\n self.connect(self.ui.vodosborTabVodosborCombobox, QtCore.SIGNAL(\"currentIndexChanged(const QString&)\"), self.vodosborTabVodosborComboboxItemChanged)\n self.connect(self.ui.vodosborTabDataList, QtCore.SIGNAL(\"currentItemChanged (QListWidgetItem*,QListWidgetItem*)\"), self.vodosborTabDataListSelectionChanged)\n self.connect(self.ui.monitorTabVodosborCombobox, QtCore.SIGNAL(\"currentIndexChanged(const QString&)\"), self.monitorTabVodosborComboboxItemChanged)\n\n # Заполнение элементов интерфейса\n self.fillDBConfigFieldsFromFile()\n#\n self.loadTabFillSattelites()\n self.loadTabFillMODIShvList()\n#\n self.ui.loadTabDownloadStatusTable.setColumnWidth(2, 170);\n\n\n # Прячем лишнее\n self.ui.loadTabProgressBar.hide()\n self.ui.loadTabDownloadGroupBox.hide()\n self.setDBStatusIndicatorsOFF()\n\n self.ui.vodosborTabProgressBar.hide()\n\n\n self.setStatsTableColumnWidths()\n\n # Папка temp\n tempPath = os.path.dirname(os.path.abspath(__file__)) + '\\\\temp'\n if not os.path.isdir(tempPath):\n os.mkdir(tempPath)\n\n\n # ------------------------------------------------------------------------ #\n # ------------------- Заполнение элементов интерфейса -------------------- #\n # ------------------------------------------------------------------------ #\n\n def loadTabFillSattelites(self):\n self.ui.loadTabSatelliteCombobox.addItems(['MODIS'])\n self.ui.vodosborTabSatelliteCombobox.addItems(['MODIS'])\n self.ui.monitorTabSatelliteCombobox.addItems(['MODIS'])\n\n def loadTabFillMODIShvList(self):\n i = 0\n while i <= 35:\n j = 0\n while j <= 17:\n if len(str(i)) == 1:\n iString = '0' + str(i)\n else:\n iString = str(i)\n if len(str(j)) == 1:\n jString = '0' + str(j)\n else:\n jString = str(j)\n hvString = 'h' + iString + 'v' + jString\n self.ui.loadTabHVCombobox.addItems([hvString])\n j += 1\n i += 1\n x, y = rusloModisLibrary.getCurrentCanvasCenterInSelectedCRS(4326)\n h, v = rusloModisLibrary.getMODIShvFromPointCoordinates(x,y,4326)\n index = int(h)*18 + int(v)\n self.ui.loadTabHVCombobox.setCurrentIndex(index)\n\n def loadTabAddCurrentHVToList (self):\n\n # Проверяем, нет ли там уже этого снимка, и добавляем в список\n item = self.ui.loadTabHVCombobox.currentText()\n items = []\n for index in xrange(self.ui.loadTabHVList.count()):\n items.append(self.ui.loadTabHVList.item(index))\n labels = [i.text() for i in items]\n if not item in labels:\n self.ui.loadTabHVList.addItems([item])\n\n def loadTabDeleteSelectedHVFromList (self):\n #items = self.ui.loadTabHVList.selectedIndexes()\n #self.ui.loadTabHVList.item\n for item in self.ui.loadTabHVList.selectedItems():\n self.ui.loadTabHVList.takeItem(self.ui.loadTabHVList.row(item))\n\n\n def getWaterObjects (self):\n dict = self.readDBAndProjectConfiguration()\n waterObjectsLayer = commonLibrary.getLayerByName(dict['water_objects_layer'])\n if waterObjectsLayer:\n values = commonLibrary.getAllValuesOfAttribute(waterObjectsLayer,dict['water_objects_name_attr'])\n return values\n else:\n return\n\n def loadTabFillAquaObjectsCombobox (self):\n values = self.getWaterObjects()\n if values:\n self.ui.loadTabAquaObjectCombobox.addItems(values)\n else:\n return\n\n def monitorTabFillWaterObjectsCombobox (self):\n values = self.getWaterObjects()\n if values:\n self.ui.monitorTabVodosborCombobox.addItems(values)\n else:\n return\n\n def loadTabGetHVFromFeatureExtent (self):\n if not self.ui.loadTabAquaObjectCombobox.currentText():\n return\n\n dict = self.readDBAndProjectConfiguration()\n waterObjectsLayer = commonLibrary.getLayerByName(dict['water_objects_layer'])\n waterFeature = commonLibrary.getFirstFeatureByAttributeValue(waterObjectsLayer,dict['water_objects_name_attr'],self.ui.loadTabAquaObjectCombobox.currentText())\n if not waterFeature:\n return\n\n waterCRS = rusloModisLibrary.getEPSGCodeFromLayer(waterObjectsLayer)\n\n hvList = rusloModisLibrary.getMODIShvListFromPolygonFeature(waterFeature, waterCRS)\n for hv in hvList:\n index = int(hv[0])*18 + int(hv[1])\n self.ui.loadTabHVCombobox.setCurrentIndex(index)\n item = self.ui.loadTabHVCombobox.currentText()\n items = []\n for index in xrange(self.ui.loadTabHVList.count()):\n items.append(self.ui.loadTabHVList.item(index))\n labels = [i.text() for i in items]\n if not item in labels:\n self.ui.loadTabHVList.addItems([item])\n\n def fillDBConfigFieldsFromFile (self):\n DBHost, DBPort, DBName, DBUser, DBPassword = self.readDBConfigFromFile()\n self.ui.ServerName.setText(str(DBHost))\n self.ui.PortNumber.setText(str(DBPort))\n self.ui.DBName.setText(str(DBName))\n self.ui.DBUserName.setText(str(DBUser))\n self.ui.DBPassword.setText(str(DBPassword))\n\n #getMODIShvFromLatLong\n\n #print extent.xMaximum()\n\n\n def setStatsTableColumnWidths(self):\n self.ui.statsTabTable.setColumnWidth(0,30)\n self.ui.statsTabTable.setColumnWidth(1,30)\n self.ui.statsTabTable.setColumnWidth(2,80)\n self.ui.statsTabTable.setColumnWidth(3,110)\n self.ui.statsTabTable.setColumnWidth(4,110)\n self.ui.statsTabTable.setColumnWidth(5,55)\n self.ui.statsTabTable.setColumnWidth(6,55)\n self.ui.statsTabTable.setColumnWidth(7,60)\n self.ui.statsTabTable.setColumnWidth(8,60)\n self.ui.statsTabTable.setColumnWidth(9,100)\n\n def statsTabFillWaterObjectsCombobox (self):\n allValues = [u'Исходные снимки']\n waterValues = self.getWaterObjects()\n if waterValues:\n allValues.extend(waterValues)\n self.ui.statsTabWaterObjectCombobox.addItems(allValues)\n else:\n self.ui.statsTabWaterObjectCombobox.addItems(allValues)\n\n def vodosborTabFillWaterObjectsCombobox (self):\n waterValues = self.getWaterObjects()\n if waterValues:\n self.ui.vodosborTabVodosborCombobox.addItems(waterValues)\n\n\n def monitorTabAddDateToList(self):\n if not self.ui.monitorTabDateListCombobox.currentText():\n return\n item = self.ui.monitorTabDateListCombobox.currentText()\n items = []\n for index in xrange(self.ui.monitorTabDateList.count()):\n items.append(self.ui.monitorTabDateList.item(index))\n labels = [i.text() for i in items]\n if not item in labels:\n self.ui.monitorTabDateList.addItems([item])\n\n def monitorTabDeleteDateFromList(self):\n for item in self.ui.monitorTabDateList.selectedItems():\n self.ui.monitorTabDateList.takeItem(self.ui.monitorTabDateList.row(item))\n\n # ------------------------------------------------------------------------ #\n # ------------------- Чтение и запись параметров из файлов---------------- #\n # ------------------------------------------------------------------------ #\n\n def openConfigurationFile(self):\n homePath = os.path.dirname(os.path.abspath(__file__))\n configPath = homePath + '/config.dat'\n os.system('notepad.exe ' + configPath)\n pass\n\n def writeDBConfigToFile (self):\n DBHost = str(self.ui.ServerName.text())\n DBPort = int(self.ui.PortNumber.text())\n DBName = str(self.ui.DBName.text())\n DBUser = str(self.ui.DBUserName.text())\n DBPassword = str(self.ui.DBPassword.text())\n path = os.path.dirname(os.path.abspath(__file__)) + '\\\\database.dat'\n f = codecs.open (path,'w', encoding=\"utf-8\")\n f.write(u'[Сервер (хост)]\\n')\n f.write(str(DBHost) + '\\n')\n f.write(u'[Номер порта]\\n')\n f.write(str(DBPort) + '\\n')\n f.write(u'[Имя базы данных]\\n')\n f.write(str(DBName) + '\\n')\n f.write(u'[Имя пользователя]\\n')\n f.write(str(DBUser) + '\\n')\n f.write(u'[Пароль]\\n')\n f.write(str(DBPassword))\n f.close()\n\n def readDBConfigFromFile (self):\n path = os.path.dirname(os.path.abspath(__file__)) + '\\\\database.dat'\n f = codecs.open (path,'r', encoding=\"utf-8\")\n\n textlines = []\n DBHost = ''\n DBPort = ''\n DBName = ''\n DBUser = ''\n DBPassword = ''\n\n for textline in f:\n textlines.append(textline)\n\n i = 0\n\n while i < len(textlines):\n if textlines[i] == u'[Сервер (хост)]\\n':\n DBHost = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'[Номер порта]\\n':\n DBPort = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'[Имя базы данных]\\n':\n DBName = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'[Имя пользователя]\\n':\n DBUser = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'[Пароль]\\n':\n DBPassword = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n\n i += 1\n\n return DBHost, DBPort, DBName, DBUser, DBPassword\n\n def readDBAndProjectConfiguration (self):\n # Возвращает словарь с параметрами проекта и базами данных\n path = os.path.dirname(os.path.abspath(__file__)) + '\\\\config.dat'\n f = codecs.open (path,'r', encoding=\"utf-8\")\n dict = {}\n textlines = []\n\n for textline in f:\n textlines.append(textline)\n\n i = 0\n\n while i < len(textlines):\n if textlines[i] == u'###[Название слоя проекта с водоёмами]\\r\\n':\n dict['water_objects_layer'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название атрибута с названиями водоёмов в соответствующем слое]\\r\\n':\n dict['water_objects_name_attr'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название схемы базы данных для хранения таблиц-растров исходных данных MODIS MOD10A2]\\r\\n':\n dict['db_original_mod10a2_scheme'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название схемы базы данных для хранения таблиц-растров данных MODIS MOD10A2 обрезанных по объекту]\\r\\n':\n dict['db_object_masks_mod10a2_scheme'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название схемы базы данных для хранения статистики по исходным снимкам MODIS MOD10A2]\\r\\n':\n dict['db_mod10a2_statistics_scheme'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название таблицы базы данных для хранения статистики по исходным снимкам MODIS MOD10A2]\\r\\n':\n dict['db_mod10a2_statistics_table'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название схемы базы данных для хранения статистики по данным MODIS MOD10A2 обрезанным по объектам]\\r\\n':\n dict['db_mask_object_mod10a2_statistics_scheme'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название таблицы базы данных для хранения статистики по данным MODIS MOD10A2 обрезанным по объектам]\\r\\n':\n dict['db_mask_object_mod10a2_statistics_table'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n if textlines[i] == u'###[Название схемы базы данных для хранения таблиц-векторных масок ледового покрытия объектов]\\r\\n':\n dict['db_lake_ice_vectors_scheme'] = re.sub(\"^\\s+|\\n|\\r|\\s+$\", '', textlines[i+1])\n\n\n\n\n\n\n i += 1\n\n f.close()\n return dict\n\n\n def checkConfigConformity(self):\n # Глобальная проверка на то, что в базе данных есть нужные схемы/таблицы с нужными полями и т.д.\n # Без неё вообще не давать подключаться!\n\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n waterObjectsLayer = commonLibrary.getLayerByName(configs['water_objects_layer'])\n if not waterObjectsLayer:\n QtGui.QMessageBox.critical(None, \"Error\", u\"В проекте отсутствует указанный в настройках слой с водоёмами\\nПроверьте настройки и добавьте соответствующий слой\")\n return 2\n\n if commonLibrary.getAllValuesOfAttribute(waterObjectsLayer, configs['water_objects_name_attr']) == 'Error':\n QtGui.QMessageBox.critical(None, \"Error\", u\"В слое с водоёмами отсутствует поле, указанное в настройках как содержащее их названия, либо в нём отсутствуют объекты. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseSchemeExistance(dboptions,configs['db_original_mod10a2_scheme']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет схемы с именем, заданным в настройках для хранения таблиц-растров исходных данных MODIS MOD10A2. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseSchemeExistance(dboptions,configs['db_object_masks_mod10a2_scheme']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет схемы с именем, заданным в настройках для хранения таблиц-растров данных MODIS MOD10A2 обрезанных по объекту. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseSchemeExistance(dboptions,configs['db_mod10a2_statistics_scheme']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет схемы с именем, заданным в настройках для хранения статистики по исходным снимкам MODIS MOD10A2. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseSchemeExistance(dboptions,configs['db_mask_object_mod10a2_statistics_scheme']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет схемы с именем, заданным в настройках для хранения статистики по данным MODIS MOD10A2 обрезанным по объектам. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseSchemeExistance(dboptions,configs['db_lake_ice_vectors_scheme']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет схемы с именем, заданным в настройках для хранения таблиц-векторных масок ледового покрытия объектов. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseTableExistance(dboptions,configs['db_mod10a2_statistics_scheme'],configs['db_mod10a2_statistics_table']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет таблицы для хранения статистики по исходным снимкам MODIS MOD10A2. \\nПроверьте настройки.\")\n return 2\n\n if not rusloModisLibrary.checkDatabaseTableExistance(dboptions,configs['db_mask_object_mod10a2_statistics_scheme'],configs['db_mask_object_mod10a2_statistics_table']):\n QtGui.QMessageBox.critical(None, \"Error\", u\"В базе данных нет таблицы для хранения статистики по данным MODIS MOD10A2 обрезанным по объектам. \\nПроверьте настройки.\")\n return 2\n\n originFieldsControl = rusloModisLibrary.checkFieldsForMODISStatsTables(dboptions,configs,'Origin')\n if originFieldsControl != 1:\n msg = u'В базе данных в таблице для хранения статистики по исходным снимкам MODIS MOD10A2 отстутствует необходимое поле: ' + originFieldsControl + u'. \\nОбратитесь к руководству пользователя и настройте базу данных.'\n QtGui.QMessageBox.critical(None, \"Error\", msg)\n return 2\n\n objectFieldsControl = rusloModisLibrary.checkFieldsForMODISStatsTables(dboptions,configs,'Objects')\n if objectFieldsControl != 1:\n msg = u'В базе данных в таблице для хранения статистики по данным MODIS MOD10A2 обрезанным по объектам отстутствует необходимое поле: ' + objectFieldsControl + u'. \\nОбратитесь к руководству пользователя и настройте базу данных.'\n QtGui.QMessageBox.critical(None, \"Error\", msg)\n return 2\n\n return 1\n pass\n\n # ------------------------------------------------------------------------ #\n # ------------------- Управление внешним видом интерфейса ---------------- #\n # ------------------------------------------------------------------------ #\n\n def setDBStatusIndicatorsOFF (self):\n self.ui.DBStatusLabel.setText(u'Не подключено')\n self.ui.DBStatusLabel.setStyleSheet(\"QLabel { color : red; }\")\n self.ui.loadTabDBStatusText.setText(u'Отсутствует связь с базой данных')\n self.ui.loadTabDBStatusText.setStyleSheet(\"QLabel { color : red; }\")\n self.ui.statsTabDBStatusText.setText(u'Отсутствует связь с базой данных')\n self.ui.statsTabDBStatusText.setStyleSheet(\"QLabel { color : red; }\")\n self.ui.vodosborTabDBStatusText.setText(u'Отсутствует связь с базой данных')\n self.ui.vodosborTabDBStatusText.setStyleSheet(\"QLabel { color : red; }\")\n self.ui.monitorTabDBStatusText.setText(u'Отсутствует связь с базой данных')\n self.ui.monitorTabDBStatusText.setStyleSheet(\"QLabel { color : red; }\")\n\n def setDBStatusIndicatorsON (self):\n self.ui.DBStatusLabel.setText(u'Подключение установлено')\n self.ui.DBStatusLabel.setStyleSheet(\"QLabel { color : green; }\")\n self.ui.loadTabDBStatusText.setText(u'Соединение с БД установлено')\n self.ui.loadTabDBStatusText.setStyleSheet(\"QLabel { color : green; }\")\n self.ui.statsTabDBStatusText.setText(u'Соединение с БД установлено')\n self.ui.statsTabDBStatusText.setStyleSheet(\"QLabel { color : green; }\")\n self.ui.vodosborTabDBStatusText.setText(u'Соединение с БД установлено')\n self.ui.vodosborTabDBStatusText.setStyleSheet(\"QLabel { color : green; }\")\n self.ui.monitorTabDBStatusText.setText(u'Соединение с БД установлено')\n self.ui.monitorTabDBStatusText.setStyleSheet(\"QLabel { color : green; }\")\n\n\n def vodosborTabVodosborComboboxItemChanged (self):\n configs = self.readDBAndProjectConfiguration()\n self.ui.vodosborTabDataList.clear()\n waterObjectsLayer = commonLibrary.getLayerByName(configs['water_objects_layer'])\n waterFeature = commonLibrary.getFirstFeatureByAttributeValue(waterObjectsLayer,configs['water_objects_name_attr'],self.ui.vodosborTabVodosborCombobox.currentText())\n\n if not waterFeature:\n return\n\n waterCRS = rusloModisLibrary.getEPSGCodeFromLayer(waterObjectsLayer)\n currentObjectHVList = rusloModisLibrary.getMODIShvListFromPolygonFeature(waterFeature,waterCRS)\n finalString = ''\n for HV in currentObjectHVList:\n hvString = rusloModisLibrary.HVtoString(int(HV[0]), int(HV[1]))\n finalString += hvString + ', '\n finalString = finalString[0:len(finalString)-2]\n self.ui.vodosborTabImageryNumbersLabel.setText(finalString)\n\n def monitorTabVodosborComboboxItemChanged(self):\n self.ui.monitorTabDateListCombobox.clear()\n self.ui.monitorTabStartDate.clear()\n self.ui.monitorTabEndDate.clear()\n\n def vodosborTabDataListSelectionChanged (self):\n self.ui.vodosborTabDataList.selectedItems()\n self.ui.vodosborTabSelectedNumberLabel.setText (str(len(self.ui.vodosborTabDataList.selectedItems())))\n\n def statsTabWaterObjectComboboxItemChanged (self):\n self.ui.statsTabTable.setRowCount(0)\n if self.ui.statsTabWaterObjectCombobox.currentText() != u'Исходные снимки':\n self.ui.statsTabAddIceLayersToProjectButton.setEnabled(True)\n self.ui.statsTabAddImageryToProjectButton.setDisabled(True)\n else:\n self.ui.statsTabAddIceLayersToProjectButton.setDisabled(True)\n self.ui.statsTabAddImageryToProjectButton.setEnabled(True)\n\n\n def monitorTabRefreshDates(self):\n\n self.ui.monitorTabStartDate.clear()\n self.ui.monitorTabEndDate.clear()\n self.ui.monitorTabDateListCombobox.clear()\n\n\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n if not self.ui.monitorTabVodosborCombobox.currentText():\n QtGui.QMessageBox.information(self, self.tr(\"Error\"),\n self.tr('Water object is not selected'))\n return\n\n dbOptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n datesList = rusloModisLibrary.getListOfAvailableDatesForObjectMask(dbOptions,configs,self.ui.monitorTabVodosborCombobox.currentText())\n datesList.sort()\n self.ui.monitorTabStartDate.addItems(datesList)\n self.ui.monitorTabEndDate.addItems(datesList)\n self.ui.monitorTabDateListCombobox.addItems(datesList)\n\n\n # ------------------------------------------------------------------------ #\n # ------------------- Работа с базой данных ------------------------------ #\n # ------------------------------------------------------------------------ #\n\n def checkDatabaseConnection(self):\n DBHost = str(self.ui.ServerName.text())\n DBPort = int(self.ui.PortNumber.text())\n DBName = str(self.ui.DBName.text())\n DBUser = str(self.ui.DBUserName.text())\n DBPassword = str(self.ui.DBPassword.text())\n try:\n connection = psycopg2.connect(\n database=DBName,\n user=DBUser,\n password=DBPassword,\n host=DBHost,\n port=DBPort\n )\n except psycopg2.Error as e:\n message = u'Невозможно подключиться к базе данных. Убедитесь в правильности введенных данных'\n self.setDBStatusIndicatorsOFF()\n QtGui.QMessageBox.critical(None, \"Error\", message)\n return\n\n if self.checkConfigConformity() == 2:\n return\n\n self.DBConnectionStatus = True\n self.setDBStatusIndicatorsON()\n\n # Заполняем водоёмы\n self.loadTabFillAquaObjectsCombobox()\n self.statsTabFillWaterObjectsCombobox()\n self.vodosborTabFillWaterObjectsCombobox()\n self.monitorTabFillWaterObjectsCombobox()\n\n try:\n self.writeDBConfigToFile()\n commonLibrary.writeLogMessage(u'Info',u'Записаны данные в database.dat')\n except:\n commonLibrary.writeLogMessage(u'Warning',u'Возникла ошибка при попытке записи данных в database.dat')\n\n\n\n def returnDBOptionsList (self):\n DBHost = str(self.ui.ServerName.text())\n DBPort = str(self.ui.PortNumber.text())\n DBName = str(self.ui.DBName.text())\n DBUser = str(self.ui.DBUserName.text())\n DBPassword = str(self.ui.DBPassword.text())\n return [DBHost,DBPort,DBName,DBUser,DBPassword]\n\n # ------------------------------------------------------------------------ #\n # ------------------- Главные кнопки ------------------------------------- #\n # ------------------------------------------------------------------------ #\n\n def loadTabLoadData (self):\n commonLibrary.clearTempDir()\n self.ui.loadTabDownloadStatusTable.setRowCount(0)\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n\n tempPath = os.path.dirname(os.path.abspath(__file__)) + '\\\\temp'\n #tempRasterPath = tempPath + '\\\\t'\n\n items = []\n for index in xrange(self.ui.loadTabHVList.count()):\n items.append(self.ui.loadTabHVList.item(index))\n places = [i.text() for i in items]\n if len(places) == 0:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбраны необходимые данные\")\n return\n\n self.ui.tabWidget.setDisabled(True)\n date1 = self.ui.loadTabStartDate.date().toString(\"yyyy.M.dd\")\n date2 = self.ui.loadTabEndDate.date().toString(\"yyyy.M.dd\")\n listOfDates = rusloModisLibrary.getListOfDatesFromInterval(date1, date2)\n listOfDatesReformated = []\n for date in listOfDates:\n newDateTemp = date.split('-')\n newDate = str(newDateTemp[0])+'.'+str(newDateTemp[1])+'.'+str(newDateTemp[2])\n listOfDatesReformated.append(newDate)\n self.ui.loadTabProgressBar.show()\n self.ui.loadTabDownloadGroupBox.show()\n\n something = False\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n\n for date in listOfDatesReformated:\n for place in places:\n h, v = rusloModisLibrary.getHVFromString(place)\n\n # Разбиваем дату\n dateUnderlinedList = date.split('.')\n dateUnderlined = dateUnderlinedList[0] + '_' + dateUnderlinedList[1] + '_' + dateUnderlinedList[2]\n\n # Проверяем на наличие в БД\n if rusloModisLibrary.checkMOD10A2OrigExistingInDatabase(dboptions,configs,int(h),int(v),dateUnderlined) == 2:\n self.ui.tabWidget.setEnabled(True)\n return\n elif rusloModisLibrary.checkMOD10A2OrigExistingInDatabase(dboptions,configs,int(h),int(v),dateUnderlined) == 3:\n self.ui.tabWidget.setEnabled(True)\n return\n elif rusloModisLibrary.checkMOD10A2OrigExistingInDatabase(dboptions,configs,int(h),int(v),dateUnderlined) == True:\n # Уже есть в БД\n self.ui.loadTabDownloadStatusTable.setRowCount(self.ui.loadTabDownloadStatusTable.rowCount()+1)\n rowPosition = self.ui.loadTabDownloadStatusTable.rowCount()\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 0, QtGui.QTableWidgetItem(place))\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 1, QtGui.QTableWidgetItem(date))\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 2, QtGui.QTableWidgetItem(u'Присутствовал в базе данных'))\n continue\n\n\n tempRasterHDFPath = tempPath + '\\\\' + 'h' + str(h) + 'v' + str(v) + '_' + str(dateUnderlined) + '.hdf'\n res = rusloModisLibrary.downloadMOD10A2ForGivenDateAndPlace(date,h,v,tempRasterHDFPath)\n if res == 2:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Невозможно установить соединение с FTP-сервером. Проверьте интернет-соединение. Не исключено, что на сервере неполадки.\")\n self.ui.tabWidget.setEnabled(True)\n self.ui.loadTabProgressBar.hide()\n return\n if res == 3:\n #print str(date) + ': data is unavailable'\n break\n if res == 4:\n #print 'Connection failed. Check it'\n QtGui.QMessageBox.critical(None, \"Error\", u\"Невозможно получить список файлов с FTP-сервера, но соединение установить удалось. Похоже, на сервере неполадки.