,,,,,,,,\n error_list = []\n while True:\n self.dev.write(\":SYSTem:ERRor?\")\n err = self.dev.read().strip()\n if err != '+0,\"No error\"':\n error_list.append(err)\n else:\n break\n\n if len(error_list) > 0:\n QMessageBox.critical(None,\"Error\", \"A error has occured: %s \" % (\"\\n%s %s `_.\n\n Args:\n in_channels (List[int]): Number of input channels per scale.\n out_channels (int): Number of output channels (used at each scale)\n num_outs (int): Number of output scales.\n start_level (int): Index of the start input backbone level used to\n build the feature pyramid. Default: 0.\n end_level (int): Index of the end input backbone level (exclusive) to\n build the feature pyramid. Default: -1, which means the last level.\n add_extra_convs (bool | str): If bool, it decides whether to add conv\n layers on top of the original feature maps. Default to False.\n If True, its actual mode is specified by `extra_convs_on_inputs`.\n If str, it specifies the source feature map of the extra convs.\n Only the following options are allowed\n\n - 'on_input': Last feat map of neck inputs (i.e. backbone feature).\n - 'on_lateral': Last feature map after lateral convs.\n - 'on_output': The last output feature map after fpn convs.\n extra_convs_on_inputs (bool, deprecated): Whether to apply extra convs\n on the original feature from the backbone. If True,\n it is equivalent to `add_extra_convs='on_input'`. If False, it is\n equivalent to set `add_extra_convs='on_output'`. Default to True.\n relu_before_extra_convs (bool): Whether to apply relu before the extra\n conv. Default: False.\n no_norm_on_lateral (bool): Whether to apply norm on lateral.\n Default: False.\n conv_cfg (dict): Config dict for convolution layer. Default: None.\n norm_cfg (dict): Config dict for normalization layer. Default: None.\n act_cfg (str): Config dict for activation layer in ConvModule.\n Default: None.\n upsample_cfg (dict): Config dict for interpolate layer.\n Default: `dict(mode='nearest')`\n\n Example:\n >>> import torch\n >>> in_channels = [2, 3, 5, 7]\n >>> scales = [340, 170, 84, 43]\n >>> inputs = [torch.rand(1, c, s, s)\n ... for c, s in zip(in_channels, scales)]\n >>> self = FPN(in_channels, 11, len(in_channels)).eval()\n >>> outputs = self.forward(inputs)\n >>> for i in range(len(outputs)):\n ... print(f'outputs[{i}].shape = {outputs[i].shape}')\n outputs[0].shape = torch.Size([1, 11, 340, 340])\n outputs[1].shape = torch.Size([1, 11, 170, 170])\n outputs[2].shape = torch.Size([1, 11, 84, 84])\n outputs[3].shape = torch.Size([1, 11, 43, 43])\n \"\"\"\n\n def __init__(self,\n in_channels,\n out_channels,\n use_dconv=False,\n kernel1=True):\n super(FPNs16C45add, self).__init__()\n assert isinstance(in_channels, list)\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.use_dconv = use_dconv\n self.kernel1 = kernel1\n self.deform_conv = DeformConv2dPack(\n out_channels,\n out_channels,\n kernel_size=3,\n padding=1, )\n if self.use_dconv:\n if self.kernel1:\n self.lateral_conv_8 = DeformConv2dPack(\n in_channels[0],\n out_channels,\n kernel_size=1,)\n\n self.lateral_conv_32 = DeformConv2dPack(\n in_channels[2],\n out_channels,\n kernel_size=1,)\n else:\n self.lateral_conv_8 = DeformConv2dPack(\n in_channels[0],\n out_channels,\n kernel_size=3,\n padding=1,)\n\n self.lateral_conv_32 = DeformConv2dPack(\n in_channels[2],\n out_channels,\n kernel_size=3,\n padding=1,)\n else:\n self.lateral_conv_8 = nn.Conv2d(\n in_channels[0],\n out_channels,\n kernel_size=1,)\n\n self.lateral_conv_32 = nn.Conv2d(\n in_channels[2],\n out_channels,\n kernel_size=1,)\n\n # default init_weights for conv(msra) and norm in ConvModule\n def init_weights(self):\n \"\"\"Initialize the weights of FPN module.\"\"\"\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n xavier_init(m, distribution='uniform')\n\n @auto_fp16()\n def forward(self, inputs):\n \"\"\"Forward function.\"\"\"\n # print(len(inputs), inputs[0].shape, inputs[1].shape, inputs[2].shape),\n\n new_input_32 = self.lateral_conv_32(inputs[2])\n\n res_x = inputs[1] + new_input_32 #\n x = self.deform_conv(res_x)\n x = x + res_x\n\n return tuple([x])\n","repo_name":"jiabeiwangTJU/DICL","sub_path":"mmdet/models/necks/fpn_single16_C45add.py","file_name":"fpn_single16_C45add.py","file_ext":"py","file_size_in_byte":5285,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"78"}
+{"seq_id":"28491064419","text":"from .emsemble.HomogeneousEmsemble import HomogeneousEmsemble\nfrom .emsemble.Emsemble import Emsemble\nfrom .feature_selection.EmsembleFS import EmsembleFS\nfrom .feature_selection.RFRFE import RFRFE\nfrom .feature_selection.MSVMRFE import MSVMRFE\nfrom .feature_selection.mRMR import mRMR\nfrom .feature_selection.Relief import Relief\nfrom .feature_selection.AGA import AGA\nfrom .classification.ThresholdClassifier import ThresholdClassifier\nfrom .classification.BaselineClassifiers import BaselineClassifiers\n\nfrom sklearn import base \nfrom sklearn.datasets import load_diabetes, load_iris, load_breast_cancer\nfrom sklearn.model_selection import train_test_split, StratifiedKFold, ShuffleSplit\nfrom sklearn.feature_selection import RFE, RFECV, SelectFromModel\nfrom sklearn.svm import SVR, SVC, LinearSVC, LinearSVR\nfrom sklearn.preprocessing import normalize, MinMaxScaler, StandardScaler, KBinsDiscretizer, label_binarize\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\nfrom sklearn.metrics import roc_curve, roc_auc_score, f1_score, precision_score, recall_score, accuracy_score, mutual_info_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport math\nfrom sklearn.ensemble import BaggingClassifier\nimport seaborn as sns\nimport statsmodels.api as sm\nimport os\nimport warnings\n\ndef make_dir_if_not_exist(directory, filename):\n new_dir = None\n if directory is not None:\n new_dir = os.path.join(directory, filename)\n if not os.path.exists(new_dir):\n os.mkdir(new_dir)\n return new_dir\n\ndef add_row(results, y_test, y_pred, base_clf, num_features, num_classes, prediction_proba):\n average='binary'\n if num_classes > 2:\n average='micro'\n auc_score = None\n if prediction_proba is not None:\n if average == 'binary':\n auc_score = roc_auc_score(y_test, y_pred) \n else:\n auc_score = []\n classes = range(num_classes)\n y_bin = label_binarize(y_test, classes=classes)\n for i in range(num_classes):\n y_temp = y_bin[:,i]\n auc_score.append(roc_auc_score(y_temp, prediction_proba[:, i]))\n auc_score = str(auc_score)\n\n results = results.append({'base clf' : base_clf,\n 'num features': num_features,\n 'accuracy' : accuracy_score(y_test, y_pred), \n 'precision': precision_score(y_test, y_pred, average=average), \n 'recall': recall_score(y_test, y_pred, average=average), \n 'auc': auc_score if auc_score is not None else -1, \n 'f1' : f1_score(y_test, y_pred, average=average)}, \n ignore_index=True)\n return results\n\ndef add_classifier(results, all_pred, all_pred_scores, \n X_train_tranformed, y_train, X_test_transformed, y_test, \n clf_temp, clf_name, \n results_params, num_classes, multiclass,\n example_indices, compare_classifiers, misclassified):\n\n clf = None\n if hasattr(clf_temp, 'reset'): # emsembles\n clf = clf_temp.reset()\n else:\n clf = base.clone(clf_temp)\n if num_classes > 2:\n if multiclass=='ovr':\n clf = OneVsRestClassifier(clf)\n else:\n clf = OneVsOneClassifier(clf)\n \n # get predictions\n clf.fit(X_train_tranformed, y_train)\n y_pred = clf.predict(X_test_transformed)\n all_pred[clf_name] = y_pred\n \n # get ids of examples that were misclassified\n if compare_classifiers == 'mcnemar':\n for i in range(len(y_pred)):\n if y_pred[i] != y_test[i]:\n misclassified[clf_name].append(example_indices[i])\n\n # get prediction probabilities \n proba = None\n if (num_classes == 2 or multiclass == 'ovr') and clf.__class__.__name__ != 'SVC':\n proba = clf.predict_proba(X_test_transformed)\n all_pred_scores[clf_name] = proba\n\n # add performance results to table\n results = add_row(results, y_test, y_pred, clf_name, len(X_train_tranformed[0]), num_classes, proba)\n if results_params is not None:\n for k in results_params.keys():\n results.loc[len(results)-1, k] = results_params[k]\n return results\n\ndef run_classifiers(results, base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, results_params,\n example_indices, compare_classifiers, misclassified):\n all_pred_scores = dict()\n all_pred = dict()\n\n # create homogeneous emsemble\n for i in range(len(base_clfs)):\n results = add_classifier(results, all_pred, all_pred_scores, \n X_train_tranformed, y_train, X_test_transformed, y_test, \n base_clfs[i] , base_clf_names[i], \n results_params, num_classes, multiclass,\n example_indices, compare_classifiers, misclassified)\n return results, all_pred, all_pred_scores\n\n\ndef create_scores_csv(num_classes, y, prediction_scores, ids, directory):\n if num_classes == 2:\n create_scores_csv_binary(y, prediction_scores, ids, directory)\n else:\n create_scores_csv_multiclass(y, prediction_scores, ids, directory)\n\ndef create_scores_csv_multiclass(y, prediction_scores, ids, directory):\n num_classes = np.max(y)\n if not os.path.exists(directory):\n os.mkdir(directory)\n for clf in prediction_scores.keys():\n scores = np.array(prediction_scores[clf])\n correct = []\n wrong = []\n for i in range(len(scores)): \n true_class = y[i] * 1.0\n predicted_class = np.argmax(scores[i]) * 1.0\n if true_class == predicted_class:\n # TP \n correct.append(np.concatenate(( [ids[i], true_class, predicted_class] , scores[i])))\n else:\n wrong.append(np.concatenate(( [ids[i], true_class, predicted_class] , scores[i])))\n \n names = ['correct', 'wrong'] \n data_columns = ['Id', 'True Class', 'Predicted Class']\n for c in range(num_classes + 1):\n data_columns.append('score class ' + str(c))\n for i, data in enumerate([correct, wrong]):\n data = sorted(data,key=lambda x: x[1])\n df = pd.DataFrame(data, columns=data_columns)\n df.to_csv(os.path.join(directory, clf + \"_\" + names[i] + \".csv\"), index=False)\n \n# find TPs with high prediction score\n# FNs with low score for prediction of 1 \ndef create_scores_csv_binary(y, prediction_scores, ids, directory):\n num_classes = np.max(y)\n if not os.path.exists(directory):\n os.mkdir(directory)\n\n for clf in prediction_scores.keys():\n scores = np.array(prediction_scores[clf])\n neg_score = scores[:,0]\n pos_score = scores[:,1]\n TP, FP, TN, FN = [], [], [], []\n for i in range(len(scores)): \n if y[i] == 1: # true class\n if pos_score[i] > neg_score[i]: #TP\n TP.append([ ids[i], neg_score[i], pos_score[i]])\n else: #FN\n FN.append([ ids[i], neg_score[i], pos_score[i]])\n else:\n if pos_score[i] > neg_score[i]: #FP \n FP.append([ ids[i], neg_score[i], pos_score[i]])\n else: # TN \n TN.append([ ids[i], neg_score[i], pos_score[i]])\n \n names = ['TP', 'FP', 'TN', 'FN'] \n data_columns = ['Id', 'Neg Score', 'Pos Score']\n for i, data in enumerate([TP, FP, TN, FN]):\n data = sorted(data,key=lambda x: x[1])\n df = pd.DataFrame(data, columns=data_columns)\n df.to_csv(os.path.join(directory, clf + \"_\" + names[i] + \".csv\"), index=False)\n\ndef run_selector_classifier(selector, all_prediction, all_prediction_scores, test_index, key_str, results, \n base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, results_params,\n compare_classifiers, misclassified):\n \n\n # run classification\n if len(X_train_tranformed[0]) > 0:\n results, temp_pred, temp_pred_scores = run_classifiers(results, base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, results_params,\n test_index, compare_classifiers, misclassified)\n # map prediction score over to all_prediction_scores\n if num_classes == 2 or multiclass == 'ovr':\n for j in range(len(test_index)):\n for name in base_clf_names:\n if name != 'SVC':\n if key_str is None:\n all_prediction_scores[name][test_index[j]] = temp_pred_scores[name][j]\n all_prediction[name][test_index[j]] = temp_pred[name][j]\n else:\n all_prediction_scores[key_str][name][test_index[j]] = temp_pred_scores[name][j]\n all_prediction[key_str][name][test_index[j]] = temp_pred[name][j]\n \n return results\n\ndef initial_all_prediction_scores(param_strs, base_clf_names, X):\n all_prediction_scores = dict()\n if param_strs is not None:\n for i in range(len(param_strs)): \n prediction_scores = dict()\n for name in base_clf_names:\n if name != 'SVC':\n prediction_scores[name] = [[0,0]] * len(X)\n all_prediction_scores[param_strs[i]] = prediction_scores\n else:\n for name in base_clf_names:\n if name != 'SVC':\n all_prediction_scores[name] = [[0,0]] * len(X)\n return all_prediction_scores\n\ndef init_misclassified_dict(base_clf_names):\n misclassified = dict()\n for name in base_clf_names:\n misclassified[name] = []\n return misclassified\n\ndef EmsembleFSClassifierPipeline(X, y, groups, \n base_clfs, base_clf_names,\n selector, combines, thresholds, cv_size, patient_ids, \n directory=None, filename=None, multiclass='ovr', \n compare_classifiers=None):\n # set number classes\n num_classes = np.max(y) + 1\n\n # make subfolder\n new_dir = make_dir_if_not_exist(directory, filename)\n\n # initial results dataframe\n results = pd.DataFrame(columns=[\"base clf\", \"combine\", \"threshold\", \"num features\", \"accuracy\", \"precision\", \"recall\", \"auc\", \"f1\"])\n \n # prediction scores\n param_strs = None\n if combines is not None:\n param_strs = [combines[i] + str(thresholds[i]) for i in range(len(combines))]\n all_prediction_scores = initial_all_prediction_scores(param_strs, base_clf_names, X)\n all_predictions = initial_all_prediction_scores(param_strs, base_clf_names, X)\n \n results_params = dict()\n\n # dict of misclassified example indices for each classifier\n misclassified = dict()\n\n selection_folds = dict()\n for param in param_strs:\n misclassified[param] = init_misclassified_dict(base_clf_names)\n selection_folds[param] = []\n \n CVSplitter = StratifiedKFold(n_splits=cv_size, random_state=None, shuffle=True)\n \n \n for train_index, test_index in CVSplitter.split(X, groups): # split based on group_number\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n groups_train = groups[train_index]\n\n # run feature selection\n if combines is not None:\n selector.fit(X_train, y_train, combine=False, groups=groups_train)\n else:\n selector.fit(X_train, y_train)\n\n if combines is not None:\n for i in range(len(combines)):\n results_params['combine'] = combines[i]\n results_params['threshold'] = thresholds[i]\n key_str = combines[i] + str(thresholds[i])\n\n selector.combine_rankings(combines[i], thresholds[i])\n selection_folds[key_str].append(selector.selection_indices)\n X_train_tranformed = selector.transform(X_train)\n X_test_transformed = selector.transform(X_test)\n \n results = run_selector_classifier(selector, all_predictions, all_prediction_scores, test_index, key_str, \n results, base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test,\n num_classes, multiclass, results_params,\n compare_classifiers, misclassified[key_str])\n\n else:\n selection_folds.append(selector.selection_indices)\n X_train_tranformed = selector.transform(X_train)\n X_test_transformed = selector.transform(X_test)\n results = run_selector_classifier(selector, all_prediction_scores, test_index, None, \n results, base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, results_params,\n compare_classifiers, misclassified)\n \n if combines is not None:\n for i in range(len(combines)):\n \n key_str = combines[i] + str(thresholds[i])\n prediction_scores = all_prediction_scores[key_str]\n if new_dir is not None:\n create_scores_csv(num_classes, y, prediction_scores, patient_ids, os.path.join(new_dir, key_str))\n else:\n if new_dir is not None:\n create_scores_csv(num_classes, y, all_prediction_scores, patient_ids, os.path.join(new_dir, filename))\n \n if compare_classifiers is not None and compare_classifiers=='mcnemar':\n for param in misclassified.keys():\n compare_all(misclassified[param], len(X), os.path.join(new_dir, param))\n \n if new_dir is not None:\n results.groupby(['base clf', 'combine', 'threshold']).mean().to_csv(os.path.join(new_dir, filename + '_mean' + \".csv\"))\n results.to_csv(os.path.join(new_dir, filename + \".csv\"))\n \n return results, all_predictions, all_prediction_scores, selection_folds\n\ndef SelectorThresholdClassifierPipeline(X, y, groups,\n cv_size, feature_selector, \n base_clfs, base_clf_names, \n directory, filename):\n new_dir = make_dir_if_not_exist(directory, filename)\n\n num_classes = np.max(y) + 1\n \n results = pd.DataFrame(columns=[\"base clf\", \"num features\", \"accuracy\", \"precision\", \"recall\", \"auc\", \"f1\"])\n CVSplitter = StratifiedKFold(n_splits=cv_size, random_state=None, shuffle=True)\n \n shuffle_splitter = ShuffleSplit(n_splits=5, test_size=.2)\n \n for train_index, test_index in CVSplitter.split(X, groups):\n X_dev, X_test = X[train_index], X[test_index]\n y_dev, y_test = y[train_index], y[test_index]\n group_dev, group_test = groups[train_index], groups[test_index]\n \n for train_index, test_index in shuffle_splitter.split(X_dev, group_dev):\n X_train, X_holdout = X[train_index], X[test_index]\n y_train, y_holdout = y[train_index], y[test_index]\n\n # Feature selection \n feature_selector.fit(X_train, y_train)\n X_train_tranformed = feature_selector.transform(X_train)\n X_holdout_transformed = feature_selector.transform(X_holdout)\n X_test_transformed = feature_selector.transform(X_test)\n \n # threshold classifier\n for i in range(len(base_clfs)):\n clf_temp = base_clfs[i]\n if hasattr(clf_temp, 'reset'): # emsembles\n clf = clf_temp.reset()\n else:\n clf = base.clone(clf_temp)\n \n threshold_clf = ThresholdClassifier(clf, multilabel=False)\n threshold_clf.fit(X_train_tranformed, y_train)\n threshold_clf.optimize_threshold(X_holdout_transformed, y_holdout)\n y_pred = threshold_clf.predict(X_test_transformed)\n results = add_row(results, y_test, y_pred, base_clf_names[i], len(X_train_tranformed[0]), num_classes, None)\n for class_i in range(len(threshold_clf.thresholds)):\n results.loc[len(results)-1, 'threshold class ' + str(class_i)] = threshold_clf.thresholds[class_i]\n \n if new_dir is not None:\n results.groupby(['base clf']).mean().to_csv(os.path.join(new_dir, filename + \"_mean\" + \".csv\"))\n results.to_csv(os.path.join(new_dir, filename + \".csv\"))\n return results\n\ndef FilterSelectorClassifierPipeline(X, y, groups,\n base_clfs, base_clf_names,\n cv_size, feature_sizes, selector, patient_ids, \n directory=None, filename=None, multiclass='ovr',\n compare_classifiers=None):\n num_classes = np.max(y) + 1\n\n new_dir = make_dir_if_not_exist(directory, filename)\n \n results = pd.DataFrame(columns=[\"base clf\", \"num features\", \"accuracy\", \"precision\", \"recall\", \"auc\", \"f1\"])\n CVSplitter = StratifiedKFold(n_splits=cv_size, random_state=None, shuffle=True)\n \n # prediction scores\n param_strs = [str(f_size) for f_size in feature_sizes]\n all_prediction_scores = initial_all_prediction_scores(param_strs, base_clf_names, X)\n all_predictions = initial_all_prediction_scores(param_strs, base_clf_names, X)\n\n # dict of misclassified example indices for each classifier \n misclassified = dict()\n selection_folds = dict()\n for param in param_strs:\n misclassified[param] = init_misclassified_dict(base_clf_names)\n selection_folds[param] = []\n\n for train_index, test_index in CVSplitter.split(X, groups):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n\n selector.fit(X_train, y_train)\n for f_size in feature_sizes:\n key_str = str(f_size)\n X_train_tranformed = selector.transform(X_train, f_size)\n X_test_transformed = selector.transform(X_test, f_size)\n selection_folds[key_str].append(selector.selected_indices[:f_size])\n # run classification\n results = run_selector_classifier(selector, all_predictions, all_prediction_scores, test_index, key_str, results, \n base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, None,\n compare_classifiers, misclassified[key_str])\n \n for f_size in feature_sizes:\n key_str = str(f_size)\n prediction_scores = all_prediction_scores[key_str]\n if new_dir is not None:\n create_scores_csv(num_classes, y, prediction_scores, patient_ids, os.path.join(new_dir, 'f_size_' + key_str))\n \n if compare_classifiers is not None and compare_classifiers=='mcnemar':\n for param in misclassified.keys():\n compare_all(misclassified[param], len(X), os.path.join(new_dir, 'f_size_' + param))\n\n if new_dir is not None:\n results.groupby(['base clf', 'num features']).mean().to_csv(os.path.join(new_dir, filename + \"_mean\" + \".csv\"))\n results.to_csv(os.path.join(new_dir, filename + \".csv\"))\n return results, all_predictions, all_prediction_scores, selection_folds\n\ndef RF_FS_CV(X, y, groups,\n n_estimators, num_features, patient_ids, \n directory=None, filename=None):\n # make subfolder\n num_classes = np.max(y) + 1\n new_dir = make_dir_if_not_exist(directory, filename)\n \n results = pd.DataFrame(columns=[\"base clf\", \"num features\", \"accuracy\", \"precision\", \"recall\", \"auc\", \"f1\"])\n CVSplitter = StratifiedKFold(n_splits=5, random_state=None, shuffle=True)\n \n prediction_scores = dict()\n prediction_scores['Random Forest'] = [[0,0]] * len(X)\n \n for train_index, test_index in CVSplitter.split(X, groups):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n \n sel = RandomForestClassifier(n_estimators=n_estimators, n_jobs=-1)\n sel.fit(X_train, y_train)\n order = sel.feature_importances_.argsort().argsort()\n for f_size in num_features:\n selected_indices = []\n min_rank_select = len(order) - f_size\n for i in range(len(order)):\n if order[i] >= min_rank_select:\n selected_indices.append(i)\n X_train_trans = X_train[:, selected_indices]\n\n clf = RandomForestClassifier(n_estimators=n_estimators, n_jobs=-1)\n clf.fit(X_train_trans, y_train)\n y_pred = clf.predict(X_test[:, selected_indices])\n #add_row(results, y_test, y_pred, base_clf, num_features, num_classes, prediction_proba):\n results = add_row(results, y_test, y_pred, 'RF', f_size, num_classes, prediction_scores['Random Forest'])\n \n y_scores = clf.predict_proba(X_test[:, selected_indices])\n for j in range(len(test_index)):\n prediction_scores['Random Forest'][test_index[j]] = y_scores[j]\n \n if new_dir is not None:\n create_scores_csv(num_classes, y, prediction_scores, patient_ids, os.path.join(new_dir, 'RF'))\n results.to_csv(os.path.join(new_dir, filename + \".csv\"))\n results.groupby(['base clf', \"num features\"]).mean().to_csv(os.path.join(new_dir, filename + '_mean' + \".csv\"))\n return results\n\ndef AGAFS(X, y, groups,\n aga, \n base_clfs, base_clf_names, \n cv_splits,\n patient_ids, directory=None, filename=None, \n multiclass='ovr', compare_classifiers=None):\n \n num_classes = np.max(y) + 1\n new_dir = make_dir_if_not_exist(directory, filename)\n\n prediction_scores = initial_all_prediction_scores(None, base_clf_names, X)\n misclassified = init_misclassified_dict(base_clf_names)\n \n results = pd.DataFrame(columns=[\"base clf\", \"num features\", \"accuracy\", \"precision\", \"recall\", \"auc\", \"f1\"])\n CVSplitter = StratifiedKFold(n_splits=cv_splits, random_state=None, shuffle=True)\n for train_index, test_index in CVSplitter.split(X, groups):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n \n aga.fit(X_train, y_train)\n selection = aga.population[np.argmax(aga.fitnesses)]\n X_train_tranformed = X_train[:, selection]\n X_test_transformed = X_test[:, selection]\n \n results, temp_pred_scores = run_classifiers(results, base_clfs, base_clf_names,\n X_train_tranformed, y_train, X_test_transformed, y_test, \n num_classes, multiclass, None,\n test_index, compare_classifiers, misclassified)\n for j in range(len(test_index)):\n for name in base_clf_names:\n if name in prediction_scores.keys():\n prediction_scores[name][test_index[j]] = temp_pred_scores[name][j]\n \n if new_dir is not None:\n create_scores_csv(num_classes, y, prediction_scores, patient_ids, os.path.join(new_dir, filename))\n results.groupby(['base clf']).mean().to_csv(os.path.join(new_dir, filename + \"_mean\" + \".csv\"))\n results.to_csv(os.path.join(new_dir, filename + \".csv\"))\n return results\n\n# Baseline classifier \ndef CV_baseline(X, y, groups,\n cv_splits, directory=None, filename=None):\n all_results = []\n clfs = [LogisticRegression(penalty='l2'), \n SVC(), \n AdaBoostClassifier(n_estimators=1000),\n RandomForestClassifier(n_estimators=1000)]\n\n CVSplitter = StratifiedKFold(n_splits=cv_splits, random_state=None, shuffle=True)\n for train_index, test_index in CVSplitter.split(X, groups):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n baseline_clfs = BaselineClassifiers(clfs)\n baseline_clfs.fit(X_train, y_train)\n y_preds = baseline_clfs.predict(X_test)\n all_results.append(baseline_clfs.get_scores(y_test, y_preds))\n all_results = pd.concat(all_results, axis=0)\n if directory is not None:\n all_results.to_csv(directory + filename)\n all_results.groupby('classifer').mean().to_csv(os.path.join(directory, filename + \".csv\"))\n return all_results\n\nfrom statsmodels.stats.contingency_tables import mcnemar\n\ndef intersection_size(lst1, lst2): \n count = 0\n for value in lst1:\n if value in lst2:\n count+=1\n return count \n\ndef reject_null(misclf, total_examples, clf_name1, clf_name2):\n size = intersection_size(misclf[clf_name1], misclf[clf_name2])\n a = [[0,0], [0,0]]\n # misclassified by both\n a[0][0] = size\n # misclassified by A\n a[0][1] = len(misclf[clf_name1]) - size\n # misclassified by B\n a[1][0] = len(misclf[clf_name2]) - size\n # not misclassified by A or B\n a[1][1] = total_examples- a[0][0] - a[0][1] - a[1][0]\n result = mcnemar(a, exact=True)\n #print('statistic=%.3f, p-value=%.3f' % (result.statistic, result.pvalue))\n #alpha = 0.05\n print(clf_name1 + \" v \" + clf_name2 + \" \" + str(result.pvalue))\n return result.pvalue\n #if result.pvalue > alpha:\n # return False\n #else:\n # return True \n \ndef compare_all(misclf, total_count, new_dir):\n clf_names = list(misclf.keys())\n sig_results = np.zeros([len(clf_names), len(clf_names)])\n for i, name in enumerate(clf_names):\n for j in range(i+1, len(clf_names)):\n result = reject_null(misclf, total_count, name, clf_names[j])\n sig_results[i][j] = result\n print(sig_results)\n # write results\n df = pd.DataFrame(sig_results, columns=clf_names)\n df['clf'] = clf_names\n if new_dir is not None:\n df.to_csv(os.path.join(new_dir, 'compare_clf.csv'), index=False)\n return sig_results","repo_name":"helenzhao093/MLMethods","sub_path":"run_trials.py","file_name":"run_trials.py","file_ext":"py","file_size_in_byte":27168,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"78"}
+{"seq_id":"16878425807","text":"# Aliasing - second name for piece of data.\n# Easier (and more useful) than making a second copy.\n# If data is immutable, aliases don't matter\n# Because data can't change.\n#But if data, can change, can lead to bugs\n\nfirst = \"Isaac\"\nsecond = first\nsecond = \"Newton\"\n\n# Not mutable, creates new string\nfirst = first + \"Newton\"\n\n# List is mutable, both first and second point to same list in memory.\n#Changes in first will affect second.\n\nfirst = [\"Isaac\"]\nsecond = first\n# Only appending list....\nfirst.append(\"Newton\")\n\n#But returns mutated list!\nprint(second)","repo_name":"MochaDreamboat/Alfy","sub_path":"FCC Python/24_Aliasing.py","file_name":"24_Aliasing.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"30855203262","text":"import torch\nfrom mmcv.cnn import ConvModule\nfrom mmcv.runner import BaseModule\nfrom torch import nn as nn\nfrom torch.nn import functional as F\n\nfrom mmdet3d.core.bbox.structures import (get_proj_mat_by_coord_type,\n points_cam2img)\nfrom ..builder import FUSION_LAYERS\nfrom . import apply_3d_transformation\nfrom .deform_layer import DeformTransLayer\nfrom .position_embedding import PositionEmbeddingSine\n\ndef get_reference_feats(img_meta,\n img_features,\n points,\n proj_mat,\n coord_type,\n img_scale_factor,\n img_crop_offset,\n img_flip,\n img_pad_shape,\n img_shape,\n aligned=True,\n padding_mode='zeros',\n align_corners=True):\n \"\"\"Obtain image features using points.\n\n Args:\n img_meta (dict): Meta info.\n img_features (torch.Tensor): 1 x C x H x W image features.\n points (torch.Tensor): Nx3 point cloud in LiDAR coordinates.\n proj_mat (torch.Tensor): 4x4 transformation matrix.\n coord_type (str): 'DEPTH' or 'CAMERA' or 'LIDAR'.\n img_scale_factor (torch.Tensor): Scale factor with shape of \\\n (w_scale, h_scale).\n img_crop_offset (torch.Tensor): Crop offset used to crop \\\n image during data augmentation with shape of (w_offset, h_offset).\n img_flip (bool): Whether the image is flipped.\n img_pad_shape (tuple[int]): int tuple indicates the h & w after\n padding, this is necessary to obtain features in feature map.\n img_shape (tuple[int]): int tuple indicates the h & w before padding\n after scaling, this is necessary for flipping coordinates.\n aligned (bool, optional): Whether use bilinear interpolation when\n sampling image features for each point. Defaults to True.\n padding_mode (str, optional): Padding mode when padding values for\n features of out-of-image points. Defaults to 'zeros'.\n align_corners (bool, optional): Whether to align corners when\n sampling image features for each point. Defaults to True.\n\n Returns:\n torch.Tensor: NxC image features sampled by point coordinates.\n \"\"\"\n\n # apply transformation based on info in img_meta\n points = apply_3d_transformation(\n points, coord_type, img_meta, reverse=True)\n\n # project points to camera coordinate\n pts_2d_with_depth = points_cam2img(points, proj_mat, with_depth=True)\n pts_depth = pts_2d_with_depth[:, -1]\n pts_2d = pts_2d_with_depth[:, :2]\n\n valid_depth_idx = (pts_depth > 0).reshape(-1,)\n\n # img transformation: scale -> crop -> flip\n # the image is resized by img_scale_factor\n img_coors = pts_2d[:, 0:2] * img_scale_factor # Nx2\n img_coors -= img_crop_offset\n\n # grid sample, the valid grid range should be in [-1,1]\n coor_x, coor_y = torch.split(img_coors, 1, dim=1) # each is Nx1\n\n if img_flip:\n # by default we take it as horizontal flip\n # use img_shape before padding for flip\n orig_h, orig_w = img_shape\n coor_x = orig_w - coor_x\n\n h, w = img_pad_shape\n coor_y = coor_y / h * 2 - 1\n coor_x = coor_x / w * 2 - 1\n\n valid_y_idx = ((coor_y >= -1) & (coor_y <= 1)).reshape(-1,)\n valid_x_idx = ((coor_x >= -1) & (coor_y <= 1)).reshape(-1,)\n\n grid = torch.cat([coor_x, coor_y],\n dim=1).unsqueeze(0).unsqueeze(0) # Nx2 -> 1x1xNx2\n\n # align_corner=True provides higher performance\n mode = 'bilinear' if aligned else 'nearest'\n point_features = F.grid_sample(\n img_features,\n grid,\n mode=mode,\n padding_mode=padding_mode,\n align_corners=align_corners) # 1xCx1xN feats\n norm_grid = (grid + 1) / 2\n\n valid_idx = valid_depth_idx & valid_y_idx & valid_x_idx\n\n return point_features.squeeze().t(), norm_grid.clone().detach(), valid_idx\n\n\n@FUSION_LAYERS.register_module()\nclass MultiVoxelDeformFusionV2(BaseModule):\n \"\"\"Fuse image features from multi-scale features.\n\n Args:\n img_channels (list[int] | int): Channels of image features.\n It could be a list if the input is multi-scale image features.\n pts_channels (int): Channels of point features\n mid_channels (int): Channels of middle layers\n out_channels (int): Channels of output fused features\n img_levels (int, optional): Number of image levels. Defaults to 3.\n coord_type (str): 'DEPTH' or 'CAMERA' or 'LIDAR'.\n Defaults to 'LIDAR'.\n conv_cfg (dict, optional): Dict config of conv layers of middle\n layers. Defaults to None.\n norm_cfg (dict, optional): Dict config of norm layers of middle\n layers. Defaults to None.\n act_cfg (dict, optional): Dict config of activatation layers.\n Defaults to None.\n activate_out (bool, optional): Whether to apply relu activation\n to output features. Defaults to True.\n fuse_out (bool, optional): Whether apply conv layer to the fused\n features. Defaults to False.\n dropout_ratio (int, float, optional): Dropout ratio of image\n features to prevent overfitting. Defaults to 0.\n aligned (bool, optional): Whether apply aligned feature fusion.\n Defaults to True.\n align_corners (bool, optional): Whether to align corner when\n sampling features according to points. Defaults to True.\n padding_mode (str, optional): Mode used to pad the features of\n points that do not have corresponding image features.\n Defaults to 'zeros'.\n lateral_conv (bool, optional): Whether to apply lateral convs\n to image features. Defaults to True.\n \"\"\"\n\n def __init__(self,\n img_channels,\n pts_channels,\n mid_channels,\n out_channels,\n img_levels=3,\n n_heads=4,\n n_points=8,\n norm=True,\n disentangle=False,\n multi_input='',\n with_pos_embed=False,\n coord_type='LIDAR',\n conv_cfg=None,\n norm_cfg=None,\n act_cfg=None,\n init_cfg=None,\n activate_out=True,\n fuse_out=False,\n dropout_ratio=0,\n aligned=True,\n align_corners=True,\n padding_mode='zeros',\n lateral_conv=True):\n super(MultiVoxelDeformFusionV2, self).__init__(init_cfg=init_cfg)\n if isinstance(img_levels, int):\n img_levels = [img_levels]\n if isinstance(img_channels, int):\n img_channels = [img_channels] * len(img_levels)\n assert isinstance(img_levels, list)\n assert isinstance(img_channels, list)\n assert len(img_channels) == len(img_levels)\n\n self.img_levels = img_levels\n self.coord_type = coord_type\n self.act_cfg = act_cfg\n self.activate_out = activate_out\n self.fuse_out = fuse_out\n self.dropout_ratio = dropout_ratio\n self.img_channels = img_channels\n self.mid_channels = mid_channels\n self.aligned = aligned\n self.align_corners = align_corners\n self.padding_mode = padding_mode\n self.n_heads = n_heads\n self.n_points = n_points\n self.with_pos_embed = with_pos_embed\n self.norm = norm\n self.multi_input = multi_input\n self.disentangle = disentangle\n if with_pos_embed:\n self.pos_embed = PositionEmbeddingSine(self.mid_channels // 2)\n if self.disentangle:\n self.img_mu_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.img_logvar_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.img_p_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.pts_mu_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.pts_logvar_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.pts_p_proj = self._generate_proj(self.mid_channels, self.mid_channels)\n self.img_recons_proj = self._generate_mapping(self.mid_channels, self.mid_channels)\n self.pts_recons_proj = self._generate_mapping(self.mid_channels, self.mid_channels)\n\n self.lateral_convs = None\n if lateral_conv:\n self.lateral_convs = nn.ModuleList()\n for i in range(len(img_channels)):\n l_conv = ConvModule(\n img_channels[i],\n mid_channels,\n 3,\n padding=1,\n conv_cfg=conv_cfg,\n norm_cfg=norm_cfg,\n act_cfg=self.act_cfg,\n inplace=False)\n self.lateral_convs.append(l_conv)\n self.img_transform = nn.Sequential(\n nn.Linear(mid_channels * len(img_channels), out_channels),\n nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),\n )\n else:\n self.img_transform = nn.Sequential(\n nn.Linear(sum(img_channels), out_channels),\n nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),\n )\n self.pts_transform = nn.Sequential(\n nn.Linear(pts_channels, out_channels),\n nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),\n )\n self.cross_att = DeformTransLayer(d_model=mid_channels, d_ffn=2 * mid_channels, \\\n n_levels=1, n_heads=self.n_heads, n_points=self.n_points,\\\n version='v2' if self.multi_input == 'concat' else 'v1')\n\n if self.fuse_out:\n self.fuse_conv = nn.Sequential(\n nn.Linear(mid_channels * 2, out_channels),\n # For pts the BN is initialized differently by default\n # TODO: check whether this is necessary\n nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),\n nn.ReLU(inplace=False))\n\n if init_cfg is None:\n self.init_cfg = [\n dict(type='Xavier', layer='Conv2d', distribution='uniform'),\n dict(type='Xavier', layer='Linear', distribution='uniform')\n ]\n \n def _generate_proj(self, in_channels, out_channels):\n return nn.Sequential(\n nn.Linear(in_channels, out_channels),\n nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01),\n nn.ReLU(inplace=False),\n nn.Linear(out_channels, out_channels),\n )\n def _generate_mapping(self, in_channels, out_channels):\n return nn.Sequential(\n nn.Linear(in_channels, out_channels),\n nn.BatchNorm1d(self.mid_channels, eps=1e-3, momentum=0.01),\n nn.ReLU(inplace=False),\n nn.Linear(out_channels, out_channels),\n )\n\n def forward(self, img_feats, voxel_coors, voxel_feats, \\\n img_metas, voxel_size, point_cloud_range):\n \"\"\"Forward function.\n\n Args:\n img_feats (list[torch.Tensor]): Image features. NOTE this should be a list of list\n pts: [list[torch.Tensor]]: A batch of points with shape N x 3.\n pts_feats (torch.Tensor): A tensor consist of point features of the\n total batch.\n img_metas (list[dict]): Meta information of images.\n\n Returns:\n torch.Tensor: Fused features of each point.\n \"\"\"\n img_pts = self.obtain_mlvl_feats(img_feats, voxel_coors, voxel_feats,\\\n img_metas, voxel_size, point_cloud_range)\n if self.disentangle:\n img_pts, losses_vae = img_pts\n img_pre_fuse = self.img_transform(img_pts)\n if self.training and self.dropout_ratio > 0:\n img_pre_fuse = F.dropout(img_pre_fuse, self.dropout_ratio)\n pts_pre_fuse = self.pts_transform(voxel_feats)\n\n # fuse_out = img_pre_fuse + pts_pre_fuse\n fuse_out = torch.cat([img_pre_fuse, pts_pre_fuse], dim=-1)\n if self.activate_out:\n fuse_out = F.relu(fuse_out)\n if self.fuse_out:\n fuse_out = self.fuse_conv(fuse_out)\n if self.disentangle:\n return fuse_out, losses_vae\n return fuse_out\n\n def obtain_mlvl_feats(self, img_feats, voxel_coors, voxel_feats,\n img_metas, voxel_size, point_cloud_range):\n \"\"\"Obtain multi-level features for each point.\n\n Args:\n img_feats (list(torch.Tensor)): Multi-scale image features produced\n by image backbone in shape (N, C, H, W).\n pts (list[torch.Tensor]): Points of each sample.\n img_metas (list[dict]): Meta information for each sample.\n\n Returns:\n torch.Tensor: Corresponding image features of each point.\n \"\"\"\n if self.lateral_convs is not None:\n img_ins = [\n lateral_conv(img_feats[i])\n for i, lateral_conv in zip(self.img_levels, self.lateral_convs)\n ]\n else:\n img_ins = img_feats\n if self.with_pos_embed:\n for ii in range(len(img_ins)):\n pos_embed = self.pos_embed(img_ins[ii])\n img_ins[ii] += pos_embed\n img_feats_per_point = []\n # Sample multi-level features\n num_camera = img_ins[0].shape[0] // len(img_metas)\n start_iter = 0\n if self.disentangle:\n recon_loss_sum = 0; kl_loss_sum = 0; num_sum = 0\n for i in range(len(img_metas)):\n mlvl_img_feats = []\n voxel_coors_per_img = voxel_coors[voxel_coors[:, 0] == i]\n x = (voxel_coors_per_img[:, 3] + 0.5) * voxel_size[0] + point_cloud_range[0]\n y = (voxel_coors_per_img[:, 2] + 0.5) * voxel_size[1] + point_cloud_range[1]\n z = (voxel_coors_per_img[:, 1] + 0.5) * voxel_size[2] + point_cloud_range[2]\n x = x.unsqueeze(-1); y = y.unsqueeze(-1); z = z.unsqueeze(-1)\n decoded_voxel_coors = torch.cat([x, y, z], dim=-1)\n num_voxels = decoded_voxel_coors.shape[0]\n voxel_feat = voxel_feats[start_iter: start_iter + num_voxels]\n for level in range(len(self.img_levels)):\n if self.disentangle:\n img_pts_feat, recon_loss, kl_loss, num_samples = self.sample_single(img_ins[level][i * num_camera: (i + 1) * num_camera],\n decoded_voxel_coors, voxel_feat, img_metas[i], level_num=level)\n mlvl_img_feats.append(img_pts_feat)\n recon_loss_sum += recon_loss; kl_loss_sum += kl_loss; num_sum += num_samples\n else:\n mlvl_img_feats.append(\n self.sample_single(img_ins[level][i * num_camera: (i + 1) * num_camera],\n decoded_voxel_coors, voxel_feat, img_metas[i], level_num=level))\n start_iter += num_voxels\n mlvl_img_feats = torch.cat(mlvl_img_feats, dim=-1)\n img_feats_per_point.append(mlvl_img_feats)\n\n img_pts = torch.cat(img_feats_per_point, dim=0)\n if self.disentangle:\n losses_vae = dict(\n recon_loss=recon_loss_sum / num_sum,\n kl_loss=kl_loss_sum / num_sum\n )\n return img_pts, losses_vae\n return img_pts\n \n def disentangle_feature(self, img_feat, pts_feat):\n img_mu = self.img_mu_proj(img_feat)\n img_logvar = self.img_logvar_proj(img_feat)\n img_p = self.img_p_proj(img_feat)\n img_std = torch.exp(0.5 * img_logvar)\n img_eps = torch.randn_like(img_std)\n img_z = img_eps * img_std + img_mu\n # img_all = torch.cat([img_p, img_z], dim=-1) # entangle to recontruct\n img_all = img_p * img_z\n img_recons = self.img_recons_proj(img_all)\n\n pts_mu = self.pts_mu_proj(pts_feat)\n pts_logvar = self.pts_logvar_proj(pts_feat)\n pts_p = self.pts_p_proj(pts_feat)\n pts_std = torch.exp(0.5 * pts_logvar)\n pts_eps = torch.randn_like(pts_std)\n pts_z = pts_eps * pts_std + pts_mu\n # pts_all = torch.cat([pts_p, pts_z], dim=-1)\n pts_all = pts_p * pts_z\n pts_recons = self.pts_recons_proj(pts_all)\n\n img_recons_loss = torch.sum(0.5 * (img_recons - img_feat) ** 2, dim=1)\n pts_recons_loss = torch.sum(0.5 * (pts_recons - pts_feat) ** 2, dim=1)\n\n img_kl_loss = -0.5 * torch.sum(1 + img_logvar - img_mu ** 2 - img_logvar.exp(), dim = 1)\n pts_kl_loss = -0.5 * torch.sum(1 + pts_logvar - pts_mu ** 2 - pts_logvar.exp(), dim = 1)\n\n recons_loss = img_recons_loss + pts_recons_loss\n kl_loss = img_kl_loss + pts_kl_loss\n # return loss shape (N, )\n return recons_loss, kl_loss, img_p, pts_p\n\n def sample_single(self, img_feats, pts, pts_feats, img_meta, level_num):\n \"\"\"Sample features from single level image feature map.\n\n Args:\n img_feats (torch.Tensor): Image feature map in shape\n (1, num_camera, C, H, W).\n pts (torch.Tensor): Points of a single sample.\n img_meta (dict): Meta information of the single sample.\n\n Returns:\n torch.Tensor: Single level image features of each point.\n \"\"\"\n # TODO: image transformation also extracted\n img_scale_factor = (\n pts.new_tensor(img_meta['scale_factor'][:2])\n if 'scale_factor' in img_meta.keys() else 1)\n img_flip = img_meta['flip'] if 'flip' in img_meta.keys() else False\n img_crop_offset = (\n pts.new_tensor(img_meta['img_crop_offset'])\n if 'img_crop_offset' in img_meta.keys() else 0)\n proj_mat_list = get_proj_mat_by_coord_type(img_meta, self.coord_type)\n num_camera = img_feats.shape[0]\n ref_points_list = []; valid_idx_list = []; ref_feats_list = []\n for camera_id in range(num_camera):\n ref_feats, ref_points, valid_idx = get_reference_feats(\n img_meta=img_meta,\n img_features=img_feats[camera_id].unsqueeze(0),\n points=pts,\n proj_mat=pts.new_tensor(proj_mat_list[camera_id]),\n coord_type=self.coord_type,\n img_scale_factor=img_scale_factor,\n img_crop_offset=img_crop_offset,\n img_flip=img_flip,\n img_pad_shape=img_meta['input_shape'][:2],\n img_shape=img_meta['img_shape'][:2],\n aligned=self.aligned,\n padding_mode=self.padding_mode,\n align_corners=self.align_corners,\n )\n ref_feats_list.append(ref_feats)\n ref_points_list.append(ref_points)\n valid_idx_list.append(valid_idx)\n\n # pts_feats = self.pts_key_proj(pts_feats.detach())\n # align pts_feats from each camera\n assign_mask = pts_feats.new_zeros((pts.shape[0]), dtype=torch.bool)\n final_img_pts = pts_feats.new_zeros((pts.shape[0], self.mid_channels))\n if self.disentangle:\n recon_loss_all = pts_feats.new_zeros((pts.shape[0], ))\n kl_loss_all = pts_feats.new_zeros((pts.shape[0], ))\n for camera_id in range(num_camera):\n valid_idx = valid_idx_list[camera_id]\n img_feat = img_feats[camera_id].unsqueeze(0)\n bs, channel_num, h, w = img_feat.shape\n flatten_img_feat = img_feat.permute(0, 2, 3, 1).\\\n reshape(bs, h * w, channel_num)\n ref_points = ref_points_list[camera_id].reshape(bs, -1, 1, 2)\n assign_idx = (~assign_mask) & valid_idx\n assign_mask |= valid_idx\n # filter invalid voxel features for redundant training\n valid_ref_points = ref_points[:, assign_idx]\n valid_ref_feats = ref_feats_list[camera_id][assign_idx]\n valid_pts_feats = pts_feats[assign_idx]\n N, Len_in, _ = flatten_img_feat.shape\n level_spatial_shapes = pts_feats.new_tensor([(h, w)], dtype=torch.long)\n level_start_index = pts_feats.new_tensor([0], dtype=torch.long)\n if self.disentangle:\n recon_loss, kl_loss, valid_ref_feats, valid_pts_feats = self.disentangle_feature(valid_ref_feats, valid_pts_feats)\n valid_ref_feats = valid_ref_feats.unsqueeze(0)\n valid_pts_feats = valid_pts_feats.unsqueeze(0)\n if self.multi_input == 'concat':\n query_feat = torch.cat([valid_ref_feats, valid_pts_feats], dim=-1)\n elif self.multi_input == 'multiply':\n query_feat = valid_ref_feats * valid_pts_feats\n elif self.multi_input == 'multiply_pts_detach':\n query_feat = valid_ref_feats * valid_pts_feats.detach()\n elif self.multi_input == 'pts':\n query_feat = valid_pts_feats\n elif self.multi_input == 'pts_detach':\n query_feat = valid_pts_feats.detach()\n elif self.multi_input == 'img':\n query_feat = valid_ref_feats\n elif self.multi_input == 'img_detach':\n query_feat = valid_ref_feats.detach()\n else:\n raise Exception\n img_pts = self.cross_att(valid_ref_feats, query_feat, valid_ref_points, \\\n flatten_img_feat, level_spatial_shapes, level_start_index).squeeze(0)\n final_img_pts[assign_idx] = img_pts\n if self.disentangle:\n recon_loss_all[assign_idx] += recon_loss\n kl_loss_all[assign_idx] += kl_loss\n if self.disentangle:\n recon_loss_all = torch.sum(recon_loss_all) / self.mid_channels ** 0.5\n kl_loss_all = torch.sum(kl_loss_all) / self.mid_channels ** 0.5\n num_valid_samples = torch.sum(assign_mask.int())\n return final_img_pts, recon_loss_all, kl_loss_all, num_valid_samples\n return final_img_pts\n","repo_name":"zehuichen123/AutoAlignV2","sub_path":"mmdet3d/models/fusion_layers/multi_voxel_deform_fusion_v2.py","file_name":"multi_voxel_deform_fusion_v2.py","file_ext":"py","file_size_in_byte":22200,"program_lang":"python","lang":"en","doc_type":"code","stars":133,"dataset":"github-code","pt":"78"}
+{"seq_id":"4098020680","text":"# binary search, sort\n\n# time O(nlogn)\n# space O(n)\n\nclass Solution:\n def numSubseq(self, nums: List[int], target: int) -> int:\n n = len(nums)\n mod = 10 ** 9 + 7\n nums.sort()\n ret = 0\n \n for i in range(n):\n j = bisect.bisect_right(nums, target-nums[i]) - 1\n if j >= i:\n ret += pow(2, j-i, mod)\n\n return ret % mod\n \n# two pointers, sort\n\n# time O(nlogn)\n# space O(1)\n\nclass Solution:\n def numSubseq(self, nums: List[int], target: int) -> int:\n n = len(nums)\n nums.sort()\n mod = 10 ** 9 + 7\n ret = 0\n l = 0\n r = n-1\n\n while l <= r:\n if nums[l] + nums[r] <= target:\n ret = (ret + pow(2, r-l, mod)) % mod\n l += 1\n else:\n r -= 1\n\n return ret\n","repo_name":"boknowswiki/mytraning","sub_path":"lc/python/1498_number_of_subsequences_that_satisfy_the_give_sum_condition.py","file_name":"1498_number_of_subsequences_that_satisfy_the_give_sum_condition.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"74584852731","text":"# Import required modules\nimport phpserialize\nimport traceback\nimport asyncio\nimport hashlib\nimport os.path\nimport subprocess\nimport urllib\nfrom datetime import datetime, timedelta\nimport inspect\nimport tempfile\nimport shutil\nfrom cogs.misc.exceptions import HoNAuthenticationError, HoNServerError\nfrom cogs.connectors.masterserver_connector import MasterServerHandler\nfrom cogs.connectors.chatserver_connector import ChatServerHandler\nfrom cogs.TCP.game_packet_lsnr import handle_clients\nfrom cogs.TCP.auto_ping_lsnr import AutoPingListener\nfrom cogs.connectors.api_server import start_api_server\nfrom cogs.game.game_server import GameServer\nfrom cogs.game.cow_master import CowMaster\nfrom cogs.handlers.commands import Commands\nfrom cogs.handlers.events import stop_event, ReplayStatus, GameStatus, GamePhase, GameServerCommands, EventBus as ManagerEventBus\nfrom cogs.misc.logger import get_logger, get_misc, get_home, get_mqtt, get_filebeat_status, get_filebeat_auth_url\nfrom pathlib import Path\nfrom cogs.game.healthcheck_manager import HealthCheckManager\nfrom enum import Enum\nfrom os.path import exists\nfrom utilities.filebeat import main as filebeat, filebeat_status, get_filebeat_auth_url\nimport random\n\nLOGGER = get_logger()\nMISC = get_misc()\nHOME_PATH = get_home()\nHON_WAS_VERSION_URL = \"http://gitea.kongor.online/administrator/KONGOR/raw/branch/main/patch/was-crIac6LASwoafrl8FrOa/x86_64/version.cfg\"\nHON_WAS_LAUNCHER_DOWNLOAD_URL = \"http://gitea.kongor.online/administrator/KONGOR/raw/branch/main/patch/was-crIac6LASwoafrl8FrOa/x86_64/hon_update_x64.zip\"\nHON_LAS_VERSION_URL = \"http://gitea.kongor.online/administrator/KONGOR/raw/branch/main/patch/las-crIac6LASwoafrl8FrOa/x86-biarch/version.cfg\"\nHON_LAS_LAUNCHER_DOWNLOAD_URL = \"http://gitea.kongor.online/administrator/KONGOR/raw/branch/main/patch/las-crIac6LASwoafrl8FrOa/x86-biarch/launcher.zip\"\n\nclass GameServerManager:\n def __init__(self, global_config, setup):\n \"\"\"\n Initializes a new GameServerManager object.\n\n Args:\n global_config (dict): A dictionary containing the global configuration for the game server.\n \"\"\"\n self.global_config = global_config\n \"\"\"\n Event Subscriptions. These are used to call other parts of the code in an event-driven approach within async functions.\n \"\"\"\n self.event_bus = ManagerEventBus()\n self.event_bus.subscribe('handle_replay_request', self.handle_replay_request)\n self.event_bus.subscribe('authenticate_to_chat_svr', self.authenticate_and_handle_chat_server)\n self.event_bus.subscribe('start_game_servers', self.start_game_servers)\n self.event_bus.subscribe('start_game_servers_task', self.start_game_servers_task)\n self.event_bus.subscribe('add_game_servers', self.create_dynamic_game_server)\n self.event_bus.subscribe('remove_game_servers', self.remove_dynamic_game_server)\n self.event_bus.subscribe('remove_game_server', self.remove_game_server)\n self.event_bus.subscribe('balance_game_server_count', self.balance_game_server_count)\n self.event_bus.subscribe('enable_game_server', self.enable_game_server)\n self.event_bus.subscribe('disable_game_server', self.disable_game_server)\n self.event_bus.subscribe('cmd_message_server', self.cmd_message_server)\n self.event_bus.subscribe('cmd_shutdown_server', self.cmd_shutdown_server)\n self.event_bus.subscribe('cmd_wake_server', self.cmd_wake_server)\n self.event_bus.subscribe('cmd_sleep_server', self.cmd_sleep_server)\n self.event_bus.subscribe('cmd_custom_command', self.cmd_custom_command)\n self.event_bus.subscribe('fork_server_from_cowmaster', self.fork_server_from_cowmaster),\n self.event_bus.subscribe('config_change_hook_actions', self.config_change_hook_actions),\n # self.event_bus.subscribe('start_gameserver_from_cowmaster', self.start_gameserver_from_cowmaster)\n self.event_bus.subscribe('patch_server', self.initialise_patching_procedure)\n self.event_bus.subscribe('update', self.update)\n self.event_bus.subscribe('check_for_restart_required', self.check_for_restart_required)\n self.event_bus.subscribe('resubmit_match_stats_to_masterserver', self.resubmit_match_stats_to_masterserver)\n self.event_bus.subscribe('update_server_start_semaphore', self.update_server_start_semaphore)\n self.tasks = {\n 'cli_handler':None,\n 'health_checks':None,\n 'autoping_listener':None,\n 'gameserver_listener':None,\n 'authentication_handler':None,\n 'gameserver_startup':None,\n 'task_cleanup': None\n }\n self.schedule_task(self.cleanup_tasks_every_30_minutes(), 'task_cleanup')\n self.schedule_task(self.heartbeat(), 'heartbeat')\n # initialise the config validator in case we need it\n self.setup = setup\n\n # set the current state of patching\n self.patching = False\n\n # preserve the current system path. We need it for a silly fix.\n self.preserved_path = os.environ[\"PATH\"]\n\n # Initialize dictionaries to store game servers and client connections\n self.server_start_semaphore = asyncio.Semaphore(self.global_config['hon_data']['svr_max_start_at_once']) # 2 max servers starting at once\n self.game_servers = {}\n self.client_connections = {}\n\n self.cowmaster = CowMaster(self.global_config['hon_data']['svr_starting_gamePort'] - 2, self.global_config)\n\n # Initialize a Commands object for sending commands to game servers\n self.commands = Commands(self.game_servers, self.client_connections, self.global_config, self.event_bus, self.cowmaster)\n # Initialise the autoping listener object\n self.auto_ping_listener = AutoPingListener(self.global_config, self.global_config['hon_data']['autoping_responder_port'])\n # Create game server instances\n LOGGER.info(f\"Manager running, starting {self.global_config['hon_data']['svr_total']} servers. Staggered start ({self.global_config['hon_data']['svr_max_start_at_once']} at a time)\")\n self.create_all_game_servers()\n\n coro = self.commands.handle_input()\n self.schedule_task(coro, 'cli_handler')\n\n # Start running health checks\n\n # Initialize MasterServerHandler and send requests\n self.chat_server_handler = None\n self.chat_server_connected = None\n self.master_server_connected = None\n self.master_server_handler = MasterServerHandler(master_server=self.global_config['hon_data']['svr_masterServer'], patch_server=self.global_config['hon_data']['svr_patchServer'], version=self.global_config['hon_data']['svr_version'], architecture=f'{self.global_config[\"hon_data\"][\"architecture\"]}', event_bus=self.event_bus)\n self.health_check_manager = HealthCheckManager(self.game_servers, self.event_bus, self.check_upstream_patch, self.resubmit_match_stats_to_masterserver, self.global_config)\n\n coro = self.health_check_manager.run_health_checks()\n self.schedule_task(coro, 'health_checks')\n\n MISC.save_last_working_branch()\n\n def cleanup_tasks(self, tasks_dict, current_time):\n for task_name, task in list(tasks_dict.items()): # Use list() to avoid \"dictionary changed size during iteration\" error\n if task is None:\n continue\n\n if not isinstance(task, asyncio.Task):\n LOGGER.error(f\"Item '{task_name}' in tasks is not a Task object.\")\n return\n\n if task.done() and task.exception() is None and task.end_time + timedelta(minutes=30) < current_time:\n del tasks_dict[task_name]\n\n async def cleanup_tasks_every_30_minutes(self):\n while True:\n current_time = datetime.now()\n # Iterate over all game servers and the manager\n for game_server in self.game_servers.values():\n self.cleanup_tasks(game_server.tasks, current_time)\n self.cleanup_tasks(self.tasks, current_time)\n for _ in range(30 * 60):\n if stop_event.is_set():\n break\n await asyncio.sleep(1)\n\n def schedule_task(self, coro, name, override = False):\n existing_task = self.tasks.get(name) # Get existing task if any\n\n if existing_task is not None:\n if not isinstance(existing_task, asyncio.Task):\n LOGGER.error(f\"Item '{name}' in tasks is not a Task object.\")\n # Choose one of the following lines, depending on your requirements:\n # raise ValueError(f\"Item '{name}' in tasks is not a Task object.\") # Option 1: raise an error\n existing_task = None # Option 2: ignore the non-Task item and overwrite it later\n\n if existing_task:\n if existing_task.done():\n if not existing_task.cancelled():\n # If the task has finished and was not cancelled, retrieve any possible exception to avoid 'unretrieved exception' warnings\n exception = existing_task.exception()\n if exception:\n LOGGER.error(f\"The previous task '{name}' raised an exception: {exception}. We are scheduling a new one.\")\n else:\n LOGGER.debug(f\"The previous task '{name}' was cancelled.\")\n else:\n if not override:\n # Task is still running\n LOGGER.debug(f\"Task '{name}' is still running, new task not scheduled.\")\n return existing_task # Return existing task\n\n # Create and register the new task\n task = asyncio.create_task(coro)\n task.add_done_callback(lambda t: setattr(t, 'end_time', datetime.now()))\n self.tasks[name] = task\n return task\n \n async def cmd_shutdown_server(self, game_server=None, force=False, delay=0, delete=False, disable=True, kill=False):\n try:\n if game_server is None: return False\n client_connection = self.client_connections.get(game_server.port, None)\n await asyncio.sleep(delay)\n if client_connection:\n if force:\n client_connection.writer.write(GameServerCommands.COMMAND_LEN_BYTES.value)\n client_connection.writer.write(GameServerCommands.SHUTDOWN_BYTES.value)\n await client_connection.writer.drain()\n LOGGER.info(f\"Command - Shutdown packet sent to GameServer #{game_server.id}. FORCED.\")\n if get_mqtt():\n get_mqtt().publish_json(\"manager/command\", {\"event_type\":\"server_shutdown_force\"})\n return True\n else:\n game_server.schedule_task(game_server.schedule_shutdown_server(delete=delete, disable=disable),'scheduled_shutdown')\n # await asyncio.sleep(0) # allow the scheduled task to be executed\n LOGGER.info(f\"Command - Shutdown packet sent to GameServer #{game_server.id}. Scheduled.\")\n if get_mqtt():\n get_mqtt().publish_json(\"manager/command\", {\"event_type\":\"server_shutdown_scheduled\"})\n return True\n else:\n # this server hasn't connected to the manager yet\n await game_server.stop_server_exe(disable=disable, delete=delete, kill=kill)\n game_server.reset_game_state()\n return True\n except Exception as e:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def cmd_wake_server(self, game_server):\n try:\n client_connection = self.client_connections.get(game_server.port, None)\n if not client_connection: return\n\n # TODO: use client_connection.send_packet() ??\n client_connection.writer.write(GameServerCommands.COMMAND_LEN_BYTES.value)\n client_connection.writer.write(GameServerCommands.WAKE_BYTES.value)\n await client_connection.writer.drain()\n\n LOGGER.info(f\"Command - Wake command sent to GameServer #{game_server.id}.\")\n except Exception as e:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def cmd_sleep_server(self, game_server):\n try:\n client_connection = self.client_connections.get(game_server.port, None)\n if not client_connection: return\n\n # TODO: use client_connection.send_packet() ??\n client_connection.writer.write(GameServerCommands.COMMAND_LEN_BYTES.value)\n client_connection.writer.write(GameServerCommands.SLEEP_BYTES.value)\n await client_connection.writer.drain()\n\n LOGGER.info(f\"Command - Sleep command sent to GameServer #{game_server.id}.\")\n except Exception as e:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def cmd_message_server(self, game_server, message):\n try:\n client_connection = self.client_connections.get(game_server.port, None)\n if client_connection is None:\n return\n\n if isinstance(message, list): message = (' ').join(message)\n message_bytes = GameServerCommands.MESSAGE_BYTES.value + message.encode('ascii') + b'\\x00'\n length = len(message_bytes)\n length_bytes = length.to_bytes(2, byteorder='little')\n\n # TODO: use client_connection.send_packet() ??\n client_connection.writer.write(length_bytes)\n client_connection.writer.write(message_bytes)\n await client_connection.writer.drain()\n LOGGER.info(f\"Command - Message command sent to GameServer #{game_server.id}.\")\n except Exception:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def cmd_custom_command(self, game_server, command, delay = 0):\n try:\n client_connection = self.client_connections.get(game_server.port, None)\n if client_connection is None:\n return\n await asyncio.sleep(delay)\n\n if isinstance(command, list): command = (' ').join(command)\n command_bytes = GameServerCommands.COMMAND_BYTES.value + command.encode('ascii') + b'\\x00'\n length = len(command_bytes)\n length_bytes = length.to_bytes(2, byteorder='little')\n\n # TODO: use client_connection.send_packet() ??\n client_connection.writer.write(length_bytes)\n client_connection.writer.write(command_bytes)\n await client_connection.writer.drain()\n if get_mqtt():\n get_mqtt().publish_json(\"manager/command\", {\"event_type\":\"custom_command\",\"command\":command})\n LOGGER.info(f\"Command - command sent to GameServer #{game_server.id}.\")\n except Exception:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def cmd_cowmaster_fork(self, instance_number, port):\n try:\n client_connection = self.client_connections.get(self.cowmaster.get_port(), None)\n\n if client_connection is None:\n return\n\n command_bytes = b'\\x28' + instance_number.to_bytes(1, \"little\") + port.to_bytes(2, \"little\") + b'\\x00'\n\n #command_bytes = b'\\x28\\x01\\x11\\x27\\x00'\n length = len(command_bytes)\n length_bytes = length.to_bytes(2, byteorder='little')\n\n client_connection.writer.write(length_bytes)\n client_connection.writer.write(command_bytes)\n await client_connection.writer.drain()\n\n LOGGER.info(f\"Command - command sent to CowMaster.\")\n except Exception:\n LOGGER.exception(f\"An error occurred while handling the {inspect.currentframe().f_code.co_name} function: {traceback.format_exc()}\")\n\n async def fork_server_from_cowmaster(self, game_server):\n try:\n await self.cowmaster.fork_new_server(game_server)\n except Exception:\n LOGGER.error(traceback.format_exc())\n\n async def start_gameserver_from_cowmaster(self, num = \"all\"):\n try:\n starting_port = self.global_config.get(\"hon_data\").get(\"svr_starting_gamePort\")\n if num == \"all\":\n number_of_instances = self.global_config.get(\"hon_data\").get(\"svr_total\")\n else:\n number_of_instances = num\n\n for i in range(number_of_instances):\n print(i)\n await self.cmd_cowmaster_fork(i+1, starting_port)\n starting_port += 1\n except Exception as e:\n LOGGER.exception(e)\n \n async def heartbeat(self):\n while not stop_event.is_set():\n # Introduce jitter: sleep for a random duration between 0 to 10 seconds\n jitter = random.uniform(0, 10)\n await asyncio.sleep(jitter)\n \n for _ in range(60):\n if stop_event.is_set():\n return\n await asyncio.sleep(1)\n if get_mqtt():\n get_mqtt().publish_json(\"manager/status\", {\"event_type\":\"heartbeat\", **self.manager_status()})\n \n def manager_status(self):\n total_free_servers = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.IDLE.value])\n total_occupied_servers = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] != GamePhase.IDLE.value])\n total_servers_in_lobby = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.IN_LOBBY.value])\n total_servers_in_picking_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.PICKING_PHASE.value])\n total_servers_in_banning_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.BANNING_PHASE.value])\n total_servers_in_game_ended_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.GAME_ENDED.value])\n total_servers_in_game_ending_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.GAME_ENDING.value])\n total_servers_in_match_started_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.MATCH_STARTED.value])\n total_servers_in_preparation_phase = len([game_server for game_server in self.game_servers.values() if game_server.game_state._state['game_phase'] == GamePhase.PREPERATION_PHASE.value])\n total_players_online = sum(game_server.game_state._state['num_clients'] for game_server in self.game_servers.values())\n \n return {\n \"total_free_servers\": total_free_servers,\n \"total_occupied_servers\": total_occupied_servers,\n \"total_servers_in_lobby\": total_servers_in_lobby,\n \"total_servers_in_picking_phase\": total_servers_in_picking_phase,\n \"total_servers_in_banning_phase\": total_servers_in_banning_phase,\n \"total_servers_in_game_ended_phase\": total_servers_in_game_ended_phase,\n \"total_servers_in_game_ending_phase\": total_servers_in_game_ending_phase,\n \"total_servers_in_match_started_phase\": total_servers_in_match_started_phase,\n \"total_servers_in_preparation_phase\": total_servers_in_preparation_phase,\n \"total_players_online\": total_players_online,\n \"total_configured_servers\": len(self.game_servers)\n }\n\n async def check_upstream_patch(self):\n if self.patching:\n LOGGER.info(\"Server patching is ongoing.. Please wait.\")\n return\n\n local_svr_version = MISC.get_svr_version(self.global_config['hon_data']['hon_executable_path'])\n\n try:\n patch_information = await self.master_server_handler.compare_upstream_patch()\n if not patch_information:\n LOGGER.error(\"Checking the upstream patch version failed, as the upstream services were unavailable.\")\n return\n if patch_information[1] != 200:\n LOGGER.error(f\"Checking the upstream patch version failed with: [{patch_information[1]}] {patch_information[0]}\")\n return\n parsed_patch_information = phpserialize.loads(patch_information[0].encode('utf-8'))\n parsed_patch_information = {key.decode() if isinstance(key, bytes) else key: (value.decode() if isinstance(value, bytes) else value) for key, value in parsed_patch_information.items()}\n LOGGER.debug(f\"Upstream patch information: {parsed_patch_information}\")\n self.latest_available_game_version = parsed_patch_information['latest']\n\n if local_svr_version != self.latest_available_game_version:\n LOGGER.info(f\"A newer patch is available. Initiating server shutdown for patching.\\n\\tUpgrading from {local_svr_version} --> {parsed_patch_information['latest']}\")\n return True\n\n return False\n\n except Exception:\n LOGGER.error(f\"{traceback.format_exc()}\")\n\n async def start_autoping_listener(self):\n LOGGER.debug(\"Starting AutoPingListener...\")\n await self.auto_ping_listener.start_listener()\n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"autoping_started\"})\n\n async def start_api_server(self):\n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"api_started\"})\n await start_api_server(self.global_config, self.game_servers, self.tasks, self.health_check_manager.tasks, self.event_bus, self.find_replay_file, port=self.global_config['hon_data']['svr_api_port'])\n\n async def start_game_server_listener(self, host, game_server_to_mgr_port):\n \"\"\"\n Starts a listener for incoming client connections on the specified host and port\n\n Args:\n host (str): the host to listen on\n game_server_to_mgr_port (int): the port to listen on\n\n Returns:\n None\n \"\"\"\n\n # Start the listener for incoming client connections\n self.game_server_lsnr = await asyncio.start_server(\n lambda *args, **kwargs: handle_clients(*args, **kwargs, game_server_manager=self),\n host, game_server_to_mgr_port\n )\n LOGGER.highlight(f\"[*] HoNfigurator Manager - Listening on {host}:{game_server_to_mgr_port} (LOCAL)\")\n \n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"gameserver_listener_started\"})\n\n await stop_event.wait()\n\n # Close all client connections\n for connection in list(self.client_connections.values()):\n await connection.close()\n\n # Close the server\n self.game_server_lsnr.close()\n await self.game_server_lsnr.wait_closed()\n\n await self.master_server_handler.close_session()\n\n LOGGER.info(\"Stopping HoNfigurator manager listener.\")\n\n def update(self):\n MISC.update_github_repository()\n MISC.save_last_working_branch()\n\n async def send_auth_request_to_masterserver(self):\n \"\"\"\n Send a request to the master server to authenticate the game server.\n\n This function sends a request to the master server to authenticate the game server using the\n replay_auth method. If the authentication is successful, it returns the parsed response.\n\n Returns:\n dict: A dictionary containing the parsed response from the master server.\n\n Raises:\n HoNAuthenticationError: If the authentication fails.