xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

17610a04c222fb0d535f11fe978558a57947af2a

4,946

py

Python

tests/test_backend.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

1

2020-05-25T22:09:40.000Z

tests/test_backend.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

null

tests/test_backend.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

null

from unittest.mock import MagicMock, patch from django.contrib.auth import get_user_model from django.test import TestCase, Client, override_settings from stagedoor.backends import EmailTokenBackend, SMSTokenBackend, StageDoorBackendMixin from stagedoor.models import AuthToken, Email, PhoneNumber, generate_token_string class StageDoorBackendMixinTests(TestCase): def test_get_user_happy_path(self): backend = StageDoorBackendMixin() user = get_user_model().objects.create() user_from_backend = backend.get_user(user_id=user.id) self.assertEqual(user, user_from_backend) def test_get_user_no_user(self): backend = StageDoorBackendMixin() user_from_backend = backend.get_user(user_id=7) self.assertEqual(None, user_from_backend) def test_authenticate_happy_path(self): backend = StageDoorBackendMixin() token_string = generate_token_string(email=True) token = AuthToken.objects.create(token=token_string) user = backend.authenticate(None, token_string) self.assertIsNotNone(user) def test_authenticate_no_token(self): backend = StageDoorBackendMixin() token_string = generate_token_string(email=True) user = backend.authenticate(None, token_string) self.assertIsNone(user) def test_single_use_token(self): backend = StageDoorBackendMixin() user = get_user_model().objects.create() user_from_backend = backend.get_user(user_id=user.id) self.assertEqual(user, user_from_backend) self.assertEqual(0, len(AuthToken.objects.all())) class EmailBackendTests(TestCase): def test_happy_path(self): email = Email.objects.create(email="hello@hellocaller.app") token_string = generate_token_string(email=True) token = AuthToken.objects.create(email=email, token=token_string) backend = EmailTokenBackend() user = backend.authenticate(None, token=token_string) self.assertIsNotNone(user) self.assertEqual("hello@hellocaller.app", user.email) email.refresh_from_db() self.assertEqual(user, email.user) def test_no_token(self): backend = EmailTokenBackend() user = backend.authenticate(None, token="hello@hellocaller.app") self.assertIsNone(user) def test_user_already_exists(self): email = Email.objects.create(email="hello@hellocaller.app") token_string = generate_token_string(email=True) token = AuthToken.objects.create(email=email, token=token_string) backend = EmailTokenBackend() user = backend.authenticate(None, token=token_string) self.assertIsNotNone(user) self.assertEqual("hello@hellocaller.app", user.email) email.refresh_from_db() self.assertEqual(user, email.user) # Now try again, and make sure we get the same user token_string = generate_token_string(email=True) token = AuthToken.objects.create(email=email, token=token_string) user = backend.authenticate(None, token=token_string) email.refresh_from_db() self.assertIsNotNone(user) self.assertEqual("hello@hellocaller.app", user.email) self.assertEqual(user, email.user) class SMSBackendTests(TestCase): def test_happy_path(self): phone_number = PhoneNumber.objects.create(phone_number="+14158675309") token_string = generate_token_string(sms=True) token = AuthToken.objects.create(phone_number=phone_number, token=token_string) backend = SMSTokenBackend() user = backend.authenticate(None, token=token_string) self.assertIsNotNone(user) phone_number = PhoneNumber.objects.get(phone_number="+14158675309") self.assertEqual(user, phone_number.user) def test_no_token(self): backend = SMSTokenBackend() user = backend.authenticate(None, token="+14158675310") self.assertIsNone(user) def test_user_already_exists(self): phone_number = PhoneNumber.objects.create(phone_number="+14158675309") token_string = generate_token_string(sms=True) token = AuthToken.objects.create(phone_number=phone_number, token=token_string) backend = SMSTokenBackend() user = backend.authenticate(None, token=token_string) self.assertIsNotNone(user) phone_number = PhoneNumber.objects.get(phone_number="+14158675309") self.assertEqual(user, phone_number.user) # Now try again, and make sure we get the same user token_string = generate_token_string(sms=True) token = AuthToken.objects.create(phone_number=phone_number, token=token_string) user = backend.authenticate(None, token=token_string) self.assertIsNotNone(user) phone_number = PhoneNumber.objects.get(phone_number="+14158675309") self.assertEqual(user, phone_number.user)

38.341085

88

0.712495

565

4,946

6.021239

0.132743

0.103469

0.061141

0.079365

0.837155

0.797472

0.783363

0.725162

0.700764

0.674897

0

0.017077

0.194905

4,946

128

89

38.640625

0.837268

0.020016

0

0.795699

0

0.040875

0.026012

0

0.247312

1

0.11828

false

0

0.053763

0

0.204301

0

null

0

1

0

1

0

null

0

7

1764688007b339099e066fde3467d3e902da9720

27,979

py

Python

src/ssnmf/ssnmf.py

lara-kassab/ssnmf

5e27d6d8daf02c08e82017fe137305823d3a8129

[ "MIT" ]

null

src/ssnmf/ssnmf.py

lara-kassab/ssnmf

5e27d6d8daf02c08e82017fe137305823d3a8129

[ "MIT" ]

null

src/ssnmf/ssnmf.py

lara-kassab/ssnmf

5e27d6d8daf02c08e82017fe137305823d3a8129

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Class and functions for training (SS)NMF model. The NMF model consists of the data matrix to be factorized, X, the factor matrices, A and S. Each model also consists of a label matrix, Y, classification factor matrix, B, and classification weight parameter, lam (although these three variables will be empty if Y is not input). These parameters define the objective function defining the model: (1) ||X - AS||_F^2 (train with mult) or (2) ||X - AS||_F^2 + lam * ||Y - BS||_F^2 (train with snmfmult) or (3) ||X - AS||_F^2 + lam * D(Y||BS) (train with klsnmfmult). Examples -------- unsupervised (1), saving errors, declaring number of iterations >>> numIters = 100 >>> model = SSNMF(numpy.random.rand(100,100),10) >>> errs = model.mult(saveerrs = True,numiters = numIters) unsupervised (1) with missing data, not saving errors, declaring number of iterations >>> numIters = 100 >>> model = SSNMF(numpy.random.rand(100,100),10, W = data['obsdata']) >>> model.mult(numiters = numIters) supervised (2), saving errors, default number of iterations >>> model = SSNMF(data['datamat'], 10, Y = data['labelmat']) >>> errs = model.snmfmult(saveerrs = True) semi-supervised (2), saving errors, default number of iterations >>> model = SSNMF(data['datamat'], 10, Y = data['labelmat'], L = data['obslabels']) >>> errs = model.snmfmult(saveerrs = True) supervised (3), not saving errors, declaring number of iterations >>> numIters = 15 >>> model = SSNMF(data['datamat'], 10, Y = data['labelmat']) >>> model.klsnmfmult(numiters = numIters) semi-supervised (3), not saving errors, declaring number of iterations and regularization parameter lam >>> numIters = 15 >>> model = SSNMF(data['datamat'], 10, lam = 0.1, Y = data['labelmat'], L = data['obslabels']) >>> model.klsnmfmult(numiters = numIters) semi-supervised (3) with missing data, not saving errors, declaring number of iterations >>> numIters = 15 >>> model = SSNMF(data['datamat'], 10, Y = data['labelmat'], W = data['obsdata'], L = data['obslabels']) >>> model.klsnmfmult(numiters = numIters) ''' import numpy as np from numpy import linalg as la class SSNMF: """ Class for (SS)NMF model. The NMF model consists of the data matrix to be factorized, X, the factor matrices, A and S. Each model also consists of a label matrix, Y, classification factor matrix, B, and classification weight parameter, lam (although these three variables will be empty if Y is not input). These parameters define the objective function defining the model: (1) ||X - AS||_F^2 (train with mult) or (2) ||X - AS||_F^2 + lam * ||Y - BS||_F^2 (train with snmfmult) or (3) ||X - AS||_F^2 + lam * D(Y||BS) (train with klsnmfmult). ... Parameters ---------- X : array Data matrix of size m x n. k : int_ Number of topics. Y : array, optional Label matrix of size p x n (default is None). W : array, optional Binary matrix of size p x n, whether the data is observed or not (default is None). L : array, optional Binary matrix of size m x n, whether the label is known or not (default is None). lam : float_, optional Weight parameter for classification term in objective (the default is 1 if Y is not None, None otherwise). A : array, optional Initialization for left factor matrix of X of size m x k (the default is a matrix with uniform random entries). S : array, optional Initialization for right factor matrix of X of size k x n (the default is a matrix with uniform random entries). B : array, optional Initialization for left factor matrix of Y of size p x k (the default is a matrix with uniform random entries if Y is not None, None otherwise). Methods ------- mult(numiters = 10, saveerrs = True) Train the unsupervised model (1) via numiters multiplicative updates. snmfmult(numiters = 10, saveerrs = True) Train the (semi-)supervised model (2) via numiters multiplicative updates. klsnmfmult(numiters = 10, saveerrs = True) Train the (semi-)supervised model (3) via numiters multiplicative updates. accuracy() Compute the classification accuracy of semi-supervised model (using Y, B, and S). kldiv() Compute the I-divergence, D(Y||BS), of semi-upervised model (using Y, B, and S). """ def __init__(self, X, k, **kwargs): self.X = X rows = np.shape(X)[0] cols = np.shape(X)[1] self.A = kwargs.get('A',np.random.rand(rows,k)) #initialize factor A self.S = kwargs.get('S',np.random.rand(k,cols)) #initialize factor S #check dimensions of X, A, and S match if rows != np.shape(self.A)[0]: raise Exception('The row dimensions of X and A are not equal.') if cols != np.shape(self.S)[1]: raise Exception('The column dimensions of X and S are not equal.') if np.shape(self.A)[1] != k: raise Exception('The column dimension of A is not equal to the input number of topics.') if np.shape(self.S)[0] != k: raise Exception('The row dimension of S is not equal to the input number of topics.') #supervision initializations (optional) self.Y = kwargs.get('Y',None) if self.Y is not None: #check dimensions of X and Y match if np.shape(self.Y)[1] != np.shape(self.X)[1]: raise Exception('The column dimensions of X and Y are not equal.') classes = np.shape(self.Y)[0] self.B = kwargs.get('B',np.random.rand(classes,k)) self.lam = kwargs.get('lam',1) #check dimensions of Y, S, and B match if np.shape(self.B)[0] != classes: raise Exception('The row dimensions of Y and B are not equal.') if np.shape(self.B)[1] != k: raise Exception('The column dimension of B is not equal to the input number of topics.') else: self.B = None self.lam = None # missing data (optional) self.W = kwargs.get('W',None) if self.W is not None: #check dimensions of X and W match if np.shape(self.W)[0] != np.shape(self.X)[0]: raise Exception('The row dimensions of X and W are not equal.') if np.shape(self.W)[1] != np.shape(self.X)[1]: raise Exception('The column dimensions of X and W are not equal.') # missing labels, semi-supervision (optional) self.L = kwargs.get('L',None) if self.L is not None: #check dimensions of Y and L match if np.shape(self.L)[0] != np.shape(self.Y)[0]: raise Exception('The row dimensions of Y and L are not equal.') if np.shape(self.L)[1] != np.shape(self.Y)[1]: raise Exception('The column dimensions of Y and L are not equal.') def mult(self,**kwargs): ''' Multiplicative updates for training unsupervised NMF model (1). Parameters ---------- numiters : int_, optional Number of iterations of updates to run (default is 10). saveerrs : bool, optional Boolean indicating whether to save model errors during iterations. eps : float_, optional Epsilon value to prevent division by zero (default is 1e-10). Returns ------- errs : array, optional If saveerrs, returns array of ||X - AS||_F for each iteration (length numiters). ''' numiters = kwargs.get('numiters', 1000) saveerrs = kwargs.get('saveerrs', False) eps = kwargs.get('eps', 1e-10) if saveerrs: errs = np.empty(numiters) #initialize error array if self.W is None: for i in range(numiters): #multiplicative updates for A and S self.A = np.multiply(np.divide(self.A,eps+ self.A @ self.S @ np.transpose(self.S)), \ self.X @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S,eps+ np.transpose(self.A) @ self.A @ self.S), \ np.transpose(self.A) @ self.X) if saveerrs: errs[i] = la.norm(self.X - self.A @ self.S, 'fro') #save reconstruction error print("Completed NMF for unsupervised learning without missing data.") if saveerrs: return [errs] elif self.W is not None: for i in range(numiters): #multiplicative updates for A and S self.A = np.multiply(np.divide(self.A,eps+ np.multiply(self.W, self.A @ self.S) @ np.transpose(self.S)), \ np.multiply(self.W,self.X) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S,eps+ np.transpose(self.A) @ np.multiply(self.W,self.A @ self.S)), \ np.transpose(self.A) @ np.multiply(self.W,self.X)) if saveerrs: errs[i] = la.norm(np.multiply(self.W,self.X) - np.multiply(self.W, self.A @ self.S), 'fro') #save reconstruction error print("Completed NMF for unsupervised learning with missing data.") if saveerrs: return [errs] def snmfmult(self,**kwargs): ''' Multiplicative updates for training semi-supervised NMF model (2). Parameters ---------- numiters : int_, optional Number of iterations of updates to run (default is 10). saveerrs : bool, optional Boolean indicating whether to save model errors during iterations. eps : float_, optional Epsilon value to prevent division by zero (default is 1e-10). Returns ------- errs : array, optional If saveerrs, returns array of ||X - AS||_F^2 + lam ||Y - BS||_F^2 for each iteration (length numiters). reconerrs : array, optional If saveerrs, returns array of ||X - AS||_F for each iteration (length numiters). classerrs : array, optional If saveerrs, returns array of ||Y - BS||_F for each iteration (length numiters). classaccs : array, optional If saveerrs, returns array of classification accuracy (computed with Y, B, S) at each iteration (length numiters). ''' numiters = kwargs.get('numiters', 1000) saveerrs = kwargs.get('saveerrs', False) eps = kwargs.get('eps', 1e-10) if saveerrs: errs = np.empty(numiters) #initialize error array reconerrs = np.empty(numiters) classerrs = np.empty(numiters) classaccs = np.empty(numiters) if self.Y is None: #if no label matrix provided, train unsupervised model instead raise Exception('Label matrix Y not provided: train with mult instead.') if self.L is None and self.W is None: # supervised learning, without missing data for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ self.A @ self.S @ np.transpose(self.S)), \ self.X @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B, eps+ self.B @ self.S @ np.transpose(self.S)), \ self.Y @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ np.transpose(self.A) @ self.A @ self.S + \ self.lam * np.transpose(self.B) @ self.B @ self.S), \ np.transpose(self.A) @ self.X + self.lam * np.transpose(self.B) \ @ self.Y) if saveerrs: reconerrs[i] = la.norm(self.X - self.A @ self.S, 'fro') classerrs[i] = la.norm(self.Y - self.B @ self.S, 'fro') errs[i] = reconerrs[i]**2 + self.lam * classerrs[i]**2 #save errors classaccs[i] = self.accuracy() print("Completed SSNMF for supervised learning without missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] elif self.L is not None and self.W is None: # semi-supervised learning, without missing data for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ self.A @ self.S @ np.transpose(self.S)), \ self.X @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B, eps+ np.multiply(self.L,self.B @ self.S) @ np.transpose(self.S)), \ np.multiply(self.L,self.Y) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ np.transpose(self.A) @ self.A @ self.S + \ self.lam * np.transpose(self.B) @ np.multiply(self.L,self.B @ self.S)), \ np.transpose(self.A) @ self.X + self.lam * np.transpose(self.B) \ @ np.multiply(self.L,self.Y)) if saveerrs: reconerrs[i] = la.norm(self.X - self.A @ self.S, 'fro') classerrs[i] = la.norm(np.multiply(self.L,self.Y) - np.multiply(self.L,self.B @ self.S), 'fro') errs[i] = reconerrs[i]**2 + self.lam * classerrs[i]**2 #save errors classaccs[i] = self.accuracy() print("Completed SSNMF for semi-supervised learning without missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] elif self.L is None and self.W is not None: # supervised learning, with missing data for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ np.multiply(self.W, self.A @ self.S) @ np.transpose(self.S)), \ np.multiply(self.W,self.X) @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B, eps+ self.B @ self.S @ np.transpose(self.S)), \ self.Y @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ np.transpose(self.A) @ np.multiply(self.W, self.A @ self.S) + \ self.lam * np.transpose(self.B) @ self.B @ self.S), \ np.transpose(self.A) @ np.multiply(self.W, self.X) + self.lam * np.transpose(self.B) \ @ self.Y) if saveerrs: reconerrs[i] = la.norm(np.multiply(self.W, self.X) - np.multiply(self.W, self.A @ self.S), 'fro') classerrs[i] = la.norm(self.Y - self.B @ self.S, 'fro') errs[i] = reconerrs[i]**2 + self.lam * classerrs[i]**2 #save errors classaccs[i] = self.accuracy() print("Completed SSNMF for supervised learning with missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] elif self.W is not None and self.L is not None: # semisupervised learning, with missing data for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ np.multiply(self.W, self.A @ self.S) @ np.transpose(self.S)), \ np.multiply(self.W,self.X) @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B, eps+ np.multiply(self.L, self.B @ self.S) @ np.transpose(self.S)), \ np.multiply(self.L, self.Y) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ np.transpose(self.A) @ np.multiply(self.W, self.A @ self.S) + \ self.lam * np.transpose(self.B) @ np.multiply(self.L,self.B @ self.S)), \ np.transpose(self.A) @ np.multiply(self.W, self.X) + self.lam * np.transpose(self.B) \ @np.multiply(self.L, self.Y)) if saveerrs: reconerrs[i] = la.norm(np.multiply(self.W, self.X) - np.multiply(self.W, self.A @ self.S), 'fro') classerrs[i] = la.norm(np.multiply(self.L, self.Y) - np.multiply(self.L, self.B @ self.S), 'fro') errs[i] = reconerrs[i]**2 + self.lam * classerrs[i]**2 #save errors classaccs[i] = self.accuracy() print("Completed SSNMF for semi-supervised learning with missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] def klsnmfmult(self,**kwargs): ''' Multiplicative updates for training semi-supervised NMF model (3). Parameters ---------- numiters : int_, optional Number of iterations of updates to run (default is 10). saveerrs : bool, optional Boolean indicating whether to save model errors during iterations. eps : float_, optional Epsilon value to prevent division by zero (default is 1e-10). Returns ------- errs : array, optional If saveerrs, returns array of ||X - AS||_F^2 + lam D(Y||BS) for each iteration (length numiters). reconerrs : array, optional If saveerrs, returns array of ||X - AS||_F for each iteration (length numiters). classerrs : array, optional If saveerrs, returns array of D(Y||BS) for each iteration (length numiters). classaccs : array, optional If saveerrs, returns array of classification accuracy (computed with Y, B, S) at each iteration (length numiters). ''' numiters = kwargs.get('numiters', 1000) saveerrs = kwargs.get('saveerrs', False) eps = kwargs.get('eps', 1e-10) if saveerrs: errs = np.empty(numiters) #initialize error array reconerrs = np.empty(numiters) classerrs = np.empty(numiters) classaccs = np.empty(numiters) if self.Y is None: #if no label matrix provided, train unsupervised model instead raise Exception('Label matrix Y not provided: train with mult instead.') classes = np.shape(self.Y)[0] cols = np.shape(self.Y)[1] if self.L is None and self.W is None: for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ self.A @ self.S @ np.transpose(self.S)), \ self.X @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B,eps+ np.ones((classes,cols)) @ np.transpose(self.S)), \ np.divide(self.Y, eps+ self.B @ self.S) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ (2 * np.transpose(self.A) @ self.A @ self.S + \ self.lam * np.transpose(self.B) @ \ np.ones((classes,cols)))),2 * np.transpose(self.A) \ @ self.X + self.lam * np.transpose(self.B) @ \ np.divide(self.Y, eps+ self.B @ self.S)) if saveerrs: reconerrs[i] = la.norm(self.X - self.A @ self.S, 'fro') classerrs[i] = self.kldiv() errs[i] = reconerrs[i]**2 + self.lam * classerrs[i] #save errors classaccs[i] = self.accuracy() print("Completed I-SSNMF for supervised learning without missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] if self.L is None and self.W is not None: for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ np.multiply(self.W, self.A @ self.S) @ np.transpose(self.S)), \ np.multiply(self.W, self.X) @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B,eps+ np.ones((classes,cols)) @ np.transpose(self.S)), \ np.divide(self.Y, eps+ self.B @ self.S) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ (2 * np.transpose(self.A) @ np.multiply(self.W, self.A @ self.S) + \ self.lam * np.transpose(self.B) @ \ np.ones((classes,cols)))),2 * np.transpose(self.A) \ @ np.multiply(self.W, self.X) + self.lam * np.transpose(self.B) @ \ np.divide(self.Y, eps+ self.B @ self.S)) if saveerrs: reconerrs[i] = la.norm(np.multiply(self.W,self.X) - np.multiply(self.W,self.A @ self.S), 'fro') classerrs[i] = self.kldiv() errs[i] = reconerrs[i]**2 + self.lam * classerrs[i] #save errors classaccs[i] = self.accuracy() print("Completed I-SSNMF for supervised learning with missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] if self.L is not None and self.W is None: for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ self.A @ self.S @ np.transpose(self.S)), \ self.X @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B,eps+ self.L @ np.transpose(self.S)), \ np.divide(np.multiply(self.L, self.Y), eps+ np.multiply(self.L,self.B @ self.S)) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ (2 * np.transpose(self.A) @ self.A @ self.S + \ self.lam * np.transpose(self.B) @ \ self.L)),2 * np.transpose(self.A) \ @ self.X + self.lam * np.transpose(self.B) @ \ np.divide(np.multiply(self.L,self.Y), eps+ np.multiply(self.L,self.B @ self.S))) if saveerrs: reconerrs[i] = la.norm(self.X - self.A @ self.S, 'fro') classerrs[i] = self.kldiv() errs[i] = reconerrs[i]**2 + self.lam * classerrs[i] #save errors classaccs[i] = self.accuracy() print("Completed I-SSNMF for semi-supervised learning without missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] if self.L is not None and self.W is not None: for i in range(numiters): #multiplicative updates for A, S, and B self.A = np.multiply(np.divide(self.A,eps+ np.multiply(self.W,self.A @ self.S) @ np.transpose(self.S)), \ np.multiply(self.W,self.X) @ np.transpose(self.S)) self.B = np.multiply(np.divide(self.B,eps+ self.L @ np.transpose(self.S)), \ np.divide(np.multiply(self.L, self.Y), eps+ np.multiply(self.L,self.B @ self.S)) @ np.transpose(self.S)) self.S = np.multiply(np.divide(self.S, eps+ (2 * np.transpose(self.A) @ np.multiply(self.W, self.A @ self.S) + \ self.lam * np.transpose(self.B) @ \ self.L)),2 * np.transpose(self.A) \ @ np.multiply(self.W,self.X) + self.lam * np.transpose(self.B) @ \ np.divide(np.multiply(self.L,self.Y), eps+ np.multiply(self.L,self.B @ self.S))) if saveerrs: reconerrs[i] = la.norm(np.multiply(self.W,self.X) - np.multiply(self.W,self.A @ self.S), 'fro') classerrs[i] = self.kldiv() errs[i] = reconerrs[i]**2 + self.lam * classerrs[i] #save errors classaccs[i] = self.accuracy() print("Completed I-SSNMF for semi-supervised learning with missing data.") if saveerrs: return [errs,reconerrs,classerrs,classaccs] def accuracy(self,**kwargs): ''' Compute accuracy of semi-supervised model (2) or (3) above. Returns ------- acc : float_ Fraction of correctly classified data points (computed with Y, B, S). ''' if self.Y is None: raise Exception('Label matrix Y not provided: model is not semi-supervised.') if self.L is None: #count number of data points which are correctly classified numdata = np.shape(self.Y)[1] numacc = 0 Yhat = self.B @ self.S for i in range(numdata): true_max = np.argmax(self.Y[:,i]) approx_max = np.argmax(Yhat[:,i]) if true_max == approx_max: numacc = numacc + 1 #return fraction of correctly classified data points acc = numacc/numdata return acc if self.L is not None: #count number of data points which are correctly classified numdata = np.shape(self.Y)[1] num_labels = numdata numacc = 0 Yhat = np.multiply(self.L, self.B @ self.S) for i in range(numdata): true_max = np.argmax(np.multiply(self.L,self.Y)[:,i]) approx_max = np.argmax(Yhat[:,i]) if (true_max == approx_max and np.multiply(self.L,self.Y)[true_max,i] != 0): numacc = numacc + 1 if (true_max == approx_max and np.multiply(self.L,self.Y)[true_max,i] == 0): num_labels = num_labels - 1 #return fraction of correctly classified data points acc = numacc/num_labels return acc def kldiv(self,**kwargs): ''' Compute I-divergence between Y and BS of semi-supervised model (most naturally (3)). Parameters ---------- eps : float_, optional Epsilon value to prevent division by zero (default is 1e-10). Returns ------- kldiv : float_ I-divergence between Y and BS. ''' eps = kwargs.get('eps', 1e-10) if self.Y is None: raise Exception('Label matrix Y not provided: model is not semi-supervised.') if self.L is None: #compute divergence Yhat = self.B @ self.S div = np.multiply(self.Y, np.log(np.divide(self.Y+eps, Yhat+eps))) - self.Y + Yhat kldiv = np.sum(np.sum(div)) return kldiv if self.L is not None: #compute divergence when there is missing labels Yhat = np.multiply(self.L,self.B @ self.S) div = np.multiply(np.multiply(self.L, self.Y), np.log(np.divide(np.multiply(self.L, self.Y)+eps, Yhat+eps))) \ -np.multiply(self.L,self.Y) + Yhat kldiv = np.sum(np.sum(div)) return kldiv

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0.08042

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0

null

0

1

0

1

0

null

0

7

17a0b7cd9a6d3d0cb3e7fc83398aaa40dc70e707

3,396

py

Python

tests/commands/create/test_create_app.py

paulproteus/briefcase

9d00dc9300cb00ef5dcd766e3a625a94b0462e51

[ "BSD-3-Clause" ]

null

tests/commands/create/test_create_app.py

paulproteus/briefcase

9d00dc9300cb00ef5dcd766e3a625a94b0462e51

[ "BSD-3-Clause" ]

null

tests/commands/create/test_create_app.py

paulproteus/briefcase

9d00dc9300cb00ef5dcd766e3a625a94b0462e51

[ "BSD-3-Clause" ]

null

from unittest import mock def test_create_app(tracking_create_command): "If the app doesn't already exist, it will be created" tracking_create_command.create_app(tracking_create_command.apps['first']) # The right sequence of things will be done assert tracking_create_command.actions == [ ('generate', tracking_create_command.apps['first']), ('support', tracking_create_command.apps['first']), ('dependencies', tracking_create_command.apps['first']), ('code', tracking_create_command.apps['first']), ('resources', tracking_create_command.apps['first']), ] # New app content has been created assert (tracking_create_command.platform_path / 'first.bundle' / 'new').exists() def test_create_existing_app_overwrite(tracking_create_command): "An existing app can be overwritten if requested" # Answer yes when asked tracking_create_command.input = mock.MagicMock(return_value='y') # Generate an app in the location. bundle_path = tracking_create_command.platform_path / 'first.bundle' bundle_path.mkdir(parents=True) with (bundle_path / 'original').open('w') as f: f.write('original template!') tracking_create_command.create_app(tracking_create_command.apps['first']) # The right sequence of things will be done assert tracking_create_command.actions == [ ('generate', tracking_create_command.apps['first']), ('support', tracking_create_command.apps['first']), ('dependencies', tracking_create_command.apps['first']), ('code', tracking_create_command.apps['first']), ('resources', tracking_create_command.apps['first']), ] # Original content has been deleted assert not (bundle_path / 'original').exists() # New app content has been created assert (bundle_path / 'new').exists() def test_create_existing_app_no_overwrite(tracking_create_command): "If you say no, the existing app won't be overwritten" # Answer no when asked tracking_create_command.input = mock.MagicMock(return_value='n') bundle_path = tracking_create_command.platform_path / 'first.bundle' bundle_path.mkdir(parents=True) with (bundle_path / 'original').open('w') as f: f.write('original template!') tracking_create_command.create_app(tracking_create_command.apps['first']) # No app creation actions will be performed assert tracking_create_command.actions == [] # Original content still exists assert (bundle_path / 'original').exists() # New app content has not been created assert not (bundle_path / 'new').exists() def test_create_existing_app_no_overwrite_default(tracking_create_command): "By default, the existing app won't be overwritten" # Answer '' (i.e., just press return) when asked tracking_create_command.input = mock.MagicMock(return_value='') bundle_path = tracking_create_command.platform_path / 'first.bundle' bundle_path.mkdir(parents=True) with (bundle_path / 'original').open('w') as f: f.write('original template!') tracking_create_command.create_app(tracking_create_command.apps['first']) assert tracking_create_command.actions == [] # Original content still exists assert (bundle_path / 'original').exists() # New app content has not been created assert not (bundle_path / 'new').exists()

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0

0.173439

3,396

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85

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null

0

1

0

1

0

null

0

7

bd6665d843367358b18f01273f4a236fd07e4e0d

46

py

Python

e2e/server_unreachable/some_test.py

testandconquer/pytest-conquer

da600c7f5bcd06aa62c5cca9b75370bf1a6ebf05

[ "MIT" ]

null

e2e/server_unreachable/some_test.py

testandconquer/pytest-conquer

da600c7f5bcd06aa62c5cca9b75370bf1a6ebf05

[ "MIT" ]

5

2018-12-27T02:52:01.000Z

2019-01-02T01:52:55.000Z

e2e/server_unreachable/some_test.py

testandconquer/pytest-conquer

da600c7f5bcd06aa62c5cca9b75370bf1a6ebf05

[ "MIT" ]

null

import time def test_a(): time.sleep(1)

7.666667

17

0.630435

8

46

3.5

0.875

0

0.028571

0.23913

46

5

18

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0

1

0.333333

true

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1

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null

0

1

0

1

0

1

0

7

bd97486db17525bb3c04503418deaca33bc13446

9,887

py

Python

tests/test_quart.py

JacobHenner/aioprometheus

d0528e832a1cdbc3503942b1b0c8925a43007500

[ "MIT" ]

104

2016-11-01T04:43:23.000Z

2022-03-09T18:15:05.000Z

tests/test_quart.py

JacobHenner/aioprometheus

d0528e832a1cdbc3503942b1b0c8925a43007500

[ "MIT" ]

35

2017-04-05T07:52:15.000Z

2022-03-13T02:29:50.000Z

tests/test_quart.py

JacobHenner/aioprometheus

d0528e832a1cdbc3503942b1b0c8925a43007500

[ "MIT" ]

18

2017-02-08T07:48:26.000Z

2022-03-01T01:16:29.000Z

import logging import sys import unittest import asynctest from aioprometheus import REGISTRY, Counter, MetricsMiddleware, formats, render try: from quart import Quart, request from aioprometheus.asgi.quart import metrics have_quart = True except ImportError: have_quart = False @unittest.skipUnless(have_quart, "Quart library is not available") class TestQuartRender(asynctest.TestCase): """Test exposing Prometheus metrics from within a Quart app""" def tearDown(self): REGISTRY.clear() async def test_render_in_quart_app(self): """check render usage in Quart app""" app = Quart(__name__) app.events_counter = Counter("events", "Number of events.") @app.route("/") async def index(): app.events_counter.inc({"path": "/"}) return "hello" @app.route("/metrics") async def handle_metrics(): content, http_headers = render(REGISTRY, request.headers.getlist("accept")) return content, http_headers # The test client also starts the web service test_client = app.test_client() # Access root to increment metric counter response = await test_client.get("/") self.assertEqual(response.status_code, 200) # Get default format response = await test_client.get("/metrics", headers={"accept": "*/*"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) # payload = await response.get_data() # Get text format response = await test_client.get("/metrics", headers={"accept": "text/plain;"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) # Get binary format response = await test_client.get( "/metrics", headers={"accept": formats.binary.BINARY_CONTENT_TYPE}, ) self.assertEqual(response.status_code, 200) self.assertIn( formats.binary.BINARY_CONTENT_TYPE, response.headers.get("content-type"), ) async def test_asgi_middleware(self): """check ASGI middleware usage in Quart app""" app = Quart(__name__) app.events_counter = Counter("events", "Number of events.") @app.route("/") async def index(): app.events_counter.inc({"path": "/"}) return "hello" # Add a route that always generates an exception @app.route("/boom") async def hello(): raise Exception("Boom") app.asgi_app = MetricsMiddleware(app.asgi_app) app.add_url_rule("/metrics", "metrics", metrics, methods=["GET"]) # The test client also starts the web service test_client = app.test_client() # Access root to increment metric counter response = await test_client.get("/") self.assertEqual(response.status_code, 200) # Get default format response = await test_client.get("/metrics", headers={"accept": "*/*"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check content self.assertIn('events{path="/"} 1', content) self.assertIn('requests_total_counter{method="GET",path="/"} 1', content) self.assertIn( 'status_codes_counter{method="GET",path="/",status_code="200"} 1', content ) self.assertIn('responses_total_counter{method="GET",path="/"} 1', content) # Access it again to confirm default metrics get incremented response = await test_client.get("/") self.assertEqual(response.status_code, 200) # Get text format response = await test_client.get("/metrics", headers={"accept": "text/plain;"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check content self.assertIn('events{path="/"} 2', content) self.assertIn('requests_total_counter{method="GET",path="/"} 2', content) self.assertIn( 'status_codes_counter{method="GET",path="/",status_code="200"} 2', content ) self.assertIn('responses_total_counter{method="GET",path="/"} 2', content) # Confirm no exception have been observed so far. self.assertNotIn("exceptions_total_counter{", content) # Access boom route to trigger exception metric update. # Silence the stderr output log generated by Quart when it captures # the exception. with self.assertLogs("quart.app", logging.ERROR): with asynctest.mock.patch.object(sys.stderr, "write") as mock_stderr: response = await test_client.get("/boom") self.assertEqual(response.status_code, 500) response = await test_client.get("/metrics", headers={"accept": "*/*"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check exception counter was NOT incremented due to Quart not # propagating exceptions out to the ASGI layer. self.assertNotIn( 'exceptions_total_counter{method="GET",path="/boom"} 1', content ) self.assertIn( 'status_codes_counter{method="GET",path="/boom",status_code="500"} 1', content, ) async def test_asgi_middleware_group_status_codes_enabled(self): """check ASGI middleware group status codes usage in FastAPI app""" app = Quart(__name__) app.events_counter = Counter("events", "Number of events.") @app.route("/") async def index(): app.events_counter.inc({"path": "/"}) return "hello" # Add a route that always generates an exception @app.route("/boom") async def hello(): raise Exception("Boom") app.asgi_app = MetricsMiddleware(app.asgi_app, group_status_codes=True) app.add_url_rule("/metrics", "metrics", metrics, methods=["GET"]) # The test client also starts the web service test_client = app.test_client() # Access root to increment metric counter response = await test_client.get("/") self.assertEqual(response.status_code, 200) # Get default format response = await test_client.get("/metrics", headers={"accept": "*/*"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check content self.assertIn('events{path="/"} 1', content) self.assertIn('requests_total_counter{method="GET",path="/"} 1', content) self.assertIn( 'status_codes_counter{method="GET",path="/",status_code="2xx"} 1', content ) self.assertIn('responses_total_counter{method="GET",path="/"} 1', content) # Access it again to confirm default metrics get incremented response = await test_client.get("/") self.assertEqual(response.status_code, 200) # Get text format response = await test_client.get("/metrics", headers={"accept": "text/plain;"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check content self.assertIn('events{path="/"} 2', content) self.assertIn('requests_total_counter{method="GET",path="/"} 2', content) self.assertIn( 'status_codes_counter{method="GET",path="/",status_code="2xx"} 2', content ) self.assertIn('responses_total_counter{method="GET",path="/"} 2', content) # Confirm no exception have been observed so far. self.assertNotIn("exceptions_total_counter{", content) # Access boom route to trigger exception metric update. # Silence the stderr output log generated by Quart when it captures # the exception. with self.assertLogs("quart.app", logging.ERROR): with asynctest.mock.patch.object(sys.stderr, "write") as mock_stderr: response = await test_client.get("/boom") self.assertEqual(response.status_code, 500) response = await test_client.get("/metrics", headers={"accept": "*/*"}) self.assertEqual(response.status_code, 200) self.assertIn( formats.text.TEXT_CONTENT_TYPE, response.headers.get("content-type"), ) payload = await response.get_data() content = payload.decode("utf-8") # Check exception counter was NOT incremented due to Quart not # propagating exceptions out to the ASGI layer. self.assertNotIn( 'exceptions_total_counter{method="GET",path="/boom"} 1', content ) self.assertIn( 'status_codes_counter{method="GET",path="/boom",status_code="5xx"} 1', content, )

36.618519

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0.616668

1,108

9,887

5.362816

0.134477

0.054527

0.057556

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0.888253

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36.754647

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false

0

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0

null

0

1

0

1

0

null

0

7

da08638632f2975d301b02c5cca682f314d45e1d

3,441

py

Python

cn/bvt/assignment-3/topo5.py

adamacosta/bvt

17f8e94105b346ddad185900df08269702e94391

[ "MIT" ]

null

cn/bvt/assignment-3/topo5.py

adamacosta/bvt

17f8e94105b346ddad185900df08269702e94391

[ "MIT" ]

null

cn/bvt/assignment-3/topo5.py

adamacosta/bvt

17f8e94105b346ddad185900df08269702e94391

[ "MIT" ]

null

# a Star topology centered on Z # D G J # \ | / # \ | / # E H K # \ | / # \ | / # F I L # \ | / # \ | / # A --- B --- C --------- Z -------- M --- N --- O # / | \ # / | \ # P S V # / | \ # / | \ # Q T W # / | \ # / | \ # R U X # topo = { 'A' : ['B'], 'B' : ['A', 'C'], 'C' : ['B', 'Z'], 'D' : ['E'], 'E' : ['D', 'F'], 'F' : ['E', 'Z'], 'G' : ['H'], 'H' : ['G', 'I'], 'I' : ['H', 'Z'], 'J' : ['K'], 'K' : ['J', 'L'], 'L' : ['K', 'Z'], 'M' : ['Z', 'N'], 'N' : ['M', 'O'], 'O' : ['N'], 'P' : ['Z', 'Q'], 'Q' : ['P', 'R'], 'R' : ['Q'], 'S' : ['Z', 'T'], 'T' : ['S', 'U'], 'U' : ['T'], 'V' : ['Z', 'W'], 'W' : ['V', 'X'], 'X' : ['W'], 'Z' : ['C', 'F', 'I', 'L', 'M', 'P', 'S', 'V']} ans = \ 'A:A0,B1,C2,D6,E5,F4,G6,H5,I4,J6,K5,L4,M4,N5,O6,P4,Q5,R6,S4,T5,U6,V4,W5,X6,Z3' + \ 'B:A1,B0,C1,D5,E4,F3,G5,H4,I3,J5,K4,L3,M3,N4,O5,P3,Q4,R5,S3,T4,U5,V3,W4,X5,Z2' + \ 'C:A2,B1,C0,D4,E3,F2,G4,H3,I2,J4,K3,L2,M2,N3,O4,P2,Q3,R4,S2,T3,U4,V2,W3,X4,Z1' + \ 'D:A6,B5,C4,D0,E1,F2,G6,H5,I4,J6,K5,L4,M4,N5,O6,P4,Q5,R6,S4,T5,U6,V4,W5,X6,Z3' + \ 'E:A5,B4,C3,D1,E0,F1,G5,H4,I3,J5,K4,L3,M3,N4,O5,P3,Q4,R5,S3,T4,U5,V3,W4,X5,Z2' + \ 'F:A4,B3,C2,D2,E1,F0,G4,H3,I2,J4,K3,L2,M2,N3,O4,P2,Q3,R4,S2,T3,U4,V2,W3,X4,Z1' + \ 'G:A6,B5,C4,D6,E5,F4,G0,H1,I2,J6,K5,L4,M4,N5,O6,P4,Q5,R6,S4,T5,U6,V4,W5,X6,Z3' + \ 'H:A5,B4,C3,D5,E4,F3,G1,H0,I1,J5,K4,L3,M3,N4,O5,P3,Q4,R5,S3,T4,U5,V3,W4,X5,Z2' + \ 'I:A4,B3,C2,D4,E3,F2,G2,H1,I0,J4,K3,L2,M2,N3,O4,P2,Q3,R4,S2,T3,U4,V2,W3,X4,Z1' + \ 'J:A6,B5,C4,D6,E5,F4,G6,H5,I4,J0,K1,L2,M4,N5,O6,P4,Q5,R6,S4,T5,U6,V4,W5,X6,Z3' + \ 'K:A5,B4,C3,D5,E4,F3,G5,H4,I3,J1,K0,L1,M3,N4,O5,P3,Q4,R5,S3,T4,U5,V3,W4,X5,Z2' + \ 'L:A4,B3,C2,D4,E3,F2,G4,H3,I2,J2,K1,L0,M2,N3,O4,P2,Q3,R4,S2,T3,U4,V2,W3,X4,Z1' + \ 'M:A4,B3,C2,D4,E3,F2,G4,H3,I2,J4,K3,L2,M0,N1,O2,P2,Q3,R4,S2,T3,U4,V2,W3,X4,Z1' + \ 'N:A5,B4,C3,D5,E4,F3,G5,H4,I3,J5,K4,L3,M1,N0,O1,P3,Q4,R5,S3,T4,U5,V3,W4,X5,Z2' + \ 'O:A6,B5,C4,D6,E5,F4,G6,H5,I4,J6,K5,L4,M2,N1,O0,P4,Q5,R6,S4,T5,U6,V4,W5,X6,Z3' + \ 'P:A4,B3,C2,D4,E3,F2,G4,H3,I2,J4,K3,L2,M2,N3,O4,P0,Q1,R2,S2,T3,U4,V2,W3,X4,Z1' + \ 'Q:A5,B4,C3,D5,E4,F3,G5,H4,I3,J5,K4,L3,M3,N4,O5,P1,Q0,R1,S3,T4,U5,V3,W4,X5,Z2' + \ 'R:A6,B5,C4,D6,E5,F4,G6,H5,I4,J6,K5,L4,M4,N5,O6,P2,Q1,R0,S4,T5,U6,V4,W5,X6,Z3' + \ 'S:A4,B3,C2,D4,E3,F2,G4,H3,I2,J4,K3,L2,M2,N3,O4,P2,Q3,R4,S0,T1,U2,V2,W3,X4,Z1' + \ 'T:A5,B4,C3,D5,E4,F3,G5,H4,I3,J5,K4,L3,M3,N4,O5,P3,Q4,R5,S1,T0,U1,V3,W4,X5,Z2' + \ 'U:A6,B5,C4,D6,E5,F4,G6,H5,I4,J6,K5,L4,M4,N5,O6,P4,Q5,R6,S2,T1,U0,V4,W5,X6,Z3' + \ 'V:A4,B3,C2,D4,E3,F2,G4,H3,I2,J4,K3,L2,M2,N3,O4,P2,Q3,R4,S2,T3,U4,V0,W1,X2,Z1' + \ 'W:A5,B4,C3,D5,E4,F3,G5,H4,I3,J5,K4,L3,M3,N4,O5,P3,Q4,R5,S3,T4,U5,V1,W0,X1,Z2' + \ 'X:A6,B5,C4,D6,E5,F4,G6,H5,I4,J6,K5,L4,M4,N5,O6,P4,Q5,R6,S4,T5,U6,V2,W1,X0,Z3' + \ 'Z:A3,B2,C1,D3,E2,F1,G3,H2,I1,J3,K2,L1,M1,N2,O3,P1,Q2,R3,S1,T2,U3,V1,W2,X3,Z0'

47.136986

82

0.408021

756

3,441

1.857143

0.236772

0.019943

0.029915

0.039886

0.717949

0.692308

0.662393

0.623932

0

0.251408

0.277536

3,441

73

83

47.136986

0.313355

0.190061

0

0.490196

0.713304

0.686913

0

1

0

false

0

1

null

0

1

0

1

0

1

0

1

0

1

null

0

8

da310f299c75d14dc54551cf69badd8a06c426a9

2,642

py

Python

vip4model/ModelCart.py

mattkjames7/vip4model

63f1fd0a566f2adcf93cdd424f5155fc62336ec3

[ "MIT" ]

1

2021-09-07T11:44:20.000Z

vip4model/ModelCart.py

mattkjames7/vip4model

63f1fd0a566f2adcf93cdd424f5155fc62336ec3

[ "MIT" ]

null

vip4model/ModelCart.py

mattkjames7/vip4model

63f1fd0a566f2adcf93cdd424f5155fc62336ec3

[ "MIT" ]

null

import numpy as np from .Model import Model,ModelScalar from ._SphHarm import _SphHarm,_SphHarmScalarCart def ModelCart(x,y,z,MaxDeg=4): ''' VIP4 Magnetic field model (see Connerney et al 1998 below). The model uses right-handed System III coordinates (I think). Inputs ====== x : float x-coordinate in Rj, R-H System III. y : float y-coordinate in Rj, R-H System III. z : float z-coordinate in Rj, R-H System III. Returns ======= Bx : float x component of magnetic field, nT. By : float y component of magnetic field, nT. Bz : float z component of magnetic field, nT. Please cite: Connerney, J. E. P., Acuña, M. H., Ness, N. F., and Satoh, T. (1998), New models of Jupiter's magnetic field constrained by the Io flux tube footprint, J. Geophys. Res., 103( A6), 11929– 11939, doi:10.1029/97JA03726. ''' #convert to spherical polar coords r = np.sqrt(x**2 + y**2 + z**2) theta = np.arccos(z/r) phi = (np.arctan2(y,x) + (2*np.pi)) % (2*np.pi) #call the model Br,Bt,Bp = Model(r,theta,phi,MaxDeg) #convert to Cartesian (hopefully correctly...) cost = np.cos(theta) sint = np.sin(theta) cosp = np.cos(phi) sinp = np.sin(phi) Bx = Br*sint*cosp + Bt*cost*cosp - Bp*sinp By = Br*sint*sinp + Bt*cost*sinp + Bp*cosp Bz = Br*cost - Bt*sint return Bx,By,Bz def ModelCartScalar(x,y,z,MaxDeg=4): ''' VIP4 Magnetic field model. The model uses right-handed System III coordinates (I think). Inputs ====== x : float x-coordinate in Rj, R-H System III. y : float y-coordinate in Rj, R-H System III. z : float z-coordinate in Rj, R-H System III. Returns ======= Bx : float x component of magnetic field, nT. By : float y component of magnetic field, nT. Bz : float z component of magnetic field, nT. If using the VIP4 model, please cite the following paper: Connerney, J. E. P., Acuña, M. H., Ness, N. F., and Satoh, T. (1998), New models of Jupiter's magnetic field constrained by the Io flux tube footprint, J. Geophys. Res., 103( A6), 11929– 11939, doi:10.1029/97JA03726.12 ''' #convert to spherical polar coords r = np.sqrt(x**2 + y**2 + z**2) theta = np.arccos(z/r) phi = (np.arctan2(y,x) + (2*np.pi)) % (2*np.pi) #call the model Br,Bt,Bp = ModelScalar(r,theta,phi,MaxDeg) #convert to Cartesian (hopefully correctly...) cost = np.cos(theta) sint = np.sin(theta) cosp = np.cos(phi) sinp = np.sin(phi) Bx = Br*sint*cosp + Bt*cost*cosp - Bp*sinp By = Br*sint*sinp + Bt*cost*sinp + Bp*cosp Bz = Br*cost - Bt*sint return Bx,By,Bz def ModelTest(x,y,z,MaxDeg=4): return _SphHarmScalarCart(x,y,z,MaxDeg)

23.175439

71

0.652536

458

2,642

3.759825

0.248908

0.075494

0.04878

0.052265

0.852497

0.84669

0.809524

0

0.041011

0.206283

2,642

113

72

23.380531

0.779208

0.632097

0

0.709677

0

1

0.096774

false

0

0.096774

0.032258

0.290323

0

null

0

1

0

1

0

null

0

7

da37afa1ca208c5cbbe4afdc3395e64ac8c1d7c9

1,044

py

Python

dev/tools/leveleditor/pandac/PandaModules.py

CrankySupertoon01/Toontown-2

60893d104528a8e7eb4aced5d0015f22e203466d

[ "MIT" ]

1

2021-02-13T22:40:50.000Z

dev/tools/leveleditor/pandac/PandaModules.py

CrankySupertoonArchive/Toontown-2

60893d104528a8e7eb4aced5d0015f22e203466d

[ "MIT" ]

1

2018-07-28T20:07:04.000Z

2018-07-30T18:28:34.000Z

dev/tools/leveleditor/pandac/PandaModules.py

CrankySupertoonArchive/Toontown-2

60893d104528a8e7eb4aced5d0015f22e203466d

[ "MIT" ]

2

2019-12-02T01:39:10.000Z

2021-02-13T22:41:00.000Z

try: from libpandaexpressModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libpandaModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libpandaphysicsModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libdirectModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libpandafxModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libpandaodeModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libotpModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise try: from libtoontownModules import * except ImportError, err: if 'DLL loader cannot find' not in str(err): raise

21.75

48

0.671456

136

1,044

5.154412

0.169118

0.079886

0.262482

0.296719

0.788873

0

0.259579

1,044

47

49

22.212766

0.906856

0

0.8

0

0.168582

0

null

0

0.4

null

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

10

16f3b9243726b77a85a24462c0c49b0a4eb40ead

796

py

Python

lesson01/luofeng/99_table.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

lesson01/luofeng/99_table.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

lesson01/luofeng/99_table.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- encoding:utf8 -*- #******************************************* # Author: LuoFeng # Date: 2019-05-18 # Filename: 99_table.py # Describe: #******************************************* # 正方形九九乘法表 for n in range(1,10): # python3 print() 函数支持 end 参数，python2 不支持, 默认值 end = '\n' for m in range(1,10): print('{} * {} = {}'.format(n, m, n*m), end='\t') print('') # 左上三角形九九乘法表 for n in range(1,10): # python3 print() 函数支持 end 参数，python2 不支持, 默认值 end = '\n' for m in range(n,10): print('{} * {} = {}'.format(n, m, n*m), end='\t') print('') # 左下三角形九九乘法表 for n in range(1,10): # python3 print() 函数支持 end 参数，python2 不支持, 默认值 end = '\n' for m in range(1,n+1): print('{} * {} = {}'.format(n, m, n*m), end='\t') print('')

26.533333

61

0.472362

114

796

3.289474

0.324561

0.112

0.106667

0.712

0.648

0

0.052885

0.21608

796

29

62

27.448276

0.548077

0.492462

0

0.75

0

0.107692

0

1

0

false

0

0.5

0

null

0

1

0

1

0

null

0

1

0

7

e508f5357db4a49c22092c74450b60b28e97ffeb

3,726

py

Python

src/dcos_e2e_cli/common/upgrade.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

63

2018-05-17T21:02:14.000Z

2021-11-15T19:18:03.000Z

src/dcos_e2e_cli/common/upgrade.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

225

2017-09-08T02:24:58.000Z

2018-05-16T12:18:58.000Z

src/dcos_e2e_cli/common/upgrade.py

jongiddy/dcos-e2e

b52ef9a1097a8fb328902064345cc6c8b0bf5779

[ "Apache-2.0" ]

21

2018-06-14T21:58:24.000Z

2021-11-15T19:18:06.000Z

""" Helpers for installing DC/OS. """ import subprocess import sys from pathlib import Path from typing import Any, Dict, Iterable, Tuple import click from halo import Halo from dcos_e2e.cluster import Cluster from dcos_e2e.node import Output from .base_classes import ClusterRepresentation from .error_handling import show_calledprocess_error def cluster_upgrade_dcos_from_path( cluster: Cluster, cluster_representation: ClusterRepresentation, ip_detect_path: Path, dcos_config: Dict[str, Any], files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]], dcos_installer: Path, doctor_message: str, enable_spinner: bool, ) -> None: """ Upgrade DC/OS on a cluster. Args: cluster: The cluster to upgrade DC/OS on. cluster_representation: A representation of the cluster. ip_detect_path: The ``ip-detect`` script to use for installing DC/OS. files_to_copy_to_genconf_dir: Pairs of host paths to paths on the installer node. These are files to copy from the host to the installer node before upgrading DC/OS. dcos_config: The DC/OS configuration to use. dcos_installer: The ``Path`` to a local DC/OS installer. doctor_message: A message which instructs the user on which command to use if installation fails. enable_spinner: Whether to enable the spinner animation. """ spinner = Halo(enabled=enable_spinner) spinner.start('Upgrading DC/OS') try: cluster.upgrade_dcos_from_path( dcos_installer=dcos_installer, dcos_config=dcos_config, ip_detect_path=ip_detect_path, files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir, output=Output.LOG_AND_CAPTURE, ) except subprocess.CalledProcessError as exc: spinner.stop() show_calledprocess_error(exc=exc) click.echo(doctor_message) cluster_representation.destroy() sys.exit(exc.returncode) spinner.succeed() def cluster_upgrade_dcos_from_url( cluster: Cluster, cluster_representation: ClusterRepresentation, ip_detect_path: Path, dcos_config: Dict[str, Any], files_to_copy_to_genconf_dir: Iterable[Tuple[Path, Path]], dcos_installer: str, doctor_message: str, enable_spinner: bool, ) -> None: """ Upgrade DC/OS on a cluster. Args: cluster: The cluster to upgrade DC/OS on. cluster_representation: A representation of the cluster. ip_detect_path: The ``ip-detect`` script to use for installing DC/OS. files_to_copy_to_genconf_dir: Pairs of host paths to paths on the installer node. These are files to copy from the host to the installer node before upgrading DC/OS. dcos_config: The DC/OS configuration to use. dcos_installer: A URL pointing to an installer. doctor_message: A message which instructs the user on which command to use if installation fails. enable_spinner: Whether to enable the spinner animation. """ spinner = Halo(enabled=enable_spinner) spinner.start('Upgrading DC/OS') try: cluster.upgrade_dcos_from_url( dcos_installer=dcos_installer, dcos_config=dcos_config, ip_detect_path=ip_detect_path, files_to_copy_to_genconf_dir=files_to_copy_to_genconf_dir, output=Output.LOG_AND_CAPTURE, ) except subprocess.CalledProcessError as exc: spinner.stop() show_calledprocess_error(exc=exc) click.echo(doctor_message) cluster_representation.destroy() sys.exit(exc.returncode) spinner.succeed()

33.267857

78

0.694042

490

3,726

5.036735

0.189796

0.02269

0.044571

0.042139

0.86953

0.843598

0

0.000708

0.241546

3,726

111

79

33.567568

0.872611

0.371981

0

0.71875

0

0.013599

0

1

0.03125

false

0

0.15625

0

0.1875

0

null

0

1

0

1

0

null

0

7

e5472bc3d527750bfb50c5e56c38d936fceee316

1,970

py

Python

examples/simple/testnode.py

DrLuke/effigy

c970d945fc22787bd6b95ec2f1dfd317876ef4dc

[ "MIT" ]

null

examples/simple/testnode.py

DrLuke/effigy

c970d945fc22787bd6b95ec2f1dfd317876ef4dc

[ "MIT" ]

null

examples/simple/testnode.py

DrLuke/effigy

c970d945fc22787bd6b95ec2f1dfd317876ef4dc

[ "MIT" ]

null

from effigy import QNodeSceneNode, NodeIO, NodeInput, NodeOutput from PyQt5.QtWidgets import QGraphicsRectItem from PyQt5.QtCore import QRectF, QPointF, Qt from PyQt5.QtGui import QPen, QColor, QBrush class TestNode(QNodeSceneNode): author = "Luke" modulename = "testmod" name = "Example Node" def addIO(self): newnode = NodeOutput(str, parent=self, name="output") newnode.setPos(20, 10) self.IO["output"] = newnode newnode = NodeInput(str, parent=self, name="input") newnode.setPos(-20, 10) self.IO["input"] = newnode def boundingRect(self): return self.mainRect.rect() def addGraphicsItems(self): self.mainRect = QGraphicsRectItem(QRectF(-15, -15, 30, 30), self) def selectedChanged(self, state): if state: self.mainRect.setPen(QPen(Qt.red)) else: self.mainRect.setPen(QPen(Qt.black)) def serialize(self): return {"testdata": self.id} def deserialize(self, data): print("This node has data: %s" % data["testdata"]) class TestNode2(QNodeSceneNode): author = "Luke" modulename = "testmod" name = "Example Node" def addIO(self): newnode = NodeOutput(int, parent=self, name="output") newnode.setPos(20, 10) self.IO["output"] = newnode newnode = NodeInput([int, str], parent=self, name="input") newnode.setPos(-25, 10) self.IO["input"] = newnode def boundingRect(self): return self.mainRect.rect() def addGraphicsItems(self): self.mainRect = QGraphicsRectItem(QRectF(-15, -15, 30, 30), self) def selectedChanged(self, state): if state: self.mainRect.setPen(QPen(Qt.red)) else: self.mainRect.setPen(QPen(Qt.black)) def serialize(self): return {"testdata": self.id} def deserialize(self, data): print("This node has data: %s" % data["testdata"])

28.142857

73

0.62132

227

1,970

5.39207

0.286344

0.078431

0.045752

0.071895

0.831699

0.830065

0.772876

0

0.024374

0.250254

1,970

70

74

28.142857

0.804333

0

0.769231

0

0.084221

0

1

0.230769

false

0

0.076923

0.538462

0.038462

0

null

0

1

0

1

0

null

0

1

0

1

0

9

e57397ac8823d3b4a2d993a40f35759fd877b2cb

160

py

Python

test/login.py

cz495969281/test007

43001233e7b61ee281c299cd66dedbd08eb667d8

[ "MIT" ]

null

test/login.py

cz495969281/test007

43001233e7b61ee281c299cd66dedbd08eb667d8

[ "MIT" ]

null

test/login.py

cz495969281/test007

43001233e7b61ee281c299cd66dedbd08eb667d8

[ "MIT" ]

null

print("------------------") print("------------------") print("------------------") print("------------------") print("------------------") num1 = 10 num2 = 20

20

27

0.23125

9

160

4.111111

0.555556

1.081081

1.216216

1.081081

0

0.040268

0.06875

160

7

28

22.857143

0.208054

0

0.714286

0

0.5625

0

1

0

false

0

0.714286

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

8

e5b5072f92e155561bc18cfadcbcd310c9fd1b61

262

py

Python

slack/python/slack_aiml_test/settings.py

m4573rn3rd/scripts

df29a79b5ed6a996ccf0357e4fe495dd05377848

[ "MIT" ]

null

slack/python/slack_aiml_test/settings.py

m4573rn3rd/scripts

df29a79b5ed6a996ccf0357e4fe495dd05377848

[ "MIT" ]

null

slack/python/slack_aiml_test/settings.py

m4573rn3rd/scripts

df29a79b5ed6a996ccf0357e4fe495dd05377848

[ "MIT" ]

null

# Slackbot API Information slack_bot_token = "xoxb-2650828670406-2670419769553-qxTzP6Sbh9tlqfYIA52wh1bZ" bot_id = "xoxb-2650828670406-2670419769553-qxTzP6Sbh9tlqfYIA52wh1bZ" # AIML FIles directory = "/aiml" learn_file = "std-startup.xml" respond = "load aiml b"

32.75

77

0.80916

29

262

7.172414

0.758621

0.163462

0.288462

0.519231

0

0.260504

0.091603

262

8

78

32.75

0.613445

0.133588

0

0.644444

0.506667

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

null

0

7

e5b72a057874a519f9464ed9ed2e2bbd59ff53aa

211

py

Python

src/events/game_start.py

ArcosJuan/Get-out-of-my-fucking-maze

ca2cfeaaeecb6c6f583ad647d020f25176170805

[ "MIT" ]

1

2022-03-12T21:38:46.000Z

src/events/game_start.py

ArcosJuan/Get-out-of-my-fucking-maze

ca2cfeaaeecb6c6f583ad647d020f25176170805

[ "MIT" ]

null

src/events/game_start.py

ArcosJuan/Get-out-of-my-fucking-maze

ca2cfeaaeecb6c6f583ad647d020f25176170805

[ "MIT" ]

null

from src.events import Event class GameStart(Event): def __init__(self, min_size): super().__init__() self.min_size = min_size def get_min_size(self): return self.min_size

