',views.checkname,name='checkname'),\n path('survey/', views.survey,name='survey'),\n path('userpattern/', views.userpattern,name='userpattern'),\n # path('voice/', views.voice,name='voice'),\n # path('voiceUnknown/', views.voiceUnknown,name='voiceUnknown'),\n # path('voiceError/', views.voiceError,name='voiceError'),\n # path('voiceContinue/', views.voiceContinue,name='voiceContinue'),\n # path('voiceContinue_con/', views.voiceContinue_con,name='voiceContinue_con'),\n path('pic_saver/',views.pic_saver,name='pic_saver'),\n path('graphmodel/', views.graphmodel,name='graphmodel'),\n path('road_direction/', views.road_direction,name='road_direction'),\n path('voicestart/', views.voicestart,name='voicestart'),\n path('voiceend/', views.voiceend,name='voiceend'),\n path('voicemode/', views.voicemode,name='voicemode')\n # path('voicecon/', views.voicecon,name='voicecon')\n # path('voicestand/', views.voicestand,name='voicestand')\n]\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"615859314","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, print_function, division\nfrom io import open\nimport unicodedata\nimport string\nimport re\nimport random\nimport os\nimport time\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nimport numpy as np\nfrom numpy.random import permutation\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch import optim\nimport torch.nn.functional as F\n\nfrom itertools import chain\n\nfrom gensim.models.word2vec import Word2Vec\nimport word2vecClassifier #JIC\n\n\nimport time\nimport math\nimport multiprocessing\n\n\nuse_cuda = torch.cuda.is_available()\nprint(\"Utilisation de la carte graphique :\",use_cuda)\n\n\ndef asMinutes(s):\n m = math.floor(s / 60)\n s -= m * 60\n return '%dm %ds' % (m, s)\n\n\ndef timeSince(since, percent):\n now = time.time()\n s = now - since\n es = s / (percent)\n rs = es - s\n return '%s (- %s)' % (asMinutes(s), asMinutes(rs))\n\n\nclass my_RNN(nn.Module):\n def __init__(self, input_size, hidden_size, model , n_layers=1):\n super(my_RNN, self).__init__()\n self.n_layers = n_layers\n self.hidden_size = hidden_size\n self.input_size = input_size\n\n self.model = model\n\n\n self.rnn = nn.RNN(input_size,hidden_size,n_layers)\n #self.linear2 = nn.Linear(hidden_size, hidden_size)\n self.linear_out = nn.Linear(hidden_size, 1)\n\n\n def forward(self, input, hidden):\n # Entrées :\n # input (variable(mat)) : les instances\n # Sortie\n # Variable(vect) : les prédictions\n output,hidden = self.rnn(input,hidden)\n return output,hidden\n\n\n\n\n def initHidden(self):\n # première couche cachée\n result = Variable(torch.zeros(1,1,self.hidden_size))\n if use_cuda:\n return result.cuda()\n else:\n return result\n\n\n\n\n def train_once(self, input_variables, target_variable, optimizer, criterion):\n # Réalise l'entraînement pour une seule phrase, et réalise la backpropagation\n # Entrées :\n # - n_epochs (int) : nombre de fois qu'on applique toutes les instance de l'ensemble d'apprentissage\n # - input_variables list of Variable(vect) : instances d'apprentissage\n # - target_variable Variable(vect(+1|-1))) : labels\n # - optimizer (pytorch object) : le résultat de optim.SGD ou optim.Adam\n # - criterion (pytorch object) : le résultat de nn.L1Loss ou nn.MSELoss\n # Sorties :\n # perte (float) : la valeur de la perte globale\n\n output_list, hidden_list = [],[]\n hidden = self.initHidden()\n\n optimizer.zero_grad()\n\n for word in input_variables :\n\n output,hidden = self(word,hidden)\n\n output_list.append(output)\n hidden_list.append(hidden)\n\n output = torch.tanh(self.linear_out(output))\n\n loss = criterion(output.view(1,-1), target_variable.view(-1))\n\n loss.backward()\n optimizer.step()\n\n return loss.data[0], output_list, hidden_list\n\n\n\n def trainIters(self, n_epochs, training_pairs, te_pairs, learning_rate, print_every=1000, eval_every = 1000):\n # Réalise l'entraînement complet, à partir des ensembles d'apprentissage\n # Entrées :\n # - n_epochs (int) : nombre de fois qu'on applique toutes les instance de l'ensemble d'apprentissage\n # - training_pairs (list of (list of (vect)), (+1|-1))) : instances d'apprentissage\n # - te_pairs (list of (Variable(vect), Variable(+1|-1))) : instances de test\n # - learning_rate (float) : devine ;)\n # - print_every (int) : imprime l'erreur moyenne toutes les print_every epochs\n # - eval_every (int) : teste le NN sur la base de test et imprime la matrice de confusion\n # Sorties :\n # none\n\n start = time.time()\n plot_losses = []\n print_loss_total = 0 # Reset every print_every\n\n optimizer = optim.SGD(self.parameters(), lr=learning_rate)\n # Autre choix possible :\n #optimizer = optim.Adam(self.parameters(), lr=learning_rate)\n\n\n criterion = nn.L1Loss()\n #criterion = nn.MSELoss()\n\n for epoch in range(1, n_epochs + 1):\n loss = 0\n\n for pair in training_pairs:\n target_variable,input_variables = pair\n\n loss,_,_ = self.train_once(input_variables, target_variable, optimizer, criterion)\n\n print_loss_total += loss\n\n\n\n if epoch % print_every == 0:\n # print the loss and time\n print_loss_avg = print_loss_total / (print_every*len(training_pairs))\n print_loss_total = 0\n print('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs),\n epoch, epoch / n_epochs * 100, print_loss_avg))\n\n if epoch % eval_every == 0:\n self.evaluateRandomly(te_pairs) # show confusion matrix on test data\n\n\n\n\n\n\n\n def evaluateRandomly(self, pairs):\n # evaluate on all pairs, print the confusion matrix\n n_successes = 0\n n_pos = 0 # also computes the proportion of positive reviews\n\n TP,TN,FP,FN = 0,0,0,0\n\n\n for pair in pairs: # replace with pairs[:n] for testing\n\n target_variable,input_variables = pair\n hidden = self.initHidden()\n output_list, hidden_list = [],[]\n\n\n for word in input_variables :\n output,hidden = self(word,hidden)\n output_list.append(output)\n hidden_list.append(hidden)\n\n output = torch.tanh(self.linear_out(output))\n\n #success = (output[int(pair[1])] == max(output))\n note = pair[0].data[0,0]\n predicted = output.data[0][0]\n\n #print('note',note)\n #print('predicted',predicted)\n success = (note*predicted > 0)\n #print('success',success[0])\n\n\n if success[0] :\n n_successes += 1\n if note>0:\n TP += 1\n else:\n TN += 1\n else:\n if note>0:\n FP += 1\n else:\n FN += 1\n\n n_pos = n_pos+1 if note==1 else n_pos\n\n print('')\n print('')\n print('Confusion matrix ')\n print()\n print(\" \\t\\t Actual class\")\n print(\" \\t\\t Pos \\t Neg\")\n print(\"Predicted Pos \\t {} \\t {}\".format(TP,FN))\n print(\" Neg \\t {} \\t {}\".format(FP,TN))\n print('')\n print('\\t \\t \\t \\t Positive reviews (%)) : ',100*n_pos/len(pairs))\n print('\\t \\t \\t \\t Success rate (%) : ',100*n_successes/len(pairs))\n\n\n\n # evaluate on all pairs, print the confusion matrix\n n_expected = n_pos+1\n #= sum([note.data[0] for (note,_) in pairs if note.data[0] == 1])\n\n predicted_scores = []\n actual = []\n\n\n for pair in pairs: # replace with pairs[:n] for testing\n\n target_variable,input_variables = pair\n hidden = self.initHidden()\n output_list, hidden_list = [],[]\n\n\n for word in input_variables :\n output,hidden = self(word,hidden)\n output_list.append(output)\n hidden_list.append(hidden)\n\n output = torch.tanh(self.linear_out(output))\n\n #success = (output[int(pair[1])] == max(output))\n note = pair[0].data[0,0]\n predicted = output.data[0,0,0]\n\n actual.append(0 if note == -1 else 1 ) # on remplace la valeur -1 par un 0,\n # ce sera plus simple pour les calculs\n predicted_scores.append(predicted)\n\n\n # find the good border\n best = int(n_expected) * [(0,-1)] # liste **triée, qui contient les n_expected meilleurs\n # éléments trouvés jusqu'ici, sous la forme de couples (indice,valeur)\n\n for i_p in range(len(predicted_scores)):\n x = predicted_scores[i_p]\n\n if x > best[-1][1] :\n # si cet élément a une meilleur score que le plus petit élément sauvegardé:\n # on cherche l'indice de la liste où il s'insère\n\n i_insert = 0 # trouver l'indice i où insérer l'élément\n while x < best[i_insert][1]:\n i_insert +=1\n best = best[:i_insert] + [(i_p,x)] + best[i_insert:-1]\n # on insère d au bon endroit pour que la liste reste triée\n\n\n predicted = np.zeros((len(actual),))\n predicted[[ind for (ind,val) in best]] = 1\n\n actual = np.array(actual)\n TP = int(sum( predicted*actual ))\n TN = int(sum( (1-predicted)*(1-actual) ))\n FP = int(sum( predicted*(1-actual) ))\n FN = int(sum( (1-predicted)*actual ))\n\n\n print('')\n print('')\n print('Confusion matrix (threshold method)')\n print()\n print(\" \\t\\t Actual class\")\n print(\" \\t\\t Pos \\t Neg\")\n print(\"Predicted Pos \\t {} \\t {}\".format(TP,FN))\n print(\" Neg \\t {} \\t {}\".format(FP,TN))\n print('')\n print('\\t \\t \\t \\t Positive reviews (%)) : ',100*int(TP+FP)/len(pairs))\n print('\\t \\t \\t \\t Success rate (%) : ',100*int(TP+TN)/len(pairs))\n\n\n\n\n# overriding getData to only load 1 folder\ndef getData(folder):\n \"\"\"\n Input:\n - folder: string of the path of a folder containing txt files.\n Output:\n - listdata: list of [Y, X] (e.g. Y = 'Positive', X = \"very cool\")\n \"\"\"\n listdata = []\n\n try :\n filenames = os.listdir(folder)\n for filename in filenames[:10]: # change here\n\n with open(os.path.join(folder, filename), 'r') as f:\n for line in f:\n\n line2 = line.strip().split('\\t')\n if len(line2) == 2:\n listdata.append(line2)\n except : # folder is a filenamewith open(os.path.join(folder, filename), 'r') as f:\n for line in folder[:10000]:\n line2 = line.strip().split('\\t')\n if len(line2) == 2:\n listdata.append(line2)\n\n return listdata\n\n\ndef folder2data(train_filename,test_filename,balanced_tr ,balanced_te, n_features):\n # Entrées :\n # - train_filename (str) : le nom du **dossier** (et non pas le nom du fichier) où se trouvent les instances d'apprentissage\n # - test_filename (str) : le nom du **dossier** (et non pas le nom du fichier) où se trouvent les instances de test\n # - balanced_tr (bool) : True si l'ensemble d'apprentissage est équilibré; False s'il est laissé tel quel\n # - balanced_te (bool) : True si l'ensemble de test est équilibré; False s'il est laissé tel quel\n # - n_features (int) : nombre de variables pour coder chaque instance\n # Sorties :\n # - cuple (new_tr_pairs, new_te_pairs):\n # new_tr_pairs : (list of (vect), (+1|-1)))\n # new_te_pairs : (list of (Variable(vect), Variable(+1|-1)))\n\n pairs = getData(train_filename)\n\n\n if balanced_tr :\n \"\"\"\n #Pour un équilibrage 75/25\n pairs_using_numbers = [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')]\n Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive']\n pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers)*3)] # différence ici\n tr_pairs = pairs_using_numbers\n \"\"\"\n #Pour un équilibrage 50/50\n pairs_using_numbers = [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral' )]\n Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive']\n pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers))]\n tr_pairs = pairs_using_numbers\n\n else :\n pairs_using_numbers = [(1,text) for (target,text) in pairs if target == 'Positive']\n pairs_using_numbers += [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')]\n tr_pairs = pairs_using_numbers\n\n pairs = getData(test_filename)\n\n if balanced_te :\n pairs_using_numbers = [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')]\n Positive_reviews = [(1,text) for (target,text) in pairs if target == 'Positive']\n pairs_using_numbers += Positive_reviews[:int(len(pairs_using_numbers))]\n te_pairs = pairs_using_numbers\n\n else :\n pairs_using_numbers = [(1,text) for (target,text) in pairs if target == 'Positive']\n pairs_using_numbers += [(-1,text) for (target,text) in pairs if (target == 'Negative' or target == 'Neutral')]\n te_pairs = pairs_using_numbers\n\n\n # print([text for (_,text) in tr_pairs[:2]])\n\n \"\"\"\n tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2))\n tfidf_vectorizer.fit([ text for (_,text) in tr_pairs+te_pairs])\n\n # fitting\n X_tr_token = tfidf_vectorizer.transform([ text for (_,text) in tr_pairs])\n X_te_token = tfidf_vectorizer.transform([ text for (_,text) in te_pairs])\n\n truncatedsvd = TruncatedSVD(n_components=n_features) # prépare à projeter les données dans un espace à n_components dimensions\n truncatedsvd.fit(X_tr_token)\n truncatedsvd.fit(X_te_token)\n\n # Réduction de dimension\n X_tr_reduced_dim = truncatedsvd.transform(X_tr_token)\n X_te_reduced_dim = truncatedsvd.transform(X_te_token)\n \"\"\"\n\n W2Vmodel = Word2Vec(sentences= [text.lower().split() for (_,text) in chain(tr_pairs,te_pairs) ] ,\n size= n_features,\n #window=self.window_size,\n negative=20,\n iter=50,\n seed=1000,\n workers=multiprocessing.cpu_count())\n\n\n new_tr_pairs = []\n for i in range(len(tr_pairs)):\n (note,text) = tr_pairs[i]\n note = Variable(torch.FloatTensor([[note]]))\n\n vect_list = [Variable(torch.FloatTensor(np.array( W2Vmodel[word.lower()]))).view(1,-1) for word in text.split() if word.lower() in W2Vmodel]\n\n new_tr_pairs.append((note,vect_list))\n\n new_te_pairs = []\n for i in range(len(te_pairs)):\n (note,text) = te_pairs[i]\n note = Variable(torch.FloatTensor([[note]]))\n\n vect_list = [Variable(torch.FloatTensor(np.array(W2Vmodel[word.lower()]))).view(1,-1) for word in text.split() if word.lower() in W2Vmodel]\n new_te_pairs.append((note,vect_list))\n\n\n\n return new_tr_pairs, new_te_pairs,W2Vmodel\n\n\n\n\n\"\"\"\ndef sentences(pairs):\n for pair in pairs:\n yield pair[1]\n\"\"\"\n\n\"\"\"\nclass sentences():\n def __init__(self,pairs):\n self.pairs = pairs\n\n def __iter__():\n retur\n\n return [pair[1] for pair in pairs]\n\"\"\"\n\"\"\"\ndef sentences(pairs):\n return [pair[1] for pair in pairs]\n\nRNN = my_RNN(n_features, hidden_size, trainW2V = iter(sentences(tr_pairs+te_pairs)), n_layers = 1)\n\"\"\"\n\n# ==================================================================\n# ================ Using the RNN in itself =========================\n# ==================================================================\n\n#training_set_folder = \"../../data/data_books_training_set\"\n#test_set_folder = \"../../data/data_books_testing_set\"\n\ntraining_set_folder = \"../../data/cleaned/Automotive_5.txt\"\ntest_set_folder = \"../../data/cleaned/Musical_Instruments_5.txt\"\n\nn_features=2\ntr_pairs,te_pairs,W2Vmodel = folder2data(training_set_folder,test_set_folder,balanced_tr=False, balanced_te=False, n_features=n_features)\n\nprint(\"instances d'entraînement\",len(tr_pairs))\nprint(\"instances de test\",len(te_pairs))\n\nhidden_size = 100\n\n\n\n\nRNN = my_RNN(n_features, hidden_size, model=W2Vmodel , n_layers = 1)\n\n#RNN.evaluateNpairs(te_pairs,1) # show some examples\n\n\nlr = 0.000005\nN_epochs = 50\nprint(\"learning rate\",lr)\nRNN.trainIters(N_epochs, tr_pairs, te_pairs, lr, 1,5)\n\n\nRNN.evaluateRandomly(te_pairs) # show global results\n\ntorch.save(RNN,'RNN_W2V')\n#cours ; cd 2eme_partie_S9/Transfer_learning/TransferLearningProject/learning/ ; python rnn_word2vec.py\n\n\"\"\"\nRNN = torch.load('RNN')\nRNN.evaluateRandomly(te_pairs)\n\"\"\"\nprint('')\nprint('')\n\nprint(' Done')\nprint('')\nprint('')\nprint('')\n","sub_path":"learning/rnn_word2vec.py","file_name":"rnn_word2vec.py","file_ext":"py","file_size_in_byte":16377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"228196963","text":"from composition import Composition\r\n\r\nmod_table = {\r\n 'Carbamidomethyl' : ['Cys', 57.0214],\r\n 'Deamidated' : ['Asn', 0.9840099999999978],\r\n 'HexNAc' : ['Asn', 203.07937],\r\n 'pyroGlu': ['Gln', -17.02655]}\r\n\r\nclass Modification:\r\n \"\"\"description of class\"\"\"\r\n\r\n def __init__(self, mod_name, mod_pos = -1, mod_num = 1, mass = 0.0, target=''):\r\n self.name = mod_name\r\n self.position = mod_pos\r\n self.number = mod_num\r\n global mod_table\r\n key = mod_table.get(mod_name)\r\n if key == None:\r\n self.mass = mass\r\n self.target = target\r\n mod_table[mod_name] = [target, mass]\r\n else:\r\n self.mass = mod_table.get(mod_name)[1]\r\n self.target = mod_table.get(mod_name)[0]\r\n","sub_path":"HH_glycopeptide - KK testing v2/modification.py","file_name":"modification.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"462738813","text":"import os\nimport numpy\nimport shutil\nimport pickle\nimport json\nimport datetime\nfrom Models.metrics import ModelMetrics, print_metrics\nfrom Utils.utils import get_abs_path, get_formatted_date\nfrom Models.reports import Report\n\n\ndef job_collector(Config, DefConfig, kwargs):\n worker = Collector(Config, DefConfig, kwargs)\n worker.run()\n\n\nclass Collector:\n def __init__(self, Config, DefConfig, kwargs):\n self.Config = Config\n if \"test_docs\" not in Config or not Config[\"results\"]:\n print (\"Documents have not been classified in this process chain.\")\n print (\"Consolidation can't be performed.\")\n return\n self.rank_threshold = 0.5\n if Config['consolidatedrank'] == \"True\":\n try:\n self.rank_threshold = float(Config[\"consolidated_rank_threshold\"])\n except ValueError:\n self.rank_threshold = 0.5\n self.testLabels = numpy.concatenate([numpy.array(x.labels).\n reshape(1,\n len(self.Config[\"predefined_categories\"])) for x in self.Config[\"test_docs\"]])\n self.qLabs = len(self.Config[\"predefined_categories\"])\n self.predictions = numpy.zeros([len(self.testLabels), self.qLabs])\n self.metrics = {}\n self.useProbabilities = False\n self.save_reports = False\n self.runtime = False\n\n def run(self):\n print (\"\\nCalculate consolidated metrics...\")\n if not self.Config[\"results\"]:\n print(\"No results to consolidate them. Consolidation can not be performed.\")\n return\n if self.Config[\"save_reports\"] == \"True\":\n if not self.Config[\"reports_path\"] or not os.path.isdir(get_abs_path(self.Config, \"reports_path\")):\n print(\"Wrong path to the folder, containing reports. Reports can not be created.\")\n else:\n self.save_reports = True\n if self.Config[\"prepare_resources_for_runtime\"] == \"True\":\n if (not self.Config[\"saved_resources_path\"] or\n not os.path.isdir(get_abs_path(self.Config, \"saved_resources_path\"))):\n print(\"Wrong path to the folder, containing resources for runtime. Resources can not be saved.\")\n else:\n self.runtime = True\n print(\"Rank threshold for consolidated results: %.2f\" % (self.rank_threshold))\n if self.save_reports or self.Config[\"show_consolidated_results\"] == \"True\":\n self.getConsolidatedResults()\n self.get_metrics()\n if self.save_reports:\n self.saveReports()\n if self.runtime:\n saved_rc_path = get_abs_path(self.Config, \"saved_resources_path\")\n if len(os.listdir(saved_rc_path)) > 0:\n print(\"Warning: folder %s is not empty. All its content will be deleted.\" % saved_rc_path)\n shutil.rmtree(saved_rc_path)\n os.makedirs(saved_rc_path, exist_ok=True)\n print(\"\\nCollect arfifacts for runtime...\")\n self.prepare_resources_for_runtime()\n\n\n def getConsolidatedResults(self):\n for key, res in self.Config[\"results\"].items():\n for i in range(len(res)):\n for j in range(self.qLabs):\n if res[i][j] == 1:\n self.predictions[i][j] += 1\n #elif res[i][j] >= self.rank_threshold:\n elif res[i][j] >= self.Config[\"ranks\"][key]:\n self.predictions[i][j] += 1\n q_models = len(self.Config[\"results\"])\n for p1 in self.predictions:\n for p in p1:\n if p >= q_models * self.rank_threshold:\n p = 1\n else:\n p = 0\n\n def get_metrics(self):\n ModelMetrics(self)\n if self.Config[\"show_consolidated_results\"] == \"True\":\n print_metrics(self)\n\n def saveReports(self):\n print (\"Save report...\")\n report = Report()\n report.requestId = self.Config[\"reqid\"]\n report.sourcesPath = self.Config[\"actual_path\"]\n report.datasetPath = self.Config[\"test_data_path\"]\n\n tokenization_options = [\"language_tokenization\", \"normalization\", \"stop_words\", \"exclude_positions\",\n \"extra_words\", \"exclude_categories\"]\n for t in tokenization_options:\n report.preprocess[t] = self.Config[t]\n for t in self.Config[\"test_docs\"]:\n report.docs[t.name] = {}\n report.docs[t.name][\"actual\"] = \",\".join(t.nlabs)\n if not self.Config[\"exclude_categories\"]:\n exclude_categories = []\n else:\n exclude_categories = self.Config[\"exclude_categories\"].split(\",\")\n cNames = [''] * (len(self.Config[\"predefined_categories\"]) - len(exclude_categories))\n for k, v in self.Config[\"predefined_categories\"].items():\n if k not in exclude_categories:\n cNames[v] = k\n report.categories = cNames\n for key, val in self.Config[\"results\"].items():\n for i in range(len(val)):\n labs = []\n for j in range(self.qLabs):\n #if val[i][j] >= self.rank_threshold:\n if val[i][j] >= self.Config[\"ranks\"][key]:\n labs.append(\"%s[%.2f]\" % (cNames[j], val[i][j]))\n report.docs[self.Config[\"test_docs\"][i].name][key] = \",\".join(labs)\n for key, val in self.Config[\"metrics\"].items():\n report.models[key] = val\n for key, val in self.Config[\"ranks\"].items():\n report.ranks[key] = val\n if len(self.Config[\"results\"]) > 1:\n for i in range(len(self.predictions)):\n labs = []\n for j in range(self.qLabs):\n if self.predictions[i][j] == 1:\n labs.append(cNames[j])\n report.docs[self.Config[\"test_docs\"][i].name][\"consolidated\"] = \",\".join(labs)\n report.models[\"consolidated\"] = self.rank_threshold\n rPath = get_abs_path(self.Config, \"reports_path\") + \"/\" + self.Config[\"reqid\"] + \".json\"\n with open(rPath, 'w', encoding=\"utf-8\") as file:\n json.dump(report.toJSON(), file, indent=4)\n file.close()\n\n def prepare_resources_for_runtime(self):\n tokenization_options = [\"language_tokenization\", \"normalization\", \"stop_words\", \"exclude_positions\",\n \"extra_words\", \"max_seq_len\", \"max_chars_seq_len\", \"single_doc_lang_tokenization_lib_path\"]\n self.Config[\"resources\"][\"tokenization\"] = {}\n ds = datetime.datetime.now()\n self.outDir = get_abs_path(self.Config, \"saved_resources_path\") + \"/\"\n for t in tokenization_options:\n if t != \"single_doc_lang_tokenization_lib_path\":\n self.Config[\"resources\"][\"tokenization\"][t] = self.Config[t]\n elif self.Config[\"language_tokenization\"] == \"True\":\n self.Config[\"resources\"][\"tokenization\"][\"single_doc_lang_tokenization_lib_path\"] = \\\n self.copyFile(get_abs_path(self.Config, \"single_doc_lang_tokenization_lib_path\"))\n isW2VNeeded = False\n for key, val in self.Config[\"resources\"][\"models\"].items():\n val[\"created_model_path\"] = self.copyFile(val[\"created_model_path\"])\n if \"w2v\" in val and val[\"w2v\"] == \"True\":\n isW2VNeeded = True\n if not isW2VNeeded and \"w2v\" in self.Config[\"resources\"]:\n self.Config[\"resources\"].pop(\"w2v\", None)\n if \"w2v\" in self.Config[\"resources\"]:\n w2vDict = {}\n isFirstLine = True\n fEmbeddings = open(self.Config[\"resources\"][\"w2v\"][\"created_model_path\"], encoding=\"utf-8\")\n for line in fEmbeddings:\n if isFirstLine == True:\n isFirstLine = False\n continue\n split = line.strip().split(\" \")\n word = split[0]\n vector = numpy.array([float(num) for num in split[1:]])\n w2vDict[word] = vector\n fEmbeddings.close()\n with open(self.Config[\"resources\"][\"w2v\"][\"created_model_path\"] + '.pkl', 'wb') as file:\n pickle.dump(w2vDict, file, pickle.HIGHEST_PROTOCOL)\n file.close()\n self.Config[\"resources\"][\"w2v\"][\"created_model_path\"] = \\\n self.copyFile(self.Config[\"resources\"][\"w2v\"][\"created_model_path\"] + '.pkl')\n if \"indexer\" in self.Config[\"resources\"]:\n self.Config[\"resources\"][\"indexer\"] = self.copyFile(self.Config[\"resources\"][\"indexer\"])\n if \"vectorizer\" in self.Config[\"resources\"]:\n self.Config[\"resources\"][\"vectorizer\"] = self.copyFile(self.Config[\"resources\"][\"vectorizer\"])\n if \"ptBertModel\" in self.Config[\"resources\"]:\n self.Config[\"resources\"][\"ptBertModel\"] = self.copyFile(self.Config[\"resources\"][\"ptBertModel\"])\n self.Config[\"resources\"][\"vocabPath\"] = self.copyFile(self.Config[\"resources\"][\"vocabPath\"])\n cNames = [''] * len(self.Config[\"predefined_categories\"])\n for k, v in self.Config[\"predefined_categories\"].items():\n cNames[v] = k\n with open(self.outDir + 'labels.txt', 'w', encoding=\"utf-8\") as file:\n file.write(\",\".join(cNames))\n file.close()\n self.Config[\"resources\"][\"labels\"] = \"labels.txt\"\n self.Config[\"resources\"][\"consolidatedRank\"] = self.rank_threshold\n with open(self.outDir + 'config.json', 'w', encoding=\"utf-8\") as file:\n json.dump(self.Config[\"resources\"], file, indent=4)\n file.close()\n de = datetime.datetime.now()\n print(\"\\nArtifacts are copied into the folder %s in %s\"%(\n get_abs_path(self.Config, \"saved_resources_path\"), get_formatted_date(ds, de)))\n\n def copyFile(self, inPath):\n dir, name = os.path.split(inPath)\n outPath = self.outDir + name\n shutil.copy(inPath, outPath)\n return name\n\n","sub_path":"TrainAndTest/Models/consolidation.py","file_name":"consolidation.py","file_ext":"py","file_size_in_byte":10078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"465839810","text":"#!/usr/bin/env python3\nimport os, sys\nimport collections\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))\n\nfrom datetime import datetime\nfrom dateutil.tz import tzlocal\nimport pytz\nimport re\nimport numpy as np\nimport json\nimport pandas as pd\n\nfrom pipeline import (lab, experiment, imaging, virus)\nimport pynwb\nfrom pynwb import NWBFile, NWBHDF5IO\n\n# ============================== SET CONSTANTS ==========================================\ndefault_nwb_output_dir = os.path.join('/data', 'NWB 2.0')\nzero_zero_time = datetime.strptime('00:00:00', '%H:%M:%S').time() # no precise time available\ninstitution = 'Janelia Research Campus'\n\n\ndef export_to_nwb(session_key, nwb_output_dir=default_nwb_output_dir, save=False, overwrite=False):\n\n this_session = (experiment.Session & session_key).fetch1()\n print(f'Exporting to NWB 2.0 for session: {this_session}...')\n # ===============================================================================\n # ============================== META INFORMATION ===============================\n # ===============================================================================\n\n # -- NWB file - a NWB2.0 file for each session\n file_name = '_'.join(\n [this_session['subject_nickname'],\n this_session['session_date'].strftime('%Y-%m-%d'),\n str(this_session['session'])])\n nwbfile = NWBFile(\n identifier=file_name,\n related_publications='http://dx.doi.org/10.1016/j.neuron.2017.05.005',\n experiment_description='Two-photon experiment recorded in {}'.format(this_session['brain_location_name']),\n session_description='Imaging session',\n session_start_time=datetime.combine(this_session['session_date'], zero_zero_time),\n file_create_date=datetime.now(tzlocal()),\n experimenter=this_session['username'],\n institution=institution,\n keywords=['motor planning', 'anterior lateral cortex', 'medial motor cortex',\n 'ALM', 'MM', 'Two-photon imaging'])\n\n # -- subject\n subj = (lab.Subject & session_key).fetch1()\n nwbfile.subject = pynwb.file.Subject(\n subject_id=this_session['subject_nickname'],\n genotype=' x '.join((lab.Subject.GeneModification\n & subj).fetch('gene_modification')),\n sex=subj['sex'],\n species=subj['species'],\n date_of_birth=datetime.combine(subj['date_of_birth'], zero_zero_time) if subj['date_of_birth'] else None)\n # -- virus\n nwbfile.virus = json.dumps([{k: str(v) for k, v in virus_injection.items() if k not in subj}\n for virus_injection in virus.VirusInjection * virus.Virus & session_key])\n\n # ===============================================================================\n # ======================== IMAGING & SEGMENTATION ===============================\n # ===============================================================================\n\n scan = (imaging.Scan & session_key).fetch1()\n\n # ---- Structural Images ----------\n images = pynwb.base.Images('images')\n\n if isinstance(scan['image_beads'], collections.Sequence):\n gcamp = pynwb.image.GrayscaleImage('GCaMP at 940nm', scan['image_gcamp'])\n images.add_image(gcamp)\n if isinstance(scan['image_ctb'], collections.Sequence):\n ctb = pynwb.image.RGBImage('CTB-647 IT', scan['image_ctb'])\n images.add_image(ctb)\n if isinstance(scan['image_beads'], collections.Sequence):\n beads = pynwb.image.GrayscaleImage('Beads PT', scan['image_beads'])\n images.add_image(beads)\n\n nwbfile.add_acquisition(images)\n\n imaging_plane = nwbfile.create_imaging_plane(\n name='Imaging plane',\n optical_channel=pynwb.ophys.OpticalChannel(\n name='green', description='green channel', emission_lambda=500.),\n description='Imaging session for PT and IT neurons during audio delay task',\n device=nwbfile.create_device(name='two-photon microscope with Thorlabs resonant galvo scannner'),\n excitation_lambda=940.,\n imaging_rate=300.,\n indicator='GCaMP6s',\n location='ALM',\n conversion=1e-6,\n unit='micrometers')\n\n # ---- Frame Time information -----\n\n frame_time = pynwb.image.TimeSeries(\n name='Frame Time',\n data=list(range(0, len(scan['frame_time']))),\n unit='a.u',\n timestamps=scan['frame_time']\n )\n nwbfile.add_acquisition(frame_time)\n\n # ----- Segementation information -----\n # link the imaging segmentation to the nwb file\n ophys = nwbfile.create_processing_module('Ophys', 'Processing result of imaging')\n img_seg = pynwb.ophys.ImageSegmentation()\n ophys.add_data_interface(img_seg)\n\n pln_seg = pynwb.ophys.PlaneSegmentation(\n name='Plane Segmentation',\n description='plane segmentation',\n imaging_plane=imaging_plane)\n\n img_seg.add_plane_segmentation([pln_seg])\n\n\n # insert ROI mask\n rois = (imaging.Scan.Roi & session_key).fetch(as_dict=True)\n\n for k, v in dict(\n roi_id='roi id',\n cell_type='PT, IT, or unknown',\n roi_trace='Trace on this session of this roi',\n neuropil_trace='Trace on this session of the neurophil',\n included='whether to include this roi into later analyses'\n ).items():\n pln_seg.add_column(name=k, description=v)\n\n\n for roi in rois:\n mask = np.zeros([512, 512])\n mask[np.unravel_index(roi['roi_pixel_list']-1, mask.shape, 'F')] = 1\n pln_seg.add_roi(\n roi_id=roi['roi_idx'],\n image_mask=mask,\n cell_type=roi['cell_type'],\n roi_trace=roi['roi_trace'],\n neuropil_trace=roi['roi_trace'],\n included=roi['inc'])\n\n # ===============================================================================\n # =============================== BEHAVIOR TRIALS ===============================\n # ===============================================================================\n\n # =============== TrialSet ====================\n # NWB 'trial' (of type dynamic table) by default comes with three mandatory attributes: 'start_time' and 'stop_time'\n # Other trial-related information needs to be added in to the trial-table as additional columns (with column name\n # and column description)\n\n dj_trial = experiment.SessionTrial * experiment.BehaviorTrial\n skip_adding_columns = experiment.Session.primary_key + ['trial_uid']\n\n if experiment.SessionTrial & session_key:\n # Get trial descriptors from TrialSet.Trial and TrialStimInfo\n trial_columns = [{'name': tag,\n 'description': re.sub('\\s+:|\\s+', ' ', re.search(\n f'(?<={tag})(.*)', str(dj_trial.heading)).group()).strip()}\n for tag in dj_trial.heading.names\n if tag not in skip_adding_columns + ['start_time', 'stop_time']]\n\n # Add new table columns to nwb trial-table for trial-label\n for c in trial_columns:\n nwbfile.add_trial_column(**c)\n\n # Add entry to the trial-table\n for trial in (dj_trial & session_key).fetch(as_dict=True):\n trial['start_time'] = float(trial['start_time'])\n trial['stop_time'] = float(trial['stop_time']) if trial['stop_time'] else 5.0\n [trial.pop(k) for k in skip_adding_columns]\n nwbfile.add_trial(**trial)\n\n # ===============================================================================\n # =============================== BEHAVIOR TRIAL EVENTS ==========================\n # ===============================================================================\n\n behav_event = pynwb.behavior.BehavioralEvents(name='BehavioralEvents')\n nwbfile.add_acquisition(behav_event)\n\n for trial_event_type in (experiment.TrialEventType & experiment.TrialEvent & session_key).fetch('trial_event_type'):\n event_times, trial_starts = (experiment.TrialEvent * experiment.SessionTrial\n & session_key & {'trial_event_type': trial_event_type}).fetch(\n 'trial_event_time', 'start_time')\n if len(event_times) > 0:\n event_times = np.hstack(event_times.astype(float) + trial_starts.astype(float))\n behav_event.create_timeseries(name=trial_event_type, unit='a.u.', conversion=1.0,\n data=np.full_like(event_times, 1),\n timestamps=event_times)\n\n # =============== Write NWB 2.0 file ===============\n if save:\n save_file_name = ''.join([nwbfile.identifier, '.nwb'])\n if not os.path.exists(nwb_output_dir):\n os.makedirs(nwb_output_dir)\n if not overwrite and os.path.exists(os.path.join(nwb_output_dir, save_file_name)):\n return nwbfile\n with NWBHDF5IO(os.path.join(nwb_output_dir, save_file_name), mode = 'w') as io:\n io.write(nwbfile)\n print(f'Write NWB 2.0 file: {save_file_name}')\n\n return nwbfile\n\n\n# ============================== EXPORT ALL ==========================================\n\nif __name__ == '__main__':\n if len(sys.argv) > 1:\n nwb_outdir = sys.argv[1]\n else:\n nwb_outdir = default_nwb_output_dir\n\n for skey in experiment.Session.fetch('KEY'):\n export_to_nwb(skey, nwb_output_dir=nwb_outdir, save=True)\n","sub_path":"pipeline/export/datajoint_to_nwb.py","file_name":"datajoint_to_nwb.py","file_ext":"py","file_size_in_byte":9415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"186991793","text":"import requests\nfrom urllib.parse import urlencode\nimport re\n\nclass News():\n def __init__(self):\n pass\n\n def weibo_hot(self):\n url = 'https://s.weibo.com/top/summary/'\n headers = {\n 'Host':'s.weibo.com',\n 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',\n }\n\n result = requests.get(url, headers=headers)\n result_dict = dict()\n hot_word_list = re.findall('.*?.*?>(.*?)', result.text, re.S)\n\n for word in hot_word_list:\n\n data = {\n 'q':word,\n 'Refer':'top'\n }\n\n result_url = 'https://s.weibo.com/weibo' + '?' + urlencode(data)\n result_dict[word] = result_url\n return result_dict\n\n","sub_path":"Raspberry/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"446567222","text":"#!/usr/bin/env python3\nimport uuid\nimport os\nimport copy\nimport json\nimport argparse\n\nfrom cyy_naive_lib.log import get_logger\n\nfrom .dataset import (\n dataset_with_indices,\n sample_subset,\n sub_dataset,\n replace_dataset_labels,\n MachineLearningPhase,\n get_dataset,\n)\nfrom .configuration import (\n get_trainer_from_configuration,\n get_inferencer_from_configuration,\n)\nfrom .hyper_gradient_trainer import HyperGradientTrainer\nfrom .reproducible_env import global_reproducible_env\n\n\ndef get_arg_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str)\n parser.add_argument(\"--model_name\", type=str)\n parser.add_argument(\"--epochs\", type=int, default=None)\n parser.add_argument(\"--batch_size\", type=int, default=None)\n parser.add_argument(\"--learning_rate\", type=float, default=None)\n parser.add_argument(\"--weight_decay\", type=float, default=None)\n parser.add_argument(\"--stop_accuracy\", type=float, default=None)\n parser.add_argument(\"--cache_size\", type=int, default=None)\n parser.add_argument(\"--model_path\", type=str, default=None)\n parser.add_argument(\"--reproducible_env_load_path\", type=str, default=None)\n parser.add_argument(\n \"--training_dataset_percentage\",\n type=float,\n default=None)\n parser.add_argument(\n \"--make_reproducible\",\n action=\"store_true\",\n default=False)\n parser.add_argument(\n \"--approx_hyper_gradient_and_momentum_dir\", type=str, default=None\n )\n parser.add_argument(\n \"--hessian_hyper_gradient_and_momentum_dir\", type=str, default=None\n )\n parser.add_argument(\n \"--hyper_gradient_sample_percentage\",\n type=float,\n default=None)\n parser.add_argument(\"--use_hessian\", action=\"store_true\", default=False)\n parser.add_argument(\n \"--use_hessian_and_approximation\", action=\"store_true\", default=False\n )\n parser.add_argument(\"--repeated_num\", type=int, default=None)\n parser.add_argument(\"--save_dir\", type=str, default=None)\n return parser\n\n\ndef get_parsed_args(parser=None):\n if parser is None:\n parser = get_arg_parser()\n args = parser.parse_args()\n if args.save_dir is None:\n args.save_dir = __create_unique_save_dir(\n os.path.join(\"models\", args.task_name))\n return args\n\n\ndef __create_unique_save_dir(save_dir: str):\n return os.path.join(save_dir, str(uuid.uuid4()))\n\n\ndef create_trainer_from_args(args):\n if args.reproducible_env_load_path is not None:\n global global_reproducible_env\n assert not global_reproducible_env.initialized\n global_reproducible_env.load(args.reproducible_env_load_path)\n args.make_reproducible = True\n\n if args.make_reproducible:\n global_reproducible_env.enable()\n global_reproducible_env.save(args.save_dir)\n\n trainer = get_task_configuration(args.task_name, True)\n if args.model_path is not None:\n trainer.load_model(args.model_path)\n\n hyper_parameter = copy.deepcopy(trainer.get_hyper_parameter())\n if args.epochs is not None:\n hyper_parameter.epochs = args.epochs\n if args.batch_size is not None:\n hyper_parameter.batch_size = args.batch_size\n if args.learning_rate is not None:\n hyper_parameter.learning_rate = args.learning_rate\n if args.weight_decay is not None:\n hyper_parameter.weight_decay = args.weight_decay\n trainer.set_hyper_parameter(hyper_parameter)\n\n if args.training_dataset_percentage is not None:\n os.makedirs(args.save_dir, exist_ok=True)\n subset_dict = sample_subset(\n trainer.training_dataset, args.training_dataset_percentage\n )\n sample_indices = sum(subset_dict.values(), [])\n trainer.training_dataset = sub_dataset(\n trainer.training_dataset, sample_indices)\n with open(\n os.path.join(args.save_dir, \"training_dataset_indices.json\"),\n mode=\"wt\",\n ) as f:\n json.dump(sample_indices, f)\n\n trainer.training_dataset = dataset_with_indices(trainer.training_dataset)\n\n if args.stop_accuracy is not None:\n trainer.stop_criterion = (\n lambda trainer, epoch, __: trainer.validation_accuracy[epoch]\n >= args.stop_accuracy\n )\n return trainer\n\n\ndef create_hyper_gradient_trainer_from_args(args):\n trainer = create_trainer_from_args(args)\n\n use_approximation = True\n if args.use_hessian:\n use_approximation = False\n if args.use_hessian_and_approximation:\n args.use_hessian = True\n use_approximation = True\n\n hyper_gradient_trainer = HyperGradientTrainer(\n trainer,\n args.cache_size,\n args.save_dir,\n hessian_hyper_gradient_and_momentum_dir=args.hessian_hyper_gradient_and_momentum_dir,\n approx_hyper_gradient_and_momentum_dir=args.approx_hyper_gradient_and_momentum_dir,\n use_hessian=args.use_hessian,\n use_approximation=use_approximation,\n )\n\n if args.hyper_gradient_sample_percentage is not None:\n subset_dict = sample_subset(\n trainer.training_dataset,\n args.hyper_gradient_sample_percentage,\n )\n sample_indices = sum(subset_dict.values(), [])\n os.makedirs(args.save_dir, exist_ok=True)\n with open(\n os.path.join(args.save_dir, \"hyper_gradient_indices.json\"),\n mode=\"wt\",\n ) as f:\n json.dump(sample_indices, f)\n get_logger().info(\"track %s samples\", len(sample_indices))\n hyper_gradient_trainer.set_computed_indices(sample_indices)\n return hyper_gradient_trainer\n\n\ndef create_validator_from_args(args):\n validator = get_task_configuration(args.task_name, False)\n if args.model_path is not None:\n validator.load_model(args.model_path)\n return validator\n\n\ndef get_randomized_label_map(args):\n randomized_label_map: dict = dict()\n with open(args.randomized_label_map_path, \"r\") as f:\n for k, v in json.load(f).items():\n randomized_label_map[int(k)] = int(v)\n return randomized_label_map\n\n\ndef get_training_dataset(args):\n dataset_name = get_task_dataset_name(args.task_name)\n training_dataset = get_dataset(dataset_name, MachineLearningPhase.Training)\n if (\n hasattr(args, \"training_dataset_indices_path\")\n and args.training_dataset_indices_path is not None\n ):\n get_logger().info(\"use training_dataset_indices_path\")\n with open(args.training_dataset_indices_path, \"r\") as f:\n subset_indices = json.load(f)\n training_dataset = sub_dataset(training_dataset, subset_indices)\n if args.randomized_label_map_path is not None:\n get_logger().info(\"use randomized_label_map_path\")\n training_dataset = replace_dataset_labels(\n training_dataset, get_randomized_label_map(args)\n )\n return training_dataset\n","sub_path":"influence_function/arg_parse.py","file_name":"arg_parse.py","file_ext":"py","file_size_in_byte":6927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"155301945","text":"import importlib\nimport datetime\nimport time\n\nfrom sqlalchemy.exc import OperationalError\nfrom sqlalchemy.sql.ddl import DDL\n\nfrom wopmars.framework.database.Base import Base\nfrom sqlalchemy import Column, Integer, String, ForeignKey, DateTime\nfrom sqlalchemy.orm import relationship, reconstructor\n\nfrom wopmars.framework.database.SQLManager import SQLManager\nfrom wopmars.framework.database.tables.IOPut import IOPut\nfrom wopmars.framework.database.tables.ModificationTable import ModificationTable\nfrom wopmars.framework.database.tables.ToolWrapper import ToolWrapper\nfrom wopmars.utils.Logger import Logger\nfrom wopmars.framework.database.tables.Type import Type\nfrom sqlalchemy.sql.functions import func\n\nclass IODbPut(IOPut, Base):\n \"\"\"\n This class extends IOPut and is specific to the input or output tables. It is the model which store the references\n to the actual tables needed by the user. The table ``wom_table`` associated with this model contains the\n following fields:\n\n - id: INTEGER - primary key - autoincrement - arbitrary ID\n - tablename: VARCHAR(255) - foreign key to the associated table: :class:`wopmars.framework.database.tables.ModificationTable.ModificationTable` - the name of the referenced table\n - model: VARCHAR(255) - the path to the model (in python notation)\n - rule_id: INTEGER - foreign key to the associated rule ID: :class:`wopmars.framework.database.tables.ToolWrapper.ToolWrapper`\n - type_id: INTEGER - foreign key to the associated type ID: :class:`wopmars.framework.database.tables.Type.Type`\n - used_at: DATE - date at which the table have been used\n \"\"\"\n __tablename__ = \"wom_table\"\n\n id = Column(Integer, primary_key=True, autoincrement=True)\n tablename = Column(String(255), ForeignKey(\"wom_modification_table.table_name\"))\n model = Column(String(255))\n rule_id = Column(Integer, ForeignKey(\"wom_rule.id\"))\n type_id = Column(Integer, ForeignKey(\"wom_type.id\"))\n used_at = Column(DateTime, nullable=True)\n\n # One table is in one rule\n rule = relationship(\"ToolWrapper\", back_populates=\"tables\", enable_typechecks=False)\n # One file has One type\n type = relationship(\"Type\", back_populates=\"tables\")\n\n modification = relationship(\"ModificationTable\", back_populates=\"tables\")\n\n # all the model names met since the begining of this instance of WopMaRS\n tablemodelnames = set()\n # al the table names met since the begining of this instance of WopMaRS\n tablenames = set()\n\n def __init__(self, model, tablename):\n \"\"\"\n self.__table is initialized to None and will contain the model of this IODbPut object.\n\n :param model: The path to the model\n :type model: str\n :param tablename: The name of the table associated with the model\n :type tablename: str\n \"\"\"\n # The file containing the table should be in PYTHONPATH\n Base.__init__(self, model=model, tablename=tablename)\n Logger.instance().debug(str(model) + \" model loaded. Tablename: \" + str(tablename))\n self.__table = None\n\n @reconstructor\n def init_on_load(self):\n \"\"\"\n This is used by SQLAlchemy to regenerate the right object when loading it from the database. Here, we need to\n get back the actual Model from the model name and store it in self.__table.\n \"\"\"\n for table in IODbPut.tablemodelnames:\n mod = importlib.import_module(table)\n try:\n if table == self.model:\n # todo tabling\n self.__table = eval(\"mod.\" + self.model.split(\".\")[-1])\n except AttributeError as e:\n raise e\n Logger.instance().debug(self.tablename + \" table class reloaded. Model: \" + self.model)\n\n def set_table(self, model):\n self.__table = model\n\n def get_table(self):\n return self.__table\n\n @staticmethod\n def create_triggers():\n \"\"\"\n Create an INSERT, UPDATE, DELETE trigger on the tables created by the user in order to store the modifications date.\n \"\"\"\n stmt = [\"INSERT\", \"UPDATE\", \"DELETE\"]\n for tablename in Base.metadata.tables:\n if tablename[:4] != \"wom_\":\n for s in stmt:\n data={\"statement\": str(s), \"tablename\": str(tablename)} \n if SQLManager.instance().__dict__['d_database_config']['db_connection'] == 'sqlite':\n sql_trigger = \"\"\"\nCREATE TRIGGER IF NOT EXISTS modification_%(tablename)s AFTER %(statement)s ON %(tablename)s\nBEGIN\nUPDATE wom_modification_table SET date = CURRENT_TIMESTAMP WHERE table_name = '%(tablename)s';\nEND;\n \"\"\"%data\n elif SQLManager.instance().__dict__['d_database_config']['db_connection'] == 'mysql':\n sql_trigger = \"\"\"\nCREATE TRIGGER IF NOT EXISTS modification_%(tablename)s_%(statement)s AFTER %(statement)s ON %(tablename)s for each row UPDATE wom_modification_table SET date = CURRENT_TIMESTAMP WHERE table_name = '%(tablename)s';\n \"\"\"%data\n obj_ddl = DDL(sql_trigger)\n SQLManager.instance().create_trigger(Base.metadata.tables[tablename], obj_ddl)\n elif SQLManager.instance().__dict__['d_database_config']['db_connection'] == 'postgresql':\n sql_trigger = \"\"\"\nCREATE OR REPLACE FUNCTION modification_%(statement)s_%(tablename)s() RETURNS TRIGGER AS $modification_%(statement)s_%(tablename)s$\nBEGIN\nUPDATE wom_modification_table SET date = CURRENT_TIMESTAMP WHERE table_name = '%(tablename)s';\nRETURN NULL; -- result is ignored since this is an AFTER trigger\nEND;\n$modification_%(statement)s_%(tablename)s$ LANGUAGE plpgsql;\nDROP TRIGGER IF EXISTS modification_%(statement)s_%(tablename)s ON \"%(tablename)s\";\nCREATE TRIGGER modification_%(statement)s_%(tablename)s AFTER INSERT ON \"%(tablename)s\" FOR EACH ROW EXECUTE PROCEDURE modification_%(statement)s_%(tablename)s();\n \"\"\"%data\n obj_ddl = DDL(sql_trigger)\n SQLManager.instance().create_trigger(Base.metadata.tables[tablename], obj_ddl)\n\n\n @staticmethod\n def set_tables_properties(tables):\n \"\"\"\n Import the models of the current execution and then associate models with IODbPut objects.\n\n :param tables: the IODbPut which need their table properties to be set.\n :type tables: ResultSet(IODbPut)\n \"\"\"\n # import models for avoid references errors between models when dealing with them\n IODbPut.import_models(set([t.model for t in tables]))\n\n for table in tables:\n # keep track of the models used in static variable of IODbPut\n IODbPut.tablemodelnames.add(table.model)\n # Associate model with the IODbPut object\n mod = importlib.import_module(table.model)\n table_model = eval(\"mod.\" + table.model.split(\".\")[-1])\n table.set_table(table_model)\n # keep track of table names used in static variable of IODbPut\n IODbPut.tablenames.add(table_model.__tablename__)\n SQLManager.instance().get_session().add(table)\n\n @staticmethod\n def get_execution_tables():\n \"\"\"\n Return all the IODbPut objects found in model IODbPut.\n\n :return: ResultSet IODbPut objects\n \"\"\"\n session = SQLManager.instance().get_session()\n execution_id = session.query(func.max(ToolWrapper.execution_id))\n return session.query(IODbPut).filter(IODbPut.rule_id == ToolWrapper.id).filter(ToolWrapper.execution_id == execution_id).all()\n\n @staticmethod\n def import_models(model_names):\n \"\"\"\n Import all the given models\n\n :param model_names: The path to the models\n :type model_names: Iterable(String)\n \"\"\"\n for t in model_names:\n Logger.instance().debug(\"IODbPut.import_models: importing \" + str(t))\n importlib.import_module(t)\n\n def is_ready(self):\n \"\"\"\n A IODbPut object is ready if its table exists and contains entries.\n\n :return: bool if the table is ready\n \"\"\"\n session = SQLManager.instance().get_session()\n try:\n results = session.query(self.__table).first()\n if results is None:\n Logger.instance().debug(\"The table \" + self.tablename + \" is empty.\")\n return False\n except OperationalError as e:\n Logger.instance().debug(\"The table \" + self.__table.__tablename__ + \" doesn't exist.\")\n return False\n except Exception as e:\n session.rollback()\n raise e\n # todo twthread\n return True\n\n def __eq__(self, other):\n \"\"\"\n Two IODbPut object are equals if their table attributes belongs to the same class and if the associated table\n has the same content\n\n :param other: IODbPut\n :return: boolean: True if the table attributes are the same, False if not\n \"\"\"\n session = SQLManager.instance().get_session()\n if self.model != other.model or self.tablename != other.tablename:\n return False\n try:\n self_results = set(session.query(self.__table).all())\n other_results = set(session.query(other.get_table()).all())\n if self_results != other_results:\n return False\n except Exception as e:\n session.rollback()\n raise e\n return True\n\n def __hash__(self):\n return id(self)\n\n def __repr__(self):\n return \"\"\n\n def __str__(self):\n return \"\"\n","sub_path":"wopmars/framework/database/tables/IODbPut.py","file_name":"IODbPut.py","file_ext":"py","file_size_in_byte":9800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"143425697","text":"\"\"\"Transition and sequence analysis of neighborhood change.\"\"\"\n\nfrom warnings import warn\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nfrom giddy.markov import Markov, Spatial_Markov\nfrom giddy.sequence import Sequence\nfrom libpysal.weights import Voronoi, lag_categorical\nfrom libpysal.weights.contiguity import Queen, Rook\nfrom libpysal.weights.distance import KNN, DistanceBand, Kernel\nfrom sklearn.cluster import AgglomerativeClustering\n\nWs = {\n \"queen\": Queen,\n \"rook\": Rook,\n \"voronoi\": Voronoi,\n \"knn\": KNN,\n \"kernel\": Kernel,\n \"distanceband\": DistanceBand,\n}\n\n\ndef transition(\n gdf,\n cluster_col,\n temporal_index=\"year\",\n unit_index=\"geoid\",\n w_type=\"rook\",\n w_options=None,\n permutations=0,\n):\n \"\"\"\n (Spatial) Markov approach to transitional dynamics of neighborhoods.\n\n Parameters\n ----------\n gdf : geopandas.GeoDataFrame or pandas.DataFrame\n Long-form geopandas.GeoDataFrame or pandas.DataFrame containing neighborhood\n attributes with a column defining neighborhood clusters.\n cluster_col : string or int\n Column name for the neighborhood segmentation, such as\n \"ward\", \"kmeans\", etc.\n temporal_index : string, optional\n Column defining time and or sequencing of the long-form data.\n Default is \"year\".\n unit_index : string, optional\n Column identifying the unique id of spatial units.\n Default is \"geoid\".\n w_type : string, optional\n Type of spatial weights type (\"rook\", \"queen\", \"knn\" or\n \"kernel\") to be used for spatial structure. Default is\n None, if non-spatial Markov transition rates are desired.\n w_options : dict\n additional options passed to a libpysal weights constructor\n (e.g. `k` for a KNN weights matrix)\n permutations : int, optional\n number of permutations for use in randomization based\n inference (the default is 0).\n\n Returns\n --------\n mar : giddy.markov.Markov instance or giddy.markov.Spatial_Markov\n if w_type=None, a classic Markov instance is returned.\n if w_type is given, a Spatial_Markov instance is returned.\n\n Examples\n --------\n >>> from geosnap import Community\n >>> columbus = Community.from_ltdb(msa_fips=\"18140\")\n >>> columbus1 = columbus.cluster(columns=['median_household_income',\n ... 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'],\n ... method='ward', n_clusters=6)\n >>> gdf = columbus1.gdf\n >>> a = transition(gdf, \"ward\", w_type=\"rook\")\n >>> a.p\n array([[0.79189189, 0.00540541, 0.0027027 , 0.13243243, 0.06216216,\n 0.00540541],\n [0.0203252 , 0.75609756, 0.10569106, 0.11382114, 0. ,\n 0.00406504],\n [0.00917431, 0.20183486, 0.75229358, 0.01834862, 0. ,\n 0.01834862],\n [0.1959799 , 0.18341709, 0.00251256, 0.61809045, 0. ,\n 0. ],\n [0.32307692, 0. , 0. , 0. , 0.66153846,\n 0.01538462],\n [0.09375 , 0.0625 , 0. , 0. , 0. ,\n 0.84375 ]])\n >>> a.P[0]\n array([[0.82119205, 0. , 0. , 0.10927152, 0.06622517,\n 0.00331126],\n [0.14285714, 0.57142857, 0.14285714, 0.14285714, 0. ,\n 0. ],\n [0.5 , 0. , 0.5 , 0. , 0. ,\n 0. ],\n [0.21428571, 0.14285714, 0. , 0.64285714, 0. ,\n 0. ],\n [0.18918919, 0. , 0. , 0. , 0.78378378,\n 0.02702703],\n [0.28571429, 0. , 0. , 0. , 0. ,\n 0.71428571]])\n \"\"\"\n if not w_options:\n w_options = {}\n assert (\n unit_index in gdf.columns\n ), f\"The unit_index ({unit_index}) column is not in the geodataframe\"\n gdf_temp = gdf.copy().reset_index()\n df = gdf_temp[[unit_index, temporal_index, cluster_col]]\n df_wide = df.pivot(\n index=unit_index, columns=temporal_index, values=cluster_col\n ).dropna()\n y = df_wide.values\n if w_type is None:\n mar = Markov(y) # class markov modeling\n else:\n geoms = gdf_temp.groupby(unit_index).first()[gdf_temp.geometry.name]\n gdf_wide = df_wide.merge(geoms, left_index=True, right_index=True)\n w = Ws[w_type].from_dataframe(gpd.GeoDataFrame(gdf_wide), **w_options)\n w.transform = \"r\"\n mar = Spatial_Markov(\n y, w, permutations=permutations, discrete=True, variable_name=cluster_col\n )\n return mar\n\n\ndef sequence(\n gdf,\n cluster_col,\n seq_clusters=5,\n subs_mat=None,\n dist_type=None,\n indel=None,\n temporal_index=\"year\",\n unit_index=\"geoid\",\n):\n \"\"\"\n Pairwise sequence analysis and sequence clustering.\n\n Dynamic programming if optimal matching.\n\n Parameters\n ----------\n gdf : geopandas.GeoDataFrame or pandas.DataFrame\n Long-form geopandas.GeoDataFrame or pandas.DataFrame containing neighborhood\n attributes with a column defining neighborhood clusters.\n cluster_col : string or int\n Column name for the neighborhood segmentation, such as\n \"ward\", \"kmeans\", etc.\n seq_clusters : int, optional\n Number of neighborhood sequence clusters. Agglomerative\n Clustering with Ward linkage is now used for clustering\n the sequences. Default is 5.\n dist_type : string\n \"hamming\": hamming distance (substitution only\n and its cost is constant 1) from sklearn.metrics;\n \"markov\": utilize empirical transition\n probabilities to define substitution costs;\n \"interval\": differences between states are used\n to define substitution costs, and indel=k-1;\n \"arbitrary\": arbitrary distance if there is not a\n strong theory guidance: substitution=0.5, indel=1.\n \"tran\": transition-oriented optimal matching. Sequence of\n transitions. Based on :cite:`Biemann:2011`.\n subs_mat : array\n (k,k), substitution cost matrix. Should be hollow (\n 0 cost between the same type), symmetric and non-negative.\n indel : float, optional\n insertion/deletion cost.\n temporal_index : string, optional\n Column defining time and or sequencing of the long-form data.\n Default is \"year\".\n unit_index : string, optional\n Column identifying the unique id of spatial units.\n Default is \"geoid\".\n\n Returns\n --------\n gdf_temp : geopandas.GeoDataFrame or pandas.DataFrame\n geopandas.GeoDataFrame or pandas.DataFrame with a new column for sequence\n labels.\n df_wide : pandas.DataFrame\n Wide-form DataFrame with k (k is the number of periods)\n columns of neighborhood types and 1 column of sequence\n labels.\n seq_dis_mat : array\n (n,n), distance/dissimilarity matrix for each pair of\n sequences\n\n Examples\n --------\n >>> from geosnap.data import Community\n >>> columbus = Community.from_ltdb(msa_fips=\"18140\")\n >>> columbus1 = columbus.cluster(columns=['median_household_income',\n ... 'p_poverty_rate', 'p_edu_college_greater', 'p_unemployment_rate'],\n ... method='ward', n_clusters=6)\n >>> gdf = columbus1.gdf\n >>> gdf_new, df_wide, seq_hamming = Sequence(gdf, dist_type=\"hamming\")\n >>> seq_hamming.seq_dis_mat[:5, :5]\n array([[0., 3., 4., 5., 5.],\n [3., 0., 3., 3., 3.],\n [4., 3., 0., 2., 2.],\n [5., 3., 2., 0., 0.],\n [5., 3., 2., 0., 0.]])\n\n \"\"\"\n assert (\n unit_index in gdf.columns\n ), f\"The unit_index ({unit_index}) column is not in the geodataframe\"\n gdf_temp = gdf.copy().reset_index()\n df = gdf_temp[[unit_index, temporal_index, cluster_col]]\n df_wide = (\n df.pivot(index=unit_index, columns=temporal_index, values=cluster_col)\n .dropna()\n .astype(\"int\")\n )\n y = df_wide.values\n seq_dis_mat = Sequence(\n y, subs_mat=subs_mat, dist_type=dist_type, indel=indel, cluster_type=cluster_col\n ).seq_dis_mat\n model = AgglomerativeClustering(n_clusters=seq_clusters).fit(seq_dis_mat)\n name_seq = dist_type + \"_%d\" % (seq_clusters)\n df_wide[name_seq] = model.labels_\n gdf_temp = gdf_temp.merge(df_wide[[name_seq]], left_on=unit_index, right_index=True)\n gdf_temp = gdf_temp.reset_index(drop=True)\n\n return gdf_temp, df_wide, seq_dis_mat\n\n\ndef predict_markov_labels(\n gdf,\n unit_index=\"geoid\",\n temporal_index=\"year\",\n cluster_col=None,\n w_type=\"rook\",\n w_options=None,\n base_year=None,\n new_colname=None,\n time_steps=1,\n increment=None,\n seed=None,\n verbose=True,\n):\n \"\"\"Predict neighborhood labels based on spatial Markov transition model\n\n Parameters\n ----------\n gdf : geopandas.GeoDataFrame\n a long-form geodataframe with a column of labels to be simulated with a spatial Markov model\n unit_index : str,\n column on dataframe that identifies unique geographic units, by default \"geoid\"\n temporal_index : str\n column on dataframe that identifies unique time periods, by default \"year\"\n cluster_col : str\n column on the dataframe that stores cluster or other labels to be simulated\n w_type : str, optional\n type of spatial weights matrix to include in the transition model, by default \"queen\"\n w_options : dict, optional\n additional keyword arguments passed to the libpysal weights constructor\n base_year : int or str, optional\n the year from which to begin simulation (i.e. the set of labels to define the first\n period of the Markov sequence)\n new_colname : str, optional\n new column name to store predicted labels under. Defaults to \"predicted\"\n time_steps : int, optional\n the number of time-steps to simulate, by default 1\n increment : str or int, optional\n styled increment each time-step referrs to. For example, for a model fitted to decadal\n Census data, each time-step refers to a period of ten years, so an increment of 10 ensures\n that the temporal index aligns appropriately with the time steps being simulated\n verbose: bool\n if true, print warnings from the label sampling process\n\n Returns\n -------\n geopandas.GeoDataFrame\n long-form geodataframe with predicted cluster labels stored in the `new_colname` column\n \"\"\"\n crs = gdf.crs\n np.random.seed(seed)\n if not new_colname:\n new_colname = \"predicted\"\n if not w_options:\n w_options = {}\n\n assert (\n cluster_col and cluster_col in gdf.columns\n ), f\"The input dataframe has no column named {cluster_col}\"\n\n assert (\n base_year\n ), \"Missing `base_year`. You must provide an initial time point with labels to begin simulation\"\n assert (\n base_year in gdf[temporal_index].unique().tolist()\n ), \"A set of observations with `temporal_index`==`base_year` must be included in the gdf\"\n\n gdf = gdf.copy()\n gdf = gdf.dropna(subset=[cluster_col]).reset_index(drop=True)\n t = transition(\n gdf,\n cluster_col,\n w_type=w_type,\n unit_index=unit_index,\n temporal_index=temporal_index,\n w_options=w_options,\n )\n\n if time_steps == 1:\n\n gdf = gdf[gdf[temporal_index] == base_year].reset_index(drop=True)\n w = Ws[w_type].from_dataframe(gdf, **w_options)\n predicted = _draw_labels(w, gdf, cluster_col, t, unit_index, verbose)\n if new_colname:\n predicted = predicted.rename(columns={cluster_col: new_colname})\n return predicted\n\n else:\n assert (\n increment\n ), \"You must set the `increment` argument to simulate multiple time steps\"\n predictions = []\n gdf = gdf[gdf[temporal_index] == base_year]\n gdf = gdf[[unit_index, cluster_col, temporal_index, gdf.geometry.name]]\n current_time = base_year + increment\n gdf = gdf.dropna(subset=[cluster_col]).reset_index(drop=True)\n w = Ws[w_type].from_dataframe(gdf, **w_options)\n predictions.append(gdf)\n\n for step in range(1, time_steps + 1):\n # use the last known set of labels to get the spatial context for each geog unit\n gdf = predictions[step - 1].copy()\n\n predicted = _draw_labels(w, gdf, cluster_col, t, unit_index, verbose)\n predicted[temporal_index] = current_time\n predictions.append(predicted)\n current_time += increment\n gdf = gpd.GeoDataFrame(pd.concat(predictions), crs=crs)\n if new_colname:\n gdf = gdf.rename(columns={cluster_col: new_colname})\n return gdf\n\n\ndef _draw_labels(w, gdf, cluster_col, markov, unit_index, verbose):\n \"\"\"Draw a random class label from the spatially-conditioned transition rates.\n\n Parameters\n ----------\n w : libpysal.weights.W\n spatial weights object\n gdf : geopandas.GeoDataFrame\n geodataframe of observations with class/cluster labels as a column\n cluster_col : string\n the column on the dataframe that holds class labels\n markov : giddy.Spatial_Markov\n an instance of a Spatial_Markov class\n unit_index : string\n the column on the dataframe that identifies unique spatial units\n\n Returns\n -------\n geopandas.GeoDataFrame\n long-form geodataframe with predicted cluster labels stored in the `new_colname` column\n \"\"\"\n gdf = gdf.copy()\n gdf = gdf.dropna(subset=[cluster_col])\n lags = lag_categorical(w, gdf[cluster_col].values)\n clusters = gdf.reset_index()[cluster_col].astype(str).values\n classes = pd.Series(markov.classes).astype(str).values\n cluster_idx = dict(zip(classes, list(range(len(classes)))))\n\n labels = {}\n for i, lag in enumerate(lags):\n # select the transition matrix using the label of unit's spatial lag\n spatial_context = np.nan_to_num(markov.P, posinf=0.0, neginf=0.0)[\n cluster_idx[lag]\n ]\n # select the class's row from the transition matrix using the unit's label\n probs = spatial_context[cluster_idx[clusters[i]]]\n probs /= (\n probs.sum()\n ) # correct for tolerance, see https://stackoverflow.com/questions/25985120/numpy-1-9-0-valueerror-probabilities-do-not-sum-to-1\n probs = np.nan_to_num(probs.flatten())\n if sum(probs) == 0:\n # in case obs have a modal neighbor never before seen in the model\n # (so all transition probs are 0)\n # fall back to the aspatial transition matrix\n if verbose:\n warn(\n f\"Falling back to aspatial transition rule for unit {gdf[unit_index][i]}\"\n )\n probs = markov.p[cluster_idx[clusters[i]]].flatten()\n\n labels[i] = np.random.choice(classes, p=probs)\n\n labels = pd.Series(labels, name=cluster_col, index=gdf.index)\n out = gdf[[unit_index, gdf.geometry.name]]\n predicted = gpd.GeoDataFrame(pd.concat([labels, out], axis=1))\n return predicted\n","sub_path":"geosnap/analyze/dynamics.py","file_name":"dynamics.py","file_ext":"py","file_size_in_byte":15547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"395956028","text":"data = {\r\n'''\r\nThe data variable above should be set to the value of the unpacked version of ObjectInformation.xnb.\r\nA quick guide is as follows:\r\n - Find the file ObjectInformation.xnb\r\n > Try C:\\Program Files (x86)\\Steam\\steamapps\\common\\Stardew Valley\\Content\\Data\r\n - Unpack ObjectInformation.xnb into a .json file\r\n > https://stardewvalleywiki.com/Modding:Editing_XNB_files#Unpack_game_files gives a good tutorial\r\n for how to do this\r\n - Copy the dictionary that appears in \"content\": { ... } and paste it into this file, so that\r\n data will be equal to that dictionary\r\n - Rename this file to object_data.py by removing the '-empty' in the filename\r\n'''\r\n}\r\n","sub_path":"ArtifactScript/object_data-empty.py","file_name":"object_data-empty.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"512909235","text":"from collections import Counter, defaultdict, OrderedDict, deque\nfrom bisect import bisect_left, bisect_right\nfrom functools import reduce, lru_cache\nfrom typing import List\nimport itertools\nimport math\nimport heapq\nimport string\ntrue = True\nfalse = False\nMIN, MAX = -0x3f3f3f3f, 0x3f3f3f3f\n#\n# @lc app=leetcode id=923 lang=python3\n#\n# [923] 3Sum With Multiplicity\n#\n# https://leetcode.com/problems/3sum-with-multiplicity/description/\n#\n# algorithms\n# Medium (35.39%)\n# Total Accepted: 17.6K\n# Total Submissions: 49.6K\n# Testcase Example: '[1,1,2,2,3,3,4,4,5,5]\\n8'\n#\n# Given an integer array A, and an integer target, return the number of tuples\n# i, j, k such that i < j < k and A[i] + A[j] + A[k] == target.\n#\n# As the answer can be very large, return it modulo 10^9 + 7.\n#\n#\n#\n# Example 1:\n#\n#\n# Input: A = [1,1,2,2,3,3,4,4,5,5], target = 8\n# Output: 20\n# Explanation:\n# Enumerating by the values (A[i], A[j], A[k]):\n# (1, 2, 5) occurs 8 times;\n# (1, 3, 4) occurs 8 times;\n# (2, 2, 4) occurs 2 times;\n# (2, 3, 3) occurs 2 times.\n#\n#\n#\n# Example 2:\n#\n#\n# Input: A = [1,1,2,2,2,2], target = 5\n# Output: 12\n# Explanation:\n# A[i] = 1, A[j] = A[k] = 2 occurs 12 times:\n# We choose one 1 from [1,1] in 2 ways,\n# and two 2s from [2,2,2,2] in 6 ways.\n#\n#\n#\n#\n#\n# Note:\n#\n#\n# 3 <= A.length <= 3000\n# 0 <= A[i] <= 100\n# 0 <= target <= 300\n#\n#\n\n\nclass Solution:\n def threeSumMulti(self, nums, t):\n cnt = [0] * 301\n for n in nums:\n cnt[n] += 1\n nums = sorted(list(set(nums)))\n ans = 0\n # print(nums, cnt)\n for i, ni in enumerate(nums):\n for j, nj in enumerate(nums[i:]):\n if ni + nj > t: break\n diff = t - ni - nj\n if diff < nj or cnt[diff] == 0:\n continue\n if j == 0:\n if diff == ni:\n ans += cnt[ni] * (cnt[ni] - 1) * (cnt[ni] - 2) // 6\n else:\n ans += cnt[ni] * (cnt[ni] - 1) * cnt[diff] // 2\n else:\n if diff == ni:\n ans += cnt[ni] * (cnt[ni] - 1) * cnt[nj] // 2\n elif diff == nj:\n ans += cnt[ni] * cnt[nj] * (cnt[nj] - 1) // 2\n else:\n ans += cnt[ni] * cnt[nj] * cnt[diff]\n # print(ni, nj, diff, ans)\n ans %= 10**9 + 7\n return ans\n\n\nsol = Solution()\n# nums, t = [1,1,2,2,2,2], 5\nnums, t = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], 8\nprint(sol.threeSumMulti(nums, t))\n","sub_path":"python_solutions/923.3sum-with-multiplicity.py","file_name":"923.3sum-with-multiplicity.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"240248369","text":"# -*- coding: utf-8 -*-\r\nfrom django.contrib import admin\r\n#from django.forms.models import *\r\nfrom django.conf import settings\r\nfrom models import Ticket, Good, Income, Article, UserData, Supplier\r\nfrom django import forms\r\nfrom models import *\r\nfrom django.forms import TextInput\r\nfrom django.forms.widgets import CheckboxInput\r\nfrom django.utils.translation import ugettext_lazy as _\r\nfrom datetime import datetime\r\n#from django.contrib.admin import SimpleListFilter\r\nimport logging\r\n#from ajax_filtered_fields.forms import ForeignKeyByRelatedField\r\n#, ManyToManyByRelatedField, AjaxForeignKeyField, ForeignKeyByLetter\r\nfrom django.utils.encoding import force_unicode\r\nfrom django.http import HttpResponseRedirect\r\nfrom chained_selects.widgets import ChainedSelectWidget\r\n\r\n\r\nclass TicketAdminForm(forms.ModelForm):\r\n #good = ForeignKeyByRelatedField(Good, \"article\")\r\n\r\n article = forms.ModelChoiceField(queryset=Article.objects.all())\r\n good = forms.ModelChoiceField(queryset=Good.objects.all(), widget=ChainedSelectWidget(\r\n parent_name='article', # имя первого селекта\r\n app_name='poluchka', # имя приложения, где лежит модель с методом\r\n model_name='article', # имя модели с методом\r\n method_name='chained_relation', # имя самого метода\r\n ))\r\n comment = forms.CharField(\r\n widget=TextInput(attrs={'size': '200'}), required=False)\r\n confirm_dublicate = forms.BooleanField(widget=CheckboxInput(\r\n ), required=False, label=_(u\"Сохранять дубликат\"))\r\n\r\n class Media:\r\n js = (\r\n settings.ADMIN_MEDIA_PREFIX + \"js/SelectBox.js\",\r\n settings.ADMIN_MEDIA_PREFIX + \"js/SelectFilter2.js\",\r\n # settings.ADMIN_MEDIA_PREFIX +'js/jquery.js',\r\n # settings.ADMIN_MEDIA_PREFIX +'js/jquery.min.js',\r\n # '/static/js/jquery-1.9.1.min.js',\r\n # '/static/js/ajax_filtered_fields.js',\r\n )\r\n css = {\r\n # 'all': ('/static/css/ajax_filtered_fields.css',)\r\n }\r\n\r\n class Meta:\r\n fields = ('user', 'dt', 'supplier', 'article', 'good',\r\n 'total', 'comment', 'confirm_dublicate')\r\n model = Ticket\r\n\r\n def __init__(self, *args, **kwargs):\r\n super(TicketAdminForm, self).__init__(*args, **kwargs)\r\n self.fields['dt'].initial = datetime.now()\r\n# if 0 == len(self.data):\r\n # чистим выборку при простом отображении формы\r\n# self.fields['good'].queryset = Good.objects.none()\r\n #получаем Категорию для товара\r\n if self.instance.pk:\r\n self.fields['article'].initial = self.instance.good.article\r\n self.fields['good'].queryset = Good.objects.filter(article=self.instance.good.article)\r\n\r\n def clean(self):\r\n cnf = self.cleaned_data.get('confirm_dublicate')\r\n if not cnf:\r\n good = self.cleaned_data.get('good')\r\n dt = self.cleaned_data.get('dt')\r\n total = self.cleaned_data.get('total')\r\n\r\n logging.debug(self.cleaned_data.get('pk'))\r\n try:\r\n tk = Ticket.objects.all().filter(good=good).filter(\r\n dt=dt).filter(total=total).exclude(pk=self.instance.id)\r\n except:\r\n # Nothing to do here, moving along.\r\n pass\r\n if tk.exists():\r\n raise forms.ValidationError(\r\n [u\"Найдены дубликаты, подтвердите добавление\", tk])\r\n return self.cleaned_data\r\n\r\n\r\nclass TicketGoodListFilter(admin.SimpleListFilter):\r\n title = _(u'Товар/услуга')\r\n parent_key = 'good__article__id__exact'\r\n\r\n # Parameter for the filter that will be used in the URL query.\r\n parameter_name = 'good_id'\r\n\r\n def lookups(self, request, model_admin):\r\n \"\"\"\r\n Returns a list of tuples. The first element in each\r\n tuple is the coded value for the option that will\r\n appear in the URL query. The second element is the\r\n human-readable name for the option that will appear\r\n in the right sidebar.\r\n \"\"\"\r\n if self.parent_key in request.GET:\r\n return [(c.id, c.label) for c in Good.objects.filter(article=request.GET[self.parent_key])]\r\n else:\r\n return ()\r\n\r\n def queryset(self, request, queryset):\r\n\r\n \"\"\"\r\n Returns the filtered queryset based on the value\r\n provided in the query string and retrievable via\r\n `self.value()`.\r\n \"\"\"\r\n # Compare the requested value (either '80s' or 'other')\r\n # to decide how to filter the queryset.key\r\n if self.value():\r\n curr_parent = Good.objects.get(pk=self.value()).article.pk\r\n logging.debug(\r\n str(curr_parent) + ' ' + request.GET[self.parent_key])\r\n if (self.parent_key in request.GET) and (curr_parent == int(request.GET[self.parent_key])):\r\n return queryset.filter(good=self.value())\r\n else:\r\n self.used_parameters[self.parameter_name] = None\r\n return queryset\r\n\r\n\r\nclass TicketAdmin(admin.ModelAdmin):\r\n form = TicketAdminForm\r\n\r\n def response_add(self, request, obj, post_url_continue='../%s/'):\r\n opts = obj._meta\r\n# pk_value = obj._get_pk_val()\r\n\r\n msg = _('The %(name)s \"%(obj)s\" was added successfully.') % {'name':\r\n force_unicode(opts.verbose_name), 'obj': force_unicode(obj)}\r\n\r\n if '_addanother' in request.POST:\r\n self.message_user(request, msg + ' ' + (_(\"You may add another %s below.\") % force_unicode(opts.verbose_name)))\r\n add_req = '?'\r\n for key in ('comment', 'user', 'supplier', 'dt', 'good', 'article'):\r\n logging.debug(getattr(obj, key))\r\n add_req += '%s=%s&' % (key, request.POST[key])\r\n return HttpResponseRedirect(request.path + add_req)\r\n\r\n return super(TicketAdmin, self).response_add(request, obj, post_url_continue)\r\n\r\n #fields = ('dt', 'user','total', 'article','good', 'comment' )\r\n #comment = forms.CharField(widget=forms.TextInput(attrs={'size':'140'}))\r\n #comment = forms.CharField(max_length=10)\r\n #comment = forms.Textarea();\r\n #comment = forms.URLField(initial='http://')\r\n #good=ForeignKeyByLetter(Article, field_name=\"label\")\r\n list_select_related = True\r\n list_display = (\r\n 'dt', 'total', 'good', 'article', 'comment', 'supplier_name', 'user')\r\n list_filter = ('dt', 'good__article', TicketGoodListFilter, 'user')\r\n list_per_page = 25\r\n date_hierarchy = 'dt'\r\n ordering = ('-dt',)\r\n save_as = True\r\n\r\nclass GoodInline(admin.TabularInline):\r\n model = Good\r\n #max_num = 3\r\n label = forms.CharField(max_length=200)\r\n extra = 0\r\n\r\nclass ArticleAdmin(admin.ModelAdmin):\r\n fieldsets = [\r\n (None, {'fields': ['label']}),\r\n ]\r\n inlines = [GoodInline]\r\n formfield_overrides = {\r\n models.CharField: {'widget': TextInput(attrs={'size':'200'})},\r\n #models.TextField: {'widget': Textarea(attrs={'rows':4, 'cols':40})},\r\n }\r\n #list_display = ('label', 'article')\r\n #list_filter = ('article',)\r\n #ordering = ('article',)\r\n\r\nclass GoodAdmin(admin.ModelAdmin):\r\n #inlines = [TicketInfoInline]\r\n list_display = ('label', 'article')\r\n list_filter = ('article',)\r\n ordering = ('article',)\r\n\r\nadmin.site.register(Income)\r\nadmin.site.register(Article, ArticleAdmin)\r\nadmin.site.register(Good, GoodAdmin)\r\nadmin.site.register(Ticket, TicketAdmin)\r\nadmin.site.register(UserData)\r\nadmin.site.register(Supplier)\r\n","sub_path":"poluchka/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":7940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"317144894","text":"from pyramid.config import Configurator\nfrom pyramid.response import Response\n\n\ndef hellow_world(request):\n return Response(\n 'Hellow world from Pyramid! \\n',\n content_type='text/plain',\n )\n\n\nconfig = Configurator()\nconfig.add_route('hello', '/hello')\nconfig.add_view(hellow_world, route_name='hello')\napp = config.make_wsgi_app()","sub_path":"pyramidapp.py","file_name":"pyramidapp.