\n \"\"\"\n mserver_auth_response = await self.master_server_handler.send_replay_auth(f\"{self.global_config['hon_data']['svr_login']}:\", hashlib.md5(self.global_config['hon_data']['svr_password'].encode()).hexdigest())\n \n if mserver_auth_response[1] != 200:\n prefix = (f\"[{mserver_auth_response[1]}] Authentication to MasterServer failed. \")\n if mserver_auth_response[1] in [401, 403]:\n LOGGER.error(f\"{prefix}Please ensure your username and password are correct in {HOME_PATH / 'config' / 'config.json'}\")\n if mserver_auth_response[1] == 401:\n msg = 'Credentials incorrect'\n else:\n msg = 'Credentials correct, but no permissions to host.'\n self.set_masterserver_status(False, msg)\n \n elif mserver_auth_response[1] > 500 and mserver_auth_response[1] < 600:\n LOGGER.error(f\"{prefix}The issue is most likely server side.\")\n self.set_masterserver_status(False,'Server side issue, master server is probably down.')\n raise HoNAuthenticationError(f\"[{mserver_auth_response[1]}] Authentication error.\")\n \n LOGGER.highlight(\"Authenticated to MasterServer.\")\n self.set_masterserver_status(True)\n\n parsed_mserver_auth_response = phpserialize.loads(mserver_auth_response[0].encode('utf-8'))\n parsed_mserver_auth_response = {key.decode(): (value.decode() if isinstance(value, bytes) else value) for key, value in parsed_mserver_auth_response.items()}\n self.master_server_handler.set_server_id(parsed_mserver_auth_response['server_id'])\n self.master_server_handler.set_cookie(parsed_mserver_auth_response['session'])\n\n return parsed_mserver_auth_response\n\n\n async def manage_upstream_connections(self, udp_ping_responder_port, retry=30):\n \"\"\"\n Authenticate the game server with the master server and connect to the chat server.\n\n This function sends a request to the master server to authenticate the game server, and then\n connects to the chat server and authenticates the game server with the chat server. It also starts\n handling packets from the chat server.\n\n Args:\n udp_ping_responder_port (int): The port to use for the UDP ping responder.\n\n Returns:\n None\n \"\"\"\n while not stop_event.is_set():\n try:\n # Send requests to the master server\n parsed_mserver_auth_response = await self.send_auth_request_to_masterserver()\n\n # Connect to the chat server and authenticate\n await self.authenticate_and_handle_chat_server(parsed_mserver_auth_response, udp_ping_responder_port)\n\n except (HoNAuthenticationError, ConnectionResetError, Exception ) as e:\n LOGGER.error(f\"{e.__class__.__name__} occurred. Retrying in {retry} seconds...\")\n self.set_chatserver_status(False, e)\n\n for _ in range(retry):\n if stop_event.is_set():\n break\n await asyncio.sleep(1)\n\n LOGGER.info(\"Stopping authentication handlers\")\n async def authenticate_and_handle_chat_server(self, parsed_mserver_auth_response, udp_ping_responder_port):\n # Create a new ChatServerHandler instance and connect to the chat server\n self.chat_server_handler = ChatServerHandler(\n parsed_mserver_auth_response[\"chat_address\"],\n parsed_mserver_auth_response[\"chat_port\"],\n parsed_mserver_auth_response[\"session\"],\n parsed_mserver_auth_response[\"server_id\"],\n username=self.global_config['hon_data']['svr_login'],\n version=self.global_config['hon_data']['svr_version'],\n region=self.global_config['hon_data']['svr_location'],\n server_name=self.global_config['hon_data']['svr_name'],\n ip_addr=self.global_config['hon_data']['svr_ip'],\n udp_ping_responder_port=udp_ping_responder_port,\n event_bus=self.event_bus\n )\n\n # connect and authenticate to chatserver\n chat_auth_response = await self.chat_server_handler.connect()\n\n if not chat_auth_response:\n raise HoNAuthenticationError(f\"Chatserver authentication failure\")\n\n LOGGER.highlight(\"Authenticated to ChatServer.\")\n \n self.set_chatserver_status(True)\n\n # Start handling packets from the chat server\n await self.chat_server_handler.handle_packets()\n\n async def resubmit_match_stats_to_masterserver(self, match_id, file_path):\n mserver_stats_response = await self.master_server_handler.send_stats_file(f\"{self.global_config['hon_data']['svr_login']}:\", hashlib.md5(self.global_config['hon_data']['svr_password'].encode()).hexdigest(), match_id, file_path)\n if not mserver_stats_response or mserver_stats_response[1] != 200 or mserver_stats_response[0] == '':\n LOGGER.error(f\"[{mserver_stats_response[1] if mserver_stats_response else 'unknown'}] Stats resubmission failed - {file_path}. Response: {mserver_stats_response[0] if mserver_stats_response else 'unknown'}\")\n if mserver_stats_response and mserver_stats_response[1] == 400 and 'title' in mserver_stats_response[0]:\n if mserver_stats_response[0] == \"One or more validation errors occurred.\":\n try:\n shutil.move(file_path, f\"{file_path}.failed\")\n except Exception:\n LOGGER.error(traceback.format_exc())\n return False\n LOGGER.info(f\"{match_id} Stats resubmission successful\")\n parsed_mserver_stats_response = phpserialize.loads(mserver_stats_response[0].encode('utf-8'))\n parsed_mserver_stats_response = {key.decode() if isinstance(key, bytes) else key: (value.decode() if isinstance(value, bytes) else value) for key, value in parsed_mserver_stats_response.items()}\n\n return True\n\n def set_server_status(self, server_type, connected, info=''):\n current_status = getattr(self, f\"{server_type}_connected\", None)\n mqtt = get_mqtt()\n\n if mqtt:\n if server_type == 'chatsv':\n mqtt.set_chatsv_state(connected)\n else:\n mqtt.set_mastersv_state(connected)\n \n if current_status != connected:\n setattr(self, f\"{server_type}_connected\", connected)\n state_info = 'connected' if connected else 'disconnected'\n if mqtt:\n mqtt.publish_json(\"manager/admin\", {\"event_type\": f\"{server_type}_state_change\", \"info\": state_info})\n\n connection_event_type = f\"{server_type}_connection_succeeded\" if connected else f\"{server_type}_connection_failed\"\n if mqtt:\n mqtt.publish_json(\"manager/admin\", {\"event_type\": connection_event_type, \"info\": str(info)})\n\n def set_chatserver_status(self, connected, info=''):\n self.set_server_status('chatsv', connected, info)\n\n def set_masterserver_status(self, connected, info=''):\n self.set_server_status('mastersv', connected, info)\n\n\n def create_all_game_servers(self):\n for id in range (1,self.global_config['hon_data']['svr_total']+1):\n port = self.global_config['hon_data']['svr_starting_gamePort'] + id - 1\n self.create_game_server(port)\n\n def create_game_server(self, game_server_port):\n \"\"\"\n Creates a new game server instance and adds it to the game server dictionary\n\n Args:\n id (int): the ID of the new game server\n game_server_port (int): the port of the new game server\n\n Returns:\n None\n \"\"\"\n id = game_server_port - self.global_config['hon_data']['svr_starting_gamePort'] + 1\n game_server = GameServer(id, game_server_port, self.global_config, self.remove_game_server, self.event_bus)\n self.game_servers[game_server_port] = game_server\n return game_server\n\n def find_next_available_ports(self):\n \"\"\"\n Finds the next available port for creating a new game server\n\n Returns:\n int or None: the next available port, or None if no ports are available\n \"\"\"\n starting_game_port = self.global_config['hon_data']['svr_starting_gamePort']\n total_allowed_servers = MISC.get_total_allowed_servers(self.global_config['hon_data']['svr_total_per_core'])\n\n for i in range(total_allowed_servers):\n game_port = starting_game_port + i\n\n if game_port not in self.game_servers:\n return game_port\n\n return None\n\n async def balance_game_server_count(self, to_add=0, to_remove=0):\n \"\"\"\n Ensures that the maximum number of game servers are running by creating new game servers\n and removing existing game servers as needed.\n\n Returns:\n None\n \"\"\"\n max_servers = self.global_config['hon_data']['svr_total']\n if to_add == \"all\":\n max_servers = MISC.get_total_allowed_servers(self.global_config['hon_data']['svr_total_per_core'])\n elif to_add > 0:\n max_servers += to_add\n\n if to_remove == \"all\":\n max_servers = 0\n elif to_remove > 0:\n max_servers -= to_remove\n\n if max_servers < 0: max_servers = 0\n self.global_config['hon_data']['svr_total'] = max_servers\n\n self.setup.validate_hon_data(self.global_config['hon_data'])\n\n idle_servers = [game_server for game_server in self.game_servers.values() if game_server.get_dict_value('status') != 3]\n occupied_servers = [game_server for game_server in self.game_servers.values() if game_server.get_dict_value('status') == 3]\n total_num_servers = len(occupied_servers) + len(idle_servers)\n num_servers_to_remove = max(total_num_servers - max_servers, 0)\n num_servers_to_create = max(max_servers - total_num_servers, 0)\n\n if num_servers_to_create > 0:\n start_servers = []\n for i in range(num_servers_to_create):\n game_port = self.find_next_available_ports()\n\n if game_port is not None:\n game_server = self.create_game_server(game_port)\n start_servers.append(game_server)\n LOGGER.info(f\"Game server created at game_port: {game_port}\")\n else:\n LOGGER.warn(\"No available ports for creating a new game server.\")\n coro = self.start_game_servers(start_servers)\n self.schedule_task(coro, 'gameserver_startup', override = True)\n\n async def remove_servers(servers, server_type):\n servers_removed = 0\n servers_to_remove = []\n for game_server in servers:\n await self.cmd_shutdown_server(game_server, delete=True)\n if not game_server.delete_me:\n servers_to_remove.append(game_server.port)\n servers_removed += 1\n if servers_removed >= num_servers_to_remove:\n break\n # for port in servers_to_remove:\n # await asyncio.sleep(0.1)\n # if port in self.game_servers:\n # game_server.cancel_tasks()\n # del self.game_servers[port]\n\n LOGGER.info(f\"Removed {servers_removed} {server_type} game servers. {total_num_servers - servers_removed} game servers are now running.\")\n return servers_removed\n\n if num_servers_to_remove > 0:\n removed_idle = await remove_servers(idle_servers, 'idle')\n num_servers_to_remove -= removed_idle\n if num_servers_to_remove > 0:\n removed_occupied = await remove_servers(occupied_servers, 'occupied')\n elif num_servers_to_remove < 0:\n LOGGER.warn(\"Number of running game servers is greater than the maximum number of game servers.\")\n\n\n async def create_dynamic_game_server(self):\n \"\"\"\n Creates new game server instances with the next available ports until the maximum number of servers is reached\n\n Returns:\n None\n \"\"\"\n max_servers = self.global_config['hon_data']['svr_total']\n running_servers = [game_server for game_server in self.game_servers.values() if game_server.started]\n num_servers_to_create = max_servers - len(running_servers)\n if num_servers_to_create <= 0:\n return\n\n start_servers = []\n for i in range(num_servers_to_create):\n game_port = self.find_next_available_ports()\n\n if game_port is not None:\n game_server = self.create_game_server(game_port)\n start_servers.append(game_server)\n LOGGER.info(f\"Game server created at game_port: {game_port}\")\n else:\n LOGGER.warn(\"No available ports for creating a new game server.\")\n\n coro = self.start_game_servers(start_servers)\n self.schedule_task(coro, 'gameserver_startup', override = True)\n\n async def check_for_restart_required(self, game_server='all'):\n if game_server == 'all':\n for game_server in self.game_servers.values():\n if game_server.params_are_different():\n await self.cmd_shutdown_server(game_server,disable=False)\n if self.cowmaster.client_connection:\n self.cowmaster.stop_cow_master(disable=False)\n await asyncio.sleep(0.1)\n game_server.enable_server()\n else:\n if game_server.params_are_different():\n await self.cmd_shutdown_server(game_server,disable=False)\n if self.cowmaster.client_connection:\n self.cowmaster.stop_cow_master(disable=False)\n await asyncio.sleep(0.1)\n game_server.enable_server()\n \n async def config_change_hook_actions(self):\n if not self.global_config['hon_data'].get('man_use_cowmaster') and self.cowmaster.client_connection:\n self.cowmaster.stop_cow_master()\n elif self.global_config['hon_data'].get('man_use_cowmaster') and not self.cowmaster.client_connection:\n await self.cowmaster.start_cow_master()\n\n async def remove_dynamic_game_server(self):\n max_servers = self.global_config['hon_data']['svr_total']\n running_servers = [game_server for game_server in self.game_servers.values() if game_server.get_dict_value('match_started') != 0]\n num_servers_to_remove = len(running_servers) - max_servers\n if num_servers_to_remove <= 0:\n return\n\n servers_removed = 0\n for game_server in running_servers:\n if await self.cmd_shutdown_server(game_server):\n del self.game_servers[game_server.port]\n servers_removed += 1\n if servers_removed >= num_servers_to_remove:\n break\n\n LOGGER.info(f\"Removed {servers_removed} game servers. {max_servers} game servers are now running.\")\n\n # count = 0\n # for i in range(num):\n # removed = False\n # for port, game_server in self.game_servers.items():\n # if not game_server.started:\n # if self.remove_game_server(game_server):\n # LOGGER.info(f\"Removed game server {port}\")\n # count += 1\n # removed = True\n # break\n # if not removed:\n # LOGGER.info(f\"No more running game servers found after removing {count} game servers.\")\n # break\n # LOGGER.info(f\"Removed a total of {count} game servers.\")\n\n def remove_game_server(self, game_server):\n \"\"\"\n Removes a game server instance with the specified port from the game server dictionary\n\n Args:\n port (int): the port of the game server to remove\n\n Returns:\n None\n \"\"\"\n for key, value in self.game_servers.items():\n if value == game_server and not game_server.started:\n game_server.cancel_tasks()\n del self.game_servers[key]\n return True\n return False\n\n def get_game_server_by_id(self, id):\n \"\"\"\n Returns the game server instance with the specified ID\n\n Args:\n id (int): the ID of the game server to get\n\n Returns:\n GameServer or None: the game server instance, or None if no game server with the specified ID exists\n \"\"\"\n return self.game_servers.get(id)\n\n def get_game_server_by_port(self, game_server_port):\n \"\"\"\n Returns the game server instance with the specified port\n\n Args:\n game_server_port (int): the port of the game server to get\n\n Returns:\n GameServer or None: the game server instance, or None if no game server with the specified port exists\n \"\"\"\n if game_server_port == self.cowmaster.get_port():\n return self.cowmaster\n\n return self.game_servers.get(game_server_port, None)\n\n async def add_client_connection(self,client_connection, port):\n \"\"\"\n Adds a client connection to the client connection dictionary with the specified port as the key\n\n Args:\n client_connection (ClientConnection): the client connection instance to add\n port (int): the port associated with the client connection\n\n Returns:\n bool: True if the client connection was added successfully, False otherwise\n \"\"\"\n if port not in self.client_connections:\n self.client_connections[port] = client_connection\n if port == self.cowmaster.get_port():\n await self.cowmaster.set_client_connection(client_connection)\n self.cowmaster.status_received.set()\n\n else:\n game_server = self.game_servers.get(port, None)\n # this is in case game server doesn't exist (config change maybe)\n if game_server:\n game_server.status_received.set()\n await game_server.set_client_connection(client_connection)\n await self.check_for_restart_required(game_server)\n # TODO\n # Create game server object here? May be happening already in game_packet_lsnr.py (handle_client_connection)\n # The instance of this happening, is for example, someone is running 10 servers. They modify the config on the fly to be 5 servers. Servers 5-10 are scheduled for shutdown, but game server objects have been destroyed.\n # since the game server isn't actually off yet, it will keep creating a connection.\n return True\n \n else:\n #TODO: raise error or happy with logger?\n if port == self.cowmaster.get_port():\n LOGGER.debug(f\"Attempting to locate duplicate CowMaster server. Looking for TCP source port ({client_connection.addr[1]}) and TCP dest port ({self.global_config['hon_data']['svr_managerPort']})\")\n cowmaster_proc = MISC.get_client_pid_by_tcp_source_port(self.global_config['hon_data']['svr_managerPort'], client_connection.addr[1])\n if cowmaster_proc:\n LOGGER.info(f\"Found duplicate CowMaster server. Killing process {cowmaster_proc.pid}\")\n cowmaster_proc.terminate()\n return\n else:\n LOGGER.warn(\"There is a duplicate CowMaster and we're unable to identify it's PID (Process ID). This won't cause issues, but there may be some wasteful RAM usage.\")\n return\n LOGGER.error(f\"A GameServer connection is already established for port {port}, this is either a dead connection, or something is very wrong (two servers with the same port).\")\n return False\n\n async def find_replay_file(self,replay_file_name):\n replay_file_paths = [Path(self.global_config['hon_data']['hon_replays_directory']) / replay_file_name]\n if self.global_config['application_data']['longterm_storage']['active']:\n replay_file_paths.append(Path(self.global_config['application_data']['longterm_storage']['location']) / replay_file_name)\n\n for replay_path in replay_file_paths:\n file_exists = Path.exists(replay_path)\n if file_exists:\n replay_file_path = replay_path\n return True,replay_file_path\n\n async def handle_replay_request(self, match_id, extension, account_id):\n replay_file_name = f\"M{match_id}.{extension}\"\n LOGGER.debug(f\"Received replay upload request.\\n\\tFile Name: {replay_file_name}\\n\\tAccount ID (requestor): {account_id}\")\n\n replay_file_paths = [Path(self.global_config['hon_data']['hon_replays_directory']) / replay_file_name]\n if self.global_config['application_data']['longterm_storage']['active']:\n replay_file_paths.append(Path(self.global_config['application_data']['longterm_storage']['location']) / replay_file_name)\n file_exists,replay_file_path = await self.find_replay_file(replay_file_name)\n\n if not file_exists:\n # Send the \"does not exist\" packet\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.DOES_NOT_EXIST)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.DOES_NOT_EXIST)\n non_existing_paths = [str(path) for path in replay_file_paths]\n LOGGER.warn(f\"Replay file {replay_file_name} does not exist. Checked: {non_existing_paths}\")\n return\n\n # Send the \"exists\" packet\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.QUEUED)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.QUEUED)\n LOGGER.debug(f\"Replay file exists ({replay_file_name}). Obtaining upload location information.\")\n\n # Upload the file and send status updates as required\n file_size = os.path.getsize(replay_file_path)\n\n upload_details = await self.master_server_handler.get_replay_upload_info(match_id, extension, self.global_config['hon_data']['svr_login'], file_size)\n\n if upload_details is None or upload_details[1] != 200:\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.GENERAL_FAILURE)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.GENERAL_FAILURE)\n LOGGER.error(f\"{replay_file_name} - Failed to obtain upload location information. HTTP Response ({upload_details[1]}):\\n\\t{upload_details[0]}\")\n return\n\n upload_details_parsed = {key.decode(): (value.decode() if isinstance(value, bytes) else value) for key, value in upload_details[0].items()}\n LOGGER.debug(f\"Uploading {replay_file_name} to {upload_details_parsed['TargetURL']}\")\n\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.UPLOADING)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.UPLOADING)\n try:\n upload_result = await self.master_server_handler.upload_replay_file(replay_file_path, replay_file_name, upload_details_parsed['TargetURL'])\n except Exception:\n LOGGER.error(f\"Error uploading replay file {replay_file_path}\")\n LOGGER.error(f\"Undefined Exception: {traceback.format_exc()}\")\n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"replay_upload_failure\",\"message\":f\"failed. Premature failure.\"})\n\n if upload_result[1] not in [204,200]:\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.GENERAL_FAILURE)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.GENERAL_FAILURE)\n LOGGER.error(f\"Replay upload failed! HTTP Upload Response ({upload_result[1]})\\n\\t{upload_result[0]}\")\n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"replay_upload_failure\",\"message\":f\"failed. HTTP response code: {upload_result[1]}\"})\n return\n # await self.event_bus.emit('replay_status_update', match_id, account_id, ReplayStatus.UPLOAD_COMPLETE)\n res = await self.chat_server_handler.create_replay_status_update_packet(match_id, account_id, ReplayStatus.UPLOAD_COMPLETE)\n LOGGER.debug(\"Replay upload completed successfully.\")\n if get_mqtt():\n get_mqtt().publish_json(\"manager/admin\", {\"event_type\":\"replay_upload_success\",\"message\":\"successful\"})\n\n\n async def remove_client_connection(self,client_connection):\n \"\"\"\n Removes a client connection from the client connection dictionary with the specified port as the key\n\n Args:\n client_connection (ClientConnection): the client connection instance to remove\n port (int): the port associated with the client connection\n\n Returns:\n None\n \"\"\"\n for key, value in self.client_connections.items():\n if value == client_connection:\n LOGGER.debug(f\"GameServer #{client_connection.id} removing connection.\")\n del self.client_connections[key]\n game_server = self.game_servers.get(key, None)\n # This is in case game server doesn't exist intentionally (maybe config changed)\n if game_server:\n game_server.reset_game_state()\n game_server.unset_client_connection()\n # indicate that the sub commands should be regenerated since the list of connected servers has changed.\n # await self.commands.initialise_commands()\n self.commands.subcommands_changed.set()\n return True\n return False\n\n def update_server_start_semaphore(self):\n max_start_at_once = self.global_config['hon_data']['svr_max_start_at_once']\n self.server_start_semaphore = asyncio.Semaphore(max_start_at_once)\n\n async def start_game_servers_task(self, game_servers):\n coro = self.start_game_servers(game_servers)\n self.schedule_task(coro, 'gameserver_startup')\n\n async def start_game_servers(self, game_servers, timeout=120, launch=False, service_recovery=False, config_reload=True):\n try:\n timeout = self.global_config['hon_data']['svr_startup_timeout']\n \"\"\"\n Start all game servers.\n\n This function starts all the game servers that were created by the GameServerManager. It\n does this by calling the start_server method of each game server object.\n\n Game servers are started using a \"semaphore\", to stagger their start to groups and not all at once.\n The timeout value may be reached, for slow servers, it may need to be adjusted in the config file if required.\n\n This function does not return anything, but can log errors or other information.\n \"\"\"\n if MISC.get_os_platform() == \"win32\":\n # this is an atrocious fix until I find a better solution.\n # on some systems, the compiled honfigurator.exe file, which is just launcher.py from cogs.misc causes issues for the opened hon_x64.exe. The exe is unable to locate one of the game dll resources.\n # I wasted a lot of time trying to troubleshoot it, launching main.py directly works fine. This is my solution until a better one comes around. It's set within the scope of the script, and doesn't modify the systems environment.\n path_list = os.environ[\"PATH\"].split(os.pathsep)\n if str(self.global_config['hon_data']['hon_install_directory'] / 'game') not in path_list:\n os.environ[\"PATH\"] = f\"{self.global_config['hon_data']['hon_install_directory'] / 'game'}{os.pathsep}{self.preserved_path}\"\n\n if launch and await self.check_upstream_patch():\n if not await self.initialise_patching_procedure(source=\"startup\"):\n return False\n\n async def start_game_server_with_semaphore(game_server, timeout):\n game_server.game_state.update({'status':GameStatus.QUEUED.value})\n async with self.server_start_semaphore:\n # Use the schedule_task method to start the server\n if game_server not in self.game_servers.values():\n return\n\n # Ensure the task is actually a Task or Future\n task = asyncio.ensure_future(game_server.schedule_task(game_server.start_server(timeout=timeout), 'start_server'))\n try:\n # Ensure asyncio.wait_for(task, timeout) and stop_event.wait() are Tasks\n wait_for_task = asyncio.create_task(asyncio.wait_for(task, timeout))\n stop_event_wait_task = asyncio.create_task(stop_event.wait())\n\n # Prepare the tasks\n tasks = [wait_for_task, stop_event_wait_task]\n\n # Wait for any task to complete\n done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)\n # If the stop_event was set, cancel the other task and return\n if stop_event.is_set():\n for task in pending:\n task.cancel()\n LOGGER.info(f\"Shutting down uninitialised GameServer #{game_server.id} due to stop event.\")\n await self.cmd_shutdown_server(game_server)\n else:\n # The game server start task completed successfully\n # LOGGER.info(f\"GameServer #{game_server.id} started successfully.\")\n pass\n except asyncio.TimeoutError:\n LOGGER.error(f\"GameServer #{game_server.id} failed to start within the timeout period.\")\n await self.cmd_shutdown_server(game_server)\n except HoNServerError:\n # LOGGER.error(f\"GameServer #{game_server.id} encountered a server error.\")\n await self.cmd_shutdown_server(game_server)\n\n start_tasks = []\n if game_servers == \"all\":\n game_servers = list(self.game_servers.values())\n\n for game_server in game_servers:\n already_running = await game_server.get_running_server()\n if already_running:\n LOGGER.info(f\"GameServer #{game_server.id} with public ports {game_server.get_public_game_port()}/{game_server.get_public_voice_port()} already running.\")\n\n if self.global_config['hon_data'].get('man_use_cowmaster') and not self.cowmaster.client_connection:\n await self.cowmaster.start_cow_master()\n\n # setup or verify filebeat configuration for match log submission\n await filebeat_status()\n if launch:\n await filebeat(self.global_config)\n\n if not self.global_config['hon_data']['svr_start_on_launch']:\n LOGGER.info(\"Waiting for manual server start up. svr_start_on_launch setting is disabled.\")\n return\n\n if not service_recovery and not get_filebeat_status()['running']:\n msg = f\"Filebeat is not running, you may not start any game servers until you finalise the setup of filebeat.\\nStatus\\n\\tInstalled: {get_filebeat_status()['installed']}\\n\\tRunning: {get_filebeat_status()['running']}\\n\\tCertificate Status: {get_filebeat_status()['certificate_status']}\\n\\tPending Auth: {True if get_filebeat_auth_url() else False}\"\n if get_filebeat_auth_url():\n print(f\"Please authorise match log submissions to continue: {get_filebeat_auth_url()}\")\n raise RuntimeError(msg)\n\n if self.global_config['hon_data'].get('man_use_cowmaster'):\n if not self.cowmaster.client_connection:\n if launch or config_reload:\n i = 0\n incr = 5\n while not self.cowmaster.client_connection:\n if not config_reload: # prevent excessive prints\n LOGGER.warn(f\"Waiting for CowMaster to connect to manager before starting servers. Waiting {i}/{timeout} seconds\")\n await asyncio.sleep(incr)\n i += incr\n if i > timeout:\n return False\n else:\n LOGGER.warn(\"Cannot start servers. Cowmaster is in use, but not yet connected to the manager. Please wait and try again\")\n return\n\n for game_server in game_servers:\n start_tasks.append(start_game_server_with_semaphore(game_server, timeout))\n\n await asyncio.gather(*start_tasks)\n\n # indicate that the sub commands should be regenerated since the list of connected servers has changed.\n asyncio.create_task(self.commands.initialise_commands())\n self.commands.subcommands_changed.set()\n\n except Exception as e:\n LOGGER.error(f\"GameServers failed to start\\n{traceback.format_exc()}\")\n if not launch:\n raise\n\n async def patch_extract_crc_from_file(self, url):\n try:\n with urllib.request.urlopen(url) as response:\n content = response.read().decode('utf-8')\n # sample: 4.10.8.0;4.10.8.honpatch;B30B80D1;hon_update_x64.zip;4DFDFDD5\n components = content.strip().split(';')\n version = components[0]\n patch = components[1]\n hon_exe_crc = components[2]\n filename = components[3]\n hon_update_exe_crc = components[-1]\n return hon_update_exe_crc\n\n except Exception as e:\n LOGGER.error(f\"URL: {url} - Error occurred while extracting CRC from file: {e}\")\n return None\n\n async def initialise_patching_procedure(self, timeout=3600, source='startup'):\n if self.patching:\n LOGGER.warn(\"Patching is already in progress.\")\n return\n\n for game_server in self.game_servers.values():\n LOGGER.debug(f\"GameServer #{game_server.id} - Initialising server shutdown for patching\")\n if game_server.started and game_server.enabled:\n await self.cmd_message_server(game_server, \"!! ANNOUNCEMENT !! This server will shutdown after the current match for patching.\")\n await self.cmd_shutdown_server(game_server)\n\n if MISC.get_proc(self.global_config['hon_data']['hon_executable_name']):\n LOGGER.debug(\"Some HoN servers are still running. Waiting until they've shut down.\")\n return\n \n if MISC.get_os_platform() == \"win32\":\n launcher_binary = 'hon_update_x64.exe'\n launcher_zip = 'hon_update_x64.zip'\n hon_version_url = HON_WAS_VERSION_URL\n launcher_download_url = HON_WAS_LAUNCHER_DOWNLOAD_URL\n else:\n launcher_binary = 'launcher'\n launcher_zip = 'launcher.zip'\n hon_version_url = HON_LAS_VERSION_URL\n launcher_download_url = HON_LAS_LAUNCHER_DOWNLOAD_URL\n\n launcher_crc = await self.patch_extract_crc_from_file(hon_version_url)\n if not launcher_crc:\n LOGGER.error(\"Patching failed.\")\n return False\n if (not exists(self.global_config['hon_data']['hon_install_directory'] / launcher_binary)) or (launcher_crc and launcher_crc.lower() != MISC.calculate_crc32(self.global_config['hon_data']['hon_install_directory'] / launcher_binary).lower()):\n LOGGER.debug(f\"Beginning to download new launcher from {launcher_download_url}\")\n try:\n temp_folder = tempfile.TemporaryDirectory()\n temp_path = temp_folder.name\n temp_zip_path = Path(temp_path) / launcher_zip\n\n download_launcher = urllib.request.urlretrieve(launcher_download_url, temp_zip_path)\n if not download_launcher:\n LOGGER.warn(f\"Newer {launcher_zip} is available, however the download failed.\\n\\t1. Please download the file manually: {launcher_download_url}\\n\\t2. Unzip the file into {self.global_config['hon_data']['hon_install_directory']}\")\n return\n\n temp_extracted_path = temp_folder.name\n extracted_file_name = MISC.unzip_file(source_zip=temp_zip_path, dest_unzip=temp_extracted_path)\n\n temp_extracted_launcher_path = Path(temp_path) / extracted_file_name[0]\n\n launcher_binary_path = self.global_config['hon_data']['hon_install_directory'] / launcher_binary\n\n LOGGER.debug(f\"Downloaded launcher files: {os.listdir(temp_extracted_path)}\")\n\n # Check if the file is in use before moving it\n try:\n shutil.move(temp_extracted_launcher_path, launcher_binary_path)\n LOGGER.debug(f\"Moved extracted launcher to HoN working directory: {launcher_binary_path}\")\n except PermissionError:\n LOGGER.warn(f\"Hon Update - the file {self.global_config['hon_data']['hon_install_directory'] / launcher_zip} is currently in use. Closing the file..\")\n process = MISC.get_proc(proc_name=launcher_zip)\n if process: process.terminate()\n try:\n shutil.move(temp_extracted_launcher_path, launcher_binary_path)\n except Exception:\n LOGGER.error(f\"HoN Update - Failed to copy downloaded {launcher_binary} into {self.global_config['hon_data']['hon_install_directory']}\\n\\t1. Please download the file manually: {launcher_download_url}\\n\\t2. Unzip the file into {self.global_config['hon_data']['hon_install_directory']}\")\n return\n\n except Exception as e:\n LOGGER.error(f\"Error occurred during file download or extraction: {e}\")\n\n patcher_executable = self.global_config['hon_data']['hon_install_directory'] / launcher_binary\n try:\n if MISC.get_os_platform() == \"win32\":\n subprocess.run([patcher_executable, \"-norun\"], timeout=timeout)\n else:\n os.chmod(patcher_executable, 0o700)\n subprocess.run([patcher_executable], timeout=timeout)\n\n if MISC.get_os_platform() == \"linux\":\n executable = \"hon-x86_64-server_KONGOR\"\n if not os.path.exists(self.global_config['hon_data']['hon_install_directory'] / executable):\n executable = \"hon-x86_64-server\"\n self.global_config['hon_data']['hon_executable_path'] = self.global_config['hon_data']['hon_install_directory'] / executable\n self.global_config['hon_data']['hon_executable_name'] = executable\n\n svr_version = MISC.get_svr_version(self.global_config['hon_data']['hon_executable_path'])\n if MISC.get_svr_version(self.global_config['hon_data']['hon_executable_path']) != self.latest_available_game_version:\n LOGGER.error(f\"Server patching failed. Current version: {svr_version}\")\n return False\n\n LOGGER.info(\"Patching successful!\")\n self.global_config['hon_data']['svr_version'] = svr_version\n if source == \"startup\":\n return True\n elif source == \"healthcheck\":\n await self.start_game_servers(\"all\")\n\n except subprocess.TimeoutExpired:\n LOGGER.warn(f\"Patching failed as it exceeded {timeout} seconds to patch resources.\")\n return False\n except Exception:\n LOGGER.error(f\"An unexpected error occured while patching: {traceback.format_exc()}\")\n return False\n finally:\n # patching is done. Whether it failed or otherwise.\n self.patching = False\n\n async def disable_game_server(self, game_server):\n game_server.disable_server()\n\n async def enable_game_server(self, game_server):\n game_server.enable_server()\n\n def start_hon_proxy():\n pass\n\n def check_hon_proxy_running():\n pass\n\n def create_hon_proxy_config():\n pass","repo_name":"HoNfigurator/HoNfigurator-Central","sub_path":"cogs/game/game_server_manager.py","file_name":"game_server_manager.py","file_ext":"py","file_size_in_byte":65485,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"78"}
+{"seq_id":"6055939866","text":"import os\n\nlist_ = [\"BOS to LAX on 2022-09-10\", \"BOS to LAX on 2022-09-10\", \"BOS to LAX on 2022-09-10\"]\npath = os.getcwd()\n#exists = os.path.exists(path+r\"\\test.txt\")\n'''\nif exists:\n file = open(\"test.txt\", \"r\")\n var = file.read()\n file.close()\n\n file2 = open(\"test2.txt\", \"w\")\n file2.write(var)\n file2.close()\nelse:\n file = open(\"test.txt\", \"w\")\n file.close()\n\n# reads the file first and saves it to the backup file,\n# code below changes the new file without modifying backup\n\nfile = open(\"test.txt\", \"w\")\nfor i in list_:\n file.write(i)\nfile.close()\n'''\nlist1 = [\"BOS to ABE on 2022-12-18\", \"BOS to AVP on 2022-12-18\", \"BOS to CHS on 2022-12-18\", \"BOS to LAX on 2022-12-18\"]\nlist2 = [\"PHL to BOS on 2022-12-23\", \"CHS to BOS on 2022-12-23\", \"EWR to BOS on 2022-12-23\", \"IAD to BOS on 2022-12-23\"]\n\nlist3 = []\nlist4 = []\n\nfor i in list1:\n list3.append(i[7:10])\n\nfor i in list2:\n list4.append(i[:3])\n\nfor i in list3:\n if i in list4:\n print(i)\n\n'''\nFrom BOS to CHS\n departure dates:\n 2022-08-20\n 2022-08-22\n 2022-08-23\n \n return dates:\n 2022-08-22\n 2022-08-23\n 2022-08-25\n \nfor i in range(206+1):\n print(\"{:.2f}\".format(i/206*100) + \"%\")\n #rint(i/206*100)\n'''\n\n\ndef percent(counter, mode):\n if mode == 1:\n return \"{:.2f}\".format(counter/206*100) + \"%\"\n\nmode = 1\n\nfor i in range(207):\n print(percent(i,\"1\"))\n\n\n","repo_name":"hprantis/United-Award","sub_path":"File.py","file_name":"File.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"69813610172","text":"from datetime import datetime\n\n\ndef is_tuple(value):\n return type(value) is tuple\n\n\ndef is_list(value):\n return type(value) is list\n\n\ndef is_tuple_or_list(value):\n return is_tuple(value) or is_list(value)\n\n\ndef convert_tuple_to_dict(tuple_value, tuple_item_names):\n count = len(tuple_item_names)\n\n if tuple_value is None:\n index = 0\n tuple_value = tuple()\n while index < count:\n tuple_value = tuple_value + (None,)\n index += 1\n\n result = dict()\n index = 0\n for item in tuple_value:\n key = tuple_item_names[index]\n result[key] = item\n index += 1\n\n return result\n\n\ndef is_empty_string(input_value):\n return (\n input_value is None\n or not isinstance(input_value, str)\n or input_value.strip() == \"\"\n )\n\n\ndef assemble_quotes(scraping_request, scraping_response):\n \"\"\"A quote is composed of a head and a body. The data of the head comes from a scraping\n request. The data of the body comes from a scrapng response.\"\"\"\n\n def is_meaningful_price(quote_body):\n price = quote_body[\"price\"]\n return price is not None\n\n quotes = []\n\n quote_head = {\n \"company_id\": scraping_request[\"company_id\"],\n \"rental_route_id\": scraping_request[\"rental_route_id\"],\n \"pick_up_date_id\": scraping_request[\"pick_up_date_id\"],\n \"pick_up_time_id\": scraping_request[\"pick_up_time_id\"],\n \"rental_duration_id\": scraping_request[\"rental_duration_id\"],\n \"created_on\": datetime.now(),\n }\n\n scraping_response = list(filter(is_meaningful_price, scraping_response))\n\n if len(scraping_response) == 0:\n quotes.append({**quote_head})\n else:\n for quote_body in scraping_response:\n quote = {**quote_head, **quote_body}\n quotes.append(quote)\n\n return quotes\n\n\ndef format_scraping_request(scraping_request):\n return (\n \"[company_id: {}, rental_route_id: {}, pick_up_date_id: {}, \"\n + \"pick_up_time_id: {}, rental_duration_id: {}]\"\n ).format(\n scraping_request[\"company_id\"],\n scraping_request[\"rental_route_id\"],\n scraping_request[\"pick_up_date_id\"],\n scraping_request[\"pick_up_time_id\"],\n scraping_request[\"rental_duration_id\"],\n )","repo_name":"zgo23/RentalsScraping","sub_path":"lib/utils/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":2284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"24117966391","text":"from bibliopixel.animation.matrix import Matrix\nfrom bibliopixel.util.util import genVector\n\n\nclass Bloom(Matrix):\n\n def __init__(self, layout, dir=True, **kwds):\n super().__init__(layout, **kwds)\n self._vector = genVector(self.width, self.height)\n self._dir = dir\n\n def pre_run(self):\n self._step = 0\n\n def step(self, amt=8):\n if self._dir:\n s = 255 - self._step\n else:\n s = self._step\n\n # this respects master brightness but is slower\n for y in range(self.height):\n for x in range(self.width):\n index = self._vector[y][x] * 255 / self.height + s\n self.layout.set(x, y, self.palette(index))\n\n self._step += amt\n if(self._step >= 255):\n self._step = 0\n","repo_name":"ManiacalLabs/BiblioPixelAnimations","sub_path":"BiblioPixelAnimations/matrix/bloom.py","file_name":"bloom.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"78"}
+{"seq_id":"17761509834","text":"import sys\nimport tensorflow as tf\nimport scipy.misc\nimport cv2\nimport utils\nsys.path.insert(0, '../model/')\nimport driving_data\n\nLOGDIR = '../save/model'\n\ndef main():\n\tfinal_mse = []\n\tmodel_configs = utils.from_recipe()\n\tfor config in model_configs:\n\t\tMODEL_TITLE = config[\"MODEL_TITLE\"]\n\n\t\tsess = tf.InteractiveSession()\n\t\tsaver = tf.train.Saver()\n\n\t\tcheckpoint_path = os.path.join(LOGDIR, MODEL_TITLE + \".ckpt\")\n\t\tsaver.restore(sess, checkpoint_path)\n\n\t\txs, ys_ = driving_data.test_xs, driving_data.test_ys\n\n\t\twith tf.name_scope('loss'):\n\t\t\tloss = tf.reduce_mean(tf.square(tf.sub(y_, y)))\n\t\t\ttf.scalar_summary('mse', loss)\n\n\t\tmse = sess.run(loss, feed_dict={x: xs, y_: ys, keep_prob: 0.8})\n\n\t\tjson_data = {\"model\": MODEL_TITLE, \"mse\": mse}\n\t\tfinal_mse.append(json_data)\n\tutils.to_json_file(final_mse, \"report\", \"final_mse.json\")\n\tbest_model, best_mse = utils.find_best_model()\n\tprint (\"Best Model is {0} With MSE {1}\".format(best_model, best_mse))\n\nif __name__ == '__main__':\n\tmain()","repo_name":"Minsu-Daniel-Kim/udacity_challenge2","sub_path":"pipeline/evaluation_pipeline.py","file_name":"evaluation_pipeline.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"10289870745","text":"import logging\nimport time\nimport json\nfrom multiprocessing.pool import ThreadPool\nfrom threading import Thread, Event, Lock\n\nclass Orchestrator:\n def __init__(self, n_workers=4, logging_interval=10):\n self.n_workers = n_workers\n self.pool = ThreadPool(processes=self.n_workers)\n # Rename worker threads\n for worker_i, worker in enumerate(self.pool._pool):\n worker.name = f\"WorkThread-{worker_i:02d}\"\n self.queued = {}\n self.active = {}\n self.thread = Thread(target=self.__start)\n self.thread.name = \"OrchThread\"\n self.lock = Lock()\n self.__stop_event = Event()\n self.last_logged = 0\n self.logging_interval = logging_interval\n self.thread.start()\n\n def __start(self):\n logging.debug(f\"Orchestrator started with {self.n_workers} workers\")\n while not self.__stop_event.is_set():\n finished_jobs = []\n # Check for job completion\n for job_name, worker in self.active.items():\n if worker.ready():\n finished_jobs.append(job_name)\n if worker.successful():\n logging.debug(f\"{job_name} finished\")\n else:\n try:\n worker.get()\n except Exception as e:\n logging.error(f\"{job_name} failed, dumping error\\n{e}\")\n while len(finished_jobs) > 0:\n self.active.pop(finished_jobs.pop())\n # Submit jobs that do not have the same job name as any active job\n self.lock.acquire()\n for job_name, job_queue in self.queued.items():\n if job_name not in self.active.keys():\n worker_func, job_args = job_queue.pop()\n self.active[job_name] = self.pool.apply_async(worker_func, job_args)\n logging.debug(f\"{job_name} submitted\")\n if len(job_queue) == 0:\n finished_jobs.append(job_name)\n while len(finished_jobs) > 0:\n self.queued.pop(finished_jobs.pop())\n # Logging\n now = time.time()\n if (now - self.last_logged) >= self.logging_interval:\n if self.active:\n logging.debug(f\"Active jobs: {', '.join(self.active)}\")\n else:\n logging.debug(f\"No active orchestrator jobs\")\n if self.queued:\n queue_lengths = [f\"{n}: {len(q)}\" for n, q in self.queued.items()]\n logging.debug(f\"Queued jobs: {', '.join(queue_lengths)}\")\n else:\n logging.debug(f\"No queued orchestrator jobs\")\n self.last_logged = now\n self.lock.release()\n\n def stop(self):\n self.pool.close()\n self.pool.terminate()\n self.clear()\n self.__stop_event.set()\n self.thread.join()\n\n def clear(self, job_name=\"\"):\n self.lock.acquire()\n if not job_name:\n self.queued = {}\n elif job_name in self.queued.keys():\n self.queued[job_name] = []\n self.lock.release()\n\n def put(self, job_name, worker_func, job_args):\n self.lock.acquire()\n if job_name in self.queued.keys():\n self.queued[job_name].insert(0, (worker_func, job_args))\n else:\n self.queued[job_name] = [(worker_func, job_args)]\n self.lock.release()\n","repo_name":"jkguiang/rucio-sense-dmm","sub_path":"dmm/orchestrator.py","file_name":"orchestrator.py","file_ext":"py","file_size_in_byte":3521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"73418326011","text":"\nfrom supp.utils import send_request\nfrom supp.config import todo\n\nimport traceback\n\ntry:\n import readline\nexcept:\n pass \n\ndef repl(prompt):\n\n while True:\n\n try:\n user_input = input(prompt)\n\n except EOFError:\n print('')\n break \n\n if not user_input.strip():\n continue\n\n input_strings = user_input.split()\n\n command = input_strings[0].lower()\n\n try:\n\n if len(input_strings) == 1:\n\n if command in ('exit', 'quit'):\n break\n\n if command == 'list':\n\n response = send_request({'operation': 'list'})\n print(response)\n continue \n\n if command == 'reset':\n\n todo['handlers'].reset()\n continue \n\n if len(input_strings) == 2:\n\n assert all(char.isdigit() for char in input_strings[1])\n key = int(input_strings[1])\n\n if command == 'set':\n\n todo['handlers'].set(key)\n continue \n\n if command == 'del':\n\n todo['handlers'].remove(key)\n continue\n\n if command == 'get':\n\n todo['handlers'].get(key)\n continue\n \n raise AssertionError\n\n except AssertionError:\n print('Usage: { {set|get|del} } | list|reset | exit|quit }')\n\n except Exception as err:\n print(err)\n traceback.print_exc()\n","repo_name":"timedu/edis2023-ex08","sub_path":"app/supp/repl.py","file_name":"repl.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"8898374097","text":"from django.conf import settings\nfrom django.contrib import messages\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.core.mail import send_mail\nfrom .forms import EmailSignupForm\nfrom .models import Signup\n\nimport json\nimport requests\n\nMAILCHIMP_API_KEY = settings.MAILCHIMP_API_KEY\nMAILCHIMP_DATA_CENTER = settings.MAILCHIMP_DATA_CENTER\nMAILCHIMP_EMAIL_LIST_ID = settings.MAILCHIMP_EMAIL_LIST_ID\n\napi_url = 'https://{dc}.api.mailchimp.com/3.0'.format(dc=MAILCHIMP_DATA_CENTER)\nmembers_endpoint = '{api_url}/lists/{list_id}/members'.format(\n api_url=api_url,\n list_id=MAILCHIMP_EMAIL_LIST_ID\n)\n\n\ndef subscribe(email):\n data = {\n \"email_address\": email,\n \"status\": \"subscribed\"\n }\n r = requests.post(\n members_endpoint,\n auth=(\"\", MAILCHIMP_API_KEY),\n data=json.dumps(data)\n )\n return r.status_code, r.json()\n\n\n\n\ndef email_list_signup(request):\n if request.method == 'POST':\n sub = Signup(email=request.POST['email'])\n sub.save()\n send_mail('Newsletter confirmation', 'confirm to get into the subscription', settings.FROM_EMAIL,[sub.email],\n connection=None, html_message='Thank you for signing up for my email newsletter! sent by celery \\\n Please complete the process by \\\n clicking here to \\\n confirm your registration .'.format(sub.email))\n return render(request, 'index.html', {'email': sub.email, 'action': 'added', 'form': EmailSignupForm()})\n else:\n return render(request, 'index.html', {'form': EmailSignupForm()})\n\n\ndef confirm(request):\n sub = Signup.objects.get(email=request.GET['email'])\n if sub.conf_num == request.GET['conf_num']:\n sub.confirmed = True\n sub.save()\n return render(request, 'index.html', {'email': sub.email, 'action': 'confirmed'})\n else:\n return render(request, 'index.html', {'email': sub.email, 'action': 'denied'})\n\ndef delete(request):\n sub = Signup.objects.get(email=request.GET['email'])\n if sub.conf_num == request.GET['conf_num']:\n sub.delete()\n return render(request, 'index.html', {'email': sub.email, 'action': 'unsubscribed'})\n else:\n return render(request, 'index.html', {'email': sub.email, 'action': 'denied'})\n","repo_name":"lunyamwi/soft-dj-mathtraining-python","sub_path":"marketing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"73980214012","text":"import tkinter as tk\r\n\r\nclass scrollableFrame(tk.Frame):\r\n \"\"\"A Tkinter scrollable frame\r\n\r\n * Use the 'interior' attribute to place widgets inside the scrollable frame\r\n * Construct and pack/place/grid normally\r\n * This frame only allows vertical scrolling\r\n \"\"\"\r\n def __init__(self, master, *args, **kw):\r\n tk.Frame.__init__(self, master, *args, **kw)\r\n\r\n # create a canvas object and a vertical scrollbar for scrolling it\r\n self.vscrollbar = tk.Scrollbar(self, orient = \"vertical\")\r\n self.vscrollbar.pack(fill = \"both\", side = \"right\")\r\n self.canvas = tk.Canvas(self, bd = 0, highlightthickness = 0,\r\n yscrollcommand = self.vscrollbar.set)#, bg = \"black\")\r\n self.canvas.pack(side = \"left\", fill = \"both\", expand = \"yes\")\r\n self.vscrollbar.config(command = self.canvas.yview)\r\n\r\n # reset the view\r\n self.resetView()\r\n\r\n # create a frame inside the canvas which will be scrolled with it\r\n self.interior = tk.Frame(self.canvas)#, bg = \"pink\")\r\n self.interior_id = self.canvas.create_window(0, 0, window = self.interior,\r\n anchor = \"nw\")\r\n\r\n self.interior.bind(\"\", self.configureInterior)\r\n self.canvas.bind(\"\", self.configureCanvas)\r\n\r\n # track changes to the canvas and frame width and sync them,\r\n # also updating the scrollbar\r\n def configureInterior(self, event):\r\n # update the scrollbars to match the size of the inner frame\r\n size = (self.interior.winfo_reqwidth(), self.interior.winfo_reqheight())\r\n self.canvas.config(scrollregion = \"0 0 %s %s\" % size)\r\n if self.interior.winfo_reqwidth() != self.canvas.winfo_width():\r\n # update the canvas's width to fit the inner frame\r\n self.canvas.config(width = self.interior.winfo_reqwidth())\r\n\r\n def configureCanvas(self, event):\r\n if self.interior.winfo_reqwidth() != self.canvas.winfo_width():\r\n # update the inner frame's width to fill the canvas\r\n self.canvas.itemconfigure(self.interior_id, width = self.canvas.winfo_width())\r\n\r\n def getInterior(self):\r\n return self.interior\r\n \r\n def resetView(self):\r\n self.canvas.xview_moveto(0)\r\n self.canvas.yview_moveto(0)\r\n","repo_name":"AyalCiobotaru/pyVolleyStat","sub_path":"scrollableFrame.py","file_name":"scrollableFrame.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"74938697851","text":"# Python3 program to validate Visa\n# Card number using regular expression\nimport re\n \n# Function to validate Visa Card\n# number using regular expression.\ndef isValidVisaCardNo(string):\n \n # Regex to check valid Visa\n # Card number\n regex = \"^4[0-9]{12}(?:[0-9]{3})?$\";\n \n # Compile the ReGex\n p = re.compile(regex);\n \n # If the string is empty\n # return false\n if (string == ''):\n return False;\n \n # Pattern class contains matcher()\n # method to find matching between\n # given string and regular expression.\n m = re.match(p, string);\n \n # Return True if the string\n # matched the ReGex else False\n if m is None:\n return False\n else:\n return True\n \n# Driver code\nif __name__ == \"__main__\":\n \n # Test Case 1\n str1 = \"4155279860457\";\n print(str1, isValidVisaCardNo(str1));\n \n # Test Case 2\n str2 = \"4155279860457201\";\n print(str2, isValidVisaCardNo(str2));\n \n # Test Case 3\n str3 = \"4155279\";\n print(str3, isValidVisaCardNo(str3));\n \n # Test Case 4\n str4 = \"6155279860457\";\n print(str4, isValidVisaCardNo(str4));\n \n # Test Case 5\n str5 = \"415a27##60457\";\n print(str5, isValidVisaCardNo(str5));\n \n# This code is contributed by AnkitRai01\n# https://www.geeksforgeeks.org/how-to-validate-visa-card-number-using-regular-expression/\n\n","repo_name":"sedwards/infrastructure_examples","sub_path":"coding/python_validator/python_validator.py","file_name":"python_validator.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"43841280420","text":"import os\nimport numpy as np\nimport pandas as pd\nimport re\n#import csv\nimport sys\nimport matplotlib.pyplot as plt\n\n''' evttiming-hist.py\n- Get histograms illustrating time per event across all completed HPC jobs\n- Extract data from central framwework logging\n'''\n\n# get parameters\np = re.search('(?<=-)[a-z]+', str(sys.argv[1]))\nparameter_str = str(p.group(0))\nnum_param = 'no. of ' + parameter_str\n\nfilecount = 0\nevtparam = []\n\n# create new results file (text)\nf = open('evttimes.txt', 'w')\n\n# traverse results directory\nfor subdir, dirs, files in os.walk('RESULTSDIR'):\n\n for file in files:\n filepath = subdir + os.sep + file\n\n if filepath.endswith('AthenaMP.log'):\n\n filecount = filecount + 1\n\n # extract lines of interest from log file\n linelist = [ line.rstrip('\\n') for line in open(filepath) if 'INFO [evt' in line ]\n\n # extract CPU time and write to file\n for line in linelist:\n m = re.search('(?<=cpu=)\\d+', line)\n cputime = int(m.group(0))/1000.0\n f.write((str(cputime)+'\\n'))\n\nprint('\\nNumber of files scanned: %d\\n') % filecount\n\n# write to CSV file\nwith open('evttimes.csv', 'w') as f:\n for item in evtparam:\n csv.writer(f).writerow([item])\n\n# plot CPU time on histogram\nplt.figure\nplt.hist(evtparam, bins=np.arange(0, 3000, 100), histtype='step', color='b', label='Time per event')\nplt.xlabel('Time interval')\nplt.ylabel('No. of events')\nplt.legend()\nplt.savefig('evttime-all.png')\n","repo_name":"awashbro/examples","sub_path":"hpc-optimisation/analysis/evttiming-hist.py","file_name":"evttiming-hist.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"6058029996","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom control import lqr\nfrom mass_spring_damper import MassSpringDamper\nfrom inverted_pendulum import InvertedPendulum\nimport cv2\n\nclass LQRController:\n \n def __init__(self, system, Q, R, visualize = False):\n \n self.system = system\n self.dt = self.system.dt\n self.K = self.calculate_K(system.A, system.B, Q, R)\n self.visualize = visualize\n \n def calculate_K(self, A, B, Q, R):\n \n K, S, E = lqr(A, B, Q, R)\n \n return K\n \n def simulate(self, totalTime, x, xRef):\n \n numForwardSimulationSteps = int(totalTime / self.dt)\n elapsedTime = 0\n xHist = np.zeros((numForwardSimulationSteps, 2))\n timeHist = np.zeros(numForwardSimulationSteps)\n uHist = np.zeros(numForwardSimulationSteps)\n \n for i in range(0, numForwardSimulationSteps):\n \n u = (-self.K @ (x - xRef)).flatten()\n if u < self.system.uMin:\n u = self.system.uMin\n if u > self.system.uMax:\n u = self.system.uMax\n x = self.system.forward_simulate(x, u)\n \n elapsedTime = elapsedTime + self.dt\n \n xHist[i, :] = x.flatten()\n timeHist[i] = elapsedTime\n uHist[i] = u\n \n if self.visualize == True:\n self.system.visualize(x)\n \n return xHist, timeHist, uHist\n \n def plot_hist(self, x, totalTime, u):\n \n self.system.plot_hist(x, totalTime, u)\n \nif __name__ == \"__main__\":\n \n system = MassSpringDamper(5.0, 1.0, 2.0, .01)\n Q = np.diag([20.0, 1.0])\n R = .001 * np.ones((1, 1)) \n controller = LQRController(system, Q, R, 0) \n xInit = np.array([[0., 0.]]).T\n xRef = np.array([[-1, 0.0]]).T\n \n xHist, timeHist, uHist = controller.simulate(20, xInit, xRef) \n controller.plot_hist(xHist, timeHist, uHist)\n \n # system = InvertedPendulum(.2, .1, 1.0, .01)\n # Q = np.diag([0.05, 1.0])\n # R = .1 * np.ones((1, 1)) \n # controller = LQRController(system, Q, R, 0) \n # xInit = np.array([[0.,.5]]).T\n # xRef = np.array([[0.0, 0.0]]).T\n \n # xHist, timeHist, uHist = controller.simulate(5, xInit, xRef) \n # controller.plot_hist(xHist, timeHist, uHist)\n \n","repo_name":"hpsanders98/linear_quadratic_regulator","sub_path":"lqr_controller.py","file_name":"lqr_controller.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"20080870807","text":"from PIL import Image\nimport random\ncolors = {\n 'A': (225,0,0), #ROOD\n 'C': (0,225,0), #GROEN\n 'G': (0,0,225), #BLAUW\n 'T': (0,0,0) #ZWART\n}\n\ncolorsList = [(225,0,0), #ROOD\n (0,225,0), #GROEN\n (0,0,225), #BLAUW\n (0,0,0) ]\n\ndef decode(char): #Vertaal character uit het alfabet naar een drie letterige code als in DNA\n if char== '\\'':\n return ['A','A','A']\n if char== ',':\n return ['A','A','C']\n if char== '2':\n return ['A','A','G']\n if char== '3':\n return ['A','A','T']\n if char== '4':\n return ['A','C','A']\n if char== '5':\n return ['A','C','C']\n if char== '6':\n return ['A','C','G']\n if char== '7':\n return ['A','C','T']\n if char== '8':\n return ['A','G','A']\n if char== '9':\n return ['A','G','C']\n if char== 'a':\n return ['A','G','G']\n if char== 'b':\n return ['A','G','T']\n if char== 'c':\n return ['A','T','A']\n if char== 'd':\n return ['A','T','C']\n if char== 'e':\n return ['A','T','G']\n if char== 'f':\n return ['A','T','T']\n if char== 'g':\n return ['C','A','A']\n if char== 'h':\n return ['C','A','C']\n if char== 'i':\n return ['C','A','G']\n if char== 'j':\n return ['C','A','T']\n if char== 'k':\n return ['C','C','A']\n if char== 'l':\n return ['C','C','C']\n if char== 'm':\n return ['C','C','G']\n if char== 'n':\n return ['C','C','T']\n if char== 'o':\n return ['C','G','A']\n if char== 'p':\n return ['C','G','C']\n if char== 'q':\n return ['C','G','G']\n if char== 'r':\n return ['C','G','T']\n if char== 's':\n return ['C','T','A']\n if char== 't':\n return ['C','T','C']\n if char== 'u':\n return ['C','T','G']\n if char== 'v':\n return ['C','T','T']\n if char== 'w':\n return ['G','A','A']\n if char== 'x':\n return ['G','A','C']\n if char== 'y':\n return ['G','A','G']\n if char== 'z':\n return ['G','A','T']\n if char== 'A':\n return ['G','C','A']\n if char== 'B':\n return ['G','C','C']\n if char== 'C':\n return ['G','C','G']\n if char== 'D':\n return ['G','C','T']\n if char== 'E':\n return ['G','G','A']\n if char== 'F':\n return ['G','G','C']\n if char== 'G':\n return ['G','G','G']\n if char== 'H':\n return ['G','G','T']\n if char== 'I':\n return ['G','T','A']\n if char== 'J':\n return ['G','T','C']\n if char== 'K':\n return ['G','T','G']\n if char== 'L':\n return ['G','T','T']\n if char== 'M':\n return ['T','A','A']\n if char== 'N':\n return ['T','A','C']\n if char== 'O':\n return ['T','A','G']\n if char== 'P':\n return ['T','A','T']\n if char== 'Q':\n return ['T','C','A']\n if char== 'R':\n return ['T','C','C']\n if char== 'S':\n return ['T','C','G']\n if char== 'T':\n return ['T','C','T']\n if char== 'U':\n return ['T','G','A']\n if char== 'V':\n return ['T','G','C']\n if char== 'W':\n return ['T','G','G']\n if char== 'X':\n return ['T','G','T']\n if char== 'Y':\n return ['T','T','A']\n if char== 'Z':\n return ['T','T','C']\n if char== '.':\n return ['T','T','G']\n if char== ' ':\n return ['T','T','T']\n\ndef addLetter(array, char): #Voegt letter code (uit decode) toe aan de lijst\n for letter in decode(char):\n array.append(letter)\n\ndef translateText(array, input): #Voegt iedere letter code uit input toe aan de lijst, met wat chatches voor special cases\n for letter in input:\n if letter != '\\n':\n addLetter(array , letter)\n\ndef createImage(x,y, array): #Zorgt voor iedere code letter in de gecoderde text de juiste kleur toe volgens colors en maakt een plaatje\n padding = int(((x*y) - len(array))/2)\n img = Image.new('RGB', (x, y))\n for i in range(x):\n for j in range(y):\n if i*y + j > padding and i*y + j - padding < len(array): \n color = array[i*y + j - padding]\n img.putpixel((j,i), colors[color])\n else:\n img.putpixel((j,i), random.choice(colorsList))#voegt een willekeurige kleuern voor en na de text\n img.save('output.png')\n\n return img\n\ntext = \"\"\"If you are reeding this you have decoed this message and you probaply see how my image could be an DNA strand. \nAlthough the image migth seem like chaos it this has a meaning. Chaos is infromation anything that isn't chaos can be simplefied \nto it's simpelest form, chaos. But if you decoded this you probaply already know this. So information is chaos. Our DNA is Chaos. \nEvery physics thoery, filosofical thought or great poets words can be discribed by chaos. On the internet there is a website \ncalled the library of babel. On this site you can search any sentences thinkable and it is already in their database. \nBut if you look through this library you will find just random text. Al these senteces you could think of already exist in a chaos.\nNow to get to my real point, we have decoded the meaning of the randomness of our DNA. But there is another purely random thing\nin our universe. That thing is qauntum particles. I don't think I or you will ever on our own find the encrypted meaning of \nthe random interactions our reality is based on, but one day we might. There is onething about this randomness we can \nalready say about this randomness. This randomness is who we are what we do and the world we live in.\"\"\"\nresult = []\n\ntranslateText(result, text)\nprint(result)\n\nwallpaper = createImage(75,75,result)\nwallpaper.show()\n","repo_name":"WillemPaternotte/Atlas-van-de-menselijke-geografie","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"71721873211","text":"import pymongo\nimport pprint\nimport json\nimport re\n\npos = [35.85835, -0.31198]\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient[\"mydb\"]\nmycol = mydb[\"adresse\"]\ntype_error = [ 'Lieu-dit', 'Sécurité et Protection', 'Santé', 'Justice', 'Administration','Autre','Service','Education', 'Hotels', 'Restauration','Banque','Sport']\ntype_comp = [\"Organisme\",\"Nom de lieu-dit\",\"Numéro\",\"Unité de batiment\",\"Voie\",\"Zone\",\"Boite postale\",\"Localité\"]\n\npos = [10 ,22]\n\n######## reshaping document ########\ndef reclasse(doc):\n k = []\n v = []\n v1 = []\n k1 = []\n ##lll = json.loads(json.dumps(doc))\n for key, value in doc.items() : \n if key == 'Composants':\n comp = value\n for key, value in doc.items() :\n k.append(key)\n v.append(value)\n abc = v[k.index('Composants')]\n for dicti in abc : \n for key, value in dicti.items() :\n if key == 'Type' :\n v1.append(value)\n if key == 'ValeurInformation' :\n k1.append(value)\n v_for_changes = \"\" \n k_for_changes = \"\" \n indx1 = 0\n indx2 = 0\n for i in range(0,len(v1)-1): \n for i in range(0,len(v1)-1): \n if i+1 > len(v1)-1 : \n break\n elif type_comp.index(v1[i])>type_comp.index(v1[i+1]) : \n k_for_changes = v1[i]\n v1[i] = v1[i+1]\n v1[i+1] = k_for_changes\n v_for_changes = comp[i]\n comp[i] = comp[i+1]\n comp[i+1] = v_for_changes\n indx1 +=1\n if indx1 == indx2:\n break\n else : \n indx2 = indx1\n continue\n \n doc.update({ \"Composants\":comp})\n return(doc)\n\n####### filtering address ########\ndef FilterAdress(input_adress): \n list = input_adress.split()\n if list[0].lower() in type_error:\n list.pop(0)\n filtred_adress = ' '.join([str(elem) for elem in list]) \n return filtred_adress\n else:\n return input_adress\n\n####### making the address function ##########\ndef make_adrss(select_id) : \n adrss = []\n sss = mycol.find( {\"Id\" :select_id} )\n\n for ss in sss : \n mkmkm = ss['Composants']\n\n for k in mkmkm : \n # using json.dumps() for cast it to string\n m = k['ValeurInformation']\n lll = json.loads(json.dumps(m))\n for key, value in lll.items() :\n if type(value) == int : \n adrss.append(value)\n else :\n adrss.append(value.capitalize())\n adrss.append(',')\n \n adrss = adrss[:-1]\n return(','.join(map(str, (FilterAdress( ' '.join(map(str, adrss)))).split(' ,'))))\n\n####### take position and give the address function ########## >>> reverse geocoding \ndef get_pos_give_adrs(pos) :\n adresse = 'none address'\n bb = mycol.aggregate([\n {\n '$geoNear': {\n 'near': { 'type': 'Point', 'coordinates': [ pos[0], pos[1] ]},\n 'distanceField': 'ObjetAdressable.PositionAdresse.Geometrie.coordinates',\n 'spherical': True, \n \"maxDistance\": 50,\n }\n }\n ])\n for i in bb :\n adresse = i ['Adresse'] \n if adresse == 'none address' : \n continue\n else : \n break\n if adresse == 'none address' : \n position = {\n \"isExist\" : False\n }\n else :\n position = {\n \"isExist\" : True,\n \"address\" : adresse,\n \"lat\" : pos[0],\n \"lon\" : pos[1]\n }\n return(position)\nprint (get_pos_give_adrs(pos)) #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n\n####### insertion function ########## \ndef insert_doc(doc) :\n doc = reclasse(doc)\n doc.update({ \"Id\" : mycol.find().count() + 1})\n mycol.insert_one(doc)\n mycol.update_one({\"Id\" : mycol.find().count()},{\"$set\" : {\"Adresse\" : make_adrss(mycol.find().count())}}) \n\n####### finding list of address ####### >> autocomplete \ndef find_adresse(adresse) : \n if adresse[0] in [' ', ',', ';','.'] :\n adresse = adresse[1:]\n\n es = [\",\",\" ,\", \", \",\" ;\",\"; \",\" \",\" \"]\n for i in range(0,len(es)) :\n adresse = ' '.join(map(str,adresse.split(es[i])))\n words_adrs = adresse.split(' ')\n val_adrs = []\n adrs = []\n sss = mycol.find({},{\"_id\":0, \"Adresse\" : 1})\n for ss in sss : \n val_adrs.append(ss[\"Adresse\"])\n adrs = val_adrs\n val_adrs = []\n for k in range(0,len(words_adrs)) : \n for kk in adrs : \n if re.search(words_adrs[k].lower(), kk.lower()) != None : \n val_adrs.append(kk)\n adrs = val_adrs\n val_adrs = []\n if len(adrs)> 10 :\n adrs = adrs[0:10]\n return (adrs) # >>> autocoplete\n\n####### take address and give the position function ########\ndef get_adrs_give_pos(adresse) : \n kkk = mycol.aggregate([\n { \"$match\": { \"Adresse\": adresse } },\n { \"$project\": { \"_id\": 0, \"coordinates\" : \"$ObjetAdressable.PositionAdresse.Geometrie.coordinates\"}},\n { \"$sort\": {\"Id\": 1}}\n ])\n for kk in kkk :\n tt = kk\n\n try:\n tt\n except NameError:\n rep ={\n \"isExist\" : False,\n \"address\": adresse,\n }\n else:\n for key , value in kk.items() :\n coord = value\n rep ={\n \"isExist\" : True,\n \"address\": adresse,\n \"lat\" : coord[0],\n \"lng\" : coord[1]\n }\n return(rep)","repo_name":"b31zakx96/route-palnner-dz","sub_path":"flask_mongodb/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"8739276805","text":"\"\"\"A suite of tools for processing FITS files.\n\n $Header: /nfs/slac/g/glast/ground/cvs/pointlike/python/uw/utilities/fitstools.py,v 1.21 2013/10/24 10:16:19 kerrm Exp $\n\n author: Matthew Kerr\n\n\"\"\"\n\nINT_TYPES = ['EVENT_CLASS','CONVERSION_TYPE']\n\n\nfrom astropy.io import fits as pyfits; pf= pyfits\nimport numpy as N; import numpy as np\nfrom types import ListType,FunctionType,MethodType\nfrom math import cos,sin,pi\nfrom skymaps import SkyDir,Gti,BinnedPhotonData,PythonUtilities\n\ndef rect_mask(lons,lats,cut_lon,cut_lat,lon_hwidth,lat_hwidth):\n \"\"\" lons -- longitude coordinate (deg.)\n lats -- latitude coordinate (deg.)\n \"\"\"\n mask = np.abs( lons - cut_lon)/np.cos(np.radians(lats)) < lon_hwidth\n return mask & (np.abs(lats - cut_lat) < lat_hwidth)\n\ndef trap_mask(ras,decs,cut_dir,radius):\n \"\"\" Make a conservative, trapezoidal cut as a precursor to a radius cut.\"\"\"\n try:\n rad_test = radius[0]\n except:\n rad_test = radius\n if rad_test > 30:\n return np.asarray([True] * len(ras)) # cut no good for large radii\n\n c1 = np.cos( np.radians(cut_dir.dec() - radius) )\n c2 = np.cos( np.radians(cut_dir.dec() + radius) )\n ra_radius = radius/np.minimum(c1,c2) #conservative\n\n mask = np.abs(ras - cut_dir.ra()) <= ra_radius\n mask &= np.abs(decs - cut_dir.dec()) <= radius\n #If cut radius overlaps the pole, keep a polar cap above cut_dir.dec()\n if np.any(cut_dir.dec()+radius > np.pi/2.) or np.any(cut_dir.dec() - radius < -N.pi/2.):\n mask |= np.abs(decs) > np.abs(cut_dir.dec())\n mask[radius > 20] = True # cut doesn't work for large radii?\n return mask\n\ndef rad_mask(ras,decs,cut_dir,radius,mask_only=False):\n \"\"\"Make a slower, exact cut on radius.\"\"\"\n ra0,dec0 = np.radians(cut_dir.ra()),np.radians(cut_dir.dec())\n ras,decs = np.radians(ras),np.radians(decs)\n cos_diffs = np.sin(decs)*np.sin(dec0)+np.cos(decs)*np.cos(dec0)*np.cos(ras-ra0)\n mask = cos_diffs > np.cos(np.radians(radius))\n if mask_only:\n return mask\n else:\n return mask,np.arccos(cos_diffs)[mask]\n\ndef get_gti_mask(ft1file,times):\n gti = Gti(ft1file)\n gti_starts,gti_stops = \\\n np.asarray([(x.minValue(),x.maxValue()) for x in gti]).transpose()\n a = np.argsort(gti_stops)\n gti_starts = gti_starts[a]; gti_stops = gti_stops[a]\n indices = np.searchsorted(gti_stops,times)\n accept = (times > gti_starts[indices]) & (times <= gti_stops[indices])\n return accept\n\ndef rad_extract(eventfiles,center,radius_function,return_cols=['PULSE_PHASE'],cuts=None,apply_GTI=True,theta_cut=66.4,zenith_cut=105,return_indices=False):\n \"\"\" Extract events with a radial cut. \n Return specified columns and perform additional boolean cuts.\n\n Return is in form of a dictionary whose keys are column names \n (and 'DIFFERENCES') and values are numpy arrays with the column \n values. These will have been concatenated if there are multiple\n FT1 files.\n\n ========= =======================================================\n Argument Description\n ========= =======================================================\n eventfiles -- a list of FT1 filenames\n center -- a SkyDir giving the center of the radial cut\n radius_function -- can be either a float specifying a cookier cutter \n radial cut, or a function taking as arguments the energy \n and event_class and speciying the radius in degrees, e.g.