21.1

48

0.64455

29

211

4.206897

0.517241

0.286885

0.270492

0.245902

0

0.265403

211

10

48

21.1

0.787097

0

1

0.333333

false

0

0.166667

0.666667

0

1

0

null

1

0

null

0

1

0

1

0

7

e5ebc4932df04eab5f87fbd870f222dde7998412

15

py

Python

app/containers/Codedash/js/data/generator/temp.py

GAUTAMRAJU15/demo

fd11b6db098d5b0dec4de74590ff5239dd614a7e

[ "MIT" ]

null

app/containers/Codedash/js/data/generator/temp.py

GAUTAMRAJU15/demo

fd11b6db098d5b0dec4de74590ff5239dd614a7e

[ "MIT" ]

4

2020-04-05T22:02:22.000Z

2022-03-24T07:58:59.000Z

app/containers/Codedash/js/data/generator/temp.py

GAUTAMRAJU15/demo

fd11b6db098d5b0dec4de74590ff5239dd614a7e

[ "MIT" ]

null

print(8%6-5/3)

7.5

14

0.6

5

15

1.8

1

0

0.285714

0.066667

15

1

15

0.357143

0

1

0

true

0

1

null

0

1

0

1

0

1

0

null

0

1

0

1

0

8

f919e1e1800f004de4f9772da02f5180f9091681

9,460

py

Python

src/pages/migrations/0002_auto_20191214_1316.py

ahsanali2000/Patientio

254896ba0386a3738b5fd13800e1d524a5c47bea

[ "BSD-3-Clause" ]

2

2020-04-19T20:13:46.000Z

2022-03-11T09:48:53.000Z

src/pages/migrations/0002_auto_20191214_1316.py

ahsanali2000/Patientio

254896ba0386a3738b5fd13800e1d524a5c47bea

[ "BSD-3-Clause" ]

1

2020-06-26T22:26:22.000Z

2020-06-26T22:28:25.000Z

src/pages/migrations/0002_auto_20191214_1316.py

ahsanali2000/Patientio

254896ba0386a3738b5fd13800e1d524a5c47bea

[ "BSD-3-Clause" ]

3

2020-06-21T20:52:12.000Z

2021-07-31T11:07:21.000Z

# Generated by Django 3.0 on 2019-12-14 08:16 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('pages', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='doctor', name='person', ), migrations.RemoveField( model_name='history', name='patient', ), migrations.RemoveField( model_name='labdoc', name='person', ), migrations.RemoveField( model_name='nurse', name='person', ), migrations.RemoveField( model_name='receptionist', name='person', ), migrations.RemoveField( model_name='report', name='patient', ), migrations.AddField( model_name='appointment', name='disease_option', field=models.CharField(blank=True, max_length=50, null=True), ), migrations.AddField( model_name='appointment', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='doctor', name='address', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AddField( model_name='doctor', name='age', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='doctor', name='email', field=models.EmailField(blank=True, max_length=254, null=True), ), migrations.AddField( model_name='doctor', name='first_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='doctor', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.AddField( model_name='doctor', name='last_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='doctor', name='phone', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AddField( model_name='doctor', name='sex', field=models.CharField(blank=True, max_length=15, null=True), ), migrations.AddField( model_name='history', name='address', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AddField( model_name='history', name='age', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='history', name='email', field=models.EmailField(blank=True, max_length=254, null=True), ), migrations.AddField( model_name='history', name='first_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='history', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.AddField( model_name='history', name='last_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='history', name='phone', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AddField( model_name='history', name='sex', field=models.CharField(blank=True, max_length=15, null=True), ), migrations.AddField( model_name='history', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='labdoc', name='address', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AddField( model_name='labdoc', name='age', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='labdoc', name='email', field=models.EmailField(blank=True, max_length=254, null=True), ), migrations.AddField( model_name='labdoc', name='first_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='labdoc', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.AddField( model_name='labdoc', name='last_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='labdoc', name='phone', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AddField( model_name='labdoc', name='sex', field=models.CharField(blank=True, max_length=15, null=True), ), migrations.AddField( model_name='nurse', name='address', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AddField( model_name='nurse', name='age', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='nurse', name='email', field=models.EmailField(blank=True, max_length=254, null=True), ), migrations.AddField( model_name='nurse', name='first_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='nurse', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.AddField( model_name='nurse', name='last_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='nurse', name='phone', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AddField( model_name='nurse', name='sex', field=models.CharField(blank=True, max_length=15, null=True), ), migrations.AddField( model_name='receptionist', name='address', field=models.CharField(blank=True, max_length=200, null=True), ), migrations.AddField( model_name='receptionist', name='age', field=models.IntegerField(blank=True, null=True), ), migrations.AddField( model_name='receptionist', name='email', field=models.EmailField(blank=True, max_length=254, null=True), ), migrations.AddField( model_name='receptionist', name='first_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='receptionist', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.AddField( model_name='receptionist', name='last_name', field=models.CharField(blank=True, max_length=150, null=True), ), migrations.AddField( model_name='receptionist', name='phone', field=models.CharField(blank=True, max_length=20, null=True), ), migrations.AddField( model_name='receptionist', name='sex', field=models.CharField(blank=True, max_length=15, null=True), ), migrations.AddField( model_name='report', name='user', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='appointment', name='doctor', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='service', name='icon', field=models.FileField(default='', upload_to='images/'), ), migrations.AlterField( model_name='service', name='image', field=models.FileField(default='', upload_to='images/'), ), migrations.DeleteModel( name='Patient', ), migrations.DeleteModel( name='Person', ), ]

33.546099

133

0.540803

891

9,460

5.612795

0.089787

0.095381

0.20236

0.237552

0.902819

0.832434

0.824835

0.772446

0

0.015868

0.333827

9,460

281

134

33.66548

0.77769

0.004545

0

0.941818

1

0

0.083165

0

1

0

false

0

0.010909

0

0.021818

0

null

0

1

0

1

0

null

0

10

0088f28f1c6652002fa08ccee3e16f6c267e08ec

2,424

py

Python

napari/components/_tests/test_world_coordinates.py

mrocklin/napari

b61d9ae570e30091a97b6c76e37cd95fe5b296b6

[ "BSD-3-Clause" ]

1

2021-04-04T21:25:04.000Z

napari/components/_tests/test_world_coordinates.py

mrocklin/napari

b61d9ae570e30091a97b6c76e37cd95fe5b296b6

[ "BSD-3-Clause" ]

1

2020-10-15T19:31:09.000Z

2020-10-15T19:39:33.000Z

napari/components/_tests/test_world_coordinates.py

mrocklin/napari

b61d9ae570e30091a97b6c76e37cd95fe5b296b6

[ "BSD-3-Clause" ]

null

import numpy as np from napari.components import ViewerModel def test_translated_images(): """Test two translated images.""" viewer = ViewerModel() np.random.seed(0) data = np.random.random((10, 10, 10)) viewer.add_image(data) viewer.add_image(data, translate=[10, 0, 0]) assert viewer.dims.range[0] == (0, 20 - 1, 1) assert viewer.dims.range[1] == (0, 10 - 1, 1) assert viewer.dims.range[2] == (0, 10 - 1, 1) assert viewer.dims.nsteps == [20, 10, 10] for i in range(viewer.dims.nsteps[0]): viewer.dims.set_current_step(0, i) assert viewer.dims.current_step[0] == i def test_scaled_images(): """Test two scaled images.""" viewer = ViewerModel() np.random.seed(0) data = np.random.random((10, 10, 10)) viewer.add_image(data) viewer.add_image(data[::2], scale=[2, 1, 1]) assert viewer.dims.range[0] == (0, 10 - 1, 1) assert viewer.dims.range[1] == (0, 10 - 1, 1) assert viewer.dims.range[2] == (0, 10 - 1, 1) assert viewer.dims.nsteps == [10, 10, 10] for i in range(viewer.dims.nsteps[0]): viewer.dims.set_current_step(0, i) assert viewer.dims.current_step[0] == i def test_scaled_and_translated_images(): """Test scaled and translated images.""" viewer = ViewerModel() np.random.seed(0) data = np.random.random((10, 10, 10)) viewer.add_image(data) viewer.add_image(data[::2], scale=[2, 1, 1], translate=[10, 0, 0]) assert viewer.dims.range[0] == (0, 20 - 2, 1) assert viewer.dims.range[1] == (0, 10 - 1, 1) assert viewer.dims.range[2] == (0, 10 - 1, 1) assert viewer.dims.nsteps == [19, 10, 10] for i in range(viewer.dims.nsteps[0]): viewer.dims.set_current_step(0, i) assert viewer.dims.current_step[0] == i def test_both_scaled_and_translated_images(): """Test both scaled and translated images.""" viewer = ViewerModel() np.random.seed(0) data = np.random.random((10, 10, 10)) viewer.add_image(data, scale=[2, 1, 1]) viewer.add_image(data, scale=[2, 1, 1], translate=[20, 0, 0]) assert viewer.dims.range[0] == (0, 40 - 2, 2) assert viewer.dims.range[1] == (0, 10 - 1, 1) assert viewer.dims.range[2] == (0, 10 - 1, 1) assert viewer.dims.nsteps == [20, 10, 10] for i in range(viewer.dims.nsteps[0]): viewer.dims.set_current_step(0, i) assert viewer.dims.current_step[0] == i

35.647059

70

0.618812

387

2,424

3.790698

0.105943

0.190866

0.218132

0.171779

0.923654

0.90184

0.861622

0.819359

0

0.084595

0.209984

2,424

67

71

36.179104

0.681462

0.05198

0

0.685185

0

0.37037

1

0.074074

false

0

0.037037

0

0.111111

0

null

0

1

0

null

0

8

dabd39aef0dadaaac78062270877c1da8518c8b7

1,809

py

Python

diventi/adventures/migrations/0007_auto_20200502_1136.py

flavoi/diven

3173ca3ca3fbedc191b8eab3639a6bceb3c442c4

[ "Apache-2.0" ]

2

2019-06-27T16:00:17.000Z

2020-08-14T07:46:05.000Z

diventi/adventures/migrations/0007_auto_20200502_1136.py

flavoi/diven

3173ca3ca3fbedc191b8eab3639a6bceb3c442c4

[ "Apache-2.0" ]

26

2020-02-15T22:39:35.000Z

2022-02-19T21:09:01.000Z

diventi/adventures/migrations/0007_auto_20200502_1136.py

flavoi/diven

3173ca3ca3fbedc191b8eab3639a6bceb3c442c4

[ "Apache-2.0" ]

1

2021-11-12T22:30:15.000Z

# Generated by Django 2.2.12 on 2020-05-02 09:36 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('adventures', '0006_auto_20200502_1130'), ] operations = [ migrations.AddField( model_name='antagonist', name='description_en', field=models.TextField(blank=True, null=True, verbose_name='description'), ), migrations.AddField( model_name='antagonist', name='description_it', field=models.TextField(blank=True, null=True, verbose_name='description'), ), migrations.AddField( model_name='antagonist', name='title_en', field=models.CharField(max_length=50, null=True, verbose_name='title'), ), migrations.AddField( model_name='antagonist', name='title_it', field=models.CharField(max_length=50, null=True, verbose_name='title'), ), migrations.AddField( model_name='antagonistgoal', name='description_en', field=models.TextField(blank=True, null=True, verbose_name='description'), ), migrations.AddField( model_name='antagonistgoal', name='description_it', field=models.TextField(blank=True, null=True, verbose_name='description'), ), migrations.AddField( model_name='antagonistgoal', name='title_en', field=models.CharField(max_length=50, null=True, verbose_name='title'), ), migrations.AddField( model_name='antagonistgoal', name='title_it', field=models.CharField(max_length=50, null=True, verbose_name='title'), ), ]

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0.588723

177

1,809

5.841808

0.248588

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0.17795

0.208897

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0

0.031177

0.290768

1,809

53

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34.132075

0.774747

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1

0

false

0

0.021277

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0.085106

0

null

0

1

0

1

0

null

0

9

dad6d9d8b61676f25e557d3c2409faf57a2068d9

531

py

Python

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Forms/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

1

2020-03-25T03:27:24.000Z

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Forms/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

extensions/.stubs/clrclasses/Autodesk/AutoCAD/Internal/Forms/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import ExColumnHeader from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import ExListView from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import HelpProvider from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import IInPlaceEditUpdater from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import InPlaceEditControl from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import LabelEllipsis from __clrclasses__.Autodesk.AutoCAD.Internal.Forms import ListViewCellEditEventArgs

66.375

84

0.894539

56

531

7.982143

0.25

0.219239

0.344519

0.454139

0.751678

0

0.052731

531

7

85

75.857143

0.888668

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

8

dae9779e1eb09d8e88bb707ec8f1d00c50c5783f

9,006

py

Python

tests/api/ranklist_row_tests.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

26

2015-06-21T16:19:44.000Z

2021-11-15T12:32:25.000Z

tests/api/ranklist_row_tests.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

5

2015-03-10T06:00:52.000Z

2020-01-18T12:59:25.000Z

tests/api/ranklist_row_tests.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

12

2015-04-24T17:16:50.000Z

2022-01-04T14:21:25.000Z

""" This module provides classes for testing RanklistRow object """ import unittest from codeforces import RanklistRow, Party, ProblemResult class RanklistRowTests(unittest.TestCase): def setUp(self): self.row = RanklistRow() def load_from_dict(self): d = { "party": { "contestId": 374, "members": [{"handle": "Deception"}], "participantType": "CONTESTANT", "ghost": False, "room": 46, "startTimeSeconds": 1387380600 }, "rank": 1, "points": 4902.0, "penalty": 0, "successfulHackCount": 11, "unsuccessfulHackCount": 1, "problemResults": [ { "points": 312.0, "rejectedAttemptCount": 1, "type": "FINAL", "bestSubmissionTimeSeconds": 4174 }, { "points": 596.0, "rejectedAttemptCount": 2, "type": "FINAL", "bestSubmissionTimeSeconds": 4583 }, { "points": 1128.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 3751 }, { "points": 1816.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 1430 }, { "points": 0.0, "rejectedAttemptCount": 0, "type": "FINAL" } ], "lastSubmissionTimeSeconds": 424242 } self.row.load_from_dict(d) self.assertEqual(Party(d['party']), self.row.party) self.assertEqual(1, self.row.rank) self.assertEqual(4902.0, self.row.points) self.assertEqual(0, self.row.penalty) self.assertEqual(11, self.row.successful_hack_count) self.assertEqual(1, self.row.unsuccessful_hack_count) self.assertEqual(list(map(ProblemResult, d['problemResults'])), self.row.problem_results) self.assertEqual(424242, self.row.last_submission_time) def load_only_required_from_dict(self): """ Required fields are: party rank points penalty successfulHackCount unsuccessfulHackCount problemResults """ d = { "party": { "contestId": 374, "members": [{"handle": "Deception"}], "participantType": "CONTESTANT", "ghost": False, "room": 46, "startTimeSeconds": 1387380600 }, "rank": 1, "points": 4902.0, "penalty": 0, "successfulHackCount": 11, "unsuccessfulHackCount": 1, "problemResults": [ { "points": 312.0, "rejectedAttemptCount": 1, "type": "FINAL", "bestSubmissionTimeSeconds": 4174 }, { "points": 596.0, "rejectedAttemptCount": 2, "type": "FINAL", "bestSubmissionTimeSeconds": 4583 }, { "points": 1128.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 3751 }, { "points": 1816.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 1430 }, { "points": 0.0, "rejectedAttemptCount": 0, "type": "FINAL" } ] } self.row.load_from_dict(d) self.assertEqual(Party(d['party']), self.row.party) self.assertEqual(1, self.row.rank) self.assertEqual(4902.0, self.row.points) self.assertEqual(0, self.row.penalty) self.assertEqual(11, self.row.successful_hack_count) self.assertEqual(1, self.row.unsuccessful_hack_count) self.assertEqual(list(map(ProblemResult, d['problemResults'])), self.row.problem_results) self.assertIsNone(self.row.last_submission_time) def test_load_from_json(self): d = { "party": { "contestId": 374, "members": [{"handle": "Deception"}], "participantType": "CONTESTANT", "ghost": False, "room": 46, "startTimeSeconds": 1387380600 }, "rank": 1, "points": 4902.0, "penalty": 0, "successfulHackCount": 11, "unsuccessfulHackCount": 1, "problemResults": [ { "points": 312.0, "rejectedAttemptCount": 1, "type": "FINAL", "bestSubmissionTimeSeconds": 4174 }, { "points": 596.0, "rejectedAttemptCount": 2, "type": "FINAL", "bestSubmissionTimeSeconds": 4583 }, { "points": 1128.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 3751 }, { "points": 1816.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 1430 }, { "points": 0.0, "rejectedAttemptCount": 0, "type": "FINAL" } ], "lastSubmissionTimeSeconds": 424242 } json = str(d).replace('False', 'false').replace("'", '"') self.row.load_from_json(json) self.assertEqual(Party(d['party']), self.row.party) self.assertEqual(1, self.row.rank) self.assertEqual(4902.0, self.row.points) self.assertEqual(0, self.row.penalty) self.assertEqual(11, self.row.successful_hack_count) self.assertEqual(1, self.row.unsuccessful_hack_count) self.assertEqual(list(map(ProblemResult, d['problemResults'])), self.row.problem_results) self.assertEqual(424242, self.row.last_submission_time) def test_load_only_required_from_json(self): """ Required fields are: party rank points penalty successfulHackCount unsuccessfulHackCount problemResults """ d = { "party": { "contestId": 374, "members": [{"handle": "Deception"}], "participantType": "CONTESTANT", "ghost": False, "room": 46, "startTimeSeconds": 1387380600 }, "rank": 1, "points": 4902.0, "penalty": 0, "successfulHackCount": 11, "unsuccessfulHackCount": 1, "problemResults": [ { "points": 312.0, "rejectedAttemptCount": 1, "type": "FINAL", "bestSubmissionTimeSeconds": 4174 }, { "points": 596.0, "rejectedAttemptCount": 2, "type": "FINAL", "bestSubmissionTimeSeconds": 4583 }, { "points": 1128.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 3751 }, { "points": 1816.0, "rejectedAttemptCount": 0, "type": "FINAL", "bestSubmissionTimeSeconds": 1430 }, { "points": 0.0, "rejectedAttemptCount": 0, "type": "FINAL" } ] } json = str(d).replace('False', 'false').replace("'", '"') self.row.load_from_json(json) self.assertEqual(Party(d['party']), self.row.party) self.assertEqual(1, self.row.rank) self.assertEqual(4902.0, self.row.points) self.assertEqual(0, self.row.penalty) self.assertEqual(11, self.row.successful_hack_count) self.assertEqual(1, self.row.unsuccessful_hack_count) self.assertEqual(list(map(ProblemResult, d['problemResults'])), self.row.problem_results) self.assertIsNone(self.row.last_submission_time) if __name__ == '__main__': unittest.main()

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6.269767

0.133333

0.064046

0.13452

0.077151

0.932987

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0

0.063677

0.428048

9,006

267

98

33.730337

0.721413

0.036087

0

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0

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0.062713

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false

0

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0

null

0

1

0

1

0

null

0

7

9712768994f5a1edb806ab5f21f076b623ff8876

21,977

py

Python

tests/integration/test_envelope.py

andrew-chang-dewitt/hoops-api

3530c5127c35742aad84df8d6a5286b9f5ad3608

[ "MIT" ]

null

tests/integration/test_envelope.py

andrew-chang-dewitt/hoops-api

3530c5127c35742aad84df8d6a5286b9f5ad3608

[ "MIT" ]

10

2021-11-02T23:31:56.000Z

2021-12-07T03:41:12.000Z

tests/integration/test_envelope.py

andrew-chang-dewitt/hoops

3530c5127c35742aad84df8d6a5286b9f5ad3608

[ "MIT" ]

null

"""Tests for /envelope routes.""" from decimal import Decimal from unittest import main, IsolatedAsyncioTestCase as TestCase from uuid import UUID from db_wrapper.model import sql # internal test dependencies from tests.helpers.application import ( get_test_client, get_token_header, ) from tests.helpers.database import ( setup_user, setup_account, setup_transactions, ) BASE_URL = "/envelope" class TestRoutePostRoot(TestCase): """Testing POST /envelope.""" async def test_valid_request(self) -> None: """Testing a valid request's response.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database) new_envelope = { "name": "envelope", } response = await client.post( BASE_URL, headers={ **get_token_header(user_id), "accept": "application/json"}, json=new_envelope) with self.subTest( msg="Responds with a status code of 201."): self.assertEqual(201, response.status_code) with self.subTest( msg="Responds with newly created Envelope's data."): body = response.json() with self.subTest(msg="Saves the given name."): self.assertEqual(body["name"], new_envelope["name"]) with self.subTest(msg="Bound to current user in auth token."): self.assertEqual(body["user_id"], str(user_id)) with self.subTest(msg="Has a UUID identifier."): self.assertTrue(UUID(body["id"])) with self.subTest(msg="Starts with zero funds."): self.assertEqual(body["total_funds"], 0) with self.subTest(msg="Saves the Envelope to the database."): body = response.json() new_id = UUID(body["id"]) await database.connect() query_result = await database.execute_and_return(sql.SQL(""" SELECT * FROM envelope WHERE id = {new_id}; """).format(new_id=sql.Literal(new_id))) await database.disconnect() result = query_result[0] with self.subTest( msg="Given envelope name & database envelope name match." ): self.assertEqual(result["name"], new_envelope["name"]) with self.subTest(msg="Binds to currently auth'd user."): self.assertEqual(result["user_id"], user_id) with self.subTest(msg="Starts with 0 funds."): self.assertEqual(result["total_funds"], 0) class TestRouteGetRoot(TestCase): """Testing GET /envelope.""" async def test_valid_request(self) -> None: """Testing a valid request's response.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") other_id = await setup_user(database, "other") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}), ('envelope', 1.00, {user_id}), ('envelope', 1.00, {user_id}), ('envelope', 1.00, {other_id}); """).format( user_id=sql.Literal(user_id), other_id=sql.Literal(other_id)) await database.connect() await database.execute(add_envelope_query) await database.disconnect() response = await client.get( BASE_URL, headers={ **get_token_header(user_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Responds w/ all Envelopes belonging to current user." ): body = response.json() self.assertEqual( len(body), 3, msg="Body should contain 3 Envelopes.") for item in body: with self.subTest(msg="Envelope has a name."): self.assertEqual(item["name"], "envelope") with self.subTest( msg="Bound to current user in auth token."): self.assertEqual(item["user_id"], str(user_id)) with self.subTest(msg="Envelope has a UUID identifier."): self.assertTrue(UUID(item["id"])) with self.subTest(msg="Envelope has funds."): self.assertEqual(item["total_funds"], 1.00) class TestRouteGetId(TestCase): """Testing GET /envelope/{id}.""" async def test_valid_request(self) -> None: """Testing a valid request's response.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] response = await client.get( f"{BASE_URL}/{envelope_id}", headers={ **get_token_header(user_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Responds with requested Envelope's data."): body = response.json() with self.subTest(msg="Includes the Envelope's name."): self.assertEqual(body["name"], "envelope") with self.subTest(msg="Bound to current user in auth token."): self.assertEqual(body["user_id"], str(user_id)) with self.subTest(msg="Has a UUID identifier."): self.assertEqual(body["id"], str(envelope_id)) with self.subTest(msg="Includes Envelope's funds."): self.assertEqual(body["total_funds"], 1) async def test_can_only_get_own_envelopes(self) -> None: """A User can't get another User's Envelopes.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") other_id = await setup_user(database, "other") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] response = await client.get( f"{BASE_URL}/{envelope_id}", headers={ **get_token_header(other_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 404."): self.assertEqual(404, response.status_code) class TestRoutePutId(TestCase): """Testing PUT /envelope/{id}.""" async def test_valid_request(self) -> None: """Testing a valid request's response.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] changes = {"name": "new name"} response = await client.put( f"{BASE_URL}/{envelope_id}", headers={ **get_token_header(user_id), "accept": "application/json"}, json=changes) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Responds with requested Envelope's updated data."): body = response.json() with self.subTest(msg="Includes the Envelope's name."): self.assertEqual(body["name"], changes["name"]) with self.subTest(msg="Saves changes to the database."): body = response.json() new_id = UUID(body["id"]) await database.connect() query_result = await database.execute_and_return(sql.SQL(""" SELECT * FROM envelope WHERE id = {new_id}; """).format(new_id=sql.Literal(new_id))) await database.disconnect() result = query_result[0] self.assertEqual(result["name"], changes["name"]) async def test_can_only_change_own_envelopes(self) -> None: """A User can't change another User's Envelopes.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") other_id = await setup_user(database, "other") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] changes = {"name": "new name"} response = await client.put( f"{BASE_URL}/{envelope_id}", headers={ **get_token_header(other_id), "accept": "application/json"}, json=changes) with self.subTest( msg="Responds with a status code of 404."): self.assertEqual(404, response.status_code) async def test_can_not_update_funds(self) -> None: """Funds must be updated via `.../funds/{amount}` endpoint.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 1.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] changes = {"total_funds": 100000} response = await client.put( f"{BASE_URL}/{envelope_id}", headers={ **get_token_header(user_id), "accept": "application/json"}, json=changes) with self.subTest( msg="Responds with a status code of 404."): self.assertEqual(422, response.status_code) class TestRoutePutFunds(TestCase): """Testing PUT /envelope/{id}/funds/{amount}.""" async def test_move_funds_from_available(self) -> None: """Testing moving funds from Available Balance.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") account_id = await setup_account(database, user_id) await setup_transactions(database, [Decimal(10)], account_id) add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 0.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] amount = 5 response = await client.put( f"{BASE_URL}/{envelope_id}/funds/{amount}", headers={ **get_token_header(user_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Responds with Envelope with updated funds."): body = response.json() self.assertEqual(body["total_funds"], 5) with self.subTest( msg="Envelope is updated in database."): query = sql.SQL(""" SELECT total_funds FROM envelope WHERE id = {envelope_id}; """).format( envelope_id=sql.Literal(envelope_id)) await database.connect() query_result = await database.execute_and_return(query) await database.disconnect() self.assertEqual(query_result[0]["total_funds"], 5) with self.subTest( msg="Can not move funds if not enough are available."): amount = 11 response = await client.put( f"{BASE_URL}/{envelope_id}/funds/{amount}", headers={ **get_token_header(user_id), "accept": "application/json"}) self.assertEqual(response.status_code, 409) async def test_move_funds_from_other_envelope(self) -> None: """Testing moving funds from a given Envelope.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") account_id = await setup_account(database, user_id) await setup_transactions(database, [Decimal(10)], account_id) add_envelopes_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('to', 0.00, {user_id}), ('from', 10.00, {user_id}) RETURNING id, name; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelopes_query) await database.disconnect() for result in query_result: if result["name"] == "from": from_envelope = result["id"] if result["name"] == "to": to_envelope = result["id"] amount = 5 response = await client.put( f"{BASE_URL}/{to_envelope}/funds/{amount}" + f"?other={from_envelope}", headers={ **get_token_header(user_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Source Envelope is updated in database."): query = sql.SQL(""" SELECT total_funds FROM envelope WHERE id = {from_envelope}; """).format( from_envelope=sql.Literal(from_envelope)) await database.connect() query_result = await database.execute_and_return(query) await database.disconnect() self.assertEqual(query_result[0]["total_funds"], 5) async def test_negative_funds_sends_from_envelope_to_other(self) -> None: """Moving negative funds takes from given envelope & gives to other.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") account_id = await setup_account(database, user_id) await setup_transactions(database, [Decimal(10)], account_id) add_envelopes_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 10.00, {user_id}), ('other', 0.00, {user_id}) RETURNING id, name; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelopes_query) await database.disconnect() for result in query_result: if result["name"] == "envelope": envelope = result["id"] if result["name"] == "other": other = result["id"] amount = -5 response = await client.put( f"{BASE_URL}/{envelope}/funds/{amount}" + f"?other={other}", headers={ **get_token_header(user_id), "accept": "application/json"}) with self.subTest( msg="Responds with a status code of 200."): self.assertEqual(200, response.status_code) with self.subTest( msg="Source Envelope is updated in database."): query = sql.SQL(""" SELECT total_funds FROM envelope WHERE id = {envelope}; """).format( envelope=sql.Literal(envelope)) await database.connect() query_result = await database.execute_and_return(query) await database.disconnect() self.assertEqual(query_result[0]["total_funds"], 5) with self.subTest( msg="Other Envelope is updated in database."): query = sql.SQL(""" SELECT total_funds FROM envelope WHERE id = {other}; """).format( other=sql.Literal(other)) await database.connect() query_result = await database.execute_and_return(query) await database.disconnect() self.assertEqual(query_result[0]["total_funds"], 5) async def test_can_not_move_funds_if_not_envough_available(self) -> None: """Can not move funds if not enough are available.""" async with get_test_client() as clients: client, database = clients user_id = await setup_user(database, "first") account_id = await setup_account(database, user_id) await setup_transactions(database, [Decimal(10)], account_id) add_envelope_query = sql.SQL(""" INSERT INTO envelope (name, total_funds, user_id) VALUES ('envelope', 0.00, {user_id}) RETURNING id; """).format( user_id=sql.Literal(user_id)) await database.connect() query_result = \ await database.execute_and_return(add_envelope_query) await database.disconnect() envelope_id = query_result[0]["id"] amount = 11 response = await client.put( f"{BASE_URL}/{envelope_id}/funds/{amount}", headers={ **get_token_header(user_id), "accept": "application/json"}) self.assertEqual(response.status_code, 409) if __name__ == "__main__": main()

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null

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1

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null

0

7

977421f580ce1d3eff1f8f153407e47511353549

56

py

Python

models/NER/models/__init__.py

GroupLe/grouple-face-tagger

5fd87c074dc50a5fc341e9f30774094a1616a87f

[ "MIT" ]

null

models/NER/models/__init__.py

GroupLe/grouple-face-tagger

5fd87c074dc50a5fc341e9f30774094a1616a87f

[ "MIT" ]

19

2021-07-22T11:18:17.000Z

2021-08-20T10:12:17.000Z

models/NER/models/__init__.py

GroupLe/grouple-face-tagger

5fd87c074dc50a5fc341e9f30774094a1616a87f

[ "MIT" ]

1

2021-07-29T11:56:03.000Z

# from .naive import NerLstm from .naive import BertLstm

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979cf2d4987ad6d61141b5095063f9e1f3cc16bc

334

py

Python

torch_ort/__init__.py

ashari4/ort

4e1a654a23fe8f73d6702bb49694f7e793059989

[ "MIT" ]

null

torch_ort/__init__.py

ashari4/ort

4e1a654a23fe8f73d6702bb49694f7e793059989

[ "MIT" ]

null

torch_ort/__init__.py

ashari4/ort

4e1a654a23fe8f73d6702bb49694f7e793059989

[ "MIT" ]

null

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------- from onnxruntime.training.ortmodule import ORTModule from onnxruntime import set_seed

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7

8ae690f5da4ecb026c964166afbb3290b5251e1e

166

py

Python

cloudcopy/server/setup.py

cloud-copy/server

1b4470adc3cfdadf7a5846667a4c905afa3ab1c3

[ "MIT" ]

null

cloudcopy/server/setup.py

cloud-copy/server

1b4470adc3cfdadf7a5846667a4c905afa3ab1c3

[ "MIT" ]

1

2020-09-25T02:24:45.000Z

cloudcopy/server/setup.py

cloud-copy/core

1b4470adc3cfdadf7a5846667a4c905afa3ab1c3

[ "MIT" ]

null

import os from .config import settings def setup_environment(): os.makedirs(settings.BASE_PATH, exist_ok=True) os.makedirs(settings.LOG_PATH, exist_ok=True)

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1

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7

c139e327255d0b592b9ad110fc033a6fa4ed36f8

6,570

py

Python

test_image_manipulation.py

shequin-joshua-9147/cs162-final-project

0edff7170ca0761b38c30774475bfecac2a18f01

[ "MIT" ]

null

test_image_manipulation.py

shequin-joshua-9147/cs162-final-project

0edff7170ca0761b38c30774475bfecac2a18f01

[ "MIT" ]

null

test_image_manipulation.py

shequin-joshua-9147/cs162-final-project

0edff7170ca0761b38c30774475bfecac2a18f01

[ "MIT" ]

null

""" Pytest the ImageStuff module. Joshua Shequin """ from image_manipulation import ImageO import numpy as np """ BASE IMAGE np.array([[[255, 0, 0, 255], [0, 255, 0, 255], [0, 0, 255, 255], [255, 255, 255, 255]], [[0, 0, 0, 255], [0, 0 ,0, 255], [255, 255, 255, 255], [255, 255, 255, 255]], [[255, 255, 255, 255], [0, 0, 255, 255], [0, 255, 0, 255], [255, 0, 0, 255]], [[247, 248, 255, 255], [255, 255, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) """ def test_clear_red(): io = ImageO("images/test_picture.png") test_case = np.array([[[0, 0, 0, 255], [0, 255, 0, 255], [0, 0, 255, 255], [0, 255, 255, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [0, 255, 255, 255], [0, 255, 255, 255]], [[0, 255, 255, 255], [0, 0, 255, 255], [0, 255, 0, 255], [0, 0, 0, 255]], [[0, 248, 255, 255], [0, 255, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.clear_red("", returnable=True) == test_case).all() def test_clear_green(): io = ImageO("images/test_picture.png") test_case = np.array([[[255, 0, 0, 255], [0, 0, 0, 255], [0, 0, 255, 255], [255, 0, 255, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [255, 0, 255, 255], [255, 0, 255, 255]], [[255, 0, 255, 255], [0, 0, 255, 255], [0, 0, 0, 255], [255, 0, 0, 255]], [[247, 0, 255, 255], [255, 0, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.clear_green("", returnable=True) == test_case).all() def test_clear_blue(): io = ImageO("images/test_picture.png") test_case = np.array([[[255, 0, 0, 255], [0, 255, 0, 255], [0, 0, 0, 255], [255, 255, 0, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [255, 255, 0, 255], [255, 255, 0, 255]], [[255, 255, 0, 255], [0, 0, 0, 255], [0, 255, 0, 255], [255, 0, 0, 255]], [[247, 248, 0, 255], [255, 255, 0, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.clear_blue("", returnable=True) == test_case).all() def test_only_red(): io = ImageO("images/test_picture.png") test_case = np.array([[[255, 0, 0, 255], [0, 0, 0, 255], [0, 0, 0, 255], [255, 0, 0, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [255, 0, 0, 255], [255, 0, 0, 255]], [[255, 0, 0, 255], [0, 0, 0, 255], [0, 0, 0, 255], [255, 0, 0, 255]], [[247, 0, 0, 255], [255, 0, 0, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.red_only("", returnable=True) == test_case).all() def test_only_green(): io = ImageO("images/test_picture.png") test_case = np.array([[[0, 0, 0, 255], [0, 255, 0, 255], [0, 0, 0, 255], [0, 255, 0, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [0, 255, 0, 255], [0, 255, 0, 255]], [[0, 255, 0, 255], [0, 0, 0, 255], [0, 255, 0, 255], [0, 0, 0, 255]], [[0, 248, 0, 255], [0, 255, 0, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.green_only("", returnable=True) == test_case).all() def test_only_blue(): io = ImageO("images/test_picture.png") test_case = np.array([[[0, 0, 0, 255], [0, 0, 0, 255], [0, 0, 255, 255], [0, 0, 255, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [0, 0, 255, 255], [0, 0, 255, 255]], [[0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]], [[0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.blue_only("", returnable=True) == test_case).all() def test_lower_half(): io = ImageO("images/test_picture.png") test_case = np.array([[[127, 0, 0, 255], [0, 127, 0, 255], [0, 0, 127, 255], [127, 127, 127, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [127, 127, 127, 255], [127, 127, 127, 255]], [[127, 127, 127, 255], [0, 0, 127, 255], [0, 127, 0, 255], [127, 0, 0, 255]], [[123, 124, 127, 255], [127, 127, 127, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.lower_half("", returnable=True) == test_case).all() def test_upper_half(): io = ImageO("images/test_picture.png") test_case = np.array([[[255, 128, 128, 255], [128, 255, 128, 255], [128, 128, 255, 255], [255, 255, 255, 255]], [[128, 128, 128, 255], [128, 128, 128, 255], [255, 255, 255, 255], [255, 255, 255, 255]], [[255, 255, 255, 255], [128, 128, 255, 255], [128, 255, 128, 255], [255, 128, 128, 255]], [[251, 252, 255, 255], [255, 255, 255, 255], [128, 128, 128, 255], [128, 128, 128, 255]]]) assert (io.upper_half("", returnable=True) == test_case).all() def test_gray_scale(): io = ImageO("images/test_picture.png") test_case = np.array([[[85, 85, 85, 255], [85, 85, 85, 255], [85, 85, 85, 255], [255, 255, 255, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [255, 255, 255, 255], [255, 255, 255, 255]], [[255, 255, 255, 255], [85, 85, 85, 255], [85, 85, 85, 255], [85, 85, 85, 255]], [[250, 250, 250, 255], [255, 255, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.gray_scale("", returnable=True) == test_case).all() def test_invert_color(): io = ImageO("images/test_picture.png") test_case = np.array([[[0, 255, 255, 255], [255, 0, 255, 255], [255, 255, 0, 255], [0, 0, 0, 255]], [[255, 255, 255, 255], [255, 255, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]], [[0, 0, 0, 255], [255, 255, 0, 255], [255, 0, 255, 255], [0, 255, 255, 255]], [[8, 7, 0, 255], [0, 0, 0, 255], [255, 255, 255, 255], [255, 255, 255, 255]]]) assert (io.invert_color("", returnable=True) == test_case).all() def test_block(): io = ImageO("images/test_picture.png") # in this case since it is 4x4 it will just block it by 1 pixel blocks and so not do anything test_case = np.array([[[255, 0, 0, 255], [0, 255, 0, 255], [0, 0, 255, 255], [255, 255, 255, 255]], [[0, 0, 0, 255], [0, 0, 0, 255], [255, 255, 255, 255], [255, 255, 255, 255]], [[255, 255, 255, 255], [0, 0, 255, 255], [0, 255, 0, 255], [255, 0, 0, 255]], [[247, 248, 255, 255], [255, 255, 255, 255], [0, 0, 0, 255], [0, 0, 0, 255]]]) assert (io.block_image("", returnable=True) == test_case).all()

51.328125

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null

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1

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1

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null

0

11

a9aa6670594291b264801f948cbae934042b2ee2

1,210

py

Python

pymicropel/test/test_helper/test_crypto.py

vkorecky/pymicropel

9333ba1d691664a01d0ec63f89ae13956f37d633

[ "Apache-2.0" ]

null

pymicropel/test/test_helper/test_crypto.py

vkorecky/pymicropel

9333ba1d691664a01d0ec63f89ae13956f37d633

[ "Apache-2.0" ]

null

pymicropel/test/test_helper/test_crypto.py

vkorecky/pymicropel

9333ba1d691664a01d0ec63f89ae13956f37d633

[ "Apache-2.0" ]

null

"""Test of cryptography.""" from pymicropel.helper.crypto import Crypto def test_encrypt_decrypt_test(): """Test encoding and decoding.""" original_str = "Sww=BRDqXPgX5ytH" exp_str_pass1 = "Ffz7WCI{MAjR hyB" exp_str_pass999999 = 'Mgf"\\BUnF@vG+ieW' cryptography = Crypto() cryptography.crypt_init(1) encoded_str = cryptography.code_string(original_str) assert exp_str_pass1 == encoded_str decoded_str = cryptography.decode_string(encoded_str) assert original_str == decoded_str cryptography.crypt_init(999999) encoded_str = cryptography.code_string(original_str) assert exp_str_pass999999 == encoded_str decoded_str = cryptography.decode_string(encoded_str) assert original_str == decoded_str cryptography.crypt_init(0) encoded_str = cryptography.code_string(original_str) assert original_str == encoded_str decoded_str = cryptography.decode_string(encoded_str) assert original_str == decoded_str cryptography.crypt_init(-1) encoded_str = cryptography.code_string(original_str) assert original_str == encoded_str decoded_str = cryptography.decode_string(encoded_str) assert original_str == decoded_str

32.702703

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1,210

5.715232

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0.202781

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1,210

36

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false

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0.038462

0

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1

0

1

0

null

0

1

0

7

a9cb7e9cb15bcd92cff640079317e84253e973d1

73,023

py

Python

tests/unittest/test_server_file.py

andy-maier/secureserveraccess

24f4817b2066401451840b3c7b308e1792eb3e60

[ "Apache-2.0" ]

1

2021-03-29T22:09:47.000Z

tests/unittest/test_server_file.py

andy-maier/secureserveraccess

24f4817b2066401451840b3c7b308e1792eb3e60

[ "Apache-2.0" ]

49

2021-03-29T20:13:28.000Z

2021-05-01T10:38:19.000Z

tests/unittest/test_server_file.py

andy-maier/secureserveraccess

24f4817b2066401451840b3c7b308e1792eb3e60

[ "Apache-2.0" ]

null

# 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. """ Test the _server_file.py module. """ from __future__ import absolute_import, print_function import os import pytest from easy_server import ServerFile, ServerFileFormatError, \ ServerFileOpenError, ServerFileUserDefinedFormatError, \ ServerFileUserDefinedSchemaError, ServerFileGroupUserDefinedFormatError, \ ServerFileGroupUserDefinedSchemaError # White box testing: We test an internal function from easy_server._server_file import _load_server_file from ..utils.simplified_test_function import simplified_test_function from ..utils.server_file_utils import easy_server_file TEST_SERVERFILE_FILEPATH = 'tests/testfiles/server.yml' TEST_SERVERFILE_FILEPATH_ABS = os.path.abspath(TEST_SERVERFILE_FILEPATH) TEST_VAULTFILE_FILEPATH = 'tests/testfiles/vault.yml' TEST_VAULTFILE_FILEPATH_ABS = os.path.abspath(TEST_VAULTFILE_FILEPATH) # Standard server and vault files that are dynamically created for testing: TEST_SERVER_FILENAME = 'server.yml' TEST_VAULT_FILENAME = 'vault.yml' TEST_VAULT_PASSWORD = 'vault' FOO_USER_DEFINED_SCHEMA = { "$schema": "http://json-schema.org/draft-07/schema#", "title": "FOO - JSON schema for user-defined items in server files", "type": "object", "required": [ "foo", ], "additionalProperties": False, "properties": { "foo": { "type": "string", "description": "The foo value", }, }, } INVALID_USER_DEFINED_SCHEMA = { "$schema": "http://json-schema.org/draft-07/schema#", "title": "Invalid JSON schema for user-defined items in server files", "type": "object_xxx", "additionalProperties": False, } TESTCASES_SF_INIT = [ # Testcases for ServerFile.__init__() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * init_args: Tuple of positional arguments to ServerFile(). # * init_kwargs: Dict of keyword arguments to ServerFile(). # * exp_serverfile_attrs: Dict with expected ServerFile attributes. # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "Order of positional parameters", dict( init_args=( TEST_SERVERFILE_FILEPATH, ), init_kwargs=dict(), exp_serverfile_attrs={ 'filepath': TEST_SERVERFILE_FILEPATH_ABS, 'user_defined_schema': None, 'group_user_defined_schema': None, 'vault_server_schema': None, }, ), None, None, True ), ( "Names of keyword arguments", dict( init_args=(), init_kwargs=dict( filepath=TEST_SERVERFILE_FILEPATH, ), exp_serverfile_attrs={ 'filepath': TEST_SERVERFILE_FILEPATH_ABS, 'user_defined_schema': None, 'group_user_defined_schema': None, 'vault_server_schema': None, }, ), None, None, True ), ( "Omitted required parameter: filepath", dict( init_args=(), init_kwargs=dict(), exp_serverfile_attrs=None, ), TypeError, None, True ), ( "File not found", dict( init_args=(), init_kwargs=dict( filepath='invalid_file', ), exp_serverfile_attrs=None, ), (ServerFileOpenError, "Cannot open server file"), None, True ), ( "Server file that references vault file", dict( init_args=(), init_kwargs=dict( filepath=TEST_SERVERFILE_FILEPATH, ), exp_serverfile_attrs={ 'filepath': TEST_SERVERFILE_FILEPATH_ABS, 'vault_file': TEST_VAULTFILE_FILEPATH_ABS, 'user_defined_schema': None, 'group_user_defined_schema': None, 'vault_server_schema': None, }, ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_INIT) @simplified_test_function def test_ServerFile_init( testcase, init_args, init_kwargs, exp_serverfile_attrs): """ Test function for ServerFile.__init__() """ # The code to be tested act_obj = ServerFile(*init_args, **init_kwargs) # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) for attr_name in exp_serverfile_attrs: exp_attr_value = exp_serverfile_attrs[attr_name] assert hasattr(act_obj, attr_name), \ "Missing attribute {0!r} in returned ServerFile object". \ format(attr_name) act_attr_value = getattr(act_obj, attr_name) assert act_attr_value == exp_attr_value, \ "Unexpected value for attribute {0!r}: Expected {1!r}, got {2!r}".\ format(attr_name, exp_attr_value, act_attr_value) TESTCASES_SF_LOAD = [ # Testcases for ServerFile._load_server_file() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * user_defined_schema: JSON schema for validating user-defined portion # of server items in server file, or None. # * group_user_defined_schema: JSON schema for validating user-defined # portion of group items in server file, or None. # * exp_data: Expected result of _load_server_file() # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger # Basic validation ( "Empty file: Missing required elements", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on top-level element.* is not of type 'object'"), None, True ), ( "Invalid YAML syntax: Mixing list and dict", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " - foo\n" " bar:\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Invalid YAML syntax"), None, True ), ( "Invalid top-level type list", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="- servers: {}\n" "- server_groups: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on top-level element: .* is not of type 'object'"), None, True ), ( "Missing required 'servers' element", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="server_groups: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on top-level element: 'servers' is a required " "property"), None, True ), ( "Invalid type for 'servers' element: list", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " - foo\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'servers': .* is not of type 'object'"), None, True ), ( "Invalid type for 'servers' element: string", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: bla\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'servers': .* is not of type 'object'"), None, True ), ( "Invalid type for 'server_groups' element: list", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups: []\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups': .* is not of type " "'object'"), None, True ), ( "Invalid type for 'server_groups' element: string", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups: bla\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups': .* is not of type " "'object'"), None, True ), ( "Invalid type of server group", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1: invalid\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1': .* is not of type " "'object'"), None, True ), ( "Missing required element 'description' in server group", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " members: []\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1': 'description' is " "a required property"), None, True ), ( "Invalid type for element 'description' in server group: list", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " description: []\n" " members: []\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1.description': " ".* is not of type 'string'"), None, True ), ( "Missing required element 'members' in server group", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " description: desc1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1': 'members' is " "a required property"), None, True ), ( "Invalid type for element 'members' in server group: string", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " description: desc1\n" " members: invalid\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1.members': " ".* is not of type 'array'"), None, True ), ( "Invalid type for server group member: dict", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " description: desc1\n" " members:\n" " - {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'server_groups.grp1.members.0': " ".* is not of type 'string'"), None, True ), ( "Invalid default null", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups: {}\n" "default: null\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Validation failed on element 'default': " "None is not of type 'string'"), None, True ), # More semantic errors ( "Server group member nickname not found", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n" "server_groups:\n" " grp1:\n" " description: desc1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Nickname 'srv1' in server group 'grp1' not found"), None, True ), ( "Default nickname not found", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: desc1\n" " members:\n" " - srv1\n" "default: srv\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data=None, ), (ServerFileFormatError, "Default nickname 'srv' not found"), None, True ), # Valid simple server files ( "Valid file with no servers and server_group+default omitted", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data={ 'servers': {}, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), ( "Valid file with one server that is default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "default: srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data={ 'servers': { 'srv1': { 'description': 'server1', 'user_defined': { 'stuff': 42, }, }, }, 'server_groups': {}, 'default': 'srv1', 'vault_file': None, }, ), None, None, True ), ( "Valid file with one server and one server group that is default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" "default: grp1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=None, exp_data={ 'servers': { 'srv1': { 'description': 'server1', 'user_defined': { 'stuff': 42, }, }, }, 'server_groups': { 'grp1': { 'description': 'group1', 'members': ['srv1'], }, }, 'default': 'grp1', 'vault_file': None, }, ), None, None, True ), # JSON schema validation of user-defined portion of server items ( "Valid file with no servers but with FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=FOO_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data={ 'servers': {}, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), ( "Valid file with two servers that satisfy FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " foo: bar1\n" " srv2:\n" " description: server2\n" " user_defined:\n" " foo: bar2\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=FOO_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data={ 'servers': { 'srv1': { 'description': 'server1', 'user_defined': { 'foo': 'bar1', }, }, 'srv2': { 'description': 'server2', 'user_defined': { 'foo': 'bar2', }, }, }, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), ( "Valid file with one server that misses user_defined element and FOO " "schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=FOO_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data=None, ), (ServerFileUserDefinedFormatError, "Missing user_defined element for server srv1"), None, True ), ( "Valid file with one server that misses property required in FOO " "schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=FOO_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data=None, ), (ServerFileUserDefinedFormatError, "Invalid format in user-defined portion of item for server srv1.*" "Validation failed on top-level of user-defined item.*" "'foo' is a required property"), None, True ), ( "Valid file with one server that has incorrect type as per FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " foo: 42\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=FOO_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data=None, ), (ServerFileUserDefinedFormatError, "Invalid format in user-defined portion of item for server srv1.*" "Validation failed on element 'foo'.*" "42 is not of type 'string'"), None, True ), ( "File with one server and invalid FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " foo: bar\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=INVALID_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data=None, ), (ServerFileUserDefinedSchemaError, "Invalid JSON schema for validating user-defined portion of server " "items in server file"), None, True ), ( "File with no servers and invalid FOO schema (not recognized)", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=INVALID_USER_DEFINED_SCHEMA, group_user_defined_schema=None, exp_data={ 'servers': {}, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), # JSON schema validation of user-defined portion of group items ( "Valid file with no servers but with FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=FOO_USER_DEFINED_SCHEMA, exp_data={ 'servers': {}, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), ( "Valid file with two groups that satisfy FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" "server_groups:\n" " grp1:\n" " description: group1\n" " user_defined:\n" " foo: bar1\n" " members:\n" " - srv1\n" " grp2:\n" " description: group2\n" " user_defined:\n" " foo: bar2\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=FOO_USER_DEFINED_SCHEMA, exp_data={ 'servers': { 'srv1': { 'description': 'server1', }, }, 'server_groups': { 'grp1': { 'description': 'group1', 'user_defined': { 'foo': 'bar1', }, 'members': ['srv1'], }, 'grp2': { 'description': 'group2', 'user_defined': { 'foo': 'bar2', }, 'members': ['srv1'], }, }, 'default': None, 'vault_file': None, }, ), None, None, True ), ( "Valid file with one group that misses user_defined element and FOO " "schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=FOO_USER_DEFINED_SCHEMA, exp_data=None, ), (ServerFileGroupUserDefinedFormatError, "Missing user_defined element for group grp1"), None, True ), ( "Valid file with one group that misses property required in FOO " "schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" "server_groups:\n" " grp1:\n" " description: group1\n" " user_defined: {}\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=FOO_USER_DEFINED_SCHEMA, exp_data=None, ), (ServerFileGroupUserDefinedFormatError, "Invalid format in user-defined portion of item for group grp1.*" "Validation failed on top-level of user-defined item.*" "'foo' is a required property"), None, True ), ( "Valid file with one group that has incorrect type as per FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" "server_groups:\n" " grp1:\n" " description: group1\n" " user_defined:\n" " foo: 42\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=FOO_USER_DEFINED_SCHEMA, exp_data=None, ), (ServerFileGroupUserDefinedFormatError, "Invalid format in user-defined portion of item for group grp1.*" "Validation failed on element 'foo'.*" "42 is not of type 'string'"), None, True ), ( "File with one server and invalid FOO schema", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" "server_groups:\n" " grp1:\n" " description: group1\n" " user_defined:\n" " foo: bar\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=INVALID_USER_DEFINED_SCHEMA, exp_data=None, ), (ServerFileGroupUserDefinedSchemaError, "Invalid JSON schema for validating user-defined portion of group " "items in server file"), None, True ), ( "File with no groups and invalid FOO schema (not recognized)", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, user_defined_schema=None, group_user_defined_schema=INVALID_USER_DEFINED_SCHEMA, exp_data={ 'servers': {}, 'server_groups': {}, 'default': None, 'vault_file': None, }, ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_LOAD) @simplified_test_function def test_ServerFile_load( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, user_defined_schema, group_user_defined_schema, exp_data): """ Test function for ServerFile._load_server_file() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: # The code to be tested act_data = _load_server_file( server_filepath, user_defined_schema, group_user_defined_schema) # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) assert act_data == exp_data TESTCASES_SF_IS_VAULT_FILE_ENCRYPTED = [ # Testcases for ServerFile.is_vault_file_encrypted() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * exp_result: Expected return valoue of is_vault_file_encrypted(). # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "No vault file", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_encrypted=None, ), None, None, True ), ( "Decrypted vault file, no password", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="vault_file: {vfn}\n" "servers:\n" " srv1:\n" " description: server1\n". \ format(vfn=TEST_VAULT_FILENAME), vault_filename=TEST_VAULT_FILENAME, vault_yaml="secrets:\n" " srv1:\n" " foo: bar\n", vault_password=None, exp_encrypted=False, ), None, None, True ), ( "Encrypted vault file, with correct password", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="vault_file: {vfn}\n" "servers:\n" " srv1:\n" " description: server1\n". \ format(vfn=TEST_VAULT_FILENAME), vault_filename=TEST_VAULT_FILENAME, vault_yaml="secrets:\n" " srv1:\n" " foo: bar\n", vault_password=TEST_VAULT_PASSWORD, exp_encrypted=True, ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_IS_VAULT_FILE_ENCRYPTED) @simplified_test_function def test_SF_is_vault_file_encrypted( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, exp_encrypted): """ Test function for ServerFile.is_vault_file_encrypted() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: esf = ServerFile(server_filepath, vault_password, use_keyring=False, use_prompting=False) # The code to be tested act_encrypted = esf.is_vault_file_encrypted() # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) assert act_encrypted == exp_encrypted TESTCASES_SF_GET_SERVER = [ # Testcases for ServerFile.get_server() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * nick: nickname input parameter for get_server(). # * exp_server_attrs: Dict with expected attributes of Server result # of get_server(). # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "No servers; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_server_attrs=None, ), (KeyError, "Server with nickname 'srv' not found"), None, True ), ( "One server; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_server_attrs=None, ), (KeyError, "Server with nickname 'srv' not found"), None, True ), ( "One server group with one server; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_server_attrs=None, ), (KeyError, "Server with nickname 'srv' not found"), None, True ), ( "One server group with one server; existing server nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv1', exp_server_attrs=dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ), None, None, True ), ( "One server group with one server; existing group nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='grp1', exp_server_attrs=None, ), (KeyError, "Server with nickname 'grp1' not found"), None, True ), ( "One server with vault file, server exists in vault file", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="vault_file: {vfn}\n" "servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n".format(vfn=TEST_VAULT_FILENAME), vault_filename=TEST_VAULT_FILENAME, vault_yaml="secrets:\n" " srv1:\n" " foo: bar\n", vault_password=None, nick='srv1', exp_server_attrs=dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, secrets={'foo': 'bar'}), ), None, None, True ), ( "One server with vault file, server does not exist in vault file", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="vault_file: {vfn}\n" "servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n".format(vfn=TEST_VAULT_FILENAME), vault_filename=TEST_VAULT_FILENAME, vault_yaml="secrets:\n" " srv2:\n" " foo: bar\n", vault_password=None, nick='srv1', exp_server_attrs=dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, secrets=None), ), None, None, True ), ( "One server with encrypted vault file", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="vault_file: {vfn}\n" "servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n".format(vfn=TEST_VAULT_FILENAME), vault_filename=TEST_VAULT_FILENAME, vault_yaml="secrets:\n" " srv1:\n" " foo: bar\n", vault_password=TEST_VAULT_PASSWORD, nick='srv1', exp_server_attrs=dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, secrets={'foo': 'bar'}), ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_GET_SERVER) @simplified_test_function def test_ServerFile_get_server( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, nick, exp_server_attrs): """ Test function for ServerFile.get_server() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: esf = ServerFile(server_filepath, vault_password, use_keyring=False, use_prompting=False) # The code to be tested act_srv = esf.get_server(nick) # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) for name in exp_server_attrs: assert getattr(act_srv, name) == exp_server_attrs[name] TESTCASES_SF_LIST_SERVERS = [ # Testcases for ServerFile.list_servers() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * nick: nickname input parameter for list_servers(). # * exp_servers_attrs: List of dicts with expected attributes of # Server objects in the result of list_servers(). # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "No servers; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_servers_attrs=None, ), (KeyError, "Server or server group with nickname 'srv' not found"), None, True ), ( "One server; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_servers_attrs=None, ), (KeyError, "Server or server group with nickname 'srv' not found"), None, True ), ( "One server group with one server; non-existing nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv', exp_servers_attrs=None, ), (KeyError, "Server or server group with nickname 'srv' not found"), None, True ), ( "One server group with one server; existing server nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='srv1', exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with one server; existing group nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='grp1', exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with two servers; existing group nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='grp1', exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ( "Nested server groups 2 levels deep; existing group nickname", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " grp2:\n" " description: group2\n" " members:\n" " - grp1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='grp2', exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ( "Nested server groups 2 levels deep; existing group nickname and " "multiple group memberships", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " grp2:\n" " description: group2\n" " members:\n" " - grp1\n" " - srv1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, nick='grp2', exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_LIST_SERVERS) @simplified_test_function def test_ServerFile_list_servers( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, nick, exp_servers_attrs): """ Test function for ServerFile.list_servers() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: esf = ServerFile(server_filepath, vault_password, use_keyring=False, use_prompting=False) # The code to be tested act_sds = esf.list_servers(nick) # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) assert len(exp_servers_attrs) == len(act_sds) sorted_exp_servers_attrs = sorted( exp_servers_attrs, key=lambda x: x['nickname']) sorted_act_sds = sorted(act_sds, key=lambda x: x.nickname) for i, exp_server_attrs in enumerate(sorted_exp_servers_attrs): act_sd = sorted_act_sds[i] for name in exp_server_attrs: assert getattr(act_sd, name) == exp_server_attrs[name] TESTCASES_SF_LIST_DEFAULT_SERVERS = [ # Testcases for ServerFile.list_default_servers() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * exp_servers_attrs: List of dicts with expected attributes of # Server objects in the result of list_default_servers(). # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "No servers, no default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[], ), None, None, True ), ( "One server; no default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[], ), None, None, True ), ( "One server; with default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "default: srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with one server; server is default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" "default: srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with one server; group is default", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" "default: grp1\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_LIST_DEFAULT_SERVERS) @simplified_test_function def test_ServerFile_list_default_servers( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, exp_servers_attrs): """ Test function for ServerFile.list_default_servers() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: esf = ServerFile(server_filepath, vault_password, use_keyring=False, use_prompting=False) # The code to be tested act_sds = esf.list_default_servers() # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) assert len(exp_servers_attrs) == len(act_sds) sorted_exp_servers_attrs = sorted( exp_servers_attrs, key=lambda x: x['nickname']) sorted_act_sds = sorted(act_sds, key=lambda x: x.nickname) for i, exp_server_attrs in enumerate(sorted_exp_servers_attrs): act_sd = sorted_act_sds[i] for name in exp_server_attrs: assert getattr(act_sd, name) == exp_server_attrs[name] TESTCASES_SF_LIST_ALL_SERVERS = [ # Testcases for ServerFile.list_all_servers() # Each list item is a testcase tuple with these items: # * desc: Short testcase description. # * kwargs: Keyword arguments for the test function: # * server_filename: Filename of server file to be created. # * server_yaml: Content of server file. # * vault_filename: Filename of vault file to be created, or None. # * vault_yaml: Content of vault file, or None. # * vault_password: Password for encryption of vault file, or None. # * exp_servers_attrs: List of dicts with expected attributes of # Server objects in the result of list_all_servers(). # * exp_exc_types: Expected exception type(s), or None. # * exp_warn_types: Expected warning type(s), or None. # * condition: Boolean condition for testcase to run, or 'pdb' for debugger ( "No servers", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers: {}\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[], ), None, None, True ), ( "One server", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with one server", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), ], ), None, None, True ), ( "One server group with two servers", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ( "Nested server groups 2 levels deep with two servers total", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " grp2:\n" " description: group2\n" " members:\n" " - grp1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ( "Nested server groups 2 levels deep with two servers total and " "multiple group memberships", dict( server_filename=TEST_SERVER_FILENAME, server_yaml="servers:\n" " srv1:\n" " description: server1\n" " user_defined:\n" " stuff: 42\n" " srv2:\n" " description: server2\n" " user_defined:\n" " stuff: 43\n" "server_groups:\n" " grp1:\n" " description: group1\n" " members:\n" " - srv1\n" " grp2:\n" " description: group2\n" " members:\n" " - grp1\n" " - srv1\n" " - srv2\n", vault_filename=None, vault_yaml=None, vault_password=None, exp_servers_attrs=[ dict( nickname='srv1', description='server1', contact_name=None, access_via=None, user_defined={'stuff': 42}, ), dict( nickname='srv2', description='server2', contact_name=None, access_via=None, user_defined={'stuff': 43}, ), ], ), None, None, True ), ] @pytest.mark.parametrize( "desc, kwargs, exp_exc_types, exp_warn_types, condition", TESTCASES_SF_LIST_ALL_SERVERS) @simplified_test_function def test_ServerFile_list_all_servers( testcase, server_filename, server_yaml, vault_filename, vault_yaml, vault_password, exp_servers_attrs): """ Test function for ServerFile.list_all_servers() """ with easy_server_file( server_filename, server_yaml, vault_filename, vault_yaml, vault_password) as server_filepath: esf = ServerFile(server_filepath, vault_password, use_keyring=False, use_prompting=False) # The code to be tested act_sds = esf.list_all_servers() # Ensure that exceptions raised in the remainder of this function # are not mistaken as expected exceptions assert testcase.exp_exc_types is None, \ "Expected exception not raised: {}". \ format(testcase.exp_exc_types) assert len(exp_servers_attrs) == len(act_sds) sorted_exp_servers_attrs = sorted( exp_servers_attrs, key=lambda x: x['nickname']) sorted_act_sds = sorted(act_sds, key=lambda x: x.nickname) for i, exp_server_attrs in enumerate(sorted_exp_servers_attrs): act_sd = sorted_act_sds[i] for name in exp_server_attrs: assert getattr(act_sd, name) == exp_server_attrs[name]

