url represented as e.g. https://www.reddit.com/\n '''\n try:\n response = requests.get(url)\n self.soup = BeautifulSoup(response.text,\"html.parser\")\n except:\n print('CONNECTION LOST OR BAD URL')\n\n\n def _get_paras(self):\n ''' private method that collects paragraphs by reading all elements\n a lot of redundancies, however, ensures all text is picked up.\n '''\n paras = self.soup.find_all('p')\n sstring = ''''''\n for text in paras:\n sstring += text.text.replace('\\n',' ') + ''' '''\n self.text = sstring\n\n def _write_paras(self):\n ''' Write the paragraphs from the data '''\n print(\"{}\\0{}\".format(self.date,self.text), file=self.file)\n self.it += 1\n\n def _write_header(self):\n ''' Write the header of the csv file '''\n print(\"date\\0text\",file=self.file)\n\n def _write_log(self,url,page):\n ''' Open and write in the log file\n\n :param url: takes URL string e.g. https://www.reddit.com/\n :param page: takes page number\n '''\n with open(self.log,'a') as file:\n print(\"{};{}\".format(url,page),file=file)\n\n def _write_log_endpage(self,url,page):\n with open(self.log_endpage,'a+') as file:\n print(\"{};{}\".format(url,page),file=file)\n\n def _writeover_log_endpage(self,url,page):\n with open(self.log_endpage,'w+') as file:\n print(\"{};{}\".format(url,page),file=file)\n\n def _check_log(self):\n ''' Checking the log from current position '''\n with open(\"log.txt\",\"r+\") as file:\n return True\n\n def _open_log(self):\n ''' Open the log and retrieve the latest entry'''\n with open('log.txt', 'r+') as f:\n lines = f.read().splitlines()\n last_line = lines[-1]\n x = last_line.split(';')\n return x[0], x[1]\n\n def _open_log_endpage(self):\n ''' Opens the log that contains the endpages for the URL'''\n with open('log_endpage.txt','r+') as f:\n lines = f.read().splitlines()\n URL = [line.split(';')[0] for line in lines]\n ENDPAGE = [line.split(';')[1] for line in lines]\n\n return URL, ENDPAGE\n\n def _check_endpage(self):\n ''' Cycling tips doesn't return a 404 error, instead has its own error page\n this function attempts to find the error page. Will return true if on error page '''\n\n return self.soup.find('div',attrs={'class':'et_pb_text_inner'})\n\n def _crawl(self,counter=1,stop_when=-1):\n '''Scowers the given URLs using a counter initialised at 1, specify otherwise (when a log\n is present)'''\n if stop_when < 0:\n for URL in self.url:\n url = URL + \"/page/{}/\".format(counter)\n print(\"URL: {}\".format(URL))\n self._get_soup(url)\n while self.soup.find('div', attrs = {'class':'et_pb_text_inner'}) == None:\n print(\"PAGE: {}\".format(counter))\n for sub_url in self.soup.find_all('div',attrs={'class':'ArchiveTilePostGrid__posts-col -clearfix'}):\n post_urls = sub_url.find_all('div',attrs={'class':'PostSnippet__image'})\n for post_url in post_urls:\n self._get_soup(post_url.find('a',href=True)['href'])\n try:\n self.date = self.soup.find('p',attrs={'class':'date'}).text\n except:\n self.date = \"nan\"\n self._get_paras()\n self._write_paras()\n if self.it % 50 == 0:\n print(f\"{self.it} texts completed\")\n # UPDATE THE URL\n self._write_log(URL,counter)\n counter+= 1\n url = URL + \"page/{}\".format(counter)\n self._get_soup(url)\n\n print(\"WHY WONT U PRINT THIS\")\n self._write_log_endpage(URL,counter - 1)\n counter = 1\n else:\n print(\"UPDATING...\")\n for URL in self.url:\n url = URL + \"page/{}/\".format(counter)\n print(\"UPDATING URL: {}\".format(URL))\n self._get_soup(url)\n print(counter, stop_when)\n while counter < stop_when + 1:\n print(\"PAGE: {}\".format(counter))\n for sub_url in self.soup.find_all('div',attrs={'class':'ArchiveTilePostGrid__posts-col -clearfix'}):\n post_urls = sub_url.find_all('div',attrs={'class':'PostSnippet__image'})\n for post_url in post_urls:\n self._get_soup(post_url.find('a',href=True)['href'])\n try:\n self.date = self.soup.find('p',attrs={'class':'date'}).text\n except:\n self.date = \"nan\"\n self._get_paras()\n self._write_paras()\n if self.it % 50 == 0:\n print(f\"{self.it} texts completed\") # UPDATE THE URL\n counter+= 1\n url = URL + \"/page/{}/\".format(counter)\n self._get_soup(url)\n\n def _run_crawl(self):\n ''' Running the webcrawler '''\n try: # Check if in log\n self._check_log()\n print(\"RUNNING FROM LATEST ENTRY\")\n url, page = self._open_log()\n self.url = self.url[self.url.index(url):] # find latest url to end.\n counter = int(page) # collect the page\n self._crawl(counter + 1,-1)\n counter = 1\n\n except:\n print(\"NO ENTRIES IN LOG\")\n self._crawl()\n\n def _run_newpage_crawl(self):\n ''' Checks and runs the crawler on new pages from the last log entries '''\n self.url, endpages = self._open_log_endpage()\n new_endpage = []\n print(self.url)\n for url, endpage in zip(self.url,endpages):\n URL = url + 'page/{}'.format(int(endpage) + 1)\n self._get_soup(URL)\n print(URL)\n new_page = 1\n\n while self.soup.find('div', attrs = {'class':'et_pb_text_inner'}) == None:\n URL = url + 'page/{}'.format(int(endpage) + new_page)\n self._get_soup(URL)\n new_page += 1\n self._crawl(1,new_page)\n\n new_endpage.append(new_page + int(endpage) - 1)\n\n print(new_endpage)\n for url, endpage in zip(self.url, new_endpage):\n self._writeover_log_endpage(url,endpage)\n", "sub_path": "SemanticAnalysis/Cyclingtipcs/CrawlCyclingtips.py", "file_name": "CrawlCyclingtips.py", "file_ext": "py", "file_size_in_byte": 7683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "536046400", "text": "# -*- coding: utf-8 -*-\n\nimport io\n\nfrom transcode.sanitize import sanitize_html\n\nfrom docutils import nodes\nfrom docutils.core import publish_parts\nfrom docutils.writers.html4css1 import Writer, HTMLTranslator\n\n\nclass TXHTMLTranslator(HTMLTranslator):\n\n def __init__(self, document):\n HTMLTranslator.__init__(self, document)\n\n def visit_raw(self, node):\n if 'html' in node.get('format', '').split():\n self.body.append(sanitize_html(node.astext()))\n\n # Keep non-HTML raw text out of output:\n raise nodes.SkipNode\n\n\ndef render(source, *args, **kwargs):\n warning_stream = io.StringIO()\n html_writer = Writer()\n html_writer.translator_class = kwargs.pop('translator_class', TXHTMLTranslator)\n result = publish_parts(\n source,\n writer=html_writer,\n writer_name='html4css1',\n settings_overrides={\n 'warning_stream': warning_stream,\n }\n )\n\n frag = result['fragment']\n output = warning_stream.getvalue()\n if output:\n bits = output.split(':', 2)\n frag = '{}\\n{}'.format(\n '[Line {}]: {}
'.format(bits[1], bits[2]),\n frag\n )\n\n return frag\n", "sub_path": "transcode/renderers/rst.py", "file_name": "rst.py", "file_ext": "py", "file_size_in_byte": 1217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "docutils.writers.html4css1.HTMLTranslator", "line_number": 12, "usage_type": "name"}, {"api_name": "docutils.writers.html4css1.HTMLTranslator.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "docutils.writers.html4css1.HTMLTranslator", "line_number": 15, "usage_type": "name"}, {"api_name": "transcode.sanitize.sanitize_html", "line_number": 19, "usage_type": "call"}, {"api_name": "docutils.nodes.SkipNode", "line_number": 22, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 22, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 26, "usage_type": "call"}, {"api_name": "docutils.writers.html4css1.Writer", "line_number": 27, "usage_type": "call"}, {"api_name": "docutils.core.publish_parts", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "464954533", "text": "#! /usr/bin/env python\n\nimport os\nfrom PIL import Image\nimport numpy as np\n\ndata_root = '/home/zeyu/data/MARS/mix_cuhk01_delicate'\n\ndef image_padding(img):\n# Resize image to square.\n\th, w, _ = img.shape # Size of image: (128, 64, 3)\n\tpadding = (h - w) / 2 # height > width\n\tside = h\n\timg_target = np.zeros(shape=(side, side, 3), dtype=img.dtype)\n\timg_target[:, padding: padding + w, :] = img\n\treturn img_target\n\ndir_list = ['occlude', 'label']\nfor dir_name in dir_list:\n\tdir_path_src = os.path.join(data_root, dir_name)\n\tdir_path_dst = os.path.join(data_root, dir_name + '_padding')\n\n\tif not os.path.exists(dir_path_dst):\n\t\tos.makedirs(dir_path_dst)\n\tfile_list = os.listdir(dir_path_src)\n\n\tnum_file = len(file_list)\n\tcount_file = 0\n\tfor file_name in file_list:\n\t\tfile_path_src = os.path.join(dir_path_src, file_name)\n\t\tfile_path_dst = os.path.join(dir_path_dst, file_name)\n\n\t\timg = np.array(Image.open(file_path_src))\n\t\timg_padding = image_padding(img)\n\t\tImage.fromarray(img_padding).save(file_path_dst)\n\n\t\tcount_file += 1\n\t\tif count_file % 1000 == 0:\n\t\t\tprint('%d / %d images in directory %s done.' % (count_file, num_file, dir_name))\n\n\tprint('Images in directory %s have been padded.' % dir_name)\n\n", "sub_path": "tools/image_padding.py", "file_name": "image_padding.py", "file_ext": "py", "file_size_in_byte": 1202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "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.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "383065910", "text": "from scvi.inference import UnsupervisedTrainer\nfrom scvi.utils import demultiply, compute_hdi, softmax\nimport os\nfrom tqdm import tqdm_notebook\nimport pandas as pd\nimport numpy as np\nimport warnings\nimport time\nimport pickle\n\n\ndef save_pickle(data, filename):\n with open(filename, \"wb\") as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n\n\ndef load_pickle(filename):\n with open(filename, \"rb\") as f:\n res = pickle.load(f)\n return res\n\n\ndef train_model(\n mdl_class,\n dataset,\n mdl_params: dict,\n train_params: dict,\n train_fn_params: dict,\n filename: str = None,\n):\n \"\"\"\n\n :param mdl_class: Class of algorithm\n :param dataset: Dataset\n :param mdl_params:\n :param train_params:\n :param train_fn_params:\n :param filename\n :return:\n \"\"\"\n # if os.path.exists(filename):\n # res = load_pickle(filename)\n # return res[\"vae\"], res[\"trainer\"]\n\n if \"test_indices\" not in train_params:\n warnings.warn(\"No `test_indices` attribute found.\")\n my_vae = mdl_class(\n n_input=dataset.nb_genes, n_batch=dataset.n_batches, **mdl_params\n )\n my_trainer = UnsupervisedTrainer(my_vae, dataset, **train_params)\n my_trainer.train(**train_fn_params)\n print(my_trainer.train_losses)\n return my_vae, my_trainer\n\n\ndef estimate_lfc_density(\n filename,\n mdl_class,\n dataset,\n mdl_params: dict,\n train_params: dict,\n train_fn_params: dict,\n sizes: list,\n n_picks: int = 10,\n n_samples: int = 500,\n label_a=0,\n label_b=1,\n importance_sampling=False,\n):\n \"\"\"\n\n \"\"\"\n\n lfcs = dict()\n my_vae, my_trainer = train_model(\n mdl_class, dataset, mdl_params, train_params, train_fn_params\n )\n post = my_trainer.test_set\n train_indices = post.data_loader.sampler.indices\n train_samples = np.random.permutation(train_indices)\n post = my_trainer.create_posterior(\n model=my_vae, gene_dataset=dataset, indices=train_samples\n )\n outputs = post.get_latents(n_samples=n_samples, other=True, device=\"cpu\")\n z, labels, scales = outputs[\"z\"], outputs[\"label\"], outputs[\"scale\"]\n weights = softmax(outputs[\"log_probas\"], axis=0)\n\n for (size_ix, size) in enumerate(tqdm_notebook(sizes)):\n lfc_size = []\n for exp in range(n_picks):\n labels = labels.squeeze()\n # Sampling cells\n where_a = np.where(labels == label_a)[0]\n where_b = np.where(labels == label_b)[0]\n where_a = where_a[np.random.choice(len(where_a), size=size)]\n where_b = where_b[np.random.choice(len(where_b), size=size)]\n # Sampling z observations\n scales_a = scales[:, where_a, :].reshape((-1, dataset.nb_genes)).numpy()\n scales_b = scales[:, where_b, :].reshape((-1, dataset.nb_genes)).numpy()\n if importance_sampling:\n weights_a = weights[:, where_a].reshape((-1)).numpy() / len(where_a)\n weights_b = weights[:, where_b].reshape((-1)).numpy() / len(where_b)\n else:\n weights_a = None\n weights_b = None\n scales_a, scales_b = demultiply(\n arr1=scales_a,\n arr2=scales_b,\n factor=3,\n weights_a=weights_a,\n weights_b=weights_b,\n )\n lfc = np.log2(scales_a) - np.log2(scales_b)\n assert not np.isnan(lfc).any(), lfc\n lfc_size.append(lfc)\n lfc_size = np.array(lfc_size)\n lfcs[size] = lfc_size\n save_pickle(lfcs, filename=filename)\n return lfcs\n\n\ndef estimate_lfc_mean(\n filename,\n mdl_class,\n dataset,\n mdl_params: dict,\n train_params: dict,\n train_fn_params: dict,\n sizes: list,\n n_picks: int = 10,\n n_samples: int = 500,\n label_a=0,\n label_b=1,\n importance_sampling=False,\n) -> dict:\n \"\"\"\n Returns LFC POINT ESTIMATES\n \"\"\"\n if os.path.exists(filename):\n return load_pickle(filename)\n lfcs = dict()\n my_vae, my_trainer = train_model(\n mdl_class, dataset, mdl_params, train_params, train_fn_params\n )\n post = my_trainer.test_set\n train_indices = post.data_loader.sampler.indices\n train_samples = np.random.permutation(train_indices)\n post = my_trainer.create_posterior(\n model=my_vae, gene_dataset=dataset, indices=train_samples\n )\n outputs = post.get_latents(n_samples=n_samples, other=True, device=\"cpu\")\n z, labels, scales = outputs[\"z\"], outputs[\"label\"], outputs[\"scale\"]\n weights = softmax(outputs[\"log_probas\"], axis=0)\n\n for (size_ix, size) in enumerate(tqdm_notebook(sizes)):\n lfc_size = []\n for exp in range(n_picks):\n labels = labels.squeeze()\n # Sampling cells\n where_a = np.where(labels == label_a)[0]\n where_b = np.where(labels == label_b)[0]\n where_a = where_a[np.random.choice(len(where_a), size=size)]\n where_b = where_b[np.random.choice(len(where_b), size=size)]\n # Sampling z observations\n scales_a = scales[:, where_a, :].reshape((-1, dataset.nb_genes)).numpy()\n scales_b = scales[:, where_b, :].reshape((-1, dataset.nb_genes)).numpy()\n if importance_sampling:\n weights_a = weights[:, where_a].reshape((-1)).numpy() / len(where_a)\n weights_b = weights[:, where_b].reshape((-1)).numpy() / len(where_b)\n else:\n weights_a = None\n weights_b = None\n scales_a, scales_b = demultiply(\n arr1=scales_a,\n arr2=scales_b,\n factor=3,\n weights_a=weights_a,\n weights_b=weights_b,\n )\n lfc = np.log2(scales_a) - np.log2(scales_b)\n # assert not np.isnan(lfc).any(), lfc\n if np.isnan(lfc).any():\n warnings.warn(\"NaN values appeared in LFCs\")\n lfc_size.append(lfc.mean(0))\n lfc_size = np.array(lfc_size)\n lfcs[size] = lfc_size\n save_pickle(lfcs, filename=filename)\n return lfcs\n\n\ndef estimate_de_proba(\n filename,\n mdl_class,\n dataset,\n mdl_params: dict,\n train_params: dict,\n train_fn_params: dict,\n sizes: list,\n delta: float = 0.5,\n n_trainings: int = 5,\n n_picks: int = 25,\n n_samples: int = 500,\n label_a=0,\n label_b=1,\n):\n \"\"\"\n\n \"\"\"\n if os.path.exists(filename):\n return np.load(filename)\n\n n_sizes = len(sizes)\n de_probas = np.zeros((n_trainings, n_sizes, n_picks, dataset.nb_genes))\n # lfcs = np.zeros((n_trainings, N_SIZES, n_picks, dataset.nb_genes, 3*n_samples))\n for training in range(n_trainings):\n my_vae, my_trainer = train_model(\n mdl_class, dataset, mdl_params, train_params, train_fn_params\n )\n post = my_trainer.test_set\n train_indices = post.data_loader.sampler.indices\n train_samples = np.random.permutation(train_indices)\n post = my_trainer.create_posterior(\n model=my_vae, gene_dataset=dataset, indices=train_samples\n )\n outputs = post.get_latents(n_samples=n_samples, other=True, device=\"cpu\")\n z, labels, scales = outputs[\"z\"], outputs[\"label\"], outputs[\"scale\"]\n\n for (size_ix, size) in enumerate(tqdm_notebook(sizes)):\n for exp in range(n_picks):\n labels = labels.squeeze()\n where_a = np.where(labels == label_a)[0]\n where_b = np.where(labels == label_b)[0]\n where_a = where_a[np.random.choice(len(where_a), size=size)]\n where_b = where_b[np.random.choice(len(where_b), size=size)]\n scales_a = scales[:, where_a, :].reshape((-1, dataset.nb_genes)).numpy()\n scales_b = scales[:, where_b, :].reshape((-1, dataset.nb_genes)).numpy()\n scales_a, scales_b = demultiply(arr1=scales_a, arr2=scales_b, factor=3)\n lfc = np.log2(scales_a) - np.log2(scales_b)\n if np.isnan(lfc).any():\n warnings.warn(\"NaN values appeared in LFCs\")\n\n pgs = np.nanmean(np.abs(lfc) >= delta, axis=0)\n de_probas[training, size_ix, exp, :] = pgs\n np.save(file=filename, arr=de_probas)\n return de_probas\n\n\ndef multi_train_estimates(\n filename,\n mdl_class,\n dataset,\n mdl_params: dict,\n train_params: dict,\n train_fn_params: dict,\n sizes: list,\n delta: float = 0.5,\n n_trainings: int = 5,\n n_picks: int = 25,\n n_samples: int = 500,\n n_samples_total: int = None,\n label_a: int = 0,\n label_b: int = 1,\n importance_sampling: bool = False,\n normalized_means: np.ndarray = None,\n compute_heldout_ll: bool = False,\n) -> pd.DataFrame:\n \"\"\"\n\n \"\"\"\n n_examples, n_genes = dataset.X.shape\n if os.path.exists(filename):\n return pd.read_pickle(filename)\n\n dfs_li = []\n local_lfc_gt = None\n local_is_de = None\n for training in range(n_trainings):\n train_time = time.time()\n my_vae, my_trainer = train_model(\n mdl_class, dataset, mdl_params, train_params, train_fn_params\n )\n train_time = time.time() - train_time\n post = my_trainer.test_set\n test_indices = post.data_loader.sampler.indices\n test_labels = dataset.labels.squeeze()[test_indices]\n\n marginal_ll = None\n if compute_heldout_ll:\n marginal_ll = my_trainer.test_set.marginal_ll(ratio_loss=True)\n # train_samples = np.random.permutation(train_indices)\n # post = my_trainer.create_posterior(\n # model=my_vae, gene_dataset=dataset, indices=test_indices\n # )\n # outputs = post.get_latents(\n # n_samples=n_samples, other=True, device=\"cpu\"\n # )\n # z, labels, scales = outputs[\"z\"], outputs[\"label\"], outputs[\"scale\"]\n # weights = softmax(outputs[\"log_probas\"], axis=0)\n\n for (size_ix, size) in enumerate(tqdm_notebook(sizes)):\n n_samples_local = (\n n_samples if n_samples_total is None else n_samples_total // size\n )\n print(n_samples_local)\n for exp in range(n_picks):\n inference_time = time.time()\n where_a = np.where(test_labels == label_a)[0]\n where_b = np.where(test_labels == label_b)[0]\n where_a = where_a[np.random.choice(len(where_a), size=size)]\n where_b = where_b[np.random.choice(len(where_b), size=size)]\n # From local indices to global\n where_a = test_indices[where_a]\n where_b = test_indices[where_b]\n\n post_a = my_trainer.create_posterior(\n model=my_vae, gene_dataset=dataset, indices=where_a\n )\n outputs_a = post_a.get_latents(\n n_samples=n_samples_local, other=True, device=\"cpu\"\n )\n z_a, labels_a, scales_a = (\n outputs_a[\"z\"],\n outputs_a[\"label\"],\n outputs_a[\"scale\"],\n )\n assert len(np.unique(labels_a.squeeze())) == 1\n\n post_b = my_trainer.create_posterior(\n model=my_vae, gene_dataset=dataset, indices=where_b\n )\n outputs_b = post_b.get_latents(\n n_samples=n_samples_local, other=True, device=\"cpu\"\n )\n z_b, labels_b, scales_b = (\n outputs_b[\"z\"],\n outputs_b[\"label\"],\n outputs_b[\"scale\"],\n )\n assert len(np.unique(labels_b.squeeze())) == 1\n scales_a = scales_a.reshape((-1, dataset.nb_genes)).numpy()\n scales_b = scales_b.reshape((-1, dataset.nb_genes)).numpy()\n\n # exp_post = my_trainer.create_posterior(\n # model=my_vae, gene_dataset=dataset, indices=test_indices\n # )\n # outputs = post.get_latents(\n # n_samples=n_samples, other=True, device=\"cpu\"\n # )\n # z, labels, scales = outputs[\"z\"], outputs[\"label\"], outputs[\"scale\"]\n # weights = softmax(outputs[\"log_probas\"], axis=0)\n\n # labels = labels.squeeze()\n # # Sampling cells\n # where_a = np.where(labels == label_a)[0]\n # where_b = np.where(labels == label_b)[0]\n # where_a = where_a[np.random.choice(len(where_a), size=size)]\n # where_b = where_b[np.random.choice(len(where_b), size=size)]\n # # Sampling z observations\n # scales_a = scales[:, where_a, :].reshape((-1, dataset.nb_genes)).numpy()\n # scales_b = scales[:, where_b, :].reshape((-1, dataset.nb_genes)).numpy()\n # if importance_sampling:\n # weights_a = weights[:, where_a].reshape((-1)) / len(where_a)\n # weights_b = weights[:, where_b].reshape((-1)) / len(where_b)\n # else:\n weights_a = None\n weights_b = None\n if normalized_means is not None:\n # overall_idx_a = test_indices[where_a]\n # overall_idx_b = test_indices[where_b]\n overall_idx_a = where_a\n overall_idx_b = where_b\n h_a = normalized_means[overall_idx_a].reshape((-1, 1, n_genes))\n h_b = normalized_means[overall_idx_b].reshape((1, -1, n_genes))\n lfc_dist = (np.log2(h_a) - np.log2(h_b)).reshape((-1, n_genes))\n local_lfc_gt = lfc_dist.mean(0)\n local_is_de = (np.abs(lfc_dist) >= delta).astype(float).mean(0)\n # scales_a, scales_b = demultiply(\n # arr1=scales_a,\n # arr2=scales_b,\n # factor=3,\n # weights_a=weights_a,\n # weights_b=weights_b\n # )\n lfc = np.log2(scales_a) - np.log2(scales_b)\n assert lfc.shape[1] == dataset.nb_genes, lfc.shape\n if np.isnan(lfc).any():\n warnings.warn(\"NaN values appeared in LFCs\")\n\n pgs = np.nanmean(np.abs(lfc) >= delta, axis=0)\n lfc_mean = np.nanmean(lfc, axis=0)\n lfc_median = np.nanmedian(lfc, axis=0)\n lfc_std = np.nanstd(lfc, axis=0)\n hdi64 = compute_hdi(lfc, credible_interval=0.64)\n hdi25 = compute_hdi(lfc, credible_interval=0.25)\n hdi50 = compute_hdi(lfc, credible_interval=0.50)\n hdi75 = compute_hdi(lfc, credible_interval=0.75)\n hdi95 = compute_hdi(lfc, credible_interval=0.95)\n hdi99 = compute_hdi(lfc, credible_interval=0.99)\n\n inference_time = time.time() - inference_time\n df = pd.DataFrame(\n dict(\n de_proba=pgs,\n lfc_mean=lfc_mean,\n lfc_median=lfc_median,\n lfc_std=lfc_std,\n hdi25_low=hdi25[:, 0],\n hdi25_high=hdi25[:, 1],\n hdi50_low=hdi50[:, 0],\n hdi50_high=hdi50[:, 1],\n hdi75_low=hdi75[:, 0],\n hdi75_high=hdi75[:, 1],\n hdi95_low=hdi95[:, 0],\n hdi95_high=hdi95[:, 1],\n hdi64_low=hdi64[:, 0],\n hdi64_high=hdi64[:, 1],\n hdi99_low=hdi99[:, 0],\n hdi99_high=hdi99[:, 1],\n )\n ).assign(\n experiment=lambda x: exp,\n sample_size=lambda x: size,\n training=lambda x: training,\n gene=np.arange(dataset.nb_genes),\n lfc_gt=local_lfc_gt,\n is_de=local_is_de,\n train_time=train_time,\n inference_time=inference_time,\n marginal_ll=marginal_ll,\n )\n dfs_li.append(df)\n df_res = pd.concat(dfs_li, ignore_index=True)\n df_res.to_pickle(filename)\n return df_res\n\n\ndef train_or_load(\n filepath, dataset, my_mdl_class, my_mdl_params, my_train_params, my_train_fn_params\n):\n if os.path.exists(filepath):\n tup = load_pickle(filepath)\n else:\n tup = train_model(\n mdl_class=my_mdl_class,\n dataset=dataset,\n mdl_params=my_mdl_params,\n train_params=my_train_params,\n train_fn_params=my_train_fn_params,\n )\n save_pickle(tup, filepath)\n return tup\n", "sub_path": "scvi_utils/lfc.py", "file_name": "lfc.py", "file_ext": "py", "file_size_in_byte": 16909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pickle.dump", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 19, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 46, "usage_type": "call"}, {"api_name": "scvi.inference.UnsupervisedTrainer", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "scvi.utils.softmax", "line_number": 86, "usage_type": "call"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "scvi.utils.demultiply", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "scvi.utils.softmax", "line_number": 153, "usage_type": "call"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 163, "usage_type": "attribute"}, {"api_name": "scvi.utils.demultiply", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 182, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 221, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 234, "usage_type": "attribute"}, {"api_name": "scvi.utils.demultiply", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 239, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 278, "usage_type": "call"}, {"api_name": "time.time", "line_number": 282, "usage_type": "call"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 300, "usage_type": "call"}, {"api_name": "time.time", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 310, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 386, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.nanmedian", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.nanstd", "line_number": 392, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 393, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 394, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 395, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 396, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 397, "usage_type": "call"}, {"api_name": "scvi.utils.compute_hdi", "line_number": 398, "usage_type": "call"}, {"api_name": "time.time", "line_number": 400, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 424, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 432, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 440, "usage_type": "attribute"}]}
{"seq_id": "60334794", "text": "\"\"\"\nThe data assimilation system (no assimilation example)\nLoad:\n x_a_init.txt\nSave:\n x_b.txt\n x_a.txt\n\"\"\"\nimport numpy as np\nfrom scipy.integrate import ode\nimport lorenz96\nfrom settings import *\n\n# load initial condition\nx_a_init = np.genfromtxt('x_a_init.txt')\n#x_a_init = np.genfromtxt('x_t.txt')[0] + 1.e-4 # using nature run value plus a small error (for test purpose)\n\n# load observations\n# y_o_save = ......\n\n# initial x_b: no values at the initial time (assign NaN)\nx_b_save = np.full((1,N), np.nan, dtype='f8')\n\n# initial x_a: from x_a_ens_init\nx_a_save = np.array([x_a_init])\n\ntt = 1\nwhile tt <= nT:\n tts = tt - 1\n Ts = tts * dT # forecast start time\n Ta = tt * dT # forecast end time (DA analysis time)\n print('Cycle =', tt, ', Ts =', round(Ts, 10), ', Ta =', round(Ta, 10))\n\n #--------------\n # forecast step\n #--------------\n\n solver = ode(lorenz96.f).set_integrator('dopri5')\n solver.set_initial_value(x_a_save[tts], Ts).set_f_params(F)\n solver.integrate(Ta)\n x_b_save = np.vstack([x_b_save, [solver.y]])\n\n #--------------\n # analysis step\n #--------------\n\n # background\n x_b = x_b_save[tt].transpose()\n\n # observation\n #y_o = ......\n\n # innovation\n #y_b = np.dot(H, x_b)\n #d = y_o - y_b\n\n # analysis scheme (No assimilation in this example)\n x_a = x_b\n\n x_a_save = np.vstack([x_a_save, x_a.transpose()])\n tt += 1\n\n# save background and analysis data\nnp.savetxt('x_b.txt', x_b_save)\nnp.savetxt('x_a.txt', x_a_save)\n", "sub_path": "das.py", "file_name": "das.py", "file_ext": "py", "file_size_in_byte": 1513, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.genfromtxt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.integrate.ode", "line_number": 38, "usage_type": "call"}, {"api_name": "lorenz96.f", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "616497873", "text": "import urllib3\nimport xml.etree.ElementTree as ET\nimport pandas as pd\nimport os\n\n# create data folder if not exist\ndata_folder = 'data'\nif not os.path.exists(data_folder):\n os.mkdir(data_folder)\n\n# get data\nhttp = urllib3.PoolManager()\nr = http.request('GET', \"https://www.vietcombank.com.vn/ExchangeRates/ExrateXML.aspx\")\n\n# parse xml\nroot = ET.fromstring(r.data)\ndate = None\narr = []\nfor child in root:\n tag, attrib = child.tag, child.attrib\n if tag == 'DateTime':\n date = child.text\n elif tag == 'Exrate':\n arr.append(child.attrib)\n \n# convert to dataframe and save to file\ndf = pd.DataFrame(arr)\ndf['date'] = date\ndf['date'] = pd.to_datetime(df['date'])\ndt = str(df['date'].iloc[0])[:10]\nfilepath = \"{}/{}.csv\".format(data_folder, dt)\ndf.to_csv(filepath, index=False)\nprint(\"save\", df.shape[0], \"rows to \", data_folder)\n", "sub_path": "vcb.py", "file_name": "vcb.py", "file_ext": "py", "file_size_in_byte": 857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.path.exists", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 12, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 16, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 16, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "71468435", "text": "import numpy as np\nimport cv2\n\ndef click(event, x, y, flgs, params):\n if event == cv2.EVENT_LBUTTONDOWN:\n b = img[y, x, 0]\n g = img[y, x, 1]\n r = img[y, x, 2]\n #print(b, g, r)\n findThis = np.uint8([[[b, g, r]]])\n hsvColor = cv2.cvtColor(findThis, cv2.COLOR_BGR2HSV)\n\n l_b = np.array([hsvColor[0][0][0] - 20, 100, 100])\n u_b = np.array([hsvColor[0][0][0] + 20, 255, 255])\n\n mask = cv2.inRange(hsv, l_b, u_b)\n res = cv2.bitwise_and(img, img, mask = mask)\n\n cv2.imshow('mask', mask)\n cv2.imshow('res', res)\n\n\"\"\"\ncv2.namedWindow('img')\ncv2.setMouseCallback('img', click)\n\n\nimg = cv2.imread('used_images_videos/detect_blob.png')\nimg = cv2.resize(img, (400, 400))\n\nhsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n \nwhile(1):\n\n cv2.imshow('img', img)\n\n k= cv2.waitKey(1) & 0xFF\n if k == 27:\n break\n\n\"\"\"\ncap = cv2.VideoCapture(0) \n\nwhile(1):\n _, frame = cap.read()\n\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n l_b = np.array([110, 100, 100])\n u_b = np.array([130, 255, 255])\n\n mask = cv2.inRange(hsv, l_b, u_b)\n res = cv2.bitwise_and(frame, frame, mask = mask)\n \n cv2.imshow('img', frame)\n cv2.imshow('mask', mask)\n cv2.imshow('res', res)\n\n k= cv2.waitKey(1) & 0xFF\n if k == 27:\n break\n\ncap.release()\ncv2.destroyAllWindows()", "sub_path": "Practice/opencv/object_dtctn_basedOnColor.py", "file_name": "object_dtctn_basedOnColor.py", "file_ext": "py", "file_size_in_byte": 1362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "81265618", "text": "\"\"\"\nTwitch bot\n\n TODO ( Soonโข ):\n * Check if user has mod/sub priviliges when using commands\n * Fetch moderator-list for channels from Twitch\n * Check that the bot actually connects to twitch and the channels on startup\n * Move commands.py and blacklist.py to json or something for easier live editing?\n * Make it so commands can take arguments\n * Allow blacklist to contain regex\n\"\"\"\n\nimport socket\nimport re\nfrom time import sleep\n\nfrom commands import commands\nfrom config import config\nfrom blacklist import blacklist\n\nclass TwitchBot():\n\n def __init__(self):\n self.sock = socket.socket()\n\n def connect(self, channels):\n \"\"\"Establish a connection with Twitch IRC and connect to channels\"\"\"\n if config['debug']:\n print(\"Connecting to Twitch\")\n\n self.sock.connect((config['host'], config['port']))\n self.sock.send(f\"PASS {config['oauth_pass']}\\r\\n\".encode(\"utf-8\"))\n self.sock.send(f\"NICK {config['nick']}\\r\\n\".encode(\"utf-8\"))\n\n for channel in channels:\n self.join_channel(channel)\n\n def run(self):\n while True:\n response = self.sock.recv(1024).decode(\"utf-8\")\n self.handle_message(response)\n sleep(2) # To prevent getting banned from sending to many messages (20 per 30sec)\n\n def join_channel(self, channel, greeting=\"/me has joined the channel\"):\n self.sock.send(f\"JOIN #{channel}\\r\\n\".encode(\"utf-8\"))\n self.send_message(greeting, channel)\n\n def respond_to_ping(self):\n self.sock.send(\"PONG :tmi.twitch.tv\\r\\n\".encode(\"utf-8\"))\n if config['debug']:\n print(\"Pinging server\")\n\n def send_message(self, message, channel):\n \"\"\"Sends a message to a Twitch channel\"\"\"\n self.sock.send(f\"PRIVMSG #{channel} :{message}\\r\\n\".encode(\"utf-8\"))\n if config['debug']:\n print(f\"OUT - {channel}: {message}\")\n\n def handle_message(self, message):\n \"\"\"Decide what to do with a message from server\"\"\"\n\n chat_message = re.compile(r\"^:\\w+!\\w+@\\w+\\.tmi\\.twitch\\.tv PRIVMSG #\\w+ :\")\n if re.match(chat_message, message): # Message is from a chat\n channel = message[1::].split(\"!\")[0]\n user = re.search(r'\\w+', message).group(0)\n message = chat_message.sub(\"\", message)[:-2]\n\n res = self.check_blacklist(message, channel)\n if res[0] != -1:\n self.timeout_user(channel, user, res[0], res[1])\n elif message[0] == \"!\":\n self.handle_commands(message[1::], channel, user)\n\n elif message == \"PING :tmi.twitch.tv\\r\\n\":\n self.respond_to_ping()\n\n def handle_commands(self, command, channel, username):\n \"\"\"Execute a command\"\"\"\n user_auth_level = self.get_user_authority_level(channel, username)\n for group in ['global', channel]:\n for auth_level in user_auth_level:\n if command in commands[group][auth_level]:\n self.send_message(commands[group][command], channel)\n\n def get_user_authority_level(self, channel, username):\n authority_levels = ['channelowner', 'mod', 'sub', 'all']\n if username == channel:\n return authority_levels\n else:\n return authority_levels[3]\n\n def check_blacklist(self, message, channel):\n \"\"\"Check if part of a message is blacklisted\"\"\"\n if channel in blacklist:\n for phrase in blacklist[channel]:\n if phrase in message:\n return blacklist[channel][phrase]\n return [-1, '']\n\n def timeout_user(self, channel, username, time, timeout_message):\n if timeout_message:\n self.send_message(timeout_message, channel)\n self.send_message(f\"/timeout {username} {time}\", channel)\n\n if config['debug']:\n print(f\"Timed out user {username} for {time} seconds.\")\n\n\nif __name__ == \"__main__\":\n bot = TwitchBot()\n bot.connect(config['channels'])\n bot.run()\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 4052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "config.config", "line_number": 28, "usage_type": "name"}, {"api_name": "config.config", "line_number": 31, "usage_type": "name"}, {"api_name": "config.config", "line_number": 32, "usage_type": "name"}, {"api_name": "config.config", "line_number": 33, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "config.config", "line_number": 50, "usage_type": "name"}, {"api_name": "config.config", "line_number": 56, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 62, "usage_type": "call"}, {"api_name": "re.match", "line_number": 63, "usage_type": "call"}, {"api_name": "re.search", "line_number": 65, "usage_type": "call"}, {"api_name": "commands.commands", "line_number": 82, "usage_type": "name"}, {"api_name": "commands.commands", "line_number": 83, "usage_type": "name"}, {"api_name": "blacklist.blacklist", "line_number": 94, "usage_type": "name"}, {"api_name": "blacklist.blacklist", "line_number": 95, "usage_type": "name"}, {"api_name": "blacklist.blacklist", "line_number": 97, "usage_type": "name"}, {"api_name": "config.config", "line_number": 105, "usage_type": "name"}, {"api_name": "config.config", "line_number": 111, "usage_type": "name"}]}
{"seq_id": "303690062", "text": "import cv2\nimport requests\nfrom detecter import Detecter\nimport numpy as np\nfrom threading import Thread\n\ndef detect() :\n global fancyFrame\n frame = fancyFrame\n if(fancyFrame is not None) :\n image_ex = np.expand_dims(frame, axis=0)\n (boxes,scores,classes,num) = detecter.detect(image_ex)\n detecter.viaulize(frame,boxes,classes,scores,threshold)\n return frame\n\ndef play() :\n global fancyDetectFrame\n print(\"detect๋ ๋์์ ์์ ์ค๋น ์๋ฃ...\")\n \n if(fancyDetectFrame is not None) :\n cv2.imshow('Video',fancyDetectFrame)\n cv2.waitKey(1)\n \n\ndef run(*args) :\n global fancyDetectFrame\n while(True) :\n fancyDetectFrame = detect()\n print(fancyDetectFrame)\n play()\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\nfancyFrame = None #detectํ ์ต์ ์ด๋ฏธ์ง๋ฅผ ๋ด๋๋ค.\nfancyDetectFrame = None #์์์ผ๋ก ๋ด๋ณด๋ด๋ ์ต์ ์ด๋ฏธ์ง\n\n#detecter์์ฑ\ndetecter = Detecter()\ndetecter.setup('./frozen_inference_graph.pb', './mscoco_label_map.pbtxt')\n\ncap = cv2.VideoCapture('C:/Users/student/Desktop/videos/car/video/video3.mp4')\nfps = cap.get(cv2.CAP_PROP_FPS)\ndelay = int(1000/fps)\nthreshold = 0.3\n\nThread(target = run).start()\nwhile(cap.isOpened()):\n ret, frame = cap.read()\n fancyFrame = frame\n cv2.waitKey(delay)\n", "sub_path": "traffic/video_detect.py", "file_name": "video_detect.py", "file_ext": "py", "file_size_in_byte": 1340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.expand_dims", "line_number": 11, "usage_type": "call"}, {"api_name": "detecter.detect", "line_number": 12, "usage_type": "call"}, {"api_name": "detecter.viaulize", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}, {"api_name": "detecter.Detecter", "line_number": 38, "usage_type": "call"}, {"api_name": "detecter.setup", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 42, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 50, "usage_type": "call"}]}
{"seq_id": "135713869", "text": "\nclass ColorMap():\n def __init__(self):\n # self.colors = [(0xFF, 0x00, 0x00), (0x00, 0x00, 0xFF), (0x00, 0xFF, 0x00),\n # (0x00, 0xFF, 0xFF), (0xFF, 0xFF, 0x00), (0xFF, 0x00, 0xFF),\n # (0xFF, 0xFF, 0xFF), (0xAA, 0x33, 0xAA), (0x68, 0x00, 0x0D),\n # (0x88, 0x88, 0xFF), (0xFF, 0xAA, 0xAA), (0xAA, 0xFF, 0xFF),\n # (0xAA, 0xAA, 0x55), (0x55, 0xFF, 0x55), (0x00, 0xA5, 0xFF)]\n\n self.colors = [(0xFF, 0x00, 0x00), (0x00, 0x00, 0xFF), (0x00, 0xFF, 0x00),\n (0x00, 0xFF, 0xFF), (0xFF, 0xFF, 0x00), (0xFF, 0x00, 0xFF),\n (0xFF, 0xAA, 0xAA), (0xAA, 0x33, 0xAA), (0x68, 0x00, 0x0D),\n (0xAA, 0xAA, 0x55), (0x55, 0xFF, 0x55), (0x00, 0xA5, 0xFF),\n (0x88, 0x88, 0xFF), (0xAA, 0xFF, 0xFF), (0xFF, 0xFF, 0xFF)\n ]\n\n self.num_color = len(self.colors)\n\n def get(self, idx):\n return self.colors[idx % self.num_color]\n\n def get_w(self, idx, w):\n color = self.colors[idx % self.num_color]\n r = int(color[0]*w)\n g = int(color[1]*w)\n b = int(color[2]*w)\n return (r, g, b)\n\ndef str_time():\n import datetime\n now = datetime.datetime.now()\n time_stamp = '_%d_%d_%d_%d_%d_%d' % ( now.year, now.month, now.day, now.hour, now.minute, now.second)\n return time_stamp", "sub_path": "utils/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}]}
{"seq_id": "116178282", "text": "import unittest\nimport os\nimport multiprocessing\nfrom unittest.mock import patch, mock_open\n\nfrom nix_review.app import main\n\nTEST_ROOT = os.path.dirname(os.path.realpath(__file__))\nDEBUG = False\n\n\nclass IgnoreArgument:\n pass\n\n\ndef read_asset(asset: str) -> str:\n with open(os.path.join(TEST_ROOT, \"assets\", asset)) as f:\n return f.read()\n\n\ndef expect_side_effects(test, arg_spec):\n arg_spec_iterator = iter(arg_spec)\n\n def side_effect(*args, **kwargs):\n try:\n (expected_args, ret) = next(arg_spec_iterator)\n if DEBUG:\n print(f\"({expected_args}) -> {ret}\")\n if expected_args is IgnoreArgument:\n return ret\n if expected_args != args[0]:\n print(args[0])\n test.assertEqual(expected_args, args[0])\n return ret\n except StopIteration:\n test.fail(\n f\"run out of calls, you can mock it with following arguments:\\n({args}, 0)\"\n )\n\n return side_effect\n\n\npkg_list = read_asset(\"package_list_after.txt\").encode(\"utf-8\")\n\n\ndef local_eval_cmds():\n return [\n (IgnoreArgument,\n mock_open(read_data=read_asset(\"github-pull-1.json\"))()),\n (IgnoreArgument,\n mock_open(read_data=read_asset(\"github-pull-1-statuses.json\"))()),\n ([\n 'git', 'fetch', '--force', 'https://github.com/NixOS/nixpkgs',\n 'master:refs/nix-review/0', 'pull/1/head:refs/nix-review/1'\n ], 0),\n (['git', 'rev-parse', '--verify', 'refs/nix-review/0'], b\"hash1\"),\n (['git', 'rev-parse', '--verify', 'refs/nix-review/1'], b\"hash2\"),\n (['git', 'worktree', 'add', './.review/pr-1', 'hash1'], 0),\n ([\n 'nix-env', '-f', './.review/pr-1', '-qaP', '--xml', '--out-path',\n '--show-trace'\n ], b\"\"),\n (['git', 'merge', '--no-commit', 'hash2'], 0),\n ([\n 'nix-env', '-f', './.review/pr-1', '-qaP', '--xml', '--out-path',\n '--show-trace', '--meta'\n ], pkg_list),\n ]\n\n\ndef borg_eval_cmds():\n return [\n (IgnoreArgument,\n mock_open(read_data=read_asset(\"github-pull-37200.json\"))()),\n (IgnoreArgument,\n mock_open(read_data=read_asset(\"github-pull-37200-statuses.json\"))()),\n (\"https://gist.githubusercontent.com/GrahamcOfBorg/4c9ebc3e608308c6096202375b0dc902/raw/\",\n read_asset(\"gist-37200.txt\").encode(\"utf-8\").split(b\"\\n\")),\n ([\n 'git', 'fetch', '--force', 'https://github.com/NixOS/nixpkgs',\n 'master:refs/nix-review/0', 'pull/37200/head:refs/nix-review/1'\n ], 0),\n (['git', 'rev-parse', '--verify', 'refs/nix-review/0'], b\"hash1\"),\n (['git', 'rev-parse', '--verify', 'refs/nix-review/1'], b\"hash2\"),\n (['git', 'worktree', 'add', './.review/pr-37200', 'hash1'], 0),\n (['git', 'merge', '--no-commit', 'hash2'], 0),\n (['nix', 'eval', '--raw', 'nixpkgs.system'], b\"x86_64-linux\"),\n ]\n\n\nbuild_cmds = [([\n 'nix', 'eval', '--json',\n '(with import {}; {\\n\\t\"pong3d\" = (builtins.tryEval \"${pong3d}\").success;\\n})'\n], b'{\"pong3d\":true}'), ([\n 'nix-shell', '--no-out-link', '--keep-going', '--max-jobs',\n str(multiprocessing.cpu_count()), '--option', 'build-use-sandbox', 'true',\n '--run', 'true', '--builders', 'ssh://joerg@10.243.29.170 aarch64-linux',\n '-p', 'pong3d'\n], 0), (['nix-shell', '-p', 'pong3d'], 0), (['git', 'worktree', 'prune'], 0)]\n\n\nclass TestStringMethods(unittest.TestCase):\n def setUp(self):\n os.chdir(os.path.join(TEST_ROOT, \"assets/nixpkgs\"))\n\n @patch('urllib.request.urlopen')\n @patch('subprocess.Popen')\n @patch('subprocess.check_call')\n @patch('subprocess.check_output')\n def test_pr_local_eval(self, mock_check_output, mock_check_call,\n mock_popen, mock_urlopen):\n effects = expect_side_effects(self, local_eval_cmds() + build_cmds)\n mock_check_call.side_effect = effects\n mock_popen.stdout.side_effect = effects\n mock_check_output.side_effect = effects\n mock_urlopen.side_effect = effects\n\n main(\"nix-review\", [\n \"--build-args\",\n '--builders \"ssh://joerg@10.243.29.170 aarch64-linux\"', \"pr\", \"1\"\n ])\n\n @patch('urllib.request.urlopen')\n @patch('subprocess.Popen')\n @patch('subprocess.check_call')\n @patch('subprocess.check_output')\n def test_pr_borg_eval(self, mock_check_output, mock_check_call, mock_popen,\n mock_urlopen):\n effects = expect_side_effects(self, borg_eval_cmds() + build_cmds)\n mock_check_call.side_effect = effects\n mock_popen.stdout.side_effect = effects\n mock_check_output.side_effect = effects\n mock_urlopen.side_effect = effects\n\n main(\"nix-review\", [\n \"--build-args\",\n '--builders \"ssh://joerg@10.243.29.170 aarch64-linux\"', \"pr\",\n \"37200\"\n ])\n\n\nif __name__ == '__main__':\n unittest.main(failfast=True)\n", "sub_path": "nix_review/tests/test_app.py", "file_name": "test_app.py", "file_ext": "py", "file_size_in_byte": 5044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "unittest.mock.mock_open", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 51, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 74, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 76, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 96, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "nix_review.app.main", "line_number": 118, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 106, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 107, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 108, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 109, "usage_type": "call"}, {"api_name": "nix_review.app.main", "line_number": 135, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 123, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 124, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 125, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 143, "usage_type": "call"}]}
{"seq_id": "330442618", "text": "import torch\nfrom torchtext import data\nimport numpy as np\nfrom torch.autograd import Variable\n\n\ndef nopeak_mask(size, opt):\n np_mask = np.triu(np.ones((1, size, size)), k=1).astype('uint8')\n if opt.use_cond2dec == True:\n cond_mask = np.zeros((1, opt.cond_dim, opt.cond_dim))\n cond_mask_upperright = np.ones((1, opt.cond_dim, size))\n cond_mask_upperright[:, :, 0] = 0\n cond_mask_lowerleft = np.zeros((1, size, opt.cond_dim))\n upper_mask = np.concatenate([cond_mask, cond_mask_upperright], axis=2)\n lower_mask = np.concatenate([cond_mask_lowerleft, np_mask], axis=2)\n np_mask = np.concatenate([upper_mask, lower_mask], axis=1)\n np_mask = Variable(torch.from_numpy(np_mask) == 0)\n if opt.device == 0:\n np_mask = np_mask.cuda()\n return np_mask\n\ndef create_masks(src, trg, cond, opt):\n torch.set_printoptions(profile=\"full\")\n src_mask = (src != opt.src_pad).unsqueeze(-2)\n cond_mask = torch.unsqueeze(cond, -2)\n cond_mask = torch.ones_like(cond_mask, dtype=bool)\n src_mask = torch.cat([cond_mask, src_mask], dim=2)\n\n if trg is not None:\n trg_mask = (trg != opt.trg_pad).unsqueeze(-2)\n if opt.use_cond2dec == True:\n trg_mask = torch.cat([cond_mask, trg_mask], dim=2)\n np_mask = nopeak_mask(trg.size(1), opt)\n if trg.is_cuda:\n np_mask.cuda()\n trg_mask = trg_mask & np_mask\n\n else:\n trg_mask = None\n return src_mask, trg_mask\n\n# patch on Torchtext's batching process that makes it more efficient\n# from http://nlp.seas.harvard.edu/2018/04/03/attention.html#position-wise-feed-forward-networks\n\nclass MyIterator(data.Iterator):\n def create_batches(self):\n if self.train:\n def pool(d, random_shuffler):\n for p in data.batch(d, self.batch_size * 100):\n p_batch = data.batch(sorted(p, key=self.sort_key), self.batch_size, self.batch_size_fn)\n for b in random_shuffler(list(p_batch)):\n yield b\n self.batches = pool(self.data(), self.random_shuffler)\n \n else:\n self.batches = []\n for b in data.batch(self.data(), self.batch_size,\n self.batch_size_fn):\n self.batches.append(sorted(b, key=self.sort_key))\n\nglobal max_src_in_batch, max_tgt_in_batch\n\ndef batch_size_fn(new, count, sofar):\n \"Keep augmenting batch and calculate total number of tokens + padding.\"\n global max_src_in_batch, max_tgt_in_batch\n if count == 1:\n max_src_in_batch = 0\n max_tgt_in_batch = 0\n max_src_in_batch = max(max_src_in_batch, len(new.src))\n max_tgt_in_batch = max(max_tgt_in_batch, len(new.trg) + 2)\n src_elements = count * max_src_in_batch\n tgt_elements = count * max_tgt_in_batch\n return max(src_elements, tgt_elements)\n", "sub_path": "Batch.py", "file_name": "Batch.py", "file_ext": "py", "file_size_in_byte": 2887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.triu", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.set_printoptions", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 32, "usage_type": "call"}, {"api_name": "torchtext.data.Iterator", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torchtext.data", "line_number": 45, "usage_type": "name"}, {"api_name": "torchtext.data.batch", "line_number": 49, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 49, "usage_type": "name"}, {"api_name": "torchtext.data.batch", "line_number": 50, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 50, "usage_type": "name"}, {"api_name": "torchtext.data.batch", "line_number": 57, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 57, "usage_type": "name"}]}
{"seq_id": "410714872", "text": "from selenium import webdriver\nfrom bs4 import BeautifulSoup\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nimport pandas as pd\n\nfrom Select_Course_Section import SelectSection\nfrom Select_Semester import SelectSemester\n\n\nclass RawDataHours:\n global df_raw_section_data\n\n def __init__(self, section, course2):\n self.section = section\n self.course2 = course2\n\n def raw_data_hours(self):\n\n def table3_function():\n table3 = table.find_all('tr')[1:]\n for j in table3:\n raw_section_data = j.find_all('td')\n row = [tr.text for tr in raw_section_data]\n row.append(self.section)\n row.append(self.course2)\n try:\n length = len(df_raw_section_data)\n df_raw_section_data.loc[length] = row\n except:\n continue\n driver.back()\n driver.back()\n\n headers = ['NO', 'Name', 'ID', 'Grade', 'Hours', 'Other', 'Section', 'Course']\n df_raw_section_data = pd.DataFrame(columns=headers)\n final_hours = driver.find_element_by_xpath('//*[@id=\"FinalGrades_form\"]/a').click()\n course = driver.find_element_by_xpath('/html/body/table/tbody/tr/td[2]/table[2]/tbody/tr[2]/td[3]').text\n element = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.ID, 'Roster_form')))\n page_source = driver.page_source\n soup = BeautifulSoup(page_source, 'lxml')\n\n table = soup.find('table', {'bgcolor': 'white', 'cellpadding': '5', 'cellspacing': '0'})\n try:\n table2 = table.find_all('th')\n table3_function()\n\n except:\n driver.back()\n driver.back()\n\n return df_raw_section_data\n\n\nclass TotalSectionHours:\n headers2 = ['Course', 'Section', 'Total']\n df_sections = pd.DataFrame(columns=headers2)\n length = 0\n\n def __init__(self, df_raw_section_data):\n self.df_raw_section_data = df_raw_section_data\n\n def total_section_hours(self):\n course = 0\n section = 0\n total_hours = 0\n section_hours = []\n print('length equals', TotalSectionHours.length)\n for i in range(len(self.df_raw_section_data)):\n section = self.df_raw_section_data.loc[i, 'Section']\n course = self.df_raw_section_data.loc[i, 'Course']\n hours = self.df_raw_section_data.loc[i, 'Hours']\n hours = hours.split()\n hours = hours[0]\n if hours == 'hours':\n hours = 0\n hours = float(hours)\n total_hours = total_hours + hours\n section_hours.append(course)\n section_hours.append(section)\n section_hours.append(total_hours)\n TotalSectionHours.df_sections.loc[TotalSectionHours.length] = section_hours\n TotalSectionHours.length = TotalSectionHours.length + 1\n print('df sections', TotalSectionHours.df_sections)\n TotalSectionHours.df_sections.to_csv('C:/Users/family/Desktop/IWPA_Section_Totals.csv')\n # return TotalSectionHours.df_sections\n\n\ndef calculate_apprecticeship_hours(year, semester):\n\n global section_and_course\n global tr_table\n global df_sections\n page_source = driver.page_source\n soup = BeautifulSoup(page_source, 'lxml')\n table = soup.find('select', {'onchange': 'New_Term(this.form)', 'name': 'term_code'})\n option_table = table.find_all('option')\n page_source = driver.page_source\n soup = BeautifulSoup(page_source, 'lxml')\n # for i in range(len(option_table)):\n s = SelectSemester(session=year + ' ' + semester, soup=soup, driver=driver)\n section_table = s.select_session()\n # section_table = soup.find('table', {'bgcolor': 'white'})\n tr_table = section_table.find_all('tr')\n for i in range(len(tr_table)):\n print(i, len(tr_table))\n s = SelectSection(P_Period='P1', driver=driver)\n section_and_course = s.selection_process(i + 1)\n print(section_and_course)\n if section_and_course == None:\n continue\n r = RawDataHours(section=section_and_course[0], course2=section_and_course[1])\n df_raw_section_data = r.raw_data_hours()\n df_empty = df_raw_section_data.empty\n if df_empty == True:\n continue\n t = TotalSectionHours(df_raw_section_data)\n t.total_section_hours()\n\n\n\n\n\n\ndriver = webdriver.Chrome(\"C:/Users/family/PycharmProjects/chromedriver.exe\")\ndriver.get('https://secure.cerritos.edu/rosters/login.cgi')\nlogin = driver.find_element_by_name('login')\nlogin.send_keys('gvasquez')\nlogin = driver.find_element_by_name('passwd')\nlogin.send_keys('Celestino80!')\nbutton = driver.find_element_by_xpath('//*[@id=\"login_form\"]/table/tbody/tr[3]/td[2]/input').click()\n# This waits for list of courses to load\nelement = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.ID, 'clform')))\n# headers = ['NO', 'Name', 'ID', 'Grade', 'Hours', 'Other', 'Section', 'Course']\n# df = pd.DataFrame(columns=headers)\npd.set_option('display.max_columns', None)\np_period = input(\"For what period would you like totals, P1, P2, P3, or Grand Total? \")\nif p_period == \"P1\":\n year = input(\"Please provide the P1 year,e.g. 2020: \")\n calculate_apprecticeship_hours(year=year, semester='Summer')\n calculate_apprecticeship_hours(year=year, semester='Fall')\n# if p_period == \"P2\":\n# P2_function()\n# if p_period == \"P3\":\n# P3_function()\n# if p_period == \"Grand Total\":\n# P1_function()\n# P3_function()\n\n\n\n# df_raw_section_data.to_csv('C:/Users/family/Desktop/IWPA_Raw_Data.csv')\n# df_sections.to_csv('C:/Users/family/Desktop/IWPA_Section_Totals.csv')\n", "sub_path": "AED.py", "file_name": "AED.py", "file_ext": "py", "file_size_in_byte": 5814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 41, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 41, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 97, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 101, "usage_type": "call"}, {"api_name": "Select_Semester.SelectSemester", "line_number": 103, "usage_type": "call"}, {"api_name": "Select_Course_Section.SelectSection", "line_number": 109, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 127, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 127, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 135, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 135, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 135, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 135, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 135, "usage_type": "name"}, {"api_name": "pandas.set_option", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "182647270", "text": "import multiprocessing\nimport time\n\n\ndef download_from_web(q):\n '''ไธ่ฝฝๆฐๆฎ'''\n # ๆจกๆไป็ฝไธไธ่ฝฝ็ๆฐๆฎ\n data = [11,22,33,44]\n\n for temp in data:\n # ๅๅฏนๅไธญๅๆฐๆฎ\n q.put(temp)\n\n print('ไธ่ฝฝ็ปๆ')\n\n\ndef analysis(q):\n '''ๆฐๆฎๅค็'''\n waitting_analysis_data = list() # ็ฉบๅ่กจ็จlist()ๅฏ่ฏปๆงๅผบ\n\n while True:\n # ไป้ๅไธญ่ทๅๆฐๆฎ\n data = q.get()\n waitting_analysis_data.append(data)\n if q.empty():\n break\n\n # ๆจกๆๆฐๆฎๅค็\n print(waitting_analysis_data)\n\n\ndef main():\n # ๅๅปบไธไธช้ๅ\n q = multiprocessing.Queue(3) # 3ไปฃ่กจ้ๅๆๅคๅญๆพ3ไธชๆฐๆฎ๏ผไธๅๅ็ฑๆไฝ็ณป็ปๅณๅฎ\n\n # ๅๅปบๅค่ฟ็จ๏ผๅฐ้ๅ็ๅผ็จๅฝๅๅฎๅไผ ๅ
ฅ\n p1 = multiprocessing.Process(target=download_from_web,args=(q,))\n p2 = multiprocessing.Process(target=analysis, args=(q,))\n p1.start()\n p2.start()\n\n\nif __name__ == '__main__':\n main()", "sub_path": "4:python้ซ็บง/ๅคๅถ็ๆไปถ/demo/mul_queue_demo.py", "file_name": "mul_queue_demo.py", "file_ext": "py", "file_size_in_byte": 986, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "multiprocessing.Queue", "line_number": 34, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 37, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "493714754", "text": "# -*- coding: utf-8 -*-\n\nimport docx\nimport sys\nimport jieba\nfrom gensim.models import Word2Vec\nimport os\nimport numpy as np\nimport pickle as pk\nimport warnings\nimport gensim\nfrom copy import deepcopy\nwarnings.filterwarnings(\"ignore\")\nfrom multiprocessing import cpu_count\n\n\nclass project(object):\n def __init__(self,path):\n self.path=path\n def load_text(self,file_path):\n doc = docx.Document(file_path)\n texts = []\n for p in doc.paragraphs:\n if len(p.text)>0:\n texts.append(p.text)\n\n # ่ฏปๅ
ฅ่กจๆ ผๅ
ๅฎน\n # for t in doc.tables:\n # for r in t.rows:\n # tmp=[]\n # for c in r.cells:\n # tmp.append(c.text.decode('utf-8'))\n # texts.append(' '.join(tmp))\n return texts\n def load_stopword(self):\n with open('stopword.txt','r') as f:\n sw=[line.strip() for line in f.readlines()]\n return sw\n\n def cal_cos(self,array1,array2):\n return np.dot(array1, array2) / (np.sqrt(np.sum(array1 ** 2)) * np.sqrt(np.sum(array2 ** 2)))\n\n def load_file(self,filename):\n with open(filename, 'rb') as f:\n original_sentences = pk.load(f)\n return original_sentences\n\n def save_file(self,texts,filename):\n with open(filename+'.pkl', 'wb') as f:\n pk.dump(texts, f, True)\n\n def dict_produce(self,texts,original_id=''):\n dict_num_text = {}\n if len(original_id)>0:\n for num, text in enumerate(texts):\n dict_num_text[original_id[num]] = text\n else:\n for num, text in enumerate(texts):\n dict_num_text[num] = text\n return dict_num_text\n\n def jie_ba(self,texts,mode='train',id_connect=False):\n '''\n ็ปๅทดๅ่ฏ๏ผๅฆๆmodeๆฏtrain ๅๅฏน่ฎญ็ป็่ฏญๆๅ่ฏ๏ผๅฆๅๅฏนๆ้ฎไฝๅ่ฏใ\n '''\n stoping_words=self.load_stopword()\n jieba_text=[]\n index = []\n if mode == 'train':\n flag = 0\n for text in texts: # ๆณจๆๆฏๅจtexts้\n blank_connect_words = ' '.join(jieba.cut(text)) # ๅฆๆ็จlcut็่ฏ ็ฉบๆ ผไผๅ ็จlistไธไธชไฝ็ฝฎใ\n list_word = [str(word) for word in blank_connect_words.strip().split() if word not in stoping_words]\n if len(list_word)>3:#ๅ่ฏไพฏๅคงไบ3ไธช ๆๆทปๅ \n jieba_text.append(list_word)\n if id_connect:\n index.append(flag)\n flag += 1\n\n # ๅฏนๆ้ฎ็ๅ่ฏ\n else:\n blank_connect_words = ' '.join(jieba.cut(texts.strip())) # ่พๅ
ฅ็ๆฏไธไธชๅฅๅญ.\n jieba_text = [word for word in blank_connect_words.strip().split()]\n if len(jieba_text) ==0:\n print (u'่พๅ
ฅ้ฎ้ขไธ่ฝไธบ็ฉบ')\n return\n return jieba_text,index\n\n def word_vector(self,index2word):\n dict_word_vector = {}\n doc_word = []\n for word in model.wv.index2word:\n dict_word_vector[word] = model[word]\n doc_word.append(word)\n return dict_word_vector\n\n def sentence_vector(self,dict_word_vector):\n '''\n :return:่ฟๅ ไธไธชๅญๅ
ธ sentences_vector{ๅๅงๅฅๅญid:ๅฅๅญ็ๅ้่กจ็คบ๏ผๆฏไธช่ฏ็ๅ้ๆฑไบๅๅผ๏ผ}\n '''\n sentences_vector={}\n # ๆญคๅค็numๅฐฑๆฏๅฏนๅบไบๆๅๅงๅฅๅญๅฏนๅบ็ๆ
ๅต\n\n sentences = self.load_file('num_jieba_text.pkl')\n #็ปๅทด็่ฏๅฆๆไธๅจword2vector็่ฏๅ
ธไธญ๏ผ่ฟๆฏไผ็ผบๅคฑ\n for origin_id, sentence_item in sentences.items():\n tmp = []\n for word in sentence_item:\n #ๆญคๅคๅฆๆๅจ่ฎญ็ป็่ฏๅ
ธ้ๆฒกๆ่ฟไธช่ฏ๏ผๅฐฑไธ็จ็ฎกไบ\n get_word=dict_word_vector.get(word)\n if get_word is not None:\n tmp.append(get_word)\n if len(tmp)!=0:\n mean_vector = np.mean(tmp, axis=0)\n sentences_vector[origin_id] = mean_vector\n return sentences_vector\n\n\n\n def question_answer(self,inputs,dict_word_vector, dict_jeiba_origid_vector,model,answer_num=3):\n '''\n :param input: ่พๅ
ฅๅไธช้ฎ้ข\n :param dict_word_vector: word2vector่ฎญ็ปๅ็่ฏๅ้๏ผๆ ผๅผๆฏdict{ๅ่ฏ๏ผๅ้}\n :param dict_jeiba_origid_vector: dict{ๆๅๅงๅฅๅญ็id:็ปๅทดๅ่ฏๅๅฅๅญ็ๅ้}\n :param model: ่พๅ
ฅ่ฎญ็ปๅฅฝ็ๆจกๅ\n :return: ่พๅ
ฅ็้ฎ้ข่ฝฌๆขไธบๅฅๅญ็ๅ้๏ผๅฏปๆพdict_origid_vectorไธญ็ธไผผๅบฆๆ้ซ็ๅฅๅญๅฏนๅบ็key๏ผ่ฟๅๅๅงๅฅๅญ[key]\n '''\n if len(inputs)==0:\n print ('่พๅ
ฅไธ่ฝไธบ็ฉบๅฆ๏ผ')\n return\n\n original_sentences=self.load_file('num_text.pkl')\n jieba_sentences = self.load_file('num_jieba_text.pkl')\n for input in inputs:\n question,_ =self.jie_ba(input, 'question') #ๆญคๆถ่ฟๅ็ๆฏ list\n word_list= list(dict_word_vector.keys())\n\n\n original_index = list(dict_jeiba_origid_vector.keys())\n dict_values = np.array(list(dict_jeiba_origid_vector.values()))\n #้ฎ้ข่ฝฌๅไธบ่ฏๅ้\n\n record_vector = [] # ็บชๅฝ้ฎ้ข็่ฏๅ้\n for item in question:\n if item in word_list:\n record_vector.append(dict_word_vector.get(item))\n if len(record_vector)==0:\n print('Q: ', input)\n print('ๆจ็้ฎ้ขๅจ่ฎญ็ป่ฏญๆไธญๅฏ่ฝๆฒกๆๅบ็ฐ่ฟ๏ผ่ฏท็กฎ่ฎคๆจ็้ฎ้ขๆ่ฟฐ\\n')\n #ๅฅๅญ็่ฏๅ้\n else:\n #ๅพๅฐ่พๅ
ฅๅฅๅญ็ๅ้\n sentence_vector = np.mean(record_vector, axis=0)\n\n lis=[]\n for i in dict_values:\n lis.append(self.cal_cos(i,sentence_vector))\n\n\n sort_list = np.argsort(lis)\n print('Q: ', input)\n\n # ไปword2vectorๅฏปๆพ็ๅ้้้๏ผ ๆพๅบๆ็ธไผผ็ไธไธช็ปๆ๏ผไฝไธบ้ฎ้ขๅจๅๆไธญ็ๅฎไฝ\n simi_num=-1\n set_len_num=-1\n for i in range(7):\n _index=original_index[sort_list[-1-i]]\n _len=len(set(jieba_sentences[_index]).intersection(set(question)))\n if _len>set_len_num:\n set_len_num=_len\n simi_num = _index\n\n # ๅฎไฝๅ็้ฎ้ข๏ผ่พๅบไนๅ็ปๅทด่ฏๅคงไบ answer_num ไธช็ๅฅๅญใ\n max_index=len(original_sentences)\n num=0\n index = simi_num + num\n while index5: #็ปๅทด่ฏๅคงไบ 5ไธชๆ่พๅบ\n # print ( 'A{}:\\t'.format(index), original_sentences[index])\n print(original_sentences[index])\n num+=1\n index = index+1\n print('')\n\n\n def pre_processing(self):\n print (u\"ๅ ่ฝฝๅ็จ่ฏ\")\n stoping_words=self.load_stopword()\n print (u\"ๅ ่ฝฝๆๆฌ\")\n texts = self.load_text(self.path) # ่ฏปๅ
ฅ็docๆไปถ็ๆฏไธ่ก\n print (u'ๅๅง่ฏญๆ็่กๆฐ')\n #่ฏญๆๅค็ๆ dict{id๏ผsentence}ๅนถไฟๅญ\n dict_num_original_senteces=self.dict_produce(texts)\n self.save_file(dict_num_original_senteces, 'num_text')\n #ๅฅๅญ็ปๅทดๅ่ฏๅนถไธไฟๅญ็ปๆ\n jieba_text,original_id=self.jie_ba(texts,id_connect=True)\n #็ปๅทดๅ่ฏๅฅๅญๅๅๅงๅฅๅญ็ๅฏนๅบๅ
ณ็ณป๏ผๅนถไธไฟๅญ\n dict_num_jieba=self.dict_produce(jieba_text,original_id)\n self.save_file(dict_num_jieba, 'num_jieba_text')\n\nclass LoadCorpora(project):\n def __init__(self, s):\n self.path = s\n def __iter__(self):\n setences =self.load_file(self.path)\n for num, texts in setences.items():\n yield ' '.join(texts).split(' ')\n\nif __name__=='__main__':\n # file_path=u\"AS.docx\"\n file_path = 'CC้ท่พพ.doc'\n ques_and_answer = project(file_path)\n\n if not os.path.exists('num_jieba_text.pkl'):\n #ๅค็ๅๅง่ฏญๆ\n ques_and_answer.pre_processing()\n\n if not os.path.exists('word2vec.model'):\n print (u'ๅผๅงๅปบ็ซๆจกๅ---')\n sentences =LoadCorpora('num_jieba_text.pkl')\n model = Word2Vec(sentences, size=250, min_count=3, workers=cpu_count(),iter=10) # ่ฏๅ้็ปดๅบฆไธบ200๏ผไธขๅผๅบ็ฐๆฌกๆฐๅฐไบmin_countๆฌก็่ฏ #ๆณจๆworkersๅจไธๅๆบๅจไธ็ๅๅผใ\n copy_model=deepcopy(model)\n copy_model.save('word2vec.model')\n\n model = Word2Vec.load('word2vec.model')\n dict_word_vector=ques_and_answer.word_vector(model.wv.index2word)\n dict_id_vector=ques_and_answer.sentence_vector(dict_word_vector)\n\n query = ['CC้ท่พพไผบๆๅ็ณป็ปๆฏไปไน',\n 'ๅคฉ็บฟๆนไฝๆไปฐ่งๅฎไฝไธๅ๏ผๆไน็ปดไฟฎ๏ผ',\n 'ไผบๆไธ่ฝๅฏๅจ๏ผๅๆด๏ผ',\n 'ๅฏๆง็ก
้ฃๆบๆไนๅ',\n 'ๅทๅดๅผๅ
ณ่ฑๆฃๅค็ๆนๆณ',\n 'ไฟฏไปฐ็ตๆบๆ
้21#ๆ
้่งฃๅณๅๆณ',\n 'ไฟฏไปฐ็ตๆบๆ
้16#ๆ
้้ฎ้ขๅบๅจๅช้',\n 'ๆงๅถๆ้ฎๅไบ๏ผๅๆด๏ผ',\n '่ญฌๅฆ็ง้ฃๅฟฝ่ณ๏ผๅๆไธๅบๆฉ้',\n 'ๆๆๆณๅฐ๏ผๅฝๅนดๆๆปๆฏ็ฌ่ช่ทๅฐๅฐๅๅป๏ผๆพ็ป็ปๆฏไบฒๅบไบไธไธชๆๆ ท็้พ้ขใ',\n 'ไฝ ไธญๅๅ็ไปไน๏ผ']\n ques_and_answer.question_answer(query,dict_word_vector, dict_id_vector,model,answer_num=1)\n", "sub_path": "็ฌฌๅไธ่/13.RNN/project_word2vec.py", "file_name": "project_word2vec.py", "file_ext": "py", "file_size_in_byte": 9710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "warnings.filterwarnings", "line_number": 13, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 41, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 45, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 72, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "gensim.models.Word2Vec", "line_number": 223, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 223, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 224, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec.load", "line_number": 227, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec", "line_number": 227, "usage_type": "name"}]}
{"seq_id": "95935431", "text": "from flask import jsonify\nfrom dao.UserDAO import UserDao\n\n\nclass UserHandler:\n\n # Dictionaries\n # user_id, first_name, last_name, email, phone\n def build_user_dict(self, row):\n result = {}\n result['user_id'] = row[0]\n result['first_name'] = row[1]\n result['last_name'] = row[2]\n result['email'] = row[3]\n result['phone'] = row[4]\n return result\n\n def build_post_dict(self, row):\n result = {}\n result['post_id'] = row[0]\n result['chat_id'] = row[1]\n result['caption'] = row[2]\n result['media_id'] = row[3]\n result['user_id'] = row[4]\n result['post_date'] = row[5]\n return result\n\n def build_credential_dict(self, row):\n result = {'UserId': row[0], 'Username': row[3]}\n return result\n\n # reaction_id, post_id, user_id reaction_date, reaction_type\n def build_user_reaction_dict(self, row):\n result = {}\n result['reaction_id'] = row[0]\n result['post_id'] = row[1]\n result['user_id'] = row[2]\n result['reaction_id'] = row[3]\n result['reaction_type'] = row[4]\n return result\n\n # Gets\n def getAllUser(self):\n dao = UserDao()\n user_list = dao.getAllUser()\n result_list = []\n for row in user_list:\n result = self.build_user_dict(row)\n result_list.append(result)\n return jsonify(User=result_list)\n\n def getUserAllReaction(self,uid):\n dao = UserDao()\n user_reaction_list = dao.getUserReaction(uid)\n result_list = []\n for row in user_reaction_list:\n result = self.build_user_reaction_dict(row)\n result_list.append(result)\n return jsonify(ReactionsByUser=result_list)\n\n def getUserById(self, uid):\n dao = UserDao()\n row = dao.getUserById(uid)\n if not row:\n return jsonify(Error=\"User Not Found\"), 404\n else:\n user = self.build_user_dict(row)\n return jsonify(User=user)\n\n # def getAllUserChats(self, uid):\n # dao = UserDao()\n # row = dao.getAllUserChats(uid)\n # if not row:\n # return jsonify(Error=\"Not Found Chats for this user.\"), 404\n # else:\n # chats = self.build_user_dict(row)\n # return jsonify(Chats=chats)\n\n def getUsername(self, uid):\n username = UserDao().getUsername(uid)\n\n if not username:\n return jsonify(Error=\"USER NOT FOUND\"), 404\n\n result = {}\n result['username'] = username\n return jsonify(Username=result)\n\n # TODO: Finish search method.\n # def searchUsers(self, args):\n # username = args.get(\"username\")\n # firstName = args.get(\"firstName\")\n # lastName = args.get(\"lastName\")\n # dao = UserDao()\n # user_list = []\n # if (len(args) == 2) and firstName and lastName:\n # user_list = dao.getUserByFirstNameAndLastName(firstName, lastName)\n # elif (len(args) == 1) and firstName:\n # user_list = dao.getUserByFirstName(firstName)\n # elif (len(args) == 1) and lastName:\n # user_list = dao.getUserByLastName(lastName)\n # elif (len(args) == 1) and username:\n # user_list = dao.getUserByUsername(username)\n # else:\n # return jsonify(Error=\"Malformed query string\"), 400\n # result_list = []\n # for row in user_list:\n # result = self.build_user_dict(row)\n # result_list.append(result)\n # return jsonify(User=result_list)\n\n def getPostsFromUser(self, uid):\n posts = UserDao().postsFromUser(uid)\n if not posts:\n return jsonify(Error=\"NOT FOUND POSTS FROM USER\"), 404\n result = []\n for p in posts:\n result.append(self.build_post_dict(p))\n return jsonify(PostsFromUser=result)\n\n # CRUDS\n def insertUser(self):\n dao = UserDao()\n user = dao.insertUser()\n result = self.build_user_dict(user)\n return jsonify(User=result), 201\n\n def updateUser(self, uid, form):\n dao = UserDao()\n user = dao.update(uid)\n if not user:\n return jsonify(Error=\"USER NOT FOUND\"), 404\n\n result = self.build_user_dict(user)\n return jsonify(User=result), 200\n\n def deleteUser(self, uid):\n return jsonify(DeleteStatus=\"OK\"), 200\n\n def getCredentials(self):\n dao = UserDao()\n result = dao.getCredentials('', '')\n return jsonify(User=self.build_credential_dict(result))\n", "sub_path": "handler/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 4568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "dao.UserDAO", "line_number": 44, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 44, "usage_type": "call"}, {"api_name": "dao.UserDAO.getAllUser", "line_number": 45, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 53, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 53, "usage_type": "call"}, {"api_name": "dao.UserDAO.getUserReaction", "line_number": 54, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 62, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 62, "usage_type": "call"}, {"api_name": "dao.UserDAO.getUserById", "line_number": 63, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 87, "usage_type": "call"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 123, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 123, "usage_type": "call"}, {"api_name": "dao.UserDAO.insertUser", "line_number": 124, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 126, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 129, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 129, "usage_type": "call"}, {"api_name": "dao.UserDAO.update", "line_number": 130, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 138, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 141, "usage_type": "name"}, {"api_name": "dao.UserDAO.UserDao", "line_number": 141, "usage_type": "call"}, {"api_name": "dao.UserDAO.getCredentials", "line_number": 142, "usage_type": "call"}, {"api_name": "dao.UserDAO", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 143, "usage_type": "call"}]}
{"seq_id": "81358001", "text": "import os\nimport re\nimport typing\n\nfrom mitmproxy import exceptions\nfrom mitmproxy import ctx\nfrom mitmproxy.addons.modifyheaders import parse_modify_spec, ModifySpec\n\n\nclass MapRemote:\n def __init__(self):\n self.replacements: typing.List[ModifySpec] = []\n\n def load(self, loader):\n loader.add_option(\n \"map_remote\", typing.Sequence[str], [],\n \"\"\"\n Replacement pattern of the form \"[/flow-filter]/regex/[@]replacement\", where\n the separator can be any character. The @ allows to provide a file path that\n is used to read the replacement string.\n \"\"\"\n )\n\n def configure(self, updated):\n if \"map_remote\" in updated:\n self.replacements = []\n for option in ctx.options.map_remote:\n try:\n spec = parse_modify_spec(option)\n try:\n re.compile(spec.subject)\n except re.error:\n raise ValueError(f\"Invalid regular expression: {spec.subject}\")\n except ValueError as e:\n raise exceptions.OptionsError(\n f\"Cannot parse map_remote option {option}: {e}\"\n ) from e\n\n self.replacements.append(spec)\n\n def request(self, flow):\n if not flow.reply.has_message:\n for spec in self.replacements:\n if spec.matches(flow):\n self.replace(flow.request, spec.subject, spec.replacement)\n\n def replace(self, obj, search, repl):\n \"\"\"\n Replaces all matches of the regex search in the url of the request with repl.\n\n Returns:\n The number of replacements made.\n \"\"\"\n if repl.startswith(b\"@\"):\n path = os.path.expanduser(repl[1:])\n try:\n with open(path, \"rb\") as f:\n repl = f.read()\n except IOError:\n ctx.log.warn(\"Could not read replacement file: %s\" % repl)\n return\n\n replacements = 0\n obj.url, replacements = re.subn(search, repl, obj.pretty_url.encode(\"utf8\", \"surrogateescape\"), flags=re.DOTALL)\n return replacements\n", "sub_path": "mitmproxy/addons/mapremote.py", "file_name": "mapremote.py", "file_ext": "py", "file_size_in_byte": 2241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "typing.List", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mitmproxy.addons.modifyheaders.ModifySpec", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx.options", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 27, "usage_type": "name"}, {"api_name": "mitmproxy.addons.modifyheaders.parse_modify_spec", "line_number": 29, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 31, "usage_type": "call"}, {"api_name": "re.error", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mitmproxy.exceptions.OptionsError", "line_number": 35, "usage_type": "call"}, {"api_name": "mitmproxy.exceptions", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx.log.warn", "line_number": 60, "usage_type": "call"}, {"api_name": "mitmproxy.ctx.log", "line_number": 60, "usage_type": "attribute"}, {"api_name": "mitmproxy.ctx", "line_number": 60, "usage_type": "name"}, {"api_name": "re.subn", "line_number": 64, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 64, "usage_type": "attribute"}]}
{"seq_id": "291785158", "text": "\"\"\"empty message\n\nRevision ID: 19460b67bc7e\nRevises: 233ada81cc9d\nCreate Date: 2020-03-29 01:39:46.138556\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '19460b67bc7e'\ndown_revision = '233ada81cc9d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('professor',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.String(length=256), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('courseprof',\n sa.Column('subject', sa.String(length=256), nullable=False),\n sa.Column('course_num', sa.Integer(), nullable=False),\n sa.Column('type', sa.String(length=8), nullable=False),\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('prof_id', sa.Integer(), nullable=False),\n sa.Column('rating', sa.Float(precision=2, asdecimal=1), nullable=True),\n sa.ForeignKeyConstraint(['prof_id'], ['professor.id'], ),\n sa.ForeignKeyConstraint(['subject', 'course_num', 'type', 'id'], ['courseoff.subject', 'courseoff.course_num', 'courseoff.type', 'courseoff.id'], ),\n sa.PrimaryKeyConstraint('subject', 'course_num', 'type', 'id', 'prof_id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('courseprof')\n op.drop_table('professor')\n # ### end Alembic commands ###\n", "sub_path": "Flask/migrations/versions/19460b67bc7e_.py", "file_name": "19460b67bc7e_.py", "file_ext": "py", "file_size_in_byte": 1483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "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.PrimaryKeyConstraint", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 42, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 42, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 43, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "631998337", "text": "#!bin/bash/env python\n\nimport smtplib\nimport os, sys\nfrom email.mime.application import MIMEApplication\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.base import MIMEBase\nfrom email.mime.text import MIMEText\nfrom email.utils import COMMASPACE, formatdate\nimport argparse\nimport logging\nimport ast\nfrom email import encoders\n\n\nclass Email(object):\n def __init__(self, send_from=None, send_to=None, subject=None, email_body=None, attachment=None, password=None,\n filename=None):\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s - %(lineno)d')\n self.logger = logging.getLogger(__name__)\n self.send_from = send_from\n self.send_to = send_to\n self.subject = subject\n self.email_body = email_body\n self.password = str(password)\n self.attachment = attachment\n self.filename = filename\n\n def send_mail(self, files=None):\n self.logger.info(\"going to send email to {} from {}\".format(self.send_to, self.send_from))\n assert isinstance(self.send_to, list), \"should be list\"\n\n # adding sendders and recievers\n msg = MIMEMultipart();\n msg['From'] = self.send_from;\n msg['To'] = COMMASPACE.join(self.send_to)\n msg['Date'] = formatdate(localtime=True);\n msg['Subject'] = self.subject\n\n print (\"-----------------------\")\n\n if self.attachment and self.filename:\n for count, filename in enumerate(self.filename):\n part = MIMEBase(\"application\", \"octet-stream\")\n part.set_payload(self.attachment[count])\n encoders.encode_base64(part)\n # Add header as key/value pair to attachment part;cl/.\n # filename= self.filename\n\n part.add_header(\n \"Content-Disposition\",\n f\"attachment; filename= {filename}\",\n )\n msg.attach(part);\n else:\n self.logger.info(\"attachment can't be None\")\n raise\n\n if self.email_body:\n part = MIMEText(self.email_body, 'plain');\n msg.attach(part)\n msg.as_string()\n else:\n self.logger.info(\"email_body can't be None\")\n raise\n\n # for outlook\n # smtp = smtplib.SMTP(\"smtp.outlook.office365.com\",587, timeout=20);smtp.starttls()\n\n # for gmail\n smtp = smtplib.SMTP(\"smtp.gmail.com\", 587, timeout=20);\n smtp.starttls()\n smtp.ehlo();\n\n # going to login\n try:\n smtp.login(self.send_from, self.password)\n smtp.sendmail(self.send_from, self.send_to, msg.as_string());\n smtp.close()\n except Exception as error:\n self.logger.info(\"task.status failed due to {}\".format(error))\n raise\n\n self.logger.info(\"task.status success\")\n\n", "sub_path": "readme_service/sent_email.py", "file_name": "sent_email.py", "file_ext": "py", "file_size_in_byte": 2956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.basicConfig", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 35, "usage_type": "call"}, {"api_name": "email.utils.COMMASPACE.join", "line_number": 37, "usage_type": "call"}, {"api_name": "email.utils.COMMASPACE", "line_number": 37, "usage_type": "name"}, {"api_name": "email.utils.formatdate", "line_number": 38, "usage_type": "call"}, {"api_name": "email.mime.base.MIMEBase", "line_number": 45, "usage_type": "call"}, {"api_name": "email.encoders.encode_base64", "line_number": 47, "usage_type": "call"}, {"api_name": "email.encoders", "line_number": 47, "usage_type": "name"}, {"api_name": "email.mime.text.MIMEText", "line_number": 61, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 72, "usage_type": "call"}]}
{"seq_id": "637686898", "text": "\"\"\"\n Pygame base template for opening a window\n\n Sample Python/Pygame Programs\n Simpson College Computer Science\n http://programarcadegames.com/\n http://simpson.edu/computer-science/\n\n Explanation video: http://youtu.be/vRB_983kUMc\n\"\"\"\n\nimport pygame\nimport random\n\n# Define some colors\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\nGREY = (127, 127, 127)\n\ndef random_color():\n\treturn random_color(colors)\n\n\n\n\npygame.init()\n\n\n\n\n# Set the width and height of the screen [width, height]\nSCREEN_WIDTH = 700\nSCREEN_HEIGHT = 500\n\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\n\npygame.display.set_caption(\"Ball Game\")\n\n\nclass Circle():\n\tdef __init__(self, mouse_position):\n\t\tself.mouse_position = mouse_position\n\t\tself.x_pos = mouse_position[0]\n\t\tself.y_pos = mouse_position[1]\n\tdef draw(self):\n\t\tglobal screen\n\t\tglobal BLACK\n\t\tpygame.draw.circle(screen, BLACK, self.mouse_position, 5)\n\tdef move(self, x_speed,y_speed, x_pos, y_pos):\n\t\t#screen.fill(WHITE)\n\t\tself.x_speed= x_speed\n\t\tself.y_speed= y_speed\n\t\tself.x_pos+= x_speed\n\t\tself.y_pos+= y_speed\n# Loop until the user clicks the close button.\ndone = False\n\n# Used to manage how fast the screen updates\nclock = pygame.time.Clock()\n\ncircle_list=[]\n\n# -------- Main Program Loop -----------\nwhile not done:\n\t# --- Main event loop\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tdone = True\n\n\n\t# --- Game logic should go here\n\tpressed = pygame.mouse.get_pressed()\t\n\tif pressed[0]== 1:\n\t\tprint (\"Here!\")\n\t\tscreen.fill(WHITE)\n\t\tmouse_position= pygame.mouse.get_pos()\n\t\tcircle = Circle(mouse_position)\n\t\tcircle_list.append(circle)\n\t\tfor circle in circle_list:\n\t\t\tcircle.draw()\n\t\tcircle.move(0,1)\n\t\t\n\t\n\t\t\n\t\n\t\t\n\n\t# Here, we clear the screen to white. Don't put other drawing commands\n\t# above this, or they will be erased with this command.\n\n\t# If you want a background image, replace this clear with blit'ing the\n\t# background image.\n\t\n\n\t# --- Drawing code should go here\n\t\n\n\t\n\n\n\n\n\t# --- Go ahead and update the screen with what we've drawn.\n\tpygame.display.flip()\n\n\t# --- Limit to 60 frames per second\n\tclock.tick(60)\n\n# Close the window and quit.\npygame.quit()\nexit() # Needed when using IDLE\n", "sub_path": "python/mouse.py", "file_name": "mouse.py", "file_ext": "py", "file_size_in_byte": 2227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pygame.init", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 113, "usage_type": "call"}]}
{"seq_id": "249963402", "text": "import collections\nimport heapq\nfrom typing import List\n\nclass Solution:\n def topKFrequent(self, nums: List[int], k: int) -> List[int]:\n \n freq = collections.Counter(nums)\n freqs_heap = []\n \n for f in freq :\n heapq.heappush(freqs_heap,(-freq[f],f))\n \n topk = []\n \n for i in range(k):\n topk.append(heapq.heappop(freqs_heap)[1])\n \n return topk\n \n ", "sub_path": "์์K๋น๋์์.py/Counter์ด์ฉ.py", "file_name": "Counter์ด์ฉ.py", "file_ext": "py", "file_size_in_byte": 469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 8, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 12, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "389632957", "text": "import pygame.draw\nimport sys\nfrom platforms import *\n\npygame.init()\nfont = pygame.font.Font(None, 60)\nfont1 = pygame.font.Font(None, 30)\nFPS = 70\nwidth = 800\nlength = 600\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nGREEN = (0, 255, 0)\n# creating display\nscreen = pygame.display.set_mode((width, length))\nclock = pygame.time.Clock()\nfinished = False\n\n\ndef level_read(file_name):\n file = open(file_name, 'r')\n plat = []\n for line in file:\n a = line.split()\n x_cord, y_cord, p_width, p_length = a\n plat.append(Platform(screen, width, length, x_cord, y_cord, p_width, p_length))\n file.close()\n return plat\n\n\ndef frame_draw(plat):\n for i in plat:\n pygame.draw.rect(screen, GREEN, i.rect)\n\n\ndef menu(screen):\n pygame.mixer.music.load('Jungle.mp3')\n pygame.mixer.music.play()\n forest_surf = pygame.image.load('forest.jpg')\n forest_rect = forest_surf.get_rect(\n bottomright=(width, length))\n screen.blit(forest_surf, forest_rect)\n tlevel_1 = font.render('Level 1', True, BLACK, WHITE)\n screen.blit(tlevel_1, (175, 50))\n tlevel_2 = font.render('Level 2', True, BLACK, WHITE)\n screen.blit(tlevel_2, (175, 150))\n tlevel_3 = font.render('Level 3', True, BLACK, WHITE)\n screen.blit(tlevel_3, (175, 250))\n tlevel_4 = font.render('Level 4', True, BLACK, WHITE)\n screen.blit(tlevel_4, (175, 350))\n tlevel_5 = font.render('Level 5', True, BLACK, WHITE)\n screen.blit(tlevel_5, (175, 450))\n tlevel_6 = font.render('Level 6', True, BLACK, WHITE)\n screen.blit(tlevel_6, (475, 50))\n tlevel_7 = font.render('Level 7', True, BLACK, WHITE)\n screen.blit(tlevel_7, (475, 150))\n tlevel_8 = font.render('Level 8', True, BLACK, WHITE)\n screen.blit(tlevel_8, (475, 250))\n tlevel_9 = font.render('Level 9', True, BLACK, WHITE)\n screen.blit(tlevel_9, (475, 350))\n tlevel_10 = font.render('Level 10', True, BLACK, WHITE)\n screen.blit(tlevel_10, (475, 450))\n tsettings = font.render('Settings', True, BLACK, WHITE)\n screen.blit(tsettings, (300, 525))\n\n\ndef quit_draw():\n tquit = font.render('Quit', True, WHITE, BLACK)\n screen.blit(tquit, (700, 0))\n\n\ndef quit_check(event, screen):\n x, y = event\n if x > 700 and x < 790 and y > 0 and y < 44:\n menu(screen)\n\n\ndef menu_choice(screen):\n for i in pygame.event.get():\n if i.type == pygame.QUIT:\n sys.exit()\n if i.type == pygame.MOUSEBUTTONDOWN:\n if i.button == 1:\n x, y = i.pos\n if x > 175 and x < 320 and y > 50 and y < 94:\n all = level_read('level_1.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 175 and x < 320 and y > 150 and y < 194:\n all = level_read('level_2.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 175 and x < 320 and y > 250 and y < 294:\n all = level_read('level_3.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 175 and x < 320 and y > 350 and y < 394:\n all = level_read('level_4.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 175 and x < 320 and y > 450 and y < 494:\n all = level_read('level_5.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 475 and x < 620 and y > 50 and y < 94:\n all = level_read('level_6.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 475 and x < 620 and y > 150 and y < 194:\n all = level_read('level_7.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 475 and x < 620 and y > 250 and y < 294:\n all = level_read('level_8.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 475 and x < 620 and y > 350 and y < 394:\n all = level_read('level_9.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 475 and x < 620 and y > 450 and y < 494:\n all = level_read('level_10.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n if x > 300 and x < 475 and y > 525 and y < 569:\n all = level_read('settings.txt')\n screen.fill(WHITE)\n frame_draw(all)\n quit_draw()\n pygame.mixer.music.pause()\n pygame.display.update()\n quit_check(i.pos, screen)\n pygame.display.update()\n pygame.display.update()\n\n\nmenu(screen)\nwhile not finished:\n clock.tick(FPS)\n menu_choice(screen)\npygame.quit()\n", "sub_path": "menu.py", "file_name": "menu.py", "file_ext": "py", "file_size_in_byte": 6149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pygame.draw.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 5, "usage_type": "name"}, {"api_name": "pygame.draw.font.Font", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.draw.font", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 6, "usage_type": "name"}, {"api_name": "pygame.draw.font.Font", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.draw.font", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 7, "usage_type": "name"}, {"api_name": "pygame.draw.display.set_mode", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 15, "usage_type": "name"}, {"api_name": "pygame.draw.time.Clock", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.draw.time", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 16, "usage_type": "name"}, {"api_name": "pygame.draw.draw.rect", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.draw.draw", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 33, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 37, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.play", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 38, "usage_type": "name"}, {"api_name": "pygame.draw.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw.image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 39, "usage_type": "name"}, {"api_name": "pygame.draw.event.get", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.draw.event", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 79, "usage_type": "name"}, {"api_name": "pygame.draw.QUIT", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 80, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.draw.MOUSEBUTTONDOWN", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 82, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 90, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 91, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 97, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 98, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 104, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 105, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 111, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 112, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 112, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 118, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 119, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 125, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 126, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 132, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 133, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 139, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 140, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 140, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 146, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 147, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 147, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 153, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 153, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 154, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 154, "usage_type": "name"}, {"api_name": "pygame.draw.mixer.music.pause", "line_number": 160, "usage_type": "call"}, {"api_name": "pygame.draw.mixer", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 160, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 161, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 161, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 163, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 163, "usage_type": "name"}, {"api_name": "pygame.draw.display.update", "line_number": 164, "usage_type": "call"}, {"api_name": "pygame.draw.display", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.draw", "line_number": 164, "usage_type": "name"}, {"api_name": "pygame.draw.quit", "line_number": 171, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 171, "usage_type": "name"}]}
{"seq_id": "382995829", "text": "# -*- coding: utf-8 -*-\n\nfrom django.db import models\nfrom django.db.models.fields.related import ForeignKey\n\nfrom django_es import Connection\n\n\nclass ESManager(models.Manager):\n\n def save(self, model_instance):\n serialized_instance = self.__serialize(model_instance)\n\n Connection.get_connection().index(\n index='test',\n doc_type=self.__get_doc_type(),\n id=model_instance.id,\n body=serialized_instance\n )\n\n def get(self, id):\n serialized_instance = Connection.get_connection().get_source(\n index='test',\n doc_type=self.__get_doc_type(),\n id=id\n )\n model_boilerplate = self.model()\n for attribute, value in serialized_instance.items():\n setattr(model_boilerplate, attribute, value)\n return model_boilerplate\n\n def delete(self, model_instance):\n Connection.get_connection().delete(\n index='test',\n doc_type=self.__get_doc_type(),\n id=model_instance.id\n )\n\n def __get_doc_type(self):\n cfg_class = self.__get_config_class()\n if hasattr(cfg_class, 'doc_type') and self.model.ElasticSearch.doc_type:\n doc_type = self.model.ElasticSearch.doc_type\n else:\n doc_type = 'model-{0}'.format(self.model.__name__)\n return doc_type\n\n def __serialize(self, model_instance):\n serialized_instance = {}\n for field in self.__get_fields():\n value = getattr(model_instance, field)\n\n if isinstance(value, models.Model):\n field += '_id'\n value = value.id\n\n serialized_instance[field] = value\n return serialized_instance\n\n def __get_fields(self):\n cfg_class = self.__get_config_class()\n if hasattr(cfg_class, 'fields') and self.model.ElasticSearch.fields:\n fields = self.model.ElasticSearch.fields\n else:\n fields = [f.name for f in self.model._meta.fields]\n return fields\n\n def __get_config_class(self):\n return self.model.ElasticSearch\n", "sub_path": "django_es/managers.py", "file_name": "managers.py", "file_ext": "py", "file_size_in_byte": 2114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.db.models.Manager", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django_es.Connection.get_connection", "line_number": 14, "usage_type": "call"}, {"api_name": "django_es.Connection", "line_number": 14, "usage_type": "name"}, {"api_name": "django_es.Connection.get_connection", "line_number": 22, "usage_type": "call"}, {"api_name": "django_es.Connection", "line_number": 22, "usage_type": "name"}, {"api_name": "django_es.Connection.get_connection", "line_number": 33, "usage_type": "call"}, {"api_name": "django_es.Connection", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "313395107", "text": "import csv\n\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\n\nclass CustomLinearRegression(object):\n _test_data_filename = 'app/static/test_data/test_data.csv'\n\n def __init__(self):\n self._model = LinearRegression()\n\n def train(self):\n inputs = []\n outputs = []\n with open(self._test_data_filename, 'r') as csvfile:\n reader = csv.DictReader(csvfile)\n next(reader)\n for row in reader:\n inputs.append(row[\"year\"])\n outputs.append(row[\"count\"])\n self._model.fit(np.array(inputs).reshape((-1, 1)), np.array(outputs))\n\n def predict(self, year):\n return int(self._model.predict(np.array([year]).reshape((-1, 1)))[0])\n", "sub_path": "app/neyronka.py", "file_name": "neyronka.py", "file_ext": "py", "file_size_in_byte": 749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "sklearn.linear_model.LinearRegression", "line_number": 11, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "653680907", "text": "import datetime\r\n\r\ndef verifica_data(data):\r\n\r\n ok = False\r\n try:\r\n datetime.datetime.strptime(data, \"%d/%m/%Y\")\r\n ok = True\r\n except:\r\n print(\"Erro: formato invรกlido. Entre com dd/mm/aaaa\") \r\n return ok\r\n\r\ndef dividir_data(data):\r\n\r\n data = data.split(\"/\")\r\n dia = int(data[0])\r\n mes = int(data[1])\r\n ano = int(data[2])\r\n return dia, mes, ano\r\n\r\ndata_ok = False\r\nwhile (not data_ok):\r\n data = input(\"Entre com o data: \")\r\n data_ok = verifica_data(data)\r\n\r\ndia, mes, ano = dividir_data(data)\r\nprint(dia, mes, ano)\r\n", "sub_path": "Lista2/Ex10 copy.py", "file_name": "Ex10 copy.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "attribute"}]}
