xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

6b97a216ca7bed17169dea75598a8db7b38aa938

3,225

py

Python

tests/conftest.py

jeromedockes/neuroquery_image_search

2222caf464de84694273a494ec2d00071b3d14a2

[ "BSD-3-Clause" ]

3

2021-01-26T20:27:24.000Z

2021-09-28T19:51:36.000Z

tests/conftest.py

jeromedockes/neuroquery_image_search

2222caf464de84694273a494ec2d00071b3d14a2

[ "BSD-3-Clause" ]

null

tests/conftest.py

jeromedockes/neuroquery_image_search

2222caf464de84694273a494ec2d00071b3d14a2

[ "BSD-3-Clause" ]

1

2021-01-21T22:27:16.000Z

from pathlib import Path import tempfile from unittest.mock import MagicMock import pytest import numpy as np import pandas as pd from scipy import sparse import nibabel import nilearn from nilearn.datasets import _testing from nilearn.datasets._testing import request_mocker # noqa: F401 def make_fake_img(): rng = np.random.default_rng(0) img = rng.random(size=(4, 3, 5)) return nibabel.Nifti1Image(img, np.eye(4)) @pytest.fixture() def fake_img(): return make_fake_img() def make_fake_data(): n_voxels, n_components, n_studies, n_terms = 23, 8, 12, 9 rng = np.random.default_rng(0) difumo_maps = rng.random((n_components, n_voxels)) difumo_maps[rng.binomial(1, 0.3, size=difumo_maps.shape).astype(int)] = 0 difumo_inverse_covariance = np.linalg.pinv(difumo_maps.dot(difumo_maps.T)) difumo_maps = sparse.csr_matrix(difumo_maps) projections = rng.random((n_studies, n_components)) term_projections = rng.random((n_terms, n_components)) articles_info = pd.DataFrame({"pmid": np.arange(n_studies) + 100}) articles_info["title"] = [ f"title {pmid}" for pmid in articles_info["pmid"] ] articles_info["pubmed_url"] = [ f"url {pmid}" for pmid in articles_info["pmid"] ] mask = np.zeros(4 * 3 * 5, dtype=int) mask[:n_voxels] = 1 mask = mask.reshape((4, 3, 5)) mask_img = nibabel.Nifti1Image(mask, np.eye(4)) doc_freq = pd.DataFrame( { "term": ["term_{i}" for i in range(n_terms)], "document_frequency": np.arange(n_terms), } ) with tempfile.TemporaryDirectory() as temp_dir: temp_dir = Path(temp_dir) sparse.save_npz(temp_dir / "difumo_maps.npz", difumo_maps) np.save( temp_dir / "difumo_inverse_covariance.npy", difumo_inverse_covariance, ) np.save(temp_dir / "projections.npy", projections) np.save(temp_dir / "term_projections.npy", term_projections) articles_info.to_csv(temp_dir / "articles-info.csv", index=False) mask_img.to_filename(str(temp_dir / "mask.nii.gz")) doc_freq.to_csv( str(temp_dir / "document_frequencies.csv"), index=False ) archive = _testing.dict_to_archive( {"neuroquery_image_search_data": temp_dir} ) return archive @pytest.fixture(autouse=True) def temp_data_dir(tmp_path_factory, monkeypatch): home_dir = tmp_path_factory.mktemp("temp_home") monkeypatch.setenv("HOME", str(home_dir)) monkeypatch.setenv("USERPROFILE", str(home_dir)) data_dir = home_dir / "neuroquery_data" data_dir.mkdir() monkeypatch.setenv("NEUROQUERY_DATA", str(data_dir)) @pytest.fixture(autouse=True, scope="function") def map_mock_requests(request_mocker): request_mocker.url_mapping[ "https://osf.io/mx3t4/download" ] = make_fake_data() return request_mocker @pytest.fixture(autouse=True) def patch_nilearn(monkeypatch): def fake_motor_task(*args, **kwargs): return {"images": [make_fake_img()]} monkeypatch.setattr( nilearn.datasets, "fetch_neurovault_motor_task", fake_motor_task ) monkeypatch.setattr("webbrowser.open", MagicMock())

32.25

78

0.68093

438

3,225

4.753425

0.321918

0.036984

0.01585

0.018732

0.074928

0.048991

0.027858

0

0.013143

0.197829

3,225

99

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32.575758

0.791651

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0.033613

0

1

0.083333

false

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0.130952

0.02381

0.27381

0

null

0

null

0

1

0

6b9e0d5209b7b62f3db7419587e03282debb98ce

524

py

Python

Praticas/pratica03/latitude_longitude.py

andrepinto42/Processamento-de-Linguagens

98facba0d1c9ca751743b1c83dca7f441aa182e9

[ "MIT" ]

1

2022-03-18T21:39:47.000Z

Praticas/pratica03/latitude_longitude.py

andrepinto42/Processamento-de-Linguagens

98facba0d1c9ca751743b1c83dca7f441aa182e9

[ "MIT" ]

null

Praticas/pratica03/latitude_longitude.py

andrepinto42/Processamento-de-Linguagens

98facba0d1c9ca751743b1c83dca7f441aa182e9

[ "MIT" ]

null

import re import sys real_num = r'[+-]?\d+(?:\.\d+)?' # Falta colocar as os paratenses para identificar o grupo correto coord = rf'$({real_num}),\s*({real_num})$' for line in sys.stdin: line = re.sub(coord,r"<point lat='\1', lon='\2' />",line) if (line): print(line) quit() # Tambem dá para executar assim coord = rf'$(?P<lat>{real_num}),\s*(?P<lon>{real_num})$' for line in sys.stdin: line = re.sub(coord,r"<point lat='\g<lat>', lon='\g<lon>' />",line) if (line): print(line)

22.782609

71

0.574427

85

524

3.482353

0.458824

0.118243

0.054054

0.094595

0.445946

0.317568

0

0.004717

0.19084

524

23

72

22.782609

0.693396

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0

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0

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false

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0.142857

0

null

0

null

0

1

0

6b9f0ffcb6c75b079a5eb98125aa38eb4f61fd76

375

py

Python

includes/prav_modules/test2.py

praveen868686/DAGAirflow-with-Python

483fffc2e7f987e523ae3653a90869a67cdad886

[ "MIT" ]

null

includes/prav_modules/test2.py

praveen868686/DAGAirflow-with-Python

483fffc2e7f987e523ae3653a90869a67cdad886

[ "MIT" ]

null

includes/prav_modules/test2.py

praveen868686/DAGAirflow-with-Python

483fffc2e7f987e523ae3653a90869a67cdad886

[ "MIT" ]

null

import pandas as pd import numpy as np def see(): m = pd.read_csv('C:/dag/Expenditure.csv') #m = pd.read_csv('C:\dag\Expendture.csv') # print(m.head()) countt= m ['Category'].value_counts(sort=True, ascending=True).to_frame() print(countt) pivottable= m.pivot_table(index=['Category'], values=['Myself'], aggfunc='sum') print(pivottable) see()

25

83

0.656

55

375

4.381818

0.618182

0.024896

0.058091

0.082988

0.116183

0

0.16

375

14

84

26.785714

0.765079

0.149333

0

0.148265

0.069401

0

1

0.111111

false

0

0.222222

0

0.333333

0.222222

0

null

0

null

0

1

0

6ba002cacb0aea0efbea0d09c9f0563aeccf4db3

4,134

py

Python

MLP/generate_args.py

AMNoureldin/COMP551-HW4

0c855372862300cc0454f144bb40b2e72ba93861

[ "Apache-2.0" ]

15

2021-03-18T03:00:15.000Z

2022-02-28T04:42:54.000Z

MLP/generate_args.py

AMNoureldin/COMP551-HW4

0c855372862300cc0454f144bb40b2e72ba93861

[ "Apache-2.0" ]

null

MLP/generate_args.py

AMNoureldin/COMP551-HW4

0c855372862300cc0454f144bb40b2e72ba93861

[ "Apache-2.0" ]

2

2021-11-05T15:50:20.000Z

2022-01-16T11:48:27.000Z

# Copyright 2021 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from datetime import datetime from utils import * #===== time stamp for experiment file names timestamp = datetime.now() timestamp = timestamp.strftime("%d-%m-%Y_%H%M") script_name = 'main' # main script to be executed #================================ # args for main script # #================================ seed= 1 # setting random seed for reproducibility #===== MODEL ===== # model_type= 'MLP1' no_bias= True # don't use biases in layers make_linear= False # linear activation function (if False, then ReLU) no_BN= True # disable BatchNorm NTK_style= True # NTK-style parametrization of the network base_width= 8 all_widths= [8, 32, 128, 216, 328, 512, 635] fract_freeze_cl= 0 # allowed fraction of all cl-layer weights that may be frozen dense_only= False # consider dense models only, no weight freezing #===== TRAINING ===== # no_ES= True # disable Early Stopping train_subset_size= 2048 # train on a subset of the train set mbs= 256 # mini-batch size max_epochs= 300 # max number of training epochs #===== DATASET ===== # dataset= 'MNIST' normalize_pixelwise= True #=== for NTK-style nets, the LR value is width-dependent # loading optimized LR values for each width from file if NTK_style: bta_avg_and_lr= torch.load('optimized_LR_for_NTK_style_MLP1.pt') # NWTF (for "Num. Weights To Freeze") is a dictionary with # key = width # val = [(nwtf_cl, nwtf_fc)_1, (nwtf_cl, nwtf_fc)_2, ...] # i.e., a list of valid combinations of weights to freeze for the respective layer (cl and fc) if dense_only: NWTF = {base_width: [(0,0)]} else: NWTF = get_NWTF(base_width, all_widths, fract_freeze_cl) #=== tags for file names bias_tag='_no_bias' if no_bias else '' NTK_tag='_NTK_style' if NTK_style else '' act_fctn='Linear' if make_linear else 'ReLU' job_configs=[] for width, val in NWTF.items(): for nwtf_cl,nwtf_fc in val: cur_base_width=width if nwtf_cl==nwtf_fc else base_width # compose name for output dir output_dir = f'{dataset}_{model_type}_{NTK_tag}' output_dir+= f'_base_{cur_base_width}_width_{width}_{act_fctn}{bias_tag}' if train_subset_size>0: output_dir+=f'_train_on_{train_subset_size}_samples' if normalize_pixelwise: output_dir+=f'_pixelwise_normalization' if NTK_style: # get LR from file lrkey=f'{cur_base_width}_{width}' lr=bta_avg_and_lr[lrkey] else: lr= 0.1 config ={ 'base_width': int(cur_base_width), 'width': int(width), 'lr': lr, 'seed': seed, 'nwtf_cl': int(nwtf_cl), 'nwtf_fc': int(nwtf_fc), 'dataset': dataset, 'normalize_pixelwise': normalize_pixelwise, 'train_subset_size': train_subset_size, 'no_ES': no_ES, 'max_epochs': max_epochs, 'mbs': mbs, 'no_bias': no_bias, 'NTK_style': NTK_style, 'make_linear': make_linear, 'no_BN': no_BN, 'output_dir': output_dir } job_configs.append(config) for config in job_configs: my_str=f'\npython -m {script_name} ' for k, v in config.items(): if isinstance(v, bool): if v: my_str+=f'--{k} ' else: my_str+=f'--{k} {v} ' print(my_str)

32.046512

94

0.618287

577

4,134

4.211438

0.37435

0.032922

0.030864

0.024691

0

0.015815

0.265844

4,134

128

95

32.296875

0.784843

0.376149

0

0.042254

0

0.174566

0.082149

0

1

0

false

0

0.042254

0

0.042254

0.014085

0

null

0

null

0

1

0

6ba35922a8da9226341017db55248451774263a5

38,712

py

Python

EasyRegression.py

pankajchejara23/EasyRegression

7f76d92c4a9d056a83bde6abc2fd6eb980602e44

[ "MIT" ]

1

2021-04-19T16:47:27.000Z

EasyRegression.py

pankajchejara23/EasyRegression

7f76d92c4a9d056a83bde6abc2fd6eb980602e44

[ "MIT" ]

null

EasyRegression.py

pankajchejara23/EasyRegression

7f76d92c4a9d056a83bde6abc2fd6eb980602e44

[ "MIT" ]

null

import pandas as pd import matplotlib.pyplot as plt import librosa import seaborn as sns from sklearn.model_selection import train_test_split import math from sklearn.model_selection import LeaveOneGroupOut from sklearn.metrics import mean_squared_error, mean_absolute_error import traceback import statistics # Regression Model from sklearn.linear_model import LinearRegression from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso from sklearn.linear_model import ElasticNet from sklearn.linear_model import Lars from sklearn.linear_model import BayesianRidge from sklearn.linear_model import SGDRegressor from sklearn.linear_model import RANSACRegressor from pyfiglet import Figlet from sklearn.model_selection import cross_val_score from joblib import dump, load from sklearn.kernel_ridge import KernelRidge from sklearn.tree import DecisionTreeRegressor from xgboost import XGBRegressor from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import AdaBoostRegressor from sklearn.ensemble import ExtraTreesRegressor from sklearn.ensemble import GradientBoostingRegressor from sklearn.ensemble import VotingRegressor from sklearn.ensemble import StackingRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import cross_val_score import statistics from sklearn.model_selection import cross_validate # Dimensionality reduction from sklearn.decomposition import PCA from sklearn import manifold import numpy as np from sklearn.model_selection import GridSearchCV from scipy.special import entr import random from sklearn import metrics from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn import metrics from art import * class EasyRegression: def __init__(self): print(text2art('Easy')) print(text2art('Regression')) self.seed = 40 self.strategy = None self.parameterFound = dict() self.configured = False self.models = None # Scalers self.std = StandardScaler() self.mmax = MinMaxScaler() self.random_state = 42 self.feature_set = dict() self.label_set = dict() self.groups = None self.datasets = None self.label = None self.flagDataset = False self.flagGroup = False self.flagParameterFind = False self.train_test = None self.cross_val = None self.leave_group = None self.leave_dataset = None self.stratified = None def loadFeature(self,feature_file,feature_type,feature_name): if len(self.feature_set) == 0: print('-------------------------------') print(' STEP : Features loading') print('-------------------------------') if feature_type not in ['ind','grp']: print('===> Error: Undefined feature type') return else: try: if feature_name in self.feature_set.keys(): print('===> Feature with name ',feature_name,' already exist. Choose a different name') return else: tmp = pd.read_csv(feature_file) if len(self.feature_set) > 0: first_feat = self.feature_set[list(self.feature_set.keys())[0]] if tmp.shape[0] != first_feat[2].shape[0]: print('===> Error: Mismatch in feature size with previously added features ',first_feat[1] ) return self.feature_set[feature_name] = [feature_type,feature_name,tmp] print('===> Feature file:',feature_file,' is loaded successfully !') print('===> Summary:') print(' #instances:',tmp.shape[0]) print(' #attributes:',tmp.shape[1]) num_cols = tmp.select_dtypes(['int64','float64']) print(' #numeric-attributes:',num_cols.shape[1]) print('') return num_cols except: print('===> Error occurred while loading the file') traceback.print_exc() def loadLabels(self,label_file): try: print('-------------------------------') print(' STEP : Labels loading') print('-------------------------------') tmp = pd.read_csv(label_file) if len(self.feature_set) > 0: first_feat = self.feature_set[list(self.feature_set.keys())[0]] if tmp.shape[0] != first_feat[2].shape[0]: print(' Error: Mismatch in feature size with loaded feature ',first_feat[1] ) return None for label in tmp.columns: self.label_set[label] = tmp[label] print('===> Label file:',label_file,' is loaded successfully !') print('===> Summary:') print(' #labels:',len(tmp.columns.tolist())) print(' labels:', tmp.columns.tolist()) print('') return tmp except: print('===> Error occurred while loading the file:',label_file) traceback.print_exc() return None def feature_name_check(self,feature_name): if feature_name not in self.feature_set.keys(): print(' Feature name:', feature_name,' is not available.') return None def label_name_check(self,label_name): if label_name not in self.label_set.keys(): print(' Label name:',label_name,' is not available.') return None def extractFeatures(self,data,cor=.80): print('-------------------------------') print(' STEP : Feature Extraction ') print('-------------------------------') correlated_features = set() features = data correlation_matrix = features.corr() for i in range(len(correlation_matrix .columns)): for j in range(i): if abs(correlation_matrix.iloc[i, j]) > cor: colname = correlation_matrix.columns[i] correlated_features.add(colname) #print('Correlated Features:') #print(correlated_features) features.drop(labels=correlated_features,axis=1,inplace=True) print('===> ',len(correlated_features),' correlated features are removed.') print('===> Final features shape:',features.shape) return features def findCorrelation(self,label_name=None,sort=True): if self.dataReady == False: print('Data is not ready yet for analysis.') return if label_name is not None: if label_name in self.labels.columns: tmp_features = self.features.copy() tmp_features[label_name] = self.labels[label_name] cor_table = tmp_features.corr() print(' Correlation ') print(' -------------------------------') print(cor_table[label_name]) print(' -------------------------------') else: if self.labels.shape[1] > 1: print(' There are more than one label available.') print(self.labels.columns) print('Deafult: first column is used to computer correlation') label_name = self.labels.columns[0] tmp_features = self.features.copy() tmp_features[label_name] = self.labels[label_name] cor_table = tmp_features.corr() print(' Correlation ') print(' -------------------------------') print(cor_table[label_name]) print(' -------------------------------') def setGroupFeatureLabels(self,feat_labels): self.group_feature_labels = feat_labels """ This function performs group-level feature computation supported fusions: Dimensionality reduction, Entropy, Gini, Average """ def getGroupFeatures(self,data): group_feature_labels = ['add','del','speak','turns'] features_group = dict() # iterate for each group-level feature for grp_feature in group_feature_labels: tmp = list() # get all column names similar to grp_feature for indiv_feature in data.columns: if grp_feature in indiv_feature: tmp.append(indiv_feature) features_group[grp_feature] = tmp.copy() return features_group # preparing gini coefficient def getGINI(self,data): """Calculate the Gini coefficient of a numpy array.""" print('-------------------------------') print(' STEP : Feature Fusion using Gini') print('-------------------------------') group_features = self.getGroupFeatures(data) gini = dict() for key in group_features.keys(): tmp = data[group_features[key]].values tmp = tmp + 0.0000001 tmp = np.sort(tmp) index = np.arange(1,tmp.shape[1]+1) n = tmp.shape[1] key = 'grp_gini_'+key gini[key] = ((np.sum((2 * index - n - 1) * tmp,axis=1)) / (n * np.sum(tmp,axis=1))) #Gini coefficient gini_features = pd.DataFrame(gini) return gini_features # Compute entropy features for individual features def getEntropy(self,data): print('-------------------------------') print(' STEP : Feature Fusion using Entropy') print('-------------------------------') group_features = self.getGroupFeatures(data) entropy = dict() for key in group_features.keys(): tmp = data[group_features[key]].values tmp = tmp tmp_sum = tmp.sum(axis=1,keepdims=True) + .0000000000001 p = tmp/tmp_sum key = 'grp_entropy_'+key entropy[key] = entr(p).sum(axis=1)/np.log(2) entropy_features = pd.DataFrame(entropy) return entropy_features """ Apply dimentionality reduction on features PCA """ def Scaling(self,data,algo): print('-------------------------------') print(' STEP : Feature Scaling') print('-------------------------------') if algo in ['std','mmax']: if algo == 'std': res = pd.DataFrame(self.std.fit_transform(data), columns=data.columns) print('===> Successfully applied Standard Scaling') return res elif algo == 'mmax': res = pd.DataFrame(self.mmax.fit_transform(data), columns=data.columns) print('===> Successfully applied MinMax Scaling') return res else: print('===> Error: Unsupported scaling method') return None def DimRed(self,algo,data,params=None): print('-------------------------------') print(' STEP : Feature fusion using DimRed') print('-------------------------------') if algo not in ['pca','mds','isomap','tsne']: print('===> Erro: Unsupported dimension reduction algorithm specified') return None else: if algo!='pca' and len(params) ==0: print('===> Error: Specify n_components/n_neighbors parameters') return None else: # Dimensionality reduction X_train, X_test, y_train, y_test = train_test_split(data,self.label_set[self.label],train_size=.7,random_state=self.seed) self.pca = PCA(random_state = self.seed) self.mds = manifold.MDS(n_components=params['n_components'],max_iter=100,n_init=1,random_state = self.seed) self.isomap = manifold.Isomap(n_neighbors=params['n_neighbors'],n_components=params['n_components']) self.tsne = manifold.TSNE(n_components=params['n_components'],init='pca',random_state = self.seed) if algo == 'pca': self.pca.fit(X_train) pca_features = self.pca.transform(data) print('===> Successfully applied PCA') pca_columns = [None] * pca_features.shape[1] for k in range(pca_features.shape[1]): pca_columns[k] = 'pca_' + str(k) return pd.DataFrame(pca_features,columns=pca_columns) if algo == 'mds': self.mds.fit(X_train) mds_features = self.mds.transform(data) mds_columns = [None] * mds_features.shape[1] for k in range(mds_features.shape[1]): mds_columns[k] = 'mds_' + str(k) print('===> Successfully applied MDS') return pd.DataFrame(mds_features,columns=mds_columns) if algo== 'isomap': self.isomap.fit(X_train) isomap_features = self.isomap.transform(data) print('===> Successfully applied ISOMAP') isomap_columns = [None] * isomap_features.shape[1] for k in range(isomap_features.shape[1]): isomap_columns[k] = 'iso_' + str(k) return pd.DataFrame(isomap_features,columns=isomap_columns) if algo=='tsne': tsne_features = self.tsne.fit_transform(data) print('===> Successfully applied t-SNE') tsne_columns = [None] * tsne_features.shape[1] for k in range(tsne_features.shape[1]): tsne_columns[k] = 'tsne_' + str(k) return pd.DataFrame(tsne_features,columns=tsne_columns) ; def loadConfiguredModules(self,modules): print('-------------------------------') print(' STEP : Configured Regression Moduel Loaded') print('-------------------------------') self.models = modules self.configured = True def regressionModelInitialize(self): print('-------------------------------') print(' STEP : Regression Moduel Initialised') print('-------------------------------') self.models = dict() self.params=dict() self.models['knn'] = KNeighborsRegressor() self.models['rf'] = RandomForestRegressor(random_state = self.seed) self.models['ada'] = AdaBoostRegressor(random_state = self.seed) self.models['gb'] = GradientBoostingRegressor(random_state = self.seed) self.models['xg'] = XGBRegressor(random_state = self.seed) self.models['mlp'] = MLPRegressor() self.models['svm'] = SVR() self.models['vot'] = VotingRegressor([('knn',self.models['knn']),('ada',self.models['ada']),('rand',self.models['rf']),('svm',self.models['svm'])]) # Preparing parameter for finding optimal parameters self.params['knn'] ={'n_neighbors':[2,3,4,5],'algorithm':['auto', 'ball_tree', 'kd_tree', 'brute']} self.params['rf'] = {'max_depth':[2,3,4,5,6],'n_estimators':[50,100,150,200],'min_samples_split':[3,4,5]} self.params['ada'] = {'learning_rate':[.01,.001,.0001],'n_estimators':[50,100,150,200],'loss':['linear', 'square', 'exponential']} self.params['gb'] = {'learning_rate':[.01,.001,.0001],'n_estimators':[50,100,150,200],'loss':['ls', 'lad', 'huber', 'quantile'],'min_samples_split':[3,4,5]} self.params['xg']={'booster':['gbtree', 'gblinear','dart']} self.params['mlp']={'solver':['lbfgs','sgd','adam'],'activation':['identity', 'logistic', 'tanh', 'relu'],'hidden_layer_sizes':[(5,5,5),(5,4,3),(10,10,5)]} k=['rbf', 'linear','poly','sigmoid'] c= [1,10,100,.1] g=[.0001,.001,.001,.01,.1] self.params['svm']=dict(kernel=k, C=c, gamma=g) print('-------------------------------------------') print('===> K-Nearest Neighbors initialized') print('===> Random Forest initialized') print('===> AdaBoost initialized') print('===> Gradient Boost initialized') print('===> XGBoost initialized') print('===> Neural Network initialized') print('===> SVM initialized') print('===> Voting classifier with KNN, AdaBoost, SVM and Random Forest') def findParametersAndEvaluate(self,data,strategy,label_name,group=None,dataset=None,cv=5): self.strategy = strategy self.results = {} print('-------------------------------') print(' STEP : Finding Parameters & Evaluate Models') print('-------------------------------') self.label_name_check(label_name) #print(self.labelset.columns) # store performance data for each strategy if (strategy == 'train_test_split' or strategy == 'all'): self.train_test = dict() for model in self.models.keys(): self.train_test[model] = None print('===> Evaluation strategy: Train and Test Split ') X_train, X_test, y_train, y_test = train_test_split(data,self.label_set[label_name],train_size=.7,random_state=self.seed) print('===> Parameters find-> Start') for model in self.models.keys(): if model == 'vot': continue if not self.configured: gd = GridSearchCV(self.models[model],self.params[model],cv=cv,scoring='neg_root_mean_squared_error') gd.fit(X_train,y_train) print(' Parameters for ',model,': ',gd.best_params_) self.models[model] = gd.best_estimator_ print('===> Parameters find-> End') test_performances = dict() print('===> Test data performance[RMSE] ') for model in self.models.keys(): self.models[model].fit(X_train,y_train) test_performances[model] = mean_squared_error(y_test,self.models[model].predict(X_test),squared=False) #print(' Model[',model,']:',test_performances[model]) self.train_test[model] = test_performances[model] print(self.train_test) self.results['train_test'] = self.train_test if (strategy == 'cross_val' or strategy == 'all'): self.cross_val = dict() cross_val = dict() for model in self.models.keys(): self.cross_val[model] = None print('==============================================') print('Evaluation strategy: Cross Validation') print('==============================================') for model in self.models.keys(): if model != 'vot' and not self.configured: print(' ==> Finding params for ',model) gd = GridSearchCV(self.models[model],self.params[model],cv=10,scoring='neg_root_mean_squared_error') gd.fit(data,self.label_set[label_name]) print(' Parameters: ',gd.best_params_) self.models[model] = gd.best_estimator_ cross_val[model] = cross_val_score(self.models[model],data,self.label_set[label_name],scoring='neg_root_mean_squared_error',cv=cv) #print(' Score[',model,']:',cross_val_scores[model]) cross_val_mean = -1 * statistics.mean(cross_val[model]) cross_val_var = statistics.variance(cross_val[model]) self.cross_val[model] = [cross_val_mean,cross_val_var] self.results['cross_val'] = self.cross_val if (strategy == 'leave_one_group_out' or strategy == 'all'): self.leave_group = dict() for model in self.models.keys(): self.leave_group[model] = None print('==============================================') print('Evaluation strategy: Leave one group out') print('==============================================') logo = LeaveOneGroupOut() n_splits = logo.get_n_splits(groups=group) error= dict() for model in self.models.keys(): error[model] = [None]*n_splits k =0 for train_index, test_index in logo.split(data,self.label_set[label_name],group): #print(test_index) X_train, y_train = data.iloc[train_index],self.label_set[label_name][train_index] X_test, y_test = data.iloc[test_index],self.label_set[label_name][test_index] for model in self.models.keys(): if model != 'vot' and not self.configured: print(' ==> Finding params for ',model) gd = GridSearchCV(self.models[model],self.params[model],cv=10,scoring='neg_root_mean_squared_error') gd.fit(X_train,y_train) print(' Parameters: ',gd.best_params_) estimator = gd.best_estimator_ self.models[model] = estimator self.models[model].fit(X_train,y_train) error[model][k] = mean_squared_error(y_test,self.models[model].predict(X_test),squared=False) #print(' Model[',model,']:',error[model]) k = k+1 for model in self.models.keys(): err_mean = statistics.mean(error[model]) err_var = statistics.variance(error[model]) self.leave_group[model] = [err_mean,err_var] self.results['leave_group'] = self.leave_group if (strategy == 'leave_one_dataset_out' or strategy == 'all'): self.leave_dataset = dict() for model in self.models.keys(): self.leave_dataset[model] = None print('==============================================') print('Evaluation strategy: Leave one dataset out') print('==============================================') logo = LeaveOneGroupOut() n_splits = logo.get_n_splits(groups=dataset) error= dict() for model in self.models.keys(): error[model] = [None]*n_splits k =0 for train_index, test_index in logo.split(data,self.label_set[label_name],dataset): X_train, y_train = data.iloc[train_index],self.label_set[label_name][train_index] X_test, y_test = data.iloc[test_index],self.label_set[label_name][test_index] for model in self.models.keys(): if model != 'vot' and not self.configured: print(' ==> Finding params for ',model) gd = GridSearchCV(self.models[model],self.params[model],cv=10,scoring='neg_root_mean_squared_error') gd.fit(X_train,y_train) #print(' Parameters: ',gd.best_params_) estimator = gd.best_estimator_ self.models[model] = estimator self.models[model].fit(X_train,y_train) error[model][k] = mean_squared_error(y_test,self.models[model].predict(X_test),squared=False) #print(' Model[',model,']:',error[model]) k = k+1 for model in self.models.keys(): err_mean = statistics.mean(error[model]) err_var = statistics.variance(error[model]) self.leave_dataset[model] = [err_mean,err_var] self.results['leave_dataset'] = self.leave_dataset if (strategy=='sorted_stratified' or strategy == 'all') : self.stratified = dict() for model in self.models.keys(): self.stratified[model] = None # idea from https://scottclowe.com/2016-03-19-stratified-regression-partitions/ print('==============================================') print('Evaluation strategy: Sorted Stratification') print('==============================================') label_df = pd.DataFrame(self.label_set) indices = label_df.sort_values(by=[label_name]).index.tolist() splits = dict() error = dict() for model in self.models.keys(): error[model] = [None]*cv for i in range(cv): splits[i] = list() for i in range(len(indices)): if i%cv == 0: pick = random.sample(range(cv),cv) cur_pick = pick.pop() splits[cur_pick].append(indices[i]) for i in range(cv): test_index = splits[i] train_index = [] for j in range(cv): if j != i: train_index = train_index + splits[j] ########################################## # Code to training model on sorted stratified set X_train, y_train = data.iloc[train_index],self.label_set[label_name][train_index] X_test, y_test = data.iloc[test_index],self.label_set[label_name][test_index] for model in self.models.keys(): if model != 'vot' and not self.configured: print(' ==> Finding params for ',model) gd = GridSearchCV(self.models[model],self.params[model],cv=10,scoring='neg_root_mean_squared_error') gd.fit(X_train,y_train) print(' Parameters: ',gd.best_params_) estimator = gd.best_estimator_ self.models[model] = estimator self.models[model].fit(X_train,y_train) error[model][i] = mean_squared_error(y_test,self.models[model].predict(X_test),squared=False) #print(' Model[',model,']:',error[model]) for model in self.models.keys(): err_mean = statistics.mean(error[model]) err_var = statistics.variance(error[model]) self.stratified[model] = [err_mean,err_var] ########################################## self.results['stratified'] = self.stratified else: print('Unsupported evaluation strategy') return None return self.results # Preparing dataframe with results for report generation """ if strategy == 'train_test_split': df = pd.DataFrame(columns = ['model','train_test]) for model in self.models.keys(): df = df.append({'model':model,'train_test':self.train_test[model]},ignore_index=True) if strategy == 'cross_val': df = pd.DataFrame(columns = ['model','train_test_mean','train_test_var']) for model in self.models.keys(): df = df.append({'model':model,'train_test_mean':self.train_test[model][0],'train_test_var':self.train_test[model][1]},ignore_index=True) if strategy == 'leave_one_group_out': df = pd.DataFrame(columns = ['model','train_test_mean','train_test_var']) for model in self.models.keys(): df = df.append({'model':model,'train_test_mean':self.train_test[model][0],'train_test_var':self.train_test[model][1]},ignore_index=True) if strategy == 'leave_one_dataset_out': df = pd.DataFrame(columns = ['model','train_test_mean','train_test_var']) for model in self.models.keys(): df = df.append({'model':model,'train_test_mean':self.train_test[model][0],'train_test_var':self.train_test[model][1]},ignore_index=True) if strategy == 'sorted_stratified': df = pd.DataFrame(columns = ['model','train_test_mean','train_test_var']) for model in self.models.keys(): df = df.append({'model':model,'train_test_mean':self.train_test[model][0],'train_test_var':self.train_test[model][1]},ignore_index=True) if strategy == 'all': df = pd.DataFrame(columns = ['model','train_test','cross_val','leave_group','leave_dataset','stratified']) for model in self.models.keys(): df = df.append({'model':model,'train_test':self.train_test[model],'cross_val':self.cross_val[model],'leave_group':self.leave_group[model],'leave_dataset':self.leave_dataset[model],'stratified':self.stratified[model]},ignore_index=True) return df """ def report(self,currentOutput,report_name=''): df = pd.DataFrame(columns = ['model','train_test','cross_val_mean','cross_val_var','leave_group_mean','leave_group_var','leave_dataset_mean','leave_dataset_var','stratified_mean','stratified_var']) for model in self.models.keys(): df = df.append({'model':model,'train_test':self.train_test[model],'cross_val_mean':self.cross_val[model][0],'cross_val_var':self.cross_val[model][1],'leave_group_mean':self.leave_group[model][0],'leave_group_var':self.leave_group[model][1],'leave_dataset_mean':self.leave_dataset[model][0],'leave_dataset_var':self.leave_dataset[model][1],'stratified_mean':self.stratified[model][0],'stratified_var':self.stratified[model][1]},ignore_index=True) filename = report_name df.to_csv(filename,index=False) print('==============================================') print(' Report Generation') print('==============================================') print(' ===> Successfully generated ') print(' ===> Results saved in easyRegress_report.csv file') def activateGroups(self,groups): self.groups = groups self.flagGroup = True def activateDatasets(self,datasets): self.datasets = datasets self.flagDataset = True def activateLabel(self,label): self.label = label def buildPipeline(self,sequence,report_name=''): """ <feature_name> : Name of feature feature_extraction: Apply feature extraction based on correlation feature_scaling: Apply feature scaling. Options: Standard, MinMax feature_fusion: Apply feature fusion. Options: gini, entropy, pca, isomap, mds, tsne load_models: Load regression models. find_evaluate: Model evaluation. Options: train_test_split, cross_validation, leave_one_group_out, leave_one_dataset_out, sorted_stratified report_results: Report results. Options: table, chart """ currentOutput = None for index, step in enumerate(sequence): label = self.label groups = self.groups datasets = self.datasets if index == 0: self.feature_name_check(step) currentOutput = self.feature_set[step][2] elif step == 'feature_extraction': results = self.extractFeatures(currentOutput) currentOutput = results elif step == 'feature_scaling_std': print(currentOutput.shape) results = self.Scaling(currentOutput,'std') currentOutput = results elif step == 'feature_scaling_mmax': results = self.Scaling(currentOutput,'mmax') currentOutput = results elif step == 'feature_fusion_pca': results = self.DimRed('pca',currentOutput,{'n_components':2,'n_neighbors':3}) currentOutput = results elif step == 'feature_fusion_mds': results = self.DimRed('mds',currentOutput,{'n_components':2,'n_neighbors':3}) currentOutput = results elif step == 'feature_fusion_isomap': results = self.DimRed('isomap',currentOutput,{'n_components':2,'n_neighbors':3}) currentOutput = results elif step == 'feature_fusion_tsne': results = self.DimRed('tsne',currentOutput,{'n_components':2,'n_neighbors':3}) currentOutput = results elif step == 'feature_fusion_entropy': results = self.getEntropy(currentOutput) currentOutput = results print(results) elif step == 'feature_fusion_gini': results = self.getGINI(currentOutput) currentOutput = results print(results) elif step == 'load_modules': self.regressionModelInitialize() elif step == 'evaluate_train_test': if label == None: print(' ====> Error: labels are not loaded') results =self.findParametersAndEvaluate(currentOutput,'train_test_split',label) currentOutput = results elif step == 'evaluate_cross_val': if label == None: print(' ====> Error: labels are not loaded') results =self.findParametersAndEvaluate(currentOutput,'cross_val',label) currentOutput = results elif step == 'evaluate_leave_group_out': if label == None: print(' ====> Error: labels are not loaded') if self.flagDataset == False: print(' ====> Error: groups ids are not loaded') results =self.findParametersAndEvaluate(currentOutput,'leave_one_group_out',label,group=groups) currentOutput = results elif step == 'evaluate_leave_dataset_out': if label == None: print(' ====> Error: labels are not loaded') if self.flagDataset == False: print(' ====> Error: datasets ids are not loaded') results =self.findParametersAndEvaluate(currentOutput,'leave_one_dataset_out',label,dataset = datasets) currentOutput = results elif step == 'evaluate_stratified': if label == None: print(' ====> Error: labels are not loaded') results =self.findParametersAndEvaluate(currentOutput,'sorted_stratified',label) currentOutput = results elif step == 'all': if label == None: print(' ====> Error: labels are not loaded') results =self.findParametersAndEvaluate(currentOutput,'all',label,group = groups, dataset = datasets) currentOutput = results elif step == 'report_csv': self.report(currentOutput,report_name) else: print(' Unsupported module ',step,' is specified')

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457

0.517462

3,813

38,712

5.084448

0.120378

0.029917

0.012379

0.017331

0.469851

0.41043

0.359983

0.325012

0.293857

0.271264

0

0.008895

0.346585

38,712

941

458

41.139214

0.757541

0.03712

0

0.3457

0

0.161192

0.045734

0

1

0.035413

false

0

0.097808

0

0.177066

0.215852

0

null

0

null

0

1

0

6ba459622ba98919bfa10ab43ed05d7011713aea

469

py

Python

Moderate/Prime Numbers/main.py

AstrorEnales/CodeEval

eae0fb471d27d3a83d544ff4a4651ed1a2076930

[ "MIT" ]

null

Moderate/Prime Numbers/main.py

AstrorEnales/CodeEval

eae0fb471d27d3a83d544ff4a4651ed1a2076930

[ "MIT" ]

null

Moderate/Prime Numbers/main.py

AstrorEnales/CodeEval

eae0fb471d27d3a83d544ff4a4651ed1a2076930

[ "MIT" ]

null

import sys lines = open(sys.argv[1], 'r') for line in lines: line = line.replace('\n', '').replace('\r', '') if len(line) > 0: n = int(line) primes = set([2]) num = 3 while num < n: if all(num % i != 0 for i in primes): primes = set(list(primes) + [num]) num = num + 1 primes = sorted(list(primes)) print(','.join([str(x) for x in primes])) lines.close()

26.055556

52

0.45629

64

469

3.34375

0.484375

0.084112

0

0.020408

0.373134

469

17

53

27.588235

0.707483

0

0.013274

0

1

0

false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

6ba4e572e52707590a52608ce4cc12b513909627

2,117

py

Python

gemtown/users/serializers.py

doramong0926/gemtown

2c39284e3c68f0cc11994bed0ee2abaad0ea06b6

[ "MIT" ]

null

gemtown/users/serializers.py

doramong0926/gemtown

2c39284e3c68f0cc11994bed0ee2abaad0ea06b6

[ "MIT" ]

5

2020-09-04T20:13:39.000Z

2022-02-17T22:03:33.000Z

gemtown/users/serializers.py

doramong0926/gemtown

2c39284e3c68f0cc11994bed0ee2abaad0ea06b6

[ "MIT" ]

null

from rest_framework import serializers from gemtown.modelphotos import models as modelphoto_models from gemtown.modelers import models as modeler_models from gemtown.musicians import models as musician_models from . import models import time class TimestampField(serializers.Field): def to_representation(self, value): return int(time.mktime(value.timetuple())) class UsernameSerializer(serializers.ModelSerializer): class Meta: model = models.User fields = ( 'username', ) class MusicianSerializer(serializers.ModelSerializer): class Meta: model = musician_models.Musician fields = ( 'id', 'nickname', 'country', ) class ModelPhotoSerializer(serializers.ModelSerializer): class Meta: model = modelphoto_models.ModelPhoto fields = ( 'file', 'photo_type', ) class ModelerSerializer(serializers.ModelSerializer): cover_image = ModelPhotoSerializer() class Meta: model = modeler_models.Modeler fields = ( 'id', 'cover_image', 'nickname', 'country', ) class UserSerializer(serializers.ModelSerializer): created_at = TimestampField() updated_at = TimestampField() followers = UsernameSerializer(many=True) followings = UsernameSerializer(many=True) musician = MusicianSerializer() modeler = ModelerSerializer() class Meta: model = models.User fields = ( 'id', 'username', 'email', 'first_name', 'last_name', 'user_class', 'gem_amount', 'musician', 'modeler', 'gender', 'profile_photo', 'country', 'mobile_number', 'mobile_country', 'followers', 'followings', 'is_superuser', 'is_staff', 'created_at', 'updated_at' )

25.817073

60

0.561171

166

2,117

7.012048

0.39759

0.111684

0.060137

0.090206

0.142612

0.051546

0

0.352858

2,117

81

61

26.135802

0.849635

0

0.309859

0

0.117313

0

1

0.014085

false

0

0.084507

0.014085

0.366197

0

null

0

null

0

1

0

6ba78991985070dc29bb6e09cbc030857e571e30

6,702

py

Python

alexa_skills/cis_diagnosis.py

paramraghavan/sls-py-alexa-color-picker

da4752442dd4ead19832930103adb9d81cfc163a

[ "MIT" ]

null

alexa_skills/cis_diagnosis.py

paramraghavan/sls-py-alexa-color-picker

da4752442dd4ead19832930103adb9d81cfc163a

[ "MIT" ]

null

alexa_skills/cis_diagnosis.py

paramraghavan/sls-py-alexa-color-picker

da4752442dd4ead19832930103adb9d81cfc163a

[ "MIT" ]

null

# -*- coding: utf-8 -*- import logging from ask_sdk_core.skill_builder import SkillBuilder from ask_sdk_core.utils import is_request_type, is_intent_name from ask_sdk_core.handler_input import HandlerInput from ask_sdk_model import Response from ask_sdk_model.ui import SimpleCard import os from alexa_skills import aws_utils CIS_SERVICE_URL = os.environ['CIS_SERVICE_URL'] CIS_AWS_ACCESS_KEY_ID = os.environ['CIS_AWS_ACCESS_KEY_ID'] CIS_AWS_SECRET_ACCESS_KEY = os.environ.get('CIS_AWS_SECRET_ACCESS_KEY') skill_name = "CISDiagnosis" help_text = ("Please tell me your medical condition. You can say " "I have cold headache.") report_slot = "report" sb = SkillBuilder() logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @sb.request_handler(can_handle_func=is_request_type("LaunchRequest")) def launch_request_handler(handler_input): """Handler for Skill Launch.""" # type: (HandlerInput) -> Response speech = "Welcome, Tell me your medical condition." handler_input.response_builder.speak( speech + " " + help_text).ask(help_text) return handler_input.response_builder.response @sb.request_handler(can_handle_func=is_intent_name("AMAZON.HelpIntent")) def help_intent_handler(handler_input): """Handler for Help Intent.""" # type: (HandlerInput) -> Response handler_input.response_builder.speak(help_text).ask(help_text) return handler_input.response_builder.response @sb.request_handler( can_handle_func=lambda handler_input: is_intent_name("AMAZON.CancelIntent")(handler_input) or is_intent_name("AMAZON.StopIntent")(handler_input)) def cancel_and_stop_intent_handler(handler_input): """Single handler for Cancel and Stop Intent.""" # type: (HandlerInput) -> Response speech_text = "Goodbye!" return handler_input.response_builder.speak(speech_text).response @sb.request_handler(can_handle_func=is_request_type("SessionEndedRequest")) def session_ended_request_handler(handler_input): """Handler for Session End.""" # type: (HandlerInput) -> Response return handler_input.response_builder.response from io import StringIO def getMedicalAnalysis(medical_report): client = aws_utils.get_boto3_client(CIS_AWS_ACCESS_KEY_ID, CIS_AWS_SECRET_ACCESS_KEY, 'comprehendmedical') response = client.detect_entities_v2( Text=medical_report ) mc_dict = {} for entity in response['Entities']: if entity["Category"] == "MEDICAL_CONDITION" and len(entity["Traits"]) > 0: #print(f'| {entity["Text"]} |{entity["Category"]} |') mc_dict[entity["Text"]] = entity["Category"] #print(mc_dict) string_buffer = StringIO() for item in mc_dict: string_buffer.write( item + ' is ' + mc_dict[item] + ' ') return string_buffer.getvalue() @sb.request_handler(can_handle_func=is_intent_name("MedicalIntent")) def my_medical_diagnosis_handler(handler_input): """Check if color is provided in slot values. If provided, then set your favorite color from slot value into session attributes. If not, then it asks user to provide the color. """ # type: (HandlerInput) -> Response slots = handler_input.request_envelope.request.intent.slots if report_slot in slots: medical_report = slots[report_slot].value speakOutput = getMedicalAnalysis(medical_report) # build json object as per the CISApi # handler_input.attributes_manager.session_attributes[color_slot_key] = fav_color speech = "Identified diseases are " + speakOutput reprompt = ("That's " + speakOutput) else: speech = "I'm not sure, please try again" reprompt = ("I'm not sure, please try again") handler_input.response_builder.speak(speech).ask(reprompt) return handler_input.response_builder.response @sb.request_handler(can_handle_func=is_intent_name("AMAZON.FallbackIntent")) def fallback_handler(handler_input): """AMAZON.FallbackIntent is only available in en-US locale. This handler will not be triggered except in that locale, so it is safe to deploy on any locale. """ # type: (HandlerInput) -> Response speech = ( "The {} skill can't help you with that. " + help_text ).format(skill_name) reprompt = (help_text) handler_input.response_builder.speak(speech).ask(reprompt) return handler_input.response_builder.response def convert_speech_to_text(ssml_speech): """convert ssml speech to text, by removing html tags.""" # type: (str) -> str s = SSMLStripper() s.feed(ssml_speech) return s.get_data() @sb.global_response_interceptor() def add_card(handler_input, response): """Add a card by translating ssml text to card content.""" # type: (HandlerInput, Response) -> None response.card = SimpleCard( title=skill_name, content=convert_speech_to_text(response.output_speech.ssml)) @sb.global_response_interceptor() def log_response(handler_input, response): """Log response from alexa service.""" # type: (HandlerInput, Response) -> None print("Alexa Response: {}\n".format(response)) @sb.global_request_interceptor() def log_request(handler_input): """Log request to alexa service.""" # type: (HandlerInput) -> None print("Alexa Request: {}\n".format(handler_input.request_envelope.request)) @sb.exception_handler(can_handle_func=lambda i, e: True) def all_exception_handler(handler_input, exception): """Catch all exception handler, log exception and respond with custom message. """ # type: (HandlerInput, Exception) -> None print("Encountered following exception: {}".format(exception)) speech = "Sorry, there was some problem. Please try again!!" handler_input.response_builder.speak(speech).ask(speech) return handler_input.response_builder.response ######## Convert SSML to Card text ############ # This is for automatic conversion of ssml to text content on simple card # You can create your own simple cards for each response, if this is not # what you want to use. from six import PY2 try: from HTMLParser import HTMLParser except ImportError: from html.parser import HTMLParser class SSMLStripper(HTMLParser): def __init__(self): self.reset() self.full_str_list = [] if not PY2: self.strict = False self.convert_charrefs = True def handle_data(self, d): self.full_str_list.append(d) def get_data(self): return ''.join(self.full_str_list) ################################################ # Handler to be provided in lambda console. lambda_handler = sb.lambda_handler()

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null

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null

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0

6ba8948db01a555810296ad83a1297622916c86e

988

py

Python

armautils_cli/smdimerge.py

KoffeinFlummi/ArmaUtils

2f1fdc8fb561fb54077f3c328d7a788e75c78dad

[ "MIT" ]

1

2015-02-19T17:31:17.000Z

armautils_cli/smdimerge.py

KoffeinFlummi/ArmaUtils

2f1fdc8fb561fb54077f3c328d7a788e75c78dad

[ "MIT" ]

null

armautils_cli/smdimerge.py

KoffeinFlummi/ArmaUtils

2f1fdc8fb561fb54077f3c328d7a788e75c78dad

[ "MIT" ]

null

#!/usr/bin/env python3 import numpy as np from PIL import Image def smdimerge(pargs, oargs): if len(pargs) != 3: return -1 path_spec, path_gloss, path_target = pargs try: spec = Image.open(path_spec).convert("RGBA") gloss = Image.open(path_gloss).convert("RGBA") except: print("Failed to read images. Please check your paths.") return 1 if spec.size != gloss.size: print("Image sizes do not match, aborting.") return 1 smdi = Image.new("RGBA", spec.size, "white") data = np.array(smdi) r,g,b,a = data.transpose() g = np.array(spec).transpose()[0] b = np.array(gloss).transpose()[0] data = np.array([r,g,b,a]).transpose() smdi = Image.fromarray(data) try: smdi.save(path_target) except: print("Failed to write final image to disk. Check permissions.") return 1 else: print("SMDI map saved at: {}".format(path_target)) return 0

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72

0.601215

140

988

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0.044293

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null

0

null

0

1

0

6baa9c56ec82f3de3e848ccc5b1bc7bfad503442

7,001

py

Python

detect_motor_test3.py

binghaohuang1/object-detective-visual-tracking

e61680a771dc13a006113d96965e59ff1bc3ce6d

[ "MIT" ]

null

detect_motor_test3.py

binghaohuang1/object-detective-visual-tracking

e61680a771dc13a006113d96965e59ff1bc3ce6d

[ "MIT" ]

null

detect_motor_test3.py

binghaohuang1/object-detective-visual-tracking

e61680a771dc13a006113d96965e59ff1bc3ce6d

[ "MIT" ]

null

#!/usr/bin/env python #!coding=utf-8 import rospy import numpy as np import PIL.Image as pilimage from sensor_msgs.msg import CompressedImage from sensor_msgs.msg import Image from std_msgs.msg import Float64 from cv_bridge import CvBridge, CvBridgeError import cv2 import time from yolo import YOLO from sensor_msgs.msg import Joy from std_msgs.msg import String from geometry_msgs.msg import Twist from tf.transformations import * from math import pi from geometry_msgs.msg import PoseStamped from std_msgs.msg import Header from sensor_msgs.msg import JointState from threading import Thread import threading global RV2_motor1_joint yolo = YOLO() bridge = CvBridge() def send(): rospy.Subscriber('/mid_camera/color/image_raw/compressed', CompressedImage, ReceiveVideo_right) rospy.spin() def ReceiveVideo_right(data): global cv_image # print(1) cv_image = bridge.compressed_imgmsg_to_cv2(data, 'bgr8') def main(): global delta_x,cv_image time.sleep(4) fps = 0 while not rospy.is_shutdown(): t1 = time.time() # 读取某一帧 frame = cv2.cvtColor(cv_image,cv2.COLOR_BGR2RGB) # 转变成Image frame = pilimage.fromarray(np.uint8(frame)) # 进行检测 frame, bbox_list, label_list = yolo.detect_image(frame) frame = np.array(frame) # RGBtoBGR满足opencv显示格式 frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR) fps = ( fps + (1./(time.time()-t1)) ) / 2 print("fps= %.2f"%(fps)) frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) print(frame.shape) cv2.imshow("video",frame) cv2.waitKey(3) # c= cv2.waitKey(1) & 0xff # if c==27: # break if type(label_list) != int: # 没检测到物体的时候，bbox_list和label_list为1 num_of_obj = len(label_list) #print('num_of_object:', num_of_obj) #确定跟踪物体与图像中点的相对坐标 for i in range(num_of_obj): if 'banana' in label_list[i]: object_center = (bbox_list[i][1]+bbox_list[i][3])*0.5 delta_x = 320-object_center #print(delta_x) #return delta_x # location_pub.publish(delta_x) #motor1_move() elif 'bed' in label_list[i]: print("yyy") pass else: print('yolo未识别到任何物体') pass def motor1_move(): time.sleep(1) global command_vel_pub_m, delta_x, RV2_motor1_joint delta_x = 0 now = rospy.Time.now() motor_vel = JointState() motor_vel.header = Header() motor_vel.header.stamp = now motor_vel.header.frame_id = "bulldog" motor_vel.name = ["motor1"] # rospy.Subscriber('/joint_states_motor',JointState,RV2_motorjointstate_callback) while not rospy.is_shutdown(): print(delta_x) #中间位判断 if -1.5 < RV2_motor1_joint < 1.5: #左转判断条件 if delta_x > 200: motor_vel.velocity = [0.48] print (motor_vel) command_vel_pub_m.publish(motor_vel) time.sleep(2) elif 80 < delta_x < 200: motor_vel.velocity = [(delta_x - 40) * 0.003] print (motor_vel) command_vel_pub_m.publish(motor_vel) time.sleep(2) #右转判断条件 elif delta_x < -200: motor_vel.velocity = [-0.48] command_vel_pub_m.publish(motor_vel) time.sleep(2) elif -200 < delta_x < -80: motor_vel.velocity = [(delta_x + 40) * 0.003] command_vel_pub_m.publish(motor_vel) time.sleep(2) #停止判断条件 elif -80 < delta_x < 80: motor_vel.velocity = [0] command_vel_pub_m.publish(motor_vel) #左限位判断条件 if 1.5 < RV2_motor1_joint: #左转判断条件 if delta_x > 80: motor_vel.velocity = [0] print (motor_vel) command_vel_pub_m.publish(motor_vel) time.sleep(2) #右转判断条件 elif delta_x < -200: motor_vel.velocity = [-0.48] command_vel_pub_m.publish(motor_vel) time.sleep(2) elif -200 < delta_x < -80: motor_vel.velocity = [(delta_x + 40) * 0.003] command_vel_pub_m.publish(motor_vel) time.sleep(2) #停止判断条件 elif -80 < delta_x < 80: motor_vel.velocity = [0] command_vel_pub_m.publish(motor_vel) time.sleep(0.5) #右限位判断条件 if RV2_motor1_joint < -1.5: #左转判断条件 if delta_x > 200: motor_vel.velocity = [0.48] print (motor_vel) command_vel_pub_m.publish(motor_vel) time.sleep(2) elif 80 < delta_x < 200: motor_vel.velocity = [(delta_x - 40) * 0.003] print (motor_vel) command_vel_pub_m.publish(motor_vel) time.sleep(2) #右转判断条件 elif delta_x < -80: motor_vel.velocity = [0] command_vel_pub_m.publish(motor_vel) time.sleep(2) #停止判断条件 elif -80 < delta_x < 80: motor_vel.velocity = [0] command_vel_pub_m.publish(motor_vel) time.sleep(0.5) else: motor_vel.velocity = [0] command_vel_pub_m.publish(motor_vel) time.sleep(0.5) #for object in vision_database_dict: # 再将opencv格式额数据转换成ros image格式的数据发布 # try: # #self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8")) # location_pub.publish(location_pub) # except CvBridgeError as e: # print('e') def RV2_motorjointstate_callback(data): # 定义RV2 motor数据全局变量，进行赋值 global RV2_motor1_joint RV2_motor1_joint = data.position[0] print(RV2_motor1_joint) if __name__ == '__main__': # 初始化ros节点 rospy.init_node("cv_bridge_test") rospy.loginfo("Starting cv_bridge_test node") global command_vel_pub_m, delta_x #创建发布者 command_vel_pub_m = rospy.Publisher('/motor_control/input/velocity', JointState, queue_size = 100, latch=True) #订阅躯干点击位置信息 rospy.Subscriber('/joint_states_motor',JointState,RV2_motorjointstate_callback) #定义yolo识别子程序 t_send = threading.Thread(target = send) t_send.start() t_main = threading.Thread(target=main) t_main.start() #time.sleep(2) # 定义躯干运动子进程 t_motor1 = threading.Thread(target = motor1_move) t_motor1.start() rospy.spin() # except KeyboardInterrupt: # print("Shutting down cv_bridge_test node.") # cv2.destroyAllWindows()

32.868545

114

0.571347

862

7,001

4.37935

0.230858

0.08053

0.058543

0.063046

0.423841

0.358146

0.352318

0.331921

0.296424

0

0.042836

0.333095

7,001

212

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0.765689

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0

0.032749

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0

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false

0.013333

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0.073333

0

null

0

null

0

1

0

6baba2b5a50d24a7e67ce4b5e3c206e3e6b416ad

7,184

py

Python

git_pull_all.py

searKing/git-pull-all

b77a1b0461cd00a5f3ccd48253a12674557302b6

[ "MIT" ]

null

git_pull_all.py

searKing/git-pull-all

b77a1b0461cd00a5f3ccd48253a12674557302b6

[ "MIT" ]

null

git_pull_all.py

searKing/git-pull-all

b77a1b0461cd00a5f3ccd48253a12674557302b6

[ "MIT" ]

null

#!/usr/bin/python import git from git import * import threading import os import sys import getopt from enum import Enum class GitCommandType(Enum): pull = 1 push = 2 nop = 3 def yes_or_no(msg: str): yes_no = input(msg + " ? [Y]es or [n]o?") yes_no = yes_no.lower() if yes_no == "yes" or yes_no == "y": return True elif yes_no == "no" or yes_no == "n": return False else: return True # is_git_dir returns if current directory has .git/ def is_git_dir(dir_path: str): repo_git_dir = os.path.join(dir_path, '.git') if not os.path.exists(repo_git_dir): return False return True def update_git_repo(git_cmd_type: GitCommandType, git_repo_dir: str, git_stash_if_have_uncommitted_changes: bool, unhandled_git_repo_dirs: list): try: git_repo = git.Repo(git_repo_dir) if git_cmd_type == GitCommandType.pull and git_repo.is_dirty(): if not git_stash_if_have_uncommitted_changes: if not yes_or_no("Repo " + git_repo_dir + " have uncommitted changes, \n\tgit reset --hard"): unhandled_git_repo_dirs.append(git_repo_dir) return try: git_repo.git.stash('save', True) except Exception as exception: print( "git stash repo:" + git_repo_dir + " Failed:\r\n git reset --hard recommended" + str(exception)) unhandled_git_repo_dirs.append(git_repo_dir) return remote_repo = git_repo.remote() print("start git %s from remote for: %s" % (git_cmd_type.name, git_repo_dir), end='') try: if git_cmd_type == GitCommandType.pull: remote_repo.pull() elif git_cmd_type == GitCommandType.push: remote_repo.push() elif git_cmd_type == GitCommandType.nop: pass else: print("") raise Exception('unrecognised git command: ' + git_cmd_type.name) except Exception as exception: print("") print( "git " + git_cmd_type.name + " repo:" + git_repo_dir + " Failed:\r\n git reset --hard recommended" + str( exception)) unhandled_git_repo_dirs.append(git_repo_dir) return print("... Done.") except NoSuchPathError as e: pass except InvalidGitRepositoryError as e: pass finally: pass def update_git_repo_thread(git_cmd_type: GitCommandType, root_path: str, git_stash_if_have_uncommitted_changes: bool, dirty_git_repo_dirs: list, git_update_thread_pools: list): if git_stash_if_have_uncommitted_changes: git_update_thread_ = threading.Thread(target=update_git_repo, args=(git_cmd_type, root_path, True, dirty_git_repo_dirs)) git_update_thread_.start() git_update_thread_pools.append(git_update_thread_) else: update_git_repo(git_cmd_type, root_path, False, dirty_git_repo_dirs) def walk_and_update(git_cmd_type: GitCommandType, root_path: str, continue_when_meet_git: bool, depth: int, max_depth: int, git_stash_if_have_uncommitted_changes: bool, dirty_git_repo_dirs: list, git_update_thread_pools: list): if depth >= max_depth: print("jump for %s too deep: depth[%d] max_depth[%d]" % (root_path, depth, max_depth)) return if is_git_dir(root_path): update_git_repo_thread(git_cmd_type, root_path, git_stash_if_have_uncommitted_changes, dirty_git_repo_dirs, git_update_thread_pools) if not continue_when_meet_git: # print("jump subdirs for %s meet git" % (root_path)) return depth = depth + 1 for root_dir, sub_dirs, sub_files in os.walk(root_path): for sub_dir in sub_dirs: walk_and_update(git_cmd_type, os.path.join(root_dir, sub_dir), continue_when_meet_git, depth, max_depth, git_stash_if_have_uncommitted_changes, dirty_git_repo_dirs, git_update_thread_pools) sub_dirs.clear() sub_files.clear() class Usage(Exception): def __init__(self, msg): self.msg = msg def main(argv=None): if argv is None: argv = sys.argv try: try: g_git_cmd_type: GitCommandType = GitCommandType.nop g_walk_paths: list = ["."] g_git_stash_if_have_uncommitted_changes: bool = False g_continue_when_meet_git: bool = False g_stop_when_meet_max_depth: int = 10 opts, args = getopt.getopt(argv[1:], "hycd:", ["help", "path", "git_stash_if_have_uncommitted_changes", "continue_when_meet_git", "stop_when_meet_max_depth=10"]) if len(args) > 0: g_git_cmd_type = GitCommandType[args[0]] if len(args) > 1: g_walk_paths = args[1:] for op, value in opts: if op == "-y": g_git_stash_if_have_uncommitted_changes = True if op == "-c": g_continue_when_meet_git = True elif op == "-d": g_stop_when_meet_max_depth = value elif op == "-h": print("=======""Usage:") print("python git_pull_all.py pull|push .") print("python git_pull_all.py -y -c -d 10 pull|push YourPath") print("python git_pull_all.py" " --git_stash_if_have_uncommitted_changes " "--continue_when_meet_git " "--stop_when_meet_max_depth=10 pull|push YourPath") print("=======") Usage("-h") sys.exit() g_dirty_git_repo_dirs = [] g_git_update_thread_pools = [] for walk_path in g_walk_paths: walk_and_update(g_git_cmd_type, walk_path, g_continue_when_meet_git, 0, g_stop_when_meet_max_depth, g_git_stash_if_have_uncommitted_changes, g_dirty_git_repo_dirs, g_git_update_thread_pools) for git_update_thread in g_git_update_thread_pools: git_update_thread.join(30) if len(g_dirty_git_repo_dirs) != 0: print('these repos have uncommitted changes or conflicts:\r\n') for dirty_repo_dir in g_dirty_git_repo_dirs: print('dir %s has uncommited changes or conflicts, please check\r\n' % (dirty_repo_dir)) print("Done git " + g_git_cmd_type.name + " all") except getopt.error as msg: raise Usage(msg) except Usage as err: print >> sys.stderr, err.msg print >> sys.stderr, "for help use --help" return 2 if __name__ == "__main__": sys.exit(main())