\")\n self.ui.tabWidget.setEnabled(True)\n self.ui.loadTabProgressBar.hide()\n return\n if res == 5:\n #print 'Cannot download file'\n break\n\n tempRasterGTiffPath = tempPath + '\\\\' + 'h' + str(h) + 'v' + str(v) + '_' + str(dateUnderlined) + '.tif'\n rusloModisLibrary.convertHDFToGTiff(tempRasterHDFPath,'MOD_Grid_Snow_500m:Maximum_Snow_Extent','HDF4_EOS:EOS_GRID',tempRasterGTiffPath)\n\n # Считаем статистику растра\n # Общая статистика\n statsDict = rusloModisLibrary.getRasterFileBasicStatistics(tempRasterGTiffPath)\n uniqueValuesDict = rusloModisLibrary.rasterUniqueValuesCount(tempRasterGTiffPath)\n #print statsDict\n #print uniqueValuesDict[0]\n # Уникальные значения\n\n\n tableName = rusloModisLibrary.HVtoString(int(h),int(v)) + '_' + str(dateUnderlined)\n rusloModisLibrary.loadRasterToPostGIS(tempRasterGTiffPath,dboptions,configs['db_original_mod10a2_scheme'],tableName)\n self.ui.loadTabDownloadStatusTable.setRowCount(self.ui.loadTabDownloadStatusTable.rowCount()+1)\n rowPosition = self.ui.loadTabDownloadStatusTable.rowCount()\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 0, QtGui.QTableWidgetItem(place))\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 1, QtGui.QTableWidgetItem(date))\n if rusloModisLibrary.checkMOD10A2OrigExistingInDatabase(dboptions,configs,int(h),int(v),dateUnderlined) == True:\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 2, QtGui.QTableWidgetItem(u'Успешно загружен'))\n rusloModisLibrary.loadMOD10A2StatisticsToPostGIS (tempRasterGTiffPath, h, v, date, dboptions, configs)\n else:\n self.ui.loadTabDownloadStatusTable.setItem(rowPosition - 1, 2, QtGui.QTableWidgetItem(u'Возникла ошибка БД'))\n\n self.ui.tabWidget.setEnabled(True)\n self.ui.loadTabProgressBar.hide()\n\n def statsTabRefreshTable(self):\n #self.ui.statsTabTable.clear()\n self.ui.statsTabTable.setRowCount(0)\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n\n dbOptions = self.returnDBOptionsList()\n configDict = self.readDBAndProjectConfiguration()\n if self.ui.statsTabWaterObjectCombobox.currentText() == u'Исходные снимки':\n # Статистика по исходным снимкам\n dataList = rusloModisLibrary.getAllMOD10A2OriginImageryFromPostGIS(dbOptions,configDict)\n self.ui.statsTabTable.setSortingEnabled(False)\n for dataEntry in dataList:\n currentImageryDict = rusloModisLibrary.getHVDataFromTableName(dataEntry)\n\n currentH = currentImageryDict['h']\n currentV = currentImageryDict['v']\n currentDate = str(currentImageryDict['year']) + '-' + str(currentImageryDict['month']) + '-' + str(currentImageryDict['day'])\n currentImageryStatsDict = rusloModisLibrary.getMOD10A2OriginImageryStatsByHVDate(dbOptions,configDict,currentH,currentV,currentDate)\n\n currentQuality = float(currentImageryStatsDict['bad_count']) / float(currentImageryStatsDict['overage_count'])\n icePercent = float(currentImageryStatsDict['lake_ice'])/float(currentImageryStatsDict['overage_count'])\n if currentQuality >= 0.20 and self.ui.statsTabBadDataCheckbox.isChecked():\n continue\n\n self.ui.statsTabTable.setRowCount(self.ui.statsTabTable.rowCount()+1)\n rowPosition = self.ui.statsTabTable.rowCount()\n\n self.ui.statsTabTable.setItem(rowPosition - 1, 0, QtGui.QTableWidgetItem(currentH))\n self.ui.statsTabTable.setItem(rowPosition - 1, 1, QtGui.QTableWidgetItem(currentV))\n self.ui.statsTabTable.setItem(rowPosition - 1, 2, QtGui.QTableWidgetItem(currentDate))\n if currentQuality >= 0.20:\n #self.ui.loadTabDownloadStatusTable.item(rowPosition - 1, 3).setBackground(QtGui.QColor(100,0,0))\n self.ui.statsTabTable.setItem(rowPosition - 1, 3, QtGui.QTableWidgetItem(u'!Неудовлетв.'))\n else:\n #self.ui.loadTabDownloadStatusTable.item(rowPosition - 1, 3).setBackground(QtGui.QColor(0,100,0))\n self.ui.statsTabTable.setItem(rowPosition - 1, 3, QtGui.QTableWidgetItem(u'Удовлетв.'))\n\n self.ui.statsTabTable.setItem(rowPosition - 1, 4, QtGui.QTableWidgetItem(str(math.trunc(currentQuality*1000)/10.0)))\n self.ui.statsTabTable.setItem(rowPosition - 1, 5, QtGui.QTableWidgetItem(str(currentImageryStatsDict['minimum'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 6, QtGui.QTableWidgetItem(str(currentImageryStatsDict['maximum'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 7, QtGui.QTableWidgetItem(str(currentImageryStatsDict['mean'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 8, QtGui.QTableWidgetItem(str(currentImageryStatsDict['stdev'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 9, QtGui.QTableWidgetItem(str(math.trunc(icePercent*1000)/10.0)))\n self.ui.statsTabTable.setItem(rowPosition - 1, 10, QtGui.QTableWidgetItem('-'))\n\n pass\n else:\n # Статистика по маскам водосборов\n objectName = self.ui.statsTabWaterObjectCombobox.currentText()\n translitName = commonLibrary.transliterateString(objectName)\n dataList = rusloModisLibrary.getAllMOD10A2ObjectMasksFromPostGIS(dbOptions,configDict,objectName)\n self.ui.statsTabTable.setSortingEnabled(False)\n #print dataList\n for dataEntry in dataList:\n curDateDict = rusloModisLibrary.getDateFromMOD10A2ObjectMaskTableName(dataEntry)\n curYear = curDateDict['year']\n curMonth = curDateDict['month']\n curDay = curDateDict['day']\n curDate = str(curYear) + '-' + str(curMonth) + '-' + str(curDay)\n currentImageryStatsDict = rusloModisLibrary.getMOD10A2ObjectMaskStatsByObjectNameAndDate(dbOptions,configDict,objectName,curYear,curMonth,curDay)\n\n #print currentImageryStatsDict\n currentQuality = float(currentImageryStatsDict['bad_count']) / float(currentImageryStatsDict['overage_count'])\n icePercent = float(currentImageryStatsDict['lake_ice'])/float(currentImageryStatsDict['overage_count'])\n if currentQuality >= 0.20 and self.ui.statsTabBadDataCheckbox.isChecked():\n continue\n\n self.ui.statsTabTable.setRowCount(self.ui.statsTabTable.rowCount()+1)\n rowPosition = self.ui.statsTabTable.rowCount()\n\n self.ui.statsTabTable.setItem(rowPosition - 1, 0, QtGui.QTableWidgetItem('-'))\n self.ui.statsTabTable.setItem(rowPosition - 1, 1, QtGui.QTableWidgetItem('-'))\n self.ui.statsTabTable.setItem(rowPosition - 1, 2, QtGui.QTableWidgetItem(curDate))\n if currentQuality >= 0.20:\n self.ui.statsTabTable.setItem(rowPosition - 1, 3, QtGui.QTableWidgetItem(u'!Неудовлетв.'))\n else:\n self.ui.statsTabTable.setItem(rowPosition - 1, 3, QtGui.QTableWidgetItem(u'Удовлетв.'))\n\n self.ui.statsTabTable.setItem(rowPosition - 1, 4, QtGui.QTableWidgetItem(str(math.trunc(currentQuality*1000)/10.0)))\n self.ui.statsTabTable.setItem(rowPosition - 1, 5, QtGui.QTableWidgetItem(str(currentImageryStatsDict['minimum'])))\n #QtGui.QMessageBox.critical(None, \"Error\", str(currentImageryStatsDict['maximum']))\n self.ui.statsTabTable.setItem(rowPosition - 1, 6, QtGui.QTableWidgetItem(str(currentImageryStatsDict['maximum'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 6, QtGui.QTableWidgetItem(str(currentImageryStatsDict['maximum'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 7, QtGui.QTableWidgetItem(str(currentImageryStatsDict['mean'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 8, QtGui.QTableWidgetItem(str(currentImageryStatsDict['stdev'])))\n self.ui.statsTabTable.setItem(rowPosition - 1, 9, QtGui.QTableWidgetItem(str(math.trunc(icePercent*1000)/10.0)))\n self.ui.statsTabTable.setItem(rowPosition - 1, 10, QtGui.QTableWidgetItem(objectName))\n self.ui.statsTabTable.setSortingEnabled(True)\n self.ui.statsTabTable.sortByColumn(2,QtCore.Qt.AscendingOrder)\n pass\n\n def statsTabAddImageryToProject(self):\n #Получаем индексы строк, в которых что-нибудь выделено\n rows = sorted(set(index.row() for index in self.ui.statsTabTable.selectedIndexes()))\n if len(rows) == 0:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбраны необходимые данные\")\n return\n\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n for row in rows:\n h = self.ui.statsTabTable.item(row,0).text()\n v = self.ui.statsTabTable.item(row,1).text()\n date = self.ui.statsTabTable.item(row,2).text()\n year = date [0:4]\n month = date [5:7]\n day = date [8:10]\n vodosbor = self.ui.statsTabTable.item(row,10).text()\n if vodosbor == '-':\n rusloModisLibrary.addToProjectOriginMOD10A2ImageryByHVDate(dboptions,configs,h,v,year,month,day)\n 500*500\n else:\n rusloModisLibrary.addToProjectMOD10A2ObjectMaskByObjectNameAndDate(dboptions,configs,self.ui.statsTabWaterObjectCombobox.currentText(),year,month,day)\n 500*500\n pass\n\n def statsTabAddIceLayersToProject(self):\n #Получаем индексы строк, в которых что-нибудь выделено\n rows = sorted(set(index.row() for index in self.ui.statsTabTable.selectedIndexes()))\n if len(rows) == 0:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбраны необходимые данные\")\n return\n\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n for row in rows:\n date = self.ui.statsTabTable.item(row,2).text()\n year = date [0:4]\n month = date [5:7]\n day = date [8:10]\n vodosbor = self.ui.statsTabTable.item(row,10).text()\n if vodosbor == '-':\n #rusloModisLibrary.addToProjectOriginMOD10A2ImageryByHVDate(dboptions,configs,h,v,year,month,day)\n #print 500*500\n pass\n else:\n rusloModisLibrary.addToProjectLakeIceVectorMaskByObjectNameAndDate(dboptions,configs,vodosbor,year,month,day)\n 500*500\n pass\n\n pass\n\n def vodosborTabRefreshData (self):\n\n\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n\n if not self.ui.vodosborTabVodosborCombobox.currentText():\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбран водоём\")\n return\n\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n\n self.ui.vodosborTabDataList.clear()\n\n waterObjectsLayer = commonLibrary.getLayerByName(configs['water_objects_layer'])\n waterFeature = commonLibrary.getFirstFeatureByAttributeValue(waterObjectsLayer,configs['water_objects_name_attr'],self.ui.vodosborTabVodosborCombobox.currentText())\n if not waterFeature:\n return\n\n waterCRS = rusloModisLibrary.getEPSGCodeFromLayer(waterObjectsLayer)\n currentObjectHVList = rusloModisLibrary.getMODIShvListFromPolygonFeature(waterFeature,waterCRS)\n\n\n imageryList = rusloModisLibrary.getAllMOD10A2OriginImageryFromPostGIS(dboptions,configs)\n HVDatesList = rusloModisLibrary.getHVDateListsFromOriginMOD10A2TableNames(imageryList)\n\n\n # Если отмечен флаг контроля качества\n\n # Собираем все даты нужных нам H и V по отдельности\n i = 0\n allDatesList = []\n while i < len(currentObjectHVList):\n curH = int(currentObjectHVList[i][0])\n curV = int(currentObjectHVList[i][1])\n curDates = []\n j = 0\n while j < len(HVDatesList):\n\n compH = int(HVDatesList[j][0])\n compV = int(HVDatesList[j][1])\n\n if curH == compH and curV == compV:\n if self.ui.vodosborTabUseOnlyGoodCheckbox.isChecked():\n currentDate = str(HVDatesList[j][2]) + '-' + str(HVDatesList[j][3]) + '-' + str(HVDatesList[j][4])\n currentImageryStatsDict = rusloModisLibrary.getMOD10A2OriginImageryStatsByHVDate(dboptions, configs, compH,compV,currentDate)\n currentQuality = float(currentImageryStatsDict['bad_count']) / float(currentImageryStatsDict['overage_count'])\n if currentQuality < 0.20:\n pass\n else:\n j += 1\n continue\n\n curDates.append([HVDatesList[j][2],HVDatesList[j][3],HVDatesList[j][4]])\n\n j += 1\n\n allDatesList.append(curDates)\n i += 1\n\n # Находим даты, которые есть для всех нужных нам H и V\n finalDates = []\n\n originDates = allDatesList[0]\n for Date in originDates:\n flag = 1\n for compDateList in allDatesList:\n flag2 = 0\n for compDate in compDateList:\n if compDate[0] == Date[0] and compDate[1] == Date[1] and compDate[2] == Date[2]:\n flag2 = 1\n if flag2 == 0:\n flag = 0\n\n if flag == 1:\n curString = Date[0] + '.' + Date[1] + '.' + Date[2]\n finalDates.append(curString)\n finalDates.sort()\n\n self.ui.vodosborTabDataList.addItems(finalDates)\n\n pass\n\n def vodosborTabRunProcessing (self):\n commonLibrary.clearTempDir()\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n\n if len(self.ui.vodosborTabDataList.selectedItems()) == 0:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбраны необходимые данные\")\n return\n\n dboptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n\n self.ui.vodosborTabProgressBar.show()\n\n waterObjectsLayer = commonLibrary.getLayerByName(configs['water_objects_layer'])\n waterFeature = commonLibrary.getFirstFeatureByAttributeValue(waterObjectsLayer,configs['water_objects_name_attr'],self.ui.vodosborTabVodosborCombobox.currentText())\n if not waterFeature:\n return\n\n sinCRS = QgsCoordinateReferenceSystem()\n sinCRS.createFromProj4(\"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs\")\n sourceCRS = waterObjectsLayer.crs()\n #rusloModisLibrary.convertVectorFeatureFromCRSToCRS(waterFeature,'Polygon',sourceCRS,sinCRS)\n\n waterCRS = rusloModisLibrary.getEPSGCodeFromLayer(waterObjectsLayer)\n currentObjectHVList = rusloModisLibrary.getMODIShvListFromPolygonFeature(waterFeature,waterCRS)\n#\n tempPath = os.path.dirname(os.path.abspath(__file__)) + '\\\\temp\\\\'\n#\n for item in self.ui.vodosborTabDataList.selectedItems():\n curYear = item.text()[0:4]\n curMonth = item.text()[5:7]\n curDay = item.text()[8:10]\n translitName = commonLibrary.transliterateString(self.ui.vodosborTabVodosborCombobox.currentText())\n newTableName = translitName + '_' + str(curYear) +'_' + str(curMonth) + '_' + str(curDay)\n schemaObjects = configs['db_object_masks_mod10a2_scheme']\n schemaOrigin = configs['db_original_mod10a2_scheme']\n\n if rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase(dboptions,configs,self.ui.vodosborTabVodosborCombobox.currentText(),curYear,curMonth,curDay) == 2:\n return\n elif rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase(dboptions,configs,self.ui.vodosborTabVodosborCombobox.currentText(),curYear,curMonth,curDay) == 3:\n return\n elif rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase(dboptions,configs,self.ui.vodosborTabVodosborCombobox.currentText(),curYear,curMonth,curDay) == True:\n #print u'Присутствовал в базе данных'\n continue\n\n\n if len(currentObjectHVList) == 1:\n originTableName = rusloModisLibrary.HVtoString(int(currentObjectHVList[0][0]),int(currentObjectHVList[0][1]))+ '_' + str(curYear) +'_' + str(curMonth) + '_' + str(curDay)\n\n rasterLayer = rusloModisLibrary.getRasterLayerFromPostGIS(dboptions,schemaOrigin,originTableName)\n rusloModisLibrary.saveRasterLayerToPathGeoTiff(rasterLayer,tempPath + originTableName + '.tif')\n\n vectorMaskTempPath = tempPath + 'vectorTempMask.shp'\n\n sinWaterFeature = rusloModisLibrary.convertVectorFeatureFromCRSToCRS(waterFeature,'Polygon',sourceCRS,sinCRS)\n\n rusloModisLibrary.createTempVectorLayerByFeature(sinWaterFeature,'Polygon',vectorMaskTempPath)\n\n rusloModisLibrary.cutRasterByVectorMaskGDAL(tempPath + originTableName + '.tif', vectorMaskTempPath,tempPath + newTableName + '.tif')\n\n curDate = str(curYear) + '-' + str(curMonth) + '-' + str(curDay)\n\n rusloModisLibrary.loadRasterToPostGIS(tempPath + newTableName + '.tif',dboptions,configs['db_object_masks_mod10a2_scheme'],newTableName)\n\n rusloModisLibrary.loadMOD10A2ObjectMaskStatisticsToPostGIS (tempPath + newTableName + '.tif',curDate,translitName,dboptions,configs)\n\n newRasterLayer = QgsRasterLayer(tempPath + newTableName + '.tif')\n rusloModisLibrary.generateLakeIceLayerForRasterLayerAndWriteToPostGIS(dboptions,configs,newRasterLayer,self.ui.vodosborTabVodosborCombobox.currentText(),curYear,curMonth,curDay,tempPath + 'vectorTempMask.shp')\n\n else:\n rusloModisLibrary.mergeListOfOriginMOD10A2RastersByHVListAndDate(dboptions,configs,currentObjectHVList,curYear,curMonth,curDay,tempPath + newTableName + '_merged_orig.tif')\n\n vectorMaskTempPath = tempPath + 'vectorTempMask.shp'\n\n sinWaterFeature = rusloModisLibrary.convertVectorFeatureFromCRSToCRS(waterFeature,'Polygon',sourceCRS,sinCRS)\n\n rusloModisLibrary.createTempVectorLayerByFeature(sinWaterFeature,'Polygon',vectorMaskTempPath)\n\n rusloModisLibrary.cutRasterByVectorMaskGDAL(tempPath + newTableName + '_merged_orig.tif', vectorMaskTempPath,tempPath + newTableName + '.tif')\n curDate = str(curYear) + '-' + str(curMonth) + '-' + str(curDay)\n\n rusloModisLibrary.loadRasterToPostGIS(tempPath + newTableName + '.tif',dboptions,configs['db_object_masks_mod10a2_scheme'],newTableName)\n\n rusloModisLibrary.loadMOD10A2ObjectMaskStatisticsToPostGIS (tempPath + newTableName + '.tif',curDate,translitName,dboptions,configs)\n\n newRasterLayer = QgsRasterLayer(tempPath + newTableName + '.tif')\n rusloModisLibrary.generateLakeIceLayerForRasterLayerAndWriteToPostGIS(dboptions,configs,newRasterLayer,self.ui.vodosborTabVodosborCombobox.currentText(),curYear,curMonth,curDay,tempPath + 'vectorTempMask.shp')\n\n pass\n self.ui.vodosborTabProgressBar.hide()\n QtGui.QMessageBox.about(None, \"Success\", u\"Обработка закончена\")\n\n\n def monitorTabRunProcessing(self):\n commonLibrary.clearTempDir()\n if self.DBConnectionStatus == False:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Отсутствует связь с базой данных\")\n return\n if not self.ui.monitorTabVodosborCombobox.currentText():\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбран водоём\")\n return\n\n dbOptions = self.returnDBOptionsList()\n configs = self.readDBAndProjectConfiguration()\n objectName = self.ui.monitorTabVodosborCombobox.currentText()\n # График от начальной до конечной даты\n if self.ui.monitorTabStartEndPlotRadioButton.isChecked():\n startDate = self.ui.monitorTabStartDate.currentText()\n endDate = self.ui.monitorTabEndDate.currentText()\n\n if endDate <= startDate:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Конечная дата не может быть меньше или равной начальной\")\n return\n\n datesAllList = rusloModisLibrary.getListOfAvailableDatesForObjectMask(dbOptions,configs,objectName)\n datesAllList.sort()\n startDateIndex = datesAllList.index(startDate)\n endDateIndex = datesAllList.index(endDate)\n # Список всех нужных дат по порядку\n datesIntervalList = datesAllList[startDateIndex:endDateIndex+1]\n\n iceStatsList = []\n\n for date in datesIntervalList:\n curYear = date[0:4]\n curMonth = date[5:7]\n curDay = date[8:10]\n curStats = rusloModisLibrary.getMOD10A2ObjectMaskStatsByObjectNameAndDate(dbOptions,configs,objectName,curYear,curMonth,curDay)\n icePercent = float(curStats['lake_ice'])/float(curStats['overage_count'])*100\n iceArea = rusloModisLibrary.getIceAreaForMOD10A2MaskObjectByObjectNameAndDate(dbOptions,configs,objectName,curYear,curMonth,curDay)\n if self.ui.monitorTabCountSquareInKmRadioButton.isChecked():\n iceStatsList.append(iceArea)\n else:\n iceStatsList.append(icePercent)\n\n rusloModisLibrary.generatePlotByDatesAndNumbers(datesIntervalList,iceStatsList,objectName)\n pass\n\n # График по списку дат\n if self.ui.monitorTabDateListPlotRadioButton.isChecked():\n items = []\n datesIntervalList = []\n iceStatsList = []\n for index in xrange(self.ui.monitorTabDateList.count()):\n items.append(self.ui.monitorTabDateList.item(index))\n dates = [i.text() for i in items]\n if len(dates) == 0:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Не выбраны необходимые данные\")\n return\n if len(dates) == 1:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Требуется выбрать по меньшей мере две даты\")\n return\n\n\n for date in dates:\n curYear = date[0:4]\n curMonth = date[5:7]\n curDay = date[8:10]\n curStats = rusloModisLibrary.getMOD10A2ObjectMaskStatsByObjectNameAndDate(dbOptions,configs,objectName,curYear,curMonth,curDay)\n datesIntervalList.append(str(curYear)+'-'+str(curMonth)+'-'+str(curDay))\n icePercent = float(curStats['lake_ice'])/float(curStats['overage_count'])*100\n iceArea = rusloModisLibrary.getIceAreaForMOD10A2MaskObjectByObjectNameAndDate(dbOptions,configs,objectName,curYear,curMonth,curDay)\n if self.ui.monitorTabCountSquareInKmRadioButton.isChecked():\n iceStatsList.append(iceArea)\n else:\n iceStatsList.append(icePercent)\n datesIntervalList.sort()\n rusloModisLibrary.generatePlotByDatesAndNumbers(datesIntervalList,iceStatsList,objectName)\n pass\n\n # Слой с нарастанием-сходом льда\n if self.ui.monitorTabIceDynamicRadioButton.isChecked():\n startDate = self.ui.monitorTabStartDate.currentText()\n endDate = self.ui.monitorTabEndDate.currentText()\n\n if endDate <= startDate:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Конечная дата не может быть меньше или равной начальной\")\n return\n\n startYear = startDate[0:4]\n startMonth = startDate[5:7]\n startDay = startDate[8:10]\n startDateVectorLayer = rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate(dbOptions,configs,objectName,startYear,startMonth,startDay)\n\n endYear = endDate[0:4]\n endMonth = endDate[5:7]\n endDay = endDate[8:10]\n endDateVectorLayer = rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate(dbOptions,configs,objectName,endYear,endMonth,endDay)\n\n #QgsMapLayerRegistry.instance().addMapLayer(startDateVectorLayer)\n #QgsMapLayerRegistry.instance().addMapLayer(endDateVectorLayer)\n\n tempPath = os.path.dirname(os.path.abspath(__file__)) + '\\\\temp\\\\'\n #a = rusloModisLibrary.differenceBetweenTwoPolygonLayers(startDateVectorLayer,endDateVectorLayer,tempPath+'diff_vector_start_end.shp')\n #b = rusloModisLibrary.differenceBetweenTwoPolygonLayers(endDateVectorLayer,startDateVectorLayer,tempPath+'diff_vector_end_start.shp')\n sinCRS = QgsCoordinateReferenceSystem()\n sinCRS.createFromProj4(\"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs\")\n memoryLayerString = \"Polygon?crs=\" + sinCRS.authid()\n memoryLayer = QgsVectorLayer(memoryLayerString, u'Освобождение и нарастание льда (' + objectName + u', ' + startDate + u' - ' + endDate + u')', \"memory\")\n a = rusloModisLibrary.twoSidesDifferenceBetweenTwoPolygonLayers(startDateVectorLayer,endDateVectorLayer,memoryLayer)\n memoryLayer.setCrs(sinCRS)\n\n # Задать стиль\n s1 = QgsFillSymbolV2.defaultSymbol(memoryLayer.geometryType())\n s1.setColor(QtGui.QColor.fromRgb(255,228,225))\n\n s2 = QgsFillSymbolV2.defaultSymbol(memoryLayer.geometryType())\n s2.setColor(QtGui.QColor.fromRgb(240,255,240))\n\n increaseStyle = QgsRendererCategoryV2('Decrease',s1,u'Освобождение льда')\n decreaseStyle = QgsRendererCategoryV2('Increase',s2,u'Нарастание льда')\n sr = QgsCategorizedSymbolRendererV2('TYPE',[increaseStyle, decreaseStyle])\n sr.setClassAttribute('TYPE')\n memoryLayer.setRendererV2(sr)\n\n QgsMapLayerRegistry.instance().addMapLayer(memoryLayer)\n memoryLayer.triggerRepaint()\n pass\n\n #Слой для TimeManager\n if self.ui.monitorTabIceTimeManagerLayerRadioButton.isChecked():\n startDate = self.ui.monitorTabStartDate.currentText()\n endDate = self.ui.monitorTabEndDate.currentText()\n if endDate <= startDate:\n QtGui.QMessageBox.critical(None, \"Error\", u\"Конечная дата не может быть меньше или равной начальной\")\n return\n\n datesAllList = rusloModisLibrary.getListOfAvailableDatesForObjectMask(dbOptions,configs,objectName)\n datesAllList.sort()\n startDateIndex = datesAllList.index(startDate)\n endDateIndex = datesAllList.index(endDate)\n\n # Список всех нужных дат по порядку\n datesIntervalList = datesAllList[startDateIndex:endDateIndex+1]\n\n sinCRS = QgsCoordinateReferenceSystem()\n sinCRS.createFromProj4(\"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs\")\n memoryTimeManagerLayerString = \"Polygon?crs=\" + sinCRS.authid()\n memoryTimeManagerLayer = QgsVectorLayer(memoryTimeManagerLayerString, 'name', \"memory\")\n memoryTimeManagerLayer.setLayerName(u'Ледовый покров (' + objectName + u') с ' + str(startDate) + u' по ' + str(endDate))\n memoryTimeManagerLayerDataProvider = memoryTimeManagerLayer.dataProvider()\n memoryTimeManagerLayerDataProvider.addAttributes([QgsField(\"ID\", QtCore.QVariant.Int),\n QgsField(\"AREA\", QtCore.QVariant.Double),\n QgsField(\"DATE\", QtCore.QVariant.Date)])\n memoryTimeManagerLayer.updateFields()\n j = 0\n for date in datesIntervalList:\n curYear = date[0:4]\n curMonth = date[5:7]\n curDay = date[8:10]\n curIceLayer = rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate(dbOptions,configs,objectName,curYear,curMonth,curDay)\n curIceLayerFeatures = curIceLayer.getFeatures()\n for curIceFeature in curIceLayerFeatures:\n if curIceFeature:\n newIceFeature = curIceFeature\n newIceFeature.setAttributes ([j, curIceFeature['AREA'], date])\n memoryTimeManagerLayerDataProvider.addFeatures([newIceFeature])\n j += 1\n\n memoryTimeManagerLayer.commitChanges()\n memoryTimeManagerLayer.updateExtents()\n memoryTimeManagerLayer.updateFields()\n memoryTimeManagerLayer.setCrs(sinCRS)\n QgsMapLayerRegistry.instance().addMapLayer(memoryTimeManagerLayer)\n\n\n\n def openConfigurationFile(self):\n homePath = os.path.dirname(os.path.abspath(__file__))\n configPath = homePath + '/config.dat'\n os.system('notepad.exe ' + configPath)\n\n\n def cancel(self):\n self.close()\n", "sub_path": "mainplugin_dialog.py", "file_name": "mainplugin_dialog.py", "file_ext": "py", "file_size_in_byte": 63877, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PyQt4.QtGui.QWidget", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QWidget.