34.922525

80

0.483478

6,698

73,023

5.034189

0.043744

0.060352

0.0484

0.054094

0.900353

0.881758

0.865862

0.858804

0.84703

0.837273

0

0.010609

0.429467

73,023

2,090

81

34.939234

0.798737

0.10194

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0.832606

0

0.000545

0.225226

0.004317

0

0.009815

1

0.003817

false

0.045802

0.003817

0

0.007634

0.000545

0

null

0

1

0

1

0

null

0

7

e759e111e8532250379574aaf174bb9a6a570515

296

py

Python

concrete_settings/contrib/sources/__init__.py

coordt/concrete-settings

b444c3f1f8cdbe30135c1978876215e04ebc7622

[ "MIT" ]

5

2020-04-25T12:18:33.000Z

2021-03-26T18:51:33.000Z

concrete_settings/contrib/sources/__init__.py

coordt/concrete-settings

b444c3f1f8cdbe30135c1978876215e04ebc7622

[ "MIT" ]

13

2019-03-20T10:42:39.000Z

2021-07-07T08:01:05.000Z

concrete_settings/contrib/sources/__init__.py

coordt/concrete-settings

b444c3f1f8cdbe30135c1978876215e04ebc7622

[ "MIT" ]

3

2020-04-25T08:53:29.000Z

2021-07-06T19:15:52.000Z

from .yaml_source import YamlSource # noqa: F401 # imported but unused from .json_source import JsonSource # noqa: F401 # imported but unused from .envvar_source import EnvVarSource # noqa: F401 # imported but unused from .python_source import PythonSource # noqa: F401 # imported but unused

59.2

75

0.783784

40

296

5.7

0.4

0.210526

0.280702

0.333333

0.491228

0.381579

0

0.048387

0.162162

296

4

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74

0.870968

0.429054

0

1

0

true

0

1

0

1

0

null

1

0

1

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null

0

1

0

1

0

1

0

7

e7abac1d75645cd7f4f620f9728743a448e6b6a9

7,369

py

Python

modules/jwtoken/handlers/verify.py

francoricci/spspid

db335f2335824ba4f7aa7a01cd15c235bc815a47

[ "MIT" ]

2

2017-09-18T07:03:14.000Z

2021-09-28T07:58:58.000Z

modules/jwtoken/handlers/verify.py

francoricci/spspid

db335f2335824ba4f7aa7a01cd15c235bc815a47

[ "MIT" ]

1

2021-09-28T08:04:05.000Z

modules/jwtoken/handlers/verify.py

francoricci/sapspid

db335f2335824ba4f7aa7a01cd15c235bc815a47

[ "MIT" ]

null

from response import ResponseObj from request import RequestObjNew import tornado.web import tornado.gen import tornado.ioloop import tornado.concurrent import logging import commonlib from jwtoken.handlers.jwtokenhandler import jwtokenHandler import asyncio import globalsObj class verifyHandler(jwtokenHandler): def __init__(self, *args, **kwds): super().__init__(*args, **kwds) #get async def get(self): self.set_header('Content-Type', 'application/json; charset=UTF-8') self.set_default_headers() response_obj = await self.verify() asyncio.ensure_future(self.writeLog(response_obj), loop = globalsObj.ioloop) self.writeResponse(response_obj) #post async def post(self): self.set_header('Content-Type', 'application/json; charset=UTF-8') self.set_default_headers() response_obj = await self.verify() asyncio.ensure_future(self.writeLog(response_obj), loop = globalsObj.ioloop) self.writeResponse(response_obj) @commonlib.inner_log async def verify(self): try: if self.request.method == 'GET': token = super(self.__class__, self).get_argument('token') elif self.request.method == 'POST': # leggi il json della richiesta temp = RequestObjNew(self.request.body) if temp.error["code"] == 2: response_obj = ResponseObj(debugMessage=temp.error["message"], httpcode=400) response_obj.setError('400') logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('Validation error. Json input error') return response_obj elif temp.error["code"] > 0: raise tornado.web.HTTPError(httpcode=503, log_message=temp.error["message"]) token = temp.request['token'] verifica = await self.dbobjJwt.execute_statment("verify_token('%s')" % token) if verifica['error'] == 0: if verifica['result'][0]['verify_token_bycod'] == None: response_obj = ResponseObj(httpcode=404) response_obj.setError('jwtoken101') elif verifica['result'][0]['verify_token_bycod']['error'] == 0: response_obj = ResponseObj(httpcode=200) response_obj.setError('200') response_obj.setResult(jose = verifica['result'][0]['verify_token_bycod']['message']) elif verifica['result'][0]['verify_token_bycod']['error'] > 0: response_obj = ResponseObj(httpcode=401, devMessage=(verifica['result'][0]['verify_token_bycod']['message'])) response_obj.setError('jwtoken100') elif verifica['error'] > 0: response_obj = ResponseObj(debugMessage=verifica['result'], httpcode=500) response_obj.setError('jwtoken105') except tornado.web.MissingArgumentError as error: response_obj = ResponseObj(debugMessage=error.log_message, httpcode=error.status_code, devMessage=error.log_message) response_obj.setError(str(error.status_code)) logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('%s'% error,exc_info=True) except Exception as inst: response_obj = ResponseObj(httpcode=500) response_obj.setError('500') logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('Exception',exc_info=True) if self.request.method == 'POST': response_obj.setID(temp.id) return response_obj class verifySamlHandler(jwtokenHandler): def __init__(self, *args, **kwds): super().__init__(*args, **kwds) #get async def get(self): self.set_header('Content-Type', 'application/json; charset=UTF-8') self.set_default_headers() response_obj = await self.verify() asyncio.ensure_future(self.writeLog(response_obj), loop = globalsObj.ioloop) self.writeResponse(response_obj) #post async def post(self): self.set_header('Content-Type', 'application/json; charset=UTF-8') self.set_default_headers() response_obj = await self.verify() asyncio.ensure_future(self.writeLog(response_obj), loop = globalsObj.ioloop) self.writeResponse(response_obj) @commonlib.inner_log async def verify(self): try: if self.request.method == 'GET': token = super(self.__class__, self).get_argument('token') elif self.request.method == 'POST': # leggi il json della richiesta temp = RequestObjNew(self.request.body) if temp.error["code"] == 2: response_obj = ResponseObj(debugMessage=temp.error["message"], httpcode=400) response_obj.setError('400') logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('Validation error. Json input error') return response_obj elif temp.error["code"] > 0: raise tornado.web.HTTPError(httpcode=503, log_message=temp.error["message"]) token = temp.request['token'] verifica = await self.dbobjJwt.execute_statment("verify_saml_bytoken('%s')" % token) if verifica['error'] == 0: if verifica['result'][0]['verify_saml_by_cod_token'] == None: response_obj = ResponseObj(httpcode=404) response_obj.setError('jwtoken101') elif verifica['result'][0]['verify_saml_by_cod_token']['error'] == 0: response_obj = ResponseObj(httpcode=200) response_obj.setError('200') response_obj.setResult(jose = verifica['result'][0]['verify_saml_by_cod_token']['message'], saml_attributes = verifica['result'][0]['verify_saml_by_cod_token']['saml_attributes']) elif verifica['result'][0]['verify_saml_by_cod_token']['error'] > 0: response_obj = ResponseObj(httpcode=401, devMessage=(verifica['result'][0]['verify_saml_by_cod_token']['message'])) response_obj.setError('jwtoken100') elif verifica['error'] > 0: response_obj = ResponseObj(debugMessage=verifica['result'], httpcode=500) response_obj.setError('jwtoken105') except tornado.web.MissingArgumentError as error: response_obj = ResponseObj(debugMessage=error.log_message, httpcode=error.status_code, devMessage=error.log_message) response_obj.setError(str(error.status_code)) logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('%s'% error,exc_info=True) except Exception as inst: response_obj = ResponseObj(httpcode=500) response_obj.setError('500') logging.getLogger(type(self).__module__+"."+type(self).__qualname__).error('Exception',exc_info=True) if self.request.method == 'POST': response_obj.setID(temp.id) return response_obj

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0.924515

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42.595376

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false

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e7b2e99747ee5a74ff3df1cb19b37a26e4a7fd5a

34,707

py

Python

code/variant_calling.py

pierson-we/wes_pipe

f24c3d2541def30b3c7ff86995c227330c4c6e15

[ "MIT" ]

null

code/variant_calling.py

pierson-we/wes_pipe

f24c3d2541def30b3c7ff86995c227330c4c6e15

[ "MIT" ]

null

code/variant_calling.py

pierson-we/wes_pipe

f24c3d2541def30b3c7ff86995c227330c4c6e15

[ "MIT" ]

null

#!/usr/bin/env python3 import subprocess import luigi import luigi.interface import os import sys import time import random import pipeline_utils import global_vars import bam_processing import misc_utils class mutect_single_normal(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() sample = luigi.Parameter() fastq_file = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() # case_dict = luigi.DictParameter() cfg = luigi.DictParameter() def requires(self): # if self.matched_n != '': # return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads)] # else: return bam_processing.index_bam(sample=self.sample, fastq_file=self.fastq_file, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'mutect', self.sample + '.vcf.gz')), luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'mutect', self.sample + '.vcf.gz.tbi'))] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) # if self.matched_n: # cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', '"%s|%s"' % (self.input()[0][0].path, self.input()[1][0].path), '-z', '-F', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/testsomatic.R', '|', './packages/VarDictJava/VarDict/var2vcf_paired.pl', '-N', '"%s|%s"' % (self.case + '_T', self.case + '_N'), '-f', '0.01', '>%s' % os.path.join(self.vcf_path, 'vardict')] # else: cmd = [self.cfg['gatk4_location'], '--java-options', '"-Xmx8g -Xms8g -XX:+UseSerialGC -Djava.io.tmpdir=%s"' % self.cfg['tmp_dir'], 'Mutect2', '-R', self.cfg['fasta_file'], '-I', self.input()[0].path, '-tumor', self.sample, '-L', self.cfg['library_bed'], '--native-pair-hmm-threads', self.max_threads, '-O', self.output()[0].path] pipeline_utils.command_call(cmd, self.output(), threads_needed=self.max_threads) class mutect_pon(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): # if self.matched_n != '': # return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads)] # else: return [mutect_single_normal(sample=case_name + '_N', fastq_file=self.case_dict[case_name]['N'], project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] def output(self): return luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'mutect', 'pon.vcf.gz')) def run(self): pipeline_utils.confirm_path(self.output().path) # if self.matched_n: # cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', '"%s|%s"' % (self.input()[0][0].path, self.input()[1][0].path), '-z', '-F', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/testsomatic.R', '|', './packages/VarDictJava/VarDict/var2vcf_paired.pl', '-N', '"%s|%s"' % (self.case + '_T', self.case + '_N'), '-f', '0.01', '>%s' % os.path.join(self.vcf_path, 'vardict')] # else: cmd = [self.cfg['gatk4_location'], 'CreateSomaticPanelOfNormals'] for normal_vcf in self.input(): cmd.append('--vcfs') cmd.append(normal_vcf[0].path) cmd.append('--output') cmd.append(self.output().path) pipeline_utils.command_call(cmd, [self.output()]) class mutect(luigi.Task): max_threads = luigi.IntParameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() project_dir = luigi.Parameter() case_dict = luigi.DictParameter() # gatk4_location = luigi.Parameter() # gatk3_location = luigi.Parameter() # dbsnp = luigi.Parameter() # germline_resource = luigi.Parameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() # fasta_dir = os.path.join('/', *luigi.Parameter().task_value('bowtie', 'fasta_file').split('/')[:-1]) cfg = luigi.DictParameter() def requires(self): if self.matched_n != '': return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), mutect_pon(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] else: return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), mutect_pon(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] def output(self): return [luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_mutect.vcf.gz')), luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_mutect.vcf.gz.tbi'))] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) if self.matched_n: cmd = [self.cfg['gatk4_location'], '--java-options', '"-Xmx8g -Xms8g -XX:+UseSerialGC -Djava.io.tmpdir=%s"' % self.cfg['tmp_dir'], 'Mutect2', '-R', self.cfg['fasta_file'], '-I', self.input()[0][0].path, '-tumor', self.case + '_T', '-I', self.input()[1][0].path, '-normal', self.case + '_N', '--germline-resource', self.cfg['germline_resource'], '--af-of-alleles-not-in-resource', '0.0000025', '-L', self.cfg['library_bed'], '-pon', self.input()[-1].path, '--native-pair-hmm-threads', self.max_threads, '-O', self.output()[0].path] else: cmd = [self.cfg['gatk4_location'], '--java-options', '"-Xmx8g -Xms8g -XX:+UseSerialGC -Djava.io.tmpdir=%s"' % self.cfg['tmp_dir'], 'Mutect2', '-R', self.cfg['fasta_file'], '-I', self.input()[0][0].path, '-tumor', self.case + '_T', '--germline-resource', self.cfg['germline_resource'], '--af-of-alleles-not-in-resource', '0.0000025', '-L', self.cfg['library_bed'], '-pon', self.input()[-1].path, '--native-pair-hmm-threads', self.max_threads, '-O', self.output()[0].path] pipeline_utils.command_call(cmd, self.output(), threads_needed=self.max_threads) class filter_mutect(luigi.Task): max_threads = luigi.IntParameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() project_dir = luigi.Parameter() case_dict = luigi.DictParameter() # gatk4_location = luigi.Parameter() # fasta_file = luigi.Parameter() # fasta_dir = os.path.join('/', *luigi.Parameter().task_value('bowtie', 'fasta_file').split('/')[:-1]) cfg = luigi.DictParameter() def requires(self): return mutect(project_dir=self.project_dir, vcf_path=self.vcf_path, case=self.case, tumor=self.tumor, matched_n=self.matched_n, max_threads=self.max_threads, case_dict=self.case_dict, cfg=self.cfg) def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_mutect_filtered' + '.vcf.gz')) def run(self): pipeline_utils.confirm_path(self.output().path) cmd = [self.cfg['gatk4_location'], '--java-options', '"-Xmx8g -Xms8g -XX:+UseSerialGC -Djava.io.tmpdir=%s"' % self.cfg['tmp_dir'], 'FilterMutectCalls', '-V', self.input()[0].path, '-O', self.output().path] pipeline_utils.command_call(cmd, [self.output()], sleep_time=1.1) # for input_file in self.input(): # input_file.remove() class sort_mutect(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() cfg = luigi.DictParameter() def requires(self): return [filter_mutect(max_threads=self.max_threads, project_dir=self.project_dir, case=self.case, tumor=self.tumor, matched_n=self.matched_n, vcf_path=self.vcf_path, case_dict=self.case_dict, cfg=self.cfg), bam_processing.picard_index(cfg=self.cfg)] def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_mutect_sorted' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) cmd = ['java', '-jar', self.cfg['picard_location'], 'SortVcf', 'I=%s' % self.input()[0].path, 'O=%s' % self.output().path, 'SEQUENCE_DICTIONARY=%s' % self.input()[1].path] pipeline_utils.command_call(cmd, [self.output()], threads_needed=self.max_threads) class scalpel_discovery(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): if self.matched_n != '': return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] else: return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] def output(self): if self.matched_n != '': return luigi.LocalTarget(os.path.join(self.vcf_path, 'scalpel', 'twopass', 'somatic.db.dir')) else: return luigi.LocalTarget(os.path.join(self.vcf_path, 'scalpel', 'variants.db.dir')) def run(self): pipeline_utils.confirm_path(self.output().path) if self.matched_n: cmd = ['./packages/scalpel-0.5.4/scalpel-discovery', '--somatic', '--normal', self.input()[1][0].path, '--tumor', self.input()[0][0].path, '--bed', self.cfg['library_bed'], '--ref', self.cfg['fasta_file'], '--two-pass', '--dir', os.path.join(self.vcf_path, 'scalpel'), '--numprocs', str(self.max_threads)] else: cmd = ['./packages/scalpel-0.5.4/scalpel-discovery', '--single', '--bam', self.input()[0][0].path, '--bed', self.cfg['library_bed'], '--ref', self.cfg['fasta_file'], '--dir', os.path.join(self.vcf_path, 'scalpel'), '--numprocs', str(self.max_threads)] pipeline_utils.command_call(cmd, [self.output()], threads_needed=self.max_threads) class scalpel_export(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() # fasta_dir = os.path.join('/', *luigi.Parameter().task_value('bowtie', 'fasta_file').split('/')[:-1]) cfg = luigi.DictParameter() def requires(self): # if self.matched_n != '': return scalpel_discovery(case=self.case, tumor=self.tumor, matched_n=self.matched_n, project_dir=self.project_dir, vcf_path=self.vcf_path, max_threads=self.max_threads, cfg=self.cfg) #, scalpel_discovery(case=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=int(self.max_threads/2))] # else: # return scalpel_discovery(case=self.case + '_T', tumor=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads) def output(self): if self.matched_n != '': return luigi.LocalTarget(os.path.join(self.vcf_path, 'scalpel', 'twopass', 'somatic.indel.vcf')) else: return luigi.LocalTarget(os.path.join(self.vcf_path, 'scalpel', 'variants.indel.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) if self.matched_n: cmd = ['./packages/scalpel-0.5.4/scalpel-export', '--somatic', '--db', self.input().path[:-4], '--bed', self.cfg['library_bed'], '--ref', self.cfg['fasta_file']] else: cmd = ['./packages/scalpel-0.5.4/scalpel-export', '--single', '--db', self.input().path[:-4], '--bed', self.cfg['library_bed'], '--ref', self.cfg['fasta_file']] pipeline_utils.command_call(cmd, [self.output()], sleep_time=1.1) # not yet tested - need to install GNU Parallel on cluster... but might be able to run local install http://git.savannah.gnu.org/cgit/parallel.git/tree/README class freebayes(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_freebayes' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) wait_time = random.uniform(0,3) time.sleep(wait_time) sys.stdout.flush() while not pipeline_utils.add_thread_count(global_vars.thread_file, 1): time.sleep(1) cmd = [self.cfg['freebayes_location'], '-f', self.cfg['fasta_file'], '-t', self.cfg['library_bed'], self.input()[0].path] print(' '.join(cmd)) sys.stdout.flush() p1 = subprocess.Popen(' '.join(cmd), stdout=subprocess.PIPE, shell=True) # outs, err = p.communicate() cmd = [self.cfg['vcffilter_location'], '-f', '"QUAL > 20"'] p2 = subprocess.Popen(' '.join(cmd), stdout=subprocess.PIPE, stdin=p1.stdout, shell=True) cmd = ['vcf-sort'] p3 = subprocess.Popen(' '.join(cmd), stdout=subprocess.PIPE, stdin=p2.stdout, shell=True) # outs, err = p.communicate() outs, err = p3.communicate() with open(self.output().path, 'wb') as f: f.write(outs) while not pipeline_utils.sub_thread_count(global_vars.thread_file, 1): time.sleep(1) class sort_freebayes(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return [freebayes(max_threads=self.max_threads, project_dir=self.project_dir, case=self.case, tumor=self.tumor, matched_n=self.matched_n, vcf_path=self.vcf_path, cfg=self.cfg), bam_processing.picard_index(cfg=self.cfg)] def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_freebayes_sorted' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) cmd = ['java', '-jar', self.cfg['picard_location'], 'SortVcf', 'I=%s' % self.input()[0].path, 'O=%s' % self.output().path, 'SEQUENCE_DICTIONARY=%s' % self.input()[1].path] pipeline_utils.command_call(cmd, [self.output()], threads_needed=self.max_threads) class vardict(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): if self.matched_n != '': return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] else: return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_vardict' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) if self.matched_n: cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', '"%s|%s"' % (self.input()[0][0].path, self.input()[1][0].path), '-th', self.max_threads, '-z', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/testsomatic.R', '|', './packages/VarDictJava/VarDict/var2vcf_paired.pl', '-N', '"%s|%s"' % (self.case + '_T', self.case + '_N'), '-f', '0.01', '>%s' % self.output().path] else: cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', self.input()[0][0].path, '-th', self.max_threads, '-z', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/teststrandbias.R', '|', './packages/VarDictJava/VarDict/var2vcf_valid.pl', '-N', self.case + '_T', '-E', '-f', '0.01', '>%s' % self.output().path] pipeline_utils.command_call(cmd, [self.output()], threads_needed=self.max_threads) class sort_vardict(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return [vardict(max_threads=self.max_threads, project_dir=self.project_dir, case=self.case, tumor=self.tumor, matched_n=self.matched_n, vcf_path=self.vcf_path, cfg=self.cfg), bam_processing.picard_index(cfg=self.cfg)] def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_vardict_sorted' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) cmd = ['java', '-jar', self.cfg['picard_location'], 'SortVcf', 'I=%s' % self.input()[0].path, 'O=%s' % self.output().path, 'SEQUENCE_DICTIONARY=%s' % self.input()[1].path] pipeline_utils.command_call(cmd, [self.output()], threads_needed=self.max_threads) # this will be pretty annoying to get up and going class varscan(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): if self.matched_n != '': return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] else: return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg)] def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_varscan' + '.vcf')) def run(self): pipeline_utils.confirm_path(self.output().path) if self.matched_n: cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', '"%s|%s"' % (self.input()[0][0].path, self.input()[1][0].path), '-z', '-F', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/testsomatic.R', '|', './packages/VarDictJava/VarDict/var2vcf_paired.pl', '-N', '"%s|%s"' % (self.case + '_T', self.case + '_N'), '-f', '0.01', '> %s' % os.path.join(self.vcf_path, 'vardict')] else: cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', self.input()[0][0].path, '-z', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/teststrandbias.R', '|', './packages/VarDictJava/VarDict/var2vcf_valid.pl', '-N', self.case + '_T', 'E', '-f', '0.01', '> %s' % os.path.join(self.vcf_path, 'vardict')] pipeline_utils.command_call(cmd, [self.output()]) class pindel(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return [bam_processing.index_bam(sample=case_name + '_N', fastq_file=self.case_dict[case_name]['N'], project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] \ + [bam_processing.index_bam(sample=case_name + '_T', fastq_file=self.case_dict[case_name]['T'], project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) for case_name in self.case_dict] def output(self): pindel_files = ['_D', '_SI', '_TD', '_INV'] #, '_LI', '_BP', return [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'pindel', 'pindel_all_samples' + ext)) for ext in pindel_files] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) with open('___pindel_bams___.txt', 'w') as f: for input_bam in self.input(): case = input_bam[0].path.split('/')[-1].split('_')[0] if '_N' in input_bam[0].path: f.write('%s %s %s\n' % (input_bam[0].path, self.cfg['insert_size'], case + '_N')) else: f.write('%s %s %s\n' % (input_bam[0].path, self.cfg['insert_size'], case + '_T')) cmd = ['./packages/pindel/pindel', '-f', self.cfg['fasta_file'], '-i', '___pindel_bams___.txt', '-T', self.max_threads, '-c', 'ALL', '-o', os.path.join(self.project_dir, 'output', 'pindel', 'pindel_all_samples')] pipeline_utils.command_call(cmd, self.output(), threads_needed=self.max_threads) os.remove('___pindel_bams___.txt') class filter_pindel(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return pindel(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return [luigi.LocalTarget(os.path.join(self.project_dir, 'output', case_name, 'variants', case_name + '_T.pindel.bed')) for case_name in self.case_dict] + \ [luigi.LocalTarget(os.path.join(self.project_dir, 'output', case_name, 'variants', case_name + '_N.pindel.bed')) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] + \ [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'all_samples', 'all_samples_pindel.tsv'))] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) wait_time = random.uniform(0,3) time.sleep(wait_time) sys.stdout.flush() while not pipeline_utils.add_thread_count(global_vars.thread_file, 1): time.sleep(1.2) sample_dict = {output.path.split('/')[-1].split('.pindel.bed')[0]: output.path for output in self.output()[:-1]} misc_utils.filter_pindel(pindel_files=[input_file.path for input_file in self.input()], sample_dict=sample_dict, project_dir=self.project_dir, all_samples_output=self.output()[-1].path, min_reads=self.cfg['pindel_min_reads'], min_qual=self.cfg['pindel_min_qual'], max_inv_length=self.cfg['pindel_max_inv_length']) while not pipeline_utils.sub_thread_count(global_vars.thread_file, 1): time.sleep(1.2) class annotate_pindel(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return filter_pindel(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): outputs = [] for input_file in self.input()[:-1]: output_file = input_file.path.split('.bed')[0] + '_final.bed' outputs.append(luigi.LocalTarget(output_file)) return outputs def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) wait_time = random.uniform(0,3) time.sleep(wait_time) sys.stdout.flush() while not pipeline_utils.add_thread_count(global_vars.thread_file, 1): time.sleep(1.2) for i, input_file in enumerate(self.input()[:-1]): cmd = 'sort-bed %s' % input_file.path # print(cmd) p1 = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) cmd = ['bedtools', 'intersect', '-wa', '-u', '-sorted', '-a', 'stdin', '-b', self.cfg['exons_bed']] cmd = ' '.join(cmd) p2 = subprocess.Popen(cmd, stdout=subprocess.PIPE, stdin=p1.stdout, shell=True) # print(cmd) cmd = ["bedmap", "--echo", "--echo-map-id-uniq", "--delim", r"'\t'", "-", self.cfg['genmap']] cmd = " ".join(cmd) # print(cmd) p3 = subprocess.Popen(cmd, stdout=subprocess.PIPE, stdin=p2.stdout, shell=True) outs, err = p3.communicate() with open(self.output()[i].path, 'wb') as f: f.write(str.encode('#gffTags\n')) f.write(outs) while not pipeline_utils.sub_thread_count(global_vars.thread_file, 1): time.sleep(1.2) class filter_pindel_old(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return pindel(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return [luigi.LocalTarget(input_file.path + '.filtered.tsv') for input_file in self.input()] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) wait_time = random.uniform(0,3) time.sleep(wait_time) sys.stdout.flush() while not pipeline_utils.add_thread_count(global_vars.thread_file, 1): time.sleep(1.2) for i, input_file in enumerate(self.input()): cmd = 'grep "ChrID" %s' % input_file.path #| awk '$17 >= 3' > $file_out p1 = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) # outs, err = p.communicate() cmd = "awk '$17>=%s'" % self.cfg['pindel_min_reads'] p2 = subprocess.Popen(cmd, stdout=subprocess.PIPE, stdin=p1.stdout, shell=True) # outs, err = p.communicate() outs, err = p2.communicate() with open(self.output()[i].path, 'wb') as f: f.write(outs) while not pipeline_utils.sub_thread_count(global_vars.thread_file, 1): time.sleep(1.2) class parse_pindel(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return filter_pindel(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return [luigi.LocalTarget(os.path.join(self.project_dir, 'output', case_name, 'variants', case_name + '_T.pindel.bed')) for case_name in self.case_dict] + \ [luigi.LocalTarget(os.path.join(self.project_dir, 'output', case_name, 'variants', case_name + '_N.pindel.bed')) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] + \ [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'all_samples', 'all_samples_pindel.tsv'))] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) wait_time = random.uniform(0,3) time.sleep(wait_time) sys.stdout.flush() while not pipeline_utils.add_thread_count(global_vars.thread_file, 1): time.sleep(1.2) sample_dict = {output.path.split('/')[-1].split('.pindel.bed')[0]: output.path for output in self.output()[:-1]} misc_utils.format_pindel(pindel_files=[input_file.path for input_file in self.input()], sample_dict=sample_dict, project_dir=self.project_dir, all_samples_output=self.output()[-1].path, min_reads=self.cfg['pindel_min_reads']) while not pipeline_utils.sub_thread_count(global_vars.thread_file, 1): time.sleep(1.2) class pindel2vcf(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return pindel(case_dict=self.case_dict, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'pindel', 'pindel_all_samples.vcf')) def run(self): pindel_input = '_'.join(self.input()[0].path.split('_')[:-1]) pipeline_utils.confirm_path(self.output().path) cmd = ['./packages/pindel/pindel2vcf', '-r', self.cfg['fasta_file'], '-G', '-R', self.cfg['base_name'], '-d', 'idk', '-P', pindel_input, '-v', self.output().path] pipeline_utils.command_call(cmd, [self.output()]) class msisensor(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() case = luigi.Parameter() tumor = luigi.Parameter() matched_n = luigi.Parameter() vcf_path = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): return bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) def output(self): return luigi.LocalTarget(os.path.join(self.vcf_path, self.case + '_T.msisensor')) def run(self): pipeline_utils.confirm_path(self.output().path) cmd = ['./packages/msisensor/binary/msisensor.linux', 'msi', '-d', './packages/msisensor/microsatellites.list', '-t', self.input()[0].path, '-e', self.cfg['library_bed'], '-o', self.output().path] # , '-b', self.max_threads pipeline_utils.command_call(cmd, [self.output()]) # , threads_needed=self.max_threads) class cnvkit(luigi.Task): max_threads = luigi.IntParameter() project_dir = luigi.Parameter() # case = luigi.Parameter() # tumor = luigi.Parameter() # matched_n = luigi.Parameter() # vcf_path = luigi.Parameter() case_dict = luigi.DictParameter() # library_bed = luigi.Parameter() # fasta_file = luigi.Parameter() cfg = luigi.DictParameter() def requires(self): # if self.matched_n != '': # return [bam_processing.index_bam(sample=self.case + '_T', fastq_file=self.tumor, project_dir=self.project_dir, max_threads=self.max_threads), bam_processing.index_bam(sample=self.case + '_N', fastq_file=self.matched_n, project_dir=self.project_dir, max_threads=self.max_threads)] # else: return [bam_processing.index_bam(sample=case_name + '_N', fastq_file=self.case_dict[case_name]['N'], project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] \ + [bam_processing.index_bam(sample=case_name + '_T', fastq_file=self.case_dict[case_name]['T'], project_dir=self.project_dir, max_threads=self.max_threads, cfg=self.cfg) for case_name in self.case_dict] def output(self): return [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'reference', 'reference.cnn'))] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_T_recalibrated.cnr' % case_name)) for case_name in self.case_dict] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_T_recalibrated.cns' % case_name)) for case_name in self.case_dict] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_T_recalibrated.targetcoverage.cnn' % case_name)) for case_name in self.case_dict] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_T_recalibrated.antitargetcoverage.cnn' % case_name)) for case_name in self.case_dict] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_N_recalibrated.targetcoverage.cnn' % case_name)) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] \ + [luigi.LocalTarget(os.path.join(self.project_dir, 'output', 'cnvkit', 'variants', '%s_N_recalibrated.antitargetcoverage.cnn' % case_name)) for case_name in self.case_dict if self.case_dict[case_name]['N'] != ''] def run(self): for output in self.output(): pipeline_utils.confirm_path(output.path) # if self.matched_n: # cmd = ['./packages/VarDictJava/build/install/VarDict/bin/VarDict', '-G', self.cfg['fasta_file'], '-f', '0.01', '-N', self.case + '_T', '-b', '"%s|%s"' % (self.input()[0][0].path, self.input()[1][0].path), '-z', '-F', '-c', '1', '-S', '2', '-E', '3', '-g', '4', self.cfg['library_bed'], '|', './packages/VarDictJava/VarDict/testsomatic.R', '|', './packages/VarDictJava/VarDict/var2vcf_paired.pl', '-N', '"%s|%s"' % (self.case + '_T', self.case + '_N'), '-f', '0.01', '>%s' % os.path.join(self.vcf_path, 'vardict')] # else: cmd = ['python3', './packages/cnvkit/cnvkit.py', 'batch', os.path.join(self.project_dir, 'output', '*', 'alignment', '*T*recalibrated.bam'), '--normal', os.path.join(self.project_dir, 'output', '*', 'alignment', '*N*recalibrated.bam'), '--targets', self.cfg['library_bed'], '--fasta', self.cfg['fasta_file'], '--output-reference', self.output()[0].path, '--output-dir', os.path.join(self.project_dir, 'output', 'cnvkit', 'variants'), '--diagram', '--scatter', '--rlibpath', './packages/R', '--annotate', './packages/cnvkit/data/refFlat_b37.txt', '--drop-low-coverage', '-p', self.max_threads] pipeline_utils.command_call(cmd, self.output(), threads_needed=self.max_threads)

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0

0.0088

0.106175

34,707

717

595

48.405858

0.745084

0.164232

0

0.718468

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0.149946

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false

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1

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null

0

1

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8

99f928d43051cdad79c8075f0cafc9ceccd47e51

352

py

Python

ruprompts/__init__.py

sberbank-ai/ru-prompts

4eeedae92cb5234c70adc787ace7cfceb76b0be0

[ "Apache-2.0" ]

30

2021-12-17T07:05:10.000Z

2022-02-22T09:38:35.000Z

ruprompts/__init__.py

sberbank-ai/ru-prompts

4eeedae92cb5234c70adc787ace7cfceb76b0be0

[ "Apache-2.0" ]

null

ruprompts/__init__.py

sberbank-ai/ru-prompts

4eeedae92cb5234c70adc787ace7cfceb76b0be0

[ "Apache-2.0" ]

2

2022-01-04T01:36:48.000Z

2022-01-04T02:00:24.000Z

from ruprompts.pipelines import ( Text2TextGenerationWithPromptPipeline, TextGenerationWithPromptPipeline, ) from ruprompts.preprocessing import Text2TextPreprocessor from ruprompts.prompt import MultiPrompt, Prompt from ruprompts.prompt_format import PromptFormat from ruprompts.prompt_provider import LSTMPromptProvider, TensorPromptProvider

39.111111

78

0.875

30

352

10.2

0.5

0.212418

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0.00627

0.09375

352

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0

true

0

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1

0

null

0

1

0

1

0

1

0

7

413937787fb2463aa5bca94811628297c341550c

7,760

py

Python

_unittests/ut_sklapi/test_onnx_speedup_cluster.py

sdpython/mlprodic

9367dacc91d35ec670c8a8a76708300a75bbc993

[ "MIT" ]

32

2018-03-04T23:33:30.000Z

2022-03-10T19:15:06.000Z

_unittests/ut_sklapi/test_onnx_speedup_cluster.py

sdpython/mlprodic

9367dacc91d35ec670c8a8a76708300a75bbc993

[ "MIT" ]

184

2017-11-30T14:10:35.000Z

2022-02-21T08:29:31.000Z

_unittests/ut_sklapi/test_onnx_speedup_cluster.py

sdpython/mlprodic

9367dacc91d35ec670c8a8a76708300a75bbc993

[ "MIT" ]

9

2019-07-24T13:18:00.000Z

2022-03-07T04:08:07.000Z

""" @brief test log(time=5s) """ from io import BytesIO import pickle import unittest from logging import getLogger import numpy from numba import NumbaWarning # import pandas # from sklearn.pipeline import make_pipeline from sklearn.exceptions import ConvergenceWarning from sklearn.cluster import KMeans from sklearn.datasets import load_iris from pyquickhelper.pycode import ExtTestCase, ignore_warnings from mlprodict.sklapi import OnnxSpeedupCluster from mlprodict.tools import get_opset_number_from_onnx from mlprodict.onnx_conv import to_onnx from mlprodict.onnxrt import OnnxInference class TestOnnxSpeedupCluster(ExtTestCase): def setUp(self): logger = getLogger('skl2onnx') logger.disabled = True def opset(self): return get_opset_number_from_onnx() @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans32(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset()) spd.fit(X, y) spd.assert_almost_equal(X, decimal=4) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans32_onnxruntime(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), runtime="onnxruntime1") spd.fit(X, y) spd.assert_almost_equal(X, decimal=4) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans32_numpy(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), runtime="numpy") spd.fit(X, y) spd.assert_almost_equal(X, decimal=4) @ignore_warnings((ConvergenceWarning, NumbaWarning)) def test_speedup_kmeans32_numba(self): data = load_iris() X, y = data.data, data.target X = X.astype(numpy.float32) spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), runtime="numba", nopython=False) spd.fit(X, y) spd.assert_almost_equal(X, decimal=4) self.assertIn("CPUDispatch", str(spd.onnxrt_.func)) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False) spd.fit(X, y) spd.assert_almost_equal(X) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64_op_version(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False) spd.fit(X, y) opset = spd.op_version self.assertGreater(self.opset(), opset['']) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64_pickle(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False) spd.fit(X, y) st = BytesIO() pickle.dump(spd, st) st2 = BytesIO(st.getvalue()) spd2 = pickle.load(st2) expected = spd.predict(X) got = spd2.predict(X) self.assertEqualArray(expected, got) expected = spd.raw_predict(X) got = spd2.raw_predict(X) self.assertEqualArray(expected, got) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64_numpy_pickle(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False, runtime="numpy") spd.fit(X, y) st = BytesIO() pickle.dump(spd, st) st2 = BytesIO(st.getvalue()) spd2 = pickle.load(st2) expected = spd.predict(X) got = spd2.predict(X) self.assertEqualArray(expected, got) expected = spd.raw_predict(X) got = spd2.raw_predict(X) self.assertEqualArray(expected, got) @ignore_warnings((ConvergenceWarning, NumbaWarning)) def test_speedup_kmeans64_numba_pickle(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False, runtime="numba", nopython=False) spd.fit(X, y) st = BytesIO() pickle.dump(spd, st) st2 = BytesIO(st.getvalue()) spd2 = pickle.load(st2) expected = spd.predict(X) got = spd2.predict(X) self.assertEqualArray(expected, got) expected = spd.raw_predict(X) got = spd2.raw_predict(X) self.assertEqualArray(expected, got) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64_onnx(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False) spd.fit(X, y) expected_label = spd.predict(X) expected_score = spd.transform(X) onx = to_onnx(spd, X[:1]) oinf = OnnxInference(onx) got = oinf.run({'X': X}) self.assertEqualArray(expected_score, got['scores']) self.assertEqualArray(expected_label, got['label']) @ignore_warnings(ConvergenceWarning) def test_speedup_kmeans64_onnx_numpy(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False, runtime='numpy') spd.fit(X, y) expected_label = spd.predict(X) expected_score = spd.transform(X) onx = to_onnx(spd, X[:1]) oinf = OnnxInference(onx) got = oinf.run({'X': X}) self.assertEqualArray(expected_score, got['scores']) self.assertEqualArray(expected_label, got['label']) @ignore_warnings((ConvergenceWarning, NumbaWarning)) def test_speedup_kmeans64_onnx_numba(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False, runtime='numba', nopython=False) spd.fit(X, y) # print(spd.numpy_code_) expected_label = spd.predict(X) expected_score = spd.transform(X) onx = to_onnx(spd, X[:1]) oinf = OnnxInference(onx) got = oinf.run({'X': X}) self.assertEqualArray(expected_score, got['scores']) self.assertEqualArray(expected_label, got['label']) @ignore_warnings((ConvergenceWarning, NumbaWarning)) def test_speedup_kmeans64_onnx_numba_python(self): data = load_iris() X, y = data.data, data.target spd = OnnxSpeedupCluster( KMeans(n_clusters=3), target_opset=self.opset(), enforce_float32=False, runtime='numba', nopython=False) spd.fit(X, y) # print(spd.numpy_code_) expected_label = spd.predict(X) expected_score = spd.transform(X) onx = to_onnx(spd, X[:1]) oinf = OnnxInference(onx) got = oinf.run({'X': X}) self.assertEqualArray(expected_score, got['scores']) self.assertEqualArray(expected_label, got['label']) if __name__ == '__main__': # TestOnnxSpeedupCluster().test_speedup_kmeans32() unittest.main()

34.336283

67

0.635438

912

7,760

5.220395

0.120614

0.010922

0.082336

0.043688

0.829658

0.820416

0.819366

0.768536

0.752363

0

0.015036

0.254381

7,760

225

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34.488889

0.807812

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0

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0

0.016125

0

0.109375

1

0.078125

false

0

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0.005208

0.161458

0

null

0

1

0

1

0

null

0

7

41428dcf6d1fb25a21a121c3fb4919bb9d19def5

46,614

py

Python

mysql_config/WebMonitoring/generators/website/tests/website_expected_size.py

raresraf/rafMetrics

21eb5e8210364bf70eee746d71c45f3e353dcb10

[ "MIT" ]

15

2019-11-03T18:01:27.000Z

2021-05-05T20:54:57.000Z

mysql_config/WebMonitoring/generators/website/tests/website_expected_size.py

raresraf/rafMetrics

21eb5e8210364bf70eee746d71c45f3e353dcb10

[ "MIT" ]

392

2019-11-09T21:28:01.000Z

2022-03-31T13:04:45.000Z

mysql_config/WebMonitoring/generators/website/tests/website_expected_size.py

raresraf/rafMetrics

21eb5e8210364bf70eee746d71c45f3e353dcb10

[ "MIT" ]

1

2021-03-11T18:35:16.000Z

EXPECTED_DAILY_WEBSITE_GENERATE_SAMPLES_QUERIES_SIZE = """delimiter // DROP PROCEDURE IF EXISTS get_daily_samples_size_websites; CREATE PROCEDURE get_daily_samples_size_websites ( IN id INT, OUT entry0 FLOAT, OUT entry1 FLOAT, OUT entry2 FLOAT, OUT entry3 FLOAT, OUT entry4 FLOAT, OUT entry5 FLOAT, OUT entry6 FLOAT, OUT entry7 FLOAT, OUT entry8 FLOAT, OUT entry9 FLOAT, OUT entry10 FLOAT, OUT entry11 FLOAT, OUT entry12 FLOAT, OUT entry13 FLOAT, OUT entry14 FLOAT, OUT entry15 FLOAT, OUT entry16 FLOAT, OUT entry17 FLOAT, OUT entry18 FLOAT, OUT entry19 FLOAT, OUT entry20 FLOAT, OUT entry21 FLOAT, OUT entry22 FLOAT, OUT entry23 FLOAT, OUT start_hour FLOAT ) BEGIN select HOUR(now()) INTO start_hour; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 23 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry0 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 23 HOUR) AND Websiteid = id limit 1); else SET entry0 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 23 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 22 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry1 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 23 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 22 HOUR) AND Websiteid = id limit 1); else SET entry1 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 22 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 21 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry2 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 22 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 21 HOUR) AND Websiteid = id limit 1); else SET entry2 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 21 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 20 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry3 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 21 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 20 HOUR) AND Websiteid = id limit 1); else SET entry3 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 20 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 19 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry4 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 20 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 19 HOUR) AND Websiteid = id limit 1); else SET entry4 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 19 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry5 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 19 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND Websiteid = id limit 1); else SET entry5 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 17 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry6 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 17 HOUR) AND Websiteid = id limit 1); else SET entry6 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 17 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 16 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry7 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 17 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 16 HOUR) AND Websiteid = id limit 1); else SET entry7 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 16 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 15 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry8 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 16 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 15 HOUR) AND Websiteid = id limit 1); else SET entry8 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 15 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 14 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry9 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 15 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 14 HOUR) AND Websiteid = id limit 1); else SET entry9 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 14 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 13 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry10 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 14 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 13 HOUR) AND Websiteid = id limit 1); else SET entry10 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 13 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry11 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 13 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND Websiteid = id limit 1); else SET entry11 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 11 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry12 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 11 HOUR) AND Websiteid = id limit 1); else SET entry12 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 11 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 10 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry13 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 11 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 10 HOUR) AND Websiteid = id limit 1); else SET entry13 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 10 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 9 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry14 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 10 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 9 HOUR) AND Websiteid = id limit 1); else SET entry14 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 9 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 8 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry15 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 9 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 8 HOUR) AND Websiteid = id limit 1); else SET entry15 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 8 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 7 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry16 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 8 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 7 HOUR) AND Websiteid = id limit 1); else SET entry16 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 7 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry17 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 7 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND Websiteid = id limit 1); else SET entry17 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 5 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry18 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 5 HOUR) AND Websiteid = id limit 1); else SET entry18 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 5 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 4 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry19 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 5 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 4 HOUR) AND Websiteid = id limit 1); else SET entry19 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 4 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 3 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry20 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 4 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 3 HOUR) AND Websiteid = id limit 1); else SET entry20 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 3 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 2 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry21 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 3 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 2 HOUR) AND Websiteid = id limit 1); else SET entry21 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 2 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 1 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry22 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 2 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 1 HOUR) AND Websiteid = id limit 1); else SET entry22 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 1 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry23 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 1 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 HOUR) AND Websiteid = id limit 1); else SET entry23 := 0; end if; END// delimiter ; """ EXPECTED_WEEKLY_WEBSITE_GENERATE_SAMPLES_QUERIES_SIZE = """delimiter // DROP PROCEDURE IF EXISTS get_weekly_samples_size_websites; CREATE PROCEDURE get_weekly_samples_size_websites ( IN id INT, OUT entry0 FLOAT, OUT entry1 FLOAT, OUT entry2 FLOAT, OUT entry3 FLOAT, OUT entry4 FLOAT, OUT entry5 FLOAT, OUT entry6 FLOAT, OUT entry7 FLOAT, OUT entry8 FLOAT, OUT entry9 FLOAT, OUT entry10 FLOAT, OUT entry11 FLOAT, OUT entry12 FLOAT, OUT entry13 FLOAT, OUT entry14 FLOAT, OUT entry15 FLOAT, OUT entry16 FLOAT, OUT entry17 FLOAT, OUT entry18 FLOAT, OUT entry19 FLOAT, OUT entry20 FLOAT, OUT entry21 FLOAT, OUT entry22 FLOAT, OUT entry23 FLOAT, OUT entry24 FLOAT, OUT entry25 FLOAT, OUT entry26 FLOAT, OUT entry27 FLOAT, OUT start_hour FLOAT ) BEGIN select HOUR(now()) INTO start_hour; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 168 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 162 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry0 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 168 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 162 HOUR) AND Websiteid = id limit 1); else SET entry0 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 162 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 156 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry1 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 162 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 156 HOUR) AND Websiteid = id limit 1); else SET entry1 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 156 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 150 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry2 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 156 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 150 HOUR) AND Websiteid = id limit 1); else SET entry2 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 150 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 144 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry3 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 150 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 144 HOUR) AND Websiteid = id limit 1); else SET entry3 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 144 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 138 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry4 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 144 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 138 HOUR) AND Websiteid = id limit 1); else SET entry4 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 138 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 132 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry5 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 138 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 132 HOUR) AND Websiteid = id limit 1); else SET entry5 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 132 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 126 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry6 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 132 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 126 HOUR) AND Websiteid = id limit 1); else SET entry6 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 126 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 120 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry7 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 126 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 120 HOUR) AND Websiteid = id limit 1); else SET entry7 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 120 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 114 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry8 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 120 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 114 HOUR) AND Websiteid = id limit 1); else SET entry8 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 114 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 108 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry9 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 114 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 108 HOUR) AND Websiteid = id limit 1); else SET entry9 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 108 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 102 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry10 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 108 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 102 HOUR) AND Websiteid = id limit 1); else SET entry10 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 102 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 96 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry11 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 102 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 96 HOUR) AND Websiteid = id limit 1); else SET entry11 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 96 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 90 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry12 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 96 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 90 HOUR) AND Websiteid = id limit 1); else SET entry12 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 90 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 84 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry13 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 90 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 84 HOUR) AND Websiteid = id limit 1); else SET entry13 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 84 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 78 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry14 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 84 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 78 HOUR) AND Websiteid = id limit 1); else SET entry14 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 78 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 72 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry15 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 78 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 72 HOUR) AND Websiteid = id limit 1); else SET entry15 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 72 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 66 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry16 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 72 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 66 HOUR) AND Websiteid = id limit 1); else SET entry16 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 66 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 60 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry17 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 66 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 60 HOUR) AND Websiteid = id limit 1); else SET entry17 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 60 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 54 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry18 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 60 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 54 HOUR) AND Websiteid = id limit 1); else SET entry18 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 54 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 48 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry19 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 54 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 48 HOUR) AND Websiteid = id limit 1); else SET entry19 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 48 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 42 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry20 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 48 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 42 HOUR) AND Websiteid = id limit 1); else SET entry20 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 42 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 36 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry21 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 42 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 36 HOUR) AND Websiteid = id limit 1); else SET entry21 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 36 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 30 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry22 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 36 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 30 HOUR) AND Websiteid = id limit 1); else SET entry22 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 30 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry23 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 30 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND Websiteid = id limit 1); else SET entry23 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry24 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND Websiteid = id limit 1); else SET entry24 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry25 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND Websiteid = id limit 1); else SET entry25 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry26 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND Websiteid = id limit 1); else SET entry26 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 HOUR) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry27 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 HOUR) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 HOUR) AND Websiteid = id limit 1); else SET entry27 := 0; end if; END// delimiter ; """ EXPECTED_MONTHLY_WEBSITE_GENERATE_SAMPLES_QUERIES_SIZE = """delimiter // DROP PROCEDURE IF EXISTS get_monthly_samples_size_websites; CREATE PROCEDURE get_monthly_samples_size_websites ( IN id INT, OUT entry0 FLOAT, OUT entry1 FLOAT, OUT entry2 FLOAT, OUT entry3 FLOAT, OUT entry4 FLOAT, OUT entry5 FLOAT, OUT entry6 FLOAT, OUT entry7 FLOAT, OUT entry8 FLOAT, OUT entry9 FLOAT, OUT entry10 FLOAT, OUT entry11 FLOAT, OUT entry12 FLOAT, OUT entry13 FLOAT, OUT entry14 FLOAT, OUT entry15 FLOAT, OUT entry16 FLOAT, OUT entry17 FLOAT, OUT entry18 FLOAT, OUT entry19 FLOAT, OUT entry20 FLOAT, OUT entry21 FLOAT, OUT entry22 FLOAT, OUT entry23 FLOAT, OUT entry24 FLOAT, OUT entry25 FLOAT, OUT entry26 FLOAT, OUT entry27 FLOAT, OUT entry28 FLOAT, OUT entry29 FLOAT, OUT entry30 FLOAT, OUT start_hour FLOAT ) BEGIN select DAY(now()) INTO start_hour; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 31 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 30 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry0 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 31 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 30 DAY) AND Websiteid = id limit 1); else SET entry0 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 30 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 29 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry1 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 30 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 29 DAY) AND Websiteid = id limit 1); else SET entry1 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 29 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 28 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry2 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 29 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 28 DAY) AND Websiteid = id limit 1); else SET entry2 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 28 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 27 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry3 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 28 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 27 DAY) AND Websiteid = id limit 1); else SET entry3 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 27 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 26 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry4 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 27 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 26 DAY) AND Websiteid = id limit 1); else SET entry4 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 26 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 25 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry5 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 26 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 25 DAY) AND Websiteid = id limit 1); else SET entry5 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 25 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 24 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry6 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 25 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 24 DAY) AND Websiteid = id limit 1); else SET entry6 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 23 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry7 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 24 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 23 DAY) AND Websiteid = id limit 1); else SET entry7 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 23 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 22 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry8 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 23 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 22 DAY) AND Websiteid = id limit 1); else SET entry8 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 22 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 21 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry9 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 22 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 21 DAY) AND Websiteid = id limit 1); else SET entry9 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 21 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 20 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry10 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 21 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 20 DAY) AND Websiteid = id limit 1); else SET entry10 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 20 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 19 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry11 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 20 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 19 DAY) AND Websiteid = id limit 1); else SET entry11 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 19 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry12 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 19 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 18 DAY) AND Websiteid = id limit 1); else SET entry12 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 17 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry13 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 18 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 17 DAY) AND Websiteid = id limit 1); else SET entry13 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 17 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 16 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry14 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 17 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 16 DAY) AND Websiteid = id limit 1); else SET entry14 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 16 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 15 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry15 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 16 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 15 DAY) AND Websiteid = id limit 1); else SET entry15 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 15 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 14 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry16 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 15 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 14 DAY) AND Websiteid = id limit 1); else SET entry16 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 14 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 13 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry17 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 14 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 13 DAY) AND Websiteid = id limit 1); else SET entry17 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 13 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry18 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 13 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 12 DAY) AND Websiteid = id limit 1); else SET entry18 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 11 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry19 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 12 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 11 DAY) AND Websiteid = id limit 1); else SET entry19 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 11 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 10 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry20 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 11 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 10 DAY) AND Websiteid = id limit 1); else SET entry20 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 10 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 9 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry21 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 10 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 9 DAY) AND Websiteid = id limit 1); else SET entry21 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 9 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 8 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry22 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 9 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 8 DAY) AND Websiteid = id limit 1); else SET entry22 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 8 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 7 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry23 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 8 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 7 DAY) AND Websiteid = id limit 1); else SET entry23 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 7 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry24 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 7 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 6 DAY) AND Websiteid = id limit 1); else SET entry24 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 5 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry25 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 6 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 5 DAY) AND Websiteid = id limit 1); else SET entry25 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 5 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 4 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry26 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 5 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 4 DAY) AND Websiteid = id limit 1); else SET entry26 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 4 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 3 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry27 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 4 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 3 DAY) AND Websiteid = id limit 1); else SET entry27 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 3 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 2 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry28 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 3 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 2 DAY) AND Websiteid = id limit 1); else SET entry28 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 2 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 1 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry29 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 2 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 1 DAY) AND Websiteid = id limit 1); else SET entry29 := 0; end if; if EXISTS(SELECT SUM(bodySize) from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 1 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 DAY) AND Websiteid = id limit 1)) then SELECT SUM(bodySize) INTO entry30 from REQUESTS where Metricid = (SELECT Metricid FROM WEBSITES_METRICS WHERE TIMESTAMP >= DATE_SUB(NOW(), INTERVAL 1 DAY) AND TIMESTAMP <= DATE_SUB(NOW(), INTERVAL 0 DAY) AND Websiteid = id limit 1); else SET entry30 := 0; end if; END// delimiter ; """