39.256831

121

0.579065

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7,184

4.127036

0.156352

0.060773

0.047356

0.044199

0.46935

0.36964

0.280716

0.213102

0.202052

0.191002

0

0.004607

0.335329

7,184

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39.472527

0.791414

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0

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0

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0.025202

0

1

0.044872

false

0.025641

0.044872

0

0.192308

0.108974

0

null

0

null

0

1

0

6bac63dfef6fa2d50e75637d8cb0e279922534d7

1,943

py

Python

03-dearsanta/dateconvert.py

hugovk/NaNoGenMo-2016

e71b333173b221066f56adcdea4fe8cfdfd4e7c7

[ "FTL" ]

null

03-dearsanta/dateconvert.py

hugovk/NaNoGenMo-2016

e71b333173b221066f56adcdea4fe8cfdfd4e7c7

[ "FTL" ]

null

03-dearsanta/dateconvert.py

hugovk/NaNoGenMo-2016

e71b333173b221066f56adcdea4fe8cfdfd4e7c7

[ "FTL" ]

null

#!/usr/bin/env python3 """ Take a timestamp like: 25/11/2016 23:05:03 Convert it to: 25 November 2016, 13:05 PST 25 November 2016, 16:05 EST 25 November 2016, 21:05 GMT 25 November 2016, 21:05 UTC 25 November 2016, 23:05 EET 26 November 2016, 02:35 IST 26 November 2016, 05:05 CST 26 November 2016, 06:05 JST 26 November 2016, 08:05 AEDT """ import argparse import pytz # pip install pytz from dateutil.parser import parse # pip install python-dateutil def utc_to_local(utc_dt, local_tz): local_dt = utc_dt.replace(tzinfo=pytz.utc).astimezone(local_tz) return local_tz.normalize(local_dt) # .normalize might be unnecessary if __name__ == "__main__": parser = argparse.ArgumentParser( description="Convert a timestamp into eight others.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument("timestamp", help="Input timestamp") args = parser.parse_args() # print(args.timestamp) indate = parse(args.timestamp, dayfirst=True, yearfirst=False) local_tz = pytz.timezone("Europe/Helsinki") # print(indate, local_tz) localdt = local_tz.localize(indate) us_pacific = pytz.timezone("US/Pacific") us_eastern = pytz.timezone("US/Eastern") london = pytz.timezone("Europe/London") india = pytz.timezone("Asia/Calcutta") china = pytz.timezone("Asia/Shanghai") japan = pytz.timezone("Asia/Tokyo") sydney = pytz.timezone("Australia/Sydney") for tz in [ us_pacific, us_eastern, london, pytz.UTC, local_tz, india, china, japan, sydney, ]: timezone_name = tz.localize(indate).tzname() local_date = localdt.astimezone(tz).strftime("%d %B %Y, %H:%M") print(f"{local_date} {timezone_name}") # x = tz.localize(indate) # print("{} ({})".format(localdt.astimezone(tz), x.tzname())) # print() # End of file

25.906667

74

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1,943

4.808429

0.43295

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74

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0

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0.135135

0.027027

0

null

0

null

0

1

0

6bad66e09e43cf21ce9d5331e922f80d18955982

799

py

Python

priv_tube/database/repositories/system_flags.py

ActionCactus/prive_tube

d6d53b87e1a91248e32532a86b23f2d2f3196c58

[ "MIT" ]

null

priv_tube/database/repositories/system_flags.py

ActionCactus/prive_tube

d6d53b87e1a91248e32532a86b23f2d2f3196c58

[ "MIT" ]

null

priv_tube/database/repositories/system_flags.py

ActionCactus/prive_tube

d6d53b87e1a91248e32532a86b23f2d2f3196c58

[ "MIT" ]

null

from priv_tube.database.models.system_flags import SystemFlags as Model from priv_tube.database import db class SystemFlags: """ Repository for interacting with the `system_flags` database table responsible for system-wide toggles. """ @staticmethod def is_enabled(setting_name: str) -> bool: flag: Model = Model.query.filter_by(flag_name=setting_name).first() return bool(flag.value) @staticmethod def enable(setting_name: str): model: Model = Model.query.filter_by(flag_name=setting_name).first() model.value = True db.session.commit() @staticmethod def disable(setting_name: str): model: Model = Model.query.filter_by(flag_name=setting_name).first() model.value = False db.session.commit()

29.592593

106

0.693367

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5.264706

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0.122905

0.078212

0.117318

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0.210263

799

26

107

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0.176471

false

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0.411765

0

null

0

null

0

1

0

6baec59b9ec0b23b54791668f80cdb75f7f78fe2

2,899

py

Python

borisat/models/rd/unnested.py

CircleOnCircles/borisat

4f170ee1a0b11f06b1c3e99f42823061d7e0028e

[ "MIT" ]

null

borisat/models/rd/unnested.py

CircleOnCircles/borisat

4f170ee1a0b11f06b1c3e99f42823061d7e0028e

[ "MIT" ]

1

2020-10-10T08:18:29.000Z

borisat/models/rd/unnested.py

CircleOnCircles/borisat

4f170ee1a0b11f06b1c3e99f42823061d7e0028e

[ "MIT" ]

null

""" { 'vNID': { 'anyType': [ '0105558096348' ] }, 'vtin': None, 'vtitleName': { 'anyType': [ 'บริษัท' ] }, 'vName': { 'anyType': [ 'โฟลว์แอคเคาท์ จำกัด' ] }, 'vSurname': { 'anyType': [ '-' ] }, 'vBranchTitleName': { 'anyType': [ 'บริษัท' ] }, 'vBranchName': { 'anyType': [ 'โฟลว์แอคเคาท์ จำกัด' ] }, 'vBranchNumber': { 'anyType': [ 0 ] }, 'vBuildingName': { 'anyType': [ 'ชุดสกุลไทย สุรวงศ์ ทาวเวอร์' ] }, 'vFloorNumber': { 'anyType': [ '11' ] }, 'vVillageName': { 'anyType': [ '-' ] }, 'vRoomNumber': { 'anyType': [ '12B' ] }, 'vHouseNumber': { 'anyType': [ '141/12' ] }, 'vMooNumber': { 'anyType': [ '-' ] }, 'vSoiName': { 'anyType': [ '-' ] }, 'vStreetName': { 'anyType': [ 'สุรวงศ์' ] }, 'vThambol': { 'anyType': [ 'สุริยวงศ์' ] }, 'vAmphur': { 'anyType': [ 'บางรัก' ] }, 'vProvince': { 'anyType': [ 'กรุงเทพมหานคร' ] }, 'vPostCode': { 'anyType': [ '10500' ] }, 'vBusinessFirstDate': { 'anyType': [ '2016/04/07' ] }, 'vmsgerr': None } """ from typing import Any from typing import Dict from typing import List from typing import Optional from typing import Union import stringcase as stringcase from loguru import logger def unnest(soap_data: Dict[str, Optional[Dict[str, List[Union[str, int]]]]], nonull:bool=True): # drop none if nonull: notnull = {k: v for k, v in soap_data.items() if v} # anytype flatten flatten = {} for k, v in notnull.items(): if k.startswith('v'): k = k[1:] k = stringcase.snakecase(k) try: flatten[k] = v['anyType'][0] if len(v['anyType']) > 1: logger.info( "please let dev. know this case exists. by creating an issue on https://github.com/CircleOnCircles/borisat/issues.") except Exception as e: logger.exception("unseen format") return flatten def get_error(unnested: Dict[str, Any]) -> Optional[str]: """ get error if any""" if error_message := unnested.get('msgerr'): return error_message else: return False

19.993103

137

0.397378

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2,899

4.592157

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0

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0.457744

2,899

144

138

20.131944

0.704835

0.588479

0

0.035714

0.142166

0

1

0.071429

false

0

0.25

0

0.428571

0

null

0

null

0

1

0

6bb3065e877b04d04efa39f8cb2dec584e2b65df

3,696

py

Python

app/utils/logger.py

janaSunrise/ZeroCOM

7197684ce708f080fe215b0a6e57c12836e4c0ab

[ "Apache-2.0" ]

6

2021-03-27T08:58:04.000Z

2021-05-23T17:07:09.000Z

app/utils/logger.py

janaSunrise/ZeroCOM

7197684ce708f080fe215b0a6e57c12836e4c0ab

[ "Apache-2.0" ]

2

2021-05-30T08:06:53.000Z

2021-06-02T17:02:06.000Z

app/utils/logger.py

janaSunrise/ZeroCOM

7197684ce708f080fe215b0a6e57c12836e4c0ab

[ "Apache-2.0" ]

null

# -- Imports -- from datetime import datetime from colorama import Back from rich.console import Console from .colors import get_bright_color, get_color # -- Mappings -- log_color_mapping = { "error": get_bright_color("RED"), "warning": get_bright_color("YELLOW"), "message": get_color("CYAN"), "success": get_bright_color("GREEN"), "info": get_bright_color("MAGENTA"), "critical": get_bright_color("RED") + Back.YELLOW, "flash": get_bright_color("BLUE"), } log_mapping = { "error": f"[{log_color_mapping['error']}%{get_color('RESET')}]", "warning": f"[{log_color_mapping['warning']}!{get_color('RESET')}]", "message": f"[{log_color_mapping['message']}>{get_color('RESET')}]", "success": f"[{log_color_mapping['success']}+{get_color('RESET')}]", "info": f"[{log_color_mapping['info']}#{get_color('RESET')}]", "critical": f"[{log_color_mapping['critical']}X{get_color('RESET')}{Back.RESET}]", "flash": f"[{log_color_mapping['flash']}-{get_color('RESET')}]", } class Logger: def __init__(self): self._console = Console() @staticmethod def _append_date(message: str) -> str: timestamp = datetime.now() timestamp = ( f"{get_bright_color('CYAN')}" f"{timestamp.hour}:{timestamp.minute}:{timestamp.second}" f"{get_bright_color('RESET')}" ) return f"[{timestamp}]{message}" def error(self, message: str, date: bool = True) -> None: log_type = "error" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message) def warning(self, message: str, date: bool = True) -> None: log_type = "warning" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message) def message(self, username: str, message: str, date: bool = True, **kwargs) -> None: log_type = "message" message_prefix = log_mapping[log_type] message_pre = f"{get_bright_color('YELLOW')} {username}{get_color('RESET')} {message_prefix} " if date: message_pre = self._append_date(message_pre) print(message_pre, end="") self._console.print(message, **kwargs) def success(self, message: str, date: bool = True) -> None: log_type = "success" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message) def info(self, message: str, date: bool = True) -> None: log_type = "info" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message) def critical(self, message: str, date: bool = True) -> None: log_type = "critical" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message) def flash(self, message: str, date: bool = True) -> None: log_type = "flash" message_prefix = log_mapping[log_type] message = f"{message_prefix} {log_color_mapping[log_type]}{message}" if date: message = self._append_date(message) print(message)

29.806452

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0.128378

0.064695

0.097043

0.126155

0.507394

0.47597

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0.232413

3,696

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0.095238

0

null

0

null

0

1

0

6bb5a3ae1541a6323233d61a349487d06b8c5714

1,907

py

Python

Simplex_Files/Dual_Problem.py

c-randall/Primal-Simplex-Method

620d7598691ed9717d2d18706c44e462f75e85c5

[ "BSD-3-Clause" ]

1

2021-12-04T12:18:17.000Z

Simplex_Files/Dual_Problem.py

c-randall/Primal-Simplex-Method

620d7598691ed9717d2d18706c44e462f75e85c5

[ "BSD-3-Clause" ]

null

Simplex_Files/Dual_Problem.py

c-randall/Primal-Simplex-Method

620d7598691ed9717d2d18706c44e462f75e85c5

[ "BSD-3-Clause" ]

2

2020-05-30T16:38:37.000Z

2022-01-22T19:50:42.000Z

""" Created on Wed Apr 3 13:07:18 2019 Author: Corey R. Randall Summary: If the user wishes to solve the Dual Problem over the Primal one, this function provides support to appropriately convert the problem into its alternate form. """ """ Import needed modules """ "-----------------------------------------------------------------------------" import numpy as np """ Function definition """ "-----------------------------------------------------------------------------" def dual_problem(user_inputs, conversion): # Extract dictionary for readibility: A = conversion['A'] b = conversion['b'] c_coeff = conversion['c_coeff'] n = conversion['n'] m = conversion['m'] n_slack = conversion['n_slack'] # Convert A, c_coeff to allow for unrestricted y (i.e. y = y' - y''): A_temp = np.repeat(A.T, 2) A_temp[1::2] = -A_temp[1::2] A = np.reshape(A_temp, [A.shape[1], 2*A.shape[0]]) A = np.hstack([A, np.identity(A.shape[0])]) b_temp = np.repeat(b.T, 2) # the obj. coeff. in (D) are b from (P) b_temp[1::2] = -b_temp[1::2] b = np.reshape(b_temp, [b.shape[1], 2*b.shape[0]]) b = np.hstack([b, np.zeros([1, A.shape[0]])]) # Ensure no negative values on RHS: for i in range(c_coeff.shape[1]): if c_coeff[0,i] < 0: # the RHS, b values, in (D) are c from (P) A[i,:] = -A[i,:] c_coeff[0,i] = -c_coeff[0,i] # Generate dictionary for outputs: dual_conversion = {} dual_conversion['A'] = A dual_conversion['b'] = c_coeff.T dual_conversion['c_coeff'] = -b dual_conversion['n'] = 2*m dual_conversion['m'] = n +n_slack dual_conversion['n_slack'] = n +n_slack dual_conversion['n_prim'] = n dual_conversion['n_slack_prim'] = n_slack return dual_conversion

31.262295

80

0.529628

273

1,907

3.56044

0.322344

0.144033

0.024691

0.09465

0.061728

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0.025228

0.251704

1,907

60

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0.655922

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0

0.162882

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false

0

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0

0.09375

0

null

0

null

0

1

0

6bbbb5f359c8e40427bcd1ee484f14b132a99a62

2,593

py

Python

gallery/m_cardloader.py

kengoon/KvGallery

4d946fa06479636411e027bfdebbb15c58c176cf

[ "MIT" ]

2

2021-05-28T13:37:07.000Z

2021-06-20T06:47:20.000Z

gallery/m_cardloader.py

kengoon/KvGallery

4d946fa06479636411e027bfdebbb15c58c176cf

[ "MIT" ]

null

gallery/m_cardloader.py

kengoon/KvGallery

4d946fa06479636411e027bfdebbb15c58c176cf

[ "MIT" ]

null

from kivy.event import EventDispatcher from kivy.metrics import dp from kivy.properties import ListProperty, StringProperty from kivymd.uix.card import MDCard from kivy.lang import Builder __all__ = "M_CardLoader" Builder.load_string( """ # kv_start <M_CardLoader>: md_bg_color: 0, 0, 0, 0 radius: [dp(10), ] ripple_behavior: True RelativeLayout: AsyncImage: id: image color: 0,0,0,0 source: root.source anim_delay: .1 allow_stretch: True keep_ratio: False nocache: True on_load: root.dispatch("on_load") canvas.before: StencilPush RoundedRectangle: pos: self.pos size: self.size radius: root.radius StencilUse canvas.after: StencilUnUse RoundedRectangle: size: self.size pos: self.pos radius: root.radius StencilPop M_AKImageLoader: id: loader radius: root.radius circle: False MDBoxLayout: id:box opacity: 0 padding: dp(10) adaptive_height: True md_bg_color: 0, 0, 0, .6 radius: [0, 0, root.radius[0], root.radius[0]] M_AKLabelLoader: text: root.text radius: root.text_radius size_hint_y: None theme_text_color: "Custom" text_color: root.text_color height: dp(20) if not self.text else self.texture_size[1] font_style: "Money" font_size: dp(16) halign:"center" # kv_end """ ) class M_CardLoader(MDCard): text = StringProperty("") text_radius = ListProperty([dp(5), ]) text_color = ListProperty([1, 1, 1, 1]) source = StringProperty("") def __init__(self, **kwargs): super().__init__(**kwargs) self.register_event_type("on_load") def on_load(self): self.ids.loader.opacity = 0 self.ids.image.color = [1, 1, 1, 1] def on_touch_down(self, touch): self.root.pause_clock() def on_touch_up(self, touch): timer = touch.time_end - touch.time_start if timer < 0.2: self.root.ids.raw.switch_tab("feeds") self.root.resume_clock() def on_release(self): self.root.ids.feeds.dispatch("on_tab_release")

27.585106

73

0.529117

288

2,593

4.559028

0.388889

0.013709

0.011424

0.018279

0.025895

0.018279

0

0.024284

0.38064

2,593

93

74

27.88172

0.793275

0

0.038657

0

1

0.178571

false

0

0.178571

0

0.535714

0

null

0

null

0

1

0

6bbc545ddb8b337163afbee7b7359f2bf1545ca8

763

py

Python

setup.py

OpenTAI/pre-commit-hooks

e123691fa26ff26d1a5f3513ee419bec6eef02ab

[ "MIT" ]

null

setup.py

OpenTAI/pre-commit-hooks

e123691fa26ff26d1a5f3513ee419bec6eef02ab

[ "MIT" ]

1

2022-02-16T10:19:25.000Z

2022-02-16T10:19:26.000Z

setup.py

OpenTAI/pre-commit-hooks

e123691fa26ff26d1a5f3513ee419bec6eef02ab

[ "MIT" ]

null

from setuptools import find_packages, setup # type: ignore def readme(): with open('./README.md', encoding='utf-8') as f: content = f.read() return content setup( name='pre_commit_hooks', version='0.1.0', description='A pre-commit hook for OpenTAI projects', long_description=readme(), long_description_content_type='text/markdown', url='https://github.com/OpenTAI/pre-commit-hooks', author='OpenTAI Team', author_email='', packages=find_packages(), python_requires='>=3.6', install_requires=['PyYAML'], entry_points={ 'console_scripts': [ 'say-hello=pre_commit_hooks.say_hello:main', 'check-copyright=pre_commit_hooks.check_copyright:main', ], }, )

26.310345

68

0.647444

93

763

5.107527

0.634409

0.094737

0.117895

0

0.009934

0.208388

763

28

69

27.25

0.77649

0.015727

0

0.351135

0.125501

0

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0.041667

false

0

0.041667

0

0.125

0

null

0

null

0

1

0

6bc548fb933264d47e42737add228df3e1a66805

3,509

py

Python

main.py

lhs9842/KNUTNoticeBot

cfc83f2abc079a660177d00da1eab288cad021b4

[ "MIT" ]

1

2022-02-23T01:54:07.000Z

main.py

lhs9842/KNUTNoticeBot

cfc83f2abc079a660177d00da1eab288cad021b4

[ "MIT" ]

null

main.py

lhs9842/KNUTNoticeBot

cfc83f2abc079a660177d00da1eab288cad021b4

[ "MIT" ]

1

2022-02-23T07:17:31.000Z

import setting import requests import threading import time import sqlite3 from bs4 import BeautifulSoup from urllib import parse public_board = [["BBSMSTR_000000000059", "일반소식"], ["BBSMSTR_000000000060", "장학안내"], ["BBSMSTR_000000000055", "학사공지사항"]] # [boardId, 게시판 명칭] db_conn = sqlite3.connect("NoticeBot.db", check_same_thread=False) db_cur = db_conn.cursor() db_cur.execute('SELECT * FROM sqlite_master WHERE type="table" AND name="final_ntt"') # 테이블 존재 여부 확인 r = db_cur.fetchall() if r: print("기존 데이터를 불러옵니다.") else: print("새로 데이터베이스를 구축합니다.") db_conn.execute('CREATE TABLE final_ntt(boardId TEXT, final_nttId TEXT)') for n in public_board: db_conn.execute('INSERT INTO final_ntt VALUES ("' + n[0] + '", "1049241")') # 초기값 부여 시 검색 대상 게시판 중 하나의 게시글 하나를 적당히 선택하여 그 게시글의 nttId로 지정할 것. 제대로 지정하지 않으면 최초 구동 시 Many Request로 텔레그램 API 서버가 오류 발생시킴. db_conn.commit() def send_message(channel, message): encode_message = parse.quote(message) url = 'https://api.telegram.org/bot' + setting.bot_token + '/sendmessage?chat_id=' + channel + '&text=' + encode_message response = requests.get(url) if response.status_code != 200: print("ERROR!!" + str(response.status_code)) def find_new_ntt(board_info): try: url = 'https://www.ut.ac.kr/cop/bbs/' + board_info[0] + '/selectBoardList.do' response = requests.get(url) if response.status_code == 200: db_cur.execute("SELECT final_nttId FROM final_ntt WHERE boardId='" + board_info[0] + "'") rows = db_cur.fetchall() final = int(rows[0][0]) html = response.text soup = BeautifulSoup(html, 'html.parser') result_id = soup.findAll('input', {'name':'nttId', 'type':'hidden'}) r_n = soup.findAll('input', {'type':'submit'}) result_name = [] for n in r_n: na = n.get('value') if (na != "검색") & (na != "등록하기"): # 최상부 검색 버튼 및 최하부 페이지 만족도 조사 부분의 submit 버튼 예외 처리 result_name.append(na) count = 0 result_name.reverse() result_id.reverse() for n in result_id: i = int(n.get('value')) if i == 0: # 최상부 검색 버튼 부분에 지정된 nttId 값 0에 대한 예외처리 break if i <= final: count += 1 continue send_message(setting.all_notice_channel, "[" + board_info[1] + "] " + result_name[count] + " : http://www.ut.ac.kr/cop/bbs/" + board_info[0] + "/selectBoardArticle.do?nttId=" + str(i)) db_conn.execute("UPDATE final_ntt SET final_nttId='" + str(i) + "' WHERE boardId='" + board_info[0] + "'") count += 1 db_conn.commit() except: now = time.localtime() message = "EXCEPT!! " + board_info[1] message += "%04d/%02d/%02d %02d:%02d:%02d" % (now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec) encode_message = parse.quote(message) url = 'https://api.telegram.org/bot' + setting.bot_token + '/sendmessage?chat_id=' + setting.admin_channel + '&text=' + encode_message response = requests.get(url) if response.status_code != 200: print("NETWORK ERROR!!" + str(response.status_code) + "\n" + message) find_new_ntt(board_info) def Bot_Start(): for c in public_board: find_new_ntt(c) threading.Timer(30, Bot_Start).start() Bot_Start()

44.987179

205

0.596751

477

3,509

4.224319

0.404612

0.035732

0.044665

0.032754

0.273449

0.206948

0.159801

0

0.032133

0.263893

3,509

78

206

44.987179

0.747967

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0

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0

0.224159

0.021713

0

1

0.041667

false

0

0.097222

0

0.138889

0.055556

0

null

0

null

0

1

0

6bc83f48988080bc745090b0af2be2b40f9b6a5e

2,041

py

Python

inference_methods_local.py

Yaakoubi/Struct-CKN

fa007fa71310866584bdf2e5b038e6663b94e965

[ "MIT" ]

1

2021-05-30T13:42:56.000Z

inference_methods_local.py

Yaakoubi/Struct-CKN

fa007fa71310866584bdf2e5b038e6663b94e965

[ "MIT" ]

null

inference_methods_local.py

Yaakoubi/Struct-CKN

fa007fa71310866584bdf2e5b038e6663b94e965

[ "MIT" ]

2

2022-03-16T22:00:30.000Z

2022-03-29T20:08:57.000Z

import ad3 import numpy as np from pystruct.inference.common import _validate_params class InferenceException(Exception): pass def inference_ad3_local(unary_potentials, pairwise_potentials, edges, relaxed=False, verbose=0, return_energy=False, branch_and_bound=False, inference_exception=None, return_marginals=False): b_multi_type = isinstance(unary_potentials, list) if b_multi_type: res = ad3.general_graph(unary_potentials, edges, pairwise_potentials, verbose=verbose, n_iterations=4000, exact=branch_and_bound) else: n_states, pairwise_potentials = \ _validate_params(unary_potentials, pairwise_potentials, edges) unaries = unary_potentials.reshape(-1, n_states) res = ad3.general_graph(unaries, edges, pairwise_potentials, verbose=verbose, n_iterations=4000, exact=branch_and_bound) unary_marginals, pairwise_marginals, energy, solver_status = res if verbose: print(solver_status) if solver_status in ["fractional", "unsolved"] and relaxed: if b_multi_type: y = (unary_marginals, pairwise_marginals) else: unary_marginals = unary_marginals.reshape(unary_potentials.shape) y = (unary_marginals, pairwise_marginals) else: if b_multi_type: if inference_exception and solver_status in ["fractional", "unsolved"]: raise InferenceException(solver_status) ly = list() _cum_n_states = 0 for unary_marg in unary_marginals: ly.append(_cum_n_states + np.argmax(unary_marg, axis=-1)) _cum_n_states += unary_marg.shape[1] y = np.hstack(ly) else: y = np.argmax(unary_marginals, axis=-1) if return_energy: return y, -energy if return_marginals: return y, unary_marginals return y

39.25

92

0.632533

227

2,041

5.374449

0.295154

0.091803

0.032787

0.029508

0.290164

0.17541

0.116393

0

0.012544

0.296913

2,041

51

93

40.019608

0.837631

0

0.25

0

0.01809

0

1

0.022727

false

0.022727

0.068182

0

0.181818

0.022727

0

null

0

null

0

1

0

6bcb30fd29a6ef624f4b1ad7d00a496c9b08cdb4

6,611

py

Python

leo/modes/lotos.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

1,550

2015-01-14T16:30:37.000Z

2022-03-31T08:55:58.000Z

leo/modes/lotos.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

2,009

2015-01-13T16:28:52.000Z

2022-03-31T18:21:48.000Z

leo/modes/lotos.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

200

2015-01-05T15:07:41.000Z

2022-03-07T17:05:01.000Z

# Leo colorizer control file for lotos mode. # This file is in the public domain. # Properties for lotos mode. properties = { "commentEnd": "*)", "commentStart": "(*", "indentNextLines": "\\s*(let|library|process|specification|type|>>).*|\\s*(\\(|\\[\\]|\\[>|\\|\\||\\|\\|\\||\\|\\[.*\\]\\||\\[.*\\]\\s*->)\\s*", } # Attributes dict for lotos_main ruleset. lotos_main_attributes_dict = { "default": "null", "digit_re": "", "escape": "", "highlight_digits": "false", "ignore_case": "true", "no_word_sep": "", } # Dictionary of attributes dictionaries for lotos mode. attributesDictDict = { "lotos_main": lotos_main_attributes_dict, } # Keywords dict for lotos_main ruleset. lotos_main_keywords_dict = { "accept": "keyword1", "actualizedby": "keyword1", "any": "keyword1", "basicnaturalnumber": "keyword2", "basicnonemptystring": "keyword2", "behavior": "keyword1", "behaviour": "keyword1", "bit": "keyword2", "bitnatrepr": "keyword2", "bitstring": "keyword2", "bool": "keyword2", "boolean": "keyword2", "choice": "keyword1", "decdigit": "keyword2", "decnatrepr": "keyword2", "decstring": "keyword2", "element": "keyword2", "endlib": "keyword1", "endproc": "keyword1", "endspec": "keyword1", "endtype": "keyword1", "eqns": "keyword1", "exit": "keyword1", "false": "literal1", "fbool": "keyword2", "fboolean": "keyword2", "for": "keyword1", "forall": "keyword1", "formaleqns": "keyword1", "formalopns": "keyword1", "formalsorts": "keyword1", "hexdigit": "keyword2", "hexnatrepr": "keyword2", "hexstring": "keyword2", "hide": "keyword1", "i": "keyword1", "in": "keyword1", "is": "keyword1", "let": "keyword1", "library": "keyword1", "nat": "keyword2", "natrepresentations": "keyword2", "naturalnumber": "keyword2", "noexit": "keyword1", "nonemptystring": "keyword2", "octdigit": "keyword2", "octet": "keyword2", "octetstring": "keyword2", "octnatrepr": "keyword2", "octstring": "keyword2", "of": "keyword1", "ofsort": "keyword1", "opnnames": "keyword1", "opns": "keyword1", "par": "keyword1", "process": "keyword1", "renamedby": "keyword1", "richernonemptystring": "keyword2", "set": "keyword2", "sortnames": "keyword1", "sorts": "keyword1", "specification": "keyword1", "stop": "keyword1", "string": "keyword2", "string0": "keyword2", "string1": "keyword2", "true": "literal1", "type": "keyword1", "using": "keyword1", "where": "keyword1", } # Dictionary of keywords dictionaries for lotos mode. keywordsDictDict = { "lotos_main": lotos_main_keywords_dict, } # Rules for lotos_main ruleset. def lotos_rule0(colorer, s, i): return colorer.match_span(s, i, kind="comment1", begin="(*", end="*)", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="",exclude_match=False, no_escape=False, no_line_break=False, no_word_break=False) def lotos_rule1(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq=">>", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule2(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="[>", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule3(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="|||", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule4(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="||", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule5(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="|[", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule6(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="]|", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule7(colorer, s, i): return colorer.match_seq(s, i, kind="operator", seq="[]", at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="") def lotos_rule8(colorer, s, i): return colorer.match_keywords(s, i) # Rules dict for lotos_main ruleset. rulesDict1 = { "(": [lotos_rule0,], "0": [lotos_rule8,], "1": [lotos_rule8,], "2": [lotos_rule8,], "3": [lotos_rule8,], "4": [lotos_rule8,], "5": [lotos_rule8,], "6": [lotos_rule8,], "7": [lotos_rule8,], "8": [lotos_rule8,], "9": [lotos_rule8,], ">": [lotos_rule1,], "@": [lotos_rule8,], "A": [lotos_rule8,], "B": [lotos_rule8,], "C": [lotos_rule8,], "D": [lotos_rule8,], "E": [lotos_rule8,], "F": [lotos_rule8,], "G": [lotos_rule8,], "H": [lotos_rule8,], "I": [lotos_rule8,], "J": [lotos_rule8,], "K": [lotos_rule8,], "L": [lotos_rule8,], "M": [lotos_rule8,], "N": [lotos_rule8,], "O": [lotos_rule8,], "P": [lotos_rule8,], "Q": [lotos_rule8,], "R": [lotos_rule8,], "S": [lotos_rule8,], "T": [lotos_rule8,], "U": [lotos_rule8,], "V": [lotos_rule8,], "W": [lotos_rule8,], "X": [lotos_rule8,], "Y": [lotos_rule8,], "Z": [lotos_rule8,], "[": [lotos_rule2,lotos_rule7,], "]": [lotos_rule6,], "a": [lotos_rule8,], "b": [lotos_rule8,], "c": [lotos_rule8,], "d": [lotos_rule8,], "e": [lotos_rule8,], "f": [lotos_rule8,], "g": [lotos_rule8,], "h": [lotos_rule8,], "i": [lotos_rule8,], "j": [lotos_rule8,], "k": [lotos_rule8,], "l": [lotos_rule8,], "m": [lotos_rule8,], "n": [lotos_rule8,], "o": [lotos_rule8,], "p": [lotos_rule8,], "q": [lotos_rule8,], "r": [lotos_rule8,], "s": [lotos_rule8,], "t": [lotos_rule8,], "u": [lotos_rule8,], "v": [lotos_rule8,], "w": [lotos_rule8,], "x": [lotos_rule8,], "y": [lotos_rule8,], "z": [lotos_rule8,], "|": [lotos_rule3,lotos_rule4,lotos_rule5,], } # x.rulesDictDict for lotos mode. rulesDictDict = { "lotos_main": rulesDict1, } # Import dict for lotos mode. importDict = {}

29.513393

149

0.570867

715

6,611

5.044755

0.244755

0.177433

0.022456

0.037427

0.440255

0.433879

0.418908

0.401164

0

0.032126

0.223113

6,611

223

150

29.64574

0.670171

0.062472

0

0.036269

0

0.250084

0.020463

0

1

0.046632

false

0

0.005181

0.046632

0.098446

0

null

0

null

0

1

0

6bce53c9abe42e1145ff3c9feca2ddc25c7666a1

275

py

Python

src/ProblemSolving/DiagonalDifference.py

Feng-Zhao/hackerrankPy

fc04f0a11cf543ad3697860eca774103593abcd5

[ "Apache-2.0" ]

null

src/ProblemSolving/DiagonalDifference.py

Feng-Zhao/hackerrankPy

fc04f0a11cf543ad3697860eca774103593abcd5

[ "Apache-2.0" ]

null

src/ProblemSolving/DiagonalDifference.py

Feng-Zhao/hackerrankPy

fc04f0a11cf543ad3697860eca774103593abcd5

[ "Apache-2.0" ]

null

def diagonalDifference(arr): a = 0 b = 0 for i in range(0, len(arr)): a += arr[i][i] b += arr[i][len(arr) - i - 1] return abs(a - b) if __name__ == '__main__': arr = [[11, 2, 4],[4, 5, 6],[10, 8, -12]] print(diagonalDifference(arr))

21.153846

45

0.490909

45

275

2.822222

0.577778

0.094488

0

0.083333

0.301818

275

12

46

22.916667

0.578125

0

0.029091

0

1

0.1

false

0

0.2

0.1

0

null

0

null

0

1

0

6bd0d6670874846404621cb14ddcf0728b11d685

1,395

py

Python

balltze_simulation/balltze_pybullet/balltze/balltze.py

Kotochleb/Balltze

55b15cb57d20f7f212293bf838e1d6cf874bb4c2

[ "MIT" ]

1

2021-09-04T03:59:01.000Z

balltze_simulation/balltze_pybullet/balltze/balltze.py

Kotochleb/Balltze

55b15cb57d20f7f212293bf838e1d6cf874bb4c2

[ "MIT" ]

null

balltze_simulation/balltze_pybullet/balltze/balltze.py

Kotochleb/Balltze

55b15cb57d20f7f212293bf838e1d6cf874bb4c2

[ "MIT" ]

null

import pybullet as p import time import numpy as np import pybullet_data from balltze_description import Balltze, BalltzeKinematics import math if __name__ == '__main__': time_step = 1./240. physicsClient = p.connect(p.GUI) p.setAdditionalSearchPath(pybullet_data.getDataPath()) p.setGravity(0,0,-9.81) p.setTimeStep(time_step) planeId = p.loadURDF('plane.urdf') robot = Balltze('../../../balltze_description/balltze_description/urdf/balltze.urdf', p, position=[0,0,0.11]) kinematics = BalltzeKinematics(None) i = 0.0 dir = 1 while True: try: ends = kinematics.body_inverse([0.0,0.0,i], [0.0,i/10,0.02], [[0.1, -0.1, -0.06],[0.1, 0.06, -0.02],[-0.1, -0.06, -0.06],[-0.1, 0.06, -0.06]]) joints = kinematics.inverse(ends) robot.set_joint_arr(np.array(joints.T).reshape(1,12)[0]) # print((kinematics.forward_leg(joints)*1000).astype(np.int64)/1000) # print(joints) # print(ends) except Exception as e: print(e) i += dir*0.0007 if i >= np.pi/10: dir = -1 if i <= -np.pi/10: dir = 1 # robot.set_joint_arr([0, -np.pi/2, np.pi/2]*4) p.stepSimulation() time.sleep(time_step) cubePos, cubeOrn = p.getBasePositionAndOrientation(robot) print(cubePos,cubeOrn) p.disconnect()

32.44186

154

0.597133

200

1,395

4.055

0.38

0.019729

0.018496

0.024661

0.073983

0.062885

0.054254

0

0.083491

0.244444

1,395

42

155

33.214286

0.685958

0.098925

0

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0

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0.052716

0

1

0

false

0

0.176471

0

0.176471

0.058824

0

null

0

null

0

1

0

6bd20797291e733d3485db7e9a7d16d42673718a

8,850

py

Python

urbit_sniffer.py

laanwj/urbit-tools

b3823d50d5ab84c0852593e3255c0d7c51de6d1c

[ "MIT" ]

18

2015-02-03T19:27:18.000Z

2021-04-04T03:03:57.000Z

urbit_sniffer.py

laanwj/urbit-tools

b3823d50d5ab84c0852593e3255c0d7c51de6d1c

[ "MIT" ]

null

urbit_sniffer.py

laanwj/urbit-tools

b3823d50d5ab84c0852593e3255c0d7c51de6d1c

[ "MIT" ]

2

2015-10-02T01:37:13.000Z

2017-06-04T03:41:49.000Z

#!/usr/bin/python3 # Copyright (c) 2014 Wladimir J. van der Laan, Visucore # Distributed under the MIT software license, see # http://www.opensource.org/licenses/mit-license.php. ''' urbit UDP sniffer Usage: urbit_sniffer.py [-p <port1>-<port2>,<port3>,...] [-i <interface>] ''' import struct, sys, io, argparse, datetime from struct import pack,unpack from binascii import b2a_hex from urbit.util import format_hexnum,from_le,to_le,dump_noun from urbit.cue import cue from urbit.pname import pname from urbit.crua import de_crua from misc.sniffer import Sniffer, PCapLoader if sys.version_info[0:2] < (3,0): print("Requires python3", file=sys.stderr) exit(1) class Args: # default args # interface we're interested in interface = b'eth0' # ports we're interested in ports = set(list(range(4000,4008)) + [13337, 41954]) # known keys for decrypting packets keys = {} # dump entire nouns show_nouns = True # show hex for decrypted packets show_raw = False # show timestamps show_timestamps = False # show keyhashes for decrypted packets always_show_keyhashes = False # constants... CRYPTOS = {0:'%none', 1:'%open', 2:'%fast', 3:'%full'} # utilities... def ipv4str(addr): '''Bytes to IPv4 address''' return '.'.join(['%i' % i for i in addr]) def crypto_name(x): '''Name for crypto algo''' if x in CRYPTOS: return CRYPTOS[x] else: return 'unk%02i' % x def hexstr(x): '''Bytes to hex string''' return b2a_hex(x).decode() def colorize(str, col): return ('\x1b[38;5;%im' % col) + str + ('\x1b[0m') # cli colors and glyphs COLOR_TIMESTAMP = 38 COLOR_RECIPIENT = 51 COLOR_IP = 21 COLOR_HEADER = 27 COLOR_VALUE = 33 COLOR_DATA = 250 COLOR_DATA_ENC = 245 v_arrow = colorize('→', 240) v_attention = colorize('>', 34) + colorize('>', 82) + colorize('>', 118) v_colon = colorize(':', 240) v_equal = colorize('=', 245) def parse_args(): args = Args() parser = argparse.ArgumentParser(description='Urbit sniffer. Dump incoming and outgoing urbit packets.') pdefault = '4000-4007,13337,41954' # update this when Args changes... idefault = args.interface.decode() parser.add_argument('-p, --ports', dest='ports', help='Ports to listen on (default: '+pdefault+')') parser.add_argument('-i, --interface', dest='interface', help='Interface to listen on (default:'+idefault+')', default=idefault) parser.add_argument('-k, --keys', dest='keys', help='Import keys from file (with <keyhash> <key> per line)', default=None) parser.add_argument('-n, --no-show-nouns', dest='show_nouns', action='store_false', help='Don\'t show full noun representation of decoded packets', default=True) parser.add_argument('-r, --show-raw', dest='show_raw', action='store_true', help='Show raw hex representation of decoded packets', default=False) parser.add_argument('-t, --show-timestamp', dest='show_timestamps', action='store_true', help='Show timestamps', default=False) parser.add_argument('-l, --read', dest='read_dump', help='Read a pcap dump file (eg from tcpdump)', default=None) parser.add_argument('--always-show-keyhashes', dest='always_show_keyhashes', help='Show keyhashes even for decrypted packets (more spammy)', default=False) r = parser.parse_args() if r.read_dump is not None: args.packet_source = PCapLoader(r.read_dump) else: args.packet_source = Sniffer(r.interface.encode()) if r.ports is not None: args.ports = set() for t in r.ports.split(','): (a,_,b) = t.partition('-') ai = int(a) bi = int(b) if b else ai args.ports.update(list(range(int(ai), int(bi)+1))) if r.keys is not None: args.keys = {} print(v_attention + ' Loading decryption keys from ' + r.keys) with open(r.keys, 'r') as f: for line in f: line = line.strip() if not line or line.startswith('#'): continue l = line.split() # filter out '.' so that keys can be copied directly args.keys[int(l[0].replace('.',''))] = int(l[1].replace('.','')) args.show_nouns = r.show_nouns args.show_raw = r.show_raw args.show_timestamps = r.show_timestamps args.always_show_keyhashes = r.always_show_keyhashes return args def dump_urbit_packet(args, timestamp, srcaddr, sport, dstaddr, dport, data): try: # Urbit header and payload urhdr = unpack('<L', data[0:4])[0] proto = urhdr & 7 mug = (urhdr >> 3) & 0xfffff yax = (urhdr >> 23) & 3 yax_bytes = 1<<(yax+1) qax = (urhdr >> 25) & 3 qax_bytes = 1<<(qax+1) crypto = (urhdr >> 27) sender = from_le(data[4:4+yax_bytes]) receiver = from_le(data[4+yax_bytes:4+yax_bytes+qax_bytes]) payload = data[4+yax_bytes+qax_bytes:] if crypto == 2: # %fast keyhash = from_le(payload[0:16]) payload = payload[16:] else: keyhash = None except (IndexError, struct.error): print('Warn: invpkt') return # Decode packet if crypto known decrypted = False if crypto in [0,1]: # %none %open decrypted = True if crypto == 2 and keyhash in args.keys: # %fast payload = from_le(payload) payload = de_crua(args.keys[keyhash], payload) payload = to_le(payload) decrypted = True # Print packet hdata = [('proto', str(proto)), ('mug', '%05x' % mug), ('crypto', crypto_name(crypto))] if keyhash is not None and (args.always_show_keyhashes or not decrypted): hdata += [('keyhash', format_hexnum(keyhash))] if srcaddr is not None: metadata = '' if args.show_timestamps: metadata += colorize(datetime.datetime.utcfromtimestamp(timestamp).strftime('%H%M%S.%f'), COLOR_TIMESTAMP) + ' ' metadata += (colorize(ipv4str(srcaddr), COLOR_IP) + v_colon + colorize(str(sport), COLOR_IP) + ' ' + colorize(pname(sender), COLOR_RECIPIENT) + ' ' + v_arrow + ' ' + colorize(ipv4str(dstaddr), COLOR_IP) + v_colon + colorize(str(dport), COLOR_IP) + ' ' + colorize(pname(receiver), COLOR_RECIPIENT)) else: metadata = (' %fore ' + # nested packet colorize(pname(sender), COLOR_RECIPIENT) + ' ' + v_arrow + ' ' + colorize(pname(receiver), COLOR_RECIPIENT)) print( metadata + v_colon + ' ' + ' '.join(colorize(key, COLOR_HEADER) + v_equal + colorize(value, COLOR_VALUE) for (key,value) in hdata)) if decrypted: # decrypted or unencrypted data if args.show_raw: print(' ' + colorize(hexstr(payload), COLOR_DATA)) cake = cue(from_le(payload)) if cake[0] == 1701998438: # %fore subpacket = to_le(cake[1][1][1]) dump_urbit_packet(args, None, None, None, None, None, subpacket) else: if args.show_nouns: sys.stdout.write(' ') dump_noun(cake, sys.stdout) sys.stdout.write('\n') else: # [sealed] print(' [' + colorize(hexstr(payload), COLOR_DATA_ENC)+']') def main(args): print(v_attention + ' Listening on ' + args.packet_source.name + ' ports ' + (',').join(str(x) for x in args.ports)) for timestamp,packet in args.packet_source: try: # IP header iph = unpack('!BBHHHBBH4s4s', packet[0:20]) ihl = (iph[0] & 15)*4 if ihl < 20: # cannot handle IP headers <20 bytes # print("Warn: invhdr") continue protocol = iph[6] srcaddr = iph[8] dstaddr = iph[9] if protocol != 17: # not UDP #print("Warn: invproto") continue # UDP header (sport, dport, ulength, uchecksum) = unpack('!HHHH', packet[ihl:ihl+8]) data = packet[ihl+8:ihl+ulength] if len(data) != (ulength-8): print("Warn: invlength") continue # invalid length packet if dport not in args.ports and sport not in args.ports: # only urbit ports continue except (IndexError, struct.error): print('Warn: invpkt') continue dump_urbit_packet(args, timestamp, srcaddr, sport, dstaddr, dport, data) if __name__ == '__main__': # Force UTF8 out sys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='utf8', line_buffering=True) sys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding='utf8', line_buffering=True) try: main(parse_args()) except KeyboardInterrupt: pass

38.146552

165

0.599887

1,135

8,850

4.567401

0.274009

0.020062

0.026235

0.007523

0.154514

0.092207

0.055941

0.039738

0.021605

0

0.026981

0.262938

8,850

231

166

38.311688

0.767592

0.110508

0

0.152542

0

0.011299

0.110585

0.008319

0

0.000896

0

1

0.039548

false

0.00565

0.050847

0.00565

0.175141

0.050847

0

null

0

null

0

1

0

6bd54aeda1cf3aa43806abaf7f0e2dafeca01c0d

1,638

py

Python

app/tests/v1/test_product.py

owezzy/StoreManager

821856c0d502b55bd499cfe9188cd4951c5b0b75

[ "MIT" ]

null

app/tests/v1/test_product.py

owezzy/StoreManager

821856c0d502b55bd499cfe9188cd4951c5b0b75

[ "MIT" ]

2

2018-10-10T22:32:35.000Z

2021-06-01T22:50:56.000Z

app/tests/v1/test_product.py

owezzy/StoreManager

821856c0d502b55bd499cfe9188cd4951c5b0b75

[ "MIT" ]

1

2018-10-25T12:42:41.000Z

import unittest import json from app.app import create_app POST_PRODUCT_URL = '/api/v1/products' GET_A_SINGLE_PRODUCT = '/api/v1/product/1' GET_ALL_PRODUCTS = '/api/v1/products' class TestProduct(unittest.TestCase): def setUp(self): """Initialize the api with test variable""" self.app = create_app('testing') self.client = self.app.test_client() self.create_product = json.dumps(dict( product_name="shoes", stock=2, price=3000 )) def test_add_product(self): """Test for post product""" resource = self.client.post( POST_PRODUCT_URL, data=self.create_product, content_type='application/json') data = json.loads(resource.data.decode()) print(data) self.assertEqual(resource.status_code, 201, msg='CREATED') self.assertEqual(resource.content_type, 'application/json') def test_get_products(self): """test we can get products""" resource = self.client.get(POST_PRODUCT_URL, data=json.dumps(self.create_product), content_type='application/json') get_data = json.dumps(resource.data.decode()) print(get_data) self.assertEqual(resource.content_type, 'application/json') self.assertEqual(resource.status_code, 200) def test_get(self): """test we can get a single products""" resource = self.client.get(GET_A_SINGLE_PRODUCT) self.assertEqual(resource.status_code, 404) if __name__ == '__main__': unittest.main()

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1,638

5.041237

0.309278

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0.054054

0

null

0

null

0

1

0

6bd88438849c3a76c7d468249205380ab8ab8c38

1,346

py

Python

cryptoshredding/s3/client.py

hupe1980/cryptoshredding

1ab5ee452c4435f486006aa2cc1a7bee440d91fe

[ "MIT" ]

null

cryptoshredding/s3/client.py

hupe1980/cryptoshredding

1ab5ee452c4435f486006aa2cc1a7bee440d91fe

[ "MIT" ]

null

cryptoshredding/s3/client.py

hupe1980/cryptoshredding

1ab5ee452c4435f486006aa2cc1a7bee440d91fe

[ "MIT" ]

null

import boto3 from botocore.client import BaseClient from ..key_store import KeyStore from .object import CryptoObject from .stream_body_wrapper import StreamBodyWrapper class CryptoS3(object): def __init__( self, client: BaseClient, key_store: KeyStore, ) -> None: self._client = client self._key_store = key_store def put_object(self, CSEKeyId: str, Bucket: str, Key: str, **kwargs): obj = CryptoObject( key_store=self._key_store, object=boto3.resource("s3").Object(Bucket, Key), ) return obj.put(CSEKeyId=CSEKeyId, **kwargs) def get_object(self, **kwargs): obj = self._client.get_object(**kwargs) obj["Body"] = StreamBodyWrapper( key_store=self._key_store, stream_body=obj["Body"], metadata=obj["Metadata"], ) return obj def __getattr__(self, name: str): """Catch any method/attribute lookups that are not defined in this class and try to find them on the provided bridge object. :param str name: Attribute name :returns: Result of asking the provided client object for that attribute name :raises AttributeError: if attribute is not found on provided bridge object """ return getattr(self._client, name)

31.302326

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0.641902

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1,346

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0.043062

0.035885

0.047847

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0.271917

1,346

42

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0.018145

0

1

0.137931

false

0

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0

0.448276

0

null

0

null

0

1

0

6bd9272def1931c3aead22a640fadc1a05f50b8f

5,627

py

Python

tests/infra/test_subnet.py

bretttegart/treadmill

812109e31c503a6eddaee2d3f2e1faf2833b6aaf

[ "Apache-2.0" ]

2

2017-10-31T18:48:20.000Z

2018-03-04T20:35:20.000Z

tests/infra/test_subnet.py

bretttegart/treadmill

812109e31c503a6eddaee2d3f2e1faf2833b6aaf

[ "Apache-2.0" ]

null

tests/infra/test_subnet.py

bretttegart/treadmill

812109e31c503a6eddaee2d3f2e1faf2833b6aaf

[ "Apache-2.0" ]

null

""" Unit test for EC2 subnet. """ import unittest import mock from treadmill.infra.subnet import Subnet class SubnetTest(unittest.TestCase): @mock.patch('treadmill.infra.connection.Connection') def test_init(self, ConnectionMock): conn_mock = ConnectionMock() Subnet.ec2_conn = Subnet.route53_conn = conn_mock subnet = Subnet( id=1, vpc_id='vpc-id', metadata={ 'Tags': [{ 'Key': 'Name', 'Value': 'goo' }] } ) self.assertEquals(subnet.vpc_id, 'vpc-id') self.assertEquals(subnet.name, 'goo') self.assertEquals(subnet.ec2_conn, conn_mock) @mock.patch('treadmill.infra.connection.Connection') def test_create_tags(self, ConnectionMock): conn_mock = ConnectionMock() conn_mock.create_tags = mock.Mock() Subnet.ec2_conn = Subnet.route53_conn = conn_mock subnet = Subnet( name='foo', id='1', vpc_id='vpc-id' ) subnet.create_tags() conn_mock.create_tags.assert_called_once_with( Resources=['1'], Tags=[{ 'Key': 'Name', 'Value': 'foo' }] ) @mock.patch('treadmill.infra.connection.Connection') def test_create(self, ConnectionMock): ConnectionMock.context.region_name = 'us-east-1' conn_mock = ConnectionMock() subnet_json_mock = { 'SubnetId': '1' } conn_mock.create_subnet = mock.Mock(return_value={ 'Subnet': subnet_json_mock }) conn_mock.create_route_table = mock.Mock(return_value={ 'RouteTable': {'RouteTableId': 'route-table-id'} }) Subnet.ec2_conn = Subnet.route53_conn = conn_mock _subnet = Subnet.create( cidr_block='172.23.0.0/24', vpc_id='vpc-id', name='foo', gateway_id='gateway-id' ) self.assertEqual(_subnet.id, '1') self.assertEqual(_subnet.name, 'foo') self.assertEqual(_subnet.metadata, subnet_json_mock) conn_mock.create_subnet.assert_called_once_with( VpcId='vpc-id', CidrBlock='172.23.0.0/24', AvailabilityZone='us-east-1a' ) conn_mock.create_tags.assert_called_once_with( Resources=['1'], Tags=[{ 'Key': 'Name', 'Value': 'foo' }] ) conn_mock.create_route_table.assert_called_once_with( VpcId='vpc-id' ) conn_mock.create_route.assert_called_once_with( RouteTableId='route-table-id', DestinationCidrBlock='0.0.0.0/0', GatewayId='gateway-id' ) conn_mock.associate_route_table.assert_called_once_with( RouteTableId='route-table-id', SubnetId='1', ) @mock.patch('treadmill.infra.connection.Connection') def test_refresh(self, ConnectionMock): conn_mock = ConnectionMock() subnet_json_mock = { 'VpcId': 'vpc-id', 'Foo': 'bar' } conn_mock.describe_subnets = mock.Mock(return_value={ 'Subnets': [subnet_json_mock] }) Subnet.ec2_conn = Subnet.route53_conn = conn_mock _subnet = Subnet(id='subnet-id', vpc_id=None, metadata=None) _subnet.refresh() self.assertEqual(_subnet.vpc_id, 'vpc-id') self.assertEqual(_subnet.metadata, subnet_json_mock) @mock.patch.object(Subnet, 'refresh') @mock.patch.object(Subnet, 'get_instances') @mock.patch('treadmill.infra.connection.Connection') def test_show(self, ConnectionMock, get_instances_mock, refresh_mock): conn_mock = ConnectionMock() Subnet.ec2_conn = Subnet.route53_conn = conn_mock _subnet = Subnet(id='subnet-id', vpc_id='vpc-id', metadata=None) _subnet.instances = None result = _subnet.show() self.assertEqual( result, { 'VpcId': 'vpc-id', 'SubnetId': 'subnet-id', 'Instances': None } ) get_instances_mock.assert_called_once_with(refresh=True, role=None) refresh_mock.assert_called_once() @mock.patch('treadmill.infra.connection.Connection') def test_persisted(self, ConnectionMock): _subnet = Subnet(id='subnet-id', metadata={'foo': 'goo'}) self.assertFalse(_subnet.persisted) _subnet.metadata['SubnetId'] = 'subnet-id' self.assertTrue(_subnet.persisted) @mock.patch('treadmill.infra.connection.Connection') def test_persist(self, ConnectionMock): ConnectionMock.context.region_name = 'us-east-1' conn_mock = ConnectionMock() Subnet.ec2_conn = Subnet.route53_conn = conn_mock conn_mock.create_subnet = mock.Mock( return_value={ 'Subnet': { 'foo': 'bar' } } ) _subnet = Subnet( id='subnet-id', metadata=None, vpc_id='vpc-id', name='subnet-name' ) _subnet.persist( cidr_block='cidr-block', gateway_id='gateway-id', ) self.assertEqual(_subnet.metadata, {'foo': 'bar'}) conn_mock.create_subnet.assert_called_once_with( VpcId='vpc-id', CidrBlock='cidr-block', AvailabilityZone='us-east-1a' )

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0

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0.316865

5,627

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1

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false

0

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0.071895

0

null

0

null

0

1

0

6bdac7bc37d6267a61f05383477b3f0ca7a95eab

5,138

py

Python

tests/building/test_tokenization.py

fossabot/langumo

2d8b30979878bb27fb07cc31879c13c5c186582c

[ "Apache-2.0" ]

7

2020-09-05T08:30:25.000Z

2021-11-01T14:07:58.000Z

tests/building/test_tokenization.py

fossabot/langumo

2d8b30979878bb27fb07cc31879c13c5c186582c

[ "Apache-2.0" ]

2

2020-09-11T14:19:47.000Z

2021-03-05T17:22:21.000Z

tests/building/test_tokenization.py

fossabot/langumo

2d8b30979878bb27fb07cc31879c13c5c186582c

[ "Apache-2.0" ]

3

2020-09-11T14:16:06.000Z

2021-10-31T14:18:10.000Z

import tempfile from langumo.building import TrainTokenizer, TokenizeSentences from langumo.utils import AuxiliaryFileManager _dummy_corpus_content = ( 'Wikipedia is a multilingual online encyclopedia created and maintained ' 'as an open collaboration project by a community of volunteer editors ' 'using a wiki-based editing system. It is the largest and most popular ' 'general reference work on the World Wide Web. It is also one of the 15 ' 'most popular websites ranked by Alexa, as of August 2020. It features ' 'exclusively free content and no commercial ads. It is hosted by the ' 'Wikimedia Foundation, a non-profit organization funded primarily through ' 'donations.\n' 'Wikipedia was launched on January 15, 2001, and was created by Jimmy ' 'Wales and Larry Sanger. Sanger coined its name as a portmanteau of the ' 'terms "wiki" and "encyclopedia". Initially an English-language ' 'encyclopedia, versions of Wikipedia in other languages were quickly ' 'developed. With 6.1 million articles, the English Wikipedia is the ' 'largest of the more than 300 Wikipedia encyclopedias. Overall, Wikipedia ' 'comprises more than 54 million articles attracting 1.5 billion unique ' 'visitors per month.\n' 'In 2005, Nature published a peer review comparing 42 hard science ' 'articles from Encyclopædia Britannica and Wikipedia and found that ' 'Wikipedia\'s level of accuracy approached that of Britannica, although ' 'critics suggested that it might not have fared so well in a similar ' 'study of a random sampling of all articles or one focused on social ' 'science or contentious social issues. The following year, Time stated ' 'that the open-door policy of allowing anyone to edit had made Wikipedia ' 'the biggest and possibly the best encyclopedia in the world, and was a ' 'testament to the vision of Jimmy Wales.\n' 'Wikipedia has been criticized for exhibiting systemic bias and for being ' 'subject to manipulation and spin in controversial topics; Edwin Black ' 'has criticized Wikipedia for presenting a mixture of "truth, half truth, ' 'and some falsehoods". Wikipedia has also been criticized for gender ' 'bias, particularly on its English-language version, where the dominant ' 'majority of editors are male. However, edit-a-thons have been held to ' 'encourage female editors and increase the coverage of women\'s topics. ' 'Facebook announced that by 2017 it would help readers detect fake news ' 'by suggesting links to related Wikipedia articles. YouTube announced a ' 'similar plan in 2018.' ) def test_subset_file_creation(): with tempfile.TemporaryDirectory() as tdir, \ AuxiliaryFileManager(f'{tdir}/workspace') as afm: corpus = afm.create() with corpus.open('w') as fp: fp.write('hello world!\n' * 100) with (TrainTokenizer(subset_size=1024) ._create_subset_file(afm, corpus) .open('r')) as fp: assert len(fp.readlines()) == 79 with (TrainTokenizer(subset_size=128) ._create_subset_file(afm, corpus) .open('r')) as fp: assert len(fp.readlines()) == 10 with (TrainTokenizer(subset_size=2000) ._create_subset_file(afm, corpus) .open('r')) as fp: assert len(fp.readlines()) == 100 def test_training_wordpiece_tokenizer(): with tempfile.TemporaryDirectory() as tdir, \ AuxiliaryFileManager(f'{tdir}/workspace') as afm: corpus = afm.create() with corpus.open('w') as fp: fp.write(_dummy_corpus_content) # Train WordPiece tokenizer and get vocabulary file. vocab = (TrainTokenizer(vocab_size=128, limit_alphabet=64, unk_token='[UNK]') .build(afm, corpus)) # Read subwords from the vocabulary file. with vocab.open('r') as fp: words = fp.readlines() # Check if the number of total words equals to vocabulary size and the # vocabulary contains unknown token. assert len(words) == 128 assert words[0].strip() == '[UNK]' def test_subword_tokenization(): with tempfile.TemporaryDirectory() as tdir, \ AuxiliaryFileManager(f'{tdir}/workspace') as afm: corpus = afm.create() with corpus.open('w') as fp: fp.write(_dummy_corpus_content) # Train WordPiece vocabulary and tokenize sentences. vocab = (TrainTokenizer(vocab_size=128, limit_alphabet=64) .build(afm, corpus)) tokenized = (TokenizeSentences(unk_token='[UNK]') .build(afm, corpus, vocab)) # Test if the tokenization is correctly applied to the corpus. Note # that the tokenizer model will normalize the sentences. with tokenized.open('r') as fp: assert (fp.read().strip().replace('##', '').replace(' ', '') == _dummy_corpus_content.lower().replace(' ', ''))

47.137615

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650

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5.201538

0.436923

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0.010352

0.01331

0.216208

0.211772

0.196983

0.169772

0

0.018243

0.253211

5,138

108

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47.574074

0.862914

0.071234

0

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0

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0

0.070588

1

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false

0

0.035294

0

0.070588

0

null

0

null

0

1

0

6bdb7b37ea55baaa1973a1fff39476ce6ea71851

12,819

py

Python

examples/BertNewsClassification/news_classifier.py

mlflow/mlflow-torchserve

91663b630ef12313da3ad821767faf3fc409345b

[ "Apache-2.0" ]