{"seq_id": "262186537", "text": "import pygame\nfrom spritesheet import *\nfrom random import choice\nfrom keyboards import *\nimport textwrap\n\n\n\nclass Feature(object):\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, offset_y):\n self.x = x\n self.y = y\n self.imagex = imagex\n self.imagey = imagey\n self.name = name\n self.width = width\n self.height = height\n self.cur_img = 0\n self.spritesheet = spritesheet\n self.img = self.spritesheet.get_image(0, 0)\n self.GameController = GameController\n self.GameData = GameData\n self.offset_y = offset_y\n\n def set_image(self, img_x, img_y):\n self.img = self.spritesheet.get_image(img_x, img_y)\n\nclass Interactable(object):\n # TODO: Make this a class???\n def __init__(self):\n pass\n\n def get_interacted_with(self):\n pass\n\n#hi\n\nclass Person(Feature):\n\n UPDATE_COLOUR_EVENT = pygame.USEREVENT + 5\n\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, facing, feature_type, offset_y = 24):\n\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, offset_y)\n\n self.activity = None\n self.facing = facing\n self.drawing_priority = 2\n self.feature_type = feature_type\n\n def update_behaviour(self, some_parameter=None):\n '''\n Updates character behavior based on timer\n :param some_parameter: some useless parameter\n :return: time until next update in milliseconds\n '''\n time_to_next_update = 500\n if self.activity == None:\n self.activity = \"be_red\"\n self.change_red()\n elif self.activity == \"be_red\":\n self.activity = \"be_green\"\n self.change_green()\n elif self.activity == \"be_green\":\n self.activity = \"be_blue\"\n self.change_blue()\n elif self.activity == \"be_blue\":\n self.activity = \"be_red\"\n self.change_red()\n\n return time_to_next_update\n\nclass Player(Person):\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData):\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, facing=\"front\", feature_type=\"Player\", offset_y=10)\n self.front = [\"assets/player/P_front_1.png\", \"assets/player/P_front_2.png\", \"assets/player/P_front_3.png\", \"assets/player/P_front_4.png\", \"assets/player/P_front_1.png\", \"assets/player/P_front_2.png\", \"assets/player/P_front_3.png\", \"assets/player/P_front_4.png\"]\n self.back = [\"assets/player/P_back_1.png\", \"assets/player/P_back_2.png\", \"assets/player/P_back_3.png\", \"assets/player/P_back_4.png\", \"assets/player/P_back_1.png\", \"assets/player/P_back_2.png\", \"assets/player/P_back_3.png\", \"assets/player/P_back_4.png\"]\n self.left = [\"assets/player/P_left_1.png\", \"assets/player/P_left_2.png\", \"assets/player/P_left_3.png\", \"assets/player/P_left_4.png\", \"assets/player/P_left_1.png\", \"assets/player/P_left_2.png\", \"assets/player/P_left_3.png\", \"assets/player/P_left_4.png\"]\n self.right = [\"assets/player/P_right_1.png\", \"assets/player/P_right_2.png\", \"assets/player/P_right_3.png\", \"assets/player/P_right_4.png\", \"assets/player/P_right_1.png\", \"assets/player/P_right_2.png\", \"assets/player/P_right_3.png\", \"assets/player/P_right_4.png\"]\n self.state = \"idle\"\n self.drawing_priority = 2\n self.step_timer = pygame.USEREVENT + 7\n\n def activate_timer(self):\n pygame.time.set_timer(self.step_timer, 60)\n\n def draw(self, screen):\n self_x =(self.imagex * self.GameData.square_size[0])+self.GameData.base_locator_x\n self_y = ((self.imagey * self.GameData.square_size[1])-self.offset_y)+self.GameData.base_locator_y\n screen.blit(self.img, [(self.imagex * self.GameData.square_size[0])+self.GameData.base_locator_x,\n ((self.imagey * self.GameData.square_size[1])-self.offset_y)+self.GameData.base_locator_y])\n\n def try_door(self):\n\n the_tile = self.get_facing_tile().object_filling\n self.GameData.positioner[self.GameController.current_room].through_door(\n self.GameData.room_list[self.GameController.current_room].door_list[the_tile])\n\n #TODO: Fix this so that it doesn't screw up when you go through a door\n def try_walk(self, direction):\n # checks mapClasses - position_manager to see if the player is acing a wall or another object\n can_move = self.GameData.positioner[self.GameController.current_room].can_move(self.GameData.player[\"Player\"])\n # moves the player a single step if they are able to\n if can_move:\n self.walk_player(direction)\n\n def turn_player(self, direction):\n if direction is Direction.LEFT:\n self.set_image(0, 3)\n self.facing = \"left\"\n elif direction is Direction.RIGHT:\n self.set_image(0, 2)\n self.facing = \"right\"\n elif direction is Direction.UP:\n self.set_image(0, 1)\n self.facing = \"back\"\n elif direction is Direction.DOWN:\n self.set_image(0, 0)\n self.facing = \"front\"\n\n def walk_player(self, direction):\n self.state = direction\n self.GameData.positioner[self.GameController.current_room].empty_tile(self)\n if direction is Direction.LEFT:\n self.x -= 1\n elif direction is Direction.RIGHT:\n self.x += 1\n elif direction is Direction.UP:\n self.y -= 1\n elif direction is Direction.DOWN:\n self.y += 1\n self.GameData.positioner[self.GameController.current_room].fill_tile(self)\n\n def walk_cycle(self):\n if self.state == Direction.LEFT:\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 3)\n self.GameController.camera[0] += 1/4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 3)\n self.GameController.camera[0] += 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(0, 3)\n\n elif self.state == Direction.RIGHT:\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 2)\n self.GameController.camera[0] -= 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 2)\n self.GameController.camera[0] -= 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(0, 2)\n\n elif self.state == Direction.DOWN:\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 0)\n self.GameController.camera[1] -= 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 0)\n self.GameController.camera[1] -= 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(0, 0)\n\n\n elif self.state == Direction.UP:\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 1)\n\n self.GameController.camera[1] += 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 1)\n self.GameController.camera[1] += 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(0, 1)\n\n def continue_walking(self):\n if self.state == Direction.LEFT:\n self.walk_cycle()\n\n elif self.state == Direction.RIGHT:\n self.walk_cycle()\n\n elif self.state == Direction.UP:\n self.walk_cycle()\n\n elif self.state == Direction.DOWN:\n self.walk_cycle()\n\n def check_if_walking(self):\n if self.state in [Direction.LEFT, Direction.RIGHT, Direction.UP, Direction.DOWN]:\n return True\n else:\n return False\n\n def get_facing_tile(self):\n\n facing_tile_y = 0\n facing_tile_x = 0\n if self.facing == \"back\":\n facing_tile_y = int(self.y - 1)\n facing_tile_x = int(self.x)\n\n elif self.facing == \"front\":\n facing_tile_y = int(self.y + 1)\n facing_tile_x = int(self.x)\n\n elif self.facing == \"left\":\n facing_tile_y = int(self.y)\n facing_tile_x = int(self.x - 1)\n\n elif self.facing == \"right\":\n facing_tile_y = int(self.y)\n facing_tile_x = int(self.x + 1)\n\n facing_tile = self.GameData.room_list[self.GameController.current_room].tiles_array[facing_tile_x][facing_tile_y]\n\n return facing_tile\n\n def interact_with(self):\n facing_tile = self.get_facing_tile()\n object_filling = facing_tile.object_filling\n filling_type = facing_tile.filling_type\n full = facing_tile.full\n\n if full:\n\n if filling_type in [\"Pixie\",\"Walker\"]:\n self.GameData.character_list[object_filling].get_interacted_with()\n\n elif filling_type == \"Prop\":\n self.GameData.prop_list[object_filling].get_interacted_with()\n\n elif filling_type == \"Door\":\n pass\n else:\n pass\n\n def set_state(self, state_to_set):\n self.state = state_to_set\n\nclass NPC(Person):\n\n NPC_TIMER_ID = 10\n \n\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, facing, feature_type, offset_y):\n\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, facing, feature_type, offset_y)\n\n self.set_state(\"idle\")\n self.facing = \"front\"\n self.initiate = pygame.USEREVENT + NPC.NPC_TIMER_ID\n self.action_clock = pygame.USEREVENT + NPC.NPC_TIMER_ID + 1\n self.drawing_priority = 2\n self.friendship = 0\n\n NPC.NPC_TIMER_ID += 2\n\n # TODO: make sets of walking behaviour types that different NPC can have (back and forth/look around/square/etc.)\n\n def turn_left(self):\n self.set_image(0, 3)\n self.facing = \"left\"\n self.set_state(\"idle\")\n self.set_state(\"idle\")\n\n def turn_right(self):\n self.set_image(1, 2)\n self.facing = \"right\"\n self.set_state(\"idle\")\n\n def turn_front(self):\n self.set_image(0, 0)\n self.facing = \"front\"\n self.set_state(\"idle\")\n\n def turn_back(self):\n self.set_image(0, 1)\n self.facing = \"back\"\n self.set_state(\"idle\")\n\n def check_if_walking(self):\n if self.state == \"walk_left\":\n self.walk_cycle()\n\n if self.state == \"walk_right\":\n self.walk_cycle()\n\n if self.state == \"walk_front\":\n self.walk_cycle()\n\n if self.state == \"walk_back\":\n self.walk_cycle()\n\n elif self.state == \"say_hi\":\n print(\"Hello everyone!\")\n self.set_state(\"idle\")\n\n def walk_left(self):\n self.set_state(\"walk_left\")\n self.GameData.positioner[self.GameController.current_room].empty_tile(self)\n self.x -= 1\n self.GameData.positioner[self.GameController.current_room].fill_tile(self)\n\n def walk_right(self):\n self.set_state(\"walk_right\")\n self.GameData.positioner[self.GameController.current_room].empty_tile(self)\n self.x += 1\n self.GameData.positioner[self.GameController.current_room].fill_tile(self)\n\n def walk_front(self):\n self.set_state(\"walk_front\")\n self.GameData.positioner[self.GameController.current_room].empty_tile(self)\n self.y += 1\n self.GameData.positioner[self.GameController.current_room].fill_tile(self)\n\n def walk_back(self):\n self.set_state(\"walk_back\")\n self.GameData.positioner[self.GameController.current_room].empty_tile(self)\n self.y -= 1\n self.GameData.positioner[self.GameController.current_room].fill_tile(self)\n\n def walk_cycle(self):\n if self.state == \"walk_left\":\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 3)\n self.imagex -= 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 3)\n self.imagex -= 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(self.cur_img, 3)\n\n elif self.state == \"walk_right\":\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 2)\n self.imagex += 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 2)\n self.imagex += 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 3\n self.set_image(self.cur_img, 2)\n\n elif self.state == \"walk_front\":\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 0)\n self.imagey += 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 0)\n\n self.imagey += 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(self.cur_img, 0)\n elif self.state == \"walk_back\":\n if 0 <= self.cur_img < 3:\n self.cur_img += 1\n self.set_image(self.cur_img, 1)\n self.imagey -= 1 / 4\n\n elif self.cur_img == (3):\n self.cur_img = 0\n self.set_image(self.cur_img, 1)\n self.imagey -= 1 / 4\n self.set_state(\"idle\")\n\n else:\n self.cur_img = 0\n self.set_image(self.cur_img, 1)\n\n def get_facing_tile(self):\n\n facing_tile_y = 0\n facing_tile_x = 0\n if self.facing == \"back\":\n facing_tile_y = int(self.y - 1)\n facing_tile_x = int(self.x)\n\n elif self.facing == \"front\":\n facing_tile_y = int(self.y + 1)\n facing_tile_x = int(self.x)\n\n elif self.facing == \"left\":\n facing_tile_y = int(self.y)\n facing_tile_x = int(self.x - 1)\n\n elif self.facing == \"right\":\n facing_tile_y = int(self.y)\n facing_tile_x = int(self.x + 1)\n\n facing_tile = self.GameData.room_list[self.GameController.current_room].tiles_array[facing_tile_x][\n facing_tile_y]\n\n return facing_tile\n\n def draw(self, screen):\n screen.blit(self.img, [((self.imagex + self.GameController.camera[0]) * self.GameData.square_size[0])\n + self.GameData.base_locator_x, ((self.imagey + self.GameController.camera[1])\n * self.GameData.square_size[\n 1] - self.offset_y) + self.GameData.base_locator_y])\n\n def set_phrase(self, phrase_to_set):\n self.current_phrase = phrase_to_set\n\n def set_state(self, state_to_set):\n self.state = state_to_set\n\nclass Pixie(NPC):\n WALK_LEFT = \"walk_left\"\n WALK_RIGHT = \"walk_right\"\n WALK_FRONT = \"walk_front\"\n WALK_BACK = \"walk_back\"\n TURNING_LEFT = \"turning_left\"\n TURNING_FRONT = \"turning_front\"\n TURNING_RIGHT = \"turning_right\"\n TURNING_BACK = \"turning_back\"\n IDLE = \"idle\"\n AVAILABLE_STATES = [WALK_BACK, WALK_RIGHT, WALK_BACK, WALK_FRONT, TURNING_BACK, TURNING_RIGHT, TURNING_FRONT, TURNING_LEFT, IDLE]\n\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, phrase):\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, facing = \"front\", feature_type=\"Pixie\", offset_y=10)\n assert self.state in self.AVAILABLE_STATES\n self.actions = [\"walk_left\", \"walk_right\", \"walk_front\", \"walk_back\", \"turning_left\", \"turning_front\", \"turning_right\", \"turning_back\"]\n self.available_actions = [\"turn\"]\n self.spritesheet = spritesheet\n self.state = \"idle\"\n self.phrase = phrase\n self.current_phrase = None\n self.speaking_queue = None #textwrap.wrap(\"Hi everyone, it's so nice to see you here today! I hope you have all been doing well\", width=30)\n\n def activate_timers(self):\n pygame.time.set_timer(self.initiate, 1000)\n pygame.time.set_timer(self.action_clock, 80)\n\n def do_activity(self):\n if self.state == \"idle\":\n result = choice(self.actions)\n\n if result == \"walk_left\":\n self.facing = \"left\"\n can_walk = self.GameData.positioner[self.GameController.current_room].can_move(self)\n if can_walk:\n self.walk_left()\n\n elif result == \"walk_right\":\n self.facing = \"right\"\n can_walk = self.GameData.positioner[self.GameController.current_room].can_move(self)\n if can_walk:\n self.walk_right()\n\n elif result == \"walk_front\":\n self.facing = \"front\"\n can_walk = self.GameData.positioner[self.GameController.current_room].can_move(self)\n if can_walk:\n self.walk_front()\n\n elif result == \"walk_back\":\n self.facing = \"back\"\n can_walk = self.GameData.positioner[self.GameController.current_room].can_move(self)\n if can_walk:\n self.walk_back()\n\n elif result == \"turning_front\":\n self.turn_front()\n\n elif result == \"turning_back\":\n self.turn_back()\n\n elif result == \"turning_left\":\n self.turn_left()\n\n elif result == \"turning_right\":\n self.turn_right()\n\n def get_interacted_with(self):\n # TODO: Fix all of this mess - make their picture pop up in their speech bubble thing\n if self.state == \"idle\":\n if self.GameData.player[\"Player\"].facing == \"back\":\n self.turn_front()\n elif self.GameData.player[\"Player\"].facing == \"front\":\n self.turn_back()\n elif self.GameData.player[\"Player\"].facing == \"left\":\n self.turn_right()\n elif self.GameData.player[\"Player\"].facing == \"right\":\n self.turn_left()\n self.GameController.set_keyboard_manager(InConversationOptions.ID)\n # self.GameController.set_menu(\"character_interact_menu\")\n self.GameController.MenuManager.character_interact_menu = True\n self.GameData.menu_list[\"character_interact_menu\"].set_talking_to(self.name)\n self.set_state(\"talking\")\n\n\n def speak(self, chosen_phrase):\n my_font = pygame.font.Font(self.GameController.font, 10)\n item = my_font.render(self.name + \":\", 1, (0, 0, 0))\n self.GameController.screen.blit(item, (\n self.GameData.overlay_list[\"text_box\"].x + 150, self.GameData.overlay_list[\"text_box\"].y + 20))\n\n my_font = pygame.font.Font(self.GameController.font, 10)\n item = my_font.render(chosen_phrase, 1, (0, 0, 0))\n self.GameController.screen.blit(item, (self.GameData.overlay_list[\"text_box\"].x + 150, self.GameData.overlay_list[\"text_box\"].y + 60))\n\n def display_name(self):\n my_font = pygame.font.Font(self.GameController.font, 10)\n item = my_font.render(self.name + \":\", 1, (0, 0, 0))\n self.GameController.screen.blit(item, (self.GameData.overlay_list[\"text_box\"].x + 150, self.GameData.overlay_list[\"text_box\"].y + 20))\n\n def test_speak(self):\n text_line = 0\n for line in self.current_phrase:\n my_font = pygame.font.Font(self.GameController.font, 10)\n item = my_font.render(line, 1, (0, 0, 0))\n self.GameController.screen.blit(item, (self.GameData.overlay_list[\"text_box\"].x + 150, self.GameData.overlay_list[\"text_box\"].y + 60 + 25*text_line))\n text_line += 1\n\n def set_current_phrase(self):\n self.current_phrase = textwrap.wrap(self.phrase, width=30)\n\n def set_speaking_queue(self):\n\n phrase_counter = 0\n self.speaking_queue = []\n for line in range(3):\n if len(self.current_phrase) > 0:\n self.speaking_queue.append(self.current_phrase[0])\n self.current_phrase.pop(0)\n\n if len(self.current_phrase) == 0:\n self.current_phrase = None\n\n def clear_speaking_queue(self):\n self.speaking_queue = None\n\n\nclass Prop(Feature):\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, size_x, size_y, offset_y=10):\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, offset_y)\n self.drawing_priority = 1\n self.size_x = size_x\n self.size_y = size_y\n self.feature_type = \"Prop\"\n\n def draw(self, screen):\n screen.blit(self.img, [((self.imagex + self.GameController.camera[0]) * self.GameData.square_size[0])\n + self.GameData.base_locator_x, ((self.imagey + self.GameController.camera[1])\n * self.GameData.square_size[1] - self.offset_y) + self.GameData.base_locator_y])\n\n\n #TODO: Fix this!\n def get_interacted_with(self):\n print(\"I'm a \" + self.name + \"!\")\n\nclass Decoration(Prop):\n def __init__(self, x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, size_x, size_y, location_list):\n super().__init__(x, y, imagex, imagey, width, height, spritesheet, name, GameController, GameData, size_x, size_y)\n self.location_list = location_list\n self.offset_y = 10\n\n def draw(self, screen):\n for location in self.location_list:\n screen.blit(self.img,[((location[0] + self.GameController.camera[0]) * self.GameData.square_size[\n 0]) + self.GameData.base_locator_x, (((location[1] + self.GameController.camera[1]) *\n self.GameData.square_size[1]) - self.offset_y) + self.GameData.base_locator_y])\n", "sub_path": "src/features.py", "file_name": "features.py", "file_ext": "py", "file_size_in_byte": 22895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pygame.USEREVENT", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.time.set_timer", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 277, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pygame.time.set_timer", "line_number": 474, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 474, "usage_type": "attribute"}, {"api_name": "pygame.time.set_timer", "line_number": 475, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 475, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 479, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 536, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 536, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 541, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 541, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 546, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 546, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 553, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 553, "usage_type": "attribute"}, {"api_name": "textwrap.wrap", "line_number": 559, "usage_type": "call"}]}
{"seq_id": "414132077", "text": "import pymongo\nimport die_rolling as die\nimport math as m\nimport modifiers\n\nclient = pymongo.MongoClient()\ndb = client.deathwatch\nphenomena = db.phenomena\nperils = db.perils\nchars = db.chars\n\ndef test():\n \"\"\"Willpower test for Psyker\"\"\"\n # This is a kind of catch-all for a psychic test\n # Which should cover most cases\n # Takes push level into account\n # and will print out the peril or phenomena if you trigger it\n # (automatically rolling 1d100 for these)\n chr = input(\"Which character? \")\n lvl = input(\"(f)ettered, (u)nfettered or (p)ushed? \")\n psy = db.chars.find_one({'name': chr})['attrib']['Psy']\n\n psy_final = 0\n if lvl == 'f':\n psy_final = m.ceil(psy/2)\n elif lvl == 'u':\n psy_final = psy\n else:\n psy_final = psy+3\n\n wp = db.chars.find_one({'name': chr})['attrib']['WP'] + (5 * psy_final)\n mods = modifiers.other()\n trgt = wp + mods\n rolled = die.roll(\"1d100\", silent=True)\n\n print()\n if trgt > rolled:\n print(\"Success!\")\n else:\n print(\"Failure...\")\n print(\"\\tRolled {} versus {} on WP test\".format(rolled, trgt))\n\n # Check both Psychic Phenomena and Perils of the Warp\n if lvl != 'f':\n check_phenomena(rolled, lvl)\n input()\n\n\ndef check_phenomena(rolled, lvl):\n dble = (str(rolled) == str(rolled)[::-1])\n high = (rolled >= 75)\n push = lvl == 'p'\n\n trigger_phenomena = dble or push or high\n\n phnm, prl, out = [], [], []\n fmt = \"\"\"\n{}\n{}\"\"\"\n\n if trigger_phenomena:\n if push:\n print(\"\\nPushing cause phenomena...\")\n else:\n print(\"\\nDoubles cause phenomena...\")\n\n # What's the roll on the phenomena table?\n phnm_roll = die.roll(\"1d100\", silent=True)\n phnm = get_phenomena(phnm_roll)\n\n # If we roll perils, use this as the output instead\n # Otherwise, just keep phnm, the phenomena\n if phnm['name'] == 'Perils of the Warp':\n prl_roll = die.roll(\"1d100\", silent=True)\n prl = get_peril(prl_roll)\n out = prl\n else:\n out = phnm\n\n if not out:\n msg = \"No phenomena. John is sad.\"\n else:\n # Made the mistake of calling it 'effect' in the phenomena\n # and 'effects' in the perils\n # and currently thinking of fast way to fix\n try:\n msg = fmt.format(out['name'], out['effect'])\n except:\n msg = fmt.format(out['name'], out['effects'])\n\n print(msg)\n\ndef get_phenomena(roll):\n # Loop through psychic phenomena table\n # if our value matches one of it's 'trigger' values (i.e we rolled 2, so 1-3 peril)\n # return that phenomena\n print(\"Rolled {} on phenomenas...\".format(roll))\n phnms = db.phenomena\n val = 0\n\n for phnm in phnms.find():\n if roll in phnm['triggers']:\n val = phnm['name']\n\n phnm = phnms.find_one({'name':val})\n\n return phnm\n\ndef get_peril(roll):\n # Exact same logic as above, but taking data from a different table\n print(\"\\nTriggered Perils of the Warp!!!\")\n print(\"Rolled {} on perils...\".format(roll))\n prls = db.perils\n val = 0\n\n for prl in prls.find():\n if roll in prl['triggers']:\n val = prl['name']\n\n prl = prls.find_one({'name':val})\n\n return prl\n", "sub_path": "psyker.py", "file_name": "psyker.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pymongo.MongoClient", "line_number": 6, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 25, "usage_type": "call"}, {"api_name": "modifiers.other", "line_number": 32, "usage_type": "call"}, {"api_name": "die_rolling.roll", "line_number": 34, "usage_type": "call"}, {"api_name": "die_rolling.roll", "line_number": 68, "usage_type": "call"}, {"api_name": "die_rolling.roll", "line_number": 74, "usage_type": "call"}]}
{"seq_id": "262251167", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport sys\nimport cg_algorithms as alg\nimport math\nfrom typing import Optional\nfrom PyQt5.QtWidgets import (\n QApplication,\n QMainWindow,\n qApp,\n QGraphicsScene,\n QGraphicsView,\n QGraphicsItem,\n QListWidget,\n QHBoxLayout,\n QWidget,\n QStyleOptionGraphicsItem,\n QColorDialog, QInputDialog, QFileDialog,\n QAction,QToolBar,QStyle,QProxyStyle)\nfrom PyQt5.QtGui import (\n QPainter, QMouseEvent, QColor, QPixmap,QTransform,QIcon,QPalette)\nfrom PyQt5.QtCore import QRectF, Qt,QSignalBlocker,QSize\n\nglobal g_penColor #used when set pencolor\ng_penColor = QColor(0,0,0) #black\n# size of canvas\nglobal g_width, g_height\ng_width = g_height = 600\n# is_draw_finish, status\nglobal g_draw_finish, g_draw_status, g_edit_status\n# for access\nglobal g_list_widget, g_window, g_canvas\nglobal g_transform\nglobal g_copy_item # ๅคๅถๅ่ฝ\n\ng_draw_finish = 1\ng_draw_status = [\"dot_line\",\"line\",\"ellipse\", \"polygon\",\"curve\"]\ng_edit_status = ['translate','rotate','scale','clip']\ng_transform = QTransform()\ng_copy_item = None\n\ndef is_close(pos0, pos1):\n \"\"\"ๅคๆญไธคไธช็นๆฏๅฆ่ถณๅค่ฟ,็จไบๅค่พนๅฝข็้ญๅๅ้ๆฉๅพๅ
\"\"\"\n if abs(pos0[0]-pos1[0])<=5 and abs(pos0[1]-pos1[1])<=5:\n return True\n else :\n return False\n\ndef atoi(s):\n \"\"\"ๅฐๅญ็ฌฆไธฒ่ฝฌๅไธบๆฐๅญ\"\"\"\n s = s[::-1]\n num = 0\n for i, v in enumerate(s):\n for j in range(0, 10):\n if v == str(j):\n num += j * (10 ** i)\n return num\n\ndef get_limit_pos(x, is_x):\n limit = g_width if is_x else g_height\n if x<0:\n xout = 0\n elif x>limit:\n xout = limit\n else:\n xout =x\n return xout\n\ndef copy_MyItem(src):\n sid = src.id\n status = src.item_type\n p_list = src.p_list[:]\n algorithm = src.algorithm\n item = MyItem(sid, status, p_list, algorithm)\n item.pixels = src.pixels[:]\n item.penColor = src.penColor\n item.paddingColor = src.paddingColor\n item.isPadding = src.isPadding\n return item\n\nclass MyCanvas(QGraphicsView):\n \"\"\"\n ็ปๅธ็ชไฝ็ฑป๏ผ็ปงๆฟ่ชQGraphicsView๏ผ้็จQGraphicsViewใQGraphicsSceneใQGraphicsItem็็ปๅพๆกๆถ\n \"\"\"\n def __init__(self, *args):\n super().__init__(*args)\n self.main_window = None\n self.list_widget = None\n self.item_dict = {}\n self.selected_id = ''\n\n self.status = ''\n self.temp_algorithm = ''\n self.temp_id = '-1'\n self.cur_id = ''\n #draw polygon need temp_item to judge start \n self.temp_item = MyItem(self.temp_id, 'noneType', \\\n [[0, 0], [0, 0]], 'noneAlg')\n # ๆๅจ็ปๅธๆถๅคๆญ็ปๅธ็็ผฉๆพๆ
ๅต\n self.is_image_scaling = 0\n\n #clear canvas\n def clear_canvas(self):\n #clear\n self.list_widget.clear()\n self.list_widget.addItem(\"clear selection\")\n self.scene().clear()\n self.main_window.reset_id()\n self.item_dict = {}\n self.selected_id = ''\n\n self.status = ''\n self.temp_algorithm = ''\n self.temp_id = '-1'\n self.cur_id = ''\n self.temp_item = MyItem(self.temp_id, 'noneType', \\\n [[0, 0], [0, 0]], 'noneAlg')\n global g_draw_finish\n g_draw_finish = 1\n global g_penColor\n g_penColor = QColor(0,0,0)\n \n def start_draw(self, status, algorithm):\n self.check_finish()\n global g_draw_finish\n g_draw_finish = 0\n self.status = status\n self.temp_algorithm = algorithm\n self.temp_id = self.main_window.get_id()\n \n def selectedItemClear(self):\n if self.selected_id == '':\n return\n self.item_dict[self.selected_id].edit_clear()\n \n def start_edit(self, status, algorithm):\n self.check_finish()\n global g_draw_finish\n g_draw_finish = 0\n self.status = status\n self.temp_algorithm = algorithm\n self.selectedItemClear()\n \n def finish_draw(self):\n global g_draw_finish\n g_draw_finish = 1\n self.main_window.id_inc()\n self.temp_id = self.main_window.get_id()\n \n def check_finish(self):\n global g_draw_finish\n if g_draw_finish == 1:\n return \n # finish the last item\n if self.status == 'polygon':\n # ๅค่พนๅฝขๆฒก็ปๅฎๅฐฑๅปๅนฒๅซ็ไบ\n if self.temp_item.p_list[0] != self.temp_item.p_list[-1]:\n tmp_p_list = self.temp_item.p_list[0][:]\n self.temp_item.p_list.append(tmp_p_list)\n self.updateScene([self.sceneRect()])\n self.temp_item.edit_type = 'polygon_fit'\n self.updateScene([self.sceneRect()])\n self.finish_draw()\n \n def clear_selection(self):\n if self.selected_id != '':\n if self.selected_id in self.item_dict.keys():\n self.item_dict[self.selected_id].selected = False\n self.item_dict[self.selected_id].update()\n self.selected_id = ''\n\n def selection_changed(self, selected):\n self.check_finish()\n self.clear_selection()\n if selected == 'clear selection':\n self.updateScene([self.sceneRect()])\n return\n self.main_window.statusBar().clearMessage()\n self.main_window.statusBar().showMessage('ๅพๅ
้ๆฉ๏ผ %s' % selected)\n strList = selected.split()\n if strList == []:\n # ้็ฝฎ็ปๅธ็ๆถๅไนไผ่ฟๅ
ฅ่ฟไธชๅฝๆฐ\n return\n self.selected_id = strList[-1]\n self.item_dict[self.selected_id].selected = True\n self.item_dict[self.selected_id].update()\n # can continously choose by click\n if self.status != 'choose':\n self.status = ''\n self.updateScene([self.sceneRect()])\n \n def choose_item(self):\n self.status = 'choose'\n \n def delete_choose(self):\n self.check_finish()\n if self.selected_id == '':\n print(\"ๅ ้ค่ฏท้ไธญๅพๅ
.\")\n return\n sid = self.selected_id\n self.clear_selection()\n g_list_widget.takeItem(g_list_widget.currentRow())\n \n item = self.item_dict[sid]\n item.item_type = 'delete'\n item.p_list = []\n item.pixels = []\n del self.item_dict[sid]\n self.updateScene([self.sceneRect()])\n \n def copy_item(self):\n self.check_finish()\n if self.selected_id == '':\n print(\"ๅคๅถ่ฏท้ไธญๅพๅ
.\")\n return\n src_item = self.item_dict[self.selected_id]\n global g_copy_item\n g_copy_item = copy_MyItem(src_item)\n \n def paste_item(self):\n self.check_finish()\n if g_copy_item == None:\n print(\"่ฏทๅ
ๅคๅถไธไธชๅพๅ
.\")\n return\n item = copy_MyItem(g_copy_item)\n self.main_window.id_inc()\n item.id = self.main_window.get_id()\n self.add_item(item)\n \n def padding(self):\n \"\"\"ๅกซๅ
\"\"\"\n self.check_finish()\n if self.selected_id == '':\n print(\"่ฏทๅ
้ๆฉไธไธชๅพๅ
.\")\n return\n item = self.item_dict[self.selected_id]\n if item.item_type not in ['polygon','ellipse']:\n print(\"ไธ่ฝๅฏนๅค่พนๅฝขๅๆคญๅไปฅๅค็ๅพๅ
ๅกซๅ
.\")\n return\n item.paddingColor = QColorDialog.getColor()\n item.isPadding = True\n self.updateScene([self.sceneRect()])\n \n def add_item(self, item):\n self.scene().addItem(item)\n self.item_dict[item.id] = item\n self.list_widget.addItem(item.item_type+\" : \"+item.id)\n self.updateScene([self.sceneRect()])\n \n def mousePressEvent(self, event: QMouseEvent) -> None:\n pos = self.mapToScene(event.localPos().toPoint())\n x = int(pos.x())\n y = int(pos.y())\n x = get_limit_pos(x, 1)\n y = get_limit_pos(y, 0)\n self.cur_id = ''\n \n # choose item by clicking canvas\n if self.status == 'choose':\n # item = g_canvas.scene().itemAt(x, y, g_transform)\n items = self.item_dict.values()\n pos = [x,y]\n # break_flg\n b_flg = False\n id = ''\n # select in canvas\n for item in items:\n if b_flg:\n break\n for coor in item.pixels:\n if is_close(coor, pos):\n b_flg = True\n self.selection_changed(item.id)\n id = item.id\n break\n # select in list_widget\n for i in range(1, g_list_widget.count()):\n widget_item = g_list_widget.item(i)\n strList = widget_item.text().split()\n if(strList[-1] == id):\n g_list_widget.setCurrentRow(i)\n QSignalBlocker(g_list_widget)\n widget_item.setSelected(True)\n break\n \n # ็นๅป่พน็ๅค้่ฟๆถๆๅจ็ปๅธ\n self.is_image_scaling = 0\n if g_width-5 <= x <= g_width+5 and g_height-5 <= y <= g_height+5:\n self.is_image_scaling = 3 \n elif g_width-5 <= x <= g_width+5: \n self.is_image_scaling = 1 \n elif g_height-5 <= y <= g_height+5: \n self.is_image_scaling = 2\n \n def press_draw():\n global g_draw_status\n if self.status not in g_draw_status:\n return ''\n if self.temp_item.id != self.main_window.get_id():\n self.temp_item = MyItem(self.temp_id, self.status, \\\n [[x, y], [x, y]], self.temp_algorithm)\n self.scene().addItem(self.temp_item)\n else:\n # needs more than two points\n if self.status == 'polygon':\n self.temp_item.p_list.append([x,y])\n elif self.status == 'curve':\n self.temp_item.p_list.insert(-1, [x,y])\n return self.temp_item.id\n \n def press_edit():\n if self.status not in g_edit_status:\n return ''\n if self.selected_id == '':\n print(\"่ฏท้ๆฉๅพๅ
.\")\n return\n sid = self.selected_id\n if self.status in ['translate','clip']:\n if self.status == 'clip' \\\n and self.item_dict[sid].item_type != 'line':\n g_window.statusBar().showMessage('ไธ่ฝ่ฃๅช้็บฟๆฎต')\n self.status = ''\n else:\n self.item_dict[sid].edit_type = self.status\n self.item_dict[sid].poi = [x,y]\n self.item_dict[sid].poi1 = [x,y]\n self.item_dict[sid].edit_algorithm = self.temp_algorithm\n self.item_dict[sid].edit_over = 0\n elif self.status in ['rotate', 'scale']:\n if self.item_dict[sid].param_cnt == 0:\n self.item_dict[sid].edit_type = self.status\n self.item_dict[sid].center = [x,y]\n self.item_dict[sid].edit_over = 0\n self.item_dict[sid].param_cnt = 1\n elif self.item_dict[sid].param_cnt == 1:\n self.item_dict[sid].poi = [x,y]\n self.item_dict[sid].poi1 = [x,y]\n self.item_dict[sid].param_cnt = 2\n else:\n self.status = ''\n self.item_dict[sid].edit_clear()\n else:\n print(\"Undefined Behavior: No such edit situation\")\n return ''\n return sid\n \n # draw or edit\n if self.is_image_scaling == 0:\n dealing_id = press_draw()\n if dealing_id != '':\n self.cur_id = dealing_id\n dealing_id = press_edit()\n if dealing_id != '':\n self.cur_id = dealing_id\n self.updateScene([self.sceneRect()])\n #self.updateScene([self.temp_item.boundingRect()])\n super().mousePressEvent(event)\n\n def mouseMoveEvent(self, event: QMouseEvent) -> None:\n pos = self.mapToScene(event.localPos().toPoint())\n xin = int(pos.x())\n yin = int(pos.y())\n x = get_limit_pos(xin, 1)\n y = get_limit_pos(yin, 0)\n \n def get_real_bound(x_in):\n \"\"\"ๅพๅฐไธ่ถ
่ฟ้ๅถ็็ๅฎ่พน็\"\"\"\n x_out = 600\n if x_in>=1000:\n x_out = 1000\n elif x_in<=100:\n x_out = 100\n else:\n x_out = x_in\n return x_out\n\n # ็ผฉๆพ็ปๅธๅ่ฝ\n if self.is_image_scaling > 0:\n global g_width, g_height\n if self.is_image_scaling == 1: \n g_width = get_real_bound(xin)\n elif self.is_image_scaling == 2:\n g_height = get_real_bound(yin)\n else: \n g_width = get_real_bound(xin)\n g_height = get_real_bound(yin)\n self.setFixedSize(g_width+10, g_height+10)\n self.scene().setSceneRect(0, 0, g_width, g_height)\n self.main_window.resize(g_width, g_height)\n \n def move_draw():\n if self.status not in g_draw_status:\n return\n if self.status in ['dot_line','line','ellipse','polygon']:\n self.temp_item.p_list[-1] = [x, y]\n elif self.status == 'curve':\n le = len(self.temp_item.p_list)\n if le <= 2: \n self.temp_item.p_list[-1] = [x, y]\n else:\n self.temp_item.p_list[-2] = [x, y]\n \n def move_edit():\n if self.selected_id == '':\n return\n sid = self.selected_id\n if self.status in ['translate','clip']:\n self.item_dict[sid].edit_type = self.status\n self.item_dict[sid].poi1 = [x,y]\n elif self.status == 'rotate' or self.status == 'scale':\n if self.item_dict[sid].param_cnt == 1:\n pass\n elif self.item_dict[sid].param_cnt == 2:\n self.item_dict[sid].poi1 = [x,y]\n elif self.item_dict[sid].param_cnt>2:\n print(\"error, rotate or scale not over\")\n \n if self.is_image_scaling == 0:\n move_draw()\n move_edit()\n self.updateScene([self.sceneRect()])\n #self.updateScene([self.temp_item.boundingRect()])\n super().mouseMoveEvent(event)\n\n def mouseReleaseEvent(self, event: QMouseEvent) -> None:\n \n def release_draw():\n if self.status not in g_draw_status:\n return\n if self.temp_id not in self.item_dict:\n self.item_dict[self.temp_id] = self.temp_item\n self.list_widget.addItem(self.status+\" : \"+self.temp_id)\n if self.status in ['dot_line','line','ellipse']:\n self.finish_draw()\n if self.status == 'polygon':\n global g_draw_finish\n g_draw_finish = 0\n else :\n if self.status == 'polygon'\\\n and is_close(self.temp_item.p_list[0], self.temp_item.p_list[-1]):\n # finish draw polygon\n # [-1] and [0] refer to the same vertex\n self.temp_item.p_list[-1] = self.temp_item.p_list[0][:]\n self.finish_draw()\n \n def release_edit():\n if self.selected_id == '':\n return\n sid = self.selected_id\n if self.status in ['translate', 'clip']:\n self.item_dict[sid].edit_over = 1\n elif self.status == 'rotate' or self.status == 'scale':\n if self.item_dict[sid].param_cnt == 1:\n pass\n elif self.item_dict[sid].param_cnt == 2:\n self.item_dict[sid].edit_over = 1 \n elif self.item_dict[sid].param_cnt>2:\n print(\"error, rotate or scale not over\")\n \n if self.is_image_scaling == 0:\n release_draw()\n release_edit()\n\n if self.is_image_scaling >0:\n self.is_image_scaling = 0\n self.updateScene([self.sceneRect()])\n super().mouseReleaseEvent(event)\n\n\nclass MyItem(QGraphicsItem):\n \"\"\"\n ่ชๅฎไนๅพๅ
็ฑป๏ผ็ปงๆฟ่ชQGraphicsItem\n \"\"\"\n def __init__(self, item_id: str, item_type: str, p_list: list, algorithm: str = '', parent: QGraphicsItem = None):\n \"\"\"\n :param item_id: ๅพๅ
ID\n :param item_type: ๅพๅ
็ฑปๅ๏ผ'line'ใ'polygon'ใ'ellipse'ใ'curve'็ญ\n :param p_list: ๅพๅ
ๅๆฐ\n :param algorithm: ็ปๅถ็ฎๆณ๏ผ'DDA'ใ'Bresenham'ใ'Bezier'ใ'B-spline'็ญ\n :param parent:\n \"\"\"\n super().__init__(parent)\n self.id = item_id # ๅพๅ
ID\n self.item_type = item_type # ๅพๅ
็ฑปๅ๏ผ'line'ใ'polygon'ใ'ellipse'ใ'curve'็ญ\n self.p_list = p_list[:] # ๅพๅ
ๅๆฐ\n self.algorithm = algorithm # ็ปๅถ็ฎๆณ๏ผ'DDA'ใ'Bresenham'ใ'Bezier'ใ'B-spline'็ญ\n self.selected = False\n \n self.penColor = g_penColor # ่ชๅทฑ็penColor\n self.pixels = [] # ่ฎฐๅฝๆๆๅ็ด ็น\n # for edit\n self.edit_type = ''\n self.edit_algorithm = '' # used only for clip\n self.poi = [0,0] # [x,y] of move\n self.poi1 = [0,0]\n self.center = [0,0] # [x,y] of center\n self.edit_over = 0 # if the transformation operation over\n self.param_cnt = 0 # some operation needs 2 mouse_press\n # for padding\n self.paddingColor = None\n self.isPadding = False\n \n def edit_clear(self):\n self.edit_type = ''\n self.edit_algorithm = ''\n self.center = [0,0]\n self.poi = [0,0]\n self.poi1 = [0,0]\n self.edit_over = 0\n self.param_cnt = 0\n \n def edit_finish(self, new_p_list):\n self.edit_clear()\n self.old_p_list = self.p_list[:]\n self.p_list = new_p_list\n \n def get_draw_pixels(self, p_list, algorithm):\n # draw figure\n result = []\n if self.item_type == 'line':\n result = alg.draw_line(p_list, algorithm)\n elif self.item_type == 'dot_line':\n result = alg.draw_dotted_line(p_list)\n elif self.item_type == 'polygon':\n # ็ป่พน\n for i in range(0,len(p_list)-1):\n result.extend(alg.draw_line([p_list[i],\\\n p_list[i+1]], algorithm))\n elif self.item_type == 'ellipse':\n result = alg.draw_ellipse(p_list)\n elif self.item_type == 'curve':\n result = alg.draw_curve(p_list, algorithm) \n return result\n \n #guiไผๅจupdate่ชๅจ่ฐ็จpaint้ๆฐ็ปๅถๆๆๅพๅ
\n def paint(self, painter: QPainter, option: QStyleOptionGraphicsItem, \\\n widget: Optional[QWidget] = ...) -> None:\n def angle(v1, v2):\n \"\"\"่ฎก็ฎv2็ธๅฏนไบv1็้กบๆถ้่งๅบฆ\n v1 = [[x0,y0],[x1,y1]], v2ๅ็\n \"\"\"\n dx1 = v1[1][0] - v1[0][0]\n dy1 = v1[1][1] - v1[0][1]\n dx2 = v2[1][0] - v2[0][0]\n dy2 = v2[1][1] - v2[0][1]\n angle1 = math.atan2(dy1, dx1)\n angle1 = int(angle1 * 180/math.pi)\n angle2 = math.atan2(dy2, dx2)\n angle2 = int(angle2 * 180/math.pi)\n ret = angle1 - angle2\n return ret\n \n def thick_draw_point(painter, poi):\n \"\"\"ๅ ็ฒ็ปๅถไธไธช็น,็จไบ่ฃๅช็บฟๆฎตๆถ้ซไบฎ้ไธญ้จๅ\"\"\"\n painter.drawPoint(*[poi[0]+1,poi[1]+1])\n painter.drawPoint(*[poi[0]+1,poi[1]-1])\n painter.drawPoint(*[poi[0]-1,poi[1]+1])\n painter.drawPoint(*[poi[0]-1,poi[1]-1])\n return\n \n def paint_small_cycle(painter, p_list):\n for poi in p_list:\n pixels = alg.draw_ellipse([[poi[0]-2,poi[1]-2],[poi[0]+2,poi[1]+2]])\n for p in pixels:\n painter.drawPoint(*p)\n return\n \n def paint_dotted_line(painter, p_list):\n pixels = alg.draw_dotted_line(p_list)\n for p in pixels:\n painter.drawPoint(*p)\n \n if self.p_list == [] or self.item_type == 'delete':\n # be deleted\n return\n \n # change p_list accoring to edit_type\n new_p_list = self.p_list\n if self.edit_type == 'translate':\n # ๆงๅถ็น\n painter.setPen(QColor(255,0,255))\n paint_small_cycle(painter, [self.poi, self.poi1])\n paint_dotted_line(painter, [self.poi, self.poi1])\n \n new_p_list = alg.translate(self.p_list, self.poi1[0]-self.poi[0], \\\n self.poi1[1]-self.poi[1])\n if self.edit_over == 1:\n # finish\n self.edit_finish(new_p_list)\n elif self.edit_type == 'rotate':\n if self.item_type == 'ellipse':\n # g_window.statusBar().clearMessage()\n print(\"Can't rotate ellipse.\")\n self.edit_finish(self.p_list)\n else:\n painter.setPen(QColor(255,0,255))\n if self.param_cnt==1:\n paint_small_cycle(painter, [self.center])\n elif self.param_cnt == 2:\n paint_small_cycle(painter, [self.center, self.poi, self.poi1])\n paint_dotted_line(painter, [self.center, self.poi])\n paint_dotted_line(painter, [self.center, self.poi1])\n # center and poi, poi1 all gotten\n theta = angle([self.center, self.poi], [self.center, self.poi1])\n new_p_list = alg.rotate(self.p_list, \\\n self.center[0], self.center[1], theta)\n if self.edit_over == 1:\n # clear\n self.edit_finish(new_p_list)\n elif self.edit_type == 'scale':\n painter.setPen(QColor(255,0,255))\n if self.param_cnt == 1:\n paint_small_cycle(painter, [self.center])\n if self.param_cnt == 2:\n paint_small_cycle(painter, [self.center, self.poi, self.poi1])\n paint_dotted_line(painter, [self.center, self.poi])\n paint_dotted_line(painter, [self.center, self.poi1])\n # ็ผฉๆพๅๆฐ, ๆ นๆฎdx็ๆฏๅผ็กฎๅฎ\n if self.poi[0]-self.center[0] == 0:\n s = 1\n else :\n s = (self.poi1[0]-self.center[0])/(self.poi[0]-self.center[0])\n new_p_list = alg.scale(self.p_list, \\\n self.center[0], self.center[1], s)\n if self.edit_over == 1:\n self.edit_finish(new_p_list)\n elif self.edit_type == 'clip':\n if self.edit_over == 0:\n # draw the clip window\n painter.setPen(QColor(0,255,0))\n painter.drawRect( self.regionRect([self.poi,self.poi1]) ) \n tmp_p_list = alg.clip(self.p_list, self.poi[0], self.poi[1],\\\n self.poi1[0], self.poi1[1], self.edit_algorithm)\n if tmp_p_list != []:\n # highlight the line in clip window\n tmp_pixels = self.get_draw_pixels(tmp_p_list,self.algorithm)\n painter.setPen(QColor(0, 255, 0))\n for p in tmp_pixels:\n thick_draw_point(painter, p)\n elif self.edit_over == 1:\n # ๅพๅฐ่ฃๅชๅ็็ซฏ็น\n new_p_list = alg.clip(self.p_list, self.poi[0], self.poi[1],\\\n self.poi1[0], self.poi1[1], self.edit_algorithm)\n self.edit_finish(new_p_list)\n if self.p_list == []:\n # ็บฟๆฎต่ขซ่ฃๅชๆฒกไบ\n self.item_type = 'delete'\n self.pixels = []\n g_canvas.clear_selection()\n g_list_widget.takeItem(g_list_widget.currentRow())\n del g_canvas.item_dict[self.id]\n #ไธ้ข่ฟๅฅๅ ไบๅ,็ปๅธๅคงๅฐๆนๅๅๅๅ ้คๅพๅ
ไผๅดฉๆบ\n # g_canvas.scene().removeItem(self)\n return\n # ๅกซๅ
\n if self.isPadding:\n painter.setPen(self.paddingColor)\n polygon_padding(painter, self)\n \n item_pixels = []\n if new_p_list != []:\n if self.id == g_canvas.cur_id:\n item_pixels = self.get_draw_pixels(new_p_list, self.algorithm)\n self.pixels = item_pixels\n else:\n item_pixels = self.pixels\n else :\n print(\"Undefined Behavior: new_p_list shouldn't be []\")\n # ็บฟๆฎต่ขซ่ฃๅชๆฒกไบ็่ฏไธ่ฏฅๅฐ่ฟไธๆญฅ\n return\n # draw\n painter.setPen(self.penColor)\n for p in item_pixels:\n painter.drawPoint(*p)\n # draw bound\n if self.selected:\n painter.setPen(QColor(255, 0, 0))\n painter.drawRect(self.regionRect(new_p_list))\n pass\n \n #็ปๅถitemๆถๆ้่ๅด\n def boundingRect(self) -> QRectF:\n x,y,w,h = self.compute_region(self.p_list)\n return QRectF(x - 1, y - 1, w + 2, h + 2)\n \n def regionRect(self, new_p_list) -> QRectF:\n x,y,w,h = self.compute_region(new_p_list)\n return QRectF(x - 1, y - 1, w + 2, h + 2)\n \n def compute_region(self, new_p_list):\n x,y,w,h = [0,0,0,0]\n # ่ฃๅช็บฟๆฎตๅๅฏ่ฝๅบ็ฐp_list็ฉบ็็ๅพๅ
\n if new_p_list == []:\n return [x,y,w,h]\n if self.item_type in ['line','ellipse','dot_line']:\n x0, y0 = new_p_list[0]\n x1, y1 = new_p_list[1]\n x = min(x0, x1)\n y = min(y0, y1)\n w = max(x0, x1) - x\n h = max(y0, y1) - y\n elif self.item_type == 'polygon' or self.item_type == 'curve':\n x, y = new_p_list[0]\n w, h = new_p_list[0]\n for i in range(len(new_p_list)):\n if x > new_p_list[i][0]:\n x = new_p_list[i][0]\n if y > new_p_list[i][1]:\n y = new_p_list[i][1]\n if w < new_p_list[i][0]:\n w = new_p_list[i][0]\n if h < new_p_list[i][1]:\n h = new_p_list[i][1]\n w = w-x\n h = h-y\n return [x,y,w,h]\n\ndef polygon_padding(painter, item):\n pixels = item.pixels[:]\n pixels.sort()\n pix = []\n vertical = []\n curx = pixels[0][0]\n for p in pixels:\n if curx == p[0]:\n vertical.append(p)\n else:\n pix.append(vertical[:])\n vertical = []\n curx = p[0]\n vertical.append(p)\n pix.append(vertical[:])\n # print(pix)\n for posList in pix:\n xpos = posList[0][0]\n parity = False\n le = len(posList)\n oldy = posList[0][1]\n for i in range(le):\n cury = posList[i][1]\n if i1000 or x<100 or y>1000 or y<100:\n print(\"x and y must in [100,1000], please input again.\")\n return\n self.canvas_widget.clear_canvas()\n self.canvas_widget.setFixedSize(x+10, y+10)\n self.scene.setSceneRect(0, 0, x, y)\n self.resize(x, y)\n global g_width, g_height\n g_width= x\n g_height = y\n \n #ไฟๅญ็ปๅธ\n def save_canvas(self):\n self.statusBar().showMessage('ไฟๅญ็ปๅธ')\n # ไปคๅไธไธชๆฒกๅฎๆๅพๅฝขๅฎๆ\n self.canvas_widget.check_finish()\n \n fname = QFileDialog.getSaveFileName(self, 'Save file',\\\n '/home/output/default','Image files (*.bmp)') \n #cancel save\n if(fname[0]==''):\n return\n # Get QRectF\n rect = self.scene.sceneRect()\n # Create a pixmap, fill with white color\n pixmap = QPixmap(g_width, g_height)\n # ่ฎพ็ฝฎ่ๆฏ็ฝ่ฒ\n pixmap.fill(QColor(255,255,255))\n # painter\n painter = QPainter(pixmap)\n # Render scene to the pixmap\n self.scene.render(painter, rect, rect)\n painter.end()\n # save bmp file\n pixmap.save(fname[0]) \n \n #ไปQColorDialogไธญ้ๅ้ข่ฒ,ๅนถ่ฎพ็ฝฎไธบpen็้ข่ฒ\n def pen_color_change(self):\n self.statusBar().showMessage('่ฎพ็ฝฎ็ป็ฌ้ข่ฒ')\n # ่ฎพ็ฝฎ็ป็ฌ้ข่ฒไนไผไปคๅไธไธชๆฒกๅฎๆๅพๅฝขๅฎๆ\n self.canvas_widget.check_finish()\n global g_penColor\n color = QColorDialog.getColor()\n g_penColor = color\n\n def choose_item(self):\n self.canvas_widget.choose_item()\n \n def delete_choose(self):\n self.canvas_widget.delete_choose()\n \n def copy_action(self):\n self.canvas_widget.copy_item()\n \n def paste_action(self):\n self.canvas_widget.paste_item()\n \n def padding_action(self):\n self.canvas_widget.padding()\n \n def get_id(self):\n _id = str(self.item_cnt)\n #self.item_cnt += 1\n return _id\n \n def id_inc(self):\n self.item_cnt += 1\n \n def reset_id(self):\n self.item_cnt = 0\n \n #DDA็ปๅถ็บฟๆฎต \n def line_dda_action(self):\n self.canvas_widget.start_draw('line', 'DDA')\n self.statusBar().showMessage('DDA็ฎๆณ็ปๅถ็บฟๆฎต')\n \n #Bresenham็ปๅถ็บฟๆฎต\n def line_bresenham_action(self):\n self.canvas_widget.start_draw('line','Bresenham')\n self.statusBar().showMessage('Bresenham็ฎๆณ็ปๅถ็บฟๆฎต')\n \n def dotted_line_action(self):\n self.canvas_widget.start_draw('dot_line','default')\n self.statusBar().showMessage('็ปๅถ่็บฟๆฎต')\n \n def polygon_bresenham_action(self):\n self.canvas_widget.start_draw('polygon','Bresenham')\n self.statusBar().showMessage('Bresenham็ฎๆณ็ปๅถๅค่พนๅฝข')\n \n def polygon_dda_action(self):\n self.canvas_widget.start_draw('polygon','DDA')\n self.statusBar().showMessage('DDA็ฎๆณ็ปๅถๅค่พนๅฝข')\n \n def ellipse_action(self):\n self.canvas_widget.start_draw('ellipse','default')\n self.statusBar().showMessage('็ปๅถๆคญๅ')\n \n def curve_bezier_action(self):\n self.canvas_widget.start_draw('curve','Bezier')\n self.statusBar().showMessage('Bezier็ฎๆณ็ปๅถๆฒ็บฟ')\n \n def curve_b_spline_action(self):\n self.canvas_widget.start_draw('curve','B-spline')\n self.statusBar().showMessage('B_spline็ฎๆณ็ปๅถๆฒ็บฟ')\n \n # ๅนณ็งป\n def translate_action(self):\n self.canvas_widget.start_edit('translate','default')\n self.statusBar().showMessage('ๅนณ็งป้ไธญๅพๅ
')\n \n # ๆ่ฝฌ\n def rotate_action(self):\n self.canvas_widget.start_edit('rotate','default')\n self.statusBar().showMessage('ๆ่ฝฌ้ไธญๅพๅ
')\n \n # ็ผฉๆพ\n def scale_action(self):\n self.canvas_widget.start_edit('scale','default')\n self.statusBar().showMessage('็ผฉๆพ้ไธญๅพๅ
')\n \n # ่ฃๅช็บฟๆฎต\n def clip_cohen_sutherland_action(self):\n self.canvas_widget.start_edit('clip','Cohen-Sutherland')\n self.statusBar().clearMessage()\n self.statusBar().showMessage('Cohen-Sutherland็ฎๆณ่ฃๅช็บฟๆฎต')\n \n def clip_liang_barsky_action(self):\n self.canvas_widget.start_edit('clip','Liang-Barsky')\n self.statusBar().clearMessage()\n self.statusBar().showMessage('Liang-Barsky็ฎๆณ่ฃๅช็บฟๆฎต')\n \n# Create a custom \"QProxyStyle\" to enlarge the QMenu icons\n#-----------------------------------------------------------\nclass MyProxyStyle(QProxyStyle):\n pass\n def pixelMetric(self, QStyle_PixelMetric, option=None, widget=None):\n\n if QStyle_PixelMetric == QStyle.PM_SmallIconSize:\n return 40\n else:\n return QProxyStyle.pixelMetric(self, QStyle_PixelMetric, option, widget)\n \nif __name__ == '__main__':\n app = QApplication(sys.argv)\n # print(dir(QGraphicsScene))\n # help(QListWidget.row)\n mw = MainWindow()\n # The proxy style should be based on an existing style,\n # like 'Windows', 'Motif', 'Plastique', 'Fusion', ...\n myStyle = MyProxyStyle('Fusion')\n app.setStyle(myStyle)\n palette1 = QPalette()\n palette1.setColor(palette1.Background, QColor(236,240,241))\n mw.setPalette(palette1)\n #mw.setWindowOpacity(0.85) # ่ฎพ็ฝฎ็ชๅฃ้ๆๅบฆ\n #mw.setAttribute(Qt.WA_TranslucentBackground) # ่ฎพ็ฝฎ็ชๅฃ่ๆฏ้ๆ\n global g_window\n g_window = mw\n mw.show()\n app.exec_()\n del app\n #sys.exit(0)", "sub_path": "CG_demo/cg_gui.py", "file_name": "cg_gui.py", "file_ext": "py", "file_size_in_byte": 41897, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "PyQt5.QtGui.QColor", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTransform", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGraphicsView", "line_number": 82, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog.getColor", "line_number": 241, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog", "line_number": 241, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QMouseEvent", "line_number": 251, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSignalBlocker", "line_number": 283, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QMouseEvent", "line_number": 360, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QMouseEvent", "line_number": 426, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGraphicsItem", "line_number": 471, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGraphicsItem", "line_number": 475, "usage_type": "name"}, {"api_name": "cg_algorithms.draw_line", "line_number": 522, "usage_type": "call"}, {"api_name": "cg_algorithms.draw_dotted_line", "line_number": 524, "usage_type": "call"}, {"api_name": "cg_algorithms.draw_line", "line_number": 528, "usage_type": "call"}, {"api_name": "cg_algorithms.draw_ellipse", "line_number": 531, "usage_type": "call"}, {"api_name": "cg_algorithms.draw_curve", "line_number": 533, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 537, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyleOptionGraphicsItem", "line_number": 537, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 538, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 538, "usage_type": "name"}, {"api_name": "math.atan2", "line_number": 547, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 548, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 549, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 550, "usage_type": "attribute"}, {"api_name": "cg_algorithms.draw_ellipse", "line_number": 564, "usage_type": "call"}, {"api_name": "cg_algorithms.draw_dotted_line", "line_number": 570, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 582, "usage_type": "call"}, {"api_name": "cg_algorithms.translate", "line_number": 586, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 597, "usage_type": "call"}, {"api_name": "cg_algorithms.rotate", "line_number": 606, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 612, "usage_type": "call"}, {"api_name": "cg_algorithms.scale", "line_number": 624, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 631, "usage_type": "call"}, {"api_name": "cg_algorithms.clip", "line_number": 633, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 638, "usage_type": "call"}, {"api_name": "cg_algorithms.clip", "line_number": 643, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 678, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 685, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 683, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 689, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 687, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 754, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QListWidget", "line_number": 764, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGraphicsScene", "line_number": 772, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 785, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 785, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 787, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 787, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 789, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 789, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 791, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 791, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 793, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 793, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 795, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 795, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 797, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 797, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 808, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 808, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 810, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 810, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 812, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 812, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 814, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 814, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 816, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 816, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 818, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 818, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 820, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 820, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 822, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 822, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QToolBar", "line_number": 824, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 825, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.LeftToolBarArea", "line_number": 826, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 826, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 838, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 839, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 839, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 841, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 841, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 843, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 843, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 845, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 845, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 848, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 848, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 851, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 851, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 853, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 853, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.qApp.quit", "line_number": 876, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.qApp", "line_number": 876, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 903, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 906, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QInputDialog.getText", "line_number": 918, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QInputDialog", "line_number": 918, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QInputDialog.getText", "line_number": 919, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QInputDialog", "line_number": 919, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 941, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 941, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 949, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 951, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 953, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog.getColor", "line_number": 966, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog", "line_number": 966, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProxyStyle", "line_number": 1057, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyle.PM_SmallIconSize", "line_number": 1061, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QStyle", "line_number": 1061, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProxyStyle.pixelMetric", "line_number": 1064, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QProxyStyle", "line_number": 1064, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 1067, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 1067, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 1075, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1076, "usage_type": "call"}]}
{"seq_id": "503510631", "text": "from flask import Blueprint\nfrom flask_restful import (\n Resource,\n\n abort,\n fields,\n marshal_with,\n reqparse,\n)\n\nfrom . import Api\nfrom app import db\nfrom models import Thing\n\nblueprint = Blueprint(name='things', import_name=__name__)\napi = Api(blueprint)\n\nthing_parser = reqparse.RequestParser()\nthing_parser.add_argument('title', type=str, required=True)\n\nthing_detail_fields = {\n 'id': fields.Integer,\n 'date_created': fields.DateTime,\n 'date_modified': fields.DateTime,\n 'title': fields.String\n}\n\nthing_list_fields = {\n 'id': fields.Integer,\n 'title': fields.String,\n}\n\nthingspeople_detail_fields = {\n 'person_id': fields.Integer\n}\n\nthingspeople_list_fields = thing_list_fields\nthingspeople_list_fields['people'] = fields.Nested(thingspeople_detail_fields)\n\n\nclass ThingResource(Resource):\n @marshal_with(thing_detail_fields)\n def get(self, thing_id):\n thing = Thing.get(id=thing_id)\n if not thing:\n abort(404, message=\"Thing {} doesn't exist\".format(thing_id))\n return thing\n\n\nclass ThingListResource(Resource):\n @marshal_with(thing_list_fields)\n def get(self):\n return db.session.query(Thing).all()\n\n @marshal_with(thing_detail_fields)\n def post(self):\n args = thing_parser.parse_args()\n return Thing.create(**args)\n\n\nclass ThingsPeopleResource(Resource):\n @marshal_with(thingspeople_list_fields)\n def get(self, thing_id):\n thing = Thing.get(id=thing_id)\n if not thing:\n abort(404, message=\"Thing {} doesn't exist\".format(thing_id))\n return thing\n\n\napi.add_resource(ThingListResource, '/')\napi.add_resource(ThingResource, '/')\napi.add_resource(ThingsPeopleResource, '//people')\n", "sub_path": "views/v1_restful/things.py", "file_name": "things.py", "file_ext": "py", "file_size_in_byte": 1758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "flask.Blueprint", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 18, "usage_type": "name"}, {"api_name": "flask_restful.fields.Integer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "flask_restful.fields.DateTime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "flask_restful.fields.DateTime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "flask_restful.fields.String", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "flask_restful.fields.Integer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "flask_restful.fields.String", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "flask_restful.fields.Integer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask_restful.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "flask_restful.fields.Nested", "line_number": 38, "usage_type": "call"}, {"api_name": "flask_restful.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 41, "usage_type": "name"}, {"api_name": "models.Thing.get", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Thing", "line_number": 44, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 46, "usage_type": "call"}, {"api_name": "flask_restful.marshal_with", "line_number": 42, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 50, "usage_type": "name"}, {"api_name": "app.db.session.query", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Thing", "line_number": 53, "usage_type": "argument"}, {"api_name": "app.db.session", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 53, "usage_type": "name"}, {"api_name": "flask_restful.marshal_with", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Thing.create", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Thing", "line_number": 58, "usage_type": "name"}, {"api_name": "flask_restful.marshal_with", "line_number": 55, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Thing.get", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Thing", "line_number": 64, "usage_type": "name"}, {"api_name": "flask_restful.abort", "line_number": 66, "usage_type": "call"}, {"api_name": "flask_restful.marshal_with", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "537714108", "text": "import time\nfrom collections import deque\n\nimport torch\nimport torch.nn.functional as F\n\nfrom envs import ObsNorm\nfrom model import ActorCritic\nfrom pong import Pong\nfrom simple_ai import PongAi\n\n\n\ndef test(rank, args, shared_model, counter, optimizer, testValue):\n torch.manual_seed(args.seed + rank)\n\n env = Pong(headless= args.headless)\n # env.seed(args.seed + rank)\n\n model = ActorCritic(1, 3)\n \n opponent = PongAi(env, 2)\n obsNorm = ObsNorm()\n model.eval()\n\n env.set_names('Player', opponent.get_name())\n state = obsNorm.prepro(env.reset()[0])\n \n state = torch.from_numpy(state)\n reward_sum = 0\n done = True\n\n start_time = time.time()\n \n # a quick hack to prevent the agent from stucking\n actions = deque(maxlen=100)\n episode_length = 0\n save_count = 0\n test_count = 0\n while True:\n testValue.put(['test']) \n env.render()\n episode_length += 1\n # Sync with the shared model\n if done:\n model.load_state_dict(shared_model.state_dict())\n cx = torch.zeros(1, 256)\n hx = torch.zeros(1, 256)\n else:\n cx = cx.detach()\n hx = hx.detach()\n\n with torch.no_grad():\n inputTensor = state.unsqueeze(0);\n value, logit, (hx, cx) = model((inputTensor, (hx, cx)))\n prob = F.softmax(logit, dim=-1)\n action = prob.max(1, keepdim=True)[1].numpy()\n\n action2 = opponent.get_action()\n \n (state, obs2), (reward, reward2), done, info = env.step((action[0,0], action2))\n done = done or episode_length >= args.max_episode_length\n reward_sum += reward\n state = obsNorm.prepro(state)\n # a quick hack to prevent the agent from stucking\n actions.append(action[0, 0])\n if actions.count(actions[0]) == 5000:\n done = True\n test_count += 1\n \n if done:\n test_count += 1\n print(\"Time {}, num steps {}, FPS {:.0f}, episode reward {}, episode length {}\".format(\n time.strftime(\"%Hh %Mm %Ss\",\n time.gmtime(time.time() - start_time)),\n counter.value, counter.value / (time.time() - start_time),\n reward_sum, episode_length))\n reward_sum = 0\n episode_length = 0\n actions.clear()\n obsNorm.reset()\n state = obsNorm.prepro(env.reset()[0])\n if save_count == 30:\n if args.save_progress:\n save_checkpoint(shared_model, optimizer, 'checkpoint/checkpoint-left-{}'.format(counter.value))\n save_count = 0\n if test_count == 20:\n save_count += 1\n test_count = 0\n time.sleep(60)\n \n state = torch.from_numpy(state)\n\ndef save_checkpoint(model, optimizer, filename='/output/checkpoint.pth.tar'):\n torch.save({\n 'model_state_dict': model.state_dict(),\n 'optimizer_state_dict': optimizer.state_dict()\n }, filename)\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "torch.manual_seed", "line_number": 15, "usage_type": "call"}, {"api_name": "pong.Pong", "line_number": 17, "usage_type": "call"}, {"api_name": "model.ActorCritic", "line_number": 20, "usage_type": "call"}, {"api_name": "simple_ai.PongAi", "line_number": 22, "usage_type": "call"}, {"api_name": "envs.ObsNorm", "line_number": 23, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 36, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 74, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 95, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 96, "usage_type": "call"}]}
{"seq_id": "70073957", "text": "import numpy as np \nimport cv2\n\nlk_params = dict(winSize = (21, 21), \n\t\t\t\t#maxLevel = 3,\n \tcriteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 30, 0.01))\n\ndef featureTracking(image_ref, image_cur, px_ref):\n \n px_ref = px_ref.reshape(-1,1,2).astype('float32')\n kp2, st, err = cv2.calcOpticalFlowPyrLK(image_ref, image_cur, px_ref, None, **lk_params) #shape: [k,2] [k,1] [k,1]\n\n st = st.reshape(st.shape[0])\n kp1 = px_ref[st == 1]\n kp2 = kp2[st == 1]\n\n return kp1, kp2", "sub_path": "visual_slam/core/geocom/features.py", "file_name": "features.py", "file_ext": "py", "file_size_in_byte": 510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "cv2.TERM_CRITERIA_EPS", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_COUNT", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.calcOpticalFlowPyrLK", "line_number": 11, "usage_type": "call"}]}
{"seq_id": "418198987", "text": "import torch\nimport gym\nfrom collections import defaultdict\n\nenv = gym.make('Blackjack-v0')\n\n\ndef gen_random_policy(n_action):\n probs = torch.ones(n_action) / n_action\n def policy_function(state):\n return probs\n return policy_function\n\nrandom_policy = gen_random_policy(env.action_space.n)\n\n\ndef run_episode(env, behavior_policy):\n r\"\"\"Run an episode given a behavior policy.\n Args:\n env: OpenAI Gym environment\n behavior_policy: behavior policy\n Returns:\n resulting states, actions and rewards for the entire episode\n \"\"\"\n\n state = env.reset()\n rewards = []\n actions = []\n states = []\n is_done = False\n\n while not is_done:\n probs = behavior_policy(state)\n action = torch.multinomial(probs, 1).item()\n actions.append(action)\n states.append(state)\n state, reward, is_done, info = env.step(action)\n rewards.append(reward)\n if is_done:\n break\n \n return states, actions, rewards\n\n\ndef mc_control_off_policy_incremental(env, gamma, n_episode, behavior_policy):\n \"\"\"Obtain the optimal policy with off-policy Monte Carlo control with incremental way of updating the Q function\n Args:\n env: OpenAI Gym environment\n gamma: discount factor\n n_episode: number of episodes\n behavior_policy: behavior policy\n Returns:\n the optimal Q-function, and optimal policy\n \"\"\"\n\n n_action = env.action_space.n\n number = defaultdict(int)\n Q = defaultdict(lambda: torch.empty(n_action))\n\n for episode in range(n_episode):\n weight = 1\n states_t, actions_t, rewards_t = run_episode(env, behavior_policy)\n return_t = 0\n \n for state_t, action_t, reward_t in zip(states_t[::-1], actions_t[::-1], rewards_t[::-1]):\n return_t = gamma * return_t + reward_t\n number[(state_t, action_t)] += 1\n Q[state_t][action_t] += (weight / number[(state_t, action_t)]) * (return_t - Q[state_t][action_t])\n if action_t != torch.argmax(Q[state_t]).item():\n break\n weight *= 1. / behavior_policy(state_t)[action_t]\n \n policy = {}\n for state, actions in Q.items():\n policy[state] = torch.argmax(actions).item()\n\n return Q, policy\n\n\ngamma = 1\nn_episode = 500000\n\noptimal_Q, optimal_policy = mc_control_off_policy_incremental(env, gamma, n_episode, random_policy)\n\noptimal_value = defaultdict(float)\nfor state, action_values in optimal_Q.items():\n optimal_value[state] = torch.max(action_values).item()\n\nprint('Optimal Q:\\n', optimal_Q)\nprint('Optimal policy:\\n', optimal_policy)\nprint('Optimal value:\\n', optimal_value)", "sub_path": "blackjack_off_policy_incremental.py", "file_name": "blackjack_off_policy_incremental.py", "file_ext": "py", "file_size_in_byte": 2689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "gym.make", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 34, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 57, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "460643408", "text": "import threading\nimport sqlite3\n\n\nALLOWED_TYPES = {\n int: 'INTEGER',\n str: 'TEXT',\n float: 'FLOAT',\n bool: 'BOOLEAN',\n}\n\n\nclass Wrapper:\n def __init__(self, wraps):\n self.wraps = wraps\n\n @classmethod\n def __class_getitem__(cls, item):\n return cls(item)\n \n def unwrap(self):\n w = self.wraps\n while isinstance(w, Wrapper):\n w = w.wraps\n return w\n\n\nclass NotNull(Wrapper):\n pass\n\n\nclass Primary(Wrapper):\n pass\n\n\nclass List(Wrapper):\n pass\n\n\nclass Auto(Wrapper):\n pass\n\n\nclass List_:\n def __init__(self, tab, record, of, items):\n self._tab = tab\n self._record = record\n self._of = of\n for i in items:\n self.append(i)\n \n def append(self, i):\n if i._id is None:\n i._table.insert(i)\n \n class Doc:\n pass\n d = Doc()\n d.cols = {\n self._tab.magic[0]: getattr(self._record, self._record._table.primary._field),\n self._tab.magic[1]: getattr(i, i._table.primary._field)\n }\n self._tab.magic[2].insert(d)\n \n def all_items(self):\n query = f'''SELECT * FROM {self._tab.magic[2]};'''\n return_value = []\n ready = threading.Event()\n\n def f():\n cur = self._tab._db.connection.cursor()\n cur.execute(query)\n dat = cur.fetchall()\n cur.close()\n return_value.append(dat)\n ready.set()\n \n self._tab._db.enqueue(f)\n ready.wait()\n dat = return_value.pop()\n\n return [self._record._table(*i) for i in dat]\n\n def __str__(self):\n return '[' + ', '.join(map(lambda x: f'<{x}>', self.all_items())) + ']'\n\n\nclass Reference:\n def __init__(self, db, table, field, type_, original=None):\n self._db = db\n self._table = table\n self._field = field\n self._type = type_\n self._original = original\n\n def get_table(self):\n return self._table\n\n def prepare(self, simple=False):\n stmt = defer = ''\n\n if self._type in ALLOWED_TYPES:\n stmt = f'{self._field} {ALLOWED_TYPES[self._type]}'\n elif isinstance(self._type, self.__class__):\n defer = f'FOREIGN KEY ({self._field}) REFERENCES {self._type._table}({self._type._field})'\n ext, d = self._type.prepare()\n if d:\n defer += ',' + d\n ext = ext.split(' ', 1)[-1]\n stmt = str(self._field) + ' ' + ext\n\n if simple:\n return str(self._field) + ' ' + ext\n\n elif isinstance(self._type, Table):\n defer = f'FOREIGN KEY ({self._field}) REFERENCES {self._type.primary}'\n ext, d = self._type.primary.prepare(True)\n if d:\n defer += ',' + d\n ext = ext.split(' ', 1)[-1]\n stmt = str(self._field) + ' ' + ext\n\n if simple:\n return str(self._field) + ' ' + ext\n\n if self._original and not simple:\n for i in self._original[::-1]:\n if isinstance(i, NotNull):\n stmt += ' NOT NULL'\n elif isinstance(i, Primary):\n stmt += ' PRIMARY KEY'\n elif isinstance(i, Auto):\n stmt += ' AUTOINCREMENT'\n\n return (stmt, defer)\n\n def __str__(self):\n if self._field:\n return f'{self._table}({self._field})'\n return self._table\n\n\nclass Record:\n def __init__(self, cols, table):\n self.__dict__['cols'] = cols\n self.__dict__['_table'] = table\n\n def get_table(self):\n return self.__dict__['_table']\n\n def __getattr__(self, attr):\n return self.__dict__['cols'][attr]\n \n def __setattr__(self, attr, value):\n self.__dict__['cols'][attr] = value\n \n def __str__(self):\n return ', '.join(f'{i}: {repr(self.__dict__[\"cols\"][i])}' for i in self.__dict__['cols'])\n \n def __repr__(self):\n return f'<{self}>'\n\n\nclass Table:\n class NoneValue:\n pass\n\n NoneValue = NoneValue()\n\n def __init__(self, db, name):\n self._db = db\n self._name = name\n\n self.primary = None\n self.fields = []\n\n def prepare(self, exist_ok=False):\n stmt = f'CREATE TABLE {\"IF NOT EXISTS \" if exist_ok else \"\"}' + self._name + '(\\n'\n\n deferred = []\n for i in self.fields:\n if i._original and list in i._original:\n continue\n s, d = i.prepare()\n if d:\n deferred.append(d)\n stmt += ' ' + s + ',\\n'\n for i in deferred:\n stmt += ' ' + i + ',\\n'\n\n stmt = stmt.strip(',\\n') + '\\n'\n stmt += ');'\n return stmt\n\n def __call__(self, *args, **kwargs):\n fields = {}\n args = list(args)\n\n if self.fields[0] == self.primary and self.primary._field == '_id':\n if len(args) < len(self.fields):\n args.insert(0, None)\n \n for i in self.fields:\n if i._original:\n for j in i._original:\n pass\n\n #if isinstance(j, Auto):\n # fields[j.unwrap()._field] = self.NoneValue\n\n for n, i in enumerate(args):\n if len(fields) == len(self.fields):\n raise AttributeError('Passed arguments fail to match table structure')\n fields[self.fields[n]] = i\n for i in kwargs:\n if i not in self.fields or i in fields:\n raise AttributeError('Passed arguments fail to match table structure')\n fields[i] = kwargs[i]\n \n lists = [i for i in fields if isinstance(fields[i], list)]\n\n _fields = {}\n for i in fields:\n _fields[i._field] = None if i in lists else fields[i]\n\n record = Record(_fields, self)\n\n for i in lists:\n record.__dict__['cols'][i._field] = List_(self, record, i, fields[i])\n return record\n\n def __str__(self):\n return self._name\n \n def select(self, **kwargs):\n if '___database_thread' in kwargs:\n ___database_thread = True\n kwargs.pop('___database_thread')\n else:\n ___database_thread = False\n \n selector, args = [], []\n fields = [i._field for i in self.fields]\n for i in kwargs:\n if i not in fields:\n raise AttributeError('No column \\'' + i + '\\'')\n selector.append(f'{i}=?')\n args.append(kwargs[i])\n\n query = f'SELECT * FROM {self._name}'\n if selector:\n query += f' WHERE ' + ' AND '.join(selector)\n\n return_value = []\n ready = threading.Event()\n\n def f():\n cur = self._db.connection.cursor()\n cur.execute(query, args)\n res = cur.fetchall()\n\n ret = []\n for i in res:\n row = []\n for j, r in zip(i, self.fields):\n if isinstance(r._type, Table):\n j = r._type.select(**{r._type.primary._field: j}, ___database_thread=True)[0]\n row.append(j)\n ret.append(self(*row))\n\n return_value.append(ret)\n ready.set()\n \n if ___database_thread:\n f()\n else:\n self._db._enqueue(f)\n ready.wait()\n return return_value[0]\n \n def insert(self, document):\n args = []\n fields = [i._field for i in self.fields]\n \n for i in document.cols:\n if i not in fields:\n raise AttributeError('No column \\'' + i + '\\'')\n for i in fields:\n if i in document.cols:\n if isinstance(document.cols[i], Record):\n pk = document.cols[i].get_table()\n pk = getattr(document.cols[i], pk.primary._field)\n args.append(pk)\n elif isinstance(document.cols[i], List_):\n pass\n else:\n args.append(document.cols[i])\n else:\n args.append(None)\n\n selector = ', '.join(['?'] * len(args))\n query = f'INSERT INTO {self._name} VALUES ({selector})'\n\n ready = threading.Event()\n def f():\n cur = self._db.connection.cursor()\n cur.execute(query, args)\n self._db.connection.commit()\n\n if self.primary:\n if self.primary._field == '_id':\n id_ = cur.lastrowid\n document._id = id_\n\n cur.close()\n ready.set()\n\n self._db._enqueue(f)\n ready.wait()\n\n\nclass DB:\n def __init__(self, path):\n self.tables = []\n\n self.connection = None\n threading.Thread(target=self._monitor, args=(path, ), daemon=True).start()\n\n self._queue = []\n self._queue_ready = threading.Event()\n self._queue_lock = threading.Lock()\n \n def _enqueue(self, f):\n with self._queue_lock:\n self._queue.append(f)\n self._queue_ready.set()\n\n def _monitor(self, path):\n self.connection = sqlite3.connect(path)\n\n while True:\n self._queue_ready.wait()\n with self._queue_lock:\n item = self._queue.pop(0)\n if not self._queue:\n self._queue_ready.clear()\n\n item()\n\n def object(self, obj_class):\n attributes = obj_class.__annotations__\n obj_class._fields = []\n table = Table(self, obj_class.__name__)\n\n for i in attributes:\n if attributes[i] not in ALLOWED_TYPES and not isinstance(attributes[i], (Table, Reference, Wrapper)):\n raise ValueError('Unsupported type:', attributes[i])\n\n original = [attributes[i]]\n while isinstance(attributes[i], Wrapper):\n if attributes[i].wraps not in ALLOWED_TYPES and not isinstance(attributes[i].wraps, (Table, Reference)):\n raise ValueError('Unsupported type:', attributes[i].wraps)\n attributes[i] = attributes[i].wraps\n original.append(attributes[i])\n\n if isinstance(original[0], List):\n if isinstance(attributes[i], Table):\n r_table = attributes[i]\n else:\n r_table = attributes[i].get_table()\n\n new_t = Table(self, f'___{table}__{r_table}')\n new_t.fields.append(\n Reference(self, new_t, str(table), int)\n )\n new_t.fields.append(\n Reference(self, new_t, str(r_table), r_table.primary)\n )\n self.tables.append(new_t)\n new_t.magic = [str(table), str(r_table), new_t]\n table.magic = [str(table), str(r_table), new_t]\n\n ref = Reference(self, table, i, Reference(self, new_t, str(table), int), [list])\n else:\n ref = Reference(self, table, i, attributes[i], original)\n\n table.fields.append(ref)\n setattr(table, i, ref)\n\n if isinstance(original[0], Primary):\n if table.primary is not None:\n raise ValueError('Table cannot have multiple primary keys!')\n table.primary = ref\n\n if table.primary is None:\n ref = Reference(self, table, '_id', int, [Auto[Primary[int]], Primary[int], int])\n table.fields.insert(0, ref)\n table._id = ref\n table.primary = ref\n\n self.tables.append(table)\n\n return table\n\n def prepare(self, exist_ok=False):\n ready = threading.Event()\n def f():\n cur = self.connection.cursor()\n for i in self.tables:\n print(i.prepare(exist_ok))\n #cur.execute(i.prepare(exist_ok))\n cur.close()\n\n ready.set()\n self._enqueue(f)\n ready.wait()\n\n", "sub_path": "voiplib/database/orm.py", "file_name": "orm.py", "file_ext": "py", "file_size_in_byte": 12078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "threading.Event", "line_number": 68, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 255, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 304, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 327, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 330, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 331, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 339, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 406, "usage_type": "call"}]}
{"seq_id": "145697029", "text": "from django.contrib.auth.models import User\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\nfrom .models import Tag, Profile, Post, Comment\nfrom .forms import SearchForm, CommentForm\nfrom .search import get_query, normalize_query\n\nposts_side = Post.objects.filter(date_published__lte=timezone.now()).order_by('-date_published')[:5]\ncomments_side = Comment.objects.filter(approved=True).order_by('-created_date')[:5]\ntags_side = Tag.objects.all().order_by('name')\nmonths_side = Post.objects.dates('date_published', 'month', order='DESC')\ncontext = {'posts_side': posts_side, 'comments_side': comments_side, 'tags_side': tags_side, 'months_side': months_side}\n\ndef main(request):\n return render(request, 'blog/main.html', context)\n\ndef blog(request):\n context['posts'] = Post.objects.filter(date_published__lte=timezone.now()).order_by('-date_published')\n return render(request, 'blog/blog.html', context)\n\ndef view_post(request, year, month, day, slug):\n context['post'] = get_object_or_404(Post, slug=slug)\n if request.method == \"POST\":\n form = CommentForm(request.POST)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.post = context['post']\n comment.save()\n context['form'] = CommentForm()\n return render(request, 'blog/view_post.html', context)\n elif request.method == \"GET\":\n context['form'] = CommentForm()\n return render(request, 'blog/view_post.html', context)\n\ndef view_day(request, year, month, day):\n context['year'] = year\n context['month'] = month\n context['day'] = day\n context['posts'] = Post.objects.filter(date_published__year=year, date_published__month=month, date_published__day=day)\n return render(request, 'blog/post_archive_day.html', context)\n\ndef view_month(request, year, month):\n context['year'] = year\n context['month'] = month\n context['posts'] = Post.objects.filter(date_published__year=year).filter(date_published__month=month)\n return render(request, 'blog/post_archive_month.html', context)\n\ndef view_year(request, year):\n context['year'] = year\n context['posts'] = Post.objects.filter(date_published__year=year)\n return render(request, 'blog/post_archive_year.html', context)\n\ndef view_tag(request, slug):\n context['tag'] = get_object_or_404(Tag, slug=slug)\n context['posts'] = context['tag'].post_set.all().exclude(date_published__iexact=None)\n return render(request, 'blog/view_tag.html', context)\n \ndef view_author(request, slug):\n profile = get_object_or_404(Profile, slug=slug)\n context['user'] = profile.user\n context['posts'] = profile.user.post_set.all().exclude(date_published__iexact=None)\n return render(request, 'blog/view_author.html', context)\n\ndef search_results(request):\n form = SearchForm(request.GET)\n if form.is_valid():\n search_str = request.GET.get('search_str')\n context['search_str'] = search_str\n context['posts'] = get_query(search_str, Post)\n else:\n posts = None\n return render(request, 'blog/search_results.html', context)\n\ndef contact(request):\n return render(request, 'blog/contact.html', context)\n\ndef notes(request):\n return render(request, 'blog/notes.html', context)\n\ndef projects(request):\n return render(request, 'blog/projects.html', context)\n", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "models.Post.objects.filter", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 8, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 8, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Comment.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Tag.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Post.objects.dates", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 18, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 22, "usage_type": "argument"}, {"api_name": "forms.CommentForm", "line_number": 24, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 45, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Tag", "line_number": 54, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Profile", "line_number": 59, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "forms.SearchForm", "line_number": 65, "usage_type": "call"}, {"api_name": "search.get_query", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 69, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}]}
{"seq_id": "380526060", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import *\n\nclass ExplicitWaitDemo1():\n def test(self):\n baseUrl = \"http://www.expedia.com\"\n driver = webdriver.Firefox()\n driver.implicitly_wait(5)\n driver.maximize_window()\n driver.get(baseUrl)\n driver.find_element(By.ID, \"tab-flight-tab-hp\").click()\n driver.find_element(By.ID, \"flight-origin-hp-flight\").send_keys(\"SFO\")\n driver.find_element(By.ID, \"flight-destination-hp-flight\").send_keys(\"NYC\")\n driver.find_element(By.ID, \"flight-departing-hp-flight\").send_keys(\"04/24/2020\")\n returnDate = driver.find_element(By.ID, \"flight-returning-hp-flight\")\n returnDate.clear()\n returnDate.send_keys(\"05/25/2020\")\n driver.find_element(By.CSS_SELECTOR, \"[data-gcw-key='hp-flight'] .btn-primary\").click()\n wait = WebDriverWait(driver, 10, poll_frequency=1, ignored_exceptions=[NoSuchElementException,ElementNotVisibleException,ElementNotSelectableException])\n element = wait.until(EC.element_to_be_clickable((By.ID, \"stopFilter_stops-0\")))\n element.click()\n\n time.sleep(2)\nff = ExplicitWaitDemo1()\nff.test()\n", "sub_path": "WaitTypes/ExplicitWait.py", "file_name": "ExplicitWait.py", "file_ext": "py", "file_size_in_byte": 1363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 11, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 15, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "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.ID", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "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.ID", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 24, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "11142507", "text": "import nltk\nfrom wikidata import WikidataEntityLookup\nfrom dbco import *\n\nwd = WikidataEntityLookup()\ndef lookupNamedEntities(namedEntityTexts):\n '''\n Given list of texts that correspond to named entities,\n return a list of dictionaries, where each dict has \n the original text, entity id, and description.\n\n Example usage:\n lookupNamedEntities(['NYC', 'New York State', 'USA'])\n should return [\n {'text': 'NYC', 'id': 'Q60', 'description': 'city in state of New York...'},\n {'text': 'New York State', 'id': 'Q1380', 'description': 'state in us..'}, ..\n ]\n '''\n returned_list = [] \n \n for i in xrange(len(namedEntityTexts)):\n entity = namedEntityTexts[i]\n entity_info = wd.searchEntities(entity)\n if entity_info is not None:\n entity_info['text'] = entity\n else:\n entity_info = {'text': entity, 'id': '-1'} # -1 is no results from wikidata\n returned_list.append(entity_info)\n \n return returned_list\n\ndef getNameEntities(text):\n sentences = nltk.sent_tokenize(text)\n tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]\n tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]\n chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)\n\n nameEntity = []\n for sentence in chunked_sentences:\n for part in sentence:\n if type(part) is nltk.Tree:\n entityTree = part.leaves()\n entity = \"\"\n for tuplePair in entityTree:\n entity += \" \"\n entity += tuplePair[0]\n entity = entity[1:]\n nameEntity.append(entity)\n nameEntity = list(set(nameEntity))\n return lookupNamedEntities(nameEntity)\n\n# def entityTester():\n# with open(\"small_sample_articles.json\") as f:\n# for line in f:\n# print(getNameEntities(json.loads(line)[\"content\"]))\n\ndef getArticlesNoEntities(limit=1000):\n articles = db.qdoc.find({ \"$query\": { \"entities\": { \"$exists\": False } }, \"$orderby\": { '_id' : -1 } }).limit(limit)\n return articles\n\n#Driver\ndef tagEntities():\n articles = getArticlesNoEntities()\n for a in articles:\n db.qdoc.update( { \"_id\": a['_id'] },{\"$set\": {\"entities\": getNameEntities(a['content'] ) } } )\n\nif __name__ == \"__main__\":\n tagEntities()\n", "sub_path": "name_entity_extraction.py", "file_name": "name_entity_extraction.py", "file_ext": "py", "file_size_in_byte": 2398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "wikidata.WikidataEntityLookup", "line_number": 5, "usage_type": "call"}, {"api_name": "nltk.sent_tokenize", "line_number": 33, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 34, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 35, "usage_type": "call"}, {"api_name": "nltk.ne_chunk_sents", "line_number": 36, "usage_type": "call"}, {"api_name": "nltk.Tree", "line_number": 41, "usage_type": "attribute"}]}
{"seq_id": "588550589", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Author: Sword York\n# GitHub: https://github.com/SwordYork/sequencing\n# No rights reserved.\n#\nimport collections\n\nimport tensorflow as tf\n\n\ndef merge_dict(d, u):\n \"\"\"\n update d using u. It is used to merge model params.\n\n :param d: old dict\n :param u: new dict\n :return:\n \"\"\"\n for k, v in u.items():\n if isinstance(v, collections.Mapping):\n r = merge_dict(d.get(k, {}), v)\n d[k] = r\n else:\n d[k] = u[k]\n return d\n\n\ndef disable_dropout(d):\n for k, v in d.items():\n if isinstance(v, collections.Mapping):\n r = disable_dropout(v)\n d[k] = r\n elif k == 'input_keep_prob' or k == 'output_keep_prob':\n d[k] = 1.0\n return d\n\n\ndef get_rnn_cell(params):\n cell_name = params['cell_name']\n state_size = params['state_size']\n num_layers = params['num_layers']\n input_keep_prob = params['input_keep_prob']\n output_keep_prob = params['output_keep_prob']\n\n cells = []\n for _ in range(num_layers):\n cell = tf.nn.rnn_cell.DropoutWrapper(\n getattr(tf.contrib.rnn, cell_name)(state_size),\n input_keep_prob, output_keep_prob)\n cells.append(cell)\n return cells\n", "sub_path": "sequencing/utils/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 1278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "collections.Mapping", "line_number": 22, "usage_type": "attribute"}, {"api_name": "collections.Mapping", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.rnn_cell.DropoutWrapper", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib", "line_number": 50, "usage_type": "attribute"}]}