103.586667

251

0.725276

6,760

46,614

4.920414

0.019675

0.129758

0.159702

0.189646

0.996753

0.99513

0.99059

0.989598

0.988966

0

0.033945

0.187905

46,614

449

252

103.817372

0.844723

0

0.587054

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0.370536

0.995924

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null

0

1

0

1

0

1

0

null

0

11

419c69d678cc9cd2f88a6701ee1de53ff5cf1815

6,683

py

Python

loldib/getratings/models/NA/na_hecarim/na_hecarim_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_hecarim/na_hecarim_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_hecarim/na_hecarim_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Hecarim_Jng_Aatrox(Ratings): pass class NA_Hecarim_Jng_Ahri(Ratings): pass class NA_Hecarim_Jng_Akali(Ratings): pass class NA_Hecarim_Jng_Alistar(Ratings): pass class NA_Hecarim_Jng_Amumu(Ratings): pass class NA_Hecarim_Jng_Anivia(Ratings): pass class NA_Hecarim_Jng_Annie(Ratings): pass class NA_Hecarim_Jng_Ashe(Ratings): pass class NA_Hecarim_Jng_AurelionSol(Ratings): pass class NA_Hecarim_Jng_Azir(Ratings): pass class NA_Hecarim_Jng_Bard(Ratings): pass class NA_Hecarim_Jng_Blitzcrank(Ratings): pass class NA_Hecarim_Jng_Brand(Ratings): pass class NA_Hecarim_Jng_Braum(Ratings): pass class NA_Hecarim_Jng_Caitlyn(Ratings): pass class NA_Hecarim_Jng_Camille(Ratings): pass class NA_Hecarim_Jng_Cassiopeia(Ratings): pass class NA_Hecarim_Jng_Chogath(Ratings): pass class NA_Hecarim_Jng_Corki(Ratings): pass class NA_Hecarim_Jng_Darius(Ratings): pass class NA_Hecarim_Jng_Diana(Ratings): pass class NA_Hecarim_Jng_Draven(Ratings): pass class NA_Hecarim_Jng_DrMundo(Ratings): pass class NA_Hecarim_Jng_Ekko(Ratings): pass class NA_Hecarim_Jng_Elise(Ratings): pass class NA_Hecarim_Jng_Evelynn(Ratings): pass class NA_Hecarim_Jng_Ezreal(Ratings): pass class NA_Hecarim_Jng_Fiddlesticks(Ratings): pass class NA_Hecarim_Jng_Fiora(Ratings): pass class NA_Hecarim_Jng_Fizz(Ratings): pass class NA_Hecarim_Jng_Galio(Ratings): pass class NA_Hecarim_Jng_Gangplank(Ratings): pass class NA_Hecarim_Jng_Garen(Ratings): pass class NA_Hecarim_Jng_Gnar(Ratings): pass class NA_Hecarim_Jng_Gragas(Ratings): pass class NA_Hecarim_Jng_Graves(Ratings): pass class NA_Hecarim_Jng_Hecarim(Ratings): pass class NA_Hecarim_Jng_Heimerdinger(Ratings): pass class NA_Hecarim_Jng_Illaoi(Ratings): pass class NA_Hecarim_Jng_Irelia(Ratings): pass class NA_Hecarim_Jng_Ivern(Ratings): pass class NA_Hecarim_Jng_Janna(Ratings): pass class NA_Hecarim_Jng_JarvanIV(Ratings): pass class NA_Hecarim_Jng_Jax(Ratings): pass class NA_Hecarim_Jng_Jayce(Ratings): pass class NA_Hecarim_Jng_Jhin(Ratings): pass class NA_Hecarim_Jng_Jinx(Ratings): pass class NA_Hecarim_Jng_Kalista(Ratings): pass class NA_Hecarim_Jng_Karma(Ratings): pass class NA_Hecarim_Jng_Karthus(Ratings): pass class NA_Hecarim_Jng_Kassadin(Ratings): pass class NA_Hecarim_Jng_Katarina(Ratings): pass class NA_Hecarim_Jng_Kayle(Ratings): pass class NA_Hecarim_Jng_Kayn(Ratings): pass class NA_Hecarim_Jng_Kennen(Ratings): pass class NA_Hecarim_Jng_Khazix(Ratings): pass class NA_Hecarim_Jng_Kindred(Ratings): pass class NA_Hecarim_Jng_Kled(Ratings): pass class NA_Hecarim_Jng_KogMaw(Ratings): pass class NA_Hecarim_Jng_Leblanc(Ratings): pass class NA_Hecarim_Jng_LeeSin(Ratings): pass class NA_Hecarim_Jng_Leona(Ratings): pass class NA_Hecarim_Jng_Lissandra(Ratings): pass class NA_Hecarim_Jng_Lucian(Ratings): pass class NA_Hecarim_Jng_Lulu(Ratings): pass class NA_Hecarim_Jng_Lux(Ratings): pass class NA_Hecarim_Jng_Malphite(Ratings): pass class NA_Hecarim_Jng_Malzahar(Ratings): pass class NA_Hecarim_Jng_Maokai(Ratings): pass class NA_Hecarim_Jng_MasterYi(Ratings): pass class NA_Hecarim_Jng_MissFortune(Ratings): pass class NA_Hecarim_Jng_MonkeyKing(Ratings): pass class NA_Hecarim_Jng_Mordekaiser(Ratings): pass class NA_Hecarim_Jng_Morgana(Ratings): pass class NA_Hecarim_Jng_Nami(Ratings): pass class NA_Hecarim_Jng_Nasus(Ratings): pass class NA_Hecarim_Jng_Nautilus(Ratings): pass class NA_Hecarim_Jng_Nidalee(Ratings): pass class NA_Hecarim_Jng_Nocturne(Ratings): pass class NA_Hecarim_Jng_Nunu(Ratings): pass class NA_Hecarim_Jng_Olaf(Ratings): pass class NA_Hecarim_Jng_Orianna(Ratings): pass class NA_Hecarim_Jng_Ornn(Ratings): pass class NA_Hecarim_Jng_Pantheon(Ratings): pass class NA_Hecarim_Jng_Poppy(Ratings): pass class NA_Hecarim_Jng_Quinn(Ratings): pass class NA_Hecarim_Jng_Rakan(Ratings): pass class NA_Hecarim_Jng_Rammus(Ratings): pass class NA_Hecarim_Jng_RekSai(Ratings): pass class NA_Hecarim_Jng_Renekton(Ratings): pass class NA_Hecarim_Jng_Rengar(Ratings): pass class NA_Hecarim_Jng_Riven(Ratings): pass class NA_Hecarim_Jng_Rumble(Ratings): pass class NA_Hecarim_Jng_Ryze(Ratings): pass class NA_Hecarim_Jng_Sejuani(Ratings): pass class NA_Hecarim_Jng_Shaco(Ratings): pass class NA_Hecarim_Jng_Shen(Ratings): pass class NA_Hecarim_Jng_Shyvana(Ratings): pass class NA_Hecarim_Jng_Singed(Ratings): pass class NA_Hecarim_Jng_Sion(Ratings): pass class NA_Hecarim_Jng_Sivir(Ratings): pass class NA_Hecarim_Jng_Skarner(Ratings): pass class NA_Hecarim_Jng_Sona(Ratings): pass class NA_Hecarim_Jng_Soraka(Ratings): pass class NA_Hecarim_Jng_Swain(Ratings): pass class NA_Hecarim_Jng_Syndra(Ratings): pass class NA_Hecarim_Jng_TahmKench(Ratings): pass class NA_Hecarim_Jng_Taliyah(Ratings): pass class NA_Hecarim_Jng_Talon(Ratings): pass class NA_Hecarim_Jng_Taric(Ratings): pass class NA_Hecarim_Jng_Teemo(Ratings): pass class NA_Hecarim_Jng_Thresh(Ratings): pass class NA_Hecarim_Jng_Tristana(Ratings): pass class NA_Hecarim_Jng_Trundle(Ratings): pass class NA_Hecarim_Jng_Tryndamere(Ratings): pass class NA_Hecarim_Jng_TwistedFate(Ratings): pass class NA_Hecarim_Jng_Twitch(Ratings): pass class NA_Hecarim_Jng_Udyr(Ratings): pass class NA_Hecarim_Jng_Urgot(Ratings): pass class NA_Hecarim_Jng_Varus(Ratings): pass class NA_Hecarim_Jng_Vayne(Ratings): pass class NA_Hecarim_Jng_Veigar(Ratings): pass class NA_Hecarim_Jng_Velkoz(Ratings): pass class NA_Hecarim_Jng_Vi(Ratings): pass class NA_Hecarim_Jng_Viktor(Ratings): pass class NA_Hecarim_Jng_Vladimir(Ratings): pass class NA_Hecarim_Jng_Volibear(Ratings): pass class NA_Hecarim_Jng_Warwick(Ratings): pass class NA_Hecarim_Jng_Xayah(Ratings): pass class NA_Hecarim_Jng_Xerath(Ratings): pass class NA_Hecarim_Jng_XinZhao(Ratings): pass class NA_Hecarim_Jng_Yasuo(Ratings): pass class NA_Hecarim_Jng_Yorick(Ratings): pass class NA_Hecarim_Jng_Zac(Ratings): pass class NA_Hecarim_Jng_Zed(Ratings): pass class NA_Hecarim_Jng_Ziggs(Ratings): pass class NA_Hecarim_Jng_Zilean(Ratings): pass class NA_Hecarim_Jng_Zyra(Ratings): pass

16.026379

46

0.77151

972

6,683

4.878601

0.151235

0.203712

0.407423

0.494728

0.808941

0

0.166243

6,683

416

47

16.064904

0.851041

0

0.498195

0

1

0

true

0.498195

0.00361

0

0.501805

0

null

1

0

null

0

1

0

1

0

8

41c43c5d04dbd397cba7e1a133160b6364bf4e79

3,658

py

Python

tests/test_unit/test_type_utils.py

best-doctor/Mario

a6c83b9f7e7558a4e71d8acb00b8d164fe8eec6f

[ "MIT" ]

12

2020-01-30T02:19:16.000Z

2022-01-20T04:00:43.000Z

tests/test_unit/test_type_utils.py

best-doctor/Mario

a6c83b9f7e7558a4e71d8acb00b8d164fe8eec6f

[ "MIT" ]

32

2019-12-07T14:06:05.000Z

2020-06-26T07:12:03.000Z

tests/test_unit/test_type_utils.py

best-doctor/Mario

a6c83b9f7e7558a4e71d8acb00b8d164fe8eec6f

[ "MIT" ]

3

2020-08-21T07:54:53.000Z

2021-01-11T12:05:48.000Z

import collections from typing import List, Dict, Mapping, Iterable, Tuple, Set, Optional, Union import pytest from super_mario.utils.types import is_instance_of_type, is_instance_of_named_tuple def test_is_instance_of_type_simple_types(): assert is_instance_of_type(1, int) assert is_instance_of_type('abc', str) assert is_instance_of_type([1, 2], list) assert is_instance_of_type({1: 2}, dict) assert not is_instance_of_type(1, str) assert not is_instance_of_type('abc', int) assert not is_instance_of_type([1, 2], dict) assert not is_instance_of_type({1: 2}, list) def test_is_instance_of_type_basic_typing_types(): assert is_instance_of_type([], List) assert is_instance_of_type({}, Dict) assert is_instance_of_type({}, Mapping) assert is_instance_of_type([], Iterable) assert not is_instance_of_type([], Dict) assert not is_instance_of_type({}, List) assert not is_instance_of_type({}, Tuple) assert not is_instance_of_type([], Set) def test_is_instance_of_type_typing_list_with_simple_types(): assert is_instance_of_type([1, 2, 3], List[int]) assert not is_instance_of_type([1, 2, 3], List[str]) assert not is_instance_of_type([1, 2, '3'], List[str]) assert not is_instance_of_type({1: 2}, List[str]) def test_is_instance_of_type_typing_dict_with_simple_types(): assert is_instance_of_type({1: '1', 2: '2'}, Dict[int, str]) assert is_instance_of_type({1: '1', 2: '2'}, Mapping[int, str]) assert not is_instance_of_type({1: '1', 2: '2'}, Dict[str, str]) assert not is_instance_of_type({1: '1', 2: '2'}, Dict[int, int]) assert not is_instance_of_type([1, 2, 3, 4], Dict[int, int]) def test_is_instance_of_type_typing_with_optionals(): assert is_instance_of_type(1, Optional[int]) assert is_instance_of_type(None, Optional[int]) assert is_instance_of_type(None, Optional[List[int]]) assert is_instance_of_type([], List[Optional[int]]) assert is_instance_of_type([1, 2], List[Optional[int]]) assert is_instance_of_type([1, 2, None], List[Optional[int]]) assert not is_instance_of_type([1, 2, 'None'], List[Optional[int]]) assert is_instance_of_type(None, Optional[Dict]) assert is_instance_of_type(None, Optional[Dict[str, Optional[int]]]) assert is_instance_of_type({'a': 1, 'b': None}, Dict[str, Optional[int]]) assert is_instance_of_type({1: 'a', None: 'b'}, Dict[Optional[int], str]) def test_is_instance_of_type_typing_with_unions(): assert is_instance_of_type(1, Union[int]) assert is_instance_of_type(1, Union[int, str]) assert is_instance_of_type('1', Union[int, str]) assert is_instance_of_type([], Union[List, Dict]) assert is_instance_of_type({}, Union[List, Dict]) assert not is_instance_of_type(set(), Union[List, Dict]) assert not is_instance_of_type([], Union[int, str]) assert is_instance_of_type([], List[Union[int]]) assert is_instance_of_type([1, 2], List[Union[int, str]]) assert is_instance_of_type([1, '2'], List[Union[int, str]]) assert not is_instance_of_type([1, '2', None], List[Union[int, str]]) assert is_instance_of_type({'a': 1, 'b': '2'}, Dict[str, Union[int, str]]) assert is_instance_of_type({1: 'a', '2': 'b'}, Dict[Union[int, str], str]) @pytest.mark.parametrize( ('named_tuple', 'expected_result'), [ (collections.namedtuple('Test', 'a, b, c')(1, 2, 3), True), ((1, 2, 3), False), ('str', False), (['str'], False), ], ) def test_is_instance_of_named_tuple(named_tuple, expected_result): assert is_instance_of_named_tuple(named_tuple) == expected_result

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68beee8640eed8e164e7e6630c62b422e7ddb650

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Python

tests/test_ldbws_session.py

grundleborg/nrewebservices

f8416afad160366c70b0579c37aa11fb175ccac1

[ "BSD-2-Clause" ]

15

2017-03-16T14:34:56.000Z

2021-09-18T23:27:03.000Z

tests/test_ldbws_session.py

grundleborg/nrewebservices

f8416afad160366c70b0579c37aa11fb175ccac1

[ "BSD-2-Clause" ]

9

2016-09-09T13:43:32.000Z

2018-12-30T19:55:09.000Z

tests/test_ldbws_session.py

grundleborg/nrewebservices

f8416afad160366c70b0579c37aa11fb175ccac1

[ "BSD-2-Clause" ]

3

2016-12-12T12:29:56.000Z

2018-06-03T22:44:51.000Z

import os import sys testsPath = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, testsPath + '/../') from suds import WebFault import os import pytest from nrewebservices.ldbws import Session API_URL = "https://lite.realtime.nationalrail.co.uk/OpenLDBWS/wsdl.aspx?ver=2016-02-16" @pytest.fixture(scope="module") def session(): return Session(API_URL) @pytest.mark.skipif(os.environ.get("NRE_LDBWS_API_KEY") is None, reason="NRE_LDBWS_API_KEY must be set to test ldbws.Session class.") class TestSession(object): def test_get_station_board_arrivals(self, session): r = session.get_station_board("PAD", rows=10, include_departures=False, include_arrivals=True) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_departures(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=False) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_arrivals_departures(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=True) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_neither(self, session): with pytest.raises(ValueError): r = session.get_station_board("PAD", rows=10, include_departures=False, include_arrivals=False) def test_get_station_board_arrivals_filtered_from(self, session): r = session.get_station_board("PAD", rows=10, include_departures=False, include_arrivals=True, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" def test_get_station_board_departures_filtered_from(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=False, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" def test_get_station_board_arrivals_departures_filtered_from(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=True, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" @pytest.mark.xfail def test_get_station_board_arrivals_filtered_to(self, session): r = session.get_station_board("PAD", rows=10, include_departures=False, include_arrivals=True, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_departures_filtered_to(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=False, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_arrivals_departures_filtered_to(self, session): r = session.get_station_board("PAD", rows=10, include_departures=True, include_arrivals=True, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_arrivals_filtered_both_filters(self, session): r = session.get_station_board("PAD", rows=10, include_departures=False, include_arrivals=True, from_filter_crs="RDG", to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" ### NEXT ### def test_get_station_board_with_details_arrivals(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=False, include_arrivals=True) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_with_details_departures(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=False) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_with_details_arrivals_departures(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=True) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None def test_get_station_board_with_details_neither(self, session): with pytest.raises(ValueError): r = session.get_station_board_with_details("PAD", rows=10, include_departures=False, include_arrivals=False) def test_get_station_board_arrivals_with_details_filtered_from(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=False, include_arrivals=True, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" def test_get_station_board_departures_with_details_filtered_from(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=False, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" def test_get_station_board_arrivals_departures_with_details_filtered_from(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=True, from_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" assert r.filter_type == "from" @pytest.mark.xfail def test_get_station_board_arrivals_with_details_filtered_to(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=False, include_arrivals=True, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_departures_with_details_filtered_to(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=False, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_arrivals_departures_with_details_filtered_to(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=True, include_arrivals=True, to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" @pytest.mark.xfail def test_get_station_board_arrivals_with_details_filtered_both_filters(self, session): r = session.get_station_board_with_details("PAD", rows=10, include_departures=False, include_arrivals=True, from_filter_crs="RDG", to_filter_crs="RDG") assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name == "Reading" assert r.filter_crs == "RDG" # TODO: Investigate why filter_type is None, not "to". assert r.filter_type == "to" def test_get_next_departures_basic(self, session): r = session.get_next_departures("PAD", ["RDG", "TWY"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 2 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" def test_get_next_departures_not_a_list(self, session): with pytest.raises(ValueError): r = session.get_next_departures("PAD", "RDG") def test_get_next_departures_too_few(self, session): with pytest.raises(ValueError): r = session.get_next_departures("PAD", []) def test_get_next_departures_too_many(self, session): with pytest.raises(ValueError): r = session.get_next_departures("PAD", ["RDG", "TWY", "PLY", "PNZ", "WAT", "WIN", "BRI", "STP", "LEI", "MIM", "OXF", "CBG", "ABY", "STS", "LSK", "CSK", "GSL", "CLJ", "WIJ", "WAT", "MKC", "BIR", "BMS", "BSH", "MYB", "OPY"]) def test_get_next_departures_repeated(self, session): r = session.get_next_departures("PAD", ["RDG", "TWY", "RDG"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 3 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" assert r.next_departures[2].crs == "RDG" def test_get_next_departures_with_details_basic(self, session): r = session.get_next_departures_with_details("PAD", ["RDG", "TWY"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 2 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" def test_get_next_departures_with_details_not_a_list(self, session): with pytest.raises(ValueError): r = session.get_next_departures_with_details("PAD", "RDG") def test_get_next_departures_with_details_too_few(self, session): with pytest.raises(ValueError): r = session.get_next_departures_with_details("PAD", []) def test_get_next_departures_with_details_too_many(self, session): with pytest.raises(ValueError): r = session.get_next_departures_with_details("PAD", ["RDG", "TWY", "PLY", "PNZ", "WAT", "WIN", "BRI", "STP", "LEI", "MIM", "OXF", "CBG", "ABY", "STS", "LSK", "CSK", "GSL", "CLJ", "WIJ", "WAT", "MKC", "BIR", "BMS", "BSH", "MYB", "OPY"]) def test_get_next_departures_with_details_repeated(self, session): r = session.get_next_departures_with_details("PAD", ["RDG", "TWY", "RDG"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 3 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" assert r.next_departures[2].crs == "RDG" def test_get_fastest_departures_basic(self, session): r = session.get_fastest_departures("PAD", ["RDG", "TWY"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 2 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" def test_get_fastest_departures_not_a_list(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures("PAD", "RDG") def test_get_fastest_departures_too_few(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures("PAD", []) def test_get_fastest_departures_too_many(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures("PAD", ["RDG", "TWY", "PLY", "PNZ", "WAT", "WIN", "BRI", "STP", "LEI", "MIM", "OXF", "CBG", "ABY", "STS", "LSK", "CSK", "GSL", "CLJ", "WIJ", "WAT", "MKC", "BIR", "BMS", "BSH", "MYB", "OPY"]) def test_get_next_fastestrtures_repeated(self, session): r = session.get_fastest_departures("PAD", ["RDG", "TWY", "RDG"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 3 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" assert r.next_departures[2].crs == "RDG" def test_get_fastest_departures_with_details_basic(self, session): r = session.get_fastest_departures_with_details("PAD", ["RDG", "TWY"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 2 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" def test_get_fastest_departures_with_details_not_a_list(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures_with_details("PAD", "RDG") def test_get_fastest_departures_with_details_too_few(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures_with_details("PAD", []) def test_get_fastest_departures_with_details_too_many(self, session): with pytest.raises(ValueError): r = session.get_fastest_departures_with_details("PAD", ["RDG", "TWY", "PLY", "PNZ", "WAT", "WIN", "BRI", "STP", "LEI", "MIM", "OXF", "CBG", "ABY", "STS", "LSK", "CSK", "GSL", "CLJ", "WIJ", "WAT", "MKC", "BIR", "BMS", "BSH", "MYB", "OPY"]) def test_get_fastest_departures_with_details_repeated(self, session): r = session.get_fastest_departures_with_details("PAD", ["RDG", "TWY", "RDG"]) assert r.location_name == "London Paddington" assert r.crs == "PAD" assert r.filter_location_name is None assert r.filter_crs is None assert r.filter_type is None assert len(r.next_departures) == 3 assert r.next_departures[0].crs == "RDG" assert r.next_departures[1].crs == "TWY" assert r.next_departures[2].crs == "RDG" def test_get_service(self, session): r = session.get_station_board("LBG", include_departures=True, include_arrivals=False) assert len(r.train_services) > 0 s = session.get_service_details(r.train_services[0].service_id) assert s.crs == "LBG" def test_get_service_invalid_id(self, session): # TODO: Wrap up SUDS errors in something more helpful in the API. with pytest.raises(WebFault): s = session.get_service_details("lalalalala")

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68bf722715551c9c887897220a7705dce3de5ca9

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py

Python

Experiments/STMeta/deprecated/Runner_supplement_external_30.py

TempAnonymous/Context_Analysis

bbeba1ed7ea7001c22a12721fc4f390d4cc01a6e

[ "MIT" ]

3

2021-06-29T06:18:18.000Z

2021-09-07T03:11:35.000Z

Experiments/STMeta/deprecated/Runner_supplement_external_30.py

TempAnonymous/Context_Analysis

bbeba1ed7ea7001c22a12721fc4f390d4cc01a6e

[ "MIT" ]

null

Experiments/STMeta/deprecated/Runner_supplement_external_30.py

TempAnonymous/Context_Analysis

bbeba1ed7ea7001c22a12721fc4f390d4cc01a6e

[ "MIT" ]

null

import os import warnings warnings.filterwarnings("ignore") ############################################# # BenchMark emb-linear-add ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_method:emb-linear-add,graph:Distance-Correlation-Interaction,mark:emb_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_method:emb-linear-add,graph:Distance-Correlation-Interaction,mark:emb_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_method:emb-linear-add,graph:Distance-Correlation-Line,mark:emb_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_method:emb-linear-add,graph:Distance-Correlation,mark:emb_linear_add,MergeIndex:1') ############################################# # BenchMark emb-linear-gating ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_method:emb-linear-gating,graph:Distance-Correlation-Interaction,mark:emb_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_method:emb-linear-gating,graph:Distance-Correlation-Interaction,mark:emb_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_method:emb-linear-gating,graph:Distance-Correlation-Line,mark:emb_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_method:emb-linear-gating,graph:Distance-Correlation,mark:emb_linear_gating,MergeIndex:1') ############################################# # BenchMark multi-linear-add ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_method:multi-linear-add,graph:Distance-Correlation-Interaction,mark:multi_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_method:multi-linear-add,graph:Distance-Correlation-Interaction,mark:multi_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_method:multi-linear-add,graph:Distance-Correlation-Line,mark:multi_linear_add,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_method:multi-linear-add,graph:Distance-Correlation,mark:multi_linear_add,MergeIndex:1') ############################################# # BenchMark multi-linear-gating ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_method:multi-linear-gating,graph:Distance-Correlation-Interaction,mark:multi_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_method:multi-linear-gating,graph:Distance-Correlation-Interaction,mark:multi_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_method:multi-linear-gating,graph:Distance-Correlation-Line,mark:multi_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_method:multi-linear-gating,graph:Distance-Correlation,mark:multi_linear_gating,MergeIndex:1') ############################################# # BenchMark lstm-not-concat ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_lstm_len:4,external_method:lstm-not-concat,graph:Distance-Correlation-Interaction,mark:lstm4_not_concat,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_lstm_len:4,external_method:lstm-not-concat,graph:Distance-Correlation-Interaction,mark:lstm4_not_concat,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_lstm_len:4,external_method:lstm-not-concat,graph:Distance-Correlation-Line,mark:lstm4_not_concat,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_lstm_len:4,external_method:lstm-not-concat,graph:Distance-Correlation,mark:lstm4_not_concat,MergeIndex:1') ############################################# # BenchMark lstm-linear-gating ############################################# os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d bike_chicago.data.yml ' '-p external_lstm_len:4,external_method:lstm-linear-gating,graph:Distance-Correlation-Interaction,mark:lstm4_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d didi_chengdu.data.yml ' '-p external_lstm_len:4,external_method:lstm-linear-gating,graph:Distance-Correlation-Interaction,mark:lstm4_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d metro_shanghai.data.yml ' '-p external_lstm_len:4,external_method:lstm-linear-gating,graph:Distance-Correlation-Line,mark:lstm4_linear_gating,MergeIndex:6') os.system('python STMeta_Obj.py -m STMeta_v1.model.yml -d chargestation_beijing.data.yml ' '-p external_lstm_len:4,external_method:lstm-linear-gating,graph:Distance-Correlation,mark:lstm4_linear_gating,MergeIndex:1')

55.046729

147

0.707131

825

5,890

4.854545

0.058182

0.080899

0.083895

0.11985

0.976779

0.93608

0.9201

0.915106

0

0.011936

0.089643

5,890

106

148

55.566038

0.734987

0.027844

0

0.705882

0

0.470588

0.853833

0.616335

0

1

0

true

0

0.039216

0

0.039216

0

null

0

1

0

1

0

1

null

0

1

0

11

68d9fe0fbefb5c6f1eca6bede9415f23a2f4dec3

1,431

py

Python

python/8.py

kstrikis/euler

a09cefc0762e093520f8c34aa059b618e3a62452

[ "Unlicense" ]

null

python/8.py

kstrikis/euler

a09cefc0762e093520f8c34aa059b618e3a62452

[ "Unlicense" ]

null

python/8.py

kstrikis/euler

a09cefc0762e093520f8c34aa059b618e3a62452

[ "Unlicense" ]

null

## kstrikis' solution for project euler problem 8 ## released under The Unlicense ## Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this product? from decimal import Decimal number = "7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450" lgprod = 0 for a in range(len(number)-12): prod = 1 for b in range(13): prod = prod * Decimal(number[a+b]) if(prod>lgprod): lgprod = prod print(lgprod)

102.214286

1,011

0.907757

68

1,431

19.102941

0.647059

0.020015

0

0.756171

0.065688

1,431

13

1,012

110.076923

0.215408

0.140461

0

0.817661

0

1

0

1

0

false

0

0.1

0

0.1

0

1

null

0

1

0

1

0

1

null

1

0

8

68e86cd8391a48d18dbbd22ad22fae5d3abbf590

9,790

py

Python

tests/sample_proof_key_exists.py

unparalleled-js/py-trie

23e77052406836dc34b8472ddbc46430250e1750

[ "MIT" ]

83

2017-11-27T09:22:39.000Z

2022-03-21T10:44:43.000Z

tests/sample_proof_key_exists.py

unparalleled-js/py-trie

23e77052406836dc34b8472ddbc46430250e1750

[ "MIT" ]

95

2017-12-05T18:06:50.000Z

2022-03-11T03:51:18.000Z

tests/sample_proof_key_exists.py

unparalleled-js/py-trie

23e77052406836dc34b8472ddbc46430250e1750

[ "MIT" ]

46

2017-11-28T16:53:17.000Z

2022-03-28T17:51:12.000Z

# flake8: NOQA: E501 # This proves that the given key (which is an account) exists on the trie rooted at this state # root. It was obtained by querying geth via the LES protocol state_root = b'Gu\xd8\x85\xf5/\x83:e\xf5\x9e0\x0b\xce\x86J\xcc\xe4.0\xc8#\xdaW\xb3\xbd\xd0).\x91\x17\xe8' key = b'\x9b\xbf\xc3\x08Z\xd0\xd47\x84\xe6\xe4S4ndG|\xac\xa3\x0f^7\xd5nv\x14\x9e\x98\x84\xe7\xc2\x97' proof = ([b'\x01\xb2\xcf/\xa7&\xef{\xec9c%\xed\xeb\x9b)\xe9n\xb5\xd5\x0e\x8c\xa9A\xc1:-{<2$)', b'\xa2\xbab\xe5J\x88\xa1\x8b\x90y\xa5yW\xd7G\x13\x16\xec\xb3\xb6\x87S9okV\xa3\rlC\xbfU', b'\xd6\x06\x92\x9e\x0b\xd310|\xbeV\x9d\xb4r\xdf0\xa5Q\xfb\xec\xb9I\x8c\x96r\x81\xeb\xefX7_l', b'\xa8\x88\xed@\x04\x7f\xa6\xbe&\x89&\x89T\t+\xac\xb8w\x8a\xebn\x16\x0c\xe1n\xb4?\xad\x14\xfdF\xff', b'\xc9\t\xd0\xaa\xb0:P\xdc\xea\xedX%\x04\x9a\xbe\x1f\x16\x0cf\xbc\x04P#@\xfd\xd60\xad\xecK\x8b\x08', b'x\xff\xb2\x9ajO\xbc\x1bjR\x80$I\xe6\x95\xf6Tow\x82\xf9\x01\xa8V\xa9\xaa4\xa6`\x88\xf9\x10', b'I\x1cQc\x8a\xeda\xf8\xd1D\x01GT)\xc9\x02O\xef\x8d\xcc\\\xf9\xe6}\x8a~\xcc\x98~\xd5\xd6\xb6', b"U'\xa2\xa0 \xe4\xb1\xb6\xc3\xcd4C_\x9c]\xb3P\xa8w\xef\x8c\xde\xc2\x02^v\xcd\x12\xed%\x89\xa5", b'(\xa6x\xfa\xbe\xc3\x9a\xae\xaa\xe9\xbcv#u\\\xdfo\x14\x9a3\xbc\x89c\xc1\xfe\xdf[{|\x02P\x03', b'\xcf5\x07\x8f3\xa9\x1f\x19Q\xbb\x11\x8a\xb0\x97\xbe\x93\xb2\xd5~\xe2\xe06\x07\xc37\x08vg\x80 BD', b'U\x8e/\x95&\n\xc5\xf1\xd4\xc3\xb9\xa84Rd\xaa\x80\xfe8\xf1\xcf G\xcc\xe3\x99\x01\x07\xceH\x9a`', b'W\x1f\xb5\x1c\xec\xf7\x0b\x86\x15\r\xf9\xf9\x94\xcd|\xe6B\x9f\xa8l\x8d]D\xf7\xba\xee:\xc0\\\x11\xb8\x08', b'\xf5i\xee)\xc4\xd24\xfc\x8f\xba\xc0vS\x1dU>\xccz\xd18\n\xa2+\n\xcf\xe2i*\xee\x18\xe8\xc1', b'\x9dmSX\x1e\xee\xf7`\x1d\x0cO\xfcF\xe4\xbd\x0cE2\x10H6\xf0\x93|\xd5z\xe7=\xebbJ\xd6', b'u\x08\x92\x08\xa5Nl\x938\x03\xa3\xe2O\xe8\xfe\xb1\xc4\x87\x8c\xb8q\x9eb\x89b\x96\x98\xd7\xf22\xb9\xa2', b'\xa6V\xb5?\xcc\xd2\xc8*ME\xe7\xcf\xf8\xad\xf8\xdb\xe7\xf8\xf6D\xd5<\x1c\x95F\x13\x0e\x06rz\xe5m', b''], [b"\xb3\x03\xa9\xc11\x87mQ\xa1I2D4jg\xfe\xd0%k\xf2\r]\xb0\x0e\xeb'\x17\xedx\xc9Uj", b'L/\r$7-\xa5\xdf x\x9c\xbc\xc4\x99\x1e\xc5\xd8\xb5\xaf\xd1\xd1\xae\xe6L\xeco\xc4\xe2RUe\r', b'\xbeSp\xf5\xef\x02\xcd\x83\xb2\x0b\xa06\xfd\xca\xbb\xed_\xf2}\xf7\xea\xb3\x84\x17\xed\xcc\x19mF\x13(\xf3', b"\xfb$IYR\x9f\x04p\x01\x1d}\x88\x0b\xed'\x8e%\x9b\xc9\xeaN_\xab\xf9\xc9\x9d\xac\xa9\xb3\t\x1eq", b'\xaab\xeb\x14\xc2\xf6}%\xaa+0\xb5\xc1\x0f< \xc5ma\xb1c\xeb\xdd\xca\xc0\x90\xe2L\x8b\xe9\xfe/', b'\x91l\x9d\xa2\x84\xbf\xc1\x05\xe2S\x0e\xc9`\xc0^}Q!\xc4ml-\xec\xf4R$\xf6\x8a\xd3\xc6\xf1j', b'\xf3\x13\xde\xe0L\xdb\x96E`Q\xdf\xa1\x13\x01b5\xe4k\xde\xde\xbf\xb10\xaf\xe61Z\xdbZ\xd47\xf4', b'\t\x81\xb0\xea*\xec\xd0\xc3\x16\xee\xed~\xdc\x98e\x90\xf2~p\xbbSY\x19\xcfl\xc4)\x01\xc2\xd9\xc91', b'-\xda%\x8a\xc5jA-\xe5 lIp\xbe\xb3h\x98\x0f\x80q\xed\xab\x89KN\xdd\xa6\xcb;\x98\xb08', b'\x13\x97\x12f\xa31\xfa}\xf1\xfe\x19\xfa\x0b\xe6\x89\x9a\xcb\xf5\xed\xf3Q\x98O=\xa3\xb0e/\xd9\x9fy\x08', b'f\xba%\xfb\xbfE\x1d]\xb3\x05\xe4$\xa5\xd2G\xecc\xe5#\x0f,\x91\x8bN9a\x8a\xd1L\x16l\xa5', b'#p\x15\x8bU\x04\x88/K|4a\xfc\x0e.Zm^{\x15uk\x8d\xe4_\xfe\xee\xae\xb99\xd1\x8e', b'C \x9f\xb3y\xf3d.\x8b\t\x1cF\x9eL\x08\x07y\x08\xb9\xe1\xffM\x87\xfd\xd6\xfd\xdb\x8f\x94\x9e\x88\xc2', b'\x17X\x1f/\x8b\x82\xf5\xe4\x02\x84}\xbe\x9bz` \x94\'"_\x9c\xff\x06\t>\x8a\xd7oK\xf9\xf5w', b'6Q\x8db\xd8\\\x84_Rin\x18\x1f\x17\x89\x7f@\xd6\xbb%>\xafa\'\x80A\xa7\xd8}d\x07"', b'\xccgm\xf7\x05\xc8\xe4G\xf4\xb3\x18\xc7\\.\x0b\xa25]\xdc\x80w\xda\xc9;\xde\x9b\x03\xa0LS\xce\x8c', b''], [b'\xe4\xd3\x15\xe0\xaa\x0f\xf9\xd0\xa6\xc2\xc8B_\xaf"0\x8c\xea;\x91\xe4E\x04\xec\x901yZ\xd6>\xadc', b'wM\xce\x16JS:\xe96\x98\x12|\xa0\xc9~G\xbb\xc7u8\xc8\x93\x9b\x05\x92yh\xaa\xda\x94NK', b'\x89\xc7\xa2\xbd\xe1\xda\x06$|\xde\x03\xd9RS\x90\x84\xe7\x05\x0cc\xdfy\xb0\xfb@\x065\xdb8\xa9\xef\x1f', b'@\x11>\xe8\xb8\x19\xb7\xc7@\x92m$\x93 \x08\xc5\x15\xbd\x97\xb0;\xf5\x05q;\xb5\xc69\xd3E\xc4\x0e', b'\xd5_ol\x05o\x8e\xf0V\xd2\xa0n\xe7CxR\xc9\x92HTQhkc\x10K\xad\xfdU\xe9\x97\x8f', b'v\x7f\xc5KB\xdaYS\xa1\xbf 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py

Python

polls/slow_import.py

mrfleap/django-turboreload

9c90ca9a46d54c421f18ff0dfcfd3e8805dd593d

[ "MIT" ]

null

polls/slow_import.py

mrfleap/django-turboreload

9c90ca9a46d54c421f18ff0dfcfd3e8805dd593d

[ "MIT" ]

null

polls/slow_import.py

mrfleap/django-turboreload

9c90ca9a46d54c421f18ff0dfcfd3e8805dd593d

[ "MIT" ]

null

import time print("Slow import starting") time.sleep(0.05) print("Slow import finished")

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py

Python

projectinfo/test.py

lifecake/Project-build-info

6594c41933af3351d5ad62510cd84f004f675f9a

[ "Apache-2.0" ]

null

projectinfo/test.py

lifecake/Project-build-info

6594c41933af3351d5ad62510cd84f004f675f9a

[ "Apache-2.0" ]

null

projectinfo/test.py

lifecake/Project-build-info

6594c41933af3351d5ad62510cd84f004f675f9a

[ "Apache-2.0" ]

null

import json # # { # "applicationId":"com.neulion.f", # "applicationName":"f", # "applicationVersion":"8.0602", # "librarys": [ # { # "libraryName":"track-core", # "libraryVersion":"6" # },{ # "libraryName":"track-ga", # "libraryVersion":"6" # },{ # "libraryName":"player", # "libraryVersion":"5.0.0" # } # ] # } tempdata = [{"package":"com.neulion.firetv.ufc.android.amazon.dev","packageName":"ufc-tv","productFlavorName":"amazon","packageVersionCode":"103","packageVersionName":"8.0613","packageTargetSdk":"27","packageMiniSdk":"21","deepLinkScheme":"amazon_ufctv","packageMappingUrl":"","libraryCoordinateList":[{"group":"com.android.tools.lint","name":"lint-gradle","currentVersion":"26.1.2"},{"group":"org.jetbrains.kotlin","name":"kotlin-annotation-processing-gradle","currentVersion":"1.2.41"},{"group":"com.crashlytics.sdk.android","name":"crashlytics","currentVersion":"2.8.0"},{"group":"com.squareup.leakcanary","name":"leakcanary-android","currentVersion":"1.5.4"},{"group":"com.squareup.leakcanary","name":"leakcanary-android-no-op","currentVersion":"1.5.4"},{"group":"org.jetbrains.kotlin","name":"kotlin-stdlib-jdk8","currentVersion":"1.2.41"},{"group":"com.google.android.gms","name":"play-services-analytics","currentVersion":"11.8.0"},{"group":"com.android.databinding","name":"baseLibrary","currentVersion":"3.1.2"},{"group":"com.neulion.android.app","name":"core","currentVersion":"0.5.3-SNAPSHOT"},{"group":"com.neulion.android.iap","name":"iap-amazon","currentVersion":"2.1.0-SNAPSHOT"},{"group":"android.arch.lifecycle","name":"extensions","currentVersion":"1.1.1"},{"group":"com.android.support","name":"support-annotations","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"adapters","currentVersion":"3.1.2"},{"group":"com.android.support","name":"recyclerview-v7","currentVersion":"27.0.2"},{"group":"com.jakewharton","name":"butterknife-compiler","currentVersion":"8.8.1"},{"group":"com.neulion.android","name":"service-v5","currentVersion":"3.0.12"},{"group":"com.android.databinding","name":"library","currentVersion":"3.1.2"},{"group":"com.android.support","name":"appcompat-v7","currentVersion":"27.0.2"},{"group":"com.android.support","name":"leanback-v17","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"compiler","currentVersion":"3.1.2"},{"group":"com.jakewharton","name":"butterknife","currentVersion":"8.8.1"},{"group":"com.neulion.android.media","name":"NeuPlayer","currentVersion":"4.7.2-SNAPSHOT"},{"group":"com.android.support","name":"design","currentVersion":"27.0.2"},{"group":"com.android.support","name":"multidex-instrumentation","currentVersion":"1.0.2"},{"group":"com.android.support","name":"cardview-v7","currentVersion":"27.0.2"},{"group":"com.neulion.android.iap","name":"iap-google","currentVersion":"2.1.0-SNAPSHOT"},{"group":"com.neulion.android.tracking","name":"tracker-ga","currentVersion":"4.3.2"},{"group":"com.neulion.android","name":"uikit-fresco","currentVersion":"1.1.12-SNAPSHOT"},{"group":"com.neulion.android","name":"appengine","currentVersion":"2.4.0"},{"group":"com.android.support","name":"multidex","currentVersion":"1.0.2"},{"group":"com.android.support","name":"support-v4","currentVersion":"27.0.2"},{"group":"com.android.support.constraint","name":"constraint-layout","currentVersion":"1.1.0"},{"group":"uk.co.chrisjenx","name":"calligraphy","currentVersion":"2.3.0"},{"group":"com.neulion.android","name":"commonparser","currentVersion":"3.0.4"}]},{"package":"com.neulion.firetv.ufc.android.amazon.dev","packageName":"ufc-tv","productFlavorName":"google","packageVersionCode":"103","packageVersionName":"8.0613","packageTargetSdk":"27","packageMiniSdk":"21","deepLinkScheme":"google_ufctv","packageMappingUrl":"","libraryCoordinateList":[{"group":"com.android.tools.lint","name":"lint-gradle","currentVersion":"26.1.2"},{"group":"org.jetbrains.kotlin","name":"kotlin-annotation-processing-gradle","currentVersion":"1.2.41"},{"group":"com.crashlytics.sdk.android","name":"crashlytics","currentVersion":"2.8.0"},{"group":"com.squareup.leakcanary","name":"leakcanary-android","currentVersion":"1.5.4"},{"group":"com.squareup.leakcanary","name":"leakcanary-android-no-op","currentVersion":"1.5.4"},{"group":"org.jetbrains.kotlin","name":"kotlin-stdlib-jdk8","currentVersion":"1.2.41"},{"group":"com.google.android.gms","name":"play-services-analytics","currentVersion":"11.8.0"},{"group":"com.android.databinding","name":"baseLibrary","currentVersion":"3.1.2"},{"group":"com.neulion.android.app","name":"core","currentVersion":"0.5.3-SNAPSHOT"},{"group":"com.neulion.android.iap","name":"iap-amazon","currentVersion":"2.1.0-SNAPSHOT"},{"group":"android.arch.lifecycle","name":"extensions","currentVersion":"1.1.1"},{"group":"com.android.support","name":"support-annotations","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"adapters","currentVersion":"3.1.2"},{"group":"com.android.support","name":"recyclerview-v7","currentVersion":"27.0.2"},{"group":"com.jakewharton","name":"butterknife-compiler","currentVersion":"8.8.1"},{"group":"com.neulion.android","name":"service-v5","currentVersion":"3.0.12"},{"group":"com.android.databinding","name":"library","currentVersion":"3.1.2"},{"group":"com.android.support","name":"appcompat-v7","currentVersion":"27.0.2"},{"group":"com.android.support","name":"leanback-v17","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"compiler","currentVersion":"3.1.2"},{"group":"com.jakewharton","name":"butterknife","currentVersion":"8.8.1"},{"group":"com.neulion.android.media","name":"NeuPlayer","currentVersion":"4.7.2-SNAPSHOT"},{"group":"com.android.support","name":"design","currentVersion":"27.0.2"},{"group":"com.android.support","name":"multidex-instrumentation","currentVersion":"1.0.2"},{"group":"com.android.support","name":"cardview-v7","currentVersion":"27.0.2"},{"group":"com.neulion.android.iap","name":"iap-google","currentVersion":"2.1.0-SNAPSHOT"},{"group":"com.neulion.android.tracking","name":"tracker-ga","currentVersion":"4.3.2"},{"group":"com.neulion.android","name":"uikit-fresco","currentVersion":"1.1.12-SNAPSHOT"},{"group":"com.neulion.android","name":"appengine","currentVersion":"2.4.0"},{"group":"com.android.support","name":"multidex","currentVersion":"1.0.2"},{"group":"com.android.support","name":"support-v4","currentVersion":"27.0.2"},{"group":"com.android.support.constraint","name":"constraint-layout","currentVersion":"1.1.0"},{"group":"uk.co.chrisjenx","name":"calligraphy","currentVersion":"2.3.0"},{"group":"com.neulion.android","name":"commonparser","currentVersion":"3.0.4"}]},{"package":"com.neulion.firetv.ufc.android.amazon.dev","packageName":"ufc-tv","productFlavorName":"prod","packageVersionCode":"103","packageVersionName":"8.0613","packageTargetSdk":"27","packageMiniSdk":"21","deepLinkScheme":"prod_ufctv","packageMappingUrl":"","libraryCoordinateList":[{"group":"com.android.tools.lint","name":"lint-gradle","currentVersion":"26.1.2"},{"group":"org.jetbrains.kotlin","name":"kotlin-annotation-processing-gradle","currentVersion":"1.2.41"},{"group":"com.crashlytics.sdk.android","name":"crashlytics","currentVersion":"2.8.0"},{"group":"com.squareup.leakcanary","name":"leakcanary-android","currentVersion":"1.5.4"},{"group":"com.squareup.leakcanary","name":"leakcanary-android-no-op","currentVersion":"1.5.4"},{"group":"org.jetbrains.kotlin","name":"kotlin-stdlib-jdk8","currentVersion":"1.2.41"},{"group":"com.google.android.gms","name":"play-services-analytics","currentVersion":"11.8.0"},{"group":"com.android.databinding","name":"baseLibrary","currentVersion":"3.1.2"},{"group":"com.neulion.android.app","name":"core","currentVersion":"0.5.3-SNAPSHOT"},{"group":"com.neulion.android.iap","name":"iap-amazon","currentVersion":"2.1.0-SNAPSHOT"},{"group":"android.arch.lifecycle","name":"extensions","currentVersion":"1.1.1"},{"group":"com.android.support","name":"support-annotations","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"adapters","currentVersion":"3.1.2"},{"group":"com.android.support","name":"recyclerview-v7","currentVersion":"27.0.2"},{"group":"com.jakewharton","name":"butterknife-compiler","currentVersion":"8.8.1"},{"group":"com.neulion.android","name":"service-v5","currentVersion":"3.0.12"},{"group":"com.android.databinding","name":"library","currentVersion":"3.1.2"},{"group":"com.android.support","name":"appcompat-v7","currentVersion":"27.0.2"},{"group":"com.android.support","name":"leanback-v17","currentVersion":"27.0.2"},{"group":"com.android.databinding","name":"compiler","currentVersion":"3.1.2"},{"group":"com.jakewharton","name":"butterknife","currentVersion":"8.8.1"},{"group":"com.neulion.android.media","name":"NeuPlayer","currentVersion":"4.7.2-SNAPSHOT"},{"group":"com.android.support","name":"design","currentVersion":"27.0.2"},{"group":"com.android.support","name":"multidex-instrumentation","currentVersion":"1.0.2"},{"group":"com.android.support","name":"cardview-v7","currentVersion":"27.0.2"},{"group":"com.neulion.android.iap","name":"iap-google","currentVersion":"2.1.0-SNAPSHOT"},{"group":"com.neulion.android.tracking","name":"tracker-ga","currentVersion":"4.3.2"},{"group":"com.neulion.android","name":"uikit-fresco","currentVersion":"1.1.12-SNAPSHOT"},{"group":"com.neulion.android","name":"appengine","currentVersion":"2.4.0"},{"group":"com.android.support","name":"multidex","currentVersion":"1.0.2"},{"group":"com.android.support","name":"support-v4","currentVersion":"27.0.2"},{"group":"com.android.support.constraint","name":"constraint-layout","currentVersion":"1.1.0"},{"group":"uk.co.chrisjenx","name":"calligraphy","currentVersion":"2.3.0"},{"group":"com.neulion.android","name":"commonparser","currentVersion":"3.0.4"}]}] print(type(tempdata)) print(len(tempdata)) for data in tempdata: print(data) print(type(data)) db = get_db() error = None if dict1['package'] is None: error = 'Missing package' elif dict1['packageName'] is None: error = 'Missing packageName' elif dict1['packageVersionCode'] is None: error = 'Missing packageVersionCode' elif dict1['libraryCoordinateList'] is None: error = 'Missing Library info' # if already exist just update. elif db.execute( 'SELECT package, packageVersionName, productFlavorName FROM Package WHERE package = ? and ' 'packageVersionName = ? and productFlavorName = ?', (dict1['package'], dict1['packageVersionName'], dict1['productFlavorName'])).fetchone() is not None: # print('Found') db.execute( 'UPDATE Package SET packageName = ?, packageVersionCode = ?,productFlavorName = ?, packageTargetSdk = ?, ' 'packageMiniSdk = ?, packageMappingUrl = ?, deepLinkScheme = ? WHERE package = ? and ' 'packageVersionName = ?', (dict1['packageName'], dict1['packageVersionCode'], dict1['productFlavorName'], dict1['packageTargetSdk'], dict1['packageMiniSdk'], dict1['packageMappingUrl'], dict1['deepLinkScheme'], dict1['package'], dict1['packageVersionName']) ) db.execute( 'UPDATE Package SET date = datetime(\'now\', \'localtime\') WHERE package = ? and ' 'packageVersionName = ?', (dict1['package'], dict1['packageVersionName']) ) db.commit() id = db.execute( 'select id from PackageLibrary WHERE package = ? and packageVersionName = ? and productFlavorName = ?', (dict1['package'], dict1['packageVersionName'], dict1['productFlavorName'])) pids = [dict(id=row[0]) for row in id.fetchall()] ids = [] for item in pids: ids.append(item['id']) # print(ids) i = 0 for dict2 in dict1['libraryCoordinateList']: db.execute( 'UPDATE PackageLibrary SET package = ?, packageName = ?, productFlavorName = ?, packageVersionName = ?, ' 'libraryGroup = ?, libraryName = ?, libraryVersion = ? WHERE package = ? and ' 'packageVersionName = ? and id = ?', (dict1['package'], dict1['packageName'], dict1['productFlavorName'], dict1['packageVersionName'], dict2['group'], dict2['name'], dict2['currentVersion'], dict1['package'], dict1['packageVersionName'], ids[i]) ) db.commit() i = i + 1 # print('Update') error = 'Project Info Updated' # print(error) # insert new data

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0.084946

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6b6142b6243371bed9fd9e9b448dcf232f11c4b5

12,541

py

Python

B2G/gecko/dom/bindings/parser/tests/test_attr.py

wilebeast/FireFox-OS

43067f28711d78c429a1d6d58c77130f6899135f

[ "Apache-2.0" ]

3

2015-08-31T15:24:31.000Z

2020-04-24T20:31:29.000Z

B2G/gecko/dom/bindings/parser/tests/test_attr.py

wilebeast/FireFox-OS

43067f28711d78c429a1d6d58c77130f6899135f

[ "Apache-2.0" ]

null

B2G/gecko/dom/bindings/parser/tests/test_attr.py

wilebeast/FireFox-OS

43067f28711d78c429a1d6d58c77130f6899135f

[ "Apache-2.0" ]

3

2015-07-29T07:17:15.000Z

2020-11-04T06:55:37.000Z

import WebIDL def WebIDLTest(parser, harness): testData = [("::TestAttr%s::b", "b", "Byte%s", False), ("::TestAttr%s::rb", "rb", "Byte%s", True), ("::TestAttr%s::o", "o", "Octet%s", False), ("::TestAttr%s::ro", "ro", "Octet%s", True), ("::TestAttr%s::s", "s", "Short%s", False), ("::TestAttr%s::rs", "rs", "Short%s", True), ("::TestAttr%s::us", "us", "UnsignedShort%s", False), ("::TestAttr%s::rus", "rus", "UnsignedShort%s", True), ("::TestAttr%s::l", "l", "Long%s", False), ("::TestAttr%s::rl", "rl", "Long%s", True), ("::TestAttr%s::ul", "ul", "UnsignedLong%s", False), ("::TestAttr%s::rul", "rul", "UnsignedLong%s", True), ("::TestAttr%s::ll", "ll", "LongLong%s", False), ("::TestAttr%s::rll", "rll", "LongLong%s", True), ("::TestAttr%s::ull", "ull", "UnsignedLongLong%s", False), ("::TestAttr%s::rull", "rull", "UnsignedLongLong%s", True), ("::TestAttr%s::str", "str", "String%s", False), ("::TestAttr%s::rstr", "rstr", "String%s", True), ("::TestAttr%s::obj", "obj", "Object%s", False), ("::TestAttr%s::robj", "robj", "Object%s", True), ("::TestAttr%s::object", "object", "Object%s", False), ("::TestAttr%s::f", "f", "Float%s", False), ("::TestAttr%s::rf", "rf", "Float%s", True)] parser.parse(""" interface TestAttr { attribute byte b; readonly attribute byte rb; attribute octet o; readonly attribute octet ro; attribute short s; readonly attribute short rs; attribute unsigned short us; readonly attribute unsigned short rus; attribute long l; readonly attribute long rl; attribute unsigned long ul; readonly attribute unsigned long rul; attribute long long ll; readonly attribute long long rll; attribute unsigned long long ull; readonly attribute unsigned long long rull; attribute DOMString str; readonly attribute DOMString rstr; attribute object obj; readonly attribute object robj; attribute object _object; attribute float f; readonly attribute float rf; }; interface TestAttrNullable { attribute byte? b; readonly attribute byte? rb; attribute octet? o; readonly attribute octet? ro; attribute short? s; readonly attribute short? rs; attribute unsigned short? us; readonly attribute unsigned short? rus; attribute long? l; readonly attribute long? rl; attribute unsigned long? ul; readonly attribute unsigned long? rul; attribute long long? ll; readonly attribute long long? rll; attribute unsigned long long? ull; readonly attribute unsigned long long? rull; attribute DOMString? str; readonly attribute DOMString? rstr; attribute object? obj; readonly attribute object? robj; attribute object? _object; attribute float? f; readonly attribute float? rf; }; interface TestAttrArray { attribute byte[] b; readonly attribute byte[] rb; attribute octet[] o; readonly attribute octet[] ro; attribute short[] s; readonly attribute short[] rs; attribute unsigned short[] us; readonly attribute unsigned short[] rus; attribute long[] l; readonly attribute long[] rl; attribute unsigned long[] ul; readonly attribute unsigned long[] rul; attribute long long[] ll; readonly attribute long long[] rll; attribute unsigned long long[] ull; readonly attribute unsigned long long[] rull; attribute DOMString[] str; readonly attribute DOMString[] rstr; attribute object[] obj; readonly attribute object[] robj; attribute object[] _object; attribute float[] f; readonly attribute float[] rf; }; interface TestAttrNullableArray { attribute byte[]? b; readonly attribute byte[]? rb; attribute octet[]? o; readonly attribute octet[]? ro; attribute short[]? s; readonly attribute short[]? rs; attribute unsigned short[]? us; readonly attribute unsigned short[]? rus; attribute long[]? l; readonly attribute long[]? rl; attribute unsigned long[]? ul; readonly attribute unsigned long[]? rul; attribute long long[]? ll; readonly attribute long long[]? rll; attribute unsigned long long[]? ull; readonly attribute unsigned long long[]? rull; attribute DOMString[]? str; readonly attribute DOMString[]? rstr; attribute object[]? obj; readonly attribute object[]? robj; attribute object[]? _object; attribute float[]? f; readonly attribute float[]? rf; }; interface TestAttrArrayOfNullableTypes { attribute byte?[] b; readonly attribute byte?[] rb; attribute octet?[] o; readonly attribute octet?[] ro; attribute short?[] s; readonly attribute short?[] rs; attribute unsigned short?[] us; readonly attribute unsigned short?[] rus; attribute long?[] l; readonly attribute long?[] rl; attribute unsigned long?[] ul; readonly attribute unsigned long?[] rul; attribute long long?[] ll; readonly attribute long long?[] rll; attribute unsigned long long?[] ull; readonly attribute unsigned long long?[] rull; attribute DOMString?[] str; readonly attribute DOMString?[] rstr; attribute object?[] obj; readonly attribute object?[] robj; attribute object?[] _object; attribute float?[] f; readonly attribute float?[] rf; }; interface TestAttrNullableArrayOfNullableTypes { attribute byte?[]? b; readonly attribute byte?[]? rb; attribute octet?[]? o; readonly attribute octet?[]? ro; attribute short?[]? s; readonly attribute short?[]? rs; attribute unsigned short?[]? us; readonly attribute unsigned short?[]? rus; attribute long?[]? l; readonly attribute long?[]? rl; attribute unsigned long?[]? ul; readonly attribute unsigned long?[]? rul; attribute long long?[]? ll; readonly attribute long long?[]? rll; attribute unsigned long long?[]? ull; readonly attribute unsigned long long?[]? rull; attribute DOMString?[]? str; readonly attribute DOMString?[]? rstr; attribute object?[]? obj; readonly attribute object?[]? robj; attribute object?[]? _object; attribute float?[]? f; readonly attribute float?[]? rf; }; """) results = parser.finish() def checkAttr(attr, QName, name, type, readonly): harness.ok(isinstance(attr, WebIDL.IDLAttribute), "Should be an IDLAttribute") harness.ok(attr.isAttr(), "Attr is an Attr") harness.ok(not attr.isMethod(), "Attr is not an method") harness.ok(not attr.isConst(), "Attr is not a const") harness.check(attr.identifier.QName(), QName, "Attr has the right QName") harness.check(attr.identifier.name, name, "Attr has the right name") harness.check(str(attr.type), type, "Attr has the right type") harness.check(attr.readonly, readonly, "Attr's readonly state is correct") harness.ok(True, "TestAttr interface parsed without error.") harness.check(len(results), 6, "Should be six productions.") iface = results[0] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttr", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttr", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "", name, type % "", readonly) iface = results[1] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttrNullable", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttrNullable", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "Nullable", name, type % "OrNull", readonly) iface = results[2] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttrArray", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttrArray", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "Array", name, type % "Array", readonly) iface = results[3] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttrNullableArray", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttrNullableArray", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "NullableArray", name, type % "ArrayOrNull", readonly) iface = results[4] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttrArrayOfNullableTypes", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttrArrayOfNullableTypes", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "ArrayOfNullableTypes", name, type % "OrNullArray", readonly) iface = results[5] harness.ok(isinstance(iface, WebIDL.IDLInterface), "Should be an IDLInterface") harness.check(iface.identifier.QName(), "::TestAttrNullableArrayOfNullableTypes", "Interface has the right QName") harness.check(iface.identifier.name, "TestAttrNullableArrayOfNullableTypes", "Interface has the right name") harness.check(len(iface.members), len(testData), "Expect %s members" % len(testData)) attrs = iface.members for i in range(len(attrs)): data = testData[i] attr = attrs[i] (QName, name, type, readonly) = data checkAttr(attr, QName % "NullableArrayOfNullableTypes", name, type % "OrNullArrayOrNull", readonly) parser = parser.reset() threw = False try: parser.parse(""" interface A { [SetterInfallible] readonly attribute boolean foo; }; """) results = parser.finish() except Exception, x: threw = True harness.ok(threw, "Should not allow [SetterInfallible] on readonly attributes")

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118

0.58839

1,312

12,541

5.619665

0.096037

0.154483

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0.024413

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12,541

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0

1

0

null

0

1

0

7

6b9751b50b6295f2007b03754c615fad69738adc

12,378

py

Python

sasuke 34/J.py

conganhhcmus/Codelearn.io

cb698924fe9eac27fb8b2cef4e03c7619cb4fe81

[ "MIT" ]

null

sasuke 34/J.py

conganhhcmus/Codelearn.io

cb698924fe9eac27fb8b2cef4e03c7619cb4fe81

[ "MIT" ]

null

sasuke 34/J.py

conganhhcmus/Codelearn.io

cb698924fe9eac27fb8b2cef4e03c7619cb4fe81

[ "MIT" ]

null

a = arr = [[False for i in range(100)] for j in range(100)] ans = 0 sl = 0 block= [] def findLargestRectangle(blockNumber): global sl global block block = blockNumber for i in block: sl += i for i in range(1,9): find(i, 1) return ans def find(r,n): global ans global a # calc result if (((n - 1) * 4) % r == 0): l = (n - 1) * 4 // r check = True for i in range(l): for j in range(r): if (not a[i][j]): check = False break if (not check): break if (check and ans < (n-1)*4): ans = (n-1)*4 # condition end if (n > sl): return #fill - backtracking for i in range(80): for j in range(r): if (not a[i][j]): if (block[0] > 0): if (not a[i + 1][j] and not a[i + 2][j] and not a[i + 3][j]): a[i][j] = True a[i + 1][j] = True a[i + 2][j] = True a[i + 3][j] = True block[0]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 2][j] = False a[i + 3][j] = False block[0]+=1 if (j + 3 < r and not a[i][j + 1] and not a[i][j + 2] and not a[i][j + 3]): a[i][j] = True a[i][j + 1] = True a[i][j + 2] = True a[i][j + 3] = True block[0]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i][j + 2] = False a[i][j + 3] = False block[0]+=1 if (block[1] > 0): if (j + 1 < r and not a[i][j + 1] and not a[i + 1][j] and not a[i + 1][j + 1]): a[i][j] = True a[i][j + 1] = True a[i + 1][j] = True a[i + 1][j + 1] = True block[1]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i + 1][j] = False a[i + 1][j + 1] = False block[1]+=1 if (block[2] > 0): if (j + 2 < r and not a[i][j + 1] and not a[i][j + 2] and not a[i + 1][j + 2]): a[i][j] = True a[i][j + 1] = True a[i][j + 2] = True a[i + 1][j + 2] = True block[2]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i][j + 2] = False a[i + 1][j + 2] = False block[2]+=1 if (j + 1 < r and not a[i][j + 1] and not a[i + 1][j] and not a[i + 2][j]): a[i][j] = True a[i][j + 1] = True a[i + 1][j] = True a[i + 2][j] = True block[2]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i + 1][j] = False a[i + 2][j] = False block[2]+=1 if (j + 2 < r and not a[i + 1][j] and not a[i + 1][j + 1] and not a[i + 1][j + 2]): a[i][j] = True a[i + 1][j] = True a[i + 1][j + 1] = True a[i + 1][j + 2] = True block[2]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j + 1] = False a[i + 1][j + 2] = False block[2]+=1 if (j > 0): if (not a[i + 1][j] and not a[i + 2][j] and not a[i + 2][j - 1]): a[i][j] = True a[i + 1][j] = True a[i + 2][j] = True a[i + 2][j - 1] = True block[2]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 2][j] = False a[i + 2][j - 1] = False block[2]+=1 if (block[3] > 0): if (j + 2 < r and not a[i][j + 1] and not a[i + 1][j + 1] and not a[i + 1][j + 2]): a[i][j] = True a[i][j + 1] = True a[i + 1][j + 1] = True a[i + 1][j + 2] = True block[3]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i + 1][j + 1] = False a[i + 1][j + 2] = False block[3]+=1 if (j > 0): if (not a[i + 1][j] and not a[i + 1][j - 1] and not a[i + 2][j - 1]): a[i][j] = True a[i + 1][j] = True a[i + 1][j - 1] = True a[i + 2][j - 1] = True block[3]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j - 1] = False a[i + 2][j - 1] = False block[3]+=1 if (block[4] > 0): if (j + 1 < r and j > 0): if(not a[i][j + 1] and not a[i + 1][j] and not a[i + 1][j - 1]): a[i][j] = True a[i][j + 1] = True a[i + 1][j] = True a[i + 1][j - 1] = True block[4]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i + 1][j] = False a[i + 1][j - 1] = False block[4]+=1 if (j + 1 < r): if (not a[i + 1][j] and not a[i + 1][j + 1] and not a[i + 2][j + 1]): a[i][j] = True a[i + 1][j] = True a[i + 1][j + 1] = True a[i + 2][j + 1] = True block[4]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j + 1] = False a[i + 2][j + 1] = False block[4]+=1 if (block[5] > 0): if (j + 2 < r and not a[i][j + 1] and not a[i][j + 2] and not a[i + 1][j]): a[i][j] = True a[i][j + 1] = True a[i][j + 2] = True a[i + 1][j] = True block[5]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i][j + 2] = False a[i + 1][j] = False block[5]+=1 if (j + 1 < r and not a[i][j + 1] and not a[i + 1][j + 1] and not a[i + 2][j + 1]): a[i][j] = True a[i][j + 1] = True a[i + 1][j + 1] = True a[i + 2][j + 1] = True block[5]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i + 1][j + 1] = False a[i + 2][j + 1] = False block[5]+=1 if (j > 1): if(not a[i + 1][j] and not a[i + 1][j - 1] and not a[i + 1][j - 2]): a[i][j] = True a[i + 1][j] = True a[i + 1][j - 1] = True a[i + 1][j - 2] = True block[5]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j - 1] = False a[i + 1][j - 2] = False block[5]+=1 if (j + 1 < r): if (not a[i + 1][j] and not a[i + 2][j] and not a[i + 2][j + 1]): a[i][j] = True a[i + 1][j] = True a[i + 2][j] = True a[i + 2][j + 1] = True block[5]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 2][j] = False a[i + 2][j + 1] = False block[5]+=1 if (block[6] > 0): if (j + 2 < r and not a[i][j + 1] and not a[i][j + 2] and not a[i + 1][j + 1]): a[i][j] = True a[i][j + 1] = True a[i][j + 2] = True a[i + 1][j + 1] = True block[6]-=1 find(r, n + 1) a[i][j] = False a[i][j + 1] = False a[i][j + 2] = False a[i + 1][j + 1] = False block[6]+=1 if (j + 1 < r and not a[i + 1][j] and not a[i + 1][j + 1] and not a[i + 2][j]): a[i][j] = True a[i + 1][j] = True a[i + 1][j + 1] = True a[i + 2][j] = True block[6]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j + 1] = False a[i + 2][j] = False block[6]+=1 if (j > 0): if (not a[i + 1][j] and not a[i + 1][j - 1] and not a[i + 2][j]): a[i][j] = True a[i + 1][j] = True a[i + 1][j - 1] = True a[i + 2][j] = True block[6]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j - 1] = False a[i + 2][j] = False block[6]+=1 if (j + 1 < r and j > 0): if(not a[i + 1][j] and not a[i + 1][j + 1] and not a[i + 1][j - 1]): a[i][j] = True a[i + 1][j] = True a[i + 1][j - 1] = True a[i + 1][j + 1] = True block[6]-=1 find(r, n + 1) a[i][j] = False a[i + 1][j] = False a[i + 1][j - 1] = False a[i + 1][j + 1] = False block[6]+=1 return print(findLargestRectangle([0,0,1,1,0,1,0]))