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"333893808","text":"__author__ = 'ooyanetomohito'\n\n\nclass Node:\n def __init__(self, x):\n self.data = x\n self.left = None\n self.right = None\n def search(node, x):\n while node:\n if node.data == x: return True\n if x < node.data:\n node = node.left\n else:\n node = node.right\n\n return False\n","sub_path":"TestSample/practice/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"469344962","text":"cond_sexo=0\r\ncond_resp =0\r\ncont_id =0\r\ncont_masc = 0\r\ncont_fem =0\r\nresp = 'S'\r\nwhile resp !='N':\r\n cond_sexo = 0\r\n idade = int(input('Digite a sua idade: '))\r\n if idade > 18:\r\n cont_id += 1\r\n while cond_sexo ==0:\r\n sexo = input('Digite o seu sexo: ').upper().strip()\r\n if sexo == 'M' or sexo == 'F':\r\n cond_sexo = 1\r\n if sexo == 'M':\r\n cont_masc += 1\r\n if sexo == 'F' and idade <20:\r\n cont_fem += 1\r\n while cond_resp == 0:\r\n resp = input('Deseja continuar [S/N]: ').upper().strip()\r\n if resp == 'S' or resp == 'N':\r\n cond_resp =1\r\n print(sexo,resp,cont_id,cont_masc,cont_fem)","sub_path":"ex069.py","file_name":"ex069.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"484981451","text":"ex4 = {\n 1 : 35,\n 2 : 36,\n 3 : 40,\n 4 : 44\n}\nwhile True:\n print('''Answer the following algebra question\nIf x = 8, then what is the value of 4(x + 3) ?''')\n for i,j in ex4.items():\n print(i,j, sep = \". \")\n choice = int(input(\"Your choice: \"))\n if(choice == 4):\n print(\"Bingo!\")\n break\n elif(choice == 1 or choice == 2 or choice == 3):\n print(\":(\")\n else:\n print(\"Only allowed 1->4. Again!\")\n","sub_path":"ass4/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"206905353","text":"import serial\nimport time\nimport threading\nimport tuxconf as tc\nimport sys\n\nclass rfid(threading.Thread):\n\n def __init__(self):\n\n threading.Thread.__init__(self)\n self.running = True\n self.device = serial.Serial(tc.serial_path,tc.serial_baud,timeout=5)\n self.tag_arrived = False\n\n\n def run(self):\n \n while self.running:\n \n line = self.device.readline().decode('ascii')\n tag_time = str(int(time.time()))\n\n if (len(line) > 2):\n print(\"[TAG] \"+line)\n self.tag_arrived = True\n \n with open(tc.serial_log, 'a') as file:\n file.write(tag_time+\",\"+line+\"\\n\")\n\n self.device.close()\n\n\ndef main():\n print(\"Start RFID scanner\")\n rfid_loop = rfid()\n rfid_loop.setDaemon(True)\n rfid_loop.start()\n print(\"Scanner started\")\n\n try:\n while True:\n continue\n except KeyboardInterrupt:\n rfid.running=False\n sys.exit()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"tuxcap_v2/rfid.py","file_name":"rfid.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"274448977","text":"#!/usr/bin/python3\n\"\"\" This is necesary ? \"\"\"\n\n\nclass Square:\n \"\"\" This is a Square class \"\"\"\n\n def __init__(self, size=0, position=(0, 0)):\n \"\"\"Example of docstring on the __init__ method.\n Args:\n size (int): Size of the square obj.\n position (tuple): tuple of position\n \"\"\"\n self.size = size\n self.position = position\n\n @property\n def size(self):\n \"\"\" property\n\n Returns:\n self size\n \"\"\"\n return self.__size\n\n @property\n def position(self):\n \"\"\" position\n\n Returns:\n self postion\n \"\"\"\n return self.__position\n\n @size.setter\n def size(self, value):\n \"\"\"Example of docstring on the __init__ method.\n Args:\n size (int): Size of the square obj.\n \"\"\"\n if type(value) is not int:\n raise TypeError(\"size must be an integer\")\n elif value < 0:\n raise ValueError(\"size must be >= 0\")\n else:\n self.__size = value\n\n @position.setter\n def position(self, value):\n if (type(value) is not tuple or len(value) is not 2 or\n type(value[0]) is not int or type(value[1]) is not int or\n value[0] < 0 or value[1] < 0):\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n else:\n self.__position = value\n\n def area(self):\n \"\"\" area of square\n\n Returns:\n area of square\n \"\"\"\n return self.__size * self.__size\n\n def my_print(self):\n \"\"\" print square \"\"\"\n if self.__size is 0:\n print(\"\")\n else:\n for i in range(self.__position[1]):\n print(\"\")\n for i in range(self.__size):\n if self.__position[0]:\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.__size)\n\n def __str__(self):\n \"\"\" print square \"\"\"\n listm = []\n if self.__size is 0:\n listm.append(\"\")\n else:\n for i in range(self.__position[1]):\n listm.append(\"\\n\")\n for i in range(0, self.__size):\n if self.__position[0]:\n listm.append(\" \" * self.__position[0])\n listm.append(\"#\" * self.__size)\n if i < self.__size - 1:\n listm.append(\"\\n\")\n return (\"\".join(listm))\n","sub_path":"0x06-python-classes/101-square.py","file_name":"101-square.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"358105467","text":"#!/usr/bin/env python\n#\n# Copyright (C) 2018 The Android Open Source Project\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\"\"\"Symbolizes stack traces from logcat.\nSee https://developer.android.com/ndk/guides/ndk-stack for more information.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport re\nimport subprocess\nimport sys\n\n\ndef find_llvm_symbolizer():\n \"\"\"Finds the NDK llvm-symbolizer(1) binary.\n\n Returns: An absolute path to llvm-symbolizer(1).\"\"\"\n\n # ndk-stack is installed to $NDK/prebuilt//bin, so from\n # `~/Downloads/android-ndk-r18/prebuilt/linux-x86_64/bin/ndk-stack`...\n # ...get `/usr/enh/Downloads/android-ndk-r18/`:\n ndk_bin = os.path.dirname(os.path.realpath(__file__))\n ndk_root = os.path.abspath(os.path.join(ndk_bin, '../../..'))\n # ...get `linux-x86_64`:\n arch = os.path.basename(os.path.abspath(os.path.join(ndk_bin, '../')))\n # And from there construct the llvm-symbolizer path.\n llvm_bin = os.path.join(ndk_root, 'toolchains', 'llvm', 'prebuilt', arch,\n 'bin')\n return os.path.join(llvm_bin, 'llvm-symbolizer')\n\n\ndef main():\n \"\"\"\"Program entry point.\"\"\"\n parser = argparse.ArgumentParser(\n description='Symbolizes Android crashes.',\n epilog='See .')\n parser.add_argument(\n '-sym',\n '--sym',\n dest='symbol_dir',\n required=True, # TODO: default to '.'?\n help='directory containing unstripped .so files')\n parser.add_argument(\n '-i',\n '-dump',\n '--dump',\n dest='input',\n default='-',\n type=argparse.FileType('r'),\n help='input filename')\n args = parser.parse_args()\n\n if not os.path.exists(args.symbol_dir):\n sys.exit('{} does not exist!\\n'.format(args.symbol_dir))\n\n cmd = [\n find_llvm_symbolizer(), '--demangle', '--functions=linkage',\n '--inlining=true', '--use-symbol-table=true'\n ]\n proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n\n banner = '*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***'\n in_crash = False\n saw_frame = False\n for line in args.input:\n line = line.rstrip()\n\n if not in_crash:\n if banner in line:\n in_crash = True\n saw_frame = False\n print('********** Crash dump: **********')\n continue\n\n for tag in ['Build fingerprint:', 'Abort message:']:\n if tag in line:\n print(line[line.find(tag):])\n continue\n\n # See Backtrace::FormatFrameData in libbacktrace.\n # We're deliberately very loose because NDK users are likely to be\n # looking at crashes on ancient OS releases.\n # TODO: support asan stacks too?\n m = re.match('.* +(#[0-9]+) +pc ([0-9a-f]+) +(([^ ]+).*)', line)\n if m:\n saw_frame = True\n frame = m.group(1)\n pc = m.group(2)\n tail = m.group(3)\n lib = m.group(4)\n\n lib_path = os.path.join(args.symbol_dir, os.path.basename(lib))\n\n out_line = '%s 0x%s %s' % (frame, pc, tail)\n indent = (out_line.find('(') + 1) * ' '\n print(out_line)\n if os.path.exists(lib_path):\n print('\"%s\" 0x%s' % (lib_path, pc), file=proc.stdin)\n while True:\n symbolizer_output = proc.stdout.readline().rstrip()\n if not symbolizer_output:\n break\n # TODO: rewrite file names based on a source path?\n print('%s%s' % (indent, symbolizer_output))\n elif saw_frame:\n in_crash = False\n print('Crash dump is completed\\n')\n\n # for asan\n asan_begin = re.compile(r\".*==\\d+==ERROR:\\sAddressSanitizer:.*\")\n asan_end = re.compile(r\".*==\\d+==ABORTING.*\")\n asan_trace = re.compile(r\".*\\s+#\\d+\\s(0x[0-9a-fA-F]+)\\s+\\((.*)\\+(0x[0-9a-fA-F]+)\\).*\")\n asan_trace_header = re.compile(r\".*WRITE of size.*\"\n r\"|.*READ of size.*\"\n r\"|.*Thread .* created by .* here:.*\"\n r\"|.*freed by thread .* here:.*\"\n r\"|.*previously allocated by thread .* here:.*\")\n\n proc.stdout.flush()\n args.input.seek(0)\n\n in_asan = False\n for line in args.input:\n line = line.rstrip()\n\n if not in_asan:\n m = asan_begin.match(line)\n if m:\n in_asan = True\n print(\"##############################ASAN BEGIN##############################\")\n continue\n\n m = asan_trace_header.match(line)\n if m:\n print(line)\n continue\n\n m = asan_trace.match(line)\n if m:\n pc = m.group(1)\n lib = m.group(2)\n addr = m.group(3)\n\n print(line)\n lib_path = os.path.join(args.symbol_dir, os.path.basename(lib))\n if os.path.exists(lib_path):\n print('\"%s\" %s' % (lib_path, addr), file=proc.stdin)\n print('--------------------------------------------------------------------------------------------------------------------------------------------------------------------')\n while True:\n symbolizer_output = proc.stdout.readline().rstrip()\n if not symbolizer_output:\n break\n print('--> %s <--' % symbolizer_output)\n print('')\n elif asan_end.match(line):\n print(\"##############################ASAN END##############################\")\n in_asan = False\n continue\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"ndk_stack_asan/ndk-stack-asan.py","file_name":"ndk-stack-asan.py","file_ext":"py","file_size_in_byte":6377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"270834330","text":"import random\nimport re\nimport numpy as np\nimport feedparser\n\ndef textParse(bigString):\n list0fTokens=re.split(r'\\W',bigString)\n return [tok.lower() for tok in list0fTokens if len(tok)>2]\n\n#遍历词汇表中的每个词并统计它在文本中出现的次数，后根据出现次数从高到低对词典进行排序，返回最高的30个单词\ndef calcMostFreq(vocabList,fullText):\n import operator\n freqDict={}\n for token in vocabList:\n freqDict[token]=fullText.count(token)\n sortedFreq=sorted(freqDict.items(),key=operator.itemgetter(1),reverse=True)\n return sortedFreq[:30]\n\ndef localWords(feed1,feed0):\n import feedparser\n docList=[];classList=[];fullText=[]\n minLen=min(len(feed1['entries']),len(feed0['entries']))\n for i in range(minLen):\n wordList=textParse(feed1['entries'][i]['summary'])\n docList.append(wordList)\n fullText.extend(wordList)\n classList.append(1)\n wordList=textParse(feed0['entries'][i]['summary'])\n docList.append(wordList)\n fullText.extend(wordList)\n classList.append(0)\n vocabList=createVocabList(docList)\n top30Words=calcMostFreq(vocabList,fullText)\n\n for pairW in top30Words:\n if pairW[0] in vocabList:\n vocabList.remove(pairW[0])\n trainingSet=list(range(2*minLen));testSet=[]\n\n for i in range(20):\n randIndex=int(random.uniform(0,len(trainingSet)))\n testSet.append(trainingSet[randIndex])\n del(trainingSet[randIndex])\n\n trainMat=[];trainClassses=[]\n\n for docIndex in trainingSet:\n trainMat.append(bagOfWords2VecMN(vocabList,docList[docIndex]))\n trainClassses.append(classList[docIndex])\n p0V,p1V,pSpam=trainNB0(np.array(trainMat),np.array(trainClassses))\n errorCount=0\n for docIndex in testSet:\n wordVector=bagOfWords2VecMN(vocabList,docList[docIndex])\n if classifyNB(np.array(wordVector),p0V,p1V,pSpam)!=classList[docIndex]:\n errorCount+=1\n print(\"the error rate is:\",float(errorCount)/len(testSet))\n return vocabList,p0V,p1V\n\n\ndef trainNB0(trainMatrix,trainCategory):\n numTrainDocs=len(trainMatrix)\n numWords=len(trainMatrix[0])\n pAbusive=sum(trainCategory)/float(numTrainDocs)\n p0Num=np.ones(numWords)#p0Num=np.zeros(numWords)\n p1Num=np.ones(numWords)#p1Num=np.zeros(numWords)\n p0Demo=2.0;p1Demo=2.0\n #p0Demo=0.0;p1Demo=0.0\n for i in range(numTrainDocs):\n if trainCategory[i]==1:\n p1Num+=trainMatrix[i]\n p1Demo+=sum(trainMatrix[i])\n else:\n p0Num+=trainMatrix[i]\n p0Demo+=sum(trainMatrix[i])\n p1Vect=p1Num/p1Demo\n p0Vect=p0Num/p0Demo\n return p0Vect,p1Vect,pAbusive\n\n#######################################朴素贝叶斯分类函数\ndef classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):\n p1=sum(vec2Classify*p1Vec)+np.log(pClass1)\n p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)\n if p1>p0:\n return 1\n else:\n return 0\n\n\ndef setOfWords2Vec(vocabList,inputSet):\n returnVec=[0]*len(vocabList) #创建一个其中所含元素都为0的向量\n for word in inputSet:\n if word in vocabList:\n returnVec[vocabList.index(word)]=1\n else:\n print(\"the word: %s is not in my vocabulary!\"%word)\n return returnVec\n\n\ndef bagOfWords2VecMN(vocabList, inputSet):\n returnVec=[0]*len(vocabList)\n for word in inputSet:\n if word in vocabList:\n returnVec[vocabList.index(word)]+=1\n return returnVec\n\ndef createVocabList(dataSet):\n vocabSet=set([]) #创建一个空集\n for document in dataSet:\n vocabSet=vocabSet | set(document) #创建两个集合的并集\n return list(vocabSet)\n\n#显示地域相关的用词\ndef getTopWords(ny,sf):\n import operator\n vocabList,p0V,p1V=localWords(ny,sf)\n topNY=[];topSF=[]\n for i in range(len(p0V)):\n if(p0V[i]>-6.0):topSF.append((vocabList[i],p0V[i]))\n if(p1V[i]>-6.0):topNY.append((vocabList[i],p1V[i]))\n sortedSF=sorted(topSF,key=lambda pair:pair[1],reverse=True)\n print(\"SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**\")\n for item in sortedSF:\n print(item[0])\n sortedNY=sorted(topNY,key=lambda pair:pair[1],reverse=True)\n print(\"NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**\")\n for item in sortedNY:\n print(item[0])\n\nif __name__ == '__main__':\n #因书本给的不能更换\n ny = feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')\n sf = feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')\n vocabList,pSF,pNY=localWords(ny,sf)\n getTopWords(ny,sf)","sub_path":"Advertisement.py","file_name":"Advertisement.py","file_ext":"py","file_size_in_byte":4615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"39846746","text":"import os\n\nimport math\nimport matplotlib.pyplot as plt\nimport torch\nfrom gym import wrappers \nimport numpy as np\nimport wandb\n\n\nfrom network import TD3Agent\nfrom utils import *\n\n\ndef train():\n # simulation of the agent solving the spacecraft attitude control problem\n env = make(\"SatelliteContinuous\")\n #logger\n wandb.init(project='Satellite-continuous',\n config={\n \"batch_size\": 128,\n \"critic_lr\": 1e-3,\n \"actor_lr\": 1e-4,\n \"max_episodes\": 10000,\n \"max_steps\": 300,\n \"gamma\": 0.99,\n \"tau\" : 1e-3,\n \"buffer_maxlen\": 100000,\n \"policy_noise\": 0.2,\n \"policy_freq\": 2,\n \"noise_clip\": 0.5,\n \"prioritized_on\": False,\n \"State\": 'angle:4, ang_rate:4, ang_vel:3',}\n )\n config = wandb.config\n\n max_episodes = config.max_episodes\n max_steps = config.max_steps\n batch_size = config.batch_size\n\n policy_noise = config.policy_noise\n policy_freq = config.policy_freq\n noise_clip = config.noise_clip\n\n gamma = config.gamma\n buffer_maxlen = config.buffer_maxlen\n tau = config.tau\n critic_lr = config.critic_lr\n actor_lr = config.actor_lr\n\n agent = TD3Agent(env, gamma, tau, buffer_maxlen, critic_lr, actor_lr, True, max_episodes * max_steps,\n policy_freq, policy_noise, noise_clip)\n # wandb.watch([agent.critic,agent.actor], log=\"all\")\n # curr_dir = os.path.abspath(os.getcwd())\n # agent = torch.load(curr_dir + \"/models/spacecraft_control_ddpg.pkl\")\n # agent.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n episode_rewards = mini_batch_train(env, agent, max_episodes, max_steps, batch_size)\n\n plt.figure()\n plt.plot(episode_rewards)\n plt.xlabel(\"Episodes\")\n plt.ylabel(\"Reward\")\n\t# plt.show()\n # plt.savefig(curr_dir + \"/results/plot_reward_hist.png\")\n\n curr_dir = os.path.abspath(os.getcwd())\n if not os.path.isdir(\"models\"):\n os.mkdir(\"models\")\n torch.save(agent, curr_dir + \"/models/spacecraft_control_ddpg.pkl\")\n\ndef evaluate():\n # simulation of the agent solving the cartpole swing-up problem\n env = make(\"SatelliteContinuous\")\n # uncomment for recording a video of simulation\n # env = wrappers.Monitor(env, './video', force=True)\n\n curr_dir = os.path.abspath(os.getcwd())\n\n agent = torch.load(curr_dir + \"/models/spacecraft_control_ddpg.pkl\",map_location='cpu')\n agent.device = torch.device('cpu')\n agent.train = False\n\n state = env.reset()\n print('The goal angle :'+ str(env.goalEuler) + \" the target multi:\" + str(env.multi))\n r = 0\n qe = np.empty((0,4))\n q = np.empty((0,4))\n w = np.empty((0,3))\n actions = np.empty((0,3))\n r_hist = np.empty((0,3)) \n\n dt = 0.1\n simutime = 30\n max_steps = int(simutime/dt) # dt is 0.1\n\n for i in range(max_steps):\n action = agent.get_action(state)\n # action = np.squeeze(action)\n next_error_state, reward, done, next_state, _ = env.step(action)\n # env.render()\n q=np.append(q,next_state[0:4].reshape(1,-1),axis=0)\n qe=np.append(qe,next_error_state[0:4].reshape(1,-1),axis=0)\n w=np.append(w,next_error_state[8:11].reshape(1,-1),axis=0)\n r += reward\n actions = np.append(actions, action.reshape(1,-1),axis=0)\n # r_hist = np.append(r_hist, np.array([-env.r1,-env.r2,-env.r3]).reshape(1,-1),axis=0)\n\n state = next_error_state\n\n env.close()\n #-------------------------------結果のプロット----------------------------------\n #region\n #show the total reward\n print(\"Total Reward is : \" + str(r))\n # データの形の整理\n q = q.reshape([-1,4])\n qe = qe.reshape([-1,4])\n w = w.reshape([-1,3])\n \n # plot the angle and action curve\n #-------------------plot settings------------------------------\n plt.rcParams['font.family'] = 'Times New Roman' # font familyの設定\n plt.rcParams['mathtext.fontset'] = 'stix' # math fontの設定\n plt.rcParams[\"font.size\"] = 10 # 全体のフォントサイズが変更されます。\n plt.rcParams['xtick.labelsize'] = 10 # 軸だけ変更されます。\n plt.rcParams['ytick.labelsize'] = 10 # 軸だけ変更されます \n plt.rcParams['xtick.direction'] = 'in' # x axis in\n plt.rcParams['ytick.direction'] = 'in' # y axis in \n plt.rcParams['axes.linewidth'] = 1.0 # axis line width\n plt.rcParams['axes.grid'] = True # make grid\n plt.rcParams[\"legend.loc\"] = \"best\" # 凡例の位置、\"best\"でいい感じのところ\n plt.rcParams[\"legend.frameon\"] = True # 凡例を囲うかどうか、Trueで囲う、Falseで囲わない\n plt.rcParams[\"legend.framealpha\"] = 1.0 # 透��度、0.0から1.0の値を入れる\n plt.rcParams[\"legend.facecolor\"] = \"white\" # 背景色\n # plt.rcParams[\"legend.edgecolor\"] = \"black\" # 囲いの色\n plt.rcParams[\"legend.fancybox\"] = True # Trueにすると囲いの四隅が丸くなる\n #-------------------------------------------------------------- \n curr_dir = os.path.abspath(os.getcwd())\n if not os.path.isdir(\"results\"):\n os.mkdir(\"results\")\n \n plt.figure(figsize=(12,5),dpi=100)\n plt.tight_layout()\n plt.subplots_adjust(wspace=0.3, hspace=0.3)\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(231)\n plt.plot(np.arange(max_steps)*dt, q[:,0],label =r\"$q_{0}$\")\n plt.plot(np.arange(max_steps)*dt, q[:,1],label =r\"$q_{1}$\")\n plt.plot(np.arange(max_steps)*dt, q[:,2],label =r\"$q_{2}$\")\n plt.plot(np.arange(max_steps)*dt, q[:,3],label =r\"$q_{3}$\")\n plt.title('Quaternion')\n plt.ylabel('quaternion value')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.tight_layout()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_quaternion.png\")\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(232)\n plt.plot(np.arange(max_steps)*dt, qe[:,0],label =r\"$q_{0}$\")\n plt.plot(np.arange(max_steps)*dt, qe[:,1],label =r\"$q_{1}$\")\n plt.plot(np.arange(max_steps)*dt, qe[:,2],label =r\"$q_{2}$\")\n plt.plot(np.arange(max_steps)*dt, qe[:,3],label =r\"$q_{3}$\")\n plt.title('Quaternion Error')\n plt.ylabel('quaternion value')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.tight_layout()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_error_quaternion.png\")\n\n angle = np.array([np.rad2deg(env.dcm2euler(env.quaternion2dcm(q[i,:]))).tolist() for i in range(max_steps)])\n angle = angle.reshape([-1,3])\n print(angle[-1,:])\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(233)\n plt.plot(np.arange(max_steps)*dt, angle[:,0],label = r\"$\\phi$\")\n plt.plot(np.arange(max_steps)*dt, angle[:,1],label = r\"$\\theta$\")\n plt.plot(np.arange(max_steps)*dt, angle[:,2],label = r\"$\\psi$\")\n # plt.title('Action')\n plt.ylabel('angle [deg]')\n plt.xlabel(r'time [s]')\n plt.legend(loc=\"lower center\", bbox_to_anchor=(0.5,1.05), ncol=3)\n plt.tight_layout()\n # plt.ylim(-20, 20)\n plt.grid(True, color='k', linestyle='dotted', linewidth=0.8)\n # plt.savefig(curr_dir + \"/results/plot_angle.png\")\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(234)\n plt.plot(np.arange(max_steps)*dt, w[:,0],label =r\"$\\omega_{x}$\")\n plt.plot(np.arange(max_steps)*dt, w[:,1],label =r\"$\\omega_{y}$\")\n plt.plot(np.arange(max_steps)*dt, w[:,2],label =r\"$\\omega_{z}$\")\n plt.title('Angular velocity')\n plt.ylabel('angular velocity [rad/s]')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.tight_layout()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_ang_vel.png\")\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(235)\n plt.plot(np.arange(max_steps)*dt, actions[:,0],label = r\"$\\tau_{x}$\")\n plt.plot(np.arange(max_steps)*dt, actions[:,1],label = r\"$\\tau_{x}$\")\n plt.plot(np.arange(max_steps)*dt, actions[:,2],label = r\"$\\tau_{x}$\")\n plt.title('Action')\n plt.ylabel('Input torque [Nm]')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.tight_layout()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_torque.png\")\n plt.savefig(curr_dir + \"/results/total_results.png\")\n\n # plt.figure(figsize=(8,4),dpi=100)\n # plt.plot(np.arange(max_steps)*dt, r_hist[:,0],label = r\"$q$ pnlty\")\n # plt.plot(np.arange(max_steps)*dt, r_hist[:,1],label = r\"$\\omega$ pnlty\")\n # plt.plot(np.arange(max_steps)*dt, r_hist[:,2],label = r\"$\\tau$ pnlty\")\n # plt.plot(np.arange(max_steps)*dt, r_hist[:,0]+r_hist[:,1]+r_hist[:,2],label = r\"$toal$\",linestyle='dotted')\n # # plt.title('Action')\n # plt.ylabel('reward')\n # plt.xlabel(r'time [s]')\n # plt.tight_layout()\n # plt.legend()\n # # plt.ylim(-20, 20)\n # plt.grid(True, color='k', linestyle='dotted', linewidth=0.8)\n # plt.savefig(curr_dir + \"/results/reward_compo.png\")\n\n plt.show()\n #endregion\n # -------------------------結果プロット終わり--------------------------------\ndef env_test():\n\n # simulation of the agent solving the cartpole swing-up problem\n env = make(\"SatelliteContinuous\")\n curr_dir = os.path.abspath(os.getcwd())\n env.reset()\n print('The goal angle :'+ str(env.goalEuler) + \" the target multi:\" + str(env.multi))\n r = 0\n qe = np.empty((0,4))\n q = np.empty((0,4))\n w = np.empty((0,3))\n actions = np.empty((0,3))\n\n kp = 0.7\n kd = 1.9\n Kp = np.array([[0,kp,0,0],\n [0,0,kp,0],\n [0,0,0,kp]])\n Kd = np.array([[kd,0,0],\n [0,kd,0],\n [0,0,kd]])\n action = np.array([0,0,0]).reshape(1,3)\n actions = np.append(actions, action,axis=0)\n\n dt = 0.1\n simutime = 50\n simulation_iterations = int(simutime/dt) -1 # dt is 0.01\n\n for i in range(1, simulation_iterations):\n action = np.squeeze(action)\n next_error_state, reward, done, next_state, _ = env.step(action)\n # env.render()\n # q=np.append(q,next_state[0].reshape(1,-1),axis=0)\n # qe=np.append(qe,next_error_state[0].reshape(1,-1),axis=0)\n # w=np.append(w,next_error_state[2].reshape(1,-1),axis=0)\n q=np.append(q,next_state[:4].reshape(1,-1),axis=0)\n qe=np.append(qe,next_error_state[:4].reshape(1,-1),axis=0)\n w=np.append(w,next_error_state[-3:].reshape(1,-1),axis=0)\n r += reward\n # state = next_state\n #----------------control law (PID controller)-----------------------\n action = -Kp@next_error_state[:4].reshape(-1,1)-Kd@next_error_state[-3:].reshape(-1,1)\n actions = np.append(actions, action.reshape(1,-1),axis=0)\n #--------------------------------------------------------------------\n\n # env.close()\n #show the total reward\n print(\"Total Reward is : \" + str(r))\n # データの形の整理\n q = q.reshape([-1,4])\n qe = qe.reshape([-1,4])\n w = w.reshape([-1,3])\n # angle = [e for i in]\n\n # plot the angle and action curve\n curr_dir = os.path.abspath(os.getcwd())\n if not os.path.isdir(\"results\"):\n os.mkdir(\"results\")\n plt.figure(figsize=(12,5),dpi=100)\n # plt.figure(figsize=(5.0,3.5),dpi=100\n plt.subplot(231)\n plt.plot(np.arange(simulation_iterations-1)*dt, q[:,0],label =r\"$q_{0}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, q[:,1],label =r\"$q_{1}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, q[:,2],label =r\"$q_{2}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, q[:,3],label =r\"$q_{3}$\")\n plt.title('Quaternion')\n plt.ylabel('quaternion value')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_angle.png\")\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(232)\n plt.plot(np.arange(simulation_iterations-1)*dt, qe[:,0],label =r\"$q_{0}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, qe[:,1],label =r\"$q_{1}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, qe[:,2],label =r\"$q_{2}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, qe[:,3],label =r\"$q_{3}$\")\n plt.title('Quaternion Error')\n plt.ylabel('quaternion value')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_angle.png\")\n\n angle = np.array([np.rad2deg(env.dcm2euler(env.quaternion2dcm(q[i,:]))).tolist() for i in range(simulation_iterations-1)])\n angle = angle.reshape([-1,3])\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(233)\n plt.plot(np.arange(simulation_iterations-1)*dt, angle[:,0],label = r\"$\\phi$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, angle[:,1],label = r\"$\\theta$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, angle[:,2],label = r\"$\\psi$\")\n # plt.title('Action')\n plt.ylabel('angle [deg]')\n plt.xlabel(r'time [s]')\n plt.legend(loc=\"lower center\", bbox_to_anchor=(0.5,1.05), ncol=3)\n plt.tight_layout()\n # plt.ylim(-20, 20)\n plt.grid(True, color='k', linestyle='dotted', linewidth=0.8)\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(234)\n plt.plot(np.arange(simulation_iterations-1)*dt, w[:,0],label =r\"$\\omega_{x}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, w[:,1],label =r\"$\\omega_{y}$\")\n plt.plot(np.arange(simulation_iterations-1)*dt, w[:,2],label =r\"$\\omega_{z}$\")\n plt.title('Angular velocity')\n plt.ylabel('angular velocity [rad/s]')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n # plt.savefig(curr_dir + \"/results/plot_angle.png\")\n\n # plt.figure(figsize=(5.0,3.5),dpi=100)\n plt.subplot(235)\n plt.plot(np.arange(simulation_iterations)*dt, actions[:,0],label = r\"$\\tau_{x}$\")\n plt.plot(np.arange(simulation_iterations)*dt, actions[:,1],label = r\"$\\tau_{x}$\")\n plt.plot(np.arange(simulation_iterations)*dt, actions[:,2],label = r\"$\\tau_{x}$\")\n plt.title('Action')\n plt.ylabel('Input torque [Nm]')\n plt.xlabel(r'time [s]')\n plt.legend()\n plt.grid(color='k', linestyle='dotted', linewidth=0.6)\n\n # plt.savefig(curr_dir + \"/results/plot_action.png\")\n plt.show()\n\n\nif __name__ == '__main__':\n plt.close()\n val = input('Enter the number 1:train 2:evaluate 3:env_test > ')\n if val == '1':\n train()\n elif val == '2':\n evaluate()\n elif val == '3':\n env_test()\n else:\n print(\"You entered the wrong number, run again and choose from 1 or 2 or 3.\")\n","sub_path":"td3/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":14608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"550540060","text":"\nimport pprint\nfrom pprint import pprint\nimport os\nimport sys\nimport time\nimport win32con\nimport win32gui\nfrom win32api import GetModuleHandle\nimport json\n\nclass WindowsBalloonTip:\n def __init__(self, title, msg):\n message_map = {win32con.WM_DESTROY: self.OnDestroy, }\n # Register the Window class.\n wc = win32gui.WNDCLASS()\n self.destroyed = False\n hinst = wc.hInstance = GetModuleHandle(None)\n wc.lpszClassName = \"PythonTaskbar\"\n wc.lpfnWndProc = message_map # could also specify a wndproc.\n class_atom = win32gui.RegisterClass(wc)\n # Create the Window.\n style = win32con.WS_OVERLAPPED | win32con.WS_SYSMENU\n self.hwnd = win32gui.CreateWindow(class_atom, \"Taskbar\", style,\n 0, 0, win32con.CW_USEDEFAULT,\n win32con.CW_USEDEFAULT, 0, 0,\n hinst, None)\n win32gui.UpdateWindow(self.hwnd)\n icon_path_name = os.path.abspath(os.path.join(sys.path[0],\n \"balloontip.ico\"))\n icon_flags = win32con.LR_LOADFROMFILE | win32con.LR_DEFAULTSIZE\n try:\n hicon = win32gui.LoadImage(hinst, icon_path_name,\n win32con.IMAGE_ICON, 0, 0, icon_flags)\n except:\n hicon = win32gui.LoadIcon(0, win32con.IDI_APPLICATION)\n flags = win32gui.NIF_ICON | win32gui.NIF_MESSAGE | win32gui.NIF_TIP\n nid = (self.hwnd, 0, flags, win32con.WM_USER+20, hicon, \"tooltip\")\n win32gui.Shell_NotifyIcon(win32gui.NIM_ADD, nid)\n win32gui.Shell_NotifyIcon(win32gui.NIM_MODIFY,\n (self.hwnd, 0, win32gui.NIF_INFO,\n win32con.WM_USER+20, hicon,\n \"Balloon tooltip\", msg, 200, title))\n # self.show_balloon(title, msg)\n time.sleep(20)\n win32gui.DestroyWindow(self.hwnd)\n win32gui.UnregisterClass(class_atom, hinst)\n self.destroyed = True\n\n def OnDestroy(self, hwnd, msg, wparam, lparam):\n nid = (self.hwnd, 0)\n win32gui.Shell_NotifyIcon(win32gui.NIM_DELETE, nid)\n win32gui.PostQuitMessage(0) # Terminate the app.\n\n def isDestroyed(self):\n return self.destroyed\n\n\ndef balloon_tip(title, msg):\n w = WindowsBalloonTip(title, msg)\n return w\n\n#############################################\n\nimport bs4, requests\n\ndef findSharePrice(url):\n res = requests.get(url)\n res.raise_for_status()\n soup = bs4.BeautifulSoup(res.text, 'html.parser')\n elems = soup.select('body > div.container.container--game.wrapper.clearfix > div.content-region.region--full.game-count.full-width > div > div > h3')\n #print('counter: '+ elems[0].text.strip())\n count = elems[0].text.strip()\n count = count.replace(\",\", \"\")\n #print('My counter: '+ count)\n return int(count)\n\nprint('Enter the threshold value for which you want to get notify: ')\nthreshold = input()\n\nwhile(1):\n actual = findSharePrice('https://www.marketwatch.com/vse')\n if(actual == int(threshold) ):\n print('Value is equal to threshold')\n balloon_tip('Threshold Value Reached...', ' Put your message here. Value is equal to threshold')\n break;\n\n","sub_path":"observer_notifier/observer_notifier.py","file_name":"observer_notifier.py","file_ext":"py","file_size_in_byte":3320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"168910766","text":"######## Exercises 1 ########\n\n#### Input and Output ####\n\n# Write a program called \"hello.py\" which asks the user for a name, and then personally greets them.\n\ni = input(\"Enter your name: \")\n\nprint(\"Hello \", i, \"!\", sep='')\n\n# Write a program called repeater.py which reads a stirng and then prints it out twice.\n\ni = input(\"Enter a string: \")\nprint(i*2, sep=' ')\n\n# Write a program called multiplyer.py which reads a number, then prints out double that number.\n\ni = int(input(\"Enter a number: \"))\nprint(i*2)\n\n#### Box Printing ####\n\n# Write a program which prints a box. You should use the plus, minus and pipe characters.\n\nprint(\"+---+\\n| |\\n+---+\\n\")\n\n# Write a program which reads a string and prints a box around it.\n\ni = input(\"Enter a string: \")\nl = len(i) + 2\n\nprint(\"+\", \"-\"*l, \"+\", sep='')\nprint(\"| \", i, \" |\", sep='')\nprint(\"+\", \"-\"*l, \"+\", sep='')\n\n#### Calculator ####\n\n# Write a program which reads two numbesr and prints their sum.\n\n#a = int(input(\"Enter a number: \"))\n#b = int(input(\"Enter a number: \"))\n#print(a + b)\n\na, b = [int(a)\n for a in input(\"Enter two numbers separated by a space: \").split()]\nprint(a + b)\n\n# Subtract\nprint(a-b)\n\n# Multiply\nprint(a*b)\n\n# Divide\nprint(a/b)\n\n# General purpose calculator\n\na, o, b = [x for x in input(\n \"Enter a mathematical expression separated by spaces: \").split()]\na = int(a)\nb = int(b)\n\nif o == \"/\" or o == \"\\\\\":\n print(a/b)\nelif o == \"+\":\n print(a+b)\nelif o == \"-\":\n print(a-b)\nelif o == \"*\" or o == \"+\":\n print(a*b)\nelse:\n print(\"Invalid input!\")\n\n\ndef calculator():\n # Calculator any notation!\n opr = []\n nmbr = []\n\n for val in input(\"Enter a mathematical expression separated by spaces: \").split():\n check = list(val)\n # print(check)\n test = True\n for char in check:\n # print(ord(char),ord(\"0\"),ord(\"9\"))\n if ord(char) not in range(ord(\"0\"), ord(\"9\")):\n test = False\n # print(test)\n if test == False:\n opr = opr + [val]\n else:\n nmbr = nmbr + [int(val)]\n\n # print(opr,nmbr)\n\n for i in range(len(opr)):\n if i == 0:\n if opr[i] == \"/\" or opr[i] == \"\\\\\":\n res = nmbr[i]/nmbr[i+1]\n elif opr[i] == \"+\":\n res = nmbr[i]+nmbr[i+1]\n elif opr[i] == \"-\":\n res = nmbr[i]-nmbr[i+1]\n elif opr[i] == \"*\" or opr[i] == \"+\":\n res = nmbr[i]*nmbr[i+1]\n else:\n return(\"Invalid input!\")\n else:\n if opr[i] == \"/\" or opr[i] == \"\\\\\":\n res = res + nmbr[i+1]\n elif opr[i] == \"+\":\n res = res + nmbr[i+1]\n elif opr[i] == \"-\":\n res = res - nmbr[i+1]\n elif opr[i] == \"*\" or opr[i] == \"+\":\n res = res * nmbr[i+1]\n else:\n return(\"Invalid input!\")\n\n print(\"Result is\", res)\n\n\ncalculator()\n","sub_path":"Python/problemSets/caculator.py","file_name":"caculator.py","file_ext":"py","file_size_in_byte":2950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"296265319","text":"from scapy.all import *\na = \" \"\n\nsrc = \"10.0.2.11\"\ndst = \"10.0.2.12\"\nsport = 11111 #random.randint(1024,65535)\ndport = 8333\nseq = 3587496741#random.randint(1587496741,9587496741)\nack = 0\npld_VERSION = '\\xf9\\xbe\\xb4\\xd9version\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00]\\xf6\\xe0\\xe2'\n\n#SYN\nip = IP(src = src, dst = dst)\nSYN = TCP(sport=sport, dport=dport, flags='S', seq = seq)\n##send(ip/SYN)\nSYNACK = sr1(ip/SYN)\n\n\n#SYN-ACK\nACK = TCP(sport=sport, dport=dport, flags='A', seq=SYNACK.ack, ack=SYNACK.seq+1)\nsend(ip/ACK)\n#FINACK=sr1(ip/ACK)\n'''\n#Bitcoin version\n#tcp = TCP(sport=sport, dport=dport, flags='PA', seq=FINACK.ack, ack=FINACK.seq+1)\ntcp = TCP(sport=sport, dport=dport, flags='PA', seq=ACK.seq+1, ack=ACK.ack)\nVERACK = sr1(ip/tcp/pld_VERSION)\n'''\n\n'''\ndef pkt_callback(pkt):\n src = \"10.0.2.12\"\n dst = \"10.0.2.7\"\n sport = \" \"\n dport = \" \"\n seq = 0\n ack = 0\n pld_VERSION = '\\xf9\\xbe\\xb4\\xd9version\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00]\\xf6\\xe0\\xe2'\n ip = IP(src = src, dst = dst)\n\n #if pkt[IP].src == \"10.0.2.15\" and pkt[IP].dst == \"10.0.2.7\":\n #pkt.show()\n try:\n if pkt[IP].src == \"10.0.2.12\" and pkt[IP].dst == \"10.0.2.7\" and pkt[TCP].payload:\n pkt.show()\n print(\"!!!!! outbound session, flags:\", pkt[TCP].flags)\n elif pkt[IP].src == \"10.0.2.12\" and pkt[IP].dst == \"10.0.2.7\" and pkt[TCP].flags == 16L:\n pkt.show()\n print(\"!!!!! outbound session, flags:\", pkt[TCP].flags) \n sport = pkt[TCP].sport\n dport = pkt[TCP].dport\n #synchronize the seq and ack\n if seq < pkt[TCP].seq or seq == pkt[TCP].seq:\n seq = pkt[TCP].seq\n #print(\"$$$seq:\", seq)\n if ack < pkt[TCP].ack or ack == pkt[TCP].ack:\n ack = pkt[TCP].ack\n #print(\"!!!!ack:\", ack)\n #construct the pkt_spoof\n tcp = TCP(sport=sport, dport=dport, flags='PA', seq=seq, ack =ack)\n pkt_spoof = ip/tcp/pld_VERSION\n print(pkt_spoof)\n send(pkt_spoof)\n print(\"!!!! $p00f scceeds!\")\n elif pkt[IP].dst == \"10.0.2.12\" and pkt[IP].src == \"10.0.2.7\" and pkt[TCP].payload:\n pkt.show()\n print(\"!!!!! inbound session, flags:\", pkt[TCP].flags)\n sport = pkt[TCP].dport\n dport = pkt[TCP].sport\n seq = pkt[TCP].ack\n ack = pkt[TCP].seq + 35\n tcp = TCP(sport=sport, dport=dport, flags='A', seq=seq, ack =ack)\n pkt_spoof = ip/tcp\n print(pkt_spoof)\n send(pkt_spoof)\n print(\"!!!! reply ack\")\n except:\n pass\n \n\nsniff(iface=\"enp0s3\",prn=pkt_callback, filter=\"tcp\", store=1)\n'''\n","sub_path":"tools/Networking/bitcoin_bogus_node.py","file_name":"bitcoin_bogus_node.py","file_ext":"py","file_size_in_byte":2768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"143829413","text":"import database\nfrom functions import path_f, cli_f\nfrom queries import city_q, team_q\nfrom classes import team\nfrom rules import sad_rules\nimport time\nimport random\nimport math\nimport traceback, sys\n\n# http://woarl.com/board/viewtopic.php?f=114&t=3326\n# max_batches = 100# Shouldn't need more than this\nconfig = {\n\t\"batches\":\t100,\n\t\"hops\":\t\t1,\n\t\"max hops\":\t20,\n}\n\ndef sad_batch(w, verbose=True):\n\tcity_dict = w.live_cities()\n\t\n\t# raise Exception(\"Alter wealth production rate\")\n\t# raise Exception(\"Make it so cities take into account what they can afford\")\n\t\n\t# Reset buys\n\ttotal_demand = 0\n\ttotal_surplus = 0\n\twealth = 0\n\tneed_len = 0\n\tcities_needing_grain = 0\n\tfor city_id, the_city in city_dict.items():\n\t\tthe_city.buys = []\n\t\tthe_city.sells = []\n\t\t\n\t\t\n\t\ttotal_demand += sum([(-a if a < 0 else 0) for r, a in the_city.goods.items()])\n\t\t# total_demand += (-the_city.goods['Grain'] if the_city.goods['Grain'] < 0 else 0)\n\t\ttotal_surplus += sum([(a if a > 0 else 0) for r, a in the_city.goods.items()])\n\t\t# total_surplus += (the_city.goods['Grain'] if the_city.goods['Grain'] > 0 else 0)\n\t\twealth += the_city.wealth\n\t\t\n\t\tif the_city.goods['Grain'] < 0:\n\t\t\tcities_needing_grain += 1\n\t\t\n\t\tneed_len += len(the_city.current_demands())\n\t\n\t# 451\n\tprint(\"Kruzen: %s\" % str(city_dict[451].goods))\n\tprint(\"Kruzen: %s\" % str(city_dict[451].wealth))\n\tprint(\"Demand: %s, Avg demand: %s\" % (total_demand, total_demand/len(city_dict.keys())))\n\tprint(\"Surplus: %s\" % total_surplus)\n\tprint(\"Wealth: %s, avg wealth: %s\" % (wealth, wealth/len(city_dict.keys())))\n\tprint(\"cities_needing_grain: %s\" % cities_needing_grain)\n\tprint(\"Avg needs: %s\" % (need_len/len(city_dict.keys())))\n\t\n\t# Find new buys\n\tfind_buys(w, city_dict, verbose)\n\t\n\t# Appove them\n\tapprove_buys(w, city_dict, verbose)\n\t\n\tsale_total = 0\n\tfor city_id, the_city in city_dict.items():\n\t\tfor s in the_city.sells:\n\t\t\t# print(s)\n\t\t\tsale_total += s[2]\n\t\n\tprint(\"Quantity sold: %s\" % sale_total)\n\t\n\t# Execute\n\texecute_buys(w, city_dict, verbose)\n\t\n\tprint(\"Batch complete\\n\")\n\ndef find_buys(w, city_dict, verbose=True):\n\t# city_dict = w.live_cities()\n\t\n\t# End point stopper\n\t# w.suppliers = {r:set() for r in sad_rules.res_list}\n\t# for city_id, the_city in city_dict.items():\n\t# \tthe_city.best_price = {}\n\t# \t\n\t# \tfor r in sad_rules.res_list:\n\t# \t\tif the_city.goods[r] > 0:\n\t# \t\t\tw.suppliers[r].add(city_id)\n\t\n\tfor city_id, the_city in city_dict.items():\n\t\tthe_city.needs_cache = the_city.current_demands()\n\t\n\t\n\tif verbose:\n\t\tit = cli_f.progressbar(range(0, config['hops']+1), \"Hopping: \", 60, True)\n\telse:\n\t\tit = range(0, config['hops']+1)\n\t\n\tfor h in it:\n\t# for h in range(0, max_hops+1):\n\t\t# for city_id, the_city in cli_f.progressbar(city_dict.items(), \"Hopping %d: \" % h, 60, True):\n\t\tfor city_id, the_city in city_dict.items():\n\t\t\t_discover(w, city_dict, the_city, h)\n\t\n\tif verbose:\n\t\tit = cli_f.progressbar(city_dict.items(), \"Finding buys: \", 60, True)\n\telse:\n\t\tit = city_dict.items()\n\t\n\tfor city_id, the_city in it:\n\t\tif the_city.wealth <= 5: continue\n\t\t\n\t\tneeds = the_city.current_demands()\n\t\t\n\t\toffers = {}\n\t\tfor r in needs:\n\t\t\toffers[r] = []\n\t\t\n\t\t# For each supply we want\n\t\tfor c, d in the_city.connections.items():\n\t\t\tif city_id in city_dict[c].connections:\n\t\t\t\tfor r in needs:\n\t\t\t\t\tif city_dict[c].best_price[config['hops']][r][0] > 1000: continue\n\t\t\t\t\t\n\t\t\t\t\t# Price is made from price saved at other city times the get_price multiplier\n\t\t\t\t\toffer_price = get_price(w, the_city, city_dict[c]) * city_dict[c].best_price[config['hops']][r][0]\n\t\t\t\t\tavailiable = the_city.wealth/len(needs)\n\t\t\t\t\tamount = min(availiable/offer_price, -the_city.goods[r])\n\t\t\t\t\toffers[r].append((offer_price, list(city_dict[c].best_price[config['hops']][r][1]) + [the_city.id], amount))\n\t\t\n\t\t# Now to turn them into Buys\n\t\tfor r in needs:\n\t\t\tavailiable = the_city.wealth/len(needs)\n\t\t\tamount_needed = -the_city.goods[r]\n\t\t\t\n\t\t\twhile amount_needed > 0 and len(offers[r]) > 0:\n\t\t\t\tbest_offer = None\n\t\t\t\tbest_price = 99999\n\t\t\t\t\n\t\t\t\tfor i, o in enumerate(offers[r]):\n\t\t\t\t\tif o[0] < best_price:\n\t\t\t\t\t\tbest_offer = i\n\t\t\t\t\t\tbest_price = o[0]\n\t\t\t\t\n\t\t\t\taccepted_offer = offers[r][best_offer]\n\t\t\t\t\n\t\t\t\tdel(offers[r][best_offer])\n\t\t\t\t\n\t\t\t\t# print(\"\")\n\t\t\t\t# print(accepted_offer)\n\t\t\t\tcity_dict[accepted_offer[1][0]].sells.append((r, accepted_offer[1], accepted_offer[2]))\n\n# Asks the city what the best price it can get for a given supply is in a certain range\n# Best price is a tuple (price, city_pathway)\n# Cost: 9999 = can't get it\n# Pathway uses a stack system, build from supplier, read from buyer\ndef _discover(w, city_dict, the_city, hops_allowed=0):\n\t# If 0 hops then we're looking at suppliers\n\tif hops_allowed == 0:\n\t\tthe_city.best_price[0] = {}\n\t\tfor r in sad_rules.res_list:\n\t\t\tif the_city.goods[r] > 0:\n\t\t\t\tthe_city.best_price[0][r] = (sad_rules.base_rate[r], [the_city.id])\n\t\t\telse:\n\t\t\t\tthe_city.best_price[0][r] = (9999, [])\n\t\t\n\t# If we can hop then we check our neighbours for their best hop -1\n\telse:\n\t\t# Start by setting best price as what we can do with 1 fewer hop\n\t\tif hops_allowed in the_city.best_price:\n\t\t\ttot = sum([a[0] for r, a in the_city.best_price[hops_allowed].items()])\n\t\t\tif tot < 5:\n\t\t\t\tprint(tot)\n\t\tthe_city.best_price[hops_allowed] = {}\n\t\t\n\t\tfor r in sad_rules.res_list:\n\t\t\tthe_city.best_price[hops_allowed][r] = the_city.best_price[hops_allowed-1][r]\n\t\t\n\t\t# Now to try it with one more hop!\n\t\tfor c, d in the_city.connections.items():\n\t\t\tif the_city.id in city_dict[c].connections:\n\t\t\t\tfor r in sad_rules.res_list:\n\t\t\t\t\tif city_dict[c].best_price[hops_allowed-1][r][0] > 1000: continue\n\t\t\t\t\t\n\t\t\t\t\tbest_price = the_city.best_price[hops_allowed-1][r][0]\n\t\t\t\t\t\n\t\t\t\t\t# Price is made from price saved at other city times the get_price multiplier\n\t\t\t\t\tnew_price = get_price(w, the_city, city_dict[c]) * city_dict[c].best_price[hops_allowed-1][r][0]\n\t\t\t\t\t\n\t\t\t\t\tif new_price < best_price:\n\t\t\t\t\t\tthe_city.best_price[hops_allowed][r] = (new_price, list(city_dict[c].best_price[hops_allowed-1][r][1]) + [the_city.id])\n\nprice_cache = {}\ndef get_price(w, buyer, seller):\n\t\"\"\"\n\tPrice is always made by the code returning a price\n\tthus any price returned already has the multipliers worked out\n\t\"\"\"\n\t\n\t# Call count: 4360709 (that's with max_hops = 9)\n\t# No cache - 18.52, time in func: 4.7, approx time per city: 2.0s\n\t# With cache - 16.87, time in func: 3.4, approx time per city: 1.7s\n\t\n\tif (buyer.id, seller.id) not in price_cache:\n\t\tprice_cache[(buyer.id, seller.id)] = (w.distance_cache[(seller.id, buyer.id)]) * (1 + w.tax_cache[(seller.team, buyer.team)]/100)\n\treturn price_cache[(buyer.id, seller.id)]\n\ndef approve_buys(w, city_dict, verbose=True):\n\tteam_dict = w.teams()\n\t\n\tif verbose:\n\t\tit = cli_f.progressbar(city_dict.items(), \"Approving buys: \", 60, True)\n\telse:\n\t\tit = city_dict.items()\n\t\n\tsales_count = 0\n\tsales_lowered = 0\n\t\n\tfor city_id, the_city in it:\n\t\tif len(the_city.sells) == 0: continue\n\t\t\n\t\t# print(\"\\n\\n\")\n\t\t# print(the_city.sells)\n\t\t# exit()\n\t\t\n\t\taccepted_sells = []\n\t\tmarked_sells = []\n\t\t\n\t\t# We need to work out which sells we're gonna do\n\t\t# preferential treatment is given to those that we have a low tax rate to\n\t\t# it's possible we can honour more than one sell\n\t\t# the sells should all be for just a single supply\n\t\t\n\t\t# If the sells are not for a single supply it will be an inefficent system as only 1 favoured is tracked\n\t\t\n\t\t# Temporary counters\n\t\tgoods = sum([(a if a > 0 else 0) for r, a in the_city.goods.items()])\n\t\tgoods_dict = dict(the_city.goods)\n\t\t\n\t\t# Keep going while we can\n\t\ti = 0\n\t\twhile goods > 0 and len(accepted_sells) < len(the_city.sells):\n\t\t\ti += 1\n\t\t\tif i > 100:\n\t\t\t\tbreak\n\t\t\t\n\t\t\tfavoured = None\n\t\t\tfavoured_tax = 9999\n\t\t\t\n\t\t\tfor i, s in enumerate(the_city.sells):\n\t\t\t\tif i in marked_sells: continue\n\t\t\t\t\n\t\t\t\t# s[1] is the path\n\t\t\t\t# 0 is us\n\t\t\t\t# 1 is the first buyer\n\t\t\t\tbuyer = s[1][1]\n\t\t\t\t\n\t\t\t\tif w.tax_cache[(the_city.team, city_dict[buyer].team)] < favoured_tax:\n\t\t\t\t\tfavoured_i = i\n\t\t\t\t\tfavoured = s\n\t\t\t\t\tfavoured_tax = w.tax_cache[(the_city.team, city_dict[buyer].team)]\n\t\t\t\n\t\t\t# print(\"Fav: %s, %s\" % (favoured_tax, str(favoured)))\n\t\t\t\n\t\t\t# Actual amount needs to be limited\n\t\t\t# marked_sells.append(favoured)\n\t\t\tmarked_sells.append(favoured_i)\n\t\t\tfinal_buyer = favoured[1][-1]\n\t\t\tr = favoured[0]\n\t\t\t\n\t\t\tsales_count += 1\n\t\t\tif goods_dict[r] < favoured[2]:\n\t\t\t\tsales_lowered += 1\n\t\t\t\n\t\t\tamount = min(goods_dict[r], favoured[2])\n\t\t\t\n\t\t\t# print(amount, \" \")\n\t\t\t\n\t\t\t# Move counters, add amount to the sell\n\t\t\tfavoured = (favoured[0], favoured[1], amount)\n\t\t\taccepted_sells.append(favoured)\n\t\t\tgoods -= amount\n\t\t\tgoods_dict[r] -= amount\n\t\t\t\n\t\t\t# goods = -100\n\t\t\n\t\tthe_city.sells = accepted_sells\n\t\t# for s in the_city.sells:\n\t\t# \tprint(s)\n\t\t# exit()\n\t\n\tprint(\"Sale count: %s\" % sales_count)\n\tprint(\"Sales lowered: %s\" % sales_lowered)\n\ndef execute_buys(w, city_dict, verbose=True):\n\t# city_dict = w.live_cities()\n\t\n\tif verbose:\n\t\tit = cli_f.progressbar(city_dict.items(), \"Executing buys: \", 60, True)\n\telse:\n\t\tit = city_dict.items()\n\t\n\tfor city_id, the_city in it:\n\t\tfor s in the_city.sells:\n\t\t\t_execute(w, city_dict, s)\n\n\ndef _execute(w, city_dict, sell):\n\t\"\"\"\n\tif self.source != None:\n\t\tself.source.execute(root=False)\n\telse:\n\t\tself.seller.goods[self.resource] -= self.amount\n\t\n\tloss = self.price - (self.price / (_distance(self.buyer, self.seller)))\n\t\n\t# Loss is paid by buyer but never reaches the seller\n\tself.seller.wealth += self.price - loss\n\tself.buyer.wealth -= self.price\n\t\n\tif root:\n\t\tself.buyer.goods[self.resource] += self.amount\n\t\"\"\"\n\t\n\t# Aliases\n\tr = sell[0]\n\troute = sell[1]\n\tamount = sell[2]\n\t\n\t# Split up the route into city pairs\n\tpath = []\n\tfor i in range(1, len(route)):\n\t\tpath.append((route[i-1], route[i]))\n\t\n\t# For each path part, redistribute wealth\n\t# price_counter = sad_rules.base_rate[r]\n\tprevious_sale = sad_rules.base_rate[r] * amount\n\tfor seller, buyer in path:\n\t\t# price_cache[(buyer.id, seller.id)] = (w.distance_cache[(seller.id, buyer.id)]) * (1 + w.tax_cache[(seller.team, buyer.team)]/100)\n\t\t\n\t\t# Sell with distance and tax taken into account\n\t\tsale_price = previous_sale * price_cache[(buyer, seller)]\n\t\t\n\t\t# Seller gets price without the distance, that's lost\n\t\tprofit = sale_price/w.distance_cache[(seller, buyer)]\n\t\t\n\t\t# Apply wealth difference\n\t\tcity_dict[seller].wealth += profit\n\t\tcity_dict[buyer].wealth -= sale_price\n\t\t\n\t\tprevious_sale = sale_price\n\t\n\t# Apply difference to first buyer and seller's goods\n\tbuyer = route[-1]\n\tseller = route[0]\n\t\n\tcity_dict[buyer].goods[r] += amount\n\tcity_dict[seller].goods[r] -= amount\n\ndef supply_and_demand(the_world):\n\tcity_dict = the_world.live_cities()\n\tteam_dict = the_world.active_teams()\n\t\n\tthe_world.mass_get_team_techs()\n\tthe_world.mass_get_team_deities()\n\tthe_world.mass_get_team_evolutions()\n\t\n\t# Randomise city production type\n\trandom.seed()\n\tr = -1\n\tfor k, v in city_dict.items():\n\t\tr += 1\n\t\tif r >= len(sad_rules.res_list): r = 0\n\t\tv.supply_good = r\n\t\t# v.supply_good = random.randrange(0, len(sad_rules.res_list))\n\t\n\t# Build a cache of taxes and borders\n\tthe_world.tax_cache = {}\n\t# the_world.border_cache = {}\n\tfor t1 in team_dict.keys():\n\t\tfor t2 in team_dict.keys():\n\t\t\tif t1 == t2:\n\t\t\t\tthe_world.tax_cache[(t1, t2)] = 0\n\t\t\telse:\n\t\t\t\tthe_world.tax_cache[(t1, t2)] = the_world.get_taxes(t1, t2)\n\t\t\t\t# the_world.border_cache[(t1, t2)] = the_world.get_border(t1, t2)\n\t\n\t# Build a distance cache, K1 going to K2\n\tthe_world.distance_cache = {}\n\tfor k1, c1 in city_dict.items():\n\t\tfor k2, c2 in city_dict.items():\n\t\t\tif k1 == k2:\n\t\t\t\tthe_world.distance_cache[(k1, k2)] = 0\n\t\t\telse:\n\t\t\t\tif k2 in c1.connections:\n\t\t\t\t\tthe_world.distance_cache[(k1, k2)] = (1 + (sad_rules.distance_percentage * c1.connections[k2]/100))\n\t\n\t# Total, Min, Max\n\tproduction = {r:[0, 9999, -9999] for r in sad_rules.res_list}\n\tdemand = {r:[0, 9999, -9999] for r in sad_rules.res_list}\n\twealth = [0, 9999, -9999]\n\t\n\tcity_count = len(city_dict.keys())\n\tcity_size = sum([c.size for i, c in city_dict.items()])/city_count\n\t\n\t# First work out demand\n\tfor city_id, the_city in city_dict.items():\n\t\tsad_rules.produce_wealth(the_world, the_city)\n\t\twealth[0] += the_city.wealth\n\t\twealth[1] = min(wealth[1], the_city.wealth)\n\t\twealth[2] = max(wealth[2], the_city.wealth)\n\t\tthe_city.satisfied = False\n\t\t\n\t\tfor r in sad_rules.res_list:\n\t\t\td = sad_rules.demand[r](the_world, the_city, the_world._teams[the_city.team])\n\t\t\tdemand[r][0] += d\n\t\t\tdemand[r][1] = min(d, demand[r][1])\n\t\t\tdemand[r][2] = max(d, demand[r][2])\n\t\t\tthe_city.goods[r] = -d\n\t\t\n\t\tif the_city.goods[\"Linen\"] > the_city.goods[\"Wool\"]:\n\t\t\tthe_city.wool_is_nice = True\n\t\t\n\t\tcity_supply = sad_rules.res_list[the_city.supply_good]\n\t\t\n\t\tp = sad_rules.supply[city_supply](the_world, the_city, the_world._teams[the_city.team])\n\t\tproduction[city_supply][0] += p\n\t\tproduction[city_supply][1] = min(p, production[city_supply][1])\n\t\tproduction[city_supply][2] = max(p, production[city_supply][2])\n\t\tthe_city.goods[city_supply] += p\n\t\t# print(sad_rules.supply[\"Grain\"](the_city, the_world._teams[the_city.team].tech_levels), sad_rules.demand[\"Grain\"](the_city, the_world._teams[the_city.team].tech_levels))\n\t\n\tpre_report = generate_sad_pre_report(the_world)\n\t\n\t# Now begin to satisfy it\n\tsatisfied = False\n\tbatches = 0\n\twhile not satisfied:\n\t\tsad_batch(the_world)\n\t\t\n\t\tsatisfied = True\n\t\tfor city_id, the_city in city_dict.items():\n\t\t\tif not the_city.satisfied and the_city.wealth > 0:\n\t\t\t\tsatisfied = False\n\t\t\n\t\t# Force a breakout\n\t\tbatches += 1\n\t\tconfig['hops'] = min(config['hops'] + 1, config['max hops'])\n\t\tif batches > config['batches']:\n\t\t\tsatisfied = True\n\t\n\tpost_report = generate_sad_post_report(the_world)\n\t\n\t# print(pre_report['res_surplus']['Grain'])\n\t\n\treturn {\n\t\t\"pre\": pre_report,\n\t\t\"post\": post_report,\n\t\t\"production\": production,\n\t\t\"demand\": demand,\n\t\t\"wealth\": wealth,\n\t}\n\ndef print_reports(the_world, report_dict, reports=['res_summary', 'production', 'demand', 'wealth']):\n\tcity_dict = the_world.cities()\n\t\n\tcity_count = len(city_dict.keys())\n\tcity_size = sum([c.size for i, c in city_dict.items()])/city_count\n\t\n\tif 'res_summary' in reports:\n\t\tprint(print_res_dict(report_dict['pre']['res_summary'],\t\tthe_world, \"''Summary''\"), \"\\n\")\n\t\n\tif 'res_surplus' in reports:\n\t\tprint(print_res_dict(report_dict['pre']['res_surplus'],\t\tthe_world, \"''Surplus''\"), \"\\n\")\n\t\n\tif 'res_demand' in reports:\n\t\tprint(print_res_dict(report_dict['pre']['res_demand'],\t\tthe_world, \"''Demand''\"), \"\\n\")\n\t\n\tif 'res_producers' in reports:\n\t\tprint(print_res_dict(report_dict['pre']['res_producers'],\tthe_world, \"''Producers'' (out of %d)\" % city_count), \"\\n\")\n\t\n\tksize = city_size/1000\n\t\n\tif 'production' in reports:\n\t\tproduction = report_dict['production']\n\t\tprint(database.shell_text(\"''Production'' Min Max Avg Ratio\"))\n\t\tfor r in sad_rules.res_list:\n\t\t\tavg = production[r][0]/city_count\n\t\t\n\t\t\tprint(\"{res:17} {min:6} {max:6} {avg:6} {ratio:6}\".format(\n\t\t\t\tres = r,\n\t\t\t\ttotal = production[r][0],\n\t\t\t\tmin = round(production[r][1], 2),\n\t\t\t\tmax = round(production[r][2], 2),\n\t\t\t\tavg = round(avg, 2),\n\t\t\t\tratio = round(avg/ksize, 2),\n\t\t\t))\n\t\tprint(\"\")\n\t\n\tif 'demand' in reports:\n\t\tdemand = report_dict['demand']\n\t\t\n\t\tprint(database.shell_text(\"\\n''Demand'' Min Max Avg Ratio\"))\n\t\tfor r in sad_rules.res_list:\n\t\t\tavg = demand[r][0]/city_count\n\t\t\n\t\t\tprint(\"{res:17} {min:6} {max:6} {avg:6} {ratio:6}\".format(\n\t\t\t\tres = r,\n\t\t\t\ttotal = demand[r][0],\n\t\t\t\tmin = round(demand[r][1], 2),\n\t\t\t\tmax = round(demand[r][2], 2),\n\t\t\t\tavg = round(avg, 2),\n\t\t\t\tratio = round(avg/ksize, 2),\n\t\t\t))\n\t\n\tif 'wealth' in reports:\n\t\twealth = report_dict['wealth']\n\t\tavg = wealth[0]/city_count\n\t\t\n\t\tprint(database.shell_text(\"\\n''Wealth''\\n Min Max Avg Ratio\"))\n\t\tprint(\"{min:6} {max:6} {avg:6} {ratio:6}\".format(\n\t\t\ttotal = wealth[0],\n\t\t\tmin = round(wealth[1], 2),\n\t\t\tmax = round(wealth[2], 2),\n\t\t\tavg = round(avg, 2),\n\t\t\tratio = round(avg/ksize, 2),\n\t\t))\n\t\n\t# Attempt at ratios\n\tif 'ratios' in reports:\n\t\tprint(database.shell_text(\"\\n''Ratios''\"))\n\t\t# print(report_dict['pre'].keys())\n\t\tproduction = report_dict['production']\n\t\tdemand = report_dict['demand']\n\t\tfor r in sad_rules.res_list:\n\t\t\tprint(\"{res:17} {ratio:6}\".format(\n\t\t\t\tres = r,\n\t\t\t\tratio = round(production[r][0]/demand[r][0], 2),\n\t\t\t))\n\ndef print_res_dict(res_dict, the_world, title=\"\", one_line=False):\n\tcity_dict = the_world.live_cities()\n\toutput = []\n\t\n\tif title != \"\":\n\t\toutput.append(database.shell_text(title))\n\t\n\tfor r in sad_rules.res_list:\n\t\toutput.append(\"{0}: {1} avg: {2}\".format(r, res_dict[r], round(res_dict[r]/len(the_world.live_cities()), 2)))\n\t\n\treturn \"\\n\".join(output)\n\ndef generate_sad_pre_report(the_world):\n\t\"\"\"Runs through the cities of the world and finds out how many of them have XYZ\"\"\"\n\tcity_dict = the_world.live_cities()\n\t\n\tres_summary\t\t= {r:0 for r in sad_rules.res_list}\n\tres_surplus\t\t= {r:0 for r in sad_rules.res_list}\n\tres_demand\t\t= {r:0 for r in sad_rules.res_list}\n\tres_producers\t= {r:0 for r in sad_rules.res_list}\n\t\n\tfor city_id, the_city in city_dict.items():\n\t\tres_producers[sad_rules.res_list[the_city.supply_good]] += 1\n\t\tfor r in sad_rules.res_list:\n\t\t\tamount = the_city.goods[r]\n\t\t\t\n\t\t\tif amount > 0:\n\t\t\t\tres_surplus[r] += amount\n\t\t\telse:\n\t\t\t\tres_demand[r] -= amount\n\t\t\tres_summary[r] += amount\n\t\n\treturn {\n\t\t\"res_producers\":\tres_producers,\n\t\t\"res_summary\":\t\tres_summary,\n\t\t\"res_surplus\":\t\tres_surplus,\n\t\t\"res_demand\":\t\tres_demand,\n\t}\n\ndef generate_sad_post_report(the_world):\n\t\"\"\"Runs through the cities of the world and finds out how many of them have XYZ\"\"\"\n\tcity_dict = the_world.live_cities()\n\t\n\treturn {}\n\n","sub_path":"functions/sad_f copy 9-11.py","file_name":"sad_f copy 9-11.py","file_ext":"py","file_size_in_byte":17478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"591483406","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nfrom datetime import datetime\nimport discord\nimport os\nimport urllib.parse\n\nimport emojis\nimport excel\nimport pss_assert\nimport pss_core as core\nimport pss_fleet as fleet\nimport pss_login as login\nimport pss_lookups as lookups\nimport pss_tournament as tourney\nimport pss_user as user\nimport settings\nimport utility as util\n\n\n# ---------- Constants ----------\n\nFLEET_KEY_NAME = 'AllianceId'\nFLEET_DESCRIPTION_PROPERTY_NAME = 'AllianceName'\n\nFLEET_SHEET_COLUMN_NAMES = [\n 'Timestamp',\n 'Fleet',\n 'Player name',\n 'Rank',\n 'Last Login Date',\n 'Trophies',\n 'Stars',\n 'Join Date',\n 'Logged in ago',\n 'Joined ago'\n]\nFLEET_SHEET_COLUMN_TYPES = [\n settings.EXCEL_COLUMN_FORMAT_DATETIME,\n None,\n None,\n None,\n settings.EXCEL_COLUMN_FORMAT_DATETIME,\n settings.EXCEL_COLUMN_FORMAT_NUMBER,\n settings.EXCEL_COLUMN_FORMAT_NUMBER,\n settings.EXCEL_COLUMN_FORMAT_DATETIME,\n None,\n None\n]\n\n\n\n\n\n\n\n\n# ---------- Helper functions ----------\n\nasync def _get_fleet_details_by_info(fleet_info: dict, fleet_users_infos: dict) -> list:\n fleet_info = await _get_fleet_info_by_id(fleet_info[FLEET_KEY_NAME])\n\n division_design_id = fleet_info['DivisionDesignId']\n fleet_name = fleet_info[FLEET_DESCRIPTION_PROPERTY_NAME]\n fleet_description = fleet_info['AllianceDescription'].strip()\n member_count = int(fleet_info['NumberOfMembers'])\n min_trophy_required = fleet_info['MinTrophyRequired']\n ranking = util.get_ranking(fleet_info['Ranking'])\n requires_approval = fleet_info['RequiresApproval'].lower() == 'true'\n stars = int(fleet_info['Score'])\n trophies = sum([int(user_info['Trophy']) for user_info in fleet_users_infos.values()])\n\n if requires_approval:\n fleet_type = 'Private'\n else:\n fleet_type = 'Public'\n division = lookups.DIVISION_DESIGN_ID_TO_CHAR[division_design_id]\n\n lines = [f'**```{fleet_name}```**```']\n if fleet_description:\n lines.append(f'{fleet_description}``````')\n lines.append(f'Ranking - {ranking}')\n lines.append(f'Min trophies - {min_trophy_required}')\n lines.append(f'Members - {member_count}')\n lines.append(f'Trophies - {util.get_reduced_number_compact(trophies)}')\n if division != '-':\n lines.append(f'Division - {division}')\n lines.append(f'Stars - {util.get_reduced_number_compact(stars)}')\n lines.append(f'Type - {fleet_type}')\n\n lines[-1] += '```'\n\n return lines\n\n\nasync def _get_fleet_info_by_name(fleet_name: str, exact: bool = True):\n fleet_infos = await _get_fleet_infos_by_name(fleet_name)\n if exact:\n for fleet_info in fleet_infos.values():\n if fleet_info[FLEET_DESCRIPTION_PROPERTY_NAME] == fleet_name:\n return fleet_info\n if fleet_infos:\n return fleet_infos[0]\n else:\n return None\n\n\nasync def _get_fleet_info_from_tournament_data(fleet_info: dict, fleet_users_infos: dict, fleet_data: dict) -> list:\n fleet_id = fleet_info[FLEET_KEY_NAME]\n if fleet_id in fleet_data.keys():\n fleet_info['Score'] = fleet_data[fleet_id]['Score']\n return await _get_fleet_details_by_info(fleet_info, fleet_users_infos)\n\n\ndef _get_fleet_sheet_lines(fleet_users_infos: dict, retrieval_date: datetime, fleet_name: str = None) -> list:\n result = [FLEET_SHEET_COLUMN_NAMES]\n\n for user_info in fleet_users_infos.values():\n logged_in_ago = retrieval_date - util.parse_pss_datetime(user_info['LastLoginDate'])\n joined_ago = retrieval_date - util.parse_pss_datetime(user_info['AllianceJoinDate'])\n if fleet_name is None and FLEET_DESCRIPTION_PROPERTY_NAME in user_info.keys():\n fleet_name = user_info[FLEET_DESCRIPTION_PROPERTY_NAME]\n line = [\n util.format_excel_datetime(retrieval_date),\n fleet_name,\n user_info[user.USER_DESCRIPTION_PROPERTY_NAME],\n user_info['AllianceMembership'],\n util.convert_pss_timestamp_to_excel(user_info['LastLoginDate']),\n int(user_info['Trophy']),\n int(user_info['AllianceScore']),\n util.convert_pss_timestamp_to_excel(user_info['AllianceJoinDate']),\n util.get_formatted_timedelta(logged_in_ago, include_relative_indicator=False),\n util.get_formatted_timedelta(joined_ago, include_relative_indicator=False)\n ]\n result.append(line)\n return result\n\n\nasync def get_full_fleet_info_as_text(fleet_info: dict, fleet_data: dict = None, user_data: dict = None, data_date: datetime = None) -> (list, list):\n \"\"\"Returns a list of lines for the post, as well as the path to the spreadsheet created\"\"\"\n fleet_name = fleet_info[FLEET_DESCRIPTION_PROPERTY_NAME]\n fleet_id = fleet_info[FLEET_KEY_NAME]\n retrieval_date = util.get_utcnow()\n fleet_users_infos = await _get_fleet_users_by_id(fleet_id)\n if fleet_users_infos:\n fleet_info = list(fleet_users_infos.values())[0]['Alliance']\n else:\n fleet_info = await _get_fleet_info_by_name(fleet_name)\n\n post_content = await _get_fleet_details_by_info(fleet_info, fleet_users_infos)\n fleet_sheet_contents = _get_fleet_sheet_lines(fleet_users_infos, retrieval_date)\n fleet_sheet_path_current = excel.create_xl_from_data(fleet_sheet_contents, fleet_name, retrieval_date, FLEET_SHEET_COLUMN_TYPES)\n file_paths = [fleet_sheet_path_current]\n\n if fleet_data and fleet_id in fleet_data.keys() and user_data and data_date:\n fleet_info = fleet_data[fleet_id]\n fleet_name = fleet_info[fleet.FLEET_DESCRIPTION_PROPERTY_NAME]\n fleet_users_infos = dict({user_id: user_info for user_id, user_info in user_data.items() if user_info['AllianceId'] == fleet_id})\n fleet_sheet_contents = _get_fleet_sheet_lines(fleet_users_infos, data_date, fleet_name)\n file_name = f'{fleet_name}_tournament-{data_date.year}-{util.get_month_short_name(data_date).lower()}.xlsx'\n fleet_sheet_path_past = excel.create_xl_from_data(fleet_sheet_contents, fleet_name, data_date, FLEET_SHEET_COLUMN_TYPES, file_name=file_name)\n file_paths.append(fleet_sheet_path_past)\n\n return post_content, file_paths\n\n\nasync def _get_search_fleets_base_path(fleet_name: str) -> str:\n access_token = await login.DEVICES.get_access_token()\n result = f'AllianceService/SearchAlliances?accessToken={access_token}&skip=0&take=100&name={util.url_escape(fleet_name)}'\n return result\n\n\nasync def _get_get_alliance_base_path(fleet_id: str) -> str:\n access_token = await login.DEVICES.get_access_token()\n result = f'AllianceService/GetAlliance?accessToken={access_token}&allianceId={fleet_id}'\n return result\n\n\nasync def _get_search_fleet_users_base_path(fleet_id: str) -> str:\n access_token = await login.DEVICES.get_access_token()\n result = f'AllianceService/ListUsers?accessToken={access_token}&skip=0&take=100&allianceId={fleet_id}'\n return result\n\n\n\n\n\n\n\n\n\n\n# ---------- Alliance info ----------\n\nasync def get_fleet_details_by_name(fleet_name: str, as_embed: bool = settings.USE_EMBEDS) -> list:\n pss_assert.valid_parameter_value(fleet_name, 'fleet_name', min_length=0)\n\n fleet_infos = list((await _get_fleet_infos_by_name(fleet_name)).values())\n return fleet_infos\n\n\ndef get_fleet_search_details(fleet_info: dict) -> str:\n fleet_name = fleet_info[FLEET_DESCRIPTION_PROPERTY_NAME]\n fleet_trophies = fleet_info['Trophy']\n fleet_stars = fleet_info['Score']\n fleet_division = int(fleet_info['DivisionDesignId'])\n trophies = f' {emojis.trophy} {fleet_trophies}'\n if fleet_division > 0:\n stars = f' {emojis.star} {fleet_stars}'\n else:\n stars = ''\n result = f'{fleet_name}{trophies}{stars}'\n return result\n\n\nasync def _get_fleet_info_by_id(fleet_id: str) -> dict:\n path = await _get_get_alliance_base_path(fleet_id)\n fleet_data_raw = await core.get_data_from_path(path)\n fleet_info = core.xmltree_to_dict3(fleet_data_raw)\n return fleet_info\n\n\nasync def _get_fleet_infos_by_name(fleet_name: str) -> dict:\n path = await _get_search_fleets_base_path(fleet_name)\n fleet_data_raw = await core.get_data_from_path(path)\n fleet_infos = core.xmltree_to_dict3(fleet_data_raw)\n return fleet_infos\n\n\nasync def _get_fleet_users_by_id(alliance_id: str) -> dict:\n path = await _get_search_fleet_users_base_path(alliance_id)\n fleet_users_data_raw = await core.get_data_from_path(path)\n fleet_users_infos = core.xmltree_to_dict3(fleet_users_data_raw)\n return fleet_users_infos\n\n\nasync def get_fleet_users_by_info(fleet_info: dict) -> dict:\n fleet_id = fleet_info[FLEET_KEY_NAME]\n return await _get_fleet_users_by_id(fleet_id)\n\n\n\n\n\n\n\n\n\n\n# ---------- Stars ----------\n\ndef get_fleet_users_stars_from_info(fleet_info: dict, fleet_users_infos: dict, retrieved_date: datetime = None) -> list:\n fleet_name = fleet_info[FLEET_DESCRIPTION_PROPERTY_NAME]\n division = lookups.DIVISION_DESIGN_ID_TO_CHAR[fleet_info['DivisionDesignId']]\n\n fleet_users_infos = util.sort_entities_by(list(fleet_users_infos.values()), [('AllianceScore', int, True), (user.USER_KEY_NAME, int, False)])\n fleet_users_infos_count = len(fleet_users_infos)\n\n lines = [f'**{fleet_name} member stars (division {division})**']\n for i, user_info in enumerate(fleet_users_infos, 1):\n stars = user_info['AllianceScore']\n user_name = util.escape_markdown(user_info[user.USER_DESCRIPTION_PROPERTY_NAME])\n if i < fleet_users_infos_count:\n difference = int(user_info['AllianceScore']) - int(fleet_users_infos[i]['AllianceScore'])\n else:\n difference = 0\n lines.append(f'**{i}.** {stars} (+{difference}) {emojis.star} {user_name}')\n\n if retrieved_date is not None:\n lines.append(util.get_historic_data_note(retrieved_date))\n\n return lines\n\n\ndef get_fleet_users_stars_from_tournament_data(fleet_info: dict, fleet_data: dict, user_data: dict, retrieved_date: datetime) -> list:\n fleet_id = fleet_info[FLEET_KEY_NAME]\n fleet_users_infos = {}\n if fleet_id in fleet_data.keys():\n fleet_info['DivisionDesignId'] = fleet_data[fleet_id]['DivisionDesignId']\n fleet_users_infos = dict({user_info[user.USER_KEY_NAME]: user_info for user_info in user_data.values() if user_info[FLEET_KEY_NAME] == fleet_id})\n return get_fleet_users_stars_from_info(fleet_info, fleet_users_infos, retrieved_date=retrieved_date)\n\n\n\n\n\n\n\n\n\n\n\n\n# ---------- Testing ----------\n\n#if __name__ == '__main__':\n# test_fleets = ['Fallen An']\n# for fleet_name in test_fleets:\n# os.system('clear')\n# is_tourney_running = tourney.is_tourney_running()\n# fleet_infos = await get_fleet_details_by_name(fleet_name)\n# lines = [get_fleet_search_details(fleet_info) for fleet_info in fleet_infos]\n# for line in lines:\n# print(line)\n","sub_path":"src/pss_fleet.py","file_name":"pss_fleet.py","file_ext":"py","file_size_in_byte":10837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"386942418","text":"print('Intorduceti un cuvant palindrom')\ncheck_word = input()\n\n\nwhile True:\n letters_list = list(check_word)\n #print(letters_list)\n reverse_letters = letters_list[::-1]\n palindrom = '' .join(reverse_letters)\n #print(palindrom)\n if check_word == palindrom:\n print(check_word, 'este un palindrom')\n break\n else:\n print(check_word, 'nu este un palindrom')\n print('Adauga alt cuvant')\n check_word = input()\n\n\n\n","sub_path":"polindrom.py","file_name":"polindrom.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"286785825","text":"# Import standard libraries.\nimport math\nimport tempfile\nimport warnings\nimport numbers\n\n# Import external libraries.\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom matplotlib.patches import Patch\nimport seaborn as sns\nimport skbio as sb\nfrom skbio.stats.ordination import OrdinationResults\nfrom scipy import stats\nfrom scipy.spatial.distance import euclidean\nfrom skbio.stats.composition import clr\n\n# Import QIIME 2 libraries.\nimport qiime2\nfrom qiime2 import Artifact\nfrom qiime2 import Metadata\nfrom qiime2 import Visualization\nimport dokdo\n\ndef taxa_cols(df):\n \"\"\"Returns metadata columns from DataFrame object.\"\"\"\n cols = []\n for col in df.columns:\n if 'Unassigned' in col:\n cols.append(col)\n elif '__' in col:\n cols.append(col)\n else:\n continue\n return cols\n\ndef _get_mf_cols(df):\n \"\"\"Returns metadata columns from DataFrame object.\"\"\"\n cols = []\n for column in df.columns:\n if 'Unassigned' in column:\n continue\n elif '__' in column:\n continue\n else:\n cols.append(column)\n return cols\n\ndef _filter_samples(df, mf, exclude_samples, include_samples):\n \"\"\"Returns DataFrame objects after sample filtering.\"\"\"\n if exclude_samples and include_samples:\n m = (\"Cannot use 'exclude_samples' and \"\n \"'include_samples' arguments together\")\n raise ValueError(m)\n elif exclude_samples:\n for x in exclude_samples:\n for y in exclude_samples[x]:\n i = mf[x] != y\n df = df.loc[i]\n mf = mf.loc[i]\n elif include_samples:\n for x in include_samples:\n i = mf[x].isin(include_samples[x])\n df = df.loc[i]\n mf = mf.loc[i]\n else:\n pass\n return (df, mf)\n\ndef _sort_by_mean(df):\n \"\"\"Returns DataFrame object after sorting taxa by mean relative abundance.\"\"\"\n a = df.div(df.sum(axis=1), axis=0)\n a = a.loc[:, a.mean().sort_values(ascending=False).index]\n return df[a.columns]\n\ndef _pretty_taxa(s):\n \"\"\"Returns pretty taxa name.\"\"\"\n if isinstance(s, matplotlib.text.Text):\n s = s.get_text()\n ranks = list(reversed(s.split(';')))\n\n for i, rank in enumerate(ranks):\n if rank in ['Others', 'Unassigned']:\n return rank\n\n if rank == '__':\n continue\n\n if rank.split('__')[1] is '':\n continue\n\n if 'uncultured' in rank:\n continue\n\n # The species name can be sometimes tricky to parse because it could\n # be full (e.g. Helicobacter pylori) or partial (e.g. pylori). In the\n # latter case, I will borrow the genus name (e.g. Helicobacter) to\n # form the full species name.\n if 's__' in rank:\n rank = rank.split('__')[1]\n\n if len(rank.split('_')) == 1:\n genus = ranks[i+1].split('__')[1].split('_')[0]\n species = rank.split('_')[0]\n rank = f'{genus} {species}'\n else:\n rank = rank.replace('_', ' ')\n\n if '__' in rank:\n rank = rank.split('__')[1]\n\n return rank\n\ndef _artist(\n ax, title=None, title_fontsize=None, xlabel=None, xlabel_fontsize=None,\n ylabel=None, ylabel_fontsize=None, zlabel=None, zlabel_fontsize=None,\n xticks=None, yticks=None, xticklabels=None, xticklabels_fontsize=None,\n yticklabels=None, yticklabels_fontsize=None, xrot=None, xha=None,\n xmin=None, xmax=None, ymin=None, ymax=None, xlog=False, ylog=False,\n hide_xtexts=False, hide_ytexts=False, hide_ztexts=False,\n hide_xlabel=False, hide_ylabel=False, hide_zlabel=False,\n hide_xticks=False, hide_yticks=False, hide_zticks=False,\n hide_xticklabels=False, hide_yticklabels=False, hide_zticklabels=False,\n show_legend=False, legend_loc='best', legend_ncol=1,\n legend_labels=None, remove_duplicates=False, legend_only=False,\n legend_fontsize=None, legend_markerscale=None, legend_lw=None,\n legend_title=None, plot_method=None\n):\n \"\"\"\n This method controls various properties of a figure.\n\n Parameters\n ----------\n ax : matplotlib.axes.Axes\n Axes object to draw the plot onto.\n title : str, optional\n Sets the figure title.\n title_fontsize : float or str, optional\n Sets the title font size.\n xlabel : str, optional\n Set the x-axis label.\n xlabel_fontsize : float or str, optional\n Sets the x-axis label font size.\n ylabel : str, optional\n Set the y-axis label.\n ylabel_fontsize : float or str, optional\n Sets the y-axis label font size.\n zlabel : str, optional\n Set the z-axis label.\n zlabel_fontsize : float or str, optional\n Sets the z-axis label font size.\n xticks : list, optional\n Positions of x-axis ticks.\n yticks : list, optional\n Positions of y-axis ticks.\n xticklabels : list, optional\n Tick labels for the x-axis.\n xticklabels_fontsize : float or str, optional\n Font size for the x-axis tick labels.\n yticklabels : list, optional\n Tick labels for the y-axis.\n yticklabels_fontsize : float or str, optional\n Font size for the y-axis tick labels.\n xrot : float, optional\n Rotation degree of tick labels for the x-axis.\n xha : str, optional\n Horizontal alignment of tick labels for the x-axis.\n xmin : float, optional\n Minimum value for the x-axis.\n xmax : float, optional\n Maximum value for the x-axis.\n ymin : float, optional\n Minimum value for the y-axis.\n ymax : float, optional\n Maximum value for the x-axis.\n xlog : bool, default: False\n Draw the x-axis in log scale.\n ylog : bool, default: False\n Draw the y-axis in log scale.\n hide_xtexts : bool, default: False\n Hides all the x-axis texts.\n hide_ytexts : bool, default: False\n Hides all the y-axis texts.\n hide_ztexts : bool, default: False\n Hides all the z-axis texts.\n hide_xlabel : bool, default: False\n Hides the x-axis label.\n hide_ylabel : bool, default: False\n Hides the y-axis label.\n hide_zlabel : bool, default: False\n Hides the z-axis label.\n hide_xticks : bool, default: False\n Hides ticks and tick labels for the x-axis.\n hide_yticks : bool, default: False\n Hides ticks and tick labels for the y-axis.\n hide_zticks : bool, default: False\n Hides ticks and tick labels for the z-axis.\n hide_xticklabels : bool, default: False\n Hides tick labels for the x-axis.\n hide_yticklabels : bool, default: False\n Hides tick labels for the y-axis.\n hide_zticklabels : bool, default: False\n Hides tick labels for the z-axis.\n show_legend : bool, default: False\n Show the figure legend.\n legend_loc : str, default: 'best'\n Legend location specified as in matplotlib.pyplot.legend.\n legend_ncol : int, default: 1\n Number of columns that the legend has.\n legend_only : bool, default: False\n Clear the figure and display the legend only.\n legend_fontsize : float or str, optional\n Sets the legend font size.\n legend_markerscale : float, optional\n Relative size of legend markers compared with the original.\n legend_lw : float, optional\n Width of the lines in the legend.\n legend_title: str, optional\n Legend title.\n plot_method : str, optional\n Name of the plotting method. This argument is internally used for\n the `alpha_rarefaction_plot` method. Not to be used by users.\n\n Returns\n -------\n matplotlib.axes.Axes\n Axes object with the plot drawn onto it.\n\n Notes\n -----\n Font size can be specified by provding a number or a string as defined in:\n {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}.