\n\n def radius(energy,event_class):\n return numpy.where(event_class,2,1)*(energy/1000)**-0.75\n\n ========= =======================================================\n Keyword Description\n ========= =======================================================\n return_cols ['RA','DEC','ENERGY','EVENT_CLASS','PULSE_PHASE'] - \n a list of FT1 column names to return\n cuts None - an optional list of boolean cuts to apply, \n e.g., ['ENERGY > 100']\n NB -- cuts not yet implemented!!\n no_cuts [False] do not apply default zenith and incidence angle cuts\n apply_GTI [True] accept or reject an event based on GTI if True; \n else ignore GTI\n return_indices [False] if True, return an array giving the index in the\n original file of each event; obviously only useful in the \n case of a single event file\n ========= =======================================================\n \"\"\"\n if not hasattr(radius_function,'__call__'):\n simple_scalar = True\n rval = radius_function\n radius_function = lambda e,event_class: rval\n else:\n simple_scalar = False\n\n eventfiles = __FITS_parse__(eventfiles)\n\n from collections import defaultdict,deque\n coldict = defaultdict(deque)\n cols = {}\n cut_cols = ['ZENITH_ANGLE','THETA','TIME']\n keys = list(set(['RA','DEC','ENERGY','CONVERSION_TYPE']+cut_cols+return_cols))\n accepted = 0\n total = 0\n\n for eventfile in eventfiles:\n #e = pf.open(eventfile,memmap=1)\n #nrows = e[1].data.shape[0]\n #e.close()\n nrows = pyfits.getheader(eventfile,'EVENTS')['NAXIS2']\n\n for key in keys:\n cols[key] = np.empty(nrows,dtype=float)\n PythonUtilities.get_float_col(cols[key],eventfile,'EVENTS',key)\n\n rad = radius_function(cols['ENERGY'],cols['CONVERSION_TYPE'])\n tmask = trap_mask(cols['RA'],cols['DEC'],center,rad)\n tmask &= (cols['ZENITH_ANGLE'] < zenith_cut) & (cols['THETA'] < theta_cut)\n if apply_GTI:\n tmask &= get_gti_mask(eventfile,cols['TIME'])\n print ('GTI will remove %d of %d photons.'%((~tmask).sum(),len(tmask)))\n if simple_scalar:\n rmask,diffs = rad_mask(cols['RA'][tmask],cols['DEC'][tmask],center,rad)\n else:\n rmask,diffs = rad_mask(cols['RA'][tmask],cols['DEC'][tmask],center,rad[tmask])\n\n for key in keys:\n coldict[key].append(cols[key][tmask][rmask])\n if return_indices:\n if 'EVENT_INDICES' not in return_cols:\n return_cols.append('EVENT_INDICES')\n coldict['EVENT_INDICES'].append(np.arange(len(tmask))[tmask][rmask])\n coldict['DIFFERENCES'].append(diffs)\n accepted += tmask.sum()\n total += len(tmask)\n\n for key in coldict.keys():\n if (key in cut_cols) and not (key in return_cols):\n cols.pop(key)\n continue\n cols[key] = np.concatenate([x for x in coldict[key]])\n if key in INT_TYPES: cols[key] = cols[key].astype(int)\n\n print ('Cuts removed %d of %d photons.'%(total-accepted,total))\n return cols\n\n#TODO: GTI\n#e.g. counts_plot(file_list,coordsys='galactic',cuts=['L > 50','L < 100','B > -60'])\ndef counts_plot(ft1files,center,fov=10,scale='log',pixels=256,coordsys='equatorial',\n cuts = None, print_locs = False):\n\n ft1 = merge_flight_data(ft1files,cuts=cuts)\n events = ft1[1]\n if coordsys == 'equatorial':\n lon,lat = N.asarray(events.data.field('RA')),N.asarray(events.data.field('DEC'))\n clon,clat = center.ra(),center.dec()\n else:\n lon,lat = N.asarray(events.data.field('L')),N.asarray(events.data.field('B'))\n clon,clat = center.l(),center.b()\n mask = rect_mask(lon,lat,clon,clat,fov/2.,fov/2.)\n\n lon = lon[mask]\n lat = lat[mask]\n\n img,x,y = N.histogram2d(lon,lat,bins=pixels)\n if scale == 'log':\n img = N.where(img > 0,N.log10(img),-1)\n from pylab import pcolor,xlabel,ylabel,imshow\n\n #pcolor(x,y,img.transpose()) #TODO -- make RA go the right way!\n imshow(img.transpose())\n xlabel( ('RA' if coordsys=='equatorial' else 'L') + ' (deg)')\n ylabel( ('DEC' if coordsys=='equatorial' else 'B') + ' (deg)')\n if print_locs:\n if coordsys == 'equatorial': print ('RA DEC ENERGY TIME EVENT_CLASS')\n else: print ('L B ENERGY TIME EVENT_CLASS')\n en = events.data.field('ENERGY')\n time = events.data.field('TIME')\n ec = events.data.field('EVENT_CLASS')\n for i in xrange(len(lon)):\n\n print ('%.2f %.2f %.2g %.10g %d'%(lon[i],lat[i],en[i],time[i],ec[i]))\n return img\n\ndef merge_flight_data(files, outputfile = None, cuts = None, fields = None):\n \"\"\"Merge FT1 or FT2 files and make cuts on the columns.\n\n If a cut is made on MET, the GTI are pruned and updated so that the exposure\n calculation will accurately reflect the MET cuts.\n\n outputfile -- [None] string argument giving output file name; if None, return the\n pyfits Table instance\n cuts -- [None] a list of logical cuts to apply to FITS columns, e.g. 'ENERGY > 100'\n fields -- not implemented\n\n NOTA BENE: the headers will not be treated correctly!!!\n \"\"\"\n\n handles = __get_handles__(files)\n table_name = handles[0][1].name #set to 'EVENTS' for FT1 or 'SC_DATA' for FT2\n\n event_table = __merge_events__(handles, table_name = table_name)\n\n if cuts is not None:\n __arbitrary_cuts__(event_table,cuts)\n interval = [event_table.data.field('TIME').min(),event_table.data.field('TIME').max()]\n else: interval = None\n\n #Overwrite data in dummy table and write it to file\n #handles[0][table_name].data = event_table.data\n #handles[0][table_name].columns = event_table.columns\n handles[0][table_name] = event_table\n\n if table_name == 'EVENTS':\n handles[0]['GTI'].data = __merge_gti__(handles,interval=interval).data\n\n if outputfile is not None: handles[0].writeto(outputfile,clobber=True)\n for x in handles: x.close()\n\n return handles[0]\n\ndef get_fields(files, fields, cuts = None, memmap = False):\n \"\"\"A lightweight version to get only certain fields of flight data.\"\"\"\n\n data = dict()\n files = __FITS_parse__(files)\n\n #Process the cuts\n f = pf.open(files[0],memmap=memmap)\n if cuts is not None:\n cut_fields = set()\n for i,cut in enumerate(cuts): #put cut columns in namespace\n tokens = [tok.strip() for tok in cut.split()]\n for name in f['EVENTS'].columns.names:\n if name in tokens:\n cut_fields.add(name)\n cuts[i] = cuts[i].replace(name,'data[\\'%s\\']'%name)\n cut_fields = list(cut_fields)\n else: cut_fields = []\n f.close()\n del(f)\n\n counts = N.empty(len(files),dtype=int)\n\n for nfi,fi in enumerate(files):\n f = pf.open(fi,memmap=memmap)\n counts[nfi] = len(f['EVENTS'].data) #f['EVENTS'].data.getshape()[0]\n f.close()\n del(f)\n\n for field in fields + cut_fields:\n data[field] = N.empty(counts.sum(),dtype=float)\n\n #Get fields\n counter = 0\n for fi,counts in zip(files,counts):\n f = pf.open(fi,memmap=memmap)\n for field in fields + cut_fields:\n #data[field] += [N.array(f['EVENTS'].data.field(field),dtype=N.float32)]\n data[field][counter:counter+counts] = f['EVENTS'].data.field(field)\n counter += counts\n f.close()\n del(f)\n\n import gc\n gc.collect()\n gc.collect()\n\n \"\"\"\n #Concatenate results\n for field in fields + cut_fields:\n data[field] = N.concatenate(data[field]).astype(float) #note hard case\n gc.collect()\n \"\"\"\n\n #Apply cuts\n if cuts is not None:\n mask = N.asarray([True]*len(data[data.keys()[0]]))\n for cut in cuts:\n mask = N.logical_and(mask,eval(cut))\n for field in fields:\n data[field] = data[field][mask]\n\n #Remove \"helper\" data for cuts\n for cut in cut_fields:\n if cut not in fields: data.pop(cut)\n\n return data\n\n\ndef FT1_to_GTI(files):\n \"\"\"Convert FT1 files to a single GTI object.\"\"\"\n handles = __get_handles__(files)\n from skymaps import Gti\n g = Gti(files[0])\n starts,stops = __merge_gti__(handles[1:],no_table = True)\n if len(starts) == 0: return g\n for i in xrange(len(starts)):\n g.insertInterval(starts[i],stops[i])\n return g\n\ndef sum_ltcubes(files,outputfile = 'summed_ltcube.fits'):\n \"\"\"Pass either a name of an ASCII file with other FITS file names or a list of filenames.\"\"\"\n\n files = __FITS_parse__(files)\n\n try:\n f = pf.open(files[0])\n header = f['EXPOSURE'].header\n exposures = [f['EXPOSURE'].data.field('COSBINS')] \n except KeyError:\n print('file %s has no EXPOSURE table: aborting'%files[0])\n return\n finally:\n f.close() \n\n for file in files[1:]:\n try:\n f = pf.open(file)\n h = f['EXPOSURE'].header\n for key in header.keys():\n if key not in ['DATE','DATE-OBS','DATE-END','TSTART','TSTOP']:\n assert(h[key]==header[key])\n exposures+=[f['EXPOSURE'].data.field('COSBINS')]\n except AssertionError:\n print('Inconsistent header values, file %s, keyword %s'%(file,key))\n return\n except KeyError:\n print('File %s has no table \"EXPOSURE\": aborting'%file)\n return\n finally:\n f.close()\n\n summed_exposure = N.array(exposures).sum(axis=0)\n null = header.get('TNULL1', None) #THB: kluge fix.\n coldef = pf.ColDefs([pf.Column(name='COSBINS',format=header['TFORM1'],null=null,array=summed_exposure)])\n exp_table = pf.new_table(coldef,header=header,nrows=exposures[0].shape[0])\n gti = merge_gti(files)\n exp_table.writeto(outputfile,clobber=True)\n gti.writeExtension(outputfile)\n\ndef merge_gti(files,table_name = 'GTI',interval = None):\n \"\"\"Return the union of Gtis specified in files, with an optional time range cut.\"\"\"\n gtis = [Gti(f,table_name) for f in files]\n new_gti = Gti(gtis[0])\n for gti in gtis:\n new_gti.combine(gti)\n\n if interval is not None:\n new_gti = new_gti.applyTimeRangeCut(*interval)\n\n return new_gti\n\ndef merge_bpd(bpd_files,outfile = None):\n \"\"\"Merge a set of BinnedPhotonData files.\n \n outfile: File to write the merged BinnedPhotonData to. If None, don't save it.\"\"\"\n bpds = [BinnedPhotonData(bf) for bf in bpd_files]\n bpds = [bpd for bpd in bpds if bpd.gti().computeOntime()>0] #Ignore entries with empty GTIs\n new_bpd = bpds[0]\n for b in bpds[1:]:\n new_bpd.add(b)\n if outfile:\n new_bpd.write(outfile)\n\n return new_bpd\n\ndef merge_lt(lt_files,outfile = 'merged_lt.fits',weighted = True):\n \"\"\"Merge a list of LivetimeCube files, handling the WEIGHTED_EXPOSURE extension.\n\n kwargs:\n outfile: File to save the merged LivetimeCube to\n weighted: If true, merge WEIGHTED_EXPOSURE tables, in addition to\n EXPOSURE.\n \"\"\"\n\n files = __FITS_parse__(lt_files)\n\n try:\n f = pf.open(files[0])\n p_header = f['PRIMARY'].header\n err = 'EXPOSURE'\n header = f['EXPOSURE'].header\n exposures = [f['EXPOSURE'].data.COSBINS]\n err = 'WEIGHTED_EXPOSURE'\n if weighted:\n w_header = f['WEIGHTED_EXPOSURE'].header\n w_exposures = [f['WEIGHTED_EXPOSURE'].data.COSBINS]\n except KeyError:\n print('file %s has no %s table: aborting'%(files[0],ext))\n return\n finally:\n f.close() \n\n for file in files[1:]:\n try:\n f = pf.open(file)\n ext = 'EXPOSURE'\n h = f['EXPOSURE'].header\n for key in header.keys():\n assert(h[key]==header[key])\n exposures+=[f['EXPOSURE'].data.COSBINS]\n\n if weighted:\n ext = 'WEIGHTED_EXPOSURE'\n hw = f['WEIGHTED_EXPOSURE'].header\n for key in w_header.keys():\n assert(hw[key]==w_header[key])\n w_exposures += [f['WEIGHTED_EXPOSURE'].data.COSBINS]\n\n except AssertionError:\n print('Inconsistent header values, file %s, extension %s, keyword %s'%(file,ext,key))\n return\n except KeyError:\n print('File %s has no table \"%s\": aborting'%(file,ext))\n return\n finally:\n f.close()\n primary = pf.PrimaryHDU(data=None,header = p_header)\n hdulist = pf.HDUList([primary])\n summed_exposure = N.array(exposures).sum(axis=0)\n null = header.get('TNULL1',None)\n coldef = pf.ColDefs([pf.Column(name='COSBINS',format=header['TFORM1'],null=null,array=summed_exposure)])\n exp_table = pf.new_table(coldef,header=header,nrows=exposures[0].shape[0])\n hdulist.append(exp_table)\n if weighted:\n summed_w_exposure = N.array(w_exposures).sum(axis=0)\n null = w_header.get('TNULL1',None)\n coldef_w = pf.ColDefs([pf.Column(name='COSBINS',format=w_header['TFORM1'],null=null,array=summed_w_exposure)])\n w_exp_table = pf.new_table(coldef_w,header=w_header,nrows=w_exposures[0].shape[0])\n hdulist.append(w_exp_table)\n hdulist.writeto(outfile,clobber=True)\n gti = merge_gti(files)\n gti.writeExtension(outfile)\n\n#EVERYTHING BELOW IS AN INTERNAL CALL.\n\ndef __FITS_parse__(files):\n \"\"\"Parse input and return a list of (FITS) filenames.\n\n files -- a glob-style wildcard, an ASCII file containing a list of filenames, a list of filenames, or a single FITS file.\"\"\"\n #This doesn't detect ndarrays.\n #if type(files) is ListType: return files\n\n #Try testing for non-string iterability instead.\n if getattr(files,'__iter__',False):\n return files\n\n try: #See if it's a FITS file\n f = pf.open(files)\n f[0]\n f.close()\n return [files]\n except:\n pass\n if files[0] == '@':\n return [line.strip() for line in file(files[1:]) if len(line)>0 and line[0]!='#']\n from glob import glob\n return glob(files)\n\ndef __get_handles__(files):\n files = __FITS_parse__(files)\n handles = [pf.open(x,memmap=1) for x in files] #some versions may need to disable memmap (=0)\n return handles\n\ndef __merge_events__(handles,table_name = 'EVENTS'):\n \"\"\"Return a FITS table of merged FT1 events. Now works for FT2 too!\"\"\"\n\n num_events = [x[table_name].data.shape[0] for x in handles]\n columns,header = __common_columns__(handles,table_name)\n event_table = pf.new_table(columns,header=header,nrows=sum(num_events))\n previous_loc = 0\n for i,handle in enumerate(handles):\n for j in xrange(len(columns)):\n name = columns[j].name\n event_table.data.field(name)[previous_loc:previous_loc+num_events[i]] = handle[table_name].data.field(name)[:]\n previous_loc += num_events[i]\n return event_table\n\ndef __common_columns__(handles,table_name = 'EVENTS'):\n \"\"\"Find the columns common to all files.\"\"\"\n #Quick kluge - return the shortest one...\n all_cols = [handle[table_name].columns for handle in handles]\n arg = N.argmin([len(col) for col in all_cols])\n return all_cols[arg],handles[arg][table_name].header\n\n \"\"\"\n selector = {}\n for col in all_cols:\n for n in col: selector[n.name] = n\n for col in all_cols:\n names = [n.name for n in col]\n for key in selector.keys():\n if not (key in names): selector.pop(key)\n print selector\n \"\"\"\n\n\n\n\ndef __merge_gti__(handles,no_table=False,interval=None):\n \"\"\"Return a FITS table of merged GTI.\"\"\"\n\n if len(handles) == 0: return ([],[])\n\n #Merge the gti and sort the results\n starts = N.concatenate([x['GTI'].data.field('START') for x in handles])\n stops = N.concatenate([x['GTI'].data.field('STOP') for x in handles])\n sorting = N.argsort(starts)\n starts = starts[sorting]\n stops = stops[sorting]\n\n if interval is not None:\n mask = N.logical_and(starts > interval[0], stops < interval[1])\n starts = N.append(interval[0],starts[mask])\n stops = N.append(stops[mask],interval[1])\n\n if no_table: return (starts,stops)\n\n #Put GTI in new table\n gti_table = pf.new_table(handles[0]['GTI'].columns,nrows = len(starts))\n gti_table.data.field('START')[:] = starts\n gti_table.data.field('STOP')[:] = stops\n\n return gti_table\n\ndef __arbitrary_cuts__(events,cuts):\n \"\"\"Perform the cuts provided as arguments.\n\n events -- a major table in FITS format such as might be returned by __merge_events__\n cuts -- a list of cuts; the final result is the intersection of all cuts. e.g,\n ['ENERGY>100','MET > 5000','MET < 10000']\n \"\"\"\n\n if cuts is None: return\n\n from numarray import array,logical_and #some installations may need the numpy version of these calls\n\n for cut in cuts: #put cut columns in namespace\n for name in events.columns.names:\n if name in cut:\n exec('%s = events.data.field(\\'%s\\')'%(name,name))\n mask = array([True]*events.data.shape[0])\n for cut in cuts: #build mask cumulatively\n exec('mask = logical_and(mask,%s)'%cut)\n\n new_table = pf.new_table(events.columns,nrows=len(mask[mask]))\n for i in xrange(len(events.columns)):\n new_table.data.field(i)[:] = events.data.field(i)[mask]\n events.data = new_table.data\n\n","repo_name":"fermi-lat/pointlike","sub_path":"python/uw/utilities/fitstools.py","file_name":"fitstools.py","file_ext":"py","file_size_in_byte":20781,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"18633586319","text":"from typing import List\n\nclass Solution:\n def numberOfArithmeticSlices_slow(self, A: List[int]) -> int:\n dp = [[False] * len(A) for _ in range(len(A))]\n cnt = 0\n for i in range(len(A)-2):\n if A[i+2]-A[i+1]==A[i+1]-A[i]:\n dp[i][i+2] = True\n cnt = cnt + 1\n print(dp)\n for k in range(3, len(A)):\n for i in range(len(A)-k):\n j = i + k\n dp[i][j] = dp[i][j-1] and dp[i+1][j]\n print('dp[{0}][{1}]={2}'.format(i,j,dp[i][j]))\n if dp[i][j]:\n cnt = cnt+1\n return cnt\n\n def numberOfArithmeticSlices(self, A: List[int]) -> int:\n res = 0\n dp = 0\n for i in range(2,len(A)):\n if A[i]-A[i-1]==A[i-1]-A[i-2]:\n dp += 1\n res += dp\n else:\n dp = 0\n return res\n\n\nif __name__ == '__main__':\n sol = Solution()\n print(sol.numberOfArithmeticSlices([1,2,3,4]))","repo_name":"ivan0703/leetcode_dp","sub_path":"413. Arithmetic Slices/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"27636203999","text":"# -*-coding:UTF-8-*-\nfrom win32com.client import Dispatch\nimport win32com.client\nfrom tkinter import *\nimport os, datetime, string\n\n\"\"\"\nauth:zhongqionglun\ntime:2019/03/29\ndescription: 统计项目中每人每天测试用例数\n\"\"\"\n\n\ndef cases_days():\n testers = member.get().split(\"|\")\n print(testers)\n xlApp = win32com.client.Dispatch('Excel.Application')\n filepath = os.getcwd() + \"\\\\\" + filename.get() + \".xls\"\n xlBook = xlApp.Workbooks.Open(filepath)\n # print(xlBook)\n xlSheet = xlBook.Worksheets(\"Worksheet\")\n dic = dict.fromkeys(testers)\n # print(dic)\n for tester in testers:\n row = 6\n numday = dict.fromkeys(list(range(1, 31)), 0)\n # print(numday)\n while True:\n if not xlSheet.Cells(row, 1).Value:\n\n dic[tester] = numday\n # print(dic)\n break\n elif (xlSheet.Cells(row, 7).Value == tester):\n # print(xlSheet.Cells(row, 7))\n # days.append(xlSheet.Cells(row, 6).Value)\n # print(days)\n for day in range(1, 31):\n # print(\"the day is : %s\" % day)\n if datetime.datetime.now().month >= 10:\n substring = \"2019-\" + str(datetime.datetime.now().month) + \"-\" + str(day)\n else:\n if day >= 10:\n substring = \"2019-0\" + str(datetime.datetime.now().month) + \"-\" + str(day)\n else:\n substring = \"2019-0\" + str(datetime.datetime.now().month) + \"-0\" + str(day)\n # print(substring)\n if not str(xlSheet.Cells(row, 6).Value).find(substring):\n numday[day] = numday.get(day) + 1\n # print(numday.get(day))\n # print(numday)\n break\n\n row += 1\n continue\n print(dic)\n\n\ndef writeexcel():\n outfilename = \"执行效率月统计结果.xlsx\"\n xlApp = win32com.client.Dispatch('Excel.Application')\n if os.path.exists(\"执行效率月统计结果.xlsx\"):\n print(\"The file is exists....\")\n # 判断shett是否存在，一个tester一个sheet#\n\n else:\n print(\"The file isn't exists....\")\n\n\nwindow = Tk()\nwindow.title('用例执行分析')\nwindow.iconbitmap('py-blue-trans-out.ico')\nform = Frame(window)\nform.pack()\nlab1 = Label(form, text='文件名：')\nfilename = StringVar()\nent1 = Entry(form, textvariable=filename)\nfilename.set(\"\")\nlab1.grid(row=0, column=0)\nent1.grid(row=0, column=1)\nlab2 = Label(window, text=\"测试成员：\")\nlab2.pack()\nmember = StringVar()\nent2 = Entry(window, width=40, textvariable=member)\nmember.set(\"\")\nent2.pack()\nbtn = Button(window, text='统计', command=cases_days)\nbtn.pack()\nwindow.mainloop()\n","repo_name":"zhongqionglun/GitLibrary","sub_path":"Testlink/cases_per_days.py","file_name":"cases_per_days.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"74616411771","text":"import pytest\nfrom harness.logger import logger\n\nlarge_file_01 = 'large_file_01'\nlarge_file_02 = 'large_file_02'\n\n@pytest.mark.skip(reason=\"using >> to concatenate files seems to hang clients\")\ndef test_concat(gkfs_daemon, gkfs_shell, file_factory):\n \"\"\"Concatenate two large files\"\"\"\n\n lf01 = file_factory.create(large_file_01, size=4.0, unit='MB')\n lf02 = file_factory.create(large_file_02, size=4.0, unit='MB')\n\n cmd = gkfs_shell.cp(lf01.pathname, gkfs_daemon.mountdir)\n assert cmd.exit_code == 0\n\n cmd = gkfs_shell.cp(lf02.pathname, gkfs_daemon.mountdir)\n assert cmd.exit_code == 0\n\n cmd = gkfs_shell.stat('--terse', gkfs_daemon.mountdir / large_file_01)\n assert cmd.exit_code == 0\n out = cmd.parsed_stdout\n assert out.size == lf01.size\n\n cmd = gkfs_shell.stat('--terse', gkfs_daemon.mountdir / large_file_02)\n assert cmd.exit_code == 0\n out = cmd.parsed_stdout\n assert out.size == lf02.size\n\n cmd = gkfs_shell.md5sum(gkfs_daemon.mountdir / large_file_01)\n assert cmd.exit_code == 0\n assert cmd.parsed_stdout.digest == lf01.md5sum()\n\n cmd = gkfs_shell.md5sum(gkfs_daemon.mountdir / large_file_02)\n assert cmd.exit_code == 0\n assert cmd.parsed_stdout.digest == lf02.md5sum()\n\n ##XXX hangs!\n cmd = gkfs_client.bash('cat', gkfs_daemon.mountdir / large_file_01, '>>', gkfs_daemon.mountdir / large_file_02)\n assert cmd.exit_code == 0\n","repo_name":"NGIOproject/old_GekkoFS_old","sub_path":"tests/integration/shell/test_concat.py","file_name":"test_concat.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"78"}
+{"seq_id":"2280126816","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2020-02-11 06:51:38\n# @Author : mutudeh (josephmathone@gmail.com)\n# @Link : ${link}\n# @Version : $Id$\n\nimport os\n\nclass Solution():\n def search(self, nums, target):\n if not nums:\n return -1\n left = 0\n right = len(nums) - 1\n\n\n while left <= right:\n mid = int((left + right)/2)\n\n if nums[mid] == target:\n return mid\n if nums[mid] >= nums[left]:\n #说明左边有序，利用左边进行判断\n if target >= nums[left] and target <= nums[mid]:\n right = mid - 1\n else:\n left = mid + 1\n else:\n # 说明右边有序,利用右边进行判断\n if target >= nums[mid] and target <= nums[right]:\n left = mid + 1\n else:\n right = mid - 1\n return -1\n\ns = Solution()\ndata = [5,1]\nprint(s.search(data,1))","repo_name":"joseph-mutu/Codes-of-Algorithms-and-Data-Structure","sub_path":"Leetcode/在旋转数组中查找-使用左边进行判断.py","file_name":"在旋转数组中查找-使用左边进行判断.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"23441221568","text":"from jinja2 import FileSystemLoader , Environment\nfrom .resource import Resource\nimport pathlib\n \nclass Instance(Resource):\n def _load_template(self):\n searchpath = pathlib.Path(__file__).parent.parent/\"templates\"\n loader = FileSystemLoader(searchpath=searchpath)\n template = Environment(loader=loader).get_template(\"instance.json.j2\")\n return template\n \n def _get_client(self):\n return self.compute.instances()\n \n def _execute_request(self):\n return self._get_client().insert(\n project=self.data.get(\"project_name\",\"\"),\n zone=self.data.get(\"zone_name\",\"\"),\n body = self.body\n ).execute()\n\n def _get_operation(self):\n return (\n self\n .compute\n .zoneOperations()\n .get(\n project=self.data.get(\"project_name\",\"\"),\n zone=self.data.get(\"zone_name\",\"\"),\n operation=self.request.get(\"name\",\"\")\n )\n )\n def get_ip(self):\n response = self._get_client().get(\n project=self.data.get(\"project_name\",\"\"),\n zone=self.data.get(\"zone_name\",\"\"),\n instance=self.data.get(\"instance_name\",\"\")\n ).execute()\n\n return {\n \"private\": (\n response\n .get(\"networkInterfaces\",[])[0]\n .get(\"networkIP\",\"\")\n ),\n \"public\": (\n response\n .get(\"networkInterfaces\",[])[0]\n .get(\"accessConfigs\",[])[0]\n .get(\"natIP\",\"\")\n )\n }","repo_name":"fermented-cat/potential-octo-guide","sub_path":"scripts/provision/resources/instance.py","file_name":"instance.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"26266316059","text":"import android\ndroide=android.Android()\n\narchivo = droide.dialogGetInput(\"Ecriba nombre de archivo\")\ndt = 100\nfin = 30000\ntiempo = 0\ndroide.startSensingTimed(2,dt)\n\nlecturas = []\ndroide.ttsSpeak(\"Inicio de recorrido\")\nimport time\nwhile tiempo <= fin:\n lecturas.append(droide.sensorsReadAccelerometer().result)\n time.sleep(dt/1000.0)\n tiempo += dt\n \ndroide.stopSensing();\ndroide.ttsSpeak(\"Fin de recorrido\")\n\nimport csv\n#c = csv.writer(open(\"MYFILE.csv\", \"wb\"))\n#c.writerow(lecturas)\nwith open(archivo.result + '.csv', 'w') as fp:\n a=csv.writer(fp,delimiter=',')\n a.writerows(lecturas)","repo_name":"Blindbandit01/ProyectoAndroidz","sub_path":"ProyectoAndroid/lania.ipython/Dia2/notebooks/D2L5.py","file_name":"D2L5.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"71756409851","text":"from django.shortcuts import render,redirect,reverse,HttpResponse\nimport datetime\nfrom django.views.generic import ListView,DetailView\nfrom comments.models import Comment\nfrom .models import Mypost,PostCategory\n# Create your views here.\n\ndef error_handle(request):\n return render(request,\"error.html\")\n\n\ndef feedContent(filepath):\n posted = filepath.path\n with open(posted,\"r\") as fp:\n return fp.read()\n\n#使用ListView显示博客列表页\nclass HomeView(ListView):\n model = Mypost\n template_name = \"myblog/index.html\"\n context_object_name = \"post_list\"\n ordering = ['-post_update_date']\n paginate_by = 5\n\n #添加附加信息\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n category_list = PostCategory.objects.exclude(name=\"undefined\")\n context[\"category_list\"] = category_list\n return context\n\ndef postview(request,post_id):\n try:\n posted = Mypost.objects.get(pk=post_id)\n except:\n #博客文章不存在 重定向至错误页面\n return redirect(reverse(\"blog:error\"))\n else:\n post_info = {}\n post_info[\"posted_id\"] = post_id\n post_info[\"post_title\"] = posted.post_title\n post_info[\"post_author\"] = posted.post_author\n post_info[\"post_date\"] = posted.post_date\n post_info[\"post_update_date\"] = posted.post_update_date\n post_info[\"post_content\"] = feedContent(posted.post_article)\n\n # 评论\n comment_obj = Comment.objects.filter(comm_posted=posted).order_by(\"comm_created_time\")\n\n #分类\n category_list = PostCategory.objects.exclude(name=\"undefined\")\n\n context = {\n \"post_info\":post_info,\n \"comment_info\":comment_obj,\n \"category_list\":category_list,\n }\n return render(request,\"myblog/blog-post.html\",context)\n\n\nclass CategoryView(ListView):\n model = Mypost\n template_name = \"myblog/index.html\"\n context_object_name = \"post_list\"\n ordering = ['-post_update_date']\n paginate_by = 5\n\n\n #与home相同，但要筛选出需要的\n def get_queryset(self):\n ca_name = self.request.GET.get('name', None)\n pkid = PostCategory.objects.get(name=ca_name)\n return Mypost.objects.filter(post_category=pkid).order_by('-post_update_date')\n\n #添加附加信息\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n category_list = PostCategory.objects.exclude(name=\"undefined\")\n context[\"category_list\"] = category_list\n return context\n# class PostView(DetailView):\n# model = Mypost\n# template_name = \"myblog/blog-post.html\"\n# context_object_name = \"post_info\"","repo_name":"Ma5ker/website","sub_path":"myblog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"70845207612","text":"#!/usr/bin/python3\n\"\"\" comparations with API request title \"\"\"\nimport operator\nimport requests\n\n\ndef count_words(subreddit, word_list, hot_list=[], after='', values={}):\n \"\"\" Request with recursion and compare the response \"\"\"\n url = 'https://www.reddit.com/r/{}/hot.json?after={}'.format(\n subreddit, after)\n data = {'user-agent': 'scraping_rec-0-0-3'}\n if len(hot_list) == 0:\n for x in range(0, len(word_list)):\n values[word_list[x]] = 0\n try:\n res = requests.get(url, headers=data,\n allow_redirects=False).json()\n query = res.get('data').get('children')\n after = res.get('data').get('after')\n for child in query:\n data = child.get('data').get('title')\n for x in range(0, len(word_list)):\n if word_list[x].lower() in data.lower():\n values[word_list[x]] += 1\n hot_list.append(data)\n if after is None:\n values_sort = sorted(values.items(), key=operator.itemgetter(1))\n for n in range(len(values_sort), 0, -1):\n if values_sort[n - 1][1] > 0:\n print(\"{}: {}\".format(values_sort[n - 1][0],\n values_sort[n - 1][1]))\n return(hot_list)\n return(count_words(subreddit, word_list, hot_list, after, values))\n except:\n return(None)\n","repo_name":"cristian0497/holberton-system_engineering-devops","sub_path":"0x16-api_advanced/100-count.py","file_name":"100-count.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"11200204344","text":"#plottting 3D surface plot f(x,y)=x^2 + y^2 using python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.cm as cm\nfrom pylab import rcParams\nrcParams['figure.figsize']=5,5\n\nfig=plt.figure()\naxes=fig.gca(projection='3d') #gca=get current axis\nx= np.linspace(-1,1,100)\ny= np.linspace(-1,1,100)\n\nxv,yv =np.meshgrid(x,y)\nz=(xv**2+yv**2)/2\n# surface_plot with color grading and color bar\np=axes.plot_surface(xv,yv,z,rstride=4,cstride=4,cmap=cm.RdBu,linewidth=0,antialiased =False)\n\nfig.colorbar(p,shrink=0.5)\naxes.set_xlabel('$x$',fontsize=15)\naxes.set_ylabel('$y$',fontsize=15)\naxes.set_zlabel('$z$',fontsize=15)\n\nplt.tight_layout()\nplt.show()\n","repo_name":"baleshwar-mahto/cse-using-python","sub_path":"surface.py","file_name":"surface.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"21133884824","text":"import logging\n\nlog = logging.getLogger(__name__)\n\n\ndef get_transaction_metadata(cursor):\n \"\"\"Return PostgreSQL-specific transaction ID.\"\"\"\n cursor.execute('SELECT txid_current();')\n txid = cursor.fetchone()[0]\n return txid\n\n\ndef is_transaction_complete(cursor, txid):\n \"\"\"Test if transaction ID is finished (committed or aborted.\"\"\"\n # See http://www.postgresql.org/docs/9.1/static/functions-info.html for\n # descriptions of the postgres system functions.\n cursor.execute('SELECT txid_current_snapshot();')\n # \"txid_snapshot's textual representation is xmin:xmax:xip_list.\n # For example 10:20:10,14,15 means xmin=10, xmax=20, xip_list=10, 14, 15.\"\n xmin, xmax, xip_list = cursor.fetchone()[0].split(':')\n xmin = long(xmin)\n xmax = long(xmax)\n xip_list = map(long, xip_list.split(',')) if xip_list else []\n\n # If the transaction is finished according to the postgres system functions,\n # but we can't find the TransactionCommitBarrier, we know the transaction is\n # aborted.\n\n # xmin is the \"earliest transaction ID (txid) that is still active. All\n # earlier transactions will either be committed and visible, or rolled\n # back and dead.\"\n if txid < xmin:\n log.info('Transaction(%lu) finished xmin', txid)\n return True\n\n # \"A txid that is xmin <= txid < xmax and not in this list was already\n # completed at the time of the snapshot, and thus either visible or dead\n # according to its commit status.\"\n if xmin <= txid and txid < xmax and txid not in xip_list:\n log.info('Transaction(%lu) finished xip_list', txid)\n return True\n\n return False\n\n\ndef get_debug_info(cursor):\n cursor.execute('SELECT txid_current_snapshot();')\n return cursor.fetchone()[0]\n","repo_name":"silasbw/django_transaction_barrier","sub_path":"django_transaction_barrier/postgresql.py","file_name":"postgresql.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"78"}
+{"seq_id":"31255779229","text":"import sys\nsys.path.append('/home/zikun/work/tracking/pysot-toolkit')\nfrom pysot.datasets import DatasetFactory\nfrom pysot.utils.region import vot_overlap\nimport numpy as np\nfrom tadt_tracker import Tadt_Tracker\nimport os\nimport torch\n\nfrom defaults import _C as cfg\nfrom backbone_v2 import build_vgg16\n\n\notb_root = 'the root path of otb benchmark'\nresult_path = 'the root path that you want to put the result'\nvgg16_model_mat_path = 'the path of the vgg model'\n\ndataset = DatasetFactory.create_dataset(name = 'OTB100', dataset_root = otb_root, load_img = False)\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nframe_counter = 0\npred_bboxes = []\nmodel = build_vgg16(cfg)\n\n\nif not os.path.exists(result_path):\n os.mkdir(result_path)\nfor video in dataset:\n #if video.name != 'CarScale':\n # continue\n print('video',video.name)\n for idx, (img_path, gt_bbox) in enumerate(video):\n print('frame:',idx)\n if idx == frame_counter:\n target_location = np.array(gt_bbox)\n tracker = Tadt_Tracker(cfg, model = model, device = device, display = False)\n tracker.initialize_tadt(img_path, target_location)\n else:\n tracker.tracking(img_path, idx)\n tracker.end_visualize()\n tracker.saving_result(result_path, video.name, zero_index = False)\n","repo_name":"ZikunZhou/TADT-python","sub_path":"tadt_otb_v2.py","file_name":"tadt_otb_v2.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":86,"dataset":"github-code","pt":"78"}
+{"seq_id":"29947274398","text":"import discord\nfrom discord.ext import commands\n\nclass MCwiki(commands.Cog):\n \n def __init__(self, client):\n self.client = client\n \n @commands.command()\n async def mcwiki(self, ctx):\n await ctx.channel.send(\"https://minecraft.gamepedia.com/Minecraft_Wiki\")\n \ndef setup(client):\n client.add_cog(MCwiki(client))","repo_name":"0rang-3/GRAPE-Discord-Bot","sub_path":"cogs/mcwiki.py","file_name":"mcwiki.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"72714441212","text":"\nimport os\n\ndef removeFile(filePath):\n files = os.listdir(filePath)\n for file in files:\n fileName = f\"{filePath}/{file}\"\n if os.path.isdir(fileName):\n removeFile(fileName)\n else:\n if \"长期更新\" in fileName :\n os.remove(fileName)","repo_name":"luofanrain/lanzou-more-upload","sub_path":"controller/removeFile.py","file_name":"removeFile.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"32529668950","text":"from typing import Optional, Callable\nimport Box2D as b2\nimport gym\nimport numpy as np\nfrom gym import spaces\nfrom gym.utils import seeding\n\nfrom .car_model import CarModel, CAR_WIDTH, CAR_HEIGHT\n\nROAD_HIGHT = 25.0\nDT = 1 / 60\n\n\nclass FrictionZoneListener(b2.b2ContactListener):\n def __init__(self, env):\n b2.b2ContactListener.__init__(self)\n self.env = env\n\n def BeginContact(self, contact):\n self._contact(contact, True)\n\n def EndContact(self, contact):\n self._contact(contact, False)\n\n def _contact(self, contact, begin):\n if (\n contact.fixtureA == self.env.friction_zone\n or contact.fixtureB == self.env.friction_zone\n ):\n if begin:\n self.env.friction_zone.touch = True\n else:\n self.env.friction_zone.touch = False\n\n\nclass CliffDaredevil(gym.Env):\n metadata = {\"render.modes\": [\"human\", \"rgb_array\"], \"video.frames_per_second\": 30}\n\n def __init__(\n self, render_mode: Optional[str] = None, friction_profile: float = 0.1\n ):\n self.contactListener_keepref = FrictionZoneListener(self)\n self.min_position = -5.0\n self.max_position = 75.0\n self.goal_zone = (50.0, 50.0 + CAR_WIDTH)\n self.cliff_edge = 0.3\n self.friction_start = 40.0\n self.friction = (\n friction_profile\n if callable(friction_profile)\n else lambda _: friction_profile\n )\n self.world = b2.b2World((0, -10), contactListener=self.contactListener_keepref)\n self._build_road()\n self.viewer = None\n self.action_space = spaces.Box(\n np.array([-1, 0]), np.array([+1, +1]), dtype=np.float32\n ) # gas, brake\n self.observation_space = spaces.Box(\n low=np.array([self.min_position, -32.0]),\n high=np.array([self.max_position, 32.0]),\n dtype=np.float32,\n ) # position, velocity\n self.car: Optional[CarModel] = None\n self.render_mode = render_mode\n self.seed()\n self.reset()\n\n def _build_road(self):\n self.ground = self.world.CreateStaticBody(\n fixtures=[\n b2.b2FixtureDef(\n shape=b2.b2EdgeShape(\n vertices=[\n (self.min_position, ROAD_HIGHT),\n (self.goal_zone[1] + self.cliff_edge, ROAD_HIGHT),\n ]\n ),\n friction=0.99,\n ),\n b2.b2FixtureDef(\n shape=b2.b2EdgeShape(\n vertices=[\n (self.friction_start, ROAD_HIGHT),\n (self.goal_zone[1], ROAD_HIGHT),\n ]\n ),\n friction=self.friction(0),\n ),\n ]\n )\n self.ground.fixtures[1].touch = False\n self.friction_zone = self.ground.fixtures[1]\n self.ground.userData = self.ground\n\n # In new Gym API, this function is deprecated\n def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n\n def step(self, action):\n truncated = False\n if action is not None:\n action = np.clip(action, -1.0, 1.0)\n self.car.gas(action[0])\n self.car.brake(action[1])\n self.car.step()\n self.world.Step(DT, 6 * 30, 2 * 30)\n x, y = self.car.hull.position\n if self.friction_zone.touch:\n progress = np.clip(\n (x - self.friction_start) / (self.goal_zone[1] - self.friction_start),\n 0.0,\n 1.0,\n )\n friction = self.friction(progress)\n self.friction_zone.friction = friction\n backward = x < self.min_position\n reward = 0\n if backward:\n reward -= 1.0\n angle = self.car.hull.angle\n terminated = y < ROAD_HIGHT or backward or np.abs(angle) > 0.9\n if self.goal_zone[0] < x < self.goal_zone[1]:\n reward += 1.0\n distance = self.goal_zone[0] - x\n reward -= np.abs(distance)\n cost = -(self.goal_zone[1] + self.cliff_edge - x)\n v = self.car.hull.linearVelocity[0]\n if self.render_mode == \"human\":\n self.render()\n return (\n np.array([x, v], np.float32),\n reward,\n terminated,\n truncated,\n {\"cost\": cost},\n )\n\n def reset(\n self,\n *,\n seed: Optional[int] = None,\n options: Optional[dict] = None,\n ):\n self._destroy()\n self.friction_zone.touch = False\n #assert type(self.friction) is Callable\n self.friction_zone.friction = self.friction(0.0)\n position = self.np_random.uniform(low=-0.1, high=0.1)\n self.car = CarModel(self.world, position, ROAD_HIGHT)\n if self.render_mode == \"human\":\n self.render()\n return self.step(None)[0], {}\n\n def _destroy(self):\n if self.car is None:\n return\n self.car.destroy()\n\n def render(self):\n mode = self.render_mode\n screen_width, screen_height = 640, 320\n if self.viewer is None:\n import cliff_daredevil.rendering as rendering\n\n self.viewer = rendering.Viewer(screen_width, screen_height)\n self.viewer.set_bounds(self.min_position, self.max_position, 0.0, 40.0)\n sky = rendering.make_polygon(\n [\n (self.min_position, 0.0),\n (self.min_position, 40.0),\n (self.max_position, 40.0),\n (self.max_position, 0.0),\n ]\n )\n sky.set_color(135 / 255, 206 / 255, 235 / 255)\n xs_ground = np.linspace(self.goal_zone[1], self.goal_zone[1] + 1.0, 25)\n ys_ground = np.linspace(ROAD_HIGHT, 0.0, 25)\n xs_ground += self.np_random.uniform(-0.75, 0.75, 25)\n ground = rendering.make_polygon(\n [(self.min_position, 0.0), (self.min_position, ROAD_HIGHT)]\n + [*zip(xs_ground, ys_ground)]\n )\n ground.set_color(237 / 255, 201 / 255, 175 / 255)\n oil = rendering.make_polyline(\n [\n (self.friction_start, ROAD_HIGHT - 0.1),\n (self.goal_zone[1], ROAD_HIGHT - 0.1),\n ]\n )\n oil.set_linewidth(2)\n xs_sea = np.linspace(self.goal_zone[1], self.max_position, 25)\n ys_sea = np.maximum(np.sin(xs_sea * 7.1) * 2.0, 0.2)\n sea = rendering.make_polygon(\n [*zip(xs_sea, ys_sea)] + [(xs_sea[-1], 0.0), (xs_sea[0], 0.0)]\n )\n sea.set_color(0, 105 / 255, 148 / 255)\n sun = rendering.make_circle(2.5)\n sun.set_color(252 / 255, 212 / 255, 64 / 255)\n sun.add_attr(rendering.Transform((65, 35)))\n car_width, car_height = CAR_WIDTH, CAR_HEIGHT\n l, r, t, b = -car_width / 2, car_width / 2, car_height, 0\n car = rendering.make_polygon([(l, b), (l, t), (r, t), (r, b)])\n self.car_transform = rendering.Transform()\n car.add_attr(self.car_transform)\n radius = car_height / 2.5\n frontwheel = rendering.make_circle(radius)\n frontwheel.add_attr(rendering.Transform(translation=(car_width / 4, 0)))\n frontwheel.add_attr(self.car_transform)\n frontwheel.set_color(0.5, 0.5, 0.5)\n frontwheel_rim = rendering.make_circle(0.3, res=30, filled=True)\n frontwheel_rim.set_color(1.0, 0.0, 0.0)\n frontwheel_rim.add_attr(rendering.Transform(translation=(radius - 0.3, 0)))\n self.frontwheel_rim_transform = rendering.Transform()\n frontwheel_rim.add_attr(self.frontwheel_rim_transform)\n backwheel = rendering.make_circle(radius)\n backwheel.add_attr(rendering.Transform(translation=(-car_width / 4, 0)))\n backwheel.add_attr(self.car_transform)\n backwheel.set_color(0.5, 0.5, 0.5)\n backwheel_rim = rendering.make_circle(0.3, res=30, filled=True)\n backwheel_rim.set_color(1.0, 0.0, 0.0)\n backwheel_rim.add_attr(rendering.Transform(translation=(radius - 0.3, 0)))\n self.backwheel_rim_transform = rendering.Transform()\n backwheel_rim.add_attr(self.backwheel_rim_transform)\n\n def make_flag(position):\n flagx = position\n flagy1 = ROAD_HIGHT\n flagy2 = flagy1 + 2.0\n flagpole = rendering.Line((flagx, flagy1), (flagx, flagy2))\n flag = rendering.FilledPolygon(\n [\n (flagx, flagy2),\n (flagx, flagy2 - 1.0),\n (flagx + 2.5, flagy2 - 0.5),\n ]\n )\n flag.set_color(0.8, 0.8, 0)\n return flag, flagpole\n\n right_flag, right_flagpole = make_flag(self.goal_zone[0])\n left_flag, left_flagpole = make_flag(self.goal_zone[1])\n self.viewer.add_geom(sky)\n self.viewer.add_geom(sea)\n self.viewer.add_geom(ground)\n self.viewer.add_geom(oil)\n self.viewer.add_geom(sun)\n self.viewer.add_geom(car)\n self.viewer.add_geom(frontwheel)\n self.viewer.add_geom(frontwheel_rim)\n self.viewer.add_geom(backwheel)\n self.viewer.add_geom(backwheel_rim)\n self.viewer.add_geom(right_flagpole)\n self.viewer.add_geom(right_flag)\n self.viewer.add_geom(left_flagpole)\n self.viewer.add_geom(left_flag)\n pos = self.car.hull.position\n self.car_transform.set_translation(*pos)\n self.car_transform.set_rotation(self.car.hull.angle)\n self.frontwheel_rim_transform.set_translation(*pos)\n self.backwheel_rim_transform.set_translation(*pos)\n self.frontwheel_rim_transform.set_translation(*self.car.wheels[0].position)\n self.frontwheel_rim_transform.set_rotation(self.car.wheels[0].angle)\n self.backwheel_rim_transform.set_rotation(self.car.wheels[1].angle)\n self.backwheel_rim_transform.set_translation(*self.car.wheels[1].position)\n return self.viewer.render(return_rgb_array=mode == \"rgb_array\")\n\n def close(self):\n if self.viewer:\n self.viewer.close()\n self.viewer = None\n\n\nif __name__ == \"__main__\":\n from pyglet.window import key\n from gym.wrappers import TimeLimit\n\n a = np.array([0.0, 0.0])\n\n def key_press(k, mod):\n global restart\n if k == 0xFF0D:\n restart = True\n if k == key.RIGHT:\n a[0] = +1.0\n if k == key.LEFT:\n a[0] = -1.0\n if k == key.SPACE:\n a[1] = +0.8\n\n def key_release(k, mod):\n if k == key.RIGHT:\n a[0] = 0\n if k == key.LEFT:\n a[0] = 0\n if k == key.SPACE:\n a[1] = 0\n\n env: gym.Env = CliffDaredevil()\n env = TimeLimit(env, 600)\n env.render()\n env.viewer.window.on_key_press = key_press\n env.viewer.window.on_key_release = key_release\n isopen = True\n while isopen:\n env.reset()\n total_reward = 0.0\n total_cost = 0.0\n steps = 0\n restart = False\n while True:\n s, r, done, truncated, info = env.step(a)\n total_reward += r\n total_cost += info[\"cost\"]\n if steps % 200 == 0 or done:\n print(\"\\naction \" + str([\"{:+0.4f}\".format(x) for x in a]))\n print(\"step {} total_reward {:+0.4f}\".format(steps, total_reward))\n print(\"step {} total_cost {:+0.4f}\".format(steps, total_cost))\n print(\"step {} cost {:+0.4f}\".format(steps, info[\"cost\"]))\n print(\"step {} reward {:+0.4f}\".format(steps, r))\n steps += 1\n isopen, _ = env.render()\n if done or restart or not isopen:\n break\n env.close()\n","repo_name":"yardenas/cliff-daredevil","sub_path":"cliff_daredevil/cliff_daredevil.py","file_name":"cliff_daredevil.py","file_ext":"py","file_size_in_byte":12200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"28160549561","text":"\"\"\"This module implements the models for the Blog core system.\"\"\"\n\nfrom app.models.acl import AccessControlMixin\nfrom app.models.patch import CommentMixin, StatusMixin, TagMixin\nfrom app.models import db\n\n\nclass Post(AccessControlMixin, StatusMixin, CommentMixin, TagMixin, db.Model):\n \"\"\"Implements the Post model.\n \"\"\"\n\n title = db.Column(db.String(32))\n sub = db.Column(db.String(128))\n user_id = db.Column(db.Integer, db.ForeignKey(u'user.id'))\n user = db.relationship('User', backref='posts', lazy='select')\n content = db.Column(db.Text())\n\n def __init__(self, title, sub, user, content):\n \"\"\"Initialize the Sketch object.\n\n Args:\n :param title: The title of the post\n :param sub: The subtitle of the post\n :param user: A user\n :param content: Content db.String\n \"\"\"\n super(Post, self).__init__()\n self.title = title\n self.sub = sub\n self.user = user\n self.content = content\n\n def __repr__(self):\n return self.title\n","repo_name":"OhBonsai/flask-boilerplate","sub_path":"app/models/blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"27607036198","text":"from collections import OrderedDict\nfrom warnings import warn\n\nfrom astropy.table import join\nimport astropy.table as table\nfrom astropy.table.operations import _join, _merge_table_meta\nfrom astropy.units import Unit\nimport numpy as np\nfrom astropy.coordinates import SkyCoord\nfrom .exceptions import MultipleResultsWarning, NotFoundWarning, NotFoundError\n\n\nclass MaskedColumn(table.MaskedColumn):\n\n def find(self, sub, start=0, end=None):\n if isinstance(self, (MaskedColumn)):\n str_array = np.array(self, dtype=str)\n index = np.core.defchararray.find(str_array, sub, start=start, end=end) != -1\n return np.where(index)[0], str_array[index]\n\n else:\n return NotImplemented\n\n def to_array(self, units=None):\n \"\"\"\n Returns the columns as a MaskedArray.\n If units are specified, convert to good units\n before returning the array.\n \"\"\"\n if units is None:\n return self.data.copy()\n else:\n # Convert to quantity array\n data = self.quantity\n # Need to stored masked values\n # (quantity arrays don't deal with masks)\n mask = self.mask\n\n # Make sure it has the good units\n data = data.to(units)\n\n # Convert to masked array\n return np.ma.array(data.value, mask=mask)\n\n\nclass Column(table.Column):\n\n def find(self, sub, start=0, end=None):\n if isinstance(self, (Column)):\n str_array = np.array(self, dtype=str)\n index = np.core.defchararray.find(str_array, sub, start=start, end=end) != -1\n return np.where(index)[0], str_array[index]\n\n else:\n return NotImplemented\n\n def to_array(self, units=None):\n \"\"\"\n Returns the columns as an array.\n If units are specified, convert to good units\n before returning the array.\n \"\"\"\n if units is None:\n return self.data.copy()\n else:\n # Convert to quantity array\n data = self.quantity\n\n # Make sure it has the good units\n data = data.to(units)\n\n # Convert to array\n return np.array(data.value)\n\n\nclass Table(table.Table):\n\n # Redefine class attributes (if not, the originals would be taken)\n Column = Column\n MaskedColumn = MaskedColumn\n\n # Set attributes\n main_col = None # Default column used to order\n log = [] # Save output when using insert_value method\n\n def __init__(self, *args, masked=True, **kwargs):\n\n super().__init__(*args, masked=masked, **kwargs)\n\n if self.masked:\n self.nan_to_mask()\n\n # New methods\n def rename_columns(self, old, new):\n\n for ko, kn in zip(old, new):\n self.rename_column(ko, kn)\n\n def nan_to_mask(self):\n \"\"\"\n Replace nan by masked array\n \"\"\"\n if self.masked:\n for k in self.keys():\n if not isinstance(self[k], SkyCoord) and self[k].dtype == float:\n self[k].mask = np.isnan(self[k]) | self[k].mask\n else:\n raise TypeError(\"Input must be a Masked Table.\" +\n \"\\n \\t Set its mask to True before calling\" +\n \" (example: t = Table(t, masked=True, copy=False)).\")\n\n def by_pl_name(self, *plName, name_key=None):\n \"\"\"\n Return the complete line of a given planet name (plName)\n \"\"\"\n position = self.get_index(*plName, name_key=name_key)\n position = np.array(position)\n\n is_valid = (position >= 0)\n\n if is_valid.any():\n out = self[position[is_valid]]\n else:\n raise NotFoundError(*plName)\n\n return out\n\n def by_plName(self, *args, **kwargs):\n \"\"\"\n Old name of by_pl_name. Just an alias\n \"\"\"\n\n return self.by_pl_name(*args, **kwargs)\n\n def get_index(self, *plName, name_key=None):\n '''\n Return the lines index where plName are located for the column given by name_key\n name_key default is given by main_col attribute of the object\n '''\n name_key = name_key or self.main_col\n\n position = []\n for pl in plName:\n try:\n position.append(int(self[name_key].find(pl)[0]))\n\n except TypeError:\n values = self[name_key].find(pl)[1]\n if len(values) > 0:\n warn(MultipleResultsWarning(pl, values))\n else:\n warn(NotFoundWarning(pl))\n position.append(-1)\n\n return position\n\n def set_main_col(self, colname=None, extension='_temp'):\n '''\n Set self.main_col and assign it to the first column.\n If colname is None, simply assign self.main_col to the\n first column.\n '''\n if self.main_col is None:\n self.main_col = colname\n elif colname is None:\n colname = self.main_col\n\n colname_temp = colname+extension\n self.rename_column(colname, colname_temp)\n self.add_column(self[colname_temp], name=colname, index=0)\n self.remove_column(colname_temp)\n\n def correct_units(self, badunits=['degrees', 'days', 'hours','jovMass', 'mags'],\n gunits=['degree', 'day', 'hour','jupiterMass', 'mag'], verbose=True,\n debug=False):\n '''\n Correct columns units for astropy units\n '''\n text_frame = \"Column {} corrected for '{}' unit (previous was '{}')\"\n\n for col in self.colnames:\n if debug:\n print(col, self[col].unit)\n\n # Skip skycoord: no unit attribute\n if isinstance(self[col], SkyCoord):\n continue\n\n # Search for bad units\n # TODO: check if unit is in list instead of loop and use dict\n # to replace\n for bunit, gunit in zip(badunits, gunits):\n if self[col].unit == bunit:\n self[col].unit = gunit\n\n # Message and log it\n self.log.append(\n text_frame.format(col, self[col].unit, bunit))\n if verbose:\n print(self.log[-1])\n print(self.log[-1])\n\n def cols_2_qarr(self, *keys):\n '''\n Returns columns given in input as astropy q_arrays\n '''\n\n warn(DeprecationWarning(\n \"This method will be removed in future versions. Do not use it.\"))\n\n out = []\n for k in keys:\n try:\n out.append(np.ma.array(self[k].data) * self[k].unit)\n except TypeError:\n out.append(np.ma.array(self[k].data))\n\n return tuple(out)\n\n def set_units(self, units, cols=None):\n '''\n Assign units to columns.\n units:\n list of units (str or astropy units) to be assign\n cols:\n list of columns names (str).\n If None is given, it takes all the keys, so Table.keys() as default\n '''\n\n if not cols:\n cols = self.keys()\n\n for col, u in zip(cols, units):\n self[col].unit = u\n\n def new_value(self, plName, col, value):\n\n names = np.array(self[self.main_col], dtype=str)\n position = np.where(names == plName)[0]\n\n self[col][position] = value\n\n def complete(self, right, key=None, join_type='left',\n add_col=True, metadata_conflicts='warn',\n verbose=True, debug=False, **kwargs):\n \"\"\"\n Add every missing data in self if present in right.\n\n join_type : 'inner': do not add new rows\n 'outer': add new rows if not present in self\n add_col: add new colums from right\n \"\"\"\n\n key = key or self.main_col\n\n # Try converting inputs to Table as needed\n if not isinstance(right, Table):\n right = Table(right)\n\n try:\n out = self._complete(right, key=key, join_type=join_type,\n add_col=add_col, verbose=verbose, debug=debug)\n except:\n warn(\"Custom table completion failed, trying default astropy join.\")\n # NOTE: This seemd to break when I tested it quickly,\n # but I needed masking anyway so I did not get to the bottom of the problem.\n # - Thomas\n col_name_map = OrderedDict()\n out = _join(\n self,\n right,\n join_type=join_type,\n col_name_map=col_name_map,\n keys=key,\n **kwargs\n )\n\n # Merge the column and table meta data. Table subclasses might override\n # these methods for custom merge behavior.\n _merge_table_meta(out, [self, right], metadata_conflicts=metadata_conflicts)\n\n return out\n\n def _complete(self, right, key=None, join_type='left', add_col=True,\n verbose=True, debug=False):\n\n if not key:\n raise ValueError('key is empty')\n\n # Save shared columns without \"key\"\n cols = intersection(self.keys(), right.keys())\n cols.remove(key)\n\n # Join tables\n join_t = join(self, right, join_type=join_type, keys=key)\n\n # Complete masked values of \"self\" if available in \"right\"\n for col in cols:\n\n # Add eventually a condition to check units!\n\n # Names of joined columns (default from join())\n col1, col2 = col + '_1', col + '_2'\n\n # Index of masked in \"self\" and not masked in \"right\"\n index = join_t[col1].mask & ~join_t[col2].mask\n\n # Reassign value\n join_t[col1].unshare_mask()\n join_t[col1][index] = join_t[col2][index]\n\n # Remove 2nd column and rename to original\n join_t[col1].name = col\n del join_t[col2]\n\n # Remove added columns from \"right\" if not wanted\n supp_cols = difference(right.keys(), self.keys())\n if debug: print(supp_cols)\n\n if not add_col and supp_cols:\n if verbose:\n print('remove non shared columns from second table')\n join_t.remove_columns(supp_cols)\n\n return join_t\n\n def complete_cols(self, col_in, col_out, name_key=None):\n '''\n Use a column from table to complete another column.\n Input:\n col_in: list of names of columns to use (list of str)\n col_out: list of names of columns to complete (list of str)\n '''\n # Take default col if none is given\n name_key = name_key or self.main_col\n\n # Def table with cols to use and rename it to cols to complete\n temp_table = Table(self[[name_key] + col_in], masked=True)\n temp_table.nan_to_mask()\n temp_table.rename_columns(col_in, col_out)\n\n # Complete with the temp_table\n return self.complete(temp_table, key=name_key)\n\n def add_calc_col(self, fct, *args, f_args=(), f_kwargs={}, col_keys=[], **kwargs):\n '''\n Add new column wich is the result of fct(table[col_keys], *f_args, **f_kwargs)\n\n args and kwargs are passed to MaskedColumn instantiation\n\n '''\n\n # Build tuple of columns inputs to fct and add to f_args\n cols = ()\n for key in col_keys:\n cols += (self[key],)\n f_args = cols + f_args\n\n # Define column and add it\n col = MaskedColumn(*args, data=fct(*f_args, **f_kwargs), **kwargs)\n self.add_column(col)\n\n def check_col_units(self, colname):\n\n col_units = self[colname].unit\n try: # Check if col_units valid\n 1. * Unit(col_units)\n except TypeError:\n print('Column has no units (unit = None)')\n except: # Enter valid unit and refresh all table\n print(\"Column units '{}' are not\".format(col_units) +\n ' recognized by astropy.\\n')\n print(\"Error message from astropy:\")\n print_unit_error(str(col_units))\n print(\"-------------------------\")\n gunit = input('***** Please enter the corresponding unit'\n + ' recognized by astropy unit: ')\n self.correct_units(badunits=[str(col_units)], gunits=[gunit])\n\n\ndef difference(left, right):\n if isinstance(left, list) and isinstance(right, list):\n return list(set(left) - set(right))\n else:\n return NotImplemented\n\n\ndef intersection(tbl, other):\n if isinstance(tbl, list):\n return list(set(tbl).intersection(other))\n else:\n return NotImplemented\n\n\ndef print_unit_error(str_unit):\n\n try:\n Unit(str_unit)\n except ValueError as e:\n print(e)\n","repo_name":"AntoineDarveau/exofile","sub_path":"exofile/table_custom.py","file_name":"table_custom.py","file_ext":"py","file_size_in_byte":12812,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"78"}
+{"seq_id":"2077249573","text":"from typing import List, Optional, Union\n\nimport numpy as np\n\nfrom . import hm\nfrom .base import Point2PointCalibrationBase\n\n\nclass Arun(Point2PointCalibrationBase):\n # Least-Squares Fitting of Two 3-D Point Sets\n # K. S. Arun, T. S. Huang, and S.D. Blostein\n # IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987\n\n @staticmethod\n def name():\n return 'Arun'\n\n def __init__(self):\n super().__init__()\n self._points_a = None\n self._points_b = None\n\n def set_points(self, points_a: np.ndarray, points_b: np.ndarray,\n weights: Optional[Union[List, np.ndarray]] = None) -> None:\n if weights is not None:\n raise RuntimeError(\"Weights are not supported by Arun\")\n if points_a.shape[0] != 3 or points_b.shape[0] != 3 or points_a.shape[1] != points_b.shape[1]:\n raise RuntimeError(\"Both point clouds must have shapes (3, n)\")\n self._points_a = points_a\n self._points_b = points_b\n\n def _calibrate(self, **_):\n if self._points_a is None or self._points_b is None:\n raise RuntimeError(\"Point data is missing\")\n return hm.analytic.solve_arun(self._points_a, self._points_b)\n","repo_name":"uulm-mrm/excalibur","sub_path":"excalibur/calibration/point2point/arun.py","file_name":"arun.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"78"}
+{"seq_id":"37026052500","text":"import os\nimport sys\nimport numpy as np\nimport glob\n\ndirectory = \".\"\nif len(sys.argv) > 1:\n directory = sys.argv[1]\nnpzglob = os.path.join(directory, \"*.npz\")\nnpzfiles = glob.glob(npzglob)\n\nnfiles = len(npzfiles)\nflaggedfiles = []\nfor idx, npz in enumerate(npzfiles):\n npzdata = np.load(npz)\n if \"sigma_in\" in npzdata.files:\n sigma = np.array(npzdata[\"sigma_in\"])\n npss = np.array(npzdata[\"npss\"])\n if np.count_nonzero(sigma) > 0 and np.count_nonzero(npss) == 0:\n flaggedfiles.append(npz)\n","repo_name":"achilleas-k/connectivity","sub_path":"checksigma.py","file_name":"checksigma.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"70873657853","text":"def corresponding_grade(grade):\n if 2.00 <= grade <= 2.99:\n return 'Fail'\n elif 3.00 <= grade <= 3.49:\n return 'Poor'\n elif 3.50 <= grade <= 4.49:\n return 'Good'\n elif 4.50 <= grade <= 5.49:\n return 'Very Good'\n elif 5.50 <= grade <= 6.00:\n return 'Excellent'\n\n\nresult = float(input())\nprint(corresponding_grade(result))\n","repo_name":"KaloyankerR/python-fundamentals-repository","sub_path":"Assignments/Functions/Lab/01. Grades.py","file_name":"01. Grades.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"17535315807","text":"import random\n\napp.stepsPerSecond = 5\napp.nextRadius = 0\n\nRect(0, 0, 400, 400)\n\ndef onStep():\n if (app.nextRadius < 200):\n # Increases the radius by 5.\n app.nextRadius += 5\n\n # Define these variables to generate new random values for the next\n # circle. Borders are at most 50 and dash values are at most 100.\n ### (HINT: redGreen is used in an rgb so it can't be bigger than 255!)\n ### Fix Your Code Here ###\n redGreen = random.randint(0,255)\n newBorderWidth = random.randint(0,50)\n dashWidth = random.randint(0,100)\n dashSpace = random.randint(0,100)\n\n # Draws the next circle with the values generated above.\n Circle(200, 200, app.nextRadius, fill=None,\n border=rgb(redGreen, 255 - redGreen, 255),\n borderWidth=newBorderWidth, dashes=(dashWidth, dashSpace))\n","repo_name":"Psycho461/-APCSP-CSAcademyANSWERS","sub_path":"4.5.3 Strange machine.py","file_name":"4.5.3 Strange machine.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"71132450813","text":"import os\r\nimport json\r\n\r\nimport pytest\r\n\r\nfrom habit import Habit\r\nimport analytics\r\n\r\n\r\nDATA_DIR = os.path.join(os.path.realpath(os.path.pardir), \"habittracker\", \"data\")\r\n\r\n\r\n@pytest.fixture()\r\ndef sample_habits_objects():\r\n sample_data_file_name = os.path.join(DATA_DIR, \"sample_data.json\")\r\n if os.path.exists(sample_data_file_name):\r\n # Read json file with example data\r\n with open(sample_data_file_name, encoding=\"utf-8\") as json_file:\r\n # Load json file containing example data\r\n sample_habits = json.load(json_file)\r\n # Create habit coming from json and save to habit database\r\n sample_habits_objects = list()\r\n for item in sample_habits[\"sample_data\"]:\r\n habit = Habit(item[\"name\"], item[\"description\"], item[\"periodicity\"])\r\n for checkoff_date in item[\"checkoffs\"]:\r\n habit.check_off(checkoff_date)\r\n sample_habits_objects.append((habit.id, habit))\r\n return sample_habits_objects\r\n else:\r\n assert False, f\"File {sample_data_file_name} doest not exist.\"\r\n\r\n\r\nclass TestListHabits:\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n def test_list_habits_valid_option_all(self, sample_habits_objects):\r\n expected_list_length = len(sample_habits_objects)\r\n expected_habit_names = [habit[1].name for habit in sample_habits_objects]\r\n actual = analytics.list_habits(sample_habits_objects)\r\n actual_habit_names = [habit[1].name for habit in actual]\r\n assert sorted(actual_habit_names) == sorted(expected_habit_names)\r\n assert len(actual) == expected_list_length\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.negative\r\n def test_list_habits_invalid_option_habits_missing(self):\r\n with pytest.raises(TypeError) as exc_info:\r\n analytics.list_habits()\r\n expected_error_message = \"list_habits() missing 1 required positional argument: 'habits'\"\r\n assert str(exc_info.value) == expected_error_message\r\n\r\n\r\nclass TestListHabitsByPeriodicity:\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n @pytest.mark.parametrize(\"expected_periodicity\", [\"DAILY\", \"WEEKLY\"])\r\n def test_list_habits_by_periodicity_valid_option_all(self, sample_habits_objects, expected_periodicity):\r\n expected_habit_names = [habit[1].name for habit in sample_habits_objects\r\n if habit[1].periodicity.name == expected_periodicity]\r\n expected_list_length = len(expected_habit_names)\r\n actual = analytics.list_habits_by_periodicity(sample_habits_objects, expected_periodicity)\r\n actual_habit_names = [habit[1].name for habit in actual]\r\n assert sorted(actual_habit_names) == sorted(expected_habit_names)\r\n assert len(actual) == expected_list_length\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.negative\r\n @pytest.mark.parametrize(\"expected_periodicity\", [\"DAILY\", \"WEEKLY\"])\r\n def test_list_habits_by_periodicity_invalid_option_habits_missing(self, expected_periodicity):\r\n with pytest.raises(TypeError) as exc_info:\r\n analytics.list_habits_by_periodicity(periodicity=expected_periodicity)\r\n expected_error_message = \"list_habits_by_periodicity() missing 1 required positional argument: 'habits'\"\r\n assert str(exc_info.value) == expected_error_message\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.negative\r\n def test_list_habits_by_periodicity_invalid_option_periodicity_missing(self, sample_habits_objects):\r\n with pytest.raises(TypeError) as exc_info:\r\n analytics.list_habits_by_periodicity(habits=sample_habits_objects)\r\n expected_error_message = \"list_habits_by_periodicity() missing 1 required positional argument: 'periodicity'\"\r\n assert str(exc_info.value) == expected_error_message\r\n\r\n\r\nclass TestGetLongestStreak:\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n def test_get_longest_streak_valid_option_all(self, sample_habits_objects):\r\n expected_longest_streak = 11\r\n actual = analytics.get_longest_streak(sample_habits_objects)\r\n assert actual == expected_longest_streak\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n def test_get_longest_streak_valid_option_all_empty_habits(self):\r\n expected_longest_streak = 0\r\n actual = analytics.get_longest_streak(habits=[])\r\n assert actual == expected_longest_streak\r\n\r\n\r\nclass TestGetLongestStreakForHabit:\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n @pytest.mark.parametrize(\"expected_longest_streaks\", [[\"Meditating\", 5],\r\n [\"Workout\", 4],\r\n [\"Drink 2 liter of water\", 11],\r\n [\"Didn't watch TV\", 1],\r\n [\"No spend\", 1]])\r\n def test_get_longest_streak_for_habit_valid_option_all(self, sample_habits_objects, expected_longest_streaks):\r\n habit_id = [habit[1].id for habit in sample_habits_objects if habit[1].name == expected_longest_streaks[0]]\r\n actual = analytics.get_longest_streak_for_habit(sample_habits_objects, habit_id[0])\r\n assert actual == expected_longest_streaks[1]\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.positive\r\n def test_get_longest_streak_for_habit_invalid_option_habit_id_not_in_objects(self, sample_habits_objects):\r\n expected_longest_streak = 0\r\n actual = analytics.get_longest_streak_for_habit(habits=sample_habits_objects, habit_id=\"42\")\r\n assert actual == expected_longest_streak\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.negative\r\n def test_get_longest_streak_for_habit_invalid_option_habits_missing(self):\r\n with pytest.raises(TypeError) as exc_info:\r\n analytics.get_longest_streak_for_habit(habit_id=\"42\")\r\n expected_error_message = \"get_longest_streak_for_habit() missing 1 required positional argument: 'habits'\"\r\n assert str(exc_info.value) == expected_error_message\r\n\r\n @pytest.mark.unit\r\n @pytest.mark.negative\r\n def test_get_longest_streak_for_habit_invalid_option_habit_id_missing(self, sample_habits_objects):\r\n with pytest.raises(TypeError) as exc_info:\r\n analytics.get_longest_streak_for_habit(habits=sample_habits_objects)\r\n expected_error_message = \"get_longest_streak_for_habit() missing 1 required positional argument: 'habit_id'\"\r\n assert str(exc_info.value) == expected_error_message\r\n","repo_name":"anjakuchenbecker/oofpp_habits_project","sub_path":"tests/test_analytics.py","file_name":"test_analytics.py","file_ext":"py","file_size_in_byte":6558,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"22495850660","text":"import os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom exp1a_psignifit_analysis import a_data_extraction, b3_plot_staircase, c_plots, \\\n d_average_participant, e_average_exp_data, make_average_plots, make_long_df, \\\n plot_w_errors_no_1probe\nfrom rad_flow_psignifit_analysis import plot_runs_ave_w_errors\nfrom psignifit_tools import get_psignifit_threshold_df, get_psig_thr_w_hue\nfrom python_tools import which_path, running_on_laptop, switch_path\n\n'''\nThis script if for checking for any differences between thr upper visual field and lower visual field.\nIt will use the already analysied RUNDATA_sorted.xlsx to do this.\nLoop through the participant run folders and append each RUNDATA-sorted.xlsx, with addition 'run' column.\nSave to P_all_runs_master_output.csv.