42.979167

103

0.230166

1,555

12,378

1.832154

0.027653

0.148122

0.097929

0.130572

0.848719

0.835381

0.833977

0.829765

0.824149

0.822745

0

0.086339

0.630393

12,378

287

104

43.12892

0.536393

0.003555

0

0.770909

0

1

0.007273

false

0

0.014545

0.003636

0

1

null

0

1

0

1

0

1

0

null

0

9

6ba14f388ea2cd8e378e871024edd16eb5c1b8c8

4,462

py

Python

vgg/vgg.py

amir-saniyan/KerasNets

6031fd19b81a41422a3fea65d793509bac01092f

[ "MIT" ]

null

vgg/vgg.py

amir-saniyan/KerasNets

6031fd19b81a41422a3fea65d793509bac01092f

[ "MIT" ]

null

vgg/vgg.py

amir-saniyan/KerasNets

6031fd19b81a41422a3fea65d793509bac01092f

[ "MIT" ]

null

import tensorflow as tf def build_model(configuration='vgg19', input_shape=(224, 224, 3), num_classes=1000, dropout_rate=0.5): if configuration not in ['vgg11', 'vgg13', 'vgg16', 'vgg19']: raise Exception('Configuration should be one of these values: vgg11, vgg13, vgg16, vgg19') inputs = tf.keras.Input(shape=input_shape) x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(inputs) if configuration == 'vgg13' or configuration == 'vgg16' or configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding='valid')(x) x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg13' or configuration == 'vgg16' or configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding='valid')(x) x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg16' or configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding='valid')(x) x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg16' or configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding='valid')(x) x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg16' or configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) if configuration == 'vgg19': x = tf.keras.layers.Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation=tf.keras.activations.relu)(x) x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding='valid')(x) x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(units=4096, activation=tf.keras.activations.relu)(x) x = tf.keras.layers.Dropout(rate=dropout_rate)(x) x = tf.keras.layers.Dense(units=4096, activation=tf.keras.activations.relu)(x) x = tf.keras.layers.Dropout(rate=dropout_rate)(x) outputs = tf.keras.layers.Dense(units=num_classes, activation=tf.keras.layers.Softmax())(x) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) return model

49.032967

102

0.607127

575

4,462

4.66087

0.114783

0.125373

0.135821

0.865672

0.85709

0

0.058685

0.240027

4,462

90

103

49.577778

0.731643

0

0.827586

0

0.05827

0

1

0.017241

false

0

0.017241

0

0.051724

0

null

0

1

0

1

0

null

0

7

6bdaa8c6396ce9b1f3be8da8a1f72271f9224e4a

62,996

py

Python

demo/adif_pb2.py

k0swe/adif-json-protobuf

90cc62e38919611754e0dca15498d0444d6d5871

[ "Apache-2.0" ]

3

2020-10-08T16:11:59.000Z

2021-08-06T05:13:36.000Z

demo/adif_pb2.py

k0swe/adif-json-protobuf

90cc62e38919611754e0dca15498d0444d6d5871

[ "Apache-2.0" ]

4

2020-10-18T00:47:19.000Z

2021-08-18T23:27:58.000Z

demo/adif_pb2.py

k0swe/adif-json-protobuf

90cc62e38919611754e0dca15498d0444d6d5871

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: adif.proto from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='adif.proto', package='adif', syntax='proto3', serialized_options=b'Z\010.;adifpb', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\nadif.proto\x12\x04\x61\x64if\x1a\x1fgoogle/protobuf/timestamp.proto\"=\n\x04\x41\x64if\x12\x1c\n\x06header\x18\x01 \x01(\x0b\x32\x0c.adif.Header\x12\x17\n\x04qsos\x18\x02 \x03(\x0b\x32\t.adif.Qso\"\x82\x01\n\x06Header\x12\x14\n\x0c\x61\x64if_version\x18\x01 \x01(\t\x12\x35\n\x11\x63reated_timestamp\x18\x02 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x12\n\nprogram_id\x18\x03 \x01(\t\x12\x17\n\x0fprogram_version\x18\x04 \x01(\t\"\x82\x07\n\x03Qso\x12&\n\x0flogging_station\x18\x01 \x01(\x0b\x32\r.adif.Station\x12(\n\x11\x63ontacted_station\x18\x02 \x01(\x0b\x32\r.adif.Station\x12&\n\x0bpropagation\x18\x03 \x01(\x0b\x32\x11.adif.Propagation\x12\x0c\n\x04\x62\x61nd\x18\x04 \x01(\t\x12\x0f\n\x07\x62\x61nd_rx\x18\x05 \x01(\t\x12\x0c\n\x04\x66req\x18\x06 \x01(\x01\x12\x0f\n\x07\x66req_rx\x18\x07 \x01(\x01\x12\x0c\n\x04mode\x18\x08 \x01(\t\x12\x0f\n\x07submode\x18\t \x01(\t\x12\x13\n\x0b\x64istance_km\x18\n \x01(\r\x12+\n\x07time_on\x18\x0b \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12,\n\x08time_off\x18\x0c \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x0e\n\x06random\x18\r \x01(\x08\x12\x14\n\x0crst_received\x18\x0e \x01(\t\x12\x10\n\x08rst_sent\x18\x0f \x01(\t\x12\x0b\n\x03swl\x18\x10 \x01(\x08\x12\x10\n\x08\x63omplete\x18\x11 \x01(\t\x12\x0f\n\x07\x63omment\x18\x12 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type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='contacted_station', full_name='adif.Qso.contacted_station', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='propagation', full_name='adif.Qso.propagation', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='band', full_name='adif.Qso.band', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='band_rx', full_name='adif.Qso.band_rx', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='freq', full_name='adif.Qso.freq', index=5, number=6, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='freq_rx', full_name='adif.Qso.freq_rx', index=6, number=7, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='mode', full_name='adif.Qso.mode', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='submode', full_name='adif.Qso.submode', index=8, number=9, type=9, cpp_type=9, label=1, 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name='time_off', full_name='adif.Qso.time_off', index=11, number=12, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='random', full_name='adif.Qso.random', index=12, number=13, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='rst_received', full_name='adif.Qso.rst_received', index=13, number=14, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, 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message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='clublog', full_name='adif.Qso.clublog', index=25, number=26, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='hrdlog', full_name='adif.Qso.hrdlog', index=26, number=27, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qrzcom', full_name='adif.Qso.qrzcom', index=27, number=28, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='eqsl', full_name='adif.Qso.eqsl', index=28, number=29, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='lotw', full_name='adif.Qso.lotw', index=29, number=30, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='card', full_name='adif.Qso.card', index=30, number=31, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='app_defined', full_name='adif.Qso.app_defined', index=31, number=32, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[_QSO_APPDEFINEDENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=250, serialized_end=1148, ) _STATION = _descriptor.Descriptor( name='Station', full_name='adif.Station', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='op_call', full_name='adif.Station.op_call', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='op_name', full_name='adif.Station.op_name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='grid_square', full_name='adif.Station.grid_square', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='latitude', full_name='adif.Station.latitude', index=3, number=4, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='longitude', full_name='adif.Station.longitude', index=4, number=5, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='power', full_name='adif.Station.power', index=5, number=6, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='rig', full_name='adif.Station.rig', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='antenna', full_name='adif.Station.antenna', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='antenna_azimuth', full_name='adif.Station.antenna_azimuth', index=8, number=9, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='antenna_elevation', full_name='adif.Station.antenna_elevation', index=9, number=10, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='owner_call', full_name='adif.Station.owner_call', index=10, number=11, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='station_call', full_name='adif.Station.station_call', index=11, number=12, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='age', full_name='adif.Station.age', index=12, number=13, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='silent_key', full_name='adif.Station.silent_key', index=13, number=14, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qsl_via', full_name='adif.Station.qsl_via', index=14, number=15, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='address', full_name='adif.Station.address', index=15, number=16, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='street', full_name='adif.Station.street', index=16, number=17, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='city', full_name='adif.Station.city', index=17, number=18, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='postal_code', full_name='adif.Station.postal_code', index=18, number=19, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='county', full_name='adif.Station.county', index=19, number=20, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='state', full_name='adif.Station.state', index=20, number=21, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='country', full_name='adif.Station.country', index=21, number=22, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='dxcc', full_name='adif.Station.dxcc', index=22, number=23, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='continent', full_name='adif.Station.continent', index=23, number=24, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='email', full_name='adif.Station.email', index=24, number=25, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='web', full_name='adif.Station.web', index=25, number=26, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cq_zone', full_name='adif.Station.cq_zone', index=26, number=27, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='itu_zone', full_name='adif.Station.itu_zone', index=27, number=28, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='darc_dok', full_name='adif.Station.darc_dok', index=28, number=29, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='fists', full_name='adif.Station.fists', index=29, number=30, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='fists_cc', full_name='adif.Station.fists_cc', index=30, number=31, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='iota', full_name='adif.Station.iota', index=31, number=32, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='iota_island_id', full_name='adif.Station.iota_island_id', index=32, number=33, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='pfx', full_name='adif.Station.pfx', index=33, number=34, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='region', full_name='adif.Station.region', index=34, number=35, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='skcc', full_name='adif.Station.skcc', index=35, number=36, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sig', full_name='adif.Station.sig', index=36, number=37, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sig_info', full_name='adif.Station.sig_info', index=37, number=38, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sota_ref', full_name='adif.Station.sota_ref', index=38, number=39, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ten_ten', full_name='adif.Station.ten_ten', index=39, number=40, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='usaca_counties', full_name='adif.Station.usaca_counties', index=40, number=41, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='uksmg', full_name='adif.Station.uksmg', index=41, number=42, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='vucc_grids', full_name='adif.Station.vucc_grids', index=42, number=43, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1151, serialized_end=1910, ) _PROPAGATION = _descriptor.Descriptor( name='Propagation', full_name='adif.Propagation', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='propagation_mode', full_name='adif.Propagation.propagation_mode', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='a_index', full_name='adif.Propagation.a_index', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='k_index', full_name='adif.Propagation.k_index', index=2, number=3, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='solar_flux_index', full_name='adif.Propagation.solar_flux_index', index=3, number=4, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ant_path', full_name='adif.Propagation.ant_path', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='force_init', full_name='adif.Propagation.force_init', index=5, number=6, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='max_bursts', full_name='adif.Propagation.max_bursts', index=6, number=7, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='meteor_shower_name', full_name='adif.Propagation.meteor_shower_name', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='nr_bursts', full_name='adif.Propagation.nr_bursts', index=8, number=11, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='nr_pings', full_name='adif.Propagation.nr_pings', index=9, number=12, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sat_mode', full_name='adif.Propagation.sat_mode', index=10, number=9, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sat_name', full_name='adif.Propagation.sat_name', index=11, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1913, serialized_end=2171, ) _CONTESTDATA = _descriptor.Descriptor( name='ContestData', full_name='adif.ContestData', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='contest_id', full_name='adif.ContestData.contest_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='serial_sent', full_name='adif.ContestData.serial_sent', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='serial_received', full_name='adif.ContestData.serial_received', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='arrl_section', full_name='adif.ContestData.arrl_section', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='station_class', full_name='adif.ContestData.station_class', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='check', full_name='adif.ContestData.check', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='precedence', full_name='adif.ContestData.precedence', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2174, serialized_end=2333, ) _CREDIT = _descriptor.Descriptor( name='Credit', full_name='adif.Credit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='credit', full_name='adif.Credit.credit', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='qsl_medium', full_name='adif.Credit.qsl_medium', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2335, serialized_end=2379, ) _UPLOAD = _descriptor.Descriptor( name='Upload', full_name='adif.Upload', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='upload_date', full_name='adif.Upload.upload_date', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='upload_status', full_name='adif.Upload.upload_status', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2381, serialized_end=2481, ) _QSL = _descriptor.Descriptor( name='Qsl', full_name='adif.Qsl', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='sent_date', full_name='adif.Qsl.sent_date', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sent_status', full_name='adif.Qsl.sent_status', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sent_via', full_name='adif.Qsl.sent_via', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='received_date', full_name='adif.Qsl.received_date', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='received_status', full_name='adif.Qsl.received_status', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='received_via', full_name='adif.Qsl.received_via', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='received_message', full_name='adif.Qsl.received_message', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=2484, serialized_end=2699, ) _ADIF.fields_by_name['header'].message_type = _HEADER _ADIF.fields_by_name['qsos'].message_type = _QSO _HEADER.fields_by_name['created_timestamp'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _QSO_APPDEFINEDENTRY.containing_type = _QSO _QSO.fields_by_name['logging_station'].message_type = _STATION _QSO.fields_by_name['contacted_station'].message_type = _STATION _QSO.fields_by_name['propagation'].message_type = _PROPAGATION _QSO.fields_by_name['time_on'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _QSO.fields_by_name['time_off'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _QSO.fields_by_name['contest'].message_type = _CONTESTDATA _QSO.fields_by_name['credit_submitted'].message_type = _CREDIT _QSO.fields_by_name['credit_granted'].message_type = _CREDIT _QSO.fields_by_name['clublog'].message_type = _UPLOAD _QSO.fields_by_name['hrdlog'].message_type = _UPLOAD _QSO.fields_by_name['qrzcom'].message_type = _UPLOAD _QSO.fields_by_name['eqsl'].message_type = _QSL _QSO.fields_by_name['lotw'].message_type = _QSL _QSO.fields_by_name['card'].message_type = _QSL _QSO.fields_by_name['app_defined'].message_type = _QSO_APPDEFINEDENTRY _UPLOAD.fields_by_name['upload_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _UPLOAD.fields_by_name['upload_status'].enum_type = _UPLOADSTATUS _QSL.fields_by_name['sent_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP _QSL.fields_by_name['received_date'].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP DESCRIPTOR.message_types_by_name['Adif'] = _ADIF DESCRIPTOR.message_types_by_name['Header'] = _HEADER DESCRIPTOR.message_types_by_name['Qso'] = _QSO DESCRIPTOR.message_types_by_name['Station'] = _STATION DESCRIPTOR.message_types_by_name['Propagation'] = _PROPAGATION DESCRIPTOR.message_types_by_name['ContestData'] = _CONTESTDATA DESCRIPTOR.message_types_by_name['Credit'] = _CREDIT DESCRIPTOR.message_types_by_name['Upload'] = _UPLOAD DESCRIPTOR.message_types_by_name['Qsl'] = _QSL DESCRIPTOR.enum_types_by_name['UploadStatus'] = _UPLOADSTATUS _sym_db.RegisterFileDescriptor(DESCRIPTOR) Adif = _reflection.GeneratedProtocolMessageType('Adif', (_message.Message,), { 'DESCRIPTOR' : _ADIF, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Adif) }) _sym_db.RegisterMessage(Adif) Header = _reflection.GeneratedProtocolMessageType('Header', (_message.Message,), { 'DESCRIPTOR' : _HEADER, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Header) }) _sym_db.RegisterMessage(Header) Qso = _reflection.GeneratedProtocolMessageType('Qso', (_message.Message,), { 'AppDefinedEntry' : _reflection.GeneratedProtocolMessageType('AppDefinedEntry', (_message.Message,), { 'DESCRIPTOR' : _QSO_APPDEFINEDENTRY, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Qso.AppDefinedEntry) }) , 'DESCRIPTOR' : _QSO, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Qso) }) _sym_db.RegisterMessage(Qso) _sym_db.RegisterMessage(Qso.AppDefinedEntry) Station = _reflection.GeneratedProtocolMessageType('Station', (_message.Message,), { 'DESCRIPTOR' : _STATION, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Station) }) _sym_db.RegisterMessage(Station) Propagation = _reflection.GeneratedProtocolMessageType('Propagation', (_message.Message,), { 'DESCRIPTOR' : _PROPAGATION, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Propagation) }) _sym_db.RegisterMessage(Propagation) ContestData = _reflection.GeneratedProtocolMessageType('ContestData', (_message.Message,), { 'DESCRIPTOR' : _CONTESTDATA, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.ContestData) }) _sym_db.RegisterMessage(ContestData) Credit = _reflection.GeneratedProtocolMessageType('Credit', (_message.Message,), { 'DESCRIPTOR' : _CREDIT, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Credit) }) _sym_db.RegisterMessage(Credit) Upload = _reflection.GeneratedProtocolMessageType('Upload', (_message.Message,), { 'DESCRIPTOR' : _UPLOAD, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Upload) }) _sym_db.RegisterMessage(Upload) Qsl = _reflection.GeneratedProtocolMessageType('Qsl', (_message.Message,), { 'DESCRIPTOR' : _QSL, '__module__' : 'adif_pb2' # @@protoc_insertion_point(class_scope:adif.Qsl) }) _sym_db.RegisterMessage(Qsl) DESCRIPTOR._options = None _QSO_APPDEFINEDENTRY._options = None # @@protoc_insertion_point(module_scope)

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d43c9f85b610e1de5ab09b60c10f3c287a9952f7

16,790

py

Python

src/pipeline/datasets/paths.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

null

src/pipeline/datasets/paths.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

1

2021-12-12T22:13:58.000Z

2021-12-17T22:49:39.000Z

src/pipeline/datasets/paths.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

null

import os ################################################################################ # ------------------------------ GERMAN CREDIT --------------------------------# ################################################################################ # ------------------------------ RAW DATA ------------------------------# GERMAN_CREDIT_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "german_credit", "german.data")) GERMAN_CREDIT_NUMERIC_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "german_credit", "german.data-numeric")) GERMAN_CREDIT_SAMPLED_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "german_credit", f"sampled_GermanCreditDataset.csv")) # ------------------------------ DEPLOYMENT (SYNTHESIZED) DATA ------------------------------# GERMAN_CREDIT_DEPLOYMENT_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_GermanCreditDataset.csv")) GERMAN_CREDIT_DEPLOYMENT_DATASET_PLUS_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_GermanCreditDataset_plus.csv")) GERMAN_CREDIT_SAMPLED_DEPLOYMENT_DATASET = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_sampled_GermanCreditDataset.csv")) # ------------------------------ TRAINING DATA ------------------------------# GERMAN_CREDIT_TRAINING_PROCESSED_DF_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset.pickle")) GERMAN_CREDIT_TRAINING_PROCESSED_DF_PLUS_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDatasetPlus.pickle")) GERMAN_CREDIT_TRAINING_FEATURE_METRIC_LIST_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_FeatureMetricsList.pickle")) GERMAN_CREDIT_RETRAINING_DF = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditConcatenatedDataFrame.pickle")) GERMAN_CREDIT_TRAINING_X_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_train.pickle")) GERMAN_CREDIT_TRAINING_Y_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_train.pickle")) GERMAN_CREDIT_TRAINING_X_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_validation.pickle")) GERMAN_CREDIT_TRAINING_Y_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_validation.pickle")) GERMAN_CREDIT_TRAINING_X_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_test.pickle")) GERMAN_CREDIT_TRAINING_Y_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_test.pickle")) GERMAN_CREDIT_RETRAINING_X_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_train.pickle")) GERMAN_CREDIT_RETRAINING_Y_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_train.pickle")) GERMAN_CREDIT_RETRAINING_X_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_validation.pickle")) GERMAN_CREDIT_RETRAINING_Y_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_validation.pickle")) GERMAN_CREDIT_RETRAINING_X_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_test.pickle")) GERMAN_CREDIT_RETRAINING_Y_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_test.pickle")) GERMAN_CREDIT_TRAINING_X_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_train_raw.pickle")) GERMAN_CREDIT_TRAINING_Y_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_train_raw.pickle")) GERMAN_CREDIT_TRAINING_X_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_validation_raw.pickle")) GERMAN_CREDIT_TRAINING_Y_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_validation_raw.pickle")) GERMAN_CREDIT_TRAINING_X_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_X_test_raw.pickle")) GERMAN_CREDIT_TRAINING_Y_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditDataset_y_test_raw.pickle")) GERMAN_CREDIT_RETRAINING_X_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_train_raw.pickle")) GERMAN_CREDIT_RETRAINING_Y_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_train_raw.pickle")) GERMAN_CREDIT_RETRAINING_X_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_validation_raw.pickle")) GERMAN_CREDIT_RETRAINING_Y_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_validation_raw.pickle")) GERMAN_CREDIT_RETRAINING_X_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_X_test_raw.pickle")) GERMAN_CREDIT_RETRAINING_Y_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "GermanCreditSampledTrainingTrainDataset_GermanCreditSampledDeploymentDataset_y_test_raw.pickle")) # ------------------------------ Backup and Testing ------------------------------# # Not-drifted SMOTENC_GERMAN_CREDIT_DEPLOYMENT_DATASET_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_GermanCreditDataset_normal.csv")) SMOTENC_GERMAN_CREDIT_DEPLOYMENT_DATASET_PLUS_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_GermanCreditDataset_plus_normal.csv")) GAN_GERMAN_CREDIT_DEPLOYMENT_DATASET_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_GermanCreditDataset_normal.csv")) GAN_GERMAN_CREDIT_DEPLOYMENT_DATASET_PLUS_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_GermanCreditDataset_plus_normal.csv")) # Drifted SMOTENC_GERMAN_CREDIT_DEPLOYMENT_DATASET_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_GermanCreditDataset_drift.csv")) SMOTENC_GERMAN_CREDIT_DEPLOYMENT_DATASET_PLUS_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_GermanCreditDataset_plus_drift.csv")) GAN_GERMAN_CREDIT_DEPLOYMENT_DATASET_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_GermanCreditDataset_drift.csv")) GAN_GERMAN_CREDIT_DEPLOYMENT_DATASET_PLUS_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_GermanCreditDataset_plus_drift.csv")) ################################################################################ # ------------------------------ BANK MARKETING ------------------------------# ################################################################################ # ------------------------------ RAW DATA ------------------------------# BANK_MARKETING_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "bank_marketing", "bank.csv")) BANK_MARKETING_FULL_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "bank_marketing", "bank-full.csv")) BANK_MARKETING_ADDITIONAL_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "bank_marketing", "bank-additional.csv")) BANK_MARKETING_ADDITIONAL_FULL_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "bank_marketing", "bank-additional-full.csv")) BANK_MARKETING_SAMPLED_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "bank_marketing", f"sampled_BankMarketingDataset.csv")) # ------------------------------ DEPLOYMENT (SYNTHESIZED) DATA ------------------------------# BANK_MARKETING_DEPLOYMENT_DATASET_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_BankMarketingDataset.csv")) BANK_MARKETING_DEPLOYMENT_DATASET_PLUS_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_BankMarketingDataset_plus.csv")) BANK_MARKETING_SAMPLED_DEPLOYMENT_DATASET = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"generated_sampled_BankMarketingDataset.csv")) # ------------------------------ TRAINING DATA ------------------------------# BANK_MARKETING_TRAINING_PROCESSED_DF_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset.pickle")) BANK_MARKETING_TRAINING_PROCESSED_DF_PLUS_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDatasetPlus.pickle")) BANK_MARKETING_TRAINING_FEATURE_METRIC_LIST_PATH = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_FeatureMetricsList.pickle")) BANK_MARKETING_RETRAINING_DF = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingConcatenatedDataFrame.pickle")) BANK_MARKETING_TRAINING_X_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_train.pickle")) BANK_MARKETING_TRAINING_Y_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_train.pickle")) BANK_MARKETING_TRAINING_X_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_validation.pickle")) BANK_MARKETING_TRAINING_Y_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_validation.pickle")) BANK_MARKETING_TRAINING_X_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_test.pickle")) BANK_MARKETING_TRAINING_Y_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_test.pickle")) BANK_MARKETING_RETRAINING_X_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_train.pickle")) BANK_MARKETING_RETRAINING_Y_TRAIN = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_train.pickle")) BANK_MARKETING_RETRAINING_X_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_validation.pickle")) BANK_MARKETING_RETRAINING_Y_VALIDATION = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_validation.pickle")) BANK_MARKETING_RETRAINING_X_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_test.pickle")) BANK_MARKETING_RETRAINING_Y_TEST = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_test.pickle")) BANK_MARKETING_TRAINING_X_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_train_raw.pickle")) BANK_MARKETING_TRAINING_Y_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_train_raw.pickle")) BANK_MARKETING_TRAINING_X_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_validation_raw.pickle")) BANK_MARKETING_TRAINING_Y_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_validation_raw.pickle")) BANK_MARKETING_TRAINING_X_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_X_test_raw.pickle")) BANK_MARKETING_TRAINING_Y_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingDataset_y_test_raw.pickle")) BANK_MARKETING_RETRAINING_X_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_train_raw.pickle")) BANK_MARKETING_RETRAINING_Y_TRAIN_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_train_raw.pickle")) BANK_MARKETING_RETRAINING_X_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_validation_raw.pickle")) BANK_MARKETING_RETRAINING_Y_VALIDATION_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_validation_raw.pickle")) BANK_MARKETING_RETRAINING_X_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_X_test_raw.pickle")) BANK_MARKETING_RETRAINING_Y_TEST_RAW = os.path.abspath(os.path.join(__file__, "..", "..", "preprocessing", "raw_files", "BankMarketingSampledTrainingTrainDataset_BankMarketingSampledDeploymentDataset_y_test_raw.pickle")) # ------------------------------ Backup and Testing ------------------------------# # Non-Drifted SMOTENC_BANK_MARKETING_DEPLOYMENT_DATASET_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_BankMarketingDataset_normal.csv")) SMOTENC_BANK_MARKETING_DEPLOYMENT_DATASET_PLUS_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_BankMarketingDataset_plus_normal.csv")) GAN_BANK_MARKETING_DEPLOYMENT_DATASET_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_BankMarketingDataset.csv_normal")) GAN_BANK_MARKETING_DEPLOYMENT_DATASET_PLUS_PATH_NORMAL = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_BankMarketingDataset_plus_normal.csv")) # Drifted SMOTENC_BANK_MARKETING_DEPLOYMENT_DATASET_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_BankMarketingDataset_drift.csv")) SMOTENC_BANK_MARKETING_DEPLOYMENT_DATASET_PLUS_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"smotenc_generated_BankMarketingDataset_plus_drift.csv")) GAN_BANK_MARKETING_DEPLOYMENT_DATASET_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_BankMarketingDataset_drift.csv")) GAN_BANK_MARKETING_DEPLOYMENT_DATASET_PLUS_PATH_DRIFT = os.path.abspath(os.path.join(__file__, "..", "..", "data_generation", "raw_files", f"gan_generated_BankMarketingDataset_plus_drift.csv"))

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0

7

2e19aa45383db9286c61545abbf7b844a606dce1

7,569

py

Python

test/test_ipam_api.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

test/test_ipam_api.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

test/test_ipam_api.py

nrfta/python-netbox-client

68ba6dd4d7306513dc1ad38f3ac59122ba4f70a8

[ "MIT" ]

null

# coding: utf-8 """ NetBox API API to access NetBox # noqa: E501 OpenAPI spec version: 2.8 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import netbox_client from netbox_client.api.ipam_api import IpamApi # noqa: E501 from netbox_client.rest import ApiException class TestIpamApi(unittest.TestCase): """IpamApi unit test stubs""" def setUp(self): self.api = netbox_client.api.ipam_api.IpamApi() # noqa: E501 def tearDown(self): pass def test_ipam_aggregates_create(self): """Test case for ipam_aggregates_create """ pass def test_ipam_aggregates_delete(self): """Test case for ipam_aggregates_delete """ pass def test_ipam_aggregates_list(self): """Test case for ipam_aggregates_list """ pass def test_ipam_aggregates_partial_update(self): """Test case for ipam_aggregates_partial_update """ pass def test_ipam_aggregates_read(self): """Test case for ipam_aggregates_read """ pass def test_ipam_aggregates_update(self): """Test case for ipam_aggregates_update """ pass def test_ipam_ip_addresses_create(self): """Test case for ipam_ip_addresses_create """ pass def test_ipam_ip_addresses_delete(self): """Test case for ipam_ip_addresses_delete """ pass def test_ipam_ip_addresses_list(self): """Test case for ipam_ip_addresses_list """ pass def test_ipam_ip_addresses_partial_update(self): """Test case for ipam_ip_addresses_partial_update """ pass def test_ipam_ip_addresses_read(self): """Test case for ipam_ip_addresses_read """ pass def test_ipam_ip_addresses_update(self): """Test case for ipam_ip_addresses_update """ pass def test_ipam_prefixes_available_ips_create(self): """Test case for ipam_prefixes_available_ips_create """ pass def test_ipam_prefixes_available_ips_read(self): """Test case for ipam_prefixes_available_ips_read """ pass def test_ipam_prefixes_available_prefixes_create(self): """Test case for ipam_prefixes_available_prefixes_create A convenience method for returning available child prefixes within a parent. # noqa: E501 """ pass def test_ipam_prefixes_available_prefixes_read(self): """Test case for ipam_prefixes_available_prefixes_read A convenience method for returning available child prefixes within a parent. # noqa: E501 """ pass def test_ipam_prefixes_create(self): """Test case for ipam_prefixes_create """ pass def test_ipam_prefixes_delete(self): """Test case for ipam_prefixes_delete """ pass def test_ipam_prefixes_list(self): """Test case for ipam_prefixes_list """ pass def test_ipam_prefixes_partial_update(self): """Test case for ipam_prefixes_partial_update """ pass def test_ipam_prefixes_read(self): """Test case for ipam_prefixes_read """ pass def test_ipam_prefixes_update(self): """Test case for ipam_prefixes_update """ pass def test_ipam_rirs_create(self): """Test case for ipam_rirs_create """ pass def test_ipam_rirs_delete(self): """Test case for ipam_rirs_delete """ pass def test_ipam_rirs_list(self): """Test case for ipam_rirs_list """ pass def test_ipam_rirs_partial_update(self): """Test case for ipam_rirs_partial_update """ pass def test_ipam_rirs_read(self): """Test case for ipam_rirs_read """ pass def test_ipam_rirs_update(self): """Test case for ipam_rirs_update """ pass def test_ipam_roles_create(self): """Test case for ipam_roles_create """ pass def test_ipam_roles_delete(self): """Test case for ipam_roles_delete """ pass def test_ipam_roles_list(self): """Test case for ipam_roles_list """ pass def test_ipam_roles_partial_update(self): """Test case for ipam_roles_partial_update """ pass def test_ipam_roles_read(self): """Test case for ipam_roles_read """ pass def test_ipam_roles_update(self): """Test case for ipam_roles_update """ pass def test_ipam_services_create(self): """Test case for ipam_services_create """ pass def test_ipam_services_delete(self): """Test case for ipam_services_delete """ pass def test_ipam_services_list(self): """Test case for ipam_services_list """ pass def test_ipam_services_partial_update(self): """Test case for ipam_services_partial_update """ pass def test_ipam_services_read(self): """Test case for ipam_services_read """ pass def test_ipam_services_update(self): """Test case for ipam_services_update """ pass def test_ipam_vlan_groups_create(self): """Test case for ipam_vlan_groups_create """ pass def test_ipam_vlan_groups_delete(self): """Test case for ipam_vlan_groups_delete """ pass def test_ipam_vlan_groups_list(self): """Test case for ipam_vlan_groups_list """ pass def test_ipam_vlan_groups_partial_update(self): """Test case for ipam_vlan_groups_partial_update """ pass def test_ipam_vlan_groups_read(self): """Test case for ipam_vlan_groups_read """ pass def test_ipam_vlan_groups_update(self): """Test case for ipam_vlan_groups_update """ pass def test_ipam_vlans_create(self): """Test case for ipam_vlans_create """ pass def test_ipam_vlans_delete(self): """Test case for ipam_vlans_delete """ pass def test_ipam_vlans_list(self): """Test case for ipam_vlans_list """ pass def test_ipam_vlans_partial_update(self): """Test case for ipam_vlans_partial_update """ pass def test_ipam_vlans_read(self): """Test case for ipam_vlans_read """ pass def test_ipam_vlans_update(self): """Test case for ipam_vlans_update """ pass def test_ipam_vrfs_create(self): """Test case for ipam_vrfs_create """ pass def test_ipam_vrfs_delete(self): """Test case for ipam_vrfs_delete """ pass def test_ipam_vrfs_list(self): """Test case for ipam_vrfs_list """ pass def test_ipam_vrfs_partial_update(self): """Test case for ipam_vrfs_partial_update """ pass def test_ipam_vrfs_read(self): """Test case for ipam_vrfs_read """ pass def test_ipam_vrfs_update(self): """Test case for ipam_vrfs_update """ pass if __name__ == '__main__': unittest.main()

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384

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null

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1

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1

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7

2e6e434b058ccda61dc7039007cc4b858b9d6577

20,436

py

Python

Practice/PythonApplication/LeetCode/Array/485.py

kushalbhola/MyStuff

3f1064866487e489af41f8662a875b9954d5d8b0

[ "Apache-2.0" ]

null

Practice/PythonApplication/LeetCode/Array/485.py

kushalbhola/MyStuff

3f1064866487e489af41f8662a875b9954d5d8b0

[ "Apache-2.0" ]

1

2020-04-29T23:00:26.000Z

Practice/PythonApplication/LeetCode/Array/485.py

kushalbhola/MyStuff

3f1064866487e489af41f8662a875b9954d5d8b0

[ "Apache-2.0" ]

null

def main(): input = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1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] output = findMaxConsecutiveOnes(input) print(output) def findMaxConsecutiveOnes(nums): globalMax = 0 count = 0 if (len(nums) == 0): return 0 for n in nums: if n == 1: count = count +n if(count > globalMax): globalMax = count elif n==0: count = 0 return globalMax if __name__ == '__main__': main()

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20,013

0.500196

10,047

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0

null

0

14

5cff7413f5a21b7876c160ba6fc46a4379049e9b

3,052

py

Python

test/test_cflapi_players.py

streibeb/cflapi

4e0bf609bab94f0f8c7f623409de7fa16bad2f78

[ "MIT" ]

4

2018-08-21T21:44:09.000Z

2020-02-18T13:09:17.000Z

test/test_cflapi_players.py

streibeb/cflapi

4e0bf609bab94f0f8c7f623409de7fa16bad2f78

[ "MIT" ]

null

test/test_cflapi_players.py

streibeb/cflapi

4e0bf609bab94f0f8c7f623409de7fa16bad2f78

[ "MIT" ]

null

from test import * PLAYER_ID = 159141 def test_get_players(response_keys): api = CFLApi(API_KEY) response = api.getPlayers() assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" def test_get_player(response_keys): api = CFLApi(API_KEY) response = api.getPlayer(PLAYER_ID) assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" assert response['data'][0]['cfl_central_id'] == PLAYER_ID def test_get_player_include_seasons(response_keys): api = CFLApi(API_KEY) response = api.getPlayer(PLAYER_ID, include='seasons') assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" assert response['data'][0]['cfl_central_id'] == PLAYER_ID assert isinstance(response['data'][0]['seasons'], dict) def test_get_player_include_game_by_game(response_keys): api = CFLApi(API_KEY) response = api.getPlayer(PLAYER_ID, include='game_by_game') assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" assert response['data'][0]['cfl_central_id'] == PLAYER_ID assert isinstance(response['data'][0]['game_by_game'], dict) def test_get_player_include_seasons_and_game_by_game(response_keys): api = CFLApi(API_KEY) response = api.getPlayer(PLAYER_ID, include=['seasons','game_by_game']) assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" assert response['data'][0]['cfl_central_id'] == PLAYER_ID assert isinstance(response['data'][0]['seasons'], dict) assert isinstance(response['data'][0]['game_by_game'], dict) def test_get_players_sort_by_birth_date_asc(response_keys): api = CFLApi(API_KEY) response = api.getPlayers(sort='birth_date') assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" firstGame, lastGame = response['data'][0], response['data'][-1] assert firstGame['birth_date'] <= lastGame['birth_date'] def test_get_players_sort_by_height_desc(response_keys): api = CFLApi(API_KEY) response = api.getPlayers(sort='-height') assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" firstGame, lastGame = response['data'][0], response['data'][-1] assert firstGame['height'] >= lastGame['height'] def test_get_players_filter_by_position_abbreviation(response_keys): api = CFLApi(API_KEY) response = api.getPlayers(filter={'position_abbreviation': {'eq': 'QB'}}) assert isinstance(response, dict) assert set(response_keys).issubset(response.keys()), "All keys should be in the response" for player in response['data']: assert player['position']['abbreviation'] == 'QB'

40.157895

93

0.724443

414

3,052

5.113527

0.120773

0.136042

0.079358

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0.771375

0

0.006894

0.144495

3,052

75

94

40.693333

0.803907

0

0.607143

0

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false

0

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0

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0

null

0

1

0

null

0

1

0

7

cf5d225b4d6d2eac6aaf83a46b7bc5747f325cde

9,541

py

Python

src/ue4nlp/ue_estimator_mahalanobis.py

AIRI-Institute/uncertainty_transformers

982b5ae8b39cb484ce3559a72f95d18f30487e38

[ "MIT" ]

null

src/ue4nlp/ue_estimator_mahalanobis.py

AIRI-Institute/uncertainty_transformers

982b5ae8b39cb484ce3559a72f95d18f30487e38

[ "MIT" ]

null

src/ue4nlp/ue_estimator_mahalanobis.py

AIRI-Institute/uncertainty_transformers

982b5ae8b39cb484ce3559a72f95d18f30487e38

[ "MIT" ]

null

import torch import numpy as np from tqdm import tqdm import time from utils.utils_heads import ( ElectraClassificationHeadIdentityPooler, BertClassificationHeadIdentityPooler, ElectraNERHeadIdentityPooler, ) from utils.utils_inference import ( is_custom_head, unpad_features, pad_scores ) from ue4nlp.mahalanobis_distance import ( mahalanobis_distance, mahalanobis_distance_relative, mahalanobis_distance_marginal, compute_centroids, compute_covariance ) import logging log = logging.getLogger() class UeEstimatorMahalanobis: def __init__(self, cls, ue_args, config, train_dataset): self.cls = cls self.ue_args = ue_args self.config = config self.train_dataset = train_dataset def __call__(self, X, y): return self._predict_with_fitted_cov(X, y) def fit_ue(self, X, y=None, X_test=None): cls = self.cls model = self.cls._auto_model log.info("****************Start fitting covariance and centroids **************") if y is None: y = self._exctract_labels(X) self._replace_model_head() X_features = self._exctract_features(X) self.class_cond_centroids = self._fit_centroids(X_features, y) self.class_cond_covariance = self._fit_covariance(X_features, y) self.fit_all_md_versions = "fit_all_md_versions" in self.ue_args.keys() and self.ue_args.fit_all_md_versions if self.fit_all_md_versions: self.train_centroid = self._fit_centroids(X_features, y, class_cond=False) self.train_covariance = self._fit_covariance(X_features, y, class_cond=False) log.info("**************Done.**********************") def _fit_covariance(self, X, y, class_cond=True): if class_cond: return compute_covariance(self.class_cond_centroids, X, y, class_cond) return compute_covariance(self.train_centroid, X, y, class_cond) def _fit_centroids(self, X, y, class_cond=True): return compute_centroids(X, y, class_cond) def _replace_model_head(self): log.info("Change classifier to Identity Pooler") cls = self.cls model = self.cls._auto_model use_paper_version = self.ue_args.get("use_paper_version", False) and not(self.ue_args.use_spectralnorm) use_activation = not use_paper_version if is_custom_head(model): model.classifier = ElectraClassificationHeadIdentityPooler(model.classifier, use_activation) else: model.classifier = BertClassificationHeadIdentityPooler(model.classifier) def _exctract_labels(self, X): return np.asarray([example["label"] for example in X]) def _exctract_features(self, X): cls = self.cls model = self.cls._auto_model try: X = X.remove_columns("label") except: X.dataset = X.dataset.remove_columns("label") X_features = cls.predict(X, apply_softmax=False, return_preds=False)[0] return X_features def _predict_with_fitted_cov(self, X, y): cls = self.cls model = self.cls._auto_model log.info("****************Compute MD with fitted covariance and centroids **************") start = time.time() if y is None: y = self._exctract_labels(X) X_features = self._exctract_features(X) end = time.time() eval_results = {} md, inf_time = mahalanobis_distance(None, None, X_features, self.class_cond_centroids, self.class_cond_covariance) sum_inf_time = inf_time + (end - start) eval_results["mahalanobis_distance"] = md.tolist() eval_results["ue_time"] = sum_inf_time log.info(f"UE time: {sum_inf_time}") if self.fit_all_md_versions: md_relative = mahalanobis_distance_relative(None, None, X_features, self.train_centroid, self.train_covariance) md_marginal = mahalanobis_distance_marginal(None, None, X_features, self.class_cond_centroids, self.class_cond_covariance, self.train_centroid, self.train_covariance) eval_results["mahalanobis_distance_relative"] = md_relative.tolist() eval_results["mahalanobis_distance_marginal"] = md_marginal.tolist() log.info("**************Done.**********************") return eval_results class UeEstimatorMahalanobisNer: def __init__(self, cls, ue_args, config, train_dataset): self.cls = cls self.ue_args = ue_args self.config = config self.train_dataset = train_dataset def __call__(self, X, y): return self._predict_with_fitted_cov(X, y) def fit_ue(self, X, y=None, X_test=None): cls = self.cls model = self.cls._auto_model log.info("****************Start fitting covariance and centroids **************") if y is None: y, y_shape = self._exctract_labels(X) self._replace_model_head() X_features = self._exctract_features(X) X_features, y = unpad_features(X_features, y) self.class_cond_centroids = self._fit_centroids(X_features, y) self.class_cond_covariance = self._fit_covariance(X_features, y) self.fit_all_md_versions = "fit_all_md_versions" in self.ue_args.keys() and self.ue_args.fit_all_md_versions if self.fit_all_md_versions: self.train_centroid = self._fit_centroids(X_features, y, class_cond=False) self.train_covariance = self._fit_covariance(X_features, y, class_cond=False) log.info("**************Done.**********************") def _fit_covariance(self, X, y, class_cond=True): if class_cond: return compute_covariance(self.class_cond_centroids, X, y, class_cond) return compute_covariance(self.train_centroid, X, y, class_cond) def _fit_centroids(self, X, y, class_cond=True): return compute_centroids(X, y, class_cond) def _replace_model_head(self): log.info("Change classifier to Identity Pooler") cls = self.cls model = self.cls._auto_model use_paper_version = self.ue_args.get("use_paper_version", False) and not(self.ue_args.use_spectralnorm) use_activation = not use_paper_version if is_custom_head(model): model.classifier = ElectraNERHeadIdentityPooler(model.classifier, use_activation) else: model.classifier = BertClassificationHeadIdentityPooler(model.classifier) def _exctract_labels(self, X): y = np.asarray([example["labels"] for example in X]) y_shape = y.shape return y.reshape(-1), y_shape def _exctract_features(self, X): cls = self.cls model = self.cls._auto_model try: X = X.remove_columns("labels") except: X.dataset = X.dataset.remove_columns("labels") X_features = cls.predict(X, apply_softmax=False, return_preds=False)[0] X_features = X_features.reshape(-1, X_features.shape[-1]) return X_features def _predict_with_fitted_cov(self, X, y): cls = self.cls model = self.cls._auto_model log.info("****************Compute MD with fitted covariance and centroids **************") start = time.time() y_pad, y_shape = self._exctract_labels(X) X_features = self._exctract_features(X) X_features, y = unpad_features(X_features, y_pad) end = time.time() eval_results = {} md, inf_time = mahalanobis_distance(None, None, X_features, self.class_cond_centroids, self.class_cond_covariance) md = pad_scores(md, np.asarray(y_pad).reshape(y_shape), y_pad) sum_inf_time = inf_time + (end - start) eval_results["mahalanobis_distance"] = md.tolist() eval_results["ue_time"] = sum_inf_time log.info(f"UE time: {sum_inf_time}") if self.fit_all_md_versions: md_relative = mahalanobis_distance_relative(None, None, X_features, self.train_centroid, self.train_covariance) md_relative = pad_scores(md_relative, np.asarray(y_pad).reshape(y_shape), y_pad) md_marginal = mahalanobis_distance_marginal(None, None, X_features, self.class_cond_centroids, self.class_cond_covariance, self.train_centroid, self.train_covariance) md_relative = pad_scores(md_relative, np.asarray(y_pad).reshape(y_shape), y_pad) eval_results["mahalanobis_distance_relative"] = md_relative.tolist() eval_results["mahalanobis_distance_marginal"] = md_marginal.tolist() log.info("**************Done.**********************") return eval_results

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0.033981

0.029873

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0.000887

0.291374

9,541

247

117

38.62753

0.791303

0

0.774725

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0.038566

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1

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0

7

d8774ec73da9b87613faccad02ae7a7d5544e1c1

32,676

py

Python

tests/test_console.py

adrienmillot/AirBnB_clone

757bdea6064f8c8a1d0fad3e8ad30d9b08b3f75a

[ "Unlicense" ]

null

tests/test_console.py

adrienmillot/AirBnB_clone

757bdea6064f8c8a1d0fad3e8ad30d9b08b3f75a

[ "Unlicense" ]

1

2021-07-01T11:29:07.000Z

tests/test_console.py

adrienmillot/AirBnB_clone

757bdea6064f8c8a1d0fad3e8ad30d9b08b3f75a

[ "Unlicense" ]

null

#!/usr/bin/python3 from os import system from models.engine.file_storage import FileStorage import unittest from io import StringIO from unittest.mock import patch from console import HBNBCommand from models import storage import os class ConsolePromptingTest(unittest.TestCase): def testPrompt(self): """ Prompt command """ self.assertEqual(HBNBCommand().prompt, "(hbnb) ") def testEmptyLine(self): """ Empty line """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("") self.assertEqual(output.getvalue().strip(), "") class ConsoleHelpTest(unittest.TestCase): def testHelpCreate(self): """ create() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help create") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Creates a new instance of BaseModel, \ saves it (to the JSON file) and prints the id.\n\n") def testHelpAll(self): """ all() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help all") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Prints all string representation of \ all instances based or not on the class name.\n\n") def testHelpDestroy(self): """ destroy() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help destroy") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Deletes an instance based on the \ class name and id (save the change into the JSON file).\n\n") def testHelpUpdate(self): """ update() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help update") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Updates an instance based on the \ class name and id by adding or updating attribute (save the \ change into the JSON file).\n\n") def testHelpShow(self): """ show() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help show") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Prints the string representation of \ an instance based on the class name and id.\n\n") def testHelpQuit(self): """ quit have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help quit") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Quit command to exit the program\n\n") def testHelpEOF(self): """ EOF command have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help EOF") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "EOF command to exit the program\n\n") def testHelpCount(self): """ count() method have help documented """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("help count") self.assertGreater(len(output.getvalue()), 0) self.assertEqual(output.getvalue(), "Update your command interpreter \ (console.py) to retrieve the number of instances of a class.\ \n\n") class ConsoleExitTest(unittest.TestCase): def testDoQuit(self): """ Quit """ with self.assertRaises(SystemExit): HBNBCommand().onecmd("quit") def testDoEOF(self): """ EOF """ with self.assertRaises(SystemExit): HBNBCommand().onecmd("EOF") class ConsoleAllTest(unittest.TestCase): @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testAllInvalidClass(self): """ all invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("all toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("toto.all()") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testAllMissingClass(self): """ all() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd(".all()") self.assertEqual(output.getvalue(), "** class name missing **\n") def testAllInstanceSpaceNotation(self): """ all instance command """ self.__allInstanceSpaceNotation("Amenity", "User") self.__allInstanceSpaceNotation("BaseModel", "User") self.__allInstanceSpaceNotation("City", "User") self.__allInstanceSpaceNotation("Place", "User") self.__allInstanceSpaceNotation("Review", "User") self.__allInstanceSpaceNotation("State", "User") self.__allInstanceSpaceNotation("User", "BaseModel") def testAllInstanceDotNotation(self): """ all() instance command """ self.__allInstanceDotNotation("Amenity", "User") self.__allInstanceDotNotation("BaseModel", "User") self.__allInstanceDotNotation("City", "User") self.__allInstanceDotNotation("Place", "User") self.__allInstanceDotNotation("Review", "User") self.__allInstanceDotNotation("State", "User") self.__allInstanceDotNotation("User", "BaseModel") def __allInstanceSpaceNotation(self, prmClassName, prmOtherClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue() with patch("sys.stdout", new=StringIO()) as output: command = "all {}".format(prmClassName) self.assertFalse(HBNBCommand().onecmd(command)) self.assertIn(prmClassName, output.getvalue().strip()) self.assertNotIn(prmOtherClassName, output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __allInstanceDotNotation(self, prmClassName, prmOtherClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue() with patch("sys.stdout", new=StringIO()) as output: command = "{}.all()".format(prmClassName) self.assertFalse(HBNBCommand().onecmd(command)) self.assertIn(prmClassName, output.getvalue().strip()) self.assertNotIn(prmOtherClassName, output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) class ConsoleCountTest(unittest.TestCase): __classes = [ 'BaseModel', 'User', 'State', 'City', 'Amenity', 'Place', 'Review' ] @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testCountMissingClass(self): """ count() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("count") self.assertEqual(output.getvalue(), "** class name missing **\n") def testCountInvalidClass(self): """ count() invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("count toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testCountInstanceSpaceNotation(self): """ count instance command. """ for className in self.__classes: self.__testCountSpaceNotation(className) def testCountInstanceDotNotation(self): """ count() instance command. """ for className in self.__classes: self.__testCountDotNotation(className) def __testCountSpaceNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "count {}".format(prmClassName))) count = int(output.getvalue()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue() with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "count {}".format(prmClassName))) self.assertEqual(output.getvalue().strip(), str(count + 1)) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __testCountDotNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.count()".format(prmClassName))) count = int(output.getvalue()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue() with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.count()".format(prmClassName))) self.assertEqual(output.getvalue().strip(), str(count + 1)) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) class ConsoleCreateTest(unittest.TestCase): __classes = [ 'BaseModel', 'User', 'State', 'City', 'Amenity', 'Place', 'Review' ] @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testCreateMissingClass(self): """ create() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("create") self.assertEqual(output.getvalue(), "** class name missing **\n") def testInvalidClass(self): """ create() invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("create toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testCreateInstance(self): """ create() Amenity """ for prmClassName in self.__classes: self.__testCreateObject(prmClassName) def __testCreateObject(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() key = "{}.{}".format(prmClassName, id) self.assertIn(key, storage.all().keys()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) class ConsoleDestroyTest(unittest.TestCase): __classes = [ 'BaseModel', 'User', 'State', 'City', 'Amenity', 'Place', 'Review' ] @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testDestroyMissingClass(self): """ destroy() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("destroy") self.assertEqual(output.getvalue(), "** class name missing **\n") with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd(".destroy()") self.assertEqual(output.getvalue(), "** class name missing **\n") def testDestroyInvalidClass(self): """ destroy() invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("destroy toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("toto.destroy()") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testDestroyMissingIdSpaceNotation(self): """ destroy missing id command """ for className in self.__classes: self.__missingIdSpaceNotation(className) def testDestroyMissingIdDotNotation(self): """ destroy() missing id command """ for className in self.__classes: self.__missingIdDotNotation(className) def testDestroyNoInstanceFoundSpaceNotation(self): """ destroy no instance command """ for className in self.__classes: self.__noInstanceFoundSpaceNotation(className) def testDestroyNoInstanceFoundDotNotation(self): """ destroy() no instance command """ for className in self.__classes: self.__noInstanceFoundDotNotation(className) def testDestroyInstanceSpaceNotation(self): """ destroy instance command """ for className in self.__classes: self.__destroyInstanceSpaceNotation(className) def testDestroyInstanceDotNotation(self): """ destroy() instance command """ for className in self.__classes: self.__destroyInstanceDotNotation(className) def __missingIdSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "destroy {}".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __missingIdDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy()".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __noInstanceFoundSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "destroy {} 1".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __noInstanceFoundDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy(1)".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __destroyInstanceSpaceNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) command = "destroy {} {}".format(prmClassName, id) self.assertFalse(HBNBCommand().onecmd(command)) self.assertNotIn(obj, storage.all()) def __destroyInstanceDotNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) command = "{}.destroy({})".format(prmClassName, id) self.assertFalse(HBNBCommand().onecmd(command)) self.assertNotIn(obj, storage.all()) def __getObj(self, prmClassName: str, prmUuid: str): return storage.all()["{}.{}".format(prmClassName, prmUuid)] class ConsoleShowTest(unittest.TestCase): __classes = [ 'BaseModel', 'User', 'State', 'City', 'Amenity', 'Place', 'Review' ] @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testShowMissingClass(self): """ show() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("show") self.assertEqual(output.getvalue(), "** class name missing **\n") def testInvalidClass(self): """ show() invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("show toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("toto.show()") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testMissingIdSpaceNotation(self): """ show missing id command """ for className in self.__classes: self.__missingIdSpaceNotation(className) def testMissingIdDotNotation(self): """ show() missing id command """ for className in self.__classes: self.__missingIdDotNotation(className) def testNoInstanceFoundSpaceNotation(self): """ show no instance command """ for className in self.__classes: self.__noInstanceFoundSpaceNotation(className) def testNoInstanceFoundDotNotation(self): """ show() no instance command """ for className in self.__classes: self.__noInstanceFoundDotNotation(className) def testShowInstanceSpaceNotation(self): """ show instance command """ for className in self.__classes: self.__showInstanceSpaceNotation(className) def testShowInstanceDotNotation(self): """ show() instance command """ for className in self.__classes: self.__showInstanceDotNotation(className) def __missingIdSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "show {}".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __missingIdDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.show()".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __noInstanceFoundSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "show {} 1".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __noInstanceFoundDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.show(1)".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __showInstanceSpaceNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) command = "show {} {}".format(prmClassName, id) self.assertFalse(HBNBCommand().onecmd(command)) self.assertEqual(obj.__str__(), output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __showInstanceDotNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) command = "{}.show({})".format(prmClassName, id) self.assertFalse(HBNBCommand().onecmd(command)) self.assertEqual(obj.__str__(), output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "destroy {} {}".format(prmClassName, id))) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __getObj(self, prmClassName: str, prmUuid: str): return storage.all()["{}.{}".format(prmClassName, prmUuid)] class ConsoleUpdateTest(unittest.TestCase): __classes = [ 'BaseModel', 'User', 'State', 'City', 'Amenity', 'Place', 'Review' ] @classmethod def setUp(self): try: os.rename("file.json", "tmp") except IOError: pass FileStorage.__objects = {} @classmethod def tearDown(self): try: os.remove("file.json") except IOError: pass try: os.rename("tmp", "file.json") except IOError: pass def testShowMissingClass(self): """ update() missing class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("update") self.assertEqual(output.getvalue(), "** class name missing **\n") def testInvalidClass(self): """ update() invalid class """ with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("update toto") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") with patch('sys.stdout', new=StringIO()) as output: HBNBCommand().onecmd("toto.update()") self.assertEqual(output.getvalue(), "** class doesn't exist **\n") def testMissingIdSpaceNotation(self): """ update missing id command """ for className in self.__classes: self.__missingIdSpaceNotation(className) def testMissingIdDotNotation(self): """ update() missing id command """ for className in self.__classes: self.__missingIdDotNotation(className) def testNoInstanceFoundSpaceNotation(self): """ update no instance command """ for className in self.__classes: self.__noInstanceFoundSpaceNotation(className) def testNoInstanceFoundDotNotation(self): """ update() no instance command """ for className in self.__classes: self.__noInstanceFoundDotNotation(className) def testMissingAttributeSpaceNotation(self): """ update() no instance command """ for className in self.__classes: self.__missingAttributeSpaceNotation(className) def testMissingAttributeDotNotation(self): """ update() no instance command """ for className in self.__classes: self.__missingAttributeDotNotation(className) def testUpdateInstanceSpaceNotation(self): """ update no instance command """ for className in self.__classes: self.__updateInstanceSpaceNotation(className) def testUpdateInstanceDotNotation(self): """ update() no instance command """ for className in self.__classes: self.__updateInstanceDotNotation(className) self.__updateInstanceWithJSONDotNotation(className) def __missingIdSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "update {}".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __missingIdDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.update()".format(prmClassName))) self.assertEqual("** instance id missing **", output.getvalue().strip()) def __noInstanceFoundSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "update {} 1".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __noInstanceFoundDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.update(1)".format(prmClassName))) self.assertEqual("** no instance found **", output.getvalue().strip()) def __missingAttributeSpaceNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() obj = self.__getObj(prmClassName, id) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "update {} {}".format(prmClassName, id))) self.assertEqual("** attribute name missing **", output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __missingAttributeDotNotation(self, prmClassName: str): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() obj = self.__getObj(prmClassName, id) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.update(\"{}\")".format(prmClassName, id))) self.assertEqual("** attribute name missing **", output.getvalue().strip()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __updateInstanceSpaceNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) self.assertNotIn("first_name", obj.__dict__.keys()) command = "update {} {} {} {}".format( prmClassName, id, "first_name", "john") self.assertFalse(HBNBCommand().onecmd(command)) self.assertEqual(obj.first_name, "john") with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __updateInstanceDotNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) self.assertNotIn("first_name", obj.__dict__.keys()) command = "{}.update(\"{}\", \"{}\", \"{}\")".format( prmClassName, id, "first_name", "john") self.assertFalse(HBNBCommand().onecmd(command)) obj = self.__getObj(prmClassName, id) self.assertIn("first_name", obj.__dict__.keys()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __updateInstanceWithJSONDotNotation(self, prmClassName): with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "create {}".format(prmClassName))) id = output.getvalue().strip() with patch("sys.stdout", new=StringIO()) as output: obj = self.__getObj(prmClassName, id) self.assertNotIn("first_name", obj.__dict__.keys()) jsonData = "{'first_name': 'john'}" command = "{}.update(\"{}\", {})".format( prmClassName, id, jsonData) self.assertFalse(HBNBCommand().onecmd(command)) obj = self.__getObj(prmClassName, id) self.assertIn("first_name", obj.__dict__.keys()) with patch("sys.stdout", new=StringIO()) as output: self.assertFalse(HBNBCommand().onecmd( "{}.destroy({})".format(prmClassName, id))) def __getObj(self, prmClassName: str, prmUuid: str): return storage.all()["{}.{}".format(prmClassName, prmUuid)]