\n \"\"\"\n if isinstance(title, str):\n ax.set_title(title, fontsize=title_fontsize)\n\n if isinstance(xlabel, str):\n ax.set_xlabel(xlabel, fontsize=xlabel_fontsize)\n\n if isinstance(ylabel, str):\n ax.set_ylabel(ylabel, fontsize=ylabel_fontsize)\n\n if isinstance(zlabel, str):\n ax.set_zlabel(zlabel, fontsize=zlabel_fontsize)\n\n if isinstance(xticks, list):\n ax.set_xticks(xticks)\n\n if isinstance(yticks, list):\n ax.set_yticks(yticks)\n\n if isinstance(xticklabels, list):\n a = len(ax.get_xticklabels())\n b = len(xticklabels)\n if a != b:\n raise ValueError(f\"Expected {a} items, but found {b}\")\n ax.set_xticklabels(xticklabels)\n\n if xticklabels_fontsize is not None:\n ax.tick_params(axis='x', which='major', labelsize=xticklabels_fontsize)\n\n if isinstance(yticklabels, list):\n a = len(ax.get_yticklabels())\n b = len(yticklabels)\n if a != b:\n raise ValueError(f\"Expected {a} items, but found {b}\")\n ax.set_yticklabels(yticklabels)\n\n if yticklabels_fontsize is not None:\n ax.tick_params(axis='y', which='major', labelsize=yticklabels_fontsize)\n\n if isinstance(xrot, numbers.Number):\n ax.set_xticklabels(ax.get_xticklabels(), rotation=xrot)\n\n if isinstance(xha, str):\n ax.set_xticklabels(ax.get_xticklabels(), ha=xha)\n\n ax.set_xlim(left=xmin, right=xmax)\n ax.set_ylim(bottom=ymin, top=ymax)\n\n if xlog:\n ax.set_xscale('log')\n\n if ylog:\n ax.set_yscale('log')\n\n if hide_xtexts:\n ax.set_xlabel('')\n ax.set_xticklabels([])\n\n if hide_ytexts:\n ax.set_ylabel('')\n ax.set_yticklabels([])\n\n if hide_ztexts:\n ax.set_zlabel('')\n ax.set_zticklabels([])\n\n if hide_xlabel:\n ax.set_xlabel('')\n\n if hide_ylabel:\n ax.set_ylabel('')\n\n if hide_zlabel:\n ax.set_zlabel('')\n\n if hide_xticks:\n ax.set_xticks([])\n\n if hide_yticks:\n ax.set_yticks([])\n\n if hide_zticks:\n ax.set_zticks([])\n\n if hide_xticklabels:\n ax.set_xticklabels([])\n\n if hide_yticklabels:\n ax.set_yticklabels([])\n\n # Control the figure legend.\n h, l = ax.get_legend_handles_labels()\n\n if legend_labels:\n a = len(legend_labels)\n b = len(l)\n if a != b:\n m = f\"Expected {b} legend labels, received {a}\"\n raise ValueError(m)\n l = legend_labels\n\n if remove_duplicates:\n if h:\n n = int(len(h) / 2)\n h, l = h[:n], l[:n]\n\n def _display_legend():\n leg = ax.legend(h, l, loc=legend_loc, ncol=legend_ncol,\n fontsize=legend_fontsize, markerscale=legend_markerscale,\n title=legend_title, title_fontsize=legend_fontsize)\n\n if plot_method == 'alpha_rarefaction_plot':\n i = 1\n else:\n i = 0\n\n if legend_lw is not None:\n for lh in leg.legendHandles[i:]:\n lh.set_linewidth(legend_lw)\n\n if legend_only:\n # The order matters.\n ax.clear()\n _display_legend()\n ax.axis('off')\n elif show_legend:\n if h:\n _display_legend()\n else:\n warnings.warn(\"No handles with labels found to put in legend.\")\n else:\n if ax.get_legend():\n ax.get_legend().remove()\n else:\n pass\n\n return ax\n\ndef _get_others_col(df, count, taxa_names, show_others):\n \"\"\"Returns DataFrame object after selecting taxa.\"\"\"\n if count is not 0 and taxa_names is not None:\n m = \"Cannot use 'count' and 'taxa_names' arguments together\"\n raise ValueError(m)\n elif count is not 0:\n if count < df.shape[1]:\n others = df.iloc[:, count-1:].sum(axis=1)\n df = df.iloc[:, :count-1]\n if show_others:\n df = df.assign(Others=others)\n else:\n pass\n elif taxa_names is not None:\n others = df.drop(columns=taxa_names).sum(axis=1)\n df = df[taxa_names]\n if show_others:\n df = df.assign(Others=others)\n else:\n pass\n\n return df\n\ndef _parse_input(input, temp_dir):\n \"\"\"Parse the input QIIME 2 object and export the files.\"\"\"\n if isinstance(input, qiime2.Artifact):\n fn = f'{temp_dir}/dokdo-temporary.qza'\n input.save(fn)\n input = fn\n Artifact.load(input).export_data(temp_dir)\n elif isinstance(input, qiime2.Visualization):\n fn = f'{temp_dir}/dokdo-temporary.qzv'\n input.save(fn)\n input = fn\n Visualization.load(input).export_data(temp_dir)\n elif isinstance(input, str) and input.endswith('.qza'):\n Artifact.load(input).export_data(temp_dir)\n elif isinstance(input, str) and input.endswith('.qzv'):\n Visualization.load(input).export_data(temp_dir)\n else:\n pass\n\ndef export(input, temp_dir):\n \"\"\"\n Export QIIME 2 data as files to a temporary directory.\n\n This method will automatically detect the type of the input file or\n object from QIIME 2 and then export the underlying data as files to the\n specified temporary directory.\n\n Parameters\n ----------\n input : str, qiime2.Artifact, or qiime2.Visualization\n Path to the input file. Or QIIME 2 object.\n temp_dir : str\n Path to the temporary directory.\n \"\"\"\n if isinstance(input, qiime2.Artifact):\n fn = f'{temp_dir}/temp.qza'\n input.save(fn)\n input = fn\n Artifact.load(input).export_data(temp_dir)\n elif isinstance(input, qiime2.Visualization):\n fn = f'{temp_dir}/temp.qzv'\n input.save(fn)\n input = fn\n Visualization.load(input).export_data(temp_dir)\n elif isinstance(input, str) and input.endswith('.qza'):\n Artifact.load(input).export_data(temp_dir)\n elif isinstance(input, str) and input.endswith('.qzv'):\n Visualization.load(input).export_data(temp_dir)\n else:\n raise TypeError(f'Incorrect input type detected: {type(input)}.')\n","sub_path":"dokdo/api/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":14018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"548392493","text":"import platform\n\nfrom conan.tools.env import Environment\nfrom conans.client.graph.graph import CONTEXT_HOST\n\n\nclass VirtualEnv:\n \"\"\" captures the conanfile environment that is defined from its\n dependencies, and also from profiles\n \"\"\"\n\n def __init__(self, conanfile):\n self._conanfile = conanfile\n self._conanfile.virtualenv = False\n\n def build_environment(self):\n \"\"\" collects the buildtime information from dependencies. This is the typical use case\n of build_requires defining information for consumers\n \"\"\"\n build_env = Environment()\n # First visit the direct build_requires\n for build_require in self._conanfile.dependencies.build_requires:\n # Lower priority, the runenv of all transitive \"requires\" of the build requires\n for require in build_require.dependencies.requires:\n build_env.compose(self._collect_transitive_runenv(require))\n # Second, the implicit self information in build_require.cpp_info\n build_env.compose(self._runenv_from_cpp_info(build_require.cpp_info))\n # Finally, higher priority, explicit buildenv_info\n if build_require.buildenv_info:\n build_env.compose(build_require.buildenv_info)\n\n # Requires in host context can also bring some direct buildenv_info\n def _collect_transitive_buildenv(d):\n r = Environment()\n for child in d.dependencies.requires:\n r.compose(_collect_transitive_buildenv(child))\n # Then the explicit self\n if d.buildenv_info:\n r.compose(d.buildenv_info)\n return r\n for require in self._conanfile.dependencies.requires:\n build_env.compose(_collect_transitive_buildenv(require))\n\n # The profile environment has precedence, applied last\n profile_env = self._conanfile.buildenv\n build_env.compose(profile_env)\n return build_env\n\n @staticmethod\n def _runenv_from_cpp_info(cpp_info):\n \"\"\" return an Environment deducing the runtime information from a cpp_info\n \"\"\"\n dyn_runenv = Environment()\n if cpp_info is None: # This happens when the dependency is a private one = BINARY_SKIP\n return dyn_runenv\n if cpp_info.bin_paths: # cpp_info.exes is not defined yet\n dyn_runenv.prepend_path(\"PATH\", cpp_info.bin_paths)\n # If it is a build_require this will be the build-os, otherwise it will be the host-os\n if cpp_info.lib_paths:\n dyn_runenv.prepend_path(\"LD_LIBRARY_PATH\", cpp_info.lib_paths)\n dyn_runenv.prepend_path(\"DYLD_LIBRARY_PATH\", cpp_info.lib_paths)\n if cpp_info.framework_paths:\n dyn_runenv.prepend_path(\"DYLD_FRAMEWORK_PATH\", cpp_info.framework_paths)\n return dyn_runenv\n\n def _collect_transitive_runenv(self, d):\n r = Environment()\n for child in d.dependencies.requires:\n r.compose(self._collect_transitive_runenv(child))\n # Apply \"d\" runenv, first the implicit\n r.compose(self._runenv_from_cpp_info(d.cpp_info))\n # Then the explicit\n if d.runenv_info:\n r.compose(d.runenv_info)\n return r\n\n def run_environment(self):\n \"\"\" collects the runtime information from dependencies. For normal libraries should be\n very occasional\n \"\"\"\n runenv = Environment()\n # At the moment we are adding \"test-requires\" (build_requires in host context)\n # to the \"runenv\", but this will be investigated\n for build_require in self._conanfile.dependencies.build_requires:\n if build_require.context == CONTEXT_HOST:\n runenv.compose(self._collect_transitive_runenv(build_require))\n for require in self._conanfile.dependencies.requires:\n runenv.compose(self._collect_transitive_runenv(require))\n\n # FIXME: Missing profile info\n result = runenv\n return result\n\n def generate(self):\n build_env = self.build_environment()\n run_env = self.run_environment()\n # FIXME: Use settings, not platform Not always defined :(\n # os_ = self._conanfile.settings_build.get_safe(\"os\")\n if build_env: # Only if there is something defined\n if platform.system() == \"Windows\":\n build_env.save_bat(\"conanbuildenv.bat\")\n else:\n build_env.save_sh(\"conanbuildenv.sh\")\n if run_env:\n if platform.system() == \"Windows\":\n run_env.save_bat(\"conanrunenv.bat\")\n else:\n run_env.save_sh(\"conanrunenv.sh\")\n","sub_path":"conan/tools/env/virtualenv.py","file_name":"virtualenv.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"74816149","text":"#!/usr/bin/env python3.2\n\n# Tentative features for /r/singapore\n# monday: eat what\n# wednesday: buy what\n# friday: do what\n\n# to be ran with cron, since it's only needed on specific days of the week\n# to use: ./weeklysubmissions.py username password \n\nimport praw\nimport sys\nimport datetime\n\ndisclaimer = \"\"\" \n---\n^(this is the resident bot moderator of /r/singapore. if you have a feedback or\nan error report, please message /u/infinitus_ about it.)\n\"\"\"\n\nsubmissions = { \n'Monday': ('Eat what?', \"\"\"\nNo idea what to eat this week? Bored of your daily lunch routine? New cafe/restaurant/hawker discovery? Post them here.\n\nWhen posting, please:\n\n* Be nice to one another\n* Agree to disagree \n\nPhotographs are encouraged. Let the salivating begin. \n\"\"\" + disclaimer),\n\n'Wednesday': ('Buy what?', \"\"\"\nWhat else is there to do in Singapore other than eating? Shopping, of course.\n\nThis thread is for sharing the deals that you've found, and purchases you've made recently. Anything goes: Clothes, watches, cars, holiday packages, pets, video games, a new house. Online shopping included.\n\nWhen posting, please:\n\n* Be nice to one another\n* Agree to disagree \n\n\n\"\"\" + disclaimer),\n\n'Friday' : ('Go where?', \"\"\" \nThe weekends are here! Where are you going this weekend? What events are happening around this island that people should know about? Post them here. \n\nWhen posting, please:\n\n* Be nice to one another\n* Agree to disagree \n\n\n\"\"\" + disclaimer)\n}\n\n# Gets the day in the word form (Monday, Tuesday..)\ndef get_current_day_of_the_week():\n dt = datetime.datetime.now()\n return dt.strftime(\"%A\")\n\n# Gets the date in the format: [Day Month]\ndef get_date_tag():\n dt = datetime.datetime.now()\n day = dt.day\n month = dt.strftime(\"%B\")\n return \"[\" +str(numeral_to_ordinal(day)) + \" \" + month + \"]\"\n\n# Converts number (1, 2, 3..) to their ordinal form (1st, 2nd, 3rd..)\ndef numeral_to_ordinal(num): \n suffix = \"\"\n if (num == 11 or num == 12 or num == 13):\n suffix = \"th\"\n elif (num % 10 == 1): \n suffix = \"st\"\n elif (num % 10 == 2):\n suffix = \"nd\"\n elif (num % 10 == 3):\n suffix = \"rd\"\n else:\n suffix = \"th\"\n\n return str(num) + suffix \n\n# Posts the thread with predefined texts, depending on the day this method was\n# called on\ndef post_thread(r):\n today = get_current_day_of_the_week()\n\n TITLE = get_date_tag() + \" \" + submissions[today][0]\n TEXT = submissions[today][1]\n SUBREDDIT = \"\" # the name only: \"subredditname\", not \"/r/subredditname\"\n \n r.submit(SUBREDDIT, TITLE, text=TEXT)\n\ndef main():\n # get username and password from the command line arguments\n user = sys.argv[1]\n pwd = sys.argv[2]\n\n r = praw.Reddit(user_agent='jrb')\n r.login(user, pwd)\n\n post_thread(r)\n\nif __name__ == '__main__':\n main()\n","sub_path":"weeklysubmissions.py","file_name":"weeklysubmissions.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"415953466","text":"from src.custom_logger import Logger\nimport re\nimport json\n\nwith open('config/training_config.json') as f:\n config = json.loads(f.read())\n\nNUMBER_OF_COLUMNS = config['numberOfColumns']\nCOLUMN_NAMES = [name for name in config['columns']]\nCOLUMN_DATA_TYPES = config['columns']\nDATA_TYPE_DICT = {'object': object, 'float': float, \"int\": int}\n\nlogger = Logger()\nlog_type = 'file_validation'\n\n\nclass Validator:\n\n @staticmethod\n def validate_file_name(filename):\n \"\"\"\n Perform Filename and filetype Validation\n :param filename: Exact Name of the file with extension\n :return: True if Filename and type are valid, else raise Exception\n \"\"\"\n pattern = r'^wafer_[0-9]{8}_[0-9]{6}\\.csv$'\n try:\n if not re.match(pattern, str(filename).lower()):\n raise Exception('TRAINING_VALIDATION : Invalid Filename/Filetype' + ':' + str(filename))\n else:\n return True\n except Exception as e:\n logger.log_file_validation(str(e))\n return False\n\n @staticmethod\n def validate_number_of_columns(df, filename):\n \"\"\"\n Validate the number of columns in the dataset\n :param df: Pandas Dataframe\n :return: True if valid, else Raise Exception\n \"\"\"\n try:\n if not len(df.columns) == NUMBER_OF_COLUMNS:\n raise Exception('TRAINING_VALIDATION : Invalid Number of Columns in file : ' + str(filename))\n else:\n return True\n except Exception as e:\n logger.log_file_validation(str(e))\n return False\n\n @staticmethod\n def validate_name_of_columns(df, filename):\n columns = df.columns\n try:\n if columns.tolist() == COLUMN_NAMES:\n return True\n else:\n raise Exception('TRAINING_VALIDATION : Invalid Columns Names in file : ' + str(filename))\n except Exception as e:\n logger.log_file_validation(str(e))\n return False\n\n @staticmethod\n def validate_column_data_type(df, filename):\n columns = df.columns\n try:\n col_data_types = df.dtypes\n for index in col_data_types.index:\n if col_data_types[index] != DATA_TYPE_DICT[COLUMN_DATA_TYPES[index]]:\n raise Exception('TRAINING_VALIDATION : Invalid columns data type in file : {}'.format(filename))\n return True\n except Exception as e:\n logger.log_file_validation(str(e))\n return False\n\n @staticmethod\n def validate_null_columns(df, filename):\n try:\n if len(df.isnull().sum()[df.isnull().sum() == len(df)]) == 0:\n return True\n else:\n raise Exception('TRAINING_VALIDATION : All Column Values are null in file : {}'.format(filename))\n except Exception as e:\n logger.log_file_validation(str(e))\n return False\n\n\n\n","sub_path":"src/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"5147200","text":"'''\nCreated on Mar 20, 2012\n\n@author: SFUser\n'''\n# -*- coding: utf-8 -*-\nimport urllib.request\nfrom html.parser import HTMLParser\n\nurl = \"http://www.google.cn\"\nf = urllib.request.urlopen(url)\nsource = f.read().decode('cp936').encode('utf-8')\nparser = HTMLParser()\nparser.feed(source)\n","sub_path":"src/web/htmlparser1.py","file_name":"htmlparser1.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"143645992","text":"from unittest.mock import patch\n\nimport flask\nimport pytest\n\nfrom app import create_app\nfrom app.constants.error_message import MISSING_TOKEN, INVALID_TOKEN\nfrom app.utils.app_exception import AuthenticationException\nfrom app.utils.token import token_required, encode_token\n\n\ndef test_request_with_missing_access_token():\n with pytest.raises(AuthenticationException) as exc_info:\n mock_header_and_test_token(None)\n assert str(exc_info.value) == MISSING_TOKEN\n\n\ndef test_request_with_invalid_token():\n with pytest.raises(AuthenticationException) as exc_info:\n mock_header_and_test_token(f'Bearer iikshf92.oifhsfds.98dfdsfh')\n assert str(exc_info.value) == INVALID_TOKEN\n\n with pytest.raises(AuthenticationException) as exc_info:\n mock_header_and_test_token(f'iikshf92.oifhsfds.98dfdsfh')\n assert str(exc_info.value) == INVALID_TOKEN\n\n with pytest.raises(AuthenticationException) as exc_info:\n mock_header_and_test_token(f'Bearer iikshf92.oifhsfds 98dfdsfh')\n assert str(exc_info.value) == INVALID_TOKEN\n\n\ndef test_token_with_invalid_user():\n access_token = encode_token({\"id\": 3})\n with pytest.raises(AuthenticationException) as exc_info:\n mock_header_and_test_token(f'Bearer {access_token}')\n assert str(exc_info.value) == INVALID_TOKEN\n\n\ndef mock_header_and_test_token(access_token):\n with patch.object(flask, 'request') as request_mock:\n request_mock.headers.get.return_value = access_token\n with create_app().app_context():\n response = token_required(lambda value: (value, 200))()\n\n request_mock.headers.get.assert_called_once_with('Authorization')\n return response[1]\n","sub_path":"flask-photo-app-master/tests/utils/test_token.py","file_name":"test_token.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"326488485","text":"import sys\n'''\n概述：处理一个列表[]，将列表中的数据输出，可以规定是否缩进、缩进深度、输出到哪里\n参数说明\n\tthe_list:列表名称\n\tindent：遇到列表嵌套列表情况是否缩进\n\tlevel：初始缩进深度\n\tfh：输出到哪里，可以是文件或屏幕上\n'''\ndef print_lol(the_list,indent=False,level=0,fh=sys.stdout):\n\tfor i in the_list:\n\t\tif isinstance(i,list):\n\t\t\tprint_lol(i,indent,level+1,fh)\n\t\telse:\n\t\t\tif indent:\n\t\t\t\tfor tab_stop in range(level):\n\t\t\t\t\tprint(\"\\t\",end='',file=fh)\n\t\t\tprint(i,file=fh)\n# 测试\nmovies=['A',1975,'B',1991,['c',['d1','d2','d3','d4','d5']]]\nnester_file=open('neste_file.txt','w')\nprint_lol(movies,fh=nester_file)\n\n\n","sub_path":"pickle操作/nester.py","file_name":"nester.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"649772343","text":"#!/usr/bin/python3\n\"\"\"\n Starts a Flask web application to listen on 0.0.0.0:5000\n\"\"\"\n\nfrom flask import Flask, render_template\nfrom models import storage\n\n\napp = Flask(__name__)\napp.url_map.strict_slashes = False\n\n\n@app.route('/hbnb_filters')\ndef all_states():\n \"\"\"\n List of all states\n \"\"\"\n states = storage.all(\"State\")\n amenities = storage.all(\"Amenity\")\n context = {'states': states, 'amenities': amenities}\n return render_template('10-hbnb_filters.html', **context)\n\n\n@app.teardown_appcontext\ndef teardown_app(exception):\n \"\"\"\n Closing the storage\n \"\"\"\n storage.close()\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=5000)\n","sub_path":"web_flask/10-hbnb_filters.py","file_name":"10-hbnb_filters.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"435679640","text":"#%%\nimport random\nimport os\nfrom shutil import copy2\n#split_folders.ratio('custom_data/*', output=\"output\", seed=1337, ratio=(.8, .2))\n\n#%%\ninput_folder = './asm_data'\nout_folder = list(0 for i in range(3))\nout_folder[0] = './asm_data_alt'\t# Train\nout_folder[1] = './asm_data_val_alt'\t# Validation\nout_folder[2] = './asm_data_test_alt'\t# Test\nratio = [1.0, 0.0, 0.0] # Train : Val : Test\nassert sum(ratio) == 1.0, \"Sum of ratio not 1\"\n#%%\ndef safe_make_dir(path):\n\tif not os.path.exists(out_dir[i]):\n\t\tos.makedirs(out_dir[i])\n\nrandom.seed(1337) # For reproducability of results\nfor root, dirs, files in os.walk(input_folder):\n\tout_dir = list(0 for i in range(3))\n\tfor f in files:\n\t\tfile_name = os.path.join(root, f)\n\t\t#print(file_name)\n\t\ttmp = root.split(os.sep)\n\t\tfor i in range(3):\n\t\t\tout_dir[i] = os.path.join(out_folder[i], tmp[-2] + '_' + tmp[-1])\n\t\t\tprint(out_dir[i])\n\t\t\tsafe_make_dir(out_dir[i])\n\t\trand = random.uniform(0, 1)\n\t\tif rand >= ratio[1] + ratio[2]:\n\t\t\tcopy2(file_name, out_dir[0])\n\t\telif rand < ratio[1] + ratio[2] and rand > ratio[2]:\n\t\t\tcopy2(file_name, out_dir[1])\n\t\telse:\n\t\t\tcopy2(file_name, out_dir[2])\n\n#%%\n","sub_path":"procedure_2/pre_processing_2.py","file_name":"pre_processing_2.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"92477660","text":"# Modules\nimport argparse\nfrom math import ceil\nfrom math import floor\nfrom math import log\n\n\ndef is_none(param):\n if param is None:\n return True\n return False\n\n\ndef is_negative(param):\n if float(param) < 0:\n return True\n return False\n\n\ndef calculate_annuity(principal, periods, interest):\n if is_none(principal) or is_none(periods) or is_none(interest)\\\n or is_negative(principal) or is_negative(periods) or is_negative(interest):\n print(\"Incorrect parameters\")\n return\n\n p = float(principal)\n n = int(periods)\n i = float(interest) / (12 * 100)\n\n annuity_payment = p * ((i * pow(1 + i, n)) / (pow(1 + i, n) - 1))\n annuity_payment = ceil(annuity_payment)\n overpayment = (n * annuity_payment) - p\n\n print(f\"Your annuity payment = {annuity_payment}!\")\n print(f\"Overpayment = {round(overpayment)}\")\n\n\ndef calculate_annuity_periods(principal, payment, interest):\n if is_none(principal) or is_none(payment) or is_none(interest) \\\n or is_negative(principal) or is_negative(payment) or is_negative(interest):\n print(\"Incorrect parameters\")\n return\n\n p = float(principal)\n i = float(interest) / (12 * 100)\n pay = float(payment)\n\n n = log(pay / (pay - i * p), i + 1)\n n = ceil(n)\n overpayment = (n * pay) - p\n\n counts = divmod(n, 12)\n years = round(counts[0])\n months = ceil(counts[1])\n\n if months == 12:\n years += 1\n months = 0\n\n if years == 0:\n if months == 1:\n print(f\"It will take {months} month to repay this loan!\")\n else:\n print(f\"It will take {months} months to repay this loan!\")\n elif months == 0:\n if years == 1:\n print(f\"It will take {years} year to repay this loan!\")\n else:\n print(f\"It will take {years} years to repay this loan!\")\n else:\n print(f\"It will take {years} \", end=\"\")\n print(\"year\" if years == 1 else \"years\", end=\"\")\n print(f\" and {months} \", end=\"\")\n print(\"month\" if months == 1 else \"months\", end=\"\")\n print(\" to repay this loan!\")\n\n print(f\"Overpayment = {round(overpayment)}\")\n\n\ndef calculate_annuity_principal(periods, payment, interest):\n if is_none(periods) or is_none(payment) or is_none(interest) \\\n or is_negative(periods) or is_negative(payment) or is_negative(interest):\n print(\"Incorrect parameters\")\n return\n\n n = int(periods)\n i = float(interest) / (12 * 100)\n pay = float(payment)\n principal = pay / ((i * pow(1 + i, n)) / (pow(1 + i, n) - 1))\n principal = floor(principal)\n overpayment = (n * pay) - principal\n\n print(f\"Your loan principal = {principal}!\")\n print(f\"Overpayment = {round(overpayment)}\")\n\n\ndef calculate_diff(principal, periods, interest):\n if is_none(principal) or is_none(periods) or is_none(interest)\\\n or is_negative(principal) or is_negative(periods) or is_negative(interest):\n print(\"Incorrect parameters\")\n return\n\n p = float(principal)\n n = int(periods)\n i = float(interest) / (12 * 100)\n\n overpayment = p\n month = 1\n for _ in range(n):\n d = (p / n) + i * (p - ((p * (month - 1)) / n))\n d = ceil(d)\n print(f\"Month {month + 1}: payment is {d}\")\n month += 1\n overpayment -= d\n overpayment = -1 * overpayment\n print(f\"\\nOverpayment = {overpayment}\")\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--type\", help=\"type of loan\")\nparser.add_argument(\"--principal\", help=\"the initial loan amount\")\nparser.add_argument(\"--periods\", help=\"amount of months to make a payment\")\nparser.add_argument(\"--payment\", help=\"payment amount per period\")\nparser.add_argument(\"--interest\", help=\"the interest rate on the loan\")\n\n# Store input\nargs = parser.parse_args()\n\n# Check: Type\nif args.type == \"annuity\":\n if args.payment is None:\n calculate_annuity(principal=args.principal, periods=args.periods, interest=args.interest)\n elif args.principal is None:\n calculate_annuity_principal(periods=args.periods, payment=args.payment, interest=args.interest)\n elif args.periods is None:\n calculate_annuity_periods(principal=args.principal, payment=args.payment, interest=args.interest)\nelif args.type == \"diff\":\n if args.payment is not None:\n print(\"Incorrect parameters\")\n else:\n calculate_diff(principal=args.principal, periods=args.periods, interest=args.interest)\nelse:\n print(\"Incorrect parameters\")\n","sub_path":"Loan Calculator/task/creditcalc/creditcalc.py","file_name":"creditcalc.py","file_ext":"py","file_size_in_byte":4512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"552003535","text":"import torch\nimport torch.nn.functional as F\nfrom torchtext import data\nfrom torchtext import datasets\nimport time\nimport random\n\ntorch.backends.cudnn.deterministic = True\n\n# General Settings\n\nRANDOM_SEED = 123\ntorch.manual_seed(RANDOM_SEED)\n\nVOCABULARY_SIZE = 20000\nLEARNING_RATE = 1e-4\nBATCH_SIZE = 128\nNUM_EPOCHS = 15\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nEMBEDDING_DIM = 128\nHIDDEN_DIM = 256\nOUTPUT_DIM = 1\n\n# Dataset\nTEXT = data.Field(tokenize = 'spacy')\nLABEL = data.LabelField(dtype = torch.float)\n# LABEL = data.LabelField()\ntrain_data, test_data = datasets.IMDB.splits(TEXT, LABEL)\ntrain_data, valid_data = train_data.split(random_state=random.seed(RANDOM_SEED),\n split_ratio=0.8)\n\nprint(f'Num Train: {len(train_data)}')\nprint(f'Num Valid: {len(valid_data)}')\nprint(f'Num Test: {len(test_data)}')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"dl_model_pytorch/Recurrent Neural Networks (RNNs)/Many-to-one: Sentiment Analysis /0_Simple RNN.py","file_name":"0_Simple RNN.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"128392045","text":"# -*- coding: utf-8 -*-\n\n# This is a simple NN (multi-layer perceptron) for testing TF on MNIST dataset.\n# Code copied from the book 'TensorFlow: 实战 Google 深度学习框架', with minor modifications.\n#\n# Author: Hao Wu, hwuu@outlook.com\n\nimport argparse\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n#\n# MNIST数据集相关的常数。\n#\n\nN_NODES_INPUT = 784 # 输入层的节点数。对于MNIST数据集，这个就等于图片的像素。\nN_NODES_OUTPUT = 10 # 输出层的节点数。这个等于类别的数目。因为在MNIST数据集中\n # 需要区分的是0~9这10个数字，所以这里输出层的节点数为10。\n\n#\n# 配置神经网络的参数。\n#\n\nN_NODES_LAYER1 = 500 # 隐藏层节点数。这里使用只有一个隐藏层的网络结构作为样例。\n # 这个隐藏层有500个节点。\nBATCH_SIZE = 100 # 一个训练batch中的训练数据个数。数字越小时，训练过程越接近\n # 随机梯度下降；数��越大时，训练越接近梯度下降。\nLEARNING_RATE = 0.5 # 学习率。\nTRAINING_STEPS = 30000 # 训练轮数。\n\n# 一个辅助函数，给定神经网络的输入和所有参数，计算神经网络的前向传播结果。在这里\n# 定义了一个使用ReLU激活函数的三层全连接神经网络。通过加入隐藏层实现了多层网络结构，\n# 通过ReLU激活函数实现了去线性化。\ndef inference(input_tensor, weights1, biases1, weights2, biases2):\n # 计算隐藏层的前向传播结果，这里使用了ReLU激活函数。\n layer1 = \\\n tf.nn.relu(\n tf.matmul(input_tensor, weights1) + \\\n biases1\n )\n # 计算输出层的前向传播结果。因为在计算损失函数时会一并计算softmax函数， \n # 所以这里不需要加入激活函数。而且不加入softmax不会影响预测结果。因为预测时\n # 使用的是不同类别对应节点输出值的相对大小，有没有softmax层对最后分类结果的\n # 计算没有影响。于是在计算整个神经网络的前向传播时可以不加入最后的softmax层。\n return \\\n tf.matmul(layer1, weights2) + \\\n biases2\n\n# 训练模型的过程。\ndef train(mnist):\n x = tf.placeholder(tf.float32, [None, N_NODES_INPUT], name='x-input')\n y_ = tf.placeholder(tf.float32, [None, N_NODES_OUTPUT], name='y-input')\n\n # 生成隐藏层的参数。\n weights1 = tf.Variable(\n tf.truncated_normal([N_NODES_INPUT, N_NODES_LAYER1], stddev=0.1))\n biases1 = tf.Variable(tf.constant(0.1, shape=[N_NODES_LAYER1]))\n # 生成输出层的参数。\n weights2 = tf.Variable(\n tf.truncated_normal([N_NODES_LAYER1, N_NODES_OUTPUT], stddev=0.1))\n biases2 = tf.Variable(tf.constant(0.1, shape=[N_NODES_OUTPUT]))\n\n # 计算在当前参数下神经网络前向传播的结果。\n y = inference(x, weights1, biases1, weights2, biases2)\n\n # 定义存储训练轮数的变量。这个变量不需要计算滑动平均值，所以这里指定这个变量为\n # 不可训练的变量(trainable=Fasle)。在使用TensorFlow训练神经网络时， \n # 一般会将代表训练轮数的变量指定为不可训练的参数。 \n global_step = tf.Variable(0, trainable=False)\n\n # 计算交叉熵作为刻画预测值和真实值之间差距的损失函数。这里使用了TensorFlow中提\n # 供的sparse_softmax_cross_entropy_with_logits函数来计算交叉熵。当分类\n # 问题只有一个正确答案时，可以使用这个函数来加速交叉熵的计算。MNIST问题的图片中\n # 只包含了0~9中的一个数字，所以可以使用这个函数来计算交叉熵损失。这个函数的第一个\n # 参数是神经网络不包括softmax层的前向传播结果，第二个是训练数据的正确答案。因为\n # 标准答案是一个长度为10的一维数组，而该函数需要提供的是一个正确答案的数字，所以需\n # 要使用tf.argmax函数来得到正确答案对应的类别编号。\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=tf.argmax(y_, 1), logits=y)\n # 计算在当前batch中所有样例的交叉熵平均值。\n cross_entropy_mean = tf.reduce_mean(cross_entropy)\n # 总损失等于交叉熵损失。\n loss = cross_entropy_mean\n # 使用tf.train.GradientDescentOptimizer优化算法来优化损失函数。\n train_step = \\\n tf.train.GradientDescentOptimizer(LEARNING_RATE) \\\n .minimize(loss, global_step=global_step)\n\n # 检验神经网络前向传播结果是否正确。tf.argmax(y, 1)\n # 计算每一个样例的预测答案。其中y是一个batch_size * 10的二维数组，每一行\n # 表示一个样例的前向传播结果。tf.argmax的第二个参数“1”表示选取最大值的操作仅在第一\n # 个维度中进行，也就是说，只在每一行选取最大值对应的下标。于是得到的结果是一个长度为\n # batch的一维数组，这个一维数组中的值就表示了每一个样例对应的数字识别结果。tf.equal\n # 判断两个张量的每一维是否相等，如果相等返回True，否则返回False。\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n # 这个运算首先将一个布尔型的数值转换为实数型，然后计算平均值。这个平均值就是模型在这\n # 一组数据上的正确率。\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n # 初始化会话并开始训练过程。\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n # 准备验证数据。一般在神经网络的训练过程中会通过验证数据来大致判断停止的\n # 条件和评判训练的效果。\n validate_feed = {\n x: mnist.validation.images, \n y_: mnist.validation.labels\n }\n\n # 准备测试数据。在真实的应用中，这部分数据在训练时是��可见的，这个数据只是作为模\n # 型优劣的最后评价标准。\n test_feed = {\n x: mnist.test.images,\n y_: mnist.test.labels\n } \n\n # 迭代地训练神经网络。\n for i in range(TRAINING_STEPS):\n # 产生这一轮使用的一个batch的训练数据，并运行训练过程。\n xs, ys = mnist.train.next_batch(BATCH_SIZE)\n _, loss_val = sess.run([train_step, loss], feed_dict={x: xs, y_: ys})\n # 每1000轮输出一次在验证数据集上的测试结果。\n if i % 1000 == 0:\n # 计算滑动平均模型在验证数据上的结果。因为MNIST数据集比较小，所以一次\n # 可以处理所有的验证数据。为了计算方便，本样例程序没有将验证数据划分为更\n # 小的batch。当神经网络模型比较复杂或者验证数据比较大时，太大的batch\n # 会导致计算时间过长甚至发生内存溢出的错误。\n validate_acc = sess.run(accuracy, feed_dict=validate_feed)\n print(\"After %d training step(s), validation accuracy \"\n \"using average model is %g (loss = %g)\" % (i, validate_acc, loss_val))\n\n\n # 在训练结束之后，在测试数据上检测神经网络模型的最终正确率。\n test_acc = sess.run(accuracy, feed_dict=test_feed)\n print(\"After %d training step(s), test accuracy using average \"\n \"model is %g\" % (TRAINING_STEPS, test_acc))\n\n# Parameters from command line.\nargs_ = None\n\n# 主程序入口。\ndef main(_):\n # 声明处理MNIST数据集的类，这个类在初始化时会自动下载数据。\n mnist = input_data.read_data_sets(args_.data_dir, one_hot=True)\n train(mnist)\n\n# TensorFlow提供的一个主程序入口，tf.app.run会调用上面定义的main函数。\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Launch TensorFlow for training.')\n parser.add_argument(\"--data-dir\", default=\"/tmp/data\",\n help='Data folder. MNIST data files will be downloaded if they do not exist.')\n args_ = parser.parse_args()\n tf.app.run()\n","sub_path":"mnist/mnist-mlp.py","file_name":"mnist-mlp.py","file_ext":"py","file_size_in_byte":8372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"134153194","text":"import requests\nimport http.client, urllib\nimport os\nimport glob\nimport time\nimport argparse\nfrom time import sleep\n#!/usr/bin/env python3\nimport subprocess\n\nos.system('modprobe w1-gpio')\nos.system('modprobe w1-therm')\n \nbase_dir = '/sys/bus/w1/devices/'\ndevice_folder = glob.glob(base_dir + '28*')[0]\ndevice_file = device_folder + '/w1_slave'\n \ndef read_temp_raw():\n f = open(device_file, 'r')\n lines = f.readlines()\n f.close()\n return lines\n \ndef read_temp():\n lines = read_temp_raw()\n while lines[0].strip()[-3:] != 'YES':\n time.sleep(0.2)\n lines = read_temp_raw()\n equals_pos = lines[1].find('t=')\n if equals_pos != -1:\n temp_string = lines[1][equals_pos+2:]\n temp_c = float(temp_string) / 1000.0\n return temp_c\n\n\n\ncar_on = True\ntemp = read_temp \ni = 0\nwhile car_on == True:\n i += 1\n #change the number after >= to change temperature that alert the phone at.\n if read_temp() >= 0:\n python3_command = \"amg88xx_still.py\"\n process = subprocess.Popen(python3_command.split(), stdout=subprocess.PIPE)\n output, error = process.communicate() # receive output from the python2 script\n r = requests.post(\"https://api.pushover.net/1/messages.json\", data={\n \"token\":\"aajaiutjvx1fwdqy15g2nqcp6av8dt\",\n \"user\":\"usqmx1mv4pams23f4nq8igh5a7okzf\",\n \"message\":\"Your vehicle has reached a DANGEROUS temperature. Please return to your vehicle.\"}, \n files={\"attachment\":open(\"/home/pi/Desktop/amg88xx_still.jpg\",\"rb\")})\n exit()\n","sub_path":"Edward_alert.py","file_name":"Edward_alert.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"214485758","text":"import sqlite3\nimport xmlrpclib,SimpleXMLRPCServer\n\nconn = sqlite3.connect('/data/issduser.db')\nc = conn.cursor()\n# Create table\n#c.execute('''create table count\n#(INT cnt)''')\ncount = 0\n\nclass Synchandler():\n\tdef add(self):\n\t\tc.execute('insert into count values (?)', count)\n\t\tconn.commit()\n\t\tcount += 1\n\t\nsynchandler = Synchandler()\nserver = SimpleXMLRPCServer.SimpleXMLRPCServer((\"192.168.0.201\",5678))\nserver.register_instance(synchandler)\nserver.serve_forever()\n\n","sub_path":"python/jsonrpc/test_rpc.py","file_name":"test_rpc.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"214167851","text":"import math\nimport re\nimport random\n#石头剪刀布游戏\ndef shitou():\n guess_list = [\"石头\", \"剪刀\", \"布\"]\n win_combination = [[\"布\", \"石头\"], [\"石头\", \"剪刀\"], [\"剪刀\", \"布\"]]\n\n while True:\n computer = random.choice(guess_list)\n people = input('请输入：石头,剪刀,布\\n').strip()\n if people not in guess_list:\n continue\n elif computer == people:\n print (\"平手，再玩一次！\")\n elif [computer, people] in win_combination:\n print (\"电脑获胜，再玩，人获胜才能退出！\")\n else:\n print (\"人获胜！\")\n break\nshitou()\n\n# 3.打印出所有的\"水仙花数\"，所谓\"水仙花数\"是指一个三位数，\n# 其各位数字立方和等于该数本身。\n# 例如：153是一个\"水仙花数\"，因为153=1的三次方＋5的三次方＋3的三次方。\ndef shuixianhua():\n for i in range(100,999):\n a=str(i)\n if math.pow(int(a[0]),3)+math.pow(int(a[1]),3)+math.pow(int(a[2]),3)==int(i):\n print(int(a))\n\ndef shuixianhua2():\n for i in range(100, 999):\n a=i%10\n b=int((i%100-a)/10)\n c=int((i%1000-b)/100)\n if math.pow(a,3)+math.pow(b,3)+math.pow(c,3)==i:\n print(i)\n\n# 4.输入一行字符，分别统计出其中英文字母、空格、数字和其它字符的个数。\ndef four():\n asd=input();\n chinazhenze = re.compile(\"[\\u4ea0-\\u9fa5a-zA-Z].*?\")\n kongge = re.compile(\" .*?\")\n shuzi = re.compile(\"[0-9].*?\")\n qitazifu = re.compile(\"[`~!@#$^&*()=|{}':;',\\\\[\\\\].<>/?~！@#￥……&*（）——|{}【】‘；：”“'。，、？].*?\")\n china = chinazhenze.findall(asd)\n konggea = kongge.findall(asd)\n shuzia = shuzi.findall(asd)\n qitazifua = qitazifu.findall(asd)\n print(len(china))\n print(len(konggea))\n print(len(shuzia))\n print(len(qitazifua))\n#s输出匹配的字符\n # print(china)\n # print(konggea)\n # print(shuzia)\n # print(qitazifua)\n","sub_path":"Based on practice/three.py","file_name":"three.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"293856831","text":"#!/usr/bin/python\nimport logging\nimport sys\n \nclass StreamToLogger(object):\n \"\"\"\n Fake file-like stream object that redirects writes to a logger instance.\n \"\"\"\n def __init__(self, logger, log_level=logging.INFO):\n self.logger = logger\n self.log_level = log_level\n self.linebuf = ''\n \n def write(self, buf):\n for line in buf.rstrip().splitlines():\n self.logger.log(self.log_level, line.rstrip())\n\nstdout_logger = logging.getLogger('STDOUT')\nsl = StreamToLogger(stdout_logger, logging.INFO)\nsys.stdout = sl\n \nstderr_logger = logging.getLogger('STDERR')\nsl = StreamToLogger(stderr_logger, logging.ERROR)\nsys.stderr = sl","sub_path":"setupLogging.py","file_name":"setupLogging.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"544644473","text":"import sys\nimport quandl\nimport json\n#import calendar\n#import pandas as pd\nimport lib.settings as STNGS\nfrom dateutil import rrule\nfrom datetime import datetime\nfrom calendar import monthrange\nimport re\n\n#set apikey for authentication against quandl\nquandl.ApiConfig.api_key = STNGS.api_Key\n\nstartDate = STNGS.startDate\nendDate = STNGS.endDate\ntickers = STNGS.tickers\n\n\"\"\"\nfunction to perform quandl query for data retrieval\n :param ticker: the ticker symbol for which data is being fetched\n :param startDate: the start date for a query\n :param endDate: the end date for a query\n :return datatable containing the ticker symbol, the date, and the open and close prices for the given date range\n\"\"\"\ndef dataQuery(ticker, startDate, endDate):\n return quandl.get_table('WIKI/PRICES', qopts={\"columns\":[\"ticker\",\"date\",\"open\",\"close\"]},ticker=ticker, date = {'gte': startDate, 'lte':endDate})\n\n\n\"\"\"\nfunction to print the results\n :param dataResults: expects a properly formatted json style object - {key:value} \n\"\"\"\ndef prettyPrint(dataResults):\n print(json.dumps(dataResults, indent=3))\n\n\n\"\"\"\nfunction to check date\n :param aDate: date to check - expect yyyy-mm-dd format\n :return boolean: True if valid date, False otherwise\n\"\"\"\ndef isDate(aDate):\n try:\n #ensure date is in yyyy-mm-dd format\n if re.match('\\d{4}-\\d{2}-\\d{2}', aDate):\n return True\n\n except ValueError:\n return False\n\n return False\n\n\n\"\"\"\nfunction to determine which security had the most days where the closing price was lower than the opening price\n :param startDate: date to start data processing - expect yyyy-mm-dd format\n :param endDate: date to end data processing - expect yyyy-mm-dd format\n :param tickers: an array of tickers to fetch data and perform calculations on - expect ['tick1','tick2',...'tickx']\n\n :return tickerCalculations: a python dictionary of tickers with calculated lists\n of month, average_open, average_close values in format\n {'ticker':[{'month':'yyyy-dd','average_open':dd.dddd,'average_close':dd.dddd}]}\n\"\"\"\ndef biggestLoser(startDate, endDate, tickers):\n #loop through tickers, perform calculations, return \n print(\"Fetching data and calculating the biggest loser\")\n lostMost = {\"symbol\": None,\"totalDays\":0}\n for ticker in tickers:\n query = dataQuery(ticker, startDate, endDate)\n difference = (query.close.sub(query.open)).tolist()\n negativeDays = sum(1 for number in difference if number < 0)\n if (negativeDays > lostMost[\"totalDays\"]):\n lostMost[\"symbol\"] = ticker\n lostMost[\"totalDays\"] = negativeDays\n\n return lostMost;\n\n\n\n\"\"\"\nfunction calculating average open and average close by month for given list of stock tickers\n :param startDate: date to start data processing - expect yyyy-mm-dd format\n :param endDate: date to end data processing - expect yyyy-mm-dd format\n :param tickers: an array of tickers to fetch data and perform calculations on - expect ['tick1','tick2',...'tickx']\n\n :return tickerCalculations: a python dictionary of tickers with calculated lists\n of month, average_open, average_close values in format\n {'ticker':[{'month':'yyyy-dd','average_open':dd.dddd,'average_close':dd.dddd}]}\n\"\"\"\ndef tickerProcessing(startDate, endDate, tickers):\n tickerCalculations = {}\n\n print('Fetching data and calculating open and close averages...')\n for ticker in tickers:\n aList = []\n #using the dateutil rrule module loop from a start date to end date by month\n for dt in rrule.rrule(rrule.MONTHLY, dtstart=startDate, until=endDate):\n year = dt.strftime(\"%Y\")\n month = dt.strftime(\"%m\")\n daysInMonth = str(monthrange(int(year),int(dt.strftime(\"%m\")))[1])\n firstDateMonth = year + \"-\" + month + \"-\" \"1\"\n lastDateMonth = year + \"-\" + month + \"-\" + daysInMonth \n\n query = dataQuery(ticker, firstDateMonth, lastDateMonth)\n dataResults = {}\n dataResults[\"month\"] = year + \"-\" + month\n dataResults[\"average_open\"] = sum(query.open)/len(query.open)\n dataResults[\"average_close\"] = sum(query.close)/len(query.close)\n\n aList.append(dataResults)\n\n tickerCalculations[ticker] = aList\n\n return tickerCalculations;\n\n\"\"\"\ndisplay a help menu for users\n\"\"\"\ndef helpMenu():\n print(\n '''\n Coding Challenge:\n Usage:\n 1) python main.py #executes the program and runs the default tickerProcessing function\n 2) python main.py -biggest-loser #executes the program and runs the biggestLoser function\n 3) python main.py --help #displays this menu; additional flags to bring up the menu are -h or -H\n '''\n )\n\n\n\"\"\"\nmain execution\n\"\"\"\n#check to see if arguments are being passed\n#if not set args to execute the main function\n#otherwise set the args variable to the arguments passed by the user\n#(argv[0] is the file path, and thus is ignored)\nargs = sys.argv[1:] if len(sys.argv) > 1 else ['executeMain']\n\n#before doing anything else we check if help is being sought\n#if so display the help menu and end the program\nif ('--help' in args) or ('-h' in args) or ('-H' in args):\n helpMenu()\n sys.exit()\n\n#we made it here, so we are not asking for help and have executed the program successfully\n#loop through the args to determine if the biggest-loser argument was passed, or if we are running the default program\nfor x in args:\n print(\"Argument: \", x)\n\n #dataValidation (ensure dates properly formatted and tickers is a list)\n if (isDate(startDate) is False) or (isDate(endDate) is False):\n print(\"Invalid date provided: yyyy-mm-dd\")\n elif type(tickers) is not list:\n print(\"The provided tickers input is not a list: ['str1','str2',...,'strx']\")\n else:\n startDate = datetime.strptime(startDate, '%Y-%m-%d')\n endDate = datetime.strptime(endDate, '%Y-%m-%d')\n tickers = tickers\n\n print('Processing...')\n\n if (x == 'executeMain'):\n prettyPrint(tickerProcessing(startDate, endDate, tickers));\n elif (x == '-biggest-loser'):\n prettyPrint(biggestLoser(startDate, endDate, tickers));\n else:\n #invalid arguments likely passed\n #exit and return to the prompt for another go\n print(\n '''\n Passed arguments invalid...\n For usage please type:\n python main.py --help\n '''\n )\n sys.exit()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"16162923","text":"\"\"\" TD PART III :\r\n#################################################################\r\n ANALYSIS AND HYPOTHESIS WITH A GENOME SCALE METABOLIC MODEL\r\nFrom the given functions, the information you got about the cobra language,\r\nand the result of previous TD part, try to answer the following questions.\r\nfiles: practical_metabolic_model.pdf\r\n\r\nFor a better understanding, you can also open the file function.py to see how some functions are built.\r\nDuring this tutorial you will use object oriented language.\r\n\"\"\"\r\n######A\r\n# 1- Load module:\r\nfrom functions import *\r\n\r\n#2- Dowload and import e_coli_core model:\r\n\r\n#3 - Print information about the model:\r\n\r\n#4- Launch the flux balance analysis without changing any parameter:\r\n\r\n#5- Print the objective value. Was it what you expected? \r\n\r\n#6- Print fluxes. How many reactions are active in this condition?\r\n\r\n#7 From the fluxes, define which metabolites are consume and produce by the model?\r\n#Verify your result with the summary function:\r\n\r\n\r\n#8- print the summary of one of these metabolites to see how it's produced and consumed:\r\n\r\n#9- Verify if the model can grow without oxygen:\r\n## Create a new model to keep initial state\r\n\r\n## Set upper bound and lower bound:\r\n\r\n## Launch fba\r\n\r\n## Conclude\r\n\r\n#10- Verify if the model can use other carbon sources : lactate or formate.\r\n## Create a copy of the model\r\n\r\n## Close the model\r\n\r\n## Change bound to allow entry of the target metabolite and verify the change is done:\r\n\r\n#11- It is possible to produce acetate and to grow? change the objective function (growth min 0.20):\r\n\r\n## Change the coefficient for ac from 1 to 0.5 for example. What do you observe?\r\n\r\n#######B\r\n#Let's look all of this in an entire model;\r\n\"\"\" In biggest model is more difficult to interpret something. Lets see if we can answer to some\r\nquestion using different functions.\r\n\"\"\"\r\n\r\n\"\"\" I - You know that you will receive a Saccharomyces cerevisiae strain that you have to grow.\r\nYou have 3 different media. Which one will you use to be sure that the strain will grow?\"\"\"\r\nMedium1 = ['ac_e','ade_e','ala__L_e','arg__L_e','asn__L_e','asp__L_e','ca2_e',\r\n 'cit_e','cys_L_e','man_e','gln__L_e','glu__L_e','h2o_e','his__L_e',\r\n 'ile__L_e','mg2_e','nad_e','nh4_e','nac_e','pi_e','pro__L_e','ser__L_e',\r\n 'so4_e','try__L_e','tyr__L_e','trp__L_e','h2_e','o2_e' ]\r\nMedium2 = ['ac_e','ade_e','ala__L_e','arg__L_e','asn__L_e','asp__L_e','ca2_e',\r\n 'cit_e','cu2_e','cys_L_e','fe2_e','fe3_e','fru_e','glc__D_e',\r\n 'gln__L_e','glu__L_e','h2o_e','his__L_e','ile__L_e','mg2_e','nad_e',\r\n 'nh4_e','nac_e','no3_e','pi_e','pro__L_e','ser__L_e','so4_e','try__L_e',\r\n 'tyr__L_e','ura_e','val__L_e','trp__L_e','h2_e','o2_e' ]\r\nMedium3 = ['4abz_e','ac_e','ace_e','ade_e','ala__L_e','arg__L_e','ascb__L_e','asn__L_e',\r\n 'asp__L_e','btn_e','cbl1_e','cit_e','cl_e','cobalt2_e',\r\n 'cu2_e','cys_L_e','fe2_e','fe3_e','fol_e','glc__D_e','glu__L_e','gly_e','gthrd_e',\r\n 'gua_e','h2o_e','hco3_e','his__L_e','i_e','ile__L_e','inost_e','k_e','leu_L_e','met__L_e','co2_e','no2_e',\r\n 'mg2_e','mn2_e','mndn_e','mobd_e','na1_e','nad_e','nh4_e','nac_e','ni2_e',\r\n 'no3_e','pi_e','pnto__R_e','pro__L_e','pydam_e','pydxn_e','rezrn_e','ribflv_e',\r\n 'ser__L_e','so4_e','thci_e','thm_e','try__L_e','tyr__L_e','ura_e','val__L_e',\r\n 'xan_e','zn2_e','h_e','trp__L_e', 'mobd_e','slnt_e', 'tungs_e','lipoate_e',\r\n 'pydx_e','o2_e' ]\r\n\r\n# Download one model from bigg database \r\n\r\n# Verify that the model can grow without changing any parameter:\r\n\r\n# Verify how the model can grow in each medium and conclude:\r\n\r\n\r\n\r\n\"\"\" II- You work for an agribusiness company specialized in yoghurt production.\r\nIn, purpose to improve the taste of some product, you want to produce the molecule RR 2 3 Butanediol which gave the butter/ creamy taste.\r\nYour company whant a biological way to produce this molecule instead of chemical.\r\nyou have 4 species in your bacteria collection:\r\n - Saccharomyces cerevisiae\r\n - Lactococcus lactis\r\n - Klebsiella pneumoniae\r\nCan you tell which species is the most likely to produce this molecule? \"\"\"\r\n\r\n# Find molecule id on Bigg database / its exchange reaction associated\r\n\r\n# Load the 3 models\r\n\r\n\r\n# Run following function for 'lactococcus lactis':\r\n#%%% = updateLactococcus(%%%)\r\n\r\n# Verify that models can grow:\r\n\r\n#for each model find biomass function position or name\r\n\r\n#=> Choice 1 :\r\n#- Update the biomass bound (>0)\r\n#- Change the objective function\r\n#- Run FBA\r\n\r\n#=> Choice 2 :\r\n#- Change the objective function with both reactions\r\n#- Run FBA\r\n\r\n\r\n# What can you conclude?\r\n\r\n\r\n# Did you check if models could spontaneously produce the molecule?\r\n\r\n\r\n\r\n","sub_path":"Python_R_Scripts/III-Anaysis.py","file_name":"III-Anaysis.py","file_ext":"py","file_size_in_byte":4804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"538871295","text":"#import numpy as np\r\nimport random\r\n\r\nimport numpy as np\r\n\r\n# Finite-State, Finite-Action Markov Decission Process\r\nclass FiniteMDP:\r\n def __init__(self, transitions, rewards, discount_factor):\r\n self.model = transitions\r\n self.rewards = rewards\r\n self.discount_factor = discount_factor\r\n\r\n def next_state(self, curr_state, action):\r\n next_states_transitions = self.model[curr_state][action]\r\n\r\n states = []\r\n probs = []\r\n for next_state, prob in next_states_transitions.items():\r\n states.append(next_state)\r\n probs.append(prob)\r\n sample = np.random.random_sample(1)[0]\r\n s = 0\r\n for i in range(len(probs)):\r\n if sample < s + probs[i]:\r\n return states[i]\r\n s = s + probs[i]\r\n\r\n def qvalue_iteration(self, num_iter=100, iteration_callback=None):\r\n q_value = {}\r\n v_value = {} # This max_a{q_value(s, a)}. This is used for caching in order to reduce running complexity.\r\n # Initialize q_values and v_values to 0\r\n\r\n for state, action_dict in self.model.items():\r\n state_actions_values = {}\r\n for action in action_dict.keys():\r\n state_actions_values[action] = 0\r\n v_value[state] = 0\r\n q_value[state] = state_actions_values\r\n \r\n # Initialize random policy\r\n #random.seed(a=42)\r\n if iteration_callback is not None:\r\n prev_policy = {}\r\n for state, action_dict in q_value.items():\r\n prev_policy[state] = random.choice(list(action_dict.keys()))\r\n\r\n for t in range(num_iter):\r\n prev_q_value = q_value\r\n for state, action_dict in self.model.items():\r\n for action, new_state_dict in action_dict.items():\r\n expected_v_next = 0\r\n for new_state, prob in new_state_dict.items(): #prob is P(state, action, new_state)\r\n if new_state is not None: # Not terminal state\r\n expected_v_next = expected_v_next + (prob * v_value[new_state])\r\n # Update Q-Value by Bellman Equation\r\n q_value[state][action] = self.rewards[state] + self.discount_factor*expected_v_next\r\n\r\n # Update v-values for next iteration outside of nested loops above.\r\n for state, action_dict in q_value.items():\r\n v_value[state] = action_dict[max(action_dict, key=action_dict.get)]\r\n\r\n if iteration_callback is not None:\r\n curr_policy = prev_policy\r\n for state, action_dict in q_value.items():\r\n best_action = max(action_dict, key=action_dict.get)\r\n if action_dict[best_action] > prev_q_value[state][prev_policy[state]]:\r\n curr_policy[state] = best_action\r\n iteration_callback(t, curr_policy, q_value, v_value)\r\n prev_policy = curr_policy\r\n\r\n # Compute final optimal policy\r\n policy = {}\r\n for state, action_dict in q_value.items():\r\n policy[state] = max(action_dict, key=action_dict.get)\r\n return (policy, q_value, v_value)\r\n\r\n\r\n","sub_path":"mlp/reinforcement/environment/fmdp.py","file_name":"fmdp.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"415672835","text":"#! /usr/bin/env python3\n\nimport numpy,pandas as pd,math,string\n\n\nclass container:\n class data:\n master=None\n def maxStorage(self):\n number='0'*10\n bytesPerLine=10\n phoneNumberLen=len(number)\n phoneNumberDigits=len(string.digits)\n maxPhoneNumbers=math.pow(phoneNumberLen,phoneNumberDigits)\n maxStorage=maxPhoneNumbers*bytesPerLine\n return maxStorage\n\n class processing:\n master=None\n def engineering(self,num):\n if 1000 > num > 0:\n #b\n return str(num)+\"b\".upper()\n elif 1000**2 > num > 1000**1: \n #k\n return str(num/(1000**1))+\"kb\".upper()\n elif 1000**3 > num > 1000**2:\n #m\n return str(num/(1000**2))+\"mb\".upper()\n elif 1000**4 > num > 1000**3:\n #g\n return str(num/(1000**3))+\"gb\".upper()\n elif 1000**5 > num > 1000**4:\n #t\n return str(num/(1000**4))+\"tb\".upper()\n elif 1000**6 > num > 1000**5:\n #p\n return str(num/(1000**5))+\"pb\".upper()\n class tasks:\n #this is where the work should be done\n #so as to not pollute the assembler\n master=None\n def run(self):\n maxStorage=self.master.data.maxStorage()\n result=self.master.processing.engineering(maxStorage)\n print(result)\n\n class void:\n master=None\n\n def assembler(self):\n #this is just to put all objects in the work area wa\n wa=self.void()\n wa.master=wa\n\n wa.data=self.data()\n wa.data.master=wa\n\n wa.processing=self.processing()\n wa.processing.master=wa\n\n wa.tasks=self.tasks()\n wa.tasks.master=wa\n wa.tasks.run()\n\ncont=container()\ncont.assembler()\n","sub_path":"classing-python3/phoneNumbers-calc.py","file_name":"phoneNumbers-calc.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"91002593","text":"#!/usr/bin/env python\nimport re\nfrom itertools import combinations_with_replacement as all_combinations\nfrom collections import Counter\n\npuzzleinput = 'day15.txt'\n\nproperties = ['capacity', 'durability', 'flavor', 'texture', 'calories']\nteaspoons = 100\ncalories_required = 500\n\nregex = r'(.*):.{10}(-?\\d+),.{12}(-?\\d+),.{8}(-?\\d+),.{9}(-?\\d+),.{10}(-?\\d+)'\n\ndef score(recipe):\n property_tally = dict(zip(properties,[0] * len(properties)))\n for item in recipe:\n for i in ingredients_list:\n if i['name'] == item[0]:\n for p in properties:\n property_tally[p] += item[1] * i[p]\n calorie_target_met = property_tally['calories'] == calories_required\n score = 1\n for p in property_tally:\n if p != 'calories':\n if property_tally[p] > 0:\n score *= property_tally[p]\n else:\n return (0, False)\n return score, calorie_target_met\n\ningredients_list = list()\nrecipes_list = list()\nbest_ignoring_calories = 0\nbest_calorie_target = 0\n\nwith open(puzzleinput) as f:\n for ingredients_text in f:\n i = list(re.match(regex, ingredients_text).groups())\n i = [i[0], int(i[1]), int(i[2]), int(i[3]), int(i[4]), int(i[5])]\n ingredient = dict(zip(['name'] + properties, i))\n ingredients_list.append(ingredient)\n\ningredient_names = [i['name'] for i in ingredients_list]\nall_ingredient_combinations = all_combinations(ingredient_names, teaspoons)\nfor combination in all_ingredient_combinations:\n recipes_list.append(Counter(combination).items())\n\nfor recipe in recipes_list:\n current_score, calorie_target_met = score(recipe)\n best_ignoring_calories = max(current_score, best_ignoring_calories)\n if calorie_target_met:\n best_calorie_target = max(current_score, best_calorie_target)\nprint('part 1: %d' % best_ignoring_calories)\nprint('part 2: %d' % best_calorie_target)\n","sub_path":"day15.py","file_name":"day15.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"79917893","text":"from django import http\nfrom django.utils.encoding import smart_unicode as u\n\nfrom olympia.amo.helpers import page_title\n\nfrom . import get_service\nfrom .forms import ShareForm\n\n\ndef share(request, obj, name, description):\n try:\n service = get_service(request.GET['service'])\n except KeyError:\n raise http.Http404()\n\n if not service:\n raise http.Http404()\n\n form = ShareForm({\n 'title': page_title({'request': request}, name),\n 'url': u(obj.get_url_path()),\n 'description': description,\n })\n form.full_clean()\n return http.HttpResponseRedirect(service.url.format(**form.cleaned_data))\n","sub_path":"src/olympia/sharing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"347539166","text":"#\n# Copyright 2012, Piston Cloud Computing, Inc.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport mox\nimport unittest\n\nfrom burrow.common import cfg\nfrom burrow.common import context\nfrom burrow.common import exc\nfrom burrow.api.sqs import controller as sqs_controller\n\nfrom tests.fakes import queue\n\nclass TestSQSController(unittest.TestCase):\n def setUp(self):\n cfg.CFG.queue_backend = 'tests.fakes.queue'\n self.context = context.RequestContext(auth_tok='tik_tok',\n user='fake_user',\n tenant='tenant_uuid')\n self.context2 = context.RequestContext(auth_tok='tik_tok',\n user='fake_user2',\n tenant='not_my_tenant')\n\n self.out_of_context = context.RequestContext(auth_tok='tik_tok',\n user='fake_user2',\n tenant='out_of_context')\n\n self.uuids = (u for u in xrange(12345, 12355))\n\n self.controller = sqs_controller.Controller()\n self.controller.queue_api.setup_fixtures() # Bring nothing to table.\n self.mox = mox.Mox()\n\n def tearDown(self):\n self.mox.UnsetStubs()\n\n def test_list_queues(self):\n self.assertEqual([], self.controller.list_queues(self.context))\n self.assertRaises(exc.QueueError, \n self.controller.receive_message,\n self.context, 'fake_queue')\n\n self.controller.queue_api.setup_fixtures(\n queues=[{'uuid': '12345',\n 'owner': 'fake_user',\n 'tenant': self.context.tenant,\n 'name': 'fake_queue',},\n {'uuid': '123456',\n 'owner': 'not_me',\n 'tenant': 'not_my_tenant',\n 'name': 'fake_queue',}])\n\n self.assertEqual(1, len(self.controller.list_queues(self.context)))\n self.assertEqual(1, len(self.controller.list_queues(\n self.context, queue_name_prefix='fake_')))\n self.assertEqual(1, len(self.controller.list_queues(self.context2)))\n self.assertEqual(0, len(self.controller.list_queues(\n self.out_of_context)))\n self.assertEqual(0, len(self.controller.list_queues(\n self.context, queue_name_prefix='empty')))\n self.assertEqual([{'queue_url': 'http://localhost/tenant_uuid/fake_queue'}],\n self.controller.list_queues(self.context))\n\n def test_create_queue(self):\n self.mox.StubOutWithMock(queue.uuid, 'uuid4')\n queue.uuid.uuid4().AndReturn(self.uuids.next())\n queue.uuid.uuid4().AndReturn(self.uuids.next())\n\n self.mox.ReplayAll()\n self.assertEqual(0, len(self.controller.list_queues(self.context)))\n self.assertEqual('12345',\n self.controller.create_queue(self.context, 'tealeaf'))\n\n self.assertEqual(1, len(self.controller.list_queues(self.context)))\n self.assertRaises(exc.QueueError, self.controller.create_queue,\n self.context, 'tealeaf')\n \n self.assertEqual('12346',\n self.controller.create_queue(self.context, 'fffffffuuuuuuuuuuuu'))\n\n self.assertEqual(2, len(self.controller.list_queues(self.context)))\n\n def test_get_queue_url(self):\n self.mox.StubOutWithMock(queue.uuid, 'uuid4')\n queue.uuid.uuid4().AndReturn(self.uuids.next())\n\n self.mox.ReplayAll()\n self.assertEqual(0, len(self.controller.list_queues(self.context)))\n self.assertEqual('12345',\n self.controller.create_queue(self.context, 'tealeaf'))\n\n self.assertEqual({'queue_url': 'http://localhost/tenant_uuid/tealeaf'}, \n self.controller.get_queue_url(self.context, 'tealeaf'))\n\n\n def test_delete_queue(self):\n self.mox.StubOutWithMock(queue.uuid, 'uuid4')\n queue.uuid.uuid4().AndReturn(self.uuids.next())\n queue.uuid.uuid4().AndReturn(self.uuids.next())\n\n self.mox.ReplayAll()\n self.assertEqual(0, len(self.controller.list_queues(self.context)))\n self.assertEqual('12345',\n self.controller.create_queue(self.context, 'tealeaf'))\n\n self.assertEqual(1, len(self.controller.list_queues(self.context)))\n self.assertRaises(exc.QueueError, self.controller.create_queue,\n self.context, 'tealeaf')\n\n self.assertEqual('12346',\n self.controller.create_queue(self.context, 'fffffffuuuuuuuuuuuu'))\n\n self.assertEqual(2, len(self.controller.list_queues(self.context)))\n self.assertIsNone(self.controller.delete_queue(self.context, 'tealeaf'))\n\n self.assertEqual(1, len(self.controller.list_queues(self.context)))\n self.assertIsNone(self.controller.delete_queue(self.context,\n 'fffffffuuuuuuuuuuuu'))\n self.assertEqual(0, len(self.controller.list_queues(self.context)))\n","sub_path":"tests/api/sqs/test_controller.py","file_name":"test_controller.py","file_ext":"py","file_size_in_byte":5801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"297911274","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Convert HTML on the official site into CSV file.\n\n1. Download HTML page. (http://www.j-league.or.jp/schedule/)\n2. Convert HTML file encoding into UTF-8.\n3. Run this script to give HTML file path as argument, and save CSV output.\n4. Post CSV file via App Engine remote API.\n5. Check some iCal feeds.\n\n $ curl \"http://jleague-calendar.appspot.com/club/fctokyo.ics\"\n\nUsage:\n webpage2csv.py [-v | -q] [-d DELIM] [-o OUTPUT]\\\n \n webpage2csv.py -h | --help\n webpage2csv.py --version\n\nOptions:\n -h --help Show this screen.\n --version Show version.\n -v --verbose Increase logging verbosity [default: False].\n -q --quiet Supress logging [default: False].\n -d DELIM --delimiter=DELIM Input field delimiter [default: ,].\n -o OUTPUT --output=OUTPUT Output file path.\n\"\"\"\n\nimport csv\nimport datetime\nimport logging\nimport logging.config\nimport os\nimport re\nimport sys\n\nimport docopt\nimport schema\nfrom clitool.textio import RowMapper\nfrom bs4 import BeautifulSoup\n\nDEFAULT_ENCODING = 'utf-8'\n\nAPPNAME = os.path.splitext(os.path.basename(__file__))[0]\nVERSION = '0.1'\nLOG_FORMAT = '%(asctime)s|%(name)s:%(levelname)s|L%(lineno)d|%(message)s'\nLOG_DATEFMT = '%Y-%m-%d %H:%M:%S'\n\nCOMMON_ARGUMENTS = { # for \"schema.Schema\" constructor\n '--help': bool,\n '--version': bool,\n '--verbose': bool,\n '--quiet': bool,\n '--delimiter': schema.And(str, lambda d: len(d) == 1)\n}\n\nLOGGING_CONFIG = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'standard': {\n 'format': LOG_FORMAT,\n 'datefmt': LOG_DATEFMT\n },\n },\n 'handlers': {\n 'default': {\n 'level': 'DEBUG',\n 'class': 'logging.StreamHandler',\n 'formatter': 'standard',\n },\n },\n 'loggers': {\n APPNAME: {\n 'handlers': ['default'],\n 'level': 'INFO',\n 'propagate': False\n }\n }\n}\n\nFIXTURE = re.compile(r\"^Ｊ[１|２] 第([\\d]+)節$\")\nDAY = re.compile(r\"^([\\d]+)月([\\d]+)日\\(.*\\)$\")\n\nCLUB_FIELDS = [\n {\"id\": \"key\", \"type\": \"string\"},\n {\"id\": \"abbr\", \"type\": \"string\"},\n {\"id\": \"display_name\", \"type\": \"string\"},\n {\"id\": \"category\", \"type\": \"string\"},\n {\"id\": \"channel\", \"type\": \"string\"},\n {\"id\": \"url\", \"type\": \"string\"},\n {\"id\": \"stadium\", \"type\": \"string\"}\n]\n\nSTADIUM_FIELDS = [\n {\"id\": \"key\", \"type\": \"string\"},\n {\"id\": \"abbr\", \"type\": \"string\"},\n {\"id\": \"name\", \"type\": \"string\"},\n {\"id\": \"display_name\", \"type\": \"string\"},\n {\"id\": \"location\", \"type\": \"string\"},\n {\"id\": \"wikipedia_url\", \"type\": \"string\"}\n]\n\nFIELDS = [\n {\"id\": \"key\", \"type\": \"string\"},\n {\"id\": \"year\", \"type\": \"integer\"},\n {\"id\": \"fixture\", \"type\": \"integer\"},\n {\"id\": \"home\", \"type\": \"string\"},\n {\"id\": \"away\", \"type\": \"string\"},\n {\"id\": \"stadium\", \"type\": \"string\"},\n {\"id\": \"kickoff\", \"type\": \"datetime\", \"format\": \"%Y-%m-%dT%H:%M:%S\"},\n {\"id\": \"status\", \"type\": \"string\"},\n {\"id\": \"description\", \"type\": \"string\"}\n]\n\n\nclass DictMapper(object):\n \"\"\"Convert dictionary object to list of strings of values.\n \"\"\"\n\n def __init__(self, fields):\n self.fields = fields\n\n def __call__(self, dt):\n out = []\n for f in self.fields:\n k, t = f['id'], f['type']\n v = dt.get(k, f.get('default', ''))\n if t == 'string':\n val = v\n elif not v:\n val = ''\n elif t == 'datetime':\n val = v.strftime(f['format'])\n elif t in ('integer', 'float'):\n val = str(v)\n elif t == 'boolean':\n m = f.get('mapping', {})\n if v in m:\n val = m[v]\n else:\n val = str(v)\n else:\n raise ValueError('Unknown type \"{}\" for \"{}\"'.format(t, k))\n out.append(val)\n return out\n\n\nclass ScheduleWriter(object):\n\n fields = FIELDS\n\n def __init__(self, stream, delimiter=','):\n self.writer = csv.writer(stream, delimiter=delimiter)\n self.mapper = DictMapper(self.fields)\n\n def head(self):\n self.writer.writerow(tuple(map(lambda f: f['id'], self.fields)))\n\n def write(self, dt):\n self.writer.writerow(self.convert(dt))\n\n def convert(self, dt):\n dt['year'] = dt['kickoff'].year\n dt['key'] = '{}-{}-{}-{}-{}'.format(\n dt['year'], dt['category'], dt['fixture'], dt['home'], dt['away'])\n if 'status' not in dt:\n dt['status'] = 'yet'\n return self.mapper(dt)\n\n\nclass ScheduleBoxParser(object):\n\n def __init__(self, writer, clubs, stadiums):\n self.writer = writer\n self.stadiums = stadiums\n self.clubs = clubs\n self.logger = logging.getLogger(APPNAME)\n\n def eat(self, soup):\n schedules = soup.find_all('div', attrs={'class': 'sche-box'})\n for schedule in schedules:\n self.parse(schedule)\n\n def parse(self, schebox):\n self.logger.debug('Start parsing')\n mday = schebox.find('h3', 'match-day').string\n m1 = DAY.match(mday)\n if m1 is None:\n self.logger.error('Invalid match day string: %s', mday)\n return\n categories = schebox.find_all('h3', 'match-cate')\n tables = schebox.find_all('div', 'sche-table')\n if len(categories) != len(tables):\n self.logger.error('Sizes of \"match-cate\" and \"sche-table\" are not' +\n 'matched at %s. %d != %d', mday, len(categories),\n len(tables))\n return\n\n year = 2014 # Magic number\n month, day = map(int, m1.groups())\n\n for ecategory, ematches in zip(categories, tables):\n scategory = ecategory.string\n # TODO: Parse \"ヤマザキナビスコカップ\"\n m2 = FIXTURE.match(scategory)\n if m2 is None:\n self.logger.info('Category is not J League: %s', scategory)\n continue\n if u'Ｊ１' in scategory:\n category = 'J1'\n elif u'Ｊ２' in scategory:\n category = 'J2'\n else:\n self.logger.fatal('regex is wrong for: %s', scategory)\n continue\n fixture = int(m2.group(1))\n for match in ematches.find_all('tr'):\n dt = {'category': category, 'fixture': fixture}\n kickoff = match.find(class_='kickoff').string\n if kickoff != u'未定':\n hour, minute = map(int, kickoff.split(':'))\n dt['kickoff'] = datetime.datetime(year, month, day,\n hour, minute)\n else:\n dt['kickoff'] = datetime.date(year, month, day)\n dt['home'] = self.parse_club(match, 'home')\n dt['away'] = self.parse_club(match, 'away')\n dt['stadium'] = self.parse_stadium(match)\n score = self.parse_score(match)\n if score is not None:\n dt['status'] = 'done'\n dt['description'] = score\n self.writer.write(dt)\n self.logger.debug('Finish parsing')\n\n def parse_club(self, match, kind):\n elem = match.find(class_=kind).find('a')\n if elem is None:\n self.logger.error('Element is not found for club: %s', kind)\n return\n if kind == 'home':\n index = 0\n elif kind == 'away':\n index = 1\n else:\n self.logger.fatal('Unknown club type in match: %s', kind)\n return\n club = self.clubs.get(elem.contents[index])\n if club is None:\n self.logger.error('Element is not found for club: %s', kind)\n return\n return club['key']\n\n def parse_stadium(self, match):\n elem = match.find(class_='stadium').find('a')\n if elem is None:\n stadium = match.find('td', class_='stadium')\n abbr = elem.string.strip().lstrip(u'（').rstrip(u'）')\n stadium = self.stadiums.get(abbr)\n if stadium is None:\n self.logger.error('Unknown stadium name: %s', abbr)\n return stadium\n else:\n return stadium['display_name']\n\n def parse_score(self, match):\n elem = match.find(class_='score').find('a')\n if elem is None:\n return\n if elem.get('href').startswith('/result/'):\n return elem.string\n # or \"href\" startswith '/live/' for live score.\n\n\ndef main():\n args = docopt.docopt(__doc__, version='{} {}'.format(APPNAME, VERSION))\n try:\n s = schema.Schema(dict({\n '--output': schema.Or(None, schema.Use(lambda f: open(f, 'w'))),\n '': os.path.isfile,\n '': os.path.isfile,\n '': os.path.isfile\n }, **COMMON_ARGUMENTS))\n args = s.validate(args)\n except schema.SchemaError as e:\n exit(e)\n\n # Setup logging verbosity.\n logging.config.dictConfig(LOGGING_CONFIG)\n logger = logging.getLogger(APPNAME)\n if args['--verbose']:\n logger.setLevel(logging.DEBUG)\n elif args['--quiet']:\n logger.setLevel(logging.CRITICAL)\n\n # Load file contents\n with open(args['']) as fp:\n soup = BeautifulSoup(fp)\n clubs = {}\n with open(args['']) as fp:\n mapper = RowMapper(CLUB_FIELDS, strict=False)\n reader = csv.reader(fp, delimiter=',')\n for r in map(mapper, reader):\n if 'abbr' in r:\n clubs[r['abbr']] = r\n stadiums = {}\n with open(args['']) as fp:\n mapper = RowMapper(STADIUM_FIELDS)\n reader = csv.reader(fp, delimiter=',')\n for r in map(mapper, reader):\n stadiums[r['abbr']] = r\n\n # Prepare output stream, and put header row.\n output_stream = args['--output'] or sys.stdout\n writer = ScheduleWriter(output_stream)\n writer.head()\n # Start parsing putting each match.\n parser = ScheduleBoxParser(writer, clubs, stadiums)\n parser.eat(soup)\n\n if not output_stream.isatty():\n output_stream.close()\n\n\nif __name__ == '__main__':\n main()\n\n# vim: set et ts=4 sw=4 cindent fileencoding=utf-8 :\n","sub_path":"scraping/webpage2csv.py","file_name":"webpage2csv.py","file_ext":"py","file_size_in_byte":10489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"39267662","text":"import os\n\n\ntemplatedir = './templates'\n\n\nclass Template(str):\n def __new__(self, string):\n if not os.path.exists(os.path.join(templatedir, string)):\n raise Exception('Template not found')\n return super().__new__(self, string)\n\n\nteam_register = Template('team_register.html')\n","sub_path":"templates.py","file_name":"templates.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"575100810","text":"#\n# -*- coding: utf-8 -*-\n# Copyright 2019 Red Hat\n# GNU General Public License v3.0+\n# (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\"\"\"\nThe junos_l3_interfaces class\nIt is in this file where the current configuration (as dict)\nis compared to the provided configuration (as dict) and the command set\nnecessary to bring the current configuration to it's desired end-state is\ncreated\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\nfrom ansible.module_utils.network.common.cfg.base import ConfigBase\nfrom ansible.module_utils.network.common.utils import to_list\nfrom ansible.module_utils.network.junos.facts.facts import Facts\nfrom ansible.module_utils.network.junos.junos import (\n locked_config, load_config, commit_configuration, discard_changes,\n tostring)\nfrom ansible.module_utils.network.common.netconf import (build_root_xml_node,\n build_child_xml_node)\n\n\nclass L3_interfaces(ConfigBase):\n \"\"\"\n The junos_l3_interfaces class\n \"\"\"\n\n gather_subset = [\n '!all',\n '!min',\n ]\n\n gather_network_resources = [\n 'l3_interfaces',\n ]\n\n def __init__(self, module):\n super(L3_interfaces, self).__init__(module)\n\n def get_l3_interfaces_facts(self):\n \"\"\" Get the 'facts' (the current configuration)\n\n :rtype: A dictionary\n :returns: The current configuration as a dictionary\n \"\"\"\n facts, _warnings = Facts(self._module).get_facts(\n self.gather_subset, self.gather_network_resources)\n l3_interfaces_facts = facts['ansible_network_resources'].get(\n 'l3_interfaces')\n if not l3_interfaces_facts:\n return []\n return l3_interfaces_facts\n\n def execute_module(self):\n \"\"\" Execute the module\n\n :rtype: A dictionary\n :returns: The result from module execution\n \"\"\"\n result = {'changed': False}\n warnings = list()\n\n existing_interfaces_facts = self.get_l3_interfaces_facts()\n\n config_xmls = self.set_config(existing_interfaces_facts)\n with locked_config(self._module):\n for config_xml in to_list(config_xmls):\n diff = load_config(self._module, config_xml, warnings)\n\n commit = not self._module.check_mode\n if diff:\n if commit:\n commit_configuration(self._module)\n else:\n discard_changes(self._module)\n result['changed'] = True\n\n if self._module._diff:\n result['diff'] = {'prepared': diff}\n\n result['commands'] = config_xmls\n\n changed_interfaces_facts = self.get_l3_interfaces_facts()\n\n result['before'] = existing_interfaces_facts\n if result['changed']:\n result['after'] = changed_interfaces_facts\n\n result['warnings'] = warnings\n return result\n\n def set_config(self, existing_l3_interfaces_facts):\n \"\"\" Collect the configuration from the args passed to the module,\n collect the current configuration (as a dict from facts)\n\n :rtype: A list\n :returns: the commands necessary to migrate the current configuration\n to the desired configuration\n \"\"\"\n want = self._module.params['config']\n have = existing_l3_interfaces_facts\n resp = self.set_state(want, have)\n return to_list(resp)\n\n def set_state(self, want, have):\n \"\"\" Select the appropriate function based on the state provided\n\n :param want: the desired configuration as a dictionary\n :param have: the current configuration as a dictionary\n :rtype: A list\n :returns: the list xml configuration necessary to migrate the current\n configuration\n to the desired configuration\n \"\"\"\n root = build_root_xml_node('interfaces')\n state = self._module.params['state']\n if state == 'overridden':\n config_xmls = self._state_overridden(want, have)\n elif state == 'deleted':\n config_xmls = self._state_deleted(want, have)\n elif state == 'merged':\n config_xmls = self._state_merged(want, have)\n elif state == 'replaced':\n config_xmls = self._state_replaced(want, have)\n\n for xml in config_xmls:\n root.append(xml)\n\n return tostring(root)\n\n def _get_common_xml_node(self, name):\n root_node = build_root_xml_node('interface')\n build_child_xml_node(root_node, 'name', name)\n intf_unit_node = build_child_xml_node(root_node, 'unit')\n return root_node, intf_unit_node\n\n def _state_replaced(self, want, have):\n \"\"\" The xml generator when state is replaced\n\n :rtype: A list\n :returns: the xml necessary to migrate the current configuration\n to the desired configuration\n \"\"\"\n intf_xml = []\n intf_xml.extend(self._state_deleted(want, have))\n intf_xml.extend(self._state_merged(want, have))\n return intf_xml\n\n def _state_overridden(self, want, have):\n \"\"\" The xml generator when state is overridden\n\n :rtype: A list\n :returns: the xml necessary to migrate the current configuration\n to the desired configuration\n \"\"\"\n intf_xml = []\n intf_xml.extend(self._state_deleted(have, have))\n intf_xml.extend(self._state_merged(want, have))\n return intf_xml\n\n def _state_merged(self, want, have):\n \"\"\" The xml generator when state is merged\n\n :rtype: A list\n :returns: the xml necessary to merge the provided into\n the current configuration\n \"\"\"\n intf_xml = []\n for config in want:\n root_node, unit_node = self._get_common_xml_node(config['name'])\n build_child_xml_node(unit_node, 'name',\n str(config['unit']))\n if config.get('ipv4'):\n self.build_ipaddr_et(config, unit_node)\n if config.get('ipv6'):\n self.build_ipaddr_et(config, unit_node, protocol='ipv6')\n intf_xml.append(root_node)\n return intf_xml\n\n def build_ipaddr_et(self, config, unit_node, protocol='ipv4',\n delete=False):\n family = build_child_xml_node(unit_node, 'family')\n inet = 'inet'\n if protocol == 'ipv6':\n inet = 'inet6'\n ip_protocol = build_child_xml_node(family, inet)\n for ip_addr in config[protocol]:\n if ip_addr['address'] == 'dhcp' and protocol == 'ipv4':\n build_child_xml_node(ip_protocol, 'dhcp')\n else:\n ip_addresses = build_child_xml_node(\n ip_protocol, 'address')\n build_child_xml_node(\n ip_addresses, 'name', ip_addr['address'])\n\n def _state_deleted(self, want, have):\n \"\"\" The xml configuration generator when state is deleted\n\n :rtype: A list\n :returns: the xml configuration necessary to remove the current\n configuration of the provided objects\n \"\"\"\n intf_xml = []\n existing_l3_intfs = [l3_intf['name'] for l3_intf in have]\n\n if not want:\n want = have\n\n for config in want:\n if config['name'] not in existing_l3_intfs:\n continue\n else:\n root_node, unit_node = self._get_common_xml_node(\n config['name'])\n build_child_xml_node(unit_node, 'name',\n str(config['unit']))\n family = build_child_xml_node(unit_node, 'family')\n ipv4 = build_child_xml_node(family, 'inet')\n intf = next(\n (intf for intf in have if intf['name'] == config['name']),\n None)\n if 'ipv4' in intf:\n if 'dhcp' in [x['address'] for x in intf.get('ipv4') if intf.get('ipv4') is not None]:\n build_child_xml_node(ipv4, 'dhcp', None, {'delete': 'delete'})\n else:\n build_child_xml_node(\n ipv4, 'address', None, {'delete': 'delete'})\n ipv6 = build_child_xml_node(family, 'inet6')\n build_child_xml_node(ipv6, 'address', None, {'delete': 'delete'})\n intf_xml.append(root_node)\n return intf_xml\n","sub_path":"env/lib/python3.9/site-packages/ansible/module_utils/network/junos/config/l3_interfaces/l3_interfaces.py","file_name":"l3_interfaces.py","file_ext":"py","file_size_in_byte":8590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"241771246","text":"n=int(input(\"Enter the number:\"))\nif n>1:\n for i in range(2,n):\n if n%i==0:\n print ('no')\n break\n \n else:\n print(\"yes\")\nelif n==1 or n==0:\n print ('neither prime nor composite')\n","sub_path":"prime66.py","file_name":"prime66.