diff --git "a/3850.jsonl" "b/3850.jsonl" new file mode 100644--- /dev/null +++ "b/3850.jsonl" @@ -0,0 +1,1679 @@ +{"seq_id":"19390205128","text":"import numpy as np \r\nfrom numpy import random as rd \r\nfrom scipy.stats import t\r\nimport time\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n#!MLE functions \r\ndef get_eps_t(xt, b, T): \r\n '''\r\n Note: this cannot handle single array (due to reshape)\r\n If want single array of x to be handled, remove reshaping of res in loop\r\n Get *all* epsilon_t following summation approach. \r\n Returns an array with all eps_t and and array with all epsilon_t^2, which we need for sigma^2.\r\n *xt = 1D array of x\r\n *b = scalar parameter \r\n *T = # of periods, i.e. len(xt)\r\n '''\r\n final_norm = np.empty((n_specs, 1))\r\n for t in range(T+1): \r\n #get x_{t-s} up to x_t\r\n #turn around order such that x_t is first element and x_1 is last \r\n sub_x = np.flip(xt[:, :t], axis = 1)\r\n sub_x_II = np.empty((n_specs, len(sub_x[0])))\r\n #loop over all different x-specifications and calculate epsilon as summation of x_t following formula\r\n for j in range(n_specs):\r\n sub_x_II[j] = np.array([b**i * x_t * (-1)**i for i, x_t in enumerate(sub_x[j])])\r\n res = np.sum(sub_x_II, axis = 1)\r\n res = np.reshape(res, (n_specs, 1))\r\n final_norm = np.hstack((final_norm, res))\r\n #drop first observation since this is just initializing, empty array\r\n final_norm = final_norm[:, 2:]\r\n return(final_norm)\r\n\r\ndef lll_hood_opt(xt, bs, T):\r\n values = np.empty((n_specs, 1))\r\n for param in bs:\r\n eps_hat = get_eps_t(xt, param, T = T)\r\n sigma = 1/(T-1) * np.sum(eps_hat**2, axis = 1)\r\n #can get sum of sqrd epsilon directly and save it\r\n res = (T-1)*np.log(1/np.sqrt(2*np.pi*sigma)) - (1/(2*sigma)) * np.sum(eps_hat**2)\r\n res = np.reshape(res, (n_specs, 1))\r\n values = np.hstack((values, res))\r\n #discard initializing empty array again\r\n values = values[:, 1:]\r\n max_i = np.argmax(values, 1)\r\n opt_param = np.empty((n_specs, 1))\r\n for i in range(n_specs): \r\n opt_param[i] = bs[max_i[i]]\r\n return(opt_param)\r\n\r\n#!replication study \r\n#set general parameters\r\nS = 100\r\nT = 1000\r\nn_specs = 3\r\nb = 0.25\r\nbs = np.arange(-0.9, 0.9+0.001, 0.05)\r\n#set up empty array where everything is saved to \r\n#this time using hstack, maybe faster but in the end need to discard first element \r\nopt_params = np.empty((n_specs, 1))\r\nstart = time.time()\r\nfor s in range(S):\r\n eps = np.empty((n_specs, T+1))\r\n eps[0] = rd.normal(0, 1, T+1)\r\n eps[1] = t.rvs(df = 5, size = T+1)\r\n eps[2] = rd.uniform(0, 1, T+1)\r\n eps[:, 0] = 0\r\n eps_lag = eps[:, :T]\r\n #start epsilon from second element because we added eps[0] = 0\r\n x = eps[:, 1:] + b*eps_lag\r\n results = lll_hood_opt(x, bs, T)\r\n opt_params = np.hstack((opt_params, results))\r\n print(s)\r\nend = time.time() \r\nprint(end-start)\r\n\r\n#get sigmas for optimal parameters by calculating them again for the given b \r\n\r\n#!calculating biases \r\ndef bias(estimate, true_val, T): \r\n '''\r\n Calculate bias as given in assignment.\r\n '''\r\n reps = len(estimate)\r\n summation = np.sum(estimate)\r\n bias = 1/reps * summation - true_val\r\n return(bias)\r\n\r\nbias_params = np.empty(n_specs)\r\nbias_variance = np.empty(n_specs)\r\ntrue_variances = np.array((1, 5/3, 1/12)) #define true variances (per hand since easier)\r\nbias(opt_params)\r\nfor i, param in enumerate(opt_params):\r\n bias_params[i] = bias(param, b, T)\r\nprint(bias_params)\r\n","repo_name":"LCruzFer/TSA","sub_path":"Assignment 9/Assignment 9_Q15_MLE.py","file_name":"Assignment 9_Q15_MLE.py","file_ext":"py","file_size_in_byte":3435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"15294327818","text":"import os\ntext_file = open(\"Output.txt\", \"w\")\nrootdir = r\"C:\\xampp\\htdocs\\maccilMarketing\\images\\contents\"\n\nfor subdir, dirs, files in os.walk(rootdir):\n\tfor file in files:\n #print os.path.join(subdir, file)\n\t\tgroups = subdir.split('\\\\');\n\t\tif len(groups) == 7:\n\t\t\tfilepath = os.sep + groups[6] + os.sep + file\n\t\telse:\n\t\t\tfilepath = os.sep + file\n\t\tif filepath.endswith(\".jpg\") and len(groups) == 7:\n\t\t\ttext_file.write(\"{'src': 'images/content/%s', 'tag': '%s'}, \\n\" % (filepath[1:], groups[6]))\ntext_file.close()","repo_name":"nick158/macCilMarketing","sub_path":"images/contents/imagemake.py","file_name":"imagemake.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"20393295441","text":"import re\nfrom sysexpert import Log, Operateur, Parenthese, Connecteur, Fait, Proposition, Regle, AnalyseurSimple\n\n\nclass AnalyseurSyntaxique(AnalyseurSimple):\n\n @staticmethod\n def analyse_symbole(symbole):\n symbole = symbole.strip()\n if re.match(r\"^[a-zA-Z]([\\w|\\s]*\\w)?$\", symbole):\n return symbole\n raise Exception(\"N'est pas un symbole valide {}\".format(symbole))\n\n @staticmethod\n def analyse_valeur(valeur):\n valeur = valeur.strip()\n try:\n return int(valeur)\n except BaseException:\n pass\n try:\n return float(valeur)\n except BaseException:\n pass\n if valeur == \"True\" or valeur == \"Vrai\":\n return True\n elif valeur == \"False\" or valeur == \"Faux\":\n return False\n else:\n return valeur\n\n @staticmethod\n def analyse_valeur_fait(valeur):\n valeur = valeur.strip()\n try:\n return int(valeur)\n except BaseException:\n pass\n try:\n return float(valeur)\n except BaseException:\n pass\n if valeur == \"True\" or valeur == \"Vrai\":\n return True\n elif valeur == \"False\" or valeur == \"Faux\":\n return False\n elif valeur.startswith(\"\\\"\") and valeur.endswith(\"\\\"\"):\n return valeur\n else:\n raise Exception(\"N'est pas une valeur valide {}\".format(valeur))\n\n @staticmethod\n def verifier_valeurs(valeurs, lexeme):\n if len(valeurs) == 0:\n raise Exception(\"il manque deux valeurs {}\".format(lexeme))\n if len(valeurs) == 1:\n raise Exception(\"il manque un valeur {}\".format(lexeme))\n if valeurs[0].strip() == \"\" or valeurs[1].strip() == \"\":\n raise Exception(\"Une valeur ne peut être vide {}\".format(lexeme))\n\n @staticmethod\n def analyse_premise(chaine):\n lexemes = []\n nb_parenthese = 0\n for lexeme in re.split(r'(&+|\\|\\|+|\\(+|\\)+)', chaine):\n if Parenthese.OUVRANT.value in lexeme:\n nb_parenthese += 1\n lexemes.append(Parenthese.OUVRANT)\n elif Parenthese.FERMANT.value in lexeme:\n nb_parenthese -= 1\n lexemes.append(Parenthese.FERMANT)\n elif '&' in lexeme:\n lexemes.append(Connecteur.ET)\n elif '||' in lexeme:\n lexemes.append(Connecteur.OU)\n elif Operateur.EGALITE.value in lexeme:\n valeurs = lexeme.split(Operateur.EGALITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(Proposition(\n valeur_gauche, Operateur.EGALITE, valeur_droite))\n elif Operateur.INEGALITE.value in lexeme:\n valeurs = lexeme.split(Operateur.INEGALITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(Proposition(\n valeur_gauche, Operateur.INEGALITE, valeur_droite))\n elif Operateur.SUPERIORITEOUEGALITE.value in lexeme:\n valeurs = lexeme.split(Operateur.SUPERIORITEOUEGALITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(\n Proposition(\n valeur_gauche,\n Operateur.SUPERIORITEOUEGALITE,\n valeur_droite))\n elif Operateur.SUPERIORITE.value in lexeme:\n valeurs = lexeme.split(Operateur.SUPERIORITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(Proposition(\n valeur_gauche, Operateur.SUPERIORITE, valeur_droite))\n elif Operateur.INFERIORITEOUEGALITE.value in lexeme:\n valeurs = lexeme.split(Operateur.INFERIORITEOUEGALITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(\n Proposition(\n valeur_gauche,\n Operateur.INFERIORITEOUEGALITE,\n valeur_droite))\n elif Operateur.INFERIORITE.value in lexeme:\n valeurs = lexeme.split(Operateur.INFERIORITE.value)\n AnalyseurSyntaxique.verifier_valeurs(valeurs, lexeme)\n valeur_gauche = AnalyseurSyntaxique.analyse_valeur(valeurs[0])\n valeur_droite = AnalyseurSyntaxique.analyse_valeur(valeurs[1])\n lexemes.append(Proposition(\n valeur_gauche, Operateur.INFERIORITE, valeur_droite))\n elif lexeme.strip() != \"\":\n raise Exception(\"Non valide {}\".format(lexeme))\n if nb_parenthese != 0:\n raise Exception(\"Il manque une parenthèse\")\n return lexemes\n\n @staticmethod\n def analyse_conclusion(chaine):\n conclusions = []\n for conclusion in chaine.split(\"&\"):\n if len(conclusion.split(\"=\")) < 2:\n raise Exception(\"Invalide {}\".format(chaine))\n symbole = AnalyseurSyntaxique.analyse_symbole(\n conclusion.split(\"=\")[0])\n valeur = AnalyseurSyntaxique.analyse_valeur_fait(\n conclusion.split(\"=\")[1])\n conclusions.append(Fait(symbole, valeur))\n return conclusions\n\n @staticmethod\n def analyse_regle(chaine):\n if len(chaine.split(\":=\")) < 2:\n raise Exception(\"Invalide {}\".format(chaine))\n conclusions = AnalyseurSyntaxique.analyse_conclusion(\n chaine.split(\":=\")[0])\n premisses = AnalyseurSyntaxique.analyse_premise(chaine.split(\":=\")[1])\n return Regle(premisses, conclusions)\n\n @staticmethod\n def analyse_fait(chaine):\n if len(chaine.split(\"=\")) < 2:\n raise Exception(\"Invalide {}\".format(chaine))\n AnalyseurSyntaxique.verifier_valeurs(chaine.split(\"=\"), chaine)\n symbole = AnalyseurSyntaxique.analyse_symbole(chaine.split(\"=\")[0])\n valeur = AnalyseurSyntaxique.analyse_valeur_fait(chaine.split(\"=\")[1])\n return Fait(symbole, valeur)\n\n @staticmethod\n def retire_commentaire(chaine):\n return chaine.split(\"#\")[0]\n\n @staticmethod\n def analyse_ligne(chaine, basedefaits, basederegles):\n if \":=\" in chaine:\n try:\n basederegles.ajouter(AnalyseurSyntaxique.analyse_regle(chaine))\n except Exception as e:\n Log.warning(\"{}\".format(e))\n elif \"=\" in chaine:\n try:\n basedefaits.ajouter(AnalyseurSyntaxique.analyse_fait(chaine))\n except Exception as e:\n Log.warning(\"{}\".format(e))\n elif chaine.strip() != \"\":\n Log.warning(\"Non reconnus {}\".format(chaine))\n\n @staticmethod\n def analyse_fichier(nom_fichier, basedefaits, basederegles):\n with open(nom_fichier, 'r', encoding=\"utf-8\") as fichier:\n for ligne in fichier:\n chaine = AnalyseurSyntaxique.retire_commentaire(\n ligne.split(\"\\n\")[0])\n AnalyseurSyntaxique.analyse_ligne(\n chaine, basedefaits, basederegles)\n fichier.close()\n","repo_name":"theodeze/SystemeExpert","sub_path":"sysexpert/analyseursyntaxique.py","file_name":"analyseursyntaxique.py","file_ext":"py","file_size_in_byte":8070,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"5096123483","text":"# -*- coding: utf-8 -*- #\n\"\"\"This provides the DuneAnalytics class implementation\"\"\"\n\nfrom requests import Session\nimport logging\n\n# --------- Constants --------- #\n\nBASE_URL = \"https://dune.com\"\nGRAPH_URL = 'https://core-hsr.dune.com/v1/graphql'\nGRAPH_URL_NEW = 'https://app-api.dune.com/v1/graphql'\n\n# --------- Constants --------- #\nlogging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s : %(levelname)s : %(funcName)-9s : %(message)s'\n)\nlogger = logging.getLogger(\"dune\")\n\n\nclass DuneAnalytics:\n \"\"\"\n DuneAnalytics class to act as python client for duneanalytics.com.\n All requests to be made through this class.\n \"\"\"\n\n def __init__(self, username, password):\n \"\"\"\n Initialize the object\n :param username: username for duneanalytics.com\n :param password: password for duneanalytics.com\n \"\"\"\n self.csrf = None\n self.auth_refresh = None\n self.token = None\n self.username = username\n self.password = password\n self.query_id = None\n self.session = Session()\n headers = {\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,'\n 'image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n 'dnt': '1',\n 'sec-ch-ua': '\"Google Chrome\";v=\"95\", \"Chromium\";v=\"95\", \";Not A Brand\";v=\"99\"',\n 'sec-ch-ua-mobile': '?0',\n 'sec-fetch-dest': 'document',\n 'sec-fetch-mode': 'cors',\n 'sec-fetch-site': 'same-site',\n 'origin': BASE_URL,\n 'upgrade-insecure-requests': '1'\n }\n self.session.headers.update(headers)\n\n def login(self):\n \"\"\"\n Try to login to duneanalytics.com & get the token\n :return:\n \"\"\"\n login_url = BASE_URL + '/auth/login'\n csrf_url = BASE_URL + '/api/auth/csrf'\n auth_url = BASE_URL + '/api/auth'\n\n # fetch login page\n self.session.get(login_url)\n\n # get csrf token\n self.session.post(csrf_url)\n self.csrf = self.session.cookies.get('csrf')\n\n # try to login\n form_data = {\n 'action': 'login',\n 'username': self.username,\n 'password': self.password,\n 'csrf': self.csrf,\n 'next': BASE_URL\n }\n\n self.session.post(auth_url, data=form_data)\n self.auth_refresh = self.session.cookies.get('auth-refresh')\n if self.auth_refresh is None:\n logger.warning(\"Login Failed!\")\n\n def fetch_auth_token(self):\n \"\"\"\n Fetch authorization token for the user\n :return:\n \"\"\"\n session_url = BASE_URL + '/api/auth/session'\n\n response = self.session.post(session_url)\n if response.status_code == 200:\n self.token = response.json().get('token')\n if self.token is None:\n logger.warning(\"Fetching Token Failed!\")\n else:\n logger.error(response.text)\n\n def query_result_id(self, query_id):\n \"\"\"\n Fetch the query result id for a query\n\n :param query_id: provide the query_id\n :return:\n \"\"\"\n query_data = {\"operationName\": \"GetResult\", \"variables\": {\"query_id\": query_id},\n \"query\": \"query GetResult($query_id: Int!, $parameters: [Parameter!]) \"\n \"{\\n get_result_v2(query_id: $query_id, parameters: $parameters) \"\n \"{\\n job_id\\n result_id\\n error_id\\n __typename\\n }\\n}\\n\"\n }\n\n self.session.headers.update({'authorization': f'Bearer {self.token}'})\n\n response = self.session.post(GRAPH_URL, json=query_data)\n if response.status_code == 200:\n data = response.json()\n logger.debug(data)\n if 'errors' in data:\n logger.error(data.get('errors'))\n return None\n result_id = data.get('data').get('get_result_v2').get('result_id')\n return result_id\n else:\n logger.error(response.text)\n return None\n\n def query_result_id_v3(self, query_id):\n \"\"\"\n Fetch the query result id for a query\n\n :param query_id: provide the query_id\n :return:\n \"\"\"\n self.query_id = query_id\n query_data = {\"operationName\": \"GetResult\", \"variables\": {\"query_id\": query_id, \"parameters\": []},\n \"query\": \"query GetResult($query_id: Int!, $parameters: [Parameter!]!) \"\n \"{\\n get_result_v3(query_id: $query_id, parameters: $parameters) \"\n \"{\\n job_id\\n result_id\\n error_id\\n __typename\\n }\\n}\\n\"\n }\n\n self.session.headers.update({'authorization': f'Bearer {self.token}'})\n\n response = self.session.post(GRAPH_URL, json=query_data)\n if response.status_code == 200:\n data = response.json()\n logger.debug(data)\n if 'errors' in data:\n logger.error(data.get('errors'))\n return None\n result_id = data.get('data').get('get_result_v3').get('result_id')\n return result_id\n else:\n logger.error(response.text)\n return None\n\n def query_result(self, result_id):\n \"\"\"\n Fetch the result for a query\n :param result_id: result id of the query\n :return:\n \"\"\"\n query_data = {\"operationName\": \"FindResultDataByResult\",\n \"variables\": {\"result_id\": result_id, \"error_id\": \"00000000-0000-0000-0000-000000000000\"},\n \"query\": \"query FindResultDataByResult($result_id: uuid!, $error_id: uuid!) \"\n \"{\\n query_results(where: {id: {_eq: $result_id}}) \"\n \"{\\n id\\n job_id\\n runtime\\n generated_at\\n columns\\n __typename\\n }\"\n \"\\n query_errors(where: {id: {_eq: $error_id}}) {\\n id\\n job_id\\n runtime\\n\"\n \" message\\n metadata\\n type\\n generated_at\\n __typename\\n }\\n\"\n \"\\n get_result_by_result_id(args: {want_result_id: $result_id}) {\\n data\\n __typename\\n }\\n}\\n\"\n }\n\n self.session.headers.update({'authorization': f'Bearer {self.token}'})\n\n response = self.session.post(GRAPH_URL, json=query_data)\n if response.status_code == 200:\n data = response.json()\n logger.debug(data)\n return data\n else:\n logger.error(response.text)\n return {}\n\n def get_execution_result(self, execution_id):\n query_data = {\"operationName\": \"GetExecution\",\n \"variables\": {\"execution_id\": execution_id, \"query_id\": self.query_id, \"parameters\": []},\n \"query\": \"query GetExecution($execution_id: String!, $query_id: Int!, $parameters: [Parameter!]!)\"\n \" {\\n get_execution(\\n execution_id: $execution_id\\n query_id: $query_id\\n \"\n \"parameters: $parameters\\n ) {\\n execution_queued {\\n execution_id\\n \"\n \"execution_user_id\\n position\\n execution_type\\n created_at\\n \"\n \"__typename\\n }\\n execution_running {\\n execution_id\\n \"\n \"execution_user_id\\n execution_type\\n started_at\\n created_at\\n \"\n \"__typename\\n }\\n execution_succeeded {\\n execution_id\\n \"\n \"runtime_seconds\\n generated_at\\n columns\\n data\\n __typename\\n }\"\n \"\\n execution_failed {\\n execution_id\\n type\\n message\\n metadata\"\n \" {\\n line\\n column\\n hint\\n __typename\\n }\\n \"\n \"runtime_seconds\\n generated_at\\n __typename\\n }\\n __typename\\n }\\n}\\n\"}\n self.session.headers.update({'authorization': f'Bearer {self.token}'})\n\n response = self.session.post(GRAPH_URL_NEW, json=query_data)\n if response.status_code == 200:\n data = response.json()\n logger.debug(data)\n return data\n else:\n logger.error(response.text)\n return {}\n","repo_name":"itzmestar/duneanalytics","sub_path":"duneanalytics/duneanalytics.py","file_name":"duneanalytics.py","file_ext":"py","file_size_in_byte":8573,"program_lang":"python","lang":"en","doc_type":"code","stars":130,"dataset":"github-code","pt":"75"} +{"seq_id":"14425465809","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio\n\nimport numpy as np\nfrom tqdm import tqdm\nimport math\nimport pdb\n\nimport utils\nimport augmentation\n\ndef train_epoch(global_step, model_params, model, optimizer, train_loaders, summary_writer):\n data_loader, reverb_loader, noise_loader = train_loaders\n\n eq_model = augmentation.MicrophoneEQ(rate=model_params.sample_rate).cuda()\n loss_meter = utils.AverageMeter('loss', fmt=':.4f')\n\n beta = np.linspace(model_params.noise_min, model_params.noise_max, model_params.noise_scales)\n noise_level = np.cumprod(1 - beta)\n noise_level = torch.tensor(noise_level.astype(np.float32))\n\n loop = tqdm(data_loader)\n for i, x in enumerate(loop):\n batch_size = x.shape[0]\n x, noise, rir = x.cuda(), next(noise_loader).cuda(), next(reverb_loader).cuda()\n clean_x, aug_x = augmentation.augment(x, rir=rir, noise=noise, eq_model=eq_model)\n t = torch.randint(0, model_params.noise_scales, [batch_size], device=clean_x.device)\n noise_scale = noise_level[t].cuda()\n noise_scale_sqrt = noise_scale**0.5\n noise = torch.randn_like(clean_x)\n noisy_x = noise_scale_sqrt[:,None,None] * clean_x + (1.0 - noise_scale[:,None,None])**0.5 * noise\n predicted = model(noisy_x, aug_x, t)\n loss = nn.L1Loss()(noise, predicted)\n loss.backward()\n optimizer.step()\n model.zero_grad()\n loss_meter.update(loss.item(), n=x.shape[0])\n loop.set_postfix_str(str(loss_meter))\n summary_writer.add_scalar('training/loss', loss.item(), global_step)\n global_step += 1\n\n return global_step, model, optimizer\n\n\ndef validate(model_params, model, val_loaders):\n data_loader, reverb_loader, noise_loader = val_loaders\n\n eq_model = augmentation.MicrophoneEQ().cuda()\n loss_meter = utils.AverageMeter('loss', fmt=':.4f')\n\n beta = np.linspace(model_params.noise_min, model_params.noise_max, model_params.noise_scales)\n alpha = 1 - beta\n alpha_t = torch.tensor(np.cumprod(1 - beta).astype(np.float32))\n \n with torch.no_grad():\n for x in data_loader: \n batch_size = x.shape[0]\n x, noise, rir = x.cuda(), next(noise_loader).cuda(), next(reverb_loader).cuda()\n clean_x, aug_x = augmentation.augment(x, rir=rir, noise=noise, eq_model=eq_model)\n t = torch.randint(0, model_params.noise_scales, [batch_size], device=clean_x.device)\n noise_scale = alpha_t[t].cuda()\n noise = torch.randn_like(clean_x)\n noisy_x = (noise_scale[:,None,None])**0.5 * clean_x + (1.0 - noise_scale[:,None,None])**0.5 * noise\n predicted = model(noisy_x, aug_x, t)\n loss = nn.L1Loss()(noise, predicted)\n loss_meter.update(loss.item(), n=x.shape[0])\n \n samples = generate(model_params, model, aug_x)\n \n return clean_x, aug_x, samples, loss_meter.avg\n\n\ndef generate(model_params, model, cond_waveform):\n beta = np.linspace(model_params.noise_min, model_params.noise_max, model_params.noise_scales)\n alpha = 1 - beta\n alpha_t = np.cumprod(alpha)\n x = torch.randn_like(cond_waveform)\n for t in range(model_params.noise_scales - 1, -1, -1):\n T = torch.tensor([t], dtype=torch.int64).repeat(cond_waveform.shape[0]).to(cond_waveform.device)\n c1 = 1/math.sqrt(alpha[t])\n c2 = beta[t]/math.sqrt(1 - alpha_t[t])\n x = c1*(x - c2*model(x, cond_waveform, T))\n if t > 0:\n sigma = math.sqrt((1 - alpha_t[t-1]) * beta[t] / (1 - alpha_t[t]))\n x += sigma*torch.randn_like(x)\n return x\n","repo_name":"nkandpa2/music_enhancement","sub_path":"training_utils/diffwave_waveform.py","file_name":"diffwave_waveform.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"75"} +{"seq_id":"6951271370","text":"from flask import Blueprint, flash, g, redirect, request, url_for\nfrom flaskr.db import get_db\nimport os\n\nbp = Blueprint('upload', __name__, url_prefix='/upload')\n\n\ndef up(_formname, db_operation, return_point):\n APP_ROOT = os.path.dirname(os.path.abspath(__file__))\n target = os.path.join(APP_ROOT, 'static/uploads/')\n if not os.path.isdir(target):\n os.mkdir(target)\n\n for upload in request.files.getlist(_formname):\n if upload is not None and upload != '':\n filename = upload.filename\n destination = \"/\".join([target, filename])\n upload.save(destination)\n\n db = get_db()\n db_operation(upload, db)\n else:\n flash('Image is required.')\n return return_point()\n\n\n@bp.route('/new_ava', methods=('POST',))\ndef new_ava():\n def operation(upload, db):\n db.execute(\n 'UPDATE user SET img = ?'\n ' WHERE id = ?',\n (upload.filename, g.user['id'])\n )\n db.commit()\n db.execute(\n 'INSERT INTO images(img, user_id) VALUES(?, ?)',\n (upload.filename, g.user['id'])\n )\n db.commit()\n\n up('file', operation, lambda: redirect(url_for('profile.settings')))\n\n flash('New avatar')\n\n return redirect(url_for('profile.settings'))\n\n\n@bp.route('/upload_file', methods=('POST',))\ndef upload_img_profile():\n def operation(upload, db):\n db.execute(\n 'INSERT INTO images(img, user_id) VALUES(?, ?)',\n (upload.filename, g.user['id'])\n )\n db.commit()\n\n up('file_img', operation, lambda: redirect(url_for('profile.people', profile_id=g.user['id'])))\n\n flash('New images for profile')\n\n return redirect(url_for('profile.people', profile_id=g.user['id']))\n\n\n@bp.route('/new_username', methods=('POST',))\ndef new_username():\n db = get_db()\n\n user = get_db().execute(\n 'SELECT id'\n ' FROM user'\n ' WHERE username = ?',\n (request.form['username'],)\n ).fetchone()\n if user is not None:\n flash('Username is taken')\n return redirect(url_for('profile.settings'))\n\n db.execute(\n 'UPDATE user SET username = ?'\n ' WHERE id = ?',\n (request.form['username'], g.user['id'])\n )\n db.commit()\n\n flash('New username profile')\n\n return redirect(url_for('profile.settings'))\n","repo_name":"SavaFeeD/my_blog_on_flask","sub_path":"flaskr/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"23971963768","text":"class TrieNode:\n def __init__(self):\n self.children = [0] * 26\n self.leaf = False\n\n\nclass Trie:\n def __init__(self):\n self.root = TrieNode()\n\n def get_index(self, ch):\n return ord('a') - ord(ch)\n\n def insert_string(self, insert_string):\n buf_root = self.root\n\n for char in insert_string:\n index = self.get_index(char)\n if buf_root.children[index]:\n buf_root = buf_root.children[index]\n\n else:\n buf_root.children[index] = TrieNode()\n buf_root = buf_root.children[index]\n\n buf_root.leaf = True\n\n def search_string(self, search_string):\n buf_root = self.root\n\n for char in search_string:\n index = self.get_index(char)\n if not buf_root.children[index]:\n return False\n buf_root = buf_root.children[index]\n\n return buf_root.leaf\n\n\nif __name__ == \"__main__\":\n trie = Trie()\n insert_string = ['hello', 'the', 'robbin', 'disco', 'disqualified', 'damaged', 'dam', 'damote']\n search_string = ['hello', 'the', 'try', 'copy', 'robbi', 'robbin', 'damaged']\n\n for strings in insert_string:\n trie.insert_string(strings)\n\n for s_string in search_string:\n print(\"Searching for string {} in trie, and it was {}\".format(s_string, trie.search_string(s_string)))\n","repo_name":"run-me/how_to_approach_cs_problem_solving","sub_path":"data_structures/08_tries/tries.py","file_name":"tries.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"73866450482","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nimport flask_excel as excel\nfrom search_factory import SearchFactory\nfrom settings import url_maps, name_maps\nfrom common.ms_logs import log\nfrom flask import Flask, render_template, jsonify, request\n\n# name_map = url_map = 'hello world'\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return render_template('index.html', url_map=url_maps, name_map=name_maps)\n\n\n@app.route('/exp_excel/')\ndef exp_excel(e_id):\n \"\"\"\n :param e_id: 根据e_id 决定下载Excel文件\n :return:\n \"\"\"\n # 根据e_id 从数据库中查询信息，返回Excel文件\n result = {\n \"专利名称\": \"邱哥钓鱼\",\n \"专利号\": \"1024\",\n \"专利类别\": \"发明\",\n \"日期\": \"2019.02.28\"\n }\n column_names = list(result.keys())\n return excel.make_response_from_dict(\n result,\n column_names=column_names,\n file_type='xls',\n file_name='专利.xls'\n )\n\n\n@app.route('/query', methods=['POST'])\ndef query():\n data = request.get_json()\n web_site = data.get('web_site')\n web_site = int(web_site) if web_site else ''\n if web_site == 0: # 专利\n patent_id = data.get('patent_id')\n patent_code = data.get('patent_code')\n # 校验参数是否完整\n if not all([patent_id, patent_code]):\n return jsonify({'code': 602, 'msg': '参数不完整！'})\n # 校验专利号是否合法\n if not re.match(r'\\d{9}|\\d{13}', patent_id):\n return jsonify({'code': 601, 'msg': '专利号不合法！'})\n post_data = {\n 'patent_id': patent_id, 'patent_code': patent_code\n }\n log.info(post_data)\n # 调用爬虫，返回抓取结果\n result = SearchFactory(int(web_site)).draw(post_data)\n time.sleep(3)\n if not result:\n return jsonify({'code': 625, 'msg': '未获取信息，稍后重试'})\n return jsonify({'code': 600, 'e_id': patent_id, 'result': result})\n elif web_site == 1: # 商标\n return jsonify({'msg': 'this is test'})\n elif web_site == 2: # 国家企业信息\n return jsonify({'msg': 'this is test'})\n elif web_site == 3: # 企查查\n return jsonify({'msg': 'this is test'})\n else:\n return jsonify({'msg': '没有选择站点'})\n\n\nif __name__ == '__main__':\n log.init_log('info', True)\n excel.init_excel(app)\n app.run(debug=True)\n","repo_name":"leslie-kung/my_search","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"20405889955","text":"from django.test import TestCase\nfrom django.shortcuts import reverse\nfrom .models import Test, Question, RunTest\nfrom .forms import AnswerForm\nfrom django.forms.models import formset_factory\n\n\nclass CreateTestCase(TestCase):\n test_name = 'My_testing_test'\n\n @classmethod\n def setUp(cls):\n super().setUpClass()\n cls.test = Test.objects.create(name=cls.test_name)\n\n def test_add_question(self):\n q1 = Question(text='what is your name?')\n q1.save()\n adding_question = {'test_id': self.test.id, 'q_id': q1.id}\n r = reverse('app_tests:add_q', kwargs=adding_question)\n request = self.client.post(r, data=adding_question)\n self.assertEqual(Test.objects.values_list('questions', flat=True).count(), 1)\n\n q2 = Question(text='what is your favorite color?')\n q2.save()\n adding_question = {'test_id': self.test.id, 'q_id': q2.id}\n r = reverse('app_tests:add_q', kwargs=adding_question)\n request = self.client.post(r, data=adding_question)\n self.assertEqual(Test.objects.values_list('questions', flat=True).count(), 2)\n\n def test_run_test(self):\n answer_factory = formset_factory(AnswerForm, min_num=2)\n runing_test_1 = {'form-TOTAL_FORMS': '3',\n 'form-INITIAL_FORMS': '0',\n 'form-MIN_NUM_FORMS': '2',\n 'form-MAX_NUM_FORMS': '1000',\n 'form-0-Answer': 'King Arthur',\n 'form-1-Answer': 'Blue',\n 'start': 'Done'}\n answer_form_set = answer_factory(runing_test_1)\n self.assertTrue(answer_form_set.is_valid())\n\n self.assertEquals(RunTest.objects.all().count(), 0)\n r = reverse('app_tests:run_test', kwargs={'test_id': self.test.id})\n request = self.client.post(r, data=runing_test_1)\n self.assertEquals(RunTest.objects.all().count(), 1)\n\n runing_test_2 = {'form-TOTAL_FORMS': '3',\n 'form-INITIAL_FORMS': '0',\n 'form-MIN_NUM_FORMS': '2',\n 'form-MAX_NUM_FORMS': '1000',\n 'form-0-Answer': '',\n 'form-1-Answer': '',\n 'start': 'Done'}\n answer_form_set = answer_factory(runing_test_2)\n self.assertFalse(answer_form_set.is_valid())\n","repo_name":"Jesyfox/pyacad_v2_tests","sub_path":"pyFormers/app_tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"31347890049","text":"import os\r\n\r\n\r\ndef find_files(suffix, path):\r\n paths = list()\r\n if os.listdir(path):\r\n directories = os.listdir(path)\r\n\r\n for directory in directories:\r\n sub_directory = os.path.join(path, directory)\r\n if os.path.isdir(sub_directory):\r\n # recursive call\r\n new_folder = find_files(suffix, sub_directory)\r\n paths.extend(new_folder)\r\n\r\n elif suffix.split('.')[1].lower() == sub_directory.split('.')[-1].lower():\r\n # if the required suffix is present append it to the paths list\r\n paths.append(sub_directory)\r\n\r\n return paths\r\n\r\n\r\ndef print_path(directory_list):\r\n if len(directory_list) == 0:\r\n print(\"No Files Found!!!!!\")\r\n else:\r\n for directory in directory_list:\r\n print(directory)\r\n\r\n\r\ntest_c = find_files('.c', './testdir')\r\nprint_path(test_c)\r\n\r\nprint(\"====================================================\")\r\n\r\ntest_h = find_files('.h', './testdir')\r\nprint_path(test_h)\r\n\r\nprint(\"====================================================\")\r\n\r\ntest_random = find_files('.py', './testdir')\r\nprint_path(test_random)\r\n","repo_name":"gssasank/DataStructuresProjects","sub_path":"Data Structures Projects/File Recursion/problem_2.py","file_name":"problem_2.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"71997880242","text":"from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom .serializers import *\nfrom .models import *\n\n\n\nclass GetCategories(APIView):\n \n\n def get(self, request):\n cat = Category.objects.all()\n\n cat = CategorySerializer(cat, many=True)\n\n return Response({\n \"status\":status.HTTP_200_OK,\n \"data\":cat.data\n })\n\n\nclass GetPostsOfACategory(APIView):\n \n\n def get(self, request, id):\n cat = Category.objects.get(id=id)\n posts = cat.posts.all()\n\n posts = PostSerializer(posts, many=True)\n\n return Response({\n \"status\":status.HTTP_200_OK,\n \"data\":posts.data\n })\n\nclass GetAllPosts(APIView):\n \n\n def get(self, request):\n posts = Post.objects.all()\n\n posts = PostSerializer(posts, many=True)\n\n return Response({\n \"status\":status.HTTP_200_OK,\n \"data\":posts.data\n })\n\n\nclass GetPostDetail(APIView):\n \n\n def get(self, request, id):\n post = Post.objects.get(id=id)\n\n post = PostSerializer(post)\n\n return Response({\n \"status\":status.HTTP_200_OK,\n \"data\":post.data\n })\n\n\nclass CommentOnPost(APIView):\n \n\n def post(self, request):\n data = CommentSerializer(request.data).data\n\n id = data['post_id']\n email = data['email']\n comment = data['comment']\n\n post = Post.objects.get(id=id)\n user = User.objects.get(email=email)\n\n comment = Comment.objects.create(\n user = user,\n text=comment\n )\n comment.save()\n\n post.comments.add(comment)\n\n post.save()\n\n return Response({\n \"status\":status.HTTP_200_OK,\n \"message\":\"Comment was successful\"\n })\n\n","repo_name":"fennin3/Recipe-App-Api","sub_path":"community/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"24867142121","text":"# Python program to implement the balanced paranthesis\r\nimport unittest\r\n\r\nclass Node:\r\n def __init__(self, data):\r\n self.data = data\r\n self.next = None\r\n\r\n# Supported Operations: push, pop, peek, is_empty\r\nclass Stack:\r\n def __init__(self):\r\n self.head = None\r\n def is_empty(self):\r\n return True if self.head is None else False\r\n def push(self, data):\r\n new_node = Node(data)\r\n if self.is_empty():\r\n self.head = new_node\r\n else:\r\n new_node.next = self.head\r\n self.head = new_node\r\n def pop(self):\r\n if self.is_empty():\r\n return None\r\n else:\r\n popped_element = self.head\r\n self.head = self.head.next\r\n popped_element.next = None\r\n return popped_element.data\r\n def peek(self):\r\n return None if self.is_empty() else self.head.data\r\n\r\ndef is_paranthesis_balanced(string):\r\n base_stack = Stack()\r\n open_bracket_types = {')': '(', '}': '{', ']': '['}\r\n open_brackets = [open_bracket_types.get(key) for key in open_bracket_types.keys()]\r\n\r\n for ch in string:\r\n if ch in open_brackets:\r\n base_stack.push(ch)\r\n elif ch in open_bracket_types.keys():\r\n popped = base_stack.pop()\r\n if not (open_bracket_types.get(ch) == popped):\r\n print (popped, open_bracket_types.get(ch))\r\n return False\r\n else:\r\n pass\r\n return True\r\n\r\n\r\nif __name__ == '__main__':\r\n input_str = '(abs)({sc}[)'\r\n print (\"Is String = %s, balanced? - %r\" % (input_str, is_paranthesis_balanced(input_str)))\r\n","repo_name":"royadityak94/InterviewPrep","sub_path":"Algorithms/Direct/balanced_paranthesis.py","file_name":"balanced_paranthesis.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"66961354","text":"from mpi4py import MPI\nimport numpy as np\n\ncomm = MPI.COMM_WORLD\nsize = comm.Get_size() # new: gives number of ranks in comm\nrank = comm.Get_rank()\n\nnumDataPerRank = 10\ndata = None\nif rank == 0:\n # np.linspace( Start, stop, Quant de numeros)\n data = np.linspace(1, size*numDataPerRank, numDataPerRank*size)\n\n# inicia um vetor com elementos vazio do tamando arg[0] | do tipo arg[1]\nrecvbuf = np.empty(numDataPerRank, dtype='d') # allocate space for recvbuf\ncomm.Scatter(data, recvbuf, root=0)\n\nprint('Rank: ',rank, ', recvbuf received: ',recvbuf)","repo_name":"ribolive/examplesPythonMpi","sub_path":"paralell_numpyScatter.py","file_name":"paralell_numpyScatter.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"29173339700","text":"#PRG1100-2022-juksekode\n\n#Introduksjon til private/ skjulte attributter\n#Vi bør sikre oss at det bare er metodene til objektet som kan endre/ aksessere\n#attributtene/ instansevariablene\n#Slik det er nå kan kode i main endre verdier på attributter til objektet\n#dvs vi kan jukse i spillet\nimport random\n\n#Mynt-klassen simulerer en mynt og hva en kan gjøre med den\nclass Mynt:\n #__init__metoden initierer objektet/ forekomsten/ instansen\n #og tilordner sideopp-atributtet (self.sideopp) startverdi via en\n #input i __init__\n #dvs setter en startverdi som ikke skal telles med\n \n def __init__(self):\n #Oppgi \"myntside\" opp før første kast\n self.sideopp=input('Hvilken side på mynten er opp før første kast: ')\n\n #Kast metoden simulerer ett kast med mynten\n #og gir sideopp-attributtet ny verdi\n def kast(self):\n if random.randint(0,1)==0:\n self.sideopp='Kron'\n else:\n self.sideopp='Mynt'\n\n #hent_sideopp metoden returnerer til enhver til\n #verdien/ (\"siste verdi\") på mynten, dvs sideopp-attributtet\n def hent_sideopp(self):\n return self.sideopp\n\n\ndef main():\n\n antall_kron=0\n antall_mynt=0\n\n #Oppretter et mynt-objekt, en forekomst/ instanse\n min_mynt=Mynt()\n\n print('Før første kast, er denne siden opp: ',min_mynt.hent_sideopp())\n\n antall_kast=int(input('Hvor mange ganger skal mynten kastes: '))\n\n for antall_kast in range(1,antall_kast+1,1):\n #Mynten kastes\n min_mynt.kast()\n\n #Resultatet av kastet skrives ut\n print('Resultatet av kast nr',antall_kast,'ble',min_mynt.hent_sideopp())\n\n #Her kommer den nye \"jukse-koden\"\n #Uansett hva som blir resultatet av kast-metoden, overstyrer vi\n #resultatet til å bli f.eks. Kron\n min_mynt.sideopp='Kron'\n print('Resultatet av kast nr',antall_kast,'ble manipulert til',min_mynt.sideopp)\n print()\n\n #Opptelling av kast\n if min_mynt.hent_sideopp()=='Kron':\n antall_kron+=1\n else:\n antall_mynt+=1\n \n print('Resultatet ble',antall_kron,'antall kron og',antall_mynt,'antall mynt')\n\nmain()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Monosakkarider/USN","sub_path":"Python/2SEMESTER/9forelesning28032022/myntkastClassJUKSEKODE.py","file_name":"myntkastClassJUKSEKODE.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"12432662777","text":"import yaml\nimport faker\nimport faker.providers\nimport random\n\nfaker.Faker.seed(0)\nrandom.seed(0)\nfake = faker.Faker()\n\nystart_provider = faker.providers.DynamicProvider(\n provider_name=\"ystart\",\n elements=[2015, 2014, 2013, 2012]\n)\nfake.add_provider(ystart_provider)\n\nhouse_provider = faker.providers.DynamicProvider(\n provider_name=\"house\",\n elements=[\"Red\", \"Yellow\", \"Blue\", \"Green\"]\n)\nfake.add_provider(house_provider)\n\nstudents = {}\n\nstudents_to_generate = 1000\n\nfor i in range(students_to_generate):\n profile = fake.profile()\n\n name = profile[\"name\"]\n\n # preferred_name username with only letters\n preferred_name = \"\".join(\n [c for c in profile[\"username\"] if c.isalpha()]\n )\n\n name_with_dots = name.replace(\" \", \".\")\n\n username_discriminator = 0\n while name_with_dots+str(username_discriminator).rjust(2, \"0\") in students:\n username_discriminator += 1\n print(name_with_dots, \"is duplicated\")\n\n username = (name_with_dots+\".