\n\nThen run psignifit on this\n'''\n\n# # loop through run folders with first 4 scripts (a, get_psignifit_threshold_df, b3, c)\n# # then run script d to get master lists and averages\n# exp_path = '/Users/nickmartin/Documents/PycharmProjects/Cardiff/exp1a_data'\n# participant_list = ['aa', 'bb', 'cc', 'dd', 'ee']\n# exp_path = '/Users/nickmartin/Documents/PycharmProjects/Cardiff/EXP1_sep4_5'\n# exp_path = r\"C:\\Users\\sapnm4\\OneDrive - Cardiff University\\PycharmProjects\\Cardiff\\EXP1_sep4_5\"\n# exp_path = r\"C:\\Users\\sapnm4\\OneDrive - Cardiff University\\PycharmProjects\\Cardiff\\Exp3_Ricco_NM_v4\"\nexp_path = r\"C:\\Users\\sapnm4\\OneDrive - Cardiff University\\PycharmProjects\\Cardiff\\exp1a_data\"\nconvert_path1 = os.path.normpath(exp_path)\nif running_on_laptop():\n convert_path1 = switch_path(convert_path1, 'mac_oneDrive')\nexp_path = convert_path1\n\nparticipant_list = ['aa', 'bb', 'cc', 'dd', 'ee', 'Nick'] # ['Kim', 'Kris', 'Simon', 'Nick']\n# participant_list = ['Nick'] # ['Kim', 'Kris', 'Simon', 'Nick']\np_idx_plus = 1\n\nn_runs = 12\nanalyse_from_run = 1\ntrim_list = []\n\n'''Part 1, get threshold for each participant and make master list'''\n\nexp_thr = []\nexp_CI_width = []\n\n# for p_idx, participant_name in enumerate(participant_list):\n#\n# root_path = os.path.join(exp_path, participant_name)\n#\n# # search to automatically get run_folder_names\n# dir_list = os.listdir(root_path)\n# run_folder_names = []\n# for i in range(n_runs): # numbers 0 to 11\n# check_dir = f'{participant_name}_{i + analyse_from_run}' # numbers 1 to 12\n# if check_dir in dir_list:\n# run_folder_names.append(check_dir)\n#\n# if len(run_folder_names) > 0:\n# print(\"running analysis for:\")\n# for i in run_folder_names:\n# print(i)\n# else:\n# print(\"no run folders found\")\n#\n#\n# # add RUNDATA-sorted to all_data\n# all_data = []\n#\n# for run_idx, run_dir in enumerate(run_folder_names):\n#\n# print(f'\\ncompiling analysis for {participant_name}, {run_dir}, {participant_name}_{run_idx+1}\\n')\n# save_path = f'{root_path}{os.sep}{run_dir}'\n#\n# # don't delete this (participant_name = participant_name),\n# # needed to ensure names go name1, name2, name3 not name1, name12, name123\n# p_name = participant_name\n#\n# # '''a'''\n# p_name = f'{participant_name}_{run_idx+1}_output.csv'\n# # p_name = f'{participant_name}{run_idx+1}'\n# # isi_list = [-1, 0, 2, 4, 6, 9, 12, 24]\n#\n# if os.path.isfile(os.path.join(save_path, 'RUNDATA-sorted.xlsx')):\n# run_data_path = os.path.join(save_path, 'RUNDATA-sorted.xlsx')\n# elif os.path.isfile(os.path.join(save_path, p_name)):\n# run_data_path = os.path.join(save_path, p_name)\n# elif os.path.isfile(os.path.join(save_path, f'{run_dir}_output.csv')):\n# run_data_path = os.path.join(save_path, f'{run_dir}_output.csv')\n# elif os.path.isfile(os.path.join(save_path, f'{participant_name}_output.csv')):\n# run_data_path = os.path.join(save_path, f'{participant_name}_output.csv')\n# else:\n# raise FileNotFoundError(f'{participant_name}, run_dir {run_dir}')\n#\n# # run_data_path = f'{save_path}{os.sep}RUNDATA-sorted.xlsx'\n#\n# # run_data_path = os.path.join(save_path, )\n#\n# if run_data_path[-4:] == 'xlsx':\n# run_data_df = pd.read_excel(run_data_path, engine='openpyxl',\n# # usecols=['ISI',\n# # 'stair',\n# # 'separation',\n# # # 'group',\n# # 'probeLum', 'trial_response', 'corner']\n# )\n# else:\n# run_data_df = pd.read_csv(run_data_path)\n# print(f\"run_data_df:\\n{run_data_df}\")\n#\n# # add isi column for multi-indexing\n# if 'run' not in list(run_data_df.columns):\n# run_data_df.insert(0, 'run', int(run_idx+1))\n# # if verbose:\n# print(f'run_data_df:\\n{run_data_df.head()}')\n#\n# # get column names to use on all_data_df\n# column_names = list(run_data_df)\n#\n# # add to all_data\n# all_data.append(run_data_df)\n#\n# # create all_data_df - reshape to 2d\n# all_data_shape = np.shape(all_data)\n# print(f'all_data_shape:\\n{all_data_shape}')\n#\n# if len(np.shape(all_data)) == 2:\n# sheets, rows, columns = np.shape(all_data)\n# all_data = np.reshape(all_data, newshape=(sheets * rows, columns))\n# # if verbose:\n# print(f'all_data reshaped from {all_data_shape} to {np.shape(all_data)}')\n# all_data_df = pd.DataFrame(all_data, columns=column_names)\n# else:\n# all_data_df = pd.concat(all_data, ignore_index=True)\n#\n# visual_field_list = ['UVF' if i < 200 else 'LVF' for i in all_data_df['corner'].to_list()]\n# all_data_df['vis_field'] = visual_field_list\n# # if verbose:\n# print(f\"all_data_df:\\n{all_data_df}\")\n#\n# sep_list = sorted(list(all_data_df['separation'].unique()))\n# print(f\"sep_list: {sep_list}\")\n#\n#\n# # # if save_all_data:\n# save_name = 'P_all_runs_master_output.csv'\n# save_csv_path = os.path.join(root_path, save_name)\n# # # if verbose:\n# print(f\"\\nsaving all_data_df to save_csv_path: {save_csv_path}\")\n# all_data_df.to_csv(save_csv_path, index=False)\n#\n#\n#\n# all_data_df = pd.read_csv(os.path.join(root_path, 'P_all_runs_master_output.csv'))\n#\n# vis_field_names = ['UVF', 'LVF']\n#\n#\n# both_vfs_thr = []\n# both_vfs_CI_width = []\n#\n# for idx, vis_field_name in enumerate(vis_field_names):\n#\n#\n# print(f'Running psignifit for {vis_field_name}')\n#\n# vis_field_df = all_data_df[all_data_df['vis_field'] == vis_field_name]\n# print(vis_field_df)\n#\n# isi_list = sorted(list(vis_field_df['ISI'].unique()))\n# print(f\"isi_list: {isi_list}\")\n#\n# sep_list = sorted(list(vis_field_df['separation'].unique()))\n# print(f\"sep_list: {sep_list}\")\n#\n#\n#\n#\n# '''get psignifit thresholds df - use stairs as sep levels rather than using groups'''\n#\n# thr_df = get_psignifit_threshold_df(root_path=exp_path,\n# p_run_name=participant_name,\n# csv_name=vis_field_df,\n# n_bins=9, q_bins=True,\n# thr_col='probeLum',\n# sep_col='separation', sep_list=sep_list,\n# isi_col='ISI', isi_list=isi_list,\n# conf_int=True, thr_type='Bayes',\n# plot_both_curves=False,\n# # cols_to_add_dict=None, save_name=f'psignifit_{vis_field_name}_ISI{ISI}_sep{separation}',\n# cols_to_add_dict=None, save_name=f'psignifit_{vis_field_name}',\n# show_plots=False, save_plots=False,\n# verbose=True)\n#\n# # thr_df['vis_field'] = vis_field_name\n# thr_df.insert(1, 'vis_field', vis_field_name)\n#\n# cond_list = thr_df['separation'].to_list()\n# if vis_field_name == 'LVF':\n# cond_list = [-.01 if i == 0 else -i for i in cond_list]\n# # thr_df['cond'] = cond_list\n# thr_df.insert(2, 'cond', cond_list)\n#\n# print(f'psignifit_{vis_field_name}:\\n{thr_df}')\n# column_names = list(thr_df)\n#\n# # add this VFs thr and CI width to list to concat with other VF\n# both_vfs_thr.append(thr_df)\n#\n# CI_width_filename = f'psignifit_{vis_field_name}_CI_width.csv'\n#\n# VF_CI_width_df = pd.read_csv(os.path.join(root_path, CI_width_filename))\n# VF_CI_width_df.insert(1, 'vis_field', vis_field_name)\n# VF_CI_width_df.insert(2, 'cond', cond_list)\n# both_vfs_CI_width.append(VF_CI_width_df)\n#\n# # progress_df = pd.concat(both_vfs_thr)\n# # save_name = 'psignifit_progress.csv'\n# # save_csv_path = os.path.join(root_path, save_name)\n# # print(f\"\\nsaving progress_df to save_csv_path:\\n{save_csv_path}\")\n# # progress_df.to_csv(save_csv_path, index=False)\n#\n#\n# # create both_vfs_df - reshape to 2d\n# both_vfs_shape = np.shape(both_vfs_thr)\n# sheets, rows, columns = np.shape(both_vfs_thr)\n# both_vfs_thr = np.reshape(both_vfs_thr, newshape=(sheets * rows, columns))\n# print(f'both_vfs_thr reshaped from {both_vfs_shape} to {np.shape(both_vfs_thr)}')\n# both_vfs_df = pd.DataFrame(both_vfs_thr, columns=column_names)\n# print(f\"both_vfs_df:\\n{both_vfs_df}\")\n#\n# save_name = 'psignifit_both_vfs.csv'\n# save_csv_path = os.path.join(root_path, save_name)\n# print(f\"\\nsaving all_data_df to save_csv_path:\\n{save_csv_path}\")\n# both_vfs_df.to_csv(save_csv_path, index=False)\n#\n# # create both_vfs_CI_width_df - reshape to 2d\n# both_vfs_CI_width_shape = np.shape(both_vfs_CI_width)\n# sheets, rows, columns = np.shape(both_vfs_CI_width)\n# both_vfs_CI_width = np.reshape(both_vfs_CI_width, newshape=(sheets * rows, columns))\n# print(f'both_vfs_thr reshaped from {both_vfs_CI_width_shape} to {np.shape(both_vfs_CI_width)}')\n# both_vfs_CI_width_df = pd.DataFrame(both_vfs_CI_width, columns=column_names)\n# print(f\"both_vfs_CI_width_df:\\n{both_vfs_CI_width_df}\")\n#\n# save_name = 'both_vfs_CI_width.csv'\n# save_csv_path = os.path.join(root_path, save_name)\n# print(f\"\\nsaving both_vfs_CI_width to save_csv_path:\\n{save_csv_path}\")\n# both_vfs_CI_width_df.to_csv(save_csv_path, index=False)\n#\n#\n# '''Load psignifit_both_vfs and check columns'''\n# # make plot to show UVF and LVF on one axis\n# psig_both_vf_df = pd.read_csv(os.path.join(root_path, 'psignifit_both_vfs.csv'))\n# print(f\"\\npsig_both_vf_df:\\n{psig_both_vf_df}\")\n#\n# '''Load both_vfs_CI_width_df and check columns'''\n# # make plot to show UVF and LVF on one axis\n# both_vfs_CI_width_df = pd.read_csv(os.path.join(root_path, 'both_vfs_CI_width.csv'))\n# print(f\"\\nboth_vfs_CI_width_df:\\n{both_vfs_CI_width_df}\")\n#\n# # change 1probe from 99 to 20\n# both_vf_columns = list(psig_both_vf_df.columns)\n# sep_list = psig_both_vf_df['separation'].to_list()\n# sep_list = [20 if i == 99 else i for i in sep_list]\n# psig_both_vf_df['separation'] = sep_list\n# both_vfs_CI_width_df['separation'] = sep_list\n#\n# if 'cond' not in both_vf_columns:\n# print(\"\\nMaking cond column\")\n# # add condition list which is equal to sep for uVF or negative sep for LVF (with -.01 instead of -0)\n# sep_list = psig_both_vf_df['separation'].to_list()\n# vf_list = psig_both_vf_df['vis_field'].to_list()\n# cond_list = []\n# for vf, sep in zip(vf_list, sep_list):\n# if vf == 'LVF':\n# if sep == 0:\n# this_cond = -.01\n# else:\n# this_cond = -sep\n# else:\n# this_cond = sep\n# print(f\"vf: {vf}, sep: {sep}, this_cond: {this_cond}\")\n# cond_list.append(this_cond)\n# print(f\"cond_list: {cond_list}\")\n# psig_both_vf_df.insert(2, 'cond', cond_list)\n# both_vfs_CI_width_df.insert(2, 'cond', cond_list)\n#\n#\n# # change 1probe from 99 to 20\n# cond_list = psig_both_vf_df['cond'].to_list()\n# cond_list = [20 if i == 99 else i for i in cond_list]\n# cond_list = [-20 if i == -99 else i for i in cond_list]\n# psig_both_vf_df['cond'] = cond_list\n# both_vfs_CI_width_df['cond'] = cond_list\n#\n#\n# save_name = 'psignifit_both_vfs.csv'\n# save_csv_path = os.path.join(root_path, save_name)\n# print(f\"\\nsaving all_data_df to save_csv_path:\\n{save_csv_path}\")\n# psig_both_vf_df.to_csv(save_csv_path, index=False)\n#\n# save_name = 'both_vfs_CI_width.csv'\n# save_csv_path = os.path.join(root_path, save_name)\n# print(f\"\\nsaving both_vfs_CI_width to save_csv_path:\\n{save_csv_path}\")\n# both_vfs_CI_width_df.to_csv(save_csv_path, index=False)\n#\n# # add participant name\n# if 'p_name' not in both_vf_columns:\n# psig_both_vf_df.insert(0, 'p_name', participant_name)\n# both_vfs_CI_width_df.insert(0, 'p_name', participant_name)\n#\n# print(f\"psig_both_vf_df:\\n{psig_both_vf_df}\")\n# exp_thr.append(psig_both_vf_df)\n# exp_CI_width.append(both_vfs_CI_width_df)\n#\n#\n#\n# # save master dfs\n# exp_thr_df = pd.concat(exp_thr)\n# save_csv_path = os.path.join(exp_path, 'MASTER_exp_VF_thr.csv')\n# exp_thr_df.to_csv(save_csv_path, index=False)\n# print(f\"exp_thr_df:\\n{exp_thr_df}\")\n#\n# # save master dfs\n# exp_CI_width_df = pd.concat(exp_CI_width)\n# save_csv_path = os.path.join(exp_path, 'MASTER_exp_VF_CI.csv')\n# exp_CI_width_df.to_csv(save_csv_path, index=False)\n# print(f\"exp_CI_width_df:\\n{exp_thr_df}\")\n#\n#\n#\n#\n#\n#\n#\n# # # make long form df\n# exp_VF_thr_df = pd.read_csv(os.path.join(exp_path, 'MASTER_exp_VF_thr.csv'))\n# print(f\"\\nexp_VF_thr_df:\\n{exp_VF_thr_df}\")\n#\n# exp_VF_thr_df.rename({'ISI_-1': 'ISI_99'}, axis=1, inplace=True)\n# exp_VF_thr_long_df = pd.wide_to_long(exp_VF_thr_df, stubnames='ISI_',\n# i=['vis_field', 'separation', 'p_name', 'cond'],\n# j='ISI',\n# sep='')\n# # exp_VF_thr_long_df.rename({'ISI val': 'ISI', 'ISI_': 'probeLum'}, axis=1, inplace=True)\n# exp_VF_thr_long_df.rename({'ISI_': 'probeLum'}, axis=1, inplace=True)\n# exp_VF_thr_long_df.reset_index(inplace=True)\n# print(f\"\\nexp_VF_thr_long_df:\\n{exp_VF_thr_long_df}\")\n#\n# # make long form CIs\n# exp_VF_CI_df = pd.read_csv(os.path.join(exp_path, 'MASTER_exp_VF_CI.csv'))\n# print(f\"\\nexp_VF_CI_df:\\n{exp_VF_CI_df}\")\n#\n#\n# exp_VF_CI_df.rename({'ISI_-1': 'ISI_99'}, axis=1, inplace=True)\n# exp_VF_CI_long_df = pd.wide_to_long(exp_VF_CI_df, stubnames='ISI_',\n# i=['vis_field', 'separation', 'p_name', 'cond'],\n# j='ISI',\n# sep='')\n# # exp_VF_CI_long_df.rename({'ISI val': 'ISI', 'ISI_': 'probeLum'}, axis=1, inplace=True)\n# exp_VF_CI_long_df.rename({'ISI_': 'CI_width'}, axis=1, inplace=True)\n# exp_VF_CI_long_df.reset_index(inplace=True)\n# print(f\"\\nexp_VF_CI_long_df:\\n{exp_VF_CI_long_df}\")\n#\n#\n# # add cond number column\n# cond_vals = sorted(exp_VF_thr_long_df['cond'].unique())\n# neg_sep_num_dict = dict(zip(cond_vals, list(range(len(cond_vals)))))\n# print(f\"\\nneg_sep_num_dict: {neg_sep_num_dict}\")\n#\n# exp_VF_thr_long_df.insert(4, 'cond_num', exp_VF_thr_long_df[\"cond\"].map(neg_sep_num_dict))\n# exp_VF_CI_long_df.insert(4, 'cond_num', exp_VF_CI_long_df[\"cond\"].map(neg_sep_num_dict))\n# print(f\"\\nexp_VF_thr_long_df:\\n{exp_VF_thr_long_df}\")\n# save_csv_path = os.path.join(exp_path, 'MASTER_exp_VF_thr_long.csv')\n# exp_VF_thr_long_df.to_csv(save_csv_path, index=False)\n#\n# save_csv_path = os.path.join(exp_path, 'MASTER_exp_VF_CI_long.csv')\n# exp_VF_CI_long_df.to_csv(save_csv_path, index=False)\n#\n# print('\\nPart 1, get threshold for each participant and make master list: finished\\n')\n\n\n\n\n\n'''Part 2: make plots\nfor each participant make plots for all data\nthresholds (with negative sep)\nDifference\n\nMake conditions plots\nJust concurrent (ISI_-1)\nJust sep (0, 2, 3, 6)\n\nAll participants\ndifference, \nJust concurrent (ISI_-1)\nJust sep (0, 2, 3, 6)\n'''\nexp_VF_thr_long_df = pd.read_csv(os.path.join(exp_path, 'MASTER_exp_VF_thr_long.csv'))\nprint(f\"\\nexp_VF_thr_long_df:\\n{exp_VF_thr_long_df}\")\n\n# get means per condition\ngroupby_sep_thr_df = exp_VF_thr_long_df.drop('p_name', axis=1)\nexp_mean_thr_long_df = groupby_sep_thr_df.groupby(['cond_num', 'ISI'], sort=True).mean()\nexp_mean_thr_long_df.reset_index(inplace=True)\nprint(f\"\\nexp_mean_thr_long_df:\\n{exp_mean_thr_long_df}\")\n\n\nexp_VF_CI_long_df = pd.read_csv(os.path.join(exp_path, 'MASTER_exp_VF_CI_long.csv'))\nprint(f\"\\nexp_VF_CI_long_df:\\n{exp_VF_CI_long_df}\")\ngroupby_sep_CI_df = exp_VF_CI_long_df.drop('p_name', axis=1)\nexp_mean_CI_long_df = groupby_sep_CI_df.groupby(['cond_num', 'ISI'], sort=True).mean()\nexp_mean_CI_long_df.reset_index(inplace=True)\n# exp_mean_CI_long_df = exp_mean_CI_long_df.CI_width.div(2, fill_value=0),\nexp_mean_CI_long_df['halved_CI'] = exp_mean_CI_long_df.CI_width.div(2, fill_value=0)\nprint(f\"\\nexp_mean_CI_long_df:\\n{exp_mean_CI_long_df}\")\n\n\nexp_mean_thr_long_df['ISI'] = [str(i) for i in exp_mean_thr_long_df['ISI'].to_list()]\nexp_mean_CI_long_df['ISI'] = [str(i) for i in exp_mean_CI_long_df['ISI'].to_list()]\nprint(f\"\\nexp_mean_thr_long_df:\\n{exp_mean_thr_long_df}\")\n\n\n\n'''Fig 1 - all data'''\nprint('\\nFig 1 - all data')\n\nfig_1_thr_df = exp_mean_thr_long_df.copy()\nfig_1_err_df = exp_mean_CI_long_df.copy()\n\n# use wide means df\nprint(f\"exp_mean_thr_long_df:\\n{exp_mean_thr_long_df}\")\nfig_1_thr_df = exp_mean_thr_long_df.pivot(index=['cond_num', 'separation', 'cond'], columns='ISI', values='probeLum')\nfig_1_thr_df.reset_index(inplace=True, drop=False)\nprint(f\"fig_1_thr_df:\\n{fig_1_thr_df}\")\n\n# fig_1_thr_df.index.name = None\nisi_col_dict = {'99': 'conc', '0': 'ISI_0', '2': 'ISI_2',\n '4': 'ISI_4', '6': 'ISI_6', '9': 'ISI_9',\n '12': 'ISI_12', '24': 'ISI_24'}\nfig_1_thr_df.rename(columns=isi_col_dict, inplace=True)\nprint(f\"fig_1_thr_df:\\n{fig_1_thr_df}\")\nfig_1_thr_df = fig_1_thr_df[['cond_num', 'separation', 'cond',\n 'conc', 'ISI_0', 'ISI_2', 'ISI_4',\n 'ISI_6', 'ISI_9', 'ISI_12', 'ISI_24']]\nfig_1_thr_df.index.name = None\nprint(f\"fig_1_thr_df:\\n{fig_1_thr_df}\")\n\nprint(f\"\\nexp_mean_CI_long_df:\\n{exp_mean_CI_long_df}\")\nwide_mean_CI_df = exp_mean_CI_long_df.pivot(index=['cond_num', 'separation', 'cond'], columns='ISI', values='halved_CI')\nwide_mean_CI_df.reset_index(inplace=True, drop=False)\nwide_mean_CI_df.index.name = None\nwide_mean_CI_df.rename(columns=isi_col_dict, inplace=True)\n\nwide_mean_CI_df = wide_mean_CI_df[['cond_num', 'separation', 'cond',\n 'conc', 'ISI_0', 'ISI_2', 'ISI_4',\n 'ISI_6', 'ISI_9', 'ISI_12', 'ISI_24']]\nwide_mean_CI_df.index.name = None\nprint(f\"wide_mean_CI_df:\\n{wide_mean_CI_df}\")\n\n\n# add cond number column\nif 'cond_num' not in list(fig_1_thr_df.columns):\n cond_vals = fig_1_thr_df['cond'].unique()\n neg_sep_num_dict = dict(zip(cond_vals, list(range(len(cond_vals)))))\n print(f\"\\nneg_sep_num_dict: {neg_sep_num_dict}\")\n\n fig_1_thr_df.insert(4, 'cond_num', exp_mean_thr_long_df[\"cond\"].map(neg_sep_num_dict))\n wide_mean_CI_df.insert(4, 'cond_num', exp_mean_CI_long_df[\"cond\"].map(neg_sep_num_dict))\n\nwide_mean_thr_w_cond_idx_df = fig_1_thr_df.set_index('cond_num')\nwide_mean_thr_w_cond_idx_df.sort_index(inplace=True)\nif 'p_name' in list(wide_mean_thr_w_cond_idx_df.columns):\n wide_mean_thr_w_cond_idx_df.drop('p_name', axis=1, inplace=True)\nif 'vis_field' in list(wide_mean_thr_w_cond_idx_df.columns):\n wide_mean_thr_w_cond_idx_df.drop('vis_field', axis=1, inplace=True)\nif 'cond' in list(wide_mean_thr_w_cond_idx_df.columns):\n wide_mean_thr_w_cond_idx_df.drop('cond', axis=1, inplace=True)\nif 'separation' in list(wide_mean_thr_w_cond_idx_df.columns):\n wide_mean_thr_w_cond_idx_df.drop('separation', axis=1, inplace=True)\nprint(f\"exp_thr_w_cond_idx_df:\\n{wide_mean_thr_w_cond_idx_df}\")\n\nwide_mean_CI_w_cond_idx_df = wide_mean_CI_df.set_index('cond_num')\nwide_mean_CI_w_cond_idx_df.sort_index(inplace=True)\n\nx_tick_vals = wide_mean_thr_w_cond_idx_df.index.tolist()\nx_tick_labels = sorted(list(exp_mean_thr_long_df['cond'].unique()))\nx_tick_labels = ['1pr' if i in [20.0, -20.0] else str(i) for i in x_tick_labels]\nx_tick_labels = ['-0' if i == '-0.01' else str(i) for i in x_tick_labels]\nx_tick_labels = [i[:-2] if i not in ['1pr', '-0'] else i for i in x_tick_labels]\n\nprint(f\"x_tick_vals: {x_tick_vals}\")\nprint(f\"x_tick_labels: {x_tick_labels}\")\n\nif 'p_name' in list(wide_mean_CI_w_cond_idx_df.columns):\n wide_mean_CI_w_cond_idx_df.drop('p_name', axis=1, inplace=True)\nif 'vis_field' in list(wide_mean_CI_w_cond_idx_df.columns):\n wide_mean_CI_w_cond_idx_df.drop('vis_field', axis=1, inplace=True)\nif 'cond' in list(wide_mean_CI_w_cond_idx_df.columns):\n wide_mean_CI_w_cond_idx_df.drop('cond', axis=1, inplace=True)\nif 'separation' in list(wide_mean_CI_w_cond_idx_df.columns):\n wide_mean_CI_w_cond_idx_df.drop('separation', axis=1, inplace=True)\nprint(f\"exp_CI_w_cond_idx_df:\\n{wide_mean_CI_w_cond_idx_df}\")\n\n\nisi_name_list = [i for i in list(wide_mean_thr_w_cond_idx_df.columns) if 'ISI_' in i]\n\nfig_1a_title = 'all data: compare UVF & LVF\\n(Errors are mean of participant CIs, per ISI)'\n\nplot_runs_ave_w_errors(fig_df=wide_mean_thr_w_cond_idx_df, error_df=wide_mean_CI_w_cond_idx_df,\n jitter=.1, error_caps=True, alt_colours=False,\n legend_names=isi_name_list,\n x_tick_vals=x_tick_vals,\n x_tick_labels=x_tick_labels,\n x_axis_label='Sep in diag pixels. Neg=LVF, Pos=UVF',\n even_spaced_x=True, fixed_y_range=False,\n fig_title=fig_1a_title, save_name='all_data_VFs.png',\n save_path=exp_path, verbose=True)\nax = plt.gca() # to get the axis\nax.axvline(x=(x_tick_vals[-1]/2), linestyle=\"-.\", color='lightgrey') # add dotted line at zero\n\nplt.show()\nplt.close()\n\n\n\n'''Fig 2, difference between UVF and LVF'''\n'''Plot shoing difference in VF for each ISI'''\nprint(f\"\\nplot diff between VFs for each ISI\")\n# for each separation value, subtract LFV from UVF for difference score.\n\nget_diff_df = exp_VF_thr_long_df.copy()\nprint(f\"get_diff_df ({get_diff_df.shape}):\\n{get_diff_df}\")\n\nLVF_df = get_diff_df.loc[get_diff_df['cond_num'] < 7]\ncond_num_list = LVF_df['cond_num'].tolist()\nISI_val_list = LVF_df.pop('ISI').tolist()\np_name_list = LVF_df.pop('p_name').tolist()\nLVF_df = LVF_df.drop(['cond', 'vis_field'], axis=1)\nLVF_df.set_index('separation', inplace=True)\n\n\nUVF_df = get_diff_df.loc[get_diff_df['cond_num'] >= 7]\nUVF_df = UVF_df.drop(['cond', 'ISI', 'vis_field', 'p_name'], axis=1)\nUVF_df.set_index('separation', inplace=True)\nprint(f\"LVF_df ({LVF_df.shape}):\\n{LVF_df}\")\nprint(f\"UVF_df ({UVF_df.shape}):\\n{UVF_df}\")\n\n# plot difference.\ndiff_df = UVF_df.subtract(LVF_df, fill_value=0)\nprint(f\"diff_df ({diff_df.shape}):\\n{diff_df}\")\n\ndiff_df['cond_num'] = cond_num_list\nif 'ISI' not in list(diff_df.columns):\n diff_df.insert(1, 'ISI', ISI_val_list)\ndiff_df = diff_df.rename(columns={'probeLum': 'thr_diff'})\n\npos_sep_vals = sorted(diff_df.index.unique())\ndiff_df.reset_index(inplace=True)\nprint(f\"diff_df ({diff_df.shape}):\\n{diff_df}\")\n\n# convert ISI column to string, to make it work as Hue\ndiff_df['ISI'] = ['conc' if i == 99 else str(i) for i in diff_df['ISI'].to_list()]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nsns.pointplot(data=diff_df, x='cond_num', y='thr_diff',\n hue='ISI',\n estimator=np.mean, errorbar='se', dodge=True, markers='.',\n errwidth=1, capsize=.2,\n )\n\nx_tick_vals = sorted(diff_df['cond_num'].unique())\nax.set_xticks(x_tick_vals)\ncond_sep_dict = {0: '0', 1: '1', 2: '2', 3: '3', 4: '6', 5: '18', 6: '1pr'}\nx_tick_labels = [cond_sep_dict[k] for k in x_tick_vals]\nax.set_xticklabels(x_tick_labels)\nprint(f\"pos_sep_vals: {pos_sep_vals}\")\nprint(f\"x_tick_labels: {x_tick_labels}\")\n\nfig_title = f'exp1a all data: diff UVF - LVF\\n(Errors are SEs of means collapsed across participants)'\nplt.title(fig_title)\nx_axis = 'Sep in diag pixels'\nax.set_xlabel(x_axis)\ny_axis = 'Threshold different (UVF - LVF)'\nax.set_ylabel(y_axis)\nax.axhline(y=0, linestyle=\"-.\", color='lightgrey') # add dotted line at zero\n\nsave_as = os.path.join(exp_path, 'diff_vfs.png')\nplt.savefig(save_as)\nplt.show()\n\n\n'''Make diff plot per participant'''\nif 'p_name' not in list(diff_df.columns):\n diff_df.insert(1, 'p_name', p_name_list)\nprint(f\"diff_df ({diff_df.shape}):\\n{diff_df}\")\n\np_name_list = set(p_name_list)\nprint(f\"p_name_list: {p_name_list}\")\n\nfor p_name in p_name_list:\n p_name_diff_df = diff_df[diff_df['p_name'] == p_name]\n\n fig, ax = plt.subplots(figsize=(10, 6))\n sns.pointplot(data=p_name_diff_df, x='cond_num', y='thr_diff',\n hue='ISI',\n estimator=np.mean, errorbar='se',\n dodge=True, markers='.',\n errwidth=1, capsize=.2,\n )\n\n x_tick_vals = sorted(diff_df['cond_num'].unique())\n ax.set_xticks(x_tick_vals)\n cond_sep_dict = {0: '0', 1: '1', 2: '2', 3: '3', 4: '6', 5: '18', 6: '1pr'}\n x_tick_labels = [cond_sep_dict[k] for k in x_tick_vals]\n ax.set_xticklabels(x_tick_labels)\n print(f\"pos_sep_vals: {pos_sep_vals}\")\n print(f\"x_tick_labels: {x_tick_labels}\")\n\n fig_title = f'exp1a {p_name}: diff UVF - LVF' \\\n # f'\\n(Errors are SEs of means collapsed across participants)'\n plt.title(fig_title)\n x_axis = 'Sep in diag pixels'\n ax.set_xlabel(x_axis)\n y_axis = 'Threshold different (UVF - LVF)'\n ax.set_ylabel(y_axis)\n ax.axhline(y=0, linestyle=\"-.\", color='lightgrey') # add dotted line at zero\n\n save_as = os.path.join(exp_path, f'{p_name}_diff_vfs.png')\n plt.savefig(save_as)\n plt.show()\n\n\n'''One plot, all participants, collapsed across ISIs'''\nfig, ax = plt.subplots(figsize=(10, 6))\nsns.pointplot(data=diff_df, x='cond_num', y='thr_diff',\n hue='p_name',\n estimator=np.mean, errorbar='se',\n dodge=True, markers='.',\n errwidth=1, capsize=.2,\n )\n\nx_tick_vals = sorted(diff_df['cond_num'].unique())\nax.set_xticks(x_tick_vals)\ncond_sep_dict = {0: '0', 1: '1', 2: '2', 3: '3', 4: '6', 5: '18', 6: '1pr'}\nx_tick_labels = [cond_sep_dict[k] for k in x_tick_vals]\nax.set_xticklabels(x_tick_labels)\nprint(f\"pos_sep_vals: {pos_sep_vals}\")\nprint(f\"x_tick_labels: {x_tick_labels}\")\n\nfig_title = f'exp1a participants: diff UVF - LVF' \\\n f'\\n(Errors are SEs of participant means collapsed across ISIs)'\nplt.title(fig_title)\nx_axis = 'Sep in diag pixels'\nax.set_xlabel(x_axis)\ny_axis = 'Threshold different (UVF - LVF)'\nax.set_ylabel(y_axis)\nax.axhline(y=0, linestyle=\"-.\", color='lightgrey') # add dotted line at zero\n\nsave_as = os.path.join(exp_path, f'participant_diff_vfs.png')\nplt.savefig(save_as)\nplt.show()\n\n\n'''just concurrent differences - mean and per participant'''\nconc_diff_df = diff_df[diff_df['ISI'] == 'conc']\nprint(f\"conc_diff_df ({conc_diff_df.shape}):\\n{conc_diff_df}\")\n\nx_tick_vals = conc_diff_df['cond_num'].unique()\n# sort variables by sorted(neg_sep_num_list) order\nneg_sep_num_array = np.array(x_tick_vals)\nprint(f\"\\nneg_sep_num_array: {neg_sep_num_array}\")\nsort_index = np.argsort(x_tick_vals)\nprint(f\"sort_index: {sort_index}\")\n\nx_tick_vals = [x_tick_vals[i] for i in sort_index]\nprint(f\"x_tick_vals: {x_tick_vals}\")\n\nx_tick_labels = conc_diff_df['cond_num'].unique()\nx_tick_labels = [x_tick_labels[i] for i in sort_index]\nx_tick_labels = ['1pr' if i == 20 else i for i in x_tick_labels]\nprint(f\"x_tick_labels: {x_tick_labels}\")\n\n# plot participant and mean differences.\nfig, ax = plt.subplots(figsize=(10, 6))\n\nsns.lineplot(data=conc_diff_df, x='cond_num', y='thr_diff', hue='p_name',\n alpha=.7)\n\nsns.pointplot(data=conc_diff_df, x='cond_num', y='thr_diff',\n estimator=np.mean, errorbar='se',\n markers='.',\n errwidth=1, capsize=.2, color='black')\n\n\nfig_title = f'Concurrent: diff UVF - LVF\\n' \\\n f'(Errors are SEs of means collapsed across participants)'\nplt.title(fig_title)\nx_axis = 'Probe separation (diag pixels)'\nax.set_xlabel(x_axis)\ny_axis = 'Threshold different (UVF - LVF)'\nax.set_ylabel(y_axis)\nax.set_xticks(x_tick_vals)\nax.set_xticklabels(x_tick_labels)\nax.axhline(y=0, linestyle=\"-.\", color='lightgrey') # add dotted line at zero\n\nsave_as = os.path.join(exp_path, 'conc_diff_vfs.png')\nplt.savefig(save_as)\nplt.show()\n\n\n\n\n'''fig 3: make ISI plots'''\nprint(\"\\nMaking ISI plots\")\n\nISI_plots_df = exp_VF_thr_long_df.copy()\nISI_er_plot_df = exp_VF_CI_long_df.copy()\nprint(f\"ISI_plots_df ({ISI_plots_df.shape}):\\n{ISI_plots_df}\")\nprint(f\"ISI_er_plot_df ({ISI_er_plot_df.shape}):\\n{ISI_er_plot_df}\")\n\nsep_vals = ISI_plots_df['separation'].unique()\nsep_num_dict = dict(zip(sep_vals, list(range(len(sep_vals)))))\nprint(f\"\\nsep_num_dict: {sep_num_dict}\")\nISI_plots_df.insert(4, 'sep_num', ISI_plots_df[\"separation\"].map(sep_num_dict))\nprint(f\"ISI_plots_df ({ISI_plots_df.shape}):\\n{ISI_plots_df}\")\n\nISI_er_plot_df.insert(4, 'sep_num', ISI_er_plot_df[\"separation\"].map(sep_num_dict))\nISI_er_plot_df['halved_CI'] = ISI_er_plot_df.CI_width.div(2, fill_value=0)\nprint(f\"ISI_er_plot_df ({ISI_er_plot_df.shape}):\\n{exp_VF_CI_long_df}\")\n\n\nISIs_to_plot = [99, 4, 12]\n\nfor this_ISI in ISIs_to_plot:\n\n ISI_name = this_ISI\n if this_ISI == 99:\n ISI_name = 'Concurrent'\n print(f\"ISI: {ISI_name} ({this_ISI})\")\n\n ISI_df = ISI_plots_df.loc[ISI_plots_df['ISI'] == this_ISI]\n print(f\"ISI_df ({ISI_df.shape}):\\n{ISI_df}\")\n\n ISI_error_df = ISI_er_plot_df[ISI_er_plot_df['ISI'] == this_ISI]\n print(f\" ISI_error_df ({ISI_error_df.shape}):\\n{ISI_error_df}\")\n\n x_tick_vals = sorted(ISI_df['sep_num'].unique())\n print(f\"\\nx_tick_vals: {x_tick_vals}\")\n\n x_tick_labels = ['1pr' if i == 20 else str(i) for i in sorted(ISI_df['separation'].unique())]\n print(f\"\\nx_tick_labels: {x_tick_labels}\")\n\n fig, ax = plt.subplots(figsize=(10, 6))\n\n # thick lines showing means with errors.\n sns.pointplot(data=ISI_df, x='sep_num', y='probeLum',\n hue='vis_field',\n errorbar='se', capsize=.05,\n scale=1.25,\n )\n\n # background faint lines showing actual thr\n sns.lineplot(data=ISI_df, x='sep_num', y='probeLum',\n hue='vis_field',\n style='p_name',\n dashes=False,\n alpha=.3,\n legend=False\n )\n\n\n ax.set_xticks(x_tick_vals)\n ax.set_xticklabels(x_tick_labels)\n plt.title(f'ISI {ISI_name}: compare UVF & LVF\\n(Error bars: SE of Participant thresholds)')\n ax.set_xlabel('Sep in diag pixels')\n ax.set_ylabel('Threshold')\n plt.savefig(os.path.join(exp_path, f'exp1a_ISI_{ISI_name}_VFs'))\n plt.show()\n\n\n\n'''figs 4, 5, 6, 6: make sep plots for sep 0, 2, 3, 6'''\nprint(\"\\nMaking separation plots\")\nISI_vals = exp_VF_thr_long_df['ISI'].unique()\nISI_num_dict = dict(zip(ISI_vals, list(range(len(ISI_vals)))))\nprint(f\"\\nISI_num_dict: {ISI_num_dict}\")\nexp_VF_thr_long_df.insert(4, 'ISI_num', exp_VF_thr_long_df[\"ISI\"].map(ISI_num_dict))\nsep_to_plot = [0, 2, 3, 6]\n\nfor this_sep in sep_to_plot:\n sep_df = exp_VF_thr_long_df[exp_VF_thr_long_df['separation'] == this_sep]\n print(f\"sep_df:\\n{sep_df}\")\n\n x_tick_vals = sep_df['ISI_num'].unique()\n x_tick_labels = ['conc' if i == 99 else str(i) for i in sep_df['ISI'].unique()]\n print(f\"\\nx_tick_vals: {x_tick_vals}\")\n print(f\"x_tick_labels: {x_tick_labels}\")\n\n\n # thick lines showing means with errors.\n sns.pointplot(data=sep_df, x='ISI_num', y='probeLum',\n hue='vis_field',\n style='p_name',\n scale=1.25,\n errorbar='se', capsize=.05,\n )\n\n # background faint lines showing actual thr\n sns.lineplot(data=sep_df, x='ISI_num', y='probeLum',\n hue='vis_field',\n style='p_name',\n dashes=False,\n alpha=.3,\n legend=False\n )\n\n ax = plt.gca() # to get the axis\n ax.set_xticks(x_tick_vals)\n ax.set_xticklabels(x_tick_labels)\n plt.title(f'Sep {this_sep}: compare UVF & LVF\\n(Error bars: SE of Participant thresholds)')\n ax.set_xlabel('ISI')\n ax.set_ylabel('Threshold')\n plt.savefig(os.path.join(exp_path, f'exp1a_sep{this_sep}_VFs'))\n plt.show()\n\n\n\n'''make plots for each participant showing there actual thresholds for concurrent'''\nprint(\"\\nMaking participant plots for concurrent probes\")\n\n# add sep num column to thr and err dfs.\nsep_vals = exp_VF_thr_long_df['separation'].unique()\nsep_num_dict = dict(zip(sep_vals, list(range(len(sep_vals)))))\nprint(f\"\\nsep_num_dict: {sep_num_dict}\")\n\nif 'sep_num' not in list(exp_VF_thr_long_df.columns):\n exp_VF_thr_long_df.insert(4, 'sep_num', exp_VF_thr_long_df[\"separation\"].map(sep_num_dict))\nprint(f\"exp_VF_thr_long_df ({exp_VF_thr_long_df.shape}):\\n{exp_VF_thr_long_df}\")\n\nif 'sep_num' not in list(exp_VF_CI_long_df.columns):\n exp_VF_CI_long_df.insert(4, 'sep_num', exp_VF_CI_long_df[\"separation\"].map(sep_num_dict))\n\n# add calved CI column to err_df\nif 'halved_CI' not in list(exp_VF_CI_long_df.columns):\n exp_VF_CI_long_df['halved_CI'] = exp_VF_CI_long_df.CI_width.div(2, fill_value=0)\nprint(f\"exp_VF_CI_long_df ({exp_VF_CI_long_df.shape}):\\n{exp_VF_CI_long_df}\")\n\n# Just concurrent data\nconc_thr_df = exp_VF_thr_long_df[exp_VF_thr_long_df['ISI'] == 99]\nprint(f\"conc_thr_df ({conc_thr_df.shape}):\\n{conc_thr_df}\")\nconc_CI_df = exp_VF_CI_long_df[exp_VF_CI_long_df['ISI'] == 99]\nprint(f\"conc_CI_df ({conc_CI_df.shape}):\\n{conc_CI_df}\")\n\n# get participant and vis_field conditions to loop through\np_name_list = conc_thr_df['p_name'].unique()\nprint(f\"p_name_list: {p_name_list}\")\nvf_list = conc_thr_df['vis_field'].unique()\nprint(f\"vf_list: {vf_list}\")\n\n# make plots\ncap_size = 7\nfor p_name in p_name_list:\n p_name_thr_df = conc_thr_df[conc_thr_df['p_name'] == p_name]\n print(f\"p_name_thr_df:\\n{p_name_thr_df}\")\n\n p_name_err_df = conc_CI_df[conc_CI_df['p_name'] == p_name]\n print(f\"p_name_err_df:\\n{p_name_err_df}\")\n\n x_tick_vals = sorted(p_name_thr_df['sep_num'].unique())\n x_tick_labels = ['1pr' if i == 20 else str(i) for i in sorted(p_name_thr_df['separation'].unique())]\n\n fig, ax = plt.subplots(figsize=(10, 6))\n for vis_field in vf_list:\n\n # dfs just for this vis field\n VF_thr_df = p_name_thr_df[p_name_thr_df['vis_field'] == vis_field]\n VF_err_df = p_name_err_df[p_name_err_df['vis_field'] == vis_field]\n\n ax.errorbar(data=VF_thr_df, x='sep_num', y='probeLum',\n yerr=VF_err_df['halved_CI'],\n marker='o', lw=3, elinewidth=2,\n capsize=cap_size)\n\n ax = plt.gca() # to get the axis\n ax.set_xticks(x_tick_vals)\n ax.set_xticklabels(x_tick_labels)\n plt.title(f'{p_name}: compare UVF & LVF\\n(Error bars: Participant 95% CIs)')\n ax.set_xlabel('ISI')\n ax.set_ylabel('Threshold')\n plt.savefig(os.path.join(exp_path, f'exp1a_{p_name}_VFs.png'))\n plt.show()\n\n\nprint('\\nexp1a_analysis_pipe_UVF_LVF finished\\n')\n","repo_name":"Nickdotmartin/Cardiff","sub_path":"Nick_scripts/UVF_LVF_exp1a_analysis_pipe.py","file_name":"UVF_LVF_exp1a_analysis_pipe.py","file_ext":"py","file_size_in_byte":35835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"10510682092","text":"import json\nimport os\nimport platform\nimport time\nfrom io import StringIO\n\nimport numpy as np\nimport pandas as pd\nimport psycopg2\nimport requests\nfrom psycopg2.extras import Json\nfrom tqdm.auto import tqdm\n\nfrom db import DB\nfrom misc.reddit_logger import root_logger\nfrom utils import batch\n\nMINUTE = 60\n\n\nclass Gather(DB):\n def __init__(self):\n super(Gather, self).__init__()\n\n self.MINUTE = 60\n self.logger = root_logger\n self.system_type = platform.system()\n self.pushshift_api_client = self.get_pushshift_client()\n\n self.prepared_statement_filepath = os.path.join(\n self.curdir_fullpath, \"prepared_statements.csv\"\n )\n\n self.pushshift_comment_ids_endpoint = (\n \"https://api.pushshift.io/reddit/submission/comment_ids\"\n )\n\n self.pushshift_submissions_endpoint = (\n \"https://api.pushshift.io/reddit/search/submission/?\"\n )\n\n def get_submissions_to_backfill(\n self, dates_absent: list, stickied: bool = True\n ) -> list:\n before_after_dates = [\n (\n int((x - pd.Timedelta(days=1)).timestamp()),\n int((x + pd.Timedelta(days=1)).timestamp()),\n )\n for x in dates_absent\n ]\n\n # for all dates absent, find out if there are more stickied submissions, and merge them back into stickied_df at the end\n all_params = [\n {\n \"subreddit\": \"wallstreetbets\",\n \"stickied\": \"true\" if stickied is True else \"false\",\n \"before\": x[1],\n \"after\": x[0],\n }\n for x in before_after_dates\n ]\n\n all_backfilled_submissions = []\n for param in tqdm(all_params):\n response = requests.get(self.pushshift_submissions_endpoint, params=param)\n\n if response.status_code == 200:\n data = response.json()\n if \"data\" in data.keys():\n all_backfilled_submissions.append(data[\"data\"])\n\n time.sleep(1)\n\n return all_backfilled_submissions\n\n @staticmethod\n def get_all_daily_discussion_thread_sub_ids_pmaw(\n last_n_days: int = 30, after_n_days: int = None\n ) -> list:\n endpoint = \"https://api.pushshift.io/reddit/search/submission/?\"\n if after_n_days:\n params = {\n \"q\": \"Daily Discussion Thread\",\n \"subreddit\": \"wallstreetbets\",\n \"before\": f\"{last_n_days}\",\n \"after\": f\"{after_n_days}\",\n }\n else:\n params = {\n \"q\": \"Daily Discussion Thread\",\n \"subreddit\": \"wallstreetbets\",\n \"before\": f\"{last_n_days}\",\n }\n\n res = requests.get(endpoint, params=params)\n\n if res.status_code == 200:\n results = res.json()\n\n all_submissions = results[\"data\"]\n\n all_daily_discussion_thread_sub_ids = []\n for sub in all_submissions:\n if \"link_flair_text\" in sub.keys():\n if sub[\"link_flair_text\"] == \"Daily Discussion\":\n all_daily_discussion_thread_sub_ids.append(sub)\n\n return all_daily_discussion_thread_sub_ids\n\n def insert_comments_from_praw(self, comment: dict) -> None:\n insert_query = f\"\"\"INSERT INTO wsb_comments({','.join(comment.keys())}) VALUES %s ON CONFLICT (id) DO NOTHING;\"\"\"\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n insert_query_2 = cur.mogrify(insert_query, tuple(comment.values()))\n print(insert_query_2)\n cur.execute(insert_query_2)\n conn.commit()\n\n def insert_submissions_from_pmaw(self, all_subreddit_submissions: list) -> None:\n all_mogrified_comments, un_mogrified_comments = [], []\n\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n for submission in tqdm(all_subreddit_submissions):\n pop_keys = [\n key\n for key in submission.keys()\n if key not in self.submission_cols\n ]\n for key in pop_keys:\n _ = submission.pop(key)\n\n for key, val in submission.items():\n if key in [\n \"all_awardings\",\n \"awarders\",\n \"author_flair_richtext\",\n \"gildings\",\n \"link_flair_richtext\",\n \"treatment_tags\",\n \"collections\",\n \"crosspost_parent_list\",\n \"poll_data\",\n ]:\n if isinstance(submission[key], dict) or isinstance(\n submission[key], list\n ):\n submission[key] = Json(json.dumps(val))\n try:\n cols_str = \",\".join(submission.keys())\n values_str = (\"%s,\" * len(submission)).rstrip(\",\")\n insert_query = f\"\"\"INSERT INTO wsb_submissions ({cols_str}) VALUES ({values_str}) ON CONFLICT (id) DO NOTHING;\"\"\"\n\n mogrified_comment = cur.mogrify(\n insert_query, list(submission.values())\n )\n all_mogrified_comments.append(mogrified_comment)\n\n except (TypeError, psycopg2.ProgrammingError) as _:\n un_mogrified_comments.append(submission)\n\n all_joined_comments = b\" \".join(all_mogrified_comments)\n\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n cur.execute(all_joined_comments)\n conn.commit()\n\n def get_daily_discussion_thread_submissions_pmaw(self, limit: int = 10000) -> list:\n posts = self.pushshift_api_client.search_submissions(\n q=\"Daily Discussion Thread\", subreddit=\"wallstreetbets\", limit=limit\n )\n submissions = [s for s in posts]\n return submissions\n\n def get_all_comment_ids_from_submission_ids_pmaw(\n self, submission_comment_ids: dict\n ) -> list:\n all_sub_id_with_comments = []\n for sub_id, comments in tqdm(submission_comment_ids.items()):\n comments_arr = self.pushshift_api_client.search_comments(ids=comments)\n for c in comments_arr:\n c[\"submission_id\"] = sub_id\n all_sub_id_with_comments.append(c)\n return all_sub_id_with_comments\n\n def get_all_comments_from_pmaw_single_thread(\n self, submissions: list = None, before: int = 30, after: int = 100\n ) -> list:\n if isinstance(submissions, list) and len(submissions) == 0:\n submissions = self.get_all_daily_discussion_thread_sub_ids_pmaw(\n last_n_days=before, after_n_days=after\n )\n elif submissions is None:\n submissions = self.get_all_daily_discussion_thread_sub_ids_pmaw(\n last_n_days=before, after_n_days=after\n )\n\n all_results = []\n for sub in tqdm(submissions):\n url = f\"{self.pushshift_comment_ids_endpoint}/{sub['id']}\"\n res = requests.get(url)\n\n if res.status_code == 200:\n all_results.append(\n {\"submission_id\": sub[\"id\"], \"comment_ids\": res.json()[\"data\"]}\n )\n\n return all_results\n\n @staticmethod\n def filter_all_submissions_for_correct_link_flair_text(\n submissions: list,\n link_flair_text: str = \"Daily Discussion\",\n ) -> tuple:\n exceptions = []\n all_dates_gathered = []\n for submission in submissions:\n if \"link_flair_text\" in list(submission.keys()):\n if submission[\"link_flair_text\"] == link_flair_text:\n try:\n date_info = pd.to_datetime(\n \"-\".join(\n submission[\"full_link\"]\n .split(\"/\")[-2]\n .split(\"_\")[-4:][1:]\n ),\n format=\"%B-%d-%Y\",\n )\n it = {\"date\": date_info, \"submission_links\": []}\n all_dates_gathered.append(it)\n except (TypeError, ValueError) as _:\n exceptions.append(submission)\n else:\n pass\n else:\n pass\n\n return all_dates_gathered, exceptions\n\n @staticmethod\n def match_correct_submission_links_with_their_dates(\n all_dates_gathered: list, submissions: list\n ) -> tuple:\n final_dates_gathered = []\n exceptions = []\n for submission in tqdm(submissions):\n if \"link_flair_text\" in submission.keys():\n if submission[\"link_flair_text\"] == \"Daily Discussion\":\n try:\n date_info = pd.to_datetime(\n \"-\".join(\n submission[\"full_link\"]\n .split(\"/\")[-2]\n .split(\"_\")[-4:][1:]\n ),\n format=\"%B-%d-%Y\",\n )\n for ele in all_dates_gathered:\n if ele[\"date\"] == date_info:\n ele[\"submission_links\"].append(submission[\"full_link\"])\n final_dates_gathered.append(ele)\n\n except (TypeError, ValueError, AssertionError) as _:\n exceptions.append(submission)\n else:\n pass\n else:\n pass\n return final_dates_gathered, exceptions\n\n def compare_submission_dates_in_pmaw_to_dates_in_db(\n self, final_dates_gathered: list\n ) -> tuple[list, pd.DataFrame]:\n all_submissions_meta_df = (\n pd.DataFrame(final_dates_gathered)\n .drop_duplicates(subset=[\"date\"], inplace=False)\n .reset_index(drop=True)\n .set_index(\"date\")\n .explode(\"submission_links\")\n .reset_index()\n )\n\n all_submissions_meta_df.columns = [\"date\", \"links_in_reddit\"]\n\n # Check if these are present in the database, and how many comments do each of these submissions have?\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n cur.execute(\n \"SELECT DISTINCT id, full_link FROM wsb_submissions WHERE link_flair_text = 'Daily Discussion';\"\n )\n sub_in_db_df = pd.DataFrame(\n cur.fetchall(), columns=[\"id\", \"links_in_db\"]\n )\n\n merged_df = pd.merge(\n left=all_submissions_meta_df,\n right=sub_in_db_df,\n left_on=\"links_in_reddit\",\n right_on=\"links_in_db\",\n how=\"outer\",\n )\n\n in_db_possible_backup_needed_df = merged_df[~merged_df[\"date\"].isna()].copy()\n in_db_possible_backup_needed_df = in_db_possible_backup_needed_df.sort_values(\n by=\"date\"\n )\n in_db_possible_backup_needed_df = in_db_possible_backup_needed_df.reset_index(\n drop=True\n )\n\n # Grab the submissions that are not there in db and push them\n submission_links_not_uploaded = set(\n in_db_possible_backup_needed_df[\"links_in_reddit\"].values.tolist()\n ) - set(in_db_possible_backup_needed_df[\"links_in_db\"].values.tolist())\n\n submission_ids = [ele.split(\"/\")[6] for ele in submission_links_not_uploaded]\n in_db_not_daily_subs_df = merged_df[merged_df[\"date\"].isna()].copy()\n\n return submission_ids, in_db_not_daily_subs_df\n\n def get_all_comment_ids_for_given_submission_ids_in_db(\n self, submission_ids: list\n ) -> list:\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n cur.execute(\n f\"\"\"SELECT submission_id, id as comment_id from comments;\"\"\"\n )\n submission_comments_df = pd.DataFrame(\n cur.fetchall(), columns=[\"submission_id\", \"comment_id\"]\n )\n\n comment_ids_present = submission_comments_df.loc[\n submission_comments_df.submission_id.isin(submission_ids), \"comment_id\"\n ].values.tolist()\n return comment_ids_present\n\n def find_missing_comment_ids_from_pmaw(\n self,\n submission_ids_to_be_refreshed: list = None,\n comment_ids_present: list = None,\n ) -> tuple[list, list]:\n \"\"\"\n Get all the backfill submissions for some submissions that might be incomplete.\n :return:\n \"\"\"\n\n all_comments, exceptions = [], []\n\n for submission_id in tqdm(submission_ids_to_be_refreshed):\n comments = self.pushshift_api_client.search_submission_comment_ids(\n ids=[submission_id], safe_exit=True, memsafe=True\n )\n\n submission_id_comments = {\n \"submission_id\": submission_id,\n \"comments\": [\n comment\n for comment in comments\n if comment not in comment_ids_present\n ],\n }\n\n if len(submission_id_comments[\"comments\"]) > 0:\n\n comments_arr = self.pushshift_api_client.search_comments(\n ids=submission_id_comments[\"comments\"]\n )\n for c in comments_arr:\n c[\"submission_id\"] = submission_id_comments[\"submission_id\"]\n all_comments.append(c)\n\n return all_comments, exceptions\n\n def insert_all_comments_to_db_using_df_pmaw(self, df: pd.DataFrame) -> None:\n \"\"\"\n For each comment in all_comments... insert that into the database.\n :param df: a list of dicts, [{... all dict properties of the wsb_comments table}]\n :return: None\n \"\"\"\n\n print(\"Formatting into correct format...\")\n to_insert_comments = []\n\n comment = df.iloc[\n 0,\n ].to_dict()\n\n comment_copy = comment.copy()\n to_pop = [\n key for key in list(comment_copy.keys()) if key not in self.comments_cols\n ]\n for key in to_pop:\n _ = comment.pop(key)\n\n # comment = {\n # \"subreddit_name_prefixed\" if k == \"subreddit\" else k: v\n # for k, v in comment.items()\n # }\n\n for i in tqdm(range(len(df))):\n comment = df.iloc[\n i,\n ].to_dict()\n comment_copy = comment.copy()\n comment[\"subreddit_name_prefixed\"] = f\"r/{comment['subreddit']}\"\n to_pop = [\n key\n for key in list(comment_copy.keys())\n if key not in self.comments_cols\n ]\n\n for key in to_pop:\n _ = comment.pop(key)\n\n for key, val in comment.items():\n if key in [\n \"awarders\",\n \"user_reports\",\n \"all_awardings\",\n \"report_reasons\",\n \"gildings\",\n \"author_flair_richtext\",\n \"treatment_tags\",\n \"mod_reports\",\n ]:\n comment[key] = Json(json.dumps(val))\n\n if isinstance(comment[key], np.bool_):\n comment[key] = bool(val)\n\n if isinstance(comment[key], np.int64):\n comment[key] = int(val)\n\n if \"edited\" in comment.keys() and isinstance(comment[\"edited\"], bool):\n comment[\"edited\"] = int(0)\n\n if \"author_patreon_flair\" in comment.keys() and isinstance(\n comment[\"author_patreon_flair\"], bool\n ):\n comment[\"author_patreon_flair\"] = \"\"\n\n if \"author_cakeday\" in comment.keys() and isinstance(\n comment[\"author_cakeday\"], np.float\n ):\n comment[\"author_cakeday\"] = bool(0)\n\n if \"author_premium\" in comment.keys() and isinstance(\n comment[\"author_premium\"], float\n ):\n comment[\"author_premium\"] = bool(0)\n\n if \"can_mod_post\" in comment.keys() and isinstance(\n comment[\"can_mod_post\"], float\n ):\n comment[\"can_mod_post\"] = bool(0)\n\n to_insert_comments.append(tuple(comment.values()))\n\n if (i % 1000000 == 0) & (i > 0):\n\n df_ = pd.DataFrame.from_records(\n to_insert_comments, columns=list(comment.keys())\n )\n\n for key in [\n \"author_flair_richtext\",\n \"gildings\",\n \"all_awardings\",\n \"awarders\",\n \"treatment_tags\",\n ]:\n if key in df_.columns.tolist():\n df_ = df_.drop(columns=[key])\n\n if \"locked\" in df_.columns:\n df_.loc[:, \"locked\"] = df_[\"locked\"].astype(bool)\n\n if \"total_awards_received\" in df_.columns:\n df_.loc[:, \"total_awards_received\"] = (\n df_[\"total_awards_received\"].fillna(0.0).astype(int)\n )\n\n df_.loc[:, \"edited\"] = df_[\"edited\"].astype(int)\n\n text_stream: StringIO = StringIO()\n df_.to_csv(path_or_buf=text_stream, header=True, index=False)\n text_stream.seek(0)\n\n print(\"-- COPY-ing into the db...\")\n focused_cols = \",\".join(df_.columns.tolist())\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n try:\n cur.copy_expert(\n sql=f\"\"\"COPY wsb_comments ({focused_cols}) FROM STDIN WITH (FORMAT CSV, DELIMITER ',', HEADER);\"\"\",\n file=text_stream,\n )\n conn.commit()\n except psycopg2.ProgrammingError as e:\n print(e)\n print(\"-- Done.\")\n\n print(\"-- Vacuuming wsb_comments...\")\n self.execute_command_in_db(command_str=\"VACUUM wsb_comments;\")\n print(\"-- Done.\")\n\n def insert_all_comments_to_db_using_list_pmaw(\n self, all_comments_list: list, skip_iterations: int = None\n ) -> None:\n \"\"\"\n For each comment in all_comments... insert that into the database.\n :param all_comments_list: A list of dicts, [{... all dict properties of the wsb_comments table}]\n :param skip_iterations: If some iterations have already been completed, then just skip past those and move on..\n :return: None\n \"\"\"\n\n print(\"formatting into correct format...\")\n comments_cols = []\n to_insert_comments = []\n\n # make a reasonable batch len\n batch_len = 50000\n\n # there are too many comments, batch them\n batched_comments = batch(all_comments_list, n=batch_len)\n if skip_iterations is not None:\n skip_iterations = int(skip_iterations)\n print(f\"-- skipping iterations: {skip_iterations}\")\n for _ in tqdm(range(skip_iterations), total=skip_iterations):\n _ = next(batched_comments)\n\n # we need to pre-calculate the total number of batches here\n total_batches = (\n int(len(all_comments_list) // batch_len) - skip_iterations\n if skip_iterations is not None\n else int(len(all_comments_list) // batch_len)\n )\n\n j = 0\n for bc in tqdm(batched_comments, total=total_batches):\n j += 1\n\n for comment in tqdm(bc, total=batch_len, leave=False):\n # we need to remove some keys from comment, but cant pop keys in a loop within a dict, so a workaround\n # is used, by using comment_copy.\n comment_copy = comment.copy()\n to_pop = [\n key\n for key in list(comment_copy.keys())\n if key not in self.comments_cols\n ]\n for key in to_pop:\n _ = comment.pop(key)\n\n comment = {\n \"subreddit_name_prefixed\" if k == \"subreddit\" else k: v\n for k, v in comment.items()\n }\n comment[\"subreddit_name_prefixed\"] = f\"r/wallstreetbets\"\n\n for key, val in comment.items():\n if key in [\n \"awarders\",\n \"user_reports\",\n \"all_awardings\",\n \"report_reasons\",\n \"gildings\",\n \"author_flair_richtext\",\n \"treatment_tags\",\n \"mod_reports\",\n ]:\n # comment[key] = json(json.dumps(val, default=vars))\n comment[key] = json.dumps(val, default=vars)\n\n if isinstance(comment[key], np.bool_):\n comment[key] = bool(val)\n\n if isinstance(comment[key], np.int64):\n comment[key] = int(val)\n\n if \"edited\" in comment.keys() and isinstance(comment[\"edited\"], bool):\n comment[\"edited\"] = int(0)\n\n if \"author_patreon_flair\" in comment.keys() and isinstance(\n comment[\"author_patreon_flair\"], bool\n ):\n comment[\"author_patreon_flair\"] = \"\"\n\n if \"author_cakeday\" in comment.keys() and isinstance(\n comment[\"author_cakeday\"], np.float\n ):\n comment[\"author_cakeday\"] = bool(0)\n\n if \"author_premium\" in comment.keys() and isinstance(\n comment[\"author_premium\"], float\n ):\n comment[\"author_premium\"] = bool(0)\n\n if \"can_mod_post\" in comment.keys() and isinstance(\n comment[\"can_mod_post\"], float\n ):\n comment[\"can_mod_post\"] = bool(0)\n\n if len(comments_cols) == 0:\n comments_cols = list(comment.keys())\n\n to_insert_comments.append(comment)\n\n # turn into dataframe to make things easier downstream.\n df_ = pd.dataframe.from_dict(to_insert_comments)\n\n text_stream = StringIO()\n for key in [\"author_flair_richtext\", \"gildings\", \"all_awardings\"]:\n if key in df_.columns.tolist():\n df_ = df_.drop(columns=[key])\n\n if \"locked\" in df_.columns:\n df_.loc[:, \"locked\"] = df_[\"locked\"].astype(bool)\n\n if \"total_awards_received\" in df_.columns:\n df_.loc[:, \"total_awards_received\"] = (\n df_[\"total_awards_received\"].fillna(0.0).astype(int)\n )\n\n df_.loc[:, \"edited\"] = df_[\"edited\"].astype(int)\n df_.to_csv(text_stream, header=True, index=False)\n text_stream.seek(0)\n\n print(\"-- copy-ing into the db...\")\n focused_cols = \",\".join(df_.columns.tolist())\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n try:\n cur.copy_expert(\n sql=f\"\"\"copy comments({focused_cols}) from stdin with (format csv, delimiter ',', header);\"\"\",\n file=text_stream,\n )\n conn.commit()\n except psycopg2.ProgrammingError as e:\n print(e)\n print(\"-- done.\")\n\n def backfill_comments(self) -> None:\n self.logger.info(\n \"-- 1/9 Fetching 'Daily Discussions Thread' submissions from pmaw ...\"\n )\n all_submissions_from_praw = self.get_daily_discussion_thread_submissions_pmaw()\n\n self.logger.info(\"-- 2/9 Updating wsb_submissions table...\")\n self.update_wsb_submissions_table(submissions=all_submissions_from_praw)\n\n self.logger.info(\"-- 3/9 Fetching distinct wsb_submissions ids...\")\n with self.get_psycopg2_conn() as conn:\n with conn.cursor() as cur:\n cur.execute(\"SELECT DISTINCT(id) FROM wsb_submissions;\")\n submission_ids_present = [i[0] for i in cur.fetchall()]\n\n self.logger.info(\n \"-- 4/9 Filtering all submissions for correct link flair text ...\"\n )\n (\n all_dates_gathered,\n submission_exceptions_1,\n ) = self.filter_all_submissions_for_correct_link_flair_text(\n submissions=all_submissions_from_praw\n )\n\n self.logger.info(\n \"-- 5/9 Matching correct submission links with their dates ...\"\n )\n (\n final_dates_gathered,\n submission_exceptions_2,\n ) = self.match_correct_submission_links_with_their_dates(\n all_dates_gathered, submissions=all_submissions_from_praw\n )\n\n self.logger.info(\"-- 6/9 Comparing submission dates in pmaw to dates in db...\")\n (\n submission_ids_to_be_refreshed,\n in_db_not_daily_sub_df,\n ) = self.compare_submission_dates_in_pmaw_to_dates_in_db(\n final_dates_gathered=final_dates_gathered\n )\n\n if len(submission_ids_to_be_refreshed) > 0:\n\n self.logger.info(\n \"-- 7/9 Getting all comment_ids for given submission ids...\"\n )\n comment_ids_present = (\n self.get_all_comment_ids_for_given_submission_ids_in_db(\n submission_ids=submission_ids_present\n )\n )\n\n self.logger.info(\"-- 8/9 Finding mission comment ids from pmaw..\")\n all_comments, comments_exceptions = self.find_missing_comment_ids_from_pmaw(\n submission_ids_to_be_refreshed=submission_ids_to_be_refreshed,\n comment_ids_present=comment_ids_present,\n )\n\n self.logger.info(\"-- 9/9 Inserting all comments to db...\")\n self.insert_all_comments_to_db_pmaw(df=pd.DataFrame(all_comments))\n else:\n self.logger.info(\"-- No submission ids to be refreshed...\")\n","repo_name":"tranquilo12/WSB","sub_path":"wsb.py","file_name":"wsb.py","file_ext":"py","file_size_in_byte":26994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"2392108762","text":"class Node:\r\n def __init__(self, data):\r\n self.data = data\r\n self.next = None\r\n\r\nclass LinkedList:\r\n def __init__(self):\r\n self.head = None\r\n \r\n\r\n def display(self):\r\n temp = self.head\r\n while temp is not None:\r\n print(temp.data)\r\n temp = temp.next\r\n\r\nlList = LinkedList()\r\nlList.head = Node(\"CHandigarh\")\r\nsecond = Node(\"Hyderabad\")\r\nthird = Node(\"Banglore\")\r\n\r\nlList.head.next = second\r\nsecond.next = third\r\n\r\nlList.display()\r\n\r\n","repo_name":"saitejamarolix/linkedlists","sub_path":"traversingLinkedList.py","file_name":"traversingLinkedList.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"27563086980","text":"from selenium.common import TimeoutException\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\n\nDELAY = 0.5\n\ndef update_stack_capacity(driver, val):\n capacity_input = driver.find_element(value=\"capacityInput\")\n capacity_input.send_keys(val)\n\n\ndef click_stack_capacity(driver):\n capacity_button = driver.find_element(value=\"capacityButton\")\n capacity_button.click()\n time.sleep(DELAY)\n\n\ndef check_stack_capacity(driver, val):\n capacity_input = driver.find_element(value=\"capacityInput\")\n if capacity_input.text != val:\n raise Exception(\"Stack capacity not matched\")\n\n\ndef stack_ops_availability(driver, state):\n stack_ops = driver.find_element(value=\"stackOps\")\n is_stack_ops_visible = stack_ops.is_displayed()\n if is_stack_ops_visible != state:\n raise Exception(\"Stack operation not found\")\n\n\ndef stack_push(driver, val):\n push_input = driver.find_element(value=\"pushInput\")\n push_input.send_keys(val)\n push_button = driver.find_element(value=\"pushButton\")\n push_button.click()\n time.sleep(DELAY)\n\n\ndef click_stack_display(driver):\n display_button = driver.find_element(value=\"displayButton\")\n display_button.click()\n time.sleep(DELAY)\n\n\ndef click_stack_pop(driver):\n display_button = driver.find_element(value=\"popButton\")\n display_button.click()\n time.sleep(DELAY)\n\n\ndef verify_stack_values(driver, values):\n stack_values = driver.find_element(value=\"stackValuesLabel\")\n if \", \".join([str(val) for val in values]) != stack_values.text:\n raise Exception(\"stack content incorrect\")\n\n\ndef click_stack_reset(driver):\n reset_button = driver.find_element(value=\"resetButton\")\n reset_button.click()\n time.sleep(DELAY)\n\n\ndef check_alert_and_accept(driver):\n try:\n WebDriverWait(driver, 4).until(EC.alert_is_present(),\n 'Timed out waiting for PA creation confirmation popup to appear.')\n\n alert = driver.switch_to.alert\n alert.accept()\n except TimeoutException as error:\n print(\"no alert\")\n raise error\n","repo_name":"dareofmeashwani/StackApp","sub_path":"tests/selenium_util.py","file_name":"selenium_util.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"6135128093","text":"from l10ntool import AbstractL10nTool\nfrom sdf import SdfEntity\nimport sys\nimport shutil\n\nclass Xtxex(AbstractL10nTool):\n _resource_type = \"xtx\"\n\n def __init__(self):\n AbstractL10nTool.__init__(self)\n\n def merge_file(self, inputfilename, outputfilename, parsed_file_ref, lang, is_forced_lang, sdfdata):\n # Special handling for en-US files\n if lang == \"en-US\":\n mod_outputfilename = outputfilename\n # mod here if needed\n self.copy_file(inputfilename, mod_outputfilename)\n return\n # merge usual lang\n sdfline = self.prepare_sdf_line(inputfilename,lang)\n if sdfline.get_id() in sdfdata:\n line = sdfdata[sdfline.get_id()].text.replace(\"\\\\n\", '\\n')\n self.make_dirs(outputfilename)\n try:\n f = open(outputfilename, \"w+\")\n f.write(line)\n except IOError:\n print(\"ERROR: Can not write file \" + outputfilename)\n sys.exit(-1)\n else:\n f.close()\n return\n # no sdf data found then copy en-US source file\n if is_forced_lang:\n self.copy_file(inputfilename, outputfilename)\n\n ##### Extract a single File\n def extract_file(self, inputfile):\n lines = []\n try:\n f = open(inputfile, \"r\")\n lines = f.readlines()\n except IOError:\n print(\"ERROR: Can not open file \" + inputfile)\n sys.exit(-1)\n else:\n f.close()\n # remove legal header\n lines = [line for line in lines if len(line) > 0 and not line[0] == '#']\n # escape all returns\n lines = [line.replace('\\n', \"\\\\n\") for line in lines]\n line = ''.join(lines)\n test = str(line)\n if len(test.strip()):\n sdf_entity = self.prepare_sdf_line(inputfile);\n sdf_entity.text = line\n return str(sdf_entity)\n else:\n return \"\"\n\n def prepare_sdf_line(self, inputfile=\"\", lang=\"\"):\n if lang == \"\":\n lang = self._source_language\n return SdfEntity(project=self._options.project_name, source_file=self.get_filename_string(inputfile),\n resource_type=self._resource_type, gid=\"none\", lid=\"none\", langid=lang,text=\"\")\n\nrun = Xtxex()\n","repo_name":"apache/openoffice","sub_path":"main/l10ntools/scripts/tool/xtxex.py","file_name":"xtxex.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","stars":834,"dataset":"github-code","pt":"78"}
+{"seq_id":"72625309052","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn import tree\n\ndef read_csv(file):\n df = pd.DataFrame.from_csv('%s.csv'%(file),sep=';',index_col=0,encoding ='ISO-8859-1')\n df = df.reset_index()\n \n return df\n \ndef convert_dataset(df):\n \n label_enco = LabelEncoder()\n \n \n df['País'] = label_enco.fit_transform(df['País'])\n df['Tipo-viagem'] = label_enco.fit_transform(df['Tipo-viagem'])\n df['Piscina'] = label_enco.fit_transform(df['Piscina'])\n df['Academia'] = label_enco.fit_transform(df['Academia'])\n df['Tenis'] = label_enco.fit_transform(df['Tenis'])\n df['Spa'] = label_enco.fit_transform(df['Spa'])\n df['Casino'] = label_enco.fit_transform(df['Casino'])\n df['Internet'] = label_enco.fit_transform(df['Internet'])\n df['Hotel'] = label_enco.fit_transform(df['Hotel'])\n \n \n return df\n\ndef convert_input(value):\n \n label_enco = LabelEncoder()\n \n #label_enco.fit_transform()\n\ndef inverse_result(result):\n \n label_enco = LabelEncoder()\n \n label_enco.fit(['Circus Circus Hotel & Casino Las Vegas','Excalibur Hotel & Casino','Monte Carlo Resort&Casino','Treasure Island- TI Hotel & Casino',\n 'Tropicana Las Vegas - A Double Tree by Hilton Hotel','Caesars Palace','The Cosmopolitan Las Vegas','The Palazzo Resort Hotel Casino',\n 'Wynn Las Vegas','Trump International Hotel Las Vegas','The Cromwell','Encore at wynn Las Vegas','Hilton Grand Vacations on the Boulevard',\n \"Marriott's Grand Chateau\",'Tuscany Las Vegas Suites & Casino','Hilton Grand Vacations at the Flamingo','Wyndham Grand Desert',\n 'The Venetian Las Vegas Hotel','Bellagio Las Vegas','Paris Las Vegas','The Westin las Vegas Hotel Casino & Spa'])\n \n r = label_enco.inverse_transform(result)\n \n return r\n\ndef predict_tree(X,y,input_):\n \n clf = tree.DecisionTreeClassifier(criterion='gini', splitter='best')\n \n pred = clf.fit(X,y).predict(input_)\n \n return inverse_result(pred)\n\ndef split_dataset(df):\n \n new_df = pd.DataFrame()\n \n new_df['País'] = df['País']\n new_df['Reviews'] = df['Reviews']\n new_df['Hotel-reviews'] = df['Hotel-reviews']\n new_df['Votes'] = df['Votes']\n new_df['Score'] = df['Score']\n new_df['Tipo-viagem'] = df['Tipo-viagem']\n new_df['Piscina'] = df['Piscina']\n new_df['Academia'] = df['Academia']\n new_df['Tenis'] = df['Tenis']\n new_df['Spa'] = df['Spa']\n new_df['Casino'] = df['Casino']\n new_df['Internet'] = df['Internet']\n new_df['Estrelas'] = df['Estrelas']\n \n train = new_df.values\n \n target = df['Hotel'].values\n \n return train,target\n\ndef main():\n df = read_csv('hotel')\n df = convert_dataset(df)\n train,target = split_dataset(df)\n pred = predict_tree(train,target,[1,36,5,20,4,1,1,0,1,1,0,0,4])\n \n print(pred)\n\n\nif __name__ == '__main__':\n \n main()\n ","repo_name":"eriksonJAguiar/Data-Analytics","sub_path":"hotel.py","file_name":"hotel.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"13625449160","text":"import requests\nimport base64\nimport random\nfrom time import sleep\nfrom time import time\n\napi_url = 'http://127.0.0.1:5000'\n\n\ndef get_admin_token():\n params = {'username': 'admin', 'password': 'password'}\n resp = requests.get(url=api_url + '/api/v1/authenticate', params=params)\n token_from_api = resp.json()['token']\n return token_from_api\n\n\ndef get_header_bearer(token_for_auth):\n header = {'Authorization': 'Bearer ' + token_for_auth}\n return header\n\n\ndef list_devices(admin_token):\n header = get_header_bearer(admin_token)\n resp = requests.get(url=api_url + '/api/v1/device', headers=header)\n devices_from_api = resp.json()\n return devices_from_api\n\n\ndef add_device(admin_token):\n header = get_header_bearer(admin_token)\n resp = requests.post(url=api_url + '/api/v1/device/{}'.format('DemoDevice'),\n json={'data': {'password': '12345'},\n 'properties': {'temperature': {'value': 0}}}, headers=header)\n return resp.status_code\n\n\ndef update_property(device_properties, device_token):\n header = get_header_bearer(device_token)\n resp = requests.post(url=api_url + '/api/v1/device/DemoDevice/details', headers=header, json=device_properties)\n return resp\n\n\ndef update_status(device_status, device_token):\n header = get_header_bearer(device_token)\n resp = requests.post(url=api_url + '/api/v1/device/DemoDevice/status', headers=header, json=device_status)\n return resp\n\n\ndef get_token_device():\n userpass = base64.b64encode(str.encode('{}:{}'.format('DemoDevice', '12345')))\n header = {'Authorization': 'Basic ' + str(userpass)[2:-1]}\n resp = requests.get(url=api_url + '/api/v1/device/DemoDevice/token', headers=header)\n token_from_api = resp.json()['token']\n return token_from_api\n\n\nprint('Logging in as an admin')\ntoken = get_admin_token()\nprint('Admin token: {}'.format(token))\n\nprint('Obtaining list of current devices from API')\ndevices = list_devices(token)\nprint('Devices:\\n{}'.format(devices))\n\n# We want to prevent from attempting to add the same device twice if script was run multiple times\ndevices_in_db = [item['name'] for item in devices]\nif 'DemoDevice' not in devices_in_db:\n print('DemoDevice not found, adding to the database')\n response_code = add_device(token)\n print('Response code (200 if success): {}'.format(response_code))\nelse:\n print('DemoDevice is already in the database! Skipping...')\n\nprint('Logging in as a device')\ntoken = get_token_device()\nprint('Device token: {}'.format(token))\n\n# we will put those into DemoDevice\nproperties_to_initalize = {\n 'temperature1': {\n 'value': 0,\n 'threshold': 50},\n 'temperature2': {\n 'value': 0,\n 'threshold': 50},\n 'temperature3': {\n 'value': 0,\n 'threshold': 50},\n 'temperature4': {\n 'value': 0,\n 'threshold': 50}\n}\n\nprint('Pushing blank properties into the device')\nresponse_code = update_property(properties_to_initalize, token)\nprint('Response code (200 if success): {}'.format(response_code))\n\n# Device needs to update all properties to easily accomodate to the design\n# IAA currently does not care about the history\n# Note that if you want to set old timestamp, you need to specify it:\nproperties_to_update = {\n 'temperature1': {\n 'value': 0},\n 'temperature2': {\n 'value': 0},\n 'temperature3': {\n 'value': 0},\n 'temperature4': {\n 'value': 0,\n 'timestamp': int(time()) - 60 * 60 * 24 * 1000}\n}\n\n# extract names of the properties\nkeys_to_update = list(properties_to_initalize.keys())\n# status list\ngeneral_status = [{'value': 'OK'},\n {'value': 'ERROR'},\n {'value': 'custom message'},\n {'value': 'OK', 'timestamp': int(time()) - 86400 * 365}]\ntry:\n while True:\n sleep(7)\n # choose random property\n key_to_update = keys_to_update[random.randint(0, len(keys_to_update) - 1)]\n # choose random value\n value = random.randint(0, 100)\n # modify poprerty dict\n properties_to_update[key_to_update]['value'] = value\n # update properties\n update_property(properties_to_update, token)\n # pick a new status\n status = general_status[random.randint(0, len(general_status) - 1)]\n # update status\n update_status(status, token)\n print('Property {} of DemoDevice was set to {} with general status: {}'.format(key_to_update, value, status))\nexcept KeyboardInterrupt:\n pass\n","repo_name":"dabku/iamalive","sub_path":"client_example/client_example.py","file_name":"client_example.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"31847049299","text":"from sqlalchemy import *\nfrom migrate import *\n\n\nfrom migrate.changeset import schema\npre_meta = MetaData()\npost_meta = MetaData()\nroom = Table('room', post_meta,\n Column('id', Integer, primary_key=True, nullable=False),\n Column('sortOrder', Integer),\n Column('roomName', String(length=30)),\n)\n\ntime_slot = Table('time_slot', post_meta,\n Column('id', Integer, primary_key=True, nullable=False),\n Column('sortOrder', Integer),\n Column('timeSlotName', String(length=30)),\n)\n\nschedule_slot = Table('schedule_slot', pre_meta,\n Column('id', INTEGER, primary_key=True, nullable=False),\n Column('talkId', INTEGER),\n Column('timeSlot', VARCHAR(length=30)),\n Column('room', VARCHAR(length=30)),\n)\n\nschedule_slot = Table('schedule_slot', post_meta,\n Column('id', Integer, primary_key=True, nullable=False),\n Column('talkId', Integer),\n Column('timeSlotId', Integer),\n Column('roomId', Integer),\n)\n\n\ndef upgrade(migrate_engine):\n # Upgrade operations go here. Don't create your own engine; bind\n # migrate_engine to your metadata\n pre_meta.bind = migrate_engine\n post_meta.bind = migrate_engine\n post_meta.tables['room'].create()\n post_meta.tables['time_slot'].create()\n pre_meta.tables['schedule_slot'].columns['room'].drop()\n pre_meta.tables['schedule_slot'].columns['timeSlot'].drop()\n post_meta.tables['schedule_slot'].columns['roomId'].create()\n post_meta.tables['schedule_slot'].columns['timeSlotId'].create()\n\n\ndef downgrade(migrate_engine):\n # Operations to reverse the above upgrade go here.\n pre_meta.bind = migrate_engine\n post_meta.bind = migrate_engine\n post_meta.tables['room'].drop()\n post_meta.tables['time_slot'].drop()\n pre_meta.tables['schedule_slot'].columns['room'].create()\n pre_meta.tables['schedule_slot'].columns['timeSlot'].create()\n post_meta.tables['schedule_slot'].columns['roomId'].drop()\n post_meta.tables['schedule_slot'].columns['timeSlotId'].drop()\n","repo_name":"PghTechFest/pypghtechfest","sub_path":"db_repository/versions/005_migration.py","file_name":"005_migration.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"21816886584","text":"def on_enter(event_data):\n \"\"\" \"\"\"\n pocs = event_data.model\n pocs.next_state = 'sleeping'\n\n pocs.say(\"Resetting the list of observations and doing some cleanup!\")\n\n # Cleanup existing observations\n try:\n pocs.observatory.scheduler.reset_observed_list()\n except Exception as e: # pragma: no cover\n pocs.logger.warning(f'Problem with cleanup: {e!r}')\n","repo_name":"panoptes/POCS","sub_path":"src/panoptes/pocs/state/states/default/housekeeping.py","file_name":"housekeeping.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"78"}