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"632886533","text":"import re\r\nfrom util.Log import Logger\r\nfrom util.ImageOperation import ImageOperation\r\nfrom services.ImageRecognition import ImageRecognition\r\n\r\n\r\nclass AssertImages:\r\n def __init__(self):\r\n self.log = Logger().logger\r\n\r\n def assert_images(self, devices, exc_img, act_img, comment):\r\n \"\"\"\r\n 断言：\r\n 1.预期图片&实际图片\r\n 2.分词识别正确\r\n \"\"\"\r\n\r\n # 校验ocr\r\n img_str = ImageRecognition().imgOCR(act_img)['words_result']\r\n self.log.info(f'ocr光学文字识别结果：{img_str}')\r\n\r\n count = 0\r\n for i in comment:\r\n res = re.search(i, str(img_str))\r\n if res:\r\n count += 1\r\n if count == len(comment):\r\n ocr_result = True\r\n else:\r\n ocr_result = False\r\n\r\n img_similar = ImageRecognition().imgSimilar(exc_img, act_img)\r\n self.log.info(f'图像相似度结果: {img_similar}')\r\n\r\n image_path = ImageOperation().save_compare_img(exc_img, act_img, devices)\r\n\r\n mes = {\r\n 'ocr_result': ocr_result,\r\n 'ocr_description': img_str,\r\n 'compare_img_path': image_path,\r\n 'similar_data': img_similar\r\n }\r\n return mes\r\n\r\n\r\nif __name__ == '__main__':\r\n a = AssertImages()\r\n path1 = r\"D:\\work\\code\\project_test\\ebuy_app\\statics\\exce_img\\e_buy_home_online.jpg\"\r\n path2 = r\"D:\\work\\code\\project_test\\ebuy_app\\statics\\exce_img\\e_buy_home_offline.jpg\"\r\n comments = [\"微信\", \"扫一扫\", \"摇一摇\", \"搜一搜\"]\r\n # print(a.assert_images(img1, img2, comment))\r\n print(a.assert_images(\"c2a1596b\", path1, path2, comments))\r\n","sub_path":"app_test_win/services/AssertImages.py","file_name":"AssertImages.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"112723964","text":"\n# Copyright (c) 2021, kang kimchhay and contributors\n# For license information, please see license.txt\n\nimport frappe\nfrom frappe.model.document import Document\n\nclass HotelBooking(Document):\n\tdef before_save(self):\n\t\tif self.from_date > self.to_date:\n\t\t\tfrappe.throw(\"From date must be smaller than to date\")\n\tdef before_submit(self):\n\t\tbooking = frappe.db.get_list(\n\t\t\t\t\"Hotel Booking\",\n\t\t\t\t\tfilters = {\n\t\t\t\t\t\t\"docstatus\":1,\n\t\t\t\t\t\t\"status\" : \"Booking\",\n\t\t\t\t\t\t\"hotel_room\": self.hotel_room\n\t\t\t\t\t})\n\t\tif 1 <= len(booking):\n\t\t\tfrappe.throw(\"Room already Booking\")\n\t\n","sub_path":"hotel_management/hotel_management/doctype/hotel_booking/hotel_booking.py","file_name":"hotel_booking.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"546451732","text":"from pomp.core.base import BaseHttpRequest, BaseHttpResponse, BaseDownloadWorker, BaseCrawlException\nfrom pomp.contrib.concurrenttools import ConcurrentDownloader\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.common.exceptions import NoSuchElementException, TimeoutException\nimport time\n\n\nclass PhantomRequest(BaseHttpRequest):\n def __init__(self, url):\n self.url = url\n self.driver_url = None\n\n def __str__(self):\n return '<{s.__class__.__name__} url: {s.url}> ' \\\n 'wdriver: {s.driver_url}'.format(s=self)\n\n\nclass PhantomResponse(BaseHttpResponse):\n def __init__(self, req, body, images=None):\n self.req = req\n self.content = body\n if images:\n self.images = images\n\n @property\n def request(self):\n return self.req\n\n\nclass PhantomWorker(BaseDownloadWorker):\n def __init__(self):\n self.pid = 'windows'\n # on linux\n # self.pid = os.getpid()\n\n def get_one(self, request):\n # attach webdriver to already started phantomjs node\n user_agent = (\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/39.0.2171.95 Safari/537.36'\n )\n dcap = DesiredCapabilities.PHANTOMJS.copy()\n dcap['phantomjs.page.settings.userAgent'] = user_agent\n driver = webdriver.Remote(\n command_executor=request.driver_url,\n desired_capabilities=dcap,\n )\n driver.get(request.url)\n # wait untill JS renders the page\n try:\n WebDriverWait(driver, 10).until(\n expected_conditions.presence_of_element_located(\n (By.XPATH, './/*[@id=\"footer\"]')\n )\n )\n except TimeoutException:\n print(request.url + ' не загрузился')\n return BaseCrawlException(request=request, exception=TimeoutException)\n image_elements = []\n\n try:\n # Этот элемент появляется на иностранных сайтах, и на некоторых русских.\n # Берлин, Дубай, Минск, Майами, Самара, Саратов, Чебоксары\n element = driver.find_element_by_xpath('.//*[@id=\"lang_select\"]')\n if element:\n opt_ru, opt_en = None, None\n for option in element.find_elements_by_tag_name('option'):\n if option.text == 'Ru':\n opt_ru = option\n elif option.text == 'En':\n opt_en = option\n if opt_ru:\n opt_ru.click()\n time.sleep(3)\n elif opt_en:\n opt_en.click()\n time.sleep(3)\n # if element:\n # select = Select(element)\n # select.deselect_all()\n # select.select_by_visible_text('En')\n # select.deselect_all()\n # select.select_by_visible_text('Ru')\n\n except NoSuchElementException:\n pass\n\n try:\n if driver.find_element_by_xpath('.//*[@id=\"quest-image\"]'):\n current_image = 'n/a'\n while current_image not in image_elements:\n \"\"\" Картинки на сайте переключаются нажатием на картинку стрелочки.\n через JS изменяется стиль элемента - меняется адрес картинки.\n Перебираем картинки и записываем их в list до тех пор, пока не начнут повторяться.\n И заодно обрезаем слева текст `background-image: url(` и справа `);`\n \"\"\"\n current_image = driver.find_element_by_xpath('.//*[@id=\"quest-image\"]').get_attribute('style')[22:][:-2]\n image_elements.append(current_image)\n driver.find_element_by_xpath('.//*[@id=\"next_img\"]').click()\n driver.implicitly_wait(4)\n # time.sleep(1)\n current_image = driver.find_element_by_xpath('.//*[@id=\"quest-image\"]').get_attribute('style')[22:][:-2]\n except NoSuchElementException:\n pass\n\n # finish - get current document body\n body = driver.execute_script('return document.documentElement.outerHTML;')\n return PhantomResponse(request, body, image_elements)\n\n\nclass PhantomDownloader(ConcurrentDownloader):\n\n def __init__(self, phantom_drivers, *args, **kwargs):\n self.drivers = phantom_drivers\n super().__init__(*args, **kwargs)\n\n def prepare(self, *args, **kwargs):\n super().prepare(*args, **kwargs)\n\n def get(self, requests):\n # associate each request with phantomjs node\n def _associate_driver_url(request):\n request.driver_url = self.drivers[0].command_executor._url\n self.drivers.rotate(1)\n return request\n\n return super().get(\n map(_associate_driver_url, requests)\n )\n\n def stop(self):\n super().stop()\n # for driver in self.drivers:\n # driver.close()\n # driver.quit()\n # for linux\n # pid = driver.service.process.pid\n # HACK - phantomjs does not exited after close and quit\n # try:\n # os.kill(pid, signal.SIGTERM)\n # except ProcessLookupError:\n # pass\n","sub_path":"phobiaru/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":5886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"420205795","text":"from selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\n#-------------------- Remove It -----------------------\n#import selenium\n#from selenium.webdriver.common.keys import Keys\n#from selenium import webdriver\n#driver=webdriver.Chrome()\n#driver.switch_to.default_content()\n#driver.find_element_by_xpath().is_selected()\n#driver.find_element_by_xpath().is_displayed()\n# -------------------------------------------------------------\n\nclass OpportunityPage:\n Icon_ShowMoreActions_XPath=\"//button[@class='slds-button slds-button_icon-border-filled']\"\n MenuItem_CreateQuoteProposal_XPath=\"//span[text()='Create Quote/Proposal']\"\n\n # Constructor\n def __init__(self, driver):\n self.driver = driver\n\n # Click on Create Quote/Proposal Menu Item from Opportunity Detail page\n def ClickShowMoreActions(self):\n print(\"---------- Method: ClickShowMoreActions\")\n element=WebDriverWait(self.driver,60).until(EC.element_to_be_clickable((By.XPATH,self.Icon_ShowMoreActions_XPath)))\n element.click()\n\n def ClickCreateProposalMenuItem(self):\n print(\"---------- Method: ClickCreateProposalMenuItem\")\n element1=WebDriverWait(self.driver, 60).until(EC.element_to_be_clickable((By.XPATH, self.MenuItem_CreateQuoteProposal_XPath)))\n element1.click()\n\n def ProposalRecordTypeClickButton(self,Button):\n print(\"---------- Method: ProposalRecordTypeClickButton\")\n btn=\"//input[@value='\"+Button+\"']\"\n RecTypPageBtn=WebDriverWait(self.driver, 60).until(EC.element_to_be_clickable((By.XPATH, btn)))\n RecTypPageBtn.click()","sub_path":"pageObjects/OpportunitiesPage.py","file_name":"OpportunitiesPage.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"318019199","text":"# Copyright 2022 Huawei Technologies Co., Ltd.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# Copyright (c) OpenMMLab. All rights reserved.\nimport numpy as np\nimport torch\n\nfrom mmdet.utils.util_random import ensure_rng\n\n\ndef random_boxes(num=1, scale=1, rng=None):\n \"\"\"Simple version of ``kwimage.Boxes.random``\n\n Returns:\n Tensor: shape (n, 4) in x1, y1, x2, y2 format.\n\n References:\n https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390\n\n Example:\n >>> num = 3\n >>> scale = 512\n >>> rng = 0\n >>> boxes = random_boxes(num, scale, rng)\n >>> print(boxes)\n tensor([[280.9925, 278.9802, 308.6148, 366.1769],\n [216.9113, 330.6978, 224.0446, 456.5878],\n [405.3632, 196.3221, 493.3953, 270.7942]])\n \"\"\"\n rng = ensure_rng(rng)\n\n tlbr = rng.rand(num, 4).astype(np.float32)\n\n tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])\n tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])\n br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])\n br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])\n\n tlbr[:, 0] = tl_x * scale\n tlbr[:, 1] = tl_y * scale\n tlbr[:, 2] = br_x * scale\n tlbr[:, 3] = br_y * scale\n\n boxes = torch.from_numpy(tlbr)\n return boxes\n","sub_path":"PyTorch/built-in/cv/detection/SSD_for_PyTorch/mmdet/core/bbox/demodata.py","file_name":"demodata.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"291087728","text":"# coding:utf-8\n\nimport json\nfrom tornado.web import RequestHandler, StaticFileHandler\n\nfrom config import r\nfrom connect import session\nfrom constants import certify_token\nfrom session import Session\nfrom user.user_modules import LoginCheck, User\n\n\nclass BaseHandler(RequestHandler):\n \"\"\"自定义基类\"\"\"\n @property\n def db(self):\n \"\"\"作为RequestHandler对象的db属性\"\"\"\n return self.application.db\n\n @property\n def redis(self):\n \"\"\"作为RequestHandler对象的redis属性\"\"\"\n return self.application.redis\n\n def prepare(self):\n \"\"\"预解析json数据\"\"\"\n if self.request.headers.get(\"Content-Type\", \"\").startswith(\"application/json\"):\n self.json_args = json.loads(self.request.body)\n else:\n self.json_args = {}\n\n def set_default_headers(self):\n \"\"\"设置默认json格式\"\"\"\n self.set_header(\"Access-Control-Allow-Headers\", \"Authorization, Content-Type, Depth, User-Agent, X-File-Size, X-Requested-With, X-Requested-By, If-Modified-Since, X-File-Name, X-File-Type, Cache-Control, Origin\")\n self.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n # self.set_header('Access-Control-Allow-Headers', 'Authorization')\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE')\n # self.set_header(\"Access-Control-Allow-Credentials\", \"true\")\n\n def get_current_user(self):\n \"\"\"判断用户是否登录\"\"\"\n if self.request.method == 'GET':\n print(\"request method is get!!!\")\n user_id = self.get_query_argument(\"user_id\", None)\n token = self.get_query_argument(\"token\", None)\n if user_id and token:\n uid = r.get(token)\n if uid:\n uid = uid.decode()\n if uid == user_id:\n user = session.query(User).filter(User.id == uid)\n if user.count() == 1:\n # return {\"user_id\": user_id, \"token\": token, \"code\": 0}\n return 0\n else:\n return 2\n else:\n login = session.query(LoginCheck).filter(LoginCheck.u_id == user_id).filter(LoginCheck.login_key != None)\n if login.count() == 1:\n return 1\n else:\n return 2\n if self.request.method == 'POST':\n print(\"request method is post!!!\")\n user_id = self.get_body_argument(\"user_id\", None)\n token = self.get_body_argument(\"token\", None)\n if user_id and token:\n uid = r.get(token)\n if uid:\n print('exists uid')\n uid = uid.decode()\n if uid == user_id:\n user = session.query(User).filter(User.id == uid)\n if user.count() == 1:\n # return {\"user_id\": user_id, \"token\": token, \"code\": 0}\n return 0\n else:\n return 2\n else:\n login = session.query(LoginCheck).filter(LoginCheck.u_id == user_id).filter(LoginCheck.login_key != None)\n if login.count() == 1:\n return 1\n else:\n return 2\n\n\n # if self.request.method == \"POST\":\n # user_id = self.get_query_argument(\"user_id\", None)\n # token = self.get_query_argument(\"token\", None)\n # if user_id and token:\n\n\n\nclass StaticFileBaseHandler(StaticFileHandler):\n \"\"\"自定义静态文件处理类, 在用户获取html页面的时候设置_xsrf的cookie\"\"\"\n def __init__(self, *args, **kwargs):\n super(StaticFileBaseHandler, self).__init__(*args, **kwargs)\n self.xsrf_token\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"t1_tornado/BaseHandler.py","file_name":"BaseHandler.py","file_ext":"py","file_size_in_byte":4030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"240067907","text":"import tensorflow as tf\nimport random\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ntf.set_random_seed(777) # for reproducibility\n\n# Check out https://www.tensorflow.org/get_started/mnist/beginners for\n# more information about the mnist dataset\n#mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\ntrain, test = tf.keras.datasets.mnist.load_data()\n\nnb_classes = 10\n\ntrain_x, train_y = train\ntrain_y = train_y.reshape([-1, 1])\ntest_x, test_y = test\ntest_y = test_y.reshape([-1, 1])\n\ntrain_x = np.reshape(train_x, [-1, 784])\ntest_x = np.reshape(test_x, [-1, 784])\n\n\n# MNIST data image of shape 28 * 28 = 784\nX = tf.placeholder(tf.float32, [None, 784])\n# 0 - 9 digits recognition = 10 classes\nY = tf.placeholder(tf.int32, [None, 1])\nY_one_hot = tf.one_hot(Y, nb_classes)\nprint(\"one_hot\", Y_one_hot)\nY_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])\nprint(\"reshape\", Y_one_hot)\n\nkeep_prob = tf.placeholder(tf.float32)\n\n# reshape X for CNN input\nX_image = tf.reshape(X, [-1, 28, 28, 1])\n\nW1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))\n\nL1 = tf.nn.conv2d(X_image, W1, [1, 1, 1, 1], padding='SAME')\nL1 = tf.nn.relu(L1)\nL1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\nL1 = tf.nn.dropout(L1, keep_prob=keep_prob)\n\nW2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))\n\nL2 = tf.nn.conv2d(L1, W2, [1, 1, 1, 1], padding='SAME')\nL2 = tf.nn.relu(L2)\nL2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\nL2 = tf.nn.dropout(L2, keep_prob=keep_prob)\n\nL2_flat = tf.reshape(L2, [-1, 7*7*64])\nW3 = tf.get_variable('W3', [7*7*64, 512], initializer=tf.contrib.layers.xavier_initializer())\nB3 = tf.Variable(tf.random_normal([512]))\nL3 = tf.nn.relu(tf.matmul(L2_flat, W3) + B3)\nL3 = tf.nn.dropout(L3, keep_prob=keep_prob)\n\nW4 = tf.get_variable('W4', [512, nb_classes], initializer=tf.contrib.layers.xavier_initializer())\nB4 = tf.Variable(tf.random_normal([nb_classes]))\n\nlogits = tf.matmul(L3, W4) + B4\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y_one_hot))\noptimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)\n\n\n\n# logits = tf.matmul(L3, W4)\n# hypothesis = tf.nn.relu(logits + b4)\n# cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=([Y_one_hot])))\n# optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)\n#\n# Test model\nis_correct = tf.equal(tf.argmax(logits, 1), tf.cast(tf.reshape(Y, [-1]), tf.int64))\n# Calculate accuracy\naccuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))\n\n# parameters\ntraining_epochs = 15\nbatch_size = 100\n\nwith tf.Session() as sess:\n # Initialize TensorFlow variables\n sess.run(tf.global_variables_initializer())\n # one_hot_train_y = tf.one_hot(train_y, nb_classes).eval()\n # one_hot_test_y = tf.one_hot(test_y, nb_classes).eval()\n # Training cycle\n for epoch in range(training_epochs):\n avg_cost = 0\n total_batch = int(int(train_x.shape[0]) / batch_size)\n\n for i in range(total_batch):\n batch_xs, batch_ys = train_x[i*batch_size:(i+1)*batch_size], \\\n train_y[i*batch_size:(i+1)*batch_size],\n c, _ = sess.run([cost, optimizer], feed_dict={X: batch_xs, Y: batch_ys, keep_prob: 0.7})\n avg_cost += c / total_batch\n\n\n print('Epoch:', '%04d' % (epoch + 1),\n 'cost =', '{:.9f}'.format(avg_cost))\n\n\n print(\"Learning finished\")\n\n # Test the model using test sets\n\n # print(\"Accuracy: \", accuracy.eval(session=sess, feed_dict={\n # X: test_x, Y: test_y}))\n\n a = sess.run([accuracy], feed_dict={X: test_x, Y: test_y, keep_prob: 1})\n print(a)\n\n # Get one and predict\n r = random.randint(0, len(test_x))\n print(\"Label: \", test_y[r])\n print(\"Prediction: \", sess.run(tf.argmax(logits, 1), feed_dict={X: np.reshape(test_x[r], [1, -1]), keep_prob: 1}))\n\n plt.imshow(\n test_x[r].reshape(28, 28),\n cmap='Greys',\n interpolation='nearest')\n plt.show()\n\n","sub_path":"mnist_with_cnn_01.py","file_name":"mnist_with_cnn_01.py","file_ext":"py","file_size_in_byte":4052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"231755471","text":"import numpy as np\nimport cv2\n\nimg0 = cv2.imread('used_images_videos/lena.jpg', 1)\nimg = np.zeros([512, 512, 3], np.uint8)\n\nimg2 = cv2.rectangle(img, (0, 0), (255, 255), (147, 96, 47), -1)\n\n\"\"\" Similarly we use circle, line, arrowedLine, polygon, ellipse etc to draw the shapes tacit from the names \"\"\"\n\nfont = cv2.FONT_HERSHEY_DUPLEX\nimg2 = cv2.putText(img2, 'Abhinav', (100, 100), font, 2, (100, 100, 20), 4, cv2.LINE_8)\n\n\ncv2.imshow('pagal', img2)\n\n# cv2.imwrite('gshape.jpg', img2)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","sub_path":"Practice/opencv/geometric_shapes.py","file_name":"geometric_shapes.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"443213564","text":"from django.core.exceptions import ObjectDoesNotExist\n\nfrom profiles.exceptions import ApiUnauthorized\nfrom back.settings import MIN_ANDROID_VERSION, MIN_IOS_VERSION\nfrom profiles.models import DeviceInfo\n\n\ndef check_version(meta):\n \"\"\"\n checks what version of the app the request is coming from\n throws a 401 if the version is lower than required\n \"\"\"\n error = ApiUnauthorized(code='WRONG_VERSION', message='Your app runs on an older version. Please update your app')\n version_header = meta.get('HTTP_X_APP_VER')\n if not version_header:\n raise error\n try:\n os, version = version_header.split('_')\n except Exception:\n raise error\n if os.lower() == 'android':\n compare_versions(MIN_ANDROID_VERSION, version.lower(), error)\n elif os.lower() == 'ios':\n compare_versions(MIN_IOS_VERSION, version.lower(), error)\n else:\n raise error\n\n\ndef compare_versions(minimum, incoming, error):\n \"\"\"\n :param minimum: version of the app that the server thinks is minimum\n :param incoming: version in the incoming request\n :param error exception to be thrown in case the version is wrong\n \"\"\"\n try:\n incoming_first, incoming_second, incoming_third = incoming.split('.')\n minimum_first, minimum_second, minimum_third = minimum.split('.')\n if minimum_first > incoming_first:\n raise error\n if minimum_second > incoming_second:\n raise error\n if minimum_third > incoming_third:\n raise error\n except Exception:\n raise error\n\n\ndef coordinates(bundle):\n meta = bundle.request.META\n profile = bundle.request.user\n lat = meta.get('HTTP_X_DEV_LAT')\n lon = meta.get('HTTP_X_DEV_LON')\n info = meta.get('HTTP_X_DEV_INFO')\n tz = meta.get('HTTP_X_DEV_TZ')\n\n # noinspection PyBroadException\n try:\n os, model = info.split('_')\n except Exception:\n os, model = None, None\n\n try:\n device = DeviceInfo.objects.get(owner=profile)\n if lat is not None:\n device.latitude = lat\n if lon is not None:\n device.longitude = lon\n if os is not None:\n device.os = os\n if model is not None:\n device.model = model\n if tz is not None:\n device.tz = tz\n except ObjectDoesNotExist:\n device = DeviceInfo.objects.create(owner=profile, latitude=lat, longitude=lon, os=os, model=model, tz=tz)\n device.save()\n","sub_path":"profiles/headers.py","file_name":"headers.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"200945464","text":"import sys\n\nif len(sys.argv) != 3:\n print(\"[-] Usage ./ecb_enc filetoencrypt outfilename\")\n exit(1)\nkey = \"A0E\"\nblocks = []\nwith open(sys.argv[1], \"rb\") as f:\n while True:\n block = f.read(16)\n out_block = b\"\"\n if len(block) == 0:\n break\n if len(block) != 16:\n block += b\"0\" * (16 - len(block))\n for i in range(len(block)):\n out_block += bytes([block[i] ^ ord(key[i % len(key)])])\n blocks.append(out_block)\nwith open(sys.argv[2], \"wb\") as f:\n for i in blocks :\n f.write(i)\n","sub_path":"security/CRYPTOGRAPHY/ecb_enc.py","file_name":"ecb_enc.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"192605100","text":"from pyspark.ml.linalg import Vectors\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.clustering import KMeans\n\ncluster_df = spark.read.csv('clustering_dataset.csv', header=True, inferSchema=True)\ncluster_df.show()\n\nvectorAssembler = VectorAssembler(inputCols=['col1', 'col2', 'col3'], outputCol='features')\nvcluster_df = vectorAssembler.transform(cluster_df)\n\nvcluster_df.show()\n\nkmeans = KMeans().setK(3)\nkmeans = kmeans.setSeed(1)\nkmodel = kmeans.fit(vcluster_df)\n\ncenters = kmodel.clusterCenters()\n\n\n# hierarchical clustering\nvcluster_df.show()\n\nfrom pyspark.ml.clustering import BisectingKMeans\nbkmeans = BisectingKMeans().setK(3)\nbkmeans = bkmeans.setSeed(1)\nbkmodel = bkmeans.fit(vcluster_df)\n\nbkcenters = bkmodel.clusterCenters()\n","sub_path":"clustering/clustering.py","file_name":"clustering.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"600485312","text":"import unittest\nfrom api.api_poem import ApiPoem\nfrom tool.read_json import ReadJson\nfrom parameterized import parameterized\n\ndef get_data():\n data = ReadJson(\"poem.json\").read_json()\n arrs = []\n arrs.append((data.get(\"url\"),\n data.get(\"page\"),\n data.get(\"count\")))\n return arrs\n\ndef get_more_data():\n datas = ReadJson(\"poem_more.json\").read_json()\n arrs = []\n for data in datas.values():\n arrs.append((data.get(\"url\"),\n data.get(\"page\"),\n data.get(\"count\")))\n return arrs\n\n\nclass TestPoem(unittest.TestCase):\n\n @parameterized.expand(get_data())\n def test_Poem(self,url,page,count):\n\n url=url\n para={\"page\":page,\"count\":count}\n\n\n data = ApiPoem().api_poem(url,para)\n data1 = data.json()[\"message\"]\n return data1\n\n\n\n","sub_path":"script/test_poem.py","file_name":"test_poem.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"145300486","text":"\n\"\"\"Draft bizlogic\n\"\"\"\n\nimport os\nfrom django.conf import settings\nfrom app.usrlib import image_utils, common\nimport shutil\nfrom app.models import Post, Tag, Year\nimport datetime\nimport re\nfrom app.bizlogic import image_bizlogic\n\n\ndef register_all_archive_posts() -> None:\n \"\"\"Extract data from __drafts, archives folder and register them into DB.\"\"\"\n\n # Get all existing post folder paths. Sorted from past to now.\n DRAFT_TOP_DIR = os.path.join(settings.BASE_DIR, '__drafts')\n draft_dirs = filter(os.path.isdir,\n map(lambda _: os.path.join(DRAFT_TOP_DIR, _),\n sorted(os.listdir(DRAFT_TOP_DIR))))\n\n # UPSERT MODE -----------------------------------------\n for dirpath in draft_dirs:\n archive_post = __create_archive_post_obj(dirpath)\n Post.objects.update_or_create(\n code=archive_post.code,\n defaults={\n 'publish_at': archive_post.publish_at,\n 'title_ja': archive_post.title_ja,\n 'title_en': archive_post.title_en,\n 'tag': archive_post.tag,\n 'year': archive_post.year,\n 'thumbnail': archive_post.thumbnail,\n 'body_ja': archive_post.body_ja,\n 'body_en': archive_post.body_en,\n 'html': archive_post.html,\n },\n )\n\n # BULK_INSERT MODE -----------------------------------------\n # Create Post object one by one.\n # Cuz images will be copied within method, they will exist before registering Post.\n # Obstructs image duplication as well.\n # post_objs = (__create_archive_post_obj(dirpath) for dirpath in draft_dirs)\n # Post.objects.bulk_create(post_objs)\n\n\ndef __create_archive_post_obj(dirpath) -> Post:\n \"\"\"Extract archive data and create Post object with it.\"\"\"\n\n # Create new image names. {0.jpg : uniquename}\n MARKDOWNX_DIR = os.path.join(settings.MEDIA_ROOT, 'markdownx')\n image_correspondence_table = {\n basename: image_utils.get_unique_image_name(basename, MARKDOWNX_DIR)\n for basename in image_utils.get_image_basenames(dirpath)\n }\n\n # Copy images to media folder with new unique names. 0.jpg -> /media/markdownx/uniquename\n for origin, new_basename in image_correspondence_table.items():\n shutil.copyfile(os.path.join(dirpath, origin),\n os.path.join(MARKDOWNX_DIR, new_basename))\n\n # Get data of archive file.\n archive_data = __extract_archive_data(dirpath)\n\n # Create thumbnail for this post.\n thumbnail_basename = (image_correspondence_table[archive_data['mainimage']]\n if archive_data['mainimage'] in image_correspondence_table\n else None)\n if thumbnail_basename:\n image_bizlogic.generate_thumbnail(thumbnail_basename)\n\n # Create Post object.\n return Post(\n # Here set time with Japan timezone, then it will be registered with UTC in DB, minus 9 hours.\n publish_at=datetime.datetime.strptime(archive_data['publishdate'] + '+0900', '%Y-%m-%d%z'),\n code=archive_data['code'],\n title_ja=archive_data['title_ja'],\n title_en=archive_data['title_en'],\n tag=Tag.objects.filter(name_ja=archive_data['tag']).first(),\n year=Year.objects.filter(code=archive_data['publishdate'][:4]).first(),\n thumbnail=thumbnail_basename,\n body_ja=__manipulate_body_content(archive_data['ja_md'], image_correspondence_table),\n body_en=__manipulate_body_content(archive_data['en_md'], image_correspondence_table),\n html=__manipulate_body_content(archive_data['ja_html'], image_correspondence_table),\n )\n\n\ndef __extract_archive_data(dirpath) -> dict:\n \"\"\"Extract data required only from archive files.\"\"\"\n\n # Each path.\n JA_MD = os.path.join(dirpath, 'ja.md')\n EN_MD = os.path.join(dirpath, 'en.md')\n JA_HTML = os.path.join(dirpath, 'ja.html')\n\n # Get markdown meta data.\n meta = common.get_markdown_metadata(JA_MD)\n\n # Extract data.\n # There are little difference between having html(older posts) and not having html(newer posts).\n has_html = os.path.isfile(JA_HTML)\n return {\n 'publishdate': meta['publishdate'][0],\n 'code': meta['code'][0],\n 'title_ja': meta['title_ja'][0],\n 'title_en': meta['title_en'][0],\n 'tag': meta['tag'][0],\n 'mainimage': meta['mainimage'][0],\n 'ja_md': '' if has_html else common.read_file(JA_MD),\n 'en_md': '' if has_html else common.read_file(EN_MD),\n 'ja_html': common.read_file(JA_HTML) if has_html else '',\n }\n\n\ndef __manipulate_body_content(string, image_correspondence_table):\n \"\"\"Manipulate body.\"\"\"\n\n # Replace image paths.\n for key, value in image_correspondence_table.items():\n string = (string\n .replace(f'', f'')\n .replace(f'src=\"{key}\"', f'src=\"/media/markdownx/{value}\"')\n )\n\n # Remove meta description in markdown file. Looks very bizlogic.\n for r in [\n r'publishdate: .*?\\n',\n r'publish: .*?\\n',\n r'code: .*?\\n',\n r'title_ja: .*?\\n',\n r'title_en: .*?\\n',\n r'tag: .*?\\n',\n r'author: .*?\\n',\n r'hash: .*?\\n',\n r'originaleid: .*?\\n',\n r'mainimage: .*?\\n',\n r'convert2html: .*?\\n',\n ]:\n string = re.sub(r, '', string)\n return string\n","sub_path":"app/bizlogic/archive_bizlogic.py","file_name":"archive_bizlogic.py","file_ext":"py","file_size_in_byte":5452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"433404258","text":"#This is a program to find the sum of primes under two million\n\nimport math\n\nsumPrimes = 10\n\nfor number in range(7,2000000,2):\n\n for test in range(3,int(round(math.sqrt(number)))+2,2):\n\n if int(str(number)[-1])==5:\n break\n \n elif test==math.sqrt(number):\n break\n\n elif test==int(round(math.sqrt(number))):\n sumPrimes = sumPrimes + number\n elif test==int(round(math.sqrt(number))+1):\n sumPrimes = sumPrimes + number\n\n elif number%test==0:\n break\n\nprint(str(sumPrimes))\n","sub_path":"Old Solutions/10_primes.py","file_name":"10_primes.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"147284215","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('perfiles', '0003_auto_20141117_1525'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='perfilestudiantes',\n name='area',\n field=models.ForeignKey(blank=True, to='perfiles.Areas', null=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='perfilestudiantes',\n name='idioma',\n field=models.ManyToManyField(to='perfiles.Idiomas', null=True, blank=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='perfilestudiantes',\n name='lenguajes',\n field=models.ManyToManyField(to='perfiles.Lenguajes', null=True, blank=True),\n preserve_default=True,\n ),\n ]\n","sub_path":"perfiles/migrations/0004_auto_20141117_1542.py","file_name":"0004_auto_20141117_1542.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"248978606","text":"# Copyright (C) 2002-2017 CERN for the benefit of the ATLAS collaboration\n\n\nfrom AthenaCommon.SystemOfUnits import *\nfrom AthenaCommon.Logging import logging\n\nfrom JetRec.JetRecConf import FastJetInterfaceTool\n\n_fastjetLog = logging.getLogger(\"FastJetInterfaceConfiguration\")\n\n# set up some enumerator values\ndef enums(name='Enum',**enums):\n return type( name, (), enums)\n\n# recognized keys\nfastjet_conf_tags = enums('FastJetConfTags',\n Strategy=[ 'default', 'Best', \n 'N2MinHeapTiled','N2Tiled', 'N2PoorTiled', 'N2Plain',\n 'N3Dumb', \n 'NlnN', 'NlnN3pi', 'NlnN4pi', 'NlnNCam4pi', 'NlnNCam2pi2R', 'NlNCam',\n 'plugin_strategy' ],\n RecombScheme = [ 'default', 'E', 'pt', 'pt2', 'Et', 'Et2', 'BIpt', 'BIpt2' ],\n Algorithm = [ 'default', 'kt', 'Kt', 'anti-kt', 'AntiKt', 'cambridge', 'CamKt',\n 'genkt', 'passive cambridge', 'passive genkt',\n 'CMSCone', 'SISCone'],\n JetAreaMethod = [ 'default', 'VoronoiArea', 'ActiveArea', \n 'ActiveAreaExplicitGhost', 'PassiveArea', '1GhostPassiveArea' ],\n SISSplitMergeScale = [ 'default', 'pttilde', 'PtTilde', 'Pt', 'Et', 'mt' ], \n )\n\n# Ghosted area parameters\nfastjet_gas = enums('FastJetGhostAreaSettings',\n def_ghost_maxrap = 6.0, #fastjet::gas::def_ghost_maxrap\n def_repeat = 1, #fastjet::gas::def_repeat\n def_ghost_area = 0.01, #fastjet::gas::def_ghost_area\n def_grid_scatter = 1.0, #fastjet::gas::def_grid_scatter\n def_kt_scatter = 0.1, #fastjet::gas::def_kt_scatter\n def_mean_ghost_kt = 1e-100,#fastjet::gas::def_mean_ghost_kt\n )\n\n# ignored keys\nconfig_ignored_keys = enums('SetupIgnoredKeys',\n ControlKeys = [\"_alreadyChecked_\",\"_locked_\",\"_ignoreUnknown_\" ])\n\n# Default FastJet configuration dictionary: \n#\n# Most keys are the same as the corresponding FastJet tags or enumerator names.\n# In addition, for backward compatibility, the following tags are recognized:\n#\n# \ndefFastJetInterfaceConfigDict = {\n # -- overall setup and process control\n 'Algorithm' : \"anti-kt\",\n 'JetAreaMethod' : \"VoronoiArea\",\n 'CalculateJetArea' : False,\n # -- kt-style parameters\n 'Strategy' : \"Best\",\n 'RecombScheme' : \"E\",\n # -- CMS cone parameters\n 'CMS_SeedThreshold' : 15.*GeV,\n # -- SIS cone parameters\n 'SIS_OverlapThreshold' : 0.75,\n 'SIS_NumPassMax' : 0,\n 'SIS_ProtojetPtMin' : 0.0,\n 'SIS_DoCaching' : False,\n 'SIS_SplitMergeScale' : 'PtTilde',\n 'SIS_SplitMergeStopScale' : 0.0,\n # -- jet algorithm parameters\n 'Radius' : 0.4, # ATLAS default\n 'Inclusive' : True, # ATLAS default\n 'InclusivePtMin' : 0.*GeV,\n 'ExclusiveDcut' : 0.5, \n 'ExclusiveNjets' : 3,\n # -- jet area calculation directives and parameters\n 'VoronoiEffectiveRfact' : 1.0, # Voronoi\n 'GhostMaxRapidity' : fastjet_gas.def_ghost_maxrap, \n 'GhostMinRapidity' : -fastjet_gas.def_ghost_maxrap, \n 'GhostRepeats' : fastjet_gas.def_repeat,\n 'GhostAreaSize' : fastjet_gas.def_ghost_area,\n 'GhostGridScatter' : fastjet_gas.def_grid_scatter,\n 'GhostKtScatter' : fastjet_gas.def_kt_scatter,\n 'GhostMeanKt' : fastjet_gas.def_mean_ghost_kt\n }\n\n# Check whole dictionary or key/value assigments and return dictionary with\n# invalid options stripped (if allowed) or exception thrown for invalid options\ndef checkAndUpdate(**options):\n # already checked\n if options.get(\"_alreadyChecked_\",False) or options.get(\"_locked_\",False):\n return options\n\n # check what to do with unknowns\n ignoreUnknown = options.pop(\"_ignoreUnknown_\",False)\n\n # check every entry\n for k in options.keys():\n if k not in defFastJetInterfaceConfigDict :\n if ignoreUnknown :\n _fastjetLog.warning(\"Option %s unknown - ignoring it!\"%(k))\n options.pop(k)\n else :\n _fastjetLog.error(\"Option %s unknown - abort configuration!\"%(k))\n raise Exception\n\n checkedOptions = dict(defFastJetInterfaceConfigDict)\n for k,v in defFastJetInterfaceConfigDict.iteritems():\n t = type(v)\n if t in ( list, set, dict ) :\n checkedOptions[k] = t(v)\n\n checkedOptions['_alreadyChecked_'] = True\n checkedOptions.update(options)\n\n # check settings for Strategy\n key = \"Strategy\"\n # print checkedOptions\n tag = checkedOptions[key]\n _fastjetLog.info(\"Test option %s\",key)\n if checkedOptions[key] not in fastjet_conf_tags.Strategy :\n _fastjetLog.error(\"Strategy \\042%s\\042 not recognized - fatal! Allowed values are: \",checkedOptions['Strategy'])\n for s in fastjet_conf_tags.Strategy :\n _fastjetLog.error(\"\\042%s\\042\",s)\n raise Exception\n \n # check settings for RecombScheme\n if checkedOptions['RecombScheme'] not in fastjet_conf_tags.RecombScheme :\n _fastjetLog.error(\"RecombScheme \\042%s\\042 not recognized - fatal! Allowed values are: \",checkedOptions['RecombScheme'])\n for s in fastjet_conf_tags.RecombScheme :\n _fastjetLog.error(\"\\042%s\\042\",s)\n raise Exception\n \n # check settings for Algorithm\n if checkedOptions['Algorithm'] not in fastjet_conf_tags.Algorithm :\n _fastjetLog.error(\"Algorithm \\042%s\\042 not recognized - fatal! Allowed values are: \",checkedOptions['Algorithm'])\n for s in fastjet_conf_tags.Algorithm :\n _fastjetLog.error(\"\\042%%s\\042\",s)\n raise Exception\n\n # check settings for JetAreaMethod\n if checkedOptions['JetAreaMethod'] not in fastjet_conf_tags.JetAreaMethod :\n _fastjetLog.error(\"JetAreaMethod \\042%s\\042 not recognized - fatal! Allowed values are: \",checkedOptions['JetAreaMethod'])\n for s in fastjet_conf_tags.JetAreaMethod :\n _fastjetLog.error(\"\\042%s\\042\",s)\n raise Exception\n\n # check settings for SIS split merge scale\n if checkedOptions['SIS_SplitMergeScale'] not in fastjet_conf_tags.SISSplitMergeScale :\n _fastjetLog.error(\"SIS_SplitMergeScale \\042%2\\042 not recognized - fatal! Allowed values are: \",checkedOptions['SIS_SplitMergeScale'])\n for s in fastjet_conf_tags.SISSplitMergeScale :\n _fastjetLog.error(\"\\042%s\\042\",s)\n raise Exception\n\n return checkedOptions\n \n\ndef getFastJetInterfaceConfig(name,**options):\n # get tool configuration \n fjTool = FastJetInterfaceTool(name)\n from AthenaCommon.AppMgr import ToolSvc\n ToolSvc += fjTool\n # check job options\n options = checkAndUpdate(**options) \n # set tool properties\n for k,v in options.iteritems():\n if k not in config_ignored_keys.ControlKeys :\n setattr(fjTool,k,v)\n # return tool configuration object\n return fjTool\n\n#def dumpFastJetInterfaceConfig(**options=**defFastJetInterfaceConfigDict):\n# # write out all attributes\n# for k,v in options.iteritems():\n# _fastjetLog.message(\"Config::%s value %s\",%(k),%(v))\n \n","sub_path":"athena/Reconstruction/Jet/JetRec/python/FastJetInterfaceConfig.py","file_name":"FastJetInterfaceConfig.py","file_ext":"py","file_size_in_byte":7599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"608668458","text":"import os\nimport csv\n\n\ncsv_path = os.path.join(\"Resources\", \"budget_data.csv\")\n\n\nwith open(csv_path, newline=\"\") as file:\n\n budget_data_reader = csv.reader(file, delimiter=\",\")\n\n csv_header = next(budget_data_reader)\n\n row_count = 0\n net_total = 0\n changes = []\n row_val = int(next(budget_data_reader)[1])\n row_val_2 = row_val\n months = []\n\n for row in budget_data_reader:\n row_count += 1\n net_total += int(row[1])\n\n\n change = int(row[1]) - row_val\n changes.append(change)\n row_val = int(row[1])\n\n months.append(row[0])\n\n greatest_increase = max(changes)\n increase_index = changes.index(greatest_increase)\n\n greatest_decrease = min(changes)\n decrease_index = changes.index(greatest_decrease)\n\n greatest_increase_month = months[increase_index]\n greatest_decrease_month = months[decrease_index]\n\n # To account for second next()\n row_count = row_count + 1\n net_total = net_total + row_val_2\n\n\n average_change = round(sum(changes) / len(changes),2)\n\n\n\n\nprint(\"Financial Analysis\")\nprint(\"----------------------------\")\nprint(f\"Total Months: {row_count}\")\nprint(f\"Total: ${net_total}\")\nprint(f\"Average Change: ${average_change}\")\nprint(f\"Greatest Increase in Profits: {greatest_increase_month} (${greatest_increase})\")\nprint(f\"Greatest Decrease in Profits: {greatest_decrease_month} (${greatest_decrease})\")\n\n\nresults = open(\"Financial_Analysis.txt\", 'w')\nresults.write(f\"\"\"Financial Analysis\n----------------------------\nTotal Months: {row_count}\nTotal: ${net_total}\nAverage Change: ${average_change}\nGreatest Increase in Profits: {greatest_increase_month} (${greatest_increase})\nGreatest Decrease in Profits: {greatest_decrease_month} (${greatest_decrease})\n\"\"\")\n","sub_path":"Homework-3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"306368086","text":"import os\nimport sys\nimport traceback\nfrom time import sleep\nfrom selenium import webdriver\nfrom PIL import Image\n\n\nclass BrowserHelper(object):\n display = None\n driver = None\n\n def __del__(self):\n self.stop()\n\n def start(self):\n \"\"\"Start the browser.\"\"\"\n if self.driver is None: \n if \"darwin\" in sys.platform:\n CHROME_DRIVER_PATH = \"drivers/mac/chromedriver\"\n elif \"win\" in sys.platform:\n CHROME_DRIVER_PATH = \"drivers/windows/chromedriver.exe\"\n else:\n CHROME_DRIVER_PATH = \"drivers/linux/chromedriver\"\n options = webdriver.ChromeOptions()\n options.add_experimental_option(\"prefs\", {\"profile.managed_default_content_settings.images\":2})\n options.add_argument(\"--headless\")\n options.add_argument('disable-infobars')\n options.add_argument(\"--disable-extensions\")\n self.driver = webdriver.Chrome(CHROME_DRIVER_PATH, chrome_options=options)\n \n def stop(self):\n \"\"\"Stop the browser.\"\"\"\n if self.driver is not None:\n self.driver.quit()\n self.driver = None\n\n def restart(self):\n \"\"\"Restart the browser.\"\"\"\n self.stop()\n self.start()\n\n def save_screenshot(self, path=\"screenshot.jpg\"):\n \"\"\"Create screenshot.\"\"\"\n self.driver.save_screenshot(path)\n\n def get_cookies(self):\n \"\"\"Return cookies.\"\"\"\n cookies = {}\n for cookie in self.driver.get_cookies():\n cookies[cookie[\"name\"]] = cookie[\"value\"]\n return cookies\n\n def element_exist(self, xpath):\n \"\"\"Check if element exist.\"\"\"\n try:\n self.driver.find_element_by_xpath(xpath)\n return True\n except:\n return False\n \n def wait_element(self, xpath, sec=10):\n \"\"\"Wait and element.\"\"\"\n i = 0\n while i < sec:\n sleep(1)\n if self.element_exist(xpath):\n return True\n \n def click(self, xpath):\n \"\"\"Click.\"\"\"\n if self.element_exist(xpath):\n self.driver.find_element_by_xpath(xpath).click()\n\n def send_keys(self, xpath, keys):\n \"\"\"Send keys.\"\"\"\n if self.element_exist(xpath):\n self.driver.find_element_by_xpath(xpath).send_keys(keys)\n\n def save_screenshot_of_element(self, xpath, path):\n \"\"\"Save screenshot of element.\"\"\"\n element = self.driver.find_element_by_xpath(xpath)\n location = element.location\n size = element.size\n self.driver.save_screenshot(path)\n im = Image.open(path)\n left = location['x']\n top = location['y']\n right = location['x'] + size['width']\n bottom = location['y'] + size['height']\n im = im.crop((left, top, right, bottom))\n im.save(path)\n \n def get_page(self, link, cookies={},check_captha=False):\n \"\"\"Get page.\"\"\"\n self.driver.get(link)\n sleep(1)\n content = self.driver.page_source\n return content\n \n def get_page_source(self):\n return self.driver.page_source\n\n def login(self, url, login, password, loginXpath, passwordXpath, submitXpath = \"\", loginButtonXpath = \"\", frameXpath = \"\"):\n \"\"\"Login into the site.\"\"\"\n if url != \"\":\n self.driver.get(url)\n if loginButtonXpath != \"\":\n self.driver.find_element_by_xpath(loginButtonXpath).click()\n if frameXpath != \"\":\n self.driver.switch_to_frame(self.driver.find_element_by_xpath(frameXpath))\n self.driver.find_element_by_xpath(loginXpath).send_keys(login)\n self.driver.find_element_by_xpath(passwordXpath).send_keys(password)\n self.driver.find_element_by_xpath(submitXpath).click()\n sleep(1)\n","sub_path":"worker/helpers/browser_helper.py","file_name":"browser_helper.py","file_ext":"py","file_size_in_byte":3805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"9905702","text":"import datetime\r\nfrom django.test import TestCase\r\nfrom .models import Anken\r\n\r\n\r\nclass AnkenModelTests(TestCase):\r\n\r\n def test_anken_has_date(self):\r\n \"\"\"\r\n 作成した日記データに日付が付与されているか確認 \r\n \"\"\"\r\n Anken.objects.create(pub_date='2021-09-14',\r\n ankenmei='A案件',\r\n iraibusho='A部署',\r\n iraisha='Aさん',\r\n nouki='2021-09-30',\r\n mitumorikousu='0',\r\n naiyou='AAA',\r\n genjouchi='0', \r\n kitaikouka='0', \r\n tantousha='W', \r\n koumoku='案件', \r\n joutai='対応中', \r\n jissekikousu='0',)\r\n actual_anken = Anken.objects.get(pub_date='2021-09-14')\r\n print(actual_anken)\r\n self.assertIsInstance(actual_anken.pub_date, datetime.date)\r\n\r\n ","sub_path":"akapp/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"79742923","text":"# START LAB EXERCISE 05\nprint('Lab Exercise 05 \\n')\n\n# [IMPORTANT NOTE]\n# The autograder in Gradescope will directly test functions and files instead of variables\n# So even though the variables printed seems right, it's possible your code didn't pass all the test cases.\n\n# In this lab, we use txt files. Be very careful that a line in txt files\n# should contain a new line \"\\n\" character at the end of this line.\n\n# PROBLEM 1 (5 Points)\n# Define a function named \"concatenate_name_type\".\n# The function accepts two arguments - one is \"file_name\", the other is \"file_type\". Both two arguments are strings.\n# For given arguments, the function should return \".\"\n# Pass two defined variables \"file_name\", \"file_type\" to the function, assign the result to \"full_file_name\"\n# Print \"full_file_name\".\n\n# BEGIN PROBLEM 1 SOLUTION\nf_name = \"file1\"\nf_type = \"txt\"\n\ndef concatenate_name_type(file_name, file_type):\n return f\"{file_name}.{file_type}\"\n\nfull_file_name = concatenate_name_type(f_name, f_type)\nprint(full_file_name)\n\n# END PROBLEM 1 SOLUTION\n\n\n# PROBLEM 2 (10 Points)\n# Define a function named \"write_into_file\"\n# The function accepts two arguments - one is \"filename\", the other is \"file_content\"\n# \"filename\" is a string and \"file_content\" is a list of strings\n# Open the file with \"full_file_name\", read all lines, store the last two lines into the variable \"last_two_lines\"\n# Make sure that there is a new line character \"\\n\" at the end of each line in \"last_two_lines\"\n# Write \"last_two_lines\" into a new file called \"file2.txt\" using the function \"write_into_file\"\n# Print \"last_two_lines\"\n\n# BEGIN PROBLEM 2 SOLUTION\n\n#def write_into_file(filename, file_content):\n #path = filename\n #file_handle = open(path)\n #lines = file_handle.readlines()\n #file_handle.close()\n #for line in lines[-2:]:\n #file_content.append(line)\n\n#last_two_lines = []\n#path = 'file2.txt'\n#file_handle = open(path, 'w')\n#write_into_file(full_file_name, last_two_lines)\n#for line in last_two_lines:\n #file_handle.write(f\"{line}\")\n#file_handle.close()\n#print(last_two_lines)\n\ndef write_into_file(filename, file_content):\n file_open = open(filename, 'w')\n #for line in file_content:\n for line in file_content:\n file_open.write(f\"{line}\")\n file_open.close()\n\nlast_two_lines = []\nfile_handle = open(full_file_name)\nfile_content = file_handle.readlines()\nprint(file_content)\nlast_two_lines = file_content[1:]\nfilename = 'file2.txt'\nwrite_into_file(filename,last_two_lines)\nprint(last_two_lines)\n\n# END PROBLEM 2 SOLUTION\n\n\n# PROBLEM 3 (10 Points)\n# Finally, put all you've learned together.\n# Open each file with file_name in \"file_name_list\" and \"file_type\",\n# read all lines and store those unique lines into the variable \"unique_lines\".\n# Make sure that there is a new line character \"\\n\" at the end of each line in \"unique_lines\".\n# Write \"unique_lines\" into a new file called \"summary.txt\" using the function \"write_into_file\".\n# Print \"unique_lines\".\n\n# BEGIN PROBLEM 3 SOLUTION\nfile_name_list = [\"file1\", \"file2\", \"file3\"]\nfile_type = \"txt\"\nunique_lines = []\nall_files = []\nall_lines=[]\nall_lines_string = []\nunique_lines_list = []\nfor file in file_name_list:\n all_files.append(concatenate_name_type(file, file_type))\nprint(all_files)\n\nfor file in all_files:\n #print(file)\n file_opening = open(file, 'r')\n all_lines.append(file_opening.readlines())\n for line in all_lines:\n for x in line:\n all_lines_string.append(x.split(','))\nprint(all_lines_string)\n\nfor line in all_lines_string:\n if line not in unique_lines_list:\n unique_lines_list.append(line)\nfor line in unique_lines_list:\n for x in line:\n unique_lines.append(x)\n\n\n#for line in unique_lines:\n #print(line)\n #file_opening.close()\nwrite_into_file('summary.txt',unique_lines)\nprint(unique_lines)\n# END PROBLEM 3 SOLUTION\n#how can we check if a value already exists in unique_lines and only append it if it doesn't already exist\n#you'll need for loops to iterate through file_name_list and the lines within each file\n# END LAB EXERCISE\n","sub_path":"lab_05.py","file_name":"lab_05.py","file_ext":"py","file_size_in_byte":4100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"325572400","text":"from data_unit.windowQueue import mData\n\nmList = [\n [1, 8], [2, 2], [3, 4], [5, 11], [6, 7]\n]\nmList.sort(key=lambda each : each[1], reverse=True)\nprint(mList)\n\ni = 0\nlength = len(mList)\nwhile (i < length):\n mList[i][1] += 1\n i += 1\nprint(mList, end=\"\\n\\n\")\n\n\nmmList = [0,1,2,0,0,3,0,0,4,5,7]\nprint(mmList)\nfor i in range (0, 5):\n oneEl = mmList.pop(0)\n print(oneEl)\n print(mmList)\n\n\n# flow : list = []\n#\n# with open(\"streamSample.txt\", \"r\") as streamSample:\n# allData = streamSample.read().replace(' ', '').replace('\\n', '')\n# print(allData)\n# for N in allData:\n# newData = mData([N])\n# flow.append(newData)\n#\n# print(\"\\nStart Printing\")\n# i = 0\n# for x in flow:\n# print(i, end=\" \")\n# print(str(x))\n# i += 1\n# print(\"\\nAgain\")\n# i = 0\n# flowLength = len(flow)\n# for i in range (0, flowLength):\n# print(i, end=\" \")\n# print(flow[i])\n# i += 1\n# print(\"\\nEnumerate\")\n# for x in list(enumerate(flow)):\n# print(x[0], end=\" \")\n# print(str(x[1]))\n# exit(1)\n\n\n","sub_path":"pytest001/src/mytest04/testanything.py","file_name":"testanything.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"472099454","text":"lista_stip = []\nwhile True:\n stipendio=int(input(\"A quanto ammonta lo stipendio del dipendente? (se viene inviata la quota -1 verrà calcolata la media) \"))\n if stipendio == -1:\n break\n else:\n lista_stip.append(stipendio)\n\nmedia = sum(lista_stip)/len(lista_stip)\nprint(\"Media stipendi: \" + str(media))\n","sub_path":"es 26 pag 73.py","file_name":"es 26 pag 73.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"143567040","text":"\n\n\n# 419. Battleships in a Board\n\n# Given an 2D board, count how many battleships are in it. \n# The battleships are represented with 'X's, empty slots are represented with '.'s. You may assume the following rules:\n\n# You receive a valid board, made of only battleships or empty slots.\n# Battleships can only be placed horizontally or vertically.\n# In other words, they can only be made of the shape 1xN (1 row, N columns) or Nx1 (N rows, 1 column), where N can be of any size.\n\n#At least one horizontal or vertical cell separates between two battleships - there are no adjacent battleships.\n# Example:\n# X..X\n# ...X\n# ...X\n# In the above board there are 2 battleships.\n\n# Invalid Example:\n# ...X\n# XXXX\n# ...X\n# This is an invalid board that you will not receive - as battleships will always have a cell separating between them.\n\n# Follow up:\n# Could you do it in one-pass, using only O(1) extra memory and without modifying the value of the board?\n\nclass countBattleships:\n def doit1(self, board):\n \"\"\"\n :type board: List[List[str]]\n :rtype: int\n \"\"\"\n # Will not working in \"invalid case\"\n if not board :\n return 0\n\n m, n = len(board), len(board[0])\n count = 0\n for i in range(m):\n for j in range(n):\n\n if board[i][j] == 'X' and (i == 0 or board[i-1][j] == '.') and (j == 0 or board[i][j-1] == '.'):\n count += 1\n\n return count\n\n\n def doit(self, board):\n \"\"\"\n :type board: List[List[str]]\n :rtype: int\n \"\"\"\n if len(board) == 0:\n return 0;\n \n shipCount = 0;\n \n for y, row in enumerate(board):\n for x, cell in enumerate(row):\n if cell == \"X\" and checkShip(board, x, y):\n shipCount += 1;\n \n return shipCount;\n \n def checkShip(board, x, y):\n\n if checkLeft(board, x, y) or checkTop(board, x, y):\n return False;\n \n if checkRight(board, x, y):\n while checkThis(board, x, y):\n if checkTop(board, x, y) or checkBottom(board, x, y):\n return False;\n x += 1; \n\n elif checkBottom(board, x, y):\n while checkThis(board, x, y):\n if checkLeft(board, x, y) or checkRight(board, x, y):\n return False;\n y += 1;\n \n return True;\n \n def checkLeft(self, board, x, y):\n return x > 0 and board[y][x - 1] == \"X\";\n \n def checkRight(self, board, x, y):\n return x < len(board[0]) - 1 and board[y][x + 1] == \"X\";\n \n def checkTop(self, board, x, y):\n return y > 0 and board[y - 1][x] == \"X\";\n \n def checkBottom(self, board, x, y):\n return y < len(board) - 1 and board[y + 1][x] == \"X\";\n \n def checkThis(self, board, x, y):\n return x >= 0 and x < len(board[0]) and y >= 0 and y < len(board) and board[y][x] == \"X\";\n\n\n\nif __name__==\"__main__\":\n\n a = [\n ['X','.','.', 'X'],\n ['.','.','.', 'X'],\n ['.','.','.', 'X']\n ]\n\n res = countBattleships().doit(a)\n \n\n a = [\n ['.','.','.', 'X'],\n ['X','X','X', 'X'],\n ['.','.','.', 'X']\n ]\n\n res = countBattleships().doit(a)\n pass\n\n ","sub_path":"PythonLeetcode/LeetCodeE/429_BattleshipsinBoard.py","file_name":"429_BattleshipsinBoard.py","file_ext":"py","file_size_in_byte":3451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"270138971","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.spatial import ConvexHull\r\nimport seaborn as sns\r\nfrom pywaffle import Waffle\r\nimport squarify\r\nimport random\r\nimport math\r\nimport matplotlib; matplotlib.use('TkAgg')\r\nimport warnings; warnings.filterwarnings('ignore')\r\nimport warnings; warnings.filterwarnings(action='once')\r\n\r\n\r\nlarge = 22; med = 16; small = 12\r\nparams = {'axes.titlesize': large,\r\n 'legend.fontsize': med,\r\n 'figure.figsize': (16, 10),\r\n 'axes.labelsize': med,\r\n 'axes.titlesize': med,\r\n 'xtick.labelsize': med,\r\n 'ytick.labelsize': med,\r\n 'figure.titlesize': large}\r\n\r\n###=============================================================================================\r\n\r\n\r\ndef Waffle_Chart(self):\r\n\r\n df_raw = pd.read_csv(\"dataVset/mpg_ggplot2.csv\")\r\n\r\n # Prepare Data\r\n df = df_raw.groupby('class').size().reset_index(name='counts')\r\n n_categories = df.shape[0]\r\n colors = [plt.cm.inferno_r(i / float(n_categories)) for i in range(n_categories)]\r\n\r\n # Draw Plot and Decorate\r\n fig = plt.figure(\r\n \"Waffle Chart\",\r\n FigureClass=Waffle,\r\n plots={\r\n '111': {\r\n 'values': df['counts'],\r\n 'labels': [\"{0} ({1})\".format(n[0], n[1]) for n in df[['class', 'counts']].itertuples()],\r\n 'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12},\r\n 'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize': 18}\r\n },\r\n },\r\n rows=7,\r\n colors=colors,\r\n figsize=(16, 9)\r\n )\r\n plt.show()\r\n\r\n###=============================================================================================\r\n\r\ndef Pie_Chart(self):\r\n df_raw = pd.read_csv(\"dataVset/mpg_ggplot2.csv\")\r\n\r\n # Prepare Data\r\n df = df_raw.groupby('class').size().reset_index(name='counts')\r\n\r\n # Draw Plot\r\n fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect=\"equal\"), dpi=80)\r\n\r\n data = df['counts']\r\n categories = df['class']\r\n explode = [0, 0, 0, 0, 0, 0.1, 0]\r\n\r\n def func(pct, allvals):\r\n absolute = int(pct / 100. * np.sum(allvals))\r\n return \"{:.1f}% ({:d} )\".format(pct, absolute)\r\n\r\n wedges, texts, autotexts = ax.pie(data,\r\n autopct=lambda pct: func(pct, data),\r\n textprops=dict(color=\"w\"),\r\n colors=plt.cm.Dark2.colors,\r\n startangle=140,\r\n explode=explode)\r\n\r\n # Decoration\r\n ax.legend(wedges, categories, title=\"Vehicle Class\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\r\n plt.setp(autotexts, size=10, weight=700)\r\n ax.set_title(\"Class of Vehicles: Pie Chart\")\r\n plt.show()\r\n\r\n###=============================================================================================\r\n\r\ndef Treemap(self):\r\n df_raw = pd.read_csv(\"dataVset/mpg_ggplot2.csv\")\r\n\r\n # Prepare Data\r\n df = df_raw.groupby('class').size().reset_index(name='counts')\r\n labels = df.apply(lambda x: str(x[0]) + \"\\n (\" + str(x[1]) + \")\", axis=1)\r\n sizes = df['counts'].values.tolist()\r\n colors = [plt.cm.Spectral(i / float(len(labels))) for i in range(len(labels))]\r\n\r\n # Draw Plot\r\n plt.figure(\"Treemap\",figsize=(12, 8), dpi=80)\r\n squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8)\r\n\r\n # Decorate\r\n plt.title('Treemap of Vechile Class')\r\n plt.axis('off')\r\n plt.show()\r\n\r\n###=============================================================================================\r\n\r\ndef Bar_Chart(self):\r\n df_raw = pd.read_csv(\"dataVset/mpg_ggplot2.csv\")\r\n\r\n # Prepare Data\r\n df = df_raw.groupby('manufacturer').size().reset_index(name='counts')\r\n n = df['manufacturer'].unique().__len__() + 1\r\n all_colors = list(plt.cm.colors.cnames.keys())\r\n random.seed(100)\r\n c = random.choices(all_colors, k=n)\r\n\r\n # Plot Bars\r\n plt.figure(\"Bar Chart\",figsize=(16, 10), dpi=80)\r\n plt.bar(df['manufacturer'], df['counts'], color=c, width=.5)\r\n for i, val in enumerate(df['counts'].values):\r\n plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom',\r\n fontdict={'fontweight': 500, 'size': 12})\r\n\r\n # Decoration\r\n plt.gca().set_xticklabels(df['manufacturer'], rotation=60, horizontalalignment='right')\r\n plt.title(\"Number of Vehicles by Manaufacturers\", fontsize=22)\r\n plt.ylabel('# Vehicles')\r\n plt.ylim(0, 45)\r\n plt.show()\r\n\r\n###=============================================================================================","sub_path":"TrafficSolutionAnalysis/TrafficRoadCal/DVFunction/Composition.py","file_name":"Composition.py","file_ext":"py","file_size_in_byte":4765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"423264845","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\nPublish a service scheduler to SNS.\n\nThis script runs on a fixed schedule to send an SNS notification to start\none of our adapters. It receives a blob of JSON from a CloudWatch timed\nevent, and publishes that to the service scheduler topic.\n\"\"\"\n\nfrom wellcome_lambda_utils.sns_utils import publish_sns_message\n\n\ndef main(event, _):\n print(f'event = {event!r}')\n message = {\n 'cluster': event['cluster'],\n 'service': event['service'],\n 'desired_count': event['desired_count'],\n }\n publish_sns_message(topic_arn=event['topic_arn'], message=message)\n","sub_path":"shared_infra/service_scheduler/src/service_scheduler.py","file_name":"service_scheduler.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"576917371","text":"import time\nimport sys\n\ndef instrucoes():\n print(\"Este é um jogo da velha.\")\n print(\"As jogadas serão passadas por coordenadas indicadas por números de 1-9\")\n print()\n print(\"|1 2 3|\")\n print(\"|4 5 6|\")\n print(\"|7 8 9|\")\n print(\"------------------------------------------------------------------------\")\n \ndef tabuleiro():\n global tabu\n tabu= [\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"]\n \n \ndef mostra():\n print(tabu[0],tabu[1],tabu[2])\n print(tabu[3],tabu[4],tabu[5])\n print(tabu[6],tabu[7],tabu[8])\n \ndef jogadas():\n for i in range(0,9):\n jog=True\n if i%2 == 0:\n j1 = int(input(\"Jogador1: \"))\n while jog:\n if tabu[j1-1]==\".\":\n tabu[j1-1]=str(\"X\")\n mostra()\n if tabu[0]==\"X\" and tabu[1]==\"X\" and tabu[2]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[3]==\"X\" and tabu[4]==\"X\" and tabu[5]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[6]==\"X\" and tabu[7]==\"X\" and tabu[8]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[0]==\"X\" and tabu[3]==\"X\" and tabu[6]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[1]==\"X\" and tabu[4]==\"X\" and tabu[7]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[2]==\"X\" and tabu[5]==\"X\" and tabu[8]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[0]==\"X\" and tabu[4]==\"X\" and tabu[8]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n elif tabu[2]==\"X\" and tabu[4]==\"X\" and tabu[6]==\"X\":\n print(\"JOGADOR 1 VENCEU!!!\")\n time.sleep(2)\n dnovo()\n else:\n pass \n jog=False\n else:\n print(\"Escolha uma casa vazia!\")\n j1 = int(input(\"Jogador1: \"))\n else:\n j2 = int(input(\"Jogador2: \"))\n while jog:\n if tabu[j2-1]==\".\":\n tabu[j2-1]=str(\"O\")\n mostra()\n if tabu[0]==\"O\" and tabu[1]==\"O\" and tabu[2]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[3]==\"O\" and tabu[4]==\"O\" and tabu[5]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[6]==\"O\" and tabu[7]==\"O\" and tabu[8]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[0]==\"O\" and tabu[3]==\"O\" and tabu[6]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[1]==\"O\" and tabu[4]==\"O\" and tabu[7]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[2]==\"O\" and tabu[5]==\"O\" and tabu[8]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[0]==\"O\" and tabu[4]==\"O\" and tabu[8]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n elif tabu[2]==\"O\" and tabu[4]==\"O\" and tabu[6]==\"O\":\n print(\"JOGADOR 2 VENCEU!!!\")\n dnovo()\n else:\n pass\n jog=False\n else:\n print(\"Escolha uma casa vazia!\")\n j2 = int(input(\"Jogador2: \"))\n\ndef dnovo():\n x=input(\"Deseja jogar de novo? s/n: \")\n if x==\"s\":\n instrucoes()\n tabuleiro()\n mostra()\n jogadas()\n elif x==\"n\":\n time.sleep(2)\n sys.exit()\n else:\n print(\"Opção inválida!\")\n print(\"Digite s para sim ou n para não\")\n dnovo()\n\n \ninstrucoes()\ntabuleiro()\nmostra()\njogadas()\n","sub_path":"jv.py","file_name":"jv.py","file_ext":"py","file_size_in_byte":4692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"504681442","text":"import numpy as np\n\n\ndef getVesselMeanWidth(dict_vesselWidth):\n ##first, get the mean vessel width for each segments\n dict_meanVesselWidth = {}\n for vesselkey in dict_vesselWidth.keys():\n segVesselWidths = dict_vesselWidth[vesselkey]\n meanwidth0 = np.mean(segVesselWidths)\n std0 = np.std(segVesselWidths)\n filteredWidths = segVesselWidths[np.abs(segVesselWidths - meanwidth0) <= np.minimum(2*std0, 2.5)]\n if len(filteredWidths) > 30: #if the width is less than 30 pixels, then remove it.\n meanWidth = np.mean(filteredWidths)\n # else:\n # meanWidth = meanwidth0\n\n dict_meanVesselWidth[vesselkey] = meanWidth #[meanWidth, meanwidth0, std0]\n\n\n return dict_meanVesselWidth","sub_path":"pulseWavePropagation/VesselWidth/GetVesselMeanWidth.py","file_name":"GetVesselMeanWidth.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"408411568","text":"import pandas as pd\nfrom utils.constants import LEVEL_1_keyword, OCCUPATION_keyword, ALSFRSR_keyword\n\n'''\nfile and data loading functions\n'''\ndef readMetricsExcelData(data_folder, file_name, sheet_name_or_index):\n file_to_open = data_folder / file_name\n #read sheet\n df = pd.read_excel(file_to_open, sheet_name=sheet_name_or_index, header=None)\n #convert all feature values to string\n df = df.astype(str)\n return df\n\n# for script a_baseline_classification.py\n\n#remove .0 from values\ndef removeFloatPartOfString(s): \n #forceful convertion to str\n if type(s) != \"str\":\n s = str(s) \n #no point, nothing to do\n if s.find(\".\") == -1:\n return s\n else:\n #remove all after the dot (inclusive)\n return s[:s.find(\".\")]\n\n#returns ndarrays and a list of features\ndef load_original_data_with_missings(file_name, features_to_keep=['ALL']):\n df = pd.read_csv(file_name, engine='python')\n # keep only the features in features_to_keep\n if features_to_keep[0] != 'ALL':\n df = df[features_to_keep]\n #convert missings to -1\n df = df.fillna(-1) \n #convert all feature values to string\n df = df.astype(str)\n #sort values by group class\n df = df.sort_values(by=['group'])\n #remove all decimal parts \n for i in range(0, df.columns.size):\n df[df.columns[i]] = df[df.columns[i]].apply(removeFloatPartOfString) \n #convert the data frame read from the csv file to a matrix\n table_X = df.values\n #table_y: last column of the file, the target class column\n table_y = table_X[:, -1]\n #cast all table_y elements to int\n table_y = table_y.astype(int)\n #remove first (subject id) and last (target class) columns of the data matrix \n #to only keep the data\n table_X = table_X[:, list(range(1,len(df.columns) - 1))]\n #cast all table_X elements to int\n table_X = table_X.astype(int)\n #get list of feature names (also removing first and last columns)\n feature_names = list(df)[1:-1]\n #return the feature data and the target data\n return table_X, table_y, feature_names\n\n# for script b_subject_biclusters_classification.py\n\n#returns ndarrays and a list of features\ndef load_matrix_data_no_missings(file_name):\n df = pd.read_csv(file_name, engine='python')\n #convert the data frame read from the csv file to a matrix\n table_X = df.values\n #table_y: last column of the file, the target class column\n table_y = table_X[:, -1]\n #cast all table_y elements to int\n table_y = table_y.astype(int)\n #remove first (subject id) and last (target class) columns of the data matrix \n #to only keep the data\n table_X = table_X[:, list(range(1,len(df.columns) - 1))]\n #cast all table_X elements to int\n table_X = table_X.astype(int)\n #get list of feature names (also removing first and last columns)\n feature_names = list(df)[1:-1]\n #return the feature data and the target data\n return table_X, table_y, feature_names\n\n# for script c_merged_data_classification.py\n\n#returns dataframe\ndef load_to_merge_original_data_with_missings(file_name, features_to_keep=['ALL']):\n df = pd.read_csv(file_name, engine='python')\n # keep only the features in features_to_keep\n if features_to_keep[0] != 'ALL':\n df = df[features_to_keep]\n #convert missings to -1\n df = df.fillna(-1) \n #convert all to string\n df = df.astype(str)\n #remove all decimal parts \n for i in range(0, df.columns.size):\n df[df.columns[i]] = df[df.columns[i]].apply(removeFloatPartOfString) \n return df\n\n#returns dataframe\ndef load_to_merge_matrix_data_no_missings(file_name):\n df = pd.read_csv(file_name, engine='python')\n return df\n\n# for baseline scripts (to translate from category values to labels)\ndef correctZeroPrefixCategories(value):\n if value not in [\"NA\", \"nan\", \"NaN\"] and 0 <= int(value) < 10:\n return \"0\" + value\n else:\n return value\n\ndef getTranslationMapCategoriesLabels(data_folder, xslx_translation_categories_labels):\n # create map\n label_map = {}\n # read file\n file_to_open = data_folder / xslx_translation_categories_labels\n # read first and only sheet (with header in the first line)\n label_data = pd.read_excel(file_to_open, sheet_name=0, header=0)\n # convert everything to strings (NaN are \"nan\" now and integers gain a decimal case :S) \n label_data = label_data.astype(str)\n #for each second column, remove all decimal parts \n for i in range(0, label_data.columns.size, 2):\n label_data[label_data.columns[i+1]] = label_data[label_data.columns[i+1]].apply(removeFloatPartOfString) \n # if column is one of ALSFRSR, remove floats from first column too\n if (ALSFRSR_keyword in label_data.columns[i]):\n label_data[label_data.columns[i]] = label_data[label_data.columns[i]].apply(removeFloatPartOfString) \n # #failsafe for Occupation (level 1) columns (that have categories like 01)\n # for i in range(0, label_data.columns.size, 2):\n # if (OCCUPATION_keyword in label_data.columns[i]) and (LEVEL_1_keyword in label_data.columns[i]):\n # label_data[label_data.columns[i+1]] = label_data[label_data.columns[i+1]].apply(correctZeroPrefixCategories) \n # iterate through all columns, two by two (first column: labels, second column: category values)\n for i in range(0, label_data.columns.size, 2):\n # get column name\n columnName = label_data.columns[i]\n # remove nan's from labels and category values' columns\n temp_labels = [x for x in label_data[label_data.columns[i]].tolist() if x != 'nan']\n temp_cat_values = [x for x in label_data[label_data.columns[i + 1]].tolist() if x != 'nan']\n # create new map\n feature_map = {}\n # add category values as keys, labels as values\n for j in range(0, len(temp_labels)):\n feature_map[temp_cat_values[j]] = temp_labels[j]\n # add feature map to label map\n label_map[columnName] = feature_map\n\n #return map\n return label_map\n\n\n\n","sub_path":"Python_code/scripts_task_2/utils/load_data_utils.py","file_name":"load_data_utils.py","file_ext":"py","file_size_in_byte":6054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"206109588","text":"#Imports\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport datetime\r\nimport os\r\nimport sys\r\n\r\nyear = datetime.datetime.today().year\r\n\r\ndef Pirates():\r\n #Outfile\r\n outfile = open('Pirates Pitching Stats.txt', 'a') \r\n #Loop 1\r\n again = 1\r\n while again == 1: \r\n #Pirates 2019 Pitching Staff\r\n fnames = ['Chris', 'Steven', 'Nick', 'Kyle', 'Michael', 'Keone', 'Nick', 'Francisco', 'Jordan', 'Joe', 'Jameson', 'Richard', 'Felipe', 'Trevor' ]\r\n lnames = ['Archer', 'Brault', 'Burdi', 'Crick', 'Feliz', 'Kela', 'Kingham', 'Liriano', 'Lyles', 'Musgrove', 'Taillon', 'Rodriguez', 'Vazquez', 'Williams'] \r\n #Counters and Variables\r\n i = 0\r\n games_won = 0\r\n games_lost = 0\r\n era = 0\r\n #ave_era = 0\r\n year = datetime.datetime.today().year\r\n #Title\r\n print(f'\\n\\nPittsburgh Pirates Pitching Staff, {year}\\n')\r\n #Loop 2\r\n while i < len(lnames):\r\n #Rodriguez is on a different html page\r\n if lnames[i] == 'Rodriguez':\r\n page = '05.shtml'\r\n else: \r\n page = '01.shtml'\r\n #Print Player Info\r\n player = lnames[i].lower()[:5]+fnames[i].lower()[:2]+page\r\n firstletter = lnames[i][:1].lower()\r\n #URL\r\n if lnames[i] == 'Vazquez':\r\n url = 'https://www.baseball-reference.com/players/r/riverfe01.shtml' \r\n else:\r\n url = 'https://www.baseball-reference.com/players/%s/%s' % (firstletter, player)\r\n response = requests.get(url)\r\n #Soup\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n #Soup Finds\r\n wins = soup.find('h4', class_='poptip', attrs={'data-tip': 'Wins'})\r\n losses = soup.find('h4', class_='poptip', attrs={'data-tip': 'Losses'})\r\n era = soup.find('h4', class_='poptip', attrs={'data-tip': '9 * ER / IP
For recent years, leaders need 1 IP
per team game played.
Bold indicates lowest ERA using current stats
Gold means awarded ERA title at end of year.'})\r\n games_pitched = soup.find('h4', class_='poptip', attrs={'data-tip': 'Games Played
This includes all times that the player appeared on the lineup card. Pitchers in non-DH games that appeared on the lineup card but didn\\'t bat will still have a game in this column.'})\r\n games_started = soup.find('h4', class_='poptip', attrs={'data-tip': 'Games Started'})\r\n games_saved = soup.find('h4', class_='poptip', attrs={'data-tip': 'Saves'})\r\n innings_pitched = soup.find('h4', class_='poptip', attrs={'data-tip': 'Innings Pitched'}) \r\n whip = soup.find('h4', class_='poptip', attrs={'data-tip': '(BB + H)/IP
For recent years, leaders need 1 IP
per team game played'}) \r\n #Soup Next Siblings\r\n wins = wins.next_sibling\r\n losses = losses.next_sibling\r\n era = era.next_sibling\r\n games_pitched = games_pitched.next_sibling\r\n games_started = games_started.next_sibling\r\n games_saved = games_saved.next_sibling\r\n innings_pitched = innings_pitched.next_sibling\r\n whip = whip.next_sibling\r\n #Print Player's name\r\n #print(lnames[i]+', '+fnames[i]+'\\n'+soup.find(itemprop='name').find_next('p').find_next('p').contents[2].strip()+'-Handed '+soup.find(itemprop='name').find_next('p').contents[2].strip()+' ('+player+')')\r\n print(lnames[i]+', '+fnames[i])\r\n outfile.write(lnames[i]+', '+fnames[i]+':\\n')\r\n #Print \r\n if wins.text == 0:\r\n print('\\tWins (YTD): 0')\r\n outfile.write('\\tWins (YTD): 0\\n')\r\n else:\r\n print('\\tWins (YTD): '+wins.text)\r\n outfile.write('\\tWins (YTD): '+wins.text+'\\n')\r\n if losses.text == 0:\r\n print('\\tLosses (YTD): 0') \r\n outfile.write('\\tLosses (YTD): 0\\n')\r\n else:\r\n print('\\tLosses (YTD): '+losses.text)\r\n outfile.write('\\tLosses (YTD): '+losses.text+'\\n') \r\n if era.text == 0:\r\n print('\\tERA (YTD): 0')\r\n outfile.write('\\tERA (YTD): 0\\n')\r\n else:\r\n print('\\tERA (YTD): '+era.text) \r\n outfile.write('\\tERA (YTD): '+era.text+'\\n')\r\n print('\\tGames Pitched (YTD): '+games_pitched.text)\r\n outfile.write('\\tGames Pitched (YTD): '+games_pitched.text+'\\n')\r\n if games_started.text == 0:\r\n print('\\tGames Started (YTD): 0')\r\n outfile.write('\\tGames Started (YTD): 0\\n')\r\n else:\r\n print('\\tGames Started (YTD): '+games_started.text)\r\n outfile.write('\\tGames Started (YTD): '+games_started.text+'\\n')\r\n if games_saved.text == 0:\r\n print('\\tGames Saved (YTD): 0')\r\n outfile.write('\\tGames Saved (YTD): 0')\r\n else:\r\n print('\\tGames Saved (YTD): '+games_saved.text)\r\n outfile.write('\\tGames Saved (YTD): '+games_saved.text+'\\n') \r\n print('\\tInnings Pitched (YTD): '+innings_pitched.text)\r\n outfile.write('\\tInnings Pitched (YTD): '+innings_pitched.text+'\\n')\r\n print('\\tWHIP (YTD): '+whip.text)\r\n outfile.write('\\tWHIP (YTD): '+whip.text+'\\n')\r\n print('-' * 45)\r\n outfile.write('-' * 52)\r\n outfile.write('\\n')\r\n #Increment\r\n games_won = games_won + int(wins.text)\r\n games_lost = games_lost + int(losses.text)\r\n i += 1\r\n #Team Stats\r\n print(f'\\nPirates Pitching Staff Record ({year} YTD):\\n')\r\n print('Games Won (YTD): ',games_won,'\\n')\r\n print('Games Lost (YTD): ',games_lost,'\\n')\r\n print(f'Games Won/Lost Record (YTD): {games_won/(games_won+games_lost):.3f}')\r\n outfile.write(f'\\nPirates Pitching Staff Record ({year} YTD):\\n')\r\n outfile.write('Games Won (YTD): '+str(games_won)+'\\n')\r\n outfile.write('Games Lost (YTD): '+str(games_lost)+'\\n')\r\n outfile.write(f'Games Won/Lost Record (YTD): {games_won/(games_won+games_lost):.3f}')\r\n #Close file\r\n outfile.close()\r\n #Again\r\n again = int(input('\\nWould you like to run again (1) or select another pitcher (2) or (3) to quit.'))\r\n if again == 2:\r\n AnotherPitcher()\r\n \r\ndef AnotherPitcher():\r\n #Loop 1\r\n again = 1\r\n while again == 1:\r\n another_pitcher_lname = input('Enter another pitcher\\'s Last Name: ')\r\n another_pitcher_fname = input('Enter another pitcher\\'s First Name: ')\r\n #Counters and Variables\r\n i = 0\r\n games_won = 0\r\n games_lost = 0\r\n era = 0\r\n year = datetime.datetime.today().year\r\n #Title\r\n print(f'\\n\\n{another_pitcher_fname} {another_pitcher_lname}, {year} Stats\\n')\r\n #Print Player Info\r\n page = '01.shtml'\r\n player = another_pitcher_lname.lower()[:5]+another_pitcher_fname.lower()[:2]+page\r\n firstletter = another_pitcher_lname[:1].lower()\r\n #URL\r\n url = 'https://www.baseball-reference.com/players/%s/%s' % (firstletter, player)\r\n response = requests.get(url)\r\n #Soup\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n #Soup Finds\r\n wins = soup.find('h4', class_='poptip', attrs={'data-tip': 'Wins'})\r\n losses = soup.find('h4', class_='poptip', attrs={'data-tip': 'Losses'})\r\n era = soup.find('h4', class_='poptip', attrs={'data-tip': '9 * ER / IP
For recent years, leaders need 1 IP
per team game played.