\" +\n str(username_discriminator).rjust(2, \"0\")).lower()\n\n if profile[\"sex\"] == \"M\":\n gender = \"male\"\n else:\n gender = \"female\"\n\n students[username] = {\n \"house\": fake.house(),\n \"name\": name,\n \"preferred_name\": preferred_name,\n \"ystart\": fake.ystart(),\n \"gender\": gender\n }\n\n# benjamin.jefferson.00:\n # house: Green\n # name: Benjamin Jefferson\n # preferred_name: Ben\n # ystart: 2013\n # gender: male\n\nstudents_db_text = yaml.dump(\n {\n \"students\": students,\n \"houses\": [\n \"Red\",\n \"Yellow\",\n \"Blue\",\n \"Green\"\n ]\n }\n)\n\nwith open(\"student_db.yaml\", \"w\") as f:\n f.write(students_db_text)\n\n\n# students_split_by_ystart = {}\n\n# for username, student in students.items():\n# if student[\"year\"] in students_split_by_ystart:\n","repo_name":"maxfire2008/sports-tracker","sub_path":"reference/generate_test_data.py","file_name":"generate_test_data.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"75"} +{"seq_id":"43338072770","text":"\"\"\"\nName: rt.py\nPython to clone and build a weather model repo and run\nregression tests.\n\"\"\"\n\n# Imports\nimport datetime\nimport logging\nimport os\n\n\ndef run(job_obj):\n \"\"\"\n Runs a baseline test for a weather model PR\n \"\"\"\n logger = logging.getLogger('RT/RUN')\n logger.info('Started running regression test')\n pr_repo_loc, repo_dir_str = clone_pr_repo(job_obj, job_obj.workdir)\n logger.info(f'pr_repo_loc is: {pr_repo_loc}')\n logger.info(f'repo_dir_str is: {repo_dir_str}')\n run_regression_test(job_obj, pr_repo_loc)\n post_process(job_obj, pr_repo_loc, repo_dir_str)\n logger.info('Finished running regression test')\n\n\ndef run_regression_test(job_obj, pr_repo_loc):\n logger = logging.getLogger('RT/RUN_REGRESSION_TEST')\n logger.info('Started run_regression_test')\n if job_obj.compiler == 'gnu':\n rt_command = [[f'export RT_COMPILER=\"{job_obj.compiler}\" && cd tests '\n '&& /bin/bash --login ./rt.sh -r -k -l rt_gnu.conf >& gnu_out',\n pr_repo_loc]]\n elif job_obj.compiler == 'intel':\n rt_command = [[f'export RT_COMPILER=\"{job_obj.compiler}\" && cd tests '\n '&& /bin/bash --login ./rt.sh -r -k >& intel_out', pr_repo_loc]]\n job_obj.run_commands(logger, rt_command)\n logger.info('Finished run_regression_test')\n\n\ndef remove_pr_data(job_obj, pr_repo_loc, repo_dir_str, rt_dir):\n logger = logging.getLogger('RT/REMOVE_PR_DATA')\n logger.info('Started remove_pr_data')\n rm_command = [\n [f'rm -rf {rt_dir}', pr_repo_loc],\n [f'rm -rf {repo_dir_str}', pr_repo_loc]\n ]\n job_obj.run_commands(logger, rm_command)\n logger.info('Finished remove_pr_data')\n\n\ndef clone_pr_repo(job_obj, workdir):\n ''' clone the GitHub pull request repo, via command line '''\n logger = logging.getLogger('RT/CLONE_PR_REPO')\n logger.info('Started clone_pr_repo')\n repo_name = job_obj.preq_dict['preq'].head.repo.full_name\n branch = job_obj.preq_dict['preq'].head.ref\n app_name = job_obj.repo[\"app_address\"].split(\"/\")[1]\n git_url = f'https://${{ghapitoken}}@github.com/{repo_name}'\n logger.debug(f'GIT URL: {git_url}')\n repo_dir_str = f'{workdir}/pr/'\\\n f'{str(job_obj.preq_dict[\"preq\"].id)}/'\\\n f'{datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")}'\n pr_repo_loc = f'{repo_dir_str}/{app_name}'\n job_obj.comment_append(f'Repo location: {pr_repo_loc}')\n create_repo_commands = [\n [f'mkdir -p \"{repo_dir_str}\"', os.getcwd()],\n [f'git clone -b {branch} {git_url} {app_name}', repo_dir_str],\n ['git submodule update --init --recursive',\n f'{repo_dir_str}/{app_name}'],\n ['git config user.email \"venita.hagerty@noaa.gov\"',\n f'{repo_dir_str}/{app_name}'],\n ['git config user.name \"venitahagerty\"',\n f'{repo_dir_str}/{app_name}']\n ]\n\n job_obj.run_commands(logger, create_repo_commands)\n\n logger.info('Finished clone_pr_repo')\n return pr_repo_loc, repo_dir_str\n\n\ndef post_process(job_obj, pr_repo_loc, repo_dir_str):\n ''' This is the callback function associated with the \"RT\" command '''\n logger = logging.getLogger('RT/POST_PROCESS')\n logger.info('Started post_process')\n rt_log = f'tests/RegressionTests_{job_obj.machine}'\\\n f'.{job_obj.compiler}.log'\n filepath = f'{pr_repo_loc}/{rt_log}'\n rt_dir, logfile_pass = process_logfile(job_obj, filepath)\n if logfile_pass:\n job_obj.comment_append('Regression test successful')\n # remove_pr_data(job_obj, pr_repo_loc, repo_dir_str, rt_dir)\n else:\n job_obj.comment_append('Regression test FAILED')\n issue_id = job_obj.send_comment_text()\n logger.debug(f'Issue comment id is {issue_id}')\n logger.info('Finished post_process')\n\n\ndef process_logfile(job_obj, logfile):\n logger = logging.getLogger('RT/PROCESS_LOGFILE')\n logger.info('Started process_logfile')\n rt_dir = []\n fail_string_list = ['Test', 'failed']\n if os.path.exists(logfile):\n with open(logfile) as f:\n for line in f:\n if all(x in line for x in fail_string_list):\n # if 'FAIL' in line and 'Test' in line:\n job_obj.comment_append(f'{line.rstrip(chr(10))}')\n elif 'working dir' in line and not rt_dir:\n rt_dir = os.path.split(line.split()[-1])[0]\n job_obj.comment_append(f'Please manually delete: '\n f'{rt_dir}')\n elif 'SUCCESSFUL' in line:\n logger.info('RT Successful')\n logger.info('Finished process_logfile')\n return rt_dir, True\n logger.critical('Log file exists but is not complete')\n job_obj.job_failed(logger, f'{job_obj.preq_dict[\"action\"]}')\n else:\n logger.critical(f'Could not find {job_obj.machine}'\n f'.{job_obj.compiler} '\n f'{job_obj.preq_dict[\"action\"]} log')\n print(f'Could not find {job_obj.machine}.{job_obj.compiler} '\n f'{job_obj.preq_dict[\"action\"]} log')\n raise FileNotFoundError\n","repo_name":"NOAA-GSL/rrfs-ci","sub_path":"tests/auto/jobs/rt.py","file_name":"rt.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"10575607903","text":"import io\nimport logging\nfrom time import sleep\nimport PIL\nimport cv2\nimport numpy as np\nfrom werkzeug.utils import secure_filename\nfrom autoclicker import capture_util\nfrom autoclicker.api.util import RequestStatus\nfrom werkzeug.security import check_password_hash, generate_password_hash\nfrom flask import (\n Blueprint, Response, flash, g, redirect, render_template, request, url_for\n)\nfrom werkzeug.exceptions import abort\nfrom autoclicker.image_util import ImageType, convert_pil_image, resize\n\nfrom autoclicker.ui.auth import login_required\nfrom autoclicker.db import get_db\nfrom autoclicker.capture_util import get_all_window_titles, get_hwnd, grab_window_content, stop_capture\nfrom autoclicker.util import Size\n\nbp = Blueprint('capture', __name__, url_prefix='/capture')\n\n\n@bp.route(\"/start/\")\n@login_required\ndef start():\n capture_util.stop_capture()\n\n title = request.args.get('title')\n running = capture_util.start_capture(title=title)\n\n return redirect(url_for('index'))\n\n\n@bp.route(\"/stop\")\n@login_required\ndef stop():\n capture_util.stop_capture()\n\n return RequestStatus.SUCCESS.value\n\n\n@bp.route(\"/window_titles\")\n@login_required\ndef window_titles():\n hwnds = capture_util.get_all_window_titles()\n\n return hwnds\n\n\ndef gather_img():\n counter = 0\n encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 30]\n\n while True:\n try:\n image = capture_util.get_image()\n\n if not image:\n return\n\n resize(image, width=512, height=512)\n _, frame = cv2.imencode(\n '.jpg', convert_pil_image(image, ImageType.OPENCV), encode_param)\n\n yield (b'--frame\\r\\nContent-Type: image/jpeg\\r\\n\\r\\n' + frame.tobytes() + b'\\r\\n')\n logging.info(f\"Streamed frame{'.' * (counter % 3) }\")\n\n counter += 1\n sleep(0.01)\n except Exception as e:\n logging.error(e)\n\n\n@bp.route(\"/stream\")\n@login_required\ndef stream():\n return Response(gather_img(), mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\n@bp.route('/preview')\n@login_required\ndef preview():\n return render_template('preview.html')\n","repo_name":"Brucknem/AutoClicker","sub_path":"autoclicker/api/capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"29677591036","text":"\"\"\" Main Module of PyChara \"\"\"\n__author__ = 'gabriel'\n\n\nfrom optparse import OptionParser\nfrom pychara.parser import Parser\nimport os\n\ndef cmdline_options():\n \"\"\" Definition of the Cmd Line Options \"\"\"\n options = OptionParser()\n options.add_option(\"-f\",\"--file\",dest = \"filename\")\n options.add_option(\"-r\",\"--recursive\", action=\"store_true\",dest=\"recursive\")\n options.add_option(\"-m\",\"--metric\", action=\"append\", dest=\"metrics\")\n options.add_option(\"-o\",\"--output\", dest=\"output\", default=\"output.xml\")\n return options\n\n\ndef main():\n \"\"\" Main Function of PyChara \"\"\"\n options = cmdline_options()\n (options, args) = options.parse_args()\n parser = Parser()\n filename = os.path.basename(options.filename)\n path = os.path.dirname(options.filename) or \".\"\n parser.add_visitor(options.metrics)\n parser.parse_files(path,filename,options.recursive)\n\nif __name__ == \"__main__\":\n main()","repo_name":"GJacobsohn/PyChara","sub_path":"pychara.py","file_name":"pychara.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"33723872897","text":"from flask import Flask, request, abort\r\nimport json\r\napp = Flask(__name__)\r\n\r\n@app.post(\"/test\")\r\n\r\ndef json_parser():\r\n try:\r\n string_to_cut = request.json['string_to_cut']\r\n except:\r\n print('POST request key not recognized')\r\n abort(400)\r\n result_string = cut_string(string_to_cut)\r\n return_json = {\r\n \"return_string\": result_string,\r\n }\r\n return json.dumps(return_json)\r\n\r\ndef cut_string(string):\r\n index = 0\r\n string_list = []\r\n for char in string:\r\n index += 1\r\n if index % 3 == 0:\r\n string_list.append(char)\r\n return \"\".join(string_list)\r\n\r\nif __name__ == '__main__':\r\n app.run(host='0.0.0.0', port=5000, debug=True)\r\n","repo_name":"harpreetgaur/flask_app","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"25655677885","text":"import jwt\nimport bcrypt\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom teami.models import User\nfrom rest_framework.views import APIView\nfrom django.views import View\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework_simplejwt.serializers import TokenObtainPairSerializer, TokenRefreshSerializer\nfrom django.contrib.auth import authenticate\nfrom drf_yasg.utils import swagger_auto_schema\n\n\n#from rest_framework.generics import ListCreateAPIView\nfrom .serializers import *\n\nclass Unique(APIView):\n @swagger_auto_schema(operation_id=\"이메일 중복 확인\")\n def post(self, request):\n data = request.data\n if User.objects.filter(email = data['email']).exists():\n return Response({\"message\": \"이메일이 존재합니다\"}, status=status.HTTP_400_BAD_REQUEST)\n return Response({\"message\": \"이 이메일은 사용 가능합니다\"}, status=status.HTTP_200_OK)\n\nclass Register(APIView):\n @swagger_auto_schema(operation_id=\"회원가입\")\n def post(self, request):\n data = request.data\n if data['password'] != data['password2']:\n return Response({\"message\": \"비밀번호가 일치하지 않습니다\"},status=status.HTTP_400_BAD_REQUEST)\n content = {\n \"email\": data['email'],\n \"password\": bcrypt.hashpw(data['password'].encode(\"utf-8\"), bcrypt.gensalt()).decode(\"utf-8\"),\n \"username\": data['username']\n }\n serializer = UserSerializer(data=content)\n if serializer.is_valid():\n user = serializer.save()\n token = TokenObtainPairSerializer.get_token(user)\n refresh_token = str(token)\n access_token = str(token.access_token)\n response = Response(\n {\n \"user\": serializer.data,\n \"message\": \"register successs\",\n \"token\": {\n \"access\": access_token,\n \"refresh\": refresh_token,\n },\n },\n status=status.HTTP_201_CREATED,\n )\n \n return response\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass Login(APIView):\n @swagger_auto_schema(operation_id=\"가입된 유저들 조회\")\n def get(self, request):\n user = User.objects.values()\n return Response({\"list\" : list(user)}, status = 200)\n\n @swagger_auto_schema(operation_id=\"로그인 (토큰 쿠키에 저장)\")\n def post(self, request):\n data = request.data\n if User.objects.filter(email = data['email']).exists() :\n user = User.objects.get(email = data['email'])\n if bcrypt.checkpw(data['password'].encode('utf-8'), user.password.encode('utf-8')) :\n serializer = UserSerializer(user)\n token = TokenObtainPairSerializer.get_token(user)\n refresh_token = str(token)\n access_token = str(token.access_token)\n response = Response(\n {\n \"user\": serializer.data,\n \"message\": \"login success\",\n \"token\": {\n \"access\": access_token,\n \"refresh\": refresh_token,\n },\n },\n status=status.HTTP_200_OK,\n )\n response.set_cookie(\"access\", access_token, httponly=True)\n response.set_cookie(\"refresh\", refresh_token, httponly=True)\n return response\n else:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n return Response({\"message\": \"존재하지 않는 계정입니다.\"}, status=status.HTTP_400_BAD_REQUEST)\n\nclass Logout(APIView):\n @swagger_auto_schema(operation_id=\"로그아웃 (토큰 쿠키에서 삭제)\")\n def post(self, request):\n data = request.data\n user = User.objects.get(email = data['email'])\n token = TokenObtainPairSerializer.get_token(user)\n response = Response(\n {\n \"message\": \"success\",\n },\n status=status.HTTP_200_OK,\n )\n response.delete_cookie(\"access\")\n response.delete_cookie(\"refresh\")\n return response","repo_name":"2022SVBootcamp-Team-I/FishyFish","sub_path":"backend/accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"8814101604","text":"\n# The server address when testing locally\nDEFAULT_WEB_SERVER = {\n \"host\": \"localhost\",\n \"protocol\": \"http\",\n \"port\": 80\n}\n\n# Interval between samples (in milliseconds)\nSAMPLE_INTERVAL = 200\n\n# Serial rate\nSERIAL_RATE = 9600\n\n# Time to wait for connection to establish (in seconds)\nCONNECTION_TIMEOUT = 10\n\n# After connection established time to wait for init (in seconds)\nCONNECTION_WAIT = 2\n","repo_name":"jkittley/Arduino-Serial-Websocket","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"1054874685","text":"# 导入需要的包\nimport cv2\nimport os\nimport numpy as np\n\nMIN_DESCRIPTOR = 32 # surprisingly enough, 2 descriptors are already enough\n\n# 计算傅里叶描述子\n\n\ndef fourierDesciptor(res):\n # Laplacian算子进行八邻域检测\n gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)\n dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)\n Laplacian = cv2.convertScaleAbs(dst)\n contour = find_contours(Laplacian) # 提取轮廓点坐标\n contour_array = contour[0][:, 0, :] # 注意这里只保留区域面积最大的轮廓点坐标\n ret_np = np.ones(dst.shape, np.uint8) # 创建黑色幕布\n ret = cv2.drawContours(\n ret_np, contour[0], -1, (255, 255, 255), 1) # 绘制白色轮廓\n cv2.imshow(\"ret\", ret)\n contours_complex = np.empty(contour_array.shape[:-1], dtype=complex)\n contours_complex.real = contour_array[:, 0] # 横坐标作为实数部分\n contours_complex.imag = contour_array[:, 1] # 纵坐标作为虚数部分\n fourier_result = np.fft.fft(contours_complex) # 进行傅里叶变换\n #fourier_result = np.fft.fftshift(fourier_result)\n descirptor_in_use = truncate_descriptor(fourier_result) # 截短傅里叶描述子\n # 绘图显示\n reconstruct(ret, descirptor_in_use)\n return ret, descirptor_in_use\n\n\ndef find_contours(Laplacian):\n # binaryimg = cv2.Canny(res, 50, 200) #二值化，canny检测\n h = cv2.findContours(Laplacian, cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE) # 寻找轮廓\n contour = h[0]\n contour = sorted(contour, key=cv2.contourArea,\n reverse=True) # 对一系列轮廓点坐标按它们围成的区域面积进行排序\n return contour\n\n# 截短傅里叶描述子\n\n\ndef truncate_descriptor(fourier_result):\n descriptors_in_use = np.fft.fftshift(fourier_result)\n\n # 取中间的MIN_DESCRIPTOR项描述子\n center_index = int(len(descriptors_in_use) / 2)\n low, high = center_index - \\\n int(MIN_DESCRIPTOR / 2), center_index + int(MIN_DESCRIPTOR / 2)\n descriptors_in_use = descriptors_in_use[low:high]\n\n descriptors_in_use = np.fft.ifftshift(descriptors_in_use)\n return descriptors_in_use\n\n# 由傅里叶描述子重建轮廓图\n\n\ndef reconstruct(img, descirptor_in_use):\n contour_reconstruct = np.fft.ifft(descirptor_in_use)\n contour_reconstruct = np.array(\n [contour_reconstruct.real, contour_reconstruct.imag])\n contour_reconstruct = np.transpose(contour_reconstruct)\n contour_reconstruct = np.expand_dims(contour_reconstruct, axis=1)\n if contour_reconstruct.min() < 0:\n contour_reconstruct -= contour_reconstruct.min()\n contour_reconstruct *= img.shape[0] / contour_reconstruct.max()\n contour_reconstruct = contour_reconstruct.astype(np.int32, copy=False)\n\n black_np = np.ones(img.shape, np.uint8) # 创建黑色幕布\n black = cv2.drawContours(\n black_np, contour_reconstruct, -1, (255, 255, 255), 1) # 绘制白色轮廓\n cv2.imshow(\"contour_reconstruct\", black)\n # cv2.imwrite('recover.png',black)\n return black\n\n# 显示ROI为二值模式\n\n\ndef binaryMask(frame, x0, y0, width, height):\n cv2.rectangle(frame, (x0, y0), (x0+width, y0+height),\n (0, 255, 0)) # 画出截取的手势框图\n roi = frame[y0:y0+height, x0:x0+width] # 获取手势框图\n cv2.imshow(\"roi\", roi) # 显示手势框图\n res = skinMask(roi) # 进行肤色检测\n cv2.imshow(\"res\", res) # 显示肤色检测后的图像\n\n ret, fourier_result = fourierDesciptor(res) # 傅里叶描述子获取轮廓点\n\n # 保存手势\n if saveImg == True and binaryMode == True:\n saveROI(res)\n elif saveImg == True and binaryMode == False:\n saveROI(roi)\n return res\n\n\n# YCrCb颜色空间的Cr分量+Otsu法阈值分割算法\ndef skinMask(roi):\n YCrCb = cv2.cvtColor(roi, cv2.COLOR_BGR2YCR_CB) # 转换至YCrCb空间\n (y, cr, cb) = cv2.split(YCrCb) # 拆分出Y,Cr,Cb值\n cr1 = cv2.GaussianBlur(cr, (5, 5), 0)\n _, skin = cv2.threshold(\n cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Ostu处理\n res = cv2.bitwise_and(roi, roi, mask=skin)\n return res\n\n\n# 保存ROI图像\ndef saveROI(img):\n global path, counter, gesturename, saveImg\n if counter > numofsamples:\n # 恢复到初始值，以便后面继续录制手势\n saveImg = False\n gesturename = ''\n counter = 0\n return\n\n counter += 1\n name = gesturename + str(counter) # 给录制的手势命名\n print(\"Saving img: \", name)\n cv2.imwrite(path+name+'.png', img) # 写入文件\n time.sleep(0.05)\n\n\n# 设置一些常用的一些参数\n# 显示的字体 大小 初始位置等\nfont = cv2.FONT_HERSHEY_SIMPLEX # 正常大小无衬线字体\nsize = 0.5\nfx = 10\nfy = 355\nfh = 18\n# ROI框的显示位置\nx0 = 300\ny0 = 100\n# 录制的手势图片大小\nwidth = 300\nheight = 300\n# 每个手势录制的样本数\nnumofsamples = 300\ncounter = 0 # 计数器，记录已经录制多少图片了\n# 存储地址和初始文件夹名称\ngesturename = ''\npath = ''\n# 标识符 bool类型用来表示某些需要不断变化的状态\nbinaryMode = False # 是否将ROI显示为而至二值模式\nsaveImg = False # 是否需要保存图片\n\n# 创建一个视频捕捉对象\ncap = cv2.VideoCapture(0) # 0为（笔记本）内置摄像头\n\nwhile(True):\n # 读帧\n ret, frame = cap.read() # 返回的第一个参数为bool类型，用来表示是否读取到帧，如果为False说明已经读到最后一帧。frame为读取到的帧图片\n # 图像翻转（如果没有这一步，视频显示的刚好和我们左右对称）\n frame = cv2.flip(frame, 2) # 第二个参数大于0：就表示是沿y轴翻转\n # 显示ROI区域 # 调用函数\n roi = binaryMask(frame, x0, y0, width, height)\n\n # 显示提示语\n cv2.putText(frame, \"Option: \", (fx, fy), font, size, (0, 255, 0)) # 标注字体\n cv2.putText(frame, \"b-'Binary mode'/ r- 'RGB mode' \",\n (fx, fy + fh), font, size, (0, 255, 0)) # 标注字体\n cv2.putText(frame, \"p-'prediction mode'\", (fx, fy + 2 * fh),\n font, size, (0, 255, 0)) # 标注字体\n cv2.putText(frame, \"s-'new gestures(twice)'\",\n (fx, fy + 3 * fh), font, size, (0, 255, 0)) # 标注字体\n cv2.putText(frame, \"q-'quit'\", (fx, fy + 4 * fh),\n font, size, (0, 255, 0)) # 标注字体\n\n key = cv2.waitKey(1) & 0xFF # 等待键盘输入，\n if key == ord('b'): # 将ROI显示为二值模式\n # binaryMode = not binaryMode\n binaryMode = True\n print(\"Binary Threshold filter active\")\n elif key == ord('r'): # RGB模式\n binaryMode = False\n\n if key == ord('i'): # 调整ROI框\n y0 = y0 - 5\n elif key == ord('k'):\n y0 = y0 + 5\n elif key == ord('j'):\n x0 = x0 - 5\n elif key == ord('l'):\n x0 = x0 + 5\n\n if key == ord('p'):\n \"\"\"调用模型开始预测\"\"\"\n print(\"using CNN to predict\")\n if key == ord('q'):\n break\n\n if key == ord('s'):\n \"\"\"录制新的手势（训练集）\"\"\"\n # saveImg = not saveImg # True\n if gesturename != '': #\n saveImg = True\n else:\n print(\"Enter a gesture group name first, by enter press 'n'! \")\n saveImg = False\n elif key == ord('n'):\n # 开始录制新手势\n # 首先输入文件夹名字\n gesturename = (input(\"enter the gesture folder name: \"))\n os.makedirs(gesturename)\n\n path = \"./\" + gesturename + \"/\" # 生成文件夹的地址 用来存放录制的手势\n\n # 展示处理之后的视频帧\n cv2.imshow('frame', frame)\n if (binaryMode):\n cv2.imshow('ROI', roi)\n else:\n cv2.imshow(\"ROI\", frame[y0:y0+height, x0:x0+width])\n\n\n# 最后记得释放捕捉\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"Honour-Van/RaspberryPILabPKU","sub_path":"Lab 7/outline.py","file_name":"outline.py","file_ext":"py","file_size_in_byte":7883,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"10792671996","text":"n = int(input())\ns = list(input())\nfor i in range(n-1):\n new_ = input()\n for j in range(len(s)):\n if s[j] == new_[j]:\n continue\n else:\n s[j] = \"?\"\n\n\nprint(\"\".join(s))","repo_name":"hyunjinee/Algorithm","sub_path":"solved.ac/python/1032.py","file_name":"1032.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"75"} +{"seq_id":"19872188647","text":"# © 2019 Numigi (tm) and all its contributors (https://bit.ly/numigiens)\n# License LGPL-3.0 or later (http://www.gnu.org/licenses/lgpl).\n\nfrom odoo.tests import common\n\n\nclass TestProjectIterationSaleInheritance(common.SavepointCase):\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.project_1 = cls.env[\"project.project\"].create({\"name\": \"P1\"})\n cls.product_a = cls.env[\"product.template\"].create(\n {\n \"name\": \"A1\",\n \"type\": \"service\",\n \"service_policy\": \"delivered_timesheet\",\n \"service_tracking\": \"project_only\",\n }\n )\n cls.partner = cls.env[\"res.partner\"].create({\"name\": \"partner\"})\n\n def test_child_project_get_sales_info_from_parent(self):\n sale_order = self.env[\"sale.order\"].create(\n {\n \"name\": \"SO001\",\n \"partner_id\": self.partner.id,\n \"order_line\": [(0, 0, {\n \"product_id\": self.product_a.product_variant_id.id,\n \"product_uom_qty\": 10,\n })]\n }\n )\n sale_order.action_confirm()\n sale_project = sale_order.project_ids[0]\n self.project_1.parent_id = sale_project.id\n self.project_1._onchange_parent_inherit_sale_object()\n self.assertEqual(self.project_1.sale_order_id, sale_project.sale_order_id)\n self.assertEqual(self.project_1.sale_line_id, sale_project.sale_line_id)\n","repo_name":"Numigi/odoo-project-addons","sub_path":"project_iteration_sale_inheritance/tests/test_project_iteration_sale_inheritance.py","file_name":"test_project_iteration_sale_inheritance.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"75"} +{"seq_id":"14819093581","text":"import csv\nimport os\nimport re\n\nimport numpy as np\nimport sklearn.model_selection\nimport tabulate\nimport yaml\n\nfrom src import *\n\nif __name__ == '__main__':\n with open(\"./config.yaml\") as f:\n config = yaml.safe_load(f)\n K_FOLD_NUM = config[\"k_fold_num\"]\n ROUND = config[\"round\"]\n VCCA_MODEL_NUM = config[\"vcca_model_num\"]\n NEIGHBOURS_NUM = config[\"neighbours_num\"]\n results = []\n # 遍历数据集文件夹\n for root, dirs, files in os.walk(\"./datasets/\"):\n for file in files:\n # 仅读取后缀名为 .data 的文件\n if re.match(r\".*\\.data\", file):\n result = [file.replace(\".data\", '')]\n dataset = []\n # 由于 .data 文件是 CSV 格式，故采用 CSV 模块读取\n with open(os.path.join(root, file), 'r') as f:\n reader = csv.reader(f)\n for i in reader:\n # 防止出现空行\n if len(i) != 0:\n dataset.append(i)\n # 预处理，包含数据集与标签的分离、缺失项填充、归一化与升维\n dataset, label = pre_process(dataset, NEIGHBOURS_NUM)\n cca_total_correct_identified = 0\n cca_total_cant_identified = 0\n vcca_total_correct_identified = 0\n vcca_total_cant_identified = 0\n # 采用十折交叉验证\n kf = sklearn.model_selection.KFold(n_splits=K_FOLD_NUM, shuffle=True)\n epoch = 1\n for i in range(ROUND):\n for train_idx, test_idx in kf.split(dataset, label):\n print(file.replace(\".data\", ''), \"Epoch\", epoch)\n epoch += 1\n model = []\n train_set, train_label = np.array(dataset[train_idx]), label[train_idx]\n test_set, test_label = np.array(dataset[test_idx]), label[test_idx]\n # 测试 CCA\n model_cca = cca(train_set, train_label)\n cca_correct_identified, cca_cant_identified = test_cca(model_cca, test_set, test_label)\n print(\"CCA: Rate:\" + str(round(cca_correct_identified / len(test_idx) * 100, 2)) + '%,',\n end=' ')\n print(\"can't identified:\", cca_cant_identified)\n cca_total_correct_identified += cca_correct_identified\n cca_total_cant_identified += cca_cant_identified\n model_vcca = vcca(train_set, train_label, VCCA_MODEL_NUM)\n vcca_correct_identified, vcca_cant_identified = test_vcca(model_vcca, test_set, test_label)\n print(\"VCCA: Rate:\" + str(round(vcca_correct_identified / len(test_idx) * 100, 2)) + '%',\n end=' ')\n print(\", can't identified:\", vcca_cant_identified)\n vcca_total_correct_identified += vcca_correct_identified\n vcca_total_cant_identified += vcca_cant_identified\n result.append(str(round(cca_total_correct_identified / (K_FOLD_NUM * ROUND) / (len(dataset) /\n K_FOLD_NUM)\n * 100, 2)) + '%')\n result.append(cca_total_cant_identified / (K_FOLD_NUM * ROUND))\n result.append(str(round(vcca_total_correct_identified / (K_FOLD_NUM * ROUND) / (len(dataset) /\n K_FOLD_NUM)\n * 100, 2)) + '%')\n result.append(vcca_total_cant_identified / (K_FOLD_NUM * ROUND))\n results.append(result)\n print(tabulate.tabulate(results, headers=[\"Dataset\", \"CCA-Avg rate\", \"CCA-Avg can't identified\", \"VCCA-Avg rate\",\n \"VCCA-Avg can't identified\"], tablefmt=\"pretty\"))\n","repo_name":"skymkmk/VCCA","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"40371298602","text":"from flask import current_app\nfrom flask import Blueprint\nfrom flask_restx import Api, Namespace\n# from flask_request_validator import *\n# from flask_request_validator.exceptions import InvalidRequestError\n# from flask_request_validator.error_formatter import demo_error_formatter\n\nfrom svc.ml_model_svc_eps import MLModelEPS\nfrom svc.ml_model_svc_eps_predict_result import MLModelEPSPredictRequest\nfrom svc.ml_model_svc_sample import MLModelSample\n\n\nblueprint = Blueprint('api_2', __name__, url_prefix='/api/v2')\napi = Api(blueprint, title='Flask API v0.2', version='0.2', description='Flask API v0.2')\n\nml_model_ns_v2 = Namespace('ml_model', 'ML model service v2')\n\n\n@api.errorhandler\ndef default_error_handler(error):\n \"\"\"\n Default error handler of api_2\n \"\"\"\n # msg = traceback.format_exc()\n # Debug_mode: Ture 인 경우에 error 항목에 interactive debugger 모드가 생성됨\n # 따라서 Debug_mode: False 인 경우만 500 의 별도 메시지 출력 필요\n status_code = getattr(error, 'code', 500)\n\n if current_app.debug is False & status_code == 500:\n msg = '관리자에게 문의'\n else:\n msg = str(error)\n\n return {'message': msg}, status_code\n\n\ndef create_ml_model_namespace():\n api.add_namespace(ml_model_ns_v2)\n ml_model_ns_v2.add_resource(MLModelSample, '/sample')\n ml_model_ns_v2.add_resource(MLModelEPS, '/eps-predict')\n ml_model_ns_v2.add_resource(MLModelEPSPredictRequest, '/eps-predict-request')\n","repo_name":"PyBack/fastapi-flask-vue-mlmodel","sub_path":"backend/views/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"3029466424","text":"from django.views.generic.base import TemplateView\nfrom django.shortcuts import render\nfrom dbmf.models import Course, CoursePrereq, Lecturer, UserTakenCourse, CRNS, TempCRNS\nfrom django.contrib.auth.decorators import login_required\n\nclass Index(TemplateView):\n template_name = 'pages/index.html'\n\n\nclass OpenSourceView(TemplateView):\n template_name = 'pages/open_source.html'\n\n\nclass AboutPage(TemplateView):\n\t\"\"\"docstring for About\"\"\"\n\ttemplate_name = 'pages/about.html'\n\n\nclass SignUpPage(TemplateView):\n template_name = 'pages/signup.html'\n\n\nclass RateLecturer(TemplateView):\n template_name = 'pages/rate_lecturer.html'\n\n def get(self, request):\n if request.user.id != None:\n lecturer_list = Lecturer.objects.all()\n return render(request, self.template_name, {'lecturers': lecturer_list})\n else:\n return render(request, 'pages/signup.html')\n def post(self, request):\n rates = request.POST.getlist('rates[]')\n i = 0\n for entry in rates:\n if entry != '0':\n lecturers = Lecturer.objects.all()\n lecturer = lecturers[i]\n total = lecturer.vote_count * float(lecturer.vote_avg)\n lecturer.vote_count += 1\n total += float(entry)\n avg = total/lecturer.vote_count\n lecturer.vote_avg = avg\n lecturer.save()\n i+=1\n\n lecturers = Lecturer.objects.all()\n for item in lecturers:\n print(\"score: \", item.vote_avg)\n return render(request,'pages/rate_lecturer.html',{'lecturers': lecturers})\n\nclass TakenCourseView(TemplateView):\n template_name = 'pages/taken_course.html'\n\n def get(self, request):\n if request.user.id != None:\n course_list = Course.objects.all()\n return render(request, 'pages/taken_course.html', {'courses': course_list})\n else:\n return render(request, 'pages/signup.html')\n def post(self, request):\n checks = request.POST.getlist('checks[]')\n\n for entry in checks:\n new = UserTakenCourse(user_id=request.user.id, course_code =entry, lecturer_id = \"11\")\n new.save()\n return render(request,'pages/open_source.html')\n\n\nclass Profile(TemplateView):\n template_name = 'pages/profile.html'\n\n def get(self, request):\n if request.user.id != None:\n course_list = UserTakenCourse.objects.all()\n real_list = list()\n for item in course_list:\n if(int(item.user_id) == int(request.user.id)):\n real_list.append(item.course_code)\n all_courses = Course.objects.all()\n new = list()\n\n for item in all_courses:\n if(item.code+'/' in real_list):\n new.append(item)\n return render(request, 'pages/profile.html', {'courses': new})\n else:\n return render(request, 'pages/signup.html')\n\n def post(self, request):\n delete_crs = request.POST.get('course_to_delete')\n print(\"delete: \", delete_crs)\n i = 0\n\n UserTakenCourse.objects.get(course_code=delete_crs+'/').delete()\n if request.user.id != None:\n course_list = UserTakenCourse.objects.all()\n real_list = list()\n for item in course_list:\n if(int(item.user_id) == int(request.user.id)):\n real_list.append(item.course_code)\n all_courses = Course.objects.all()\n new = list()\n\n for item in all_courses:\n if(item.code+'/' in real_list):\n new.append(item)\n return render(request, 'pages/profile.html', {'courses': new})\n else:\n return render(request, 'pages/signup.html')\n\n\nclass GeneratorSchedule(TemplateView):\n template_name = 'pages/schedule_generator.html'\n\nclass CreateSchedule(TemplateView):\n\n template_name = 'pages/create_schedule.html'\n def get(self,request):\n TempCRNS.objects.all().delete()\n lecturers = Lecturer.objects.all()\n\n crn_lists=CRNS.objects.all()\n print(\"list: \", crn_lists)\n print(\"-----------\")\n pre = CoursePrereq.objects.all()\n taken_course = UserTakenCourse.objects.all()\n pre_c1 = list()\n pre_c2 = list()\n user_taken = list()\n for i in pre:\n pre_c1.append(i.code1)\n pre_c2.append(i.code2)\n for i in taken_course:\n print(\"taken: \", i.course_code)\n print(\"user_id: \", i.user_id)\n print(\"curr_user_id: \", request.user.id)\n\n if int(i.user_id) == int(request.user.id):\n user_taken.append(i.course_code)\n print(\"yeah, taken: \", i.course_code)\n\n\n for x in crn_lists:\n for y in lecturers:\n if x.lecturer == y.name:\n x.avgScore = y.vote_avg\n x.save()\n crn_lists=CRNS.objects.all()\n print(\"list: \", crn_lists)\n for x in crn_lists:\n if x.course_code in pre_c1:\n print(\"in\")\n index = pre_c1.index(x.course_code)\n print(\"index: \", index)\n print(\"taken_code: \", x.course_code)\n if pre_c2[index]+'/' in user_taken:\n print(\"Yes taken: \", x.course_code)\n new = TempCRNS(course_name=x.course_name, course_code = x.course_code, day = x.day, time = x.time,room= x.room, building = x.building, lecturer= x.lecturer, crn = x.crn, time_start=x.time_start ,time_stop = x.time_stop, avgScore = x.avgScore, capacity = x.capacity, enrolled = x.enrolled)\n new.save()\n else:\n new = TempCRNS(course_name=x.course_name, course_code = x.course_code, day = x.day, time = x.time,room= x.room, building = x.building, lecturer= x.lecturer, crn = x.crn, time_start=x.time_start ,time_stop = x.time_stop, avgScore = x.avgScore, capacity = x.capacity, enrolled = x.enrolled)\n new.save()\n\n\n\n crn_lists=TempCRNS.objects.all().order_by('course_name', '-avgScore')\n\n #crn_lists=CRNS.objects.all().order_by('course_name', '-avgScore')\n #TempCRNS.objects.all().delete()\n return render(request,self.template_name,{'crns':crn_lists})\n\n def post(self,request):\n checks = request.POST.getlist('checks[]')\n desired_lists = []\n\n\n for x in checks:\n desired_lists.append((CRNS.objects.get(crn__contains = x)))\n\n return render(request,'pages/schedule_generator.html',{'conc':desired_lists})\n","repo_name":"inkayat/BeeScheduled","sub_path":"pages/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"73408833203","text":"import torch\nimport torch.nn as nn\nfrom .sa import Self_Attention\nfrom .ca import Cross_Attention\nfrom .rn import Relation_Network, cos_similar\n\nfrom options import Option\n\nclass Model(nn.Module):\n def __init__(self, args):\n super(Model, self).__init__()\n\n self.args = args\n\n self.sa = Self_Attention(d_model=args.d_model, cls_number=args.cls_number, pretrained=args.pretrained)\n self.ca = Cross_Attention(args=args, h=args.head, n=args.number, d_model=args.d_model, d_ff=args.d_ff, dropout=0.1)\n self.rn = Relation_Network(args.anchor_number, dropout=0.1)\n self.conv2d = nn.Conv2d(768, 512, 2, 2)\n\n\n def forward(self, sk, im, stage='train', only_sa=False):\n\n if stage == 'train':\n\n sk_im = torch.cat((sk, im), dim=0)\n sa_fea, left_tokens, idxs = self.sa(sk_im) # [4b, 197, 768]\n ca_fea = self.ca(sa_fea) # [4b, 197, 768]\n\n cls_fea = ca_fea[:, 0] # [4b, 1, 768]\n token_fea = ca_fea[:, 1:] # [4b, 196, 768]\n batch = token_fea.size(0)\n\n token_fea = token_fea.view(batch, 768, 14, 14)\n down_fea = self.conv2d(token_fea)\n down_fea = down_fea.view(batch, 512, 7*7)\n down_fea = down_fea.transpose(1, 2) # [4b, 49, 512]\n\n sk_fea = down_fea[:batch // 2]\n im_fea = down_fea[batch // 2:]\n cos_scores = cos_similar(sk_fea, im_fea) # [2b, 49, 49]\n cos_scores = cos_scores.view(batch // 2, -1)\n rn_scores = self.rn(cos_scores) # [2b, 1]\n\n # print('cls_fea:', cls_fea.size())\n # print('rn_scores:', cls_fea.size())\n return cls_fea, rn_scores\n\n else:\n\n if only_sa:\n sa_fea, left_tokens, idxs = self.sa(sk) # [b, 197, 768]\n return sa_fea, idxs\n else:\n sk_im = torch.cat((sk, im), dim=0)\n ca_fea = self.ca(sk_im) # [2b, 197, 768]\n\n cls_fea = ca_fea[:, 0] # [2b, 1, 768]\n token_fea = ca_fea[:, 1:] # [2b, 196, 768]\n batch = token_fea.size(0)\n\n token_fea = token_fea.view(batch, 768, 14, 14)\n down_fea = self.conv2d(token_fea)\n down_fea = down_fea.view(batch, 512, 7 * 7)\n down_fea = down_fea.transpose(1, 2) # [2b, 49, 512]\n\n sk_fea = down_fea[:batch // 2]\n im_fea = down_fea[batch // 2:]\n cos_scores = cos_similar(sk_fea, im_fea) # [b, 49, 49]\n cos_scores = cos_scores.view(batch // 2, -1)\n rn_scores = self.rn(cos_scores) # [b, 49, 49]\n\n # print('cls_fea:', cls_fea.size())\n # print('rn_scores:', cls_fea.size())\n return cls_fea, rn_scores\n\nif __name__ == '__main__':\n args = Option().parse()\n sk = torch.rand((4, 224, 224))\n im = torch.rand((4, 224, 224))\n model = Model(args)\n cls_fea, rn_scores = model(sk, im)\n","repo_name":"buptLinfy/ZSE-SBIR","sub_path":"model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"75"} +{"seq_id":"40546129842","text":"import csv\nimport array as arr\nimport string\n\nfile1 = open('needHx.txt', 'r')\nneedLines = file1.readlines()\nfile1.close\nfile2 = open('00_countries.txt', 'r')\nbaseLines = file2.readlines()\n\nkeyedCodes = []\n\nneedCodes = []\n\nfor line in needLines:\n code = line.split('-')[0].split(':')[-1].strip()\n needCodes.append(code)\n\n\nfor line in baseLines:\n code = line.split('=')[0].strip()\n if code in needCodes:\n country = line.split('.txt')[0].split('/')[1]\n keyedCodes.append([code, country])\n\n \nfor pair in keyedCodes:\n code = pair[0]\n name = pair[1]\n file3 = open(code + \" - \" + name + '.txt', 'w')\n file3.write('capital=1')\n file3.close\n","repo_name":"danieI/hoi4Mod","sub_path":"image/newCountry.py","file_name":"newCountry.