{"seq_id": "181935650", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 29 00:34:41 2020\n\n@author: IลIK\n\"\"\"\n\n#%%\n#Kรผtรผphaneler\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.impute import SimpleImputer\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LinearRegression\n\n#%%\n#Verilerin Okunmasฤฑ\n\nveriler = pd.read_csv(\"eksikveriler.csv\")\n\nulkeler = veriler.iloc[:,0:1].values\nboy_kilo_yas = veriler.iloc[:,1:4].values\ncinsiyet = veriler[['cinsiyet']]\nboy = veriler.iloc[:,1:2]\n\n#%%\n#Verilerin รn ฤฐลlemesi\n\nimputer = SimpleImputer(missing_values = np.nan, strategy = \"mean\")\nboy_kilo_yas = imputer.fit_transform(boy_kilo_yas)\n\n##Kategorize Edildi\nle = preprocessing.LabelEncoder()\nohe = preprocessing.OneHotEncoder()\n\nulkeler = ohe.fit_transform(ulkeler).toarray()\ncinsiyet = ohe.fit_transform(cinsiyet).toarray()\n\n##Dataframe Dรถnรผลtรผrรผldรผ\nulkeler = pd.DataFrame(data = ulkeler, index = range(22), columns = [\"fr\", \"tr\", \"us\"])\ncinsiyet = pd.DataFrame(data = cinsiyet[:,0:1], index = range(22), columns = [\"cinsiyet\"])\nboy_kilo_yas = pd.DataFrame(data = boy_kilo_yas, index = range(22), columns = [\"boy\", \"kilo\", \"yas\"])\n\n##DataFrameler birleลtirildi\nsonuc = pd.concat([ulkeler, boy_kilo_yas], axis = 1)\nsonuc = pd.concat([sonuc, cinsiyet], axis = 1)\n\nulke_boy_kilo_yas = pd.concat([ulkeler, boy_kilo_yas], axis = 1)\nulke_kilo_yas_cinsiyet = pd.concat([sonuc.iloc[:,0:3], sonuc.iloc[:,4:]], axis = 1)\n\n##Eฤitilmesi (Train Test)\nx_train, x_test, y_train, y_test = train_test_split(ulke_boy_kilo_yas, cinsiyet, test_size = 0.33, random_state = 0)\n\n##StandardScaler\nsc = StandardScaler()\nX_train = sc.fit_transform(x_train)\nX_test =sc.fit_transform(x_test)\n'''\n##StandardScaler\nsc = StandardScaler()\nX_train = sc.fit_transform(x_train)\nX_test = sc.fit_transform(x_test)\nY_train = sc.fit_transform(y_train)\nY_test = sc.fit_transform(y_test)\n'''\nlr = LinearRegression()\nlr.fit(x_train, y_train)\n\n##tahmin\ntahmin = lr.predict(x_test)\n\n\n\n##Boyun bulunmasฤฑ\nlr2 = LinearRegression()\nx_train, x_test, y_train, y_test = train_test_split(ulke_kilo_yas_cinsiyet, boy, test_size = 0.33, random_state = 0)\n\nlr2.fit(x_train, y_train)\ntahmin2 = lr2.predict(x_test)\n\n\n\n\n#%%\n#Gรถrselleลtirme\nx_train = x_train.sort_index() #ฤฐndexleri random bir ลekilde olan dataframe sฤฑralฤฑ hhale dรถnรผลtรผrรผr\ny_train = y_train.sort_index()\n\nplt.plot(x_train, y_train)\nplt.plot(x_test, tahmin)\n", "sub_path": "Algoritma Sablonlarฤฑ/Regression/deneme_multipleLinear_Boy_ornek.py", "file_name": "deneme_multipleLinear_Boy_ornek.py", "file_ext": "py", "file_size_in_byte": 2519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 38, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 39, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "418039145", "text": "import cv2\nimport argparse\nimport os\n\ndef get_args():\n parser = argparse.ArgumentParser(description='Capture image.')\n parser.add_argument('--camera_index', type=int, default=0, help='Camera index.')\n parser.add_argument('--save_dir', type=str, default='./data/images/', help='Save dir')\n parser.add_argument('--width', type=int, help='Video width.')\n parser.add_argument('--height', type=int, help='Video height.')\n \n return parser.parse_args()\n\nif __name__ == '__main__':\n \n args = get_args()\n \n assert(len(args.save_dir) > 0)\n\n cap = cv2.VideoCapture(args.camera_index)\n\n w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) if args.width == None else args.width\n h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) if args.height == None else args.height\n \n if not os.path.exists(args.save_dir):\n os.mkdir(args.save_dir)\n\n idx = 0\n while True:\n ret, frame = cap.read()\n cv2.imshow('Capture', frame)\n\n if cv2.waitKey(1) & 0xFF == ord('s'): \n file_name = args.save_dir + str(idx) + '.png'\n cv2.imwrite(file_name, frame) \n print('Saved image to ', file_name)\n idx += 1 \n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cap.release()\n", "sub_path": "image_capture.py", "file_name": "image_capture.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "73727368", "text": "from typing import List\n\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass PrintBinaryTree:\n def print_tree(self, root: TreeNode) -> List[List[str]]:\n # Get the height of the tree\n self.height = self.get_height(root)\n print(self.height)\n d = 2 ** self.height - 1\n # build the matrix\n result = [[\"\" for i in range(d)] for j in range(self.height)]\n # draw on the matrix\n self.draw(root, result, self.height, d // 2)\n return result\n\n def draw(self, node, result, height, position):\n # draw the node\n result[self.height - height][position] = str(node.val)\n\n # if this is the lowest level, we are done\n if height == 1:\n return\n\n # calculate the depth of the current node\n node_depth = self.height - height + 1\n\n # how much away should we place the child node(s)\n child_sep = 2 ** (self.height - node_depth - 1)\n\n # recursively draw the children node (DFS)\n if node.left is not None:\n child_pos = position - child_sep\n self.draw(node.left, result, height - 1, child_pos)\n if node.right is not None:\n child_pos = position + child_sep\n self.draw(node.right, result, height - 1, child_pos)\n\n def get_height(self, node):\n if node.left is None and node.right is None:\n return 1\n\n if node.left is None and node.right is not None:\n return 1 + self.get_height(node.right)\n\n if node.left is not None and node.right is None:\n return 1 + self.get_height(node.left)\n\n if node.left is not None and node.right is not None:\n return 1 + max(self.get_height(node.left), self.get_height(node.right))\n", "sub_path": "src/trees/printBinaryTree.py", "file_name": "printBinaryTree.py", "file_ext": "py", "file_size_in_byte": 1826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "typing.List", "line_number": 12, "usage_type": "name"}]}
{"seq_id": "580723358", "text": "from discord.errors import DiscordException\nfrom ..__imports__ import *\nfrom ..settings import *\nfrom .discord_init import DiscordInit\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n def __init__(self, source, *, data, volume=0.5, ytdl_format_options=ytdl_format_options):\n super().__init__(source, volume)\n youtube_dl.utils.bug_reports_message = lambda: ''\n self.ytdl = youtube_dl.YoutubeDL(ytdl_format_options)\n self.data = data\n self.title = data.get('title')\n self.url = data.get('url')\n\n @classmethod\n async def from_url(self, cls, url, *, loop=None, stream=False):\n loop = loop or asyncio.get_event_loop()\n data = await loop.run_in_executor(None, lambda: self.ytdl.extract_info(url, download=not stream))\n\n if 'entries' in data:\n # take first item from a playlist\n data = data['entries'][0]\n\n filename = data['url'] if stream else self.ytdl.prepare_filename(data)\n return cls(discord.FFmpegPCMAudio(filename, **ffmpeg_options), data=data)\n\nclass MusicMixin(DiscordInit, commands.Cog):\n StartTime = 0\n lastPod = None\n\n @commands.command()\n async def join(self, ctx, *, channel: discord.VoiceChannel):\n if ctx.voice_client is not None:\n return await ctx.voice_client.move_to(channel)\n await channel.connect()\n\n\n @commands.command(pass_context=True, aliases=['oldp', 'olds'])\n async def play(self, ctx, *, url=\"https://youtu.be/dQw4w9WgXcQ\"):\n \n # DISABLED\n return\n #await ctx.message.add_reaction('๐ง') \n if (\"youtube.com\" in str(url) or \"youtu.be\"):\n async with ctx.typing():\n player = await YTDLSource.from_url(url=url, loop=self.client.loop, stream=True)\n ctx.voice_client.play(player, after=None)\n if(str(url) == \"https://youtu.be/dQw4w9WgXcQ\"):\n embed = discord.Embed(title=\"You need to give a url!\", colour=discord.Colour(0xff5065), url=url, description=player.title)\n embed.set_image(url=\"https://i.imgur.com/xrBXtFh.png\")\n else:\n embed = discord.Embed(title=\"Playing from Youtube\", colour=discord.Colour(0xff5065), url=url, description=player.title)\n y = re.search(\"/?v=(.{,11})\", url).groups()[0]\n try:\n embed.set_image(url=f\"https://img.youtube.com/vi/{y}/0.jpg\")\n except:\n pass\n else:\n embed = discord.Embed(title=f\"Searching : {str(url)}\", colour=discord.Colour(0xff5065))\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=self.client.loop, stream=True)\n ctx.voice_client.play(player, after=None)\n\n embed.set_author(name=ctx.message.author.name,icon_url=ctx.message.author.avatar_url)\n embed.set_footer(text=self.name, icon_url=self.avatar)\n await ctx.reply(embed=embed)\n\n @commands.command(pass_context=True, aliases=['oldpl'])\n async def lofi(self, ctx, *, url=\"https://youtu.be/5qap5aO4i9A\"):\n # DISABLED\n return\n #await ctx.message.add_reaction('๐ง')\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=self.client.loop, stream=True)\n ctx.voice_client.play(player, after=None)\n embed = discord.Embed(title=\"Playing from Youtube\", colour=discord.Colour(0xff5065), url=url, description=player.title)\n embed.set_image(url=\"https://i.ytimg.com/vi/5qap5aO4i9A/maxresdefault.jpg\")\n embed.set_author(name=ctx.message.author.name,icon_url=ctx.message.author.avatar_url)\n embed.set_footer(text=self.name, icon_url=self.avatar)\n await ctx.reply(embed=embed)\n\n @commands.command(aliases=['oldpodp'])\n async def podplay(self,ctx,epno=0): \n \n # DISABLED\n return\n \n podepi = epno\n if(self.lastPod == None):\n embed = discord.Embed(colour=discord.Colour(\n 0xbd10e0), description=\" \")\n embed.set_thumbnail(url=self.avatar)\n embed.set_author(name=self.name, url=self.avatar,icon_url=self.avatar)\n embed.add_field(name=f\"No Recent Podcast Searches\",value=f\"search for podcast using {self.pre}pod\",inline=False)\n embed.set_thumbnail(url=self.avatar)\n await ctx.send(embed=embed)\n else:\n currentpod = self.lastPod\n try:\n await self.playPodcast(ctx,podepi=podepi,currentpod=currentpod)\n embed = discord.Embed(title=currentpod.GetEpisodeDetails(podepi)['title'],\n colour=discord.Colour(0xb8e986), url=currentpod.GetEpisodeDetails(podepi)['link'],\n description=currentpod.GetEpisodeDetails(podepi)['summsary'],\n inline=False)\n embed.set_thumbnail(url=currentpod.PodcastImage(podepi))\n embed.set_author(name=self.name,icon_url=self.avatar)\n embed.set_footer(text=currentpod.GetEpisodeDetails(podepi)['title'],icon_url=self.avatar)\n await ctx.send(embed=embed)\n except AttributeError:\n await ctx.send(\"You aren't in voice channel m8\")\n\n @commands.command(aliases=['oldpodcast'])\n async def pod(self,ctx , * , strparse = \" \"): \n \n # DISABLED\n await ctx.message.add_reaction('โ')\n await ctx.message.reply(\"This feature is currently disabled\")\n return\n \n \n if(':' in strparse):\n podname_,num = strparse.replace(' ','').split(':')\n podepi = int(num)\n elif(not strparse == ' '):\n podname_ = strparse.replace(' ','')\n podepi = None\n else:\n podname_ = ' '\n podepi = None\n\n try:\n start = int(podname_.split('-')[1])\n podname = podname_.split('-')[0]\n except:\n start = 0\n podname = podname_\n if(podname == \" \"):\n embed = discord.Embed(colour=discord.Colour(0x91ff), description=\"Podcast Section\")\n embed.set_thumbnail(url=self.avatar)\n embed.set_author(name=self.name, icon_url=self.avatar)\n embed.add_field(name=f\"{self.pre}pod\", value=\"This very command you ran\",inline=False)\n embed.add_field(name=f\"{self.pre}pod [Name of Podcast]\",value=\"Searches for the Podcast and shows Episodes related to it.\", inline=False)\n embed.add_field(name=f\"{self.pre}pod [Name of Podcast] : [Selection Number] or {self.pre}podp [Selection No]\", value=\"Play the podcast selection , default 0 plays the latest available episode\",inline=False)\n embed.add_field(name=f\"{self.pre}stop or {self.pre}dc\",value=\"Stop and Disconnect\\n Sadly Haven't Implemented any Pause for now\", inline=False)\n if(podepi == None and not podname == \" \"):\n await ctx.send(f'Searching ๐')\n embed = discord.Embed(colour=discord.Colour(\n 0xbd10e0), description=\" \")\n embed.set_thumbnail(url=self.avatar)\n embed.set_author(name=self.name, url=self.avatar,\n icon_url=self.avatar)\n try:\n k = ph.PodSearch(podname)\n except json.JSONDecodeError:\n embed.add_field(name=f\"Corrupted Feed\",value=\"Command raised an exception: JSONDecodeError\",inline=False)\n return\n except:\n embed.add_field(name=f\"Somewhere Something went wrong\",\n value=r\"I have 0 clue what the hell happened rn ยฏ\\_(ใ)_/ยฏ\", inline=False)\n return\n await ctx.message.add_reaction('โณ')\n if(not k['name'] == \"Podcast Not Found\"):\n currentpod = ph.Podcast(k['name'], k['rss'])\n self.lastPod = currentpod\n paginationsize = ph.Pagination(k['count'],5)\n ind = 0 + 5*start\n for episode_ in currentpod.ListEpisodes()[start:start+5]:\n embed.add_field(name=f\"{ind} : \"+episode_,value=k['date'],inline=False)\n ind += 1\n if(paginationsize > 1):\n embed.add_field(name=\"Change Page\",value=f\"```{self.pre}pod {podname} - [Page_Number]```\")\n embed.set_footer(text=f\"Page {start}/{paginationsize}\", icon_url=self.avatar)\n try:\n embed.set_thumbnail(url=k['image'])\n except:\n embed.set_thumbnail(url=self.avatar)\n else:\n embed.add_field(name=f\"No Podcasts Found\",value=\"itunes returned no results\",inline=False)\n embed.set_thumbnail(url=self.avatar)\n\n embed.set_thumbnail(url=currentpod.PodcastImage(podepi))\n embed.set_author(name=self.name,icon_url=self.avatar)\n embed.set_footer(text=k['name'],icon_url=self.avatar)\n\n await ctx.send(embed=embed)\n\n async def playPodcast(self, context, podepi, currentpod):\n try:\n await context.voice_client.disconnect()\n except:\n pass\n if context.voice_client is None:\n if context.author.voice.channel:\n await context.author.voice.channel.connect()\n\n guild = context.guild\n voice_client: discord.VoiceClient = discord.utils.get(self.client.voice_clients, guild=guild)\n _source_ = currentpod.GetEpisodeMp3(podepi)\n audio_source = discord.FFmpegPCMAudio(_source_)\n if not voice_client.is_playing():\n voice_client.play(audio_source, after=None)\n\n @commands.command(aliases=['oldfuckoff', 'olddc' , 'olddisconnect'])\n async def stop(self, ctx ):\n if(ctx.author.voice.channel):\n await ctx.message.add_reaction('๐')\n embed = discord.Embed( title=f\"Exiting\", description=f\"played\" ,colour=discord.Colour(0xff5065))\n embed.set_author(name=ctx.message.author.name,icon_url=ctx.message.author.avatar_url)\n embed.set_footer(text=self.client.user.name,icon_url=self.client.user.avatar_url)\n await ctx.voice_client.disconnect()\n return await ctx.reply(embed=embed)\n else:\n await ctx.message.add_reaction('โ')\n embed = discord.Embed( title=f\"you are not in the voice channel\", colour=discord.Colour(0xff5065))\n embed.set_author(name=ctx.message.author.name,icon_url=ctx.message.author.avatar_url)\n embed.set_footer(text=self.client.user.name,icon_url=self.client.user.avatar_url)\n return await ctx.reply(embed=embed)\n\n @lofi.before_invoke\n @play.before_invoke\n async def ensure_voice(self, ctx):\n \n # DISABLED\n await ctx.message.add_reaction('โ')\n await ctx.message.reply(\"This feature is currently disabled\")\n return\n \n if ctx.voice_client is None:\n if ctx.author.voice.channel:\n self.StartTime = ttime.time()\n print(\"timer started\")\n await ctx.author.voice.channel.connect()\n else:\n embed = discord.Embed(title=f\"{ctx.message.author.mention} is not connected to any Voice channel\", colour=discord.Colour(0xff5065))\n embed.set_author(name=ctx.message.author.name,icon_url=ctx.message.author.avatar_url)\n embed.set_footer(text=self.client.user.name,icon_url=self.client.user.avatar_url)\n return await ctx.send(embed=embed)\n\n elif ctx.voice_client.is_playing():\n ctx.voice_client.stop()\n\ndef setup(bot):\n bot.add_cog(MusicMixin(bot))\n", "sub_path": "mainbot/bot_mixins/musicbot.py", "file_name": "musicbot.py", "file_ext": "py", "file_size_in_byte": 11790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "discord.errors.PCMVolumeTransformer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "discord.errors", "line_number": 6, "usage_type": "name"}, {"api_name": "discord.errors.FFmpegPCMAudio", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 25, "usage_type": "name"}, {"api_name": "discord_init.DiscordInit", "line_number": 27, "usage_type": "name"}, {"api_name": "discord.errors.VoiceChannel", "line_number": 32, "usage_type": "attribute"}, {"api_name": "discord.errors", "line_number": 32, "usage_type": "name"}, {"api_name": "discord.errors.Embed", "line_number": 49, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 49, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 49, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 52, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 59, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 59, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 59, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 76, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 90, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 90, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 90, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 101, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 102, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 102, "usage_type": "name"}, {"api_name": "discord.errors.Embed", "line_number": 138, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 138, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 138, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 147, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 147, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 147, "usage_type": "call"}, {"api_name": "discord.errors.VoiceClient", "line_number": 197, "usage_type": "attribute"}, {"api_name": "discord.errors", "line_number": 197, "usage_type": "name"}, {"api_name": "discord.errors.utils.get", "line_number": 197, "usage_type": "call"}, {"api_name": "discord.errors.utils", "line_number": 197, "usage_type": "attribute"}, {"api_name": "discord.errors.FFmpegPCMAudio", "line_number": 199, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 199, "usage_type": "name"}, {"api_name": "discord.errors.Embed", "line_number": 207, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 207, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 207, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 214, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 214, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 214, "usage_type": "call"}, {"api_name": "discord.errors.Embed", "line_number": 234, "usage_type": "call"}, {"api_name": "discord.errors", "line_number": 234, "usage_type": "name"}, {"api_name": "discord.errors.Colour", "line_number": 234, "usage_type": "call"}]}
{"seq_id": "458737594", "text": "#!/usr/bin/python\n\n\ndef outlierCleaner(data):\n \"\"\"\n Clean away the 10% of points that have the largest\n residual errors (difference between the prediction\n and the actual net worth).\n\n Return a list of tuples named cleaned_data where \n each tuple is of the form (age, net_worth, error).\n \"\"\"\n \n print(\"hello\")\n cleaned_data = []\n \n data[3]=0\n ### your code goes here \n from scipy.spatial import distance\n import pandas as pd\n print(type(data))\n for i in range(0, len(data.index)):\n a = (data[1][i] , data[0][i])\n b = (data[1][i], data[2][i])\n data[3][i] = distance.euclidean(a,b)\n \n print(data)\n sdata = data.sort(columns=3, ascending=False)\n sdata = sdata.reset_index()\n cl_data = sdata[int(len(sdata)*0.1):]\n \n cl_data = cl_data.loc[:,[1,2,3]]\n\n return cl_data\n #return cleaned_data\n\n", "sub_path": "outliers/outlier_cleaner.py", "file_name": "outlier_cleaner.py", "file_ext": "py", "file_size_in_byte": 909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "scipy.spatial.distance.euclidean", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 25, "usage_type": "name"}]}
{"seq_id": "189858384", "text": "\n# -*- coding: utf-8 -*-\nfrom config import db\n\n\nclass SpiderDeploy(db.Model):\n __tablename__ = \"spider_deploy\"\n\n deploy_id = db.Column(db.Integer, primary_key=True, nullable=False)\n description = db.Column(db.String(30))\n deployed_at = db.Column(db.DateTime)\n created_at = db.Column(db.DateTime)\n updated_at = db.Column(db.DateTime)\n\n def __init__(self, description,deployed_at,created_at,updated_at):\n '''Constructor'''\n self.description=description\n self.deployed_at=deployed_at\n self.created_at=created_at\n self.updated_at=updated_at\n\n\n def __repr__(self):\n return 'deploy_id : %s' % self.deploy_id\n\n\n# Client and database attributes dictionary\nclinetHead = {u'deployId', u'description', u'deployedAt', u'createdAt', u'updatedAt'}\ntableChangeDic = {\n \"deployId\":\"deploy_id\",\n \"description\":\"description\",\n \"deployedAt\":\"deployed_at\",\n \"createdAt\":\"created_at\",\n \"updatedAt\":\"updated_at\"\n}\n\nintList = {u'deployId'}\n\n# db.create_all()\n", "sub_path": "boss_service/models/Boss/SpiderDeploy.py", "file_name": "SpiderDeploy.py", "file_ext": "py", "file_size_in_byte": 1018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "config.db.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 6, "usage_type": "name"}, {"api_name": "config.db.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "config.db", "line_number": 9, "usage_type": "name"}, {"api_name": "config.db.Integer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "config.db.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "config.db", "line_number": 10, "usage_type": "name"}, {"api_name": "config.db.String", "line_number": 10, "usage_type": "call"}, {"api_name": "config.db.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "config.db", "line_number": 11, "usage_type": "name"}, {"api_name": "config.db.DateTime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "config.db", "line_number": 12, "usage_type": "name"}, {"api_name": "config.db.DateTime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "config.db", "line_number": 13, "usage_type": "name"}, {"api_name": "config.db.DateTime", "line_number": 13, "usage_type": "attribute"}]}
{"seq_id": "567116206", "text": "from django import forms\nfrom django.core.validators import *\nfrom .models import *\n\n\nQuestionChoice = (\n ('Multiple Choice','Multiple Choice'), ('Boolean Type','Boolean Type'), ('Rating Scale','Rating Scale'),\n ('Text/Paragraph','Text/Paragraph'), ('Number Input','Number Input'), ('Picture Input','Picture Input'),\n)\n\nSourceChoice = (\n ('1','Manually'),('2','Import CSV file'),\n)\n\n\nclass TeacherForm(forms.ModelForm):\n Name = forms.CharField(label='Teacher Name')\n Description = forms.CharField(label='Task Description')\n NumberOfQuestions = forms.IntegerField(label='No. of questions')\n Source = forms.ChoiceField(label=\"How would you like provide data \",widget=forms.RadioSelect,choices=SourceChoice)\n File = forms.FileField(label=\"Choose file to upload(*Not Requied in manual mode)\",required=False)\n class Meta:\n model= Project\n fields= '__all__'\n\n\nclass TaskForm(forms.ModelForm):\n Question = forms.CharField(label='Question',max_length=500,required=True)\n QuestionType = forms.ChoiceField(label='Question Type',choices=QuestionChoice)\n AnswerOptions = forms.CharField(label='Answer Option',)\n class Meta:\n model = Task\n fields= ['Question','QuestionType','AnswerOptions']\n\n\nclass StudentMultipleChoiceForm(forms.ModelForm):\n def __init__(self, user, *args, **kwargs):\n super(StudentMultipleChoiceForm, self).__init__(*args, **kwargs)\n teacher = Temp.objects.latest('TeacherName')\n question_list=list(Temp.objects.filter(TeacherName=teacher.TeacherName))\n choice=question_list[0].AnswerOptions.split(',')\n MultipleChoice =(\n (choice[0],choice[0]),(choice[1],choice[1]),(choice[2],choice[2]),(choice[3],choice[3]),\n )\n self.fields['Answers'] = forms.ChoiceField(label=\"Answer\",widget=forms.RadioSelect,choices= MultipleChoice)\n class Meta:\n model=Answer\n fields=['Answers']\n\nclass StudentBooleanChoiceForm(forms.ModelForm):\n def __init__(self, user,*args, **kwargs):\n super(StudentBooleanChoiceForm, self).__init__(*args, **kwargs)\n teacher = Temp.objects.latest('TeacherName')\n question_list=list(Temp.objects.filter(TeacherName=teacher.TeacherName))\n choice=question_list[0].AnswerOptions.split(',')\n MultipleChoice =(\n (choice[0],choice[0]),(choice[1],choice[1]),\n )\n self.fields['Answers'] = forms.ChoiceField(label=\"Answer\",widget=forms.RadioSelect,choices= MultipleChoice)\n class Meta:\n model=Answer\n fields=['Answers']\n\nclass StudentRatingScaleForm(forms.ModelForm):\n def __init__(self, user, *args, **kwargs):\n super(StudentRatingScaleForm, self).__init__(*args, **kwargs)\n teacher = Temp.objects.latest('TeacherName')\n question_list=list(Temp.objects.filter(TeacherName=teacher.TeacherName))\n ratings=question_list[0].AnswerOptions.split(',')\n RatingChoice = []\n for i in range(0,len(ratings)):\n RatingChoice.append((ratings[i],ratings[i]))\n self.fields['Answers'] = forms.ChoiceField(label=\"Answer\",widget=forms.RadioSelect,choices= RatingChoice)\n class Meta:\n model=Answer\n fields=['Answers']\n\nclass StudentParagraphForm(forms.ModelForm):\n def __init__(self,user, *args, **kwargs):\n super(StudentParagraphForm, self).__init__(*args, **kwargs)\n teacher = Temp.objects.latest('TeacherName')\n question_list=list(Temp.objects.filter(TeacherName=teacher.TeacherName))\n max_characters=question_list[0].AnswerOptions\n self.fields['Answers'] = forms.CharField(label=\"Answer\",widget=forms.Textarea,max_length=int(max_characters))\n class Meta:\n model=Answer\n fields=['Answers']\n\nclass StudentNumberInputForm(forms.ModelForm):\n def __init__(self, user, *args, **kwargs):\n super(StudentNumberInputForm, self).__init__(*args, **kwargs)\n teacher = Temp.objects.latest('TeacherName')\n question_list=list(Temp.objects.filter(TeacherName=teacher.TeacherName))\n number_range=question_list[0].AnswerOptions.split(',')\n self.fields['Answers'] = forms.IntegerField(label=\"Answer\",validators=[MaxValueValidator(int(number_range[1])),\n MinValueValidator((int(number_range[0])))])\n class Meta:\n model=Answer\n fields=['Answers']\n\n# class StudentPictureInputForm(forms.ModelForm):\n# def __init__(self, user,question_list, *args, **kwargs):\n# super(StudentPictureInputForm, self).__init__(*args, **kwargs)\n#\n# self.fields['Choice'] = forms.\n# class Meta:\n# model=Answer\n# fields=[]\n\n", "sub_path": "sm_task/app/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 4640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.forms.ModelForm", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.forms.FileField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 28, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 36, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.forms.ModelForm", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 50, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 59, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.forms.ModelForm", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 64, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 73, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 73, "usage_type": "attribute"}, {"api_name": "django.forms.ModelForm", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 78, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 84, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 84, "usage_type": "attribute"}, {"api_name": "django.forms.ModelForm", "line_number": 89, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 89, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "35971121", "text": "#! /volume1/@appstore/python3/bin/python3\n#coding=utf-8\n#%%\nimport numpy as np\nimport pandas as pd\nfrom pyecharts.charts import Kline, Line, Bar, EffectScatter, Boxplot, Grid\nfrom pyecharts import options as opts\nfrom pyecharts.globals import SymbolType, CurrentConfig\nfrom datetime import datetime, timedelta\nfrom pytz import timezone\nimport sys\nfrom DataAgent import DataAgent\nfrom DBObj import Stock_poll, Index_poll\nfrom Radar import Radar\nimport libs\nimport config as cfg\nfrom itertools import product\n\nclass Analytics:\n def __init__ (self,daObj):\n self.da = daObj\n self.poll_aggHistRec = pd.DataFrame() # sec code ็บงๅซ็่ๅๆฐๆฎ๏ผ้ๅๅ
ๅซ่ฏๅธๅๆๆฐ\n self.poll_aggStkPoll = pd.DataFrame() # poll time index ็บงๅซ็่ๅๆฐๆฎ๏ผๅชๅ
ๅซ่ก็ฅจ\n self.poll_index = pd.DataFrame()\n self.poll_stock = pd.DataFrame()\n self.pollchart = None\n self.kchart = None\n self.macrochart = None\n self.kchartdf = pd.DataFrame() # k็บฟๅพๅๅงๆฐๆฎ\n\n def candlechart(self,str_tscode=None,subset='masterCandle',dat_cursor=None,MA=[5,10,21], renderfile=False) -> Grid:\n str_tscode = DataAgent.formatCode(str_tscode)\n int_chartsize = 500\n if str_tscode == False or str_tscode is None:\n print('invalid sec code provided...')\n return False\n dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)) if dat_cursor is None else dat_cursor\n closestTradeDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )\n tpl_dateindex = tuple(str(i) for i in self.da.df_calTbl.loc[self.da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-int_chartsize:] )\n\n # ่ฟๆฅๅจๅญๅฏน่ฑก\n if subset=='masterCandle': \n data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')\n else:\n data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')\n \n # ๅๅงๅไบคๆๆฅ่กจๆ ผ๏ผไฝไธบๆ ๅไบคๆๆฅๅบๅjoinไธช่กไบคๆๆฐๆฎ\n df_toPlot = data_store['masterCandle'].loc[data_store['masterCandle']['ts_code']==str_tscode]\n df_dateindex = pd.DataFrame({'trade_caldate': tpl_dateindex}).astype(int)\n data_store.close()\n try:\n str_secname = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0]\n str_sectype = self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['sec_type'].values[0]\n except Exception as e:\n return libs.log_csv(str_cat='WARNING',str_op='candlechart',str_desc='security code {} does not exist in basic info table...'.format(str_tscode))\n str_plotname = '{} - {}'.format(str_tscode,str_secname)\n\n # ไบคๆๆฅๆถ้ด่ฝดๆ ๅๅ๏ผ่กฅๅ
จๅ็ๆฅ,ๅฝๆฅliveๆฐๆฎ็ญk็บฟ\n '''------ ่ทๅๅฎๆถไบคๆๆฐๆฎ้จๅ--------------------'''\n int_liveDate = self.da.int_findClosestTradeDate(datetime.strftime(datetime.now().astimezone(timezone(cfg.STR_TIMEZONE)),'%Y%m%d') )\n if int_liveDate<=closestTradeDate and int_liveDate>df_toPlot['trade_date'].max():\n df_liveQuote = self.da.query('quote_now',[self.da.formatCode(str_tscode,False),])\n if len(df_liveQuote)>0:\n df_liveQuote.rename(columns={'open':'adj_open',\n 'high':'adj_high',\n 'low':'adj_low',\n 'price':'adj_close',\n 'volume':'vol',\n 'amount':'amount'}, inplace=True)\n df_liveQuote=df_liveQuote.astype(dtype= {'adj_open':'float','adj_high':'float','adj_low':'float','adj_close':'float','vol':'float','amount':'float'}) \n df_liveQuote['trade_date'] = int_liveDate\n df_liveQuote['ts_code'] = str_tscode\n df_liveQuote['vol'] = df_liveQuote['vol']/100 if str_sectype=='stk' or str_tscode[:3]=='399' else df_liveQuote['vol']\n df_liveQuote['amount'] = df_liveQuote['amount']/1000\n df_toPlot = df_toPlot.append(df_liveQuote[['ts_code','trade_date','adj_open','adj_high','adj_low','adj_close','vol','amount']],sort=False)\n '''------ ็ปๆ ่ทๅๅฎๆถไบคๆๆฐๆฎ้จๅ--------------------'''\n\n df_toPlot = pd.merge(df_dateindex, df_toPlot, left_on='trade_caldate', right_on='trade_date', how='left')\n df_toPlot['close'].fillna(method='ffill',inplace=True)\n df_toPlot['ts_code'].fillna(method='ffill',inplace=True)\n df_toPlot['klineSML'].fillna('NNNN',inplace=True)\n df_toPlot['adj_close'].fillna(method='ffill',inplace=True)\n df_toPlot['vol'].fillna(value=0,inplace=True)\n df_toPlot['amount'].fillna(value=0,inplace=True)\n df_toPlot['open'].fillna(df_toPlot['close'],inplace=True)\n df_toPlot['high'].fillna(df_toPlot['close'],inplace=True)\n df_toPlot['low'].fillna(df_toPlot['close'],inplace=True)\n df_toPlot['adj_open'].fillna(df_toPlot['adj_close'],inplace=True)\n df_toPlot['adj_high'].fillna(df_toPlot['adj_close'],inplace=True)\n df_toPlot['adj_low'].fillna(df_toPlot['adj_close'],inplace=True)\n self.kchartdf = df_toPlot #่พๅบ่ณๅฏ่ฎฟ้ฎๅฏน่ฑกๅฑๆงไธญ\n\n '''-----------------็ปๅพ้จๅ---------------------'''\n lst_ohlcv=df_toPlot.loc[:,['adj_open','adj_close','adj_low','adj_high']].values.tolist()\n lst_vol=list(df_toPlot['vol'].values)\n lst_amount=list(df_toPlot['amount'].values)\n lst_peaks=list(abs(df_toPlot['peaks'].values))\n # lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['pivots'].values)]\n lst_pivotdown=[float('nan') if i<0 else i for i in list(df_toPlot['valid_pivots'].values)]\n # lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['pivots'].values)]\n lst_pivotup=[float('nan') if i>0 else abs(i) for i in list(df_toPlot['valid_pivots'].values)]\n lst_xaxis=list(df_toPlot['trade_caldate'].astype(str))\n\n def calculate_ma(day_count: int, d):\n result: List[Union[float, str]] = []\n for i in range(len(d)):\n if i < day_count:\n result.append(\"-\")\n continue\n sum_total = 0.0\n for j in range(day_count):\n sum_total += float(d[i - j][1])\n result.append(abs(float(\"%.3f\" % (sum_total / day_count))))\n return result\n\n kline = (Kline()\n .add_xaxis(lst_xaxis)\n .add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,\n markpoint_opts=opts.MarkPointOpts(\n data=[opts.MarkPointItem(type_=\"min\",value_dim=\"close\"),\n opts.MarkPointItem(type_=\"max\",value_dim=\"close\")],\n symbol_size = [20, 20], #่กจ็คบๆ ่ฎฐๅฎฝไธบ 20๏ผ้ซไธบ 10\n ), \n itemstyle_opts=opts.ItemStyleOpts(\n color=\"#ec0000\",\n color0=\"#00da3c\",\n border_color=\"#ec0000\",\n border_color0=\"#00da3c\",\n ),)\n .set_global_opts(\n title_opts=opts.TitleOpts(\n title=str_plotname,\n subtitle='MA='+str(MA),\n ),\n xaxis_opts=opts.AxisOpts(type_=\"category\"),\n yaxis_opts=opts.AxisOpts(\n is_scale=True,\n splitarea_opts=opts.SplitAreaOpts(\n is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)\n ),\n ),\n legend_opts=opts.LegendOpts(is_show=True, pos_top='10%', pos_left=\"center\"),\n datazoom_opts=[\n opts.DataZoomOpts(\n is_show=False,\n type_=\"inside\",\n xaxis_index=[0,1,2],\n range_start=75,\n range_end=100,\n ),\n opts.DataZoomOpts(\n is_show=True,\n xaxis_index=[0,1,2],\n type_=\"slider\",\n pos_top=\"90%\",\n range_start=75,\n range_end=100,\n ),\n ],\n tooltip_opts=opts.TooltipOpts(\n trigger=\"axis\",\n axis_pointer_type=\"cross\",\n background_color=\"rgba(245, 245, 245, 0.8)\",\n border_width=1,\n border_color=\"#ccc\",\n textstyle_opts=opts.TextStyleOpts(color=\"#000\",font_size=10),\n ),\n # ้ด้็ปฟ้ณ้็บข\n visualmap_opts=opts.VisualMapOpts(\n is_show=False,\n dimension=2,\n series_index=list(map(lambda x: x+len(MA),[4,5])), #ๅจๆ็ฎๅบvolๅamtๆฑ็ถๅพ็series index\n is_piecewise=True,\n pieces=[\n {\"value\": 1, \"color\": \"#ec0000\"},\n {\"value\": -1, \"color\": \"#00da3c\"},\n ],\n ), \n axispointer_opts=opts.AxisPointerOpts(\n is_show=True,\n link=[{\"xAxisIndex\": \"all\"}],\n label=opts.LabelOpts(background_color=\"#777\"),\n ),\n brush_opts=opts.BrushOpts(\n x_axis_index=\"all\",\n brush_link=\"all\",\n out_of_brush={\"colorAlpha\": 0.1},\n brush_type=\"lineX\",\n ),\n )\n )\n trendline = (\n Line()\n .add_xaxis(lst_xaxis)\n .add_yaxis('้ซไฝ็น', lst_peaks,\n itemstyle_opts=opts.ItemStyleOpts(color=\"green\"))\n )\n for i in MA:\n if i is not None:\n trendline.add_yaxis(\n series_name='MA'+str(i),\n y_axis=calculate_ma(day_count=i, d=lst_ohlcv),\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),\n label_opts=opts.LabelOpts(is_show=False),\n )\n trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))\n \n keyPoints = (\n EffectScatter()\n .add_xaxis(lst_xaxis)\n .add_yaxis(\"ๆซ่ท้ซ\", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color=\"purple\"))\n .add_yaxis(\"ๆซๅไฝ\", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color=\"blue\"))\n .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n )\n vol_bar = (\n Bar()\n .add_xaxis(lst_xaxis)\n .add_yaxis(\n series_name=\"ไบคๆ้\",\n yaxis_data=[\n [i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]\n for i in range(len(lst_vol))\n ],\n xaxis_index=1,\n yaxis_index=1,\n label_opts=opts.LabelOpts(is_show=False),\n )\n .set_global_opts(\n xaxis_opts=opts.AxisOpts(\n type_=\"category\",\n is_scale=True,\n grid_index=1,\n boundary_gap=False,\n axisline_opts=opts.AxisLineOpts(is_on_zero=False),\n axistick_opts=opts.AxisTickOpts(is_show=False),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n axislabel_opts=opts.LabelOpts(is_show=False),\n split_number=20,\n min_=\"ๆไฝ\",\n max_=\"ๆ้ซ\",\n ),\n yaxis_opts=opts.AxisOpts(\n grid_index=1,\n is_scale=True,\n split_number=2,\n axislabel_opts=opts.LabelOpts(is_show=False),\n axisline_opts=opts.AxisLineOpts(is_show=False),\n axistick_opts=opts.AxisTickOpts(is_show=False),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n ),\n legend_opts=opts.LegendOpts(is_show=False),\n )\n # .add_yaxis(\"ไบคๆ้\", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color=\"#456A76\"))\n )\n amnt_bar = (\n Bar()\n .add_xaxis(lst_xaxis)\n .add_yaxis(\n series_name=\"ไบคๆ้ข\",\n yaxis_data=[\n [i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]\n for i in range(len(lst_amount))\n ],\n xaxis_index=2,\n yaxis_index=2,\n label_opts=opts.LabelOpts(is_show=False),\n )\n .set_global_opts(\n xaxis_opts=opts.AxisOpts(\n type_=\"category\",\n is_scale=True,\n grid_index=2,\n boundary_gap=False,\n axisline_opts=opts.AxisLineOpts(is_on_zero=False),\n axistick_opts=opts.AxisTickOpts(is_show=False),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n axislabel_opts=opts.LabelOpts(is_show=False),\n split_number=20,\n min_=\"ๆไฝ\",\n max_=\"ๆ้ซ\",\n ),\n yaxis_opts=opts.AxisOpts(\n grid_index=2,\n is_scale=True,\n split_number=2,\n axislabel_opts=opts.LabelOpts(is_show=False),\n axisline_opts=opts.AxisLineOpts(is_show=False),\n axistick_opts=opts.AxisTickOpts(is_show=False),\n splitline_opts=opts.SplitLineOpts(is_show=False),\n ),\n legend_opts=opts.LegendOpts(is_show=False),\n )\n # .add_yaxis(\"ไบคๆ้ข\", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color=\"#456A76\"))\n )\n priceChart = kline.overlap(trendline).overlap(keyPoints)\n gridChart = Grid()\n gridChart.add(\n priceChart,\n grid_opts=opts.GridOpts(pos_left=\"10%\", pos_right=\"8%\", pos_bottom='40%'),\n )\n gridChart.add(\n vol_bar,\n grid_opts=opts.GridOpts(pos_left=\"10%\", pos_right=\"8%\", pos_top=\"60%\", height='15%'),\n )\n gridChart.add(\n amnt_bar,\n grid_opts=opts.GridOpts(pos_left=\"10%\", pos_right=\"8%\", pos_top=\"75%\"),\n )\n\n fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')\n gridChart.render(fname) if renderfile else None\n self.kchart = gridChart # ๅฐ็ปๆ่พๅบๅฐanalyticsๅฏน่ฑกๅฑๆงไธญ็จไบfunctionไปฅๅค็่ฐ็จ\n return self\n\n def pollsummary(self,subset='masterCandle',dat_cursor=None,renderfile=False):\n # ๅๅงๅไบคๆๆถ้ดx่ฝด\n heartbeat = 300\n # ๅฆๆๆชๆไพๅค็ๆถ้ดๆธธๆ ๏ผๅ้ป่ฎคไฝฟ็จๆ่ฟไบคๆๆฅ่ณๆถ็ๆถ็ๆถ้ด็ๆฐๆฎ\n if dat_cursor is None:\n dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))\n int_latestViewDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )\n int_cursorDateTime = int(str(int_latestViewDate)+'150100')\n int_cursorDateStart = int(str(int_latestViewDate)+'000000')\n # ๅฆๆไพๅค็ๆธธๆ ๆถ้ด๏ผๅ่ฏฅๆถ้ดไธบๆธธๆ ๅกๅฐบ็ๆๅๆถ้ด๏ผๅฆๆฅๆไธบ้ไบคๆๆฅๅ่ชๅจ่ฐๆดไธบๆ่ฟไบคๆๆฅ็ๆถ็ๆถ้ด\n else:\n int_cursorDate = datetime.strftime(dat_cursor,'%Y%m%d')\n int_latestViewDate = self.da.int_findClosestTradeDate(int_cursorDate)\n if int_cursorDate==int_latestViewDate: # ๅฆ็ธ็ญ่ฏดๆๆไพๆฅๆไธบๆๆไบคๆๆฅ๏ผๅไฝฟ็จๆไพ็ๅฐๆถๅ้็งไธบๆธธๆ ็ป็นๆถ้ด๏ผๅฆๅๅฐ่ชๅจๆฟๆขไธบๆ่ฟไบคๆๆฅ็ๆถ็ๆถ้ด็น\n int_cursorDateTime = int(datetime.strftime(dat_cursor,'%Y%m%d%H%M%S'))\n int_cursorDateStart = int(datetime.strftime(dat_cursor,'%Y%m%d')+'000000')\n else:\n int_cursorDateTime = int(str(int_latestViewDate) +'150100')\n int_cursorDateStart = int(str(int_latestViewDate) +'000000')\n beatRange = Radar.pop_pulseRange(int_latestViewDate,heartbeat)\n df_stockPoll = pd.read_sql(DataAgent.dbsession.query(Stock_poll).filter(Stock_poll.time_index>=int_cursorDateStart,\n Stock_poll.time_index<=int_cursorDateTime,\n Stock_poll.volume!=0).statement, \n DataAgent.dbsession.bind)\n df_indexPoll = pd.read_sql(DataAgent.dbsession.query(Index_poll).filter(Index_poll.time_index>=int_cursorDateStart,\n Index_poll.time_index<=int_cursorDateTime,\n Index_poll.volume!=0).statement, \n DataAgent.dbsession.bind)\n '''\n data_store = libs.tryOpenH5(cfg.H5_FILE_POLL,mode='r')\n if 'stockPoll' in data_store:\n df_stockPoll = data_store['stockPoll']\n df_stockPoll = df_stockPoll[df_stockPoll['volume']!=0] # ่ฟๆปคๆๆฐๆฎไธญ้ไบคๆไธญ็่ฏๅธ\n df_stockPoll = df_stockPoll[(df_stockPoll['time_index']<=int_cursorDateTime)&(df_stockPoll['time_index']>=int_cursorDateStart)] # ่ฟๆปคๆๅ็ปๅพๆ้ดๆ ๅ
ณ็ๆฐๆฎ\n else:\n df_stockPoll = pd.DataFrame()\n df_indexPoll = data_store['indexPoll'] if 'indexPoll' in data_store else pd.DataFrame()\n data_store.close()\n '''\n\n data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')\n # ๅฆๆๆ็
ง่พๅ
ฅๆฅๆๆฒกๆๆพๅฐๅฝๆฅไปปไฝ็ไธญๆฐๆฎ๏ผๅไปๅ
ฅๅบๅๅฒๆฐๆฎไธญๆพๅฐๅฝๆฅๆถ็ๆฐๆฎ็จไบๅกซๅ
\n if len(df_indexPoll)==0:\n df_indexPoll = data_store['masterCandle'][data_store['masterCandle']['trade_date']==int_latestViewDate]\n df_indexPoll['time_index'] = int_cursorDateTime\n df_indexPoll['pct_change'] = round(df_indexPoll['adj_close']/df_indexPoll['pre_close']*100,2)\n df_indexPoll['per'] = np.nan\n if len(df_stockPoll)==0:\n df_stockPoll = data_store['masterCandle'][data_store['masterCandle']['trade_date']==int_latestViewDate]\n df_stockPoll['time_index'] = int_cursorDateTime\n df_stockPoll['pct_change'] = round((df_stockPoll['adj_close']-df_stockPoll['pre_close'])/df_stockPoll['pre_close']*100,2)\n df_stockPoll.rename(columns={'pe': 'per'}, inplace=True)\n # ๅฆๆๅ
ฅๅบๅๅฒๆฐๆฎไธญไปๆชๆพๅฐไปปไฝๆฐๆฎๅ้ๅบๅๆ็จๅบ\n if any([len(df_stockPoll)==0,len(df_indexPoll)==0]):\n data_store.close()\n print('stockPoll or indexPoll does not exist for the cursor date in the offline data store, process exiting...')\n return False\n \n ''' ------่ฏปๅsecurity code็บงๅซ็่ๅๆฐๆฎ------- '''\n if subset=='masterCandle': \n pass #data_store = libs.tryOpenH5(cfg.H5_FILE_PRE,mode='r')\n else:\n data_store.close() # ๅ
ณ้ญไนๅๆๅผ็master data file\n data_store = libs.tryOpenH5('{}{}.dat'.format(cfg.H5_FILE_PATH,subset),mode='r')\n df_histRec = data_store['AggByCode']\n data_store.close()\n self.poll_aggHistRec = df_histRec # sec code ็บงๅซ็่ๅๆฐๆฎ๏ผ้ๅๅ
ๅซ่ฏๅธๅๆๆฐ,่พๅบ่ณๅฏน่ฑกๅ้\n ''' ------่ฏปๅsecurity code็บงๅซ็่ๅ็ปๆ------- '''\n\n sr_intBeatIndex = pd.Series(beatRange.strftime('%Y%m%d%H%M%S').astype('int64')).rename(index='time_index')\n df_index = pd.merge(sr_intBeatIndex,df_indexPoll,how='left',left_on='time_index',right_on='time_index',suffixes=['','1'])\n \n df_stock = pd.merge(sr_intBeatIndex,df_stockPoll,how='left',left_on='time_index',right_on='time_index',suffixes=['','1'])\n df_stock = pd.merge(df_stock,df_histRec,how='left',left_on='ts_code',right_index=True,suffixes=['','1'])\n\n self.poll_index = df_index #ๅฐ็ปๆ่พๅบ่ณanalyiticsๅฏน่ฑก็จไบๆนๆณๅค็่ฎฟ้ฎ\n self.poll_stock = df_stock #ๅฐ็ปๆ่พๅบ่ณanalyiticsๅฏน่ฑก็จไบๆนๆณๅค็่ฎฟ้ฎ\n # libs.df_csv(cfg.PATH_BUGFILE,(df_stock,))\n ''' --------ๆๆby time index็บงๅซ็่ๅๅจ่ฟ้ๅฎๆ -----'''\n def popPctList(x):\n # ็งป้คๆถจ่ทๅน
่ถ
่ฟ+- 10%็, ๆฐ่กไธๅ10%ๆถจ่ทๅน
้ๅถๅ ๆญคไผๅนฒๆฐbox chartๆพ็คบ\n return [0]*3 if len(x)<3 else [i for i in x if abs(i)<11]\n def bigBox(x, up=True):\n # ไธญ้ณ๏ผ้ด๏ผไปฅไธk็บฟๆฐ้\n if up:\n return list(x.loc[(x['box_size']>0) & (x['klineSML'].str.match(DataAgent.re_bigUpBoxPat))]['ts_code'])\n else:\n return list(x.loc[(x['box_size']<0) & (x['klineSML'].str.match(DataAgent.re_bigDnBoxPat))]['ts_code'])\n def breakThrough(x,up=True):\n if up: # ๆพๅฐไธ็ฉฟๆๆๆซ่ท้ซ็น็\n return list(x.loc[x['close']>=x['validPiv_dnHigh']]['ts_code'])\n else: # ๆพๅฐ่ท็ ดๆๆๆซๅไฝ็น็\n return list(x.loc[x['close']<=x['validPiv_upLow']]['ts_code']) \n df_stockBeatGrp = df_stock.groupby('time_index')\n df_stockBeatRollup = df_stockBeatGrp.agg({'ts_code': ['count'],\n 'pct_change': [popPctList],\n 'per': ['median']}) # functionๆพๅ
ฅไธญๆฌๅทๅ
ไผ็ๆไธคไธชๅฑ็บงๅๅ๏ผๆนไพฟๅ้ขjoinๆๆฐๅๅ.ๅฆๆ ไธญๆฌๅทๅฐๅชไบง็ไธๅฑๅๅ\n df_stockBeatRollup.columns = ['_'.join(x) for x in df_stockBeatRollup.columns.ravel()]\n df_stockBeatRollup['upThruList'] = df_stockBeatGrp.apply(lambda x: breakThrough(x,up=True)) if len(df_histRec) > 0 else np.nan\n df_stockBeatRollup['dnThruList']= df_stockBeatGrp.apply(lambda x: breakThrough(x,up=False)) if len(df_histRec) > 0 else np.nan\n df_stockBeatRollup['upThruCount'] = df_stockBeatRollup.apply(lambda row: len(row['upThruList']),axis=1) if len(df_histRec) > 0 else np.nan\n df_stockBeatRollup['dnThruCount']= df_stockBeatRollup.apply(lambda row: len(row['dnThruList']),axis=1) if len(df_histRec) > 0 else np.nan\n\n df_stockBeatRollup['upBigBoxList'] = df_stockBeatGrp.apply(lambda x: bigBox(x,up=True))\n df_stockBeatRollup['dnBigBoxList'] = df_stockBeatGrp.apply(lambda x: bigBox(x,up=False))\n df_stockBeatRollup['upBigBoxCount'] = df_stockBeatRollup.apply(lambda row: len(row['upBigBoxList']),axis=1) if len(df_histRec) > 0 else np.nan\n df_stockBeatRollup['dnBigBoxCount'] = df_stockBeatRollup.apply(lambda row: len(row['dnBigBoxList']),axis=1) if len(df_histRec) > 0 else np.nan \n ''' --------by time index็บงๅซ็่ๅ็ปๆ -------------------------------'''\n \n df_stockBeatRollup.astype(dtype= {'ts_code_count':'int32','upThruCount':'int32','dnThruCount':'int32','upBigBoxCount':'int32','dnBigBoxCount':'int32'})\n self.poll_aggStkPoll = df_stockBeatRollup.loc[df_stockBeatRollup['ts_code_count']>0] # ๅชๅฐๆๆ็ปๆ่พๅบๅฐanalyticsๅฏน่ฑกๅฑๆงไธญ็จไบfunctionไปฅๅค็่ฐ็จ\n\n ''' ------------------ไฝๅพๅบๅ---------------------------'''\n per_median = round(self.poll_aggStkPoll.loc[self.poll_aggStkPoll.index==self.poll_aggStkPoll.index.max()].per_median.values[0],2)\n \n lst_xaxis = ['{}:{}:{}'.format(i.hour,i.minute,i.second) for i in beatRange]\n marketSummaryBoxPlot = Boxplot()\n lst_yaxis = np.around(marketSummaryBoxPlot.prepare_data(df_stockBeatRollup['pct_change_popPctList'].values), decimals=2).tolist()\n \n marketSummaryBoxPlot.add_xaxis(lst_xaxis)\n marketSummaryBoxPlot.add_yaxis(\"%\", lst_yaxis)\n marketSummaryBoxPlot.set_global_opts(title_opts=opts.TitleOpts(title='ๆดไฝๆถจ่ทๅน
ๅๅธ - {} (ๆๅๅธ็็ไธญไฝๆฐ:{})'.format(int_latestViewDate,str(per_median))),\n yaxis_opts=opts.AxisOpts(name=\"ๆถจๅน
\",min_=-10,max_=10),\n xaxis_opts=opts.AxisOpts(name=\"\",\n axislabel_opts=opts.LabelOpts(is_show=False),\n axisline_opts=opts.AxisLineOpts(is_show=True),\n axistick_opts=opts.AxisTickOpts(is_show=True),\n splitline_opts=opts.SplitLineOpts(is_show=False),),\n legend_opts=opts.LegendOpts(is_show=False),\n )\n \n lst_upThruBar = df_stockBeatRollup['upThruCount'].values.tolist()\n lst_dnThruBar = df_stockBeatRollup['dnThruCount'].values.tolist()\n lst_bigUpBoxBar = df_stockBeatRollup['upBigBoxCount'].values.tolist()\n lst_bigDnBoxBar = df_stockBeatRollup['dnBigBoxCount'].values.tolist()\n \n pivotCountChart = (\n Bar()\n .add_xaxis(lst_xaxis)\n .add_yaxis(\n series_name=\"ๅไธ็ช็ ดๆฐ้\",\n yaxis_data=lst_upThruBar,\n label_opts=opts.LabelOpts(is_show=False),\n itemstyle_opts=opts.ItemStyleOpts(color=\"#FD625E\"),\n )\n .add_yaxis(\n series_name=\"ๅไธ็ช็ ดๆฐ้\",\n xaxis_index=1,\n yaxis_index=1,\n yaxis_data=lst_dnThruBar,\n label_opts=opts.LabelOpts(is_show=False),\n itemstyle_opts=opts.ItemStyleOpts(color=\"#01B8AA\"),\n )\n .add_yaxis(\n series_name=\"้ฟ้ณ็บฟๆฐ้\",\n xaxis_index=1,\n yaxis_index=1,\n yaxis_data=lst_bigUpBoxBar,\n label_opts=opts.LabelOpts(is_show=False),\n itemstyle_opts=opts.ItemStyleOpts(color=\"red\"),\n )\n .add_yaxis(\n series_name=\"้ฟ้ด็บฟๆฐ้\",\n xaxis_index=1,\n yaxis_index=1,\n yaxis_data=lst_bigDnBoxBar,\n label_opts=opts.LabelOpts(is_show=False),\n itemstyle_opts=opts.ItemStyleOpts(color=\"green\"),\n )\n .set_global_opts(\n xaxis_opts=opts.AxisOpts(name=\"ๅฐๆถ/ๅ้\",),\n yaxis_opts=opts.AxisOpts(\n axislabel_opts=opts.LabelOpts(is_show=True),\n axisline_opts=opts.AxisLineOpts(is_show=True),\n axistick_opts=opts.AxisTickOpts(is_show=True),\n splitline_opts=opts.SplitLineOpts(is_show=True),\n ),\n legend_opts=opts.LegendOpts(is_show=True,pos_bottom='0%', pos_left=\"center\"),\n )\n )\n gridChart = Grid()\n gridChart.add(\n marketSummaryBoxPlot,\n grid_opts=opts.GridOpts(pos_left=\"10%\", pos_right=\"8%\", pos_bottom='45%'),\n )\n gridChart.add(\n pivotCountChart,\n grid_opts=opts.GridOpts(pos_left=\"10%\", pos_right=\"8%\", pos_top=\"55%\"),\n )\n fname = '{}marketSummary{}.html'.format(cfg.PATH_ANAFILE,int_latestViewDate)\n gridChart.render(fname) if renderfile else None \n return gridChart\n\n def corelationMacro(self,dat_cursor=None,lst_tscode=['399002.SZ'],lst_macro=['shibor.on','PIMon.cpi'],lst_findmedian=['pe',()],renderfile=False) -> Line:\n lst_findmedian[1] = tuple(self.da.df_stockTbl['ts_code']) if lst_findmedian[1] is None else lst_findmedian[1]\n\n if dat_cursor is None:\n dat_cursor = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))\n int_latestViewDate = self.da.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )\n df_baseCal = self.da.df_calTbl.loc[self.da.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()\n lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')]\n\n linechart = (\n Line()\n .add_xaxis(lst_xaxis)\n )\n\n for code in lst_tscode:\n str_tscode = DataAgent.formatCode(code)\n df_target = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)\n if len(df_target)>0:\n df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')\n else:\n continue\n linename = '{} - {}'.format(str_tscode,self.da.df_lookupTbl.loc[self.da.df_lookupTbl['ts_code']==str_tscode]['name'].values[0])\n lst_values = list(df_toPlot['close'])\n lst_values = list(Analytics.normByRange(lst_values))\n \n linechart.add_yaxis(linename, lst_values,\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),\n label_opts=opts.LabelOpts(is_show=False),\n itemstyle_opts=opts.ItemStyleOpts(\n color=\"#00da3c\",\n ),\n )\n try:\n lst_pe = df_toPlot['pe'].values.tolist()\n lst_pb = df_toPlot['pb'].values.tolist()\n lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]\n linechart.add_yaxis(str_tscode+\"_PE\", lst_pe, yaxis_index=1, #ๅๆฐไฝฟ็จๅณy่ฝด\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=1),\n label_opts=opts.LabelOpts(is_show=False),\n )\n linechart.add_yaxis(str_tscode+\"_PE/PB\", lst_pepb, yaxis_index=1, #ๅๆฐไฝฟ็จๅณy่ฝด\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=1),\n label_opts=opts.LabelOpts(is_show=False),\n )\n except:\n continue\n\n df_shibor = self.da.query('load_shibor')\n # print('Supported shibor columns: {}'.format(df_shibor.columns))\n df_pimon = self.da.query('load_piMon')\n # print('Supported macro eco columns: {}'.format(df_pimon.columns))\n \n for item in lst_macro:\n try:\n topic,colname = item.split('.')\n except:\n print('WARNING! input code {} not understood...'.format(item))\n if topic=='shibor':\n df_target = df_shibor\n maxarr = df_target.max(axis=0, skipna=True).values[1:]\n minarr = df_target.min(axis=0, skipna=True).values[1:]\n deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable\n elif topic=='pimon':\n df_target = df_pimon\n deltaRng = None\n else:\n print('WARNING! subject code {} is not understood...'.format(topic))\n if len(df_target)>0:\n df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')\n df_toPlot[colname] = df_toPlot[colname].bfill()\n else:\n continue\n linename = item\n lst_values = list(df_toPlot[colname])\n lst_values = list(Analytics.normByRange(lst_values,delta=deltaRng))\n linechart.add_yaxis(linename, lst_values, # ๆๆ็ญๆฏไพ็ผฉๆพ็ๆ็บฟ้ฝๆพๅจlinechart้๏ผไฝฟ็จๅทฆไพงy่ฝด\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),\n label_opts=opts.LabelOpts(is_show=False),\n )\n str_cat = lst_findmedian[0]\n df_result = pd.DataFrame()\n for str_tscode in lst_findmedian[1]:\n try:\n df = self.da.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]\n print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\\r')\n except Exception as e:\n print('WARNING! {} csv file not found...'.format(str_tscode))\n continue\n df_result = pd.concat([df_result,df])\n df_result.dropna(subset=[str_cat],inplace=True)\n df_result = df_result.loc[df_result['trade_date']>19940301]\n # libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))\n # print(df_result.groupby('trade_date')['ts_code'].count())\n df_target = df_result.groupby('trade_date')[str_cat].median()\n if len(df_target)>0:\n df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')\n linename = '{}-median'.format(str_cat)\n lst_values = list(df_toPlot[str_cat])\n lst_values = list(lst_values)\n linechart.add_yaxis(linename, lst_values, yaxis_index=1,\n is_smooth=True,\n is_hover_animation=False,\n linestyle_opts=opts.LineStyleOpts(width=1),\n label_opts=opts.LabelOpts(is_show=False),\n )\n linechart.extend_axis( # ๆๆไฟๆๅๆฏไพ็ๆ็บฟ้ฝๆพๅจlinechart2้๏ผไฝฟ็จๅณไพงy่ฝด\n yaxis=opts.AxisOpts(\n name=\"ๅๆฐ\",\n type_=\"value\",\n position=\"right\",\n axisline_opts=opts.AxisLineOpts(\n linestyle_opts=opts.LineStyleOpts(color=\"black\")\n ),\n axislabel_opts=opts.LabelOpts(formatter=\"{value}ๅ\"),\n )\n )\n linechart.set_global_opts(\n title_opts=opts.TitleOpts(\n title='ๅฎ่งๆฏ่พ',\n ),\n xaxis_opts=opts.AxisOpts(type_=\"category\"),\n yaxis_opts=opts.AxisOpts(name=\"็ญๆฏไพ\",\n is_scale=True,\n ),\n legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', pos_left=\"center\"),\n datazoom_opts=[\n opts.DataZoomOpts(\n is_show=False,\n type_=\"inside\",\n xaxis_index=[0],\n range_start=75,\n range_end=100,\n ),\n opts.DataZoomOpts(\n is_show=True,\n xaxis_index=[0],\n type_=\"slider\",\n pos_top=\"90%\",\n range_start=75,\n range_end=100,\n ),\n ],\n tooltip_opts=opts.TooltipOpts(\n trigger=\"axis\",\n axis_pointer_type=\"cross\",\n background_color=\"rgba(245, 245, 245, 0.8)\",\n border_width=1,\n border_color=\"#ccc\",\n textstyle_opts=opts.TextStyleOpts(color=\"#000\",font_size=10),\n ),\n axispointer_opts=opts.AxisPointerOpts(\n is_show=True,\n link=[{\"xAxisIndex\": \"all\"}],\n label=opts.LabelOpts(background_color=\"#777\"),\n ),\n brush_opts=opts.BrushOpts(\n x_axis_index=\"all\",\n brush_link=\"all\",\n out_of_brush={\"colorAlpha\": 0.1},\n brush_type=\"lineX\",\n ),\n )\n fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')\n linechart.render(fname)\n else:\n pass\n \n fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')\n linechart.render(fname) if renderfile else None\n self.macrochart = linechart #่พๅบ่ณๅฏ่ฎฟ้ฎๅฏน่ฑกๅฑๆงไธญ\n return self\n\n def corelationAnalysis(self,str_reportName='',tpl_dateindex=None,tpl_baselist=None,tpl_targetlist=None):\n fname = '{}{}_corelation.csv'.format(cfg.PATH_ANAFILE,str_reportName)\n\n # scope up all target trade dates into a list\n if tpl_dateindex is None:\n return\n \n # define benchmarking securities, can be indice or stock\n if tpl_baselist is None:\n tpl_baselist = ('000001.SH','000300.SH','000905.SH','399006.SZ')\n # specify the target securities to compare with benchmarks\n if tpl_targetlist is None:\n tpl_targetlist = tuple(self.da.df_stockTbl['ts_code'])\n\n # populate all possible pairs specified by the benchmark and target scope\n analyticScope = tuple(product(tpl_baselist,tpl_targetlist))\n print('{} pairs are in scope for analysis'.format(len(analyticScope)))\n\n # specify result summary table structure\n df_result = pd.DataFrame(columns=['str_targetcode','str_basecode','sampleSize','validSampleRatio','CorelatePerc'])\n \n str_basecode = ''\n df_base = pd.DataFrame()\n\n #define benchmarking price, can be against 'high', 'low', 'open', 'close'\n str_price = 'close'\n for pair in analyticScope:\n # ๅ
่ฏปๅๅบๅ่ฏๅธ/ๆๆฐ็ๆฐๆฎ๏ผๅพช็ฏไธญๅ็ฐๆฐ็ๅบๅ่ฏๅธๅๅฏนๆฌกๅ้้ๆฐ่ตๅผ๏ผๅฆๅบๅๆชๅๅๅช้ๅฐๆฏ่พๆ ็ๅ้้ๆฐ่ตๅผ\n if str_basecode != pair[0]:\n str_basecode,str_targetcode = pair\n df_base = self.da.query('load_alldaily', enumerate=False, tpl_dateindex=tpl_dateindex, ts_code=str_basecode) \n if len(df_base)<1:\n continue\n # ่ฎก็ฎๅบๆฏ่กๆฐๆฎๅไธ่กๆฐๆฎ็ๅทฎๅผ\n df_base['delta_'+str_price]=df_base[str_price]-df_base[str_price].shift(1)\n df_base['delta_'+str_price].fillna(value=0,inplace=True)\n else:\n str_targetcode = pair[1]\n\n df_target = self.da.query('load_alldaily', enumerate=False, tpl_dateindex=tpl_dateindex, ts_code=str_targetcode)\n \n if len(df_target)<1:\n continue\n\n # ๆ นๆฎไนๅๅถๅฎ็ไปทๆ ผ็ง็ฑป่ฟ่กๅทฎไปท่ฎก็ฎ\n df_target = DataAgent.fq(df_target)\n df_target['delta_'+str_price]=df_target['adj_'+str_price]-df_target['adj_'+str_price].shift(1)\n df_target['delta_'+str_price].fillna(value=0,inplace=True)\n \n df_comb = pd.DataFrame.merge(df_base, right=df_target, how='left',left_on='trade_date', right_on='trade_date',suffixes=['_base','_target'])\n # ๅฆbaseๅtarget็deltaไน็งฏไธบๆญฃๆฐ่ฏดๆๆญฃ็ธๅ
ณ๏ผๅไนๅ่ด็ธๅ
ณ\n df_comb['corelation'] = df_comb.apply(lambda x: 1 if x['delta_'+str_price+'_base']*x['delta_'+str_price+'_target']>0 else (-1 if x['delta_'+str_price+'_base']*x['delta_'+str_price+'_target']<0 else 0), axis=1)\n \n # ๆต้ๆๆๆฐๆฎๅ ๆฏ\n sampleSize = len(df_comb)\n #ๆฐๅบๆๆญฃ๏ผ่ด็ธๅ
ณ็ป่ฎบ็ไบคๆๆฅไธชๆฐ๏ผ -1ไปฃ่กจ่ด็ธๅ
ณ๏ผ0ไปฃ่กจๆ ๆฐๆฎๆๆ ๅๅ๏ผ1ไปฃ่กจๆญฃ็ธๅ
ณ\n corelationGrp = df_comb.groupby('corelation')['trade_date'].count()\n #ๆๆๆ ทๆฌๆปๆฐๅๅปๆ ๆฐๆฎๆๆ ๅๅ็ๆ ทๆฌๆฐ้ๅณๅฏๅพๅฐๆๆๆ ทๆฌๆฐ้\n validSampleSize = (sampleSize - corelationGrp.loc[0]) if 0 in corelationGrp.index else sampleSize\n validSampleRatio = round(validSampleSize/sampleSize,2) if sampleSize!=0 else 0\n \n #ๆญฃ็ธๅ
ณๆ ทๆฌๆฐ้ๅ ๆฏ\n CorelateCount = corelationGrp.loc[1] if 1 in corelationGrp.index else 0\n CorelatePerc = round(CorelateCount/validSampleSize,2) if validSampleSize!=0 else 0\n \n resultRow = [str_targetcode,str_basecode,sampleSize,validSampleRatio,CorelatePerc]\n df_result.loc[len(df_result)] = resultRow\n\n print('done pair:{}-{}'.format(str_basecode,str_targetcode))\n \n libs.df_csv(fname,(df_result,))\n print('Corelation analysis done and result saved to {}'.format(fname))\n \n @staticmethod\n def normByRange(lst_input,delta=None,int_start=0,int_end=1000,):\n length = abs(int_end - int_start)\n delta = np.nanmax(lst_input) - np.nanmin(lst_input) if delta==None else delta\n return map(lambda x: round(((x- np.nanmin(lst_input))*length/delta),0), lst_input)\n\nif __name__ == \"__main__\" :\n '''--------------------- General Parameters Secion -----------------'''\n an = Analytics(DataAgent(liveNet=False))\n dat_cursor = datetime(2019,4,16,15,1,tzinfo=timezone(cfg.STR_TIMEZONE))\n starttime = datetime.now().astimezone(timezone(cfg.STR_TIMEZONE))\n closestTradeDate = an.da.int_findClosestTradeDate(datetime.strftime(starttime,'%Y%m%d') )\n '''--------------------- General Parameters Secion -----------------'''\n \n '''--------------------- correlation analysis secion -----------------'''\n # int_startbound,int_endbound = 20180101,20190115\n # tpl_dateindex = tuple(str(i) for i in da.df_calTbl['cal_date'] if i>=int_startbound and i<=int_endbound)\n tpl_dateindex = tuple(str(i) for i in an.da.df_calTbl.loc[an.da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-30:] )\n tpl_baselist = ('000300.SH','399006.SZ')\n tpl_targetlist = tuple(an.da.df_stockTbl['ts_code']) \n # an.corelationAnalysis(str_reportName='stock',tpl_dateindex=tpl_dateindex,tpl_baselist=tpl_baselist,tpl_targetlist=tpl_targetlist)\n '''----------------- correlation analysis secion ends ----------------'''\n\n '''--------------------- Candle chart generation secion -----------------'''\n # int_startbound,int_endbound = 20180101,20190115\n # tpl_dateindex = tuple(str(i) for i in da.df_calTbl['cal_date'] if i>=int_startbound and i<=int_endbound)\n # tpl_dateindex = tuple(str(i) for i in da.df_calTbl.loc[da.df_calTbl['cal_date'] <= closestTradeDate]['cal_date'][-250:] )\n # an.candlechart('399006',subset='masterCandle',dat_cursor=None, MA=[5,10,21,60],renderfile=True)\n '''----------------- Cadle chart generation secion ends ----------------'''\n\n '''--------------------- Poll Msrket Summary secion -----------------'''\n # da.resampleStore(dat_cursor,tpl_targetlist=None)\n # an.pollsummary(renderfile=True)\n # libs.df_csv(cfg.PATH_BUGFILE,(df_stockBeatRollup,))\n '''----------------- Poll Msrket Summary secion ends ----------------'''\n \n '''--------------------- Macro Analysis -----------------'''\n # an.corelationMacro(lst_tscode=['000001.SH','000001.SZ'],lst_macro=['shibor.1w','pimon.cpi'],lst_findmedian=['pe',('000001.SZ',)],renderfile=True)\n '''--------------------- Macro Analysis ends -----------------'''\n ", "sub_path": "tsaisendo/analytics.py", "file_name": "analytics.py", "file_ext": "py", "file_size_in_byte": 43244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.formatCode", "line_number": 32, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 37, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "libs.tryOpenH5", "line_number": 43, "usage_type": "call"}, {"api_name": "config.H5_FILE_PRE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "libs.tryOpenH5", "line_number": 45, "usage_type": "call"}, {"api_name": "config.H5_FILE_PATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "libs.log_csv", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 60, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pandas.merge", "line_number": 78, "usage_type": "call"}, {"api_name": "pyecharts.charts.Kline", "line_number": 116, "usage_type": "call"}, {"api_name": "pyecharts.options.MarkPointOpts", "line_number": 119, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 119, "usage_type": "name"}, {"api_name": "pyecharts.options.MarkPointItem", "line_number": 120, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 120, "usage_type": "name"}, {"api_name": "pyecharts.options.MarkPointItem", "line_number": 121, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 121, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 124, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 124, "usage_type": "name"}, {"api_name": "pyecharts.options.TitleOpts", "line_number": 131, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 131, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 135, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 135, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 136, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 136, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitAreaOpts", "line_number": 138, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 138, "usage_type": "name"}, {"api_name": "pyecharts.options.AreaStyleOpts", "line_number": 139, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 139, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 142, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 142, "usage_type": "name"}, {"api_name": "pyecharts.options.DataZoomOpts", "line_number": 144, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 144, "usage_type": "name"}, {"api_name": "pyecharts.options.DataZoomOpts", "line_number": 151, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 151, "usage_type": "name"}, {"api_name": "pyecharts.options.TooltipOpts", "line_number": 160, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 160, "usage_type": "name"}, {"api_name": "pyecharts.options.TextStyleOpts", "line_number": 166, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 166, "usage_type": "name"}, {"api_name": "pyecharts.options.VisualMapOpts", "line_number": 169, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 169, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisPointerOpts", "line_number": 179, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 179, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 182, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 182, "usage_type": "name"}, {"api_name": "pyecharts.options.BrushOpts", "line_number": 184, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 184, "usage_type": "name"}, {"api_name": "pyecharts.charts.Line", "line_number": 193, "usage_type": "call"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 196, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 196, "usage_type": "name"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 205, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 205, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 206, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 206, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 208, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 208, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 209, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 209, "usage_type": "name"}, {"api_name": "pyecharts.charts.EffectScatter", "line_number": 212, "usage_type": "call"}, {"api_name": "pyecharts.globals.SymbolType.ARROW", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pyecharts.globals.SymbolType", "line_number": 214, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 214, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 214, "usage_type": "name"}, {"api_name": "pyecharts.globals.SymbolType.ARROW", "line_number": 215, "usage_type": "attribute"}, {"api_name": "pyecharts.globals.SymbolType", "line_number": 215, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 215, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 215, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 216, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 216, "usage_type": "name"}, {"api_name": "pyecharts.charts.Bar", "line_number": 219, "usage_type": "call"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 229, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 229, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 232, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 232, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 237, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 237, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 238, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 238, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 239, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 239, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 240, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 240, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 245, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 245, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 249, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 249, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 250, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 250, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 251, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 251, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 252, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 252, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 254, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 254, "usage_type": "name"}, {"api_name": "pyecharts.charts.Bar", "line_number": 259, "usage_type": "call"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 269, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 269, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 272, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 272, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 277, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 277, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 278, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 278, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 279, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 279, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 280, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 280, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 285, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 285, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 289, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 289, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 290, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 290, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 291, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 291, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 292, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 292, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 294, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 294, "usage_type": "name"}, {"api_name": "pyecharts.charts.Grid", "line_number": 299, "usage_type": "call"}, {"api_name": "pyecharts.options.GridOpts", "line_number": 302, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 302, "usage_type": "name"}, {"api_name": "pyecharts.options.GridOpts", "line_number": 306, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 306, "usage_type": "name"}, {"api_name": "pyecharts.options.GridOpts", "line_number": 310, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 310, "usage_type": "name"}, {"api_name": "config.PATH_ANAFILE", "line_number": 313, "usage_type": "attribute"}, {"api_name": "pyecharts.charts.Grid", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 323, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 323, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 323, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 323, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 324, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 324, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 329, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 332, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 332, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 333, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 333, "usage_type": "name"}, {"api_name": "Radar.Radar.pop_pulseRange", "line_number": 337, "usage_type": "call"}, {"api_name": "Radar.Radar", "line_number": 337, "usage_type": "name"}, {"api_name": "pandas.read_sql", "line_number": 338, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.dbsession.query", "line_number": 338, "usage_type": "call"}, {"api_name": "DBObj.Stock_poll", "line_number": 338, "usage_type": "argument"}, {"api_name": "DataAgent.DataAgent.dbsession", "line_number": 338, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 338, "usage_type": "name"}, {"api_name": "DBObj.Stock_poll.time_index", "line_number": 338, "usage_type": "attribute"}, {"api_name": "DBObj.Stock_poll.time_index", "line_number": 339, "usage_type": "attribute"}, {"api_name": "DBObj.Stock_poll", "line_number": 339, "usage_type": "name"}, {"api_name": "DBObj.Stock_poll.volume", "line_number": 340, "usage_type": "attribute"}, {"api_name": "DBObj.Stock_poll", "line_number": 340, "usage_type": "name"}, {"api_name": "DataAgent.DataAgent.dbsession", "line_number": 341, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 341, "usage_type": "name"}, {"api_name": "pandas.read_sql", "line_number": 342, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.dbsession.query", "line_number": 342, "usage_type": "call"}, {"api_name": "DBObj.Index_poll", "line_number": 342, "usage_type": "argument"}, {"api_name": "DataAgent.DataAgent.dbsession", "line_number": 342, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 342, "usage_type": "name"}, {"api_name": "DBObj.Index_poll.time_index", "line_number": 342, "usage_type": "attribute"}, {"api_name": "DBObj.Index_poll.time_index", "line_number": 343, "usage_type": "attribute"}, {"api_name": "DBObj.Index_poll", "line_number": 343, "usage_type": "name"}, {"api_name": "DBObj.Index_poll.volume", "line_number": 344, "usage_type": "attribute"}, {"api_name": "DBObj.Index_poll", "line_number": 344, "usage_type": "name"}, {"api_name": "DataAgent.DataAgent.dbsession", "line_number": 345, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 345, "usage_type": "name"}, {"api_name": "libs.tryOpenH5", "line_number": 358, "usage_type": "call"}, {"api_name": "config.H5_FILE_PRE", "line_number": 358, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 364, "usage_type": "attribute"}, {"api_name": "libs.tryOpenH5", "line_number": 381, "usage_type": "call"}, {"api_name": "config.H5_FILE_PATH", "line_number": 381, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 387, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 388, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 390, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 391, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.re_bigUpBoxPat", "line_number": 403, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 403, "usage_type": "name"}, {"api_name": "DataAgent.DataAgent.re_bigDnBoxPat", "line_number": 405, "usage_type": "attribute"}, {"api_name": "DataAgent.DataAgent", "line_number": 405, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 416, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 417, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 418, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 419, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 423, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 424, "usage_type": "attribute"}, {"api_name": "pyecharts.charts.Boxplot", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 435, "usage_type": "call"}, {"api_name": "pyecharts.options.TitleOpts", "line_number": 439, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 439, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 440, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 440, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 441, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 441, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 442, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 442, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 443, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 443, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 444, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 444, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 445, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 445, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 446, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 446, "usage_type": "name"}, {"api_name": "pyecharts.charts.Bar", "line_number": 455, "usage_type": "call"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 460, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 460, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 461, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 461, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 468, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 468, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 469, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 469, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 476, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 476, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 477, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 477, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 484, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 484, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 485, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 485, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 488, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 488, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 489, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 489, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 490, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 490, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 491, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 491, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisTickOpts", "line_number": 492, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 492, "usage_type": "name"}, {"api_name": "pyecharts.options.SplitLineOpts", "line_number": 493, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 493, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 495, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 495, "usage_type": "name"}, {"api_name": "pyecharts.charts.Grid", "line_number": 498, "usage_type": "call"}, {"api_name": "pyecharts.options.GridOpts", "line_number": 501, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 501, "usage_type": "name"}, {"api_name": "pyecharts.options.GridOpts", "line_number": 505, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 505, "usage_type": "name"}, {"api_name": "config.PATH_ANAFILE", "line_number": 507, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 515, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 515, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 515, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 515, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 516, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 516, "usage_type": "name"}, {"api_name": "pyecharts.charts.Line", "line_number": 521, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.formatCode", "line_number": 526, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent", "line_number": 526, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 529, "usage_type": "call"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 539, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 539, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 540, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 540, "usage_type": "name"}, {"api_name": "pyecharts.options.ItemStyleOpts", "line_number": 541, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 541, "usage_type": "name"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 552, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 552, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 553, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 553, "usage_type": "name"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 558, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 558, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 559, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 559, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 585, "usage_type": "call"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 595, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 595, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 596, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 596, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 599, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 607, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 614, "usage_type": "call"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 621, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 621, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 622, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 622, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 625, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 625, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisLineOpts", "line_number": 629, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 629, "usage_type": "name"}, {"api_name": "pyecharts.options.LineStyleOpts", "line_number": 630, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 630, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 632, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 632, "usage_type": "name"}, {"api_name": "pyecharts.options.TitleOpts", "line_number": 636, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 636, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 639, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 639, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisOpts", "line_number": 640, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 640, "usage_type": "name"}, {"api_name": "pyecharts.options.LegendOpts", "line_number": 643, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 643, "usage_type": "name"}, {"api_name": "pyecharts.options.DataZoomOpts", "line_number": 645, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 645, "usage_type": "name"}, {"api_name": "pyecharts.options.DataZoomOpts", "line_number": 652, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 652, "usage_type": "name"}, {"api_name": "pyecharts.options.TooltipOpts", "line_number": 661, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 661, "usage_type": "name"}, {"api_name": "pyecharts.options.TextStyleOpts", "line_number": 667, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 667, "usage_type": "name"}, {"api_name": "pyecharts.options.AxisPointerOpts", "line_number": 669, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 669, "usage_type": "name"}, {"api_name": "pyecharts.options.LabelOpts", "line_number": 672, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 672, "usage_type": "name"}, {"api_name": "pyecharts.options.BrushOpts", "line_number": 674, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 674, "usage_type": "name"}, {"api_name": "config.PATH_ANAFILE", "line_number": 681, "usage_type": "attribute"}, {"api_name": "config.PATH_ANAFILE", "line_number": 686, "usage_type": "attribute"}, {"api_name": "pyecharts.charts.Line", "line_number": 511, "usage_type": "name"}, {"api_name": "config.PATH_ANAFILE", "line_number": 692, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 706, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 710, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 713, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent.fq", "line_number": 736, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent", "line_number": 736, "usage_type": "name"}, {"api_name": "pandas.DataFrame.merge", "line_number": 740, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 740, "usage_type": "attribute"}, {"api_name": "libs.df_csv", "line_number": 761, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.nanmin", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.nanmin", "line_number": 768, "usage_type": "call"}, {"api_name": "DataAgent.DataAgent", "line_number": 772, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 773, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 773, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 773, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 774, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 774, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 774, "usage_type": "call"}, {"api_name": "config.STR_TIMEZONE", "line_number": 774, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 775, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 775, "usage_type": "name"}]}
{"seq_id": "441975551", "text": "import json\nimport logging\nimport socket\n\nimport survillo.components.servos.micro_servo_sg90 as sg90\nimport survillo.components.servos.micro_servo_ts90a as ts90a\nimport survillo.crane.controlled_servo as cs\n\n_IP = '0.0.0.0'\n_PORT = 10003\n_HORIZONTAL_SERVO_PIN = 17\n_VERTICAL_SERVO_PIN = 27\n\n_logger = logging.getLogger(__name__)\n\n\nclass ServoServer:\n\n def __init__(self, horizontal_servo, vertical_servo):\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server_socket:\n _logger.debug('Server socket created')\n\n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n server_socket.bind((_IP, _PORT))\n server_socket.listen(1)\n\n while True:\n _logger.debug('Server socket waiting for incoming connections')\n\n client_socket, from_address = server_socket.accept()\n with client_socket:\n _logger.debug('Connection established with {}'.format(from_address))\n\n while True:\n data = client_socket.recv(1024)\n\n if not data:\n break\n\n shutdown = False\n\n msg = data.decode('utf-8')\n for split_msg in msg.strip().split():\n\n if not split_msg:\n _logger.warning('Empty string received from server')\n continue\n\n _logger.debug('Command from client: {}'.format(split_msg))\n\n if split_msg == 'q':\n # Shut down the servos.\n horizontal_servo.set_position(None)\n vertical_servo.set_position(None)\n shutdown = True\n break\n\n try:\n json_message = json.loads(split_msg)\n except json.JSONDecodeError as err:\n _logger.error('Could not json-decode: {}'.format(split_msg))\n continue\n\n position_x = float(json_message.get('px'))\n position_y = float(json_message.get('py'))\n if position_x is None or position_y is None:\n _logger.warning('String does not match expected pattern: {}'.format(split_msg))\n continue\n\n # The horizontal servo is upside down.\n horizontal_servo.set_position(normalized_position=-position_x)\n vertical_servo.set_position(normalized_position=position_y)\n\n if shutdown:\n break\n\n _logger.debug('Closing client socket')\n\n\ndef main():\n horizontal_servo = cs.ControlledServo(pin=_HORIZONTAL_SERVO_PIN,\n min_pulse_width_s=sg90.MIN_PULSE_WIDTH_s,\n max_pulse_width_s=sg90.MAX_PULSE_WIDTH_s,\n frame_width_s=sg90.FRAME_WIDTH_s,\n nominal_max_angle_deg=sg90.MAX_ANGLE_deg,\n range_deg=None,\n default_angle_deg=0.0)\n\n vertical_servo = cs.ControlledServo(pin=_VERTICAL_SERVO_PIN,\n min_pulse_width_s=ts90a.MIN_PULSE_WIDTH_s,\n max_pulse_width_s=ts90a.MAX_PULSE_WIDTH_s,\n frame_width_s=ts90a.FRAME_WIDTH_s,\n nominal_max_angle_deg=ts90a.MAX_ANGLE_deg,\n range_deg=[-90, -60],\n default_angle_deg=-90)\n\n ServoServer(horizontal_servo=horizontal_servo, vertical_servo=vertical_servo)\n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.DEBUG)\n main()\n", "sub_path": "test/servo_server.py", "file_name": "servo_server.py", "file_ext": "py", "file_size_in_byte": 4173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 20, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 20, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 60, "usage_type": "attribute"}, {"api_name": "survillo.crane.controlled_servo.ControlledServo", "line_number": 81, "usage_type": "call"}, {"api_name": "survillo.crane.controlled_servo", "line_number": 81, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_sg90.MIN_PULSE_WIDTH_s", "line_number": 82, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_sg90", "line_number": 82, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_sg90.MAX_PULSE_WIDTH_s", "line_number": 83, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_sg90", "line_number": 83, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_sg90.FRAME_WIDTH_s", "line_number": 84, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_sg90", "line_number": 84, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_sg90.MAX_ANGLE_deg", "line_number": 85, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_sg90", "line_number": 85, "usage_type": "name"}, {"api_name": "survillo.crane.controlled_servo.ControlledServo", "line_number": 89, "usage_type": "call"}, {"api_name": "survillo.crane.controlled_servo", "line_number": 89, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_ts90a.MIN_PULSE_WIDTH_s", "line_number": 90, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_ts90a", "line_number": 90, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_ts90a.MAX_PULSE_WIDTH_s", "line_number": 91, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_ts90a", "line_number": 91, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_ts90a.FRAME_WIDTH_s", "line_number": 92, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_ts90a", "line_number": 92, "usage_type": "name"}, {"api_name": "survillo.components.servos.micro_servo_ts90a.MAX_ANGLE_deg", "line_number": 93, "usage_type": "attribute"}, {"api_name": "survillo.components.servos.micro_servo_ts90a", "line_number": 93, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 101, "usage_type": "attribute"}]}
{"seq_id": "601288916", "text": "import pymongo\nimport re\nfrom datetime import date, timedelta\n# ------------------------------------------\nfrom ToolPack import tools\nfrom KeyGraph_Approach import common_phrases\n\n\ndef split_sentences(doc):\n p_sent_tokenizer = tools.load_pickle(\"/home/iraklis/PycharmProjects/AllTheNews/Pivot_Files/\"\n \"Classifiers/PunktSentenceTokenizer.pickle\")\n sentences = p_sent_tokenizer.sentences_from_text(doc)\n return sentences\n\n\n# Clean your documents with this method before searching for the noun phrases\ndef document_cleaning(doc_sentences):\n stop_words = common_phrases.PhraseDetection.load_stopwords()\n documents = list()\n doc_range = list()\n index = 0\n doc_number = 0\n\n for line in doc_sentences:\n # line_to_lowercase = line.lower()\n clean_line = re.sub('[^A-Za-z0-9]+', ' ', line) # line_to_lowercase\n documents.append(clean_line)\n index += 1\n doc_range.append(index)\n doc_number += 1\n\n documents = [doc.strip() for doc in documents]\n clean_documents = list()\n\n for doc in documents:\n clean_documents.append(' '.join([word for word in doc.split() if word not in stop_words]))\n\n documents = clean_documents[:]\n\n return documents\n\n\ndef gather_documents(c_week):\n # Getting data from the mongo database\n client = pymongo.MongoClient()\n # Database name is minedNews\n db = client.all_the_news\n\n path = \"/home/iraklis/PycharmProjects/AllTheNews/KeyGraph_Approach/Week_docs_sentences/\"\n week_clean_sents = list()\n with open(path + c_week + '_ids.txt', 'w') as week_file:\n\n for document in db[c_week].find({}, no_cursor_timeout=True):\n line_sentences = split_sentences(document[\"text\"])\n for sent in line_sentences:\n week_file.write(sent + \"\\n\")\n clean_sent = document_cleaning(line_sentences)\n week_clean_sents.append(clean_sent)\n tools.save_pickle(path + c_week + \"sents_of_doc.pickle\", week_clean_sents)\n\n\ndef gather_documents_per_day():\n # Getting data from the mongo database\n client = pymongo.MongoClient()\n # Database name is minedNews\n db = client.all_the_news\n\n path = \"/home/iraklis/PycharmProjects/AllTheNews/KeyGraph_Approach/Day_doc_sentences/\"\n start_day = date(2016, 1, 4)\n stop_day = date(2017, 1, 1)\n c_day = start_day\n c_week = str(start_day.isocalendar()[0]) + \"-\" + str(start_day.isocalendar()[1])\n\n while c_day < stop_day:\n day_clean_sents = list()\n current_date = str(c_day.year) + \"-\" + str(c_day.month) + \"-\" + str(c_day.day)\n print(current_date)\n with open(path + current_date + '_ids.txt', 'w') as week_file:\n for document in db[c_week].find({\"date\": current_date}, no_cursor_timeout=True):\n line_sentences = split_sentences(document[\"text\"])\n for sent in line_sentences:\n week_file.write(sent + \"\\n\")\n clean_sent = document_cleaning(line_sentences)\n if clean_sent:\n day_clean_sents.append(clean_sent)\n else:\n print(\"Got one on:\", current_date, \" \", c_week)\n print(document[\"_id\"])\n c_day += timedelta(1)\n c_week = str(c_day.isocalendar()[0]) + \"-\" + str(c_day.isocalendar()[1])\n tools.save_pickle(path + current_date + \"_sents_of_doc.pickle\", day_clean_sents)\n\n\nif __name__ == \"__main__\":\n # gather_documents_per_day()\n a = tools.load_pickle(\"/home/iraklis/PycharmProjects/AllTheNews/KeyGraph_Approach/\"\n \"Day_doc_sentences/2016-1-4_sents_of_doc.pickle\")\n print()\n\n", "sub_path": "KeyGraph_Approach/day_code/day_doc_preprocess.py", "file_name": "day_doc_preprocess.py", "file_ext": "py", "file_size_in_byte": 3684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "ToolPack.tools.load_pickle", "line_number": 10, "usage_type": "call"}, {"api_name": "ToolPack.tools", "line_number": 10, "usage_type": "name"}, {"api_name": "KeyGraph_Approach.common_phrases.PhraseDetection.load_stopwords", "line_number": 18, "usage_type": "call"}, {"api_name": "KeyGraph_Approach.common_phrases.PhraseDetection", "line_number": 18, "usage_type": "attribute"}, {"api_name": "KeyGraph_Approach.common_phrases", "line_number": 18, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 26, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 45, "usage_type": "call"}, {"api_name": "ToolPack.tools.save_pickle", "line_number": 59, "usage_type": "call"}, {"api_name": "ToolPack.tools", "line_number": 59, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "ToolPack.tools.save_pickle", "line_number": 91, "usage_type": "call"}, {"api_name": "ToolPack.tools", "line_number": 91, "usage_type": "name"}, {"api_name": "ToolPack.tools.load_pickle", "line_number": 96, "usage_type": "call"}, {"api_name": "ToolPack.tools", "line_number": 96, "usage_type": "name"}]}