Bold indicates lowest ERA using current stats
Gold means awarded ERA title at end of year.'})\r\n games_pitched = soup.find('h4', class_='poptip', attrs={'data-tip': 'Games Played
This includes all times that the player appeared on the lineup card. Pitchers in non-DH games that appeared on the lineup card but didn\\'t bat will still have a game in this column.'})\r\n games_started = soup.find('h4', class_='poptip', attrs={'data-tip': 'Games Started'})\r\n games_saved = soup.find('h4', class_='poptip', attrs={'data-tip': 'Saves'})\r\n innings_pitched = soup.find('h4', class_='poptip', attrs={'data-tip': 'Innings Pitched'}) \r\n whip = soup.find('h4', class_='poptip', attrs={'data-tip': '(BB + H)/IP
For recent years, leaders need 1 IP
per team game played'}) \r\n #Soup Next Siblings\r\n wins = wins.next_sibling\r\n losses = losses.next_sibling\r\n era = era.next_sibling\r\n games_pitched = games_pitched.next_sibling\r\n games_started = games_started.next_sibling\r\n games_saved = games_saved.next_sibling\r\n innings_pitched = innings_pitched.next_sibling\r\n whip = whip.next_sibling\r\n #Print Player's name\r\n #print(another_pitcher_lname+', '+another_pitcher_fname+'\\n'+soup.find(itemprop='name').find_next('p').find_next('p').contents[2].strip()+'-Handed '+soup.find(itemprop='name').find_next('p').contents[2].strip()+' ('+player+')')\r\n print(another_pitcher_lname+', '+another_pitcher_fname)\r\n #Print \r\n print('Wins (YTD): 0') if wins.text == 0 else print('Wins (YTD): '+wins.text)\r\n print('Losses (YTD): 0') if losses.text == 0 else print('Losses (YTD): '+losses.text)\r\n print('ERA (YTD): 0') if era.text == 0 else print('ERA (YTD): '+era.text) \r\n print('Games Pitched (YTD): '+games_pitched.text)\r\n print('Games Started (YTD): 0') if games_started.text == 0 else print('Games Started (YTD): '+games_started.text)\r\n print('Games Saved (YTD): 0') if games_saved.text == 0 else print('Games Saved (YTD): '+games_saved.text)\r\n print('Innings Pitched (YTD): '+innings_pitched.text)\r\n print('WHIP (YTD): '+whip.text)\r\n print('-' * 45)\r\n #Increment\r\n games_won = games_won + int(wins.text)\r\n games_lost = games_lost + int(losses.text)\r\n i += 1\r\n #Again\r\n again = int(input('\\nWould you like to run again? Type (1) or (2) to Quit.'))\r\n if again not in [1, 2, 3]:\r\n print('You must enter a digit between 1 and 2. Please enter again: ')\r\n if again == 1:\r\n Main()\r\n elif again == 2:\r\n quit\r\n \r\ndef OpenFile():\r\n os.system ('notepad.exe Pirates Pitching Stats.txt')\r\n \r\ndef Main():\r\n \r\n stats = int(input('Enter (1) for the Pirates Pitching Stats (Writes data to: Pittsburgh Pirates Stats.txt), (2) to look-up another Pitcher\\'s stats, (3) Open Pittsburgh Pirates Stats.txt: '))\r\n if stats == 1:\r\n Pirates()\r\n elif stats == 2:\r\n AnotherPitcher()\r\n elif stats == 3: \r\n OpenFile()\r\n elif stats not in [1, 2, 3]:\r\n print('You must enter a digit between 1 and 3. Please enter again: ')\r\n stats = int(input('Enter (1) for the Pirates Pitching Stats (Writes data to: Pittsburgh Pirates Stats.txt), (2) to look-up another Pitcher\\'s stats, (3) Open Pittsburgh Pirates Stats.txt: '))\r\n if stats == 1:\r\n Pirates()\r\n elif stats == 2:\r\n AnotherPitcher()\r\n elif stats == 3: \r\n OpenFile()\r\n \r\n#Main Function \r\nMain()","sub_path":"Pirates Pitching Stats - MLB Pitcher Scrape.py","file_name":"Pirates Pitching Stats - MLB Pitcher Scrape.py","file_ext":"py","file_size_in_byte":11466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"393168301","text":"import random\n\nclass Hash:\n\n\n def randomizer(self):\n return self.salt\n\n def __init__(self, keys):\n self.lower_characters = ['q', 'w', 'e', 'r', 't', 'y', 'u', 'i', 'o', 'p', 'a',\n 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'z', 'x', 'c', 'v', 'b', 'n', 'm']\n self.upper_characters = ['Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'I', 'O', 'P', 'A',\n 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'Z', 'X', 'C', 'V', 'B', 'N', 'M']\n self.numbers_characters = ['1', '2', '3',\n '4', '5', '6', '7', '8', '9', '0']\n self.special_chars = ['!', '@', '#', '$', '%', '^', '&',\n '*', '(', ')', '-', '_', '+', '=', ',', ' ', ';', ':']\n self.reference_characters = self.lower_characters + \\\n self.upper_characters + self.numbers_characters + self.special_chars\n self.combined = ['a', 'x', 'T', 'F', 'B', '8', '*', ';', 'p', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'w', 'z', 'c', 'v', 'b', 'n', 'm', 'Q', 'W', 'E', 'e', 'R', 'Y', 'U', 'I', 'O', 'P', 'A', 'S', 'r', 'D',\n 'G', 'H', 'J', 'K', 'L', 'Z', 'X', 'C', 't', 'V', 'N', 'M', '1', '2', '3', '4', '5', '6', 'y', '7', '9', '0', '!', '@', '#', '$', '%', '^', 'u', '&', '(', ')', '-', '_', '+', '=', ',', 'i', ' ', ':', 'o', 'q']\n\n # Generates a float from the key which will be used to randomize all characters, numbers and special symbols\n self.salt = ''\n for key in keys:\n self.salt = self.salt + str(self.combined.index(key))\n self.salt = int(self.salt)/10**len(self.salt)\n\n # Uses a float generated from the key to randomize the characters\n random.shuffle(self.reference_characters, self.randomizer)\n\n def encrypt(self, message):\n encrypted_message = ''\n for message_character in message:\n target = self.reference_characters.index(message_character)\n encrypted_message = encrypted_message + self.combined[target]\n return encrypted_message\n\n def decrypt(self, message):\n decrypted_message = ''\n for encrypted_message_character in message:\n target = self.combined.index(encrypted_message_character)\n decrypted_message = decrypted_message + \\\n self.reference_characters[target]\n return decrypted_message\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"326661547","text":"import numpy as np\nimport matplotlib.pyplot as plt\n#\nflow_label = np.load('flow_label.npy')\nflow_predict_distill = np.load('flow_distill.npy')\nflow_predict_student = np.load('flow_student.npy')\nflow_predict_student_no_weather = np.load('flow_student_no_weather.npy')\n\n\n# MSE 时刻平均(最后一维是时间)\nmse_distill = np.mean(np.mean(np.mean(np.square(flow_label - flow_predict_distill), axis=0), axis=0), axis=0)\nmse_student = np.mean(np.mean(np.mean(np.square(flow_label - flow_predict_student), axis=0), axis=0), axis=0)\nmse_student_no_weather = np.mean(np.mean(np.mean(np.square(flow_label - flow_predict_student_no_weather), axis=0), axis=0), axis=0)\n\n# RMSE\nrmse_distill = np.sqrt(mse_distill)\nrmse_student = np.sqrt(mse_student)\nrmse_student_no_weather = np.sqrt(mse_student_no_weather)\n\n# MAE\nmae_distill = np.mean(np.mean(np.mean(np.abs(flow_label - flow_predict_distill), axis=0), axis=0), axis=0)\nmae_student = np.mean(np.mean(np.mean(np.abs(flow_label - flow_predict_student), axis=0), axis=0), axis=0)\nmae_student_no_weather = np.mean(np.mean(np.mean(np.abs(flow_label - flow_predict_student_no_weather), axis=0), axis=0), axis=0)\n\n# MAPE\ndef cal_mape(flow_label, flow_predict):\n diff = np.abs(flow_label - flow_predict)\n diff_ratio = diff / flow_label\n # diff_ratio[diff_ratio == np.math.nan] = 0.0\n diff_ratio[diff_ratio == np.math.inf] = 0.0\n return np.mean(np.mean(np.mean(diff_ratio, axis=0), axis=0), axis=0)\n\nmape_distill = cal_mape(flow_label, flow_predict_distill)\nmape_student = cal_mape(flow_label, flow_predict_student)\nmape_student_no_weather = cal_mape(flow_label, flow_predict_student_no_weather)\n\nablation_option = 'teacher'#'weather'\nindex = 'MAPE' #'RMSE'\n\nf = {\n 'family': 'Times New Roman',\n 'weight': 'normal',\n 'size': 22\n}\nf_legend = {\n 'family': 'Times New Roman',\n 'weight': 'normal',\n 'size': 17\n}\nbar_width = 0.2 # 条形宽度\nT = [\"10\", \"20\", \"30\", \"40\", \"50\", \"60\"]\nif ablation_option == 'teacher':\n # teacher\n index_distill = np.arange(len(rmse_distill)) # 的横坐标\n index_student = index_distill + bar_width # 横坐标\n if index == 'RMSE':\n plt.bar(index_distill, height=rmse_distill, width=bar_width, color='b', label='own')\n plt.bar(index_student, height=rmse_student, width=bar_width, color='c', label='student')\n plt.ylabel('RMSE', fontdict=f) # 纵坐标轴标题\n plt.ylim(2.45, 2.58)\n elif index == 'MAE':\n plt.bar(index_distill, height=mae_distill, width=bar_width, color='b', label='own')\n plt.bar(index_student, height=mae_student, width=bar_width, color='c', label='student')\n plt.ylabel('MAE', fontdict=f) # 纵坐标轴标题\n plt.ylim(1.72, 1.83)\n elif index == 'MAPE':\n plt.bar(index_distill, height=mape_distill, width=bar_width, color='b', label='own')\n plt.bar(index_student, height=mape_student, width=bar_width, color='c', label='student')\n plt.ylabel('MAPE', fontdict=f) # 纵坐标轴标题\n plt.ylim(0.34,0.39)\n plt.legend(['ST-KDN', 'ST-KDN-NT'], prop=f_legend,loc=2) # 显示图例\n # plt.title('Effect of Teacher Guidance', fontdict=f1) # 图形标题\n\nelif ablation_option == 'weather':\n index_student = np.arange(len(rmse_student)) # 的横坐标\n index_student_no_weather = index_student + bar_width # 横坐标\n if index == 'RMSE':\n plt.bar(index_student, height=rmse_student, width=bar_width, color='b', label='student')\n plt.bar(index_student_no_weather, height=rmse_student_no_weather, width=bar_width, color='c', label='NoWeather')\n plt.ylabel('RMSE', fontdict=f) # 纵坐标轴标题\n plt.ylim(2.45, 2.6)\n elif index == 'MAE':\n plt.bar(index_student, height=mae_student, width=bar_width, color='b', label='student')\n plt.bar(index_student_no_weather, height=mae_student_no_weather, width=bar_width, color='c', label='NoWeather')\n plt.ylabel('MAE', fontdict=f) # 纵坐标轴标题\n plt.ylim(1.76,1.82)\n elif index == 'MAPE':\n plt.bar(index_student, height=mape_student, width=bar_width, color='b', label='student')\n plt.bar(index_student_no_weather, height=mape_student_no_weather, width=bar_width, color='c', label='NoWeather')\n plt.ylabel('MAPE', fontdict=f) # 纵坐标轴标题\n plt.ylim(0.35,0.40)\n plt.legend(['ST-KDN-NT','ST-KDN-NTW'],prop=f_legend,loc=2) # 显示图例\n # plt.title('Effect of Weather Feature Modeling',fontdict=f1) # 图形标题\n\nplt.subplots_adjust(left=0.2,bottom=0.2)\nplt.xticks(index_student + bar_width / 2, T, fontproperties='Times New Roman',\n size=22) #\nplt.yticks(fontproperties='Times New Roman', size=22)\nplt.xlabel('Time/min', fontdict=f)\n\n\nplt.show()","sub_path":"result_analyse/plot_ablation.py","file_name":"plot_ablation.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"457676437","text":"import re\n\ndef max_min_normalize(all_datapoints, datapoint):\n\treturn ( datapoint - min(all_datapoints) ) / ( max(all_datapoints) - min(all_datapoints) )\n\nclass DataSet:\n\n\tdef __init__(self, matrix, name=\"DataSet\"):\n\t\tself.data = matrix\n\t\tself.name = name\n\n\n\tdef getAttribute(self, index):\n\t\td = []\n\t\tfor i in range(0, len(self.data)):\n\t\t\td += [self.data[i][index]]\n\t\treturn d\n\n\tdef getNumInstances(self):\n\t\treturn len(self.data)\n\n\tdef getNumAttributes(self):\n\t\treturn len(self.data[0])\n\n\tdef normalize(self, normFunction=max_min_normalize):\n\t\tfor i in range(0, len(self.data[0])):\n\t\t\tattribute = self.getAttribute(i)\n\t\t\tif type(attribute[0]) != float:\n\t\t\t\t\tcontinue\n\t\t\tfor j in range(0, len(attribute)):\n\t\t\t\tself.data[j][i] = normFunction(attribute, attribute[j])\n\n\tdef simb2num(self, columnIndex):\n\t\tif type(self.data[0][columnIndex]) != str:\n\t\t\treturn\n\t\tclss = set()\n\t\tfor li in range(0, len(self.data)):\n\t\t\tclss.add(self.data[li][columnIndex])\n\t\tdic = {}\n\t\ti = 0\n\t\tfor cls in clss:\n\t\t\tdic[cls] = i\n\t\t\ti += 1\n\t\tfor li in range(0, len(self.data)):\n\t\t\tself.data[li][columnIndex] = dic[self.data[li][columnIndex]]\n\n\t@staticmethod\n\tdef readFromArff(arffFileName):\n\t\tdata_matrix = []\n\t\tf = open(arffFileName)\n\t\tds = f.read()\n\t\tf.close()\n\t\tname = \"\"\n\t\tlines = \"\"\n\t\ttry:\n\t\t\tname = re.search(\"\\@relation\\s+([\\w\\d]+)\", ds, re.I).group(1)\n\t\texcept Exception:\n\t\t\tname = \"DataSet\"\n\t\ttry:\n\t\t\tlines = ds.split(\"@data\")[1]\n\t\t\tlines = lines.split(\"\\n\")\n\t\texcept Exception:\n\t\t\tprint(\"File does not conform with ARFF file format\")\n\t\t\treturn\n\t\tdata = []\n\t\tfor line in lines:\n\t\t\tif line == \"\":\n\t\t\t\tcontinue\n\t\t\tdataline = []\n\t\t\tattrs = line.split(\",\")\n\t\t\tfor attr in attrs:\n\t\t\t\tat = ''\n\t\t\t\ttry:\n\t\t\t\t\tat = float(attr)\n\t\t\t\texcept ValueError:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tat = str(attr)\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tcontinue\n\t\t\t\tdataline += [at]\n\t\t\tdata += [dataline]\n\t\treturn DataSet(data, name)\n","sub_path":"pyRecog/dataaccess/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"403959198","text":"import re\nimport logging\nfrom collections import OrderedDict, namedtuple\nfrom functools import total_ordering\nfrom delphin._exceptions import XmrsStructureError\nfrom .config import (\n IVARG_ROLE, CONSTARG_ROLE,\n HANDLESORT, CVARSORT, ANCHOR_SORT, QUANTIFIER_SORT,\n QEQ\n)\n\n# VARIABLES, LNKS, and HOOKS\n\n@total_ordering\nclass MrsVariable(object):\n \"\"\"An MrsVariable has an id (vid), sort, and sometimes properties.\n\n MrsVariables combine an integer variable ID (or *vid*)) with a\n string sortal type (*sort*). In MRS and RMRS, variables may be the\n bearers of the properties of an |EP| (thus the name \"variable\n properties\"). MrsVariables are used for several purposes:\n\n * **intrinsic variables** (aka IVs)\n * **handles** (labels or holes)\n * **anchors** (a nodeid with a sort)\n * **variable argument values** (IVs, labels, holes, or\n underspecified variables for unexpressed arguments)\n\n Example:\n\n Within an Xmrs structure, a *vid* must be unique. MrsVariables\n can then be compared using either the sort and the vid or the vid\n by itself. For example, ``v1`` and ``v2`` below are not equal\n despite having the same vid (so they should not appear in the same\n Xmrs structure), but they are both equal to their shared vid of\n ``1``. Also note that an MrsVariable can be compared to its string\n representation.\n\n >>> v1 = MrsVariable(vid=1, sort='x')\n >>> v2 = MrsVariable(vid=1, sort='e')\n >>> v1 == v2\n False\n >>> v1 == 1\n True\n >>> v2 == 1\n True\n >>> v1 == 'x1'\n True\n >>> v1 == 'x2'\n False\n >>> v1 == 'e1'\n False\n\n Args:\n vid: an number for the variable ID\n sort: a string for the sortal type\n properties: a dictionary of variable properties\n Returns:\n an instantiated MrsVariable object\n Raises:\n ValueError: when *vid* is not castable to an int\n \"\"\"\n def __init__(self, vid, sort, properties=None):\n # vid is the number of the name (e.g. 1, 10003)\n self.vid = int(vid)\n # sort is the letter(s) of the name (e.g. h, x)\n self.sort = sort\n if sort == HANDLESORT and properties:\n pass # handles cannot have properties. Log this?\n self.properties = properties or OrderedDict()\n\n @classmethod\n def from_string(cls, varstring):\n \"\"\"\n Construct an |MrsVariable| by its string representation.\n\n Args:\n varstring: a string containing the sort and vid of an\n MrsVariable, such as \"x1\" or \"event3\"\n Returns:\n an instantiated MrsVariable object if the string represents\n an MrsVariable, or None otherwise\n \"\"\"\n try:\n sort, vid = MrsVariable.sort_vid_split(varstring)\n return cls(vid, sort)\n except (ValueError, TypeError):\n return None\n\n @classmethod\n def anchor(cls, vid):\n return cls(vid, ANCHOR_SORT)\n\n def __eq__(self, other):\n # try both as MrsVariables\n try:\n return self.vid == other.vid and self.sort == other.sort\n except AttributeError:\n pass # other is not an MrsVariable\n # attempt as string\n try:\n sort, vid = MrsVariable.sort_vid_split(other)\n return self.sort == sort and self.vid == int(vid)\n except (ValueError, TypeError):\n pass # doesn't match a variable\n # try again as vid only\n try:\n vid = int(other)\n return self.vid == vid\n except (ValueError, TypeError):\n pass # nope.. return False\n return False\n\n def __lt__(self, other):\n vid1 = self.vid\n # only compare vids for lt\n try:\n return vid1 < int(other)\n except (ValueError, TypeError):\n pass # not an int or MrsVariable\n # try as a string\n try:\n sort, vid2 = MrsVariable.sort_vid_split(other)\n return vid1 < int(vid2)\n except (ValueError, TypeError):\n pass # not a string... no good output\n raise ValueError('Cannot compare MrsVariable to {} of type {}'\n .format(str(other), type(other)))\n\n def __int__(self):\n return self.vid\n\n def __hash__(self):\n return hash(str(self))\n\n def __repr__(self):\n return ''.format(\n self.sort, self.vid, id(self)\n )\n\n def __str__(self):\n return '{}{}'.format(str(self.sort), str(self.vid))\n\n @property\n def sortinfo(self):\n \"\"\"\n Return the properties including a mapping of \"cvarsort\" to\n the sort of the MrsVariable. Sortinfo is used in DMRS objects,\n which don't have variables, in order to capture the sortal type\n of a |Node|.\n \"\"\"\n # FIXME: currently gets CVARSORT even if the var is not a IV\n sortinfo = OrderedDict([(CVARSORT, self.sort)])\n sortinfo.update(self.properties)\n return sortinfo\n\n @staticmethod\n def sort_vid_split(vs):\n try:\n sort, vid = re.match(r'^(\\w*\\D)(\\d+)$', vs).groups()\n return sort, vid\n except AttributeError:\n raise ValueError('Invalid variable string: {}'.format(str(vs)))\n\n\n# I'm not sure this belongs here, but anchors are MrsVariables...\nclass AnchorMixin(object):\n @property\n def anchor(self):\n \"\"\"\n The anchor of the |EP|, |Node|, or |Argument| is just the\n nodeid wrapped in an MrsVariable. In |Xmrs| functions, integer\n nodeids are used instead of anchors.\n \"\"\"\n if self.nodeid is not None:\n return MrsVariable(vid=self.nodeid, sort=ANCHOR_SORT)\n return None\n\n @anchor.setter\n def anchor(self, anchor):\n self.nodeid = anchor.vid\n\n\nclass VarGenerator(object):\n \"\"\"Simple class to produce MrsVariables, incrementing the vid for\n each one.\"\"\"\n\n def __init__(self, starting_vid=1):\n self.vid = starting_vid\n\n def new(self, sort, properties=None):\n v = MrsVariable(self.vid, sort, properties=properties)\n self.vid += 1\n return v\n\n\nclass Lnk(object):\n \"\"\"\n Lnk objects link predicates to the surface form in one of several\n ways, the most common of which being the character span of the\n original string.\n\n Args:\n data: the Lnk specifiers, whose quality depends on *type*\n type: the way the Lnk relates the semantics to the surface form\n\n Note:\n\n Valid *types* and their associated *data* shown in the table\n below.\n\n ========= =================================================\n type data\n ========= =================================================\n charspan a tuple of start and end character positions from\n the surface string\n chartspan a tuple of start and end parse chart vertices\n tokens a list of token identifiers\n edge an edge identifier\n ========= =================================================\n\n Example:\n\n Lnk objects should be created using the classmethods:\n\n >>> Lnk.charspan(0,5)\n '<0:5>'\n >>> Lnk.chartspan(0,5)\n '<0#5>'\n >>> Lnk.tokens([0,1,2])\n '<0 1 2>'\n >>> Lnk.edge(1)\n '<@1>'\n\n \"\"\"\n\n # These types determine how a lnk on an EP or MRS are to be\n # interpreted, and thus determine the data type/structure of the\n # lnk data.\n CHARSPAN = 0 # Character span; a pair of offsets\n CHARTSPAN = 1 # Chart vertex span: a pair of indices\n TOKENS = 2 # Token numbers: a list of indices\n EDGE = 3 # An edge identifier: a number\n\n def __init__(self, data, type):\n if type not in (Lnk.CHARSPAN, Lnk.CHARTSPAN, Lnk.TOKENS, Lnk.EDGE):\n raise ValueError('Invalid lnk type: {}'.format(type))\n self.type = type\n self.data = data\n\n @classmethod\n def charspan(cls, start, end):\n \"\"\"\n Create a Lnk object for a character span.\n\n Args:\n start: the initial character position (cfrom)\n end: the final character position (cto)\n \"\"\"\n return cls((int(start), int(end)), Lnk.CHARSPAN)\n\n @classmethod\n def chartspan(cls, start, end):\n \"\"\"\n Create a Lnk object for a chart span.\n\n Args:\n start: the initial chart vertex\n end: the final chart vertex\n \"\"\"\n return cls((int(start), int(end)), Lnk.CHARTSPAN)\n\n @classmethod\n def tokens(cls, tokens):\n \"\"\"\n Create a Lnk object for a token range.\n\n Args:\n tokens: a list of token identifiers\n \"\"\"\n return cls(tuple(map(int, tokens)), Lnk.TOKENS)\n\n @classmethod\n def edge(cls, edge):\n \"\"\"\n Create a Lnk object for an edge (used internally in generation).\n\n Args:\n edge: an edge identifier\n \"\"\"\n return cls(int(edge), Lnk.EDGE)\n\n def __str__(self):\n if self.type == Lnk.CHARSPAN:\n return '<{}:{}>'.format(self.data[0], self.data[1])\n elif self.type == Lnk.CHARTSPAN:\n return '<{}#{}>'.format(self.data[0], self.data[2])\n elif self.type == Lnk.EDGE:\n return '<@{}>'.format(self.data)\n elif self.type == Lnk.TOKENS:\n return '<{}>'.format(' '.join(self.data))\n\n def __repr__(self):\n return ''.format(str(self), id(self))\n\n def __eq__(self, other):\n return self.type == other.type and self.data == other.data\n\n\nclass LnkMixin(object):\n \"\"\"\n A mixin class for predications (|EPs| or |Nodes|) or full |Xmrs|\n objects, which are the types that can be linked to surface strings.\n This class provides the :py:attr:`~delphin.mrs.lnk.LnkMixin.cfrom`\n and :py:attr:`~delphin.mrs.lnk.LnkMixin.cto` properties so they are\n always available (defaulting to a value of -1 if there is no lnk or\n if the lnk is not a Lnk.CHARSPAN type).\n \"\"\"\n @property\n def cfrom(self):\n \"\"\"\n The initial character position in the surface string. Defaults\n to -1 if there is no valid cfrom value.\n \"\"\"\n cfrom = -1\n try:\n if self.lnk.type == Lnk.CHARSPAN:\n cfrom = self.lnk.data[0]\n except AttributeError:\n pass # use default cfrom of -1\n return cfrom\n\n @property\n def cto(self):\n \"\"\"\n The final character position in the surface string. Defaults\n to -1 if there is no valid cto value.\n \"\"\"\n cto = -1\n try:\n if self.lnk.type == Lnk.CHARSPAN:\n cto = self.lnk.data[1]\n except AttributeError:\n pass # use default cto of -1\n return cto\n\n\nclass Hook(object):\n \"\"\"\n A container class for TOP, INDEX, and XARG.\n\n This class simply encapsulates three variables associated with an\n |Xmrs| object, and none of the arguments are required.\n\n Args:\n top: the global top handle\n index: the semantic index\n xarg: the external argument (not likely used for a full |Xmrs|)\n ltop: an alternate spelling of top (top is preferred)\n \"\"\"\n def __init__(self, top=None, index=None, xarg=None, ltop=None):\n self.top = top or ltop\n self.index = index\n self.xarg = xarg\n\n def __repr__(self):\n return ''.format(\n self.top, self.index, self.xarg, id(self)\n )\n\n def __eq__(self, other):\n if not isinstance(other, Hook):\n return False\n return (\n self.top == other.top and\n self.index == other.index and\n self.xarg == other.xarg\n )\n\n # for compatibility\n @property\n def ltop(self):\n return self.top\n\n @ltop.setter\n def ltop(self, value):\n self.top = value\n\n\n# ARGUMENTS, LINKS, and CONSTRAINTS\n\nclass Argument(AnchorMixin):\n \"\"\"\n An argument of an \\*MRS predicate.\n\n Args:\n nodeid: the nodeid of the node with the argument\n argname: the name of the argument (sometimes called \"rargname\")\n value: the MrsVariable or constant value of the argument\n \"\"\"\n\n INTRINSIC_ARG = 0 # ARG0, conventionally\n VARIABLE_ARG = 1 # The value is the ARG0 of some other EP\n HANDLE_ARG = 2 # The value is a handle (supertype of next two)\n LABEL_ARG = 3 # The value is the label of some other EP(s)\n HCONS_ARG = 4 # The value is the hi variable of an HCONS\n CONSTANT_ARG = 5 # The value is a constant (e.g. a string)\n\n def __init__(self, nodeid, argname, value):\n self.nodeid = nodeid\n self.argname = argname\n self.value = value\n self._type = None\n\n def __repr__(self):\n return ''.format(\n self.nodeid, self.argname, self.value, id(self)\n )\n\n def __eq__(self, other):\n # ignore missing nodeid?\n # argname is case insensitive\n snid = self.nodeid\n onid = other.nodeid\n return (\n (None in (snid, onid) or snid == onid) and\n self.argname.lower() == other.argname.lower() and\n self.value == other.value\n )\n\n @classmethod\n def mrs_argument(cls, argname, value):\n return cls(None, argname, value)\n\n @classmethod\n def rmrs_argument(cls, anchor, argname, value):\n return cls(anchor.vid, argname, value)\n\n def infer_argument_type(self, xmrs=None):\n if self.argname == IVARG_ROLE:\n return Argument.INTRINSIC_ARG\n elif isinstance(self.value, MrsVariable):\n if self.value.sort == HANDLESORT:\n # if there's no xmrs given, then use HANDLE_ARG as it\n # is the supertype of LABEL_ARG and HCONS_ARG\n if xmrs is not None:\n if xmrs.get_hcons(self.value) is not None:\n return Argument.HCONS_ARG\n else:\n return Argument.LABEL_ARG\n else:\n return Argument.HANDLE_ARG\n else:\n return Argument.VARIABLE_ARG\n else:\n return Argument.CONSTANT_ARG\n\n @property\n def type(self):\n if self._type is None:\n self._type = self.infer_argument_type()\n return self._type\n\n @type.setter\n def type(self, value):\n self._type = value\n\n\nclass Link(object):\n \"\"\"DMRS-style Links are a way of representing arguments without\n variables. A Link encodes a start and end node, the argument\n name, and label information (e.g. label equality, qeq, etc).\"\"\"\n def __init__(self, start, end, argname=None, post=None):\n self.start = int(start)\n self.end = int(end)\n self.argname = argname\n self.post = post\n\n def __repr__(self):\n return ' #{}) at {}>'.format(\n self.start, self.argname or '', self.post, self.end, id(self)\n )\n\n\nclass HandleConstraint(object):\n \"\"\"A relation between two handles.\"\"\"\n\n def __init__(self, hi, relation, lo):\n self.hi = hi\n self.relation = relation\n self.lo = lo\n\n @classmethod\n def qeq(cls, hi, lo):\n return cls(hi, QEQ, lo)\n\n def __eq__(self, other):\n return (self.hi == other.hi and\n self.relation == other.relation and\n self.lo == other.lo)\n\n def __hash__(self):\n return hash(repr(self))\n\n def __repr__(self):\n return ''.format(\n str(self.hi), self.relation, str(self.lo), id(self)\n )\n\nIndividualConstraint = namedtuple('IndividualConstraint',\n ['target', 'relation', 'clause'])\n\n# PREDICATES AND PREDICATIONS\n\n\nclass Pred(object):\n \"\"\"\n A semantic predicate.\n\n Args:\n predtype: the type of predicate; valid values are grammarpred,\n stringpred, or realpred, although in practice one won't use\n this constructor directly, but instead use one of the\n classmethods\n lemma: the lemma of the predicate\n pos: the part-of-speech; a single, lowercase character\n sense: the (often omitted) sense of the predicate\n Returns:\n an instantiated Pred object\n\n Preds come in three flavors:\n\n * **grammar preds** (gpreds): preds defined in a semantic hierarchy\n in the grammar, and are not necessarily tied to a lexical entry;\n grammar preds may not begin with a leading underscore\n * **real preds** (realpreds): preds that are defined as the\n composition of a lemma, a part-of-speech (pos), and sometimes a\n sense---parts-of-speech are always single characters, and senses\n may be numbers or string descriptions\n * **string preds** (spreds): a string (often double-quoted) that\n represents a real pred; string preds must begin with a leading\n underscore\n\n While MRS representations may distinguish real preds and string\n preds, in pyDelphin they are equivalent. All well-formed predicates,\n when represented as strings, end with ``_rel``, but in practice this\n may not be true (some may end in ``_relation``, or have no such\n suffix).\n\n Example:\n\n Preds are compared using their string representations.\n Surrounding quotes (double or single) are ignored, and\n capitalization doesn't matter. In addition, preds may be\n compared directly to their string representations:\n\n >>> p1 = Pred.stringpred('_dog_n_1_rel')\n >>> p2 = Pred.realpred(lemma='dog', pos='n', sense='1')\n >>> p3 = Pred.grammarpred('dog_n_1_rel')\n >>> p1 == p2\n True\n >>> p1 == '_dog_n_1_rel'\n True\n >>> p1 == p3\n False\n \"\"\"\n pred_re = re.compile(\n r'_?(?P.*?)_' # match until last 1 or 2 parts\n r'((?P[a-z])_)?' # pos is always only 1 char\n r'((?P([^_\\\\]|(?:\\\\.))+)_)?' # no unescaped _s\n r'(?Prel(ation)?)$', # NB only _rel is valid\n re.IGNORECASE\n )\n # Pred types (used mainly in input/output, not internally in pyDelphin)\n GRAMMARPRED = 0 # only a string allowed (quoted or not)\n REALPRED = 1 # may explicitly define lemma, pos, sense\n STRINGPRED = 2 # quoted string form of realpred\n\n def __init__(self, predtype, lemma=None, pos=None, sense=None):\n \"\"\"Extract the lemma, pos, and sense (if applicable) from a pred\n string, if given, or construct a pred string from those\n components, if they are given. Treat malformed pred strings\n as simple preds without extracting the components.\"\"\"\n # GRAMMARPREDs and STRINGPREDs are given by strings (with or without\n # quotes). STRINGPREDs have an internal structure (defined here:\n # http://moin.delph-in.net/RmrsPos), but basically:\n # _lemma_pos(_sense)?_rel\n # Note that sense is optional. The initial underscore is meaningful.\n self.type = predtype\n self.lemma = lemma\n self.pos = pos\n self.sense = str(sense) if sense is not None else sense\n self.string = None # set by class methods\n\n def __eq__(self, other):\n\n if isinstance(other, Pred):\n other = other.string\n return self.string.strip('\"\\'') == other.strip('\"\\'')\n\n def __str__ (self):\n return self.string\n\n def __repr__(self):\n return ''.format(self.string, id(self))\n\n def __hash__(self):\n return hash(self.string)\n\n @classmethod\n def stringpred(cls, predstr):\n lemma, pos, sense, end = Pred.split_pred_string(predstr.strip('\"\\''))\n pred = cls(Pred.STRINGPRED, lemma=lemma, pos=pos, sense=sense)\n pred.string = predstr\n return pred\n\n @classmethod\n def grammarpred(cls, predstr):\n lemma, pos, sense, end = Pred.split_pred_string(predstr.strip('\"\\''))\n pred = cls(Pred.GRAMMARPRED, lemma=lemma, pos=pos, sense=sense)\n pred.string = predstr\n return pred\n\n @staticmethod\n def string_or_grammar_pred(predstr):\n if predstr.strip('\"').lstrip(\"'\").startswith('_'):\n return Pred.stringpred(predstr)\n else:\n return Pred.grammarpred(predstr)\n\n @classmethod\n def realpred(cls, lemma, pos, sense=None):\n pred = cls(Pred.REALPRED, lemma=lemma, pos=pos, sense=sense)\n string_tokens = list(filter(bool, [lemma, pos, str(sense or '')]))\n pred.string = '_'.join([''] + string_tokens + ['rel'])\n return pred\n\n @staticmethod\n def split_pred_string(predstr):\n \"\"\"\n Extract the components from a pred string and log errors for any\n malformedness.\n\n Args:\n predstr: a predicate string\n\n Examples:\n\n >>> Pred.split_pred_string('_dog_n_1_rel')\n ('dog', 'n', '1', 'rel')\n >>> Pred.split_pred_string('quant_rel')\n ('quant', None, None, 'rel')\n \"\"\"\n if not predstr.lower().endswith('_rel'):\n logging.debug('Predicate does not end in \"_rel\": {}'\n .format(predstr))\n match = Pred.pred_re.search(predstr)\n if match is None:\n logging.debug('Unexpected predicate string: {}'.format(predstr))\n return (predstr, None, None, None)\n # _lemma_pos(_sense)?_end\n return (match.group('lemma'), match.group('pos'),\n match.group('sense'), match.group('end'))\n\n @staticmethod\n def is_valid_pred_string(predstr, suffix_required=True):\n \"\"\"\n Return True if the given predicate string represents a valid\n Pred, False otherwise. If suffix_required is False,\n abbreviated Pred strings will be accepted (e.g. _dog_n_1\n instead of _dog_n_1_rel)\n \"\"\"\n predstr = predstr.strip('\"').lstrip(\"'\")\n if (not suffix_required and\n predstr.rsplit('_', 1)[-1] not in ('rel', 'relation')):\n predstr += '_rel'\n return Pred.pred_re.match(predstr) is not None\n\n @staticmethod\n def normalize_pred_string(predstr):\n \"\"\"\n Make pred strings more consistent by removing quotes and using\n the _rel suffix.\n \"\"\"\n predstr = predstr.strip('\"').lstrip(\"'\")\n match = (Pred.pred_re.match(predstr) or\n Pred.pred_re.match(predstr + '_rel'))\n if match:\n d = match.groupdict()\n tokens = [d['lemma']]\n if d['pos']:\n tokens.append(d['pos'])\n if d['sense']:\n tokens.append(d['sense'])\n tokens.append('rel')\n return '_'.join(tokens)\n return None\n\n def short_form(self):\n \"\"\"\n Return the pred string without quotes or a _rel suffix.\n\n Example:\n\n >>> p = Pred.stringpred('\"_cat_n_1_rel\"')\n >>> p.short_form()\n '_cat_n_1'\n \"\"\"\n return self.string.strip('\"').lstrip(\"'\").rsplit('_', 1)[0]\n\n def is_quantifier(self):\n return self.pos == QUANTIFIER_SORT\n\n\n@total_ordering\nclass Node(LnkMixin):\n \"\"\"\n A very simple predication for DMRSs. Nodes don't have |Arguments|\n or labels like |EPs|, but they do have a\n :py:attr:`~delphin.mrs.node.Node.carg` property for constant\n arguments, and their sortal type is given by the `cvarsort` value\n on their property mapping.\n\n Args:\n nodeid: node identifier\n pred: node's |Pred|\n sortinfo: node properties (with cvarsort)\n lnk: links pred to surface form or parse edges\n surface: surface string\n base: base form\n carg: constant argument string\n \"\"\"\n\n def __init__(self, nodeid, pred, sortinfo=None,\n lnk=None, surface=None, base=None, carg=None):\n self.nodeid = int(nodeid) if nodeid is not None else None\n self.pred = pred\n # sortinfo is the properties plus cvarsort\n self.sortinfo = OrderedDict(sortinfo or [])\n self.lnk = lnk\n self.surface = surface\n self.base = base\n self.carg = carg\n # accessor method\n self.get_property = self.sortinfo.get\n\n def __repr__(self):\n return ''.format(\n self.nodeid, self.pred.string, str(self.lnk), id(self)\n )\n\n def __eq__(self, other):\n # not doing self.__dict__ == other.__dict__ right now, because\n # functions like self.get_property show up there\n snid = self.nodeid\n onid = other.nodeid\n return ((None in (snid, onid) or snid == onid) and\n self.pred == other.pred and\n # make one side a regular dict for unordered comparison\n dict(self.sortinfo.items()) == other.sortinfo and\n self.lnk == other.lnk and\n self.surface == other.surface and\n self.base == other.base and\n self.carg == other.carg)\n\n def __lt__(self, other):\n x1 = (self.cfrom, self.cto, -self.is_quantifier(), self.pred.lemma)\n try:\n x2 = (other.cfrom, other.cto, -other.is_quantifier(),\n other.pred.lemma)\n return x1 < x2\n except AttributeError:\n return False # comparing Node to non-Node means Node is greater?\n\n @property\n def cvarsort(self):\n \"\"\"\n The sortal type of the predicate.\n \"\"\"\n return self.sortinfo.get(CVARSORT)\n\n @cvarsort.setter\n def cvarsort(self, value):\n self.sortinfo[CVARSORT] = value\n\n @property\n def properties(self):\n \"\"\"\n The properties of the Node (without `cvarsort`, so it's the set\n of properties a corresponding |EP| would have).\n \"\"\"\n return OrderedDict((k, v) for (k, v) in self.sortinfo.items()\n if k != CVARSORT)\n\n def is_quantifier(self):\n \"\"\"\n Return True if the Node is a quantifier, or False otherwise.\n \"\"\"\n return self.pred.is_quantifier()\n\n\n@total_ordering\nclass ElementaryPredication(LnkMixin, AnchorMixin):\n \"\"\"\n An elementary predication (EP) combines a predicate with various\n structural semantic properties.\n\n EPs must have a |Pred| and a |MrsVariable| *label*. Well-formed EPs\n will have an intrinsic argument (e.g. ARG0) on their *args* list,\n which specifies the intrinsic variable (IV), though it is not\n required by pyDelphin. However, some methods use an index of IVs to\n calculate semantic structure, so the absence of an intrinsic\n argument could cause unexpected behavior.\n\n Args:\n pred: The |Pred| of the EP\n label: label handle\n anchor: an |MrsVariable| anchor or int nodeid\n args: a list of the EP's |Arguments|\n lnk: |Lnk| object associated with the pred\n surface: surface string\n base: base form\n \"\"\"\n\n def __init__(self, pred, label, anchor=None, args=None,\n lnk=None, surface=None, base=None):\n self.label = label\n # first args, then can get IV\n self.argdict = OrderedDict((a.argname, a) for a in (args or []))\n # Only fill in other attributes if pred is given, otherwise ignore.\n # This behavior is to help enable the from_node classmethod.\n self._node = None\n if pred is not None:\n iv = self.iv\n self._node = Node(\n anchor.vid if anchor else None,\n pred,\n sortinfo=iv.sortinfo if iv else None,\n lnk=lnk,\n surface=surface,\n base=base,\n carg=self.carg\n )\n\n @classmethod\n def from_node(cls, label, node, args=None):\n ep = cls(None, label, args=args)\n ep._node = node\n return ep\n\n def __repr__(self):\n return ''.format(\n self.pred.string, str(self.iv or '?'), id(self)\n )\n\n def __eq__(self, other):\n return (self.label == other.label and\n self.argdict == other.argdict and\n self._node == other._node)\n\n def __lt__(self, other):\n try:\n return self._node < other._node\n except AttributeError:\n return False # comparing EP to non-EP means EP is greater?\n\n # these properties provide an interface to the node attributes\n\n @property\n def nodeid(self):\n return self._node.nodeid\n\n @nodeid.setter\n def nodeid(self, value):\n self._node.nodeid = value\n # also update the args' nodeids\n for arg in self.argdict.values():\n arg.nodeid = value\n\n @property\n def pred(self):\n return self._node.pred\n\n @pred.setter\n def pred(self, value):\n self._node.pred = value\n\n @property\n def sortinfo(self):\n return self.iv.sortinfo\n\n @property\n def lnk(self):\n return self._node.lnk\n\n @lnk.setter\n def lnk(self, value):\n self._node.lnk = value\n\n @property\n def surface(self):\n return self._node.surface\n\n @surface.setter\n def surface(self, value):\n self._node.surface = value\n\n @property\n def base(self):\n return self._node.base\n\n @base.setter\n def base(self, value):\n self._node.base = value\n\n # carg property intentionally left out. It should be accessed from\n # the arg list (see the property below)\n\n # these properties are specific to the EP's qualities\n\n @property\n def intrinsic_variable(self):\n return self.arg_value(IVARG_ROLE)\n\n #: A synonym for :py:meth:`intrinsic_variable`\n iv = intrinsic_variable\n\n @property\n def properties(self):\n try:\n return self.iv.properties\n except AttributeError: # in case iv is None\n return OrderedDict()\n\n @property\n def carg(self):\n return self.arg_value(CONSTARG_ROLE)\n\n @property\n def args(self):\n return list(self.argdict.values())\n\n def get_arg(self, rargname):\n return self.argdict.get(rargname)\n\n def arg_value(self, rargname):\n try:\n arg = self.argdict[rargname]\n return arg.value\n except KeyError:\n return None\n\n def add_argument(self, arg):\n if arg.nodeid is None:\n arg.nodeid = self.nodeid\n elif arg.nodeid != self.nodeid:\n raise XmrsStructureError(\n \"Argument's nodeid must match the EP's (or be None).\"\n )\n if arg.argname in self.argdict:\n raise XmrsStructureError(\n \"Argument with role {} already exists in the EP.\"\n .format(arg.argname)\n )\n self.argdict[arg.argname] = arg\n\n def is_quantifier(self):\n return self.pred.is_quantifier()\n","sub_path":"delphin/mrs/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":30954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"30595869","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('tombs', '0009_tombimage'),\n ('geo', '0005_auto_20151123_1434'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='gpstracksession',\n name='graveyard',\n field=models.ForeignKey(to='tombs.Graveyard', default=1, verbose_name='graveyard', related_name='gps_tracks'),\n preserve_default=False,\n ),\n ]\n","sub_path":"backend/geo/migrations/0006_gpstracksession_graveyard.py","file_name":"0006_gpstracksession_graveyard.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"385924053","text":"\nfrom django.conf.urls import url,include\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom django.views.decorators.cache import cache_page\nfrom bookstore import views\n\nurlpatterns = format_suffix_patterns([\n url(r'^$', views.api_root),\n url(r'^books/$',\n views.BookList.as_view(),\n name='book-list'),\n url(r'^books/(?P[0-9]+)/$',\n views.BookDetail.as_view(),\n name='book-detail'),\n\n url(r'^genres/$',\n views.GenreList.as_view(),\n name='genre-list'),\n url(r'^genres/(?P[0-9]+)/$',\n views.GenreDetail.as_view(),\n name='genre-detail'),\n\n])\n\n\n# Login and logout views for the browsable API\nurlpatterns += [\n url(r'^api-auth/', include('rest_framework.urls',\n namespace='rest_framework')),\n\n]\n\n\n","sub_path":"bookstore/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"184747539","text":"import uuid\n\nfrom twisted.trial import unittest\nfrom twisted.internet.task import Clock\nfrom smpp.pdu_builder import DeliverSM, BindTransceiverResp\nfrom smpp.pdu import unpack_pdu\n\nfrom vumi.tests.utils import FakeRedis\nfrom vumi.transports.smpp.clientserver.client import (\n EsmeTransceiver,\n EsmeCallbacks,\n KeyValueBase,\n KeyValueStore,\n ESME)\nfrom vumi.transports.smpp.clientserver.config import ClientConfig\n\n\nclass KeyValueStoreTestCase(unittest.TestCase):\n\n def setUp(self):\n self.kvs = KeyValueStore()\n KeyValueBase.register(self.kvs.__class__)\n self.prefix = \"smpp_test_%s\" % uuid.uuid4()\n\n def tearDown(self):\n pass\n\n def run_all_tests_on_instance(self, instance):\n self.kvs = instance\n KeyValueBase.register(instance.__class__)\n self.test_implements_abstract()\n self.test_set_get_delete()\n self.test_incr()\n\n def test_instance_test(self):\n newKeyValueStoreTestCase = KeyValueStoreTestCase()\n newKeyValueStoreTestCase.prefix = \"smpp_test_%s\" % uuid.uuid4()\n instance = KeyValueStore()\n newKeyValueStoreTestCase.run_all_tests_on_instance(instance)\n\n def test_implements_abstract(self):\n self.assertTrue(issubclass(KeyValueStore, KeyValueBase))\n self.assertTrue(isinstance(self.kvs, KeyValueBase))\n\n def test_set_get_delete(self):\n key1 = \"%s#cookie\" % self.prefix\n\n try:\n self.assertEqual(self.kvs.get(key1), None)\n self.kvs.set(key1, \"monster\")\n self.assertEqual(self.kvs.get(key1), \"monster\")\n self.kvs.set(key1, \"crumbles\")\n self.assertNotEqual(self.kvs.get(key1), \"monster\")\n self.assertEqual(self.kvs.get(key1), \"crumbles\")\n self.assertEqual(self.kvs.delete(key1), True)\n self.assertEqual(self.kvs.get(key1), None)\n\n except:\n self.kvs.delete(key1)\n raise\n\n def test_incr(self):\n key1 = \"%s#counter\" % self.prefix\n\n try:\n self.assertEqual(self.kvs.get(key1), None)\n self.assertEqual(self.kvs.incr(key1), 1)\n self.kvs.set(key1, 1)\n self.assertEqual(self.kvs.incr(key1), 2)\n self.kvs.set(key1, \"1\")\n self.assertEqual(self.kvs.incr(key1), 2)\n self.kvs.delete(key1)\n self.assertEqual(self.kvs.incr(key1), 1)\n self.assertEqual(self.kvs.incr(key1), 2)\n self.assertEqual(self.kvs.incr(key1), 3)\n self.assertEqual(self.kvs.delete(key1), True)\n\n except:\n self.kvs.delete(key1)\n raise\n\n\nclass FakeTransport(object):\n def __init__(self):\n self.connected = True\n\n def loseConnection(self):\n self.connected = False\n\n\nclass FakeEsmeTransceiver(EsmeTransceiver):\n\n def __init__(self, *args, **kwargs):\n EsmeTransceiver.__init__(self, *args, **kwargs)\n self.transport = FakeTransport()\n self.clock = Clock()\n self.callLater = self.clock.callLater\n\n def send_pdu(self, *args):\n pass\n\n\nclass EsmeSequenceNumberTestCase(unittest.TestCase):\n\n def test_sequence_rollover(self):\n config = ClientConfig(host=\"127.0.0.1\", port=\"0\",\n system_id=\"1234\", password=\"password\")\n esme = FakeEsmeTransceiver(config, FakeRedis(), None)\n self.assertEqual(1, esme.get_seq())\n esme.get_next_seq()\n self.assertEqual(2, esme.get_seq())\n esme.set_seq(4004004004)\n self.assertEqual(4004004004, esme.get_seq())\n esme.get_next_seq()\n self.assertEqual(1, esme.get_seq())\n\n\nclass EsmeTransceiverTestCase(unittest.TestCase):\n def get_esme(self, **callbacks):\n config = ClientConfig(host=\"127.0.0.1\", port=\"0\",\n system_id=\"1234\", password=\"password\")\n esme_callbacks = EsmeCallbacks(**callbacks)\n esme = FakeEsmeTransceiver(config, FakeRedis(), esme_callbacks)\n return esme\n\n def get_sm(self, msg, data_coding=3):\n sm = DeliverSM(1, short_message=msg, data_coding=data_coding)\n return unpack_pdu(sm.get_bin())\n\n def test_deliver_sm_simple(self):\n \"\"\"A simple message should be delivered.\"\"\"\n def cb(**kw):\n self.assertEqual(u'hello', kw['short_message'])\n\n esme = self.get_esme(deliver_sm=cb)\n esme.handle_deliver_sm(self.get_sm('hello'))\n\n def test_deliver_sm_ucs2(self):\n \"\"\"A UCS-2 message should be delivered.\"\"\"\n def cb(**kw):\n self.assertEqual(u'hello', kw['short_message'])\n\n esme = self.get_esme(deliver_sm=cb)\n esme.handle_deliver_sm(self.get_sm('\\x00h\\x00e\\x00l\\x00l\\x00o', 8))\n\n def test_bad_sm_ucs2(self):\n \"\"\"An invalid UCS-2 message should be discarded.\"\"\"\n def cb(**kw):\n self.assertEqual(bad_msg, kw['short_message'])\n self.flushLoggedErrors()\n\n esme = self.get_esme(deliver_sm=cb)\n bad_msg = '\\n\\x00h\\x00e\\x00l\\x00l\\x00o'\n esme.handle_deliver_sm(self.get_sm(bad_msg, 8))\n\n def test_bind_timeout(self):\n esme = self.get_esme()\n esme.connectionMade()\n\n self.assertEqual(True, esme.transport.connected)\n self.assertNotEqual(None, esme._lose_conn)\n\n esme.clock.advance(esme.smpp_bind_timeout)\n\n self.assertEqual(False, esme.transport.connected)\n self.assertEqual(None, esme._lose_conn)\n\n def test_bind_no_timeout(self):\n esme = self.get_esme()\n esme.connectionMade()\n\n self.assertEqual(True, esme.transport.connected)\n self.assertNotEqual(None, esme._lose_conn)\n\n esme.handle_bind_transceiver_resp(unpack_pdu(\n BindTransceiverResp(1).get_bin()))\n\n self.assertEqual(True, esme.transport.connected)\n self.assertEqual(None, esme._lose_conn)\n esme.lc_enquire.stop()\n\n\nclass ESMETestCase(unittest.TestCase):\n\n def setUp(self):\n self.client_config = ClientConfig(\n host='localhost',\n port=2775,\n system_id='test_system',\n password='password',\n )\n self.kvs = None\n self.esme_callbacks = None\n self.esme = ESME(self.client_config, self.kvs,\n self.esme_callbacks)\n\n def test_bind_as_transceiver(self):\n self.esme.bindTransciever()\n","sub_path":"vumi/transports/smpp/clientserver/tests/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":6397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"354889888","text":"import os\n\nfrom reframe.core.pipeline import RunOnlyRegressionTest\nfrom reframe.settings import settings\nfrom reframe.utility.functions import standard_threshold\nfrom reframe.utility.parsers import StatefulParser\n\n\nclass VASPBaseCheck(RunOnlyRegressionTest):\n def __init__(self, name, **kwargs):\n super().__init__(name, os.path.dirname(__file__), **kwargs)\n\n # Uncomment and set the valid prog. environments for your site\n # self.valid_prog_environs = [ 'PrgEnv-intel' ]\n\n # Uncomment and adjust to load the VASP module\n # self.modules = [ 'VASP' ]\n\n self.sanity_patterns = {\n '-': {\n '1 F=\\s+(?P\\S+)': [\n ('result', float,\n lambda value, **kwargs:\n standard_threshold(\n value, (-.85026214E+03, -1e-5, 1e-5)))\n ],\n }\n }\n\n self.keep_files = ['OUTCAR']\n self.parser = StatefulParser(standard_threshold)\n self.perf_patterns = {\n 'OUTCAR': {\n '(?PGeneral timing and accounting)': [\n ('perf_section', str, self.parser.on)\n ],\n 'Total CPU time used \\(sec\\):\\s+(?P\\S+)': [\n ('perf', float, self.parser.match)\n ]\n }\n }\n\n self.reference = {\n # Uncomment and adjust the references for your systems/partitions\n # 'cpusys' : {\n # 'perf' : (213, None, 0.10)\n # },\n # 'gpusys' : {\n # 'perf' : (71.0, None, 0.10)\n # },\n '*': {\n 'perf_section': None,\n }\n }\n\n # Uncomment and set the maintainers and/or tags\n # self.maintainers = [ 'me' ]\n # self.tags = { 'example' }\n\n def setup(self, partition, environ, **job_opts):\n super().setup(partition, environ, **job_opts)\n\n # Needed from VASP to avoid segfaults\n self.job.pre_run = ['ulimit -s unlimited']\n\n\nclass VASPGPUCheck(VASPBaseCheck):\n def __init__(self, **kwargs):\n super().__init__('vasp_gpu_check', **kwargs)\n\n # Uncomment and adjust for your gpu systems\n # self.valid_systems = ['gpusys']\n\n self.descr = 'VASP GPU check'\n\n # Reset sources dir relative to the SCS apps prefix\n self.sourcesdir = os.path.join(self.sourcesdir, 'gpu')\n\n self.executable = 'vasp_gpu'\n self.variables = {'CRAY_CUDA_MPS': '1'}\n\n # Uncomment and adjust for your site\n # self.num_tasks = 16\n # self.num_tasks_per_node = 1\n self.num_gpus_per_node = 1\n\n\nclass VASPCPUCheck(VASPBaseCheck):\n def __init__(self, **kwargs):\n super().__init__('vasp_cpu_check', **kwargs)\n\n self.descr = 'VASP CPU check'\n\n # Uncomment and adjust for your gpu systems\n # self.valid_systems = [ 'cpusys' ]\n\n # Reset sources dir relative to the SCS apps prefix\n self.sourcesdir = os.path.join(self.sourcesdir, 'cpu')\n self.executable = 'vasp_std'\n\n # Uncomment and adjust for your site\n # self.use_multithreading = True\n # self.num_tasks = 32\n # self.num_tasks_per_node = 2\n\n\ndef _get_checks(**kwargs):\n return [VASPGPUCheck(**kwargs), VASPCPUCheck(**kwargs)]\n","sub_path":"examples/apps/vasp/vasp_check.py","file_name":"vasp_check.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"40017228","text":"#! /usr/bin/python\nfrom __future__ import print_function\nfrom random import *\nimport sys\nimport redis\nimport time\nsys.path.append('..')\n#from orderbook import OrderBook\n\nmyredis = redis.Redis(host='localhost', port=6379, db=0)\n\ndef generate_new_buy(trade_id):\n return {'type' : 'limit', \n 'side' : 'bid', \n 'quantity' : randint(1,1000),\n 'price' : randint(900,1050),\n 'trade_id' : trade_id}\n \ndef generate_cross_buy(trade_id):\n return {'type' : 'limit', \n 'side' : 'bid', \n 'quantity' : randint(1,1000), \n 'price' : randint(1055,1200),\n 'trade_id' : trade_id}\n\ndef generate_new_sell(trade_id):\n return {'type' : 'limit', \n 'side' : 'ask', \n 'quantity' : randint(1,1000), \n 'price' : randint(1055,1200),\n 'trade_id' : trade_id}\n \ndef generate_cross_sell(trade_id):\n return {'type' : 'limit', \n 'side' : 'ask', \n 'quantity' : randint(1,1000),\n 'price' : randint(900,1050),\n 'trade_id' : trade_id}\n\ndef gen_orders(nb_orders_prefilled, verbose = False):\n \n o_id = 0\n orders = []\n for trade_id in range(nb_orders_prefilled):\n orders.append( generate_new_buy(trade_id) )\n orders.append( generate_new_buy(trade_id) )\n orders.append( generate_cross_buy(trade_id) )\n orders.append( generate_new_sell(trade_id) )\n orders.append( generate_new_sell(trade_id) )\n\n orders.append( generate_new_sell(trade_id) )\n orders.append( generate_new_sell(trade_id) )\n orders.append( generate_cross_sell(trade_id) )\n orders.append( generate_new_sell(trade_id) )\n orders.append( generate_new_sell(trade_id) )\n \n for order in orders:\n newitem = []\n\n newitem.append(str(o_id))\n newitem.append(order[\"type\"][0].upper())\n newitem.append(order[\"side\"][0].upper())\n newitem.append(str(order[\"quantity\"]))\n newitem.append(str(order[\"price\"]))\n newitem.append(str(order[\"trade_id\"]))\n newitem.append(str(current_milli_time()))\n\n insertstring = \"-\".join(newitem)\n myredis.rpush(\"order\", insertstring)\n\n o_id = o_id + 1\n print (o_id)\n \ndef current_milli_time():\n return int(round(time.time() * 1000))\n\ndef main(argv):\n \n # nb buys and sells to prefill orderbook\n gen_orders(100)\n\n #pprint(data)\n pass\n\nif __name__ == \"__main__\":\n main(sys.argv)\n\n\n","sub_path":"HelperScripts/genOrders.py","file_name":"genOrders.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"241557891","text":"#!/usr/bin/env python3\n\nfrom flask import Flask, render_template, request, redirect\nimport hash_it\nimport hash_crack\nimport time\n\n#default (starting) values of the variables used in the app\nhashes=[]\ncracked=\"Not cracked (yet)\"\ncrack_time=\"n/a\"\n\napp = Flask(__name__)\napp.config['APPLICATION_ROOT'] = \"/fun-with-hashes\"\nprefix=\"/fun-with-hashes\"\n\n@app.route(prefix+'/')\ndef hasher():\n\n\treturn render_template(\"hash.html\", hashes = hashes, cracked = cracked, crack_time = crack_time, prefix=prefix)\n\n#when the user clicks on the \"Hish Hash\" button, the key from the input box is passed to the hasher module\n@app.route(prefix+'/hish-hash', methods = ['POST'])\ndef hish_hash():\n\n\tif request.method == \"POST\":\n\n\t\tsource_string=request.form['source_string']\n\n\t\tdel hashes[:]\n\t\t#hashes.clear()\n\n\t\thashes.append({\"type\":\"MD5\", \"digest\": hash_it.hash_md5(source_string)})\n\t\thashes.append({\"type\":\"SHA1\", \"digest\": hash_it.hash_sha1(source_string)})\n\t\thashes.append({\"type\":\"SHA224\", \"digest\": hash_it.hash_sha224(source_string)})\n\t\thashes.append({\"type\":\"SHA256\", \"digest\": hash_it.hash_sha256(source_string)})\n\t\thashes.append({\"type\":\"SHA384\", \"digest\": hash_it.hash_sha384(source_string)})\n\t\thashes.append({\"type\":\"SHA512\", \"digest\": hash_it.hash_sha512(source_string)})\n\n\treturn redirect(prefix+'/')\n\n#When the user clicks on the \"Crick Crack\" button, the hash from the input box and the chosen hash function are passed to the cracker module\n@app.route(prefix+'/crick-crack', methods = ['POST'])\ndef crick_crack():\n\n\tstart = time.time()\n\n\t#varibales should are used by the main function (hasher) as well\n\tglobal cracked\n\tglobal crack_time\n\t\n\tif request.method == \"POST\":\n\n\t\ttarget_hash=request.form['target_hash'].strip()\n\t\tcracked=hash_crack.crack_it(target_hash, request.form[\"hash_type\"])\n\n\tend = time.time()\n\t\n\tcrack_time = str(end - start)\n\n\treturn redirect(prefix+'/')\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', debug = True)\n","sub_path":"hash_web.py","file_name":"hash_web.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"458288219","text":"import pygame\nimport sys\nfrom settings import Settings\nfrom background import Background\nfrom player import Player \nfrom weapon import Weapon \nfrom score import Score \nfrom pygame.sprite import Group\nfrom pygame.sprite import Sprite\nfrom random import randint\nfrom button import Button\nisGirl = False\n\ndef onKeyDown(screen, player, key, running):\n\tif running == False and key == pygame.K_TAB:\n\t\treturn True\n\tif player == None:\n\t\treturn \n\tif key == pygame.K_DOWN:\n\t\tplayer.offset(player.profile.playerSpeed)\n\telif key == pygame.K_UP:\n\t\tplayer.offset(-player.profile.playerSpeed)\n\telif key == pygame.K_SPACE:\n\t\tif len(player.weapons) < 3:\n\t\t\tplayer.attack()\n\telif key == pygame.K_h:\n\t\tglobal isGirl \n\t\tif isGirl:\n\t\t\tisGirl = False\n\t\telse:\n\t\t\tisGirl = True\n\ndef onKeyUp(player, running):\n\tif running:\n\t\tplayer.offset(0)\n\ndef handleEvents(screen, player, event, playButton, running):\n\tif event.type == pygame.QUIT:\n\t\tpygame.quit()\n\t\tsys.exit()\n\telif event.type == pygame.KEYDOWN:\n\t\treturn onKeyDown(screen, player, event.key, running)\n\telif event.type == pygame.KEYUP:\n\t\tonKeyUp(player, running)\n\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\treturn playButton.check(pygame.mouse.get_pos())\n\nrunning = False\nprofile = Settings()\npygame.init()\nscreen = pygame.display.set_mode((profile.screen_width, profile.screen_height))\npygame.display.set_caption('Konoha Crush')\n\nwall = Background(screen, 'images/konohaCrush.jpg')\nforest = Background(screen, 'images/background.jpg')\nplayButton = Button(screen, 'Play', (0, 0, 0), (screen.get_rect().centerx, screen.get_rect().centery))\nmessage = Button(screen, 'Click TAB to play', (255, 255, 255), (screen.get_rect().centerx, screen.get_rect().bottom))\nopeningImage = True\n\nreqInit = True\nvillain, hero = None, None\n\nwhile True:\n\tfor event in pygame.event.get():\n\t\tif handleEvents(screen, hero, event, playButton, running) == True:\n\t\t\trunning = True\n\t\t\topeningImage = False\n\n\tif running == False:\n\t\tif openingImage: \n\t\t\twall.show()\n\t\telse: \n\t\t\tforest.show()\n\t\t\thero.show()\n\t\t\tvillain.show()\n\t\tplayButton.show()\n\t\tmessage.show()\n\telse:\n\t\tif reqInit:\n\t\t\thero = Player(screen, 'images/naruto.png', 'images/rasengan.png', True, 'images/girl.png', 'images/love.png')\n\t\t\tvillain = Player(screen, 'images/snake.png', 'images/sword.png', False)\n\t\t\treqInit = False\n\t\tif isGirl:\n\t\t\thero.changeGirl()\n\t\telse:\n\t\t\thero.changeBoy()\n\n\t\tforest.show()\n\t\thero.update(villain)\n\t\tvillain.update(hero)\n\t\thero.show()\t\t\t\n\t\tvillain.show()\n\t\tif hero.get_points() <= 0 or villain.get_points() <= 0: \n\t\t\trunning = False\n\t\t\treqInit = True\n\t\t\tisGirl = False\n\t\t\tif hero.get_points() > 0:\n\t\t\t\tstatus = 'won'\n\t\t\telse:\n\t\t\t\tstatus = 'lost'\n\t\t\tplayButton = Button(screen, 'Naruto ' + status, (255, 255, 255), (screen.get_rect().centerx, screen.get_rect().centery))\n\tpygame.display.flip()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"245721717","text":"'''\nTask \nRead a line of input from stdin and save it to a variable, . Then print the contents of to stdout.\n\nInput Format\n\nA single line containing sentence .\n\nConstraints\n\nOutput Format\n\nPrint the contents of to stdout.\n\nSample Input\n\nHow many chickens does it take to cross the road?\nSample Output\n\nHow many chickens does it take to cross the road?\n\n'''\n\ndef read():\n s = input();\n return s\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"Python/Introduction_Reading Raw_Input .py","file_name":"Introduction_Reading Raw_Input .py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"500391840","text":"from datetime import datetime, timedelta\r\n\r\nfrom flask import g, request, render_template\r\n\r\nfrom . import main\r\nfrom ..forms import SearchForm\r\nfrom ..models import Project\r\n\r\n\r\n@main.before_app_request\r\ndef before_request():\r\n g.previous_url = request.referrer\r\n g.search_form = SearchForm()\r\n\r\n\r\n@main.route(\"/\")\r\ndef index():\r\n page = request.args.get(\"page\", 1, type=int)\r\n yesterday = datetime.today() - timedelta(days=1)\r\n projects = (\r\n Project.query.filter(Project.created >= yesterday)\r\n .order_by(Project.created.desc())\r\n .paginate(page=page, per_page=2)\r\n )\r\n\r\n return render_template(\"index.html\", title=\"Home\", projects=projects)\r\n","sub_path":"shareproject/main/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"517494515","text":"from django.db import models\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom django.conf import settings\nfrom django.db.models import F\n\nfrom jsonfield import JSONField\n\nimport random\nimport json\n\nfrom experiments.dateutils import now\nfrom experiments import conf\n\n\nCONTROL_STATE = 0\nENABLED_STATE = 1\nTRACK_STATE = 3\n\nSTATES = (\n (CONTROL_STATE, 'Default/Control'),\n (ENABLED_STATE, 'Enabled'),\n (TRACK_STATE, 'Track'),\n)\n\nclass Counter(models.Model):\n key = models.CharField(primary_key=True, max_length=128)\n hash = models.CharField( max_length=96)\n field = models.CharField( max_length=20)\n count = models.IntegerField(default=0,blank=False,null=False)\n\nclass Count(): \n @classmethod\n def next(cls, hash=\"default\", field=\"default\", count = 1):\n key = \"%s:%s\" % (hash, field)\n try:\n Counter.objects.filter(pk = key).update(count = (F('count') + count))\n return Counter.objects.get(pk = key).count\n except Counter.DoesNotExist:\n return Counter.objects.create(key = key, hash=hash, field=field, count = count).count\n\n @classmethod\n def get(cls, hash=\"default\", field=\"default\"):\n key = \"%s:%s\" % (hash, field)\n try:\n return Counter.objects.get(pk = key).count\n except Counter.DoesNotExist:\n return Counter.objects.create(key = key, hash=hash, field=field, count = 0).count\n\n @classmethod\n def getall(cls, hash=\"default\"):\n try:\n return Counter.objects.filter(hash = hash)\n except Counter.DoesNotExist:\n return None\n\n\n @classmethod\n def delete(cls, hash=\"default\"):\n try:\n objects = Counter.objects.filter(hash = hash)\n \n for o in objects:\n o.delete()\n \n objects = Counter.objects.filter(hash = hash)\n return None\n except Counter.DoesNotExist:\n return None\n\n @classmethod\n def deletefield(cls, hash=\"default\", field=\"default\"):\n key = \"%s:%s\" % (hash, field)\n try:\n return Counter.objects.get(pk = key).delete()\n except Counter.DoesNotExist:\n return None\n \n @classmethod\n def len(cls, hash=\"default\"):\n try:\n return Counter.objects.filter(hash = hash).count()\n except Counter.DoesNotExist:\n return 0\n \n @classmethod\n def next_hex(cls, key = 'default:default'):\n return hex(Counter.next(key)).replace('0x', '').replace('L', '')\n \n def __unicode__(self):\n return u'%s = %s' % (self.pk, self.count)\n \n\nclass Experiment(models.Model):\n name = models.CharField(primary_key=True, max_length=128)\n description = models.TextField(default=\"\", blank=True, null=True)\n alternatives = JSONField(default={}, blank=True)\n relevant_chi2_goals = models.TextField(default=\"\", null=True, blank=True)\n relevant_mwu_goals = models.TextField(default=\"\", null=True, blank=True)\n\n state = models.IntegerField(default=CONTROL_STATE, choices=STATES)\n\n start_date = models.DateTimeField(default=now, blank=True, null=True, db_index=True)\n end_date = models.DateTimeField(blank=True, null=True)\n\n def is_displaying_alternatives(self):\n if self.state == CONTROL_STATE:\n return False\n elif self.state == ENABLED_STATE:\n return True\n elif self.state == TRACK_STATE:\n return True\n else:\n raise Exception(\"Invalid experiment state %s!\" % self.state)\n\n def is_accepting_new_users(self):\n if self.state == CONTROL_STATE:\n return False\n elif self.state == ENABLED_STATE:\n return True\n elif self.state == TRACK_STATE:\n return False\n else:\n raise Exception(\"Invalid experiment state %s!\" % self.state)\n\n def ensure_alternative_exists(self, alternative, weight=None):\n if alternative not in self.alternatives:\n self.alternatives[alternative] = {}\n self.alternatives[alternative]['enabled'] = True\n self.save()\n if weight is not None and 'weight' not in self.alternatives[alternative]:\n self.alternatives[alternative]['weight'] = float(weight)\n self.save()\n\n @property\n def default_alternative(self):\n for alternative, alternative_conf in self.alternatives.items():\n if alternative_conf.get('default'):\n return alternative\n return conf.CONTROL_GROUP\n\n def set_default_alternative(self, alternative):\n for alternative_name, alternative_conf in self.alternatives.items():\n if alternative_name == alternative:\n alternative_conf['default'] = True\n elif 'default' in alternative_conf:\n del alternative_conf['default']\n\n def random_alternative(self):\n if all('weight' in alt for alt in self.alternatives.values()):\n return weighted_choice([(name, details['weight']) for name, details in self.alternatives.items()])\n else:\n return random.choice(list(self.alternatives.keys()))\n\n def __unicode__(self):\n return self.name\n\n def to_dict(self):\n data = {\n 'name': self.name,\n 'start_date': self.start_date,\n 'end_date': self.end_date,\n 'state': self.state,\n 'description': self.description,\n 'relevant_chi2_goals': self.relevant_chi2_goals,\n 'relevant_mwu_goals': self.relevant_mwu_goals,\n 'default_alternative': self.default_alternative,\n 'alternatives': ','.join(self.alternatives.keys()),\n }\n return data\n\n def to_dict_serialized(self):\n return json.dumps(self.to_dict(), cls=DjangoJSONEncoder)\n\n\nclass Enrollment(models.Model):\n \"\"\" A participant in a split testing experiment \"\"\"\n user = models.ForeignKey(getattr(settings, 'AUTH_USER_MODEL', 'auth.User'))\n experiment = models.ForeignKey(Experiment)\n enrollment_date = models.DateTimeField(auto_now_add=True)\n last_seen = models.DateTimeField(null=True)\n alternative = models.CharField(max_length=50)\n\n class Meta:\n unique_together = ('user', 'experiment')\n\n def __unicode__(self):\n return u'%s - %s' % (self.user, self.experiment)\n\n\ndef weighted_choice(choices):\n total = sum(w for c, w in choices)\n r = random.uniform(0, total)\n upto = 0\n for c, w in choices:\n upto += w\n if upto >= r:\n return c\n\n\n","sub_path":"experiments/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"630169812","text":"from django.conf.urls import url\n\nfrom blog import views\nurlpatterns = [\n url(r'^$', views.index, name=\"blog_index\"),\n url(r'^detail/(?P\\d+)/$', views.detail, name=\"blog_detail\"),\n url(r'^aboutme$', views.aboutme, name=\"aboutme\"),\n url(r'^tag/(?P\\d+)/(?P\\d+)/$', views.tagindex, name=\"blog_tagindex\"),\n url(r'^cata/(?P\\d+)/(?P\\d+)/$', views.cataindex, name=\"blog_cataindex\"),\n ]\n","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"243404240","text":"import pandas as pd\nfrom sklearn.neighbors import KNeighborsClassifier\n\nimport classes.KnnNetwork as KnnNetwork\n\n\n# Import iris dataset\ndata = pd.read_csv(\"assets/data/iris.csv\")\n\nkn = KnnNetwork.KnnNetwork()\n\n#print(\"Single entry\", data.iloc[0])\n#print(\"Several entries\", data.iloc[:5])\n#print(\"Several keys\", data.iloc[:5, :-1])\n#print(\"Several names\", data.iloc[:5, -1:])\n#print(data.head())\n\n\ndef processData(data):\n\tkeys = [ x[-1] for x in data.values.tolist() ]\n\tentries = [ x[:-1] for x in data.values.tolist() ]\n\treturn list(zip(keys, entries))\n\ntrainingSet = processData(data.iloc[:105])\nvalidationSet = processData(data.iloc[105:135])\ntestSet = processData(data.iloc[135:])\n\nkn.train(trainingSet)\n\ncorrectTraining = 0\ndef runOwn(data, k, printLabel):\n\tcorrect = 0\n\tfor key, values in data:\n\t\tif kn.guess(values, k)[0] == key:\n\t\t\tcorrect += 1\n\n\tprint(printLabel, correct, \"/\", len(data))\n\ndef runSciKit(valData, k, printLabel):\n\tcorrect = 0\n\tclazz = KNeighborsClassifier(n_neighbors=k)\n\tclazz.fit(data.iloc[:105, 0:4], data.iloc[:105, -1])\n\tfor key, values in valData:\n\t\tif clazz.predict([values])[0] == key:\n\t\t\tcorrect += 1\n\n\tprint(printLabel, correct, \"/\", len(valData))\n\n\ntest = [7.2, 3.6, 5.1, 2.5]\n\n#clazz = KNeighborsClassifier(n_neighbors=5)\n#clazz.fit(data.iloc[:105, 0:4], data.iloc[:105, -1])\n#for key, values in validationSet:\n#\tprint(\"Own\", kn.guess(values, 5), \" with actual \", key)\n#\tprint(\"SciKit\", clazz.predict([values]), clazz.kneighbors([values])[1], \" with actual\", key)\nfor x in range(1, 8, 2):\n\trunOwn(validationSet, x, \"Own\" + str(x))\n\trunSciKit(validationSet, x, \"SciKit\" + str(x))\n\n#print(kn.guess(validationSet[0][1], 3))\n\n","sub_path":"first-steps/k_nearest_neighbour/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"549093433","text":"# Copyright 2018 Intel Corporation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# -----------------------------------------------------------------------------\n\nimport unittest\n\nfrom sawtooth_sdk.consensus.zmq_service import ZmqService\nfrom sawtooth_sdk.messaging.future import Future\nfrom sawtooth_sdk.messaging.future import FutureResult\nfrom sawtooth_sdk.protobuf import consensus_pb2\nfrom sawtooth_sdk.protobuf.validator_pb2 import Message\n\n\nclass TestService(unittest.TestCase):\n def setUp(self):\n self.mock_stream = unittest.mock.Mock()\n self.service = ZmqService(\n stream=self.mock_stream,\n timeout=10)\n\n def _make_future(self, message_type, content):\n fut = Future('test')\n fut.set_result(FutureResult(\n message_type=message_type,\n content=content))\n return fut\n\n def test_send_to(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_SEND_TO_RESPONSE,\n content=consensus_pb2.ConsensusSendToResponse(\n status=consensus_pb2.ConsensusSendToResponse.OK\n ).SerializeToString())\n\n self.service.send_to(\n receiver_id=b'receiver_id',\n message_type='message_type',\n payload=b'payload')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_SEND_TO_REQUEST,\n content=consensus_pb2.ConsensusSendToRequest(\n message_type='message_type',\n content=b'payload',\n receiver_id=b'receiver_id').SerializeToString())\n\n def test_broadcast(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_BROADCAST_RESPONSE,\n content=consensus_pb2.ConsensusBroadcastResponse(\n status=consensus_pb2.ConsensusBroadcastResponse.OK\n ).SerializeToString())\n\n self.service.broadcast(\n message_type='message_type',\n payload=b'payload')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_BROADCAST_REQUEST,\n content=consensus_pb2.ConsensusBroadcastRequest(\n message_type='message_type',\n content=b'payload').SerializeToString())\n\n def test_initialize_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_INITIALIZE_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusInitializeBlockResponse(\n status=consensus_pb2.ConsensusInitializeBlockResponse.OK\n ).SerializeToString())\n\n self.service.initialize_block(previous_id=b'test')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_INITIALIZE_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusInitializeBlockRequest(\n previous_id=b'test').SerializeToString())\n\n def test_summarize_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_SUMMARIZE_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusSummarizeBlockResponse(\n status=consensus_pb2.ConsensusSummarizeBlockResponse.OK,\n summary=b'summary').SerializeToString())\n\n result = self.service.summarize_block()\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_SUMMARIZE_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusSummarizeBlockRequest()\n .SerializeToString())\n\n self.assertEqual(result, b'summary')\n\n def test_finalize_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_FINALIZE_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusFinalizeBlockResponse(\n status=consensus_pb2.ConsensusFinalizeBlockResponse.OK,\n block_id=b'block_id').SerializeToString())\n\n result = self.service.finalize_block(data=b'test')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_FINALIZE_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusFinalizeBlockRequest(\n data=b'test').SerializeToString())\n\n self.assertEqual(result, b'block_id')\n\n def test_cancel_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_CANCEL_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusCancelBlockResponse(\n status=consensus_pb2.ConsensusCancelBlockResponse.OK\n ).SerializeToString())\n\n self.service.cancel_block()\n\n request = consensus_pb2.ConsensusCancelBlockRequest()\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_CANCEL_BLOCK_REQUEST,\n content=request.SerializeToString())\n\n def test_check_blocks(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_CHECK_BLOCKS_RESPONSE,\n content=consensus_pb2.ConsensusCheckBlocksResponse(\n status=consensus_pb2.ConsensusCheckBlocksResponse.OK\n ).SerializeToString())\n\n self.service.check_blocks(priority=[b'test1', b'test2'])\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_CHECK_BLOCKS_REQUEST,\n content=consensus_pb2.ConsensusCheckBlocksRequest(\n block_ids=[b'test1', b'test2']).SerializeToString())\n\n def test_commit_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_COMMIT_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusCommitBlockResponse(\n status=consensus_pb2.ConsensusCommitBlockResponse.OK\n ).SerializeToString())\n\n self.service.commit_block(block_id=b'test')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_COMMIT_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusCommitBlockRequest(\n block_id=b'test').SerializeToString())\n\n def test_ignore_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_IGNORE_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusIgnoreBlockResponse(\n status=consensus_pb2.ConsensusIgnoreBlockResponse.OK\n ).SerializeToString())\n\n self.service.ignore_block(block_id=b'test')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_IGNORE_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusIgnoreBlockRequest(\n block_id=b'test').SerializeToString())\n\n def test_fail_block(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_FAIL_BLOCK_RESPONSE,\n content=consensus_pb2.ConsensusFailBlockResponse(\n status=consensus_pb2.ConsensusFailBlockResponse.OK\n ).SerializeToString())\n\n self.service.fail_block(block_id=b'test')\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_FAIL_BLOCK_REQUEST,\n content=consensus_pb2.ConsensusFailBlockRequest(\n block_id=b'test').SerializeToString())\n\n def test_get_blocks(self):\n block_1 = consensus_pb2.ConsensusBlock(\n block_id=b'block1',\n previous_id=b'block0',\n signer_id=b'signer1',\n block_num=1,\n payload=b'test1')\n\n block_2 = consensus_pb2.ConsensusBlock(\n block_id=b'block2',\n previous_id=b'block1',\n signer_id=b'signer2',\n block_num=2,\n payload=b'test2')\n\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_BLOCKS_GET_RESPONSE,\n content=consensus_pb2.ConsensusBlocksGetResponse(\n status=consensus_pb2.ConsensusBlocksGetResponse.OK,\n blocks=[block_1, block_2]).SerializeToString())\n\n blocks = self.service.get_blocks(block_ids=[b'id1', b'id2'])\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_BLOCKS_GET_REQUEST,\n content=consensus_pb2.ConsensusBlocksGetRequest(\n block_ids=[b'id1', b'id2']).SerializeToString())\n\n self.assertEqual({\n block_id: (\n block.previous_id,\n block.signer_id,\n block.block_num,\n block.payload)\n for block_id, block in blocks.items()\n }, {\n b'block1': (b'block0', b'signer1', 1, b'test1'),\n b'block2': (b'block1', b'signer2', 2, b'test2'),\n })\n\n def test_get_chain_head(self):\n block = consensus_pb2.ConsensusBlock(\n block_id=b'block',\n previous_id=b'block0',\n signer_id=b'signer',\n block_num=1,\n payload=b'test')\n\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_CHAIN_HEAD_GET_RESPONSE,\n content=consensus_pb2.ConsensusChainHeadGetResponse(\n status=consensus_pb2.ConsensusChainHeadGetResponse.OK,\n block=block).SerializeToString())\n\n chain_head = self.service.get_chain_head()\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_CHAIN_HEAD_GET_REQUEST,\n content=consensus_pb2.ConsensusChainHeadGetRequest()\n .SerializeToString())\n\n self.assertEqual(chain_head.block_id, b'block')\n self.assertEqual(chain_head.previous_id, b'block0')\n self.assertEqual(chain_head.signer_id, b'signer')\n self.assertEqual(chain_head.block_num, 1)\n self.assertEqual(chain_head.payload, b'test')\n\n def test_get_settings(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_SETTINGS_GET_RESPONSE,\n content=consensus_pb2.ConsensusSettingsGetResponse(\n status=consensus_pb2.ConsensusSettingsGetResponse.OK,\n entries=[\n consensus_pb2.ConsensusSettingsEntry(\n key='key1',\n value='value1'),\n consensus_pb2.ConsensusSettingsEntry(\n key='key2',\n value='value2')]).SerializeToString())\n\n entries = self.service.get_settings(\n block_id=b'test',\n settings=['test1', 'test2'])\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_SETTINGS_GET_REQUEST,\n content=consensus_pb2.ConsensusSettingsGetRequest(\n block_id=b'test',\n keys=['test1', 'test2']).SerializeToString())\n\n self.assertEqual(\n entries, {\n 'key1': 'value1',\n 'key2': 'value2',\n })\n\n def test_get_state(self):\n self.mock_stream.send.return_value = self._make_future(\n message_type=Message.CONSENSUS_STATE_GET_RESPONSE,\n content=consensus_pb2.ConsensusStateGetResponse(\n status=consensus_pb2.ConsensusStateGetResponse.OK,\n entries=[\n consensus_pb2.ConsensusStateEntry(\n address='address1',\n data=b'data1'),\n consensus_pb2.ConsensusStateEntry(\n address='address2',\n data=b'data2')]).SerializeToString())\n\n entries = self.service.get_state(\n block_id=b'test',\n addresses=['test1', 'test2'])\n\n self.mock_stream.send.assert_called_with(\n message_type=Message.CONSENSUS_STATE_GET_REQUEST,\n content=consensus_pb2.ConsensusStateGetRequest(\n block_id=b'test',\n addresses=['test1', 'test2']).SerializeToString())\n\n self.assertEqual(\n entries, {\n 'address1': b'data1',\n 'address2': b'data2',\n })\n","sub_path":"tests/test_zmq_service.py","file_name":"test_zmq_service.py","file_ext":"py","file_size_in_byte":12818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"481445878","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n__author__ = 'Mike'\n\n'''scrapy初始url的两种方法\n 一种是常量start_urls,并需要定义一个方法parse()\n 另一种是直接定义一个方法：start_request() \n'''\n\nclass simpleUrl(scrapy.Spider):\n name = \"simpleUrl\"\n\n '''无需定义start_request的写法，只要定义一个start_url'''\n start_urls = ['http://lab.scrapyd.cn/page/1/',\n 'http://lab.scrapyd.cn/page/2/', ]\n\n #如果简写url，必须定义parse方法\n\n def parse(self, response):\n page = response.url.split(\"/\")[-2]\n filename = 'mingyan-%s.html' % page\n with open(filename, 'wb') as f:\n f.write(response.body) # 刚才请求的页面去哪里了？