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"19087526484","text":"#Sorting Algorithm Visualizer\r\n#Created by Hamzah Shahid\r\n\r\nimport pygame\r\nfrom random import randint\r\nimport pygame_widgets\r\nfrom sys import exit\r\n\r\n#initialize pygame\r\npygame.init()\r\n\r\n#initial array size\r\narraySize = 10\r\ndata = []\r\nprogress = []\r\n\r\n#initial bool\r\nisSorted = False\r\ndone = False\r\n\r\n#define colors\r\nWHITE = (255, 255, 255)\r\nGREEN = (0, 255, 0)\r\nRED = (255, 0, 0)\r\nBLACK = (0,0,0)\r\n\r\n#create screen\r\nsize = (900, 600)\r\ngameDisplay = pygame.display.set_mode(size)\r\npygame.display.set_caption(\"Sorting Algorithm Visualizer\")\r\n\r\n#create sorting visualizer surface\r\nbarSurface = pygame.Surface((900, 400))\r\nbarSurface.fill((50,50,50))\r\ngameDisplay.blit(barSurface, (0, 200))\r\nbarSurfaceUpdateRect = pygame.Rect(50, 200,800, 400)\r\n\r\n#create slider used to adjust the array size\r\nslider = pygame_widgets.Slider(gameDisplay, 650, 100, 200, 50, min=10, max=100, step=10, initial = 10)\r\n\r\n# create a clock to control how fast the screen updates\r\nclock = pygame.time.Clock()\r\n\r\n#fixes the size of the data when adjusting the slider\r\ndef fixSize(newSize):\r\n data = arrayInit(newSize, 20)\r\n return data\r\n\r\n#draws the bars after each step in the sorting algorithm\r\ndef drawRect(data, progress):\r\n #clears the screen \r\n barSurface.fill((50,50,50))\r\n\r\n #title \r\n titleCenter = ((450), (50))\r\n textSurf, textRect = text_objects(\"Sorting Algorithm Visualizer\", largeText, BLACK, titleCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n \r\n #determines which color to make the bars\r\n for i in range(arraySize):\r\n color = BLACK\r\n if progress[i] == 1:\r\n color = WHITE\r\n elif progress[i] == 2:\r\n color = RED\r\n else:\r\n color = GREEN\r\n \r\n #draws the rectangle\r\n pygame.draw.rect(barSurface, color, pygame.Rect((50 + (750/arraySize)*i, 350 - 15*data[i], (300/arraySize), 15*data[i])))\r\n \r\n #pushes the update to the screen\r\n gameDisplay.blit(barSurface, (0, 200))\r\n pygame.display.update(barSurfaceUpdateRect)\r\n\r\n#code for selection sort algorithm \r\ndef selectionSort():\r\n for i in range(arraySize): \r\n\r\n #find the smallest element in the array from the unsorted elements\r\n smallestNumIndex = i \r\n for j in range(i+1, arraySize): \r\n if data[j] < data[smallestNumIndex]: \r\n smallestNumIndex = j \r\n \r\n progress[j] = 2\r\n progress[i] = 2\r\n drawRect(data, progress)\r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n # Swap the found minimum element with the element at the beggining \r\n data[i], data[smallestNumIndex] = data[smallestNumIndex], data[i]\r\n progress[j] = 1\r\n progress[i] = 3 \r\n drawRect(data, progress)\r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n #code to allow the user to exit the program midway through sorting\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n\r\n#code for bubble sort algorithm\r\ndef bubbleSort():\r\n for i in range(arraySize - 1):\r\n for j in range(0, arraySize - i - 1):\r\n if data[j] > data[j+1]:\r\n progress[j] = 2\r\n progress[j+1] = 2\r\n drawRect(data, progress)\r\n \r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n t = data[j]\r\n data[j] = data[j+1]\r\n data[j+1] = t\r\n\r\n progress[j] = 1\r\n progress[j+1] = 1\r\n drawRect(data, progress)\r\n \r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n #code to allow the user to exit the program midway through sorting\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n \r\n if arraySize - i >= 0:\r\n progress[arraySize -1 - i] = 3 \r\n \r\n drawRect(data, progress)\r\n progress[0] = 3\r\n\r\n#code for insertion sort algorithm\r\ndef insertSort():\r\n for i in range(1, arraySize): \r\n key = data[i] \r\n\r\n j = i-1\r\n while j >=0 and key < data[j] : \r\n progress[j+1] = 2\r\n progress[j] = 2\r\n drawRect(data, progress)\r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n data[j+1] = data[j] \r\n\r\n progress[j] = 1\r\n progress[j+1] = 1\r\n drawRect(data, progress)\r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n j -= 1\r\n\r\n data[j+1] = key \r\n drawRect(data, progress)\r\n pygame.time.delay(int(2500/arraySize))\r\n\r\n #code to allow the user to exit the program midway through sorting\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n \r\n #colors the bars green after the sorting is complete\r\n for i in range(arraySize):\r\n progress[i] = 3\r\n drawRect(data, progress)\r\n pygame.time.delay(50)\r\n\r\n#initialize the array used for sorting\r\ndef arrayInit(size, maxNum):\r\n newData = []\r\n for i in range(size):\r\n value = randint(0, maxNum)\r\n newData.insert(i, value)\r\n progress.insert(i, 1)\r\n #return the new data\r\n return newData\r\n\r\n#method used to create text\r\ndef text_objects(text, font, color, text_center):\r\n rendered = font.render(text, True, color)\r\n return rendered, rendered.get_rect(center = text_center)\r\n\r\n#creates the interactive buttons found at the top of the screen\r\ndef create_buttons():\r\n #steup the text components\r\n bbSortCenter = ((50 + (100/2)), (100 + (50/2)))\r\n insertSortCenter = ((200 + (100/2), (100 + (50/2))))\r\n selectionSortCenter = ((350 + (100/2), (100 + (50/2))))\r\n randomCenter = ((500 + (100/2), (100 + (50/2))))\r\n arraySizeCenter = ((650 + 100), 75)\r\n\r\n #static buttons\r\n pygame.draw.rect(gameDisplay, GREEN, (50, 100, 100, 50))\r\n pygame.draw.rect(gameDisplay, GREEN, (200, 100, 100, 50))\r\n pygame.draw.rect(gameDisplay, GREEN, (350, 100, 100, 50))\r\n pygame.draw.rect(gameDisplay, GREEN, (500, 100, 100, 50))\r\n\r\n #bubble sort button\r\n\r\n #if user is rolling over the button\r\n if 50 + 100 > mouse[0] > 50 and 100 + 50 > mouse[1] > 50: \r\n pygame.draw.rect(gameDisplay, RED, (50, 100, 100, 50))\r\n textSurf, textRect = text_objects(\"Bubble Sort\", smallText, BLACK, bbSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n if click[0] == 1 and not isSorted:\r\n return 1\r\n\r\n #insert sort button\r\n\r\n #if user is rolling over the button\r\n if 200 + 100 > mouse[0] > 200 and 100 + 50 > mouse[1] > 50:\r\n pygame.draw.rect(gameDisplay, RED, (200, 100, 100, 50))\r\n textSurf, textRect = text_objects(\"Insert Sort\", smallText, BLACK, insertSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n if click[0] == 1 and not isSorted:\r\n return 2 \r\n \r\n #selection sort button\r\n\r\n #if user is rolling over the button\r\n if 350 + 100 > mouse[0] > 350 and 100 + 50 > mouse[1] > 50:\r\n pygame.draw.rect(gameDisplay, RED, (350, 100, 100, 50))\r\n textSurf, textRect = text_objects(\"Selection Sort\", superSmallText, BLACK, selectionSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n if click[0] == 1 and not isSorted:\r\n return 3 \r\n \r\n #randomize button\r\n\r\n #if user is rolling over the button\r\n if 500 + 100 > mouse[0] > 500 and 100 + 50 > mouse[1] > 50:\r\n pygame.draw.rect(gameDisplay, RED, (500, 100, 100, 50))\r\n textSurf, textRect = text_objects(\"New Data\", smallText, BLACK, randomCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n if click[0] == 1:\r\n return 4 \r\n\r\n #button text\r\n textSurf, textRect = text_objects(\"Bubble Sort\", smallText, BLACK, bbSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n textSurf, textRect = text_objects(\"Insert Sort\", smallText, BLACK, insertSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n textSurf, textRect = text_objects(\"Selection Sort\", superSmallText, BLACK, selectionSortCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n textSurf, textRect = text_objects(\"New Data\", smallText, BLACK, randomCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n textSurf, textRect = text_objects(\"Set Size\", largeText, BLACK, arraySizeCenter)\r\n gameDisplay.blit(textSurf, textRect)\r\n\r\n\r\n#initialize arrays\r\ndata = arrayInit(arraySize, 20) \r\n\r\n# loop until the user exits the program\r\n\r\nwhile not done:\r\n # loop for every event that occurs in the program\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n done = True\r\n pygame.quit()\r\n exit()\r\n \r\n # fill display\r\n gameDisplay.fill(WHITE)\r\n\r\n # event listeners\r\n mouse = pygame.mouse.get_pos()\r\n click = pygame.mouse.get_pressed()\r\n slider.listen(event)\r\n slider.draw()\r\n\r\n # fonts\r\n largeText = pygame.font.SysFont('comicsansms', 25)\r\n smallText = pygame.font.SysFont('comicsansms', 15)\r\n superSmallText = pygame.font.SysFont('comicsansms', 12)\r\n\r\n # code to draw objects\r\n flag = create_buttons()\r\n drawRect(data, progress)\r\n\r\n # program logic\r\n\r\n #updates the array as soon as the value is changed\r\n newSize = slider.getValue()\r\n if newSize != arraySize:\r\n data = fixSize(newSize)\r\n arraySize = newSize\r\n isSorted = False\r\n \r\n #code to handle button presses\r\n if flag == 1:\r\n bubbleSort()\r\n isSorted = True\r\n if flag == 2:\r\n insertSort()\r\n isSorted = True\r\n if flag == 3:\r\n selectionSort()\r\n isSorted = True\r\n if flag == 4:\r\n data = arrayInit(arraySize, 20)\r\n isSorted = False\r\n\r\n # set the clock speed to 60 times per second \r\n clock.tick(60) \r\n\r\n # update the screen\r\n pygame.display.flip()\r\n\r\n#needed to quit game correctly\r\npygame.quit()\r\nexit()","repo_name":"ShahidHamzah/Sorting-Algorithm-Visualizer","sub_path":"sorter.py","file_name":"sorter.py","file_ext":"py","file_size_in_byte":10540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"13572152236","text":"from rejson import Client, Path\nimport json\nimport time\nfrom datetime import date, datetime, timedelta\nfrom timeloop import Timeloop\nimport os\nimport requests\n\n# start timeloop\ntl = Timeloop()\n\nBACKEND_SERVICE_URL = \"http://localhost:8080\" if os.getenv(\n 'BACKEND_SERVICE_URL') == None else os.getenv('BACKEND_SERVICE_URL')\nTWEET_SERVICE_URL = \"http://localhost:8081\" if os.getenv(\n 'TWEET_SERVICE_URL') == None else os.getenv('TWEET_SERVICE_URL')\nREDIS_HOST = \"localhost\" if os.getenv(\n 'REDIS_HOST') == None else os.getenv('REDIS_HOST')\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\nUPLOAD_FOLDER = dir_path+\"/filestore\"\n\n# connect to queue\nrj = Client(host=REDIS_HOST, port=6379, decode_responses=True)\ntweetRequest = {'tweetRequest': []}\nprofileRequest = {'profileRequest': []}\n\n\ndef checkifObjectExist():\n if rj.jsonget('user', Path.rootPath()) == None or rj.jsonget('user', Path('.profileRequest')) == None:\n rj.jsonset('user', Path.rootPath(), profileRequest)\n\n if rj.jsonget('tweet', Path.rootPath()) == None or rj.jsonget('tweet', Path('.tweetRequest')) == None:\n rj.jsonset('tweet', Path.rootPath(), tweetRequest)\n\n\ndef profileAction(requestPath, method, headers, filename, userform):\n files = []\n if len(filename) > 0 and os.path.exists(UPLOAD_FOLDER+'/'+filename) and os.path.isfile(UPLOAD_FOLDER+'/'+filename):\n file = ('profilePic', open(UPLOAD_FOLDER+'/'+filename, 'rb'))\n files.append(file)\n os.remove(UPLOAD_FOLDER+'/'+filename)\n else:\n files.append(('profilePic', ''))\n\n payload = {'userForm': userform}\n resp = requests.request(\n method=method,\n url=BACKEND_SERVICE_URL+requestPath,\n headers=headers,\n data=payload,\n files=files,\n allow_redirects=False\n )\n return resp\n\n\ndef tweetAction(requestPath, method, headers, filename, tweetForm):\n files = []\n\n if method == 'DELETE':\n resp = requests.request(\n method=method,\n url=TWEET_SERVICE_URL+requestPath,\n headers=headers,\n allow_redirects=False\n )\n return resp\n\n if filename and len(filename) > 0 and os.path.exists(UPLOAD_FOLDER+'/'+filename) and os.path.isfile(UPLOAD_FOLDER+'/'+filename):\n file = ('file', open(UPLOAD_FOLDER+'/'+filename, 'rb'))\n files.append(file)\n os.remove(UPLOAD_FOLDER+'/'+filename)\n else:\n files.append(('file', ''))\n\n payload = {'tweetForm': tweetForm}\n resp = requests.request(\n method=method,\n url=TWEET_SERVICE_URL+requestPath,\n headers=headers,\n data=payload,\n files=files,\n allow_redirects=False\n )\n return resp\n\n\ndef commentAction(requestPath, method, headers, body):\n resp = requests.request(\n method=method,\n url=TWEET_SERVICE_URL+requestPath,\n headers=headers,\n json=body,\n allow_redirects=False\n )\n return resp\n\n# every 100 seconds, print groups\n\n\n@tl.job(interval=timedelta(seconds=10))\ndef profileService():\n # get contents\n checkifObjectExist()\n for obj in rj.jsonget('user', Path('.profileRequest')):\n print(obj)\n rj.jsonarrpop('user', Path('.profileRequest'))\n res = profileAction(obj['requestPath'], obj['method'],\n obj['headers'], obj['file'], obj['form'])\n print(res.text.encode('utf8'))\n\n\n@tl.job(interval=timedelta(seconds=20))\ndef tweetService():\n # get contents\n checkifObjectExist()\n for obj in rj.jsonget('tweet', Path('.tweetRequest')):\n print(obj)\n rj.jsonarrpop('tweet', Path('.tweetRequest'))\n if(obj['service'] == 'comment'):\n res = commentAction(obj['requestPath'],\n obj['method'], obj['headers'], obj['body'])\n else:\n form = None if 'form' not in obj else obj['form']\n file = None if 'file' not in obj else obj['file']\n res = tweetAction(obj['requestPath'],\n obj['method'], obj['headers'], file, form)\n print(res.text.encode('utf8'))\n\n\nif __name__ == \"__main__\":\n tl.start(block=True)\n","repo_name":"rishijatania/Twitter-App","sub_path":"tweet-queue/middleware/worker-app.py","file_name":"worker-app.py","file_ext":"py","file_size_in_byte":4160,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"34737160847","text":"#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a human agent to control the ego vehicle via keyboard\n\"\"\"\n\nimport time\nimport json\nfrom threading import Thread\nimport cv2\nimport numpy as np\n\ntry:\n import pygame\n from pygame.locals import K_DOWN\n from pygame.locals import K_LEFT\n from pygame.locals import K_RIGHT\n from pygame.locals import K_SPACE\n from pygame.locals import K_UP\n from pygame.locals import K_a\n from pygame.locals import K_d\n from pygame.locals import K_s\n from pygame.locals import K_w\n from pygame.locals import K_q\nexcept ImportError:\n raise RuntimeError('cannot import pygame, make sure pygame package is installed')\n\nimport carla\n\nfrom leaderboard.autoagents.autonomous_agent import AutonomousAgent, Track\n\n\ndef get_entry_point():\n return 'HumanAgent'\n\nclass HumanInterface(object):\n\n \"\"\"\n Class to control a vehicle manually for debugging purposes\n \"\"\"\n\n def __init__(self):\n self._width = 800\n self._height = 600\n self._surface = None\n\n pygame.init()\n pygame.font.init()\n self._clock = pygame.time.Clock()\n self._display = pygame.display.set_mode((self._width, self._height), pygame.HWSURFACE | pygame.DOUBLEBUF)\n pygame.display.set_caption(\"Human Agent\")\n\n def run_interface(self, input_data):\n \"\"\"\n Run the GUI\n \"\"\"\n\n # process sensor data\n image_center = input_data['Center'][1][:, :, -2::-1]\n\n # display image\n self._surface = pygame.surfarray.make_surface(image_center.swapaxes(0, 1))\n if self._surface is not None:\n self._display.blit(self._surface, (0, 0))\n pygame.display.flip()\n\n def _quit(self):\n pygame.quit()\n\n\nclass HumanAgent(AutonomousAgent):\n\n \"\"\"\n Human agent to control the ego vehicle via keyboard\n \"\"\"\n\n current_control = None\n agent_engaged = False\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n self.track = Track.SENSORS\n\n self.agent_engaged = False\n self._hic = HumanInterface()\n self._controller = KeyboardControl(path_to_conf_file)\n self._prev_timestamp = 0\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n \"\"\"\n\n sensors = [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Center'},\n {'type': 'sensor.speedometer', 'reading_frequency': 20, 'id': 'speed'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n self.agent_engaged = True\n self._hic.run_interface(input_data)\n\n control = self._controller.parse_events(timestamp - self._prev_timestamp)\n self._prev_timestamp = timestamp\n\n return control\n\n def destroy(self):\n \"\"\"\n Cleanup\n \"\"\"\n self._hic._quit = True\n\n\nclass KeyboardControl(object):\n\n \"\"\"\n Keyboard control for the human agent\n \"\"\"\n\n def __init__(self, path_to_conf_file):\n \"\"\"\n Init\n \"\"\"\n self._control = carla.VehicleControl()\n self._steer_cache = 0.0\n self._clock = pygame.time.Clock()\n\n # Get the mode\n if path_to_conf_file:\n\n with (open(path_to_conf_file, \"r\")) as f:\n lines = f.read().split(\"\\n\")\n self._mode = lines[0].split(\" \")[1]\n self._endpoint = lines[1].split(\" \")[1]\n\n # Get the needed vars\n if self._mode == \"log\":\n self._log_data = {'records': []}\n\n elif self._mode == \"playback\":\n self._index = 0\n self._control_list = []\n\n with open(self._endpoint) as fd:\n try:\n self._records = json.load(fd)\n self._json_to_control()\n except json.JSONDecodeError:\n pass\n else:\n self._mode = \"normal\"\n self._endpoint = None\n\n def _json_to_control(self):\n\n # transform strs into VehicleControl commands\n for entry in self._records['records']:\n control = carla.VehicleControl(throttle=entry['control']['throttle'],\n steer=entry['control']['steer'],\n brake=entry['control']['brake'],\n hand_brake=entry['control']['hand_brake'],\n reverse=entry['control']['reverse'],\n manual_gear_shift=entry['control']['manual_gear_shift'],\n gear=entry['control']['gear'])\n self._control_list.append(control)\n\n def parse_events(self, timestamp):\n \"\"\"\n Parse the keyboard events and set the vehicle controls accordingly\n \"\"\"\n # Move the vehicle\n if self._mode == \"playback\":\n self._parse_json_control()\n else:\n self._parse_vehicle_keys(pygame.key.get_pressed(), timestamp*1000)\n\n # Record the control\n if self._mode == \"log\":\n self._record_control()\n\n return self._control\n\n def _parse_vehicle_keys(self, keys, milliseconds):\n \"\"\"\n Calculate new vehicle controls based on input keys\n \"\"\"\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return \n elif event.type == pygame.KEYUP:\n if event.key == K_q:\n self._control.gear = 1 if self._control.reverse else -1\n self._control.reverse = self._control.gear < 0\n\n if keys[K_UP] or keys[K_w]:\n self._control.throttle = 0.6\n else:\n self._control.throttle = 0.0\n\n steer_increment = 3e-4 * milliseconds\n if keys[K_LEFT] or keys[K_a]:\n self._steer_cache -= steer_increment\n elif keys[K_RIGHT] or keys[K_d]:\n self._steer_cache += steer_increment\n else:\n self._steer_cache = 0.0\n\n steer_cache = min(0.95, max(-0.95, self._steer_cache))\n self._control.steer = round(self._steer_cache, 1)\n self._control.brake = 1.0 if keys[K_DOWN] or keys[K_s] else 0.0\n self._control.hand_brake = keys[K_SPACE]\n\n def _parse_json_control(self):\n\n if self._index < len(self._control_list):\n self._control = self._control_list[self._index]\n self._index += 1\n else:\n print(\"JSON file has no more entries\")\n\n def _record_control(self):\n new_record = {\n 'control': {\n 'throttle': self._control.throttle,\n 'steer': self._control.steer,\n 'brake': self._control.brake,\n 'hand_brake': self._control.hand_brake,\n 'reverse': self._control.reverse,\n 'manual_gear_shift': self._control.manual_gear_shift,\n 'gear': self._control.gear\n }\n }\n\n self._log_data['records'].append(new_record)\n\n def __del__(self):\n # Get ready to log user commands\n if self._mode == \"log\" and self._log_data:\n with open(self._endpoint, 'w') as fd:\n json.dump(self._log_data, fd, indent=4, sort_keys=True)\n","repo_name":"autonomousvision/transfuser","sub_path":"leaderboard/leaderboard/autoagents/human_agent.py","file_name":"human_agent.py","file_ext":"py","file_size_in_byte":8281,"program_lang":"python","lang":"en","doc_type":"code","stars":882,"dataset":"github-code","pt":"75"} +{"seq_id":"35682800830","text":"\r\n\r\n\r\n\r\n\r\n\r\ndef cut(message):\r\n key = message\r\n while len(key) > 0 and not key[0].isalpha():\r\n key = key[1:]\r\n while len(key) > 0 and not key[-1].isalpha():\r\n key = key[:-1]\r\n return key\r\n\r\nwith open(\"aspell_wordlist.txt\", \"r\") as f: \r\n content = f.read()\r\n print(50000)\r\nfor x in content.split()[:25000]: \r\n print(\"insert\",cut(x))\r\nfor x in content.split()[:25000]:\r\n print(\"find\", cut(x))\r\n","repo_name":"Kubciooo/4SEM","sub_path":"AiSD/Lista 4/konwersja.py","file_name":"konwersja.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"75069093043","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 2 22:13:40 2018\n\n@author: Chat\n\"\"\"\nimport praw\nimport os\nfrom urllib import request\nimport configparser \nimport ast\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n\nreddit = praw.Reddit(client_id=config['default']['client_id'],\n client_secret=config['default']['client_secret'],\n password=config['default']['password'],\n user_agent=config['default']['user_agent'],\n username=config['default']['username'])\n\nprint(\"Account logged in: \", reddit.user.me())\nSUBREDDIT = str(config['default']['SUBREDDIT'])\nflair = str(config['default']['Flair'])\nfilter_words = ast.literal_eval(config['default']['filter_words'])\nLIMIT = int(config['default']['LIMIT'])\n\nif not os.path.isfile(\"already_fetched.txt\"):\n already_fetched = []\nelse:\n with open(\"already_fetched.txt\", \"r\") as f:\n already_fetched = f.read()\n already_fetched = already_fetched.split(\"\\n\")\n already_fetched = list(filter(None, already_fetched))\n \ndef image_fetch(already_fetched, flair, filter_words):\n subreddit = reddit.subreddit(SUBREDDIT)\n new_posts = subreddit.new(limit=LIMIT)\n for submission in new_posts:\n if submission.link_flair_text == flair and (filter_word_check(filter_words, submission.title)) and submission.id not in already_fetched:\n url = submission.url\n try:\n f = open(str(submission.author)+\".jpg\", 'wb')\n f.write(request.urlopen(url).read())\n f.close() \n except:\n print(\"Silently Failing. Can't save file\")\n already_fetched.append(submission.id)\n with open(\"already_fetched.txt\", \"w\") as f:\n for x in already_fetched:\n f.write(x + \"\\n\")\n \ndef filter_word_check(filter_words, title):\n for x in filter_words:\n if x in title.lower():\n return True\n else:\n return False\nimage_fetch(already_fetched, flair, filter_words)\n ","repo_name":"jcsumlin/Reddit-Image-Scraper","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"11150716296","text":"\"\"\"6.009 Lab 8A: carlae Interpreter\"\"\"\n\nimport sys\n\n\nclass EvaluationError(Exception):\n \"\"\"Exception to be raised if there is an error during evaluation.\"\"\"\n pass\n\n\ndef tokenize(source):\n \"\"\"\n Splits an input string into meaningful tokens (left parens, right parens,\n other whitespace-separated values). Returns a list of strings.\n\n Arguments:\n source (str): a string containing the source code of a carlae\n expression\n \"\"\"\n lines = source.split('\\n')\n for i in range(len(lines)):\n lines[i] = lines[i][:lines[i].index(';')] if ';' in lines[i] else lines[i]\n \n tokens = []\n for line in lines:\n i = 0\n while i < len(line):\n if line[i] in ['(', ')']:\n tokens.append(line[i])\n elif line[i] != ' ':\n j = i + 1\n while j < len(line) and line[j] not in ['(', ')', ' ']:\n j += 1\n tokens.append(line[i:j])\n i = j-1\n i += 1\n return tokens\n \ndef parse_helper(tokens):\n output = []\n i = 0\n \n while i < len(tokens):\n token = tokens[i]\n if token == ')':\n raise SyntaxError('mismatched parentheses: closed without openning')\n elif token == '(': #is nested sublist\n open_n = 1\n closed_n = 0\n pos = i\n try:\n while closed_n < open_n:\n pos += 1\n if tokens[pos] == '(':\n open_n += 1\n elif tokens[pos] == ')':\n closed_n += 1\n except:\n raise SyntaxError('mismatched parentheses: not closed')\n \n output += [parse_helper(tokens[i+1:pos])]\n i = pos\n elif token.isdigit(): #its an int\n output.append(int(token))\n elif token[1:].isdigit() and token[0] == '-': #negative int\n output.append(int(token))\n elif token.replace('.', '').isdigit() and token.count('.') == 1: #float\n output.append(float(token))\n elif token.replace('.', '').replace('-', '').isdigit() and token.count('.') == 1 and token[0] == '-': #negative float\n output.append(float(token))\n else:\n output.append(token)\n \n i+=1\n \n return output\n\n\ndef parse(tokens):\n \"\"\"\n Parses a list of tokens, constructing a representation where:\n * symbols are represented as Python strings\n * numbers are represented as Python ints or floats\n * S-expressions are represented as Python lists\n\n Arguments:\n tokens (list): a list of strings representing tokens\n \"\"\"\n return parse_helper(tokens)[0]\n \n \n \n \n \n\n\ncarlae_builtins = {\n '+': sum,\n '-': lambda args: -args[0] if len(args) == 1 else (args[0] - sum(args[1:])),\n}\n\n\ndef evaluate(tree):\n \"\"\"\n Evaluate the given syntax tree according to the rules of the carlae\n language.\n\n Arguments:\n tree (type varies): a fully parsed expression, as the output from the\n parse function\n \"\"\"\n raise NotImplementedError\n\n\nif __name__ == '__main__':\n test = ';add the numbers 2 and 3\\n(+ ; this expression\\n 2 ; spans multiple\\n 3 ; lines\\n)'\n test = '(define circle-area (lambda (r) (* 3.14 (* r r))))'\n tokens = tokenize(test)\n print(tokens)\n parsed= parse(tokens)\n print(parsed)","repo_name":"gabe-madonna/labs","sub_path":"lab8A/lab_7.5.py","file_name":"lab_7.5.py","file_ext":"py","file_size_in_byte":3499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"5370317873","text":"import json\r\n\r\ntry:\r\n with open(\"users.json\") as f:\r\n users = json.load(f)\r\nexcept FileNotFoundError:\r\n users = []\r\n\r\nwhile True:\r\n print(\"1-new user\")\r\n print(\"2-exit\")\r\n choice = int(input(\"Pick one: \"))\r\n\r\n if choice == 1:\r\n user = {}\r\n user[\"full_name\"] = input(\"Give full name: \")\r\n user[\"username\"] = input(\"Give username: \")\r\n user[\"password\"] = input(\"Give password: \")\r\n user[\"role\"] = input(\"Give role (admin, user): \")\r\n users.append(user)\r\n elif choice == 2:\r\n with open(\"users.json\", \"w\") as f:\r\n json.dump(users, f)\r\n break\r\n","repo_name":"psounis/python","sub_path":"lesson15/exercise09.admin.py","file_name":"exercise09.admin.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"75"} +{"seq_id":"39097153270","text":"import unittest, numpy\n\nfrom binf.tests.pdf import MockBinfPDF\nfrom binf.samplers import BinfState\nfrom binf.samplers.gibbs import GibbsSampler\nfrom binf.samplers.hmc import HMCSampler\n\nclass MockSampler(object):\n\n def __init__(self, variable_name):\n\n self.pdf = None\n self._state = 5.0\n self.variable_name = variable_name\n\n @property\n def state(self):\n return self._state\n @state.setter\n def state(self, value):\n self._state = value\n\n @property\n def last_draw_stats(self):\n\n return {self.variable_name: {'testlastdrawstats{}'.format(self.state): self.state}}\n\n @property\n def sampling_stats(self):\n\n return {'testsamplingstats{}'.format(self.state): self.state}\n\n def sample(self):\n\n if 'y' in self.pdf.parameters:\n return self.state * 2.0 * self.pdf['y'].value\n else:\n return self.state * 2.0 * self.pdf['x'].value\n\n\nclass testGibbsSampler(unittest.TestCase):\n\n def _create_sampler(self):\n\n return GibbsSampler(MockBinfPDF(), BinfState({'x': 2.0, 'y': 3.0}),\n {'x': MockSampler('x'), 'y': MockSampler('y')})\n\n def testSetup_conditional_pdfs(self):\n\n gips = self._create_sampler()\n\n self.assertEqual(len(gips._conditional_pdfs), 2)\n self.assertTrue('x' in gips._conditional_pdfs)\n self.assertTrue('y' in gips._conditional_pdfs)\n self.assertEqual(gips._conditional_pdfs['x']['y'].value, 3.0)\n self.assertEqual(gips._conditional_pdfs['y']['x'].value, 2.0)\n self.assertEqual(len(gips._conditional_pdfs['x'].variables), 1)\n self.assertTrue('y' in gips.subsamplers['x'].pdf.parameters)\n self.assertEqual(gips.subsamplers['x'].pdf['y'].value, 3.0)\n self.assertEqual(len(gips._conditional_pdfs['y'].variables), 1)\n self.assertTrue('x' in gips.subsamplers['y'].pdf.parameters)\n self.assertEqual(gips.subsamplers['y'].pdf['x'].value, 2.0)\n\n def testUpdate_conditional_pdf_params(self):\n\n gips = self._create_sampler()\n \n gips.state.update_variables(x=5.0)\n gips._update_conditional_pdf_params()\n self.assertEqual(gips._conditional_pdfs['y']['x'].value, 5.0)\n\n def testUpdate_samplers(self):\n\n gips = self._create_sampler()\n \n new_sampler = MockSampler('x')\n new_sampler.pdf = MockBinfPDF()\n new_sampler.pdf['ParamA'].set(23.0)\n gips.update_samplers(x=new_sampler)\n\n self.assertEqual(gips.subsamplers['x'].pdf['ParamA'].value, 23.0)\n\n def testUpdate_subsampler_states(self):\n\n gips = GibbsSampler(MockBinfPDF(), BinfState({'x': 2.0, 'y': 3.0}),\n {'x': MockSampler('x'), \n 'y': HMCSampler(MockBinfPDF(), 1.0, 0.1, 12)})\n\n gips.state.update_variables(x=5.0)\n gips.state.update_variables(y=2.3)\n gips._update_subsampler_states()\n\n self.assertEqual(gips.subsamplers['x'].state, 5.0)\n self.assertEqual(gips.subsamplers['y'].state, 2.3)\n\n def testUpdate_state(self):\n\n gips = self._create_sampler()\n \n gips._update_state(x=34.0)\n\n self.assertEqual(gips.state.variables['x'], 34.0)\n\n def testSample(self):\n\n gips = self._create_sampler()\n gips._update_state(x=0.5)\n sample = gips.sample()\n\n self.assertEqual(sample.variables, gips.state.variables)\n self.assertEqual(sample.variables['x'], 3.0)\n self.assertEqual(sample.variables['y'], 18.0)\n\n def testGet_last_draw_stats(self):\n\n gips = self._create_sampler()\n \n stats = gips.last_draw_stats\n self.assertEqual(len(stats), 2)\n self.assertTrue('x' in stats)\n self.assertTrue('y' in stats)\n self.assertTrue('testlastdrawstats2.0' in stats['x'])\n self.assertTrue('testlastdrawstats3.0' in stats['y'])\n\n def testSamplingStats(self):\n\n gips = self._create_sampler()\n stats = gips.sampling_stats\n\n self.assertTrue('testsamplingstats2.0' in stats)\n self.assertTrue('testsamplingstats3.0' in stats)\n \n\nif __name__ == '__main__':\n\n unittest.main()\n \n \n","repo_name":"simeoncarstens/binf","sub_path":"binf/tests/samplers/gibbs.py","file_name":"gibbs.py","file_ext":"py","file_size_in_byte":4195,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"19877127488","text":"# -*- coding: utf-8 -*-\n# refer to https://github.com/pyimgui/pyimgui/tree/master/imgui/integrations\n# refer to https://github.com/seanchas116/qtimgui\n# refer to https://github.com/pedohorse/lifeblood/blob/dev/src/lifeblood_viewer/nodeeditor.py\n\nfrom __future__ import absolute_import\n\nimport imgui\nfrom imgui.integrations.opengl import ProgrammablePipelineRenderer\n\nfrom PySide6.QtOpenGLWidgets import QOpenGLWidget\nfrom PySide6.QtCore import QObject, QEvent, Qt, QDateTime\nfrom PySide6.QtWidgets import QApplication\nfrom PySide6.QtGui import QMouseEvent, QWheelEvent, QKeyEvent, QCursor\n\n\nclass PySide6Renderer(QObject, ProgrammablePipelineRenderer):\n key_map = {\n Qt.Key_Tab: imgui.KEY_TAB,\n Qt.Key_Left: imgui.KEY_LEFT_ARROW,\n Qt.Key_Right: imgui.KEY_RIGHT_ARROW,\n Qt.Key_Up: imgui.KEY_UP_ARROW,\n Qt.Key_Down: imgui.KEY_DOWN_ARROW,\n Qt.Key_PageUp: imgui.KEY_PAGE_UP,\n Qt.Key_PageDown: imgui.KEY_PAGE_DOWN,\n Qt.Key_Home: imgui.KEY_HOME,\n Qt.Key_End: imgui.KEY_END,\n Qt.Key_Insert: imgui.KEY_INSERT,\n Qt.Key_Delete: imgui.KEY_DELETE,\n Qt.Key_Backspace: imgui.KEY_BACKSPACE,\n Qt.Key_Space: imgui.KEY_SPACE,\n Qt.Key_Enter: imgui.KEY_ENTER,\n Qt.Key_Return: imgui.KEY_ENTER,\n Qt.Key_Escape: imgui.KEY_ESCAPE,\n Qt.Key_A: imgui.KEY_A,\n Qt.Key_C: imgui.KEY_C,\n Qt.Key_V: imgui.KEY_V,\n Qt.Key_X: imgui.KEY_X,\n Qt.Key_Y: imgui.KEY_Y,\n Qt.Key_Z: imgui.KEY_Z,\n }\n cursor_map = {\n imgui.MOUSE_CURSOR_ARROW: Qt.CursorShape.ArrowCursor,\n imgui.MOUSE_CURSOR_TEXT_INPUT: Qt.CursorShape.IBeamCursor,\n imgui.MOUSE_CURSOR_RESIZE_ALL: Qt.CursorShape.SizeAllCursor,\n imgui.MOUSE_CURSOR_RESIZE_NS: Qt.CursorShape.SizeVerCursor,\n imgui.MOUSE_CURSOR_RESIZE_EW: Qt.CursorShape.SizeHorCursor,\n imgui.MOUSE_CURSOR_RESIZE_NESW: Qt.CursorShape.SizeBDiagCursor,\n imgui.MOUSE_CURSOR_RESIZE_NWSE: Qt.CursorShape.SizeFDiagCursor,\n imgui.MOUSE_CURSOR_HAND: Qt.CursorShape.PointingHandCursor,\n imgui.MOUSE_CURSOR_NOT_ALLOWED: Qt.CursorShape.ForbiddenCursor\n }\n\n def __init__(self, window: QOpenGLWidget) -> None:\n QObject.__init__(self)\n ProgrammablePipelineRenderer.__init__(self)\n self._gui_time: float = 0.0\n self._mouse_pressed: list[bool] = [False, False, False]\n self._mouse_wheel: float = 0.0\n self.widget = window\n for value in self.key_map.values():\n self.io.key_map[value] = value\n self.io.set_clipboard_text_fn = self.setClipboard\n self.io.get_clipboard_text_fn = self.getClipboard\n self.widget.installEventFilter(self)\n\n def process_inputs(self) -> None:\n io = self.io\n io.display_size = self.widget.size().width(), self.widget.size().height()\n io.display_fb_scale = self.widget.devicePixelRatioF(), self.widget.devicePixelRatioF()\n\n current_time = QDateTime().currentMSecsSinceEpoch()/1000.0\n if (self._gui_time > 0.0) and ((current_time - self._gui_time) > 0.0):\n io.delta_time = current_time - self._gui_time\n else:\n io.delta_time = 1.0/60.0\n self._gui_time = current_time\n\n if self.widget.isActiveWindow():\n pos = self.widget.mapFromGlobal(QCursor.pos())\n io.mouse_pos = pos.x(), pos.y()\n else:\n io.mouse_pos = -1, -1\n\n for i in range(3):\n io.mouse_down[i] = self._mouse_pressed[i]\n io.mouse_wheel = self._mouse_wheel\n self._mouse_wheel = 0.0\n self.updateCursorShape()\n\n def onMousePressedChange(self, event: QMouseEvent) -> None:\n button = event.buttons()\n if button & Qt.LeftButton:\n self._mouse_pressed[0] = True\n else:\n self._mouse_pressed[0] = False\n if button & Qt.RightButton:\n self._mouse_pressed[1] = True\n else:\n self._mouse_pressed[1] = False\n if button & Qt.MiddleButton:\n self._mouse_pressed[2] = True\n else:\n self._mouse_pressed[2] = False\n\n def onWheel(self, event: QWheelEvent) -> None:\n deltay = event.pixelDelta().y()\n if deltay != 0:\n self._mouse_wheel += float(deltay) / (5.0 * imgui.get_text_line_height())\n else:\n self._mouse_wheel += float(event.angleDelta().y()) / 120.0\n\n def onKeyPressRelease(self, event: QKeyEvent) -> None:\n key_pressed = (event.type() == QEvent.KeyPress)\n key = event.key()\n imgui_key = self.key_map.get(key)\n if imgui_key is not None:\n self.io.keys_down[imgui_key] = key_pressed\n\n if key_pressed:\n text = event.text()\n if len(text) == 1:\n self.io.add_input_character(ord(text))\n\n self.io.key_ctrl = event.modifiers() & Qt.ControlModifier\n self.io.key_shift = event.modifiers() & Qt.ShiftModifier\n self.io.key_alt = event.modifiers() & Qt.AltModifier\n self.io.key_super = event.modifiers() & Qt.MetaModifier\n\n def eventFilter(self, watched: QObject, event: QEvent) -> bool:\n if event.type() in (QEvent.MouseButtonPress, QEvent.MouseButtonRelease):\n self.onMousePressedChange(QMouseEvent(event))\n elif event.type() == QEvent.Wheel:\n self.onWheel(QWheelEvent(event))\n elif event.type() in (QEvent.KeyPress, QEvent.KeyRelease):\n self.onKeyPressRelease(QKeyEvent(event))\n return QObject.eventFilter(self, watched, event)\n\n def setClipboard(self, text: str) -> None:\n QApplication.clipboard().setText(text)\n\n def getClipboard(self) -> str:\n return QApplication.clipboard().text()\n\n def updateCursorShape(self) -> None:\n if self.io.config_flags & imgui.CONFIG_NO_MOUSE_CURSOR_CHANGE:\n return\n\n imgui_cursor = imgui.get_mouse_cursor()\n if self.io.mouse_draw_cursor or (imgui_cursor == imgui.MOUSE_CURSOR_NONE):\n self.widget.setCursor(Qt.CursorShape.BlankCursor)\n else:\n qt_cursor = self.cursor_map.get(imgui_cursor)\n if qt_cursor is not None:\n self.widget.setCursor(qt_cursor)\n else:\n self.widget.setCursor(Qt.CursorShape.ArrowCursor)\n","repo_name":"EagleEatApple/glskeleton","sub_path":"glskeleton/qtimgui/pyside6.py","file_name":"pyside6.py","file_ext":"py","file_size_in_byte":6308,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"75"} +{"seq_id":"42667283616","text":"from typing import Callable, Any, Tuple, Mapping\n\nfrom pyquibbler import Assignment\nfrom pyquibbler.inversion.invert import invert\nfrom pyquibbler.path.data_accessing import deep_set\nfrom pyquibbler.utilities.get_original_func import get_original_func\nfrom pyquibbler.path_translation.source_func_call import SourceFuncCall\nfrom pyquibbler.path import PathComponent\n\n\ndef inverse(func: Callable, indices: Any, value: Any, args: Tuple[Any, ...] = None, kwargs: Mapping[str, Any] = None,\n empty_path: bool = False, assignment: Assignment = None):\n func = get_original_func(func)\n if indices is not None and empty_path is True:\n raise Exception(\"The indices cannot be set if empty path is True\")\n\n args = args or tuple()\n kwargs = kwargs or {}\n previous_value = SourceFuncCall.from_(func, args, kwargs).run()\n assignment = assignment or Assignment(path=[PathComponent(indices)] if not empty_path else [], value=value)\n inversals = invert(\n func_call=SourceFuncCall.from_(func, args, kwargs),\n previous_result=previous_value,\n assignment=assignment\n )\n\n return ({\n inversal.source: deep_set(inversal.source.value, inversal.assignment.path, inversal.assignment.value)\n for inversal in inversals\n },\n inversals)\n\n","repo_name":"Technion-Kishony-lab/quibbler","sub_path":"tests/functional/inversion/inverters/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":303,"dataset":"github-code","pt":"75"} +{"seq_id":"21152117472","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\n\nclass Utilities:\n @staticmethod\n def read_images(*images, cvtColor_code=cv2.COLOR_BGR2RGB):\n result = [cv2.cvtColor(cv2.imread(image), cvtColor_code) for image in images]\n return result if len(result) > 1 else result[0]\n\n @staticmethod\n def show_images(*images, cmap=\"gray\", figsize=(12, 12)):\n n = len(images)\n if n == 1:\n fig, axis = plt.subplots(figsize=figsize)\n axis.imshow(images[0], cmap=cmap)\n elif n == 2:\n fig, axis = plt.subplots(ncols=2, figsize=figsize)\n axis[0].imshow(images[0], cmap=cmap)\n axis[1].imshow(images[1], cmap=cmap)\n\n else:\n columns = int(np.ceil(np.sqrt(n)))\n rows = int(np.ceil(n / columns))\n\n fig, axis = plt.subplots(rows, columns, figsize=figsize)\n\n k = 0\n for i in range(rows):\n for j in range(columns):\n if k < n:\n axis[i][j].imshow(images[k], cmap=cmap)\n k += 1\n else:\n axis[i][j].axis(\"off\")\n\n plt.tight_layout()\n plt.show()\n return fig, axis\n\n @staticmethod\n def read_and_show(\n *images, cvtColor_code=cv2.COLOR_BGR2RGB, cmap=\"gray\", figsize=(12, 12)\n ):\n results = Utilities.read_images(*images, cvtColor_code=cvtColor_code)\n if isinstance(results, list):\n Utilities.show_images(*results, cmap=cmap, figsize=figsize)\n else:\n Utilities.show_images(results, cmap=cmap, figsize=figsize)\n return results\n","repo_name":"Repsajsov/OpenCVUtilities","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"6183682486","text":"#!