{"seq_id": "256394574", "text": "import numpy as np\nfrom shapely.ops import transform\nimport os\nfrom tempfile import NamedTemporaryFile\nfrom shapely.geometry import shape, box\nfrom pyveda.utils import from_bounds\nimport numpy as np\nfrom skimage.draw import polygon\nfrom skimage.io import imread\n\n\nclass NDImageHandler(object):\n _default_dtype = np.float32\n\n @staticmethod\n def _payload_handler(*args, **kwargs):\n return NDImageHandler._bytes_to_array(*args, **kwargs)\n\n @staticmethod\n def _on_fail(shape=(3, 256, 256), dtype=np.uint8):\n return np.zeros(shape, dtype=dtype)\n\n @staticmethod\n def _bytes_to_array(bstring):\n if bstring is None:\n return on_fail()\n try:\n fd = NamedTemporaryFile(prefix='veda', suffix='.tif', delete=False)\n fd.file.write(bstring)\n fd.file.flush()\n fd.close()\n arr = imread(fd.name)\n if len(arr.shape) == 3:\n arr = np.rollaxis(arr, 2, 0)\n else:\n arr = np.expand_dims(arr, axis=0)\n except Exception as e:\n arr = NDImageHandler._on_fail()\n finally:\n fd.close()\n os.remove(fd.name)\n return arr\n\n\nclass BaseLabelHandler(object):\n @staticmethod\n def _get_transform(bounds, height, width):\n return from_bounds(*bounds, width, height)\n\n @staticmethod\n def _parse_response(res):\n return res['properties']['label']\n\n @staticmethod\n def _payload_handler(*args, **kwargs):\n raise NotImplementedError\n\n\nclass ClassificationHandler(BaseLabelHandler):\n _default_dtype = np.uint8\n\n @staticmethod\n def _payload_handler(item, klasses=[], **kwargs):\n payload = ClassificationHandler._parse_response(item)\n return [payload[klass] for klass in klasses]\n\n\nclass SegmentationHandler(BaseLabelHandler):\n _default_dtype = np.float32\n\n @staticmethod\n def _payload_handler(*args, **kwargs):\n return SegmentationHandler._handle_pixel_payload(*args, **kwargs)\n\n @staticmethod\n def _handle_pixel_payload(item, klasses=[], out_shape=None, **kwargs):\n payload = SegmentationHandler._parse_response(item)\n if len(out_shape) == 3:\n out_shape = out_shape[-2:]\n out_array = np.zeros(out_shape)\n value = 1\n for klass in klasses:\n shapes = payload[klass]\n try:\n out_array += rasterize(((shape(g), value) for g in shapes), out_shape=out_shape)\n except Exception as e:\n pass\n return out_array\n\n @staticmethod\n def _handle_geo_payload(item, imshape):\n out_shape = imshape\n if len(imshape) == 3:\n out_shape = imshape[-2:]\n out_array = np.zeros(out_shape)\n value = 1\n for k, features in item['data']['label'].items():\n try:\n out_array += SegmentationHandler._create_mask(features, value, out_shape)\n value += 1\n except Exception as e: # I think this is ValueError from rasterio but need check\n pass\n return out_array\n\n @staticmethod\n def _create_mask(shapes, value, _shape):\n mask = np.zeros(_shape, dtype=np.uint8)\n for f in shapes:\n coords = f['coordinates'][0]\n r, c = zip(*[(x,y) for x,y in coords])\n rr, cc = polygon(np.array(r), np.array(c))\n mask[rr, cc] = value\n\n\nclass ObjDetectionHandler(BaseLabelHandler):\n _default_dtype = np.float32\n\n @staticmethod\n def _payload_handler(*args, **kwargs):\n return ObjDetectionHandler._handle_pixel_payload(*args, **kwargs)\n\n @staticmethod\n def _handle_pixel_payload(item, klasses=[], out_shape=None, **kwargs):\n payload = ObjDetectionHandler._parse_response(item)\n return [payload[klass] for klass in klasses]\n\n @staticmethod\n def _handle_geo_payload(item, imshape):\n out_shape = imshape\n if len(imshape) == 3:\n out_shape = imshape[-2:]\n xfm = BaseLabelHandler._get_transform(item['data']['bounds'], *out_shape)\n labels = []\n for k, features in item['data']['label'].items():\n class_labels = []\n for i, f in enumerate(features):\n b = shape(f).bounds\n ll, ur = ~xfm * (b[0],b[1]), ~xfm * (b[2],b[3])\n class_labels.append([*ll, *ur])\n labels.append(class_labels)\n return labels\n\n", "sub_path": "pyveda/fetch/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 4459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.float32", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "shapely.geometry.shape", "line_number": 21, "usage_type": "argument"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 28, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.rollaxis", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 36, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 41, "usage_type": "call"}, {"api_name": "pyveda.utils.from_bounds", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "shapely.geometry.shape", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 107, "usage_type": "attribute"}, {"api_name": "skimage.draw.polygon", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 116, "usage_type": "attribute"}, {"api_name": "shapely.geometry.shape", "line_number": 137, "usage_type": "call"}]}
{"seq_id": "642457957", "text": "import json\nimport os\nimport sys\nfrom string import Template\n\nimport sublime\n\nfrom .lib import plistlib\n\nsublime_settings = \"Preferences.sublime-settings\"\nscope_name = \"colored.comments.color.\"\nMSG = Template(\n \"\"\"\nWould you like to change your color scheme to '$scheme'?\nTo permanently disable this prompt, set 'prompt_new_color_scheme' \nto false in the Colored Comments settings.\"\"\"\n)\n\nsublime_default_cs = [\n \"Mariana.sublime-color-scheme\",\n \"Celeste.sublime-color-scheme\",\n \"Monokai.sublime-color-scheme\",\n \"Breakers.sublime-color-schem\",\n \"Sixteen.sublime-color-scheme\",\n]\n\n\nclass ColorManager:\n def __init__(self, new_color_scheme_path, tags, view, settings, regenerate, log):\n self.new_color_scheme_path = new_color_scheme_path\n self.view = view\n self.sublime_pref = None\n self.tags = tags\n self.settings = settings\n self.regenerate = regenerate\n self.log = log\n self.color_scheme = str()\n self.update_preferences = True\n self.awaiting_feedback = False\n\n def get_update_pref(self):\n return self.update_preferences\n\n def get_awaiting_feedback(self):\n return self.awaiting_feedback\n\n def set_awaiting_feedback(self, status):\n self.awaiting_feedback = status\n\n def _add_colors_to_scheme(self, color_scheme, is_json):\n scheme_rule_key = \"rules\" if is_json else \"settings\"\n settings = color_scheme[scheme_rule_key]\n scope_exist = bool()\n updates = bool()\n\n for tag in self.tags:\n curr_tag = self.tags[tag]\n if not curr_tag.get(\"color\", False):\n continue\n\n name = _get_color_property(\"name\", curr_tag)\n background = _get_color_property(\"background\", curr_tag)\n foreground = _get_color_property(\"foreground\", curr_tag)\n if False in [name, background, foreground]:\n continue\n\n scope = \"{}{}\".format(scope_name, name.lower().replace(\" \", \".\"))\n\n for setting in settings:\n if \"scope\" in setting and setting[\"scope\"] == scope:\n scope_exist = True\n\n if not scope_exist:\n updates = True\n entry = dict()\n entry[\"name\"] = \"[Colored Comments] {}\".format(name.title())\n entry[\"scope\"] = scope\n if is_json:\n entry[\"foreground\"] = foreground\n entry[\"background\"] = background\n else:\n entry[\"settings\"] = dict()\n entry[\"settings\"][\"foreground\"] = foreground\n entry[\"settings\"][\"background\"] = background\n\n settings.append(entry)\n color_scheme[scheme_rule_key] = settings\n return updates, color_scheme\n\n def _create_custom_color_scheme_directory(self):\n path = os.path.join(sublime.packages_path(), self.new_color_scheme_path)\n if not os.path.exists(path):\n os.makedirs(path)\n return path\n\n def create_user_custom_theme(self):\n if self.awaiting_feedback:\n return\n self.awaiting_feedback = True\n if not self.tags:\n self.awaiting_feedback = False\n return\n\n self.sublime_pref = sublime.load_settings(sublime_settings)\n color_scheme = self.sublime_pref.get(\"color_scheme\")\n if self.regenerate and self.settings.get(\"old_color_scheme\", \"\") != \"\":\n color_scheme = self.settings.get(\"old_color_scheme\", \"\")\n\n self.settings.set(\"old_color_scheme\", color_scheme)\n sublime.save_settings(\"colored_comments.sublime-settings\")\n cs_base = os.path.basename(color_scheme)\n\n if cs_base[0:16] != \"Colored Comments\":\n cs_base = \"{}{}\".format(\"Colored Comments-\", cs_base)\n\n custom_color_base = self._create_custom_color_scheme_directory()\n new_cs_absolute = os.path.join(custom_color_base, cs_base)\n self.color_scheme = \"{}{}{}{}\".format(\n \"Packages/\", self.new_color_scheme_path, \"/\", cs_base\n )\n print(self.color_scheme)\n\n updates, loaded_scheme, is_json = self.load_color_scheme(color_scheme)\n\n if self.regenerate or updates or color_scheme != self.color_scheme:\n try:\n os.remove(new_cs_absolute)\n except OSError as ex:\n self.log.debug(str(ex))\n pass\n if is_json:\n with open(new_cs_absolute, \"w\") as outfile:\n json.dump(loaded_scheme, outfile, indent=4)\n else:\n with open(new_cs_absolute, \"wb\") as outfile:\n outfile.write(plistlib.dumps(loaded_scheme))\n\n if color_scheme != self.color_scheme:\n if sublime.ok_cancel_dialog(\n MSG.substitute(scheme=self.color_scheme), \"Confirm\"\n ):\n self.sublime_pref.set(\"color_scheme\", self.color_scheme)\n sublime.save_settings(\"Preferences.sublime-settings\")\n self.settings.set(\"prompt_new_color_scheme\", False)\n sublime.save_settings(\"colored_comments.sublime-settings\")\n self.update_preferences = False\n self.awaiting_feedback = False\n\n def load_color_scheme(self, scheme):\n is_json = bool()\n try:\n if scheme in sublime_default_cs:\n scheme = \"{}{}\".format(\"Packages/Color Scheme - Default/\", scheme)\n scheme_content = sublime.load_binary_resource(scheme)\n except Exception as ex:\n sublime.error_message(\n \" \".join(\n [\n \"An error occured while reading color\",\n \"scheme file. Please check the console\",\n \"for details.\",\n ]\n )\n )\n self.log.debug(\n \"[Colored Comments]: {} - {}\".format(\n self.load_color_scheme.__name__, ex\n )\n )\n raise\n if scheme.endswith(\".sublime-color-scheme\"):\n is_json = True\n updates, color_scheme = self._add_colors_to_scheme(\n sublime.decode_value(scheme_content.decode(\"utf-8\")), is_json\n )\n elif scheme.endswith(\".tmTheme\"):\n updates, color_scheme = self._add_colors_to_scheme(\n plistlib.loads(bytes(scheme_content)), is_json\n )\n else:\n sys.exit(1)\n return updates, color_scheme, is_json\n\n\ndef _get_color_property(property, tags):\n if not tags[\"color\"].get(property, False):\n return False\n return tags[\"color\"][property]\n", "sub_path": "color_manager.py", "file_name": "color_manager.py", "file_ext": "py", "file_size_in_byte": 6732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "string.Template", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sublime.packages_path", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "sublime.load_settings", "line_number": 104, "usage_type": "call"}, {"api_name": "sublime.save_settings", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "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.remove", "line_number": 127, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 133, "usage_type": "call"}, {"api_name": "lib.plistlib.dumps", "line_number": 136, "usage_type": "call"}, {"api_name": "lib.plistlib", "line_number": 136, "usage_type": "name"}, {"api_name": "sublime.ok_cancel_dialog", "line_number": 139, "usage_type": "call"}, {"api_name": "sublime.save_settings", "line_number": 143, "usage_type": "call"}, {"api_name": "sublime.save_settings", "line_number": 145, "usage_type": "call"}, {"api_name": "sublime.load_binary_resource", "line_number": 154, "usage_type": "call"}, {"api_name": "sublime.error_message", "line_number": 156, "usage_type": "call"}, {"api_name": "sublime.decode_value", "line_number": 174, "usage_type": "call"}, {"api_name": "lib.plistlib.loads", "line_number": 178, "usage_type": "call"}, {"api_name": "lib.plistlib", "line_number": 178, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 181, "usage_type": "call"}]}
{"seq_id": "230874698", "text": "from __future__ import print_function\nimport urllib2, urllib\nfrom bs4 import BeautifulSoup, Comment\nimport os\nimport glob\n\nliveFileDir = '/wiki/liveArticles/' # dir of your live Wikipedia articles downloaded from scrapeWikiVital.py\nstaticFileDir = '/VOLUMES/LIBRARYBOX/static-Wikipedia-100' # dir of where you want the static copies of the Wikipedia articles\nmediaFileDir = staticFileDir+'/wiki/en/' # the static articles will be placed within a /wiki/en/ for the English versions\n\nif not os.path.exists(mediaFileDir):\n\t\ttry:\n\t\t\tos.makedirs(mediaFileDir)\n\t\texcept:\n\t\t\tpass\n\ndef convertWikiArticle(filename):\n\tglobal liveFileDir, staticFileDir, mediaFileDir\n\tfrenchlink = ''\n\n\twith open(filename,'r') as f:\n\t\ts = f.read()\n\n\tarticleName = filename\n\tarticleName = urllib.unquote(articleName).decode('utf8').strip()\n\tarticleName = articleName.replace('.'+liveFileDir,'').strip()\n\tprint(articleName)\n\n\tsoup = BeautifulSoup(s)\n\n\tfor div in soup.findAll('table', { 'class' : 'navbox' }):\n\t\tdiv.extract()\n\n\tfor div in soup.findAll('table', { 'class' : 'persondata' }):\n\t\tdiv.extract()\n\n\tfor div in soup.findAll('div', { 'class' : 'noprint' }):\n\t\tdiv.extract()\n\n\tfor div in soup.findAll('ul', { 'class' : 'noprint' }):\n\t\tdiv.extract()\t\t\n\n\t#for div in soup.findAll('div', { 'class' : 'thumb' }):\n\t#\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'class' : 'hatnote' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'class' : 'after-portlet' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'mw-head' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'p-logo' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'catlinks' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'p-interaction' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'p-tb' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'siteNotice' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'jump-to-nav' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'p-coll-print_export' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'id' : 'section_SpokenWikipedia' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'class' : 'ns-0' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('div', { 'class' : 'topicon' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('ul', { 'id' : 'footer-places' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('link', { 'rel' : 'dns-prefetch' }):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('link', { 'rel' : 'stylesheet' }):\n\t\tdiv.extract()\t\t\n\n\t#for div in soup.findAll('table', { 'class' : 'plainlinks' }):\n\t#\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('script'):\n\t\tdiv.extract()\t\t\n\n\tfor div in soup.findAll('style'):\n\t\tdiv.extract()\t\n\n\tfor div in soup.findAll('span', { 'class' : 'mw-editsection' }):\n\t\tdiv.extract()\t\n\n\tcomments = soup.findAll(text=lambda text:isinstance(text, Comment))\n\t[comment.extract() for comment in comments]\n\n\tstyletag = soup.new_tag('link')\n\tstyletag.attrs['rel'] = 'stylesheet'\n\tstyletag.attrs['href'] = '../css/style.css'\n\tsoup.head.append(styletag)\n\n\tfor div in soup.findAll('a', { 'class' : 'external text'}):\n\t\tdiv['target'] = '_blank'\n\n\tfor div in soup.findAll('a', { 'dir' : 'ltr'}):\n\t\t#div.unwrap()\n\t\tpass\n\n\tfor div in soup.findAll('a', { 'lang' : 'fr' , 'hreflang' : 'fr'}):\n\t\tfrenchlink = div['href']\n\t\tprint('FrenchLink: '+frenchlink) # get the French link for the article to download, and write it to a file to get later\n\t\t\n\t\tif frenchlink != '':\n\t\t\twith open(\"lists/listFrenchArticles2Download.txt\", \"a\") as myfile:\n\t\t\t\tmyfile.write('http:'+frenchlink+'\\n')\n\t\telse:\n\t\t\twith open(\"lists/missingVitalFrenchArticles.txt\", \"a\") as myfile:\n\t\t\t\tmyfile.write(articleName+'\\n')\n\n\tfor div in soup.findAll('li', { 'id' : 'footer-info-copyright'}):\n\t\tdiv.clear()\n\t\tdiv.append('Text is available under the ')\n\t\tnew_tag = soup.new_tag(\"a\", href=\"Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License.html\")\n\t\tnew_tag.string = 'Creative Commons Attribution-ShareAlike License'\n\t\tdiv.append(new_tag)\n\t\tdiv.append('; additional copyright terms may apply to images, audio and video. Please see this article on wikipedia.org for copyright information.')\n\n\t# Clear and then rebuild the navigation sidebar; needs to be rewritten in reverse order\n\tfor div in soup.findAll('div', { 'id' : 'p-navigation' }):\n\t\tdiv.clear()\n\n\t\tnew_tag = soup.new_tag('h3')\n\t\tnew_tag.string = 'Search Wikipedia'\n\t\tdiv.append(new_tag)\n\t\tdiv.h3['id']='p-navigation-label'\n\n\n\t\tnew_tag = soup.new_tag('input')\n\t\t#new_tag.string = 'Search'\n\t\tdiv.append(new_tag)\n\t\tdiv.input['id']='searchInput'\n\t\tdiv.input['type']='search'\n\t\tdiv.input['name']='search'\n\t\tdiv.input['accesskey']='f'\n\t\tdiv.input['placeholder']='Search'\n\t\tdiv.input['style']='margin-left:10px;margin-bottom:10px;width:130px;'\n\n\n\t\tnew_tag = soup.new_tag('h3')\n\t\tnew_tag.string = 'Navigation'\n\t\tdiv.append(new_tag)\n\t\tdiv.h3['id']='p-navigation-label'\n\n\t\tnew_tag = soup.new_tag('div', { 'class' : \"body\" })\n\t\tdiv.append(new_tag)\n\t\tdiv.div['class']='body'\n\n\t\tnew_tag = soup.new_tag('ul')\n\t\tdiv.div.append(new_tag)\n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.div.ul.append(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Mathematics')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Mathematics.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Technology')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Technology.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Physical sciences')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Physical_sciences.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Biology and health sciences')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Biology_and_health_sciences.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Society and social sciences')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Society_and_social_sciences.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Everyday life')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Everyday_life.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Philosophy and religion')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Philosophy_and_religion.html'\n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Arts')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Arts.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('Geography')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_Geography.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('History')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_History.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = ('People')\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Subject_People.html' \n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = 'Main Page'\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/Main_Page.html'\n\n\t# Clear and then rebuild the language sidebar\n\tfor div in soup.findAll('div', { 'id' : 'p-lang' }):\n\t\tdiv.clear()\n\t\tnew_tag = soup.new_tag('h3')\n\t\tnew_tag.string = 'Languages'\n\t\tdiv.append(new_tag)\n\t\tdiv.h3['id']='p-lang-label'\n\t\tnew_tag = soup.new_tag('div', { 'class' : \"body\" })\n\t\tdiv.append(new_tag)\n\t\tdiv.div['class']='body'\n\t\tnew_tag = soup.new_tag('ul')\n\t\tdiv.div.append(new_tag)\n\n\n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.div.ul.append(new_tag)\n\n\n\t\tif frenchlink != '':\n\t\t\tnew_tag = soup.new_tag('a')\t\n\t\t\tdiv.li.append(new_tag)\n\t\t\tdiv.li.a['href']='../fr/'+articleName # needs to be changed to whatever the French title is\n\t\t\tnew_tag.string = (u'Fran\\u00E7ais')\n\t\telse:\n\t\t\tnew_tag = soup.new_tag('span')\n\n\n\n\t\tnew_tag = soup.new_tag('li')\n\t\tdiv.li.insert_before(new_tag)\n\t\tnew_tag = soup.new_tag('a')\n\t\tnew_tag.string = 'English'\n\t\tdiv.li.append(new_tag)\n\t\tdiv.li.a['href']='../en/'+articleName+'.html'\n\t\t\n\n\tfor div in soup.find('div', { 'id' : 'mw-content-text' }).findAll('a'):\n\n\t\threflink = str(div['href'].encode('utf-8'))\n\t\tif hreflink[:1] != '#' and '/wiki/' in hreflink:\n\t\t\tdiv['href'] = div['href'].replace('/wiki/','../en/')\n\t\t\tdiv['href'] += '.html'\n\n\t\n\ttry:\n\t\tfor div in soup.find('div', { 'id' : 'mw-content-text' }).findAll('img'):\n\t\t\t\n\t\t\timglink = str(div['src'].encode('utf-8'))\n\t\t\tprint(imglink)\n\t\t\t#imglink = imglink.replace('//upload.wikimedia.org/wikipedia/','../')\n\t\t\t#print(imglink)\n\t\t\tif not '?' in imglink:\n\t\t\t\twith open(\"lists/listInfoBoxImgs.txt\", \"a\") as myfile:\n\t\t\t\t\tmyfile.write(imglink+'\\n')\n\n\texcept:\n\t\tpass\t\n\n\ttry:\n\t\tfor div in soup.find('div', { 'id' : 'mw-content-text' }).findAll('source'):\n\t\t\t\n\t\t\togglink = str(div['src'].encode('utf-8'))\n\t\t\tprint(ogglink)\n\t\t\t#ogglink = ogglink.replace('//upload.wikimedia.org/wikipedia/','../')\n\t\t\t#print(imglink)\n\n\t\t\twith open(\"lists/listInfoBoxOggs.txt\", \"a\") as myfile:\n\t\t\t\tmyfile.write(ogglink+'\\n')\n\n\texcept:\n\t\tpass\t\t\t\n\n\n\t\n\n\n\tsoup.prettify()\n\t\n\n\tnew_soup = str(soup)\n\tnew_soup = new_soup.replace('//upload.wikimedia.org/wikipedia/','../')\n\t\n\n\ttry:\n\t\tpath = staticFileDir # I ran out of space on my laptop, so I'm just going to throw all these files on a USB\n\t\tf = open(path+'/wiki/en/'+articleName,'w')\n\t\tprint(new_soup,file=f)\n\t\tf.close\n\texcept:\n\t\tprint('Static save failed!!!')\n\ni=0\nfor infile in glob.glob( os.path.join('./wiki/liveArticles/', '*.html') ):\n\ti+=1\n\tprint('Article #'+str(i))\n\tprint(infile)\n\tconvertWikiArticle(infile)\n\n", "sub_path": "convertWiki2Static.py", "file_name": "convertWiki2Static.py", "file_ext": "py", "file_size_in_byte": 10062, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.unquote", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "bs4.Comment", "line_number": 106, "usage_type": "argument"}, {"api_name": "glob.glob", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}]}
{"seq_id": "503224886", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/lib/python2.5/site-packages/mougeon/gui/hildon/portrait.py\n# Compiled at: 2012-03-01 11:28:37\nimport dbus, dbus.glib, hildon, osso, gettext\n_ = gettext.gettext\nfrom mougeon.common import version\nversion.getInstance().submitRevision('$Revision: 3 $')\n\nclass FremantleRotation(object):\n \"\"\"thp's screen rotation for Maemo 5\n\nSimply instantiate an object of this class and let it auto-rotate\nyour StackableWindows depending on the device orientation.\n\nIf you need to relayout a window, connect to its \"configure-event\"\nsignal and measure the ratio of width/height and relayout for that.\n\nYou can set the mode for rotation to AUTOMATIC (default), NEVER or\nALWAYS with the set_mode() method.\n\"\"\"\n (AUTOMATIC, NEVER, ALWAYS) = range(3)\n MODE_CAPTIONS = (\n _('Automatic'), _('Landscape'), _('Portrait'))\n (_PORTRAIT, _LANDSCAPE) = ('portrait', 'landscape')\n _ENABLE_ACCEL = 'req_accelerometer_enable'\n _DISABLE_ACCEL = 'req_accelerometer_disable'\n _MCE_SERVICE = 'com.nokia.mce'\n _MCE_REQUEST_PATH = '/com/nokia/mce/request'\n _MCE_REQUEST_IF = 'com.nokia.mce.request'\n KBD_SLIDER = '/sys/devices/platform/gpio-switch/slide/state'\n _KBD_OPEN = 'open'\n _KBD_CLOSED = 'closed'\n\n def __init__(self, app_name, main_window=None, version='1.0', mode=0):\n \"\"\"Create a new rotation manager\n\napp_name ... The name of your application (for osso.Context)\nmain_window ... The root window (optional, hildon.StackableWindow)\nversion ... The version of your application (optional, string)\nmode ... Initial mode for this manager (default: AUTOMATIC)\n\"\"\"\n self._orientation = None\n self._main_window = main_window\n self._stack = hildon.WindowStack.get_default()\n self._mode = -1\n self._last_dbus_orientation = None\n self._keyboard_state = self._get_keyboard_state()\n app_id = ('-').join((app_name, self.__class__.__name__))\n self._osso_context = osso.Context(app_id, version, False)\n program = hildon.Program.get_instance()\n program.connect('notify::is-topmost', self._on_topmost_changed)\n system_bus = dbus.Bus.get_system()\n system_bus.add_signal_receiver(self._on_orientation_signal, signal_name='sig_device_orientation_ind', dbus_interface='com.nokia.mce.signal', path='/com/nokia/mce/signal')\n system_bus.add_signal_receiver(self._on_keyboard_signal, signal_name='Condition', dbus_interface='org.freedesktop.Hal.Device', path='/org/freedesktop/Hal/devices/platform_slide')\n self.set_mode(mode)\n return\n\n def get_mode(self):\n \"\"\"Get the currently-set rotation mode\n\nThis will return one of three values: AUTOMATIC, ALWAYS or NEVER.\n\"\"\"\n return self._mode\n\n def set_mode(self, new_mode):\n \"\"\"Set the rotation mode\n\nYou can set the rotation mode to AUTOMATIC (use hardware rotation\ninfo), ALWAYS (force portrait) and NEVER (force landscape).\n\"\"\"\n if new_mode not in (self.AUTOMATIC, self.ALWAYS, self.NEVER):\n raise ValueError('Unknown rotation mode')\n if self._mode != new_mode:\n if self._mode == self.AUTOMATIC:\n self._last_dbus_orientation = self._orientation\n self._send_mce_request(self._DISABLE_ACCEL)\n if new_mode == self.NEVER:\n self._orientation_changed(self._LANDSCAPE)\n elif new_mode == self.ALWAYS and self._keyboard_state != self._KBD_OPEN:\n self._orientation_changed(self._PORTRAIT)\n elif new_mode == self.AUTOMATIC:\n self._orientation_changed(self._last_dbus_orientation)\n self._send_mce_request(self._ENABLE_ACCEL)\n self._mode = new_mode\n\n def _send_mce_request(self, request):\n rpc = osso.Rpc(self._osso_context)\n rpc.rpc_run(self._MCE_SERVICE, self._MCE_REQUEST_PATH, self._MCE_REQUEST_IF, request, use_system_bus=True)\n\n def _on_topmost_changed(self, program, property_spec):\n if self._mode == self.AUTOMATIC:\n if program.get_is_topmost():\n self._send_mce_request(self._ENABLE_ACCEL)\n else:\n self._send_mce_request(self._DISABLE_ACCEL)\n\n def _get_main_window(self):\n if self._main_window:\n return self._main_window\n else:\n windows = self._stack.get_windows()\n if windows:\n return windows[(-1)]\n else:\n return\n return\n\n def _orientation_changed(self, orientation):\n if self._orientation == orientation:\n return\n flags = 0\n if orientation != self._LANDSCAPE:\n flags |= hildon.PORTRAIT_MODE_SUPPORT\n if orientation == self._PORTRAIT:\n flags |= hildon.PORTRAIT_MODE_REQUEST\n window = self._get_main_window()\n if window is not None:\n hildon.hildon_gtk_window_set_portrait_flags(window, flags)\n self._orientation = orientation\n return\n\n def _get_keyboard_state(self):\n try:\n return open(self.KBD_SLIDER).read().strip()\n except IOError:\n return self._KBD_CLOSED\n\n def _keyboard_state_changed(self):\n state = self._get_keyboard_state()\n if state == self._KBD_OPEN:\n self._orientation_changed(self._LANDSCAPE)\n elif state == self._KBD_CLOSED:\n if self._mode == self.AUTOMATIC:\n self._orientation_changed(self._last_dbus_orientation)\n elif self._mode == self.ALWAYS:\n self._orientation_changed(self._PORTRAIT)\n self._keyboard_state = state\n\n def _on_keyboard_signal(self, condition, button_name):\n if condition == 'ButtonPressed' and button_name == 'cover':\n self._keyboard_state_changed()\n\n def _on_orientation_signal(self, orientation, stand, face, x, y, z):\n if orientation in (self._PORTRAIT, self._LANDSCAPE):\n if self._mode == self.AUTOMATIC and self._keyboard_state != self._KBD_OPEN:\n self._orientation_changed(orientation)\n self._last_dbus_orientation = orientation", "sub_path": "pycfiles/mougeon-0.1.0dev-r48.linux-armv7l.tar/portrait.py", "file_name": "portrait.py", "file_ext": "py", "file_size_in_byte": 6256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "gettext.gettext", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mougeon.common.version.getInstance", "line_number": 10, "usage_type": "call"}, {"api_name": "mougeon.common.version", "line_number": 10, "usage_type": "name"}, {"api_name": "hildon.WindowStack.get_default", "line_number": 47, "usage_type": "call"}, {"api_name": "hildon.WindowStack", "line_number": 47, "usage_type": "attribute"}, {"api_name": "osso.Context", "line_number": 52, "usage_type": "call"}, {"api_name": "mougeon.common.version", "line_number": 52, "usage_type": "argument"}, {"api_name": "hildon.Program.get_instance", "line_number": 53, "usage_type": "call"}, {"api_name": "hildon.Program", "line_number": 53, "usage_type": "attribute"}, {"api_name": "dbus.Bus.get_system", "line_number": 55, "usage_type": "call"}, {"api_name": "dbus.Bus", "line_number": 55, "usage_type": "attribute"}, {"api_name": "osso.Rpc", "line_number": 90, "usage_type": "call"}, {"api_name": "hildon.PORTRAIT_MODE_SUPPORT", "line_number": 116, "usage_type": "attribute"}, {"api_name": "hildon.PORTRAIT_MODE_REQUEST", "line_number": 118, "usage_type": "attribute"}, {"api_name": "hildon.hildon_gtk_window_set_portrait_flags", "line_number": 121, "usage_type": "call"}]}
{"seq_id": "359265704", "text": "from flask import Flask, request, jsonify, json, make_response, redirect, session, send_from_directory\nfrom flask_mysqldb import MySQL\nimport datetime\nfrom flask_cors import CORS\nfrom flask_mail import Mail,Message\n\nfrom werkzeug.security import generate_password_hash, check_password_hash\n\nimport threading\nimport asyncio\nimport secrets\nimport os, string, random\nfrom fastai import *\nfrom fastai.vision import *\nfrom fastai.callbacks.hooks import *\nimport os\nimport numpy as np\nimport cv2\nimport imutils\nimport subprocess\n\n\nUPLOAD_FOLDER = '/home/rahul/Music/car_backend/uploads/'\nMODEL_FOLDER = '/home/rahul/Music/car_backend/model/'\n\n\nALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}\napp = Flask(__name__, static_url_path='')\napp.secret_key = secrets.token_urlsafe(16)\nCORS(app)\n\napp.debug = True\napp.env = 'development'\n# app.config['SESSION_TYPE'] = 'filesystem'\napp.config['MYSQL_HOST'] = 'localhost'\napp.config['MYSQL_USER'] = 'root'\napp.config['MYSQL_PASSWORD'] = 'Root@123'\napp.config['MYSQL_DB'] = 'meta_org'\napp.config['MAIL_SERVER'] = 'smtp.gmail.com' #'server229.web-hosting.com'\napp.config['MAIL_PORT'] = 587\napp.config['MAIL_USERNAME'] = 'funto236@gmail.com'#'support@metaorigins.com'\napp.config['MAIL_DEFAULT_SENDER'] = 'funto@gmail.com'#'support@metaorigins.com'\napp.config['MAIL_PASSWORD'] = 'funto@123'#'UwuW[X0zSP^@'\napp.config['MAIL_USE_TLS'] = True\napp.config['MAIL_USE_SSL'] = False\nmysql = MySQL(app)\nmail = Mail(app)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['MODEL_FOLDER'] = MODEL_FOLDER\n\n@app.route('/')\ndef hello_world():\n return 'Hello World!'\n\n@app.route('/file/')\ndef send_js(path):\n return send_from_directory('uploads', path)\n\n\n@app.route('/login', methods=['GET','POST'])\ndef login():\n try:\n if request.method == 'POST':\n email = request.form['email']\n password = request.form['password']\n cur = mysql.connection.cursor()\n query = \"SELECT password,first_name,last_name,id FROM users WHERE email ='\"+email+\"'\"\n cur.execute(query)\n data = cur.fetchall()\n # print(data)\n if len(data) and check_password_hash(data[0][0], password):\n session['user_id'] = data[0][3]\n send_data = {'success': True, 'first_name': data[0][1], 'last_name': data[0][2], 'id': data[0][3]}\n print(session)\n return make_response(jsonify(send_data), 200)\n else:\n send_data = {'success': False}\n return make_response(jsonify(send_data), 200)\n except Exception as e:\n print(str(e))\n send_data = {'success': False}\n return make_response(jsonify(send_data), 200)\n\n\n@app.route('/register', methods=['POST'])\ndef register():\n try:\n if request.method == 'POST':\n first_name = request.form['first_name']\n last_name = request.form['last_name']\n email = request.form['email']\n phone = request.form['phone']\n company = request.form['company']\n location = request.form['location']\n password = generate_password_hash('Meta@123')\n cur = mysql.connection.cursor()\n cur.execute(\"INSERT INTO users (first_name, last_name,password, email, phone, company, location,created_at) \"\n \"VALUES (%s, %s ,%s, %s,%s, %s ,%s, %s)\",\n (first_name, last_name, password, email, phone, company, location, datetime.datetime.now()))\n mysql.connection.commit()\n cur.close()\n # msg = Message('Welcome', recipients=[email])\n # thread1 = threading.Thread(target=mail.send(msg))\n # thread1.start()\n message = \"hi,\"+first_name+\"
you have successfully created account on Metaorigin Labs.your username \"+email+\", password is Meta@123.
thank you\"\n\n subject = \"welcome,\"\n msg = Message(recipients=[email],\n html=message,\n subject=subject)\n # mail.send(msg)\n thread1 = threading.Thread(target=mail.send(msg))\n thread1.start()\n send_data = {'success': True}\n return make_response(jsonify(send_data), 200)\n except Exception as e:\n print(str(e))\n send_data = {'success': False}\n return make_response(jsonify(send_data), 200)\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\ndef randomString(stringLength=10):\n \"\"\"Generate a random string of fixed length \"\"\"\n letters = string.ascii_letters\n return ''.join(random.choice(letters) for i in range(stringLength))\n\n@app.route('/damage_check', methods=['POST'])\ndef damage_check():\n try:\n if request.method == 'POST':\n user_id = request.form['user_id']\n if not user_id:\n user_id = 1\n\n file = request.files['fileUpload']\n if file and allowed_file(file.filename):\n uid = randomString(10)\n filename = file.filename\n ext = filename.split(\".\")[-1]\n name = filename.split(\".\")[:-1]\n file_name = name[0]+'_'+uid+'.'+ext\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], file_name))\n orig_img = file_name\n learn = setup_learner()\n img = open_image(app.config['UPLOAD_FOLDER']+'/'+file_name)\n out_put = infer_imagewithcontours(img, learn)\n # print(out_put)\n ext = filename.split(\".\")[-1]\n name = filename.split(\".\")[:-1]\n file_name = name[0] + '_'+uid+'_' + '_final' + '.' + ext\n # img = open_image(app.config['UPLOAD_FOLDER']+'/'+file_name)\n permission_path = os.path.join(app.config['UPLOAD_FOLDER'], file_name)\n\n out_put = cv2.cvtColor(out_put*255, cv2.COLOR_BGR2RGB)\n cv2.imwrite(app.config['UPLOAD_FOLDER']+'/'+file_name, out_put)\n process_img = file_name\n\n print(\"***************\")\n print(permission_path)\n subprocess.call(['chmod', 'a+rwx', permission_path])\n print(\"**************end*************\")\n\n cur = mysql.connection.cursor()\n cur.execute(\n \"INSERT INTO images (user_id,original_img, process_img,created_at) \"\n \"VALUES (%s, %s ,%s, %s)\",\n (user_id, orig_img, process_img, datetime.datetime.now()))\n mysql.connection.commit()\n cur.close()\n\n send_data = {'success': True, 'image': 'http://localhost:5000/file/'+file_name}\n return make_response(jsonify(send_data), 200)\n except Exception as e:\n print(str(e))\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n print(exc_type, fname, exc_tb.tb_lineno)\n send_data = {'success': False}\n return make_response(jsonify(send_data), 200)\n\ndef setup_learner():\n try:\n # learn = load_learner(app.config['MODEL_FOLDER'], 'scratch_detector.pkl')\n print(\"Entering setup learner!\")\n path = app.config['MODEL_FOLDER']\n file = 'scratch_detector.pkl'\n test = None\n tfm_y=None\n source = Path(app.config['MODEL_FOLDER'])/'scratch_detector.pkl' if is_pathlike('scratch_detector.pkl') else 'scratch_detector.pkl'\n print(\"took up the source\")\n state = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)\n model = state.pop('model')\n src = LabelLists.load_state(path, state.pop('data'))\n if test is not None: src.add_test(test, tfm_y=tfm_y)\n data = src.databunch()\n cb_state = state.pop('cb_state')# **db_kwargs\n clas_func = state.pop('cls')\n res = clas_func(data, model, **state)\n res.callback_fns = state['callback_fns'] #to avoid duplicates\n res.callbacks = [load_callback(c,s, res) for c,s in cb_state.items()]\n return res\n # return learn\n except RuntimeError as e:\n if len(e.args) > 0 and 'CPU-only machine' in e.args[0]:\n print(e)\n message = \"\\n\\nThis model was trained with an old version of fastai and will not work in a CPU environment.\\n\\nPlease update the fastai library in your training environment and export your model again.\\n\\nSee instructions for 'Returning to work' at https://course.fast.ai.\"\n raise RuntimeError(message)\n else:\n raise\n\n \ndef acc_cars(input, target):\n target = target.squeeze(1)\n return (input.argmax(dim=1)==target).float().mean()\ndef infer_imagewithcontours(img,learn):\n mask,_,_ = learn.predict(img)\n # erode_kernel = np.ones((3,3))\n mask_copy = mask.data\n np_mask = np.asarray(mask_copy).reshape(448, 448)\n # np_mask = cv2.erode(np.uint8(np_mask), erode_kernel, 1)\n cnts = cv2.findContours(np.uint8(np_mask), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n cnts = imutils.grab_contours(cnts)\n image_copy = img.resize(448).data\n image_copy= np.asarray(image_copy)\n out= np.transpose(image_copy, (1, 2, 0))\n # for c in cnts:\n # out = cv2.drawContours(out, [c], -1, (0, 255, 0), 2)\n out = coordinate_bbox_generator(cnts, out)\n # print(type(out))\n # alpha = 0.3\n # out = alpha * np_mask.reshape(448,448,1) + (1-alpha) * out.get()\n # return out\n return out\n\ndef doOverlap(l1, r1, l2, r2): \n # If one rectangle is on doOverlap(l1, r1, l2, r2)left side of other \n if(l1[0] > r2[0] or l2[0] > r1[0]): \n return False\n \n # If one rectangle is above other \n if(l1[1] > r2[1] or l2[1] > r1[1]): \n return False\n \n return True\n\ndef overlap_handler(l1, r1, l2, r2):\n l3 = (min(l1[0], l2[0]), min(l1[1], l2[1]))\n r3 = (max(r1[0], r2[0]), max(r1[1], r2[1]))\n return l3, r3\n\n# def coordinate_bbox_generator(cnt, img):\n# for box in cnt:\n# x_coord, y_coord = [], []\n# for item in box:\n# x_coord.append(item[0][0])\n# y_coord.append(item[0][1])\n# r = x_coord\n# c = y_coord\n# # print(img)\n# c1 = (int(min(r)), int(min(c)))\n# # print(c1)\n# c2 = (int(max(r)), int(max(c)))\n# # print(c1)\n# # print(c2)\n# if c2[0]-c1[0] > 3 and c2[1] - c1[1] > 3:\n# img = cv2.UMat(img).get()\n# img = cv2.rectangle(img, c1, c2, (255, 0, 0), 1)\n# # print(111111111)\n# # print(img)\n# return img\n\n\n\ndef coordinate_bbox_generator(cnt, img):\n filtered_box = []\n for box in cnt:\n x_coord, y_coord = [], []\n for item in box:\n # print(item)\n x_coord.append(item[0][0])\n y_coord.append(item[0][1])\n # print(x_coord)\n r = x_coord\n c = y_coord\n # print(img)\n c1 = (min(r), min(c))\n # print(c1)\n c2 = (max(r), max(c))\n # print(c2)\n if (c2[0]-c1[0]>3 and c2[1]-c1[1]>3):\n filtered_box.append([c1, c2])\n # img = cv2.rectangle(img, c1, c2, (0, 0, 255), 1)\n print(len(filtered_box))\n for idx1 in range(len(filtered_box)):\n final_box = filtered_box[idx1]\n # print(idx1)\n for idx2 in range(len(filtered_box)):\n # print(idx1, idx2)\n item1, item2 = filtered_box[idx1], filtered_box[idx2]\n # print(*item1, *item2)\n if idx1==idx2:\n continue\n elif doOverlap(*item1, *item2):\n # print(\"Yippee!!\")\n filtered_box[idx2] = overlap_handler(*item1, *item2)\n # print(len(filtered_box))\n for item in filtered_box:\n img = cv2.UMat(img).get()\n # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = cv2.rectangle(img, *item, (255, 0, 0), 1)\n return img\n\nif __name__ == '__main__':\n app.run(debug=True,host='0.0.0.0')\n", "sub_path": "ajax_form/templates/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 11936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "flask.Flask", "line_number": 28, "usage_type": "call"}, {"api_name": "secrets.token_urlsafe", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 30, "usage_type": "call"}, {"api_name": "flask_mysqldb.MySQL", "line_number": 46, "usage_type": "call"}, {"api_name": "flask_mail.Mail", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "werkzeug.security.check_password_hash", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "argument"}, {"api_name": "flask.make_response", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "werkzeug.security.generate_password_hash", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask_mail.Message", "line_number": 108, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 128, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 158, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 159, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 171, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 171, "usage_type": "attribute"}, {"api_name": "flask.make_response", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "flask.make_response", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 223, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 225, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 225, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 225, "usage_type": "attribute"}, {"api_name": "imutils.grab_contours", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 229, "usage_type": "call"}, {"api_name": "cv2.UMat", "line_number": 312, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 314, "usage_type": "call"}]}
{"seq_id": "60340867", "text": "from django.conf.urls import patterns, url\n\nfrom timetable import views\n\nurlpatterns = patterns('',\n url(r'^$', views.index, name='index'),\n url(r'^compose/(?P\\w+)$', views.timetable_compose, name='timetable_compose'),\n\n url(r'render_course_list/$', views.render_course_list, name='render_course_list'),\n url(r'render_calendar/$', views.render_calendar, name='render_calendar'),\n url(r'assign_lesson/$', views.assign_lesson, name='assign_lesson'),\n)\n", "sub_path": "timetable/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "timetable.views.index", "line_number": 6, "usage_type": "attribute"}, {"api_name": "timetable.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "timetable.views.timetable_compose", "line_number": 7, "usage_type": "attribute"}, {"api_name": "timetable.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "timetable.views.render_course_list", "line_number": 9, "usage_type": "attribute"}, {"api_name": "timetable.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "timetable.views.render_calendar", "line_number": 10, "usage_type": "attribute"}, {"api_name": "timetable.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "timetable.views.assign_lesson", "line_number": 11, "usage_type": "attribute"}, {"api_name": "timetable.views", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "352880895", "text": "from django.shortcuts import render, redirect\nfrom .forms import RegisterForm\nfrom .models import User, Profile\n\n\ndef home(request):\n return render(request, 'app1/index.html')\n\n\n# Register\ndef register(request):\n form = RegisterForm\n if request.method == 'POST':\n form = form(request.POST)\n if form.is_valid():\n form.save()\n return redirect('app1:login')\n return render(request, 'app1/register.html', {'forms': form})\n\n\n# Profile View\ndef profile(request, id=None):\n context = {}\n try:\n user = User.objects.get(id=id)\n user = Profile.objects.get(user__email=user)\n context['userprofile'] = user\n except User.DoesNotExist:\n error = 'This User Does Not Exist'\n return render(request, 'page404.html', {'error': error})\n return render(request, 'app1/profile.html', context)\n\n\n# Delete User\ndef delete_user(request, id=None):\n if 'delete_user' in request.POST and id is not None:\n User.objects.get(id=id).delete()\n return redirect('app1:register')\n", "sub_path": "UserApp/More than three users/project/app1/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.shortcuts.render", "line_number": 7, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 12, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Profile.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 26, "usage_type": "name"}, {"api_name": "models.User.DoesNotExist", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "33106102", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n femagtools.asm\n ~~~~~~~~~~~~~~\n\n Reading ASM files\n\nSlots Qr:\n\n Qr <= 1.25 Qs\n Qr != Qs +/- 2p\n Qr != Qs\n Qr != Qs +/- 1\n\n\"\"\"\nimport pathlib\nimport logging\nimport logging.config\nimport numpy as np\nimport lmfit\n\nlogger = logging.getLogger('femagtools.asm')\n\n\ndef imcur(s, w1, u1, r1, ls1, lh, ls2, r2):\n \"\"\"return currents i1r, i1i, i2r, i2i\"\"\"\n xs1 = w1*ls1\n xh = w1*lh\n x1 = xs1+xh\n xs2 = w1*ls2\n x2 = xs2+xh\n # solve voltage equations for steady state\n A = np.array((\n (r1, -x1, 0, -xh),\n (x1, r1, xh, 0),\n (0, -s*xh, r2, -s*x2),\n (s*xh, 0, s*x2, r2)))\n return np.linalg.solve(A, np.array((u1, 0, 0, 0)))\n\n\ndef torque(p, w1, u1, s, r1, ls1, lh, ls2, r2):\n \"\"\"return torque\"\"\"\n if s == 0:\n return 0\n i2 = imcur(s, w1, u1, r1, ls1, lh, ls2, r2)[2:]\n return 3*p*r2/s/w1*(i2[0]**2 + i2[1]**2)\n\n\ndef fit_current(w1, u1, slip, r1, ls1, lh, i1, cosphi):\n def imcurr(s, ls2, r2):\n return np.array(\n [x + 1j*y\n for x, y in [imcur(sx, w1, u1, r1, ls1, lh, ls2, r2)[:2]\n for sx in s]])\n model = lmfit.model.Model(imcurr)\n ls2_guess = 0.0\n r2_guess = np.mean(\n [u1/i1x*sx\n for i1x, sx in zip(i1, slip) if abs(i1x) > 1e-6])\n params = model.make_params(r2=r2_guess, ls2=ls2_guess)\n guess = lmfit.models.update_param_vals(params, model.prefix)\n i1c = np.array([x*pf - 1j*x*np.sqrt(1-pf**2)\n for x, pf in zip(i1, cosphi)])\n r = model.fit(i1c, params=guess, s=slip, verbose=True)\n return r.params['ls2'].value, r.params['r2'].value\n\n\nvalmap = {\n 'Stator windings voltage (RMS)[V]': 'u1',\n 'Wdgs-connection: 0': 'wdgconn',\n 'Nominal frequency': 'f1',\n 'Stator phase winding resistamce': 'r1',\n 'Stator phase winding resistance': 'r1',\n 'Stator phase end-winding reactance': 'xs1',\n 'Stator phase winding wires/slot side': 'num_wires',\n 'Effect. air gap length [mm]': 'lfe',\n 'Effect. rotor bar length (+end) [mm]': 'rbarlen',\n 'Number of Phases': 'num_phases',\n 'Number of Pole pairs': 'p',\n 'Number of Poles simulated': 'p_gen',\n 'Number of parallel windings': 'num_par_wdgs',\n 'Rotor Lamination [kg]': 'lamweight',\n 'Rotor Conductors [kg]': 'conweight',\n 'MC-File used in calculation': 'mcfile',\n 'Losses[W/kg] in MC-File': 'felosscoeff',\n 'Max. No. Iterations': 'maxiters',\n 'Change of Perm. max %': 'permchg'}\n\n\ndef _read_sections(lines):\n \"\"\"return list of ASM sections\n\n sections are either surrounded by lines starting with '[***'\n or by starting with any 'Input data' or 'Simulation Results'\n Args:\n param lines (list) lines of ASM file to read\n\n Returns:\n list of sections\n \"\"\"\n\n section = []\n for line in lines:\n if ('[****' in line or\n 'Input data' in line or\n 'Simulation Results' in line):\n if section:\n # skip empty lines\n i = 0\n try:\n while not section[i]:\n i = i+1\n except IndexError:\n i = i-1\n yield section[i:]\n section = []\n else:\n section.append(line.strip())\n yield section\n\n\ndef read_input_data(content):\n r = dict()\n for l in content:\n if '=' in l or ':' in l:\n d = '=' if '=' in l else ':'\n k, v = [s.strip() for s in l.split(d)[:2]]\n if k == 'Wdgs-connection: 0':\n c = int(float(l.split()[-1]))\n r[valmap[k]] = ['open', 'star', 'delta'][c]\n else:\n r[valmap[k]] = float(v)\n elif '\\t' in l:\n k, v = [s.strip() for s in l.split('\\t')[:2]]\n if valmap[k] == 'mcfile':\n r['mcfile'] = v\n else:\n r[valmap[k]] = float(v)\n\n for k in ('num_phases', 'p', 'p_gen',\n 'num_par_wdgs', 'maxiters'):\n if k in r:\n r[k] = int(r[k])\n return r\n\n\ndef read_simulation_results(content):\n unit = 1e3\n resmap = {\n 'Torque = P2/(s.omega) [Nm]': 'Tp2',\n 'Rotor-Losses P2 [kW]': 'p2',\n 'Rotor-Losses P2 [W]': 'p2',\n 'Stator FE Pfe1 [kW]': 'pfe1',\n 'Stator FE Pfe1 [W]': 'pfe1'}\n r = dict(s=[], T=[], u1=[], i1=[], p1=[], cosphi=[],\n f1=[], pfe1=[], p2=[], Tp2=[])\n for l in content:\n if l.startswith('S LIP'):\n if l.find('POWER[W]') > -1:\n unit = 1\n continue\n a = l.split()\n if len(a) == 7:\n for k, v in zip(r.keys(), a):\n r[k].append(float(v))\n elif a:\n a = l.split(':')[-1].split()\n if len(a) == 1:\n try:\n k = resmap[l.split(':')[0]]\n r[k].append(float(a[0]))\n except KeyError:\n logger.warning('Key %s ignored', l.split(':')[0])\n if unit > 1:\n for k in ('p1', 'p2', 'pfe1'):\n r[k] = [x*unit for x in r[k]]\n r['s'] = [s/100 for s in r['s']]\n return r\n\n\ndef parident(w1, u1, i1, cosphi, s, r1, ls1, lh):\n \"\"\"returns equivalent circuit parameters: r2, ls\"\"\"\n logger.info(\"w1 %s u1 %s i1 %s cosphi %s s %s r1 %s ls1 %s lh %s\",\n w1, u1, i1, cosphi, s, r1, ls1, lh)\n ls2, r2 = fit_current(w1, u1, s,\n r1, ls1, lh, i1, cosphi)\n\n xi = w1*(ls1+lh - lh**2/(ls2+lh))\n return dict(r2=r2, ls2=ls2, sk=r2/np.sqrt(xi**2 + r1**2))\n\n\ndef read(arg):\n \"\"\"read asm file\n\n Args:\n filename or content (list of str) the text lines of the ASM file\n \"\"\"\n r = {}\n if isinstance(arg, str):\n lines = pathlib.Path(arg).read_text().split('\\n')\n elif isinstance(arg, pathlib.Path):\n lines = arg.read_text().split('\\n')\n else:\n lines = arg\n for s in _read_sections(lines):\n if not s:\n continue\n title = s[0].split(':')[0].strip()\n\n if 'FEMAG Classic Version' in title:\n r['version'] = s[0].split(':')[-1].replace(' Version ', '').strip()\n elif 'Project File name' in title:\n r['project'] = s[1].strip()\n elif 'Number of Nodes' in title:\n pass\n elif 'File name' in title:\n r['filename'] = s[0].split(':')[-1].strip()\n elif 'Date' in title:\n d = s[0].split(':')[1].strip().split()\n dd, MM, yy = d[0].split('.')\n hh, mm = ''.join(d[1:-1]).split('.')\n r['date'] = '{}-{}-{}T{:02}:{:02}'.format(\n yy, MM, dd, int(hh), int(mm))\n elif 'Stator windings' in title:\n r.update(read_input_data(s))\n else:\n r.update(read_simulation_results(s))\n r['pcu'] = [3*x**2*r['r1'] for x in r['i1']]\n if r['pfe1']:\n r['pltotal'] = [sum(x) for x in zip(r['pcu'], r['pfe1'], r['p2'])]\n else:\n r['pltotal'] = [sum(x) for x in zip(r['pcu'], r['p2'])]\n\n w1 = 2*np.pi*r['f1'][0]\n r['ls1'] = r.pop('xs1')/w1\n u1 = r['u1'][0]\n i1 = r['i1']\n cosphi = r['cosphi']\n if r['wdgconn'] == 'star':\n u1 = u1/np.sqrt(3)\n if r['wdgconn'] == 'delta':\n i1 = [x/np.sqrt(3) for x in i1]\n\n r['lh'] = u1/i1[0]/w1 - r['ls1']\n if len(i1) > 2:\n r.update(parident(w1, u1, i1, cosphi,\n r['s'], r['r1'], r['ls1'], r['lh']))\n return r\n\n\nif __name__ == \"__main__\":\n import sys\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(message)s')\n r = read(sys.argv[1])\n print(r)\n", "sub_path": "src/femagtools/asm.py", "file_name": "asm.py", "file_ext": "py", "file_size_in_byte": 7706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "lmfit.model.Model", "line_number": 55, "usage_type": "call"}, {"api_name": "lmfit.model", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "lmfit.models.update_param_vals", "line_number": 61, "usage_type": "call"}, {"api_name": "lmfit.models", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 189, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 200, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 234, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 242, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 253, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 253, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 255, "usage_type": "attribute"}]}
{"seq_id": "439059345", "text": "#!/usr/bin/env python\r\nimport paramiko\r\nimport os\r\nimport subprocess\r\nimport commands\r\n\r\nssh_servidor = '127.1.1.1'\r\nssh_usuario = 'usuario'\r\nssh_clave = 'contrasena'\r\nssh_puerto = 36835 \r\ncomando = 'show cable modem summary'\r\nconexion = paramiko.Transport((ssh_servidor, ssh_puerto))\r\nparamiko.util.log_to_file(\"filename.log\")\r\nconexion.connect(username = ssh_usuario, password = ssh_clave)\r\ncanal = conexion.open_session()\r\ncanal.exec_command(comando)\r\nsalida = canal.makefile('rb', -1).readlines()\r\narchivo=open('datos.txt', 'w')\r\nfalla = False\r\n\r\ndef envio_correo(falla):\r\n\tif falla:\r\n\t\tcont = 1\r\n\t\twhile cont == 1:\r\n\t\t\tejecutar = commands.getoutput(\"./correo.py\")\r\n\t\t\tcont += 2\r\n\telse:\r\n\t\tarchivo.write(\"no hay nodos alarmados\")\r\n\r\nfor linea in salida:\r\n\tif len(linea) == 104 and linea.startswith(\" \"):\r\n\t\tinterface = linea[0:2]\r\n\t\tdatoint = int(interface)\r\n\t\tporcentaje = linea[66:69]\r\n\t\tdato2 = int(porcentaje)\r\n\t\tif dato2 < 30 and datoint < 7:\r\n\t\t\tarchivo.write(linea)\r\n\t\t\tfalla = True\r\nenvio_correo(falla)\t\t\t\r\narchivo.close()\r\nif salida:\r\n\tprint(\"ok\")\r\nelse:\r\n\tprint(canal.makefile_stderr('rb', -1).readlines())\r\n\r\n\r\nconexion.close()\r\n", "sub_path": "verificar.py", "file_name": "verificar.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "paramiko.Transport", "line_number": 12, "usage_type": "call"}, {"api_name": "paramiko.util.log_to_file", "line_number": 13, "usage_type": "call"}, {"api_name": "paramiko.util", "line_number": 13, "usage_type": "attribute"}, {"api_name": "commands.getoutput", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "236259317", "text": "# create Dunbrack coordinates\nimport numpy as np\nfrom calcDihedral import calcDihedral\nfrom create_clash_list import create_clash_list\nfrom Bio.PDB import*\nimport Bio.PDB as pdb\n\nparser=PDBParser()\nfile1 = '/Users/jmorte02/Documents/Projects/Dun_coordinates/Val_coordinates.txt'\ns = parser.get_structure(\"my_pdb\", \"Val_example.pdb\")\nio=PDBIO()\nio.set_structure(s)\nmodel = s[0]\nchain = model\natoms = [a for a in chain.get_atoms() if pdb.is_aa(a.parent)]\n\n\nn_sample = 10\n# Load bonds\nbonds = np.loadtxt('Val_bonds.txt', dtype=int) \nangles = np.loadtxt('Val_angles.txt', dtype=int)\nhbonds = np.loadtxt('Val_Hbond.txt', dtype=int)\n# fix bonds and angles to 0 indexing\nbonds = bonds - 1\nangles = angles - 1\nhbonds = hbonds - 1\n\nall_bl = np.zeros([bonds.shape[0], n_sample])\nall_angles = np.zeros([angles.shape[0], n_sample])\nall_dihedral = np.zeros([9, n_sample])\ncounter = 0\n\n# dihedral positions\nphi_index = np.array([4, 6, 8, 20])\npsi_index = np.array([6, 8, 20, 22])\nchi1_index = np.array([6, 8, 10, 12])\nCH3_1_index = np.array([8, 10, 12, 13])\nCH3_2_index = np.array([8, 10, 16, 17])\nend_CH3_1_index = np.array([0, 1, 4, 6])\nend_CH3_2_index = np.array([20, 22, 24, 25])\nomega_1_index = np.array([1, 4, 6, 8])\nomega_2_index = np.array([8, 20, 22, 24])\n\nradii = np.loadtxt('Val_radii.txt')\n# Create clash list\nclash_list = create_clash_list(28, bonds, angles, radii)\n# Get radii^2\nradii_sum = radii[clash_list[:, 0]] + radii[clash_list[:, 1]]\n\n\n# modify H-bond radii\nfor i in range(0, hbonds.shape[0]):\n ind1 = np.isin(clash_list[:, 0], hbonds[i, 0])\n ind2 = np.isin(clash_list[:, 1], hbonds[i, 1])\n radii_sum[ind1&ind2] = 1.5\n print(clash_list[ind1&ind2, :])\n\n\nradii_2 = radii_sum * radii_sum\nall_time = np.zeros([n_sample, 1])\nall_E = np.zeros([n_sample, 1])\n\ndun_data = np.loadtxt(file1, usecols=np.arange(0, n_sample*3))\n\nfor i in range(0, n_sample):\n this_coord = dun_data[:, i * 3 : (i+1) * 3]\n #print(this_coord.shape)\n # Fix atom ordering\n coord = np.zeros([28, 3])\n coord[1:22, :] = this_coord[1:22, :]\n coord[1, :] = this_coord[0, :]\n coord[2:4, :] = coord[0, :]\n coord[4, :] = this_coord[1, :]\n coord[5, :] = this_coord[2, :]\n coord[6, :] = this_coord[3, :]\n coord[7, :] = this_coord[10, :]\n coord[8, :] = this_coord[4, :]\n coord[9, :] = this_coord[11, :]\n coord[10, :] = this_coord[7, :]\n coord[11, :] = this_coord[12, :]\n coord[12, :] = this_coord[8, :]\n coord[13:16, :] = this_coord[13:16, :]\n coord[16, :] = this_coord[9, :]\n coord[17:20, :] = this_coord[16:19, :]\n coord[20:22, :] = this_coord[5:7, :]\n #print(this_coord[19:22, :])\n coord[22, :] = this_coord[19, :]\n coord[23, :] = this_coord[21, :]\n coord[24, :] = this_coord[20, :]\n #coord = coord.T\n #print(coord[0,:])\n # Check clashes\n diff_pos = coord[clash_list[:, 0], :] - coord[clash_list[:, 1], :]\n sum_2 = np.sum(np.square(diff_pos), 1)\n ind0 = sum_2 < radii_2\n ind1 = sum_2 > 0\n s_r_6 = np.power(radii_2[ind0&ind1] / sum_2[ind0&ind1], 3)\n E = np.power(1 - s_r_6, 2)\n total_E = np.sum(E)\n all_E[i] = total_E / 72.0\n #print(clash_list[ind0&ind1,:])\n np.savetxt('../Dun_coordinates/Val_coordinates_' + str(i) + '.txt', coord, fmt = '%6.3f')\n\n parents = []\n counter = 0;\n for a in chain.get_atoms() :\n if pdb.is_aa(a.parent):\n parents.append(a.parent)\n counter = counter + 1;\n #xyzs = [(a.coord) for a in atoms]\n #xyzarr = np.array(xyzs)\n xyzarr = this_coord\n id_counter = 1\n # Write to PDB file\n f = open('../Dun_coordinates/Val_coordinates_' + str(i) + '.pdb', 'w')\n for i in range(0, len(atoms)):\n new_res = parents[i].get_id()[1];\n if atoms[i].get_name() == 'N':\n id_counter = id_counter+1\n if len(atoms[i].get_name())<4:\n f.write('{:6s}{:5d} {:<4}{:3s} {:1s}{:4d}{:1s} {:8.3f}{:8.3f}{:8.3f}{:6.2f}{:6.2f} {:>2s} \\n'.format('ATOM', i, atoms[i].get_name(), parents[i].get_resname(),atoms[i].get_full_id()[2], id_counter, '',xyzarr[i][0], xyzarr[i][1], xyzarr[i][2], atoms[i].get_occupancy(), atoms[i].get_bfactor(),atoms[i].get_name()[0] ))\n else:\n f.write('{:6s}{:5d} {:<4} {:3s} {:1s}{:4d}{:1s} {:8.3f}{:8.3f}{:8.3f}{:6.2f}{:6.2f} {:>2s} \\n'.format('ATOM', i, atoms[i].get_name(), parents[i].get_resname(),atoms[i].get_full_id()[2], id_counter, '',xyzarr[i][0], xyzarr[i][1], xyzarr[i][2], atoms[i].get_occupancy(), atoms[i].get_bfactor(),atoms[i].get_name()[0] ))\t\t\t\t\n f.close()\n\n # # Call reduce to add hydrogen atoms\n # reduce1 = reduce_folder + \"./reduce -Trim -quiet \" + folder_name + file_name + \"_ordered_l_u.pdb>\"+ folder_name + file_name + \"_noH.pdb\"\n # reduce2 = reduce_folder + \"./reduce -quiet \" + folder_name + file_name + \"_noH.pdb>\" +folder_name + file_name + \"_H_l_u.pdb\"\n # os.system(reduce1)\n # os.system(reduce2)\n\n\n# # calculate bonds\n# diff_pos = coord[bonds[:, 0], :] - coord[bonds[:, 1], :]\n# all_bl[:, i] = np.sqrt(np.sum(np.square(diff_pos), 1))\n\n# # calculate angles\n# for angle_loop in range(0, angles.shape[0]):\n# ba = coord[angles[angle_loop, 0], :] - coord[angles[angle_loop, 1], :]\n# bc = coord[angles[angle_loop, 2], :] - coord[angles[angle_loop, 1], :]\n\n# cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))\n# all_angles[angle_loop, i] = np.arccos(cosine_angle) * 180 / np.pi\n\n# # calcualte dihedrals\n# all_dihedral[0, i] = calcDihedral(phi_index, coord)\n# all_dihedral[1, i] = calcDihedral(psi_index, coord)\n# all_dihedral[2, i] = calcDihedral(chi1_index, coord)\n# all_dihedral[3, i] = calcDihedral(CH3_1_index, coord)\n# all_dihedral[4, i] = calcDihedral(CH3_2_index, coord)\n# all_dihedral[5, i] = calcDihedral(end_CH3_1_index, coord)\n# all_dihedral[6, i] = calcDihedral(end_CH3_2_index, coord)\n# all_dihedral[7, i] = calcDihedral(omega_1_index, coord)\n# all_dihedral[8, i] = calcDihedral(omega_2_index, coord)\n\n# np.savetxt('../Dun_coordinates/Energy.txt', all_E)\n# np.savetxt('../Dun_coordinates/all_bonds.txt', all_bl, fmt='%5.2f')\n# np.savetxt('../Dun_coordinates/all_angles.txt', all_angles, fmt='%6.2f')\n# np.savetxt('../Dun_coordinates/all_dihedral.txt', all_dihedral, fmt='%7.2f')", "sub_path": "Code/make_dun_coordinates.py", "file_name": "make_dun_coordinates.py", "file_ext": "py", "file_size_in_byte": 6301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "Bio.PDB.is_aa", "line_number": 15, "usage_type": "call"}, {"api_name": "Bio.PDB", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 44, "usage_type": "call"}, {"api_name": "create_clash_list.create_clash_list", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 102, "usage_type": "call"}, {"api_name": "Bio.PDB.is_aa", "line_number": 107, "usage_type": "call"}, {"api_name": "Bio.PDB", "line_number": 107, "usage_type": "name"}]}
{"seq_id": "542556689", "text": "from flask import Flask, jsonify, render_template, make_response\nimport csv\n\napp = Flask(__name__)\n\n# results = [{'state':'Arizona', 'district': '1', 'cand1': 'A', 'cand2': 'B', 'party1':'dem', 'party2': 'rep','pred': 0.7, 'time': 0}, {'state':'Massachusetts', 'district': '1', 'cand1': 'A', 'cand2': 'B', 'party1':'dem', 'party2': 'rep','pred': 0.25, 'time': 0}]\n\nwith open('FakeData-Sheet1.csv') as f:\n reader = csv.DictReader(f)\n data = list(reader)\n\nresults = []\nassert len(data)% 2 == 0\nparty = {\"R\": \"rep\", \"D\": \"dem\"}\ncolor = {\"Republican\": 1, \"Democratic\": 0}\nfor i in range(0, int(len(data)), 2):\n # print(data[i][\"State\"], \"H\")\n # print(data[i+1][\"State\"], \"H\")\n # print(data[i][\"State\"]==data[i+1][\"State\"])\n # assert data[i][\"State\"] == data[i+1][\"State\"]\n # assert data[i][\"District\"] == data[i+1][\"District\"]\n entry = {}\n entry[\"pred1\"] = float(data[i][\"Prob Winning\"])\n entry[\"pred2\"] = float(data[i+1][\"Prob Winning\"])\n entry[\"party1\"] = party[data[i][\"Party\"]]\n entry[\"cand1\"] = data[i][\"Candidate\"]\n entry[\"party2\"] = party[data[i+1][\"Party\"]]\n entry[\"cand2\"] = data[i+1][\"Candidate\"]\n entry[\"state\"] = data[i][\"State\"].rstrip()\n entry[\"district\"] = data[i][\"District\"]\n results.append(entry)\n\naffiliations = {}\nwith open('States-by-Affiliation-Sheet1.csv') as f:\n reader = csv.reader(f)\n for row in reader:\n affiliation = row[1].split()\n # if len(affiliation) == 1:\n # print(row[0].rstrip())\n if affiliation[0] in color:\n affiliations[row[0].rstrip()] = color[affiliation[0]]\n else:\n affiliations[row[0].rstrip()] = 0.5\n # else:\n\ntimedata = []\ntimes = ['2019-12-07', '2019-11-07', '2019-10-07', '2019-09-07', '2019-08-07']\n\nwith open('{}.csv'.format(times[0])) as f:\n reader = csv.DictReader(f)\n reader = list(reader)\n for entry in reader:\n entry[\"party\"] = party[entry[\"party\"]]\n # for time in times:\n # entry[\"time\"] = time\n # entry[\"prob\"] = entry[time]\n # timedata.append(entry)\n # print(reader)\n # for i in range(0, int(len(reader)), 2):\n # entry = {}\n # entry['party'] = party[reader[i]['party']]\n # entry['state'] = reader[i]['state']\n # entry['district'] = reader[i]['district']\n\n # print(entry)\n # for time in times:\n # timedata.append()\n # pass\n\nresults = []\nassert len(reader)% 2 == 0\nfor i in range(0, int(len(reader)), 2):\n # print(data[i][\"State\"], \"H\")\n # print(data[i+1][\"State\"], \"H\")\n # print(data[i][\"State\"]==data[i+1][\"State\"])\n # assert data[i][\"State\"] == data[i+1][\"State\"]\n # assert data[i][\"District\"] == data[i+1][\"District\"]\n entry = {}\n entry[\"pred1\"] = float(reader[i][times[0]]) if reader[i][\"party\"] == \"dem\" else 1-float(reader[i][times[0]])\n entry[\"pred2\"] = float(reader[i+1][times[0]]) if reader[i+1][\"party\"] == \"dem\" else 1-float(reader[i+1][times[0]])\n entry[\"party1\"] = reader[i][\"party\"]\n entry[\"cand1\"] = reader[i][\"person\"]\n entry[\"party2\"] = reader[i+1][\"party\"]\n entry[\"cand2\"] = reader[i+1][\"person\"]\n entry[\"state\"] = reader[i][\"state\"].rstrip()\n entry[\"district\"] = reader[i][\"district\"]\n results.append(entry)\n\n@app.route('/', methods = ['GET'])\ndef home():\n return make_response(jsonify({'results':results, 'affiliations': affiliations, 'timedata': reader, 'times': times}), 200)\n\n@app.route('/a', methods = ['GET'])\ndef page():\n return render_template('/visualization.html')\n\nif __name__== '__main__':\n app.jinja_env.auto_reload = True\n app.config['TEMPLATES_AUTO_RELOAD'] = True\n # app.config['TEMPLATES_AUTO_RELOAD'] = True\n app.config['STATIC_AUTO_RELOAD'] = True\n app.run(debug=True, extra_files=['/static','/templates'])", "sub_path": "election_prediction-master/final_lab/backend.py", "file_name": "backend.py", "file_ext": "py", "file_size_in_byte": 3898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 9, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 35, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}]}
{"seq_id": "101688823", "text": "from gi.repository import Gtk, GtkSource\nclass Status:\n def __init__(self):\n self.connect('view-created', lambda _, view:\n view.connect('draw', self.draw_status))\n self.connect('key-pressed', lambda _:\n self.current_view.queue_draw())\n\n self.command_prefix = []\n self.connect('key-handler-reset', lambda w: self.command_prefix.clear())\n self.connect('key-handler-prefix', lambda w, c: self.command_prefix.append(c))\n\n def draw_status(self, view, cr):\n rect = view.get_allocation()\n cr.select_font_face('Times')\n cr.set_font_size(256)\n cr.set_source_rgb(0.2, 0.2, 0.2)\n cr.move_to(rect.width / 3, rect.height / 2)\n # operation_mode\n if self.operation_mode == self.COMMAND:\n cr.show_text('C')\n # command\n cr.set_font_size(128)\n t = ''.join(self.command_prefix)\n if self.n != 0: t = str(self.n) + t\n cr.show_text(t)\n # selection_mode\n cr.move_to(rect.width / 3 + 50, rect.height / 2 - 50)\n if self.selection_mode == self.CHAR:\n cr.show_text('c')\n elif self.selection_mode == self.LINE:\n cr.show_text('l')\n elif self.selection_mode == self.RECT:\n cr.show_text('r')\n # current column\n buf = view.get_buffer()\n cursor_rect = view.get_iter_location(buf.get_iter_at_mark(buf.get_insert()))\n cr.set_source_rgb(0, 0.5, 0)\n cr.set_line_width(2)\n x, _ = view.buffer_to_window_coords(Gtk.TextWindowType.WIDGET, cursor_rect.x, 0)\n cr.move_to(x, 0)\n cr.line_to(x, rect.height)\n cr.stroke()\n", "sub_path": "core_status.py", "file_name": "core_status.py", "file_ext": "py", "file_size_in_byte": 1512, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "gi.repository.Gtk.TextWindowType", "line_number": 40, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 40, "usage_type": "name"}]}
{"seq_id": "355612081", "text": "\ndef parse_vips(description):\n \"\"\"Return metadata from vips image description as dict.\"\"\"\n if not description.startswith(''):\n raise ValueError('invalid MetaSeries image description')\n\n import xml.etree.ElementTree as etree\n import re\n\n root = etree.fromstring(description)\n #ns = re.match('\\{(.*)\\}', root.tag).group(1)\n ns = re.match('\\{(.*)\\}', root.tag).group(0)\n \n #image = root.find('image')\n #etree.dump(image)\n #print(image)\n #quit()\n\n types = {\n 'float': float,\n 'int': int,\n 'bool': lambda x: asbool(x, 'on', 'off'),\n 'VipsRefString' : str,\n 'gint' : int,\n 'gdouble' : float\n\n }\n\n # def is_convertible_to_float(value):\n # try:\n # float(value)\n # return True\n # except:\n # return False\n\n def parse(root, result):\n for image in root.findall(f'{ns}properties'):\n for prop in image.findall(f'{ns}property'):\n value = prop.find(f\"{ns}value\")\n result[prop.find(f\"{ns}name\").text] = types[value.get(\"type\")](value.text)\n \n return result\n\n adict = parse(root, {})\n if 'Description' in adict:\n adict['Description'] = adict['Description'].replace('
', '\\n')\n return adict", "sub_path": "hist/read_vips.py", "file_name": "read_vips.py", "file_ext": "py", "file_size_in_byte": 1280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "xml.etree.ElementTree.fromstring", "line_number": 10, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 10, "usage_type": "name"}, {"api_name": "re.match", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "214999502", "text": "#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nModule that contains implementation for custom PySide/PyQt windows\n\"\"\"\n\nfrom __future__ import print_function, division, absolute_import\n\nimport os\nimport uuid\nfrom collections import defaultdict\n\nfrom Qt.QtCore import *\nfrom Qt.QtWidgets import *\nfrom Qt.QtGui import *\n\nimport appdirs\n\nfrom tpDcc.libs import qt\nimport tpDcc as tp\nfrom tpDcc import register\nfrom tpDcc.libs.python import path, folder\nfrom tpDcc.libs.qt.core import qtutils, animation, theme, statusbar, dragger, resizers, settings as qt_settings\nfrom tpDcc.libs.qt.widgets import layouts\n\n\nclass WindowContents(QFrame, object):\n \"\"\"\n Widget that defines the core contents of frameless window\n Can be used to custom CSS for frameless windows contents\n \"\"\"\n\n def __init__(self, parent=None):\n super(WindowContents, self).__init__(parent=parent)\n\n\nclass BaseWindow(QMainWindow, object):\n\n closed = Signal()\n\n WindowName = 'New Window'\n\n def __init__(self, parent=None, **kwargs):\n\n main_window = tp.Dcc.get_main_window()\n if parent is None:\n parent = main_window\n\n window_id = kwargs.get('id', None)\n self._theme = None\n self._docks = list()\n self._toolbars = dict()\n self._dpi = kwargs.get('dpi', 1.0)\n self._fixed_size = kwargs.get('fixed_size', False)\n self._init_width = kwargs.get('width', 600)\n self._init_height = kwargs.get('height', 800)\n self._has_main_menu = False\n self._show_status_bar = kwargs.pop('show_statusbar', True)\n self._init_menubar = kwargs.pop('init_menubar', False)\n self._settings = kwargs.pop('settings', None)\n self._prefs_settings = kwargs.pop('preferences_settings', None)\n self._enable_save_position = True\n self._initial_pos_override = None\n self._signals = defaultdict(list)\n self._force_disable_saving = False\n win_settings = kwargs.pop('settings', None)\n prefs_settings = kwargs.pop('preferences_settings', None)\n auto_load = kwargs.get('auto_load', True)\n\n if not hasattr(self, 'WindowId'):\n if window_id:\n self.WindowId = window_id\n else:\n self._force_disable_saving = True\n self.WindowId = str(uuid.uuid4())\n\n super(BaseWindow, self).__init__(parent)\n\n self.setObjectName(str(self.WindowId))\n self.setWindowTitle(kwargs.get('title', self.WindowName))\n self.setWindowIcon(kwargs.get('icon', tp.ResourcesMgr().icon('tpdcc')))\n\n self.resize(self._init_width, self._init_height)\n self.center(self._init_width, self._init_height)\n\n # Load base generic window UI\n self._base_ui()\n\n # Load custom window UI\n self.ui()\n self.setup_signals()\n\n self.load_settings(settings=win_settings)\n\n if auto_load:\n self.load_theme()\n else:\n self.reload_stylesheet()\n\n # ============================================================================================================\n # OVERRIDES\n # ============================================================================================================\n\n def menuBar(self):\n return self._menubar\n\n def show(self, *args, **kwargs):\n \"\"\"\n Shows the window and load its position\n :param self:\n :param args:\n :param kwargs:\n \"\"\"\n\n super(BaseWindow, self).show()\n self.load_window_position()\n\n return self\n\n def closeEvent(self, event):\n self.save_settings()\n self.closed.emit()\n for child in self.findChildren(QWidget):\n child.close()\n self.setParent(None)\n self.deleteLater()\n\n def addDockWidget(self, area, dock_widget, orientation=Qt.Horizontal, tabify=True):\n \"\"\"\n Overrides base QMainWindow addDockWidet function\n :param QDockWidgetArea area: area where dock will be added\n :param QDockWidget dock_widget: dock widget to add\n :param Qt.Orientation orientation: orientation fo the dock widget\n :param bool tabify: Whether or not dock widget can be tabbed\n \"\"\"\n\n self._docks.append(dock_widget)\n if self._has_main_menu:\n self._view_menu.addAction(dock_widget.toggleViewAction())\n\n if tabify:\n for current_dock in self._docks:\n if self.dockWidgetArea(current_dock) == area:\n self.tabifyDockWidget(current_dock, dock_widget)\n dock_widget.setVisible(True)\n dock_widget.setFocus()\n dock_widget.raise_()\n return\n\n super(BaseWindow, self).addDockWidget(area, dock_widget, orientation)\n\n # ============================================================================================================\n # BASE\n # ============================================================================================================\n\n def process_events(self):\n \"\"\"\n Forces Qt application to update GUI between calculations\n \"\"\"\n\n QApplication.processEvents()\n\n def center(self, width=None, height=None):\n \"\"\"\n Centers window to the center of the desktop\n :param width: int\n :param height: int\n \"\"\"\n\n geometry = self.frameGeometry()\n if width:\n geometry.setWidth(width)\n if height:\n geometry.setHeight(height)\n\n desktop = QApplication.desktop()\n pos = desktop.cursor().pos()\n screen = desktop.screenNumber(pos)\n center_point = desktop.screenGeometry(screen).center()\n geometry.moveCenter(center_point)\n self.window().setGeometry(geometry)\n\n def center_to_parent(self, parent_geometry=None, child_geometry=None):\n \"\"\"\n Centers current window to its parent\n :param parent_geometry:\n :param child_geometry:\n \"\"\"\n\n if parent_geometry is None or child_geometry is None:\n base_window = self\n if parent_geometry is None:\n try:\n parent_geometry = base_window.parent().frameGeometry()\n except AttributeError:\n parent_geometry = QApplication.desktop().screenGeometry()\n if child_geometry is None:\n child_geometry = base_window.frameGeometry()\n\n self.move(\n parent_geometry.x() + (parent_geometry.width() - child_geometry.width()) / 2,\n parent_geometry.y() + (parent_geometry.height() - child_geometry.height()) / 2\n )\n\n def fade_close(self):\n \"\"\"\n Closes the window with a fade animation\n \"\"\"\n\n animation.fade_window(start=1, end=0, duration=400, object=self, on_finished=self.close)\n\n def show_ok_message(self, message, msecs=None):\n \"\"\"\n Set an ok message to be displayed in the status bar\n :param message: str\n :param msecs: int\n \"\"\"\n\n self._status_bar.show_ok_message(message=message, msecs=msecs)\n\n def show_info_message(self, message, msecs=None):\n \"\"\"\n Set an info message to be displayed in the status bar\n :param message: str\n :param msecs: int\n \"\"\"\n\n self._status_bar.show_info_message(message=message, msecs=msecs)\n\n def show_warning_message(self, message, msecs=None):\n \"\"\"\n Set a warning message to be displayed in the status widget\n :param message: str\n :param msecs: int\n \"\"\"\n\n self._status_bar.show_warning_message(message=message, msecs=msecs)\n\n def show_error_message(self, message, msecs=None):\n \"\"\"\n Set an error message to be displayed in the status widget\n :param message: str\n :param msecs: int\n \"\"\"\n\n self._status_bar.show_error_message(message=message, msecs=msecs)\n\n # ============================================================================================================\n # UI\n # ============================================================================================================\n\n def get_main_layout(self):\n \"\"\"\n Returns the main layout being used by the window\n :return: QLayout\n \"\"\"\n\n main_layout = QVBoxLayout()\n main_layout.setContentsMargins(2, 2, 2, 2)\n main_layout.setSpacing(2)\n\n return main_layout\n\n def ui(self):\n \"\"\"\n Function used to define UI of the window\n \"\"\"\n\n pass\n\n def _base_ui(self):\n \"\"\"\n Internal function that setup basic window UI\n \"\"\"\n\n self.setDockNestingEnabled(True)\n self.setDocumentMode(True)\n self.setDockOptions(QMainWindow.AllowNestedDocks | QMainWindow.AnimatedDocks | QMainWindow.AllowTabbedDocks)\n self.setTabPosition(Qt.AllDockWidgetAreas, QTabWidget.North)\n\n # Central Widget\n\n central_widget = QWidget(parent=self)\n self.setCentralWidget(central_widget)\n self._central_layout = layouts.VerticalLayout(margins=(0, 0, 0, 0), spacing=0)\n central_widget.setLayout(self._central_layout)\n\n self._top_widget = QWidget()\n self._top_layout = layouts.VerticalLayout(margins=(0, 0, 0, 0), spacing=0)\n self._top_widget.setLayout(self._top_layout)\n\n # Status Bar\n #\n # self.statusBar().showMessage('')\n # self.statusBar().setSizeGripEnabled(not self._fixed_size)\n # self._status_bar = self.STATUS_BAR_WIDGET(self)\n # self.statusBar().addWidget(self._status_bar)\n # self.statusBar().setVisible(self._show_status_bar)\n\n # MenuBar\n self._menubar = QMenuBar()\n if self._init_menubar:\n self._has_main_menu = True\n self._file_menu = self.menuBar().addMenu('File')\n self._view_menu = self.menuBar().addMenu('View')\n self._exit_action = QAction(self)\n self._exit_action.setText('Close')\n self._exit_action.setShortcut('Ctrl + Q')\n self._exit_action.setIcon(tp.ResourcesMgr().resource.icon('close_window'))\n self._exit_action.setToolTip('Close application')\n self._file_menu.addAction(self._exit_action)\n self._exit_action.triggered.connect(self.fade_close)\n for i in self._docks:\n self._view_menu.addAction(i.toggleViewAction())\n self._top_layout.addWidget(self._menubar)\n\n self.main_widget = WindowContents()\n self.main_layout = self.get_main_layout()\n self.main_widget.setLayout(self.main_layout)\n\n self._central_layout.addWidget(self._top_widget)\n self._central_layout.addWidget(self.main_widget)\n\n # ============================================================================================================\n # SIGNALS\n # ============================================================================================================\n\n def setup_signals(self):\n \"\"\"\n Override in derived class to setup signals\n This function is called after ui() function is called\n \"\"\"\n\n pass\n\n def signal_connect(self, signal, fn, group=None):\n \"\"\"\n Adds a new signal for the given group\n :param signal:\n :param fn:\n :param group:\n \"\"\"\n\n self._signals[group].append((signal, fn))\n signal.connect(fn)\n\n return fn\n\n def signal_disconnect(self, group):\n \"\"\"\n Disconnects and returns all functions for a current group\n :param group:\n :return: list\n \"\"\"\n\n signals = list()\n for (signal, fn) in self._signals.pop(group, list()):\n try:\n signal.disconnect(fn)\n except RuntimeError:\n pass\n else:\n signals.append((signal, fn))\n\n return signals\n\n def signal_pause(self, *groups):\n \"\"\"\n Pauses a certain set of signals during execution\n :param groups: list\n \"\"\"\n\n if not groups:\n groups = self._signals\n\n signal_cache = dict()\n for group in groups:\n signal_cache[group] = self.signal_disconnect(group)\n\n yield\n\n for group in groups:\n for signal, fn in signal_cache[group]:\n self.signal_connect(signal, fn, group=group)\n\n # ============================================================================================================\n # SETTINGS\n # ============================================================================================================\n\n def settings(self):\n \"\"\"\n Returns window settings\n :return: QtSettings\n \"\"\"\n\n return self._settings\n\n def default_settings(self):\n \"\"\"\n Returns default settings values\n :return: dict\n \"\"\"\n\n return {}\n\n def set_settings(self, settings):\n \"\"\"\n Set window settings\n :param settings:\n \"\"\"\n\n self._settings = settings\n\n def_settings = self.default_settings()\n\n def_geometry = self.settings().get_default_value('geometry', self.objectName().upper())\n geometry = self.settings().getw('geometry', def_geometry)\n if geometry:\n self.restoreGeometry(geometry)\n\n # Reposition window in the center of the screen if the window is outside of the screen\n geometry = self.geometry()\n x = geometry.x()\n y = geometry.y()\n width = self._init_width or geometry.width()\n height = self._init_height or geometry.height()\n screen_geo = QApplication.desktop().screenGeometry()\n screen_width = screen_geo.width()\n screen_height = screen_geo.height()\n if x <= 0 or y <= 0 or x >= screen_width or y >= screen_height:\n self.center(width, height)\n\n def_window_state = self.settings().get_default_value('windowState', self.objectName().upper())\n window_state = self.settings().getw('windowState', def_window_state)\n if window_state:\n self.restoreState(window_state)\n\n def load_settings(self, settings=None):\n \"\"\"\n Loads window settings from disk\n \"\"\"\n\n settings = settings or self.settings()\n if not settings:\n settings = self._settings\n if not settings:\n self._settings = qt_settings.QtSettings(filename=self.get_settings_file(), window=self)\n self._settings.setFallbacksEnabled(False)\n if not self._prefs_settings:\n self._prefs_settings = self._settings\n return self.set_settings(self._settings)\n\n return self.set_settings(settings)\n\n def save_settings(self, settings=None):\n \"\"\"\n Saves window settings\n \"\"\"\n\n settings = settings or self.settings()\n if not settings:\n return\n\n settings.setw('geometry', self.saveGeometry())\n settings.setw('saveState', self.saveState())\n settings.setw('windowState', self.saveState())\n\n return settings\n\n def get_settings_path(self):\n \"\"\"\n Returns path where window settings are stored\n :return: str\n \"\"\"\n\n return path.clean_path(os.path.join(appdirs.user_data_dir(), self.WindowId))\n\n def get_settings_file(self):\n \"\"\"\n Returns file path of the window settings file\n :return: str\n \"\"\"\n\n return path.clean_path(os.path.expandvars(os.path.join(self.get_settings_path(), 'settings.cfg')))\n\n def enable_save_window_position(self, enable):\n \"\"\"\n Enables or disables the storage of window position in settings\n :param enable: bool\n \"\"\"\n\n self._enable_save_position = enable\n\n def load_window_position(self):\n pass\n # if self._initial_pos_override is not None:\n # x, y = self._initial_pos_override()\n # x, y =\n\n def save_window_position(self):\n print('Saving window position ...')\n\n # ============================================================================================================\n # DPI\n # ============================================================================================================\n\n def dpi(self):\n \"\"\"\n Return the current dpi for the window\n :return: float\n \"\"\"\n\n return float(self._dpi)\n\n def set_dpi(self, dpi):\n \"\"\"\n Sets current dpi for the window\n :param dpi: float\n \"\"\"\n\n self._dpi = dpi\n\n # ============================================================================================================\n # THEME\n # ============================================================================================================\n\n def load_theme(self):\n \"\"\"\n Loads window theme\n \"\"\"\n\n def_settings = self.default_settings()\n def_theme_settings = def_settings.get('theme', dict())\n accent_color = self.settings().getw('theme/accent_color') or def_theme_settings.get('accent_color')\n background_color = self.settings().getw('theme/background_color') or def_theme_settings.get('background_color')\n accent_color = 'rgb(%d, %d, %d, %d)' % accent_color.getRgb() if isinstance(\n accent_color, QColor) else accent_color\n background_color = 'rgb(%d, %d, %d, %d)' % background_color.getRgb() if isinstance(\n background_color, QColor) else background_color\n\n theme_settings = dict()\n if accent_color:\n theme_settings['accent_color'] = accent_color\n if background_color:\n theme_settings['background_color'] = background_color\n\n self.set_theme_settings(theme_settings)\n\n def theme(self):\n \"\"\"\n Returns the current theme\n :return: Theme\n \"\"\"\n\n if not self._theme:\n return None\n\n return self._theme\n\n def set_theme(self, theme):\n \"\"\"\n Sets current window theme\n :param theme: Theme\n \"\"\"\n\n self._theme = theme\n self._theme.updated.connect(self.reload_stylesheet)\n self.reload_stylesheet()\n\n def set_theme_settings(self, settings):\n \"\"\"\n Sets the theme settings from the given settings\n :param settings: dict\n \"\"\"\n\n current_theme = self._settings.get('theme', 'default')\n new_theme = tp.ResourcesMgr().theme(current_theme)\n if not new_theme:\n new_theme = theme.Theme()\n new_theme.set_settings(settings)\n self.set_theme(new_theme)\n\n def reload_stylesheet(self):\n \"\"\"\n Reloads the stylesheet to the current theme\n \"\"\"\n\n current_theme = self.theme()\n current_theme.set_dpi(self.dpi())\n stylesheet = current_theme.stylesheet()\n self.setStyleSheet(stylesheet)\n\n # TODO: This operation is VERY heavy. Find a better way of doing this\n # all_widgets = qtutils.iterate_children(self.main_widget, qobj_class=QObject)\n # for w in all_widgets:\n # if hasattr(w, 'setStyleSheet'):\n # w.setStyleSheet(stylesheet)\n # w.update()\n\n # ============================================================================================================\n # TOOLBAR\n # ============================================================================================================\n\n def add_toolbar(self, name, area=Qt.TopToolBarArea):\n \"\"\"\n Adds a new toolbar to the window\n :return: QToolBar\n \"\"\"\n\n new_toolbar = QToolBar(name)\n self.addToolBar(area, new_toolbar)\n\n return new_toolbar\n\n # ============================================================================================================\n # DOCK\n # ============================================================================================================\n\n def add_dock(self, name, widget=None, pos=Qt.LeftDockWidgetArea, tabify=True):\n \"\"\"\n Adds a new dockable widget to the window\n :param name: str, name of the dock widget\n :param widget: QWidget, widget to add to the dock\n :param pos: Qt.WidgetArea\n :param tabify: bool, Wheter the new widget should be tabbed to existing docks\n :return: QDockWidget\n \"\"\"\n\n if widget:\n dock_name = ''.join([widget.objectName(), 'Dock'])\n else:\n dock_name = name + 'Dock'\n\n existing_dock = self.find_dock(dock_name)\n if existing_dock:\n existing_dock.raise_()\n\n dock = DockWidget(title=name, parent=self, floating=False)\n dock.setObjectName(dock_name)\n if widget is not None:\n dock.setWidget(widget)\n self.addDockWidget(pos, dock, tabify=tabify)\n\n return dock\n\n def set_active_dock_tab(self, dock_widget):\n \"\"\"\n Sets the current active dock tab depending on the given dock widget\n :param dock_widget: DockWidget\n \"\"\"\n\n tab_bars = self.findChildren(QTabBar)\n for bar in tab_bars:\n count = bar.count()\n for i in range(count):\n data = bar.tabData(i)\n widget = qtutils.to_qt_object(data, qobj=type(dock_widget))\n if widget == dock_widget:\n bar.setCurrentIndex(i)\n\n def find_dock(self, dock_name):\n \"\"\"\n Returns the dock widget based on the object name passed\n :param str dock_name: dock objectName to find\n :return: QDockWidget or None\n \"\"\"\n\n for dock in self._docks:\n if dock.objectName() == dock_name:\n return dock\n\n return None\n\n def _parent_override(self):\n \"\"\"\n Internal function that overrides parent functionality to make sure that proper parent attributes are used\n in dockable windows\n \"\"\"\n\n # Make sure this function is inherited\n return super(MainWindow, self)\n\n # ============================================================================================================\n # INTERNAL\n # ============================================================================================================\n\n def _load_ui_from_file(self, ui_file):\n \"\"\"\n Internal function that loads given UI file\n :param ui_file: str\n :return: QWidget or None\n \"\"\"\n\n if not os.path.isfile(ui_file):\n return None\n\n loaded_ui = qtutils.load_ui(ui_file=ui_file)\n\n return loaded_ui\n\n\nclass MainWindow(BaseWindow, object):\n \"\"\"\n Main class to create windows\n \"\"\"\n\n dockChanged = Signal(object)\n windowResizedFinished = Signal()\n framelessChanged = Signal(object)\n windowReady = Signal()\n clearedInstance = Signal()\n\n STATUS_BAR_WIDGET = statusbar.StatusWidget\n DRAGGER_CLASS = dragger.WindowDragger\n\n _WINDOW_INSTANCES = dict()\n\n def __init__(self, parent=None, **kwargs):\n\n self._setup_resizers()\n\n self._preference_widgets_classes = list()\n self._toolset = kwargs.get('toolset', None)\n self._transparent = kwargs.get('transparent', False)\n self._config = kwargs.pop('config', None)\n self._dockable = getattr(self, 'WindowDockable', False)\n self._was_docked = False\n self._window_loaded = False\n self._window_closed = False\n self._current_docked = None\n\n super(MainWindow, self).