response.body就代表刚才下载的页面\n self.log('保存文件: %s' % filename)","sub_path":"articlespider/articlespider/spiders/simpleUrl.py","file_name":"simpleUrl.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"352770409","text":"#!/usr/bin/env python\n# Author: Angus Allen.\n# Date: 13 July 2016.\n# Summary: Executes queries on the tournament database for the tournament project.\n# No extra credits attempted\n\nimport psycopg2\nimport bleach\n\ndef connect():\n \"\"\"Connect to the PostgreSQL database. Returns a database connection.\"\"\"\n return psycopg2.connect(\"dbname=tournament\")\n\ndef deleteMatches():\n \"\"\"Remove all the match records from the database.\"\"\"\n db = connect()\n c = db.cursor()\n c.execute('DELETE FROM matches')\n db.commit()\n db.close()\n\ndef deletePlayers():\n \"\"\"Remove all the player records from the database.\"\"\"\n db = connect()\n c = db.cursor()\n c.execute('DELETE FROM players')\n db.commit()\n db.close()\n\ndef countPlayers():\n \"\"\"Returns the number of players currently registered.\"\"\"\n db = connect()\n c = db.cursor()\n c.execute('SELECT COALESCE(count(*),0) FROM players')\n rows = c.fetchall()\n db.close()\n for row in rows:\n result = row[0]\n return result\n\ndef registerPlayer(name):\n \"\"\"Adds a player to the tournament database.\n\n The database assigns a unique serial id number for the player. (This\n should be handled by your SQL database schema, not in your Python code.)\n\n Args:\n name: the player's full name (need not be unique).\n \"\"\"\n db = connect()\n c = db.cursor()\n c.execute('INSERT INTO players (player_name) VALUES (%s)', (name,))\n db.commit()\n db.close()\n\ndef playerStandings():\n \"\"\"Returns a list of the players and their win records, sorted by wins.\n\n The first entry in the list should be the player in first place, or a player\n tied for first place if there is currently a tie.\n\n Returns:\n A list of tuples, each of which contains (id, name, wins, matches):\n id: the player's unique id (assigned by the database)\n name: the player's full name (as registered)\n wins: the number of matches the player has won\n matches: the number of matches the player has played\n \"\"\"\n standings = []\n db = connect()\n c = db.cursor()\n \"\"\"Query to be executed below joins two new tables with the following names:\n win_rank\n loss_count\n\n win_rank is itself the output table from a join of a new table\n called win_count on the table called players\n \"\"\"\n c.execute('SELECT '\n ' win_rank.id, '\n ' win_rank.name, '\n ' win_rank.wins, '\n ' COALESCE(loss_count.losses, 0) AS losses '\n 'FROM '\n ' (SELECT players.player_id AS id, '\n ' players.player_name AS name, '\n ' COALESCE(win_count.wins, 0) AS wins '\n ' FROM '\n ' (SELECT winner_id, '\n ' COUNT(winner_id) AS wins '\n ' FROM matches '\n ' GROUP BY winner_id) AS win_count '\n ' FULL OUTER JOIN '\n ' players '\n ' ON players.player_id = win_count.winner_id) AS win_rank '\n 'FULL OUTER JOIN '\n ' (SELECT loser_id, '\n ' COUNT(loser_id) AS losses '\n ' FROM matches '\n ' GROUP BY loser_id) AS loss_count '\n 'ON win_rank.id = loss_count.loser_id '\n 'ORDER BY win_rank.wins DESC')\n\n rows = c.fetchall()\n db.close()\n for row in rows:\n total_matches = int(row[2]) + int(row[3])\n player_tuple = (row[0],row[1],int(row[2]),total_matches)\n standings.append(player_tuple)\n return standings\n\ndef reportMatch(winner, loser):\n \"\"\"Records the outcome of a single match between two players.\n\n Args:\n winner: the id number of the player who won\n loser: the id number of the player who lost\n \"\"\"\n db = connect()\n c = db.cursor()\n c.execute('INSERT INTO matches (winner_id, loser_id) '\n ' VALUES (%s, %s)', (winner, loser,))\n db.commit()\n db.close()\n\ndef swissPairings():\n \"\"\"Returns a list of pairs of players for the next round of a match.\n\n Assuming that there are an even number of players registered, each player\n appears exactly once in the pairings. Each player is paired with another\n player with an equal or nearly-equal win record, that is, a player adjacent\n to him or her in the standings.\n\n Returns:\n A list of tuples, each of which contains (id1, name1, id2, name2)\n id1: the first player's unique id\n name1: the first player's name\n id2: the second player's unique id\n name2: the second player's name\n \"\"\"\n standings = playerStandings()\n num = int(countPlayers())\n pairings = []\n players_per_match = 2\n next_in_standings = 1\n if num > 0:\n for i in range (num):\n if i % players_per_match == 0:\n id1 = standings[i][0]\n name1 = standings[i][1]\n id2 = standings[i+next_in_standings][0]\n name2 = standings[i+next_in_standings][1]\n pair = (id1, name1, id2, name2)\n pairings.append(pair)\n return pairings\n","sub_path":"tournament.py","file_name":"tournament.py","file_ext":"py","file_size_in_byte":5174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"533649771","text":"import glob\nimport logging\nimport pickle\nimport pyltp\nimport time\n\nimport pymysql\nfrom bert_serving.client import BertClient\nimport numpy as np\nimport json\nfrom annoy import AnnoyIndex\n\n\n# from service.client import BertClient\nfrom gensim import similarities\n\nfrom scipy.linalg import norm\n\nclass sentenceParser():\n def __init__(self):\n\n self.bc = BertClient(ip='192.168.1.103')\n self.splitter = pyltp.SentenceSplitter()\n # fin = open('./resources/ninteenth_sents.txt', 'r', encoding='UTF-8')\n #\n # self.lines = fin.readlines()\n # fin.close()\n\n self.annoyIndex=AnnoyIndex(768)\n # self.annoyIndex.load('./mod/annoy_19th.model')\n self.annoyIndex.load('./mod/annoy_19th_vedio.mod')\n load_file=open('./resources/19th_vedio.bin','rb')\n self.sents=pickle.load(load_file)\n self.lines=[sent['articleContent'] for sent in self.sents]\n\n def find_19th_org(self,content,threadhold=0.9):\n sents = list(self.splitter.split(content))\n sents_encode=self.bc.encode(sents)\n result=[]\n for i,sencode in enumerate(sents_encode):\n sentindex,dis=self.annoyIndex.get_nns_by_vector(sencode,1,include_distances=True)\n print(sents[i]+str(np.cos(dis[0])))\n if(np.cos(dis[0])>threadhold):\n result.append({'org':self.lines[sentindex[0]].strip(),'subcontent':sents[i],'score':np.cos(dis[0]),'video':self.sents[sentindex[0]] if self.sents[sentindex[0]] else {} })\n return result\n\n\n def connect_wxremit_db(self):\n return pymysql.connect(host='127.0.0.1',\n port=3306,\n user='root',\n password='',\n database='xinhua',\n charset='utf8')\n\n\n def query_country_name(self, cc2):\n sql_str = (\"SELECT distinct(FILE_UUID),txt\"\n + \" FROM e20190313\"\n + \" WHERE txt like '%s' group by FILE_UUID,txt limit 100\" % cc2)\n logging.info(sql_str)\n\n con = self.connect_wxremit_db()\n cur = con.cursor()\n cur.execute(sql_str)\n rows = cur.fetchall()\n cur.close()\n con.close()\n\n # assert len(rows) == 1, 'Fatal error: country_code does not exists!'\n return rows\n def prepare_vedio(self):\n f = glob.glob('./resources/19/*.txt')\n sents=[]\n for file in f:\n print(file)\n with open(file,'r') as fin:\n lines=fin.readlines()\n for line in lines:\n line=line.replace('\\'','\"')\n js = json.loads(line)\n # jsonp = jsonpath.jsonpath(js, \"$..news.*\")\n sents.append(js)\n #应该做成KEY vALUE\n self.sents=sents#[{str(i):sent} for i,sent in enumerate(sents)]\n annoyIndex = AnnoyIndex(768)\n for i,sent in enumerate(sents):\n encode = self.bc.encode([sent['articleContent']])[0]\n # sent_id=int(sent['sentenceId'][12:])\n annoyIndex.add_item(i,encode)\n annoyIndex.build(10)\n annoyIndex.save('./mod/annoy_19th_vedio.mod')\n fou=open('./resources/19th_vedio.bin','wb')\n pickle.dump(self.sents,fou)\n fou.close()\n # fou = np.array(self.sents, dtype=np.float32)\n # fou.tofile(\"./resources/19th_vedio.bin\")\n\n\n def prepare(self):\n fin=open('./resources/ninteenth','r',encoding='UTF-8')\n sentfile=open('./resources/ninteenth_sents.txt','w',encoding='UTF-8')\n\n '''\n AnnoyIndex(f, metric='angular') returns a new index that’s read-write and stores vector of f dimensions. Metric can be \"angular\", \"euclidean\", \"manhattan\", \"hamming\", or \"dot\".\n '''\n annoyIndex = AnnoyIndex(768)\n i=0\n sents_encode=[]\n lines=fin.readlines()\n for line in lines:\n line=line.strip()\n if line!='':\n sents=list(self.splitter.split(line))\n encodes=self.bc.encode(sents)\n\n for sent in zip(sents,encodes):\n sents_encode.append(sent[1])\n sentfile.write(sent[0]+'\\n')\n annoyIndex.add_item(i, sent[1])\n i+=1\n # # sents_encode.append({'content':sent[0],'encode':sent[1]})\n annoyIndex.build(100)\n annoyIndex.save('./mod/annoy_19th.model')\n\n fin.close()\n sentfile.close()\n\n fou=np.array(sents_encode,dtype=np.float32)\n fou.tofile(\"./resources/19.bin\")\n # fou=open('./resources/ninteenth_encode.txt','w',encoding='UTF-8')\n # for index,sent in enumerate(sents_encode):\n # fou.write(str(index)+'\\t'+sent['content']+'\\t'+str(sent['encode'])+'\\n')\n # fou.close()\n # import numpy as np\n # dis1 = np.dot(a[1], b[1]) / (norm(a[1]) * norm(b[1]))\n # dis2 = np.dot(b[0], c[0]) / (norm(b[0]) * norm(c[0]))\n # print(dis1, dis2)\n\n\n def proccess_main(self):\n # self.encodes=np.fromfile('./resources/19.bin',dtype=np.float32)\n # print(encodes)\n content='习近平总书记在党的十九大报告中指出，中国秉持共商共建共享的全球治理观，积极参与全球治理体系改革和建设，不断贡献中国智慧和力量。'\n content='世界正处于大发展大变革大调整时期，和平与发展仍然是时代主题'\n content='我们愿同意方共建“一带一路”，发挥两国“一带一路”合作的历史、文化、区位等优势，把“一带一路”互联互通建设同意大利“北方港口建设计划”、“投资意大利计划”等对接，在海上、陆地、航空、航天、文化等多个维度打造新时期的“一带一路”'\n # content='同志们：\n content='党的十九大报告指出：“我国社会主要矛盾已经转化为人民日益增长的美好生活需要和不平衡不充分的发展之间的矛盾”。从群众的需求上看，美好生活日益多样化，不仅包括物质方面的需求，还包括非物质需求。2017年12月份，厦门开始全力打造“五安工程”，从群众最关心、最直接的“家安、路安、食安、业安、心安”等平安需求着力，为打造最具安全感城市加油助力。'\n # self.getSimilray(content)\n # print('*'*15+content)\n # self.getSimilray2(content)\n print(self.find_19th_org(content))\n\n\n def getSimilray2(self,content):\n current = self.bc.encode([content])\n # annoyIndex=AnnoyIndex(768)\n # for i,encode in enumerate(self.encodes.reshape(-1, 768)):\n # annoyIndex.add_item(i,encode)\n # annoyIndex.build(100)\n result,distance=self.annoyIndex.get_nns_by_vector(current[0],5,include_distances=True)\n orgs=[]\n print(content)\n for i,r in enumerate(result):\n print(str(r)+' '+str(np.cos(distance[i])))\n # print(self.lines[r])\n # orgs.append(self.lines[r])\n orgs.append(self.sents[r])\n print(self.sents[r])\n\n distance=np.array(np.cos(distance)).tolist()\n return orgs,distance\n\n def getSimilray1(self,content):\n # None\n # current = self.bc.encode([content])\n # index=similarities.MatrixSimilarity(self.encodes.reshape(-1, 768),num_features=768,chunksize=768)\n # sim=index[current[0]]\n # sim=sorted(enumerate(sim),key=lambda item:item[1],reverse=True)\n # for i in sim[:-5]:\n # print(i)\n # print(self.lines[i[0]])\n current = self.bc.encode([content])\n score=np.sum(current[0]*self.encodes.reshape(-1, 768),axis=1)/np.linalg.norm(self.encodes.reshape(-1, 768),axis=1)\n topk=np.argsort(score)[::-1][:5]\n print(topk)\n\n\n\n def getSimilray(self,content):\n current=self.bc.encode([content])\n dtype = [('index',np.int), ('score',np.float32)]\n\n all=[]\n\n for index,encode in enumerate(self.encodes.reshape(-1,768)):\n distance=np.dot(current[0],encode)/(norm(current[0])*norm(encode))\n # if(distance>top[1]):\n all.append([index,distance])\n # all = np.array(all, dtype=dtype)\n # top= np.sort(all,order='score')\n top=sorted(all,key=lambda elem:elem[1],reverse=True)\n\n for i in top[:5]:\n print(i)\n print(self.lines[i[0]])\n\n def test(self):\n # feat=np.random.random((100000,4096))\n # annoyIndex = AnnoyIndex(4096)\n # annoyIndex.on_disk_build('a')\n # for i,v in enumerate(feat):\n # annoyIndex.add_item(i,v)\n # for i,v in enumerate(feat):\n # annoyIndex.add_item(i,v)\n t=time.time()\n #\n # annoyIndex.build(100)\n # print(time.time()-t)\n annoyIndex = AnnoyIndex(4096)\n annoyIndex.load('a')\n print(annoyIndex.get_nns_by_item(0,5))\n print(time.time() - t)\n\n def test1(self):\n rows=self.query_country_name('%')\n annoyIndex = AnnoyIndex(768)\n # for i,row in enumerate(rows):\n # encode=self.bc.encode([row[1]])\n # annoyIndex.add_item(i,encode[0])\n # annoyIndex.build(10)\n # annoyIndex.save('articles')\n annoyIndex.load('articles')\n result,index=annoyIndex.get_nns_by_item(10,5,include_distances=True)\n print(rows[10])\n print(np.cos(index))\n for i in result:\n\n print(rows[i])\n\nsentP=sentenceParser()\n# sentP.prepare()\n# sentP.prepare_vedio()\n\nsentP.proccess_main()\n# sentP.test1()\n","sub_path":"ninteenth.py","file_name":"ninteenth.py","file_ext":"py","file_size_in_byte":9661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"162980754","text":"#Shared\nimport time\nfrom botocore.exceptions import WaiterError as WaiterError\n\ndef dateconverter(o):\n import datetime\n if isinstance(o, datetime.datetime):\n return o.__str__()\n\nimport json\nimport sys\n\ndef json2str(o):\n return json.dumps(o, default=dateconverter, indent=2, sort_keys=True)\n\ndef jsonprint(o):\n my_print(json2str(0)+'\\n')\n\ndef jsonprint_err(o):\n my_print_err(json2str(0)+'\\n')\n\ndef my_print_err(str):\n red = '\\033[01;31m'\n sys.stderr.write(red+str+'\\n')\n\ndef my_print(str):\n green = '\\033[01;32m'\n sys.stdout.write(green+str+'\\n')\n\n\ndef create_changeset_and_wait(cf, stackName, file, params, type):\n with open(file, 'r') as f:\n cs_response = cf.create_change_set(\n StackName=stackName,\n TemplateBody=f.read(),\n Parameters=params,\n Capabilities=['CAPABILITY_NAMED_IAM'],\n ChangeSetType=type,\n ChangeSetName=stackName + \"-cs-\" +str(int(time.time()))\n )\n # print(str(cs_response))\n change_set_name = cs_response['Id']\n waiter = cf.get_waiter('change_set_create_complete')\n my_print(\"Waiting chagne set '%s' to be created\" % change_set_name)\n try:\n waiter.wait(ChangeSetName=change_set_name)\n my_print(\"Change Set '%s' created\" % change_set_name)\n # Execute change set\n try:\n ex_response = cf.execute_change_set(ChangeSetName=change_set_name)\n my_print(\"Change Set '%s' executed.\" % change_set_name )\n try:\n if type == 'UPDATE':\n stackWaiter = cf.get_waiter('stack_update_complete')\n else:\n stackWaiter = cf.get_waiter('stack_create_complete')\n my_print(\"Waiting stack '%s' creation to complete\" % stackName)\n stackWaiter.wait(StackName=stackName)\n my_print(\"Stack '%s' created\" % stackName)\n # DONE\n except:\n my_print_err(\"Failed to %s stack: '%s'\" % (type, stackName))\n jsonprint_err(cf.describe_stack_events(StackName=stackName))\n sys.exit()\n\n except:\n my_print_err(\"Failed to execute '%s' Change Set\" % change_set_name)\n jsonprint_err(cf.describe_change_set(ChangeSetName=change_set_name))\n\n\n except WaiterError as e:\n error_msg = e.last_response['StatusReason']\n if error_msg in ['No updates are to be performed.', 'The submitted information didn\\'t contain changes. Submit different information to create a change set.'] :\n my_print(\"%s. Going to delete change set %s\" % (error_msg, change_set_name))\n # don't need to keep ChangeSet that contains no changes\n response = cf.delete_change_set( ChangeSetName=change_set_name, StackName=stackName)\n my_print(\"Change set %s for stack %s has been deleted\" % (change_set_name, stackName))\n else:\n my_print_err(\"Unexpected WaiterError error: %s. %s\" % (stackName, error_msg))\n sys.exit()\n\n\n# def create_stack_and_wait(cf, name, file, params):\n# with open(file, 'r') as f:\n# result = cf.create_stack(\n# StackName=name,\n# TemplateBody=f.read(),\n# Parameters=params,\n# Capabilities=['CAPABILITY_NAMED_IAM']\n# )\n# my_print(\"Requesting stack %s creation.\" % name)\n# jsonprint(result)\n# waiter = cf.get_waiter('stack_create_complete')\n# my_print(\"Waiting stack '%s' to be created\" % name)\n# try:\n# waiter.wait(StackName=name)\n# except:\n# my_print_err(\"Failed to create stack: '%s'\" % name)\n# jsonprint_err(cf.describe_stack_events(StackName=name))\n# # TODO: throw exception istead of just EXIT\n# sys.exit()\n# else:\n# my_print(\"Stack '%s' created\" % name)\n# return cf.describe_stacks(StackName=name)\n\n\n# def update_stack_and_wait(cf, name, file, params):\n# with open(file, 'r') as f:\n# result = cf.update_stack(\n# StackName=name,\n# TemplateBody=f.read(),\n# Parameters=params,\n# Capabilities=['CAPABILITY_NAMED_IAM']\n# )\n# my_print(\"Requesting stack %s update.\" % name)\n# jsonprint(result)\n# waiter = cf.get_waiter('stack_update_complete')\n# my_print(\"Waiting stack '%s' to be updated\" % name)\n# try:\n# waiter.wait(StackName=name)\n# except:\n# my_print_err(\"Failed to update stack: '%s'\" % name)\n# jsonprint_err(cf.describe_stack_events(StackName=name))\n# # TODO: throw exception istead of just EXIT\n# sys.exit()\n# else:\n# my_print(\"Stack '%s' updated\" % name)\n# return cf.describe_stacks(StackName=name)\n","sub_path":"my.py","file_name":"my.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"613780136","text":"import sys\nimport re\nimport os\n\n\ndef main():\n\n fn1 = sys.argv[1]\n fn2 = sys.argv[2]\n f1 = open(fn1, 'ru')\n f2 = open(fn2, 'w')\n\n line = f1.readline()\n while line:\n f2.write(re.sub(r':[123456789]\\d*', r':1', line))\n line = f1.readline()\n \n f1.close()\n f2.close()\n\n \nif __name__ == '__main__':\n main()\n","sub_path":"hw2/q2/binarization.py","file_name":"binarization.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"147916138","text":"#!/usr/bin/env python3\n\nimport os\n\nfps = ['30', '60', '120']\nbitrate = ['20M', '40M', '60M', '80M']\ncodec = ['XVID', 'WebM', 'H264']\n\nfor c in codec:\n for b in bitrate:\n for f in fps:\n cmd = \"./ffmpeg.py -f \" + f + \" -b \" + b + \" -c \" + c + \" -o output_\" + c.lower() + \"_\" + f + \"fps_\" + b + \".mkv\"\n os.system(cmd)\n","sub_path":"scripts/create-video.py","file_name":"create-video.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"175053194","text":"# __author__ : slade\n# __time__ : 17/12/21\n\nimport pandas as pd\nimport numpy as np\nfrom xgboost.sklearn import XGBClassifier\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.linear_model.logistic import LogisticRegression\nfrom sklearn.grid_search import GridSearchCV\n\n# load data\nX_train = pd.read_csv('ensemble_X_train.csv').iloc[:, 1:]\nY_train = pd.read_csv('ensemble_Y_train.csv', header=None).iloc[:, 1:]\nX_test = pd.read_csv('ensemble_X_test.csv').iloc[:, 1:]\nY_test = pd.read_csv('ensemble_Y_test.csv', header=None).iloc[:, 1:]\nY_train = np.array(Y_train).ravel()\nY_test = np.array(Y_test).ravel()\n\n\n# define the correction rate , the res1 is the positive case rate , the res2 is the correction rate\ndef metrics_spec(actual_data, predict_data, cutoff=0.5):\n actual_data = np.array(actual_data)\n predict_data = np.array(predict_data)\n bind_data = np.c_[actual_data, predict_data]\n res1 = 1.0 * (bind_data[bind_data[:, 0] == 1][:, 1] >= cutoff).sum() / bind_data[bind_data[:, 0] == 1].shape[0]\n res2 = 1.0 * (\n (bind_data[bind_data[:, 0] == 1][:, 1] >= cutoff).sum() + (\n bind_data[bind_data[:, 0] == 0][:, 1] < cutoff).sum()) / \\\n bind_data.shape[0]\n return res1, res2\n\n\n# if you have read the article 'Kaggle-TianChi分类问题相关纯算法理论剖析', you may know the suggestion of tuning methods , let's follow\n\n# you can adjust scale_weight_suggestion = (len(Y_train) - Y_train.sum()) / Y_train.sum() to balance your scale between positive cases and negtive cases\n# get the n_estimators and learning_rate first\n# if necessary ,increasing param:cv can increase the confidence degree of the current model's result\nparam_test = {\n 'learning_rate': [0.1, 0.3, 0.9],\n 'n_estimators': [50, 100, 300, 500]\n}\ngsearch = GridSearchCV(\n estimator=XGBClassifier(\n learning_rate=0.1,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n seed=27),\n param_grid=param_test,\n scoring='roc_auc',\n n_jobs=4,\n iid=False,\n cv=2)\ngsearch.fit(X_train, Y_train)\nprint(gsearch.best_params_)\n# {'learning_rate': 0.1, 'n_estimators': 100}\n# we should also consider the training speed of each process,sometimes we can sacrifice some effect to improve the speed. but don't worry , you can also retrain the two param at last if needed\n\n\n# get subsample next\nparam_test1 = {\n 'subsample': [0.6, 0.7, 0.8, 0.9]\n}\ngsearch1 = GridSearchCV(\n estimator=XGBClassifier(\n learning_rate=0.1,\n n_estimators=100,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n seed=27),\n param_grid=param_test1,\n scoring='roc_auc',\n n_jobs=4,\n iid=False,\n cv=2)\ngsearch1.fit(X_train, Y_train)\nprint(gsearch1.best_params_)\n# {'subsample': 0.7}\n\n# if you want your model more accurate , you can calculate the accuration at your test set after each training process\n# Compared with the last time at your test set if the accuracy rate decline, you should follow actions from the article guide 'Kaggle-TianChi分类问题相关纯算法理论剖析' to adjust the params\n\n# i have train the max_leaf_nodes and min_weight_fraction_leaf privately but it doesn't work ,so we skip it. get min_samples_split and max_depth result directly\nparam_test2 = {\n 'max_depth': [3, 5, 7],\n 'min_child_weight': [0.8, 1, 1.2]\n\n}\ngsearch2 = GridSearchCV(\n estimator=XGBClassifier(\n learning_rate=0.1,\n n_estimators=100,\n subsample=0.7,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n seed=27),\n param_grid=param_test2,\n scoring='roc_auc',\n n_jobs=4,\n iid=False,\n cv=2)\ngsearch2.fit(X_train, Y_train)\nprint(gsearch2.best_params_)\n# {'max_depth': 3, 'min_child_weight': 0.8}\n\n# train colsample_bytree next\nparam_test3 = {\n 'colsample_bytree': [0.6, 0.7, 0.8, 0.9]\n}\ngsearch3 = GridSearchCV(\n estimator=XGBClassifier(\n learning_rate=0.1,\n n_estimators=100,\n max_depth=3,\n subsample=0.8,\n min_child_weight=0.7,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n seed=27),\n param_grid=param_test3,\n scoring='roc_auc',\n n_jobs=4,\n iid=False,\n cv=2)\ngsearch3.fit(X_train, Y_train)\nprint(gsearch3.best_params_)\n# {'colsample_bytree': 0.7}\n\n# reg_lambda and reg_alpha at last\nparam_test4 = {\n 'reg_lambda': [0.1, 0.3, 0.9, 3],\n 'reg_alpha': [0.1, 0.3, 0.9, 3]\n}\ngsearch4 = GridSearchCV(\n estimator=XGBClassifier(\n learning_rate=0.1,\n n_estimators=100,\n max_depth=3,\n subsample=0.7,\n min_child_weight=0.8,\n colsample_bytree=0.7,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n seed=27),\n param_grid=param_test4,\n scoring='roc_auc',\n n_jobs=4,\n iid=False,\n cv=2)\ngsearch4.fit(X_train, Y_train)\nprint(gsearch4.best_params_)\n# {'reg_alpha': 0.3, 'reg_lambda': 0.1}\n\n\n# for short, we skip the process of training the max_features and the process of training the pairs between learning_rate and n_estimators,but if u want to train a nice model these ways should be added at your process.\n# with the same reason，i skip the code '鞍点逃逸' and '极限探索' ,follow the methods mentioned at the article 'Kaggle&TianChi分类问题相关纯算法理论剖析' ,try it by yourself\n\n# define the final param\nclf = XGBClassifier(\n learning_rate=0.1,\n n_estimators=100,\n max_depth=3,\n subsample=0.7,\n min_child_weight=0.8,\n colsample_bytree=0.7,\n objective='binary:logistic',\n scale_pos_weight=1.002252816020025,\n reg_alpha=0.3,\n reg_lambda=0.1,\n seed=27\n)\n\n# train the values\nmodel_sklearn = clf.fit(X_train, Y_train)\ny_bst = model_sklearn.predict_proba(X_test)[:, 1]\nmetrics_spec(Y_train, model_sklearn.predict_proba(X_train)[:, 1])\nmetrics_spec(Y_test, y_bst)\n\n# make new features\n# we can get the spare leaf nodes for the input of stacking\ntrain_new_feature = clf.apply(X_train)\ntest_new_feature = clf.apply(X_test)\nenc = OneHotEncoder()\nenc.fit(train_new_feature)\ntrain_new_feature2 = np.array(enc.transform(train_new_feature).toarray())\ntest_new_feature2 = np.array(enc.transform(test_new_feature).toarray())\nres_data = pd.DataFrame(np.c_[Y_train, train_new_feature2])\nres_data.columns = ['f' + str(x) for x in range(res_data.shape[1])]\nres_test = pd.DataFrame(np.c_[Y_test, test_new_feature2])\nres_test.columns = ['f' + str(x) for x in range(res_test.shape[1])]\n\n# stacking a model , it can be logistic or fm, nerual network and they will come to be beyond all expectations\n# attention points of the stacking model can be obtained from the article mentioned at the top of the code\nlr = LogisticRegression(C=1, penalty='l2', max_iter=100, solver='sag', multi_class='ovr')\nmodel_lr = lr.fit(res_data.iloc[:, 1:], res_data['f0'])\ny_train_lr = model_lr.predict_proba(res_data.iloc[:, 1:])[:, 1]\ny_test_lr = model_lr.predict_proba(res_test.iloc[:, 1:])[:, 1]\nres = metrics_spec(Y_test, y_test_lr)\ncorrect_rank = X_train.columns\n\n\n\n# save models, you will load them if u want to deploy a trained model\nfrom sklearn.externals import joblib\n\njoblib.dump(model_sklearn, 'model_sklearn.pkl')\njoblib.dump(correct_rank, 'correct_rank.pkl')\njoblib.dump(enc, 'enc.pkl')\njoblib.dump(model_lr, 'model_lr.pkl')\n\n\n\n# 算法评估 ks值\n# ks_xgb_lr = np.c_[Y_test,y_test_lr]\n# ks_xgb_lr = sorted(ks_xgb_lr , key = lambda x : x[1],reverse = True)\n# ks_xgb_lr = pd.DataFrame(ks_xgb_lr)\n# for i in range(9):\n# \tend = (i+1)*break_cut\n# \tres1 = 1.0*ks_xgb_lr.iloc[:end,:][ks_xgb_lr.iloc[:end,0]==0].shape[0]/ks_xgb_lr[ks_xgb_lr.iloc[:,0]==0].shape[0]\n# \tres2 = 1.0*ks_xgb_lr.iloc[:end,:][ks_xgb_lr.iloc[:end,0]==1].shape[0]/ks_xgb_lr[ks_xgb_lr.iloc[:,0]==1].shape[0]\n# \tres = res2-res1\n# \tprint(res1,res2,res)\n","sub_path":"Ensemble/Stacking_xgboost_logistic_regression.py","file_name":"Stacking_xgboost_logistic_regression.py","file_ext":"py","file_size_in_byte":7834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"111577910","text":"import requests\r\nimport urllib\r\nimport json\r\nimport sys\r\nimport traceback\r\n\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.chrome.options import Options\r\n\r\nimport traceback\r\nimport time\r\n\r\n\r\nclass Wst:\r\n \r\n empCount = 0\r\n\r\n def __init__(self):\r\n pass\r\n \r\n def call_ws(self, url): \r\n try: \r\n\r\n response = requests.get(url)\r\n\r\n todos = json.loads(response.text)\r\n \r\n return(json.dumps(todos, sort_keys=True, indent=4, separators=(',', ': ')))\r\n \r\n #for todo in todos[\"teams\"]:\r\n # self.responseEdit.append(todo[\"name\"] ) \r\n # self.responseEdit.append(todo[\"_links\"][\"self\"][\"href\"]) \r\n # self.responseEdit.append(todo[\"_links\"][\"fixtures\"][\"href\"]) \r\n # self.responseEdit.append(todo[\"_links\"][\"players\"][\"href\"]) \r\n # self.responseEdit.append(todo[\"crestUrl\"])\r\n # self.responseEdit.append(\"\") \r\n except Exception:\r\n return(\"Unexpected error:\", sys.exc_info())\r\n raise \r\n \r\n def popuniKladionicu(self): \r\n print('starting browser')\r\n try:\r\n chrome_options = Options() \r\n #chrome_options.add_argument('--headless')\r\n chrome_options.add_argument('--ignore-certificate-errors')\r\n chrome_options.add_argument(\"--test-type\")\r\n chrome_options.binary_location = 'C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe' \r\n \r\n chrome_options.add_argument(\"--disable-popup-blocking\"); \r\n \r\n driver = webdriver.Chrome(chrome_options = chrome_options)\r\n \r\n driver.get('https://apex.oracle.com/pls/apex/f?p=51345:LOGIN_DESKTOP:17023163753850:::::&tz=2:00')\r\n \r\n emailid=driver.find_element_by_id(\"P101_USERNAME\")\r\n emailid.send_keys(\"branko\")\r\n\r\n\r\n passw=driver.find_element_by_id(\"P101_PASSWORD\")\r\n passw.send_keys(\"kkk\")\r\n \r\n signin=driver.find_element_by_id(\"B27311205195543441998\")\r\n signin.click() \r\n \r\n try:\r\n alert = driver.switch_to_alert()\r\n alert.accept()\r\n except:\r\n print('No alert')\r\n \r\n unos=driver.find_element_by_id(\"B37164898911002989583\")\r\n unos.click() \r\n \r\n time.sleep(10) \r\n \r\n \r\n trs = driver.find_element_by_class_name('t-Report-report').find_elements_by_tag_name('tr')\r\n \r\n #h = driver.find_element_by_class_name('t-Body-contentInner').text\r\n \r\n #print(h)\r\n \r\n print( len(trs) )\r\n \r\n for tr in trs:\r\n print('node:', tr)\r\n \r\n tds = tr.find_elements_by_tag_name('td')\r\n \r\n \r\n for td in tds:\r\n header = td.get_attribute('headers')\r\n txt = td.get_attribute('textContent')\r\n \r\n txt = txt.encode('utf-8')\r\n \r\n print(header, ': ', txt)\r\n \r\n if header == 'HOME_REZ':\r\n inp = td.find_element_by_tag_name('input')\r\n inp_value = inp.get_attribute('value')\r\n if inp_value == '':\r\n inp.send_keys('2')\r\n \r\n #inp.send_keys('\\b')\r\n #inp.send_keys('\\b')\r\n \r\n \r\n if header == 'GUEST_REZ':\r\n inp = td.find_element_by_tag_name('input')\r\n inp_value = inp.get_attribute('value')\r\n if inp_value == '':\r\n inp.send_keys('1') \r\n \r\n #inp.send_keys('\\b')\r\n #inp.send_keys('\\b') \r\n \r\n \r\n save=driver.find_element_by_id(\"B37172830720567434957\")\r\n save.click() \r\n \r\n \r\n except:\r\n print(\"Unexpected error:\", sys.exc_info()) \r\n traceback.print_exc() ","sub_path":"my_project/root/nested/wst.py","file_name":"wst.py","file_ext":"py","file_size_in_byte":4468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"58936388","text":"#! /usr/bin/python3\n# kmwho\n\nfrom __future__ import print_function\n\ndef solvecase():\n K,C,S = map(int, input().strip().split() )\n Smin = (K+C-1)//C\n if S < Smin:\n return \"IMPOSSIBLE\"\n kpow = [ K**i for i in range(C+1) ]\n nums = []\n for j in range( min(S,Smin) ):\n nums.append( tuple( sorted( (C*j + i) % K for i in range(C) ) ) )\n vals = [ str( 1 + sum( num[i]*kpow[C-i-1] for i in range(C) )) for num in nums ]\n #print( nums )\n return \" \".join( vals )\n\ndef solve():\n T = int(input())\n for t in range(T):\n res = solvecase()\n print( \"Case #\" + str(t+1) + \": \" + str(res) )\n\ndef main():\n\tsolve()\n\n\nmain()\n","sub_path":"codes/CodeJamCrawler/16_0_4/kmwho/smallD.py","file_name":"smallD.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"337278449","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Mengxuan Chen\n@emails: CHENMENGXUAN163@pingan.com.cn\n@description:\n # 互换收益计算\n@revise log:\n 2020.11.03 创建程序\n 2020.11.04 平均持仓成本用FIFO法计算\n 股票和股指期货分别计算\n 封装函数\n 2020.11.05 股票和股指期货合并计算\n 计算固定端收益\n 整体修复日期和index的bug\n 修复FIFO\n 测试：\n 2020.11.06 测试并修改FIFO\n 股指期货价格adj=股指期货价格*合约乘数\n 修复当天未交易当时有持仓的浮盈计算\n 修复当天交易所有持仓股票的情况\n 单独处理正好完全清仓的情况\n\"\"\"\n# In[]\nimport pandas as pd\nimport numpy as np\nimport os\nimport re\nimport datetime\nimport time\nimport warnings\n\nwarnings.filterwarnings('ignore')\nfrom WindPy import *\nw.start()\n\n\ndef extractData(Date, Codes, type='stock'):\n '''\n 使用wind API 接口提取数据，股票对应的是收盘价数据，股指期货对应结算价数据\n :param Date:\n :param Codes:\n :return:\n '''\n codes = ','.join(Codes)\n if isinstance(Date, str) == True:\n pass\n else:\n Date = datetime.strftime(Date, \"%Y-%m-%d\")\n if type == 'stock':\n tradeDate = \"tradeDate=\" + str(Date).replace('-', '') + \";priceAdj=U;cycle=D\"\n data = w.wss(codes, \"close\", tradeDate)\n elif type == 'futures':\n tradeDate = \"tradeDate=\" + str(Date).replace('-', '') + \";cycle=D\"\n data = w.wss(codes, \"settle\", tradeDate)\n else:\n raise ValueError('we do not have this type of product!')\n name = w.wss(codes, \"sec_name\", tradeDate)\n df1 = pd.DataFrame(name.Data[0], columns=name.Fields, index=name.Codes)\n df2 = pd.DataFrame(data.Data[0], columns=data.Fields, index=data.Codes)\n df = pd.concat([df1, df2], axis=1)\n df.reset_index(inplace=True)\n df.columns = ['证券代码', '证券简称', '收盘价']\n return df\n\ndef extractContractmultiplier(Codes):\n '''\n # 提取期货合约的合约乘数\n :param Date:\n :param Codes:\n :return:\n '''\n codes = ','.join(Codes)\n data = w.wss(codes, \"contractmultiplier\")\n df = pd.DataFrame(data.Data[0], columns=data.Fields, index=data.Codes)\n return df\n\ndef nameNorm(code, type):\n '''\n 对每天输入数据的股票代码进行标准化处理\n :param code:\n :param type:\n :return:\n '''\n if type == 'stock':\n code = str(int(code))\n if len(code) < 6:\n code = (6 - len(code)) * '0' + code\n else:\n pass\n if code[0] == '6':\n code = code + '.SH'\n elif code[0:3] == '688':\n code = code + '.SZ'\n else:\n code = code + '.SZ'\n elif type == 'futures':\n code = str(code)\n code = code + '.CFE'\n else:\n raise ValueError('we do not have this type of product!')\n return code\n\n\ndef FIFO(df):\n '''\n # 使用FIFO方法计算成本价格\n :param df:\n :return:\n '''\n if len(df.loc[(df['是否卖出'] == False) & (df['剩余数量'] > 0)]) == len(df.loc[(df['是否卖出'] == False)]):\n df.loc[(df['是否卖出'] == False) & (df['剩余数量'] > 0),'剩余数量'] = \\\n df.loc[(df['是否卖出'] == False) & (df['剩余数量'] > 0),'交易数量']\n dff = df.loc[(df['剩余数量'] > 0)].append(df.iloc[-1])\n # df = df.loc[~df['是否卖出'] == True]\n sum = 0\n xx = 0\n for kk, ii in enumerate(list(dff['交易数量'])):\n if sum > abs(dff['交易数量'].iloc[-1]):\n xx = kk\n break\n sum += ii\n if float(xx) > 0:\n df_x = dff.iloc[0:int(xx)]\n a = (df_x['交易价格'].iloc[:-1] * df_x['剩余数量'].iloc[:-1] ).sum()\n b = df_x['交易价格'].iloc[-1] * (abs(df['交易数量'].iloc[-1]) - df_x['剩余数量'].iloc[:-1].sum())\n c = abs(df['交易数量'].iloc[-1])\n df['成本价格'].iloc[-1] = (a+b) / c\n # 先计算最近一笔的剩余数量，再将之前吃掉的所有单的剩余数量设为0\n d = (abs(df['交易数量'].iloc[-1]) - df_x['剩余数量'].iloc[:-1].sum())\n df['剩余数量'].iloc[0:int(xx)][-1:] = df_x['剩余数量'].iloc[-1] - d\n df['剩余数量'].iloc[0:int(xx)][:-1] = 0\n else:\n # 正好完全清仓的情况要单独处理\n df_x = dff.iloc[0:1]\n a = (df_x['交易价格'].iloc[:-1] * df_x['剩余数量'].iloc[:-1] ).sum()\n b = df_x['交易价格'].iloc[-1] * (abs(df['交易数量'].iloc[-1]) - df_x['剩余数量'].iloc[:-1].sum())\n c = abs(df['交易数量'].iloc[-1])\n df['成本价格'].iloc[-1] = (a+b) / c\n # 先计算最近一笔的剩余数量，再将之前吃掉的所有单的剩余数量设为0\n d = (abs(df['交易数量'].iloc[-1]) - df_x['剩余数量'].iloc[:-1].sum())\n df['剩余数量'].iloc[0:1][-1:] = df_x['剩余数量'].iloc[-1] - d\n df['剩余数量'].iloc[0:1][:-1] = 0\n return df\n\n\ndef calToday(Date, Data, Type):\n '''\n # 计算当天汇总情况\n :param Date:\n :param Data:\n :param Div:\n :param type:\n :return:\n '''\n # 对Data数据集删除空行，对证券代码标准化\n Data.dropna(inplace=True, subset=['证券代码'])\n Data['证券代码'] = Data['证券代码'].apply(lambda x: nameNorm(x, type=Type))\n\n Data['成交数量方向'] = ''\n if Type == 'stock':\n Data['产品ID'] = Data['证券代码']\n\n Data.loc[Data['委托方向'] == '卖出', '成交数量方向'] = -1 * Data.loc[Data['委托方向'] == '卖出', '成交数量']\n Data.loc[Data['委托方向'] == '买入', '成交数量方向'] = Data.loc[Data['委托方向'] == '买入', '成交数量']\n\n elif Type == 'futures':\n Data.loc[Data['委托方向'] == '卖出开仓', '产品ID'] = Data.loc[Data['委托方向'] == '卖出开仓', '证券代码'] + '.PUT'\n Data.loc[Data['委托方向'] == '卖出平仓', '产品ID'] = Data.loc[Data['委托方向'] == '卖出平仓', '证券代码'] + '.PUT'\n Data.loc[Data['委托方向'] == '买入开仓', '产品ID'] = Data.loc[Data['委托方向'] == '买入开仓', '证券代码'] + '.CALL'\n Data.loc[Data['委托方向'] == '买入平仓', '产品ID'] = Data.loc[Data['委托方向'] == '买入平仓', '证券代码'] + '.CALL'\n\n Data.loc[Data['委托方向'] == '卖出开仓', '成交数量方向'] = Data.loc[Data['委托方向'] == '卖出开仓', '成交数量']\n Data.loc[Data['委托方向'] == '买入开仓', '成交数量方向'] = Data.loc[Data['委托方向'] == '买入开仓', '成交数量']\n Data.loc[Data['委托方向'] == '卖出平仓', '成交数量方向'] = -1 * Data.loc[Data['委托方向'] == '卖出平仓', '成交数量']\n Data.loc[Data['委托方向'] == '买入平仓', '成交数量方向'] = -1 * Data.loc[Data['委托方向'] == '买入平仓', '成交数量']\n\n num = pd.DataFrame(Data.groupby(['产品ID'])['成交数量方向'].sum())\n price = pd.DataFrame(Data.groupby(['产品ID'])['成交价格'].sum())\n fee = pd.DataFrame(Data.groupby(['产品ID'])['佣金'].sum()\n + Data.groupby(['产品ID'])['过户费'].sum()\n + Data.groupby(['产品ID'])['交割费'].sum()\n + Data.groupby(['产品ID'])['经手费'].sum()\n + Data.groupby(['产品ID'])['结算费'].sum()\n + Data.groupby(['产品ID'])['交易费'].sum()\n + Data.groupby(['产品ID'])['证管费'].sum()\n + Data.groupby(['产品ID'])['其他费用'].sum()\n + Data.groupby(['产品ID'])['全额过户费'].sum())\n tax = pd.DataFrame(Data.groupby(['产品ID'])['印花税'].sum())\n\n tclose = extractData(Date, Data['证券代码'].drop_duplicates().to_list())\n tfutures_multiplier = extractContractmultiplier(Data['证券代码'].drop_duplicates().to_list())\n\n if Type == 'futures':\n tclose_ = tclose.copy()\n tclose__ = tclose.copy()\n tclose_['证券代码'] = tclose_['证券代码'] + '.PUT'\n tclose__['证券代码'] = tclose__['证券代码'] + '.CALL'\n tclose = tclose_.append(tclose__)\n\n tfutures_multiplier_ = tfutures_multiplier.copy()\n tfutures_multipliere__ = tfutures_multiplier.copy()\n tfutures_multiplier_.index = tfutures_multiplier_.index + '.PUT'\n tfutures_multipliere__.index = tfutures_multipliere__.index + '.CALL'\n tfutures_multiplier = tfutures_multiplier_.append(tfutures_multipliere__)\n\n elif Type == 'stock':\n tfutures_multiplier = pd.DataFrame([1] * len(tclose),index= Data['产品ID'].drop_duplicates())\n\n close = pd.DataFrame(np.array(tclose['收盘价']), index=tclose['证券代码'])\n today = pd.concat([num, price, fee, tax, close, price, tfutures_multiplier], axis=1)\n today = today.reset_index()\n today.columns = ['证券代码', '交易数量', '交易价格', '交易费用', '印花税', '收盘价', '成本价格', '合约乘数']\n return today\n\n\ndef divCal(Div, Today):\n '''\n 考虑现金分红和股票分红\n :param Div:\n :param Today:\n :return:\n '''\n # 对分红数据集进行预处理\n Div['证券代码'] = Div['证券代码'].apply(lambda x: nameNorm(x, type='stock'))\n\n # 现金分红\n divCash = Div[Div['发生业务'] == '红利到帐']\n divCash.index = divCash['证券代码']\n divCash = divCash['发生金额']\n divCash = divCash.reset_index()\n divCash.columns = ['证券代码', '现金红利']\n TOday = pd.merge(divCash, Today, how='outer')\n\n # 股票分红\n divStock = data_div[data_div['发生业务'] == '红股上市']\n divStock.index = divStock['证券代码']\n divStock = divStock['发生数量']\n divStock = divStock.reset_index()\n divStock.columns = ['证券代码', '股票股利']\n TODAY = pd.merge(divStock, TOday, how='outer')\n return TODAY\n\n\ndef todayPerform(Date,Position,Today,Type):\n '''\n\n :param Position:\n :param Today:\n :return:\n '''\n Today['日期'] = Date\n Today.fillna(0,inplace=True)\n asset_not_trade = []\n\n if Type == 'futures':\n for m in Position['证券代码'].apply(lambda x: x[:10]).drop_duplicates().to_list():\n if m not in Today.loc[Today['交易数量'] != 0]['证券代码'].apply(lambda x: x[:10]).drop_duplicates().to_list():\n asset_not_trade.append(m)\n ydate = Position['日期'].drop_duplicates().to_list()[-1]\n asset_not_trade_ = asset_not_trade.copy()\n\n if len(asset_not_trade) != 0:\n tclose_not_trade = extractData(Date,asset_not_trade_)\n multiplier = extractContractmultiplier(tclose_not_trade['证券代码'].drop_duplicates().to_list())\n\n tclose_not_trade_ = tclose_not_trade.copy()\n tclose_not_trade__ = tclose_not_trade.copy()\n tclose_not_trade_['证券代码'] = tclose_not_trade_['证券代码'] + '.PUT'\n tclose_not_trade__['证券代码'] = tclose_not_trade__['证券代码'] + '.CALL'\n tclose_not_trade = tclose_not_trade_.append(tclose_not_trade__)\n tclose_not_trade = pd.DataFrame(np.array(tclose_not_trade['收盘价']), index=tclose_not_trade['证券代码'])\n\n multiplier_ = multiplier.copy()\n multiplier__ = multiplier.copy()\n multiplier_.index = multiplier_.index + '.PUT'\n multiplier__.index = multiplier__.index + '.CALL'\n multiplier = multiplier_.append(multiplier__)\n else:\n pass\n\n elif Type == 'stock':\n for m in Position['证券代码'].drop_duplicates().to_list():\n if m not in Today.loc[Today['交易数量'] != 0]['证券代码'].to_list():\n asset_not_trade.append(m)\n ydate = Position['日期'].drop_duplicates().to_list()[-1]\n\n if len(asset_not_trade) != 0:\n asset_not_trade_ = pd.DataFrame(asset_not_trade).iloc[:, 0].apply(lambda x: x[:10]).to_list()\n close_ = extractData(Date, asset_not_trade)\n tclose_not_trade = pd.DataFrame(np.array(close_['收盘价']), index=close_['证券代码'])\n multiplier = pd.DataFrame([1] * len(tclose_not_trade),index=close_['证券代码'])\n else:\n pass\n\n if len(asset_not_trade) != 0:\n nanlist = pd.DataFrame([0] * len(tclose_not_trade),index=tclose_not_trade.index)\n asset_not_trade_today = pd.concat([nanlist,nanlist,nanlist,nanlist,tclose_not_trade,nanlist,multiplier],axis=1)\n asset_not_trade_today.reset_index(inplace=True)\n asset_not_trade_today.columns = ['证券代码','交易数量', '交易价格','交易费用','印花税',\n '收盘价','成本价格','合约乘数']\n asset_not_trade_today['日期'] = Date\n\n Today_all = pd.concat([asset_not_trade_today,Today],axis = 0)\n else:\n Today_all = Today.copy()\n\n Position = pd.concat([Position,Today_all],axis = 0)\n\n Position.index = list(range(len(Position.index)))\n Position['交易数量'].fillna(0, inplace=True)\n Position['股票股利'].fillna(0, inplace=True)\n Position.loc[:, '持仓数量'] = np.array(pd.DataFrame(Position.groupby(['证券代码'])['交易数量'].cumsum())['交易数量']) + \\\n np.array(pd.DataFrame(Position.groupby(['证券代码'])['股票股利'].fillna(0).cumsum())['股票股利'])\n Position['现金红利'].fillna(0, inplace=True)\n # Position.loc[:,'累计现金红利'] = np.array(pd.DataFrame(Position.groupby(['证券代码'])['现金红利'].cumsum())['现金红利'])\n\n Position['是否卖出'] = Position['交易数量'] < 0\n Position.loc[Position['交易数量'] == 0, '是否卖出'] = np.nan\n Position_sell = Position.loc[(Position['日期'] == Date) & (Position['是否卖出'] ==True)]['证券代码']\n Position_buy = Position.loc[(Position['日期'] == Date) & (Position['是否卖出'] ==False)]['证券代码']\n for stockj in Position_buy.to_list():\n Position.loc[Position['证券代码'] == stockj, '剩余数量'] = \\\n Position.loc[Position['证券代码'] == stockj, '交易数量']\n for stocki in Position_sell.to_list():\n Position.loc[Position['证券代码']== stocki,:] = \\\n FIFO(Position.loc[(Position['证券代码']== stocki)]) # & Position['交易数量'] != 0])\n\n Position['前收盘价'] = Position.groupby(['证券代码'])['收盘价'].shift(1)\n Position.loc[Position['日期'] ==Date,'浮盈'] = Position.loc[Position['日期'] ==Date,:].apply(\n lambda x: x['合约乘数'] * x['持仓数量'] *(x['收盘价']-x['前收盘价']),axis = 1)\n\n if Type == 'stock':\n Position.loc[Position['日期'] ==Date,'实盈'] = Position.loc[Position['日期'] ==Date,:].apply(\n lambda x:x['现金红利'] + abs(x['交易数量']) * (x['交易价格']-x['成本价格']),axis = 1)\n\n\n elif Type == 'futures':\n Position['期权类型'] = Position['证券代码'].apply(lambda x: x.split('.',2)[2])\n\n Position.loc[(Position['日期'] == Date) & (Position['期权类型'] == 'CALL'), '实盈'] = \\\n Position.loc[Position['日期'] == Date, :].apply(\n lambda x: x['交易数量'] * (x['收盘价'] - x['交易价格']), axis=1)\n\n Position.loc[(Position['日期'] == Date) & (Position['期权类型'] == 'PUT'), '实盈'] = \\\n Position.loc[Position['日期'] == Date, :].apply(\n lambda x: - x['交易数量'] * (x['收盘价'] - x['交易价格']), axis=1)\n else:\n raise ValueError('we do not have this type of product!')\n Position.loc[(Position['日期'] == Date)].index = [Date] * len(Position.loc[(Position['日期'] == Date)])\n return Position\n\n\ndef sum(Date, Position, Position_futures):\n '''\n\n :param Date:\n :param Position:\n :param Position_futures:\n :return:\n '''\n Position.index = Position['日期']\n Position = Position.iloc[:,1:]\n Position_futures.index = Position_futures['日期']\n Position_futures = Position_futures.iloc[:,1:]\n sum_today = Position.loc[Position.index == Date, :].apply(lambda x: x.sum())\n sum_today_futures = Position_futures.loc[Position_futures.index == Date, :].apply(lambda x: x.sum())\n\n sum = Position.apply(lambda x: x.sum())\n sum_futures = Position_futures.apply(lambda x: x.sum())\n\n balance = sum_today['实盈'] + sum_today['现金红利'] - sum_today['交易费用'] - sum_today['印花税'] \\\n + sum_today_futures['实盈'] + sum_today_futures['现金红利'] - sum_today_futures['交易费用'] - sum_today_futures['印花税']\n\n all_sum_ = pd.DataFrame({'日期':Date,\n '账户余额变动': balance,\n '当日现金红利': sum_today['现金红利'] + sum_today_futures['现金红利'],\n '当日浮盈': sum_today['浮盈'] + sum_today_futures['浮盈'],\n '当日实盈': sum_today['实盈'] + sum_today_futures['实盈'],\n '当日交易佣金': sum_today['交易费用'] + sum_today_futures['交易费用'],\n '当日印花税': sum_today['印花税'] + sum_today_futures['印花税'],\n '累计现金红利': sum['现金红利'] + sum_futures['现金红利'],\n '浮盈': sum_today['浮盈'] + sum_today_futures['浮盈'],\n '累计实盈': sum['实盈'] + sum_futures['实盈'],\n '累计交易佣金': sum['交易费用'] + sum_futures['交易费用'],\n '累计印花税': sum['印花税'] + sum_futures['印花税']},\n index = [Date])\n\n return all_sum_\n\n\ndef fixLeg(Begin, End, Principal, Rate):\n '''\n\n :param Begin:\n :param End:\n :param Principal:\n :param Rate:\n :return:\n '''\n if isinstance(Begin, str) == True:\n begin = datetime.strptime(Begin, \"%Y-%m-%d\")\n else:\n begin = Begin\n\n if isinstance(End, str) == True:\n end = datetime.strptime(End, \"%Y-%m-%d\")\n else:\n end = End\n interval_days = end - begin\n Interest = Principal * ((1 + Rate) ** (interval_days.days / 365) - 1)\n return Interest\n\n\nif __name__ == '__main__':\n # In[]\n if os.path.exists('./result/all_position.xlsx') == True:\n all_position_ = pd.read_excel('./result/all_position.xlsx')\n else:\n all_position_ = pd.DataFrame(columns=['日期','证券代码','股票股利','现金红利','交易数量',\n '交易价格','交易费用','印花税','收盘价','剩余数量',\n '成本价格','累计现金红利','浮盈','实盈'])\n if os.path.exists('./result/all_positionfutures.xlsx') == True:\n all_position_futures = pd.read_excel('./result/all_positionfutures.xlsx')\n else:\n all_position_futures = pd.DataFrame(columns=['日期','证券代码','股票股利','现金红利','交易数量',\n '交易价格','交易费用','印花税','收盘价','剩余数量',\n '成本价格','累计现金红利','浮盈','实盈'])\n all_sum = pd.read_excel('./result/all_sum.xlsx')\n ydate = all_sum.iloc[-1,0]\n # In[]\n if os.path.exists('./data/新综合信息查询_成交回报明细（股票）.xls') == True:\n data_stock = pd.read_excel('./data/新综合信息查询_成交回报明细（股票）.xls')\n tdate_ = data_stock['日期'].drop_duplicates().dropna() # 取计算当天的日期\n tdate = tdate_.iloc[0]\n today = calToday(Date=tdate, Data=data_stock, Type='stock')\n if os.path.exists('./data/综合信息查询_资金流水20201104.xls') == True:\n data_div = pd.read_excel('./data/综合信息查询_资金流水20201104.xls').dropna(subset=['证券代码'])\n today = divCal(Div=data_div, Today=today)\n else:\n print('ATTENTION: we did not have cash div or stock div today!')\n else:\n today = all_position_.loc[all_position_['日期'] == ydate, '证券代码':'收盘价']\n tdate = input('请输入日期（格式为xxxx-xx-xx）:')\n today['日期'] = [tdate] * len(today)\n tclose = extractData(tdate, today['证券代码'].drop_duplicates().to_list())\n today['收盘价'] = tclose['收盘价']\n print('ATTENTION: we did not trade stocks today!')\n\n # In[]\n if os.path.exists('./data/新综合信息查询_成交回报明细（股指）.xls') == True:\n data_futures = pd.read_excel('./data/新综合信息查询_成交回报明细（股指）.xls').dropna(subset=['证券代码'])\n tdate_ = data_futures['日期'].drop_duplicates().dropna() # 取计算当天的日期\n tdate = tdate_.iloc[0]\n today_futures = calToday(Date=tdate, Data=data_futures, Type='futures')\n else:\n today_futures = all_position_futures.loc[all_position_futures['日期'] == ydate, '证券代码':'收盘价']\n tdate = input('请输入日期（格式为xxxx-xx-xx）:')\n today_futures['日期'] = [tdate] * len(today_futures)\n tclose_futures = extractData(tdate, today_futures['证券代码'].drop_duplicates().to_list())\n today_futures['收盘价'] = tclose_futures['收盘价']\n print('ATTENTION: we did not trade futures today!')\n\n # In[]\n #\n all_position = todayPerform(Date=tdate, Position=all_position_, Today=today, Type='stock')\n all_position_futures = todayPerform(Date=tdate, Position=all_position_futures, Today=today_futures, Type='futures')\n\n all_position.to_excel('./result/all_position.xlsx',index = False)\n all_position_futures.to_excel('./result/all_positionfutures.xlsx',index = False)\n\n # # In[]\n all_sum_ = sum(Date=tdate, Position=all_position, Position_futures=all_position_futures)\n all_sum = all_sum.append(all_sum_)\n all_sum.to_excel('./result/all_sum.xlsx',index = False)\n print(all_sum_)\n\n # In[]\n rate = (313687.579294248 / 383717693.51 + 1) ** (365 / 4) - 1\n fix = fixLeg(Begin='2020-11-01', End=tdate, Principal=383717693.51, Rate=rate)\n print('fix', '%.2f' % fix)\n print('TRS', '%.2f' % (all_sum['账户余额变动'].sum() - fix))\n\n","sub_path":"TRS_test202011061746.py","file_name":"TRS_test202011061746.py","file_ext":"py","file_size_in_byte":22618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"92092275","text":"#!/usr/bin/env python3\n# Copyright (c) 2018 The Zcash developers\n# Copyright (c) 2020-2021 The PIVX Core developers\n# Distributed under the MIT software license, see the accompanying\n# file COPYING or https://www.opensource.org/licenses/mit-license.php .\n\nfrom decimal import Decimal\nfrom time import sleep\n\nfrom test_framework.test_framework import PivxTestFramework\nfrom test_framework.util import (\n assert_equal,\n assert_raises_rpc_error,\n connect_nodes,\n disconnect_nodes,\n satoshi_round,\n get_coinstake_address,\n wait_until,\n)\n\n\n# Test wallet behaviour with Sapling addresses\nclass WalletSaplingTest(PivxTestFramework):\n\n def set_test_params(self):\n self.num_nodes = 4\n saplingUpgrade = ['-nuparams=v5_shield:201']\n self.extra_args = [saplingUpgrade, saplingUpgrade, saplingUpgrade, saplingUpgrade]\n self.extra_args[0].append('-sporkkey=932HEevBSujW2ud7RfB1YF91AFygbBRQj3de3LyaCRqNzKKgWXi')\n\n def check_tx_in_mempool(self, txids):\n self.sync_mempools()\n mempool = self.nodes[0].getrawmempool(True)\n for txid in txids:\n assert txid in mempool\n\n def wait_for_spork(self, fEnabled, spork_id):\n sleep(2)\n for i in range(self.num_nodes):\n wait_until(lambda: self.is_spork_active(i, spork_id) == fEnabled, timeout=5)\n\n def run_test(self):\n self.nodes[0].generate(2)\n self.sync_all()\n assert_equal(self.nodes[1].getblockcount(), 202)\n taddr1 = self.nodes[1].getnewaddress()\n saplingAddr0 = self.nodes[0].getnewshieldaddress()\n saplingAddr1 = self.nodes[1].getnewshieldaddress()\n\n # Verify addresses\n assert(saplingAddr0 in self.nodes[0].listshieldaddresses())\n assert(saplingAddr1 in self.nodes[1].listshieldaddresses())\n\n # Verify balance\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('0'))\n assert_equal(self.nodes[1].getshieldbalance(saplingAddr1), Decimal('0'))\n assert_equal(self.nodes[1].getreceivedbyaddress(taddr1), Decimal('0'))\n\n recipients = [{\"address\": saplingAddr0, \"amount\": Decimal('10')}]\n\n # Try fee too low\n fee_too_low = 0.001\n self.log.info(\"Trying to send a transaction with fee too low...\")\n assert_raises_rpc_error(-4, \"Fee set (%.3f) too low. Must be at least\" % fee_too_low,\n self.nodes[0].rawshieldsendmany,\n \"from_transparent\", recipients, 1, fee_too_low)\n\n # Try fee too high.\n fee_too_high = 20\n self.log.info(\"Good. It was not possible. Now try a tx with fee too high...\")\n assert_raises_rpc_error(-4, \"The transaction fee is too high: %.2f >\" % fee_too_high,\n self.nodes[0].rawshieldsendmany,\n \"from_transparent\", recipients, 1, fee_too_high)\n\n # Trying to send a rawtx with low fee directly\n self.log.info(\"Good. It was not possible. Now try with a raw tx...\")\n self.restart_node(0, extra_args=self.extra_args[0]+['-minrelaytxfee=0.0000001'])\n rawtx_hex = self.nodes[0].rawshieldsendmany(\"from_transparent\", recipients, 1)\n self.restart_node(0, extra_args=self.extra_args[0])\n connect_nodes(self.nodes[0], 1)\n assert_raises_rpc_error(-26, \"insufficient fee\",\n self.nodes[0].sendrawtransaction, rawtx_hex)\n self.log.info(\"Good. Not accepted in the mempool.\")\n\n # Fixed fee\n fee = 0.05\n\n # Node 0 shields some funds\n # taddr -> Sapling\n self.log.info(\"TX 1: shield funds from specified transparent address.\")\n mytxid1 = self.nodes[0].shieldsendmany(get_coinstake_address(self.nodes[0]), recipients, 1, fee)\n\n # shield more funds automatically selecting the transparent inputs\n self.log.info(\"TX 2: shield funds from any transparent address.\")\n mytxid2 = self.nodes[0].shieldsendmany(\"from_transparent\", recipients, 1, fee)\n self.check_tx_in_mempool([mytxid1, mytxid2])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # shield more funds creating and then sending a raw transaction\n self.log.info(\"TX 3: shield funds creating and sending raw transaction.\")\n tx_hex = self.nodes[0].rawshieldsendmany(\"from_transparent\", recipients, 1, fee)\n\n # Check SPORK_20 for sapling maintenance mode\n SPORK_20 = \"SPORK_20_SAPLING_MAINTENANCE\"\n self.activate_spork(0, SPORK_20)\n self.wait_for_spork(True, SPORK_20)\n assert_raises_rpc_error(-26, \"bad-tx-sapling-maintenance\",\n self.nodes[0].sendrawtransaction, tx_hex)\n self.log.info(\"Good. Not accepted when SPORK_20 is active.\")\n\n # Try with RPC...\n assert_raises_rpc_error(-8, \"SHIELD in maintenance (SPORK 20)\",\n self.nodes[0].shieldsendmany, \"from_transparent\", recipients, 1, fee)\n\n # Disable SPORK_20 and retry\n sleep(5)\n self.deactivate_spork(0, SPORK_20)\n self.wait_for_spork(False, SPORK_20)\n mytxid3 = self.nodes[0].sendrawtransaction(tx_hex)\n self.log.info(\"Good. Accepted when SPORK_20 is not active.\")\n self.check_tx_in_mempool([mytxid3])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Verify balance\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('30'))\n assert_equal(self.nodes[1].getshieldbalance(saplingAddr1), Decimal('0'))\n assert_equal(self.nodes[1].getreceivedbyaddress(taddr1), Decimal('0'))\n self.log.info(\"Balances check out\")\n\n # Now disconnect the block, activate SPORK_20, and try to reconnect it\n disconnect_nodes(self.nodes[0], 1)\n tip_hash = self.nodes[0].getbestblockhash()\n self.nodes[0].invalidateblock(tip_hash)\n assert tip_hash != self.nodes[0].getbestblockhash()\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('20'))\n self.log.info(\"Now trying to connect block with shield tx, when SPORK_20 is active\")\n self.activate_spork(0, SPORK_20)\n self.nodes[0].reconsiderblock(tip_hash)\n assert tip_hash != self.nodes[0].getbestblockhash() # Block NOT connected\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('20'))\n self.log.info(\"Good. Not possible.\")\n\n # Deactivate SPORK_20 and reconnect\n sleep(1)\n self.deactivate_spork(0, SPORK_20)\n self.nodes[0].reconsiderblock(tip_hash)\n self.nodes[0].syncwithvalidationinterfacequeue()\n assert_equal(tip_hash, self.nodes[0].getbestblockhash()) # Block connected\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('30'))\n self.log.info(\"Reconnected after deactivation of SPORK_20. Balance restored.\")\n connect_nodes(self.nodes[0], 1)\n\n # Node 0 sends some shield funds to node 1\n # Sapling -> Sapling\n # -> Sapling (change)\n self.log.info(\"TX 4: shield transaction from specified sapling address.\")\n recipients4 = [{\"address\": saplingAddr1, \"amount\": Decimal('10')}]\n mytxid4 = self.nodes[0].shieldsendmany(saplingAddr0, recipients4, 1, fee)\n self.check_tx_in_mempool([mytxid4])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Send more shield funds (this time with automatic selection of the source)\n self.log.info(\"TX 5: shield transaction from any sapling address.\")\n recipients5 = [{\"address\": saplingAddr1, \"amount\": Decimal('5')}]\n mytxid5 = self.nodes[0].shieldsendmany(\"from_shield\", recipients5, 1, fee)\n self.check_tx_in_mempool([mytxid5])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Send more shield funds (with create + send raw transaction)\n self.log.info(\"TX 6: shield raw transaction.\")\n tx_hex = self.nodes[0].rawshieldsendmany(\"from_shield\", recipients5, 1, fee)\n mytxid6 = self.nodes[0].sendrawtransaction(tx_hex)\n self.check_tx_in_mempool([mytxid6])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Shield more funds to a different address to verify multi-source notes spending\n saplingAddr2 = self.nodes[0].getnewshieldaddress()\n self.log.info(\"TX 7: shield funds to later verify multi source notes spending.\")\n recipients = [{\"address\": saplingAddr2, \"amount\": Decimal('10')}]\n mytxid7 = self.nodes[0].shieldsendmany(get_coinstake_address(self.nodes[0]), recipients, 1, fee)\n self.check_tx_in_mempool([mytxid7])\n\n self.nodes[2].generate(5)\n self.sync_all()\n\n # Verify multi-source notes spending\n tAddr0 = self.nodes[0].getnewaddress()\n self.log.info(\"TX 8: verifying multi source notes spending.\")\n recipients = [{\"address\": tAddr0, \"amount\": Decimal('11')}]\n mytxid8 = self.nodes[0].shieldsendmany(\"from_shield\", recipients, 1, fee)\n self.check_tx_in_mempool([mytxid8])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Verify balance\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('4.9')) # 30 received - (20 sent + 0.15 fee) - 4.95 sent\n assert_equal(self.nodes[1].getshieldbalance(saplingAddr1), Decimal('20')) # 20 received\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr2), Decimal('3.9')) # 10 received - 10 sent + 3.9 change\n assert_equal(self.nodes[1].getreceivedbyaddress(taddr1), Decimal('0'))\n assert_equal(self.nodes[0].getshieldbalance(), Decimal('8.8'))\n self.log.info(\"Balances check out\")\n\n # Node 1 sends some shield funds to node 0, as well as unshielding\n # Sapling -> Sapling\n # -> taddr\n # -> Sapling (change)\n self.log.info(\"TX 10: deshield funds from specified sapling address.\")\n recipients7 = [{\"address\": saplingAddr0, \"amount\": Decimal('8')}]\n recipients7.append({\"address\": taddr1, \"amount\": Decimal('10')})\n mytxid7 = self.nodes[1].shieldsendmany(saplingAddr1, recipients7, 1, fee)\n self.check_tx_in_mempool([mytxid7])\n\n self.nodes[2].generate(1)\n self.sync_all()\n\n # Verify balance\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('12.9')) # 4.9 prev balance + 8 received\n assert_equal(self.nodes[1].getshieldbalance(saplingAddr1), Decimal('1.95')) # 20 prev balance - (18 sent + 0.05 fee)\n assert_equal(self.nodes[1].getreceivedbyaddress(taddr1), Decimal('10'))\n self.log.info(\"Balances check out\")\n\n # Verify existence of Sapling related JSON fields\n resp = self.nodes[0].getrawtransaction(mytxid7, 1)\n assert_equal(Decimal(resp['valueBalance']), Decimal('10.05')) # 20 shield input - 8 shield spend - 1.95 change\n assert_equal(len(resp['vShieldSpend']), 3)\n assert_equal(len(resp['vShieldOutput']), 2)\n assert('bindingSig' in resp)\n shieldedSpend = resp['vShieldSpend'][0]\n assert('cv' in shieldedSpend)\n assert('anchor' in shieldedSpend)\n assert('nullifier' in shieldedSpend)\n assert('rk' in shieldedSpend)\n assert('proof' in shieldedSpend)\n assert('spendAuthSig' in shieldedSpend)\n shieldedOutput = resp['vShieldOutput'][0]\n assert('cv' in shieldedOutput)\n assert('cmu' in shieldedOutput)\n assert('ephemeralKey' in shieldedOutput)\n assert('encCiphertext' in shieldedOutput)\n assert('outCiphertext' in shieldedOutput)\n assert('proof' in shieldedOutput)\n self.log.info(\"Raw transaction decoding checks out\")\n\n # Verify importing a spending key will update the nullifiers and witnesses correctly\n self.log.info(\"Checking exporting/importing a spending key...\")\n sk0 = self.nodes[0].exportsaplingkey(saplingAddr0)\n saplingAddrInfo0 = self.nodes[2].importsaplingkey(sk0, \"yes\")\n assert_equal(saplingAddrInfo0[\"address\"], saplingAddr0)\n assert_equal(self.nodes[2].getshieldbalance(saplingAddrInfo0[\"address\"]), Decimal('12.9'))\n sk1 = self.nodes[1].exportsaplingkey(saplingAddr1)\n saplingAddrInfo1 = self.nodes[2].importsaplingkey(sk1, \"yes\")\n assert_equal(saplingAddrInfo1[\"address\"], saplingAddr1)\n assert_equal(self.nodes[2].getshieldbalance(saplingAddrInfo1[\"address\"]), Decimal('1.95'))\n\n # Verify importing a viewing key will update the nullifiers and witnesses correctly\n self.log.info(\"Checking exporting/importing a viewing key...\")\n extfvk0 = self.nodes[0].exportsaplingviewingkey(saplingAddr0)\n saplingAddrInfo0 = self.nodes[3].importsaplingviewingkey(extfvk0, \"yes\")\n assert_equal(saplingAddrInfo0[\"address\"], saplingAddr0)\n assert_equal(Decimal(self.nodes[3].getshieldbalance(saplingAddrInfo0[\"address\"], 1, True)), Decimal('12.9'))\n extfvk1 = self.nodes[1].exportsaplingviewingkey(saplingAddr1)\n saplingAddrInfo1 = self.nodes[3].importsaplingviewingkey(extfvk1, \"yes\")\n assert_equal(saplingAddrInfo1[\"address\"], saplingAddr1)\n assert_equal(self.nodes[3].getshieldbalance(saplingAddrInfo1[\"address\"], 1, True), Decimal('1.95'))\n # no balance in the wallet\n assert_equal(self.nodes[3].getshieldbalance(), Decimal('0'))\n # watch only balance\n assert_equal(self.nodes[3].getshieldbalance(\"*\", 1, True), Decimal('14.85'))\n\n # Now shield some funds using sendmany\n self.log.info(\"TX11: Shielding coins to multiple destinations with sendmany RPC...\")\n prev_balance = self.nodes[0].getbalance()\n recipients8 = {saplingAddr0: Decimal('8'), saplingAddr1: Decimal('1'), saplingAddr2: Decimal('0.5')}\n mytxid11 = self.nodes[0].sendmany(\"\", recipients8)\n self.check_tx_in_mempool([mytxid11])\n self.log.info(\"Done. Checking details and balances...\")\n\n # Decrypted transaction details should be correct\n pt = self.nodes[0].viewshieldtransaction(mytxid11)\n fee = pt[\"fee\"]\n assert_equal(pt['txid'], mytxid11)\n assert_equal(len(pt['spends']), 0)\n assert_equal(len(pt['outputs']), 3)\n found = [False] * 3\n for out in pt['outputs']:\n assert_equal(pt['outputs'].index(out), out['output'])\n if out['address'] == saplingAddr0:\n assert_equal(out['outgoing'], False)\n assert_equal(out['value'], Decimal('8'))\n found[0] = True\n elif out['address'] == saplingAddr1:\n assert_equal(out['outgoing'], True)\n assert_equal(out['value'], Decimal('1'))\n found[1] = True\n else:\n assert_equal(out['address'], saplingAddr2)\n assert_equal(out['outgoing'], False)\n assert_equal(out['value'], Decimal('0.5'))\n found[2] = True\n assert_equal(found, [True] * 3)\n\n # Verify balance\n self.nodes[2].generate(1)\n self.sync_all()\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('20.9')) # 12.9 prev balance + 8 received\n assert_equal(self.nodes[1].getshieldbalance(saplingAddr1), Decimal('2.95')) # 1.95 prev balance + 1 received\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr2), Decimal('4.4')) # 3.9 prev balance + 0.5 received\n # Balance of node 0 is: prev_balance - 1 PIV (+fee) sent externally + 250 PIV matured coinbase\n assert_equal(self.nodes[0].getbalance(), satoshi_round(prev_balance + Decimal('249') - Decimal(fee)))\n\n # Now shield some funds using sendtoaddress\n self.log.info(\"TX12: Shielding coins with sendtoaddress RPC...\")\n prev_balance = self.nodes[0].getbalance()\n mytxid12 = self.nodes[0].sendtoaddress(saplingAddr0, Decimal('10'))\n self.check_tx_in_mempool([mytxid12])\n self.log.info(\"Done. Checking details and balances...\")\n\n # Decrypted transaction details should be correct\n pt = self.nodes[0].viewshieldtransaction(mytxid12)\n fee = pt[\"fee\"]\n assert_equal(pt['txid'], mytxid12)\n assert_equal(len(pt['spends']), 0)\n assert_equal(len(pt['outputs']), 1)\n out = pt['outputs'][0]\n assert_equal(out['address'], saplingAddr0)\n assert_equal(out['outgoing'], False)\n assert_equal(out['value'], Decimal('10'))\n\n # Verify balance\n self.nodes[2].generate(1)\n self.sync_all()\n assert_equal(self.nodes[0].getshieldbalance(saplingAddr0), Decimal('30.9')) # 20.9 prev balance + 10 received\n\n self.log.info(\"All good.\")\n\n\nif __name__ == '__main__':\n WalletSaplingTest().main()\n","sub_path":"test/functional/sapling_wallet.py","file_name":"sapling_wallet.py","file_ext":"py","file_size_in_byte":16788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"240527026","text":"import json\nimport wtf\nimport wtf.typ\n\nclass Object:\n\n __slots__ = (\"__dict__\", \"__path__\", \"_type\")\n\n def __init__(self):\n super().__init__()\n self._type = wtf.typ.get_type(self)\n\n def __iter__(self):\n return iter(self.__dict__)\n\n def __len__(self):\n return len(self.__dict__)\n\n def __str__(self):\n return json.dumps(self, default=default, indent=4, sort_keys=True)\n\n def json(self):\n return json.dumps(self, default=default, sort_keys=True)\n\ndef default(o):\n if isinstance(o, Object):\n return vars(o)\n if isinstance(o, dict):\n return o.items()\n if isinstance(o, list):\n return iter(o)\n if type(o) in [str, True, False, int, float]:\n return o\n return repr(o)\n\ndef edit(o, setter):\n if not setter:\n setter = {}\n count = 0\n for key, value in items(setter):\n count += 1\n if \",\" in value:\n value = value.split(\",\")\n if value in [\"True\", \"true\"]:\n set(o, key, True)\n elif value in [\"False\", \"false\"]:\n set(o, key, False)\n else:\n set(o, key, value)\n return count\n\ndef eq(o1, o2):\n if isinstance(o2, (Dict, dict)):\n return o1.__dict__ == o2.__dict__\n return False\n\ndef format(o, keys=None, full=False):\n if keys is None:\n keys = vars(o).keys()\n res = []\n txt = \"\"\n for key in keys:\n if \"ignore\" in dir(o) and key in o.ignore:\n continue\n val = get(o, key, None)\n if not val:\n continue\n val = str(val)\n if key == \"text\":\n val = val.replace(\"\\\\n\", \"\\n\")\n if full:\n res.append(\"%s=%s \" % (key, val))\n else:\n res.append(val)\n for val in res:\n txt += \"%s%s\" % (val.strip(), \" \")\n return txt.strip()\n\ndef get(o, key, default=None):\n try:\n return o[key]\n except (TypeError, KeyError):\n try:\n return o.__dict__[key]\n except (AttributeError, KeyError):\n return getattr(o, key, default)\n\ndef items(o):\n try:\n return o.__dict__.items()\n except AttributeError:\n return o.items()\n \ndef keys(o):\n return o.__dict__.keys()\n\ndef ne(o1, o2):\n return o1.__dict__ != o2.__dict__\n\ndef search(o, match={}):\n res = False\n for key, value in items(match):\n val = get(o, key, None)\n if val:\n if not value:\n res = True\n continue\n if value in str(val):\n res = True\n continue\n else:\n res = False\n break\n else:\n res = False\n break\n return res\n\ndef set(o, key, val):\n setattr(o, key, val)\n\ndef setter(o, d):\n if not d:\n d = {}\n count = 0\n for key, value in d.items():\n if \",\" in value:\n value = value.split(\",\")\n otype = type(value)\n if value in [\"True\", \"true\"]:\n set(o, key, True)\n elif value in [\"False\", \"false\"]:\n set(o, key, False)\n elif otype == list:\n set(o, key, value)\n elif otype == str:\n set(o, key, value)\n else:\n setattr(o, key, value)\n count += 1\n return count\n\ndef sliced(o, keys=None):\n t = type(o)\n val = t()\n if not keys:\n keys = o.keys()\n for key in keys:\n try:\n val[key] = o[key]\n except KeyError:\n pass\n return val\n\ndef typed(o):\n return update(type(o)(), o)\n\ndef update(o1, o2, keys=None, skip=False):\n if not o2:\n return o1\n for key in o2:\n val = get(o2, key)\n if keys and key not in keys:\n continue\n if skip and not val:\n continue\n set(o1, key, val)\n\ndef update2(o1, o2):\n try:\n o1.__dict__.update(o2)\n except:\n o1.update(o2)\n\ndef values(o):\n return o.