+{"seq_id":"5952428597","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n This implements the nwis data retrieval. \n The data retrieval uses the already exiting python library from https://github.com/USGS-python/dataretrieval. \n The original code is modified to collect hourly measurements and interact with the data retriever BMI. A validity check is added to inform BMI if there is a measurement. \n This code is useful for extracting observation from Nextgen framework.\n \n contact\n ----------\n fwolkeba@crimson.ua.edu\n butlerz@oregonstate.edu\n \n Required inputs\n ----------\n site: USGS site ID e.g. 01123000\n start start of the requested data e.g. 2017-05-15\n parameterCd:\"00060\", parameterCd is constant to collect only flow measurements \n end: End of the requested e.g. 2017-05-15\n\n outputs\n ----------\n Flow: Hourly flow measurement of the requested station\n validity: Binary value indicating availability of measurement (0= no measurement, 1 there is a valid measurement)\n Attributes\n\n References\n ----------\n https://github.com/USGS-python/dataretrieval\n\n \"\"\"\n\n\nimport dataretrieval.nwis as nwis\nimport pandas as pd\nimport numpy as np\npd.options.mode.chained_assignment = None\n\n\nclass USGS:\n\n def __init__(self):\n super(USGS, self).__init__()\n\n def run_usgs(self, u):\n\n # -------------------------------------------------------------------\n\n sites = u.sites\n start = u.start\n end = u.end\n site = nwis.get_record(sites=u.sites, parameterCd='00060',\n start=u.start, end=u.end)\n\n # -------------------------------------------------------------------\n # change the extacted values to data frame to do computation on\n # -------------------------------------------------------------------\n\n #site = pd.DataFrame(site_data[0])\n site.reset_index(inplace=True) # reset index to grab station date\n site['datetime'] = pd.to_datetime(site['datetime'], utc=True,\n format='%Y-%m-%d %H:%M:%S') # transfer to utc so same time throughout\n site_copy = site.copy()\n site_copy['datetime'] = pd.to_datetime(site_copy['datetime'],\n utc=True, format='%Y-%m-%d %H:%M:%S') # convert datetime to average every hour\n site_copy.index = site_copy['datetime'] # index so can pull date time in resample\n site_avg = site_copy.select_dtypes(include=['float64', 'int64']).resample('H').mean()\n #site_avg = site_copy.resample('H').mean() # Average every hour based on datetime\n site_avg.reset_index(inplace=True) # reset index again to have datetime\n site_avg.columns = ['Date', 'Flow']\n\n # check validity of extracted data\n\n site_avg.loc[site_avg['Flow'] >= 0, 'validity'] = 1 # if value positive, consider\n site_avg.loc[site_avg['Flow'] < 0, 'validity'] = 0 # if less than zero, not realistic\n site_avg.loc[site_avg['Flow'].isnull() == True, 'validity'] = 0 # if NaN not availible\n\n # Output results to csv file\n\n #site_avg.to_csv('USGS_' + str(sites) + '_obs_streamflow.csv',\n # index=False)\n\n # to check if the code runs on the framework\n\n u.flow = site_avg['Flow']\n u.validity = site_avg['validity']\n\n return","repo_name":"NWC-CUAHSI-Summer-Institute/data_assimilation_with_bmi","sub_path":"observations/usgs.py","file_name":"usgs.py","file_ext":"py","file_size_in_byte":3679,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"78"}
+{"seq_id":"27443925362","text":"import math\nimport os\nfrom math import floor\n\nimport pygame as pg\n\nfrom core.classes import WVec, BlockSelection, WBounds, PixVec\nfrom core.funcs import w_to_pix_shift, pix_to_w_shift, light_level_to_color_int\nfrom core.constants import BLOCK_PIX_SIZE, PLAYER_S_POS, FULLSCREEN, C_KEY, CAM_FPS, CAM_DEFAULT_SCALE, \\\n CAM_SCALE_BOUNDS, DIR_TO_ANGLE, GUI_PATH, ACTION_MAX_DISTANCE, PIX_ORIGIN, HOTBAR_S_POS\n\n\nclass Camera:\n _ZOOM_SPEED = 1.05\n _VEL_DAMPING_FACTOR = 0.5\n _POS_DAMPING_FACTOR = 0.1\n _ZOOM_VEL_DAMPING_FACTOR = 0.1\n _SCALE_COLLISION_DAMPING_FACTOR = 0.75\n\n def __init__(self):\n self._pos = WVec()\n self._req_pos = WVec(self._pos)\n\n self._vel = WVec()\n self._req_vel = WVec(self._vel)\n\n self._zoom_vel = 1.0\n self._req_zoom_vel = 1.0\n\n self._scale = CAM_DEFAULT_SCALE\n\n if FULLSCREEN:\n self._screen = pg.display.set_mode((0, 0), pg.FULLSCREEN)\n else:\n self._screen = pg.display.set_mode((1280, 720))\n self._pix_size = PixVec(self._screen.get_size())\n\n self.selected_block_w_pos = WVec(self._pos)\n self.selected_space_w_pos = WVec(self._pos)\n self._block_selector_surf = pg.image.load(os.path.join(GUI_PATH, \"block_selector.png\")).convert()\n self._block_selector_space_only_surf = pg.image.load(os.path.join(GUI_PATH, \"block_selector_space_only.png\")).convert()\n\n # Surfs to reuse\n self._world_max_surf_scaled = pg.Surface((0, 0))\n self._player_surf_scaled = pg.Surface((0, 0))\n self._player_surf_scaled.set_colorkey(C_KEY)\n self._block_selector_surf_scaled = pg.Surface((0, 0))\n self._block_selector_surf_scaled.set_colorkey(C_KEY)\n\n self._clock = pg.time.Clock()\n self._font = pg.font.SysFont(pg.font.get_default_font(), 24)\n\n # ==== GET DATA ====\n\n @property\n def w_size(self):\n return self._pix_size / self._scale\n\n @property\n def w_view(self):\n \"\"\"World referred part of the world visible on screen. \"\"\"\n return WBounds(\n min=self._pos - self.w_size * PLAYER_S_POS,\n max=self._pos + self.w_size * (1-PLAYER_S_POS),\n )\n\n @property\n def _mouse_w_pos(self):\n mouse_pix_shift = PixVec(pg.mouse.get_pos())\n mouse_w_shift = pix_to_w_shift(\n mouse_pix_shift,\n PixVec(),\n self._pix_size,\n dest_pivot=self._pix_size * PLAYER_S_POS,\n scale=self._scale,\n )\n mouse_w_pos = mouse_w_shift + self._pos\n return mouse_w_pos\n\n @property\n def is_zooming(self):\n return not math.isclose(self._zoom_vel, 1)\n\n def _select_block(self, action_w_pos: WVec, world, *, substeps=5, max_rays=3) -> BlockSelection:\n \"\"\"Return selection based on player position and mouse position.\n Selection is one selected block and one selected space.\n \"\"\"\n\n if (self._mouse_w_pos - action_w_pos).norm() > ACTION_MAX_DISTANCE:\n return BlockSelection(None, None)\n\n selection: BlockSelection\n selection = world.get_block_pos_and_space_pos(\n action_w_pos,\n self._mouse_w_pos,\n ACTION_MAX_DISTANCE,\n substeps=substeps,\n max_rays=max_rays,\n )\n\n if selection.block_w_pos is not None:\n return selection\n\n selection = world.get_intersected_block_pos_and_space_pos(\n action_w_pos,\n self._mouse_w_pos,\n ACTION_MAX_DISTANCE,\n substeps=substeps,\n )\n\n return selection\n\n # ==== DRAW ====\n\n def draw_world(self, max_surf, max_view_pos: WVec):\n max_surf_scaled_pix_size = floor(PixVec(max_surf.get_size()) * (self._scale / BLOCK_PIX_SIZE))\n if self._world_max_surf_scaled.get_size() != max_surf_scaled_pix_size:\n self._world_max_surf_scaled = pg.transform.scale(self._world_max_surf_scaled, max_surf_scaled_pix_size)\n self._world_max_surf_scaled = pg.transform.scale(max_surf, max_surf_scaled_pix_size, self._world_max_surf_scaled)\n\n w_shift = max_view_pos - self._pos\n pix_shift = w_to_pix_shift(\n w_shift,\n max_surf_scaled_pix_size,\n self._pix_size,\n dest_pivot=self._pix_size * PLAYER_S_POS,\n scale=self._scale\n )\n\n self._screen.blit(self._world_max_surf_scaled, pix_shift)\n\n def draw_player(self, anim_surf, player_pos: WVec, sky_light):\n anim_surf.draw_and_tick(sky_light)\n\n surf_scaled_pix_size = floor(anim_surf.w_size * self._scale)\n if self._player_surf_scaled.get_size() != surf_scaled_pix_size:\n self._player_surf_scaled = pg.transform.scale(self._player_surf_scaled, surf_scaled_pix_size)\n self._player_surf_scaled = pg.transform.scale(anim_surf.surf, surf_scaled_pix_size, self._player_surf_scaled)\n\n w_shift = player_pos - self._pos\n pix_shift = w_to_pix_shift(\n w_shift,\n surf_scaled_pix_size,\n self._pix_size,\n source_pivot=surf_scaled_pix_size * PixVec(0.5, 0.0),\n dest_pivot=self._pix_size * PLAYER_S_POS,\n scale=self._scale\n )\n\n self._screen.blit(self._player_surf_scaled, pix_shift)\n\n def draw_block_selector(self, action_w_pos: WVec, world):\n selection: BlockSelection\n selection = self._select_block(action_w_pos, world)\n if selection.block_w_pos is None:\n self.selected_block_w_pos = None\n self.selected_space_w_pos = None\n return\n\n self.selected_block_w_pos = selection.block_w_pos\n self.selected_space_w_pos = self.selected_block_w_pos + selection.space_w_pos_shift\n\n surf_pix_size = floor(PixVec(self._scale, self._scale))\n if not selection.space_only:\n surf = self._block_selector_surf\n else:\n surf = self._block_selector_space_only_surf\n\n if self._block_selector_surf_scaled.get_size() != surf_pix_size:\n self._block_selector_surf_scaled = pg.transform.scale(self._block_selector_surf_scaled, surf_pix_size)\n self._block_selector_surf_scaled = pg.transform.scale(surf, surf_pix_size, self._block_selector_surf_scaled)\n self._block_selector_surf_scaled = pg.transform.rotate(self._block_selector_surf_scaled, DIR_TO_ANGLE[selection.space_w_pos_shift])\n\n w_shift = floor(self.selected_block_w_pos) - self._pos\n\n if selection.space_only:\n self.selected_block_w_pos = None\n\n pix_shift = w_to_pix_shift(\n w_shift,\n surf_pix_size,\n self._pix_size,\n source_pivot=PixVec(-1, 1),\n dest_pivot=self._pix_size * PLAYER_S_POS,\n scale=self._scale)\n\n self._screen.blit(self._block_selector_surf_scaled, pix_shift)\n\n def draw_hotbar(self, surf, pix_shift):\n self._screen.blit(surf, self._pix_size * (1-HOTBAR_S_POS) - pix_shift)\n\n def draw_debug_info(self):\n fps_surf = self._font.render(f\"{self._clock.get_fps():.1f}\", True, (255, 255, 255))\n self._screen.blit(fps_surf, (20, 20))\n\n def display_flip_and_clock_tick(self):\n pg.display.flip()\n self._clock.tick(CAM_FPS)\n\n # ==== REQUEST MOVEMENTS ====\n\n def req_zoom_in(self):\n self._req_zoom_vel = self._ZOOM_SPEED\n\n def req_zoom_out(self):\n self._req_zoom_vel = 1 / self._ZOOM_SPEED\n\n def req_zoom_stop(self):\n self._req_zoom_vel = 1.0\n\n def req_move(self, pos):\n self._req_vel = pos - self._pos\n\n # ==== APPLY MOVEMENTS ====\n\n def set_transforms(self, pos: WVec, vel: WVec = WVec()):\n self._pos = WVec(pos)\n self._req_pos = WVec(self._pos)\n\n self._vel = WVec(vel)\n self._req_vel = WVec(self._vel)\n\n def move(self, threshold=0.001):\n self._vel += (self._req_vel - self._vel) * self._VEL_DAMPING_FACTOR\n self._pos += self._vel * self._POS_DAMPING_FACTOR ** (1 / (1 + abs(self._vel)))\n # (1/(1+speed)) is 1 when speed is 0 and is 0 when speed is +inf.\n # This is so that the CAM_POS_DAMPING_FACTOR is only applied at low speeds.\n\n self._zoom_vel *= (self._req_zoom_vel / self._zoom_vel) ** self._ZOOM_VEL_DAMPING_FACTOR\n if CAM_SCALE_BOUNDS[0] > self._scale:\n self._zoom_vel = 1.0\n self._scale = CAM_SCALE_BOUNDS[0] * (1+threshold)\n if self._scale > CAM_SCALE_BOUNDS[1]:\n self._zoom_vel = 1.0\n self._scale = CAM_SCALE_BOUNDS[1] / (1+threshold)\n self._scale *= self._zoom_vel\n","repo_name":"AndreiToroplean/mineflat","sub_path":"graphics/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":8620,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"20785570180","text":"import unittest as ut\n\ndef listNodeBuilder(numbers):\n nodes = [ListNode(x) for x in numbers]\n\n root = nodes[0]\n copy_root = root\n for x in range(1, len(nodes)):\n root.next = nodes[x]\n root = root.next\n\n return copy_root\n\nclass TestSolutionMethods(ut.TestCase):\n def test_toNumber(self):\n numbers = [ 2, 4, 3 ]\n root = listNodeBuilder(numbers)\n s = Solution()\n self.assertEqual(342, s.toNumber(root))\n\n def testAddTwoNumbers(self):\n number1, number2 = listNodeBuilder([2, 4, 3]), listNodeBuilder([5, 6, 4])\n result = listNodeBuilder([7, 0, 8])\n\n s = Solution()\n\n self.assertEqual(s.toNumber(result),\n s.toNumber(s.addTwoNumbers(number1, number2)))\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n total = str(self.toNumber(l1) + self.toNumber(l2))\n\n node_element = [ListNode(x) for x in reversed(total)]\n root = node_element[0]\n copy_root = root\n for x in range(1, len(node_element)):\n root.next = node_element[x]\n root = root.next\n\n return copy_root\n\n def toNumber(self, x):\n number = 0\n numberes = []\n while x.next is not None:\n numberes.append(x.val)\n x = x.next\n numberes.append(x.val) # add last one\n numberes = reversed(numberes)\n for x in numberes:\n number = int(number) * 10 + int(x)\n\n return number\n\nif __name__ == '__main__':\n ut.main()","repo_name":"coding-and-trading/PracticeEveryDay","sub_path":"LeetCode/addTwoNumbers.py","file_name":"addTwoNumbers.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"70180349694","text":"import pprint\n\nfrom pconf import Pconf\nimport os\n\nfrom ioflow.configure.get_configure_path_from_argv import get_configure_path_from_argv\n\n\ndef guess_configure_file_type(file_name):\n file_extension_mapping = {\".json\": \"json\", \".yaml\": \"yaml\", \".yml\": \"yaml\"}\n\n _, file_extension = os.path.splitext(file_name)\n\n if file_extension in file_extension_mapping:\n return file_extension_mapping[file_extension]\n\n raise ValueError(file_extension)\n\n\ndef find_best_file_candidate(candidate_list):\n for candidate in candidate_list:\n if os.path.exists(candidate):\n return candidate\n\n return None\n\n\ndef read_configure(return_empty=False) -> dict:\n # set return_empty to True for not read config from env\n # which can prevent unexpected result\n # e.g. './configure.json' is not for this app, but for other using\n if return_empty:\n return {}\n\n default_configure_candidate = [\n \".\".join([\"./configure\", ext]) for ext in [\"yaml\", \"yml\", \"json\"]\n ]\n builtin_configure_candidate = [\n \".\".join([\"./builtin_configure\", ext]) for ext in [\"yaml\", \"yml\", \"json\"]\n ]\n\n default_configure = find_best_file_candidate(default_configure_candidate)\n builtin_configure = find_best_file_candidate(builtin_configure_candidate)\n\n active_configure_file = get_configure_path_from_argv()\n if not active_configure_file:\n active_configure_file = os.getenv(\"_DEFAULT_CONFIG_FILE\", default_configure)\n\n builtin_configure_file = os.getenv(\"_BUILTIN_CONFIG_FILE\", builtin_configure)\n\n # Note: this is a safeguard, before using any Pconf function, do execute this\n # In case former Pconf usage influence current usage\n # which will lead to a hidden and wired bug\n Pconf.clear()\n\n # disable read configure from environment\n # Pconf.env()\n\n active_configure_file_abs_path = os.path.realpath(active_configure_file)\n\n if not os.path.exists(active_configure_file):\n msg = \"default configure file is not found! CWD: {}; activate_config: {}; builtin_configure: {}\".format(\n os.getcwd(), active_configure_file, builtin_configure_file\n )\n print(msg)\n raise ValueError(msg)\n else:\n print(\n \">>> Using configure read from file: {}\".format(\n active_configure_file_abs_path\n )\n )\n\n file_encoding = guess_configure_file_type(active_configure_file_abs_path)\n Pconf.file(active_configure_file, file_encoding)\n\n # try loading builtin configure file\n if builtin_configure_file and os.path.exists(builtin_configure_file):\n print(\"loading builtin configure from {}\".format(builtin_configure_file))\n file_encoding = guess_configure_file_type(builtin_configure_file)\n Pconf.file(builtin_configure_file, encoding=file_encoding)\n else:\n print(\">>> builtin configure file is not found!\")\n\n # Get all the config values parsed from the sources\n config = Pconf.get()\n\n # NOTE: clean Pconf for later brand new use\n Pconf.clear()\n\n print(\"++\" * 8, \"configure\", \"++\" * 8)\n pprint.pprint(config)\n\n return config\n\n # sys.exit(0)\n\n # return {\n # 'corpus': {\n # 'train': './data/train.conllz',\n # 'test': './data/test.conllz'\n # },\n # 'model': {\n # 'shuffle_pool_size': 10,\n # 'batch_size': 32,\n # 'epochs': 20,\n # 'arch': {}\n # }\n # }\n\n\nread_config = read_configure # alias\n","repo_name":"hot-vs-cool/ioflow","sub_path":"ioflow/configure/read_configure.py","file_name":"read_configure.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"70657497212","text":"import argparse\nimport os\nimport time\n\nimport nengo_dl\nfrom nengo.learning_rules import PES\nfrom nengo.params import Default\nfrom nengo.processes import WhiteSignal\nfrom sklearn.metrics import mean_squared_error\n\nfrom memristor_nengo.extras import *\nfrom memristor_nengo.learning_rules import mPES\n\nsetup()\n\n# Should not be useful for NengoDL>=3.3.0\n# tf.compat.v1.disable_eager_execution()\n# tf.compat.v1.disable_control_flow_v2()\n\nparser = argparse.ArgumentParser()\nparser.add_argument( \"-f\", \"--function\", default=\"x\",\n help=\"The function to learn. Default is x\" )\nparser.add_argument( \"-i\", \"--inputs\", default=[ \"sine\", \"sine\" ], nargs=\"*\", choices=[ \"sine\", \"white\" ],\n help=\"The input signals [learning, testing]. Default is sine\" )\nparser.add_argument( \"-t\", \"--timestep\", default=0.001, type=int )\nparser.add_argument( \"-S\", \"--simulation_time\", default=30, type=int )\nparser.add_argument( \"-N\", \"--neurons\", nargs=\"*\", default=[ 10 ], action=\"store\", type=int,\n help=\"The number of neurons used in the Ensembles [pre, post, error]. Default is 10\" )\nparser.add_argument( \"-D\", \"--dimensions\", default=3, type=int,\n help=\"The number of dimensions of the input signal\" )\nparser.add_argument( \"-n\", \"--noise\", nargs=\"*\", default=0.15, type=float,\n help=\"The noise on the simulated memristors [R_0, R_1, c, R_init] Default is 0.15\" )\nparser.add_argument( \"-g\", \"--gain\", default=1e4, type=float ) # default chosen by parameter search experiments\nparser.add_argument( \"-l\", \"--learning_rule\", default=\"mPES\", choices=[ \"mPES\", \"PES\" ] )\nparser.add_argument( \"-P\", \"--parameters\", default=Default, type=float,\n help=\"The parametrs of simualted memristors. For now only the exponent c\" )\nparser.add_argument( \"-b\", \"--backend\", default=\"nengo_dl\", choices=[ \"nengo_dl\", \"nengo_core\" ] )\nparser.add_argument( \"-o\", \"--optimisations\", default=\"run\", choices=[ \"run\", \"build\", \"memory\" ] )\nparser.add_argument( \"-s\", \"--seed\", default=None, type=int )\nparser.add_argument( \"--plot\", default=0, choices=[ 0, 1, 2, 3 ], type=int,\n help=\"0: No visual output, 1: Show plots, 2: Save plots, 3: Save data\" )\nparser.add_argument( \"--verbosity\", default=2, choices=[ 0, 1, 2 ], type=int,\n help=\"0: No textual output, 1: Only numbers, 2: Full output\" )\nparser.add_argument( \"-pd\", \"--plots_directory\", default=\"../data/\",\n help=\"Directory where plots will be saved. Default is ../data/\" )\nparser.add_argument( \"-d\", \"--device\", default=\"/cpu:0\",\n help=\"/cpu:0 or /gpu:[x]\" )\nparser.add_argument( \"-lt\", \"--learn_time\", default=3 / 4, type=float )\nparser.add_argument( '--probe', default=1, choices=[ 0, 1, 2 ], type=int,\n help=\"0: probing disabled, 1: only probes to calculate statistics, 2: all probes active\" )\n\n# TODO read parameters from conf file https://docs.python.org/3/library/configparser.html\nargs = parser.parse_args()\nseed = args.seed\ntf.random.set_seed( seed )\nnp.random.seed( seed )\nfunction_string = \"lambda x: \" + args.function\nfunction_to_learn = eval( function_string )\nif len( args.inputs ) not in (1, 2):\n parser.error( 'Either give no values for action, or two, not {}.'.format( len( args.inputs ) ) )\nif len( args.inputs ) == 1:\n if args.inputs[ 0 ] == \"sine\":\n input_function_train = input_function_test = Sines( period=4 )\n if args.inputs[ 0 ] == \"white\":\n input_function_train = input_function_test = WhiteSignal( period=60, high=5, seed=seed )\nif len( args.inputs ) == 2:\n if args.inputs[ 0 ] == \"sine\":\n input_function_train = Sines( period=4 )\n if args.inputs[ 0 ] == \"white\":\n input_function_train = WhiteSignal( period=60, high=5, seed=seed )\n if args.inputs[ 1 ] == \"sine\":\n input_function_test = Sines( period=4 )\n if args.inputs[ 1 ] == \"white\":\n input_function_test = WhiteSignal( period=60, high=5, seed=seed )\ntimestep = args.timestep\nsim_time = args.simulation_time\nif len( args.neurons ) not in range( 1, 3 ):\n parser.error( 'Either give no values for action, or one, or three, not {}.'.format( len( args.neurons ) ) )\nif len( args.neurons ) == 1:\n pre_n_neurons = post_n_neurons = error_n_neurons = args.neurons[ 0 ]\nif len( args.neurons ) == 2:\n pre_n_neurons = error_n_neurons = args.neurons[ 0 ]\n post_n_neurons = args.neurons[ 1 ]\nif len( args.neurons ) == 3:\n pre_n_neurons = args.neurons[ 0 ]\n post_n_neurons = args.neurons[ 1 ]\n error_n_neurons = args.neurons[ 2 ]\ndimensions = args.dimensions\nnoise_percent = args.noise\ngain = args.gain\nexponent = args.parameters\nlearning_rule = args.learning_rule\nbackend = args.backend\noptimisations = args.optimisations\nprogress_bar = False\nprintlv1 = printlv2 = lambda *a, **k: None\nif args.verbosity >= 1:\n printlv1 = print\nif args.verbosity >= 2:\n printlv2 = print\n progress_bar = True\nplots_directory = args.plots_directory\ndevice = args.device\nprobe = args.probe\ngenerate_plots = show_plots = save_plots = save_data = False\nif args.plot >= 1:\n generate_plots = True\n show_plots = True\n probe = 2\nif args.plot >= 2:\n save_plots = True\nif args.plot >= 3:\n save_data = True\n\n# TODO give better names to folders or make hierarchy\nif save_plots or save_data:\n dir_name, dir_images, dir_data = make_timestamped_dir( root=plots_directory + learning_rule + \"/\" )\n\nlearn_time = int( sim_time * args.learn_time )\nn_neurons = np.amax( [ pre_n_neurons, post_n_neurons ] )\nif optimisations == \"build\":\n optimize = False\n sample_every = timestep\n simulation_discretisation = 1\nelif optimisations == \"run\":\n optimize = True\n sample_every = timestep\n simulation_discretisation = 1\nelif optimisations == \"memory\":\n optimize = False\n sample_every = timestep * 100\n simulation_discretisation = n_neurons\nprintlv2( f\"Using {optimisations} optimisation\" )\n\nmodel = nengo.Network( seed=seed )\nwith model:\n nengo_dl.configure_settings( inference_only=True )\n # Create an input node\n input_node = nengo.Node(\n output=SwitchInputs( input_function_train,\n input_function_test,\n switch_time=learn_time ),\n size_out=dimensions\n )\n \n # Shut off learning by inhibiting the error population\n stop_learning = nengo.Node( output=lambda t: t >= learn_time )\n \n # Create the ensemble to represent the input, the learned output, and the error\n pre = nengo.Ensemble( pre_n_neurons, dimensions=dimensions, seed=seed )\n post = nengo.Ensemble( post_n_neurons, dimensions=dimensions, seed=seed )\n error = nengo.Ensemble( error_n_neurons, dimensions=dimensions, radius=2, seed=seed )\n \n # Connect pre and post with a communication channel\n # the matrix given to transform is the initial weights found in model.sig[conn][\"weights\"]\n # the initial transform has not influence on learning because it is overwritten by mPES\n # the only influence is on the very first timesteps, before the error becomes large enough\n conn = nengo.Connection(\n pre.neurons,\n post.neurons,\n transform=np.zeros( (post.n_neurons, pre.n_neurons) )\n )\n \n # Apply the learning rule to conn\n if learning_rule == \"mPES\":\n conn.learning_rule_type = mPES(\n noisy=noise_percent,\n gain=gain,\n seed=seed,\n exponent=exponent )\n if learning_rule == \"PES\":\n conn.learning_rule_type = PES()\n printlv2( \"Simulating with\", conn.learning_rule_type )\n \n # Provide an error signal to the learning rule\n nengo.Connection( error, conn.learning_rule )\n \n # Compute the error signal (error = actual - target)\n nengo.Connection( post, error )\n \n # Subtract the target (this would normally come from some external system)\n nengo.Connection( pre, error, function=function_to_learn, transform=-1 )\n \n # Connect the input node to ensemble pre\n nengo.Connection( input_node, pre )\n \n nengo.Connection(\n stop_learning,\n error.neurons,\n transform=-20 * np.ones( (error.n_neurons, 1) ) )\n \n # essential ones are used to calculate the statistics\n if probe > 0:\n pre_probe = nengo.Probe( pre, synapse=0.01, sample_every=sample_every )\n post_probe = nengo.Probe( post, synapse=0.01, sample_every=sample_every )\n if probe > 1:\n input_node_probe = nengo.Probe( input_node, sample_every=sample_every )\n error_probe = nengo.Probe( error, synapse=0.01, sample_every=sample_every )\n learn_probe = nengo.Probe( stop_learning, synapse=None, sample_every=sample_every )\n weight_probe = nengo.Probe( conn, \"weights\", synapse=None, sample_every=sample_every )\n post_spikes_probe = nengo.Probe( post.neurons, sample_every=sample_every )\n if isinstance( conn.learning_rule_type, mPES ):\n pos_memr_probe = nengo.Probe( conn.learning_rule, \"pos_memristors\", synapse=None,\n sample_every=sample_every )\n neg_memr_probe = nengo.Probe( conn.learning_rule, \"neg_memristors\", synapse=None,\n sample_every=sample_every )\n\n# Create the Simulator and run it\nprintlv2( f\"Backend is {backend}, running on \", end=\"\" )\nif backend == \"nengo_core\":\n printlv2( \"CPU\" )\n cm = nengo.Simulator( model, seed=seed, dt=timestep, optimize=optimize, progress_bar=progress_bar )\nif backend == \"nengo_dl\":\n printlv2( device )\n cm = nengo_dl.Simulator( model, seed=seed, dt=timestep, progress_bar=progress_bar, device=device )\nstart_time = time.time()\nwith cm as sim:\n for i in range( simulation_discretisation ):\n printlv2( f\"\\nRunning discretised step {i + 1} of {simulation_discretisation}\" )\n sim.run( sim_time / simulation_discretisation )\nprintlv2( f\"\\nTotal time for simulation: {time.strftime( '%H:%M:%S', time.gmtime( time.time() - start_time ) )} s\" )\n\nif probe > 0:\n # essential statistics\n y_true = sim.data[ pre_probe ][ int( (learn_time / timestep) / (sample_every / timestep) ):, ... ]\n y_pred = sim.data[ post_probe ][ int( (learn_time / timestep) / (sample_every / timestep) ):, ... ]\n # MSE after learning\n printlv2( \"MSE after learning [f(pre) vs. post]:\" )\n mse = mean_squared_error( function_to_learn( y_true ), y_pred, multioutput='raw_values' )\n printlv1( mse.tolist() )\n # Correlation coefficients after learning\n correlation_coefficients = correlations( function_to_learn( y_true ), y_pred )\n printlv2( \"Pearson correlation after learning [f(pre) vs. post]:\" )\n printlv1( correlation_coefficients[ 0 ] )\n printlv2( \"Spearman correlation after learning [f(pre) vs. post]:\" )\n printlv1( correlation_coefficients[ 1 ] )\n printlv2( \"Kendall correlation after learning [f(pre) vs. post]:\" )\n printlv1( correlation_coefficients[ 2 ] )\n printlv2( \"MSE-to-rho after learning [f(pre) vs. post]:\" )\n printlv1( mse_to_rho_ratio( mse, correlation_coefficients[ 1 ] ) )\n\nif probe > 1:\n # Average\n printlv2( \"Weights average after learning:\" )\n printlv1( np.average( sim.data[ weight_probe ][ -1, ... ] ) )\n \n # Sparsity\n printlv2( \"Weights sparsity at t=0 and after learning:\" )\n printlv1( gini( sim.data[ weight_probe ][ 0 ] ), end=\" -> \" )\n printlv1( gini( sim.data[ weight_probe ][ -1 ] ) )\n\nplots = { }\nif generate_plots and probe > 1:\n plotter = Plotter( sim.trange( sample_every=sample_every ), post_n_neurons, pre_n_neurons, dimensions,\n learn_time,\n sample_every,\n plot_size=(13, 7),\n dpi=300,\n pre_alpha=0.3\n )\n plots[ \"results_smooth\" ] = plotter.plot_results( sim.data[ input_node_probe ], sim.data[ pre_probe ],\n sim.data[ post_probe ],\n error=\n sim.data[ post_probe ] -\n function_to_learn( sim.data[ pre_probe ] ),\n smooth=True )\n plots[ \"results\" ] = plotter.plot_results( sim.data[ input_node_probe ], sim.data[ pre_probe ],\n sim.data[ post_probe ],\n error=\n sim.data[ post_probe ] -\n function_to_learn( sim.data[ pre_probe ] ),\n smooth=False )\n plots[ \"post_spikes\" ] = plotter.plot_ensemble_spikes( \"Post\", sim.data[ post_spikes_probe ],\n sim.data[ post_probe ] )\n plots[ \"weights\" ] = plotter.plot_weight_matrices_over_time( sim.data[ weight_probe ], sample_every=sample_every )\n \n plots[ \"testing_smooth\" ] = plotter.plot_testing( function_to_learn( sim.data[ pre_probe ] ),\n sim.data[ post_probe ],\n smooth=True )\n plots[ \"testing\" ] = plotter.plot_testing( function_to_learn( sim.data[ pre_probe ] ), sim.data[ post_probe ],\n smooth=False )\n if n_neurons <= 10 and learning_rule == \"mPES\":\n plots[ \"weights_mpes\" ] = plotter.plot_weights_over_time( sim.data[ pos_memr_probe ],\n sim.data[ neg_memr_probe ] )\n plots[ \"memristors\" ] = plotter.plot_values_over_time( sim.data[ pos_memr_probe ], sim.data[ neg_memr_probe ],\n value=\"resistance\" )\n\nif save_plots:\n assert generate_plots and probe > 1\n \n for fig in plots.values():\n fig.savefig( dir_images + str( i ) + \".pdf\" )\n # fig.savefig( dir_images + str( i ) + \".png\" )\n \n print( f\"Saved plots in {dir_images}\" )\n\nif save_data:\n save_weights( dir_data, sim.data[ weight_probe ] )\n print( f\"Saved NumPy weights in {dir_data}\" )\n \n save_results_to_csv( dir_data, sim.data[ input_node_probe ], sim.data[ pre_probe ], sim.data[ post_probe ],\n sim.data[ post_probe ] - function_to_learn( sim.data[ pre_probe ] ) )\n save_memristors_to_csv( dir_data, sim.data[ pos_memr_probe ], sim.data[ neg_memr_probe ] )\n print( f\"Saved data in {dir_data}\" )\n\n# TODO save output txt with metrics\n\nif show_plots:\n assert generate_plots and probe > 1\n \n for fig in plots.values():\n fig.show()\n","repo_name":"thomastiotto/Learning-to-approximate-functions-using-niobium-doped-strontium-titanate-memristors","sub_path":"experiments/mPES.py","file_name":"mPES.py","file_ext":"py","file_size_in_byte":14882,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"78"}
+{"seq_id":"11985141024","text":"#!/usr/bin/env python\n\nimport math\nimport mpmath\nimport numpy as np\nfrom context import floripy\nfrom floripy.mathutils.xform import *\n\n\n#mpmath.mp.dps = 128\npi = math.pi\n\n\ndef get_force_torque_prolate(a, c, d, u_inf, omega_inf, E_inf):\n if c == 'zero':\n print('Reduction of prolate to a line not possible.')\n return float('nan'), float('nan')\n e = math.sqrt((a+c)*(a-c))/a\n e2 = e*e\n e3 = e2*e\n e5 = e3*e2\n one_m_e2 = 1.0 - e2\n one_p_e2 = 1.0 + e2\n two_m_e2 = 2.0 - e2\n two_e2_m_three = 2.0*e2 - 3.0\n three_m_e2 = 3.0 - e2\n three_e2_m_one = 3.0*e2 - 1.0\n three_m_five_e2 = 3.0 - 5.0*e2\n\n L = math.log1p(e) - math.log1p(-e)\n XA = (8.0/3)*e3/(-2*e + one_p_e2*L)\n YA = (16.0/3)*e3/(2*e + three_e2_m_one*L)\n XC = (4.0/3)*e3*one_m_e2/(2*e - one_m_e2*L)\n YC = (4.0/3)*e3*two_m_e2/(-2*e + one_p_e2*L)\n YH = (4.0/3)*e5/(-2*e + one_p_e2*L)\n\n epsilon = np.zeros((3,3,3))\n epsilon[1,2,0] = 1.0\n epsilon[2,1,0] = -1.0\n epsilon[0,2,1] = -1.0\n epsilon[2,0,1] = 1.0\n epsilon[0,1,2] = 1.0\n epsilon[1,0,2] = -1.0\n\n dd = np.outer(d,d)\n I = np.identity(3)\n A = 6*pi*a*(XA*dd + YA*(I-dd))\n force = np.dot(A, u_inf)\n C = 8*pi*(a**3)*(XC*dd + YC*(I-dd))\n torque = np.dot(C, omega_inf) - 8*pi*a**3*YH*np.einsum('ijl,l,k,jk',\n epsilon, d, d, E_inf)\n return force, torque\n\n\ndef get_force_torque_oblate(a, c, d, u_inf, omega_inf, E_inf):\n if c == 'zero':\n XA = 8/(3*pi)\n YA = 16/(9*pi)\n XC = 4/(3*pi)\n YC = 4/(3*pi)\n YH = -4/(3*pi)\n else:\n e = math.sqrt((a+c)*(a-c))/a\n e2 = e*e\n e3 = e2*e\n e5 = e2*e3\n one_m_e2 = 1.0 - e2\n two_m_e2 = 2.0 - e2\n sqrt_one_m_e2 = math.sqrt(one_m_e2)\n C = math.atan(e/sqrt_one_m_e2)\n XA = (4.0/3)*e3/((2*e2-1)*C + e*sqrt_one_m_e2)\n YA = (8.0/3)*e3/((2*e2+1)*C - e*sqrt_one_m_e2)\n XC = (2.0/3)*e3/(C - e*sqrt_one_m_e2)\n YC = (2.0/3)*e3*two_m_e2/(e*sqrt_one_m_e2 - (1-2*e2)*C)\n YH = (-2.0/3)*e5/(e*sqrt_one_m_e2 - (1-2*e2)*C)\n\n epsilon = np.zeros((3,3,3))\n epsilon[1,2,0] = 1.0\n epsilon[2,1,0] = -1.0\n epsilon[0,2,1] = -1.0\n epsilon[2,0,1] = 1.0\n epsilon[0,1,2] = 1.0\n epsilon[1,0,2] = -1.0\n\n dd = np.outer(d,d)\n I = np.identity(3)\n K = 6*pi*a*(XA*dd + YA*(I-dd))\n force = np.dot(K, u_inf)\n Omega_0 = 8*pi*(a**3)*(XC*dd + YC*(I-dd))\n torque = np.dot(Omega_0, omega_inf) - 8*pi*a**3*YH*np.einsum('ijl,l,k,jk',\n epsilon, d, d, E_inf)\n return force, torque\n\n\ndef get_force_torque_ellipsoid(a, b, c, dcm, u_inf, omega_inf, E_inf):\n if c == 'zero' and b == 'zero':\n print('RF and RJ ill-defined for b = c = 0.')\n return float('nan'), float('nan')\n asq = a**2\n bsq = b**2\n if c == 'zero':\n csq = 0.0\n else:\n csq = c**2\n\n asq_alpha_1 = (2*asq/3.0)*mpmath.elliprj(asq, bsq, csq, asq)\n bsq_alpha_2 = (2*bsq/3.0)*mpmath.elliprj(asq, bsq, csq, bsq)\n if c == 'zero':\n csq_alpha_3 = 0.0\n else:\n csq_alpha_3 = (2*csq/3.0)*mpmath.elliprj(asq, bsq, csq, csq)\n \n chi = 2*mpmath.elliprf(asq, bsq, csq)\n\n A = np.zeros((3,3))\n C = np.zeros((3,3))\n H_tilde = np.zeros((3,3,3))\n\n A[0,0] = 16*pi/(chi + asq_alpha_1)\n A[1,1] = 16*pi/(chi + bsq_alpha_2)\n A[2,2] = 16*pi/(chi + csq_alpha_3)\n A = shift_tensor2_dcm(A, dcm, forward=False)\n\n C[0,0] = (16*pi/3)*(bsq+csq)/(bsq_alpha_2 + csq_alpha_3)\n C[1,1] = (16*pi/3)*(csq+asq)/(asq_alpha_1 + csq_alpha_3)\n C[2,2] = (16*pi/3)*(asq+bsq)/(asq_alpha_1 + bsq_alpha_2)\n C = shift_tensor2_dcm(C, dcm, forward=False)\n\n H_tilde[0,1,2] = (16*pi/3)*bsq/(bsq_alpha_2 + csq_alpha_3)\n H_tilde[0,2,1] = -(16*pi/3)*csq/(bsq_alpha_2 + csq_alpha_3)\n H_tilde[1,2,0] = (16*pi/3)*csq/(asq_alpha_1 + csq_alpha_3)\n H_tilde[1,0,2] = -(16*pi/3)*asq/(asq_alpha_1 + csq_alpha_3)\n H_tilde[2,0,1] = (16*pi/3)*asq/(asq_alpha_1 + bsq_alpha_2)\n H_tilde[2,1,0] = -(16*pi/3)*bsq/(asq_alpha_1 + bsq_alpha_2)\n H_tilde = shift_tensor3_dcm(H_tilde, dcm, forward=False)\n\n force = np.dot(A, u_inf)\n torque = np.dot(C, omega_inf) + np.einsum('ijk,jk', H_tilde, E_inf)\n return force, torque\n\n\naxis, angle = get_rand_axis_angle()\ndcm = axis_angle_to_dcm(axis, angle)\n#dcm = np.identity(3)\n\n#Streaming Velocity\nu_inf = np.random.random((3,))\n#u_inf = np.array([1, 0, 0]) #np.random.random((3,))\n#omega_inf = np.zeros((3,))\n#E_inf = np.zeros((3,3))\nomega_inf = np.random.random((3,))\nE_inf = np.random.random((3,3))\nE_inf = (E_inf + E_inf.T)/2\n\nprint('Velocity field')\nprint('--------------')\nprint('Streaming velocity: ', u_inf)\nprint()\nprint('Angular velocity: ', omega_inf)\nprint()\nprint('Strain rate tensor: ', E_inf[0,:])\nprint(' ', E_inf[1,:])\nprint(' ', E_inf[2,:])\nprint()\n\nprint('Direction cosine matrix: ', dcm[0,:])\nprint(' ', dcm[1,:])\nprint(' ', dcm[2,:])\nprint()\n\n#Ellipsoid/sphere\nprint('Ellipsoid --> Sphere')\nprint('=============================\\n')\na = 0.5\nb = 0.5\nc = 0.5\n\nforce, torque = get_force_torque_ellipsoid(a, b, c, dcm, u_inf,\n omega_inf, E_inf)\nprint('Ellipsoid ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\nforce = 6*pi*a*u_inf\ntorque = 8*pi*a**3*omega_inf\nprint('Sphere ({0})'.format(a))\nprint('-------------------------')\nprint('Force (6*pi*r*u_inf): ', force)\nprint('Torque (8*pi*r^3*omega_inf): ', torque)\nprint()\n\n#Ellipsoid/oblate\nprint('Ellipsoid --> Oblate')\nprint('=============================\\n')\na = 0.5\nb = 0.5\nc = 0.15\n\nforce, torque = get_force_torque_ellipsoid(a, b, c, dcm, u_inf,\n omega_inf, E_inf)\nprint('Ellipsoid ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\nd = rotate_vector_dcm(np.array([0,0,1]), dcm)\nforce, torque = get_force_torque_oblate(a, c, d, u_inf,\n omega_inf, E_inf)\nprint('Oblate ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\n#Ellipsoid/prolate\nprint('Ellipsoid --> Prolate')\nprint('=============================\\n')\na = 1.0\nb = 0.5\nc = 0.5\n\nforce, torque = get_force_torque_ellipsoid(a, b, c, dcm, u_inf,\n omega_inf, E_inf)\nprint('Ellipsoid ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\nd = rotate_vector_dcm(np.array([1,0,0]), dcm)\nforce, torque = get_force_torque_prolate(a, c, d, u_inf,\n omega_inf, E_inf)\nprint('Prolate ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\n\n#Elliptic disc/circular disc\nprint('Elliptic disc --> Circular disc')\nprint('=============================\\n')\na = 0.5\nb = 0.5\nc = 'zero'\n\nforce, torque = get_force_torque_ellipsoid(a, b, c, dcm, u_inf,\n omega_inf, E_inf)\nprint('Elliptic disc ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\nd = rotate_vector_dcm(np.array([0,0,1]), dcm)\nforce, torque = get_force_torque_oblate(a, c, d, u_inf,\n omega_inf, E_inf)\nprint('Circular disc ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\n\n#Ellipsoid/needle\nprint('Ellipsoid --> Needle')\nprint('=============================\\n')\na = 1.0\nb = 'zero'\nc = 'zero'\n\nforce, torque = get_force_torque_ellipsoid(a, b, c, dcm, u_inf,\n omega_inf, E_inf)\nprint('Ellipsoid ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\nd = rotate_vector_dcm(np.array([1,0,0]), dcm)\nforce, torque = get_force_torque_prolate(a, c, d, u_inf,\n omega_inf, E_inf)\nprint('Needle ({0}, {1}, {2})'.format(a,b,c))\nprint('-------------------------')\nprint('Force: ', force)\nprint('Torque: ', torque)\nprint()\n\n\n","repo_name":"saridut/FloriPy","sub_path":"hydrodynamics/ellipsoid_rf.py","file_name":"ellipsoid_rf.py","file_ext":"py","file_size_in_byte":8165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"74755801212","text":"from pyrogram.types import (\r\n InlineKeyboardButton,\r\n InlineKeyboardMarkup,\r\n InlineQueryResultPhoto,\r\n)\r\nfrom youtubesearchpython.__future__ import VideosSearch\r\n\r\nfrom AnonXMusic import app\r\nfrom AnonXMusic.utils.inlinequery import answer\r\nfrom config import BANNED_USERS\r\n\r\n\r\n@app.on_inline_query(~BANNED_USERS)\r\nasync def inline_query_handler(client, query):\r\n text = query.query.strip().lower()\r\n answers = []\r\n if text.strip() == \"\":\r\n try:\r\n await client.answer_inline_query(query.id, results=answer, cache_time=10)\r\n except:\r\n return\r\n else:\r\n a = VideosSearch(text, limit=20)\r\n result = (await a.next()).get(\"result\")\r\n for x in range(15):\r\n title = (result[x][\"title\"]).title()\r\n duration = result[x][\"duration\"]\r\n views = result[x][\"viewCount\"][\"short\"]\r\n thumbnail = result[x][\"thumbnails\"][0][\"url\"].split(\"?\")[0]\r\n channellink = result[x][\"channel\"][\"link\"]\r\n channel = result[x][\"channel\"][\"name\"]\r\n link = result[x][\"link\"]\r\n published = result[x][\"publishedTime\"]\r\n description = f\"{views} | {duration} ᴍɪɴᴜᴛᴇs | {channel} | {published}\"\r\n buttons = InlineKeyboardMarkup(\r\n [\r\n [\r\n InlineKeyboardButton(\r\n text=\"ʏᴏᴜᴛᴜʙᴇ 🎄\",\r\n url=link,\r\n )\r\n ],\r\n ]\r\n )\r\n searched_text = f\"\"\"\r\n❄ ᴛɪᴛʟᴇ : {title} \r\n\r\n⏳ ᴅᴜʀᴀᴛɪᴏɴ : {duration} ᴍɪɴᴜᴛᴇs\r\n👀 ᴠɪᴇᴡs : {views}\r\n🎥 ᴄʜᴀɴɴᴇʟ : {channel} \r\n⏰ ᴘᴜʙʟɪsʜᴇᴅ ᴏɴ : {published}\r\n\r\n\r\n➻ ɪɴʟɪɴᴇ sᴇᴀʀᴄʜ ᴍᴏᴅᴇ ʙʏ {app.name} -ftp <0 or 1>\")\r\n print(\" |filename : error file to look for error\")\r\n print(\" |ftp : 1 to upload the image to ftp, default is 0 \")\r\n print(\" |h : print usage\")\r\n exit(1)\r\n\r\ndef main():\r\n global logger\r\n error_filename =''\r\n recipename_filename = ''\r\n status_code = -1\r\n marker = 0\r\n #print(len(sys.argv))\r\n if len(sys.argv) < 5 :\r\n usage()\r\n\r\n opts, args = getopt.getopt(sys.argv[1:], \"r:h:f:ftp:\")\r\n for o, a in opts:\r\n if o == '-f':\r\n error_filename = a\r\n marker = 1\r\n elif o == '-ftp':\r\n marker = 2\r\n elif o == '-r':\r\n recipename_filename = a\r\n\r\n access = 0o755\r\n #log folder\r\n log_folder = os.path.join(os.getcwd(), \"logs\")\r\n if not os.path.exists(log_folder):\r\n os.mkdir(log_folder, access)\r\n\r\n log_folder = os.path.join(log_folder, append_timestamp(log_file))\r\n log_folder = log_folder + \".txt\"\r\n\r\n # Create and configure logger\r\n logging.basicConfig(filename= log_folder,\r\n format='%(asctime)s %(message)s',\r\n filemode='w')\r\n\r\n # Creating an object\r\n logger = logging.getLogger()\r\n\r\n # Setting the threshold of logger to DEBUG\r\n logger.setLevel(logging.DEBUG)\r\n if os.path.exists(error_filename):\r\n logger.info(\"mains: error filename: \" + error_filename + \"Recipe filename: \" + recipename_filename)\r\n status_code , command_file = handle_knowerror(error_filename,recipename_filename)\r\n logger.info(\"main: handle_knowerror returncode: \" + str(status_code) + \" Filename: \" + \\\r\n \"None\" if command_file==\"\" else command_file)\r\n print(command_file)\r\n exit(status_code)\r\n else:\r\n logger.info(\"handle_knowerror: Error file not found \" + error_filename)\r\n\r\n exit(1)\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"BharathSundaram/StreamaBT","sub_path":"pythonsrc/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"20491316522","text":"import APIHandler\n\n\"\"\"\nFields:\n:id: course ID (eg: ENGL101)\n:name: course name\n:description: course description\n:credits: course credits\n:gen_ed: list of gen eds the course fulfills\n:dept_id: the first part of the course id (eg: ENGL)\n:prereqs: course prerequisites\n\"\"\"\nclass Course:\n def __init__(self, course_id):\n API = APIHandler.API()\n course_info = API.get_course_by_id(course_id)\n\n self.id = course_info[0]['course_id']\n self.name = course_info[0][\"name\"]\n self.description = course_info[0][\"description\"]\n self.credits = int(course_info[0]['credits'])\n self.gen_ed = course_info[0][\"gen_ed\"]\n self.dept_id = course_info[0][\"dept_id\"]\n self.prereqs = []\n if course_info[0]['relationships']['prereqs'] != None:\n raw_prereqs = course_info[0]['relationships']['prereqs']\n id_indices = []\n for i in range(len(raw_prereqs)):\n if raw_prereqs[i].isdigit() and raw_prereqs[i - 1].isalpha():\n id_indices.append(i)\n for id_index in id_indices:\n self.prereqs.append(raw_prereqs[id_index - 4 : id_index + 3])\n\n def __eq__(self, __o: object) -> bool:\n if type(__o) is Course:\n return self.id == __o.id\n elif type(__o) is str:\n return self.id == __o\n else:\n return False\n\n def __repr__(self) -> str:\n return self.id\n","repo_name":"abarkker/OpenSourcery2022","sub_path":"course.py","file_name":"course.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"78"}
+{"seq_id":"31002348820","text":"import os\r\nfrom tkinter import *\r\nimport time\r\nfrom datetime import date\r\nimport math\r\nimport webbrowser\r\nimport random\r\n##\r\n\r\ndef movieslist():\r\n f=open(\"Project1.txt\")\r\n m=f.read()\r\n print(m)\r\n\r\n\r\n\r\n##\r\ndef seereview():\r\n f=open(\"addreview.txt\",'r')\r\n print(f.read())\r\n n=int(input(\"Enter 999 to go back\"))\r\n while n!=999:\r\n n=int(input(\"Plz enter a specific value(999 for back)\"))\r\n\r\n mrt()\r\n \r\n \r\n \r\n##\r\ndef addreview():\r\n f=open(\"addreview.txt\",'a')\r\n print(\"Please type your review about the movie:\")\r\n s=\"\"\r\n name=input(\"Enter your name:\")\r\n s+=(name+\":\\n\")\r\n m=input(\"Line 1: (999 for previous page)\\n\")\r\n \r\n i=2\r\n while m!='999':\r\n print(\"Line \",i,\": (999 for previous page)\")\r\n m=input()\r\n s+=(m+\"\\n\")\r\n i+=1\r\n s=s[0:len(s)-4]\r\n s+=\"\\n\"\r\n f.write(s)\r\n print(\"Review Added !!\\n\")\r\n print(\"Redirecting to HomePage ......\")\r\n time.sleep(2)\r\n home()\r\n\r\n##\r\n\r\n \r\n \r\n \r\n \r\n \r\n## \r\ns=[\"www.youtube.com/watch?v=vOCM9wztBYQ\",\"www.youtube.com/watch?v=zAGVQLHvwOY\",\"www.youtube.com/watch?v=KyhrrdpA2YA\",\"www.youtube.com/watch?v=GWxF84mWfxs\",\"www.youtube.com/watch?v=tsxemFX0a7k\"] \r\ndef trailer():\r\n a=int(input(\"1.Asuran (90%)\\n2.Joker (88%)\\n3.Sye Raa (85%)\\n4.Pailwaan (83%)\\n5.Chichchore (88%)\\n6.Back to Previous Page\\n\"))\r\n if a==1 or a==2 or a==3 or a==4 or a==5:\r\n webbrowser.open(s[a-1],new=0)#Trailer\r\n mrt()\r\n elif a==6:\r\n mrt()\r\n \r\n \r\n \r\n## \r\ndef mrt():\r\n print(\"MOVIES TRAILERS AND REVIEWS\\n\\n1.Movie Trailer\\n2.Movie Review\\n3.Back to HomePage\")\r\n n=int(input())\r\n if n==1:\r\n trailer()\r\n elif n==2:\r\n n2=int(input(\"1.User Reviews\\n2.Add Review\\n3.Back\"))\r\n if n2==1:\r\n seereview()\r\n elif n2==2:\r\n addreview()\r\n elif n2==3:\r\n mrt()\r\n elif n==3:\r\n home()\r\n \r\n \r\n \r\n \r\n## \r\ndef confirm():\r\n \r\n n=int(input(\"Enter Movie to book : \"))\r\n \r\n if n==1:\r\n print(\"Asuran (U/A)\\nAction, Drama\\nCast : Dhanush,Manju Warrier,Ken Karunas,Teejay,Prakash Raj,Pasupathy,Naren,Pawan\\nDirector : Vetri Maaran\\nMusic Composer : G V Prakash Kumar\\nLanguage : Tamil, Malayalam\\nDuration : 2 hrs 21 mins\")\r\n print(\"\\nTrailer - \")\r\n s=int(input(\"Enter 0 to confirm and 999 to previous page:\\n\"))\r\n if s==0:\r\n book(n)\r\n elif s==999:\r\n movieslist()\r\n confirm()\r\n elif n==2:\r\n print(\"Joker (A)\\nCrime,Fantasy,Thriller\\nCast : Joaquin, Robert, Zazie, Brian Tyree\\nDirector : Todd Philips\\nMusic Composer : Hildur\\nLanguage : English\\nDuration : 2 hrs 16 mins\")\r\n s=int(input(\"Enter 0 to confirm and 999 to previous page:\\n\"))\r\n if s==0:\r\n book(n)\r\n elif s==999:\r\n movieslist()\r\n confirm()\r\n elif n==3:\r\n print(\"SyeRaa Narasimha Reddy (U)\\nAction,Drama,Historical\\nCast : Chiranjeevi,Kicha Sudeep, Vijay Sethupathi,Jagapati Babu,Nayantara\\nDirector : Surender Reddy\\nMusic Composer : Amit Trivedi\\nLanguage : Telugu,Kannada,Tamil,Hindi\\nDuration : 3 hrs 5 mins\")\r\n s=int(input(\"Enter 0 to confirm and 999 to previous page:\\n\"))\r\n if s==0:\r\n book(n)\r\n elif s==999:\r\n movieslist()\r\n confirm()\r\n elif n==4:\r\n print(\"Pailwaan (U/A)\\nAction, Drama\\nCast : Kicha Sudeep,Sunil Shetty,Sushant Singh,Akansha Singh\\nDirector : S Krishna \\nMusic Composer : Arjun Janya\\nLanguage : Kannada, Telugu, Tamil, Malayalam\\nDuration : 2 hrs 46 mins\")\r\n s=int(input(\"Enter 0 to confirm and 999 to previous page:\\n\"))\r\n if s==0:\r\n book(n)\r\n elif s==999:\r\n movieslist()\r\n confirm()\r\n elif n==5:\r\n print(\"Chichhorre (U)\\nComedy,Romantic,Drama\\nCast : Sushant Singh, Shradda Kapoor, Varun Sharma, Tushar Pandey\\nDirector : Nithesh Tiwari\\nMusic Composer : Pritam\\nLanguage : Hindi\\nDuration : 2 hrs 29 mins\")\r\n s=int(input(\"Enter 0 to confirm and 999 to previous page:\"))\r\n if s==0:\r\n book(n)\r\n elif s==999:\r\n movieslist()\r\n confirm()\r\n elif n==999:\r\n home()\r\n \r\n \r\n##\r\nmlist=[\"Asuran\",\"Joker\",\"Sye Raa Narasimha Reddy\",\"Pailwaan\",\"Chichchore\"]\r\ntkprice=[250,450,300,300,400]\r\nslotlist=[\"7:00 am to 10:15 am\",\"11:00 am to 02:15 pm\",\"3:00 pm to 06:15 pm\",\"7:00 pm to 10:15 pm\"]\r\ndef book(temp):\r\n tseats=\"\"\r\n n=temp\r\n fprice=0\r\n pc=tkprice[n-1]+150\r\n gc=tkprice[n-1]\r\n sc=tkprice[n-1]-100\r\n slot=int(input(\"enter your slot :\\n1.07:00 am to 10:15 am\\n2.11:00 am to 02:15 pm\\n3.03:00 pm to 06:15 pm\\n4.07:00 pm to 10:15 pm\\n\"))\r\n nseats=int(input(\"Enter required number of seats :\"))\r\n print(\"Platinum Class -> Rs.\",pc,\"\\nGold Class -> Rs.\",gc,\"\\nSilver Class -> Rs.\",sc,\"\\nEnter P/G/S :\")\r\n classtype=input()\r\n if classtype in 'pP':\r\n fprice=pc\r\n elif classtype in 'gG':\r\n fprice=gc\r\n elif classtype in 'sS':\r\n fprice=sc\r\n seatselection(nseats)\r\n print(\"Please enter the seats you wish to book :\")\r\n seattemp=nseats\r\n while seattemp:\r\n sn=input(\"Enter Seat Number :\")\r\n tseats+=(sn+\" \")\r\n seattemp-=1\r\n \r\n fprice=fprice*nseats\r\n fpricetax=fprice*0.18\r\n total=fprice+fpricetax\r\n invoice(nseats,total,n,tseats,slot)\r\n\r\n\r\n##\r\n\r\ndef seatselection(nseats):\r\n n=nseats\r\n \r\n s=[' * ',' $ ']\r\n seatrow=['A','','B','','C','','D','','E','','F','','G','','H','','I','','J','']\r\n seatcolumn=range(1,21)\r\n seats=[[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]\r\n print(\"A to J - Rows 1 to 20 - Columns\")\r\n for i in range(20):\r\n for j in seatcolumn:\r\n if i%2==0:\r\n if j<10:\r\n seats[i].append(seatrow[i]+\" \"+str(j))\r\n else:\r\n seats[i].append(seatrow[i]+str(j))\r\n else:\r\n seats[i].append(random.choice(s))\r\n\r\n for i in range(len(seats)):\r\n if i%2==0:\r\n print(seats[i])\r\n else:\r\n print(seats[i],'\\n')\r\n\r\n print(\" * -> Unbooked\\n $ -> Booked\")\r\n\r\n \r\n\r\n \r\n##\r\ndef invoice(n,t,mvn,ts,sl):\r\n slot=sl\r\n tseats=ts\r\n nseats=n\r\n gtotal=t\r\n ntotal=gtotal/1.18\r\n gst=gtotal-ntotal\r\n moviename=mlist[mvn-1]\r\n print(\"\\n\"*10)\r\n \r\n print(\" RND CINEMAS\")\r\n print(\"Date/Time :\",time.ctime())\r\n print(\"Ticket No:\",random.randint(1,850),\" Screen No:\",random.randint(1,5))\r\n print(\"-----------------------------------\\n\")\r\n print(\"Movie : \",moviename)\r\n print(\"Slot : \",slotlist[slot-1])\r\n print(\"Number of seats : \",nseats)\r\n print(\"Seats : \",tseats)\r\n print(\"Net Total : Rs.\",round(ntotal,2))\r\n print(\"GST 18 % : Rs.\",round(gst,2))\r\n print(\"Grand Total : Rs.\",round(gtotal,2))\r\n print(\"-----------------------------------\")\r\n print(\"\\n\\n\")\r\n\r\n q=int(input(\"1.Print Receipt\\n2.Cancel (move to HomePage)\"))\r\n if q==1:\r\n time.sleep(1)\r\n print(\"Sending details to Printer - EPSON ML 380 Series\")\r\n time.sleep(1)\r\n print(\"Printing .....\")\r\n time.sleep(3)\r\n print(\"Thank you for Booking ! Enjoying watching the movie and dont forget to rate us !\")\r\n time.sleep(1)\r\n print(\"Redirecting to HomePage ......\\n\")\r\n time.sleep(2)\r\n home()\r\n elif q==2:\r\n print(\"Redirecting to HomePage ......\\n\")\r\n time.sleep(2)\r\n home()\r\n\r\n##\r\ndef snacks():\r\n pricesnacks=0\r\n popcorn=[160,180,190]\r\n pizza=[130,180]\r\n burger=[80,120]\r\n kfc=[300,650]\r\n beverage=[40,95,40,90]\r\n l=[]\r\n lp=[]\r\n \r\n print(\"Welcome to RND Food Corner !! \\n\\n\")\r\n print(\"What would you like to have ?\")\r\n n=int(input(\"1.Popcorn\\n2.Pizza\\n3.Burger\\n4.Chicken(KFC)\\n5.Beverages\\n6.Back to homepage\"))\r\n while n!=6:\r\n if n==1:\r\n n1=int(input(\" Types Small Big\\n1.Salty - Rs.160 Rs.210\\n2.Cheesy - Rs.180 Rs.240\\n3.Caramel - Rs.190 Rs.270\\n4.Back\\n\"))\r\n n1size=int(input(\"1.Big\\n2.Small\"))\r\n if n1==1:\r\n if n1size==1:\r\n pricesnacks+=(popcorn[0]+50)\r\n l.append(\"Big Salty Popcorn\")\r\n lp.append(popcorn[0]+50)\r\n else:\r\n pricesnacks+=popcorn[0]\r\n l.append(\"Small Salty Popcorn\")\r\n lp.append(popcorn[0])\r\n elif n1==2:\r\n if n1size==1:\r\n pricesnacks+=(popcorn[1]+60)\r\n l.append(\"Big Cheesy Popcorn\")\r\n lp.append(popcorn[1]+60)\r\n else:\r\n pricesnacks+=popcorn[1]\r\n l.append(\"Small Cheesy Popcorn\")\r\n lp.append(popcorn[1])\r\n elif n1==3:\r\n if n1size==1:\r\n pricesnacks+=(popcorn[2]+80)\r\n l.append(\"Big Caramel Popcorn\")\r\n lp.append(popcorn[2]+80)\r\n else:\r\n pricesnacks+=popcorn[2]\r\n l.append(\"Small Caramel Popcorn\")\r\n lp.append(popcorn[2])\r\n elif n1==4:\r\n n=int(input(\"1.Popcorn\\n2.Pizza\\n3.Burger\\n4.Chicken(KFC)\\n5.Beverages\\n6.Back to homepage\"))\r\n \r\n \r\n elif n==2:\r\n n2=int(input(\" Types Small Large \\n1.Veg Pizza - Rs.130 Rs.200\\n2.Chicken Pizza - Rs.180 Rs.240\\n3.Back\"))\r\n n2size=int(input(\"1.Small\\n2.Large\"))\r\n if n2==1:\r\n if n2size==1:\r\n pricesnacks+=pizza[0]\r\n l.append(\"Veg Pizza Small\")\r\n lp.append(pizza[0])\r\n \r\n else:\r\n pricesnacks+=(pizza[0]+70)\r\n l.append(\"Veg Pizza Big\")\r\n lp.append(pizza[0]+70)\r\n elif n2==2:\r\n if n2size==1:\r\n pricesnacks+=pizza[1]\r\n l.append(\"Chicken Pizza Small\")\r\n lp.append(pizza[1])\r\n else:\r\n pricesnacks+=(pizza[1]+60)\r\n l.append(\"Chicken Pizza Big\")\r\n lp.append(pizza[1]+60)\r\n elif n3==3:\r\n snacks()\r\n \r\n elif n==3:\r\n n3=int(input(\" Types Small Large \\n1.Veg Burger - Rs.80 Rs.100\\n2.Chicken Burger - Rs.120 Rs.150\\n3.Back\"))\r\n n3size=int(input(\"1.Small\\n2.Large\"))\r\n if n3==1:\r\n if n3size==1:\r\n pricesnacks+=burger[0]\r\n l.append(\"Veg Burger Small\")\r\n lp.append(burger[0])\r\n else:\r\n pricesnacks+=(burger[0]+20)\r\n l.append(\"Veg Burger Big\")\r\n lp.append(burger[0]+20)\r\n elif n3==2:\r\n if n3size==1:\r\n pricesnacks+=burger[1]\r\n l.append(\"Chicken Burger Small\")\r\n lp.append(burger[1])\r\n else:\r\n pricesnacks+=(burger[1]+30)\r\n l.append(\"Chicken Burger Big\")\r\n lp.append(burger[1]+30)\r\n elif n3==3:\r\n snacks()\r\n\r\n elif n==4:\r\n n4=int(input(\" Type Rate \\n1.4 legs - Rs.300\\n2.10 legs - Rs.650\"))\r\n if n4==1:\r\n pricesnacks+=kfc[0]\r\n l.append(\"KFC Chicken 4 legs\")\r\n lp.append(kfc[0])\r\n else:\r\n pricesnacks+=kfc[1]\r\n l.append(\"KFC Chicken 10 legs\")\r\n lp.append(kfc[1])\r\n \r\n elif n==5:\r\n n5=int(input(\" Type Rate \\n1.Pepsi (200ml) - Rs.40\\n2.Pepsi (900ml) - Rs.95\\n3.Coke (250ml) - Rs.40\\n4.Coke (900ml) - Rs.90\\n5.Back\"))\r\n if n5==1:\r\n pricesnacks+=beverage[n5-1]\r\n l.append(\"Pepsi (200ml)\")\r\n lp.append(beverage[0])\r\n elif n5==2:\r\n pricesnacks+=beverage[n5-1]\r\n l.append(\"Pepsi (900ml)\")\r\n lp.append(beverage[1])\r\n elif n5==3:\r\n pricesnacks+=beverage[n5-1]\r\n l.append(\"Coke (250ml)\")\r\n lp.append(beverage[2])\r\n elif n5==4:\r\n pricesnacks+=beverage[n5-1]\r\n l.append(\"Coke (900ml)\")\r\n lp.append(beverage[3])\r\n elif n5==5:\r\n snacks()\r\n print(\"Items added :\")\r\n for i in range(len(l)):\r\n print(i+1,\".\",l[i],\" - Rs.\",lp[i])\r\n choice=int(input(\"1.Continue to buy further\\n2.Terminate\"))\r\n if choice==1:\r\n n=int(input(\"1.Popcorn\\n2.Pizza\\n3.Burger\\n4.Chicken(KFC)\\n5.Beverages\\n6.Back to homepage\"))\r\n continue\r\n else:\r\n break\r\n snacksbill(l,lp,pricesnacks)\r\n\r\n##\r\ndef snacksbill(templ,templp,temppricesnacks):\r\n l=templ\r\n lp=templp\r\n pricesnacks=temppricesnacks\r\n \r\n print(\" RND Food Corner\")\r\n print(\" taste never ends....\")\r\n print(\"--------------------------------------------------\")\r\n print(\"Date / Time:\",time.ctime())\r\n print(\"Invoice:\",random.randint(450,950))\r\n print(\"--------------------------------------------------\")\r\n for i in range(len(l)):\r\n print(i+1,\".\",l[i],\" - Rs.\",lp[i])\r\n print(\" Net : Rs.\",round(pricesnacks,2))\r\n print(\" GST(18%): Rs. \",round(pricesnacks*0.18,2))\r\n print(\" Total: Rs.\",round(pricesnacks*1.18,2),\"\\n\\n\")\r\n print(\"--------------------------------------------------\")\r\n \r\n choice=int(input(\"1.Print Receipt\\n2.Cancel and Re-order\\n3.Back to Homepage\"))\r\n if choice==1:\r\n time.sleep(1)\r\n print(\"Sending details to Printer - EPSON ML 380 Series\")\r\n time.sleep(1)\r\n print(\"Printing .....\")\r\n time.sleep(3)\r\n print(\"Thank you for Ordering ! Enjoying watching the movie and dont forget to rate us !\")\r\n time.sleep(1)\r\n print(\"Redirecting to HomePage ......\\n\")\r\n time.sleep(2)\r\n home()\r\n elif choice==2:\r\n print(\"Current order cancelled !\\nRedirecting to Snacks Page ...\\n\")\r\n time.sleep(2)\r\n snacks()\r\n elif choice==3:\r\n print(\"Redirecting to HomePage ......\\n\")\r\n time.sleep(3)\r\n home()\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n#### \r\ndef home():\r\n print(\" Welcome to RND CINEMAS !!\")\r\n print(\" get into reality.....\")\r\n print(\"\\n\"*2)\r\n n=int(input(\"1.Book a Movie Ticket\\n2.Movie Reviews and Trailers\\n3.Snacks and Refreshments\\n4.Exit\\n\"))\r\n if n==1:\r\n movieslist()\r\n confirm()\r\n elif n==2:\r\n mrt()\r\n elif n==3:\r\n snacks()\r\n elif n==4:\r\n print(\"Thank you :) Hope you enjoy using our app !\")\r\n \r\n\r\n\r\nhome()\r\n\r\n\r\n","repo_name":"dheerajsreddy/Movie-Booking","sub_path":"Project1.py","file_name":"Project1.py","file_ext":"py","file_size_in_byte":15952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"1400418738","text":"import pandas\r\nimport turtle\r\nfrom quizz import StatesQuiz\r\n\r\nTITLE = \"U.S. States Game\"\r\nIMAGE_PATH = \"./blank_states_img.gif\"\r\nMISSED_STATES = \"./missed_states.csv\"\r\nRESOLUTION = (500, 725)\r\n\r\ndef screen_setup():\r\n screen.title(TITLE)\r\n screen.addshape(IMAGE_PATH)\r\n screen.setup(height = RESOLUTION[0], width = RESOLUTION[1])\r\n turtle.shape(IMAGE_PATH)\r\n\r\n\r\nscreen = turtle.Screen()\r\nscreen_setup()\r\nquiz = StatesQuiz()\r\nis_game_on = True\r\n\r\nwhile is_game_on:\r\n my_answer = screen.textinput(title = \"Guess the State\", \r\n prompt = f\"{quiz.no_correct_answers}/{quiz.number_of_all_states} States Correct\").title()\r\n if my_answer == \"Exit\":\r\n break\r\n quiz.answer(my_answer)\r\n is_game_on = quiz.continue_game()\r\n\r\nmissed_answers = pandas.Series(quiz.missed_states()) \r\nmissed_answers.to_csv(MISSED_STATES)\r\n\r\n#getting coordinates from a map\r\n#def get_mouse_click_cor(x,y):\r\n# print(x,y)\r\n#turtle.onscreenclick(get_mouse_click_cor)\r\n\r\nturtle.mainloop()\r\n\r\n","repo_name":"Aevise/Python-projects","sub_path":"PY_USStatesQuizz/PY_USStatesQuizz/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"5589313705","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Feb 20 16:00:51 2020\r\n\r\n@author: nacer\r\n\"\"\"\r\n\r\nimport os\r\n\r\n## Global Vars\r\n\r\n#Path of input files\r\nPATH = \"./\"\r\n\r\n#List all input file names\r\nFILES = []\r\nfor r, d, f in os.walk(PATH):\r\n for file in f:\r\n if '.txt' in file and \"solution\" not in file:\r\n FILES.append(os.path.join(file))\r\n\r\n''' \r\n Choose the files we'll work with\r\n 1 - a_example.txt\r\n 2 - b_read_on.txt\r\n 3 - c_incunabula.txt\r\n 4 - d_tough_choices.txt\r\n 5 - e_so_many_books.txt\r\n 6 - f_libraries_of_the_world.txt\r\n'''\r\nfor i in range(len(FILES)):\r\n print(i+1,\"-\",FILES[i])\r\nchoice = int(input(\"Choose a file : \"))\r\nFILE = FILES[choice-1]\r\n\r\ndef read_input(file):\r\n f = open(file,\"r\")\r\n text = f.read()\r\n lines = text.split(\"\\n\")\r\n \r\n B,L,D = lines[0].split(\" \")\r\n BL = list( map(int, lines[1].split(\" \")))\r\n LL= []\r\n for i in range(int(L)):\r\n LL += [ list(map(int,lines[2*i+2].split(\" \"))) + [ list(map(int,lines[2*i+3].split(\" \"))) ] ]\r\n return int(B),int(L),int(D), BL, LL\r\n\r\nB,L,D,BL,LL = read_input(FILE)\r\n\r\n# Building a better BL list\r\nBS = [list(range(len(BL))),BL]\r\nBS = list(zip(*BS))\r\nBS.sort(key=lambda x: x[1])\r\nBS = list(zip(*BS))\r\n\r\ndef scorify_library(library):\r\n \"\"\"\r\n The aim is to give the libraries a score, that will enable to order them later on\r\n \"\"\"\r\n NB = library[0]\r\n BD = library[2]\r\n SB = library_total_book_score(library)\r\n DR = library[1]\r\n library_scoring = (D - DR) * BD * (SB/NB)\r\n return library_scoring\r\n\r\n\r\n\r\ndef library_total_book_score(library):\r\n book_ids = library[3]\r\n total_library_book_score = 0\r\n for id in book_ids:\r\n total_library_book_score += BL[id]\r\n return total_library_book_score\r\n\r\n\r\n# Scores --> list of tuple (id lib, score)\r\nscores = []\r\nfor i in range(len(LL)):\r\n scores += [( i, scorify_library(LL[i]) ) ]\r\nscores.sort(key=lambda tup: tup[1])\r\n\r\ndef compute_available_days():\r\n available_libraries = []\r\n availability_day = 0\r\n while len(scores)>0:\r\n library_id_score = scores.pop()\r\n library_id = library_id_score[0]\r\n DR = LL[library_id][1]\r\n availability_day += DR\r\n if availability_day > D:\r\n continue\r\n else:\r\n entry = (library_id,availability_day)\r\n available_libraries.append(entry)\r\n return available_libraries\r\n\r\nAvL = compute_available_days() # Availability of libraries following the scoring\r\n\r\ndef available_libs(d):\r\n AvLD = []\r\n day = AvL[0][1]\r\n i = 0\r\n ln = len(AvL)\r\n while i 0:\r\n AvLD += [AvL[i]]\r\n day = AvL[i][1]\r\n i+=1\r\n return AvLD\r\n\r\nScB = [] #Scanned books\r\nScBpL = [] #Scanned books per lib\r\nLL2 = LL.copy()\r\n\r\ndef available_books(n_lib):\r\n return len(LL2[n_lib][3])\r\n\r\nfor d in range(D):\r\n print(\"Day %d/%d\\t\\t%.2f%%\" % (d,D,d/D*100))\r\n AvLD = available_libs(d)\r\n for lib in AvLD:\r\n id_lib = lib[0]\r\n scan_rate = LL2[ id_lib ][2]\r\n \r\n for book in LL2[id_lib][3]:\r\n if scan_rate == 0:\r\n continue\r\n \r\n if book in ScB:\r\n LL2[id_lib][3].remove(book)\r\n else :\r\n ScBpL += [ (book,id_lib) ]\r\n ScB += [book]\r\n scan_rate -= 1\r\n \r\nLIBS = available_libs(D)\r\nnbr_libraries_for_sign_up = len(LIBS)\r\nlibraries_submission = [ [] ]*nbr_libraries_for_sign_up\r\n\r\n\r\nlibrary_dict = {}\r\ntemp_lib = 0\r\n\r\nfor book in ScBpL:\r\n if book[1] not in library_dict.keys(): \r\n library_dict[book[1]]=[]\r\n library_dict[book[1]].append(book[0])\r\n\r\nOUT = \"Solutions/\" + FILE[:-4] + \"_solution.txt\"\r\n\r\nf = open(OUT,\"w+\")\r\nf.write(str(nbr_libraries_for_sign_up))\r\nf.write(\"\\n\")\r\nfor lib in LIBS:\r\n BOOKS = library_dict[lib[0]]\r\n f.write(str(lib[0]))\r\n f.write(\" \")\r\n f.write(str(len(BOOKS)))\r\n f.write(\"\\n\")\r\n for b in BOOKS:\r\n f.write(str(b))\r\n f.write(\" \")\r\n f.write(\"\\n\")\r\nf.close()","repo_name":"NacerSebtiMS/Google_Hashcode_2020_Qualification","sub_path":"SIN_solution.py","file_name":"SIN_solution.py","file_ext":"py","file_size_in_byte":4104,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"23980189901","text":"def elemSum(list,sum):\n if list == []:\n return sum\n else:\n sum += list[0]\n return elemSum(list[1:],sum)\n\nsum=0\nlista1=[1,2,3]\nlista2=[1,2]\nprint( elemSum(lista1, sum))\nprint( elemSum(lista2, sum))\n","repo_name":"lfgbs/IA","sub_path":"Guiaoprog/1/elemSum.py","file_name":"elemSum.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"829428580","text":"import json, os\nimport cPickle as pickle\n\nDBLog = \"data/db.txt\"\nDBPick = \"data/db.p\"\ndata = dict()\n\ndef load_data():\n\tglobal data\n\tif not os.path.exists(DBPick):\n\t\tdata = dict()\n\telse:\n\t\tdata = pickle.load(open(DBPick, \"rb\"))\n\tif not os.path.exists(DBLog):\n\t\topen(DBLog, 'w').close()\n\t\n\twith open(DBLog, \"r\") as myfile:\n\t\tfor l in myfile.readlines():\n\t\t\tdat = json.loads(l)\n\t\t\tact = dat[\"action\"]\n\t\t\tif act == \"add\":\n\t\t\t\tadd(dat[\"key\"], dat[\"value\"], False)\n\t\t\telif act == \"remove\":\n\t\t\t\tremove(dat[\"key\"], False)\n\t\n\tpickle.dump(data, open(DBPick, \"wb\"))\n\t\n\tos.remove(DBLog)\n\tmyfile = open(DBLog, \"w\").close()\n\treturn myfile\n\t\ndef write_f(mstr):\n\twith open(DBLog, 'a') as file:\n\t\tfile.write(mstr + \"\\n\")\n\ndef all():\n\treturn data\n\ndef get(key):\n\treturn data[key]\n\ndef add(key, value, store = True):\n\tdata[key] = value\n\tif store:\n\t\twrite_f(json.dumps({\"key\":key, \"action\":\"add\", \"value\":value }))\n\ndef remove(key, store = True):\n\tif key not in data:\n\t\treturn\n\tdel data[key]\n\tif store:\n\t\twrite_f(json.dumps({\"key\":key, \"action\":\"remove\" }))\n\n\nfile = load_data()","repo_name":"hbirler/datalovr","sub_path":"store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"2010160970","text":"import argparse\nimport ast\nimport webbrowser\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nimport math\nimport re\nfrom collections import namedtuple\nfrom datetime import datetime, timedelta\nfrom urllib.request import urlopen\nfrom xml.etree import ElementTree\nimport unicodedata\n\n\nTABLE_TEMPLATE = '''\n\n'''\n\nINSIDE_TABLE = '''\n\n Activitat \n \n\n {0.name} \n \n\n Lloc \n Direcció \n Numero \n Dia \n Hora \n Edat \n \n\n {0.place} \n {0.address} \n {0.number} \n {0.day} \n {0.hour} \n {0.age} \n \n{1}\n'''\n\nSTATIONS_ROW = '''\n\n Estació \n Distància (metres) \n '''\n\nSTATIONS_TEMPLATE = '''\n\n {0.name} \n {0.distance} \n '''\n\nHTML_DOC_TEMPLATE = '''\n\n\n Query Results \n 0 and zero_proof[-2] > 0):\n pass\n else:\n counter_change += 1\n if counter_change == 0:\n x = x * 3\n x_neg = x_neg * 3\n zero_proof.clear()\n else:\n x = x * 2\n x_neg = x_neg * 2\n break\n iter_count += 1\n if iter_count == 6:\n messagebox.showerror(title=\"Error de Convergencia\", message=\"La ecuacion insertada no presenta \"\n \"raices reales.\\n\")\n return\n\n graph_x.set_xlim([x_neg, x])\n for value in range(x_neg, x, step):\n x_value.append(value)\n y_value.append(f(value))\n if math.isnan(y_value[-1]):\n x_value.pop()\n y_value.pop()\n\n # To get a smoothed curve for the graph\n x_lin = array(x_value)\n y_lin = array(y_value)\n\n x_smooth = linspace(min(x_lin), max(x_lin), 300) # Create 300? points between min and max (another values?)\n spline = make_interp_spline(x_lin, y_lin, k=3) # Make a splin, k is for grade of smoothness\n y_smooth = spline(x_smooth) # Need to verify, maybe smooth the y values for x smoothed values\n ################################################################\n\n # graph_x.plot(x_value, y_value, color=\"red\")\n graph_x.plot(x_smooth, y_smooth, color=\"red\")\n graph_x.axhline(0, color='black')\n graph_x.axvline(0, color='black')\n graph_x.set_xlabel('x', fontsize=16)\n graph_x.set_ylabel('F(x)', fontstyle=\"oblique\", fontsize=16)\n graph_x.grid()\n\n canvas.draw()\n\n except ZeroDivisionError:\n messagebox.showwarning(title=\"Advertencia: Fallo en Expresiones\",\n message=\"Por favor verifique lo siguiente:\\n\\n\"\n \"# Utilizar como unica variable la letra x (minuscula).\\n\"\n \"# Hay funciones o constantes mal indicadas.\")\n except ValueError:\n messagebox.showwarning(title=\"Advertencia: Error de Sintaxis\",\n message=\"La ecuacion esta mal representada, por favor verifique lo siguiente:\\n\\n\"\n \"# Uso incorrecto de los parentesis.\\n\"\n \"# Uso incorrecto de signos matematicos.\")\n return\n\n\ndef activate_Bisection(): # Call bisection method from M_Biseccion module\n Bisection_Results(root, to_centerx, to_centery, entry_equation, entry_tol)\n return\n\n\ndef activate_FalsePos(): # Call false position method from M_FalsaPos_M module\n FalsePos_Results(root, to_centerx, to_centery, entry_equation, entry_tol)\n return\n\n\ndef activate_FixPoint(): # Call false position method from M_PuntoFijo module\n FixedPoint_Results(root, to_centerx, to_centery, entry_equation, entry_tol)\n return\n\n\ndef help_text(): # Just a guide to input an equation in a correct way\n messagebox.showinfo(title=\"¿Como introducir una ecuacion?\",\n message=\"# La ecuacion a ingresar debe contener maximo una variable identificada como 'x'\"\n \" por lo que su expresion matematica debe estar en funcion de x.\\n\\n\"\n \"# Los decimales se representan mediante el punto (.).\\n\"\n \"# Las operaciones de multiplicacion debe representarse correctamente con el simbolo \"\n \"(*).\\n\\n \"\n \" Ejemplo: 2x (forma incorrecta) => 2*x (forma correcta).\\n\\n\"\n \"# La representacion de potencias viene dado por ** (doble signo de multiplicacion).\\n\"\n \" Ejemplo: x**2 => x^2 o como se leeria 'x al cuadrado'.\\n\\n\"\n \"# Las funciones trigometricas se escriben tal como se ha definido su abreviacion y su\"\n \"valor objetivo debe ir siempre entre parentesis.\\n\"\n \"Ejemplo:\\n\"\n \" sin(x) => Funcion Seno (se aplico su abreviacion como sin).\\n\"\n \" cos(x) => Funcion Coseno.\\n\"\n \" tan(x) => Funcion Tangente.\\n\"\n \" csc(x) => Funcion Cosecante.\\n\"\n \" sec(x) => Funcion Secante.\\n\"\n \" cot(x) => Funcion Cotangente.\\n\"\n \" Asi mismo es posible usar las funciones inversas e hiperbolicas:\\n\"\n \" asin, sinh, asinh, acos, cosh, acosh, atan, tanh, atanh,acot, coth,\\n\"\n \" acoth, asec, sech, asech, acsc, csch, acsch.\\n\\n\"\n \"# Las constantes pi y euler se expresan como (pi) y (E), respectivamente.\\n\\n\"\n \"# La raiz cuadrada se puede expresar mediante sqrt() o tambien es posible expresar \"\n \"cualquier tipo de radicacion en su forma de potencia.\\n \"\n \" Ejemplo:\\n\"\n \" sqrt(x) = x**(1/2) | sqrt(4) = 4**(1/2).\\n\"\n \" x**(1/3) => Raiz Cubica\")\n return\n\n\ndef change_limx():\n def assert_values():\n val = [[\"Limite Inferior\", lower_entry.get()], [\"Limite Superior\", higher_entry.get()],\n [\"Paso\", step_entry.get()]]\n i = 0\n\n # To avoid some error in input cells like exchange of limits, Step less than 0 and not integer\n try:\n\n for i in range(len(val)):\n val[i][1] = int(val[i][1])\n if val[0][1] >= val[1][1]:\n messagebox.showwarning(title=\"Limites Erroneos\",\n message=\"El Limite Inferior es mayor o igual al Limite Superior.\\n\"\n \"Cerciorese que los valores correspondan a lo indicado.\",\n parent=limit_window)\n return\n elif val[2][1] <= 0:\n messagebox.showwarning(title=\"Paso no Valido\",\n message=\"El valor de Paso no puede ser menor o igual a 0.\", parent=limit_window)\n return\n elif int(val[2][1]) > numpy.abs((int(val[1][1])) - (int(val[0][1]))) // 4 + 1:\n messagebox.showwarning(title=\"Fallo en Metodo Grafico\",\n message=f\"El valor de Paso ingresado no genera el numero de puntos minimo para \"\n f\"generar un grafico suavizado.\\n \"\n f\"Para los limites seleccionados, es posible utilizar un Paso \"\n f\"maximo de {numpy.abs((int(val[1][1])) - (int(val[0][1]))) // 4 + 1}.\",\n parent=limit_window)\n return\n\n graph_function(val[0][1], val[1][1] + val[2][1], val[2][1], True)\n\n except ValueError:\n if val[i][1] == \"\":\n messagebox.showwarning(title=\"Valor Vacio\", message=f\"No se digito valor alguno para {val[i][0]}.\",\n parent=limit_window)\n\n elif \" \" in val[i][1]:\n messagebox.showwarning(title=\"Error en Numero\",\n message=f\"El valor para {val[i][0]} contiene al menos un espacio \"\n f\"entre digitos.\",\n parent=limit_window)\n\n elif val[i][1].isupper() or val[i][1].islower() or val[i][1].count(\".