40

2020-11-13T02:08:10.000Z

2022-03-27T07:41:57.000Z

examples/BertNewsClassification/news_classifier.py

Ideas2IT/mlflow-torchserve

d6300fb73f16d74ee2c7718c249faf485c4f3b62

[ "Apache-2.0" ]

23

2020-11-16T11:28:01.000Z

2021-09-23T11:28:24.000Z

examples/BertNewsClassification/news_classifier.py

Ideas2IT/mlflow-torchserve

d6300fb73f16d74ee2c7718c249faf485c4f3b62

[ "Apache-2.0" ]

15

2020-11-13T10:25:25.000Z

2022-02-01T10:13:20.000Z

# pylint: disable=W0221 # pylint: disable=W0613 # pylint: disable=E1102 # pylint: disable=W0223 import shutil from collections import defaultdict import numpy as np import pandas as pd import torch import torch.nn.functional as F from sklearn.model_selection import train_test_split from torch import nn from torch.utils.data import Dataset, DataLoader from transformers import ( BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup, ) import argparse import os from tqdm import tqdm import requests import torchtext.datasets as td import mlflow.pytorch class_names = ["World", "Sports", "Business", "Sci/Tech"] class AGNewsDataset(Dataset): """ Constructs the encoding with the dataset """ def __init__(self, reviews, targets, tokenizer, max_len): self.reviews = reviews self.targets = targets self.tokenizer = tokenizer self.max_len = max_len def __len__(self): return len(self.reviews) def __getitem__(self, item): review = str(self.reviews[item]) target = self.targets[item] encoding = self.tokenizer.encode_plus( review, add_special_tokens=True, max_length=self.max_len, return_token_type_ids=False, padding="max_length", return_attention_mask=True, return_tensors="pt", truncation=True, ) return { "review_text": review, "input_ids": encoding["input_ids"].flatten(), "attention_mask": encoding["attention_mask"].flatten(), "targets": torch.tensor(target, dtype=torch.long), } class NewsClassifier(nn.Module): def __init__(self, args): super(NewsClassifier, self).__init__() self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.PRE_TRAINED_MODEL_NAME = "bert-base-uncased" self.EPOCHS = args.max_epochs self.df = None self.tokenizer = None self.df_train = None self.df_val = None self.df_test = None self.train_data_loader = None self.val_data_loader = None self.test_data_loader = None self.optimizer = None self.total_steps = None self.scheduler = None self.loss_fn = None self.BATCH_SIZE = 16 self.MAX_LEN = 160 self.NUM_SAMPLES_COUNT = args.num_samples n_classes = len(class_names) self.VOCAB_FILE_URL = args.vocab_file self.VOCAB_FILE = "bert_base_uncased_vocab.txt" self.drop = nn.Dropout(p=0.2) self.bert = BertModel.from_pretrained(self.PRE_TRAINED_MODEL_NAME) for param in self.bert.parameters(): param.requires_grad = False self.fc1 = nn.Linear(self.bert.config.hidden_size, 512) self.out = nn.Linear(512, n_classes) def forward(self, input_ids, attention_mask): """ :param input_ids: Input sentences from the batch :param attention_mask: Attention mask returned by the encoder :return: output - label for the input text """ pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask).pooler_output output = F.relu(self.fc1(pooled_output)) output = self.drop(output) output = self.out(output) return output @staticmethod def process_label(rating): rating = int(rating) return rating - 1 def create_data_loader(self, df, tokenizer, max_len, batch_size): """ :param df: DataFrame input :param tokenizer: Bert tokenizer :param max_len: maximum length of the input sentence :param batch_size: Input batch size :return: output - Corresponding data loader for the given input """ ds = AGNewsDataset( reviews=df.description.to_numpy(), targets=df.label.to_numpy(), tokenizer=tokenizer, max_len=max_len, ) return DataLoader(ds, batch_size=batch_size, num_workers=4) def prepare_data(self): """ Creates train, valid and test dataloaders from the csv data """ td.AG_NEWS(root="data", split=("train", "test")) extracted_files = os.listdir("data/AG_NEWS") train_csv_path = None for fname in extracted_files: if fname.endswith("train.csv"): train_csv_path = os.path.join(os.getcwd(), "data/AG_NEWS", fname) self.df = pd.read_csv(train_csv_path) self.df.columns = ["label", "title", "description"] self.df.sample(frac=1) self.df = self.df.iloc[: self.NUM_SAMPLES_COUNT] self.df["label"] = self.df.label.apply(self.process_label) if not os.path.isfile(self.VOCAB_FILE): filePointer = requests.get(self.VOCAB_FILE_URL, allow_redirects=True) if filePointer.ok: with open(self.VOCAB_FILE, "wb") as f: f.write(filePointer.content) else: raise RuntimeError("Error in fetching the vocab file") self.tokenizer = BertTokenizer(self.VOCAB_FILE) RANDOM_SEED = 42 np.random.seed(RANDOM_SEED) torch.manual_seed(RANDOM_SEED) self.df_train, self.df_test = train_test_split( self.df, test_size=0.1, random_state=RANDOM_SEED, stratify=self.df["label"] ) self.df_val, self.df_test = train_test_split( self.df_test, test_size=0.5, random_state=RANDOM_SEED, stratify=self.df_test["label"] ) self.train_data_loader = self.create_data_loader( self.df_train, self.tokenizer, self.MAX_LEN, self.BATCH_SIZE ) self.val_data_loader = self.create_data_loader( self.df_val, self.tokenizer, self.MAX_LEN, self.BATCH_SIZE ) self.test_data_loader = self.create_data_loader( self.df_test, self.tokenizer, self.MAX_LEN, self.BATCH_SIZE ) def setOptimizer(self): """ Sets the optimizer and scheduler functions """ self.optimizer = AdamW(model.parameters(), lr=1e-3, correct_bias=False) self.total_steps = len(self.train_data_loader) * self.EPOCHS self.scheduler = get_linear_schedule_with_warmup( self.optimizer, num_warmup_steps=0, num_training_steps=self.total_steps ) self.loss_fn = nn.CrossEntropyLoss().to(self.device) def startTraining(self, model): """ Initialzes the Traning step with the model initialized :param model: Instance of the NewsClassifier class """ history = defaultdict(list) best_accuracy = 0 for epoch in range(self.EPOCHS): print(f"Epoch {epoch + 1}/{self.EPOCHS}") train_acc, train_loss = self.train_epoch(model) print(f"Train loss {train_loss} accuracy {train_acc}") val_acc, val_loss = self.eval_model(model, self.val_data_loader) print(f"Val loss {val_loss} accuracy {val_acc}") history["train_acc"].append(train_acc) history["train_loss"].append(train_loss) history["val_acc"].append(val_acc) history["val_loss"].append(val_loss) if val_acc > best_accuracy: torch.save(model.state_dict(), "best_model_state.bin") best_accuracy = val_acc def train_epoch(self, model): """ Training process happens and accuracy is returned as output :param model: Instance of the NewsClassifier class :result: output - Accuracy of the model after training """ model = model.train() losses = [] correct_predictions = 0 for data in tqdm(self.train_data_loader): input_ids = data["input_ids"].to(self.device) attention_mask = data["attention_mask"].to(self.device) targets = data["targets"].to(self.device) outputs = model(input_ids=input_ids, attention_mask=attention_mask) _, preds = torch.max(outputs, dim=1) loss = self.loss_fn(outputs, targets) correct_predictions += torch.sum(preds == targets) losses.append(loss.item()) loss.backward() nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) self.optimizer.step() self.scheduler.step() self.optimizer.zero_grad() return ( correct_predictions.double() / len(self.train_data_loader) / self.BATCH_SIZE, np.mean(losses), ) def eval_model(self, model, data_loader): """ Validation process happens and validation / test accuracy is returned as output :param model: Instance of the NewsClassifier class :param data_loader: Data loader for either test / validation dataset :result: output - Accuracy of the model after testing """ model = model.eval() losses = [] correct_predictions = 0 with torch.no_grad(): for d in data_loader: input_ids = d["input_ids"].to(self.device) attention_mask = d["attention_mask"].to(self.device) targets = d["targets"].to(self.device) outputs = model(input_ids=input_ids, attention_mask=attention_mask) _, preds = torch.max(outputs, dim=1) loss = self.loss_fn(outputs, targets) correct_predictions += torch.sum(preds == targets) losses.append(loss.item()) return correct_predictions.double() / len(data_loader) / self.BATCH_SIZE, np.mean(losses) def get_predictions(self, model, data_loader): """ Prediction after the training step is over :param model: Instance of the NewsClassifier class :param data_loader: Data loader for either test / validation dataset :result: output - Returns prediction results, prediction probablities and corresponding values """ model = model.eval() review_texts = [] predictions = [] prediction_probs = [] real_values = [] with torch.no_grad(): for d in data_loader: texts = d["review_text"] input_ids = d["input_ids"].to(self.device) attention_mask = d["attention_mask"].to(self.device) targets = d["targets"].to(self.device) outputs = model(input_ids=input_ids, attention_mask=attention_mask) _, preds = torch.max(outputs, dim=1) probs = F.softmax(outputs, dim=1) review_texts.extend(texts) predictions.extend(preds) prediction_probs.extend(probs) real_values.extend(targets) predictions = torch.stack(predictions).cpu() prediction_probs = torch.stack(prediction_probs).cpu() real_values = torch.stack(real_values).cpu() return review_texts, predictions, prediction_probs, real_values if __name__ == "__main__": parser = argparse.ArgumentParser(description="PyTorch BERT Example") parser.add_argument( "--max_epochs", type=int, default=5, metavar="N", help="number of epochs to train (default: 14)", ) parser.add_argument( "--num_samples", type=int, default=15000, metavar="N", help="Number of samples to be used for training " "and evaluation steps (default: 15000) Maximum:100000", ) parser.add_argument( "--vocab_file", default="https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", help="Custom vocab file", ) parser.add_argument( "--model_save_path", type=str, default="models", help="Path to save mlflow model" ) args = parser.parse_args() mlflow.start_run() model = NewsClassifier(args) model = model.to(model.device) model.prepare_data() model.setOptimizer() model.startTraining(model) print("TRAINING COMPLETED!!!") test_acc, _ = model.eval_model(model, model.test_data_loader) print(test_acc.item()) y_review_texts, y_pred, y_pred_probs, y_test = model.get_predictions( model, model.test_data_loader ) print("\n\n\n SAVING MODEL") if os.path.exists(args.model_save_path): shutil.rmtree(args.model_save_path) mlflow.pytorch.save_model( model, path=args.model_save_path, requirements_file="requirements.txt", extra_files=["class_mapping.json", "bert_base_uncased_vocab.txt"], ) mlflow.end_run()

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6bde8a8095cceac04979671e29124bd410698f7c

3,716

py

Python

src/toil/lib/aws/utils.py

rupertnash/toil

fd805d5fa14cca98f2bc64b322a4b546e163d6c9

[ "Apache-2.0" ]

6

2018-05-27T05:09:11.000Z

2020-07-01T17:02:40.000Z

src/toil/lib/aws/utils.py

rupertnash/toil

fd805d5fa14cca98f2bc64b322a4b546e163d6c9

[ "Apache-2.0" ]

1

2020-07-01T18:31:30.000Z

2020-07-08T14:03:39.000Z

src/toil/lib/aws/utils.py

rupertnash/toil

fd805d5fa14cca98f2bc64b322a4b546e163d6c9

[ "Apache-2.0" ]

1

2020-04-06T15:04:44.000Z

# Copyright (C) 2015-2021 Regents of the University of California # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from typing import Optional from toil.lib.misc import printq from toil.lib.retry import retry from toil.lib import aws try: from boto.exception import BotoServerError except ImportError: BotoServerError = None # AWS/boto extra is not installed logger = logging.getLogger(__name__) @retry(errors=[BotoServerError]) def delete_iam_role(role_name: str, region: Optional[str] = None, quiet: bool = True): from boto.iam.connection import IAMConnection iam_client = aws.client('iam', region_name=region) iam_resource = aws.resource('iam', region_name=region) boto_iam_connection = IAMConnection() role = iam_resource.Role(role_name) # normal policies for attached_policy in role.attached_policies.all(): printq(f'Now dissociating policy: {attached_policy.name} from role {role.name}', quiet) role.detach_policy(PolicyName=attached_policy.name) # inline policies for attached_policy in role.policies.all(): printq(f'Deleting inline policy: {attached_policy.name} from role {role.name}', quiet) # couldn't find an easy way to remove inline policies with boto3; use boto boto_iam_connection.delete_role_policy(role.name, attached_policy.name) iam_client.delete_role(RoleName=role_name) printq(f'Role {role_name} successfully deleted.', quiet) @retry(errors=[BotoServerError]) def delete_iam_instance_profile(instance_profile_name: str, region: Optional[str] = None, quiet: bool = True): iam_resource = aws.resource('iam', region_name=region) instance_profile = iam_resource.InstanceProfile(instance_profile_name) for role in instance_profile.roles: printq(f'Now dissociating role: {role.name} from instance profile {instance_profile_name}', quiet) instance_profile.remove_role(RoleName=role.name) instance_profile.delete() printq(f'Instance profile "{instance_profile_name}" successfully deleted.', quiet) @retry(errors=[BotoServerError]) def delete_sdb_domain(sdb_domain_name: str, region: Optional[str] = None, quiet: bool = True): sdb_client = aws.client('sdb', region_name=region) sdb_client.delete_domain(DomainName=sdb_domain_name) printq(f'SBD Domain: "{sdb_domain_name}" successfully deleted.', quiet) @retry(errors=[BotoServerError]) def delete_s3_bucket(bucket: str, region: Optional[str], quiet: bool = True): printq(f'Deleting s3 bucket in region "{region}": {bucket}', quiet) s3_client = aws.client('s3', region_name=region) s3_resource = aws.resource('s3', region_name=region) paginator = s3_client.get_paginator('list_object_versions') for response in paginator.paginate(Bucket=bucket): versions = response.get('Versions', []) + response.get('DeleteMarkers', []) for version in versions: printq(f" Deleting {version['Key']} version {version['VersionId']}", quiet) s3_client.delete_object(Bucket=bucket, Key=version['Key'], VersionId=version['VersionId']) s3_resource.Bucket(bucket).delete() printq(f'\n * Deleted s3 bucket successfully: {bucket}\n\n', quiet)

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null

0

null

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0

6bdeffb0d14dc1d4dca20f695831236be20df06b

3,389

py

Python

src/data/processed_data.py

Victoradukwu/titanic

18a4e8fe7dbe755a946512ca71b1d2a2f5932c64

[ "MIT" ]

null

src/data/processed_data.py

Victoradukwu/titanic

18a4e8fe7dbe755a946512ca71b1d2a2f5932c64

[ "MIT" ]

null

src/data/processed_data.py

Victoradukwu/titanic

18a4e8fe7dbe755a946512ca71b1d2a2f5932c64

[ "MIT" ]

null

import os import numpy as np import pandas as pd def read_data(): raw_data_path = os.path.join(os.path.pardir, 'data', 'raw') train_file_path = os.path.join(raw_data_path, 'train.csv') test_file_path = os.path.join(raw_data_path, 'test.csv') train_df = pd.read_csv(train_file_path, index_col = 'PassengerId') test_df = pd.read_csv(test_file_path, index_col = 'PassengerId') test_df['Survived'] = -100 df = pd.concat([train_df, test_df], sort=-False, axis=0) return df def process_data(df): return(df .assign(Title = lambda x: x.Name.map(get_title)) .pipe(fill_missing_values) .assign(Fare_Bin = lambda x: pd.qcut(x.Fare, 4, labels=['very_low', 'low', 'high', 'very_high'])) .assign(AgeState = lambda x: np.where(x.Age >= 18, 'Adult', 'Child')) .assign(FamilySize = lambda x: x.Parch + x.SibSp + 1) .assign(IsMother = lambda x: np.where(((x.Age > 18) & (x.Parch > 0) & (x.Title != 'Miss') & (x.Sex == 'female')), 1,0)) .assign(Cabin = lambda x: np.where(x.Cabin == 'T', np.nan, x.Cabin)) .assign(Deck = lambda x: x.Cabin.map(get_deck)) .assign(IsMale = lambda x: np.where(x.Sex == 'male', 1, 0)) .pipe(pd.get_dummies, columns=['Deck', 'Pclass', 'Title', 'Fare_Bin', 'Embarked', 'AgeState']) .drop(['Cabin', 'Name', 'Ticket', 'Parch', 'SibSp', 'Sex'], axis=1) .pipe(reorder_columns) ) # modify the function to reduce number of titles and return more meaningful functions def get_title(name): title_map = { 'mr': 'Mr', 'mrs': 'Mrs', 'mme': 'Mrs', 'ms': 'Mrs', 'miss': 'Miss', 'mlle': 'Miss', 'master': 'Master', 'don': 'Sir', 'rev': 'Sir', 'sir': 'Sir', 'jonkheer': 'Sir', 'dr': 'Officer', 'major': 'Officer', 'capt': 'Office', 'col': 'Officer', 'lady': 'Lady', 'the countess': 'Lady', 'dona': 'Lady' } first_name_with_title = name.split(',')[1] raw_title = first_name_with_title.split('.')[0] title = raw_title.strip().lower() return title_map[title] def get_deck(cabin): return np.where(pd.notnull(cabin), str(cabin)[0].upper(), 'Z') def fill_missing_values(df): #Embarked df.Embarked.fillna('C', inplace=True) # Fare median_fare = df[(df.Pclass == 3) & (df.Embarked == 'S')]['Fare'].median() df.Fare.fillna(median_fare, inplace=True) #Age title_age_median = df.groupby('Title').Age.transform('median') df.Age.fillna(title_age_median, inplace=True) return df def reorder_columns(df): columns = [column for column in df.columns if column != 'Survived'] columns = ['Survived'] + columns df = df[columns] return df def write_data(df): processed_data_path = os.path.join(os.path.pardir, 'data', 'processed') write_train_path = os.path.join(processed_data_path, 'train.csv') write_test_path = os.path.join(processed_data_path, 'test.csv') df.loc[df.Survived != -100].to_csv(write_train_path) columns = [column for column in df.columns if column != 'Survived'] df.loc[df.Survived == -100][columns].to_csv(write_test_path) if __name__ == '__main__': df = read_data() df = process_data(df) write_data(df)

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0.025039

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false

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0.194805

0

null

0

null

0

1

0

6be02c0cdca45be2d0933fe3cbd070df05fee26e

59,336

py

Python

clustergrammer/upload_pages/clustergrammer_old.py

delosrogers/clustergrammer-web

14102cfca328214d3bc8285e8331663fe0e5fad4

[ "MIT" ]

5

2018-04-04T16:25:06.000Z

2021-04-10T23:47:20.000Z

clustergrammer/upload_pages/clustergrammer_old.py

delosrogers/clustergrammer-web

14102cfca328214d3bc8285e8331663fe0e5fad4

[ "MIT" ]

8

2016-07-16T02:55:12.000Z

2022-02-02T16:42:17.000Z

clustergrammer/upload_pages/clustergrammer_old.py

delosrogers/clustergrammer-web

14102cfca328214d3bc8285e8331663fe0e5fad4

[ "MIT" ]

4

2019-05-28T08:52:41.000Z

2021-01-11T22:14:48.000Z

# define a class for networks class Network(object): ''' Networks have two states: the data state where they are stored as: matrix and nodes and a viz state where they are stored as: viz.links, viz.row_nodes, viz. col_nodes. The goal is to start in a data-state and produce a viz-state of the network that will be used as input to clustergram.js. ''' def __init__(self): # network: data-state self.dat = {} self.dat['nodes'] = {} self.dat['nodes']['row'] = [] self.dat['nodes']['col'] = [] # node_info holds the orderings (ini, clust, rank), classification ('cl'), # and other general information self.dat['node_info'] = {} for inst_rc in self.dat['nodes']: self.dat['node_info'][inst_rc] = {} self.dat['node_info'][inst_rc]['ini'] = [] self.dat['node_info'][inst_rc]['clust'] = [] self.dat['node_info'][inst_rc]['rank'] = [] self.dat['node_info'][inst_rc]['info'] = [] # classification is specifically used to color the class triangles self.dat['node_info'][inst_rc]['cl'] = [] self.dat['node_info'][inst_rc]['value'] = [] # initialize matrix self.dat['mat'] = [] # mat_info is an optional dictionary # so I'm not including it by default # network: viz-state self.viz = {} self.viz['row_nodes'] = [] self.viz['col_nodes'] = [] self.viz['links'] = [] def load_tsv_to_net(self, filename): f = open(filename,'r') lines = f.readlines() f.close() self.load_lines_from_tsv_to_net(lines) def pandas_load_tsv_to_net(self, file_buffer): ''' A user can add category information to the columns ''' import pandas as pd # get lines and check for category and value info lines = file_buffer.getvalue().split('\n') # check for category info in headers cat_line = lines[1].split('\t') add_cat = False if cat_line[0] == '': add_cat = True tmp_df = {} if add_cat: # read in names and categories tmp_df['mat'] = pd.read_table(file_buffer, index_col=0, header=[0,1]) else: # read in names only tmp_df['mat'] = pd.read_table(file_buffer, index_col=0, header=0) # save to self self.df_to_dat(tmp_df) # add categories if necessary if add_cat: cat_line = [i.strip() for i in cat_line] self.dat['node_info']['col']['cl'] = cat_line[1:] # make a dict of columns in categories ########################################## col_in_cat = {} for i in range(len(self.dat['node_info']['col']['cl'])): inst_cat = self.dat['node_info']['col']['cl'][i] inst_col = self.dat['nodes']['col'][i] if inst_cat not in col_in_cat: col_in_cat[inst_cat] = [] # collect col names for categories col_in_cat[inst_cat].append(inst_col) # save to node_info self.dat['node_info']['col_in_cat'] = col_in_cat def load_lines_from_tsv_to_net(self, lines): import numpy as np # get row/col labels and data from lines for i in range(len(lines)): # get inst_line inst_line = lines[i].rstrip().split('\t') # strip each element inst_line = [z.strip() for z in inst_line] # get column labels from first row if i == 0: tmp_col_labels = inst_line # add the labels for inst_elem in range(len(tmp_col_labels)): # skip the first element if inst_elem > 0: # get the column label inst_col_label = tmp_col_labels[inst_elem] # add to network data self.dat['nodes']['col'].append(inst_col_label) # get row info if i > 0: # save row labels self.dat['nodes']['row'].append(inst_line[0]) # get data - still strings inst_data_row = inst_line[1:] # convert to float inst_data_row = [float(tmp_dat) for tmp_dat in inst_data_row] # save the row data as an array inst_data_row = np.asarray(inst_data_row) # initailize matrix if i == 1: self.dat['mat'] = inst_data_row # add rows to matrix if i > 1: self.dat['mat'] = np.vstack( ( self.dat['mat'], inst_data_row ) ) def load_l1000cds2(self, l1000cds2): import scipy import numpy as np # process gene set result if 'upGenes' in l1000cds2['input']['data']: # add the names from all the results all_results = l1000cds2['result'] # grab col nodes - input sig and drugs self.dat['nodes']['col'] = [] for i in range(len(all_results)): inst_result = all_results[i] self.dat['nodes']['col'].append(inst_result['name']+'#'+str(i)) self.dat['node_info']['col']['value'].append(inst_result['score']) for type_overlap in inst_result['overlap']: self.dat['nodes']['row'].extend( inst_result['overlap'][type_overlap] ) self.dat['nodes']['row'] = sorted(list(set(self.dat['nodes']['row']))) # initialize the matrix self.dat['mat'] = scipy.zeros([ len(self.dat['nodes']['row']), len(self.dat['nodes']['col']) ]) # fill in the matrix with l10000 data ######################################## # fill in gene sigature as first column for i in range(len(self.dat['nodes']['row'])): inst_gene = self.dat['nodes']['row'][i] # get gene index inst_gene_index = self.dat['nodes']['row'].index(inst_gene) # if gene is in up add 1 otherwise add -1 if inst_gene in l1000cds2['input']['data']['upGenes']: self.dat['node_info']['row']['value'].append(1) else: self.dat['node_info']['row']['value'].append(-1) # save the name as a class for i in range(len(self.dat['nodes']['col'])): self.dat['node_info']['col']['cl'].append(self.dat['nodes']['col'][i]) # swap keys for aggravate and reverse if l1000cds2['input']['aggravate'] == False: # reverse gene set up_type = 'up/dn' dn_type = 'dn/up' else: # mimic gene set up_type = 'up/up' dn_type = 'dn/dn' # loop through drug results for inst_result_index in range(len(all_results)): inst_result = all_results[inst_result_index] # for non-mimic if up/dn then it should be negative since the drug is dn # for mimic if up/up then it should be positive since the drug is up for inst_dn in inst_result['overlap'][up_type]: # get gene index inst_gene_index = self.dat['nodes']['row'].index(inst_dn) # save -1 to gene row and drug column if up_type == 'up/dn': self.dat['mat'][ inst_gene_index, inst_result_index ] = -1 else: self.dat['mat'][ inst_gene_index, inst_result_index ] = 1 # for non-mimic if dn/up then it should be positive since the drug is up # for mimic if dn/dn then it should be negative since the drug is dn for inst_up in inst_result['overlap'][dn_type]: # get gene index inst_gene_index = self.dat['nodes']['row'].index(inst_up) # save 1 to gene row and drug column if dn_type == 'dn/up': self.dat['mat'][ inst_gene_index, inst_result_index ] = 1 else: self.dat['mat'][ inst_gene_index, inst_result_index ] = -1 # process a characteristic direction vector result else: all_results = l1000cds2['result'] # get gene names self.dat['nodes']['row'] = l1000cds2['input']['data']['up']['genes'] + l1000cds2['input']['data']['dn']['genes'] # save gene expression values tmp_exp_vect = l1000cds2['input']['data']['up']['vals'] + l1000cds2['input']['data']['dn']['vals'] for i in range(len(self.dat['nodes']['row'])): self.dat['node_info']['row']['value'].append(tmp_exp_vect[i]) # gather result names for i in range(len(all_results)): inst_result = all_results[i] # add result to list self.dat['nodes']['col'].append(inst_result['name']+'#'+str(i)) self.dat['node_info']['col']['cl'].append(inst_result['name']) # reverse signature, score [1,2] if l1000cds2['input']['aggravate'] == False: self.dat['node_info']['col']['value'].append( inst_result['score']-1 ) else: self.dat['node_info']['col']['value'].append( 1 - inst_result['score'] ) # concat up and down lists inst_vect = inst_result['overlap']['up'] + inst_result['overlap']['dn'] inst_vect = np.transpose(np.asarray(inst_vect)) inst_vect = inst_vect.reshape(-1,1) # initialize or add to matrix if type(self.dat['mat']) is list: self.dat['mat'] = inst_vect else: self.dat['mat'] = np.hstack(( self.dat['mat'], inst_vect)) def load_vect_post_to_net(self, vect_post): import numpy as np # get all signatures (a.k.a. columns) sigs = vect_post['columns'] # get all rows from signatures all_rows = [] all_sigs = [] for inst_sig in sigs: # gather sig names all_sigs.append(inst_sig['col_name']) # get column col_data = inst_sig['data'] # gather row names for inst_row_data in col_data: # get gene name all_rows.append( inst_row_data['row_name'] ) # get unique sorted list of genes all_rows = sorted(list(set(all_rows))) all_sigs = sorted(list(set(all_sigs))) print( 'found ' + str(len(all_rows)) + ' rows' ) print( 'found ' + str(len(all_sigs)) + ' columns\n' ) # save genes and sigs to nodes self.dat['nodes']['row'] = all_rows self.dat['nodes']['col'] = all_sigs # initialize numpy matrix of nans self.dat['mat'] = np.empty((len(all_rows),len(all_sigs))) self.dat['mat'][:] = np.nan is_up_down = False if 'is_up_down' in vect_post: if vect_post['is_up_down'] == True: is_up_down = True if is_up_down == True: self.dat['mat_up'] = np.empty((len(all_rows),len(all_sigs))) self.dat['mat_up'][:] = np.nan self.dat['mat_dn'] = np.empty((len(all_rows),len(all_sigs))) self.dat['mat_dn'][:] = np.nan # loop through all signatures and rows # and place information into self.dat for inst_sig in sigs: # get sig name inst_sig_name = inst_sig['col_name'] # get row data col_data = inst_sig['data'] # loop through column for inst_row_data in col_data: # add row data to signature matrix inst_row = inst_row_data['row_name'] inst_value = inst_row_data['val'] # find index of row and sig in matrix row_index = all_rows.index(inst_row) col_index = all_sigs.index(inst_sig_name) # save inst_value to matrix self.dat['mat'][row_index, col_index] = inst_value if is_up_down == True: self.dat['mat_up'][row_index, col_index] = inst_row_data['val_up'] self.dat['mat_dn'][row_index, col_index] = inst_row_data['val_dn'] def load_data_file_to_net(self, filename): # load json from file to new dictionary inst_dat = self.load_json_to_dict(filename) # convert dat['mat'] to numpy array and add to network self.load_data_to_net(inst_dat) def load_data_to_net(self, inst_net): ''' load data into nodes and mat, also convert mat to numpy array''' self.dat['nodes'] = inst_net['nodes'] self.dat['mat'] = inst_net['mat'] # convert to numpy array self.mat_to_numpy_arr() def export_net_json(self, net_type, indent='no-indent'): ''' export json string of dat ''' import json from copy import deepcopy if net_type == 'dat': exp_dict = deepcopy(self.dat) # convert numpy array to list if type(exp_dict['mat']) is not list: exp_dict['mat'] = exp_dict['mat'].tolist() elif net_type == 'viz': exp_dict = self.viz # make json if indent == 'indent': exp_json = json.dumps(exp_dict, indent=2) else: exp_json = json.dumps(exp_dict) return exp_json def write_json_to_file(self, net_type, filename, indent='no-indent'): import json # get dat or viz representation as json string if net_type == 'dat': exp_json = self.export_net_json('dat', indent) elif net_type == 'viz': exp_json = self.export_net_json('viz', indent) # save to file fw = open(filename, 'w') fw.write( exp_json ) fw.close() def set_node_names(self, row_name, col_name): '''give names to the rows and columns''' self.dat['node_names'] = {} self.dat['node_names']['row'] = row_name self.dat['node_names']['col'] = col_name def mat_to_numpy_arr(self): ''' convert list to numpy array - numpy arrays can not be saved as json ''' import numpy as np self.dat['mat'] = np.asarray( self.dat['mat'] ) def swap_nan_for_zero(self): import numpy as np self.dat['mat'][ np.isnan( self.dat['mat'] ) ] = 0 def filter_row_thresh( self, row_filt_int, filter_type='value' ): ''' Remove rows from matrix that do not meet some threshold value: The default filtering is value, in that at least one value in the row has to be higher than some threshold. num: Rows can be filtered by the number of non-zero values it has. sum: Rows can be filtered by the sum of the values ''' import scipy import numpy as np # max vlue in matrix mat = self.dat['mat'] max_mat = abs(max(mat.min(), mat.max(), key=abs)) # maximum number of measurements max_num = len(self.dat['nodes']['col']) mat_abs = abs(mat) sum_row = np.sum(mat_abs, axis=1) max_sum = max(sum_row) # transfer the nodes nodes = {} nodes['row'] = [] nodes['col'] = self.dat['nodes']['col'] # transfer the 'info' part of node_info if necessary node_info = {} node_info['row'] = [] node_info['col'] = self.dat['node_info']['col']['info'] # filter rows ################################# for i in range(len(self.dat['nodes']['row'])): # get row name inst_nodes_row = self.dat['nodes']['row'][i] # get node info - disregard ini, clust, and rank orders if len(self.dat['node_info']['row']['info']) > 0: inst_node_info = self.dat['node_info']['row']['info'][i] # get absolute value of row data row_vect = np.absolute(self.dat['mat'][i,:]) # value: is there at least one value over cutoff ################################################## if filter_type == 'value': # calc cutoff cutoff = row_filt_int * max_mat # count the number of values above some thresh found_tuple = np.where(row_vect >= cutoff) if len(found_tuple[0])>=1: # add name nodes['row'].append(inst_nodes_row) # add info if necessary if len(self.dat['node_info']['row']['info']) > 0: node_info['row'].append(inst_node_info) elif filter_type == 'num': num_nonzero = np.count_nonzero(row_vect) # use integer number of non-zero measurements cutoff = row_filt_int * 10 if num_nonzero>= cutoff: # add name nodes['row'].append(inst_nodes_row) # add info if necessary if len(self.dat['node_info']['row']['info']) > 0: node_info['row'].append(inst_node_info) elif filter_type == 'sum': inst_row_sum = sum(abs(row_vect)) if inst_row_sum > row_filt_int*max_sum: # add name nodes['row'].append(inst_nodes_row) # add info if necessary if len(self.dat['node_info']['row']['info']) > 0: node_info['row'].append(inst_node_info) # cherrypick data from self.dat['mat'] ################################## # filtered matrix filt_mat = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_up' in self.dat: filt_mat_up = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) filt_mat_dn = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_info' in self.dat: # initialize filtered mat_info dictionary with tuple keys filt_mat_info = {} # loop through the rows for i in range(len(nodes['row'])): inst_row = nodes['row'][i] # loop through the cols for j in range(len(nodes['col'])): inst_col = nodes['col'][j] # get row and col index pick_row = self.dat['nodes']['row'].index(inst_row) pick_col = self.dat['nodes']['col'].index(inst_col) # cherrypick ############### filt_mat[i,j] = self.dat['mat'][pick_row, pick_col] if 'mat_up' in self.dat: filt_mat_up[i,j] = self.dat['mat_up'][pick_row, pick_col] filt_mat_dn[i,j] = self.dat['mat_dn'][pick_row, pick_col] if 'mat_info' in self.dat: filt_mat_info[str((i,j))] = self.dat['mat_info'][str((pick_row,pick_col))] # save nodes array - list of node names self.dat['nodes'] = nodes # save node_info array - list of node infos self.dat['node_info']['row']['info'] = node_info['row'] self.dat['node_info']['col']['info'] = node_info['col'] # overwrite with new filtered data self.dat['mat'] = filt_mat # overwrite with up/dn data if necessary if 'mat_up' in self.dat: self.dat['mat_up'] = filt_mat_up self.dat['mat_dn'] = filt_mat_dn # overwrite mat_info if necessary if 'mat_info' in self.dat: self.dat['mat_info'] = filt_mat_info print( 'final mat shape' + str(self.dat['mat'].shape ) + '\n') def filter_col_thresh( self, cutoff, min_num_meet ): ''' remove rows and columns from matrix that do not have at least min_num_meet instances of a value with an absolute value above cutoff ''' import scipy import numpy as np # transfer the nodes nodes = {} nodes['row'] = self.dat['nodes']['row'] nodes['col'] = [] # transfer the 'info' part of node_info if necessary node_info = {} node_info['row'] = self.dat['node_info']['row']['info'] node_info['col'] = [] # add cols with non-zero values ################################# for i in range(len(self.dat['nodes']['col'])): # get col name inst_nodes_col = self.dat['nodes']['col'][i] # get node info - disregard ini, clust, and rank orders if len(self.dat['node_info']['col']['info']) > 0: inst_node_info = self.dat['node_info']['col']['info'][i] # get col vect col_vect = np.absolute(self.dat['mat'][:,i]) # check if there are nonzero values found_tuple = np.where(col_vect >= cutoff) if len(found_tuple[0])>=min_num_meet: # add name nodes['col'].append(inst_nodes_col) # add info if necessary if len(self.dat['node_info']['col']['info']) > 0: node_info['col'].append(inst_node_info) # cherrypick data from self.dat['mat'] ################################## # filtered matrix filt_mat = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_up' in self.dat: filt_mat_up = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) filt_mat_dn = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_info' in self.dat: # initialize filtered mat_info dictionary with tuple keys filt_mat_info = {} # loop through the rows for i in range(len(nodes['row'])): inst_row = nodes['row'][i] # loop through the cols for j in range(len(nodes['col'])): inst_col = nodes['col'][j] # get row and col index pick_row = self.dat['nodes']['row'].index(inst_row) pick_col = self.dat['nodes']['col'].index(inst_col) # cherrypick ############### filt_mat[i,j] = self.dat['mat'][pick_row, pick_col] if 'mat_up' in self.dat: filt_mat_up[i,j] = self.dat['mat_up'][pick_row, pick_col] filt_mat_dn[i,j] = self.dat['mat_dn'][pick_row, pick_col] if 'mat_info' in self.dat: filt_mat_info[str((i,j))] = self.dat['mat_info'][str((pick_row,pick_col))] # save nodes array - list of node names self.dat['nodes'] = nodes # save node_info array - list of node infos self.dat['node_info']['row']['info'] = node_info['row'] self.dat['node_info']['col']['info'] = node_info['col'] # overwrite with new filtered data self.dat['mat'] = filt_mat # overwrite with up/dn data if necessary if 'mat_up' in self.dat: self.dat['mat_up'] = filt_mat_up self.dat['mat_dn'] = filt_mat_dn # overwrite mat_info if necessary if 'mat_info' in self.dat: self.dat['mat_info'] = filt_mat_info print( 'final mat shape' + str(self.dat['mat'].shape ) + '\n') def filter_network_thresh( self, cutoff, min_num_meet ): ''' remove rows and columns from matrix that do not have at least min_num_meet instances of a value with an absolute value above cutoff ''' import scipy import numpy as np # transfer the nodes nodes = {} nodes['row'] = [] nodes['col'] = [] # transfer the 'info' part of node_info if necessary node_info = {} node_info['row'] = [] node_info['col'] = [] # add rows with non-zero values ################################# for i in range(len(self.dat['nodes']['row'])): # get row name inst_nodes_row = self.dat['nodes']['row'][i] # get node info - disregard ini, clust, and rank orders if len(self.dat['node_info']['row']['info']) > 0: inst_node_info = self.dat['node_info']['row']['info'][i] # get row vect row_vect = np.absolute(self.dat['mat'][i,:]) # check if there are nonzero values found_tuple = np.where(row_vect >= cutoff) if len(found_tuple[0])>=min_num_meet: # add name nodes['row'].append(inst_nodes_row) # add info if necessary if len(self.dat['node_info']['row']['info']) > 0: node_info['row'].append(inst_node_info) # add cols with non-zero values ################################# for i in range(len(self.dat['nodes']['col'])): # get col name inst_nodes_col = self.dat['nodes']['col'][i] # get node info - disregard ini, clust, and rank orders if len(self.dat['node_info']['col']['info']) > 0: inst_node_info = self.dat['node_info']['col']['info'][i] # get col vect col_vect = np.absolute(self.dat['mat'][:,i]) # check if there are nonzero values found_tuple = np.where(col_vect >= cutoff) if len(found_tuple[0])>=min_num_meet: # add name nodes['col'].append(inst_nodes_col) # add info if necessary if len(self.dat['node_info']['col']['info']) > 0: node_info['col'].append(inst_node_info) # cherrypick data from self.dat['mat'] ################################## # filtered matrix filt_mat = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_up' in self.dat: filt_mat_up = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) filt_mat_dn = scipy.zeros([ len(nodes['row']), len(nodes['col']) ]) if 'mat_info' in self.dat: # initialize filtered mat_info dictionary with tuple keys filt_mat_info = {} # loop through the rows for i in range(len(nodes['row'])): inst_row = nodes['row'][i] # loop through the cols for j in range(len(nodes['col'])): inst_col = nodes['col'][j] # get row and col index pick_row = self.dat['nodes']['row'].index(inst_row) pick_col = self.dat['nodes']['col'].index(inst_col) # cherrypick ############### filt_mat[i,j] = self.dat['mat'][pick_row, pick_col] if 'mat_up' in self.dat: filt_mat_up[i,j] = self.dat['mat_up'][pick_row, pick_col] filt_mat_dn[i,j] = self.dat['mat_dn'][pick_row, pick_col] if 'mat_info' in self.dat: filt_mat_info[str((i,j))] = self.dat['mat_info'][str((pick_row,pick_col))] # save nodes array - list of node names self.dat['nodes'] = nodes # save node_info array - list of node infos self.dat['node_info']['row']['info'] = node_info['row'] self.dat['node_info']['col']['info'] = node_info['col'] # overwrite with new filtered data self.dat['mat'] = filt_mat # overwrite with up/dn data if necessary if 'mat_up' in self.dat: self.dat['mat_up'] = filt_mat_up self.dat['mat_dn'] = filt_mat_dn # overwrite mat_info if necessary if 'mat_info' in self.dat: self.dat['mat_info'] = filt_mat_info print( 'final mat shape' + str(self.dat['mat'].shape ) + '\n') def keep_max_num_links(self, keep_num_links): print('\trun keep_max_num_links') max_mat_value = abs(self.dat['mat']).max() # check the total number of links inst_thresh = 0 inst_pct_max = 0 inst_num_links = (abs(self.dat['mat'])>inst_thresh).sum() print('initially there are '+str(inst_num_links)+' links ') print('there are initially '+str(inst_num_links)+'\n') thresh_fraction = 100 while (inst_num_links > keep_num_links): # increase the threshold as a pct of max value in mat inst_pct_max = inst_pct_max + 1 # increase threshold inst_thresh = max_mat_value*(float(inst_pct_max)/thresh_fraction) # check the number of links above the curr threshold inst_num_links = (abs(self.dat['mat'])>inst_thresh).sum() print('there are '+str(inst_num_links)+ ' links at threshold '+str(inst_pct_max)+'pct and value of ' +str(inst_thresh)+'\n') # if there are no links then increas thresh back up if inst_num_links == 0: inst_pct_max = inst_pct_max - 1 inst_thresh = max_mat_value*(float(inst_pct_max)/thresh_fraction) print('final number of links '+str(inst_num_links)) # replace values that are less than thresh with zero self.dat['mat'][ abs(self.dat['mat']) < inst_thresh] = 0 # return number of links return (abs(self.dat['mat'])>inst_thresh).sum() def cluster_row_and_col(self, dist_type='cosine', linkage_type='average', dendro=True, \ run_clustering=True, run_rank=True): ''' cluster net.dat and make visualization json, net.viz. optionally leave out dendrogram colorbar groups with dendro argument ''' import scipy import numpy as np from scipy.spatial.distance import pdist from copy import deepcopy # do not make dendrogram is you are not running clusttering if run_clustering == False: dendro = False # make distance matrices ########################## # get number of rows and columns from self.dat num_row = len(self.dat['nodes']['row']) num_col = len(self.dat['nodes']['col']) # initialize distance matrices row_dm = scipy.zeros([num_row,num_row]) col_dm = scipy.zeros([num_col,num_col]) # make copy of matrix tmp_mat = deepcopy(self.dat['mat']) # calculate distance matrix row_dm = pdist( tmp_mat, metric=dist_type ) col_dm = pdist( tmp_mat.transpose(), metric=dist_type ) # prevent negative values row_dm[row_dm < 0] = float(0) col_dm[col_dm < 0] = float(0) # initialize clust order clust_order = self.ini_clust_order() # initial ordering ################### clust_order['row']['ini'] = range(num_row, -1, -1) clust_order['col']['ini'] = range(num_col, -1, -1) # cluster if run_clustering == True: clust_order['row']['clust'], clust_order['row']['group'] = \ self.clust_and_group(row_dm, linkage_type=linkage_type) clust_order['col']['clust'], clust_order['col']['group'] = \ self.clust_and_group(col_dm, linkage_type=linkage_type) # rank if run_rank == True: clust_order['row']['rank'] = self.sort_rank_nodes('row') clust_order['col']['rank'] = self.sort_rank_nodes('col') # save clustering orders to node_info if run_clustering == True: self.dat['node_info']['row']['clust'] = clust_order['row']['clust'] self.dat['node_info']['col']['clust'] = clust_order['col']['clust'] else: self.dat['node_info']['row']['clust'] = clust_order['row']['ini'] self.dat['node_info']['col']['clust'] = clust_order['col']['ini'] if run_rank == True: self.dat['node_info']['row']['rank'] = clust_order['row']['rank'] self.dat['node_info']['col']['rank'] = clust_order['col']['rank'] else: self.dat['node_info']['row']['rank'] = clust_order['row']['ini'] self.dat['node_info']['col']['rank'] = clust_order['col']['ini'] # transfer ordereings # row self.dat['node_info']['row']['ini'] = clust_order['row']['ini'] self.dat['node_info']['row']['group'] = clust_order['row']['group'] # col self.dat['node_info']['col']['ini'] = clust_order['col']['ini'] self.dat['node_info']['col']['group'] = clust_order['col']['group'] #!! disabled temporarily # if len(self.dat['node_info']['col']['cl']) > 0: # self.calc_cat_clust_order() # make the viz json - can optionally leave out dendrogram self.viz_json(dendro) def calc_cat_clust_order(self): from clustergrammer import Network from copy import deepcopy col_in_cat = self.dat['node_info']['col_in_cat'] # alpha order categories all_cats = sorted(col_in_cat.keys()) # cluster each category ############################## # calc clustering of each category all_cat_orders = [] # this is the ordering of the columns based on their category, not # including their clustering order on top of their category tmp_col_names_list = [] for inst_cat in all_cats: inst_cols = col_in_cat[inst_cat] # keep a list of the columns tmp_col_names_list.extend(inst_cols) cat_net = deepcopy(Network()) cat_net.dat['mat'] = deepcopy(self.dat['mat']) cat_net.dat['nodes'] = deepcopy(self.dat['nodes']) # get dataframe, to simplify column filtering cat_df = cat_net.dat_to_df() # get subset of dataframe sub_df = {} sub_df['mat'] = cat_df['mat'][inst_cols] # load back to dat cat_net.df_to_dat(sub_df) try: cat_net.cluster_row_and_col('cos') inst_cat_order = cat_net.dat['node_info']['col']['clust'] except: inst_cat_order = range(len(cat_net.dat['nodes']['col'])) prev_order_len = len(all_cat_orders) # add previous order length to the current order number inst_cat_order = [i+prev_order_len for i in inst_cat_order] all_cat_orders.extend(inst_cat_order) # sort tmp_col_names_lust by the integers in all_cat_orders names_col_cat_clust = [x for (y,x) in sorted(zip(all_cat_orders,tmp_col_names_list))] # calc category-cluster order ############################## final_order = [] for i in range(len(self.dat['nodes']['col'])): # get the rank of the col in the order of col_nodes inst_col_name = self.dat['nodes']['col'][i] inst_col_num = names_col_cat_clust.index(inst_col_name) final_order.append(inst_col_num) self.dat['node_info']['col']['cl_index'] = final_order def clust_and_group( self, dm, linkage_type='average' ): import scipy.cluster.hierarchy as hier # calculate linkage Y = hier.linkage( dm, method=linkage_type ) Z = hier.dendrogram( Y, no_plot=True ) # get ordering inst_clust_order = Z['leaves'] all_dist = self.group_cutoffs() # generate distance cutoffs inst_groups = {} for inst_dist in all_dist: inst_key = str(inst_dist).replace('.','') inst_groups[inst_key] = hier.fcluster(Y, inst_dist*dm.max(), 'distance') inst_groups[inst_key] = inst_groups[inst_key].tolist() return inst_clust_order, inst_groups def sort_rank_node_values( self, rowcol ): import numpy as np from operator import itemgetter from copy import deepcopy # make a copy of nodes and node_info inst_nodes = deepcopy(self.dat['nodes'][rowcol]) inst_vals = deepcopy(self.dat['node_info'][rowcol]['value']) tmp_arr = [] for i in range(len(inst_nodes)): inst_dict = {} # get name of the node inst_dict['name'] = inst_nodes[i] # get value inst_dict['value'] = inst_vals[i] tmp_arr.append(inst_dict) # sort dictionary by value tmp_arr = sorted( tmp_arr, key=itemgetter('value') ) # get list of sorted nodes tmp_sort_nodes = [] for inst_dict in tmp_arr: tmp_sort_nodes.append( inst_dict['name'] ) # get the sorted index sort_index = [] for inst_node in inst_nodes: sort_index.append( tmp_sort_nodes.index(inst_node) ) return sort_index def sort_rank_nodes( self, rowcol ): import numpy as np from operator import itemgetter from copy import deepcopy # make a copy of node information inst_nodes = deepcopy(self.dat['nodes'][rowcol]) inst_mat = deepcopy(self.dat['mat']) sum_term = [] for i in range(len(inst_nodes)): inst_dict = {} # get name of the node inst_dict['name'] = inst_nodes[i] # sum values of the node if rowcol == 'row': inst_dict['total'] = np.sum(inst_mat[i,:]) else: inst_dict['total'] = np.sum(inst_mat[:,i]) # add this to the list of dicts sum_term.append(inst_dict) # sort dictionary by number of terms sum_term = sorted( sum_term, key=itemgetter('total'), reverse=False ) # get list of sorted nodes tmp_sort_nodes = [] for inst_dict in sum_term: tmp_sort_nodes.append(inst_dict['name']) # get the sorted index sort_index = [] for inst_node in inst_nodes: sort_index.append( tmp_sort_nodes.index(inst_node) ) return sort_index def viz_json(self, dendro=True): ''' make the dictionary for the clustergram.js visualization ''' # get dendrogram cutoff distances all_dist = self.group_cutoffs() # make nodes for viz ##################### # make rows and cols for inst_rc in self.dat['nodes']: for i in range(len( self.dat['nodes'][inst_rc] )): inst_dict = {} inst_dict['name'] = self.dat['nodes'][inst_rc][i] inst_dict['ini'] = self.dat['node_info'][inst_rc]['ini'][i] #!! clean this up so I do not have to get the index here inst_dict['clust'] = self.dat['node_info'][inst_rc]['clust'].index(i) inst_dict['rank'] = self.dat['node_info'][inst_rc]['rank'][i] # add node class cl if len(self.dat['node_info'][inst_rc]['cl']) > 0: inst_dict['cl'] = self.dat['node_info'][inst_rc]['cl'][i] # add node class cl_index if 'cl_index' in self.dat['node_info'][inst_rc] > 0: inst_dict['cl_index'] = self.dat['node_info'][inst_rc]['cl_index'][i] # add node class val if len(self.dat['node_info'][inst_rc]['value']) > 0: inst_dict['value'] = self.dat['node_info'][inst_rc]['value'][i] # add node information # if 'info' in self.dat['node_info'][inst_rc]: if len(self.dat['node_info'][inst_rc]['info']) > 0: inst_dict['info'] = self.dat['node_info'][inst_rc]['info'][i] # group info if dendro==True: inst_dict['group'] = [] for tmp_dist in all_dist: # read group info in correct order tmp_dist = str(tmp_dist).replace('.','') inst_dict['group'].append( float( self.dat['node_info'][inst_rc]['group'][tmp_dist][i] ) ) # append dictionary to list of nodes self.viz[inst_rc+'_nodes'].append(inst_dict) # links ######## for i in range(len( self.dat['nodes']['row'] )): for j in range(len( self.dat['nodes']['col'] )): if abs( self.dat['mat'][i,j] ) > 0: inst_dict = {} inst_dict['source'] = i inst_dict['target'] = j inst_dict['value'] = self.dat['mat'][i,j] # add up/dn values if necessary if 'mat_up' in self.dat: inst_dict['value_up'] = self.dat['mat_up'][i,j] if 'mat_up' in self.dat: inst_dict['value_dn'] = self.dat['mat_dn'][i,j] # add information if necessary - use dictionary with tuple key # each element of the matrix needs to have information if 'mat_info' in self.dat: # use tuple string inst_dict['info'] = self.dat['mat_info'][str((i,j))] # add highlight if necessary - use dictionary with tuple key if 'mat_hl' in self.dat: inst_dict['highlight'] = self.dat['mat_hl'][i,j] # append link self.viz['links'].append( inst_dict ) def df_to_dat(self, df): import numpy as np import pandas as pd self.dat['mat'] = df['mat'].values self.dat['nodes']['row'] = df['mat'].index.tolist() self.dat['nodes']['col'] = df['mat'].columns.tolist() # check if there is category information in the column names if type(self.dat['nodes']['col'][0]) is tuple: self.dat['nodes']['col'] = [i[0] for i in self.dat['nodes']['col']] if 'mat_up' in df: self.dat['mat_up'] = df['mat_up'].values self.dat['mat_dn'] = df['mat_dn'].values def dat_to_df(self): import numpy as np import pandas as pd df = {} # always return 'mat' dataframe df['mat'] = pd.DataFrame(data = self.dat['mat'], columns=self.dat['nodes']['col'], index=self.dat['nodes']['row']) if 'mat_up' in self.dat: df['mat_up'] = pd.DataFrame(data = self.dat['mat_up'], columns=self.dat['nodes']['col'], index=self.dat['nodes']['row']) df['mat_dn'] = pd.DataFrame(data = self.dat['mat_dn'], columns=self.dat['nodes']['col'], index=self.dat['nodes']['row']) return df def make_filtered_views(self, dist_type='cosine', run_clustering=True, \ dendro=True, views=['filter_row_sum','N_row_sum'], calc_col_cats=True, \ linkage_type='average'): from copy import deepcopy ''' This will calculate multiple views of a clustergram by filtering the data and clustering after each filtering. This filtering will keep the top N rows based on some quantity (sum, num-non-zero, etc). ''' print('running make_filtered_views') print('dist_type '+str(dist_type)) # get dataframe dictionary of network and remove rows/cols with all zero values df = self.dat_to_df() # each row or column must have at least one non-zero value threshold = 0.0001 df = self.df_filter_row(df, threshold) df = self.df_filter_col(df, threshold) # calculate initial view with no row filtering ################################################## # swap back in the filtered df to dat self.df_to_dat(df) # cluster initial view self.cluster_row_and_col(dist_type=dist_type, linkage_type=linkage_type, \ run_clustering=run_clustering, dendro=dendro) # set up views all_views = [] # generate views for each column category (default to only one) all_col_cat = ['all_category'] # check for column categories and check whether category specific clustering # should be calculated if len(self.dat['node_info']['col']['cl']) > 0 and calc_col_cats: tmp_cats = sorted(list(set(self.dat['node_info']['col']['cl']))) # gather all col_cats all_col_cat.extend(tmp_cats) for inst_col_cat in all_col_cat: # make a copy of df to send to filters send_df = deepcopy(df) # add N_row_sum views if 'N_row_sum' in views: print('add N top views') all_views = self.add_N_top_views( send_df, all_views, dist_type=dist_type, current_col_cat=inst_col_cat ) if 'filter_row_sum' in views: all_views = self.add_pct_top_views( send_df, all_views, dist_type=dist_type, current_col_cat=inst_col_cat ) # add views to viz self.viz['views'] = all_views print('finished make_filtered_views') def add_pct_top_views(self, df, all_views, dist_type='cosine', \ current_col_cat='all_category'): from clustergrammer import Network from copy import deepcopy import numpy as np # make a copy of the network so that filtering is not propagated copy_net = deepcopy(self) # filter columns by category if necessary - do this on df, which is a copy if current_col_cat != 'all_category': keep_cols = copy_net.dat['node_info']['col_in_cat'][current_col_cat] df['mat'] = copy_net.grab_df_subset(df['mat'], keep_rows='all', keep_cols=keep_cols) # gather category key is_col_cat = False if len(self.dat['node_info']['col']['cl']) > 0 and current_col_cat=='all_category': is_col_cat = True cat_key_col = {} for i in range(len(self.dat['nodes']['col'])): cat_key_col[ self.dat['nodes']['col'][i] ] = self.dat['node_info']['col']['cl'][i] # filter between 0% and 90% of some threshoold all_filt = range(10) all_filt = [i/float(10) for i in all_filt] # row filtering values mat = deepcopy(df['mat']) sum_row = np.sum(mat, axis=1) max_sum = max(sum_row) for inst_filt in all_filt: cutoff = inst_filt * max_sum # make a copy of the network so that filtering is not propagated copy_net = deepcopy(self) # make copy of df inst_df = deepcopy(df) # filter row in df inst_df = copy_net.df_filter_row(inst_df, cutoff, take_abs=False) # filter columns by category if necessary if current_col_cat != 'all_category': keep_cols = copy_net.dat['node_info']['col_in_cat'][current_col_cat] inst_df['mat'] = copy_net.grab_df_subset(inst_df['mat'], keep_rows='all', keep_cols=keep_cols) if 'mat_up' in inst_df: # grab up and down data inst_df['mat_up'] = copy_net.grab_df_subset(inst_df['mat_up'], keep_rows='all', keep_cols=keep_cols) inst_df['mat_dn'] = copy_net.grab_df_subset(inst_df['mat_dn'], keep_rows='all', keep_cols=keep_cols) # ini net net = deepcopy(Network()) # transfer to dat net.df_to_dat(inst_df) # add col categories if necessary if is_col_cat: inst_col_cats = [] for inst_col_name in copy_net.dat['nodes']['col']: inst_col_cats.append( cat_key_col[inst_col_name] ) # transfer category information net.dat['node_info']['col']['cl'] = inst_col_cats # add col_in_cat net.dat['node_info']['col_in_cat'] = copy_net.dat['node_info']['col_in_cat'] # try to cluster try: try: # cluster net.cluster_row_and_col(dist_type=dist_type,run_clustering=True) except: # cluster net.cluster_row_and_col(dist_type=dist_type,run_clustering=False) # add view inst_view = {} inst_view['filter_row_sum'] = inst_filt inst_view['dist'] = 'cos' inst_view['col_cat'] = current_col_cat inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = net.viz['row_nodes'] inst_view['nodes']['col_nodes'] = net.viz['col_nodes'] all_views.append(inst_view) except: print('\t*** did not cluster pct filtered view') return all_views def add_N_top_views(self, df, all_views, dist_type='cosine',\ current_col_cat='all_category'): from clustergrammer import Network from copy import deepcopy # make a copy of hte network copy_net = deepcopy(self) # filter columns by category if necessary if current_col_cat != 'all_category': keep_cols = copy_net.dat['node_info']['col_in_cat'][current_col_cat] df['mat'] = copy_net.grab_df_subset(df['mat'], keep_rows='all', keep_cols=keep_cols) # gather category key is_col_cat = False if len(self.dat['node_info']['col']['cl']) > 0 and current_col_cat=='all_category': is_col_cat = True cat_key_col = {} for i in range(len(self.dat['nodes']['col'])): cat_key_col[ self.dat['nodes']['col'][i] ] = self.dat['node_info']['col']['cl'][i] # keep the following number of top rows keep_top = ['all',500,400,300,200,100,90,80,70,60,50,40,30,20,10] # get copy of df and take abs value, cell line cols and gene rows df_abs = deepcopy(df['mat']) # transpose to get gene columns df_abs = df_abs.transpose() # sum the values of the genes in the cell lines tmp_sum = df_abs.sum(axis=0) # take absolute value to keep most positive and most negative rows tmp_sum = tmp_sum.abs() # sort rows by value tmp_sum.sort(ascending=False) rows_sorted = tmp_sum.index.values.tolist() for inst_keep in keep_top: # initialize df tmp_df = deepcopy(df) # filter columns by category if necessary if current_col_cat != 'all_category': keep_cols = copy_net.dat['node_info']['col_in_cat'][current_col_cat] tmp_df['mat'] = copy_net.grab_df_subset(tmp_df['mat'], keep_rows='all', keep_cols=keep_cols) if 'mat_up' in df: # grab up and down data tmp_df['mat_up'] = copy_net.grab_df_subset(tmp_df['mat_up'], keep_rows='all', keep_cols=keep_cols) tmp_df['mat_dn'] = copy_net.grab_df_subset(tmp_df['mat_dn'], keep_rows='all', keep_cols=keep_cols) if inst_keep < len(rows_sorted) or inst_keep == 'all': # initialize netowrk net = deepcopy(Network()) # filter the rows if inst_keep != 'all': # get the labels of the rows that will be kept keep_rows = rows_sorted[0:inst_keep] # filter the matrix tmp_df['mat'] = tmp_df['mat'].ix[keep_rows] if 'mat_up' in tmp_df: tmp_df['mat_up'] = tmp_df['mat_up'].ix[keep_rows] tmp_df['mat_dn'] = tmp_df['mat_dn'].ix[keep_rows] # filter columns - some columns may have all zero values tmp_df = self.df_filter_col(tmp_df,0.001) # transfer to dat net.df_to_dat(tmp_df) else: net.df_to_dat(tmp_df) # add col categories if necessary if is_col_cat: inst_col_cats = [] for inst_col_name in self.dat['nodes']['col']: inst_col_cats.append( cat_key_col[inst_col_name] ) # transfer category information net.dat['node_info']['col']['cl'] = inst_col_cats # add col_in_cat net.dat['node_info']['col_in_cat'] = copy_net.dat['node_info']['col_in_cat'] # try to cluster try: try: # cluster net.cluster_row_and_col(dist_type,run_clustering=True) except: # cluster net.cluster_row_and_col(dist_type,run_clustering=False) # add view inst_view = {} inst_view['N_row_sum'] = inst_keep inst_view['dist'] = 'cos' inst_view['col_cat'] = current_col_cat inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = net.viz['row_nodes'] inst_view['nodes']['col_nodes'] = net.viz['col_nodes'] all_views.append(inst_view) except: print('\t*** did not cluster N filtered view') return all_views def fast_mult_views(self, dist_type='cos', run_clustering=True, dendro=True): import numpy as np import pandas as pd from clustergrammer import Network from copy import deepcopy ''' This will use Pandas to calculte multiple views of a clustergram Currently, it is only filtering based on row-sum and it is disregarding link information (used to add click functionality). ''' # gather category key is_col_cat = False if len(self.dat['node_info']['col']['cl']) > 0: is_col_cat = True cat_key_col = {} for i in range(len(self.dat['nodes']['col'])): cat_key_col[ self.dat['nodes']['col'][i] ] = self.dat['node_info']['col']['cl'][i] # get dataframe dictionary of network and remove rows/cols with all zero values df = self.dat_to_df() # each row or column must have at least one non-zero value threshold = 0.001 df = self.df_filter_row(df, threshold) df = self.df_filter_col(df, threshold) # calculate initial view with no row filtering ################################################# # swap back in filtered df to dat self.df_to_dat(df) # cluster initial view self.cluster_row_and_col('cos',run_clustering=run_clustering, dendro=dendro) # set up views all_views = [] # set up initial view inst_view = {} inst_view['filter_row_sum'] = 0 inst_view['dist'] = 'cos' inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = self.viz['row_nodes'] inst_view['nodes']['col_nodes'] = self.viz['col_nodes'] # add view with no filtering all_views.append(inst_view) # filter between 0% and 90% of some threshoold all_filt = range(10) all_filt = [i/float(10) for i in all_filt] # row filtering values mat = self.dat['mat'] mat_abs = abs(mat) sum_row = np.sum(mat_abs, axis=1) max_sum = max(sum_row) for inst_filt in all_filt: # skip zero filtering if inst_filt > 0: cutoff = inst_filt * max_sum # filter row df = self.df_filter_row(df, cutoff, take_abs=True) print('\tfiltering at cutoff ' + str(inst_filt) + ' mat shape: ' + str(df['mat'].shape)) # ini net net = deepcopy(Network()) # transfer to dat net.df_to_dat(df) # add col categories if necessary if is_col_cat: inst_col_cats = [] for inst_col_name in self.dat['nodes']['col']: inst_col_cats.append( cat_key_col[inst_col_name] ) net.dat['node_info']['col']['cl'] = inst_col_cats # try to cluster try: # cluster net.cluster_row_and_col('cos') # add view inst_view = {} inst_view['filter_row_sum'] = inst_filt inst_view['dist'] = 'cos' inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = net.viz['row_nodes'] inst_view['nodes']['col_nodes'] = net.viz['col_nodes'] all_views.append(inst_view) except: print('\t*** did not cluster filtered view') # add views to viz self.viz['views'] = all_views print('\tfinished fast_mult_views') def make_mult_views(self, dist_type='cos',filter_row=['value'], filter_col=False, run_clustering=True, dendro=True): ''' This will calculate multiple views of a clustergram by filtering the data and clustering after each fitlering. By default row filtering will be turned on and column filteirng will not. The filtering steps are defined as a percentage of the maximum value found in the network. ''' from clustergrammer import Network from copy import deepcopy # filter between 0% and 90% of some to be determined value all_filt = range(10) all_filt = [i/float(10) for i in all_filt] # cluster default view self.cluster_row_and_col('cos', run_clustering=run_clustering, dendro=dendro) self.viz['views'] = [] all_views = [] # Perform row filterings ########################### if len(filter_row) > 0: # perform multiple types of row filtering ########################################### for inst_type in filter_row: for row_filt_int in all_filt: # initialize new net net = deepcopy(Network()) net.dat = deepcopy(self.dat) # filter rows net.filter_row_thresh(row_filt_int, filter_type=inst_type) # filter columns since some columns might be all zero net.filter_col_thresh(0.001,1) # try to cluster - will not work if there is one row try: # cluster net.cluster_row_and_col('cos') inst_name = 'filter_row'+'_'+inst_type # add view inst_view = {} inst_view[inst_name] = row_filt_int inst_view['dist'] = 'cos' inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = net.viz['row_nodes'] inst_view['nodes']['col_nodes'] = net.viz['col_nodes'] all_views.append(inst_view) except: print('\t***did not cluster filtered view') # Default col Filtering ########################### inst_meet = 1 if filter_col == True: # col filtering ##################### for col_filt in all_filt: # print(col_filt) # initialize new net net = deepcopy(Network()) net.dat = deepcopy(self.dat) filt_value = col_filt * max_mat # filter cols net.filter_col_thresh(filt_value, inst_meet) # try to cluster - will not work if there is one col try: # cluster net.cluster_row_and_col('cos') # add view inst_view = {} inst_view['filter_col'] = col_filt inst_view['dist'] = 'cos' inst_view['nodes'] = {} inst_view['nodes']['row_nodes'] = net.viz['row_nodes'] inst_view['nodes']['col_nodes'] = net.viz['col_nodes'] all_views.append(inst_view) except: print('did not cluster filtered view') # add views to viz self.viz['views'] = all_views @staticmethod def df_filter_row(df, threshold, take_abs=True): ''' filter rows in matrix at some threshold and remove columns that have a sum below this threshold ''' import pandas as pd from copy import deepcopy from clustergrammer import Network net = Network() # take absolute value if necessary if take_abs == True: df_copy = deepcopy(df['mat'].abs()) else: df_copy = deepcopy(df['mat']) ini_rows = df_copy.index.values.tolist() # transpose df df_copy = df_copy.transpose() # sum the values of the rows tmp_sum = df_copy.sum(axis=0) # take absolute value to keep most positive and most negative rows tmp_sum = tmp_sum.abs() # sort rows by value tmp_sum.sort(ascending=False) # filter series using threshold tmp_sum = tmp_sum[tmp_sum>threshold] # get keep_row names keep_rows = sorted(tmp_sum.index.values.tolist()) if len(keep_rows) < len(ini_rows): # grab the subset of the data df['mat'] = net.grab_df_subset(df['mat'], keep_rows=keep_rows) if 'mat_up' in df: # grab up and down data df['mat_up'] = net.grab_df_subset(df['mat_up'], keep_rows=keep_rows) df['mat_dn'] = net.grab_df_subset(df['mat_dn'], keep_rows=keep_rows) return df @staticmethod def df_filter_col(df, threshold, take_abs=True): ''' filter columns in matrix at some threshold and remove rows that have all zero values ''' import pandas from copy import deepcopy from clustergrammer import Network net = Network() # take absolute value if necessary if take_abs == True: df_copy = deepcopy(df['mat'].abs()) else: df_copy = deepcopy(df['mat']) # filter columns to remove columns with all zero values # transpose df_copy = df_copy.transpose() df_copy = df_copy[df_copy.sum(axis=1) > threshold] # transpose back df_copy = df_copy.transpose() # filter rows df_copy = df_copy[df_copy.sum(axis=1) > 0] # get df ready for export if take_abs == True: inst_rows = df_copy.index.tolist() inst_cols = df_copy.columns.tolist() df['mat'] = net.grab_df_subset(df['mat'], inst_rows, inst_cols) else: # just transfer the copied data df['mat'] = df_copy return df @staticmethod def grab_df_subset(df, keep_rows='all', keep_cols='all'): if keep_cols != 'all': # filter columns df = df[keep_cols] if keep_rows != 'all': # filter rows df = df.ix[keep_rows] return df @staticmethod def load_gmt(filename): f = open(filename, 'r') lines = f.readlines() f.close() gmt = {} # loop through the lines of the gmt for i in range(len(lines)): # get the inst line, strip off the new line character inst_line = lines[i].rstrip() inst_term = inst_line.split('\t')[0] # get the elements inst_elems = inst_line.split('\t')[2:] # save the drug-kinase sets gmt[inst_term] = inst_elems return gmt @staticmethod def load_json_to_dict(filename): ''' load json to python dict and return dict ''' import json f = open(filename, 'r') inst_dict = json.load(f) f.close() return inst_dict @staticmethod def save_dict_to_json(inst_dict, filename, indent='no-indent'): import json # save as a json fw = open(filename, 'w') if indent == 'indent': fw.write( json.dumps(inst_dict, indent=2) ) else: fw.write( json.dumps(inst_dict) ) fw.close() @staticmethod def ini_clust_order(): rowcol = ['row','col'] orderings = ['clust','rank','group','ini'] clust_order = {} for inst_node in rowcol: clust_order[inst_node] = {} for inst_order in orderings: clust_order[inst_node][inst_order] = [] return clust_order @staticmethod def threshold_vect_comparison(x, y, cutoff): import numpy as np # x vector ############ # take absolute value of x x_abs = np.absolute(x) # this returns a tuple found_tuple = np.where(x_abs >= cutoff) # get index array found_index_x = found_tuple[0] # y vector ############ # take absolute value of y y_abs = np.absolute(y) # this returns a tuple found_tuple = np.where(y_abs >= cutoff) # get index array found_index_y = found_tuple[0] # get common intersection found_common = np.intersect1d(found_index_x, found_index_y) # apply cutoff thresh_x = x[found_common] thresh_y = y[found_common] # return the threshold data return thresh_x, thresh_y @staticmethod def group_cutoffs(): # generate distance cutoffs all_dist = [] for i in range(11): all_dist.append(float(i)/10) return all_dist @staticmethod def find_dict_in_list(list_dict, search_value, search_string): ''' find a dict in a list of dicts by searching for a value ''' # get all the possible values of search_value all_values = [d[search_value] for d in list_dict] # check if the search value is in the keys if search_string in all_values: # find the dict found_dict = (item for item in list_dict if item[search_value] == search_string).next() else: found_dict = {} # return the found dictionary return found_dict