/usr/bin/env python\n\n#from sys import _OptExcInfo\nfrom distutils import dist\nfrom os import remove\nfrom numpy.core.numeric import Inf\nfrom numpy.distutils.npy_pkg_config import _read_config_imp\nfrom numpy.f2py.crackfortran import true_intent_list\nimport rospy\nfrom sensor_msgs.msg import LaserScan\nfrom std_msgs.msg import String\n\nimport random\nimport math \n\nfrom nav_msgs.msg import Odometry\nfrom tf import transformations\n\nfrom visualization_msgs.msg import Marker\nfrom geometry_msgs.msg import Point\nfrom geometry_msgs.msg import Twist\n\nimport numpy as np\n\nangle_inc = 0.00872664619237\nA = 0\nB = 0\nC = 0\n\nposition_ = Point()\nyaw_ = 0\nstate_ = 0\nbot_state_ = 0\ndes_point_ = Point()\n\ndes_point_.z = 0\ndes_yaw = 0\nyaw_prec_ = 0.15#math.pi/90#90\ndist_prec_ = 0.5\npub = None\ncount = 2\n\nmap = [0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,\n 0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,\n 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n 1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n 0,1,1,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,\n 0,0,1,1,0,0,1,1,1,1,1,1,1,0,0,0,0,0,\n 0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,\n 0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1,\n 0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1,\n 0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,\n 0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,\n 0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,\n 0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,\n 0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,\n 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,\n 0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,1,0,\n 0,0,0,0,0,0,0,1,1,1,0,0,0,1,1,1,1,0,\n 0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,1,1]\n\nobst_map = np.zeros(shape=(20,18))\n\nclass node():\n\tdef __init__(self,par=None,pos=None):\n\t\tself.gcost = 0\n\t\tself.hcost = 0\n\t\tself.fcost = 0\n\t\tself.parent = par\n\t\tself.posit = pos\n\t\n\tdef __eq__(self,an):\n\t\treturn self.posit == an.posit\n\ndef astar(map,start,end):\n\n\trows = len(map)\n\tcols = len(map[rows-1])\n\t#rospy.loginfo(rows)\n\t#rospy.loginfo(cols)\n\tstart_node = node(None,start)\n\n\n\tstart_node.gcost = 0\n\tstart_node.hcost = 0\n\tstart_node.fcost = 0\n\t\n\n\tend_node = node(None,end)\n\n\tend_node.gcost = 0\n\tend_node.hcost = 0\n\tend_node.fcost = 0\n\n\t#start_node.hcost = get_distance(start_node,end_node)\n\t\n\topen_nodes =[]\n\tvisited_nodes =[]\n\topen_nodes.append(start_node)\n\n\twhile(len(open_nodes)>0):\n\t\t\n\t\t#minf = Inf\n\t\trospy.loginfo(len(open_nodes))\n\t\tid = 0\n\t\tcurrent = open_nodes[0]\n\t\tfor i in range(len(open_nodes)):\n\t\t\tif open_nodes[i].fcosto.gcost:\n\t\t\t\t\t\n\t\t\t\t\t#rospy.loginfo(\"same\")\n\t\t\t\t\tvar1= True\n\t\t\t\t\tbreak\n\t\t\t\telif child == o and child.gcost yaw_prec_):\n\t\tif (change_yaw > 0):\n\t\t\tyaw.angular.z = 0.5*math.fabs(change_yaw)#0.3 \n\t\telse :\n\t\t\tyaw.angular.z = -0.5*math.fabs(change_yaw)#-0.3\n\t\tpub.publish(yaw)\n\telse:\n\t\tchange_state(1)\n\ndef straight(point):\n\tglobal position_,yaw_\n\tglobal des_yaw\n\t\n\tmove = Twist()\n\t\n\t\n\tdist = math.sqrt(pow((point.y-position_.y),2)+pow((point.x-position_.x),2))\n\tdes_yaw = math.atan(point.y - position_.y)/(point.x - position_.x)\n\tchange_yaw = des_yaw - yaw_\n\t#rospy.loginfo(\"came to straight\"+str(dist)+\" des_yaw \"+str(des_yaw)+\" x \"+str(position_.x)+\" y \"+str(position_.y))\n\tif (dist > dist_prec_):\n\t\t#rospy.loginfo(\"move forward\")\n\t\tmove.linear.x = 0.8*dist\n\t\tpub.publish(move)\n\telse:\n\t\t#rospy.loginfo(\"stop\")\n\t\tchange_state(2)\n\n\tif (math.fabs(change_yaw) > yaw_prec_):\n\t\t#rospy.loginfo(\"Change yaw\")\n\t\tchange_state(0)\n\t\n\ndef done():\n\tstop = Twist()\n\tstop.linear.x = 0.0\n\tstop.angular.x = 0.0\n\tpub.publish(stop)\n\ndef change_state(state):\n\tglobal state_\n\tstate_=state\n\t#rospy.loginfo(\"Changed state to \"+str(state))\n\nif __name__ == '__main__':\n\trospy.init_node('astar')\n\tglobal des_point_,state_,pub\n\tsub_odom = rospy.Subscriber(\"/odom\",Odometry,call_odom)\n\tpub = rospy.Publisher(\"/cmd_vel\",Twist,queue_size=1)\n\tendx = -1*(rospy.get_param('y')-10) #1#7#1#rospy.get_param('x') \n\tendy = math.floor(rospy.get_param('x') + 9)#13#6#13.5#rospy.get_param('y') - 10\n\t\n\tstartx = 12#8#0#8#-8 + 9\n\tstarty = 1#1#0#1#-2 - 10\n\n\trate = rospy.Rate(30)\n\n\tfor i in range(len(map)):\n\t\tif map[i]==1:\n\t\t\tobst_map[int(i/18)][i%18] = 1\n\t\n\t#rospy.loginfo(startx,starty,endx,endy)\n\tresult = astar(obst_map,(startx,starty),(endx,endy))\n\tpath = []#[(-8, -2), (-7, -2), (-6, -2), (-5, -2), (-4, -3), (-3, -4), (-2, -5), (-1, -5), (0, -4), (0, -3), (0, -2), (0, -1), (0, 0), (0, 1), (1, 2), (2, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (4, 9)]#[(-8, -2), (-7, -2), (-6, -2), (-5, -2), (-4, -2), (-3, -3), (-2, -4), (-1, -3), (0, -2), (0, -1), (0, 0), (0, 1), (1, 2), (2, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (4, 9)] #[]\n\tresult.reverse()\n\tfor i in result : \n \t\t\ta = i[1]-9\n \t\t\tb = -i[0]+10\n \t\t\tpath.append((a,b))\n\n\trospy.loginfo(path)\n\t\n\t\n\t\n\twhile not rospy.is_shutdown():\n\t\tif count == len(path)-1:\n\t\t\tstate_ = 3\n\t\tdes_point_.x = path[count][0]#4.5\n\t\tdes_point_.y = path[count][1]#9.0\n\t\t#rospy.loginfo(str(state_))\n\t\tpoint_to_point()\n\t\t\n\t\trate.sleep()\n\t\n\n\t\n","repo_name":"danalex7212/Projects","sub_path":"A-star Path Planning in ROS python/astar.py","file_name":"astar.py","file_ext":"py","file_size_in_byte":8942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"32813741063","text":"from django.test import TestCase\n\nfrom common import setup\nfrom retractions.management.commands import contactable_authors\nfrom retractions.models import Author, AuthorAlias, RetractedPaper\n\n\nclass CommandsTestCase(TestCase):\n def setUp(self):\n self.c = contactable_authors.Command()\n setup.setup_logger(2)\n\n def test_update_citing_paper_normal(self):\n retracted_paper = RetractedPaper.objects.create(\n pmid=\"123\", scopus_id=\"abcd\", title=\"Foo bar\"\n )\n\n # Test a paper that is not an erratum.\n data = {\n \"authors\": [\n {\n \"ce:given-name\": \"Denise F.\",\n \"preferred-name\": {\n \"ce:given-name\": \"Denise F.\",\n \"ce:initials\": \"D.F.\",\n \"ce:surname\": \"Blake\",\n \"ce:indexed-name\": \"Blake D.\",\n },\n \"@seq\": \"1\",\n \"ce:initials\": \"D.F.\",\n \"@_fa\": \"true\",\n \"@type\": \"auth\",\n \"ce:e-address\": {\n \"$\": \"drsblakeinoz@bigpond.com\",\n \"@type\": \"email\",\n },\n \"ce:surname\": \"Blake\",\n \"@auid\": \"23484157000\",\n \"ce:indexed-name\": \"Blake D.F.\",\n },\n {\n \"ce:given-name\": \"Derelle A.\",\n \"preferred-name\": {\n \"ce:given-name\": \"Derelle A.\",\n \"ce:initials\": \"D.A.\",\n \"ce:surname\": \"Young\",\n \"ce:indexed-name\": \"Young D.\",\n },\n \"@seq\": \"2\",\n \"ce:initials\": \"D.A.\",\n \"@_fa\": \"true\",\n \"@type\": \"auth\",\n \"ce:surname\": \"Young\",\n \"@auid\": \"55539598300\",\n \"ce:indexed-name\": \"Young D.A.\",\n },\n {\n \"ce:given-name\": \"Lawrence H.\",\n \"preferred-name\": {\n \"ce:given-name\": \"Lawrence H.\",\n \"ce:initials\": \"L.H.\",\n \"ce:surname\": \"Brown\",\n \"ce:indexed-name\": \"Brown L.\",\n },\n \"@seq\": \"3\",\n \"ce:initials\": \"L.H.\",\n \"@date-locked\": \"2017-03-09T15:31:16.567\",\n \"@_fa\": \"true\",\n \"@type\": \"auth\",\n \"ce:surname\": \"Brown\",\n \"@auid\": \"7404220468\",\n \"ce:indexed-name\": \"Brown L.H.\",\n },\n ]\n }\n\n self.c._update_retracted_paper(\"abcd\", data)\n retracted_paper.refresh_from_db()\n\n # Check author objects saved correctly\n a = Author.objects.all()\n self.assertEqual(len(a), 3)\n aa = AuthorAlias.objects.all()\n self.assertEqual(len(aa), 3)\n a = AuthorAlias.objects.get(surname=\"Blake\")\n self.assertEqual(a.author.auid, \"23484157000\")\n self.assertEqual(a.given_name, \"Denise F.\")\n self.assertEqual(a.email_address, \"drsblakeinoz@bigpond.com\")\n","repo_name":"ebmdatalab/retractobot","sub_path":"retractions/tests/commands/test_contactable_authors.py","file_name":"test_contactable_authors.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"13369522526","text":"from slackclient import SlackClient\nimport os\nimport json\nimport logging\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\nslack_client = SlackClient(os.getenv(\"SLACK_TOKEN\"))\nsc_bot = SlackClient(os.getenv(\"SLACK_BOT_TOKEN\"))\nSLACK_CHANNEL = os.getenv(\"SLACK_CHANNEL\", \"builds_test\")\nSLACK_BOT_NAME = os.getenv(\"SLACK_BOT_NAME\", \"PipelineBot\")\nSLACK_BOT_ICON = os.getenv(\"SLACK_BOT_ICON\", \":robot_face:\")\n\n\ndef find_slack_message_for_update(pipeline_execution_id):\n channel_id = find_channel_id(SLACK_CHANNEL)\n slack_messages = get_slack_messages_from_channel(channel_id=channel_id)\n\n for message in slack_messages:\n if message.get('username', '') != SLACK_BOT_NAME:\n continue\n\n attachments = message.get('attachments', [])\n for attachment in attachments:\n if 'footer' not in attachment:\n continue\n \n if attachment['footer'] == pipeline_execution_id:\n return message\n\n return None\n\n\ndef find_channel_id(channel_name):\n res = slack_client.api_call(\"channels.list\", exclude_archived=1)\n\n if 'error' in res:\n if not isinstance(res['error'], str):\n err_message = ''\n else:\n err_message = res['error']\n raise ValueError(f'can not read channel list. error message from slack:{err_message}')\n\n channels = res['channels']\n\n for channel in channels:\n if channel['name'] == channel_name:\n return channel['id']\n\n raise ValueError(f'can not find channel. channel name:{channel_name}')\n\n\ndef get_slack_messages_from_channel(channel_id):\n res = slack_client.api_call('channels.history', channel=channel_id)\n\n if 'error' in res:\n if not isinstance(res['error'], str):\n err_message = ''\n else:\n err_message = res['error']\n raise ValueError(f'can not read channel list. error message from slack:{err_message}')\n\n return res['messages']\n\n\ndef update_message(channel_id, message_id, attachments):\n res = slack_client.api_call(\n \"chat.update\",\n channel=channel_id,\n ts=message_id,\n icon_emoji=SLACK_BOT_ICON,\n username=SLACK_BOT_NAME,\n attachments=attachments\n )\n\n if 'error' in res:\n if not isinstance(res['error'], str):\n err_message = ''\n else:\n err_message = res['error']\n raise ValueError(f'can update message. error message from slack:{err_message}')\n\n return res\n\n\ndef send_message(channel_id, attachments):\n res = slack_client.api_call(\n \"chat.postMessage\",\n channel=channel_id,\n icon_emoji=SLACK_BOT_ICON,\n username=SLACK_BOT_NAME,\n attachments=attachments\n )\n\n if 'error' in res:\n if not isinstance(res['error'], str):\n err_message = ''\n else:\n err_message = res['error']\n raise ValueError(f'can update message. error message from slack:{err_message}')\n\n return res\n","repo_name":"peoplefund-tech/codepipeline-slack-integration","sub_path":"slack_helper.py","file_name":"slack_helper.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"75"} +{"seq_id":"32165418707","text":"\nfrom openpyxl import Workbook\nimport xlsxwriter\nimport constants\n\n\ndef saveCveToFile(filename, cves):\n workbook = xlsxwriter.Workbook(filename)\n worksheet = workbook.add_worksheet()\n\n i = 0\n for cve in cves:\n\n str = parseCve(cve)\n\n if i == 0:\n k = 0\n for key in str.split('\\r\\n')[0].split('\\t'):\n worksheet.write(i, k, key)\n k += 1\n i += 1\n\n values = str.split('\\r\\n')[-1]\n print(values.split('\\t'))\n k = 0\n for value in values.split('\\t'):\n worksheet.write(i, k, value)\n k += 1\n\n i += 1\n\n workbook.close()\n\n\ndef saveCpeToFile(filename, cpes):\n workbook = xlsxwriter.Workbook(filename)\n worksheet = workbook.add_worksheet()\n\n i = 0\n for cpe in cpes:\n\n str = parseCpe(cpe)\n\n if i == 0:\n k = 0\n for key in str.split('\\r\\n')[0].split('\\t'):\n worksheet.write(i, k, key)\n k += 1\n i += 1\n\n values = str.split('\\r\\n')[-1]\n print(values.split('\\t'))\n k = 0\n for value in values.split('\\t'):\n worksheet.write(i, k, value)\n k += 1\n\n i += 1\n\n workbook.close()\n\n\ndef parseCpe(cpe):\n return str(CpeInfo(cpe))\n\n\ndef parseCve(cve):\n return str(CveInfo(cve))\n\n\nclass CpeInfo:\n\n cpe23Uri = None\n lastModifiedDate = None\n deprecatedBy = None\n deprecated = None\n titles = None\n refs = None\n vulnerabilities = None\n\n def __init__(self, jsonData):\n self.cpe23Uri = jsonData[\"cpe23Uri\"]\n self.lastModifiedDate = jsonData[\"lastModifiedDate\"]\n self.deprecatedBy = jsonData[\"deprecatedBy\"]\n self.deprecated = jsonData[\"deprecated\"]\n self.titles = jsonData[\"titles\"]\n self.refs = jsonData[\"refs\"]\n self.vulnerabilities = jsonData[\"vulnerabilities\"]\n\n def __str__(self) -> str:\n\n paramStr = ''\n\n paramKeys = [\"cpe23Uri\", \"lastModifiedDate\",\n \"deprecated\", \"titles\", \"refs\", \"vulnerabilities\"]\n paramValues = []\n\n for param in paramKeys:\n if param == \"cpe23Uri\":\n paramValues.append(self.cpe23Uri)\n elif param == \"lastModifiedDate\":\n paramValues.append(self.lastModifiedDate)\n elif param == \"deprecated\":\n paramValues.append(str(self.deprecated))\n elif param == \"titles\":\n tmpTitle = \"\"\n for title in self.titles:\n if len(tmpTitle) > 0:\n tmpTitle += ','\n tmpTitle += title['title']\n paramValues.append(tmpTitle)\n elif param == \"refs\":\n tmpRef = \"\"\n for ref in self.refs:\n if len(tmpRef) > 0:\n tmpRef += ','\n tmpRef += ref['type'] + ':' + ref['ref']\n\n paramValues.append(tmpRef)\n\n elif param == \"vulnerabilities\":\n tmpStr = ''\n for cve in self.vulnerabilities:\n if len(tmpStr) > 0:\n tmpStr += ','\n tmpStr += cve\n\n paramValues.append(tmpStr)\n\n paramStr += \"\\t\".join(paramKeys).upper()\n paramStr += '\\r\\n'+'\\t'.join(paramValues)\n\n return paramStr\n\n\nclass CveInfo:\n cve = None\n configurations = None\n impact = None\n publishedDate = None\n lastModifiedDate = None\n\n def __init__(self, jsonData):\n self.cve = jsonData[\"cve\"]\n self.configurations = jsonData[\"configurations\"]\n self.impact = jsonData[\"impact\"]\n self.publishedDate = jsonData[\"publishedDate\"]\n self.lastModifiedDate = jsonData[\"lastModifiedDate\"]\n\n def __str__(self):\n\n paramStr = ''\n paramKeys = [\"cveId\", \"cweId\", \"description\",\n \"cvssV3\", \"baseScore\", \"baseSeverity\", \"exploitabilityScore\", \"impactScore\", \"publishedDate\", \"lastModifiedDate\", \"version\"]\n paramValues = []\n\n for param in paramKeys:\n if param == 'publishedDate':\n publishedDate = self.publishedDate\n paramValues.append(publishedDate)\n elif param == 'version':\n versions = ''\n for node in self.configurations['nodes']:\n for cpeMatch in node['cpe_match']:\n currentUri = cpeMatch['cpe23Uri'].split(':')\n constUri = constants.cpe23uri.split(\":\")\n if \":\".join(currentUri[0:5]) == \":\".join(constUri[0:5]):\n if 'versionStartIncluding' in cpeMatch.keys() and 'versionEndIncluding' in cpeMatch.keys():\n if len(versions) > 0:\n versions += ','\n versions += cpeMatch['versionStartIncluding'] + \\\n '-'+cpeMatch['versionEndIncluding']\n elif 'versionStartIncluding' in cpeMatch.keys() and 'versionEndIncluding' not in cpeMatch.keys():\n if len(versions) > 0:\n versions += ','\n versions += cpeMatch['versionStartIncluding']+'-'+'*'\n elif 'versionStartIncluding' not in cpeMatch.keys() and 'versionEndIncluding' in cpeMatch.keys():\n if len(versions) > 0:\n versions += ','\n versions += '*'+'-' + \\\n cpeMatch['versionEndIncluding']\n elif 'versionStartIncluding' not in cpeMatch.keys() and 'versionEndIncluding' not in cpeMatch.keys():\n if len(versions) > 0:\n versions += ','\n versions += currentUri[5]\n paramValues.append(versions)\n elif param == 'lastModifiedDate':\n lastModifiedDate = self.lastModifiedDate\n paramValues.append(lastModifiedDate)\n elif param == \"cveId\":\n cveId = self.cve[\"CVE_data_meta\"][\"ID\"]\n paramValues.append(cveId)\n elif param == \"cweId\":\n cweId = self.cve[\"problemtype\"]['problemtype_data'][0]['description'][0][\"value\"]\n paramValues.append(cweId)\n elif param == \"description\":\n description = self.cve[\"description\"][\"description_data\"][0][\"value\"]\n paramValues.append(description)\n elif param == \"cvssV3\":\n cvssV3 = self.impact['baseMetricV2']['cvssV2']['vectorString']\n if 'baseMetricV3' in self.impact.keys():\n cvssV3 = self.impact['baseMetricV3']['cvssV3']['vectorString']\n paramValues.append(cvssV3)\n elif param == 'baseScore':\n baseScore = str(\n self.impact['baseMetricV2']['cvssV2']['baseScore'])\n if 'baseMetricV3' in self.impact.keys():\n baseScore = str(\n self.impact['baseMetricV3']['cvssV3']['baseScore'])\n paramValues.append(baseScore)\n elif param == \"baseSeverity\":\n baseSeverity = self.impact['baseMetricV2']['severity']\n if 'baseMetricV3' in self.impact.keys():\n baseSeverity = self.impact['baseMetricV3']['cvssV3']['baseSeverity']\n paramValues.append(baseSeverity)\n elif param == \"exploitabilityScore\":\n exploitabilityScore = str(\n self.impact['baseMetricV2']['exploitabilityScore'])\n if 'baseMetricV3' in self.impact.keys():\n exploitabilityScore = str(\n self.impact['baseMetricV3']['exploitabilityScore'])\n paramValues.append(exploitabilityScore)\n elif param == \"impactScore\":\n impactScore = str(self.impact['baseMetricV2']['impactScore'])\n if 'baseMetricV3' in self.impact.keys():\n impactScore = str(\n self.impact['baseMetricV3']['impactScore'])\n paramValues.append(impactScore)\n\n paramStr += \"\\t\".join(paramKeys).upper()\n paramStr += '\\r\\n'+'\\t'.join(paramValues)\n\n return paramStr\n","repo_name":"Dha0/CPETools","sub_path":"fetchData/results.py","file_name":"results.py","file_ext":"py","file_size_in_byte":8504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"25307091620","text":"from typing import Callable, Generator, Iterable\nfrom pathlib import Path\n\nfrom ..utils.misc import iter_bytes_read\n\nfrom .common import IAssetSrc, IExtensionSrc\n\nfrom ..utils.matching import CriteriaMatcher\nfrom ..utils.extension import (\n get_asset_from_vsix,\n get_version,\n get_version_asset,\n get_vsix_manifest,\n sanitize_extension,\n sort_extensions,\n)\n\nfrom ..models import *\n\ntry:\n from .gallery import ExternalGallery\n\n class MirrorExtensionSrc(IExtensionSrc):\n def __init__(self, src: str = None) -> None:\n super().__init__()\n self._gallery = ExternalGallery(src)\n\n def _sanitize_extension(self, ext: GalleryExtension):\n return ext\n\n def generate_page(\n self,\n criteria: \"list[GalleryCriterium]\",\n flags: GalleryFlags,\n assetTypes: \"list[str]\",\n page: int = 1,\n pageSize: int = 10,\n sortBy: SortBy = SortBy.NoneOrRelevance,\n sortOrder: SortOrder = SortOrder.Default,\n ) -> Generator[\n GalleryExtension, None, \"list[GalleryExtensionQueryResultMetadata]\"\n ]:\n resp = self._gallery.extension_query(\n {\n \"filters\": [\n {\n \"criteria\": criteria,\n \"pageNumber\": page,\n \"pageSize\": pageSize,\n \"sortBy\": sortBy,\n \"sortOrder\": sortOrder,\n }\n ],\n \"assetTypes\": assetTypes,\n \"flags\": flags,\n },\n )\n\n for ext in resp[\"results\"][0][\"extensions\"]:\n yield self._sanitize_extension(ext)\n return resp[\"results\"][0][\"resultMetadata\"]\n\nexcept ModuleNotFoundError:\n pass\n\n\nclass IterExtensionSrc(IExtensionSrc):\n def __init__(self, exts: Iterable[GalleryExtension]) -> None:\n super().__init__()\n self._exts = exts\n\n def iter(self):\n return self._exts\n\n def _sanitize_extension(\n self, flags: GalleryFlags, assetTypes: \"list[str]\", ext: GalleryExtension\n ):\n return sanitize_extension(flags, assetTypes, ext)\n\n def generate_page(\n self,\n criteria: \"list[GalleryCriterium]\",\n flags: GalleryFlags,\n assetTypes: \"list[str]\",\n page: int = 1,\n pageSize: int = 10,\n sortBy: SortBy = SortBy.NoneOrRelevance,\n sortOrder: SortOrder = SortOrder.Default,\n *,\n short_on_qty: bool = False,\n ) -> Generator[GalleryExtension, None, \"list[GalleryExtensionQueryResultMetadata]\"]:\n matcher: CriteriaMatcher = CriteriaMatcher(criteria)\n matched = 0\n start = ((page or 1) - 1) * pageSize\n end = start + pageSize\n cats = {}\n\n for ext in sort_extensions(self.iter(), sortOrder, sortBy):\n if (\n GalleryFlags.ExcludeNonValidated in flags\n and \"validated\" not in ext[\"flags\"]\n ):\n continue\n if matcher.is_match(ext):\n matched += 1\n for cat in ext.get(\"categories\", []):\n cats[cat] = cats.get(cat, 0) + 1\n if matched > start and matched <= end:\n yield self._sanitize_extension(flags, assetTypes, ext)\n if matched >= end and short_on_qty:\n break\n\n return [\n {\n \"metadataType\": \"ResultCount\",\n \"metadataItems\": [\n {\"name\": \"TotalCount\", \"count\": matched},\n ],\n },\n {\n \"metadataType\": \"Categories\",\n \"metadataItems\": [\n {\"name\": cat, \"count\": count} for cat, count in cats.items()\n ],\n },\n ]\n\n\nclass ProxyExtensionSrc(IExtensionSrc):\n def __init__(\n self,\n src: IExtensionSrc,\n proxy_url: Callable[[str, str, GalleryExtension, GalleryExtensionVersion], str],\n ) -> None:\n super().__init__()\n self.src = src\n self.proxy_url = proxy_url\n\n def generate_page(\n self,\n criteria: \"list[GalleryCriterium]\",\n flags: GalleryFlags,\n assetTypes: \"list[str]\",\n page: int = 1,\n pageSize: int = 10,\n sortBy: SortBy = SortBy.NoneOrRelevance,\n sortOrder: SortOrder = SortOrder.Default,\n ) -> Generator[GalleryExtension, None, \"list[GalleryExtensionQueryResultMetadata]\"]:\n gen = self.src.generate_page(\n criteria, flags, assetTypes, page, pageSize, sortBy, sortOrder\n )\n while True:\n try:\n ext: GalleryExtension = next(gen)\n for ver in ext.get(\"versions\", []):\n for uri in [\"assetUri\", \"fallbackAssetUri\"]:\n if uri in ver:\n ver[uri] = self.proxy_url(ver[uri], uri, ext, ver)\n yield ext\n except StopIteration as ex:\n return ex.value\n\n\nclass CachedGallerySrc(IterExtensionSrc, IAssetSrc):\n _exts: \"dict[str, GalleryExtension]\"\n _uid_map: \"dict[str, str]\"\n\n def __init__(self, asset_target: \"str|Callable[[], str]\" = None) -> None:\n self._uid_map = {}\n asset_target = asset_target or None\n self._asset_target = (\n (lambda: asset_target) if isinstance(asset_target, str) else asset_target\n )\n\n def _get_uid(self, id: \"str|GalleryExtension\"):\n if isinstance(id, str):\n if \".\" in id:\n return id\n else:\n return self._uid_map.get(id.lower())\n else:\n return f'{id[\"publisher\"][\"publisherName\"]}.{id[\"extensionName\"]}'\n\n def _load(self) -> Iterable[GalleryExtension]:\n return []\n\n def _sanitize_extension(\n self, flags: GalleryFlags, assetTypes: \"list[str]\", ext: GalleryExtension\n ):\n ext = super()._sanitize_extension(flags, assetTypes, ext)\n asset_target = self._asset_target() if self._asset_target else \"/\"\n if not asset_target.endswith(\"/\"):\n asset_target += \"/\"\n for ver in ext.get(\"versions\", []):\n ver[\"assetUri\"] = ver[\"fallbackAssetUri\"] = asset_target + self.asset_path(\n ext[\"extensionId\"], ver[\"version\"]\n )\n return ext\n\n def iter(self):\n return self._exts.values()\n\n def get_extension(\n self,\n extensionId: str,\n flags: GalleryFlags = GalleryFlags.IncludeAssetUri\n | GalleryFlags.IncludeCategoryAndTags\n | GalleryFlags.IncludeFiles\n | GalleryFlags.IncludeInstallationTargets\n | GalleryFlags.IncludeStatistics\n | GalleryFlags.IncludeVersionProperties\n | GalleryFlags.IncludeVersions,\n assetTypes: \"list[str]\" = [],\n ):\n extuid = self._get_uid(extensionId)\n ext = self._exts.get(extuid, None)\n if ext:\n return self._sanitize_extension(flags, assetTypes, ext)\n\n def asset_path(self, extensionId: str, version: str) -> \"str|None\":\n uid = self._get_uid(extensionId)\n if uid:\n return f\"{uid}/{version}\"\n\n def get_extension_asset(self, extensionId: str, version: \"str | None\", asset: str):\n path = self.asset_path(extensionId, version)\n if path:\n return self.get_asset(path, asset)\n return None, None\n\n def get_asset(self, src: str, asset: AssetType):\n return None, None\n\n def reload(self):\n import semver\n\n self._exts: \"dict[str, GalleryExtension]\" = {}\n for ext in self._load():\n uid = self._get_uid(ext)\n _ext = self._exts.get(uid, None)\n\n if _ext:\n for ver in ext[\"versions\"]:\n dup = False\n v = ver[\"version\"]\n for _ver in _ext[\"versions\"]:\n _v = _ver[\"version\"]\n if _v == v:\n dup = True\n if not dup:\n _ext[\"versions\"].append(ver)\n else:\n self._exts[uid] = ext\n self._uid_map[ext[\"extensionId\"]] = uid\n\n for ext in self._exts.values():\n try:\n ext[\"versions\"].sort(\n key=lambda v: semver.Version.parse(v[\"version\"]), reverse=True\n )\n except:\n ext[\"versions\"].sort(key=lambda v: v[\"version\"], reverse=True)\n\n\nclass LocalGallerySrc(CachedGallerySrc):\n def __init__(self, path: str, id_cache: str = None, asset_target=None) -> None:\n self._path = Path(path)\n self._path.mkdir(exist_ok=True, parents=True)\n self._ids_cache = Path(id_cache) if id_cache else self._path / \"ids.json\"\n super().__init__(asset_target)\n self.reload()\n\n def get_asset(self, path: str, asset: \"str|AssetType\"):\n assets = self.assets.get(path, None)\n if assets:\n vsix = self._path / assets[AssetType.VSIX]\n if asset == AssetType.VSIX:\n return iter_bytes_read(vsix), vsix\n else:\n return get_asset_from_vsix(vsix, asset, assets_map=assets)\n\n return None, None\n\n def _load(self):\n import json, uuid\n from ..utils.extension import gallery_ext_from_manifest\n\n self.assets: \"dict[str, dict[AssetType, str]]\" = {}\n ids = (\n json.loads(self._ids_cache.read_text()) if self._ids_cache.exists() else {}\n )\n\n for file in self._path.iterdir():\n if file.suffix == \".vsix\":\n manifest = get_vsix_manifest(file)\n ext = gallery_ext_from_manifest(manifest)\n uid = self._get_uid(ext)\n ext[\"extensionId\"] = ids.setdefault(uid, str(uuid.uuid4()))\n ext[\"publisher\"][\"publisherId\"] = ids.setdefault(\n ext[\"publisher\"][\"publisherName\"], str(uuid.uuid4())\n )\n ext[\"versions\"][0][\"assetUri\"] = str(file.name)\n ext[\"versions\"][0][\"fallbackAssetUri\"] = str(file.name)\n ext[\"versions\"][0][\"flags\"] += \" validated\"\n ext[\"flags\"] += \" validated\"\n ext[\"versions\"][0][\"files\"].append(\n {\"source\": file.name, \"assetType\": AssetType.VSIX.value}\n )\n self.assets[self.asset_path(uid, ext[\"versions\"][0][\"version\"])] = {\n f[\"assetType\"]: f[\"source\"] for f in ext[\"versions\"][0][\"files\"]\n }\n yield ext\n\n self._ids_cache.write_text(json.dumps(ids))\n","repo_name":"jose-pr/vscode-alt-marketplace","sub_path":"src/components/sources.py","file_name":"sources.py","file_ext":"py","file_size_in_byte":10783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"74691984882","text":"import sys\nfrom collections import deque\nfrom itertools import combinations\n\ninput = sys.stdin.readline\ndirection = [(0, 1), (-1, 0), (0, -1), (1, 0)]\n\ndef get_pos():\n\tempty = []\n\tvirus = []\n\tfor i in range(n):\n\t\tfor j in range(m):\n\t\t\tif arr[i][j] == 0:\n\t\t\t\tempty.append((i, j))\n\t\t\telif arr[i][j] == 2:\n\t\t\t\tvirus.append((i, j))\n\treturn empty, virus\n\ndef set_wall(comb):\n\tfor x, y in comb:\n\t\tarr[x][y] = 1\n\ndef collapse_wall(comb):\n\tfor x, y in comb:\n\t\tarr[x][y] = 0\n\t\t\t\t\ndef bfs(virus):\n q = deque(virus)\n visited = [[False] * m for _ in range(n)]\n count = len(virus)\n while q:\n q_size = len(q)\n for _ in range(q_size):\n x, y = q.popleft()\n visited[x][y] = True\n for dx, dy in direction:\n nx = dx + x\n ny = dy + y\n if (0 <= nx < n) and (0 <= ny < m):\n if arr[nx][ny] == 0 and not visited[nx][ny]:\n visited[nx][ny] = True\n q.append((nx, ny))\n count += 1\n return count\n\n#입력\nn, m = map(int, input().split())\narr = [list(map(int, input().split())) for _ in range(n)]\n\nempty, virus = get_pos()\ncombs = combinations(empty, 3)\n\ncount = int(1e9)\n\nfor comb in combs:\n\tset_wall(comb)\n\t\n\ttemp = bfs(virus)\n\tif temp < count:\n\t\tcount = temp\n\tcollapse_wall(comb)\n\nwall = n * m - (len(empty) + len(virus))\nprint(n * m - (count + wall + 3))","repo_name":"chanwooleeme/baekjoon","sub_path":"BOJ-with-tony9420/9.Graph Traversal/10.14502.py","file_name":"10.14502.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"38203059143","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport sqlite3\nimport pymongo\n\n\"\"\"\nExercise 5.7\nSqlite version of Exercise:\nWe have 3 SELECT clauses. Numbered from first/outer to last/most inner: (1), (2) and (3). \n\n(3) collects all ProductNames ordered by the customer 'ALFKI'\n(2) collects all customers who have ordered any of the ProductNames from (1). This is GROUP BY CustomerID and ProductName\n(1) selects distinct CustomerID from (2) and counts it. This is GROUP BY CustomerID, ORDER BY the count and select the first 5 records\n\n\"\"\"\ncon = sqlite3.connect(\"northwind.db\")\ncon.text_factory = lambda x: str(x, 'latin1')\ncur = con.cursor()\ncur.execute(\"\"\"SELECT DISTINCT outerSelect.cID, count(outerSelect.cID) as no_cID FROM \n (SELECT Orders.CustomerID as cID from Orders\n INNER JOIN 'Order Details' on Orders.OrderID = 'Order Details'.OrderID \n INNER JOIN Products on 'Order Details'.ProductID = Products.ProductID \n WHERE Products.ProductName in \n (SELECT DISTINCT Products.ProductName as pName FROM Orders \n INNER JOIN 'Order Details' on Orders.OrderID = 'Order Details'.OrderID \n INNER JOIN Products on 'Order Details'.ProductID = Products.ProductID \n WHERE Orders.CustomerID = 'ALFKI')\n GROUP BY cID, Products.ProductName) outerSelect\n WHERE outerSelect.cID != \"ALFKI\"\n GROUP BY outerSelect.cID ORDER BY no_cID DESC LIMIT 5\"\"\")\n\ncustomers = cur.fetchall() \ncon.close()\n\nprint(\"Sqlite part:\\n----------------\")\nfor cust, freq in customers:\n print(\"{} products was bought by {}\".format(freq, cust))\n\n\n\nprint(\"\\nMongoDB part:\\n---------------\")\nclient = pymongo.MongoClient('localhost', 27017)\ndb = client[\"Northwind\"]\ncustomer_collection = db[\"customers\"]\norder_collection = db[\"orders\"]\norder_details_collection = db[\"order-details\"]\nproducts_collection = db[\"products\"]\n\nALFKI_products = []\n\nfor ALFKI_order in order_collection.find({'CustomerID': 'ALFKI'}):\n ALFKI_products.extend(order_details_collection.find({'OrderID':ALFKI_order[\"OrderID\"]}).distinct(\"ProductID\"))\n\ncustomer_product_dict = {}\n\nfor order in order_collection.find({\"CustomerID\":{\"$ne\":\"ALFKI\"}}):\n for order_detail in order_details_collection.find({\n \"$and\":[\n {\"OrderID\":order[\"OrderID\"]},\n {\"ProductID\":{\"$in\":ALFKI_products}} \n ]}):\n customer_product_dict.setdefault(order[\"CustomerID\"],[]).append(order_detail[\"ProductID\"])\n\nmost_bought_customers = sorted([(customer,len(set(products))) for (customer,products) in customer_product_dict.items()], key=lambda x: x[1], reverse=True)[:5]\nfor customer,count in most_bought_customers:\n print(\"{} products was bought by {}\".format(count, customer))","repo_name":"handiandi/Computational-Tools-for-Big-Data","sub_path":"Lesson 5/exercise5-7.py","file_name":"exercise5-7.py","file_ext":"py","file_size_in_byte":3049,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"39562310764","text":"# clean aerin1.txt for initial testing\n\n# 8/30/21: epiphany\n# just only take the lines after the ones beginning with \"[\"\n\nlines = list()\ncleanList = list()\n\nwith open('txtfiles/just-hanging-out 2.txt', 'r', encoding=\"utf8\") as a: # first, read txt file into a list\n lines = a.readlines()\n\n\nfor i in range(len(lines)): # each line; i = line number\n if lines[i][0] == \"[\" and lines[i+1] != \"\\n\": # the second part is just for weird blank spaces (stickers?)\n cleanList.append(lines[i+1][:-1]) # take next line; take off \\n at end\n\nprint (cleanList)\n\nwith open('txtfiles/fullchatclean.txt', 'w', encoding=\"utf8\") as w: # 'w' write\n for i in range(len(cleanList)):\n w.write(cleanList[i]+\"\\n\") # write list item\n i+=1\n\n","repo_name":"UCSD-CSE-SPIS-2021/botproject-alex-jeannie","sub_path":"cleaner1.py","file_name":"cleaner1.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"36441439937","text":"import torch.nn.functional as F\r\nimport torch\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\nimport torchvision.models as models\r\nfrom torchvision import transforms, utils\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport torch.optim as optim\r\nimport os\r\n# torch.cuda.set_device(gpu_id)#使用GPU\r\nimport torchvision\r\ndef default_loader(path):\r\n return Image.open(path).convert('RGB')\r\n\r\n\r\nclass MyDataset(Dataset):\r\n def __init__(self, txt, transform=torchvision.transforms.ToTensor(), target_transform=None, loader=default_loader):\r\n super(MyDataset, self).__init__()\r\n fh = open(txt, 'r')\r\n imgs = []\r\n for line in fh:\r\n line = line.strip('\\n')\r\n line = line.rstrip('\\n')\r\n words = line.split()\r\n imgs.append((words[0], words[1]))\r\n\r\n self.imgs = imgs\r\n self.transform = transform\r\n self.target_transform = target_transform\r\n self.loader = loader\r\n\r\n def __getitem__(self, index):\r\n fn, label = self.imgs[index]\r\n img = self.loader(fn)\r\n label_img = self.loader(label)\r\n if self.transform is not None:\r\n img = self.transform(img)\r\n label_img = self.transform(label_img)\r\n return img, label_img\r\n\r\n def __len__(self):\r\n return len(self.imgs)\r\n\r\n\r\n\r\ntrain_transforms = transforms.Compose([\r\n transforms.RandomResizedCrop((227, 227)),\r\n transforms.ToTensor(),\r\n])\r\ntext_transforms = transforms.Compose([\r\n transforms.RandomResizedCrop((227, 227)),\r\n transforms.ToTensor(),\r\n])\r\n\r\n\r\n","repo_name":"YuSha-FIAS/denoise","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"19527091232","text":"# Урок 1. Ввод-Вывод, операторы ветвления\n# Задача 1\n# Найдите сумму цифр трехзначного числа.\n# Пример:\n# 123 -> 6 (1 + 2 + 3)\n# 100 -> 1 (1 + 0 + 0)\n\nn = int(input())\na = n // 100\nb = n // 10 % 10\nc = n % 10\nprint(a + b + c)","repo_name":"Adbord/Python_Seminar","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30483520419","text":"from copy import deepcopy\nfrom functools import partial\nfrom queue import Queue, Empty\nfrom typing import Any, Optional, Union\n\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n\nfrom PIL import Image\nfrom PIL.ImageQt import ImageQt\nfrom torchvision.transforms.functional import to_pil_image\n\nfrom .image_display import ImageWidget\n\nfrom ..parameters import parameters_to_yaml, yaml_to_parameters, convert_params, set_parameter_defaults\nfrom .thread_wrapper import ImageTrainingThread\nfrom .statistics import Statistics\nfrom .yaml_editor import YamlEditor\n\n\nclass Experiment(QWidget):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._queue = Queue()\n self.trainer = ImageTrainingThread(self._queue)\n self._training_parameters = None\n self._create_widgets()\n self._call_queue_processing()\n\n def _create_widgets(self):\n grid = QGridLayout(self)\n\n l = QVBoxLayout()\n grid.addLayout(l, 0, 0)\n self.editor = YamlEditor()\n l.addWidget(self.editor)\n\n self.log_display = QPlainTextEdit()\n self.log_display.setMaximumHeight(200)\n font = QFont(\"Mono\")\n font.setPointSize(7)\n self.log_display.setFont(font)\n l.addWidget(self.log_display)\n #grid.addWidget(self.log_display, 2, 0, 1, 2)\n\n bar = QToolBar()\n l.addWidget(bar)\n self.tool_buttons = dict()\n for id, name in (\n (\"start\", self.tr(\"&Start\")),\n (\"update\", self.tr(\"&Update\")),\n (\"stop\", self.tr(\"Sto&p\")),\n ):\n b = QToolButton()\n b.setText(name)\n bar.addWidget(b)\n b.clicked.connect(partial(self.slot_tool_button, id))\n self.tool_buttons[id] = b\n\n l = QVBoxLayout()\n grid.addLayout(l, 0, 1)\n self.image_display = ImageWidget()\n l.addWidget(self.image_display)\n\n self.statistics = Statistics()\n grid.addWidget(self.statistics, 1, 0, 1, 2)\n\n def create_actions(self, menu: QMenu):\n self.editor.create_actions(menu)\n\n def get_parameters(self) -> Optional[dict]:\n \"\"\"\n Return the currently trained parameters\n \"\"\"\n if self._training_parameters:\n params = deepcopy(self._training_parameters)\n else:\n params = self._get_parameters(for_training=False)\n if params:\n for key in (\n \"verbose\", \"output\", \"snapshot_interval\",\n \"start_epoch\", \"device\"\n ):\n params.pop(key, None)\n return params\n\n def get_image(self) -> Optional[QImage]:\n return self.image_display.image\n\n def get_config_header(self) -> str:\n stats = self.trainer.running_counts()\n if stats:\n run_time = stats['training_seconds']\n epoch = stats['training_epochs']\n else:\n run_time = self.trainer._trainer.training_seconds\n epoch = self.trainer._trainer.epoch\n return self.trainer._trainer.get_config_header(run_time=run_time, epoch=epoch)\n\n def slot_tool_button(self, id: str):\n # print(\"SLOT\", id)\n parameters = self._get_parameters()\n\n if id == \"start\" and parameters:\n self.trainer.create()\n self.trainer.start_training(parameters)\n self._training_parameters = deepcopy(parameters)\n\n elif id == \"update\" and parameters:\n self.trainer.create()\n self.trainer.update_parameters(parameters)\n self._training_parameters = deepcopy(parameters)\n\n elif id == \"stop\":\n self.trainer.pause_training()\n\n def _process_queue_message(self, name: str, data: Any):\n # print(\"FROM TRAINER:\", name, data)\n if name == \"snapshot\":\n image = to_pil_image(data)\n self.image_display.set_image(image)\n\n elif name == \"log\":\n self.log_display.appendPlainText(data)\n\n elif name == \"progress\":\n self._add_stats(data)\n\n elif name == \"started\":\n pass\n\n elif name == \"stopped\":\n pass\n\n def halt(self):\n self.trainer.destroy()\n\n def set_parameters(self, parameters: Union[str, dict]):\n self.editor.set_parameters(parameters)\n\n def _get_parameters(self, for_training: bool = True) -> Optional[dict]:\n params = self.editor.get_parameters()\n\n if params and for_training:\n params[\"verbose\"] = 2\n params[\"snapshot_interval\"] = 1.\n\n return params\n\n def _call_queue_processing(self):\n QTimer.singleShot(100, self._process_queue)\n\n def _process_queue(self):\n for i in range(20):\n try:\n message = self._queue.get_nowait()\n self._process_queue_message(message.name, message.data)\n except Empty:\n break\n\n self._call_queue_processing()\n\n def _add_stats(self, stats: dict):\n self.statistics.add_stats(stats)\n","repo_name":"defgsus/clipig","sub_path":"src/gui/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":5075,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"75"} +{"seq_id":"20417222925","text":"\"\"\"\nProvides a map panel for showing device locations.