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d8a07a91a62ea805fcd842b06bbfc0b66c527af4

2,058

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Python

usaspending_api/reporting/migrations/0006_auto_20210423_1715.py

ststuck/usaspending-api

b13bd5bcba0369ff8512f61a34745626c3969391

[ "CC0-1.0" ]

217

2016-11-03T17:09:53.000Z

2022-03-10T04:17:54.000Z

usaspending_api/reporting/migrations/0006_auto_20210423_1715.py

Hk92a/usaspending-api

25daa9dbc30835b8f4b4c797c592ba9ecc78ca00

[ "CC0-1.0" ]

622

2016-09-02T19:18:23.000Z

2022-03-29T17:11:01.000Z

usaspending_api/reporting/migrations/0006_auto_20210423_1715.py

Hk92a/usaspending-api

25daa9dbc30835b8f4b4c797c592ba9ecc78ca00

[ "CC0-1.0" ]

93

2016-09-07T20:28:57.000Z

2022-02-25T00:25:27.000Z

# Generated by Django 2.2.18 on 2021-04-23 17:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('reporting', '0005_auto_20210202_2235'), ] operations = [ migrations.AlterField( model_name='reportingagencyoverview', name='linked_assistance_awards', field=models.IntegerField(null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='linked_procurement_awards', field=models.IntegerField(null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='total_budgetary_resources', field=models.DecimalField(decimal_places=2, max_digits=23, null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='total_diff_approp_ocpa_obligated_amounts', field=models.DecimalField(decimal_places=2, max_digits=23, null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='total_dollars_obligated_gtas', field=models.DecimalField(decimal_places=2, max_digits=23, null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='unlinked_assistance_c_awards', field=models.IntegerField(null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='unlinked_assistance_d_awards', field=models.IntegerField(null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='unlinked_procurement_c_awards', field=models.IntegerField(null=True), ), migrations.AlterField( model_name='reportingagencyoverview', name='unlinked_procurement_d_awards', field=models.IntegerField(null=True), ), ]

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null

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0

1

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null

0

9

d8ddcb47d320e4af2507c3590177f331e78e3547

101,921

py

Python

app/productdb/tests/test_productdb_api_views.py

gaetoleole/product-database

191b304600d6f069d57ab3d0c28886e7e6545231

[ "MIT" ]

null

app/productdb/tests/test_productdb_api_views.py

gaetoleole/product-database

191b304600d6f069d57ab3d0c28886e7e6545231

[ "MIT" ]

null

app/productdb/tests/test_productdb_api_views.py

gaetoleole/product-database

191b304600d6f069d57ab3d0c28886e7e6545231

[ "MIT" ]

null

""" Test suite for the productdb.api_views module """ import pytest from urllib.parse import quote import requests from django.utils.dateformat import DateFormat from django.utils.formats import get_format from django.conf import settings from django.contrib.auth.models import User, Permission from django.core.urlresolvers import reverse from django.utils.datetime_safe import date, datetime from mixer.backend.django import mixer from rest_framework import status from rest_framework.authtoken.models import Token from rest_framework.test import APIClient from app.config.models import NotificationMessage from app.productdb.models import Vendor, ProductGroup, Product, ProductList, ProductMigrationOption, \ ProductMigrationSource pytestmark = pytest.mark.django_db AUTH_USER = { "username": "api", "password": "api" } SUPER_USER = { "username": "pdb_admin", "password": "pdb_admin" } REST_TOKEN_AUTH = reverse("productdb:api-token-auth") REST_VENDOR_LIST = reverse("productdb:vendors-list") REST_VENDOR_DETAIL = REST_VENDOR_LIST + "%d/" REST_PRODUCT_GROUP_LIST = reverse("productdb:productgroups-list") REST_PRODUCT_GROUP_COUNT = REST_PRODUCT_GROUP_LIST + "count/" REST_PRODUCT_GROUP_DETAIL = REST_PRODUCT_GROUP_LIST + "%d/" REST_PRODUCT_LIST = reverse("productdb:products-list") REST_PRODUCT_COUNT = REST_PRODUCT_LIST + "count/" REST_PRODUCT_DETAIL = REST_PRODUCT_LIST + "%d/" REST_PRODUCTLIST_LIST = reverse("productdb:productlists-list") REST_PRODUCTLIST_DETAIL = REST_PRODUCTLIST_LIST + "%d/" REST_PRODUCTMIGRATIONSOURCE_LIST = reverse("productdb:productmigrationsources-list") REST_PRODUCTMIGRATIONSOURCE_DETAIL = REST_PRODUCTMIGRATIONSOURCE_LIST + "%d/" REST_PRODUCTMIGRATIONOPTION_LIST = reverse("productdb:productmigrationoptions-list") REST_PRODUCTMIGRATIONOPTION_DETAIL = REST_PRODUCTMIGRATIONOPTION_LIST + "%d/" REST_NOTIFICATIONMESSAGES_LIST = reverse("productdb:notificationmessages-list") REST_NOTIFICATIONMESSAGES_DETAIL = REST_NOTIFICATIONMESSAGES_LIST + "%d/" COMMON_API_ENDPOINT_BEHAVIOR = [ REST_VENDOR_LIST, REST_VENDOR_DETAIL % 1, REST_PRODUCT_GROUP_LIST, REST_PRODUCT_GROUP_DETAIL % 1, REST_PRODUCTLIST_LIST, REST_PRODUCTLIST_DETAIL % 1, REST_PRODUCTMIGRATIONSOURCE_LIST, REST_PRODUCTMIGRATIONSOURCE_DETAIL % 1, REST_PRODUCTMIGRATIONOPTION_LIST, REST_PRODUCTMIGRATIONOPTION_DETAIL % 1, REST_NOTIFICATIONMESSAGES_LIST ] @pytest.fixture def common_api_endpoint_objects(): """DB objects for the common API endpoint tests""" mixer.blend("productdb.ProductGroup") mixer.blend("productdb.ProductMigrationSource") mixer.blend("productdb.ProductMigrationOption") @pytest.mark.usefixtures("common_api_endpoint_objects") @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestCommonAPIEndpoint: """Test Django REST Framework API behavior""" def test_unauthorized_access(self): client = APIClient() for url in COMMON_API_ENDPOINT_BEHAVIOR: response = client.get(url) assert response.status_code == status.HTTP_401_UNAUTHORIZED, "Unauthorized access not allowed by default" assert response["Content-Type"] == "application/json", "Should use JSON by default" assert response.json() == {"detail": "Authentication credentials were not provided."} def test_invalid_authentication(self): client = APIClient() client.login(username="api", password="invalid password") for url in COMMON_API_ENDPOINT_BEHAVIOR: response = client.get(url) assert response.status_code == status.HTTP_401_UNAUTHORIZED assert response.json() == {'detail': 'Authentication credentials were not provided.'} def test_invalid_permissions(self): client = APIClient() client.login(**AUTH_USER) for url in COMMON_API_ENDPOINT_BEHAVIOR: response = client.post(url) assert response.status_code == status.HTTP_403_FORBIDDEN assert response.json() == {'detail': 'You do not have permission to perform this action.'} def test_xml_renderer(self): """smoke test to verify, that the XML renderer works. Only an XML renderer is implemented therefore write operations using XML are not possible""" for e in range(1, 50): p = mixer.blend("productdb.Product") test_queries = [ REST_VENDOR_LIST, REST_PRODUCT_GROUP_LIST, REST_PRODUCTLIST_LIST, REST_PRODUCTMIGRATIONSOURCE_LIST, REST_PRODUCTMIGRATIONOPTION_LIST, ] client = APIClient() client.login(**AUTH_USER) for url in test_queries: response = client.get(url + "?format=xml") assert response.status_code == status.HTTP_200_OK def test_page_size(self): for e in range(1, 50): mixer.blend("productdb.Product") client = APIClient() client.login(**AUTH_USER) # default page size is 25 response = client.get(REST_PRODUCT_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert jdata["pagination"]["page_records"] == 25, "default page size is 25" assert jdata["pagination"]["total_records"] == 50, "total records should be all products" # test custom page size response = client.get(REST_PRODUCT_LIST + "?page_size=40") assert response.status_code == status.HTTP_200_OK jdata = response.json() assert jdata["pagination"]["page_records"] == 40, "should contain 40 elements" assert jdata["pagination"]["total_records"] == 50, "total records should be all products" def test_token_authentication(self): for e in range(1, 50): mixer.blend("productdb.Product") client = APIClient() client.login(**AUTH_USER) # get a token by posting the username and password to the API endpoint response = client.post(REST_TOKEN_AUTH, data=dict(**AUTH_USER)) assert response.status_code == status.HTTP_200_OK assert "token" in response.json() @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestVendorAPIEndpoint: """ Django REST Framework API endpoint tests for the Vendor model """ def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_VENDOR_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_VENDOR_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "pagination": { "page": 1, "page_records": 3, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 3 }, "data": [ { "name": "unassigned", "id": 0, "url": "http://testserver/productdb/api/v1/vendors/0/" }, { "name": "Cisco Systems", "id": 1, "url": "http://testserver/productdb/api/v1/vendors/1/" }, { "name": "Juniper Networks", "id": 2, "url": "http://testserver/productdb/api/v1/vendors/2/" } ] } client = APIClient() client.login(**AUTH_USER) response = client.get(REST_VENDOR_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_vendor") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_vendor") client = APIClient() client.login(username=test_user, password=test_user) response = client.post(REST_VENDOR_LIST, data={"name": "Awesome Vendor"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "POST" not allowed.'} assert Vendor.objects.count() == 3, "no additional vendor is created" def test_change_access_with_permission(self): # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="change_vendor") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.change_vendor") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_VENDOR_DETAIL % 1, data={"name": "renamed vendor"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "PUT" not allowed.'} def test_delete_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="delete_vendor") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.delete_vendor") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_VENDOR_DETAIL % 1) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "DELETE" not allowed.'} assert Vendor.objects.count() == 3, "no vendor was deleted" def test_delete_unassigned_vendor_as_superuser(self): # not possible due to limitations in the model implementation client = APIClient() client.login(**SUPER_USER) response = client.delete(REST_VENDOR_DETAIL % 0) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "DELETE" not allowed.'} assert Vendor.objects.count() == 3, "no vendor was deleted" def test_search_field(self): """ search field implementation contains a regular expression search on the vendor name field :return: """ expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "name": "Cisco Systems", "id": 1, "url": "http://testserver/productdb/api/v1/vendors/1/" } ] } mixer.blend("productdb.Vendor", name="CCCCCCCi") client = APIClient() client.login(**AUTH_USER) # verify the use of regular expressions response = client.get(REST_VENDOR_LIST + "?search=" + quote("^Ci")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_fields(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "name": "Cisco Systems", "id": 1, "url": "http://testserver/productdb/api/v1/vendors/1/" } ] } client = APIClient() client.login(**AUTH_USER) # use ID field filter (exact match) response = client.get(REST_VENDOR_LIST + "?id=1") assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # use name field response = client.get(REST_VENDOR_LIST + "?name=" + quote("Cisco Systems")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # call with empty result response = client.get(REST_VENDOR_LIST + "?name=" + quote("Cisco")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 0, "should return nothing, because an exact match is required" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestProductMigrationOptionAPIEndpoint: """ Django REST Framework API endpoint tests for the ProductMigrationOption model """ def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_PRODUCTMIGRATIONOPTION_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_PRODUCTMIGRATIONOPTION_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "migration_source": 1, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement", "id": 1, "product": 1, "comment": "", }, { "migration_source": 1, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement2", "id": 2, "product": 2, "comment": "", } ] } p1 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=1, product_id="B") p2 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=2, product_id="A") pmg = mixer.blend("productdb.ProductMigrationSource", name="Cisco", id=1) pmo1 = mixer.blend("productdb.ProductMigrationOption", product=p1, migration_source=pmg, replacement_product_id=expected_result["data"][0]["replacement_product_id"], comment="") pmo2 = mixer.blend("productdb.ProductMigrationOption", product=p2, migration_source=pmg, replacement_product_id=expected_result["data"][1]["replacement_product_id"], comment="") expected_result["data"][0]["id"] = pmo1.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pmo1.id expected_result["data"][0]["comment"] = pmo1.comment expected_result["data"][1]["id"] = pmo2.id expected_result["data"][1]["url"] = expected_result["data"][1]["url"] % pmo2.id expected_result["data"][1]["comment"] = pmo2.comment client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_productmigrationoption") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_productmigrationoption") client = APIClient() client.login(username=test_user, password=test_user) response = client.post(REST_PRODUCTMIGRATIONOPTION_LIST, data={"replacement_id": "Awesome Product Migration Option"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "POST" not allowed.'} assert ProductMigrationOption.objects.count() == 0, "no additional product migration option is created" def test_change_access_with_permission(self): # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="change_productmigrationoption") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.change_productmigrationoption") mixer.blend("productdb.productmigrationoption") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCTMIGRATIONOPTION_DETAIL % 1, data={"comment": "renamed product migration source"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "PUT" not allowed.'} def test_delete_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="delete_productmigrationoption") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.delete_productmigrationoption") mixer.blend("productdb.productmigrationoption") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_PRODUCTMIGRATIONOPTION_DETAIL % 1) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "DELETE" not allowed.'} assert ProductMigrationOption.objects.count() == 1, "no product migration option was deleted" def test_search_field(self): """ search field contains a regular expression on the product id of the migration option and on the replacement product id :return: """ expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "migration_source": 1, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement", "id": 1, "product": 1, "comment": "", }, { "migration_source": 1, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement2", "id": 2, "product": 2, "comment": "", } ] } p1 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=1, product_id="A1") p2 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=2, product_id="A2") p3 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=3, product_id="B1") pmg = mixer.blend("productdb.ProductMigrationSource", name="Cisco", id=1) pmo1 = mixer.blend("productdb.ProductMigrationOption", product=p1, migration_source=pmg, replacement_product_id=expected_result["data"][0]["replacement_product_id"], comment="") pmo2 = mixer.blend("productdb.ProductMigrationOption", product=p2, migration_source=pmg, replacement_product_id=expected_result["data"][1]["replacement_product_id"], comment="") mixer.blend("productdb.ProductMigrationOption", product=p3, migration_source=pmg, replacement_product_id=expected_result["data"][1]["replacement_product_id"], comment="") expected_result["data"][0]["id"] = pmo1.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pmo1.id expected_result["data"][0]["comment"] = pmo1.comment expected_result["data"][1]["id"] = pmo2.id expected_result["data"][1]["url"] = expected_result["data"][1]["url"] % pmo2.id expected_result["data"][1]["comment"] = pmo2.comment client = APIClient() client.login(**AUTH_USER) # verify the use of regular expressions response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?search=" + quote("^A")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_fields(self): expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "migration_source": 1, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement1", "id": 1, "product": 1, "comment": "", }, { "migration_source": 2, "migration_product_info_url": None, "url": "http://testserver/productdb/api/v1/productmigrationoptions/%d/", "replacement_product_id": "replacement2", "id": 2, "product": 2, "comment": "", } ] } p1 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=1, product_id="A1") p2 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=2, product_id="A2") p3 = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1), id=3, product_id="B1") pmg = mixer.blend("productdb.ProductMigrationSource", name="Cisco", id=1) pmg2 = mixer.blend("productdb.ProductMigrationSource", name="Other", id=2) pmo1 = mixer.blend("productdb.ProductMigrationOption", product=p1, migration_source=pmg, replacement_product_id=expected_result["data"][0]["replacement_product_id"], comment="") pmo2 = mixer.blend("productdb.ProductMigrationOption", product=p2, migration_source=pmg2, replacement_product_id=expected_result["data"][1]["replacement_product_id"], comment="") mixer.blend("productdb.ProductMigrationOption", product=p3, migration_source=pmg2, replacement_product_id=expected_result["data"][1]["replacement_product_id"], comment="") expected_result["data"][0]["id"] = pmo1.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pmo1.id expected_result["data"][0]["comment"] = pmo1.comment expected_result["data"][1]["id"] = pmo2.id expected_result["data"][1]["url"] = expected_result["data"][1]["url"] % pmo2.id expected_result["data"][1]["comment"] = pmo2.comment client = APIClient() client.login(**AUTH_USER) # use ID field filter (exact match) response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?id=%s" % pmo1.id) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" mod_res = expected_result mod_res["pagination"] = { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 } del mod_res["data"][1] assert jdata == expected_result, "unexpected result from API endpoint" # use replacement_product_id field filter (startswith match) response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?replacement_product_id=" + quote("replacement1")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == mod_res, "unexpected result from API endpoint" # use product field filter (startswith match) response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?product=" + quote("A1")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == mod_res, "unexpected result from API endpoint" # use migration_source field filter (startswith match) response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?migration_source=" + quote("Cisco")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == mod_res, "unexpected result from API endpoint" # call with empty result response = client.get(REST_PRODUCTMIGRATIONOPTION_LIST + "?replacement_product_id=" + quote("invalid")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 0, "should return nothing, because an exact match is required" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestProductMigrationSourceAPIEndpoint: """ Django REST Framework API endpoint tests for the ProductMigrationSource model """ def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_PRODUCTMIGRATIONSOURCE_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_PRODUCTMIGRATIONSOURCE_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "id": 1, "preference": 50, "url": "http://testserver/productdb/api/v1/productmigrationsources/1/", "description": "My description", "name": "Cisco", }, { "id": 2, "preference": 50, "url": "http://testserver/productdb/api/v1/productmigrationsources/2/", "description": "My other description", "name": "other", } ] } [mixer.blend("productdb.ProductMigrationSource", **expected_result["data"][i]) for i in range(0, len(expected_result["data"]))] client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCTMIGRATIONSOURCE_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_productmigrationsource") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_productmigrationsource") client = APIClient() client.login(username=test_user, password=test_user) response = client.post(REST_PRODUCTMIGRATIONSOURCE_LIST, data={"name": "Awesome Product Migration Source"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "POST" not allowed.'} assert ProductMigrationSource.objects.count() == 0, "no additional vendor is created" def test_change_access_with_permission(self): # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="change_productmigrationsource") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.change_productmigrationsource") mixer.blend("productdb.productmigrationsource") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCTMIGRATIONSOURCE_DETAIL % 1, data={"name": "renamed product migration source"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "PUT" not allowed.'} def test_delete_access_with_permission(self): test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="delete_productmigrationsource") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.delete_productmigrationsource") mixer.blend("productdb.productmigrationsource") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_PRODUCTMIGRATIONSOURCE_DETAIL % 1) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "DELETE" not allowed.'} assert ProductMigrationSource.objects.count() == 1, "no product migration source was deleted" def test_search_field(self): """ search field implementation contains a regular expression search on the vendor name field :return: """ expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "name": "Cisco Systems", "url": "http://testserver/productdb/api/v1/productmigrationsources/%d/", "id": 1, "preference": 50, "description": None, } ] } pmg = mixer.blend("productdb.productmigrationsource", name="Cisco Systems") expected_result["data"][0]["id"] = pmg.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pmg.id mixer.blend("productdb.productmigrationsource", name="Other PMG") client = APIClient() client.login(**AUTH_USER) # verify the use of regular expressions response = client.get(REST_PRODUCTMIGRATIONSOURCE_LIST + "?search=" + quote("^Ci")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_fields(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "description": None, "id": 1, "name": "Cisco Systems", "preference": 50, "url": "http://testserver/productdb/api/v1/productmigrationsources/%d/" } ] } pmg = mixer.blend("productdb.productmigrationsource", name="Cisco Systems") expected_result["data"][0]["id"] = pmg.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pmg.id mixer.blend("productdb.productmigrationsource", name="Other PMG") client = APIClient() client.login(**AUTH_USER) # use ID field filter (exact match) response = client.get(REST_PRODUCTMIGRATIONSOURCE_LIST + "?id=%s" % pmg.id) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # use name field filter (exact match) response = client.get(REST_PRODUCTMIGRATIONSOURCE_LIST + "?name=" + quote("Cisco Systems")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # call with empty result response = client.get(REST_PRODUCTMIGRATIONSOURCE_LIST + "?name=" + quote("Cisco")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 0, "should return nothing, because an exact match is required" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestProductGroupAPIEndpoint: """Django REST Framework API endpoint tests for the Product Group model""" def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_PRODUCT_GROUP_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_PRODUCT_GROUP_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "data": [ { "url": "http://testserver/productdb/api/v1/productgroups/1/", "name": "product group 1", "id": 1, "vendor": 0 }, { "url": "http://testserver/productdb/api/v1/productgroups/2/", "name": "product group 2", "id": 2, "vendor": 0 }, { "url": "http://testserver/productdb/api/v1/productgroups/3/", "name": "product group 3", "id": 3, "vendor": 0 } ], "pagination": { "page_records": 3, "last_page": 1, "url": { "next": None, "previous": None }, "page": 1, "total_records": 3 } } mixer.blend("productdb.ProductGroup", name="product group 1") mixer.blend("productdb.ProductGroup", name="product group 2") mixer.blend("productdb.ProductGroup", name="product group 3") client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCT_GROUP_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" # adjust ID values from Database for c in range(0, 3): expected_result["data"][c]["id"] = ProductGroup.objects.get(name="product group %d" % (c+1)).id expected_result["data"][c]["url"] = "http://testserver/productdb/api/v1/productgroups/%d/" % expected_result["data"][c]["id"] assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): test_user = "user" test_product_group_name = "Test Product Group" expected_result = { "vendor": 1, "name": test_product_group_name, "url": "http://testserver/productdb/api/v1/productgroups/1/", "id": 1 } u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_productgroup") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_productgroup") client = APIClient() client.login(username=test_user, password=test_user) # create with name response = client.post(REST_PRODUCT_GROUP_LIST, data={"name": test_product_group_name}) assert response.status_code == status.HTTP_400_BAD_REQUEST assert response.json() == {'vendor': ['This field is required.']} # create with name name and Vendor ID response = client.post(REST_PRODUCT_GROUP_LIST, data={"name": test_product_group_name, "vendor": 1}) assert response.status_code == status.HTTP_201_CREATED # adjust ID values from Database expected_result["id"] = ProductGroup.objects.get(name=test_product_group_name).id expected_result["url"] = "http://testserver/productdb/api/v1/productgroups/%d/" % expected_result["id"] assert response.json() == expected_result, "Should provide the new product group" def test_change_access_with_permission(self): test_product_group = "renamed product group" pg = mixer.blend("productdb.ProductGroup", name="product group") expected_result = { "url": "http://testserver/productdb/api/v1/productgroups/%d/", "vendor": 0, "name": test_product_group, "id": 0 } # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="change_productgroup") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.change_productgroup") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCT_GROUP_DETAIL % pg.id, data={"name": test_product_group}) assert response.status_code == status.HTTP_200_OK # adjust pk value expected_result["id"] = ProductGroup.objects.get(name=test_product_group).id expected_result["url"] = expected_result["url"] % expected_result["id"] assert response.json() == expected_result def test_delete_access_with_permission(self): pg = mixer.blend("productdb.ProductGroup") assert ProductGroup.objects.count() == 1 test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="delete_productgroup") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.delete_productgroup") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_PRODUCT_GROUP_DETAIL % pg.id) assert response.status_code == status.HTTP_204_NO_CONTENT assert ProductGroup.objects.count() == 0 def test_count_endpoint(self): mixer.blend("productdb.ProductGroup", name="product group 1") mixer.blend("productdb.ProductGroup", name="product group 2") mixer.blend("productdb.ProductGroup", name="product group 3") assert ProductGroup.objects.count() == 3 client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCT_GROUP_COUNT) assert response.status_code == status.HTTP_200_OK assert response.json() == {'count': 3} def test_search_field(self): expected_result = { "pagination": { "page": 1, "page_records": 5, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 5 }, "data": [ { "vendor": 0, "id": 0, "name": "product group 0", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, { "vendor": 0, "id": 0, "name": "product group 1", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, { "vendor": 0, "id": 0, "name": "product group 2", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, { "vendor": 0, "id": 0, "name": "product group 3", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, { "vendor": 0, "id": 0, "name": "product group 4", "url": "http://testserver/productdb/api/v1/productgroups/%d/" } ] } for e in range(0, 5): ProductGroup.objects.create(name="product group %d" % e) client = APIClient() client.login(**AUTH_USER) # verify the use of regular expressions response = client.get(REST_PRODUCT_GROUP_LIST + "?search=" + quote("^product group \d+$")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 5, "Should contain five elements" # adjust pk values for e in range(0, 5): expected_result["data"][e]["id"] = ProductGroup.objects.get(name="product group %d" % e).id expected_result["data"][e]["url"] = expected_result["data"][e]["url"] % expected_result["data"][e]["id"] assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_id_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "vendor": 0, "id": 1, "name": "TBD", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, ] } pg = mixer.blend("productdb.ProductGroup", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = pg.id expected_result["data"][0]["vendor"] = pg.vendor.id expected_result["data"][0]["name"] = pg.name expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert ProductGroup.objects.count() == 1 client = APIClient() client.login(**AUTH_USER) # use ID field filter (exact match) response = client.get(REST_PRODUCT_GROUP_LIST + "?id=%d" % pg.id) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_name_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "vendor": 0, "id": 1, "name": "", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, ] } pg = mixer.blend("productdb.ProductGroup", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = pg.id expected_result["data"][0]["vendor"] = pg.vendor.id expected_result["data"][0]["name"] = pg.name expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert ProductGroup.objects.count() == 1 client = APIClient() client.login(**AUTH_USER) # use name field (exact match) response = client.get(REST_PRODUCT_GROUP_LIST + "?name=" + quote(pg.name)) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_vendor_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "vendor": 0, "id": 1, "name": "TBD", "url": "http://testserver/productdb/api/v1/productgroups/%d/" }, ] } pg = mixer.blend("productdb.ProductGroup", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = pg.id expected_result["data"][0]["vendor"] = pg.vendor.id expected_result["data"][0]["name"] = pg.name expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert ProductGroup.objects.count() == 1 client = APIClient() client.login(**AUTH_USER) # use vendor field (startswith) response = client.get(REST_PRODUCT_GROUP_LIST + "?vendor=" + quote("Cisco")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestProductAPIEndpoint: """Django REST Framework API endpoint tests for the Product model""" today_string = DateFormat(datetime.now()).format(get_format(settings.SHORT_DATE_FORMAT)) def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_PRODUCT_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_PRODUCT_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 0, "list_price": "12.32", "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": self.today_string } ] } p = mixer.blend("productdb.Product", list_price=12.32) expected_result["data"][0]["id"] = p.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % p.id expected_result["data"][0]["product_id"] = p.product_id client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCT_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata["pagination"]["total_records"] == 1, "unexpected result from API endpoint" assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): test_user = "user" test_product_id = "Test Product ID" expected_result = { "currency": "USD", "end_of_service_contract_renewal": None, "eol_reference_url": None, "url": "http://testserver/productdb/api/v1/products/%d/", "eol_reference_number": None, "product_group": None, "end_of_sale_date": None, "description": "", "vendor": 0, "tags": "", "list_price": None, "eol_ext_announcement_date": None, "eox_update_time_stamp": None, "end_of_new_service_attachment_date": None, "end_of_support_date": None, "end_of_sw_maintenance_date": None, "end_of_sec_vuln_supp_date": None, "end_of_routine_failure_analysis": None, "id": 0, "product_id": test_product_id, "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_product") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_product") client = APIClient() client.login(username=test_user, password=test_user) # create with name response = client.post(REST_PRODUCT_LIST, data={"product_id": test_product_id}) assert response.status_code == status.HTTP_201_CREATED, response.content.decode() # adjust ID values from Database expected_result["id"] = Product.objects.get(product_id=test_product_id).id expected_result["url"] = "http://testserver/productdb/api/v1/products/%d/" % expected_result["id"] assert response.json() == expected_result, "Should provide the new product" def test_create_product_with_lc_state_sync_field(self): test_user = "user" test_product_id = "Test Product ID" expected_result = { "currency": "USD", "end_of_service_contract_renewal": None, "eol_reference_url": None, "url": "http://testserver/productdb/api/v1/products/%d/", "eol_reference_number": None, "product_group": None, "end_of_sale_date": None, "description": "", "vendor": 0, "tags": "", "list_price": None, "eol_ext_announcement_date": None, "eox_update_time_stamp": None, "end_of_new_service_attachment_date": None, "end_of_support_date": None, "end_of_sw_maintenance_date": None, "end_of_sec_vuln_supp_date": None, "end_of_routine_failure_analysis": None, "id": 0, "product_id": test_product_id, "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_product") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_product") client = APIClient() client.login(username=test_user, password=test_user) # create with name response = client.post(REST_PRODUCT_LIST, data={"product_id": test_product_id, "lc_state_sync": True}) assert response.status_code == status.HTTP_201_CREATED # adjust ID values from Database expected_result["id"] = Product.objects.get(product_id=test_product_id).id expected_result["url"] = "http://testserver/productdb/api/v1/products/%d/" % expected_result["id"] assert response.json() == expected_result, "Should provide the new product" def test_change_lc_state_sync(self): p = mixer.blend("productdb.Product", product_id="product ID") expected_result = { "currency": "USD", "end_of_service_contract_renewal": None, "eol_reference_url": None, "url": "http://testserver/productdb/api/v1/products/%d/", "eol_reference_number": None, "product_group": None, "end_of_sale_date": None, "description": "", "vendor": 0, "tags": "", "list_price": None, "eol_ext_announcement_date": None, "eox_update_time_stamp": None, "end_of_new_service_attachment_date": None, "end_of_support_date": None, "end_of_sw_maintenance_date": None, "end_of_sec_vuln_supp_date": None, "end_of_routine_failure_analysis": None, "id": 0, "product_id": p.product_id, "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) perm = Permission.objects.get(codename="change_product") assert perm is not None u.user_permissions.add(perm) u.save() assert u.has_perm("productdb.change_product") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCT_DETAIL % p.id, data={ "product_id": p.product_id, "lc_state_sync": True }) assert response.status_code == status.HTTP_200_OK # adjust pk value expected_result["id"] = Product.objects.get(product_id="product ID").id expected_result["url"] = expected_result["url"] % expected_result["id"] assert response.json() == expected_result def test_change_access_with_permission(self): p = mixer.blend("productdb.Product", product_id="product ID") test_renamed_product = "renamed product" expected_result = { "currency": "USD", "end_of_service_contract_renewal": None, "eol_reference_url": None, "url": "http://testserver/productdb/api/v1/products/%d/", "eol_reference_number": None, "product_group": None, "end_of_sale_date": None, "description": "", "vendor": 0, "tags": "", "list_price": None, "eol_ext_announcement_date": None, "eox_update_time_stamp": None, "end_of_new_service_attachment_date": None, "end_of_support_date": None, "end_of_sw_maintenance_date": None, "end_of_sec_vuln_supp_date": None, "end_of_routine_failure_analysis": None, "id": 0, "product_id": test_renamed_product, "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) perm = Permission.objects.get(codename="change_product") assert perm is not None u.user_permissions.add(perm) u.save() assert u.has_perm("productdb.change_product") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCT_DETAIL % p.id, data={"product_id": test_renamed_product}) assert response.status_code == status.HTTP_200_OK # adjust pk value expected_result["id"] = Product.objects.get(product_id=test_renamed_product).id expected_result["url"] = expected_result["url"] % expected_result["id"] assert response.json() == expected_result def test_change_product_group(self): v1 = Vendor.objects.get(id=1) v2 = Vendor.objects.get(id=2) invalid_pg = mixer.blend("productdb.ProductGroup", name="invalid product group", vendor=v2) valid_pg = mixer.blend("productdb.ProductGroup", name="valid product group", vendor=v1) p = mixer.blend("productdb.Product", product_id="product ID", vendor=v1) expected_result = { "currency": "USD", "end_of_service_contract_renewal": None, "eol_reference_url": None, "url": "http://testserver/productdb/api/v1/products/%d/" % p.id, "eol_reference_number": None, "product_group": valid_pg.id, "end_of_sale_date": None, "description": "", "vendor": v1.id, "tags": "", "list_price": None, "eol_ext_announcement_date": None, "eox_update_time_stamp": None, "end_of_new_service_attachment_date": None, "end_of_support_date": None, "end_of_sw_maintenance_date": None, "end_of_sec_vuln_supp_date": None, "end_of_routine_failure_analysis": None, "id": p.id, "product_id": p.product_id, "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } client = APIClient() client.login(**SUPER_USER) # try to associate the product to a product group of a different vendor response = client.put(REST_PRODUCT_DETAIL % p.id, data={ "product_id": p.product_id, "product_group": invalid_pg.id }) assert response.status_code == status.HTTP_400_BAD_REQUEST jdata = response.json() assert len(jdata) == 1, "Should contain a single error message" assert "product_group" in jdata assert "Invalid product group, group and product must be associated to the same vendor" in str(jdata) # try to associate the product to a product group of the same vendor response = client.put(REST_PRODUCT_DETAIL % p.id, data={ "product_id": p.product_id, "product_group": valid_pg.id }) assert response.status_code == status.HTTP_200_OK assert response.json() == expected_result def test_delete_access_with_permission(self): p = mixer.blend("productdb.Product") assert Product.objects.count() == 1 test_user = "user" u = User.objects.create_user(test_user, "", test_user) perm = Permission.objects.get(codename="delete_product") assert perm is not None u.user_permissions.add(perm) u.save() assert u.has_perm("productdb.delete_product") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_PRODUCT_DETAIL % p.id) assert response.status_code == status.HTTP_204_NO_CONTENT assert Product.objects.count() == 0 def test_count_endpoint(self): mixer.blend("productdb.Product", name="product 1") mixer.blend("productdb.Product", name="product 2") mixer.blend("productdb.Product", name="product 3") assert Product.objects.count() == 3 client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCT_COUNT) assert response.status_code == status.HTTP_200_OK assert response.json() == {'count': 3} def test_search_field_by_product_id(self): expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 21", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None }, { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } for e in range(0, 5): Product.objects.create(product_id="test product %d" % e) for e in range(1, 3): p = Product.objects.create(product_id="product 2%d" % e) expected_result["data"][e-1]["id"] = p.id expected_result["data"][e-1]["url"] = expected_result["data"][e-1]["url"] % p.id client = APIClient() client.login(**AUTH_USER) # search by product ID (with regular expression response = client.get(REST_PRODUCT_LIST + "?search=" + quote("^product \d+$")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == len(expected_result["data"]), \ "Should contain the same amount of elements as the expected result" assert jdata == expected_result, "unexpected result from API endpoint" def test_search_field_by_product_description(self): expected_result = { "pagination": { "page": 1, "page_records": 2, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 2 }, "data": [ { "id": 0, "list_price": None, "description": "my search description", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 21", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None }, { "id": 0, "list_price": None, "description": "other search description", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } for e in range(0, 5): Product.objects.create(product_id="test product %d" % e, description=str(e)) for e in range(1, 3): p = Product.objects.create( product_id="product 2%d" % e, description=expected_result["data"][e-1]["description"] ) expected_result["data"][e-1]["id"] = p.id expected_result["data"][e-1]["url"] = expected_result["data"][e-1]["url"] % p.id client = APIClient() client.login(**AUTH_USER) # search by product ID (with regular expression response = client.get(REST_PRODUCT_LIST + "?search=" + quote("^\w+ search description$")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == len(expected_result["data"]), \ "Should contain the same amount of elements as the expected result" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_id_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1)) p = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = p.id expected_result["data"][0]["vendor"] = p.vendor.id expected_result["data"][0]["product_id"] = p.product_id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert Product.objects.count() == 2 client = APIClient() client.login(**AUTH_USER) # use vendor field (startswith) response = client.get(REST_PRODUCT_LIST + "?id=" + str(p.id)) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_product_id_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1)) p = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = p.id expected_result["data"][0]["vendor"] = p.vendor.id expected_result["data"][0]["product_id"] = p.product_id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert Product.objects.count() == 2 client = APIClient() client.login(**AUTH_USER) # use product_id (exact match) response = client.get(REST_PRODUCT_LIST + "?product_id=" + quote(p.product_id)) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" # use incomplete product_id response = client.get(REST_PRODUCT_LIST + "?product_id=" + quote(p.product_id[:5])) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 0, "Should return no element" def test_filter_vendor_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=2)) p = mixer.blend("productdb.Product", vendor=Vendor.objects.get(id=1)) expected_result["data"][0]["id"] = p.id expected_result["data"][0]["vendor"] = p.vendor.id expected_result["data"][0]["product_id"] = p.product_id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert Product.objects.count() == 2 client = APIClient() client.login(**AUTH_USER) # use vendor field (startswith) response = client.get(REST_PRODUCT_LIST + "?vendor=" + quote("Cisco")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" def test_filter_product_group_field(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 0, "list_price": None, "description": "", "eol_reference_url": None, "eol_ext_announcement_date": None, "url": "http://testserver/productdb/api/v1/products/%d/", "end_of_sec_vuln_supp_date": None, "end_of_service_contract_renewal": None, "end_of_support_date": None, "eol_reference_number": None, "end_of_sw_maintenance_date": None, "tags": "", "vendor": 0, "product_id": "product 22", "end_of_routine_failure_analysis": None, "end_of_sale_date": None, "eox_update_time_stamp": None, "product_group": None, "end_of_new_service_attachment_date": None, "currency": "USD", "lc_state_sync": False, "internal_product_id": None, "update_timestamp": self.today_string, "list_price_timestamp": None } ] } v1 = Vendor.objects.get(id=1) v2 = Vendor.objects.get(id=2) mixer.blend( "productdb.Product", vendor=v2, product_group=mixer.blend("productdb.ProductGroup", vendor=v2) ) pg = mixer.blend("productdb.ProductGroup", vendor=v1) p = mixer.blend("productdb.Product", vendor=v1, product_group=pg) expected_result["data"][0]["id"] = p.id expected_result["data"][0]["vendor"] = p.vendor.id expected_result["data"][0]["product_id"] = p.product_id expected_result["data"][0]["product_group"] = pg.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % expected_result["data"][0]["id"] assert Product.objects.count() == 2 client = APIClient() client.login(**AUTH_USER) # use product_group field (exact match) response = client.get(REST_PRODUCT_LIST + "?product_group=" + quote(pg.name)) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 1, "Expect a single entry in the result" assert jdata == expected_result, "unexpected result from API endpoint" # use incomplete product_group field response = client.get(REST_PRODUCT_LIST + "?product_group=" + quote(pg.name[:5])) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata["pagination"]["total_records"] == 0, "Should return no element" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestProductListAPIEndpoint: """Django REST framework API endpoint tests for the Product List model""" TEST_PRODUCTS = [ "Product A", "Product B", "Product C", "Product D", "Product E" ] TEST_PRODUCT_LIST_NAME = "Test Product List" def create_test_data(self): for e in self.TEST_PRODUCTS: mixer.blend("productdb.Product", product_id=e) def create_test_product_list(self): self.create_test_data() u = User.objects.get(username="pdb_admin") pl = mixer.blend( "productdb.ProductList", name=self.TEST_PRODUCT_LIST_NAME, description="Test Liste\nJust a test list", string_product_list="\n".join(self.TEST_PRODUCTS), update_user=u ) return pl.id def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_PRODUCTLIST_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_PRODUCTLIST_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): self.create_test_data() expected_result = { "pagination": { "page_records": 1, "total_records": 1, "url": { "previous": None, "next": None }, "page": 1, "last_page": 1 }, "data": [ { "id": 0, "name": "TestList", "description": "Test Liste\nJust a test list", "string_product_list": self.TEST_PRODUCTS, "update_date": "", "contact_email": "", "url": "http://testserver/productdb/api/v1/productlists/%d/" } ] } u = User.objects.get(username="api") pl = mixer.blend( "productdb.ProductList", name=expected_result["data"][0]["name"], description=expected_result["data"][0]["description"], string_product_list="\n".join(expected_result["data"][0]["string_product_list"]), update_user=u ) expected_result["data"][0]["id"] = pl.id expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pl.id expected_result["data"][0]["update_date"] = pl.update_date.strftime("%Y-%m-%d") expected_result["data"][0]["contact_email"] = u.email client = APIClient() client.login(**AUTH_USER) response = client.get(REST_PRODUCTLIST_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata["pagination"]["total_records"] == 1, "unexpected result from API endpoint" assert jdata == expected_result, "unexpected result from API endpoint" # access first element of the list response = client.get(jdata["data"][0]["url"]) assert response.status_code == status.HTTP_200_OK assert jdata["data"][0] == response.json() def test_add_access_with_permission(self): """add action through API for Product List not supported""" self.create_test_data() test_user = "user" test_product_list_id = "Test Product List" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_productlist") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.add_productlist") client = APIClient() client.login(username=test_user, password=test_user) # create with name response = client.post(REST_PRODUCTLIST_LIST, data={"name": test_product_list_id}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED assert response.json() == {'detail': 'Method "POST" not allowed.'} assert ProductList.objects.count() == 0, "no product list was created" def test_change_access_with_permission(self): id = self.create_test_product_list() # create a user with permissions test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="change_productlist") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.change_productlist") client = APIClient() client.login(username=test_user, password=test_user) response = client.put(REST_PRODUCTLIST_DETAIL % id, data={"name": "renamed product list"}) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "PUT" not allowed.'} def test_delete_access_with_permission(self): id = self.create_test_product_list() test_user = "user" u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="delete_productlist") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("productdb.delete_productlist") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_PRODUCTLIST_DETAIL % id) assert response.status_code == status.HTTP_405_METHOD_NOT_ALLOWED, "API endpoint is always read only" assert response.json() == {'detail': 'Method "DELETE" not allowed.'} assert ProductList.objects.count() == 1, "no product list was deleted" def test_filter_fields(self): pl_id = self.create_test_product_list() mixer.blend("productdb.ProductList", name="Product List", string_product_list="Product A") expected_result = { "pagination": { "page_records": 1, "total_records": 1, "url": { "previous": None, "next": None }, "page": 1, "last_page": 1 }, "data": [ { "id": 0, "name": self.TEST_PRODUCT_LIST_NAME, "description": "Test Liste\nJust a test list", "string_product_list": self.TEST_PRODUCTS, "update_date": "", "contact_email": "admin@localhost.localhost", "url": "http://testserver/productdb/api/v1/productlists/%d/" } ] } expected_result["data"][0]["id"] = pl_id expected_result["data"][0]["update_date"] = date.today().strftime("%Y-%m-%d") expected_result["data"][0]["url"] = expected_result["data"][0]["url"] % pl_id client = APIClient() client.login(**AUTH_USER) # use ID field filter (exact match) response = client.get(REST_PRODUCTLIST_LIST + "?id=%d" % pl_id) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # use name field (contains) response = client.get(REST_PRODUCTLIST_LIST + "?name=" + quote(self.TEST_PRODUCT_LIST_NAME.lower())) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" # use description field (contains) response = client.get(REST_PRODUCTLIST_LIST + "?description=" + quote("Just a test")) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not provided" assert jdata == expected_result, "unexpected result from API endpoint" @pytest.mark.usefixtures("import_default_users") @pytest.mark.usefixtures("import_default_vendors") class TestNotificationMessageAPIEndpoint: """Django REST Framework API endpoint tests for the NotificationMessage model""" today_string = DateFormat(datetime.now()).format(get_format(settings.SHORT_DATE_FORMAT)) def test_token_authentication(self, live_server): token, _ = Token.objects.get_or_create(user=User.objects.get(username=AUTH_USER["username"])) response = requests.get(live_server + REST_NOTIFICATIONMESSAGES_LIST, headers={ "Authorization": "Token %s" % token.key }) assert response.status_code == status.HTTP_200_OK, response.text response = requests.get(live_server + REST_NOTIFICATIONMESSAGES_LIST, headers={ "Authorization": "Token invalid_token" }) assert response.status_code == status.HTTP_401_UNAUTHORIZED def test_read_access_with_authenticated_user(self): expected_result = { "pagination": { "page": 1, "page_records": 1, "url": { "next": None, "previous": None }, "last_page": 1, "total_records": 1 }, "data": [ { "id": 1, "title": "FooBar", "type": "INFO", "summary_message": "Test", "detailed_message": "Test", "created": "" } ] } nm = mixer.blend("config.NotificationMessage") expected_result["data"][0]["id"] = nm.id expected_result["data"][0]["title"] = nm.title expected_result["data"][0]["summary_message"] = nm.summary_message expected_result["data"][0]["detailed_message"] = nm.detailed_message expected_result["data"][0]["created"] = nm.created.isoformat().replace("+00:00", "Z") client = APIClient() client.login(**AUTH_USER) response = client.get(REST_NOTIFICATIONMESSAGES_LIST) assert response.status_code == status.HTTP_200_OK jdata = response.json() assert "pagination" in jdata, "pagination information not provided" assert "data" in jdata, "data branch not found in result" assert jdata["pagination"]["total_records"] == 1, "unexpected result from API endpoint" print(jdata) assert jdata == expected_result, "unexpected result from API endpoint" def test_add_access_with_permission(self): test_user = "user" expected_result = { "id": 1, "title": "FooBar", "type": "INFO", "summary_message": "summary message", "detailed_message": "detailed message", "created": None } u = User.objects.create_user(test_user, "", test_user) p = Permission.objects.get(codename="add_notificationmessage") assert p is not None u.user_permissions.add(p) u.save() assert u.has_perm("config.add_notificationmessage") client = APIClient() client.login(username=test_user, password=test_user) # create with name response = client.post(REST_NOTIFICATIONMESSAGES_LIST, data={ "title": expected_result["title"], "summary_message": expected_result["summary_message"], "detailed_message": expected_result["detailed_message"] }) assert response.status_code == status.HTTP_201_CREATED, response.content.decode() # adjust ID values from Database nm_obj = NotificationMessage.objects.get(title=expected_result["title"]) expected_result["id"] = nm_obj.id expected_result["created"] = nm_obj.created.isoformat().replace("+00:00", "Z") assert response.json() == expected_result, "Should provide the new notification message" def test_delete_access_with_permission(self): nm = mixer.blend("config.NotificationMessage") assert NotificationMessage.objects.count() == 1 test_user = "user" u = User.objects.create_user(test_user, "", test_user) perm = Permission.objects.get(codename="delete_notificationmessage") assert perm is not None u.user_permissions.add(perm) u.save() assert u.has_perm("config.delete_notificationmessage") client = APIClient() client.login(username=test_user, password=test_user) response = client.delete(REST_NOTIFICATIONMESSAGES_DETAIL % nm.id) assert response.status_code == status.HTTP_204_NO_CONTENT assert NotificationMessage.objects.count() == 0

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37,320

py

Python

stats_scripts/mpmath/functions/theta.py

michalkouril/altanalyze

e721c79c56f7b0022516ff5456ebaa14104c933b

[ "Apache-2.0" ]

625

2015-01-07T04:56:25.000Z

2022-03-28T16:30:27.000Z

stats_scripts/mpmath/functions/theta.py

michalkouril/altanalyze

e721c79c56f7b0022516ff5456ebaa14104c933b

[ "Apache-2.0" ]

322

2015-01-01T15:19:37.000Z

2022-03-27T05:07:51.000Z

stats_scripts/mpmath/functions/theta.py

michalkouril/altanalyze

e721c79c56f7b0022516ff5456ebaa14104c933b

[ "Apache-2.0" ]

160

2015-01-25T01:16:52.000Z

2022-03-21T14:44:20.000Z

from .functions import defun, defun_wrapped @defun def _jacobi_theta2(ctx, z, q): extra1 = 10 extra2 = 20 # the loops below break when the fixed precision quantities # a and b go to zero; # right shifting small negative numbers by wp one obtains -1, not zero, # so the condition a**2 + b**2 > MIN is used to break the loops. MIN = 2 if z == ctx.zero: if (not ctx._im(q)): wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) x2 = (x*x) >> wp a = b = x2 s = x2 while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp s += a s = (1 << (wp+1)) + (s << 1) s = ctx.ldexp(s, -wp) else: wp = ctx.prec + extra1 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp-1) are = bre = x2re aim = bim = x2im sre = (1<<wp) + are sim = aim while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp sre += are sim += aim sre = (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) else: if (not ctx._im(q)) and (not ctx._im(z)): wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) x2 = (x*x) >> wp a = b = x2 c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) cn = c1 = ctx.to_fixed(c1, wp) sn = s1 = ctx.to_fixed(s1, wp) c2 = (c1*c1 - s1*s1) >> wp s2 = (c1 * s1) >> (wp - 1) cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp s = c1 + ((a * cn) >> wp) while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp s += (a * cn) >> wp s = (s << 1) s = ctx.ldexp(s, -wp) s *= ctx.nthroot(q, 4) return s # case z real, q complex elif not ctx._im(z): wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = x2re aim = bim = x2im c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) cn = c1 = ctx.to_fixed(c1, wp) sn = s1 = ctx.to_fixed(s1, wp) c2 = (c1*c1 - s1*s1) >> wp s2 = (c1 * s1) >> (wp - 1) cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp sre = c1 + ((are * cn) >> wp) sim = ((aim * cn) >> wp) while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp sre += ((are * cn) >> wp) sim += ((aim * cn) >> wp) sre = (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) #case z complex, q real elif not ctx._im(q): wp = ctx.prec + extra2 x = ctx.to_fixed(ctx._re(q), wp) x2 = (x*x) >> wp a = b = x2 prec0 = ctx.prec ctx.prec = wp c1, s1 = ctx.cos_sin(z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) #c2 = (c1*c1 - s1*s1) >> wp c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) #s2 = (c1 * s1) >> (wp - 1) s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 sre = c1re + ((a * cnre) >> wp) sim = c1im + ((a * cnim) >> wp) while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 sre += ((a * cnre) >> wp) sim += ((a * cnim) >> wp) sre = (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) # case z and q complex else: wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = x2re aim = bim = x2im prec0 = ctx.prec ctx.prec = wp # cos(z), sin(z) with z complex c1, s1 = ctx.cos_sin(z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 n = 1 termre = c1re termim = c1im sre = c1re + ((are * cnre - aim * cnim) >> wp) sim = c1im + ((are * cnim + aim * cnre) >> wp) n = 3 termre = ((are * cnre - aim * cnim) >> wp) termim = ((are * cnim + aim * cnre) >> wp) sre = c1re + ((are * cnre - aim * cnim) >> wp) sim = c1im + ((are * cnim + aim * cnre) >> wp) n = 5 while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 termre = ((are * cnre - aim * cnim) >> wp) termim = ((aim * cnre + are * cnim) >> wp) sre += ((are * cnre - aim * cnim) >> wp) sim += ((aim * cnre + are * cnim) >> wp) n += 2 sre = (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) s *= ctx.nthroot(q, 4) return s @defun def _djacobi_theta2(ctx, z, q, nd): MIN = 2 extra1 = 10 extra2 = 20 if (not ctx._im(q)) and (not ctx._im(z)): wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) x2 = (x*x) >> wp a = b = x2 c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) cn = c1 = ctx.to_fixed(c1, wp) sn = s1 = ctx.to_fixed(s1, wp) c2 = (c1*c1 - s1*s1) >> wp s2 = (c1 * s1) >> (wp - 1) cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp if (nd&1): s = s1 + ((a * sn * 3**nd) >> wp) else: s = c1 + ((a * cn * 3**nd) >> wp) n = 2 while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp if nd&1: s += (a * sn * (2*n+1)**nd) >> wp else: s += (a * cn * (2*n+1)**nd) >> wp n += 1 s = -(s << 1) s = ctx.ldexp(s, -wp) # case z real, q complex elif not ctx._im(z): wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = x2re aim = bim = x2im c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp) cn = c1 = ctx.to_fixed(c1, wp) sn = s1 = ctx.to_fixed(s1, wp) c2 = (c1*c1 - s1*s1) >> wp s2 = (c1 * s1) >> (wp - 1) cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp if (nd&1): sre = s1 + ((are * sn * 3**nd) >> wp) sim = ((aim * sn * 3**nd) >> wp) else: sre = c1 + ((are * cn * 3**nd) >> wp) sim = ((aim * cn * 3**nd) >> wp) n = 5 while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp if (nd&1): sre += ((are * sn * n**nd) >> wp) sim += ((aim * sn * n**nd) >> wp) else: sre += ((are * cn * n**nd) >> wp) sim += ((aim * cn * n**nd) >> wp) n += 2 sre = -(sre << 1) sim = -(sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) #case z complex, q real elif not ctx._im(q): wp = ctx.prec + extra2 x = ctx.to_fixed(ctx._re(q), wp) x2 = (x*x) >> wp a = b = x2 prec0 = ctx.prec ctx.prec = wp c1, s1 = ctx.cos_sin(z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) #c2 = (c1*c1 - s1*s1) >> wp c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) #s2 = (c1 * s1) >> (wp - 1) s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if (nd&1): sre = s1re + ((a * snre * 3**nd) >> wp) sim = s1im + ((a * snim * 3**nd) >> wp) else: sre = c1re + ((a * cnre * 3**nd) >> wp) sim = c1im + ((a * cnim * 3**nd) >> wp) n = 5 while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if (nd&1): sre += ((a * snre * n**nd) >> wp) sim += ((a * snim * n**nd) >> wp) else: sre += ((a * cnre * n**nd) >> wp) sim += ((a * cnim * n**nd) >> wp) n += 2 sre = -(sre << 1) sim = -(sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) # case z and q complex else: wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = x2re aim = bim = x2im prec0 = ctx.prec ctx.prec = wp # cos(2*z), sin(2*z) with z complex c1, s1 = ctx.cos_sin(z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp c2im = (c1re*c1im - s1re*s1im) >> (wp - 1) s2re = (c1re*s1re - c1im*s1im) >> (wp - 1) s2im = (c1re*s1im + c1im*s1re) >> (wp - 1) t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if (nd&1): sre = s1re + (((are * snre - aim * snim) * 3**nd) >> wp) sim = s1im + (((are * snim + aim * snre)* 3**nd) >> wp) else: sre = c1re + (((are * cnre - aim * cnim) * 3**nd) >> wp) sim = c1im + (((are * cnim + aim * cnre)* 3**nd) >> wp) n = 5 while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if (nd&1): sre += (((are * snre - aim * snim) * n**nd) >> wp) sim += (((aim * snre + are * snim) * n**nd) >> wp) else: sre += (((are * cnre - aim * cnim) * n**nd) >> wp) sim += (((aim * cnre + are * cnim) * n**nd) >> wp) n += 2 sre = -(sre << 1) sim = -(sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) s *= ctx.nthroot(q, 4) if (nd&1): return (-1)**(nd//2) * s else: return (-1)**(1 + nd//2) * s @defun def _jacobi_theta3(ctx, z, q): extra1 = 10 extra2 = 20 MIN = 2 if z == ctx.zero: if not ctx._im(q): wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) s = x a = b = x x2 = (x*x) >> wp while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp s += a s = (1 << wp) + (s << 1) s = ctx.ldexp(s, -wp) return s else: wp = ctx.prec + extra1 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) sre = are = bre = xre sim = aim = bim = xim while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp sre += are sim += aim sre = (1 << wp) + (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) return s else: if (not ctx._im(q)) and (not ctx._im(z)): s = 0 wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) a = b = x x2 = (x*x) >> wp c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) c1 = ctx.to_fixed(c1, wp) s1 = ctx.to_fixed(s1, wp) cn = c1 sn = s1 s += (a * cn) >> wp while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp s += (a * cn) >> wp s = (1 << wp) + (s << 1) s = ctx.ldexp(s, -wp) return s # case z real, q complex elif not ctx._im(z): wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = xre aim = bim = xim c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) c1 = ctx.to_fixed(c1, wp) s1 = ctx.to_fixed(s1, wp) cn = c1 sn = s1 sre = (are * cn) >> wp sim = (aim * cn) >> wp while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp sre += (are * cn) >> wp sim += (aim * cn) >> wp sre = (1 << wp) + (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) return s #case z complex, q real elif not ctx._im(q): wp = ctx.prec + extra2 x = ctx.to_fixed(ctx._re(q), wp) a = b = x x2 = (x*x) >> wp prec0 = ctx.prec ctx.prec = wp c1, s1 = ctx.cos_sin(2*z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) sre = (a * cnre) >> wp sim = (a * cnim) >> wp while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 sre += (a * cnre) >> wp sim += (a * cnim) >> wp sre = (1 << wp) + (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) return s # case z and q complex else: wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = xre aim = bim = xim prec0 = ctx.prec ctx.prec = wp # cos(2*z), sin(2*z) with z complex c1, s1 = ctx.cos_sin(2*z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) sre = (are * cnre - aim * cnim) >> wp sim = (aim * cnre + are * cnim) >> wp while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 sre += (are * cnre - aim * cnim) >> wp sim += (aim * cnre + are * cnim) >> wp sre = (1 << wp) + (sre << 1) sim = (sim << 1) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) return s @defun def _djacobi_theta3(ctx, z, q, nd): """nd=1,2,3 order of the derivative with respect to z""" MIN = 2 extra1 = 10 extra2 = 20 if (not ctx._im(q)) and (not ctx._im(z)): s = 0 wp = ctx.prec + extra1 x = ctx.to_fixed(ctx._re(q), wp) a = b = x x2 = (x*x) >> wp c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) c1 = ctx.to_fixed(c1, wp) s1 = ctx.to_fixed(s1, wp) cn = c1 sn = s1 if (nd&1): s += (a * sn) >> wp else: s += (a * cn) >> wp n = 2 while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp if nd&1: s += (a * sn * n**nd) >> wp else: s += (a * cn * n**nd) >> wp n += 1 s = -(s << (nd+1)) s = ctx.ldexp(s, -wp) # case z real, q complex elif not ctx._im(z): wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = xre aim = bim = xim c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp) c1 = ctx.to_fixed(c1, wp) s1 = ctx.to_fixed(s1, wp) cn = c1 sn = s1 if (nd&1): sre = (are * sn) >> wp sim = (aim * sn) >> wp else: sre = (are * cn) >> wp sim = (aim * cn) >> wp n = 2 while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp if nd&1: sre += (are * sn * n**nd) >> wp sim += (aim * sn * n**nd) >> wp else: sre += (are * cn * n**nd) >> wp sim += (aim * cn * n**nd) >> wp n += 1 sre = -(sre << (nd+1)) sim = -(sim << (nd+1)) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) #case z complex, q real elif not ctx._im(q): wp = ctx.prec + extra2 x = ctx.to_fixed(ctx._re(q), wp) a = b = x x2 = (x*x) >> wp prec0 = ctx.prec ctx.prec = wp c1, s1 = ctx.cos_sin(2*z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) if (nd&1): sre = (a * snre) >> wp sim = (a * snim) >> wp else: sre = (a * cnre) >> wp sim = (a * cnim) >> wp n = 2 while abs(a) > MIN: b = (b*x2) >> wp a = (a*b) >> wp t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if (nd&1): sre += (a * snre * n**nd) >> wp sim += (a * snim * n**nd) >> wp else: sre += (a * cnre * n**nd) >> wp sim += (a * cnim * n**nd) >> wp n += 1 sre = -(sre << (nd+1)) sim = -(sim << (nd+1)) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) # case z and q complex else: wp = ctx.prec + extra2 xre = ctx.to_fixed(ctx._re(q), wp) xim = ctx.to_fixed(ctx._im(q), wp) x2re = (xre*xre - xim*xim) >> wp x2im = (xre*xim) >> (wp - 1) are = bre = xre aim = bim = xim prec0 = ctx.prec ctx.prec = wp # cos(2*z), sin(2*z) with z complex c1, s1 = ctx.cos_sin(2*z) ctx.prec = prec0 cnre = c1re = ctx.to_fixed(ctx._re(c1), wp) cnim = c1im = ctx.to_fixed(ctx._im(c1), wp) snre = s1re = ctx.to_fixed(ctx._re(s1), wp) snim = s1im = ctx.to_fixed(ctx._im(s1), wp) if (nd&1): sre = (are * snre - aim * snim) >> wp sim = (aim * snre + are * snim) >> wp else: sre = (are * cnre - aim * cnim) >> wp sim = (aim * cnre + are * cnim) >> wp n = 2 while are**2 + aim**2 > MIN: bre, bim = (bre * x2re - bim * x2im) >> wp, \ (bre * x2im + bim * x2re) >> wp are, aim = (are * bre - aim * bim) >> wp, \ (are * bim + aim * bre) >> wp t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp cnre = t1 cnim = t2 snre = t3 snim = t4 if(nd&1): sre += ((are * snre - aim * snim) * n**nd) >> wp sim += ((aim * snre + are * snim) * n**nd) >> wp else: sre += ((are * cnre - aim * cnim) * n**nd) >> wp sim += ((aim * cnre + are * cnim) * n**nd) >> wp n += 1 sre = -(sre << (nd+1)) sim = -(sim << (nd+1)) sre = ctx.ldexp(sre, -wp) sim = ctx.ldexp(sim, -wp) s = ctx.mpc(sre, sim) if (nd&1): return (-1)**(nd//2) * s else: return (-1)**(1 + nd//2) * s @defun def _jacobi_theta2a(ctx, z, q): """ case ctx._im(z) != 0 theta(2, z, q) = q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=-inf, inf) max term for minimum (2*n+1)*log(q).real - 2* ctx._im(z) n0 = int(ctx._im(z)/log(q).real - 1/2) theta(2, z, q) = q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=n0, inf) + q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n, n0-1, -inf) """ n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2) e2 = ctx.expj(2*z) e = e0 = ctx.expj((2*n+1)*z) a = q**(n*n + n) # leading term term = a * e s = term eps1 = ctx.eps*abs(term) while 1: n += 1 e = e * e2 term = q**(n*n + n) * e if abs(term) < eps1: break s += term e = e0 e2 = ctx.expj(-2*z) n = n0 while 1: n -= 1 e = e * e2 term = q**(n*n + n) * e if abs(term) < eps1: break s += term s = s * ctx.nthroot(q, 4) return s @defun def _jacobi_theta3a(ctx, z, q): """ case ctx._im(z) != 0 theta3(z, q) = Sum(q**(n*n) * exp(j*2*n*z), n, -inf, inf) max term for n*abs(log(q).real) + ctx._im(z) ~= 0 n0 = int(- ctx._im(z)/abs(log(q).real)) """ n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q)))) e2 = ctx.expj(2*z) e = e0 = ctx.expj(2*n*z) s = term = q**(n*n) * e eps1 = ctx.eps*abs(term) while 1: n += 1 e = e * e2 term = q**(n*n) * e if abs(term) < eps1: break s += term e = e0 e2 = ctx.expj(-2*z) n = n0 while 1: n -= 1 e = e * e2 term = q**(n*n) * e if abs(term) < eps1: break s += term return s @defun def _djacobi_theta2a(ctx, z, q, nd): """ case ctx._im(z) != 0 dtheta(2, z, q, nd) = j* q**1/4 * Sum(q**(n*n + n) * (2*n+1)*exp(j*(2*n + 1)*z), n=-inf, inf) max term for (2*n0+1)*log(q).real - 2* ctx._im(z) ~= 0 n0 = int(ctx._im(z)/log(q).real - 1/2) """ n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2) e2 = ctx.expj(2*z) e = e0 = ctx.expj((2*n + 1)*z) a = q**(n*n + n) # leading term term = (2*n+1)**nd * a * e s = term eps1 = ctx.eps*abs(term) while 1: n += 1 e = e * e2 term = (2*n+1)**nd * q**(n*n + n) * e if abs(term) < eps1: break s += term e = e0 e2 = ctx.expj(-2*z) n = n0 while 1: n -= 1 e = e * e2 term = (2*n+1)**nd * q**(n*n + n) * e if abs(term) < eps1: break s += term return ctx.j**nd * s * ctx.nthroot(q, 4) @defun def _djacobi_theta3a(ctx, z, q, nd): """ case ctx._im(z) != 0 djtheta3(z, q, nd) = (2*j)**nd * Sum(q**(n*n) * n**nd * exp(j*2*n*z), n, -inf, inf) max term for minimum n*abs(log(q).real) + ctx._im(z) """ n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q)))) e2 = ctx.expj(2*z) e = e0 = ctx.expj(2*n*z) a = q**(n*n) * e s = term = n**nd * a if n != 0: eps1 = ctx.eps*abs(term) else: eps1 = ctx.eps*abs(a) while 1: n += 1 e = e * e2 a = q**(n*n) * e term = n**nd * a if n != 0: aterm = abs(term) else: aterm = abs(a) if aterm < eps1: break s += term e = e0 e2 = ctx.expj(-2*z) n = n0 while 1: n -= 1 e = e * e2 a = q**(n*n) * e term = n**nd * a if n != 0: aterm = abs(term) else: aterm = abs(a) if aterm < eps1: break s += term return (2*ctx.j)**nd * s @defun def jtheta(ctx, n, z, q, derivative=0): if derivative: return ctx._djtheta(n, z, q, derivative) z = ctx.convert(z) q = ctx.convert(q) # Implementation note # If ctx._im(z) is close to zero, _jacobi_theta2 and _jacobi_theta3 # are used, # which compute the series starting from n=0 using fixed precision # numbers; # otherwise _jacobi_theta2a and _jacobi_theta3a are used, which compute # the series starting from n=n0, which is the largest term. # TODO: write _jacobi_theta2a and _jacobi_theta3a using fixed-point if abs(q) > ctx.THETA_Q_LIM: raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM) extra = 10 if z: M = ctx.mag(z) if M > 5 or (n == 1 and M < -5): extra += 2*abs(M) cz = 0.5 extra2 = 50 prec0 = ctx.prec try: ctx.prec += extra if n == 1: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._jacobi_theta2(z - ctx.pi/2, q) else: ctx.dps += 10 res = ctx._jacobi_theta2a(z - ctx.pi/2, q) else: res = ctx._jacobi_theta2(z - ctx.pi/2, q) elif n == 2: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._jacobi_theta2(z, q) else: ctx.dps += 10 res = ctx._jacobi_theta2a(z, q) else: res = ctx._jacobi_theta2(z, q) elif n == 3: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._jacobi_theta3(z, q) else: ctx.dps += 10 res = ctx._jacobi_theta3a(z, q) else: res = ctx._jacobi_theta3(z, q) elif n == 4: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._jacobi_theta3(z, -q) else: ctx.dps += 10 res = ctx._jacobi_theta3a(z, -q) else: res = ctx._jacobi_theta3(z, -q) else: raise ValueError finally: ctx.prec = prec0 return res @defun def _djtheta(ctx, n, z, q, derivative=1): z = ctx.convert(z) q = ctx.convert(q) nd = int(derivative) if abs(q) > ctx.THETA_Q_LIM: raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM) extra = 10 + ctx.prec * nd // 10 if z: M = ctx.mag(z) if M > 5 or (n != 1 and M < -5): extra += 2*abs(M) cz = 0.5 extra2 = 50 prec0 = ctx.prec try: ctx.prec += extra if n == 1: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd) else: ctx.dps += 10 res = ctx._djacobi_theta2a(z - ctx.pi/2, q, nd) else: res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd) elif n == 2: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._djacobi_theta2(z, q, nd) else: ctx.dps += 10 res = ctx._djacobi_theta2a(z, q, nd) else: res = ctx._djacobi_theta2(z, q, nd) elif n == 3: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._djacobi_theta3(z, q, nd) else: ctx.dps += 10 res = ctx._djacobi_theta3a(z, q, nd) else: res = ctx._djacobi_theta3(z, q, nd) elif n == 4: if ctx._im(z): if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))): ctx.dps += extra2 res = ctx._djacobi_theta3(z, -q, nd) else: ctx.dps += 10 res = ctx._djacobi_theta3a(z, -q, nd) else: res = ctx._djacobi_theta3(z, -q, nd) else: raise ValueError finally: ctx.prec = prec0 return +res