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1

0

6be3f12ed534c88956efb0cde9bfba8da5449ad9

1,113

py

Python

server/face_recogniser.py

fvalle1/ai_server

0bbb2b842a329ca8bbeb6529a2008b61dcc77cc0

[ "MIT" ]

1

2021-03-10T15:37:21.000Z

server/face_recogniser.py

fvalle1/ai_server

0bbb2b842a329ca8bbeb6529a2008b61dcc77cc0

[ "MIT" ]

null

server/face_recogniser.py

fvalle1/ai_server

0bbb2b842a329ca8bbeb6529a2008b61dcc77cc0

[ "MIT" ]

null

import cv2 as cv from model import model class face_recogniser(model): def __init__(self): super() self.net = cv.dnn.readNet('/home/pi/inception/face-detection-adas-0001.xml','/home/pi/inception/face-detection-adas-0001.bin') self.net.setPreferableTarget(cv.dnn.DNN_TARGET_MYRIAD) def add_face_rectangle(self, frame): # Prepare input blob and perform an inference. blob = cv.dnn.blobFromImage(frame, size=(672, 384), ddepth=cv.CV_8U) self.net.setInput(blob) out = self.net.forward() # Draw detected faces on the frame. for detection in out.reshape(-1, 7): confidence = float(detection[2]) xmin = int(detection[3] * frame.shape[1]) ymin = int(detection[4] * frame.shape[0]) xmax = int(detection[5] * frame.shape[1]) ymax = int(detection[6] * frame.shape[0]) if confidence > 0.5: cv.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0)) return frame def process(self, frame): return self.add_face_rectangle(frame)

39.75

134

0.615454

150

1,113

4.486667

0.5

0.041605

0.044577

0.056464

0.106984

0

0.040964

0.254268

1,113

27

135

41.222222

0.76988

0.070081

0

0.091085

0

1

0.136364

false

0

0.090909

0.045455

0.363636

0

null

0

null

0

1

0

6be4827cb6db3797b4e0960a8f9afb82862b44ab

2,783

py

Python

tests/test_cpymadtools/test_generators.py

fsoubelet/PyhDToolk

aef9d5f1fac2d4c4307d50ba5c53f6a5eb635a66

[ "MIT" ]

5

2020-05-28T09:16:01.000Z

2021-12-27T18:59:15.000Z

tests/test_cpymadtools/test_generators.py

fsoubelet/PyhDToolk

aef9d5f1fac2d4c4307d50ba5c53f6a5eb635a66

[ "MIT" ]

71

2020-02-20T20:32:43.000Z

2022-03-24T17:04:28.000Z

tests/test_cpymadtools/test_generators.py

fsoubelet/PyhDToolk

aef9d5f1fac2d4c4307d50ba5c53f6a5eb635a66

[ "MIT" ]

2

2021-09-28T16:01:06.000Z

2022-03-16T19:04:23.000Z

import random import pytest from pyhdtoolkit.cpymadtools.generators import LatticeGenerator class TestLatticeGenerator: def test_base_cas_lattice_generation(self): base_cas_lattice = LatticeGenerator.generate_base_cas_lattice() assert isinstance(base_cas_lattice, str) assert len(base_cas_lattice) == 1493 def test_onesext_cas_lattice(self): onesext_cas_lattice = LatticeGenerator.generate_onesext_cas_lattice() assert isinstance(onesext_cas_lattice, str) assert len(onesext_cas_lattice) == 2051 def test_oneoct_cas_lattice(self): oneoct_cas_lattice = LatticeGenerator.generate_oneoct_cas_lattice() assert isinstance(oneoct_cas_lattice, str) assert len(oneoct_cas_lattice) == 2050 def test_tripleterrors_study_reference(self): tripleterrors_study_reference = LatticeGenerator.generate_tripleterrors_study_reference() assert isinstance(tripleterrors_study_reference, str) assert len(tripleterrors_study_reference) == 1617 @pytest.mark.parametrize( "randseed, tferror", [ ("", ""), ("95", "195"), ("105038", "0.001"), (str(random.randint(0, 1e7)), str(random.randint(0, 1e7))), (random.randint(0, 1e7), random.randint(0, 1e7)), ], ) def test_tripleterrors_study_tferror_job(self, randseed, tferror): tripleterrors_study_tferror_job = LatticeGenerator.generate_tripleterrors_study_tferror_job( rand_seed=randseed, tf_error=tferror, ) assert isinstance(tripleterrors_study_tferror_job, str) assert len(tripleterrors_study_tferror_job) == 2521 + len(str(randseed)) + len(str(tferror)) assert f"eoption, add, seed = {randseed};" in tripleterrors_study_tferror_job assert f"B2r = {tferror};" in tripleterrors_study_tferror_job @pytest.mark.parametrize( "randseed, mserror", [ ("", ""), ("95", "195"), ("105038", "0.001"), (str(random.randint(0, 1e7)), str(random.randint(0, 1e7))), (random.randint(0, 1e7), random.randint(0, 1e7)), ], ) def test_tripleterrors_study_mserror_job(self, randseed, mserror): tripleterrors_study_mserror_job = LatticeGenerator.generate_tripleterrors_study_mserror_job( rand_seed=randseed, ms_error=mserror, ) assert isinstance(tripleterrors_study_mserror_job, str) assert len(tripleterrors_study_mserror_job) == 2384 + len(str(randseed)) + len(str(mserror)) assert f"eoption, add, seed = {randseed};" in tripleterrors_study_mserror_job assert f"ealign, ds := {mserror} * 1E-3 * TGAUSS(GCUTR);" in tripleterrors_study_mserror_job

42.166667

100

0.680201

308

2,783

5.824675

0.201299

0.190635

0.06243

0.075808

0.389075

0.218506

0.181717

0.12709

0

0.037207

0.217751

2,783

65

101

42.815385

0.786863

0

0.25

0

0.06935

0

0.285714

1

0.107143

false

0

0.053571

0

0.178571

0

null

0

null

0

1

0

6be4e81e9d40d83f06f1cfa243fa8007e8370a2f

1,789

py

Python

preprocessing/act_bed_construction.py

shtoneyan/sea-lion

7e1ce9a18a147eea42e6172a2329d696f6e6aef9

[ "MIT" ]

1

2022-02-10T21:21:32.000Z

preprocessing/act_bed_construction.py

shtoneyan/sea-lion

7e1ce9a18a147eea42e6172a2329d696f6e6aef9

[ "MIT" ]

null

preprocessing/act_bed_construction.py

shtoneyan/sea-lion

7e1ce9a18a147eea42e6172a2329d696f6e6aef9

[ "MIT" ]

null

import pandas as pd import re import sys #read bed file #constructure acitivity table #output tfr file def main(): bed_file = sys.argv[1] act_table = sys.argv[2] data = pd.read_csv(act_table,sep = '\t') data.rename(columns={'Unnamed: 0':'loci'}, inplace=True) chrom = [i.split(':')[0] for i in list(data.loci)] coord = [re.split(':()',i)[-1] for i in list(data.loci)] start = [i.split('(')[0].split('-')[0] for i in coord] end = [i.split('(')[0].split('-')[1] for i in coord] strand = [i[-2] for i in coord] data = data.drop(columns=['loci']) # chrom = [i.split(':')[0] for i in list(data.loci)] # start = [re.split(':|-',i)[1] for i in list(data.loci)] # end = [re.split(":|-",i)[2] for i in list(data.loci)] # clean_end = [i[:-3] for i in end] # strand = [i[-2] for i in end] data['chrom'] = chrom data['start'] = start data['end'] = end data['strand'] = strand cols = data.columns.tolist() cols = cols[-4:]+cols[:-4] data = data[cols] output_act = act_table.split('.txt')[0]+'.bed' data.to_csv(output_act,sep='\t',index = False) ############################################################## def align_seqs_scores_1hot(seq_vecs, seq_scores, sort=True): if sort: seq_headers = sorted(seq_vecs.keys()) else: seq_headers = seq_vecs.keys() # construct lists of vectors train_scores = [] train_seqs = [] for header in seq_headers: train_seqs.append(seq_vecs[header]) train_scores.append(seq_scores[header]) # stack into matrices train_seqs = np.vstack(train_seqs) train_scores = np.vstack(train_scores) return train_seqs, train_scores if __name__ == '__main__': main()

28.854839

66

0.567915

260

1,789

3.757692

0.296154

0.040942

0.061412

0.051177

0.188332

0.174002

0.126919

0.116684

0

0.013768

0.228619

1,789

61

67

29.327869

0.694203

0.196758

0

0.047619

0

1

0.052632

false

0

0.078947

0

0.157895

0

null

0

null

0

1

0

6be8cef9f2811735b313ed8611fa3362dad56bc1

2,072

py

Python

aiserver/backup/simpleserver2.py

hirasaki1985/Oreilly_deepLearning

378b60ccec67dc616669fcd65ad14c7eddae6767

[ "MIT" ]

null

aiserver/backup/simpleserver2.py

hirasaki1985/Oreilly_deepLearning

378b60ccec67dc616669fcd65ad14c7eddae6767

[ "MIT" ]

null

aiserver/backup/simpleserver2.py

hirasaki1985/Oreilly_deepLearning

378b60ccec67dc616669fcd65ad14c7eddae6767

[ "MIT" ]

null

import sys, socket import json import cgi try: from StringIO import StringIO except ImportError: from io import StringIO import numpy as np from http.server import BaseHTTPRequestHandler, HTTPServer from modules.controller import Controller # setting host = '' port = 8000 class MyHandler(BaseHTTPRequestHandler): def do_POST(self): print("simpleserver do_POST exec()") if self.path.endswith('favicon.ico'): return; self.controller = Controller() # request form = self.getRequestData() print(type(form)) # logic #logicResult = "" logicResult = self.controller.webLogic(form) # make result result = self.makeResponseData(logicResult) # send self.sendResponse(result) return def getRequestData(self): # POST されたフォームデータを解析する form = cgi.FieldStorage( fp=self.rfile, headers=self.headers, environ={'REQUEST_METHOD':'POST', 'CONTENT_TYPE':'png', }) print(form) #image = {"test":"requestData"} return form def makeResponseData(self, result): print("### simpleserver makeResponseData exec") #result = {"test":"responseData"} print(result) print(type(result)) return result def sendResponse(self, result): print("### simpleserver sendResponse exec") self.send_response(200) self.send_header('Content-type', 'text/json') self.send_header('Access-Control-Allow-Origin', 'http://deeplearning.local.com') self.end_headers() #self.wfile.flush() self.wfile.write(str(result).encode('UTF-8')) self.wfile.close() return try: server = HTTPServer((host, port), MyHandler) server.serve_forever() except KeyboardInterrupt: print ('^C received, shutting down the web server') server.socket.close()

25.9

89

0.586873

200

2,072

6.035

0.465

0.042254

0.024855

0.044739

0

0.005587

0.30888

2,072

79

90

26.227848

0.837291

0.074807

0

0.075472

0

0.145594

0.014778

0

1

0.075472

false

0

0.169811

0

0.339623

0.150943

0

null

0

null

0

1

0

6bedb7df070733efc849ec3f70b009e7c5d82ea3

1,214

py

Python

get_machine_id.py

Server-Factory/Parallels-Utils

9b5c724b59832abf0506c5f632b0122573e71cd7

[ "Apache-2.0" ]

1

2021-01-01T23:24:31.000Z

get_machine_id.py

Server-Factory/Parallels-Utils

9b5c724b59832abf0506c5f632b0122573e71cd7

[ "Apache-2.0" ]

null

get_machine_id.py

Server-Factory/Parallels-Utils

9b5c724b59832abf0506c5f632b0122573e71cd7

[ "Apache-2.0" ]

null

#!/usr/bin/python import subprocess import sys def main(): if len(sys.argv) > 1: machines = dict() image = sys.argv[1] row_home = "Home:" row_id_open = "{" row_id_close = "}" output = subprocess.check_output(['prlctl', 'list', '-a', '-i']) items = str(output).split("ID:") for item in items: if row_home in item: home = "" machine_id = "" rows = item.strip().split('\\n') for row in rows: if row_id_open in row.strip() and row_id_close in row.strip(): machine_id = row.replace(row_id_open, "").replace(row_id_close, "").strip() if row_home in row: home = row.replace(row_home, "").strip() machines[home] = machine_id for machine_id in machines: if machine_id.startswith(image): machine = machines[machine_id] print(machine) sys.exit(0) print("Unknown_ID") sys.exit(1) else: print("No image path provided") sys.exit(1) if __name__ == "__main__": main()

28.904762

99

0.490939

142

1,214

3.971831

0.338028

0.053191

0.047872

0.039007

0

0.006684

0.383855

1,214

41

100

29.609756

0.747326

0.01318

0

0.058824

0

0.055973

0

1

0.029412

false

0

0.058824

0

0.088235

0

null

0

null

0

1

0

6bf3a11f4eaf5ef256dd41b159fcdf1ed04aaca8

12,158

py

Python

transform/preprocess/student_preprocess.py

WillianFuks/papis19

479c5460218c8f02716dbd5c2b0b9121a4328ab0

[ "Apache-2.0" ]

4

2019-06-24T13:20:22.000Z

2020-11-12T01:19:02.000Z

transform/preprocess/student_preprocess.py

WillianFuks/papis19

479c5460218c8f02716dbd5c2b0b9121a4328ab0

[ "Apache-2.0" ]

7

2019-12-16T21:55:20.000Z

2022-02-10T00:16:54.000Z

transform/preprocess/student_preprocess.py

WillianFuks/papis19

479c5460218c8f02716dbd5c2b0b9121a4328ab0

[ "Apache-2.0" ]

8

2019-06-24T12:27:51.000Z

2021-04-20T18:33:24.000Z

# Copyright 2019 Willian Fuks # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import, division, print_function import os import sys import argparse import six import tensorflow as tf import tensorflow_transform as tft import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions import tensorflow_transform.beam.impl as tft_beam from tensorflow_transform.tf_metadata import dataset_metadata, dataset_schema from tensorflow_transform.beam import impl as beam_impl from tensorflow_transform.coders import example_proto_coder from tensorflow_transform.beam.tft_beam_io import transform_fn_io import ast import six import preprocess.metadata as metadata import tempfile if not six.PY2: sys.exit("ERROR: Must use Python2.7") def build_bq_query(filename, project_id, init_date, end_date): query = open(filename).read().format(project_id=project_id, init_date=init_date, end_date=end_date) return query def build_pipeline_options(args): """ Apache Beam Pipelines must receive a set of options for setting how the engine should run. Args ---- args: argparse.Namespace Returns ------- pipeline_options: defines how to run beam job. """ options = {} options['runner'] = args.runner if args.temp_location: options['temp_location'] = args.temp_location if args.project: options['project'] = args.project if args.staging_location: options['staging_location'] = args.staging_location if args.job_name: options['job_name'] = args.job_name if args.max_num_workers: options['max_num_workers'] = args.max_num_workers if args.machine_type: options['machine_type'] = args.machine_type options.update({'save_main_session': True}) options.update({'setup_file': './setup.py'}) pipeline_options = PipelineOptions(**options) return pipeline_options class FlattenInteractionsFn(beam.DoFn): def process(self, element): """ flattens table """ for hit in element[1]: yield {'customer_id': element[0], 'sku': hit['sku'], 'action': hit['action']} def preprocess_fn(dictrow): return { 'customer_id': tft.string_to_int(dictrow['customer_id'], vocab_filename='customers_mapping'), 'sku': tft.string_to_int(dictrow['sku'], vocab_filename='skus_mapping'), 'action': dictrow['action'] } def aggregate_customers_sessions(sessions): """ Receives as input what products customers interacted with and returns their final aggregation. Args ---- sessions: list of list of dicts. List where each element is a list of dict of type: [{'action': '', 'sku': ''}] Returns ------- results: list of dicts Each resulting dict is aggregated on the sku and action level (repeating clauses are filtered out). """ result = [] for session in sessions: for hit in session: result.append(hit) return [dict(t) for t in {tuple(d.items()) for d in result}] def build_final_results(row): """ row = (customer_id, [{sku:, action}, {sku:, action}]) """ skus_list = [e['sku'] for e in row[1]] actions_list = [e['action'] for e in row[1]] return { 'customer_id': row[0], 'skus_list': skus_list, 'actions_list': actions_list } def build_test_results(row): """ ('customer2', {'test': [{'skus_list': [1, 1], 'actions_list': ['AddedToBasket', 'Browsed'], 'customer_id': 'customer2'}], 'train': [{'skus_list': [1, 1], 'actions_list': ['AddedToBasket', 'Browsed'], 'customer_id': 'customer2'}]}) """ result = {} result['customer_id'] = row[0] inner_dicts = row[1] # customers that had empty interactions after filtering out test dataset. if not inner_dicts['test']: return # customers that were not present in training data. if not inner_dicts['train']: return test_dict = inner_dicts['test'][0] result['skus_list'] = test_dict['skus_list'] result['actions_list'] = test_dict['actions_list'] train_dict = inner_dicts['train'][0] result['trained_skus_list'] = train_dict['skus_list'] result['trained_actions_list'] = train_dict['actions_list'] return result def read_input_data(args, pipeline, flag): """ Reads train and test pipelines. args: input args. pipeline: input pipeline where all transformations will take place. flag: either train or test. """ if args.input_sql: train_query = build_bq_query(args.input_sql, args.project, args.train_init_date, args.train_end_date) test_query = build_bq_query(args.input_sql, args.project, args.test_init_date, args.test_end_date) data = ( pipeline | '{} read'.format(flag) >> beam.io.Read(beam.io.BigQuerySource( query=train_query if flag == 'train' else test_query, use_standard_sql=True) ) ) else: data = ( pipeline | '{} read'.format(flag) >> beam.io.ReadFromText( args.input_train_data if flag == 'train' else args.input_test_data ) | '{} to json'.format(flag) >> beam.Map(lambda x: ast.literal_eval(x)) ) data = ( data | '{} filter empty hits'.format(flag) >> beam.Filter(lambda x: x['hits']) | '{} prepare customer grouping'.format(flag) >> beam.Map(lambda x: ( x['customer_id'], [{'action': e['action'], 'sku': e['productSku']} for e in x['hits'] if e['action'] in ['Browsed', 'AddedToBasket']]) ) | '{} group customers'.format(flag) >> beam.GroupByKey() | '{} aggregate customers sessions'.format(flag) >> beam.Map(lambda x: ( x[0], aggregate_customers_sessions(x[1])) ) | '{} flatten'.format(flag) >> beam.ParDo(FlattenInteractionsFn()) ) return data def write_total_distinct_keys_to_file(data, filename, key): """ Counts how many distinct items of "key" is present in data. Key here is either sku or customer_id. Args ---- data: pcollection. filename: where to write results to. key: on which value to count for. """ _ = ( data | 'get {}'.format(key) >> beam.Map(lambda x: x[key]) | 'group {}'.format(key) >> beam.RemoveDuplicates() | 'count {}'.format(key) >> beam.combiners.Count.Globally() | 'write {}'.format(key) >> beam.io.WriteToText(filename) ) def write_tfrecords(data, schema, filename, name): """ Converts input pcollection into a file of tfrecords following schema. Args ---- data: pcollection. schema: dataset_schema from tensorflow transform. name: str to identify operations. """ _ = ( data | '{} tfrecords write'.format(name) >> beam.io.tfrecordio.WriteToTFRecord( filename, coder=example_proto_coder.ExampleProtoCoder(dataset_schema.Schema(schema))) ) def aggregate_transformed_data(transformed_data, flag): """ One of the final steps into our pipelining transformations where data that has been transformed (in our case, skus went from string names to integer indices) is aggregated on the user level. transformed_data: pcollection. flag: identifies train or test Returns ------- transformed_data aggregated on user level. """ if flag == 'test': transformed_data = ( transformed_data | 'test filter out invalid skus' >> beam.Filter(lambda x: x['sku'] != -1) ) transformed_agg_data = ( transformed_data | '{} prepare grouping'.format(flag) >> beam.Map(lambda x: ( x['customer_id'], {'sku': x['sku'], 'action': x['action']}) ) | '{} transformed agg group'.format(flag) >> beam.GroupByKey() | '{} final results'.format(flag) >> beam.Map(lambda x: build_final_results(x)) ) return transformed_agg_data def aggregate_final_test_data(train_data, test_data): """ Joins train dataset with test so that only customers that we can make recommendations are present in final dataset. Remember that, in order to make them, we need to know a priori what customers interacted with. That's why we join the train data so we know customers preferences when we need to interact with them with our system. """ data = ( { 'train': train_data | 'train prepare customer key' >> beam.Map(lambda x: ( x['customer_id'], x)), 'test': test_data | 'test prepare customer key' >> beam.Map(lambda x: ( x['customer_id'], x)) } | 'cogroup' >> beam.CoGroupByKey() | 'build final rows' >> beam.Map(build_test_results) | 'filter customers out of test' >> beam.Filter(lambda x: x) ) return data def run_tft_pipeline(args): """ This is where all the data we have available in our database is processed and transformed into Tensorflow tfrecords for later training and testing. The code runs in distributed manner automatically in the engine choosen by the `runner` argument in input. """ pipeline_options = build_pipeline_options(args) temp_tft_folder = ( tempfile.mkdtemp(dir='/tmp/') if not args.tft_temp else args.tft_temp ) tft_transform_folder = ( tempfile.mkdtemp(dir='/tmp/') if not args.tft_transform else args.tft_transform ) with beam.Pipeline(options=pipeline_options) as pipeline: with beam_impl.Context(temp_dir=temp_tft_folder): train_data = read_input_data(args, pipeline, 'train') write_total_distinct_keys_to_file(train_data, args.nitems_filename, 'sku') train_dataset = (train_data, metadata.RAW_DATA_METADATA) (train_data, transformed_train_metadata), transform_fn = ( train_dataset | beam_impl.AnalyzeAndTransformDataset(preprocess_fn) ) _ = ( transform_fn | 'WriteTransformFn' >> transform_fn_io.WriteTransformFn(tft_transform_folder) ) train_data = aggregate_transformed_data( train_data, 'train' ) write_tfrecords(train_data, metadata.OUTPUT_TRAIN_SCHEMA, args.output_train_filename, 'output train') test_data = read_input_data(args, pipeline, 'test') test_dataset = (test_data, metadata.RAW_DATA_METADATA) (test_data, _) = ( (test_dataset, transform_fn) | beam_impl.TransformDataset()) test_data = aggregate_transformed_data( test_data, 'test' ) test_data = aggregate_final_test_data( train_data, test_data ) write_tfrecords(test_data, metadata.OUTPUT_TEST_SCHEMA, args.output_test_filename, 'output test') def main(): args = parse_args() run_tft_pipeline(args) if __name__ == '__main__': main()

31.661458

89

0.618358

1,450

12,158

4.991034

0.230345

0.017963

0.02128

0.015476

0.148404

0.11151

0.086638

0.07434

0.063562

0.052784

0

0.003408

0.276032

12,158

383

90

31.744125

0.818791

0.246833

0

0.122642

0

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0

1

0.066038

false

0

0.084906

0.004717

0.207547

0.004717

0

null

0

null

0

1

0

6bf66439de1fb3ae4352db93cc562648f32d838f

751

py

Python

leetcode/p690.py

mythnc/lab

9f69482a063e3cfce2ce8832c2ef1425658c31b9

[ "MIT" ]

null

leetcode/p690.py

mythnc/lab

9f69482a063e3cfce2ce8832c2ef1425658c31b9

[ "MIT" ]

null

leetcode/p690.py

mythnc/lab

9f69482a063e3cfce2ce8832c2ef1425658c31b9

[ "MIT" ]

null

# https://leetcode.com/problems/employee-importance/ """ # Definition for Employee. class Employee: def __init__(self, id: int, importance: int, subordinates: List[int]): self.id = id self.importance = importance self.subordinates = subordinates """ class Solution: def getImportance(self, employees: List['Employee'], id: int) -> int: table = {} for e in employees: table[e.id] = e result = table[id].importance q = [] q.append(table[id].subordinates) while len(q) > 0: ids = q.pop() for id_ in ids: result += table[id_].importance q.append(table[id_].subordinates) return result

28.884615

74

0.559254

82

751

5.036585

0.378049

0.067797

0.062954

0.11138

0.239709

0

0.001961

0.320905

751

25

75

30.04

0.807843

0.356858

0

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0

1

0.071429

false

0

0.214286

0

0.428571

0

null

0

null

0

1

0

6bf727625ea059c4f0a7f91f758f96269dcdd254

3,916

py

Python

python/ranking/examples/fess/train_model.py

codelibs/logana

48b475e9fd5224821bfba7d41e755d8d64806651

[ "Apache-2.0" ]

2

2020-09-30T12:42:28.000Z

2020-11-04T01:34:20.000Z

python/ranking/examples/fess/train_model.py

codelibs/logana

48b475e9fd5224821bfba7d41e755d8d64806651

[ "Apache-2.0" ]

null

python/ranking/examples/fess/train_model.py

codelibs/logana

48b475e9fd5224821bfba7d41e755d8d64806651

[ "Apache-2.0" ]

null

import dataclasses import datetime import gzip import json import logging import os from typing import Any, Dict import numpy as np import tensorflow as tf from absl import flags from loganary.ranking.common import NumpyJsonEncoder, setup_logging, setup_seed from loganary.ranking.model import ( RankingModel, RankingModelConfig, RankingModelEmbeddingField, RankingModelField, ) flags.DEFINE_string("train_path", None, "Path of .tfrecords file for training.") flags.DEFINE_string("eval_path", None, "Path of .tfrecords file for evaluation.") flags.DEFINE_string("keyword_path", None, "Path of vocabulary file for keyword field.") flags.DEFINE_string("title_path", None, "Path of vocabulary file for title field.") flags.DEFINE_string("model_path", None, "Path of trained model files.") flags.DEFINE_integer("num_train_steps", 15000, "The number of train steps.") flags.DEFINE_list("hidden_layer_dims", ["64", "32", "16"], "Sizes for hidden layers.") flags.DEFINE_integer( "keyword_embedding_dim", 20, "Dimention of an embedding for keyword field." ) flags.DEFINE_integer( "title_embedding_dim", 20, "Dimention of an embedding for title field." ) flags.DEFINE_integer("batch_size", 32, "Batch size.") flags.DEFINE_integer("list_size", 100, "List size.") flags.DEFINE_float("learning_rate", 0.05, "Learning rate.") flags.DEFINE_integer("group_size", 10, "Group size.") flags.DEFINE_float("dropout_rate", 0.8, "Dropout rate.") flags.DEFINE_bool("verbose", False, "Set a logging level as debug.") FLAGS = flags.FLAGS logger = logging.getLogger(__name__) def main(_) -> None: setup_seed() setup_logging(FLAGS.verbose) now_str = datetime.datetime.now().strftime("%Y%m%d%H%M") model_path: str = f"{FLAGS.model_path}/{now_str}" config: RankingModelConfig = RankingModelConfig( model_path=model_path, train_path=FLAGS.train_path, eval_path=FLAGS.eval_path, context_fields=[ RankingModelEmbeddingField( name="keyword", vocabulary_file=FLAGS.keyword_path, dimension=FLAGS.keyword_embedding_dim, ), ], example_fields=[ RankingModelEmbeddingField( name="title", vocabulary_file=FLAGS.title_path, dimension=FLAGS.title_embedding_dim, ), ], label_field=RankingModelField( name="relevance", column_type="numeric", default_value=-1, ), num_train_steps=FLAGS.num_train_steps, hidden_layer_dims=FLAGS.hidden_layer_dims, batch_size=FLAGS.batch_size, list_size=FLAGS.list_size, learning_rate=FLAGS.learning_rate, group_size=FLAGS.group_size, dropout_rate=FLAGS.dropout_rate, ) logger.info(f"Config: {config}") model: RankingModel = RankingModel(config) result = model.train() logger.info(f"Result: {result}") export_model_path: str = model.save_model() saved_model_path: str = f"{model_path}/saved_model" os.rename(export_model_path, saved_model_path) logger.info(f"Output Model Path: {saved_model_path}") with gzip.open(f"{model_path}/result.json.gz", mode="wt", encoding="utf-8") as f: config_dict: Dict[str, Any] = dataclasses.asdict(config) del config_dict["eval_metric"] f.write( json.dumps( { "config": config_dict, "result": result, }, ensure_ascii=False, cls=NumpyJsonEncoder, ) ) if __name__ == "__main__": flags.mark_flag_as_required("train_path") flags.mark_flag_as_required("eval_path") flags.mark_flag_as_required("keyword_path") flags.mark_flag_as_required("title_path") flags.mark_flag_as_required("model_path") tf.compat.v1.app.run()

34.350877

87

0.671348

484

3,916

5.163223

0.293388

0.066026

0.043217

0.028011

0.186074

0.123249

0.080032

0.031212

0

0.009785

0.217058

3,916

113

88

34.654867

0.805284

0

0.088235

0

0.223442

0.025536

0

1

0.009804

false

0

0.117647

0

0.127451

0

null

0

null

0

1

0

d40116f3963560e5bc86c79d514d9d85b3020138

22,630

py

Python

build-fortress.py

DFIRmadness/infosec-fortress

cc20c5c5ecf5194fdd270e7accdf927b71ed2952

[ "MIT" ]

33

2021-06-22T01:42:06.000Z

2022-03-27T14:41:44.000Z

build-fortress.py

ED-209-MK7/infosec-fortress

cc20c5c5ecf5194fdd270e7accdf927b71ed2952

[ "MIT" ]

1

2021-06-24T09:10:03.000Z

2021-06-28T13:25:59.000Z

build-fortress.py

ED-209-MK7/infosec-fortress

cc20c5c5ecf5194fdd270e7accdf927b71ed2952

[ "MIT" ]

5

2021-06-23T08:04:22.000Z

2022-03-27T14:41:45.000Z

#!/bin/python3 ''' Title: build-fortress.py Purpose: Build the infosec-fortress Author: James Smith (DFIRmadness) Contributors: Check the github page. Notes: Beta Version: 0.5 Usage: ./build-fortress.py Functions: + apt update + dist upgrade + install base packages + create /opt/infosec-fortress + start log + install starter packages (min. pkgs to let script run) + install the REMnux Distribution + install SIFT + install base security packages + install Metasploit Framework + install wordlists + install and update exploitdb (searchsploit) + log2Timeline + elasticsearch containers + powershell Core (turns out its part of REMnux) + install impacket + install enum4linux + enum4linux https://github.com/cddmp/enum4linux-ng + display message about updating ZAP and Burp after reboot ''' # Globals PKG_MGR = 'apt' FORTRESS_DIR = '/opt/infosec-fortress/' BUILD_LOG = 'build-fortress.log' LOG = FORTRESS_DIR + BUILD_LOG # Minimal Package list to get started starterPackagesList = [ 'net-tools', 'curl', 'git' ] # List of packages to have APT install. Change if you want. You break it you buy it. aptPackageList = [ 'tmux', 'torbrowser-launcher', 'nmap', 'smbclient', 'locate', 'radare2-cutter', 'snort', 'dirb', 'gobuster', 'medusa', 'masscan', 'whois', 'libjenkins-htmlunit-core-js-java', 'autopsy', 'hashcat', 'kismet', 'kismet-plugins', 'airgraph-ng', 'wifite', 'dnsenum', 'dnsmap', 'ettercap-common', 'ettercap-graphical', 'netdiscover', 'sqsh', 'install nfs-common' ] # List of packages to have SNAP install. Change if you want. You break it you buy it. snapPackageList = [ 'chromium', 'sqlmap', 'john-the-ripper' ] # Snaps that need --classic # Avoid these. It's better to scrape a git for the latest and install. Zaproxy is a great example. snapClassicPackageList =[ #'zaproxy' ] ######################################################## # Colors GREEN = '\033[32m' RED = '\033[31m' YELLOW = '\033[33m' NOCOLOR = '\033[m' from datetime import datetime from getpass import getpass from hashlib import sha1 from os import geteuid,path,makedirs from os.path import expanduser from subprocess import run from urllib.request import urlopen from requests import get from re import search # Check that the user is root def checkIfRoot(): if geteuid() != 0: print(RED + '[!] You need sudo/root permissions to run this... exiting.' + NOCOLOR) exit(0) # Check for internet connection def checkForInternet(): try: check = urlopen('https://www.google.com', timeout=3.0) print(GREEN +'[+] Internet connection looks good!' + NOCOLOR) except: print(RED + '[-] Internet connection looks down. You will need internet for this to run (most likely). Fix and try again.' + NOCOLOR) exit(1) def initNotice(): print('[!] This script requires user input once or twice.\n\ [!] It is not completely "Set and Forget".') nullInput = input('Hit Enter.') # Get starting Disk Room def freeSpaceStart(): # Needs Regex Impovement with RE Search. Non Gig sized systems will break this. global FREE_SPACE_START_INT freeSpaceStart = run(['df -h /'],shell=True,capture_output=True).stdout.decode().split('G')[2].strip() writeToLog('[i] Gigs of Free Space on / at the Start of the build: ' + freeSpaceStart + 'G') FREE_SPACE_START_INT = float(freeSpaceStart) return(FREE_SPACE_START_INT) def freeSpaceEnd(): # Needs Regex Impovement with RE Search. Non Gig sized systems will break this. freeSpaceEnd = run(['df -h /'],shell=True,capture_output=True).stdout.decode().split('G')[2].strip() writeToLog('[i] Gigs of Free Space on / at the Start of the build: ' + freeSpaceEnd + 'G') freeSpaceEndInt = float(freeSpaceEnd) spaceUsed = FREE_SPACE_START_INT - freeSpaceEndInt writeToLog('[i] Gigs of Space used for InfoSec-Fortress Buildout: ' + str(spaceUsed) + 'G') # Check/Inform about for unattended upgrade def informAboutUnattendedUpgade(): print('[!][!][!][!][!][!][!][!]\nUnattended Upgades firing while this script is running will break it.\ \nKill or complete the upgrades if you recently booted or rebooted. Then continue.\ \nIT MAY REQUIRE A REBOOT! If so, kill this script. Reboot. Run the updates. Run this script again.') nullInput = input('Hit any key to continue.') def createFortressDir(FORTRESS_DIR): print('[*] Creating InfoSec Fortress Dir at:',FORTRESS_DIR) try: makedirs(FORTRESS_DIR, exist_ok=True) except FileExistsError: print('[i] ' + FORTRESS_DIR + ' already exists. Continuing.') except Exception as e: print('[-] Error creating the ' + FORTRESS_DIR + '. Error ' + str(e)) def startLogFile(): try: now = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") if not path.isfile(LOG): with open(LOG, 'a') as log: log.write(now + " - Log Started.\n") return('Succeeded') else: with open(LOG, 'a') as log: log.write(now + " - Log Started. Strange, the log file appears to exist already? Continuing anyways.\n") return('Succeeded') except: return('Failed') # For now just simply exit here exit(1) def writeToLog(stringToLog): now = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") with open(LOG, 'a') as log: log.write(now + " - " + stringToLog + '\n') if '[+]' in stringToLog: print('\n' + GREEN + stringToLog + NOCOLOR + '\n----------------------------------------------------------\n') elif '[-]' in stringToLog: print('\n' + RED + stringToLog + NOCOLOR + '\n----------------------------------------------------------\n') elif '[i]' in stringToLog + NOCOLOR: print('\n' + YELLOW + stringToLog + NOCOLOR + '\n----------------------------------------------------------\n') else: print('\n' + stringToLog + '\n----------------------------------------------------------\n') def buildStarterPackageList(): listOfPackagesCommand = '' for package in starterPackagesList: listOfPackagesCommand = (listOfPackagesCommand + ' ' + package).strip() return(listOfPackagesCommand) def buildAptPackageList(): listOfPackagesCommand = '' for package in aptPackageList: listOfPackagesCommand = (listOfPackagesCommand + ' ' + package).strip() return(listOfPackagesCommand) def buildSnapPackageList(): listOfPackagesCommand = '' for package in snapPackageList: listOfPackagesCommand = (listOfPackagesCommand + ' ' + package).strip() return(listOfPackagesCommand) def buildSnapClassicPackagesList(): listOfPackagesCommand = '' for package in snapClassicPackageList: listOfPackagesCommand = (listOfPackagesCommand + ' ' + package).strip() return(listOfPackagesCommand) # apt update def updateOS(): #writeToLog('[+] Beginning OS updates...') try: run(['/usr/bin/apt','update']) except Exception as e: writeToLog('[-] APT Updating failed. Fix and try again. Error:',str(e)) exit(1) try: run(['/usr/bin/apt','upgrade','-y']) except Exception as e: writeToLog('[-] APT Updating failed. Fix and try again. Error:',str(e)) exit(1) try: run(['/usr/bin/apt','dist-upgrade','-y']) except Exception as e: writeToLog('[-] APT Updating failed. Fix and try again. Error:',str(e)) exit(1) # Minimal packages def installStarterPackages(): starterPackages = buildStarterPackageList() writeToLog('[*] Attempting installation of the following starter packages: ' + starterPackages) try: run(['/usr/bin/apt install -y ' + starterPackages],shell=True) writeToLog('[+] Starter Packages installed.') except Exception as e: writeToLog('[-] Starter Packages installation failed:',str(e)) # the REMnux Distribution def installREMnux(): writeToLog('[+] Installing REMnux. This will take quite awhile. Verify the hash from the site later.') try: run(['/usr/bin/wget https://REMnux.org/remnux-cli'],shell=True) run(['/usr/bin/mv remnux-cli remnux'],shell=True) run(['/usr/bin/chmod +x remnux'],shell=True) run(['/usr/bin/mv remnux /usr/local/bin'],shell=True) run(['/usr/local/bin/remnux install --mode=addon'],shell=True) writeToLog('[+] REMnux Added On (downloaded and ran).') except Exception as e: writeToLog('[-] Something went wrong during the REMnux install. Error: ' + str(e)) # Install SIFT def installSIFTPackages(): writeToLog('[*] Finding latest SIFT Release.') try: latestLinkPage = get('https://github.com/sans-dfir/sift-cli/releases/latest').text.splitlines() latestSIFTBinLine = [match for match in latestLinkPage if "sift-cli-linux" in match][0].split('"')[1] latestSIFTBin = 'https://github.com/' + latestSIFTBinLine #latestSIFTBin = search('https:.*sift-cli-linux',latestSIFTBinLine)[0] writeToLog('[+] latest SIFT BIN: ' + latestSIFTBin) except Exception as e: writeToLog('[-] latest SIFT Bin not found. Error: ' + str(e)) return writeToLog('[*] Installing SIFT Packages.') try: run(['/usr/bin/curl -Lo /usr/local/bin/sift ' + latestSIFTBin],shell=True) run(['/usr/bin/chmod +x /usr/local/bin/sift'],shell=True) run(['/usr/local/bin/sift install --mode=packages-only'],shell=True) writeToLog('[+] SIFT Packages installed (downloaded and ran).') except Exception as e: writeToLog('[-] Installation of SIFT Packages had an error. Error: '+str(e)) # install base packages def installAPTandSNAPPackages(): print('[i] If Wireshark asks - say YES non-super users can capture packets.\n\n\ [i] When snort asks about a monitoring interface enter lo.\n\ [i] Setting the interface to "lo" (no quotes) sets it for local use.\n\ [i] Set any private network for the "home" network.\n\n\ [i] KISMET - Say YES to the sticky bit. Add your username to the Kismet Goup at the prompt.') nullInput = input('Hit Enter.') aptPackages = buildAptPackageList() snapPackages = buildSnapPackageList() snapClassicPackages = buildSnapClassicPackagesList() writeToLog('[*] Attempting installation of the following ATP packages: ' + aptPackages) try: run(['/usr/bin/apt install -y ' + aptPackages],shell=True) writeToLog('[+] APT Packages installed.') except Exception as e: writeToLog('[-] APT Packages installation failed:',str(e)) writeToLog('[*] Attempting installation of the following Snap Packages: ' + snapPackages) try: run(['/usr/bin/snap install ' + snapPackages],shell=True) writeToLog('[+] Snap Packages installed.') except Exception as e: writeToLog('[-] Snap packages installation failed:',str(e)) if len(snapClassicPackages) == 0: writeToLog('[*] No snap classics to install.') return writeToLog('[*] Attempting installation of the following Snap Classic Packages: ' + snapClassicPackages) for package in snapClassicPackageList: try: run(['/usr/bin/snap install --classic ' + package],shell=True) writeToLog('[+] Snap Classic ' + package + ' installed.') except Exception as e: writeToLog('[-] Snap packages ' + package + ' failed:',str(e)) # Swap Netcats # Change out netcat-bsd for netcat-traditional def swapNetcat(): writeToLog('[*] Attempting to trade out netcat-bsd for netcat-traditional') try: run(['/usr/bin/apt purge -y netcat-openbsd'],shell=True) run(['/usr/bin/apt install -y netcat-traditional'],shell=True) writeToLog('[+] netcat-traditional installed.') except Exception as e: writeToLog('[-] Installation of netcat-traditional failed. Error: '+str(e)) # Metasploit Framework def installMSF(): writeToLog('[+] Installing Metasploit Framework.') try: run(['/usr/bin/curl https://raw.githubusercontent.com/rapid7/metasploit-omnibus/master/config/templates/metasploit-framework-wrappers/msfupdate.erb > msfinstall'],shell=True) run(['/usr/bin/chmod 755 msfinstall'],shell=True) run(['./msfinstall'],shell=True) writeToLog('[+] MSF Installed Successfully.') except Exception as e: writeToLog('[-] Something went wrong during the MSF install. Error: ' + str(e)) # Install wordlists # Git clone the default wordlists # Add Rockyou2021 # Add fuzzing list for burp/SQLI (xplatform.txt) def installWordlists(): # Error handling using git in this way (with run) sucks. writeToLog('[*] Installing Wordlists to /usr/share/wordlists') makedirs('/usr/share/wordlists/', exist_ok=True) try: run(['/usr/bin/git clone https://github.com/3ndG4me/KaliLists.git /usr/share/wordlists/'],shell=True) run(['/usr/bin/rm /usr/share/wordlists/README.md'],shell=True) run(['/usr/bin/gunzip /usr/share/wordlists/rockyou.txt.gz'],shell=True) writeToLog('[+] Kali default wordlists added and unpacked.') except Exception as e: writeToLog('[-] There was an error installing Kali default wordlists. Error: ' + str(e)) try: run(['/usr/bin/wget https://raw.githubusercontent.com/fuzzdb-project/fuzzdb/master/attack/sql-injection/detect/xplatform.txt \ -O /usr/share/wordlists/xplatform.txt'],shell=True) writeToLog('[+] Xplatform.txt SQLI Validation list added.') except Exception as e: writeToLog('[-] There was an error adding xplatform.txt. Error: ' + str(e)) #Install exploit-db def installExploitDb(): writeToLog('[*] Installing ExploitDB.') try: run(['/usr/bin/git clone https://github.com/offensive-security/exploitdb.git /opt/exploitdb'],shell=True) run(['/usr/bin/ln -sf /opt/exploitdb/searchsploit /usr/local/bin/searchsploit'],shell=True) writeToLog('[+] Exploit DB Added.') except Exception as e: writeToLog('[-] There was an error installing ExploitDB. Error: ' + str(e)) try: writeToLog('[*] Updating ExploitDB...') run(['/usr/local/bin/searchsploit -u'],shell=True) writeToLog('[+] Exploit DB Updated.') except Exception as e: writeToLog('[-] There was an error updating ExploitDB. Error: ' + str(e)) # elasticsearch containers? # powershell Core # REMnux already installs it. #def installPosh(): # writeToLog('[*] Installing Powershell.') # try: # run(['/usr/bin/apt-get update\ # && /usr/bin/apt-get install -y wget apt-transport-https software-properties-common\ # && /usr/bin/wget -q https://packages.microsoft.com/config/ubuntu/20.04/packages-microsoft-prod.deb\ # && /usr/bin/dpkg -i packages-microsoft-prod.deb\ # && /usr/bin/apt-get update\ # && /usr/bin/add-apt-repository universe\ # && /usr/bin/apt-get install -y powershell'],shell=True) # writeToLog('[+] Powershell installed.') # except Exception as e: # writeToLog('[-] There was an error installing Powershell. Error: ' + str(e)) # Install Impacket def installImpacket(): writeToLog('[*] Installing Impacket.') try: run(['/usr/bin/git clone https://github.com/SecureAuthCorp/impacket.git /opt/impacket'],shell=True) run(['/usr/bin/python3 -m pip install /opt/impacket/.'],shell=True) # It seems that it takes running this twice to get it to complete run(['/usr/bin/python3 -m pip install /opt/impacket/.'],shell=True) writeToLog('[+] Impacket Installed.') except Exception as e: writeToLog('[-] There was an error installing Impacket. Error: ' + str(e)) # enum4Linux def installEnum(): writeToLog('[*] Installing Enum4Linux.') try: run(['/usr/bin/git clone https://github.com/CiscoCXSecurity/enum4linux.git /opt/enum4linux'],shell=True) run(['/usr/bin/ln -sf /opt/enum4linux/enum4linux.pl /usr/local/bin/enum4linux.pl'],shell=True) writeToLog('[+] Enum4Linux Installed.') except Exception as e: writeToLog('[-] There was an error installing Enum4Linux. Error: ' + str(e)) # enum4linux def installEnumNG(): writeToLog('[*] Installing Enum4Linux-ng.') try: run(['/usr/bin/git clone https://github.com/cddmp/enum4linux-ng /opt/enum4linux-ng'],shell=True) run(['/usr/bin/ln -sf /opt/enum4linux-ng/enum4linux-ng.py /usr/local/bin/enum4linux-ng.py'],shell=True) writeToLog('[+] Enum4Linux-ng Installed.') except Exception as e: writeToLog('[-] There was an error installing Enum4Linux-ng. Error: ' + str(e)) # Install WebShells def installWebShells(): writeToLog('[*] Installing Kali\'s Webshells') try: run(['/usr/bin/git clone https://gitlab.com/kalilinux/packages/webshells.git /usr/share/webshells'],shell=True) writeToLog('[+] Kali\'s WebShells Cloned to /usr/share/webshells') except Exception as e: writeToLog('[-] There was an error installing Enum4Linux. Error: ' + str(e)) # Install Windows Resources def installWindowsResources(): writeToLog('[*] Installing Kali\'s Windows Resources') try: run(['/usr/bin/git clone https://gitlab.com/kalilinux/packages/windows-binaries.git /usr/share/windows-resources'],shell=True) writeToLog('[+] Kali\'s Windows Resources Cloned to /usr/share/webshells') except Exception as e: writeToLog('[-] There was an error installing Enum4Linux. Error: ' + str(e)) # Install Bloodhound def installBloodhound(): writeToLog('[*] Finding latest Blood Hound Release.') try: latestLinkPage = get('https://github.com/BloodHoundAD/BloodHound/releases/latest').text.splitlines() latestBloodHoundZip = [match for match in latestLinkPage if "BloodHound-linux-x64.zip" in match][0].split('"')[1] writeToLog('[+] latest Blood Hound Zip at: ' + latestBloodHoundZip) except Exception as e: writeToLog('[-] latest Blood Hound Zip not found. Error: ' + str(e)) return writeToLog('[*] Installing Bloodhound...') try: run(['/usr/bin/curl -Lo /tmp/bloodhound.zip https://github.com' + latestBloodHoundZip],shell=True) run(['/usr/bin/unzip -o /tmp/bloodhound.zip -d /opt/'],shell=True) except Exception as e: writeToLog('[-] Bloodhound not installed. Error: ' + str(e)) # Find and install latest Zaproxy def installZaproxy(): writeToLog('[*] Finding latest Zaproxy Release.') try: latestLinkPage = get('https://github.com/zaproxy/zaproxy/releases/latest').text.splitlines() latestZapDeb = [match for match in latestLinkPage if "_all.deb" in match][0].split('"')[1] writeToLog('[+] latest Zaproxy Zip at: ' + latestZapDeb) except Exception as e: writeToLog('[-] latest Zaproxy Zip not found. Error: ' + str(e)) return writeToLog('[*] Installing Zaproxy...') try: run(['/usr/bin/curl -Lo /tmp/zaproxy.deb ' + latestZapDeb],shell=True) run(['/usr/bin/dpkg -i /tmp/zaproxy.deb'],shell=True) except Exception as e: writeToLog('[-] Zaproxy not installed. Error: ' + str(e)) def installZeek(): # instll Zeek writeToLog('[*] Installing Zeek...') try: run(['/usr/bin/echo \'deb http://download.opensuse.org/repositories/security:/zeek/xUbuntu_20.04/ /\' | sudo tee /etc/apt/sources.list.d/security:zeek.list'],shell=True) run(['/usr/bin/curl -fsSL https://download.opensuse.org/repositories/security:zeek/xUbuntu_20.04/Release.key | gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/security_zeek.gpg > /dev/null'],shell=True) run(['/usr/bin/apt update'],shell=True) run(['/usr/bin/apt -y install zeek'],shell=True) except Exception as e: writeToLog('[-] Zeek not installed. Error: ' + str(e)) # add /opt/zeek/bin to the path permanently try: writeToLog('[i] Writing Zeeks path to the current users bashrc. You may need to manually add: \'export PATH=$PATH:/opt/zeek/bin\' to yours.') run(['/usr/bin/echo "export PATH=$PATH:/opt/zeek/bin" >> ~/.bashrc'],shell=True) run(['export PATH=$PATH:/opt/zeek/bin'],shell=True) except Exception as e: writeToLog('[-] Zeek path not added. Error: ' + str(e)) # display log def displayLog(): print('[*] The following activities were logged:\n') with open(LOG,'r') as log: allLines = log.readlines() for line in allLines: print(line.strip()) # display fortress artwork def displayImage(): try: run(['/usr/bin/curl -Lo ' + FORTRESS_DIR + 'fortress.jpg https://dfirmadness.com/wp-content/uploads/2021/06/infosec-fortress-2500.jpg'],shell=True) run(['/usr/bin/eog ' + FORTRESS_DIR + 'fortress.jpg'],shell=True) run(['/usr/bin/rm ' + FORTRESS_DIR + 'fortress.jpg'],shell=True) except: return # display message about updating ZAP and Burp after reboot def giveUserNextSteps(): print(GREEN + '[+]' + '-----------------------------------------------------------------------------------' + NOCOLOR) print(GREEN + '[+]' + '------------------------ ! Script Complete ! --------------------------------------' + NOCOLOR) print('\n\n[!] REBOOT the system. After Reboot you will want to run Burp, Zap and Ghidra. Each will ask you to update.\ \n You should update these. If they have you download a .deb file you simple run ' + GREEN + 'dpkg -i foo.deb' + NOCOLOR + '.\ \n Don\'t forget to run: \'echo "export PATH=$PATH:/opt/zeek/bin" >> ~/.bashrc\' to add the Zeek bins to your user (non-root) path') nullInput = input('Hit Enter.') # Re-enable unattended upgrade #Only needed if auto kill of unattended upgrades is added def main(): checkIfRoot() checkForInternet() initNotice() informAboutUnattendedUpgade() createFortressDir(FORTRESS_DIR) startLogFile() freeSpaceStart() updateOS() installStarterPackages() installREMnux() installSIFTPackages() installAPTandSNAPPackages() swapNetcat() installMSF() installWordlists() installExploitDb() installImpacket() installEnum() installEnumNG() installWebShells() installWindowsResources() installBloodhound() installZaproxy() installZeek() freeSpaceEnd() displayLog() displayImage() giveUserNextSteps() exit(0) main() if __name__== "__main__": main()