\") >= 2 or \\\n val[i][1].count(\"+\", 1) >= 1 or val[i][1].count(\"-\", 1) >= 1:\n messagebox.showwarning(title=\"Valor Invalido\",\n message=f\"El valor para {val[i][0]} no es un numero real.\\n\"\n f\"Modifique el valor a un Numero Entero.\\n\",\n parent=limit_window)\n\n elif len(val[i][1].split(\".\", 1)) == 2 and (\n val[i][1].split(\".\", 1)[0].startswith(\n (\"+\", \"-\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"))) and \\\n val[i][1].split(\".\", 1)[1].isdigit():\n messagebox.showwarning(title=\"Numero no Valido\",\n message=f\"Se ha ingresado '{val[i][1]}' para {val[i][0]}.\\nRecuerde que debe \"\n f\"ingresar solo Numeros Enteros.\",\n parent=limit_window)\n\n elif not val[i][1].isalnum() and (val[i][1].count(\".\") <= 1) and (\n val[i][1].startswith((\"+\", \"-\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"))):\n messagebox.showwarning(title=\"Error Aritmetico\",\n message=f\"El valor para {val[i][0]} contiene uno o varios caracteres \"\n f\"desconcidos.\\n \"\n f\"Modifique el valor a un Numero Entero.\\n\",\n parent=limit_window)\n\n else:\n messagebox.showwarning(title=\"Error de Validacion\",\n message=f\"El valor de {val[i][0]} contiene datos que imposibilitan su \"\n f\"procesamiento.\\n \"\n \"Por favor verifique que los datos ingresados sea correcto.\",\n parent=limit_window)\n ################################################################\n return\n\n limit_window = Toplevel(root)\n limit_window.title(\"Dominio de la Funcion\")\n limit_window.configure(background=\"beige\")\n\n window_width = 500\n window_height = 150\n centerx = root.winfo_screenwidth() / 2 - window_width / 2\n centery = root.winfo_screenheight() / 2 - window_height / 2\n limit_window.geometry(f\"{window_width}x{window_height}+{int(centerx) - 280}+{int(centery) - 100}\")\n\n label_style = ttk.Style()\n label_style.configure(\"BW.TLabel\", background=\"beige\")\n label_style.map(\"BW.TLabel\", background=[(\"active\", \"beige\")])\n\n info_text = ttk.Label(limit_window,\n text=\"Establezca el dominio de la funcion digitando sus limites a continuacion :\\n\"\n \" (Todos los campos deben llenarse con numeros enteros)\",\n style=\"BW.TLabel\")\n info_text.grid(row=0, column=0, columnspan=5, padx=10, pady=5)\n\n lower_x = ttk.Label(limit_window, text=\"Limite Inferior =\", style=\"BW.TLabel\")\n lower_x.grid(row=1, column=0, pady=5)\n\n lower_entry = ttk.Entry(limit_window)\n lower_entry.grid(row=1, column=1, pady=5)\n\n higher_x = ttk.Label(limit_window, text=\"Limite Superior =\", style=\"BW.TLabel\")\n higher_x.grid(row=2, column=0, pady=5)\n\n higher_entry = ttk.Entry(limit_window)\n higher_entry.grid(row=2, column=1, pady=5)\n\n step_label = ttk.Label(limit_window, text=\"Paso =\", style=\"BW.TLabel\")\n step_label.grid(row=3, column=0)\n\n step_entry = ttk.Entry(limit_window)\n step_entry.grid(row=3, column=1)\n\n plot_button2 = ttk.Button(limit_window, text=\"Plot\", command=assert_values)\n plot_button2.grid(row=2, column=2)\n\n return\n\n\n# Initiation of tkinter window\nroot = themed_tk.ThemedTk()\nroot.get_themes()\nroot.set_theme(\"radiance\")\nroot.title(\"Raices de Ecuaciones\")\nroot.configure(bg=\"beige\")\n################################################################\n\n# A way to center tkinter window on screen\nroot_width = 850\nroot_height = 600\nscreen_width = root.winfo_screenwidth()\nscreen_height = root.winfo_screenheight()\nto_centerx = screen_width / 2 - root_width / 2\nto_centery = screen_height / 2 - root_height / 2\n\nroot.geometry(f\"{root_width}x{root_height}+{int(to_centerx)}+{int(to_centery) - 20}\")\n################################################################\n\n# root.minsize(800, 600)\n\nlabel_equation = ttk.Label(root, text=\" Escriba la ecuacion : \", font=(\"Helvetica\", 18, \"bold\"), borderwidth=5,\n relief=\"sunken\")\nlabel_equation.grid(row=0, column=0, pady=10)\n\nentry_equation = ttk.Entry(root, width=25, font=(\"Helvetica\", 14))\nentry_equation.grid(row=1, column=0, padx=10, pady=5)\n\n# To put background color of all ttk object to the same color as background color of window (radiance theme cause color\n# alteration)\nstyle_backgttk = ttk.Style()\nstyle_backgttk.configure(\"TButton\", background=\"beige\")\nstyle_backgttk.map(\"TButton\", background=[(\"active\", \"beige\")])\n################################################################\n\ngraph_Button = ttk.Button(root, text=\"Plot\", width=5, command=graph_function)\ngraph_Button.grid(row=2, column=0, padx=10)\n\nlabel_fx = ttk.Label(root, text=\"F(x) =\")\nlabel_fx.config(background=\"beige\", font=(\"font name\", 12, \"bold\"))\nlabel_fx.place(x=10, y=105)\n\n# The way to adjust an image (png,etc) to a specific size (this case on button frame)\nhelp_pic = Image.open(help_icon_dir)\nresized_help = help_pic.resize((30, 30), Image.ANTIALIAS)\nhelp_icon = ImageTk.PhotoImage(resized_help)\n\ngraph_pic = Image.open(graph_icon_dir)\nresized_graph = graph_pic.resize((65, 65), Image.ANTIALIAS)\ngraph_icon = ImageTk.PhotoImage(resized_graph)\n################################################################\n\nhelp_button = Button(root, image=help_icon, highlightthickness=5, bd=0, background=\"beige\", command=help_text,\n cursor=\"hand2\")\nhelp_button.place(x=27, y=130)\nfloat_help = Hovertip(help_button, \"Ayuda para ingresar \\nuna ecuacion\", hover_delay=100)\n\ngraphIcon_button = Button(root, image=graph_icon, highlightthickness=0, bd=1, background=\"beige\", command=change_limx,\n cursor=\"hand2\")\ngraphIcon_button.place(x=260, y=140)\nfloat_graph = Hovertip(graphIcon_button, \"Seleccionar limites\", hover_delay=100)\n\nlabel_note_eq = ttk.Label(root, text=\" Nota : La variable a utilizar en la ecuacion debe ser ' x '.\", borderwidth=10,\n relief=\"solid\", foreground=\"red\")\nlabel_note_eq.grid(row=3, column=0, padx=10, pady=5)\n\n# To create the two orange(coral) bars to enclose all method button\nsquare_line1 = Canvas(root, height=2, width=300, background=\"coral\", bd=0, highlightthickness=0, borderwidth=8,\n relief=\"raised\")\nsquare_line1.place(x=50, y=270) # Top bar\nsquare_line2 = Canvas(root, height=2, width=300, background=\"coral\", bd=0, highlightthickness=0, borderwidth=8,\n relief=\"raised\")\nsquare_line2.place(x=50, y=550) # Bottom bar\n################################################################\n\nlabel_MSelection = ttk.Label(root, text=\"Seleccione el metodo para encontrar la raiz:\", background=\"beige\")\nlabel_MSelection.grid(row=4, column=0)\n\nlabel_tol = ttk.Label(root, text=\"(Tolerancia(%)= )\", background=\"beige\")\nlabel_tol.place(x=115, y=312)\nentry_tol = ttk.Entry(root, width=6)\nentry_tol.place(x=225, y=312)\n\n# From here all the method button are placed\nm_Bisection = ttk.Button(root, text=\"Metodo de Biseccion\", command=activate_Bisection, width=19)\nm_Bisection.grid(row=5, column=0)\n\nm_FalsaPos = ttk.Button(root, text=\"M. Falsa Posicion Mod.\", command=activate_FalsePos, width=19)\nm_FalsaPos.place(x=108, y=375)\n\nm_FixPoint = ttk.Button(root, text=\"Metodo Punto Fijo\", command=activate_FixPoint, width=19)\nm_FixPoint.place(x=108, y=410)\n################################################################\n\n# Code for add a graph draw on window mode --Figure,FigureCanvasTkAgg,NavigationToolbar2Tk are the most important--\nx_value = []\ny_value = []\n# Figure is responsible of the size white frame where graph is placed (figsize is the size frame, dpi relate to zoom?¿)\nfig = Figure(figsize=(6, 6), dpi=70, edgecolor=\"tomato\", facecolor=\"azure\", linewidth=4,\n subplotpars=SubplotParams(0.15))\ngraph_x = fig.add_subplot(1, 1, 1) # Add the graph in the frame\ngraph_x.axhline(0, color='black')\ngraph_x.axvline(0, color='black')\ngraph_x.set_xlabel('x', fontsize=16)\ngraph_x.set_ylabel('F(x)', fontstyle=\"oblique\", fontsize=16)\ngraph_x.grid()\n\n# I'm not sure about this but it allows to draw the graph in the white frame\ncanvas = FigureCanvasTkAgg(fig, master=root)\ncanvas.get_tk_widget().configure(highlightcolor=\"red\")\ncanvas.draw()\ncanvas.get_tk_widget().grid()\n\n# This allows to place the toolbar buttons to handle the graph\ntoolbar_frame = tkinter.Frame(root)\ntoolbar = NavigationToolbar2Tk(canvas, toolbar_frame)\ntoolbar.update()\ntoolbar.configure(background=\"beige\")\ntoolbar._message_label.config(background=\"beige\")\n# toolbar._message_label.pack_configure(side=tkinter.LEFT)\n# for button in toolbar.winfo_children():\n# button.configure(background=\"dark gray\")\ncanvas.get_tk_widget().grid(row=0, column=2, rowspan=8, columnspan=8, padx=10, pady=10)\ntoolbar_frame.grid(row=8, column=2, rowspan=8, columnspan=8)\n################################################################\n\nroot.mainloop()\n","repo_name":"HansJP96/Raices-Ecuaciones","sub_path":"GUI_User.py","file_name":"GUI_User.py","file_ext":"py","file_size_in_byte":19523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"5321440748","text":"from django.contrib.auth.models import User\nfrom .models import Board,Topic,Post\nfrom django.views import View\n# Create your views here.\nfrom django.shortcuts import render, get_object_or_404,redirect,HttpResponseRedirect,reverse\nfrom django.views.decorators.http import require_http_methods\nfrom rest_framework.decorators import api_view\n\nfrom rest_framework.views import APIView\nfrom .serializers import BoardSerializ\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .forms import BoardForm\nfrom django.contrib.auth import authenticate,login,logout\nfrom rest_framework.permissions import IsAuthenticated\n\n\ndef user_login(request):\n context={}\n if request.method==\"POST\":\n username=request.POST['username']\n password=request.POST['password']\n user=authenticate(request,username=username,password=password)\n if user:\n login(request,user)\n return HttpResponseRedirect(reverse('home'))\n\n else:\n context[\"error\"]=\"provide valid credentials!\"\n return render(request, 'login.html', context)\n\n else:\n return render(request,'login.html',context)\n\n\ndef user_logout(request):\n logout(request)\n return HttpResponseRedirect(reverse('login'))\n\n\n\nclass HelloView(APIView):\n permission_classes = (IsAuthenticated,) # <-- And here\n def get(self, request):\n content = {'message': 'Hello, World!'}\n return Response(content)\n@require_http_methods(['GET'])\ndef home(request):\n boards=Board.objects.all()\n return render(request,'home.html',{'boards':boards})\n\n\n\n\ndef board_topics(request,pk):\n board = get_object_or_404(Board, pk=pk)\n return render(request, 'topics.html', {'board': board})\n\n\n\n\n\n\ndef new_topic(request,pk):\n board=get_object_or_404(Board,pk=pk)\n user=User.objects.first()\n if request.method=='POST':\n subject=request.POST['subject']\n message=request.POST['message']\n\n topic=Topic.objects.create(\n subject=subject,\n board=board,\n starter=user\n )\n post=Post.objects.create(\n message=message,\n topic=topic,\n created_by=user\n )\n\n return render(request,'new_topic.html',{'board':board})\n\n\ndef update(request):\n board=Board.objects.all().update( description='This ....')\n return redirect('home')\n\n\n\n@api_view(['GET'])\ndef boardList(request):\n board=Board.objects.all()\n serializer=BoardSerializ(board,many=True)\n return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef boardCreate(request):\n serializer=BoardSerializ(data=request.data)\n serializer.validate_name(request.data['name'])\n if serializer.is_valid(raise_exception=True):\n serializer.save()\n\n return Response (serializer.data)\n\n\n\n@api_view(['PUT'])\ndef boardUpdate(request,pk):\n board=Board.objects.get(id=pk)\n serializer=BoardSerializ(instance=board,data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response (serializer.data)\n\n@api_view(['delete'])\ndef boardDelete(request,pk):\n board=Board.objects.filter(id=pk).first()\n board.delete()\n return Response ('Deleted!')\n\n\n\n\n\n\n\n\n\n\n\n\ndef creat_board(request):\n form = BoardForm()\n if request.method=='POST':\n form = BoardForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context={'form':form}\n return render(request,'new_board.html',context)\n\ndef UpdateBoard(request,pk):\n board=Board.objects.get(id=pk)\n form = BoardForm(instance=board)\n if request.method=='POST':\n form = BoardForm(request.POST,instance=board)\n if form.is_valid():\n form.save()\n return redirect('/')\n\n context={'form':form}\n\n return render(request, 'new_board.html', context)\n\n\ndef DeleteBoard(request,pk):\n board = Board.objects.get(id=pk)\n if request.method=='POST':\n board.delete()\n return redirect('/')\n\n context={'item':board}\n return render(request,'delete.html',context)","repo_name":"yaqoot9/djano_project","sub_path":"boards/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"3801329818","text":"import logging\nfrom sys import getsizeof\nfrom concurrent.futures.thread import ThreadPoolExecutor\n\nimport boto3\nfrom boto3.dynamodb.conditions import Key\nfrom boto3.exceptions import ResourceNotExistsError\nfrom typing import List, Optional, Any, Dict, Tuple\n\nfrom botocore.exceptions import ClientError\n\nfrom StructNoSQL.tables_clients.backend.dynamodb_utils import DynamoDBUtils\nfrom StructNoSQL.tables_clients.backend.models import GlobalSecondaryIndex, PrimaryIndex, CreateTableQueryKwargs, \\\n GetItemResponse, Response, EXPRESSION_MAX_BYTES_SIZE\nfrom StructNoSQL.models import DatabasePathElement, FieldPathSetter, MapItemInitializer, \\\n MapItemInitializerContainer, QueryMetadata\nfrom StructNoSQL.practical_logger import message_with_vars\nfrom StructNoSQL.utils.data_processing import navigate_into_data_with_field_path_elements\n\n\nclass DynamoDbCoreAdapter:\n _EXISTING_DATABASE_CLIENTS = {}\n PAY_PER_REQUEST = \"PAY_PER_REQUEST\"\n PROVISIONED = \"PROVISIONED\"\n\n def __init__(\n self, table_name: str, region_name: str, primary_index: PrimaryIndex,\n create_table: bool = True, billing_mode: str = PAY_PER_REQUEST,\n global_secondary_indexes: List[GlobalSecondaryIndex] = None,\n boto_session: Optional[boto3.Session] = None\n ):\n self.table_name = table_name\n self.primary_index = primary_index\n self.create_table = create_table\n self.billing_mode = billing_mode\n self.global_secondary_indexes = global_secondary_indexes\n self._global_secondary_indexes_hash_keys = []\n if self.global_secondary_indexes is not None:\n for secondary_index in self.global_secondary_indexes:\n self._global_secondary_indexes_hash_keys.append(secondary_index.hash_key_name)\n\n # We store the database clients in a static variable, so that if we init the class with\n # the same region_name, we do not need to wait for a new initialization of the client.\n if region_name in self._EXISTING_DATABASE_CLIENTS.keys():\n self.dynamodb = self._EXISTING_DATABASE_CLIENTS[region_name]\n # print(f\"Re-using the already created dynamodb client for region {region_name}\")\n elif \"default\" in self._EXISTING_DATABASE_CLIENTS.keys():\n self.dynamodb = self._EXISTING_DATABASE_CLIENTS[\"default\"]\n # print(f\"Re-using the already created dynamodb client for the default region\")\n else:\n # print(f\"Initializing the {self}. For local development, make sure that you are connected to internet.\"\n # f\"\\nOtherwise the DynamoDB client will get stuck at initializing the {self}\")\n\n used_boto_session: boto3.Session = boto3.Session() if boto_session is None else boto_session\n dynamodb_regions = used_boto_session.get_available_regions('dynamodb')\n if region_name in dynamodb_regions:\n self.dynamodb = used_boto_session.resource('dynamodb', region_name=region_name)\n self._EXISTING_DATABASE_CLIENTS[region_name] = self.dynamodb\n else:\n self.dynamodb = used_boto_session.resource('dynamodb')\n self._EXISTING_DATABASE_CLIENTS['default'] = self.dynamodb\n logging.debug(\n f\"Warning ! The specified dynamodb region_name {region_name} is not a valid region_name.\"\n f\"The dynamodb client has been initialized without specifying the region.\")\n\n self._create_table_if_not_exists()\n\n def _create_table_if_not_exists(self) -> None:\n \"\"\"\n Creates table in Dynamodb resource if it doesn't exist and create_table is set as True.\n :raises: PersistenceException: When `create_table` fails on dynamodb resource.\n \"\"\"\n if self.create_table:\n create_table_query_kwargs = CreateTableQueryKwargs(table_name=self.table_name, billing_mode=self.billing_mode)\n create_table_query_kwargs.add_hash_key(\n key_name=self.primary_index.hash_key_name,\n key_python_variable_type=self.primary_index.hash_key_variable_python_type\n )\n if self.primary_index.sort_key_name is not None and self.primary_index.sort_key_variable_python_type is not None:\n create_table_query_kwargs.add_hash_key(\n key_name=self.primary_index.sort_key_name,\n key_python_variable_type=self.primary_index.sort_key_variable_python_type\n )\n\n if self.global_secondary_indexes is not None:\n create_table_query_kwargs.add_all_global_secondary_indexes(global_secondary_indexes=self.global_secondary_indexes)\n try:\n self.dynamodb.create_table(**create_table_query_kwargs.data)\n except Exception as e:\n if e.__class__.__name__ != \"ResourceInUseException\":\n raise Exception(f\"Request to create the table if not exist failed: Exception of type {type(e).__name__} occurred {str(e)}\")\n\n def put_record(self, item_dict: dict) -> bool:\n serialized_item_dict = DynamoDBUtils.python_to_dynamodb(python_object=item_dict)\n try:\n table = self.dynamodb.Table(self.table_name)\n response = table.put_item(Item=serialized_item_dict)\n return True if response is not None else False\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} doesn't exist. Failed to save attributes to DynamoDb table.\")\n except Exception as e:\n print(f\"Failed to save attributes to DynamoDb table. Exception of type {type(e).__name__} occurred: {str(e)}\")\n return False\n\n def delete_record(self, indexes_keys_selectors: dict) -> bool:\n try:\n table = self.dynamodb.Table(self.table_name)\n response_data: Optional[dict] = table.delete_item(Key=indexes_keys_selectors)\n return True if response_data is not None else False\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} doesn't exist. Failed to delete_record in DynamoDb table.\")\n except Exception as e:\n print(e)\n return False\n\n def remove_record(self, indexes_keys_selectors: dict) -> Optional[dict]:\n try:\n table = self.dynamodb.Table(self.table_name)\n response_data: dict = table.delete_item(Key=indexes_keys_selectors, ReturnValues='ALL_OLD')\n if response_data is None:\n return None\n response_attributes: Optional[dict] = response_data.get('Attributes', None)\n if response_attributes is None:\n return None\n return DynamoDBUtils.dynamodb_to_python(response_attributes)\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} doesn't exist. Failed to remove_record in DynamoDb table.\")\n except Exception as e:\n print(e)\n return None\n\n def get_item_by_primary_key(self, index_name: str, key_value: any, fields_path_elements: Optional[List[List[DatabasePathElement]]]) -> Optional[GetItemResponse]:\n if fields_path_elements is not None:\n kwargs = self._fields_paths_elements_to_expressions(fields_path_elements=fields_path_elements)\n else:\n kwargs = {}\n kwargs['Key'] = {index_name: key_value}\n kwargs['ConsistentRead'] = True\n\n try:\n table = self.dynamodb.Table(self.table_name)\n response = table.get_item(**kwargs)\n if 'Item' in response:\n processed_item = DynamoDBUtils.dynamodb_to_python(dynamodb_object=response['Item'])\n return GetItemResponse(item=processed_item, success=True)\n else:\n return GetItemResponse(item=None, success=False)\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} do not exist or in the process of being created. Failed to get attributes from DynamoDb table.\")\n except Exception as e:\n print(f\"Failed to retrieve attributes from DynamoDb table. Exception of type {type(e).__name__} occurred: {str(e)}\")\n return None\n\n def check_if_item_exist_by_primary_key(self, index_name: str, key_value: str, fields_path_elements: Optional[List[str]]) -> Optional[bool]:\n raise Exception(\"Not implemented\") # todo: implement\n\n def _execute_update_query(self, query_kwargs_dict: dict, allow_validation_exception: bool = False) -> Optional[Response]:\n try:\n table = self.dynamodb.Table(self.table_name)\n response = table.update_item(**query_kwargs_dict)\n return Response(response)\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} do not exist or in the process of being created. Failed to get attributes from DynamoDb table.\")\n except ClientError as e:\n print(f\"{e} - No element has been found for the update query : {query_kwargs_dict}\")\n if allow_validation_exception and e.response['Error']['Code'] == 'ValidationException':\n return Response({})\n return None\n except Exception as e:\n print(f\"Failed to update attributes in DynamoDb table. Exception of type {type(e).__name__} occurred: {str(e)}\")\n return None\n\n def add_data_elements_to_list(self, index_name: str, key_value: Any, object_path: str, element_values: List[dict]) -> Optional[Response]:\n serialized_elements_values: List[Any] = DynamoDBUtils.python_to_dynamodb(python_object=element_values)\n kwargs = {\n 'TableName': self.table_name,\n 'Key': {index_name: key_value},\n 'ReturnValues': \"UPDATED_NEW\",\n 'UpdateExpression': f\"SET {object_path} = list_append(if_not_exists({object_path}, :emptyList), :newItems)\",\n # The if_not_exists inside the list_append, will create an empty\n # list before adding the newItems, only if the field do not exist.\n 'ExpressionAttributeValues': {\n ':newItems': serialized_elements_values,\n ':emptyList': []\n }\n }\n return self._execute_update_query(query_kwargs_dict=kwargs)\n\n def remove_data_elements_from_list(self, index_name: str, key_value: Any, list_object_path: str, indexes_to_remove: list) -> Optional[Response]:\n kwargs = {\n 'TableName': self.table_name,\n 'Key': {index_name: key_value},\n 'ReturnValues': \"UPDATED_NEW\"\n }\n update_expression = \"REMOVE \"\n for i, index_in_database_list in enumerate(indexes_to_remove):\n update_expression += f\"{list_object_path}[{index_in_database_list}]\"\n if i + 1 < len(indexes_to_remove):\n update_expression += \", \"\n kwargs['UpdateExpression'] = update_expression\n\n return self._execute_update_query(query_kwargs_dict=kwargs)\n\n def remove_data_elements_from_map(\n self, index_name: str, key_value: Any,\n targets_path_elements: List[List[DatabasePathElement]],\n retrieve_removed_elements: bool = False\n ) -> Optional[Dict[str, Any]]:\n\n consumed_targets_path_elements: List[List[DatabasePathElement]] = []\n expression_attribute_names_dict = {}\n update_expression = \"REMOVE \"\n\n for i_target, target in enumerate(targets_path_elements):\n current_target_num_path_elements = len(target)\n current_setter_attribute_names = {}\n current_remover_update_expression = \"\"\n\n for i_path_element, path_element in enumerate(target):\n current_path_key = f\"#target{i_target}_pathKey{i_path_element}\"\n current_remover_update_expression += current_path_key\n current_setter_attribute_names[current_path_key] = path_element.element_key\n if i_path_element + 1 < current_target_num_path_elements:\n current_remover_update_expression += \".\"\n\n complete_update_expression_bytes_size = getsizeof(update_expression)\n current_setter_update_expression_bytes_size = getsizeof(current_remover_update_expression)\n update_expression_bytes_size_if_setter_is_added = complete_update_expression_bytes_size + current_setter_update_expression_bytes_size\n if update_expression_bytes_size_if_setter_is_added < EXPRESSION_MAX_BYTES_SIZE:\n if i_target > 0:\n update_expression += \", \"\n expression_attribute_names_dict = {**expression_attribute_names_dict, **current_setter_attribute_names}\n update_expression += current_remover_update_expression\n consumed_targets_path_elements.append(target)\n else:\n break\n\n update_query_kwargs = {\n 'TableName': self.table_name,\n 'Key': {index_name: key_value},\n 'ReturnValues': \"UPDATED_OLD\" if retrieve_removed_elements is True else \"NONE\",\n 'UpdateExpression': update_expression,\n 'ExpressionAttributeNames': expression_attribute_names_dict,\n }\n response = self._execute_update_query(query_kwargs_dict=update_query_kwargs, allow_validation_exception=True)\n if response is None:\n return None\n\n # Even if we return an empty output_response_attributes dict, we do not want to return None instead of a dict, because since this function only returns a dict, a return\n # value of None indicates that the operation failed. Where as, for example in delete operation, we will not request the removed attributes from the database, which will\n # give us an empty output_response_attributes dict, but the delete operation will base itself on the presence of the dict to judge if the operation failed or not.\n output_response_attributes: dict = DynamoDBUtils.dynamodb_to_python(response.attributes) if response.attributes is not None else {}\n if len(targets_path_elements) == len(consumed_targets_path_elements):\n return output_response_attributes\n else:\n for current_target_path_elements in consumed_targets_path_elements:\n targets_path_elements.remove(current_target_path_elements)\n\n request_continuation_response_attributes = self.remove_data_elements_from_map(\n index_name=index_name, key_value=key_value,\n targets_path_elements=targets_path_elements,\n retrieve_removed_elements=retrieve_removed_elements\n )\n combined_output_response_attributes = {\n **(output_response_attributes or {}),\n **(request_continuation_response_attributes or {})\n }\n return combined_output_response_attributes\n\n @staticmethod\n def _add_database_path_element_to_string_expression(\n base_string: Optional[str], database_path_element: DatabasePathElement, path_key: str\n ) -> Tuple[str, Dict[str, str]]:\n\n output_string: str = base_string or str()\n output_expression_attribute_names: Dict[str, str] = {}\n\n if isinstance(database_path_element.element_key, str):\n if len(output_string) > 0:\n output_string += \".\"\n output_string += path_key\n output_expression_attribute_names[path_key] = database_path_element.element_key\n elif isinstance(database_path_element.element_key, int):\n # If the element_key is an int, it means we try to access an index of a list or set. We can right away use the index access\n # quotation ( [$index] instead of .$attributeName ) and not need to pass the index as an attribute name. Note, that anyway,\n # boto3 will only accept strings as attribute names, and trying to pass an int or float as attribute name, will crash the request.\n output_string += f\"[{database_path_element.element_key}]\"\n\n return output_string, output_expression_attribute_names\n\n @staticmethod\n def _add_database_path_element_to_string_path(base_string: Optional[str], database_path_element: DatabasePathElement) -> str:\n output_string: str = base_string or str()\n if isinstance(database_path_element.element_key, str):\n if len(output_string) > 0:\n output_string += \".\"\n output_string += database_path_element.element_key\n elif isinstance(database_path_element.element_key, int):\n # See comment on similar block code in function _add_database_path_element_to_string_expression\n output_string += f\"[{database_path_element.element_key}]\"\n return output_string\n\n @staticmethod\n def _prepare_map_initialization(\n i: int, current_path_element: DatabasePathElement, overriding_init_value: Any,\n previous_element_initializer_container: Optional[MapItemInitializerContainer] = None\n ) -> MapItemInitializer:\n\n current_path_key = f\"#pathKey{i}\"\n current_path_target = str() if previous_element_initializer_container is None else previous_element_initializer_container.item.path_target\n\n current_path_target, new_expression_attribute_names = DynamoDbCoreAdapter._add_database_path_element_to_string_expression(\n base_string=current_path_target, database_path_element=current_path_element, path_key=current_path_key\n )\n expression_attribute_names = (\n {**previous_element_initializer_container.item.expression_attribute_names, **new_expression_attribute_names}\n if previous_element_initializer_container is not None else new_expression_attribute_names\n )\n\n item_default_value = overriding_init_value or current_path_element.get_default_value()\n return MapItemInitializer(\n path_target=current_path_target, last_item_element_key=current_path_element.element_key,\n item_default_value=item_default_value, expression_attribute_names=expression_attribute_names\n )\n\n def initialize_element_in_map_target(\n self, index_name: str, key_value: Any, initializer: MapItemInitializer, is_last_path_element: bool = False\n ) -> Optional[Response]:\n\n base_query_kwargs = {\n 'TableName': self.table_name,\n 'Key': {index_name: key_value},\n 'ExpressionAttributeNames': initializer.expression_attribute_names,\n 'ExpressionAttributeValues': {\n ':item': initializer.item_default_value\n },\n 'ReturnValues': 'UPDATED_NEW' if is_last_path_element else 'NONE'\n # We do not need to waste retrieving the UPDATED_NEW value when we know we we\n # will it only for the last path element (we use right after the loop ended)\n }\n current_update_expression = f\"SET {initializer.path_target} = if_not_exists({initializer.path_target}, :item)\"\n current_set_potentially_missing_object_query_kwargs = {**base_query_kwargs, 'UpdateExpression': current_update_expression}\n response = self._execute_update_query(query_kwargs_dict=current_set_potentially_missing_object_query_kwargs)\n if is_last_path_element is True:\n # If the last item attribute value in the database does (retrieved thanks to the \"UPDATED_NEW\" ReturnValues),\n # do not equal its default_value (which is either the field type default value, or the\n # last_item_custom_overriding_init_value parameter), we want to initialize again the item, because this means\n # that the attribute existed, so the SET update expression with if_not_exists did not performed, and we do\n # not currently have the value we want in the database. We perform this operation only for the last path\n # element in the request, to avoid overriding and scratching list or dictionary that are expected to exist.\n field_existing_attribute_in_database = response.attributes.get(initializer.last_item_element_key) if response is not None else None\n if field_existing_attribute_in_database is None or field_existing_attribute_in_database != initializer.item_default_value:\n # It is possible for the field to already exist, and yet, to not have\n current_update_expression = f\"SET {initializer.path_target} = :item\"\n current_override_existing_object_value_query_kwargs = {**base_query_kwargs, 'UpdateExpression': current_update_expression}\n override_object_value_response = self._execute_update_query(query_kwargs_dict=current_override_existing_object_value_query_kwargs)\n return override_object_value_response\n return response\n\n def _construct_update_data_element_to_map_query_kwargs(\n self, index_name: str, key_value: Any, field_path_elements: List[DatabasePathElement], value: Any, return_old_value: bool = False\n ) -> dict:\n\n expression_attribute_names_dict: Dict[str, str] = {}\n update_expression = \"SET \"\n\n for i, path_element in enumerate(field_path_elements):\n current_path_key = f\"#pathKey{i}\"\n update_expression += current_path_key\n expression_attribute_names_dict[current_path_key] = path_element.element_key\n if i + 1 < len(field_path_elements):\n update_expression += \".\"\n else:\n update_expression += \" = :item\"\n\n serialized_value = DynamoDBUtils.python_to_dynamodb(python_object=value)\n update_query_kwargs = {\n \"TableName\": self.table_name,\n \"Key\": {index_name: key_value},\n \"ReturnValues\": \"NONE\" if return_old_value is not True else \"UPDATED_OLD\",\n \"UpdateExpression\": update_expression,\n \"ExpressionAttributeNames\": expression_attribute_names_dict,\n \"ExpressionAttributeValues\": {\n \":item\": serialized_value\n }\n }\n return update_query_kwargs\n\n def set_update_data_element_to_map_with_default_initialization(\n self, index_name: str, key_value: Any, field_path_elements: List[DatabasePathElement], value: Any, return_old_value: bool = False\n ) -> Optional[Response]:\n update_query_kwargs = self._construct_update_data_element_to_map_query_kwargs(\n index_name=index_name, key_value=key_value, field_path_elements=field_path_elements, value=value, return_old_value=return_old_value\n )\n return self._execute_update_query_with_initialization_if_missing(\n index_name=index_name, key_value=key_value, update_query_kwargs=update_query_kwargs,\n setters=[FieldPathSetter(field_path_elements=field_path_elements, value_to_set=value)],\n )\n\n def set_update_data_element_to_map_without_default_initialization(\n self, index_name: str, key_value: Any, field_path_elements: List[DatabasePathElement], value: Any\n ) -> Optional[Response]:\n update_query_kwargs = self._construct_update_data_element_to_map_query_kwargs(\n index_name=index_name, key_value=key_value, field_path_elements=field_path_elements, value=value\n )\n return self._execute_update_query(query_kwargs_dict=update_query_kwargs)\n\n @staticmethod\n def _setters_to_tidied_initializers(setters: List[FieldPathSetter]) -> Dict[str, MapItemInitializerContainer]:\n root_initializers_containers: Dict[str, MapItemInitializerContainer] = {}\n all_initializers_containers: Dict[str, MapItemInitializerContainer] = {}\n\n for setter in setters:\n current_absolute_target_path: str = \"\"\n last_map_initializer_container: Optional[MapItemInitializerContainer] = None\n setter_serialized_value_to_set = DynamoDBUtils.python_to_dynamodb(python_object=setter.value_to_set)\n\n for i_path, path_element in enumerate(setter.field_path_elements):\n current_absolute_target_path = DynamoDbCoreAdapter._add_database_path_element_to_string_path(\n base_string=current_absolute_target_path, database_path_element=path_element\n )\n existing_container: Optional[MapItemInitializerContainer] = all_initializers_containers.get(current_absolute_target_path, None)\n if existing_container is None:\n overriding_init_value: Optional[Any] = setter_serialized_value_to_set if i_path + 1 >= len(setter.field_path_elements) else None\n current_map_initializer = DynamoDbCoreAdapter._prepare_map_initialization(\n i=i_path, current_path_element=path_element, overriding_init_value=overriding_init_value,\n previous_element_initializer_container=last_map_initializer_container\n )\n current_map_initializer_container = MapItemInitializerContainer(item=current_map_initializer, nexts_in_line=dict())\n all_initializers_containers[current_absolute_target_path] = current_map_initializer_container\n # We must use the current_absolute_target_path as key, because the all_initializers_containers dict is a flattened dict.\n\n # We can use the element_key instead of the absolute path as key for both the root_initializers_containers and last_map_initializer_container\n # dict's since they are layered dictionaries, unlike the all_initializers_containers dict which is a flattened dict.\n if last_map_initializer_container is None:\n # If the last_map_initializer_container is None, this means that we are in the first iteration of the\n # field_path_elements loop, which means that the current_map_initializer_container is a root initializer.\n root_initializers_containers[path_element.element_key] = current_map_initializer_container\n else:\n # If the last_map_initializer_container is not None, we know that our current_map_initializer_container is not\n # a root initializer, and needs to be set in the nexts_in_line variable of the last_map_initializer_container.\n last_map_initializer_container.nexts_in_line[path_element.element_key] = current_map_initializer_container\n\n last_map_initializer_container = current_map_initializer_container\n else:\n last_map_initializer_container = existing_container\n logging.info(message_with_vars(\n message=\"Successfully tidied duplicate DatabasePathElement initializer\",\n vars_dict={\n 'trimmedPathElementKey': path_element.element_key,\n 'trimmedSetterSerializedValueToSet': setter_serialized_value_to_set,\n 'existingContainerItemPathTarget': existing_container.item.path_target,\n 'existingContainerItemDefaultValue': existing_container.item.item_default_value\n }\n ))\n return root_initializers_containers\n\n def _initializer_task_executor(self, index_name: str, key_value: str, initializer_container: MapItemInitializerContainer) -> Optional[Response]:\n # todo: group fields initialization in single request\n is_last_path_element = not len(initializer_container.nexts_in_line) > 0\n initialization_response = self.initialize_element_in_map_target(\n index_name=index_name, key_value=key_value,\n initializer=initializer_container.item,\n is_last_path_element=is_last_path_element\n )\n if initialization_response is None:\n logging.error(message_with_vars(\n message=\"Initialized a field after a set/update multiple data elements in map request had failed.\",\n vars_dict={'initializer_container': initializer_container}\n ))\n\n if len(initializer_container.nexts_in_line) > 0:\n results = self._run_initializers_in_executors(\n index_name=index_name, key_value=key_value,\n initializers=initializer_container.nexts_in_line\n )\n return initialization_response\n\n def _run_initializers_in_executors(self, index_name: str, key_value: str, initializers: Dict[str, MapItemInitializerContainer]) -> Optional[List[Optional[Response]]]:\n initializers_values = initializers.values()\n num_initializers = len(initializers_values)\n with ThreadPoolExecutor(max_workers=num_initializers) as executor:\n results: List[Optional[Response]] = [executor.submit(\n self._initializer_task_executor, index_name, key_value, item\n ).result() for i, item in enumerate(initializers_values)]\n return results\n\n def _execute_update_query_with_initialization_if_missing(\n self, index_name: str, key_value: Any, update_query_kwargs: dict, setters: List[FieldPathSetter]\n ) -> Optional[Response]:\n\n response = self._execute_update_query(query_kwargs_dict=update_query_kwargs)\n if response is None:\n # If the response is None, it means that one of the path of the target path has not been found and need to be initialized.\n database_paths_initializers = self._setters_to_tidied_initializers(setters=setters)\n results = self._run_initializers_in_executors(\n index_name=index_name, key_value=key_value,\n initializers=database_paths_initializers\n )\n last_response = results[-1]\n # This function can only be triggered if there is at least one item in setters List, so we do not need to worry about accessing\n # the last element in result List without checking if the len of the setters or of the results is superior to zero.\n return last_response\n return response\n\n def set_update_multiple_data_elements_to_map(\n self, index_name: str, key_value: Any, setters: List[FieldPathSetter], return_old_values: bool\n ) -> Optional[Response]:\n\n if not len(setters) > 0:\n # If we tried to run the query with no object setter,\n # she will crash when executed. So we return None.\n return None\n\n update_query_kwargs = {\n 'TableName': self.table_name,\n 'Key': {index_name: key_value},\n 'ReturnValues': \"UPDATED_OLD\" if return_old_values is True else \"NONE\"\n }\n update_expression = \"SET \"\n expression_attribute_names_dict, expression_attribute_values_dict = {}, {}\n\n consumed_setters: List[FieldPathSetter] = []\n for i_setter, current_setter in enumerate(setters):\n current_setter_update_expression = \"\"\n current_setter_attribute_names, current_setter_attribute_values = {}, {}\n setter_serialized_value_to_set = DynamoDBUtils.python_to_dynamodb(python_object=current_setter.value_to_set)\n\n for i_path, current_path_element in enumerate(current_setter.field_path_elements):\n current_path_key = f\"#setter{i_setter}_pathKey{i_path}\"\n current_setter_update_expression, new_expression_attribute_names = DynamoDbCoreAdapter._add_database_path_element_to_string_expression(\n base_string=current_setter_update_expression, database_path_element=current_path_element, path_key=current_path_key\n )\n current_setter_attribute_names = {**current_setter_attribute_names, **new_expression_attribute_names}\n\n if i_path >= (len(current_setter.field_path_elements) - 1):\n current_setter_update_expression += f\" = :item{i_setter}\"\n current_setter_attribute_values[f\":item{i_setter}\"] = setter_serialized_value_to_set\n\n complete_update_expression_bytes_size = getsizeof(update_expression)\n # complete_update_query_attribute_names_bytes_size = getsizeof(expression_attribute_names_dict)\n # complete_update_query_attribute_values_bytes_size = getsizeof(expression_attribute_values_dict)\n current_setter_update_expression_bytes_size = getsizeof(current_setter_update_expression)\n # current_setter_attribute_names_bytes_size = getsizeof(current_setter_attribute_names)\n # current_setter_attribute_values_bytes_size = getsizeof(current_setter_attribute_values)\n update_expression_bytes_size_if_setter_is_added = complete_update_expression_bytes_size + current_setter_update_expression_bytes_size\n # attributes_names_bytes_size_if_setter_is_added = complete_update_query_attribute_names_bytes_size + current_setter_attribute_names_bytes_size\n # attributes_values_bytes_size_if_setter_is_added = complete_update_query_attribute_values_bytes_size + current_setter_attribute_values_bytes_size\n if update_expression_bytes_size_if_setter_is_added < EXPRESSION_MAX_BYTES_SIZE:\n if i_setter > 0:\n update_expression += \", \"\n update_expression += current_setter_update_expression\n expression_attribute_names_dict = {**expression_attribute_names_dict, **current_setter_attribute_names}\n expression_attribute_values_dict = {**expression_attribute_values_dict, **current_setter_attribute_values}\n consumed_setters.append(current_setter)\n else:\n print(message_with_vars(\n message=\"Update operation expression size has reached over 4kb. \"\n \"The operation will be divided in a secondary operation (which could also be divided)\",\n vars_dict={\n 'index_name': index_name, 'key_value': key_value,\n 'setters': setters, 'update_expression': update_expression,\n 'current_setter_update_expression': current_setter_update_expression,\n 'current_setter_attribute_names': current_setter_attribute_names,\n 'current_setter_attribute_values': current_setter_attribute_values\n }\n ))\n break\n\n update_query_kwargs['UpdateExpression'] = update_expression\n update_query_kwargs['ExpressionAttributeValues'] = expression_attribute_values_dict\n if len(expression_attribute_names_dict) > 0:\n update_query_kwargs['ExpressionAttributeNames'] = expression_attribute_names_dict\n\n response = self._execute_update_query_with_initialization_if_missing(\n index_name=index_name, key_value=key_value,\n update_query_kwargs=update_query_kwargs, setters=consumed_setters\n )\n if len(consumed_setters) == len(setters):\n return response\n else:\n for setter in consumed_setters:\n setters.remove(setter)\n return self.set_update_multiple_data_elements_to_map(\n index_name=index_name, key_value=key_value,\n setters=setters, return_old_values=return_old_values\n )\n\n def query_response_by_key(\n self, index_name: str, key_value: Any,\n fields_path_elements: Optional[List[List[DatabasePathElement]]] = None,\n filter_expression: Optional[Any] = None, pagination_records_limit: Optional[int] = None,\n exclusive_start_key: Optional[str] = None, **additional_kwargs\n ) -> Response:\n if fields_path_elements is not None:\n kwargs = self._fields_paths_elements_to_expressions(fields_path_elements=fields_path_elements)\n else:\n kwargs = {}\n if additional_kwargs is not None:\n kwargs = {**kwargs, **additional_kwargs}\n\n kwargs['KeyConditionExpression'] = Key(index_name).eq(key_value)\n if index_name != self.primary_index.hash_key_name:\n # If the queried index is the primary_index, it must\n # not be specified, otherwise the request will fail.\n kwargs['IndexName'] = index_name\n\n if filter_expression is not None:\n kwargs['FilterExpression'] = filter_expression\n\n if pagination_records_limit is not None:\n kwargs['Limit'] = pagination_records_limit\n\n if exclusive_start_key is not None:\n kwargs['ExclusiveStartKey'] = exclusive_start_key\n\n try:\n table = self.dynamodb.Table(self.table_name)\n response = table.query(**kwargs)\n return Response(DynamoDBUtils.dynamodb_to_python(response))\n except ResourceNotExistsError:\n raise Exception(f\"DynamoDb table {self.table_name} do not exist or in the process\"\n \"of being created. Failed to get attributes from DynamoDb table.\")\n except Exception as e:\n raise Exception(f\"Failed to retrieve attributes from DynamoDb table.\"\n f\"Exception of type {type(e).__name__} occurred: {str(e)}\")\n\n def query_items_by_key(\n self, index_name: str, key_value: Any, fields_path_elements: List[List[DatabasePathElement]],\n filter_expression: Optional[Any] = None, pagination_records_limit: Optional[int] = None,\n exclusive_start_key: Optional[str] = None, **additional_kwargs\n ) -> Tuple[Optional[List[Any]], QueryMetadata]:\n response = self.query_response_by_key(\n index_name=index_name, key_value=key_value, fields_path_elements=fields_path_elements,\n filter_expression=filter_expression, pagination_records_limit=pagination_records_limit,\n exclusive_start_key=exclusive_start_key, **additional_kwargs\n )\n if response is None:\n return None, QueryMetadata(count=0, has_reached_end=True, last_evaluated_key=None)\n query_metadata = QueryMetadata(\n count=response.count,\n has_reached_end=response.has_reached_end,\n last_evaluated_key=response.last_evaluated_key\n )\n return response.items, query_metadata\n\n output: List[Any] = []\n for record_item_data in response.items:\n if isinstance(record_item_data, dict):\n if is_multi_selector is not True:\n field_path_elements: List[DatabasePathElement]\n output.append(navigate_into_data_with_field_path_elements(\n data=record_item_data, field_path_elements=field_path_elements,\n num_keys_to_navigation_into=len(field_path_elements)\n ))\n else:\n field_path_elements: Dict[str, List[DatabasePathElement]]\n output.append(self._unpack_multiple_retrieved_fields(\n item_data=record_item_data, fields_path_elements=field_path_elements,\n num_keys_to_stop_at_before_reaching_end_of_item=0, metadata=False\n ))\n return output, query_metadata\n\n def query_single_item_by_key(\n self, index_name: str, key_value: Any,\n fields_path_elements: Optional[List[List[DatabasePathElement]]] = None,\n filter_expression: Optional[Any] = None\n ) -> Optional[dict]:\n # Yes, a query request is heavier than a get request that we could do with the _get_item_by_primary_key function.\n # Yet, in a get request, we cannot specify an index_name to query on. So, the _query_single_item_by_key should be\n # used when we want to get an item based on another index that the primary one. Otherwise, use _get_item_by_primary_key\n response = self.query_response_by_key(\n index_name=index_name, key_value=key_value,\n fields_path_elements=fields_path_elements,\n filter_expression=filter_expression, pagination_records_limit=1\n )\n if response.count == 1:\n return response.items[0]\n elif not response.count > 0:\n print(\"No item has been found\")\n return None\n elif not response.count > 1:\n print(\"More than one item has been found. Returning first item.\")\n return response.items[0]\n\n def get_or_query_single_item(self, index_name: str, key_value: str, fields_path_elements: List[List[DatabasePathElement]]) -> Optional[dict]:\n if self.primary_index.hash_key_name == index_name:\n response: Optional[GetItemResponse] = self.get_item_by_primary_key(\n index_name=index_name, key_value=key_value, fields_path_elements=fields_path_elements\n )\n return response.item if response is not None else None\n else:\n if index_name not in self._global_secondary_indexes_hash_keys:\n print(message_with_vars(\n message=\"A index_name was not the primary_index index_name, and was not found in the \"\n \"global_secondary_indexes hash_keys. Database query not executed, and None is being returned.\",\n vars_dict={\n 'primary_index.hash_key_name': self.primary_index.hash_key_name,\n '_global_secondary_indexes_hash_keys': self._global_secondary_indexes_hash_keys,\n 'index_name': index_name, 'key_value': key_value, \"fields_path_elements\": fields_path_elements\n }\n ))\n return None\n else:\n response_items: Optional[List[dict]] = self.query_response_by_key(\n index_name=index_name, key_value=key_value,\n fields_path_elements=fields_path_elements, pagination_records_limit=1\n ).items\n if isinstance(response_items, list) and len(response_items) > 0:\n return response_items[0]\n else:\n return None\n\n def get_data_in_path_target(\n self, index_name: str, key_value: str,\n field_path_elements: List[DatabasePathElement],\n num_keys_to_navigation_into: int\n ) -> Optional[any]:\n\n response_item = self.get_or_query_single_item(\n index_name=index_name, key_value=key_value,\n fields_path_elements=[field_path_elements]\n )\n return navigate_into_data_with_field_path_elements(\n data=response_item, field_path_elements=field_path_elements,\n num_keys_to_navigation_into=num_keys_to_navigation_into\n )\n\n def get_value_in_path_target(self, index_name: str, key_value: str, field_path_elements: List[DatabasePathElement]) -> Optional[any]:\n return self.get_data_in_path_target(\n index_name=index_name, key_value=key_value,\n field_path_elements=field_path_elements,\n num_keys_to_navigation_into=len(field_path_elements)\n )\n\n def get_item_in_path_target(self, index_name: str, key_value: str, field_path_elements: List[DatabasePathElement]) -> Optional[dict]:\n return self.get_data_in_path_target(\n index_name=index_name, key_value=key_value,\n field_path_elements=field_path_elements,\n num_keys_to_navigation_into=len(field_path_elements) - 1\n )\n\n @staticmethod\n def _unpack_multiple_retrieved_fields(\n item_data: dict, fields_path_elements: Dict[str, List[DatabasePathElement]],\n num_keys_to_stop_at_before_reaching_end_of_item: int, metadata: bool = False\n ):\n output: Dict[str, Any] = {}\n for field_path_key, field_item_path_elements in fields_path_elements.items():\n # All the values of each requested items will be inside the response_item dict. We just need\n # to navigate inside of the response_item with the field_path_elements for each requested\n # item, and package that in an output dict that will use the key of the requested items.\n if len(field_item_path_elements) > 0:\n num_keys_to_navigation_into: int = len(field_item_path_elements) - num_keys_to_stop_at_before_reaching_end_of_item\n navigated_item: Optional[Any] = navigate_into_data_with_field_path_elements(\n data=item_data, field_path_elements=field_item_path_elements,\n num_keys_to_navigation_into=num_keys_to_navigation_into\n )\n output[field_path_key] = navigated_item if metadata is not True else {\n 'value': navigated_item, 'field_path_elements': field_item_path_elements\n }\n return output\n\n def get_data_from_multiple_fields_in_path_target(\n self, key_value: str, fields_path_elements: Dict[str, List[DatabasePathElement]],\n num_keys_to_stop_at_before_reaching_end_of_item: int, index_name: Optional[str] = None,\n metadata: bool = False\n ) -> Optional[Dict[str, Any]]:\n\n response_item: Optional[dict] = self.get_or_query_single_item(\n index_name=index_name, key_value=key_value,\n fields_path_elements=list(fields_path_elements.values())\n )\n if response_item is None:\n return None\n\n return self._unpack_multiple_retrieved_fields(\n item_data=response_item, fields_path_elements=fields_path_elements,\n num_keys_to_stop_at_before_reaching_end_of_item=num_keys_to_stop_at_before_reaching_end_of_item,\n metadata=metadata\n )\n\n def get_values_in_multiple_path_target(\n self, index_name: str, key_value: str,\n fields_path_elements: Dict[str, List[DatabasePathElement]],\n metadata: bool = False\n ):\n return self.get_data_from_multiple_fields_in_path_target(\n index_name=index_name, key_value=key_value,\n fields_path_elements=fields_path_elements,\n num_keys_to_stop_at_before_reaching_end_of_item=0, metadata=metadata\n )\n\n def get_items_in_multiple_path_target(\n self, index_name: str, key_value: str,\n fields_path_elements: Dict[str, List[DatabasePathElement]],\n metadata: bool = False\n ):\n return self.get_data_from_multiple_fields_in_path_target(\n index_name=index_name, key_value=key_value,\n fields_path_elements=fields_path_elements,\n num_keys_to_stop_at_before_reaching_end_of_item=1, metadata=metadata\n )\n\n @staticmethod\n def _add_to_filter_expression(expression, condition):\n if expression is None:\n return condition\n return expression & condition\n\n @staticmethod\n def _fields_paths_elements_to_expressions(fields_path_elements: List[List[DatabasePathElement]]) -> dict:\n output_kwargs = {}\n expression_attribute_names = {}\n projection_expression = \"\"\n\n # In DynamoDB, when trying to get some fields, certain type of values (like values with - in them,\n # or big numbers like an UUID), can not work and provoke in error while executing the query. In\n # order to fix that, we need to pass the variable name in the ExpressionAttributeNames instead of\n # putting it directly in the ProjectionExpression. Yet, when using a map path like myMap.data.item1\n # we must declare each path element (myMap, data, item1) as separate ExpressionAttributeNames,\n # otherwise it will not work. So, we define an 'id' for each path of each attribute name\n # (f\"#f{i_field}_{i_path}\") we add our path as the dict value, and then we build the condition\n # expression to use the ids of our attributes names.\n for i_field, field_elements in enumerate(fields_path_elements):\n for i_path_element, path_element in enumerate(field_elements):\n if i_path_element > 0:\n last_field_path_element = field_elements[i_path_element - 1]\n if last_field_path_element.default_type == list:\n projection_expression += f'[{path_element.element_key}]'\n # We set the index of a list directly inside the projection expression, because a variable can\n # be passed the expression_attribute_names only if it is a str, where as our index will be an int.\n continue\n elif last_field_path_element.default_type == set:\n raise Exception(\"Selection by index in set type's not yet supported\")\n\n # If we reach this point, the continue keyword has not been triggered, which means that the current\n # path_element has not yet been handled by a special case handler, like an list index handler.\n current_field_path_expression_name = f\"#f{i_field}_{i_path_element}\"\n expression_attribute_names[current_field_path_expression_name] = path_element.element_key\n projection_expression += f'{\".\" if i_path_element > 0 else \"\"}{current_field_path_expression_name}'\n\n if i_field + 1 < len(fields_path_elements):\n projection_expression += \", \"\n\n if len(expression_attribute_names) > 0:\n output_kwargs[\"ExpressionAttributeNames\"] = expression_attribute_names\n if projection_expression.replace(\" \", \"\") != \"\":\n output_kwargs[\"ProjectionExpression\"] = projection_expression\n\n return output_kwargs\n\n","repo_name":"inoftrobinson/StructNoSQL","sub_path":"StructNoSQL/tables_clients/backend/dynamodb_core.py","file_name":"dynamodb_core.py","file_ext":"py","file_size_in_byte":49644,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"78"}
+{"seq_id":"21884289854","text":"from abc import ABC, abstractmethod\nfrom collections.abc import Sequence\nfrom dataclasses import dataclass\nfrom typing import (\n Any,\n Generic,\n Iterable,\n Iterator,\n Optional,\n TypeVar,\n cast,\n no_type_check,\n)\n\n\nclass LinkedListError(Exception):\n pass\n\n\nclass EmptyLinkedList(LinkedListError):\n \"\"\"Is raised when the operation is not allowed on an empty linked list\"\"\"\n\n def __str__(self):\n return \"LinkedList is empty\"\n\n\nclass NotFoundDataError(LinkedListError):\n def __init__(self, data: Any):\n self.data = data\n super().__init__(data)\n\n def __str__(self):\n return f\"Data: {self.data} is not found in here!\"\n\n\nT = TypeVar(\"T\")\n\n\nclass LinkedList(Generic[T], ABC):\n def __init__(self, head: Optional[\"T\"] = None):\n if head is not None:\n self.head: \"Node[T]\" = (\n _ensure_node(head)\n if isinstance(self, SinglyLinkedList)\n else _ensure_dnode(head)\n )\n self._size = 1\n else:\n self.head = head if isinstance(self, SinglyLinkedList) else head\n self._size = 0\n self.tail: Optional[\"Node\"] = self.head\n\n def __iter__(self) -> Iterator[T]:\n if self.head is None:\n raise EmptyLinkedList()\n\n data, next_item = self.head.data, self.head.next_item\n yield data\n while next_item is not None:\n data, next_item = next_item.data, next_item.next_item\n yield data\n\n def iternodes(self) -> Iterator[\"Node[T]\"]:\n if self.head is None:\n raise EmptyLinkedList()\n\n node, next_node = self.head, self.head.next_item\n yield node\n while next_node is not None:\n node, next_node = next_node, next_node.next_item\n yield node\n\n @abstractmethod\n def pop(self, index: Optional[int] = None) -> \"T\":\n pass\n\n @abstractmethod\n def popleft(self) -> \"T\":\n pass\n\n def remove(self, index: Optional[int] = None) -> None:\n self.pop(index)\n\n def removeleft(self) -> None:\n self.popleft()\n\n\n# Singly LinkedList implementation\n@dataclass\nclass Node(Generic[T]):\n \"\"\"Simple node representation\n\n each node is like:\n --------------------\n | data | next_item |\n --------------------\n in which the data is the actual data the node is containing and next_item\n is a reference the next node.\n \"\"\"\n\n data: Any\n next_item: Optional[\"Node\"] = None\n\n def __hash__(self):\n return hash((self.data, self.next_item))\n\n\nclass SinglyLinkedList(LinkedList[T], Sequence):\n \"\"\"Singly Linked List implementation\n\n Attributes:\n head: is the head node of the linked list, defaults to None.\n tail: is the tail node of the linked list, default to `head`.\n _size: holds the size of linked list, size is the number of nodes which are connected to each other.\n\n Methods:\n append: adds a node to the end of the linked list, the newest node becomes the `tail`.\n appendleft: add a node to the head of the linked list, the newses node becomes the `head`.\n pop: deletes and return the tail, or a desired index\n popleft: deletes and returns the head of the linked list.\n remove: just like pop but does not return.\n removeleft: deletes the head of the linked list.\n insert: insert a data to a specific index\n extend: just like list.extend\n\n Behaviours:\n '__eq__', '__ne__'\n '__hash__', hash(obj)\n '__iter__', iter(obj) (for i in obj)\n '__getitem__', obj[0], obj[:10]\n '__contains__', ob in obj, ob not in obj\n '__init__', SLL(...)\n '__len__', len(obj)\n '__repr__', print, repr\n '__reversed__', reversed(obj)\n 'count', obj.count(ob)\n 'index', obj.index(ob)\n \"\"\"\n\n @no_type_check\n def __getitem__(self, item: int | slice) -> \"T\":\n if not self:\n raise EmptyLinkedList()\n\n self_iter = iter(self)\n data = None\n\n if isinstance(item, int):\n item = item if item >= 0 else item + self._size\n if item == 0:\n return self.head.data\n\n for _ in range(item + 1):\n try:\n data = next(self_iter)\n except StopIteration:\n raise IndexError(\"Index out of range\") from None\n else:\n return data\n\n # item is a slice now\n return [\n self[index]\n for index in range(\n item.start if item.start >= 0 else self._size + item.start,\n item.stop,\n item.step,\n )\n ]\n\n def __len__(self):\n return self._size\n\n def append(self, data: Any):\n \"\"\"Append a node to the end of SinglyLinkedList\n\n If `data` is not a node: wrap it in a node.\n \"\"\"\n\n # Check if we have a head or not\n # if no head is available -> head is None and tail is None too\n data = _ensure_node(data)\n if self.head is None:\n self.tail = self.head = data\n else:\n self.tail.next_item = data\n self.tail = data\n assert self.tail.next_item is None\n self._size += 1\n\n def appendleft(self, data: Any):\n \"\"\"Append a node to the start of the SinglyLinkedList\n\n This node will be the `head`\n \"\"\"\n\n # Check if we have a head or not\n # if no head is available -> head is Node and tail is None too\n # then, we have to just append the data to out SinglyLinkedList :)\n if self.head is None:\n self.append(data)\n return\n\n data = _ensure_node(data)\n former_head = self.head\n self.head = data\n self.head.next_item = former_head\n self._size += 1\n\n def pop(self, index: Optional[int] = None) -> \"T\":\n \"\"\"Pop the tail, or a desired index of sll\"\"\"\n if len(self) == 0:\n raise EmptyLinkedList()\n\n if index is None:\n if len(self) == 1:\n return self.popleft()\n\n for node in self.iternodes():\n if node.next_item == self.tail:\n # this is the node before the tail:\n to_ret = self.tail\n self.tail = node\n self.tail.next_item = None\n self._size -= 1\n return to_ret.data\n\n if isinstance(index, int):\n if index == 0:\n return self.popleft()\n\n to_ret = self[index] # may raise error :)\n\n before_and_self_nodes = []\n\n for idx, node in enumerate(self.iternodes()):\n if idx == index - 1:\n before_and_self_nodes.append(node)\n elif idx == index:\n before_and_self_nodes.append(node)\n\n before_node, self_node = before_and_self_nodes\n after_node = self_node.next_item\n before_node.next_item = after_node\n self._size -= 1\n return to_ret # type: ignore\n\n def popleft(self) -> \"T\":\n \"\"\"Pop the head\"\"\"\n if self.head is None:\n raise EmptyLinkedList()\n\n former_head = self.head\n newer_head = self.head.next_item\n self.head = newer_head\n self._size -= 1\n return former_head.data\n\n def insert(self, index: int, data: Any):\n \"\"\"Insert a node at the given\n\n >>> a = [1]\n >>> a.insert(0, 0)\n >>> # a -> [0, 1]\n \"\"\"\n\n if self._size == 0:\n self.append(data)\n return\n\n # if index == 0: it will be inserted at the head\n if index == 0:\n self.appendleft(data)\n return\n\n where_data = self[index]\n former_node = self.head\n\n while True:\n if former_node.next_item.data == where_data:\n _ = _ensure_node(data)\n later_node = former_node.next_item\n former_node.next_item = _\n _.next_item = later_node\n self._size += 1\n return\n former_node = former_node.next_item\n\n def extend(self, data: Iterable):\n for item in data:\n self.append(_ensure_node(item))\n\n def __repr__(self):\n return f\"{self.__class__.__name__}(head={self.head})\"\n\n\n# TODO: finish CLL\n# Circularly LinkedList implementation\nclass CircularlyLinkedList(SinglyLinkedList[\"T\"], Sequence):\n def __init__(self, data: Optional[T] = None):\n super().__init__(data)\n if data is not None:\n self.tail.next_item = self.head\n\n def append(self, data: Any):\n super().append(data)\n self.tail.next_item = self.head\n\n def appendleft(self, data: Any):\n super().appendleft(data)\n self.tail.next_item = self.head\n\n def pop(self, index: Optional[int] = None) -> \"T\":\n \"\"\"Pop the tail, or a desired index of sll\"\"\"\n if len(self) == 0:\n raise EmptyLinkedList()\n\n if index is None:\n got = super().pop(None)\n self.tail.next_item = self.head\n return got\n\n if isinstance(index, int):\n if index == 0:\n return self.popleft()\n\n to_ret = self[index] # may raise error :)\n\n before_and_self_nodes = []\n\n for idx, node in enumerate(self.iternodes()):\n if idx == len(self) + 1:\n break\n if idx == index - 1:\n before_and_self_nodes.append(node)\n elif idx == index:\n before_and_self_nodes.append(node)\n\n before_node, self_node = before_and_self_nodes\n after_node = self_node.next_item\n before_node.next_item = after_node\n self._size -= 1\n self.tail.next_item = self.head\n\n return to_ret\n\n def popleft(self) -> \"T\":\n got = super().popleft()\n self.tail.next_item = self.head\n return got\n\n\n# Doubly LinkedList implementation\n\n\nclass DNode(Node[\"T\"]):\n \"\"\"Class to create a two-way node\n\n each d-node is like:\n --------------------------------\n | prev_item | data | next_item |\n -------------------------------\n\n But the API is designed to instantiate a DNode as:\n dnode = DNode(data, prev_item=None, next_item=None)\n This is much better than DNode(prev_item, data, next_item=None)\n \"\"\"\n\n prev_item: Optional[\"DNode\"] = None\n\n def __hash__(self):\n return hash((self.data, self.prev_item, self.next_item))\n\n\nclass DoublyLinkedList(LinkedList, Sequence, Generic[T]):\n \"\"\"Doubly Linked List implementation\n\n Attributes:\n head: the head of dll -> prev_item is always None, Default: None.\n tail: the tail of dll -> next_item is always None, Default: `head`\n _size holds the size of the dll\n\n Methods:\n append: adds a dnode to the end dll, the newest dnode becomes the tail\n appendleft: adds a dnode to the head, the newest dnode becomes the head\n\n \"\"\"\n\n def append(self, data: Any):\n \"\"\"Append a Dnode to DoublyLinkedList\n\n Ensure data is a Dnode.\n \"\"\"\n\n data = _ensure_dnode(data)\n if self.head is None:\n self.tail = self.head = data\n else:\n self.tail.next_item = data\n data.prev_item = self.tail\n self.tail = data\n assert self.tail.next_item is None\n self._size += 1\n\n def appendleft(self, data: Any):\n \"\"\"Append at the head of DoublyLinkedList\n\n Ensure data is a Dnode\n \"\"\"\n\n if self.head is None:\n self.append(data)\n return\n\n data = _ensure_dnode(data)\n former_head = cast(\"DNode\", self.head)\n self.head = data\n self.head.next_item = former_head\n former_head.prev_item = cast(\"DNode\", self.head)\n self._size += 1\n\n def pop(self, index: Optional[int] = None) -> \"T\":\n \"\"\"Pop the tail\"\"\"\n if len(self) == 0:\n raise EmptyLinkedList()\n\n if len(self) == 1:\n # one dnode in out dll\n to_ret = self.tail\n self.head = self.tail = None\n self._size -= 1\n return to_ret.data\n\n former_tail = self.tail\n newest_tail = cast(DNode, self.tail).prev_item\n newest_tail.next_item = None\n self.tail = newest_tail\n self._size -= 1\n return former_tail.data\n\n @no_type_check\n def popleft(self) -> \"T\":\n \"\"\"Pop the head\"\"\"\n if len(self) == 0:\n raise EmptyLinkedList()\n\n if len(self) == 1:\n return self.pop()\n\n former_head = self.head\n newest_head = self.head.next_item\n newest_head.prev_item = None\n self.head = newest_head\n self._size -= 1\n return former_head.data\n\n @no_type_check\n def __getitem__(self, index: int | slice) -> \"T\":\n pass\n\n def __len__(self):\n return self._size\n\n def __repr__(self):\n return f\"{self.__class__.