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Python

nidaqmx/tests/test_stream_digital_readers_writers.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

252

2017-03-22T02:43:16.000Z

2022-03-27T14:44:44.000Z

nidaqmx/tests/test_stream_digital_readers_writers.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

133

2017-03-21T20:57:59.000Z

2022-03-31T16:08:12.000Z

nidaqmx/tests/test_stream_digital_readers_writers.py

stafak/nidaqmx-python

f354d7971b21074c120c6f298dbbf4a5e0e4f4f4

[ "MIT" ]

124

2017-04-01T18:35:24.000Z

2022-03-25T06:30:00.000Z

import numpy import pytest import random import time import nidaqmx from nidaqmx.constants import ( LineGrouping) from nidaqmx.stream_readers import ( DigitalSingleChannelReader, DigitalMultiChannelReader) from nidaqmx.stream_writers import ( DigitalSingleChannelWriter, DigitalMultiChannelWriter) from nidaqmx.tests.fixtures import x_series_device from nidaqmx.tests.helpers import generate_random_seed from nidaqmx.tests.test_read_write import TestDAQmxIOBase from nidaqmx.utils import flatten_channel_string class TestDigitalSingleChannelReaderWriter(TestDAQmxIOBase): """ Contains a collection of pytest tests that validate the digital single channel readers and writers in the NI-DAQmx Python API. These tests use only a single X Series device by both writing to and reading from ONLY the digital output lines. """ @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_one_line(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) do_line = random.choice(x_series_device.do_lines).name with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_line, line_grouping=LineGrouping.CHAN_PER_LINE) writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) # Generate random values to test. values_to_test = [bool(random.getrandbits(1)) for _ in range(10)] values_read = [] for value_to_test in values_to_test: writer.write_one_sample_one_line(value_to_test) time.sleep(0.001) values_read.append(reader.read_one_sample_one_line()) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_multi_line(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_lines = random.randint(2, len(x_series_device.do_lines)) do_lines = random.sample(x_series_device.do_lines, number_of_lines) with nidaqmx.Task() as task: task.do_channels.add_do_chan( flatten_channel_string([d.name for d in do_lines]), line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) # Generate random values to test. values_to_test = numpy.array( [bool(random.getrandbits(1)) for _ in range(number_of_lines)]) writer.write_one_sample_multi_line(values_to_test) time.sleep(0.001) values_read = numpy.zeros(number_of_lines, dtype=numpy.bool) reader.read_one_sample_multi_line(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( not any([d.do_port_width <= 8 for d in x_series_device().do_ports]), reason="Requires digital port with at most 8 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_byte(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) do_port = random.choice( [d for d in x_series_device.do_ports if d.do_port_width <= 8]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = [int(random.getrandbits(do_port.do_port_width)) for _ in range(10)] writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) values_read = [] for value_to_test in values_to_test: writer.write_one_sample_port_byte(value_to_test) time.sleep(0.001) values_read.append(reader.read_one_sample_port_byte()) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( not any([d.do_port_width <= 16 for d in x_series_device().do_ports]), reason="Requires digital port with at most 16 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_uint16(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) do_port = random.choice( [do for do in x_series_device.do_ports if do.do_port_width <= 16]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = [int(random.getrandbits(do_port.do_port_width)) for _ in range(10)] writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) values_read = [] for value_to_test in values_to_test: writer.write_one_sample_port_uint16(value_to_test) time.sleep(0.001) values_read.append(reader.read_one_sample_port_uint16()) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( not any([d.do_port_width <= 32 for d in x_series_device().do_ports]), reason="Requires digital port with at most 32 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_uint32(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) do_port = random.choice( [do for do in x_series_device.do_ports if do.do_port_width <= 32]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = [int(random.getrandbits(do_port.do_port_width)) for _ in range(10)] writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) values_read = [] for value_to_test in values_to_test: writer.write_one_sample_port_uint32(value_to_test) time.sleep(0.001) values_read.append(reader.read_one_sample_port_uint32()) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( not any([d.do_port_width <= 8 for d in x_series_device().do_ports]), reason="Requires digital port with at most 8 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_byte(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) do_port = random.choice( [d for d in x_series_device.do_ports if d.do_port_width <= 8]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)], dtype=numpy.uint8) writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) task.start() writer.write_many_sample_port_byte(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros(number_of_samples, dtype=numpy.uint8) reader.read_many_sample_port_byte( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ values_to_test[-1] for _ in range(number_of_samples)] numpy.testing.assert_array_equal(values_read, expected_values) @pytest.mark.skipif( not any([d.do_port_width <= 16 for d in x_series_device().do_ports]), reason="Requires digital port with at most 16 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_uint16(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) do_port = random.choice( [d for d in x_series_device.do_ports if d.do_port_width <= 16]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)], dtype=numpy.uint16) writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) task.start() writer.write_many_sample_port_uint16(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros(number_of_samples, dtype=numpy.uint16) reader.read_many_sample_port_uint16( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ values_to_test[-1] for _ in range(number_of_samples)] numpy.testing.assert_array_equal(values_read, expected_values) @pytest.mark.skipif( not any([d.do_port_width <= 32 for d in x_series_device().do_ports]), reason="Requires digital port with at most 32 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_uint32(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) do_port = random.choice( [d for d in x_series_device.do_ports if d.do_port_width <= 32]) with nidaqmx.Task() as task: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)], dtype=numpy.uint32) writer = DigitalSingleChannelWriter(task.out_stream) reader = DigitalSingleChannelReader(task.in_stream) task.start() writer.write_many_sample_port_uint32(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros(number_of_samples, dtype=numpy.uint32) reader.read_many_sample_port_uint32( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ values_to_test[-1] for _ in range(number_of_samples)] numpy.testing.assert_array_equal(values_read, expected_values) class TestDigitalMultiChannelReaderWriter(TestDAQmxIOBase): """ Contains a collection of pytest tests that validate the digital multi channel readers and writers in the NI-DAQmx Python API. These tests use only a single X Series device by utilizing the internal loopback routes on the device. """ @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_one_line(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_channels = random.randint(2, len(x_series_device.do_lines)) do_lines = random.sample(x_series_device.do_lines, number_of_channels) with nidaqmx.Task() as task: task.do_channels.add_do_chan( flatten_channel_string([d.name for d in do_lines]), line_grouping=LineGrouping.CHAN_PER_LINE) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) # Generate random values to test. values_to_test = numpy.array( [bool(random.getrandbits(1)) for _ in range(number_of_channels)]) writer.write_one_sample_one_line(values_to_test) time.sleep(0.001) values_read = numpy.zeros(number_of_channels, dtype=numpy.bool) reader.read_one_sample_one_line(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_multi_line(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) num_lines = random.randint(2, 4) number_of_channels = random.randint( 2, numpy.floor(len(x_series_device.do_lines) / float(num_lines))) all_lines = random.sample(x_series_device.do_lines, num_lines * number_of_channels) with nidaqmx.Task() as task: for i in range(number_of_channels): do_lines = all_lines[i * num_lines:(i + 1) * num_lines] task.do_channels.add_do_chan( flatten_channel_string([d.name for d in do_lines]), line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) # Generate random values to test. values_to_test = numpy.array( [[bool(random.getrandbits(1)) for _ in range(num_lines)] for _ in range(number_of_channels)]) writer.write_one_sample_multi_line(values_to_test) time.sleep(0.001) values_read = numpy.zeros( (number_of_channels, num_lines), dtype=numpy.bool) reader.read_one_sample_multi_line(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( len([d.do_port_width <= 8 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 8 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_byte(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 8] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(d.do_port_width)) for d in do_ports], dtype=numpy.uint8) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) writer.write_one_sample_port_byte(values_to_test) time.sleep(0.001) values_read = numpy.zeros(number_of_channels, dtype=numpy.uint8) reader.read_one_sample_port_byte(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( len([d.do_port_width <= 16 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 16 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_uint16(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 16] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(d.do_port_width)) for d in do_ports], dtype=numpy.uint16) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) writer.write_one_sample_port_uint16(values_to_test) time.sleep(0.001) values_read = numpy.zeros(number_of_channels, dtype=numpy.uint16) reader.read_one_sample_port_uint16(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( len([d.do_port_width <= 32 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 32 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_one_sample_port_uint32(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 32] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [int(random.getrandbits(d.do_port_width)) for d in do_ports], dtype=numpy.uint32) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) writer.write_one_sample_port_uint32(values_to_test) time.sleep(0.001) values_read = numpy.zeros(number_of_channels, dtype=numpy.uint32) reader.read_one_sample_port_uint32(values_read) numpy.testing.assert_array_equal(values_read, values_to_test) @pytest.mark.skipif( len([d.do_port_width <= 8 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 8 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_byte(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 8] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [[int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)] for do_port in do_ports], dtype=numpy.uint8) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) task.start() writer.write_many_sample_port_byte(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros( (number_of_channels, number_of_samples), dtype=numpy.uint8) reader.read_many_sample_port_byte( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ [values_to_test[i, -1] for _ in range(number_of_samples)] for i in range(number_of_channels)] numpy.testing.assert_array_equal(values_read, expected_values) @pytest.mark.skipif( len([d.do_port_width <= 16 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 16 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_uint16(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 16] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [[int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)] for do_port in do_ports], dtype=numpy.uint16) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) task.start() writer.write_many_sample_port_uint16(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros( (number_of_channels, number_of_samples), dtype=numpy.uint16) reader.read_many_sample_port_uint16( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ [values_to_test[i, -1] for _ in range(number_of_samples)] for i in range(number_of_channels)] numpy.testing.assert_array_equal(values_read, expected_values) @pytest.mark.skipif( len([d.do_port_width <= 32 for d in x_series_device().do_ports]) < 2, reason="Requires 2 digital ports with at most 32 lines.") @pytest.mark.parametrize('seed', [generate_random_seed()]) def test_many_sample_port_uint32(self, x_series_device, seed): # Reset the pseudorandom number generator with seed. random.seed(seed) number_of_samples = random.randint(2, 20) all_ports = [d for d in x_series_device.do_ports if d.do_port_width <= 32] number_of_channels = random.randint(2, len(all_ports)) do_ports = random.sample(all_ports, number_of_channels) with nidaqmx.Task() as task: for do_port in do_ports: task.do_channels.add_do_chan( do_port.name, line_grouping=LineGrouping.CHAN_FOR_ALL_LINES) # Generate random values to test. values_to_test = numpy.array( [[int(random.getrandbits(do_port.do_port_width)) for _ in range(number_of_samples)] for do_port in do_ports], dtype=numpy.uint32) writer = DigitalMultiChannelWriter(task.out_stream) reader = DigitalMultiChannelReader(task.in_stream) task.start() writer.write_many_sample_port_uint32(values_to_test) time.sleep(0.001) # Since we're writing to and reading from ONLY the digital # output lines, we can't use sample clocks to correlate the # read and write sampling times. Thus, we essentially read # the last value written multiple times. values_read = numpy.zeros( (number_of_channels, number_of_samples), dtype=numpy.uint32) reader.read_many_sample_port_uint32( values_read, number_of_samples_per_channel=number_of_samples) expected_values = [ [values_to_test[i, -1] for _ in range(number_of_samples)] for i in range(number_of_channels)] numpy.testing.assert_array_equal(values_read, expected_values)

42.279294

78

0.648709

3,393

26,340

4.728264

0.046272

0.026928

0.047871

0.028985

0.955183

0.952877

0.939974

0.931933

0.929627

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0

0.013697

0.273804

26,340

622

79

42.347267

0.825021

0.116363

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null

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1

0

1

0

null

0

7

992bce62bae104c54b56a1b18bb10eb1fb319093

12,413

py

Python

test/test_remove_documents.py

ShaneKilkelly/bedquilt

beaee513a015ed0dd633b738517b33eb7c4c42a3

[ "MIT" ]

288

2015-04-20T18:14:39.000Z

2021-10-30T01:35:44.000Z

test/test_remove_documents.py

ShaneKilkelly/bedquilt

beaee513a015ed0dd633b738517b33eb7c4c42a3

[ "MIT" ]

21

2015-04-13T12:48:40.000Z

2017-05-27T12:41:10.000Z

test/test_remove_documents.py

ShaneKilkelly/bedquilt

beaee513a015ed0dd633b738517b33eb7c4c42a3

[ "MIT" ]

19

2015-11-03T09:25:00.000Z

2021-05-01T00:28:02.000Z

import testutils import json import string import psycopg2 class TestRemoveDocumnts(testutils.BedquiltTestCase): def test_remove_on_empty_collection(self): self.cur.execute(""" select bq_create_collection('people'); """) _ = self.cur.fetchall() self.cur.execute(""" select bq_remove('people', '{"age": 22}') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_one_on_empty_collection(self): self.cur.execute(""" select bq_create_collection('people'); """) _ = self.cur.fetchall() self.cur.execute(""" select bq_remove_one('people', '{"age": 22}') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_on_non_existant_collection(self): self.cur.execute(""" select bq_remove('people', '{"age": 22}') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_one_on_non_existant_collection(self): self.cur.execute(""" select bq_remove_one('people', '{"age": 22}') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_hitting_single_document(self): sarah = {'_id': "sarah@example.com", 'name': "Sarah", 'city': "Glasgow", 'age': 34, 'likes': ['icecream', 'cats']} mike = {'_id': "mike@example.com", 'name': "Mike", 'city': "Edinburgh", 'age': 32, 'likes': ['cats', 'crochet']} jill = {'_id': "jill@example.com", 'name': "Jill", 'city': "Glasgow", 'age': 32, 'likes': ['code', 'crochet']} darren = {'_id': "darren@example.com", 'name': "Darren", 'city': "Manchester"} self._insert('people', sarah) self._insert('people', mike) self._insert('people', jill) self._insert('people', darren) self.cur.execute(""" select bq_remove('people', '{"age": 34}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (1,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (mike,), (jill,), (darren,) ]) def test_remove_hitting_many_document(self): sarah = {'_id': "sarah@example.com", 'name': "Sarah", 'city': "Glasgow", 'age': 34, 'likes': ['icecream', 'cats']} mike = {'_id': "mike@example.com", 'name': "Mike", 'city': "Edinburgh", 'age': 32, 'likes': ['cats', 'crochet']} jill = {'_id': "jill@example.com", 'name': "Jill", 'city': "Glasgow", 'age': 32, 'likes': ['code', 'crochet']} darren = {'_id': "darren@example.com", 'name': "Darren", 'city': "Manchester"} self._insert('people', sarah) self._insert('people', mike) self._insert('people', jill) self._insert('people', darren) self.cur.execute(""" select bq_remove('people', '{"age": 32}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (2,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,), (darren,) ]) def test_remove_one_documents(self): sarah = {'_id': "sarah@example.com", 'name': "Sarah", 'city': "Glasgow", 'age': 34, 'likes': ['icecream', 'cats']} mike = {'_id': "mike@example.com", 'name': "Mike", 'city': "Edinburgh", 'age': 32, 'likes': ['cats', 'crochet']} jill = {'_id': "jill@example.com", 'name': "Jill", 'city': "Glasgow", 'age': 32, 'likes': ['code', 'crochet']} darren = {'_id': "darren@example.com", 'name': "Darren", 'city': "Manchester"} self._insert('people', sarah) self._insert('people', mike) self._insert('people', jill) self._insert('people', darren) # remove_one a single document matching a wide query self.cur.execute(""" select bq_remove_one('people', '{"age": 32}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (1,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,), (jill,), (darren,) ]) # remove_one a single document matching a specific query self.cur.execute(""" select bq_remove_one('people', '{"name": "Darren"}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (1,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,), (jill,) ]) # remove_one a single document matching an _id self.cur.execute(""" select bq_remove_one('people', '{"_id": "jill@example.com"}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (1,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,) ]) def test_remove_one_by_id_on_non_existant_collection(self): self.cur.execute(""" select bq_remove_one_by_id('people', 'jill@example.com'); """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_one_by_id_on_empty_collection(self): self.cur.execute(""" select bq_create_collection('people'); """) _ = self.cur.fetchall() self.cur.execute(""" select bq_remove_one_by_id('people', 'jill@example.com'); """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_one_by_id(self): sarah = {'_id': "sarah@example.com", 'name': "Sarah", 'city': "Glasgow", 'age': 34, 'likes': ['icecream', 'cats']} mike = {'_id': "mike@example.com", 'name': "Mike", 'city': "Edinburgh", 'age': 32, 'likes': ['cats', 'crochet']} jill = {'_id': "jill@example.com", 'name': "Jill", 'city': "Glasgow", 'age': 32, 'likes': ['code', 'crochet']} darren = {'_id': "darren@example.com", 'name': "Darren", 'city': "Manchester"} self._insert('people', sarah) self._insert('people', mike) self._insert('people', jill) self._insert('people', darren) # remove an existing document self.cur.execute(""" select bq_remove_one_by_id('people', 'jill@example.com') """) result = self.cur.fetchall() self.assertEqual(result, [ (1,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,), (mike,), (darren,) ]) # remove a document which is not in collection self.cur.execute(""" select bq_remove_one_by_id('people', 'xxxxx') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) self.cur.execute(""" select bq_find('people', '{}'); """) result = self.cur.fetchall() self.assertEqual(result, [ (sarah,), (mike,), (darren,) ]) def test_remove_many_by_ids_on_non_existant_collection(self): self.cur.execute(""" select bq_remove_many_by_ids('people', '["one", "three"]'); """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_many_by_ids_on_empty_collection(self): self.cur.execute(""" select bq_create_collection('people'); """) _ = self.cur.fetchall() self.cur.execute(""" select bq_remove_many_by_ids('people', '["one", "three"]'); """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) def test_remove_many_by_ids(self): sarah = {'_id': "sarah@example.com", 'name': "Sarah", 'city': "Glasgow", 'age': 34, 'likes': ['icecream', 'cats']} mike = {'_id': "mike@example.com", 'name': "Mike", 'city': "Edinburgh", 'age': 32, 'likes': ['cats', 'crochet']} jill = {'_id': "jill@example.com", 'name': "Jill", 'city': "Glasgow", 'age': 32, 'likes': ['code', 'crochet']} darren = {'_id': "darren@example.com", 'name': "Darren", 'city': "Manchester"} self._insert('people', sarah) self._insert('people', mike) self._insert('people', jill) self._insert('people', darren) self.cur.execute(""" select bq_remove_many_by_ids('people', '["nope", "nope_two"]') """) result = self.cur.fetchall() self.assertEqual(result, [ (0,) ]) self.cur.execute(""" select bq_count('people', '{}') """) result = self.cur.fetchall() self.assertEqual(result, [ (4,) ]) self.cur.execute(""" select bq_remove_many_by_ids('people', '["darren@example.com", "mike@example.com"]') """) result = self.cur.fetchall() self.assertEqual(result, [ (2,) ]) self.cur.execute(""" select bq_count('people', '{}') """) result = self.cur.fetchall() self.assertEqual(result, [ (2,) ]) self.cur.execute(""" select bq_find('people', '{}') """) result = self.cur.fetchall() self.assertEqual( map(lambda r: r[0]['_id'], result), ['sarah@example.com', 'jill@example.com'])

30.42402

92

0.418916

1,035

12,413

4.846377

0.077295

0.086523

0.123604

0.927233

0.920853

0.91248

0.891946

0.874402

0

0.009115

0.425522

12,413

407

93

30.498772

0.694293

0.017965

0

0.846377

0

0.26067

0.044731

0

0.078261

1

0.037681

false

0

0.011594

0

0.052174

0

null

0

1

0

1

0

null

0

7

993d9997a490057a2a0060e5e439dc1789e8136a

3,496

py

Python

tutorials/organic_synthesis_figures/organic_spaces.py

leewaymay/839_fonduer

1692f018ef113d88dca4ede69cc2ead55b7b1003

[ "Apache-2.0" ]

1

2018-05-31T02:44:00.000Z

tutorials/organic_synthesis_figures/organic_spaces.py

leewaymay/839_fonduer

1692f018ef113d88dca4ede69cc2ead55b7b1003

[ "Apache-2.0" ]

null

tutorials/organic_synthesis_figures/organic_spaces.py

leewaymay/839_fonduer

1692f018ef113d88dca4ede69cc2ead55b7b1003

[ "Apache-2.0" ]

null

from __future__ import print_function from builtins import chr from builtins import str from builtins import range from difflib import SequenceMatcher import re from fonduer.candidates import OmniNgrams from fonduer.models import TemporaryImplicitSpan class OmniNgramsProd(OmniNgrams): def __init__(self, parts_by_doc=None, n_max=1, expand=False, split_tokens=' '): """:param parts_by_doc: a dictionary d where d[document_name.upper()] = [partA, partB, ...]""" OmniNgrams.__init__(self, n_max=n_max, split_tokens=' ') self.parts_by_doc = parts_by_doc self.expander = lambda x: [x] def apply(self, session, context): for ts in OmniNgrams.apply(self, session, context): value = ts.get_span() yield TemporaryImplicitSpan( sentence=ts.sentence, char_start=ts.char_start, char_end=ts.char_end, expander_key=u'prod_expander', position=0, text=value, words=[value], lemmas=[value], pos_tags=[ts.get_attrib_tokens('pos_tags')[-1]], ner_tags=[ts.get_attrib_tokens('ner_tags')[-1]], dep_parents=[ts.get_attrib_tokens('dep_parents')[-1]], dep_labels=[ts.get_attrib_tokens('dep_labels')[-1]], page=[ts.get_attrib_tokens('page')[-1]] if ts.sentence.is_visual() else [None], top=[ts.get_attrib_tokens('top')[-1]] if ts.sentence.is_visual() else [None], left=[ts.get_attrib_tokens('left')[-1]] if ts.sentence.is_visual() else [None], bottom=[ts.get_attrib_tokens('bottom')[-1]] if ts.sentence.is_visual() else [None], right=[ts.get_attrib_tokens('right')[-1]] if ts.sentence.is_visual() else [None], meta=None) # Added by Wei Li class OmniNgramsOrganic(OmniNgrams): # def __init__(self, n_max=2, split_tokens=None): # OmniNgrams.__init__(self, n_max=n_max, split_tokens=None) def apply(self, session, context): for ts in OmniNgrams.apply(self, session, context): value = ts.get_span() yield TemporaryImplicitSpan( sentence=ts.sentence, char_start=ts.char_start, char_end=ts.char_end, expander_key=u'N/A', position=0, text=value, words=[value], lemmas=[value], pos_tags=[ts.get_attrib_tokens('pos_tags')[-1]], ner_tags=[ts.get_attrib_tokens('ner_tags')[-1]], dep_parents=[ts.get_attrib_tokens('dep_parents')[-1]], dep_labels=[ts.get_attrib_tokens('dep_labels')[-1]], page=[ts.get_attrib_tokens('page')[-1]] if ts.sentence.is_visual() else [None], top=[ts.get_attrib_tokens('top')[-1]] if ts.sentence.is_visual() else [None], left=[ts.get_attrib_tokens('left')[-1]] if ts.sentence.is_visual() else [None], bottom=[ts.get_attrib_tokens('bottom')[-1]] if ts.sentence.is_visual() else [None], right=[ts.get_attrib_tokens('right')[-1]] if ts.sentence.is_visual() else [None], meta=None)

42.120482

102

0.556064

417

3,496

4.386091

0.215827

0.054675

0.108256

0.167305

0.730454

0.689995

0

0.009236

0.31865

3,496

82

103

42.634146

0.758606

0.061499

0

0.722222

0

0.041552

0

1

0.041667

false

0

0.111111

0

0.180556

0.013889

0

null

0

1

0

1

0

1

0

null

0

7

995b110980d3af59c9919d34412586d5ee92b8b4

1,565

py

Python

src/layers/padding.py

uw-bionlp/ards

e9fc27f7034cc6b54f0ccdba4a58377948cf0258

[ "BSD-3-Clause" ]

null

src/layers/padding.py

uw-bionlp/ards

e9fc27f7034cc6b54f0ccdba4a58377948cf0258

[ "BSD-3-Clause" ]

null

src/layers/padding.py

uw-bionlp/ards

e9fc27f7034cc6b54f0ccdba4a58377948cf0258

[ "BSD-3-Clause" ]

null

import torch import logging from torch.nn import ConstantPad3d, ConstantPad2d, ConstantPad1d def pad3D(x, max_seq_count): ''' pad first dimension (sequence count) of documents ''' # current sentence count seq_count = x.shape[0] # append zeros, if sequence count too low if seq_count < max_seq_count: padding_back = max_seq_count - seq_count pad = ConstantPad3d((0, 0, 0, 0, 0, padding_back), 0) x = pad(x) # truncate document elif seq_count > max_seq_count: x = x[:max_seq_count] return x def pad2D(x, max_seq_count): ''' pad first dimension (sequence count) of documents ''' # current sentence count seq_count = x.shape[0] # append zeros, if sequence count too low if seq_count < max_seq_count: padding_back = max_seq_count - seq_count pad = ConstantPad2d((0, 0, 0, padding_back), 0) x = pad(x) # truncate document elif seq_count > max_seq_count: x = x[:max_seq_count] return x def pad1D(x, max_seq_count): ''' pad first dimension (sequence count) of documents ''' # current sentence count seq_count = x.shape[0] # append zeros, if sequence count too low if seq_count < max_seq_count: padding_back = max_seq_count - seq_count pad = ConstantPad1d((0, padding_back), 0) x = pad(x) # truncate document elif seq_count > max_seq_count: x = x[:max_seq_count] return x

23.014706

65

0.607029

212

1,565

4.254717

0.174528

0.239468

0.182927

0.079823

0.845898

0

0.022243

0.310543

1,565

67

66

23.358209

0.813716

0.251118

0

0.7

0

1

0.1

false

0

0.1

0

0.3

0

null

1

0

1

0

null

0

8

41d134ec886e3474471ede005be82ee6c61fb0ef

16,652

py

Python

paida-3.2.1_2.10.1/paida/paida_core/IFitData.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

paida-3.2.1_2.10.1/paida/paida_core/IFitData.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

paida-3.2.1_2.10.1/paida/paida_core/IFitData.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

from paida.paida_core.PAbsorber import * from paida.paida_core.IRangeSet import * from paida.paida_core.PExceptions import * from paida.paida_core.IHistogram1D import * from paida.paida_core.IHistogram2D import * from paida.paida_core.IHistogram3D import * from paida.paida_core.ICloud1D import * from paida.paida_core.ICloud2D import * from paida.paida_core.ICloud3D import * from paida.paida_core.IProfile1D import * from paida.paida_core.IProfile2D import * from paida.paida_core.IDataPointSet import * from paida.paida_core.ITuple import * from paida.paida_core.IEvaluator import * import paida.paida_core.PTypes as PTypes import types class IFitData: def __init__(self): self._connection = [] self._range = [] self._binned = None self._dataDescription = '' def create1DConnection(self, data1, data2 = None, data3 = None): centers = [] weights = [] errorsP = [] errorsM = [] if isinstance(data1, IHistogram1D) and (data2 == None) and (data3 == None): self._binned = True histogram = data1 xAxis = histogram.axis() for xBinNumber in range(xAxis.bins()): weight = histogram.binHeight(xBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 else: xCenter = histogram.binMean(xBinNumber) centers.append([xCenter]) weights.append(weight) error = histogram._binError2(xBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, ICloud1D) and (data2 == None) and (data3 == None): self._binned = False cloud = data1 for entryNumber in range(cloud.entries()): xCenter = cloud.value(entryNumber) centers.append([xCenter]) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(data1, IProfile1D) and (data2 == None) and (data3 == None): self._binned = True profile = data1 xAxis = profile.axis() for xBinNumber in range(xAxis.bins()): weight = profile.binHeight(xBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 else: xCenter = profile.binMean(xBinNumber) centers.append([xCenter]) weights.append(weight) error = profile._binError2(xBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, IDataPointSet) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType): self._binned = True dataPointSet = data1 xIndex = data2 valIndex = data3 for offset in range(dataPointSet.size()): dataPoint = dataPointSet.point(offset) x = dataPoint.coordinate(xIndex) y = dataPoint.coordinate(valIndex) centers.append([x.value()]) weights.append(y.value()) errorsP.append(y.errorPlus()**2) errorsM.append(y.errorMinus()**2) else: raise TypeError('Invalid arguments.') self._connection = [centers, weights, errorsP, errorsM] self._range.append(IRangeSet()) def create2DConnection(self, data1, data2 = None, data3 = None, data4 = None): centers = [] weights = [] errorsP = [] errorsM = [] if isinstance(data1, IHistogram2D) and (data2 == None) and (data3 == None) and (data4 == None): self._binned = True histogram = data1 xAxis = histogram.xAxis() yAxis = histogram.yAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): weight = histogram.binHeight(xBinNumber, yBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 else: xCenter = histogram.binMeanX(xBinNumber, yBinNumber) yCenter = histogram.binMeanY(xBinNumber, yBinNumber) centers.append([xCenter, yCenter]) weights.append(weight) error = histogram._binError2(xBinNumber, yBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, IHistogram2D) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and (data4 == None): self._binned = True histogram = data1 xIndex = data2 yIndex = data3 xAxis = histogram.xAxis() yAxis = histogram.yAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): weight = histogram.binHeight(xBinNumber, yBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 else: xCenter = histogram.binMeanX(xBinNumber, yBinNumber) yCenter = histogram.binMeanY(xBinNumber, yBinNumber) center = [None, None] center[xIndex] = xCenter center[yIndex] = yCenter centers.append(center) weights.append(weight) error = histogram._binError2(xBinNumber, yBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, ICloud2D) and (data2 == None) and (data3 == None) and (data4 == None): self._binned = False cloud = data1 for entryNumber in range(cloud.entries()): xCenter = cloud.valueX(entryNumber) yCenter = cloud.valueY(entryNumber) centers.append([xCenter, yCenter]) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(data1, ICloud2D) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and (data4 == None): self._binned = False cloud = data1 xIndex = data2 yIndex = data3 for entryNumber in range(cloud.entries()): center = [None, None] center[xIndex] = cloud.valueX(entryNumber) center[yIndex] = cloud.valueY(entryNumber) centers.append(center) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(data1, IProfile2D) and (data2 == None) and (data3 == None) and (data4 == None): self._binned = True profile = data1 xAxis = profile.xAxis() yAxis = profile.yAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): weight = profile.binHeight(xBinNumber, yBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 else: xCenter = profile.binMeanX(xBinNumber, yBinNumber) yCenter = profile.binMeanY(xBinNumber, yBinNumber) centers.append([xCenter, yCenter]) weights.append(weight) error = profile._binError2(xBinNumber, yBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, IProfile2D) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and (data4 == None): self._binned = True profile = data1 xIndex = data2 yIndex = data3 xAxis = profile.xAxis() yAxis = profile.yAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): weight = profile.binHeight(xBinNumber, yBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 else: xCenter = profile.binMeanX(xBinNumber, yBinNumber) yCenter = profile.binMeanY(xBinNumber, yBinNumber) center = [None, None] center[xIndex] = xCenter center[yIndex] = yCenter centers.append(center) weights.append(weight) error = profile._binError2(xBinNumber, yBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, IDataPointSet) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and isinstance(data4, types.IntType): self._binned = True dataPointSet = data1 xIndex = data2 yIndex = data3 valIndex = data4 for offset in range(dataPointSet.size()): dataPoint = dataPointSet.point(offset) x = dataPoint.coordinate(xIndex) y = dataPoint.coordinate(yIndex) z = dataPoint.coordinate(valIndex) centers.append([x.value(), y.value()]) weights.append(z.value()) errorsP.append(z.errorPlus()**2) errorsM.append(z.errorMinus()**2) else: raise TypeError('Invalid arguments.') self._connection = [centers, weights, errorsP, errorsM] self._range.append(IRangeSet()) self._range.append(IRangeSet()) def create3DConnection(self, data1, data2 = None, data3 = None, data4 = None, data5 = None): centers = [] weights = [] errorsP = [] errorsM = [] if isinstance(data1, IHistogram3D) and (data2 == None) and (data3 == None) and (data4 == None) and (data5 == None): self._binned = True histogram = data1 xAxis = histogram.xAxis() yAxis = histogram.yAxis() zAxis = histogram.zAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): for zBinNumber in range(zAxis.bins()): weight = histogram.binHeight(xBinNumber, yBinNumber, zBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 zCenter = (zAxis.binLowerEdge(zBinNumber) + zAxis.binUpperEdge(zBinNumber)) / 2.0 else: xCenter = histogram.binMeanX(xBinNumber, yBinNumber, zBinNumber) yCenter = histogram.binMeanY(xBinNumber, yBinNumber, zBinNumber) zCenter = histogram.binMeanZ(xBinNumber, yBinNumber, zBinNumber) centers.append([xCenter, yCenter, zCenter]) weights.append(weight) error = histogram._binError2(xBinNumber, yBinNumber, zBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, IHistogram3D) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and isinstance(data4, types.IntType) and (data5 == None): self._binned = True histogram = data1 xIndex = data2 yIndex = data3 zIndex = data4 xAxis = histogram.xAxis() yAxis = histogram.yAxis() zAxis = histogram.zAxis() for xBinNumber in range(xAxis.bins()): for yBinNumber in range(yAxis.bins()): for zBinNumber in range(zAxis.bins()): weight = histogram.binHeight(xBinNumber, yBinNumber, zBinNumber) if weight == 0.0: xCenter = (xAxis.binLowerEdge(xBinNumber) + xAxis.binUpperEdge(xBinNumber)) / 2.0 yCenter = (yAxis.binLowerEdge(yBinNumber) + yAxis.binUpperEdge(yBinNumber)) / 2.0 zCenter = (zAxis.binLowerEdge(zBinNumber) + zAxis.binUpperEdge(zBinNumber)) / 2.0 else: xCenter = histogram.binMeanX(xBinNumber, yBinNumber, zBinNumber) yCenter = histogram.binMeanY(xBinNumber, yBinNumber, zBinNumber) zCenter = histogram.binMeanZ(xBinNumber, yBinNumber, zBinNumber) center = [None, None, None] center[xIndex] = xCenter center[yIndex] = yCenter center[zIndex] = zCenter centers.append(center) weights.append(weight) error = histogram._binError2(xBinNumber, yBinNumber, zBinNumber) errorsP.append(error) errorsM.append(error) elif isinstance(data1, ICloud3D) and (data2 == None) and (data3 == None) and (data4 == None) and (data5 == None): self._binned = False cloud = data1 for entryNumber in range(cloud.entries()): xCenter = cloud.valueX(entryNumber) yCenter = cloud.valueY(entryNumber) zCenter = cloud.valueZ(entryNumber) centers.append([xCenter, yCenter, zCenter]) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(data1, ICloud3D) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and isinstance(data4, types.IntType) and (data5 == None): self._binned = False cloud = data1 xIndex = data2 yIndex = data3 zIndex = data4 for entryNumber in range(cloud.entries()): center = [None, None, None] center[xIndex] = cloud.valueX(entryNumber) center[yIndex] = cloud.valueY(entryNumber) center[zIndex] = cloud.valueZ(entryNumber) centers.append(center) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(data1, IDataPointSet) and isinstance(data2, types.IntType) and isinstance(data3, types.IntType) and isinstance(data4, types.IntType) and isinstance(data5, types.IntType): self._binned = True dataPointSet = data1 xIndex = data2 yIndex = data3 zIndex = data4 valIndex = data5 for offset in range(dataPointSet.size()): dataPoint = dataPointSet.point(offset) x = dataPoint.coordinate(xIndex) y = dataPoint.coordinate(yIndex) z = dataPoint.coordinate(zIndex) val = dataPoint.coordinate(valIndex) centers.append([x.value(), y.value(), z.value()]) weights.append(val.value()) errorsP.append(val.errorPlus()**2) errorsM.append(val.errorMinus()**2) else: raise TypeError('Invalid arguments.') self._connection = [centers, weights, errorsP, errorsM] self._range.append(IRangeSet()) self._range.append(IRangeSet()) self._range.append(IRangeSet()) def createConnection(self, data1 = None, data2 = None, data3 = None): centers = [] weights = [] errorsP = [] errorsM = [] if isinstance(data1, ITuple) and hasattr(data2, '__iter__') and (data3 == None): self._binned = False iTuple = data1 colData = data2 length = iTuple.rows() if length == -1: raise TypeError('This ITuple has no row.') iTuple.start() if isinstance(colData[0], types.StringTypes): columnIndices = [] evaluatorCs = [] for columnName in colData: columnIndex = iTuple.findColumn(columnName) columnIndices.append(columnIndex) columnType = iTuple.columnType(columnIndex) if columnType == PTypes.Double: evaluatorCs.append(iTuple.getDouble) elif columnType == PTypes.Float: evaluatorCs.append(iTuple.getFloat) elif columnType == PTypes.Int: evaluatorCs.append(iTuple.getInt) elif columnType == PTypes.Short: evaluatorCs.append(iTuple.getShort) elif columnType == PTypes.Long: evaluatorCs.append(iTuple.getLong) elif columnType == PTypes.Char: evaluatorCs.append(iTuple.getChar) elif columnType == PTypes.Byte: evaluatorCs.append(iTuple.getByte) elif columnType == PTypes.Boolean: evaluatorCs.append(iTuple.getBoolean) elif columnType == PTypes.String: evaluatorCs.append(iTuple.getString) elif columnType == PTypes.Object: evaluatorCs.append(iTuple.getObject) elif columnType == PTypes.Tuple: evaluatorCs.append(iTuple.getTuple) else: raise TypeError('Illegal column type %s' % columnType) while iTuple.next(): center = [] for i, columnIndex in enumerate(columnIndices): center.append(evaluatorCs[i](columnIndex)) centers.append(center) weights.append(None) errorsP.append(None) errorsM.append(None) elif isinstance(colData[0], IEvaluator): evaluatorCs = [] for evaluator in colData: evaluator.initialize(iTuple) evaluatorCs.append(evaluator.evaluateDouble) while iTuple.next(): center = [] for evaluatorC in evaluatorCs: center.append(evaluatorC()) centers.append(center) weights.append(None) errorsP.append(None) errorsM.append(None) else: raise TypeError('Illegal list data type.') self._connection = [centers, weights, errorsP, errorsM] for i in range(len(colData)): self._range.append(IRangeSet()) elif isinstance(data1, IDataPointSet) and hasattr(data2, '__iter__') and isinstance(data3, types.IntType): self._binned = True dataPointSet = data1 indices = data2 valIndex = data3 for offset in range(dataPointSet.size()): dataPoint = dataPointSet.point(offset) val = dataPoint.coordinate(valIndex) center = [] if indices == []: center.append(offset) else: for i in indices: center.append(dataPoint.coordinate(i).value()) centers.append(center) weights.append(val.value()) errorsP.append(val.errorPlus()**2) errorsM.append(val.errorMinus()**2) self._connection = [centers, weights, errorsP, errorsM] for i in range(len(indices)): self._range.append(IRangeSet()) else: raise TypeError('Invalid arguments.') def reset(self): self._connection = [] self._range = [] self._binned = None self._dataDescription = '' def dimension(self): return len(self._range) def dataDescription(self): return self._dataDescription def range(self, i): try: return self._range[i] except IndexError: raise TypeError('The Range[%d] does not exist.' % i)

35.887931

188

0.69325

1,870

16,652

6.132086

0.093583

0.016482

0.023546

0.021976

0.80954

0.755211

0.746926

0.734368

0.700706

0.665736

0

0.016193

0.187845

16,652

463

189

35.965443

0.831707

0

0.705336

0

0.01111

0

1

0.020882

false

0

0.037123

0.00464

0.067285

0

null

0

1

0

1

0

null

0

7

5aaca73fd77168cc61f1abe7535ffbe52c651777

8,001

py

Python

locust/test/test_distribution.py

AliAnsariArshad/locust

e6a0e94943fad197df540cc79b357500d1f04f1b

[ "MIT" ]

null

locust/test/test_distribution.py

AliAnsariArshad/locust

e6a0e94943fad197df540cc79b357500d1f04f1b

[ "MIT" ]

null

locust/test/test_distribution.py

AliAnsariArshad/locust

e6a0e94943fad197df540cc79b357500d1f04f1b

[ "MIT" ]

null

import time import unittest from locust import User from locust.distribution import weight_users class TestDistribution(unittest.TestCase): def test_distribution_no_user_classes(self): user_classes_count = weight_users(user_classes=[], user_count=0) self.assertDictEqual(user_classes_count, {}) user_classes_count = weight_users(user_classes=[], user_count=1) self.assertDictEqual(user_classes_count, {}) def test_distribution_equal_weights_and_fewer_amount_than_user_classes(self): class User1(User): weight = 1 class User2(User): weight = 1 class User3(User): weight = 1 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=0) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 0, "User3": 0}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=1) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 0, "User3": 0}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=2) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 0}) def test_distribution_equal_weights(self): class User1(User): weight = 1 class User2(User): weight = 1 class User3(User): weight = 1 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=3) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 1}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=4) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 1, "User3": 1}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=5) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 2, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=6) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 2, "User3": 2}) def test_distribution_unequal_and_unique_weights_and_fewer_amount_than_user_classes(self): class User1(User): weight = 1 class User2(User): weight = 2 class User3(User): weight = 3 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=0) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 0, "User3": 0}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=1) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 0, "User3": 1}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=2) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 1, "User3": 1}) def test_distribution_unequal_and_unique_weights(self): class User1(User): weight = 1 class User2(User): weight = 2 class User3(User): weight = 3 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=3) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 1}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=4) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=5) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 2, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=6) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 2, "User3": 3}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=10) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 3, "User3": 5}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=11) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 4, "User3": 5}) def test_distribution_unequal_and_non_unique_weights_and_fewer_amount_than_user_classes(self): class User1(User): weight = 1 class User2(User): weight = 2 class User3(User): weight = 2 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=0) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 0, "User3": 0}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=1) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 1, "User3": 0}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=2) self.assertDictEqual(user_classes_count, {"User1": 0, "User2": 1, "User3": 1}) def test_distribution_unequal_and_non_unique_weights(self): class User1(User): weight = 1 class User2(User): weight = 2 class User3(User): weight = 2 user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=3) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 1}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=4) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 1, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=5) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 2, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=6) self.assertDictEqual(user_classes_count, {"User1": 1, "User2": 3, "User3": 2}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=10) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 4, "User3": 4}) user_classes_count = weight_users(user_classes=[User1, User2, User3], user_count=11) self.assertDictEqual(user_classes_count, {"User1": 2, "User2": 5, "User3": 4}) def test_distribution_large_number_of_users(self): class User1(User): weight = 5 class User2(User): weight = 55 class User3(User): weight = 37 class User4(User): weight = 2 class User5(User): weight = 97 class User6(User): weight = 41 class User7(User): weight = 33 class User8(User): weight = 19 class User9(User): weight = 19 class User10(User): weight = 34 class User11(User): weight = 78 class User12(User): weight = 76 class User13(User): weight = 28 class User14(User): weight = 62 class User15(User): weight = 69 for user_count in range(1044523783783, 1044523783783 + 1000): ts = time.perf_counter() user_classes_count = weight_users( user_classes=[ User1, User2, User3, User4, User5, User6, User7, User8, User9, User10, User11, User12, User13, User14, User15, ], user_count=user_count, ) delta_ms = 1e3 * (time.perf_counter() - ts) self.assertEqual(sum(user_classes_count.values()), user_count) self.assertLessEqual(delta_ms, 100)

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5ab5cd0abd96d341534b4e7e4537ee747176cd97

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py

Python

morpheus/test_normalized_matrix.py

amirsh/MorpheusPy

8eda959e71a3b377c3f6629802bad2bd4f5a5ee6

[ "Apache-2.0" ]

12

2018-10-04T08:27:33.000Z

2022-01-11T15:41:29.000Z

morpheus/test_normalized_matrix.py

amirsh/MorpheusPy

8eda959e71a3b377c3f6629802bad2bd4f5a5ee6

[ "Apache-2.0" ]

3

2020-09-22T16:18:51.000Z

2021-12-28T19:01:00.000Z

morpheus/test_normalized_matrix.py

amirsh/MorpheusPy

8eda959e71a3b377c3f6629802bad2bd4f5a5ee6

[ "Apache-2.0" ]

4

2019-12-13T17:52:19.000Z

2021-12-17T12:43:44.000Z

# Copyright 2018 Side Li and Arun Kumar # 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 numpy as np import scipy.sparse as sp import sklearn.preprocessing as preprocess from numpy.testing import ( run_module_suite, assert_equal, assert_almost_equal ) import normalized_matrix as nm import utils class TestNormalizedMatrix(object): s = np.matrix([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0], [9.0, 1.0]]) k = [np.array([0, 1, 1, 0, 1])] r = [np.matrix([[1.1, 2.2], [3.3, 4.4]])] m = np.matrix([[1.0, 2.0, 1.1, 2.2], [4.0, 3.0, 3.3, 4.4], [5.0, 6.0, 3.3, 4.4], [8.0, 7.0, 1.1, 2.2], [9.0, 1.0, 3.3, 4.4]]) n_matrix = nm.NormalizedMatrix(s, r, k) def test_add(self): n_matrix = self.n_matrix local_matrix = n_matrix + 1 assert_equal(local_matrix.ent_table, self.s + 1) assert_equal(local_matrix.att_table[0], self.r[0] + 1) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix + np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s + np.matrix([1.0, 2.0])) assert_equal(local_matrix.att_table[0], self.r[0] + np.matrix([1.1, 2.2])) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = 1 + n_matrix assert_equal(local_matrix.ent_table[0], 1 + self.s[0]) assert_equal(local_matrix.att_table[0], 1 + self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.matrix([1.0, 2.0, 1.1, 2.2]) + n_matrix assert_equal(local_matrix.ent_table, np.matrix([1.0, 2.0]) + self.s) assert_equal(local_matrix.att_table[0], np.matrix( [1.1, 2.2]) + self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix + 1 local_matrix += 1 assert_equal(local_matrix.ent_table[0], 2 + self.s[0]) assert_equal(local_matrix.att_table[0], 2 + self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix + np.matrix([1.0, 2.0, 1.1, 2.2]) local_matrix += np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s + np.matrix([1.0, 2.0]) * 2) assert_equal(local_matrix.att_table[0], self.r[0] + np.matrix([1.1, 2.2]) * 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.add(n_matrix, 1) assert_equal(local_matrix.ent_table, self.s + 1) assert_equal(local_matrix.att_table[0], self.r[0] + 1) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.add(1, n_matrix) assert_equal(local_matrix.ent_table, self.s + 1) assert_equal(local_matrix.att_table[0], self.r[0] + 1) assert_equal(local_matrix.kfkds[0], self.k[0]) def test_sub(self): n_matrix = self.n_matrix local_matrix = n_matrix - 1 assert_equal(local_matrix.ent_table[0], self.s[0] - 1) assert_equal(local_matrix.att_table[0], self.r[0] - 1) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix - np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s - np.matrix([1.0, 2.0])) assert_equal(local_matrix.att_table[0], self.r[0] - np.matrix([1.1, 2.2])) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = 1 - n_matrix assert_equal(local_matrix.ent_table[0], 1 - self.s[0]) assert_equal(local_matrix.att_table[0], 1 - self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.matrix([1.0, 2.0, 1.1, 2.2]) - n_matrix assert_equal(local_matrix.ent_table, np.matrix([1.0, 2.0]) - self.s) assert_equal(local_matrix.att_table[0], np.matrix( [1.1, 2.2]) - self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix - 1 local_matrix -= 1 assert_equal(local_matrix.ent_table[0], self.s[0] - 2) assert_equal(local_matrix.att_table[0], self.r[0] - 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix - np.matrix([1.0, 2.0, 1.1, 2.2]) local_matrix -= np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s - np.matrix([1.0, 2.0]) * 2) assert_equal(local_matrix.att_table[0], self.r[0] - np.matrix([1.1, 2.2]) * 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.subtract(n_matrix, 1) assert_equal(local_matrix.ent_table, self.s - 1) assert_equal(local_matrix.att_table[0], self.r[0] - 1) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.subtract(1, n_matrix) assert_equal(local_matrix.ent_table, 1 - self.s) assert_equal(local_matrix.att_table[0], 1 - self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) def test_mul(self): n_matrix = self.n_matrix local_matrix = n_matrix * 2 assert_equal(local_matrix.ent_table[0], self.s[0] * 2) assert_equal(local_matrix.att_table[0], self.r[0] * 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = 2 * n_matrix assert_equal(local_matrix.ent_table[0], 2 * self.s[0]) assert_equal(local_matrix.att_table[0], 2 * self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = 2 * n_matrix local_matrix *= 2 assert_equal(local_matrix.ent_table[0], 4 * self.s[0]) assert_equal(local_matrix.att_table[0], 4 * self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.multiply(n_matrix, 2) assert_equal(local_matrix.ent_table, self.s * 2) assert_equal(local_matrix.att_table[0], self.r[0] * 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.multiply(2, n_matrix) assert_equal(local_matrix.ent_table, 2 * self.s) assert_equal(local_matrix.att_table[0], 2 * self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) def test_div(self): n_matrix = self.n_matrix local_matrix = n_matrix / 2 assert_equal(local_matrix.ent_table, self.s / 2) assert_equal(local_matrix.att_table[0], self.r[0] / 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix / np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s / np.matrix([1.0, 2.0])) assert_equal(local_matrix.att_table[0], self.r[0] / np.matrix([1.1, 2.2])) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = 2 / n_matrix assert_equal(local_matrix.ent_table, 2 / self.s) assert_equal(local_matrix.att_table[0], 2 / self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.matrix([1.0, 2.0, 1.1, 2.2]) / n_matrix assert_equal(local_matrix.ent_table, np.matrix([1.0, 2.0]) / self.s) assert_equal(local_matrix.att_table[0], np.matrix( [1.1, 2.2]) / self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix / 2 local_matrix /= 2 assert_equal(local_matrix.ent_table, self.s / 4) assert_equal(local_matrix.att_table[0], self.r[0] / 4) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = n_matrix / np.matrix([1.0, 2.0, 1.1, 2.2]) local_matrix /= np.matrix([1.0, 2.0, 1.1, 2.2]) assert_equal(local_matrix.ent_table, self.s / np.power(np.matrix([1.0, 2.0]), 2)) assert_equal( local_matrix.att_table[0], self.r[0] / np.power(np.matrix([1.1, 2.2]), 2)) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.divide(n_matrix, 2) assert_equal(local_matrix.ent_table, self.s / 2) assert_equal(local_matrix.att_table[0], self.r[0] / 2) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.divide(2, n_matrix) assert_equal(local_matrix.ent_table, 2 / self.s) assert_equal(local_matrix.att_table[0], 2 / self.r[0]) assert_equal(local_matrix.kfkds[0], self.k[0]) def test_pow(self): n_matrix = self.n_matrix local_matrix = n_matrix ** 2 assert_equal(local_matrix.ent_table, np.power(self.s, 2)) assert_equal(local_matrix.att_table[0], np.power(self.r[0], 2)) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.power(n_matrix, 2) assert_equal(local_matrix.ent_table, np.power(self.s, 2)) assert_equal(local_matrix.att_table[0], np.power(self.r[0], 2)) assert_equal(local_matrix.kfkds[0], self.k[0]) local_matrix = np.power(2, n_matrix) assert_equal(local_matrix.ent_table, np.power(2, self.s)) assert_equal(local_matrix.att_table[0], np.power(2, self.r[0])) assert_equal(local_matrix.kfkds[0], self.k[0]) def test_transpose(self): n_matrix = self.n_matrix assert_equal(n_matrix.T.T.sum(axis=0), n_matrix.sum(axis=0)) assert_equal(np.array_equal(n_matrix.T.sum( axis=0), n_matrix.sum(axis=0)), False) def test_inverse(self): n_matrix = self.n_matrix assert_almost_equal(n_matrix.I, self.n_matrix.I) def test_row_sum(self): n_matrix = self.n_matrix assert_almost_equal(n_matrix.sum(axis=1), self.m.sum(axis=1)) def test_row_sum_trans(self): n_matrix = nm.NormalizedMatrix(self.s, self.r, self.k, trans=True) assert_almost_equal(n_matrix.sum(axis=1), self.m.T.sum(axis=1)) def test_col_sum(self): n_matrix = self.n_matrix assert_almost_equal(n_matrix.sum(axis=0), self.m.sum(axis=0)) def test_row_col_trans(self): n_matrix = nm.NormalizedMatrix(self.s, self.r, self.k, trans=True) assert_almost_equal(n_matrix.sum(axis=0), self.m.T.sum(axis=0)) def test_sum(self): n_matrix = self.n_matrix assert_almost_equal(n_matrix.sum(), self.m.sum()) def test_lmm(self): n_matrix = self.n_matrix x = np.matrix([[1.0], [2.0], [3.0], [4.0]]) assert_equal(n_matrix * x, self.m * x) def test_lmm_trans(self): n_matrix = self.n_matrix.T x = np.matrix([[1.0], [2.0], [3.0], [4.0], [5.0]]) assert_almost_equal(n_matrix * x, self.m.T * x) def test_rmm(self): n_matrix = self.n_matrix x = np.matrix([[1.0, 2.0, 3.0, 4.0, 5.0]]) assert_almost_equal(x * n_matrix, x * self.m) def test_rmm_trans(self): n_matrix = self.n_matrix x = np.matrix([[1.0, 2.0, 3.0, 4.0]]) assert_equal(x * n_matrix.T, x * self.m.T) def test_cross_prod(self): n_matrix = self.n_matrix.T * self.n_matrix assert_almost_equal(n_matrix, self.m.T * self.m) n_matrix = np.multiply(self.n_matrix.T, self.n_matrix) assert_almost_equal(n_matrix, self.m.T * self.m) s = np.matrix([[1.0, 2.0], [4.0, 3.0], [ 5.0, 6.0], [8.0, 7.0], [9.0, 1.0]]) k = [np.array([0, 1, 1, 0, 1]), np.array([0, 1, 1, 1, 0])] r = [np.matrix([[1.1, 2.2], [3.3, 4.4]]), np.matrix([[0.1, 0.2], [0.3, 0.4]])] n_matrix = nm.NormalizedMatrix(s, r, k) m = np.hstack([s, r[0][k[0]], r[1][k[1]]]) assert_almost_equal(n_matrix.T * n_matrix, m.T * m) n_matrix = nm.NormalizedMatrix(s, [sp.coo_matrix(ri) for ri in r], k) assert_almost_equal((n_matrix.T * n_matrix).toarray(), m.T * m) def test_cross_prod_trans(self): n_matrix = self.n_matrix.T n_matrix = n_matrix.T * n_matrix assert_almost_equal(n_matrix, self.m * self.m.T) n_matrix = nm.NormalizedMatrix( self.s, [sp.coo_matrix(att) for att in self.r], self.k).T n_matrix = n_matrix.T * n_matrix assert_almost_equal(n_matrix, self.m * self.m.T) def test_cross_prod_hess(self): n_matrix = self.n_matrix assert_almost_equal(n_matrix._cross_prod_hess( np.arange(4)), self.m.dot(np.arange(4).reshape(-1, 1) * self.m.T)) n_matrix = nm.NormalizedMatrix( self.s, [sp.coo_matrix(att) for att in self.r], self.k) assert_almost_equal(n_matrix._cross_prod_hess( np.arange(4)), self.m.dot(np.arange(4).reshape(-1, 1) * self.m.T)) def test_cross_prod_hess_tran(self): n_matrix = self.n_matrix.T assert_almost_equal(n_matrix._cross_prod_hess( np.arange(5)), self.m.T.dot(np.arange(5).reshape(-1, 1) * self.m)) n_matrix = nm.NormalizedMatrix( self.s, [sp.coo_matrix(att) for att in self.r], self.k).T assert_almost_equal(n_matrix._cross_prod_hess( np.arange(5)), self.m.T.dot(np.arange(5).reshape(-1, 1) * self.m)) def test_max(self): n_matrix = self.n_matrix assert_equal(n_matrix.max(), self.m.max()) assert_equal(n_matrix.max(axis=0), self.m.max(axis=0)) def test_min(self): n_matrix = self.n_matrix assert_equal(n_matrix.min(), self.m.min()) assert_equal(n_matrix.min(axis=0), self.m.min(axis=0)) def test_mean(self): n_matrix = self.n_matrix assert_equal(n_matrix.mean(), self.m.mean()) assert_equal(n_matrix.mean(axis=0), self.m.mean(axis=0)) def test_var(self): n_matrix = self.n_matrix assert_equal(n_matrix.var(), self.m.var()) assert_equal(n_matrix.var(axis=0), self.m.var(axis=0)) def test_std(self): n_matrix = self.n_matrix assert_equal(n_matrix.std(), self.m.std()) assert_equal(n_matrix.std(axis=0), self.m.std(axis=0)) def test_mean_centering(self): n_matrix = utils.mean_centering(self.n_matrix) assert_equal(np.hstack((n_matrix.ent_table, n_matrix.att_table[0][n_matrix.kfkds[0]])), self.m - self.m.mean()) n_matrix = utils.mean_centering(self.n_matrix, axis=0) scaler = preprocess.StandardScaler(with_std=False) scaler.fit(self.m) assert_equal(np.hstack((n_matrix.ent_table, n_matrix.att_table[0][n_matrix.kfkds[0]])), scaler.transform(self.m)) def test_standardization(self): n_matrix = utils.standardization(self.n_matrix) assert_equal(np.hstack((n_matrix.ent_table, n_matrix.att_table[0][n_matrix.kfkds[0]])), (self.m - self.m.mean()) / self.m.std()) n_matrix = utils.standardization(self.n_matrix, axis=0) scaler = preprocess.StandardScaler() scaler.fit(self.m) assert_equal(np.hstack((n_matrix.ent_table, n_matrix.att_table[0][n_matrix.kfkds[0]])), scaler.transform(self.m)) def test_normalization(self): n_matrix = utils.normalization(self.n_matrix) assert_equal(np.hstack((n_matrix.ent_table, n_matrix.att_table[0][n_matrix.kfkds[0]])), (self.m - self.m.mean()) / (self.m.max() - self.m.min())) def test_imputation(self): s = np.matrix([[1.0, np.nan], [4.0, 3.0], [ 5.0, 6.0], [8.0, 7.0], [9.0, 1.0]]) k = self.k r = [np.matrix([[np.nan, 2.2], [3.3, 4.4]])] m = np.hstack([s, r[0][k[0]]]) m[np.isnan(m)] = np.nanmean(m) n_matrix = nm.NormalizedMatrix(s, r, k) assert_equal(utils.imputation(n_matrix).sum(axis=0), m.sum(axis=0)) s = np.matrix([[1.0, np.nan], [4.0, 3.0], [ 5.0, 6.0], [8.0, 7.0], [9.0, 1.0]]) k = [np.array([0, 1, 1, 0, 1]), np.array([0, 1, 1, 0, 1])] r = [np.matrix([[np.nan, 2.2], [3.3, 4.4]]), np.matrix([[np.nan, 2.2], [3.3, 4.4]])] m = np.hstack([s, r[0][k[0]], r[1][k[1]]]) mean = np.nanmean(m, axis=0) inds = np.where(np.isnan(m)) m[inds] = np.take(mean, inds[1]) n_matrix = nm.NormalizedMatrix(s, r, k) assert_almost_equal(utils.imputation( n_matrix, axis=0).sum(axis=0), m.sum(axis=0)) if __name__ == "__main__": run_module_suite()