__init__", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QWidget", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 27, "usage_type": "name"}, {"api_name": "rusloModis_mainmenu.Ui_Dialog", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 33, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 45, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.SIGNAL", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 74, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getCurrentCanvasCenterInSelectedCRS", "line_number": 103, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMODIShvFromPointCoordinates", "line_number": 104, "usage_type": "call"}, {"api_name": "commonLibrary.getLayerByName", "line_number": 128, "usage_type": "call"}, {"api_name": "commonLibrary.getAllValuesOfAttribute", "line_number": 130, "usage_type": "call"}, {"api_name": "commonLibrary.getLayerByName", "line_number": 154, "usage_type": "call"}, {"api_name": "commonLibrary.getFirstFeatureByAttributeValue", "line_number": 155, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getEPSGCodeFromLayer", "line_number": 159, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMODIShvListFromPolygonFeature", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 233, "usage_type": "call"}, {"api_name": "os.system", "line_number": 235, "usage_type": "call"}, {"api_name": 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"PyQt4.QtGui.QMessageBox", "line_number": 795, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 795, "usage_type": "name"}, {"api_name": "commonLibrary.getLayerByName", "line_number": 803, "usage_type": "call"}, {"api_name": "commonLibrary.getFirstFeatureByAttributeValue", "line_number": 804, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getEPSGCodeFromLayer", "line_number": 808, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMODIShvListFromPolygonFeature", "line_number": 809, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getAllMOD10A2OriginImageryFromPostGIS", "line_number": 812, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getHVDateListsFromOriginMOD10A2TableNames", "line_number": 813, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMOD10A2OriginImageryStatsByHVDate", "line_number": 834, "usage_type": "call"}, {"api_name": "commonLibrary.clearTempDir", "line_number": 873, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 875, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 875, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 875, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 879, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 879, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 879, "usage_type": "name"}, {"api_name": "commonLibrary.getLayerByName", "line_number": 887, "usage_type": "call"}, {"api_name": "commonLibrary.getFirstFeatureByAttributeValue", "line_number": 888, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getEPSGCodeFromLayer", "line_number": 897, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMODIShvListFromPolygonFeature", "line_number": 898, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 900, "usage_type": "call"}, {"api_name": "os.path", "line_number": 900, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 900, "usage_type": "call"}, {"api_name": "commonLibrary.transliterateString", "line_number": 906, "usage_type": "call"}, {"api_name": "rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase", "line_number": 911, "usage_type": "call"}, {"api_name": "rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase", "line_number": 913, "usage_type": "call"}, {"api_name": "rusloModisLibrary.checkMOD10A2ObjectMaskExistingInDatabase", "line_number": 915, "usage_type": "call"}, {"api_name": "rusloModisLibrary.HVtoString", "line_number": 921, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getRasterLayerFromPostGIS", "line_number": 923, "usage_type": "call"}, {"api_name": "rusloModisLibrary.saveRasterLayerToPathGeoTiff", "line_number": 924, "usage_type": "call"}, {"api_name": "rusloModisLibrary.convertVectorFeatureFromCRSToCRS", "line_number": 928, "usage_type": "call"}, {"api_name": "rusloModisLibrary.createTempVectorLayerByFeature", "line_number": 930, "usage_type": "call"}, {"api_name": "rusloModisLibrary.cutRasterByVectorMaskGDAL", "line_number": 932, "usage_type": "call"}, {"api_name": "rusloModisLibrary.loadRasterToPostGIS", "line_number": 936, "usage_type": "call"}, {"api_name": "rusloModisLibrary.loadMOD10A2ObjectMaskStatisticsToPostGIS", "line_number": 938, "usage_type": "call"}, {"api_name": "rusloModisLibrary.generateLakeIceLayerForRasterLayerAndWriteToPostGIS", "line_number": 941, "usage_type": "call"}, {"api_name": "rusloModisLibrary.mergeListOfOriginMOD10A2RastersByHVListAndDate", "line_number": 944, "usage_type": "call"}, {"api_name": "rusloModisLibrary.convertVectorFeatureFromCRSToCRS", "line_number": 948, "usage_type": "call"}, {"api_name": "rusloModisLibrary.createTempVectorLayerByFeature", "line_number": 950, "usage_type": "call"}, {"api_name": "rusloModisLibrary.cutRasterByVectorMaskGDAL", "line_number": 952, "usage_type": "call"}, {"api_name": "rusloModisLibrary.loadRasterToPostGIS", "line_number": 955, "usage_type": "call"}, {"api_name": "rusloModisLibrary.loadMOD10A2ObjectMaskStatisticsToPostGIS", "line_number": 957, "usage_type": "call"}, {"api_name": "rusloModisLibrary.generateLakeIceLayerForRasterLayerAndWriteToPostGIS", "line_number": 960, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox.about", "line_number": 964, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 964, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 964, "usage_type": "name"}, {"api_name": "commonLibrary.clearTempDir", "line_number": 968, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 970, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 970, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 970, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 973, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 973, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 973, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 985, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 985, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 985, "usage_type": "name"}, {"api_name": "rusloModisLibrary.getListOfAvailableDatesForObjectMask", "line_number": 988, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getMOD10A2ObjectMaskStatsByObjectNameAndDate", "line_number": 1001, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getIceAreaForMOD10A2MaskObjectByObjectNameAndDate", "line_number": 1003, "usage_type": "call"}, {"api_name": "rusloModisLibrary.generatePlotByDatesAndNumbers", "line_number": 1009, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 1021, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 1021, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1021, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 1024, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 1024, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1024, "usage_type": "name"}, {"api_name": "rusloModisLibrary.getMOD10A2ObjectMaskStatsByObjectNameAndDate", "line_number": 1032, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getIceAreaForMOD10A2MaskObjectByObjectNameAndDate", "line_number": 1035, "usage_type": "call"}, {"api_name": "rusloModisLibrary.generatePlotByDatesAndNumbers", "line_number": 1041, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 1050, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 1050, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1050, "usage_type": "name"}, {"api_name": "rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate", "line_number": 1056, "usage_type": "call"}, {"api_name": "rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate", "line_number": 1061, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 1066, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1066, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1066, "usage_type": "call"}, {"api_name": "qgis.core.QgsVectorLayer", "line_number": 1072, "usage_type": "call"}, {"api_name": "rusloModisLibrary.twoSidesDifferenceBetweenTwoPolygonLayers", "line_number": 1073, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QColor.fromRgb", "line_number": 1078, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QColor", "line_number": 1078, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1078, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QColor.fromRgb", "line_number": 1081, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QColor", "line_number": 1081, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1081, "usage_type": "name"}, {"api_name": "qgis.core.QgsMapLayerRegistry.instance", "line_number": 1089, "usage_type": "call"}, {"api_name": "qgis.core.QgsMapLayerRegistry", "line_number": 1089, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QMessageBox.critical", "line_number": 1098, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QMessageBox", "line_number": 1098, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 1098, "usage_type": "name"}, {"api_name": "rusloModisLibrary.getListOfAvailableDatesForObjectMask", "line_number": 1101, "usage_type": "call"}, {"api_name": "qgis.core.QgsVectorLayer", "line_number": 1112, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.QVariant", "line_number": 1115, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 1115, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.QVariant", "line_number": 1116, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 1116, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.QVariant", "line_number": 1117, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 1117, "usage_type": "name"}, {"api_name": "rusloModisLibrary.getLakeIceVectorMaskByObjectNameAndDate", "line_number": 1124, "usage_type": "call"}, {"api_name": "qgis.core.QgsMapLayerRegistry.instance", "line_number": 1137, "usage_type": "call"}, {"api_name": "qgis.core.QgsMapLayerRegistry", "line_number": 1137, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 1142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1142, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 1142, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1144, "usage_type": "call"}]} {"seq_id": "543537734", "text": "import json\nimport numpy as np\nimport torch\nimport os\nimport shutil\nfrom torchvision.models import vgg11, resnet18\n\ndef load_config(path):\n with open(path, 'r') as file:\n config = json.load(file)\n\n if config[\"data\"]['place'] == \"department\":\n config[\"data\"][\"min_pos\"] = torch.tensor([-1.4, -5.2, 1.3]).cuda()\n config[\"data\"][\"max_pos\"] = torch.tensor([0.5, -3.3, 1.8]).cuda()\n config[\"data\"][\"number_images\"]\n assert True == False, \"Need to put number of images \"\n config[\"sampler\"][\"no_fly_zone\"] = None\n\n elif config[\"data\"][\"place\"] == \"living_room\":\n config[\"data\"][\"min_pos\"] = torch.tensor([-1.3, -0.5, 0.2,0.]).cuda()\n config[\"data\"][\"max_pos\"] = torch.tensor([1.8, 1.4, 1.7,270.]).cuda()\n if config[\"sampler\"][\"no_fly\"] == \"True\":\n config[\"sampler\"][\"no_fly_zone\"] = torch.tensor([[[0.5,-0.5,0.2],[1.7,1.1,0.9]],[[-1.3,0.5,0.1],[-0.1,1.7,1.1]]]).cuda()\n elif config[\"sampler\"][\"no_fly\"] == \"False\":\n config[\"sampler\"][\"no_fly_zone\"] = None\n \n return config\n\n\ndef write_to_file(path,content):\n log_file = open(path,'a')\n log_file.write(content)\n log_file.flush()\n log_file.close()\n\ndef create_directories(exp_path):\n os.mkdir(exp_path)\n os.mkdir(exp_path + '/visualisations')\n os.mkdir(exp_path + '/visualisations/poses')\n os.mkdir(exp_path + '/visualisations/history')\n os.mkdir(exp_path + '/checkpoints')\n shutil.copy(exp_path +'/../../config.json',exp_path +'/config.json')\n shutil.copytree(exp_path +'/../../code',exp_path +'/code')\n", "sub_path": "pose/code/utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 1637, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 23, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 41, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 42, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 43, "usage_type": "call"}]} {"seq_id": "390034351", "text": "from graph import *\nfrom tkinter import *\nfrom tabulate import tabulate\n\nOPTIONS = [\n 'Матрица смежности',\n 'Матрица инцидентности',\n 'Степень вершин',\n 'Изолированные и висячие',\n 'Матрица растояний',\n 'Матрица доступности',\n 'Простые циклы',\n 'BFS',\n 'DFS'\n]\n\n\ndef strong_connections():\n colors = ['yellow', 'orange', 'blue', 'green', 'red']\n for i in graph.get_cycles():\n color = colors.pop()\n image_of_graph.change_vertex_color(i, vertex_color=color)\n\n\ndef full_update(path):\n global gif, image_of_graph, graph\n graph = Graph()\n graph.read_file(path)\n image_of_graph = GraphImage (graph)\n image_of_graph.fill_graph()\n image_of_graph.process_image()\n gif = PhotoImage(file=\"source/image.gif\")\n image_label.configure(image=gif)\n left_label.configure(text='')\n right_label.configure(text=get_text_label_2())\n\n\ndef load_case_1():\n full_update('source/case_1.txt')\n\n\ndef load_case_2():\n full_update('source/case_2.txt')\n\n\ndef load_case_3():\n full_update('source/case_3.txt')\n\n\ndef adjacency_matrix_out():\n image_of_graph.change_vertex_color(range(graph.vertices))\n return graph.wrap(graph.adjacency_matrix, 'v', 'v')\n\n\ndef incidence_matrix_out():\n image_of_graph.change_vertex_color(range(graph.vertices))\n return graph.wrap(graph.incidence_matrix, 'v', 'e')\n\n\ndef vertex_power_out():\n image_of_graph.change_vertex_color(range(graph.vertices))\n matrix_list = []\n for i in range(graph.vertices):\n matrix_list.append(graph.get_vertex_power(i))\n return graph.wrap(matrix_list, custom_horizontal=['d-', 'd+'])\n\n\ndef isolated_vertices_out():\n image_of_graph.change_vertex_color(graph.get_isolated(), vertex_color='red')\n image_of_graph.change_vertex_color(graph.get_pendant(), vertex_color='green')\n pre_string = 'Изолированные:\\n'\n for i in graph.get_isolated():\n pre_string += 'v' + str(i + 1) + '\\t'\n pre_string += '\\nВисячие:\\n'\n for i in graph.get_pendant():\n pre_string += 'v' + str(i + 1) + '\\t'\n return pre_string\n\n\ndef distance_matrix_out():\n image_of_graph.change_vertex_color(range(graph.vertices))\n pre_string = graph.wrap(graph.get_distance_matrix(), 'v', 'v')\n return pre_string\n\n\ndef availability_matrix_out():\n image_of_graph.change_vertex_color(range(graph.vertices))\n pre_string = graph.wrap(graph.get_availability_matrix(), 'v', 'v')\n return pre_string\n\n\ndef bfs_out():\n str_out = tabulate(graph.bfs_protocol(0))\n return str_out\n\n\ndef dfs_out():\n str_out = tabulate(graph.dfs_protocol(0))\n return str_out\n\n\ndef cycles_out():\n pre_string = ''\n image_of_graph.change_vertex_color(range(graph.vertices))\n for cycle in graph.get_cycles():\n pre_string += ' '.join([str(i + 1) for i in cycle])[::-1] + '\\n'\n return pre_string\n\n\ndef storeys_out():\n pre_string = ''\n for storey in graph.get_storeys().keys():\n pre_string += 'Ярус: ' + str(storey) + '\\n'\n for vertex in graph.get_storeys()[storey]:\n pre_string += 'v' + str(vertex + 1) + ' '\n pre_string += '\\n'\n return pre_string\n\n\ndef topological_sort_out():\n return graph.topological_sort()\n\n\ndef graph_type():\n type = int(graph.connectivity_type())\n if type == 1:\n pre_string = 'Сильно связный'\n elif type == 2:\n pre_string = 'Односторенне-связый'\n elif type == 3:\n pre_string = 'Слабо связный'\n else:\n pre_string = 'Несвязный'\n return pre_string\n\n\ndef get_text_label_1():\n global gif\n choiсe = variable.get()\n output_string = ''\n if choiсe == OPTIONS[0]:\n output_string = adjacency_matrix_out()\n elif choiсe == OPTIONS[1]:\n output_string = incidence_matrix_out()\n elif choiсe == OPTIONS[2]:\n output_string = vertex_power_out()\n elif choiсe == OPTIONS[3]:\n output_string = isolated_vertices_out()\n elif choiсe == OPTIONS[4]:\n output_string = distance_matrix_out()\n elif choiсe == OPTIONS[5]:\n output_string = availability_matrix_out()\n elif choiсe == OPTIONS[6]:\n output_string = cycles_out()\n elif choiсe == OPTIONS[7]:\n output_string = bfs_out()\n elif choiсe == OPTIONS[8]:\n output_string = dfs_out()\n\n strong_connections()\n gif = PhotoImage(file=\"source/image.gif\")\n image_label.configure(image=gif)\n left_label.configure(text='\\n' + output_string)\n\n\ndef get_text_label_2():\n pre_string = ''\n pre_string += graph_type()\n pre_string += '\\nРадиус: ' + str(graph.get_radius()) + '\\n'\n pre_string += 'Диаметр: ' + str(graph.get_diameter()) + '\\n'\n pre_string += 'Центр: \\n'\n for i in graph.get_centers():\n pre_string += 'v' + str(i + 1) + ' '\n pre_string += '\\n\\n' + storeys_out()\n\n pre_string += '\\nТопологическая сортировка\\n' + str(topological_sort_out())\n return pre_string\n\n\ngraph = Graph()\ngraph.read_file('source/case_1.txt')\n\nimage_of_graph = GraphImage(graph)\nimage_of_graph.fill_graph()\nimage_of_graph.process_image()\n\n\nroot = Tk()\n\n# Create window frames\nf_1 = Frame(root)\nf_2 = Frame(root)\nf_2_1 = Frame(f_2)\nf_2_1_1 = Frame(f_2_1)\nf_2_1_2 = Frame(f_2_1)\nf_2_2 = Frame(f_2)\nf_3 = Frame(root)\n\n# Case botton\nbutton_case_1 = Button(root, text='case 1', command=load_case_1)\nbutton_case_2 = Button(root, text='case 2', command=load_case_2)\nbutton_case_3 = Button(root, text='case 3', command=load_case_3)\n\n# Label with image of graph\ngif = PhotoImage(file='source/image.gif')\nimage_label = Label(f_1, image=gif, width=400)\n\n# Left button\nbutton = Button(f_2_1_1, text='Вычислить', command=get_text_label_1)\n\n# Dropdown list\nvariable = StringVar(root)\nvariable.set(OPTIONS[0])\noption = OptionMenu(f_2_1_2, variable, *OPTIONS)\n\n# Label with printed matrix\nleft_label = Label(f_2_2, text='\\n', font=('Monaco', 20), justify=LEFT)\n\n# Properties label\nright_label = Label(f_3, text=get_text_label_2(), font=('Monaco', 16), justify=LEFT)\n\n# Pack frames\nf_1.pack(side=LEFT)\nf_2.pack(side=LEFT)\nf_2_1.pack()\nf_2_1_1.pack(side=LEFT)\nf_2_1_2.pack(side=RIGHT)\nf_2_2.pack()\nf_3.pack(side=LEFT)\n\n# Pack widgets\nbutton_case_1.pack()\nbutton_case_2.pack()\nbutton_case_3.pack()\nimage_label.pack()\noption.pack()\nbutton.pack()\nleft_label.pack()\nright_label.pack()\n\nroot.mainloop()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6516, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "graph.get_cycles", "line_number": 20, "usage_type": "call"}, {"api_name": "graph.read_file", "line_number": 28, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 51, "usage_type": "attribute"}, {"api_name": "graph.wrap", "line_number": 52, "usage_type": "call"}, {"api_name": "graph.adjacency_matrix", "line_number": 52, "usage_type": "attribute"}, {"api_name": "graph.vertices", "line_number": 56, "usage_type": "attribute"}, {"api_name": "graph.wrap", "line_number": 57, "usage_type": "call"}, {"api_name": "graph.incidence_matrix", "line_number": 57, "usage_type": "attribute"}, {"api_name": "graph.vertices", "line_number": 61, "usage_type": "attribute"}, {"api_name": "graph.vertices", "line_number": 63, "usage_type": "attribute"}, {"api_name": "graph.get_vertex_power", "line_number": 64, "usage_type": "call"}, {"api_name": "graph.wrap", "line_number": 65, "usage_type": "call"}, {"api_name": "graph.get_isolated", "line_number": 69, "usage_type": "call"}, {"api_name": "graph.get_pendant", "line_number": 70, "usage_type": "call"}, {"api_name": "graph.get_isolated", "line_number": 72, "usage_type": "call"}, {"api_name": "graph.get_pendant", "line_number": 75, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 81, "usage_type": "attribute"}, {"api_name": "graph.wrap", "line_number": 82, "usage_type": "call"}, {"api_name": "graph.get_distance_matrix", "line_number": 82, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 87, "usage_type": "attribute"}, {"api_name": "graph.wrap", "line_number": 88, "usage_type": "call"}, {"api_name": "graph.get_availability_matrix", "line_number": 88, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 93, "usage_type": "call"}, {"api_name": "graph.bfs_protocol", "line_number": 93, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 98, "usage_type": "call"}, {"api_name": "graph.dfs_protocol", "line_number": 98, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 104, "usage_type": "attribute"}, {"api_name": "graph.get_cycles", "line_number": 105, "usage_type": "call"}, {"api_name": "graph.get_storeys", "line_number": 112, "usage_type": "call"}, {"api_name": "graph.get_storeys", "line_number": 114, "usage_type": "call"}, {"api_name": "graph.topological_sort", "line_number": 121, "usage_type": "call"}, {"api_name": "graph.connectivity_type", "line_number": 125, "usage_type": "call"}, {"api_name": "graph.get_radius", "line_number": 169, "usage_type": "call"}, {"api_name": "graph.get_diameter", "line_number": 170, "usage_type": "call"}, {"api_name": "graph.get_centers", "line_number": 172, "usage_type": "call"}, {"api_name": "graph.read_file", "line_number": 181, "usage_type": "call"}]} {"seq_id": "133061557", "text": "from lark import Lark\nimport torch.utils.data\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom enum import Enum\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom torch.utils.tensorboard import SummaryWriter\n\n\nclass lalrDataset(torch.utils.data.Dataset):\n \"\"\"LALR dataset.