\n\nFor more details about this component, please refer to the documentation at\nhttps://home-assistant.io/components/map/\n\"\"\"\nDOMAIN = 'map'\n\n\nasync def async_setup(hass, config):\n \"\"\"Register the built-in map panel.\"\"\"\n await hass.components.frontend.async_register_built_in_panel(\n 'map', 'map', 'mdi:account-location')\n return True\n","repo_name":"jest-community/jest-pytest","sub_path":"src/__tests__/integration/home-assistant/homeassistant/components/map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"75"} +{"seq_id":"2584217696","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Oct 3 18:55:08 2022\r\n\r\n@author: grace\r\n\"\"\"\r\n\r\n\"\"\"\r\ndef saludo(nombre):\r\n print(\"Hola!\",nombre,\"\\n\")\r\n\r\nsaludo(\"Grace\")\r\nsaludo(\"Andrea\")\r\n\"\"\"\r\n\r\ndef saludo2(nom1, nom2):\r\n print(\"Hola!\",nom1)\r\n print(\"Hola!\",nom2,\"\\n\"*2)\r\nsaludo2(\"Juan\",\"Carlos\")\r\nsaludo2(\"Ana\",\"María\")","repo_name":"GraceStephanieP/Scribs-curso-de-Python-Essential","sub_path":"S6_3(Funcion_saludo).py","file_name":"S6_3(Funcion_saludo).py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"14742568386","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#house price prediction multiple regression \nimport pandas as pd \n\n\n# In[2]:\n\n\ndf=pd.read_csv(\"E:\\\\Django\\\\HousePricePrediction\\\\static\\\\csv\\\\Housingdata.csv\")\n\n\n# In[3]:\n\n\n#df.head()\n\n\n# In[4]:\n\n\nimport seaborn as sns \n\n\n# In[5]:\n\n\nx=df.drop([\"date\",\"id\",\"sqft_lot\",\"sqft_basement\",\"zipcode\",\"sqft_living15\",\"sqft_lot15\"],axis=1)\n\n\n# In[6]:\n\n\nx.head()\n\n\n# In[7]:\n\n\n#split the data \nfrom sklearn.model_selection import train_test_split \n\n\n# In[8]:\n\n\nX=x.drop(\"price\",axis=1)\n\n\n# In[9]:\n\n\ny=x[[\"price\"]]\n\n\n# In[10]:\n\n\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.50)\n\n\n# In[11]:\n\n\n#Analyze the algorithm\nfrom sklearn.linear_model import LinearRegression\n\n\n# In[12]:\n\n\n#Initialize the algorithm\nreg=LinearRegression()\n\n\n# In[14]:\n\n\nreg.fit(X_train,y_train)\n\n\n# In[15]:\n\n\npred=reg.predict(X_test)\n\n\n# In[16]:\n\n\n#pred\n\n\n# In[17]:\n\n\nfrom sklearn.metrics import r2_score\n\n\n# In[18]:\n\n\nr2_score(pred,y_test)\n\n\n# In[19]:\n\n\nsns.heatmap(y.isnull())\n\n\n# In[20]:\n\n\n#x.isnull().sum()\n\n\n# In[22]:\n\ndef predict_price(bedroom,bathroom,sliving,sabove,ybuilt,yrenovated,view,floors,lat,long,waterfront,grade):\n array_val = [int(bedroom),int(bathroom),int(sliving),int(sabove),int(ybuilt),int(yrenovated),int(view),int(floors),int(lat),int(long),int(waterfront),int(grade)] \n print(reg.predict([[3,2.25,2570,7242,2.0,0,0,3,7,2170,400,1951,1991,98125,47,-122,1690,3]]))\n #val = reg.predict([array_val])\n return 1\n\n# In[ ]:\n\n\n\n\n","repo_name":"PriyalaxmiUdayakumar/House-Price-Prediction","sub_path":"ml_code.py","file_name":"ml_code.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"42928254040","text":"N = int(input())\n\nlst = [0] * 41\nlst[0:1] = [0,1]\n\ndef fibonacci(n):\n\tif n <= 1:\n\t\treturn lst[n]\n\telse:\n\t\tif lst[n] == 0:\n\t\t\tlst[n] = fibonacci(n-1) + fibonacci(n - 2)\n\t\treturn lst[n]\n\nfor repeat in range(0,N):\n\tp = int(input())\n\n\tif p == 0:\n\t\tprint(\"1 0\")\n\telse:\n\t\tprint(\"%d %d\"%(fibonacci(p-1),fibonacci(p)))","repo_name":"inggan/Baekjoon_Online_Judge","sub_path":"1003.py","file_name":"1003.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"72412086643","text":"# Definition for a binary tree node.\n\n\n\nclass Node:\n\n def __init__(self, data):\n\n self.left = None\n self.right = None\n self.data = data\n\n def insert(self, data):\n if self.data:\n if data < self.data:\n if self.left is None:\n self.left = Node(data)\n else:\n self.left.insert(data)\n elif data > self.data:\n if self.right is None:\n self.right = Node(data)\n else:\n self.right.insert(data)\n else:\n self.data = data\n\n def PrintTree(self):\n if self.left:\n self.left.PrintTree()\n print( self.data),\n if self.right:\n self.right.PrintTree()\n\n# Use the insert method to add nodes\nroot = Node(1)\nroot.insert(None)\nroot.insert(2)\nroot.insert(3)\nroot.PrintTree()\n\n\n\nclass Solution(object):\n def inorderTraversal(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: List[int]\n \"\"\"\n\ntree = [1, None, 2, 3]\n\n# if __name__ == '__main__':\n# print Solution().inorderTraversal(root)\n","repo_name":"jsverch/practice","sub_path":"leet94.py","file_name":"leet94.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"31530503310","text":"#!/usr/bin/python\nimport datetime\nimport math\n\n\nclass sfloat(object):\n def __init__(self, exp, mant):\n self.exponent = exp\n self.mantissa = mant\n\n def encode(self, buffer, idx):\n buffer[idx] = self.mantissa & 0x00ff\n buffer[idx + 1] = (self.exponent << 4) | ((self.mantissa & 0x0f00) >> 8)\n\n @classmethod\n def decode(cls, buffer, idx):\n exp = (buffer[idx + 1] >> 4) & 0x0f\n mant = ((buffer[idx + 1] & 0x0f) << 8) | buffer[idx]\n return cls(exp, mant)\n\n @classmethod\n def default(cls):\n return cls(0, 0)\n\n def __eq__(self, other):\n return (self.exponent == other.exponent) and (self.mantissa == other.mantissa)\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n return \"%s(exponent=0x%02X, mantissa=0x%04X)\" % (self.__class__.__name__, self.exponent, self.mantissa)\n\n def __str__(self):\n return self.__repr__()\n\n def assertValue(self, expected, name):\n if self != expected:\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(name, self, expected))\n\n\nclass bleFloat(object):\n def __init__(self, exp, mant):\n self.exponent = exp\n self.mantissa = mant\n\n @classmethod\n def decode(cls, buffer, idx):\n exp = buffer[idx + 3]\n mant = (buffer[idx + 2] << 16) | (buffer[idx + 1] << 8) | buffer[idx]\n return cls(exp, mant)\n\n @classmethod\n def default(cls):\n return cls(0, 0)\n\n def __eq__(self, other):\n return (self.exponent == other.exponent) and (self.mantissa == other.mantissa)\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n return \"%s(exponent=0x%02X, mantissa=0x%06X)\" % (self.__class__.__name__, self.exponent, self.mantissa)\n\n def __str__(self):\n return self.__repr__()\n\n def assertValue(self, expected, name):\n if self != expected:\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(name, self, expected))\n\n def value(self):\n exp = self.exponent\n if exp > 127:\n exp = -(256 - exp)\n return self.mantissa * math.pow(10, exp)\n\n\nclass DateTime(datetime.datetime):\n MIN_YEAR = 1582\n\n def __init__(self, year, month, day, hour, minute, second):\n if year < self.MIN_YEAR:\n raise ValueError(\"Timestamp year out of range (%r)\" % year)\n datetime.datetime.__init__(year, month, day, hour, minute, second)\n\n @classmethod\n def decode(cls, buffer, idx):\n year = (buffer[idx + 1] << 8) + buffer[idx]\n month = buffer[idx + 2]\n day = buffer[idx + 3]\n hour = buffer[idx + 4]\n minute = buffer[idx + 5]\n second = buffer[idx + 6]\n\n return cls(year, month, day, hour, minute, second)\n\n @classmethod\n def default(cls):\n return cls(cls.MIN_YEAR, 1, 1, 0, 0, 0)\n\n def assertValue(self, expected, name):\n if self != expected:\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(name, str(self), str(expected)))\n\n\nclass CscMeasurement(object):\n FLAG_WHEEL_REV_DATA = 0x01\n FLAG_CRANK_REV_DATA = 0x02\n FLAG_UNUSED = ~(FLAG_WHEEL_REV_DATA | FLAG_CRANK_REV_DATA)\n\n def __init__(self, flags, cumulWheelRev, wheelEvtTime, cumulCrankRev, crankEvtTime):\n if (flags & self.FLAG_UNUSED) != 0:\n raise ValueError(\"RscMeasurement.flags RFU bit(s) set ({0})\".format(flags))\n self.flags = flags\n self.cumulWheelRev = cumulWheelRev\n self.wheelEvtTime = wheelEvtTime\n self.cumulCrankRev = cumulCrankRev\n self.crankEvtTime = crankEvtTime\n\n @classmethod\n def decode(cls, buffer, idx):\n flags = buffer[idx]\n offset = 1\n if (flags & cls.FLAG_WHEEL_REV_DATA) != 0:\n cumulWheelRev = (buffer[idx + offset + 3] << 24) + (buffer[idx + offset + 2] << 16) + (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n offset += 4\n wheelEvtTime = (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n offset += 2\n else:\n cumulWheelRev = 0\n wheelEvtTime = 0\n\n if (flags & cls.FLAG_CRANK_REV_DATA) != 0:\n cumulCrankRev = (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n offset += 2\n crankEvtTime = (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n offset += 2\n else:\n cumulCrankRev = 0\n crankEvtTime = 0\n return cls(flags, cumulWheelRev, wheelEvtTime, cumulCrankRev, crankEvtTime)\n\n def __eq__(self, other):\n if (self.flags != other.flags) :\n return False\n if ((self.flags & self.FLAG_WHEEL_REV_DATA) != 0) and ((self.cumulWheelRev != other.cumulWheelRev) or (self.wheelEvtTime != other.wheelEvtTime) ):\n return False\n if ((self.flags & self.FLAG_CRANK_REV_DATA) != 0) and ((self.cumulCrankRev != other.cumulCrankRev) or (self.crankEvtTime != other.crankEvtTime) ):\n return False\n return True\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n if (self.flags & self.FLAG_WHEEL_REV_DATA) != 0:\n cumulWheelRevStr = \"{0}\".format(self.cumulWheelRev)\n wheelEvtTimeStr = \"{0}\".format(self.wheelEvtTime)\n else:\n cumulWheelRevStr = \"\"\n wheelEvtTimeStr = \"\"\n\n if (self.flags & self.FLAG_CRANK_REV_DATA) != 0:\n cumulCrankRevStr = \"{0}\".format(self.cumulCrankRev)\n crankEvtTimeStr = \"{0}\".format(self.crankEvtTime)\n else:\n cumulCrankRevStr = \"\"\n crankEvtTimeStr = \"\"\n return \"{0}(flags=0x{1:02X}, cumulWheelRev={2}, wheelEvtTime={3}, cumulCrankRev={4}, crankEvtTime={5})\".format(\n self.__class__.__name__, self.flags, cumulWheelRevStr, wheelEvtTimeStr,\n cumulCrankRevStr, crankEvtTimeStr)\n\n def __str__(self):\n return self.__repr__()\n\n def __assertFlagValue(self, expected, flag, flagName):\n if (self.flags & flag) != (expected.flags & flag):\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(flagName, (self.flags & flag) != 0, (expected.flags & flag) != 0))\n\n def assertValue(self, expected, name):\n self.__assertFlagValue(expected, self.FLAG_WHEEL_REV_DATA, name + \".flags.FLAG_WHEEL_REV_DATA\")\n self.__assertFlagValue(expected, self.FLAG_CRANK_REV_DATA, name + \".flags.FLAG_CRANK_REV_DATA\")\n if (self.flags & self.FLAG_WHEEL_REV_DATA) != 0:\n if self.cumulWheelRev != expected.cumulWheelRev:\n raise ValueError(\"Unexpected value for {0}.cumulWheelRev (was {1}, expected {2})\".format(name, self.cumulWheelRev, expected.cumulWheelRev))\n if self.wheelEvtTime != expected.wheelEvtTime:\n raise ValueError(\"Unexpected value for {0}.wheelEvtTime (was {1}, expected {2})\".format(name, self.wheelEvtTime, expected.wheelEvtTime))\n if (self.flags & self.FLAG_CRANK_REV_DATA) != 0:\n if self.cumulCrankRev != expected.cumulCrankRev:\n raise ValueError(\"Unexpected value for {0}.cumulCrankRev (was {1}, expected {2})\".format(name, self.cumulCrankRev, expected.cumulCrankRev))\n if self.crankEvtTime != expected.crankEvtTime:\n raise ValueError(\"Unexpected value for {0}.crankEvtTime (was {1}, expected {2})\".format(name, self.crankEvtTime, expected.crankEvtTime))\n\n def isWheelRevDataIncluded(self):\n return (self.flags & self.FLAG_WHEEL_REV_DATA) != 0\n\n def isCrankRevDataIncluded(self):\n return (self.flags & self.FLAG_CRANK_REV_DATA) != 0\n\nclass RscMeasurement(object):\n FLAG_INST_STRIDE_LEN = 0x01\n FLAG_TOTAL_DIST = 0x02\n FLAG_RUNNING = 0x04\n FLAG_UNUSED = ~(FLAG_INST_STRIDE_LEN | FLAG_TOTAL_DIST | FLAG_RUNNING)\n\n def __init__(self, flags, instSpeed, instCadence, instStrideLen, totalDist):\n if (flags & self.FLAG_UNUSED) != 0:\n raise ValueError(\"RscMeasurement.flags RFU bit(s) set ({0})\".format(flags))\n self.flags = flags\n self.instSpeed = instSpeed\n self.instCadence = instCadence\n self.instStrideLen = instStrideLen\n self.totalDist = totalDist\n\n @classmethod\n def decode(cls, buffer, idx):\n flags = buffer[idx]\n instSpeed = (buffer[idx + 2] << 8) + buffer[idx + 1]\n instCadence = buffer[idx + 3]\n offset = 4\n if (flags & cls.FLAG_INST_STRIDE_LEN) != 0:\n instStrideLen = (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n offset += 2\n else:\n instStrideLen = 0\n if (flags & cls.FLAG_TOTAL_DIST) != 0:\n totalDist = (buffer[idx + offset + 3] << 24) + (buffer[idx + offset + 2] << 16) + (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n else:\n totalDist = 0\n return cls(flags, instSpeed, instCadence, instStrideLen, totalDist)\n\n def __eq__(self, other):\n if (self.flags != other.flags) or (self.instSpeed != other.instSpeed) or (self.instCadence != other.instCadence):\n return False\n if ((self.flags & self.FLAG_INST_STRIDE_LEN) != 0) and (self.instStrideLen != other.instStrideLen):\n return False\n if ((self.flags & self.FLAG_TOTAL_DIST) != 0) and (self.totalDist != other.totalDist):\n return False\n return True\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n if (self.flags & self.FLAG_INST_STRIDE_LEN) != 0:\n instStrideLenStr = \"{0}\".format(self.instStrideLen)\n else:\n instStrideLenStr = \"\"\n if (self.flags & self.FLAG_TOTAL_DIST) != 0:\n totalDistStr = \"{0}\".format(self.totalDist)\n else:\n totalDistStr = \"\"\n return \"{0}(flags=0x{1:02X}, instSpeed={2}, instCadence={3}, instStrideLen={4}, totalDist={5})\".format(\n self.__class__.__name__, self.flags, self.instSpeed,\n self.instCadence, instStrideLenStr, totalDistStr)\n\n def __str__(self):\n return self.__repr__()\n\n def __assertFlagValue(self, expected, flag, flagName):\n if (self.flags & flag) != (expected.flags & flag):\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(flagName, (self.flags & flag) != 0, (expected.flags & flag) != 0))\n\n def assertValue(self, expected, name):\n self.__assertFlagValue(expected, self.FLAG_INST_STRIDE_LEN, name + \".flags.INST_STRIDE_LEN\")\n self.__assertFlagValue(expected, self.FLAG_TOTAL_DIST, name + \".flags.TOTAL_DIST\")\n self.__assertFlagValue(expected, self.FLAG_RUNNING, name + \".flags.RUNNING\")\n if self.instSpeed != expected.instSpeed:\n raise ValueError(\"Unexpected value for {0}.instSpeed (was {1}, expected {2})\".format(name, self.instSpeed, expected.instSpeed))\n if self.instCadence != expected.instCadence:\n raise ValueError(\"Unexpected value for {0}.instCadence (was {1}, expected {2})\".format(name, self.instCadence, expected.instCadence))\n if (self.flags & self.FLAG_INST_STRIDE_LEN) != 0:\n if self.instStrideLen != expected.instStrideLen:\n raise ValueError(\"Unexpected value for {0}.instStrideLen (was {1}, expected {2})\".format(name, self.instStrideLen, expected.instStrideLen))\n if (self.flags & self.FLAG_TOTAL_DIST) != 0:\n if self.totalDist != expected.totalDist:\n raise ValueError(\"Unexpected value for {0}.totalDist (was {1}, expected {2})\".format(name, self.totalDist, expected.totalDist))\n\n def isInstStrideLenIncluded(self):\n return (self.flags & self.FLAG_INST_STRIDE_LEN) != 0\n\n def isTotalDistIncluded(self):\n return (self.flags & self.FLAG_TOTAL_DIST) != 0\n\n def isRunning(self):\n return (self.flags & self.FLAG_RUNNING) != 0\n\n\nclass BloodPressureMeasurement(object):\n FLAG_UNIT_KPA = 0x01\n FLAG_TIME_STAMP = 0x02\n FLAG_PULSE_RATE = 0x04\n FLAG_USER_ID = 0x08\n FLAG_MEAS_STATUS = 0x10\n FLAG_UNUSED = ~(FLAG_UNIT_KPA | FLAG_TIME_STAMP | FLAG_PULSE_RATE | FLAG_USER_ID | FLAG_MEAS_STATUS)\n\n def __init__(self, flags, systolic, diastolic, meanArterial, timeStamp, pulseRate, userId, measStatus):\n if (flags & self.FLAG_UNUSED) != 0:\n raise ValueError(\"BloodPressureMeasurement.flags RFU bit(s) set ({0})\".format(flags))\n if (flags & self.FLAG_MEAS_STATUS) != 0:\n if (measStatus & 0xffc0) != 0:\n raise ValueError(\"RFU bits are set in BloodPressureMeasurement.MeasurementStatus (%r)\" % measStatus)\n pulseRateRangeDetectionFlags = (measStatus >> 3) & 0x0003\n if pulseRateRangeDetectionFlags == 3:\n raise ValueError(\"RFU value used in BloodPressureMeasurement.MeasurementStatus.PulseRateRangeDetectionFlags (%r)\" % measStatus)\n self.flags = flags\n self.systolic = systolic\n self.diastolic = diastolic\n self.meanArterial = meanArterial\n self.timeStamp = timeStamp\n self.pulseRate = pulseRate\n self.userId = userId\n self.measStatus = measStatus\n\n @classmethod\n def decode(cls, buffer, idx):\n # Extract flags\n flags = buffer[idx]\n\n # Validate data length\n expectedLength = 7\n if (flags & cls.FLAG_TIME_STAMP) != 0:\n expectedLength += 7\n if (flags & cls.FLAG_PULSE_RATE) != 0:\n expectedLength += 2\n if (flags & cls.FLAG_USER_ID) != 0:\n expectedLength += 1\n if (flags & cls.FLAG_MEAS_STATUS) != 0:\n expectedLength += 2\n maxLen = len(buffer) - idx\n if maxLen < expectedLength:\n raise ValueError(\"Invalid data length (flags = {0}, expected length = {0}, actual Length = {1})\".format(flags, expectedLength, maxLen))\n\n # Extract mandatory fields\n systolic = sfloat.decode(buffer, idx + 1)\n diastolic = sfloat.decode(buffer, idx + 3)\n meanArterial = sfloat.decode(buffer, idx + 5)\n\n # Extract conditional fields\n offset = 7\n if (flags & cls.FLAG_TIME_STAMP) != 0:\n timeStamp = DateTime.decode(buffer, idx + offset)\n offset += 7\n else:\n timeStamp = DateTime.default()\n\n if (flags & cls.FLAG_PULSE_RATE) != 0:\n pulseRate = sfloat.decode(buffer, idx + offset)\n offset += 2\n else:\n pulseRate = sfloat.default()\n\n if (flags & cls.FLAG_USER_ID) != 0:\n userId = buffer[idx + offset]\n offset += 1\n else:\n userId = 255\n\n if (flags & cls.FLAG_MEAS_STATUS) != 0:\n measStatus = (buffer[idx + offset + 1] << 8) + buffer[idx + offset]\n else:\n measStatus = 0xFFFF\n\n return cls(flags, systolic, diastolic, meanArterial, timeStamp, pulseRate, userId, measStatus)\n\n def __eq__(self, other):\n if (self.flags != other.flags) or (self.systolic != other.systolic) or (self.diastolic != other.diastolic) or (self.meanArterial != other.meanArterial):\n return False\n if ((self.flags & self.FLAG_TIME_STAMP) != 0) and (self.timeStamp != other.timeStamp):\n return False\n if ((self.flags & self.FLAG_PULSE_RATE) != 0) and (self.pulseRate != other.pulseRate):\n return False\n if ((self.flags & self.FLAG_USER_ID) != 0) and (self.userId != other.userId):\n return False\n if ((self.flags & self.FLAG_MEAS_STATUS) != 0) and (self.measStatus != other.measStatus):\n return False\n return True\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n if (self.flags & self.FLAG_TIME_STAMP) != 0:\n timeStampStr = \"{0}\".format(str(self.timeStamp))\n else:\n timeStampStr = \"\"\n if (self.flags & self.FLAG_PULSE_RATE) != 0:\n pulseRateStr = \"{0}\".format(self.pulseRate)\n else:\n pulseRateStr = \"\"\n if (self.flags & self.FLAG_USER_ID) != 0:\n userIdStr = \"{0}\".format(self.userId)\n else:\n userIdStr = \"\"\n if (self.flags & self.FLAG_MEAS_STATUS) != 0:\n measStatusStr = \"0x{0:04X}\".format(self.measStatus)\n else:\n measStatusStr = \"\"\n return \"{0}(flags=0x{1:02X}, systolic={2}, diastolic={3}, meanArterial={4}, timeStamp={5}, pulseRate={6}, userId={7}, measStatus={8})\".format(\n self.__class__.__name__, self.flags, self.systolic, self.diastolic,\n self.meanArterial, timeStampStr, pulseRateStr, userIdStr, measStatusStr)\n\n def __str__(self):\n return self.__repr__()\n\n def __assertFlagValue(self, expected, flag, flagName):\n if (self.flags & flag) != (expected.flags & flag):\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(flagName, (self.flags & flag) != 0, (expected.flags & flag) != 0))\n\n def assertValue(self, expected, name):\n self.__assertFlagValue(expected, self.FLAG_UNIT_KPA, name + \".flags.UNIT_KPA\")\n self.__assertFlagValue(expected, self.FLAG_TIME_STAMP, name + \".flags.TIME_STAMP\")\n self.__assertFlagValue(expected, self.FLAG_PULSE_RATE, name + \".flags.PULSE_RATE\")\n self.__assertFlagValue(expected, self.FLAG_USER_ID, name + \".flags.USER_ID\")\n self.__assertFlagValue(expected, self.FLAG_MEAS_STATUS, name + \".flags.MEAS_STATUS\")\n self.systolic.assertValue(expected.systolic, name + \".systolic\")\n self.diastolic.assertValue(expected.diastolic, name + \".diastolic\")\n self.meanArterial.assertValue(expected.meanArterial, name + \".meanArterial\")\n if (self.flags & self.FLAG_TIME_STAMP) != 0:\n self.timeStamp.assertValue(expected.timeStamp, name + \".timeStamp\")\n if (self.flags & self.FLAG_PULSE_RATE) != 0:\n self.pulseRate.assertValue(expected.pulseRate, name + \".pulseRate\")\n if (self.flags & self.FLAG_USER_ID) != 0:\n if self.userId != expected.userId:\n raise ValueError(\"Unexpected value for {0}.userId (was {1}, expected {2})\".format(name, self.userId, expected.userId))\n if (self.flags & self.FLAG_MEAS_STATUS) != 0:\n if self.measStatus != expected.measStatus:\n raise ValueError(\"Unexpected value for {0}.measStatus (was {1}, expected {2})\".format(name, self.measStatus, expected.measStatus))\n\n def isUnitKpa(self):\n return (self.flags & self.FLAG_UNIT_KPA) != 0\n\n def isTimeStampIncluded(self):\n return (self.flags & self.FLAG_TIME_STAMP) != 0\n\n def isPulseRateIncluded(self):\n return (self.flags & self.FLAG_PULSE_RATE) != 0\n\n def isUserIdIncluded(self):\n return (self.flags & self.FLAG_USER_ID) != 0\n\n def isMeasurementStatusIncluded(self):\n return (self.flags & self.FLAG_MEAS_STATUS) != 0\n\n\nclass TemperatureMeasurement(object):\n FLAG_UNIT_FAHRENHEIT = 0x01\n FLAG_TIME_STAMP = 0x02\n FLAG_TEMP_TYPE = 0x04\n FLAG_UNUSED = ~(FLAG_UNIT_FAHRENHEIT | FLAG_TIME_STAMP | FLAG_TEMP_TYPE)\n\n def __init__(self, flags, tempMeasurement, timeStamp, tempType):\n if (flags & self.FLAG_UNUSED) != 0:\n raise ValueError(\"TemperatureMeasurement.flags RFU bit(s) set ({0})\".format(flags))\n self.flags = flags\n self.tempMeasurement = tempMeasurement\n self.timeStamp = timeStamp\n self.tempType = tempType\n\n @classmethod\n def decode(cls, buffer, idx):\n # Extract flags\n flags = buffer[idx]\n\n # Validate data length\n expectedLength = 5\n if (flags & cls.FLAG_TIME_STAMP) != 0:\n expectedLength += 7\n if (flags & cls.FLAG_TEMP_TYPE) != 0:\n expectedLength += 1\n maxLen = len(buffer) - idx\n if maxLen < expectedLength:\n raise ValueError(\"Invalid data length (flags = {0}, expected length = {0}, actual Length = {1})\".format(flags, expectedLength, maxLen))\n\n # Extract mandatory fields\n tempMeasurement = bleFloat.decode(buffer, idx + 1)\n\n # Extract conditional fields\n offset = 5\n if (flags & cls.FLAG_TIME_STAMP) != 0:\n timeStamp = DateTime.decode(buffer, idx + offset)\n offset += 7\n else:\n timeStamp = DateTime.default()\n\n if (flags & cls.FLAG_TEMP_TYPE) != 0:\n tempType = buffer[idx + offset]\n else:\n tempType = 0\n\n return cls(flags, tempMeasurement, timeStamp, tempType)\n\n def __eq__(self, other):\n if (self.flags != other.flags) or (self.tempMeasurement != other.tempMeasurement):\n return False\n if ((self.flags & self.FLAG_TIME_STAMP) != 0) and (self.timeStamp != other.timeStamp):\n return False\n if ((self.flags & self.FLAG_TEMP_TYPE) != 0) and (self.tempType != other.tempType):\n return False\n return True\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __repr__(self):\n if (self.flags & self.FLAG_TIME_STAMP) != 0:\n timeStampStr = \"{0}\".format(str(self.timeStamp))\n else:\n timeStampStr = \"\"\n if (self.flags & self.FLAG_TEMP_TYPE) != 0:\n tempTypeStr = \"{0}\".format(self.userId)\n else:\n tempTypeStr = \"\"\n return \"{0}(flags=0x{1:02X}, tempMeasurement={2}, timeStamp={3}, tempType={4})\".format(\n self.__class__.__name__, self.flags, self.tempMeasurement, timeStampStr, tempTypeStr)\n\n def __str__(self):\n return self.__repr__()\n\n def __assertFlagValue(self, expected, flag, flagName):\n if (self.flags & flag) != (expected.flags & flag):\n raise ValueError(\"Unexpected value for {0} (was {1}, expected {2})\".format(flagName, (self.flags & flag) != 0, (expected.flags & flag) != 0))\n\n def assertValue(self, expected, name):\n self.__assertFlagValue(expected, self.FLAG_UNIT_FAHRENHEIT, name + \".flags.FLAG_UNIT_FAHRENHEIT\")\n self.__assertFlagValue(expected, self.FLAG_TIME_STAMP, name + \".flags.TIME_STAMP\")\n self.__assertFlagValue(expected, self.FLAG_TEMP_TYPE, name + \".flags.FLAG_TEMP_TYPE\")\n self.systolic.tempMeasurement(expected.tempMeasurement, name + \".tempMeasurement\")\n if (self.flags & self.FLAG_TIME_STAMP) != 0:\n self.timeStamp.assertValue(expected.timeStamp, name + \".timeStamp\")\n if (self.flags & self.FLAG_TEMP_TYPE) != 0:\n if self.tempType != expected.tempType:\n raise ValueError(\"Unexpected value for {0}.tempType (was {1}, expected {2})\".format(name, self.tempType, expected.tempType))\n\n def isUnitFahrenheit(self):\n return (self.flags & self.FLAG_UNIT_FAHRENHEIT) != 0\n\n def isTimeStampIncluded(self):\n return (self.flags & self.FLAG_TIME_STAMP) != 0\n\n def isTempTypeIncluded(self):\n return (self.flags & self.FLAG_TEMP_TYPE) != 0\n","repo_name":"NordicPlayground/ble-optiboot","sub_path":"tests/system_tests/common_memu/ble/central/bleChar.py","file_name":"bleChar.py","file_ext":"py","file_size_in_byte":23210,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"75"} +{"seq_id":"15689986466","text":"import cv2\nimport numpy as np\n\n\ndef cvt_label_to_image(labels):\n \"\"\"\n 레이블링된 전처리 이미지를 시각화\n 0은 검은색, 나머지는 Hue값 돌아가면서 사용\n \"\"\"\n image = np.zeros((len(labels), len(labels[0]), 3), 'uint8')\n for r, line in enumerate(labels):\n for c, item in enumerate(line):\n if item == 0:\n image[r][c] = [0, 0, 0]\n else:\n image[r][c] = [(item * 5) % 180, 255, 255]\n image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n return image\n\n\ndef watershed(opening):\n sure_bg = cv2.dilate(opening, kernel, iterations=3)\n dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)\n ret, sure_fg = cv2.threshold(dist_transform, 0.7 / 100 * dist_transform.max(), 255, 0)\n sure_fg = np.uint8(sure_fg)\n\n unknown = cv2.subtract(sure_bg, sure_fg)\n\n _, markers = cv2.connectedComponents(sure_fg, connectivity=4)\n markers = markers + 1\n markers[unknown == 255] = 0\n\n markers = cv2.watershed(img, markers)\n markers[markers == -1] = 0\n markers[markers == 1] = 0\n\n _, just_labels = cv2.connectedComponents(opening, connectivity=4)\n _, labels, stats, _ = cv2.connectedComponentsWithStats(np.uint8(markers), connectivity=4)\n\n return just_labels, labels, len(stats)\n\n\nimg = cv2.imread('cropped.jpg')\n\nlab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)\nl, a, b = cv2.split(lab)\nret, thresh = cv2.threshold(b, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\ncnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\ncnts = cnts[0] if len(cnts) == 2 else cnts[1]\nfor c in cnts:\n flood_filled = cv2.drawContours(thresh, [c], 0, (255, 255, 255), -1)\n\nkernel = np.ones((3, 3), np.uint8)\nopening = cv2.morphologyEx(flood_filled, cv2.MORPH_OPEN, kernel, iterations=2)\n\nth_list = np.arange(20, 80, 0.01)\ncounts = np.zeros_like(th_list)\nfor idx, th in enumerate(th_list):\n _, _, counts[idx] = watershed(opening)\n\nth = th_list[np.argmax(counts)]\n\njust_labels, labels, count = watershed(opening)\n\njust_label_img = cvt_label_to_image(just_labels)\nlabel_img = cvt_label_to_image(labels)\nstack = np.hstack((just_label_img, label_img))\ncv2.imshow('win', stack)\ncv2.waitKey(0)\n","repo_name":"dlinky/image_process_tools","sub_path":"watershed.py","file_name":"watershed.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"5641464641","text":"import functools\nimport numpy as np\n\nTRAIN_PATH = 'data/train.csv'\nTEST_PATH = 'data/test.csv'\n\n'''\nthis function reverse a dictionary given the condition that both the keys and the values are unique\n'''\ndef reverse_dict(d):\n return {d[key]: key for key in d}\n\n'''\nthis decorator time the execution of a given function\n'''\ndef timed(f):\n import time\n def g():\n t=time.time() # get the current time\n f()\n # print the current time - the recorded time (which is the elapsed time in seconds)\n print(\"le temps d'excution de f est {t:.0f}\".format(t=(time.time()) - t))\n return g\n\ndef some_bullshit(y):\n import matplotlib.pyplot as plt\n import scipy.fftpack\n\n # Number of samplepoints\n N = len(y)\n # sample spacing\n T = 1/250\n x = np.linspace(0.0, N*T, N)\n yf = scipy.fftpack.fft(y)\n xf = scipy.fftpack.fftfreq(N,T)\n xs = scipy.fftpack.fftshift(xf)\n yshift = scipy.fftpack.fftshift(yf)\n print(yf)\n fig, ax = plt.subplots()\n plt.xlim(-8, 8)\n a=np.array(yshift)\n a=np.abs(yshift)\n \n b=np.r_[True, a[1:] < a[:-1]] & np.r_[a[:-1] < a[1:], True]\n b=a[b]\n b.sort()\n \n ax.plot(xs,1.0/N*np.abs(yshift))\n print(b)\n plt.show()\n ","repo_name":"SalahEddineLahniche/MLC-Kaggle-2017","sub_path":"old/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"35521707419","text":"from . import logout_access_blueprint\nfrom ..models import InvalidToken\nfrom backend.constants import JSON_MIMETYPE\nfrom flask import jsonify\n\nfrom flask_jwt_extended import (\n get_jwt,\n jwt_required,\n)\n\n\n@logout_access_blueprint.route(\"/api/logout/access\", methods=[\"POST\"])\n@jwt_required()\ndef logout_access():\n jti = get_jwt()[\"jti\"]\n\n try:\n invalid_token = InvalidToken(jti=jti)\n invalid_token.save()\n\n return (jsonify({\"success\": True}), 200, JSON_MIMETYPE)\n\n except Exception as err:\n return (jsonify({\"error\": str(err)}), 400, JSON_MIMETYPE)\n","repo_name":"randy-concepcion/microblog","sub_path":"backend/backend/logout_access/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"30399011297","text":"import math as _math\nfrom abc import ABCMeta, abstractmethod\nfrom functools import reduce\n\n_log2 = lambda x: _math.log2(x)\n_ln = _math.log\n\n_product = lambda s: reduce(lambda x, y: x * y, s)\n\n_SMALL = 1e-20\n\ntry:\n from scipy.stats import fisher_exact\nexcept ImportError:\n\n def fisher_exact(*_args, **_kwargs):\n raise NotImplementedError\n\n\n### Indices to marginals arguments:\n\nNGRAM = 0\n\"\"\"Marginals index for the ngram count\"\"\"\n\nUNIGRAMS = -2\n\"\"\"Marginals index for a tuple of each unigram count\"\"\"\n\nTOTAL = -1\n\"\"\"Marginals index for the number of words in the data\"\"\"\n\n\nclass NgramAssocMeasures(metaclass=ABCMeta):\n \"\"\"\n An abstract class defining a collection of generic association measures.\n Each public method returns a score, taking the following arguments::\n\n score_fn(count_of_ngram,\n (count_of_n-1gram_1, ..., count_of_n-1gram_j),\n (count_of_n-2gram_1, ..., count_of_n-2gram_k),\n ...,\n (count_of_1gram_1, ..., count_of_1gram_n),\n count_of_total_words)\n\n See ``BigramAssocMeasures`` and ``TrigramAssocMeasures``\n\n Inheriting classes should define a property _n, and a method _contingency\n which calculates contingency values from marginals in order for all\n association measures defined here to be usable.\n \"\"\"\n\n _n = 0\n\n @staticmethod\n @abstractmethod\n def _contingency(*marginals):\n \"\"\"Calculates values of a contingency table from marginal values.\"\"\"\n raise NotImplementedError(\n \"The contingency table is not available\" \"in the general ngram case\"\n )\n\n @staticmethod\n @abstractmethod\n def _marginals(*contingency):\n \"\"\"Calculates values of contingency table marginals from its values.\"\"\"\n raise NotImplementedError(\n \"The contingency table is not available\" \"in the general ngram case\"\n )\n\n @classmethod\n def _expected_values(cls, cont):\n \"\"\"Calculates expected values for a contingency table.\"\"\"\n n_all = sum(cont)\n bits = [1 << i for i in range(cls._n)]\n\n # For each contingency table cell\n for i in range(len(cont)):\n # Yield the expected value\n yield (\n _product(\n sum(cont[x] for x in range(2**cls._n) if (x & j) == (i & j))\n for j in bits\n )\n / (n_all ** (cls._n - 1))\n )\n\n @staticmethod\n def raw_freq(*marginals):\n \"\"\"Scores ngrams by their frequency\"\"\"\n return marginals[NGRAM] / marginals[TOTAL]\n\n @classmethod\n def student_t(cls, *marginals):\n \"\"\"Scores ngrams using Student's t test with independence hypothesis\n for unigrams, as in Manning and Schutze 5.3.1.\n \"\"\"\n return (\n marginals[NGRAM]\n - _product(marginals[UNIGRAMS]) / (marginals[TOTAL] ** (cls._n - 1))\n ) / (marginals[NGRAM] + _SMALL) ** 0.5\n\n @classmethod\n def chi_sq(cls, *marginals):\n \"\"\"Scores ngrams using Pearson's chi-square as in Manning and Schutze\n 5.3.3.\n \"\"\"\n cont = cls._contingency(*marginals)\n exps = cls._expected_values(cont)\n return sum((obs - exp) ** 2 / (exp + _SMALL) for obs, exp in zip(cont, exps))\n\n @staticmethod\n def mi_like(*marginals, **kwargs):\n \"\"\"Scores ngrams using a variant of mutual information. The keyword\n argument power sets an exponent (default 3) for the numerator. No\n logarithm of the result is calculated.\n \"\"\"\n return marginals[NGRAM] ** kwargs.get(\"power\", 3) / _product(\n marginals[UNIGRAMS]\n )\n\n @classmethod\n def pmi(cls, *marginals):\n \"\"\"Scores ngrams by pointwise mutual information, as in Manning and\n Schutze 5.4.\n \"\"\"\n return _log2(marginals[NGRAM] * marginals[TOTAL] ** (cls._n - 1)) - _log2(\n _product(marginals[UNIGRAMS])\n )\n\n @classmethod\n def likelihood_ratio(cls, *marginals):\n \"\"\"Scores ngrams using likelihood ratios as in Manning and Schutze 5.3.4.\"\"\"\n cont = cls._contingency(*marginals)\n return 2 * sum(\n obs * _ln(obs / (exp + _SMALL) + _SMALL)\n for obs, exp in zip(cont, cls._expected_values(cont))\n )\n\n @classmethod\n def poisson_stirling(cls, *marginals):\n \"\"\"Scores ngrams using the Poisson-Stirling measure.\"\"\"\n exp = _product(marginals[UNIGRAMS]) / (marginals[TOTAL] ** (cls._n - 1))\n return marginals[NGRAM] * (_log2(marginals[NGRAM] / exp) - 1)\n\n @classmethod\n def jaccard(cls, *marginals):\n \"\"\"Scores ngrams using the Jaccard index.\"\"\"\n cont = cls._contingency(*marginals)\n return cont[0] / sum(cont[:-1])\n\n\nclass BigramAssocMeasures(NgramAssocMeasures):\n \"\"\"\n A collection of bigram association measures. Each association measure\n is provided as a function with three arguments::\n\n bigram_score_fn(n_ii, (n_ix, n_xi), n_xx)\n\n The arguments constitute the marginals of a contingency table, counting\n the occurrences of particular events in a corpus. The letter i in the\n suffix refers to the appearance of the word in question, while x indicates\n the appearance of any word. Thus, for example:\n\n - n_ii counts ``(w1, w2)``, i.e. the bigram being scored\n - n_ix counts ``(w1, *)``\n - n_xi counts ``(*, w2)``\n - n_xx counts ``(*, *)``, i.e. any bigram\n\n This may be shown with respect to a contingency table::\n\n w1 ~w1\n ------ ------\n w2 | n_ii | n_oi | = n_xi\n ------ ------\n ~w2 | n_io | n_oo |\n ------ ------\n = n_ix TOTAL = n_xx\n \"\"\"\n\n _n = 2\n\n @staticmethod\n def _contingency(n_ii, n_ix_xi_tuple, n_xx):\n \"\"\"Calculates values of a bigram contingency table from marginal values.\"\"\"\n (n_ix, n_xi) = n_ix_xi_tuple\n n_oi = n_xi - n_ii\n n_io = n_ix - n_ii\n return (n_ii, n_oi, n_io, n_xx - n_ii - n_oi - n_io)\n\n @staticmethod\n def _marginals(n_ii, n_oi, n_io, n_oo):\n \"\"\"Calculates values of contingency table marginals from its values.\"\"\"\n return (n_ii, (n_oi + n_ii, n_io + n_ii), n_oo + n_oi + n_io + n_ii)\n\n @staticmethod\n def _expected_values(cont):\n \"\"\"Calculates expected values for a contingency table.\"\"\"\n n_xx = sum(cont)\n # For each contingency table cell\n for i in range(4):\n yield (cont[i] + cont[i ^ 1]) * (cont[i] + cont[i ^ 2]) / n_xx\n\n @classmethod\n def phi_sq(cls, *marginals):\n \"\"\"Scores bigrams using phi-square, the square of the Pearson correlation\n coefficient.\n \"\"\"\n n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)\n\n return (n_ii * n_oo - n_io * n_oi) ** 2 / (\n (n_ii + n_io) * (n_ii + n_oi) * (n_io + n_oo) * (n_oi + n_oo)\n )\n\n @classmethod\n def chi_sq(cls, n_ii, n_ix_xi_tuple, n_xx):\n \"\"\"Scores bigrams using chi-square, i.e. phi-sq multiplied by the number\n of bigrams, as in Manning and Schutze 5.3.3.\n \"\"\"\n (n_ix, n_xi) = n_ix_xi_tuple\n return n_xx * cls.phi_sq(n_ii, (n_ix, n_xi), n_xx)\n\n @classmethod\n def fisher(cls, *marginals):\n \"\"\"Scores bigrams using Fisher's Exact Test (Pedersen 1996). Less\n sensitive to small counts than PMI or Chi Sq, but also more expensive\n to compute. Requires scipy.\n \"\"\"\n\n n_ii, n_io, n_oi, n_oo = cls._contingency(*marginals)\n\n (odds, pvalue) = fisher_exact([[n_ii, n_io], [n_oi, n_oo]], alternative=\"less\")\n return pvalue\n\n @staticmethod\n def dice(n_ii, n_ix_xi_tuple, n_xx):\n \"\"\"Scores bigrams using Dice's coefficient.\"\"\"\n (n_ix, n_xi) = n_ix_xi_tuple\n return 2 * n_ii / (n_ix + n_xi)\n\n\nclass TrigramAssocMeasures(NgramAssocMeasures):\n \"\"\"\n A collection of trigram association measures. Each association measure\n is provided as a function with four arguments::\n\n trigram_score_fn(n_iii,\n (n_iix, n_ixi, n_xii),\n (n_ixx, n_xix, n_xxi),\n n_xxx)\n\n The arguments constitute the marginals of a contingency table, counting\n the occurrences of particular events in a corpus. The letter i in the\n suffix refers to the appearance of the word in question, while x indicates\n the appearance of any word. Thus, for example:\n\n - n_iii counts ``(w1, w2, w3)``, i.e. the trigram being scored\n - n_ixx counts ``(w1, *, *)``\n - n_xxx counts ``(*, *, *)``, i.e. any trigram\n \"\"\"\n\n _n = 3\n\n @staticmethod\n def _contingency(n_iii, n_iix_tuple, n_ixx_tuple, n_xxx):\n \"\"\"Calculates values of a trigram contingency table (or cube) from\n marginal values.\n >>> TrigramAssocMeasures._contingency(1, (1, 1, 1), (1, 73, 1), 2000)\n (1, 0, 0, 0, 0, 72, 0, 1927)\n \"\"\"\n (n_iix, n_ixi, n_xii) = n_iix_tuple\n (n_ixx, n_xix, n_xxi) = n_ixx_tuple\n n_oii = n_xii - n_iii\n n_ioi = n_ixi - n_iii\n n_iio = n_iix - n_iii\n n_ooi = n_xxi - n_iii - n_oii - n_ioi\n n_oio = n_xix - n_iii - n_oii - n_iio\n n_ioo = n_ixx - n_iii - n_ioi - n_iio\n n_ooo = n_xxx - n_iii - n_oii - n_ioi - n_iio - n_ooi - n_oio - n_ioo\n\n return (n_iii, n_oii, n_ioi, n_ooi, n_iio, n_oio, n_ioo, n_ooo)\n\n @staticmethod\n def _marginals(*contingency):\n \"\"\"Calculates values of contingency table marginals from its values.