40.996377

210

0.644057

2,677

22,630

5.428838

0.220022

0.021056

0.027868

0.035918

0.369573

0.328287

0.25824

0.1895

0.163628

0.121861

0

0.005682

0.198939

22,630

551

211

41.07078

0.796006

0.136677

0

0.26699

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0.376436

0.050796

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1

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false

0.002427

0.021845

0

0.116505

0.043689

0

null

0

null

0

1

0

d4051d0a2b00e9998b74b852734dd381524320f3

955

py

Python

substance/constants.py

philraj/substance

c68c8343e22fd2ac1e83b7567140c2a20f417984

[ "Apache-2.0" ]

null

substance/constants.py

philraj/substance

c68c8343e22fd2ac1e83b7567140c2a20f417984

[ "Apache-2.0" ]

null

substance/constants.py

philraj/substance

c68c8343e22fd2ac1e83b7567140c2a20f417984

[ "Apache-2.0" ]

null

class Constants(object): class ConstError(TypeError): pass def __init__(self, **kwargs): for name, value in list(kwargs.items()): super(Constants, self).__setattr__(name, value) def __setattr__(self, name, value): if name in self.__dist__: raise self.ConstError("Can't rebind const(%s)" % name) self.__dict__[name] = value def __delattr__(self, name): if name in self.__dict__: raise self.ConstError("Can't unbind const(%s)" % name) raise NameError(name) Tables = Constants( BOXES="boxes" ) DefaultEngineBox = 'turbulent/substance-box:1.0' EngineStates = Constants( RUNNING="running", STOPPED="stopped", SUSPENDED="suspended", UNKNOWN="unknown", INEXISTENT="inexistent" ) Syncher = Constants( UP=">>", DOWN="<<", BOTH="<>" ) Orchestrators = Constants( DOCKWRKR="dockwrkr", COMPOSE="docker-compose" )

20.76087

66

0.618848

102

955

5.519608

0.509804

0.063943

0.042629

0.081705

0

0.00277

0.243979

955

45

67

21.222222

0.777008

0

0.150943

0.028302

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1

0.088235

false

0.029412

0

0.147059

0

null

0

null

0

1

0

d4051f0da6a3085bed81c035499b45d737816f30

12,482

py

Python

scripts/identify_taxonomic_trees.py

AtilioA/wikidata-evaluation-based-on-ontologies

b726cc40a80312e92e7aa42fc24f1eee21bc40be

[ "Unlicense" ]

2

2020-12-06T21:57:36.000Z

2020-12-11T16:07:00.000Z

scripts/identify_taxonomic_trees.py

AtilioA/wikidata-evaluation-based-on-ontologies

b726cc40a80312e92e7aa42fc24f1eee21bc40be

[ "Unlicense" ]

null

scripts/identify_taxonomic_trees.py

AtilioA/wikidata-evaluation-based-on-ontologies

b726cc40a80312e92e7aa42fc24f1eee21bc40be

[ "Unlicense" ]

null

import time import json import sys from pathlib import Path from pprint import pprint import wikidata_utils from graphviz import Digraph NL = "\n" def find_subclasses_between(subclass, superclass): # Query Stardog for subclasses subclassesJSON = wikidata_utils.query_subclasses_stardog(superclass, subclass)[ "results" ]["bindings"] subclassesList = [] try: # Parse JSON for results subclassesList = [result["entity"]["value"] for result in subclassesJSON] # Look for QID in all the strings subclassesList = wikidata_utils.regex_match_QID(subclassesList) except: pass print(f"Subclasses between '{subclass}' and '{superclass}':\n{subclassesList}") # print(subclassLabels) try: # Remove superclass from the list (it is included by SPARQL) subclassesList.remove(superclass) except: pass # Return reversed list so we can use it immediately in the right order with graphviz return list(reversed(subclassesList)) def graph_from_superclasses_dict(treesDictFilename, **kwargs): # PROBLEM: Given a dictionary with entities, their superclasses and subclasses, create a "maximal" graph that displays the relation between entities dotsTime = int(time.time()) # Optional argument; if it exists, will include only entities from the ranking rankingEntities = kwargs.get("rankingEntities", None) useRandomColors = kwargs.get("useRandomColors", None) remainingEntities = set(rankingEntities) totalEntities = len(remainingEntities) with open(Path(treesDictFilename), "r+", encoding="utf8") as dictFile: entitiesDict = json.load(dictFile) # Filter out entities without any subclasses in the ranking # Entities of interest here are entities without superclasses or whose superclasses are themselves entitiesDict = dict( filter( lambda x: x[1]["subclasses"] != [] and (x[1]["superclasses"] == [] or [x[0]] == x[1]["superclasses"]), entitiesDict.items(), ) ) keepEntity = "1" keptDict = {} pprint(entitiesDict.keys()) while(len(keepEntity) > 0): if not keptDict: keepEntity = input("What entity to generate graphs for? [Enter] for All: ") else: keepEntity = input("What entity to generate graphs for? [Enter] to leave: ") if keepEntity: kept = entitiesDict.pop(keepEntity) keptDict[keepEntity] = kept else: break print(f"Kept {keepEntity}") if keptDict: entitiesDict = keptDict # Number of entities to be processed print(f"{len(entitiesDict)} superclasses") nodesDict = {} for entity in entitiesDict.items(): # Get label for each main entity entityLabel = wikidata_utils.get_entity_label(entity[0]) nSubclasses = len(entity[1]["subclasses"]) print(f"\nBuilding graph for {entity[0]} ({entityLabel}).") print(f"{entityLabel.capitalize()} has at least {nSubclasses} subclasses from the ranking.\n") # Create graph for each main entity nodesep = "0.1" ranksep = "0.5" if nSubclasses > 50: nodesep = "0.15" ranksep = "1" dot = Digraph( comment=entityLabel, strict=True, encoding="utf8", graph_attr={"nodesep": nodesep, "ranksep": ranksep, "rankdir": "BT"}, ) # Create a bigger node for each main entity dot.node(f"{entityLabel}\n{entity[0]}", fontsize="24") # Add entity QID to nodes' dict nodesDict[entity[0]] = True print( f"{totalEntities - len(remainingEntities)} entities (of {totalEntities}) from the ranking processed so far." ) for subclass in entity[1]["subclasses"]: # Get label for each subclass subclassLabel = wikidata_utils.get_entity_label(subclass) # If label is unavailable, use ID if subclassLabel != "Label unavailable": subclassNodeLabel = f"{subclassLabel}\n{subclass}" else: subclassNodeLabel = subclass print( f'Finding subclasses between "{subclassLabel}" and "{entityLabel}"...' ) # Get random color for nodes and edges argsColor = "#111111" if useRandomColors: argsColor = wikidata_utils.random_color_hex() edgeLabel = None if not nodesDict.get(subclass, False): # Create subclass node dot.node(f"{subclassLabel}\n{subclass}", color=argsColor) # Add subclass QID to nodes' dict nodesDict[subclass] = True # Query intermediary entities between "subclass" and "entity" (returns ordered list) subclassesBetween = find_subclasses_between(subclass, entity[0]) # Default styling for intermediary subclasses subclassNodeArgs = { "shape": "square", "color": "#777777", "fontsize": "10", "fontcolor": "#555555", } # remainingEntitiesLastIteration = {totalEntities - len(remainingEntities)} if rankingEntities: # Filter out subclasses that aren't from the ranking subclassesBetween = { subclass: True for subclass in subclassesBetween if subclass in rankingEntities } print(f"Subclasses between: {subclassesBetween}") # Use no particular styling instead subclassNodeArgs = {} # edgeLabel = "P279+" if subclassesBetween: # Get labels for each subclass in between subclassLabels = [ wikidata_utils.get_entity_label(subclass) for subclass in list(subclassesBetween) ] # Connect "main" subclass to its immediate superclass print( f"(First) Marking {subclassNodeLabel.split(NL)[0]} ({subclassNodeLabel.split(NL)[1]}) as subclass of {subclassLabels[-1]} ({list(subclassesBetween)[-1]})" ) dot.edge( subclassNodeLabel, f"{subclassLabels[-1]}\n{list(subclassesBetween)[-1]}", label=edgeLabel, color=argsColor, arrowhead="o", ) try: remainingEntities.remove(list(subclassesBetween)[-1]) except KeyError: pass for i, subclassBetween in enumerate(subclassesBetween): if not nodesDict.get(subclassBetween, False): # Create node for each subclass dot.node( f"{subclassLabels[i]}\n{subclassBetween}", **subclassNodeArgs, color=argsColor, ) # Add intermediary entity QID to nodes' dict nodesDict[subclassBetween] = True for i, subclassBetween in enumerate(list(subclassesBetween)[:-1]): # Connect each subclass to its immediate superclass # First, check if they should be connected for j, entityAbove in enumerate(list(subclassesBetween)[i:]): checkSubclass = list(subclassesBetween)[i] checkSubclassLabel = subclassLabels[i] if i == 0: checkSubclass = subclass checkSubclassLabel = subclassLabel isSubclass = wikidata_utils.query_subclass_stardog( entityAbove, checkSubclass, transitive=True )["results"]["bindings"][0]["isSubclass0"]["value"] isSubclass = isSubclass.lower() == "true" print( f" (For) Is {checkSubclass} subclass of {entityAbove}? {isSubclass}" ) if isSubclass: print( f" Marking {checkSubclassLabel} ({checkSubclass}) as subclass of {subclassLabels[i + j]} ({entityAbove})" ) dot.edge( f"{checkSubclassLabel}\n{checkSubclass}", f"{subclassLabels[i + j]}\n{entityAbove}", label=edgeLabel, color=argsColor, arrowhead="o", ) try: remainingEntities.remove(checkSubclass) except KeyError: pass try: remainingEntities.remove(entityAbove) except KeyError: pass # if totalEntities - len(remainingEntities) > remainingEntitiesLastIteration: print( f"{totalEntities - len(remainingEntities)} entities (of {totalEntities}) from the ranking processed so far." ) # Connect the topmost superclass to the main superclass, i.e., the entity print( f"(Last) Marking {subclassLabels[0]} as subclass of {entityLabel}" ) dot.edge( f"{subclassLabels[0]}\n{list(subclassesBetween)[0]}", f"{entityLabel}\n{entity[0]}", label=edgeLabel, color=argsColor, arrowhead="o", ) else: # If there are no subclasses in between, connect subclass and entity directly print( f"Joining {subclassNodeLabel.split(NL)[0]} ({subclassNodeLabel.split(NL)[1]}) and {entityLabel} ({entity[0]})" ) dot.edge( subclassNodeLabel, f"{entityLabel}\n{entity[0]}", label=edgeLabel, color=argsColor, arrowhead="o", ) try: remainingEntities.remove(subclass) except KeyError: pass # Not having graphviz properly installed might raise an exception try: if rankingEntities: u = dot.unflatten(stagger=5) # Break graphs into more lines u.render(f"output/dots/dots_{dotsTime}/AP1_{dot.comment}.gv") else: u = dot.unflatten(stagger=5) # Break graphs into more lines u.render( f"output/dots/dots_{dotsTime}/AP1_{dot.comment}_intermediary.gv" ) except: print("\nVerify your Graphviz installation or Digraph args!\n") pass try: remainingEntities.remove(entity[0]) except KeyError: pass print(remainingEntities) def get_ranking_entity_set(rankingFile): entityList = parse_ranking_file(rankingFile) return set(entityList) def parse_ranking_file(rankingFile): lines = rankingFile.readlines() lines = list(map(lambda line: line.strip(), lines)) # Look for the QID in all strings rankEntities = wikidata_utils.regex_match_QID(lines) return rankEntities if __name__ == "__main__": try: fileIn = Path(sys.argv[2]) except: fileIn = Path("output/ranking/AP1_minus_Q23958852_ranking.txt") with open(fileIn, "r") as rankingFile: entities = parse_ranking_file(rankingFile) # entitiesSet = get_ranking_entity_set(rankingFile) # graph_from_superclasses_dict( # "output/AP1_occurrence.json", rankingEntities=entities # ) graph_from_superclasses_dict( "output/AP1_trees.json", rankingEntities=entities )

37.371257

174

0.539337

1,098

12,482

6.068306

0.250455

0.012607

0.010506

0.016809

0.199009

0.153084

0.123668

0.10866

0.070839

0

0.011031

0.375421

12,482

333

175

37.483483

0.843638

0.162875

0

0.284483

0

0.008621

0.194041

0.072177

0

1

0.017241

false

0.034483

0.030172

0

0.060345

0.077586

0

null

0

null

0

1

0

d4064fd610eae924f03892b2d599dd0687c7269d

913

py

Python

dist/weewx-4.0.0b5/bin/weewx/junk.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

10

2017-01-05T17:30:48.000Z

2021-09-18T15:04:20.000Z

dist/weewx-4.0.0b5/bin/weewx/junk.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

2

2019-07-21T10:48:42.000Z

2022-02-16T20:36:45.000Z

dist/weewx-4.0.0b5/bin/weewx/junk.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

12

2017-01-05T18:50:30.000Z

2021-10-05T07:35:45.000Z

import weewx class MyTypes(object): def get_value(self, obs_type, record, db_manager): if obs_type == 'dewpoint': if record['usUnits'] == weewx.US: return weewx.wxformulas.dewpointF(record.get('outTemp'), record.get('outHumidity')) elif record['usUnits'] == weewx.METRIC or record['usUnits'] == weewx.METRICWX: return weewx.wxformulas.dewpointC(record.get('outTemp'), record.get('outHumidity')) else: raise ValueError("Unknown unit system %s" % record['usUnits']) else: raise weewx.UnknownType(obs_type) class MyVector(object): def get_aggregate(self, obs_type, timespan, aggregate_type=None, aggregate_interval=None): if obs_type.starts_with('ch'): "something" else: raise weewx.UnknownType(obs_type)

32.607143

99

0.591457

97

913

5.443299

0.463918

0.079545

0.102273

0.083333

0.257576

0

0.292443

913

28

100

32.607143

0.817337

0

0.25

0

0.11488

0

1

0.1

false

0

0.05

0

0.35

0

null

0

null

0

1

0

d407929c4b0bf64d15071879c336b090bd7b4eb9

1,580

py

Python

vision_api_batch.py

swowko51/pfch_humanvsmachine_2019

ba5a4c0db804e62892a28c72ba2d2180c6e44282

[ "MIT" ]

null

vision_api_batch.py

swowko51/pfch_humanvsmachine_2019

ba5a4c0db804e62892a28c72ba2d2180c6e44282

[ "MIT" ]

null

vision_api_batch.py

swowko51/pfch_humanvsmachine_2019

ba5a4c0db804e62892a28c72ba2d2180c6e44282

[ "MIT" ]

null

import io import os import pandas as pd import re # Imports the Google Cloud client library from google.cloud import vision from google.cloud.vision import types # Set Google API authentication os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "INSERT_FULL_KEY_FILE_PATH" # --------------------------------------------------------------------- # Retrieve labels for a batch of images and create a dataframe # Folder where images are stored ImageFolder = "INSERT_FULL_FOLDER_PATH" # Placeholders to store data ImageID = [] Description = [] # Instantiates a client ImageLabels = pd.DataFrame() client = vision.ImageAnnotatorClient() # Get labels and scores for every image in folder for file in os.listdir(ImageFolder): filename = os.path.basename(file).split('.jpg')[0] # Get image ID image_file = io.open(ImageFolder+file, 'rb') # Open image content = image_file.read() # Read image into memory image = types.Image(content=content) response = client.label_detection(image=image) # Gets response from API for image labels = response.label_annotations # Get labels from response Nlabels = len(labels) # Get the number of labels that were returned for i in range(0, Nlabels): # For each label we will store the MID, label, and score ImageID.append(filename) # Keep track Image ID Description.append(labels[i].description) # Store label # Put Image ID and label into data frame ImageLabels["imageid"] = ImageID ImageLabels["desc"] = Description ImageLabels.groupby(ImageID) # print(ImageLabels) Export = ImageLabels.to_json (r'test2.json',orient='records')

28.727273

85

0.73038

215

1,580

5.302326

0.469767

0.028947

0.026316

0

0.002214

0.142405

1,580

54

86

29.259259

0.839114

0.393671

0

0.119403

0.083156

0

1

0

false

0

0.222222

0

0.222222

0

null

0

null

0

1

0

d407d7b9d817bbd5e0d8ab001619f9db240a997f

1,556

py

Python

examples/ElasticsearchDomain.py

hmain/troposphere-fork

815ee739bcf3d024e1aef5caeca4e4d63e85e98e

[ "BSD-2-Clause" ]

null

examples/ElasticsearchDomain.py

hmain/troposphere-fork

815ee739bcf3d024e1aef5caeca4e4d63e85e98e

[ "BSD-2-Clause" ]

null

examples/ElasticsearchDomain.py

hmain/troposphere-fork

815ee739bcf3d024e1aef5caeca4e4d63e85e98e

[ "BSD-2-Clause" ]

null

# Converted from Elasticsearch Domain example located at: # http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#d0e51519 from troposphere import Template, constants from troposphere.elasticsearch import Domain, EBSOptions from troposphere.elasticsearch import ElasticsearchClusterConfig from troposphere.elasticsearch import SnapshotOptions templ = Template() templ.add_description('Elasticsearch Domain example') es_domain = templ.add_resource(Domain( 'ElasticsearchDomain', DomainName="ExampleElasticsearchDomain", ElasticsearchClusterConfig=ElasticsearchClusterConfig( DedicatedMasterEnabled=True, InstanceCount=2, ZoneAwarenessEnabled=True, InstanceType=constants.ELASTICSEARCH_M3_MEDIUM, DedicatedMasterType=constants.ELASTICSEARCH_M3_MEDIUM, DedicatedMasterCount=3 ), EBSOptions=EBSOptions(EBSEnabled=True, Iops=0, VolumeSize=20, VolumeType="gp2"), SnapshotOptions=SnapshotOptions(AutomatedSnapshotStartHour=0), AccessPolicies={'Version': '2012-10-17', 'Statement': [{ 'Effect': 'Allow', 'Principal': { 'AWS': '*' }, 'Action': 'es:*', 'Resource': '*' }]}, AdvancedOptions={"rest.action.multi.allow_explicit_index": "true"} )) print(templ.to_json())

36.186047

111

0.633676

118

1,556

8.271186

0.584746

0.061475

0.086066

0.104508

0

0.020318

0.272494

1,556

42

112

37.047619

0.841873

0.105398

0

0.134726

0.04611

0

1

0

false

0

0.117647

0

0.117647

0.029412

0

null

0

null

0

1

0

d409f34383374dd7cd26218ce4cee29f4ca7b7c0

3,917

py

Python

footprint/models/audio.py

arthurtofani/footprint

572401d4cba3299ae9915fca2a7d08ea1a3a9bc4

[ "MIT" ]

null

footprint/models/audio.py

arthurtofani/footprint

572401d4cba3299ae9915fca2a7d08ea1a3a9bc4

[ "MIT" ]

null

footprint/models/audio.py

arthurtofani/footprint

572401d4cba3299ae9915fca2a7d08ea1a3a9bc4

[ "MIT" ]

null

from collections import defaultdict import librosa import os import numpy as np import h5py class Audio: filename = None project = None bucket = None tempo = None beats = None features = None tokens = None loaded_from_cache = False has_changed = False def __init__(self, filename, project): self.filename = filename self.project = project self.features = defaultdict() self.tokens = defaultdict() self.signal_has_changed = False self.feature_has_changed = False self.token_has_changed = False self.y = None self.sr = None def load(self): if self.project.cache_features: self.__load_features_from_cache() self.__load_tokens_from_cache() def add_feature(self, feature_name, feature): self.features[feature_name] = feature self.feature_has_changed = True def add_tokens(self, tokens_key, tokens): self.tokens[tokens_key] = tokens self.token_has_changed = True def persist(self): if self.project.cache_features and self.feature_has_changed: self.persist_features() if self.project.cache_tokens and self.token_has_changed: self.persist_tokens() if self.project.cache_signal and self.signal_has_changed: self.persist_signal() def signal(self): if self.y is None: self.y, self.sr = self.__load_signal() return (self.y, self.sr) def cleanup(self): self.y = None self.sr = None def persist_features(self): self.__create_cache_folder() print('dumping features', self.filename) with h5py.File(self.cache_filename('features'), "w") as f: for key in self.features.keys(): f.create_dataset(key, data=self.features[key]) self.feature_has_changed = False def persist_tokens(self): print('dumping tokens', self.filename) with h5py.File(self.cache_filename('tokens'), "w") as f: for key in self.tokens.keys(): f.attrs[key] = self.tokens[key] self.token_has_changed = False def persist_signal(self): self.__create_cache_folder() print('dumping audio', self.filename) with h5py.File(self.cache_filename('audio'), "w") as f: f.create_dataset('y', data=self.y) f.attrs["sr"] = self.sr self.signal_has_changed = False def clean_cache(self, file_type_str): if self.cache_filename_exists(): os.remove(self.cache_filename(file_type_str)) def __load_signal(self): return self.__load_signal_from_cache() or self.__load_signal_from_file() def __load_signal_from_file(self): print('loading signal from file - %s' % self.filename) self.y, self.sr = librosa.load(self.filename) self.signal_has_changed = True return (self.y, self.sr) def __load_signal_from_cache(self): if not self.cache_filename_exists('audio'): return None print('loading signal from cache - %s' % self.filename) with h5py.File(self.cache_filename('audio'), 'r') as f: self.y = np.array(f['y']) self.sr = f.attrs["sr"] return (self.y, self.sr) def __load_features_from_cache(self): if not self.cache_filename_exists('features'): return with h5py.File(self.cache_filename('features'), 'r') as f: for k in f.keys(): self.features[k] = np.array(f[k]) def __load_tokens_from_cache(self): if not self.cache_filename_exists('tokens'): return with h5py.File(self.cache_filename('tokens'), 'r') as f: for k in f.attrs.keys(): self.tokens[k] = f.attrs[k] self.token_has_changed = False def cache_filename(self, file_type_str): return self.__cache_folder() + ('/%s.hdf5' % file_type_str) def cache_filename_exists(self, file_type_str): return os.path.isfile(self.cache_filename(file_type_str)) def __create_cache_folder(self): os.makedirs(self.__cache_folder(), exist_ok=True) def __cache_folder(self): fld = self.project.cache_folder return fld + self.filename

29.900763

76

0.693643

570

3,917

4.5

0.135088

0.054581

0.079532

0.037427

0.40117

0.316179

0.263938

0.115789

0.083821

0

0.002528

0.191984

3,917

130

77

30.130769

0.807899

0

0.165138

0

0.045698

0

1

0.183486

false

0

0.045872

0.027523

0.412844

0.045872

0

null

0

null

0

1

0

d40a02528d58fe92a0117b78210b7cc191ea3776

2,634

py

Python

src/solr.py

ielm/CSCI6964

5ab46ea68d9a1bf6192a2f3cbae0acea0e3696c8

[ "MIT" ]

null

src/solr.py

ielm/CSCI6964

5ab46ea68d9a1bf6192a2f3cbae0acea0e3696c8

[ "MIT" ]

null

src/solr.py

ielm/CSCI6964

5ab46ea68d9a1bf6192a2f3cbae0acea0e3696c8

[ "MIT" ]

null

import json import os import urllib.request, urllib.parse from collections import OrderedDict from typing import List, Union, Dict class SOLR: """ Wrapper for the SOLR retrieval engine. """ def __init__( self, host: str = "localhost", port: int = 8983, cookie: dict = None, collection: str = "trec", file_number: int = 100, ir_model: str = "DFR", ): self.host = host if self.host is None: self.host = ( os.environ["SOLR_HOST"] if "SOLR_HOST" in os.environ else "localhost" ) self.port = port if self.port is None: self.port = ( int(os.environ["SOLR_PORT"]) if "SOLR_PORT" in os.environ else 8983 ) self.cookie = cookie self.collection = collection if self.collection is None: self.collection = ( os.environ["SOLR_COLLECTION"] if "SOLR_COLLECTION" in os.environ else "trec" ) self.file_number = file_number if self.file_number is None: self.file_number = ( int(os.environ["SOLR_FILENUMBER"]) if "SOLR_FILENUMBER" in os.environ else 100 ) self.ir_model = ir_model if self.ir_model is None: self.ir_model = ( os.environ["SOLR_IRMODEL"] if "SOLR_IRMODEL" in os.environ else "DFR" ) def __rget(self, path: str = "select?", params: Union[Dict, None] = None): url = f"http://{self.host}:{str(self.port)}/solr/{self.collection}/{path}" def __format_param(key): values = params[key] if type(values) is not list: values = [values] return "&".join(map(lambda value: key + "=" + str(value), values)) if len(params) > 0: url = f"{url}?{'&'.join(map(__format_param, params.keys()))}" request = urllib.request.urlopen(url) return json.load(request)["response"]["docs"] def query( self, query: str, fields: List[str] = None, rows: int = 15, sort: str = "score asc", ): params = { "fl": ( urllib.parse.quote(" ".join(fields)) if fields is not None else "docno%2Cscore%2Cdoctext&" ), "q": f"doctext%3A({query})", "rows": f"{rows}", "sort": urllib.parse.quote(sort), } return self.__rget(path="select", params=params)

27.154639

85

0.509112

296

2,634

4.415541

0.290541

0.06886

0.038256

0.057383

0

0.012063

0.370539

2,634

96

86

27.4375

0.776236

0.014427

0

0.054054

0

0.14186

0.022868

0

1

0.054054

false

0

0.067568

0

0.175676

0

null

0

null

0

1

0

d40bd0e8a76f18b193a91aefda5569f8c67d163b

3,773

py

Python

modules/slack.py

KTH/Jan-Ove

661acf5843b7b6dd459b0dddd64cffe27840e3c8

[ "MIT" ]

null

modules/slack.py

KTH/Jan-Ove

661acf5843b7b6dd459b0dddd64cffe27840e3c8

[ "MIT" ]

null

modules/slack.py

KTH/Jan-Ove

661acf5843b7b6dd459b0dddd64cffe27840e3c8

[ "MIT" ]

null

__author__ = 'tinglev@kth.se' import os import re import logging from slackclient import SlackClient def init(): #global CLIENT, BOT_ID log = logging.getLogger(__name__) client = SlackClient(os.environ.get('SLACK_BOT_TOKEN')) auth_test = client.api_call("auth.test") log.debug('Auth test response: %s', auth_test) bot_id = auth_test["user_id"] log.debug('Bot ID is "%s"', bot_id) client.rtm_connect(with_team_state=False, auto_reconnect=True) return client def mention_to_user_id(mention): mention_regex = r'^<@(.+)>$' matches = re.search(mention_regex, mention) if matches: return matches.group(1) return None def user_id_to_mention(user_id): return f'<@{user_id}>' def get_rtm_messages(events): messages = [] for event in events: if event["type"] == "message": messages.append(event) return messages def message_is_command(message): try: trigger_text = os.environ.get('BOT_TRIGGER') or '!pingis' log = logging.getLogger(__name__) trigger_regex = r'^{0} (.+)'.format(trigger_text) matches = re.search(trigger_regex, message['text']) if matches and matches.group(1): return matches.group(1).strip(), message['user'], message['channel'] except Exception as err: log.debug('Edited message ignored "%s". Error: "%s".', message, err) return (None, None, None) def send_ephemeral(slack_client, channel, user, message, default_message=None): log = logging.getLogger(__name__) log.debug('Sending eph to ch "%s" user "%s" msg "%s"', channel, user, message) slack_client.api_call( "chat.postEphemeral", channel=channel, user=user, text=message or default_message ) def get_user_info(slack_client, slack_user_id): log = logging.getLogger(__name__) log.debug('Calling "users.info" on slack api') user = slack_client.api_call( 'users.info', user=slack_user_id ) log.debug('Got user %s', user) return user def get_user_list(slack_client): log = logging.getLogger(__name__) log.debug('Calling "users.list" on slack api') result = slack_client.api_call( 'users.list' ) #log.debug('Response from api was: %s', result) return result def get_user_from_user_list(user_list, user_id): log = logging.getLogger(__name__) if not 'members' in user_list: return None for user in user_list['members']: if 'id' in user and user['id'] == user_id: log.debug('Found user %s in user_list', user_id) return user return None def get_user_image_url(user): imv_version = 'image_192' log = logging.getLogger(__name__) if 'user' in user and 'profile' in user['user']: if imv_version in user['user']['profile']: log.debug('Found user image for user %s', user['user']['id']) return user['user']['profile'][imv_version] return None def send_message(slack_client, channel, message, default_message=None): log = logging.getLogger(__name__) log.debug('Sending msg to ch "%s" msg "%s"', channel, message) response = slack_client.api_call( "chat.postMessage", channel=channel, text=message or default_message ) log.debug('Response from api was: %s', response) def send_block_message(slack_client, channel, blocks): log = logging.getLogger(__name__) log.debug('Sending block message to ch "%s" blocks "%s"', channel, blocks) response = slack_client.api_call( "chat.postMessage", channel=channel, blocks=blocks ) log.debug('Response from api was: %s', response) def rtm_read(slack_client): return slack_client.rtm_read()

31.974576

82

0.655712

511

3,773

4.594912

0.213307

0.0477

0.072828

0.08816

0.286201

0.212095

0.197189

0.169506

0.132879

0.053663

0

0.002377

0.219454

3,773

117

83

32.247863

0.794907

0.017758

0

0.257426

0

0.171166

0

1

0.128713

false

0

0.039604

0.019802

0.316832

0

null

0

null

0

1

0

d40cd5794c364cbfbd47cd7ffe66063081da15e8

952

py

Python

gitconfig_parser/parser.py

seanfisk/gitconfig-parser

f747a4263bdd7fc4ffd6b6a0fe34ef7cf7e935a8

[ "MIT" ]

null

gitconfig_parser/parser.py

seanfisk/gitconfig-parser

f747a4263bdd7fc4ffd6b6a0fe34ef7cf7e935a8

[ "MIT" ]

null

gitconfig_parser/parser.py

seanfisk/gitconfig-parser

f747a4263bdd7fc4ffd6b6a0fe34ef7cf7e935a8

[ "MIT" ]

2

2019-03-07T04:55:29.000Z

2019-03-28T00:59:55.000Z

""":mod:`gitconfig_parser.parser` -- Parser implementation """ from pyparsing import ( OneOrMore, restOfLine, Group, ZeroOrMore, CharsNotIn, Suppress, Word, alphanums, Literal, pythonStyleComment) def build_parser(): key = Word(alphanums).setResultsName('key') value = restOfLine.setParseAction( lambda string, location, tokens: tokens[0].strip() ).setResultsName('value') property_ = Group(key + Suppress(Literal('=')) + value) properties = Group(OneOrMore(property_)).setResultsName('properties') section_name = (Suppress('[') + OneOrMore(CharsNotIn(']')) + Suppress(']')).setResultsName('section') section = Group(section_name + properties) ini_file = ZeroOrMore(section).setResultsName('sections') ini_file.ignore(pythonStyleComment) return ini_file def parse_file(file_): parser = build_parser() return parser.parseWithTabs().parseFile(file_, parseAll=True)

35.259259

73

0.697479

91

952

7.153846

0.472527

0.032258

0

0.001259

0.165966

952

26

74

36.615385

0.81864

0.057773

0

0.041573

0

1

0.105263

false

0

0.052632

0

0.263158

0

null

0

null

0

1

0

d40d751818208fbb5ac12dea45e5d857cfe9c460

1,641

py

Python

chroma-manager/tests/unit/chroma_api/test_command.py

GarimaVishvakarma/intel-chroma

fdf68ed00b13643c62eb7480754d3216d9295e0b

[ "MIT" ]

null

chroma-manager/tests/unit/chroma_api/test_command.py

GarimaVishvakarma/intel-chroma

fdf68ed00b13643c62eb7480754d3216d9295e0b

[ "MIT" ]

null

chroma-manager/tests/unit/chroma_api/test_command.py

GarimaVishvakarma/intel-chroma

fdf68ed00b13643c62eb7480754d3216d9295e0b

[ "MIT" ]

null

from chroma_core.models import Command from chroma_core.models.host import ManagedHost from chroma_core.services.job_scheduler.job_scheduler_client import JobSchedulerClient import mock from tests.unit.chroma_api.chroma_api_test_case import ChromaApiTestCase class TestCommandResource(ChromaApiTestCase): def test_host_lists(self): """Test that commands which take a list of hosts as an argument are get the host URIs converted to host IDs (for use with HostListMixin)""" from chroma_api.urls import api hosts = [] for i in range(0, 2): address = 'myserver_%d' % i host = ManagedHost.objects.create( address = address, fqdn = address, nodename = address) hosts.append(host) with mock.patch("chroma_core.services.job_scheduler.job_scheduler_client.JobSchedulerClient.command_run_jobs", mock.Mock(return_value = Command.objects.create().id)): response = self.api_client.post("/api/command/", data={ 'message': "Test command", 'jobs': [ { 'class_name': 'UpdateNidsJob', 'args': {'hosts': [api.get_resource_uri(h) for h in hosts]} } ] }) self.assertEqual(response.status_code, 201) host_ids = "[%s]" % ", ".join([str(h.id) for h in hosts]) JobSchedulerClient.command_run_jobs.assert_called_once_with([{'class_name': 'UpdateNidsJob', 'args': {'host_ids': host_ids}}], 'Test command')

42.076923

154

0.604509

184

1,641

5.201087

0.472826

0.041797

0.043887

0.041797

0.100313

0

0.004325

0.295551

1,641

38

155

43.184211

0.823529

0.081048

0

0.149364

0.060951

0

0.066667

1

0.033333

false

0

0.2

0

0.266667

0

null

0

null

0

1

0

d40e05ca54d2f05415844295db6aa87aa4fcad8b

743

py

Python

tps/problems/forms/export.py

akmohtashami/tps-web

9dab3ffe97c21f658be30ce2f2711dd93e4ba60f

[ "MIT" ]

5

2019-02-26T06:10:43.000Z

2021-07-24T17:11:45.000Z

tps/problems/forms/export.py

akmohtashami/tps-web

9dab3ffe97c21f658be30ce2f2711dd93e4ba60f

[ "MIT" ]

3

2019-08-15T13:56:03.000Z

2021-06-10T18:43:16.000Z

tps/problems/forms/export.py

jonathanirvings/tps-web

46519347d4fc8bdced9b5bceb6cdee5ea4e508f2

[ "MIT" ]

2

2018-12-28T13:12:59.000Z

2020-12-25T18:42:13.000Z

from django import forms from problems.models import ExportPackage class ExportForm(forms.ModelForm): class Meta: model = ExportPackage fields = ('exporter', 'export_format',) def __init__(self, *args, **kwargs): self.problem = kwargs.pop('problem') self.revision = kwargs.pop('revision') self.creator = kwargs.pop('user') super(ExportForm, self).__init__(*args, **kwargs) def save(self, **kwargs): export_package = super(ExportForm, self).save(commit=False) export_package.problem = self.problem export_package.commit_id = self.revision.commit_id export_package.creator = self.creator export_package.save() return export_package

30.958333

67

0.664872

83

743

5.746988

0.39759

0.163522

0.079665

0

0.224764

743

23

68

32.304348

0.828125

0

0.053836

0

1

0.111111

false

0

0.111111

0

0.388889

0

null

0

null

0

1

0

d4112f52a0cbfaf422eb8c4063d5420c3f79065c

851

py

Python

presenterServer.py

eliahvo/presenter-app

9d5b90e15590316b1c769b94d1d88f62ea594f7e

[ "MIT" ]

null

presenterServer.py

eliahvo/presenter-app

9d5b90e15590316b1c769b94d1d88f62ea594f7e

[ "MIT" ]

null

presenterServer.py

eliahvo/presenter-app

9d5b90e15590316b1c769b94d1d88f62ea594f7e

[ "MIT" ]

null

import socket import sys from pynput.keyboard import Key, Controller import time TCP_IP = sys.argv[1] TCP_PORT = 5005 BUFFER_SIZE = 20 # Normally 1024, but we want fast response keyboard = Controller() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind((TCP_IP, TCP_PORT)) s.listen(1) print("Server: ", TCP_IP) while 1: try: print("Waiting...") conn, addr = s.accept() print('Connection address: ', addr) data = conn.recv(BUFFER_SIZE) if not data: break print("received data:", data,) if data == b'forward': keyboard.press(Key.right) keyboard.release(Key.right) elif data == b'backward': keyboard.press(Key.left) keyboard.release(Key.left) except KeyboardInterrupt: if conn: conn.close() print("W: interrupt received, stopping…") break finally: # clean up conn.close() #conn.close()

19.790698

60

0.690952

124

851

4.693548

0.532258

0.025773

0.054983

0

0.018519

0.175088

851

43

61

19.790698

0.806268

0.07168

0

0.060606

0

0.125794

0

1

0

false

0

0.121212

0

0.121212

0.151515

0

null

0

null

0

1

0

d4137fb795db6c1885430d3738fc61716e37a474

3,226

py

Python

newserver/server.py

packedbread/hack.moscow

d00aeabd5b46d1c95990e181cb08895e9a4e2ff7

[ "MIT" ]

null

newserver/server.py

packedbread/hack.moscow

d00aeabd5b46d1c95990e181cb08895e9a4e2ff7

[ "MIT" ]

2

2021-03-09T22:13:16.000Z

2021-05-10T18:42:36.000Z

newserver/server.py

packedbread/hack.moscow

d00aeabd5b46d1c95990e181cb08895e9a4e2ff7

[ "MIT" ]

null

from concurrent.futures import ProcessPoolExecutor from storage import ClientStorage from aiohttp import web, hdrs import aiofiles import tempfile import asyncio import logging import shutil import os MAX_FILE_SIZE = 20 * 1024 * 1024 PROCESS_POOL_SIZE = 8 STATIC_PATH = '../client/dist' TEMP_PATH = 'temp' logger = logging.getLogger('Server') logging.basicConfig( level=logging.CRITICAL, format='[%(levelname)s] %(name)s: %(message)s', ) clients = ClientStorage clients.loop = loop = asyncio.get_event_loop() clients.pool = pool = ProcessPoolExecutor(PROCESS_POOL_SIZE) app = web.Application() routes = web.RouteTableDef() # Index page @routes.get('/') async def index(_): return web.FileResponse(STATIC_PATH + '/index.html') # Upload multiple files @routes.post('/upload') @routes.route('OPTIONS', '/upload') async def upload(request: web.Request): if request.method == 'OPTIONS': return web.Response(status=200) reader = await request.multipart() tempdir = tempfile.mkdtemp(dir=TEMP_PATH) files = [] while True: field = await reader.next() if field is None: break if field.name != 'files[]': continue size = 0 path = os.path.join(tempdir, str(len(files))) file = await aiofiles.open(path, mode='wb') while True: if size > MAX_FILE_SIZE: shutil.rmtree(tempdir, ignore_errors=True) raise web.Response(status=403, text='Too large file') chunk = await field.read_chunk() if not chunk: break size += len(chunk) await file.write(chunk) await file.flush() await file.close() files.append(path) if not files: return web.Response(status=400, text='No files') client = ClientStorage() future = client.handle_upload(files) asyncio.ensure_future(future, loop=loop) logging.critical('New client storage: ' + client.uid) return web.Response(status=200, text=client.uid) @routes.post('/next') async def get_next(request): try: data = await request.json() except: data = {'time': 0} if not clients.clients: return web.HTTPNotFound() # Could get client by uid client = next(iter(clients.clients.values())) result = { 'status': client.status, 'ready': client.status == 'ready', } if client.status == 'ready': from_, to = client.next_jump(data['time']) result['from'], result['to'] = from_, to return web.json_response(data=result) # Disable CORS globally @app.on_response_prepare.append async def on_prepare(_, response): response.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = '*' response.headers[hdrs.ACCESS_CONTROL_ALLOW_METHODS] = 'OPTIONS, GET, POST' response.headers[hdrs.ACCESS_CONTROL_ALLOW_HEADERS] = ( 'Content-Type, Access-Control-Allow-Headers, Authorization, X-Requested-With' ) if __name__ == '__main__': # Cleanup temp dir shutil.rmtree(TEMP_PATH, ignore_errors=True) os.mkdir(TEMP_PATH) # Serve static & register routes routes.static('/', STATIC_PATH) app.add_routes(routes) # Start port = os.getenv('PORT', 5000) web.run_app(app, port=port)

27.810345

85

0.6646

404

3,226

5.180693

0.376238

0.0258

0.032489

0.032967

0.077879

0.053034

0

0.011399

0.211407

3,226

115

86

28.052174

0.811321

0.040918

0

0.023256

0

0.096889

0.009397

0

1

0

false

0

0.104651

0

0.151163

0

null

0

null

0

1

0

d414c13a693261e067977fffbcfd18adb9931eb1

881

py

Python

src/adafruit-circuitpython-bundle-4.x-mpy-20190713/examples/miniesptool_esp32nina.py

mbaaba/solar_panel

42059d8c61320494ad1298065dbc50cd9b3bd51e

[ "MIT" ]

1

2020-04-13T16:10:53.000Z

infra/libs-400rc2-20190512/examples/miniesptool_esp32nina.py

jadudm/feather-isa

b7419e6698c3f64be4d8122656eb8124631ca859

[ "MIT" ]

null

infra/libs-400rc2-20190512/examples/miniesptool_esp32nina.py

jadudm/feather-isa

b7419e6698c3f64be4d8122656eb8124631ca859

[ "MIT" ]

null

import time import board import busio from digitalio import DigitalInOut, Direction # pylint: disable=unused-import import adafruit_miniesptool print("ESP32 Nina-FW") uart = busio.UART(board.TX, board.RX, baudrate=115200, timeout=1) resetpin = DigitalInOut(board.D5) gpio0pin = DigitalInOut(board.D6) esptool = adafruit_miniesptool.miniesptool(uart, gpio0pin, resetpin, flashsize=4*1024*1024) esptool.sync() print("Synced") print("Found:", esptool.chip_name) if esptool.chip_name != "ESP32": raise RuntimeError("This example is for ESP32 only") esptool.baudrate = 912600 print("MAC ADDR: ", [hex(i) for i in esptool.mac_addr]) # Note: Make sure to use the LATEST nina-fw binary release! esptool.flash_file("NINA_W102-1.3.1.bin",0x0,'3f9d2765dd3b7b1eab61e1eccae73e44') esptool.reset() time.sleep(0.5)

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0.190476

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null

0

null

0

1

0

d418716dae9acd4334a9f34e0f2074dd64298ebb

451

py

Python

sqlalchemy/test_delete.py

WebSofter/lessnor

f5843a29f443126d30955a2fe4e7f3cadb216ad8

[ "MIT" ]

null

sqlalchemy/test_delete.py

WebSofter/lessnor

f5843a29f443126d30955a2fe4e7f3cadb216ad8

[ "MIT" ]

null

sqlalchemy/test_delete.py

WebSofter/lessnor

f5843a29f443126d30955a2fe4e7f3cadb216ad8

[ "MIT" ]

null

#Import our configuration functions from models import Film from config import get_session #Get instances for working with DB session = get_session() #***********************Working with db******************** #Update spcify row v1 film = session.query(Film).filter(Film.id == 3).one() session.delete(film) session.commit() #Update spcify row v2 films = Film.__table__.delete().where(Film.id.in_([2, 4, 5])) session.execute(films) session.commit()

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0.085809

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0.222222

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null

0

null

0

1

0

d41946ca90fe6073895898eb60b2f62a025bcdcd

1,045

py

Python

kakakucom-scrape/recover.py

GINK03/itmedia-scraping

5afbe06dd0aa12db1694a2b387aa2eeafb20e981

[ "MIT" ]

16

2018-02-06T14:43:41.000Z

2021-01-23T05:07:33.000Z

kakakucom-scrape/recover.py

GINK03/itmedia-scraping

5afbe06dd0aa12db1694a2b387aa2eeafb20e981

[ "MIT" ]

null

kakakucom-scrape/recover.py

GINK03/itmedia-scraping

5afbe06dd0aa12db1694a2b387aa2eeafb20e981

[ "MIT" ]

4

2018-01-16T13:50:43.000Z

2019-12-16T19:45:54.000Z

import glob import json import pickle import gzip import os import hashlib import re import bs4 import concurrent.futures names = set([name.split('/').pop() for name in glob.glob('hrefs/*')]) size = len(names) def _map(arg): urls = set() index, size, name = arg print(index, '/', size, name) try: html = gzip.decompress(open(f'htmls/{name}', 'rb').read()).decode() except Exception as ex: return [] soup = bs4.BeautifulSoup(html) for a in soup.find_all('a', href=True): href = a.get('href') href = re.sub(r'\?.*?', '', href) href = '/'.join(filter(lambda x:'='not in x, href.split('/'))) #print(href) if 'https://book.dmm.com' in href: urls.add(href) return urls urls = set() args = [(index, size, name) for index, name in enumerate(names)] _map(args[0]) with concurrent.futures.ProcessPoolExecutor(max_workers=16) as exe: for _urls in exe.map(_map, args): for url in _urls: urls.add(url) print(urls) open('urls.pkl.gz', 'wb').write(gzip.compress(pickle.dumps(urls)))

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71

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0

0.333333

0.055556

0

null

0

null

0

1

0

d41ba959656db47ebcf8cbb11038ffe513c678ca

7,339

py

Python

scrounger/core/session.py

NORD-Function/IOS-tools

dd393666aa1ba1117d1c472cfdef4d0b18216904

[ "BSD-3-Clause" ]

217

2018-08-23T12:00:45.000Z

2022-01-20T12:09:09.000Z

scrounger/core/session.py

Warlockk/scrounger

dd393666aa1ba1117d1c472cfdef4d0b18216904

[ "BSD-3-Clause" ]

18

2018-08-24T09:28:54.000Z

2020-05-19T04:49:54.000Z

scrounger/core/session.py

Warlockk/scrounger

dd393666aa1ba1117d1c472cfdef4d0b18216904

[ "BSD-3-Clause" ]

48

2018-08-23T12:51:45.000Z

2022-01-20T12:09:02.000Z

""" Module that holds all information of a session of scrounger """ # custom module imports from sys import path as _path # config imports from scrounger.utils.config import _SCROUNGER_HOME class Session(object): _name = "" _rows, _columns = 128, 80 options = {} global_options = {} devices = {} results = {} exceptions = [] # unused _available_modules = None _module_instance = None _current_module = None _module_class = None prompt = None def __init__(self, name): from os import popen, path # helper functions from scrounger.utils.general import execute # used to find the available modules import scrounger.modules self._name = name self._rows, self._columns = popen('stty size', 'r').read().split() self._rows, self._columns = int(self._rows), int(self._columns) if self._columns < 128: self._columns = 128 # need to add / to then replace it modules_path = "{}/".format(scrounger.modules.__path__[0]) modules = execute("find {} -name '*.py'".format(modules_path)) self._available_modules = [ module.replace(modules_path, "").replace(".py", "") for module in modules.split("\n") if module and "__" not in module ] # add custom modules modules_path = "{}/modules/".format(_SCROUNGER_HOME) modules = execute("find {} -name \"*.py\"".format(modules_path)) # add path to sys.path _path.append(modules_path) self._available_modules += [ module.replace(modules_path, "").replace(".py", "") for module in modules.split("\n") if module and "__" not in module ] # fix for macos self._available_modules = [ module[1:] if module.startswith("/") else module for module in sorted(self._available_modules) ] # public vars to be used by calling modules self.options = {} self.global_options = { "debug": "False", "device": "", "output": "", "verbose": "False" } self.devices = {} self.results = {} self.exceptions = [] # unused self.prompt = None # initialize private vars self._module_instance = None self._current_module = None self._module_class = None def modules(self): """ Returns the available modules :return: returns a list with the available modules """ return self._available_modules def back(self): """Returns to the main state""" self._module_instance = None self._current_module = None self._module_class = None def use(self, module): self._current_module = module if module.startswith("custom/"): self._module_class = __import__("{}".format( module.replace("/", ".")), fromlist=["Module"]) else: self._module_class = __import__("scrounger.modules.{}".format( module.replace("/", ".")), fromlist=["Module"]) if not hasattr(self._module_class, "Module"): self._current_module = None self._module_class = None raise Exception("Missing `Module` class") self._module_instance = self._module_class.Module() if not hasattr(self._module_class.Module, "meta") or not hasattr( self._module_instance, "options"): self._module_instance = None self._current_module = None self._module_class = None raise Exception("Missing required variables") def module_options(self): """ Returns the options dict for the current module or None if no module is active :return: a dict with the required options """ if self._module_instance: return self._module_instance.options return None def module(self): """ Returns the current active module or None if no module is active :return: a str with the current module """ return self._current_module def instance(self): """ Returns an instance with the current active module or None if no module is active :return: an object representing an inatance of the current active module """ return self._module_instance def name(self): """ Returns the name of a session :return: a str with the session name """ return self._name def to_dict(self): """ Returns a dict representing the current sesssion :return: a dict representing the session """ return { "name": self._name, "devices": [ { "id": self.devices[device]["device"].device_id(), "type": self.devices[device]["type"], "no": device } for device in self.devices ], "results": self.results, # TODO: if object, need to reproduce it "global": self.global_options, "options": self.options, "current": self._current_module, "prompt": self.prompt } def __str__(self): return "Session {}".format(self.name()) def load_sessions(filename): """ Loads a list of sessions from a file :param str filename: the file path to load the sessions from :return: a list of Session objects """ from scrounger.core.device import IOSDevice, AndroidDevice from scrounger.utils.general import file_exists from json import loads if not file_exists(filename): return [] with open(filename, "r") as fp: content = fp.read() sessions = [] try: json_sessions = loads(content) except Exception as e: # error loading sessions files return [] for json_session in json_sessions["sessions"]: session = Session(json_session["name"]) for json_device in json_session["devices"]: if json_device["type"] == "ios": device = IOSDevice(json_device["id"]) else: device = AndroidDevice(json_device["id"]) session.devices[json_device["no"]] = { "device": device, "type": json_device["type"] } session.results = json_session["results"] session.global_options = json_session["global"] session.options = json_session["options"] if json_session["current"]: session.use(json_session["current"]) session.prompt = json_session["prompt"] sessions += [session] return sessions def save_sessions(sessions, filename): """ Saves a list of session into a file :param list sessions: a list of Session objects :param str filename: the filepath to save the sessions to :return: nothing """ from json import dumps with open(filename, "w") as fp: fp.write(dumps( {"sessions": [session.to_dict() for session in sessions]} ))

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0.571331

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0.020756

0.241166

0.193971

0.157153

0

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0.326884

7,339

256

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0

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0

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0

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0

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false

0

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0.312925

0

null

0

null

0

1

0

d41d1b57c833f3f45020982a1362ffc5bb62328a

8,703

py

Python

deepmath/treegen/cnf_model_test.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

830

2016-11-07T21:46:27.000Z

2022-03-23T08:01:03.000Z

deepmath/treegen/cnf_model_test.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

26

2016-11-07T22:06:31.000Z

2022-02-16T00:18:29.000Z

deepmath/treegen/cnf_model_test.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

168

2016-11-07T21:48:55.000Z

2022-03-19T02:47:14.000Z

# Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for treegen.cnf_model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import json import tensorflow as tf from deepmath.treegen import cnf_model from deepmath.treegen import cnf_model_test_lib flags = tf.flags FLAGS = flags.FLAGS class CnfModelTest(tf.test.TestCase): # From l102_finseq_1, in test0.jsonl # v7_ordinal1(X1) | ~m1_subset_1(X1, k4_ordinal1()) tiny_expr = json.loads( '''{"clauses": [{"positive": true, "params": [{"var": "X1"}], "pred": "v7_ordinal1"}, {"positive": false, "params": [{"var": "X1"}, {"params": [], "func": "k4_ordinal1"}], "pred": "m1_subset_1"}]}''') # From l102_modelc_2, in test0.jsonl huge_expr = json.loads( '''{"clauses": [{"positive": true, "params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X1"}, {"var": "X1"}], "pred": "r5_modelc_2"}, {"positive": false, "equal": [{"params": [{"var": "X1"}, {"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}], "func": "k1_funct_1"}, {"params": [{"var": "X1"}, {"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}], "func": "k1_funct_1"}]}, {"positive": false, "params": [{"var": "X1"}, {"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}, {"params": [], "func": "esk4_0"}], "pred": "epred2_3"}, {"positive": false, "params": [{"var": "X1"}], "pred": "v7_ordinal1"}, {"positive": false, "params": [{"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}], "pred": "v3_modelc_2"}, {"positive": false, "params": [{"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}, {"params": [], "func": "k5_numbers"}], "pred": "m2_finseq_1"}, {"positive": false, "params": [{"params": [{"params": [], "func": "esk4_0"}, {"var": "X1"}, {"var": "X2"}, {"var": "X3"}], "func": "esk2_4"}], "pred": "v1_modelc_2"}, {"positive": false, "params": [{"params": [], "func": "esk4_0"}, {"params": [], "func": "esk5_0"}, {"var": "X1"}], "pred": "r4_modelc_2"}, {"positive": false, "params": [{"var": "X1"}, {"params": [{"params": [{"params": [], "func": "k9_modelc_2"}, {"params": [{"params": [], "func": "esk4_0"}], "func": "u1_struct_0"}], "func": "k2_zfmisc_1"}], "func": "k1_zfmisc_1"}], "pred": "m1_subset_1"}, {"positive": false, "params": [{"var": "X1"}, {"params": [{"params": [{"params": [], "func": "k15_modelc_2"}, {"params": [{"params": [], "func": "esk4_0"}], "func": "u1_modelc_2"}], "func": "k2_zfmisc_1"}], "func": "k1_zfmisc_1"}], "pred": "m1_subset_1"}, {"positive": false, "params": [{"var": "X1"}, {"params": [], "func": "k9_modelc_2"}, {"params": [{"params": [], "func": "esk4_0"}], "func": "u1_struct_0"}], "pred": "v1_funct_2"}, {"positive": false, "params": [{"var": "X1"}, {"params": [], "func": "k15_modelc_2"}, {"params": [{"params": [], "func": "esk4_0"}], "func": "u1_modelc_2"}], "pred": "v1_funct_2"}, {"positive": false, "params": [{"var": "X1"}], "pred": "v1_funct_1"}, {"positive": false, "params": [{"var": "X1"}], "pred": "v1_funct_1"}]}''') def testSeqModelMemorizesTinyExpr(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, num_iterations=200, extra_hparams='depth=1', model_class=cnf_model.CNFSequenceModel) def testSeqModelMemorizesTinyExprMaskedXent(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, num_iterations=200, extra_hparams='depth=1,masked_xent=true', model_class=cnf_model.CNFSequenceModel) def testSeqModelWorksWithTinyHugeExpr(self): cnf_model_test_lib.test_memorization( self, [self.tiny_expr, self.huge_expr], num_iterations=1, model_class=cnf_model.CNFSequenceModel) def testSeqModelWorksWithTinyHugeExprMaskedXent(self): cnf_model_test_lib.test_memorization( self, [self.tiny_expr, self.huge_expr], num_iterations=1, extra_hparams='masked_xent=true', model_class=cnf_model.CNFSequenceModel) def testTreeModelMemorizesTinyExprStdFixedZ(self): cnf_model_test_lib.test_memorization(self, self.tiny_expr) def testTreeModelMemorizesTinyExprStdVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='objective=vae,min_kl_weight=1') def testTreeModelMemorizesTinyExprStdIwaeMcSamples2(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='objective=iwae,min_kl_weight=1,mc_samples=2') def testTreeModelMemorizesTinyExprStdVaeMix(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='objective=vae_mix,batch_size=3', num_iterations=150) def testTreeModelMemorizesTinyExprAuxLstmFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[aux_lstm]') def testTreeModelMemorizesTinyExprUncondSibFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[uncond_sib]') def testTreeModelMemorizesTinyExprGatedSigmoidFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated],gate_type=sigmoid') def testTreeModelMemorizesTinyExprGatedSoftmaxFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated],gate_type=softmax') def testTreeModelMemorizesTinyExprGatedTiedFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated],gate_tied=true') def testTreeModelMemorizesTinyExprAuxLstmUncondSibFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[aux_lstm,uncond_sib]') def testTreeModelMemorizesTinyExprAuxLstmVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, num_iterations=80, extra_hparams='model_variants=[aux_lstm],objective=vae,min_kl_weight=1') def testTreeModelMemorizesTinyExprAuxLstmGatedUncondSibFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[aux_lstm,gated,uncond_sib]') def testTreeModelMemorizesTinyExprGatedUncondSibVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated,uncond_sib]') def testTreeModelMemorizesTinyExprAuxLstmGatedLayerNormUncondSibVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[aux_lstm,gated,layer_norm,uncond_sib]') def testTreeModelMemorizesTinyExprGatedLayerNormUncondSibVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated,layer_norm,uncond_sib]') def testTreeModelWorksWithTinyExprTanhMostVariationssVae(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, num_iterations=1, extra_hparams='model_variants=[gated,layer_norm,rev_read,uncond_sib],' 'act_fn=tanh,objective=vae,min_kl_weight=1') def testTreeModelMemorizesTinyExprMostVariationsDeepFixedZ(self): cnf_model_test_lib.test_memorization( self, self.tiny_expr, extra_hparams='model_variants=[gated,layer_norm,rev_read,uncond_sib],' 'highway_layers=5,op_hidden=1') if __name__ == '__main__': tf.test.main()

42.871921

80

0.649087

1,005

8,703

5.316418

0.20597

0.041924

0.04941

0.061763

0.618566

0.615759

0.579075

0.537339

0.529478

0.467153

0

0.025963

0.167988

8,703

202

81

43.084158

0.711918

0.091693

0

0.5

0

0.153846

0.14762

0

1

0.177966

false

0

0.059322

0

0.262712

0.008475

0

null

0

null

0

1

0

d41dac3657c44fb744404a9ec259d063e17765ee

3,905

py

Python

codes/utils.py

k1101jh/Alpha-Zero

f2a83f430f186ad0633e38baa31c2a042e1305b6

[ "Apache-2.0" ]

1

2021-11-12T13:36:47.000Z

codes/utils.py

k1101jh/Alpha-Zero

f2a83f430f186ad0633e38baa31c2a042e1305b6

[ "Apache-2.0" ]

null

codes/utils.py

k1101jh/Alpha-Zero

f2a83f430f186ad0633e38baa31c2a042e1305b6

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import os import sys import io import importlib import math from typing import Dict, Iterable, List, Optional, Tuple from games.game_types import Move, Player from games.game_types import Point from games.game_types import game_name_dict from games.game_types import game_state_dict sys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='utf-8') sys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding='utf-8') COLS = 'ABCDEFGHJKLMNOPQRSTUVWXYZ' EMPTY = 0 STONE_TO_CHAR = { EMPTY: '━╋━', Player.black.value: ' ○ ', Player.white.value: ' ● ', } def get_rule_constructor(game_name: str, rule_name: str): module = importlib.import_module(f'games.{game_name_dict[game_name]}.rule') constructor = getattr(module, rule_name) return constructor def get_game_state_constructor(name: str): module = importlib.import_module(f'games.{game_name_dict[name]}.{game_name_dict[name]}_game_state') constructor = getattr(module, game_state_dict[name]) return constructor def print_turn(game_state) -> None: print(f'{game_state.player.name} turn!') sys.stdout.flush() def print_move(player_move: Move) -> None: if player_move is not None: player = player_move[0] move = player_move[1] if move.is_pass: move_str = 'passes' else: move_str = '%s%d' % (COLS[move.point.col], move.point.row + 1) print('%s %s' % (player, move_str)) sys.stdout.flush() def print_board(board) -> None: board_size = board.get_board_size() for row in range(board_size - 1, -1, -1): bump = " " if row <= board_size else "" line = [] for col in range(0, board_size): stone = board.get(Point(row=row, col=col)) line.append(STONE_TO_CHAR[stone]) print('%s%2d %s' % (bump, row + 1, ''.join(line))) print(' ' + ' '.join(COLS[:board_size])) sys.stdout.flush() def print_visit_count(visit_counts: Optional[Iterable[int]]) -> None: if visit_counts is not None: board_size = int(math.sqrt(len(visit_counts))) for row in range(board_size - 1, -1, -1): bump = " " if row <= board_size else "" print('\n%s%2d' % (bump, row + 1), end='') for col in range(0, board_size): visit_count = visit_counts[row * board_size + col] print('%4d ' % (visit_count), end='') print('') print(' ' + ' '.join(COLS[:board_size])) sys.stdout.flush() def print_winner(winner: Player) -> None: if winner is Player.both: print("DRAW!!!") else: print(winner.name, "WINS!!!") sys.stdout.flush() def point_from_coords(coords: Tuple[int, int]) -> Point: col = COLS.index(coords[0]) row = int(coords[1:]) - 1 return Point(row=row, col=col) def is_on_grid(point: Point, board_size: int) -> bool: """[summary] check point is on grid Args: point (Point): [description] board_size (int): Size of board. Returns: bool: Is point on board. """ return 0 <= point.row < board_size and 0 <= point.col < board_size def get_agent_filename(game_name: str, version: int, postfix: str = "", prefix: str = "") -> str: cur_file_path = os.path.abspath(__file__) project_path = os.path.dirname(os.path.dirname(cur_file_path)) dir_path = os.path.join(project_path, f'trained_models/{game_name}') file_name = f'{postfix}-v{version}{prefix}.pth' os.makedirs(dir_path, exist_ok=True) return os.path.join(dir_path, file_name) def copy_list(input_list: List) -> List: ret = input_list.copy() for idx, item in enumerate(ret): ret[idx] = item return ret def copy_dict(input_dict: Dict) -> Dict: ret = input_dict.copy() for key, value in ret.items(): ret[key] = value return ret