\"\"\"\n\n def __init__(self, lines_s, lines_p):\n self.lines_s=lines_s.copy()\n self.lines_p=lines_p.copy()\n self.length_s=[]\n self.length_p=[]\n for line in self.lines_s:\n self.length_s.append(len(line))\n for line in self.lines_p:\n self.length_p.append(len(line))\n assert self.length_s == self.length_p\n max_lenght = np.max(self.length_s)\n '''sets for chars'''\n self.s_chars = ['_'] + sorted(set(''.join(self.lines_s)))\n self.p_chars = ['_'] + sorted(set(''.join(self.lines_p)))\n ''' padding '''\n for i in range(len(self.lines_s)):\n while len(self.lines_s[i]) < max_lenght+1:\n self.lines_s[i] += '_'\n for i in range(len(self.lines_p)):\n while len(self.lines_p[i]) < max_lenght+1:\n self.lines_p[i] += '_'\n ''' int2chars and chars2int dictionaries '''\n self.int2s_char = enumerate(self.s_chars)\n self.s_char2int = {char: ind for ind, char in self.int2s_char}\n self.s_int2char = {self.s_char2int[char]: char for char in self.s_char2int}\n self.int2p_char = enumerate(self.p_chars)\n self.p_char2int = {char: ind for ind, char in self.int2p_char}\n ''' creating index value arrays'''\n input_seq = []\n target_seq = []\n for i in range(len(self.lines_p)):\n input_seq.append([self.p_char2int[character] for character in self.lines_p[i]])\n target_seq.append([self.s_char2int[character] for character in self.lines_s[i]])\n '''one hot encoding'''\n dict_size = len(self.p_char2int)\n seq_len = max_lenght+1\n batch_size = len(self.lines_p)\n self.input_seq = self.one_hot_encode(input_seq, dict_size, seq_len, batch_size)\n self.input_seq=torch.tensor(self.input_seq).cuda(0)\n dict_size = len(self.s_char2int)\n self.target_seq = self.one_hot_encode(target_seq, dict_size, seq_len, batch_size)\n self.target_seq=torch.tensor(self.target_seq).cuda(0)\n\n def __len__(self):\n return self.input_seq.shape[0]\n\n def __getitem__(self, idx):\n return [self.input_seq[idx],self.target_seq[idx],]\n\n def lastpadindex(self,batch):\n \"I'm sure there's a more clever way to do this..\"\n input = batch[0]\n target = batch[1]\n for i in range(input.shape[1]):\n if (input[:, i].equal(input[:, -1])):\n return input[:, :i + 1], target[:, :i + 1]\n return input, target\n def get_s_chars(self):\n return set(self.s_chars)\n def get_s_char2int(self):\n return self.s_char2int\n def get_tokenmap(self):\n return [self.s_int2char[i] for i in sorted(self.s_int2char.keys())]\n def one_hot_encode(self,sequence, dict_size, seq_len, batch_size):\n ''' Creating a multi-dimensional array of zeros with the desired output shape '''\n features = np.zeros((batch_size, seq_len, dict_size), dtype=np.float32)\n\n ''' Replacing the 0 at the relevant character index with a 1 to represent that character '''\n for i in range(batch_size):\n for u in range(seq_len):\n features[i, u, sequence[i][u]] = 1\n return features\n\n\n\n\nclass EncoderRNN(nn.Module):\n def __init__(self, input_size, hidden_size):\n super(EncoderRNN, self).__init__()\n self.hidden_size = hidden_size\n self.gru = nn.GRU(input_size, hidden_size, batch_first=True,bidirectional=True)\n\n def forward(self, input):\n output, hidden = self.gru(input)\n return output, hidden\n\n def reinit(self):\n '''Reinitialize weights'''\n\n def weights_init(l):\n if hasattr(l, 'weight') and isinstance(l.weight, torch.Tensor):\n nn.init.xavier_uniform_(l.weight.data)\n if hasattr(l, 'bias') and isinstance(l.bias, torch.Tensor):\n nn.init.uniform_(l.bias)\n\n self.apply(weights_init)\n\n\n'''5s_rna_data files'''\nwith open(\"/home/rishal/lalrnn/5s_data/5s_shortlisted/dbn_can.txt\") as f:\n lines_s_5srna = [line.strip() for line in f.readlines()]\nwith open(\"/home/rishal/lalrnn/5s_data/5s_shortlisted/seq_can.txt\") as f:\n lines_p_5srna = [line.strip() for line in f.readlines()]\nf.close()\n\nlines_p_5srna_train1, lines_p_5srna_test, lines_s_5srna_train1, lines_s_5srna_test = train_test_split(lines_p_5srna, lines_s_5srna, test_size=0.1, random_state=42)\nlines_p_5srna_train, lines_p_5srna_val, lines_s_5srna_train, lines_s_5srna_val = train_test_split(lines_p_5srna_train1, lines_s_5srna_train1, test_size=0.22, random_state=42)\n\n'''srp_rna_data files'''\nwith open(\"/home/rishal/lalrnn/srp_data/srp_shortlisted/dbn_can.txt\") as f:\n lines_s_srprna = [line.strip() for line in f.readlines()]\nwith open(\"/home/rishal/lalrnn/srp_data/srp_shortlisted/seq_can.txt\") as f:\n lines_p_srprna = [line.strip() for line in f.readlines()]\nf.close()\n\nlines_p_srprna_train1, lines_p_srprna_test, lines_s_srprna_train1, lines_s_srprna_test = train_test_split(lines_p_srprna, lines_s_srprna, test_size=0.1, random_state=42)\nlines_p_srprna_train, lines_p_srprna_val, lines_s_srprna_train, lines_s_srprna_val = train_test_split(lines_p_srprna_train1, lines_s_srprna_train1, test_size=0.22, random_state=42)\n\n'''trna_data files'''\nwith open(\"/home/rishal/lalrnn/trna_data/trna_shortlisted/dbn_can.txt\") as f:\n lines_s_trna = [line.strip() for line in f.readlines()]\nwith open(\"/home/rishal/lalrnn/trna_data/trna_shortlisted/seq_can.txt\") as f:\n lines_p_trna = [line.strip() for line in f.readlines()]\nf.close()\n\nlines_p_trna_train1, lines_p_trna_test, lines_s_trna_train1, lines_s_trna_test = train_test_split(lines_p_trna, lines_s_trna, test_size=0.1, random_state=42)\nlines_p_trna_train, lines_p_trna_val, lines_s_trna_train, lines_s_trna_val = train_test_split(lines_p_trna_train1, lines_s_trna_train1, test_size=0.22, random_state=42)\n\nfor i in range(len(lines_s_trna_test)):\n if i==36:\n print(lines_s_trna_test[i],lines_p_trna_test[i])\n\ngrammar='''?e: DOT\n | LPARENA RPARENU\n | LPARENC RPARENG\n | LPARENG RPARENC\n | LPARENG RPARENU\n | LPARENU RPARENG\n | LPARENU RPARENA\n | e LPARENA e RPARENU \n | e LPARENC e RPARENG \n | e LPARENG e RPARENC \n | e LPARENG e RPARENU \n | e LPARENU e RPARENG \n | e LPARENU e RPARENA \n | e DOT\n | LPARENA e RPARENU\n | LPARENC e RPARENG\n | LPARENG e RPARENC\n | LPARENG e RPARENU\n | LPARENU e RPARENG\n | LPARENU e RPARENA\n | e LPARENA RPARENU\n | e LPARENC RPARENG\n | e LPARENG RPARENC\n | e LPARENG RPARENU\n | e LPARENU RPARENG\n | e LPARENU RPARENA\nDOT: \".\"\nLPARENA: \"A\"\nLPARENC: \"C\"\nLPARENG: \"G\"\nLPARENU: \"U\"\nRPARENA: \"a\"\nRPARENC: \"c\"\nRPARENG: \"g\"\nRPARENU: \"u\"\n'''\ntest_data=lalrDataset(lines_s_srprna_test,lines_p_srprna_test)\ntestloader = torch.utils.data.DataLoader(test_data, batch_size=1, shuffle=False, num_workers=0)\nparser = Lark(grammar, start='e', parser='lalr')\ntokenmap = [str(t.pattern).replace(r'\\\\', '').strip(\"'\") for t in parser.terminals]\ntokenmap.append(\"_\")\nassert set(tokenmap)==test_data.get_s_chars()\ntokenmap=test_data.get_tokenmap()\n\nfrom lalrnn_all_lets import SimpleGenerativeLALRNN\n#decoder = DecoderRNN(100,4)\ndecoder = SimpleGenerativeLALRNN(grammar, 'e', tokenmap, '_', test_data.get_s_char2int())\nencoder = EncoderRNN(5,300)\nencoder.cuda(0)\ndecoder.cuda(0)\n\ncheckpoint=torch.load('/home/rishal/lalrnn/lets_l300_bi_dropout2_wd1e5_best.pth.tar')\nencoder.load_state_dict(checkpoint['encoder_state_dict'])\ndecoder.load_state_dict(checkpoint['decoder_state_dict'])\n\nppv_list=[]\nsensitivity_list=[]\nvalid_num=0\nlalrnn=True\nfor i, data in enumerate(testloader):\n if lalrnn==True:\n decoder.set_test()\n decoder.eval()\n if i==36:\n print('lol')\n input,target = test_data.lastpadindex(data)\n encoder_outputs, encoder_hidden = encoder(input)\n encoder_hidden = (encoder_hidden[0] +\n encoder_hidden[1])\n decoder_input=target\n #encoder_hidden = encoder_hidden.squeeze(0)\n decoder_hidden = encoder_hidden\n decoder_output, _ = decoder(decoder_input, decoder_hidden,input)\n else:\n input, target = test_data.lastpadindex(data)\n encoder_outputs, encoder_hidden = encoder(input)\n decoder_output=decoder.predict(encoder_hidden,target.size(1))\n string1=torch.argmax(decoder_output[:, :target.size(1), :], dim=2)\n #print(string1)\n string1=string1.squeeze(0)\n string2=torch.argmax(target,dim=2)\n string2=string2.squeeze(0)\n str1=[]\n str2=[]\n for x in range(len(string1)):\n str1.append(tokenmap[string1[x]])\n str1=''.join(str1)\n print('predicted')\n print(str1)\n for x in range(len(string2)):\n str2.append(tokenmap[string2[x]])\n str2=''.join(str2)\n print('true')\n print(str2)\n str2=str2.replace('_','')\n str1=str1.replace('_','')\n stack_s1 = []\n stack_s2 = []\n pairs_s1 = set()\n pairs_s2 = set()\n str1 = list(str1)\n str2 = list(str2)\n assert len(str1) == len(str2)\n for i in range(len(str1)):\n if str1[i] == '(' or str1[i]=='[' or str1[i]=='{' or str1[i].isupper():\n stack_s1.append(i)\n if str2[i] == '(' or str2[i]=='[' or str2[i]=='{' or str2[i].isupper():\n stack_s2.append(i)\n if str1[i] == ')' or str1[i]==']' or str1[i]=='}' or str1[i].islower():\n pairs_s1.add((stack_s1[-1], i))\n stack_s1.pop()\n if str2[i] == ')'or str2[i]==']' or str2[i]=='}' or str2[i].islower():\n pairs_s2.add((stack_s2[-1], i))\n stack_s2.pop()\n TP = len(pairs_s1 & pairs_s2)\n FP = len(pairs_s1 - pairs_s2)\n FN = len(pairs_s2 - pairs_s1)\n #print(TP,FP,FN)\n if TP+FP==0:\n FP=1\n if TP+FN==0:\n FN=1\n ppv_list.append(TP/(TP+FP))\n #print(ppv_list)\n sensitivity_list.append(TP/(TP+FN))\n\nprint('validity ', valid_num/(i+1))\nppv_list=np.array(ppv_list)\nsensitivity_list=np.array(sensitivity_list)\nf1=(2*ppv_list*sensitivity_list)/(ppv_list+sensitivity_list)\nf1=np.nan_to_num(f1)\nprint('ppv',ppv_list.mean())\nprint('sensitivity',sensitivity_list.mean())\nprint('f1',f1.mean())", "sub_path": "prediction_canon.py", "file_name": "prediction_canon.py", "file_ext": "py", "file_size_in_byte": 10472, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.utils", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 108, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 141, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 185, "usage_type": "attribute"}, {"api_name": "lark.Lark", "line_number": 186, "usage_type": "call"}, {"api_name": "lalrnn_all_lets.SimpleGenerativeLALRNN", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 278, "usage_type": "call"}]} {"seq_id": "302648006", "text": "\"\"\"empty message\n\nRevision ID: b48beb44344\nRevises: 1407298ea779\nCreate Date: 2016-01-05 09:46:11.005519\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'b48beb44344'\ndown_revision = '1407298ea779'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('role_id', sa.Integer(), nullable=True))\n op.drop_column('users', 'role')\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('role', sa.INTEGER(), nullable=True))\n op.drop_column('users', 'role_id')\n ### end Alembic commands ###\n", "sub_path": "migrations/versions/b48beb44344_.py", "file_name": "b48beb44344_.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "alembic.op.add_column", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}]} {"seq_id": "540428991", "text": "from fc.qemu.main import daemonize\nimport datetime\nimport sys\nimport subprocess\nimport time\nimport shlex\n\n\ndef run_supervised(cmd, name, logfile):\n daemonize()\n log = open(logfile, 'a+', buffering=0)\n now = datetime.datetime.now().isoformat()\n log.write('{} - starting command {}\\n'.format(now, cmd))\n s = subprocess.Popen(\n shlex.split(cmd), close_fds=True, stdin=None, stdout=log, stderr=log)\n now = datetime.datetime.now().isoformat()\n log.write('{} - command has PID {}\\n'.format(now, s.pid))\n exit_code = s.wait()\n now = datetime.datetime.now().isoformat()\n log.write('{} - command exited with exit code {}\\n'.format(now, exit_code))\n\n\nif __name__ == '__main__':\n run_supervised(*sys.argv[1:])\n", "sub_path": "src/fc/qemu/hazmat/supervise.py", "file_name": "supervise.py", "file_ext": "py", "file_size_in_byte": 739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "fc.qemu.main.daemonize", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 14, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "528607473", "text": "import cv2\nimport numpy as np\n\ndrawing = False\nix, iy = -1, -1\nsel_rect_endpoint = []\ntagged = np.empty((0, 4), dtype='int')\n\n\ndef tag_players(image, dx=0.2, dy=0.3):\n img = cv2.resize(image, (0, 0), fx=dx, fy=dy)\n\n def draw_rectangle(event, x, y, flags, param):\n global ix, iy, drawing, tagged, sel_rect_endpoint\n\n if event == cv2.EVENT_LBUTTONDOWN:\n drawing = True\n ix, iy = x, y\n\n elif event == cv2.EVENT_MOUSEMOVE and drawing:\n sel_rect_endpoint = (x, y)\n\n elif event == cv2.EVENT_LBUTTONUP:\n cv2.rectangle(img, (ix, iy), (x, y), (0, 0, 255), 1)\n tagged = np.vstack((tagged, np.array([ix, iy, x, y])))\n drawing = False\n sel_rect_endpoint = []\n\n win_name = 'Tagging players'\n cv2.namedWindow(win_name)\n cv2.setMouseCallback(win_name, draw_rectangle, None)\n\n while 1:\n if sel_rect_endpoint and drawing:\n clone = img.copy()\n cv2.rectangle(clone, (ix, iy), sel_rect_endpoint, (0, 0, 255), 1)\n cv2.imshow(win_name, clone)\n else:\n cv2.imshow(win_name, img)\n if cv2.waitKey(20) & 0xFF == 27:\n break\n cv2.destroyAllWindows()\n return tagged\n\nsample = cv2.imread('output/images/sample.jpg')\nprint(tag_players(sample))", "sub_path": "tagging.py", "file_name": "tagging.py", "file_ext": "py", "file_size_in_byte": 1316, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.empty", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEMOVE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_LBUTTONUP", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 45, "usage_type": "call"}]} {"seq_id": "410657662", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n''' writes nodes csv to nodes sql table '''\n\nimport sqlite3\nimport csv\nfrom pprint import pprint\n# modified from code posted to Udacity forums\n\nsqlite_file = 'osm.db' # name of the sqlite database file\n\n# Connect to the database\nconn = sqlite3.connect(sqlite_file)\n# Get a cursor object\ncur = conn.cursor()\ncur.execute('''DROP TABLE IF EXISTS nodes''')\nconn.commit()\n# Create the table, specifying the column names and data types:\ncur.execute('''\n CREATE TABLE nodes (\n id INTEGER PRIMARY KEY NOT NULL,\n lat REAL,\n lon REAL,\n user TEXT,\n uid INTEGER,\n version INTEGER,\n changeset INTEGER,\n timestamp TEXT\n );\n ''')\n# commit the changes\nconn.commit()\n# Read in the csv file as a dictionary, format the\n# data as a list of tuples:\nwith open('nodes.csv','rb') as fin:\n dr = csv.DictReader(fin) # comma is default delimiter\n to_db = [(i['id'].decode(\"utf-8\"), i['lat'].decode(\"utf-8\"),i['lon'].decode(\"utf-8\"), i['user'].decode(\"utf-8\"), i['uid'].decode(\"utf-8\"), i['version'].decode(\"utf-8\"), i['changeset'].decode(\"utf-8\"), i['timestamp'].decode(\"utf-8\")) for i in dr]\n# insert the formatted data\ncur.executemany(\"INSERT INTO nodes(id, lat, lon, user, uid, version, changeset, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?);\", to_db)\n# commit the changes\nconn.commit()\ncur.execute('SELECT * FROM nodes')\nall_rows = cur.fetchall()\nprint('1):')\npprint(all_rows)\nconn.close()\n", "sub_path": "DAND/OpenStreetMap/to_sql_nodes.py", "file_name": "to_sql_nodes.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 36, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 45, "usage_type": "call"}]} {"seq_id": "271867571", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttypes.fields import GenericForeignKey\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom utils.models import BaseUtilityModel\n\n\nclass Rating(BaseUtilityModel):\n user = models.ForeignKey(\n 'auth.User',\n related_name='rating_user'\n )\n content_type = models.ForeignKey(\n ContentType,\n db_index=True\n )\n object_id = models.IntegerField(\n db_index=True\n )\n content_object = GenericForeignKey(\n 'content_type',\n 'object_id'\n )\n date_rated = models.DateField(\n blank=True,\n null=True\n )\n rating_text = models.TextField(\n blank=True,\n null=True\n )\n rating = models.IntegerField(\n blank=True,\n validators=[MaxValueValidator(100),\n MinValueValidator(1)]\n )\n\n class Meta:\n verbose_name = 'rating'\n verbose_name_plural = 'ratings'\n unique_together = ('user', 'object_id',)\n\n def __str__(self):\n return 'rating: %s | %s' % (self.rating, self.rating_text[0:20])\n\n", "sub_path": "rating/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "utils.models.BaseUtilityModel", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.fields.GenericForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.DateField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 35, "usage_type": "call"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 36, "usage_type": "call"}]} {"seq_id": "629050376", "text": "# -*- coding: utf-8 -*-\n\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('microdevices', '0003'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='organmodel',\n name='model_image',\n field=models.ImageField(null=True, upload_to=b'models', blank=True),\n ),\n ]\n", "sub_path": "microdevices/migrations/0004.py", "file_name": "0004.py", "file_ext": "py", "file_size_in_byte": 386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} {"seq_id": "137135912", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nThis script contains functions for loading and analysis of burrito data\n\"\"\"\n\nimport numpy as np\nimport scipy as sp\nimport pandas as pd\n\ndef load_burritos(filename='burrito_current.csv',\n delete_unreliable = True):\n # Load all data\n df = pd.read_csv(filename)\n df.Location = df.Location.str.lower()\n \n # Delete unreliable ratings\n if delete_unreliable:\n \n # Binarize unreliable\n df.Unreliable = df.Unreliable.map({'x':1,'X':1,1:1})\n df.Unreliable = df.Unreliable.fillna(0)\n \n # Select only reliable ratings from dataframe\n import pandasql\n q = \"\"\"\n SELECT\n *\n FROM\n df\n WHERE\n unreliable == 0\n \"\"\"\n df = pandasql.sqldf(q.lower(), locals())\n\n return df", "sub_path": "burrito/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandasql.sqldf", "line_number": 33, "usage_type": "call"}]} {"seq_id": "161805337", "text": "import logging\n\nimport requests\n\nfrom pajbot.models.command import Command\nfrom pajbot.modules import BaseModule\nfrom pajbot.modules import ModuleSetting\n\nlog = logging.getLogger(__name__)\n\n\nclass PNSLModule(BaseModule):\n\n ID = __name__.split(\".\")[-1]\n NAME = \"Run P&SL lists\"\n DESCRIPTION = \"Run P&SL lists through the !runpnsl command\"\n CATEGORY = \"Moderation\"\n SETTINGS = [\n ModuleSetting(\n key=\"level\",\n label=\"Level required to use the command\",\n type=\"number\",\n required=True,\n placeholder=\"\",\n default=750,\n constraints={\"min_value\": 500, \"max_value\": 2000},\n ),\n ModuleSetting(\n key=\"per_chunk\",\n label=\"How many lines to process per chunk\",\n type=\"number\",\n required=True,\n placeholder=\"\",\n default=30,\n constraints={\"min_value\": 1, \"max_value\": 500},\n ),\n ModuleSetting(\n key=\"chunk_delay\",\n label=\"Delay between chunks (in seconds)\",\n type=\"number\",\n required=True,\n placeholder=\"\",\n default=30,\n constraints={\"min_value\": 5, \"max_value\": 60},\n ),\n ]\n\n def __init__(self, bot):\n super().__init__(bot)\n\n self.pnsl_token = None\n\n if bot:\n if \"pnsl\" in bot.config:\n self.pnsl_token = bot.config[\"pnsl\"].get(\"token\", None)\n\n def run_pnsl(self, bot, source, message, event, args):\n base_url = \"https://bot.tetyys.com/api/v1/BotLists\"\n\n if not self.pnsl_token:\n bot.whisper(source, f\"Missing P&SL token in config.ini. talk to @{bot.admin} BabyRage\")\n return False\n\n guid = message.replace(\"https://bot.tetyys.com/BotList/\", \"\")\n\n headers = {\"Authorization\": f\"Bearer {self.pnsl_token}\"}\n\n res = requests.get(base_url + \"/\" + guid, headers=headers)\n\n if not res.ok:\n error_data = res.json()\n bot.whisper(source, f\"Something went wrong with the P&SL request: {error_data['errors']['Guid'][0]}\")\n return False\n\n privmsg_list = res.text.splitlines()\n\n log.info(f\"[P&SL] User {source.name} running list {guid} with {len(privmsg_list)} entries\")\n\n bot.privmsg_arr_chunked(\n privmsg_list, per_chunk=self.settings[\"per_chunk\"], chunk_delay=self.settings[\"chunk_delay\"]\n )\n\n def load_commands(self, **options):\n self.commands[\"runpnsl\"] = Command.raw_command(\n self.run_pnsl,\n delay_all=20,\n delay_user=20,\n level=self.settings[\"level\"],\n description=\"Run a P&SL list\",\n command=\"runpnsl\",\n )\n self.commands[\"pnslrun\"] = self.commands[\"runpnsl\"]\n", "sub_path": "pajbot/modules/pnsl.py", "file_name": "pnsl.py", "file_ext": "py", "file_size_in_byte": 2812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "pajbot.modules.BaseModule", "line_number": 12, "usage_type": "name"}, {"api_name": "pajbot.modules.ModuleSetting", "line_number": 19, "usage_type": "call"}, {"api_name": "pajbot.modules.ModuleSetting", "line_number": 28, "usage_type": "call"}, {"api_name": "pajbot.modules.ModuleSetting", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 68, "usage_type": "call"}, {"api_name": "pajbot.models.command.Command.raw_command", "line_number": 84, "usage_type": "call"}, {"api_name": "pajbot.models.command.Command", "line_number": 84, "usage_type": "name"}]} {"seq_id": "8190338", "text": "#!BPY\r\n \r\n# Une cyclide de Dupin C3, format fil de fer, avec les quatres droites. En couleur...