\n >>> TrigramAssocMeasures._marginals(1, 0, 0, 0, 0, 72, 0, 1927)\n (1, (1, 1, 1), (1, 73, 1), 2000)\n \"\"\"\n n_iii, n_oii, n_ioi, n_ooi, n_iio, n_oio, n_ioo, n_ooo = contingency\n return (\n n_iii,\n (n_iii + n_iio, n_iii + n_ioi, n_iii + n_oii),\n (\n n_iii + n_ioi + n_iio + n_ioo,\n n_iii + n_oii + n_iio + n_oio,\n n_iii + n_oii + n_ioi + n_ooi,\n ),\n sum(contingency),\n )\n\n\nclass QuadgramAssocMeasures(NgramAssocMeasures):\n \"\"\"\n A collection of quadgram association measures. Each association measure\n is provided as a function with five arguments::\n\n trigram_score_fn(n_iiii,\n (n_iiix, n_iixi, n_ixii, n_xiii),\n (n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),\n (n_ixxx, n_xixx, n_xxix, n_xxxi),\n n_all)\n\n The arguments constitute the marginals of a contingency table, counting\n the occurrences of particular events in a corpus. The letter i in the\n suffix refers to the appearance of the word in question, while x indicates\n the appearance of any word. Thus, for example:\n\n - n_iiii counts ``(w1, w2, w3, w4)``, i.e. the quadgram being scored\n - n_ixxi counts ``(w1, *, *, w4)``\n - n_xxxx counts ``(*, *, *, *)``, i.e. any quadgram\n \"\"\"\n\n _n = 4\n\n @staticmethod\n def _contingency(n_iiii, n_iiix_tuple, n_iixx_tuple, n_ixxx_tuple, n_xxxx):\n \"\"\"Calculates values of a quadgram contingency table from\n marginal values.\n \"\"\"\n (n_iiix, n_iixi, n_ixii, n_xiii) = n_iiix_tuple\n (n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix) = n_iixx_tuple\n (n_ixxx, n_xixx, n_xxix, n_xxxi) = n_ixxx_tuple\n n_oiii = n_xiii - n_iiii\n n_ioii = n_ixii - n_iiii\n n_iioi = n_iixi - n_iiii\n n_ooii = n_xxii - n_iiii - n_oiii - n_ioii\n n_oioi = n_xixi - n_iiii - n_oiii - n_iioi\n n_iooi = n_ixxi - n_iiii - n_ioii - n_iioi\n n_oooi = n_xxxi - n_iiii - n_oiii - n_ioii - n_iioi - n_ooii - n_iooi - n_oioi\n n_iiio = n_iiix - n_iiii\n n_oiio = n_xiix - n_iiii - n_oiii - n_iiio\n n_ioio = n_ixix - n_iiii - n_ioii - n_iiio\n n_ooio = n_xxix - n_iiii - n_oiii - n_ioii - n_iiio - n_ooii - n_ioio - n_oiio\n n_iioo = n_iixx - n_iiii - n_iioi - n_iiio\n n_oioo = n_xixx - n_iiii - n_oiii - n_iioi - n_iiio - n_oioi - n_oiio - n_iioo\n n_iooo = n_ixxx - n_iiii - n_ioii - n_iioi - n_iiio - n_iooi - n_iioo - n_ioio\n n_oooo = (\n n_xxxx\n - n_iiii\n - n_oiii\n - n_ioii\n - n_iioi\n - n_ooii\n - n_oioi\n - n_iooi\n - n_oooi\n - n_iiio\n - n_oiio\n - n_ioio\n - n_ooio\n - n_iioo\n - n_oioo\n - n_iooo\n )\n\n return (\n n_iiii,\n n_oiii,\n n_ioii,\n n_ooii,\n n_iioi,\n n_oioi,\n n_iooi,\n n_oooi,\n n_iiio,\n n_oiio,\n n_ioio,\n n_ooio,\n n_iioo,\n n_oioo,\n n_iooo,\n n_oooo,\n )\n\n @staticmethod\n def _marginals(*contingency):\n \"\"\"Calculates values of contingency table marginals from its values.\n QuadgramAssocMeasures._marginals(1, 0, 2, 46, 552, 825, 2577, 34967, 1, 0, 2, 48, 7250, 9031, 28585, 356653)\n (1, (2, 553, 3, 1), (7804, 6, 3132, 1378, 49, 2), (38970, 17660, 100, 38970), 440540)\n \"\"\"\n (\n n_iiii,\n n_oiii,\n n_ioii,\n n_ooii,\n n_iioi,\n n_oioi,\n n_iooi,\n n_oooi,\n n_iiio,\n n_oiio,\n n_ioio,\n n_ooio,\n n_iioo,\n n_oioo,\n n_iooo,\n n_oooo,\n ) = contingency\n\n n_iiix = n_iiii + n_iiio\n n_iixi = n_iiii + n_iioi\n n_ixii = n_iiii + n_ioii\n n_xiii = n_iiii + n_oiii\n\n n_iixx = n_iiii + n_iioi + n_iiio + n_iioo\n n_ixix = n_iiii + n_ioii + n_iiio + n_ioio\n n_ixxi = n_iiii + n_ioii + n_iioi + n_iooi\n n_xixi = n_iiii + n_oiii + n_iioi + n_oioi\n n_xxii = n_iiii + n_oiii + n_ioii + n_ooii\n n_xiix = n_iiii + n_oiii + n_iiio + n_oiio\n\n n_ixxx = n_iiii + n_ioii + n_iioi + n_iiio + n_iooi + n_iioo + n_ioio + n_iooo\n n_xixx = n_iiii + n_oiii + n_iioi + n_iiio + n_oioi + n_oiio + n_iioo + n_oioo\n n_xxix = n_iiii + n_oiii + n_ioii + n_iiio + n_ooii + n_ioio + n_oiio + n_ooio\n n_xxxi = n_iiii + n_oiii + n_ioii + n_iioi + n_ooii + n_iooi + n_oioi + n_oooi\n\n n_all = sum(contingency)\n\n return (\n n_iiii,\n (n_iiix, n_iixi, n_ixii, n_xiii),\n (n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),\n (n_ixxx, n_xixx, n_xxix, n_xxxi),\n n_all,\n )\n\n\nclass ContingencyMeasures:\n \"\"\"Wraps NgramAssocMeasures classes such that the arguments of association\n measures are contingency table values rather than marginals.\n \"\"\"\n\n def __init__(self, measures):\n \"\"\"Constructs a ContingencyMeasures given a NgramAssocMeasures class\"\"\"\n self.__class__.__name__ = \"Contingency\" + measures.__class__.__name__\n for k in dir(measures):\n if k.startswith(\"__\"):\n continue\n v = getattr(measures, k)\n if not k.startswith(\"_\"):\n v = self._make_contingency_fn(measures, v)\n setattr(self, k, v)\n\n @staticmethod\n def _make_contingency_fn(measures, old_fn):\n \"\"\"From an association measure function, produces a new function which\n accepts contingency table values as its arguments.\n \"\"\"\n\n def res(*contingency):\n return old_fn(*measures._marginals(*contingency))\n\n res.__doc__ = old_fn.__doc__\n res.__name__ = old_fn.__name__\n return res\n","repo_name":"nltk/nltk","sub_path":"nltk/metrics/association.py","file_name":"association.py","file_ext":"py","file_size_in_byte":15656,"program_lang":"python","lang":"en","doc_type":"code","stars":12541,"dataset":"github-code","pt":"75"} +{"seq_id":"28736693516","text":"T = int(input())\nfor case in range(T):\n N, K = input().split()\n N = int(N)\n K = int(K)\n PROB = list(map(float, input().split()))\n PROB.sort()\n \n MAXPROB = 0\n for i in range(0, K+1):\n PROBS = [0 for i in range(N+10)]\n PROBS[0] = 1\n for p in PROB[:i]:\n for k in range(K, 0, -1):\n PROBS[k] = PROBS[k-1] * p + PROBS[k] * (1-p)\n PROBS[0] = PROBS[0] * (1-p)\n \n for p in PROB[N-K+i:]:\n for k in range(K, 0, -1):\n PROBS[k] = PROBS[k-1] * p + PROBS[k] * (1-p)\n PROBS[0] = PROBS[0] * (1-p)\n\n MAXPROB = max(MAXPROB, PROBS[K//2])\n \n print(\"Case #%d: %.7f\" % (case+1, MAXPROB))","repo_name":"DaHuO/Supergraph","sub_path":"codes/CodeJamCrawler/16_4_2/Funghi/Untitled.py","file_name":"Untitled.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"74795277043","text":"import pygame\n\nfrom states.base_game_state import BaseGameState\nfrom game_state_manager import GameStateManager\nfrom gui_manager import GUIManager\nfrom gui_elements.text_input import TextInput\nfrom gui_elements.text_label import TextLabel\nfrom states.login_state import LoginState\nfrom settings import *\n\n\nclass IPState(BaseGameState):\n def __init__(self, state_manager: GameStateManager):\n super().__init__(state_manager)\n\n def set_next_state(ip_address):\n next_state = LoginState(self.state_manager, ip_address.strip())\n next_state.set_self(exclusive=True)\n\n self.gui_manager = GUIManager(self.state_manager.screen, self.state_manager.base_font_path)\n\n self.text_label = TextLabel(\n self.gui_manager,\n 'text_label',\n pygame.Rect((SCREEN_WIDTH - 900) // 2, (SCREEN_HEIGHT - 60) // 2 - 100, 900, 60),\n text='Введите IP адрес',\n color=pygame.Color('limegreen'),\n font_size=60\n )\n\n self.text_input = TextInput(\n self.gui_manager,\n 'ip_input',\n pygame.Rect((SCREEN_WIDTH - 900) // 2, (SCREEN_HEIGHT - 60) // 2, 900, 60),\n pygame.Color('yellow'),\n pygame.Color('lightgray'),\n font_size=20,\n callback=set_next_state\n )\n\n def process_event(self, event):\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_TAB:\n self.text_input.active = True\n self.gui_manager.process_event(event)\n\n def update(self):\n self.gui_manager.update()\n \n def process_quit(self):\n pass\n","repo_name":"DanyaIzm/pygame_online_chat","sub_path":"client/states/ip_state.py","file_name":"ip_state.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"70208350643","text":"from django.db import models\n\nfrom backend.base_models import BaseModel\n\n\nclass Measure(BaseModel):\n title = models.CharField(\n max_length=128,\n verbose_name=\"Наименование\",\n blank=False,\n null=False\n )\n\n title_short = models.CharField(\n max_length=8,\n verbose_name=\"Наименование (кратко)\",\n blank=False,\n null=False\n ) \n \n class Meta:\n verbose_name = \"Измерение\"\n verbose_name_plural = \"Измерения\"\n\n def __str__(self):\n return self.title\n","repo_name":"LiberBear/diplom","sub_path":"delivery/models/Measure.py","file_name":"Measure.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"13291238220","text":"from django.shortcuts import render, redirect, reverse\nfrom django.views import View\nfrom django.contrib.auth import login, logout\nfrom candidate.forms import CandidateLoginForm, CandidateRegisterForm, UpdateCandidateForm\nfrom accounts.models import User, Candidate, Event\nfrom lib.authentication import CandidateLoginRequiredMixin\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate\nfrom datetime import datetime, time, timedelta\n\n# Create your views here.\nclass RegisterCandidateView(View):\n def get(self, request):\n \"\"\"Render candidate Registration page.\n \"\"\"\n if request.user.is_authenticated:\n return redirect(reverse('candidate:candidate_events', args=[request.user.id]))\n form = CandidateRegisterForm()\n return render(request, 'register_candidate.html', {'form':form})\n\n def post(self, request):\n \"\"\"\n Handle candidate registration.\n \"\"\"\n\n form = CandidateRegisterForm(request.POST)\n if form.is_valid():\n try:\n user = User.objects.get(email=form.cleaned_data['email'])\n messages.add_message(request, messages.ERROR, 'Candidate with same email already exists!')\n return render(request, 'register_candidate.html', {'form': form})\n except User.DoesNotExist:\n user = User.objects.create_user(\n email=form.cleaned_data['email'],\n password=form.cleaned_data['password']\n )\n candidate = Candidate.objects.create(name=form.cleaned_data['name'], user=user, stack=request.POST.getlist('stack'))\n return redirect(reverse('candidate:candidate_login'))\n\n return render(request, 'register_candidate.html', {'form': form})\n\nclass CandidateLoginView(View):\n def get(self, request):\n \"\"\"Render candidate login page.\n \"\"\"\n\n if request.user.is_authenticated:\n return redirect(reverse('candidate:candidate_events', args=[request.user.id]))\n\n form = CandidateLoginForm()\n return render(request, 'login_candidate.html')\n\n def post(self, request):\n \"\"\"\n Handle candidate login.\n \"\"\"\n\n form = CandidateLoginForm(request.POST)\n if form.is_valid():\n candidate_user = authenticate(email=form.cleaned_data['email'], password=form.cleaned_data['password'])\n\n if candidate_user:\n login(request, candidate_user, backend='lib.authentication.UserBackend')\n return redirect(reverse('candidate:candidate_events', args=[candidate_user.id]))\n\n messages.add_message(request, messages.ERROR, 'Invalid Login Credentials')\n return render(request, 'login_candidate.html', {'form': form})\n\nclass CandidateDetailsView(CandidateLoginRequiredMixin, View):\n def get(self, request, candidate_id):\n \"\"\"\n Render Candidate details page.\n \"\"\"\n\n candidate = Candidate.objects.get(user=request.user)\n context = {'candidate': candidate}\n\n return render(request, 'candidate_details.html', context)\n\nclass CandidateEventsView(CandidateLoginRequiredMixin, View):\n def get(self, request, candidate_id):\n \"\"\"\n Render Candidate's events page.\n \"\"\"\n\n candidate = Candidate.objects.get(user=request.user)\n upcoming_event = Event.objects.filter(candidate=candidate)\n context = {'upcoming_events':upcoming_event}\n return render(request, 'candidate_events.html', context)\n\n\nclass EditCandidateView(CandidateLoginRequiredMixin, View):\n \"\"\"\n Edit a Candidate object\n \"\"\"\n\n def get(self, request, candidate_id):\n\n form = UpdateCandidateForm()\n candidate = Candidate.objects.get(user=request.user)\n context = {'form':form, 'candidate':candidate}\n return render(request, 'edit_candidate.html', context)\n\n def post(self, request, candidate_id):\n form = UpdateCandidateForm(request.POST)\n candidate = Candidate.objects.get(user=request.user)\n if form.is_valid():\n candidate.name = form.cleaned_data['name']\n candidate.required_passes = request.POST.getlist('stack')\n candidate.save()\n messages.add_message(request, messages.SUCCESS, 'Candidate updated successfully')\n return redirect(reverse('candidate:candidate_details', args=[candidate.id]))\n else:\n messages.add_message(request, messages.ERROR, 'Error updating candidate, please select relevant fields')\n return redirect(reverse('candidate:edit_details', args=[candidate.id]))\n context = {'form':form, 'candidate':candidate}\n return render(request, 'edit_candidate.html', context)\n\n\nclass CandidateLogoutView(CandidateLoginRequiredMixin, View):\n def get(self, request):\n \"\"\"\n Logout candidate.\n \"\"\"\n\n request.session.flush()\n logout(request)\n return redirect(reverse('candidate:candidate_login'))","repo_name":"madewithkode/vgg_test","sub_path":"src/candidate/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"18528288929","text":"import json\nwith open('s2orc/sample/pdf_parses/sample.jsonl','r') as f:\n data = [json.loads(line) for line in f]\n\ncnt = 0\nwith open(\"sample_s2orc.txt\", \"w\") as f:\n for i in data[:1000]:\n body_text = i['body_text']\n abstract = []\n body = [] \n if len(i['abstract']) > 0: \n for j in i['abstract']:\n if j['section']=='Abstract':\n abstract.append(\"ABSTRACT : \"+j['text'])\n\n for j in body_text:\n if ('introduction' in j['section'].strip().lower() or 'conclusion' in j['section'].strip().lower() and len(j['text'])!=0):\n body.append(j['section']+\" : \"+j['text'])\n\n if len(body)!=0 or len(abstract) != 0:\n f.write(i['paper_id']+\"\\n\")\n [f.write(k+'\\n') for k in abstract]\n [f.write(k+'\\n') for k in body]\n f.write(\"\\n\")","repo_name":"AEHUSPHAM/UniMol","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"178784650","text":"import numpy as np\nfrom collections import defaultdict\n\nclass Agent:\n # I used Expected Sarsa with MC Control to get probability, so that we can do exploration and exploitation\n \n def __init__(self, nA=6, epsilon=0.004, alpha=0.1, gamma=1.0, divisor = 400):\n \"\"\" Initialize agent.\n\n Params\n ======\n - nA: number of actions available to the agent\n \"\"\"\n self.nA = nA\n self.Q = defaultdict(lambda: np.zeros(self.nA))\n self.epsilon = epsilon\n self.alpha = alpha\n self.gamma = gamma\n self.divisor = divisor\n \n def select_action(self, state, i_episode):\n \"\"\" Given the state, select an action.\n\n Params\n ======\n - state: the current state of the environment\n\n Returns\n =======\n - action: an integer, compatible with the task's action space\n \"\"\"\n \n \n #epsilon = 1.0 / (i_episode + 0.3) #seems returns the highest reward when it sets to this line, and the other epsilon in the other method is set as epsilon = 1.0 / (i_episode)\n \n epsilon = 1.0 / (i_episode) \n\n # adding the value of divisor in the method select_action AND method step, seems will yield to lower average reward. So divisor 800 has a higher reward than 8000\n \n policy = np.ones(self.nA) * epsilon / self.nA\n policy[np.argmax(self.Q[state])] = 1 - epsilon + epsilon / self.nA \n \n #the function get_probs above (3 lines above), allows us to do exploration-exploitation\n \n # pick next action A\n action = np.random.choice(np.arange(self.nA), p=policy)\n \n \n return action\n \n \n \"\"\"\n Epsilon must be a number between 0 and 1. If Epsilon is small, then the agent will be greedy. Conversely, if the Epsilon is big, then the agent will explore various actions\n \"\"\"\n \n \n def step(self, state, action, reward, next_state, done, i_episode):\n \"\"\" Update the agent's knowledge, using the most recently sampled tuple.\n\n Params\n ======\n - state: the previous state of the environment\n - action: the agent's previous choice of action\n - reward: last reward received\n - next_state: the current state of the environment\n - done: whether the episode is complete (True or False)\n \"\"\"\n \n \"\"\"\n The step-size parameter α\\alphaα must satisfy 0<α≤10 < \\alpha \\leq 10<α≤1. Higher values of α\\alphaα will result in faster learning, but values of α\\alphaα that are too high can prevent MC control from converging to π∗\\pi_*π∗​.\n \n the higher is the gamma, the more the agent cares about the FUTURE REWARD. Conversely, the less is the gamma, the more the agent cares about the MOST IMMEDIATE REWARD\n Gamma value is always between 0 and 1\n \"\"\"\n \n \n epsilon = 1.0 / (i_episode)\n \n #I think this is the way to select the policy. It is used to obtain the action probabilities corresponding to epsilon-greedy policy.\n next_policy = np.ones(self.nA) * self.epsilon / self.nA \n \n \n \n next_policy[np.argmax(self.Q[state])] = 1 - epsilon + epsilon / self.nA \n \n \n #Essentially, next_policy is deemed as the best policy that should be selected by the agent\n \n #In expected sarsa, it uses np.dot(self.Q[next_state], next_policy)\n self.Q[state][action] = self.Q[state][action] + (self.alpha * (reward + (self.gamma * np.dot(self.Q[next_state], next_policy) - self.Q[state][action]))) \n \n","repo_name":"GlorianY/Udacity_Machine_Learning_Nanodegree","sub_path":"Reinforcement_Learning_Projects/Taxi_Reinforcement Learning_Agent/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":3674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"26926826554","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nimport dlib\r\n\r\n# load face detector\r\nface_detector = cv2.CascadeClassifier('./models/haarcascade_frontalface_default.xml')\r\n\r\n# load landmark detector\r\nlandmark_model = dlib.shape_predictor('./models/shape_predictor_68_face_landmarks.dat')\r\n\r\n# eye facial landmarks\r\nFACIAL_LANDMARKS_IDXS = {\r\n \"left_eye\": [36, 37, 38, 39, 40, 41],\r\n \"right_eye\": [42, 43, 44, 45, 46, 47]\r\n}\r\n\r\n# convert landmarks shape to numpy array\r\ndef shape2numpy(shape):\r\n xy = [(shape.part(i).x, shape.part(i).y,) for i in range(68)]\r\n return np.array(xy, dtype='float32')\r\n\r\n\r\ndef align_image(image, leftEyePos=(0.32, 0.32), faceSize=200, minSizeFaceDetector=(100, 100)):\r\n # convert to grayscale for face detector\r\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\r\n # detect faces\r\n bboxes = face_detector.detectMultiScale(gray, 1.1, 2, minSize=minSizeFaceDetector)\r\n\r\n # if no face was found return empty array\r\n if len(bboxes) == 0: return []\r\n \r\n # take face if there is only one\r\n if len(bboxes) == 1: bbox_idx = 0\r\n # take largest face otherwise\r\n else: bbox_idx = np.argmax(bboxes[:, 2])\r\n\r\n # get bounding box coordinates and width/height of face\r\n (x, y, w, h) = bboxes[bbox_idx]\r\n\r\n # get face crop\r\n face = image[y:y + h, x:x + w]\r\n\r\n # get landmarks\r\n shape = landmark_model(face, dlib.rectangle(0, 0, face.shape[0], face.shape[1]))\r\n shape_np = shape2numpy(shape)\r\n\r\n # only get eyes\r\n leftEyePts = shape_np[FACIAL_LANDMARKS_IDXS[\"left_eye\"]]\r\n rightEyePts = shape_np[FACIAL_LANDMARKS_IDXS[\"right_eye\"]]\r\n\r\n # get center points\r\n leftEyeCenter = leftEyePts.mean(axis=0).astype(\"int\")\r\n rightEyeCenter = rightEyePts.mean(axis=0).astype(\"int\")\r\n\r\n # compute angle\r\n dY = rightEyeCenter[1] - leftEyeCenter[1]\r\n dX = rightEyeCenter[0] - leftEyeCenter[0]\r\n angle = np.degrees(np.arctan2(dY, dX))\r\n\r\n # get desired eye position\r\n desiredRightEyeX = 1.0 - leftEyePos[0]\r\n\r\n # compute scaling factor\r\n dist = np.sqrt((dX ** 2) + (dY ** 2))\r\n desiredDist = (desiredRightEyeX - leftEyePos[0])\r\n desiredDist *= faceSize\r\n scale = desiredDist / dist\r\n\r\n # compute center between both eyes\r\n eyesCenter = (\r\n (leftEyeCenter[0] + rightEyeCenter[0]) // 2,\r\n (leftEyeCenter[1] + rightEyeCenter[1]) // 2\r\n )\r\n\r\n # get rotation matrix\r\n M = cv2.getRotationMatrix2D((int(eyesCenter[0]), int(eyesCenter[1])), angle, scale)\r\n\r\n # update the translation component of the matrix\r\n tX = faceSize * 0.5\r\n tY = faceSize * leftEyePos[1]\r\n M[0, 2] += (tX - eyesCenter[0])\r\n M[1, 2] += (tY - eyesCenter[1])\r\n\r\n # apply affine transformation\r\n (w, h) = (faceSize, faceSize)\r\n output = cv2.warpAffine(face, M, (w, h), flags=cv2.INTER_CUBIC)\r\n\r\n return output\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n # load image\r\n img = cv2.imread('./data/multiple.jpg')\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n\r\n # align it\r\n aligned_img = align_image(img)\r\n\r\n # show results\r\n plt.subplot(1,2,1)\r\n plt.imshow(img)\r\n plt.title('Original')\r\n plt.axis('off')\r\n plt.subplot(1,2,2)\r\n plt.imshow(aligned_img)\r\n plt.title('Aligned')\r\n plt.axis('off')\r\n plt.show()","repo_name":"hra00/Explainable-AI","sub_path":"FaceAligner.py","file_name":"FaceAligner.py","file_ext":"py","file_size_in_byte":3287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"10566440308","text":"import pandas as pd\nimport os\n\nfrom tqdm import tqdm\n\nfrom util.dataTool import getStandard4gen, getStandard4gen_enhanced\n\n\ndef replace_name(s: str) -> str:\n replace_dict = {'【目的/意义】': '【目的】',\n '【方法/过程】': '【方法】',\n '【研究目的】': '【目的】',\n '【研究方法】': '【方法】',\n '【研究设计/方法】': '【方法】'}\n\n for k, v in replace_dict.items():\n s = s.replace(k, v)\n\n return s\n\n\ndef getStandard(file_name):\n path = 'data/all'\n\n data = pd.DataFrame()\n for file in tqdm(os.listdir(path), desc='Reading excel'):\n data = data.append(pd.read_excel(path + '/' + file))\n\n result = list()\n for idx, item in tqdm(data.iterrows(), desc='Extracting'):\n if str(item['title'])[0: 2] == '基于' and '综述' not in str(item['title']) and ('[' == str(item['abstract'])[0] or '【' == str(item['abstract'])[0]):\n result.append([item['title'],\n item['abstract'].replace('[', '【').replace(']', '】').replace(' ', ''),\n item['keywords']])\n\n result = pd.DataFrame(result, columns=['title', 'abstract', 'keywords'])\n result = result.sample(frac=1)\n\n result['abstract'] = result['abstract'].apply(replace_name)\n\n result.to_excel(file_name, index=False)\n\n\ndef write2txt(content_list, title_list, name):\n with open(name, 'w', encoding='utf8') as file:\n file.write('text_a' + '\\t' + 'label' + '\\n')\n\n for c, t in zip(content_list, title_list):\n file.write(str(c).strip() + '\\t' + str(t).strip() + '\\n')\n\n\nif __name__ == '__main__':\n # getStandard('standard.xlsx')\n # getStandard('all_corpus.xlsx')\n\n c, t, keywords = getStandard4gen_enhanced('all_corpus.xlsx')\n\n res = list()\n for key_list in keywords:\n for k in key_list:\n res.append(k)\n\n with open('keywords.txt', 'w', encoding='utf8') as file:\n for k in set(res):\n file.write(str(k).strip() + '\\n')\n\n # content, title = getStandard4gen('all_corpus.xlsx')\n # write2txt(content, title, 'all_corpus.txt')\n\n # cv = 0.15\n # test_index = int(len(content) * cv)\n #\n # # train\n # content_train = content[test_index:]\n # title_train = title[test_index:]\n # write2txt(content_train, title_train, 'p2_t2t_standard2_train.txt')\n #\n # # test\n # content_test = content[0: test_index]\n # title_test = title[0: test_index]\n # write2txt(content_test, title_test, 'p2_t2t_standard2_test.txt')","repo_name":"Hipkevin/paperExtraction","sub_path":"preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"31018709362","text":"import numpy as np\r\nimport scipy.interpolate as spi\r\n\r\n\r\ndef run_off(length, x, loss_factor, d_max, multiplier, multiplier_b, rainfall):\r\n \"\"\"\r\n Takes the rainfall and the relevent runoff parameters and uses them to\r\n generate the surface runoff and the baseflow runoff as numpy arrays\r\n based on a loss model. The multipliers give more flexibility to the\r\n scaling of the hydrographs than using a fixed catchment size.\r\n \"\"\"\r\n \r\n # defines the groundwater depth and the surface runoff arrays and initial conditions\r\n depth = np.zeros(length)\r\n flow = np.zeros(length)\r\n loss = np.zeros(length)\r\n depth[0] = 0\r\n \r\n \r\n for i in range(1,length):\r\n \r\n loss[i] = loss_factor*(depth[i-1]/d_max)**x\r\n\r\n flow[i] = (rainfall[i]*(depth[i-1]/d_max)**x)\r\n \r\n depth[i] = depth[i-1] + rainfall[i] - (rainfall[i]*(depth[i-1]/d_max)**x) \r\n \r\n if depth[i] > d_max:\r\n depth[i] = d_max\r\n \r\n if depth[i] > loss[i-1]:\r\n depth[i] = depth[i] - loss[i-1]\r\n \r\n if depth[i] < loss[i-1]:\r\n depth[i] = 0\r\n \r\n flow = flow * multiplier\r\n loss = loss * multiplier_b\r\n \r\n return flow, loss\r\n\r\n\r\ndef unit_hydrograph(flow, loss, x_array, Tp, Tm, Te, Vm, min_flow):\r\n \"\"\"\r\n A unit hydrograph routing module that routes the surface runoff and \r\n baseflow runoff. The unit hydrograph is very flexible with a variable \r\n time to peak, time to mid falling limb, magnitude of mid falling limb \r\n and the time to end. Returns the routed surface and base flow and the \r\n combined flow prediction as numpy arrays.\r\n \"\"\"\r\n \r\n x = (0, Tp, Tm, Te)\r\n y = (0, 1, Vm, 0)\r\n \r\n f = spi.interp1d(x,y)\r\n \r\n x1 = np.arange(0, Te, 0.25, dtype=float)\r\n \r\n y1 = f(x1)\r\n \r\n Q_predicted = np.zeros(len(x_array))\r\n Q_surface = np.zeros(len(x_array))\r\n Q_base = np.zeros(len(x_array))\r\n \r\n for i in range(len(x_array)-len(y1)):\r\n \r\n temp1 = flow[i]*y1\r\n temp2 = loss[i]*y1\r\n temp1 = np.pad(temp1, (i, len(x_array)-i-len(y1)), 'constant', constant_values=(0, 0))\r\n temp2 = np.pad(temp2, (i, len(x_array)-i-len(y1)), 'constant', constant_values=(0, 0))\r\n \r\n Q_surface += temp1\r\n Q_base += temp2\r\n \r\n Q_predicted = Q_surface + Q_base + min_flow\r\n \r\n return Q_predicted, Q_surface, Q_base\r\n\r\n\r\ndef model_run(time, rainfall, params): \r\n \"\"\"\r\n Used to run the runoff and unit hydrograph models.\r\n \"\"\"\r\n length = len(time)\r\n \r\n runoff, loss = run_off(length, params[0], params[1], params[2], params[3], params[4], rainfall)\r\n\r\n Q_predicted, Q_surface, Q_base = unit_hydrograph(runoff, loss, time, params[5], params[6], params[7], params[8], params[9])\r\n \r\n return runoff, loss, Q_predicted, Q_surface, Q_base","repo_name":"calumpeden/river-level-forecasting","sub_path":"model_code.py","file_name":"model_code.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"28766143792","text":"from copy import deepcopy\nfrom datetime import date, datetime\nfrom decimal import Decimal\nfrom typing import Generic, Iterator, List, Optional, Sequence, TypeVar, Union\n\nfrom fastapi import Depends, FastAPI, Header, HTTPException, Query, status\nfrom fastapi.responses import PlainTextResponse\n\nfrom bson.objectid import ObjectId\nfrom dateutil import relativedelta\nfrom pydantic import BaseModel, conint, Field\nfrom pydantic.generics import GenericModel\nfrom pymongo import MongoClient\nfrom pymongo.client_session import ClientSession as MongoSession\n\nfrom ..adapters.mongo import mongo, PyObjectId\nfrom ..adapters.repository import HistoricResponseType, IndicatorsResponseType\nfrom ..domain.commands import CreateRevenue\nfrom ..domain.models import Revenue\nfrom ..domain.utils import generate_pydantic_model\nfrom ..service_layer import messagebus\nfrom ..service_layer.unit_of_work import MongoUnitOfWork\nfrom ..settings import DATABASE_URL, SECRET_KEY\n\n# region: dependencies\n\n\ndef get_session() -> Iterator[MongoSession]: # pragma: no cover\n with mongo.client.start_session() as session:\n yield session\n\n\nasync def verify_key(x_key: str = Header(...)) -> None:\n if x_key != SECRET_KEY:\n raise HTTPException(status_code=400, detail=\"X-Key header invalid\")\n\n\n# endregion: dependencies\n\n# region: app definitions\n\napp = FastAPI(dependencies=[Depends(verify_key)])\n\n\n@app.on_event(\"startup\")\ndef on_startup(): # pragma: no cover\n mongo.client = MongoClient(host=DATABASE_URL)\n\n\n@app.on_event(\"shutdown\")\ndef on_startup(): # pragma: no cover\n mongo.client.close()\n\n\n# endregion: app definitions\n\n# region: typing\n\nRevenueReadModel = generate_pydantic_model(\n Revenue, default_field_definitions={\"id\": (PyObjectId, Field(alias=\"_id\"))}\n)\nRevenueReadModel.Config.json_encoders = {ObjectId: lambda oid: str(oid)}\n\nT = TypeVar(\"T\")\n\n\nclass Page(GenericModel, Generic[T]):\n items: Sequence[T]\n total: conint(ge=0) # type: ignore\n page: conint(ge=1) # type: ignore\n size: conint(ge=1) # type: ignore\n\n\nclass HistoricResponse(BaseModel):\n historic: List[HistoricResponseType]\n avg: Decimal\n\n\n# endregion: typing\n\n# region: helpers\n\n\ndef insert_zeros_if_no_data_in_monthly_historic_data(\n historic: List[HistoricResponseType],\n) -> List[HistoricResponseType]:\n if len(historic) == 13:\n return historic\n\n _historic, diffs = deepcopy(historic), 0\n for idx, (current, _next) in enumerate(zip(historic[:], historic[1:])):\n current_date = datetime.strptime(current[\"date\"], \"%m/%Y\").date()\n delta = relativedelta.relativedelta(\n dt1=datetime.strptime(_next[\"date\"], \"%m/%Y\").date(),\n dt2=current_date,\n )\n\n diff_months = delta.months + (12 * delta.years)\n for diff in range(1, diff_months):\n new_date = current_date + relativedelta.relativedelta(months=diff)\n _historic.insert(\n idx + diff + diffs,\n {\"date\": f\"{new_date.month}/{new_date.year}\", \"total\": 0},\n )\n diffs += diff_months - 1\n return _historic\n\n\n# endregion: helpers\n\n# region: endpoints\n\n\n@app.get(\"/revenues/reports/historic\", response_model=HistoricResponse)\ndef revenue_historic_endpoint(\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n try:\n avg_dict = uow.revenues.query.avg().next()\n except StopIteration:\n avg_dict = {\"avg\": Decimal()}\n\n historic = insert_zeros_if_no_data_in_monthly_historic_data(uow.revenues.query.historic())\n today = datetime.utcnow().replace(day=1).date()\n\n if len(historic) == 12:\n historic.append({\"date\": f\"{today.month}/{today.year}\", \"total\": historic[-1][\"total\"]})\n return {\"historic\": historic, **avg_dict}\n\n\n@app.get(\"/revenues/reports/indicators\", response_model=IndicatorsResponseType)\ndef revenue_indicators_endpoint(\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n return uow.revenues.query.indicators()\n\n\n@app.get(\"/revenues/{revenue_id}\", response_model=RevenueReadModel)\ndef get_revenue_endpoint(\n revenue_id: PyObjectId,\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n revenue = uow.revenues.query.get(revenue_id=revenue_id)\n if revenue is None:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Revenue not found\")\n\n return revenue\n\n\n@app.get(\"/revenues\", response_model=Page[RevenueReadModel])\ndef list_revenues_endpoint(\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n page: int = Query(default=1, ge=1),\n size: int = Query(default=5, ge=1, le=100),\n description: Optional[str] = None,\n start_date: Union[date, None, str] = None,\n end_date: Union[date, None, str] = None,\n ordering: Optional[str] = None,\n):\n # fastapi considers a query param equals to `?start_date=` as an empty string\n # so this endpoint would return \"422 Unprocessable Entity\" for `date` params.\n # NOTE: for a bigger project, consider using a middleware\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n cursor = uow.revenues.query.list(\n description=description,\n start_date=start_date or None,\n end_date=end_date or None,\n sort=ordering or \"-created_at\",\n )\n return mongo.paginate(cursor=cursor, total=uow.revenues.query.count(), page=page, size=size)\n\n\n@app.delete(\n \"/revenues/{revenue_id}\",\n status_code=status.HTTP_204_NO_CONTENT,\n response_class=PlainTextResponse,\n)\ndef delete_revenue_endpoint(\n revenue_id: PyObjectId,\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n result = uow.revenues.delete(revenue_id=revenue_id)\n uow.commit()\n if not result:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Revenue not found\")\n\n\n@app.post(\"/revenues\", status_code=status.HTTP_202_ACCEPTED)\nasync def create_revenue_endpoint(\n data: CreateRevenue,\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n messagebus.handle(message=data, uow=MongoUnitOfWork(user_id=user_id, session=session))\n\n\n@app.patch(\"/revenues/{revenue_id}\", response_model=RevenueReadModel)\nasync def update_revenue_endpoint(\n revenue_id: PyObjectId,\n data: CreateRevenue,\n user_id: int = Header(...),\n session: MongoSession = Depends(get_session),\n):\n with MongoUnitOfWork(user_id=user_id, session=session) as uow:\n result = uow.revenues.update(revenue_id=revenue_id, revenue=Revenue(**data.dict()))\n uow.commit()\n if result is None:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Revenue not found\")\n return result\n\n\n# endregion: endpoints\n","repo_name":"MuriloScarpaSitonio/multi-sources-financial-control","sub_path":"revenues/src/entrypoints/fastapi_app.py","file_name":"fastapi_app.py","file_ext":"py","file_size_in_byte":7155,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"25388563579","text":"from django.db import models\n\nfrom erp.models import BaseModel\n\n# Create your models here.\n\n\nclass Customer(BaseModel):\n first_name = models.CharField(max_length=200, null=True, blank=True)\n middle_name = models.CharField(max_length=200, null=True, blank=True)\n last_name = models.CharField(max_length=200, null=True, blank=True)\n date_of_birth = models.DateField(max_length=200, null=True, blank=True)\n address = models.CharField(max_length=200, null=True, blank=True)\n occupation = models.CharField(max_length=200, null=True, blank=True)\n company = models.CharField(max_length=200, null=True, blank=True)\n phone_number = models.CharField(max_length=200, null=True, blank=True)\n email = models.EmailField(max_length=200, null=True, blank=True)\n\n class Meta:\n verbose_name = \"customer\"\n verbose_name_plural = \"customers\"\n ordering = ['-created_at']\n permissions = []\n\n def __str__(self):\n return f\"{self.first_name} {self.last_name}\"\n\n @classmethod\n def get_all_customers(cls):\n customers = Customer.objects.all().values()\n return customers\n\n @classmethod\n def get_customer(cls, **kwargs):\n customer = Customer.objects.filter(**kwargs).values()\n return customer\n\n @classmethod\n def create_customer(cls, **kwargs):\n customer = None\n try:\n customer = Customer.objects.create(**kwargs)\n except Exception as e:\n print(f\"Failed to create customer. Error below: \\n {e}\")\n return customer\n\n @classmethod\n def update_customer(cls, customer_id, **kwargs):\n customer = None\n try:\n customer = Customer.objects.filter(id=customer_id).update(**kwargs)\n except Exception as e:\n print(f\"Failed to update customer. Error below: \\n {e}\")\n return customer\n\n @classmethod\n def delete_customer(cls, customer_id):\n customer = None\n try:\n customer = Customer.objects.filter(id=customer_id).delete()\n except Exception as e:\n print(f\"Failed to delete customer. Error below: \\n {e}\")\n return customer\n\n @classmethod\n def delete_all_customers(cls):\n customer = None\n try:\n customer = Customer.objects.all().delete()\n except Exception as e:\n print(f\"Failed to delete customer. Error below: \\n {e}\")\n return customer\n","repo_name":"ALIYUABUBAKAR23/learning-and-dev","sub_path":"api/crm/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"27108318230","text":"from django.test import TestCase\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom files.models import File\n\n\nclass URLTestCase(TestCase):\n def test_file_list_url(self):\n \"\"\"\n Проверяет, что URL для списка файлов возвращает статус 200 (OK).\n \"\"\"\n url = reverse(\"file-list\")\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_file_detail_url(self):\n \"\"\"\n Проверяет, что URL для деталей файла возвращает статус 200 (OK) после создания файла с id=10.\n \"\"\"\n File.objects.create(id=10, file=\"test_file.txt\")\n url = reverse(\"file-detail\", args=[10])\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n","repo_name":"Leenominai/test_picasso","sub_path":"backend/filemanager/api/tests/test_urls.py","file_name":"test_urls.