__name__}(head={self.head})\"\n\n\nclass StrSinglyLinkedList(SinglyLinkedList[str]):\n pass\n\n\nclass IntSinglyLinkedList(SinglyLinkedList[int]):\n pass\n\n\nclass StrDoublyLinkedList(DoublyLinkedList[str]):\n pass\n\n\nclass IntDoublyLinkedList(DoublyLinkedList[int]):\n pass\n\n\ndef _ensure_node(data: Any) -> \"Node\":\n if isinstance(data, Node):\n return data\n return Node(data)\n\n\ndef _ensure_dnode(data: Any) -> \"DNode\":\n if isinstance(data, DNode):\n return data\n return DNode(data)\n\n\n# TODO: fix no_type_check mypy errors\n","repo_name":"mahdihaghverdi/DrFatemiDataStructureClass","sub_path":"dsp/datastructures/linkedlists.py","file_name":"linkedlists.py","file_ext":"py","file_size_in_byte":13785,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"34511192958","text":"import cv2\nimport os\nimport numpy as np\nfrom scipy.ndimage import maximum_filter, minimum_filter\nimport argparse\nimport time\nimport math\n\nfrom HeadMaskGenerator import load_mask\n\n# debug draw\nimport matplotlib.pyplot as plt\n\n'''\ncalculate all MRI image score in the dataset\nreturn individual score for each image\n\ninput => masks_img, foreground_img\noutput => average score of current dataset\n'''\ndef mriqa(masks, imgs):\n _, _, n = masks.shape\n scores = []\n num_mask = len(masks)\n cnt = 0\n for i in range(n):\n img = imgs[:,:,i]\n mask = masks[:,:,i]\n if (np.sum(mask) / (mask.shape[0] * mask.shape[1]) < 0.2):\n scores.append(0.0)\n cnt += 1\n continue\n # most images are polluted\n if cnt > num_mask / 4:\n return [0.0]\n # normalize img\n norm_img = cv2.normalize(img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n\n gray = norm_img\n # contrast feature img\n window = (13, 13)\n maximg = maximum_filter(gray, size = window)\n minimg = minimum_filter(gray, size = window)\n contrast_img = maximg - minimg\n # image moment\n grayscale_moment = cv2.moments(gray)['nu20']\n contrast_moment = cv2.moments(contrast_img)['nu20']\n # binary image\n fgmg = (gray > grayscale_moment).astype(np.int)\n fcmg = (gray > contrast_moment).astype(np.int)\n fcmc = (contrast_img > contrast_moment).astype(np.int)\n fgmc = (contrast_img > grayscale_moment).astype(np.int)\n\n # print(np.sum(fgmg))\n # print(np.sum(fcmg))\n # print(np.sum(fcmc))\n # print(np.sum(fgmc))\n # fig = plt.figure(figsize=(10,10))\n # fig.add_subplot(2,2,1)\n # plt.imshow(fgmg, cmap=plt.cm.bone)\n # fig.add_subplot(2,2,2)\n # plt.imshow(fcmg, cmap=plt.cm.bone)\n # fig.add_subplot(2,2,3)\n # plt.imshow(fcmc, cmap=plt.cm.bone)\n # fig.add_subplot(2,2,4)\n # plt.imshow(fgmc, cmap=plt.cm.bone)\n # plt.show()\n # luminance contrast quality score\n q11 = fcmg & fgmg\n q1 = np.sum(mask * q11) / max(np.sum(fcmg), np.sum(fgmg)) if max(np.sum(fcmg), np.sum(fgmg)) != 0 else 0\n\n # texture score\n q22 = fgmc & fcmc\n q2 = np.sum(mask * q22) / max(np.sum(fgmc), np.sum(fgmg)) if max(np.sum(fgmc), np.sum(fgmg)) != 0 else 0\n\n # texture contrast quality score\n q33 = fgmc & fcmc\n q3 = np.sum(mask * q33) / np.sum(mask) if np.sum(mask) != 0 else 0\n\n # lightness quality score\n q44 = fcmg & fgmg\n q4 = np.sum(mask * q44) / np.sum(mask) if np.sum(mask) != 0 else 0\n\n # print(f\"{q1},{q2},{q3},{q4}\")\n # weight\n w1 = w2 = w4 = 0.1\n w3 = 0.7\n Q = w1 * q1 + w2 * q2 + w3 * q3 + w4 * q4\n\n if not math.isnan(Q):\n scores.append(Q)\n return scores\n\n# load mask img and dicom img file name\ndef fileImgLoad(Opath):\n dcms_path = []\n masks_path = []\n for file in os.listdir(Opath):\n exten = file.split('.')[1]\n if exten == \"dcm\":\n dcms_path.append(Opath + file)\n elif exten == \"dcmactImg\":\n masks_path.append(Opath + file)\n \n return masks_path, dcms_path\n\n# read image\ndef ImgLoader(masks_path, dcms_path):\n masks = []\n fgs = []\n first = True\n for i,mask in enumerate(masks_path):\n mask_img = cv2.imread(mask, cv2.IMREAD_GRAYSCALE)\n # norm_image = cv2.normalize(mask_img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n if first:\n first = False\n w, h = mask_img.shape\n masks = np.zeros((w, h, len(masks_path)))\n masks[:,:,i] = mask_img\n \n first = True\n for i,fg in enumerate(dcms_path):\n fg_img = cv2.imread(fg, cv2.IMREAD_GRAYSCALE)\n # norm_image = cv2.normalize(fg_img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n if first:\n first = False\n w, h = fg_img.shape\n fgs = np.zeros((w, h, len(dcms_path)))\n fgs[:,:,i] = fg_img\n \n return masks, fgs\n\n\n# load generated mask image and dicomimg\ndef load_existing_mask(Opath):\n masks_path, dcms_path = fileImgLoad(Opath)\n # read \n masks, imgs = ImgLoader(masks_path, dcms_path)\n return masks, imgs\n\n# slice property\n# from f(x) = e^(-pi*(x-1)^2)\ndef slice_score(slice_thickness, slice_spacing):\n p = slice_thickness / slice_spacing\n return np.exp(-np.pi * (p - 1)**2)\n\n# weight control for final score\ndef final_score(img_score, slice_score):\n return img_score * 0.7 + slice_score * 0.3\n\ndef directory_score(directory, hdr):\n \"\"\"\n :param directory: Input directory containg MRI images\n :return: metric (img_quality_score, img_slice_score, num_slices, total_score, time_consuming)\n \"\"\"\n print(f\"Starting process directory {directory}\")\n masks = []\n imgs = []\n thickness = 0\n\n masks, imgs, distance, thickness = load_mask(directory, hdr)\n scores = mriqa(masks, imgs)\n img_score = sum(scores) / len(scores)\n s_score = slice_score(float(thickness), float(distance)) if distance != 0 and thickness != 0 else 0\n score = final_score(img_score, s_score)\n\n print(f\"Processed the directory {directory}\")\n return (img_score, s_score, len(scores), score)\n\n\n# Unit Test\nif __name__ == \"__main__\":\n # parsing command line arguments \n # Input path of MRI image dataset\n # Output result of foreground image \n parser = argparse.ArgumentParser()\n parser.add_argument(\"-f\", \"--hdr\", type=bool)\n parser.add_argument(\"-i\", \"--input\")\n parser.add_argument(\"-o\", \"--output\")\n args = parser.parse_args()\n \n if args.input is None or args.output is None:\n print(\"Please python mriqa.py -i for calculate dataset quality score\")\n\n Ipath = args.input\n Opath = args.output\n HDREnable = args.hdr\n # Ipath = \"../data/series_216_SGE_fs_ax_113_2.55_256x256/\"\n # Ipath = \"../pydicom-playground/data/\"\n # Opath = \"../bfg/OutputImg-Larry-8/\"\n # Opath = \"./OutputImg-sample\"\n\n\n\n masks = []\n imgs = []\n thickness = 0\n\n if not os.path.exists(Opath):\n os.makedirs(Opath)\n\n masks, imgs, distance, thickness = load_mask(Ipath, Opath)\n\n scores = mriqa(masks, imgs)\n img_score = sum(scores) / len(scores)\n s_score = slice_score(float(thickness), float(distance))\n score = final_score(img_score, s_score)\n print(f\"img_score is: {img_score}, slice_score is: {s_score}\")\n print(f\"The dataset quality is {score}\")\n print(f\"The number of slice is {len(scores)}\")\n\n # check difference between new generated img and existing img\n\n # masks_e, imgs_e = load_existing_mask(Opath)\n # masks_n, imgs_n = load_mask(Ipath, Opath)\n # _,_,n = masks_e.shape\n # for i in range(n):\n # m_e = masks_e[:,:,i]\n # m_e = cv2.normalize(m_e, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n # m_n = masks_n[:,:,i]\n # m_d = m_e - m_n\n\n # i_e = imgs_e[:,:,i]\n # i_n = imgs_n[:,:,i]\n # i_d = i_e - i_n","repo_name":"davidzhangxm/3DMIP-MRIQC","sub_path":"mriqa.py","file_name":"mriqa.py","file_ext":"py","file_size_in_byte":7165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"37572550211","text":"\n# 定义不可达距离\n_ = float('inf')\n\n\ndef read(dir):\n lst = []\n with open(dir) as f:\n while True:\n line = f.readline().split()\n if not line:\n break\n l = [int(i) for i in line]\n lst.append(l)\n # lst = lst[1:]\n return lst\n\n\nlst = [[6, 3, 2], [4, 5, 6], [5, 3], [3, 1]]\nans = [_ for i in range(lst[0][0])]\n\nfor fix in lst[2:]:\n ans[fix[1]-1] = fix[0]\n\nranked = lst[1].copy()\nwhile ranked:\n rank = 0\n if len(ranked) == 1:\n for i in range(len(ans)):\n if ans[-(i+1)] == _:\n ans[-(i+1)] = ranked[rank]\n ranked.remove(ranked[rank])\n break\n break\n if ranked[rank] not in ans:\n for pos in range(len(ans)):\n if pos+len(ranked) == len(ans)+1:\n # [0, 1, 2, 3, _, _], [5, 6]\n ans[pos] = ranked[rank]\n ranked.remove(ranked[rank])\n rank = 0\n break\n if ans[pos] == _ and ans[pos+1] == ranked[rank+1]:\n print('ans[pos+1]:', ans[pos+1])\n print('ranked[rank+1]:', ranked[rank+1])\n print('len(ans)-(pos+1):', len(ans)-(pos+1))\n # for i in range(len(ans)-(pos+1)):\n # if ans[-i] == _:\n # ans[-(i+1)] = ranked[rank]\n # ranked.remove(ranked[rank])\n # rank = 0\n # break\n break\n else:\n ranked.remove(ranked[rank])\n\n\nprint(ans)","repo_name":"saflhsajkhbgr/My-journey-on-preparing-for-USACO","sub_path":"MilkingOrder.py","file_name":"MilkingOrder.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"6712472026","text":"import os\nimport tensorflow as tf\nimport pandas as pd\nimport nltk as nl\nimport numpy as np\n\ncwd = os.getcwd()\nos.chdir('/Users/clementmanger/Desktop/Thesis/Word2Vec')\n# cwd\n\ndf = pd.DataFrame.from_csv('ReviewsFiction.csv', sep = '|', header=0)\nlines = df['Review Text']\n\n#figure out document lengths\nlength = []\nfor f in df['Review Text']:\n length.append(len(nl.word_tokenize(f)))\nMAX_DOCUMENT_LENGTH = max(length)\n\n# create vocabulary\n\nvocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)\n\nx = np.array(list(vocab_processor.fit_transform(lines)))\n\nvocabulary = vocab_processor.vocabulary_\n\nvocab_size = len(vocabulary)\n\n#check if the vocabulary actually has any words in it. it feels like vocab may have the length, but no actual words in it\n\n#this is the idea, and it comes from https://github.com/cahya-wirawan/cnn-text-classification-tf/blob/master/data_helpers.py\n# with open('GoogleNews-vectors-negative300.bin', 'rb') as f:\n# header = f.readline()\n# vocab_size, vector_size = map(int, header.split())\n# print(vocab_size, vector_size)\n\n\ndef load_embedding_vectors_word2vec(vocabulary, filename, binary):\n # load embedding_vectors from the word2vec\n encoding = 'utf-8'\n with open(filename, \"rb\") as f:\n header = f.readline()\n vocab_size, vector_size = map(int, header.split())\n # initial matrix with random uniform\n embedding_vectors = np.random.uniform(-0.25, 0.25, (len(vocabulary), vector_size))\n if binary:\n binary_len = np.dtype('float32').itemsize * vector_size\n for line_no in range(vocab_size):\n word = []\n while True:\n ch = f.read(1)\n if ch == b' ':\n break\n if ch == b'':\n raise EOFError(\"unexpected end of input; is count incorrect or file otherwise damaged?\")\n if ch != b'\\n':\n word.append(ch)\n word = str(b''.join(word), encoding=encoding, errors='strict')\n idx = vocabulary.get(word)\n if idx != 0:\n embedding_vectors[idx] = np.fromstring(f.read(binary_len), dtype='float32')\n else:\n f.seek(binary_len, 1)\n else:\n for line_no in range(vocab_size):\n line = f.readline()\n if line == b'':\n raise EOFError(\"unexpected end of input; is count incorrect or file otherwise damaged?\")\n parts = str(line.rstrip(), encoding=encoding, errors='strict').split(\" \")\n if len(parts) != vector_size + 1:\n raise ValueError(\"invalid vector on line %s (is this really the text format?)\" % (line_no))\n word, vector = parts[0], list(map('float32', parts[1:]))\n idx = vocabulary.get(word)\n if idx != 0:\n embedding_vectors[idx] = vector\n f.close()\n return embedding_vectors\n\ninitW = load_embedding_vectors_word2vec(vocabulary, 'GoogleNews-vectors-negative300.bin', True)\n\nW = tf.Variable(tf.random_uniform([vocab_size, 300], -1.0, 1.0),name=\"W\")\n\nW.assign(initW)\n\ntf.nn.embedding_lookup(W, features)\n\n#\n#\n# def load_embedding_vectors_glove(vocabulary, filename, vector_size):\n# # load embedding_vectors from the glove\n# # initial matrix with random uniform\n# embedding_vectors = np.random.uniform(-0.25, 0.25, (len(vocabulary), vector_size))\n# f = open(filename)\n# for line in f:\n# values = line.split()\n# word = values[0]\n# vector = np.asarray(values[1:], dtype=\"float32\")\n# idx = vocabulary.get(word)\n# if idx != 0:\n# embedding_vectors[idx] = vector\n# f.close()\n# return embedding_vectors\n","repo_name":"CjemGit/FictionNon","sub_path":"W2Vtest.py","file_name":"W2Vtest.py","file_ext":"py","file_size_in_byte":3848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"36296637483","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass PoetryModel(nn.Module):\n def __init__(self, vocab_size, embedding_dim, hidden_dim):\n super(PoetryModel, self).__init__()\n self.vocab_size = vocab_size\n self.embedding_dim = embedding_dim\n self.hidden_dim = hidden_dim\n self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)\n self.lstm = nn.LSTM(self.embedding_dim, self.hidden_dim, num_layers=2)\n self.linear = nn.Linear(self.hidden_dim, self.vocab_size)\n\n def forward(self, input, hidden=None):\n sentence_length, batch_size = input.size()\n if hidden == None:\n h_0 = input.data.new(2, batch_size, self.hidden_dim).fill_(0).float()\n c_0 = input.data.new(2, batch_size, self.hidden_dim).fill_(0).float()\n else:\n h_0, c_0 = hidden\n \n embeds = self.embedding(input)\n output, hidden = self.lstm(embeds, (h_0, c_0))\n output = self.linear(output.view(sentence_length * batch_size, -1))\n return output, hidden","repo_name":"swimmtest/deep-learning-assignments","sub_path":"Auto-writingPoem/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"418525635","text":"import sys\nimport os\nimport time\n\nsys.path.append(os.getcwd()[:os.getcwd().index('implementations')])\n\nfrom keras import backend as K\nfrom keras import optimizers\nfrom keras.layers import Conv2D, Activation, BatchNormalization, UpSampling2D, Lambda\nfrom keras.models import Sequential\nfrom random import shuffle\n\nfrom keras.utils import HDF5Matrix\n\nfrom implementations.support_scripts.common import data_to_onehot, H5Choose\nfrom implementations.support_scripts.image_processing import ImageDownloader\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n\nb_size = 8\ndir_name = \"../small_dataset\"\nlist_dir = os.listdir(dir_name)\nshuffle(list_dir)\nlist_dir = list_dir\nnum_classes = 400\nn_epochs = 1000\n\nmodel = Sequential()\n\n# conv1_1\nmodel.add(Conv2D(64, (3, 3), padding=\"same\", input_shape=(256, 256, 1)))\nmodel.add(Activation(\"relu\"))\n\n# conv1_2\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\n# conv2_1\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\n# conv2_2\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(BatchNormalization(axis=3)) # todo: check if really axis 1 since data has last axis for chanel\nmodel.add(Activation(\"relu\"))\n\nmodel.add(Conv2D(400, (1, 1), padding=\"same\"))\n\n\n# multidimensional softmax\ndef custom_softmax(x):\n x = K.reshape(x, (b_size * 256 * 256, num_classes))\n x = K.softmax(x)\n x = K.reshape(x, (b_size, 256, 256, num_classes))\n return x\n\nmodel.add(Activation(custom_softmax))\n\n\n# sgd = optimizers.SGD(lr=10, momentum=0.0, decay=0, nesterov=False)\nopt = optimizers.Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\nmodel.compile(optimizer=opt,\n loss=\"mean_squared_error\")\n\nmodel.summary()\n\n\nsave_every_n_epoch = 50\nstart_from = 100\n\n# start image downloader\nid = ImageDownloader(\"../h5_data\", \"imp5_\", mode=\"common\")\nid.setDaemon(True) # thread die when main thread die\nid.start()\n\nfile_picker = H5Choose(dir=\"../h5_data\")\n\ntry:\n for epoch in range(n_epochs):\n # Instantiating HDF5Matrix for the training set, which is a slice of the first 150 elements\n file = file_picker.pick_next(id)\n X_train = HDF5Matrix(file, 'grayscale')\n y_train = HDF5Matrix(file, 'ab_hist')\n\n print(\"Epoch \" + str(epoch) + \"/\" + str(n_epochs))\n start = time.time()\n for b in range(len(y_train) // b_size):\n i, j = b * b_size, (b+1) * b_size\n\n a = data_to_onehot(y_train[i:j])\n model.train_on_batch(X_train[i:j], a)\n print(\"Spent: \" + str(time.time() - start))\n if epoch % 5 == 4:\n print(model.evaluate(X_train[:8], data_to_onehot(y_train[:8]), batch_size=8))\n if epoch % 10 == 9:\n model.save_weights(\"../weights/implementation5-\" + str(epoch) + \".h5\")\n\nexcept (KeyboardInterrupt, SystemExit):\n id.stop()\n sys.exit()\n\nid.stop()","repo_name":"PrimozGodec/DeepColorization","sub_path":"implementations/implementation5.py","file_name":"implementation5.py","file_ext":"py","file_size_in_byte":3615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"11719826258","text":"from lxml import etree\nfrom urllib import parse\nimport requests\nimport json\nimport re\nfrom interval import Interval\n\nbaseUrl = 'http://121.194.213.115/swyt/jxcdkbcx.php' #基于校园内网地址进行爬取\nua = 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36'\n\n# http://121.194.213.72/ 教务处网站 获取实时教学周\nweek = 10\n\nurlDic = {\n 'xnxq':\"'2020-20211'\"#查询日期,默认为2020-2021第一学期\n #'jxcdmc'为教室get请求头\n # \"%CD%C5%D6%FD%BD%A3%C2%A5\"是由\"团铸剑楼\"gbk编码得来\n}\nclassRoomNumZhuJian = ['101','102','104','105','106','108','110','111','112',\n '201','202','204','205','206','207','208','210',\n '301','302','303','304','305','306','307','308','309',\n '401','402','403','404','405','406','407','408','409','410','411',\n '501','502','503','504','505','506','507','508','509','510','511']#铸剑楼教室\n\nclassRoomNumZhong = ['103','104','107','110','112','113',\n '203','204','205','206','207','208','210','211',\n '303','304','305','306','307','308',\n '407','408',\n '503','504','505','506','507','510',\n '603','607',\n '703','704','705','707','708']#中楼教室\n\nclassRoomNumXi = ['102','103','104','105','106','109',\n '202','203','204','205','206','209',\n '302','303','304','305','306','309',\n '402','403','404','405','406','409',\n '502','503','504','505','506','509',]#西配楼教室\n\n#测试用教室号\n# classRoomNumZhuJian = ['101']#铸剑楼教室\n# classRoomNumZhong = ['103']#中楼教室\n# classRoomNumXi = ['102']#西配楼教室\n\npathPool = [#上午1、2节\n \"//table[@class='table table-bordered table-striped table-condensed']//tr[1]//td[2]/text()\",\n \"//table[@class='table table-bordered table-striped table-condensed']//tr[1]//td[3]/text()\",\n \"//table[@class='table table-bordered table-striped table-condensed']//tr[1]//td[4]/text()\",\n \"//table[@class='table table-bordered table-striped table-condensed']//tr[1]//td[5]/text()\",\n \"//table[@class='table table-bordered table-striped table-condensed']//tr[1]//td[6]/text()\",\n #上午3、4节\n \"//body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[2]/td[2]/text()\",\n \"//body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[2]/td[3]/text()\",\n \"//body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[2]/td[4]/text()\",\n \"//body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[2]/td[5]/text()\",\n \"//body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[2]/td[6]/text()\",\n #下午1、2节\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[3]/td[2]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[3]/td[3]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[3]/td[4]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[3]/td[5]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[3]/td[6]/text()\",\n #下午3、4节\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[4]/td[2]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[4]/td[3]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[4]/td[4]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[4]/td[5]/text()\",\n \"/html[1]/body[1]/div[3]/div[1]/div[1]/div[3]/div[1]/div[2]/table[1]/tbody[1]/tr[4]/td[6]/text()\",\n]\n\n# 定义一个变量\njsontext = {'data':[]}\n\n#生成当前教学楼所有教室的url池，以便下一步爬取\ndef creatUrlPool(roomNumSelect):\n urlPool = [] #url池, 存放需要遍历的url \n if roomNumSelect is classRoomNumZhuJian:\n for roomNum in roomNumSelect:\n urlPool.append(\"{}?{}&jxcdmc=%27%CD%C5%D6%FD%BD%A3%C2%A5{}%27\".format(baseUrl,parse.urlencode(urlDic),roomNum))#铸剑楼拼接字符串\n elif roomNumSelect is classRoomNumZhong:\n for roomNum in roomNumSelect:\n urlPool.append(\"{}?{}&jxcdmc=%27%CD%C5%D3%FD%BE%AF%D6%D0%C2%A5{}%27\".format(baseUrl,parse.urlencode(urlDic),roomNum))#中楼拼接字符串\n elif roomNumSelect is classRoomNumXi:\n for roomNum in roomNumSelect:\n urlPool.append(\"{}?{}&jxcdmc=%27%CD%C5%D3%FD%BE%AF%CE%F7%C2%A5{}%27\".format(baseUrl,parse.urlencode(urlDic),roomNum))#西配楼拼接字符串\n return urlPool\n\ndef RegStr(string):\n global week\n Reg1 = r'\\d-\\d\\d'\n Reg2 = r'\\d-\\d'\n if re.search(Reg1,string) is None:\n it = re.search(Reg2,string)\n else:\n it = re.search(Reg1,string)\n\n numberList = str(it.group()).split('-')\n for i in range(len(numberList)):\n numberList[i] = int(numberList[i])\n \n #若有课，返回1；无课，返回0\n return 1 if week in Interval(numberList[0],numberList[1]) else 0\n\n\n#开始爬取\ndef getResponse(roomNumSelect):\n am12, am34, pm12, pm34 = [], [], [], []\n urlPool = creatUrlPool(roomNumSelect)\n\n for classRoomIndex,url in enumerate(urlPool):\n with requests.get(url,headers={'User-agent':ua}) as response:\n content = response.text #HTML内容\n html = etree.HTML(content)\n\n count = 0\n for path in pathPool:\n pathTemp = html.xpath(path)\n count+=1\n pathFlag = 1#默认置1\n\n # max(list, key=len, default='')\n #检测文本\n if len(pathTemp)==0:\n pathFlag=0\n else:\n pathTemp = max(pathTemp, key=len, default='')\n pathFlag = RegStr(pathTemp)\n\n #结果检测完毕\n #append(0)为占位符,表示有课\n if count<=5:\n if pathFlag==0:#pathFlag为0，代表无课\n am12.append(int(roomNumSelect[classRoomIndex]))\n else:#pathFlag不为0，代表有课。向结果数组添加0，以示占位。\n am12.append(0)\n elif 5 maybe RCE? :D)\n count2 (int): second counter, max: 999 (buffer overflow, if more! -> maybe RCE? :D)\n\n Returns:\n _type_: byte array of the command which needs to be sent to the device\n \"\"\"\n try:\n bytearray_count1 = struct.pack(\"!h\", int(count1))\n bytearray_count2 = struct.pack(\"!h\", int(count2))\n return bytearray(\n [\n 8,\n 0,\n 10,\n 128,\n int(bytearray_count1[1]) % 256,\n int(bytearray_count1[0]) % 256,\n int(bytearray_count2[1]) % 256,\n int(bytearray_count2[0]) % 256,\n ]\n )\n except BaseException as error:\n logging.error(\"could not update the scoreboard: {}\".format(error))\n","repo_name":"derkalle4/python3-idotmatrix-client","sub_path":"core/idotmatrix/scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"74527277052","text":"import os\nimport cv2\nfrom zipfile import ZipFile\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nfrom itertools import combinations\n\ndef imagePreprocess(image,size):# Size in format img_width,img_height\n image=cv2.resize(image, size) \n #(thresh, image) = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # grayscale to binary using threshold\n image = image/255\n return image\ndef getData(loc,name_file,size,dic):\n img_list = []\n l = len(name_file)\n counter = 0\n for name in name_file:\n if counter==int(l/4):\n print(\"25% Completed..\")\n elif counter==int(l/2):\n print(\"50% Completed..\")\n elif counter==int(3*l/2):\n print(\"75% Completed..\")\n counter+=1\n\n try:\n img = cv2.imread(os.path.join(loc,name),0)\n img = imagePreprocess(img,size)\n img = img.reshape((size[0],size[1],1))\n img_list.append(name)\n dic[name] = img\n except:\n print(\"Couldn't import \",name,\"in Location:\",loc)\n continue\n print(\"100% Completed\")\n return img_list \n\n \n# Dataset 1\ndef getNames1(loc):\n real_loc = os.path.join(loc,'genuine')\n forge_loc= os.path.join(loc,'forged')\n real_names = os.listdir(real_loc)\n forge_names= os.listdir(forge_loc)\n # Sorting forged list because it is not in order of elements\n # Sort it in order of \"Last 2 Digits\" (Excluding \".png\") which denote who's sign it is \n forge_names = sorted(forge_names,key= lambda x: int(x[-6:-4]))\n return real_names,forge_names\n\ndef getImages(loc,size,dic):\n print(\"Getting Dataset-1 Data and Saving inside the Dictionary..\")\n real_names,forge_names = getNames1(loc)\n print(\"Getting Genuine Images..\")\n real_img = getData(os.path.join(loc,'genuine'),real_names,size,dic)\n print(\"Getting Forged Images..\")\n forge_img= getData(os.path.join(loc,'forged'),forge_names,size,dic)\n return np.asarray(real_img),np.asarray(forge_img)\n\n\n# Dataset 2\ndef getImages2(loc,size,dic):\n print(\"Getting Dataset2 Data..\")\n real_names = os.listdir(os.path.join(loc,'full_org'))\n forg_names = os.listdir(os.path.join(loc,'full_forg'))\n img_real = getData(os.path.join(loc,'full_org'),real_names,size,dic)\n img_forg = getData(os.path.join(loc,'full_forg'),forg_names,size,dic)\n print(\"Data Import Complete!\")\n return (np.asarray(img_real),np.asarray(img_forg))\n\n\ndef getDataset3(data,zipobject,dic,size):\n lis = []\n for c in range(0,len(data)):\n if c == len(data)//2:print(\"50% Complete\")\n if c == len(data)//4:print(\"25% Complete\")\n if c == 3*len(data)//4:print(\"75% Complete\")\n i = data[c]\n img = np.asarray(Image.open(zipobject.open('BHSig260/Hindi/'+ i)))\n img = imagePreprocess(img ,size)\n img = img.reshape((size[0],size[1],1))\n lis.append(i)\n dic[i] = img\n print('100% Complete')\n return lis\n\n# Dataset 3\ndef returnPairList(pairfile):\n x1,x2,y=[],[],[]\n for i in pairfile:\n t = i.split(' ')\n x1.append(t[0])\n x2.append(t[1])\n y.append(int(t[2]))\n return x1,x2,y\n\ndef getHindi(path,size,images_dictionary):\n real_list=[]\n forge_lis=[]\n with ZipFile(path, 'r') as z: \n Fdata = z.read('BHSig260/Hindi/list.forgery').decode(\"utf-8\").split(\"\\n\")\n Gdata = z.read('BHSig260/Hindi/list.genuine').decode(\"utf-8\").split(\"\\n\")\n Fdata = Fdata[0:-1]\n Gdata = Gdata[0:-1]\n pairs = z.read('BHSig260/Hindi/Hindi_pairs.txt').decode(\"utf-8\").split(\"\\n\")\n pairs=pairs[0:-1]\n print(\"Getting Genuine Data..\")\n real_list=getDataset3(Gdata,z,images_dictionary,size)\n print(\"Getting Forged Data..\")\n forge_list=getDataset3(Fdata,z,images_dictionary,size)\n return real_list,forge_list\n\ndef makeHindiPairs(real,forged):\n x1,x2,y = [],[],[]\n for i in range(0,160):\n fstart = i*30\n gstart = i*24\n for j in range(gstart,gstart+24):\n for k in range(j+1,gstart+24):\n x1.append(real[j])\n x2.append(real[k])\n y.append(1)\n for k in range(fstart,fstart+30):\n x1.append(real[j])\n x2.append(forged[k])\n y.append(0)\n return x1,x2,y\n\n\ndef makePairs(real_img,forged_img,no_of_writers):\n y=[]\n x1=[]\n x2=[]\n length = len(real_img) # Length of both is supposed to be same\n for i in range(0,length,no_of_writers): # Real-Real samples\n combs = list(combinations(range(i,i+no_of_writers),2))\n for each in combs:\n x1.append(real_img[each[0]])\n x2.append(real_img[each[1]])\n y.append(1)\n x1.append(real_img[each[0]])\n x2.append(forged_img[each[1]])\n y.append(0)\n return [np.asarray(x1),np.asarray(x2),np.asarray(y)]\n","repo_name":"SiddheshSingh/Signature-Verification","sub_path":"data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":4772,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"}
+{"seq_id":"41836170488","text":"import os\nfrom subprocess import Popen, PIPE\n\nimport schedule\nimport time\nfrom threading import Thread\n\nimport json\nfrom flask import Flask, request, abort\nfrom flask_mail import Mail, Message\n\napp = Flask(__name__)\n\n# Webhook, Scheduled\napp.config[\"MODE\"] = os.environ.get(\"MODE\", None)\n\nif app.config[\"MODE\"] == \"cron\":\n print(\"[+] Starting in cron mode\")\n # If no interval is passed, it will be executed hourly\n app.config[\"INTERVAL\"] = int(os.environ.get(\"INTERVAL\", 3600))\n\n# Notifications\napp.config[\"NOTIFY\"] = os.environ.get(\"NOTIFY\", None)\nif app.config[\"NOTIFY\"] == \"email\":\n # Mail configurations\n app.config[\"MAIL_TITLE\"] = os.environ[\"MAIL_TITLE\"]\n app.config[\"MAIL_SERVER\"] = os.environ[\"MAIL_SERVER\"]\n app.config[\"MAIL_PORT\"] = int(os.environ[\"MAIL_PORT\"])\n app.config[\"MAIL_USE_SSL\"] = bool(os.environ[\"MAIL_USE_SSL\"])\n app.config[\"MAIL_USERNAME\"] = os.environ[\"MAIL_USERNAME\"]\n app.config[\"MAIL_PASSWORD\"] = os.environ[\"MAIL_PASSWORD\"]\n\n mail = Mail(app)\n\n sender = os.environ[\"MAIL_SENDER\"]\n recipient = os.environ[\"MAIL_RECIPIENT\"]\n mailTitle = os.environ[\"MAIL_TITLE\"]\n \nif not os.path.isfile(\"/task.sh\"):\n print(\"[+] /task.sh or /task.py not found, please specify a task\")\n exit(-1)\n\ndef scheduler():\n # Run for the very first time, so we get a result\n print(\"[+] Starting the cron scheduller\")\n task()\n schedule.every(app.config[\"INTERVAL\"]).seconds.do(task)\n while True:\n schedule.run_pending()\n time.sleep(1)\n\ndef task():\n print(\"[+] Executing the task\")\n proc = Popen([\"/task.sh\"], stdout=PIPE, stderr=PIPE)\n stdout, stderr = proc.communicate()\n exitcode = proc.returncode\n print(\"[+] Task done, output: \")\n print(stdout)\n print(stderr)\n body = stdout + \"\\n\\n\\n\" + stderr\n sendEmail(mailTitle, body)\n \ndef sendEmail(title, body):\n msg = Message(title,\n sender=sender,\n recipients=[recipient])\n msg.body = body \n mail.send(msg)\n\n@app.route(\"/\")\ndef index():\n return \"This is a service, and you are using it wrong !\"\n\nif __name__ == \"__main__\":\n schedulerThread = Thread(target=scheduler)\n schedulerThread.daemon = True\n schedulerThread.start()\n #app.run(host='0.0.0.0', port=8080,threaded=True)\n schedulerThread.join()\n","repo_name":"opsxcq/docker-task","sub_path":"src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"17780896602","text":"import os\nimport json\n\nimport luigi\nimport numpy as np\nimport z5py\n\nfrom cluster_tools.copy_volume import CopyVolumeLocal, CopyVolumeSlurm\nfrom paintera_tools import convert_to_paintera_format, set_default_roi, set_default_block_shape\nfrom mmpb.default_config import write_default_global_config\nfrom common import RAW_PATH, RAW_KEY, PAINTERA_PATH, PAINTERA_KEY, TMP_PATH, ROI_PATH, LABEL_MAPPING_PATH\n\n\ndef write_max_id(path, key, max_id):\n with z5py.File(path) as f:\n ds = f[key]\n ds.attrs['maxId'] = max_id\n\n\ndef copy_watersheds(input_path, input_key,\n output_path, output_key,\n copy_ids, tmp_folder, target, max_jobs,\n offset=None, insert_mode=False):\n task = CopyVolumeLocal if target == 'local' else CopyVolumeSlurm\n config_dir = os.path.join(tmp_folder, 'configs')\n os.makedirs(config_dir, exist_ok=True)\n\n config = task.default_task_config()\n config.update({'insert_mode': insert_mode, 'offset': offset})\n\n if copy_ids is None:\n with z5py.File(PAINTERA_PATH, 'r') as f:\n max_id = f[PAINTERA_KEY].attrs['maxId']\n else:\n config.update({'value_list': copy_ids.tolist()})\n max_id = int(copy_ids.max())\n\n with open(os.path.join(config_dir, 'copy_volume.config'), 'w') as f:\n json.dump(config, f)\n\n t = task(tmp_folder=tmp_folder, max_jobs=max_jobs, config_dir=config_dir,\n input_path=input_path, input_key=input_key,\n output_path=output_path, output_key=output_key,\n prefix='copy-ws')\n ret = luigi.build([t], local_scheduler=True)\n assert ret, \"Copy failed\"\n\n write_max_id(output_path, output_key, max_id)\n\n\ndef make_proofreading_project(project_folder, tmp_folder,\n assignments, block_labels, block_roi,\n target, max_jobs):\n\n if len(block_labels) == 0:\n return\n # don't do anything if we have a paintera project already\n if os.path.exists(os.path.join(project_folder, 'attributes.json')):\n return\n\n os.makedirs(project_folder, exist_ok=True)\n config_dir = os.path.join(tmp_folder, 'configs')\n\n roi_begin, roi_end = block_roi\n write_default_global_config(config_dir, roi_begin, roi_end)\n with open(os.path.join(config_dir, 'global.config'), 'r') as f:\n block_shape = json.load(f)['block_shape']\n\n data_path = os.path.join(project_folder, 'data.n5')\n f = z5py.File(data_path)\n f.require_group('volumes')\n\n # make a link to the raw data\n raw_out_key = 'volumes/raw'\n if raw_out_key not in f:\n print(\"Make raw symlink\")\n raw_in = os.path.join(RAW_PATH, RAW_KEY)\n raw_out = os.path.join(data_path, raw_out_key)\n os.symlink(raw_in, raw_out)\n\n # get the relevant fragment segment assignments for this block\n print(\"Get assignment mask\")\n assignment_mask = np.isin(assignments[:, 1], block_labels)\n assert assignment_mask.sum() > 0\n block_assignments = assignments[assignment_mask]\n assert block_assignments.shape[0] == assignment_mask.sum()\n assert block_assignments.shape[1] == 2\n print(\"Sub assignments have the shape:\", block_assignments.shape)\n\n # copy the relevant part of the fragment segment assignment\n print(\"Copy the assignments\")\n g_out = f.require_group('volumes/paintera')\n save_assignments = block_assignments.T\n ds_ass = g_out.require_dataset('fragment-segment-assignment', shape=save_assignments.shape,\n chunks=save_assignments.shape, compression='gzip',\n dtype='uint64')\n ds_ass[:] = save_assignments\n\n # copy the relevant parts of the watersheds\n print(\"Copy the watersheds\")\n ws_ids = block_assignments[:, 0]\n copy_watersheds(PAINTERA_PATH, os.path.join(PAINTERA_KEY, 'data/s0'),\n data_path, 'volumes/watershed',\n ws_ids, tmp_folder, target, max_jobs)\n\n # make the paintera data\n res = [0.025, 0.01, 0.01]\n restrict_sets = [-1, -1, 5, 4,\n 4, 3, 3, 1]\n print(\"Make new paintera data\")\n set_default_roi(roi_begin, roi_end)\n set_default_block_shape(block_shape)\n convert_to_paintera_format(data_path, raw_out_key,\n 'volumes/watershed', 'volumes/paintera',\n label_scale=1, resolution=res,\n tmp_folder=tmp_folder, target=target, max_jobs=max_jobs,\n max_threads=16, convert_to_label_multisets=True,\n restrict_sets=restrict_sets)\n\n\n# make the appropriate sub-volume and paintera project for each block\ndef make_proofreading_projects(root, labels_to_blocks, rois_to_blocks, target, max_jobs):\n os.makedirs(root, exist_ok=True)\n tmp_root = './tmps'\n os.makedirs(tmp_root, exist_ok=True)\n\n with z5py.File(TMP_PATH, 'r') as f:\n assignments = f['node_labels/fragment-segment-assignment2'][:]\n\n n_blocks = len(labels_to_blocks)\n # block_ids = range(1, n_blocks + 1)\n block_ids = range(3, 17)\n\n for block_id in block_ids:\n print(\"Make project\", block_id, \"/\", n_blocks + 1)\n project_folder = os.path.join(root, 'project%02i' % block_id)\n tmp_folder = os.path.join(tmp_root, 'tmp_project%i' % block_id)\n make_proofreading_project(project_folder, tmp_folder,\n assignments, labels_to_blocks[block_id],\n rois_to_blocks[block_id], target, max_jobs)\n\n\ndef make_subdivision(root, target, max_jobs):\n with open(LABEL_MAPPING_PATH, 'r') as f:\n labels_to_blocks = json.load(f)\n labels_to_blocks = {int(k): v for k, v in labels_to_blocks.items()}\n\n with open(ROI_PATH, 'r') as f:\n rois_to_blocks = json.load(f)\n rois_to_blocks = {int(k): v for k, v in rois_to_blocks.items()}\n\n make_proofreading_projects(root, labels_to_blocks, rois_to_blocks, target, max_jobs)\n\n\nif __name__ == '__main__':\n root = '/g/arendt/EM_6dpf_segmentation/corrections_and_proofreading/paintera_projects'\n target = 'local'\n max_jobs = 48\n make_subdivision(root, target, max_jobs)\n","repo_name":"mobie/platybrowser-project","sub_path":"segmentation/correction/make_proofreading_projects.py","file_name":"make_proofreading_projects.py","file_ext":"py","file_size_in_byte":6196,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"78"}
+{"seq_id":"12802282395","text":"'''\ncopy('story.txt', 'story_copy.txt') # None\n# expect the contents of story.txt and story_copy.txt to be the same\n'''\n\ndef copy(file, cpy_file):\n with open(file) as filehandle:\n data = filehandle.read()\n\n with open(cpy_file, 'w') as copyfile:\n copyfile.write(data)\n\n\ncopy('cf1.txt', 'cf2.txt')\n","repo_name":"lavkushsingh31/Python_Programs","sub_path":"cpy.py","file_name":"cpy.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"38282443774","text":"import sys\nimport lib.definition as proc\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\n\nimport processes.foundation as fd\nimport processes.fitModel as fM\nimport processes.histogramAction as hA\n\nfrom lmfit import Model\n\n\ndef fit_energy(energy_df):\n counts, bins, bars = plt.hist(energy_df['cal_energy'], histtype='step', bins=1000)\n\n i = np.argmax(counts)\n\n lower = hA.find_nearest_bin(bins, bins[i]-5)\n upper = hA.find_nearest_bin(bins, bins[i]+5)\n ydata = counts[lower:upper]\n xdata = bins[lower:upper]\n\n\n \n gmodel = Model(fM.lingaus)\n #gmodel = Model(gaus)\n i = np.argmax(ydata)\n #params = gmodel.make_params(A=700, m1=315.5, s1=0.5, H_tail=-0.000001, H_step=1, tau=-0.5, slope=-6, intrcpt=180)\n params = gmodel.make_params(a1=1700, m1=xdata[i], s1=1.5, slope=0.0, intrcpt=0.0)\n #params['s1'].vary = False\n result = gmodel.fit(ydata,params, x=xdata)\n\n sigma1 = result.params['s1'].value\n fw1 = 2.355*sigma1\n err = result.params['s1'].stderr\n err1 = err*2.355\n energy = result.params['m1'].value\n print(fw1)\n\n return sigma1, err, np.abs(fw1), err1, result.params['m1'].value\n\ndef main():\n base_min = 1800\n base_max = 10000\n base_lis = np.asarray([x for x in np.arange(base_min, base_max, 200)])\n l=0\n sig_lis1 = []\n fw_lis1 = []\n sig_error_lis1 = []\n fw_error_lis1 = []\n energy_lis1 = []\n sig_lis2 = []\n fw_lis2 = []\n sig_error_lis2 = []\n fw_error_lis2 = []\n energy_lis2 = []\n sig_lis3 = []\n fw_lis3 = []\n sig_error_lis3 = []\n fw_error_lis3 = []\n energy_lis3 = []\n sig_lis4 = []\n fw_lis4 = []\n sig_error_lis4 = []\n fw_error_lis4 = []\n energy_lis4 = []\n for base_line in base_lis:\n energy_df = pd.read_csv(r'baseVsNoiseCSV1723SubWave/energyArr' + str(base_line) + '.csv')\n energy_df = energy_df[energy_df[\"trapEmax\"]<200]\n energy_df[\"cal_energy\"] = energy_df[\"trapEmax\"]\n energy_df[\"true_energy\"] = 5.812\n energy_df[\"base_line\"] = base_line\n\n sig, sig_error, fw, fw_error, energy = fit_energy(energy_df)\n sig_lis1.append(sig)\n sig_error_lis1.append(sig_error)\n fw_lis1.append(fw)\n fw_error_lis1.append(fw_error)\n energy_lis1.append(energy-(5.812))\n\n\n for base_line in base_lis:\n energy_df = pd.read_csv(r'baseVsNoiseCSV1723SubTrap/energyArr' + str(base_line) + '.csv')\n energy_df = energy_df[energy_df[\"trapEmax\"]<20]\n energy_df[\"cal_energy\"] = energy_df[\"trapEmax\"]\n energy_df[\"true_energy\"] = 5.812\n energy_df[\"base_line\"] = base_line\n\n sig, sig_error, fw, fw_error, energy = fit_energy(energy_df)\n sig_lis2.append(sig)\n sig_error_lis2.append(sig_error)\n fw_lis2.append(fw)\n fw_error_lis2.append(fw_error)\n energy_lis2.append(energy-(5.812))\n \n for base_line in base_lis:\n energy_df = pd.read_csv(r'baseVsNoiseCSV1723WaveletSubWave/energyArr' + str(base_line) + '.csv')\n energy_df = energy_df[energy_df[\"trapEmax\"]<20]\n energy_df[\"cal_energy\"] = energy_df[\"trapEmax\"]\n energy_df[\"true_energy\"] = 5.812\n energy_df[\"base_line\"] = base_line\n\n sig, sig_error, fw, fw_error, energy = fit_energy(energy_df)\n sig_lis3.append(sig)\n sig_error_lis3.append(sig_error)\n fw_lis3.append(fw)\n fw_error_lis3.append(fw_error)\n energy_lis3.append(energy-(5.812))\n \n \"\"\"\n for base_line in base_lis:\n energy_df = pd.read_csv(r'baseVsNoiseCSV1726/energyArr' + str(base_line) + '.csv')\n energy_df = energy_df[energy_df[\"trapEmax\"]<20]\n energy_df[\"cal_energy\"] = energy_df[\"trapEmax\"]*0.09458385296-0.09174633738\n energy_df[\"true_energy\"] = 5.812*0.09458385296-0.09174633738\n energy_df[\"base_line\"] = base_line\n\n sig, sig_error, fw, fw_error, energy = fit_energy(energy_df)\n sig_lis4.append(sig)\n sig_error_lis4.append(sig_error)\n fw_lis4.append(fw)\n fw_error_lis4.append(fw_error)\n energy_lis4.append(energy-(5.812*0.09458385296-0.09174633738))\n \"\"\"\n\n df1 = pd.DataFrame({\n \"Base_line\":pd.Series(base_lis),\n \"sigma\":pd.Series(sig_lis1),\n \"sigma_error\":pd.Series(sig_error_lis1),\n \"FWHM\":pd.Series(fw_lis1),\n \"FWHM_error\":pd.Series(fw_error_lis1),\n \"Energy_diff\":pd.Series(energy_lis1)\n })\n\n df2 = pd.DataFrame({\n \"Base_line\":pd.Series(base_lis),\n \"sigma\":pd.Series(sig_lis2),\n \"sigma_error\":pd.Series(sig_error_lis2),\n \"FWHM\":pd.Series(fw_lis2),\n \"FWHM_error\":pd.Series(fw_error_lis2),\n \"Energy_diff\":pd.Series(energy_lis2)\n })\n\n df3 = pd.DataFrame({\n \"Base_line\":pd.Series(base_lis),\n \"sigma\":pd.Series(sig_lis3),\n \"sigma_error\":pd.Series(sig_error_lis3),\n \"FWHM\":pd.Series(fw_lis3),\n \"FWHM_error\":pd.Series(fw_error_lis3),\n \"Energy_diff\":pd.Series(energy_lis3)\n })\n\n \"\"\"\n df4 = pd.DataFrame({\n \"Base_line\":pd.Series(base_lis),\n \"sigma\":pd.Series(sig_lis4),\n \"sigma_error\":pd.Series(sig_error_lis4),\n \"FWHM\":pd.Series(fw_lis4),\n \"FWHM_error\":pd.Series(fw_error_lis4),\n \"Energy_diff\":pd.Series(energy_lis4)\n })\n \"\"\"\n\n fig = go.Figure()\n\n fig.add_trace(go.Scatter(x=df1[\"Base_line\"], y=df1[\"FWHM\"], name=\"Subtract Before Trap\", \n error_y=dict(\n type='data',\n array=np.asarray(df1[\"FWHM_error\"]),\n visible=True\n )))\n fig.add_trace(go.Scatter(x=df2[\"Base_line\"], y=df2[\"FWHM\"], name=\"Subtract After Trap\", \n error_y=dict(\n type='data',\n array=np.asarray(df2[\"FWHM_error\"]),\n visible=True\n )))\n fig.add_trace(go.Scatter(x=df3[\"Base_line\"], y=df3[\"FWHM\"], name=\"Wavelet Filtered Subtract \", \n error_y=dict(\n type='data',\n array=np.asarray(df3[\"FWHM_error\"]),\n visible=True\n )))\n \"\"\"\n fig.add_trace(go.Scatter(x=df4[\"Base_line\"], y=df4[\"FWHM\"], name=\"Det1726\", \n error_y=dict(\n type='data',\n array=np.asarray(df4[\"FWHM_error\"]),\n visible=True\n )))\n \"\"\"\n\n \"\"\"\n fig.add_vline(x=8500, annotation_text=\"Max Baseline 9 Channels\", annotation_font_size=20)\n fig.add_vline(x=15000, annotation_text=\"Max Baseline 5 Channels\", annotation_font_size=20)\n fig.update_xaxes(title=\"Length of BaseLine [Clocks]\", title_font_size=20)\n fig.update_yaxes(title=\"FWHM [keV]\", title_font_size=20)\n \"\"\"\n fig.update_xaxes(title=\"Length of BaseLine [Clocks]\", title_font_size=20)\n fig.update_yaxes(title=\"FWHM [ADC]\", title_font_size=20)\n\n fig.update_layout(\n title=\"FWHM Vs Length of Baseline For Different Methods\",\n title_font_size=20,\n font=dict(\n family=\"Courier New, monospace\",\n size=20\n )\n )\n fig.show()\n\n\n\nif __name__==\"__main__\":\n main()","repo_name":"jbrowni2/geminiAnalysis","sub_path":"baseline/plotFWHMVsBaseline.py","file_name":"plotFWHMVsBaseline.py","file_ext":"py","file_size_in_byte":6951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"41459635181","text":"import numpy as np\nimport torch\nfrom torch.autograd import Variable\n\n\ndef get_data():\n train_x = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779,\n 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997,\n 5.654, 9.27, 3.1])\n train_y = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366,\n 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904,\n 2.42, 2.94, 1.3])\n dtype = torch.FloatTensor\n X = Variable(torch.from_numpy(train_x).type(dtype), requires_grad=False).view(17, 1)\n Y = Variable(torch.from_numpy(train_y).type(dtype), requires_grad=False)\n return X, Y\n\n\ndef get_weights():\n w = Variable(torch.randn(1), requires_grad=True)\n b = Variable(torch.randn(1), requires_grad=True)\n return w, b\n\n\ndef simple_network(x, w, b):\n y_pred = torch.matmul(x, w) + b\n return y_pred\n\n\ndef loss_fn(y, y_pred, w, b):\n loss = (y_pred - y).pow(2).sum()\n for param in [w, b]:\n if not param.grad is None:\n param.grad.data.zero_()\n loss.backward()\n return loss.data[0]\n\n\n# 优化器\ndef optimize(learning_rate, w, b):\n w.data -= learning_rate * w.grad.data\n b.data -= learning_rate * b.grad.data\n\n\n","repo_name":"sunyinggang/dailyCode","sub_path":"python/PyTorch-DeepLearning/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"17512491161","text":"# -*- coding: utf-8 -*-\n\"\"\"Goal: determine the fairness of the games.\n\nCompute win percentage for true and for the starter such that:\n t_win_% == 1 - f_win_%\n start_win_% == 1 - second_win_%\nand decide if they are fair.\n\nDetailed approach:\nFor every config file, play a bunch of games.\nCollect game ending data.\nCompute intermediate data so mean and standard dev can be computed.\n\nCreated on Sun Jul 23 11:29:10 2023\n@author: Ann\"\"\"\n\nimport argparse\nimport enum\nimport math\nimport os\nimport random\nimport sys\nimport time\n\nimport pandas as pd\nimport scipy\nimport tqdm\n\nfrom context import ai_player\nfrom context import man_config\n\nfrom game_log import game_log\nfrom game_interface import WinCond\n\n\n\n# %%\n\nALL = 'all'\n\n\nWIN_SCORE = 4\nTIE_SCORE = 2\n\nEXPECTED_VAL = WIN_SCORE * 0.5\n\n\n# %% column names\n\n# raw tally collumns\n\nMAX_TURNS = 'max_turns'\n\n# W_starter_winner\nW_F_F = 'w_f_f'\nW_F_T = 'w_f_t'\nW_T_F = 'w_t_f'\nW_T_T = 'w_t_t'\n\n# in order by starter, winner\nWINNER_COL = [W_F_F, W_F_T, W_T_F, W_T_T]\n\n# TIE_starter\nTIE_T = 'tie_t'\nTIE_F = 'tie_f'\n\n# in order by starter\nTIE_COL = [TIE_F, TIE_T]\n\n# two sum columns\nSCORE = 'score'\nSCORE_2 = 'score_2' # squared\n\nST_SCORE = 'st_score'\nST_SCORE_2 = 'st_score_2'\n\nINT_COLUMNS = [MAX_TURNS] + WINNER_COL + TIE_COL + \\\n [SCORE, SCORE_2, ST_SCORE, ST_SCORE_2]\n\nMEAN = 'mean'\nSTDEV = 'stddev'\n\nWIN_PCT = 'win_pct'\nSTR_PCT = 'str_pct'\nTIE_PCT = 'tie_pct'\nFLT_COLUMNS = [WIN_PCT, STR_PCT, TIE_PCT]\n\n# fair columsn\nWIN_FAIR = 'win_fair'\nSTARTER_FAIR = 'starter_fair'\n\nBOOL_COLUMNS = [WIN_FAIR, STARTER_FAIR]\n\n\n# %% files, fields and defaults\n\nPATH = '../GameProps/'\nBAD_CFG = 'all_params.txt'\n\nINDEX = [fname[:-4] for fname in os.listdir(PATH) if fname != BAD_CFG]\n\nFIELDS = {\n 'allow_rule': 0,\n 'blocks': False,\n 'capsamedir': False,\n 'capt_max': 0,\n 'capt_min': 0,\n 'capt_on': '',\n 'capt_rturn': False,\n 'capttwoout': False,\n 'child_cvt': 0,\n 'child_rule': 0,\n 'child_type': 0,\n 'crosscapt': False,\n 'evens': False,\n 'goal': 0,\n 'gparam_one': 0,\n 'grandslam': 0,\n 'min_move': 1,\n 'mlaps': 0,\n 'move_one': False,\n 'moveunlock': False,\n 'multicapt': False,\n 'mustpass': False,\n 'mustshare': False,\n 'no_sides': False,\n 'nocaptfirst': False,\n 'oppsidecapt': False,\n 'pickextra': 0,\n 'prescribed': 0,\n 'round_fill': 0,\n 'round_starter': 0,\n 'rounds': False,\n 'skip_start': False,\n 'sow_direct': 1,\n 'sow_own_store': False,\n 'sow_rule': 0,\n 'sow_start': False,\n 'start_pattern': 0,\n 'stores': False,\n 'udir_holes': '',\n 'visit_opp': False,\n 'xc_sown': False,\n 'xcpickown': 0,\n }\n\n\n# %% score and collect\n\nclass GameResult(enum.Enum):\n \"\"\"Game results.\"\"\"\n\n WIN = WinCond.WIN.value\n TIE = WinCond.TIE.value\n MAX_TURNS = enum.auto()\n\n\ndef score_game(data, gname, starter, result, winner):\n \"\"\"Collect sum and sum squared for scores for winner\n and for starter's wins.\"\"\"\n\n score = 0\n if result == GameResult.WIN.value and winner:\n score = WIN_SCORE\n elif result == GameResult.TIE.value:\n score = TIE_SCORE\n data.loc[gname, SCORE] += score\n data.loc[gname, SCORE_2] += score * score\n\n score = 0\n if result == GameResult.WIN.value and winner == starter:\n score = WIN_SCORE\n elif result == GameResult.TIE.value:\n score = TIE_SCORE\n data.loc[gname, ST_SCORE] += score\n data.loc[gname, ST_SCORE_2] += score * score\n\n\ndef result_name(starter, result, winner):\n \"\"\"Lookup the column name for the result.\"\"\"\n\n if result == GameResult.WIN.value:\n return WINNER_COL[starter * 2 + winner]\n\n if result == GameResult.TIE.value:\n return TIE_COL[starter]\n\n if result == GameResult.MAX_TURNS.value:\n return MAX_TURNS\n\n assert f'Unexpedect game result {result}.'\n\n\n# %% build data frame\n\n\ndef build_data_frame():\n \"\"\"Build a data frame with all the desired columns,\n Include key game data (optional), raw game result tallys, two sum values\n for win percents, and fairness columns.\"\"\"\n\n if cargs.no_params:\n data = pd.DataFrame(index=cargs.game)\n\n else:\n ginfo = dict()\n for gname in cargs.game:\n gdict = man_config.read_game(PATH + gname + '.txt')\n cons_gd = dict(**gdict['game_constants'], **gdict['game_info'])\n del cons_gd['about']\n del cons_gd['name']\n ginfo[gname] = cons_gd\n data = pd.DataFrame.from_dict(ginfo).transpose()\n\n for name, dval in FIELDS.items():\n if name in data:\n data[name] = data[name].fillna(dval)\n\n dlen = len(cargs.game)\n\n for col in INT_COLUMNS:\n data[col] = [0] * dlen\n for col in FLT_COLUMNS:\n data[col] = [0.0] * dlen\n for col in BOOL_COLUMNS:\n data[col] = [False] * dlen\n\n return data\n\n\n# %% fairness tests\n\ndef std_dev(xsum, x_sqr_sum, nbr):\n \"\"\"Use two sum formula to compute standard deviation.\"\"\"\n\n return math.sqrt((x_sqr_sum - ((xsum * xsum) / nbr)) / (nbr - 1))\n\n\ndef fail_to_reject(data, gname, tag, confidence=0.95):\n \"\"\"H0: prob of win = 0.5 Ha: prob of win != 0.5\n\n Failing to reject - means we didn't reject H0\n -- not enough evidence to prove it false\"\"\"\n\n nbr_games = cargs.nbr_runs - data.loc[gname, MAX_TURNS]\n if nbr_games <= 1:\n return False\n\n mean = data.loc[gname, tag] / nbr_games\n stdev = std_dev(data.loc[gname, tag], data.loc[gname, tag + '_2'],\n nbr_games)\n if abs(stdev) < 0.00001:\n return False\n\n test_stat = (mean - EXPECTED_VAL) / (stdev / math.sqrt(nbr_games))\n crit_value = scipy.stats.norm.ppf(1 - (1 - confidence) / 2)\n\n return test_stat < crit_value\n\n\ndef evals(gname, data):\n \"\"\"Compute win_fair and starter_fair.\"\"\"\n\n data.loc[gname, WIN_FAIR] = fail_to_reject(data, gname, SCORE)\n data.loc[gname, STARTER_FAIR] = fail_to_reject(data, gname, ST_SCORE)\n\n nbr_games = cargs.nbr_runs - data.loc[gname, MAX_TURNS]\n data.loc[gname, WIN_PCT] = data.loc[gname, SCORE] / (WIN_SCORE * nbr_games)\n data.loc[gname, STR_PCT] = data.loc[gname, ST_SCORE] / (WIN_SCORE * nbr_games)\n data.loc[gname, TIE_PCT] = \\\n (data.loc[gname, TIE_T] + data.loc[gname, TIE_F]) / nbr_games\n\n\n# %% play and collect\n\ndef test_one_game(game, pdict):\n \"\"\"Play one game, return the result as\n outcome (win, tie, or max turns) and winner (if one)\"\"\"\n\n if cargs.ai_player:\n tplayer = ai_player.AiPlayer(game, pdict)\n fplayer = ai_player.AiPlayer(game, pdict)\n\n for _ in range(5000 if game.info.rounds else 500):\n\n if not cargs.ai_player:\n moves = game.get_moves()\n assert moves, \"Game didn't end right.\"\n move = random.choice(moves)\n else:\n game_log.active = False\n if game.turn:\n move = tplayer.pick_move()\n else:\n move = fplayer.pick_move()\n game_log.active = cargs.save_logs\n\n cond = game.move(move)\n if cond in (WinCond.WIN, WinCond.TIE):\n break\n if cond in (WinCond.ROUND_WIN, WinCond.ROUND_TIE):\n if game.new_game(cond, new_round_ok=True):\n return cond.value, game.turn\n game_log.turn(0, 'Start Game', game)\n\n if game.info.mustpass:\n game.test_pass()\n\n else:\n return GameResult.MAX_TURNS.value, None\n\n return cond.value, game.turn\n\n\ndef play_one_config(gname, data):\n \"\"\"For one game configuration, play cargs.nbr_runs number of games.\n Start half with True and half with False.\n Tally raw results and accumualte data for mean/std_dev (score).\"\"\"\n\n for cnt in tqdm.tqdm(range(cargs.nbr_runs)):\n\n game, pdict = man_config.make_game(PATH + gname + '.txt')\n if cnt < cargs.nbr_runs // 2:\n starter = game.turn = True\n else:\n starter = game.turn = False\n\n result, winner = test_one_game(game, pdict)\n\n # raw tally\n col = result_name(starter, result, winner)\n data.loc[gname, col] += 1\n\n score_game(data, gname, starter, result, winner)\n if cargs.save_logs:\n game_log.save(f'Fair Games Simulated.\\n{cargs}\\n'\n + f'Starter: {starter}\\n\\n'\n + game.params_str())\n game_log.new()\n time.sleep(1)\n\n # print(game.params_str())\n\n\ndef play_them_all(data):\n\n for gname in cargs.game:\n print(gname)\n play_one_config(gname, data)\n evals(gname, data)\n\n data.to_csv(f'data/{cargs.output}.csv')\n\n\n# %% command line args\n\ndef define_parser():\n \"\"\"Define the command line arguements.\"\"\"\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--game', action='append',\n choices=list(INDEX) + [ALL],\n help=\"\"\"Select the games to simulate. Use multiple\n options to select multiple games.\"\"\")\n\n parser.add_argument('--nbr_runs', action='store',\n default=10, type=int,\n help=\"\"\"Select the number of games to simulate.\n Default: %(default)s\"\"\")\n\n parser.add_argument('--ai_player', action='store_true',\n help=\"\"\"Use the minimaxer ai_player.\n Default: %(default)s\"\"\")\n\n parser.add_argument('--no_params', action='store_true',\n help=\"\"\"Don't game parameters in output file.\n Default: %(default)s\"\"\")\n\n parser.add_argument('--save_logs', action='store_true',\n help=\"\"\"Save the game logs. Only one game maybe\n selected and nbr_games must be < 50.\n Games will be slowed to 1 per second.\n Default: %(default)s\"\"\")\n\n parser.add_argument('--output', action='store',\n default='junk',\n help=\"\"\"Output file. Default: %(default)s\"\"\")\n\n # TODO add confidence\n\n return parser\n\n\ndef process_command_line():\n\n global cargs\n\n parser = define_parser()\n try:\n cargs = parser.parse_args()\n except argparse.ArgumentError:\n parser.print_help()\n sys.exit()\n\n if not cargs.game:\n cargs.game = INDEX\n if cargs.save_logs and (len(cargs.game) > 1 or cargs.nbr_runs > 50):\n print(\"save_logs only valid for <= 1 game and <= 50 runs.\")\n sys.exit()\n\n game_log.active = cargs.save_logs\n game_log.level = game_log.STEP\n\n print(cargs)\n\n# %%\n\nprocess_command_line()\n\ndata = build_data_frame()\nplay_them_all(data)\n","repo_name":"StoneShark/MancalaGames","sub_path":"analysis/fair_games.py","file_name":"fair_games.py","file_ext":"py","file_size_in_byte":10936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"36523557746","text":"# Open the file with the list of strings\nwith open('3.in') as f:\n # Read all the lines from the file\n lines = f.readlines()\n\n# Initialize the total score to 0\nscore = 0\n\n# Loop through each group of three strings\nfor i in range(0, len(lines), 3):\n # Get the current group of three strings\n s1, s2, s3 = lines[i:i+3]\n\n # Strip whitespace from the strings\n s1 = s1.strip()\n s2 = s2.strip()\n s3 = s3.strip()\n\n # Find the common characters in the three strings\n common_chars = set(s1).intersection(s2, s3)\n\n # Loop through each common character\n for c in common_chars:\n # Convert the character to a number\n if c.islower():\n # Convert lowercase letters to numbers between 1 and 26\n num = ord(c) - ord('a') + 1\n elif c.isupper():\n # Convert uppercase letters to numbers between 27 and 53\n num = ord(c) - ord('A') + 27\n\n # Add the number to the total score\n score += num\n\n# Print the total score\nprint(score)","repo_name":"msturm/aoc2022","sub_path":"3/gpt3.py","file_name":"gpt3.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"4453114193","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport shutil\nimport torch\nimport onnx\nimport pycuda\nimport tensorrt\nimport pycuda.driver as cuda\nimport pycuda.autoinit\n\n\n# Simple helper data class that's a little nicer to use than a 2-tuple.\nclass HostDeviceMem(object):\n def __init__(self, host_mem, device_mem):\n self.host = host_mem\n self.device = device_mem\n\n def __str__(self):\n return \"Host:\\n\" + str(self.host) + \"\\nDevice:\\n\" + str(self.device)\n\n def __repr__(self):\n return self.__str__()\n\n\ndef allocate_buffers(engine, batch_size):\n inputs = []\n outputs = []\n bindings = []\n stream = cuda.Stream()\n for binding in engine:\n size = tensorrt.volume(engine.get_binding_shape(binding)) * batch_size\n dtype = tensorrt.nptype(engine.get_binding_dtype(binding))\n # Allocate host and device buffers\n host_mem = cuda.pagelocked_empty(size, dtype)\n device_mem = cuda.mem_alloc(host_mem.nbytes)\n # Append the device buffer to device bindings.\n bindings.append(int(device_mem))\n # Append to the appropriate list.\n if engine.binding_is_input(binding):\n inputs.append(HostDeviceMem(host_mem, device_mem))\n else:\n outputs.append(HostDeviceMem(host_mem, device_mem))\n return inputs, outputs, bindings, stream\n\n","repo_name":"YonghaoHe/LFD-A-Light-and-Fast-Detector","sub_path":"lfd/deployment/tensorrt/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":408,"dataset":"github-code","pt":"78"}
+{"seq_id":"40082503135","text":"import numpy as np\r\nfrom matplotlib import pyplot as plt\r\nfrom scipy.integrate import solve_ivp\r\nimport os,time\r\nimport Lagrange, Lyapunov_init, Lyapunov_diff\r\n\r\n#共通変数,関数\r\nfrom util import (\r\n mu,\r\n dirnum,\r\n dirgraph,\r\n func,\r\n event_y0pm,\r\n event_y0mp,\r\n Lyapunov_half,\r\n Lyapunov\r\n)\r\n\r\n#エネルギー計算\r\ndef Energy(mu,x):\r\n r1 = np.sqrt((x[0]+mu)**2+x[1]**2)\r\n r2 = np.sqrt((x[0]-1+mu)**2+x[1]**2)\r\n #エネルギー\r\n E = (x[2]**2+x[3]**2)/2-(x[0]**2+x[1]**2)/2 -(1-mu)/r1 -mu/r2 -mu*(1-mu)/2\r\n return E\r\n\r\n#Contination\r\ndef Contination(mu,x1,x2,energy):\r\n energy_init = Energy(mu, x1)\r\n energy_re = Energy(mu, x2)\r\n delta = x2 - x1\r\n print(energy_re)\r\n if abs(energy_re - energy)<1e-10:\r\n return x2\r\n elif (energy_init - energy)*(energy_re - energy)<0:\r\n delta = delta*0.5\r\n x2 = Lyapunov_diff.diff_corr(mu, x1+delta)\r\n return Contination(mu, x1, x2, energy)\r\n else:\r\n x1 = x2\r\n x2 = Lyapunov_diff.diff_corr(mu, x1+delta)\r\n return Contination(mu, x1, x2, energy)\r\n\r\n#L1,数値の保存も含めたcontination\r\ndef Con_init(mu, xe, energy):\r\n #ラグランジュ点の判別\r\n if xe>1-mu:\r\n file_name = 'L2_init.npy'\r\n elif xe>0:\r\n file_name = 'L1_init.npy'\r\n else:\r\n file_name = 'L3_init.npy'\r\n #値の検索\r\n if file_name in os.listdir(dirnum):\r\n initdata = np.load(dirnum+file_name, allow_pickle=True).item()\r\n if energy in initdata.keys():\r\n return initdata[energy]\r\n else:\r\n initdata = dict()\r\n\r\n init_x1 = Lyapunov_init.lyap_init(mu, 0.001, xe)\r\n init_x2 = Lyapunov_init.lyap_init(mu, 0.005, xe)\r\n x_diff1 = Lyapunov_diff.diff_corr(mu, init_x1)\r\n x_diff2 = Lyapunov_diff.diff_corr(mu, init_x2)\r\n x_contination = Contination(mu, x_diff1, x_diff2, energy)\r\n #値の保存\r\n initdata[energy] = x_contination\r\n np.save(dirnum+file_name,initdata)\r\n\r\n return x_contination\r\n\r\ndef main():\r\n t1 = time.time()\r\n L = Lagrange.Lagrangepoint(mu)\r\n E = [-1.59, -1.57, -1.55, -1.53, -1.51]\r\n color = ['b', 'c', 'g', 'k', 'm']\r\n\r\n #図の設定\r\n fig = plt.figure(figsize=(6,5))\r\n ax = fig.add_subplot()\r\n for i in range(len(E)):\r\n #初期条件の計算\r\n L1_con = Con_init(mu, L[0][0], E[i])\r\n L2_con = Con_init(mu, L[0][1], E[i])\r\n #積分計算\r\n y1 = Lyapunov(L1_con)\r\n y2 = Lyapunov(L2_con)\r\n ax.plot(y1[0], y1[1], color=color[i], label='E='+str(E[i]))\r\n ax.plot(y2[0], y2[1], color=color[i])\r\n ax.plot(L[0][0],L[1][0],'rx')\r\n ax.plot(L[0][1],L[1][1],'rx')\r\n ax.plot(1-mu, 0, marker='.', markersize=10, color='y')\r\n ax.set_xlabel('$\\it{x}$',fontsize=25,fontstyle='italic')\r\n ax.set_ylabel('y',fontsize=25)\r\n fig.gca().set_aspect('equal')\r\n ax.set_title('Contination')\r\n ax.legend(bbox_to_anchor=(1, 1), loc='upper left')\r\n plt.tight_layout()\r\n\r\n #グラフの保存\r\n fig.savefig(dirgraph+\"Contination.png\")\r\n\r\n t2 = time.time()\r\n print(f'経過時間:{t2-t1}s')\r\n plt.show()\r\n\r\nif __name__=='__main__':\r\n main()","repo_name":"MendyTaka/graduation-thesis","sub_path":"PCR3BP_Tube_ver2/Lyapunov/Lyapunov_con.py","file_name":"Lyapunov_con.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"2344649092","text":"def commaDelimited(l):\n commaList = ''\n for i in range(len(l)):\n if len(l) == 1:\n commaList += 'and ' + l[0]\n else:\n commaList += l[0] + ', '\n del l[0]\n return commaList\n\nspam = ['apples', 'bananas', 'tofu', 'cats']\nprint(commaDelimited(spam))\n","repo_name":"rickymccallum87/automate-the-boring-stuff","sub_path":"commaList.py","file_name":"commaList.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"74125215612","text":"from math import tanh,cosh\nfrom numpy import linspace\nfrom pylab import plot,show\n\naccuracy = 1e-16\n\ndef arctanh(u):\n x = 0.0\n delta = 1.0\n while abs(delta)>accuracy:\n delta = (tanh(x)-u)*cosh(x)**2\n x -= delta\n return x\n\n# upoints = linspace(-0.99,0.99,100)\n# xpoints = []\n# for u in upoints:\n# xpoints.append(arctanh(u))\n# plot(upoints,xpoints)\n# show()\n\na=0\nb=100\nx = 0.3\nN = 300\nh = (b-a)/N\n\nwhile arctanh(x)>accuracy:\n\tdf=(arctanh(x+h)-arctanh(x))/h\n\tx=x-arctanh(x)/df\nprint(x)","repo_name":"dgrin1/p304_2022_examples","sub_path":"petra_atanh.py","file_name":"petra_atanh.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"78"}