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556

3,905

4.235612

0.232014

0.061147

0.029724

0.036093

0.22845

0.164756

0.140977

0.121444

0

0.008317

0.230218

3,905

131

104

29.80916

0.773453

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0

0.206522

0

0.080898

0.056006

0

1

0.130435

false

0.021739

0.130435

0

0.336957

0.163043

0

null

0

null

0

1

0

d41fccb7523d2734e1207ea00040f62a02a41e0c

1,545

py

Python

obdlib/uart.py

s-s-boika/obdlib

5b0b35276575a522d20858b6993a9bebf0acc968

[ "MIT" ]

9

2015-07-14T07:15:58.000Z

2021-06-03T01:42:19.000Z

obdlib/uart.py

s-s-boika/obdlib

5b0b35276575a522d20858b6993a9bebf0acc968

[ "MIT" ]

null

obdlib/uart.py

s-s-boika/obdlib

5b0b35276575a522d20858b6993a9bebf0acc968

[ "MIT" ]

4

2015-07-15T09:05:46.000Z

2022-02-06T04:28:53.000Z

DEFAULT_BAUDRATE = 38400 import sys if (hasattr(sys, 'implementation') and sys.implementation.name == 'micropython'): # if using pyBoard from pyb import UART as uart_base else: from serial import Serial as uart_base from obdlib.logging import logger class UART(object): def __init__(self): self.bus_name = uart_base.__name__ self.bus = None self.map = {} def connection(self, port, baudrate=DEFAULT_BAUDRATE): try: self.bus = uart_base(port, baudrate) self._mapping() except Exception as err: # logging exception logger.error(err) return None return self def __getattr__(self, item): def args_wrapper(*args, **kwargs): try: response = getattr(self.bus, item)(*args, **kwargs) except AttributeError: response = self._invoke_mapping(item, *args, **kwargs) return response return args_wrapper def _invoke_mapping(self, method, *args, **kwargs): try: item = self.map[self.bus_name][method] return getattr(self.bus, item)(*args, **kwargs) if item else None except KeyError: raise Exception( "Unregistered method or attribute {}".format(method)) def _mapping(self): self.map = { "UART": { "close": "deinit", "flushInput": "", "flushOutput": "" }, }

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77

0.55534

160

1,545

5.1875

0.36875

0.050602

0.043373

0.06747

0

0.004946

0.345631

1,545

56

78

27.589286

0.816024

0.022006

0

0.068182

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0

1

0.136364

false

0

0.090909

0

0.363636

0

null

0

null

0

1

0

d423ccff923c193b1018a53c447911773c46989d

1,352

py

Python

4-gen_diffs.py

lcmd-epfl/Local_Kernel_Regression

5be1b01fb347a8b8473ffe62a2f2d1fe2c1c4157

[ "MIT" ]

null

4-gen_diffs.py

lcmd-epfl/Local_Kernel_Regression

5be1b01fb347a8b8473ffe62a2f2d1fe2c1c4157

[ "MIT" ]

null

4-gen_diffs.py

lcmd-epfl/Local_Kernel_Regression

5be1b01fb347a8b8473ffe62a2f2d1fe2c1c4157

[ "MIT" ]

null

import numpy as np import time import sklearn as sk from sklearn import metrics import gc import sys import pickle init_t = time.time() at = sys.argv[1] ref_envs = np.load('./data/train_envs.npy', allow_pickle=True).item()[at] reps_dict = np.load('./data/red_reps_dict.npy', allow_pickle=True) attypes = np.load('./data/attypes.npy', allow_pickle=True) gc.collect() t1 = time.time() with open('progress_gen__diffs_{}.txt'.format(at), 'a') as file: file.write('Reps loaded, time: {} \n'.format(time.time() - t1)) atom_projections = [] with open('progress_gen__diffs_{}.txt'.format(at), 'a') as file: file.write('Starting train products at {} \n'.format(time.time() - init_t)) atom_diffs = [] t1 = time.time() for i in range(len(reps_dict[:])): repd = reps_dict[i] rep_at_envs = repd[at] if len(rep_at_envs) > 0: atom_diff = sk.metrics.pairwise_distances( rep_at_envs, ref_envs, n_jobs=-1).T else: atom_diff = [] atom_diffs.append(atom_diff) if i % 100 == 0: with open('progress_gen__diffs_{}.txt'.format(at), 'a') as file: file.write(' Train mol {}, cost: {} \n'.format( i, time.time() - t1)) t1 = time.time() with open('./euclideans/{}_diffs.npy'.format(at), "wb") as fp: pickle.dump(atom_diffs, fp)

22.163934

79

0.623521

207

1,352

3.874396

0.338164

0.069825

0.037406

0.067332

0.225686

0.190773

0

0.011246

0.210799

1,352

60

80

22.533333

0.740394

0

0.157895

0

0.189349

0.109467

0

1

0

false

0

0.184211

0

0.184211

0

null

0

null

0

1

0

d426fe625d6ef6fff91e3cb4328137b87944afc4

6,540

py

Python

crypto.py

aivalja/Crypto-Scrooge

98fb08f3eb1008de66e02b30a70f6e772c095a48

[ "MIT" ]

null

crypto.py

aivalja/Crypto-Scrooge

98fb08f3eb1008de66e02b30a70f6e772c095a48

[ "MIT" ]

null

crypto.py

aivalja/Crypto-Scrooge

98fb08f3eb1008de66e02b30a70f6e772c095a48

[ "MIT" ]

null

from json import loads import argparse from urllib.request import urlopen from pprint import pprint from datetime import datetime def main(): parser = argparse.ArgumentParser() parser.add_argument("start_date", help="Start date in format DD.MM.YYYY", type=lambda s: datetime.strptime(s, '%d.%m.%Y')) parser.add_argument("end_date", help="End date in format DD.MM.YYYY", type=lambda s: datetime.strptime(s, '%d.%m.%Y')) args = parser.parse_args() start_date = args.start_date end_date = args.end_date if(start_date > end_date): parser.error("start_date must be before end_date") # Can be changed to use different coins coin = "bitcoin" coin_history = get_price_history(start_date, end_date, coin) price_history = parse_data_history(coin_history, start_date, end_date, "prices") longest_bear = get_longest_bear(price_history) volume_history = parse_data_history(coin_history, start_date, end_date, "total_volumes") optimal_investment_dates = get_optimal_investment(price_history) highest_volume = get_highest_volume(volume_history) print(f"Longest bearish trend: {longest_bear[0]}") print( f"Highest volume: {highest_volume[0]} on " f"{highest_volume[1].strftime('%d.%m.%Y')}" ) if(optimal_investment_dates[0] == 0): print(f"Optimal investment dates: do not invest in this period") else: print( f"Optimal investment dates: buy on " f"{optimal_investment_dates[0].strftime('%d.%m.%Y')} and sell on " f"{optimal_investment_dates[1].strftime('%d.%m.%Y')}" ) def get_highest_volume(volume_history): """Returns the date with highest trading volume. Return is format: [highest volume, date with highest volume]. """ highest_volume = 0 highest_volume_timestamp = 0 for timestamp in volume_history: if(volume_history[timestamp] > highest_volume): highest_volume = volume_history[timestamp] highest_volume_timestamp = timestamp return([highest_volume, datetime.fromtimestamp(highest_volume_timestamp / 1000)]) def get_optimal_investment(price_history): """Returns optimal dates to buy and sell given coin""" sorted_history = sorted(price_history, key=price_history.get) largest_profit = 0 largest_profit_dates = [0,0] complete = False current_end_price = 0 current_start_price = 0 current_time = 0 current_iteration = 0 # Loop from largest price down for current_end_time in reversed(sorted_history): current_end_price = price_history[current_end_time] if(current_end_price - price_history[sorted_history[0]] < largest_profit): # With current end price not possible to find better deal break # Loop from smallest price up for current_start_time in sorted_history: current_start_price = price_history[current_start_time] if(current_end_price - current_start_price > largest_profit): if(current_end_time > current_start_time): # Found start price that is before end price, best deal # with current end_price largest_profit = current_end_price - current_start_price largest_profit_dates = [ datetime.fromtimestamp(current_start_time / 1000), datetime.fromtimestamp(current_end_time / 1000) ] break else: # Largest possible profit is smaller than previously found break return(largest_profit_dates) def get_longest_bear(price_history): """Get longest bear trend length and dates Return is format: [longest bear length, start date, end date]. """ previous_price = 0 longest_bear_start = 0 longest_bear_end = 0 longest_bear_length = 0 current_bear_start = 0 current_bear_length = 0 for timestamp in price_history: if(price_history[timestamp] < previous_price): current_bear_length += 1 else: current_bear_start = timestamp current_bear_length = 0 if(current_bear_length > longest_bear_length): longest_bear_length = current_bear_length longest_bear_start = current_bear_start longest_bear_end = timestamp previous_price = price_history[timestamp] return([ longest_bear_length, datetime.fromtimestamp(longest_bear_start / 1000), datetime.fromtimestamp(longest_bear_end / 1000) ]) def parse_data_history(data, start_date, end_date, data_type): """Parse price history to include only datapoints closest to midnight UTC.""" datapoints = {} previous_value = data[data_type][0] expected_timestamp = int(datetime.timestamp(start_date) * 1000) end_timestamp = int(datetime.timestamp(end_date) * 1000) for k in data[data_type]: # Timestamp is correct if(k[0] == expected_timestamp): datapoints[k[0]] = k[1] # Set expected timestamp to next day expected_timestamp += 86400000 elif(k[0] > expected_timestamp): # Current value was closer to midnight than previous if(abs(k[0] - expected_timestamp) < abs(previous_value[0] - expected_timestamp)): datapoints[k[0]] = k[1] #Previous value was closer to midnight else: datapoints[previous_value[0]] = previous_value[1] expected_timestamp += 86400000 if(end_timestamp < expected_timestamp): break previous_value = k return datapoints def get_price_history(start_date, end_date, coin): """Get price history for given coin and date period.""" # 3600 added to end date in order to # make sure end date midnight is also included with urlopen(f"https://api.coingecko.com/api/v3/coins/{coin}" f"/market_chart/range?vs_currency=eur&from=" f"{str(datetime.timestamp(start_date) - 3600)}&to=" f"{str(datetime.timestamp(end_date) + 3600)}") as response: response_content = response.read() response_content.decode('utf-8') json_response = loads(response_content) return json_response if __name__ == '__main__': main()

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92

0.647706

805

6,540

4.991304

0.201242

0.050772

0.023892

0.031857

0.260577

0.106023

0.050274

0

0.019665

0.269113

6,540

167

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39.161677

0.820921

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0

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0

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0.04

0

null

0

null

0

1

0

d4276c833da8da729c76c47b1baa7513e58dc63c

4,504

py

Python

main.py

pechhenka/itmo_parser_table

8cefcad00290539872759c6a4dc8050e03333a1f

[ "Unlicense" ]

null

main.py

pechhenka/itmo_parser_table

8cefcad00290539872759c6a4dc8050e03333a1f

[ "Unlicense" ]

null

main.py

pechhenka/itmo_parser_table

8cefcad00290539872759c6a4dc8050e03333a1f

[ "Unlicense" ]

null

import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) url = "https://abit.itmo.ru/bachelor/rating_rank/all/261/" required_name = 'Шараев Павел Ильдарович' def write_to_file(result): global required_name import xlsxwriter with xlsxwriter.Workbook('result.xlsx') as workbook: worksheet = workbook.add_worksheet('Таблица') worksheet.write_row(0, 0, ['Номер', 'Номер в конкурсной группе', 'Условие поступления', '№ п/п', 'Номер заявления', 'ФИО', 'Вид', 'М', 'Р', 'И', 'ЕГЭ+ИД', 'ЕГЭ', 'ИД', 'Наличие согласия на зачисление', 'Преимущественное право', 'Олимпиада', 'Статус']) data_format1 = workbook.add_format({'bg_color': '#16de69'}) gray = workbook.add_format({'bg_color': '#dbdbdb'}) white = workbook.add_format({'bg_color': '#ffffff'}) current_color = gray last_color = white last_cond = result[0][0] j = 1 for i in range(len(result)): if i > 0 and result[i][0] == last_cond: j += 1 else: j = 1 current_color, last_color = last_color, current_color last_cond = result[i][0] if required_name == result[i][3]: worksheet.write_row(i + 1, 0, [i + 1, j] + result[i], data_format1) else: worksheet.write_row(i + 1, 0, [i + 1, j] + result[i], current_color) def cmp_items(a, b): def convert(l): condtion_key = {'без вступительных испытаний': 4, 'на бюджетное место в пределах особой квоты': 3, 'на бюджетное место в пределах целевой квоты': 2, 'по общему конкурсу': 1, 'на контрактной основе': 0} l[0] = condtion_key[l[0]] l[8] = int(l[8] or 0) l[11] = 1 if l[11] == 'Да' else 0 l[12] = 1 if l[12] == 'Да' else 0 return l a = convert(a.copy()); b = convert(b.copy()) r = 0 if a[11] > b[11]: r = -1 # Наличие согласия на зачисление elif a[11] < b[11]: r = 1 else: if a[0] > b[0]: r = -1 # Условие поступления (бви, контракт ...) elif a[0] < b[0]: r = 1 else: if a[12] > b[12]: r = -1 # Преимущественное право elif a[12] > b[12]: r = 1 else: if a[8] > b[8]: r = -1 # ЕГЭ+ИД elif a[8] < b[8]: r = 1 else: r = 0 return r last_condition = '' def parse_row(row): def to_int_possible(a): try: r = int(a) except: r = '' return r global last_condition cells = row.find_all('td') if len(cells) == 15: last_condition = cells[0].getText() cells = row.find_all('td', {'rowspan': None}) condition = last_condition number_1 = int(cells[0].getText()) number_2 = int(cells[1].getText()) full_name = cells[2].getText() mode = cells[3].getText() m = to_int_possible(cells[4].getText()) r = to_int_possible(cells[5].getText()) i = to_int_possible(cells[6].getText()) exam_and_ia = to_int_possible(cells[7].getText()) exam = to_int_possible(cells[8].getText()) ia = to_int_possible(cells[9].getText()) agreement = cells[10].getText() advantage = cells[11].getText() olympiad = cells[12].getText() status = cells[13].getText() res = [condition, number_1, number_2, full_name, mode, m, r, i, exam_and_ia, exam, ia, agreement, advantage, olympiad, status] return res def main(): print('Скачиваю страницу:', url) import requests r = requests.get(url, verify=False) # получаем страницу print('Ищу таблицу') from bs4 import BeautifulSoup soup = BeautifulSoup(r.text, features='html.parser') # парсим таблицу rows = soup.find_all('tr', {'class': None}) # получаем строки print('Начинаю парсить таблицу') result = [] for row in rows: res = parse_row(row) result.append(res) print('Ранжирую таблицу') from functools import cmp_to_key result = sorted(result, key=cmp_to_key(cmp_items)) print('Вывожу таблицу в файл') write_to_file(result) print('Готово!') if __name__ == '__main__': main()

30.026667

118

0.538854

577

4,504

4.071057

0.317158

0.006811

0.03874

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0.140485

0.049383

0.025543

0

0.036634

0.327265

4,504

149

119

30.228188

0.737954

0.033082

0

0.165289

0

0.133425

0

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false

0

0.041322

0

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0.049587

0

null

0

null

0

1

0

d4284956c70586476c90480dd8b13432ac59b14e

3,503

py

Python

makeplotz.py

Munfred/scutils

9c6b6ce078c0b2aeb7a9f801933cf767197f4efd

[ "Unlicense" ]

null

makeplotz.py

Munfred/scutils

9c6b6ce078c0b2aeb7a9f801933cf767197f4efd

[ "Unlicense" ]

null

makeplotz.py

Munfred/scutils

9c6b6ce078c0b2aeb7a9f801933cf767197f4efd

[ "Unlicense" ]

null

### EXAMPLE JOB SUBMISISON ### # sbatch -A SternbergGroup --gres gpu --mem=64000 -t 15:00:00 --ntasks 10 --nodes 1 --job-name "bcbg" --wrap "python bcbg.py" ### FILENAME CHANGE BEFORE RUNNING ### model_name= 'blah' adata_file='../blah.h5ad' ###################################### import sys import warnings; warnings.simplefilter('ignore') import os import numpy as np import pandas as pd import json import matplotlib.pyplot as plt from scvi.dataset import GeneExpressionDataset from scvi.models import VAE from scvi.inference import UnsupervisedTrainer import torch import anndata import scvi import datetime import plotly.express as px import plotly.graph_objects as go from anndata import AnnData from umap import UMAP from fastTSNE import TSNE from fastTSNE.callbacks import ErrorLogger import plotnine as p print('Starting makeplotz with model:', model_name) ##### PLOTTING FUNCTIONS ###### def isnotebook(): # return false if not running on a notebook to avoid drawing and wasting time try: shell = get_ipython().__class__.__name__ if shell == 'ZMQInteractiveShell': return True # Jupyter notebook or qtconsole elif shell == 'TerminalInteractiveShell': return False # Terminal running IPython else: return False # Other type (?) except NameError: return False # Probably standard Python interpreter def derplot(adata=None, filename='derplot',embedding='tsne',feature='sample_type_tech', size=(12, 12), save=False, draw=False, psize=3): start = datetime.datetime.now() p.options.figure_size = size savename=filename +'.' + embedding + '.' + feature + '.png' print(start.strftime("%H:%M:%S"), 'Starting ... \t',savename, ) p.theme_set(p.theme_classic()) pt = \ p.ggplot(p.aes(embedding +'0', embedding + '1', color=feature), adata.obs) \ + p.geom_point(size=psize, alpha = 1, stroke = 0 ) \ + p.guides(color = p.guide_legend(override_aes={'size': 15})) if isnotebook() and draw: pt.draw() if save: pt.save(savename, format='png', dpi=200) end = datetime.datetime.now() delta = end-start print(start.strftime("%H:%M:%S"), str(int(delta.total_seconds())), 's to make: \t', savename) return(pt) def wraplot(adata=None, filename='wraplot',embedding='tsne',feature='sample_type_tech', size=(12, 12), color=None, save=False, draw=False, psize=3): start = datetime.datetime.now() p.options.figure_size = size savename = filename +'.' + embedding + '.' + feature + '.' + str(color) + '.png' if color==None: color=feature savename = filename +'.' + embedding + '.' + feature + '.png' print(start.strftime("%H:%M:%S"), 'Starting ... \t',savename, ) pt = ( p.ggplot(p.aes(x= embedding+'0', y=embedding+'1', color=color), adata.obs) + p.geom_point(color='lightgrey', shape = '.', data=adata.obs.drop(feature, axis = 1)) + p.geom_point(shape='.', size=psize, alpha = 1, stroke = 0 ) + p.theme_minimal() + p.facet_wrap('~' + feature ) + p.guides(color = p.guide_legend(override_aes={'size': 10})) ) if isnotebook() and draw: pt.draw() if save: pt.save(savename, format='png', dpi=200) end = datetime.datetime.now() delta = end-start print(start.strftime("%H:%M:%S"), str(int(delta.total_seconds())), 's to make: \t', savename) return(pt)

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126

0.633172

451

3,503

4.847007

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0.034767

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3,503

98

127

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null

0

null

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1

0

d42854dec66dab2bfdf36e91642dcd8421c577f7

1,244

py

Python

examples/injecting_existing_instance/injecting_existing_instance_example.py

allrod5/injectable

74e640f0911480fb06fa97c1a468c3863541c0fd

[ "MIT" ]

71

2018-02-05T04:12:27.000Z

2022-02-15T23:08:16.000Z

examples/injecting_existing_instance/injecting_existing_instance_example.py

Euraxluo/injectable

74e640f0911480fb06fa97c1a468c3863541c0fd

[ "MIT" ]

104

2018-02-06T23:37:36.000Z

2021-08-25T04:50:15.000Z

examples/injecting_existing_instance/injecting_existing_instance_example.py

Euraxluo/injectable

74e640f0911480fb06fa97c1a468c3863541c0fd

[ "MIT" ]

13

2019-02-10T18:52:50.000Z

2022-01-26T17:12:35.000Z

""" In this example you'll see how to supply an already-initialized instance as injectable. For whatever reason we have already initialized an instance of ``Application`` and assigned it to the ``app`` variable so we use the :meth:`injectable_factory <injectable.injectable_factory>` decorator in a lambda which in turn just returns the existing ``app``. Now our ``InjectingExistingInstance`` example class can be injected with our existing ``Application`` instance. .. seealso:: The :meth:`injectable_factory <injectable.injectable_factory>` decorator can also be used in regular functions and not just in lambdas. The :ref:`factory_example` shows how to use it. """ # sphinx-start from examples import Example from examples.injecting_existing_instance.app import Application from injectable import autowired, Autowired, load_injection_container class InjectingExistingInstance(Example): @autowired def __init__( self, app: Autowired(Application), ): self.app = app def run(self): print(self.app.number) # 42 def run_example(): load_injection_container() example = InjectingExistingInstance() example.run() if __name__ == "__main__": run_example()

27.644444

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1,244

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1,244

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0.166667

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0.055556

0

null

0

null

0

1

0

d42903bc52bf571f31d31df1a834976bfcbfa6a0

2,736

py

Python

fHDHR/device/tuners/__init__.py

crackers8199/fHDHR_Youtube-IHOPKC

e1878d972ffba96ff813690630e30e2de8b5f504

[ "WTFPL" ]

null

fHDHR/device/tuners/__init__.py

crackers8199/fHDHR_Youtube-IHOPKC

e1878d972ffba96ff813690630e30e2de8b5f504

[ "WTFPL" ]

null

fHDHR/device/tuners/__init__.py

crackers8199/fHDHR_Youtube-IHOPKC

e1878d972ffba96ff813690630e30e2de8b5f504

[ "WTFPL" ]

null

from fHDHR.exceptions import TunerError from .tuner import Tuner class Tuners(): def __init__(self, fhdhr, epg, channels): self.fhdhr = fhdhr self.channels = channels self.epg = epg self.max_tuners = int(self.fhdhr.config.dict["fhdhr"]["tuner_count"]) self.tuners = {} for i in range(1, self.max_tuners + 1): self.tuners[i] = Tuner(fhdhr, i, epg) def tuner_grab(self, tuner_number): if int(tuner_number) not in list(self.tuners.keys()): self.fhdhr.logger.error("Tuner %s does not exist." % str(tuner_number)) raise TunerError("806 - Tune Failed") # TunerError will raise if unavailable self.tuners[int(tuner_number)].grab() return tuner_number def first_available(self): if not self.available_tuner_count(): raise TunerError("805 - All Tuners In Use") for tunernum in list(self.tuners.keys()): try: self.tuners[int(tunernum)].grab() except TunerError: continue else: return tunernum raise TunerError("805 - All Tuners In Use") def tuner_close(self, tunernum): self.tuners[int(tunernum)].close() def status(self): all_status = {} for tunernum in list(self.tuners.keys()): all_status[tunernum] = self.tuners[int(tunernum)].get_status() return all_status def available_tuner_count(self): available_tuners = 0 for tunernum in list(self.tuners.keys()): tuner_status = self.tuners[int(tunernum)].get_status() if tuner_status["status"] == "Inactive": available_tuners += 1 return available_tuners def inuse_tuner_count(self): inuse_tuners = 0 for tunernum in list(self.tuners.keys()): tuner_status = self.tuners[int(tunernum)].get_status() if tuner_status["status"] == "Active": inuse_tuners += 1 return inuse_tuners def get_stream_info(self, stream_args): stream_args["channelUri"] = self.channels.get_channel_stream(str(stream_args["channel"])) if not stream_args["channelUri"]: raise TunerError("806 - Tune Failed") channelUri_headers = self.fhdhr.web.session.head(stream_args["channelUri"]).headers stream_args["true_content_type"] = channelUri_headers['Content-Type'] if stream_args["true_content_type"].startswith(tuple(["application/", "text/"])): stream_args["content_type"] = "video/mpeg" else: stream_args["content_type"] = stream_args["true_content_type"] return stream_args

31.448276

97

0.612208

325

2,736

4.963077

0.236923

0.080595

0.048357

0.049597

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0.199628

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2,736

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0.283333

0

null

0

null

0

1

0

d42f67e9e0f814fed2def88800445e02b82253cc

401

py

Python

ARC/arc001-arc050/arc040/a.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

2

2020-06-12T09:54:23.000Z

2021-05-04T01:34:07.000Z

ARC/arc001-arc050/arc040/a.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

961

2020-06-23T07:26:22.000Z

2022-03-31T21:34:52.000Z

ARC/arc001-arc050/arc040/a.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- def main(): n = int(input()) r_count = 0 b_count = 0 for i in range(n): si = input() r_count += si.count('R') b_count += si.count('B') if r_count > b_count: print('TAKAHASHI') elif r_count < b_count: print('AOKI') else: print('DRAW') if __name__ == '__main__': main()

16.708333

33

0.456359

52

401

3.211538

0.480769

0.143713

0.131737

0.143713

0.203593

0

0.012195

0.386534

401

23

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false

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0.1875

0

null

0

null

0

1

0

d4305b27996382bcf34ae4f2d283d939605b8ef7

6,622

py

Python

labs/lab3/lab3b.py

MITLLRacecar/racecar-parth-kocheta

6e244575b83e312880c5540342a380364032d326

[ "MIT" ]

1

2021-08-01T17:06:39.000Z

labs/lab3/lab3b.py

MITLLRacecar/racecar-parth-kocheta

6e244575b83e312880c5540342a380364032d326

[ "MIT" ]

null

labs/lab3/lab3b.py

MITLLRacecar/racecar-parth-kocheta

6e244575b83e312880c5540342a380364032d326

[ "MIT" ]

null

""" Copyright MIT and Harvey Mudd College MIT License Summer 2020 Lab 3B - Depth Camera Cone Parking """ ######################################################################################## # Imports ######################################################################################## import sys import cv2 as cv import numpy as np from typing import Any, Tuple, List, Optional from nptyping import NDArray from enum import IntEnum sys.path.insert(0, "../../library") import racecar_core import racecar_utils as rc_utils ######################################################################################## # Global variables ######################################################################################## # Sets up the racecar object rc = racecar_core.create_racecar() # >> Constants # The smallest contour we will recognize as a valid contour MIN_CONTOUR_AREA = 30 # The HSV range for the color orange, stored as (hsv_min, hsv_max) ORANGE = ((10, 100, 100), (20, 255, 255)) # >> Variables speed = 0.0 # The current speed of the car angle = 0.0 # The current angle of the car's wheels contour_center = None # The (pixel row, pixel column) of contour contour_area = 0 # The area of contour # Add any global variables here isParked = False # Set to true once the car has stopped around 30cm in front of the cone ######################################################################################## # Functions ######################################################################################## class State(IntEnum): search = 0 approach = 1 curState = State.search def update_contour(): """ Finds contours in the current color image and uses them to update contour_center and contour_area """ global contour_center global contour_area image = rc.camera.get_color_image() if image is None: contour_center = None contour_area = 0 else: # Find all of the orange contours contours = rc_utils.find_contours(image, ORANGE[0], ORANGE[1]) # Select the largest contour contour = rc_utils.get_largest_contour(contours, MIN_CONTOUR_AREA) if contour is not None: # Calculate contour information contour_center = rc_utils.get_contour_center(contour) contour_area = rc_utils.get_contour_area(contour) # Draw contour onto the image rc_utils.draw_contour(image, contour) rc_utils.draw_circle(image, contour_center) else: contour_center = None contour_area = 0 def get_mask( image: NDArray[(Any, Any, 3), np.uint8], hsv_lower: Tuple[int, int, int], hsv_upper: Tuple[int, int, int] ) -> NDArray[Any, Any]: """ Returns a mask containing all of the areas of image which were between hsv_lower and hsv_upper. Args: image: The image (stored in BGR) from which to create a mask. hsv_lower: The lower bound of HSV values to include in the mask. hsv_upper: The upper bound of HSV values to include in the mask. """ # Convert hsv_lower and hsv_upper to numpy arrays so they can be used by OpenCV hsv_lower = np.array(hsv_lower) hsv_upper = np.array(hsv_upper) # TODO: Use the cv.cvtColor function to switch our BGR colors to HSV colors image = cv.cvtColor(image, cv.COLOR_BGR2HSV) # TODO: Use the cv.inRange function to highlight areas in the correct range mask = cv.inRange(image, hsv_lower, hsv_upper) return mask def start(): """ This function is run once every time the start button is pressed """ # Have the car begin at a stop rc.drive.stop() # Print start message print(">> Lab 3B - Depth Camera Cone Parking") def update(): """ After start() is run, this function is run every frame until the back button is pressed """ # TODO: Park the car 30 cm away from the closest orange cone. global speed global angle global curState # Search for contours in the current color image update_contour() print(curState) imgX = rc.camera.get_width() if contour_center is not None: angle = rc_utils.remap_range(contour_center[1],0,imgX,-1,1) if contour_center is None: curState == State.search angle = 1 if curState == State.search: angle = 1 speed = 0.2 if contour_center is not None: curState = State.approach depth_image = rc.camera.get_depth_image() depth_image_adjust = (depth_image - 0.01) % 9999 depth_image_adjust_blur = cv.GaussianBlur(depth_image_adjust, (11,11), 0) image = rc.camera.get_color_image() mask = get_mask(image, ORANGE[0], ORANGE[1]) masked_depth_image = cv.bitwise_and(depth_image, depth_image, mask=mask) top_left_inclusive = (0, 0) bottom_right_exclusive = ((rc.camera.get_height() * 4 // 5) , rc.camera.get_width()) cropped_image = rc_utils.crop(masked_depth_image, top_left_inclusive, bottom_right_exclusive) closest_pixel = rc_utils.get_closest_pixel(cropped_image) distance = cropped_image[closest_pixel[0], closest_pixel[1]] rc.display.show_depth_image(cropped_image, points=[closest_pixel]) if curState == State.approach: if distance < 29: speed = rc_utils.remap_range(distance, 0, 30, -1, 0) print("backing") elif distance < 30: speed = 0 angle = 0 elif distance > 30 and distance < 100: speed = rc_utils.remap_range(distance, 30, 1000, 0, 1) elif distance > 100: speed = 0.5 rc.drive.set_speed_angle(speed, angle) # Print the current speed and angle when the A button is held down if rc.controller.is_down(rc.controller.Button.Y): isParked = False print("not parke") if rc.controller.is_down(rc.controller.Button.A): print("Speed:", speed, "Angle:", angle) # Print the center and area of the largest contour when B is held down if rc.controller.is_down(rc.controller.Button.B): if contour_center is None: print("No contour found") else: print("Center:", contour_center, "Area:", contour_area) pass ######################################################################################## # DO NOT MODIFY: Register start and update and begin execution ######################################################################################## if __name__ == "__main__": rc.set_start_update(start, update, None) rc.go()

31.235849

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6,622

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false

0.009346

0.074766

0

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0.065421

0

null

0

null

0

1

0

d4318826121e4a0a63a9ff18a3888788c700b50d

19,272

py

Python

ska-tmc/ska-tmc-sdpsubarrayleafnode-mid/src/ska_tmc_sdpsubarrayleafnode_mid/sdp_subarray_leaf_node.py

ska-telescope/tmc-prototype

4138274e933d4b05f7fe9fc34a11c417b6d0d336

[ "BSD-3-Clause" ]

3

2019-01-10T11:49:36.000Z

2019-07-19T03:32:52.000Z

ska-tmc/ska-tmc-sdpsubarrayleafnode-mid/src/ska_tmc_sdpsubarrayleafnode_mid/sdp_subarray_leaf_node.py

ska-telescope/tmc-prototype

4138274e933d4b05f7fe9fc34a11c417b6d0d336

[ "BSD-3-Clause" ]

19

2019-01-07T14:50:26.000Z

2019-10-02T13:25:23.000Z

ska-tmc/ska-tmc-sdpsubarrayleafnode-mid/src/ska_tmc_sdpsubarrayleafnode_mid/sdp_subarray_leaf_node.py

ska-telescope/tmc-prototype

4138274e933d4b05f7fe9fc34a11c417b6d0d336

[ "BSD-3-Clause" ]

1

2018-12-21T13:39:23.000Z

# -*- coding: utf-8 -*- # # This file is part of the SdpSubarrayLeafNode project # # # # Distributed under the terms of the BSD-3-Clause license. # See LICENSE.txt for more info. """ SDP Subarray Leaf node is to monitor the SDP Subarray and issue control actions during an observation. It also acts as a SDP contact point for Subarray Node for observation execution. """ # PROTECTED REGION ID(sdpsubarrayleafnode.additionnal_import) ENABLED START # # Third party imports import os # PyTango imports import tango import threading from tango import DebugIt, AttrWriteType, ApiUtil from tango.server import run, command, device_property, attribute # Additional imports from ska.base import SKABaseDevice from ska.base.control_model import HealthState, ObsState from ska.base.commands import ResultCode from tmc.common.tango_client import TangoClient from tmc.common.tango_server_helper import TangoServerHelper from . import const, release from .assign_resources_command import AssignResources from .release_resources_command import ReleaseAllResources from .configure_command import Configure from .scan_command import Scan from .endscan_command import EndScan from .end_command import End from .abort_command import Abort from .restart_command import Restart from .obsreset_command import ObsReset from .telescope_on_command import TelescopeOn from .telescope_off_command import TelescopeOff from .reset_command import ResetCommand from .device_data import DeviceData from .exceptions import InvalidObsStateError # PROTECTED REGION END # // SdpSubarrayLeafNode.additionnal_import __all__ = [ "SdpSubarrayLeafNode", "main", "AssignResources", "const", "release", "ReleaseAllResources", "TelescopeOn", "TelescopeOff", "Configure", "Abort", "Restart", "ObsReset", "Scan", "End", "EndScan", "ResetCommand" ] # pylint: disable=unused-argument,unused-variable, implicit-str-concat class SdpSubarrayLeafNode(SKABaseDevice): """ SDP Subarray Leaf node is to monitor the SDP Subarray and issue control actions during an observation. :Device Properties: SdpSubarrayFQDN: FQDN of the SDP Subarray Tango Device Server. :Device Attributes: receiveAddresses: This attribute is used for testing purposes. In the unit test cases it is used to provide FQDN of receiveAddresses attribute from SDP. activityMessage: String providing information about the current activity in SDP Subarray Leaf Node. activeProcessingBlocks: This is a attribute from SDP Subarray which depicts the active Processing Blocks in the SDP Subarray. """ # ----------------- # Device Properties # ----------------- SdpSubarrayFQDN = device_property( dtype="str", doc="FQDN of the SDP Subarray Tango Device Server." ) # ---------- # Attributes # ---------- receiveAddresses = attribute( dtype="str", access=AttrWriteType.READ_WRITE, doc="This attribute is used for testing purposes. In the unit test cases, " "it is used to provide FQDN of receiveAddresses attribute from SDP.", ) activityMessage = attribute( dtype="str", access=AttrWriteType.READ_WRITE, doc="String providing information about the current activity in SDP Subarray Leaf Node", ) activeProcessingBlocks = attribute( dtype="str", doc="This is a attribute from SDP Subarray which depicts the active Processing Blocks in " "the SDP Subarray.", ) class InitCommand(SKABaseDevice.InitCommand): """ A class for the TMC SdpSubarrayLeafNode's init_device() method. """ def do(self): """ Initializes the attributes and properties of the SdpSubarrayLeafNode. return: A tuple containing a return code and a string message indicating status. The message is for information purpose only. rtype: (ResultCode, str) """ super().do() device = self.target self.this_server = TangoServerHelper.get_instance() self.this_server.set_tango_class(device) device.attr_map = {} device.attr_map["receiveAddresses"] = "" device.attr_map["activeProcessingBlocks"] = "" device.attr_map["activityMessage"] = "" # Initialise attributes device._sdp_subarray_health_state = HealthState.OK device._build_state = "{},{},{}".format( release.name, release.version, release.description ) device._version_id = release.version # Create DeviceData class instance device_data = DeviceData.get_instance() device.device_data = device_data standalone_mode = os.environ.get("STANDALONE_MODE") self.logger.info("Device running in standalone_mode:%s", standalone_mode) ApiUtil.instance().set_asynch_cb_sub_model(tango.cb_sub_model.PUSH_CALLBACK) log_msg = f"{const.STR_SETTING_CB_MODEL}{ApiUtil.instance().get_asynch_cb_sub_model()}" self.logger.debug(log_msg) self.this_server.write_attr( "activityMessage", const.STR_SDPSALN_INIT_SUCCESS, False ) # Initialise Device status device.set_status(const.STR_SDPSALN_INIT_SUCCESS) self.logger.info(const.STR_SDPSALN_INIT_SUCCESS) return (ResultCode.OK, const.STR_SDPSALN_INIT_SUCCESS) # --------------- # General methods # --------------- def always_executed_hook(self): # PROTECTED REGION ID(SdpSubarrayLeafNode.always_executed_hook) ENABLED START # """Internal construct of TANGO.""" # PROTECTED REGION END # // SdpSubarrayLeafNode.always_executed_hook def delete_device(self): # PROTECTED REGION ID(SdpSubarrayLeafNode.delete_device) ENABLED START # """Internal construct of TANGO.""" # PROTECTED REGION END # // SdpSubarrayLeafNode.delete_device # ------------------ # Attributes methods # ------------------ def read_receiveAddresses(self): # PROTECTED REGION ID(SdpSubarrayLeafNode.receiveAddresses_read) ENABLED START # """Internal construct of TANGO. Returns the Receive Addresses. receiveAddresses is a forwarded attribute from SDP Master which depicts State of the SDP.""" return self.attr_map["receiveAddresses"] # PROTECTED REGION END # // SdpSubarrayLeafNode.receiveAddresses_read def write_receiveAddresses(self, value): # PROTECTED REGION ID(SdpSubarrayLeafNode.receiveAddresses_read) ENABLED START # """Internal construct of TANGO. Sets the Receive Addresses. receiveAddresses is a forwarded attribute from SDP Master which depicts State of the SDP.""" self.attr_map["receiveAddresses"] = value # PROTECTED REGION END # // SdpSubarrayLeafNode.receiveAddresses_read def read_activityMessage(self): # PROTECTED REGION ID(SdpSubarrayLeafNode.activityMessage_read) ENABLED START # """Internal construct of TANGO. Returns Activity Messages. activityMessage is a String providing information about the current activity in SDP Subarray Leaf Node""" return self.attr_map["activityMessage"] # PROTECTED REGION END # // SdpSubarrayLeafNode.activityMessage_read def write_activityMessage(self, value): # PROTECTED REGION ID(SdpSubarrayLeafNode.activityMessage_write) ENABLED START # """Internal construct of TANGO. Sets the activity message. activityMessage is a String providing information about the current activity in SDP Subarray Leaf Node""" self.update_attr_map("activityMessage", value) # PROTECTED REGION END # // SdpSubarrayLeafNode.activityMessage_write def update_attr_map(self, attr, val): # PROTECTED REGION ID(SdpSubarrayLeafNode.update_attr_map) ENABLED START # """This method updates attribute value in attribute map. Once a thread has acquired a lock, subsequent attempts to acquire it are blocked, until it is released.""" lock = threading.Lock() lock.acquire() self.attr_map[attr] = val lock.release() # PROTECTED REGION END # // SdpSubarrayLeafNode.update_attr_map def read_activeProcessingBlocks(self): # PROTECTED REGION ID(SdpSubarrayLeafNode.activeProcessingBlocks_read) ENABLED START # """Internal construct of TANGO. Returns Active Processing Blocks.activeProcessingBlocks is a forwarded attribute from SDP Subarray which depicts the active Processing Blocks in the SDP Subarray""" return self.attr_map["activeProcessingBlocks"] # PROTECTED REGION END # // SdpSubarrayLeafNode.activeProcessingBlocks_read # -------- # Commands # -------- def is_telescope_on_allowed(self): """ Checks Whether this command is allowed to be run in current device state. return: True if this command is allowed to be run in current device state. rtype: boolean raises: DevF ailed if this command is not allowed to be run in current device state. """ handler = self.get_command_object("TelescopeOn") return handler.check_allowed() @command() @DebugIt() def TelescopeOn(self): """ Sets the opState to ON. :param argin: None :return: None """ handler = self.get_command_object("TelescopeOn") handler() def is_telescope_off_allowed(self): """ Checks Whether this command is allowed to be run in current device state. return: True if this command is allowed to be run in current device state. rtype: boolean raises: DevF ailed if this command is not allowed to be run in current device state. """ handler = self.get_command_object("TelescopeOff") return handler.check_allowed() @command() @DebugIt() def TelescopeOff(self): """ Sets the opState to Off. :param argin: None :return: None """ handler = self.get_command_object("TelescopeOff") handler() @command() @DebugIt() def Abort(self): """ Invoke Abort on SdpSubarrayLeafNode. """ handler = self.get_command_object("Abort") handler() def is_Abort_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean raises: DevFailed if this command is not allowed to be run in current device state """ handler = self.get_command_object("Abort") return handler.check_allowed() @command( dtype_in=("str"), doc_in="The input JSON string consists of information related to id, max_length, scan_types" " and processing_blocks.", ) @DebugIt() def AssignResources(self, argin): """ Assigns resources to given SDP subarray. """ handler = self.get_command_object("AssignResources") try: self.validate_obs_state() except InvalidObsStateError as error: self.logger.exception(error) tango.Except.throw_exception( const.ERR_DEVICE_NOT_IN_EMPTY_IDLE, const.ERR_ASSGN_RESOURCES, "SdpSubarrayLeafNode.AssignResources()", tango.ErrSeverity.ERR, ) handler(argin) def is_AssignResources_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean """ handler = self.get_command_object("AssignResources") return handler.check_allowed() def is_Configure_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean """ handler = self.get_command_object("Configure") return handler.check_allowed() @command( dtype_in=("str"), doc_in="The JSON input string consists of scan type.", ) @DebugIt() def Configure(self, argin): """ Invokes Configure on SdpSubarrayLeafNode. """ handler = self.get_command_object("Configure") handler(argin) def is_End_allowed(self): """ Checks whether this command is allowed to be run in current device state. return: True if this command is allowed to be run in current device state. rtype: boolean """ handler = self.get_command_object("End") return handler.check_allowed() @command() @DebugIt() def End(self): """This command invokes End command on SDP subarray to end the current Scheduling block.""" handler = self.get_command_object("End") handler() def is_EndScan_allowed(self): """ Checks whether this command is allowed to be run in current device state. return: True if this command is allowed to be run in current device state. rtype: boolean """ handler = self.get_command_object("EndScan") return handler.check_allowed() @command() @DebugIt() def EndScan(self): """ Invokes EndScan on SdpSubarrayLeafNode. """ handler = self.get_command_object("EndScan") handler() @command() @DebugIt() def ObsReset(self): """ Invoke ObsReset command on SdpSubarrayLeafNode. """ handler = self.get_command_object("ObsReset") handler() def is_ObsReset_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean """ handler = self.get_command_object("ObsReset") return handler.check_allowed() def is_ReleaseAllResources_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean raises: DevFailed if this command is not allowed to be run in current device state """ handler = self.get_command_object("ReleaseAllResources") return handler.check_allowed() @command() @DebugIt() def ReleaseAllResources(self): """ Invokes ReleaseAllResources command on SdpSubarrayLeafNode. """ handler = self.get_command_object("ReleaseAllResources") handler() @command() @DebugIt() def Restart(self): """ Invoke Restart command on SdpSubarrayLeafNode. """ handler = self.get_command_object("Restart") handler() def is_Restart_allowed(self): """ Checks whether this command is allowed to be run in current device state return: True if this command is allowed to be run in current device state rtype: boolean raises: DevFailed if this command is not allowed to be run in current device state """ handler = self.get_command_object("Restart") return handler.check_allowed() def is_Scan_allowed(self): """ Checks whether this command is allowed to be run in current device state. return: True if this command is allowed to be run in current device state. rtype: boolean """ handler = self.get_command_object("Scan") return handler.check_allowed() @command( dtype_in=("str"), doc_in="The JSON input string consists of SB ID.", ) @DebugIt() def Scan(self, argin): """Invoke Scan command to SDP subarray.""" handler = self.get_command_object("Scan") handler(argin) def validate_obs_state(self): self.this_server = TangoServerHelper.get_instance() sdp_subarray_fqdn = self.this_server.read_property("SdpSubarrayFQDN")[0] sdp_sa_client = TangoClient(sdp_subarray_fqdn) if sdp_sa_client.get_attribute("obsState").value in [ ObsState.EMPTY, ObsState.IDLE, ]: self.logger.info( "SDP subarray is in required obstate,Hence resources to SDP can be assign." ) else: self.logger.error("Subarray is not in EMPTY obstate") log_msg = "Error in device obstate." self.this_server.write_attr("activityMessage", log_msg, False) raise InvalidObsStateError("SDP subarray is not in EMPTY obstate.") def init_command_objects(self): """ Initialises the command handlers for commands supported by this device. """ super().init_command_objects() # Create device_data class object device_data = DeviceData.get_instance() args = (device_data, self.state_model, self.logger) self.register_command_object("AssignResources", AssignResources(*args)) self.register_command_object("ReleaseAllResources", ReleaseAllResources(*args)) self.register_command_object("Scan", Scan(*args)) self.register_command_object("End", End(*args)) self.register_command_object("Restart", Restart(*args)) self.register_command_object("Configure", Configure(*args)) self.register_command_object("EndScan", EndScan(*args)) self.register_command_object("Abort", Abort(*args)) self.register_command_object("ObsReset", ObsReset(*args)) self.register_command_object("TelescopeOff", TelescopeOff(*args)) self.register_command_object("TelescopeOn", TelescopeOn(*args)) self.register_command_object("Reset", ResetCommand(*args)) # ---------- # Run server # ---------- def main(args=None, **kwargs): # PROTECTED REGION ID(SdpSubarrayLeafNode.main) ENABLED START # """ Runs the SdpSubarrayLeafNode :param args: Arguments internal to TANGO :param kwargs: Arguments internal to TANGO :return: SdpSubarrayLeafNode TANGO object """ # PROTECTED REGION ID(SdpSubarrayLeafNode.main) ENABLED START # ret_val = run((SdpSubarrayLeafNode,), args=args, **kwargs) return ret_val # PROTECTED REGION END # // SdpSubarrayLeafNode.main if __name__ == "__main__": main()

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0.14204

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0

null

0

null

0

1

0

d431a229e5bc2ae5e5df2bc300491d02cd7bb9da

4,255

py

Python

src/app.py

qq20004604/qq-robot

f109a774666efcd0cf36ed4da0d79f53b32b1577

[ "Apache-2.0" ]

8

2019-05-20T03:30:27.000Z

2020-09-24T13:16:53.000Z

src/app.py

qq20004604/qq-robot

f109a774666efcd0cf36ed4da0d79f53b32b1577

[ "Apache-2.0" ]

null

src/app.py

qq20004604/qq-robot

f109a774666efcd0cf36ed4da0d79f53b32b1577

[ "Apache-2.0" ]

2

2020-09-24T13:16:56.000Z

2020-12-25T09:44:48.000Z

from aiocqhttp import CQHttp from datetime import datetime from sendmsg import SendMsg from loadData import LoadData import threading import time # windows本机运行本脚本与coolq的配置 # HOST = '127.0.0.1' # PORT = 7788 # 这个url是发送给docker容器里的coolq # 举例来说，假如docker命令有这样的 -p 3542:9000 -p 15700:5700 # 9000 是coolq暴露的页面访问地址（这里映射到了外面的3542，所以外界通过3542端口访问） # 而5700是是coolq接受数据的端口（即是这个python服务发送给coolq的数据），这里映射到了15700， # 所以外界通过15700端口发送信息给coolq BASEURL = 'http://127.0.0.1:15700/' bot = CQHttp(api_root=BASEURL) d = { # '博客': 'https://blog.csdn.net/qq20004604', # 'github': 'https://github.com/qq20004604', # 'nginx': 'https://github.com/qq20004604/nginx-demo', # 'django': 'https://github.com/qq20004604/Python3_Django_Demo', # 'docker': 'https://github.com/qq20004604/docker-learning', # 'webpack': 'https://github.com/qq20004604/webpack-study', # 'react': 'https://github.com/qq20004604/react-demo', # 'vue': 'github： https://github.com/qq20004604/vue-scaffold\n博客专栏（1.x）：https://blog.csdn.net/qq20004604/article/category/6381182', # '笔记': 'https://github.com/qq20004604/notes', # 'demo': 'https://github.com/qq20004604/some_demo', # '海外服务器': 'https://manage.hostdare.com/aff.php?aff=939\n这个可以做私人服务器（不需要备案），也可以找群主询问如何架设SS server的方法。', # 'QQ 机器人': 'https://github.com/qq20004604/qq-robot', # '架构': 'https://juejin.im/post/5cea1f705188250640005472', # 'es6': 'https://blog.csdn.net/qq20004604/article/details/78014684', # 'vue脚手架': 'https://github.com/qq20004604/Vue-with-webpack', # 'react脚手架': 'https://github.com/qq20004604/react-with-webpack', # 'Macbook常用软件': 'https://github.com/qq20004604/when-you-get-new-Macbook', # 'python的django与mysql交互': 'https://blog.csdn.net/qq20004604/article/details/89934212' } ld = LoadData() def log(context, filename='./log.log'): with open(filename, 'a', encoding='utf-8') as f: f.write('time:%s, sender:%s, message_type:%s, user_id:%s, content:%s\n' % ( datetime.now(), context['sender']['nickname'], context['message_type'], context['sender']['user_id'], context['raw_message'])) @bot.on_message() async def handle_msg(context): msg = context['message'] # print(msg) ''' # print(str(context)) 内容示例如下 {'font': 1473688, 'message': '#help', 'message_id': 528, 'message_type': 'private', 'post_type': 'message', 'raw_message': '#help', 'self_id': 2691365658, 'sender': {'age': 30, 'nickname': '零零水', 'sex': 'male', 'user_id': 20004604}, 'sub_type': 'friend', 'time': 1558283078, 'user_id': 20004604} ''' result = '' isindict = False isinhelp = False for k in d: if ('#' + k) in msg: result += d[k] + '\n' isindict = True if '#help' in msg: result += '你可以使用以下命令~记得前面带上#喔\n' isinhelp = True for k in d: result += '#' + k + '\n' # 默认词典要求给star if isindict is True: result += "记得给star！" # 只要是词典之一，则打印日志 if isindict is True or isinhelp is True: log(context) return {'reply': result} @bot.on_notice('group_increase') async def handle_group_increase(context): await bot.send(context, message='欢迎新人～可以输入#help来向我查询所有命令喔', at_sender=True, auto_escape=True) @bot.on_request('group', 'friend') async def handle_request(context): return {'approve': True} SendMsg(BASEURL) def mixin_dict(): global d minutes = 0 while True: # 1 分钟更新一次 minutes = minutes + 1 if minutes % 60 == 0: print('%s hours pass' % (minutes / 60)) ld_dict = ld.load_search_info() d = {**ld_dict} time.sleep(60) t1 = threading.Thread(target=mixin_dict, name='loop') t1.start() # docker的配置 HOST = '172.18.0.1' PORT = 12399 # 这里是coolq接收到qq信息，然后发送到这个python服务的端口。 # 所以也就是这个python服务，接收到这个消息的端口 # 在 coolq 的docker容器里，这个是在 */coolq/app/io.github.richardchien.coolqhttpapi/config/(qq号).ini 里配置的 # 由于容器不能通过 127.0.0.1 直接访问宿主机的端口，因此，需要通过执行 ip addr show docker0 命令来查看宿主机的端口 # 举例来说，我的server执行这个命令，获得的宿主机的 ip 是 172.18.0.1 （即，容器访问 172.18.0.1 这个地址是访问宿主机） # 于是修改那个ini配置文件：post_url = http://172.18.0.1:34519 # 这里的host可以保持要和那个ip地址保持一样，port也是 bot.run(host=HOST, port=PORT)

31.518519

135

0.647239

526

4,255

5.171103

0.448669

0.052574

0.066912

0.114706

0.118015

0.041544

0.029412

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0.102394

0.185194

4,255

134

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31.753731

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0.016129

0

null

0

null

0

1

0

d43272fbb73b6c6b6b7fe45a8e85aee3304ff420

1,359

py

Python

src/9/enforcing_type_checking_on_a_function_using_a_decorator/example.py

tuanavu/python-gitbook

948a05e065b0f40afbfd22f697dff16238163cde

[ "MIT" ]

14

2017-05-20T04:06:46.000Z

2022-01-23T06:48:45.000Z

src/9/enforcing_type_checking_on_a_function_using_a_decorator/example.py

tuanavu/python-gitbook

948a05e065b0f40afbfd22f697dff16238163cde

[ "MIT" ]

1

2021-06-10T20:17:55.000Z

src/9/enforcing_type_checking_on_a_function_using_a_decorator/example.py

tuanavu/python-gitbook

948a05e065b0f40afbfd22f697dff16238163cde

[ "MIT" ]

15

2017-03-29T17:57:33.000Z

2021-08-24T02:20:08.000Z

from inspect import signature from functools import wraps def typeassert(*ty_args, **ty_kwargs): def decorate(func): # If in optimized mode, disable type checking if not __debug__: return func # Map function argument names to supplied types sig = signature(func) bound_types = sig.bind_partial(*ty_args, **ty_kwargs).arguments @wraps(func) def wrapper(*args, **kwargs): bound_values = sig.bind(*args, **kwargs) # Enforce type assertions across supplied arguments for name, value in bound_values.arguments.items(): if name in bound_types: if not isinstance(value, bound_types[name]): raise TypeError( 'Argument {} must be {}'.format(name, bound_types[name]) ) return func(*args, **kwargs) return wrapper return decorate # Examples @typeassert(int, int) def add(x, y): return x + y @typeassert(int, z=int) def spam(x, y, z=42): print(x, y, z) if __name__ == '__main__': print(add(2,3)) try: add(2, 'hello') except TypeError as e: print(e) spam(1, 2, 3) spam(1, 'hello', 3) try: spam(1, 'hello', 'world') except TypeError as e: print(e)

26.134615

84

0.557027

166

1,359

4.421687

0.415663

0.054496

0.021798

0.038147

0.065395

0

0.012209

0.337013

1,359

51

85

26.647059

0.802442

0.108904

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0.041494

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0.078947

1

0.131579

false

0

0.052632

0.026316

0.315789

0.105263

0

null

0

null

0

1

0

d433b3494ae66595b5f31b0ee2375f4a611bbe3e

2,230

py

Python

muk_web_theme/muk_dms_actions/tests/test_file.py

marionumza/vocal_v12

480990e919c9410903e06e7813ee92800bd6a569

[ "Unlicense" ]

null

muk_web_theme/muk_dms_actions/tests/test_file.py

marionumza/vocal_v12

480990e919c9410903e06e7813ee92800bd6a569

[ "Unlicense" ]

null

muk_web_theme/muk_dms_actions/tests/test_file.py

marionumza/vocal_v12

480990e919c9410903e06e7813ee92800bd6a569

[ "Unlicense" ]

1

2021-05-05T07:59:08.000Z

################################################################################### # # Copyright (C) 2017 MuK IT GmbH # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ################################################################################### import os import base64 import logging from odoo.exceptions import AccessError, ValidationError from odoo.addons.muk_utils.tests.common import multi_users from odoo.addons.muk_dms.tests.common import setup_data_function from odoo.addons.muk_dms.tests.test_file import FileTestCase _path = os.path.dirname(os.path.dirname(__file__)) _logger = logging.getLogger(__name__) class FileActionTestCase(FileTestCase): def setUp(self): super(FileActionTestCase, self).setUp() self.action = self.env['muk_dms_actions.action'].sudo() @multi_users(lambda self: self.multi_users()) @setup_data_function(setup_func='_setup_test_data') def test_available_actions(self): self.action.create({'name': "Test 01"}) self.action.create({'name': "Test 02", 'is_limited_to_single_file': True}) self.action.create({'name': "Test 03", 'criteria_directory': self.new_root_directory.id}) self.action.create({'name': "Test 04", 'criteria_directory': self.new_sub_directory.id}) self.assertTrue(len(self.new_file_root_directory.actions) == 3) self.assertTrue(len(self.new_file_root_directory.actions_multi) == 2) self.assertTrue(len(self.new_file_sub_directory.actions) == 4) self.assertTrue(len(self.new_file_sub_directory.actions_multi) == 3)

44.6

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0.233999

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0

0.010133

0.159193

2,230

50

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44.6

0.7648

0.309865

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0

0.166667

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0.083333

false

0

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0.416667

0

null

0

null

0

1

0

d4343b5d871290ec2f4ca3f7e5cf287d0875613d

2,500

py

Python

populate.py

yueguoguo/shopify-flask-example

dc556aeac21bfcd9bbd5fdeece5cd4827e090055

[ "Apache-2.0" ]

null

populate.py

yueguoguo/shopify-flask-example

dc556aeac21bfcd9bbd5fdeece5cd4827e090055

[ "Apache-2.0" ]

null

populate.py

yueguoguo/shopify-flask-example

dc556aeac21bfcd9bbd5fdeece5cd4827e090055

[ "Apache-2.0" ]

null

# Mock method to populate data to a Shopify store # This is used only for development purpose import io import requests import random from typing import List from time import sleep import shopify from faker import Faker ACCESS_TOKEN = "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" # Temporary use only API_VERSION = "2020-10" SHOP_URL = "fromairstore.myshopify.com" class Populate: def __init__(self, access_token: str, shop_url: str, api_version: str): """ Initialize a populate object Args: access_token (str): shopify API access token shop_url (str): shopify shop URL api_version (str): shopify API version """ self.token = access_token self.shop_url = shop_url self.api_version = api_version random.seed(42) session = shopify.Session(shop_url, api_version, access_token) shopify.ShopifyResource.activate_session(session) self.existing_customers = None self.existing_products = None def get_customers(self) -> List: if not self.existing_customers: self.existing_customers = shopify.Customer.find() return self.existing_customers def get_products(self) -> List: if not self.existing_products: self.existing_products = shopify.Product.find() return self.existing_products def generate_customer(self): """Add customers with random fake information to the shop """ fake = Faker() names = fake.name().split(' ') customer = shopify.Customer() customer.first_name = names[0] customer.last_name = names[1] customer.email = "{0}{1}@gmail.com".format(names[0], names[1]) customer.save() def generate_products(self): """Generate fake products """ NotImplemented def generate_order(self): """Generate an order for a customer to purchase a product """ customer = random.choice(self.get_customers()) product = random.choice(self.get_products()) order = shopify.Order() order.customer = { "first_name": customer.first_name, "last_name": customer.last_name, "email": customer.email } order.fulfillment_status = "fulfilled" order.line_items = [ { "title": product.title, "quantity": 1, "price": product.price_range() } ] order.save() if __name__ == "__main__": populator = Populate(access_token=ACCESS_TOKEN, shop_url=SHOP_URL, api_version=API_VERSION) # generate 5 fake customers for _ in range(5): populator.generate_customer() sleep(0.5) # generate 10 fake orders with random customer and product for _ in range(5): populator.generate_order() sleep(1)

23.809524

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0.029027

0.06033

0

0.019212

0.1672

2,500

104

93

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0

1

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false

0

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0

0.25

0

null

0

null

0

1

0

d43594d2c1b672c278943517c110acaf9fee2089

1,067

py

Python

examples/VNH5019_example.py

being24/VNH5019

1030050d363991e43f63befbb052c423c3470156

[ "MIT" ]

null

examples/VNH5019_example.py

being24/VNH5019

1030050d363991e43f63befbb052c423c3470156

[ "MIT" ]

null

examples/VNH5019_example.py

being24/VNH5019

1030050d363991e43f63befbb052c423c3470156

[ "MIT" ]

null

# !/usr/bin/env python3 # -*- coding: utf-8 -*- import time from logging import DEBUG, ERROR, FATAL, INFO, WARN import pigpio from VNH5019_driver import VNH5019 as MOTOR if __name__ == "__main__": count = 0.0 one_count = 360 * 4 / (64 * 50) pi = pigpio.pi() motor0 = MOTOR( pi, driver_out1=20, driver_out2=21, encoder_in1=5, encoder_in2=6, pwm_channel=0, gear_ratio=150, logging_level=WARN) motor1 = MOTOR( pi, driver_out1=23, driver_out2=24, encoder_in1=27, encoder_in2=22, pwm_channel=1, gear_ratio=50, logging_level=WARN) time.sleep(3) motor0.rotate_motor(pwm_duty_cycle=500, rotation_angle=180) motor1.rotate_motor(pwm_duty_cycle=500, rotation_angle=180) #motor0.drive(pwm_duty_cycle=4095) #motor1.drive(pwm_duty_cycle=4095) time.sleep(3) print("-" * 10) print(motor0.get_current_angle()) print(motor1.get_current_angle()) # メモ I制御を導入して平滑化したい、ゲインにスピードの逆数かけるのやめたい

20.921569

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0.200318

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0

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0

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0.090909

0

null

0

null

0

1

0

d43f4c8e8daff28f9449dbacb0f541d11f998d5f

326

py

Python

stockUpdater/update.py

SamrathPalSingh/stockmarketwebsite

ea91647e25066c1a5c7f48015dccd19117428e9b

[ "MIT" ]

null

stockUpdater/update.py

SamrathPalSingh/stockmarketwebsite

ea91647e25066c1a5c7f48015dccd19117428e9b

[ "MIT" ]

9

2020-05-05T18:43:29.000Z

2021-09-22T18:58:59.000Z

stockUpdater/update.py

SamrathPalSingh/stockmarketwebsite

ea91647e25066c1a5c7f48015dccd19117428e9b

[ "MIT" ]

null

from .stock_prediction_logic.analyse import start_analysis from home.models import stock def updateStocks(): # start_analysis() obj = stock.objects.get(stockSymbol = "AAPL") obj.macd_trend = 'be' obj.rank = int(4) import datetime now = datetime.datetime.now() obj.volume = str(now) obj.save()

25.076923

58

0.687117

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0.4

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null

0

null

0

1

0

d43fb21fc63432d8db4d6094061fd258a4b6f29b

790

py

Python

train.py

jindal2309/conv-ai-model

2238780bdc01965a97726edaeb834cda7ed73867

[ "MIT" ]

null

train.py

jindal2309/conv-ai-model

2238780bdc01965a97726edaeb834cda7ed73867

[ "MIT" ]

null

train.py

jindal2309/conv-ai-model

2238780bdc01965a97726edaeb834cda7ed73867

[ "MIT" ]

null

from torch.utils.data.dataloader import DataLoader from dataloader import ConvAIDataset from utils import combine_contexts from vocab.text import BPEVocab max_seq_len = 512 train_data = 'data/train_self_revised_no_cands.txt' bpe_vocab_path = 'vocab/bpe.vocab' bpe_codes_path = 'vocab/bpe.code' params = {'batch_size': 64, 'shuffle': True, 'num_workers': 2, 'collate_fn': combine_contexts} if __name__ == '__main__': vocab = BPEVocab.from_files(bpe_vocab_path, bpe_codes_path) dataset = ConvAIDataset(filename=train_data, max_seq_len=max_seq_len, bpe_vocab=vocab) dataloader = DataLoader(dataset, **params) for i, (contexts, targets) in enumerate(dataloader): print(i, contexts, targets) exit(0)

30.384615

94

0.702532

103

790

5.048544

0.495146

0.061538

0.051923

0

0.011111

0.202532

790

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0.055556

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null

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null

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1

0

d440b9f21f95bdb0021bcda32bda78128d97dda1

1,415

py

Python

leetcode/binary_tree_level _order_traversal_leetcode_102/binary_tree_level _order_traversal.py

Williano/Interview-Prep

0ad688637215080c7e4d26c640d74c89227e7cfb

[ "MIT" ]

null

leetcode/binary_tree_level _order_traversal_leetcode_102/binary_tree_level _order_traversal.py

Williano/Interview-Prep

0ad688637215080c7e4d26c640d74c89227e7cfb

[ "MIT" ]

null

leetcode/binary_tree_level _order_traversal_leetcode_102/binary_tree_level _order_traversal.py

Williano/Interview-Prep

0ad688637215080c7e4d26c640d74c89227e7cfb

[ "MIT" ]

null

""" Leetcode No: 102 Title: Binary Tree Level Order Traversal Description: Given the root of a binary tree, return the level order traversal of its nodes' values. (i.e., from left to right, level by level). Example 1: Input: root = [3,9,20,null,null,15,7] Output: [[3],[9,20],[15,7]] Example 2: Input: root = [1] Output: [[1]] Example 3: Input: root = [] Output: [] """ from typing import Optional, List from collections import deque class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def level_order(self, root: Optional[TreeNode]) -> List[List[int]]: if root is None: return root tree_queue = deque() tree_queue.append(root) level_order_traversal = [] while tree_queue: tree_levels = [] for _ in range(len(tree_queue)): current_node = tree_queue.popleft() if current_node: tree_levels.append(current_node.val) if current_node.left: tree_queue.append(current_node.left) if current_node.right: tree_queue.append(current_node.right) level_order_traversal.append(tree_levels) return level_order_traversal

21.769231

76

0.579505

176

1,415

4.494318

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0.079646

0.120101

0.055626

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1,415

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0.076923

0

0.307692

0

null

0

null

0

1

0

d4430fca441867f9bd80ffce1daeda8281c206ea

5,785

py

Python

Validation/Performance/test/ThreadTest.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

3

2018-08-24T19:10:26.000Z

2019-02-19T11:45:32.000Z

Validation/Performance/test/ThreadTest.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

7

2016-07-17T02:34:54.000Z

2019-08-13T07:58:37.000Z

Validation/Performance/test/ThreadTest.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

5

2018-08-21T16:37:52.000Z

2020-01-09T13:33:17.000Z

#!/usr/bin/env python #A script to test threading concepts/implementations from __future__ import print_function import threading,time,sys #The following used from past bad experience with multithreading in Python def _cleanup(): pass #Define the "thread" class, of which each thread is a instance class TestThread(threading.Thread): #Constructor with 2 arguments def __init__(self,Name,Cpu,**kwargs): #Name and Cpu are equivalent to any number of options necessary to instantiate the Test() self.Name=Name self.Cpu=Cpu self.kwargs=kwargs threading.Thread.__init__(self) #Actual function executed at the invocation of thread's start() method def run(self): #Instantiate the test of class Test() self.Test=Test(self.Name,self.Cpu,**(self.kwargs)) #This is the function used to really activate the test #Launch it! self.Test.runTest() return #Define the class Test, of which each test (executed in a thread or not) is an instance class Test: #Constructor with 2 optional arguments def __init__(self,Name="N/A",Cpu="N/A",**kwargs): self.Name=Name self.Cpu=Cpu self.kwargs=kwargs #Initializing some list to keep timestamps for a silly test self.Times=[] print("Initializing Test() instance, value of Name is %s and valud of Cpu is %s"%(self.Name,self.Cpu)) #Silly functions to get back the Name and Cpu arguments originally passed to the Test object def getName(self): return self.Name def getCpu(self): return self.Cpu #The actual test function def runTest(self): print("I am thread Test and I was invoked with arguments Name %s, Cpu %s and optional keyword arguments %s"%(self.Name,self.Cpu,self.kwargs)) self.time=0 while self.time<10: self.Times.append(time.ctime()) time.sleep(1) self.time+=1 print(self.Times) if self.kwargs: print("Testing keyword arguments handling with function invocation") test(**(self.kwargs)) return #Test function for arguments fun ahi="AHI!" def test(cpu='N/A',perfsuitedir=ahi,IgProfEvents='N/A',IgProfCandles='N/A',cmsdriverOptions='N/A',stepOptions='N/A',string="IgProf",profilers='N/A',bypasshlt='N/A',userInputFile='N/A'): print(cpu) print(perfsuitedir) print(userInputFile) #print "Value of Available is: %s"%Available #Playing with classes for variable scope tests: class Pippo: def __init__(self): self.a=0 self.b=1 def test1(self,d): print(d) def test2(self): self.e=self.Pluto(self) self.e.testscope() class Pluto: def __init__(self,mother): self.Me="Pluto" self.mother=mother def testscope(self): #print a #print self.a self.mother.test1(self.Me) def main(): #Testing threading concepts ;) #First set that all 4 cores are available: Available=['0','1','2','3'] #Then populate the list of tests to do: #This list should be a list of arguments with which to run simpleGenReport (except the cpu). TestToDo=['Pippo','Pluto','Paperino','Minnie','Qui','Quo','Qua','Zio Paperone','Banda Bassotti','Archimede','Topolino'] #Now let's set up an infinite loop that will go through the TestToDo list, submit a thread per cpu available from the Available list #using pop. activeThreads={} while True: #If there are cores available and tests to run: print("Main while loop:") print(Available) print(TestToDo) #Logic based on checking for TestToDo first: if TestToDo: print("Still folllowing %s tests to do:"%len(TestToDo)) print(TestToDo) #Test available cores: if Available: print("Hey there is at least one core available!") print(Available) cpu=Available.pop() print("Let's use core %s"%cpu) threadArgument=TestToDo.pop() print("Let's submit job %s on core %s"%(threadArgument,cpu)) print("Instantiating thread") print("Testing the keyword arguments with:") kwargs={'cpu':3,'perfsuitedir':"work",'userInputFile':'TTBAR_GEN,FASTSIM.root'} print(kwargs) threadToDo=TestThread(threadArgument,cpu,**kwargs) print("Starting thread %s"%threadToDo) threadToDo.start() print("Appending thread %s to the list of active threads"%threadToDo) activeThreads[cpu]=threadToDo #If there is no available core, pass, there will be some checking of activeThreads, a little sleep and then another check. else: pass #Test activeThreads: for cpu in activeThreads.keys(): if activeThreads[cpu].isAlive(): pass elif cpu not in Available: print("About to append cpu %s to Available list"%cpu) Available.append(cpu) if set(Available)==set(['0','1','2','3']) and not TestToDo: break else: print("Sleeping and checking again...") time.sleep(1) #Check we broke out of the infinite loop! print("WHEW! We're done... all TestToDo are done...") print(Available) print(TestToDo) #Next: check scenarios #1-many more TestToDo than Available cores #Test 1 done successfully. #2-complicated Test() class that calls other functions with args #3-What happens on the machine with top #4-What if they get killed or hang?