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"14054294306","text":"input = open(\"./day3/input.txt\", \"r\")\nrows = []\n\nfor line in input:\n rows.append(line)\n\ninput.close()\n\n## PART 1\n\nbase_index = 0\nnum_trees = 0\nrow_length = len(rows[0]) - 1\n\nfor row in rows:\n value = row[base_index % row_length]\n num_trees += value == '#'\n base_index += 3\n\nprint(\"Number of trees: \" + str(num_trees))\n\n## PART 2\n\nbase_index = 0\nnum_trees = 0\nrow_length = len(rows[0]) - 1\n\ntraverse_right_amount = [1, 3, 5, 7, 1]\nonly_evens = [False, False, False, False, True]\nanswer = 1\ntrees = []\n\nfor i in range(len(traverse_right_amount)):\n base_index = 0\n num_trees = 0\n\n for j in range(len(rows)):\n # skip if odd row (proxy for traversing down two)\n if only_evens[i] and j % 2 != 0:\n continue\n\n row = rows[j]\n value = row[base_index % row_length]\n num_trees += value == '#'\n base_index += traverse_right_amount[i]\n\n trees.append(num_trees)\n answer *= num_trees\n\nprint(\"Number of trees: \" + str(answer))","repo_name":"tmonfre/advent-of-code-2020","sub_path":"day3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"20646354508","text":"from glob import glob\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nfrom matplotlib.gridspec import GridSpec\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nfrom matplotlib.pyplot import Line2D\nfrom matplotlib.ticker import MaxNLocator\nimport pandas as pd\n\n\ndef get_data(infile, stat):\n df = pd.read_hdf(infile)\n x = df.index\n y = df[stat].values\n yerr_min = y - df[stat + '_err'].values\n yerr_max = y + df[stat + '_err'].values\n return x, y, yerr_min, yerr_max\n\n\nif __name__ == '__main__':\n # Figure parameters\n nrows = 15\n ncols = 8\n figsize = (24, 24)\n # Plots and labels parameters\n statistics = ['var', 'skew', 'kurt', 'skew_norm', 'kurt_norm']\n ylabels = dict(var='Variance $(\\sigma^2)$ [mK$^2$]',\n skew='Skewness $(m_3 / \\sigma^3)$',\n kurt='Excess Kurtosis $(m_4 / \\sigma^4 - 3)$')\n xlims = (139.915, 195.235)\n ylims = dict(var=(-0.1, 1.0), skew=(-1, 1.5), kurt=(-1, 2.5))\n bandwidths = np.arange(1, 16) # rows\n telescopes = ['hera37', 'hera61', 'hera91', 'hera127',\n 'hera169', 'hera217', 'hera271', 'hera331'] # cols\n axlabels = np.array(\n ['{:s} {:d} MHz'.format(tel, bw)\n for tel in telescopes for bw in bandwidths]\n )\n axlabels.shape = (nrows, ncols)\n bbox = dict(boxstyle=\"round\", fc=\"none\")\n\n # Legend parameters\n handlers = [\n Line2D([], [], linestyle='--', color='black', linewidth=1),\n Line2D([], [], linestyle='-', color='black', linewidth=1),\n Patch(color='0.7'),\n Patch(color='0.55')\n ]\n labels = ['Windowing', 'Binning', 'Error (Windowing)', 'Error (Binning)']\n\n # Get the list of input files\n stats_dir = '/Users/piyanat/research/pdf_paper/new_stats/'\n df_files_window = np.array(\n [stats_dir + '{:s}_gauss_stats_df_bw{:.2f}MHz_windowing.h5'\n .format(tel, bw) for bw in bandwidths for tel in telescopes ]\n )\n df_files_bin = np.array(\n [stats_dir + '{:s}_gauss_stats_df_bw{:.2f}MHz_binning.h5'\n .format(tel, bw) for bw in bandwidths for tel in telescopes]\n )\n df_files_window.shape = (nrows, ncols)\n df_files_bin.shape = (nrows, ncols)\n # print(df_files_bin)\n\n # Loop over stats and plot\n for stat in statistics:\n # Init figure\n fig, ax = plt.subplots(nrows, ncols, sharex=True, sharey=True,\n figsize=figsize,\n gridspec_kw=dict(wspace=0, hspace=0))\n\n # Plot spectral varying cases\n for i in range(nrows):\n for j in range(ncols):\n x1, y1, y1err_min, y1err_max = get_data(\n df_files_window[i, j], stat\n )\n x2, y2, y2err_min, y2err_max = get_data(\n df_files_bin[i, j], stat\n )\n ax[i, j].fill_between(x1, y1err_min, y1err_max, color='0.7')\n ax[i, j].fill_between(x2, y2err_min, y2err_max, color='0.55')\n ax[i, j].plot(x1, y1, 'k--', linewidth=1)\n ax[i, j].plot(x2, y2, 'k-', linewidth=1)\n if i == nrows - 1:\n ax[i, j].set_xlabel(telescopes[j])\n if j == 0:\n ax[i, j].set_ylabel('{:d} MHz'.format(bandwidths[i]))\n # ax[i, j].text(0.03, 0.8, axlabels[i, j],\n # transform=ax[i, j].transAxes, bbox=bbox)\n\n ax[i, j].grid()\n ax[0, 0].set_xlim(*xlims)\n ax[0, 0].set_ylim(*ylims[stat])\n # Axes labels\n fig.text(0.01, 0.5, ylabels[stat], rotation='vertical',\n horizontalalignment='left', verticalalignment='center')\n fig.text(0.5, 0.01, 'Frequency [MHz]', horizontalalignment='center',\n verticalalignment='bottom')\n # fig.text(0.5, 0.99, 'Ionized Fraction', horizontalalignment='center',\n # verticalalignment='top')\n\n # Legend\n plt.figlegend(handles=handlers, labels=labels, loc='upper center',\n ncol=4, fontsize='medium')\n\n # Tidy up\n fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.98])\n fig.canvas.draw()\n # plt.show()\n fig.savefig(stats_dir + 'heraXX_{:s}.pdf'.format(stat), dpi=200)\n plt.close()\n","repo_name":"piyanatk/visual","sub_path":"beam1p/plot_stats_grid2.py","file_name":"plot_stats_grid2.py","file_ext":"py","file_size_in_byte":4332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"38317489054","text":"from typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\nfrom dash import Dash, Input, Output, State, no_update\nfrom dash.exceptions import PreventUpdate\nfrom webviz_config.common_cache import CACHE\n\nfrom webviz_subsurface._models import ObservationModel\nfrom webviz_subsurface._providers import EnsembleTableProvider\n\nfrom ..figures.plotly_line_plot import PlotlyLinePlot\n\n\ndef build_figure(\n app: Dash,\n get_uuid: Callable,\n tableproviders: Dict[str, EnsembleTableProvider],\n observationmodel: Optional[ObservationModel],\n parameterproviders: Dict[str, EnsembleTableProvider],\n colors: Dict,\n) -> None:\n @app.callback(\n Output(\n {\"id\": get_uuid(\"clientside\"), \"plotly_attribute\": \"plotly_data\"}, \"data\"\n ),\n Output(\n {\"id\": get_uuid(\"clientside\"), \"plotly_attribute\": \"initial_layout\"}, \"data\"\n ),\n Input(get_uuid(\"stored_x_value\"), \"data\"),\n Input(\n {\n \"id\": get_uuid(\"data_selectors\"),\n \"data_attribute\": \"y\",\n \"source\": \"table\",\n },\n \"value\",\n ),\n Input(\n {\n \"id\": get_uuid(\"data_selectors\"),\n \"data_attribute\": \"ensemble\",\n \"source\": \"table\",\n },\n \"value\",\n ),\n Input(\n {\n \"id\": get_uuid(\"data_selectors\"),\n \"data_attribute\": \"parameter\",\n \"source\": \"parameter\",\n },\n \"value\",\n ),\n Input(get_uuid(\"traces\"), \"value\"),\n Input(get_uuid(\"observations\"), \"value\"),\n Input(get_uuid(\"highlight-realizations\"), \"value\"),\n Input(get_uuid(\"mode\"), \"value\"),\n Input({\"id\": get_uuid(\"parameter-filter\"), \"type\": \"data-store\"}, \"data\"),\n )\n # pylint: disable=too-many-locals, too-many-arguments\n def _update_plot(\n x_column_name: str,\n y_column_name: str,\n ensemble_names: Union[List, str],\n parameter_name: str,\n traces: List,\n show_obs: List,\n highlight_realizations: List[int],\n mode: str,\n parameter_filter: Optional[Dict],\n ) -> Union[Tuple]:\n if not x_column_name:\n raise PreventUpdate\n highlight_realizations = (\n [int(real) for real in highlight_realizations]\n if highlight_realizations\n else []\n )\n if not ensemble_names:\n return [], no_update\n ensemble_names = (\n [ensemble_names] if not isinstance(ensemble_names, list) else ensemble_names\n )\n real_filter = {} if parameter_filter is None else parameter_filter\n\n csv_dframe = get_table_data(\n tableproviders=tableproviders,\n ensemble_names=ensemble_names,\n table_column_names=[x_column_name, y_column_name],\n realization_filter=real_filter,\n )\n if parameter_name is not None:\n parameter_dframe = get_table_data(\n tableproviders=parameterproviders,\n ensemble_names=ensemble_names,\n table_column_names=[parameter_name],\n realization_filter=real_filter,\n )\n df = pd.merge(csv_dframe, parameter_dframe, on=[\"ENSEMBLE\", \"REAL\"])\n else:\n df = csv_dframe\n if df.empty:\n return [], {\"title\": \"No data found with current filter\"}\n figure = PlotlyLinePlot(\n xaxis_title=x_column_name, yaxis_title=y_column_name, ensemble_colors=colors\n )\n if \"Realizations\" in traces:\n figure.add_realization_traces(\n dframe=df,\n x_column=x_column_name,\n y_column=y_column_name,\n color_column=parameter_name,\n highlight_reals=highlight_realizations,\n opacity=0.5 if len(traces) > 1 else None,\n mode=mode,\n )\n traces.remove(\"Realizations\")\n\n if traces:\n stat_df = calc_series_statistics(df, [y_column_name], x_column_name)\n figure.add_statistical_lines(\n dframe=stat_df,\n x_column=x_column_name,\n y_column=y_column_name,\n traces=traces,\n mode=mode,\n )\n if show_obs and observationmodel is not None:\n observations = observationmodel.get_observations_for_attribute(\n attribute=y_column_name, value=x_column_name\n )\n if observations is not None:\n figure.add_observations(\n observations=observations, x_value=x_column_name\n )\n\n fig = figure.get_figure()\n data = fig[\"data\"]\n layout = fig[\"layout\"]\n return data, layout\n\n @app.callback(\n Output(get_uuid(\"highlight-realizations\"), \"value\"),\n Input(get_uuid(\"clear-highlight-realizations\"), \"n_clicks\"),\n )\n def _clear_real_highlight(_click: int) -> Optional[List]:\n if _click is not None:\n return []\n raise PreventUpdate\n\n @app.callback(\n Output(get_uuid(\"stored_x_value\"), \"data\"),\n Output(get_uuid(\"traces\"), \"options\"),\n Output(get_uuid(\"traces\"), \"value\"),\n Output(get_uuid(\"statistics_warning\"), \"children\"),\n Input(\n {\n \"id\": get_uuid(\"data_selectors\"),\n \"data_attribute\": \"x\",\n \"source\": \"table\",\n },\n \"value\",\n ),\n State(\n {\n \"id\": get_uuid(\"data_selectors\"),\n \"data_attribute\": \"ensemble\",\n \"source\": \"table\",\n },\n \"value\",\n ),\n State(get_uuid(\"traces\"), \"value\"),\n State({\"id\": get_uuid(\"parameter-filter\"), \"type\": \"data-store\"}, \"data\"),\n )\n def _update_statistics_options(\n x_column_name: str,\n ensemble_names: List,\n current_values: List,\n parameter_filter: Optional[Dict],\n ) -> Tuple[str, List[Dict], List, str]:\n \"\"\"Deactivate statistics calculations if x-axis is varying between realizations\"\"\"\n if not ensemble_names:\n raise PreventUpdate\n real_filter = {} if parameter_filter is None else parameter_filter\n csv_dframe = get_table_data(\n tableproviders=tableproviders,\n ensemble_names=ensemble_names,\n table_column_names=[x_column_name],\n realization_filter=real_filter,\n )\n for _ens, ens_df in csv_dframe.groupby(\"ENSEMBLE\"):\n realizations = list(ens_df[\"REAL\"].unique())\n equal_x = all(\n (\n np.all(\n ens_df.loc[ens_df[\"REAL\"] == real][x_column_name].values\n == ens_df.loc[ens_df[\"REAL\"] == realizations[0]][\n x_column_name\n ].values\n )\n for real in realizations\n )\n )\n if not equal_x:\n return (\n x_column_name,\n [\n {\"label\": \"Realizations\", \"value\": \"Realizations\"},\n {\n \"label\": \"Mean\",\n \"value\": \"Mean\",\n \"disabled\": True,\n },\n {\n \"label\": \"P10/P90\",\n \"value\": \"P10/P90\",\n \"disabled\": True,\n },\n {\n \"label\": \"Low/High\",\n \"value\": \"Low/High\",\n \"disabled\": True,\n },\n ],\n [\"Realizations\"] if \"Realizations\" in current_values else [],\n \"⚠️ Cannot calculate statistics as x-axis varies between realizations\",\n )\n return (\n x_column_name,\n [\n {\"label\": val, \"value\": val}\n for val in [\"Realizations\", \"Mean\", \"P10/P90\", \"Low/High\"]\n ],\n current_values,\n \"\",\n )\n\n\n@CACHE.memoize()\ndef calc_series_statistics(\n df: pd.DataFrame, vectors: list, refaxis: str = \"DATE\"\n) -> pd.DataFrame:\n \"\"\"Calculate statistics for given vectors over the ensembles\n refaxis is used if another column than DATE should be used to groupby.\n \"\"\"\n\n # Invert p10 and p90 due to oil industry convention.\n def p10(x: List[float]) -> np.floating:\n return np.nanpercentile(x, q=90)\n\n def p90(x: List[float]) -> np.floating:\n return np.nanpercentile(x, q=10)\n\n # Calculate statistics, ignoring NaNs.\n stat_df = (\n df[[\"ENSEMBLE\", refaxis] + vectors]\n .groupby([\"ENSEMBLE\", refaxis])\n .agg([np.nanmean, np.nanmin, np.nanmax, p10, p90])\n .reset_index() # level=[\"label\", refaxis], col_level=0)\n )\n # Rename nanmin, nanmax and nanmean to min, max and mean.\n col_stat_label_map = {\n \"nanmin\": \"min\",\n \"nanmax\": \"max\",\n \"nanmean\": \"mean\",\n \"p10\": \"high_p10\",\n \"p90\": \"low_p90\",\n }\n stat_df.rename(columns=col_stat_label_map, level=1, inplace=True)\n\n return stat_df\n\n\n@CACHE.memoize()\ndef get_table_data(\n tableproviders: Dict[str, EnsembleTableProvider],\n ensemble_names: List,\n table_column_names: List,\n realization_filter: Dict[str, List],\n) -> pd.DataFrame:\n dfs = []\n for ens_name in ensemble_names:\n if not realization_filter.get(ens_name):\n dframe = pd.DataFrame(columns=[\"ENSEMBLE\", \"REAL\"] + table_column_names)\n else:\n provider = tableproviders[ens_name]\n dframe = provider.get_column_data(\n table_column_names, realizations=realization_filter.get(ens_name)\n )\n dframe[\"ENSEMBLE\"] = ens_name\n dfs.append(dframe)\n if len(dfs) > 0:\n return pd.concat(dfs)\n return pd.DataFrame()\n","repo_name":"equinor/webviz-subsurface","sub_path":"webviz_subsurface/plugins/_line_plotter_fmu/controllers/build_figure.py","file_name":"build_figure.py","file_ext":"py","file_size_in_byte":10192,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"75"} +{"seq_id":"21016156956","text":"# === Imports ===\nimport time\nimport numpy as np\nimport random\n\nimport pipes\nimport Live_Plot_Data_Point as livePlotter\nfrom utilities import DataLoggerUtility as dlu\n\n\n\n# === Main ===\ndef run(parameters, smu_systems, arduino_systems, share=None):\n\t# This script uses the default SMU, which is the first one in the list of SMU systems\n\tsmu_names = list(smu_systems.keys())\n\tsmu_instance = smu_systems[smu_names[0]]\n\n\t# Get shorthand name to easily refer to configuration parameters\n\tfr_parameters = parameters['runConfigs']['FreeRun']\n\n\t# Print the starting message\n\tprint('Free running: min V_DS='+str(fr_parameters['drainVoltageMinimum'])+'V, max V_DS='+str(fr_parameters['drainVoltageMaximum'])+'V, min V_GS='+str(fr_parameters['gateVoltageMinimum'])+'V, max V_GS='+str(fr_parameters['gateVoltageMaximum'])+'V')\n\tsmu_instance.setComplianceCurrent(fr_parameters['complianceCurrent'])\n\n\t# === START ===\n\tprint('Beginning to free run.')\n\tresults = runFree( smu_instance,\n\t\t\t\t\t\tpointLimit=fr_parameters['pointLimit'],\n\t\t\t\t\t\tgateVoltageMinimum=fr_parameters['gateVoltageMinimum'],\n\t\t\t\t\t\tgateVoltageMaximum=fr_parameters['gateVoltageMaximum'],\n\t\t\t\t\t\tdrainVoltageMinimum=fr_parameters['drainVoltageMinimum'],\n\t\t\t\t\t\tdrainVoltageMaximum=fr_parameters['drainVoltageMaximum'],\n\t\t\t\t\t\tdelayBetweenMeasurements=fr_parameters['delayBetweenMeasurements'],\n\t\t\t\t\t\tstepsBetweenMeasurements=fr_parameters['stepsBetweenMeasurements'],\n\t\t\t\t\t\tgateVoltageDistribution=fr_parameters['gateVoltageDistribution'],\n\t\t\t\t\t\tdrainVoltageDistribution=fr_parameters['drainVoltageDistribution'],\n\t\t\t\t\t\tgateVoltageDistributionParameter=fr_parameters['gateVoltageDistributionParameter'],\n\t\t\t\t\t\tdrainVoltageDistributionParameter=fr_parameters['drainVoltageDistributionParameter'],\n\t\t\t\t\t\tshare=share)\n\t\n\t# Ramp down channels\n\tsmu_instance.rampDownVoltages()\n\t# === COMPLETE ===\n\n\t# Copy parameters and add in the test results\n\tjsonData = dict(parameters)\n\n\treturn jsonData\n\n# === Data Collection ===\ndef runFree(smu_instance, pointLimit, gateVoltageMinimum, gateVoltageMaximum, drainVoltageMinimum, drainVoltageMaximum, delayBetweenMeasurements=0, stepsBetweenMeasurements=10, gateVoltageDistribution='random', drainVoltageDistribution='random', gateVoltageDistributionParameter=2, drainVoltageDistributionParameter=2, share=None):\n\tindex = 0\n\t\n\t# Define possible distributions\n\tdistributions = {\n\t\t'random': lambda minimum, maximum, index, factor: random.uniform(minimum, maximum),\n\t\t'striped': lambda minimum, maximum, index, factor: random.choice(np.linspace(minimum, maximum, factor)),\n\t\t'looped': lambda minimum, maximum, index, factor: (list(np.linspace(minimum, maximum, factor)) + list(np.linspace(maximum, minimum, factor)))[index % (2*factor)]\n\t}\n\tnextGateVoltage = distributions[gateVoltageDistribution]\n\tnextDrainVoltage = distributions[drainVoltageDistribution]\n\t\n\t# Decide if this Free Run should be limited or unlimited\n\ttry:\n\t\tint(pointLimit)\n\t\tunlimited = False\n\texcept:\n\t\tunlimited = True\n\tprint('Running in unlimited mode.') if(unlimited) else print('Running with point limit: ' + str(pointLimit))\n\t\n\t# Begin continuously applying randomly selected voltages from their distributions and taking measurements (until an interrupt is triggers by pipes, or a limit is reached)\n\twhile(unlimited or (index < pointLimit)):\n\t\t# Send a progress message\n\t\tpipes.progressUpdate(share, 'Free Run Point', start=0, current=index, end=(index if(unlimited) else pointLimit), enableAbort=True)\n\n\t\t# Get drain and gate voltage from random distributions\n\t\tgateVoltage = nextGateVoltage(gateVoltageMinimum, gateVoltageMaximum, index, gateVoltageDistributionParameter)\n\t\tdrainVoltage = nextDrainVoltage(drainVoltageMinimum, drainVoltageMaximum, index, drainVoltageDistributionParameter)\n\n\t\t# Apply bias voltages\n\t\tif(stepsBetweenMeasurements <= 1):\n\t\t\tsmu_instance.setVgs(gateVoltage)\n\t\t\tsmu_instance.setVds(drainVoltage)\n\t\telse:\n\t\t\tsmu_instance.rampGateVoltageTo(gateVoltage, steps=stepsBetweenMeasurements)\n\t\t\tsmu_instance.rampDrainVoltageTo(drainVoltage, steps=stepsBetweenMeasurements)\n\n\t\t# If delayBetweenMeasurements is non-zero, wait before taking the measurement\n\t\tif(delayBetweenMeasurements > 0):\n\t\t\ttime.sleep(delayBetweenMeasurements)\n\n\t\t# Take Measurement and save it\n\t\tmeasurement = smu_instance.takeMeasurement()\n\n\t\t# Send a data message\n\t\tpipes.livePlotUpdate(share, plots=\n\t\t[livePlotter.createLiveDataPoint(plotID='Transfer Curve', \n\t\t\t\t\t\t\t\t\t\tlabels=['Drain Current'],\n\t\t\t\t\t\t\t\t\t\txValues=[measurement['V_gs']], \n\t\t\t\t\t\t\t\t\t\tyValues=[measurement['I_d']], \n\t\t\t\t\t\t\t\t\t\txAxisTitle='Gate Voltage (V)', \n\t\t\t\t\t\t\t\t\t\tyAxisTitle='Drain Current (A)', \n\t\t\t\t\t\t\t\t\t\tyscale='linear', \n\t\t\t\t\t\t\t\t\t\tplotMode='markers',\n\t\t\t\t\t\t\t\t\t\tenumerateLegend=False,\n\t\t\t\t\t\t\t\t\t\ttimeseries=False),\n\t\t livePlotter.createLiveDataPoint(plotID='Subthreshold Curve', \n\t\t \t\t\t\t\t\t\t\tlabels=['Drain Current'],\n\t\t\t\t\t\t\t\t\t\txValues=[measurement['V_gs']], \n\t\t\t\t\t\t\t\t\t\tyValues=[abs(measurement['I_d'])], \n\t\t\t\t\t\t\t\t\t\txAxisTitle='Gate Voltage (V)', \n\t\t\t\t\t\t\t\t\t\tyAxisTitle='Drain Current (A)', \n\t\t\t\t\t\t\t\t\t\tyscale='log', \n\t\t\t\t\t\t\t\t\t\tplotMode='markers',\n\t\t\t\t\t\t\t\t\t\tenumerateLegend=False,\n\t\t\t\t\t\t\t\t\t\ttimeseries=False),\n\t\t livePlotter.createLiveDataPoint(plotID='Output Curve', \n\t\t \t\t\t\t\t\t\t\tlabels=['Drain Current'],\n\t\t\t\t\t\t\t\t\t\txValues=[measurement['V_ds']], \n\t\t\t\t\t\t\t\t\t\tyValues=[measurement['I_d']], \n\t\t\t\t\t\t\t\t\t\txAxisTitle='Drain Voltage (V)', \n\t\t\t\t\t\t\t\t\t\tyAxisTitle='Drain Current (A)', \n\t\t\t\t\t\t\t\t\t\tyscale='linear', \n\t\t\t\t\t\t\t\t\t\tplotMode='markers',\n\t\t\t\t\t\t\t\t\t\tenumerateLegend=False,\n\t\t\t\t\t\t\t\t\t\ttimeseries=False),\n\t\t])\n\t\t\n\t\t# Increment loop index\n\t\tindex += 1\n\t\n\t# Send a final progress update if the loop is ever exited (for example, if not running in unlimited mode)\n\tpipes.progressUpdate(share, 'Free Run Point', start=0, current=index, end=(index if(unlimited) else pointLimit), enableAbort=True)\n\t\t\t\t\t\n\treturn {\n\t\t\n\t}\n\n\n\n\t","repo_name":"stevennoyce/AutexysHost","sub_path":"source/procedures/Free_Run.py","file_name":"Free_Run.py","file_ext":"py","file_size_in_byte":5828,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"39141419914","text":"# check utils zdecomp\n\n\ndef izmat_zdecomp():\n import numpy as np\n from limetr.special_mat import izmat\n\n ok = True\n tol = 1e-10\n # setup problem\n # -------------------------------------------------------------------------\n k = 3\n n = [5, 2, 4]\n\n z_list = []\n tr_u_list = []\n tr_s_list = []\n for i in range(len(n)):\n z_list.append(np.random.randn(n[i], k))\n u, s, vt = np.linalg.svd(z_list[-1], full_matrices=False)\n tr_u_list.append(u)\n tr_s_list.append(s)\n\n z = np.vstack(z_list)\n tr_u = np.hstack([u.reshape(u.size, order='F') for u in tr_u_list])\n tr_s = np.hstack(tr_s_list)\n\n my_u = np.zeros(tr_u.size)\n my_s = np.zeros(tr_s.size)\n\n nz = [z_sub.shape[0] for z_sub in z_list]\n nu = [u_sub.size for u_sub in tr_u_list]\n ns = [s_sub.size for s_sub in tr_s_list]\n\n izmat.zdecomp(nz, nu, ns, z, my_u, my_s)\n\n\n if not ok:\n print('err in zdecomp')\n print('err:', err)\n\n return ok\n","repo_name":"ihmeuw-msca/burden-of-proof","sub_path":"limetr/tests/izmat_zdecomp.py","file_name":"izmat_zdecomp.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"75"} +{"seq_id":"27316236481","text":"import xml.dom.minidom\nfrom os.path import join\nimport os\nimport torch\n\nfrom mega_core.structures.bounding_box import BoxList\n# transpose\nFLIP_LEFT_RIGHT = 0\nFLIP_TOP_BOTTOM = 1\n\n\nlabelDict = {\"n02691156\": 1, \"n02419796\": 2, \"n02131653\": 3, \"n02834778\": 4, \"n01503061\": 5, \"n02924116\": 6, \"n02958343\": 7,\n \"n02402425\": 8, \"n02084071\": 9, \"n02121808\": 10, \"n02503517\": 11, \"n02118333\": 12, \"n02510455\": 13, \"n02342885\": 14, \"n02374451\": 15,\n \"n02129165\": 16, \"n01674464\": 17, \"n02484322\": 18, \"n03790512\": 19, \"n02324045\": 20, \"n02509815\": 21, \"n02411705\": 22,\n \"n01726692\": 23, \"n02355227\": 24, \"n02129604\": 25, \"n04468005\": 26, \"n01662784\": 27, \"n04530566\": 28, \"n02062744\": 29, \"n02391049\": 30}\n\ngtPrediction = []\nfor video in sorted(os.listdir(\"ILSVRC2015/Annotations/VID/val/\")):\n annoDir = \"ILSVRC2015/Annotations/VID/val/%s\" % video\n infoDir = \"videoInfo/%s\" % video\n for fileName in sorted(os.listdir(annoDir)):\n # 左上角右下角与图片大小\n dom = xml.dom.minidom.parse(join(annoDir, fileName)).documentElement\n width = dom.getElementsByTagName(\"width\")[0].childNodes[0].data\n height = dom.getElementsByTagName(\"height\")[0].childNodes[0].data\n objectLists = dom.getElementsByTagName(\"object\")\n allBBox = []\n scores = []\n labels = []\n for BBoxObject in objectLists:\n xmin = int(BBoxObject.getElementsByTagName(\n \"xmin\")[0].childNodes[0].data)\n ymin = int(BBoxObject.getElementsByTagName(\n \"ymin\")[0].childNodes[0].data)\n xmax = int(BBoxObject.getElementsByTagName(\n \"xmax\")[0].childNodes[0].data)\n ymax = int(BBoxObject.getElementsByTagName(\n \"ymax\")[0].childNodes[0].data)\n name = BBoxObject.getElementsByTagName(\n \"name\")[0].childNodes[0].data\n allBBox.append([xmin, ymin, xmax, ymax])\n scores.append(1)\n labels.append(labelDict[name])\n if allBBox == []:\n allBBox.append([0, 0, 0, 0])\n scores.append(0)\n labels.append(1)\n\n bboxList = BoxList(allBBox, (width, height))\n bboxList.add_field(\"scores\", torch.Tensor(scores))\n bboxList.add_field(\"labels\", torch.Tensor(labels).int())\n gtPrediction.append(bboxList)\ntorch.save(gtPrediction, \"gtPrediction.pth\")\n","repo_name":"aqluheng/MultiRef","sub_path":"scirptsBak/transformPrediction.py","file_name":"transformPrediction.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30399756427","text":"import fractions\nimport math\nfrom collections import Counter\n\nfrom nltk.util import ngrams\n\n\ndef sentence_nist(references, hypothesis, n=5):\n \"\"\"\n Calculate NIST score from\n George Doddington. 2002. \"Automatic evaluation of machine translation quality\n using n-gram co-occurrence statistics.\" Proceedings of HLT.\n Morgan Kaufmann Publishers Inc. https://dl.acm.org/citation.cfm?id=1289189.1289273\n\n DARPA commissioned NIST to develop an MT evaluation facility based on the BLEU\n score. The official script used by NIST to compute BLEU and NIST score is\n mteval-14.pl. The main differences are:\n\n - BLEU uses geometric mean of the ngram overlaps, NIST uses arithmetic mean.\n - NIST has a different brevity penalty\n - NIST score from mteval-14.pl has a self-contained tokenizer\n\n Note: The mteval-14.pl includes a smoothing function for BLEU score that is NOT\n used in the NIST score computation.\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n ... 'forever', 'hearing', 'the', 'activity', 'guidebook',\n ... 'that', 'party', 'direct']\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will', 'forever',\n ... 'heed', 'Party', 'commands']\n\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n\n >>> sentence_nist([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS\n 3.3709...\n\n >>> sentence_nist([reference1, reference2, reference3], hypothesis2) # doctest: +ELLIPSIS\n 1.4619...\n\n :param references: reference sentences\n :type references: list(list(str))\n :param hypothesis: a hypothesis sentence\n :type hypothesis: list(str)\n :param n: highest n-gram order\n :type n: int\n \"\"\"\n return corpus_nist([references], [hypothesis], n)\n\n\ndef corpus_nist(list_of_references, hypotheses, n=5):\n \"\"\"\n Calculate a single corpus-level NIST score (aka. system-level BLEU) for all\n the hypotheses and their respective references.\n\n :param references: a corpus of lists of reference sentences, w.r.t. hypotheses\n :type references: list(list(list(str)))\n :param hypotheses: a list of hypothesis sentences\n :type hypotheses: list(list(str))\n :param n: highest n-gram order\n :type n: int\n \"\"\"\n # Before proceeding to compute NIST, perform sanity checks.\n assert len(list_of_references) == len(\n hypotheses\n ), \"The number of hypotheses and their reference(s) should be the same\"\n\n # Collect the ngram coounts from the reference sentences.\n ngram_freq = Counter()\n total_reference_words = 0\n for (\n references\n ) in list_of_references: # For each source sent, there's a list of reference sents.\n for reference in references:\n # For each order of ngram, count the ngram occurrences.\n for i in range(1, n + 1):\n ngram_freq.update(ngrams(reference, i))\n total_reference_words += len(reference)\n\n # Compute the information weights based on the reference sentences.\n # Eqn 2 in Doddington (2002):\n # Info(w_1 ... w_n) = log_2 [ (# of occurrences of w_1 ... w_n-1) / (# of occurrences of w_1 ... w_n) ]\n information_weights = {}\n for _ngram in ngram_freq: # w_1 ... w_n\n _mgram = _ngram[:-1] # w_1 ... w_n-1\n # From https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v13a.pl#L546\n # it's computed as such:\n # denominator = ngram_freq[_mgram] if _mgram and _mgram in ngram_freq else denominator = total_reference_words\n # information_weights[_ngram] = -1 * math.log(ngram_freq[_ngram]/denominator) / math.log(2)\n #\n # Mathematically, it's equivalent to the our implementation:\n if _mgram and _mgram in ngram_freq:\n numerator = ngram_freq[_mgram]\n else:\n numerator = total_reference_words\n information_weights[_ngram] = math.log(numerator / ngram_freq[_ngram], 2)\n\n # Micro-average.\n nist_precision_numerator_per_ngram = Counter()\n nist_precision_denominator_per_ngram = Counter()\n l_ref, l_sys = 0, 0\n # For each order of ngram.\n for i in range(1, n + 1):\n # Iterate through each hypothesis and their corresponding references.\n for references, hypothesis in zip(list_of_references, hypotheses):\n hyp_len = len(hypothesis)\n\n # Find reference with the best NIST score.\n nist_score_per_ref = []\n for reference in references:\n _ref_len = len(reference)\n # Counter of ngrams in hypothesis.\n hyp_ngrams = (\n Counter(ngrams(hypothesis, i))\n if len(hypothesis) >= i\n else Counter()\n )\n ref_ngrams = (\n Counter(ngrams(reference, i)) if len(reference) >= i else Counter()\n )\n ngram_overlaps = hyp_ngrams & ref_ngrams\n # Precision part of the score in Eqn 3\n _numerator = sum(\n information_weights[_ngram] * count\n for _ngram, count in ngram_overlaps.items()\n )\n _denominator = sum(hyp_ngrams.values())\n _precision = 0 if _denominator == 0 else _numerator / _denominator\n nist_score_per_ref.append(\n (_precision, _numerator, _denominator, _ref_len)\n )\n # Best reference.\n precision, numerator, denominator, ref_len = max(nist_score_per_ref)\n nist_precision_numerator_per_ngram[i] += numerator\n nist_precision_denominator_per_ngram[i] += denominator\n l_ref += ref_len\n l_sys += hyp_len\n\n # Final NIST micro-average mean aggregation.\n nist_precision = 0\n for i in nist_precision_numerator_per_ngram:\n precision = (\n nist_precision_numerator_per_ngram[i]\n / nist_precision_denominator_per_ngram[i]\n )\n nist_precision += precision\n # Eqn 3 in Doddington(2002)\n return nist_precision * nist_length_penalty(l_ref, l_sys)\n\n\ndef nist_length_penalty(ref_len, hyp_len):\n \"\"\"\n Calculates the NIST length penalty, from Eq. 3 in Doddington (2002)\n\n penalty = exp( beta * log( min( len(hyp)/len(ref) , 1.0 )))\n\n where,\n\n `beta` is chosen to make the brevity penalty factor = 0.5 when the\n no. of words in the system output (hyp) is 2/3 of the average\n no. of words in the reference translation (ref)\n\n The NIST penalty is different from BLEU's such that it minimize the impact\n of the score of small variations in the length of a translation.\n See Fig. 4 in Doddington (2002)\n \"\"\"\n ratio = hyp_len / ref_len\n if 0 < ratio < 1:\n ratio_x, score_x = 1.5, 0.5\n beta = math.log(score_x) / math.log(ratio_x) ** 2\n return math.exp(beta * math.log(ratio) ** 2)\n else: # ratio <= 0 or ratio >= 1\n return max(min(ratio, 1.0), 0.0)\n","repo_name":"nltk/nltk","sub_path":"nltk/translate/nist_score.py","file_name":"nist_score.py","file_ext":"py","file_size_in_byte":7737,"program_lang":"python","lang":"en","doc_type":"code","stars":12541,"dataset":"github-code","pt":"75"} +{"seq_id":"31320936167","text":"#load(\"@bazel_tools//tools/build_defs/repo:git.bzl\", \"git_repository\")\nload(\"@bazel_tools//tools/build_defs/repo:http.bzl\", \"http_archive\")\n\ndef bazel_deps_repository(name):\n commit = \"89a34bf761e48883abb65922443e036ee26bf696\"\n url = \"https://github.com/mjbots/bazel_deps/archive/{}.zip\".format(commit)\n sha256 = \"7f220121e919319b35dccfe592256d68ea682dceb9d3739044d1355af659b452\"\n print(\"---sss--- Loading repository from url\", url)\n archive = http_archive(\n name = \"com_github_mjbots_bazel_deps\",\n url = url,\n # Try the following empty sha256 hash first, then replace with whatever\n # bazel says it is looking for once it complains.\n sha256 = sha256,\n strip_prefix = \"bazel_deps-{}\".format(commit),\n )\n print (\"---sss--- result of loading\", archive)\n#def bazel_deps_repository(name):\n# native.local_repository(\n# name=\"com_github_mjbots_bazel_deps\",\n# path=\"/home/gsasha/work/beaglebone/third_party/bazel_deps\"\n# )\n","repo_name":"gsasha/ledagent","sub_path":"tools/workspace/bazel_deps/repository.bzl","file_name":"repository.bzl","file_ext":"bzl","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"20673219609","text":"import numpy as np\nfrom math import cos,sin\nimport math\nimport time\nimport matplotlib.pyplot as plt\nimport tbglib\n\ns0 = np.array([[1, 0],[ 0, 1]])\nsx = np.array([[0, 1],[ 1, 0]])\nsy = np.array([[0, -1j],[1j, 0]])\nsz = np.array([[1, 0],[0, -1]])\n\n\ndef build_lattice(radius):\n size = math.ceil(radius)\n lattice = [] \n # 0,0 coordinate in terms of q1, q2 in layer 0\n if tbglib.norm(tbglib.coords([0,0])) < radius:\n lattice.append([0,0,0])\n if tbglib.norm(tbglib.coords([1,0])) < radius:\n lattice.append([1,0,1])\n for i in range(-size,size):\n for j in range(-size,size):\n if i==j==0:\n continue\n x = i*tbglib.g1_coeff[0] + j*tbglib.g2_coeff[0]\n y = i*tbglib.g1_coeff[1] + j*tbglib.g2_coeff[1]\n if tbglib.norm(tbglib.coords([x,y])) < radius:\n lattice.append([x,y,0])\n if tbglib.norm(tbglib.coords([x+1,y])) < radius:\n lattice.append([x+1,y,1])\n # layer symmetric way of doing it - for each layer consider\n # k + G where |G| server -> subprocess -> back1 -> back2) and maybe also need to create a new process , which could take a long period of upto 1ms.Thus ,this method is recomended if and only if you can save more time than that ,for example if you would like to compute some kind of recursived fibonacci sequence with a maximum depth of 40 using pypy etc.\n'''\n\nclass MagicianAssistant:\n\n def __init__(self , code):\n self._code = code\n\ndef magicwarp(func:Callable) -> MagicianAssistant:\n # solution below has some namespace problem\n '''func_id = sys._getframe().f_locals['func']\n for key ,ids in sys._getframe().f_globals.items():\n if ids == func_id:\n target_name = key ;break\n else:\n raise AttributeError(\"Didnot find lambda function\")'''\n if func.__name__ != '':\n raise TypeError('Currently only lambda functions is supported for magic call')\n active = inspect.getframeinfo(inspect.currentframe().f_back)[3][0]\n active = active[active.index(\".magicwarp(\")+11:]\n target_name = re.match('[a-zA-Z_$][a-zA-Z0-9_$]*',active)\n if target_name:\n target_name = target_name.group()\n else:\n raise AttributeError(\"Didnot find input name\")\n with open(sys.argv[0]) as f:\n cont = f.read()\n pat = re.compile(f'{target_name}.*=.*lambda.*:.+?\\n').findall(cont)\n if pat:\n for mat in pat:\n if 'target_name' not in mat:\n targstr = mat[:-1] \n targstr = targstr[targstr.index('=')+1:].strip() ; break\n else:\n raise AttributeError(\"Didnot find lambda function\")\n else:\n raise AttributeError(\"Didnot find lambda function\")\n\n return MagicianAssistant(targstr)\n\n@autocheck\ndef magiccall(self, assistant:MagicianAssistant) -> Callable:\n def warper(*args , **kwargs):\n if kwargs:\n raise AttributeError(\"kwargs not allowed in magiccall\")\n if args:\n args = args[0]\n if isinstance(args,int):\n args = f'I{args}\\n'\n elif isinstance(args,str):\n args = f'V{args}\\np0\\n'\n else:\n args = \"\"\n sending = b'\\x80\\x01' + f\"c__builtin__\\neval\\n(S'{assistant._code}'\\ntR({args}tR.\".encode()\n # print(f\"sending magic call : {sending}\")\n return partial(self._sync_call , funcname = 'magiccall' , args = ([sending],),kwargs = {})()\n return warper","repo_name":"GoodManWEN/easyrpc","sub_path":"easyrpc/blackmagic.py","file_name":"blackmagic.py","file_ext":"py","file_size_in_byte":3629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"32957341184","text":"import sys\r\nfrom random import choice, randint\r\n\r\nimport pygame\r\n\r\nfrom jugador import Jugador\r\nfrom marcianos import Marciano,NaveEnemiga\r\nfrom laser import Laser\r\n\r\n\r\nclass Background(pygame.sprite.Sprite):\r\n def __init__(self, image_file, location):\r\n pygame.sprite.Sprite.__init__(self) #call Sprite initializer\r\n self.image = pygame.image.load('resources/fUG2oM.jpg')\r\n self.rect = self.image.get_rect()\r\n self.rect.left, self.rect.top = location\r\n\r\n\r\nclass Juego:\r\n def __init__(self):\r\n jugador_sprite = Jugador((ancho_pantalla / 2, altura_pantalla),ancho_pantalla,7)\r\n self.jugador = pygame.sprite.GroupSingle(jugador_sprite)\r\n\r\n #Alien\r\n self.marcianos = pygame.sprite.Group()\r\n self.inicio_marcianos(filas=6,colum=8)\r\n self.direccion_marciano =3\r\n self.marciano_lasers= pygame.sprite.Group()\r\n\r\n #ExtraNave\r\n self.extraNave= pygame.sprite.GroupSingle()\r\n self.extraNave_spawn= randint(400,800)\r\n\r\n def inicio_marcianos(self,filas,colum,dist_x= 60,dist_y = 50,compensa_x= 350,compensa_y= 100):\r\n for fila_index, fila in enumerate(range(filas)):\r\n for col_index, col in enumerate(range(colum)):\r\n x = col_index * dist_x + compensa_x\r\n y = fila_index * dist_y + compensa_y\r\n\r\n marciano_sprite = Marciano(x,y)\r\n self.marcianos.add(marciano_sprite)\r\n def posicion_marcianos (self):\r\n total_marcianos = self.marcianos.sprites()\r\n for marciano in total_marcianos:\r\n if marciano.rect.right >= ancho_pantalla:\r\n self.direccion_marciano = -1\r\n self.marciano_mueveAbajo(3)\r\n elif marciano.rect.left <= 0:\r\n self.direccion_marciano=1\r\n self.marciano_mueveAbajo(3)\r\n def marciano_mueveAbajo (self,distancia):\r\n if self.marcianos:\r\n for marciano in self.marcianos.sprites():\r\n marciano.rect.y += distancia\r\n def marciano_disparo(self):\r\n if self.marcianos.sprites():\r\n random_marciano=choice(self.marcianos.sprites())\r\n sprite_laser = Laser(random_marciano.rect.center,3 ,altura_pantalla)\r\n self.marciano_lasers.add(sprite_laser)\r\n print('HOla')\r\n def extraNave_tiempo(self):\r\n self.extraNave_spawn -=1\r\n if self.extraNave_spawn <= 0:\r\n self.extraNave.add(NaveEnemiga(['right','left'],ancho_pantalla))\r\n def run(self):\r\n self.jugador.update()\r\n self.marcianos.update(self.direccion_marciano)\r\n self.posicion_marcianos()\r\n self.extraNave_tiempo()\r\n self.extraNave.update()\r\n\r\n self.marciano_lasers.update()\r\n\r\n self.jugador.sprite.lasers.draw(pantalla)\r\n\r\n self.jugador.draw(pantalla)\r\n self.marcianos.draw(pantalla)\r\n self.marciano_lasers.draw(pantalla)\r\n self.extraNave.draw(pantalla)\r\n\r\n\r\nif __name__ == '__main__':\r\n pygame.init()\r\n ancho_pantalla = 1200\r\n altura_pantalla = 800\r\n\r\n pantalla = pygame.display.set_mode((ancho_pantalla, altura_pantalla))\r\n clock = pygame.time.Clock()\r\n juego=Juego()\r\n BackGround = Background('resources/fUG2oM.jpg', [0, 0])\r\n\r\n\r\n marcianolaser = pygame.USEREVENT +1\r\n pygame.time.set_timer(marcianolaser,800)\r\n\r\n while True:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n if event.type ==marcianolaser:\r\n juego.marciano_disparo()\r\n\r\n pantalla.fill([30,30,30])\r\n pantalla.blit(BackGround.image, BackGround.rect)\r\n juego.run()\r\n\r\n pygame.display.flip()\r\n clock.tick(60)\r\n","repo_name":"javialvarez450/PyGame-PMDM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3774,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"22534912123","text":"\"\"\"SQRA model.\"\"\"\n\nimport numpy as np\n\nfrom ._functions import _sqra\nfrom .qra import QRA\n\n\nclass SQRA(QRA):\n r\"\"\"A class that represents the SQRA model.\n\n The SQRA model is a QRA model with a loss function smoothed by a kernel density estimator:\n\n .. math::\n \\hat{\\beta_k} = \\underset{\\beta \\in \\mathbb{R}^n}{\\operatorname{argmin}} \\left\\{ \\sum_{i=1}^{t} \\left( H \\cdot \\phi \\left( \\frac{Y_i - X_i \\beta}{H} \\right) + \\left( k - \\Phi \\left( - \\frac{Y_i - X_i \\beta}{H}\\right) \\right) \\left( Y_i - X_i \\beta \\right) \\right) \\right\\}\n\n where :math:`H` is a bandwidth parameter.\n \"\"\"\n\n def __init__(\n self, quantile: float = 0.5, H: float = None, fit_intercept: bool = False\n ) -> None:\n \"\"\"Initialize the SQRA model.\n\n :param quantile: quantile\n :type quantile: float\n :param fit_intercept: True if fit intercept in model, defaults to False\n :type fit_intercept: bool, optional\n :param H: smoothing parameter called the bandwidth, must be positive\n real number; if None, it is automatically estimated using Scott's\n (or Silverman's) rule-of-thumb\n :type H: float\n \"\"\"\n super().__init__(quantile=quantile, fit_intercept=fit_intercept)\n self.H = H\n\n def fit(self, X: np.array, y: np.array):\n \"\"\"Fit the model to the data.\n\n :param X: input matrix\n :type X: np.array\n :param y: dependent variable\n :type y: np.array\n :return: fitted model\n :rtype: SQRA\n \"\"\"\n beta = _sqra(X, y, self.quantile, self.H, self.fit_intercept)\n self._assign_coef_and_intercept(beta)\n return self\n","repo_name":"zakrzewow/remodels","sub_path":"src/remodels/qra/sqra.py","file_name":"sqra.