+{"seq_id":"9587054383","text":"import mysql.connector\n\ndef insertCustomer(line):\n mydb = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"Python@12\",\n database=\"ecomm\")\n mycursor = mydb.cursor()\n word_list = line.split(\",\")\n sql = \"INSERT INTO customers (name, address, phone_no, pwd, data) VALUES (%s, %s, %s, %s, %s)\"\n values = (word_list[1].strip(),word_list[2].strip(),word_list[0].strip(),word_list[3].strip(),word_list[4].strip())\n mycursor.execute(sql, values)\n mydb.commit()\n print(mycursor.rowcount, \"record inserted.\")\n\nwith open(\"customer_data.txt\",\"r\") as myfile:\n line = myfile.readline()\n while(line):\n try:\n insertCustomer(line)\n except:\n print(\"Could not insert line : {}\".format(line))\n continue\n finally:\n line = myfile.readline()\n","repo_name":"rahusriv/python_tutorial","sub_path":"python_batch_2/project1/load_customer_1.py","file_name":"load_customer_1.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"37753541486","text":"\nimport gevent\nfrom gevent import monkey\nfrom gevent.queue import Queue, Empty\nfrom gevent.event import Event\nmonkey.patch_all(thread=False)\nfrom time import sleep\nimport json\nimport cv2\nfrom threading import Thread, Event\nimport signal\nimport numpy as np\nfrom flask import Flask, render_template, Response, request\nfrom flask_sockets import Sockets\nimport logging\nimport os\nimport humanize\nfrom collections import deque\nfrom recorder import Recorder\nfrom gameplay.stateful import Gameplay\nfrom datetime import datetime, timedelta\nfrom visualization import Visualizer\nfrom image_recognition import ImageRecognizer, ImageRecognition\nfrom grabber import PanoramaGrabber\nfrom remoterf import RemoteRF\nfrom config_manager import ConfigManager\n\n\n\n# Get Gevent websocket patched for Python3 here:\n# https://bitbucket.org/noppo/gevent-websocket/\n# hg update python3-support\n# sudo python3 setup.py install\n\nlogger = logging.getLogger(\"flask\")\n\n# Queue messages from bootstrap\nlog_queue = deque(maxlen=1000)\nwebsockets = set()\n\napp = Flask(__name__)\ntry:\n with open(\"/etc/machine-id\", \"r\") as fh:\n app.config['SECRET_KEY'] = fh.read()\nexcept:\n app.config['SECRET_KEY'] = 'secret!'\nsockets = Sockets(app)\n\n\n\n\n# Build pipeline\ngrabber = PanoramaGrabber() # config read from ~/.robovision/grabber.conf\nimage_recognizer = ImageRecognizer(grabber)\ngameplay = Gameplay(image_recognizer)\nrf = RemoteRF(gameplay, \"/dev/ttyACM0\")\nvisualizer = Visualizer(image_recognizer, framedrop=1)\nrecorder = Recorder(grabber)\n\ndef generator():\n visualizer.enable()\n queue = visualizer.get_queue()\n while True:\n try:\n buf, resized, frame, r = queue.get_nowait()\n except Empty:\n sleep(0.001) # Fix this stupid thingie\n continue\n else:\n yield b'--frame\\r\\nContent-Type: image/jpeg\\r\\n\\r\\n'\n yield buf\n yield b'\\r\\n\\r\\n'\n\n@app.route('/combined/')\ndef video_combined(type_str):\n TYPES = ['VIDEO', 'DEBUG', 'COMBO']\n return Response(generator(), mimetype='multipart/x-mixed-replace; boundary=frame')\n\n@app.route('/')\ndef group():\n return render_template(\n 'group.html',\n )\n\n@app.route('/logging')\ndef logging_view():\n return render_template('logging.html')\n\n@sockets.route('/')\ndef command(websocket):\n x = 0\n y = 0\n w = 0\n\n for buf in log_queue:\n websocket.send(buf)\n\n game_config = ConfigManager.instance(\"game\")\n\n def get_game_options():\n return [\n (\"field_id\", [game_config.get_value(\"global\", \"field_id\"),\"A\",\"B\",\"Z\"]),\n (\"robot_id\", [game_config.get_value(\"global\", \"robot_id\"),\"A\",\"B\"]),\n (\"target goal color\", [game_config.get_value(\"global\", \"target goal color\"),\"yellow\",\"blue\"]),\n (\"gameplay status\", [game_config.get_value(\"global\", \"gameplay status\"),\"disabled\", \"enabled\"]),\n ]\n \n game_options = get_game_options()\n\n settings_packet = json.dumps(dict(\n action = \"settings-packet\",\n sliders = ConfigManager.as_list(\"imgrec\"),\n options = game_options\n ))\n websocket.send(settings_packet)\n\n while not websocket.closed:\n websockets.add(websocket)\n\n gevent.sleep(0.01)\n\n msg = websocket.receive()\n\n if not msg:\n websockets.remove(websocket)\n logger.info(\"WebSocket connection presumably closed, %d left connected\" % len(websockets))\n break\n\n response = json.loads(msg)\n action = response.pop(\"action\", None)\n if not action:\n logger.info(\"Unknown action\")\n continue\n\n\n if action == \"gamepad\":\n controls = response.pop(\"data\")\n x = controls.pop(\"controller0.axis0\", x) * 0.99\n y = controls.pop(\"controller0.axis1\", y) * 0.99\n w = controls.pop(\"controller0.axis2\", w) * 0.7\n\n # Kick the ball\n if controls.get(\"controller0.button7\", None):\n gameplay.arduino.kick()\n\n # Toggle autonomy\n if controls.get(\"controller0.button4\", None):\n gameplay.toggle()\n\n # Manual control of the robot\n if not gameplay.alive:\n gameplay.arduino.set_xyw(x,-y,-w)\n\n # TODO: slders\n elif action == \"record_toggle\":\n print(\"TOGGLING RECORDER\")\n recorder.toggle()\n elif action == \"record_enable\":\n recorder.enable()\n elif action == \"record_disable\":\n recorder.disable()\n elif action == \"set_settings\":\n for k, v in response.items():\n ConfigManager.set_config_value(k, v)\n print(response.items())\n elif action == \"set_options\":\n for k, v in response.items():\n game_config.get_option(\"global\", k).set_value(v)\n game_config.save()\n print(response.items())\n else:\n logger.error(\"Unhandled action: %s\", action)\n websockets.remove(websocket)\n logger.info(\"WebSocket connection closed, %d left connected\", len(websockets))\n return b\"\"\n\nclass MyEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime):\n return obj.strftime(\"%Y-%m-%dT%H:%M:%S.%f\")[:-3] + \"Z\"\n if isinstance(obj, timedelta):\n f = obj.total_seconds()\n mins = f // 60\n return \"%02d:%06.03f\" % (mins, f % 60)\n return json.JSONEncoder.default(self, obj)\n\n\nclass WebsocketLogHandler(logging.Handler):\n def __init__(self):\n logging.Handler.__init__(self)\n self.started = datetime.utcnow()\n\n def emit(self, record):\n timestamp = datetime.utcfromtimestamp(record.created)\n buf = json.dumps(dict(\n action = \"log-entry\",\n created = timestamp,\n uptime = timestamp - self.started,\n message = record.msg % record.args,\n severity = record.levelname.lower()), cls=MyEncoder)\n log_queue.append(buf)\n for websocket in websockets:\n websocket.send(buf)\n\ndef main():\n logger.info(\"Starting robovision\")\n\n logging.basicConfig(\n filename=\"/tmp/robovision.log\",\n level=logging.INFO)\n\n ws_handler = WebsocketLogHandler()\n handler = logging.StreamHandler()\n handler.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n handler.setFormatter(formatter)\n for facility in \"grabber\", \"recognition\", \"cli\", \"flask\", \"arduino\", \"gameplay\", \"threading\", \"recorder\":\n logging.getLogger(facility).addHandler(handler)\n logging.getLogger(facility).addHandler(ws_handler)\n logging.getLogger(facility).setLevel(logging.DEBUG)\n\n from gevent import pywsgi\n from geventwebsocket.handler import WebSocketHandler\n\n ip, port = ('0.0.0.0', 5000)\n if os.getuid() == 0:\n port = 80\n\n\n server = pywsgi.WSGIServer((ip, port), app, handler_class=WebSocketHandler)\n logger.info(\"Started server at http://{}:{}\".format(ip, port))\n\n # Quick'n'diry hacks\n image_recognizer.grabber = grabber\n image_recognizer.websockets = websockets\n\n # Start all threads\n image_recognizer.start()\n gameplay.start()\n grabber.start()\n recorder.start()\n visualizer.start()\n rf.start()\n\n # Register threads for monitoring\n from managed_threading import ThreadManager\n manager = ThreadManager()\n manager.register(gameplay)\n manager.register(recorder)\n manager.register(grabber)\n manager.register(visualizer)\n manager.register(image_recognizer)\n manager.start()\n\n # Enable some threads\n image_recognizer.enable()\n visualizer.enable()\n #if gameplay.is_enabled:\n #gameplay.enable()\n server.serve_forever()\n\nif __name__ == '__main__':\n main()\n\n#start_server = websockets.serve(time, '0.0.0.0', 5001)\n#asyncio.get_event_loop().run_until_complete(start_server)\n#asyncio.get_event_loop().run_forever()\n","repo_name":"iplayfast/zoidberg","sub_path":"robovision/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"2950749132","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom indexers import BM25Indexer, Word2VecIndexer, FastTextIndexer, BERTIndexer\nimport time\n\napp = Flask(__name__)\n\n# Определение путей к файлам индексов и моделей\nBM25_PICKLE = \"data/bm25_index.pkl\"\nW2V_MODEL_PATH = \"data/word2vec.bin\"\nW2V_EMBEDDINGS = \"data/w2v_embeddings.npy\"\nFT_MODEL_PATH = \"data/fasttext.model\"\nFT_EMBEDDINGS = \"data/ft_embeddings.npy\"\nBERT_MODEL_PATH = \"ai-forever/sbert_large_nlu_ru\"\nBERT_EMBEDDINGS = \"data/sbert_embeddings.npy\"\nCORPUS_PATH = \"data/corpus.txt\"\n\n# Инициализация и загрузка индексов при импорте модуля\nbm25_indexer = BM25Indexer()\nbm25_indexer.load_index(BM25_PICKLE)\n\nword2vec_indexer = Word2VecIndexer()\nword2vec_indexer.load_index(W2V_EMBEDDINGS, W2V_MODEL_PATH, CORPUS_PATH)\n\nfasttext_indexer = FastTextIndexer()\nfasttext_indexer.load_index(FT_EMBEDDINGS, FT_MODEL_PATH, CORPUS_PATH)\n\nbert_indexer = BERTIndexer(BERT_MODEL_PATH)\nbert_indexer.load_index(BERT_EMBEDDINGS, CORPUS_PATH)\n\n# Словарь, сопоставляющий имена индексаторов с их объектами\nindexers = {\n 'bm25': bm25_indexer,\n 'word2vec': word2vec_indexer,\n 'fasttext': fasttext_indexer,\n 'bert': bert_indexer\n}\n\n@app.route('/')\ndef index():\n return render_template('index.html') # здесь будет ваше основное описание и кнопка для перехода на страницу поиска\n\n@app.route('/search')\ndef search():\n return render_template('search.html') # здесь будет форма для ввода поискового запроса и возможно выбор индексатора\n\n@app.route('/results', methods=['POST'])\ndef results():\n search_query = request.form['query']\n selected_indexer_key = request.form['indexer'] # Это строка, такая как \"bm25\", \"word2vec\" и т.д.\n\n # Получаем соответствующий объект индексатора из словаря\n selected_indexer = indexers[selected_indexer_key]\n\n start_time = time.time()\n # Теперь вы можете использовать выбранный индексатор для поиска\n results = selected_indexer.search(search_query)\n end_time = time.time()\n\n search_time = end_time - start_time\n\n return render_template('results.html', query=search_query, results=results, time=search_time)\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"elinkamaeva/Search-Engine","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"36257349903","text":"import serial\nimport pynput\n\nfrom serial.tools import list_ports\nfrom pynput.keyboard import Key, Listener, KeyCode\n\nVALID_JOINTS = {'LOW', 'UP', 'JAWR', 'JAWW'}\nisInAutomation = 0\n\nports = [p.device for p in list_ports.comports()]\n\nprint(\"Available ports for this session:\")\nfor i in range(len(ports)):\n print(\"[\"+str(i+1)+\"] \"+str(ports[i]))\n\nport_selection = int(input(\"Enter a number for the port. (Defaults at 1) >> \") or 1)\nactive_port = ports[port_selection-1]\nactive_baudrate = int(input(\"Enter a baudrate for serial communication. (Defaults at 115200) >> \") or \"115200\")\n\nsession = serial.Serial(active_port, baudrate = active_baudrate)\nsession.write(\"PING\".encode(\"utf-8\"))\n#while 1:\n# if session.read_until(\"PONG\"):\n# break\n\nprint(\"Connection established with... [\" + str(ports[port_selection-1]) + \"] ...with a baudrate of \" + str(active_baudrate))\n\nlow = 0\nup = 0\njawr = 0\njaww = 0\n\n# Resets the arm to default angles, invoke when connection is established, when a new set of actions is applied.\ndef reset_rotation():\n global low, up, jawr, jaww\n session.write(\"RST\".encode(\"utf-8\"))\n print(\"RST\")\n low = 90\n up = 90\n jawr = 90\n jaww = 90\n\ndef single_rotation(joint, operator):\n global low, up, jawr, jaww\n i = 0\n\n if joint not in VALID_JOINTS:\n raise ValueError(\"Joint \" + str(joint) + \" is not a VALID_JOINT\")\n if int(operator) not in {0, 1}:\n raise ValueError(\"Operator can only be 0 (for -) or 1 (for +)\")\n if int(operator) == 0:\n i = -1\n elif int(operator) == 1:\n i = 1\n\n if joint == 'LOW':\n if low + i > 180 or low + i < 0:\n return 0\n else:\n low += i\n print(\"ROT \" + joint + \":\" + str(low))\n if joint == 'UP':\n if up + i > 180 or up + i < 0:\n return 0\n else:\n up += i\n print(\"ROT \" + joint + \":\" + str(up))\n if joint == 'JAWR':\n if jawr + i > 180 or jawr + i < 0:\n return 0\n else:\n jawr += i\n print(\"ROT \" + joint + \":\" + str(jawr))\n if joint == 'JAWW':\n if jaww + i > 180 or jaww + i < 0:\n return 0\n else:\n jaww += i\n print(\"ROT \" + joint + \":\" + str(jaww))\n\n session.write((\"ROT \" + joint + \":\" + str(operator)).encode(\"utf-8\"))\n \ndef pose_rotation(*argpos):\n global low, up, jawr, jaww\n if len(argpos) != 4:\n raise ValueError(\"Pose rotation takes only 4 arguments.\")\n session.write((\"ACT \" + str(argpos[0]) + \",\" + str(argpos[1]) + \",\" + str(argpos[2]) + \",\" + str(argpos[3])).encode(\"utf-8\"))\n print(\"ACT \" + str(argpos[0]) + \",\" + str(argpos[1]) + \",\" + str(argpos[2]) + \",\" + str(argpos[3]))\n\n# Stops the immediate arm actions, respond to user manual movement.\ndef stop_rotation():\n session.write(\"STP\".encode(\"utf-8\"))\n print(\"STP\")\n\nreset_rotation()\n\ndef on_press(key):\n try:\n if key.char == 'w':\n single_rotation('UP', 1)\n if key.char == 's':\n single_rotation('UP', 0)\n if key.char == 'a':\n single_rotation('JAWR', 0)\n if key.char == 'd':\n single_rotation('JAWR', 1)\n if key.char == 'q':\n single_rotation('JAWW', 0)\n if key.char == 'e':\n single_rotation('JAWW', 1)\n except AttributeError: \n if key is Key.up:\n single_rotation('LOW', 1)\n if key is Key.down:\n single_rotation('LOW', 0)\n \ndef on_release(key):\n if key is Key.esc:\n return False\n\nwith Listener(on_press=on_press, on_release=on_release, suppress=True) as Listener:\n Listener.join()","repo_name":"GToidZ/portfolio","sub_path":"02-Mechanical-Arm-Prototyping/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3650,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"40368692168","text":"#!/usr/bin/env python3\n\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see '.format(self.__path, hex(id(self)), self.__values)\n\n def __contains__(self, key):\n return key in self.__values\n\n def keys(self):\n return list(self.__values.keys())\n\n def get_path(self):\n return self.__path\n\n\nclass Request(object):\n \"\"\"\n A class for dumping dubbo request body.\n All types can be dumped:\n * boolean\n * int\n * long\n * double\n * string\n * object\n \"\"\"\n\n def __init__(self, request):\n self.__body = request\n self.__classes = []\n self.types = [] # 泛型\n self.invoke_id = get_invoke_id()\n\n def encode(self):\n \"\"\"\n 把请求序列化为字节数组\n :return:\n \"\"\"\n request_body = self._encode_request_body()\n invoke_id = list(bytearray(struct.pack('!q', self.invoke_id)))\n request_head = DEFAULT_REQUEST_META + invoke_id + get_request_body_length(request_body)\n return bytearray(request_head + request_body)\n\n def _get_parameter_types(self, arguments):\n \"\"\"\n 针对所有的参数计算得到参数类型字符串\n :param arguments:\n :return:\n \"\"\"\n parameter_types = ''\n # 判断并得出参数的类型\n for argument in arguments:\n parameter_types += self._get_class_name(argument)\n return parameter_types\n\n def _get_class_name(self, _class):\n \"\"\"\n 根据一个字段的类型得到其在Java中对应类的全限定名\n 转换规则：https://stackoverflow.com/a/3442100/4614538\n :param _class:\n :return:\n \"\"\"\n if isinstance(_class, bool): # bool类型的判断必须放在int类型判断的前面\n return 'Z'\n elif isinstance(_class, int):\n if MIN_INT_32 <= _class <= MAX_INT_32:\n return 'I'\n else:\n return 'J'\n elif isinstance(_class, float):\n return 'D'\n elif isinstance(_class, str):\n return 'L' + 'java/lang/String' + ';'\n elif isinstance(_class, Object):\n path = _class.get_path()\n path = 'L' + path.replace('.', '/') + ';'\n return path\n elif isinstance(_class, list):\n if len(_class) == 0:\n raise HessianTypeError('Method parameter {} is a list but length is zero'.format(_class))\n return '[' + self._get_class_name(_class[0])\n else:\n raise HessianTypeError('Unknown argument type: {0}'.format(_class))\n\n def _encode_request_body(self):\n \"\"\"\n 对所有的已知的参数根据dubbo协议进行编码\n :return:\n \"\"\"\n dubbo_version = self.__body['dubbo_version']\n path = self.__body['path']\n version = self.__body['version']\n method = self.__body['method']\n arguments = self.__body['arguments']\n\n body = []\n body.extend(self._encode_single_value(dubbo_version))\n body.extend(self._encode_single_value(path))\n body.extend(self._encode_single_value(version))\n body.extend(self._encode_single_value(method))\n body.extend(self._encode_single_value(self._get_parameter_types(arguments)))\n for argument in arguments:\n body.extend(self._encode_single_value(argument))\n\n attachments = {\n 'path': path,\n 'interface': path,\n 'version': version\n }\n # attachments参数以H开头，以Z结尾\n body.append(ord('H'))\n for key in list(attachments.keys()):\n value = attachments[key]\n body.extend(self._encode_single_value(key))\n body.extend(self._encode_single_value(value))\n body.append(ord('Z'))\n\n # 因为在上面的逻辑中没有对byte大小进行检测，所以在这里进行统一的处理\n for i in range(len(body)):\n body[i] = body[i] & 0xff\n return body\n\n @staticmethod\n def _encode_bool(value):\n \"\"\"\n 对bool类型进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n if value:\n result.append(ord('T'))\n else:\n result.append(ord('F'))\n return result\n\n @staticmethod\n def _encode_int(value):\n \"\"\"\n 对整数进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n # 超出int类型范围的值则转化为long类型\n # 这里问题在于对于落在int范围内的数字，我们无法判断其是long类型还是int类型，所以一律认为其是int类型\n if value > MAX_INT_32 or value < MIN_INT_32:\n result.append(ord('L'))\n result.extend(list(bytearray(struct.pack('!q', value))))\n return result\n\n if -0x10 <= value <= 0x2f:\n result.append(value + 0x90)\n elif -0x800 <= value <= 0x7ff:\n result.append(0xc8 + (value >> 8))\n result.append(value)\n elif -0x40000 <= value <= 0x3ffff:\n result.append(0xd4 + (value >> 16))\n result.append(value >> 8)\n result.append(value)\n else:\n result.append(ord('I'))\n result.append(value >> 24)\n result.append(value >> 16)\n result.append(value >> 8)\n result.append(value)\n return result\n\n @staticmethod\n def _encode_float(value):\n \"\"\"\n 对浮点类型进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n int_value = int(value)\n if int_value == value:\n if int_value == 0:\n result.append(0x5b)\n return result\n elif int_value == 1:\n result.append(0x5c)\n return result\n elif -0x80 <= int_value < 0x80:\n result.append(0x5d)\n result.append(int_value)\n return result\n elif -0x8000 <= int_value < 0x8000:\n result.append(0x5e)\n result.append(int_value >> 8)\n result.append(int_value)\n return result\n\n mills = int(value * 1000)\n if 0.001 * mills == value and MIN_INT_32 <= mills <= MAX_INT_32:\n result.append(0x5f)\n result.append(mills >> 24)\n result.append(mills >> 16)\n result.append(mills >> 8)\n result.append(mills)\n return result\n\n bits = double_to_long_bits(value)\n result.append(ord('D'))\n result.append(bits >> 56)\n result.append(bits >> 48)\n result.append(bits >> 40)\n result.append(bits >> 32)\n result.append(bits >> 24)\n result.append(bits >> 16)\n result.append(bits >> 8)\n result.append(bits)\n return result\n\n @staticmethod\n def _encode_utf(value):\n \"\"\"\n 对字符串进行编码，编码格式utf-8\n 参��方法：com.alibaba.com.caucho.hessian.io.Hessian2Output#printString\n :param value:\n :return:\n \"\"\"\n result = []\n for v in value:\n ch = ord(v)\n if ch < 0x80:\n result.append(ch & 0xff)\n elif ch < 0x800:\n result.append((0xc0 + ((ch >> 6) & 0x1f)) & 0xff)\n result.append((0x80 + (ch & 0x3f)) & 0xff)\n else:\n result.append((0xe0 + ((ch >> 12) & 0xf)) & 0xff)\n result.append((0x80 + ((ch >> 6) & 0x3f)) & 0xff)\n result.append((0x80 + (ch & 0x3f)) & 0xff)\n return result\n\n def _encode_str(self, value):\n \"\"\"\n 对一个字符串进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n # 在进行网络传输操作时一律使用unicode进行操作\n # if isinstance(value, str):\n # value = value.decode('utf-8')\n length = len(value)\n if length <= 0x1f:\n result.append(0x00 + length)\n elif length <= 0x3ff:\n result.append(0x30 + (length >> 8))\n result.append(length)\n else:\n result.append(ord('S'))\n result.append(length >> 8)\n result.append(length)\n\n result.extend(self._encode_utf(value))\n return result\n\n def _encode_object(self, value):\n \"\"\"\n 对一个对象进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n path = value.get_path()\n field_names = list(value.keys())\n\n if path not in self.__classes:\n result.append(ord('C'))\n result.extend(self._encode_single_value(path))\n\n result.extend(self._encode_single_value(len(field_names)))\n\n for field_name in field_names:\n result.extend(self._encode_single_value(field_name))\n self.__classes.append(path)\n class_id = self.__classes.index(path)\n if class_id <= 0xf:\n class_id += 0x60\n class_id &= 0xff\n result.append(class_id)\n else:\n result.append(ord('O'))\n result.extend(self._encode_single_value(class_id))\n for field_name in field_names:\n result.extend(self._encode_single_value(value[field_name]))\n return result\n\n def _encode_list(self, value):\n \"\"\"\n 对一个列表进行编码\n :param value:\n :return:\n \"\"\"\n result = []\n length = len(value)\n if length == 0:\n # 没有值则无法判断类型，一律返回null\n return self._encode_single_value(None)\n if isinstance(value[0], bool):\n _type = '[boolean'\n elif isinstance(value[0], int):\n _type = '[int'\n elif isinstance(value[0], float):\n _type = '[double'\n elif isinstance(value[0], str):\n _type = '[string'\n elif isinstance(value[0], Object):\n _type = '[object'\n else:\n raise HessianTypeError('Unknown list type: {}'.format(value[0]))\n if length < 0x7:\n result.append(0x70 + length)\n if _type not in self.types:\n self.types.append(_type)\n result.extend(self._encode_single_value(_type))\n else:\n result.extend(self._encode_single_value(self.types.index(_type)))\n else:\n result.append(0x56)\n if _type not in self.types:\n self.types.append(_type)\n result.extend(self._encode_single_value(_type))\n else:\n result.extend(self._encode_single_value(self.types.index(_type)))\n result.extend(self._encode_single_value(length))\n for v in value:\n if type(value[0]) != type(v):\n raise HessianTypeError('All elements in list must be the same type, first type'\n ' is {0} but current type is {1}'.format(type(value[0]), type(v)))\n result.extend(self._encode_single_value(v))\n return result\n\n def _encode_single_value(self, value):\n \"\"\"\n 根据hessian协议对单个变量进行编码\n :param value:\n :return:\n \"\"\"\n # 布尔类型\n if isinstance(value, bool):\n return self._encode_bool(value)\n # 整型（包括长整型）\n elif isinstance(value, int):\n return self._encode_int(value)\n # 浮点类型\n elif isinstance(value, float):\n return self._encode_float(value)\n # 字符串类型\n elif isinstance(value, str):\n return self._encode_str(value)\n # 对象类型\n elif isinstance(value, Object):\n return self._encode_object(value)\n # 列表(list)类型，不可以使用tuple替代\n elif isinstance(value, list):\n return self._encode_list(value)\n # null\n elif value is None:\n return [ord('N')]\n else:\n raise HessianTypeError('Unknown argument type: {}'.format(value))\n\n\ndef get_request_body_length(body):\n \"\"\"\n 获取body的长度，并将其转为长度为4个字节的字节数组\n :param body:\n :return:\n \"\"\"\n return list(bytearray(struct.pack('!i', len(body))))\n\n\nif __name__ == '__main__':\n pass\n","repo_name":"BSTester/dubbo-python","sub_path":"dubbo/codec/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":13444,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"78"}
+{"seq_id":"11236320346","text":"from django.conf.urls import url\nfrom django.contrib.auth.views import login, logout\nfrom . import views\n\nurlpatterns = [\n url(r'^app/?$', views.base, name='base'),\n url(r'^foo/?$', views.foo, name='foo'),\n url(r'^new_user/?$', views.new_user, name='new_user'),\n url(r'^logs/?$', views.history_view, name='history'),\n url(r'^clear_logs/?$', views.clear_logs, name='clear_logs'),\n url(r'^unauthorized/?$', views.unauthorized, name='unauthorized'),\n url(r'^user_profile/(?P[0-9]+)$', views.user_profile, name='user_profile'),\n url(r'^$', login, {\n 'template_name': 'app/login.html'\n }, name='login')\n]\n","repo_name":"rkruszynski/behave_presentation","sub_path":"django-app/training-app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"78"}
+{"seq_id":"589606940","text":"class DFA:\n def __init__(self, states, transitionTable, acceptTable, sigma ):\n self.states = states\n self.transitionTable = transitionTable\n self.acceptTable = acceptTable\n self.sigma = sigma\n \n # abbaaab\n def process(self, inputD):\n try:\n state = 0\n for c in inputD:\n state = self.transitionTable[state][self.sigma.index(c)]\n return self.acceptTable[state];\n except:\n return False\n\n @classmethod\n def inputDataForm(cls):\n n, m = map(int, input().split()) # [5, 2]\n states = list(map(int, input().split())) #[0, 1, 2, 3, 4]\n sigma = input().split() #[a, b]\n endStates = list(map(int, input().split())) #[2,4]\n acceptTable = [False for i in range(n)] # [False, False, False, False, False]\n for end in endStates:\n acceptTable[end] = True\n # [False, False, True, False, True]\n transitionTable = {}\n for i in range(n):\n transitionTable[i] = [0, 0]\n \n for i in range(n*m):\n start, inpData, des =input().split()\n # 0 a 1\n start = int(start) # 0\n des = int(des) # 1\n transitionTable[start][sigma.index(inpData)] = des\n\n return cls(states, transitionTable, acceptTable, sigma)\n\ndfa = DFA.inputDataForm()\ninputData = input()\n\nif dfa.process(inputData):\n print(\"YES\")\nelse:\n print(\"NO\")","repo_name":"minhkhacoder/ct287","sub_path":"DFA.py","file_name":"DFA.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"17780479652","text":"import os\nimport json\nimport luigi\nimport numpy as np\n\nfrom cluster_tools.mutex_watershed import MwsWorkflow\nfrom cluster_tools.postprocess import SizeFilterWorkflow\nfrom cluster_tools.thresholded_components.threshold import ThresholdLocal, ThresholdSlurm\nfrom cluster_tools.workflows import MulticutStitchingWorkflow\n\nimport elf.parallel\nfrom elf.io import open_file\nfrom z5py.util import copy_dataset\n\nfrom mmpb.default_config import get_default_shebang\nfrom mmpb.segmentation.network.prediction import prefilter_blocks\n\n\ndef make_global_config(mask_path, mask_key, shape, block_shape, tmp_folder, n_threads):\n config_folder = os.path.join(tmp_folder, 'configs')\n os.makedirs(config_folder, exist_ok=True)\n\n config = MwsWorkflow.get_config()['global']\n shebang = get_default_shebang()\n block_list_path = os.path.join(tmp_folder, 'blocks.json')\n prefilter_blocks(mask_path, mask_key,\n shape, block_shape,\n block_list_path, n_threads=n_threads)\n config.update({'shebang': shebang,\n 'block_shape': block_shape,\n 'block_list_path': block_list_path})\n\n with open(os.path.join(config_folder, 'global.config'), 'w') as f:\n json.dump(config, f)\n\n\ndef run_mws(offsets, path, fg_key, aff_key, out_key,\n tmp_folder, target, max_jobs,\n strides=[6, 6, 6], halo=[16, 32, 32]):\n\n config_folder = os.path.join(tmp_folder, 'configs')\n configs = MwsWorkflow.get_config()\n\n config = configs['mws_blocks']\n config.update({'strides': strides, 'time_limit': 360, 'mem_limit': 12,\n 'randomize_strides': True, 'noise_level': 1e-4})\n with open(os.path.join(config_folder, 'mws_blocks.config'), 'w') as f:\n json.dump(config, f)\n\n task = MwsWorkflow(tmp_folder=tmp_folder, max_jobs=max_jobs,\n target=target, config_dir=config_folder,\n input_path=path, input_key=aff_key,\n output_path=path, output_key=out_key,\n mask_path=path, mask_key=fg_key,\n offsets=offsets, halo=halo)\n ret = luigi.build([task], local_scheduler=True)\n if not ret:\n raise RuntimeError(\"MWS failed\")\n\n\ndef make_fg_mask(path, fg_key, fg_mask_out_key, tmp_folder, target, max_jobs):\n task = ThresholdLocal if target == 'local' else ThresholdSlurm\n\n threshold = .5\n config_folder = os.path.join(tmp_folder, 'configs')\n t = task(tmp_folder=tmp_folder, config_dir=config_folder, max_jobs=max_jobs,\n input_path=path, input_key=fg_key,\n output_path=path, output_key=fg_mask_out_key,\n threshold=threshold, threshold_mode='greater')\n ret = luigi.build([t], local_scheduler=True)\n if not ret:\n raise RuntimeError(\"Threshold failed\")\n\n\ndef find_bounding_boxes(seg_path, seg_key, n_threads, scale_factor):\n with open_file(seg_path, 'r') as f:\n ds = f[seg_key]\n ds.n_threads = n_threads\n chunks = ds.chunks\n seg = ds[:]\n\n unique_segs = elf.parallel.unique(seg, block_shape=chunks,\n n_threads=n_threads, verbose=True)[1:]\n\n bbs = []\n # could use more efficient impl from scipy/skimage\n for seg_id in unique_segs:\n where_seg = np.where(seg == seg_id)\n bb = tuple(slice(int(ws.min()) * sf,\n (int(ws.max()) + 1) * sf) for ws, sf in zip(where_seg, scale_factor))\n bbs.append(bb)\n\n return bbs\n\n\ndef set_bounding_box(tmp_folder, bb):\n config_path = os.path.join(tmp_folder, 'configs', 'global.config')\n with open(config_path, 'r') as f:\n config = json.load(f)\n\n # clear block list path\n config['block_list_path'] = None\n\n # set the bounding box\n bb_start = [b.start for b in bb]\n bb_stop = [b.stop for b in bb]\n config.update({'roi_begin': bb_start, 'roi_end': bb_stop})\n with open(config_path, 'w') as f:\n json.dump(config, f)\n\n\ndef stitching_multicut(offsets, path, aff_key, ws_key,\n tmp_folder, config_folder, target, max_jobs):\n task = MulticutStitchingWorkflow\n\n exp_path = os.path.join(tmp_folder, 'data.n5')\n assignment_key = 'node_labels/cilia'\n out_key = 'volumes/stitched_cilia'\n\n configs = task.get_config()\n config = configs['probs_to_costs']\n config.update({'weight_edges': True})\n with open(os.path.join(config_folder, 'probs_to_costs.config'), 'w') as f:\n json.dump(config, f)\n\n config = configs['block_edge_features']\n config.update({'offsets': offsets[:6]})\n with open(os.path.join(config_folder, 'block_edge_features.config'), 'w') as f:\n json.dump(config, f)\n\n task_names = ['merge_sub_graphs', 'merge_edge_features']\n for tname in task_names:\n config = configs[tname]\n config.update({'time_limit': 120, 'mem_limit': 32})\n with open(os.path.join(config_folder, '%s.config' % tname), 'w') as f:\n json.dump(config, f)\n\n config = configs['stitching_multicut']\n config.update({'time_limit': 600, 'mem_limit': 128, 'threads_per_job': 8})\n with open(os.path.join(config_folder, 'stitching_multicut.config'), 'w') as f:\n json.dump(config, f)\n\n t = task(tmp_folder=tmp_folder, config_dir=config_folder,\n max_jobs=max_jobs, target=target,\n input_path=path, input_key=aff_key,\n labels_path=path, labels_key=ws_key,\n assignment_path=exp_path, assignment_key=assignment_key,\n problem_path=exp_path,\n output_path=exp_path, output_key=out_key)\n ret = luigi.build([t], local_scheduler=True)\n if not ret:\n raise RuntimeError(\"Multicut stitching failed\")\n\n\nclass CopyAndOffset(luigi.Task):\n out_path = luigi.Parameter()\n exp_path = luigi.Parameter()\n path = luigi.Parameter()\n seg_out_key = luigi.Parameter()\n offset = luigi.IntParameter()\n out_key = luigi.Parameter()\n n_threads = luigi.IntParameter()\n bb_start = luigi.ListParameter()\n bb_stop = luigi.ListParameter()\n\n @staticmethod\n def add_non_zero(arr, val):\n arr[arr != 0] += val\n return arr\n\n def run(self):\n bb = tuple(slice(sta, sto) for sta, sto in zip(self.bb_start, self.bb_stop))\n with open_file(self.exp_path) as fin, open_file(self.path) as fout:\n ds_in, ds_out = fin[self.seg_out_key], fout[self.out_key]\n\n # apply offset to the segmentation if it is not 0\n if self.offset > 0:\n print(\"Add offsets ...\")\n # FIXME something with parallel chunk access is off and leads to segfaults,\n # so for now we just run this single threaded\n elf.parallel.apply_operation(ds_in, self.offset, self.add_non_zero,\n n_threads=1, roi=bb, verbose=True)\n\n # copy to the output\n print(\"Copy dataset ...\")\n copy_dataset(self.exp_path, self.path, self.seg_out_key, self.out_key,\n n_threads=self.n_threads, roi=bb)\n\n # find new offset\n print(\"Find new offset ...\")\n offset = elf.parallel.max(ds_out, n_threads=self.n_threads, roi=bb,\n verbose=True)\n\n with open(self.out_path, 'w') as f:\n json.dump({'offset': int(offset)}, f)\n print(\"Copy and offset done ...\")\n\n def output(self):\n return luigi.LocalTarget(self.out_path)\n\n\ndef postprocess_and_write(path, out_key, bb, tmp_folder, config_dir,\n target, max_jobs, n_threads, offset, min_size):\n task = SizeFilterWorkflow\n\n exp_path = os.path.join(tmp_folder, 'data.n5')\n seg_in_key = 'volumes/stitched_cilia'\n seg_out_key = 'volumes/filtered_cilia'\n\n # filter the segmentation objects by size\n t = task(tmp_folder=tmp_folder, config_dir=config_dir,\n max_jobs=max_jobs, target=target,\n input_path=exp_path, input_key=seg_in_key,\n output_path=exp_path, output_key=seg_out_key,\n size_threshold=min_size)\n ret = luigi.build([t], local_scheduler=True)\n if not ret:\n raise RuntimeError(\"Size filtering failed\")\n\n out_path = os.path.join(tmp_folder, 'copy_and_offset.json')\n t = CopyAndOffset(out_path=out_path, exp_path=exp_path, path=path,\n seg_out_key=seg_out_key, out_key=out_key, offset=offset,\n n_threads=n_threads,\n bb_start=[b.start for b in bb],\n bb_stop=[b.stop for b in bb])\n ret = luigi.build([t], local_scheduler=True)\n if not ret:\n raise RuntimeError(\"Copy anf offset failed\")\n\n with open(out_path) as f:\n offset = json.load(f)['offset']\n\n return offset\n\n\ndef get_scale_factor(path, key, mask_path, mask_key):\n with open_file(path, 'r') as f, open_file(mask_path, 'r') as fm:\n shape = f[key].shape\n mask_shape = fm[mask_key].shape\n scale_factor = [int(round(sh / float(ms), 0)) for sh, ms in zip(shape, mask_shape)]\n return scale_factor\n\n\ndef cilia_segmentation_workflow(offsets, path,\n fg_key, aff_key, fg_mask_out_key, out_key,\n mask_path, mask_key,\n tmp_folder, target, max_jobs, n_threads):\n\n size_threshold = 1000\n # preparation: find blocks we need to segment and write the global config\n with open_file(path, 'r') as f:\n shape = f[fg_key].shape\n block_shape = [64, 256, 256]\n make_global_config(mask_path, mask_key, shape, block_shape, tmp_folder, n_threads)\n\n # make mask for the foreground\n print(\"Make foreground mask ...\")\n make_fg_mask(path, fg_key, fg_mask_out_key, tmp_folder, target, max_jobs)\n\n # run block-wise mws\n print(\"Run mutex watershed ...\")\n run_mws(offsets, path, fg_mask_out_key, aff_key, out_key,\n tmp_folder, target, max_jobs)\n\n # find bounding box(es) of current segments mask and set it\n scale_factor = get_scale_factor(path, fg_key, mask_path, mask_key)\n bbs = find_bounding_boxes(mask_path, mask_key, n_threads, scale_factor)\n\n offset = 0\n # run multicut for the bounding boxes\n config_folder = os.path.join(tmp_folder, 'configs')\n print(\"Run stitching multicuts ...\")\n for ii, bb in enumerate(bbs):\n print(\"for bounding box\", ii, \":\", bb)\n set_bounding_box(tmp_folder, bb)\n tmp_folder_mc = os.path.join(tmp_folder, 'tmps_mc', 'tmp_%i' % ii)\n os.makedirs(tmp_folder_mc, exist_ok=True)\n stitching_multicut(offsets, path, aff_key, out_key,\n tmp_folder_mc, config_folder, target, max_jobs)\n\n print(\"Postprocess and write result ...\")\n # run filters and update offset\n offset = postprocess_and_write(path, out_key, bb,\n tmp_folder_mc, config_folder,\n target, max_jobs, n_threads,\n offset, size_threshold)\n","repo_name":"mobie/platybrowser-project","sub_path":"mmpb/segmentation/cilia/segment.py","file_name":"segment.py","file_ext":"py","file_size_in_byte":11063,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"78"}
+{"seq_id":"1058401381","text":"from datetime import datetime\nfrom pathlib import Path\nfrom threading import Thread\n\nfrom flaky import flaky\n\nfrom compiler_gym.util.runfiles_path import create_user_logs_dir\nfrom tests.test_main import main\n\npytest_plugins = [\"tests.pytest_plugins.common\"]\n\n\n@flaky # Unlikely event that timestamps change\ndef test_create_user_logs_dir(temporary_environ, tmpdir):\n tmpdir = Path(tmpdir)\n temporary_environ[\"COMPILER_GYM_LOGS\"] = str(tmpdir)\n\n dir = create_user_logs_dir(\"foo\")\n now = datetime.now()\n\n assert dir.parent.parent == tmpdir / \"foo\"\n\n year, month, day = dir.parent.name.split(\"-\")\n assert int(year) == now.year\n assert int(month) == now.month\n assert int(day) == now.day\n\n hour, minute, second = dir.name.split(\"-\")\n assert int(hour) == now.hour\n assert int(minute) == now.minute\n assert int(second) == now.second\n\n\ndef test_create_user_logs_dir_multithreaded(temporary_environ, tmpdir):\n tmpdir = Path(tmpdir)\n temporary_environ[\"COMPILER_GYM_LOGS\"] = str(tmpdir)\n\n class MakeDir(Thread):\n def __init__(self):\n super().__init__()\n self.dir = None\n\n def run(self):\n self.dir = create_user_logs_dir(\"foo\")\n\n def join(self):\n super().join()\n return self.dir\n\n threads = [MakeDir() for _ in range(5)]\n for t in threads:\n t.start()\n\n dirs = [t.join() for t in threads]\n\n # Every directory should be unique.\n print(dirs)\n assert len(set(dirs)) == len(dirs)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"facebookresearch/CompilerGym","sub_path":"tests/util/runfiles_path_test.py","file_name":"runfiles_path_test.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","stars":821,"dataset":"github-code","pt":"78"}
+{"seq_id":"40539571659","text":"import json\nfrom collections import namedtuple, Counter\nimport string, base64, binascii, zlib\nimport datetime\n\n#\n# Helpers\n#\n\nclass cached_property(property):\n def __get__(self, instance, owner):\n if instance is None:\n return super().__get__(instance, owner)\n\n s = '_cached_' + self.fget.__name__\n try: return getattr(instance, s)\n except AttributeError: pass\n\n ret = super().__get__(instance, owner)\n setattr(instance, s, ret)\n return ret\n\n def __set__(self, instance, value):\n raise AttributeError()\n\n def __delete__(self, instance):\n if instance is not None:\n s = '_cached_' + self.fget.__name__\n try: delattr(instance, s)\n except AttributeError: pass\n super().__delete__(instance)\n\n\nclass count_trues(object):\n \"\"\"Kinda like sum(), but only for the specific case of taking a sum()\n of booleans and then applying comparison operators. The advantage is\n that it doesn't go through the whole iterator if it doesn't need to.\n For example, in `t = count_trues(i%2 for i in range(99999))`, comparing\n `t` with 8 using any comparison operator will take 16 to 18 iterations.\n \"\"\"\n def __init__(self, iterable):\n self.count = 0\n self.iterable = iterable\n\n def __gt__(self, other):\n if self.count > other:\n return True\n for i in self.iterable:\n if i:\n self.count += 1\n if self.count > other:\n return True\n return False\n\n def __ge__(self, other):\n if self.count >= other:\n return True\n for i in self.iterable:\n if i:\n self.count += 1\n if self.count >= other:\n return True\n return False\n\n def __lt__(self, other):\n return not self.__ge__(other)\n\n def __le__(self, other):\n return not self.__gt__(other)\n\n def __eq__(self, other):\n return self.__ge__(other) and self.__le__(other)\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n\ndef maybe_decode_base64(s):\n \"\"\"Returns None if s doesn't look like base64 (even if it's decodeable).\n Otherwise, return the decoded string and the altchars used.\"\"\"\n s = s.strip()\n charset = frozenset(s)\n if len(s) < 4:\n return None\n if not charset.issubset(string.ascii_letters + string.digits + '+/-_.=\\n'):\n return None\n if len(charset.intersection('+/-_.=')) > 3:\n return None\n\n # Looks decodable, lets decode it.\n altchars = ''.join(charset.intersection('+/_-.'))[:2]\n altchars = sorted(altchars)\n if len(altchars) == 0:\n altchars = '+/'\n elif len(altchars) == 1:\n altchars = {\n '+': '+/',\n '/': '+/',\n '-': '-_',\n '_': '-_',\n '.': '._',\n }[altchars]\n\n try:\n decoded = base64.b64decode(s + '===', altchars)\n except:\n return None\n\n # Ending in the right number of '='s is a good tell.\n if s.endswith('=') and not s.endswith('===') and ('=' not in s.rstrip('=')):\n return decoded, altchars\n\n # So is decoding to all printables or mostly letters.\n if len(decoded) > 10 and charset.issubset(string.printable):\n return decoded, altchars\n if len(decoded) >= 4:\n good_chars = frozenset(string.ascii_letters + string.digits + ' _.,')\n if sum(c in good_chars for c in decoded) > len(decoded) * 0.8:\n return decoded, altchars\n\n # Super good compressability is also a sign.\n if float(len(zlib.compress(decoded))) / len(decoded) <= 0.40:\n return decoded, altchars\n\n # decoded is apparently binary junk. Make sure the input really\n # \"looks like\" base64 before returning it. Specifically, we\n # check that the breakdown of digits to lowercase to capitals looks\n # roughly even.\n if not charset.intersection(string.digits): return None\n if not charset.intersection(string.ascii_lowercase): return None\n if not charset.intersection(string.ascii_uppercase): return None\n\n count = Counter(s)\n del count['\\n']\n del count['=']\n del count['A'] # Strings of null bytes are often more common, that's fine.\n grand_total = sum(count.values())\n digits = sum(count[c] for c in string.digits)\n lower = sum(count[c] for c in string.ascii_lowercase)\n upper = sum(count[c] for c in string.ascii_uppercase)\n total = digits + lower + upper\n if total < grand_total * 0.9: # Too many other chars to be base64!\n return None\n\n # Multipled by inverse expected probability of seeing that letter.\n digits = digits / 10.0 / total * 63\n lower = lower / 26.0 / total * 63\n upper = upper / 25.0 / total * 63\n if all(i > 0.5 for i in (digits, lower, upper)):\n return decoded, altchars\n\n\n#\n# Classes for representing the \"inferred type\" of a single string.\n#\n\nclass StrTypeBase:\n __slots__ = ()\n\nclass StrBool(namedtuple('StrBool', 'true false'), StrTypeBase):\n def invert(self, val):\n return self.true if val else self.false\n __slots__ = ()\n\nclass StrDate(namedtuple('StrDate', 'kind'), StrTypeBase):\n def invert(self, val):\n return val.isoformat()\n __slots__ = ()\n\nclass StrJSON(namedtuple('StrJSON', 'type'), StrTypeBase):\n def invert(self, val):\n return json.dumps(val)\n __slots__ = ()\n\nclass StrHex(namedtuple('StrHex', 'upper'), StrTypeBase):\n def invert(self, val):\n if isinstance(val, str):\n val = val.encode('utf-8')\n return binascii.hexlify(val).decode('utf-8')\n __slots__ = ()\n\nclass StrB64(namedtuple('StrB64', 'altchars'), StrTypeBase):\n def invert(self, val):\n # Lots of utf-8 hax. We don't expect to handle bytes well anyways.\n if isinstance(val, str):\n val = val.encode('utf-8')\n return base64.b64encode(val, altchars=self.altchars.encode('utf-8')).decode('utf-8')\n __slots__ = ()\n\nclass StrNum(namedtuple('StrNum', ''), StrTypeBase):\n def invert(self, val):\n return str(val)\n __slots__ = ()\n\nclass StrList(namedtuple('StrList', 'before delimiter after'), StrTypeBase):\n def invert(self, val):\n return self.before + self.delimiter.join(val) + self.after\n __slots__ = ()\n\nclass StrStr(namedtuple('StrStr', ''), StrTypeBase):\n def invert(self, val):\n return val\n __slots__ = ()\n\nclass StringGuesser:\n \"\"\"Class for staring really hard at strings and deciding what they are.\"\"\"\n def __init__(self, s):\n self.s = s\n self.stripped = s.strip()\n self.stripped_lower = self.stripped.lower()\n\n # Currently, the properties are non-None if the value can be interpreted\n # as the given type and **is likely to be the given type**. (For example,\n # \"abcd\" will currently return None on .hexbytes).\n # This is kinda bad cuz interpretation should be separate from guessing,\n # but whatever, not going to change this right now.\n\n @cached_property\n def isodate(self):\n # Parse ISO dates, ignoring fractional seconds or timezones cuz I'm lazy.\n stripped = self.stripped\n\n if ':' not in stripped:\n return None, None\n\n # Find end before fractional seconds or timezone\n colon = stripped.index(':')\n end = len(stripped)\n for c in '.Z+- ':\n try:\n end = min(end, stripped.index(c, colon))\n except (IndexError, ValueError):\n continue\n # Sanity check that after-the-end looks close enough.\n # Probably gonna be just a colon left or timezone name.\n if len(stripped[end:].strip(' .0123456789Z+-')) <= 4:\n try:\n val = datetime.datetime.strptime(stripped[:end], \"%Y-%m-%dT%H:%M:%S\")\n except ValueError:\n return None, None\n return StrDate('iso'), val\n\n return None, None\n\n @cached_property\n def boolean(self):\n if self.stripped_lower not in (\"true\", \"yes\", \"false\", \"no\"):\n return None, None\n\n info = (\n (\"true\", \"false\"),\n (\"True\", \"False\"),\n (\"TRUE\", \"FALSE\"),\n (\"yes\", \"no\"),\n (\"Yes\", \"No\"),\n (\"YES\", \"NO\"),\n )\n for i in info:\n if self.stripped in i:\n return StrBool(*i), self.stripped == i[0]\n else:\n # weird, couldn't match case right, just use lowercase.\n if self.stripped_lower in i:\n return StrBool(*i), self.stripped_lower == i[0]\n assert False, \"Unreachable\"\n\n @cached_property\n def hexbytes(self):\n cset = frozenset(self.stripped_lower)\n if self.stripped_lower.count(' ') > len(self.stripped_lower)/8:\n return None, None\n if len(cset) >= 3 and cset.issubset('0123456789abcdef _-') and not cset.issubset('abcdef _-'):\n try:\n decoded = binascii.unhexlify(self.stripped_lower.replace(' ', '').replace('_','').replace('-',''))\n except:\n return None, None\n\n # StrHex doesn't represent any space/underscore/dash patterns\n upper = 0\n for c in self.stripped:\n if c in 'abcdef':\n upper -= 1\n elif c in 'ABCDEF':\n upper += 1\n return StrHex(upper > 0), decoded\n\n return None, None\n\n @cached_property\n def base64bytes(self):\n ret = maybe_decode_base64(self.stripped)\n if ret is None:\n return None, None\n decoded, altchars = ret\n return StrB64(altchars), decoded\n\n @cached_property\n def json(self):\n try:\n x = json.loads(self.s)\n except:\n return None, None\n else:\n typename = type(x).__name__\n # We handle non-compoud json types with the other things.\n if typename not in ('dict', 'list'):\n return None, None\n return StrJSON(type=typename), x\n\n @cached_property\n def number(self):\n asint, asfloat = None, None\n try:\n asint = int(self.stripped)\n except ValueError:\n pass\n try:\n asfloat = float(self.stripped)\n except ValueError:\n pass\n\n if asint is not None:\n return StrNum(), asint\n elif asfloat is not None:\n return StrNum(), asfloat\n else:\n return None, None\n\n @cached_property\n def list(self):\n def comma_list(s, delimiter):\n if delimiter not in s:\n return None\n before, after = '', ''\n if s[:1] in '([<' and s[-1:] in ')]>':\n before, after = s[:1], s[-1:]\n s = s[1:-1]\n\n res = [StringGuesser(i) for i in s.split(delimiter)]\n if len(res) < 4 and count_trues(i.nonbasic for i in res) < 2:\n # If there's not many elements and less than two look like anything,\n # it's probably just a string with the delimiter in it.\n return None\n return StrList(before, delimiter, after), res\n\n def newline_list(s):\n after = '\\n' if s.endswith('\\n') else ''\n s = s.strip('\\n')\n if '\\n' not in s:\n return None\n res = [StringGuesser(i) for i in s.split('\\n')]\n if len(res) >= 4 and count_trues(i.basic for i in res) >= len(res)/2:\n # If over half the lines seem like just strings, it's\n # probably just a string with newlines in it.\n return None\n return StrList('', '\\n', after), res\n\n def whitespace_list(s):\n after = '\\n' if s.endswith('\\n') else ''\n s = s.strip().split()\n if len(s) <= 1:\n return None\n res = [StringGuesser(i) for i in s]\n if count_trues(i.basic for i in res) >= len(res)/2:\n # If over half the lines seem like just strings, it's\n # probably just a string with spaces in it.\n return None\n # fixme: ...we just assume space was the delimiter. Could do better.\n return StrList('', ' ', after), res\n\n # Trying them serially like this seems to makes sense; we could\n # return multiple possibilities but it doesn't seem worth it.\n s = self.s\n return newline_list(s) or comma_list(s, '\\t') or comma_list(s, ':') or comma_list(s, ',') or whitespace_list(s) or (None, None)\n\n @property\n def nonbasic(self):\n \"\"\"True if we get any sort of inference result beyond 'just some string'.\"\"\"\n return not self.basic\n\n @property\n def basic(self):\n \"\"\"True if there's no inference result besides 'just some string'.\"\"\"\n return isinstance(self.best_type, StrStr)\n\n @property\n def best(self):\n if self.boolean[0] is not None: return self.boolean\n if self.number[0] is not None: return self.number\n if self.isodate[0] is not None: return self.isodate\n if self.hexbytes[0] is not None: return self.hexbytes\n if self.base64bytes[0] is not None: return self.base64bytes\n if self.json[0] is not None: return self.json\n if self.list[0] is not None: return self.list\n return StrStr(), self.s\n\n @property\n def best_type(self):\n return self.best[0]\n","repo_name":"mruck/athena","sub_path":"fuzzer/guesstype/guessstring.py","file_name":"guessstring.py","file_ext":"py","file_size_in_byte":12114,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"5839346383","text":"import cv2\nimport numpy as np\nimport time\n\npeople=0\n\n# DOWNLOAD THE BELOW FILES FROM http://bluerabbit.me/yolov3.zip\nclass args:\n image = 'opencv_frame.png'\n config = 'yolov3.cfg'\n weights = 'yolov3.weights'\n classes = 'yolov3.txt'\n\n\n\ndef get_output_layers(net):\n layer_names = net.getLayerNames()\n output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n return output_layers\n\ndef draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):\n global people\n label = str(classes[class_id])\n color = COLORS[class_id]\n cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)\n cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n #print('Label:',label, 'Confidence:',confidence, 'Location:',(x, y, x_plus_w, y_plus_h))\n if(label=='person'):\n people = people + 1\n\ncam = cv2.VideoCapture(0)\ncv2.namedWindow(\"CamCapture\")\nimg_counter = 0\nwhile True:\n ret, frame = cam.read()\n cv2.imshow(\"CamCapture\", frame)\n if not ret:\n break\n k = cv2.waitKey(1)\n\n if k%256 == 27:\n # WHEN ESC KEY PRESSED\n print(\"Escape hit, closing...\")\n break\n else:\n # PRESS SPACE BAR\n time.sleep(5)\n img_name = \"opencv_frame.png\".format(img_counter)\n cv2.imwrite(img_name, frame)\n print(\"{} written!\".format(img_name))\n img_counter += 1\n image = cv2.imread(args.image)\n Width = image.shape[1]\n Height = image.shape[0]\n scale = 0.00392\n classes = None\n with open(args.classes, 'r') as f:\n classes = [line.strip() for line in f.readlines()]\n\n COLORS = np.random.uniform(0, 255, size=(len(classes), 3))\n net = cv2.dnn.readNet(args.weights, args.config)\n blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)\n net.setInput(blob)\n outs = net.forward(get_output_layers(net))\n class_ids = []\n confidences = []\n boxes = []\n conf_threshold = 0.5\n nms_threshold = 0.4\n\n for out in outs:\n for detection in out:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n if confidence > 0.5:\n center_x = int(detection[0] * Width)\n center_y = int(detection[1] * Height)\n w = int(detection[2] * Width)\n h = int(detection[3] * Height)\n x = center_x - w / 2\n y = center_y - h / 2\n class_ids.append(class_id)\n confidences.append(float(confidence))\n boxes.append([x, y, w, h])\n\n indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)\n\n for i in indices:\n i = i[0]\n box = boxes[i]\n x = box[0]\n y = box[1]\n w = box[2]\n h = box[3]\n draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h))\n\n cv2.imshow(\"object detection\", image)\n print(people)\n people = 0\n\n cv2.waitKey(1000)\n cv2.imwrite(\"object-detection.jpg\", image)\n cv2.destroyAllWindows()\n time.sleep(5)\n\ncam.release()\n\n","repo_name":"shelwyn/Detect_objects_with_YOLO","sub_path":"Detect.py","file_name":"Detect.py","file_ext":"py","file_size_in_byte":3353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"8865319378","text":"import flet as ft\nfrom repath import match\n\nfrom views import MainView, BookView\n\n\ndef main(page: ft.Page):\n page.title = \"Library Manager\"\n page.vertical_alignment = ft.MainAxisAlignment.CENTER\n # page.client_storage.clear()\n\n page.window_width = 900\n page.window_max_width = 900\n page.window_min_width = 900\n\n page.window_height = 600\n page.window_max_height = 600\n page.window_min_height = 600\n\n page.window_maximizable = False\n page.padding = 0\n\n def route_change(route: ft.RouteChangeEvent):\n page.views.clear()\n router = ft.TemplateRoute(page.route)\n\n if route.data == \"/main\":\n page.views.append(\n MainView(page=page, route=\"/main\")\n )\n\n if router.match(\"/book/:uuid\"):\n book_info = list(filter(lambda b: b['uuid'] == router.uuid, page.client_storage.get(\"books\")))[0]\n page.views.append(\n BookView(page=page, route=\"/book\", book_info=book_info)\n )\n\n page.update()\n\n page.on_route_change = route_change\n page.go(\"/main\")\n\n\nif __name__ == \"__main__\":\n ft.app(\n target=main\n )\n","repo_name":"DenisZhmakin/LibraryManager","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"}
+{"seq_id":"976910519","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n# --------------------------------------------------------------------------\nchrome_driver_path = \"C:\\devlopment\\chromedriver_win32\\chromedriver\"\ndriver = webdriver.Chrome(executable_path=chrome_driver_path)\n# driver.get(\"https://en.wikipedia.org/wiki/Main_Page\")\ndriver.get(\"http://secure-retreat-92358.herokuapp.com/\")\n# --------------------------------------------------------------------------\n# click\n# article_numbers = driver.find_element(By.CSS_SELECTOR, \"#articlecount > a:nth-child(1)\")\n# # or\n# click_portal = driver.find_element(By.LINK_TEXT, \"All portals\")\n# click_portal.click()\n\n# test\n# type_in = driver.find_element(By.CSS_SELECTOR, \"#searchInput\")\n# type_in.send_keys(\"Python\")\n# type_in.send_keys(Keys.ENTER)\n\n# Sign up\nfname = driver.find_element(By.CSS_SELECTOR, \"body > form > input.form-control.top\")\nlname = driver.find_element(By.CSS_SELECTOR, \"body > form > input.form-control.middle\")\nemail = driver.find_element(By.CSS_SELECTOR, \"body > form > input.form-control.bottom\")\nsubmit = driver.find_element(By.CSS_SELECTOR, \"body > form > button\")\nfname.send_keys(\"Priyansh\")\nlname.send_keys(\"Sharma\")\nemail.send_keys(\"piyuindia4@gmail.com\")\nsubmit.click()\n","repo_name":"Priyanshsharma21/Python","sub_path":"web_dev_py/Selanium/intraction.py","file_name":"intraction.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"}
+{"seq_id":"71497996733","text":"from __future__ import annotations\n\nfrom typing import MutableMapping, MutableSequence\n\nfrom google.protobuf import field_mask_pb2 # type: ignore\nfrom google.protobuf import timestamp_pb2 # type: ignore\nimport proto # type: ignore\n\n__protobuf__ = proto.module(\n package=\"google.cloud.resourcemanager.v3\",\n manifest={\n \"Folder\",\n \"GetFolderRequest\",\n \"ListFoldersRequest\",\n \"ListFoldersResponse\",\n \"SearchFoldersRequest\",\n \"SearchFoldersResponse\",\n \"CreateFolderRequest\",\n \"CreateFolderMetadata\",\n \"UpdateFolderRequest\",\n \"UpdateFolderMetadata\",\n \"MoveFolderRequest\",\n \"MoveFolderMetadata\",\n \"DeleteFolderRequest\",\n \"DeleteFolderMetadata\",\n \"UndeleteFolderRequest\",\n \"UndeleteFolderMetadata\",\n },\n)\n\n\nclass Folder(proto.Message):\n r\"\"\"A folder in an organization's resource hierarchy, used to\n organize that organization's resources.\n\n Attributes:\n name (str):\n Output only. The resource name of the folder. Its format is\n ``folders/{folder_id}``, for example: \"folders/1234\".\n parent (str):\n Required. The folder's parent's resource name. Updates to\n the folder's parent must be performed using\n [MoveFolder][google.cloud.resourcemanager.v3.Folders.MoveFolder].\n display_name (str):\n The folder's display name. A folder's display name must be\n unique amongst its siblings. For example, no two folders\n with the same parent can share the same display name. The\n display name must start and end with a letter or digit, may\n contain letters, digits, spaces, hyphens and underscores and\n can be no longer than 30 characters. This is captured by the\n regular expression:\n ``[\\p{L}\\p{N}]([\\p{L}\\p{N}_- ]{0,28}[\\p{L}\\p{N}])?``.\n state (google.cloud.resourcemanager_v3.types.Folder.State):\n Output only. The lifecycle state of the folder. Updates to\n the state must be performed using\n [DeleteFolder][google.cloud.resourcemanager.v3.Folders.DeleteFolder]\n and\n [UndeleteFolder][google.cloud.resourcemanager.v3.Folders.UndeleteFolder].\n create_time (google.protobuf.timestamp_pb2.Timestamp):\n Output only. Timestamp when the folder was\n created.\n update_time (google.protobuf.timestamp_pb2.Timestamp):\n Output only. Timestamp when the folder was\n last modified.\n delete_time (google.protobuf.timestamp_pb2.Timestamp):\n Output only. Timestamp when the folder was\n requested to be deleted.\n etag (str):\n Output only. A checksum computed by the\n server based on the current value of the folder\n resource. This may be sent on update and delete\n requests to ensure the client has an up-to-date\n value before proceeding.\n \"\"\"\n\n class State(proto.Enum):\n r\"\"\"Folder lifecycle states.\n\n Values:\n STATE_UNSPECIFIED (0):\n Unspecified state.\n ACTIVE (1):\n The normal and active state.\n DELETE_REQUESTED (2):\n The folder has been marked for deletion by\n the user.\n \"\"\"\n STATE_UNSPECIFIED = 0\n ACTIVE = 1\n DELETE_REQUESTED = 2\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n parent: str = proto.Field(\n proto.STRING,\n number=2,\n )\n display_name: str = proto.Field(\n proto.STRING,\n number=3,\n )\n state: State = proto.Field(\n proto.ENUM,\n number=4,\n enum=State,\n )\n create_time: timestamp_pb2.Timestamp = proto.Field(\n proto.MESSAGE,\n number=5,\n message=timestamp_pb2.Timestamp,\n )\n update_time: timestamp_pb2.Timestamp = proto.Field(\n proto.MESSAGE,\n number=6,\n message=timestamp_pb2.Timestamp,\n )\n delete_time: timestamp_pb2.Timestamp = proto.Field(\n proto.MESSAGE,\n number=7,\n message=timestamp_pb2.Timestamp,\n )\n etag: str = proto.Field(\n proto.STRING,\n number=8,\n )\n\n\nclass GetFolderRequest(proto.Message):\n r\"\"\"The GetFolder request message.\n\n Attributes:\n name (str):\n Required. The resource name of the folder to retrieve. Must\n be of the form ``folders/{folder_id}``.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n\n\nclass ListFoldersRequest(proto.Message):\n r\"\"\"The ListFolders request message.\n\n Attributes:\n parent (str):\n Required. The name of the parent resource whose folders are\n being listed. Only children of this parent resource are\n listed; descendants are not listed.\n\n If the parent is a folder, use the value\n ``folders/{folder_id}``. If the parent is an organization,\n use the value ``organizations/{org_id}``.\n\n Access to this method is controlled by checking the\n ``resourcemanager.folders.list`` permission on the\n ``parent``.\n page_size (int):\n Optional. The maximum number of folders to\n return in the response. The server can return\n fewer folders than requested. If unspecified,\n server picks an appropriate default.\n page_token (str):\n Optional. A pagination token returned from a previous call\n to ``ListFolders`` that indicates where this listing should\n continue from.\n show_deleted (bool):\n Optional. Controls whether folders in the\n [DELETE_REQUESTED][google.cloud.resourcemanager.v3.Folder.State.DELETE_REQUESTED]\n state should be returned. Defaults to false.\n \"\"\"\n\n parent: str = proto.Field(\n proto.STRING,\n number=1,\n )\n page_size: int = proto.Field(\n proto.INT32,\n number=2,\n )\n page_token: str = proto.Field(\n proto.STRING,\n number=3,\n )\n show_deleted: bool = proto.Field(\n proto.BOOL,\n number=4,\n )\n\n\nclass ListFoldersResponse(proto.Message):\n r\"\"\"The ListFolders response message.\n\n Attributes:\n folders (MutableSequence[google.cloud.resourcemanager_v3.types.Folder]):\n A possibly paginated list of folders that are\n direct descendants of the specified parent\n resource.\n next_page_token (str):\n A pagination token returned from a previous call to\n ``ListFolders`` that indicates from where listing should\n continue.\n \"\"\"\n\n @property\n def raw_page(self):\n return self\n\n folders: MutableSequence[\"Folder\"] = proto.RepeatedField(\n proto.MESSAGE,\n number=1,\n message=\"Folder\",\n )\n next_page_token: str = proto.Field(\n proto.STRING,\n number=2,\n )\n\n\nclass SearchFoldersRequest(proto.Message):\n r\"\"\"The request message for searching folders.\n\n Attributes:\n page_size (int):\n Optional. The maximum number of folders to\n return in the response. The server can return\n fewer folders than requested. If unspecified,\n server picks an appropriate default.\n page_token (str):\n Optional. A pagination token returned from a previous call\n to ``SearchFolders`` that indicates from where search should\n continue.\n query (str):\n Optional. Search criteria used to select the folders to\n return. If no search criteria is specified then all\n accessible folders will be returned.\n\n Query expressions can be used to restrict results based upon\n displayName, state and parent, where the operators ``=``\n (``:``) ``NOT``, ``AND`` and ``OR`` can be used along with\n the suffix wildcard symbol ``*``.\n\n The ``displayName`` field in a query expression should use\n escaped quotes for values that include whitespace to prevent\n unexpected behavior.\n\n ::\n\n | Field | Description |\n |-------------------------|----------------------------------------|\n | displayName | Filters by displayName. |\n | parent | Filters by parent (for example: folders/123). |\n | state, lifecycleState | Filters by state. |\n\n Some example queries are:\n\n - Query ``displayName=Test*`` returns Folder resources\n whose display name starts with \"Test\".\n - Query ``state=ACTIVE`` returns Folder resources with\n ``state`` set to ``ACTIVE``.\n - Query ``parent=folders/123`` returns Folder resources\n that have ``folders/123`` as a parent resource.\n - Query ``parent=folders/123 AND state=ACTIVE`` returns\n active Folder resources that have ``folders/123`` as a\n parent resource.\n - Query ``displayName=\\\\\"Test String\\\\\"`` returns Folder\n resources with display names that include both \"Test\" and\n \"String\".\n \"\"\"\n\n page_size: int = proto.Field(\n proto.INT32,\n number=1,\n )\n page_token: str = proto.Field(\n proto.STRING,\n number=2,\n )\n query: str = proto.Field(\n proto.STRING,\n number=3,\n )\n\n\nclass SearchFoldersResponse(proto.Message):\n r\"\"\"The response message for searching folders.\n\n Attributes:\n folders (MutableSequence[google.cloud.resourcemanager_v3.types.Folder]):\n A possibly paginated folder search results.\n the specified parent resource.\n next_page_token (str):\n A pagination token returned from a previous call to\n ``SearchFolders`` that indicates from where searching should\n continue.\n \"\"\"\n\n @property\n def raw_page(self):\n return self\n\n folders: MutableSequence[\"Folder\"] = proto.RepeatedField(\n proto.MESSAGE,\n number=1,\n message=\"Folder\",\n )\n next_page_token: str = proto.Field(\n proto.STRING,\n number=2,\n )\n\n\nclass CreateFolderRequest(proto.Message):\n r\"\"\"The CreateFolder request message.\n\n Attributes:\n folder (google.cloud.resourcemanager_v3.types.Folder):\n Required. The folder being created, only the\n display name and parent will be consulted. All\n other fields will be ignored.\n \"\"\"\n\n folder: \"Folder\" = proto.Field(\n proto.MESSAGE,\n number=2,\n message=\"Folder\",\n )\n\n\nclass CreateFolderMetadata(proto.Message):\n r\"\"\"Metadata pertaining to the Folder creation process.\n\n Attributes:\n display_name (str):\n The display name of the folder.\n parent (str):\n The resource name of the folder or\n organization we are creating the folder under.\n \"\"\"\n\n display_name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n parent: str = proto.Field(\n proto.STRING,\n number=2,\n )\n\n\nclass UpdateFolderRequest(proto.Message):\n r\"\"\"The request sent to the\n [UpdateFolder][google.cloud.resourcemanager.v3.Folder.UpdateFolder]\n method.\n\n Only the ``display_name`` field can be changed. All other fields\n will be ignored. Use the\n [MoveFolder][google.cloud.resourcemanager.v3.Folders.MoveFolder]\n method to change the ``parent`` field.\n\n Attributes:\n folder (google.cloud.resourcemanager_v3.types.Folder):\n Required. The new definition of the Folder. It must include\n the ``name`` field, which cannot be changed.\n update_mask (google.protobuf.field_mask_pb2.FieldMask):\n Required. Fields to be updated. Only the ``display_name``\n can be updated.\n \"\"\"\n\n folder: \"Folder\" = proto.Field(\n proto.MESSAGE,\n number=1,\n message=\"Folder\",\n )\n update_mask: field_mask_pb2.FieldMask = proto.Field(\n proto.MESSAGE,\n number=2,\n message=field_mask_pb2.FieldMask,\n )\n\n\nclass UpdateFolderMetadata(proto.Message):\n r\"\"\"A status object which is used as the ``metadata`` field for the\n Operation returned by UpdateFolder.\n\n \"\"\"\n\n\nclass MoveFolderRequest(proto.Message):\n r\"\"\"The MoveFolder request message.\n\n Attributes:\n name (str):\n Required. The resource name of the Folder to move. Must be\n of the form folders/{folder_id}\n destination_parent (str):\n Required. The resource name of the folder or organization\n which should be the folder's new parent. Must be of the form\n ``folders/{folder_id}`` or ``organizations/{org_id}``.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n destination_parent: str = proto.Field(\n proto.STRING,\n number=2,\n )\n\n\nclass MoveFolderMetadata(proto.Message):\n r\"\"\"Metadata pertaining to the folder move process.\n\n Attributes:\n display_name (str):\n The display name of the folder.\n source_parent (str):\n The resource name of the folder's parent.\n destination_parent (str):\n The resource name of the folder or\n organization to move the folder to.\n \"\"\"\n\n display_name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n source_parent: str = proto.Field(\n proto.STRING,\n number=2,\n )\n destination_parent: str = proto.Field(\n proto.STRING,\n number=3,\n )\n\n\nclass DeleteFolderRequest(proto.Message):\n r\"\"\"The DeleteFolder request message.\n\n Attributes:\n name (str):\n Required. The resource name of the folder to be deleted.\n Must be of the form ``folders/{folder_id}``.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n\n\nclass DeleteFolderMetadata(proto.Message):\n r\"\"\"A status object which is used as the ``metadata`` field for the\n ``Operation`` returned by ``DeleteFolder``.\n\n \"\"\"\n\n\nclass UndeleteFolderRequest(proto.Message):\n r\"\"\"The UndeleteFolder request message.\n\n Attributes:\n name (str):\n Required. The resource name of the folder to undelete. Must\n be of the form ``folders/{folder_id}``.\n \"\"\"\n\n name: str = proto.Field(\n proto.STRING,\n number=1,\n )\n\n\nclass UndeleteFolderMetadata(proto.Message):\n r\"\"\"A status object which is used as the ``metadata`` field for the\n ``Operation`` returned by ``UndeleteFolder``.\n\n \"\"\"\n\n\n__all__ = tuple(sorted(__protobuf__.manifest))\n","repo_name":"googleapis/google-cloud-python","sub_path":"packages/google-cloud-resource-manager/google/cloud/resourcemanager_v3/types/folders.py","file_name":"folders.py","file_ext":"py","file_size_in_byte":14945,"program_lang":"python","lang":"en","doc_type":"code","stars":4415,"dataset":"github-code","pt":"78"}
+{"seq_id":"74845180731","text":"'''\r\nRHYTHMATICA is a simple rhythm game, desinged by Kokosei J a.k.a Wonjun Jung.\r\nCopyright (C) 2019, Wonjun Jung\r\n\r\nThis program is free software: you can redistribute it and/or modify\r\nit under the terms of the GNU General Public License as published by\r\nthe Free Software Foundation, either version 3 of the License, or\r\n(at your option) any later version.\r\n\r\nThis program is distributed in the hope that it will be useful,\r\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\r\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\nGNU General Public License for more details.\r\n\r\nYou should have received a copy of the GNU General Public License\r\nalong with this program. If not, see ? identifier ( identifier ? ] | identifier? : )\n # (note the identifier is *optional* when there is a ':'!)\n _oc_message = re.compile(r'\\[\\s*[a-zA-Z_]\\w*\\s+'\n r'(?:[a-zA-Z_]\\w*\\s*\\]|'\n r'(?:[a-zA-Z_]\\w*)?:)')\n\n class GeneratedObjectiveCVariant(baselexer):\n \"\"\"\n Implements Objective-C syntax on top of an existing C family lexer.\n \"\"\"\n\n tokens = {\n 'statements': [\n (r'@\"', String, 'string'),\n (r'@(YES|NO)', Number),\n (r\"@'(\\\\.|\\\\[0-7]{1,3}|\\\\x[a-fA-F0-9]{1,2}|[^\\\\\\'\\n])'\", String.Char),\n (r'@(\\d+\\.\\d*|\\.\\d+|\\d+)[eE][+-]?\\d+[lL]?', Number.Float),\n (r'@(\\d+\\.\\d*|\\.\\d+|\\d+[fF])[fF]?', Number.Float),\n (r'@0x[0-9a-fA-F]+[Ll]?', Number.Hex),\n (r'@0[0-7]+[Ll]?', Number.Oct),\n (r'@\\d+[Ll]?', Number.Integer),\n (r'@\\(', Literal, 'literal_number'),\n (r'@\\[', Literal, 'literal_array'),\n (r'@\\{', Literal, 'literal_dictionary'),\n (words((\n '@selector', '@private', '@protected', '@public', '@encode',\n '@synchronized', '@try', '@throw', '@catch', '@finally',\n '@end', '@property', '@synthesize', '__bridge', '__bridge_transfer',\n '__autoreleasing', '__block', '__weak', '__strong', 'weak', 'strong',\n 'copy', 'retain', 'assign', 'unsafe_unretained', 'atomic', 'nonatomic',\n 'readonly', 'readwrite', 'setter', 'getter', 'typeof', 'in',\n 'out', 'inout', 'release', 'class', '@dynamic', '@optional',\n '@required', '@autoreleasepool'), suffix=r'\\b'),\n Keyword),\n (words(('id', 'instancetype', 'Class', 'IMP', 'SEL', 'BOOL',\n 'IBOutlet', 'IBAction', 'unichar'), suffix=r'\\b'),\n Keyword.Type),\n (r'@(true|false|YES|NO)\\n', Name.Builtin),\n (r'(YES|NO|nil|self|super)\\b', Name.Builtin),\n # Carbon types\n (r'(Boolean|UInt8|SInt8|UInt16|SInt16|UInt32|SInt32)\\b', Keyword.Type),\n # Carbon built-ins\n (r'(TRUE|FALSE)\\b', Name.Builtin),\n (r'(@interface|@implementation)(\\s+)', bygroups(Keyword, Text),\n ('#pop', 'oc_classname')),\n (r'(@class|@protocol)(\\s+)', bygroups(Keyword, Text),\n ('#pop', 'oc_forward_classname')),\n # @ can also prefix other expressions like @{...} or @(...)\n (r'@', Punctuation),\n inherit,\n ],\n 'oc_classname': [\n # interface definition that inherits\n ('([a-zA-Z$_][\\w$]*)(\\s*:\\s*)([a-zA-Z$_][\\w$]*)?(\\s*)({)',\n bygroups(Name.Class, Text, Name.Class, Text, Punctuation),\n ('#pop', 'oc_ivars')),\n ('([a-zA-Z$_][\\w$]*)(\\s*:\\s*)([a-zA-Z$_][\\w$]*)?',\n bygroups(Name.Class, Text, Name.Class), '#pop'),\n # interface definition for a category\n ('([a-zA-Z$_][\\w$]*)(\\s*)(\\([a-zA-Z$_][\\w$]*\\))(\\s*)({)',\n bygroups(Name.Class, Text, Name.Label, Text, Punctuation),\n ('#pop', 'oc_ivars')),\n ('([a-zA-Z$_][\\w$]*)(\\s*)(\\([a-zA-Z$_][\\w$]*\\))',\n bygroups(Name.Class, Text, Name.Label), '#pop'),\n # simple interface / implementation\n ('([a-zA-Z$_][\\w$]*)(\\s*)({)',\n bygroups(Name.Class, Text, Punctuation), ('#pop', 'oc_ivars')),\n ('([a-zA-Z$_][\\w$]*)', Name.Class, '#pop')\n ],\n 'oc_forward_classname': [\n ('([a-zA-Z$_][\\w$]*)(\\s*,\\s*)',\n bygroups(Name.Class, Text), 'oc_forward_classname'),\n ('([a-zA-Z$_][\\w$]*)(\\s*;?)',\n bygroups(Name.Class, Text), '#pop')\n ],\n 'oc_ivars': [\n include('whitespace'),\n include('statements'),\n (';', Punctuation),\n (r'\\{', Punctuation, '#push'),\n (r'\\}', Punctuation, '#pop'),\n ],\n 'root': [\n # methods\n (r'^([-+])(\\s*)' # method marker\n r'(\\(.*?\\))?(\\s*)' # return type\n r'([a-zA-Z$_][\\w$]*:?)', # begin of method name\n bygroups(Punctuation, Text, using(this),\n Text, Name.Function),\n 'method'),\n inherit,\n ],\n 'method': [\n include('whitespace'),\n # TODO unsure if ellipses are allowed elsewhere, see\n # discussion in Issue 789\n (r',', Punctuation),\n (r'\\.\\.\\.', Punctuation),\n (r'(\\(.*?\\))(\\s*)([a-zA-Z$_][\\w$]*)',\n bygroups(using(this), Text, Name.Variable)),\n (r'[a-zA-Z$_][\\w$]*:', Name.Function),\n (';', Punctuation, '#pop'),\n (r'\\{', Punctuation, 'function'),\n ('', Text, '#pop'),\n ],\n 'literal_number': [\n (r'\\(', Punctuation, 'literal_number_inner'),\n (r'\\)', Literal, '#pop'),\n include('statement'),\n ],\n 'literal_number_inner': [\n (r'\\(', Punctuation, '#push'),\n (r'\\)', Punctuation, '#pop'),\n include('statement'),\n ],\n 'literal_array': [\n (r'\\[', Punctuation, 'literal_array_inner'),\n (r'\\]', Literal, '#pop'),\n include('statement'),\n ],\n 'literal_array_inner': [\n (r'\\[', Punctuation, '#push'),\n (r'\\]', Punctuation, '#pop'),\n include('statement'),\n ],\n 'literal_dictionary': [\n (r'\\}', Literal, '#pop'),\n include('statement'),\n ],\n }\n\n def analyse_text(text):\n if _oc_keywords.search(text):\n return 1.0\n elif '@\"' in text: # strings\n return 0.8\n elif re.search('@[0-9]+', text):\n return 0.7\n elif _oc_message.search(text):\n return 0.8\n return 0\n\n def get_tokens_unprocessed(self, text):\n from pygments.lexers._cocoa_builtins import COCOA_INTERFACES, \\\n COCOA_PROTOCOLS, COCOA_PRIMITIVES\n\n for index, token, value in \\\n baselexer.get_tokens_unprocessed(self, text):\n if token is Name or token is Name.Class:\n if value in COCOA_INTERFACES or value in COCOA_PROTOCOLS \\\n or value in COCOA_PRIMITIVES:\n token = Name.Builtin.Pseudo\n\n yield index, token, value\n\n return GeneratedObjectiveCVariant\n\n\nclass ObjectiveCLexer(objective(CLexer)):\n \"\"\"\n For Objective-C source code with preprocessor directives.\n \"\"\"\n\n name = 'Objective-C'\n aliases = ['objective-c', 'objectivec', 'obj-c', 'objc']\n filenames = ['*.m', '*.h']\n mimetypes = ['text/x-objective-c']\n priority = 0.05 # Lower than C\n\n\nclass ObjectiveCppLexer(objective(CppLexer)):\n \"\"\"\n For Objective-C++ source code with preprocessor directives.\n \"\"\"\n\n name = 'Objective-C++'\n aliases = ['objective-c++', 'objectivec++', 'obj-c++', 'objc++']\n filenames = ['*.mm', '*.hh']\n mimetypes = ['text/x-objective-c++']\n priority = 0.05 # Lower than C++\n\n\nclass LogosLexer(ObjectiveCppLexer):\n \"\"\"\n For Logos + Objective-C source code with preprocessor directives.\n\n .. versionadded:: 1.6\n \"\"\"\n\n name = 'Logos'\n aliases = ['logos']\n filenames = ['*.x', '*.xi', '*.xm', '*.xmi']\n mimetypes = ['text/x-logos']\n priority = 0.25\n\n tokens = {\n 'statements': [\n (r'(%orig|%log)\\b', Keyword),\n (r'(%c)\\b(\\()(\\s*)([a-zA-Z$_][\\w$]*)(\\s*)(\\))',\n bygroups(Keyword, Punctuation, Text, Name.Class, Text, Punctuation)),\n (r'(%init)\\b(\\()',\n bygroups(Keyword, Punctuation), 'logos_init_directive'),\n (r'(%init)(?=\\s*;)', bygroups(Keyword)),\n (r'(%hook|%group)(\\s+)([a-zA-Z$_][\\w$]+)',\n bygroups(Keyword, Text, Name.Class), '#pop'),\n (r'(%subclass)(\\s+)', bygroups(Keyword, Text),\n ('#pop', 'logos_classname')),\n inherit,\n ],\n 'logos_init_directive': [\n ('\\s+', Text),\n (',', Punctuation, ('logos_init_directive', '#pop')),\n ('([a-zA-Z$_][\\w$]*)(\\s*)(=)(\\s*)([^);]*)',\n bygroups(Name.Class, Text, Punctuation, Text, Text)),\n ('([a-zA-Z$_][\\w$]*)', Name.Class),\n ('\\)', Punctuation, '#pop'),\n ],\n 'logos_classname': [\n ('([a-zA-Z$_][\\w$]*)(\\s*:\\s*)([a-zA-Z$_][\\w$]*)?',\n bygroups(Name.Class, Text, Name.Class), '#pop'),\n ('([a-zA-Z$_][\\w$]*)', Name.Class, '#pop')\n ],\n 'root': [\n (r'(%subclass)(\\s+)', bygroups(Keyword, Text),\n 'logos_classname'),\n (r'(%hook|%group)(\\s+)([a-zA-Z$_][\\w$]+)',\n bygroups(Keyword, Text, Name.Class)),\n (r'(%config)(\\s*\\(\\s*)(\\w+)(\\s*=\\s*)(.*?)(\\s*\\)\\s*)',\n bygroups(Keyword, Text, Name.Variable, Text, String, Text)),\n (r'(%ctor)(\\s*)({)', bygroups(Keyword, Text, Punctuation),\n 'function'),\n (r'(%new)(\\s*)(\\()(\\s*.*?\\s*)(\\))',\n bygroups(Keyword, Text, Keyword, String, Keyword)),\n (r'(\\s*)(%end)(\\s*)', bygroups(Text, Keyword, Text)),\n inherit,\n ],\n }\n\n _logos_keywords = re.compile(r'%(?:hook|ctor|init|c\\()')\n\n def analyse_text(text):\n if LogosLexer._logos_keywords.search(text):\n return 1.0\n return 0\n\n\nclass SwiftLexer(ObjectiveCLexer):\n \"\"\"\n For `Swift