\r\n# chaque cercle est un seul mesh\r\n# on isole le cas des cercles complets, plutot que de trainer des si alors sinon compliques\r\n# 10/11/2014 : mesh ok, on rajoute les couleurs\r\n# et les quatre droites\r\n__author__ = \"francesco de comite\"\r\n__version__ = \"1.0 2014/11/05\"\r\n__url__=\"Website, www.lifl.fr/decomite\"\r\n \r\n \r\n##############################################################\r\n# load the modules used in the script\r\nimport bpy\r\nimport math\r\nimport mathutils\r\nfrom mathutils import *\r\n\r\nfrom math import *\r\n##############################################################\r\n# une sphere\r\n# R : rayon\r\n# n : nombre de meridiens\r\n# p : nombre de paralleles\r\n\r\ndef sphere(R,n,p):\r\n me=bpy.data.meshes.new('sphere')\r\n coords=[[0,0,0] for i in range(n*p+2)]\r\n faces=[] \r\n \r\n # Les points\r\n for i in range(p): #les points sur un parallele\r\n zc=pi/2-(i+1)*pi/(p+1)\r\n for j in range(n):\r\n coords[j+i*n]=[R*cos(2*j*pi/n)*cos(zc),R*sin(2*j*pi/n)*cos(zc),R*sin(zc)]\r\n coords[n*p]=[0,0,R]\r\n coords[n*p+1]=[0,0,-R]\r\n \r\n #les faces\r\n # Calottes\r\n for i in range(n):\r\n faces.append([i,(i+1)%n,n*p])\r\n faces.append([i+(p-1)*n,n*p+1,(i+1)%n+(p-1)*n])\r\n \r\n # relier un parallele au suivant\r\n for i in range(p-1):\r\n for j in range(n):\r\n faces.append([(j+1)%n+n*i,j+n*i,j+n*(i+1)])\r\n faces.append([(j+1)%n+n*i,j+n*(i+1),(j+1)%n+n*(i+1)])\r\n \r\n me.from_pydata(coords,[],faces) \r\n me.update(calc_edges=True) \r\n return me \r\n \r\n \r\n\r\n\r\n#un tore\r\n# R : grand rayon\r\n# r : petit rayon\r\n# n : nombre de sections\r\n# p : nombre de points sur une section\r\ndef tore(R,r,n,p):\r\n me=bpy.data.meshes.new('tore')\r\n coords=[[0,0,0] for i in range(n*p)]\r\n faces=[]\r\n for i in range(n): \r\n for j in range(p):\r\n coords[i*p+j]=[(R+r*cos(2*j*pi/p))*cos(2*i*pi/n),(R+r*cos(2*j*pi/p))*sin(2*i*pi/n),r*sin(2*j*pi/p)]\r\n for i in range(n):\r\n #relier la couronne numero i a la couronne (i+1)%n\r\n depart=i*p\r\n arrivee=((i+1)*p)%(n*p)\r\n for j in range(p):\r\n faces.append([depart+j,arrivee+j,depart+(j+1)%p])\r\n faces.append([depart+(j+1)%p,arrivee+j,arrivee+(j+1)%p])\r\n \r\n me.from_pydata(coords,[],faces) \r\n me.update(calc_edges=True) \r\n return me \r\n \r\n# un cylindre\r\ndef cylindre(r,nbFaces,l):\r\n \r\n me=bpy.data.meshes.new('cylindre')\r\n coords=[[0,0,0] for i in range(2*nbFaces+2)]\r\n faces=[]\r\n\r\n coords[2*nbFaces]=[0,0,0]\r\n coords[2*nbFaces+1]=[0,l,0]\r\n \r\n for i in range(0,nbFaces):\r\n coords[i]=[r*cos(2*i*pi/nbFaces),0,r*sin(2*i*pi/nbFaces)]\r\n coords[i+nbFaces]=[r*cos(2*i*pi/nbFaces),l,r*sin(2*i*pi/nbFaces)]\r\n \r\n for i in range(0,nbFaces):\r\n faces.append([i,(i+1)%nbFaces,2*nbFaces]) \r\n faces.append([i+nbFaces,2*nbFaces+1,nbFaces+(i+1)%nbFaces])\r\n faces.append([(i+1)%nbFaces,i,i+nbFaces])\r\n faces.append([i+nbFaces,nbFaces+(i+1)%nbFaces,(i+1)%nbFaces])\r\n \r\n me.from_pydata(coords,[],faces) # edges or faces should be [], or you ask for problems\r\n me.update(calc_edges=True) \r\n return me\r\n \r\n# construit un cylindre et l'oriente dans l'espace\r\ndef cylindreOriente(p1,p2,rayon,nbFaces):\r\n \r\n # use the class constructors from Blender to form vectors for p1 and p2\r\n p1 = Vector(p1)\r\n p2 = Vector(p2)\r\n # form a vector that points in the direction from p1 to p2\r\n dir = p2-p1 \r\n # get the length of the line we want that goes from p1 to p2\r\n length = dir.length\r\n me=cylindre(rayon,nbFaces,length)\r\n dir.normalize()\r\n u = dir\r\n uu = Vector([0,1.0,0])\r\n if abs(u.angle(uu))>1e-6:\r\n v=u.cross(uu)\r\n A=Matrix.Rotation(-u.angle(uu),4,v)\r\n else:\r\n A = Matrix((\r\n (1,0,0,0),\r\n (0,1,0,0),\r\n (0,0,1,0),\r\n (0,0,0,1)))\r\n \r\n # apply the transform to the cylinder \r\n \r\n me.transform(A)\r\n trans=mathutils.Matrix.Translation(p1)\r\n me.transform(trans)\r\n return me\r\n\r\n#Construit une couronne de points\r\ndef couronneOrientee(p1,p2,rayon,nbFaces):\r\n # use the class constructors from Blender to form vectors for p1 and p2\r\n p1 = Vector(p1)\r\n p2 = Vector(p2)\r\n # form a vector that points in the direction from p1 to p2\r\n dir = p2-p1 \r\n # get the length of the line we want that goes from p1 to p2\r\n length = dir.length\r\n me=[[0,0,0] for i in range(nbFaces)]\r\n for i in range(nbFaces):\r\n me[i]=[rayon*cos(2*i*pi/nbFaces),0,rayon*sin(2*i*pi/nbFaces)]\r\n dir.normalize()\r\n u = dir\r\n uu = Vector([0,1.0,0])\r\n if abs(u.angle(uu))>1e-6:\r\n v=u.cross(uu)\r\n A=Matrix.Rotation(-u.angle(uu),4,v)\r\n else:\r\n A = Matrix((\r\n (1,0,0,0),\r\n (0,1,0,0),\r\n (0,0,1,0),\r\n (0,0,0,1)))\r\n \r\n # apply the transform to the cylinder \r\n trans=mathutils.Matrix.Translation(p1)\r\n for i in range(nbFaces):\r\n vecti=Vector((me[i][0],me[i][1],me[i][2]))\r\n vecti.rotate(A)\r\n vecti=vecti+p1\r\n me[i]=[vecti.x,vecti.y,vecti.z]\r\n return me \r\n \r\n \r\n \r\nmaxbox=15\r\nrayon=0.135\r\n\r\nnbAlpha=48 # forcement multiple de 4...\r\nnbTheta=400\r\nnbFaces=10\r\nindice=0\r\nmemoire=[[0,0,0] for i in range(2*nbAlpha)]\r\n\r\n# les parametres de la cyclide\r\np=2\r\nq=-9\r\nomega=q-sqrt(q*q-p*q)\r\nk=p*p-p*q\r\nxOmega=(2*q*q-(p+2*q)*sqrt(q*q-p*q))/(2*q)\r\nr=abs((q-p+sqrt(q*q-p*q))/2);\r\nR=abs((q-p)*(q+sqrt(q*q-p*q))/(2*q)); \r\nRr=sqrt(R*R-r*r); \r\nk=p*p-p*q; \r\n\r\n# les sous programmes auxiliaires (cf L. Garnier)\r\n\r\ndef den1(ct,theta,epsilon):\r\n global xOmega,r,R,omega\r\n valeur=xOmega*xOmega+R*R+r*r+omega*omega-2*xOmega*omega+2*R*r*cos(ct)-2*xOmega*epsilon*r*sin(theta)\r\n return valeur\r\n\r\ndef den2(ct,theta,epsilon):\r\n global xOmega,Rr,r,omega\r\n valeur=-2*xOmega*cos(theta)*sin(ct)*Rr+2*epsilon*r*omega*sin(theta)+2*omega*cos(theta)*sin(ct)*Rr\r\n return valeur\r\n \r\ndef den3(ct,theta,epsilon):\r\n global xOmega,R,omega\r\n valeur=-2*xOmega*R*epsilon*cos(ct)*sin(theta)+2*R*epsilon*omega*cos(ct)*sin(theta)\r\n return valeur\r\n \r\ndef denom(ct,theta,epsilon):\r\n global k\r\n valeur=den1(ct,theta,epsilon)+den2(ct,theta,epsilon)+den3(ct,theta,epsilon)\r\n \r\n return k/valeur\r\n\r\ndef valX(ct,theta,epsilon):\r\n global xOmega,omega,Rr,r,R\r\n f1=xOmega\r\n f2=epsilon*r*sin(theta)\r\n f3=omega\r\n f4=Rr*cos(theta)*sin(ct)\r\n f5=epsilon*R*cos(ct)*sin(theta)\r\n total=f1-f2-f3-f4-f5\r\n return omega+total*denom(ct,theta,epsilon)\r\n\r\ndef valY(ct,theta,epsilon):\r\n global R,r\r\n f1=epsilon*R*cos(ct)*cos(theta)\r\n f2=epsilon*r*cos(theta)\r\n f3=Rr*sin(ct)*sin(theta)\r\n total=-f1-f2+f3\r\n return (-total*denom(ct,theta,epsilon))\r\n \r\ndef valZ(ct,theta,epsilon):\r\n global r\r\n total=r*sin(ct);\r\n return total*denom(ct,theta,epsilon)\r\n\r\n\r\n# Pour arreter les cylindres aux frontieres de la sphere englobante\r\ndef modifCoef(v1,v2,rayon):\r\n vA=v1[0]*v1[0]+v1[1]*v1[1]+v1[2]*v1[2]\r\n vB=v2[0]*v2[0]+v2[1]*v2[1]+v2[2]*v2[2]\r\n vC=v1[0]*v2[0]+v1[1]*v2[1]+v1[2]*v2[2]\r\n delta=(vC-vB)*(vC-vB)-(vA+vB-2*vC)*(vB-rayon*rayon)\r\n alpha0=(-(vC-vB)-sqrt(delta))/(vA+vB-2*vC)\r\n alpha1=(-(vC-vB)+sqrt(delta))/(vA+vB-2*vC)\r\n if (alpha0>=0)and(alpha0<=1):\r\n return alpha0\r\n else:\r\n return alpha1\r\n \r\n\r\ndef distance(t1,t2):\r\n ax=t1[0]-t2[0]\r\n ax=ax*ax\r\n ay=t1[1]-t2[1]\r\n ay=ay*ay\r\n az=t1[2]-t2[2]\r\n az=az*az\r\n return sqrt(ax+ay+az)\r\n \r\n#quand tous les point du cercle sont dans la sphere, on fait un tore clos. \r\ndef makeSimiliTorus(path,rayon,nbFaces):\r\n coords=[]\r\n faces=[]\r\n me=bpy.data.meshes.new('victor')\r\n for i in range(len(path)):\r\n tably=couronneOrientee(path[i],path[(i+1)%len(path)],rayon,nbFaces)\r\n for j in range(len(tably)):\r\n coords.append(tably[j])\r\n \r\n # Construire les faces\r\n for i in range(len(path)):\r\n # calculer le decalage pour eviter les etranglements\r\n # ca ne marche pas vraiment TODO\r\n temoin=coords[i*nbFaces]\r\n indice_challenger=((i+1)%len(path))*nbFaces\r\n decalageMin=0\r\n \r\n challenger=coords[indice_challenger]\r\n distMin=distance(temoin,challenger)\r\n # TODO : pas tres au point, et pas utile\r\n for decalage in range(nbFaces):\r\n challenger=coords[indice_challenger+decalage]\r\n distCourante=distance(temoin,challenger)\r\n if(distCourante<distMin):\r\n decalageMin=decalage\r\n distMin=distCourante\r\n \r\n decalageMin=0\r\n\r\n for j in range(nbFaces): # Normales en cours\r\n #faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces])\r\n faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces])\r\n\r\n faces.append([((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces,((i+1)%len(path))*nbFaces+((j+1+decalageMin)%nbFaces),i*nbFaces+(j+1)%nbFaces])\r\n me.from_pydata(coords,[],faces)\r\n me.update(calc_edges=True) \r\n return me \r\n # fin de makeSimiliTorus \r\n \r\n \r\n \r\n# Pour faire le contour ferme\r\ndef makeSimiliTorus2(path,rayon,nbFaces):\r\n coords=[]\r\n faces=[]\r\n me=bpy.data.meshes.new('victor')\r\n for i in range(len(path)):\r\n tably=couronneOrientee(path[i],path[(i+1)%len(path)],rayon,nbFaces)\r\n for j in range(len(tably)):\r\n coords.append(tably[j])\r\n \r\n # Construire les faces\r\n for i in range(len(path)):\r\n # calculer le decalage pour eviter les etranglements\r\n temoin=coords[i*nbFaces]\r\n indice_challenger=((i+1)%len(path))*nbFaces\r\n decalageMin=0\r\n \r\n challenger=coords[indice_challenger]\r\n \r\n # TODO : pas tres au point, et pas utile (en fait, vaut mieux laisser un decalage de zero...\r\n #14/11/2014 : on refait le decalage\r\n # toujours pas tres efficace : il semble que les deux derniers doivent etre tournes de nbFaces/2\r\n d1min=0\r\n d2min=0\r\n distMin=distance(temoin,challenger)\r\n for decalage1 in range(nbFaces):\r\n surCercle1=coords[i*nbFaces+decalage1]\r\n debut2=coords[indice_challenger]\r\n for decalage2 in range(nbFaces):\r\n # distance entre i+d1min et j+d2min\r\n surCercle2=coords[indice_challenger+decalage2]\r\n distCourante=distance(surCercle1,surCercle2)\r\n if(distCourante<distMin):\r\n distMin=distCourante\r\n d1min=decalage1\r\n d2min=decalage2\r\n decalageMin=(d2min-d1min+nbFaces)%nbFaces \r\n print(i,\" \",d1min,\" \",d2min,\" \",decalageMin) \r\n \r\n if(i<len(path)-2):\r\n for j in range(nbFaces):\r\n #faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces]) #originale\r\n faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces])\r\n faces.append([((i+1)%len(path))*nbFaces+(j+decalageMin)%nbFaces,((i+1)%len(path))*nbFaces+((j+1+decalageMin)%nbFaces),i*nbFaces+(j+1)%nbFaces]) #originale\r\n #traitement particulier pour les deux derniers segments...\r\n if(i==len(path)-2):\r\n decalage=decalageMin-1\r\n for j in range(nbFaces): # Normales OK\r\n #faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces]) #originale\r\n faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces]) \r\n faces.append([((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces,((i+1)%len(path))*nbFaces+((nbFaces-j-1-1+decalage)%nbFaces),i*nbFaces+(j+1)%nbFaces]) #originale\r\n #faces.append([((i+1)%len(path))*nbFaces+((nbFaces-j-1-1+decalage)%nbFaces),((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces,i*nbFaces+(j+1)%nbFaces]) \r\n if(i==len(path)-1):\r\n decalage=decalageMin-1\r\n for j in range(nbFaces): # Normales OK\r\n faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces]) #originale\r\n #faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces]) \r\n #faces.append([((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces,((i+1)%len(path))*nbFaces+((nbFaces-j-1-1+decalage)%nbFaces),i*nbFaces+(j+1)%nbFaces]) #originale\r\n faces.append([((i+1)%len(path))*nbFaces+((nbFaces-j-1-1+decalage)%nbFaces),((i+1)%len(path))*nbFaces+(nbFaces-j-1+decalage)%nbFaces,i*nbFaces+(j+1)%nbFaces]) \r\n me.from_pydata(coords,[],faces)\r\n me.update(calc_edges=True) \r\n return me \r\n # fin de makeSimiliTorus2 \r\n\r\n# construire un tube a partir d'un tableau de points \r\n# aussitot qu'on repere que tous les points sont presents, on pass la main a un autre programme\r\ndef makeMesh(path,radio,nbFaces,alpha,epsilon):\r\n global nbTheta,maxbox,memoire,indice\r\n coords=[]\r\n faces=[]\r\n \r\n me=bpy.data.meshes.new('myMesh')\r\n fin=len(path)-1\r\n complet=1\r\n # A quel angle theta=2*indiceDepart*pi/nbTheta correspond la premiere valeur du tableau\r\n indiceDepart=0\r\n # Purger le tableau \r\n nbZero=0\r\n for i in range(len(path)):\r\n if(path[i].length==0):\r\n nbZero=nbZero+1\r\n if(nbZero==len(path)):\r\n print(\"rien dans le tableau\")\r\n return \r\n \r\n if(nbZero==0):\r\n return makeSimiliTorus(path,radio,nbFaces)\r\n \r\n # cas des cercles incomplets\r\n \r\n # tableau avec des trous\r\n complet=0\r\n # tant qu'il y a des zeros a gauche, decaler a gauche \r\n while(path[0].length==0):\r\n #decaler le tableau vers la gauche\r\n indiceDepart=indiceDepart+1\r\n tmp=path[0]\r\n for i in range(len(path)-1):\r\n path[i]=path[i+1]\r\n path[len(path)-1]=tmp\r\n #tant qu'il y a quelque chose a droite, decaler a droite\r\n while(path[len(path)-1].length!=0):\r\n indiceDepart=indiceDepart-1\r\n tmp=path[len(path)-1]\r\n for i in range(len(path)-1):\r\n j=len(path)-i-1\r\n path[j]=path[j-1]\r\n path[0]=tmp \r\n while(path[fin].length==0):\r\n fin=fin-1\r\n # de l'indice zero a l'indice fin (inclus), la liste des points du tube \r\n # pour des angles theta=2*(indiceDepart)*pi/nbTheta jusqu'a theta=2*(indiceDepart+fin)*pi/nbTheta\r\n # todo : si le tube n'est pas ferme, rajouter des bouts de tube pour arriver au bord, memoriser la position de cette extremite...\r\n \r\n \r\n #rajouter un point a la fin\r\n \r\n theta1=2*(indiceDepart+fin)*pi/nbTheta\r\n theta2=2*(1+indiceDepart+fin)*pi/nbTheta\r\n vx=valX(theta1,alpha,epsilon)\r\n vy=valY(theta1,alpha,epsilon)\r\n vz=valZ(theta1,alpha,epsilon)\r\n point1=Vector((vx,vy,vz))\r\n \r\n vx=valX(theta2,alpha,epsilon)\r\n vy=valY(theta2,alpha,epsilon)\r\n vz=valZ(theta2,alpha,epsilon)\r\n point2=Vector((vx,vy,vz))\r\n valcoef=modifCoef(point1,point2,maxbox)\r\n fin=fin+1\r\n path[fin]=valcoef*point1+(1-valcoef)*point2 \r\n memoire[indice]=path[fin]\r\n indice=indice+1\r\n \r\n #rajouter un point au debut\r\n # todo que faire quand on ne peu pas ???\r\n if(nbZero!=1):\r\n theta1=2*(indiceDepart-1)*pi/nbTheta\r\n theta2=2*(indiceDepart)*pi/nbTheta\r\n vx=valX(theta1,alpha,epsilon)\r\n vy=valY(theta1,alpha,epsilon)\r\n vz=valZ(theta1,alpha,epsilon)\r\n point1=Vector((vx,vy,vz))\r\n \r\n vx=valX(theta2,alpha,epsilon)\r\n vy=valY(theta2,alpha,epsilon)\r\n vz=valZ(theta2,alpha,epsilon)\r\n point2=Vector((vx,vy,vz))\r\n valcoef=modifCoef(point1,point2,maxbox)\r\n for i in reversed(range(fin+1)):\r\n path[i+1]=path[i]\r\n path[0]=valcoef*point1+(1-valcoef)*point2 \r\n memoire[indice]=path[0]\r\n indice=indice+1\r\n fin=fin+1 \r\n \r\n \r\n \r\n for i in range(fin):\r\n \r\n tably=couronneOrientee(path[i],path[i+1],radio,nbFaces)\r\n for j in range(len(tably)):\r\n coords.append(tably[j])\r\n \r\n # la derniere couronne (a l'envers)\r\n tably=couronneOrientee(path[fin],path[fin-1],radio,nbFaces)\r\n for j in reversed(range(len(tably))):\r\n coords.append(tably[j])\r\n # Construire les faces\r\n for i in range(fin):\r\n # calculer le decalage pour eviter les etranglements\r\n temoin=coords[i*nbFaces]\r\n indice_challenger=(i+1)*nbFaces\r\n decalageMin=0\r\n challenger=coords[indice_challenger]\r\n distMin=distance(temoin,challenger)\r\n for decalage in range(nbFaces):\r\n challenger=coords[indice_challenger+decalage]\r\n distCourante=distance(temoin,challenger)\r\n if(distCourante<distMin):\r\n decalageMin=decalage\r\n distMin=distCourante\r\n \r\n \r\n \r\n if(i!=fin-1): \r\n decalageMin=0\r\n for j in range(nbFaces):\r\n #faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),(i+1)*nbFaces+(j+decalageMin)%nbFaces]) # originale\r\n faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,(i+1)*nbFaces+(j+decalageMin)%nbFaces])\r\n faces.append([(i+1)*nbFaces+(j+decalageMin)%nbFaces,(i+1)*nbFaces+((j+1+decalageMin)%nbFaces),i*nbFaces+(j+1)%nbFaces]) #originale\r\n else:\r\n #print(\"decalage :\",decalageMin)\r\n for j in range(nbFaces): # Normales OK\r\n #faces.append([i*nbFaces+j,i*nbFaces+((j+1)%nbFaces),(i+1)*nbFaces+(j+decalageMin)%nbFaces])# originale\r\n faces.append([i*nbFaces+((j+1)%nbFaces),i*nbFaces+j,(i+1)*nbFaces+(j+decalageMin)%nbFaces])\r\n faces.append([(i+1)*nbFaces+(j+decalageMin)%nbFaces,(i+1)*nbFaces+((j+1+decalageMin)%nbFaces),i*nbFaces+(j+1)%nbFaces]) #originale \r\n \r\n # toutes les faces sont finies sauf les couvercles\r\n \r\n coords.append([path[0][0],path[0][1],path[0][2]]) # de numero (fin+1)*nbFaces\r\n coords.append([path[fin][0],path[fin][1],path[fin][2]]) # de numero (fin+1)*nbFaces+1 \r\n for j in range(nbFaces):\r\n # fabriquer des triangles pour les couvercles \r\n faces.append([j,(j+1)%nbFaces,(fin+1)*nbFaces])\r\n faces.append([j+fin*nbFaces,(j+1)%nbFaces+fin*nbFaces,(fin+1)*nbFaces+1])\r\n \r\n me.from_pydata(coords,[],faces)\r\n me.update(calc_edges=True) \r\n return me \r\n \r\ndef ordonner(tableau,indice):\r\n for i in range(indice):\r\n for j in range(indice-1):\r\n valj=atan2(tableau[j][1],tableau[j][2])\r\n valjp1=atan2(tableau[j+1][1],tableau[j+1][2]) \r\n if(valj>valjp1):\r\n tmp=tableau[j]\r\n tableau[j]=tableau[j+1]\r\n tableau[j+1]=tmp\r\n return \r\n \r\ndef colorize(myMesh,myColor):\r\n # Create a single Material that respect Vertex Color\r\n mat = bpy.data.materials.new('VertexMat')\r\n mat.use_vertex_color_paint = True\r\n mat.use_vertex_color_light = True\r\n \r\n \r\n \r\n # Create new 'Col' Vertex Color Layer\r\n myMesh.vertex_colors.new()\r\n \r\n # Vertex colour data\r\n vertexColor = myMesh.vertex_colors[0].data\r\n faces = myMesh.polygons\r\n \r\n # Assign colours to verts (loop every faces)\r\n # Script Snippet from Blender Artist\r\n #Fixer la couleur de tous les sommets d'une meme lunule\r\n j = 0\r\n for face in faces:\r\n for idx in face.loop_indices:\r\n vertexColor[j].color = myColor\r\n j += 1\r\n return\r\n# fin de colorize \r\n \r\n \r\n#test\r\nprint(\"debut/n\")\r\n\r\nscn=bpy.context.scene\r\n\r\n\r\ncouleurFamille1=[0,0,1] # bleu\r\ncouleurFamille2=[1,165/255,0] #orange\r\ncouleurDroiteP=[0,1,0] # vert\r\ncouleurDroiteQ=[1,1,0] # jaune\r\ncouleurContour=[0.5,0,0] # marron\r\n\r\nfirst=1\r\nnumero=0\r\n\r\ncatenaName='velo'\r\n\r\n# Memoriser tous les points de coupure, pour les ordonner et les relier a la fin \r\nmemoire=[0 for i in range(800)]\r\nindice=0\r\n\r\n\r\n\r\nfor ind2 in range(nbAlpha):\r\n \r\n alphy=2*ind2*pi/nbAlpha\r\n #le cas des droites\r\n if alphy==pi/2:\r\n alphy=0.9999*alphy\r\n if alphy==3*pi/2:\r\n alphy=0.9999*alphy\r\n # memoriser tous les points d'un cercle de Villarceau, 0 si on sort des bornes \r\n cheminDirect=[Vector((0,0,0)) for i in range(nbTheta)] \r\n cheminInverse=[Vector((0,0,0)) for i in range(nbTheta)] \r\n \r\n for index in range(nbTheta):\r\n theta=2*index*pi/nbTheta\r\n vx=valX(theta,alphy,1)\r\n vy=valY(theta,alphy,1)\r\n vz=valZ(theta,alphy,1)\r\n \r\n \r\n depart=Vector((vx,vy,vz))\r\n \r\n \r\n if depart.length<=maxbox:\r\n cheminDirect[index]=depart\r\n \r\n \r\n \r\n # deuxieme cercle pour le meme alpha \r\n vx=valX(theta,alphy,-1)\r\n vy=valY(theta,alphy,-1)\r\n vz=valZ(theta,alphy,-1)\r\n \r\n thetaS=theta+2*pi/nbTheta\r\n vxS=valX(thetaS,alphy,-1)\r\n vyS=valY(thetaS,alphy,-1)\r\n vzS=valZ(thetaS,alphy,-1)\r\n \r\n depart=Vector((vx,vy,vz))\r\n \r\n if (depart.length<=maxbox):\r\n cheminInverse[index]=depart\r\n \r\n \r\n # On a cree deux chemins, pas forcement complets, on les transforme en tubes fermes aux extremites\r\n \r\n if(ind2!=(nbAlpha//4)):\r\n cercle1=makeMesh(cheminDirect,rayon,nbFaces,alphy,1)\r\n else:\r\n cercle1=makeMesh(cheminDirect,2*rayon,nbFaces,alphy,1)\r\n \r\n colorize(cercle1,couleurFamille1)\r\n \r\n if(first==1):\r\n ob=bpy.data.objects.new(catenaName+str(numero),cercle1)\r\n bpy.context.scene.objects.link(ob) \r\n bpy.context.scene.objects.active = ob\r\n numero+=1\r\n first=0\r\n else:\r\n localOb=bpy.data.objects.new(catenaName+str(numero),cercle1)\r\n numero+=1\r\n scn.objects.link(localOb)\r\n \r\n if(ind2!=(3*nbAlpha//4)):\r\n cercle2=makeMesh(cheminInverse,rayon,nbFaces,alphy,-1)\r\n else:\r\n cercle2=makeMesh(cheminInverse,2*rayon,nbFaces,alphy,-1)\r\n \r\n colorize(cercle2,couleurFamille2)\r\n \r\n if(first==1):\r\n ob=bpy.data.objects.new(catenaName+str(numero),cercle2)\r\n bpy.context.scene.objects.link(ob) \r\n bpy.context.scene.objects.active = ob\r\n numero+=1\r\n first=0\r\n else:\r\n localOb=bpy.data.objects.new(catenaName+str(numero),cercle2)\r\n numero+=1\r\n scn.objects.link(localOb)\r\n \r\n\r\n#for index in range(indice): \r\n #print(index,\" \",memoire[index],\" \",atan2(memoire[index][1],memoire[index][2]))\r\n \r\n \r\nordonner(memoire,indice)\r\n\r\n# On a besoin de ça pour avoir un tableau de la bonne taille : il y a surement une meilleure solution...\r\nnewMemoire=[[0,0,0] for i in range(indice)]\r\n \r\nfor index in range(indice): \r\n #print(index,\" \",memoire[index],\" \",atan2(memoire[index][1],memoire[index][2]))\r\n newMemoire[index]=memoire[index]\r\n \r\ncontour=makeSimiliTorus2(newMemoire,rayon*1.2,nbFaces) \r\ncolorize(contour,couleurContour)\r\nlocalOb=bpy.data.objects.new(catenaName+str(numero),contour)\r\nnumero+=1\r\nscn.objects.link(localOb)\r\n\r\n\r\n# les deux droites p et q\r\n# Droite P\r\nextremiteMoinsP=Vector((p,0,-maxbox+rayon))\r\nextremitePlusP=Vector((p,0,maxbox-rayon))\r\ndroiteP=cylindreOriente(extremiteMoinsP,extremitePlusP,2*rayon,nbFaces)\r\ncolorize(droiteP,couleurDroiteP)\r\nlocalOb=bpy.data.objects.new(catenaName+str(numero),droiteP)\r\nnumero+=1\r\nscn.objects.link(localOb)\r\n\r\n\r\n# Droite Q\r\nextremiteMoinsQ=Vector((q,-24.23/2+rayon,0))\r\nextremitePlusQ=Vector((q,24.23/2-rayon,0))\r\ndroiteQ=cylindreOriente(extremiteMoinsQ,extremitePlusQ,2*rayon,nbFaces)\r\ncolorize(droiteQ,couleurDroiteQ)\r\nlocalOb=bpy.data.objects.new(catenaName+str(numero),droiteQ)\r\nnumero+=1\r\nscn.objects.link(localOb)\r\n\r\n\r\n\r\nbpy.ops.object.select_pattern(extend=False, pattern=catenaName+'*', case_sensitive=False)\r\nbpy.ops.object.join() ", "sub_path": "Voronoi/src/test/cyclideC3V3.py", "file_name": "cyclideC3V3.py", "file_ext": "py", "file_size_in_byte": 24245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "bpy.data.meshes.new", "line_number": 28, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 28, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 65, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 65, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 86, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 86, "usage_type": "attribute"}, {"api_name": "mathutils.Matrix.Translation", "line_number": 134, "usage_type": "call"}, {"api_name": "mathutils.Matrix", "line_number": 134, "usage_type": "attribute"}, {"api_name": "mathutils.Matrix.Translation", "line_number": 164, "usage_type": "call"}, {"api_name": "mathutils.Matrix", "line_number": 164, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 268, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 268, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 310, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 310, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 377, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 377, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 530, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 530, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 558, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 631, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 631, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.objects.link", "line_number": 632, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 632, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 633, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 637, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 637, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 649, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 649, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.objects.link", "line_number": 650, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 650, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 651, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 655, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 655, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 675, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 675, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 686, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 686, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 696, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 696, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_pattern", "line_number": 702, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 702, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.join", "line_number": 703, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 703, "usage_type": "attribute"}]} {"seq_id": "616346687", "text": "# pip install beautifulsoup4\r\n# pip install lxml\r\n\r\nfrom bs4 import BeautifulSoup\r\n\r\nwith open(\"sample.html\", \"r\") as html_file:\r\n content = html_file.read()\r\n soup = BeautifulSoup(content, 'lxml')\r\n course_cards = soup.findAll(\"div\", class_=\"card\")\r\n for course in course_cards:\r\n course_name = course.h5.text\r\n course_price = course.a.text.split()[-1]\r\n print(f\"{course_name} costs {course_price}\")\r\n\r\n# headers = {\"User-Agent\": \"Mozilla/5.0\"}\r\n# content is bytes, text is text - content is better\r\n# find, find_all\r\n# select_one, select\r\n# # id\r\n# . class\r\n# tag\r\n# find and .get(\"attr\")\r\n\r\n# bs4.element\r\n# bs4.element.ResultSet\r\n\r\n# str.ljust(30)\r\n\r\n# string=\"xx\"\r\n\r\n# find_parents\r\n# find_parent\r\n# .get_text(strip=True)\r\n\r\n# ul\r\n# ol\r\n# li\r\n\r\n# table\r\n# tr\r\n# th/td\r\n\r\n# div section span input a p\r\n\r\n# find_next_sibling/s\r\n# find_previous_sibling/s\r\n\r\n# find_next()\r\n# find_all_next()\r\n\r\n# find_previous()\r\n# find_all_previous()\r\n", "sub_path": "PYTHON/PYTHON_BEAUTIFULSOUP/1_bs4_on_html.py", "file_name": "1_bs4_on_html.py", "file_ext": "py", "file_size_in_byte": 971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}]} {"seq_id": "307472735", "text": "import argparse\nfrom PIL import Image\n\nfrom vietocr.tool.predictor import Predictor\nfrom vietocr.tool.config import Cfg\nimport sys \nsys.path.insert(0, './')\nfrom char import character\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--img', required=True, help='foo help')\n parser.add_argument('--config', required=True, help='foo help')\n\n args = parser.parse_args()\n config_base = Cfg.load_config_from_file(\"config/base.yml\")\n config = Cfg.load_config_from_file(args.config)\n config_base.update(config)\n config = config_base\n\n config['vocab'] = character\n\n\n detector = Predictor(config)\n\n img = Image.open(args.img)\n s = detector.predict(img)\n\n print(s)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "vietocr/predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 754, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.insert", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "vietocr.tool.config.Cfg.load_config_from_file", "line_number": 15, "usage_type": "call"}, {"api_name": "vietocr.tool.config.Cfg", "line_number": 15, "usage_type": "name"}, {"api_name": "vietocr.tool.config.Cfg.load_config_from_file", "line_number": 16, "usage_type": "call"}, {"api_name": "vietocr.tool.config.Cfg", "line_number": 16, "usage_type": "name"}, {"api_name": "char.character", "line_number": 20, "usage_type": "name"}, {"api_name": "vietocr.tool.predictor.Predictor", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}]} {"seq_id": "4012783", "text": "r\"\"\"\nDownload resources used for various purposes.