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0.313984

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0.015419

0.012616

0.053546

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0

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0.278479

5,785

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false

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0.28

0

null

0

null

0

1

0

d44510e870ea216676fc2c3138e5db6f9bb2ae40

3,301

py

Python

gui/plot.py

kklmn/ParSeq

46178c21c3ee39b84bbf3d80bcd9f93128ace9e2

[ "MIT" ]

3

2018-11-19T07:14:25.000Z

2020-07-28T17:20:14.000Z

gui/plot.py

kklmn/ParSeq

46178c21c3ee39b84bbf3d80bcd9f93128ace9e2

[ "MIT" ]

null

gui/plot.py

kklmn/ParSeq

46178c21c3ee39b84bbf3d80bcd9f93128ace9e2

[ "MIT" ]

2

2019-03-25T09:36:11.000Z

2021-12-19T07:52:38.000Z

# -*- coding: utf-8 -*- __author__ = "Konstantin Klementiev" __date__ = "16 Feb 2019" # !!! SEE CODERULES.TXT !!! import os from silx.gui import qt from silx.gui import plot as splot from ..core import singletons as csi class Plot1D(splot.PlotWindow): def __init__(self, parent=None, backend=None, position=True): super(Plot1D, self).__init__(parent=parent, backend=backend, resetzoom=True, autoScale=True, logScale=True, grid=True, curveStyle=True, colormap=False, aspectRatio=False, yInverted=False, copy=True, save=True, print_=True, control=True, position=position, roi=False, mask=False, fit=False) if parent is None: self.setWindowTitle('Plot1D') action = self.getFitAction() action.setXRangeUpdatedOnZoom(True) action.setFittedItemUpdatedFromActiveCurve(True) def graphCallback(self, ddict=None): """This callback is going to receive all the events from the plot.""" if ddict is None: ddict = {} if ddict['event'] in ["legendClicked", "curveClicked"]: if ddict['button'] == "left": self.activateCurve(ddict['label']) qt.QToolTip.showText(self.cursor().pos(), ddict['label']) def activateCurve(self, label): alias = os.path.splitext(label)[0] for item in csi.allLoadedItems: if item.alias == alias: break else: return index = csi.model.indexFromItem(item) csi.selectionModel.setCurrentIndex( index, qt.QItemSelectionModel.ClearAndSelect | qt.QItemSelectionModel.Rows) class Plot2D(splot.Plot2D): pass class Plot3D(splot.StackView): posInfo = [ ('Position', None), # None is callback fn set after instantiation ('Value', None)] # None is callback fn set after instantiation def setCustomPosInfo(self): p = self._plot._positionWidget._fields[0] self._plot._positionWidget._fields[0] = (p[0], p[1], self._imagePos) p = self._plot._positionWidget._fields[1] self._plot._positionWidget._fields[1] = (p[0], p[1], self._imageVal) def _imageVal(self, x, y): "used for displaying pixel value under cursor" activeImage = self.getActiveImage() if activeImage is not None: data = activeImage.getData() height, width = data.shape # print(width, height, x, y) x = int(x) y = int(y) return data[y][x] if 0 <= x < width and 0 <= y < height else '' return '-' def _imagePos(self, x, y): "used for displaying pixel coordinates under cursor" img_idx = self._browser.value() if self._perspective == 0: dim0, dim1, dim2 = img_idx, int(y), int(x) elif self._perspective == 1: dim0, dim1, dim2 = int(y), img_idx, int(x) elif self._perspective == 2: dim0, dim1, dim2 = int(y), int(x), img_idx return '{0}, {1}, {2}'.format(dim0, dim1, dim2)

37.511364

77

0.565586

364

3,301

5.016484

0.395604

0.017525

0.048193

0.061336

0.191676

0.075575

0.044907

0

0.018775

0.322327

3,301

87

78

37.942529

0.797497

0.097849

0

0.068211

0

1

0.085714

false

0.014286

0.057143

0

0.257143

0.014286

0

null

0

null

0

1

0

d44834c9eaa5437119fdcf7514ce64d66090c5cb

576

py

Python

src/test_app.py

perylemke/immutable_infra

9e7ce2fec2ace5d2efdb598883483142b02ceac0

[ "Apache-2.0" ]

10

2019-05-06T20:48:48.000Z

2020-10-30T21:30:23.000Z

src/test_app.py

perylemke/immutable_infra

9e7ce2fec2ace5d2efdb598883483142b02ceac0

[ "Apache-2.0" ]

null

src/test_app.py

perylemke/immutable_infra

9e7ce2fec2ace5d2efdb598883483142b02ceac0

[ "Apache-2.0" ]

null

from app import app import json import socket import os.path # The first and the second test validate json structure def test_get_status_code(): app.config["TESTING"] = True with app.test_client() as client: response = client.get("/request") assert response.status_code == 200 def test_get_status_msg(): app.config["TESTING"] = True host = socket.gethostname() with app.test_client() as client: response = client.get("/request") assert response.json == { 'response': "Respeitem o isolamento social!" }

27.428571

56

0.661458

74

576

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0.472973

0.048387

0.053763

0.086022

0.33871

0

0.006803

0.234375

576

21

57

27.428571

0.836735

0.092014

0

0.352941

0

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0

0.117647

1

0.117647

false

0

0.235294

0

0.352941

0

null

0

null

0

1

0

d44a00f74c482c7832f425339e2417ab5c29c7e3

35,663

py

Python

lingvodoc/scripts/docx_import.py

SegFaulti4/lingvodoc

8b296b43453a46b814d3cd381f94382ebcb9c6a6

[ "Apache-2.0" ]

null

lingvodoc/scripts/docx_import.py

SegFaulti4/lingvodoc

8b296b43453a46b814d3cd381f94382ebcb9c6a6

[ "Apache-2.0" ]

1

2021-07-26T09:52:46.000Z

lingvodoc/scripts/docx_import.py

Winking-maniac/lingvodoc

f037bf0e91ccdf020469037220a43e63849aa24a

[ "Apache-2.0" ]

null

# Standard library imports. import ast import getopt import logging import pprint import re import sys # External imports. import docx import pympi import pyramid.paster as paster # Project imports. from lingvodoc.models import ( DBSession, Dictionary, ) # Setting up logging, if we are not being run as a script. if __name__ != '__main__': log = logging.getLogger(__name__) log.debug('module init') def levenshtein( snippet_str, snippet_index, word_str, __debug_levenshtein_flag__ = False): """ Matches word string to the snippet string via adjusted Levenshtein matching, with no penalties for snippet string skipping before and after match. """ d = {(0, j): (j, 1e256) for j in range(len(word_str) + 1)} for i in range(len(snippet_str) - snippet_index): d[(i + 1, 0)] = (0, 1e256) minimum_distance = len(word_str) minimum_begin_index = 0 minimum_end_index = 0 for i in range(1, len(snippet_str) - snippet_index + 1): if __debug_levenshtein_flag__: log.debug( 'd[{0}, 0]: {1}'.format(i, d[(i, 0)])) for j in range(1, len(word_str) + 1): # Matching current characters of the word and snippet strings. s_distance, s_begin_index = d[i - 1, j - 1] substitution_value = s_distance + ( 0 if snippet_str[snippet_index + i - 1] == word_str[j - 1] else 1) substitution_index = min(s_begin_index, i - 1) # Skipping current character from the snippet string. d_distance, d_begin_index = d[i - 1, j] deletion_value = d_distance + ( 1 if j < len(word_str) else 0) deletion_index = d_begin_index # Skipping current character from the word string. i_distance, i_begin_index = d[i, j - 1] insertion_value = i_distance + 1 insertion_index = i_begin_index # Getting minimum. minimum_value = min( substitution_value, deletion_value, insertion_value) if minimum_value == deletion_value: operation_index = 1 minimum_index = deletion_index elif minimum_value == insertion_value: operation_index = 2 minimum_index = insertion_index else: operation_index = 0 minimum_index = substitution_index d[(i, j)] = (minimum_value, minimum_index) # Showing edit distance computation details. if __debug_levenshtein_flag__: log.debug( '\nd[{0}, {1}] (\'{18}\' & \'{14}\'): {4}' '\n d[{5}, {6}] (\'{2}\' & \'{3}\'): {9} + {10}{11} (\'{12}\', \'{13}\')' '\n d[{5}, {1}] (\'{2}\' & \'{14}\'): {15} + {16}{17}' '\n d[{0}, {6}] (\'{18}\' & \'{3}\'): {19} + 1{20}'.format( i, j, snippet_str[snippet_index : snippet_index + i - 1] + '|' + snippet_str[snippet_index + i - 1], word_str[: j - 1] + '|' + word_str[j - 1], d[(i, j)][0], i - 1, j - 1, snippet_str[snippet_index : snippet_index + i - 1] + '|', word_str[: j - 1] + '|', d[(i - 1, j - 1)][0], 0 if snippet_str[snippet_index + i - 1] == word_str[j - 1] else 1, '*' if operation_index == 0 else '', snippet_str[snippet_index + i - 1], word_str[j - 1], word_str[:j] + '|', d[(i - 1, j)][0], 1 if j < len(word_str) else 0, '*' if operation_index == 1 else '', snippet_str[snippet_index : snippet_index + i] + '|', d[(i, j - 1)][0], '*' if operation_index == 2 else '')) # Checking if we have a new best matching. if d[i, len(word_str)][0] < minimum_distance: minimum_distance, minimum_begin_index = d[i, len(word_str)] minimum_end_index = i if minimum_distance == 0: break return ( minimum_distance, minimum_begin_index, minimum_end_index) def prepare_match_string(cell_str): """ Processes string for matching, finding and marking portions in parentheses to be considered as optional during matching. """ chr_list = [] chr_index = 0 for match in re.finditer(r'$[^()]*?$', cell_str): for chr in re.sub( r'\W+', '', cell_str[chr_index : match.start()]): chr_list.append((chr, False)) for chr in re.sub( r'\W+', '', match.group(0)): chr_list.append((chr, True)) chr_index = match.end() for chr in re.sub( r'\W+', '', cell_str[chr_index:]): chr_list.append((chr, False)) return chr_list def format_match_string(marked_chr_list): """ Formats list of marked characters as a string. """ chr_list = [] mark_prev = False for chr, mark in marked_chr_list: if mark != mark_prev: chr_list.append('(' if mark else ')') chr_list.append(chr) mark_prev = mark if mark_prev: chr_list.append(')') return ''.join(chr_list) class State(object): """ State of snippet table parsing. """ def __init__(self, snippet_str, cell_list, row_index): """ Initialization with the contents of the first snippet string. """ self.snippet_count = 0 self.snippet_chain = None self.snippet_str = snippet_str self.row_index = row_index self.row_list = [cell_list] self.d0 = [] self.d1 = [0.999 * i for i in range(len(self.snippet_str) + 1)] self.word_list = [] self.word_str = [] self.total_value = 0 self.snippet_value = 0 def process_row( self, row_str, cell_list, row_index, __debug_flag__ = False): """ Processing another data string, splitting into a state when it's a word string and another state when it's a new snippet string. """ # First, assuming that this data string is the next snippet string. if row_str: copy = State(row_str, cell_list, row_index) copy.snippet_chain = ( (tuple(self.row_list), self.row_index), self.snippet_chain) copy.snippet_count = self.snippet_count + 1 copy.total_value = self.total_value + self.d1[-1] yield copy # Second, assuming that this data string is a word string. len_prev = len(self.word_str) self.word_list.append(row_str) self.word_str += row_str self.row_list.append(cell_list) # Updating Levenshtein alignment of snippet words to the snippet string. for i in range(len(row_str)): self.d0 = self.d1 self.d1 = [len_prev + i + 1] for j in range(len(self.snippet_str)): # Matching current characters of the snippet string and the word string. s_cost = 0 if self.snippet_str[j][0] == row_str[i][0] else 1 if s_cost and (self.snippet_str[j][1] or row_str[i][1]): s_cost = 0.001 s_value = self.d0[j] + s_cost # Skipping current character either from the snippet string or from the word string. d_value = self.d1[j] + (0.000999 if self.snippet_str[j][1] else 0.999) i_value = self.d0[j + 1] + (0.001 if row_str[i][1] else 1) self.d1.append(min(s_value, d_value, i_value)) # Showing debug info, if required. if __debug_flag__: log.debug(( format_match_string(self.snippet_str[:j]), format_match_string(self.word_str[:len_prev + i]), self.d0[j], self.snippet_str[j][0], row_str[i][0], round(s_value, 6))) log.debug(( format_match_string(self.snippet_str[:j]), format_match_string(self.word_str[:len_prev + i + 1]), self.d1[j], self.snippet_str[j][0], round(d_value, 6))) log.debug(( format_match_string(self.snippet_str[:j + 1]), format_match_string(self.word_str[:len_prev + i]), self.d0[j + 1], row_str[i][0], round(i_value, 6))) log.debug(( format_match_string(self.snippet_str[:j + 1]), format_match_string(self.word_str[:len_prev + i + 1]), round(min(s_value, d_value, i_value), 6))) log.debug(self.d1) # Updating alignment value. if len(self.word_str) <= 0: self.snippet_value = 0 elif len(self.word_str) > len(self.snippet_str): self.snippet_value = self.d1[-1] else: self.snippet_value = min( self.d1[len(self.word_str) : 2 * len(self.word_str)]) yield self def beam_search_step( state_list, cell_str, cell_list, row_index, beam_width, __debug_beam_flag__ = False): """ Another step of alignment beam search. """ if not state_list: return [State( cell_str, cell_list, row_index)] # Sorting parsing states by the snippet they are parsing. state_dict = {} for state in state_list: for state_after in state.process_row( cell_str, cell_list, row_index): index = state_after.row_index # Leaving only states with the best snippet histories. if (index not in state_dict or state_after.total_value < state_dict[index][0]): state_dict[index] = (state_after.total_value, [state_after]) elif state_after.total_value == state_dict[index][0]: state_dict[index][1].append(state_after) state_list = [] for value, state_after_list in state_dict.values(): state_list.extend(state_after_list) # Showing snippet alignment beam search state, if required. if __debug_beam_flag__: log.debug('\n' + pprint.pformat([( round(state.total_value + state.snippet_value, 6), state.snippet_count, format_match_string(state.snippet_str), '|'.join( format_match_string(word_str) for word_str in state.word_list)) for state in state_list], width = 384)) # Leaving only a number of best states. state_list.sort(key = lambda state: (state.total_value + state.snippet_value, state.snippet_count)) return state_list[:beam_width] def parse_table( row_list, limit = None, __debug_beam_flag__ = False): """ Tries to parse snippet data represented as a table. """ # Removing any snippet alignment marks, if we have any. for cell_list in row_list: for i in range(len(cell_list)): match = re.match(r'$__\d+__$\s*', cell_list[i]) if match: cell_list[i] = cell_list[i][match.end():] state_list = [] beam_width = 32 # Going through snippet data. for row_index, cell_list in enumerate(row_list[1:], 1): if limit and row_index > limit: break if not any(cell_list[:3]): continue cell_str = ( prepare_match_string( cell_list[0].lower())) # Updating alignment search on another row. state_list = ( beam_search_step( state_list, cell_str, cell_list, row_index, beam_width, __debug_beam_flag__)) # Returning final parsing search state. return state_list def parse_by_paragraphs( row_list, limit = None, __debug_flag__ = False, __debug_beam_flag__ = False): """ Tries to parse snippet data with paragraph separation inside table cells. """ # Splitting row texts by paragraphs. line_row_list = [] line_row_count = 0 for cell_list in row_list[1:]: if limit and line_row_count >= limit: break paragraph_list_list = [ re.split(r'[^\S\n]*\n\s*', text) for text in cell_list] how_many = max( len(paragraph_list) for paragraph_list in paragraph_list_list[:3]) # Iterating over aligned paragraphs in adjacent cells. line_rank_list = [] for i in range(how_many): line_cell_list = [] for paragraph_list in paragraph_list_list: if i < len(paragraph_list): # Removing snippet alignment mark, if there is one present. cell_str = paragraph_list[i] match = re.match(r'$__\d+__$\s*', cell_str) line_cell_list.append( cell_str[match.end():] if match else cell_str) else: line_cell_list.append('') # Another line row, if it is non-empty. if any(line_cell_list): line_rank_list.append(line_cell_list) line_row_count += 1 if limit and line_row_count >= limit: break line_row_list.append(line_rank_list) # Showing what we have, if required. if __debug_flag__: log.debug( '\nrow_list:\n{0}'.format( pprint.pformat( row_list, width = 196))) state_list = [] beam_width = 32 line_row_count = 0 # Going through snippet data. for row_index, line_rank_list in enumerate(line_row_list): if limit and line_row_count >= limit: break for line_index, line_cell_list in enumerate(line_rank_list): line_cell_str = ( prepare_match_string( line_cell_list[0].lower())) # Updating alignment search on another row. state_list = ( beam_search_step( state_list, line_cell_str, line_cell_list, (row_index, line_index), beam_width, __debug_beam_flag__)) # Returning final parsing search state. return state_list def main_import(args): """ Test import of 5-tier data from a Docx file. """ opt_list, arg_list = ( getopt.gnu_getopt(args, '', [ 'all-tables', 'check-docx-file=', 'check-file=', 'debug', 'debug-beam', 'debug-eaf', 'eaf-file=', 'limit=', 'modify-docx-file', 'no-db', 'separate-by-paragraphs'])) opt_dict = dict(opt_list) # Parsing command-line options. docx_path = arg_list[0] check_file_path = opt_dict.get('--check-file') check_docx_file_path = opt_dict.get('--check-docx-file') eaf_file_path = opt_dict.get('--eaf-file') limit = ( ast.literal_eval(opt_dict['--limit']) if '--limit' in opt_dict else None) modify_docx_flag = '--modify-docx-file' in opt_dict separate_by_paragraphs_flag = '--separate-by-paragraphs' in opt_dict __debug_flag__ = '--debug' in opt_dict __debug_beam_flag__ = '--debug-beam' in opt_dict __debug_eaf_flag__ = '--debug-eaf' in opt_dict # Processing specified Docx file. log.debug( '\ndocx_path: {0}'.format(docx_path)) document = docx.Document(docx_path) if len(document.tables) <= 0: raise NotImplementedError # Accessing info of the first table, or all tables, depending on the options. # # Counting only unique cells because apparently some .docx documents can have repeating cells in their # structure. row_list = [] table_list = ( document.tables if '--all-tables' in opt_dict else document.tables[:1]) for table_index, table in enumerate(table_list): column_count = len(set(table.rows[0].cells)) row_count = len(set(table.columns[0].cells)) table_cell_list = list(table._cells) source_cell_list = [] source_cell_set = set() for cell in table_cell_list: if cell not in source_cell_set: source_cell_list.append(cell) source_cell_set.add(cell) # Checking for non-uniform rows / columns. if len(source_cell_list) != column_count * row_count: log.error( '\nTable ({}): rows and / or columns are uneven, ' '{} rows, {} columns, {3} != {1} * {2} cells.'.format( table_index, row_count, column_count, len(source_cell_list))) raise NotImplementedError row_list.extend( [cell.text for cell in source_cell_list[ i * column_count : (i + 1) * column_count]] for i in range(row_count)) log.debug( '\ntable ({}): {} columns, {} rows, {} cells'.format( table_index, column_count, row_count, len(source_cell_list))) # Processing this info. header_list = row_list[0] log.debug( '\nheader: {0}'.format(header_list)) if separate_by_paragraphs_flag: state_list = parse_by_paragraphs( row_list, limit, __debug_flag__, __debug_beam_flag__) else: state_list = parse_table( row_list, limit, __debug_beam_flag__) # Showing final alignment search state, if required. if __debug_beam_flag__: log.debug('\n' + pprint.pformat([( round(state.total_value + state.snippet_value, 6), state.snippet_count, format_match_string(state.snippet_str), '|'.join( format_match_string(word_str) for word_str in state.word_list)) for state in state_list], width = 384)) # Getting all parsed snippets, if we need them. if (eaf_file_path is not None or check_file_path is not None or check_docx_file_path is not None or modify_docx_flag): if not state_list: log.debug('\nno data') return best_state = state_list[0] snippet_chain = ( (tuple(best_state.row_list), best_state.row_index), best_state.snippet_chain) snippet_list = [] # Compiling snippet list, showing it, if required. while snippet_chain is not None: (row_tuple, row_index), snippet_chain = snippet_chain snippet_list.append((list(row_tuple), row_index)) snippet_list.reverse() if __debug_flag__: log.debug( '\nsnippet_list:\n{0}'.format( pprint.pformat( snippet_list, width = 196))) # Saving parsed alignment, if required. if check_file_path is not None: with open( check_file_path, 'w', encoding = 'utf-8') as check_file: check_file.write('\n') # Showing each parsed snippet. for i, (snippet_value_list, snippet_value_index) in enumerate(snippet_list): check_file.write( '{0}\n'.format(i + 1)) value = snippet_value_list[0] check_file.write( (value if isinstance(value, str) else value[0]) + '\n') for value in snippet_value_list[1:]: check_file.write(' ' + (value if isinstance(value, str) else value[0]) + '\n') check_file.write('\n') # Saving parsing alignment as Docx file, if required. if check_docx_file_path is not None: if separate_by_paragraphs_flag: raise NotImplementedError check_docx = docx.Document() check_table = check_docx.add_table( rows = row_count - 1 + len(snippet_list), cols = 3) table_cell_list = check_table._cells table_cell_index = 0 # Exporting all parsed snippets with their numbers. for i, (snippet_row_list, snippet_row_index) in enumerate(snippet_list): table_cell_list[table_cell_index].text = '{0}'.format(i + 1) table_cell_index += 3 for cell_list in snippet_row_list: for table_cell, snippet_cell in zip( table_cell_list[table_cell_index : table_cell_index + 3], cell_list): table_cell.text = snippet_cell table_cell_index += 3 check_docx.save(check_docx_file_path) # Saving parsed snippets as the standard 5-tier EAF structure. if eaf_file_path is not None: log.debug('\n' + pprint.pformat(snippet_list, width = 196)) eaf = pympi.Elan.Eaf() eaf.add_linguistic_type('text_top_level') eaf.add_linguistic_type('symbolic_association', 'Symbolic_Association', False) eaf.add_linguistic_type('word_translation_included_in', 'Included_In') eaf.remove_linguistic_type('default-lt') # Showing linguistic types info, if required. if __debug_eaf_flag__: log.debug( '\nget_linguistic_type_names(): {0}'.format(eaf.get_linguistic_type_names())) log.debug(''.join( '\nget_parameters_for_linguistic_type({0}): {1}'.format( repr(name), eaf.get_parameters_for_linguistic_type(name)) for name in eaf.get_linguistic_type_names())) eaf.add_tier('text', 'text_top_level') eaf.add_tier('other text', 'symbolic_association', 'text') eaf.add_tier('literary translation', 'symbolic_association', 'text') eaf.add_tier('translation', 'word_translation_included_in', 'text') eaf.add_tier('transcription', 'symbolic_association', 'translation') eaf.add_tier('word', 'symbolic_association', 'translation') eaf.remove_tier('default') # Showing tier info, if required. if __debug_eaf_flag__: log.debug( '\nget_tier_names(): {0}'.format(eaf.get_tier_names())) log.debug(''.join( '\nget_parameters_for_tier({0}): {1}'.format( repr(name), eaf.get_parameters_for_tier(name)) for name in eaf.get_tier_names())) # Compiling annotation data. step = 75 position = step for snippet_value_list, snippet_value_index in snippet_list: # Snippet base texts. text, text_other, text_translation = snippet_value_list[0] duration = len(text) * step eaf.add_annotation( 'text', position, position + duration, text) eaf.add_ref_annotation( 'other text', 'text', position, text_other) eaf.add_ref_annotation( 'literary translation', 'text', position, text_translation) # Preparing to create annotations for snippet words. translation_position = position translation_length = ( sum(len(text_list[0] or text_list[2] or text_list[1]) for text_list in snippet_value_list[1:]) + len(snippet_value_list) - 2) translation_position = position translation_step = duration // translation_length # Snippet words. for text_list in snippet_value_list[1:]: word, word_other, translation = text_list translation_duration = ( round( max(len(word or translation or word_other), 1) * translation_step)) eaf.add_annotation( 'translation', translation_position, translation_position + translation_duration, translation) eaf.add_ref_annotation( 'transcription', 'translation', translation_position, word_other) eaf.add_ref_annotation( 'word', 'translation', translation_position, word) translation_position += ( translation_duration + translation_step) # Ready to go to the next snippet. position += duration + step # Showing annotation info, if required. if __debug_eaf_flag__: log.debug(''.join( '\nget_annotation_data_for_tier({0}):\n{1}'.format( repr(name), eaf.get_annotation_data_for_tier(name)[:4]) for name in eaf.get_tier_names())) eaf.header['TIME_UNITS'] = 'milliseconds' eaf.to_file(eaf_file_path) # Modifying source Docx file with alignment marks, if required. if modify_docx_flag: if not separate_by_paragraphs_flag: for i, (snippet_row_list, snippet_row_index) in enumerate(snippet_list): mark_str = '(__{0}__)\n'.format(i + 1) cell_index = snippet_row_index * column_count for j, cell in enumerate( source_cell_list[cell_index : cell_index + 3]): # Right now can't do something like # # cell.paragraphs[0].insert_paragraph_before(mark_str), # # because, if there is a mark there already, we should delete it, and tracking this # deletion across all possible paragraphs and runs in the cell is too high complexity. cell.text = mark_str + snippet_row_list[0][j] document.save(docx_path) # When tables are separated by paragraphs. else: snippet_index = 0 snippet_row_index, snippet_rank_index = snippet_list[snippet_index][1] for row_index, cell_list in enumerate(row_list[1:]): # Along the lines of data extraction from such tables, see 'parse_by_paragraphs()' function. paragraph_list_list = [ re.split(r'([^\S\n]*\n\s*)', text) for text in cell_list] for i, paragraph_list in enumerate(paragraph_list_list): paragraph_list.append('') paragraph_list_list[i] = list( zip(paragraph_list[::2], paragraph_list[1::2])) line_list_list = [[] for text in cell_list] how_many = max( len(paragraph_list) for paragraph_list in paragraph_list_list[:3]) # Iterating over aligned paragraphs in adjacent cells. line_rank_count = 0 for i in range(how_many): line_cell_list = [] if (snippet_index is not None and row_index == snippet_row_index and line_rank_count == snippet_rank_index): mark_str = '(__{0}__)\n'.format(snippet_index + 1) for line_list in line_list_list: line_list.append(mark_str) # Next snippet. snippet_index += 1 if snippet_index >= len(snippet_list): snippet_index = None else: snippet_row_index, snippet_rank_index = ( snippet_list[snippet_index][1]) for paragraph_list, line_list in zip( paragraph_list_list, line_list_list): if i < len(paragraph_list): # Removing previous snippet alignment mark, if there is one present. cell_str, separator_str = paragraph_list[i] match = re.match(r'$__\d+__$\s*', cell_str) if match: cell_str = cell_str[match.end():] line_cell_list.append(cell_str) line_list.append(cell_str + separator_str) # Another line row, if it is non-empty. if any(line_cell_list): line_rank_count += 1 else: for line_list in line_list_list: line_list.pop() # Replacing contents of another table cell. cell_index = (row_index + 1) * column_count for cell, line_list in zip( source_cell_list[cell_index : cell_index + 3], line_list_list): match = re.fullmatch( r'(.*?)[^\S\n]*\n[^\S\n]*', line_list[0], re.DOTALL) cell.text = match.group(1) if match else line_list[0] # Splitting text into distinct paragraphs because otherwise at least LibreOffice writer # starts to take too much time to process resulting documents. for line in line_list[1:]: match = re.fullmatch( r'(.*?)[^\S\n]*\n[^\S\n]*', line, re.DOTALL) cell.add_paragraph( match.group(1) if match else line, 'Normal') # Saving Docx file updates. document.save(docx_path) def main_eaf(args): """ Showing structure of a specified Eaf file. """ for eaf_path in args: log.debug( '\neaf_path: {0}'.format(eaf_path)) eaf = pympi.Elan.Eaf(eaf_path) log.debug( '\nget_controlled_vocabulary_names(): {0}'.format(eaf.get_controlled_vocabulary_names())) log.debug( '\nget_external_ref_names(): {0}'.format(eaf.get_external_ref_names())) log.debug( '\nget_languages(): {0}'.format(eaf.get_languages())) log.debug( '\nget_lexicon_ref_names(): {0}'.format(eaf.get_lexicon_ref_names())) log.debug( '\nget_licenses(): {0}'.format(eaf.get_licenses())) log.debug( '\nget_linguistic_type_names(): {0}'.format(eaf.get_linguistic_type_names())) log.debug( '\nget_linked_files(): {0}'.format(eaf.get_linked_files())) log.debug( '\nget_locales(): {0}'.format(eaf.get_locales())) log.debug( '\nget_properties(): {0}'.format(eaf.get_properties())) log.debug( '\nget_secondary_linked_files(): {0}'.format(eaf.get_secondary_linked_files())) log.debug( '\nget_tier_names(): {0}'.format(eaf.get_tier_names())) log.debug('\n' + pprint.pformat(eaf.linguistic_types, width = 196)) # L-type and tier parameters. log.debug(''.join( '\nget_parameters_for_linguistic_type({0}): {1}'.format( repr(name), eaf.get_parameters_for_linguistic_type(name)) for name in eaf.get_linguistic_type_names())) log.debug(''.join( '\nget_tier_ids_for_linguistic_type({0}): {1}'.format( repr(name), eaf.get_tier_ids_for_linguistic_type(name)) for name in eaf.get_linguistic_type_names())) log.debug(''.join( '\nget_parameters_for_tier({0}): {1}'.format( repr(name), eaf.get_parameters_for_tier(name)) for name in eaf.get_tier_names())) # Select annotations. log.debug(''.join( '\nget_annotation_data_for_tier({0}):\n{1}'.format( repr(name), eaf.get_annotation_data_for_tier(name)[:4]) for name in eaf.get_tier_names())) # Average time interval per character. total_duration = 0 total_length = 0 for name in eaf.get_tier_names(): tier_duration = 0 tier_length = 0 for annotation in eaf.get_annotation_data_for_tier(name): begin, end, text = annotation[:3] tier_duration += end - begin tier_length += len(text) log.debug( '\ntier {0}: {1:.3f} / {2} -> {3:.3f}'.format( repr(name), tier_duration / 1000.0, tier_length, tier_duration / (tier_length * 1000))) total_duration += tier_duration total_length += tier_length log.debug( '\ntotal: {0:.3f} / {1} -> {2:.3f}'.format( total_duration / 1000.0, total_length, total_duration / (total_length * 1000))) # If we are being run as a script. if __name__ == '__main__': if (len(sys.argv) > 1 and sys.argv[1] == '-config'): # We have a configuration file; initializing DB, if required, and logging. config_path = sys.argv[2] if sys.argv[3] != '-no-db': pyramid_env = paster.bootstrap(config_path) arg_list = sys.argv[3:] else: arg_list = sys.argv[4:] paster.setup_logging(config_path) log = logging.getLogger(__name__) else: # No config file, so just logging to stdout. arg_list = sys.argv[1:] log_root = logging.getLogger() log_root.setLevel(logging.DEBUG) log_handler = logging.StreamHandler(sys.stdout) log_handler.setLevel(logging.DEBUG) log_formatter = ( logging.Formatter( '%(asctime)s %(levelname)-5.5s [%(name)s][%(threadName)s] ' '%(pathname)s:%(lineno)d: %(message)s')) log_handler.setFormatter(log_formatter) log_root.addHandler(log_handler) log = logging.getLogger(__name__) # Doing what we need. if len(arg_list) <= 0: log.info( '\nPlease specify a command to execute.') elif arg_list[0] == 'import': main_import(arg_list[1:]) elif arg_list[0] == 'eaf': main_eaf(arg_list[1:]) else: log.warn( '\nUnknown command \'{0}\'.'.format(arg_list[0]))

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d44da7d127e1ce8cb7767fe40398dec1469b4993

4,509

py

Python

mindhome_alpha/erpnext/demo/user/stock.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/demo/user/stock.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

null

mindhome_alpha/erpnext/demo/user/stock.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:39:01.000Z

# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # License: GNU General Public License v3. See license.txt from __future__ import print_function, unicode_literals import frappe, random, erpnext from frappe.desk import query_report from erpnext.stock.stock_ledger import NegativeStockError from erpnext.stock.doctype.serial_no.serial_no import SerialNoRequiredError, SerialNoQtyError from erpnext.stock.doctype.batch.batch import UnableToSelectBatchError from erpnext.stock.doctype.delivery_note.delivery_note import make_sales_return from erpnext.stock.doctype.purchase_receipt.purchase_receipt import make_purchase_return def work(): frappe.set_user(frappe.db.get_global('demo_manufacturing_user')) make_purchase_receipt() make_delivery_note() make_stock_reconciliation() submit_draft_stock_entries() make_sales_return_records() make_purchase_return_records() def make_purchase_receipt(): if random.random() < 0.6: from erpnext.buying.doctype.purchase_order.purchase_order import make_purchase_receipt report = "Purchase Order Items To Be Received" po_list =list(set([r[0] for r in query_report.run(report)["result"] if r[0]!="Total"]))[:random.randint(1, 10)] for po in po_list: pr = frappe.get_doc(make_purchase_receipt(po)) if pr.is_subcontracted=="Yes": pr.supplier_warehouse = "Supplier - WPL" pr.posting_date = frappe.flags.current_date pr.insert() try: pr.submit() except NegativeStockError: print('Negative stock for {0}'.format(po)) pass frappe.db.commit() def make_delivery_note(): # make purchase requests # make delivery notes (if possible) if random.random() < 0.6: from erpnext.selling.doctype.sales_order.sales_order import make_delivery_note report = "Ordered Items To Be Delivered" for so in list(set([r[0] for r in query_report.run(report)["result"] if r[0]!="Total"]))[:random.randint(1, 3)]: dn = frappe.get_doc(make_delivery_note(so)) dn.posting_date = frappe.flags.current_date for d in dn.get("items"): if not d.expense_account: d.expense_account = ("Cost of Goods Sold - {0}".format( frappe.get_cached_value('Company', dn.company, 'abbr'))) try: dn.insert() dn.submit() frappe.db.commit() except (NegativeStockError, SerialNoRequiredError, SerialNoQtyError, UnableToSelectBatchError): frappe.db.rollback() def make_stock_reconciliation(): # random set some items as damaged from erpnext.stock.doctype.stock_reconciliation.stock_reconciliation \ import OpeningEntryAccountError, EmptyStockReconciliationItemsError if random.random() < 0.4: stock_reco = frappe.new_doc("Stock Reconciliation") stock_reco.posting_date = frappe.flags.current_date stock_reco.company = erpnext.get_default_company() stock_reco.get_items_for("Stores - WPL") if stock_reco.items: for item in stock_reco.items: if item.qty: item.qty = item.qty - round(random.randint(1, item.qty)) try: stock_reco.insert(ignore_permissions=True, ignore_mandatory=True) stock_reco.submit() frappe.db.commit() except OpeningEntryAccountError: frappe.db.rollback() except EmptyStockReconciliationItemsError: frappe.db.rollback() def submit_draft_stock_entries(): from erpnext.stock.doctype.stock_entry.stock_entry import IncorrectValuationRateError, \ DuplicateEntryForWorkOrderError, OperationsNotCompleteError # try posting older drafts (if exists) frappe.db.commit() for st in frappe.db.get_values("Stock Entry", {"docstatus":0}, "name"): try: ste = frappe.get_doc("Stock Entry", st[0]) ste.posting_date = frappe.flags.current_date ste.save() ste.submit() frappe.db.commit() except (NegativeStockError, IncorrectValuationRateError, DuplicateEntryForWorkOrderError, OperationsNotCompleteError): frappe.db.rollback() def make_sales_return_records(): if random.random() < 0.1: for data in frappe.get_all('Delivery Note', fields=["name"], filters={"docstatus": 1}): if random.random() < 0.1: try: dn = make_sales_return(data.name) dn.insert() dn.submit() frappe.db.commit() except Exception: frappe.db.rollback() def make_purchase_return_records(): if random.random() < 0.1: for data in frappe.get_all('Purchase Receipt', fields=["name"], filters={"docstatus": 1}): if random.random() < 0.1: try: pr = make_purchase_return(data.name) pr.insert() pr.submit() frappe.db.commit() except Exception: frappe.db.rollback()

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d45013e19ee94ead3bc3ab1cecb032b98f2fafda

720

py

Python

posts/templatetags/paginator.py

ujlbu4/vas3k.club

1ec907cf7e5ae3a74059cde8729ca0b3e2d55a3e

[ "MIT" ]

496

2020-04-24T04:20:32.000Z

2022-03-31T21:55:57.000Z

posts/templatetags/paginator.py

ujlbu4/vas3k.club

1ec907cf7e5ae3a74059cde8729ca0b3e2d55a3e

[ "MIT" ]

642

2020-04-24T11:54:13.000Z

2022-03-26T15:41:06.000Z

posts/templatetags/paginator.py

ujlbu4/vas3k.club

1ec907cf7e5ae3a74059cde8729ca0b3e2d55a3e

[ "MIT" ]

243

2020-04-24T11:49:11.000Z

2022-03-24T18:38:48.000Z

from django import template register = template.Library() @register.inclusion_tag("common/paginator.html") def paginator(items): adjacent_pages = 4 num_pages = items.paginator.num_pages page = items.number start_page = max(page - adjacent_pages, 1) if start_page <= 3: start_page = 1 end_page = page + adjacent_pages + 1 if end_page >= num_pages - 1: end_page = num_pages + 1 page_numbers = [n for n in range(start_page, end_page) if 0 < n <= num_pages] return { "items": items, "page_numbers": page_numbers, "show_first": 1 not in page_numbers, "show_last": num_pages not in page_numbers, "num_pages": num_pages, }

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null

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d4504bcd464e9711733f6e23b9e032a1b7ce8a80

18,784

py

Python

svb/prior.py

physimals/svb

9c04309ef1fc1d5f81103a50a01e3bf4c8f4ea44

[ "Apache-2.0" ]

3

2022-01-08T12:50:25.000Z

2022-03-22T00:55:17.000Z

svb/prior.py

physimals/svb

9c04309ef1fc1d5f81103a50a01e3bf4c8f4ea44

[ "Apache-2.0" ]

1

2020-10-16T15:27:46.000Z

2020-10-16T15:59:48.000Z

svb/prior.py

physimals/svb

9c04309ef1fc1d5f81103a50a01e3bf4c8f4ea44

[ "Apache-2.0" ]

null

""" Definition of prior distribution """ import numpy as np try: import tensorflow.compat.v1 as tf except ImportError: import tensorflow as tf from .utils import LogBase from .dist import Normal PRIOR_TYPE_NONSPATIAL = "N" PRIOR_TYPE_SPATIAL_MRF = "M" def get_prior(param, data_model, **kwargs): """ Factory method to return a vertexwise prior """ prior = None if isinstance(param.prior_dist, Normal): if param.prior_type == "N": prior = NormalPrior(data_model.n_vertices, param.prior_dist.mean, param.prior_dist.var, **kwargs) elif param.prior_type == "M": prior = MRFSpatialPrior(data_model.n_vertices, param.prior_dist.mean, param.prior_dist.var, **kwargs) elif param.prior_type == "M2": prior = MRF2SpatialPrior(data_model.n_vertices, param.prior_dist.mean, param.prior_dist.var, **kwargs) elif param.prior_type == "Mfab": prior = FabberMRFSpatialPrior(data_model.n_vertices, param.prior_dist.mean, param.prior_dist.var, **kwargs) elif param.prior_type == "A": prior = ARDPrior(data_model.n_vertices, param.prior_dist.mean, param.prior_dist.var, **kwargs) if prior is not None: return prior else: raise ValueError("Can't create prior type %s for distribution %s - unrecognized combination" % (param.prior_type, param.prior_dist)) class Prior(LogBase): """ Base class for a prior, defining methods that must be implemented """ def mean_log_pdf(self, samples): """ :param samples: A tensor of shape [W, P, S] where W is the number of parameter vertices, P is the number of parameters in the prior (possibly 1) and S is the number of samples :return: A tensor of shape [W] where W is the number of parameter vertices containing the mean log PDF of the parameter samples provided """ raise NotImplementedError() def log_det_cov(self): raise NotImplementedError() class NormalPrior(Prior): """ Prior based on a vertexwise univariate normal distribution """ def __init__(self, nvertices, mean, var, **kwargs): """ :param mean: Prior mean value :param var: Prior variance """ Prior.__init__(self) self.name = kwargs.get("name", "NormalPrior") self.nvertices = nvertices self.scalar_mean = mean self.scalar_var = var self.mean = tf.fill([nvertices], mean, name="%s_mean" % self.name) self.var = tf.fill([nvertices], var, name="%s_var" % self.name) self.std = tf.sqrt(self.var, name="%s_std" % self.name) def mean_log_pdf(self, samples): """ Mean log PDF for normal distribution Note that ``term1`` is a constant offset when the prior variance is fixed and hence in earlier versions of the code this was neglected, along with other constant offsets such as factors of pi. However when this code is inherited by spatial priors and ARD the variance is no longer fixed and this term must be included. """ dx = tf.subtract(samples, tf.reshape(self.mean, [self.nvertices, 1, 1])) # [W, 1, N] z = tf.div(tf.square(dx), tf.reshape(self.var, [self.nvertices, 1, 1])) # [W, 1, N] term1 = self.log_tf(-0.5*tf.log(tf.reshape(self.var, [self.nvertices, 1, 1])), name="term1") term2 = self.log_tf(-0.5*z, name="term2") log_pdf = term1 + term2 # [W, 1, N] mean_log_pdf = tf.reshape(tf.reduce_mean(log_pdf, axis=-1), [self.nvertices]) # [W] return mean_log_pdf def __str__(self): return "Non-spatial prior (%f, %f)" % (self.scalar_mean, self.scalar_var) class FabberMRFSpatialPrior(NormalPrior): """ Prior designed to mimic the 'M' type spatial prior in Fabber. Note that this uses update equations for ak which is not in the spirit of the stochastic method. 'Native' SVB MRF spatial priors are also defined which simply treat the spatial precision parameter as an inference variable. This code has been verified to generate the same ak estimate given the same input as Fabber, however in practice it does not optimize to the same value. We don't yet know why. """ def __init__(self, nvertices, mean, var, idx=None, post=None, nn=None, n2=None, **kwargs): """ :param mean: Tensor of shape [W] containing the prior mean at each parameter vertex :param var: Tensor of shape [W] containing the prior variance at each parameter vertex :param post: Posterior instance :param nn: Sparse tensor of shape [W, W] containing nearest neighbour lists :param n2: Sparse tensor of shape [W, W] containing second nearest neighbour lists """ NormalPrior.__init__(self, nvertices, mean, var, name="FabberMRFSpatialPrior") self.idx = idx # Save the original vertexwise mean and variance - the actual prior mean/var # will be calculated from these and also the spatial variation in neighbour vertices self.fixed_mean = self.mean self.fixed_var = self.var # nn and n2 are sparse tensors of shape [W, W]. If nn[A, B] = 1 then A is # a nearest neighbour of B, and similarly for n2 and second nearest neighbours self.nn = nn self.n2 = n2 # Set up spatial smoothing parameter calculation from posterior and neighbour lists self._setup_ak(post, nn, n2) # Set up prior mean/variance self._setup_mean_var(post, nn, n2) def __str__(self): return "Spatial MRF prior (%f, %f)" % (self.scalar_mean, self.scalar_var) def _setup_ak(self, post, nn, n2): # This is the equivalent of CalculateAk in Fabber # # Some of this could probably be better done using linalg # operations but bear in mind this is one parameter only self.sigmaK = self.log_tf(tf.matrix_diag_part(post.cov)[:, self.idx], name="sigmak") # [W] self.wK = self.log_tf(post.mean[:, self.idx], name="wk") # [W] self.num_nn = self.log_tf(tf.sparse_reduce_sum(self.nn, axis=1), name="num_nn") # [W] # Sum over vertices of parameter variance multiplied by number of # nearest neighbours for each vertex trace_term = self.log_tf(tf.reduce_sum(self.sigmaK * self.num_nn), name="trace") # [1] # Sum of nearest and next-nearest neighbour mean values self.sum_means_nn = self.log_tf(tf.reshape(tf.sparse_tensor_dense_matmul(self.nn, tf.reshape(self.wK, (-1, 1))), (-1,)), name="wksum") # [W] self.sum_means_n2 = self.log_tf(tf.reshape(tf.sparse_tensor_dense_matmul(self.n2, tf.reshape(self.wK, (-1, 1))), (-1,)), name="contrib8") # [W] # vertex parameter mean multipled by number of nearest neighbours wknn = self.log_tf(self.wK * self.num_nn, name="wknn") # [W] swk = self.log_tf(wknn - self.sum_means_nn, name="swk") # [W] term2 = self.log_tf(tf.reduce_sum(swk * self.wK), name="term2") # [1] gk = 1 / (0.5 * trace_term + 0.5 * term2 + 0.1) hk = tf.multiply(tf.to_float(self.nvertices), 0.5) + 1.0 self.ak = self.log_tf(tf.identity(gk * hk, name="ak")) def _setup_mean_var(self, post, nn, n2): # This is the equivalent of ApplyToMVN in Fabber contrib_nn = self.log_tf(8*self.sum_means_nn, name="contrib_nn") # [W] contrib_n2 = self.log_tf(-self.sum_means_n2, name="contrib_n2") # [W] spatial_mean = self.log_tf(contrib_nn / (8*self.num_nn), name="spatial_mean") spatial_prec = self.log_tf(self.num_nn * self.ak, name="spatial_prec") self.var = self.log_tf(1 / (1/self.fixed_var + spatial_prec), name="%s_var" % self.name) #self.var = self.fixed_var self.mean = self.log_tf(self.var * spatial_prec * spatial_mean, name="%s_mean" % self.name) #self.mean = self.fixed_mean + self.ak class MRFSpatialPrior(Prior): """ Prior which performs adaptive spatial regularization based on the contents of neighbouring vertices using the Markov Random Field method This uses the same formalism as the Fabber 'M' type spatial prior but treats the ak as a parameter of the optimization. """ def __init__(self, nvertices, mean, var, idx=None, post=None, nn=None, n2=None, **kwargs): Prior.__init__(self) self.name = kwargs.get("name", "MRFSpatialPrior") self.nvertices = nvertices self.mean = tf.fill([nvertices], mean, name="%s_mean" % self.name) self.var = tf.fill([nvertices], var, name="%s_var" % self.name) self.std = tf.sqrt(self.var, name="%s_std" % self.name) # nn is a sparse tensor of shape [W, W]. If nn[A, B] = 1 then A is # a nearest neighbour of B self.nn = nn # Set up spatial smoothing parameter calculation from posterior and neighbour lists # We infer the log of ak. self.logak = tf.Variable(-5.0, name="log_ak", dtype=tf.float32) self.ak = self.log_tf(tf.exp(self.logak, name="ak")) def mean_log_pdf(self, samples): r""" mean log PDF for the MRF spatial prior. This is calculating: :math:`\log P = \frac{1}{2} \log \phi - \frac{\phi}{2}\underline{x^T} D \underline{x}` """ samples = tf.reshape(samples, (self.nvertices, -1)) # [W, N] self.num_nn = self.log_tf(tf.sparse_reduce_sum(self.nn, axis=1), name="num_nn") # [W] dx_diag = self.log_tf(tf.reshape(self.num_nn, (self.nvertices, 1)) * samples, name="dx_diag") # [W, N] dx_offdiag = self.log_tf(tf.sparse_tensor_dense_matmul(self.nn, samples), name="dx_offdiag") # [W, N] self.dx = self.log_tf(dx_diag - dx_offdiag, name="dx") # [W, N] self.xdx = self.log_tf(samples * self.dx, name="xdx") # [W, N] term1 = tf.identity(0.5*self.logak, name="term1") term2 = tf.identity(-0.5*self.ak*self.xdx, name="term2") log_pdf = term1 + term2 # [W, N] mean_log_pdf = tf.reshape(tf.reduce_mean(log_pdf, axis=-1), [self.nvertices]) # [W] # Gamma prior if we care #q1, q2 = 1, 100 #mean_log_pdf += (q1-1) * self.logak - self.ak / q2 return mean_log_pdf def __str__(self): return "MRF spatial prior" class ARDPrior(NormalPrior): """ Automatic Relevance Determination prior """ def __init__(self, nvertices, mean, var, **kwargs): NormalPrior.__init__(self, nvertices, mean, var, **kwargs) self.name = kwargs.get("name", "ARDPrior") self.fixed_var = self.var # Set up inferred precision parameter phi self.logphi = tf.Variable(tf.log(1/self.fixed_var), name="log_phi", dtype=tf.float32) self.phi = self.log_tf(tf.exp(self.logphi, name="phi")) self.var = 1/self.phi self.std = tf.sqrt(self.var, name="%s_std" % self.name) def __str__(self): return "ARD prior" class MRF2SpatialPrior(Prior): """ Prior which performs adaptive spatial regularization based on the contents of neighbouring vertices using the Markov Random Field method This uses the same formalism as the Fabber 'M' type spatial prior but treats the ak as a parameter of the optimization. It differs from MRFSpatialPrior by using the PDF formulation of the PDF rather than the matrix formulation (the two are equivalent but currently we keep both around for checking that they really are!) FIXME currently this does not work unless sample size=1 """ def __init__(self, nvertices, mean, var, idx=None, post=None, nn=None, n2=None, **kwargs): Prior.__init__(self) self.name = kwargs.get("name", "MRF2SpatialPrior") self.nvertices = nvertices self.mean = tf.fill([nvertices], mean, name="%s_mean" % self.name) self.var = tf.fill([nvertices], var, name="%s_var" % self.name) self.std = tf.sqrt(self.var, name="%s_std" % self.name) # nn is a sparse tensor of shape [W, W]. If nn[A, B] = 1 then A is # a nearest neighbour of B self.nn = nn # We need the number of samples to implement the log PDF function self.sample_size = kwargs.get("sample_size", 5) # Set up spatial smoothing parameter calculation from posterior and neighbour lists self.logak = tf.Variable(-5.0, name="log_ak", dtype=tf.float32) self.ak = self.log_tf(tf.exp(self.logak, name="ak")) def mean_log_pdf(self, samples): samples = tf.reshape(samples, (self.nvertices, -1)) # [W, N] self.num_nn = self.log_tf(tf.sparse_reduce_sum(self.nn, axis=1), name="num_nn") # [W] expanded_nn = tf.sparse_concat(2, [tf.sparse.reshape(self.nn, (self.nvertices, self.nvertices, 1))] * self.sample_size) xj = expanded_nn * tf.reshape(samples, (self.nvertices, 1, -1)) #xi = tf.reshape(tf.sparse.to_dense(tf.sparse.reorder(self.nn)), (self.nvertices, self.nvertices, 1)) * tf.reshape(samples, (1, self.nvertices, -1)) xi = expanded_nn * tf.reshape(samples, (1, self.nvertices, -1)) #xi = tf.sparse.transpose(xj, perm=(1, 0, 2)) neg_xi = tf.SparseTensor(xi.indices, -xi.values, dense_shape=xi.dense_shape ) dx2 = tf.square(tf.sparse.add(xj, neg_xi), name="dx2") sdx = tf.sparse.reduce_sum(dx2, axis=0) # [W, N] term1 = tf.identity(0.5*self.logak, name="term1") term2 = tf.identity(-self.ak * sdx / 4, name="term2") log_pdf = term1 + term2 # [W, N] mean_log_pdf = tf.reshape(tf.reduce_mean(log_pdf, axis=-1), [self.nvertices]) # [W] return mean_log_pdf def __str__(self): return "MRF2 spatial prior" class ConstantMRFSpatialPrior(NormalPrior): """ Prior which performs adaptive spatial regularization based on the contents of neighbouring vertices using the Markov Random Field method This is equivalent to the Fabber 'M' type spatial prior """ def __init__(self, nvertices, mean, var, idx=None, nn=None, n2=None, **kwargs): """ :param mean: Tensor of shape [W] containing the prior mean at each parameter vertex :param var: Tensor of shape [W] containing the prior variance at each parameter vertex :param post: Posterior instance :param nn: Sparse tensor of shape [W, W] containing nearest neighbour lists :param n2: Sparse tensor of shape [W, W] containing second nearest neighbour lists """ NormalPrior.__init__(self, nvertices, mean, var, name="MRFSpatialPrior") self.idx = idx # Save the original vertexwise mean and variance - the actual prior mean/var # will be calculated from these and also the spatial variation in neighbour vertices self.fixed_mean = self.mean self.fixed_var = self.var # nn and n2 are sparse tensors of shape [W, W]. If nn[A, B] = 1 then A is # a nearest neighbour of B, and similarly for n2 and second nearest neighbours self.nn = nn self.n2 = n2 def __str__(self): return "Spatial MRF prior (%f, %f) - const" % (self.scalar_mean, self.scalar_var) def update_ak(self, post_mean, post_cov): # This is the equivalent of CalculateAk in Fabber # # Some of this could probably be better done using linalg # operations but bear in mind this is one parameter only self.sigmaK = post_cov[:, self.idx, self.idx] # [W] self.wK = post_mean[:, self.idx] # [W] self.num_nn = np.sum(self.nn, axis=1) # [W] # Sum over vertices of parameter variance multiplied by number of # nearest neighbours for each vertex trace_term = np.sum(self.sigmaK * self.num_nn) # [1] # Sum of nearest and next-nearest neighbour mean values self.sum_means_nn = np.matmul(self.nn, np.reshape(self.wK, (-1, 1))) # [W] self.sum_means_n2 = np.matmul(self.n2, tf.reshape(self.wK, (-1, 1))) # [W] # vertex parameter mean multipled by number of nearest neighbours wknn = self.wK * self.num_nn # [W] swk = wknn - self.sum_means_nn # [W] term2 = np.sum(swk * self.wK) # [1] gk = 1 / (0.5 * trace_term + 0.5 * term2 + 0.1) hk = float(self.nvertices) * 0.5 + 1.0 self.ak = gk * hk self.log.info("%s: ak=%f", self.name, self.ak) def _setup_mean_var(self, post_mean, post_cov): # This is the equivalent of ApplyToMVN in Fabber contrib_nn = self.log_tf(8*self.sum_means_nn, name="contrib_nn") # [W] contrib_n2 = self.log_tf(-self.sum_means_n2, name="contrib_n2") # [W] spatial_mean = self.log_tf(contrib_nn / (8*self.num_nn), name="spatial_mean") spatial_prec = self.log_tf(self.num_nn * self.ak, name="spatial_prec") self.var = self.log_tf(1 / (1/self.fixed_var + spatial_prec), name="%s_var" % self.name) #self.var = self.fixed_var self.mean = self.log_tf(self.var * spatial_prec * spatial_mean, name="%s_mean" % self.name) #self.mean = self.fixed_mean + self.ak class FactorisedPrior(Prior): """ Prior for a collection of parameters where there is no prior covariance In this case the mean log PDF can be summed from the contributions of each parameter """ def __init__(self, priors, **kwargs): Prior.__init__(self) self.priors = priors self.name = kwargs.get("name", "FactPrior") self.nparams = len(priors) means = [prior.mean for prior in self.priors] variances = [prior.var for prior in self.priors] self.mean = self.log_tf(tf.stack(means, axis=-1, name="%s_mean" % self.name)) self.var = self.log_tf(tf.stack(variances, axis=-1, name="%s_var" % self.name)) self.std = tf.sqrt(self.var, name="%s_std" % self.name) self.nvertices = priors[0].nvertices # Define a diagonal covariance matrix for convenience self.cov = tf.matrix_diag(self.var, name='%s_cov' % self.name) def mean_log_pdf(self, samples): nvertices = tf.shape(samples)[0] mean_log_pdf = tf.zeros([nvertices], dtype=tf.float32) for idx, prior in enumerate(self.priors): param_samples = tf.slice(samples, [0, idx, 0], [-1, 1, -1]) param_logpdf = prior.mean_log_pdf(param_samples) mean_log_pdf = tf.add(mean_log_pdf, param_logpdf) return mean_log_pdf def log_det_cov(self): """ Determinant of diagonal matrix is product of diagonal entries """ return tf.reduce_sum(tf.log(self.var), axis=1, name='%s_log_det_cov' % self.name)