__init__(parent=parent, **kwargs)\n\n frameless = kwargs.get('frameless', True)\n self.set_frameless(frameless)\n if not frameless:\n self.set_resizer_active(False)\n self._dragger.set_dragging_enabled(False)\n self._dragger.set_window_buttons_state(False)\n else:\n self._dragger.set_dragging_enabled(True)\n self._dragger.set_window_buttons_state(True)\n self._dragger._toggle_frameless.setChecked(self.is_frameless())\n\n # We set the window title after UI is created\n self.setWindowTitle(kwargs.get('title', 'tpDcc'))\n self.setWindowIcon(kwargs.get('icon', tp.ResourcesMgr().icon('tpdcc')))\n\n MainWindow._WINDOW_INSTANCES[self.WindowId] = {\n 'window': self\n }\n\n self.windowReady.connect(lambda: setattr(self, '_window_loaded', True))\n\n # ============================================================================================================\n # PROPERTIES\n # ============================================================================================================\n\n @property\n def widget(self):\n \"\"\"\n Returns widget\n \"\"\"\n\n return self._widget\n\n # ============================================================================================================\n # CLASS METHODS\n # ============================================================================================================\n\n @classmethod\n def instance(cls, parent=None, **kwargs):\n pass\n\n @classmethod\n def clear_window_instance(cls, window_id):\n \"\"\"\n Closes the last class instance\n :param window_id:\n :return:\n \"\"\"\n\n inst = cls._WINDOW_INSTANCES.pop(window_id, None)\n if inst is not None:\n try:\n inst['window'].clearedInstance.emit()\n except RuntimeError as exc:\n tp.logger.error('Error while clearing window instance: {} | {}'.format(window_id, exc))\n\n return inst\n\n @classmethod\n def clear_window_instances(cls):\n \"\"\"\n Closes every loaded window\n \"\"\"\n\n for window_id in tuple(cls._WINDOW_INSTANCES):\n cls.clear_window_instance(window_id)\n\n # ============================================================================================================\n # OVERRIDES\n # ============================================================================================================\n\n def showEvent(self, event):\n if self.docked() != self._current_docked:\n self._current_docked = self.docked()\n self.dockChanged.emit(self._current_docked)\n\n super(MainWindow, self).showEvent(event)\n\n def closeEvent(self, event):\n self._window_closed = True\n self.unregister_callbacks()\n self.clear_window_instance(self.WindowId)\n super(MainWindow, self).closeEvent(event)\n\n def setWindowIcon(self, icon):\n if self.is_frameless() or (hasattr(self, '_dragger') and self._dragger):\n self._dragger.set_icon(icon)\n super(MainWindow, self).setWindowIcon(icon)\n\n def setWindowTitle(self, title):\n if self.is_frameless() or (hasattr(self, '_dragger') and self._dragger):\n self._dragger.set_title(title)\n super(MainWindow, self).setWindowTitle(title)\n\n def show(self, *args, **kwargs):\n \"\"\"\n Shows the window and load its position\n :param self:\n :param args:\n :param kwargs:\n \"\"\"\n\n super(MainWindow, self).show()\n self.windowReady.emit()\n\n return self\n\n # ============================================================================================================\n # UI\n # ============================================================================================================\n\n def ui(self):\n \"\"\"\n Function used to define UI of the window\n \"\"\"\n\n super(MainWindow, self).ui()\n\n for r in self._resizers:\n r.setParent(self)\n\n # Dragger\n self._dragger = self.DRAGGER_CLASS(window=self)\n self._top_layout.insertWidget(0, self._dragger)\n\n for r in self._resizers:\n r.windowResizedFinished.connect(self.windowResizedFinished)\n self.set_resize_directions()\n\n grid_layout = layouts.GridLayout()\n grid_layout.setHorizontalSpacing(0)\n grid_layout.setVerticalSpacing(0)\n grid_layout.setContentsMargins(0, 0, 0, 0)\n grid_layout.addWidget(self._top_widget, 1, 1, 1, 1)\n grid_layout.addWidget(self.main_widget, 2, 1, 1, 1)\n grid_layout.addWidget(self._top_left_resizer, 0, 0, 1, 1)\n grid_layout.addWidget(self._top_resizer, 0, 1, 1, 1)\n grid_layout.addWidget(self._top_right_resizer, 0, 2, 1, 1)\n grid_layout.addWidget(self._left_resizer, 1, 0, 2, 1)\n grid_layout.addWidget(self._right_resizer, 1, 2, 2, 1)\n grid_layout.addWidget(self._bottom_left_resizer, 3, 0, 1, 1)\n grid_layout.addWidget(self._bottom_resizer, 3, 1, 1, 1)\n grid_layout.addWidget(self._bottom_right_resizer, 3, 2, 1, 1)\n grid_layout.setColumnStretch(1, 1)\n grid_layout.setRowStretch(2, 1)\n\n self._central_layout.addLayout(grid_layout)\n\n # Shadow effect for window\n # BUG: This causes some rendering problems when using other shadow effects in child widgets of the window\n # BUG: Also detected problems when updating wigets (tree views, web browsers, etc)\n # https://bugreports.qt.io/browse/QTBUG-35196\n # shadow_effect = QGraphicsDropShadowEffect(self)\n # shadow_effect.setBlurRadius(qtutils.dpi_scale(15))\n # shadow_effect.setColor(QColor(0, 0, 0, 150))\n # shadow_effect.setOffset(qtutils.dpi_scale(0))\n # self.setGraphicsEffect(shadow_effect)\n\n for r in self._resizers:\n r.windowResizedFinished.connect(self.windowResizedFinished)\n\n if self._toolset:\n self.main_layout.addWidget(self._toolset)\n\n # ============================================================================================================\n # SIGNALS\n # ============================================================================================================\n\n def register_callback(self, callback_type, fn):\n \"\"\"\n Registers the given callback with the given function\n :param callback_type: tpDcc.DccCallbacks\n :param fn: Python function to be called when callback is emitted\n \"\"\"\n\n if type(callback_type) in [list, tuple]:\n callback_type = callback_type[0]\n\n if callback_type not in tp.callbacks():\n tp.logger.warning('Callback Type: \"{}\" is not valid! Aborting callback creation ...'.format(callback_type))\n return\n\n from tpDcc.managers import callbacks\n return callbacks.CallbacksManager.register(callback_type=callback_type, fn=fn, owner=self)\n\n def unregister_callbacks(self):\n \"\"\"\n Unregisters all callbacks registered by this window\n \"\"\"\n\n from tpDcc.managers import callbacks\n callbacks.CallbacksManager.unregister_owner_callbacks(owner=self)\n\n # ============================================================================================================\n # BASE\n # ============================================================================================================\n\n def exists(self):\n \"\"\"\n Returns whether or not this window exists\n :return: bool\n \"\"\"\n\n return True\n\n def is_loaded(self):\n \"\"\"\n Returns whether or not this window has been already loaded\n :return: bool\n \"\"\"\n\n return self._window_loaded and not self.is_closed()\n\n def is_closed(self):\n \"\"\"\n Returns whether or not this window has been closed\n \"\"\"\n\n return self._window_closed\n\n def is_frameless(self):\n \"\"\"\n Returns whether or not frameless functionality for this window is enable or not\n :return: bool\n \"\"\"\n\n return self.window().windowFlags() & Qt.FramelessWindowHint == Qt.FramelessWindowHint\n\n def set_frameless(self, flag):\n \"\"\"\n Sets whether frameless functionality is enabled or not\n :param flag: bool\n :param show: bool\n \"\"\"\n\n window = self.window()\n\n if flag and not self.is_frameless():\n window.setAttribute(Qt.WA_TranslucentBackground)\n if qtutils.is_pyside2() or qtutils.is_pyqt5():\n window.setWindowFlags(window.windowFlags() | Qt.FramelessWindowHint | Qt.NoDropShadowWindowHint)\n window.setWindowFlags(window.windowFlags() ^ Qt.WindowMinMaxButtonsHint)\n else:\n window.setWindowFlags(Qt.Window | Qt.FramelessWindowHint)\n window.setWindowFlags(window.windowFlags() ^ Qt.WindowMinMaxButtonsHint)\n self.set_resizer_active(True)\n elif not flag and self.is_frameless():\n window.setAttribute(Qt.WA_TranslucentBackground)\n if qtutils.is_pyside2() or qtutils.is_pyqt5():\n window.setWindowFlags(window.windowFlags() | Qt.FramelessWindowHint | Qt.NoDropShadowWindowHint)\n else:\n self.setWindowFlags(Qt.Window | Qt.FramelessWindowHint)\n self.set_resizer_active(False)\n\n window.show()\n\n # ============================================================================================================\n # RESIZERS\n # ============================================================================================================\n\n def set_resizer_active(self, flag):\n \"\"\"\n Sets whether resizers are enable or not\n :param flag: bool\n \"\"\"\n\n if flag:\n for r in self._resizers:\n r.show()\n else:\n for r in self._resizers:\n r.hide()\n\n def set_resize_directions(self):\n \"\"\"\n Sets the resize directions for the resizer widget of this window\n \"\"\"\n\n self._top_resizer.set_resize_direction(resizers.ResizeDirection.Top)\n self._bottom_resizer.set_resize_direction(resizers.ResizeDirection.Bottom)\n self._right_resizer.set_resize_direction(resizers.ResizeDirection.Right)\n self._left_resizer.set_resize_direction(resizers.ResizeDirection.Left)\n self._top_left_resizer.set_resize_direction(resizers.ResizeDirection.Left | resizers.ResizeDirection.Top)\n self._top_right_resizer.set_resize_direction(resizers.ResizeDirection.Right | resizers.ResizeDirection.Top)\n self._bottom_left_resizer.set_resize_direction(resizers.ResizeDirection.Left | resizers.ResizeDirection.Bottom)\n self._bottom_right_resizer.set_resize_direction(\n resizers.ResizeDirection.Right | resizers.ResizeDirection.Bottom)\n\n def get_resizers_height(self):\n \"\"\"\n Returns the total height of the vertical resizers\n :return: float\n \"\"\"\n\n resizers = [self._top_resizer, self._bottom_resizer]\n total_height = 0\n for r in resizers:\n if not r.isHidden():\n total_height += r.minimumSize().height()\n\n return total_height\n\n def get_resizers_width(self):\n \"\"\"\n Returns the total widht of the horizontal resizers\n :return: float\n \"\"\"\n\n resizers = [self._left_resizer, self._right_resizer]\n total_width = 0\n for r in resizers:\n if not r.isHidden():\n total_width += r.minimumSize().width()\n\n return total_width\n\n def _setup_resizers(self):\n \"\"\"\n Internal function that setup window resizers\n \"\"\"\n\n self._top_resizer = resizers.VerticalResizer()\n self._bottom_resizer = resizers.VerticalResizer()\n self._right_resizer = resizers.HorizontalResizer()\n self._left_resizer = resizers.HorizontalResizer()\n self._top_left_resizer = resizers.CornerResizer()\n self._top_right_resizer = resizers.CornerResizer()\n self._bottom_left_resizer = resizers.CornerResizer()\n self._bottom_right_resizer = resizers.CornerResizer()\n\n self._resizers = [\n self._top_resizer, self._top_right_resizer, self._right_resizer, self._bottom_right_resizer,\n self._bottom_resizer, self._bottom_left_resizer, self._left_resizer, self._top_left_resizer\n ]\n\n # ============================================================================================================\n # PREFERENCES SETTINGS\n # ============================================================================================================\n\n def preferences_settings(self):\n \"\"\"\n Returns window preferences settings\n :return: QtSettings\n \"\"\"\n\n return self._prefs_settings\n\n def set_preferences_settings(self, prefs_settings):\n \"\"\"\n Sets window preference settings\n :param prefs_settings:\n \"\"\"\n\n self._prefs_settings = prefs_settings\n\n def register_preference_widget_class(self, widget_class):\n \"\"\"\n Function used to registere preference widgets\n \"\"\"\n\n if not hasattr(widget_class, 'CATEGORY'):\n qt.logger.warning(\n 'Impossible to register Category Wigdet Class \"{}\" because it does not '\n 'defines a CATEGORY attribute'.format(widget_class))\n return\n\n registered_prefs_categories = [pref.CATEGORY for pref in self._preference_widgets_classes]\n if widget_class.CATEGORY in registered_prefs_categories:\n qt.logger.warning(\n 'Impossible to register Category Widget Class \"{}\" because its CATEGORY \"{}\" its '\n 'already registered!'.format(widget_class, widget_class.CATEGORY))\n return\n\n self._preference_widgets_classes.append(widget_class)\n\n # ============================================================================================================\n # DOCK\n # ============================================================================================================\n\n def dockable(self, raw=False):\n \"\"\"\n Returns whether or not the window is dockable\n :param raw: bool, If True, get current state of the window, otherwise get current setting\n :return: bool\n \"\"\"\n\n if not raw and self._was_docked is not None:\n return self._was_docked\n\n return self._dockable\n\n def set_dockable(self, dockable, override=False):\n \"\"\"\n Sets whether or not this window is dockable\n :param dockable: bool\n :param override: bool, If the dockable raw value should be set.\n Only should be used if the dock state has changed\n \"\"\"\n\n if override:\n self._was_docked = self._dockable = dockable\n else:\n self._was_docked = self._dockable\n self._dockable = dockable\n self.save_window_position()\n\n def docked(self):\n \"\"\"\n Returns whether or not this window is currently docked\n :return: bool\n \"\"\"\n\n if not self.dockable():\n return False\n\n raise NotImplementedError('docked function is not implemented!')\n\n def is_floating(self):\n \"\"\"\n Returns whether or not this window is floating\n :return: bool\n \"\"\"\n\n return tp.Dcc.is_window_floating(self.WindowId)\n\n # ============================================================================================================\n # INTERNAL\n # ============================================================================================================\n\n def _settings_validator(self, **kwargs):\n \"\"\"\n Validator used for the settings dialog\n :param kwargs: dict\n \"\"\"\n\n fields = list()\n\n clr = kwargs.get(\"accentColor\")\n if clr and self.theme().accent_color().to_string() != clr:\n self.theme().set_accent_color(clr)\n\n clr = kwargs.get(\"backgroundColor\")\n if clr and self.theme().background_color().to_string() != clr:\n self.theme().set_background_color(clr)\n\n return fields\n\n def _settings_accepted(self, **kwargs):\n \"\"\"\n Function that is called when window settings dialog are accepted\n :param kwargs: dict\n \"\"\"\n\n if not self.settings():\n return\n\n theme_name = self.theme().name()\n accent_color = kwargs.get('accentColor', self.theme().accent_color().to_string())\n background_color = kwargs.get('backgroundColor', self.theme().background_color().to_string())\n if theme_name:\n self.settings().setw('theme/name', theme_name)\n self.settings().setw('theme/accentColor', accent_color)\n self.settings().setw('theme/backgroundColor', background_color)\n self.settings().sync()\n\n self.load_theme()\n\n def _setup_theme_preferences(self):\n\n from tpDcc.libs.qt.core import preferences\n from tpDcc.libs.qt.widgets import formwidget\n\n accent_color = self.theme().accent_color().to_string()\n background_color = self.theme().background_color().to_string()\n settings_validator = self._settings_validator\n settings_accepted = self._settings_accepted\n\n class ThemeCategoryWidget(preferences.CategoryWidgetBase, object):\n\n CATEGORY = 'Theme'\n\n def __init__(self, parent=None):\n super(ThemeCategoryWidget, self).__init__(parent=parent)\n\n self.main_layout = QVBoxLayout()\n self.main_layout.setContentsMargins(2, 2, 2, 2)\n self.main_layout.setSpacing(2)\n self.setLayout(self.main_layout)\n\n form = {\n \"title\": \"Theme\",\n \"description\": \"Theme Colors\",\n \"layout\": \"vertical\",\n \"schema\": [\n {\n \"name\": \"accentColor\",\n \"type\": \"color\",\n \"value\": accent_color,\n \"colors\": [\n \"rgb(230, 80, 80, 255)\",\n \"rgb(230, 125, 100, 255)\",\n \"rgb(230, 120, 40)\",\n \"rgb(240, 180, 0, 255)\",\n \"rgb(80, 200, 140, 255)\",\n \"rgb(50, 180, 240, 255)\",\n \"rgb(110, 110, 240, 255)\",\n ]\n },\n {\n \"name\": \"backgroundColor\",\n \"type\": \"color\",\n \"value\": background_color,\n \"colors\": [\n \"rgb(40, 40, 40)\",\n \"rgb(68, 68, 68)\",\n \"rgb(80, 60, 80)\",\n \"rgb(85, 60, 60)\",\n \"rgb(60, 75, 75)\",\n \"rgb(60, 64, 79)\",\n \"rgb(245, 245, 255)\",\n ]\n },\n ],\n \"validator\": settings_validator,\n \"accepted\": settings_accepted\n }\n\n self._dlg = formwidget.FormDialog(parent=parent, form=form)\n self._dlg.setMinimumWidth(300)\n self._dlg.setMinimumHeight(300)\n self._dlg.setMaximumWidth(400)\n self._dlg.setMaximumHeight(400)\n self._dlg.accept_button().setText('Save')\n self._dlg.accept_button().setVisible(False)\n self._dlg.reject_button().setVisible(False)\n self._dlg.show()\n self.main_layout.addWidget(self._dlg)\n\n theme_prefs_widget = ThemeCategoryWidget(parent=self._preferences_window)\n\n return theme_prefs_widget\n\n\nclass DetachedWindow(QMainWindow):\n \"\"\"\n Class that incorporates functionality to create detached windows\n \"\"\"\n\n windowClosed = Signal(object)\n\n class DetachPanel(QWidget, object):\n widgetVisible = Signal(QWidget, bool)\n\n def __init__(self, parent=None):\n super(DetachedWindow.DetachPanel, self).__init__(parent=parent)\n\n self.main_layout = QVBoxLayout()\n self.setLayout(self.main_layout)\n\n def set_widget_visible(self, widget, visible):\n self.setVisible(visible)\n self.widgetVisible.emit(widget, visible)\n\n def set_widget(self, widget):\n qtutils.clear_layout(self.main_layout)\n self.main_layout.addWidget(widget)\n widget.show()\n\n class SettingGroup(object):\n global_group = ''\n\n def __init__(self, name):\n self.name = name\n self.settings = QSettings()\n\n def __enter__(self):\n if self.global_group:\n self.settings.beginGroup(self.global_group)\n self.settings.beginGroup(self.name)\n return self.settings\n\n def __exit__(self, *args):\n if self.global_group:\n self.settings.endGroup()\n self.settings.endGroup()\n self.settings.sync()\n\n @staticmethod\n def load_basic_window_settings(window, window_settings):\n window.restoreGeometry(window_settings.value('geometry', QByteArray()))\n window.restoreState(window_settings.value('windowstate', QByteArray()))\n try:\n window.split_state = window_settings.value('splitstate', '')\n except TypeError:\n window.split_state = ''\n\n def __init__(self, title, parent):\n self.tab_idx = -1\n super(DetachedWindow, self).__init__(parent=parent)\n\n self.main_widget = self.DetachPanel()\n self.setCentralWidget(self.main_widget)\n\n self.setWindowTitle(title)\n self.setWindowModality(Qt.NonModal)\n self.sgroup = self.SettingGroup(title)\n with self.sgroup as config:\n self.SettingGroup.load_basic_window_settings(self, config)\n\n self.statusBar().hide()\n\n def closeEvent(self, event):\n with self.sgroup as config:\n config.setValue('detached', False)\n self.windowClosed.emit(self)\n self.deleteLater()\n\n def moveEvent(self, event):\n super(DetachedWindow, self).moveEvent(event)\n self.save_settings()\n\n def resizeEvent(self, event):\n super(DetachedWindow, self).resizeEvent(event)\n self.save_settings()\n\n def set_widget_visible(self, widget, visible):\n self.setVisible(visible)\n\n def set_widget(self, widget):\n self.main_widget.set_widget(widget=widget)\n\n def save_settings(self, detached=True):\n with self.sgroup as config:\n config.setValue('detached', detached)\n config.setValue('geometry', self.saveGeometry())\n config.setValue('windowstate', self.saveState())\n\n\nclass DockWindow(QMainWindow, object):\n \"\"\"\n Class that with dock functionality. It's not intended to use as main window (use MainWindow for that) but for\n being inserted inside a window and have a widget with dock functionality in the main layout of that window\n \"\"\"\n\n class DockWidget(QDockWidget, object):\n def __init__(self, name, parent=None, window=None):\n super(DockWindow.DockWidget, self).__init__(name, parent)\n\n self.setWidget(window)\n\n # region Override Functions\n def setWidget(self, widget):\n \"\"\"\n Sets the window instance of the dockable main window\n \"\"\"\n\n super(DockWindow.DockWidget, self).setWidget(widget)\n\n if widget and issubclass(widget.__class__, MainWindow):\n # self.setFloating(True)\n self.setWindowTitle(widget.windowTitle())\n self.visibilityChanged.connect(self._visibility_changed)\n\n widget.setWindowFlags(Qt.Widget)\n widget.setParent(self)\n widget.windowTitleChanged.connect(self._window_title_changed)\n\n # endregion\n\n # region Private Functions\n def _visibility_changed(self, state):\n \"\"\"\n Process QDockWidget's visibilityChanged signal\n \"\"\"\n\n # TODO: Implement export widget properties functionality\n # widget = self.widget()\n # if widget:\n # widget.export_settings()\n\n def _window_title_changed(self, title):\n \"\"\"\n Process BaseWindow's windowTitleChanged signal\n :param title: str, new title\n \"\"\"\n\n self.setWindowTitle(title)\n\n _last_instance = None\n\n def __init__(self, name='BaseWindow', title='DockWindow', use_scrollbar=False, parent=None):\n self.main_layout = self.get_main_layout()\n self.__class__._last_instance = self\n super(DockWindow, self).__init__(parent)\n\n self.docks = list()\n self.connect_tab_change = True\n self.use_scrollbar = use_scrollbar\n\n self.setObjectName(name)\n self.setWindowTitle(title)\n self.statusBar().setSizeGripEnabled(False)\n self.statusBar().hide()\n\n self.ui()\n\n self.tab_change_hide_show = True\n\n def keyPressEvent(self, event):\n return\n\n def get_main_layout(self):\n \"\"\"\n Function that generates the main layout used by the widget\n Override if necessary on new widgets\n :return: QLayout\n \"\"\"\n\n return QVBoxLayout()\n\n def ui(self):\n \"\"\"\n Function that sets up the ui of the widget\n Override it on new widgets (but always call super)\n \"\"\"\n\n main_widget = QWidget()\n if self.use_scrollbar:\n scroll = QScrollArea()\n scroll.setWidgetResizable(True)\n scroll.setWidget(main_widget)\n self._scroll_widget = scroll\n main_widget.setSizePolicy(QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding))\n self.setCentralWidget(scroll)\n else:\n self.setCentralWidget(main_widget)\n\n main_widget.setLayout(self.main_layout)\n self.main_widget = main_widget\n\n self.main_layout.expandingDirections()\n self.main_layout.setContentsMargins(0, 0, 0, 0)\n self.main_layout.setSpacing(0)\n\n # ==========================================================================================\n\n # TODO: Check if we should put this on constructor\n # self.main_widget.setSizePolicy(QSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum))\n # self.centralWidget().hide()\n\n self.setTabPosition(Qt.TopDockWidgetArea, QTabWidget.West)\n self.setDockOptions(self.AnimatedDocks | self.AllowTabbedDocks | self.AllowNestedDocks)\n\n def set_active_dock_tab(self, dock_widget):\n \"\"\"\n Sets the current active dock tab depending on the given dock widget\n :param dock_widget: DockWidget\n \"\"\"\n\n tab_bars = self.findChildren(QTabBar)\n for bar in tab_bars:\n count = bar.count()\n for i in range(count):\n data = bar.tabData(i)\n widget = qtutils.to_qt_object(data, qobj=type(dock_widget))\n if widget == dock_widget:\n bar.setCurrentIndex(i)\n\n def add_dock(self, widget, name, pos=Qt.TopDockWidgetArea, tabify=True):\n docks = self._get_dock_widgets()\n for dock in docks:\n if dock.windowTitle() == name:\n dock.deleteLater()\n dock.close()\n dock_widget = self.DockWidget(name=name, parent=self)\n # dock_widget.setSizePolicy(QSizePolicy(QSizePolicy.Maximum, QSizePolicy.Minimum))\n dock_widget.setAllowedAreas(pos)\n dock_widget.setWidget(widget)\n\n self.addDockWidget(pos, dock_widget)\n\n if docks and tabify:\n self.tabifyDockWidget(docks[-1], dock_widget)\n\n dock_widget.show()\n dock_widget.raise_()\n\n tab_bar = self._get_tab_bar()\n if tab_bar:\n if self.connect_tab_change:\n tab_bar.currentChanged.connect(self._on_tab_changed)\n self.connect_tab_change = False\n\n return dock_widget\n\n def _get_tab_bar(self):\n children = self.children()\n for child in children:\n if isinstance(child, QTabBar):\n return child\n\n def _get_dock_widgets(self):\n found = list()\n for child in self.children():\n if isinstance(child, QDockWidget):\n found.append(child)\n\n return found\n\n def _on_tab_changed(self, index):\n if not self.tab_change_hide_show:\n return\n\n docks = self._get_dock_widgets()\n\n docks[index].hide()\n docks[index].show()\n\n\nclass SubWindow(MainWindow, object):\n \"\"\"\n Class to create sub windows\n \"\"\"\n\n def __init__(self, parent=None, **kwargs):\n super(SubWindow, self).__init__(parent=parent, frameless=False, **kwargs)\n\n\nclass DirectoryWindow(MainWindow, object):\n \"\"\"\n Window that stores variable to store current working directory\n \"\"\"\n\n def __init__(self, parent=None, **kwargs):\n self.directory = None\n super(DirectoryWindow, self).__init__(parent=parent, frameless=False, **kwargs)\n\n def set_directory(self, directory):\n \"\"\"\n Sets the directory of the window. If the given folder does not exists, it will created automatically\n :param directory: str, new directory of the window\n \"\"\"\n\n self.directory = directory\n\n if not path.is_dir(directory=directory):\n folder.create_folder(name=None, directory=directory)\n\n\nclass DockWidget(QDockWidget, object):\n \"\"\"\n Base docked widget\n \"\"\"\n\n def __init__(self, title, parent=None, floating=False):\n super(DockWidget, self).__init__(title, parent)\n\n self.setFloating(floating)\n self.setFeatures(\n QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable | QDockWidget.DockWidgetClosable)\n\n\nclass DockWindowContainer(DockWidget, object):\n \"\"\"\n Docked Widget used to dock windows inside other windows\n \"\"\"\n\n def __init__(self, title):\n super(DockWindowContainer, self).__init__(title)\n\n def closeEvent(self, event):\n if self.widget():\n self.widget().close()\n super(DockWindowContainer, self).closeEvent(event)\n\n\nregister.register_class('Window', MainWindow)\n", "sub_path": "tpDcc/libs/qt/core/window.py", "file_name": "window.py", "file_ext": "py", "file_size_in_byte": 54420, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "tpDcc.Dcc.get_main_window", "line_number": 46, "usage_type": "call"}, {"api_name": "tpDcc.Dcc", "line_number": 46, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 65, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 76, "usage_type": "call"}, {"api_name": "tpDcc.ResourcesMgr", "line_number": 82, "usage_type": "call"}, {"api_name": "Qt.QtCore.Horizontal", "line_number": 129, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 129, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.animation.fade_window", "line_number": 211, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.animation", "line_number": 211, "usage_type": "name"}, {"api_name": "Qt.QtCore.AllDockWidgetAreas", "line_number": 280, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 280, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.widgets.layouts.VerticalLayout", "line_number": 286, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.widgets.layouts", "line_number": 286, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.widgets.layouts.VerticalLayout", "line_number": 290, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.widgets.layouts", "line_number": 290, "usage_type": "name"}, {"api_name": "tpDcc.ResourcesMgr", "line_number": 310, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.settings.QtSettings", "line_number": 448, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.settings", "line_number": 448, "usage_type": "name"}, {"api_name": "tpDcc.libs.python.path.clean_path", "line_number": 477, "usage_type": "call"}, {"api_name": "tpDcc.libs.python.path", "line_number": 477, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path", "line_number": 477, "usage_type": "attribute"}, {"api_name": "appdirs.user_data_dir", "line_number": 477, "usage_type": "call"}, {"api_name": "tpDcc.libs.python.path.clean_path", "line_number": 485, "usage_type": "call"}, {"api_name": "tpDcc.libs.python.path", "line_number": 485, "usage_type": "name"}, {"api_name": "os.path.expandvars", "line_number": 485, "usage_type": "call"}, {"api_name": "os.path", "line_number": 485, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 485, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.theme", "line_number": 567, "usage_type": "name"}, {"api_name": "tpDcc.ResourcesMgr", "line_number": 578, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.theme.Theme", "line_number": 580, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.theme", "line_number": 580, "usage_type": "name"}, {"api_name": "Qt.QtCore.TopToolBarArea", "line_number": 605, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 605, "usage_type": "name"}, {"api_name": "Qt.QtCore.LeftDockWidgetArea", "line_number": 620, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 620, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.to_qt_object", "line_number": 658, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 658, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 695, "usage_type": "call"}, {"api_name": "os.path", "line_number": 695, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.qtutils.load_ui", "line_number": 698, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 698, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.statusbar.StatusWidget", "line_number": 714, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.statusbar", "line_number": 714, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.dragger.WindowDragger", "line_number": 715, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.dragger", "line_number": 715, "usage_type": "name"}, {"api_name": "tpDcc.ResourcesMgr", "line_number": 748, "usage_type": "call"}, {"api_name": "tpDcc.logger.error", "line_number": 789, "usage_type": "call"}, {"api_name": "tpDcc.logger", "line_number": 789, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.widgets.layouts.GridLayout", "line_number": 864, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.widgets.layouts", "line_number": 864, "usage_type": "name"}, {"api_name": "tpDcc.callbacks", "line_number": 913, "usage_type": "call"}, {"api_name": "tpDcc.logger.warning", "line_number": 914, "usage_type": "call"}, {"api_name": "tpDcc.logger", "line_number": 914, "usage_type": "attribute"}, {"api_name": "tpDcc.managers.callbacks.CallbacksManager.register", "line_number": 918, "usage_type": "call"}, {"api_name": "tpDcc.managers.callbacks.CallbacksManager", "line_number": 918, "usage_type": "attribute"}, {"api_name": "tpDcc.managers.callbacks", "line_number": 918, "usage_type": "name"}, {"api_name": "tpDcc.managers.callbacks.CallbacksManager.unregister_owner_callbacks", "line_number": 926, "usage_type": "call"}, {"api_name": "tpDcc.managers.callbacks.CallbacksManager", "line_number": 926, "usage_type": "attribute"}, {"api_name": "tpDcc.managers.callbacks", "line_number": 926, "usage_type": "name"}, {"api_name": "Qt.QtCore.FramelessWindowHint", "line_number": 961, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 961, "usage_type": "name"}, {"api_name": "Qt.QtCore.WA_TranslucentBackground", "line_number": 973, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 973, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.is_pyside2", "line_number": 974, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 974, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.is_pyqt5", "line_number": 974, "usage_type": "call"}, {"api_name": "Qt.QtCore.FramelessWindowHint", "line_number": 975, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 975, "usage_type": "name"}, {"api_name": "Qt.QtCore.NoDropShadowWindowHint", "line_number": 975, "usage_type": "attribute"}, {"api_name": "Qt.QtCore.WindowMinMaxButtonsHint", "line_number": 976, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 976, "usage_type": "name"}, {"api_name": "Qt.QtCore.Window", "line_number": 978, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 978, "usage_type": "name"}, {"api_name": "Qt.QtCore.FramelessWindowHint", "line_number": 978, "usage_type": "attribute"}, {"api_name": "Qt.QtCore.WindowMinMaxButtonsHint", "line_number": 979, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 979, "usage_type": "name"}, {"api_name": "Qt.QtCore.WA_TranslucentBackground", "line_number": 982, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 982, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.is_pyside2", "line_number": 983, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 983, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.is_pyqt5", "line_number": 983, "usage_type": "call"}, {"api_name": "Qt.QtCore.FramelessWindowHint", "line_number": 984, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 984, "usage_type": "name"}, {"api_name": "Qt.QtCore.NoDropShadowWindowHint", "line_number": 984, "usage_type": "attribute"}, {"api_name": "Qt.QtCore.Window", "line_number": 986, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 986, "usage_type": "name"}, {"api_name": "Qt.QtCore.FramelessWindowHint", "line_number": 986, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1013, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1013, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1014, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1014, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1015, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1015, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1016, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1016, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1017, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1017, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1018, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1018, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1019, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1019, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.ResizeDirection", "line_number": 1021, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1021, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1029, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1031, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1043, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1045, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.VerticalResizer", "line_number": 1056, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1056, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.VerticalResizer", "line_number": 1057, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1057, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.HorizontalResizer", "line_number": 1058, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1058, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.HorizontalResizer", "line_number": 1059, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1059, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.CornerResizer", "line_number": 1060, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1060, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.CornerResizer", "line_number": 1061, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1061, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.CornerResizer", "line_number": 1062, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1062, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.resizers.CornerResizer", "line_number": 1063, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.resizers", "line_number": 1063, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.logger.warning", "line_number": 1096, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.logger", "line_number": 1096, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt", "line_number": 1096, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.logger.warning", "line_number": 1103, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.logger", "line_number": 1103, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt", "line_number": 1103, "usage_type": "name"}, {"api_name": "tpDcc.Dcc.is_window_floating", "line_number": 1158, "usage_type": "call"}, {"api_name": "tpDcc.Dcc", "line_number": 1158, "usage_type": "attribute"}, {"api_name": "tpDcc.libs.qt.core.qtutils.clear_layout", "line_number": 1299, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 1299, "usage_type": "name"}, {"api_name": "Qt.QtCore.NonModal", "line_number": 1339, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 1339, "usage_type": "name"}, {"api_name": "Qt.QtCore.Widget", "line_number": 1398, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 1398, "usage_type": "name"}, {"api_name": "Qt.QtCore.TopDockWidgetArea", "line_number": 1485, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 1485, "usage_type": "name"}, {"api_name": "tpDcc.libs.qt.core.qtutils.to_qt_object", "line_number": 1499, "usage_type": "call"}, {"api_name": "tpDcc.libs.qt.core.qtutils", "line_number": 1499, "usage_type": "name"}, {"api_name": "Qt.QtCore.TopDockWidgetArea", "line_number": 1503, "usage_type": "attribute"}, {"api_name": "Qt.QtCore", "line_number": 1503, "usage_type": "name"}, {"api_name": "tpDcc.libs.python.path.is_dir", "line_number": 1580, "usage_type": "call"}, {"api_name": "tpDcc.libs.python.path", "line_number": 1580, "usage_type": "name"}, {"api_name": "tpDcc.libs.python.folder.create_folder", "line_number": 1581, "usage_type": "call"}, {"api_name": "tpDcc.libs.python.folder", "line_number": 1581, "usage_type": "name"}, {"api_name": "tpDcc.register.register_class", "line_number": 1611, "usage_type": "call"}, {"api_name": "tpDcc.register", "line_number": 1611, "usage_type": "name"}]}
{"seq_id": "179077415", "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: /usr/local/lib/python3.6/dist-packages/pyxrd/generic/models/lines/experimental_line.py\n# Compiled at: 2020-03-07 03:51:50\n# Size of source mod 2**32: 18243 bytes\nimport logging\nlogger = logging.getLogger(__name__)\nimport numpy as np\nfrom scipy.integrate import trapz\nfrom scipy.interpolate import UnivariateSpline\nfrom mvc.models.properties.tools import modify\nfrom mvc.models.properties import FloatProperty, LabeledProperty, IntegerProperty, FloatChoiceProperty, IntegerChoiceProperty, SetActionMixin, SignalMixin\nfrom pyxrd.data import settings\nfrom pyxrd.generic.io import storables\nfrom pyxrd.calculations.math_tools import smooth, add_noise\nfrom pyxrd.generic.models.base import DataModel\nfrom .pyxrd_line import PyXRDLine\n\n@storables.register()\nclass ExperimentalLine(PyXRDLine):\n\n class Meta(PyXRDLine.Meta):\n store_id = 'ExperimentalLine'\n\n specimen = property(DataModel.parent.fget, DataModel.parent.fset)\n color = modify((PyXRDLine.color), default=(settings.EXPERIMENTAL_COLOR),\n inherit_from='parent.parent.display_exp_color')\n lw = modify((PyXRDLine.lw), default=(settings.EXPERIMENTAL_LINEWIDTH),\n inherit_from='parent.parent.display_exp_lw')\n ls = modify((PyXRDLine.ls), default=(settings.EXPERIMENTAL_LINESTYLE),\n inherit_from='parent.parent.display_exp_ls')\n marker = modify((PyXRDLine.marker), default=(settings.EXPERIMENTAL_MARKER),\n inherit_from='parent.parent.display_exp_marker')\n cap_value = FloatProperty(default=0.0,\n text='Cap value',\n persistent=True,\n visible=True,\n widget_type='float_entry',\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n\n @property\n def max_display_y(self):\n max_value = super(ExperimentalLine, self).max_display_y\n if self.cap_value > 0:\n if not (self.num_columns > 2 and len(self.z_data)):\n max_value = min(max_value, self.cap_value)\n return max_value\n\n bg_position = FloatProperty(default=0.0,\n text='Background offset',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n bg_scale = FloatProperty(default=1.0,\n text='Background scale',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n bg_pattern = LabeledProperty(default=None,\n text='Background pattern',\n persistent=False,\n visible=False,\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n bg_type = IntegerChoiceProperty(default=0,\n text='Background type',\n choices=(settings.PATTERN_BG_TYPES),\n persistent=False,\n visible=True,\n signal_name='visuals_changed',\n set_action_name='find_bg_position',\n mix_with=(\n SignalMixin, SetActionMixin))\n\n def get_bg_type_lbl(self):\n return settings.PATTERN_BG_TYPES[self.bg_type]\n\n smooth_type = IntegerChoiceProperty(default=0,\n text='Smooth type',\n choices=(settings.PATTERN_SMOOTH_TYPES),\n persistent=False,\n visible=True,\n signal_name='visuals_changed',\n set_action_name='setup_smooth_variables',\n mix_with=(\n SignalMixin, SetActionMixin))\n smooth_pattern = None\n smooth_degree = IntegerProperty(default=0,\n text='Smooth degree',\n persistent=False,\n visible=True,\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n noise_fraction = FloatProperty(default=0.0,\n text='Noise fraction',\n persistent=False,\n visible=True,\n widget_type='spin',\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n shift_value = FloatProperty(default=0.0,\n text='Shift value',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n shift_position = FloatChoiceProperty(default=0.42574,\n text='Shift position',\n choices=(settings.PATTERN_SHIFT_POSITIONS),\n persistent=False,\n visible=True,\n signal_name='visuals_changed',\n set_action_name='setup_shift_variables',\n mix_with=(\n SignalMixin, SetActionMixin))\n peak_startx = FloatProperty(default=0.0,\n text='Peak properties start position',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n set_action_name='update_peak_properties',\n mix_with=(\n SetActionMixin,))\n peak_endx = FloatProperty(default=0.0,\n text='Peak properties end position',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n set_action_name='update_peak_properties',\n mix_with=(\n SetActionMixin,))\n peak_fwhm_result = FloatProperty(default=0.0,\n text='Peak FWHM value',\n persistent=False,\n visible=True,\n widget_type='label')\n peak_area_result = FloatProperty(default=0.0,\n text='Peak area value',\n persistent=False,\n visible=True,\n widget_type='label')\n peak_properties_pattern = LabeledProperty(default=None,\n text='Peak properties pattern',\n persistent=False,\n visible=False,\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n strip_startx = FloatProperty(default=0.0,\n text='Strip peak start position',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n set_action_name='update_strip_pattern',\n mix_with=(\n SetActionMixin,))\n strip_endx = FloatProperty(default=0.0,\n text='Strip peak end position',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n set_action_name='update_strip_pattern',\n mix_with=(\n SetActionMixin,))\n stripped_pattern = LabeledProperty(default=None,\n text='Strip peak pattern',\n persistent=False,\n visible=False,\n signal_name='visuals_changed',\n mix_with=(\n SignalMixin,))\n noise_level = FloatProperty(default=0.0,\n text='Strip peak noise level',\n persistent=False,\n visible=True,\n widget_type='float_entry',\n set_action_name='update_strip_pattern_noise',\n mix_with=(\n SetActionMixin,))\n\n def __init__(self, cap_value=0.0, *args, **kwargs):\n \"\"\"\n Valid keyword arguments for a ExperimentalLine are:\n cap_value: the value (in raw counts) at which to cap\n the experimental pattern \n \"\"\"\n (super(ExperimentalLine, self).__init__)(*args, **kwargs)\n self.cap_value = cap_value\n\n def remove_background(self):\n with self.data_changed.hold_and_emit():\n bg = None\n if self.bg_type == 0:\n bg = self.bg_position\n else:\n if self.bg_type == 1:\n if self.bg_pattern is not None:\n if not (self.bg_position == 0 and self.bg_scale == 0):\n bg = self.bg_pattern * self.bg_scale + self.bg_position\n if bg is not None:\n if self.data_y.size > 0:\n self.data_y[:, 0] -= bg\n self.clear_bg_variables()\n\n def find_bg_position(self):\n try:\n self.bg_position = np.min(self.data_y)\n except ValueError:\n return 0.0\n\n def clear_bg_variables(self):\n with self.visuals_changed.hold_and_emit():\n self.bg_pattern = None\n self.bg_scale = 0.0\n self.bg_position = 0.0\n\n def smooth_data(self):\n with self.data_changed.hold_and_emit():\n if self.smooth_degree > 0:\n degree = int(self.smooth_degree)\n self.data_y[:, 0] = smooth(self.data_y[:, 0], degree)\n self.smooth_degree = 0.0\n\n def setup_smooth_variables(self):\n with self.visuals_changed.hold_and_emit():\n self.smooth_degree = 5.0\n\n def clear_smooth_variables(self):\n with self.visuals_changed.hold_and_emit():\n self.smooth_degree = 0.0\n\n def add_noise(self):\n with self.data_changed.hold_and_emit():\n if self.noise_fraction > 0:\n noisified = add_noise(self.data_y[:, 0], self.noise_fraction)\n self.set_data(self.data_x, noisified)\n self.noise_fraction = 0.0\n\n def clear_noise_variables(self):\n with self.visuals_changed.hold_and_emit():\n self.noise_fraction = 0.0\n\n def shift_data(self):\n with self.data_changed.hold_and_emit():\n if self.shift_value != 0.0:\n if settings.PATTERN_SHIFT_TYPE == 'Linear':\n self.data_x = self.data_x - self.shift_value\n if self.specimen is not None:\n with self.specimen.visuals_changed.hold():\n for marker in self.specimen.markers:\n marker.position = marker.position - self.shift_value\n\n elif settings.PATTERN_SHIFT_TYPE == 'Displacement':\n position = self.specimen.goniometer.get_t_from_nm(self.shift_position)\n displacement = 0.5 * self.specimen.goniometer.radius * self.shift_value / np.cos(position / 180 * np.pi)\n correction = 2 * displacement * np.cos(self.data_x / 2 / 180 * np.pi) / self.specimen.goniometer.radius\n self.data_x = self.data_x - correction\n self.shift_value = 0.0\n\n def setup_shift_variables(self):\n with self.visuals_changed.hold_and_emit():\n position = self.specimen.goniometer.get_2t_from_nm(self.shift_position)\n if position > 0.1:\n max_x = position + 0.5\n min_x = position - 0.5\n condition = (self.data_x >= min_x) & (self.data_x <= max_x)\n section_x, section_y = np.extract(condition, self.data_x), np.extract(condition, self.data_y[:, 0])\n try:\n actual_position = section_x[np.argmax(section_y)]\n except ValueError:\n actual_position = position\n\n self.shift_value = actual_position - position\n\n def clear_shift_variables(self):\n with self.visuals_changed.hold_and_emit():\n self.shift_value = 0\n\n peak_bg_slope = 0.0\n avg_starty = 0.0\n avg_endy = 0.0\n\n def update_peak_properties(self):\n with self.visuals_changed.hold_and_emit():\n if self.peak_endx < self.peak_startx:\n self.peak_endx = self.peak_startx + 1.0\n return\n condition = (self.data_x >= self.peak_startx - 0.1) & (self.data_x <= self.peak_startx + 0.1)\n section = np.extract(condition, self.data_y[:, 0])\n self.avg_starty = np.min(section)\n condition = (self.data_x >= self.peak_endx - 0.1) & (self.data_x <= self.peak_endx + 0.1)\n section = np.extract(condition, self.data_y[:, 0])\n self.avg_endy = np.min(section)\n self.peak_bg_slope = (self.avg_starty - self.avg_endy) / (self.peak_startx - self.peak_endx)\n condition = (self.data_x >= self.peak_startx) & (self.data_x <= self.peak_endx)\n section_x = np.extract(condition, self.data_x)\n section_y = np.extract(condition, self.data_y)\n bg_curve = self.peak_bg_slope * (section_x - self.peak_startx) + self.avg_starty\n self.peak_area_result = abs(trapz(section_y, x=section_x) - trapz(bg_curve, x=section_x))\n fwhm_curve = section_y - bg_curve\n peak_half_max = np.max(fwhm_curve) * 0.5\n spline = UnivariateSpline(section_x, (fwhm_curve - peak_half_max), s=0)\n roots = spline.roots()\n self.peak_fwhm_result = np.abs(roots[0] - roots[(-1)]) if len(roots) >= 2 else 0\n self.peak_properties_pattern = (\n section_x, bg_curve, section_y, roots, spline(roots) + peak_half_max)\n\n def clear_peak_properties_variables(self):\n with self.visuals_changed.hold_and_emit():\n self._peak_startx = 0.0\n self._peak_properties_pattern = None\n self._peak_endx = 0.0\n self.peak_properties = 0.0\n\n def strip_peak(self):\n with self.data_changed.hold_and_emit():\n if self.stripped_pattern is not None:\n stripx, stripy = self.stripped_pattern\n indeces = ((self.data_x >= self.strip_startx) & (self.data_x <= self.strip_endx)).nonzero()[0]\n np.put(self.data_y[:, 0], indeces, stripy)\n self._strip_startx = 0.0\n self._stripped_pattern = None\n self.strip_endx = 0.0\n\n strip_slope = 0.0\n avg_starty = 0.0\n avg_endy = 0.0\n block_strip = False\n\n def update_strip_pattern_noise(self):\n with self.visuals_changed.hold_and_emit():\n condition = (self.data_x >= self.strip_startx) & (self.data_x <= self.strip_endx)\n section_x = np.extract(condition, self.data_x)\n noise = self.avg_endy * 2 * ((np.random.rand)(*section_x.shape) - 0.5) * self.noise_level\n section_y = self.strip_slope * (section_x - self.strip_startx) + self.avg_starty + noise\n self.stripped_pattern = (section_x, section_y)\n\n def update_strip_pattern(self):\n with self.visuals_changed.hold_and_emit():\n if self.strip_endx < self.strip_startx:\n self.strip_endx = self.strip_startx + 1.0\n return\n if not self.block_strip:\n self.block_strip = True\n condition = (self.data_x >= self.strip_startx - 0.1) & (self.data_x <= self.strip_startx + 0.1)\n section = np.extract(condition, self.data_y[:, 0])\n self.avg_starty = np.average(section)\n noise_starty = 2 * np.std(section) / self.avg_starty\n condition = (self.data_x >= self.strip_endx - 0.1) & (self.data_x <= self.strip_endx + 0.1)\n section = np.extract(condition, self.data_y[:, 0])\n self.avg_endy = np.average(section)\n noise_endy = 2 * np.std(section) / self.avg_starty\n self.strip_slope = (self.avg_starty - self.avg_endy) / (self.strip_startx - self.strip_endx)\n self.noise_level = (noise_starty + noise_endy) * 0.5\n self.update_strip_pattern_noise()\n\n def clear_strip_variables(self):\n with self.visuals_changed.hold_and_emit():\n self._strip_startx = 0.0\n self._strip_pattern = None\n self.strip_start_x = 0.0", "sub_path": "pycfiles/PyXRD-0.8.4.linux-x86_64.tar/experimental_line.cpython-36.py", "file_name": "experimental_line.cpython-36.py", "file_ext": "py", "file_size_in_byte": 14772, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 22, "usage_type": "name"}, {"api_name": "pyxrd_line.PyXRDLine.Meta", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 24, "usage_type": "name"}, {"api_name": "pyxrd.generic.models.base.DataModel.parent", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pyxrd.generic.models.base.DataModel", "line_number": 27, "usage_type": "name"}, {"api_name": "mvc.models.properties.tools.modify", "line_number": 28, "usage_type": "call"}, {"api_name": "pyxrd_line.PyXRDLine.color", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 28, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.EXPERIMENTAL_COLOR", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "mvc.models.properties.tools.modify", "line_number": 30, "usage_type": "call"}, {"api_name": "pyxrd_line.PyXRDLine.lw", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 30, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.EXPERIMENTAL_LINEWIDTH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 30, "usage_type": "name"}, {"api_name": "mvc.models.properties.tools.modify", "line_number": 32, "usage_type": "call"}, {"api_name": "pyxrd_line.PyXRDLine.ls", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 32, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.EXPERIMENTAL_LINESTYLE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 32, "usage_type": "name"}, {"api_name": "mvc.models.properties.tools.modify", "line_number": 34, "usage_type": "call"}, {"api_name": "pyxrd_line.PyXRDLine.marker", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pyxrd_line.PyXRDLine", "line_number": 34, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.EXPERIMENTAL_MARKER", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 34, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 36, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 43, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 53, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 60, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 61, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 68, "usage_type": "name"}, {"api_name": "mvc.models.properties.LabeledProperty", "line_number": 69, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 75, "usage_type": "name"}, {"api_name": "mvc.models.properties.IntegerChoiceProperty", "line_number": 76, "usage_type": "call"}, {"api_name": "pyxrd.data.settings.PATTERN_BG_TYPES", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 78, "usage_type": "name"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 84, "usage_type": "name"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 84, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.PATTERN_BG_TYPES", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 87, "usage_type": "name"}, {"api_name": "mvc.models.properties.IntegerChoiceProperty", "line_number": 89, "usage_type": "call"}, {"api_name": "pyxrd.data.settings.PATTERN_SMOOTH_TYPES", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 91, "usage_type": "name"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 97, "usage_type": "name"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 97, "usage_type": "name"}, {"api_name": "mvc.models.properties.IntegerProperty", "line_number": 99, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 105, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 106, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 113, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 114, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 121, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatChoiceProperty", "line_number": 122, "usage_type": "call"}, {"api_name": "pyxrd.data.settings.PATTERN_SHIFT_POSITIONS", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 124, "usage_type": "name"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 130, "usage_type": "name"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 130, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 131, "usage_type": "call"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 138, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 139, "usage_type": "call"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 146, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 147, "usage_type": "call"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 152, "usage_type": "call"}, {"api_name": "mvc.models.properties.LabeledProperty", "line_number": 157, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 163, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 164, "usage_type": "call"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 171, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 172, "usage_type": "call"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 179, "usage_type": "name"}, {"api_name": "mvc.models.properties.LabeledProperty", "line_number": 180, "usage_type": "call"}, {"api_name": "mvc.models.properties.SignalMixin", "line_number": 186, "usage_type": "name"}, {"api_name": "mvc.models.properties.FloatProperty", "line_number": 187, "usage_type": "call"}, {"api_name": "mvc.models.properties.SetActionMixin", "line_number": 194, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 222, "usage_type": "call"}, {"api_name": "pyxrd.calculations.math_tools.smooth", "line_number": 236, "usage_type": "call"}, {"api_name": "pyxrd.calculations.math_tools.add_noise", "line_number": 250, "usage_type": "call"}, {"api_name": "pyxrd.data.settings.PATTERN_SHIFT_TYPE", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 261, "usage_type": "name"}, {"api_name": "pyxrd.data.settings.PATTERN_SHIFT_TYPE", "line_number": 268, "usage_type": "attribute"}, {"api_name": "pyxrd.data.settings", "line_number": 268, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.extract", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 312, "usage_type": "call"}, {"api_name": "scipy.integrate.trapz", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 316, "usage_type": "call"}, {"api_name": "scipy.interpolate.UnivariateSpline", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.put", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 349, "usage_type": "attribute"}, {"api_name": "numpy.extract", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.extract", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 367, "usage_type": "call"}, {"api_name": "pyxrd.generic.io.storables.register", "line_number": 21, "usage_type": "call"}, {"api_name": "pyxrd.generic.io.storables", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "526424457", "text": "# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef barh(x, y, filename, figsize=(10, 10), title=None, footer=None):\n plt.figure(figsize=figsize)\n R = range(len(x))\n\n rects = plt.barh(\n R,\n y,\n height=.8,\n color='#4682B4',\n alpha=.8)\n\n for i, rect in enumerate(rects):\n width = rect.get_width()\n label = ' ' + str(y[i])\n plt.text(width + 0.25,\n rect.get_y() + rect.get_height() / 2.,\n label,\n va='center',\n fontsize=13,\n color='#666666')\n\n # Move y ticks down a bit to align with the bars.\n ypos = [r + 0.35 for r in R]\n\n # Fix possible problems with unicode chars.\n labels = [l.decode('utf-8') for l in x]\n\n plt.yticks(ypos, labels)\n\n # Hide x tick labels.\n plt.xticks(np.arange(0, 5, 1), [''])\n\n # Hide borders around plot.\n ax = plt.axes()\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.spines['left'].set_visible(False)\n ax.spines['bottom'].set_visible(False)\n\n if title:\n plt.title(title.decode('utf-8'), color='#444444')\n\n if footer:\n ax.text(\n max(y) / 2,\n -.2,\n footer.decode('utf-8'),\n fontsize=12.5,\n va='top',\n color='#444444')\n\n plt.savefig(filename, bbox_inches='tight')", "sub_path": "utils/graphs.py", "file_name": "graphs.py", "file_ext": "py", "file_size_in_byte": 1422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.barh", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]}
{"seq_id": "221096526", "text": "import bezier\nimport numpy as np\nimport appendTools\nimport scalingSkeleton as ss\nimport math\nimport makeStandardCircle as mc\nimport derivativeTools\nimport makeTriangle as mt\n\nclass InputError(Exception):\n def __init__(self, expression, message):\n self.expression = expression\n self.message = message\n\ndef getPoint(points, pIndex, offset=1):\n return points[(pIndex + offset) % len(points)]\n\ndef getBezierCurve(points, pIndex):\n p = [getPoint(points, pIndex, i) for i in range(4)]\n nodes = np.asfortranarray([\n list(map(float, [p[i].x for i in range(4)])),\n list(map(float, [p[i].y for i in range(4)])),\n ])\n return bezier.Curve(nodes, degree=3)\n\ndef appendPointByRate(contour, points, rate):\n if points[0].type == points[1].type == 'line':\n appendTools.appendPointRateLine(contour, points[:2], rate)\n return False\n else:\n appendTools.appendPointRate(contour, points, rate)\n return True\n \ndef getEdgeSegment(contour):\n edges = {}\n for i in range(len(contour.segments)):\n if contour.segments[i].type == 'line':\n edges[i] = contour.segments[i]\n else:\n nextSegment = contour.segments[(i+1) % len(contour.segments)]\n if nextSegment.type == 'line':\n edges[i] = contour.segments[i]\n # For kiyuk Only...\n elif nextSegment.type == 'curve':\n idx = contour.segments[i].onCurve.index\n if getDegree(contour.points[idx-1], contour.points[idx+1], contour.segments[i].onCurve) >= 10:\n edges[i] = contour.segments[i]\n return edges\n\ndef segToPointIndex(contour, segIndex):\n p = [p for p in contour.segments[segIndex].points if p.type != 'offcurve'][0]\n return contour.points.index(p)\n\ndef findNextPoint(points, pIndex, cnt=1):\n for i in range(cnt):\n pIndex += 1\n while points[pIndex].type == 'offcurve':\n pIndex = (pIndex+1) % len(points)\n return pIndex\n\ndef getPointRates(points, start, length):\n conlen = 0\n rates = []\n rates.append(getBezierCurve(points, start).length / length)\n conlen += rates[-1] * length\n while length > conlen:\n start = findNextPoint(points, start)\n rates.append(getBezierCurve(points, start).length / length)\n conlen += rates[-1] * length\n return rates\n\ndef applySkeletonAfterDelete(origin, os, oe, skel, ss, se):\n if os > oe:\n oe += len(origin.points)\n if ss > se:\n se += len(skel.points)\n control = 0\n # remove = [origin.points[i] for i in range(os+1, oe) if origin.points[i].type != 'offcurve']\n # while remove:\n # p = remove.pop()\n # origin.removeSegment(pToSeg(origin, p), True)\n # control -= 1\n skelRate = [getBezierCurve(skel.points, i % len(skel.points)).length for i in range(ss, se) if skel.points[i].type != 'offcurve']\n skelLen = sum(skelRate)\n skelRate = list(map(lambda x: x / skelLen, skelRate))\n cnt = 0\n newRate = 0\n for i in range(len(skelRate)-1):\n if cnt:\n newRate = ((skelLen*skelRate[i]) - (skelLen*skelRate[i-1])) / (skelLen - (skelLen*skelRate[i-1]))\n else:\n newRate = skelRate[i]\n appendPointByRate(origin, [origin.points[(findNextPoint(origin.points, os, cnt)+k) % len(origin.points)] for k in range(4)], newRate)\n control += 1\n cnt += 1\n derivativeTools.appendPointByDerivative(origin.points, (os+(cnt*3)) % len(origin.points), origin)\n cnt += 1\n control += 1\n return control\n\ndef applySkeleton(origin, os, oe, skel, ss, se):\n if os > oe:\n oe += len(origin.points)\n if ss > se:\n se += len(skel.points)\n temp = 0\n cnt = 0\n comlen = 0\n remove = [origin.points[i] for i in range(os+1, oe) if origin.points[i].type != 'offcurve']\n curIndex = os\n skelRate = [getBezierCurve(skel.points, i % len(skel.points)).length for i in range(ss, se) if skel.points[i].type != 'offcurve']\n skelLen = sum(skelRate)\n skelRate = list(map(lambda x: x / skelLen, skelRate))\n originRate = [getBezierCurve(origin.points, i % len(origin.points)).length for i in range(os, oe) if origin.points[i].type != 'offcurve']\n originLen = sum(originRate)\n originRate = list(map(lambda x: x / originLen, originRate))\n for j in range(len(skelRate)-1):\n curlen = sum(skelRate[:j+1]) * originLen\n while curlen > comlen:\n cnt += 1\n if cnt > 1:\n temp = comlen \n comlen = sum(originRate[:curIndex-os+cnt]) * originLen\n appendPointByRate(origin, [origin.points[(findNextPoint(origin.points, curIndex, cnt-1)+k) % len(origin.points)] for k in range(4)], (curlen-temp) / (comlen-temp))\n originRate = getPointRates(origin.points, os, originLen)\n cnt = 0\n comlen = 0\n while remove:\n p = remove.pop()\n origin.removeSegment(pToSeg(origin, p), True)\n # p.selected = True\n # origin.getParent().removeSelection()\n\ndef getRemovePoints(start, end, contour):\n removes = [contour.points[i] for i in range(start+1, end) if contour.points[i].type != 'offcurve']\n return removes\n\ndef length(a, b):\n return math.sqrt(pow(a[0]-b[0], 2) + pow(a[1]-b[1], 2))\n \ndef getDegree(a, b, center):\n t1 = b.x - center.x\n t2 = a.x - center.x\n if not t1:\n t1 += 1\n if not t2:\n t2 += 1\n return abs(math.degrees(math.atan((b.y-center.y)/t1)-math.atan((a.y-center.y)/t2)))\n \ndef getPointAngle(points, pIndex):\n cnt1, cnt2 = -1, 1\n while True:\n p = getPoint(points, pIndex, cnt1)\n if p.type == 'offcurve':\n cnt1 -= 1\n else:\n break\n while True:\n p = getPoint(points, pIndex, cnt2)\n if p.type == 'offcurve':\n cnt2 += 1\n else:\n break\n return getDegree(getPoint(points, pIndex, cnt1), getPoint(points, pIndex, cnt2), points[pIndex])\n \ndef getFlattestSegment(contour, segIndexes):\n dic = {}\n for i in segIndexes:\n dic[i] = getPointAngle(contour.points, segToPointIndex(contour, i))\n return [k for k, v in dic.items() if min(dic.values()) == v][0]\n\ndef getFlatSegment(contour, segIndexes):\n result = []\n for i in segIndexes:\n if getPointAngle(contour.points, segToPointIndex(contour, i)) < 5:\n result.append(i)\n return result\n\ndef getRealPoint(segment):\n for p in segment.points:\n if p.type != 'offcurve':\n return p\n\ndef pToSeg(contour, point):\n for i in range(len(contour.segments)):\n if point in contour.segments[i].points:\n return contour.segments[i].index\n\ndef contourInside(baseContour, subjectContour):\n points = [p.anchor for p in subjectContour.bPoints]\n for p in points:\n if not baseContour.pointInside(p):\n return False\n return True\n\ndef setStart(contour):\n p = []\n for s in contour.segments:\n p.append(getRealPoint(s))\n p = list(filter(lambda x: x.y == max(map(lambda y: y.y, p)), p))\n p = list(filter(lambda x: x.x == min(map(lambda y: y.x, p)), p))\n if contour.points[0] != p[0]:\n contour.setStartSegment(pToSeg(contour, p[0])+1)\n\ndef setPenPairToOrder(name, padding):\n if padding == 0:\n return name\n temp = [\"'penPair':'\", \"'dependX':'\", \"'dependY':'\"]\n for t in temp:\n if t not in name:\n continue\n index = name.find(t) + len(t)\n penPair = name[index:name.find(\"'\", index)]\n newPenPair = penPair[0] + str(int(penPair[1:-1]) + padding) + penPair[-1]\n name = name.replace(t + penPair, t + newPenPair)\n return name\n\ndef applyAttribute(target, skel, padding):\n for i in range(len(skel.segments)):\n sp = skel.points[segToPointIndex(skel, i)]\n tp = target.points[segToPointIndex(target, i)]\n if sp.type != 'offcurve' and tp.type != 'offcurve':\n tp.name = setPenPairToOrder(sp.name, padding)\n \ndef applyAttributeSegment(target, targetSegIndex, skel, skelSegIndex, padding):\n for i, j in zip(targetSegIndex, skelSegIndex):\n target.points[segToPointIndex(target, i)].name = setPenPairToOrder(skel.points[segToPointIndex(skel, j)].name, padding)\n\ndef applyAttributeAll(ori, skel):\n for o, s in zip(ori.points, skel.points):\n o.name = s.name\n\ndef preprocess(contour):\n if not contour._get_clockwise():\n contour.reverse()\n for c in contour.getParent().contours:\n if contour == c:\n continue\n if contourInside(c, contour):\n contour.reverse()\n break\n setStart(contour)\n \ndef calculateMiddlePoints(edgeSegmentKeys, length):\n count = 0\n for i in range(len(edgeSegmentKeys)):\n diff = (edgeSegmentKeys[i] - edgeSegmentKeys[i-1]) % length\n if diff > 1:\n count += diff - 1\n return count\n\ndef isAllLine(contour):\n for p in contour.points:\n if p.type != 'line':\n return False\n return True\n \ndef getPenPair(point):\n try:\n idx = point.name.find(\"'penPair':\")\n if point.name[idx+14].isalpha():\n return point.name[idx+11:idx+15]\n else:\n return point.name[idx+11:idx+14]\n except AttributeError:\n raise InputError(point.getParent().getParent().name + \".glif: contours[\" + point.getParent().index + \"].points[\" + point.index + \"]\", \"It has no penPair attribute\")\n \ndef getMaxPenPair(contour):\n maxPenPair = 0\n for p in contour.points:\n if p.name is None:\n continue\n maxPenPair = max(maxPenPair, int(getPenPair(p)[1:-1]))\n return maxPenPair\n\nif __name__ == '__main__':\n f = CurrentFont()\n sf = OpenFont(\"/Users/font/Desktop/WorkSpace_SJ/Projects/CharacterSkeleton/YOON740_skeletons.ufo\", False)\n skel = sf.getGlyph('k').contours[0]\n skelEdge = getEdgeSegment(skel)\n\n # # skel changer\n # skel1 = sf.getGlyph('b').contours[0]\n # skelEdge1 = getEdgeSegment(skel1)\n # skel2 = sf.getGlyph('b').contours[1]\n # skelEdge2 = getEdgeSegment(skel2)\n # skel3 = sf.getGlyph('b').contours[1]\n # skelEdge3 = getEdgeSegment(skel3)\n # skel4 = sf.getGlyph('b').contours[1]\n # skelEdge4 = getEdgeSegment(skel4)\n # skel5 = sf.glyphs[28].contours[0]\n # skelEdge5 = getEdgeSegment(skel5)\n # # skel changer end\n cnt = 0\n setting = False\n for o in f.glyphOrder:\n g = f.getGlyph(o)\n # if int(g.name[3:], 16) >= 0xC544 and int(g.name[3:], 16) <= 0xC78E:\n \n if g.name == '110f_05':\n setting = True\n if cnt == 1:\n setting = False\n if setting and cnt < 1:\n cnt += 1\n print(g.name)\n \n # ใ
์ผ ๋\n # mc.makeStandardCircle(*mc.classifyInOutCircle(g[0], g[1]))\n # continue\n \n # ์์ท, ์์์ท, ์ง์, ์์ง์, ์น์์ผ ๋\n # if len(g.contours[0].points) == len(skel1.points):\n # skel = skel1\n # skelEdge = skelEdge1\n # elif len(g.contours[0].points) == len(skel2.points):\n # skel = skel2\n # skelEdge = skelEdge2\n # if 'g' in skel.getParent().name:\n # # mt.findAndMake(g)\n # preprocess(g.contours[0])\n # applyAttributeAll(g.contours[0], skel)\n # continue\n # skelSet = 0 # skel changer\n # ss.scaleToTarget(g.contours[0], g.contours[1]) # set skel size to origin\n padding = 0\n for ori in g.contours:\n # # skel changer\n # if skelSet == 0 or skelSet == 1:\n # skel = skel1\n # skelEdge = skelEdge1\n # elif skelSet == 2 or skelSet == 3:\n # if skelSet == 3:\n # padding = getMaxPenPair(g.contours[0])\n # else:\n # padding = 0\n # skel = skel2\n # skelEdge = skelEdge2\n # elif skelSet == 2:\n # padding = 0\n # skel = skel3\n # skelEdge = skelEdge3\n # elif skelSet == 3:\n # padding = 6\n # skel = skel4\n # skelEdge = skelEdge4\n # elif skelSet == 4:\n # skel = skel5\n # skelEdge = skelEdge5\n # skelSet += 1\n # # skel changer end\n \n # # To apply all of attribute in skeleton\n # if isAllLine(ori):\n # oriEdge = getEdgeSegment(ori)\n # applyAttributeSegment(ori, list(oriEdge.keys()), skel, list(skelEdge.keys()), padding)\n # # padding += int(len(ori._get_bPoints()) / 2)\n # continue\n # else:\n # applyAttributeAll(ori, skel)\n # continue\n # #\n \n preprocess(ori)\n oriEdge = getEdgeSegment(ori)\n oriFlat = getFlatSegment(ori, list(oriEdge.keys()))\n while oriFlat:\n temp = oriFlat.pop()\n del(oriEdge[temp])\n if len(oriEdge) % 2:\n del(oriEdge[getFlattestSegment(ori, list(oriEdge.keys()))])\n if isAllLine(ori):\n applyAttributeSegment(ori, list(oriEdge.keys()), skel, list(skelEdge.keys()), padding)\n padding += getMaxPenPair(ori) # int(len(ori._get_bPoints()) / 2)\n continue\n keyControl = 0\n controlOriEdge = list(oriEdge)\n \n # For proper operation (get derivative and find opposite side)\n numOfMiddlePoints = calculateMiddlePoints(list(skelEdge.keys()), len(skel.bPoints))\n \n # remove points\n remove = []\n for i in range(len(oriEdge)):\n edges = list(oriEdge.keys())\n if abs(edges[i] - edges[i-1]) != 1:\n remove += getRemovePoints(segToPointIndex(ori, edges[i-1]), segToPointIndex(ori, edges[i]), ori)\n while remove:\n p = remove.pop()\n ori.removeSegment(pToSeg(ori, p), True)\n oriEdge = getEdgeSegment(ori)\n controlOriEdge = list(oriEdge)\n \n for i in range(len(oriEdge)):\n # preOriIndex = ori.segments.index(list(oriEdge.values())[i-1])\n # oriIndex = ori.segments.index(list(oriEdge.values())[i])\n # preSkelIndex = skel.segments.index(list(skelEdge.values())[i-1])\n # skelIndex = skel.segments.index(list(skelEdge.values())[i])\n if numOfMiddlePoints <= 0:\n break\n if keyControl:\n controlOriEdge = [e+keyControl if e >= controlOriEdge[i-1] else e for e in controlOriEdge]\n preOriIndex = controlOriEdge[i-1]\n oriIndex = controlOriEdge[i]\n keyControl = 0\n else:\n preOriIndex = controlOriEdge[i-1]\n oriIndex = controlOriEdge[i]\n preSkelIndex = list(skelEdge.keys())[i-1]\n skelIndex = list(skelEdge.keys())[i]\n # if skeleton's and original's edge gap is different\n # if (oriIndex-preOriIndex) % len(ori.segments) < (skelIndex-preSkelIndex) % len(skel.segments):\n if (oriIndex-preOriIndex) % len(ori.segments) != 1 or (skelIndex-preSkelIndex) % len(skel.segments) != 1:\n # # To append points first before delete origin points\n # keyControl += applySkeleton(ori, segToPointIndex(ori, preOriIndex), segToPointIndex(ori, oriIndex), skel, segToPointIndex(skel, preSkelIndex), segToPointIndex(skel, skelIndex))\n # # To delete points first before append points\n keyControl = applySkeletonAfterDelete(ori, segToPointIndex(ori, preOriIndex), segToPointIndex(ori, oriIndex), skel, segToPointIndex(skel, preSkelIndex), segToPointIndex(skel, skelIndex))\n numOfMiddlePoints -= 2\n applyAttribute(ori, skel, padding)\n padding += getMaxPenPair(ori) # int(len(ori._get_bPoints()) / 2)", "sub_path": "backup/src/applySkeleton.py", "file_name": "applySkeleton.py", "file_ext": "py", "file_size_in_byte": 16617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "numpy.asfortranarray", "line_number": 20, "usage_type": "call"}, {"api_name": "bezier.Curve", "line_number": 24, "usage_type": "call"}, {"api_name": "appendTools.appendPointRateLine", "line_number": 28, "usage_type": "call"}, {"api_name": "appendTools.appendPointRate", "line_number": 31, "usage_type": "call"}, {"api_name": "derivativeTools.appendPointByDerivative", "line_number": 96, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 139, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 148, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 148, "usage_type": "call"}]}
{"seq_id": "53828897", "text": "from discord.ext import commands\nimport discord\nimport json\n\nwith open(\"botconfig.json\") as conf:\n config = json.load(conf)\nprefix = config[\"prefix\"]\nclient = commands.Bot(command_prefix=prefix)\n\nasync def math(client,message,mess_args):\n defined_characters = set(\"1234567890+-*/!()<>=\")\n equation = \"\".join(mess_args)\n if set(equation).issubset(defined_characters):\n try:\n answer = eval(equation)\n evaled = \"Question:```py\\n{}```\\nAnswer```py\\n{}```\".format(str(equation), str(answer))\n math_embed = discord.Embed(title=\"Math\", description=evaled, colour=0x22ffaa)\n await client.send_message(message.channel, content=None, embed=math_embed)\n except:\n math_embed = discord.Embed(title=\"Math\", description=\"Hey! I'm not **that** good at maths\", colour=0x22ffaa)\n await client.send_message(message.channel, content=None, embed=math_embed)\n else:\n math_embed = discord.Embed(title=\"Math\", description=\"Watcha trying to make me cook?\", colour=0x22ffaa)\n await client.send_message(message.channel, content=None, embed=math_embed)\n", "sub_path": "commands/cmath.py", "file_name": "cmath.py", "file_ext": "py", "file_size_in_byte": 1134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "discord.ext.commands.Bot", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "286085473", "text": "# Files can be downlooaded here : https://github.com/hello2all/GTSRB_Keras_STN/blob/master/input/download_data.md\nroot_data = \"/home/diego/qdata/datasets/traffic_signs/\"\ntrain_data = root_data + \"train.p\"\nvalid_data = root_data + \"valid.p\"\ntest_data = root_data + \"test.p\"\nresults = root_data + \"results/\"\n\n\nimport sys\nsys.path.append('..')\nimport os, json\nfrom glob import glob\nimport numpy as np\nfrom scipy import misc, ndimage\nfrom scipy.ndimage.interpolation import zoom\n\nimport keras\nfrom keras.callbacks import ModelCheckpoint\nfrom keras import backend as K\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.models import Sequential\nfrom keras.layers.merge import Concatenate\nfrom keras.layers.core import Flatten, Dense, Dropout, Lambda\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D\nfrom keras.layers.pooling import GlobalAveragePooling2D\nfrom keras.optimizers import Adam\nfrom keras.preprocessing import image\nfrom keras.applications.xception import Xception\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg19 import VGG19\nfrom keras.applications.resnet50 import ResNet50\n\nfrom keras.backend import tf as k\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom deep_learning.utils import *\nimport importlib\nimport deep_learning.utils2;\nfrom deep_learning.utils2 import *\n\n# Load pickled data\nimport pickle\n\nwith open(train_data, mode='rb') as f:\n train = pickle.load(f)\nwith open(test_data, mode='rb') as f:\n test = pickle.load(f)\nwith open(valid_data, mode='rb') as f:\n valid = pickle.load(f)\n\nX_train, y_train = train['features'], train['labels']\nX_valid, y_valid = valid['features'], valid['labels']\nX_test, y_test = test['features'], test['labels']\n\nlimit_mem()\n\nX_train = X_train.astype('float32')\nX_valid = X_valid.astype('float32')\nX_test = X_test.astype('float32')\nX_train /= 255\nX_valid /= 255\nX_test /= 255\nprint('X_train shape:', X_train.shape)\nprint(X_train.shape[0], 'train samples')\nprint(X_valid.shape[0], 'valid samples')\nprint(X_test.shape[0], 'test samples')\n\n\nfrom collections import Counter\ntrain_label_counter = Counter(y_train)\n\ntrain_counter = Counter(y_train)\norder = list(zip(*train_counter.most_common()))[0]\n\nf, ax = plt.subplots(figsize=(12, 4))\nax = sns.countplot(x=y_train, order=order, color='lightblue', ax=ax, label=\"train\")\n\n_ = ax.set_title('Class distribution')\n_ = ax.legend(ncol=2, loc=\"upper right\", frameon=True)\n\nY_train = np_utils.to_categorical(y_train, 43)\nY_valid = np_utils.to_categorical(y_valid, 43)\nY_test = np_utils.to_categorical(y_test, 43)\n\nprint (Y_train.shape)\n\nnb_train_samples = X_train.shape[0]\nnb_valid_samples = X_valid.shape[0]\nbatch_size = 32\nsteps_per_epoch = nb_train_samples // batch_size\nvalid_steps = nb_valid_samples // batch_size\n\ndef relu(x): return Activation('relu')(x)\ndef dropout(x, p): return Dropout(p)(x) if p else x\ndef bn(x): return BatchNormalization()(x)\ndef relu_bn(x): return relu(bn(x))\n\ndef conv(x, nf, sz, wd, p):\n x = Conv2D(nf, (sz, sz), kernel_initializer=\"he_uniform\", padding='same',\n kernel_regularizer=regularizers.l2(wd))(x)\n return dropout(x,p)\n\ndef conv_block(x, nf, bottleneck=False, p=None, wd=0):\n x = relu_bn(x)\n if bottleneck: x = relu_bn(conv(x, nf * 4, 1, wd, p))\n return conv(x, nf, 3, wd, p)\n\n\ndef dense_block(x, nb_layers, growth_rate, bottleneck=False, p=None, wd=0):\n if bottleneck: nb_layers //= 2\n for i in range(nb_layers):\n b = conv_block(x, growth_rate, bottleneck=bottleneck, p=p, wd=wd)\n x = merge([x,b], mode='concat', concat_axis=-1)\n return x\n\n\ndef transition_block(x, compression=1.0, p=None, wd=0):\n nf = int(x.get_shape().as_list()[-1] * compression)\n x = relu_bn(x)\n x = conv(x, nf, 1, wd, p)\n return AveragePooling2D((2, 2), strides=(2, 2))(x)\n\n\ndef create_dense_net(nb_classes, img_input, depth=40, nb_block=3,\n growth_rate=12, nb_filter=16, bottleneck=False, compression=1.0, p=None, wd=0,\n activation='softmax'):\n assert activation == 'softmax' or activation == 'sigmoid'\n assert (depth - 4) % nb_block == 0\n nb_layers_per_block = int((depth - 4) / nb_block)\n nb_layers = [nb_layers_per_block] * nb_block\n\n x = conv(img_input, nb_filter, 3, wd, 0)\n for i, block in enumerate(nb_layers):\n x = dense_block(x, block, growth_rate, bottleneck=bottleneck, p=p, wd=wd)\n if i != len(nb_layers) - 1:\n x = transition_block(x, compression=compression, p=p, wd=wd)\n\n x = relu_bn(x)\n x = GlobalAveragePooling2D()(x)\n return Dense(nb_classes, activation=activation, kernel_regularizer=regularizers.l2(wd))(x)\n\n\n#input_shape = (32,32,3)\n#img_input = Input(shape=input_shape)\n\n#x = create_dense_net(43, img_input, depth=100, nb_filter=16, compression=0.5,\n # bottleneck=True, p=0.2, wd=1e-4)\n\nmodel = keras.models.load_model(results+'first_model.hp5')\n\nK.set_value(model.optimizer.lr, 0.1)\n\n# checkpoint\nfilepath=results+\"weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\ncallbacks_list = [checkpoint]\n\nmodel.fit(X_train, y_train, 64, 20, verbose=1, validation_data=(X_valid, y_valid), callbacks=callbacks_list)\n\n\n\nmodel.save(results+'second_model.hp5')\n", "sub_path": "deep_learning/train_learn_6.py", "file_name": "train_learn_6.py", "file_ext": "py", "file_size_in_byte": 5348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 51, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 72, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.pooling.GlobalAveragePooling2D", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 151, "usage_type": "attribute"}, {"api_name": "keras.backend.set_value", "line_number": 153, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 153, "usage_type": "name"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 157, "usage_type": "call"}]}
{"seq_id": "192509796", "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: /home/fsc/work/devel/flamingo/flamingo/plugins/menu/menu.py\n# Compiled at: 2020-04-24 07:27:20\n# Size of source mod 2**32: 2745 bytes\nimport logging, os\nfrom flamingo.core.errors import MultipleObjectsReturned, ObjectDoesNotExist\nfrom flamingo.core.data_model import Content, Q\nlogger = logging.getLogger('flamingo.plugins.Menu')\n\nclass Menu:\n THEME_PATHS = [\n os.path.join(os.path.dirname(__file__), 'theme')]\n\n def templating_engine_setup(self, context, templating_engine):\n\n def is_active(content, menu_item):\n return False\n\n def is_dict(v):\n return isinstance(v, dict)\n\n def is_list(v):\n return isinstance(v, list)\n\n templating_engine.env.globals['is_active'] = is_active\n templating_engine.env.globals['is_dict'] = is_dict\n templating_engine.env.globals['is_list'] = is_list\n\n def contents_parsed(self, context):\n\n def resolve_links(menu):\n for item in menu:\n name, url = item\n if isinstance(url, list):\n resolve_links(url)\n else:\n logger.debug('resolving %s', item[1])\n try:\n if isinstance(item[1], Content):\n logger.debug('resolving skipped')\n return\n if isinstance(item[1], str):\n lookup = Q(path=(item[1]))\n else:\n if not isinstance(item[1], Q):\n lookup = Q(item[1])\n else:\n lookup = item[1]\n item[1] = context.contents.get(lookup)\n logger.debug('%s -> %s', lookup, item[1])\n except ObjectDoesNotExist:\n logger.error('no content with %s %s found', 'path' if isinstance(lookup, str) else 'lookup', lookup or repr(lookup))\n except MultipleObjectsReturned:\n logger.error('multiple contents found with %s %s found', 'path' if isinstance(lookup, str) else 'lookup', lookup or repr(lookup))\n\n if not hasattr(context.settings, 'MENU'):\n context.settings.MENU = {'main': []}\n else:\n if isinstance(context.settings.MENU, list):\n context.settings.MENU = {'main': context.settings.MENU}\n else:\n if 'main' not in context.settings.MENU:\n context.settings.MENU['main'] = []\n for menu_name, menu in context.settings.MENU.items():\n resolve_links(menu)", "sub_path": "pycfiles/flamingo-1.2.tar/menu.cpython-37.py", "file_name": "menu.cpython-37.py", "file_ext": "py", "file_size_in_byte": 2820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "flamingo.core.data_model.Content", "line_number": 42, "usage_type": "argument"}, {"api_name": "flamingo.core.data_model.Q", "line_number": 46, "usage_type": "call"}, {"api_name": "flamingo.core.data_model.Q", "line_number": 48, "usage_type": "argument"}, {"api_name": "flamingo.core.data_model.Q", "line_number": 49, "usage_type": "call"}, {"api_name": "flamingo.core.errors.ObjectDoesNotExist", "line_number": 54, "usage_type": "name"}, {"api_name": "flamingo.core.errors.MultipleObjectsReturned", "line_number": 56, "usage_type": "name"}]}
{"seq_id": "189876815", "text": "import os\nimport datetime\n\nfrom configurations import Configuration, values\n\n\nclass Common(Configuration):\n\n BASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\n ENVIRONMENT = values.Value(environ_prefix=None, default='DEVELOPMENT')\n\n SECRET_KEY = values.SecretValue(environ_prefix=None)\n\n DEBUG = values.BooleanValue(False)\n\n TEMPLATE_DEBUG = values.BooleanValue(DEBUG)\n\n ALLOWED_HOSTS = ['*']\n\n # Application definition\n\n INSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.sites',\n\n # Third party\n 'south',\n 'rest_framework',\n 'django_extensions',\n 'reversion',\n 'django_gravatar',\n 'django_countries',\n 'djrill',\n 'taggit',\n 'djangosecure',\n 'corsheaders',\n 'django_filters',\n\n # Apps\n 'bookshub.users',\n 'bookshub.books',\n 'bookshub.contact',\n 'bookshub.report',\n 'bookshub.offers',\n 'bookshub.cart',\n )\n\n MIDDLEWARE_CLASSES = (\n 'djangosecure.middleware.SecurityMiddleware',\n 'corsheaders.middleware.CorsMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n )\n\n ROOT_URLCONF = 'bookshub.urls'\n\n WSGI_APPLICATION = 'bookshub.wsgi.application'\n\n # Database\n # https://docs.djangoproject.com/en/1.6/ref/settings/#databases\n\n DATABASES = values.DatabaseURLValue(\n 'sqlite:///{}'.format(os.path.join(BASE_DIR, 'db.sqlite3')))\n\n # Internationalization\n # https://docs.djangoproject.com/en/1.6/topics/i18n/\n LANGUAGE_CODE = 'en-us'\n\n TIME_ZONE = 'UTC'\n\n USE_I18N = True\n\n USE_L10N = True\n\n USE_TZ = True\n\n # Static files (CSS, JavaScript, Images)\n # https://docs.djangoproject.com/en/1.6/howto/static-files/\n\n STATIC_ROOT = 'staticfiles'\n STATIC_URL = '/static/'\n\n # STATICFILES_DIRS = (\n # os.path.join(BASE_DIR, 'static'),\n # )\n\n # TEMPLATE_DIRS = (\n # os.path.join(BASE_DIR, 'templates'),\n # )\n\n SITE_ID = 1\n\n MANDRILL_API_KEY = values.Value(environ_prefix=None)\n\n EMAIL_BACKEND = 'djrill.mail.backends.djrill.DjrillBackend'\n\n DEFAULT_FROM_EMAIL = values.Value()\n EMAIL_HOST = values.Value()\n EMAIL_HOST_USER = values.Value()\n EMAIL_HOST_PASSWORD = values.Value()\n EMAIL_PORT = values.IntegerValue()\n EMAIL_USE_TLS = values.BooleanValue(False)\n\n SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\n # Django REST framework\n REST_FRAMEWORK = {\n 'DEFAULT_PERMISSION_CLASSES': (\n 'rest_framework.permissions.IsAuthenticated',\n ),\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'bookshub.users.authentication.JWTAuthentication',\n 'bookshub.users.authentication.SessionAuthentication',\n ),\n 'DEFAULT_RENDERER_CLASSES': (\n 'rest_framework.renderers.JSONRenderer',\n 'rest_framework.renderers.BrowsableAPIRenderer',\n ),\n 'DEFAULT_FILTER_BACKENDS': (\n 'rest_framework.filters.DjangoFilterBackend',\n ),\n 'EXCEPTION_HANDLER':\n 'bookshub.utils.exceptions.custom_exception_handler',\n 'PAGINATE_BY': 25,\n }\n\n JWT_AUTH = {\n 'JWT_PAYLOAD_HANDLER':\n 'bookshub.utils.jwt_handlers.jwt_payload_handler',\n 'JWT_EXPIRATION_DELTA': datetime.timedelta(days=200),\n 'JWT_REFRESH_EXPIRATION_DELTA': datetime.timedelta(days=201),\n 'JWT_ALLOW_REFRESH': True,\n }\n\n AUTH_USER_MODEL = 'users.User'\n\n LOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"handlers\": {\n \"console\": {\n \"level\": \"INFO\",\n \"class\": \"logging.StreamHandler\",\n },\n },\n \"loggers\": {\n \"django\": {\n \"handlers\": [\"console\"],\n }\n }\n }\n\n # CORS settings\n CORS_ORIGIN_ALLOW_ALL = True\n\n #Email\n BOOKSHUB_EMAIL = values.Value(environ_prefix=None, default='DEVELOPMENT')\n\n #ISBNDB API KEY\n ISBNDB_API_KEY = values.Value(environ_prefix=None, default='DEVELOPMENT')\n\n\nclass Development(Common):\n\n DEBUG = True\n\n TEMPLATE_DEBUG = DEBUG\n\n # Development-only installed apps\n Common.INSTALLED_APPS += (\n 'debug_toolbar',\n 'rest_framework_swagger',\n )\n\n SWAGGER_SETTINGS = {\n \"exclude_namespaces\": [],\n \"api_version\": '0.3',\n \"enabled_methods\": [\n 'get',\n 'post',\n 'put',\n 'patch',\n 'delete'\n ],\n \"is_authenticated\": False,\n \"is_superuser\": False,\n }\n\n PROTOCOL = 'http'\n\n # Django Debug Toolbar\n DEBUG_TOOLBAR_PATCH_SETTINGS = values.BooleanValue(\n environ_prefix=None, default=False)\n\n # Dummy cache for development\n CACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.dummy.DummyCache',\n }\n }\n\n\nclass Testing(Development):\n LOGGING_CONFIG = None\n\n # Database Settings\n DATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(Common.BASE_DIR, 'testing_db.sqlite3'),\n }\n }\n\n # Password Hashers\n PASSWORD_HASHERS = (\n 'django.contrib.auth.hashers.MD5PasswordHasher',\n )\n\n # South\n SOUTH_TESTS_MIGRATE = False\n\n # Debug Toolbar\n DEBUG_TOOLBAR_PATCH_SETTINGS = False\n\n\nclass Production(Common):\n DEBUG_TOOLBAR_PATCH_SETTINGS = False\n\n # django-secure settings\n PROTOCOL = 'https'\n SESSION_COOKIE_SECURE = True\n SECURE_SSL_REDIRECT = True\n SECURE_HSTS_SECONDS = 31536000\n SECURE_HSTS_INCLUDE_SUBDOMAINS = True\n SECURE_FRAME_DENY = True\n SECURE_CONTENT_TYPE_NOSNIFF = True\n SECURE_BROWSER_XSS_FILTER = True\n", "sub_path": "bookshub/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 6253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "configurations.Configuration", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "configurations.values.Value", "line_number": 11, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 11, "usage_type": "name"}, {"api_name": "configurations.values.SecretValue", "line_number": 13, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 13, "usage_type": "name"}, {"api_name": "configurations.values.BooleanValue", "line_number": 15, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 15, "usage_type": "name"}, {"api_name": "configurations.values.BooleanValue", "line_number": 17, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 17, "usage_type": "name"}, {"api_name": "configurations.values.DatabaseURLValue", "line_number": 72, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 72, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "configurations.values.Value", "line_number": 103, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 103, "usage_type": "name"}, {"api_name": "configurations.values.Value", "line_number": 107, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 107, "usage_type": "name"}, {"api_name": "configurations.values.Value", "line_number": 108, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 108, "usage_type": "name"}, {"api_name": "configurations.values.Value", "line_number": 109, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 109, "usage_type": "name"}, {"api_name": "configurations.values.Value", "line_number": 110, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 110, "usage_type": "name"}, {"api_name": "configurations.values.IntegerValue", "line_number": 111, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 111, "usage_type": "name"}, {"api_name": "configurations.values.BooleanValue", "line_number": 112, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 112, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 141, "usage_type": "call"}, {"api_name": "configurations.values.Value", "line_number": 167, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 167, "usage_type": "name"}, {"api_name": "configurations.values.Value", "line_number": 170, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 170, "usage_type": "name"}, {"api_name": "configurations.values.BooleanValue", "line_number": 202, "usage_type": "call"}, {"api_name": "configurations.values", "line_number": 202, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}]}