__dict__.values()\n\ndef xdir(o, skip=\"\"):\n for k in dir(o):\n if skip and skip in k:\n continue\n yield k\n","sub_path":"wtf/obj.py","file_name":"obj.py","file_ext":"py","file_size_in_byte":4061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"368037579","text":"import prediction as pred\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom itertools import islice\nimport matplotlib\n#matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport matplotlib.colorbar as mplcb\nfrom matplotlib.colors import LogNorm\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport sys\nimport cProfile, pstats, io\nimport copy\nimport csv\nimport mmap\nimport os\n\n__author__ = 'Pierce Rixon'\nselect = 1 #up to 5\n\nnull = None\n\n\ndef main():\n #ws='whitespace'\n #ts='timescale'\n #f='frequency'\n #bw='bandwidth'\n #fn='frame_no'\n \n\n print('Running Analysis Suite')\n\n matplotlib.style.use('ggplot')\n\n root = tk.Tk()\n root.withdraw()\n \n #Use metadata to strip the 'Total whitespace' message out of the end of each csv file selected\n #metadata()\n\n print('Select configuration file')\n cfgfile = filedialog.askopenfilename(filetypes = [(\"config files\",\"*.cfg\")])\n\n with open(cfgfile) as cfg:\n config = cfg.read().splitlines()\n\n #occupancy(config)\n #occupancy_v2(config)\n occupancy_csv(config) #NOT WORKING FOR LARGE DATASETS 100MB+\n #dutycycle(config)\n #PSD(config)\n\n #print('Select window file')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")]) #('Window_dump.csv') \n \n #pred.pred(filename)\n\n #hexbin(filename)\n #analyse(filename,select) #For analysis package\n #dev_sim(filename) #For device simulator\n #legacy_sns(filename)\n #legacy(filename)\n\n\n\ndef metadata():\n \n print('Select files to remove \\'total whitespace\\' message and dump to metadata file')\n print('because im lazy, if the file does not have \\'total whitespace\\' at the end, it will take 5ever for the function to return')\n filenames = filedialog.askopenfilenames(filetypes = [(\"csv files\",\"*.csv\")])\n filelist = list(filenames)\n\n mpath = os.path.dirname(filelist[0])+'/metadata.txt'\n\n print(mpath)\n\n print('number of files: {}'.format(len(filelist)))\n #print(filelist)\n\n meta_file = open(mpath, 'w+')\n meta_file.write('Number of files: {}\\n'.format(len(filelist)))\n\n for f in filelist:\n meta_file.write(f)\n # meta_file.write('\\n')\n with open(f, 'r+') as file:\n file.seek(0, os.SEEK_END)\n pos = file.tell() - 1\n end = file.tell() - 1\n\n while pos > 0 and file.read(1) != \"T\":\n pos -= 1\n file.seek(pos, os.SEEK_SET)\n\n pos -= 1\n file.seek(pos, os.SEEK_SET)\n\n if pos > 0:\n string = file.read(end-pos)\n print(string)\n meta_file.write(string+'\\n\\n')\n file.seek(pos, os.SEEK_SET)\n file.truncate()\n else:\n print('File does not have Total Whitespace message')\n file.close()\n\ndef PSD(config):\n print('Now running PSD plot, ensure a PSD dataset is selected')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")])\n dataset = pd.read_csv(filename, header=0)\n\n c_freq = np.float(config[2])\n #config[3] has the filter bandwidth, which is not active for a WBX daughterboard, as its locked to 40MHz\n c_bandwidth = np.float(config[4])\n\n print(dataset)\n\n d_len = dataset['avg'].size\n\n print(d_len)\n\n freq = np.linspace(0,d_len-1,d_len)\n \n fig = plt.figure(figsize=(22,10))\n fig.subplots_adjust(left=0.08, bottom=0.12, right=.97, top=.95)\n ax = fig.add_subplot(1,1,1) \n\n for spine in ['left','right','top','bottom']:\n ax.spines[spine].set_color('k')\n \n ax.tick_params(which = 'major', width=1, length=3, color='k')\n ax.tick_params(which = 'minor', width=.5, length=1, color='k')\n \n ax.yaxis.set_ticks_position('left')\n ax.xaxis.set_ticks_position('bottom')\n \n start = (c_freq - (c_bandwidth*.5))/1e6\n stop = (c_freq + (c_bandwidth*.5))/1e6\n ax.set_xticklabels(np.linspace(start,stop,11))\n ax.set_xticks(np.linspace(0,d_len-1,11))\n ax.set_xlim(0,d_len-1)\n ax.set_ylim(-130,-50)\n\n #ax.set_title('Power Spectrum Density', fontsize=36)\n ax.set_xlabel('Frequency (MHz)', fontsize=(36+6))\n ax.set_ylabel('Power (dBm)', fontsize=(36+6))\n ax.tick_params(axis='both', labelsize=(26+6))\n\n ax.plot(freq,dataset['max'], color='r', label=\"Max\")\n ax.plot(freq,dataset['avg'], color='k', label=\"Avg\")\n ax.plot(freq,dataset['min'], color='b', label=\"Min\")\n\n ax.axvline(x=d_len*.1, color='k', linestyle='--')\n ax.axvline(x=d_len*.9, color='k', linestyle='--')\n\n handles, labels = ax.get_legend_handles_labels()\n\n ax.legend(handles,labels,fontsize=24)\n plt.rc('axes', labelsize=(36+6)) \n plt.show() \n\n # here we are plotting stacked slices on top of eachother, plotting frequency vs time (in frames)\ndef occupancy_csv(config):\n print('Now running occupancy plot, ensure a bandwidth dataset is selected')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")])\n \n with open(filename, 'r') as r:\n next(r)\n dataset = [[int(x) for x in rec] for rec in csv.reader(r, delimiter=',')]\n #dataset = pd.read_csv(filename, header=0)\n\n c_freq = np.float(config[2])\n #config[3] has the filter bandwidth, which is not active for a WBX daughterboard, as its locked to 40MHz\n c_bandwidth = np.float(config[4])\n\n resolution = 131072 #this will have to be modified depending on how the FFT is computed\n\n framemax = np.amax(dataset, axis=0)[4] #frame_no\n print(framemax)\n #framemax = np.max(dataset['frame_no'])\n\n hslices = 512 #number of horizontal slices\n vslices = 1024 #number of vertical slices\n \n hscans = int(framemax/hslices) + 1\n vscans = int((resolution*0.8)/vslices) #this should be an integer, if it isnt, tough :)\n\n freq_arry = np.zeros(resolution*0.8)\n indent = int(resolution * 0.1)\n\n mesh = np.zeros((hslices+1,vslices))\n\n hidx = 0\n count = 0\n\n #row:idx,timescale,frequency,bandwidth,whitespace,frame_no\n for row in dataset:\n #as the rows are sorted by frame number, we can just iterate through them\n if row[4] > (hidx+1)*hscans:\n\n #zero array\n if hidx%100 == 0 : print(hidx)\n for i in range(vslices):\n mesh[hidx,i] = (np.sum(freq_arry[i*vscans:((i+1)*vscans - 1)])/hscans - (vscans-1))*-1\n hidx = hidx + 1\n freq_arry.fill(0)\n \n count = count + 1\n freq_arry[row[1] - indent : row[1]+row[2]-1 - indent] += 1\n\n if count%100000 == 0:\n print(count)\n\n #drop lastrow\n #for i in range(vslices):\n # mesh[hidx,i] = (np.sum(freq_arry[i*vscans:((i+1)*vscans - 1)])/hscans - vscans)*-1\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1) \n \n for spine in ['left','right','top','bottom']:\n ax.spines[spine].set_color('k')\n \n ax.tick_params(which = 'major', width=1, length=3, color='k')\n ax.tick_params(which = 'minor', width=.5, length=1, color='k')\n \n ax.yaxis.set_ticks_position('left')\n ax.xaxis.set_ticks_position('bottom')\n\n #plt.subplot(111)\n meshm = np.ma.masked_where(mesh < 1, mesh)\n m = ax.pcolormesh(meshm, vmin=1, vmax=np.amax(mesh), cmap='gnuplot_r')\n \n ax.axis([0,vslices,0,hidx])\n ax.set_xticks(np.linspace(1,vslices,5))#, 26316, 52632])\n\n start = (c_freq - (c_bandwidth*.4))/1e6\n stop = (c_freq + (c_bandwidth*.4))/1e6\n ax.set_xticklabels(np.linspace(start,stop,5)/60)\n #ax.set_ticks(True)\n \n \n plt.colorbar(m,ax=ax)\n #plt.grid(True, which='major', axis='both', linestyle='-', color='none')\n \n ax.set_title('Spectrum Occupancy')\n ax.set_xlabel('Frequency (MHz)')\n ax.set_ylabel('Time (min)')\n\n ax.set_yticks(np.linspace(0,hidx,num=12))\n ax.set_yticklabels(np.around(np.linspace(0,hidx,num=12)*.005*hscans,decimals=2))\n\n plt.tight_layout()\n plt.show()\n\ndef occupancy(config):\n print('Now running occupancy plot, ensure a bandwidth dataset is selected')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")])\n dataset = pd.read_csv(filename, header=0)\n\n c_freq = np.float(config[2])\n #config[3] has the filter bandwidth, which is not active for a WBX daughterboard, as its locked to 40MHz\n c_bandwidth = np.float(config[4])\n\n resolution = 131072 #this will have to be modified depending on how the FFT is computed\n\n framemax = np.max(dataset['frame_no'])\n\n hslices = 512 #number of horizontal slices\n vslices = 1024 #number of vertical slices\n \n hscans = int(framemax/hslices) + 1\n vscans = int((resolution*0.8)/vslices) #this should be an integer, if it isnt, tough :)\n\n freq_arry = np.zeros(resolution*0.8)\n indent = int(resolution * 0.1)\n\n mesh = np.zeros((hslices+1,vslices))\n\n hidx = 0\n count = 0\n\n #row:idx,timescale,frequency,bandwidth,whitespace,frame_no\n for row in dataset.itertuples():\n #as the rows are sorted by frame number, we can just iterate through them\n if row[5] > (hidx+1)*hscans:\n\n #zero array\n if hidx%100 == 0: print(hidx)\n for i in range(vslices):\n mesh[hidx,i] = (np.sum(freq_arry[i*vscans:((i+1)*vscans - 1)])/hscans - (vscans-1))*-1\n hidx = hidx + 1\n freq_arry.fill(0)\n \n count = count + 1\n freq_arry[row[2] - indent : row[2]+row[3]-1 - indent] += 1\n\n if count%100000 == 0:\n print(count)\n\n #drop lastrow\n #for i in range(vslices):\n # mesh[hidx,i] = (np.sum(freq_arry[i*vscans:((i+1)*vscans - 1)])/hscans - vscans)*-1\n fig = plt.figure(figsize=(22,10))\n fig.subplots_adjust(left=0.07, bottom=0.12, right=1.08, top=.97)\n ax = fig.add_subplot(1,1,1) \n \n for spine in ['left','right','top','bottom']:\n ax.spines[spine].set_color('k')\n \n ax.tick_params(which = 'major', width=1, length=3, color='k')\n ax.tick_params(which = 'minor', width=.5, length=1, color='k')\n \n ax.yaxis.set_ticks_position('left')\n ax.xaxis.set_ticks_position('bottom')\n\n #plt.subplot(111)\n meshm = np.ma.masked_where(mesh < 1, mesh)\n m = ax.pcolormesh(meshm, vmin=1, vmax=np.amax(mesh), cmap='inferno_r')\n \n ax.axis([0,vslices,0,hidx])\n ax.set_xticks(np.linspace(1,vslices,5))#, 26316, 52632])\n\n start = (c_freq - (c_bandwidth*.4))/1e6\n stop = (c_freq + (c_bandwidth*.4))/1e6\n ax.set_xticklabels(np.linspace(start,stop,5))\n #ax.set_ticks(True)\n \n cb = plt.colorbar(m,ax=ax)\n #labelstr = 'Observation Density (p/'+str(hscans*vscans)+')'\n labelstr = 'Observation Density'\n cb.set_label(labelstr, fontsize=(26+6))\n cb.ax.tick_params(labelsize=(20+6))\n #plt.grid(True, which='major', axis='both', linestyle='-', color='none')\n \n #ax.set_title('Spectrum Occupancy',fontsize=)\n ax.set_xlabel('Frequency (MHz)', fontsize=(36+6))\n ax.set_ylabel('Time (min)', fontsize=(36+6))\n ax.tick_params(axis='both', labelsize=(26+6))\n\n ax.set_yticks(np.linspace(0,hidx,num=11))\n #ax.set_yticklabels(np.around(np.linspace(0,hidx,num=12)*.005*hscans/60,decimals=1))\n ax.set_yticklabels(np.around(np.linspace(0,5,num=11),decimals=1))\n\n plt.rc('axes', labelsize=(32+6)) \n plt.show()\n\ndef occupancy_v2(config):\n\n#Occupancy is a measure of duration\n print('Now running occupancy plot. This will work for any partitioned dataset')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")])\n dataset = pd.read_csv(filename, header=0)\n\n c_freq = np.float(config[2])\n #config[3] has the filter bandwidth, which is not active for a WBX daughterboard, as its locked to 40MHz\n c_bandwidth = np.float(config[4])\n\n resolution = 131072 #this will have to be modified depending on how the FFT is computed\n\n framemax = np.max(dataset['frame_no'])\n\n hslices = 1001 #number of horizontal slices\n vslices = 2001 #number of vertical slices\n \n hscans = int(framemax/hslices) + 1\n vscans = np.ceil((resolution*0.8)/vslices) #this should be an integer, if it isnt, tough :)\n\n indent = int(resolution * 0.1)\n\n #mesh[row,cols]\n mesh = np.full((hslices-1,vslices-1),np.int(hscans*vscans), dtype=np.int)\n\n count = 0\n\n #Condition the dataframe accordingly\n dataset['start'] = dataset['frame_no'].subtract(dataset['timescale']-1)\n dataset['frequency'] = dataset['frequency'].subtract(indent)\n \n print(dataset)\n print(\"hscans: {}\".format(hscans))\n print(\"vscans: {}\".format(vscans))\n # 0 1 2 3 4 5 6\n #row:idx,timescale,frequency,bandwidth,whitespace,frame_no,start\n for row in dataset.itertuples():\n\n\n #try slicing lel\n\n #mesh[np.int((row[6] - 1)/hscans) : np.int((row[2] - 1)/vscans), \\\n # np.int((row[6] + row[1] - 1)/hscans) : np.int((row[2] + row[3] - 1)/vscans)] += 1\n\n x = np.floor(row[2]/vscans)\n y = np.floor(row[6]/hscans)\n\n d = np.int(np.ceil(row[1]/hscans))\n b = np.int(np.ceil(row[3]/vscans))\n\n for i in range(d):\n for j in range(b):\n\n cell = vscans * hscans\n\n #weight = \n w_x = 0\n w_y = 0\n\n w_xl = row[2] - (x + j) * vscans \n w_xh = row[2] + row[3]-1 - (x + j + 1) * vscans\n\n if w_xl > 0 and w_xh > 0:\n w_x = vscans - w_xl + 1\n elif w_xl > 0 and w_xh <= 0:\n w_x = row[3]\n elif w_xh > 0 and w_xl <= 0:\n w_x = vscans\n else: \n w_x = row[2] + row[3]-1 - (x + j) * vscans\n\n w_yl = row[6] - (y + i) * hscans\n w_yh = row[6] + row[1]-1 - (y + i + 1) * hscans\n\n if w_yl > 0 and w_yh > 0:\n w_y = hscans - w_yl + 1\n elif w_yl > 0 and w_yh <= 0:\n w_y = row[1]\n elif w_yh > 0 and w_yl <= 0:\n w_y = hscans\n else:\n w_y = row[6] + row[1]-1 - (y + i) * hscans\n \n #print(row)\n #print(\"x:{},y:{} j{},i{} wx{} wy{}\".format(x,y,j,i,w_x,w_y))\n\n mesh[y + i, x + j] -= w_y*w_x\n \n count = count + 1\n \n if count%100 == 0:\n print(count)\n\n\n\n #Determine the starting time of a partition\n\n #print(\"Start added to dataset \\n {}\".format(dataset))\n\n ##perform thresholding\n #dataset['start'] = np.ceil(dataset['start'].div(hscans))\n ##dataset['start'] = np.ceil(dataset['start']) \n \n #dataset['frequency'] = np.ceil(dataset['frequency'].subtract(indent).div(vscans))\n ##dataset['frequency'] = np.ceil(dataset['frequency'])\n \n #dataset['bandwidth'] = np.ceil(dataset['bandwidth'].div(vscans))\n ##dataset['bandwidth'] = np.ceil(dataset['bandwidth'])\n \n #dataset['timescale'] = np.ceil(dataset['timescale'].div(hscans))\n ##dataset['timescale'] = np.ceil(dataset['timescale'])\n\n\n #dataset = dataset.sort_values(['frequency','start'], ascending=True)\n\n #print(\"Thresholded dataset \\n {}\".format(dataset))\n\n #dataset_g = dataset.groupby(['start','frequency','timescale','bandwidth']).size().reset_index().rename(columns={0:'count'})\n\n #print(dataset_g)\n\n ##dataset = dataset.sort_values(['frequency','start'], ascending=True).reset_index()\n \n ##print(\"Sorted by frequency \\n {}\".format(dataset))\n ##Grouping must be the last operation, otherwise numpy functions will not work correctly\n\n\n\n ##row:idx,start,frequency,timescale,bandwidth,count\n #for row in dataset_g.itertuples():\n \n # for i in range(np.int(row[3])):\n # for j in range(np.int(row[4])):\n # mesh[np.int(row[1]) + i - 1, np.int(row[2]) + j - 1] += np.int(row[5])\n # count += 1\n\n # if count%10000 == 0:\n # print(count)\n\n #drop lastrow\n #for i in range(vslices):\n # mesh[hidx,i] = (np.sum(freq_arry[i*vscans:((i+1)*vscans - 1)])/hscans - vscans)*-1\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1) \n \n for spine in ['left','right','top','bottom']:\n ax.spines[spine].set_color('k')\n \n ax.tick_params(which = 'major', width=1, length=3, color='k')\n ax.tick_params(which = 'minor', width=.5, length=1, color='k')\n \n ax.yaxis.set_ticks_position('left')\n ax.xaxis.set_ticks_position('bottom')\n\n #plt.subplot(111)\n meshm = np.ma.masked_where(mesh < 1, mesh)\n m = ax.pcolormesh(meshm, vmin=1, vmax=np.amax(mesh), cmap='gnuplot_r')\n \n ax.axis([0,vslices,0,hslices])\n ax.set_xticks(np.linspace(1,vslices,5))#, 26316, 52632])\n\n start = (c_freq - (c_bandwidth*.4))/1e6\n stop = (c_freq + (c_bandwidth*.4))/1e6\n ax.set_xticklabels(np.linspace(start,stop,5))\n #ax.set_ticks(True)\n \n \n plt.colorbar(m,ax=ax)\n #plt.grid(True, which='major', axis='both', linestyle='-', color='none')\n \n ax.set_title('Spectrum Occupancy')\n ax.set_xlabel('Frequency (MHz)')\n ax.set_ylabel('Time (s)')\n\n #ax.set_yticks(np.linspace(0,hidx,num=12))\n #ax.set_yticklabels(np.around(np.linspace(0,hidx,num=12)*.005*hscans,decimals=2))\n\n plt.tight_layout()\n plt.show()\n\n\n\n#new improved spicy hexbin with distributions :D\ndef hexbin(filename):\n print('Now running hexbin plot, ensure a partitioned dataset is selected')\n dataset = pd.read_csv(filename, header=0)\n\n #matplotlib.rcParams.update({'font.size': 21}\n #plt.figure(figsize=(6,8), dpi=600)\n\n #xmax = np.power(10,np.ceil(np.log10(np.max(dataset['bandwidth']))))*.5\n bwmax = np.max(dataset['bandwidth'])\n print(bwmax*190)\n if np.power(10,np.ceil(np.log10(bwmax))) > 4*bwmax:\n xmax = np.power(10,np.ceil(np.log10(bwmax))) / 4\n elif np.power(10,np.ceil(np.log10(bwmax))) > 2*bwmax:\n xmax = np.power(10,np.ceil(np.log10(bwmax))) / 2\n else: \n xmax = np.power(10,np.ceil(np.log10(bwmax)))\n\n if xmax < 25000: xmax = 25000 #This is here as 4.75MHz is the minimax (smallest max) x axis value. \n \n print(xmax*190)\n\n# xmin = 1 #Just for the BW=1 TS=1 dataset\n# ymin = 1\n xmin = 50\n #ymax = np.maximum(1e6,np.power(10,np.ceil(np.log10(np.max(dataset['timescale'])))))\n ymax = np.power(10,np.ceil(np.log10(np.max(dataset['timescale'])))) #for 5min segment\n ymin = np.min(dataset['timescale'])\n\n ##cheekybonusbits\n #array = np.histogram(dataset['timescale'], bins=np.logspace(np.log10(ymin), np.log10(ymax), num=200))\n #print(array)\n #x = array[1]\n #y = array[0]\n #print(x)\n #print(y)\n #cr_y = np.cumsum(y[::-1])[::-1] \n #print(cr_y)\n #print(\"len x: {}, len y: {}\".format(len(x[:-1]),len(cr_y)))\n #plt.figure()\n #ax = plt.subplot(111)\n #ax.scatter(x[:-1],cr_y)\n ##ax.set_yscale('log')\n #ax.set_xscale('log')\n #plt.show()\n\n #create figure hook, assign figure size and adjust padding around borders\n fig = plt.figure(figsize=(22,14))\n fig.subplots_adjust(left=0.1, bottom=0.09, right=.98, top=.98)\n\n #arrange the various axes nicely using gridspec\n gs=gridspec.GridSpec(5,6)\n gs.update(wspace = 0.03, hspace= 0.03)\n\n ax1 = plt.subplot(gs[1:,:-1])\n ax2 = plt.subplot(gs[1:,-1])\n ax3 = plt.subplot(gs[0,:-1])\n ax4 = gs[0,-1]\n\n gs2=gridspec.GridSpecFromSubplotSpec(12,16,ax4)\n cbax = plt.subplot(gs2[4,1:-1])\n\n #create the hexbin plot and a hook for the colourmap\n cbmap = ax1.hexbin(dataset['bandwidth'], dataset['timescale'], mincnt=1, xscale='log', yscale='log', cmap='inferno', norm=matplotlib.colors.LogNorm(), reduce_C_function=np.sum)\n ax1.axis([xmin, xmax, ymin, ymax])\n \n #populate the appropriate spines\n for spine in ['left','bottom']:\n ax1.spines[spine].set_color('k')\n \n ax2.spines['bottom'].set_color('k')\n ax3.spines['left'].set_color('k')\n \n #set tick sizes, colours and lengths\n ax1.tick_params(which = 'major', width=1, length=4, color='k')\n ax1.tick_params(which = 'minor', width=1, length=2, color='k')\n\n #manual xtick placement\n ax1.set_xticks([66, 132, 263, 526, 1316, 2632, 5263, 13158])#, 26316])#, 52632])\n ax1.set_xticklabels([r'12.5kHz', r'25kHz', r'50kHz', r'100kHz', r'250kHz', r'500kHz', r'1MHz', r'2.5MHz', r'5MHz', r'10MHz'])\n ax1.xaxis.set_ticks_position('bottom')\n ax1.set_xlabel('Bandwidth', fontsize=(24+18))\n \n #manual ytick placement\n ax1.set_yticks([10,19,48,95,190,477,1907,5722,11444,57220])#,171661,686646])\n ax1.set_yticklabels([r'50ms',r'100ms',r'250ms',r'500ms',r'1s',r'2.5s',r'10s',r'30s',r'1m',r'5m',r'15m',r'1h'])\n ax1.yaxis.set_ticks_position('left')\n ax1.set_ylabel('Duration', fontsize=(24+18))\n ax1.tick_params(axis='both', labelsize=(16+18))\n \n #ax1.set_title(\"With a log color scale\")\n #cbax = mplcb.make_axes_gridspec(ax4)\n\n cb = plt.colorbar(cbmap,cax=cbax, orientation='horizontal')\n cb.outline.set_visible(True)\n cb.outline.set_edgecolor('black') \n cb.set_label('Observation Density', fontsize=(14+12))\n cbax.xaxis.set_label_position('top')\n cb.ax.tick_params(labelsize=(14+12))\n #cb.set_ticks([np.log10(1),np.log10(10),np.log10(50),np.log10(100),np.log10(500)])\n #cb.set_ticklabels([1,10,50,100,500])\n\n #Timescale histogram \n ax2hist = ax2.hist(dataset['timescale'], bins=np.logspace(np.log10(ymin), np.log10(ymax), num=100), orientation='horizontal', log=True, color='k')\n ax2.set_yscale('log')\n ax2.set_xlim(1,np.power(10,np.ceil(np.log10(np.amax(ax2hist[0])))))\n\n ax2.yaxis.set_visible(False)\n ax2.xaxis.set_ticks_position('bottom')\n ax2.tick_params(which = 'major', width=1, length=4, color='k')\n ax2.tick_params(which = 'minor', width=1, length=2, color='k')\n \n ax2.tick_params(axis='both', labelsize=(14+18))\n\n #Better bandwidth histogram\n ax3.hist(dataset['bandwidth'], bins=np.logspace(np.log10(xmin), np.log10(xmax), num=200), log=True, color='k')\n ax3.set_xscale('log')\n ax3.set_xlim(xmin, xmax)\n #ax3.axis([xmin, xmax, 1, ymax])\n \n ax3.xaxis.set_visible(False)\n ax3.yaxis.set_ticks_position('left')\n ax3.tick_params(which = 'major', width=1, length=4, color='k')\n ax3.tick_params(which = 'minor', width=1, length=2, color='k')\n\n ax3.tick_params(axis='both', labelsize=(14+18))\n\n plt.rc('axes', labelsize=(20+18)) \n #dpi=plt.gcf().dpi is CRITICAL for saving an image that looks identical to the one displayed in plt.show()\n #plt.savefig('Basic.png', dpi=plt.gcf().dpi)\n plt.show()\n\n # here we are plotting totals of duration per frequency as a percentage of total duration\ndef dutycycle(filename):\n print('Now running dutycycle plot, ensure a duration dataset is selected')\n filename = filedialog.askopenfilename(filetypes = [(\"csv files\",\"*.csv\")])\n dataset = pd.read_csv(filename, header=0, dtype=np.int)\n \n resolution = 131072 #this will have to be modified depending on how the FFT is computed\n\n framemax = np.max(dataset['frame_no'])\n\n vslices = 1024\n vscans = int((resolution*0.8)/vslices)+1 #this should be an integer, if it isnt, tough :)\n \n newrez = int(resolution*0.8) + 1\n \n freq_arry = np.zeros(newrez)\n indent = int(resolution * 0.1)\n\n count = 0\n #row:idx,timescale,frequency,bandwidth,whitespace,frame_no\n for row in dataset.itertuples():\n #freq_arry[int((row[2] - indent)/vscans)] += row[1]\n freq_arry[row[2] - indent] += row[1]\n count = count + 1\n \n if count%100000 == 0:\n print(count)\n \n #now manipulate freq_arry, yes its a roundabout way, but ohwell\n #normalise\n freq_arry /= framemax #normalise\n freq_arry *= 100 #out of 100\n freq_arry -= 100 #whitespace is positive, so incumbent activity here will now be negative\n freq_arry *= -1 #make incumbent activity positive\n\n freq_max = np.zeros(vslices)\n freq_avg = np.zeros(vslices)\n for i in range(newrez):\n if freq_arry[i] > freq_max[int(i/vscans)]:\n freq_max[int(i/vscans)] = freq_arry[i]\n\n freq_avg[int(i/vscans)] += freq_arry[i]/vscans\n\n \n fig = plt.figure()\n ax = fig.add_subplot(1,1,1) \n\n freq = np.linspace(0,vslices-1,vslices)\n# ax.scatter(freq,freq_max, edgecolor = '', c='k')\n# ax.scatter(freq,freq_avg, edgecolor = '', c='r')\n\n ax.plot(freq,freq_max, c='k')\n ax.plot(freq,freq_avg, c='r')\n\n ax.set_title('Duty Cycle')\n ax.set_xlabel('Frequency')\n ax.set_ylabel('Percentage')\n\n ax.axis([0,vslices,0,100])\n\n plt.show()\n\n\n\n\ndef analyse(filename,test):\n \n print('Now running test: {}'.format(test))\n dataset = pd.read_csv(filename, header=0)#, converters={0: np.int32, 1: np.int32, 2: np.int32, 3: np.int32, 4: np.int32}, dtype=np.int32)\n\n print(dataset.dtypes)\n #converters={'timescale': np.int32, 'frequency': np.int32, 'bandwidth': np.int32,'whitespace': np.int32, 'frame_no': np.int32}\n #cols:[0] timescale, [1] frequency, [2] bandwidth, [3] whitespace, [4] frame_no\n #in a loop (iterator), [0] = index, [1] = timescale ...\n print(dataset.columns)\n cols = dataset.columns\n\n \n nbins = 131072\n\n \n if(test == 1):\n #1. BW vs TS density plot, with TS and BW histograms/KDEs on the top and right hand side of the density plot\n\n bwmax = np.max(dataset[cols[2]])\n tsmax = np.max(dataset[cols[0]])\n\n print('BWMax: {}, TSMax: {}'.format(bwmax,tsmax))\n\n bwtsframe = pd.concat([dataset[cols[2]],dataset[cols[0]]], axis=1, keys=[cols[2],cols[0]])\n print(bwtsframe)\n\n #bwtsframe_red = bwtsframe.drop_duplicates()\n bwtsframe_red = bwtsframe.groupby([cols[2], cols[0]]).size().reset_index().rename(columns={0:'count'})\n print(bwtsframe_red)\n\n bwtsframe_red['ws_count'] = bwtsframe_red[cols[2]] * bwtsframe_red[cols[0]] * bwtsframe_red['count']\n\n print(bwtsframe_red)\n\n subprogram = 3\n ### PLOTTING THINGS HERE ###\n if (subprogram == 1):\n\n fig3 = plt.figure()\n axh = fig3.add_subplot(2,1,1)\n axh2 = fig3.add_subplot(2,1,2)\n\n bwtsframe[cols[2]].hist(ax=axh, bins = 200, bottom = .1, log = True)\n axh.set_yscale('log')\n axh.set_xlim(0,30000)\n axh.set_ylim(.1,1e8)\n #axh.set_xscale('log')\n\n bwtsframe[cols[0]].hist(ax=axh2, bins = 200, bottom = .1, log = True)\n axh2.set_yscale('log')\n axh2.set_xlim(0,60000)\n axh2.set_ylim(.1,1e8)\n #axh2.set_xscale('log')\n\n axh.set_title('Bandwidth Histogram')\n axh.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n axh.set_ylabel('Density')\n\n axh2.set_title('Duration Histogram')\n axh2.set_xlabel('Timescale (5.3ms/unit)')\n axh2.set_ylabel('Density')\n\n plt.show()\n\n ##ax1 = fig.add_subplot(1,2,1) #convention (row,col,idx)\n ##ax2 = fig.add_subplot(1,2,2)\n fig = plt.figure()\n ax = fig.add_subplot(2,1,1)\n ax2 = fig.add_subplot(2,1,2)\n # plt.pcolormesh(mesh, norm=LogNorm(vmin=1, vmax=np.amax(mesh)), cmap='inferno')\n \n #plt.scatter(dataset[cols[2]],dataset[cols[0]])\n\n sc1 = ax.scatter(bwtsframe_red[cols[2]],bwtsframe_red[cols[0]],edgecolor='',c=bwtsframe_red['count'],cmap='inferno',norm=LogNorm(vmin=1,vmax=bwtsframe_red['count'].max()))\n sc2 = ax2.scatter(bwtsframe_red[cols[2]],bwtsframe_red[cols[0]],edgecolor='',c=bwtsframe_red['ws_count'],cmap='inferno',norm=LogNorm(vmin=1,vmax=bwtsframe_red['ws_count'].max()))\n\n #plt.hist2d(dataset[cols[2]],dataset[cols[0]],bins=1000)\n plt.colorbar(sc1,ax=ax)\n plt.colorbar(sc2,ax=ax2)\n\n #ticks = np.arange(0, bwmax, 6)\n #labels = range(ticks.size)\n #plt.xticks(ticks, labels)\n\n ax.set_yscale('log')\n ax.set_xscale('log')\n ax.set_ylim(1,1e5)\n ax.set_xlim(10,1e5)\n ax2.set_yscale('log')\n ax2.set_xscale('log')\n ax2.set_ylim(1,1e5)\n ax2.set_xlim(10,1e5)\n #ax.set_xlim(0,pxls)\n #ax.set_ylim(0,pxls)\n\n ax.set_title('Window Count')\n ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax.set_ylabel('Timescale (5.3ms/unit)')\n\n ax2.set_title('Whitespace Density')\n ax2.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax2.set_ylabel('Timescale (5.3ms/unit)')\n \n plt.show()\n\n if (subprogram == 2):\n #this second set of tests provides a plot of number of observations on the y axis versus the timescale or bandwidth. \n\n # wsvfframe = wsvfframe.groupby(['frequency','bandwidth'])['timescale'].sum().reset_index()\n\n bwsum = bwtsframe_red.groupby(['bandwidth'])['ws_count'].sum().reset_index()\n print('bwsum printout')\n print(bwsum)\n\n tssum = bwtsframe_red.groupby(['timescale'])['ws_count'].sum().reset_index()\n print('tssum printout')\n print(tssum)\n\n \n fig = plt.figure()\n ax = fig.add_subplot(2,2,2)\n ax2 = fig.add_subplot(2,2,4)\n ax.scatter(bwsum['bandwidth'],bwsum['ws_count'],edgecolor='')\n ax2.scatter(tssum['timescale'],tssum['ws_count'],edgecolor='')\n\n ax.set_yscale('log')\n ax.set_xscale('log')\n ax.set_xlim(1,1e5)\n ax.set_ylim(1,1e9)\n \n ax2.set_yscale('log')\n ax2.set_xscale('log')\n ax2.set_xlim(1,1e5)\n ax2.set_ylim(1,1e10)\n\n ax.set_title('Whitespace Distribution vs Bandwidth')\n ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax.set_ylabel('Unique Whitespace')\n\n ax2.set_title('Whitespace Distribution vs Timescale')\n ax2.set_xlabel('Timescale (5.3ms/unit)')\n ax2.set_ylabel('Unique Whitespace')\n\n ## window count plots - number of unique window observations\n\n bwhist = dataset[cols[2]]\n tshist = dataset[cols[0]]\n\n bwhist = bwhist.groupby(bwhist).size().reset_index().rename(columns={0:'count'})\n\n tshist = tshist.groupby(tshist).size().reset_index().rename(columns={0:'count'})\n\n print(bwhist)\n\n axa = fig.add_subplot(2,2,1)\n ax2a = fig.add_subplot(2,2,3)\n axa.scatter(bwhist[cols[2]],bwhist['count'],edgecolor='')\n ax2a.scatter(tshist[cols[0]],tshist['count'],edgecolor='')\n\n axa.set_yscale('log')\n axa.set_xscale('log')\n axa.set_xlim(1,1e5)\n axa.set_ylim(1,1e6)\n \n ax2a.set_yscale('log')\n ax2a.set_xscale('log')\n ax2a.set_xlim(1,1e5)\n ax2a.set_ylim(1,1e6)\n\n axa.set_title('Window Bandwidth Distribution')\n axa.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n axa.set_ylabel('Number of Observations')\n\n ax2a.set_title('Window Duration Distribution')\n ax2a.set_xlabel('Timescale (5.3ms/unit)')\n ax2a.set_ylabel('Number of Observations')\n\n plt.show()\n\n if (subprogram == 3):\n\n bwsum = bwtsframe_red.groupby(['bandwidth'])['ws_count'].sum().reset_index()\n print('bwsum printout')\n print(bwsum)\n\n tssum = bwtsframe_red.groupby(['timescale'])['ws_count'].sum().reset_index()\n print('tssum printout')\n print(tssum)\n\n numops = bwtsframe.groupby(['timescale']).size().reset_index().rename(columns={0:'count'})\n\n print(numops)\n \n\n tsmax = np.max(bwtsframe_red['timescale'])\n xmax = np.power(10,np.ceil(np.log10(tsmax)))\n\n ts_cumsum = np.cumsum(tssum['ws_count'][::-1])[::-1] \n\n ymax = np.power(10,np.ceil(np.log10(np.max(ts_cumsum))))\n\n print('Partitioned Whitespace: {}'.format(ts_cumsum[0]))\n\n delta = np.diff(ts_cumsum)\n delta = delta*-1\n print(delta)\n\n fig = plt.figure()\n ax = fig.add_subplot(3,1,1)\n ax2 = fig.add_subplot(3,1,2)\n #ax.scatter(bwsum['bandwidth'],bwsum['ws_count'],edgecolor='') \n ax.scatter(numops['timescale'],numops['count'],edgecolor='')\n ax2.scatter(tssum['timescale'],ts_cumsum,edgecolor='')\n ax2.scatter(tssum['timescale'],tssum['ws_count'],edgecolor='',color='r')\n #ax2.scatter(tssum['timescale'],ts_cumsum,edgecolor='')\n\n ax.set_yscale('log')\n ax.set_xscale('log')\n ax.set_xlim(1,xmax)\n ax.set_ylim(.1,1e4)\n \n ax2.set_yscale('log')\n ax2.set_xscale('log')\n ax2.set_xlim(1,xmax)\n ax2.set_ylim(1,ymax)\n\n #ax.set_title('Whitespace Distribution vs Bandwidth')\n #ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n #ax.set_ylabel('Unique Whitespace')\n \n ax.set_title('Whitespace Observation Count vs Timescale')\n ax.set_xlabel('Timescale (5.3ms/unit)')\n ax.set_ylabel('Observation Count')\n\n ax2.set_title('Cumulative Whitespace vs Timescale')\n ax2.set_xlabel('Timescale (5.3ms/unit)')\n ax2.set_ylabel('Unique Whitespace')\n\n z = np.polyfit(tssum['timescale'],tssum['ws_count'], 1)\n p = np.poly1d(z)\n\n ax2.plot(tssum['timescale'],p(tssum['timescale']), 'k--')\n\n z2 = np.polyfit(np.log(tssum['timescale']),tssum['ws_count'], 2)\n p2 = np.poly1d(z2)\n ax2.plot(tssum['timescale'],p2(np.log(tssum['timescale'])), 'g--')\n\n ax3 = fig.add_subplot(3,1,3)\n\n perh_cumsum = (ts_cumsum/np.max(ts_cumsum)) *100\n ax3.scatter(tssum['timescale'],perh_cumsum, edgecolor='')\n ax3.set_xscale('log')\n ax3.set_xlim(1,xmax)\n\n z3 = np.polyfit(np.log(tssum['timescale']),perh_cumsum, 3)\n p3 = np.poly1d(z3)\n\n ax3.plot(tssum['timescale'],p3(np.log(tssum['timescale'])), 'k--')\n ## window count plots - number of unique window observations\n plt.show()\n\n #elif(test == 8):\n #BW vs WS and TS vs WS analysis - Test 1 is the complementary analysis of this test\n #we want to plot both unique whitespace incurred as well as total whitespace as the x axis decreases\n\n \n\n elif(test == 2):\n #2. WS vs Bins (real frequency - may need to preserve band starting frequency ....) \n #Series 1 - WS per bin due to partitioning algorithm\n #Series 2 - WS per bin based on the unpartitioned spectrum\n \n subprogram = 3\n\n wsvfframe = pd.concat([dataset[cols[1]],dataset[cols[2]],dataset[cols[0]]], axis=1, keys=[cols[1],cols[2],cols[0]])\n\n #wfc:[0] frequency, [1] bandwidth, [2] timescale\n #in a loop (iterator), [0] = index, [1] = frequency ...\n wfc = wsvfframe.columns\n print(wfc)\n \n freq = np.linspace(0,nbins-1,nbins)\n\n print(freq)\n relws = np.zeros(nbins)\n compws = np.zeros(nbins)\n diffarry = np.zeros(nbins)\n \n wsvfframe = wsvfframe.groupby(['frequency','bandwidth'])['timescale'].sum().reset_index()\n\n for row in wsvfframe.itertuples():\n relws[row[1]:row[1]+row[2]] += row[3]\n\n \n if True:\n fig = plt.figure()\n ax = fig.add_subplot(2,1,1)\n axa = fig.add_subplot(2,1,2)\n \n ax.scatter(freq,relws,edgecolor='')\n\n relwsS = np.sort(relws)\n\n axa.scatter(freq,relwsS[::-1],edgecolor='')\n\n axa.set_title('Whitespace Frequency Distribution CCDF')\n axa.set_xlabel('Descending Magnitude Ordered Bin Count')\n axa.set_ylabel('Whitespace Units over Observation Period')\n\n ax.set_title('Whitespace Frequency Distribution')\n ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax.set_ylabel('Whitespace Units')\n\n plt.show()\n\n ### comaprison dataset NOTE: BW must = 1 for this dataset to be valid ###\n\n print('Select comparison windowset. ENSURE BW = 1')\n bw_1_file = filedialog.askopenfilename()\n\n bw_1_dataset = pd.read_csv(bw_1_file, header=0, dtype=np.int32)\n bcols = bw_1_dataset.columns\n\n bw_1_dataset = bw_1_dataset.groupby(by=['frequency'])['timescale'].sum().reset_index()\n\n for row in bw_1_dataset.itertuples():\n compws[row[1]] = row[2]\n \n #### test zone ####\n\n ### Profile the thing :)\n #pr = cProfile.Profile()\n #pr.enable()\n ### ... do something ...\n #pr.disable()\n #s = io.StringIO()\n #sortby = 'cumulative'\n #ps = pstats.Stats(pr, stream=s).sort_stats(sortby)\n #ps.print_stats()\n #print(s.getvalue()) \n\n ##### mmm tests ####\n\n diffarry = np.subtract(compws,relws)\n\n dfcomp = pd.DataFrame(data={'rel': relws, 'comp': compws})\n \n print(dfcomp)\n dfcomp = dfcomp.sort_values(['comp','rel'],ascending=False)\n\n print(dfcomp)\n\n d2comp = np.array([compws,relws])\n d2comp[d2comp[:,0].argsort()]\n\n if False:\n figz = plt.figure()\n \n axz = figz.add_subplot(2,1,1)\n axz.scatter(freq,dfcomp['comp'],edgecolor='',label='Absolute Spectrum')\n axz.scatter(freq,dfcomp['rel'],edgecolor='',c='r',label='Windowed Spectrum')\n axz.legend()\n\n dfcomp = dfcomp.sort_values(['rel','comp'],ascending=False)\n\n axza = figz.add_subplot(2,1,2)\n axza.scatter(freq,dfcomp['comp'],edgecolor='',label='Absolute Spectrum')\n axza.scatter(freq,dfcomp['rel'],edgecolor='',c='r',label='Windowed Spectrum')\n axza.legend()\n\n plt.show()\n\n if False:\n\n fig = plt.figure()\n ax = fig.add_subplot(3,2,1)\n ax2 = fig.add_subplot(3,2,3)\n ax3 = fig.add_subplot(3,2,5)\n\n axa = fig.add_subplot(3,2,2)\n ax2a = fig.add_subplot(3,2,4)\n ax3a = fig.add_subplot(3,2,6)\n #2.1 Companion plot to WS vs Bins, - % covered vs Bins, showing the amount of real spectrum covered by the partitioned algo as a percentage\n\n ax.scatter(freq,relws,edgecolor='')\n ax2.scatter(freq,diffarry,edgecolor='')\n # ax3.scatter(freq,compws,edgecolor='',label='Absolute Spectrum')\n ax3.scatter(bw_1_dataset['frequency'],bw_1_dataset['timescale'],edgecolor='',label='Absolute Spectrum')\n ax3.scatter(freq,relws,edgecolor='',c='r',label='Windowed Spectrum')\n\n #CDFs?\n\n relwsS = np.sort(relws)\n diffarryS = np.sort(diffarry)\n compwsS = np.sort(compws) \n\n # compwspad_r = np.resize(compwspad,(1,131072))\n # compwsS = np.sort(compwspad_r)\n # relwsS = np.sort(relws)\n\n axa.scatter(freq,relwsS[::-1],edgecolor='')\n axa.set_title('Whitespace Frequency Distribution CCDF')\n axa.set_xlabel('Descending Magnitude Ordered Bin Count')\n axa.set_ylabel('Whitespace Units over Observation Period')\n ax2a.scatter(freq,diffarryS[::-1],edgecolor='')\n ax2a.set_title('Whitespace Frequency Distribution Difference CCDF')\n ax2a.set_xlabel('Descending Magnitude Ordered Bin Count')\n ax2a.set_ylabel('Whitespace Units Difference over Observation Period')\n \n ax3a.set_title('Whitespace Frequency Distribution CCDF')\n ax3a.set_xlabel('Descending Magnitude Ordered Bin Count')\n ax3a.set_ylabel('Whitespace Units over Observation Period')\n ax3a.scatter(freq,compwsS[::-1],edgecolor='',label='Absolute Spectrum')\n \n # ax3a.scatter(freq,compwsS[::-1],edgecolor='',label='Absolute Spectrum')\n ax3a.scatter(freq,relwsS[::-1],edgecolor='',c='r',label='Windowed Spectrum')\n ax3a.legend()\n\n # ax.set_yscale('log')\n # ax.set_xscale('log')\n # ax2.set_yscale('log')\n # ax2.set_xscale('log')\n\n ax.set_title('Whitespace Frequency Distribution')\n ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax.set_ylabel('Whitespace Units')\n\n ax2.set_title('Missed Window Coverage (Absolute minus Windowed)')\n ax2.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax2.set_ylabel('Number of Whitespace Units Difference')\n\n ax3.set_title('Window Coverage Comparison')\n ax3.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax3.set_ylabel('Whitespace Units')\n ax3.legend()\n plt.show()\n\n\n\n\n elif(test == 3):\n\n #3. Instantaneous bandwidth (total) vs Time (real - in frames) \n #Possible second series showing the number of instantaneous windows/fragmentation - fragmentation is probably more interesting\n \n #cols:[0] timescale, [1] frequency, [2] bandwidth, [3] whitespace, [4] frame_no\n bwvt = pd.concat([dataset[cols[2]],dataset[cols[0]],dataset[cols[4]]], axis=1, keys=[cols[2],cols[0],cols[4]])\n\n #bcols:[0] bandwidth, [1] timescale, [2] frame_no\n bcols = bwvt.columns\n\n maxframe = np.max(bwvt[bcols[2]])\n bwspread = np.zeros(maxframe)\n\n time = np.linspace(0,maxframe-1,maxframe)\n print(time)\n\n for row in bwvt.itertuples():\n bwspread[row[3]-1-row[2]:row[3]] += row[1]\n\n #bwspread[maxframe-1] = 0 #clear the final value as it gives an improper indication of continuous bandwidths\n\n fig = plt.figure()\n ax = fig.add_subplot(2,1,1)\n ax.scatter(time,bwspread,edgecolor='')\n\n #ax.set_yscale('log')\n #ax.set_xscale('log')\n\n ax.set_title('Bandwidth over Time')\n ax.set_xlabel('Continuous Time (5.3ms/unit)')\n ax.set_ylabel('Aggregate Instantaneous Bandwidth (190Hz/bin)')\n \n plt.show()\n\n #4. Average window duration vs Time - Showing the average window persistance (in frames) with respect to time (in frames) - this may be confusing?\n\n elif(test == 4):\n\n #cols:[0] timescale, [1] frequency, [2] bandwidth, [3] whitespace, [4] frame_no\n dvt = pd.concat([dataset[cols[0]],dataset[cols[4]]], axis=1, keys=[cols[0],cols[4]])\n\n #dcols:[0] timescale, [1] frame_no\n dcols = dvt.columns\n\n maxframe = np.max(dvt[dcols[1]])\n tavgspread = np.zeros(maxframe)\n ttotalspread = np.zeros(maxframe)\n\n time = np.linspace(0,maxframe-1,maxframe)\n\n wincount = np.zeros(maxframe)\n \n print(dvt)\n\n currframe = 0\n count = 1\n\n for row in dvt.itertuples():\n if row[2] > currframe:\n \n tavgspread[currframe-1] /= count\n\n wincount[currframe] = count\n count=1\n currframe = row[2]\n else: count += 1\n\n tavgspread[row[2]-1] += row[1]\n ttotalspread[row[2]-1] += row[1]\n\n tavgspread[maxframe-1] /= count #clear the final value as it gives an improper indication of continuous bandwidths\n ttotalspread[maxframe-1] = 0\n wincount[maxframe-1] = 0\n\n fig = plt.figure()\n ax = fig.add_subplot(3,2,1)\n ax2 = fig.add_subplot(3,2,3)\n ax3 = fig.add_subplot(3,2,5)\n\n axa = fig.add_subplot(3,2,2)\n ax2a = fig.add_subplot(3,2,4)\n ax3a = fig.add_subplot(3,2,6)\n\n ax.scatter(time,tavgspread,edgecolor='')\n ax2.scatter(time,ttotalspread,edgecolor='')\n ax3.scatter(time,wincount,edgecolor='')\n\n #ax.set_yscale('log')\n #ax.set_xscale('log')\n\n ax.set_title('Window Duration over Time')\n ax.set_xlabel('Continuous Time (5.3ms/unit)')\n ax.set_ylabel('Average Window Duration (5.3ms/unit)')\n\n ax2.set_title('Aggregate Window Duration over Time')\n ax2.set_xlabel('Continuous Time (5.3ms/unit)')\n ax2.set_ylabel('Total Aggregate Window Duration (5.3ms/unit)')\n\n ax3.set_title('Total Windows over Time')\n ax3.set_xlabel('Continuous Time (5.3ms/unit)')\n ax3.set_ylabel('Number of Windows')\n \n tavgsS = np.sort(tavgspread)\n ttotsS = np.sort(ttotalspread)\n wincS = np.sort(wincount)\n \n axa.scatter(time,tavgsS[::-1],edgecolor='')\n axa.set_title('Window Duration over Time CCDF')\n axa.set_xlabel('Descending Magnitude Observation Index')\n axa.set_ylabel('Average Window Duration (5.3ms/unit)')\n\n ax2a.scatter(time,ttotsS[::-1],edgecolor='')\n ax2a.set_title('Aggregate Window Duration over Time CCDF')\n ax2a.set_xlabel('Descending Magnitude Observation Index')\n ax2a.set_ylabel('Total Aggregate Window Duration (5.3ms/unit)')\n \n ax3a.set_title('Total Windows over Time CCDF')\n ax3a.set_xlabel('Descending Magnitude Observation Index')\n ax3a.set_ylabel('Window Count')\n ax3a.scatter(time,wincS[::-1],edgecolor='')\n\n plt.show()\n\n\n\n\n if (test == 5):\n\n #5. Quality vs Bins - quality computed using weighting algorithm and assigning those weights to the relative bin(s) spanned by the appropriate window. - Sum the total weights, and/or average weights and display those?\n\n \n #cols:[0] timescale, [1] frequency, [2] bandwidth, [3] whitespace, [4] frame_no\n qvf = pd.concat([dataset[cols[0]],dataset[cols[2]],dataset[cols[1]]], axis=1, keys=[cols[0],cols[2],cols[1]])\n\n #qcols: [0] timescale, [1] bandwidth, [2] frequency\n qcols = qvf.columns\n\n freq = np.linspace(0,nbins-1,nbins)\n qualarry = np.zeros(nbins)\n\n tmin = 10\n bmin = 66 #These should already be adhered to\n\n for row in qvf.itertuples():\n qual = (row[1]-tmin)/tmin * (row[2]/bmin)\n if qual > 1:\n qualarry[row[3]:row[3]+row[2]-1] += qual\n\n fig = plt.figure()\n ax = fig.add_subplot(2,1,1)\n ax.scatter(freq,qualarry,edgecolor='')\n\n qualarryord = np.sort(qualarry)\n# qualarryord = qualarryord[::-1]\n ax2 = fig.add_subplot(2,1,2)\n ax2.scatter(freq,qualarryord[::-1],edgecolor='')\n\n #ax.set_yscale('log')\n #ax.set_xscale('log')\n\n ax.set_title('Quality per Bin over Observation Period')\n ax.set_xlabel('Frquency in Bins (190Hz/bin)')\n ax.set_ylabel('Total Bin Quality')\n\n ax2.set_title('Quality per Bin over Observation Period Ordered')\n ax2.set_xlabel('Count (Eventually as a percentage)')\n ax2.set_ylabel('Total Bin Quality')\n\n plt.show()\n\ndef legacy_sns (filename):\n\n wins = pd.read_csv(filename, header=0) #, chunksize = chnk)\n print(wins.columns)\n\n #cols:timescale,frequency,bandwidth,whitespace,frame_no\n cols = wins.columns\n \n #print(wins.head(10))\n #ts = wins.sort_values([cols[0],cols[2]])\n \n #uniq = pd.unique(wins[cols[0]].values.ravel())#, columns = [cols[0],cols[3]])\n #ts_sort = np.c_[uniq,np.zeros(uniq.size)] \n #ts_sum = np.empty_like(ts_sort)\n\n ##Row[0] is the index given by the dataframe\n ##Here the unique TS is used as the index, where the unique whitespace value is summed for each TS \n ##TS is already sorted ascending, by nature of the detection\n ## THIS WILL ONLY WORK IF EVERY TS IS OBSERVED !!!BAD!!!\n #for row in wins.itertuples():\n # ts_sort[row[1]-1,1] += row[4]\n\n \n sns.jointplot(wins[cols[2]],wins[cols[0]],kind=\"hex\",color=\"#4CB391\", stat_func=None)\n sns.jointplot(x='bandwidth',y='timescale',data = wins,kind=\"kde\",color=\"#4CB391\", stat_func=None)\n\n plt.savefig(\"heatmap.png\")\n plt.show()\n\ndef legacy(filename):\n\n wins = pd.read_csv(filename, header=0) #, chunksize = chnk)\n print(wins.columns)\n\n #cols:timescale,frequency,bandwidth,whitespace,frame_no\n cols = wins.columns\n \n #print(wins.head(10))\n #ts = wins.sort_values([cols[0],cols[2]])\n \n #uniq = pd.unique(wins[cols[0]].values.ravel())#, columns = [cols[0],cols[3]])\n #ts_sort = np.c_[uniq,np.zeros(uniq.size)] \n #ts_sum = np.empty_like(ts_sort)\n\n ##Row[0] is the index given by the dataframe\n ##Here the unique TS is used as the index, where the unique whitespace value is summed for each TS \n ##TS is already sorted ascending, by nature of the detection\n ## THIS WILL ONLY WORK IF EVERY TS IS OBSERVED !!!BAD!!!\n #for row in wins.itertuples():\n # ts_sort[row[1]-1,1] += row[4]\n\n \n #sns.jointplot(wins[cols[2]],wins[cols[0]],kind=\"hex\",color=\"#4CB391\", stat_func=None)\n #sns.jointplot(x='bandwidth',y='timescale',data = wins,kind=\"kde\",color=\"#4CB391\", stat_func=None)\n #plt.show()\n\n x = max(wins[cols[2]])\n y = max(wins[cols[0]])\n fn = max(wins[cols[4]])\n\n nbins = 131072\n\n print('Max Bandwidth: {} Max Timescale: {}'.format(x,y))\n\n mesh = np.zeros((x,y))\n num_frames = np.zeros(fn)\n\n freqdensity = np.zeros(nbins)\n\n #print(mesh)\n\n uniq = pd.unique(wins[cols[2]].values.ravel())\n uniq.sort()\n\n ws_sort = np.c_[uniq,np.zeros(uniq.size)] \n ws_sum = np.empty_like(ws_sort)\n\n count = 0\n totalws = 0\n #Row[0] is the index given by the dataframe\n for row in wins.itertuples():\n mesh[row[3]-1,row[1]-1] += row[4] \n #temp = np.where(ws_sort[:,0]==row[3])\n #ws_sort[temp,1] += row[4]\n totalws += row[4]\n ## mesh[row[1]-1,row[3]-1] += row[4]\n \n #populate frequency density array\n #for i in range(row[2],row[2]+row[3]-1):\n # freqdensity[i]+=row[1] \n\n count += 1\n if (count%100000 == 0): print('Up to: {}'.format(count))\n\n print('Total Whitespace: ')\n print(totalws)\n\n fig = plt.figure()\n ##ax1 = fig.add_subplot(1,2,1) #convention (row,col,idx)\n ##ax2 = fig.add_subplot(1,2,2)\n\n ##pd.DataFrame.hist(wins,column=cols[0],bins=(x+1),log=True,ax=ax1)\n ##ax1.set_xlim([0,50])\n ##ax1.set_title('Timescale Density')\n ##ax1.set_xlabel('Timescale')\n ##ax1.set_ylabel('# Observations')\n\n ##ax2.plot(num_frames)\n ##ax2.set_ylim([0,100])\n ##ax2.set_title('Whitespace Channels')\n ##ax2.set_xlabel('Frame Number')\n ##ax2.set_ylabel('# Whitespace Channels')\n ##plt.show()\n\n #print('Mesh populated')\n #print(mesh)\n \n ax = fig.add_subplot(1,1,1)\n plt.pcolormesh(mesh, norm=LogNorm(vmin=1, vmax=np.amax(mesh)))\n plt.colorbar()\n #ax.set_yscale('log')\n #ax.set_xscale('log')\n ax.set_xlim(1,x)\n ax.set_ylim(1,y)\n ax.set_title('Whitespace Density')\n ax.set_xlabel('Bandwidth in Bins (190Hz/bin)')\n ax.set_ylabel('Timescale (5.3ms/unit)')\n\n #ax1 = fig.add_subplot(2,1,1)\n #ax1.plot(freqdensity)\n ##ax1.set_xlim([0,50])\n #ax1.set_title('Whitespace Density per Bin')\n #ax1.set_xlabel('Frequency in Bins (190Hz/bin)')\n #ax1.set_ylabel('Whitespace')\n\n plt.show()\n\n #ts_sum = ts_sort\n # np.copyto(ts_sum,ts_sort)\n np.copyto(ws_sum,ws_sort)\n \n #df = pd.DataFrame(data=ts_sort,columns=[cols[0],cols[3]])\n #print(df)\n\n #for i in range(uniq.size-1, 0, -1):\n # ts_sum[i-1,1] += ts_sum[i,1]\n #print(ts_sum[i,1])\n for i in range(uniq.size-1, 0, -1):\n ws_sum[i-1,1] += ws_sum[i,1]\n\n print('\\nws_sort:')\n print(ws_sort)\n print('\\nws_sum:')\n print(ws_sum)\n\n sns.jointplot(ws_sort[:,0],ws_sort[:,1],kind=\"hex\",color=\"#4CB391\")\n plt.show()\n\n #for i in range(len(wins)):\n # set1 = np.array(wins[wins.columns[0::2]])[i]\n\n #wins.plot(x=cols[0],y=cols[3],kind='scatter')\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n #ax.plot(ts_sort)\n #ax.set_yscale('log')\n #plt.semilogy(ts_sort[0], ts_sort[1], ts_sum[0], ts_sum[1])\n ax.semilogy(ws_sort[:,0], ws_sort[:,1], label='Unique Whitespace')\n ax.semilogy(ws_sum[:,0], ws_sum[:,1], label='Cumulative Whitespace')\n\n ax.set_yscale('symlog') #super important to plot '0' values\n ax.legend()\n plt.show()\n\n\n\n\n#BEGIN THE SIMULATOR - Should put this in a separate file, but visualstudio is mean when it comes to selecting the file to execute etc\n\nclass Device:\n def __init__(self, ts, bw, id):\n self.ts = ts\n self.bw = bw\n self.id = id\n \n self.freq = 0\n\n self.run_time = 50 #this number should be modified based on access model\n\n self.sched_time = 0\n\n self.total_sched_time = 0 #total running time\n self.last_scheduled = 0 \n self.waiting_time = 0 #total waiting time IF communication interrupted\n #maybe need a scheduling flag?\n\n #FIXME\n def schedule(self, freq, bandwidth, startframe):\n if bandwidth < self.bw:\n print('DEVICE {} SCHEDULING ERROR, BW TOO SMALL'.format(self.id))\n else: \n self.freq = freq\n self.waiting_time = self.waiting_time + (startframe - (self.last_scheduled - 1))\n self.sched_time = startframe\n\n def deschedule(self, endframe):\n if endframe - self.sched_time < self.run_time:\n print(\"device {} only ran for {}. Required runtime: {}\".format(self.id,endframe-self.sched_time,self.run_time))\n\n self.total_sched_time = self.total_sched_time + (endframe - self.sched_time) \n self.last_scheduled = endframe\n\n def totalTime(self):\n return self.total_sched_time\n\n def totalWait(self):\n return self.waiting_time\n\n def getFreq(self):\n return self.freq\n\n def getBW(self):\n return self.bw\n\n def getID(self):\n return self.id\n\n def getRemianingRun(self, frame):\n return self.run_time - (frame - self.sched_time)\n\n #will not implement getTS yet, as we have no true concept of TS as everything is running without a-priori knowledge\n\n#class DeviceCreator\n\nclass Slice: #has been supersceeded by simply using a dataframe\n def __init__(self,frequency,bandwidth):\n self.freq = frequency\n self.bw = bandwidth\n \n def getFreq(self):\n return self.freq\n\n def getBW(self):\n return self.bw\n\nclass SpectrumEntity:\n def __init__(self,filename):\n self.framecount = 0\n self.dataset = pd.read_csv(filename, header=0)#, converters={0: np.int32, 1: np.int32, 2: np.int32, 3: np.int32, 4: np.int32}, dtype=np.int32)\n\n self.framemax = np.max(self.dataset['frame_no'])\n self.dataset = self.dataset.sort_values(['frequency'], ascending=True)\n self.dataset['start'] = self.dataset['frame_no'].subtract(self.dataset['timescale']-1)\n\n self.state = self.dataset.groupby(self.dataset['start'])\n #self.stateGroups = dict(list(self.state))\n\n self.spect_df = pd.DataFrame()\n #self.sliceList = []\n\n self.sliceFrame = pd.DataFrame()\n\n #when nextFrame is called check that result is not null\n def getNextFrame(self):\n\n if self.framecount == self.framemax:\n print(\"what we gon do now?\")\n return null\n\n #for i in range(13):\n #clear current buffer\n #del self.sliceList[:]\n self.framecount = self.framecount + 1\n\n #create frame list of slices\n \n # for key, group in self.state:\n # if key < 13:\n # print(key)\n # #print(group)\n # self.spect_df = self.spect_df.append(group)\n # self.spect_df = self.spect_df[self.spect_df.frame_no > key] #this is pretty inefficient\n # print(self.spect_df) #this should be a self contained dataframe of the current windows, just take a slice from this dataset and gg\n\n if self.framecount in self.state.groups:\n self.spect_df = self.spect_df.append(self.state.get_group(self.framecount))\n self.spect_df = self.spect_df[self.spect_df.frame_no > self.framecount]\n #self.dataset = self.dataset.sort_values(['frequency'], ascending=True)\n self.spect_df = self.spect_df.sort_values(['frequency'], ascending=True)\n else: \n self.spect_df = self.spect_df[self.spect_df.frame_no > self.framecount] #this is pretty inefficient\n\n #slice spect_df\n #print(self.spect_df)\n self.sliceFrame = self.spect_df[['frequency','bandwidth']].reset_index(drop=True)\n \n if False: \n print(self.framecount)\n print(self.spect_df)\n print(self.sliceFrame)\n\n #check for adjacency - maybe later, currently ignore adjacency\n\n\n #slices should be sorted as the generation of them is procedural\n #return self.sliceList\n return self.sliceFrame\n \nclass DeviceQueue:\n def __init__(self,numDev):\n self.queue = []\n\n #create the devices to place in the queue\n #ts and bw could be generated uniquely for each device, currently we are using a placeholder\n self.ts = 11\n self.bw = 66\n \n for i in range(numDev):\n self.queue.append(Device(self.ts,self.bw,i))\n\n print(\"{} devices created\".format(len(self.queue)))\n\n #this is called by the scheduler\n def push(self,dev,frame):\n #push takes in a device object and adds it to the queue\n dev.deschedule(frame)\n self.queue.append(dev) \n\n def peek(self):\n if not self.queue:\n return null\n else:\n return self.queue[0]\n\n def search(self,bw,time):\n for dev in self.queue:\n if dev.getBW <= bw:\n temp = dev\n self.queue.remove(dev)\n return temp\n break\n\n def pop(self):\n if not self.queue:\n return null\n else:\n return self.queue.popleft()\n\n def len(self):\n return len(self.queue)\n\nclass Scheduler:\n def __init__(self,filename,numDev):\n #create spectrum entity\n #create UDQ\n #create devices (inform queue of number of devices to populate)\n \n #implement logging\n \n self.RSL = [] #remainingSpectrumList\n self.ADL = [] #allocatedDeviceList\n\n self.currentFrame = 1\n\n #devices created within queue object\n #self.UDQ = DeviceQueue(numDev) #working around the queue as it does not seem efficient\n\n self.UDL = [] #unallocatedDeviceList\n self.ts = 11\n self.bw = 66\n \n for i in range(numDev):\n self.UDL.append(Device(self.ts,self.bw,i))\n\n print(\"{} devices created\".format(len(self.UDL)))\n\n #slices prepared within SE object\n self.SE = SpectrumEntity(filename)\n \n self.run_list()\n\n #implement operational loop within here\n def run(self):\n #check SE for spectrumstate\n self.spectrumState = self.SE.getNextFrame()\n\n #print(self.spectrumState)\n\n adevs = len(self.ADL)\n adevIdx = 0\n\n udevs = len(self.UDL)\n udevIdx = 0\n\n remainBW = 0\n self.tempADL = self.ADL.copy()\n\n uFlag = False\n noAlloc = False\n\n #compare spectrum state with ADL to ensure allocations still valid\n while not self.spectrumState.empty:\n #print('Frame number: {}'.format(self.currentFrame))\n\n if self.ADL:\n for row in self.spectrumState.itertuples(): #iterate through all of the available spectrum\n #row['frequency'], row['bandwidth']\n\n uBW = row['bandwidth']\n uFreq = row['frequency'] #beginnign of the unallocated frequency for that particular slice\n\n for adev in range(adevIdx,len(self.ADL)): #FIX ALL THESE TO HAVE CORRECT INDEXATION!!!!!!!!!!!\n\n #still need to take into account duration expired cases\n \n #check frequency and bandwidth \n \n #if we are in to the slice and the previous slice was not befitting\n if adev.getFreq + adev.getBW < uFreq and uFlag:\n #device needs to be deallocated\n uFlag = False\n self.UDL.append(adev)\n self.tempADL.remove(adev)\n\n adevIdx = adevIdx + 1\n continue \n\n if adev.getFreq + adev.getBW <= row['frequency'] + row['bandwidth'] and adev.getFreq >= row['frequency']:\n #device exists within slice\n\n #this does not work correctly for fractioned slices methinks.\n if uFreq != adev.getFreq:\n print('Gap in allocation detected. Dev start: {}, window start {}'.format(adev.getFreq,uFreq))\n self.RSL.append([uFreq,uFreq - dev.getFreq], columns=['frequency','bandwidth'])\n uBW = uBW - (uFreq - dev.getFreq)\n \n uFreq = adev.getFreq + adev.getBW #this will be one position after the end of the allocation, i.e. the next position not conflicting with the current device\n uBW = uBW - adev.getBW\n\n if uBW < 0:\n print('Well, we have a negative bandwidth here, thats not good ....')\n elif uBW == 0:\n print('Slice completely allocated')\n adevIdx = adevIdx + 1\n continue\n else: \n print('Allocation valid')\n \n elif dev.getFreq >= row['frequency'] + row['bandwidth']:\n #device does not exist in this slice, thus remaining slice is free\n print('Device {} does not exist in this slice, remaining slice: {} - validate with uBW: {}'.format(adevIdx,row['frequency']+row['bandwidth']-uFreq,uBW))\n\n adevIdx = adevIdx #this device still needs to be checked\n\n self.RSL.append([uFreq,uBW], columns=['frequency','bandwidth'])\n uFlag = True;\n \n break #currently allocated devices do not exist in this slice\n\n\n #other devices may also not exist within this bound ... probably should not break then ..\n #not if they are sorted.\n\n adevIdx = adevIdx + 1\n #update RSL\n\n #deallocate conflicting devices\n \n #for udev in range(self.UDQ.len):\n else: \n noAlloc = True\n\n print('Allocated devices checked: {}. Devices removed: {}'.format(len(self.ADL), len(self.ADL) - len(self.tempADL)))\n self.ADL = self.tempADL.copy() #I REALLY dislike this, however its annoying to remove elements from a list as you iterate over it\n\n if self.UDL:\n #unallocated devices, interrogate unallocated list then find potential slots for each device waiting\n if noAlloc:\n self.RSL = self.spectrumState.copy()\n\n for udev in range(len(self.UDL)):\n print(udev)\n #to schedule device increment the frequency by the devices bandwidth and subtract the available bandwidth for that opportunity\n print(self.RSL[self.RSL['frequency'] >= self.UDL[udev].getBW()].iloc[0:2,0:2]) \n print(self.RSL[self.RSL['frequency'] >= self.UDL[udev].getBW()].iloc[0:2,0:2].values.tolist()) \n\n\n #generate RSL\n\n #check RSL against UDQ and update RSL accordingly\n\n #perform logging on current state\n\n #sort ADL by frequency to reduce number of comparisons\n\n #loop again until we have exhausted the spectrum\n self.currentFrame = self.currentFrame + 1\n self.spectrumState = self.SE.getNextFrame()\n\n if self.spectrumState.empty:\n print(\"End of spectrum reached!\")\n break\n\n print(\"should output things here, plots etc\")\n #finish up\n print(\"scheduler terminating\")\n\n def run_list(self):\n #check SE for spectrumstate\n self.spectrumState = self.SE.getNextFrame().values.tolist()\n\n #print(self.spectrumState)\n remainBW = 0\n adevIdx = 0\n\n noAlloc = False\n\n removed = 0\n #compare spectrum state with ADL to ensure allocations still valid\n while self.spectrumState:\n #print('Frame number: {}'.format(self.currentFrame))\n\n \n if self.ADL:\n i = 0\n for row in self.spectrumState: #iterate through all of the available spectrum\n #row['frequency'], row['bandwidth']\n\n uFreq = row[0] #beginnign of the unallocated frequency for that particular slice\n uBW = row[1]\n \n while i < len(self.ADL):\n #for adev in islice(self.ADL,adevIdx,None): #FIX ALL THESE TO HAVE CORRECT INDEXATION!!!!!!!!!!!\n\n #check frequency and bandwidth \n #if we are in to the slice and the previous slice was not befitting\n if self.ADL[i].getFreq() + self.ADL[i].getBW() < uFreq:\n #device needs to be descheduled\n print('Deallocating: dev {} from [{},{}]'.format(self.ADL[i].getID(),self.ADL[i].getFreq(),self.ADL[i].getBW()))\n self.ADL[i].deschedule(self.currentFrame)\n self.UDL.append(self.ADL[i])\n del self.ADL[i]\n\n #i = i - 1\n removed = removed + 1\n continue \n\n uFlag = False\n\n #still need to take into account duration expired cases\n if self.ADL[i].getRemianingRun(self.currentFrame) <= 0:\n #device needs to be descheduled\n print('Runtime expired: dev {} from [{},{}]'.format(self.ADL[i].getID(),self.ADL[i].getFreq(),self.ADL[i].getBW()))\n self.ADL[i].deschedule(self.currentFrame)\n self.UDL.append(self.ADL[i])\n del self.ADL[i]\n\n #i = i - 1\n removed = removed + 1\n continue \n\n elif self.ADL[i].getFreq() >= row[0] + row[1]:\n #device does not exist in this slice, thus remaining slice is free\n #print('Device {} does not exist in this slice, remaining bandwidth in slice: {} - validate with uBW: {}'.format(adev.getID(),row[0]+row[1]-uFreq,uBW))\n\n self.RSL.append([uFreq,uBW])\n\n #this device still needs to be checked\n break #currently allocated devices do not exist in this slice\n\n elif self.ADL[i].getFreq() + self.ADL[i].getBW() <= row[0] + row[1] and self.ADL[i].getFreq() >= row[0]:\n #device exists within slice\n\n #this does not work correctly for fractioned slices methinks.\n if uFreq != self.ADL[i].getFreq():\n print('Gap in allocation detected. Dev start: {}, window start {}'.format(self.ADL[i].getFreq(),uFreq))\n self.RSL.append([uFreq,uFreq - self.ADL[i].getFreq()])\n uBW = uBW - (uFreq - self.ADL[i].getFreq())\n \n uFreq = self.ADL[i].getFreq() + self.ADL[i].getBW() #this will be one position after the end of the allocation, i.e. the next position not conflicting with the current device\n uBW = uBW - self.ADL[i].getBW()\n\n if uBW < 0:\n print('Well, we have a negative bandwidth here, thats not good ....')\n elif uBW == 0:\n print('Slice completely allocated!')\n #else: \n #print('Allocation valid')\n\n #end of loop and device is valid, increment i\n i = i + 1\n\n #remaining spectrum is unoccupied\n if i == len(self.ADL):\n self.RSL.append([row[0],row[1]])\n\n #for udev in range(self.UDQ.len):\n\n #sort newly populated RSL\n adevIdx = 0\n #print('Remaining Spectrum List')\n #print(self.RSL)\n self.RSL.sort(key=lambda x: x[0])\n #print(self.RSL)\n else: \n noAlloc = True\n\n allocated = 0\n if self.UDL:\n #unallocated devices, interrogate unallocated list then find potential slots for each device waiting\n if noAlloc:\n self.RSL = self.spectrumState.copy()\n noAlloc = False\n\n #for udev in self.UDL:\n # iterations = iterations + 1\n # #to schedule device increment the frequency by the devices bandwidth and subtract the available bandwidth for that opportunity\n # #print(self.RSL[self.RSL['frequency'] >= self.UDL[udev].getBW()].iloc[0:2,0:2]) \n # #print(self.RSL[self.RSL['frequency'] >= self.UDL[udev].getBW()].iloc[0:2,0:2].values.tolist()) \n # for row in self.RSL:\n # if row[1] >= udev.getBW():\n # #print(row)\n # udev.schedule(row[0],row[1],self.currentFrame)\n # row[1] = row[1] - udev.getBW()\n # row[0] = row[0] + udev.getBW()\n # self.ADL.append(udev)\n # self.UDL.remove(udev) #wont work\n # break \n\n i = 0\n offset = 0\n while i < len(self.UDL):\n #to schedule device increment the frequency by the devices bandwidth and subtract the available bandwidth for that opportunity\n \n #this looks very C and not at all pythonic :)\n for row in self.RSL:\n if row[1] >= self.UDL[i].getBW():\n #print(i)\n #print(offset\n print('Allocating {} to [{},{}]'.format(self.UDL[i].getID(),row[0],row[1]))\n self.UDL[i].schedule(row[0],row[1],self.currentFrame) \n row[0] = row[0] + self.UDL[i].getBW()\n row[1] = row[1] - self.UDL[i].getBW()\n self.ADL.append(self.UDL[i])\n del self.UDL[i] #might work\n #offset = offset + 1\n i = i - 1\n allocated = allocated + 1\n break \n i = i + 1\n \n #print('Iterations of self.UDL: {}, self.UDL size: {}'.format(i,len(self.UDL)))\n print('Original spectrum:')\n print(self.spectrumState)\n print('Remaining spectrum for frame {}:'.format(self.currentFrame)) \n print(self.RSL)\n\n #generate RSL\n\n #check RSL against UDQ and update RSL accordingly\n\n #perform logging on current state\n\n #sort ADL by frequency to reduce number of comparisons\n\n #loop again until we have exhausted the spectrum\n self.currentFrame = self.currentFrame + 1\n self.spectrumState = self.SE.getNextFrame().values.tolist()\n\n print('Frame {} - Allocated devices checked: {}. Devices removed: {}. Devices reallocated: {}. Devices unallocated: {}'.format(self.currentFrame-1,len(self.ADL), removed, allocated, len(self.UDL)))\n removed = 0\n del self.RSL[:]\n\n if not self.spectrumState:\n print(\"End of spectrum reached!\")\n break\n\n print(\"should output things here, plots etc\")\n #finish up\n print(\"scheduler terminating\")\n \n\n\n \n\n\ndef dev_sim(filename):\n\n print('Now running Device Simulation')\n\n\n runtest = Scheduler(filename,200)\n\n print('Device simulation ended')\n dataset = pd.read_csv(filename, header=0)#, converters={0: np.int32, 1: np.int32, 2: np.int32, 3: np.int32, 4: np.int32}, dtype=np.int32)\n\n #print(dataset.dtypes)\n\n #cols:[0] timescale, [1] frequency, [2] bandwidth, [3] whitespace, [4] frame_no\n #in a loop (iterator), [0] = index, [1] = timescale ...\n print(dataset.columns)\n cols = dataset.columns\n \n nbins = 131072\n\n #This set of tests are designed to simulate channel occupancy/utilisation of secondary devces accessing the whitespace spectrum, given particular access requirements.\n #These requirements are (initially): Bandwidth, minimum timescale.\n #The tests will be performed accross the number of devices operating and the resulting utilisation of the spectrum\n #Note that complex scheduling algorithms will not be explored (at least initially), as this is an extremely deep topic within itself. \n\n #the resulting plots will be: Spectrum utilisation/total throughput versus number of devices, with a family of curves, each curve will either be a variance on device required timescale or bandwidth. \n #a comparison plot for this will also be generated detailing the per device throughput and how it decays as a function of device density.\n\n #it is also of note that these models do not take into account channel capacity models, instead raw spectrum resources are focused upon and is of the unit resource blocks. Where a resource block is a 1TS * 1BW segment. (The TS may be 5ms, and BW be 12.5kHz) ...\n\n #will also need to perform these analyses as a function of time, as the sectrum changes over time, so classic, throughput vs device plots are not entirely valid here, as there is an additional variable in play. \n\n framemax = np.max(dataset['frame_no'])\n dev_arry = np.zeros(framemax)\n\n bwmin = 66;\n tsmin = 10; #these will not be required, as the windowing function already uses these basic values as the windows are observed.\n\n dev_bw = 66 #bandwidth for each device\n dev_ts = 11 #timescale for each device\n\n #use dataset, a subset here is not required\n\n dataset = dataset.sort_values(['frequency'], ascending=True)\n dataset['start'] = dataset['frame_no'].subtract(dataset['timescale']-1)\n\n #print('Printing frame 1')\n #print(dataset.loc[dataset['start'] == 1])\n\n ds_grp = dataset.groupby(dataset['start'])\n #ds_grp = ds_grp['frequency'].apply(lambda x: x.sort_values(ascending=False))\n\n #for c in ds_grp.groups: \n # print(c)\n #print(ds_grp.get_group(1))\n\n cur_frame = 1\n spect_df = pd.DataFrame()\n for key, group in ds_grp:\n if key < 13:\n print(key)\n #print(group)\n spect_df = spect_df.append(group)\n spect_df = spect_df[spect_df.frame_no > key] #this is pretty inefficient\n print(spect_df)\n #key is the identifier for the group - this is the start number\n #group becomes the entires within the group, ordered with ascending frequency\n #diff = \n #cur_frame = key\n\n\n\nif __name__ == \"__main__\":\n sys.exit(int(main() or 0))","sub_path":"Analysis_Suite.py","file_name":"Analysis_Suite.py","file_ext":"py","file_size_in_byte":76615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"128686892","text":"#!/usr/bin/env python\n\nfrom optparse import OptionParser\nimport os\nimport pipes\nimport sys\nimport fnmatch\nfrom os import listdir\nfrom os.path import isfile, join\nimport glob\nimport subprocess\n\nsys.path.append(os.path.join('.', 'tools', 'ninja', 'misc'))\nimport ninja_syntax\n\n\nclass cd:\n def __init__(self, newPath):\n self.newPath = newPath\n\n def __enter__(self):\n self.savedPath = os.getcwd()\n os.chdir(self.newPath)\n\n def __exit__(self, etype, value, traceback):\n os.chdir(self.savedPath)\n\nclass BuildSystem:\n instance = None\n def __init__(self, options = []):\n if not BuildSystem.instance:\n BuildSystem.instance = BuildSystem.__BuildSystem(options)\n\n def __getattr__(self, name):\n return getattr(self.instance, name)\n\n class __BuildSystem:\n def __init__(self, options):\n self.options = options\n self.targets = []\n self.buildfile = open('build.ninja', 'w')\n self.ninja = ninja_syntax.Writer(self.buildfile)\n self.ninja.variable('configure_args', ' '.join(sys.argv[1:]))\n\n env_keys = set(['CC', 'CXX', 'AR'])\n configure_env = dict((k, os.environ[k]) for k in os.environ if k in env_keys)\n config_str = ''\n if configure_env:\n config_str = ' '.join([k + '=' + pipes.quote(configure_env[k]) for k in configure_env])\n self.ninja.variable('configure_env', config_str + '$ ')\n\n self.ninja.variable('cc', configure_env.get('CC', 'gcc'))\n self.ninja.variable('cxx', configure_env.get('CXX', 'g++'))\n self.ninja.variable('ar', configure_env.get('AR', 'ar'))\n\n self.ninja.rule('gcc',\n command='$cc -MMD -MT $out -MF $out.d $cflags -c $in -o $out ',\n depfile='$out.d',\n deps='gcc',\n description='CC $out')\n\n self.ninja.rule('gxx',\n command='$cxx -MMD -MT $out -MF $out.d $cxxflags -c $in -o $out ',\n depfile='$out.d',\n deps='gcc',\n description='CXX $out')\n\n self.ninja.rule('ar', command='$ar rcs $out $in', description='AR $out')\n self.ninja.rule('link', command='$cxx $in $ldflags $include -o $out', description='LINK $out')\n\n def add_target(self, target):\n self.targets.append(target)\n self.build(target)\n return target\n\n def build_dir(self, filename):\n return os.path.join(os.path.join('.', 'build'), filename)\n\n def gcc(self, sources, **kwargs) :\n lib_obj = []\n for src in sources:\n lib_obj += self.ninja.build(self.build_dir(src + '.o'), 'gcc', src, **kwargs)\n return lib_obj\n\n def gxx(self, sources, **kwargs) :\n lib_obj = []\n for src in sources:\n lib_obj += self.ninja.build(self.build_dir(src + '.o'), 'gxx', src, **kwargs)\n return lib_obj\n\n def ar(self, lib_name, name, **kwargs):\n self.ninja.build(self.build_dir(lib_name), 'ar', name, **kwargs)\n\n def link(self, outfile, name, **kwargs):\n if GetPlatform() == 'win':\n outfile += '.exe'\n else:\n outfile += '.out'\n self.ninja.build(self.build_dir(outfile), 'link', name, **kwargs)\n\n def build(self, target):\n if self.options.no_os:\n target.NoOS()\n\n if self.options.no_rng:\n target.NoRNG()\n\n if self.options.development:\n target.Development()\n\n if self.options.debug:\n target.Debug()\n else:\n target.Release()\n\n if self.options.coverity:\n target.Coverity()\n\n if self.options.no_os == False:\n if (GetPlatform() == 'win'):\n target.Windows()\n elif(GetPlatform() == 'linux'):\n target.Linux()\n\n target.build()\n target.link()\n\nclass BuildFlags(object):\n def __init__(self):\n self.name = ''\n self.src_cc = []\n self.src_cxx = []\n self.objs = []\n self.cflags = ['-std=c11']\n self.cxxflags = ['-std=gnu++11']\n self.ldflags = []\n self.include = []\n\n def Development(self):\n common_warnings = ['-Wall', '-Wextra', '-Wpedantic']\n self.cflags += ['-Wbad-function-cast', '-Wc99-c11-compat', '-Wc++-compat', '-Wmissing-prototypes', '-Wnested-externs' ] + common_warnings\n self.cxxflags += ['-Wnonnull','-Wmissing-include-dirs', '-Wswitch-default', '-Wunused', '-Wuninitialized',\n '-fstrict-overflow', '-Wstrict-overflow=5',\n '-ftree-vrp', '-Warray-bounds', '-Wtrampolines', '-Wfloat-equal',\n '-Wundef', '-Wshadow', '-Wunsafe-loop-optimizations', '-Wcast-qual', '-Wcast-align', '-Wconversion',\n '-Wzero-as-null-pointer-constant', '-Wuseless-cast', '-Wlogical-op', '-Waggregate-return',\n '-Wmissing-declarations', '-Wmissing-field-initializers', '-Wredundant-decls', '-Winline', '-Wstack-protector'\n ] + common_warnings\n\n def Debug(self):\n debug_flags = ['-g3','-O0']\n self.cflags += debug_flags\n self.cxxflags += debug_flags\n self.name += '_d'\n\n def Release(self):\n release_flags = ['-O3', '-DNDEBUG', '-s']\n self.cflags += release_flags\n self.cxxflags += release_flags\n\n def Coverity(self):\n coverity_flags = ['-fprofile-arcs', '-ftest-coverage']\n self.cflags += coverity_flags\n self.cxxflags += coverity_flags\n self.ldflags += ['-lgcov']\n\n def NoOS(self):\n self.cflags += ['-DNO_OS_DEPENDENCE']\n self.cxxflags += ['-DNO_OS_DEPENDENCE']\n\n def NoRNG(self):\n self.cflags += ['-DOS_RNG_AVAILABLE']\n self.cxxflags += ['-DOS_RNG_AVAILABLE']\n\n def Linux(self):\n self.ldflags += ['-lpthread']\n self.cflags += ['-D_X86INTRIN_H_INCLUDED']\n self.cxxflags += ['-D_X86INTRIN_H_INCLUDED']\n\n def Windows(self):\n self.cflags += ['-D_WIN32_WINNT=0x0501']\n self.cxxflags += ['-D_WIN32_WINNT=0x0501']\n self.ldflags += ['-lws2_32', '-lwsock32']\n\n def build(self):\n self.objs += BuildSystem().gcc(self.src_cc, variables = [('cflags', self.cflags + self.include)])\n self.objs += BuildSystem().gxx(self.src_cxx, variables = [('cxxflags', self.cxxflags + self.include)])\n\nclass BuildCryptoPPLibrary(BuildFlags):\n def __init__(self):\n BuildFlags.__init__(self)\n self.name = 'libcryptopp'\n self.src_cc = glob.glob(os.path.join('.', 'src', '*.c')) + glob.glob(os.path.join('.', 'src', '*', '*.c'))\n self.src_cxx = glob.glob(os.path.join('.', 'src', '*.cpp')) + glob.glob(os.path.join('.', 'src', '*', '*.cpp'))\n self.include += ['-I' + os.path.join('.', 'include')]\n\n def link(self):\n BuildSystem().ar(self.name + '.a', self.objs)\n\nclass BuildCryptoPPTests(BuildFlags):\n def __init__(self, libcrypto):\n BuildFlags.__init__(self)\n self.libcrypto = libcrypto\n self.name = 'crypt_test'\n self.src_cc = glob.glob(os.path.join('.', 'tests', 'cryptopp', '*.c')) + glob.glob(os.path.join('.', 'tests', 'cryptopp', '*', '*.c'))\n self.src_cxx = glob.glob(os.path.join('.', 'tests', 'cryptopp', '*.cpp')) + glob.glob(os.path.join('.', 'tests', 'cryptopp', '*', '*.cpp'))\n self.include += self.libcrypto.include\n\n def link(self):\n BuildSystem().link(self.name, self.objs + self.libcrypto.objs, variables = [('ldflags', self.ldflags)])\n\n\nclass BuildCryptoPPTestsUnit(BuildFlags):\n def __init__(self, libcrypto):\n BuildFlags.__init__(self)\n self.libcrypto = libcrypto\n self.name = 'crypt_test_unit'\n self.src_cc = glob.glob(os.path.join('.', 'tests', 'unit_tests', '*.c')) + glob.glob(os.path.join('.', 'tests', 'unit_tests', '*', '*.c'))\n self.src_cxx = glob.glob(os.path.join('.', 'tests', 'unit_tests', '*.cpp')) + glob.glob(os.path.join('.', 'tests', 'unit_tests', '*', '*.cpp'))\n self.include += ['-I' + os.path.join('.', 'tools', 'cppunit', 'include')] + self.libcrypto.include + ['-I' + os.path.join('.', 'tests', 'unit_tests')]\n self.ldflags += ['-L' + os.path.join('.', 'tools', 'cppunit', 'src', 'cppunit', '.libs'), '-Wl,-Bstatic', '-lcppunit', '-Wl,-Bdynamic']\n\n def build(self):\n if not os.path.isfile(os.path.join('.', 'tools', 'cppunit', 'src', 'cppunit', '.libs', 'libcppunit.a')):\n with cd(os.path.join('.', 'tools', 'cppunit')):\n subprocess.call(['./configure'])\n make_process = subprocess.Popen('make', stderr=subprocess.STDOUT)\n if make_process.wait() != 0:\n exit(1)\n BuildFlags.build(self)\n\n def link(self):\n BuildSystem().link(self.name, self.objs + self.libcrypto.objs, variables = [('ldflags', self.ldflags)])\n\ndef GetPlatform():\n if sys.platform.startswith('cygwin') or sys.platform.startswith('win'):\n return 'win'\n elif sys.platform.startswith('darwin'):\n return 'mac'\n elif sys.platform.startswith('linux'):\n return 'linux'\n else:\n raise Error(\"Unknown platform: %s\" % sys.platform)\n\ndef main():\n parser = OptionParser()\n parser.add_option('--debug', action='store_true', help='enable debugging extras', default=False)\n parser.add_option('--no_os', action='store_true', help='remove os dependencies', default=False)\n parser.add_option('--no_rng', action='store_true', help='remove rng', default=False)\n parser.add_option('--build', action='store_true', help='compile code', default=False)\n parser.add_option('--coverity', action='store_true', help='coverity flags', default=False)\n parser.add_option('--development', action='store_true', help='development flags', default=False)\n\n parser.add_option('--coverity_test', action='store_true', help='perform coverity test', default=False)\n\n (options, args) = parser.parse_args()\n if args:\n print('ERROR: extra unparsed command-line arguments:', args)\n sys.exit(1)\n\n if options.build:\n build_path = os.path.join('tools', 'ninja')\n build_script = 'ninja'\n if (GetPlatform() == 'win'):\n build_script += '.exe'\n with cd(build_path):\n if not os.path.isfile(os.path.join(build_script)):\n subprocess.call(['python', 'configure.py', '--bootstrap'])\n subprocess.call([os.path.join(build_path, build_script)])\n exit(0)\n\n if options.coverity_test:\n gcovr_script = os.path.join('.', 'tools', 'gcovr', 'scripts', 'gcovr')\n subprocess.call(['python', gcovr_script, '-r', '.', '--html', '--html-details', '-o', 'coverity_result/'])\n exit(0)\n\n BuildSystem(options)\n libcryptopp = BuildSystem().add_target(BuildCryptoPPLibrary())\n BuildSystem().add_target(BuildCryptoPPTests(libcryptopp))\n BuildSystem().add_target(BuildCryptoPPTestsUnit(libcryptopp))\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":10282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"58006066","text":"# Lambda functions or anonymous functions\n# 1. It is a function without name\n# 2.It should have only one expression\n# 3.It can take multiple arguments\n# def minus(x,y):\n# return x-y\n# print(minus(8,7))\n\n# minus = lambda x,y : x-y #one liner function\n# print(minus(8,7))\n\n# Both are same only.