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null

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1

0

d451e78026744df79562580a63a5aa68b269af10

1,123

py

Python

setup.py

open-contracting/extensions-data-collector

041f7b44de60b3d241e4891da1bb1c1cb8ce9ec4

[ "BSD-3-Clause" ]

null

setup.py

open-contracting/extensions-data-collector

041f7b44de60b3d241e4891da1bb1c1cb8ce9ec4

[ "BSD-3-Clause" ]

23

2018-06-29T15:34:41.000Z

2018-11-03T13:29:49.000Z

setup.py

open-contracting/extensions-data-collector

041f7b44de60b3d241e4891da1bb1c1cb8ce9ec4

[ "BSD-3-Clause" ]

null

from setuptools import setup, find_packages with open('README.rst') as f: long_description = f.read() setup( name='ocdsextensionsdatacollector', version='0.0.1', author='Open Contracting Partnership, Open Data Services', author_email='data@open-contracting.org', url='https://github.com/open-contracting/extensions-data-collector', description='Collects data about OCDS extensions into a machine-readable format', license='BSD', packages=find_packages(), long_description=long_description, install_requires=[ 'Babel', 'ocds-babel>=0.0.3', 'ocdsextensionregistry>=0.0.5', 'polib', 'requests', 'Sphinx==1.5.1', ], extras_require={ 'test': [ 'coveralls', 'pytest', 'pytest-cov', ], }, classifiers=[ 'License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3.6', ], entry_points={ 'console_scripts': [ 'ocdsextensionsdatacollector = ocdsextensionsdatacollector.cli.__main__:main', ], }, )

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null

0

null

0

1

0

d4531e43111597e9d512203074171f5484832a7b

423

py

Python

server_side_python/DetectionAndTracking.py

Tpierga/2I_ProjetDetectionIA

39ba28a1d98bf7f0e908ee6cab933b219cc98977

[ "MIT" ]

null

server_side_python/DetectionAndTracking.py

Tpierga/2I_ProjetDetectionIA

39ba28a1d98bf7f0e908ee6cab933b219cc98977

[ "MIT" ]

null

server_side_python/DetectionAndTracking.py

Tpierga/2I_ProjetDetectionIA

39ba28a1d98bf7f0e908ee6cab933b219cc98977

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import cv2 def detect_body(frame): body_img = frame.copy() body_classifier = cv2.CascadeClassifier("haarcascade_fullbody.xml") gray = cv2.cvtColor(body_img, cv2.COLOR_BGR2GRAY) bodies = body_classifier.detectMultiScale(gray) for (x, y, w, h) in bodies: cv2.rectangle(body_img, (x, y), (x+w, y+h), (255, 0, 0), 8) return body_img

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null

0

null

0

1

0

d453fbe6cb754cbcc0bc6074f266abb4c1ebf8ac

1,333

py

Python

packages/infra_libs/infra_libs/_command_line_linux.py

xswz8015/infra

f956b78ce4c39cc76acdda47601b86794ae0c1ba

[ "BSD-3-Clause" ]

null

packages/infra_libs/infra_libs/_command_line_linux.py

xswz8015/infra

f956b78ce4c39cc76acdda47601b86794ae0c1ba

[ "BSD-3-Clause" ]

7

2022-02-15T01:11:37.000Z

2022-03-02T12:46:13.000Z

packages/infra_libs/infra_libs/_command_line_linux.py

NDevTK/chromium-infra

d38e088e158d81f7f2065a38aa1ea1894f735ec4

[ "BSD-3-Clause" ]

null

# Copyright 2016 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import ctypes import ctypes.util import sys _CACHED_CMDLINE_LENGTH = None def set_command_line(cmdline): """Replaces the commandline of this process as seen by ps.""" # Get the current commandline. argc = ctypes.c_int() argv = ctypes.POINTER(ctypes.c_char_p)() ctypes.pythonapi.Py_GetArgcArgv(ctypes.byref(argc), ctypes.byref(argv)) global _CACHED_CMDLINE_LENGTH if _CACHED_CMDLINE_LENGTH is None: # Each argument is terminated by a null-byte, so the length of the whole # thing in memory is the sum of all the argument byte-lengths, plus 1 null # byte for each. _CACHED_CMDLINE_LENGTH = sum( len(argv[i]) for i in range(0, argc.value)) + argc.value # Pad the cmdline string to the required length. If it's longer than the # current commandline, truncate it. if len(cmdline) >= _CACHED_CMDLINE_LENGTH: new_cmdline = ctypes.c_char_p(cmdline[:_CACHED_CMDLINE_LENGTH-1] + '\0') else: new_cmdline = ctypes.c_char_p(cmdline.ljust(_CACHED_CMDLINE_LENGTH, '\0')) # Replace the old commandline. libc = ctypes.CDLL(ctypes.util.find_library('c')) libc.memcpy(argv.contents, new_cmdline, _CACHED_CMDLINE_LENGTH)

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0

null

0

null

0

1

0

d4563d756cea51df08af818dd4f5a3f61d84a870

231

py

Python

4.py

CarineGhisiCadorin/infosatc-lp-avaliativo-02

0018df4f52c0659611c484c909ff4bbf925c450a

[ "MIT" ]

null

4.py

CarineGhisiCadorin/infosatc-lp-avaliativo-02

0018df4f52c0659611c484c909ff4bbf925c450a

[ "MIT" ]

null

4.py

CarineGhisiCadorin/infosatc-lp-avaliativo-02

0018df4f52c0659611c484c909ff4bbf925c450a

[ "MIT" ]

null

lista = [1,2,3,4,5,6,7,8,9,10] print(lista) lista2 = [11, 12, 13] print(lista2) lista_completa = lista + lista2 print(lista_completa) #Ou listaA = [1,2,3,4,5,6,7,8,9,10] listaB = [11, 12, 13] listaA = listaB.copy() print(listaA)

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null

0

null

0

1

0

d457da1f72c081a9943cc4f083598a70a7403071

14,240

py

Python

soundsig/discriminate.py

theunissenlab/sounsig

fca413aa71ce6fec079c59e615e328e6781d1d35

[ "MIT" ]

22

2017-08-05T12:41:49.000Z

2022-01-24T23:14:59.000Z

soundsig/discriminate.py

theunissenlab/sounsig

fca413aa71ce6fec079c59e615e328e6781d1d35

[ "MIT" ]

3

2017-07-06T19:23:54.000Z

2020-10-13T10:41:27.000Z

soundsig/discriminate.py

theunissenlab/sounsig

fca413aa71ce6fec079c59e615e328e6781d1d35

[ "MIT" ]

6

2017-05-13T18:41:23.000Z

2022-01-24T23:15:01.000Z

import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA from sklearn.ensemble import RandomForestClassifier as RF from sklearn.model_selection import StratifiedKFold from scipy.stats import binom def discriminatePlot(X, y, cVal, titleStr='', figdir='.', Xcolname = None, plotFig = False, removeTickLabels = False, testInd = None): # Frederic's Robust Wrapper for discriminant analysis function. Performs lda, qda and RF afer error checking, # Generates nice plots and returns cross-validated # performance, stderr and base line. # X np array n rows x p parameters # y group labels n rows # rgb color code for each data point - should be the same for each data beloging to the same group # titleStr title for plots # figdir is a directory name (folder name) for figures # Xcolname is a np.array or list of strings with column names for printout display # returns: ldaScore, ldaScoreSE, qdaScore, qdaScoreSE, rfScore, rfScoreSE, nClasses # Global Parameters CVFOLDS = 10 MINCOUNT = 10 MINCOUNTTRAINING = 5 # figdir = '/Users/frederictheunissen/Documents/Data/Julie/Acoustical Analysis/Figures Voice' # Initialize Variables and clean up data classes, classesCount = np.unique(y, return_counts = True) # Classes to be discriminated should be same as ldaMod.classes_ goodIndClasses = np.array([n >= MINCOUNT for n in classesCount]) goodInd = np.array([b in classes[goodIndClasses] for b in y]) if testInd is not None: # Check for goodInd - should be an np.array of dtype=bool # Transform testInd into an index inside xGood and yGood testIndx = testInd.nonzero()[0] goodIndx = goodInd.nonzero()[0] testInd = np.hstack([ np.where(goodIndx == testval)[0] for testval in testIndx]) trainInd = np.asarray([i for i in range(len(goodIndx)) if i not in testInd]) yGood = y[goodInd] XGood = X[goodInd] cValGood = cVal[goodInd] classes, classesCount = np.unique(yGood, return_counts = True) nClasses = classes.size # Number of classes or groups # Do we have enough data? if (nClasses < 2): print ('Error in ldaPLot: Insufficient classes with minimun data (%d) for discrimination analysis' % (MINCOUNT)) return -1, -1, -1, -1 , -1, -1, -1, -1, -1 if testInd is None: cvFolds = min(min(classesCount), CVFOLDS) if (cvFolds < CVFOLDS): print ('Warning in ldaPlot: Cross-validation performed with %d folds (instead of %d)' % (cvFolds, CVFOLDS)) else: cvFolds = 1 # Data size and color values nD = XGood.shape[1] # number of features in X nX = XGood.shape[0] # number of data points in X cClasses = [] # Color code for each class for cl in classes: icl = (yGood == cl).nonzero()[0][0] cClasses.append(np.append(cValGood[icl],1.0)) cClasses = np.asarray(cClasses) # Use a uniform prior myPrior = np.ones(nClasses)*(1.0/nClasses) # Perform a PCA for dimensionality reduction so that the covariance matrix can be fitted. nDmax = int(np.fix(np.sqrt(nX//5))) if nDmax < nD: print ('Warning: Insufficient data for', nD, 'parameters. PCA projection to', nDmax, 'dimensions.' ) nDmax = min(nD, nDmax) pca = PCA(n_components=nDmax) Xr = pca.fit_transform(XGood) print ('Variance explained is %.2f%%' % (sum(pca.explained_variance_ratio_)*100.0)) # Initialise Classifiers ldaMod = LDA(n_components = min(nDmax,nClasses-1), priors = myPrior, shrinkage = None, solver = 'svd') qdaMod = QDA(priors = myPrior) rfMod = RF() # by default assumes equal weights # Perform CVFOLDS fold cross-validation to get performance of classifiers. ldaYes = 0 qdaYes = 0 rfYes = 0 cvCount = 0 if testInd is None: skf = StratifiedKFold(n_splits = cvFolds) skfList = skf.split(Xr, yGood) else: skfList = [(trainInd,testInd)] for train, test in skfList: # Enforce the MINCOUNT in each class for Training trainClasses, trainCount = np.unique(yGood[train], return_counts=True) goodIndClasses = np.array([n >= MINCOUNTTRAINING for n in trainCount]) goodIndTrain = np.array([b in trainClasses[goodIndClasses] for b in yGood[train]]) # Specity the training data set, the number of groups and priors yTrain = yGood[train[goodIndTrain]] XrTrain = Xr[train[goodIndTrain]] trainClasses, trainCount = np.unique(yTrain, return_counts=True) ntrainClasses = trainClasses.size # Skip this cross-validation fold because of insufficient data if ntrainClasses < 2: continue goodInd = np.array([b in trainClasses for b in yGood[test]]) if (goodInd.size == 0): continue # Fit the data trainPriors = np.ones(ntrainClasses)*(1.0/ntrainClasses) ldaMod.priors = trainPriors qdaMod.priors = trainPriors ldaMod.fit(XrTrain, yTrain) qdaMod.fit(XrTrain, yTrain) rfMod.fit(XrTrain, yTrain) ldaYes += np.around((ldaMod.score(Xr[test[goodInd]], yGood[test[goodInd]]))*goodInd.size) qdaYes += np.around((qdaMod.score(Xr[test[goodInd]], yGood[test[goodInd]]))*goodInd.size) rfYes += np.around((rfMod.score(Xr[test[goodInd]], yGood[test[goodInd]]))*goodInd.size) cvCount += goodInd.size # Refit with all the data for the plots ldaMod.priors = myPrior qdaMod.priors = myPrior Xrr = ldaMod.fit_transform(Xr, yGood) # Check labels for a, b in zip(classes, ldaMod.classes_): if a != b: print ('Error in ldaPlot: labels do not match') # Check the within-group covariance in the rotated space # covs = [] # for group in classes: # Xg = Xrr[yGood == group, :] # covs.append(np.atleast_2d(np.cov(Xg,rowvar=False))) # withinCov = np.average(covs, axis=0, weights=myPrior) # Print the five largest coefficients of first 3 DFA MAXCOMP = 3 # Maximum number of DFA componnents MAXWEIGHT = 5 # Maximum number of weights printed for each componnent ncomp = min(MAXCOMP, nClasses-1) nweight = min(MAXWEIGHT, nD) # The scalings_ has the eigenvectors of the LDA in columns and the pca.componnents has the eigenvectors of PCA in columns weights = np.dot(ldaMod.scalings_[:,0:ncomp].T, pca.components_) print('LDA Weights:') for ic in range(ncomp): idmax = np.argsort(np.abs(weights[ic,:]))[::-1] print('DFA %d: '%ic, end = '') for iw in range(nweight): if Xcolname is None: colstr = 'C%d' % idmax[iw] else: colstr = Xcolname[idmax[iw]] print('%s %.3f; ' % (colstr, float(weights[ic, idmax[iw]]) ), end='') print() if plotFig: dimVal = 0.8 # Overall diming of background so that points can be seen # Obtain fits in this rotated space for display purposes ldaMod.fit(Xrr, yGood) qdaMod.fit(Xrr, yGood) rfMod.fit(Xrr, yGood) XrrMean = Xrr.mean(0) # Make a mesh for plotting x1, x2 = np.meshgrid(np.arange(-6.0, 6.0, 0.1), np.arange(-6.0, 6.0, 0.1)) xm1 = np.reshape(x1, -1) xm2 = np.reshape(x2, -1) nxm = np.size(xm1) Xm = np.zeros((nxm, Xrr.shape[1])) Xm[:,0] = xm1 if Xrr.shape[1] > 1 : Xm[:,1] = xm2 for ix in range(2,Xrr.shape[1]): Xm[:,ix] = np.squeeze(np.ones((nxm,1)))*XrrMean[ix] XmcLDA = np.zeros((nxm, 4)) # RGBA values for color for LDA XmcQDA = np.zeros((nxm, 4)) # RGBA values for color for QDA XmcRF = np.zeros((nxm, 4)) # RGBA values for color for RF # Predict values on mesh for plotting based on the first two DFs yPredLDA = ldaMod.predict_proba(Xm) yPredQDA = qdaMod.predict_proba(Xm) yPredRF = rfMod.predict_proba(Xm) # Transform the predictions in color codes maxLDA = yPredLDA.max() for ix in range(nxm) : cWeight = yPredLDA[ix,:] # Prob for all classes cWinner = ((cWeight == cWeight.max()).astype('float')) # Winner takes all # XmcLDA[ix,:] = np.dot(cWeight, cClasses)/nClasses XmcLDA[ix,:] = np.dot(cWinner*cWeight, cClasses) XmcLDA[ix,3] = (cWeight.max()/maxLDA)*dimVal # Plot the surface of probability plt.figure(facecolor='white', figsize=(10,4)) plt.subplot(131) Zplot = XmcLDA.reshape(np.shape(x1)[0], np.shape(x1)[1],4) plt.imshow(Zplot, zorder=0, extent=[-6, 6, -6, 6], origin='lower', interpolation='none', aspect='auto') if nClasses > 2: plt.scatter(Xrr[:,0], Xrr[:,1], c=cValGood, s=40, zorder=1) else: plt.scatter(Xrr,(np.random.rand(Xrr.size)-0.5)*12.0 , c=cValGood, s=40, zorder=1) plt.title('%s: LDA %d/%d' % (titleStr, ldaYes, cvCount)) plt.axis('square') plt.xlim((-6, 6)) plt.ylim((-6, 6)) plt.xlabel('DFA 1') plt.ylabel('DFA 2') if removeTickLabels: ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] empty_string_labels = ['']*len(labels) ax.set_xticklabels(empty_string_labels) labels = [item.get_text() for item in ax.get_yticklabels()] empty_string_labels = ['']*len(labels) ax.set_yticklabels(empty_string_labels) # Transform the predictions in color codes maxQDA = yPredQDA.max() for ix in range(nxm) : cWeight = yPredQDA[ix,:] # Prob for all classes cWinner = ((cWeight == cWeight.max()).astype('float')) # Winner takes all # XmcLDA[ix,:] = np.dot(cWeight, cClasses)/nClasses XmcQDA[ix,:] = np.dot(cWinner*cWeight, cClasses) XmcQDA[ix,3] = (cWeight.max()/maxQDA)*dimVal # Plot the surface of probability plt.subplot(132) Zplot = XmcQDA.reshape(np.shape(x1)[0], np.shape(x1)[1],4) plt.imshow(Zplot, zorder=0, extent=[-6, 6, -6, 6], origin='lower', interpolation='none', aspect='auto') if nClasses > 2: plt.scatter(Xrr[:,0], Xrr[:,1], c=cValGood, s=40, zorder=1) else: plt.scatter(Xrr,(np.random.rand(Xrr.size)-0.5)*12.0 , c=cValGood, s=40, zorder=1) plt.title('%s: QDA %d/%d' % (titleStr, qdaYes, cvCount)) plt.xlabel('DFA 1') plt.ylabel('DFA 2') plt.axis('square') plt.xlim((-6, 6)) plt.ylim((-6, 6)) if removeTickLabels: ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] empty_string_labels = ['']*len(labels) ax.set_xticklabels(empty_string_labels) labels = [item.get_text() for item in ax.get_yticklabels()] empty_string_labels = ['']*len(labels) ax.set_yticklabels(empty_string_labels) # Transform the predictions in color codes maxRF = yPredRF.max() for ix in range(nxm) : cWeight = yPredRF[ix,:] # Prob for all classes cWinner = ((cWeight == cWeight.max()).astype('float')) # Winner takes all # XmcLDA[ix,:] = np.dot(cWeight, cClasses)/nClasses # Weighted colors does not work XmcRF[ix,:] = np.dot(cWinner*cWeight, cClasses) XmcRF[ix,3] = (cWeight.max()/maxRF)*dimVal # Plot the surface of probability plt.subplot(133) Zplot = XmcRF.reshape(np.shape(x1)[0], np.shape(x1)[1],4) plt.imshow(Zplot, zorder=0, extent=[-6, 6, -6, 6], origin='lower', interpolation='none', aspect='auto') if nClasses > 2: plt.scatter(Xrr[:,0], Xrr[:,1], c=cValGood, s=40, zorder=1) else: plt.scatter(Xrr,(np.random.rand(Xrr.size)-0.5)*12.0 , c=cValGood, s=40, zorder=1) plt.title('%s: RF %d/%d' % (titleStr, rfYes, cvCount)) plt.xlabel('DFA 1') plt.ylabel('DFA 2') plt.axis('square') plt.xlim((-6, 6)) plt.ylim((-6, 6)) if removeTickLabels: ax = plt.gca() labels = [item.get_text() for item in ax.get_xticklabels()] empty_string_labels = ['']*len(labels) ax.set_xticklabels(empty_string_labels) labels = [item.get_text() for item in ax.get_yticklabels()] empty_string_labels = ['']*len(labels) ax.set_yticklabels(empty_string_labels) plt.show() plt.savefig('%s/%s.png' % (figdir,titleStr), format='png', dpi=1000) # Results ldaYes = int(ldaYes) qdaYes = int(qdaYes) rfYes = int(rfYes) p = 1.0/nClasses ldaP = 0 qdaP = 0 rfP = 0 for k in range(ldaYes, cvCount+1): ldaP += binom.pmf(k, cvCount, p) for k in range(qdaYes, cvCount+1): qdaP += binom.pmf(k, cvCount, p) for k in range(rfYes, cvCount+1): rfP += binom.pmf(k, cvCount, p) print ("Number of classes %d. Chance level %.2f %%" % (nClasses, 100.0/nClasses)) print ("%s LDA: %.2f %% (%d/%d p=%.4f)" % (titleStr, 100.0*ldaYes/cvCount, ldaYes, cvCount, ldaP)) print ("%s QDA: %.2f %% (%d/%d p=%.4f)" % (titleStr, 100.0*qdaYes/cvCount, qdaYes, cvCount, qdaP)) print ("%s RF: %.2f %% (%d/%d p=%.4f)" % (titleStr, 100.0*rfYes/cvCount, rfYes, cvCount, rfP)) return ldaYes, qdaYes, rfYes, cvCount, ldaP, qdaP, rfP, nClasses, weights

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0

null

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0

d457ff551e1af72008fb3c2b85fdd39fc33404b0

4,477

py

Python

backend_receitas/core/views.py

gugact/backend_web

32b72ec460c1b6bae63bfd391c87b0c4bf644821

[ "Apache-2.0" ]

null

backend_receitas/core/views.py

gugact/backend_web

32b72ec460c1b6bae63bfd391c87b0c4bf644821

[ "Apache-2.0" ]

null

backend_receitas/core/views.py

gugact/backend_web

32b72ec460c1b6bae63bfd391c87b0c4bf644821

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.shortcuts import render from rest_framework import viewsets from rest_framework.views import APIView from rest_framework.response import Response from rest_framework import status from django.http import * from django.contrib.auth import * from django.contrib.auth.decorators import login_required from django.core.exceptions import ObjectDoesNotExist from rest_framework.exceptions import APIException #from rest_framework.permissions import IsAuthenticatedOrReadOnly from .models import * from .serializers import * from itertools import chain # Create your views here. class RecipeDetails(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) def get_object(self, pk): try: return Recipe.objects.get(pk=pk) except Recipe.DoesNotExist: raise Http404 def get(self, request, pk, format=None): recipe = self.get_object(pk) serializer = RecipeSerializer(recipe) return Response(serializer.data) #FALTA TRATAMENTO DE IMAGENS class RecipeRegister(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) def post(self, request, format=None): serializer = RecipeSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class UserDuplicationError(APIException): status_code = status.HTTP_409_CONFLICT default_detail = u'Duplicate user' class ProfileSignUp(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) #see if User already exists def get_object(self, data): try: retrievedUser = User.objects.filter(username = data) raise UserDuplicationError() except User.DoesNotExist: return True def post(self, request, format=None): self.get_object(request.data['email']) createdUser = User.objects.create_user(request.data['email'], None, request.data['password']) request.data.pop('email', None) request.data.pop('password', None) request.data['user'] = createdUser.pk serializer = CreateProfileSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class ProfileLogin(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) def get_object(self, data): try: retrievedUser = authenticate(username=data['email'], password=data['password']) if retrievedUser is not None: user = Profile.objects.get(user = retrievedUser) print("achou usuario" + retrievedUser.username) return user else: print("NAO achou usuario") raise Http404 except User.DoesNotExist: raise Http404 def post(self, request, format=None): print("request body: " +request.data['email'] + " " + request.data['password']) profile = self.get_object(request.data) serializer = ProfileSerializer(profile) return Response(serializer.data) class MostThreeRecentRecipeFromEveryCategory(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) def get(self, request, format=None): categories = Category.objects.all() listOfRecipes = [] for cat in categories: recipes = Recipe.objects.filter(category = cat)[:3] listOfRecipes.append(recipes) qs = list(chain.from_iterable(listOfRecipes)) serializer = ThreeRecentSerializer(qs, many=True) return Response(serializer.data) class RecipesFromCategory(APIView): #permission_classes = (IsAuthenticatedOrReadOnly,) def get_object(self, pk): try: category = Category.objects.get(pk=pk) return Recipe.objects.filter(category = category) except Category.DoesNotExist: raise Http404 def get(self, request, pk, format=None): print("PK: " +pk) recipes = self.get_object(pk) serializer = CategorySerializer(recipes, many=True) print(serializer.data) return Response(serializer.data)

33.162963

101

0.682153

465

4,477

6.473118

0.270968

0.040199

0.063787

0.097674

0.36711

0.278405

0.232226

0.213621

0.192027

0

0.008391

0.228055

4,477

134

102

33.410448

0.862558

0.101854

0

0.333333

0

0.03093

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1

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false

0.043011

0.150538

0

0.483871

0.053763

0

null

0

null

0

1

0

d459cbe1a695f12684fad0e549571c60487537ba

736

py

Python

src/predict.py

tarasowski/starbucks-offer-engine

51b80e5b390b58427d842964867a5db2aed6dda6

[ "MIT" ]

1

2020-02-19T06:59:30.000Z

src/predict.py

tarasowski/starbucks-offer-engine

51b80e5b390b58427d842964867a5db2aed6dda6

[ "MIT" ]

null

src/predict.py

tarasowski/starbucks-offer-engine

51b80e5b390b58427d842964867a5db2aed6dda6

[ "MIT" ]

null

import joblib from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score def predict(data): clf, X_test, y_test = data y_pred = clf.predict(X_test) print(f'Accuracy: {round(accuracy_score(y_test, y_pred) * 100, 2)}%') print(f'F1 Score: {round(f1_score(y_test, y_pred) * 100, 2)}%') print(f'Recall Score: {round(precision_score(y_test, y_pred) * 100, 2)}%') print(f'Precision Score: {round(recall_score(y_test, y_pred) * 100, 2)}%') def main(params): return predict(params) if __name__ == '__main__': model = joblib.load('../models/model.pkl') S_test = joblib.load('../models/S_test.pkl') y_test = joblib.load('../models/y_test.pkl') main((model, S_test, y_test))

35.047619

83

0.677989

117

736

3.974359

0.282051

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0.094624

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0.16129

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736

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0

null

0

null

0

1

0

d45bb49795fcb2f093014163608b945bdcaafd58

3,238

py

Python

examples/optimize_polyfaces.py

hh-wu/ezdxf

62509ba39b826ee9b36f19c0a5abad7f3518186a

[ "MIT" ]

1

2021-06-05T09:15:15.000Z

examples/optimize_polyfaces.py

luoyu-123/ezdxf

40963a2010028f87846241e08434f43ab421f3fb

[ "MIT" ]

null

examples/optimize_polyfaces.py

luoyu-123/ezdxf

40963a2010028f87846241e08434f43ab421f3fb

[ "MIT" ]

null

# Purpose: open example files with big polyface models # Created: 23.04.2014 # Copyright (c) 2014-2020, Manfred Moitzi # License: MIT License import time from pathlib import Path import ezdxf from ezdxf.render import MeshVertexMerger SRCDIR = Path(r'D:\Source\dxftest\CADKitSamples') OUTDIR = Path('~/Desktop/Outbox').expanduser() def optimize_polyfaces(polyfaces): count = 0 runtime = 0 vertex_diff = 0 print("start optimizing...") for polyface in polyfaces: count += 1 start_vertex_count = len(polyface) start_time = time.time() polyface.optimize() end_time = time.time() end_vertex_count = len(polyface) runtime += end_time - start_time vertex_diff += start_vertex_count - end_vertex_count print(f"removed {vertex_diff} vertices in {runtime:.2f} seconds.") def optimize(name: str): filename = SRCDIR / name new_filename = OUTDIR / ('optimized_' + name) print(f'opening DXF file: {filename}') start_time = time.time() doc = ezdxf.readfile(filename) msp = doc.modelspace() end_time = time.time() print(f'time for reading: {end_time - start_time:.1f} seconds') print(f"DXF version: {doc.dxfversion}") print(f"Database contains {len(doc.entitydb)} entities.") polyfaces = (polyline for polyline in msp.query('POLYLINE') if polyline.is_poly_face_mesh) optimize_polyfaces(polyfaces) print(f'saving DXF file: {new_filename}') start_time = time.time() doc.saveas(new_filename) end_time = time.time() print(f'time for saving: {end_time - start_time:.1f} seconds') def save_as(name): filename = SRCDIR / name print(f'opening DXF file: {filename}') start_time = time.time() doc = ezdxf.readfile(filename) msp = doc.modelspace() end_time = time.time() print(f'time for reading: {end_time - start_time:.1f} seconds') print(f"DXF version: {doc.dxfversion}") print(f"Database contains {len(doc.entitydb)} entities.") polyfaces = (polyline for polyline in msp.query('POLYLINE') if polyline.is_poly_face_mesh) # create a new documents doc1 = ezdxf.new() msp1 = doc1.modelspace() doc2 = ezdxf.new() msp2 = doc2.modelspace() for polyface in polyfaces: b = MeshVertexMerger.from_polyface(polyface) b.render(msp1, dxfattribs={ 'layer': polyface.dxf.layer, 'color': polyface.dxf.color, }) b.render_polyface(msp2, dxfattribs={ 'layer': polyface.dxf.layer, 'color': polyface.dxf.color, }) new_filename = OUTDIR / ('mesh_' + name) print(f'saving as mesh DXF file: {new_filename}') start_time = time.time() doc1.saveas(new_filename) end_time = time.time() print(f'time for saving: {end_time - start_time:.1f} seconds') new_filename = OUTDIR / ('recreated_polyface_' + name) print(f'saving as polyface DXF file: {new_filename}') start_time = time.time() doc2.saveas(new_filename) end_time = time.time() print(f'time for saving: {end_time - start_time:.1f} seconds') if __name__ == '__main__': optimize('fanuc-430-arm.dxf') optimize('cnc machine.dxf') save_as('fanuc-430-arm.dxf')

32.38

94

0.661519

424

3,238

4.891509

0.252358

0.092575

0.069431

0.04918

0.51109

0.493732

0.492285

0.441659

0.391514

0

0.016484

0.213095

3,238

100

95

32.38

0.797488

0.048178

0

0.4875

0

0.270393

0.010075

0

1

0.0375

false

0

0.05

0

0.0875

0.2

0

null

0

null

0

1

0

d45c7405d2149d4710720762085175c95dab0d57

1,576

py

Python

glashammer/bundles/contrib/dev/firephp.py

passy/glashammer-rdrei

9e56952d70b961d8945707469aad9cfe97c4e7b7

[ "MIT" ]

1

2016-07-04T15:23:59.000Z

glashammer/bundles/contrib/dev/firephp.py

passy/glashammer-rdrei

9e56952d70b961d8945707469aad9cfe97c4e7b7

[ "MIT" ]

null

glashammer/bundles/contrib/dev/firephp.py

passy/glashammer-rdrei

9e56952d70b961d8945707469aad9cfe97c4e7b7

[ "MIT" ]

null

# adapted from http://code.cmlenz.net/diva/browser/trunk/diva/ext/firephp.py # (c) 2008 C. M. Lenz, Glashammer Developers from time import time from logging import Handler from simplejson import dumps from glashammer.utils import local from glashammer.utils.log import add_log_handler LEVEL_MAP = {'DEBUG': 'LOG', 'WARNING': 'WARN', 'CRITICAL': 'ERROR'} PREFIX = 'X-FirePHP-Data-' def init_firephp(): # one-time initialisation per request local.firephp_log = [] def inject_firephp_headers(response): prefix = PREFIX if not hasattr(response, 'headers'): # an httpexception or some other weird response return for i, record in enumerate(local.firephp_log): if i == 0: response.headers[prefix + '100000000001'] = '{' response.headers[prefix + '300000000001'] = '"FirePHP.Firebug.Console":[' response.headers[prefix + '399999999999'] = ',["__SKIP__"]],' response.headers[prefix + '999999999999'] = '"__SKIP__":"__SKIP__"}' secs = str(int(time()))[-3:] msgid = '3' + secs + ('%08d' % (i + 2)) msg = dumps(record) if i != 0: msg = ',' + msg response.headers[PREFIX + msgid] = msg def emit(level, record): try: local.firephp_log.append((LEVEL_MAP.get(level.upper()), record)) except AttributeError: pass def setup_firephp(app): app.connect_event('wsgi-call', init_firephp) app.connect_event('response-start', inject_firephp_headers) app.connect_event('log', emit) setup_app = setup_firephp

29.735849

85

0.64467

191

1,576

5.157068

0.486911

0.091371

0.106599

0

0.04789

0.218274

1,576

52

86

30.307692

0.751623

0.126269

0

0.14515

0.03574

0

1

0.114286

false

0.028571

0.142857

0

0.285714

0

null

0

null

0

1

0

d45e087825bbd78e5f7c80d653a07a247d6d0607

5,667

py

Python

neural machine translation/data_load.py

quanganh1997polytechnique/Project-DL-Seq2Seq

de7b118ec865af60ee44c5a463f59ac548e76794

[ "MIT" ]

null

neural machine translation/data_load.py

quanganh1997polytechnique/Project-DL-Seq2Seq

de7b118ec865af60ee44c5a463f59ac548e76794

[ "MIT" ]

null

neural machine translation/data_load.py

quanganh1997polytechnique/Project-DL-Seq2Seq

de7b118ec865af60ee44c5a463f59ac548e76794

[ "MIT" ]

null

""" ** deeplean-ai.com ** created by :: GauravBh1010tt contact :: gauravbhatt.deeplearn@gmail.com """ from __future__ import unicode_literals, print_function, division import math import re import os import numpy as np import torch import random import warnings from io import open import unicodedata import matplotlib.pyplot as plt from torch.autograd import Variable import time def asMinutes(s): m = math.floor(s / 60) s -= m * 60 return '%dm %02ds' % (m, s) def timeSince(since, percent): now = time.time() s = now - since es = s / (percent) rs = es - s return '%s (- %s)' % (asMinutes(s), asMinutes(rs)) warnings.simplefilter('ignore') plt.rcParams['figure.figsize'] = (8, 8) np.random.seed(42) torch.manual_seed(0) torch.cuda.manual_seed(0) use_cuda = torch.cuda.is_available() import zipfile zip_ref = zipfile.ZipFile('data.zip', 'r') zip_ref.extractall() zip_ref.close() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # torch.cuda.set_device(1) SOS_token = 0 EOS_token = 1 class Lang: def __init__(self, name): self.name = name self.word2index = {} self.word2count = {} self.index2word = {0: "SOS", 1: "EOS"} self.n_words = 2 # Count SOS and EOS def addSentence(self, sentence): for word in sentence.split(' '): self.addWord(word) def addWord(self, word): if word not in self.word2index: self.word2index[word] = self.n_words self.word2count[word] = 1 self.index2word[self.n_words] = word self.n_words += 1 else: self.word2count[word] += 1 # Turn a Unicode string to plain ASCII, thanks to # http://stackoverflow.com/a/518232/2809427 def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' ) # Lowercase, trim, and remove non-letter characters def normalizeString(s): s = unicodeToAscii(s.lower().strip()) s = re.sub(r"([.!?])", r" \1", s) s = re.sub(r"[^a-zA-Z.!?]+", r" ", s) return s def readLangs(lang1, lang2, reverse=False): print("Reading lines...") # Read the file and split into lines lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\ read().strip().split('\n') # Split every line into pairs and normalize pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines] # Reverse pairs, make Lang instances if reverse: pairs = [list(reversed(p)) for p in pairs] input_lang = Lang(lang2) output_lang = Lang(lang1) else: input_lang = Lang(lang1) output_lang = Lang(lang2) return input_lang, output_lang, pairs MAX_LENGTH = 10 eng_prefixes = ( "i am ", "i m ", "he is", "he s ", "she is", "she s", "you are", "you re ", "we are", "we re ", "they are", "they re " ) def filterPair(p,reverse): return len(p[0].split(' ')) < MAX_LENGTH and \ len(p[1].split(' ')) < MAX_LENGTH and \ p[reverse].startswith(eng_prefixes) def filterPairs(pairs, reverse): if reverse: reverse = 1 else: reverse = 0 return [pair for pair in pairs if filterPair(pair,reverse)] def prepareData(lang1, lang2, reverse=False): input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse) print("Read %s sentence pairs" % len(pairs)) pairs = filterPairs(pairs,reverse) print("Trimmed to %s sentence pairs" % len(pairs)) print("Counting words...") for pair in pairs: input_lang.addSentence(pair[0]) output_lang.addSentence(pair[1]) print("Counted words:") print(input_lang.name, input_lang.n_words) print(output_lang.name, output_lang.n_words) return input_lang, output_lang, pairs def indexesFromSentence(lang, sentence): return [lang.word2index[word] for word in sentence.split(' ')] def tensorFromSentence(lang, sentence): indexes = indexesFromSentence(lang, sentence) indexes.append(EOS_token) return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1) def tensorsFromPair(pair, input_lang, output_lang): input_tensor = tensorFromSentence(input_lang, pair[0]) target_tensor = tensorFromSentence(output_lang, pair[1]) return (input_tensor, target_tensor) def as_minutes(s): m = math.floor(s / 60) s -= m * 60 return '%dm %ds' % (m, s) def time_since(since, percent): now = time.time() s = now - since es = s / (percent) rs = es - s return '%s (- %s)' % (as_minutes(s), as_minutes(rs)) def indexes_from_sentence(lang, sentence): return [lang.word2index[word] for word in sentence.split(' ')] def variable_from_sentence(lang, sentence): indexes = indexes_from_sentence(lang, sentence) indexes.append(EOS_token) var = Variable(torch.LongTensor(indexes).view(-1, 1)) # print('var =', var) if use_cuda: var = var.cuda() return var def variables_from_pair(pair, input_lang, output_lang): input_variable = variable_from_sentence(input_lang, pair[0]) target_variable = variable_from_sentence(output_lang, pair[1]) return (input_variable, target_variable) def save_checkpoint(epoch, model, optimizer, directory, \ filename='best.pt'): checkpoint=({'epoch': epoch+1, 'model': model.state_dict(), 'optimizer' : optimizer.state_dict() }) try: torch.save(checkpoint, os.path.join(directory, filename)) except: os.mkdir(directory) torch.save(checkpoint, os.path.join(directory, filename))

26.85782

77

0.640551

767

5,667

4.6206

0.273794

0.030474

0.021163

0.026806

0.228273

0.172122

0.106095

0.080135

0

0.018889

0.224634

5,667

211

78

26.85782

0.787665

0.072878

0

0.164474

0

0.062607

0

1

0.131579

false

0

0.092105

0.026316

0.335526

0.052632

0

null

0

null

0

1

0

d45fdaebbda1324d20d94461daceb3de9d2ddda0

9,041

py

Python

tests/channels/mock.py

febuiles/two1-python

88704487dba7715f97a0980781d4c0efb2ea7fc4

[ "BSD-2-Clause-FreeBSD" ]

415

2016-06-10T00:46:55.000Z

2021-10-16T00:56:06.000Z

tests/channels/mock.py

febuiles/two1-python

88704487dba7715f97a0980781d4c0efb2ea7fc4

[ "BSD-2-Clause-FreeBSD" ]

25

2016-06-11T13:48:59.000Z

2021-01-05T11:19:30.000Z

tests/channels/mock.py

febuiles/two1-python

88704487dba7715f97a0980781d4c0efb2ea7fc4

[ "BSD-2-Clause-FreeBSD" ]

109

2016-06-11T05:17:05.000Z

2021-12-22T11:02:22.000Z

import codecs import two1.bitcoin as bitcoin import two1.bitcoin.utils as utils import two1.channels.server as server import two1.channels.blockchain as blockchain import two1.channels.statemachine as statemachine class MockTwo1Wallet: """Mock Two1 Wallet interface for unit testing. See two1.wallet.Two1Wallet for API.""" PRIVATE_KEY = bitcoin.PrivateKey.from_bytes( codecs.decode("83407377a24a5cef75dedb0445d2da3a5389ed34c0f0c57266b1ed0a5ebb30c1", 'hex_codec')) "Customer private key." MOCK_UTXO_SCRIPT_PUBKEY = bitcoin.Script.build_p2pkh(PRIVATE_KEY.public_key.hash160()) MOCK_UTXO = bitcoin.Hash("3d3834fb69654cea89f9b086642b867c4cb9c86cc0a4cc1972924370dd54de19") MOCK_UTXO_INDEX = 1 "Mock utxo to make deposit transaction." def get_change_public_key(self): return self.PRIVATE_KEY.public_key def build_signed_transaction( self, addresses_and_amounts, use_unconfirmed=False, insert_into_cache=False, fees=None, expiration=0): address = list(addresses_and_amounts.keys())[0] amount = addresses_and_amounts[address] inputs = [bitcoin.TransactionInput(self.MOCK_UTXO, self.MOCK_UTXO_INDEX, bitcoin.Script(), 0xffffffff)] outputs = [bitcoin.TransactionOutput(amount, bitcoin.Script.build_p2sh(utils.address_to_key_hash(address)[1]))] tx = bitcoin.Transaction(bitcoin.Transaction.DEFAULT_TRANSACTION_VERSION, inputs, outputs, 0x0) tx.sign_input(0, bitcoin.Transaction.SIG_HASH_ALL, self.PRIVATE_KEY, self.MOCK_UTXO_SCRIPT_PUBKEY) return [tx] def get_private_for_public(self, public_key): assert bytes(public_key) == bytes(self.PRIVATE_KEY.public_key) return self.PRIVATE_KEY def broadcast_transaction(self, transaction): return MockBlockchain.broadcast_tx(MockBlockchain, transaction) @property def testnet(self): return False class MockPaymentChannelServer(server.PaymentChannelServerBase): """Mock Payment Channel Server interface for unit testing.""" PRIVATE_KEY = bitcoin.PrivateKey.from_bytes( codecs.decode("9d1ad8f765996474ff478ef65692a95dba0af2e24cd9e2cb6dfeee52ce2d38e8", 'hex_codec')) "Merchant private key." blockchain = None "Merchant blockchain interface." channels = {} "Retained server-side channels state across instantiations of this payment channel server \"client\"." def __init__(self, url=None): """Instantiate a Mock Payment Channel Server interface for the specified URL. Args: url (str): URL of Mock server. Returns: MockPaymentChannelServer: instance of MockPaymentChannelServer. """ super().__init__() self._url = url def get_info(self): return {'public_key': codecs.encode(self.PRIVATE_KEY.public_key.compressed_bytes, 'hex_codec').decode('utf-8')} def open(self, deposit_tx, redeem_script): # Deserialize deposit tx and redeem script deposit_tx = bitcoin.Transaction.from_hex(deposit_tx) deposit_txid = str(deposit_tx.hash) redeem_script = statemachine.PaymentChannelRedeemScript.from_bytes(codecs.decode(redeem_script, 'hex_codec')) # Validate redeem_script assert redeem_script.merchant_public_key.compressed_bytes == self.PRIVATE_KEY.public_key.compressed_bytes # Validate deposit tx assert len(deposit_tx.outputs) == 1, "Invalid deposit tx outputs." output_index = deposit_tx.output_index_for_address(redeem_script.hash160()) assert output_index is not None, "Missing deposit tx P2SH output." assert deposit_tx.outputs[output_index].script.is_p2sh(), "Invalid deposit tx output P2SH script." assert deposit_tx.outputs[output_index].script.get_hash160() == redeem_script.hash160(), "Invalid deposit tx output script P2SH address." # nopep8 self.channels[deposit_txid] = {'deposit_tx': deposit_tx, 'redeem_script': redeem_script, 'payment_tx': None} def pay(self, deposit_txid, payment_tx): # Deserialize payment tx payment_tx = bitcoin.Transaction.from_hex(payment_tx) # Validate payment tx redeem_script = self.channels[deposit_txid]['redeem_script'] assert len(payment_tx.inputs) == 1, "Invalid payment tx inputs." assert len(payment_tx.outputs) == 2, "Invalid payment tx outputs." assert bytes(payment_tx.inputs[0].script[-1]) == bytes(self.channels[deposit_txid]['redeem_script']), "Invalid payment tx redeem script." # nopep8 # Validate payment is greater than the last one if self.channels[deposit_txid]['payment_tx']: output_index = payment_tx.output_index_for_address(self.PRIVATE_KEY.public_key.hash160()) assert output_index is not None, "Invalid payment tx output." assert payment_tx.outputs[output_index].value > self.channels[deposit_txid]['payment_tx'].outputs[output_index].value, "Invalid payment tx output value." # nopep8 # Sign payment tx assert redeem_script.merchant_public_key.compressed_bytes == self.PRIVATE_KEY.public_key.compressed_bytes, "Public key mismatch." # nopep8 sig = payment_tx.get_signature_for_input(0, bitcoin.Transaction.SIG_HASH_ALL, self.PRIVATE_KEY, redeem_script)[0] # nopep8 # Update input script sig payment_tx.inputs[0].script.insert(1, sig.to_der() + bitcoin.utils.pack_compact_int(bitcoin.Transaction.SIG_HASH_ALL)) # nopep8 # Verify signature output_index = self.channels[deposit_txid]['deposit_tx'].output_index_for_address(redeem_script.hash160()) assert payment_tx.verify_input_signature(0, self.channels[deposit_txid]['deposit_tx'].outputs[output_index].script), "Payment tx input script verification failed." # nopep8 # Save payment tx self.channels[deposit_txid]['payment_tx'] = payment_tx # Return payment txid return str(payment_tx.hash) def status(self, deposit_txid): return {} def close(self, deposit_txid, deposit_txid_signature): # Assert a payment has been made to this chanel assert self.channels[deposit_txid]['payment_tx'], "No payment tx exists." # Verify deposit txid singature public_key = self.channels[deposit_txid]['redeem_script'].customer_public_key assert public_key.verify(deposit_txid.encode(), bitcoin.Signature.from_der(deposit_txid_signature)), "Invalid deposit txid signature." # nopep8 # Broadcast to blockchain self.blockchain.broadcast_tx(self.channels[deposit_txid]['payment_tx'].to_hex()) # Return payment txid return str(self.channels[deposit_txid]['payment_tx'].hash) class MockBlockchain(blockchain.BlockchainBase): """Mock Blockchain interface for unit testing.""" _blockchain = {} """Global blockchain state accessible by other mock objects.""" def __init__(self): """Instantiate a Mock blockchain interface. Returns: MockBlockchain: instance of MockBlockchain. """ # Reset blockchain state for key in list(MockBlockchain._blockchain.keys()): del MockBlockchain._blockchain[key] # Stores transactions as # { # "<txid>": { # "tx": <serialized tx>, # "confirmations": <number of confirmations>, # "outputs_spent": [ # "<txid>" or None, # ... # ] # }, # ... # } def mock_confirm(self, txid, num_confirmations=1): self._blockchain[txid]['confirmations'] = num_confirmations def check_confirmed(self, txid, num_confirmations=1): if txid not in self._blockchain: return False return self._blockchain[txid]['confirmations'] >= num_confirmations def lookup_spend_txid(self, txid, output_index): if txid not in self._blockchain: return None if output_index >= len(self._blockchain[txid]['outputs_spent']): raise IndexError('Output index out of bounds.') return self._blockchain[txid]['outputs_spent'][output_index] def lookup_tx(self, txid): if txid not in self._blockchain: return None return self._blockchain[txid]['tx'] def broadcast_tx(self, tx): txobj = bitcoin.Transaction.from_hex(tx) txid = str(txobj.hash) if txid in self._blockchain: return txid self._blockchain[txid] = {"tx": tx, "confirmations": 0, "outputs_spent": [None] * len(txobj.outputs)} # Mark spent outputs in other blockchain transactions for other_txid in self._blockchain: for txinput in txobj.inputs: if str(txinput.outpoint) == other_txid: self._blockchain[other_txid]['outputs_spent'][txinput.outpoint_index] = txid

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0.442478

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null

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null

0

1

0

d46043e7e3ee4e0fd2cb4b7196675dd558bf6307

2,256

py

Python

dev/Gems/CloudGemMetric/v1/AWS/common-code/LoadTest/LoadTest__CloudGemMetric.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

8

2019-10-07T16:33:47.000Z

2020-12-07T03:59:58.000Z

dev/Gems/CloudGemMetric/v1/AWS/common-code/LoadTest/LoadTest__CloudGemMetric.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

null

dev/Gems/CloudGemMetric/v1/AWS/common-code/LoadTest/LoadTest__CloudGemMetric.py

jeikabu/lumberyard

07228c605ce16cbf5aaa209a94a3cb9d6c1a4115

[ "AML" ]

5

2020-08-27T20:44:18.000Z

2021-08-21T22:54:11.000Z

# All or portions of this file Copyright (c) Amazon.com, Inc. or its affiliates or # its licensors. # # For complete copyright and license terms please see the LICENSE at the root of this # distribution (the "License"). All use of this software is governed by the License, # or, if provided, by the license below or the license accompanying this file. Do not # remove or modify any license notices. This file is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # # $Revision$ from __future__ import print_function from cloud_gem_load_test.service_api_call import ServiceApiCall from data_generator import DataGenerator import metric_constant as c # # Load Test Transaction Handler registration # def add_transaction_handlers(handler_context, transaction_handlers): service_api_name = c.RES_GEM_NAME + '.ServiceApi' base_url = handler_context.mappings.get(service_api_name, {}).get('PhysicalResourceId') if not base_url: raise RuntimeError('Missing PhysicalResourceId for ' + service_api_name) transaction_handlers.append(ServiceStatus(base_url)) transaction_handlers.append(ProduceMessage(base_url)) # # Check for the service status of Cloud Gem Under Test # class ServiceStatus(ServiceApiCall): def __init__(self, base_url): ServiceApiCall.__init__(self, name=c.RES_GEM_NAME + '.ServiceStatus', method='get', base_url=base_url, path='/service/status') # # Produce Metric Messages # class ProduceMessage(ServiceApiCall): def __init__(self, base_url): ServiceApiCall.__init__(self, name=c.RES_GEM_NAME + '.ProduceMessage', method='post', base_url=base_url, path='/producer/produce/message?compression_mode=NoCompression&sensitivity_type=Insensitive&payload_type=JSON') def build_request(self): request = ServiceApiCall.build_request(self) request['body'] = { 'data': build_metric_data() } return request # # Build the metric data object needed for the metric producer request body # def build_metric_data(): print('Building metric event data') data_generator = DataGenerator() return data_generator.json(1)

35.809524

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289

2,256

5.49827

0.422145

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0.026432

0.020768

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0

0.000544

0.18484

2,256

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36.387097

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0.068966

0

null

0

null

0

1

0

d463a1c535606eac893445765a893c90912ac9f7

756

py

Python

python/django-app/config/urls.py

mp5maker/library

b4d2eea70ae0da9d917285569031edfb4d8ab9fc

[ "MIT" ]

null

python/django-app/config/urls.py

mp5maker/library

b4d2eea70ae0da9d917285569031edfb4d8ab9fc

[ "MIT" ]

23

2020-08-15T15:18:32.000Z

2022-02-26T13:49:05.000Z

python/django-app/config/urls.py

mp5maker/library

b4d2eea70ae0da9d917285569031edfb4d8ab9fc

[ "MIT" ]

null

from django.contrib import admin from django.urls import path, include from django.conf import settings from django.conf.urls.static import (static, ) urlpatterns = [ path('admin/', admin.site.urls), path('api-auth/', include('rest_framework.urls', namespace="rest_framework")), path('api/v1/', include('superhero.urls', namespace="superhero")), path('api/v1/', include('movie.urls', namespace="movie")), ] if settings.DEBUG: import debug_toolbar urlpatterns = [ path('__debug__/', include(debug_toolbar.urls)), ] + urlpatterns urlpatterns += static(settings.MEDIA_URL, document=settings.MEDIA_ROOT) if 'silk' in settings.INSTALLED_APPS: urlpatterns += [path('silk/', include('silk.urls', namespace='silk'))]

34.363636

82

0.702381

92

756

5.652174

0.347826

0.076923

0.053846

0.061538

0

0.003072

0.138889

756

22

83

34.363636

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1

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false

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0

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0

null

0

null

0

1

0

d463c99b03f786a3e4037e0d14d949c40fda1d7c

1,492

py

Python

config.py

proteus1991/RawVSR

56686859498a07c83fde191fa1fc109d7aafb3da

[ "MIT" ]

24

2021-01-05T02:34:09.000Z

2022-03-15T12:26:21.000Z

config.py

baowenbo/RawVSR

56686859498a07c83fde191fa1fc109d7aafb3da

[ "MIT" ]

3

2021-01-11T17:43:58.000Z

2021-02-04T19:59:36.000Z

config.py

baowenbo/RawVSR

56686859498a07c83fde191fa1fc109d7aafb3da

[ "MIT" ]

4

2021-01-25T08:45:04.000Z

2021-12-22T09:14:35.000Z

""" paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference file: config.py author: Xiaohong Liu date: 17/09/19 """ def get_config(args): scale = args.scale_ratio save_tag = args.save_image if scale not in [2, 4]: raise Exception('scale {} is not supported!'.format(scale)) opt = {'train': {'dataroot_GT': './dataset/train/1080p_gt_rgb', 'dataroot_LQ': './dataset/train/1080p_lr_d_raw_{}'.format(scale), 'lr': 2e-4, 'num_epochs': 100, 'N_frames': 7, 'n_workers': 12, 'batch_size': 24 if scale == 4 else 8, 'GT_size': 256, 'LQ_size': 256 // scale, 'scale': scale, 'phase': 'train', }, 'test': {'dataroot_GT': './dataset/test/1080p_gt_rgb', 'dataroot_LQ': './dataset/test/1080p_lr_d_raw_{}'.format(scale), 'N_frames': 7, 'n_workers': 12, 'batch_size': 2, 'phase': 'test', 'save_image': save_tag, }, 'network': {'nf': 64, 'nframes': 7, 'groups': 8, 'back_RBs': 4}, 'dataset': {'dataset_name': 'RawVD' } } return opt

31.083333

91

0.432306

150

1,492

4.08

0.526667

0.053922

0.055556

0.058824

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0.060642

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false

0

0.060606

0

null

0

null

0

1

0

d4672e7716b611a83d36c41530dceaaadca832b5

4,018

py

Python

src/datasets/emnist.py

bjfranks/Classification-AD

4eecd6648bb6b54662944921924c8960c2ca236c

[ "MIT" ]

27

2020-05-30T16:27:31.000Z

2022-03-28T16:45:25.000Z

src/datasets/emnist.py

bjfranks/Classification-AD

4eecd6648bb6b54662944921924c8960c2ca236c

[ "MIT" ]

3

2021-04-22T10:01:55.000Z

2022-01-13T02:50:31.000Z

src/datasets/emnist.py

bjfranks/Classification-AD

4eecd6648bb6b54662944921924c8960c2ca236c

[ "MIT" ]

7

2020-06-15T16:31:23.000Z

2022-03-23T09:33:32.000Z

from torch.utils.data import Subset from PIL import Image from torchvision.datasets import EMNIST from base.torchvision_dataset import TorchvisionDataset from PIL.ImageFilter import GaussianBlur import numpy as np import torch import torchvision.transforms as transforms import random class EMNIST_Dataset(TorchvisionDataset): def __init__(self, root: str, split: str = 'letters', normal_class: int = 1, outlier_exposure: bool = False, oe_n_classes: int = 26, blur_oe: bool = False, blur_std: float = 1.0, seed: int = 0): super().__init__(root) self.image_size = (1, 28, 28) self.n_classes = 2 # 0: normal, 1: outlier self.shuffle = True self.split = split random.seed(seed) # set seed if outlier_exposure: self.normal_classes = None self.outlier_classes = list(range(1, 27)) self.known_outlier_classes = tuple(random.sample(self.outlier_classes, oe_n_classes)) else: # Define normal and outlier classes self.normal_classes = tuple([normal_class]) self.outlier_classes = list(range(1, 27)) self.outlier_classes.remove(normal_class) self.outlier_classes = tuple(self.outlier_classes) # EMNIST preprocessing: feature scaling to [0, 1] transform = [] if blur_oe: transform += [transforms.Lambda(lambda x: x.filter(GaussianBlur(radius=blur_std)))] transform += [transforms.ToTensor()] transform = transforms.Compose(transform) target_transform = transforms.Lambda(lambda x: int(x in self.outlier_classes)) # Get train set train_set = MyEMNIST(root=self.root, split=self.split, train=True, transform=transform, target_transform=target_transform, download=True) if outlier_exposure: idx = np.argwhere(np.isin(train_set.targets.cpu().data.numpy(), self.known_outlier_classes)) idx = idx.flatten().tolist() train_set.semi_targets[idx] = -1 * torch.ones(len(idx)).long() # set outlier exposure labels # Subset train_set to selected classes self.train_set = Subset(train_set, idx) self.train_set.shuffle_idxs = False self.test_set = None else: # Subset train_set to normal_classes idx = np.argwhere(np.isin(train_set.targets.cpu().data.numpy(), self.normal_classes)) idx = idx.flatten().tolist() train_set.semi_targets[idx] = torch.zeros(len(idx)).long() self.train_set = Subset(train_set, idx) # Get test set self.test_set = MyEMNIST(root=self.root, split=self.split, train=False, transform=transform, target_transform=target_transform, download=True) class MyEMNIST(EMNIST): """ Torchvision EMNIST class with additional targets for the outlier exposure setting and patch of __getitem__ method to also return the outlier exposure target as well as the index of a data sample. """ def __init__(self, *args, **kwargs): super(MyEMNIST, self).__init__(*args, **kwargs) self.semi_targets = torch.zeros_like(self.targets) self.shuffle_idxs = False def __getitem__(self, index): """Override the original method of the EMNIST class. Args: index (int): Index Returns: tuple: (image, target, semi_target, index) """ img, target, semi_target = self.data[index], int(self.targets[index]), int(self.semi_targets[index]) # doing this so that it is consistent with all other datasets # to return a PIL Image img = Image.fromarray(img.numpy(), mode='L') if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target, semi_target, index

39.009709

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4.971944

0.262525

0.041919

0.050786

0.017735

0.25393

0.212011

0.188634

0.112858

0.079

0

0.007797

0.265804

4,018

102

118

39.392157

0.83322

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0

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0

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0

1

0.048387

false

0

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