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30832327337","text":"import os\nimport argparse\nimport deepmatcher_textual as dm\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-dataset', required=True)\nparser.add_argument('-type', required=True, choices=('StructuralWithValue', 'Structural', 'Textual', 'Dirty', 'Dirty1', 'Dirty2'))\nparser.add_argument('-batch_size', default=16, type=int)\nparser.add_argument('-epoch', default=10, type=int)\nparser.add_argument('-pos_neg', action='store_true')\nopt = parser.parse_args()\n\ndataset = opt.dataset\ndata_type = opt.type\nepoch = opt.epoch\nis_pos_neg = opt.pos_neg\nbatch_size = opt.batch_size\n\ndata_dir = os.path.join(\"dataset\", data_type, dataset)\n\ntrain, validation, test = dm.data.process(\n path=data_dir,\n cache='train_cache.pth',\n train='train.csv',\n validation='valid.csv',\n test='test.csv',\n embeddings_cache_path='./dataset',\n use_magellan_convention=True,\n ignore_columns=['ltable_id', 'rtable_id']\n)\n\npos_neg_ratio = None\nif is_pos_neg:\n test_labels = list(test.label)\n pos_neg_ratio = int((len(test_labels) - sum(test_labels)) / sum(test_labels))\n pos_neg_ratio = max(1, pos_neg_ratio)\n print(\"[Info] pos_neg_ratio: \", pos_neg_ratio)\n\nmodel = dm.MCANModel(attr_comparator='concat-mul-diff', classifier='2-layer-highway')\n\nmodel.run_train(\n train,\n validation,\n epochs=epoch,\n batch_size=batch_size,\n best_save_path='mcan_model.pth',\n pos_neg_ratio=pos_neg_ratio\n)\n\nmodel.run_eval(test)\n","repo_name":"lzzppp/DERT","sub_path":"text_er/deepmatcher/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"75"} +{"seq_id":"5051348507","text":"from __future__ import absolute_import, print_function, division\n\n__author__ = \"Perry Kundert\"\n__email__ = \"perry@hardconsulting.com\"\n__copyright__ = \"Copyright (c) 2015 Hard Consulting Corporation\"\n__license__ = \"Dual License: GPLv3 (or later) and Commercial (see LICENSE)\"\n\nimport argparse\nimport json\nimport logging\nimport re\nimport socket\nimport sys\nimport traceback\n\nimport web\nfrom ezpwd_reed_solomon import ezcod\nfrom ezpwd_reed_solomon import __version_info__ as version_max\nversion_min\t\t\t= (0,0,0)\n\naddress\t\t\t\t= ( '0.0.0.0', 80 )\t# --bind [i'face][:port] HTTP bind address\nanalytics\t\t\t= None\t\t\t# --analytics '...' Google Analytics {'id':...}\nlog\t\t\t\t= logging.getLogger( \"ezcod_api\" )\nlog_cfg\t\t\t\t= {\n \"level\":\tlogging.WARNING,\n \"datefmt\":\t'%m-%d %H:%M:%S',\n \"format\":\t'%(asctime)s.%(msecs).03d %(thread)16x %(name)-8.8s %(levelname)-8.8s %(funcName)-10.10s %(message)s',\n}\n\n# \n# The Web API, implemented using web.py\n# \n# \n\ndef deduce_encoding( available, environ, accept=None ):\n \"\"\"Deduce acceptable encoding from HTTP Accept: header:\n\n Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\n\n If it remains None (or the supplied one is unrecognized), the\n caller should fail to produce the desired content, and return an\n HTML status code 406 Not Acceptable.\n\n If no Accept: encoding is supplied in the environ, the default\n (first) encoding in order is used.\n\n We don't test a supplied 'accept' encoding against the HTTP_ACCEPT\n settings, because certain URLs have a fixed encoding. For\n example, /some/url/blah.json always wants to return\n \"application/json\", regardless of whether the browser's Accept:\n header indicates it is acceptable. We *do* however test the\n supplied 'accept' encoding against the 'available' encodings,\n because these are the only ones known to the caller.\n\n Otherwise, return the first acceptable encoding in 'available'. If no\n matching encodings are avaliable, return the (original) None.\n \"\"\"\n if accept:\n # A desired encoding; make sure it is available\n accept\t\t= accept.lower()\n if accept not in available:\n accept\t= None\n return accept\n\n # No predefined accept encoding; deduce preferred available one. Accept:\n # may contain */*, */json, etc. If multiple matches, select the one with\n # the highest Accept: quality value (our present None starts with a quality\n # metric of 0.0). Test available: [\"application/json\", \"text/html\"],\n # vs. HTTP_ACCEPT\n # \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\" Since\n # earlier matches are for the more preferred encodings, later matches must\n # *exceed* the quality metric of the earlier.\n accept\t\t= None # may be \"\", False, {}, [], ()\n HTTP_ACCEPT\t\t= environ.get( \"HTTP_ACCEPT\", \"*/*\" ).lower() if environ else \"*/*\"\n quality\t\t= 0.0\n for stanza in HTTP_ACCEPT.split( ',' ):\n # application/xml;q=0.9\n q\t\t= 1.0\n for encoding in reversed( stanza.split( ';' )):\n if encoding.startswith( \"q=\" ):\n q\t= float( encoding[2:] )\n for avail in available:\n match\t= True\n for a, t in zip( avail.split( '/' ), encoding.split( '/' )):\n if a != t and t != '*':\n match = False\n if match:\n log.debug( \"Found %16s == %-16s;q=%.1f %s %-16s;q=%.1f\",\n avail, encoding, q,\n '> ' if q > quality else '<=',\n accept, quality )\n if q > quality:\n quality\t= q\n accept\t= avail\n return accept\n\n\ndef http_exception( framework, status, message ):\n \"\"\"Return an exception appropriate for the given web framework,\n encoding the HTTP status code and message provided.\n \"\"\"\n if framework and framework.__name__ == \"web\":\n if status == 404:\n return framework.NotFound( message )\n\n if status == 406:\n class NotAcceptable( framework.NotAcceptable ):\n def __init__(self, message):\n self.message = '; '.join( [self.message, message] )\n framework.NotAcceptable.__init__(self)\n return NotAcceptable( message )\n\n if status == 500:\n exc\t\t\t= framework.InternalError()\n if message:\n exc.message\t= '; '.join( [exc.message, message] )\n return exc\n\n return Exception( \"%d %s\" % ( status, message ))\n\n\ndef ezcod_to_dict( codec ):\n dct\t\t\t\t= dict( (k,getattr( codec, k )) for k in [\n 'latitude',\n 'latitude_error',\n 'longitude',\n 'longitude_error',\n 'accuracy',\n 'confidence',\n 'certainty',\n 'precision',\n ] )\n dct['ezcod']\t\t= str( codec )\n return dct\n\n\ndef ezcod_decode( cod, parity=None, precision=None ):\n \"\"\"Convert an \"EZCOD\" string w/1-3 parity into a dict containing its decoded details. Will raise\n Exception on failure to decode the supplied EZCOD. The desired parity and precision are then\n used to produce the response (allowing us to convert an incoming EZCOD to another parity or\n precision -- with the side-effect of losing any EZCOD decoding accuracy and certainty details,\n of course).\n\n \"\"\"\n if precision is None:\n precision\t\t= 0\n precision\t\t\t= int( precision )\n if parity is None:\n parity\t\t\t= 0\n parity\t\t\t= int( parity )\n if not parity:\n parity\t\t\t= 1\n\n match\t\t\t= re.match( r'[- a-zA-Z0-9_?]+(?:[.!]([- a-zA-Z0-9_?]+))?', cod )\n assert match, \\\n \"Invalid ezcod=%s; must be 1-12 [0-9A-Z] symbols w/ [ -] space and [_?] erasures, w/ one [.!] separator\" % e\n\n # Try to identify the number of R-S parity symbols supplied. If not known (no [.!]) assume one.\n # We could support longer EZCODs (more parity) by defaulting to using longest supported codec...\n ezcod_parity\t\t= 1\n if match.group( 1 ):\n ezcod_parity\t\t= len( match.group( 1 ))\n codecs\t\t\t= {\n 1: ezcod.ezcod_3_10,\n 2: ezcod.ezcod_3_11,\n 3: ezcod.ezcod_3_12,\n }\n assert ezcod_parity in codecs, \"Unsupported EZCOD supplied w/ %d parity symbols\" % ezcod_parity\n assert parity in codecs, \"Unsupported EZCOD desired w/ %d parity symbols\" % parity\n cdc\t\t\t\t= codecs[ezcod_parity]( str( match.group() ))\n if ( ezcod_parity != parity ) or ( precision and precision != cdc.precision ):\n # A different parity desired, and possibly a different precision\n cdc\t\t\t= codecs[parity]( cdc.latitude, cdc.longitude,\n precision or cdc.precision )\n return ezcod_to_dict( cdc )\n\n\ndef latlon_encode( latlon=None, lat=None, lon=None, precision=None, parity=None ):\n \"\"\"Convert a lat, lon or \"lat,lon\" string into a dict containing its encoded details w/ the provided\n precision (0 = default) and parity (must be in range [1,3]). Will raise Exception on failure.\n\n \"\"\"\n if precision is None:\n precision\t\t= 0\n precision\t\t\t= int( precision )\n if parity is None:\n parity\t\t\t= 0\n parity\t\t\t= int( parity )\n if not parity:\n parity\t\t\t= 1\n \n if latlon:\n assert lat is None and lon is None, \\\n \"\"\"Cannot supply both lat, lon and \"lat,long\" string\"\"\"\n match\t\t\t= re.match( r'(-?[0-9]+(?:.[0-9]*)?)\\s*,\\s*(-?[0-9]+(?:.[0-9]*))?', latlon )\n assert match, \\\n \"Invalid latlon=%s; must be two simple float values separated by [,] \" % latlon\n lat,lon\t\t\t= match.group( 1, 2 )\n assert lat is not None and lon is not None, \\\n \"Must supply both latitude and longitude\"\n lat\t\t\t\t= float( lat )\n lon\t\t\t\t= float( lon )\n codecs\t\t\t= {\n 1: ezcod.ezcod_3_10,\n 2: ezcod.ezcod_3_11,\n 3: ezcod.ezcod_3_12,\n }\n assert parity in codecs, \"Unsupported EZCOD w/ %d parity symbols\" % parity\n cdc\t\t\t\t= codecs[parity]( lat, lon, precision )\n return ezcod_to_dict( cdc )\n\n\ndef api_request( version, path, queries, environ, accept, data=None, framework=web ):\n \"\"\"An EZCOD API request either comes from the GET query or POST form variables, or from the POST\n body JSON payload. The follow query/form variables are accepted:\n \n ezcod\t== [- a-zA-Z0-9_?]+(?:[.!][- a-zA-Z0-9_?]+)?\n latlon\t== (-?[0-9]+(?:.[0-9]*)?)\\s*,\\s*(-?[0-9]+(?:.[0-9]*)?\n\n If a JSON body payload is provided, it is expected to be a dict and may have the following keys:\n\n ezcod\n or:\n latitude\n longitude\n\n The response JSON (default) or HTML (if the client's Accept: header does not allow JSON) will\n always contain the following:\n\n ezcod\n latitude\n longitude\n latitude_error\n longitude_error\n accuracy\n certainty\n\n \"\"\"\n cod\t\t\t\t= queries.pop( 'ezcod', None )\n latlon\t\t\t= queries.pop( 'latlon', None )\n lat\t\t\t\t= None\n lon\t\t\t\t= None\n precision\t\t\t= queries.pop( 'precision', 0 )\n parity\t\t\t= queries.pop( 'parity', None )\n assert ( bool( cod ) ^ bool( latlon ) ) ^ bool( data ), \\\n \"Supply exactly one of ezcod=, latlon= query/form variable, or JSON payload\"\n assert not queries, \\\n \"Unrecognized queries: %s\" % \", \".join( queries.keys() )\n\n try:\n # Ensure supplied path and version are recognized. Pretty simple for now (no supported API\n # path, all known versions supported identically)...\n assert not path, \\\n \"Unrecognized API path: %s\" % path\n version_info\t\t= tuple( map( int, version.split( '.' )))\n assert version_min <= version_info <= version_max, \\\n \"Unrecognized API version: %s (%r)\" % ( '.'.join( map( str, version_info )), version_info )\n\n # GET query options, POST form variables support single requests only\n if cod:\n results\t\t= ezcod_decode( cod=cod, precision=precision, parity=parity )\n elif latlon:\n results\t\t= latlon_encode( latlon=latlon, lat=lat, lon=lon,\n precision=precision, parity=parity )\n elif data:\n # POST payload JSON supports or [ , ... ]: get either lat/lon or EZCOD\n # cod from each one.\n json_data\t\t= json.loads( data )\n results\t\t= []\n for j in json_data if type(json_data) is list else [ json_data ]:\n assert ( 'ezcod' in j ) ^ ( 'latlon' in j) ^ ( 'latitude' in j and 'longitude' in j ), \\\n \"API POST body JSON must supply either ezcod, latlon or latitude/longitude: %s\" % data\n j_cod\t\t= j.pop( 'ezcod', None )\n j_latlon\t= j.pop( 'latlon', None )\n j_lat\t\t= j.pop( 'latitude', None )\n j_lon\t\t= j.pop( 'longitude', None )\n j_precision\t= j.pop( 'precision', None ) or precision\n j_parity\t= j.pop( 'parity', None ) or parity\n assert not j, \\\n \"Unrecognized API POST payload JSON keys: %s\" % \", \".join( j.keys() )\n if j_cod:\n results += [ ezcod_decode( cod=j_cod,\n precision=j_precision, parity=j_parity ) ]\n else:\n results += [ latlon_encode( latlon=j_latlon, lat=j_lat, lon=j_lon,\n precision=j_precision, parity=j_parity ) ]\n # And convert a single back to single results\n if type( json_data ) is not list:\n results\t\t= results[0]\n else:\n raise NotImplementedError( \"Invalid API request\" )\n\n except Exception as exc:\n log.warning( \"Exception: %s\", exc )\n log.info( \"Exception Stack: %s\", traceback.format_exc() )\n raise http_exception( framework, 500, str( exc ))\n\n accept\t\t\t= deduce_encoding([ \"application/json\", \"text/javascript\", \"text/plain\",\n \"text/html\" ],\n environ=environ, accept=accept )\n\n if accept and accept in ( \"application/json\", \"text/javascript\", \"text/plain\" ):\n response\t\t= \"\"\n callback\t\t= queries and queries.get( 'callback', \"\" ) or \"\"\n if callback:\n response\t\t= callback + \"( \"\n response += json.dumps( results, sort_keys=True, indent=4 )\n if callback:\n response += \" )\"\n elif accept and accept in ( \"text/html\" ):\n render\t\t\t= web.template.render( \"static/templates/\", base=\"layout\",\n globals={ 'analytics': analytics } )\n resultslist\t\t= results if type( results ) is list else [results]\n response\t\t= render.keylist( {\n 'title':\t\t\"EZCOD Position\",\n 'keys':\t\tlist( sorted( resultslist[0].keys() )),\n 'list':\t\tresultslist,\n } )\n else:\n # Invalid encoding requested. Return appropriate 406 Not Acceptable\n message\t\t\t= \"Invalid encoding: %s, for Accept: %s\" % (\n accept, environ.get( \"HTTP_ACCEPT\", \"*.*\" ))\n raise http_exception( framework, 406, message )\n\n return accept,response\n\n\nclass trailing_slash:\n def GET( self, path ):\n web.seeother( path )\n\n\nclass favicon:\n def GET( self ):\n \"\"\"Always permanently redirect favicon.ico requests to our favicon.{ico,png}.\n The reason we do this instead of putting a is because\n all *other* requests from browsers (ie. api/... ) returning non-HTML\n response Content-Types such as application/json *also* request\n favicon.ico, and we don't have an HTML to specify any icon link.\n Furthermore, they continue to request it 'til satisfied, so we do a 301\n Permanent Redirect to satisfy the browser and prevent future requests.\n So, this is the most general way to handle the favicon.ico\"\"\"\n web.redirect( '/static/icons/favicon.ico' )\n\n\nclass home:\n def GET( self ):\n \"\"\"Forward to an appropriate start page. Detect if behind a\n proxy, and use the original forwarded host.\n \"\"\"\n \"\"\"\n # print json.dumps(web.ctx, skipkeys=True, default=repr, indent=4,)\n proxy\t\t\t= web.ctx.environ.get( \"HTTP_X_FORWARDED_HOST\", \"\" )\n if proxy:\n proxy\t\t= \"http://\" + proxy\n target\t\t\t= proxy + \"/static/index.html\"\n web.seeother( target )\n \"\"\"\n environ\t\t\t= web.ctx.environ\n queries\t\t\t= web.input()\n accept\t\t\t= None\n accept\t\t\t= deduce_encoding([ \"text/html\" ], environ=environ, accept=accept )\n if accept and accept in ( \"text/html\" ):\n render\t\t= web.template.render( \"static/templates/\", base=\"layout\",\n globals={ 'analytics': analytics } )\n response\t\t= render.map( {} )\n content\t\t= accept\n else:\n # Invalid encoding requested. Return appropriate 406 Not Acceptable\n message\t\t= \"Invalid encoding: %s, for Accept: %s\" % (\n accept, environ.get( \"HTTP_ACCEPT\", \"*.*\" ))\n raise http_exception( web, 406, message )\n\n web.header( \"Content-Type\", content )\n return response\n\n\nclass api:\n def GET( self, version, path, data=None ):\n environ\t\t\t= web.ctx.environ\n queries\t\t\t= web.input()\n accept\t\t\t= None\n if path and path.endswith( \".json\" ):\n path\t\t= path[:-5]\n accept\t\t= \"application/json\"\n log.warning( \"Queries: %r\", queries )\n content,response\t= api_request(\n version=version, path=path, queries=queries, environ=environ, accept=accept, data=data )\n\n web.header( \"Cache-Control\", \"no-cache\" )\n web.header( \"Content-Type\", content )\n return response\n\n def POST( self, version, path ):\n # form data is in web.input(), just like GET queries, but there could be body data\n return self.GET( version, path, data=web.data() )\n\n\ndef web_api( urls, http=None ):\n \"\"\"Get the required web.py classes from the global namespace. The iface:port must always passed on\n argv[1] to use app.run(), so use lower-level web.httpserver.runsimple interface, so we can bind\n to the supplied http address.\"\"\"\n try:\n app\t\t\t= web.application( urls, globals() )\n web.httpserver.runsimple( app.wsgifunc(), http )\n log.info( \"Web API started on %s:%s\",\n http[0] if http else None, http[1] if http else None )\n except socket.error:\n log.error( \"Could not bind to %s:%s for web API\",\n http[0] if http else None, http[1] if http else None )\n except Exception as exc:\n log.error( \"Web API server on %s:%s failed: %s\",\n http[0] if http else None, http[1] if http else None, exc )\n\n\ndef main( argv=None ):\n ap\t\t\t\t= argparse.ArgumentParser(\n description = \"Provide an EZCOD API Web Server\",\n epilog = \"\" )\n\n ap.add_argument( '-v', '--verbose',\n default=0, action=\"count\",\n help=\"Display logging information.\" )\n ap.add_argument( '-b', '--bind',\n default=( \"%s:%d\" % address ),\n help=\"HTTP interface[:port] to bind (default: %s:%d)\" % (\n address[0], address[1] ))\n ap.add_argument( '-a', '--analytics',\n default=None,\n help=\"Google Analytics ID (if any)\" )\n ap.add_argument( '-p', '--prefix',\n default=None,\n help=\"App URL prefix (optional)\" )\n ap.add_argument( '-l', '--log',\n help=\"Log file, if desired\" )\n args\t\t\t= ap.parse_args( argv )\n\n # Deduce interface:port address to bind, and correct types (default is address, above)\n http\t\t\t= args.bind.split( ':' )\n assert 1 <= len( http ) <= 2, \"Invalid --address []:[}: %s\" % args.bind\n http\t\t\t= ( str( http[0] ) if http[0] else address[0],\n int( http[1] ) if len( http ) > 1 and http[1] else address[1] )\n if args.analytics:\n global analytics\n analytics\t\t= { 'id': args.analytics }\n\n if args.log:\n # Output logging to a file, and handle UNIX-y log file rotation via 'logrotate', which sends\n # signals to indicate that a service's log file has been moved/renamed and it should re-open\n log_cfg['filename']\t= args.log\n\n logging.basicConfig( **log_cfg )\n\n api_path\t\t\t= [ '' ]\t# Ensure a leading '/...' after join\n if args.prefix:\n api_path.append( args.prefix )\n api_path.append( r\"v([0-9]+(?:.[0-9]+)*)(.*)?\" )\n\n # \n # The web.py url endpoints, and their classes\n # \n urls\t\t\t= (\n \"(/.*)/\",\t\t\t\t\"trailing_slash\",\n \"/favicon.ico\",\t\t\t\t\"favicon\",\n \"/?\",\t\t\t\t\t\"home\",\n \"/index.html\",\t\t\t\t\"home\",\n \"/\".join( api_path ),\t\t\t\"api\",\n )\n\n try:\n web_api( urls=urls, http=http )\n except KeyboardInterrupt:\n log.warning( \"Quitting\" )\n return 0\n except Exception as exc:\n log.warning( \"Exception: %s\", exc )\n return 1\n\nif __name__ == \"__main__\":\n sys.exit( main() )\n","repo_name":"pjkundert/ezpwd-reed-solomon","sub_path":"examples/ezcod_api/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":19461,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"75"} +{"seq_id":"1558453520","text":"# SPLA\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport csv\n\ntest_x = np.array([[0,0],[1,0],[2,1],[0,1],[1,2]])\ntest_class = np.array([1,1,1,-1,-1])\na = np.array([-1.5,5,-1])\nlr = 1\ni=0\n\nf_csv = open('data2.csv','w')\nwhile(i<20):\n print(\"Epoch%d:\"%(i))\n for n in range(len(test_class)):\n y_k = np.append(np.array([1]),test_x[n])\n g_x = np.dot(a,y_k)\n print(\"g(x)= \",g_x)\n if g_x<0:\n temp=-1\n if g_x>0:\n temp=1\n a_old = a\n if not temp==test_class[n]:\n a = a + lr*test_class[n]*y_k\n else:\n a=a_old\n\n \n print(\"a= \",a)\n \n for n in range(len(test_class)):\n if test_class[n] == 1:\n plt.plot(test_x[n][0],test_x[n][1],'bx')\n if test_class[n] == -1:\n plt.plot(test_x[n][0],test_x[n][1],'ro')\n temp_x = np.arange(0, 2.1, 0.1)\n plot_x = []\n plot_y = []\n for x in temp_x:\n if a[2] ==0:\n temp_y = x\n x = -(a[0]/a[1])\n else:\n temp_y = -(a[1]/a[2])*x-(a[0]/a[2])\n if temp_y<3.1 and temp_y>-3.1:\n plot_x.append(x)\n plot_y.append(temp_y)\n plt.plot(plot_x, plot_y)\n plt.show()\n \n n = 0\n i=i+1","repo_name":"wpddmcmc/Pattern-Recognition","sub_path":"Python/SPLA(Sequential Perceptron).py","file_name":"SPLA(Sequential Perceptron).py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"11645676450","text":"from trakt.core.helpers import from_iso8601\r\nfrom trakt.objects.core.helpers import update_attributes\r\nfrom trakt.objects.media import Media\r\n\r\n\r\nclass Video(Media):\r\n def __init__(self, client, keys=None, index=None):\r\n super(Video, self).__init__(client, keys, index)\r\n\r\n self.last_watched_at = None\r\n self.collected_at = None\r\n self.paused_at = None\r\n\r\n self.plays = None\r\n self.progress = None\r\n\r\n # Flags\r\n self.is_watched = None\r\n self.is_collected = None\r\n\r\n def _update(self, info=None, is_watched=None, is_collected=None, **kwargs):\r\n super(Video, self)._update(info, **kwargs)\r\n\r\n update_attributes(self, info, [\r\n 'plays',\r\n 'progress'\r\n ])\r\n\r\n # Set timestamps\r\n if 'last_watched_at' in info:\r\n self.last_watched_at = from_iso8601(info.get('last_watched_at'))\r\n\r\n if 'collected_at' in info:\r\n self.collected_at = from_iso8601(info.get('collected_at'))\r\n\r\n if 'paused_at' in info:\r\n self.paused_at = from_iso8601(info.get('paused_at'))\r\n\r\n # Set flags\r\n if is_watched is not None:\r\n self.is_watched = is_watched\r\n\r\n if is_collected is not None:\r\n self.is_collected = is_collected\r\n","repo_name":"der-tkw/traktforalfred","sub_path":"trakt/objects/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"75"} +{"seq_id":"41665691634","text":"\"\"\"\nThis module defines the Helper and HelperGUI classes for the Quantrolab application.\nA Helper is a NON-GUI plugin that adds functionalities to the Quantrolab integrated development environment (IDE).\nA HelperGUI is a GUI plugin that adds functionalities to the Quantrolab IDE.\nHelpers and HelperGUIs can be either independant applications from the IDE, or be run inside the IDE Coderunner to share the same global variables as the scripts.\nThey can be Singletons (that exist in only one exemplary) or not.\nA HelperGUI can be stand-alone or be the GUI layer of a Helper associated to it.\n In case of Helper-HelperGUI association,\n - both classes can be in the same or in different files.\n - the HelperGUI is usually in charge of loading the Helper if it is not already loaded.\nThe possible strategies of interaction with Helpers and HelperGUIs are the following:\n 1) Helper without HelperGUI => Users interact with the Helper through scripts in the IDE\n 2) Stand alone HelperGUI\n 2a) => User interacts with the HelperGUI only through the graphical user interface;\n 2b) => User does not interact with the HelperGUI through the graphical user interface but through scripts, and the GUI is only a display;\n 2c) => User can interact from both the graphical user interface and from scripts in the IDE (difficult to program in a reliable way).\n 3) Associated Helper and HelperGUI\n 3a) User interacts only with Helper through scripts, HelperGUI receive messages from the Helper and is only a display.\n 3b) => Both scripts of the IDE and the HelperGUI are clients and send commands to the Helper;\n After both types of interaction, the HelperGUI receives messages from the Helper and update its GUI\n This strategy is powerful but difficult to program.\n\nExample 1: The DataMgr and DataManager form a couple of associated Helper-HelperGUI implementing strategy 3b to help\n users managing datacubes (the base data structure of Quantrolab)\nExample 2: The LoopMgr and LoopManager form a couple of associated Helper-HelperGUI implementing strategy 3b to help\n users managing Smartloops.\n\nRemarks:\n - a HelperGUI and its associate Helper have strong references to each others.\n\"\"\"\nimport sys\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom application.lib.base_classes1 import Debugger, Reloadable\nfrom application.lib.com_classes import Subject, Observer\nfrom application.ide.widgets.observerwidget import ObserverWidget\n\n\nclass Helper(Debugger, Reloadable, Subject, Observer, object):\n \"\"\"\n Class for a Quantrolab's non-gui helper.\n \"\"\"\n\n def __init__(self, name=None, parent=None, globals={}):\n Reloadable.__init__(self)\n Subject.__init__(self)\n Observer.__init__(self)\n Debugger.__init__(self)\n self._name = name\n self._parent = parent\n self._globals = globals\n self._gui = None\n\n def __del__(self):\n self.notify('deleted')\n\n\nclass HelperGUI(Debugger, Reloadable, Subject, ObserverWidget, QMainWindow, object):\n \"\"\"\n Class for a helper with a Qt graphical interface (QMainWindow).\n HelperGUI is deleted when closing its main window.\n What to do with associate ?\n \"\"\"\n\n def __init__(self, name=None, parent=None, globals={}, helper=None):\n Reloadable.__init__(self)\n Subject.__init__(self)\n ObserverWidget.__init__(self)\n Debugger.__init__(self)\n QMainWindow.__init__(self, parent)\n self.setAttribute(Qt.WA_DeleteOnClose)\n self._parent = parent\n self._globals = globals\n self._name = name\n menubar = self.menuBar()\n helperMenu = menubar.addMenu('Helper')\n helperMenu.addAction(QAction('Reload this GUI helper...', self, triggered=self.reloadHelperGUI))\n self.setHelper(helper)\n if self._helper is not None:\n commandText = 'Reload associate ' + self._helper.__class__.__name__ + '...'\n helperMenu.addAction(QAction(commandText, self, triggered=self.reloadHelper))\n helperMenu.addAction(QAction('Close this GUI helper...', self, triggered=self.close))\n\n def setHelper(self, helper):\n self._helper = helper # GUI helpers have a strong reference to their associate\n if self._helper is not None:\n # attach the gui to its associate to communicate with it\n self.debugPrint('attaching', self._helper, 'to', self)\n self._helper.attach(self)\n self._helper._gui = self # create or update a _gui attribute to the associate\n\n def reloadHelperGUI(self):\n reply = QMessageBox.question(self,\n 'Confirm reload ...',\n 'This GUI helper will be reloaded. Are you sure?',\n QMessageBox.Ok | QMessageBox.Cancel)\n if reply == QMessageBox.Ok:\n self.reloadClass()\n\n def reloadHelper(self):\n reply = QMessageBox.question(self,\n 'Confirm reload ...',\n 'The associate helper ' + self._helper.__class__.__name__ +\n ' will be reloaded. Are you sure?',\n QMessageBox.Ok | QMessageBox.Cancel)\n if reply == QMessageBox.Ok:\n self._helper.reloadClass()\n\n def window2Front(self):\n self.showNormal()\n self.activateWindow()\n\n def showEvent(self, event):\n # determine the whole display geometry\n desktop = QDesktopWidget()\n rect = QRect() # available geometry\n for i in range(desktop.screenCount()):\n rect = rect.united(desktop.availableGeometry(i))\n x1, y1, x2, y2 = rect.getCoords()\n # Read the settings\n settings = QSettings()\n key = ''\n if self._name is not None:\n key += self._name + '/'\n key2 = key + 'size'\n if settings.contains(key2):\n size = settings.value(key2, self.size()).toSize()\n self.resize(size)\n size = self.frameSize()\n w, h = size.width(), size.height()\n key1 = key + 'pos' # read the previous top-left corner\n if settings.contains(key1):\n pos = settings.value(key1, self.pos()).toPoint()\n x, y = pos.x(), pos.y()\n if x < x1: # and move it back to inside the desktop if needed\n pos.setX(x1)\n elif x + w > x2:\n pos.setX(max(x1, x2 - w))\n if y < y1:\n pos.setY(y1)\n elif y + h > y2:\n pos.setY(max(0, y2 - h))\n if rect.contains(pos):\n self.move(pos)\n\n def closeEvent(self, event):\n \"\"\"\n reply = QMessageBox.question(self,\n \"Confirm Helper Panel Exit...\",\n \"Helper Panel will be closed and deleted. Are you sure?\",\n QMessageBox.Ok | QMessageBox.Cancel)\n\n if reply == QMessageBox.Ok:\n if self._helper is not None:\n self.debugPrint('detaching', self._helper, 'from', self)\n self._helper.detach(self) # remove the two references to this gui\n self._helper._gui = None\n event.accept()\n self.notify(property='closing',)\n else:\n event.ignore()\n \"\"\"\n key = ''\n if self._name is not None:\n key += self._name + '/'\n QSettings().setValue(key + 'pos', self.pos())\n QSettings().setValue(key + 'size', self.size())\n","repo_name":"denisvion/quantrolab-old","sub_path":"application/lib/helper_classes.py","file_name":"helper_classes.py","file_ext":"py","file_size_in_byte":7729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"13011020848","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport os\nimport numpy as np\nimport imageio\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\n\nfrom input_ops import create_input_ops\nfrom config import argparser\nfrom util import log\n\n\nclass Evaler(object):\n\n def __init__(self, config, model, dataset):\n self.config = config\n self.model = model\n self.train_dir = config.train_dir\n log.info(\"self.train_dir = %s\", self.train_dir)\n\n # --- input ops ---\n self.batch_size = config.batch_size\n\n self.dataset = dataset\n\n _, self.batch = create_input_ops(dataset, self.batch_size,\n is_training=False,\n shuffle=False)\n\n self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)\n self.step_op = tf.no_op(name='step_no_op')\n\n # --- vars ---\n all_vars = tf.trainable_variables()\n log.warn(\"********* var ********** \")\n slim.model_analyzer.analyze_vars(all_vars, print_info=True)\n\n tf.set_random_seed(123)\n\n session_config = tf.ConfigProto(\n allow_soft_placement=True,\n gpu_options=tf.GPUOptions(allow_growth=True),\n device_count={'GPU': 1},\n )\n self.session = tf.Session(config=session_config)\n\n # --- checkpoint and monitoring ---\n self.saver = tf.train.Saver(max_to_keep=100)\n\n self.checkpoint = config.checkpoint\n if self.checkpoint is None and self.train_dir:\n self.checkpoint = tf.train.latest_checkpoint(self.train_dir)\n log.info(\"Checkpoint path : %s\", self.checkpoint)\n elif self.checkpoint is None:\n log.warn(\"No checkpoint is given. Just random initialization :-)\")\n self.session.run(tf.global_variables_initializer())\n else:\n log.info(\"Checkpoint path : %s\", self.checkpoint)\n\n def eval_run(self):\n # load checkpoint\n if self.checkpoint:\n self.saver.restore(self.session, self.checkpoint)\n log.info(\"Loaded from checkpoint!\")\n\n log.infov(\"Start Inference and Evaluation\")\n\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(self.session,\n coord=coord, start=True)\n\n use_test_id_list = self.config.data_id_list is not None\n\n try:\n if use_test_id_list:\n with open(self.config.data_id_list, 'r') as id_list_path:\n id_list = id_list_path.readlines()\n self.id_list = [id.strip().split(' ') for id in id_list]\n\n if self.config.plot_image:\n if not os.path.exists(self.config.output_dir):\n os.makedirs(self.config.output_dir)\n\n if self.config.loss or self.config.plot_image:\n loss_all = []\n time_all = 0\n step = None\n s = 0\n continue_evaluate = True\n while continue_evaluate:\n # get testing batch\n if use_test_id_list:\n batch_id_list = self.id_list[self.batch_size*s:self.batch_size*(s+1)]\n batch_chunk = self.get_batch_chunk(batch_id_list)\n else:\n batch_chunk = self.get_batch_chunk()\n\n # inference\n step, loss, img, batch_id, step_time = \\\n self.run_single_step(batch_chunk, step=s)\n\n # plot images\n if self.config.plot_image:\n if use_test_id_list:\n for i in range(self.batch_size):\n for img_key in img.keys():\n model_name = batch_id_list[i][0].split('_')[0]\n target_id = '_'.join(batch_id_list[i][0].split('_')[1:])\n source_id = '-'.join(['_'.join(id.split('_')[1:])\n for id in batch_id_list[i][1:]])\n img_name = '{}_target_{}_source_{}_{}.png'.format(\n model_name, target_id, source_id, img_key)\n if self.config.plot_image:\n imageio.imwrite(os.path.join(\n self.config.output_dir, img_name),\n img[img_key][i])\n else:\n raise ValueError('Plotting images requires an id list.')\n\n loss_all.append(np.array(loss.values()))\n time_all += step_time\n\n s += 1\n if use_test_id_list:\n continue_evaluate = s < len(self.id_list)/self.batch_size\n else:\n continue_evaluate = s < self.config.max_eval_steps\n\n # report loss\n if not self.config.quiet:\n loss_avg = np.average(np.stack(loss_all), axis=0)\n self.log_message(\n s, loss_avg, loss.keys(), time_all,\n write_summary=self.config.write_summary,\n summary_file=self.config.summary_file,\n final_step=not continue_evaluate,\n )\n\n except Exception as e:\n coord.request_stop(e)\n\n log.warning('Completed Evaluation.')\n\n coord.request_stop()\n try:\n coord.join(threads, stop_grace_period_secs=3)\n except RuntimeError as e:\n log.warn(str(e))\n\n def get_batch_chunk(self, id_batch_list=None):\n if id_batch_list is not None:\n image = []\n pose = []\n id = []\n for id_data in id_batch_list:\n img, p = self.dataset.get_data_by_id(id_data)\n image.append(img)\n pose.append(p)\n id.append(id_data[0])\n batch_chunk = {\n 'image': np.stack(image, axis=0),\n 'camera_pose': np.stack(pose, axis=0),\n 'id': np.stack(id, axis=0)\n }\n else:\n batch_chunk = self.session.run(self.batch)\n return batch_chunk\n\n def run_single_step(self, batch_chunk, step=None, is_train=False):\n _start_time = time.time()\n\n [step, loss, img, _] = self.session.run(\n [self.global_step, self.model.eval_loss,\n self.model.eval_img, self.step_op],\n feed_dict=self.model.get_feed_dict(batch_chunk, step=step,\n is_training=is_train)\n )\n\n _end_time = time.time()\n\n return step, loss, img, batch_chunk['id'][0], (_end_time - _start_time)\n\n def log_message(self, step, loss, loss_key, time, write_summary=False,\n summary_file=None, is_train=False, final_step=False):\n loss_str = \"\"\n for key, i in sorted(zip(loss_key, range(len(loss_key)))):\n loss_str += \"{}:{loss: .5f}\\n\".format(\n loss_key[i], loss=loss[i] if 'loss' not in loss_key[i] else loss[i]/2*3)\n log_fn = (is_train and log.info or log.infov)\n if self.config.data_id_list is None:\n data_str = 'Total datapoint: {}'.format(\n self.batch_size*self.config.max_eval_steps)\n else:\n data_str = 'Total datapoint: {} from {}'.format(\n len(self.id_list), self.config.data_id_list)\n report_tag = \"[Final Avg Report] {data_str}\\n\" if final_step \\\n else \"[{split_mode:5s} step {step:5d}]\\n\".format(\n split_mode=('Report'), step=step)\n msg = (\n report_tag +\n \"[Loss]\\n{loss_str}\" +\n \"[Time] ({time:.3f} sec)\"\n ).format(split_mode=('Report'),\n step=step,\n data_str=data_str,\n loss_str=loss_str,\n time=time)\n log_fn(msg)\n if final_step:\n log.infov(\"Checkpoint: %s\", self.checkpoint)\n log.infov(\"Dataset: %s\", self.config.dataset)\n if write_summary:\n log.infov(\"Write the summary to: %s\", summary_file)\n final_msg = 'Checkpoint: {}\\nDataset: {}\\n{}{}'.format(\n self.checkpoint, self.config.dataset,\n '' if self.config.data_id_list is None else 'Id list: {}\\n'.format(\n self.config.data_id_list),\n msg)\n with open(summary_file, 'w') as f:\n f.write(final_msg)\n\n\ndef main():\n\n config, model, _, dataset_test = argparser(is_train=False)\n\n evaler = Evaler(config, model, dataset_test)\n\n log.warning(\"dataset: %s\", config.dataset)\n evaler.eval_run()\n\nif __name__ == '__main__':\n main()\n","repo_name":"shaohua0116/Multiview2Novelview","sub_path":"evaler.py","file_name":"evaler.py","file_ext":"py","file_size_in_byte":9238,"program_lang":"python","lang":"en","doc_type":"code","stars":200,"dataset":"github-code","pt":"75"} +{"seq_id":"33852097050","text":"import tkinter\nfrom random import choice, randint\nfrom time import time\n\nball_inition_number = 10\nball_click_count = 0\nclick_count = 0\nball_min_radius = 15\nball_max_radius = 40\nball_avaiable_color = ['green','blue','red','yellow','#F0F','black','gray','#0FF']\nDX = []\nDY = []\nGame = True\nwg = 500\nhg = 500\n\ndef click_ball(event):\n \"\"\"функция обработки события клика мышкой\"\"\"\n global label, Game, ball_click_count, click_count\n if Game:\n obj = canvas.find_closest(event.x, event.y)\n x1, y1, x2, y2 = canvas.coords(obj)\n click_count += 1\n if x1 <= event.x <= x2 and y1 <=event.y <= y2:\n canvas.delete(obj)\n ball_click_count += 1\n if ball_click_count == ball_inition_number:\n label['text']='GAME OVER! (точность ' +str((ball_click_count * 10000 // click_count) / 100) + \\\n '%, время: ' + str(round(time() - t)) + 'c.)'\n Game = False\n if Game: label['text']='Всего выстрелов:' + str(click_count) + '. Попаданий: ' + str(ball_click_count)\n else: exit()\n\ndef move_all_balls(event):\n \"\"\"функция движения объектов канвы\"\"\"\n for obj in canvas.find_all():\n canvas.move(obj, DX[obj-1], DY[obj-1])\n x1, y1, x2, y2 = canvas.coords(obj)\n if x1 + DX[obj-1] <=0 or x2 + DX[obj-1] >=canvas.winfo_width():\n DX[obj-1] =- DX[obj-1]\n if y1 + DY[obj-1] <=0 or y2 + DY[obj-1] >=canvas.winfo_height():\n DY[obj-1] =- DY[obj-1]\n\ndef create_random_ball():\n \"\"\"функция создания объектов (шариков)\n вместе со списками смещений каждого объекта\"\"\"\n R = randint(ball_min_radius, ball_max_radius)\n x = randint(10,int(canvas['width'])-2*R-10)\n y = randint(10,int(canvas['height'])-2*R-10)\n canvas.create_oval(x,y, x+2*R, y+2*R, fill=random_color())\n dx = randint(-2, 2)\n dy = randint(-2, 2)\n while (dx == 0 and dy == 0):\n dx = randint(-2, 2)\n dy = randint(-2, 2)\n DX.append(dx)\n DY.append(dy)\n\ndef random_color():\n return choice(ball_avaiable_color)\n\ndef init_ball_catch_game():\n for i in range(ball_inition_number):\n create_random_ball()\n\ndef init_main_window():\n \"\"\"функция инициализации игрового поля\"\"\"\n global root, canvas, label\n root = tkinter.Tk()\n root.title('Balls')\n root.geometry(str(wg)+'x'+str(hg+20))\n canvas = tkinter.Canvas(root, background=\"white\", width=wg, height=hg)\n canvas.bind('