{"seq_id": "461183786", "text": "# coding=utf-8\nfrom __future__ import absolute_import, print_function\nimport argparse\nimport os\nimport sys\nimport torch.utils.data\nfrom torch.backends import cudnn\nfrom torch.autograd import Variable\nimport models\nimport losses\nfrom utils import RandomIdentitySampler, mkdir_if_missing, logging, display\nimport DataSet\ncudnn.benchmark = True\n\n\ndef main(args):\n\n # ่ฎญ็ปๆฅๅฟไฟๅญ\n log_dir = os.path.join('checkpoints', args.log_dir)\n mkdir_if_missing(log_dir)\n\n sys.stdout = logging.Logger(os.path.join(log_dir, 'log.txt'))\n display(args)\n\n if args.r is None:\n model = models.create(args.net, Embed_dim=args.dim)\n # load part of the model\n model_dict = model.state_dict()\n # print(model_dict)\n if args.net == 'bn':\n pretrained_dict = torch.load('pretrained_models/bn_inception-239d2248.pth')\n else:\n pretrained_dict = torch.load('pretrained_models/inception_v3_google-1a9a5a14.pth')\n\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}\n\n model_dict.update(pretrained_dict)\n\n model.load_state_dict(model_dict)\n else:\n # resume model\n model = torch.load(args.r)\n\n model = model.cuda()\n\n torch.save(model, os.path.join(log_dir, 'model.pkl'))\n print('initial model is save at %s' % log_dir)\n\n # fine tune the model: the learning rate for pre-trained parameter is 1/10\n new_param_ids = set(map(id, model.Embed.parameters()))\n\n new_params = [p for p in model.parameters() if\n id(p) in new_param_ids]\n\n base_params = [p for p in model.parameters() if\n id(p) not in new_param_ids]\n param_groups = [\n {'params': base_params, 'lr_mult': 0.1},\n {'params': new_params, 'lr_mult': 1.0}]\n\n optimizer = torch.optim.Adam(param_groups, lr=args.lr,\n weight_decay=args.weight_decay)\n criterion = losses.create(args.loss, alpha=args.alpha, k=args.k).cuda()\n\n data = DataSet.create(args.data, root=None, test=False)\n train_loader = torch.utils.data.DataLoader(\n data.train, batch_size=args.BatchSize,\n sampler=RandomIdentitySampler(data.train, num_instances=args.num_instances),\n drop_last=True, num_workers=args.nThreads)\n\n for epoch in range(args.start, args.epochs):\n running_loss = 0.0\n for i, data in enumerate(train_loader, 0):\n inputs, labels = data\n # wrap them in Variable\n inputs = Variable(inputs.cuda())\n labels = Variable(labels).cuda()\n\n optimizer.zero_grad()\n\n embed_feat = model(inputs)\n\n loss, inter_, dist_ap, dist_an = criterion(embed_feat, labels)\n if args.orth > 0:\n loss = orth_reg(model, loss, cof=args.orth)\n loss.backward()\n optimizer.step()\n running_loss += loss.data[0]\n if epoch == 0 and i == 0:\n print(50*'#')\n print('Train Begin -- HA-HA-HA')\n\n print('[Epoch %05d]\\t Loss: %.3f \\t Accuracy: %.3f \\t Pos-Dist: %.3f \\t Neg-Dist: %.3f'\n % (epoch + 1, running_loss, inter_, dist_ap, dist_an))\n\n if epoch % args.save_step == 0:\n torch.save(model, os.path.join(log_dir, '%d_model.pkl' % epoch))\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='KNN-Softmax Training')\n\n # hype-parameters\n parser.add_argument('-lr', type=float, default=1e-4, help=\"learning rate of new parameters\")\n parser.add_argument('-BatchSize', '-b', default=128, type=int, metavar='N',\n help='mini-batch size (1 = pure stochastic) Default: 256')\n parser.add_argument('-num_instances', default=8, type=int, metavar='n',\n help=' number of samples from one class in mini-batch')\n parser.add_argument('-dim', default=512, type=int, metavar='n',\n help='dimension of embedding space')\n parser.add_argument('-alpha', default=30, type=int, metavar='n',\n help='hyper parameter in KNN Softmax')\n parser.add_argument('-k', default=16, type=int, metavar='n',\n help='number of neighbour points in KNN')\n\n # network\n parser.add_argument('-data', default='cub', required=True,\n help='path to Data Set')\n parser.add_argument('-net', default='bn')\n parser.add_argument('-loss', default='branch', required=True,\n help='loss for training network')\n parser.add_argument('-epochs', default=600, type=int, metavar='N',\n help='epochs for training process')\n parser.add_argument('-save_step', default=50, type=int, metavar='N',\n help='number of epochs to save model')\n\n # Resume from checkpoint\n parser.add_argument('-r', default=None,\n help='the path of the pre-trained model')\n parser.add_argument('-start', default=0, type=int,\n help='resume epoch')\n\n # basic parameter\n parser.add_argument('-log_dir', default=None,\n help='where the trained models save')\n parser.add_argument('--nThreads', '-j', default=4, type=int, metavar='N',\n help='number of data loading threads (default: 2)')\n parser.add_argument('--momentum', type=float, default=0.9)\n parser.add_argument('--weight-decay', type=float, default=2e-4)\n\n main(parser.parse_args())\n\n\n\n\n", "sub_path": "Going/Code/Deep_metric-master/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 5529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "torch.backends.cudnn.benchmark", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 13, "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": "utils.mkdir_if_missing", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.logging.Logger", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.logging", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.display", "line_number": 23, "usage_type": "call"}, {"api_name": "models.create", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.utils.data.load", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.utils.data.load", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.utils.data.load", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.utils.data.save", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.utils.data.optim.Adam", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.utils.data.optim", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 61, "usage_type": "name"}, {"api_name": "losses.create", "line_number": 63, "usage_type": "call"}, {"api_name": "DataSet.create", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.utils.data.utils.data.DataLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 66, "usage_type": "name"}, {"api_name": "utils.RandomIdentitySampler", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.utils.data.save", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 100, "usage_type": "call"}]}
{"seq_id": "118287547", "text": "from typing import Tuple\nimport pandas as pd\nfrom pathlib import Path\nimport json\nimport argparse\n\nimport pickle\nimport logging\nimport lightgbm as lgb\nimport sys\nimport os\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom evaluation import uAUC\n\nlogger = logging.getLogger('lgb_new')\n\nfrom pandarallel import pandarallel\n\npandarallel.initialize()\n\nORIGINAL_ROOT = Path('../data/wechat_algo_data1')\nTG_FEATURE_ROOT = Path('../tg_feature')\nTG_FEATURE_CONFIG = TG_FEATURE_ROOT / 'feature_config.json'\nTG_FEATURE_USER = TG_FEATURE_ROOT / 'userid_breakdown_scale.csv'\nTG_FEATURE_FEED = TG_FEATURE_ROOT / 'feedid_breakdown_scale.csv'\nLGB_FEATURE_ROOT = Path('../lgb_feature')\nLGB_OUT_OFFLINE_ROOT = Path('../lgb_out_offline')\nLGB_OUT_ONLINE_ROOT = Path('../lgb_out_online')\nLGB_TRAIN_OFFLINE = LGB_FEATURE_ROOT / 'train_offline.csv'\nLGB_TEST_OFFLINE = LGB_FEATURE_ROOT / 'test_offline.csv'\nLGB_TRAIN_ONLINE = LGB_FEATURE_ROOT / 'train_online.csv'\nLGB_TEST_ONLINE = LGB_FEATURE_ROOT / 'test_online.csv'\nLGB_TRAIN_OFFLINE_PKL = LGB_FEATURE_ROOT / 'train_offline.pkl'\nLGB_TEST_OFFLINE_PKL = LGB_FEATURE_ROOT / 'test_offline.pkl'\nLGB_TRAIN_ONLINE_PKL = LGB_FEATURE_ROOT / 'train_online.pkl'\nLGB_TEST_ONLINE_PKL = LGB_FEATURE_ROOT / 'test_online.pkl'\nACTION_LIST_ALL = [\"read_comment\", \"like\", \"click_avatar\", \"forward\", \"comment\", \"follow\", \"favorite\"]\nACTION_LIST_PRE = [\"read_comment\", \"like\", \"click_avatar\", \"forward\"]\nFEA_FEED = 'feedid,authorid,videoplayseconds,bgm_song_id,bgm_singer_id'.split(',')\nACTION_WEIGHT = { 'read_comment': 4, 'like': 3, 'click_avatar': 2, 'forward': 1 }\n\n\nLGB_PARAMS = {\n 'boosting_type': 'gbdt',\n 'objective': 'binary',\n 'metric': 'auc',\n 'max_depth': -1,\n 'num_leaves': 31,\n 'learning_rate': 0.25,\n 'feature_fraction': 0.9,\n 'bagging_fraction': 0.8,\n 'bagging_freq': 5,\n 'verbose': 0,\n 'random_state': 42,\n 'n_jobs': -1,\n 'force_col_wise': True,\n # 'two_round': True\n}\n\ndef init_logging():\n logging.basicConfig(\n level=logging.INFO,\n format=\"%(asctime)s [%(levelname)s] %(message)s\",\n handlers=[logging.FileHandler(\"debug.log\"), logging.StreamHandler()],\n )\n\ndef get_original_data(with_feedinfo: bool) -> Tuple[pd.DataFrame, pd.DataFrame]:\n logger.info('getting original data')\n user_action = pd.read_csv(ORIGINAL_ROOT / 'user_action.csv')\n test_a = pd.read_csv(ORIGINAL_ROOT / 'test_a.csv')\n\n with open(TG_FEATURE_ROOT / 'userid_map.pkl', 'rb') as f:\n userid_map = pickle.load(f)\n\n with open(TG_FEATURE_ROOT / 'feedid_map.pkl', 'rb') as f:\n feedid_map = pickle.load(f)\n\n # merge feed info\n feed_info = pd.read_csv(ORIGINAL_ROOT / 'feed_info.csv')[FEA_FEED]\n if with_feedinfo:\n user_action = user_action.merge(feed_info, how='left', on='feedid')\n test_a = test_a.merge(feed_info, how='left', on='feedid')\n\n user_action['userid'] = user_action['userid'].parallel_apply(lambda x: userid_map[x])\n user_action['feedid'] = user_action['feedid'].parallel_apply(lambda x: feedid_map[x])\n test_a['userid'] = test_a['userid'].parallel_apply(lambda x: userid_map[x])\n test_a['feedid'] = test_a['feedid'].parallel_apply(lambda x: feedid_map[x])\n\n return user_action, test_a\n\ndef prepare_tg_dense_features() -> Tuple[pd.DataFrame, pd.DataFrame]:\n with open(TG_FEATURE_CONFIG, 'r') as f:\n feature_config = json.load(f)\n\n logger.info('preparing tg dense features')\n \n user_dense_feature_names = { **(feature_config['user_breakdown']), **(feature_config['duration_breakdown']) }\n user_dense_feature_names = user_dense_feature_names.keys()\n user_dense_feature_names = [x.replace('{}', '.*') for x in user_dense_feature_names] + ['userid', 'date_']\n\n feed_dense_feature_names = { **(feature_config['feed_breakdown']), **(feature_config['device_breakdown']) }\n feed_dense_feature_names = feed_dense_feature_names.keys()\n feed_dense_feature_names = [x.replace('{}', '.*') for x in feed_dense_feature_names] + ['feedid', 'date_']\n\n user_dense_features = pd.read_csv(TG_FEATURE_USER)\n user_dense_features = user_dense_features.filter(user_dense_features)\n \n feed_dense_features = pd.read_csv(TG_FEATURE_FEED)\n feed_dense_features = feed_dense_features.filter(feed_dense_features) \n\n return user_dense_features, feed_dense_features\n\ndef prepare_train_test_data(user_action: pd.DataFrame, test_a: pd.DataFrame, user_dense_features: pd.DataFrame, feed_dense_features: pd.DataFrame):\n train_offline = user_action[(user_action['date_'] < 14) & (user_action['date_'] >= 7)]\n test_offline = user_action[user_action['date_'] == 14]\n # user_dense_features_14 = user_dense_features[user_dense_features['date_'] == 14]\n # feed_dense_features_14 = feed_dense_features[feed_dense_features['date_'] == 14]\n train_offline = train_offline.merge(user_dense_features, how='left', on=['userid', 'date_'])\n train_offline = train_offline.merge(feed_dense_features, how='left', on=['feedid', 'date_'])\n test_offline = test_offline.merge(user_dense_features, how='left', on=['userid', 'date_'])\n test_offline = test_offline.merge(feed_dense_features, how='left', on=['feedid', 'date_'])\n\n logger.info('writing train&test offline data, train shape: {}, test shape: {}'.format(train_offline.shape, test_offline.shape)) \n train_offline.to_pickle(LGB_TRAIN_OFFLINE_PKL)\n test_offline.to_pickle(LGB_TEST_OFFLINE_PKL)\n\n\n user_dense_features_15 = user_dense_features[user_dense_features['date_'] == 15]\n feed_dense_features_15 = feed_dense_features[feed_dense_features['date_'] == 15]\n # last week\n train_online = user_action[(user_action['date_'] <= 14) & (user_action['date_'] > 7)] # all data\n train_online = train_online.merge(user_dense_features, how='left', on=['userid', 'date_'])\n train_online = train_online.merge(feed_dense_features, how='left', on=['feedid', 'date_'])\n \n test_online = test_a.merge(user_dense_features_15, how='left', on=['userid'])\n test_online = test_online.merge(feed_dense_features_15, how='left', on=['feedid'])\n\n logger.info('writing train&test online data, train shape: {}, test shape: {}'.format(train_online.shape, test_online.shape))\n train_online.to_pickle(LGB_TRAIN_ONLINE_PKL)\n test_online.to_pickle(LGB_TEST_ONLINE_PKL)\n\n\ndef offline_train(phases: str):\n # load train data\n # label = 'read_comment'\n # for label in ACTION_LIST_PRE:\n phases = phases.split(',')\n logger.info('loading offline train&test data')\n train_on = pd.read_pickle(LGB_TRAIN_OFFLINE_PKL)\n test_on = pd.read_pickle(LGB_TEST_OFFLINE_PKL)\n aucs = {}\n boost_round = {}\n for label in phases:\n logger.info('-------------------------')\n logger.info('PHASE: {}'.format(label))\n logger.info('-------------------------')\n # only read_comment for now\n ACTION_LIST = ACTION_LIST_ALL.copy()\n ACTION_LIST.remove(label)\n train_on_phase = train_on.drop(['date_', 'play', 'stay'] + ACTION_LIST, axis=1)\n y_train_on = train_on_phase[label]\n x_train_on = train_on_phase.drop(label, axis=1)\n\n test_on_phase = test_on.drop(['date_', 'play', 'stay'] + ACTION_LIST, axis=1)\n y_test_on = test_on_phase[label]\n x_test_on = test_on_phase.drop(label, axis=1)\n\n logger.info('getting train & val dataset')\n\n ul = x_test_on['userid'].tolist()\n dtrain = lgb.Dataset(x_train_on, label=y_train_on)\n dval = lgb.Dataset(x_test_on, label=y_test_on)\n\n logger.info('going to train')\n lgb_model = lgb.train(\n LGB_PARAMS,\n dtrain,\n num_boost_round=10000,\n valid_sets=[dval],\n early_stopping_rounds=50,\n verbose_eval=50,\n )\n\n\n pred = lgb_model.predict(x_test_on, num_iteration=lgb_model.best_iteration)\n logger.info('best iteration: {}, best score: {}'.format(lgb_model.best_iteration, lgb_model.best_score))\n v = uAUC(y_test_on.tolist(), pred.tolist(), ul)\n logger.info('uAUC: {}'.format(v))\n aucs[label] = v\n boost_round[label] = lgb_model.best_iteration\n\n logger.debug('features: {}'.format(x_train_on.columns.tolist()))\n importance_split = lgb_model.feature_importance('split')\n logger.debug('feature importance split: {}'.format(importance_split))\n importance_gain = lgb_model.feature_importance('gain')\n logger.debug('feature importance gain: {}'.format(importance_gain))\n\n logger.info('save them to file')\n str = 'features: {}\\nfeature importance (split): {}\\nfeature importance (gain): {}\\n\\n'.format(\n x_train_on.columns.tolist(), importance_split, importance_gain\n )\n\n xobj = { x[0]: {'split': x[1], 'gain': x[2] } for x in list(zip(x_train_on.columns.tolist(), importance_split, importance_gain)) }\n\n with open(LGB_OUT_OFFLINE_ROOT / 'feature_importance_{}.txt'.format(label), 'w', encoding='utf8') as f:\n f.write(str)\n f.flush()\n\n with open(LGB_OUT_OFFLINE_ROOT / 'feature_importance_{}.json'.format(label), 'w', encoding='utf8') as f:\n f.write(\"{}\".format(xobj).replace(\"'\", '\"'))\n f.flush()\n \n \n logger.info('saving model: {}'.format(label))\n # cannot use PosixPath as parameter to 'save_model'\n lgb_model.save_model((LGB_OUT_OFFLINE_ROOT / 'lgb_{}.lgb_model'.format(label)).as_posix())\n lgb.plot_importance(lgb_model, figsize=(30, 180)).figure.savefig(LGB_OUT_OFFLINE_ROOT / 'lgb_importance_{}.png'.format(label))\n\n # calculate uAUC\n numerator = 0\n denominator = 0\n for label in phases:\n numerator += ACTION_WEIGHT[label] * aucs[label]\n denominator += ACTION_WEIGHT[label]\n \n logger.info('offline uAUC calculated: {}'.format(numerator / denominator))\n\n # save boost_round\n with open(LGB_OUT_OFFLINE_ROOT / 'boost_round.json', 'w', encoding='utf8') as f:\n json.dump(boost_round, f)\n\n pass\n\ndef online_train(runid: str):\n logger.info('loading online train&test data')\n train_on = pd.read_pickle(LGB_TRAIN_ONLINE_PKL)\n test_on = pd.read_pickle(LGB_TEST_ONLINE_PKL)\n\n submit = pd.read_csv(ORIGINAL_ROOT / 'test_a.csv')\n for label in ACTION_LIST_PRE:\n logger.info('-------------------------')\n logger.info('PHASE: {}'.format(label))\n logger.info('-------------------------')\n # only read_comment for now\n ACTION_LIST = ACTION_LIST_ALL.copy()\n ACTION_LIST.remove(label)\n train_on_phase = train_on.drop(['date_', 'play', 'stay'] + ACTION_LIST, axis=1)\n y_train_on = train_on_phase[label]\n x_train_on = train_on_phase.drop(label, axis=1)\n\n # no label in test\n # test_on_phase = test_on.drop(['date_', 'play', 'stay'] + ACTION_LIST, axis=1)\n # y_test_on = test_on_phase[label]\n # x_test_on = test_on_phase.drop(label, axis=1)\n x_test_on = test_on\n x_test_on = x_test_on[x_train_on.columns]\n\n logger.info('getting train & val dataset')\n\n ul = x_test_on['userid'].tolist()\n dtrain = lgb.Dataset(x_train_on, label=y_train_on)\n # dval = dtrain\n\n logger.info('loading best boost round when offline train')\n with open(LGB_OUT_OFFLINE_ROOT / 'boost_round.json', 'r', encoding='utf8') as f:\n boost_round = json.load(f)\n\n logger.info('going to train')\n lgb_model = lgb.train(\n LGB_PARAMS,\n dtrain,\n num_boost_round=boost_round[label],\n valid_sets=[dtrain],\n early_stopping_rounds=50,\n verbose_eval=50,\n )\n\n\n pred = lgb_model.predict(x_test_on, num_iteration=lgb_model.best_iteration)\n submit[label] = pred\n logger.info('best iteration: {}, best score: {}'.format(lgb_model.best_iteration, lgb_model.best_score))\n # there's no label for online predict\n # v = uAUC(y_test_on.tolist(), pred.tolist(), ul)\n # logger.info('uAUC: {}'.format(v))\n\n logger.debug('features: {}'.format(x_train_on.columns.tolist()))\n importance_split = lgb_model.feature_importance('split')\n logger.debug('feature importance split: {}'.format(importance_split))\n importance_gain = lgb_model.feature_importance('gain')\n logger.debug('feature importance gain: {}'.format(importance_gain))\n\n logger.info('save them to file')\n str = 'features: {}\\nfeature importance (split): {}\\nfeature importance (gain): {}\\n\\n'.format(\n x_train_on.columns.tolist(), importance_split, importance_gain\n )\n\n xobj = { x[0]: {'split': x[1], 'gain': x[2] } for x in list(zip(x_train_on.columns.tolist(), importance_split, importance_gain)) }\n\n with open(LGB_OUT_ONLINE_ROOT / 'feature_importance_{}_{}.txt'.format(label, runid), 'w', encoding='utf8') as f:\n f.write(str)\n f.flush()\n\n with open(LGB_OUT_ONLINE_ROOT / 'feature_importance_{}_{}.json'.format(label, runid), 'w', encoding='utf8') as f:\n f.write(\"{}\".format(xobj).replace(\"'\", '\"'))\n f.flush()\n \n \n logger.info('saving model: {}'.format(label))\n lgb_model.save_model((LGB_OUT_ONLINE_ROOT / 'lgb_{}_{}.lgb_model'.format(label, runid)).as_posix())\n lgb.plot_importance(lgb_model, figsize=(30, 180)).figure.savefig(LGB_OUT_ONLINE_ROOT / 'lgb_importance_{}_{}.png'.format(label, runid))\n\n # save submit\n submit.to_csv(LGB_OUT_ONLINE_ROOT / 'submit_lgb_{}.csv'.format(runid), index=False)\n pass\n\ndef parse_args() -> argparse.Namespace:\n p = argparse.ArgumentParser()\n p.add_argument('--mode', type=str, choices=['process', 'offline_train', 'online_train'])\n p.add_argument('--phase', type=str)\n p.add_argument('--with_feedinfo', action='store_true')\n p.add_argument('--runid', type=str)\n return p.parse_args()\n\ndef main():\n init_logging()\n args = vars(parse_args())\n if args['mode'] == 'process':\n user_action, test_a = get_original_data(args['with_feedinfo'])\n user_dense_features, feed_dense_features = prepare_tg_dense_features()\n prepare_train_test_data(user_action, test_a, user_dense_features, feed_dense_features)\n elif args['mode'] == 'offline_train':\n offline_train(args['phase'])\n elif args['mode'] == 'online_train':\n online_train(args['runid'])\n pass\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "lgb_new.py", "file_name": "lgb_new.py", "file_ext": "py", "file_size_in_byte": 14499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"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": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "pandarallel.pandarallel.initialize", "line_number": 19, "usage_type": "call"}, {"api_name": "pandarallel.pandarallel", "line_number": 19, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 62, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 70, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 73, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 67, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 93, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 108, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 91, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 150, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 171, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 172, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 175, "usage_type": "call"}, {"api_name": "evaluation.uAUC", "line_number": 187, "usage_type": "call"}, {"api_name": "lightgbm.plot_importance", "line_number": 217, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 230, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 239, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 261, "usage_type": "call"}, {"api_name": "json.load", "line_number": 266, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 269, "usage_type": "call"}, {"api_name": "lightgbm.plot_importance", "line_number": 310, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 317, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 316, "usage_type": "attribute"}]}
{"seq_id": "60843480", "text": "import re\n\nfrom bs4 import BeautifulSoup\n\nfrom mainapp.models import Group\nfrom parserapp.parser.scrappers.rozklad.utils import RozkladRetryException\n\nRE_NAME_CATHEDRA = re.compile(r'(.*) \\((.*)\\)')\n\n\nasync def get_teacher(session, url):\n async with session.post(url) as resp:\n html = await resp.text()\n\n def find_groups():\n for td in soup.find_all('td'):\n if td.find(class_='disLabel'):\n groups = list(td.children)[-1]\n groups = [i.group(0) for i in Group.RE_GROUP_CODE.finditer(groups)]\n yield from groups\n\n RozkladRetryException.check_html(html)\n soup = BeautifulSoup(html, features='lxml')\n header = soup.find(id='ctl00_MainContent_lblHeader')\n text = header.text.split('ะฒะธะบะปะฐะดะฐั: ')[1]\n\n full_name, cathedras_names = RE_NAME_CATHEDRA.search(text).groups()\n cathedras_names = cathedras_names.split(', ')\n\n return full_name, cathedras_names, find_groups()\n", "sub_path": "parserapp/parser/scrappers/rozklad/teachers.py", "file_name": "teachers.py", "file_ext": "py", "file_size_in_byte": 964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "re.compile", "line_number": 8, "usage_type": "call"}, {"api_name": "mainapp.models.Group.RE_GROUP_CODE.finditer", "line_number": 19, "usage_type": "call"}, {"api_name": "mainapp.models.Group.RE_GROUP_CODE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mainapp.models.Group", "line_number": 19, "usage_type": "name"}, {"api_name": "parserapp.parser.scrappers.rozklad.utils.RozkladRetryException.check_html", "line_number": 22, "usage_type": "call"}, {"api_name": "parserapp.parser.scrappers.rozklad.utils.RozkladRetryException", "line_number": 22, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "199297869", "text": "\"\"\"\n main parser function\n --sub_path: ๋ถ์ผ๋ณ ํด๋\n --dir_type: ๋ฌธ์ ํ์
๋ณ root directory. - TM(์ฉ์ด์ง)/AD(ํ์ ์ ๋ณด์๋ฌธํ์ผ)/์ง์ ์ ๋๊ฒฝ๋ก์
๋ ฅ\n\"\"\"\n\nimport argparse\nfrom pathlib import Path\n\nfrom parsers import xlsx_parser, pptx_parser, docx_mix_parser, docx_separate_parser, pdf_parser\n\nfrom utils.regex_functions import *\nfrom utils.common_functions import *\n\nTM_ROOT = str(Path.home()) + \"/Lexcode/AI แแ
กแจแแ
ณแธแแ
ญแผ แแ
กแซแแ
งแผ แแ
กแฏแแ
ฎแผแแ
ต แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ - แแ
ตแซแแ
ฉแผแแ
ตแแ
ณแผ แแ
กแจแแ
ณแธ DB แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ - แแ
ตแซแแ
ฉแผแแ
ตแแ
ณแผ แแ
กแจแแ
ณแธ DB แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ/1. แแ
ฏแซแแ
ฉแซDB(แ
แ
ฆแจแแ
ณแแ
ฉแแ
ณ)/\"\nDOC_ROOT = str(Path.home()) + \"/Lexcode/AI แแ
กแจแแ
ณแธแแ
ญแผ แแ
กแซแแ
งแผ แแ
กแฏแแ
ฎแผแแ
ต แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ - แแ
ตแซแแ
ฉแผแแ
ตแแ
ณแผ แแ
กแจแแ
ณแธ DB แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ - แแ
ตแซแแ
ฉแผแแ
ตแแ
ณแผ แแ
กแจแแ
ณแธ DB แแ
ฎแแ
ฎแจ แแ
ขแแ
ฅแฏ/3.แแ
กแจแแ
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ฅแผแแ
ฉ/\"\n\n\n# parameter variables\nparser = argparse.ArgumentParser(\n description=\"Parsing .docx files to HTML raw text, then convert to excel file.\"\n)\n\nparser.add_argument(\n \"-s\",\n \"--sub_path\",\n type=str,\n required=True,\n help=\" sub-directory for each category document files\",\n)\n\nparser.add_argument(\n \"-r\",\n \"--root_type\",\n type=str,\n default=\"TM\",\n help=\" TM(default) or AD or type root_path manually. ex)/Users/twigfarm/LexcodeDrive/ํ์ ์ ๋ณด/\",\n)\n\nargs = parser.parse_args()\n\nsub_path = args.sub_path\nroot_type = args.root_type\n\n# ์์
ํ ๋ชจ๋ ํ์ผ ๋ถ๋ฌ์ค๊ธฐ\ndef read_directory(root_type, sub_path):\n\n if root_type == \"TM\":\n root_dir = TM_ROOT\n elif root_type == \"AD\":\n root_dir = DOC_ROOT\n else:\n root_dir = root_type\n\n # read and categorize file type\n xlsx_file_list = get_filename_list(root_dir, sub_path, \".xlsx\")\n xls_file_list = get_filename_list(root_dir, sub_path, \".xls\")\n xlsx_files_list = xlsx_file_list + xls_file_list\n\n pptx_files_list = get_filename_list(root_dir, sub_path, \".pptx\")\n docx_files_list = get_filename_list(root_dir, sub_path, \".docx\")\n pdf_file_list = get_filename_list(root_dir, sub_path, \".pdf\")\n\n # return excel, pptx, docx file list\n return (xlsx_files_list, pptx_files_list, docx_files_list, pdf_file_list)\n # return pptx_files_list\n\n\n# RUN Parser\n# 0:xlsx, 1:pptx, 2:docx\nall_file_lists = read_directory(root_type, sub_path)\n\n# pass each file type\nxlsx_parser.xlsx_to_excel(all_file_lists[0], sub_path)\npptx_parser.pptx_to_excel(all_file_lists[1], sub_path)\ndocx_ko_files, docx_en_files, docx_mix_files, docx_non_lists = get_tm_doc_type(\n all_file_lists[2]\n)\ndocx_separate_parser.docx_separate_to_excel(docx_ko_files, sub_path)\ndocx_mix_parser.docx_mix_to_excel(docx_mix_files, sub_path)\n# pdf_parser.pdf_text_to_excel(all_file_lists[3], sub_path)\n", "sub_path": "18_Natural_Processing/NIA_NER_SCRIPT/parser_start.py", "file_name": "parser_start.py", "file_ext": "py", "file_size_in_byte": 3055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pathlib.Path.home", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 20, "usage_type": "call"}, {"api_name": "parsers.xlsx_parser.xlsx_to_excel", "line_number": 74, "usage_type": "call"}, {"api_name": "parsers.xlsx_parser", "line_number": 74, "usage_type": "name"}, {"api_name": "parsers.pptx_parser.pptx_to_excel", "line_number": 75, "usage_type": "call"}, {"api_name": "parsers.pptx_parser", "line_number": 75, "usage_type": "name"}, {"api_name": "parsers.docx_separate_parser.docx_separate_to_excel", "line_number": 79, "usage_type": "call"}, {"api_name": "parsers.docx_separate_parser", "line_number": 79, "usage_type": "name"}, {"api_name": "parsers.docx_mix_parser.docx_mix_to_excel", "line_number": 80, "usage_type": "call"}, {"api_name": "parsers.docx_mix_parser", "line_number": 80, "usage_type": "name"}]}
{"seq_id": "649319749", "text": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom penelope.core.models import DBSession\nfrom penelope.core.lib.helpers import unicodelower\n\nlog = logging.getLogger(__name__)\n\n\ndef is_inside_penelope():\n \"\"\"\n This is a way to sense whether the components\n are run inside a web application or in trac-admin.\n \"\"\"\n if DBSession.bind:\n return True\n return False\n\n\ndef fix_connection_init():\n \"\"\"\n patch per gestire postgresql 9.x dve l'eccezione nella gestione degli\n schema e' cambiata (TODO: segnalare sul trac di trac.edgewall.org)\n \"\"\"\n\n import psycopg2\n from psycopg2 import DataError, ProgrammingError as PGSchemaError\n from trac.db.util import ConnectionWrapper\n from trac.db.postgres_backend import assemble_pg_dsn, PostgreSQLConnection\n\n # l'eccezione ProgrammingError cambia in PGSchemaError\n def PostgreSQLConnection__init__(self, path, log=None, user=None, password=None, host=None,\n port=None, params={}):\n if path.startswith('/'):\n path = path[1:]\n if 'host' in params:\n host = params['host']\n\n cnx = psycopg2.connect(assemble_pg_dsn(path, user, password, host, port))\n\n cnx.set_client_encoding('UNICODE')\n try:\n self.schema = None\n if 'schema' in params:\n self.schema = params['schema']\n cnx.cursor().execute('SET search_path TO %s', (self.schema,))\n cnx.commit()\n # except (DataError, ProgrammingError):\n except PGSchemaError:\n cnx.rollback()\n except DataError:\n cnx.rollback()\n ConnectionWrapper.__init__(self, cnx, log)\n\n PostgreSQLConnection.__init__ = PostgreSQLConnection__init__\n\n\ndef fix_get_custom_fields():\n \"\"\"\n patch per custom field con dati su por\n \"\"\"\n\n import copy\n from trac.ticket.api import TicketSystem\n from penelope.core.models.dashboard import Project\n\n # TODO: cache ?\n # TODO: generalizzare\n def TicketSystem_get_custom_fields(self):\n if not is_inside_penelope(): # we are in trac-admin\n return self.custom_fields\n\n custom_fields = copy.deepcopy(self.custom_fields)\n project_id = self.config.get('por-dashboard', 'project-id')\n project = None\n if project_id:\n project = DBSession().query(Project).get(project_id)\n for field in custom_fields:\n if project and field['name'] == 'customerrequest':\n field['options'] = [cr.id for cr in sorted(project.customer_requests, key=unicodelower)]\n field['descriptions'] = dict([(cr.id, cr.name) for cr in project.customer_requests])\n # TODO: rimuovere il metodo sotto che invalida la cache, trovare\n # un altro modo per mantenere aggiornati i dati\n self.reset_ticket_fields()\n return custom_fields\n\n TicketSystem.get_custom_fields = TicketSystem_get_custom_fields\n\n\ndef fix_send_user_error():\n \"\"\"\n patch per 401/403\n \"\"\"\n\n import sys\n from genshi.builder import Fragment, tag\n from trac.util.text import exception_to_unicode\n from trac.web.api import RequestDone\n from trac.util.text import to_unicode\n from trac.util.translation import _, tag_\n import trac.web.main\n\n def send_user_error(req, env, e):\n # See trac/web/api.py for the definition of HTTPException subclasses.\n if env:\n env.log.warn('[%s] %s' % (req.remote_addr, exception_to_unicode(e)))\n try:\n # We first try to get localized error messages here, but we\n # should ignore secondary errors if the main error was also\n # due to i18n issues\n title = _(\"Error\")\n if e.reason:\n if title.lower() in e.reason.lower():\n title = e.reason\n else:\n title = _(\"Error: %(message)s\", message=e.reason)\n except Exception:\n title = 'Error'\n # The message is based on the e.detail, which can be an Exception\n # object, but not a TracError one: when creating HTTPException,\n # a TracError.message is directly assigned to e.detail\n if isinstance(e.detail, Exception): # not a TracError\n message = exception_to_unicode(e.detail)\n elif isinstance(e.detail, Fragment): # markup coming from a TracError\n message = e.detail\n else:\n message = to_unicode(e.detail)\n data = {'title': title, 'type': 'TracError', 'message': message,\n 'frames': [], 'traceback': None}\n if e.code == 403 and req.authname == 'anonymous':\n # TRANSLATOR: ... not logged in, you may want to 'do so' now (link)\n do_so = tag.a(_(\"do so\"), href=req.href.login())\n req.chrome['notices'].append(\n tag_(\"You are currently not logged in. You may want to \"\n \"%(do_so)s now.\", do_so=do_so))\n # MONKEY PATCH HERE !!!\n e.code = 401\n try:\n req.send_error(sys.exc_info(), status=e.code, env=env, data=data)\n except RequestDone:\n pass\n\n trac.web.main._send_user_error = send_user_error\n\n\ndef fix_get_known_users():\n \"\"\"\n patch per known_user su por\n \"\"\"\n\n from trac.env import Environment\n from penelope.core.models.dashboard import Project, User\n\n # TODO: cache ?\n # TODO: esistono api piu' semplici su por per la stessa richiesta?\n def Environment_get_known_users(self, cnx=None):\n project_id = self.config.get('por-dashboard', 'project-id')\n project = None\n if project_id:\n db = DBSession()\n project = db.query(Project).get(project_id)\n for user in db.query(User).all():\n if user.roles_in_context(project):\n yield user.login, user.fullname, user.email\n\n Environment.get_known_users = Environment_get_known_users\n\n\ndef fix_customer_request_changelog_description():\n \"\"\"\n Look up Customer Request description while creating change summaries (both web and email)\n \"\"\"\n\n from trac.ticket.web_ui import TicketModule\n from penelope.core.models.dashboard import CustomerRequest\n\n _grouped_changelog_entries = TicketModule.grouped_changelog_entries\n def TicketModule_grouped_changelog_entries(self, ticket, db, when=None):\n ret = _grouped_changelog_entries(self, ticket, db, when)\n for item in ret:\n try:\n cr = item['fields']['customerrequest']\n qry = DBSession().query(CustomerRequest)\n old_cr = qry.get(cr['old'])\n new_cr = qry.get(cr['new'])\n cr['old'] = old_cr.name if old_cr else cr['old']\n cr['new'] = new_cr.name if new_cr else cr['new']\n except KeyError:\n pass\n\n yield item\n\n TicketModule.grouped_changelog_entries = TicketModule_grouped_changelog_entries\n\n\ndef fix_customer_request_dropdown():\n \"\"\"\n Renders the CR dropdown options grouping them by state\n \"\"\"\n\n from trac.ticket.web_ui import TicketModule\n from penelope.core.models.dashboard import CustomerRequest\n\n cr_order = ['estimated', 'created','scheduled', 'achieved', 'invoiced']\n\n def cr_sortkey(cr):\n try:\n return cr_order.index(cr.workflow_state)\n except (AttributeError, ValueError):\n return -1\n\n contract_order = ['active', 'draft','done']\n\n def contract_sortkey(group):\n try:\n return contract_order.index(group['label'].workflow_state)\n except (AttributeError, ValueError):\n return -1\n\n def prepare_customerrequest_options(field, newticket):\n qry = DBSession.query(CustomerRequest)\n options = field['options']\n customer_requests = [qry.get(op) for op in options]\n if newticket:\n customer_requests = [cr for cr in customer_requests if cr.workflow_state in ['created', 'estimated']]\n\n groups = {}\n NO_CONTRACT = 'No contract available'\n for cr in sorted(customer_requests, key=cr_sortkey):\n if cr is None: # the CR has probably been deleted\n continue\n contract = cr.contract and cr.contract or NO_CONTRACT\n groups.setdefault(contract, {\n 'label': contract,\n 'options': [],\n 'descriptions': [],\n })\n groups[contract]['options'].append(cr.id)\n groups[contract]['descriptions'].append(cr.name)\n field['options'] = []\n field['descriptions'] = []\n field['optgroups'] = sorted(groups.values(), key=contract_sortkey)\n field['optional'] = True\n\n _prepare_fields = TicketModule._prepare_fields\n def TicketModule_prepare_fields(self, req, ticket):\n ret = _prepare_fields(self, req, ticket)\n for field in ret:\n if field['name'] == 'customerrequest':\n if not ticket.id:\n newticket = True\n else:\n newticket = False\n prepare_customerrequest_options(field, newticket)\n\n return ret\n\n TicketModule._prepare_fields = TicketModule_prepare_fields\n\n\ndef fix_filter_email_recipents():\n \"\"\"\n FIX: per evitare mail al customer relativamente a commenti privati e ticket sensibili\n \"\"\"\n from trac.ticket.notification import TicketNotifyEmail\n from trac.ticket.web_ui import TicketModule\n from trac.perm import PermissionSystem\n try:\n import privatecomments; privatecomments\n HAS_PRIVATECOMMENTS = True\n except ImportError:\n HAS_PRIVATECOMMENTS = False\n\n\n TicketNotifyEmail._orig_get_recipients = TicketNotifyEmail.get_recipients\n\n def TicketNotifyEmail_get_recipients(self, tktid):\n (torecipients, ccrecipients) = self._orig_get_recipients(tktid)\n perm = PermissionSystem(self.env)\n # sensitivetickets\n if self.ticket['sensitive'] == '1':\n def has_sensisitive_perm(username):\n return perm.get_user_permissions(username).get('SENSITIVE_VIEW')\n torecipients = filter(has_sensisitive_perm, torecipients)\n ccrecipients = filter(has_sensisitive_perm, ccrecipients)\n # privatecomments\n if HAS_PRIVATECOMMENTS:\n privatecomment = False\n for mod in TicketModule(self.env).grouped_changelog_entries(self.ticket, self.db, self.modtime):\n cursor = self.db.cursor()\n sql = 'SELECT private FROM private_comment WHERE ticket_id=%d AND comment_id=%d AND private>0'\n cursor.execute(sql % (int(self.ticket.id), int(mod.get('cnum'))))\n if cursor.cursor.rowcount:\n privatecomment = True\n cursor.close()\n if privatecomment:\n def has_privatecommente_perm(username):\n return perm.get_user_permissions(username).get('PRIVATE_COMMENT_PERMISSION')\n torecipients = filter(has_privatecommente_perm, torecipients)\n ccrecipients = filter(has_privatecommente_perm, ccrecipients)\n return (torecipients, ccrecipients)\n\n TicketNotifyEmail.get_recipients = TicketNotifyEmail_get_recipients\n\n\ndef fix_notification_props():\n \"\"\"\n Change the table of ticket properties printed in notifications.\n \"\"\"\n from trac.ticket.notification import TicketNotifyEmail\n\n def iter_props(self):\n tkt = self.ticket\n yield '--'\n\n for f in tkt.fields:\n fname = f['name']\n\n if fname in ['summary', 'cc', 'time', 'changetime', 'sensitive', 'esogeno', 'customerrequest', 'stats_exclude']:\n continue\n\n fval = tkt[fname] or ''\n if fname in ['owner', 'reporter']:\n fval = self.obfuscate_email(fval)\n\n flabel = f['label']\n\n if not fval:\n continue\n\n if f['type'] == 'textarea':\n yield u'%s:' % flabel\n for line in fval.split('\\n'):\n yield u' %s' % line\n else:\n yield u'%s: %s' % (flabel, fval)\n\n\n def TicketNotifyEmail_format_props(self):\n return '\\n'.join(iter_props(self))\n\n TicketNotifyEmail.format_props = TicketNotifyEmail_format_props\n\n\nlog.info(\"Monkey patch\")\nfix_connection_init()\nfix_get_custom_fields()\nfix_send_user_error()\nfix_get_known_users()\nfix_customer_request_changelog_description()\nfix_customer_request_dropdown()\nfix_filter_email_recipents()\nfix_notification_props()\n", "sub_path": "penelope/trac/monkey.py", "file_name": "monkey.py", "file_ext": "py", "file_size_in_byte": 12694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "penelope.core.models.DBSession.bind", "line_number": 15, "usage_type": "attribute"}, {"api_name": "penelope.core.models.DBSession", "line_number": 15, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 39, "usage_type": "call"}, {"api_name": "trac.db.postgres_backend.assemble_pg_dsn", "line_number": 39, "usage_type": "call"}, {"api_name": "psycopg2.ProgrammingError", "line_number": 49, "usage_type": "name"}, {"api_name": "psycopg2.DataError", "line_number": 51, "usage_type": "name"}, {"api_name": "trac.db.util.ConnectionWrapper.__init__", "line_number": 53, "usage_type": "call"}, {"api_name": "trac.db.util.ConnectionWrapper", "line_number": 53, "usage_type": "name"}, {"api_name": "trac.db.postgres_backend.PostgreSQLConnection.__init__", "line_number": 55, "usage_type": "attribute"}, {"api_name": "trac.db.postgres_backend.PostgreSQLConnection", "line_number": 55, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 73, "usage_type": "call"}, {"api_name": "penelope.core.models.dashboard.Project", "line_number": 77, "usage_type": "argument"}, {"api_name": "penelope.core.models.DBSession", "line_number": 77, "usage_type": "call"}, {"api_name": "penelope.core.lib.helpers.unicodelower", "line_number": 80, "usage_type": "name"}, {"api_name": "trac.ticket.api.TicketSystem.get_custom_fields", "line_number": 87, "usage_type": "attribute"}, {"api_name": "trac.ticket.api.TicketSystem", "line_number": 87, "usage_type": "name"}, {"api_name": "trac.util.text.exception_to_unicode", "line_number": 106, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 111, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 116, "usage_type": "call"}, {"api_name": "trac.util.text.exception_to_unicode", "line_number": 123, "usage_type": "call"}, {"api_name": "genshi.builder.Fragment", "line_number": 124, "usage_type": "argument"}, {"api_name": "trac.util.text.to_unicode", "line_number": 127, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 132, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 132, "usage_type": "name"}, {"api_name": "trac.util.translation._", "line_number": 132, "usage_type": "call"}, {"api_name": "trac.util.translation.tag_", "line_number": 134, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 139, "usage_type": "call"}, {"api_name": "trac.web.api.RequestDone", "line_number": 140, "usage_type": "name"}, {"api_name": "trac.db.util.web", "line_number": 143, "usage_type": "attribute"}, {"api_name": "trac.db.util", "line_number": 143, "usage_type": "name"}, {"api_name": "penelope.core.models.DBSession", "line_number": 160, "usage_type": "call"}, {"api_name": "penelope.core.models.dashboard.Project", "line_number": 161, "usage_type": "argument"}, {"api_name": "penelope.core.models.dashboard.User", "line_number": 162, "usage_type": "argument"}, {"api_name": "trac.env.Environment.get_known_users", "line_number": 166, "usage_type": "attribute"}, {"api_name": "trac.env.Environment", "line_number": 166, "usage_type": "name"}, {"api_name": "trac.ticket.web_ui.TicketModule.grouped_changelog_entries", "line_number": 177, "usage_type": "attribute"}, {"api_name": "trac.ticket.web_ui.TicketModule", "line_number": 177, "usage_type": "name"}, {"api_name": "penelope.core.models.dashboard.CustomerRequest", "line_number": 183, "usage_type": "argument"}, {"api_name": "penelope.core.models.DBSession", "line_number": 183, "usage_type": "call"}, {"api_name": "trac.ticket.web_ui.TicketModule.grouped_changelog_entries", "line_number": 193, "usage_type": "attribute"}, {"api_name": "trac.ticket.web_ui.TicketModule", "line_number": 193, "usage_type": "name"}, {"api_name": "penelope.core.models.DBSession.query", "line_number": 221, "usage_type": "call"}, {"api_name": "penelope.core.models.dashboard.CustomerRequest", "line_number": 221, "usage_type": "argument"}, {"api_name": "penelope.core.models.DBSession", "line_number": 221, "usage_type": "name"}, {"api_name": "trac.ticket.web_ui.TicketModule._prepare_fields", "line_number": 245, "usage_type": "attribute"}, {"api_name": "trac.ticket.web_ui.TicketModule", "line_number": 245, "usage_type": "name"}, {"api_name": "trac.ticket.web_ui.TicketModule._prepare_fields", "line_number": 258, "usage_type": "attribute"}, {"api_name": "trac.ticket.web_ui.TicketModule", "line_number": 258, "usage_type": "name"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail._orig_get_recipients", "line_number": 275, "usage_type": "attribute"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail", "line_number": 275, "usage_type": "name"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail.get_recipients", "line_number": 275, "usage_type": "attribute"}, {"api_name": "trac.perm.PermissionSystem", "line_number": 279, "usage_type": "call"}, {"api_name": "trac.ticket.web_ui.TicketModule", "line_number": 289, "usage_type": "call"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail.get_recipients", "line_number": 303, "usage_type": "attribute"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail", "line_number": 303, "usage_type": "name"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail.format_props", "line_number": 342, "usage_type": "attribute"}, {"api_name": "trac.ticket.notification.TicketNotifyEmail", "line_number": 342, "usage_type": "name"}]}
{"seq_id": "649202647", "text": "import hashlib\nimport json\n\nclass NewCosmetics:\n def __init__(self, data):\n self.build = data.get('currentVersion')\n self.previousBuild = data.get('previousVersion')\n self.hash = hashlib.md5(self.build.encode()).hexdigest()\n self.items = [Cosmetic(i) for i in data.get('items')]\n\n def json(self):\n return json.dumps(self, default=lambda o: o.__dict__)\n\nclass Cosmetic:\n def __init__(self, data):\n self.id = data.get('id')\n self.name = data.get('name')\n self.description = data.get('description')\n\n self.type = CosmeticType(data)\n self.rarity = CosmeticRarity(data)\n self.series = CosmeticSeries(data) if data.get('series') else None\n self.set = CosmeticSet(data) if data.get('set') else None\n self.introduction = None\n self.images = Images(data.get('icons', {}))\n self.variants = data.get('variants')\n self.gameplayTags = [i for i in data.get('gameplayTags', [])] if data.get('gameplayTags') else []\n\nclass CosmeticType:\n def __init__(self, data):\n self.value = data.get('backendType')\n self.displayValue = data.get('shortDescription')\n\nclass CosmeticRarity:\n def __init__(self, data):\n self.backendValue = data.get('backendRarity')\n self.value = self.backendValue.split('::')[1].lower()\n self.displayValue = data.get('rarity')\n\nclass CosmeticSeries:\n def __init__(self, data):\n self.backendValue = data.get('backendRarity')\n self.value = data.get('series').get('name')\n self.image = data.get('icons').get('series')\n\nclass CosmeticSet:\n def __init__(self, data):\n self.value = data.get('set')\n self.text = data.get('setText')\n\nclass Images:\n def __init__(self, data):\n self.smallIcon = None\n self.icon = data.get('icon')\n self.featured = data.get('featured')\n\nclass Build:\n def __init__(self, data):\n self.build = data.get('version')\n self.mainKey = data.get('mainKey')\n dKeys = data.get('dynamicKeys')\n self.dynamicKeys = [DynamicKey(i, dKeys[i]) for i in dKeys.keys()]\n \n def json(self):\n return json.dumps(self, default=lambda o: o.__dict__)\n\nclass DynamicKey:\n def __init__(self, pakFilename, key):\n self.pakFilename = pakFilename\n self.pakGuid = None\n self.key = key", "sub_path": "Rest/Models/BenBot.py", "file_name": "BenBot.py", "file_ext": "py", "file_size_in_byte": 2366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "hashlib.md5", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "108238595", "text": "import jwt, time\n\nclass LFAuthToken(object):\n \"\"\"Class to create tokens for auth with Livefyre services.\"\"\"\n def __init__(self, user, domain, key, display_name=None, duration=86400):\n self.domain = domain\n self.duration = duration\n self.key = key\n self.user = user\n self.display_name = display_name\n \n def __str__(self):\n \"\"\"Return the generated token string.\"\"\"\n return self.token\n \n @property\n def token(self):\n \"\"\"Create a signed token from inputs.\"\"\"\n token = dict(expires=self.duration + time.time(),\n user_id=self.user,\n domain=self.domain)\n if self.display_name:\n token['display_name'] = self.display_name\n return jwt.encode(token, self.key)", "sub_path": "livefyre/client/token.py", "file_name": "token.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "jwt.encode", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "7351756", "text": "from django.conf.urls import patterns, url, include\nfrom rest_framework.urlpatterns import format_suffix_patterns\n\nfrom artfence import views\n\nurlpatterns = format_suffix_patterns(patterns('',\n\turl(r'^search/$', views.search.as_view(), name='search'),\n\turl(r'^register_user/$', views.register_user.as_view(), name='register_user'),\n\turl(r'^login/$', views.log_in.as_view(), name='log_in'),\n\turl(r'^post/$', views.post.as_view(), name='post'),\n\turl(r'^profile_search/$', views.profile_search.as_view(), name='profile_search'),\n\turl(r'^view_post/$', views.view_post.as_view(), name='view_post'),\n\turl(r'^delete_post/$', views.delete_post.as_view(), name='delete_post'),\n\turl(r'^upvote/$', views.upvote.as_view(), name='upvote'),\n url(r'^api-token-auth/', 'rest_framework.authtoken.views.obtain_auth_token')\n \n \n \n \n \n))\n\n", "sub_path": "artfence/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "rest_framework.urlpatterns.format_suffix_patterns", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "artfence.views.search.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "artfence.views.search", "line_number": 7, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "artfence.views.register_user.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "artfence.views.register_user", "line_number": 8, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "artfence.views.log_in.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "artfence.views.log_in", "line_number": 9, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "artfence.views.post.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "artfence.views.post", "line_number": 10, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "artfence.views.profile_search.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "artfence.views.profile_search", "line_number": 11, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "artfence.views.view_post.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "artfence.views.view_post", "line_number": 12, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "artfence.views.delete_post.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "artfence.views.delete_post", "line_number": 13, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "artfence.views.upvote.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "artfence.views.upvote", "line_number": 14, "usage_type": "attribute"}, {"api_name": "artfence.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "459886150", "text": "\"\"\"\nThis file defines functions and classes related to the registration and implementation of callbacks via python.\nIt currently contains:\n- callbacks allowing V-Ray to support the AOV functionality of Maya Render Setup 2017 system.\n\"\"\"\n\nimport maya.cmds as cmds\nimport maya.mel as mel\nimport maya.api.OpenMaya as OpenMaya\nimport maya.app.renderSetup.model.rendererCallbacks as rc\nimport maya.app.renderSetup.model.selector as selector\nimport maya.app.renderSetup.model.utils as utils\nimport maya.app.renderSetup.model.renderSetup as renderSetup\n\nVRAY_RENDER_ELEMENT_NODE_TYPES = [\"VRayRenderElement\", \"VRayRenderElementSet\"]\n\ndef vrayGetExistingRenderElementsInScene():\n \"\"\" Returns a list of names of the existing render elements in the scene. \"\"\"\n renderElementNames = []\n for reType in VRAY_RENDER_ELEMENT_NODE_TYPES:\n renderElementNames.extend(cmds.ls(exactType=reType))\n return renderElementNames\n \ndef isVRayRenderElementType(nodeType):\n \"\"\" Returns a boolean result depending on whether the given nodeType matches any V-Ray render element node type. \"\"\"\n if not nodeType:\n return False\n return nodeType in VRAY_RENDER_ELEMENT_NODE_TYPES\n\nclass VRayRenderElementStrategy(selector.Strategy):\n \"\"\" The class provides a strategy for finding V-Ray render element nodes from a given input Selection. \"\"\"\n\n @staticmethod\n def create(filterType, customs):\n # strategy of type VRayRenderElementStrategy will be returned only if the filter type is set to \"Custom\"\n # and at least one of the custom node types matches a V-Ray render element node type.\n if filterType == selector.Filters.kCustom and reduce(lambda out, nodeType: out or isVRayRenderElementType(nodeType), customs, False):\n return VRayRenderElementStrategy()\n return None\n \n def items(self, selection):\n return filter(lambda nodeName: isVRayRenderElementType(cmds.nodeType(nodeName)), selection.names())\n \n def onConnectionChanged(self, selectorNode, srcPlug, dstPlug, made):\n depNode = OpenMaya.MFnDependencyNode(dstPlug.node())\n if isVRayRenderElementType(depNode.typeName):\n selectorNode.selectionChanged()\n\n def isTraversingConnections(self):\n return True\n\nclass VRayAOVChildSelector(selector.SimpleSelector):\n \"\"\" \n This selector is a node that is responsible for filtering V-Ray render element nodes\n in a child AOV collection in Render Setup. It derives from SimpleSelector and exists\n to override its strategy() method in order to fix improper behaviour ignoring nodes of type=VRayRenderElement.\n \"\"\"\n \n kTypeId = OpenMaya.MTypeId(0x00126CC0)\n kTypeName = 'vrayAOVChildSelector'\n \n def strategy(self, dataBlock=None):\n if dataBlock or not self._strategy:\n filterType = self.getFilterType(dataBlock)\n customs = self.getTypeFilters() if filterType == selector.Filters.kCustom else []\n theStrategy = VRayRenderElementStrategy.create(filterType, customs)\n self._strategy = [] if theStrategy is None else theStrategy\n return self._strategy\n\nclass VRayAOVCallbacks(rc.AOVCallbacks):\n \"\"\"\n Implements the AOV callbacks presented in Render Setup 2017 to provide support for export/import of V-Ray AOV data to an external file,\n opening the render settings window to \"Render Elements\" tab and organizing overrides of render elements into collections.\n \"\"\"\n \n def encode(self):\n \"\"\" Encodes/exports the AOV information. \"\"\"\n basicNodeExporter = rc.BasicNodeExporter()\n \n aovsDataJSON = {}\n renderElementNodes = []\n renderElementNames = vrayGetExistingRenderElementsInScene()\n\n for elemName in renderElementNames:\n basicNodeExporter.setNodes([elemName])\n # Passing the render element name to the exporter is enough in general but here we will set it ignore attributes\n # that are not created by this RE itself. This will be done removing the user defined from all the attributes.\n allPlugs = cmds.listAttr(elemName)\n userDefinedPlugs = cmds.listAttr(elemName, userDefined=True)\n for plug in userDefinedPlugs:\n allPlugs.remove(plug)\n plugsToIgnore = []\n for plug in allPlugs:\n fullAttrName = '{}.{}'.format(elemName, plug)\n plugsToIgnore.append(fullAttrName)\n basicNodeExporter.setPlugsToIgnore(plugsToIgnore)\n renderElementNodes.append( { elemName : basicNodeExporter.encode() })\n \n aovsDataJSON[\"renderElements\"] = renderElementNodes\n return aovsDataJSON\n \n def decode(self, aovsData, decodeType):\n \"\"\"\n Decodes/imports the AOV information.\n aovsData - The AOV data to decode\n decodeType - Overwrite, Merge\n Overwrite mode - specifies that the import should delete all existing AOV information before doing the import.\n Merge mode - overwrites any AOVs with the same name, imports AOVs with different names, and leaves alone any other pre-existing AOVs in the scene.\n \"\"\"\n \n if decodeType == self.DECODE_TYPE_OVERWRITE:\n existingRenderElements = vrayGetExistingRenderElementsInScene()\n for elemName in existingRenderElements:\n mel.eval('vrayRemoveRenderElement \"{}\"'.format(elemName))\n \n basicNodeExporter = rc.BasicNodeExporter()\n renderElements = aovsData[\"renderElements\"]\n for renderElement in renderElements:\n for elemName, elemData in renderElement.iteritems():\n fullAttrName = '{}.vrayClassType'.format(elemName)\n vrayClassType = elemData[fullAttrName]\n createdNodeName = mel.eval('vrayAddRenderElement \"{}\"'.format(vrayClassType))\n if createdNodeName != elemName:\n cmds.rename(createdNodeName, elemName)\n basicNodeExporter.decode(elemData)\n \n def displayMenu(self):\n \"\"\" This function is called to display the Render Elements tab for V-Ray renderer in unifiedRenderGlobalsWindow. \"\"\"\n \n mel.eval('unifiedRenderGlobalsWindow')\n mel.eval('setCurrentTabInRenderGlobalsWindow(\\\"Render Elements\\\")')\n mel.eval('fillSelectedTabForCurrentRenderer')\n \n def getAOVName(self, aovNode):\n \"\"\" From a given AOV node, returns the AOV name. The returned result is the collection name for the render element associated with aovNode.\"\"\"\n \n nodeType = cmds.nodeType(aovNode)\n if nodeType in VRAY_RENDER_ELEMENT_NODE_TYPES:\n return '{}_col'.format(aovNode)\n return aovNode\n\n def getCollectionSelector(self, selectorName):\n \"\"\" This function is called to create the selector for the AOV collection and determines the node types allowed for it. \"\"\"\n \n selectorName = cmds.createNode(selector.SimpleSelector.kTypeName, name=selectorName, skipSelect=True)\n selectorNode = utils.nameToUserNode(selectorName)\n selectorNode.setPattern(\"*\")\n selectorNode.setFilterType(selector.Filters.kCustom)\n customFilterStr = ' '.join(VRAY_RENDER_ELEMENT_NODE_TYPES)\n selectorNode.setCustomFilterValue(customFilterStr)\n return selectorName\n \n def getChildCollectionSelector(self, selectorName, aovName):\n \"\"\"\n This function is called to create the selector for the AOV child collection for the given render element.\n It uses a custom selector to filter the allowed types for the V-Ray render element associated with aovName.\n \"\"\"\n selectorName = cmds.createNode(VRayAOVChildSelector.kTypeName, name=selectorName, skipSelect=True)\n selectorNode = utils.nameToUserNode(selectorName)\n aovNodeName = aovName[:-len('_col')] if aovName[-len('_col'):] == '_col' else aovName\n nodeType = cmds.nodeType(aovNodeName)\n selectorNode.setFilterType(selector.Filters.kCustom)\n selectorNode.setCustomFilterValue(nodeType)\n \n newSelection = OpenMaya.MSelectionList()\n newSelection.clear()\n newSelection.add(aovNodeName)\n selectorNode.staticSelection.setWithoutExistenceCheck(newSelection)\n return selectorName\n \n def getChildCollectionSelectorAOVNodeFromDict(self, d):\n \"\"\" This function returns the child selector AOV node name from the provided dictionary. \"\"\"\n return d[\"selector\"][\"vrayAOVChildSelector\"][\"staticSelection\"]\n\ndef vrayRegisterPythonCallbacks():\n \"\"\" The function registers the node VRayAOVChildSelector and the class VRayAOVCallbacks.\"\"\"\n renderSetup.registerNode(VRayAOVChildSelector)\n # registering the set of AOV callbacks with the Render Setup system\n vrayAOVCallbacks = VRayAOVCallbacks()\n rc.registerCallbacks(\"vray\", rc.CALLBACKS_TYPE_AOVS, vrayAOVCallbacks)\n \ndef vrayUnregisterPythonCallbacks():\n \"\"\" The function unregisters the node VRayAOVChildSelector and the AOV callbacks for Render Setup. \"\"\"\n rc.unreregisterCallbacks(\"vray\", rc.CALLBACKS_TYPE_AOVS)\n renderSetup.unregisterNode(VRayAOVChildSelector)\n", "sub_path": "WitPipeline/MayaPlugs/Vray1/3.5/2017/scripts/vray/rendererCallbacks.py", "file_name": "rendererCallbacks.py", "file_ext": "py", "file_size_in_byte": 9180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "maya.cmds.ls", "line_number": 21, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 21, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.Strategy", "line_number": 30, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 30, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.Filters", "line_number": 37, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 37, "usage_type": "name"}, {"api_name": "maya.cmds.nodeType", "line_number": 42, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 42, "usage_type": "name"}, {"api_name": "maya.api.OpenMaya.MFnDependencyNode", "line_number": 45, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya", "line_number": 45, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.SimpleSelector", "line_number": 52, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 52, "usage_type": "name"}, {"api_name": "maya.api.OpenMaya.MTypeId", "line_number": 59, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya", "line_number": 59, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.Filters", "line_number": 65, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 65, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.AOVCallbacks", "line_number": 70, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks", "line_number": 70, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.BasicNodeExporter", "line_number": 78, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.listAttr", "line_number": 88, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 88, "usage_type": "name"}, {"api_name": "maya.cmds.listAttr", "line_number": 89, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 89, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 114, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 114, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.BasicNodeExporter", "line_number": 116, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks", "line_number": 116, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 122, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 122, "usage_type": "name"}, {"api_name": "maya.cmds.rename", "line_number": 124, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 124, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 130, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 130, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 131, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 131, "usage_type": "name"}, {"api_name": "maya.mel.eval", "line_number": 132, "usage_type": "call"}, {"api_name": "maya.mel", "line_number": 132, "usage_type": "name"}, {"api_name": "maya.cmds.nodeType", "line_number": 137, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 137, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 145, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 145, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.SimpleSelector", "line_number": 145, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 145, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.utils.nameToUserNode", "line_number": 146, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.utils", "line_number": 146, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.Filters", "line_number": 148, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 148, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 158, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 158, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.utils.nameToUserNode", "line_number": 159, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.utils", "line_number": 159, "usage_type": "name"}, {"api_name": "maya.cmds.nodeType", "line_number": 161, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 161, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.selector.Filters", "line_number": 162, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.selector", "line_number": 162, "usage_type": "name"}, {"api_name": "maya.api.OpenMaya.MSelectionList", "line_number": 165, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya", "line_number": 165, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.renderSetup.registerNode", "line_number": 177, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.renderSetup", "line_number": 177, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.registerCallbacks", "line_number": 180, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks", "line_number": 180, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.CALLBACKS_TYPE_AOVS", "line_number": 180, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.unreregisterCallbacks", "line_number": 184, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks", "line_number": 184, "usage_type": "name"}, {"api_name": "maya.app.renderSetup.model.rendererCallbacks.CALLBACKS_TYPE_AOVS", "line_number": 184, "usage_type": "attribute"}, {"api_name": "maya.app.renderSetup.model.renderSetup.unregisterNode", "line_number": 185, "usage_type": "call"}, {"api_name": "maya.app.renderSetup.model.renderSetup", "line_number": 185, "usage_type": "name"}]}
{"seq_id": "380116509", "text": "# Copyright The IETF Trust 2015, All Rights Reserved\n\nfrom django.db import models\nfrom django.template import Template, Context\n\nfrom email.utils import parseaddr\nfrom ietf.utils.mail import formataddr\nfrom ietf.person.models import Email\n\nimport debug # pyflakes:ignore\n\nfrom ietf.group.models import Role\n\ndef clean_duplicates(addrlist):\n address_info = {}\n for a in addrlist:\n (name,addr) = parseaddr(a)\n # This collapses duplicate addresses to one, using (arbitrarily) the\n # name from the last one:\n address_info[addr] = (name, a)\n addresses = []\n for addr, info in address_info.items():\n name, a = info\n if (name,addr)==('',''):\n addresses.append(a)\n elif name:\n addresses.append(formataddr((name,addr)))\n else:\n addresses.append(addr)\n return addresses\n\nclass MailTrigger(models.Model):\n slug = models.CharField(max_length=32, primary_key=True)\n desc = models.TextField(blank=True)\n to = models.ManyToManyField('Recipient', blank=True, related_name='used_in_to')\n cc = models.ManyToManyField('Recipient', blank=True, related_name='used_in_cc')\n\n class Meta:\n ordering = [\"slug\"]\n\n def __unicode__(self):\n return self.slug\n\nclass Recipient(models.Model):\n slug = models.CharField(max_length=32, primary_key=True)\n desc = models.TextField(blank=True)\n template = models.TextField(null=True, blank=True)\n\n class Meta:\n ordering = [\"slug\"]\n\n def __unicode__(self):\n return self.slug\n\n def gather(self, **kwargs):\n retval = []\n if hasattr(self,'gather_%s'%self.slug):\n retval.extend(eval('self.gather_%s(**kwargs)'%self.slug))\n if self.template:\n rendering = Template('{%% autoescape off %%}%s{%% endautoescape %%}'%self.template).render(Context(kwargs))\n if rendering:\n retval.extend([x.strip() for x in rendering.split(',')])\n\n return clean_duplicates(retval)\n\n def gather_doc_group_chairs(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n doc=kwargs['doc']\n if doc.group and doc.group.type.slug in ['wg','rg','ag',]:\n addrs.append('%s-chairs@ietf.org'%doc.group.acronym)\n return addrs\n\n def gather_doc_group_delegates(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n doc=kwargs['doc']\n if doc.group and doc.group.type.slug in ['wg','rg','ag',]:\n addrs.extend(doc.group.role_set.filter(name='delegate').values_list('email__address',flat=True))\n return addrs\n\n def gather_doc_group_mail_list(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n doc=kwargs['doc']\n if doc.group.type.slug in ['wg','rg','ag',]:\n if doc.group.list_email:\n addrs.append(doc.group.list_email)\n return addrs\n\n def gather_doc_affecteddoc_authors(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n for reldoc in kwargs['doc'].related_that_doc(('conflrev','tohist','tois','tops')):\n addrs.extend(Recipient.objects.get(slug='doc_authors').gather(**{'doc':reldoc.document}))\n return addrs\n\n def gather_doc_affecteddoc_group_chairs(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n for reldoc in kwargs['doc'].related_that_doc(('conflrev','tohist','tois','tops')):\n addrs.extend(Recipient.objects.get(slug='doc_group_chairs').gather(**{'doc':reldoc.document}))\n return addrs\n\n def gather_doc_affecteddoc_notify(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n for reldoc in kwargs['doc'].related_that_doc(('conflrev','tohist','tois','tops')):\n addrs.extend(Recipient.objects.get(slug='doc_notify').gather(**{'doc':reldoc.document}))\n return addrs\n\n def gather_conflict_review_stream_manager(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n for reldoc in kwargs['doc'].related_that_doc(('conflrev',)):\n addrs.extend(Recipient.objects.get(slug='doc_stream_manager').gather(**{'doc':reldoc.document}))\n return addrs\n\n def gather_conflict_review_steering_group(self,**kwargs):\n addrs = []\n if 'doc' in kwargs:\n for reldoc in kwargs['doc'].related_that_doc(('conflrev',)):\n if reldoc.document.stream_id=='irtf':\n addrs.append('\"Internet Research Steering Group\" ')\n return addrs\n\n def gather_group_steering_group(self,**kwargs):\n addrs = []\n sg_map = dict( wg='\"The IESG\" ', rg='\"Internet Research Steering Group\" ' )\n if 'group' in kwargs and kwargs['group'].type_id in sg_map:\n addrs.append(sg_map[kwargs['group'].type_id])\n return addrs \n\n def gather_stream_managers(self, **kwargs):\n addrs = []\n manager_map = dict(ise = '',\n irtf = '',\n ietf = '',\n iab = '')\n if 'streams' in kwargs:\n for stream in kwargs['streams']:\n if stream in manager_map:\n addrs.append(manager_map[stream])\n return addrs\n\n def gather_doc_stream_manager(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n addrs.extend(Recipient.objects.get(slug='stream_managers').gather(**{'streams':[kwargs['doc'].stream_id]}))\n return addrs\n\n def gather_doc_non_ietf_stream_manager(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n doc = kwargs['doc']\n if doc.stream_id and doc.stream_id != 'ietf':\n addrs.extend(Recipient.objects.get(slug='stream_managers').gather(**{'streams':[doc.stream_id,]}))\n return addrs\n\n def gather_group_responsible_directors(self, **kwargs):\n addrs = []\n if 'group' in kwargs:\n group = kwargs['group']\n if not group.acronym=='none':\n addrs.extend(group.role_set.filter(name='ad').values_list('email__address',flat=True))\n if group.type_id=='rg':\n addrs.extend(Recipient.objects.get(slug='stream_managers').gather(**{'streams':['irtf']}))\n return addrs\n\n def gather_group_secretaries(self, **kwargs):\n addrs = []\n if 'group' in kwargs:\n group = kwargs['group']\n if not group.acronym=='none':\n addrs.extend(group.role_set.filter(name='secr').values_list('email__address',flat=True))\n return addrs\n\n def gather_doc_group_responsible_directors(self, **kwargs):\n addrs = []\n if 'doc' in kwargs:\n group = kwargs['doc'].group\n if group and not group.acronym=='none':\n addrs.extend(Recipient.objects.get(slug='group_responsible_directors').gather(**{'group':group}))\n return addrs\n\n def gather_submission_authors(self, **kwargs):\n \"\"\"\n Returns a list of name and email, e.g.: [ 'Ano Nymous ' ]\n Is intended for display use, not in email context.\n \"\"\"\n addrs = []\n if 'submission' in kwargs:\n submission = kwargs['submission']\n addrs.extend([\"%s <%s>\" % (author[\"name\"], author[\"email\"]) for author in submission.authors if author.get(\"email\")])\n return addrs\n\n def gather_submission_group_chairs(self, **kwargs):\n addrs = []\n if 'submission' in kwargs: \n submission = kwargs['submission']\n if submission.group: \n addrs.extend(Recipient.objects.get(slug='group_chairs').gather(**{'group':submission.group}))\n return addrs\n\n def gather_submission_confirmers(self, **kwargs):\n \"\"\"If a submitted document is revising an existing document, the confirmers \n are the authors of that existing document, and the chairs if the document is\n a working group document and the author list has changed. Otherwise, the confirmers\n are the authors and submitter of the submitted document.\"\"\"\n\n addrs=[]\n if 'submission' in kwargs:\n submission = kwargs['submission']\n doc=submission.existing_document()\n if doc:\n old_authors = [ author for author in doc.documentauthor_set.all() if author.email ]\n\n addrs.extend([ author.formatted_email() for author in old_authors])\n\n old_author_email_set = set(author.email.address for author in old_authors)\n new_author_email_set = set(author[\"email\"] for author in submission.authors if author.get(\"email\"))\n\n if doc.group and old_author_email_set != new_author_email_set:\n if doc.group.type_id in ['wg','rg','ag']:\n addrs.extend(Recipient.objects.get(slug='group_chairs').gather(**{'group':doc.group}))\n elif doc.group.type_id in ['area']:\n addrs.extend(Recipient.objects.get(slug='group_responsible_directors').gather(**{'group':doc.group}))\n else:\n pass\n if doc.stream_id and doc.stream_id not in ['ietf']:\n addrs.extend(Recipient.objects.get(slug='stream_managers').gather(**{'streams':[doc.stream_id]}))\n else:\n # This is a bit roundabout, but we do it to get consistent and unicode-compliant\n # email names for known persons, without relying on the name parsed from the\n # draft (which might be ascii, also for persons with non-ascii names)\n emails = [ Email.objects.filter(address=author['email']).first() or author for author in submission.authors if author.get('email') ]\n addrs.extend([ e.formatted_email() if isinstance(e, Email) else formataddr((e[\"name\"], e[\"email\"])) for e in emails ] )\n submitter_email = submission.submitter_parsed()[\"email\"]\n if submitter_email and not submitter_email in [ parseaddr(a)[1] for a in addrs ]:\n addrs.append(submission.submitter)\n return addrs\n\n def gather_submission_group_mail_list(self, **kwargs):\n addrs=[]\n if 'submission' in kwargs:\n submission = kwargs['submission']\n if submission.group: \n addrs.extend(Recipient.objects.get(slug='group_mail_list').gather(**{'group':submission.group}))\n return addrs\n\n def gather_rfc_editor_if_doc_in_queue(self, **kwargs):\n addrs=[]\n if 'doc' in kwargs:\n doc = kwargs['doc']\n if doc.get_state_slug(\"draft-rfceditor\") is not None:\n addrs.extend(Recipient.objects.get(slug='rfc_editor').gather(**{}))\n return addrs\n\n def gather_doc_discussing_ads(self, **kwargs):\n addrs=[]\n if 'doc' in kwargs:\n doc = kwargs['doc']\n active_ballot = doc.active_ballot()\n if active_ballot:\n for ad, pos in active_ballot.active_ad_positions().iteritems():\n if pos and pos.pos_id == \"discuss\":\n addrs.append(ad.role_email(\"ad\").address)\n return addrs\n\n def gather_ipr_updatedipr_contacts(self, **kwargs):\n addrs=[]\n if 'ipr' in kwargs:\n ipr = kwargs['ipr']\n for rel in ipr.updates:\n if rel.target.submitter_email:\n addrs.append(rel.target.submitter_email)\n elif hasattr(rel.target,'ietfer_email') and rel.target.ietfer_email:\n addrs.append(rel.target.ietfer_email)\n return addrs\n \n def gather_ipr_updatedipr_holders(self, **kwargs):\n addrs=[]\n if 'ipr' in kwargs:\n ipr = kwargs['ipr']\n for disc in ipr.recursively_updates():\n if hasattr(ipr,'holder_contact_email') and ipr.holder_contact_email:\n addrs.append(ipr.holder_contact_email)\n return addrs\n\n def gather_doc_ipr_group_or_ad(self, **kwargs):\n \"\"\"A document's group email list if the document is a group document, \n otherwise, the document's AD if the document is active, otherwise \n the IETF chair\"\"\"\n addrs=[]\n if 'doc' in kwargs:\n doc=kwargs['doc']\n if doc.group and doc.group.acronym == 'none':\n if doc.ad and doc.get_state_slug('draft')=='active':\n addrs.extend(Recipient.objects.get(slug='doc_ad').gather(**kwargs))\n else:\n addrs.extend(Role.objects.filter(group__acronym='gen',name='ad').values_list('email__address',flat=True))\n else:\n addrs.extend(Recipient.objects.get(slug='doc_group_mail_list').gather(**kwargs)) \n return addrs\n\n def gather_liaison_manager(self, **kwargs):\n addrs=[]\n if 'group' in kwargs:\n group=kwargs['group']\n addrs.extend(group.role_set.filter(name='liaiman').values_list('email__address',flat=True))\n return addrs\n\n def gather_session_requester(self, **kwargs):\n addrs=[]\n if 'session' in kwargs:\n session = kwargs['session']\n addrs.append(session.requested_by.role_email('chair').address)\n return addrs\n\n def gather_review_team_ads(self, **kwargs):\n addrs=[]\n if 'review_req' in kwargs:\n review_req = kwargs['review_req']\n if review_req.team.parent:\n for role in review_req.team.parent.role_set.filter(name='ad'):\n addrs.append(role.email.address)\n return addrs\n", "sub_path": "ietf/mailtrigger/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 13885, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "email.utils.parseaddr", "line_number": 17, "usage_type": "call"}, {"api_name": "ietf.utils.mail.formataddr", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "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"}, {"api_name": "django.db.models.Model", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.template.Template", "line_number": 60, "usage_type": "call"}, {"api_name": "django.template.Context", "line_number": 60, "usage_type": "call"}, {"api_name": "ietf.person.models.Email.objects.filter", "line_number": 236, "usage_type": "call"}, {"api_name": "ietf.person.models.Email.objects", "line_number": 236, "usage_type": "attribute"}, {"api_name": "ietf.person.models.Email", "line_number": 236, "usage_type": "name"}, {"api_name": "ietf.person.models.Email", "line_number": 237, "usage_type": "argument"}, {"api_name": "ietf.utils.mail.formataddr", "line_number": 237, "usage_type": "call"}, {"api_name": "email.utils.parseaddr", "line_number": 239, "usage_type": "call"}, {"api_name": "ietf.group.models.Role.objects.filter", "line_number": 301, "usage_type": "call"}, {"api_name": "ietf.group.models.Role.objects", "line_number": 301, "usage_type": "attribute"}, {"api_name": "ietf.group.models.Role", "line_number": 301, "usage_type": "name"}]}
{"seq_id": "128509885", "text": "# import necessary packages\nimport imutils\nimport cv2\nimport numpy as np\n\ndef objCenter(rects, frameCenter):\n\t# check to see if a object was found\n\tif len(rects) > 0:\n\t\t# find the largest contour in the mask, then use\n\t\t# it to compute the minimum enclosing circle and\n\t\t# centroid\n\t\tc = max(rects, key=cv2.contourArea)\n\t\t((x, y), radius) = cv2.minEnclosingCircle(c)\n\t\tM = cv2.moments(c)\n\t\tcenter = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\n\t\tobjectX = center[0]\n\t\tobjectY = center[1]\n\n\t\t# return the center (x, y)-coordinates of the face\n\t\treturn ((objectX, objectY), radius)\n\n\t# otherwise no faces were found, so return the center of the\n\t# frame\n\treturn (frameCenter, None)\n", "sub_path": "src/common_tracking_application/objcenter.py", "file_name": "objcenter.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "cv2.contourArea", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.minEnclosingCircle", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "251927983", "text": "from django.conf.urls.defaults import patterns, include, handler404, handler500\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\nhandler404 = 'catalog.views.view_404'\nhandler500 = 'catalog.views.view_500'\n\nurlpatterns = patterns('',\n (r'', include('catalog.urls')),\n (r'', include('twitter.urls')),\n (r'^adminka/doc/', include('django.contrib.admindocs.urls')),\n (r'^adminka/', include(admin.site.urls)), \n )\n\n\n\n\n\nfrom django.conf import settings\nif settings.DEBUG:\n urlpatterns += patterns('',\n (r'^robots.txt$', 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT, 'path': \"robots.txt\"}),\n (r'^favicon.ico$', 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT, 'path': \"favicon.ico\"}),\n (r'^static/(?P.*)$', 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT}),\n )", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 4, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.handler404", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.handler500", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.patterns", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.include", "line_number": 13, "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.conf.settings.DEBUG", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.defaults.patterns", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}]}
{"seq_id": "624748189", "text": "import os\nimport pandas as pd\nimport json\nfrom common import *\n\nall_book_names = set()\nfor filename in os.listdir(\"raw_data/book/\"):\n\tprint(\"Reading file\", filename)\n\tcsv = pd.read_csv(\"raw_data/book/\" + filename)\n\tfor _, row in csv.iterrows():\n\t\tbook_name = process_book_name(row[\"Name\"])\n\t\tall_book_names.add(book_name)\n\nprint(\"Ended reading files\")\nprint(\"Started creating id dict\")\nbook_id_dict = {}\nfor i, e in enumerate(all_book_names):\n\tbook_id_dict[e] = i\n\nprint(\"Saving id dict\")\nwith open('book_ids.json', 'w') as fp:\n json.dump(book_id_dict, fp)", "sub_path": "2020_1/DW-DCC189/FinalTP/Pre-process data/create_book_id.py", "file_name": "create_book_id.py", "file_ext": "py", "file_size_in_byte": 557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.listdir", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "355340412", "text": "from yaml import load\nfrom exp.pendulum_experiment import PendulumExperiment\nfrom control.random_policy import RandomPolicy\nfrom model.elbo_loss import ELBOLoss\nfrom model.variational_koopman import VariationalKoopman\nfrom exp.dataset import TrajectoryDataset\nimport argparse\nimport utils\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch\nimport numpy as np\n\nimport pickle\n\nExperimentList = {'Pendulum': PendulumExperiment}\nActivationList = {'ELU': nn.ELU, 'ReLU': nn.ReLU, 'Sigmoid': nn.Sigmoid, 'Tanh': nn.Tanh}\nLossList = {'ELBOLoss': ELBOLoss}\nOptimList = {'Adam': optim.Adam, 'RMSProp': optim.RMSprop, 'SGD': optim.SGD}\nPolicyList = {'RandomPolicy': RandomPolicy}\n\ndef load_TF_weights(net, net_weights):\n param = net_weights['values']\n\n net.feat_extractor[0].weight.data = torch.from_numpy(param[0]).to(torch.float).t()\n net.feat_extractor[0].bias.data = torch.from_numpy(param[1]).to(torch.float)\n net.feat_extractor[2].weight.data = torch.from_numpy(param[2]).to(torch.float).t()\n net.feat_extractor[2].bias.data = torch.from_numpy(param[3]).to(torch.float)\n\n net.temporal_encoder_lstm.LSTM_f.cell.gates_mlp.weight.data = torch.from_numpy(param[4]).to(torch.float).t()\n net.temporal_encoder_lstm.LSTM_f.cell.gates_mlp.bias.data = torch.from_numpy(param[5]).to(torch.float)\n net.temporal_encoder_lstm.LSTM_b.cell.gates_mlp.weight.data = torch.from_numpy(param[6]).to(torch.float).t()\n net.temporal_encoder_lstm.LSTM_b.cell.gates_mlp.bias.data = torch.from_numpy(param[7]).to(torch.float)\n\n net.temporal_encoder_mlp[0].weight.data = torch.from_numpy(param[8]).to(torch.float).t()\n net.temporal_encoder_mlp[0].bias.data = torch.from_numpy(param[9]).to(torch.float)\n net.temporal_encoder_mlp[2].weight.data = torch.from_numpy(param[10]).to(torch.float).t()\n net.temporal_encoder_mlp[2].bias.data = torch.from_numpy(param[11]).to(torch.float)\n\n net.init_basis_mlp[0].weight.data = torch.from_numpy(param[12]).to(torch.float).t()\n net.init_basis_mlp[0].bias.data = torch.from_numpy(param[13]).to(torch.float)\n net.init_basis_mlp[2].weight.data = torch.from_numpy(param[14]).to(torch.float).t()\n net.init_basis_mlp[2].bias.data = torch.from_numpy(param[15]).to(torch.float)\n\n net.basis_encoder_gru.gates_mlp.weight.data = torch.from_numpy(param[24]).to(torch.float).t()\n net.basis_encoder_gru.gates_mlp.bias.data = torch.from_numpy(param[25]).to(torch.float)\n net.basis_encoder_gru.candidate_mlp.weight.data = torch.from_numpy(param[26]).to(torch.float).t()\n net.basis_encoder_gru.candidate_mlp.bias.data = torch.from_numpy(param[27]).to(torch.float)\n\n net.basis_encoder_MLP[0].weight.data = torch.from_numpy(param[20]).to(torch.float).t()\n net.basis_encoder_MLP[0].bias.data = torch.from_numpy(param[21]).to(torch.float)\n net.basis_encoder_MLP[2].weight.data = torch.from_numpy(param[22]).to(torch.float).t()\n net.basis_encoder_MLP[2].bias.data = torch.from_numpy(param[23]).to(torch.float)\n\n net.basis_inference_MLP[0].weight.data = torch.from_numpy(param[16]).to(torch.float).t()\n net.basis_inference_MLP[0].bias.data = torch.from_numpy(param[17]).to(torch.float)\n net.basis_inference_MLP[2].weight.data = torch.from_numpy(param[18]).to(torch.float).t()\n net.basis_inference_MLP[2].bias.data = torch.from_numpy(param[19]).to(torch.float)\n\n net.prior_MLP[0].weight.data = torch.from_numpy(param[28]).to(torch.float).t()\n net.prior_MLP[0].bias.data= torch.from_numpy(param[29]).to(torch.float)\n net.prior_MLP[2].weight.data= torch.from_numpy(param[30]).to(torch.float).t()\n net.prior_MLP[2].bias.data= torch.from_numpy(param[31]).to(torch.float)\n\n net.decoder[0].weight.data = torch.from_numpy(param[32]).to(torch.float).t()\n net.decoder[0].bias.data = torch.from_numpy(param[33]).to(torch.float)\n net.decoder[2].weight.data = torch.from_numpy(param[34]).to(torch.float).t()\n net.decoder[2].bias.data = torch.from_numpy(param[35]).to(torch.float)\n\nif __name__ == '__main__':\n # Parse command line arguments\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', type=str, help='Configuration file to load')\n args = parser.parse_args()\n\n # Load configuration file\n config = load(open(args.config, 'r'))\n obs_dim = config['obs_dim']\n latent_dim = config['latent_dim']\n control_dim = config['control_dim']\n del config['obs_dim']\n del config['latent_dim']\n del config['control_dim']\n\n config['subseq_len'] = 64\n config['predict_len'] = 32\n\n # Set random\n np.random.seed(config['seed'])\n torch.manual_seed(config['seed'])\n\n # Build feature extractor\n if config['feat_extractor_type'] == 'MLP':\n feat_size = config['feat_extractor_size']\n feat_act = ActivationList[config['feat_extractor_act']]\n feat_extractor = utils.construct_MLP(obs_dim, latent_dim, feat_size, feat_act)\n else:\n raise ValueError('Invalid type specified for feature extractor.')\n\n # Build decoder\n if config['decoder_type'] == 'MLP':\n decoder_size = config['decoder_size']\n decoder_act = ActivationList[config['decoder_act']]\n decoder = utils.construct_MLP(latent_dim, obs_dim, decoder_size, decoder_act)\n else:\n raise ValueError('Invalid type specified for decoder.')\n\n # Build the network\n rnn_size = config['rnn_size']\n transform_size = config['transform_size']\n inference_size = config['inference_size']\n prior_size = config['prior_size']\n del config['rnn_size']\n del config['transform_size']\n del config['inference_size']\n del config['prior_size']\n\n shift_x = torch.zeros(obs_dim)\n scale_x = torch.ones(obs_dim)\n net = VariationalKoopman(feat_extractor, decoder, latent_dim, control_dim, rnn_size, transform_size,\n inference_size, prior_size, shift_x=shift_x, scale_x=scale_x, **config)\n\n # Import network weights\n net_weights = pickle.load(open('net.pkl', 'rb'))\n load_TF_weights(net, net_weights)\n\n # Weight on final state in the reconstruction loss\n if config['reconst_final_weight'] is not None:\n config['reconst_weight'] = torch.ones(config['subseq_len']-config['predict_len'])\n config['reconst_weight'][-1] = config['reconst_final_weight']\n else:\n config['reconst_weight'] = None\n\n # Build loss function\n loss_func = LossList[config['loss_func']]\n loss = loss_func(**config)\n\n # Get optimizer parameters\n optim = OptimList[config['optim']]\n optim_args = {'lr': config['lr']}\n\n # Get rollout policy\n if config['policy'] == 'RandomPolicy':\n policy = RandomPolicy(control_dim, config['policy_range'])\n else:\n raise ValueError['Invalid type specified for rollout policy.']\n\n # Load ReplayMemory dataset\n replay_buffer = pickle.load(open('replay_memory.pkl', 'rb'))\n x, x_val = torch.from_numpy(replay_buffer['x']), torch.from_numpy(replay_buffer['x_val'])\n u, u_val = torch.from_numpy(replay_buffer['u']), torch.from_numpy(replay_buffer['u_val'])\n x_test = torch.from_numpy(np.expand_dims(replay_buffer['x_test'], axis=0))\n u_test = torch.from_numpy(np.expand_dims(replay_buffer['u_test'], axis=0))\n shift_x, scale_x = torch.from_numpy(replay_buffer['shift_x']), torch.from_numpy(replay_buffer['scale_x'])\n shift_u, scale_u = torch.from_numpy(replay_buffer['shift_u']), torch.from_numpy(replay_buffer['scale_u'])\n\n dataset = TrajectoryDataset()\n dataset.states = torch.cat((x, x_val, x_test)).to(torch.float)\n dataset.obs = torch.cat((x, x_val, x_test)).to(torch.float)\n dataset.controls = torch.cat((u, u_val, u_test)).to(torch.float)\n dataset.scale_x = scale_x.to(torch.float)\n dataset.scale_u = scale_u.to(torch.float)\n dataset.shift_x = shift_x.to(torch.float)\n dataset.shift_u = shift_u.to(torch.float)\n dataset.train_idx = np.arange(0, x.shape[0])\n dataset.val_idx = np.arange(x.shape[0], x.shape[0]+x_val.shape[0])\n dataset.test_idx = [x.shape[0]+x_val.shape[0]]\n dataset.normalize = True\n config['dataset'] = dataset\n\n # Construct the experiment\n torch.set_printoptions(precision=10)\n exp_type = ExperimentList[config['env']]\n del config['env']\n exp = exp_type(net, loss, optimizer=optim, optim_args=optim_args, shuffle=False, **config)\n\n # Train model\n exp.train_model()", "sub_path": "launch_dvkm_experiment_from_replay.py", "file_name": "launch_dvkm_experiment_from_replay.py", "file_ext": "py", "file_size_in_byte": 8348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "exp.pendulum_experiment.PendulumExperiment", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ELU", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn.Tanh", "line_number": 17, "usage_type": "attribute"}, {"api_name": "model.elbo_loss.ELBOLoss", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.optim.RMSprop", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 19, "usage_type": "attribute"}, {"api_name": "control.random_policy.RandomPolicy", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 68, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 72, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.construct_MLP", "line_number": 96, "usage_type": "call"}, {"api_name": "utils.construct_MLP", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 119, "usage_type": "call"}, {"api_name": "model.variational_koopman.VariationalKoopman", "line_number": 120, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 139, "usage_type": "name"}, {"api_name": "control.random_policy.RandomPolicy", "line_number": 144, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 155, "usage_type": "call"}, {"api_name": "exp.dataset.TrajectoryDataset", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 158, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 159, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 161, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.set_printoptions", "line_number": 172, "usage_type": "call"}, {"api_name": "exp.pendulum_experiment", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.optim", "line_number": 175, "usage_type": "name"}, {"api_name": "exp.pendulum_experiment.train_model", "line_number": 178, "usage_type": "call"}, {"api_name": "exp.pendulum_experiment", "line_number": 178, "usage_type": "name"}]}