\n\nUsage:\n python download_resources.py \\\n --game_num 5961 \\\n --data_root path/to/folder/containing/all/game/data/ \\\n --purpose calibration\n\"\"\"\n\nimport argparse\nimport os\n\nfrom src.util import s3_util\n\n\ndef download_spotlight_resources(game_num, local_game_folder):\n \"\"\"\n Download resources used for running spotlight.\n\n Args:\n game_num Game number\n local_game_folder Path to folder containing game resources\n \"\"\"\n s3_util.download_calibration_folder(game_num, local_game_folder)\n s3_util.download_color_classification_folder(game_num, local_game_folder)\n s3_util.download_object_detector_folder_no_videos(\n game_num, local_game_folder)\n s3_util.download_object_tracker_folder(game_num, local_game_folder)\n s3_util.download_results_folder(game_num, local_game_folder)\n s3_util.download_spotlight_folder(game_num, local_game_folder)\n s3_util.download_stitch_folder_no_videos(game_num, local_game_folder)\n\n\ndef download_calibration_resources(game_num, local_game_folder):\n \"\"\"\n Download resources used for calibration.\n\n Args:\n game_num Game number\n local_game_folder Path to folder containing game resources\n \"\"\"\n s3_util.download_calibration_folder(game_num, local_game_folder)\n s3_util.download_results_folder(game_num, local_game_folder)\n s3_util.download_stitch_folder_no_videos(game_num, local_game_folder)\n\n\nif __name__ == \"__main__\":\n\n ap = argparse.ArgumentParser()\n ap.add_argument(\n \"--game_num\", required=True, type=int,\n help=\"Game number to use\")\n ap.add_argument(\n \"--data_root\", required=True, type=str,\n help=\"Folder containing game data\")\n ap.add_argument(\n \"--purpose\", required=True, type=str,\n choices=[\"calibration\", \"spotlight\"])\n args = vars(ap.parse_args())\n game_num = args[\"game_num\"]\n data_root = args[\"data_root\"]\n purpose = args[\"purpose\"]\n\n assert os.path.isdir(data_root)\n\n local_game_folder = os.path.join(data_root, \"game_{0}\".format(game_num))\n\n if purpose == \"calibration\":\n download_calibration_resources(game_num, local_game_folder)\n elif purpose == \"spotlight\":\n download_spotlight_resources(game_num, local_game_folder)\n", "sub_path": "download_resources.py", "file_name": "download_resources.py", "file_ext": "py", "file_size_in_byte": 2338, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "src.util.s3_util.download_calibration_folder", "line_number": 25, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 25, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_color_classification_folder", "line_number": 26, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 26, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_object_detector_folder_no_videos", "line_number": 27, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 27, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_object_tracker_folder", "line_number": 29, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 29, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_results_folder", "line_number": 30, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 30, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_spotlight_folder", "line_number": 31, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 31, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_stitch_folder_no_videos", "line_number": 32, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 32, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_calibration_folder", "line_number": 43, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 43, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_results_folder", "line_number": 44, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 44, "usage_type": "name"}, {"api_name": "src.util.s3_util.download_stitch_folder_no_videos", "line_number": 45, "usage_type": "call"}, {"api_name": "src.util.s3_util", "line_number": 45, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}]} {"seq_id": "603905390", "text": "# coding: UTF-8\r\n\r\nimport numpy as np\r\nimport cv2\r\nfrom matplotlib import pyplot as plt\r\n\r\n#img = cv2.imread('simple.jpg',0)\r\nimg = cv2.imread('simple2.jpg',0)\r\n\r\n#まずデフォルト値でFASTオブジェクトを作る\r\nfast = cv2.FastFeatureDetector_create()\r\n\r\n# キーポイントを求めて描画する\r\nkp = fast.detect(img,None)\r\nimg2 = cv2.drawKeypoints(img, kp,None,color=(255,0,0))\r\n\r\n# デフォルトのパラメタをすべて表示\r\nprint(\"Threshold: \", fast.getThreshold()) #閾値の設定\r\nprint(\"nonmaxSuppression: \", fast.getNonmaxSuppression()) #最大値抑制を適用するか\r\nprint(\"neighborhood: \", fast.getType()) #周辺領域の選択\r\nprint(\"Total Keypoints with nonmaxSuppression: \", len(kp))\r\n\r\n#cv2.imwrite('fast_true.png',img2)\r\ncv2.imwrite('fast_true2.png',img2)\r\n\r\n# nonmaxSuppressionを停止\r\nfast.setNonmaxSuppression(0)\r\nkp = fast.detect(img,None)\r\n\r\nprint(\"Total Keypoints without nonmaxSuppression: \", len(kp))\r\n\r\nimg3 = cv2.drawKeypoints(img, kp,None,color=(255,0,0))\r\n\r\n#cv2.imwrite('fast_false.png',img3)\r\ncv2.imwrite('fast_false2.png',img3)", "sub_path": "fast_sample.py", "file_name": "fast_sample.py", "file_ext": "py", "file_size_in_byte": 1096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.FastFeatureDetector_create", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.drawKeypoints", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.drawKeypoints", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 35, "usage_type": "call"}]} {"seq_id": "271250278", "text": "\"\"\"empty message\n\nRevision ID: 40bc44aaf9dd\nRevises: 201626a58dfd\nCreate Date: 2017-01-02 18:22:33.100981\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '40bc44aaf9dd'\ndown_revision = '201626a58dfd'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('last_message',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('fb_uid', sa.Integer(), nullable=True),\n sa.Column('last_user_message', sa.Unicode(), nullable=True),\n sa.Column('last_bot_response', sa.Unicode(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('last_message')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/40bc44aaf9dd_.py", "file_name": "40bc44aaf9dd_.py", "file_ext": "py", "file_size_in_byte": 880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Unicode", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Unicode", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} {"seq_id": "192398940", "text": "from inspect import stack\nfrom pathlib import Path\n\n\ndef abs_path_str_from_rel_to_this_file( path : str ) :\n caller_frame = stack()[1]\n caller_file_path = caller_frame.filename\n caller_directory = Path( caller_file_path ).parent\n full_path = caller_directory / path\n abs_path = full_path.resolve()\n abs_path_str = abs_path.as_posix()\n return abs_path_str", "sub_path": "path.py", "file_name": "path.py", "file_ext": "py", "file_size_in_byte": 375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "inspect.stack", "line_number": 6, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}]} {"seq_id": "221369892", "text": "#!/usr/bin/env python3\n\nimport sys\nimport traceback\nimport time\nimport serial\nimport serial.tools.list_ports\n\n# feature toggles\nusb = False\nuart = True\n\n# if all the following are False then exit right away\nlocal = False\ncompetition = True\n# dynamic not working properly, keep at False\ndynamic = False\n\n# note: since this program is in /usr/bin/ on the OBC\n# it was necessary to also add the connection.py\n# class in /usr/bin and change the following line to\n# from connection import Connection\n# for the startup service to work properly\nfrom robot.comms.connection import Connection\nfrom robot.comms.uart import Uart\n\n# returns current time in milliseconds\ncurrent_millis = lambda: int(round(time.time() * 1000))\n\ndef get_commands_list():\n return \"\"\"'q': quit\\n\n'w': forward\\n\n's': back\\n\n'a': left\\n\n'd': right\\n\n'i': increase throttle speed\\n\n'j': decrease throttle speed\\n\n'o': increase steering speed\\n\n'k': decrease steering speed\\n\n'l': list commands\\n\\n\"\"\"\n\n# returns current time in milliseconds\ncurrent_millis = lambda: int(round(time.time() * 1000))\n\nif len(sys.argv) == 2:\n ROVER_PORT = int(sys.argv[1])\nelif len(sys.argv) >= 3:\n print(\n \"too many arguments, one optional argument is the port number, otherwise default to 5010\"\n )\n print(\"example usage: python ServerListener.py <port>\")\n\n\nif not local and not competition and not dynamic:\n print(\"local, competition and dynamic flags set to false, exiting\")\n sys.exit(0)\n\n\nif usb:\n # set up connection to arduino\n ports = list(serial.tools.list_ports.comports())\n is_rover = False\n\n if len(ports) == 1:\n print(\"1 USB device detected\")\n port = ports[0].name\n ser = serial.Serial('/dev/' + port, 9600)\n\n print(\"clearing buffer\")\n while ser.in_waiting:\n print(ser.readline().decode())\n\n for i in 0, 3:\n who = \"\"\n print(\"identifying MCU\")\n ser.write(str.encode(\"who\\n\"))\n\n # CRITICAL: give time for MCU to respond\n time.sleep(1)\n\n while ser.in_waiting:\n who = ser.readline().decode()\n print(\"who: \" + who)\n\n if who.strip() == \"rover\":\n print(\"Rover MCU identified!\")\n is_rover = True\n elif len(ports) == 2:\n print(\"2 USB devices detected\")\n port = ports[1].name\n ser = serial.Serial('/dev/' + port, 9600)\n\n print(\"clearing buffer\")\n while ser.in_waiting:\n print(ser.readline().decode())\n\n for i in 0, 3:\n who = \"\"\n print(\"identifying MCU\")\n ser.write(str.encode(\"who\\n\"))\n\n # CRITICAL: give time for MCU to respond\n time.sleep(1)\n\n while ser.in_waiting:\n who = ser.readline().decode()\n print(\"who: \" + who)\n\n if who.strip() == \"rover\":\n print(\"Rover MCU identified!\")\n is_rover = True\n\n if not is_rover:\n port = ports[0].name\n ser = serial.Serial('/dev/' + port, 9600)\n\n print(\"clearing buffer\")\n while ser.in_waiting:\n print(ser.readline().decode())\n\n for i in 0, 3:\n who = \"\"\n print(\"identifying MCU\")\n ser.write(str.encode(\"who\\n\"))\n\n # CRITICAL: give time for MCU to respond\n time.sleep(1)\n\n while ser.in_waiting:\n who = ser.readline().decode()\n print(\"who: \" + who)\n\n if who.strip() == \"rover\":\n print(\"Rover MCU identified!\")\n is_rover = True\n\n else:\n print(\"No USB devices recognized, exiting\")\n sys.exit(0)\n\n if is_rover:\n print(\"Connected to port: \" + port)\n else:\n print(\"Incorrect MCU connected, terminating listener\")\n sys.exit(0)\nelif uart:\n u = Uart(\"/dev/ttySAC0\", 9600, timeout=1)\n\n# for local testing\nif local:\n ROVER_IP = \"127.0.0.1\"\n ROVER_PORT = 5020\n BASE_IP = ROVER_IP\n BASE_PORT = 5025\n# for competition\nelif competition:\n ROVER_IP = \"172.16.1.30\"\n ROVER_PORT = 5030\n BASE_IP = \"172.16.1.20\"\n BASE_PORT = ROVER_PORT\n# attempt to get: physicial ip, which should not need connection to internet to work\n#elif dynamic:\n# ROVER_IP = ni.ifaddresses(ni.interfaces()[1])[AF_INET][0]['addr']\n\nprint(\"ROVER_IP: \" + ROVER_IP)\nprint(\"BASE_IP: \" + BASE_IP)\n\n# Create connection object to send/receive data with base-station\nreceiver = Connection(\"rover_drive_receiver\", ROVER_IP, ROVER_PORT)\nsender = Connection(\"rover_feedback_sender\", BASE_IP, BASE_PORT)\n\nprint(\"Rover server listening on port {} \\n\".format(ROVER_PORT))\n\nprint(\"Ready for incoming drive cmds!\\n\")\n\nprint(get_commands_list())\n\nkey_list = ['w', 'a', 's', 'd', 'i', 'j', 'l', 'o', 'k', 'm', 'n', 't', 'b', 'q']\n# Arduino sketch considers this value to be the signal for the motors to not move\nREST = 49.5\n\n# initialize throttle/steering speeds to 0\nthrottle_speed = 0\nsteering_speed = 0\n\n# impose safety limits, theoretical limit at 49.5\nMIN_THROTTLE_SPEED = 0\nMAX_THROTTLE_SPEED = 25\nMIN_STEERING_SPEED = 0\nMAX_STEERING_SPEED = 39\n\n# for controlling command throughput\nlast_cmd_sent = 0 # to keep track of the time of the last command sent\nTHROTTLE_TIME = 25 # time to wait before receiving next command\n\n\nwhile True:\n #while ser.in_waiting:\n # print(ser.readline().decode())\n\n try:\n # only receive commands if last command was sent\n # THROTTLE_TIME ago (in milliseconds)\n if current_millis() - last_cmd_sent > THROTTLE_TIME:\n\n command = receiver.receive()\n\n # for throttle speed, no backwards\n if command in key_list:\n print(\"OPERATOR command: \" + command + \" recognized\\n\")\n\n if command == 'w':\n feedback = \"cmd: w --> Forward\\n\"\n command = str(REST + throttle_speed) + \":\" + str(REST) + \"\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n last_cmd_sent = current_millis()\n\n elif command == 'a':\n feedback = \"cmd: a --> Left\\n\"\n command = str(REST + throttle_speed) + \":\" + str(REST - steering_speed) + \"\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n last_cmd_sent = current_millis()\n\n elif command == 's':\n feedback = \"cmd: s --> Back\\n\"\n command = str(REST - throttle_speed) + \":\" + str(REST) + \"\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n last_cmd_sent = current_millis()\n\n elif command == 'd':\n feedback = \"cmd: d --> Right\"\n command = str(REST + throttle_speed) + \":\" + str(REST + steering_speed) + \"\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n last_cmd_sent = current_millis()\n\n elif command == 'm':\n feedback = \"cmd: m --> enable motor controls\"\n command = \"activate\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n elif command == 'n':\n feedback = \"cmd: n --> disable motor controls\"\n command = \"deactivate\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n # 't' --> reset to 0 on release key, otherwise motor keeps spinning\n # 45.5:45.5\n elif command == 't':\n feedback = \"cmd: t --> stop moving\"\n command = str(REST) + \":\" + str(REST) + \"\\n\"\n feedback += \"\\ncommand: \" + str(command)\n print(feedback)\n sender.send(feedback)\n\n if usb:\n ser.write(str.encode(command))\n elif uart:\n u.tx(command)\n\n last_cmd_sent = current_millis()\n\n elif command == 'i':\n feedback = \"cmd: i --> Increment throttle speed\"\n\n if throttle_speed < MAX_THROTTLE_SPEED:\n throttle_speed += 0.5\n feedback += \"\\nthrottle speed: \" + str(throttle_speed)\n else:\n feedback += \"\\nthrottle speed upper limit reached\"\n\n print(feedback)\n sender.send(feedback)\n\n elif command == 'j':\n feedback = \"cmd: j --> Decrement throttle speed\"\n\n if throttle_speed > MIN_THROTTLE_SPEED:\n throttle_speed -= 0.5\n feedback += \"\\nthrottle speed: \" + str(throttle_speed)\n else:\n feedback += \"\\nthrottle speed lower limit reached\"\n\n print(feedback)\n sender.send(feedback)\n\n elif command == 'o':\n feedback = \"cmd: o --> Increment steering speed\"\n\n if steering_speed < MAX_STEERING_SPEED:\n steering_speed += 0.5\n feedback += \"\\nsteering speed: \" + str(steering_speed)\n else:\n feedback += \"\\nsteering speed upper limit reached\"\n\n print(feedback)\n sender.send(feedback)\n\n elif command == 'k':\n feedback = \"cmd: k --> Decrement steering speed\"\n\n if steering_speed > MIN_STEERING_SPEED:\n steering_speed -= 0.5\n feedback += \"\\nsteering speed: \" + str(steering_speed)\n else:\n feedback += \"\\nsteering speed lower limit reached\"\n\n print(feedback)\n sender.send(feedback)\n\n elif command == 'q':\n feedback = \"\\nTerminating connection.\"\n\n print(feedback)\n sender.send(feedback)\n\n break\n\n elif command == 'l':\n print(get_commands_list())\n sender.send(get_commands_list())\n\n elif command == 'b':\n if usb:\n data = \"\"\n while ser.in_waiting:\n data += ser.readline().decode()\n\n print(data)\n sender.send(data)\n elif uart:\n data = u.rx()\n print(data)\n sender.send(data)\n else:\n print(\"UNKOWN command \" + command + \"\\n\")\n\n if usb:\n # flush buffer to avoid overflowing it\n ser.reset_input_buffer()\n ser.reset_output_buffer()\n\n except Exception:\n if usb:\n ser.close()\n print(\"Exception in user code:\")\n print(\"-\"*60)\n traceback.print_exc(file=sys.stdout)\n print(\"-\"*60)\n break\n", "sub_path": "robot/rospackages/src/task_handler/scripts/RoverCommandListener.py", "file_name": "RoverCommandListener.py", "file_ext": "py", "file_size_in_byte": 12556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 61, "usage_type": "call"}, {"api_name": "serial.tools", "line_number": 61, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 67, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 115, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 145, "usage_type": "call"}, {"api_name": "robot.comms.uart.Uart", "line_number": 147, "usage_type": "call"}, {"api_name": "robot.comms.connection.Connection", "line_number": 169, "usage_type": "call"}, {"api_name": "robot.comms.connection.Connection", "line_number": 170, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 393, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 393, "usage_type": "attribute"}]} {"seq_id": "524538948", "text": "from django.shortcuts import render\nfrom .models import Product, Category\nfrom django.db.models import Q\nfrom django.core.paginator import Paginator, InvalidPage, PageNotAnInteger, EmptyPage\nfrom .forms import walleSearchForm\nfrom haystack import forms\nfrom haystack.query import SearchQuerySet\n\ndef index(request):\n clo_list = Product.objects.all()\n clo_list = get_page(request, clo_list)\n categories = Category.objects.filter(parent=None)\n search_from = walleSearchForm()\n return render(request, 'store/index.html', locals())\n\ndef get_page(request, clo_list):\n pagintor = Paginator(clo_list, 4)\n try:\n page = int(request.GET.get('page',1))\n clo_list = pagintor.page(page)\n except (EmptyPage, InvalidPage,Paginator):\n clo_list = pagintor.page(1)\n return clo_list\n\ndef product_detail(request, id):\n try:\n clo = Product.objects.get(pk=id)\n except Product.DoesNotExist:\n return render(request, 'store/error.html', {'error': '商品不存在'})\n return render(request, 'store/single_product.html', locals())\n\ndef category(request, id):\n try:\n cat = Category.objects.get(pk=id)\n except Category.DoesNotExist:\n return render(request, 'store/error.html', {'error': '分类不存在'})\n\n clo_list = Product.active_objects.filter(category=cat)\n clo_list= get_page(request,clo_list)\n categories = Category.objects.filter(parent=None)\n return render(request, 'store/index.html', locals())\n\ndef search(request):\n categories = Category.objects.filter(parent=None)\n search_from = walleSearchForm(request.GET)\n\n if search_from.is_valid():\n keyword = search_from.cleaned_data['keyword']\n query = SearchQuerySet()\n sqs = query.auto_query(keyword)\n clo_list = []\n for s in sqs:\n clo = Product.objects.get(pk=s.pk)\n if clo:\n clo_list.append(clo)\n clo_list = get_page(request,clo_list)\n\n return render(request, 'store/index.html', locals())", "sub_path": "store/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "models.Product.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Category.objects.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 12, "usage_type": "name"}, {"api_name": "forms.walleSearchForm", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 21, "usage_type": "name"}, {"api_name": "django.core.paginator.InvalidPage", "line_number": 21, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Product.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Product.DoesNotExist", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Category.objects.get", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Category.DoesNotExist", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Product.active_objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Product.active_objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Category.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Category.objects.filter", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 44, "usage_type": "name"}, {"api_name": "forms.walleSearchForm", "line_number": 45, "usage_type": "call"}, {"api_name": "haystack.query.SearchQuerySet", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Product.