\n\n##################################################################################################\n#Uses of lambda function filter, map and reduce\nnums=[4,5,8,2,7,1,6,9,10]\n# way 1 :\n# def is_even(n):\n# return n%2==0 :\n#\n# iss func ko bss fiter k sath hmlog ko use krna tha ar khi nhi..so y to write such\n# long code jb hame iss reuse nhi krna h to ham lambda func use kr skte h\n\n#way 2:\nis_even = lambda n :n%2==0\n#program to fetch all the even nos from this list:\n#so here we are using an inbuilt method called filter which will take a function (to decide on what basis we need to filter)\n#and a iterable it can be list tuple or anything..(to decide on which item we have to apply the filter)\nevens=list(filter(is_even,nums))\nprint(evens)\n\n#or\n\neven=list(filter(lambda n :n%2==0,nums))\nprint(even)\n########################################################################################\n# 2.map : lets say we got all the even nos and our job is to double the nos\n# in simple words whenever we want to change each value in an iterable(list,tuple..)\n# we use\n# if map:\n#\n# #this is same as big data concept we take a chunk of data and we\n# filter the data , then we map it and try to reduce it.\n\n","sub_path":"lambda funcs.py","file_name":"lambda funcs.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"453876266","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\n\ndef readData(images_file, labels_file):\n x = np.loadtxt(images_file, delimiter=',')\n y = np.loadtxt(labels_file, delimiter=',')\n return x, y\n\n\ndef softmax(x):\n \"\"\"\n Compute softmax function for input. \n Use tricks from previous assignment to avoid overflow\n \"\"\"\n # YOUR CODE HERE\n (m, n) = x.shape\n s = np.zeros(x.shape)\n for i in range(m):\n for j in range(n):\n x_minum = np.exp(x[i] - x[i][j])\n s[i][j] = 1. / x_minum.sum()\n # END YOUR CODE\n return s\n\n\ndef sigmoid(x):\n \"\"\"\n Compute the sigmoid function for the input here.\n \"\"\"\n # YOUR CODE HERE\n s = 1./ (1 + np.exp(-x))\n # END YOUR CODE\n return s\n\n\ndef forward_prop(data, labels, params):\n \"\"\"\n return hidder layer, output(softmax) layer and loss\n \"\"\"\n W1 = params['W1']\n b1 = params['b1']\n W2 = params['W2']\n b2 = params['b2']\n\n # YOUR CODE HERE\n z1 = data.dot(W1) + b1\n h = sigmoid(z1)\n z2 = h.dot(W2) + b2\n y = softmax(z2)\n cost = compute_cost(y, labels)\n # END YOUR CODE\n return h, y, cost\n\n\ndef backward_prop(data, labels, params):\n \"\"\"\n return gradient of parameters\n \"\"\"\n W1 = params['W1']\n b1 = params['b1']\n W2 = params['W2']\n b2 = params['b2']\n\n # YOUR CODE HERE\n lamda = 0.0001\n (x, h, y) = data\n m = x.shape[0]\n gradb2 = np.sum(y - labels, axis = 0) / m\n\n gradW2 = np.dot(h.transpose(), y-labels) / m + 2*lamda*W2\n \n gradb1 = np.dot(y-labels, W2.transpose()) * (h *(1-h))\n gradW1 = np.dot(x.transpose(), gradb1) / m + 2*lamda*W1\n gradb1 = np.sum(gradb1, axis = 0) / m\n \n # END YOUR CODE\n\n grad = {}\n grad['W1'] = gradW1\n grad['W2'] = gradW2\n grad['b1'] = gradb1\n grad['b2'] = gradb2\n\n return grad\n\n\ndef nn_train(trainData, trainLabels, devData, devLabels):\n (m, n) = trainData.shape\n num_hidden = 300\n num_output = 10\n learning_rate = 5\n params = {}\n\n # YOUR CODE HERE\n\n # initialize params\n W1 = np.random.standard_normal((n, num_hidden))\n W2 = np.random.standard_normal((num_hidden, num_output))\n b1 = np.zeros(num_hidden)\n b2 = np.zeros(num_output)\n params['W1'] = W1\n params['b1'] = b1\n params['W2'] = W2\n params['b2'] = b2\n\n loss_train = []\n accuracy_train = []\n loss_val = []\n accuracy_val = []\n num_epochs = 30\n batch_size = 1000\n num_iters = int(m / batch_size)\n for epoch in range(num_epochs):\n loss_epoch_train = 0\n correct_epoch_train = 0\n for iter in range(num_iters):\n input = trainData[iter * batch_size: (iter + 1) * batch_size]\n labels = trainLabels[iter * batch_size: (iter + 1) * batch_size]\n\n h, output, loss = forward_prop(input, labels, params)\n \n grad = backward_prop((input, h, output), labels, params)\n \n loss_epoch_train += loss * batch_size\n correct_epoch_train += (np.argmax(output, axis=1) == np.argmax(\n labels, axis=1)).sum()\n\n # update params\n W1 = params['W1']\n b1 = params['b1']\n W2 = params['W2']\n b2 = params['b2']\n gradW1 = grad['W1']\n gradW2 = grad['W2']\n gradb1 = grad['b1']\n gradb2 = grad['b2']\n \n W1 = W1 - learning_rate * gradW1\n b1 = b1 - learning_rate * gradb1\n W2 = W2 - learning_rate * gradW2\n b2 = b2 - learning_rate * gradb2\n params['W1'] = W1\n params['b1'] = b1\n params['W2'] = W2\n params['b2'] = b2\n\n\n loss_train.append(loss_epoch_train / m)\n accuracy_train.append(correct_epoch_train / m)\n print(\"Epoch %d: training loss: %f, training accuracy: %f\" % (epoch+1, loss_train[epoch], accuracy_train[epoch]))\n\n # test on validation set\n loss_epoch_val, accuracy_epoch_val = nn_test(devData, devLabels, params)\n loss_val.append(loss_epoch_val)\n accuracy_val.append(accuracy_epoch_val)\n print(\"val loss: %f, val accuracy: %f\" % (loss_val[epoch], accuracy_val[epoch]))\n\n pickle.dump(params, open(\"params_reg.p\", \"wb\" ))\n\n # draw\n epochs = range(1, num_epochs + 1)\n plt.figure(111)\n handles = []\n curve1, = plt.plot(epochs, loss_train, label='training loss')\n curve2, = plt.plot(epochs, loss_val, label='validation loss')\n handles.append(curve1)\n handles.append(curve2)\n plt.legend(handles=handles)\n plt.xlabel('number of epochs')\n plt.ylabel('loss')\n plt.title('loss v.s. epochs (regularization)')\n # plt.show()\n plt.savefig(\"p1_3.png\")\n\n plt.figure(112)\n handles = []\n curve1, = plt.plot(epochs, accuracy_train, label='training accuracy')\n curve2, = plt.plot(epochs, accuracy_val, label='validation accuracy')\n handles.append(curve1)\n handles.append(curve2)\n plt.legend(handles=handles)\n plt.xlabel('number of epochs')\n plt.ylabel('accuracy')\n plt.title('accuracy v.s. epochs (regularization)')\n # plt.show()\n plt.savefig('p1_4.png')\n\n # END YOUR CODE\n\n return params\n\n\ndef nn_test(data, labels, params):\n h, output, cost = forward_prop(data, labels, params)\n accuracy = compute_accuracy(output, labels)\n return cost, accuracy\n\n\ndef compute_accuracy(output, labels):\n accuracy = (np.argmax(output, axis=1) == np.argmax(\n labels, axis=1)).sum() * 1. / labels.shape[0]\n return accuracy\n\n\ndef compute_cost(output, labels):\n y_log = np.log(output)\n cost = 0\n for i in range(output.shape[0]):\n cost += -y_log[i].dot(labels[i]).sum()\n return 1. * cost / output.shape[0]\n\n\ndef one_hot_labels(labels):\n one_hot_labels = np.zeros((labels.size, 10))\n one_hot_labels[np.arange(labels.size), labels.astype(int)] = 1\n return one_hot_labels\n\n\ndef main():\n np.random.seed(100)\n trainData, trainLabels = readData('images_train.csv', 'labels_train.csv')\n\n # np.savetxt('images_debug.csv', trainData[:3000], delimiter=',')\n # np.savetxt('labels_debug.csv', trainLabels[:3000], delimiter=',')\n # return\n\n trainLabels = one_hot_labels(trainLabels)\n p = np.random.permutation(60000)\n trainData = trainData[p, :]\n trainLabels = trainLabels[p, :]\n\n devData = trainData[0:10000, :]\n devLabels = trainLabels[0:10000, :]\n trainData = trainData[10000:, :]\n trainLabels = trainLabels[10000:, :]\n\n # trainData, trainLabels = readData('images_debug.csv', 'labels_debug.csv')\n\n # trainLabels = one_hot_labels(trainLabels)\n # p = np.random.permutation(3000)\n # trainData = trainData[p, :]\n # trainLabels = trainLabels[p, :]\n\n # devData = trainData[0:2000, :]\n # devLabels = trainLabels[0:2000, :]\n # trainData = trainData[2000:, :]\n # trainLabels = trainLabels[2000:, :]\n\n mean = np.mean(trainData)\n std = np.std(trainData)\n trainData = (trainData - mean) / std\n devData = (devData - mean) / std\n\n testData, testLabels = readData('images_test.csv', 'labels_test.csv')\n testLabels = one_hot_labels(testLabels)\n testData = (testData - mean) / std\n\n params = nn_train(trainData, trainLabels, devData, devLabels)\n\n # readyForTesting = False\n # if readyForTesting:\n # accuracy = nn_test(testData, testLabels, params)\n # print('Test accuracy: %f' % accuracy)\n\nif __name__ == '__main__':\n main()\n","sub_path":"hw4/nn_starter.py","file_name":"nn_starter.py","file_ext":"py","file_size_in_byte":7436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"173545629","text":"################################################\n# File: hanoi.py\n# Writer: Dor Roter\n# Login: dor.roter\n# Exercise: ------\n# More:\n# Consulted: -----\n# Internet:\n# Notes:\n################################################\nimport sys\n\n\nclass MockHanoi:\n def __init__(self):\n self.counter = 0\n self.moves = list()\n\n def move(self, src, dst):\n self.counter += 1\n self.moves.append((src, dst))\n\n def is_minimum(self, i):\n if i >= 1:\n return self.counter == ((2 ** i) - 1)\n else:\n return self.counter == 0\n\n # returns false if any moves where wrong\n def _run(self, disks):\n towers = dict()\n towers['A'] = [i for i in range(disks, 0, -1)]\n towers['B'] = list()\n towers['C'] = list()\n\n FROM = 0\n TO = 1\n for move in self.moves:\n disk = towers[move[FROM]].pop()\n if len(towers[move[TO]]) > 0 and \\\n disk > towers[move[TO]][-1]:\n # larger disk\n return False\n towers[move[TO]].append(disk)\n\n if len(towers['A']) == 0 and (len(towers['B']) == disks or\n len(towers['C']) == disks):\n return True\n return False\n\n def validate_moves(self, disks):\n if not self.moves or disks <= 0:\n return self.counter == 0\n try:\n return self._run(disks)\n except:\n print(sys.exc_info()[0])\n return False\n","sub_path":"hanoi.py","file_name":"hanoi.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"351464537","text":"#!/usr/bin/env python\n########################## 70 ########################################\nfrom kzpy3.vis3 import *\nimport default_values\nimport rospy\nimport torch\nimport torch.autograd\nimport cv_bridge\nfrom sensor_msgs.msg import Image\nimport kzpy3.VT_net2__1June2019.rectangles as rectangles\n\nexec(identify_file_str)\n\nfull_width,full_height = 168,94\nmeta_width,meta_height = 41,23\n\nbridge = cv_bridge.CvBridge()\n\nnum_rectangle_patterns = 4\nRectangles = rectangles.Random_black_white_rectangle_collection(\n num_rectangle_patterns=num_rectangle_patterns\n)\n\ndef Camera_Shot(data): #######################################\n D = {}\n\n img = bridge.imgmsg_to_cv2(data,'rgb8')\n #mci(img)\n if shape(img)[0] > 94:\n img = cv2.resize(img,(full_width,full_height),interpolation=0)\n cr('img = cv2.resize(img,(full_width,full_height),interpolation=0)')\n \n D['img'] = img\n if not default_values.P['MOCK_ARDUINO_VERSION']:\n D['ts'] = data.header.stamp.secs + \\\n data.header.stamp.nsecs / 10.0**9\n D['seq'] = data.header.seq\n else:\n D['ts'] = time.time()\n\n return D\n\n\n\ndef Quartet(name='no_name'):\n D = {}\n D['name'] = name\n for side in ['left','right']:\n D[side] = {}\n for when in ['now','prev']:\n D[side][when] = {}\n for size_ in ['full','small']:\n D[side][when][size_] = None\n\n D['ready'] = True\n\n def _function_display(\n delay_blank=0,\n delay_prev=0,\n delay_now=0,\n scale=4,\n size_=None):\n\n shape_ = np.shape(D['left']['now'][size_])\n\n width,height = shape_[1],shape_[0]\n img_now = np.zeros(\n (height,2*width+int(width/16),3),np.uint8) + 127\n img_prev = img_now.copy()\n img_blank = img_now.copy()\n img_now[:,:width,:] = D['right']['now'][size_]\n img_now[:,-width:,:] = D['left']['now'][size_]\n img_prev[:,:width,:] = D['right']['prev'][size_]\n img_prev[:,-width:,:] = D['left']['prev'][size_]\n img_blank[:,:width,:] = 0*D['right']['prev'][size_]\n img_blank[:,-width:,:] = 0*D['left']['prev'][size_]\n if delay_blank > 0:\n mci(img_blank,\n scale=scale,\n delay=delay_blank,\n title='Quartet '+D['name'])\n if delay_prev > 0:\n mci(img_prev,\n scale=scale,\n delay=delay_prev,\n title='Quartet '+D['name'])\n if delay_now > 0:\n mci(img_now,\n scale=scale,\n delay=delay_now,\n title='Quartet '+D['name'])\n\n def _function_add_rectangles(rectangles_xys,backup_parameter):\n xys4 = rectangles_xys.reshape(len(rectangles_xys)/4,4)\n #cy(xys4)\n xys4_prev = xys4.copy()\n xys4_prev[:,1] += 0.0375\n #cm(-1)\n I = {\n 'now':{\n 'R':D['right']['now']['full'],\n 'L':D['left']['now']['full'],\n },\n 'prev':{\n 'R':D['right']['prev']['full'],\n 'L':D['left']['prev']['full'],\n },\n }\n #cm(0)\n Xys = {\n 'now': xys4,\n 'prev': xys4_prev,\n }\n for when in ['now','prev']:\n rectangles.paste_rectangles_into_drive_images(\n Xys[when],\n I[when],\n Rectangles,\n backup_parameter,\n )\n #cm(1)\n D['right']['now']['full'] = I['now']['R']\n D['left']['now']['full'] = I['now']['L']\n D['right']['prev']['full'] = I['prev']['R']\n D['left']['prev']['full'] = I['prev']['L']\n #mci(I['now']['L'],title='add_rectangles')\n #mci(D['right']['now']['full'],title='add_rectangles')\n\n\n def _function_to_torch(size_='full'):\n listoftensors = []\n for when in ['prev','now']:\n for side in (['left','right']):\n listoftensors.append(\n torch.from_numpy(D[side][when][size_]))\n camera_data = torch.cat(listoftensors, 2)\n camera_data = camera_data.cuda().float()/255. - 0.5\n camera_data = torch.transpose(camera_data, 0, 2)\n camera_data = torch.transpose(camera_data, 1, 2)\n camera_data = camera_data.unsqueeze(0)\n\n return camera_data\n\n def _function_from_torch(net_cuda,channel=0,offset=0):\n net_data = net_cuda.data.cpu().numpy()\n configs = (\n ('left','prev'),\n ('right','prev'),\n ('left','now'),\n ('right','now'),\n )\n for i in rlen(configs):\n a = 3*i\n b = 3*(i+1)-1\n\n \n\n c = net_data[channel,offset+a:offset+b+1,:,:]\n\n ccm(1,shape(c))\n\n assert shape(c) == (3,full_height,full_width) \\\n or shape(c) == (3,meta_height,meta_width)\n\n c = c.transpose(1,2,0) \n\n assert shape(c) == (full_height,full_width,3) \\\n or shape(c) == (meta_height,meta_width,3)\n\n ccm(2,shape(c))\n\n c = z55(c) # now in rgb\n if shape(c)[0] > 30:\n size_ = 'full'\n else:\n size_ = 'small'\n ccm(size_)\n side = configs[i][0]\n when = configs[i][1]\n D[side][when][size_] = c\n \"\"\"\n if shape(c)[0] > 30:\n D['size_'] = 'full'\n else:\n D['size_'] = 'small'\n ccm(D['size_'])\n side = configs[i][0]\n when = configs[i][1]\n D[side][when][D['size_']] = c\n \"\"\"\n D['display'] = _function_display\n D['to_torch'] = _function_to_torch\n D['from_torch'] = _function_from_torch\n D['add_rectangles'] = _function_add_rectangles\n\n return D\n\n\n\ndef ZED(): #######################################\n D={}\n D['full_shape'] = (full_height,full_width)\n D['small_shape'] = (meta_height,meta_width)\n D['left_list'] = []\n D['right_list'] = []\n D['left_ready'] = False\n D['stats'] = {}\n D['stats']['call'] = 0\n D['stats']['success'] = 0\n D['stats']['fail a'] = 0\n D['stats']['fail b'] = 0\n D['stats']['fail c'] = 0\n\n def _function_limit_list_lengths(max_len,min_len):\n for list_side in ['left_list','right_list']:\n if len(D[list_side]) > max_len:\n D[list_side] = D[list_side][-min_len:]\n\n def _function_build_quartet(label_frames=False):\n\n D['stats']['call'] += 1\n\n if False:#default_values.P['MOCK_ARDUINO_VERSION']:\n\n Q = Quartet(name='from ROS')\n\n Q['left']['now']['full'] = \\\n D['left_list'][-1]['img']\n\n Q['right']['now']['full'] = \\\n D['right_list'][-1]['img']\n\n Q['left']['prev']['full'] = \\\n D['left_list'][-2]['img']\n\n Q['right']['prev']['full'] = \\\n D['right_list'][-2]['img']\n\n D['stats']['success']+=1\n\n return Q\n\n else:\n try:\n\n for i in [-1,-2,-3]:\n\n dt_now = D['left_list'][i]['ts'] \\\n - D['right_list'][-1]['ts']\n if (dt_now > -0.01 and dt_now < 0.02) or default_values.P['MOCK_ARDUINO_VERSION']:\n break\n else:\n D['stats']['fail c']+=1\n return None\n\n dt_left = D['left_list'][i]['ts'] \\\n - D['left_list'][i-1]['ts']\n\n dt_right = D['right_list'][-1]['ts'] \\\n - D['right_list'][-2]['ts']\n \n if (dt_left > 0.025 and dt_left < 0.04) or default_values.P['MOCK_ARDUINO_VERSION']:\n if (dt_right > 0.025 and dt_right < 0.04) or default_values.P['MOCK_ARDUINO_VERSION']:\n\n Q = Quartet(name='from ROS')\n\n\n if label_frames:\n\n Q['left']['now']['full'] = \\\n D['left_list'][i]['img'].copy()\n\n Q['right']['now']['full'] = \\\n D['right_list'][-1]['img'].copy()\n\n Q['left']['prev']['full'] = \\\n D['left_list'][i-1]['img'].copy()\n\n Q['right']['prev']['full'] = \\\n D['right_list'][-2]['img'].copy()\n\n else:\n\n Q['left']['now']['full'] = \\\n D['left_list'][i]['img']\n\n Q['right']['now']['full'] = \\\n D['right_list'][-1]['img']\n\n Q['left']['prev']['full'] = \\\n D['left_list'][i-1]['img']\n\n Q['right']['prev']['full'] = \\\n D['right_list'][-2]['img']\n\n\n if label_frames:\n for side in ['left','right']:\n for when in ['now','prev']:\n color = (0,255,0)\n if when == 'now':\n color = (255,0,0)\n cv2.putText(\n Q[side][when]['full'],\n d2s(side,when),\n (10,20),\n cv2.FONT_HERSHEY_SIMPLEX,\n 1.0,color,2)\n\n\n Q['left']['now']['small'] = \\\n cv2.resize(Q['left']['now']['full'],\n (meta_width,meta_height),interpolation=0)\n Q['right']['now']['small'] = \\\n cv2.resize(Q['right']['now']['full'],\n (meta_width,meta_height),interpolation=0)\n Q['left']['prev']['small'] = \\\n cv2.resize(Q['left']['prev']['full'],\n (meta_width,meta_height),interpolation=0)\n Q['right']['prev']['small'] = \\\n cv2.resize(Q['right']['prev']['full'],\n (meta_width,meta_height),interpolation=0)\n\n D['stats']['success']+=1\n\n return Q\n else:\n D['stats']['fail a']+=1\n return None\n else:\n D['stats']['fail b']+=1\n return None\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n file_name = os.path.split(\n exc_tb.tb_frame.f_code.co_filename)[1]\n CS_('Exception!',emphasis=True)\n CS_(d2s(exc_type,file_name,exc_tb.tb_lineno),\n emphasis=False)\n return None\n\n D['limit_list_lengths'] = _function_limit_list_lengths\n D['build_quartet'] = _function_build_quartet\n\n return D\n\n\nZed = ZED()\ndef left_callback(data):\n Zed['left_list'].append(Camera_Shot(data))\n Zed['limit_list_lengths'](6,4)\n Zed['left_ready'] = True\n\n\ndef right_callback(data):\n Zed['right_list'].append(Camera_Shot(data))\n\n\n\n\n\n\n\n\nrospy.Subscriber(\n \"/bair_car/zed/right/image_rect_color\",\n Image,\n right_callback,\n queue_size = 1)\n\nrospy.Subscriber(\n \"/bair_car/zed/left/image_rect_color\",\n Image,\n left_callback,\n queue_size = 1)\n\n\n\nQUIT = False\ndef maintain_quartet_list(Q_list): ##############################\n\n cb('*** starting maintain_quartet_list(Q_list) thread. ***')\n hz = Timer(60)\n print_timer = Tr(60)\n timer = Timer()\n while True:\n if rospy.is_shutdown():\n break\n if QUIT == True:\n break\n if True:#try:\n\n if len(Zed['left_list']) > 3 and Zed['left_ready']:\n\n Zed['left_ready'] = False\n Q = Zed['build_quartet']()\n #clp('Q == None',Q == None)\n if Q != None:\n Q_list.append(Q)\n\n while len(Q_list) > 3:\n Q_list.pop(0)\n hz.freq(\" (camera.py) \")\n print_timer.message(\n d2s(\" (camera.py)\",\n dp(timer.time()),'seconds',\n dp(100*Zed['stats']['success']/\n (1.0*Zed['stats']['call'])),'%'))\n else:\n time.sleep(1/10000.)\n else:#except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n file_name = os.path.split(\n exc_tb.tb_frame.f_code.co_filename)[1]\n CS_('Exception!',emphasis=True)\n CS_(d2s(exc_type,file_name,exc_tb.tb_lineno),\n emphasis=False)\n\n cg('\\nExiting maintain_quartet_list thread.\\n')\n\n\n\nQ_list = []\nthreading.Thread(target=maintain_quartet_list,args=[Q_list]).start()\n\n\n\"\"\"\n\nif __name__ == '__main__':\n\n rospy.init_node('camera',anonymous=True,disable_signals=True)\n\n metadata = torch.from_numpy(zeros((1,256,23,41)))\n metadata = metadata.cuda().float()\n metadata = torch.autograd.Variable(metadata)\n\n camera_data = torch.from_numpy(zeros((1,12,23,41)))\n camera_data = camera_data.cuda().float()\n camera_data = torch.autograd.Variable(camera_data)\n\n hz = Timer(10)\n wait = Timer()\n wait2 = Timer(10)\n size_ = 'full'\n\n while True:\n\n if rospy.is_shutdown():\n break\n\n if QUIT == True:\n break\n\n if wait.time() > 10:\n if wait2.check():\n cr('wait.time() =',int(wait.time()))\n wait2.reset()\n try:\n if len(Q_list) > 0:\n Q = Q_list[-1]\n if Q['ready']:\n Q['ready'] = False\n #Q['display'](1000,1000,1000,size_,4)\n hz.freq(' (main) ')\n wait.reset()\n camera_data = Q['to_torch'](size_=size_)\n U = Quartet(name='from torch '+size_)\n if size_ == 'small':\n metadata[0,128+1+4:128+1+4+12,:,:] = camera_data\n offset = 128+1+4 \n U['from_torch'](metadata,offset=offset)\n else:\n offset = 0 \n U['from_torch'](camera_data,offset=offset) #\n\n U['display'](delay_now=1,size_=size_,scale=4)\n continue\n time.sleep(1./100000.)\n \n except KeyboardInterrupt:\n QUIT = True\n cr('\\n\\n*** KeyboardInterrupt ***\\n')\n time.sleep(1)\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n file_name = os.path.split(\n exc_tb.tb_frame.f_code.co_filename)[1]\n CS_('Exception!',emphasis=True)\n CS_(d2s(exc_type,file_name,exc_tb.tb_lineno),\n emphasis=False)\n #QUIT = True\n #cr('\\n\\n*** Exception ***\\n')\n #time.sleep(1)\n \n\"\"\"\n \n\n#EOF","sub_path":"Cars/j26June2019__/nodes/network_utils/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":15410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"514156842","text":"# python RNNLM.py train.txt test.txt 1\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nimport numpy as np\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\nfrom collections import Counter, OrderedDict\nimport sys\nfrom datetime import datetime\n\nfile1 = \"/exp/data/SEAME/train.txt\"\nfile2 = \"/exp/data/SEAME/dev.txt\"\nfile3 = \"/exp/data/SEAME/test.txt\"\n\nfile4 = \"data/clwe.txt\" # File containing embedding \n\nindex1 = []\nindex2 = []\nindex3 = []\nmodel_init_path = \"/home/vishwajeet/exp/src/models/clwe-rnn_model-5-freeze.ckpt\"\nmodel_save_path = model_init_path\nmodel_restore_path = model_init_path\n\nclass OrderedCounter(Counter, OrderedDict):\n\t'Counter that remembers the order elements are first seen'\n\tdef __repr__(self):\n\t\treturn '%s(%r)' % (self.__class__.__name__,\n\t\t\tOrderedDict(self))\n\tdef __reduce__(self):\n\t\treturn self.__class__, (OrderedDict(self),)\n\ndef make_combined_data(filename):\n\n\tfile = open(filename,'r')\n\tdata = []\n\tfor line in file:\n\t\tline = line.rstrip()\n\t\tfor word in line.split():\n\t\t\tdata.append(word)\n\t\tdata.append(\"\")\n\tfile.close()\n\treturn np.array(data)\n\ndef make_wordid_map(data, k):\n\t\"\"\"\n\tk is the number of least frequently occuring words in the training \n\tset that will be treated as so as to facilitate good estimates of words\n\t\"\"\"\n\n\tcounter = OrderedCounter(data)\n\tcommon_words = counter.most_common()\n\ttotal_words = sum(counter.values())\n\t\n\titem_to_id = dict()\n\tleast_word_dict = dict(common_words[:-k-1:-1])\n\titem_to_id[\"\"] = len(item_to_id)\n\ti = 1\n\tfor word in counter:\n\t\tif word not in least_word_dict.keys():\n\t\t\titem_to_id[word] = i\n\t\t\ti += 1\n\t\telse:\n\t\t\titem_to_id[word] = 0\n\t\t\t\n\treturn item_to_id\n\ndef encode(data, wordid_map):\n\n\twordid_list = []\n\tfor word in data:\n\t\tif word in wordid_map.keys():\n\t\t\twordid_list.append(wordid_map[word])\n\t\telse:\n\t\t\twordid_list.append(wordid_map[''])\n\treturn np.array(wordid_list)\n\ndef make_batch(index,data, wordid_map, batch_index, batch_size, num_steps):\n\ttemp_index = [i+batch_index*num_steps for i in index]\n\ttemp_index2 = [i+batch_index*num_steps+1 for i in index]\n\ttotal_batch = [i for i in data[temp_index]]\n\ttotal_batch = encode(total_batch, wordid_map)\n\ttotal_batch_2 = [i for i in data[temp_index2]]\n\ttotal_batch_2 = encode(total_batch_2, wordid_map)\n\tbatch_x = []\n\tbatch_y = []\n\tfor i in range(0,batch_size*num_steps,num_steps):\n\t\ttemp = total_batch[i:i+num_steps]\n\t\ttemp2 = total_batch_2[i:i+num_steps]\n\t\tbatch_x.append(temp)\n\t\tbatch_y.append(temp2)\n\treturn (batch_x,batch_y)\n\ndef get_batch(index,data,wordid_map ,batch_index, batch_size, num_steps):\n\n\treturn make_batch(index,data, wordid_map, batch_index, batch_size, num_steps)\n\ndef initialize_index(batch_size,num_steps,length):\n\tt = length//(batch_size*num_steps)\n\tindex = range(batch_size)\n\ttemp = []\n\t[temp.extend(range(i*t*num_steps,i*t*num_steps+num_steps)) for i in index]\n\treturn temp\n\ndef get_embedding(filename, wordid_map):\n\tembedding = np.zeros([word_vocab_size, rnn_size])\n\tembedding_mask = [[True]*rnn_size for _ in range(word_vocab_size)]\n\n\tword_count = 0\n\tfile = open(filename, 'r')\n\tfor index, line in enumerate(file):\n\t\tline = line.rstrip().split(\" \")\n\t\tword = line[0]\n\t\tline = line[1:]\n\n\t\tif word in wordid_map.keys():\n\t\t\ttry:\n\t\t\t\tembedding[wordid_map[word]] = list(map(float, line))\n\t\t\t\tembedding_mask[wordid_map[word]] = [False]*rnn_size\n\t\t\t\tword_count += 1\n\t\t\texcept ValueError:\n\t\t\t\tprint(word, index)\n\n\tprint(\"The number of words found in the wiki corpus: %d\" % word_count)\n\treturn embedding, embedding_mask\n\nbatch_size = 32\nnum_steps = 32\nnum_hidden_units = 512\nrnn_size = 300\nnum_hidden_layers = 2\ngrad_clip = 5\nmomentum = 0.95\ninit_scale = 0.1\nlearning_rate = 0.001\nepoch = 100\nunk_word_k = 1400\nword_vocab_size = 24635 - unk_word_k + 1 # Total distinct words - the least words not being considered plus \ngradient_flow = False\n\ndata = make_combined_data(file1)\ndev_data = make_combined_data(file2)\ntest_data = make_combined_data(file3)\n\nindex1 = initialize_index(batch_size,num_steps,len(data))\nindex2 = initialize_index(batch_size,num_steps,len(dev_data))\nindex3 = initialize_index(batch_size,num_steps,len(test_data))\n\nwordid_map = make_wordid_map(data, unk_word_k)\nembedding_trained, mask = get_embedding(file4, wordid_map)\n\nwith tf.device('/gpu:0'):\n\n\tinput_data = tf.placeholder(tf.int32, shape=[batch_size, num_steps])\n\ttarget = tf.placeholder(tf.int32, shape=[batch_size, num_steps])\n\tkeep_prob = tf.placeholder(tf.float32)\n\tembedding_mask = tf.constant(mask)\n\tembedding_init = tf.constant(embedding_trained, dtype=tf.float32)\n\tembedding = tf.Variable(embedding_init, name=\"embedding\")\n\tinputs = tf.nn.embedding_lookup(embedding, input_data)\n\tdef rnn_cell():\n\t\treturn tf.contrib.rnn.DropoutWrapper(\n\t\t\trnn.BasicLSTMCell(num_hidden_units,reuse=False)\n\t\t\t,output_keep_prob=keep_prob\n\t\t\t,variational_recurrent=True\n\t\t\t,dtype=tf.float32)\n\n\tcells = rnn.MultiRNNCell([rnn_cell() for _ in range(num_hidden_layers)])\n\trnn_initial_state = cells.zero_state(batch_size, dtype=tf.float32)\n\toutputs, final_state = tf.nn.dynamic_rnn(cells,inputs,initial_state=rnn_initial_state,dtype=tf.float32)\n\t\n\toutputs = tf.reshape(tf.concat(outputs,1),[-1,num_hidden_units])\n\tsoftmax_w = tf.get_variable(\"softmax_w\", [num_hidden_units, word_vocab_size])\n\tsoftmax_b = tf.get_variable(\"softmax_b\", [word_vocab_size])\n\n\tlogits = tf.matmul(outputs,softmax_w) + softmax_b\n\tlogits = tf.reshape(logits, [batch_size, num_steps, word_vocab_size])\t\n\n\tloss = tf.contrib.seq2seq.sequence_loss(logits\n\t\t\t\t\t\t\t\t\t\t\t, target\n\t\t\t\t\t\t\t\t\t\t\t, tf.ones([batch_size, num_steps]\n\t\t\t\t\t\t\t\t\t\t\t, dtype=tf.float32)\n\t\t\t\t\t\t\t\t\t\t\t, average_across_timesteps=True\n\t\t\t\t\t\t\t\t\t\t\t, average_across_batch=False)\n\n\tcost = tf.reduce_sum(loss) / num_steps\n\n\ttvars = tf.trainable_variables()\n\tgrads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), grad_clip)\n\n\t# optimizer = tf.train.AdamOptimizer(learning_rate)\n\toptimizer = tf.train.GradientDescentOptimizer(1.0)\n\n\tif not gradient_flow: \n\t\t# Fix some embeddings in the matrix\n\t\tgrad_dash = grads[0] * tf.cast(embedding_mask, dtype=grads[0].dtype)\n\t\tgrads[0] = grad_dash\n\n\ttrain_op = optimizer.apply_gradients(zip(grads, tvars))\n\t\n\tprint (\"Network Created\")\n\tinitializer = tf.random_uniform_initializer(-init_scale, init_scale) \n\tsaver = tf.train.Saver()\n\t\n\n\tprint (\"Training Started\")\n\tinit = tf.global_variables_initializer()\n\n\tconfig=tf.ConfigProto(allow_soft_placement=True)\n\tconfig.gpu_options.allow_growth = True\n\twith tf.Session(config=config) as sess:\n\t\tif not os.path.isfile(model_restore_path+\".meta\"):\n\t\t\tsess.run(init)\n\t\t\tsave_path = saver.save(sess, model_init_path)\n\t\t\tprint(\"Model saved in file: %s\" % save_path)\n\n\t\t\n\t\ttt = 0\n\t\tsaver.restore(sess,model_restore_path)\n\t\twhile tt < epoch :\n\t\t\tprint (\"Epoch %d : \" % tt)\n\t\t\tstep = 0\n\t\t\ttotal_cost = 0.0\n\t\t\tstate = sess.run(rnn_initial_state)\n\t\t\twhile (step+1)*batch_size*num_steps < len(data):\n\t\t\t\tbatch_x, batch_y = get_batch(index1,data, wordid_map ,step, batch_size, num_steps)\n\t\t\t\tstate,train_cost,_ = sess.run([final_state,cost,train_op],\n\t\t\t\t\t\t\t\t\t\t\tfeed_dict = {input_data:batch_x,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttarget:batch_y,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\trnn_initial_state: state,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tkeep_prob :0.4})\n\t\t\t\ttotal_cost += train_cost\n\t\t\t\tstep += 1\n\t\t\tprint (\"Training perplexity %f\" % np.exp(total_cost/step))\n\t\t\tstep = 0\n\t\t\ttotal_cost = 0.0\n\t\t\tstate = sess.run(rnn_initial_state)\n\t\t\twhile (step+1)*batch_size*num_steps < len(dev_data):\n\t\t\t\tbatch_x, batch_y = get_batch(index2,dev_data, wordid_map ,step, batch_size, num_steps)\n\t\t\t\tstate,dev_cost = sess.run([final_state,cost], \n\t\t\t\t\t\t\t\t\t\t\tfeed_dict = {input_data:batch_x,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttarget:batch_y, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\trnn_initial_state: state,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tkeep_prob : 1.0})\n\t\t\t\ttotal_cost += dev_cost\n\t\t\t\tstep += 1\n\n\t\t\ttotal_cost = np.exp(total_cost/step)\n\t\t\tprint(\"Dev Perplexity %f\" % total_cost)\n\t\t\ttt +=1\n\t\t\tsave_path = saver.save(sess, model_save_path)\n\t\t\t# print(\"Checkpoint at \" + str(datetime.now()))\n\n\t\tstep = 0\n\t\ttotal_cost = 0.0\n\t\tstate = sess.run(rnn_initial_state)\n\t\twhile (step+1)*batch_size*num_steps < len(test_data):\n\t\t\tbatch_x, batch_y = get_batch(index3,test_data, wordid_map ,step, batch_size, num_steps)\n\t\t\tstate, test_cost = sess.run([final_state,cost],\n\t\t\t\t\t\t\t\t\t\tfeed_dict = {input_data:batch_x,\n\t\t\t\t\t\t\t\t\t\t\t\t\ttarget:batch_y,\n\t\t\t\t\t\t\t\t\t\t\t\t\trnn_initial_state: state,\n\t\t\t\t\t\t\t\t\t\t\t\t\tkeep_prob :1.0})\n\n\t\t\ttotal_cost += test_cost\n\t\t\tstep += 1\n\t\tprint (\"Testing perplexity %f\" % np.exp(total_cost/step))\n","sub_path":"server_code/clwe-rnnlm.py","file_name":"clwe-rnnlm.py","file_ext":"py","file_size_in_byte":8412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"148725925","text":"def maxScore(cardPoints, k) -> int:\n n = len(cardPoints)\n now = n-k\n end = n\n if(k>=n):\n return sum(cardPoints)\n card = cardPoints*2\n n*=2\n total = 0\n max_point = 0\n while(now <= end):\n if (total == 0):\n total = sum(card[now:now+k])\n else:\n total = total- card[now-1] + card[now+k-1]\n max_point = max(total,max_point)\n now+=1\n return max_point\nprint(maxScore([1,2,3,4,5,6,1],3))\nprint(maxScore([2,2,2],2))\nprint(maxScore( [1,79,80,1,1,1,200,1],3))\nprint(maxScore( [96,90,41,82,39,74,64,50,30],8))\n# 1 2 3 4 5 6 1 1 2 3 4 5 6 1 ","sub_path":"problemset/1423/1423.py","file_name":"1423.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"233861946","text":"import argparse\nimport collections\nimport csv\n\n# For the meaning of these identifiers, read https://openflights.org/data.html\nAirport = collections.namedtuple('Airport', ['id', 'name', 'city', 'country', 'faa_iata', 'icao', 'lat', 'long', 'alt', 'utc_offset', 'dst', 'tz', 'type', 'source'])\nAirline = collections.namedtuple('Airline', ['id', 'name', 'alias', 'iata', 'icao', 'callsign', 'country', 'active'])\nRoute = collections.namedtuple('Route', ['airline', 'airline_id', 'source_airport', 'source_airport_id', 'dest_airport', 'dest_airport_id', 'codeshare', 'stops', 'equipment'])\n\ndef load_data():\n with open('airports.dat') as f:\n airports = {}\n for line in csv.reader(f):\n airport = Airport._make(line)\n airports[airport.id] = airport\n\n with open('airlines.dat') as f:\n airlines = {}\n for line in csv.reader(f):\n airline = Airline._make(line)\n airlines[airline.id] = airline\n\n with open('routes.dat') as f:\n # top-level keyed by source airport ID, next level keyed by destination airport ID\n routes = collections.defaultdict(lambda: collections.defaultdict(list))\n for line in csv.reader(f):\n route = Route._make(line)\n routes[route.source_airport][route.dest_airport].append(route)\n\n return airports, airlines, routes\n\n# def get_adjacent_airports(routes, airport):\n# return itertools.chain(*(.values() for x in d.values()))\n\ndef find_flights(routes, source_airport, destination_airport, max_segments):\n # We implement a basic BFS algorithm for following the routes\n # Taken from http://eddmann.com/posts/depth-first-search-and-breadth-first-search-in-python/\n queue = [(source_airport, [source_airport])]\n while queue:\n airport, path = queue.pop(0)\n if len(path) > max_segments:\n return\n for next_airport in set(routes[airport].keys()) - set(path):\n if next_airport == destination_airport:\n yield path + [next_airport]\n else:\n queue.append((next_airport, path + [next_airport]))\n\ndef build_parser():\n parser = argparse.ArgumentParser(description='Find flights.')\n parser.add_argument('source', help='source airport (e.g. SFO)')\n parser.add_argument('destination', help='destination airport (e.g. JFK)')\n parser.add_argument('segments', type=int, help='maximum number of segments')\n return parser\n\nif __name__ == '__main__':\n import sys\n parser = build_parser()\n args = parser.parse_args(sys.argv[1:])\n\n _airports, _airlines, routes = load_data()\n for flight in find_flights(routes, args.source, args.destination, args.segments):\n print(' -> '.join(flight))\n","sub_path":"solutions/lab-8/flights.py","file_name":"flights.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"133872287","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# from torch.nn import conv, Linear\n# from torch.nn.utils import _pair\n\n# from .utils import *\n\nfrom typing import Tuple\ntry:\n from .more_ops import *\nexcept (ModuleNotFoundError, ImportError):\n from more_ops import *\n\n\n@torch.jit.script\ndef channel_shuffle(x, n_group: int):\n \"\"\"\n 通道扰乱操作\n \"\"\"\n B, C = x.shape[:2]\n shape2 = list(x.shape[2:])\n x = x.reshape([B, n_group, C // n_group] + shape2)\n x = x.transpose(1, 2)\n x = x.reshape([B, C] + shape2)\n return x\n\n\n@torch.jit.script\ndef resize_ref(x, shortpoint, method: str = 'bilinear', align_corners: bool = None):\n \"\"\"\n :type x: torch.Tensor\n :type shortpoint: torch.Tensor\n :type method: str\n :type align_corners: bool\n \"\"\"\n hw = shortpoint.shape[2:4]\n ihw = x.shape[2:4]\n if hw != ihw:\n x = torch.nn.functional.interpolate(x, hw, mode=method, align_corners=align_corners)\n return x\n\n\n@torch.jit.script\ndef add_coord(x: torch.Tensor):\n \"\"\"\n 增加两层坐标层\n \"\"\"\n b, c, h, w = x.shape\n\n y_coord = torch.linspace(-1, 1, h, dtype=x.dtype, device=x.device)\n y_coord = y_coord.reshape(1, 1, -1, 1)\n y_coord = y_coord.repeat(b, 1, 1, w)\n\n x_coord = torch.linspace(-1, 1, w, dtype=x.dtype, device=x.device)\n x_coord = x_coord.reshape(1, 1, 1, -1)\n x_coord = x_coord.repeat(b, 1, h, 1)\n\n o = torch.cat((x, y_coord, x_coord), 1)\n return o\n\n\n@torch.jit.script\ndef pixelwise_norm(x, eps: float = 1e-8):\n \"\"\"\n Pixelwise feature vector normalization.\n :param x: input activations volume\n :param eps: small number for numerical stability\n :return: y => pixel normalized activations\n \"\"\"\n return x * x.pow(2).mean(dim=1, keepdim=True).add(eps).rsqrt()\n\n\n@torch.jit.script\ndef flatten(x):\n y = x.reshape(x.shape[0], -1)\n return y\n\n\n@torch.jit.script\ndef adaptive_instance_normalization(content_feat, style_feat):\n assert (content_feat.size()[:2] == style_feat.size()[:2])\n\n ss = style_feat.shape\n cs = content_feat.shape\n\n style_mean = style_feat.mean((2, 3), keepdim=True)\n style_std = style_feat.reshape(ss[0], ss[1], -1).std(2, unbiased=False).reshape_as(style_mean)\n content_mean = content_feat.mean((2, 3), keepdim=True)\n content_std = content_feat.reshape(cs[0], cs[1], -1).std(2, unbiased=False).reshape_as(content_mean)\n\n normalized_feat = (content_feat - content_mean) / (content_std + 1e-8)\n return normalized_feat * style_std + style_mean\n\n\n# mod from https://github.com/NVlabs/stylegan/blob/master/training/networks_stylegan.py\n@torch.jit.script\ndef minibatch_stddev(x, group_size: int = 4, num_new_features: int = 1, eps: float = 1e-8):\n group_size = group_size if group_size < x.shape[0] else x.shape[0]\n s = x.shape\n y = x.reshape(group_size, -1, num_new_features, s[1] // num_new_features, s[2], s[3])\n y = y - y.mean(dim=0, keepdim=True)\n y = y.pow(2).mean(dim=0)\n y = (y + eps).sqrt()\n y = y.mean(dim=(2, 3, 4), keepdim=True)\n y = y.mean(dim=2)\n y = y.repeat(group_size, 1, s[2], s[3])\n return torch.cat((x, y), dim=1)\n\n\n@torch.jit.script\ndef pixelshuffle(x: torch.Tensor, factor_hw: Tuple[int, int]):\n pH = factor_hw[0]\n pW = factor_hw[1]\n y = x\n B, iC, iH, iW = y.shape\n oC, oH, oW = iC // (pH * pW), iH * pH, iW * pW\n y = y.reshape(B, oC, pH, pW, iH, iW)\n y = y.permute(0, 1, 4, 2, 5, 3) # B, oC, iH, pH, iW, pW\n y = y.reshape(B, oC, oH, oW)\n return y\n\n\n@torch.jit.script\ndef pixelshuffle_invert(x: torch.Tensor, factor_hw: Tuple[int, int]):\n pH = factor_hw[0]\n pW = factor_hw[1]\n y = x\n B, iC, iH, iW = y.shape\n oC, oH, oW = iC * (pH * pW), iH // pH, iW // pW\n y = y.reshape(B, iC, oH, pH, oW, pW)\n y = y.permute(0, 1, 3, 5, 2, 4) # B, iC, pH, pW, oH, oW\n y = y.reshape(B, oC, oH, oW)\n return y\n\n\n@torch.jit.script\ndef one_hot(class_array: torch.Tensor, class_num: int, dim: int = -1, dtype: torch.dtype = torch.int32):\n '''\n 可将[D1, D2, D3, ..., DN] 矩阵转换为 [D1, D2, ..., DN, D(N+1)] 的独热矩阵\n :param class_array: [D1, D2, ..., DN] 类别矩阵\n :param class_num: 类别数量\n :param dim:\n :param dtype:\n :return: y => onehot array\n '''\n a = torch.arange(class_num, dtype=torch.int32, device=class_array.device)\n for _ in range(class_array.ndim):\n a = torch.unsqueeze(a, 0)\n b = (class_array[..., None] == a).to(dtype)\n if dim != -1:\n b = torch.movedim(b, -1, dim)\n return b\n\n\n@torch.jit.script\ndef one_hot_invert(onehot_array, dim: int = -1, dtype: torch.dtype = torch.int32):\n '''\n 上面one_hot的逆操作\n 可将[D1, D2, D3, ..., DN] 的独热矩阵转换为 [D1, D2, ..., D(N-1)] 的类别矩阵\n :param onehot_array: [D1, D2, ..., DN] 独热矩阵\n :param dim:\n :param dtype:\n :return: y => class array\n '''\n class_arr = torch.max(onehot_array, dim)[1]\n class_arr = class_arr.to(dtype)\n return class_arr\n","sub_path":"model_utils_torch/ops.py","file_name":"ops.py","file_ext":"py","file_size_in_byte":5008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"585269222","text":"\n# training and testing settings\nmodel = dict(\n train_cfg=dict(\n assigner=dict(\n type='GridAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0)),\n test_cfg=dict(\n nms_pre=1000,\n min_bbox_size=0,\n score_thr=0.05,\n conf_thr=0.005,\n nms=dict(type='nms', iou_threshold=0.45),\n max_per_img=100))\n","sub_path":"configs/edgeailite/_xbase_/hyper_params/yolov3_config.py","file_name":"yolov3_config.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"126419863","text":"class Solution(object):\n def sortColors(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: void Do not return anything, modify nums in-place instead.\n \"\"\"\n zero = 0\n sec = len(nums) - 1\n i = 0\n while i <= sec:\n while nums[i] == 2 and i < sec:\n nums[i], nums[sec] = nums[sec], nums[i]\n sec -= 1\n while nums[i] == 0 and i > zero:\n nums[i], nums[zero] = nums[zero], nums[i]\n zero += 1\n i += 1","sub_path":"75_SortColors.py","file_name":"75_SortColors.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"492992485","text":"# -*- coding: utf-8 -*-\n\"\"\"bugzilla(\\*bugs, skip_when=None, xfail_when=None, unskip={}, ignore=[]): Marker for bugzilla\n\nIntelligent bugzilla integration py.test plugin. Specifically tuned for cfme_tests.\nYou can specify possibly unlimited amount of bugs. Each bug is then examined by machinery, that\nresolves dupe bugs and clones in such way that it generates all possible instances of the bug\nthat can be then checked. This means you don't have to specify all variants of the bug. You just\nspecify one, it doesn't matter whether the original or any clone or dupe, and it will be expanded.\n\nThese conditions apply:\n\n* If the bug is open, test will be skipped.\n* If POST/MODIFIED and upstream, it checks build date of the appliance vs. date of last change\n of the bug. If the change was sooner than appliance build, the test is not skipped.\n* If POST/MODIFIED and downstream, it is skipped.\n\nAfter these checks, custom checks follow. We have three hooks, ``skip_when``, ``xfail_when`` and\n``unskip``. Each of the hooks is executed per-bug and receives variables via parameters. You specify\nparameters, test machinery injects them. These are available:\n\n* bugs (all bugs for the test item)\n* appliance_version\n* appliance_downstream\n* bug (current bug)\n\nAlso fixtures (funcargs) for the test are injected into the parameters (if present). Sometimes it is\nnot possible to retrieve them, when you face such thing just ping me and I will investigate further.\n\nThe order of function parameters does not matter.\n\nThe ``bug`` objects have the specified version fields (as in cfme_data.yaml) converted to\n:py:class:`utils.version.LooseVersion`. If those fields are specified as \"---\" or \"unspecified\",\nthey return None instead of :py:class:`utils.version.LooseVersion`.\n\nThe ``unskip`` hook is a little bit different. It is a dict of ``bug_id: function``, where if a bug\nis marked to be skipped by any of the machinery that marks it as skipped, it will look in the dict\nand if it finds a bug id specified there, it then calls the function associated with the ID. If\nthe function retuns True, the test will be unmarked as skipped.\n\nxfailing has precedence over skipping.\n\nExample:\n\n.. code-block:: python\n\n @pytest.mark.parametrize(\"something_parametrized\", [1,2,3])\n @pytest.mark.bugzilla(\n 1234, 2345, 3456,\n xfail_when=lambda bug, appliance_version: bug.fixed_in > appliance_version,\n unskip={\n # Something easy\n 1234: lambda bug: bug.something == \"foo\",\n # This works too. Will be never skipped on this bug's conditions.\n 2345: True,\n # Do not skip if fixture `something_parametrized` is not 1\n 3456: lambda something_parametrized: something_parametrized != 1\n })\n def test_something(bugs, something_parametrized):\n pass\n\n @pytest.mark.bugzilla # Needed so far, it stores bugzilla instance into the test for using it\n def test_something2(bug):\n if bug(123).status in {\"ON_QA\", \"ON_DEV\", \"ASSIGNED\"}:\n do_some_workaround()\n do_tests()\n\nMaintainer and responsible person: mfalesni\n\"\"\"\nimport pytest\nimport xmlrpclib\nfrom random import choice\nfrom urlparse import urlparse\n\nfrom fixtures.terminalreporter import reporter\nfrom utils import kwargify as _kwargify\nfrom utils.bz import Bugzilla\nfrom utils.conf import cfme_data\nfrom utils.log import logger\nfrom utils.version import appliance_is_downstream, current_version\n\n_bugs_cache = {}\n\n\ndef kwargify(f):\n \"\"\"Convert function having only positional args to a function taking dictionary.\n\n If you pass False or None, a function which always returns False is returned.\n If you pass True, a function which always returns True is returned.\n \"\"\"\n if f is None or f is False:\n f = lambda: False\n elif f is True:\n f = lambda: True\n\n return _kwargify(f)\n\n\ndef pytest_configure(config):\n config.addinivalue_line(\"markers\", __doc__.splitlines()[0])\n\n\n@pytest.mark.trylast\ndef pytest_collection_modifyitems(session, config, items):\n if not config.getvalue(\"bugzilla\"):\n return\n\n terminalreporter = reporter(config)\n terminalreporter.write(\"\\nChecking bugs in Bugzilla...\\n\")\n bz = Bugzilla.from_config()\n progress = (\"-\", \"\\\\\", \"|\", \"/\") # Very simple eye-candy to not pollute tests output\n progressbar = 0\n last_line_length = 0\n\n try:\n for item in filter(lambda item: item.get_marker(\"bugzilla\") is not None, items):\n marker = item.get_marker(\"bugzilla\")\n terminalreporter.write(\"\\r{}\".format(last_line_length * \" \"))\n terminalreporter.write(\"\\r{}: {}\".format(progress[progressbar], item.name))\n progressbar = (progressbar + 1) % len(progress)\n last_line_length = 3 + len(item.name)\n item._ignore_bugs = set(map(int, marker.kwargs.get(\"ignore\", set([]))))\n item._bugzilla_bugs = set(\n filter(lambda b: b is not None, map(\n lambda bug_id: bz.resolve_blocker(\n bug_id, ignore_bugs=item._ignore_bugs), marker.args)))\n item._skip_func = kwargify(marker.kwargs.get(\"skip_when\", None))\n item._xfail_func = kwargify(marker.kwargs.get(\"xfail_when\", None))\n item._unskip_dict = {}\n for bug_id, function in marker.kwargs.get(\"unskip\", {}).iteritems():\n item._unskip_dict[bug_id] = kwargify(function)\n terminalreporter.write(\"\\n\")\n terminalreporter.write(\"\\r{} bugs retrieved\\n\".format(bz.bug_count))\n terminalreporter.write(\"All bugs summary:\\n\")\n for bug in bz.bugs:\n terminalreporter.write(\"#{}:{} - {}\\n\".format(bug.id, bug.status, bug.summary))\n except xmlrpclib.Fault as exception:\n # It can happen that the user account does not have required rights.\n if exception.faultCode == 102:\n terminalreporter.write(\"\\n\\n======= !!!BAILING OUT. NOT ENOUGH RIGHTS!!! =======\\n\")\n # remove any possible bugzilla markings in the test items so that does not get tested\n for item in filter(lambda item: item.get_marker(\"bugzilla\") is not None, items):\n if hasattr(item, \"_bugzilla_bugs\"):\n delattr(item, \"_bugzilla_bugs\")\n terminalreporter.write(\"======= !!!BUGZILLA INTEGRATION DISABLED!!! =======\\n\")\n\n\n@pytest.mark.tryfirst\ndef pytest_runtest_setup(item):\n if not hasattr(item, \"_bugzilla_bugs\"):\n return\n\n if not item._bugzilla_bugs:\n return\n\n skippers = set([])\n xfailers = set([])\n\n for bug, forceskip in item._bugzilla_bugs:\n if forceskip or bug.is_opened:\n skippers.add(bug)\n if bug.upstream_bug:\n if not appliance_is_downstream() and bug.can_test_on_upstream:\n skippers.remove(bug)\n\n # Custom skip/xfail handler\n global_env = dict(\n bugs=map(lambda b: b[0], item._bugzilla_bugs),\n appliance_version=current_version(),\n appliance_downstream=appliance_is_downstream(),\n )\n # We will now extend the env with fixtures, so they can be used in the guard functions\n # We will however add only those that are not in the global_env otherwise we could overwrite\n # our own stuff.\n if hasattr(item, \"callspec\"):\n params = item.callspec.params\n else:\n # Some of the test items do not have this, so fall back\n # This can cause some problems if the fixtures are used in the guards in this case, but\n # that will tell use where is the problem and we can then find it out properly.\n params = {}\n for funcarg, value in params.iteritems():\n if funcarg not in global_env:\n global_env[funcarg] = value\n for bug, _ in item._bugzilla_bugs:\n local_env = {\"bug\": bug}\n local_env.update(global_env)\n if item._skip_func(**local_env):\n skippers.add(bug.id)\n if item._xfail_func(**local_env):\n xfailers.add(bug.id)\n\n # Separate loop for unskipping\n discards = []\n for root_bug, _ in item._bugzilla_bugs:\n # Check skippers\n resolved_bug = root_bug.bugzilla.resolve_blocker(\n root_bug.id, ignore_bugs=item._ignore_bugs)[0]\n if resolved_bug not in skippers:\n continue\n bug_id = resolved_bug.id\n # If we can't find the bug id, refer to the original ID (remember, the bug is expanded)\n if bug_id not in item._unskip_dict:\n bug_id = root_bug.id\n if bug_id not in item._unskip_dict:\n continue\n local_env = {\"bug\": resolved_bug}\n local_env.update(global_env)\n if item._unskip_dict[bug_id](**local_env):\n discards.append(resolved_bug)\n for bug in discards:\n skippers.discard(bug)\n\n # We now have to resolve what to do with this test item\n # xfailing takes precedence over skipping (xfail is via custom function)\n if xfailers:\n message = \"Marking as xfail due to these bugs: {}\".format(\", \".join(map(str, xfailers)))\n logger.info(message)\n item.add_marker(pytest.mark.xfail(reason=message))\n elif skippers:\n bz_url = urlparse(choice(list(skippers)).bugzilla.bugzilla.url)\n message = \"Skipping due to these bugs:\\n{}\".format(\n \"\\n\".join([\n \"{}: {} ({}://{}/show_bug.cgi?id={})\".format(\n bug.status, bug.summary, bz_url.scheme, bz_url.netloc, bug.id)\n for bug\n in set(skippers)\n ])\n )\n logger.info(message)\n pytest.skip(message)\n else:\n logger.info(\"No action required by Bugzilla for {}. All good!\".format(item.nodeid))\n\n\ndef pytest_addoption(parser):\n group = parser.getgroup('Bugzilla integration')\n group.addoption('--bugzilla',\n action='store_true',\n default=cfme_data.get(\"bugzilla\", {}).get(\"enabled\", False),\n dest='bugzilla',\n help='Enable Bugzilla support.')\n\n\nclass BugMock(object):\n \"\"\"Class used when Bugzilla integration is off or the fixtures are used on unmarked tests.\"\"\"\n def __getattr__(self, attr):\n return False\n\n def __cmp__(self, other):\n return False\n\n def __eq__(self, other):\n return False\n\n\n@pytest.fixture(scope=\"function\")\ndef bug(request):\n \"\"\"Fixture, that when called provides specific bug. No machinery that changes the ID is involved\n\n Usage:\n\n @pytest.mark.bugzilla(1234)\n # or just @pytest.mark.bugzilla if you want no generic skipping and so\n def test_something(bug):\n if bug(345).status is \"blabla\":\n foo()\n bar()\n baz()\n\n It works only on ``bugzilla``-marked tests so far. After I find some neat 'global' store in\n py.test, I will modify it to be usable everywhere.\n\n If bugzilla integration is disabled, it returns BugMock instance which answers False on each\n comparison, equality or attribute.\n \"\"\"\n try:\n return lambda bug: Bugzilla.from_config().resolve_blocker(bug)[0]\n except AttributeError:\n return lambda *args, **kwargs: BugMock()\n","sub_path":"markers/bz.py","file_name":"bz.py","file_ext":"py","file_size_in_byte":11179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"236888022","text":"from decimal import Decimal\n\nfrom recipemd.data import RecipeParser, Recipe, Ingredient, Amount\n\n\ndef extract(url,soup):\n\tif not 'chefkoch.de' in url:\n\t\treturn\n\n\t# title\n\ttitle = soup.find('h1', attrs={'class': 'page-title'}).text\n\tif title == 'Fehler: Seite nicht gefunden' or title == 'Fehler: Rezept nicht gefunden':\n\t\traise ValueError('No recipe found, check URL')\n\t# summary\n\tsummaryTag = soup.find('div', attrs={'class': 'summary'})\n\tsummary = summaryTag.text if summaryTag else None\n\t# servings\n\tservings= soup.find('input', attrs={'id':'divisor'}).attrs['value']\n\tyields=[Amount(Decimal(servings), f'Portion{\"en\" if int(servings) > 1 else \"\"}')]\n\n\t# tags\n\ttags=[]\n\ttagcloud=soup.find('ul', attrs={'class':'tagcloud'})\n\tfor tag in tagcloud.find_all('a'):\n\t\ttags.append(tag.text)\n\t# ingredients\n\ttable = soup.find('table', attrs={'class': 'incredients'})\n\trows = table.find_all('tr')\n\n\tingreds=[]\n\tfor row in rows:\n\t\tcols = row.find_all('td')\n\t\tcols = [s.text.strip() for s in cols]\n\t\tamount = RecipeParser.parse_amount(cols[0])\n\t\tingreds.append(Ingredient(name=cols[1],amount=amount))\n\t# instructions\n\tinstruct = soup.find('div', attrs={'id': 'rezept-zubereitung'}).text # only get text\n\tinstruct = instruct.strip() # remove leadin and ending whitespace\n\t# write to file\n\treturn Recipe(title=title, ingredients=ingreds, instructions=instruct, description=summary, tags=tags, yields=yields)\n","sub_path":"recipemd_extract/plugins/chefkoch.py","file_name":"chefkoch.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"486817084","text":"#!/usr/bin/env python3\n\"\"\"\nProvides a `bind` function to plugins so they can simply bind a function to a queue.\n\"\"\"\n\nimport logging\nfrom json import loads, dumps, decoder\nfrom os import environ\nfrom os.path import join\nimport urllib\nimport time\n\nimport pika\nfrom minio import Minio\nfrom minio.error import AccessDenied\n\n\nclass ProcessingError(Exception):\n \"\"\"Exception raised for errors during processing.\n\n Attributes:\n message -- explanation of the error\n \"\"\"\n\n def __init__(self, message: str, job_id: str=None, task_id: str=None, extra: dict=None):\n super(ProcessingError, self).__init__(message)\n self.message = message\n self.extra = extra or {}\n self.meta = {\"job_id\": job_id, \"task_id\": task_id}\n\n\ndef bind(function: callable, name: str, version=\"1.0.0\"):\n \"\"\"binds a function to the input message queue\"\"\"\n\n logger = logging.getLogger(\"nanowire-plugin\")\n\n parameters = pika.ConnectionParameters(\n host=environ[\"AMQP_HOST\"],\n port=int(environ[\"AMQP_PORT\"]),\n credentials=pika.PlainCredentials(environ[\"AMQP_USER\"], environ[\"AMQP_PASS\"]),\n heartbeat_interval=600)\n\n connection = pika.BlockingConnection(parameters)\n input_channel = connection.channel()\n output_channel = connection.channel()\n\n minio_client = Minio(\n environ[\"MINIO_HOST\"] + \":\" + environ[\"MINIO_PORT\"],\n access_key=environ[\"MINIO_ACCESS\"],\n secret_key=environ[\"MINIO_SECRET\"],\n secure=True if environ[\"MINIO_SCHEME\"] == \"https\" else False)\n minio_client.set_app_info(name, version)\n\n minio_client.list_buckets()\n\n monitor_url = environ[\"MONITOR_URL\"]\n\n logger.info(\"initialised nanowire lib\", extra={\n \"monitor_url\": monitor_url,\n \"minio\": environ[\"MINIO_HOST\"],\n \"rabbit\": environ[\"AMQP_HOST\"]\n })\n\n def send(chan, method, properties, body: str):\n \"\"\"unwraps a message and calls the user function\"\"\"\n\n logger.info(\"consumed message\", extra={\n \"chan\": chan,\n \"method\": method,\n \"properties\": properties})\n\n raw = body.decode(\"utf-8\")\n payload = loads(raw)\n validate_payload(payload, name)\n set_status(monitor_url,\n payload[\"nmo\"][\"job\"][\"job_id\"],\n payload[\"nmo\"][\"task\"][\"task_id\"],\n name + \".consumed\", error)\n\n next_plugin = get_next_plugin(name, payload[\"nmo\"][\"job\"][\"workflow\"])\n if next_plugin is None:\n logger.info(\"this is the final plugin\", extra={\n \"job_id\": payload[\"nmo\"][\"job\"][\"job_id\"],\n \"task_id\": payload[\"nmo\"][\"task\"][\"task_id\"]})\n\n path = join(\n payload[\"nmo\"][\"task\"][\"task_id\"],\n \"input\",\n \"source\",\n payload[\"nmo\"][\"source\"][\"name\"])\n\n if not minio_client.bucket_exists(payload[\"nmo\"][\"job\"][\"job_id\"]):\n raise ProcessingError(\n \"job_id does not have a bucket\",\n job_id=payload[\"nmo\"][\"job\"][\"job_id\"],\n task_id=payload[\"nmo\"][\"task\"][\"task_id\"])\n\n url = minio_client.presigned_get_object(payload[\"nmo\"][\"job\"][\"job_id\"], path)\n\n # calls the user function to mutate the JSON-LD data\n\n result = function(payload[\"nmo\"], payload[\"jsonld\"], url)\n\n # if there are issues, just use the input and carry on the pipeline\n\n if result is None:\n logger.error(\"return value is None\")\n result = payload[\"jsonld\"]\n\n if not isinstance(result, dict):\n logger.error(\"return value must be of type dict, not %s\", type(result))\n result = payload[\"jsonld\"]\n\n if \"jsonld\" in result:\n result = result[\"jsonld\"]\n else:\n result = result\n\n payload[\"jsonld\"] = result\n\n logger.info(\"finished running user code\", extra={\n \"job_id\": payload[\"nmo\"][\"job\"][\"job_id\"],\n \"task_id\": payload[\"nmo\"][\"task\"][\"task_id\"]})\n\n input_channel.basic_ack(method.delivery_tag)\n\n if next_plugin:\n output_channel.queue_declare(\n next_plugin,\n False,\n True,\n False,\n False,\n )\n output_channel.basic_publish(\n \"\",\n next_plugin,\n dumps(payload)\n )\n\n return {\n \"job_id\": payload[\"nmo\"][\"job\"][\"job_id\"],\n \"task_id\": payload[\"nmo\"][\"task\"][\"task_id\"]\n }\n\n logger.info(\"consuming from\", extra={\"queue\": name})\n\n try:\n while True:\n queue_state = input_channel.queue_declare(name, False, True, False, False)\n if queue_state.method.message_count == 0:\n time.sleep(3)\n continue\n\n method_frame, header_frame, body = input_channel.basic_get(name)\n if (method_frame, header_frame, body) == (None, None, None):\n time.sleep(3)\n continue # queue empty\n\n if body is None:\n logger.error(\"body received was empty\")\n time.sleep(3)\n continue # body empty\n\n meta = {\"job_id\": None, \"task_id\": None}\n error = \"\"\n\n try:\n meta = send(input_channel, method_frame, header_frame, body)\n\n except ProcessingError as exp:\n input_channel.basic_reject(method_frame.delivery_tag, False)\n logger.exception(\"Processing Error: \" + exp.message,\n extra={**exp.meta, **exp.extra})\n\n error = exp.message\n meta = exp.meta\n\n except Exception as exp:\n input_channel.basic_reject(method_frame.delivery_tag, False)\n logger.exception(exp)\n\n finally:\n if meta[\"job_id\"] is not None and meta[\"task_id\"] is not None:\n set_status(monitor_url, meta[\"job_id\"], meta[\"task_id\"], name + \".done\", error)\n\n except pika.exceptions.RecursionError as exp:\n connection.close()\n raise exp\n\n\ndef validate_payload(payload: dict, name: str) -> bool:\n \"\"\"ensures payload includes the required metadata and this plugin is in there\"\"\"\n\n if \"nmo\" not in payload:\n raise ProcessingError(\"no job in nmo\")\n\n if \"job\" not in payload[\"nmo\"]:\n raise ProcessingError(\"no job in nmo\")\n\n if \"task\" not in payload[\"nmo\"]:\n raise ProcessingError(\"no task in nmo\")\n\n if not ensure_this_plugin(name, payload[\"nmo\"][\"job\"][\"workflow\"]):\n raise ProcessingError(\n \"declared plugin name does not match workflow\",\n job_id=payload[\"nmo\"][\"job\"][\"job_id\"],\n task_id=payload[\"nmo\"][\"task\"][\"task_id\"])\n\n\ndef ensure_this_plugin(this_plugin: str, workflow: list)->bool:\n \"\"\"ensures the current plugin is present in the workflow\"\"\"\n for workpipe in workflow:\n if workpipe[\"config\"][\"name\"] == this_plugin:\n return True\n return False\n\n\ndef get_next_plugin(this_plugin: str, workflow: list) -> str:\n \"\"\"returns the next plugin in the sequence\"\"\"\n found = False\n for workpipe in workflow:\n if not found:\n if workpipe[\"config\"][\"name\"] == this_plugin:\n found = True\n else:\n return workpipe[\"config\"][\"name\"]\n\n return None\n\n\ndef set_status(monitor_url: str, job_id: str, task_id: str, name: str, error: str):\n \"\"\"sends a POST request to the monitor to notify it of task position\"\"\"\n req = urllib.request.Request(\n urllib.parse.urljoin(\n monitor_url,\n \"/v3/task/status/%s/%s\" % (job_id, task_id)),\n data=dumps({\n \"t\": int(time.time()),\n \"id\": task_id,\n \"p\": name,\n \"e\": error\n }).encode(),\n headers={\n \"Content-Type\": \"application/json\"\n })\n urllib.request.urlopen(req)\n","sub_path":"nanowire_plugin/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"378901586","text":"# Definition for a binary tree node.\nclass TreeNode(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\nclass BST(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n # LC700 https://leetcode.com/problems/search-in-a-binary-search-tree/\n def searchBST(self, root, val):\n if root is None:\n return None \n if val == root.val: \n return root \n elif val < root.val:\n return self.searchBST(root.left, val)\n else:\n return self.searchBST(root.right, val)\n\n # LC701 https://leetcode.com/problems/insert-into-a-binary-search-tree/\n def insertIntoBST(self, root, val):\n if root is None:\n return TreeNode(val)\n # if val == root.val:\n # BST 中一般不会插入已存在元素\n elif val < root.val:\n root.left = self.insertIntoBST(root.left, val)\n else:\n root.right = self.insertIntoBST(root.right, val)\n return root\n \n # LC450 https://leetcode.com/problems/delete-node-in-a-bst/\n # 情况 1：A 恰好是末端节点，两个子节点都为空，那么它可以当场去世了。\n # 情况 2：A 只有一个非空子节点，那么它要让这个孩子接替自己的位置。\n # 情况 3：A 有两个子节点，麻烦了，为了不破坏 BST 的性质，A 必须找到左子树中最大的那个节点，或者右子树中最小的那个节点来接替自己。\n def deleteNode(self, root, key):\n if root is None:\n return None \n if root.val == key:\n if root.left is None and root.right is None:\n return None \n elif root.left is None:\n return root.right\n elif root.right is None:\n return root.left\n else: # root.left is not None and root.right is not None:\n # First find the right most leaf of the left sub-tree\n rightmost_LeftSubTree = root.left \n while rightmost_LeftSubTree.right:\n rightmost_LeftSubTree = rightmost_LeftSubTree.right\n rightmost_LeftSubTree.right = root.right\n return root.left\n elif key < root.val:\n root.left = self.deleteNode(root.left, key)\n else:\n root.right = self.deleteNode(root.right, key)\n return root\n\n # LC98 https://leetcode.com/problems/validate-binary-search-tree\n def isValidBST(self, root):\n def helper(root, floor, ceil): \n if root is None:\n return True\n if root.val <= floor or root.val >= ceil:\n return False\n if root.left and root.left.val >= root.val:\n return False \n if root.right and root.right.val <= root.val:\n return False\n else:\n return helper(root.left, floor, min(ceil, root.val)) and helper(root.right, max(floor, root.val), ceil)\n\n return helper(root, -2**31-1, 2**31+1)\n","sub_path":"Framework/BST.py","file_name":"BST.py","file_ext":"py","file_size_in_byte":3170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"367091899","text":"from unittest import TestCase\nfrom io import BytesIO\nimport copy\n\nfrom vertx import Request\n\n\nclass RequestTestCase(TestCase):\n\n def test_string_representation(self):\n env = mock_env()\n env['REQUEST_METHOD'] = 'POST'\n env['PATH_INFO'] = '/dashboard/products'\n request = Request(env)\n self.assertEqual(str(request), '')\n self.assertEqual(repr(request), '')\n\n def test_request_method(self):\n env = mock_env()\n request = Request(env)\n env['REQUEST_METHOD'] = 'POST'\n request = Request(env)\n self.assertEqual(request.method, 'POST')\n\n def test_url(self):\n env = mock_env()\n env['wsgi.url_scheme'] = 'https'\n env['HTTP_HOST'] = 'myserver.com:8080'\n env['PATH_INFO'] = '/dashboard/products'\n env['QUERY_STRING'] = 'page=1&order=price'\n request = Request(env)\n self.assertEqual(request.url, 'https://myserver.com:8080/dashboard/products?page=1&order=price')\n\n def test_base_url(self):\n env = mock_env()\n env['wsgi.url_scheme'] = 'https'\n env['HTTP_HOST'] = 'myserver.com:8080'\n env['PATH_INFO'] = '/dashboard/products'\n env['QUERY_STRING'] = 'page=1&order=price'\n request = Request(env)\n self.assertEqual(request.base_url, 'https://myserver.com:8080')\n\n def test_scheme(self):\n env = mock_env()\n env['wsgi.url_scheme'] = 'https'\n request = Request(env)\n self.assertEqual(request.scheme, 'https')\n\n def test_host(self):\n env = mock_env()\n env['HTTP_HOST'] = 'myserver.com:8080'\n request = Request(env)\n self.assertEqual(request.host, 'myserver.com:8080')\n\n def test_path(self):\n env = mock_env()\n env['PATH_INFO'] = '/dashboard/products'\n request = Request(env)\n self.assertEqual(request.path, '/dashboard/products')\n\n def test_query_string(self):\n env = mock_env()\n env['QUERY_STRING'] = 'page=1&order=price'\n request = Request(env)\n self.assertEqual(request.query_string, 'page=1&order=price')\n\n def test_body(self):\n env = mock_env()\n env['wsgi.input'].write(b'Hello World')\n env['wsgi.input'].seek(0)\n request = Request(env)\n self.assertEqual(request.body, b'Hello World')\n\n def test_query(self):\n env = mock_env()\n env['QUERY_STRING'] = 'page=1&order=price'\n request = Request(env)\n self.assertEqual(request.query, {'page': '1', 'order': 'price'})\n\n def test_headers(self):\n env = mock_env()\n env['HTTP_AUTH'] = 'token'\n env['HTTP_X_FORWARDED_FOR'] = '203.0.113.195, 70.41.3.18, 150.172.238.178'\n request = Request(env)\n self.assertEqual(request.headers, {\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n 'accept-encoding': 'gzip, deflate, sdch',\n 'accept-language': 'pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4',\n 'auth': 'token',\n 'connection': 'keep-alive',\n 'host': 'localhost:8000',\n 'upgrade-insecure-requests': '1',\n 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36',\n 'x-forwarded-for': '203.0.113.195, 70.41.3.18, 150.172.238.178',\n })\n\n def test_header_keys_are_case_insensitive(self):\n env = mock_env()\n env['HTTP_AUTH'] = 'token'\n request = Request(env)\n self.assertEqual(request.headers['auTH'], 'token')\n\n def test_cookies(self):\n env = mock_env()\n env['HTTP_COOKIE'] = 'foo=bar; bar=biz'\n request = Request(env)\n self.assertEqual(request.cookies, {'foo': 'bar', 'bar': 'biz'})\n\n def test_empty_cookies(self):\n env = mock_env()\n request = Request(env)\n self.assertEqual(request.cookies, {})\n\n def test_cookie_with_special_characters(self):\n env = mock_env()\n env['HTTP_COOKIE'] = 'token=\"abc/\\\\073\\\\054~\\\\341\\\\347[\\'!\\\\\"\\\\\"]\"'\n request = Request(env)\n self.assertEqual(request.cookies, {'token': 'abc/;,~áç[\\'!\"\"]'})\n\n def test_ip(self):\n env = mock_env()\n env['REMOTE_ADDR'] = '127.0.0.1'\n request = Request(env)\n self.assertEqual(request.ip, '127.0.0.1')\n\n def test_ip_with_x_forwarded_for_header(self):\n env = mock_env()\n env['HTTP_X_FORWARDED_FOR'] = '203.0.113.195, 70.41.3.18, 150.172.238.178'\n request = Request(env)\n self.assertEqual(request.ip, '203.0.113.195')\n\n def test_referer(self):\n env = mock_env()\n env['HTTP_REFERER'] = 'http://localhost:8000/app/hello'\n request = Request(env)\n self.assertEqual(request.referer, 'http://localhost:8000/app/hello')\n\n def test_empty_referer(self):\n env = mock_env()\n request = Request(env)\n self.assertIsNone(request.referer)\n\n def test_user_agent(self):\n env = mock_env()\n request = Request(env)\n self.assertEqual(request.user_agent, 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36')\n\n def test_empty_user_agent(self):\n env = mock_env()\n env.pop('HTTP_USER_AGENT')\n request = Request(env)\n self.assertEqual(request.user_agent, None)\n\n\n\ndef mock_env():\n return copy.deepcopy(sample_env)\n\n\nsample_env = {\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n 'HTTP_ACCEPT_ENCODING': 'gzip, deflate, sdch',\n 'HTTP_ACCEPT_LANGUAGE': 'pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4',\n 'HTTP_CONNECTION': 'keep-alive',\n 'HTTP_HOST': 'localhost:8000',\n 'HTTP_UPGRADE_INSECURE_REQUESTS': '1',\n 'HTTP_USER_AGENT': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36',\n 'PATH_INFO': '',\n 'QUERY_STRING': '',\n 'RAW_URI': '',\n 'REMOTE_ADDR': '127.0.0.1',\n 'REMOTE_PORT': '54130',\n 'REQUEST_METHOD': 'GET',\n 'SCRIPT_NAME': '',\n 'SERVER_NAME': '127.0.0.1',\n 'SERVER_PORT': '8000',\n 'SERVER_PROTOCOL': 'HTTP/1.1',\n 'SERVER_SOFTWARE': 'gunicorn/19.6.0',\n 'wsgi.errors': BytesIO(),\n 'wsgi.file_wrapper': BytesIO(),\n 'wsgi.input': BytesIO(),\n 'wsgi.multiprocess': False,\n 'wsgi.multithread': False,\n 'wsgi.run_once': False,\n 'wsgi.url_scheme': 'http',\n 'wsgi.version': (1, 0),\n}\n","sub_path":"tests/test_request.py","file_name":"test_request.py","file_ext":"py","file_size_in_byte":6555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"542265710","text":"import time\nimport ptah\nimport transaction\nfrom ptah import config\nfrom datetime import datetime\n\nfrom base import Base\n\n\nclass TestContent(Base):\n\n def tearDown(self):\n config.cleanup_system(self.__class__.__module__)\n super(TestContent, self).tearDown()\n\n def _make_app(self):\n global ApplicationRoot\n class ApplicationRoot(ptah.cms.ApplicationRoot):\n __type__ = ptah.cms.Type('app')\n\n ApplicationRoot.__type__.cls = ApplicationRoot\n\n return ApplicationRoot\n\n def test_content_path(self):\n import ptah.cms\n self._setRequest(self._makeRequest())\n\n class MyContent(ptah.cms.Content):\n\n __mapper_args__ = {'polymorphic_identity': 'mycontent'}\n __uri_factory__ = ptah.UriFactory('mycontent')\n\n ApplicationRoot = self._make_app()\n\n factory = ptah.cms.ApplicationFactory(\n ApplicationRoot, '/app1', 'root', 'Root App')\n\n root = factory(self.request)\n\n content = MyContent(__name__='test',\n __parent__ = root,\n __path__ = '%stest/'%root.__path__)\n c_uri = content.__uri__\n ptah.cms.Session.add(content)\n\n self.assertTrue(\n content.__name__ == 'test')\n\n self.assertTrue(\n content.__resource_url__(self.request, {}) == '/app1/test/')\n transaction.commit()\n\n # same content inside same root but in different app factory\n\n factory2 = ptah.cms.ApplicationFactory(\n ApplicationRoot, '/app2', 'root', 'Root App')\n root = factory2(self.request)\n\n c = ptah.cms.Session.query(MyContent).filter(\n MyContent.__uri__ == c_uri).one()\n\n self.assertTrue(\n c.__resource_url__(self.request, {}) == '/app2/test/')\n\n def test_content_events(self):\n import ptah.cms\n\n class MyContent(ptah.cms.Content):\n __mapper_args__ = {'polymorphic_identity': 'mycontent'}\n __uri_factory__ = ptah.UriFactory('mycontent')\n\n content = MyContent()\n\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n self.assertTrue(isinstance(content.created, datetime))\n self.assertTrue(isinstance(content.modified, datetime))\n time.sleep(0.1)\n\n config.notify(ptah.cms.ContentModifiedEvent(content))\n self.assertTrue(content.modified != content.created)\n\n def test_content_set_owner_on_create(self):\n import ptah, ptah.cms\n\n class MyContent(ptah.cms.Content):\n __mapper_args__ = {'polymorphic_identity': 'mycontent'}\n __uri_factory__ = ptah.UriFactory('mycontent')\n\n content = MyContent()\n\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n self.assertEqual(content.__owner__, None)\n\n ptah.authService.set_userid('user')\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n self.assertEqual(content.__owner__, 'user')\n\n def test_content_info(self):\n import ptah, ptah.cms\n\n class MyContent(ptah.cms.Content):\n __mapper_args__ = {'polymorphic_identity': 'mycontent'}\n __uri_factory__ = ptah.UriFactory('mycontent')\n\n content = MyContent()\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n info = content.info()\n self.assertIn('__name__', info)\n self.assertIn('__type__', info)\n\n class MyContent(ptah.cms.Content):\n __type__ = ptah.cms.Type('mycontent', 'MyContent')\n\n content = MyContent()\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n info = content.info()\n self.assertIn('title', info)\n self.assertIn('description', info)\n\n def test_content_update(self):\n import ptah, ptah.cms\n\n class MyContent(ptah.cms.Content):\n __type__ = ptah.cms.Type('mycontent', 'MyContent')\n\n content = MyContent()\n config.notify(ptah.cms.ContentCreatedEvent(content))\n\n modified = content.modified\n time.sleep(0.1)\n\n content.update(title='Test title')\n info = content.info()\n\n self.assertEqual(info['title'], 'Test title')\n self.assertEqual(content.title, 'Test title')\n self.assertTrue(content.modified > modified)\n\n def test_content_delete(self):\n import ptah, ptah.cms\n\n class MyContent(ptah.cms.Content):\n __type__ = ptah.cms.Type('mycontent', 'MyContent')\n\n class MyContainer(ptah.cms.Container):\n __type__ = ptah.cms.Type('container', 'Container')\n\n content = MyContent()\n\n self.assertRaises(ptah.cms.Error, content.delete)\n\n container = MyContainer()\n container['content'] = content\n\n content.delete()\n self.assertEqual(container.keys(), [])\n","sub_path":"ptah/cms/tests/test_content.py","file_name":"test_content.py","file_ext":"py","file_size_in_byte":4797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
| ![](\"doi.png\")