{"seq_id": "482386426", "text": "import psycopg2 as sql_client\nfrom psycopg2 import sql\nimport os\nimport math\nfrom units import Man, Army\nfrom buildings import Generator\nfrom constants import GOLD_COIN, GENERATOR_COST, SOLDIER_COST, ARMORY_COST, ITEM_PRICE_MAP\nfrom base import Base, BUILDINGS_SQL\nfrom items import ITEMS_SQL\nfrom units import UNITS_SQL\nimport sys\n\nADMIN_LIST = [199256185201885184]\n\n\nclass RaidersManager(object):\n def __init__(self):\n self.conn = sql_client.connect(os.environ[\"DATABASE_URL\"], sslmode=\"require\")\n # self.conn = sql_client.connect(\"game.db\")\n self.active_players = {}\n self.create_tables()\n self.populate_players()\n\n def command_list(self):\n admin_commands = {\n \"register\": {\"player\": {\"base\": self.register_player}}\n }\n view_commands = {\n \"summary\": self.view_base,\n \"shop\": self.view_shop\n }\n build_commands = {\n \"generator\": self.build_generator,\n \"armory\": self.build_armory\n }\n hire_commands = {\n \"soldier\": self.hire_soldier\n }\n buy_commands = {\n \"sword\": self.buy_sword\n }\n base_commands = {\n \"view\": view_commands,\n \"build\": build_commands,\n \"buy\": buy_commands,\n \"hire\": hire_commands\n }\n top_level = {\n \"!base\": base_commands,\n \"!check_gold\": self.check_gold,\n \"!gather_gold\": self.gather_gold\n }\n return top_level\n\n def check_registration(self, player_name):\n if player_name not in self.active_players.keys():\n return False, \"You are not registered, please contact an admin to register\"\n else:\n return True, \"\"\n\n def register_player(self, admin_name: int, player_name: str, player_id):\n player_id = str(player_id)\n if admin_name not in ADMIN_LIST:\n return \"Invalid access, you are not an admin\"\n try:\n c = self.conn.cursor()\n c.execute(\"INSERT INTO players VALUES (%s,%s,%s,3)\", (player_id, player_name, 0))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n try:\n c = self.conn.cursor()\n for item_name in ITEMS_SQL.keys():\n c.execute(\"INSERT INTO items VALUES (%s, %s, %s)\", (player_id, item_name, 0))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n try:\n c = self.conn.cursor()\n for unit_name in UNITS_SQL.keys():\n c.execute(\"INSERT INTO garrison VALUES (%s, %s, %s)\", (player_id, unit_name, 0))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n sys.stdout.flush()\n else:\n self.conn.commit()\n\n try:\n c = self.conn.cursor()\n for building_name in BUILDINGS_SQL.keys():\n c.execute(\"INSERT INTO buildings VALUES (%s, %s, %s)\", (player_id, building_name, 0))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n base = Base(generators=[Generator(), Generator(), Generator()])\n self.active_players[player_name] = Player(player_name, base, player_id)\n return \"Player registered, {} registered!\".format(player_name)\n\n def populate_players(self):\n player_cur = self.conn.cursor()\n player_cur.execute(\"SELECT player_id, name, gold, generators FROM players\")\n try:\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n player_list = player_cur.fetchall()\n if player_list:\n for player in player_list:\n player_id = player[0]\n player_name = player[1]\n player_gold = player[2]\n player_generators = player[3]\n player_buildings = []\n items, buildings, garrison = self.load_player(player_id)\n generators = [Generator()] * player_generators\n base = Base(garrison=garrison, buildings=buildings, generators=generators, items=items)\n player_obj = Player(player_name, base, player_id)\n self.active_players[player_name] = player_obj\n\n def load_player(self, player_id):\n items = self.get_items(player_id)\n buildings = self.get_buildings(player_id)\n garrison = self.get_garrison(player_id)\n return items, buildings, garrison\n\n def get_buildings(self, player_id):\n cur = self.conn.cursor()\n cur.execute(\"SELECT name, amount FROM buildings WHERE player_id = %s\", (player_id,))\n all_buildings = cur.fetchall()\n player_buildings = []\n for building in all_buildings:\n print(\"IN BUILDINGS\")\n print((building[1], building[0]))\n sys.stdout.flush()\n if building[1]:\n building_name = building[0]\n building_obj = BUILDINGS_SQL[building_name]\n player_buildings.extend([building_obj() for i in range(building[1])])\n return player_buildings\n\n def get_items(self, player_id):\n cur = self.conn.cursor()\n cur.execute(\"SELECT name, amount FROM items WHERE player_id = %s\", (player_id,))\n items = cur.fetchall()\n player_items = []\n for item in items:\n print(\"IN ITEMS\")\n print((item[1], item[0]))\n sys.stdout.flush()\n if item[1]:\n item_name = item[0]\n item_obj = ITEMS_SQL[item_name]\n player_items.extend([item_obj() for i in range(item[1])])\n return player_items\n\n def get_garrison(self, player_id):\n cur = self.conn.cursor()\n cur.execute(\"SELECT name, amount FROM garrison WHERE player_id = %s\", (player_id,))\n units = cur.fetchall()\n player_units = []\n for unit in units:\n print(\"IN ITEMS\")\n print((unit[1], unit[0]))\n sys.stdout.flush()\n if unit[1]:\n unit_id = unit[0]\n unit_obj = UNITS_SQL[unit_id]\n player_units.extend([unit_obj() for i in range(unit[1])])\n return player_units\n\n def save_table(self, table_name, player_id, component_name, number):\n c = self.conn.cursor()\n try:\n c.execute(\n sql.SQL(\"UPDATE {} SET amount = %s WHERE player_id = %s and name = %s\")\n .format(sql.Identifier(table_name)),\n (number, player_id, component_name))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n return \"In save_table {}, table: {}\".format(e.pgerror, table_name)\n else:\n self.conn.commit()\n return True\n\n def save_buildings(self, player_id, base):\n buildings = {}\n for building in base.buildings:\n if building.name in buildings:\n buildings[building.name] += 1\n else:\n buildings[building.name] = 1\n for building in buildings.keys():\n num = buildings[building]\n self.save_table('buildings', player_id, building, num)\n\n def save_garrison(self, player_id, base):\n garrison = {}\n for unit in base.garrison:\n if unit.name in garrison:\n garrison[unit.name] += 1\n else:\n garrison[unit.name] = 1\n for unit in garrison.keys():\n num = garrison[unit]\n self.save_table('garrison', player_id, unit, num)\n\n def save_items(self, player_id, base):\n items = {}\n for item in base.items:\n if item.name in items:\n items[item.name] += 1\n else:\n items[item.name] = 1\n for item in items.keys():\n num = items[item]\n self.save_table('items', player_id, item, num)\n\n def save_player(self, player):\n base = player.base\n player_id = str(player.id)\n name = player.name\n garrison = base.garrison\n items = base.items\n generators = len(base.generators)\n self.save_buildings(player_id, base)\n self.save_garrison(player_id, base)\n self.save_items(player_id, base)\n try:\n c = self.conn.cursor()\n c.execute(\"UPDATE players SET generators = %s WHERE player_id = %s\", (generators, player_id))\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n return \"In save_player {}\".format(e.pgerror)\n else:\n self.conn.commit()\n\n def save_players(self):\n for player in self.active_players.values():\n self.save_player(player)\n return None\n\n def create_tables(self):\n # try:\n # c = self.conn.cursor()\n # c.execute('''DROP TABLE IF EXISTS players''')\n # c.execute('''DROP TABLE IF EXISTS items''')\n # c.execute('''DROP TABLE IF EXISTS buildings''')\n # c.execute('''DROP TABLE IF EXISTS garrison''')\n # self.conn.commit()\n # except Exception as e:\n # print(e)\n # print(e.pgerror)\n # sys.stdout.flush()\n # self.conn.rollback()\n # else:\n # self.conn.commit()\n try:\n c = self.conn.cursor()\n c.execute('''CREATE TABLE IF NOT EXISTS players\n (player_id varchar PRIMARY KEY, name varchar, gold integer, generators integer)''')\n c.execute('''CREATE TABLE IF NOT EXISTS items\n (player_id varchar, name varchar, amount integer)''')\n c.execute('''CREATE TABLE IF NOT EXISTS buildings\n (player_id varchar, name varchar, amount integer)''')\n c.execute('''CREATE TABLE IF NOT EXISTS garrison\n (player_id varchar, name varchar, amount integer)''')\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n\n def get_player(self, player_name):\n for player in self.active_players.values():\n if player_name == player.name:\n return player\n\n def can_afford(self, player, cost):\n c = self.conn.cursor()\n gold = player.check_gold(c)\n if gold >= cost:\n gold -= cost\n self.set_gold(player.id, gold)\n return True, gold\n else:\n return False, gold\n\n def check_gold(self, player_name):\n c = self.conn.cursor()\n player = self.get_player(player_name)\n gold = player.check_gold(c)\n return gold\n\n def build_generator(self, player_name):\n player = self.get_player(player_name)\n can_build, gold = self.can_afford(player, GENERATOR_COST)\n if can_build:\n player.base.build_generator()\n return \"You built a generator, you now have {} {}\".format(gold, GOLD_COIN)\n else:\n return \"You do not have enough to build a generator, you have {} {}\".format(gold, GOLD_COIN)\n\n def build_armory(self, player_name):\n player = self.get_player(player_name)\n can_build, gold = self.can_afford(player, ARMORY_COST)\n if can_build:\n player.base.build_armory()\n return \"You built an armory, you now have {} {}\".format(gold, GOLD_COIN)\n else:\n return \"You do not have enough to build an armory, you have {} {}\".format(gold, GOLD_COIN)\n\n def buy_sword(self, player_name: str, item=\"sword\") -> str:\n return self.buy_item(player_name, item)\n\n def buy_item(self, player_name, item):\n item = item.lower()\n if item not in ITEM_PRICE_MAP:\n return \"Item not recognized\"\n player = self.get_player(player_name)\n can_build, gold = self.can_afford(player, ITEM_PRICE_MAP[item])\n if can_build:\n success = player.base.buy_item(item)\n if success:\n return \"You bought a {}, you now have {} {}\".format(item, gold, GOLD_COIN)\n else:\n return \"Cannot buy {}, that item is not available to you\".format(item)\n else:\n return \"You do not have enough to build a {}, you have {} {}\".format(item, gold, GOLD_COIN)\n\n def hire_soldier(self, player_name):\n player = self.get_player(player_name)\n can_build, gold = self.can_afford(player, SOLDIER_COST)\n if can_build:\n player.base.hire_soldier()\n return \"You hired a soldier, you now have {} {}\".format(gold, GOLD_COIN)\n else:\n return \"You do not have enough to hire a soldier, you have {} {}\".format(gold, GOLD_COIN)\n\n def view_base(self, player_name):\n player = self.get_player(player_name)\n return player.base.view_base()\n\n def view_shop(self, player_name):\n player = self.get_player(player_name)\n return player.base.view_shop()\n\n def gather_gold(self, player_name):\n player = self.get_player(player_name)\n gold_gained = player.base.empty_generators()\n c = self.conn.cursor()\n c.execute(\"SELECT gold FROM players WHERE name = %s\", (player_name,))\n total_gold = c.fetchone()[0]\n total_gold += gold_gained\n c.execute(\"UPDATE players SET gold = %s WHERE player_id = %s\", (total_gold, player.id))\n try:\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n return \"You have {} gold, you've gained {} gold since your last check\".format(total_gold, gold_gained)\n\n def gather_gold_all(self):\n for player_name in self.active_players.keys():\n self.gather_gold(player_name)\n return True\n\n def set_gold(self, player_id, total_gold):\n c = self.conn.cursor()\n c.execute(\"UPDATE players SET gold = %s WHERE player_id = %s\", (total_gold, player_id))\n try:\n self.conn.commit()\n except Exception as e:\n print(e)\n print(e.pgerror)\n sys.stdout.flush()\n self.conn.rollback()\n else:\n self.conn.commit()\n\n def battle(self, offense_units: Army, defense_units: Army):\n offense_unit = offense_units.next()\n defense_unit = defense_units.next()\n offense_win = None\n while offense_units.men_remaining() and defense_units.men_remaining():\n offense_win = self.fight(offense_unit, defense_unit)\n if offense_win:\n defense_unit = defense_units.next()\n else:\n offense_unit = offense_units.next()\n offense_units_lost = offense_units.clear_dead()\n defense_units_lost = defense_units.clear_dead()\n return offense_units_lost, defense_units_lost, offense_win\n\n def fight(self, offense_unit: Man, defense_unit: Man):\n defense_ttk = self._get_ttk(defense_unit, offense_unit)\n offense_ttk = self._get_ttk(offense_unit, defense_unit)\n if offense_ttk < defense_ttk:\n self.fight_results(offense_unit, defense_unit, offense_ttk) # offense win\n return True\n else:\n self.fight_results(defense_unit, offense_unit, defense_ttk) # defense win\n return False\n\n def fight_results(self, winning_unit, losing_unit, winning_ttk):\n losing_unit.kill()\n losing_damage = math.floor(winning_ttk * losing_unit.damage)\n if losing_damage == winning_unit.health:\n losing_damage = math.floor(winning_unit.health * .1)\n winning_unit.injure(losing_damage)\n\n def _get_ttk(self, attacking_unit: Man, defending_unit: Man):\n swings = defending_unit.health() // attacking_unit.damage\n if defending_unit.health() % swings:\n swings += 1\n ttk = swings / attacking_unit.attack_speed\n return ttk\n\n\nclass Player(object):\n def __init__(self, name, base, id):\n self.name = name\n self.base = base\n self.id = id\n\n @property\n def name(self):\n return self._name\n\n @name.setter\n def name(self, value):\n if not value:\n raise ValueError(\"Name cannot be none or empty\")\n self._name = value\n\n @property\n def base(self):\n return self._base\n\n @base.setter\n def base(self, value):\n self._base = value\n\n @property\n def id(self):\n return self._id\n\n @id.setter\n def id(self, value):\n self._id = value\n\n def check_gold(self, c):\n c.execute(\"SELECT gold FROM players WHERE name = %s\", (self.name,))\n return c.fetchone()[0]\n", "sub_path": "raiders.py", "file_name": "raiders.py", "file_ext": "py", "file_size_in_byte": 17733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "psycopg2.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 72, "usage_type": "attribute"}, {"api_name": "items.ITEMS_SQL.keys", "line_number": 76, "usage_type": "call"}, {"api_name": "items.ITEMS_SQL", "line_number": 76, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 82, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 82, "usage_type": "attribute"}, {"api_name": "units.UNITS_SQL.keys", "line_number": 88, "usage_type": "call"}, {"api_name": "units.UNITS_SQL", "line_number": 88, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 94, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 96, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 96, "usage_type": "attribute"}, {"api_name": "base.BUILDINGS_SQL.keys", "line_number": 102, "usage_type": "call"}, {"api_name": "base.BUILDINGS_SQL", "line_number": 102, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 108, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 108, "usage_type": "attribute"}, {"api_name": "base.Base", "line_number": 112, "usage_type": "call"}, {"api_name": "buildings.Generator", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 124, "usage_type": "attribute"}, {"api_name": "buildings.Generator", "line_number": 137, "usage_type": "call"}, {"api_name": "base.Base", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 156, "usage_type": "attribute"}, {"api_name": "base.BUILDINGS_SQL", "line_number": 159, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 171, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 171, "usage_type": "attribute"}, {"api_name": "items.ITEMS_SQL", "line_number": 174, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 186, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 186, "usage_type": "attribute"}, {"api_name": "units.UNITS_SQL", "line_number": 189, "usage_type": "name"}, {"api_name": "psycopg2.sql.SQL", "line_number": 197, "usage_type": "call"}, {"api_name": "psycopg2.sql", "line_number": 197, "usage_type": "name"}, {"api_name": "psycopg2.sql.Identifier", "line_number": 198, "usage_type": "call"}, {"api_name": "psycopg2.sql", "line_number": 198, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 204, "usage_type": "attribute"}, {"api_name": "base.buildings", "line_number": 213, "usage_type": "attribute"}, {"api_name": "buildings.keys", "line_number": 218, "usage_type": "call"}, {"api_name": "base.garrison", "line_number": 224, "usage_type": "attribute"}, {"api_name": "base.items", "line_number": 235, "usage_type": "attribute"}, {"api_name": "items.keys", "line_number": 240, "usage_type": "call"}, {"api_name": "base.garrison", "line_number": 248, "usage_type": "attribute"}, {"api_name": "base.items", "line_number": 249, "usage_type": "attribute"}, {"api_name": "base.generators", "line_number": 250, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 261, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 261, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 301, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 301, "usage_type": "attribute"}, {"api_name": "constants.GENERATOR_COST", "line_number": 329, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 332, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 334, "usage_type": "argument"}, {"api_name": "constants.ARMORY_COST", "line_number": 338, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 341, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 343, "usage_type": "argument"}, {"api_name": "constants.ITEM_PRICE_MAP", "line_number": 350, "usage_type": "name"}, {"api_name": "constants.ITEM_PRICE_MAP", "line_number": 353, "usage_type": "name"}, {"api_name": "constants.GOLD_COIN", "line_number": 357, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 361, "usage_type": "argument"}, {"api_name": "constants.SOLDIER_COST", "line_number": 365, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 368, "usage_type": "argument"}, {"api_name": "constants.GOLD_COIN", "line_number": 370, "usage_type": "argument"}, {"api_name": "sys.stdout.flush", "line_number": 393, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 393, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 412, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 412, "usage_type": "attribute"}, {"api_name": "units.Army", "line_number": 417, "usage_type": "name"}, {"api_name": "units.Man", "line_number": 431, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 443, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 445, "usage_type": "call"}, {"api_name": "units.Man", "line_number": 448, "usage_type": "name"}, {"api_name": "base.setter", "line_number": 476, "usage_type": "attribute"}]}
{"seq_id": "79615980", "text": "# -*- coding: utf-8 -*-\nimport requests\nimport json\nimport os \nimport pandas as pd\nimport folium \nfrom folium import plugins\nfrom folium.plugins import MarkerCluster\nimport re\nimport random\nimport datetime\nimport pandas as pd\nfrom stations import Station, bfs\nfrom functions import get_arrows, get_bearing\nimport time\n\n# from beautifultable import BeautifulTable\n# ์์\n# ์งํ์ฒ ์ขํ ๊ตฌํ๊ธฐ\ndef make_subway():\n # ๋ค๋ฅธ ๋ฐ์ดํฐ๋ก ๋ถ์ฐ ์งํ์ฒ ์ญ ์ฃผ์๋ฅผ ํตํด ์ขํ ํ๋ํ๊ธฐ\n df=pd.read_csv('/content/Busan_subway.csv')\n\n # ํ์ํ ์๋ฃ๋ง GET\n sub_name=df['์ญ๋ช
']\n sub_add=df['์ญ์ฃผ์']\n sub=pd.concat([sub_name,sub_add],axis=1)\n\n # ์๋ฃํ ํ์ผ ์ ์ฅ\n sub.to_csv('/content/Busan_subway.csv')\n\n KAKAO_API_KEY='725d861358777ff504771605f8c53f68'\n url = '''https://dapi.kakao.com/v2/local/search/address.json?query={0}'''.format('๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ์ค์๋๋ก 1927-1')\n headers={'Authorization': 'KakaoAK {0}'.format(KAKAO_API_KEY)}\n res=requests.get(url, headers=headers)\n res=res.json()\n\n x = res['documents'][0]['road_address']['x']\n y = res['documents'][0]['road_address']['y']\n # ์นด์นด์ค API ์ด์ฉํ์ฌ ์ฃผ์์
๋ ฅ ํ ์ขํ๋ฐ๊ธฐ\n subway_x=[]\n subway_y=[]\n i=0\n while i ์๋ฌ๋ ๊ฒ์ด๋ฏ๋ก ํ์ธ\n print(sub[sub['๊ฒฝ๋']==0]['์ญ์ฃผ์'])\n\n print(sub.loc[36,'์ญ์ฃผ์'])\n\n # ์๋ฌ๋ ์ฃผ์ ์์ ํ ๋ค์ ๋๋ฆฌ๊ธฐ\n sub.loc[36,'์ญ์ฃผ์']='๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ์ค์๋๋ก 1927-1'\n sub.loc[37,'์ญ์ฃผ์']='๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ์ค์๋๋ก 2019-1'\n sub.loc[38,'์ญ์ฃผ์']='๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ์ค์๋๋ก 2107'\n sub.loc[106,'์ญ์ฃผ์']='๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ๋ฐ์ก๋ก 387'\n sub.loc[107,'์ญ์ฃผ์']='๋ถ์ฐ๊ด์ญ์ ๊ธ์ ๊ตฌ ๋ฐ์ก๋ก 465'\n\n # ํ์ธํด๋ณด๋ ์ ๋ถ False์\n print(sub['๊ฒฝ๋']==0)\n\n # ์ ์ฅํ๊ธฐ\n sub.to_csv('/content/Busan_subway.csv')\n\n##### ๊ตฌ์ฒญ๋ณ ํ์ง์ ์ ์ฒดํฌ\ndef update_save_guinfo():\n gu_info=pd.read_csv('/content/Gucheong_info(20_03_06).csv',encoding='euc-kr')\n i=0\n while i 76๋ช
์ค ๊ฑฐ์ฃผ์งํ์ธ ๋ถ๊ฐ ํ๋ช
์ ์ธํ๊ณ ๋ค ๋จ\n print(gu_info['count'].sum())\n\n # ๋ ์ง ์ต์ ํ ์์ผ ์ ์ฅํ๊ธฐ\n gu_info.to_csv('/content/Gucheong_info(20_03_06).csv')\n\n\n##### ์ ์ฒด ํ์ง์ ๋ฐ์ดํฐ ๊ฐ์ ธ์จ ํ ์๋ ๊ฒฝ๋ ์
๋ ฅํ๊ธฐ\n\ndefinite = pd.read_csv('/content/patient_info(20_03_01).csv',encoding='utf-8')\n\n# ํ์ง์๋ค์ ๋์ ์ฃผ์๋ฅผ ์
๋ ฅํ์ฌ ์๋ ๊ฒฝ๋๋ฅผ GETํ์ฌ ๋ฐ์ดํฐํ๋ ์์ ์ถ๊ฐํ๊ธฐ\n# ์นด์นด์ค API ์ด์ฉํ์ฌ ์ฃผ์์
๋ ฅ ํ ์ขํ๋ฐ๊ธฐ\ni=0\nwhile i= self.WARNING_LEVEL:\n # All reST failures preventing doc publishing go to reports\n # and thus will result to failed checkdocs run\n reports.append(message)\n return result\n\n def rst2html(value):\n \"\"\" Run rst2html translation \"\"\"\n parts = publish_parts(source=value, writer_name=\"html4css1\")\n return parts['whole']\n\n text = open(path).read()\n # Monkeypatch docutils for simple error/warning output support\n orignal_system_message = utils.Reporter.system_message\n utils.Reporter.system_message = system_message\n old_stderr = sys.stderr\n sys.stderr = open(os.devnull, \"w\")\n try:\n rst2html(text)\n utils.Reporter.system_message = orignal_system_message\n return reports\n finally:\n sys.stderr.close()\n sys.stderr = old_stderr\n\nname = os.path.basename(__file__).split(\".\")[0]\nUSAGE = \"usage: python -m %s path ...\" % name\n\nif __name__ == \"__main__\":\n argv = sys.argv\n if len(argv) == 1 or (len(argv) == 2 and argv[1] == \"--help\"):\n print(USAGE)\n else:\n for path in argv[1:]:\n reports = rstvalidator(path)\n if reports:\n print(path)\n print(\"\\n\".join(reports))\n sys.exit(1)\n", "sub_path": "py_modules/rstvalidator.py", "file_name": "rstvalidator.py", "file_ext": "py", "file_size_in_byte": 1799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "docutils.core.publish_parts", "line_number": 29, "usage_type": "call"}, {"api_name": "docutils.utils.Reporter", "line_number": 34, "usage_type": "attribute"}, {"api_name": "docutils.utils", "line_number": 34, "usage_type": "name"}, {"api_name": "docutils.utils.Reporter", "line_number": 35, "usage_type": "attribute"}, {"api_name": "docutils.utils", "line_number": 35, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.devnull", "line_number": 37, "usage_type": "attribute"}, {"api_name": "docutils.utils.Reporter", "line_number": 40, "usage_type": "attribute"}, {"api_name": "docutils.utils", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.stderr.close", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 44, "usage_type": "attribute"}, {"api_name": "public.public", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "10552222", "text": "\nimport brian2 as b2\nfrom neurodynex.hodgkin_huxley import HH\n#b2.defaultclock.dt = 1 * b2.ms\n#import brian2 as b2\nsecond = 1000*b2.units.ms\nb2.A = 1000000000000*b2.pA\nb2.units.V = 1000.0*b2.units.mV\n# Hodgkin Huxley parameters\n'''\n# Parameters\nCm = 1*b2.ufarad*cm**-2 * area\ngl = 5e-5*siemens*cm**-2 * area\nEl = -65*mV\nEK = -90*mV\nENa = 50*mV\ng_na = 100*msiemens*cm**-2 * area\ng_kd = 30*msiemens*cm**-2 * area\nVT = -63*mV\n'''\n#b2.A = 1000000000000*b2.pA\nfrom neurodynex.tools import plot_tools, input_factory\nimport io\nimport math\nimport pdb\nfrom numba import jit\nimport numpy as np\nfrom .base import *\nimport quantities as pq\n\nfrom quantities import mV as qmV\nfrom quantities import ms as qms\nfrom quantities import V as qV\n#pq.PREFERRED = [pq.mV, pq.pA, pq.UnitQuantity('femtocoulomb', 1e-15*pq.C, 'fC')]\n\nSLOW_ZOOM = False\n#, ms, s, us, ns, V\nimport matplotlib as mpl\nfrom neuronunit.capabilities import spike_functions as sf\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nfrom elephant.spike_train_generation import threshold_detection\n\n\ngetting_started = False\ntry:\n import asciiplotlib as apl\n fig = apl.figure()\n fig.plot([1,0], [0,1])\n ascii_plot = True\n import gc\n\nexcept:\n ascii_plot = False\nimport numpy\ntry:\n brian2.clear_cache('cython')\nexcept:\n pass\n\nfrom neuronunit.capabilities.spike_functions import get_spike_waveforms\n\ndef simulate_HH_neuron_local(input_current=None,\n st=None,\n El=None,\\\n EK=None,\n ENa=None,\n gl=None,\\\n gK=None,\n gNa=None,\n C=None,\n Vr=None):\n # code lifted from:\n # /usr/local/lib/python3.5/dist-packages/neurodynex/hodgkin_huxley\n #input_current = I_stim #= #stim, simulation_time=st)\n \"\"\"A Hodgkin-Huxley neuron implemented in Brian2.\n\n Args:\n input_current (TimedArray): Input current injected into the HH neuron\n st (float): Simulation time [seconds]\n\n Returns:\n StateMonitor: Brian2 StateMonitor with recorded fields\n [\"vm\", \"I_e\", \"m\", \"n\", \"h\"]\n\n https://brian2.readthedocs.io/en/stable/examples/IF_curve_Hodgkin_Huxley.html\n area = 20000*umetre**2\n Cm = 1*ufarad*cm**-2 * area\n gl = 5e-5*siemens*cm**-2 * area\n El = -65*mV\n EK = -90*mV\n ENa = 50*mV\n g_na = 100*msiemens*cm**-2 * area\n g_kd = 30*msiemens*cm**-2 * area\n VT = -63*mV\n \"\"\"\n area = 20000*b2.umetre**2\n Cm = float(C) *b2.ufarad*b2.cm**-2 * area\n VT = -63*b2.mV\n assert float(El)<0.0\n # The model\n eqs = ('''\n dvm/dt = (gl*(El-vm) - g_na*(m*m*m)*h*(vm-ENa) - g_kd*(n*n*n*n)*(vm-EK) + input_current)/Cm : volt\n dm/dt = 0.32*(mV**-1)*4*mV/exprel((13.*mV-vm+VT)/(4*mV))/ms*(1-m)-0.28*(mV**-1)*5*mV/exprel((vm-VT-40.*mV)/(5*mV))/ms*m : 1\n dn/dt = 0.032*(mV**-1)*5*mV/exprel((15.*mV-vm+VT)/(5*mV))/ms*(1.-n)-.5*exp((10.*mV-vm+VT)/(40.*mV))/ms*n : 1\n dh/dt = 0.128*exp((17.*mV-vm+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-vm+VT)/(5.*mV)))/ms*h : 1\n input_current : amp\n ''')\n # Threshold and refractoriness are only used for spike counting\n neuron = b2.NeuronGroup(1, eqs,\n threshold='v > -40*mV',\n refractory='v > -40*mV',\n method='exponential_euler')\n neuron.vm = El#*b2.units.mV\n \n #neuron.v = El\n #neuron = b2.NeuronGroup(1, eqs, method=\"exponential_euler\")\n # parameter initialization\n #neuron.m = 0.05\n #neuron.h = 0.60\n #neuron.n = 0.32\n\n #spike_monitor = b2.SpikeMonitor(neuron)\n # tracking parameters\n st_mon = b2.StateMonitor(neuron, [\"vm\"], record=True)\n\n # running the simulation\n neuron = b2.Network(neuron)\n neuron.add(st_mon)\n # dur0 = 0.1*second\n neuron.I = '0.0*nA'\n\n dur0 = int(input_current['delay'])*second\n neuron.run(dur0)\n amp = input_current['amp']\n \n neuron.I = str(amp)+str('nA')\n dur1 = int(input_current['delay']+input_current['duration'])*second\n neuron.run(dur1)\n dur2 = 0.2*second\n neuron.I = '0.0*nA'\n neuron.run(dur2)\n import pdb\n pdb.set_trace()\n vm_b = neuron.vm\n vm_b = [ float(i) for i in vm_b ]\n vm_b = AnalogSignal(vm_b,units = pq.V,sampling_period = float(0.001) * pq.s)\n self.vM = vm_b\n \"\"\"\n eqs =\n I_e = input_current(t,i) : amp\n membrane_Im = I_e + gNa*m**3*h*(ENa-vm) + \\\n gl*(El-vm) + gK*n**4*(EK-vm) : amp\n alphah = .07*exp(-.05*vm/mV)/ms : Hz\n alpham = .1*(25*mV-vm)/(exp(2.5-.1*vm/mV)-1)/mV/ms : Hz\n alphan = .01*(10*mV-vm)/(exp(1-.1*vm/mV)-1)/mV/ms : Hz\n betah = 1./(1+exp(3.-.1*vm/mV))/ms : Hz\n betam = 4*exp(-.0556*vm/mV)/ms : Hz\n betan = .125*exp(-.0125*vm/mV)/ms : Hz\n dh/dt = alphah*(1-h)-betah*h : 1\n dm/dt = alpham*(1-m)-betam*m : 1\n dn/dt = alphan*(1-n)-betan*n : 1\n dvm/dt = membrane_Im/C : volt\n \"\"\"\n\n return st_mon,selfvM,vm\n\n \"\"\"\n state_dic = st_mon.get_states()\n vm = state_dic['vm']\n v_nan = []\n for v in vm:\n v = v*1000.0\n if np.isnan(v):\n v_nan.append(-65.0*b2.units.mV)\n else:\n v_nan.append(v)\n vM = AnalogSignal(v_nan,units = pq.mV,sampling_period = float(0.001) * pq.s)\n \"\"\"\n #vM = AnalogSignal(v_nan,units = pq.mV,sampling_period = 1*pq.ms)#b2.defaultclock.dt*pq.s)\n '''\n try:\n \tvM.rescale_prefered()\n except:\n\t\timport pdb\n\t\tpdb.set_trace()\n '''\n\ngetting_started = False\nclass BHHBackend(Backend):\n \n name = 'BHH'\n \n def init_backend(self, attrs=None, cell_name='thembi',\n current_src_name='spanner', DTC=None,\n debug = False):\n \n super(BHHBackend,self).init_backend()\n\n self.model._backend.use_memory_cache = False\n self.current_src_name = current_src_name\n self.cell_name = cell_name\n self.vM = None\n self.attrs = attrs\n self.debug = debug\n self.temp_attrs = None\n self.n_spikes = None\n self.verbose = False\n\n\n if type(attrs) is not type(None):\n self.set_attrs(attrs)\n self.sim_attrs = attrs\n\n if type(DTC) is not type(None):\n if type(DTC.attrs) is not type(None):\n self.set_attrs(DTC.attrs)\n if hasattr(DTC,'current_src_name'):\n self._current_src_name = DTC.current_src_name\n if hasattr(DTC,'cell_name'):\n self.cell_name = DTC.cell_name\n\n def get_spike_count(self):\n #if np.max(self.vM)>20.0*np.mean(self.vM):\n #thresh = threshold_detection(self.vM,np.max(self.vM)-0.10*np.max(self.vM))\n thresh = threshold_detection(self.vM,0.0*pq.mV)\n\n #else:\n # thresh = []\n return len(thresh)\n\n def set_stop_time(self, stop_time = 650*pq.ms):\n \"\"\"Sets the simulation duration\n stopTimeMs: duration in milliseconds\n \"\"\"\n self.tstop = float(stop_time.rescale(pq.ms))\n\n\n def get_membrane_potential(self):\n \"\"\"Must return a neo.core.AnalogSignal.\n And must destroy the hoc vectors that comprise it.\n \"\"\"\n\n return self.vM\n\n def set_attrs(self,attrs):\n\n self.HH = None\n self.HH = HH\n\n if len(attrs):\n self.El = attrs['El'] * b2.units.mV\n self.EK = attrs['EK'] * b2.units.mV\n self.ENa = attrs['ENa'] * b2.units.mV\n self.gl = attrs['gl'] * b2.units.msiemens\n self.gK = attrs['gK'] * b2.units.msiemens\n self.gNa = attrs['gNa'] * b2.units.msiemens\n self.C = attrs['C'] * b2.units.ufarad\n self.Vr = attrs['Vr']\n\n\n self.model.attrs.update(attrs)\n if attrs is None:\n #b2.defaultclock.dt = 1 * b2.ms\n\n self.HH =HH\n\n\n\n def inject_square_current(self, current):#, section = None, debug=False):\n \"\"\"Inputs: current : a dictionary with exactly three items, whose keys are: 'amplitude', 'delay', 'duration'\n Example: current = {'amplitude':float*pq.pA, 'delay':float*pq.ms, 'duration':float*pq.ms}}\n where \\'pq\\' is a physical unit representation, implemented by casting float values to the quanitities \\'type\\'.\n Description: A parameterized means of applying current injection into defined\n Currently only single section neuronal models are supported, the neurite section is understood to be simply the soma.\n\n \"\"\"\n #b2.defaultclock.dt = 1 * b2.ms\n self.state_monitor = None\n #self.spike_monitor = None\n self.HH = None\n self.HH = HH\n attrs = copy.copy(self.model.attrs)\n if self.model.attrs is None or not len(self.model.attrs):\n self.HH = HH\n else:\n self.set_attrs(attrs)\n if 'injected_square_current' in current.keys():\n c = current['injected_square_current']\n else:\n c = current\n\n\n duration = int(c['duration'])#/10.0)#/dt#/dt.rescale('ms')\n delay = int(c['delay'])#/10.0)#/dt#.rescale('ms')\n pre_current = int(duration)+100\n amp = c['amplitude']#.rescale('uA')\n\n\n \"\"\"\n #amplitude = amp.simplified#/1000000.0\n params = {'delay':delay, 'duration':duration, 'amp':amp,'':}\n getting_started = False\n if getting_started == False:\n stim = input_factory.get_step_current(delay, duration, b2.ms, amp * b2.pA)\n st = (duration+delay+100)* b2.ms\n else:\n stim = input_factory.get_step_current(10, 7, b2.ms, 45.0 * b2.nA)\n\n st = 70 * b2.ms\n\t\"\"\"\n if self.model.attrs is None or not len(self.model.attrs):\n\n self.HH = HH\n self.state_monitor,self.vM = self.HH.simulate_HH_neuron(I_stim = {'delay':delay,'duration':duration,'amp':amp}, simulation_time=st)\n\n else:\n if self.verbose:\n print(attrs)\n self.set_attrs(attrs)\n\n (self.state_monitor,self.vM,vm) = simulate_HH_neuron_local(\n El = attrs['El'] * b2.units.V,\n EK = attrs['EK'] * b2.units.V,\n ENa = attrs['ENa'] * b2.units.V,\n gl = attrs['gl'] * b2.units.msiemens,\n gK = attrs['gK'] * b2.units.msiemens,\n gNa = attrs['gNa'] * b2.units.msiemens,\n C = attrs['C'] * b2.units.ufarad,\n Vr = attrs['Vr'],\n input_current = {'delay':delay,'duration':duration,'amp':amp}\n )\n #params = params)\n\n #self.state_monitor.clock.dt = 1 *b2.ms\n self.dt = self.state_monitor.clock.dt\n\n self.attrs = attrs\n\n if ascii_plot:\n SLOW_ZOOM = False\n if SLOW_ZOOM and self.get_spike_count()>=1 :\n\n vm = get_spike_waveforms(self.vM)\n else:\n vm = self.vM\n t = [float(f) for f in vm.times]\n v = [float(f) for f in vm.magnitude]\n fig = apl.figure()\n fig.plot(t, v, label=str('brian HH: ')+str(vm.units)+str(current['amplitude']), width=100, height=20)\n\n fig.show()\n gc.collect()\n fig = None\n\n fig = None\n return self.vM\n\n def _backend_run(self):\n results = None\n results = {}\n results['vm'] = self.vM\n results['t'] = self.vM.times\n results['run_number'] = results.get('run_number',0) + 1\n return results\n", "sub_path": "neuronunit/models/backends/bhh_dynamics.py", "file_name": "bhh_dynamics.py", "file_ext": "py", "file_size_in_byte": 11461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "brian2.units", "line_number": 6, "usage_type": "attribute"}, {"api_name": "brian2.A", "line_number": 7, "usage_type": "attribute"}, {"api_name": "brian2.pA", "line_number": 7, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 40, "usage_type": "call"}, {"api_name": "asciiplotlib.figure", "line_number": 48, "usage_type": "call"}, {"api_name": "brian2.clear_cache", "line_number": 57, "usage_type": "call"}, {"api_name": "brian2.umetre", "line_number": 97, "usage_type": "attribute"}, {"api_name": "brian2.ufarad", "line_number": 98, "usage_type": "attribute"}, {"api_name": "brian2.cm", "line_number": 98, "usage_type": "attribute"}, {"api_name": "brian2.mV", "line_number": 99, "usage_type": "attribute"}, {"api_name": "brian2.NeuronGroup", "line_number": 110, "usage_type": "call"}, {"api_name": "brian2.StateMonitor", "line_number": 125, "usage_type": "call"}, {"api_name": "brian2.Network", "line_number": 128, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 144, "usage_type": "call"}, {"api_name": "quantities.V", "line_number": 147, "usage_type": "attribute"}, {"api_name": "quantities.s", "line_number": 147, "usage_type": "attribute"}, {"api_name": "elephant.spike_train_generation.threshold_detection", "line_number": 226, "usage_type": "call"}, {"api_name": "quantities.mV", "line_number": 226, "usage_type": "attribute"}, {"api_name": "quantities.ms", "line_number": 232, "usage_type": "attribute"}, {"api_name": "quantities.ms", "line_number": 236, "usage_type": "attribute"}, {"api_name": "neurodynex.hodgkin_huxley.HH", "line_number": 249, "usage_type": "name"}, {"api_name": "brian2.units", "line_number": 252, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 253, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 254, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 255, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 256, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 257, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 258, "usage_type": "attribute"}, {"api_name": "neurodynex.hodgkin_huxley.HH", "line_number": 266, "usage_type": "name"}, {"api_name": "neurodynex.hodgkin_huxley.HH", "line_number": 282, "usage_type": "name"}, {"api_name": "neurodynex.hodgkin_huxley.HH", "line_number": 285, "usage_type": "name"}, {"api_name": "neurodynex.hodgkin_huxley.HH", "line_number": 314, "usage_type": "name"}, {"api_name": "brian2.units", "line_number": 323, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 324, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 325, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 326, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 327, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 328, "usage_type": "attribute"}, {"api_name": "brian2.units", "line_number": 329, "usage_type": "attribute"}, {"api_name": "neuronunit.capabilities.spike_functions.get_spike_waveforms", "line_number": 344, "usage_type": "call"}, {"api_name": "asciiplotlib.figure", "line_number": 349, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 353, "usage_type": "call"}]}
{"seq_id": "231270258", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\n ่ๆฌๅ:\nCreated on 2018--\n@author:David Yisun\n@group:data\n\"\"\"\n\n# -*- coding:utf8 -*-\n\nimport requests\nimport re\nimport time\n\nheaders = {\n 'Accept': '*/*',\n 'Accept-Language': 'zh-CN,zh;q=0.8',\n 'User-Agent': 'Mozilla/5.0 (X11; Fedora; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36',\n 'Hosts': 'hm.baidu.com',\n 'Referer': 'http://www.xicidaili.com/nn',\n 'Connection': 'keep-alive'\n}\n\n# ๆๅฎ็ฌๅ่ๅด๏ผ่ฟ้ๆฏ็ฌฌ1~1000้กต๏ผ\nfor i in range(1,1000):\n\n url = 'http://www.xicidaili.com/nn/' + str(i)\n req = requests.get(url=url, headers=headers)\n res = req.text\n\n # ๆๅipๅ็ซฏๅฃ\n ip_list = re.findall(\"(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}).*?(\\d{2,6})\", res, re.S)\n\n # ๅฐๆๅ็ipๅ็ซฏๅฃๅๅ
ฅๆไปถ\n f = open(\"ip2.txt\",\"a+\")\n for li in ip_list:\n ip = li[0] + ':' + li[1] + '\\n'\n print(ip)\n f.write(ip)\n\n time.sleep(2) # ๆฏ็ฌๅไธ้กตๆๅไธค็ง\n", "sub_path": "crawler_utils/crawl_xici_proxy/demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 1031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 33, "usage_type": "call"}, {"api_name": "re.S", "line_number": 33, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}]}
{"seq_id": "576651411", "text": "from flask import Flask\n\napp=Flask(__name__)\n\n@app.route('/')\ndef index():\n return \"\"\"\nloading\n\n\"\"\"\n\nimport time\nimport tornado.web\nfrom tornado.websocket import WebSocketHandler\nfrom tornado.ioloop import PeriodicCallback,IOLoop\nimport tornado.wsgi\n\nclass NowHandler(WebSocketHandler):\n clients=set()\n @staticmethod\n def echo_now():\n for client in NowHandler.clients:\n client.write_message(time.ctime())\n \n def open(self):\n NowHandler.clients.add(self)\n \n def on_close(self):\n NowHandler.clients.remove(self)\n \n\nwsgi_app=tornado.wsgi.WSGIContainer(app)\n\napplication=tornado.web.Application([\n (r'/now',NowHandler),\n (r'.*',tornado.web.FallbackHandler,{'fallback':wsgi_app })\n])\n\n\nPeriodicCallback(NowHandler.echo_now,1000).start()\n\napplication.listen(5000)\nIOLoop.instance().start()", "sub_path": "all-gists/1441063/snippet.py", "file_name": "snippet.py", "file_ext": "py", "file_size_in_byte": 1294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "tornado.websocket.WebSocketHandler", "line_number": 33, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 38, "usage_type": "call"}, {"api_name": "tornado.web.wsgi.WSGIContainer", "line_number": 47, "usage_type": "call"}, {"api_name": "tornado.web.wsgi", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 47, "usage_type": "name"}, {"api_name": "tornado.web.web.Application", "line_number": 49, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 49, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 51, "usage_type": "name"}, {"api_name": "tornado.ioloop.PeriodicCallback", "line_number": 55, "usage_type": "call"}, {"api_name": "tornado.ioloop.IOLoop.instance", "line_number": 58, "usage_type": "call"}, {"api_name": "tornado.ioloop.IOLoop", "line_number": 58, "usage_type": "name"}]}
{"seq_id": "112835348", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.interpolate as spint\nimport os\nfrom gen_var import pel_pot, dt, t_static, lp \nimport electron as el\nmydir = './static_outputs_phi'\n\ntitle = 'stopped_electron_density'\n\nsave_path = os.path.join(os.path.expanduser('~'), 'Pictures/t_static')\nfilelist = [ f for f in os.listdir(save_path) if f.startswith(title)]\nfor f in filelist:\n os.remove(os.path.join(save_path, f))\n\ne_mid, e_bins, mb = el.dist_calc(el.e_dist, el.ener_res, el.e_bar)\n\nfig, ax = plt.subplots()\nfor p in range(0,lp,10 ):\n thing2 = np.loadtxt(os.path.join(mydir,'terminal_energy_t'+str(t_static)+'pot'+str(pel_pot[p]) +'.txt'))\n thing3 = np.loadtxt(os.path.join(mydir,'stop_point_t' + str(t_static) +'pot'+str(pel_pot[p])+'.txt'))\n\n ind = np.where(thing3[:,1]==0)\n x = ind[0]\n x = x[0]\n sum1 = np.sum(e_bins[x:])\n\n thing = np.loadtxt(os.path.join(mydir,'density_t' +str(t_static) +'pot'+str(pel_pot[p])+'.txt'))\n sum2 = np.sum(thing[:,1])\n check = sum1 + sum2\n print('This should equal 1 : ' + str(check))\n ax.plot(thing[:,0], thing[:,1], label = r'$\\phi_{\\mathrm{pel}} = $' + str(pel_pot[p]))\n\n#ax.set_yscale('log')\nax.set_xlabel(r'$\\tilde{r}$', fontsize = 12)\nax.set_ylabel(r'$\\tilde{\\rho_e}$', fontsize = 12, rotation = 0)\nax.yaxis.set_label_coords(-0.07, 0.45)\nax.xaxis.set_label_coords(0.46, - 0.03)\nplt.title(r'Electron density in cloud at time $\\tilde{t} = $' + str(t_static*dt) + ' for varying pellet potentials')\nplt.legend()\nplt.savefig(save_path+'/'+title+'.png', format = 'png', dpi = 1600)\nplt.show()\n\n", "sub_path": "one_iteration/PLOT_density_varying_phi.py", "file_name": "PLOT_density_varying_phi.py", "file_ext": "py", "file_size_in_byte": 1580, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"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.expanduser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 14, "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": "electron.dist_calc", "line_number": 16, "usage_type": "call"}, {"api_name": "electron.e_dist", "line_number": 16, "usage_type": "attribute"}, {"api_name": "electron.ener_res", "line_number": 16, "usage_type": "attribute"}, {"api_name": "electron.e_bar", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "gen_var.lp", "line_number": 19, "usage_type": "argument"}, {"api_name": "numpy.loadtxt", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "gen_var.t_static", "line_number": 20, "usage_type": "argument"}, {"api_name": "gen_var.pel_pot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "gen_var.t_static", "line_number": 21, "usage_type": "argument"}, {"api_name": "gen_var.pel_pot", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "gen_var.t_static", "line_number": 28, "usage_type": "argument"}, {"api_name": "gen_var.pel_pot", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "gen_var.pel_pot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "gen_var.t_static", "line_number": 39, "usage_type": "name"}, {"api_name": "gen_var.dt", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]}
{"seq_id": "319164024", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@author: nermin.bibic\n\"\"\"\n\n# import standard modules\nfrom datetime import datetime, timedelta\nimport csv\nimport sys\nimport os\n\n# import non-standard modules\nimport requests\nfrom bs4 import BeautifulSoup\n\n# command line arguments\noutput_path = sys.argv[1] # csv file\nerror_path = sys.argv[2] # csv file\nnum_days = int(sys.argv[3]) # number of days to scrape, including today\n\n# Initialization --------------------------------------------------------------\n\ntodays_date = datetime.now().date() # datetime.date object\n\n# get the dates\ndates = [] # list of dates of form 'DD-Mon-YYYY'\nfor i in range(num_days):\n date = todays_date + timedelta(days=i)\n pair = date.strftime(\"%A %d-%b-%Y\").split(' ')\n if pair[0] not in ['Saturday', 'Sunday']:\n dates.append(pair[1])\n\n# define the header for the output file\noutput_header = ['company_name', 'symbol', 'symbol_url', 'event',\n 'is_earnings', 'date', 'time', 'date_time', 'details']\n\n# define the header for the error file\nerror_header = ['page_date', 'error_type', 'error_message']\n\n# Scrape ----------------------------------------------------------------------\n\nfor date in dates:\n url_format = \"http://www.rttnews.com/corpinfo/ConferenceCalls.aspx?Date={}\"\n url = url_format.format(date)\n try:\n # get a bs4 parsed source code of the page\n response = requests.get(url) # Response object\n source_code = response.content # Bytes object\n soup = BeautifulSoup(source_code, 'html.parser') # BeautifulSoup object\n\n # get a list of bs4.element.Tag objects each representing a row of data\n rows = soup.select(\"div.lContent div.ecoCalContent, div.ecoCalAltContent\")\n\n # skip the page if it has no data\n if len(rows) == 0:\n print(\"No rows in page with url having date %s\" % date)\n continue\n\n # scrape each row of the page\n output_data = [] # this will be a list of lists of data\n for row in rows:\n try: # get the company\n company_name = row.select('div[data-th=\"Company\"]')[0].text.strip()\n except: # an empty list would be out of range\n company_name = ''\n\n try: # get the symbol\n symbol = row.select('div[data-th=\"Symbol\"]')[0].text.strip()\n except:\n symbol = ''\n\n try: # get the symbol URL\n symbol_url = row.select('div[data-th=\"Symbol\"] a')[0]['href'].strip()\n except:\n symbol_url = ''\n\n try: # get the event\n event = row.select('div[data-th=\"Event\"]')[0].text.strip()\n except:\n event = ''\n\n try: # get the time (may need to reformat)\n time = row.select('div[data-th=\"Time\"]')[0].text.strip()\n except:\n time = ''\n\n try: # get the conference call details\n details = row.select('div[data-th=\"Details\"]')[0].text.strip()\n except:\n details = ''\n else:\n details = ' '.join(details.split()) # remove extra spaces\n\n # add an earnings flag\n if 'Earnings' in event or 'Financial' in event:\n is_earnings = 1\n else:\n is_earnings = 0\n\n # add a datetime field\n try:\n date_time = date + ' ' + time\n date_time = datetime.strptime(date_time, \"%d-%b-%Y %I:%M %p\")\n except:\n date_time = ''\n else:\n date_time = date_time.strftime(\"%Y-%m-%d %H:%M:%S\") # for sql\n\n # order the scraped data into a list\n output_row = [company_name, symbol, symbol_url, event, is_earnings,\n date, time, date_time, details]\n\n # prepare the data for sql also\n for index, field in enumerate(output_row):\n if field == '':\n output_row[index] = None\n\n # append the row to the output data\n output_data.append(output_row)\n\n # write the rows in the output data to the output file, creating the\n # file and writing the header if the file does not already exist\n if not os.path.isfile(output_path):\n with open(output_path, 'a', newline='',\n encoding='utf-8') as output_file:\n csvwriter = csv.writer(output_file) # create a csv writer\n csvwriter.writerow(output_header)\n csvwriter.writerows(output_data) # None is written as ''\n else:\n with open(output_path, 'a', newline='',\n encoding='utf-8') as output_file:\n csvwriter = csv.writer(output_file)\n csvwriter.writerows(output_data)\n\n except Exception as e:\n print(\"Error: Page with date %s\" % date)\n error_row = [date, type(e), e]\n\n # write the error row to the error file, creating the file and writing\n # the header if the file does not already exist\n if not os.path.isfile(error_path):\n with open(error_path, 'a', newline='') as error_file:\n csvwriter = csv.writer(error_file)\n csvwriter.writerow(error_header)\n csvwriter.writerow(error_row)\n else:\n with open(error_path, 'a', newline='') as error_file:\n csvwriter = csv.writer(error_file)\n csvwriter.writerow(error_row)\n\n else:\n # log the result\n log_string = \"Wrote {} rows from page with url having date {}\"\n print(log_string.format(len(output_data), date))\n", "sub_path": "Projects/conference_calls/scrape_from_rttnews.py", "file_name": "scrape_from_rttnews.py", "file_ext": "py", "file_size_in_byte": 5680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 47, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 126, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 143, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 148, "usage_type": "call"}]}
{"seq_id": "325195219", "text": "#!/usr/bin/env python3\n'''\nThe classes produced by ImageItem are the various types of items that can be\ninstalled into an image. The compiler will verify that the specified items\nhave all of their requirements satisfied, and will then apply them in\ndependency order.\n\nTo understand how the methods `provides()` and `requires()` affect\ndependency resolution / installation order, start with the docblock at the\ntop of `provides.py`.\n'''\nimport enum\nimport hashlib\nimport itertools\nimport json\nimport os\nimport subprocess\nimport tempfile\nimport sys\n\nfrom typing import Iterable, List, Mapping, NamedTuple, Optional, Set\n\nfrom . import mount_item\nfrom . import procfs_serde\n\nfrom .enriched_namedtuple import (\n metaclass_new_enriched_namedtuple, NonConstructibleField,\n)\nfrom .provides import ProvidesDirectory, ProvidesDoNotAccess, ProvidesFile\nfrom .requires import require_directory, require_file\nfrom .subvolume_on_disk import SubvolumeOnDisk\n\nfrom common import nullcontext\nfrom subvol_utils import Subvol\nfrom artifacts_dir import find_repo_root\n\n# This path is off-limits to regular image operations, it exists only to\n# record image metadata and configuration. This is at the root, instead of\n# in `etc` because that means that `FilesystemRoot` does not have to provide\n# `etc` and determine its permissions. In other words, a top-level \"meta\"\n# directory makes the compiler less opinionated about other image content.\n#\n# NB: The trailing slash is significant, making this a protected directory,\n# not a protected file.\nMETA_DIR = 'meta/'\n\n@enum.unique\nclass PhaseOrder(enum.Enum):\n '''\n With respect to ordering, there are two types of operations that the\n image compiler performs against images.\n\n (1) Regular additive operations are naturally ordered with respect to\n one another by filesystem dependencies. For example: we must create\n /usr/bin **BEFORE** copying `:your-tool` there.\n\n (2) Everything else, including:\n - RPM installation, which has a complex internal ordering, but\n simply needs needs a definitive placement as a block of `yum`\n operations -- due to `yum`'s complexity & various scripts, it's\n not desirable to treat installs as regular additive operations.\n - Path removals. It is simplest to perform them in bulk, without\n interleaving with other operations. Removals have a natural\n ordering with respect to each other -- child before parent, to\n avoid tripping \"assert_exists\" unnecessarily.\n\n For the operations in (2), this class sets a justifiable deteriminstic\n ordering for black-box blocks of operations, and assumes that each\n individual block's implementation will order its internals sensibly.\n\n Phases will be executed in the order listed here.\n\n The operations in (1) are validated, dependency-sorted, and built after\n all of the phases have built.\n\n IMPORTANT: A new phase implementation MUST:\n - handle pre-existing protected paths via `_protected_path_set`\n - emit `ProvidesDoNotAccess` if it provides new protected paths\n - ensure that `_protected_path_set` in future phases knows how to\n discover these protected paths by inspecting the filesystem.\n See `ParentLayerItem`, `RemovePathsItem`, and `MountItem` for examples.\n\n Future: the complexity around protected paths is a symptom of a lack of\n a strong runtime abstraction. Specifically, if `Subvol.run_as_root`\n used mount namespaces and read-only bind mounts to enforce protected\n paths (as is done today in `yum-from-snapshot`), then it would not be\n necessary for the compiler to know about them.\n '''\n # This actually creates the subvolume, so it must preced all others.\n PARENT_LAYER = enum.auto()\n # Precedes REMOVE_PATHS because RPM removes **might** be conditional on\n # the presence or absence of files, and we don't want that extra entropy\n # -- whereas file removes fail or succeed predictably. Precedes\n # RPM_INSTALL somewhat arbitrarily, since _gen_multi_rpm_actions\n # prevents install-remove conflicts between features.\n RPM_REMOVE = enum.auto()\n RPM_INSTALL = enum.auto()\n # This MUST be a separate phase that comes after all the regular items\n # because the dependency sorter has no provisions for eliminating\n # something that another item `provides()`.\n #\n # By having this phase be last, we also allow removing files added by\n # RPM_INSTALL. The downside is that this is a footgun. The upside is\n # that e.g. cleaning up yum log & caches can be done as an\n # `image_feature` instead of being code. We might also use this to\n # remove e.g. unnecessary parts of excessively monolithic RPMs.\n REMOVE_PATHS = enum.auto()\n\n\nclass LayerOpts(NamedTuple):\n layer_target: str\n yum_from_snapshot: str\n build_appliance: str\n\n\nclass ImageItem(type):\n 'A metaclass for the types of items that can be installed into images.'\n def __new__(metacls, classname, bases, dct):\n\n # Future: `deepfrozen` has a less clunky way of doing this\n def customize_fields(kwargs):\n fn = dct.get('customize_fields')\n if fn:\n fn(kwargs)\n return kwargs\n\n # Some items, like RPM actions, are not sorted by dependencies, but\n # get a fixed installation order. The absence of a phase means the\n # item is ordered via the topo-sort in `dep_graph.py`.\n class PhaseOrderBase:\n __slots__ = ()\n\n def phase_order(self):\n return None\n\n return metaclass_new_enriched_namedtuple(\n __class__,\n ['from_target'],\n metacls, classname, (PhaseOrderBase,) + bases, dct,\n customize_fields\n )\n\n\ndef _make_path_normal_relative(orig_d: str) -> str:\n '''\n In image-building, we want relative paths that do not start with `..`,\n so that the effects of ImageItems are confined to their destination\n paths. For convenience, we accept absolute paths, too.\n '''\n # lstrip so we treat absolute paths as image-relative\n d = os.path.normpath(orig_d).lstrip('/')\n if d == '..' or d.startswith('../'):\n raise AssertionError(f'path {orig_d} cannot start with ../')\n # This is a directory reserved for image build metadata, so we prevent\n # regular items from writing to it. `d` is never absolute here.\n # NB: This check is redundant with `ProvidesDoNotAccess(path=META_DIR)`,\n # this is just here as a fail-fast backup.\n if (d + '/').startswith(META_DIR):\n raise AssertionError(f'path {orig_d} cannot start with {META_DIR}')\n return d\n\n\ndef _coerce_path_field_normal_relative(kwargs, field: str):\n d = kwargs.get(field)\n if d is not None:\n kwargs[field] = _make_path_normal_relative(d)\n\n\ndef _make_rsync_style_dest_path(dest: str, source: str) -> str:\n '''\n rsync convention for a destination: \"ends/in/slash/\" means \"copy\n into this directory\", \"does/not/end/with/slash\" means \"copy with\n the specified filename\".\n '''\n\n # Normalize after applying the rsync convention, since this would\n # remove any trailing / in 'dest'.\n return _make_path_normal_relative(\n os.path.join(dest,\n os.path.basename(source)) if dest.endswith('/') else dest\n )\n\n\ndef _maybe_popen_zstd(path):\n 'Use this as a context manager.'\n if path.endswith('.zst'):\n return subprocess.Popen([\n 'zstd', '--decompress', '--stdout', path,\n ], stdout=subprocess.PIPE)\n return nullcontext()\n\n\ndef _open_tarfile(path):\n 'Wraps tarfile.open to add .zst support. Use this as a context manager.'\n import tarfile # Lazy since only this method needs it.\n with _maybe_popen_zstd(path) as maybe_proc:\n if maybe_proc is None:\n return tarfile.open(path)\n else:\n return tarfile.open(fileobj=maybe_proc.stdout, mode='r|')\n\n\ndef _hash_tarball(tarball: str, algorithm: str) -> str:\n 'Returns the hex digest'\n algo = hashlib.new(algorithm)\n with open(tarball, 'rb') as f:\n for chunk in iter(lambda: f.read(4096), b''):\n algo.update(chunk)\n return algo.hexdigest()\n\n\nclass TarballItem(metaclass=ImageItem):\n fields = ['into_dir', 'tarball', 'hash', 'force_root_ownership']\n\n def customize_fields(kwargs): # noqa: B902\n algorithm, expected_hash = kwargs['hash'].split(':')\n actual_hash = _hash_tarball(kwargs['tarball'], algorithm)\n if actual_hash != expected_hash:\n raise AssertionError(\n f'{kwargs} failed hash validation, got {actual_hash}'\n )\n _coerce_path_field_normal_relative(kwargs, 'into_dir')\n assert kwargs['force_root_ownership'] in [True, False], kwargs\n\n def provides(self):\n with _open_tarfile(self.tarball) as f:\n for item in f:\n path = os.path.join(\n self.into_dir, _make_path_normal_relative(item.name),\n )\n if item.isdir():\n # We do NOT provide the installation directory, and the\n # image build script tarball extractor takes pains (e.g.\n # `tar --no-overwrite-dir`) not to touch the extraction\n # directory.\n if os.path.normpath(\n os.path.relpath(path, self.into_dir)\n ) != '.':\n yield ProvidesDirectory(path=path)\n else:\n yield ProvidesFile(path=path)\n\n def requires(self):\n yield require_directory(self.into_dir)\n\n def build(self, subvol: Subvol):\n with _maybe_popen_zstd(self.tarball) as maybe_proc:\n subvol.run_as_root([\n 'tar',\n # Future: Bug: `tar` unfortunately FOLLOWS existing symlinks\n # when unpacking. This isn't dire because the compiler's\n # conflict prevention SHOULD prevent us from going out of\n # the subvolume since this TarballItem's provides would\n # collide with whatever is already present. However, it's\n # hard to state that with complete confidence, especially if\n # we start adding support for following directory symlinks.\n '-C', subvol.path(self.into_dir),\n '-x',\n # Block tar's weird handling of paths containing colons.\n '--force-local',\n # The uid:gid doing the extraction is root:root, so by default\n # tar would try to restore the file ownership from the archive.\n # In some cases, we just want all the files to be root-owned.\n *(['--no-same-owner'] if self.force_root_ownership else []),\n # The next option is an extra safeguard that is redundant\n # with the compiler's prevention of `provides` conflicts.\n # It has two consequences:\n #\n # (1) If a file already exists, `tar` will fail with an error.\n # It is **not** an error if a directory already exists --\n # otherwise, one would never be able to safely untar\n # something into e.g. `/usr/local/bin`.\n #\n # (2) Less obviously, the option prevents `tar` from\n # overwriting the permissions of `directory`, as it\n # otherwise would.\n #\n # Thanks to the compiler's conflict detection, this should\n # not come up, but now you know. Observe us clobber the\n # permissions without it:\n #\n # $ mkdir IN OUT\n # $ touch IN/file\n # $ chmod og-rwx IN\n # $ ls -ld IN OUT\n # drwx------. 2 lesha users 17 Sep 11 21:50 IN\n # drwxr-xr-x. 2 lesha users 6 Sep 11 21:50 OUT\n # $ tar -C IN -czf file.tgz .\n # $ tar -C OUT -xvf file.tgz\n # ./\n # ./file\n # $ ls -ld IN OUT\n # drwx------. 2 lesha users 17 Sep 11 21:50 IN\n # drwx------. 2 lesha users 17 Sep 11 21:50 OUT\n #\n # Adding `--keep-old-files` preserves `OUT`'s metadata:\n #\n # $ rm -rf OUT ; mkdir out ; ls -ld OUT\n # drwxr-xr-x. 2 lesha users 6 Sep 11 21:53 OUT\n # $ tar -C OUT --keep-old-files -xvf file.tgz\n # ./\n # ./file\n # $ ls -ld IN OUT\n # drwx------. 2 lesha users 17 Sep 11 21:50 IN\n # drwxr-xr-x. 2 lesha users 17 Sep 11 21:54 OUT\n '--keep-old-files',\n '-f', ('-' if maybe_proc else self.tarball),\n ], stdin=(maybe_proc.stdout if maybe_proc else None))\n\n\ndef _generate_tarball(\n temp_dir: str, generator: bytes, generator_args: List[str],\n) -> str:\n # API design notes:\n #\n # 1) The generator takes an output directory, not a file, because we\n # prefer not to have to hardcode the extension of the output file in\n # the TARGETS file -- that would make it more laborious to change\n # the compression format. Instead, the generator prints the path to\n # the created tarball to stdout. This does not introduce\n # nondeterminism, since the tarball name cannot affect the result of\n # its extraction.\n #\n # Since TARGETS already hardcodes a content hash, requiring the name\n # would not be far-fetched, this approach just seemed cleaner.\n #\n # 2) `temp_dir` is last since this allows the use of inline scripts via\n # `generator_args` with e.g. `/bin/bash`.\n #\n # Future: it would be best to sandbox the generator to limit its\n # filesystem writes. At the moment, we trust rule authors not to abuse\n # this feature and write stuff outside the given directory.\n tarball_filename = subprocess.check_output([\n generator, *generator_args, temp_dir,\n ]).decode()\n assert tarball_filename.endswith('\\n'), (generator, tarball_filename)\n tarball_filename = os.path.normpath(tarball_filename[:-1])\n assert (\n not tarball_filename.startswith('/')\n and not tarball_filename.startswith('../')\n ), tarball_filename\n return os.path.join(temp_dir, tarball_filename)\n\n\ndef tarball_item_factory(\n exit_stack, *, generator: str = None, tarball: str = None,\n generator_args: List[str] = None, **kwargs,\n):\n assert (generator is not None) ^ (tarball is not None)\n # Uses `generator` to generate a temporary `tarball` for `TarballItem`.\n # The file is deleted when the `exit_stack` context exits.\n #\n # NB: With `generator`, identical constructor arguments to this factory\n # will create different `TarballItem`s, so if we needed item\n # deduplication to work across inputs, this is broken. However, I don't\n # believe the compiler relies on that. If we need it, it should not be\n # too hard to share the same tarball for all generates with the same\n # command -- you'd add a global map of ('into_dir', 'command') ->\n # tarball, perhaps using weakref hooks to refcount tarballs and GC them.\n if generator:\n tarball = _generate_tarball(\n exit_stack.enter_context(tempfile.TemporaryDirectory()),\n generator,\n generator_args or [],\n )\n return TarballItem(**kwargs, tarball=tarball)\n\n\nclass HasStatOptions:\n '''\n Helper for setting `stat (2)` options on files, directories, etc, which\n we are creating inside the image. Interfaces with `StatOptions` in the\n image build tool.\n '''\n __slots__ = ()\n # `mode` can be an integer fully specifying the bits, or a symbolic\n # string like `u+rx`. In the latter case, the changes are applied on\n # top of mode 0.\n #\n # The defaut mode 0755 is good for directories, and OK for files. I'm\n # not trying adding logic to vary the default here, since this really\n # only comes up in tests, and `image_feature` usage should set this\n # explicitly.\n fields = [('mode', 0o755), ('user', 'root'), ('group', 'root')]\n\n def _mode_impl(self):\n return ( # The symbolic mode must be applied after 0ing all bits.\n f'{self.mode:04o}' if isinstance(self.mode, int)\n else f'a-rwxXst,{self.mode}'\n )\n\n def build_stat_options(self, subvol: Subvol, full_target_path: str):\n # `chmod` lacks a --no-dereference flag to protect us from following\n # `full_target_path` if it's a symlink. As far as I know, this\n # should never occur, so just let the exception fly.\n subvol.run_as_root(['test', '!', '-L', full_target_path])\n # -R is not a problem since it cannot be the case that we are\n # creating a directory that already has something inside it. On\n # the plus side, it helps with nested directory creation.\n subvol.run_as_root([\n 'chmod', '-R', self._mode_impl(),\n full_target_path\n ])\n subvol.run_as_root([\n 'chown', '--no-dereference', '-R', f'{self.user}:{self.group}',\n full_target_path,\n ])\n\n\nclass CopyFileItem(HasStatOptions, metaclass=ImageItem):\n fields = ['source', 'dest']\n\n def customize_fields(kwargs): # noqa: B902\n kwargs['dest'] = _make_rsync_style_dest_path(\n kwargs['dest'], kwargs['source']\n )\n\n def provides(self):\n yield ProvidesFile(path=self.dest)\n\n def requires(self):\n yield require_directory(os.path.dirname(self.dest))\n\n def build(self, subvol: Subvol):\n dest = subvol.path(self.dest)\n subvol.run_as_root(['cp', self.source, dest])\n self.build_stat_options(subvol, dest)\n\n\nclass SymlinkItem(HasStatOptions):\n fields = ['source', 'dest']\n\n def _customize_fields_impl(kwargs): # noqa: B902\n _coerce_path_field_normal_relative(kwargs, 'source')\n\n kwargs['dest'] = _make_rsync_style_dest_path(\n kwargs['dest'], kwargs['source']\n )\n\n def build(self, subvol: Subvol):\n dest = subvol.path(self.dest)\n # Source is always absolute inside the image subvolume\n source = os.path.join('/', self.source)\n subvol.run_as_root(\n ['ln', '--symbolic', '--no-dereference', source, dest]\n )\n\n\nclass SymlinkToDirItem(SymlinkItem, metaclass=ImageItem):\n customize_fields = SymlinkItem._customize_fields_impl\n\n def provides(self):\n yield ProvidesDirectory(path=self.dest)\n\n def requires(self):\n yield require_directory(self.source)\n yield require_directory(os.path.dirname(self.dest))\n\n\nclass SymlinkToFileItem(SymlinkItem, metaclass=ImageItem):\n customize_fields = SymlinkItem._customize_fields_impl\n\n def provides(self):\n yield ProvidesFile(path=self.dest)\n\n def requires(self):\n yield require_file(self.source)\n yield require_directory(os.path.dirname(self.dest))\n\n\nclass MakeDirsItem(HasStatOptions, metaclass=ImageItem):\n fields = ['into_dir', 'path_to_make']\n\n def customize_fields(kwargs): # noqa: B902\n _coerce_path_field_normal_relative(kwargs, 'into_dir')\n _coerce_path_field_normal_relative(kwargs, 'path_to_make')\n\n def provides(self):\n inner_dir = os.path.join(self.into_dir, self.path_to_make)\n while inner_dir != self.into_dir:\n yield ProvidesDirectory(path=inner_dir)\n inner_dir = os.path.dirname(inner_dir)\n\n def requires(self):\n yield require_directory(self.into_dir)\n\n def build(self, subvol: Subvol):\n outer_dir = self.path_to_make.split('/', 1)[0]\n inner_dir = subvol.path(os.path.join(self.into_dir, self.path_to_make))\n subvol.run_as_root(['mkdir', '-p', inner_dir])\n self.build_stat_options(\n subvol, subvol.path(os.path.join(self.into_dir, outer_dir)),\n )\n\n\n# NB: When we split `items.py`, this can just be merged with `mount_item.py`.\nclass MountItem(metaclass=ImageItem):\n fields = [\n 'mountpoint',\n ('build_source', NonConstructibleField),\n ('runtime_source', NonConstructibleField),\n ('is_directory', NonConstructibleField),\n ('is_repo_root', NonConstructibleField),\n # The next two are always None, their content moves into the above\n # `NonConstructibleField`s\n 'target',\n 'mount_config',\n ]\n\n def customize_fields(kwargs): # noqa: B902\n target = kwargs.pop('target')\n cfg = kwargs.pop('mount_config')\n assert (target is None) ^ (cfg is None), \\\n f'Exactly one of `target` or `mount_config` must be set in {kwargs}'\n if cfg is not None:\n cfg = cfg.copy() # We must not mutate our input!\n else:\n with open(os.path.join(target, 'mountconfig.json')) as f:\n cfg = json.load(f)\n\n kwargs['is_repo_root'] = cfg.pop('is_repo_root', False)\n default_mountpoint = cfg.pop('default_mountpoint', None)\n\n mountpoint = kwargs.get('mountpoint')\n if kwargs['is_repo_root']:\n assert default_mountpoint is None, (f'default_mountpoint: '\n '{default_mountpoint} '\n 'must not be set')\n assert mountpoint is None, (f'mountpoint: {mountpoint} '\n 'must not be set')\n kwargs['mountpoint'] = find_repo_root(sys.argv[0])\n assert kwargs['mountpoint'][0] == '/', (f'repo_root: '\n '{kwargs[\"mountpoint\"]} '\n 'must start from /')\n kwargs['mountpoint'] = kwargs['mountpoint'][1:]\n else:\n if mountpoint is None: # Missing or None => use default\n kwargs['mountpoint'] = default_mountpoint\n if kwargs['mountpoint'] is None:\n raise AssertionError(f'MountItem {kwargs} lacks mountpoint')\n _coerce_path_field_normal_relative(kwargs, 'mountpoint')\n\n kwargs['is_directory'] = cfg.pop('is_directory')\n assert (kwargs['is_directory'] or\n not kwargs['is_repo_root']), f'cannot host_file_mount repo_root'\n\n build_source = cfg.pop('build_source')\n if kwargs['is_repo_root']:\n assert build_source['source'] is None, (f'source: '\n '{build_source[\"source\"]} '\n 'must not be set')\n build_source['source'] = os.path.join('/', kwargs['mountpoint'])\n\n kwargs['build_source'] = mount_item.BuildSource(\n **build_source\n )\n if kwargs['build_source'].type == 'host' and not (\n kwargs['from_target'].startswith('//fs_image/features/host_mounts')\n or kwargs['from_target'].startswith('//fs_image/compiler/test')\n or kwargs['from_target'].startswith('//fs_image/build_appliance')\n ):\n raise AssertionError(\n 'Host mounts cause containers to be non-hermetic and fragile, '\n 'so they must be located under `fs_image/features/host_mounts` '\n 'to enable close review by the owners of `fs_image`.'\n )\n\n # This is supposed to be the run-time equivalent of `build_source`,\n # but for us it's just an opaque JSON blob that the runtime wants.\n # Hack: We serialize this back to JSON since the compiler expects\n # items to be hashable, and the source WILL contain dicts.\n runtime_source = cfg.pop('runtime_source', None)\n # Future: once runtime_source grows a schema, use it here?\n if (runtime_source and runtime_source.get('type') == 'host'):\n raise AssertionError(\n f'Only `build_source` may specify host mounts: {kwargs}'\n )\n kwargs['runtime_source'] = json.dumps(runtime_source, sort_keys=True)\n\n assert cfg == {}, f'Unparsed fields in {kwargs} mount_config: {cfg}'\n # These must be set to appease enriched_namedtuple\n kwargs['target'] = None\n kwargs['mount_config'] = None\n\n def provides(self):\n # For now, nesting of mounts is not supported, and we certainly\n # cannot allow regular items to write inside a mount.\n yield ProvidesDoNotAccess(path=self.mountpoint)\n\n def requires(self):\n # We don't require the mountpoint itself since it will be shadowed,\n # so this item just makes it with default permissions.\n yield require_directory(os.path.dirname(self.mountpoint))\n\n def build_resolves_targets(\n self, *,\n subvol: Subvol,\n target_to_path: Mapping[str, str],\n subvolumes_dir: str,\n ):\n mount_dir = os.path.join(\n mount_item.META_MOUNTS_DIR, self.mountpoint, mount_item.MOUNT_MARKER\n )\n for name, data in (\n # NB: Not exporting self.mountpoint since it's implicit in the path.\n ('is_directory', self.is_directory),\n ('build_source', self.build_source._asdict()),\n ('runtime_source', json.loads(self.runtime_source)),\n ):\n procfs_serde.serialize(data, subvol, os.path.join(mount_dir, name))\n source_path = self.build_source.to_path(\n target_to_path=target_to_path,\n subvolumes_dir=subvolumes_dir,\n )\n # Support mounting directories and non-directories... This check\n # follows symlinks for the mount source, which seems correct.\n is_dir = os.path.isdir(source_path)\n assert is_dir == self.is_directory, self\n if is_dir:\n mkdir_opts = ['--mode=0755']\n if self.is_repo_root:\n mkdir_opts.append('-p')\n # NB: if is_repo_root, mkdir below will create a non-portable dir\n # like /home/username/fbsource/fbcode in subvol layer, but such\n # a layer should never be published as a package.\n subvol.run_as_root([\n 'mkdir', *mkdir_opts, subvol.path(self.mountpoint)\n ])\n else: # Regular files, device nodes, FIFOs, you name it.\n # `touch` lacks a `--mode` argument, but the mode of this\n # mountpoint will be shadowed anyway, so let it be whatever.\n subvol.run_as_root(['touch', subvol.path(self.mountpoint)])\n mount_item.ro_rbind_mount(source_path, subvol, self.mountpoint)\n\n\ndef _protected_path_set(subvol: Optional[Subvol]) -> Set[str]:\n '''\n Identifies the protected paths in a subvolume. Pass `subvol=None` if\n the subvolume doesn't yet exist (for FilesystemRoot).\n\n All paths will be relative to the image root, no leading /. If a path\n has a trailing /, it is a protected directory, otherwise it is a\n protected file.\n\n Future: The trailing / convention could be eliminated, since any place\n actually manipulating these paths can inspect what's on disk, and act\n appropriately. If the convention proves burdensome, this is an easy\n change -- mostly affecting this file, and `yum_from_snapshot.py`.\n '''\n paths = {META_DIR}\n if subvol is not None:\n # NB: The returned paths here already follow the trailing / rule.\n for mountpoint in mount_item.mountpoints_from_subvol_meta(subvol):\n paths.add(mountpoint.lstrip('/'))\n # Never absolute: yum-from-snapshot interprets absolute paths as host paths\n assert not any(p.startswith('/') for p in paths), paths\n return paths\n\n\ndef _is_path_protected(path: str, protected_paths: Set[str]) -> bool:\n # NB: The O-complexity could obviously be lots better, if needed.\n for prot_path in protected_paths:\n # Handle both protected files and directories. This test is written\n # to return True even if `prot_path` is `/path/to/file` while `path`\n # is `/path/to/file/oops`.\n if (path + '/').startswith(\n prot_path + ('' if prot_path.endswith('/') else '/')\n ):\n return True\n return False\n\n\ndef _ensure_meta_dir_exists(subvol: Subvol):\n subvol.run_as_root([\n 'mkdir', '--mode=0755', '--parents', subvol.path(META_DIR),\n ])\n\n\nclass ParentLayerItem(metaclass=ImageItem):\n fields = ['path']\n\n def phase_order(self):\n return PhaseOrder.PARENT_LAYER\n\n def provides(self):\n parent_subvol = Subvol(self.path, already_exists=True)\n\n protected_paths = _protected_path_set(parent_subvol)\n for prot_path in protected_paths:\n yield ProvidesDoNotAccess(path=prot_path)\n\n provided_root = False\n # We need to traverse the parent image as root, so that we have\n # permission to access everything.\n for type_and_path in parent_subvol.run_as_root([\n # -P is the analog of --no-dereference in GNU tools\n #\n # Filter out the protected paths at traversal time. If one of\n # the paths has a very large or very slow mount, traversing it\n # would have a devastating effect on build times, so let's avoid\n # looking inside protected paths entirely. An alternative would\n # be to `send` and to parse the sendstream, but this is ok too.\n 'find', '-P', self.path, '(', *itertools.dropwhile(\n lambda x: x == '-o', # Drop the initial `-o`\n itertools.chain.from_iterable([\n # `normpath` removes the trailing / for protected dirs\n '-o', '-path', os.path.join(self.path, os.path.normpath(p))\n ] for p in protected_paths),\n ), ')', '-prune', '-o', '-printf', '%y %p\\\\0',\n ], stdout=subprocess.PIPE).stdout.split(b'\\0'):\n if not type_and_path: # after the trailing \\0\n continue\n filetype, abspath = type_and_path.decode().split(' ', 1)\n relpath = os.path.relpath(abspath, self.path)\n\n # We already \"provided\" this path above, and it should have been\n # filtered out by `find`.\n assert not _is_path_protected(relpath, protected_paths), relpath\n\n # Future: This provides all symlinks as files, while we should\n # probably provide symlinks to valid directories inside the\n # image as directories to be consistent with SymlinkToDirItem.\n if filetype in ['b', 'c', 'p', 'f', 'l', 's']:\n yield ProvidesFile(path=relpath)\n elif filetype == 'd':\n yield ProvidesDirectory(path=relpath)\n else: # pragma: no cover\n raise AssertionError(f'Unknown {filetype} for {abspath}')\n if relpath == '.':\n assert filetype == 'd'\n provided_root = True\n\n assert provided_root, 'parent layer {} lacks /'.format(self.path)\n\n def requires(self):\n return ()\n\n @classmethod\n def get_phase_builder(\n cls, items: Iterable['ParentLayerItem'], layer_opts: LayerOpts,\n ):\n parent, = items\n assert isinstance(parent, ParentLayerItem), parent\n\n def builder(subvol: Subvol):\n parent_subvol = Subvol(parent.path, already_exists=True)\n subvol.snapshot(parent_subvol)\n # This assumes that the parent has everything mounted already.\n mount_item.clone_mounts(parent_subvol, subvol)\n _ensure_meta_dir_exists(subvol)\n\n return builder\n\n\nclass FilesystemRootItem(metaclass=ImageItem):\n 'A simple item to endow parent-less layers with a standard-permissions /'\n fields = []\n\n def phase_order(self):\n return PhaseOrder.PARENT_LAYER\n\n def provides(self):\n yield ProvidesDirectory(path='/')\n for p in _protected_path_set(subvol=None):\n yield ProvidesDoNotAccess(path=p)\n\n def requires(self):\n return ()\n\n @classmethod\n def get_phase_builder(\n cls, items: Iterable['FilesystemRootItem'], layer_opts: LayerOpts,\n ):\n parent, = items\n assert isinstance(parent, FilesystemRootItem), parent\n\n def builder(subvol: Subvol):\n subvol.create()\n # Guarantee standard / permissions. This could be a setting,\n # but in practice, probably any other choice would be wrong.\n subvol.run_as_root(['chmod', '0755', subvol.path()])\n subvol.run_as_root(['chown', 'root:root', subvol.path()])\n _ensure_meta_dir_exists(subvol)\n\n return builder\n\n\ndef gen_parent_layer_items(target, parent_layer_json, subvolumes_dir):\n if not parent_layer_json:\n yield FilesystemRootItem(from_target=target) # just provides /\n else:\n with open(parent_layer_json) as infile:\n yield ParentLayerItem(\n from_target=target,\n path=SubvolumeOnDisk.from_json_file(infile, subvolumes_dir)\n .subvolume_path(),\n )\n\n\nclass RemovePathAction(enum.Enum):\n assert_exists = 'assert_exists'\n if_exists = 'if_exists'\n\n\nclass RemovePathItem(metaclass=ImageItem):\n fields = ['path', 'action']\n\n def customize_fields(kwargs): # noqa: B902\n _coerce_path_field_normal_relative(kwargs, 'path')\n kwargs['action'] = RemovePathAction(kwargs['action'])\n\n def phase_order(self):\n return PhaseOrder.REMOVE_PATHS\n\n def __sort_key(self):\n return (self.path, {action: idx for idx, action in enumerate([\n # We sort in reverse order, so by putting \"if\" first we allow\n # conflicts between \"if_exists\" and \"assert_exists\" items to be\n # resolved naturally.\n RemovePathAction.if_exists,\n RemovePathAction.assert_exists,\n ])}[self.action])\n\n @classmethod\n def get_phase_builder(\n cls, items: Iterable['RemovePathItem'], layer_opts: LayerOpts,\n ):\n # NB: We want `remove_paths` not to be able to remove additions by\n # regular (non-phase) items in the same layer -- that indicates\n # poorly designed `image.feature`s, which should be refactored. At\n # present, this is only enforced implicitly, because all removes are\n # done before regular items are even validated or sorted. Enforcing\n # it explicitly is possible by peeking at `DependencyGraph.items`,\n # but the extra complexity doesn't seem worth the faster failure.\n\n # NB: We could detect collisions between two `assert_exists` removes\n # early, but again, it doesn't seem worth the complexity.\n\n def builder(subvol: Subvol):\n protected_paths = _protected_path_set(subvol)\n # Reverse-lexicographic order deletes inner paths before\n # deleting the outer paths, thus minimizing conflicts between\n # `remove_paths` items.\n for item in sorted(\n items, reverse=True, key=lambda i: i.__sort_key(),\n ):\n if _is_path_protected(item.path, protected_paths):\n # For META_DIR, this is never reached because of\n # _make_path_normal_relative's check, but for other\n # protected paths, this is required.\n raise AssertionError(\n f'Cannot remove protected {item}: {protected_paths}'\n )\n # This ensures that there are no symlinks in item.path that\n # might take us outside of the subvolume. Since recursive\n # `rm` does not follow symlinks, it is OK if the inode at\n # `item.path` is a symlink (or one of its sub-paths).\n path = subvol.path(item.path, no_dereference_leaf=True)\n if not os.path.lexists(path):\n if item.action == RemovePathAction.assert_exists:\n raise AssertionError(f'Path does not exist: {item}')\n elif item.action == RemovePathAction.if_exists:\n continue\n else: # pragma: no cover\n raise AssertionError(f'Unknown {item.action}')\n subvol.run_as_root([\n 'rm', '-r',\n # This prevents us from making removes outside of the\n # per-repo loopback, which is an important safeguard.\n # It does not stop us from reaching into other subvols,\n # but since those have random IDs in the path, this is\n # nearly impossible to do by accident.\n '--one-file-system',\n path,\n ])\n pass\n\n return builder\n\n\nclass RpmAction(enum.Enum):\n install = 'install'\n # It would be sensible to have a 'remove' that fails if the package is\n # not already installed, but `yum` doesn't seem to support that, and\n # implementing it manually is a hassle.\n remove_if_exists = 'remove_if_exists'\n\n\nRPM_ACTION_TYPE_TO_YUM_CMD = {\n # We do NOT want people specifying package versions, releases, or\n # architectures via `image_feature`s. That would be a sure-fire way to\n # get version conflicts. For the cases where we need version pinning,\n # we'll add a per-layer \"version picker\" concept.\n RpmAction.install: 'install-n',\n # The way `yum` works, this is a no-op if the package is missing.\n RpmAction.remove_if_exists: 'remove-n',\n}\n\n\n# These items are part of a phase, so they don't get dependency-sorted, so\n# there is no `requires()` or `provides()` or `build()` method.\nclass RpmActionItem(metaclass=ImageItem):\n fields = ['name', 'action']\n\n def customize_fields(kwargs): # noqa: B902\n kwargs['action'] = RpmAction(kwargs['action'])\n\n def phase_order(self):\n return {\n RpmAction.install: PhaseOrder.RPM_INSTALL,\n RpmAction.remove_if_exists: PhaseOrder.RPM_REMOVE,\n }[self.action]\n\n @classmethod\n def get_phase_builder(\n cls, items: Iterable['RpmActionItem'], layer_opts: LayerOpts,\n ):\n # Do as much validation as possible outside of the builder to give\n # fast feedback to the user.\n assert (layer_opts.yum_from_snapshot is not None or\n layer_opts.build_appliance is not None), (\n f'`image_layer` {layer_opts.layer_target} must set '\n '`yum_from_repo_snapshot or build_appliance`'\n )\n assert (layer_opts.yum_from_snapshot is None or\n layer_opts.build_appliance is None), (\n f'`image_layer` {layer_opts.layer_target} must not set '\n '`both yum_from_repo_snapshot and build_appliance`'\n )\n\n action_to_rpms = {action: set() for action in RpmAction}\n rpm_to_actions = {}\n for item in items:\n assert isinstance(item, RpmActionItem), item\n action_to_rpms[item.action].add(item.name)\n actions = rpm_to_actions.setdefault(item.name, [])\n actions.append((item.action, item.from_target))\n # Raise when a layer has multiple actions for one RPM -- even\n # when all actions are the same. This can be relaxed if needed.\n if len(actions) != 1:\n raise RuntimeError(\n f'RPM action conflict for {item.name}: {actions}'\n )\n\n def builder(subvol: Subvol):\n for action, rpms in action_to_rpms.items():\n if not rpms:\n continue\n # Future: `yum-from-snapshot` is actually designed to run\n # unprivileged (but we have no nice abstraction for this).\n if layer_opts.build_appliance is None:\n subvol.run_as_root([\n # Since `yum-from-snapshot` variants are generally\n # Python binaries built from this very repo, in\n # @mode/dev, we would run a symlink-PAR from the\n # buck-out tree as `root`. This would leave behind\n # root-owned `__pycache__` directories, which would\n # break Buck's fragile cleanup, and cause us to leak old\n # build artifacts. This eventually runs the host out of\n # disk space. Un-deletable *.pyc files can also\n # interfere with e.g. `test-image-layer`, since that\n # test relies on there being just one `create_ops`\n # subvolume in `buck-image-out` with the \"received UUID\"\n # that was committed to VCS as part of the test\n # sendstream.\n 'env', 'PYTHONDONTWRITEBYTECODE=1',\n layer_opts.yum_from_snapshot,\n *sum((\n ['--protected-path', d]\n for d in _protected_path_set(subvol)\n ), []),\n '--install-root', subvol.path(), '--',\n RPM_ACTION_TYPE_TO_YUM_CMD[action],\n # Sort ensures determinism even if `yum` is\n # order-dependent\n '--assumeyes', '--', *sorted(rpms),\n ])\n else:\n '''\n ## Future\n - implement image feature \"manifold_support\" with all\n those bind-mounts below in mounts = [...]\n - add features = [\"manifold_support\"] to fb_build_appliance\n - call nspawn_in_subvol() instead of run_as_root() below\n '''\n svol = Subvol(\n layer_opts.build_appliance,\n already_exists=True,\n )\n mountpoints = mount_item.mountpoints_from_subvol_meta(svol)\n bind_mount_args = sum((\n [b'--bind-ro=' + svol.path(mp).replace(b':', b'\\\\:') +\n b':' + b'/' + mp.encode()]\n for mp in mountpoints\n ), [])\n protected_path_args = ' '.join(sum((\n ['--protected-path', d]\n for d in _protected_path_set(subvol)\n ), []))\n # Without this, nspawn would look for the host systemd's\n # cgroup setup, which breaks us in continuous integration\n # containers, which may not have a `systemd` in the host\n # container.\n subvol.run_as_root([\n 'env', 'UNIFIED_CGROUP_HIERARCHY=yes',\n 'systemd-nspawn',\n '--quiet',\n f'--directory={layer_opts.build_appliance}',\n '--register=no',\n '--keep-unit',\n '--ephemeral',\n b'--bind=' + subvol.path().replace(b':', b'\\\\:') +\n b':/mnt',\n '--bind-ro=/dev/fuse',\n '--bind-ro=/etc/fbwhoami',\n '--bind-ro=/etc/smc.tiers',\n '--bind-ro=/var/facebook/rootcanal',\n *bind_mount_args,\n '--capability=CAP_NET_ADMIN',\n 'sh',\n '-c',\n (\n 'mkdir -p /mnt/var/cache/yum; '\n 'mount --bind /var/cache/yum /mnt/var/cache/yum; '\n '/usr/bin/yum-from-fb-snapshot '\n f'{protected_path_args}'\n ' --install-root /mnt -- '\n f'{RPM_ACTION_TYPE_TO_YUM_CMD[action]} '\n '--assumeyes -- '\n f'{\" \".join(sorted(rpms))}'\n )\n ])\n\n return builder\n", "sub_path": "fs_image/compiler/items.py", "file_name": "items.py", "file_ext": "py", "file_size_in_byte": 44497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "enum.Enum", "line_number": 48, "usage_type": "attribute"}, {"api_name": "enum.auto", "line_number": 90, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 96, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 97, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 107, "usage_type": "call"}, {"api_name": "enum.unique", "line_number": 47, "usage_type": "attribute"}, {"api_name": "typing.NamedTuple", "line_number": 110, "usage_type": "name"}, {"api_name": "enriched_namedtuple.metaclass_new_enriched_namedtuple", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 187, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 189, "usage_type": "attribute"}, {"api_name": "common.nullcontext", "line_number": 190, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 198, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 200, "usage_type": "call"}, {"api_name": "hashlib.new", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "provides.ProvidesDirectory", "line_number": 239, "usage_type": "call"}, {"api_name": "provides.ProvidesFile", "line_number": 241, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 244, "usage_type": "call"}, {"api_name": "subvol_utils.Subvol", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 312, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path", "line_number": 337, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 347, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 362, "usage_type": "call"}, {"api_name": "subvol_utils.Subvol", "line_number": 392, "usage_type": "name"}, {"api_name": "provides.ProvidesFile", "line_number": 419, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path", "line_number": 422, "usage_type": "attribute"}, {"api_name": "subvol_utils.Subvol", "line_number": 424, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 440, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path", "line_number": 443, "usage_type": "attribute"}, {"api_name": "provides.ProvidesDirectory", "line_number": 453, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 456, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 457, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 457, "usage_type": "call"}, {"api_name": "os.path", "line_number": 457, "usage_type": "attribute"}, {"api_name": "provides.ProvidesFile", "line_number": 464, "usage_type": "call"}, {"api_name": "requires.require_file", "line_number": 467, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path", "line_number": 468, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 479, "usage_type": "call"}, {"api_name": "os.path", "line_number": 479, "usage_type": "attribute"}, {"api_name": "provides.ProvidesDirectory", "line_number": 481, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 482, "usage_type": "call"}, {"api_name": "os.path", "line_number": 482, "usage_type": "attribute"}, {"api_name": "requires.require_directory", "line_number": 485, "usage_type": "call"}, {"api_name": "subvol_utils.Subvol", "line_number": 487, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 489, "usage_type": "call"}, {"api_name": "os.path", "line_number": 489, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 492, "usage_type": "call"}, {"api_name": "os.path", "line_number": 492, "usage_type": "attribute"}, {"api_name": "enriched_namedtuple.NonConstructibleField", "line_number": 500, "usage_type": "name"}, {"api_name": "enriched_namedtuple.NonConstructibleField", "line_number": 501, "usage_type": "name"}, {"api_name": "enriched_namedtuple.NonConstructibleField", "line_number": 502, "usage_type": "name"}, {"api_name": "enriched_namedtuple.NonConstructibleField", "line_number": 503, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 518, "usage_type": "call"}, {"api_name": "os.path", "line_number": 518, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 519, "usage_type": "call"}, {"api_name": "artifacts_dir.find_repo_root", "line_number": 531, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 531, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 552, "usage_type": "call"}, {"api_name": "os.path", "line_number": 552, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 578, "usage_type": "call"}, {"api_name": "provides.ProvidesDoNotAccess", "line_number": 588, "usage_type": "call"}, {"api_name": "requires.require_directory", "line_number": 593, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 593, "usage_type": "call"}, {"api_name": "os.path", "line_number": 593, "usage_type": "attribute"}, {"api_name": "subvol_utils.Subvol", "line_number": 597, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 598, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 601, "usage_type": "call"}, {"api_name": "os.path", "line_number": 601, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 608, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 610, "usage_type": "call"}, {"api_name": "os.path", "line_number": 610, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 617, "usage_type": "call"}, {"api_name": "os.path", "line_number": 617, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 636, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 636, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 636, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 660, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 673, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 686, "usage_type": "call"}, {"api_name": "provides.ProvidesDoNotAccess", "line_number": 690, "usage_type": "call"}, {"api_name": "itertools.dropwhile", "line_number": 703, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 705, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 705, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path", "line_number": 707, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 707, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 710, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 714, "usage_type": "call"}, {"api_name": "os.path", "line_number": 714, "usage_type": "attribute"}, {"api_name": "provides.ProvidesFile", "line_number": 724, "usage_type": "call"}, {"api_name": "provides.ProvidesDirectory", "line_number": 726, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 740, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 745, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 746, "usage_type": "call"}, {"api_name": "provides.ProvidesDirectory", "line_number": 763, "usage_type": "call"}, {"api_name": "provides.ProvidesDoNotAccess", "line_number": 765, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 772, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 777, "usage_type": "name"}, {"api_name": "subvolume_on_disk.SubvolumeOnDisk.from_json_file", "line_number": 795, "usage_type": "call"}, {"api_name": "subvolume_on_disk.SubvolumeOnDisk", "line_number": 795, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 800, "usage_type": "attribute"}, {"api_name": "typing.Iterable", "line_number": 826, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 839, "usage_type": "name"}, {"api_name": "os.path.lexists", "line_number": 859, "usage_type": "call"}, {"api_name": "os.path", "line_number": 859, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 881, "usage_type": "attribute"}, {"api_name": "typing.Iterable", "line_number": 916, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 945, "usage_type": "name"}, {"api_name": "subvol_utils.Subvol", "line_number": 986, "usage_type": "call"}]}