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}]} {"seq_id": "6825632", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 20 18:08:07 2019\r\n\r\n@author: skgpc\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom itertools import chain\r\n#%%\r\nfrom features_stats import get_all_tokens\r\nfrom features_stats import get_words\r\nfrom features_stats import get_punct\r\nfrom features_stats import average_word_length\r\nfrom features_stats import average_sent_length\r\nfrom features_stats import check_sarcsymbol\r\nfrom features_stats import count_uppercase\r\nfrom features_stats import get_verb_lemmas\r\nfrom features_stats import get_punct_average\r\nfrom features_stats import get_sentiment\r\nfrom features_stats import get_indiv_punct\r\nfrom features_stats import relative_count_wordtypes\r\nfrom features_stats import get_entities\r\n#%%\r\ndef get_average_features(dictionary):\r\n \"\"\"Take dictionary, extract specific features (see below for full list) and\r\n return summary as a dataframe: \r\n (1) Average Word Count\r\n (2) Average Sentence Count\r\n (3) Punctuation Richness\r\n (4) Sarcasm Symbol\r\n (5) Upper-case Words\r\n (6) Verb Lemmas\r\n (7) Sentiment Classification\"\"\"\r\n \r\n #Get all tokens\r\n tokens = get_all_tokens(dictionary)\r\n \r\n #Get list of ONLY words (no punct)\r\n word_list = get_words(tokens)\r\n\r\n #Get list of ONLY punct (no words)\r\n punct_list = get_punct(tokens)\r\n\r\n #Create df for total, full returns for irony\r\n total_train= pd.DataFrame({'Ironic Comment Parsed':tokens})\r\n total_train[\"Tokens\"] = word_list\r\n total_train[\"Punctuation\"] = punct_list\r\n total_train.head()\r\n \r\n #(1) AVERAGE WORD LENGTH\r\n average_word_leng = []\r\n for comment in word_list:\r\n average_word_leng.append(average_word_length(comment))\r\n \r\n #Create DataFrame for Summary of Irony STATS\r\n summary= pd.DataFrame({\"Average Word Length\": average_word_leng})\r\n\r\n\r\n #(2) AVERAGE SENTENCE LENGTH\r\n average_sent_leng = []\r\n for x in tokens:\r\n average_sent_leng.append(average_sent_length(x))\r\n\r\n #Add to Summary of Irony STATS df\r\n summary[\"Average Sentence Length\"] = average_sent_leng\r\n summary.head()\r\n\r\n #(3) AVERAGE NUMBER OF SARCASM SYMBOL (/s)\r\n sarcfunc = []\r\n for x in tokens:\r\n sarcfunc.append(check_sarcsymbol(x))\r\n\r\n sarcsymb_list = [] \r\n for l in sarcfunc:\r\n if len(l) >= 1:\r\n sarcsymb_list.append(l)\r\n else:\r\n sarcsymb_list.append([0])\r\n\r\n #Remove list layer \r\n sarcsymb_list = list(chain.from_iterable(sarcsymb_list))\r\n\r\n #Add result to Ironic Summary DF\r\n summary[\"Sarcasm Symbol (/s)\"] = sarcsymb_list\r\n\r\n #(4) AVERAGE NUMBER OF UPPER CASE WORDS (total)\r\n uppercase_list = []\r\n for b in tokens:\r\n uppercase_list.append((count_uppercase(b)))\r\n \r\n #Remove list layer \r\n uppercase_list = list(chain.from_iterable(uppercase_list))\r\n\r\n #Add result to Ironic Summary DF\r\n summary[\"Uppercase Average\"] = uppercase_list\r\n summary.head()\r\n\r\n #(5) AVERAGE PUNCTUATION RICHNESS\r\n punct_avg = get_punct_average(punct_list, tokens)\r\n\r\n #Add result to Ironic Summary DF\r\n summary[\"Punctuation Richness\"] = punct_avg\r\n summary.head()\r\n\r\n #(6) AVERAGE NUMBER OF LEMMAS\r\n lemma_list = []\r\n for doc in tokens:\r\n lemma_list.append(get_verb_lemmas(doc))\r\n \r\n summary[\"Verb Lemma Average\"] = lemma_list\r\n summary.head()\r\n\r\n #(7) SENTIMENT CLASSIFICATION\r\n #1 = positive, -1 = negative\r\n\r\n sentiment = get_sentiment(dictionary)\r\n\r\n summary[\"Sentiment Classification\"] = sentiment \r\n\r\n #replace NAN values\r\n summary = summary.replace(np.nan, 0)\r\n return summary\r\n\r\n\r\n#%%\r\ndef get_indivpunct(dictionary):\r\n \"\"\"Take dictionary, extract punctuation marks and return summary as a \r\n dataframe\"\"\"\r\n \r\n #Get all tokens\r\n tokens = get_all_tokens(dictionary)\r\n \r\n average_indivpunc_list = []\r\n for x in tokens:\r\n average_indivpunc_list.append(get_indiv_punct(x))\r\n\r\n #Create Summary DF for each individual Punctuation Mark\r\n summary_indivpunct = pd.DataFrame(average_indivpunc_list)\r\n summary_indivpunct = summary_indivpunct.replace(np.nan, 0)\r\n \r\n return summary_indivpunct\r\n#%%\r\ndef get_pos(dictionary):\r\n \r\n #Get all tokens\r\n tokens = get_all_tokens(dictionary)\r\n\r\n average_pos_list = []\r\n for comment in tokens:\r\n average_pos_list.append(relative_count_wordtypes(comment))\r\n\r\n #Create Summary DF for POS\r\n summary_pos = pd.DataFrame(average_pos_list)\r\n \r\n #replace NAN values\r\n summary_pos = summary_pos.replace(np.nan, 0)\r\n \r\n return summary_pos\r\n#%%\r\ndef get_NER(dictionary):\r\n \r\n #Get all tokens\r\n tokens = get_all_tokens(dictionary)\r\n \r\n #(2.10) AVERAGE FOR ALL NAMED ENTITIES \r\n named_entity_list = []\r\n for comment in tokens:\r\n named_entity_list.append(get_entities(comment))\r\n \r\n\r\n #Create Summary DF for all Named Entities \r\n summary_namedentity = pd.DataFrame(named_entity_list)\r\n \r\n #replace NAN values\r\n summary_namedentity = summary_namedentity.replace(np.nan, 0)\r\n \r\n return summary_namedentity", "sub_path": "extracting_script.py", "file_name": "extracting_script.py", "file_ext": "py", "file_size_in_byte": 5111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "features_stats.get_all_tokens", "line_number": 37, "usage_type": "call"}, {"api_name": "features_stats.get_words", "line_number": 40, "usage_type": "call"}, {"api_name": "features_stats.get_punct", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "features_stats.average_word_length", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "features_stats.average_sent_length", "line_number": 63, "usage_type": "call"}, {"api_name": "features_stats.check_sarcsymbol", "line_number": 72, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 82, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 82, "usage_type": "name"}, {"api_name": "features_stats.count_uppercase", "line_number": 90, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 93, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 93, "usage_type": "name"}, {"api_name": "features_stats.get_punct_average", "line_number": 100, "usage_type": "call"}, {"api_name": "features_stats.get_verb_lemmas", "line_number": 109, "usage_type": "call"}, {"api_name": "features_stats.get_sentiment", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 122, "usage_type": "attribute"}, {"api_name": "features_stats.get_all_tokens", "line_number": 132, "usage_type": "call"}, {"api_name": "features_stats.get_indiv_punct", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 140, "usage_type": "attribute"}, {"api_name": "features_stats.get_all_tokens", "line_number": 147, "usage_type": "call"}, {"api_name": "features_stats.relative_count_wordtypes", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 157, "usage_type": "attribute"}, {"api_name": "features_stats.get_all_tokens", "line_number": 164, "usage_type": "call"}, {"api_name": "features_stats.get_entities", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 176, "usage_type": "attribute"}]} {"seq_id": "432460833", "text": "import os, sys, csv\nimport boto3, shutil\nimport pandas as pd\nfrom pprint import pprint as pp\n\n\t\t\ne=sys.exit\t\nlinesep\t= '\\n'\ncolsep\t= ','\t\n\t\t\ns3\t\t\t= boto3.client('s3')\nbucket \t\t= 'crossix-test'\nfile_name \t= 'cust_list2.csv'\ncolname \t= 'customer_name'\nto_dir \t= r'N:\\Analysis\\unique_customers.txt'\n\nsql_stmt = \"\"\"SELECT S.%s FROM s3object S LIMIT 10\"\"\" % colname\n\n\nimport tempfile\n\nfrom_dir =tempfile.gettempdir()\n#e()\n\n\nreq = s3.select_object_content(\n Bucket\t= bucket,\n Key\t\t= file_name,\n ExpressionType\t= 'SQL',\n Expression\t\t= sql_stmt,\n InputSerialization \t= {'CSV': {'FileHeaderInfo': 'USE'}},\n OutputSerialization = {'CSV': {}},\n)\n\nout=set()\t\t\t\t\nif 1:\n\t#get usique customer names\n\tfor event in req['Payload']:\n\t\tif 'Records' in event:\n\t\t\trr=event['Records']['Payload'].decode('utf-8')\n\t\t\t#pp(rr)\n\t\t\tfor rec in rr.split(linesep):\n\t\t\t\tif rec:\n\t\t\t\t\trow=rec.split(colsep)\n\t\t\t\t\tcn = row[0].strip()\n\t\t\t\t\tout.add(cn)\n\t\t\t\t\n\n\tprint('Unique customer names:')\n\tpp(out)\n\ttmpfn = os.path.join(from_dir, 'unique_cn.csv')\n\n\t#write to local tmp file\n\twith open(tmpfn, mode='w') as fh:\n\n\t\t\n\t\tcsvw = csv.writer(fh, delimiter = ',', quotechar = '\"', lineterminator = '\\n', quoting=csv.QUOTE_MINIMAL)\n\t\tfor cn in out:\t\t\t\n\t\t\tcsvw.writerow([cn])\n\n\t\n\tassert tmpfn\n\n\tprint(to_dir)\n\t#copy to network share\n\tst = shutil.copy(tmpfn, to_dir) #r'c:\\tmp') \n\n\te(0)\n", "sub_path": "aload/sel.py", "file_name": "sel.py", "file_ext": "py", "file_size_in_byte": 1358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.exit", "line_number": 7, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 11, "usage_type": "call"}, {"api_name": "tempfile.gettempdir", "line_number": 22, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 57, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 66, "usage_type": "call"}]} {"seq_id": "293592420", "text": "import socket\nimport threading\nimport os\nimport sys\nimport tkinter as tk\nfrom tkinter import simpledialog\n#import PyQt5\n#from PyQt5 import QtCore, QtGui, QtWidgets\n#from PyQt5.QWidgets import QApplication, QMainWindow\n\nimport emoji\n\n#number of users in chatroom\nnumUsers = 0\n\n##############\n# Server\n##############\n\nclass Server(threading.Thread):\n def __init__(self, host, port):\n #holds the connections of server to client\n self.connections = []\n self.host = \"127.0.0.1\"\n self.port = 1060\n super().__init__()\n\n def run(self):\n #attempt to connect socket and port\n serverSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n serverSock.bind((\"127.0.0.1\", 1060))\n serverSock.listen(1)\n #announce in terminal what we are listening on\n print('Connected on:', serverSock.getsockname())\n #attempt to connect user(s) to server\n while True:\n sc, sockname = serverSock.accept()\n print('user connects from {} to {}'.format(\n sc.getpeername(), sc.getsockname()))\n #new thread\n server_socket = ServerSocket(sc, sockname, self)\n server_socket.start()\n # Add thread to connections\n self.connections.append(server_socket)\n print('Server gets messages from user from:', sc.getpeername())\n\n def messageSend(self, message, source):\n #sends all other users usernames message\n for connection in self.connections:\n if connection.sockname != source:\n connection.send(message)\n\n def remove_connection(self, connection):\n #remove connnnection from server socket\n self.connections.remove(connection)\n\n\nclass ServerSocket(threading.Thread):\n #support socket connection\n def __init__(self, sc, sockname, server):\n self.sc = sc\n self.sockname = sockname\n self.server = server\n super().__init__()\n\n def run(self):\n #return user message\n while True:\n message = self.sc.recv(1024).decode('ascii')\n if message:\n #send users message\n self.server.messageSend(message, self.sockname)\n else:\n self.sc.close()\n #server.remove_connection(self)\n return\n\n def send(self, message):\n #Send user message to server.\n self.sc.sendall(message.encode('ascii'))\n\n##############\n# Client\n##############\n\nclass Send(threading.Thread):\n def __init__(self, socket, name):\n self.socket = socket\n self.username = name\n super().__init__()\n\n\nclass ClientRecieve(threading.Thread):\n #get threads from server\n def __init__(self, socket, name):\n super().__init__()\n self.socket = socket\n self.username = name\n self.messages = None\n\n def run(self):\n #gets messages from server and displays on GUI\n message = self.socket.recv(1024).decode('ascii')\n print(\"line 104: \" + message)\n #check message is valid\n if message:\n if self.messages:\n self.messages.insert(tk.END, (message))\n print(\"line 109: \" + message)\n else:\n print(\"ISSUE\")\n\n\n#GUI support for client and server\n\n\nclass ChatroomGUI:\n #get ports/hosts\n def __init__(self, host, port):\n self.host = host\n self.port = port\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.username = None\n self.messages = None\n\n #Starting up the client server connection\n def start(self):\n #Connect host and port\n print(\"Connecting to \" + str(self.host) + \":\" + str(self.port))\n self.socket.connect((self.host, self.port))\n print(emoji.emojize(\"Success! :thumbs_up:\"))\n\n #Get name from user\n self.username = simpledialog.askstring(\n \"Input\", \"What is your name?\", parent=tk.Tk())\n\n #threads\n send = Send(self.socket, self.username)\n receive = ClientRecieve(self.socket, self.username)\n #start thread connections\n send.start()\n receive.start()\n\n #announce that another user joined chatroom\n self.socket.sendall(\"{} has joined. Say hi!\".format(\n self.username).encode(\"ascii\"))\n print(\"\\rNote: you can leave the chatroom by typing 'q'!\\n\")\n return receive\n\n def send(self, msgTextBox):\n #message from user written in textbox in GUI\n message = msgTextBox.get()\n #remove message from textbox after user hits send\n msgTextBox.delete(0, tk.END)\n #place message (if valid, meaning there is a message) in gui box\n print(\"156 message: \" + message)\n if len(message) > 0:\n self.messages.insert(tk.END, \"{}: {}\".format(\n self.username, (message)))\n\n # quit classroom: user must type \"q\"\n if message == \"q\":\n #send to socket that user is leaving chat\n self.socket.sendall(\"{} has left the chat.\".format(\n self.username).encode(\"ascii\"))\n #close socket\n self.socket.close()\n os._exit(0)\n else:\n self.socket.sendall(\"{}: {}\".format(\n self.username, (message)).encode(\"ascii\"))\n\n\ndef main(host, port):\n #GUI of program\n client = ChatroomGUI(host, port)\n receive = client.start()\n\n #Create window for gui\n window = tk.Tk()\n\n #dimensions of window\n window.geometry(\"300x200\")\n\n #Title\n window.title(\"Socket Server Chatroom\")\n\n #Box of TEXT messages\n MsgBox = tk.Frame(master=window)\n messages = tk.Listbox(master=MsgBox)\n messages.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True)\n client.messages = messages\n receive.messages = messages\n MsgBox.grid(row=0, column=3)\n\n #Textbox for input\n colOne = tk.Frame(master=window)\n #accept single-line text strings from a user.\n msgTextBox = tk.Entry(master=colOne)\n msgTextBox.pack(side=tk.RIGHT, expand=True)\n #input of text message\n msgTextBox.bind(\"<Return>\", lambda x: client.send(msgTextBox))\n colOne.grid(row=6, column=3)\n\n #Send button\n sendMsgButton = tk.Button(\n master=window,\n text=\"Send\",\n width=8,\n command=lambda: client.send(msgTextBox)\n )\n sendMsgButton.grid(row=6, column=1)\n\n #deploy\n window.mainloop()\n\n\n#start up program\nif __name__ == '__main__':\n\n #get input from user(s)\n\n hosting = simpledialog.askstring(\n \"Input\", \"Create Host connection? (yes/no)\", parent=tk.Tk())\n #start up server on socket 127.0.0.1 and port 1060\n if (hosting == \"yes\" or hosting == \"Yes\" or hosting == \"y\"):\n (Server(\"127.0.0.1\", int(1060))).start()\n\n socketValue = simpledialog.askstring(\n \"Input\", \"Type socket value:\", parent=tk.Tk())\n portValue = simpledialog.askstring(\n \"Input\", \"Type port value:\", parent=tk.Tk())\n #start main with users given socket and port values\n #main((socketValue), int(portValue))\n main(\"127.0.0.1\", int(1060))\n", "sub_path": "notworking.py", "file_name": "notworking.py", "file_ext": "py", "file_size_in_byte": 7107, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "threading.Thread", "line_number": 20, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 30, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 58, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 86, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 108, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 122, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 122, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 122, "usage_type": "attribute"}, {"api_name": "emoji.emojize", "line_number": 131, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 134, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 134, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 135, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 168, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 180, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 189, "usage_type": "call"}, {"api_name": "tkinter.Listbox", "line_number": 190, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 191, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 191, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 197, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 199, "usage_type": "call"}, {"api_name": "tkinter.RIGHT", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 206, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 223, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 223, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 224, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 229, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 229, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 230, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 231, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 231, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 232, "usage_type": "call"}]} {"seq_id": "365946896", "text": "import json\nfrom pprint import pprint\n\nwith open (\"newsafr.json\") as f:\n data = json.load(data_file)\n pprint (data)\n\n\ndef count_length (text):\n text_list = text.split(\" \")\n text_set = set()\n for i in text_list:\n if len(i) > 6:\n text_set.add(i)\n word_value = {}\n for i in text_set:\n count = 0\n for j in text_list:\n if i == j:\n count += 1\n word_value[i] = count\n return word_value\n", "sub_path": "23.py", "file_name": "23.py", "file_ext": "py", "file_size_in_byte": 468, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 6, "usage_type": "call"}]} {"seq_id": "102413419", "text": "from at import physics\nimport matlab\nimport numpy\nimport pytest\n\n\n@pytest.mark.parametrize('dp', (0, 1e-8, 1e-7, 1e-6))\n@pytest.mark.parametrize('refpts', (None, [1], [1, 2, 3]))\ndef test_find_orbit4(engine, ml_lattice, py_lattice, dp, refpts):\n # Matlab call\n ml_refpts = (matlab.double([]) if refpts is None else\n matlab.double(list(r + 1 for r in refpts)))\n ml_orbit4 = engine.findorbit4(ml_lattice, dp, ml_refpts)\n py_ml_orbit4 = numpy.asarray(ml_orbit4)\n\n # Python call\n py_orbit4 = physics.find_orbit4(py_lattice, dp, refpts)\n\n numpy.testing.assert_almost_equal(py_ml_orbit4, py_orbit4.T)\n\n\n@pytest.mark.parametrize('dp', (0.0, 1e-8, 1e-7, 1e-6))\n@pytest.mark.parametrize('refpts', (None, [1], [1, 2, 3], [145]))\ndef test_find_m44(engine, ml_lattice, py_lattice, dp, refpts):\n # Matlab call\n ml_refpts = (matlab.double([]) if refpts is None else\n matlab.double(list(r + 1 for r in refpts)))\n ml_m44, ml_mstack = engine.findm44(ml_lattice, dp, ml_refpts, nargout=2)\n py_ml_m44 = numpy.asarray(ml_m44)\n\n # Python call\n py_m44, py_mstack = physics.find_m44(py_lattice, dp, refpts)\n\n py_mstack = numpy.squeeze(py_mstack)\n # Matches to 5 d.p.\n numpy.testing.assert_almost_equal(py_ml_m44, py_m44.T, decimal=5)\n assert py_mstack.T.shape == tuple(numpy.asarray(ml_mstack).shape)\n # Matches to 5 d.p.\n numpy.testing.assert_almost_equal(py_mstack.T, numpy.asarray(ml_mstack), decimal=5)\n", "sub_path": "pyat/test_matlab/test_cmp_physics.py", "file_name": "test_cmp_physics.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "matlab.double", "line_number": 11, "usage_type": "call"}, {"api_name": "matlab.double", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 14, "usage_type": "call"}, {"api_name": "at.physics.find_orbit4", "line_number": 17, "usage_type": "call"}, {"api_name": "at.physics", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matlab.double", "line_number": 26, "usage_type": "call"}, {"api_name": "matlab.double", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "at.physics.find_m44", "line_number": 32, "usage_type": "call"}, {"api_name": "at.physics", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "182728307", "text": "# -*- coding: utf-8 -*-\n\nimport sys\nsys.path.append('../../ia/')\nimport logging\nimport os\n\nlogging.basicConfig(filename=os.path.join(os.path.dirname(os.path.abspath(__file__)), \"log.log\"), filemode='w', level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__.split('.')[0])\n\nimport communication\nfrom socket import *\nimport time\nimport threading\n\nmyHost = ''\nmyPort = 2001\n\n#init comm\ncom = communication.CommunicationGlobale()\nsock = socket(AF_INET, SOCK_STREAM) # create a TCP socket\nsock.bind((myHost, myPort)) # bind it to the server port\nsock.listen(5) # allow 5 simultaneous\n\ndef update():\n\tif len(sys.argv) == 2:\n\t\taddr = sys.argv[1]\n\telse:\n\t\taddr = 2\n\tprint(\"Position update on adress \" + str(addr))\n\twhile 1:\n\t\tcom.sendOrderAPI(addr, 'A_GET_POS')\n\t\tret = -1\n\t\twhile ret == -1:\n\t\t\tret = com.readOrdersAPI()\n\t\t\ttime.sleep(0.01)\n\t\tdata = \":\".join(str(el) for el in ret[2])\n\t\tprint(data)\n\t\tconnection.send(bytes(data, 'utf-8'))\n\t\ttime.sleep(0.1)\n\n\nwhile 1:\n\t# wait for next client to connect\n\tprint(\"Ready, waiting for socket connection\")\n\tglobal connection\n\tglobal address\n\tconnection, address = sock.accept() # connection is a new socket\n\tprint(\"Connection established\")\n\tthreading.Thread(target=update).start()\n\twhile 1:\n\t\tdata_rec = connection.recv(1024) # receive up to 1K bytes\n\t\tif data_rec:\n\t\t\tdata_rec = [data.split(':') for data in str(data_rec, 'utf-8').split('!')[:-1]] #conversion chaine en liste\n\t\t\tfor data in data_rec:\n\t\t\t\tdata[0] = int(data[0])\n\t\t\t\t#conversion data\n\t\t\t\tif data[1] == 'A_GOTOA': #deux int un float\n\t\t\t\t\tdata[2] = int(data[2])\n\t\t\t\t\tdata[3] = int(data[3])\n\t\t\t\t\tdata[4] = float(data[4])\n\t\t\t\telif data[1] == 'A_PIDA' or data[1] == 'A_PIDD' or data[1] == 'A_ROT' or data[1] == 'A_ACCMAX': #all float\n\t\t\t\t\tfor i in range(2,len(data)):\n\t\t\t\t\t\tdata[i] = float(data[i])\n\t\t\t\telif data[1] == 'A_GOTO': #all int\n\t\t\t\t\tfor i in range(2,len(data)):\n\t\t\t\t\t\tdata[i] = int(data[i])\n\t\t\t\telif data[1] == 'A_PWM': #all int\n\t\t\t\t\tfor i in range(2,len(data)):\n\t\t\t\t\t\tdata[i] = int(data[i])\n\t\t\t\telif data[1] == 'A_SET_POS': #all int\n\t\t\t\t\tdata[2] = int(data[2])\n\t\t\t\t\tdata[3] = int(data[3])\n\t\t\t\t\tdata[4] = float(data[4])\n\n\t\t\t\tif data[1] == 'A_GOTO' or data[1] == 'A_GOTOA' or data[1] == 'A_ROT' or data[1] == 'A_PWM':\n\t\t\t\t\tdata.insert(2, 0) #ajout id\n\t\t\t\t\n\t\t\t\tprint('Data : ' + str(data))\n\t\t\t\tcom.sendOrderAPI(data[0], data[1], *data[2:]) \n\t\telse:\n\t\t\tbreak\n\tconnection.close() # close socket\n", "sub_path": "GUI/controlGUI/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "communication.CommunicationGlobale", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 50, "usage_type": "call"}]} {"seq_id": "180539421", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 28 06:57:05 2020\n\n@author: anavr\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport pyComtrade\nimport numpy as np\nfrom scipy import signal, fftpack\nfrom tkinter import *\nfrom tkinter import filedialog\nfrom inspect import *\n\n# app\n#funcion para cargar\n\nglobal comtradeObj\ncomtradeObj = pyComtrade.ComtradeRecord()\n\n#------------------ FUNCIONES DEL PROGRAMA -------------------------------\n# Para abrir archivo cfg\ndef abrir_cfg():\n \n global ar\n \n ar=filedialog.askopenfilename(title=\"Abrir cfg\")#,filetypes=((\"Archivos COMTRADE .cfg y .dat\",\".