{"seq_id": "135610648", "text": "import pygame\nimport math\nimport json\nimport sys\nimport Loader\n\nfrom Const import *\n\nfrom Classes.Particle import Particle\nfrom Classes.Emmiter import Emmiter\n\npygame.init()\n\npygame.display.set_caption('Particle System')\nscreen = pygame.display.set_mode((800, 600))\n\nclock = pygame.time.Clock()\n\nbackground = pygame.Surface((800, 600))\nbackground.fill(pygame.Color('#000000'))\n\nprint(\"LOADED\")\n\n\ndef getAngle(start, end):\n \"\"\"\n\n :param start: ะะพัะดะธะฝะฐัั 1 ัะพัะบะธ\n :param end: ะะพะพัะดะธะฝะฐัั 2 ัะพัะบะธ\n :return: ะฃะณะพะป ะฒ ะณัะฐะดััะฐั
ะผะตะถะดั ะฟััะผะพะน ะฟัะพั
ะพะดััะตะน ัะตัะตะท [start,end] ะธ ะณะพัะธะทะพะฝัะฐะปัะฝะพะน ะฟััะผะพะน\n \"\"\"\n vector = [start[0] - end[0], start[1] - end[1]]\n if vector[0] == 0:\n vector[0] = 1\n angle = -round(math.degrees(math.atan(vector[1] / vector[0])))\n if vector[0] > 0:\n return angle + 180\n else:\n return angle\n\n\ndef setupEmmiter():\n '''\n\n :return: ะกะฟะธัะพะบ ั ะบะพะพัะดะธะฝะฐัะฐะผะธ ัะผะผะธัะฐัะฐ ะธ ะธะณะปะพะผ ะธัะฟััะบะฐะฝะธั ัะฐััะธั\n '''\n checking = True\n gottem = False\n start = [None, None, None]\n\n while checking:\n\n for i in pygame.event.get():\n if i.type == pygame.MOUSEBUTTONDOWN and not gottem:\n start[0] = pygame.mouse.get_pos()\n gottem = True\n elif i.type == pygame.MOUSEBUTTONUP:\n start[1] = pygame.mouse.get_pos()\n checking = False\n if start[0]:\n screen.fill(BLACK)\n pygame.draw.line(screen, WHITE, start[0], pygame.mouse.get_pos(), 3)\n clock.tick(FPS)\n pygame.display.update()\n start[2] = round(math.hypot(abs(start[0][0] - start[1][0]),\n abs(start[0][1] - start[1][1])))\n start[1] = getAngle(start[0], start[1])\n # print(start)\n return start\n\n\ndef createParticle():\n '''\n ะะพะปัะทะพะฒะฐัะตะปั ัะพะทะดะฐัั ะฝะพะฒัั ัะฐััะธัั ะธ ะพะฝะฐ ัะพั
ัะฐะฝัะตััั ะฒ ัะฐะนะปะต \\{login}\\presets.json\n :return: None\n '''\n\n name = input(\"ะะฒะตะดะธัะต ะฝะฐะทะฒะฐะฝะธะต: \")\n size = int(input(\"ะะฒะตะดะธัะต ัะฐะทะผะตั: \"))\n lifespan = int(input(\"ะะฒะตะดะธัะต ะฟัะพะดะพะปะถะธัะตะปัะฝะพััั ะถะธะทะฝะธ: \"))\n color = [R, G, B] = [int(x) for x in input(\"ะะฒะตะดะธัะต ัะฒะตั ะฒ ัะพัะผะฐัะต rgb - '255 0 255' : \").split()]\n\n # imgsrc = None\n\n shape = input(\"ะะฒะตะดะธัะต ัะพัะผั(cir,tri,sqr,star): \")\n\n newParticle = {\n \"name\": name,\n \"size\": size,\n \"lifespan\": lifespan,\n \"color\": color,\n \"shape\": shape\n }\n directory = sys.argv[0][0:-7] + \"Accounts\\\\\" + loaderData[0]\n path = directory + \"\\\\presets.json\"\n\n oldPresets = {}\n with open(path) as presets:\n oldPresets = json.load(presets)\n oldPresets[\"particles\"].append(newParticle)\n\n with open(path, \"w\") as presets:\n json.dump(oldPresets, presets)\n input(\"ะงะฐััะธัะฐ ัะพั
ัะฐะฝะตะฝะฐ! ะะฐะถะผะธัะต 'Enter'\")\n\n\ndef setupParticle():\n \"\"\"\n ะะฑัะฐะฑะพัะบะฐ ัะพะทะดะฐะฝะธั/ะทะฐะณััะทะบะธ ัะฐััะธั\n :returns: ะัะพัะพัะธะฟ ะพะฑัะตะบัะฐ ะบะปะฐััะฐ Particle ะธะปะธ ัะพะพะฑัะตะฝะธะต ะพะฑ ะพัะธะฑะบะต\n \"\"\"\n ans = input(\"ะฅะพัะธัะต ัะพะทะดะฐัั ะฝะพะฒัั ัะฐััะธัั? [Y/N]\\n\").upper()\n\n directory = sys.argv[0][0:-7] + \"Accounts\\\\\" + loaderData[0]\n path = directory + \"\\\\presets.json\"\n\n if ans == \"Y\":\n createParticle()\n return setupParticle()\n elif ans == \"N\":\n with open(path) as presets:\n data = json.load(presets)\n print(\"ะกะฟะธัะพะบ ะดะพัััะฟะฝัั
ัะฐััะธั: \")\n for p in data[\"particles\"]:\n print(\"#%d: %s\" % (data[\"particles\"].index(p), p))\n index = int(input(\"ะัะฑะตัะธัะต ะฝะพะผะตั: \").lower())\n loadedParticle = data[\"particles\"][index]\n # print(loadedParticle)\n particle = Particle(loadedParticle[\"size\"],\n loadedParticle[\"lifespan\"],\n loadedParticle[\"color\"],\n loadedParticle[\"shape\"])\n return particle\n return print(\"ERROR\")\n\n\ndef main():\n testParticle = setupParticle() # ะฃััะฐะฝะพะฒะบะฐ ะฟะพะทะธัะธะธ ะธ ัะณะปะฐ ะธัะฟััะบะฐะฝะธั ะดะปั ัะผะผะธัะตัะฐ\n start = setupEmmiter() # ะฃััะฐะฝะพะฒะบะฐ ัะผะผะธัะตัะฐ\n testEmmiter = Emmiter(testParticle, # ะัะพัะพ-ะงะฐััะธัะฐ\n start[0], # ะกัะฐััะพะฒะฐั ะฟะพะทะธัะธั\n start[1] + 10, # ะฃะณะพะป 1\n start[1] - 10, # ะฃะณะพะป 2\n 20, # ะกะบะพัะพััั\n 1.1,\n None) # ะัะฐะฒะธัะฐัะธั\n\n\n isRunning = True\n # ะณะปะฐะฒะฝัะน ัะธะบะป\n while isRunning:\n # ะะฐะดัั ะฒ ัะตะบัะฝะดั\n pygame.time.delay(17*2)\n # screen.blit(bg, (0, 0))\n screen.blit(background, (0, 0))\n # screen.blit(horse, (500, 400))\n # ะะฑัะฐะฑะพัะบะฐ ัะพะฑััะธะน\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n isRunning = False\n if event.type == pygame.MOUSEBUTTONDOWN:\n # print(pygame.mouse.get_pos())\n pass\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n pass\n testEmmiter.emmit_new()\n # --------------\n testEmmiter.emmit_old()\n # --------------\n pygame.display.update()\n\n# ----------------------------------------------- #\n\nloaderData = Loader.launch()\n\nif __name__ == \"__main__\":\n main()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "pygame.init", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 20, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 35, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONUP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 64, "usage_type": "attribute"}, {"api_name": "math.hypot", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 94, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 99, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 114, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 122, "usage_type": "call"}, {"api_name": "Classes.Particle.Particle", "line_number": 129, "usage_type": "call"}, {"api_name": "Classes.Emmiter.Emmiter", "line_number": 140, "usage_type": "call"}, {"api_name": "pygame.time.delay", "line_number": 153, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 171, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 171, "usage_type": "attribute"}, {"api_name": "Loader.launch", "line_number": 175, "usage_type": "call"}]}
{"seq_id": "89042853", "text": "\n# coding: utf-8\n\n# In[37]:\n\n\nimport nibabel as nb\nimport numpy as np\nimport glob\nfrom scipy import ndimage as nd\nimport keras\n\npath = \"/Users/SB/Desktop/[0-9][0-9][0-9][0-9][0-9]/*nii.gz\"\nfiles = glob.glob(path)\n\nempty_array = np.zeros((20,20,10))\ndata_array = np.zeros((256, 256, 124))\nfactor = [w/float(f) for w, f in zip(empty_array.shape, data_array.shape)]\nprint(\"resampling factor:\", factor)\n\n#Test for single file\n#for file in files:\n #img = nb.load(file)\n #data = img.get_data()\n # or #data = img.dataobj\n #data = np.array(data)\n #print(data.shape)\n #resampled_data = nd.interpolation.zoom(data, zoom=factor)\n #resampled_data.shape\n #resampled_data.size\n### Create NIfTI out of resampled data to visualize\n#affine = np.diag([1, 2, 3, 1])\n#array_img = nb.Nifti1Image(resampled_data, affine)\n#nb.save(array_img, '/Users/SB/Desktop/array_image.nii.gz')\n \n\n\n# In[38]:\n\n\nx_train = []\ny_train = []\ninput_train = \"/Users/SB/Desktop/ML/CNN_Keras/midus2_nifti/input/train/*\" \ninput_train = glob.glob(input_train)\nfor input in input_train:\n if \"old\" in input:\n img = nb.load(input)\n data = img.get_data()\n resampled_old = nd.interpolation.zoom(data, zoom=factor)\n x_train.append(resampled_old)\n y_train.append(int(1))\n elif \"young\" in input:\n img = nb.load(input)\n data = img.get_data()\n resampled_young = nd.interpolation.zoom(data, zoom=factor)\n x_train.append(resampled_young)\n y_train.append(int(0))\n \nx_train = np.asarray(x_train)\nx_train = np.expand_dims(x_train, axis=4)\ny_train = keras.utils.to_categorical(y_train, num_classes=2)\nx_train.shape\n\n\n# In[39]:\n\n\nx_test = []\ny_test = []\ninput_test = \"/Users/SB/Desktop/ML/CNN_Keras/midus2_nifti/input/validation/*\" \ninput_test = glob.glob(input_test)\n\nfor input in input_test:\n if \"old\" in input:\n img = nb.load(input)\n data = img.get_data()\n resampled_old = nd.interpolation.zoom(data, zoom=factor)\n x_test.append(resampled_old)\n y_test.append(int(1))\n elif \"young\" in input:\n img = nb.load(input)\n data = img.get_data()\n resampled_young = nd.interpolation.zoom(data, zoom=factor)\n x_test.append(resampled_young)\n y_test.append(int(0))\n \nx_test = np.asarray(x_test)\nx_test = np.expand_dims(x_test, axis=4)\ny_test = keras.utils.to_categorical(y_test, num_classes=2)\nx_test.shape\n\n\n# In[47]:\n\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv3D, MaxPooling3D\nfrom keras.optimizers import SGD\n\n### Generate dummy data\n# (# of subjects, x, y, z, # of channels (3 in case of RGB))\n# x_train = np.random.random((1000, 10, 10, 10, 1))\n# 2 categories, (# of subjects, # of channels, num_classes= number of label)\n# y_train = keras.utils.to_categorical(np.random.randint(2, size=(1000, 1)), num_classes=2)\n# x_test = np.random.random((100, 10, 10, 10, 1))\n# y_test = keras.utils.to_categorical(np.random.randint(2, size=(100, 1)), num_classes=2)\n\n### Convolutional Neural Network (Four-Layer deep CNN)\nmodel = Sequential()\n# input: 10x10x10 images with 1 channels -> (10, 10, 10, 1) tensors.\n# this applies 32 convolution filters (kernels) of size 2x2x2 each.\nmodel.add(Conv3D(32, (2, 2, 2), activation='relu', input_shape=(20, 20, 10, 1)))\nmodel.add(Conv3D(32, (2, 2, 2), activation='relu'))\nmodel.add(MaxPooling3D(pool_size=(2, 2, 2)))\n# Dropout after pooling with probabily of 0.25 \nmodel.add(Dropout(0.25))\n\n# Swtiching to 64 kernels per convolution after the first pooling layer\nmodel.add(Conv3D(64, (2, 2, 2), activation='relu'))\nmodel.add(Conv3D(64, (2, 2, 2), activation='relu'))\nmodel.add(MaxPooling3D(pool_size=(2, 2, 2)))\n# Dropout after pooling with probabily of 0.25 \nmodel.add(Dropout(0.25))\n\n### This portion is Multilayer Perceptron(MLP)\n# Flatten to to 2D, apply Fully Connected Layer(FC), ReLU(with dropout), softmax\nmodel.add(Flatten())\n# Dense(256) is a fully-connected layer with 256 hidden units\nmodel.add(Dense(256, activation='relu'))\nmodel.add(Dropout(0.5))\n# the FC layer of the MLP will have 2 neurons\nmodel.add(Dense(2, activation='softmax'))\n\nsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\nmodel.compile(loss='binary_crossentropy', optimizer=sgd)\n\n# In each iteration - consider 20 training examples at once\n# num_epochs - iterate 3 times over the entire traning set\n# validation_split - levae 10% of the data for validation\nmodel.fit(x_train, y_train, batch_size=10, epochs=5)#, validation_split=0.1)\naccuracy = model.evaluate(x_test, y_test, batch_size=10)\n\nprint ('Test Score:', accuracy)\n\n", "sub_path": "analysis/Keras/Keras_CNN_T1w.py", "file_name": "Keras_CNN_T1w.py", "file_ext": "py", "file_size_in_byte": 4646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "glob.glob", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 44, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.zoom", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 49, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 49, "usage_type": "name"}, {"api_name": "nibabel.load", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.zoom", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 55, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 61, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 71, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 75, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.zoom", "line_number": 77, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 77, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 77, "usage_type": "name"}, {"api_name": "nibabel.load", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.zoom", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 83, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 89, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Conv3D", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Conv3D", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling3D", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Conv3D", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.layers.Conv3D", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling3D", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 128, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 135, "usage_type": "call"}]}
{"seq_id": "399205947", "text": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.metrics import mean_squared_error\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout, CuDNNLSTM, CuDNNGRU\nfrom sklearn.preprocessing import MinMaxScaler\nimport matplotlib.pyplot as plt\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n path = os.path.join(dirname, filename)\n print(path)\n data = pd.read_csv(path)\n data = data.loc[:, 'value']\n all_data = list(map(float, list(data)))\n\n\n dd =np.array(all_data).reshape(-1,1)\n scaler = MinMaxScaler()\n scaler.fit(dd)\n dd = scaler.transform(dd)\n all_data=dd.reshape(1, -1)[0]\n \n trainx = []\n trainy = []\n for i in range(190, len(all_data)):\n trainx.append(all_data[i - 190:i - 10])\n trainy.append(all_data[i - 10:i])\n print(len(trainx), len(trainy))\n \n trainx = np.array(trainx).reshape(-1, 180, 1)\n trainy = np.array(trainy)\n \n index = int(len(all_data) * 0.75)\n \n testx = np.array(trainx[index:])\n testy = np.array(trainy[index:])\n trainx = np.array(trainx[:index])\n trainy = np.array(trainy[:index])\n \n print('trainx.shape = ', trainx.shape)\n print('testx.shape = ', testx.shape)\n print('trainy.shape = ', trainy.shape)\n print('testy.shape = ', testy.shape)\n \n model = Sequential()\n model.add(CuDNNLSTM(100, input_shape=(180, 1)))\n model.add(Dense(10))\n model.compile(optimizer='adam', loss='mse')\n model.fit(trainx, trainy, batch_size=1, epochs=2, verbose=1)\n \n \n \n res = model.predict(testx)\n model.evaluate(res,testy)\n \n testx = testx.reshape(-1, 180)\n plot_raw = []\n for x in testx[:-1]:\n plot_raw.append(x[0])\n plot_raw.extend(testx[-1])\n \n plot_raw=np.array(plot_raw).reshape(-1,1)\n plot_raw=scaler.inverse_transform(plot_raw)\n plot_raw=plot_raw.reshape(1,-1)[0]\n \n \n plot_pred = []\n for x in res[::10]:\n plot_pred.extend(x)\n \n plot_pred=np.array(plot_pred).reshape(-1,1)\n plot_pred = scaler.inverse_transform(plot_pred)\n plot_pred=plot_pred.reshape(1,-1)[0]\n \n plt.title(path)\n plt.figure(figsize=(10,8))\n plt.plot(list(range(len(plot_raw))), plot_raw)\n plt.plot(list(range(180, len(plot_pred) + 180)), plot_pred)\n plt.show()\n\n", "sub_path": "time_predict/LSTMpredict-gpu.py", "file_name": "LSTMpredict-gpu.py", "file_ext": "py", "file_size_in_byte": 3089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "os.walk", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}]}
{"seq_id": "618392341", "text": "# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass CNNIntSpider(scrapy.Spider):\n name = 'CNN_int'\n allowed_domains = ['edition.cnn.com/world']\n start_urls = ['https://edition.cnn.com/world/']\n\n def parse(self, response):\n headers = response.xpath(\"//*[@id = 'world-zone-2']//h3[@class = 'cd__headline']/a/@href\").extract()\n for header in headers:\n absolute_url = response.urljoin(header)\n yield scrapy.Request(absolute_url, callback=self.parse_news, dont_filter=True)\n\n def parse_news(self, response):\n\n title = response.css('h1::text').extract_first()\n\n posted_date = response.xpath(\"//p[@class = 'update-time']/text()\").extract_first()\n\n story_body_introduction = response.xpath(\"//p[@class = 'zn-body__paragraph speakable']//text()\").extract_first()\n\n img_url = response.xpath(\"//img[@class = 'media__image media__image--responsive']/@data-src-mini\").extract_first()\n\n story_list = response.xpath('//*[@class=\"zn-body__paragraph speakable\"]//text()').extract()\n information_src = \"CNN\"\n\n img_url = 'https:' + img_url if img_url else img_url\n\n full_story = \"\"\n story_list.pop(0)\n for story in story_list:\n full_story += story\n if full_story[-1] == '.':\n full_story += \"\\n\\t\"\n\n yield {\n 'title': title,\n 'img_url': img_url,\n 'img_copyright': None,\n 'story_body_introduction': story_body_introduction,\n 'story_list': full_story,\n 'posted_date': posted_date,\n 'posted_time': None,\n 'information_src': information_src\n }\n", "sub_path": "ethio_news_cralwer/spiders/CNN_int.py", "file_name": "CNN_int.py", "file_ext": "py", "file_size_in_byte": 1674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "475948239", "text": "\nimport calendar, random\nimport requests, re, json\nfrom time import sleep\nfrom bs4 import BeautifulSoup\nfrom loremipsum import get_sentences\nfrom datetime import datetime\nfrom random import randint\n\n\n# database code copied and modified from Scott (Suk Hoon Kim)\nimport psycopg2 # psycopg2 docs link: http://initd.org/psycopg/docs/usage.html\nimport os\n\nfrom lmnop_project.settings import DATABASES\n\n# DB connection setup\n# HOST = os.environ['DB_URL']\n# DATABASE = os.environ['DATABASE']\n# USER = os.environ['USER']\nHOST = DATABASES['default']['HOST']\nDATABASE = DATABASES['default']['NAME']\nUSER = DATABASES['default']['USER']\nPASSWORD = os.environ['LMNOP_DB_PW']\n\nconnection = psycopg2.connect(host=HOST, database=DATABASE, user=USER, password=PASSWORD)\ncur = connection.cursor()\n\n\n#############\n# webscrapper logic by Jeremy\nMAXPAGES = 10\n\nartist_list = []\nshow_list = []\nvenue_list = []\nnote_list = []\nuser_list = []\n\nartist_json = []\nvenue_json = []\nshow_json = []\nnote_json = []\nuser_json = []\n\n\ndef main():\n '''This program makes calls to Eventful.com to get pages of Minneapolis area musical events,\n cleans the data, and generates JSON files cooresponding the LMNOP project DB tables:\n Artists, Venues, Shows, and Notes (Notes are generated for testing purposes - they will\n not be part of the API call process. Each time this program is run using the stored raw_data_list, a new and\n different Notes JSON file will be generated due to the randomness of how the file is built.)\n\n Any existing JSON files will be overwritten with new data\n\n Exit = -5 There was an error calling the web site for data\n Exit = -10 Error writing out the JSON files\n '''\n\n\n # Note that getting data from the web and scrapping the pages is extremely slow\n # getting and scrapping 100 shows takes several minutes\n # uncomment this line and comment out the following call to getTestData() to run against the web site.\n raw_data_list = getMPLS()\n\n # Test list used to save time during development of this program - list built from live data\n # comment out following line and uncomment above function call to run web scrapping for live data\n # raw_data_list = getTestData()\n\n # for line in raw_data_list:\n # print(line)\n\n # formats the raw line of data and prints in formatted columns\n if len(raw_data_list) > 0:\n scrubData(raw_data_list)\n\n # This function builds lists and dictionaries (JSON) from the raw data list\n buildJSON()\n\n # this truncate command deletes all entries in the specified table.\n # in a production environment this would not be done when new webscrapped data is added to the db\n with connection:\n try:\n # testing delete table entries\n cur.execute('''TRUNCATE TABLE lmn_note RESTART IDENTITY CASCADE ''')\n cur.execute('''TRUNCATE TABLE lmn_show RESTART IDENTITY CASCADE ''')\n cur.execute('''TRUNCATE TABLE lmn_venue RESTART IDENTITY CASCADE ''')\n cur.execute('''TRUNCATE TABLE lmn_artist RESTART IDENTITY CASCADE ''')\n connection.commit()\n\n except psycopg2.Error as e:\n return e\n\n for entry in artist_json:\n add_to_artist_db(entry)\n for entry in venue_json:\n add_to_venue_db(entry)\n for entry in show_json:\n add_to_show_db(entry)\n\n # the following function is no longer needed. the database loading has been incorporated into this module.\n # print(user_json)\n # This function writes the dictionary lists to formatted JSON files and places the files in the fixtures\n # sub-directory\n # writeOutJSON()\n\n\ndef getMPLS():\n '''This function calls eventful.com for Minneapolis area muscial events:\n an example url - http://minneapolis.eventful.com/events/categories/music#!page_number=1&category=music\n Each page contains 10 events.\n The number of pages called for is controled by MAXPAGES variable.\n For each page called the returned results are parsed for a string containing all of the required information,\n example: 'Bon Jovi on Apr 28, 2018 in Saint Paul, MN at Xcel Energy Center.'\n each parsed string is appended to the raw_data_list list.\n '''\n\n raw_data_list = []\n\n print(\"Getting web data from %s pages total, please be patient.\" % MAXPAGES)\n for page in range(1, MAXPAGES+1):\n # result = requests.get(\"http://minneapolis.eventful.com/events/categories/music\")\n httpString = \"http://minneapolis.eventful.com/events/categories/music?page_number=\" + str(\n page) + \"&category=music\"\n print(httpString)\n try:\n result = requests.get(httpString)\n sleep(1) # pause Python to allow return from call, not sure if this is needed\n print(result.status_code)\n except Exception as e:\n print('An exception happened while get web data: %s' % e)\n exit (-5)\n\n if result.status_code == 200:\n c = result.content # page returned\n soup = BeautifulSoup(c, \"html.parser\") # parse the page into beautiful soup format\n samples2 = soup.find_all(\"a\", 'tn-frame') # extract all a tags with class contains 'tn-frame'\n #print(samples2)\n\n for item in samples2:\n myURL = item.get('href')\n myURL = \"http:\" + myURL\n result = requests.get(myURL) # call Eventful.com for each individual event's data\n c = result.content\n minisoup = BeautifulSoup(c, \"html.parser\")\n samples3 = minisoup.find(\"meta\", {\"name\": \"description\"})['content'] # extract string with required\n # event information\n\n # we only want entries with one occurence of the literals \"on\", \"in\", and \"at\"\n # anything else will break the Regex code\n valid_data = validate(samples3)\n\n if valid_data:\n raw_data_list.append(samples3)\n else:\n print(\"There was a problem getting web data. Web call return code = %s\" % result.status_code)\n print(\"Web address: %s \" % httpString)\n\n return raw_data_list\n\n\ndef validate(samples3):\n '''Some entries from the web site may be so malformed the Regex won't work.\n If more than one occurence of \"on\", \"in\", or \"at\" reject the entry'''\n\n # https://stackoverflow.com/questions/19848353/number-of-occurrences-of-a-substring-in-a-string\n if ( (samples3.count(' on ') == 1) and\n (samples3.count(' in ') == 1) and\n (samples3.count(' at ') == 1) ):\n return True\n\n return False\n\n\ndef scrubData(raw_data_list):\n '''This function iterates thru the raw_data_list and cleans (scrubs) the data using Regex to removed\n unwanted characters and split the main string apart into components destined for different tables.'''\n\n # print()\n # print(\"Events scrapped from Eventful.com: \")\n # print()\n line_count = 0\n for line in raw_data_list:\n # print(line_count)\n # print(\"'\" + line + \"',\") ### builds raw line scrapped from web for development test data\n # example line='Counting Crows & Live - Band on Sep 16, 2018 in Prior Lake, MN(Minneapolis / Saint Paul metro area) at Mystic Lake Casino Hotel.'\n\n # regex to remove 'amp;' from some artist names, is html for &\n line1 = re.sub('amp;', '', line)\n\n # regex removes period at the end of line (immediately following venue name)\n line2 = re.sub('\\.', '', line1)\n\n # ['Taylor Swift', ' on ', 'Sep 1, 2018 in Minneapolis, MN at US Bank Stadium']\n myGroup = re.split(r'( on +)', line2)\n # myGroup[0]=artist\n\n # ['Sep 1, 2018', ' in ', 'Minneapolis, MN', ' at ', 'US Bank Stadium']\n date_cityst_venue = re.split(r'( in +| at +)', myGroup[2])\n # date_cityst_venue[0]=date\n # date_cityst_venue[2]=city, state\n # date_cityst_venue[4]=venue\n\n # regex splits apart city, state field\n myCitySt = re.split(r'(, )', date_cityst_venue[2])\n # myCitySt[0]=city\n\n # regex splits apart state abbrev. from \"(Minneapolis / Saint Paul metro area)\" literal\n myState = re.split(r'(\\(.*\\))', myCitySt[2])\n # myState[0]=state abbrev.\n\n #print('%80s %15s %15s %4s %40s' % (myGroup[0], date_cityst_venue[0], myCitySt[0], myState[0], date_cityst_venue[4][:30])\n # + (' field sizes: %d %d %d %d %d' % (len(myGroup[0]), len(date_cityst_venue[0]), len(myCitySt[0]), len(myState[0]), len(date_cityst_venue[4][:40]))))\n\n line_count += 1\n\n artist_list.append(myGroup[0])\n\n show_list.append(date_cityst_venue[0])\n\n venue_list.append((date_cityst_venue[4][:40],myCitySt[0],myState[0]))\n\n # print()\n # print(\"%s events found for Minneapolis area.\" % line_count)\n\n\n\ndef buildJSON():\n '''This function creates five json format dictionaries: artists, venues, shows, users, notes\n In some cases, in order to determine the foreign key ID, a mini-routine is called to scan the foreign\n list (table) for the matching element.\n In some cases where duplicate entries are possible, such as venues, the list is searched for an existing element.\n '''\n\n # build artist json list\n num = 0\n afirst_time = True\n for item in artist_list:\n if afirst_time:\n num += 1\n artist_json.append({\"model\":\"lmn.artist\", \"pk\":num, \"fields\": {\"name\":item}})\n afirst_time = False\n else:\n afirst_time = False\n for aitem in artist_json:\n if aitem[\"fields\"][\"name\"] in item:\n #print(\"In build artist table duplicate = \" + aitem[\"fields\"][\"name\"])\n found = True\n break\n else:\n found = False\n if not found:\n num += 1\n artist_json.append({\"model\": \"lmn.artist\", \"pk\": num, \"fields\": {\"name\": item}})\n\n # build venue json list\n num = 0\n first_time = True\n for item in venue_list:\n\n if first_time:\n num = 1\n venue_json.append(\n {\"model\": \"lmn.venue\", \"pk\": num, \"fields\": {\"name\": item[0], \"city\": item[1], \"state\": item[2]}})\n first_time = False\n else:\n found = False\n #print('value searching for: ' + item[0] + '***')\n for vitem in venue_json:\n #print('value in venue dictionary: ' + vitem['fields']['name'] + '***')\n if vitem['fields']['name'] in item[0]:\n found = True\n #print(\"******************** found *********************\")\n break\n else:\n found = False\n\n if not found:\n num += 1\n venue_json.append({\"model\":\"lmn.venue\", \"pk\":num, \"fields\": {\"name\": item[0], \"city\": item[1], \"state\": item[2]}})\n\n # build show json list\n num = 0\n for item in show_list:\n\n num += 1\n venue_key = getVenue(venue_list[num-1][0])\n artist_key = getArtist(artist_list[num-1])\n #print(\"returned artist key number: \" + str(artist_key))\n #day_string = datetime.strptime(item, '%b %d, %Y')\n\n d = datetime.strptime(item, '%b %d, %Y')\n day_string = d.strftime('%Y-%m-%d')\n\n # print(day_string)\n # print(item)\n\n if venue_key > 0 and artist_key > 0:\n show_json.append({\"model\":\"lmn.show\", \"pk\":num, \"fields\": {\"show_date\": str(day_string), \"artist\": artist_key, \"venue\": venue_key }})\n else:\n print(\"error: venue or artist for show was not found\")\n\n\n buildUsers()\n\n buildNotes()\n\n\ndef getArtist(artist):\n '''Get the artist Primary Key ID of an existing artist, or return zero if not found'''\n num = 0\n for aitem in artist_json:\n\n num += 1\n if aitem['fields']['name'] in artist:\n return num\n\n return 0\n\n\ndef getVenue(venue):\n '''Get the venue Primary Key ID of an existing venue, or return zero if not found'''\n num = 0\n for vitem in venue_json:\n num += 1\n #print('value in venue dictionary: ' + vitem['fields']['name'] + '***')\n if vitem['fields']['name'] in venue:\n\n #print(\"******************** found venue key *********************\")\n return num\n\n return 0 # error, venue was not found\n\n\ndef buildUsers():\n ''' Users are created for testing purposes and for use in creating Notes.'''\n\n user_json.append({\"model\": \"auth.user\", \"pk\": 1, \"fields\": {\"username\": \"kevin\", \"first_name\": \"Kevin\", \"last_name\": \"Kevin-last\", \"email\": \"xcv123@aaa.com\", \"password\": \"pbkdf2_sha256$100000$FmE0uvtW02K6$lRqwDwm/E0M/0Fgb9v09ZyH4p5simN6vBLy+KyzqDOU=\"}})\n user_json.append({\"model\": \"auth.user\", \"pk\": 2, \"fields\": {\"username\": \"bob\", \"first_name\": \"Bob\", \"last_name\": \"Bob-last\", \"email\": \"zxcv456@aaa.com\", \"password\": \"pbkdf2_sha256$100000$3m7XTcsWfgZE$8FRAIG1EL8d+QxuKKNWeKo4F5aEpRqtxnSeyJbeJwe0=\"}})\n user_json.append({\"model\": \"auth.user\", \"pk\": 3, \"fields\": {\"username\": \"mary\", \"first_name\": \"Mary\", \"last_name\": \"Mary-last\", \"email\": \"zxcv567@aaa.com\", \"password\": \"pbkdf2_sha256$100000$YsLjt3vB2eme$Qj8+si71Zgh3eM0TBpskpbD2OMVEmg1gtAzczYKfQLY=\"}})\n user_json.append({\"model\": \"auth.user\", \"pk\": 4, \"fields\": {\"username\": \"jane\", \"first_name\": \"Jane\", \"last_name\": \"Jane-last\", \"email\": \"zxcv123@aaa.com\", \"password\": \"pbkdf2_sha256$100000$FmE0uvtW02K6$lRqwDwm/E0M/0Fgb9v09ZyH4p5simN6vBLy+KyzqDOU=\"}})\n user_json.append({\"model\": \"auth.user\", \"pk\": 5, \"fields\": {\"username\": \"bill\", \"first_name\": \"Bill\", \"last_name\": \"Bill-last\", \"email\": \"wer456@aaa.com\", \"password\": \"pbkdf2_sha256$100000$3m7XTcsWfgZE$8FRAIG1EL8d+QxuKKNWeKo4F5aEpRqtxnSeyJbeJwe0=\"}})\n user_json.append({\"model\": \"auth.user\", \"pk\": 6, \"fields\": {\"username\": \"betty\", \"first_name\": \"Betty\", \"last_name\": \"Betty-last\", \"email\": \"wer567@aaa.com\", \"password\": \"pbkdf2_sha256$100000$YsLjt3vB2eme$Qj8+si71Zgh3eM0TBpskpbD2OMVEmg1gtAzczYKfQLY=\"}})\n\n # for line in user_json:\n # print(line)\n\n # pbkdf2_sha256$100000$FmE0uvtW02K6$lRqwDwm/E0M/0Fgb9v09ZyH4p5simN6vBLy+KyzqDOU=\n # password: asdfasdf users: kevin jane\n\n # pbkdf2_sha256$100000$3m7XTcsWfgZE$8FRAIG1EL8d+QxuKKNWeKo4F5aEpRqtxnSeyJbeJwe0=\n # password: zxcvzxcv users: bob bill\n\n # pbkdf2_sha256$100000$YsLjt3vB2eme$Qj8+si71Zgh3eM0TBpskpbD2OMVEmg1gtAzczYKfQLY=\n # password: qwerqwer users: mary betty\n\n\ndef buildNotes():\n '''After the users list is generated, Notes can be built.\n Notes generated with random text\n The user writing the note is determined randomly\n A data in the specified month is determined random - any day within the specified month, month can be changed\n Every other show will get between 1 and 4 notes.\n '''\n\n SENTENCECOUNT = 1250\n #LINESPERPAGE = 10\n STEP = 2\n NUMUSERS = 6\n RANDYEAR = 2018\n RANDMONTH = 4\n\n sentences_list = []\n sentences = get_sentences(SENTENCECOUNT, start_with_lorem=True)\n\n # this routine uses \"from loremipsum import get_sentences\"\n for sentence in sentences:\n line1 = re.sub(\"b'\", '', sentence)\n line2 = re.sub(\"'\", '', line1)\n sentences_list.append(line2)\n\n recNum = 0\n for num in range(1, len(show_list), STEP):\n for innerNum in range(randint(1,4)):\n recNum += 1\n rand = randint(1, NUMUSERS)\n pop1 = sentences_list.pop()\n myDate = randomdate(RANDYEAR, RANDMONTH)\n #print(myDate)\n nText = sentences_list.pop() + sentences_list.pop() + sentences_list.pop() + sentences_list.pop()\n note_json.append({\"model\": \"lmn.note\", \"pk\": recNum, \"fields\": {\"show\": num, \"user\": rand, \"title\": pop1, \"text\": nText, \"posted_date\": str(myDate)}})\n\n # for item in note_json:\n # print(item)\n\n\n# https://stackoverflow.com/questions/45833559/generate-random-date-in-a-particular-month-and-year\ndef randomdate(year, month):\n '''Returns a date somewhere within the year/month provided'''\n dates = calendar.Calendar().itermonthdates(year, month)\n return random.choice([date for date in dates if date.month == month])\n\n\n\ndef writeOutJSON():\n '''Format and write JSON files from json dictionary lists'''\n\n try:\n\n with open('fixtures/artist.json', 'w') as fp:\n # prettify the json using json.dumps\n json_string = json.dumps(artist_json, indent=4)\n fp.write(json_string)\n with open('fixtures/venue.json', 'w') as fp:\n json_string = json.dumps(venue_json, indent=4)\n fp.write(json_string)\n with open('fixtures/show.json', 'w') as fp:\n json_string = json.dumps(show_json, indent=4)\n fp.write(json_string)\n with open('fixtures/user.json', 'w') as fp:\n json_string = json.dumps(user_json, indent=4)\n fp.write(json_string)\n with open('fixtures/note.json', 'w') as fp:\n json_string = json.dumps(note_json, indent=4)\n fp.write(json_string)\n\n\n except Exception as e:\n print(\"file error writing out JSON file: % e\" % e)\n exit(-10)\n\n\n\ndef getTestData():\n # live data as of 04/06/2018\n raw_data_list = [\n 'Bon Jovi on Apr 28, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Maroon 5: Red Pill Blues Tour 2018 on Sep 18, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Rod Stewart & Cyndi Lauper on Aug 15, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Journey & Def Leppard on Jul 27, 2018 in Minneapolis, MN at Target Field.',\n 'Bruno Mars & Cardi B on Sep 11, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Justin Timberlake on Sep 28, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Keyshia Cole and Tank on Oct 27, 2018 in Minneapolis, MN at Orpheum Theatre.',\n 'Vans Warped Tour 2018 on Jul 22, 2018 in Shakopee, MN(Minneapolis / Saint Paul metro area) at Canterbury Park.',\n 'Metallica on Sep 4, 2018 in Minneapolis, MN at Target Center.',\n 'Sugarland on Aug 24, 2018 in Saint Paul, MN at Minnesota State Fair.',\n 'Jason Mraz on Aug 28, 2018 in Saint Paul, MN at Minnesota State Fair Grandstand.',\n 'Counting Crows & Live - Band on Sep 16, 2018 in Prior Lake, MN(Minneapolis / Saint Paul metro area) at Mystic Lake Casino Hotel.',\n 'Tech N9ne on Apr 21, 2018 in Minneapolis, MN at The Armory.',\n 'Kenny Chesney: Trip Around the Sun Tour on May 5, 2018 in Minneapolis, MN at U.S. Bank Stadium.',\n 'MercyMe & Tenth Avenue North on Apr 13, 2018 in Minneapolis, MN at Target Center.',\n 'Joan Baez on Oct 6, 2018 in Minneapolis, MN at State Theatre.',\n 'The Pretenders on Jul 16, 2018 in Minneapolis, MN at State Theatre.',\n 'Luke Bryan, Sam Hunt & Jon Pardi on Jul 21, 2018 in Minneapolis, MN at Target Field.',\n 'Zac Brown Band & OneRepublic on Aug 10, 2018 in Minneapolis, MN at Target Field.',\n 'Bruno Mars & Cardi B on Sep 12, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Foo Fighters on Oct 18, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Justin Timberlake - The Man Of The Woods Tour on Sep 29, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Paramore & Foster The People on Jul 5, 2018 in Minneapolis, MN at The Armory.',\n 'Taylor Swift on Sep 1, 2018 in Minneapolis, MN at U.S. Bank Stadium.',\n 'An Acoustic Evening w/ Andrew McMahon in the Wilderness & Friends on Apr 13, 2018 in Minneapolis, MN at Varsity Theater.',\n 'Robert Earl Keen on Apr 12, 2018 in Minneapolis, MN at Varsity Theater.',\n 'CALEXICO on Apr 23, 2018 in Minneapolis, MN at Fine Line Music Cafe. with special guest RYLEY WALKER18+ SHOW โย please review our minor policy under F.A....',\n 'Franz Ferdinand on Apr 27, 2018 in Minneapolis, MN at First Avenue.',\n 'Tim McGraw & Faith Hill on Jul 7, 2018 in Minneapolis, MN at Target Center.',\n 'Panic! At The Disco, Hayley Kiyoko & Arizona on Jul 11, 2018 in Minneapolis, MN at Target Center.',\n 'Leon Bridges on Sep 20, 2018 in Saint Paul, MN at Palace Theatre St. Paul.',\n 'Ringo Starr And His All Starr Band on Sep 23, 2018 in Saint Paul, MN at Ordway Center for the Performing Arts.',\n 'Elton John on Feb 22, 2019 in Minneapolis, MN at Target Center.',\n 'Alice Cooper on Aug 30, 2018 in Saint Paul, MN at Ordway Center for the Performing Arts.',\n 'Needtobreathe, Johnnyswim & Forest Blakk on Sep 7, 2018 in Minneapolis, MN at The Armory.',\n 'O.A.R. & Matt Nathanson on Sep 7, 2018 in Prior Lake, MN(Minneapolis / Saint Paul metro area) at Mystic Lake Casino Hotel.',\n 'Josh Groban & Idina Menzel on Nov 2, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Elton John on Feb 21, 2019 in Minneapolis, MN at Target Center.',\n '3 Doors Down & Collective Soul on Jul 26, 2018 in Minneapolis, MN at The Armory.',\n 'Rise Against (18+ Event) on Sep 6, 2018 in Minneapolis, MN at The Armory.',\n 'Lyle Lovett and His Large Band on Aug 29, 2018 in Minneapolis, MN at State Theatre.',\n 'On The Run II: Beyonce & Jay-Z on Aug 8, 2018 in Minneapolis, MN at US Bank Stadium.',\n 'Jorja Smith on May 1, 2018 in Minneapolis, MN at Fine Line Music Cafe.',\n 'Camila Cabello on Apr 20, 2018 in Minneapolis, MN at State Theatre.',\n 'Marco Antonio Solis: Y La Historia Continua on Apr 21, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Voices for Vision - Featuring Kat Perkins & Blind Joe on Apr 21, 2018 in Minneapolis, MN at State Theatre.',\n 'AJR on Apr 8, 2018 in Minneapolis, MN at Music Hall Minneapolis.',\n 'Brian Fallon on Apr 17, 2018 in Minneapolis, MN at Music Hall Minneapolis.',\n 'Ty Segall Tickets (18+ Event) on Apr 7, 2018 in Minneapolis, MN at 7th Street Entry.',\n 'Jethro Tull on Aug 31, 2018 in Minneapolis, MN at State Theatre.',\n 'TAUK on Apr 12, 2018 in Minneapolis, MN at Fine Line Music Cafe.',\n 'Albert Hammond Jr Tickets (21+ Event) on Apr 7, 2018 in Saint Paul, MN at Turf Club.',\n 'Echosmith on Apr 13, 2018 in Minneapolis, MN at First Avenue.',\n 'Dessa on Apr 6, 2018 in Minneapolis, MN at First Avenue.',\n 'Charlie Puth: The Voicenotes Tour on Aug 8, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Sam Smith on Aug 14, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Margo Price on Apr 14, 2018 in Minneapolis, MN at First Avenue.',\n 'The Decemberists on Apr 7, 2018 in Saint Paul, MN at Palace Theatre St. Paul.',\n 'Jack White on Aug 6, 2018 in Minneapolis, MN at The Armory.',\n 'James Bay on Oct 2, 2018 in Minneapolis, MN at State Theatre.',\n 'Kate Nash on Apr 19, 2018 in Minneapolis, MN at First Avenue.',\n 'Smashing Pumpkins on Aug 19, 2018 in Saint Paul, MN at Xcel Energy Center.',\n 'Erasure - World Be Gone Tour on Jul 29, 2018 in Minneapolis, MN at State Theatre.',\n 'Wyclef Jean on Apr 13, 2018 in Minneapolis, MN at The Pourhouse.',\n 'U Wanna Dance *Swing*Blues*Cajun*Zydeco* on Apr 6, 2018 in Minneapolis, MN at Minneapolis, Minnesota, United States. http://www.uwannadance.com is a FRE...',\n 'Monster Jam on Apr 7, 2018 in Minneapolis, MN at US Bank Stadium. Tickets are now on sale for Monster Jamยฎ at U.S. Bank Stadium in Minneapolis on Saturd...',\n 'Thursday Morning Artist Series on Apr 12, 2018 in Minneapolis, MN at MacPhail Center for Music. Complimentary Coffee and Donuts at 10:00 am Tickets $15,...',\n 'Cradle of Filth on Apr 14, 2018 in Minneapolis, MN at Music Hall Minneapolis.',\n 'Irish Singer Mรกirรญn Uรญ Chรฉide in Concert on Apr 7, 2018 in Saint Paul, MN at Celtic Junction Arts Center. The Traditional Singers Club presents a concer...',\n 'The sword on Apr 7, 2018 in Minneapolis, MN at Skyway Theatre. Skyway Theatre Presents The Sword Saturday, April 7th, 2018 @ Skyway Theatre :::: w/ supp...',\n 'Ed Sheeran: 2018 North American Stadium Tour on Oct 20, 2018 in Minneapolis, MN at U.S. Bank Stadium.',\n 'Taylor Swift on Aug 31, 2018 in Minneapolis, MN at U.S. Bank Stadium.',\n 'The Belfast Cowboys on Apr 6, 2018 in Minneapolis, MN at The Hook and Ladder Theater & Lounge. This nine-piece band has been playing in and around Minne...',\n 'The Eagles, Jimmy Buffett and The Coral Reefer Band on Jun 30, 2018 in Minneapolis, MN at Target Field.',\n 'Tony Bennett with - special guest Antonia Bennett on May 10, 2018 in Minneapolis, MN at State Theatre.',\n 'Todrick Hall on Apr 8, 2018 in Minneapolis, MN at Varsity Theater.',\n 'Tonic Sol-fa and Shaun Johnson Big Band Experience on Apr 12, 2018 in Burnsville, MN(Minneapolis / Saint Paul metro area) at Burnsville Performing Arts ...',\n '5 Seconds of Summer on Apr 15, 2018 in Minneapolis, MN at Varsity Theater.',\n 'BRIAN FALLON & THE HOWLING WEATHER on Apr 17, 2018 in Minneapolis, MN at Music Hall Minneapolis.',\n 'Coast Modern on Apr 9, 2018 in Saint Paul, MN at Amsterdam Bar and Hall.',\n 'SiriusXM Presents Alt Nation's Advanced Placement Tour on Apr 19, 2018 in Saint Paul, MN at Amsterdam Bar and Hall.',\n 'Janine on Apr 11, 2018 in Saint Paul, MN at Amsterdam Bar and Hall.',\n 'KC & the Sunshine Band on Apr 12, 2018 in Prior Lake, MN(Minneapolis / Saint Paul metro area) at Mystic Lake Casino Hotel.',\n 'Decemberists Tickets (18+ Event) on Apr 6, 2018 in Saint Paul, MN at Palace Theatre St. Paul.',\n 'KC and the Sunshine Band Tickets (18+ Event) on Apr 12, 2018 in Prior Lake, MN(Minneapolis / Saint Paul metro area) at Mystic Lake Casino Hotel.',\n 'Cradle of Filth Tickets (18+ Event) on Apr 14, 2018 in Minneapolis, MN at Music Hall Minneapolis.',\n 'Matt and Kim Tickets (18+ Event) on Apr 16, 2018 in Minneapolis, MN at First Avenue.',\n 'TAUK Tickets (18+ Event) on Apr 12, 2018 in Minneapolis, MN at First Avenue.',\n 'Missio Tickets (21+ Event) on Apr 9, 2018 in Saint Paul, MN at Turf Club.',\n 'Moose Blood on Apr 12, 2018 in Burnsville, MN(Minneapolis / Saint Paul metro area) at The Garage Burnsville. with Lydia, Souvenirs',\n 'Progressive Nightclub on Apr 9, 2018 in Saint Paul, MN at Cinema Ballroom. Samba',\n \"Chad Prather on Apr 27, 2018 in Minneapolis, MN at The Women's Club of Minneapolis.\",\n 'Dumbfoundead on Apr 29, 2018 in Minneapolis, MN at Loring Pasta Bar.',\n 'Robyn Hitchcock on Apr 26, 2018 in Saint Paul, MN at Turf Club.',\n 'Lord Huron on Apr 22, 2018 in Saint Paul, MN at Palace Theatre St. Paul.',\n 'George Ezra on Apr 30, 2018 in Minneapolis, MN at First Avenue.',\n 'Khruangbin on Apr 21, 2018 in Saint Paul, MN at Turf Club.',\n 'X Ambassadors on May 1, 2018 in Saint Paul, MN at Myth.',\n 'Trampled By Turtles on May 4, 2018 in Saint Paul, MN at Palace Theatre St. Paul.',\n 'Unknown Mortal Orchestra Tickets (18+ Event) on May 4, 2018 in Minneapolis, MN at First Avenue.'\n ]\n return raw_data_list\n\n\n# this section of database table loading code is based on a module written by classmate Scott (Suk Hoon Kim).\n\n# add artist data\ndef add_to_artist_db(data):\n # Insert queries\n\n artist_insert = \"\"\"\n INSERT INTO lmn_artist(name)\n VALUES (%s)\n ON CONFLICT DO NOTHING;\n \"\"\"\n\n # contraints re-created to prevent the addition\n constraints = '''\n ALTER TABLE lmn_artist DROP CONSTRAINT IF EXISTS lmn_artist_name_key;\n ALTER TABLE lmn_artist ADD CONSTRAINT lmn_artist_name_key UNIQUE (name);\n '''\n\n with connection:\n try:\n\n # Drop/add constraints so there will be no same artist/show added to the table\n cur.execute(constraints)\n connection.commit()\n\n artist_name = data['fields']['name']\n print(artist_name)\n cur.execute(artist_insert, (artist_name,))\n connection.commit()\n\n except psycopg2.Error as e:\n return e\n\n\n# add venue data\ndef add_to_venue_db(data):\n # Insert queries\n venue_insert = \"\"\"\n INSERT INTO lmn_venue(name, city, state)\n VALUES (%s, %s, %s)\n ON CONFLICT DO NOTHING;\n \"\"\"\n\n with connection:\n try:\n\n venue_name = data['fields']['name']\n city = data['fields']['city']\n state = data['fields']['state']\n\n cur.execute(venue_insert, (venue_name, city, state))\n connection.commit()\n\n except psycopg2.Error as e:\n return e\n\n\n# add show data\ndef add_to_show_db(data):\n\n # Insert queries\n\n show_insert = \"\"\"\n INSERT INTO lmn_show(show_date, artist_id, venue_id)\n VALUES (\n %s, \n (SELECT id FROM lmn_artist WHERE name=%s LIMIT 1), \n (SELECT id FROM lmn_venue WHERE name=%s LIMIT 1)\n )\n ON CONFLICT DO NOTHING;\n \"\"\"\n\n # contraints re-created to prevent the addition\n constraints = '''\n ALTER TABLE lmn_show DROP CONSTRAINT IF EXISTS show;\n ALTER TABLE lmn_show ADD CONSTRAINT show UNIQUE (show_date, artist_id, venue_id);\n '''\n\n with connection:\n try:\n # Drop/add constraints so there will be no same artist/show added to the table\n cur.execute(constraints)\n connection.commit()\n\n show_date = data['fields']['show_date']\n artist = get_value_artist_info(data['fields']['artist'])\n venue = get_value_venue_info(data['fields']['venue'])\n\n cur.execute(show_insert, (show_date, artist, venue))\n connection.commit()\n\n except psycopg2.Error as e:\n return e\n\n\n################## this following note table logic has not been implemented DON\"T USE THIS Note Code ############\n\n# add note data\ndef add_to_note_db(data):\n\n # Insert queries\n note_insert = \"\"\"\n INSERT INTO lmn_note(title, text, posted_date, show_id, user_id)\n VALUES (\n %s, %s, %date,\n (SELECT id FROM lmn_show WHERE name=%s LIMIT 1), \n (SELECT id FROM lmn_user WHERE name=%s LIMIT 1)\n )\n ON CONFLICT DO NOTHING;\n \"\"\"\n\n # contraints re-created to prevent the addition\n\n # need note constraints????\n\n\n constraints = '''\n ALTER TABLE lmn_note DROP CONSTRAINT IF EXISTS note;\n ALTER TABLE lmn_note ADD CONSTRAINT note UNIQUE (show_id, user_id);\n '''\n\n with connection:\n try:\n # Drop/add constraints so there will be no same artist/show added to the table\n cur.execute(constraints)\n connection.commit()\n\n show_date = data['fields']['posted_date']\n title = data['fields']['title']\n text = data['fields']['text']\n show_id = get_value_showid_info(data['fields']['show'])\n user_id = get_value_userid_info(data['fields']['user'])\n\n cur.execute(note_insert, (title, text, show_date, show_id, user_id))\n connection.commit()\n\n\n except psycopg2.Error as e:\n return e\n\n\ndef get_value_artist_info(art_id):\n\n for item in artist_json:\n if item['pk'] == art_id:\n return item['fields']['name']\n\n exit(-20) # error. matching artist in artist list not found\n\n\ndef get_value_venue_info(ven_id):\n\n for item in venue_json:\n if item['pk'] == ven_id:\n return item['fields']['name']\n\n exit(-21) # error. matching venue in venue list not found\n\n\ndef get_value_showid_info(id):\n pass\n\n\ndef get_value_userid_info(id):\n pass\n\n\n\nif __name__ == '__main__':\n main()\n\n# https://stackoverflow.com/questions/11205386/python-beautifulsoup-get-an-attribute-value-based-on-the-name-attribute/11205758\n", "sub_path": "webscrapper.py", "file_name": "webscrapper.py", "file_ext": "py", "file_size_in_byte": 32155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "lmnop_project.settings.DATABASES", "line_number": 21, "usage_type": "name"}, {"api_name": "lmnop_project.settings.DATABASES", "line_number": 22, "usage_type": "name"}, {"api_name": "lmnop_project.settings.DATABASES", "line_number": 23, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "psycopg2.Error", "line_number": 91, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 127, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 128, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 136, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 143, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 145, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 189, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 192, "usage_type": "call"}, {"api_name": "re.split", "line_number": 195, "usage_type": "call"}, {"api_name": "re.split", "line_number": 199, "usage_type": "call"}, {"api_name": "re.split", "line_number": 205, "usage_type": "call"}, {"api_name": "re.split", "line_number": 209, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 292, "usage_type": "name"}, {"api_name": "loremipsum.get_sentences", "line_number": 374, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 378, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 379, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 384, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 386, "usage_type": "call"}, {"api_name": "calendar.Calendar", "line_number": 400, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 401, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 412, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 415, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 418, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 421, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 424, "usage_type": "call"}, {"api_name": "psycopg2.Error", "line_number": 571, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 594, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 632, "usage_type": "attribute"}, {"api_name": "psycopg2.Error", "line_number": 678, "usage_type": "attribute"}]}
{"seq_id": "319922895", "text": "#!/usr/bin/env python3\n# coding=utf8\n# version: 1.0.0\n\nimport re\n\nhairTags =['orange hair', 'white hair', 'aqua hair', 'gray hair',\n 'green hair', 'red hair', 'purple hair', 'pink hair',\n 'blue hair', 'black hair', 'brown hair', 'blonde hair']\n\neyesTags = ['gray eyes', 'black eyes', 'orange eyes', 'pink eyes',\n 'yellow eyes', 'aqua eyes', 'purple eyes', 'green eyes',\n 'brown eyes', 'red eyes', 'blue eyes', 'padding']\n\ntoHairIdx = {v: i for i, v in enumerate(hairTags)}\n\ntoEyesIdx = {v: i for i, v in enumerate(eyesTags)}\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch import Tensor\n\ndef createVariable(tensor, use_cuda, volatile=False, **kwargs):\n var = Variable(tensor, volatile=volatile, **kwargs)\n return var.cuda() if use_cuda else var\n\ndef toList(x):\n if isinstance(x, Variable):\n return x.data.cpu().numpy().tolist()\n if isinstance(x, Tensor):\n return x.cpu().numpy().tolist()\n\nimport numpy as np\nfrom PIL import Image\ndef illum(x):\n r, g, b = x[:,:,0], x[:,:,1], x[:,:,2]\n l = r * 0.299 + g * 0.587 + b * 0.114\n return l\n\ndef toImage(x):\n x = x.transpose(1, 2, 0)\n if x.shape[2] == 3:\n I = illum(x)\n else:\n x = x.reshape(x.shape[:2])\n I = x\n img = np.clip(x, -1, 2)\n img = np.clip((img - I.mean()) / (I.std() + 1e-20) / 3 + 0.667, 0, 1)\n img = (img * 255).astype(np.uint8)\n img = Image.fromarray(img)\n\n org = np.clip(x, 0, 1)\n org = (org * 255).astype(np.uint8)\n org = Image.fromarray(org)\n\n return img, org\n", "sub_path": "hw4/pcom1/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "torch.autograd.Variable", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 28, "usage_type": "argument"}, {"api_name": "torch.Tensor", "line_number": 30, "usage_type": "argument"}, {"api_name": "numpy.clip", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 49, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 54, "usage_type": "name"}]}
{"seq_id": "648381532", "text": "\"\"\"GAOGDLV URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\n\nfrom GAOGDS import views as base_views\n\nurlpatterns = [\n url(r'^admin/', include(admin.site.urls)),\n url(r'^$', base_views.index,name='index'),\n url(r'^login/$', base_views.account_login, name='login'),\n url(r'^logout/$', base_views.account_logout, name='logout'),\n url(r'^service/(?P(.*)$)?', base_views.service, name='service'),\n url(r'^file/(?P(.*)$)?', base_views.file, name='file'),\n url(r'^group', base_views.group, name='group'),\n]\n", "sub_path": "GAOGDLV/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "GAOGDS.views.index", "line_number": 22, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "GAOGDS.views.account_login", "line_number": 23, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "GAOGDS.views.account_logout", "line_number": 24, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "GAOGDS.views.service", "line_number": 25, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "GAOGDS.views.file", "line_number": 26, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "GAOGDS.views.group", "line_number": 27, "usage_type": "attribute"}, {"api_name": "GAOGDS.views", "line_number": 27, "usage_type": "name"}]}
{"seq_id": "602858852", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport logging\nimport requests\n\nfrom lxml import html\nfrom urllib2 import urlopen, URLError, urlparse\nfrom publisher.models import PublisherModel\n\n# Optional packages\ntry:\n import cssutils\n cssutils.log.setLevel(logging.FATAL)\nexcept (ImportError, ):\n cssutils = None\ntry:\n from html5print import CSSBeautifier\nexcept (ImportError, ):\n CSSBeautifier = None\n\nfrom bs4 import BeautifulSoup\n\nfrom django.db import models\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core.files.base import ContentFile\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttypes.fields import GenericForeignKey\n\ntry:\n from django.template import Template, Context\nexcept ImportError: # Django < 1.8\n from django.template.loader import Template, Context\nfrom django.utils.safestring import mark_safe\n\nfrom .settings import ARCHIVE_PAGE_ASYNCHRONOUSLY, METADATA_FORM, STORAGE, RELATIONS, FILE_TYPE_CHOICES\nfrom .compatible import ModelFormField\nfrom .archiveurl import relative_to_full_url\nfrom .tasks import archive_page_components\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ArchivePageXpathForm(forms.Form):\n title = forms.CharField(\n required=True,\n initial='/html/head/title')\n content = forms.CharField(\n required=True,\n initial='/html/body')\n\n\nclass ArchivedPage(PublisherModel):\n \"\"\"\n Part of an HTML page\n \"\"\"\n url = models.CharField(\n _('URL'),\n max_length=255,\n db_index=True)\n original_url = models.CharField(\n _('original URL'),\n max_length=255)\n title = models.CharField(\n _('title'),\n blank=True,\n max_length=200)\n content = models.TextField(\n _('content'),\n blank=True)\n head = models.TextField(\n _('head'),\n blank=True)\n template_name = models.CharField(\n _('template name'),\n max_length=70,\n blank=True,\n help_text=_(\"\"\"Example: 'vintage/contact_page.html'. If this\n isn't provided, the system will use 'vintage/wpf.html'.\"\"\"))\n metadata = ModelFormField(form=METADATA_FORM)\n xpaths = ModelFormField(form=ArchivePageXpathForm)\n refetch = models.BooleanField(default=False)\n\n class Meta:\n verbose_name = _('archived page')\n verbose_name_plural = _('archived pages')\n ordering = ('url',)\n unique_together = (('url', 'publisher_is_draft'),\n ('original_url', 'publisher_is_draft'))\n\n def __unicode__(self):\n return \"%s -- %s\" % (self.url, self.title)\n\n @models.permalink\n def get_absolute_url(self):\n return ('vintage_detail', (), {'url': self.url.lstrip('/')})\n\n def clone_relations(self, src_obj, dst_obj):\n '''\n Overrides PublisherModel.clone_relations() which is called at end of .publish().\n * publishes src_obj associated files.\n * clones ArchivedPageRelation for src_obj to dst_obj.\n '''\n # Clear all Files for dst_obj\n dst_obj.files.clear()\n # Publish each of src_obj's files, if not already published,\n # and link each published-file to dst_obj.\n for src_file in src_obj.files.all():\n if src_file.publisher_linked:\n dst_file = src_file.publisher_linked\n else:\n try:\n dst_file = ArchivedFile.objects.get(original_url=src_file.original_url,\n publisher_is_draft=False)\n except ArchivedFile.DoesNotExist:\n src_file.publish()\n dst_file = src_file.publisher_linked\n dst_file.archivedpages.add(dst_obj)\n # Clone all ArchivedPageRelations\n dst_obj.relations.all().delete()\n for src_relation in src_obj.relations.all():\n dst_relation = ArchivedPageRelation(\n archivedpage=dst_obj,\n content_type=src_relation.content_type,\n object_id=src_relation.object_id,\n alias=src_relation.alias,\n )\n dst_relation.save()\n\n def get_files(self, file_type=None):\n \"\"\"Get associated files, optional file type\"\"\"\n if file_type:\n return self.files.filter(file_type=file_type)\n return self.files.all()\n\n def get_page(self):\n page = requests.get(self.original_url)\n tree = html.fromstring(page.content)\n xcontent = tree.xpath(self.xpaths['content'])[0]\n\n try:\n # Get the title\n self.title = html.tostring(tree.xpath(self.xpaths['title'])[0])\n except (Exception, ) as exc:\n logger.exception('Error parsing title: %s', unicode(exc))\n\n try:\n # Get the head\n self.head = html.tostring(tree.xpath('/html/head')[0])\n except (Exception, ):\n logger.exception('Error parsing head: %s', unicode(exc))\n\n # Get main content\n self.content = self.get_content(xcontent)\n\n def get_content(self, xhtml):\n return html.tostring(xhtml)\n\n @property\n def published_state(self):\n if not self.publisher_linked:\n return 'draft'\n if self.is_dirty:\n return 'unpublished changes'\n return 'published'\n\n def update_links(self, save=True):\n \"\"\"\n Parse through the saved document and make sure the links to the existing\n site are archived.\n \"\"\"\n soup = BeautifulSoup(self.content, \"html.parser\")\n links = soup.find_all('a')\n for tag in links:\n if not tag.has_key('href'): # NOQA\n continue\n\n href = tag['href'].strip()\n if href.startswith('{'):\n continue\n if href.startswith('javascript'):\n continue\n if '.mp3' in href:\n # It's a Link pointing to an audio file.\n if not href.startswith('{'):\n url = self.get_original_file(href, file_type='audio')\n tag['href'] = url\n elif ('.png' in href or\n '.jpg' in href or\n '.jpeg' in href or\n '.gif' in href):\n # It's a Link pointing to an image.\n if not href.startswith('{'):\n url = self.get_original_file(href, file_type='image')\n tag['href'] = url\n elif ('.doc' in href or\n '.docx' in href or\n '.pdf' in href or\n '.xls' in href):\n # It's a link pointing to a document.\n if not href.startswith('{'):\n url = self.get_original_file(href, file_type='document')\n tag['href'] = url\n else:\n # It's a regular Link pointing to url for a page.\n url = relative_to_full_url(self.original_url, href)\n try:\n ap = ArchivedPage.objects.get(original_url=url, publisher_is_draft=True)\n url = \"{%% url 'vintage_detail' url='%s' %%}\" % ap.url.lstrip('/')\n except (ArchivedPage.DoesNotExist, ):\n logger.warning('Unable to get archived page: %s', url)\n tag['href'] = url\n\n self.content = unicode(soup.prettify())\n if save:\n self.save()\n\n def strip_js(self, save=True):\n soup = BeautifulSoup(self.content, \"html.parser\")\n scripts = soup.find_all('script')\n [s.extract() for s in scripts]\n self.content = unicode(soup.prettify())\n if save:\n self.save()\n\n def update_audio_tags(self, save=True):\n \"\"\"\n Parse through the saved document and make sure the images are archived.\n \"\"\"\n soup = BeautifulSoup(self.content, \"html.parser\")\n audio_tags = soup.find_all('audio')\n for tag in audio_tags:\n src = tag['src'].strip()\n if not src.startswith('{'):\n url = self.get_original_file(src, file_type='audio')\n tag['src'] = url\n self.content = unicode(soup.prettify())\n if save:\n self.save()\n\n def update_images(self, save=True):\n \"\"\"\n Parse through the saved document and make sure the images are archived.\n \"\"\"\n soup = BeautifulSoup(self.content, \"html.parser\")\n images = soup.find_all('img')\n for tag in images:\n src = tag['src'].strip()\n if not src.startswith('{'):\n url = self.get_original_file(src, file_type='image')\n tag['src'] = url\n self.content = unicode(soup.prettify())\n if save:\n self.save()\n\n def update_css(self, save=True):\n \"\"\"\n Parse the head of the document for css files.\n \"\"\"\n soup = BeautifulSoup(self.head, \"html.parser\")\n # All the link head tags\n css = soup.find_all('link')\n for i, link in enumerate(css):\n # Make sure we are dealing with stylesheets\n if 'stylesheet' in link.get('rel', []):\n # The url of the stylesheet\n src = link['href'].strip()\n # if this has special markup, skip\n if not src.startswith('{'):\n # Generate the local file as editable\n url = self.get_original_file(\n src, editable=True,\n update_func=self.update_internal_css,\n pretty=CSSBeautifier.beautify,\n pretty_args=[4], # Indent\n order=i,\n file_type='css')\n link['href'] = url\n styles = soup.find_all('style')\n for style in styles:\n style.string = self.update_internal_css(self.original_url, style.string)\n self.head = unicode(soup.prettify())\n if save:\n self.save()\n\n def update_internal_css(self, original_path, content):\n \"\"\"Given the contents of the css file, parse out any urls so we\n have them locally. Note: that if cssutils is not installed it\n will return the content as is.\n\n \"\"\"\n def replace_url(url):\n \"\"\"This is called with the cssutils.replaceUrls function\"\"\"\n parsed = urlparse.urlparse(original_path)\n # In the case where the path contains .., we make the url proper\n if url.startswith('..'):\n url = os.path.abspath(\n os.path.join(\n os.path.dirname(parsed.path), url))\n # Build the full url with domain so we can download it\n url = '{}://{}{}'.format(parsed.scheme, parsed.netloc, url)\n # Generate the local file for the url\n return self.get_original_file(url)\n\n # We can parse the css urls if cssutils is available\n if cssutils:\n sheet = cssutils.parseString(content)\n cssutils.replaceUrls(sheet, replace_url)\n return sheet.cssText\n return content\n\n def get_original_file(self, path, **kwargs):\n \"\"\"\n Given a full or partial path, download and create the archivedfile.\n Return the url path instance\n \"\"\"\n editable = kwargs.get('editable', False)\n update_func = kwargs.get('update_func', None)\n pretty = kwargs.get('pretty', None)\n pretty_args = kwargs.get('pretty_args', [])\n order = kwargs.get('order', 0)\n file_type = kwargs.get('file_type', '')\n\n if path.startswith('//'):\n path = 'http:%s' % path\n parsed = urlparse.urlparse(path.strip())\n path = relative_to_full_url(self.original_url, path.strip())\n\n # Get or create the archived file\n af, _ = ArchivedFile.objects.get_or_create(original_url=path,\n publisher_is_draft=self.publisher_is_draft)\n af.order = order\n af.file_type = file_type\n af.save()\n\n try:\n try:\n file_content = urlopen(path).read()\n except (Exception, ) as exc:\n logger.exception('Error opening file: %s', unicode(exc))\n file_content = ''\n\n try:\n # Save the file contents\n af.content.save(\n os.path.basename(parsed.path), ContentFile(file_content))\n except (Exception,) as exc:\n logger.exception('Error saving file: %s', unicode(exc))\n return ''\n\n # If we want the content of the file to be editable, we create an\n # addtional model record\n if editable:\n try:\n source, _ = ArchivedSourceFile.objects.get_or_create(\n archivedfile=af)\n\n # Optinal function to handle the file contents\n if update_func:\n file_content = update_func(path, file_content)\n\n # Optional function to prettyify the file contents\n if pretty:\n file_content = pretty(file_content, *pretty_args)\n\n source.source = file_content\n source.save()\n except (Exception, ) as exc:\n logger.exception(\n 'Error creating editable srouce: %s', unicode(exc))\n except URLError:\n return path\n\n self.files.add(af)\n return '{{ STATIC_URL }}%s' % af.content.url\n\n def save(self, *args, **kwargs):\n if (not self.content) or self.refetch:\n if not self.id:\n super(ArchivedPage, self).save(*args, **kwargs)\n if ARCHIVE_PAGE_ASYNCHRONOUSLY:\n archive_page_components.delay(self)\n else:\n archive_page_components(self)\n\n super(ArchivedPage, self).save(*args, **kwargs)\n\n if RELATIONS:\n def get_related_content_type(self, content_type):\n \"\"\"\n Get all related items of the specified content type\n \"\"\"\n return self.relations.filter(content_type__name=content_type)\n\n def get_relation_type(self, relation_type):\n \"\"\"\n Get all relations of the specified relation type\n \"\"\"\n return self.relations.filter(alias__iexact=relation_type)\n\n def get_content_object(self, field):\n app_label, model = field[1].split('.')\n ctype = ContentType.objects.get(app_label=app_label, model=model)\n ar = self.get_related_content_type(ctype.name)\n if len(ar) > 0:\n return ar[0].content_object\n else:\n return None\n\n\ndef get_upload_path(instance, filename):\n \"\"\"\n Return the path based on the primary_key of the related page\n \"\"\"\n from urlparse import urlparse\n parsed = urlparse(instance.original_url)\n directory_name = os.path.normpath(\n os.path.join(\n 'vintage',\n parsed.netloc,\n os.path.dirname(parsed.path).strip('/'))\n )\n new_filename = os.path.normpath(\n instance.content.storage.get_valid_name(\n os.path.basename(filename)))\n return os.path.join(directory_name, new_filename)\n\n\nclass ArchivedFile(PublisherModel):\n \"\"\"\n A non-html file used in an Archived Page, such as a file\n \"\"\"\n archivedpages = models.ManyToManyField(ArchivedPage, related_name='files')\n original_url = models.CharField(\n _('original URL'),\n max_length=255)\n content = models.FileField(\n max_length=255,\n upload_to=get_upload_path,\n storage=STORAGE())\n file_type = models.CharField(\n _('type'), choices=FILE_TYPE_CHOICES,\n blank=True,\n max_length=25)\n order = models.IntegerField(default=0)\n\n class Meta:\n ordering = ('order', )\n unique_together = (('original_url', 'publisher_is_draft'), )\n\n def __unicode__(self):\n return self.original_url\n\n def clone_relations(self, src_obj, dst_obj):\n '''\n When published(), runs this at the end to keep relations nsync:\n * Leaving this here as a note, be careful adding logic in here,\n initially tried cloning file-page relationships from here with strange\n results. That work is being done in ArchivedPage.clone_relations().\n '''\n pass\n\n @property\n def published_state(self):\n if not self.publisher_linked:\n return 'draft'\n if self.is_dirty:\n return 'unpublished changes'\n return 'published'\n\n\nclass ArchivedSourceFile(models.Model):\n archivedfile = models.OneToOneField(ArchivedFile)\n source = models.TextField(_('source'))\n\n def save(self, *args, **kwargs):\n # Only update the source file if the `source` field has data\n from django.core.files.base import ContentFile\n\n if self.source:\n path = self.archivedfile.original_url\n if path.startswith('//'):\n path = 'http:%s' % path\n\n # Render the source content\n source = mark_safe(Template(self.source).render(Context({})))\n\n # Fetch the storage and name of the content field\n storage = self.archivedfile.content.storage\n file_name = self.archivedfile.content.file.name\n try:\n # generate the archived filed using the source\n storage.save(file_name, ContentFile(source))\n except (Exception, ) as exc:\n logger.exception('Error writing source file: %s', unicode(exc))\n\n super(ArchivedSourceFile, self).save(*args, **kwargs)\n\n\nif RELATIONS:\n ARCHIVEDPAGE_RELATION_LIMITS = reduce(lambda x, y: x | y, RELATIONS)\nelse:\n ARCHIVEDPAGE_RELATION_LIMITS = []\n\n\nclass ArchivedPageRelationManager(models.Manager):\n def get_content_type(self, content_type):\n \"\"\"\n Get all the items of the given content type related to this item.\n \"\"\"\n qs = self.get_query_set()\n return qs.filter(content_type__name=content_type)\n\n def get_relation_type(self, relation_type):\n \"\"\"\n Get all the items of the given relationship type related to this item.\n \"\"\"\n qs = self.get_query_set()\n return qs.filter(relation_type=relation_type)\n\n\nclass ArchivedPageRelation(models.Model):\n \"\"\"Related item\"\"\"\n archivedpage = models.ForeignKey(\n ArchivedPage, related_name='relations')\n content_type = models.ForeignKey(\n ContentType, limit_choices_to=ARCHIVEDPAGE_RELATION_LIMITS)\n object_id = models.PositiveIntegerField(\n blank=False, null=False)\n content_object = GenericForeignKey(\n ct_field='content_type', fk_field='object_id')\n alias = models.CharField(\n verbose_name=_(\"Alias\"), max_length=200, blank=True, null=True,\n help_text=_(\"A generic text field to tag a relation, \"\n \"like 'leadphoto'.\"))\n\n objects = ArchivedPageRelationManager()\n\n def __unicode__(self):\n return u\"ArchivedPageRelation\"\n", "sub_path": "vintage/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 19309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "cssutils.log.setLevel", "line_number": 14, "usage_type": "call"}, {"api_name": "cssutils.log", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.FATAL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "html5print.CSSBeautifier", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 50, "usage_type": "name"}, {"api_name": "publisher.models.PublisherModel", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 77, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 80, "usage_type": "call"}, {"api_name": "compatible.ModelFormField", "line_number": 82, "usage_type": "call"}, {"api_name": "settings.METADATA_FORM", "line_number": 82, "usage_type": "name"}, {"api_name": "compatible.ModelFormField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 87, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models.permalink", "line_number": 96, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 139, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 140, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 140, "usage_type": "name"}, {"api_name": "lxml.html.tostring", "line_number": 145, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 145, "usage_type": "name"}, {"api_name": "lxml.html.tostring", "line_number": 151, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 151, "usage_type": "name"}, {"api_name": "lxml.html.tostring", "line_number": 159, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 159, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 174, "usage_type": "call"}, {"api_name": "archiveurl.relative_to_full_url", "line_number": 208, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 221, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 232, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 247, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 262, "usage_type": "call"}, {"api_name": "html5print.CSSBeautifier.beautify", "line_number": 276, "usage_type": "attribute"}, {"api_name": "html5print.CSSBeautifier", "line_number": 276, "usage_type": "name"}, {"api_name": "urllib2.urlparse.urlparse", "line_number": 296, "usage_type": "call"}, {"api_name": "urllib2.urlparse", "line_number": 296, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path", "line_number": 299, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "attribute"}, {"api_name": "cssutils.parseString", "line_number": 309, "usage_type": "call"}, {"api_name": "cssutils.replaceUrls", "line_number": 310, "usage_type": "call"}, {"api_name": "urllib2.urlparse.urlparse", "line_number": 328, "usage_type": "call"}, {"api_name": "urllib2.urlparse", "line_number": 328, "usage_type": "name"}, {"api_name": "archiveurl.relative_to_full_url", "line_number": 329, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 332, "usage_type": "name"}, {"api_name": "urllib2.urlopen", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 348, "usage_type": "call"}, {"api_name": "os.path", "line_number": 348, "usage_type": "attribute"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 348, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 357, "usage_type": "name"}, {"api_name": "urllib2.URLError", "line_number": 373, "usage_type": "name"}, {"api_name": "settings.ARCHIVE_PAGE_ASYNCHRONOUSLY", "line_number": 383, "usage_type": "name"}, {"api_name": "tasks.archive_page_components.delay", "line_number": 384, "usage_type": "call"}, {"api_name": "tasks.archive_page_components", "line_number": 384, "usage_type": "name"}, {"api_name": "tasks.archive_page_components", "line_number": 386, "usage_type": "call"}, {"api_name": "settings.RELATIONS", "line_number": 390, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get", "line_number": 405, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 405, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 405, "usage_type": "name"}, {"api_name": "urlparse.urlparse", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path", "line_number": 419, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 423, "usage_type": "call"}, {"api_name": "os.path", "line_number": 423, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path", "line_number": 425, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path", "line_number": 427, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "attribute"}, {"api_name": "publisher.models.PublisherModel", "line_number": 431, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 435, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 435, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 436, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 436, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 437, "usage_type": "call"}, {"api_name": "django.db.models.FileField", "line_number": 439, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 439, "usage_type": "name"}, {"api_name": "settings.STORAGE", "line_number": 442, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 443, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 443, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 444, "usage_type": "call"}, {"api_name": "settings.FILE_TYPE_CHOICES", "line_number": 444, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 447, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 447, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 474, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 474, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 475, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 475, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 476, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 476, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 476, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 488, "usage_type": "call"}, {"api_name": "django.template.loader.Template", "line_number": 488, "usage_type": "call"}, {"api_name": "django.template.loader.Context", "line_number": 488, "usage_type": "call"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 495, "usage_type": "call"}, {"api_name": "settings.RELATIONS", "line_number": 502, "usage_type": "name"}, {"api_name": "settings.RELATIONS", "line_number": 503, "usage_type": "argument"}, {"api_name": "django.db.models.Manager", "line_number": 508, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 508, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 524, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 524, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 526, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 526, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 528, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 529, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 528, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 530, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 530, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.fields.GenericForeignKey", "line_number": 532, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 534, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 534, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 535, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 536, "usage_type": "call"}]}
{"seq_id": "620549373", "text": " \nfrom itertools import islice, chain\nfrom collections import Counter\n\nclass WordleSolver:\n\n points = {\n 's': 61, 'e': 61, 'a': 52, 'r': 39, 'o': 38, 'i': 34,\n 'l': 32, 't': 30, 'n': 26, 'd': 23, 'u': 22, 'c': 19,\n 'p': 18, 'y': 18, 'm': 17, 'h': 16, 'g': 14, 'b': 14,\n 'k': 11, 'f': 10, 'w': 9, 'v': 6, 'z': 2, 'x': 2,\n 'j': 2, 'q': 1\n }\n \n def __init__(\n self, \n infile:iter, \n wordlength:int = 5, \n resetpoints:bool = False,\n run:bool=True,\n ):\n '''\n Initiates WordleSolver instance attributes and runs run method.\n\n :param infile: Iterable of words to be searched through.\n All words should be the length specified\n in wordlength.\n\n :param wordlength: Length of words that to be put in Wordle.\n Defaults to 5.\n\n :param resetpoints: Whether or not to reinitialize how words are\n scored based on words in infile.\n '''\n self.infile = infile\n if resetpoints:\n self.setpoints()\n\n self.length = wordlength\n\n self.green = []\n self.yellow = {position: set() for position in range(self.length)}\n self.cum_yel = set()\n self.black = set()\n\n self.results = {}\n self.result = \"\"\n self.topscore = 0\n\n self.action = -1\n\n self.attempts = 1\n if run:\n self.run()\n\n def __str__(self):\n string = (f'green: {self.green}\\n'\n + f'yellow: {self.yellow}\\n'\n + f'{self.cum_yel}\\n'\n + f'black: {self.black}\\n')\n return string\n\n def findqualifiers(self):\n '''Fills results with words that meet Wordle requiremets.'''\n self.results.clear()\n\n for word in self.infile:\n score = 0\n for i, (greenlet, wordlet) in (\n enumerate(zip(self.green, word))\n ):\n if (\n (greenlet != '' and greenlet != wordlet)\n or (wordlet in self.yellow[i])\n or (wordlet in self.black)\n ):\n score = -1\n\n for yellowlet in self.cum_yel:\n if yellowlet not in word:\n score = -1\n\n if score != -1:\n self.results[word] = 0\n\n print(f'\\n{len(self.results)} results found')\n\n def assignscores(self):\n '''Gives each word in results a score based on its letters.'''\n for word in self.results:\n self.results[word] = 0\n lets_present = set()\n for let in word:\n if let not in lets_present:\n self.results[word] += self.points[let]\n lets_present.add(let)\n else:\n self.results[word] -= self.points[let]\n\n self.results = dict(sorted(\n self.results.items(),\n key=lambda item: item[1],\n reverse=True,\n ))\n\n def gettophit(self) -> str:\n '''Sets, prints, and returns result with highest value.'''\n self.result = max(self.results, key=self.results.get)\n self.topscore = max(self.results.values())\n print(self.result, self.topscore, '\\n')\n return self.result\n\n def evaluate(self, colors: str, word: str):\n '''Sets instance attributes according to given colors.'''\n self.green.clear()\n for i, (c, w) in enumerate(zip(colors, word)):\n if c == 'g':\n self.green.append(w)\n if c in self.black:\n self.black.remove(c)\n else:\n self.green.append('')\n\n if c == 'y':\n self.yellow[i].add(w)\n\n if c == 'b':\n self.black.add(w)\n\n self.cum_yel = set.union(*self.yellow.values())\n\n self.black.difference_update(self.green)\n self.black.difference_update(self.cum_yel)\n\n for i, let in enumerate(self.green):\n if let:\n self.yellow[i].difference_update([let])\n\n def askaction(self, prevact3:bool=False) -> str:\n '''\n Determines available actions, displays them, and asks user\n what they want to do.\n\n :param prevact3: Whether or not the user previously selected\n action 3.\n '''\n options = {\"1\": \"use word\",\n \"2\": \"reroll\",\n \"3\": \"display more results\",\n \"4\": \"manually chose word\",\n \"5\": \"end program\",\n }\n\n if len(self.results) <= 1:\n del options['2']\n del options['3']\n\n if prevact3:\n del options['1']\n try:\n del options['2']\n except KeyError:\n pass\n\n for option, instruction in options.items():\n print(f'({option}) - {instruction}')\n\n while True:\n answer = input('Enter number: ')\n if answer in options:\n self.action = answer\n return self.action\n\n print('Error: Invalid input')\n\n def askeval(self) -> str:\n '''Asks user for and returns colors given by Wordle'''\n while True:\n evl = input(f'Enter colors for word {self.result}: ')\n evl = evl.lower()\n\n if len(evl) == self.length:\n for let in evl:\n if let not in {'g','y','b',}:\n break\n else:\n return evl\n print('Error: Invalid input')\n\n def askword(self) -> str:\n '''Asks for and returns word entered by user'''\n while True:\n word = input('Enter word: ')\n word = word.lower()\n\n if len(word) == self.length:\n return word\n\n print(f'Error: Word must be {self.length} letters')\n\n def checkcont(self) -> bool:\n '''Checks if program should be ended'''\n if (self.green and '' not in self.green) or self.action == '5':\n print(f'Program ended on {self.attempts} attempts')\n return False\n return True\n\n def gettopfive(self) -> dict:\n '''Prints and removes top five results'''\n result_slice = dict(islice(self.results.items(), 5))\n print(f'\\n{result_slice}\\n')\n [self.results.pop(key) for key in result_slice.keys()]\n return result_slice\n\n def run(self):\n while True:\n self.findqualifiers()\n self.assignscores()\n self.gettophit()\n while True:\n self.action = self.askaction(prevact3=self.action=='3')\n\n if self.action == '1':\n self.evaluate(self.askeval(), self.result)\n break\n\n elif self.action == '2':\n del self.results[self.result]\n print()\n self.gettophit()\n\n elif self.action == '3':\n self.gettopfive()\n\n elif self.action == '4':\n self.result = self.askword()\n self.evaluate(self.askeval(), self.result)\n break\n \n elif self.action == '5':\n break\n\n if not self.checkcont():\n break\n\n self.attempts += 1\n\n # d = self.__dict__.copy()\n # d.pop('infile')\n # print(d)\n\n def setpoints(self):\n '''Resets the points attribute according to words in infile.'''\n all_lets = list(chain(*self.infile))\n let_count = dict(Counter(all_lets))\n minimum = min(let_count.values())\n for let in let_count:\n let_count[let] //= minimum\n # Sort dictionary into descending order by value\n # just to look nice\n let_count = dict(sorted(\n let_count.items(),\n key=lambda item: item[1],\n reverse=True,\n ))\n self.points = let_count\n return self.points\n \n# Make a set of five letter words from file\nwith open('words.txt','r') as f:\n wordsfile = f.read().split('\\n')\n\nfiveLet = [word for word in wordsfile if len(word) == 5]\n\napp = WordleSolver(fiveLet)\n", "sub_path": "WordleSolver_v4-2.py", "file_name": "WordleSolver_v4-2.py", "file_ext": "py", "file_size_in_byte": 8384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "itertools.islice", "line_number": 210, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 254, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 255, "usage_type": "call"}]}
{"seq_id": "64455300", "text": "# -*- coding: utf-8 -*-\nfrom google.appengine.datastore.datastore_query import Cursor\nfrom google.appengine.ext import ndb\nfrom collections import OrderedDict\nfrom bp_includes.lib.basehandler import BaseHandler\nfrom bp_includes.models import LogEmail\nfrom google.appengine.api import users as g_users #https://cloud.google.com/appengine/docs/python/refdocs/modules/google/appengine/api/users#get_current_user\n\n\nclass AdminLogsEmailsHandler(BaseHandler):\n def get(self):\n p = self.request.get('p')\n q = self.request.get('q')\n c = self.request.get('c')\n forward = True if p not in ['prev'] else False\n cursor = Cursor(urlsafe=c)\n\n if q:\n qry = LogEmail.query(LogEmail.to == q.lower())\n else:\n qry = LogEmail.query()\n\n PAGE_SIZE = 50\n if forward:\n emails, next_cursor, more = qry.order(LogEmail.key).fetch_page(PAGE_SIZE, start_cursor=cursor)\n if next_cursor and more:\n self.view.next_cursor = next_cursor\n if c:\n self.view.prev_cursor = cursor.reversed()\n else:\n emails, next_cursor, more = qry.order(-LogEmail.key).fetch_page(PAGE_SIZE, start_cursor=cursor)\n emails = list(reversed(emails))\n if next_cursor and more:\n self.view.prev_cursor = next_cursor\n self.view.next_cursor = cursor.reversed()\n\n def pager_url(p, cursor):\n params = OrderedDict()\n if q:\n params['q'] = q\n if p in ['prev']:\n params['p'] = p\n if cursor:\n params['c'] = cursor.urlsafe()\n return self.uri_for('admin-logs-emails', **params)\n\n self.view.pager_url = pager_url\n self.view.q = q\n\n params = {\n \"list_columns\": [('when', 'Fecha'),\n ('to', 'Destinatario'),\n ('subject', 'Asunto'),\n # ('sender', 'Sender'),\n # ('body', 'Body')\n ],\n \"emails\": emails,\n \"count\": qry.count()\n }\n params['nickname'] = g_users.get_current_user().email().lower()\n return self.render_template('usage/admin_logs_emails.html', **params)\n\n\nclass AdminLogsEmailViewHandler(BaseHandler):\n def get(self, email_id):\n try:\n emaildata = LogEmail.get_by_id(long(email_id))\n if emaildata:\n params = {\n 'emailinfo': emaildata\n }\n params['nickname'] = g_users.get_current_user().email().lower()\n return self.render_template('usage/admin_logs_email_view.html', **params)\n except ValueError:\n pass\n self.abort(404)\n", "sub_path": "bp_admin/logsemails.py", "file_name": "logsemails.py", "file_ext": "py", "file_size_in_byte": 2804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "bp_includes.lib.basehandler.BaseHandler", "line_number": 10, "usage_type": "name"}, {"api_name": "google.appengine.datastore.datastore_query.Cursor", "line_number": 16, "usage_type": "call"}, {"api_name": "bp_includes.models.LogEmail.query", "line_number": 19, "usage_type": "call"}, {"api_name": "bp_includes.models.LogEmail", "line_number": 19, "usage_type": "name"}, {"api_name": "bp_includes.models.LogEmail.to", "line_number": 19, "usage_type": "attribute"}, {"api_name": "bp_includes.models.LogEmail.query", "line_number": 21, "usage_type": "call"}, {"api_name": "bp_includes.models.LogEmail", "line_number": 21, "usage_type": "name"}, {"api_name": "bp_includes.models.LogEmail.key", "line_number": 25, "usage_type": "attribute"}, {"api_name": "bp_includes.models.LogEmail", "line_number": 25, "usage_type": "name"}, {"api_name": "bp_includes.models.LogEmail.key", "line_number": 31, "usage_type": "attribute"}, {"api_name": "bp_includes.models.LogEmail", "line_number": 31, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 38, "usage_type": "call"}, {"api_name": "google.appengine.api.users.get_current_user", "line_number": 60, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 60, "usage_type": "name"}, {"api_name": "bp_includes.lib.basehandler.BaseHandler", "line_number": 64, "usage_type": "name"}, {"api_name": "bp_includes.models.LogEmail.get_by_id", "line_number": 67, "usage_type": "call"}, {"api_name": "bp_includes.models.LogEmail", "line_number": 67, "usage_type": "name"}, {"api_name": "google.appengine.api.users.get_current_user", "line_number": 72, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 72, "usage_type": "name"}]}
{"seq_id": "355344130", "text": "#!/usr/bin/python\n\"\"\"\n#################################################################\nScript Header\n\n$Id: $ US27698\n\nCopyright (c) 2016-2017 Cisco Systems, Inc.\n\nName:\n cmCC27698_3pcc_BS_IOT_Interop_IPv6_275_BroadWorksHangsUpBeforeAnswer.py\n\nAuthor:\n Vishnu Prasad B(vishnpra@cisco.com)\n\nPurpose:\n\n This test case verifies a BroadWorks user originated call registered with\n IPv6 that is hung up by the BroadWorks user before it is answered.\n\nDescription:\n\n 1: Originate a call from BroadWorks User to the DUT.\n DUT Do not answer the call.\n BroadWorks User Disconnect the call before the DUT answers.\n\nTest bed requirement:\n 1: 2 3pcc phones\n 2: Both phone should register successfully before running script\n\nTest Steps:\n 1. Phone B calls to Phone A(DUT)\n 2. Phone B disconnects the call\n Verify:\n 1. Phone A(DUT) will get ring from Phone B\n 2. Call is disconnected\n 3. Verify the SIP signaling to and from the DUT.\n 4.Make sure the DUT sends all messages to the primary Application Server.\n 5.Verify that 180 ringing sent\n 6.Verify that 487 is sent\n 7.Verify ACK\n\n# Known Bugs:\n\"\"\"\n\nimport tng\nimport logging\nfrom tng.api import concurrent\nfrom tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended\\\n import wait_for_ccapi_call_states\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.mpp.tshark_helper import TsharkHelper\nfrom tng_sl.contrib.setup_helper import SetupHelpersTestCase\n\nlog = logging.getLogger(\"IPv6_BW_Hangsup_Before_Answer\")\n\n\nclass BWHangsupBeforeAnswer(SetupHelpersTestCase, tng.api.TestCase):\n\n helpers = (PhoneConfigHelper, PhoneLineRegHelper, TsharkHelper)\n helper_num_devices = 2\n\n def setUp(self):\n log.info(\"Start of setUp\")\n concurrent([\n self.oPhone1.ui.set_param_value,\n self.oPhone2.ui.set_param_value],\n {'IP Mode': 'IPv6 Only', 'SIP IP Preference': 'IPv6'})\n self.serverproxy = self.toolkit.get_test_env_info(\n section='bsoft', parameter_name=\"as_ip_addr6\")\n log.info(\"End of setUp\")\n\n def test_ipv6_bw_hangsup_before_answer(self):\n log.info(\"Start of IPv6_BW_Hangsup_Before_Answer\")\n log.info('Start tshark on linux')\n\n dut_ip = self.oPhone1.ui.get_param_value(\"Current IP_IPV6\")\n filter_cmd = ('port sip and host {}'.format(dut_ip))\n capture_file = self.tshark.tshark_start(filter_cmd)\n\n log.info(\"Phone2 dial Phone1's number: {}\".format(self.user_id1))\n self.oPhone2.ccapi.dial('null', self.user_id1, '', 1, 0, 1)\n # check ophone1 ringout status and oPhone2 ringing status\n wait_for_ccapi_call_states(\n self.devices, (\"RINGING\", \"PROCEEDING\"))\n\n log.info(\"Phone2 disconnects the call\")\n self.oPhone2.ccapi.hangUp('0000')\n # check two phones are in idle status\n wait_for_ccapi_call_states(self.devices, (\"IDLE\", \"IDLE\"), timeout=20)\n\n log.info('Stop tshark on Linux')\n self.tshark.tshark_stop()\n\n # tshark analysis\n log.info(\"Start tshark analysis\")\n received_msgs = self.tshark.tshark_read(\n file=capture_file, protocol='sip')\n\n expected_msgs = dict()\n expected_msgs['frame_src'] = [dut_ip, dut_ip, self.serverproxy]\n expected_msgs['frame_dst'] = [\n self.serverproxy, self.serverproxy, dut_ip]\n expected_msgs['frame_proto'] = ['SIP', 'SIP', 'SIP']\n expected_msgs['frame_data'] = [\n 'Status: 180 Ringing',\n 'Status: 487 Request Terminated',\n 'Request: ACK']\n\n result_src = self.tshark.tshark_call_flow(\n expected=expected_msgs, received=received_msgs)\n\n self.assertTrue(result_src, \"Messages from DUT doesnt match\")\n log.info(\n \"Successfully verified traces for BroadWorksHangsUpBeforeAnswer\")\n log.info(\"Tshark analysis stopped\")\n log.info(\"Stop of IPv6_BW_Hangsup_Before_Answer\")\n\n\ndef main():\n tng.api.runner()\n\nif __name__ == '__main__':\n tng.run(main)\n", "sub_path": "common/IOT/Broadsoft_Interop/section_14/cmCC27698_3pcc_BS_IOT_Interop_IPv6_275_BroadWorksHangsUpBeforeAnswer.py", "file_name": "cmCC27698_3pcc_BS_IOT_Interop_IPv6_275_BroadWorksHangsUpBeforeAnswer.py", "file_ext": "py", "file_size_in_byte": 4146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "84", "api": [{"api_name": "logging.getLogger", "line_number": 56, "usage_type": "call"}, {"api_name": "tng_sl.contrib.setup_helper.SetupHelpersTestCase", "line_number": 59, "usage_type": "name"}, {"api_name": "tng.api", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tng_sl.contrib.mpp.phone_line_reg_helper.PhoneConfigHelper", "line_number": 61, "usage_type": "name"}, {"api_name": "tng_sl.contrib.mpp.phone_line_reg_helper.PhoneLineRegHelper", "line_number": 61, "usage_type": "name"}, {"api_name": "tng_sl.contrib.mpp.tshark_helper.TsharkHelper", "line_number": 61, "usage_type": "name"}, {"api_name": "tng.api.concurrent", "line_number": 66, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.wait_for_ccapi_call_states", "line_number": 85, "usage_type": "call"}, {"api_name": "tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended.wait_for_ccapi_call_states", "line_number": 91, "usage_type": "call"}, {"api_name": "tng.api.runner", "line_number": 122, "usage_type": "call"}, {"api_name": "tng.api", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tng.run", "line_number": 125, "usage_type": "call"}]}
![\"wiฤcej\"](\"/img/see_more.png\")
\r\n \r\n