\"))) \n \n #fs=\n messagebox.showinfo(\"Se cargó el archivo .cfg de la siguiente dirección:\\n\", ar)\n \n# Para abrir archivo dat\ndef abrir_dat():\n \n global dat\n \n dat=filedialog.askopenfilename(title=\"Abrir cfg\")#,filetypes=((\"Archivos COMTRADE .cfg y .dat\",\".\"))) \n \n #fs=\n messagebox.showinfo(\"Se cargó el archivo .dat de la siguiente dirección:\\n\", dat)\n \n# Carga las entradas del programa\ndef cargar(): \n global e1,e2,e3,e4,e5, ratio_V, ratio_C\n e1=en1.get()\n e2=en2.get()\n e3=en3.get()\n e4=en4.get()\n e5=en5.get()\n \n if e1 == \"Seleccionar\":\n messagebox.showerror(\"Error\",\"Debe seleccionar la frecuencia de muestro del relé\")\n else:\n e1 = int(e1)\n \n if e2==\"\" and e3==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación de voltaje\")\n elif e2==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación del lado primario de voltaje\")\n elif e3==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación del lado secundario de voltaje\")\n else:\n e2 = int(e2)\n e3 = int(e3)\n \n if e4==\"\" and e5==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación de corriente\")\n elif e4==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación del lado primario de corriente\")\n elif e5==\"\":\n messagebox.showerror(\"Error\",\"Debe ingresar la relación de transformación del lado secundario de corriente\")\n else:\n e4 = int(e4)\n e5 = int(e5)\n \n if side.get() == 0:\n messagebox.showerror(\"Error\",\"Debe seleccionar el lado del transformador que desea visualizar\")\n elif side.get()==1:\n ratio_V = e2/e3\n ratio_C = e4/e5\n elif side.get()==2:\n ratio_V = 1\n ratio_C = 1\n \n DFP()\n \n# Funciones para cada etapa\n# ETAPA DE SUBMUESTREO\ndef subsampling(time,data,fs_comtrade,fk,fs_user_cycle):\n # time is the vector of time\n # data is the vector with the signal\n # fs_comtrade is the sample rate from the comtrade file\n # fk is the frequency of the system\n # fs_user_cycle is the sample rate given by user\n N1 = fs_user_cycle\n fs_cycle = fs_comtrade/fk\n N=np.int(fs_cycle)\n N_tot = np.int(len(data)/fs_cycle)\n new_data = [0]\n new_time = [0]\n for i in np.arange(N_tot):\n xi=data[i*N:i*N+N]\n ti=time[i*N:i*N+N]\n new_data[i*N1:i*N1+N1] = signal.resample(xi, N1)\n new_time[i*N1:i*N1+N1] = np.linspace(ti[0], ti[-1], N1, endpoint=False)\n \n return (new_time,new_data)\n\n# ETAPA DE DIGITALIZACION\ndef quantizer(data, quantizing_bits):\n # data is the vector with the signal\n # quantizing_bits is the number of bits for the converter\n # Quantizer - S&H and ADC\n quantizing_levels = 2 ** quantizing_bits\n quantizing_step = (np.max(data)-np.min(data)) / quantizing_levels\n quantizing_signal = np.round (data / quantizing_step) * quantizing_step;\n \n return quantizing_signal\n\n#ETAPA DE DFT\ndef DFT(time, data, fk, fs_user_cycle):\n # time is the vector of time\n # data is the vector with the signal\n # fk is the frequency of the system\n # fs_user_cycle is the sample rate given by user\n \n N=np.int(fs_user_cycle)\n N_tot = len(data)-N\n Xc = [0]*N_tot\n Xs = [0]*N_tot\n t = [0]*N_tot\n \n for i in np.arange(N_tot):\n xi=data[i:i+N]\n t[i]=time[i]\n Xc_sum = 0\n Xs_sum = 0\n for k in np.arange(N):\n Xc_temp=xi[k]*np.cos(2*np.pi*k/(N))\n Xc_sum=Xc_sum+Xc_temp\n Xs_temp=xi[k]*np.sin(2*np.pi*k/(N))\n Xs_sum=Xs_sum+Xs_temp\n \n Xc[i]= 2/(N*np.sqrt(2))*Xc_sum\n Xs[i]= 2/(N*np.sqrt(2))* Xs_sum\n \n return t, Xc, Xs\n \n# Realiza todo el proceso de DSP\ndef DFP():\n # Definición de variables globales para resultados\n global time, voltages, currents, time_sub, V_sub, C_sub, dig_V_sub, dig_C_sub, t, X_V, X_C, Y_V, Y_C, Xc_V, Xs_V, Xc_C, Xs_C, fs_user_cycle\n \n comtradeObj.read(ar,dat)\n N = comtradeObj['endsamp'][-1]\n #sampling_freq=comtradeObj['samp'][-1]\n fs_comtrade=comtradeObj['samp'][-1]\n fk = comtradeObj['line_freq']\n time = comtradeObj.get_timestamps()\n voltages = np.empty(([len(time),3]))\n currents = np.empty(([len(time),3]))\n # Reading voltaje and currents\n voltages[:,0] = comtradeObj['A'][16]['values']\n voltages[:,1] = comtradeObj['A'][17]['values']\n voltages[:,2] = comtradeObj['A'][18]['values']\n \n currents[:,0] = comtradeObj['A'][0]['values']\n currents[:,1] = comtradeObj['A'][1]['values']\n currents[:,2] = comtradeObj['A'][2]['values']\n for i in np.arange(6):\n if i<3:\n # voltages[:,i] = comtradeObj['A'][i]['values']\n for j in np.arange(len(voltages[:,i])):\n voltages[j,i]= voltages[j,i] * ratio_V\n else:\n # currents[:,i-3] = comtradeObj['A'][i]['values']\n for j in np.arange(len(currents[:,i-3])):\n currents[j,i-3]= currents[j,i-3] * ratio_C\n \n # Submuestreo\n fs_user_cycle = e1 # ESTA DATO VIENE DE LA INTERFAZ\n N_tot = np.int(N*fk/fs_comtrade)*fs_user_cycle\n V_sub = np.empty(([N_tot,3]))\n C_sub = np.empty(([N_tot,3]))\n time_sub = np.empty(([N_tot,6]))\n for i in np.arange(6):\n if i<3:\n time_sub[:,i], V_sub[:,i] = subsampling(time,voltages[:,i],fs_comtrade,fk,fs_user_cycle)\n else:\n time_sub[:,i], C_sub[:,i-3] = subsampling(time,currents[:,i-3],fs_comtrade,fk,fs_user_cycle)\n \n # Digitalización\n quantizing_bits_V = 12 # Valor típico: 12 (Voltaje)\n quantizing_bits_C = 16 # Valor típico: 16 (Corriente)\n dig_V_sub = np.empty(([N_tot,3]))\n dig_C_sub = np.empty(([N_tot,3]))\n for i in np.arange(6):\n if i<3:\n dig_V_sub[:,i] = quantizer(V_sub[:,i], quantizing_bits_V)\n else:\n dig_C_sub[:,i-3] = quantizer(C_sub[:,i-3], quantizing_bits_C)\n \n # DFT\n N_tot_DTF = np.int(N_tot-fs_user_cycle)\n Xc_V = np.empty(([N_tot_DTF,3]))\n Xs_V = np.empty(([N_tot_DTF,3]))\n Xc_C = np.empty(([N_tot_DTF,3]))\n Xs_C = np.empty(([N_tot_DTF,3]))\n X_V = np.empty(([N_tot_DTF,3]))\n Y_V = np.empty(([N_tot_DTF,3]))\n X_C = np.empty(([N_tot_DTF,3]))\n Y_C = np.empty(([N_tot_DTF,3]))\n t = np.empty(([N_tot_DTF,6]))\n for i in np.arange(6):\n if i<3:\n t[:,i], Xc_V[:,i], Xs_V[:,i] = DFT(time_sub[:,i], dig_V_sub[:,i], fk, fs_user_cycle)\n X_V[:,i] = np.sqrt(np.power(Xc_V[:,i],2)+np.power(Xs_V[:,i],2))\n ajus = np.pi\n if Xc_V[-1,i]>0 and Xs_V[-1,i]<0:\n ajus = 2*np.pi\n elif Xc_V[-1,i]>0 and Xs_V[-1,i]>0:\n ajus = 0\n Y_V[:,i] = (np.arctan(Xs_V[:,i]/Xc_V[:,i])+ajus)*180/np.pi\n else:\n t[:,i], Xc_C[:,i-3], Xs_C[:,i-3] = DFT(time_sub[:,i], dig_C_sub[:,i-3], fk, fs_user_cycle)\n X_C[:,i-3] = np.sqrt(np.power(Xc_C[:,i-3],2)+np.power(Xs_C[:,i-3],2))\n ajus = np.pi\n if Xc_C[-1,i-3]>0 and Xs_C[-1,i-3]<0:\n ajus = 2*np.pi\n elif Xc_C[-1,i-3]>0 and Xs_C[-1,i-3]>0:\n ajus = 0\n Y_C[:,i-3] = (np.arctan(Xs_C[:,i-3]/Xc_C[:,i-3])+ajus)*180/np.pi\n \n# ------------------------- Funciones para los Botones --------------------\ndef seniales_COMTRADE(): \n f, axarr = plt.subplots(1, 2, figsize =(16, 4))\n f.suptitle('Lectura del archivo COMTRADE', y=1, fontsize=16)\n \n axarr[0].plot(time, voltages[:,0], 'b-', label='Phase A')\n axarr[0].plot(time, voltages[:,1], 'r-', label='Phase B')\n axarr[0].plot(time, voltages[:,2], 'g-', label='Phase C')\n axarr[0].set_xlabel('Time [sec]')\n axarr[0].set_ylabel('Voltage [V]')\n axarr[0].grid()\n axarr[0].legend()\n\n axarr[1].plot(time, currents[:,0], 'b-', label='Phase A')\n axarr[1].plot(time, currents[:,1], 'r-', label='Phase B')\n axarr[1].plot(time, currents[:,2], 'g-', label='Phase C')\n axarr[1].set_xlabel('Time [sec]')\n axarr[1].set_ylabel('Current [A]')\n axarr[1].grid()\n axarr[1].legend()\n plt.show()\n \ndef submuestreo_boton():\n # PLOTING -----------------------------------------------------------------\n f, axarr = plt.subplots(3, 2, figsize =(16, 10))\n f.suptitle('Submuestreo de las señales', y=0.92, fontsize=16)\n \n # Plot Voltages\n axarr[0,0].plot(time, voltages[:,0], 'b-', label='Phase A')\n axarr[0,0].plot( time_sub[:,0], V_sub[:,0], 'co-', label='Phase A resampled')\n axarr[1,0].plot(time, voltages[:,1], 'r-', label='Phase B')\n axarr[1,0].plot( time_sub[:,1], V_sub[:,1], 'mo-', label='Phase B resampled')\n axarr[2,0].plot(time, voltages[:,1], 'g-', label='Phase C')\n axarr[2,0].plot( time_sub[:,2], V_sub[:,2], 'yo-', label='Phase C resampled')\n \n # Plot Currents\n axarr[0,1].plot(time, currents[:,0], 'b-', label='Phase A')\n axarr[0,1].plot( time_sub[:,3], C_sub[:,0], 'co-', label='Phase A resampled')\n axarr[1,1].plot(time, currents[:,1], 'r-', label='Phase B')\n axarr[1,1].plot( time_sub[:,4], C_sub[:,1], 'mo-', label='Phase B resampled')\n axarr[2,1].plot(time, currents[:,2], 'g-', label='Phase C')\n axarr[2,1].plot( time_sub[:,5], C_sub[:,2], 'yo-', label='Phase C resampled')\n \n for i in np.arange(3):\n axarr[i,0].set_xlabel('Time [sec]')\n axarr[i,0].set_ylabel('Voltage [V]')\n axarr[i,0].grid()\n axarr[i,0].legend()\n axarr[i,1].set_xlabel('Time [sec]')\n axarr[i,1].set_ylabel('Current [A]')\n axarr[i,1].grid()\n axarr[i,1].legend()\n plt.show()\n \ndef digitalizacion_boton():\n # PLOTING -----------------------------------------------------------------\n f, axarr = plt.subplots(3, 2, figsize =(16, 10))\n f.suptitle('Digitalización de la señal', y=0.92, fontsize=16)\n \n # Plot Voltages\n axarr[0,0].plot(time_sub[:,0][0:fs_user_cycle*3], V_sub[:,0][0:fs_user_cycle*3], 'b-', label='Phase A')\n axarr[0,0].plot( time_sub[:,0][0:fs_user_cycle*3], dig_V_sub[:,0][0:fs_user_cycle*3], 'c-', label='Phase A digital')\n axarr[1,0].plot(time_sub[:,1][0:fs_user_cycle*3], V_sub[:,1][0:fs_user_cycle*3], 'r-', label='Phase B')\n axarr[1,0].plot( time_sub[:,1][0:fs_user_cycle*3], dig_V_sub[:,1][0:fs_user_cycle*3], 'm-', label='Phase B digital')\n axarr[2,0].plot(time_sub[:,2][0:fs_user_cycle*3], V_sub[:,2][0:fs_user_cycle*3], 'g-', label='Phase C')\n axarr[2,0].plot( time_sub[:,2][0:fs_user_cycle*3], dig_V_sub[:,2][0:fs_user_cycle*3], 'y-', label='Phase C digital')\n \n # Plot Currents\n axarr[0,1].plot(time_sub[:,3][0:fs_user_cycle*3], C_sub[:,0][0:fs_user_cycle*3], 'b-', label='Phase A')\n axarr[0,1].plot( time_sub[:,3][0:fs_user_cycle*3], dig_C_sub[:,0][0:fs_user_cycle*3], 'c-', label='Phase A digital')\n axarr[1,1].plot(time_sub[:,4][0:fs_user_cycle*3], C_sub[:,1][0:fs_user_cycle*3], 'r-', label='Phase B')\n axarr[1,1].plot( time_sub[:,4][0:fs_user_cycle*3], dig_C_sub[:,1][0:fs_user_cycle*3], 'm-', label='Phase B digital')\n axarr[2,1].plot(time_sub[:,5][0:fs_user_cycle*3], C_sub[:,2][0:fs_user_cycle*3], 'g-', label='Phase C')\n axarr[2,1].plot( time_sub[:,5][0:fs_user_cycle*3], dig_C_sub[:,2][0:fs_user_cycle*3], 'y-', label='Phase C digital')\n \n for i in np.arange(3):\n axarr[i,0].set_xlabel('Time [sec]')\n axarr[i,0].set_ylabel('Voltage [V]')\n axarr[i,0].grid()\n axarr[i,0].legend()\n axarr[i,1].set_xlabel('Time [sec]')\n axarr[i,1].set_ylabel('Current [A]')\n axarr[i,1].grid()\n axarr[i,1].legend()\n \n plt.show()\n \ndef DFT_boton():\n # PLOTING -----------------------------------------------------------------\n f, axarr = plt.subplots(3, 2, figsize =(16, 10))\n f.suptitle('DFT En Magnitud', y=0.92, fontsize=16)\n \n # Plot Voltages\n #axarr[0,0].plot(time_sub[:,0], V_sub[:,0], 'b-', label='Phase A')\n axarr[0,0].plot( t[:,0], X_V[:,0], 'c-', label='Phase A FFT(mag)')\n #axarr[1,0].plot(time_sub[:,1], V_sub[:,1], 'r-', label='Phase B')\n axarr[1,0].plot( t[:,1], X_V[:,1], 'm-', label='Phase B FFT(mag)')\n #axarr[2,0].plot(time_sub[:,2], V_sub[:,2], label='Phase C')\n axarr[2,0].plot( t[:,2], X_V[:,2], 'y-', label='Phase C FFT(mag)')\n \n # Plot Currents\n #axarr[0,1].plot(time_sub[:,3], C_sub[:,0], 'b-', label='Phase A')\n axarr[0,1].plot( t[:,3], X_C[:,0], 'c-', label='Phase A FFT(mag)')\n #axarr[1,1].plot(time_sub[:,4], C_sub[:,1], 'r-', label='Phase B')\n axarr[1,1].plot( t[:,4], X_C[:,1], 'm-', label='Phase B FFT(mag)')\n #axarr[2,1].plot(time_sub[:,5], C_sub[:,2], 'g-', label='Phase C')\n axarr[2,1].plot( t[:,5], X_C[:,2], 'y-', label='Phase C FFT(mag)')\n \n for i in np.arange(3):\n axarr[i,0].set_xlabel('Time [sec]')\n axarr[i,0].set_ylabel('Voltage [V]')\n axarr[i,0].grid()\n axarr[i,0].legend()\n axarr[i,1].set_xlabel('Time [sec]')\n axarr[i,1].set_ylabel('Current [A]')\n axarr[i,1].grid()\n axarr[i,1].legend()\n \n plt.show()\n \n # Ploting angle\n f, axarr = plt.subplots(3, 2, figsize =(16, 10))\n f.suptitle('DFT En Fase', y=0.92, fontsize=16)\n \n # Plot Voltages\n #axarr[0,0].plot(time_sub[:,0], V_sub[:,0], 'b-', label='Phase A')\n axarr[0,0].plot( t[:,0], Y_V[:,0], 'c-', label='Phase A FFT[ang(rad)]')\n #axarr[1,0].plot(time_sub[:,1], V_sub[:,1], 'r-', label='Phase B')\n axarr[1,0].plot( t[:,1], Y_V[:,1], 'm-', label='Phase B FFT[ang(rad)]')\n #axarr[2,0].plot(time_sub[:,2], V_sub[:,2], label='Phase C')\n axarr[2,0].plot( t[:,2], Y_V[:,2], 'y-', label='Phase C FFT[ang(rad)]')\n \n # Plot Currents\n #axarr[0,1].plot(time_sub[:,3], C_sub[:,0], 'b-', label='Phase A')\n axarr[0,1].plot( t[:,3], Y_C[:,0], 'c-', label='Phase A FFT[ang(rad)]')\n #axarr[1,1].plot(time_sub[:,4], C_sub[:,1], 'r-', label='Phase B')\n axarr[1,1].plot( t[:,4], Y_C[:,1], 'm-', label='Phase B FFT[ang(rad)]')\n #axarr[2,1].plot(time_sub[:,5], C_sub[:,2], 'g-', label='Phase C')\n axarr[2,1].plot( t[:,5], Y_C[:,2], 'y-', label='Phase C FFT[ang(rad)]')\n \n for i in np.arange(3):\n axarr[i,0].set_xlabel('Time [sec]')\n axarr[i,0].set_ylabel('Angle (°)')\n axarr[i,0].grid()\n axarr[i,0].legend()\n axarr[i,1].set_xlabel('Time [sec]')\n axarr[i,1].set_ylabel('Angle (°)')\n axarr[i,1].grid()\n axarr[i,1].legend()\n plt.show()\n \ndef fasores_boton():\n # Creando la figura\n fig, ax = plt.subplots(1, 2, figsize =(16, 6))\n fig.suptitle('Diagrama fasorial de Voltaje y Corriente', y=0.95, fontsize=16)\n \n lim_axis_V = np.max([np.float(X_V[-1:,0]), np.float(X_V[-1:,1]),np.float(X_V[-1:,2])])\n lim_axis_C = np.max([np.float(X_C[-1:,0]), np.float(X_C[-1:,1]),np.float(X_C[-1:,2])])\n # Creando el punto de origen para los vectores\n x_pos = [0, 0,0] \n y_pos = [0, 0,0]\n \n ax[0].quiver(x_pos, y_pos, Xc_V[-1,0], Xs_V[-1,0], angles='xy', scale_units = 'xy', scale=1, color=['b'], label='Fase A') \n ax[0].quiver(x_pos, y_pos, Xc_V[-1,1], Xs_V[-1,1], angles='xy', scale_units = 'xy', scale=1, color=['r'], label='Fase B') \n ax[0].quiver(x_pos, y_pos, Xc_V[-1,2], Xs_V[-1,2], angles='xy', scale_units = 'xy', scale=1, color=['g'], label='Fase C') \n ax[0].axis([-1.2*lim_axis_V, 1.2*lim_axis_V, -1.2*lim_axis_V, 1.2*lim_axis_V]) \n ax[0].set_title('Voltaje [V]')\n ax[0].legend() #<-- Se nombran las leyendas\n ax[0].grid(b=True, which='major') #<-- plot grid lines\n \n ax[1].quiver(x_pos, y_pos, Xc_C[-1,0], Xs_C[-1,0], angles='xy', scale_units = 'xy', scale=1, color=['b'], label='Fase A') \n ax[1].quiver(x_pos, y_pos, Xc_C[-1,1], Xs_C[-1,1], angles='xy', scale_units = 'xy', scale=1, color=['r'], label='Fase B') \n ax[1].quiver(x_pos, y_pos, Xc_C[-1,2], Xs_C[-1,2], angles='xy', scale_units = 'xy', scale=1, color=['g'], label='Fase C') \n ax[1].axis([-1.2*lim_axis_C, 1.2*lim_axis_C, -1.2*lim_axis_C, 1.2*lim_axis_C]) \n ax[1].set_title('Corriente [A]')\n ax[1].legend() #<-- Se nombran las leyendas\n ax[1].grid(b=True, which='major') #<-- plot grid lines\n plt.show()\n \n # Mostrando las fases en la interfaz\n label_fas0.grid(row=3,column=0)\n label_fas1.config(text=['Voltaje fase A:', \"{:.2f}\".format(np.double(X_V[-1:,0])), 'V', \"{:.2f}\".format(np.double(Y_V[-1:,0])),'°'])\n label_fas1.grid(row=4,column=0)\n label_fas2.config(text=['Voltaje fase B:', \"{:.2f}\".format(np.double(X_V[-1:,1])), 'V', \"{:.2f}\".format(np.double(Y_V[-1:,1])),'°'])\n label_fas2.grid(row=5,column=0)\n label_fas3.config(text=['Voltaje fase C:', \"{:.2f}\".format(np.double(X_V[-1:,2])), 'V', \"{:.2f}\".format(np.double(Y_V[-1:,1])),'°'])\n label_fas3.grid(row=6,column=0)\n label_fas4.config(text=['Corriente fase A:', \"{:.2f}\".format(np.double(X_C[-1:,0])), 'A', \"{:.2f}\".format(np.double(Y_C[-1:,0])),'°'])\n label_fas4.grid(row=7,column=0)\n label_fas5.config(text=['Corriente fase B:', \"{:.2f}\".format(np.double(X_C[-1:,1])), 'A', \"{:.2f}\".format(np.double(Y_C[-1:,1])),'°'])\n label_fas5.grid(row=8,column=0)\n label_fas6.config(text=['Corriente fase C:', \"{:.2f}\".format(np.double(X_C[-1:,2])), 'A', \"{:.2f}\".format(np.double(Y_C[-1:,2])),'°'])\n label_fas6.grid(row=9,column=0)\n#------------------------------------------------------------------\n# MAIN\nraiz=Tk()\nraiz.title(\"Modulo DSP para relés\")\n #raiz.geometry(\"650x380\")\nx_frame = 720\ny_frame = 500\nraiz.geometry(\"{width}x{height}\".format(width=x_frame, height=y_frame)) \nraiz.resizable(True, True) \n# mifr= Frame()\n# mifr.pack(side=TOP, fill=BOTH, expand=Y)\n# #mifr.config()\n# mifr.config(width=\"650\",height=\"380\")\n# mifr.config(cursor=\"star\")\nbmenu=Menu(raiz)\nraiz.config(menu=bmenu)\n\n#-------------------------------- FRAME 1 -------------------------------\nframe1 = LabelFrame(raiz, text=\"Entradas\",height= y_frame,width =x_frame,padx=5, labelanchor=N)\nframe1.config(cursor=\"star\")\nframe1.pack(expand = 'no', fill = 'both') \n\nx_frame11 = 350\nframe11 = LabelFrame(frame1, text=\"Selección del archivo COMTRADE\",fg=\"red\",height= 100,width =x_frame11,padx=15)\nframe11.grid(column=0, row=0, padx=10, pady=10)\nlabel11 = Label(frame11, text = 'Instrucciones: Se deben seleccionar los archivos .cfg y .dat')\nlabel11.place(x = 0, y = 5) \n\nBotonAbrircfg = Button(frame11,text=\"Abrir .cfg\",command=abrir_cfg)\nBotonAbrircfg.place(x = x_frame11/6, y = 30) \nBotonAbrirdat = Button(frame11,text=\"Abrir .dat\",command=abrir_dat)\nBotonAbrirdat.place(x = 3*x_frame11/6, y = 30) \n\nx_frame12 = 250\nframe12 = LabelFrame(frame1, text=\"Selección de parámetros de entrada\",fg=\"red\",height= 200,width =x_frame12,padx=15)\nframe12.grid(column=1, row=0, columnspan=1, rowspan=2, padx=10, pady=10)\n\nt1=Label(frame12,text=\"Seleccione la frecuencia \\nde muestro del relé\",fg=\"green\")\nt1.grid(row=0,column=0,sticky=\"w\",pady=\"20\")\nen1=StringVar()\nd=OptionMenu(frame12, en1, \"4\",\"8\",\"16\",\"32\",\"64\")\nd.grid(row=0,column=1,sticky=\"w\",padx=\"20\",pady=\"20\")\nen1.set(\"Seleccionar\")\n\n\nframe121 = LabelFrame(frame12, text=\"Relación de transformadores de instrumentación\",fg=\"Blue\",height= 200,width =x_frame12,padx=15)\nframe121.grid(column=0, row=1, columnspan=2, rowspan=1, padx=10, pady=10)\n\n# Relación de transformadores de instrumentación voltaje le falta dividir entre prim y secundario\nt2=Label(frame121,text=\"Relacion de Voltaje\")\nt2.grid(row=0,column=0,sticky=\"w\")\nen2=Entry(frame121, width=7)\nen2.grid(row=0,column=1)\nt3=Label(frame121,text=\":\")\nt3.grid(row=0,column=2)\nen3=Entry(frame121, width=7)\nen3.grid(row=0,column=3)\n\n# Relación de transformadores de instrumentación corriente le falta dividir entre prim y secundario\nt21=Label(frame121,text=\"Relacion de Corriente\")\nt21.grid(row=1,column=0,sticky=\"w\")\nen4=Entry(frame121, width=7)\nen4.grid(row=1,column=1)\nt31=Label(frame121,text=\":\")\nt31.grid(row=1,column=2)\nen5=Entry(frame121, width=7)\nen5.grid(row=1,column=3)\n\nb1=Button(frame12,text=\"cargar valores\",command=cargar)\nb1.grid(row=2,column=0,columnspan=2, rowspan=1)\n\nx_frame13 = 350\nframe13 = LabelFrame(frame1, text=\"Visualización en el transformador\",fg=\"red\",height= 100,width =x_frame13,padx=15)\nframe13.grid(column=0, row=1, padx=10, pady=0)\nlabel13 = Label(frame13, text = 'Seleccione el lado que desea ver la señal del transformador')\nlabel13.place(x = 0, y = 5) \n\nside = IntVar()\nrad_trafo1 = Radiobutton(frame13,text=\"Primario\", variable = side, value=1)\nrad_trafo1.place(x = x_frame13/6, y = 30) \nrad_trafo2 = Radiobutton(frame13,text=\"Secundario\", variable = side, value=2)\nrad_trafo2.place(x = 3*x_frame13/6, y = 30) \n\n#-------------------------------- FRAME 2 -------------------------------\nframe2 = LabelFrame(raiz, text=\"Resultados del procesamiento\",height= y_frame,width =x_frame,padx=5, labelanchor=N)\nframe2.config(cursor=\"star\")\nframe2.pack(expand = 'no', fill = 'both') \n\nx_frame21 = 350\nframe21 = LabelFrame(frame2, text=\"Detalle por etapa\",fg=\"red\",height= 100,width =x_frame21,padx=15)\nframe21.grid(column=0, row=0, padx=10, pady=10)\nlabel21 = Label(frame21, text = 'Seleccione la etapa que desea detallar')\nlabel21.grid(row=0,column=0,columnspan=3)\n\nbi=Button(frame21,text=\"Señales de entrada\",command=seniales_COMTRADE)#carga los archivos\nbi.grid(row=1,column=0)\nb2=Button(frame21,text=\"Submuestreo\",command=submuestreo_boton)\nb2.grid(row=1,column=1)\n#etapa de mostrar señal digitalizada\nb3=Button(frame21,text=\"Digitalizacion\",command=digitalizacion_boton)\nb3.grid(row=1,column=2)\n#etapa de ventaneo\nb3=Button(frame21,text=\"DFT\",command=DFT_boton)\nb3.grid(row=2,column=1)\n\n\n\nx_frame22 = 250\nframe22 = LabelFrame(frame2, text=\"Señal Procesada\",fg=\"red\",height= 100,width =x_frame22,padx=15)\nframe22.grid(column=1, row=0, padx=10, pady=10)\nlabel22 = Label(frame22, text = 'En este módulo se muestran las señales resultantes')\nlabel22.grid(row=0,column=0)\n\nb3=Button(frame22,text=\"Fasores\",command=fasores_boton)\nb3.grid(row=1,column=0)\n\nlabel_fas0 = Label(frame22, text = 'Los fasores resultantes en rms son:')\nlabel_fas0.grid_forget()\nlabel_fas1 = Label(frame22, text = 'Aqui se mostrara el fasor 1',fg=\"blue\")\nlabel_fas1.grid_forget()\nlabel_fas2 = Label(frame22, text = 'Aqui se mostrara el fasor 2',fg=\"red\")\nlabel_fas2.grid_forget()\nlabel_fas3 = Label(frame22, text = 'Aqui se mostrara el fasor 3',fg=\"green\")\nlabel_fas3.grid_forget()\nlabel_fas4 = Label(frame22, text = 'Aqui se mostrara el fasor 1',fg=\"blue\")\nlabel_fas4.grid_forget()\nlabel_fas5 = Label(frame22, text = 'Aqui se mostrara el fasor 2',fg=\"red\")\nlabel_fas5.grid_forget()\nlabel_fas6 = Label(frame22, text = 'Aqui se mostrara el fasor 3',fg=\"green\")\nlabel_fas6.grid_forget()\n\n\n\nbarchiv=Menu(bmenu,tearoff=0)\nbhelp=Menu(bmenu,tearoff=0)\nbarchiv.add_command(label=\"Abrir archivo \")#carga los archivos\nbarchiv.add_command(label=\"Guardar\")\nbmenu.add_cascade(label=\"Archivo\",menu=barchiv)\n#help\n\nbhelp.add_command(label=\"Descripción\")\nbmenu.add_cascade(label=\"Ayuda\",menu=bhelp)\n\n\n\nraiz.mainloop()\n\n\n", "sub_path": "TC2/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 23708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pyComtrade.ComtradeRecord", "line_number": 20, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 28, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.int", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.signal.resample", "line_number": 105, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 233, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, 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