39.300699

95

0.60344

2,845

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3.362742

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0.118532

0.160552

0.220759

0.86443

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0.823247

0.80715

0.761054

0.745166

0

0.053303

0.237782

16,860

428

96

39.392523

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0

null

0

1

0

null

0

1

0

8

851594dabaf62af9a28e0b5be3244437b101c0fb

55

py

Python

examples/python/hello_world.py

12tqian/verification-helper-1

bfdb9a357b451a1385e5327417b3d17168ad2d51

[ "MIT" ]

88

2020-05-03T03:29:01.000Z

2022-03-01T09:12:44.000Z

examples/python/hello_world.py

12tqian/verification-helper-1

bfdb9a357b451a1385e5327417b3d17168ad2d51

[ "MIT" ]

158

2019-11-25T16:48:06.000Z

2020-05-02T14:39:56.000Z

examples/python/hello_world.py

12tqian/verification-helper-1

bfdb9a357b451a1385e5327417b3d17168ad2d51

[ "MIT" ]

40

2020-05-05T09:26:03.000Z

2022-03-13T16:14:41.000Z

def get_hello_world() -> str: return "Hello World"

18.333333

29

0.672727

8

55

4.375

0.75

0.571429

0

0.2

55

2

30

27.5

0.795455

0

0.2

0

1

0.5

true

0

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1

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1

0

1

0

null

0

1

0

1

0

7

85163ecbf36a453edd7f30109e9926d0ea8877f2

169

py

Python

demo.py

chuzcjoe/dms_detection

f8edd2251df561808caf717a06bd04dca039b740

[ "MIT" ]

4

2019-07-26T07:41:34.000Z

2019-11-03T18:44:27.000Z

demo.py

chuzcjoe/distracted_driver_detection

f8edd2251df561808caf717a06bd04dca039b740

[ "MIT" ]

1

2020-01-21T20:12:23.000Z

2020-01-21T20:12:35.000Z

release/demo.py

chuzcjoe/distracted_driver_detection

f8edd2251df561808caf717a06bd04dca039b740

[ "MIT" ]

null

from detect import Detection #from Detection import detect print(Detection().detect('test.jpg')) print(Detection().detect('test2.jpg')) #Detection().detect('test.jpg')

24.142857

38

0.751479

22

169

5.772727

0.363636

0.354331

0.314961

0.346457

0

0.006369

0.071006

169

6

39

28.166667

0.802548

0.343195

0

0.155963

0

1

0

true

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0.333333

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0.333333

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0

null

0

1

0

1

0

1

0

8

517e6846831c47ae5af9e391348dfdd3a4ee0cf6

570

py

Python

eval_covid20cases_timm-regnetx_002_GridDistortion.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_covid20cases_timm-regnetx_002_GridDistortion.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

eval_covid20cases_timm-regnetx_002_GridDistortion.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

import os ls=["python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_0_GridDistortion.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_1_GridDistortion.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_2_GridDistortion.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_3_GridDistortion.yml", "python main.py --configs configs/eval_covid20cases_unetplusplus_timm-regnetx_002_4_GridDistortion.yml", ] for l in ls: os.system(l)

51.818182

108

0.854386

80

570

5.7125

0.3

0.109409

0.131291

0.207877

0.892779

0

0.055659

0.054386

570

11

109

51.818182

0.792208

0

0.884413

0.665499

0

1

0

false

0

0.111111

0

0.111111

0

null

0

1

0

1

null

0

9

517f6e00126117133317e35a15c52cac286da631

5,061

py

Python

tests/core/test_fetcher.py

dclayton-godaddy/aws-okta-processor

ec7603a194ab24d40c2aee4f05e4f87296a880d5

[ "MIT" ]

null

tests/core/test_fetcher.py

dclayton-godaddy/aws-okta-processor

ec7603a194ab24d40c2aee4f05e4f87296a880d5

[ "MIT" ]

null

tests/core/test_fetcher.py

dclayton-godaddy/aws-okta-processor

ec7603a194ab24d40c2aee4f05e4f87296a880d5

[ "MIT" ]

null

from datetime import datetime from unittest import mock from tests.test_base import TestBase from tests.test_base import SAML_RESPONSE from mock import patch, call from mock import MagicMock from aws_okta_processor.commands.authenticate import Authenticate from aws_okta_processor.core.fetcher import SAMLFetcher # Need to add actual tests class TestFetcher(TestBase): @patch("botocore.client") @patch('aws_okta_processor.core.fetcher.print_tty') @patch('aws_okta_processor.core.fetcher.Okta') def test_fetcher( self, mock_okta, mock_print_tty, mock_client ): self.OPTIONS["--role"] = "arn:aws:iam::2:role/Role-One" mock_okta().get_saml_response.return_value = SAML_RESPONSE mock_cache = MagicMock() authenticate = Authenticate(self.OPTIONS) fetcher = SAMLFetcher(authenticate, cache=mock_cache) fetcher.fetch_credentials() @patch("boto3.client") @patch('aws_okta_processor.core.fetcher.print_tty') @patch('aws_okta_processor.core.fetcher.prompt.print_tty') @patch('aws_okta_processor.core.fetcher.prompt.input', return_value='1') @patch('aws_okta_processor.core.fetcher.Okta') def test_fetcher_should_filter_accounts( self, mock_okta, mock_prompt, mock_prompt_print_tty, mock_print_tty, mock_client ): def assume_role_side_effect(*args, **kwargs): if kwargs['RoleArn'] == 'arn:aws:iam::1:role/Role-One': return { 'Credentials': { 'AccessKeyId': 'test-key1', 'SecretAccessKey': 'test-secret1', 'SessionToken': 'test-token1', 'Expiration': datetime(2020, 4, 17, 12, 0, 0, 0) } } raise RuntimeError('invalid RoleArn') self.OPTIONS["--account-alias"] = '1*' self.OPTIONS["--pass"] = 'testpass' mock_c = mock.Mock() mock_c.assume_role_with_saml.side_effect = assume_role_side_effect mock_okta().get_saml_response.return_value = SAML_RESPONSE mock_client.return_value = mock_c authenticate = Authenticate(self.OPTIONS) fetcher = SAMLFetcher(authenticate, cache={}) creds = fetcher.fetch_credentials() self.assertDictEqual({ 'AccessKeyId': 'test-key1', 'Expiration': '2020-04-17T12:00:00', 'SecretAccessKey': 'test-secret1', 'SessionToken': 'test-token1' }, creds) self.assertEqual(5, mock_prompt_print_tty.call_count) MagicMock.assert_has_calls(mock_prompt_print_tty, [ call('Select AWS Role:'), call('Account: 1', indents=0), call('[ 1 ] Role-One', indents=1), call('[ 2 ] Role-Two', indents=1), call('Selection: ', newline=False) ]) @patch("boto3.client") @patch('aws_okta_processor.core.fetcher.print_tty') @patch('aws_okta_processor.core.fetcher.prompt.print_tty') @patch('aws_okta_processor.core.fetcher.prompt.input', return_value='1') @patch('aws_okta_processor.core.fetcher.Okta') def test_fetcher_should_prompt_all_accounts( self, mock_okta, mock_prompt, mock_prompt_print_tty, mock_print_tty, mock_client ): def assume_role_side_effect(*args, **kwargs): if kwargs['RoleArn'] == 'arn:aws:iam::1:role/Role-One': return { 'Credentials': { 'AccessKeyId': 'test-key1', 'SecretAccessKey': 'test-secret1', 'SessionToken': 'test-token1', 'Expiration': datetime(2020, 4, 17, 12, 0, 0, 0) } } raise RuntimeError('invalid RoleArn') self.OPTIONS["--pass"] = 'testpass' mock_c = mock.Mock() mock_c.assume_role_with_saml.side_effect = assume_role_side_effect mock_okta().get_saml_response.return_value = SAML_RESPONSE mock_client.return_value = mock_c authenticate = Authenticate(self.OPTIONS) fetcher = SAMLFetcher(authenticate, cache={}) creds = fetcher.fetch_credentials() self.assertDictEqual({ 'AccessKeyId': 'test-key1', 'Expiration': '2020-04-17T12:00:00', 'SecretAccessKey': 'test-secret1', 'SessionToken': 'test-token1' }, creds) self.assertEqual(7, mock_prompt_print_tty.call_count) MagicMock.assert_has_calls(mock_prompt_print_tty, [ call('Select AWS Role:'), call('Account: 1', indents=0), call('[ 1 ] Role-One', indents=1), call('[ 2 ] Role-Two', indents=1), call('Account: 2', indents=0), call('[ 3 ] Role-One', indents=1), call('Selection: ', newline=False) ])

35.391608

76

0.589212

547

5,061

5.210238

0.184644

0.039298

0.067368

0.077193

0.861404

0.825614

0.806667

0.782105

0

0.025154

0.293025

5,061

143

77

35.391608

0.771381

0.004742

0

0.781513

0

0.232129

0.099087

0

0.05042

1

0.042017

false

0.016807

0.067227

0

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0

null

0

1

0

1

0

null

0

7

51ace97bf7b8e6b0445efe966ab2ed31dd65af4f

83

py

Python

regulus/models/__init__.py

yarden-livnat/regulus

ab78a5ef7d2dcdc95f5f8e16dfe58c8db296812f

[ "BSD-3-Clause" ]

3

2018-03-16T20:47:48.000Z

2020-01-07T15:58:18.000Z

regulus/models/__init__.py

yarden-livnat/regulus.py

ab78a5ef7d2dcdc95f5f8e16dfe58c8db296812f

[ "BSD-3-Clause" ]

3

2018-03-25T07:18:39.000Z

2020-05-06T19:41:38.000Z

regulus/models/__init__.py

yarden-livnat/regulus.py

ab78a5ef7d2dcdc95f5f8e16dfe58c8db296812f

[ "BSD-3-Clause" ]

3

2018-03-16T20:48:05.000Z

2018-08-30T20:38:00.000Z

from .linear_model import * from .quadratic_model import * from .inv_reg import *

16.6

30

0.771084

12

83

5.083333

0.583333

0.360656

0.491803

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true

0

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0

null

0

1

0

1

0

1

0

7

51e66c72f1854aa2c99c7c11621d792b4bacc228

59,774

py

Python

pypureclient/flashblade/FB_2_1/api/arrays_api.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

14

2018-12-07T18:30:27.000Z

2022-02-22T09:12:33.000Z

pypureclient/flashblade/FB_2_1/api/arrays_api.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

28

2019-09-17T21:03:52.000Z

2022-03-29T22:07:35.000Z

pypureclient/flashblade/FB_2_1/api/arrays_api.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

15

2020-06-11T15:50:08.000Z

2022-03-21T09:27:25.000Z

# coding: utf-8 """ FlashBlade REST API A lightweight client for FlashBlade REST API 2.1, developed by Pure Storage, Inc. (http://www.purestorage.com/). OpenAPI spec version: 2.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # python 2 and python 3 compatibility library import six from typing import List, Optional from .. import models class ArraysApi(object): def __init__(self, api_client): self.api_client = api_client def api21_arrays_eula_get_with_http_info( self, continuation_token=None, # type: str filter=None, # type: str limit=None, # type: int offset=None, # type: int sort=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.EulaGetResponse """GET arrays/eula List the End User Agreement and signature. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_eula_get_with_http_info(async_req=True) >>> result = thread.get() :param str continuation_token: An opaque token used to iterate over a collection. The token to use on the next request is returned in the `continuation_token` field of the result. :param str filter: Exclude resources that don't match the specified criteria. :param int limit: Limit the size of the response to the specified number of resources. A `limit` of `0` can be used to get the number of resources without getting all of the resources. It will be returned in the `total_item_count` field. If a client asks for a page size larger than the maximum number, the request is still valid. In that case the server just returns the maximum number of items, disregarding the client's page size request. :param int offset: The offset of the first resource to return from a collection. :param list[str] sort: Sort the response by the specified fields (in descending order if '-' is appended to the field name). NOTE: If you provide a sort you will not get a `continuation_token` in the response. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: EulaGetResponse If the method is called asynchronously, returns the request thread. """ if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api21_arrays_eula_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api21_arrays_eula_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'limit' in params: query_params.append(('limit', params['limit'])) if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/eula', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EulaGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_eula_patch_with_http_info( self, eula=None, # type: models.Eula async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.EulaResponse """PATCH arrays/eula Modifies the signature on the End User Agreement. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_eula_patch_with_http_info(eula, async_req=True) >>> result = thread.get() :param Eula eula: (required) :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: EulaResponse If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] # verify the required parameter 'eula' is set if eula is None: raise TypeError("Missing the required parameter `eula` when calling `api21_arrays_eula_patch`") collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'eula' in params: body_params = params['eula'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/eula', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EulaResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_factory_reset_token_delete_with_http_info( self, async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> None """Delete a factory reset token Deletes any existing token that could be used to perform a factory reset on the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_factory_reset_token_delete_with_http_info(async_req=True) >>> result = thread.get() :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/factory-reset-token', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_factory_reset_token_get_with_http_info( self, continuation_token=None, # type: str filter=None, # type: str limit=None, # type: int offset=None, # type: int sort=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayFactoryResetTokenGetResponse """List factory reset tokens Displays a list of tokens used to perform a factory reset on the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_factory_reset_token_get_with_http_info(async_req=True) >>> result = thread.get() :param str continuation_token: An opaque token used to iterate over a collection. The token to use on the next request is returned in the `continuation_token` field of the result. :param str filter: Exclude resources that don't match the specified criteria. :param int limit: Limit the size of the response to the specified number of resources. A `limit` of `0` can be used to get the number of resources without getting all of the resources. It will be returned in the `total_item_count` field. If a client asks for a page size larger than the maximum number, the request is still valid. In that case the server just returns the maximum number of items, disregarding the client's page size request. :param int offset: The offset of the first resource to return from a collection. :param list[str] sort: Sort the response by the specified fields (in descending order if '-' is appended to the field name). NOTE: If you provide a sort you will not get a `continuation_token` in the response. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayFactoryResetTokenGetResponse If the method is called asynchronously, returns the request thread. """ if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api21_arrays_factory_reset_token_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api21_arrays_factory_reset_token_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'limit' in params: query_params.append(('limit', params['limit'])) if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/factory-reset-token', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayFactoryResetTokenGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_factory_reset_token_post_with_http_info( self, async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayFactoryResetTokenResponse """Create a factory reset token Creates a token that can be used to perform a factory reset on the array. Factory reset tokens can only be created after the array has been prepared for reset (e.g., all file systems, buckets, and snapshots must first be eradicated). After a token has been created, operations that would take the array out of the prepared state (e.g., creating file systems) are disabled until all tokens have been deleted. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_factory_reset_token_post_with_http_info(async_req=True) >>> result = thread.get() :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayFactoryResetTokenResponse If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/factory-reset-token', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayFactoryResetTokenResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_get_with_http_info( self, continuation_token=None, # type: str filter=None, # type: str limit=None, # type: int offset=None, # type: int sort=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayGetResponse """GET arrays List array attributes such as the array name, ID, version, and NTP servers. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_get_with_http_info(async_req=True) >>> result = thread.get() :param str continuation_token: An opaque token used to iterate over a collection. The token to use on the next request is returned in the `continuation_token` field of the result. :param str filter: Exclude resources that don't match the specified criteria. :param int limit: Limit the size of the response to the specified number of resources. A `limit` of `0` can be used to get the number of resources without getting all of the resources. It will be returned in the `total_item_count` field. If a client asks for a page size larger than the maximum number, the request is still valid. In that case the server just returns the maximum number of items, disregarding the client's page size request. :param int offset: The offset of the first resource to return from a collection. :param list[str] sort: Sort the response by the specified fields (in descending order if '-' is appended to the field name). NOTE: If you provide a sort you will not get a `continuation_token` in the response. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayGetResponse If the method is called asynchronously, returns the request thread. """ if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api21_arrays_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api21_arrays_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'limit' in params: query_params.append(('limit', params['limit'])) if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_http_specific_performance_get_with_http_info( self, end_time=None, # type: int resolution=None, # type: int start_time=None, # type: int async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayHttpSpecificPerformanceGet """GET arrays/http-specific-performance List the HTTP performance metrics of the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_http_specific_performance_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayHttpSpecificPerformanceGet If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_http_specific_performance_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_http_specific_performance_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_http_specific_performance_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/http-specific-performance', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayHttpSpecificPerformanceGet', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_nfs_specific_performance_get_with_http_info( self, end_time=None, # type: int resolution=None, # type: int start_time=None, # type: int async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayNfsSpecificPerformanceGet """GET arrays/nfs-specific-performance List the NFS performance metrics of the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_nfs_specific_performance_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayNfsSpecificPerformanceGet If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_nfs_specific_performance_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_nfs_specific_performance_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_nfs_specific_performance_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/nfs-specific-performance', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayNfsSpecificPerformanceGet', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_patch_with_http_info( self, array=None, # type: list async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayResponse """PATCH arrays Modify the general configuration of the array including banner text, array name, NTP servers, and time zone. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_patch_with_http_info(array, async_req=True) >>> result = thread.get() :param Array array: (required) :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayResponse If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] # verify the required parameter 'array' is set if array is None: raise TypeError("Missing the required parameter `array` when calling `api21_arrays_patch`") collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'array' in params: body_params = params['array'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_performance_get_with_http_info( self, end_time=None, # type: int protocol=None, # type: str resolution=None, # type: int start_time=None, # type: int async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayPerformanceGetResponse """GET arrays/performance Lists the overall performance metrics of the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_performance_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param str protocol: Display the performance of a specified protocol. Valid values are `all`, `HTTP`, `SMB`, `NFS`, and `S3`. If not specified, defaults to `all`, which will provide the combined performance of all available protocols. :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayPerformanceGetResponse If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_performance_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_performance_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_performance_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'protocol' in params: query_params.append(('protocol', params['protocol'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/performance', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayPerformanceGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_performance_replication_get_with_http_info( self, end_time=None, # type: int resolution=None, # type: int start_time=None, # type: int type=None, # type: str async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayPerformanceReplicationGetResp """GET arrays/performance/replication List replication performance metrics. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_performance_replication_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param str type: Display the metric of a specified object type. Valid values are `all`, `file-system`, and `object-store`. If not specified, defaults to `all`. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayPerformanceReplicationGetResp If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_performance_replication_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_performance_replication_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_performance_replication_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) if 'type' in params: query_params.append(('type', params['type'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/performance/replication', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayPerformanceReplicationGetResp', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_s3_specific_performance_get_with_http_info( self, end_time=None, # type: int resolution=None, # type: int start_time=None, # type: int async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArrayS3SpecificPerformanceGetResp """GET arrays/s3-specific-performance List the S3 performance metrics of the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_s3_specific_performance_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArrayS3SpecificPerformanceGetResp If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_s3_specific_performance_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_s3_specific_performance_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_s3_specific_performance_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/s3-specific-performance', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArrayS3SpecificPerformanceGetResp', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_space_get_with_http_info( self, end_time=None, # type: int resolution=None, # type: int start_time=None, # type: int type=None, # type: str async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArraySpaceGetResponse """GET arrays/space List available and used storage space on the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_space_get_with_http_info(async_req=True) >>> result = thread.get() :param int end_time: When the time window ends (in milliseconds since epoch). :param int resolution: The desired ms between samples. Available resolutions may depend on data type, `start_time` and `end_time`. In general `1000`, `30000`, `300000`, `1800000`, `7200000`, and `86400000` are possible values. :param int start_time: When the time window starts (in milliseconds since epoch). :param str type: Display the metric of a specified object type. Valid values are `array`, `file-system`, and `object-store`. If not specified, defaults to `array`. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArraySpaceGetResponse If the method is called asynchronously, returns the request thread. """ params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'end_time' in params and params['end_time'] < 0: raise ValueError("Invalid value for parameter `end_time` when calling `api21_arrays_space_get`, must be a value greater than or equal to `0`") if 'resolution' in params and params['resolution'] < 0: raise ValueError("Invalid value for parameter `resolution` when calling `api21_arrays_space_get`, must be a value greater than or equal to `0`") if 'start_time' in params and params['start_time'] < 0: raise ValueError("Invalid value for parameter `start_time` when calling `api21_arrays_space_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'end_time' in params: query_params.append(('end_time', params['end_time'])) if 'resolution' in params: query_params.append(('resolution', params['resolution'])) if 'start_time' in params: query_params.append(('start_time', params['start_time'])) if 'type' in params: query_params.append(('type', params['type'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/space', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArraySpaceGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api21_arrays_supported_time_zones_get_with_http_info( self, continuation_token=None, # type: str filter=None, # type: str limit=None, # type: int names=None, # type: List[str] offset=None, # type: int sort=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.ArraysSupportedTimeZonesGetResponse """GET arrays/supported-time-zones List supported time zones for the array. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api21_arrays_supported_time_zones_get_with_http_info(async_req=True) >>> result = thread.get() :param str continuation_token: An opaque token used to iterate over a collection. The token to use on the next request is returned in the `continuation_token` field of the result. :param str filter: Exclude resources that don't match the specified criteria. :param int limit: Limit the size of the response to the specified number of resources. A `limit` of `0` can be used to get the number of resources without getting all of the resources. It will be returned in the `total_item_count` field. If a client asks for a page size larger than the maximum number, the request is still valid. In that case the server just returns the maximum number of items, disregarding the client's page size request. :param list[str] names: A comma-separated list of resource names. If there is not at least one resource that matches each of the elements of `names`, then an error is returned. :param int offset: The offset of the first resource to return from a collection. :param list[str] sort: Sort the response by the specified fields (in descending order if '-' is appended to the field name). NOTE: If you provide a sort you will not get a `continuation_token` in the response. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: ArraysSupportedTimeZonesGetResponse If the method is called asynchronously, returns the request thread. """ if names is not None: if not isinstance(names, list): names = [names] if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api21_arrays_supported_time_zones_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api21_arrays_supported_time_zones_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'limit' in params: query_params.append(('limit', params['limit'])) if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = ['AuthorizationHeader'] return self.api_client.call_api( '/api/2.1/arrays/supported-time-zones', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ArraysSupportedTimeZonesGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, )

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0

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59,774

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0

null

0

1

0

1

0

null

0

7

cfbaff7d5c4fbb162ed5254a9891033b9b65207c

866

py

Python

test_autolens/analysis/test_setup.py

Jammy2211/AutoLens

bc132a21d1a52248f08f198474e29f985e365d85

[ "MIT" ]

114

2018-03-05T07:31:47.000Z

2022-03-08T06:40:52.000Z

test_autolens/lens/model/test_setup.py

Jammy2211/PyAutoLens

728100a3bf13f89f35030724aa08593ab44e65eb

[ "MIT" ]

143

2018-01-31T09:57:13.000Z

2022-03-16T09:41:05.000Z

test_autolens/analysis/test_setup.py

Jammy2211/AutoLens

bc132a21d1a52248f08f198474e29f985e365d85

[ "MIT" ]

33

2018-01-31T12:15:57.000Z

2022-01-08T18:31:02.000Z

import autolens as al class TestSetupHyper: def test__hyper_galaxies_names_for_lens_and_source(self): setup = al.SetupHyper(hyper_galaxies_lens=False, hyper_galaxies_source=False) assert setup.hyper_galaxies is False assert setup.hyper_galaxy_names == None setup = al.SetupHyper(hyper_galaxies_lens=True, hyper_galaxies_source=False) assert setup.hyper_galaxies is True assert setup.hyper_galaxy_names == ["lens"] setup = al.SetupHyper(hyper_galaxies_lens=False, hyper_galaxies_source=True) assert setup.hyper_galaxies is True assert setup.hyper_galaxy_names == ["source"] setup = al.SetupHyper(hyper_galaxies_lens=True, hyper_galaxies_source=True) assert setup.hyper_galaxies is True assert setup.hyper_galaxy_names == ["lens", "source"]

39.363636

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0.714781

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866

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0.823328

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0

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866

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0.2

0

null

1

0

null

0

1

0

10

320447eccb2cacfa63377525dfe40603d20662d7

118

py

Python

src/yggscr/exceptions.py

architek/yggscr

a0130f9374c4e4e3e3f397a8e0588b3852fcba24

[ "ISC" ]

2

2019-02-09T03:36:03.000Z

2020-09-29T17:04:39.000Z

src/yggscr/exceptions.py

architek/yggscr

a0130f9374c4e4e3e3f397a8e0588b3852fcba24

[ "ISC" ]

6

2018-08-05T21:59:22.000Z

2019-02-20T20:45:07.000Z

src/yggscr/exceptions.py

architek/yggscr

a0130f9374c4e4e3e3f397a8e0588b3852fcba24

[ "ISC" ]

1

2019-04-15T19:24:39.000Z

class YggException(Exception): pass class LoginFailed(Exception): pass class TooManyFailedLogins(Exception): pass

14.75

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118

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0.5

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0.375

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118

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true

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null

0

1

0

1

0

7

5c584372008a55c56425e2e1241ebac016da0017

14,890

py

Python

pvae/models/architectures.py

jacv050/capturing-implicit-hierarchical-structure

c461f069e058a338ead2520ea9ae0d0fa9ae4f0a

[ "MIT" ]

4

2021-11-23T07:24:16.000Z

2021-12-13T14:25:25.000Z

pvae/models/architectures.py

jacv050/capturing-implicit-hierarchical-structure

c461f069e058a338ead2520ea9ae0d0fa9ae4f0a

[ "MIT" ]

null

pvae/models/architectures.py

jacv050/capturing-implicit-hierarchical-structure

c461f069e058a338ead2520ea9ae0d0fa9ae4f0a

[ "MIT" ]

2

2021-12-28T10:27:47.000Z

2022-01-24T12:41:59.000Z

import torch import torch.nn as nn import torch.nn.functional as F from numpy import prod from pvae.utils import Constants from pvae.ops.manifold_layers import GeodesicLayer, MobiusLayer, LogZero, ExpZero, GyroplaneConvLayer def extra_hidden_layer(hidden_dim, non_lin): return nn.Sequential(nn.Linear(hidden_dim, hidden_dim), non_lin) class EncLinear(nn.Module): """ Usual encoder """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim, prior_iso): super(EncLinear, self).__init__() self.manifold = manifold self.data_size = data_size modules = [] modules.append(nn.Sequential(nn.Linear(prod(data_size), hidden_dim), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.enc = nn.Sequential(*modules) self.fc21 = nn.Linear(hidden_dim, manifold.coord_dim) self.fc22 = nn.Linear(hidden_dim, manifold.coord_dim if not prior_iso else 1) def forward(self, x): e = self.enc(x.view(*x.size()[:-len(self.data_size)], -1)) mu = self.fc21(e) # flatten data return mu, F.softplus(self.fc22(e)) + Constants.eta, self.manifold class DecLinear(nn.Module): """ Usual decoder """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecLinear, self).__init__() self.data_size = data_size modules = [] modules.append(nn.Sequential(nn.Linear(manifold.coord_dim, hidden_dim), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.dec = nn.Sequential(*modules) self.fc31 = nn.Linear(hidden_dim, prod(data_size)) def forward(self, z): d = self.dec(z) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class EncWrapped(nn.Module): """ Usual encoder followed by an exponential map """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim, prior_iso): super(EncWrapped, self).__init__() self.manifold = manifold self.data_size = data_size modules = [] modules.append(nn.Sequential(nn.Linear(prod(data_size), hidden_dim), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.enc = nn.Sequential(*modules) self.fc21 = nn.Linear(hidden_dim, manifold.coord_dim) self.fc22 = nn.Linear(hidden_dim, manifold.coord_dim if not prior_iso else 1) def forward(self, x): e = self.enc(x.view(*x.size()[:-len(self.data_size)], -1)) mu = self.fc21(e) # flatten data mu = self.manifold.expmap0(mu) return mu, F.softplus(self.fc22(e)) + Constants.eta, self.manifold class DecWrapped(nn.Module): """ Usual encoder preceded by a logarithm map """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecWrapped, self).__init__() self.data_size = data_size self.manifold = manifold modules = [] modules.append(nn.Sequential(nn.Linear(manifold.coord_dim, hidden_dim), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.dec = nn.Sequential(*modules) self.fc31 = nn.Linear(hidden_dim, prod(data_size)) def forward(self, z): z = self.manifold.logmap0(z) d = self.dec(z) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class DecGeo(nn.Module): """ First layer is a Hypergyroplane followed by usual decoder """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecGeo, self).__init__() self.data_size = data_size modules = [] modules.append(nn.Sequential(GeodesicLayer(manifold.coord_dim, hidden_dim, manifold), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.dec = nn.Sequential(*modules) self.fc31 = nn.Linear(hidden_dim, prod(data_size)) def forward(self, z): d = self.dec(z) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class EncMob(nn.Module): """ Last layer is a Mobius layers """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim, prior_iso): super(EncMob, self).__init__() self.manifold = manifold self.data_size = data_size modules = [] modules.append(nn.Sequential(nn.Linear(prod(data_size), hidden_dim), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.enc = nn.Sequential(*modules) self.fc21 = MobiusLayer(hidden_dim, manifold.coord_dim, manifold) self.fc22 = nn.Linear(hidden_dim, manifold.coord_dim if not prior_iso else 1) def forward(self, x): e = self.enc(x.view(*x.size()[:-len(self.data_size)], -1)) # flatten data mu = self.fc21(e) # flatten data mu = self.manifold.expmap0(mu) return mu, F.softplus(self.fc22(e)) + Constants.eta, self.manifold class DecMob(nn.Module): """ First layer is a Mobius Matrix multiplication """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecMob, self).__init__() self.data_size = data_size modules = [] modules.append(nn.Sequential(MobiusLayer(manifold.coord_dim, hidden_dim, manifold), LogZero(manifold), non_lin)) modules.extend([extra_hidden_layer(hidden_dim, non_lin) for _ in range(num_hidden_layers - 1)]) self.dec = nn.Sequential(*modules) self.fc31 = nn.Linear(hidden_dim, prod(data_size)) def forward(self, z): d = self.dec(z) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class DecBernouilliWrapper(nn.Module): """ Wrapper for Bernoulli likelihood """ def __init__(self, dec): super(DecBernouilliWrapper, self).__init__() self.dec = dec def forward(self, z): mu, _ = self.dec.forward(z) return torch.tensor(1.0).to(z.device), mu ################################################################################ # # Hyperbolic VAE encoders/decoders # ################################################################################ class EncLinearConv(nn.Module): """ 3d convolutional encoder """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim, prior_iso, posterior=None, num_mixtures=None): super(EncLinearConv, self).__init__() self.manifold = manifold self.data_size = data_size self.posterior = posterior self.num_mixtures = num_mixtures modules = [] modules.append(nn.Conv3d(in_channels=1, out_channels=16, kernel_size=(5, 5, 5), padding=(2, 2, 2))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=16, out_channels=32, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=32, out_channels=64, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=64, out_channels=128, kernel_size=(5, 5, 5))) modules.append(nn.ReLU()) modules.append(nn.Flatten()) modules.append(nn.Sequential(nn.Linear(1024, hidden_dim), non_lin)) self.enc = nn.Sequential(*modules) self.fc21 = nn.Linear(hidden_dim, manifold.coord_dim) self.fc22 = nn.Linear(hidden_dim, manifold.coord_dim if not prior_iso else 1) def forward(self, x): e = self.enc(x) mu = self.fc21(e) # flatten data return mu, F.softplus(self.fc22(e)) + Constants.eta, self.manifold #, self.posterior, self.num_mixtures class DecLinearConv(nn.Module): """ 3d convolutional decoder """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecLinearConv, self).__init__() self.data_size = data_size self.lin = nn.Sequential(nn.Linear(manifold.coord_dim, 8), non_lin) # hidden_dim modules = [] modules.append(nn.ConvTranspose3d(in_channels=1, out_channels=64, kernel_size=(5, 5, 5), padding=(2, 2, 2))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=64, out_channels=32, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=32, out_channels=16, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) self.dec = nn.Sequential(*modules) dim_modules = [] dim_modules.append(nn.ConvTranspose3d(in_channels=1, out_channels=1, kernel_size=(1, 1, 1), padding=(0, 0, 0))) dim_modules.append(nn.ReLU()) self.dim_reduction = nn.Sequential(*dim_modules) fc_modules = [] fc_modules.append(nn.ConvTranspose3d(in_channels=16, out_channels=1, kernel_size=(5, 5, 5))) fc_modules.append(nn.ReLU()) self.fc31 = nn.Sequential(*fc_modules) def forward(self, z): l = self.lin(z) v = l.view(-1, 1, 2, 2, 2) r = self.dim_reduction(v) d = self.dec(r) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class EncWrappedConv(nn.Module): """ 3D convolutional encoder followed by an exponential map """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim, prior_iso, posterior=None, num_mixtures=None): super(EncWrappedConv, self).__init__() self.manifold = manifold self.data_size = data_size self.posterior = posterior self.num_mixtures = num_mixtures modules = [] modules.append(nn.Conv3d(in_channels=1, out_channels=16, kernel_size=(5, 5, 5), padding=(2, 2, 2))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=16, out_channels=32, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=32, out_channels=64, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.Conv3d(in_channels=64, out_channels=128, kernel_size=(5, 5, 5))) modules.append(nn.ReLU()) modules.append(nn.Flatten()) modules.append(nn.Sequential(nn.Linear(1024, hidden_dim), non_lin)) self.enc = nn.Sequential(*modules) self.fc21 = nn.Linear(hidden_dim, manifold.coord_dim) self.fc22 = nn.Linear(hidden_dim, manifold.coord_dim if not prior_iso else 1) def forward(self, x): e = self.enc(x) mu = self.fc21(e) # flatten data mu = self.manifold.expmap0(mu) return mu, F.softplus(self.fc22(e)) + Constants.eta, self.manifold #, self.posterior, self.num_mixtures class DecWrappedConv(nn.Module): """ 3d convolutional decoder preceded by a logarithm map """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecWrappedConv, self).__init__() self.data_size = data_size self.manifold = manifold self.lin = nn.Sequential(nn.Linear(manifold.coord_dim, 8), non_lin) # hidden_dim modules = [] modules.append(nn.ConvTranspose3d(in_channels=1, out_channels=64, kernel_size=(5, 5, 5), padding=(2, 2, 2))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=64, out_channels=32, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=32, out_channels=16, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) self.dec = nn.Sequential(*modules) dim_modules = [] dim_modules.append(nn.ConvTranspose3d(in_channels=1, out_channels=1, kernel_size=(1, 1, 1), padding=(0, 0, 0))) dim_modules.append(nn.ReLU()) self.dim_reduction = nn.Sequential(*dim_modules) fc_modules = [] fc_modules.append(nn.ConvTranspose3d(in_channels=16, out_channels=1, kernel_size=(5, 5, 5))) fc_modules.append(nn.ReLU()) self.fc31 = nn.Sequential(*fc_modules) def forward(self, z): z = self.manifold.logmap0(z) l = self.lin(z) v = l.view(-1, 1, 2, 2, 2) r = self.dim_reduction(v) d = self.dec(r) mu = self.fc31(d).view(*z.size()[:-1], *self.data_size) # reshape data return mu, torch.ones_like(mu) class DecGyroConv(nn.Module): """ 3d convolutional decoder preceded by a logarithm map """ def __init__(self, manifold, data_size, non_lin, num_hidden_layers, hidden_dim): super(DecGyroConv, self).__init__() self.data_size = data_size self.manifold = manifold self.hidden_dim = hidden_dim gyro_modules = [] gyro_modules.append(GyroplaneConvLayer(in_features=manifold.coord_dim, out_channels=hidden_dim, kernel_size=1, manifold=manifold)) gyro_modules.append(nn.ReLU()) self.gyro_conv = nn.Sequential(*gyro_modules) dim_modules = [] dim_modules.append(nn.ConvTranspose3d(in_channels=300, out_channels=1, kernel_size=(1, 1, 1), padding=(0, 0, 0))) dim_modules.append(nn.ReLU()) self.dim_reduction = nn.Sequential(*dim_modules) modules = [] modules.append(nn.ConvTranspose3d(in_channels=1, out_channels=64, kernel_size=(5, 5, 5), padding=(2, 2, 2))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=64, out_channels=32, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) modules.append(nn.ConvTranspose3d(in_channels=32, out_channels=16, kernel_size=(5, 5, 5), padding=(1, 1, 1))) modules.append(nn.ReLU()) self.dec = nn.Sequential(*modules) fc_modules = [] fc_modules.append(nn.ConvTranspose3d(in_channels=16, out_channels=1, kernel_size=(5, 5, 5))) fc_modules.append(nn.ReLU()) self.fc31 = nn.Sequential(*fc_modules) def forward(self, z): batch = z.shape[1] g = self.gyro_conv(z) v = self.dim_reduction(g) d = self.dec(v) mu = self.fc31(d).view(-1, batch, *self.data_size) # reshape data return mu, torch.ones_like(mu)

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7a221d0a621296724c129a11629e14ae82a18639

4,426

py

Python

ase.py

gillanggans7/DdosXerXez7

e49054c8f3d49f6d869f3df94aac7ee4dfecd79d

[ "Apache-2.0" ]

null

ase.py

gillanggans7/DdosXerXez7

e49054c8f3d49f6d869f3df94aac7ee4dfecd79d

[ "Apache-2.0" ]

null

ase.py

gillanggans7/DdosXerXez7

e49054c8f3d49f6d869f3df94aac7ee4dfecd79d

[ "Apache-2.0" ]

1

2019-05-07T11:47:28.000Z

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11

7a5874d6c182281450672b2b31fd35bdb010a7eb

818

py

Python

exercises/18_sinbucles.py

ChromeOwO/Curso-B-sico-de-Python-Platzi

d97d99f25b22d3ec2f4bcadbe4f53c87d3077587

[ "MIT" ]

3

2021-05-29T23:30:56.000Z

2021-06-05T15:16:11.000Z

exercises/18_sinbucles.py

ChromeOwO/Curso-Basico-de-Python-Platzi

d97d99f25b22d3ec2f4bcadbe4f53c87d3077587

[ "MIT" ]

null

exercises/18_sinbucles.py

ChromeOwO/Curso-Basico-de-Python-Platzi

d97d99f25b22d3ec2f4bcadbe4f53c87d3077587

[ "MIT" ]

3

2021-07-21T20:03:16.000Z

2021-07-23T15:04:19.000Z

contador = 0 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 1 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 2 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 3 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 4 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 5 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 6 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 7 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador)) contador = 8 print("2 elevado a " + str(contador) + " es igual a: " + str(2 ** contador))

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8f8d0d6c3d4a815e150314a56d2ce7c420a0c208

766

py

Python

2021/bsidesahmedabad/dlppp/dlppp/solve.py

HaroldHH/My-CTF-Solutions

7baca0df1ca96a00de77a1a113a0011c43ad6ab8

[ "MIT" ]

null

2021/bsidesahmedabad/dlppp/dlppp/solve.py

HaroldHH/My-CTF-Solutions

7baca0df1ca96a00de77a1a113a0011c43ad6ab8

[ "MIT" ]

null

2021/bsidesahmedabad/dlppp/dlppp/solve.py

HaroldHH/My-CTF-Solutions

7baca0df1ca96a00de77a1a113a0011c43ad6ab8

[ "MIT" ]

null

from math import ceil, sqrt from Crypto.Util.number import long_to_bytes # Reference : https://bomotodo.wordpress.com/2017/04/09/asis-quals-2017-dlp-158-points/ p = 0xa1c8e1e9b2301cb1f5d424ec6d959d7f275e11507b2177d55f3dc1268c9a3164b72832f362975023f09623814f80fe0ffad179d0e51c40b8a1f882d1f5f28e71 y = 0x6fa0fcc8c9c5f695a5709243698d7640c27c45352375919d538137333ab3a2c748cae5e7c1294d6ffc4007476f6fec6421c992f9fe1919b381306300caa2260953e48f2ec0de7b8c6417faa42001a748b1b367f5211095ddd6bf4e681f7e7ad787e0a7f562f6f0307d6a8d7e8d18cd59bd7572f0c4f430f0fd4fc61503b203f3bcd6dd0b0f84bbdbd42126d95b525fe77e4be62c6dbd083dbcaa284b20a9ea6faf9cbaf20dd88b0180417c9021fa1dcb52b2348c4376bd6b9b38a6c860086af flag = ((y % (p**2)) - 1)//p print("[+] Flag : " + str(long_to_bytes(flag)))

69.636364

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8907c4e3355ea5e8cb189f280fbb26162a96c422

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py

Python

z2/part2/interactive/jm/random_normal_1/151224417.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

1

2020-04-16T12:13:47.000Z

z2/part2/interactive/jm/random_normal_1/151224417.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:50:15.000Z

2020-05-19T14:58:30.000Z

z2/part2/interactive/jm/random_normal_1/151224417.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:45:13.000Z

2020-06-09T19:18:31.000Z

from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 151224417 """ """ random actions, total chaos """ board = gamma_new(8, 8, 6, 5) assert board is not None assert gamma_move(board, 1, 5, 7) == 1 assert gamma_move(board, 1, 5, 7) == 0 assert gamma_move(board, 2, 1, 5) == 1 assert gamma_move(board, 3, 0, 4) == 1 assert gamma_busy_fields(board, 3) == 1 assert gamma_move(board, 4, 5, 7) == 0 assert gamma_move(board, 4, 1, 0) == 1 assert gamma_busy_fields(board, 4) == 1 assert gamma_move(board, 5, 4, 7) == 1 assert gamma_move(board, 6, 7, 0) == 1 assert gamma_move(board, 1, 0, 5) == 1 assert gamma_busy_fields(board, 1) == 2 assert gamma_move(board, 2, 4, 6) == 1 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 1, 4) == 1 assert gamma_move(board, 4, 0, 0) == 1 board539143392 = gamma_board(board) assert board539143392 is not None assert board539143392 == ("....51..\n" "....2...\n" "12......\n" "33......\n" "........\n" "........\n" "........\n" "44.....6\n") del board539143392 board539143392 = None assert gamma_move(board, 5, 1, 0) == 0 assert gamma_move(board, 6, 0, 4) == 0 assert gamma_move(board, 1, 0, 3) == 1 assert gamma_move(board, 1, 5, 1) == 1 board965567065 = gamma_board(board) assert board965567065 is not None assert board965567065 == ("....51..\n" "....2...\n" "12......\n" "33......\n" "1.......\n" "........\n" ".....1..\n" "44.....6\n") del board965567065 board965567065 = None assert gamma_move(board, 2, 2, 1) == 1 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 7, 2) == 1 assert gamma_free_fields(board, 3) == 50 assert gamma_move(board, 4, 6, 3) == 1 assert gamma_move(board, 4, 7, 4) == 1 assert gamma_move(board, 5, 5, 1) == 0 assert gamma_move(board, 5, 6, 1) == 1 assert gamma_move(board, 6, 3, 7) == 1 assert gamma_move(board, 1, 2, 0) == 1 assert gamma_move(board, 1, 7, 0) == 0 assert gamma_move(board, 2, 2, 7) == 1 board809363724 = gamma_board(board) assert board809363724 is not None assert board809363724 == ("..2651..\n" "....2...\n" "12......\n" "33.....4\n" "1.....4.\n" ".......3\n" "..2..15.\n" "441....6\n") del board809363724 board809363724 = None assert gamma_move(board, 3, 4, 3) == 1 assert gamma_move(board, 4, 0, 0) == 0 assert gamma_move(board, 5, 6, 0) == 1 assert gamma_free_fields(board, 5) == 42 board825369756 = gamma_board(board) assert board825369756 is not None assert board825369756 == ("..2651..\n" "....2...\n" "12......\n" "33.....4\n" "1...3.4.\n" ".......3\n" "..2..15.\n" "441...56\n") del board825369756 board825369756 = None assert gamma_move(board, 6, 2, 4) == 1 assert gamma_move(board, 6, 2, 7) == 0 assert gamma_move(board, 1, 2, 5) == 0 assert gamma_move(board, 1, 3, 6) == 0 assert gamma_move(board, 2, 5, 7) == 0 assert gamma_move(board, 2, 1, 2) == 1 assert gamma_move(board, 3, 3, 5) == 1 assert gamma_move(board, 4, 0, 4) == 0 assert gamma_move(board, 5, 7, 0) == 0 assert gamma_move(board, 5, 3, 3) == 1 assert gamma_move(board, 6, 4, 5) == 1 assert gamma_move(board, 1, 3, 2) == 0 assert gamma_move(board, 2, 1, 4) == 0 assert gamma_move(board, 3, 1, 1) == 1 assert gamma_move(board, 4, 2, 6) == 1 assert gamma_move(board, 4, 5, 5) == 1 assert gamma_move(board, 5, 0, 4) == 0 assert gamma_move(board, 6, 5, 2) == 1 assert gamma_move(board, 6, 4, 4) == 1 board847746538 = gamma_board(board) assert board847746538 is not None assert board847746538 == ("..2651..\n" "..4.2...\n" "12.364..\n" "336.6..4\n" "1..53.4.\n" ".2...6.3\n" ".32..15.\n" "441...56\n") del board847746538 board847746538 = None assert gamma_move(board, 1, 4, 2) == 0 assert gamma_move(board, 1, 5, 0) == 1 assert gamma_move(board, 2, 1, 0) == 0 assert gamma_move(board, 2, 3, 0) == 0 assert gamma_golden_move(board, 2, 7, 4) == 0 assert gamma_move(board, 3, 3, 5) == 0 assert gamma_move(board, 4, 0, 4) == 0 assert gamma_free_fields(board, 4) == 12 assert gamma_move(board, 5, 6, 2) == 1 assert gamma_free_fields(board, 5) == 30 assert gamma_move(board, 6, 6, 5) == 0 assert gamma_golden_possible(board, 6) == 1 assert gamma_move(board, 1, 6, 7) == 1 assert gamma_free_fields(board, 1) == 9 assert gamma_move(board, 2, 5, 7) == 0 assert gamma_move(board, 2, 2, 3) == 0 assert gamma_move(board, 3, 7, 3) == 1 assert gamma_move(board, 4, 6, 3) == 0 assert gamma_move(board, 5, 1, 0) == 0 assert gamma_move(board, 5, 7, 1) == 1 assert gamma_move(board, 1, 2, 4) == 0 assert gamma_move(board, 1, 6, 1) == 0 assert gamma_free_fields(board, 1) == 9 assert gamma_golden_possible(board, 1) == 1 assert gamma_move(board, 3, 2, 5) == 1 assert gamma_move(board, 4, 2, 2) == 0 assert gamma_move(board, 4, 2, 3) == 0 assert gamma_move(board, 5, 0, 4) == 0 assert gamma_move(board, 5, 7, 1) == 0 assert gamma_free_fields(board, 5) == 26 assert gamma_move(board, 6, 1, 4) == 0 assert gamma_move(board, 6, 7, 1) == 0 assert gamma_busy_fields(board, 6) == 6 assert gamma_move(board, 1, 2, 4) == 0 assert gamma_move(board, 2, 3, 5) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_move(board, 3, 6, 5) == 0 assert gamma_move(board, 4, 2, 3) == 0 assert gamma_move(board, 5, 6, 0) == 0 assert gamma_move(board, 5, 5, 0) == 0 assert gamma_move(board, 6, 2, 3) == 1 assert gamma_move(board, 6, 2, 5) == 0 assert gamma_golden_possible(board, 6) == 1 assert gamma_move(board, 1, 0, 3) == 0 assert gamma_move(board, 2, 0, 3) == 0 assert gamma_golden_move(board, 2, 3, 0) == 0 assert gamma_move(board, 3, 6, 4) == 0 assert gamma_move(board, 4, 5, 7) == 0 assert gamma_free_fields(board, 4) == 9 assert gamma_move(board, 5, 0, 2) == 1 assert gamma_move(board, 5, 3, 7) == 0 assert gamma_busy_fields(board, 5) == 7 assert gamma_move(board, 6, 5, 6) == 0 assert gamma_move(board, 6, 2, 7) == 0 assert gamma_move(board, 1, 1, 2) == 0 assert gamma_move(board, 2, 2, 3) == 0 assert gamma_move(board, 3, 7, 7) == 0 assert gamma_move(board, 4, 7, 1) == 0 assert gamma_move(board, 5, 1, 0) == 0 assert gamma_move(board, 6, 1, 0) == 0 assert gamma_move(board, 6, 0, 6) == 0 board308154158 = gamma_board(board) assert board308154158 is not None assert board308154158 == ("..26511.\n" "..4.2...\n" "123364..\n" "336.6..4\n" "1.653.43\n" "52...653\n" ".32..155\n" "441..156\n") del board308154158 board308154158 = None assert gamma_move(board, 1, 4, 3) == 0 assert gamma_move(board, 1, 4, 0) == 1 assert gamma_move(board, 2, 6, 0) == 0 assert gamma_move(board, 2, 1, 4) == 0 assert gamma_move(board, 3, 4, 5) == 0 assert gamma_move(board, 4, 0, 4) == 0 assert gamma_move(board, 5, 2, 4) == 0 assert gamma_move(board, 5, 4, 3) == 0 assert gamma_move(board, 6, 6, 7) == 0 assert gamma_move(board, 1, 2, 5) == 0 assert gamma_move(board, 1, 5, 2) == 0 assert gamma_golden_possible(board, 1) == 1 assert gamma_move(board, 2, 6, 0) == 0 assert gamma_move(board, 2, 5, 5) == 0 assert gamma_free_fields(board, 2) == 7 assert gamma_move(board, 3, 1, 0) == 0 assert gamma_busy_fields(board, 3) == 8 assert gamma_move(board, 4, 6, 5) == 1 assert gamma_move(board, 5, 6, 6) == 1 assert gamma_free_fields(board, 5) == 5 assert gamma_move(board, 6, 7, 6) == 0 assert gamma_move(board, 6, 1, 6) == 0 assert gamma_free_fields(board, 6) == 7 assert gamma_golden_possible(board, 1) == 1 assert gamma_move(board, 2, 6, 0) == 0 assert gamma_move(board, 2, 2, 6) == 0 assert gamma_busy_fields(board, 2) == 5 assert gamma_move(board, 3, 4, 6) == 0 assert gamma_move(board, 3, 0, 7) == 0 assert gamma_move(board, 4, 4, 6) == 0 assert gamma_golden_move(board, 4, 6, 6) == 1 assert gamma_busy_fields(board, 5) == 7 assert gamma_move(board, 6, 2, 0) == 0 assert gamma_move(board, 2, 7, 1) == 0 assert gamma_move(board, 3, 7, 0) == 0 assert gamma_free_fields(board, 3) == 6 assert gamma_move(board, 5, 6, 1) == 0 assert gamma_move(board, 5, 0, 6) == 1 assert gamma_move(board, 6, 6, 3) == 0 assert gamma_move(board, 6, 0, 6) == 0 assert gamma_move(board, 1, 7, 7) == 1 assert gamma_move(board, 2, 4, 5) == 0 assert gamma_move(board, 2, 5, 1) == 0 assert gamma_busy_fields(board, 2) == 5 assert gamma_move(board, 3, 1, 1) == 0 assert gamma_busy_fields(board, 3) == 8 assert gamma_move(board, 4, 1, 5) == 0 assert gamma_move(board, 5, 2, 5) == 0 assert gamma_free_fields(board, 5) == 5 assert gamma_move(board, 6, 6, 7) == 0 assert gamma_move(board, 6, 1, 3) == 1 assert gamma_golden_possible(board, 6) == 1 assert gamma_move(board, 1, 6, 3) == 0 assert gamma_move(board, 1, 6, 7) == 0 assert gamma_move(board, 2, 4, 3) == 0 assert gamma_move(board, 2, 2, 3) == 0 assert gamma_free_fields(board, 2) == 6 assert gamma_move(board, 3, 4, 6) == 0 assert gamma_move(board, 4, 7, 5) == 1 assert gamma_move(board, 5, 2, 4) == 0 assert gamma_move(board, 6, 4, 5) == 0 assert gamma_move(board, 6, 4, 7) == 0 assert gamma_move(board, 1, 4, 6) == 0 assert gamma_move(board, 1, 3, 7) == 0 assert gamma_golden_move(board, 1, 3, 2) == 0 assert gamma_move(board, 2, 3, 5) == 0 assert gamma_move(board, 2, 7, 2) == 0 assert gamma_move(board, 3, 2, 2) == 0 assert gamma_move(board, 4, 1, 0) == 0 assert gamma_move(board, 4, 1, 5) == 0 assert gamma_move(board, 5, 3, 6) == 0 assert gamma_move(board, 6, 3, 7) == 0 assert gamma_golden_move(board, 6, 0, 0) == 0 assert gamma_move(board, 1, 7, 1) == 0 assert gamma_move(board, 1, 2, 0) == 0 assert gamma_move(board, 2, 6, 1) == 0 assert gamma_move(board, 3, 4, 5) == 0 assert gamma_move(board, 3, 2, 0) == 0 assert gamma_move(board, 5, 4, 3) == 0 assert gamma_move(board, 5, 3, 4) == 1 assert gamma_free_fields(board, 5) == 4 assert gamma_move(board, 6, 6, 7) == 0 assert gamma_golden_move(board, 6, 2, 1) == 0 assert gamma_move(board, 1, 5, 2) == 0 assert gamma_move(board, 2, 4, 1) == 0 assert gamma_move(board, 3, 6, 5) == 0 assert gamma_move(board, 4, 2, 4) == 0 assert gamma_move(board, 6, 4, 5) == 0 assert gamma_move(board, 6, 2, 4) == 0 assert gamma_busy_fields(board, 6) == 8 assert gamma_free_fields(board, 6) == 5 assert gamma_move(board, 1, 2, 0) == 0 assert gamma_move(board, 2, 0, 3) == 0 assert gamma_move(board, 2, 4, 6) == 0 gamma_delete(board)

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8f233fae9c9ead7b9772084976e5b2720f042e5a

57,621

py

Python

notebooks/py/dev_4_bayes_poisson.py

agalea91/nhl-goalie-pull-optimization

7e57d50163c5f96a22dd5afd96c6e1ba5487c600

[ "MIT" ]

null

notebooks/py/dev_4_bayes_poisson.py

agalea91/nhl-goalie-pull-optimization

7e57d50163c5f96a22dd5afd96c6e1ba5487c600

[ "MIT" ]

null

notebooks/py/dev_4_bayes_poisson.py

agalea91/nhl-goalie-pull-optimization

7e57d50163c5f96a22dd5afd96c6e1ba5487c600

[ "MIT" ]

2

2019-06-06T10:37:48.000Z

2021-03-31T18:28:43.000Z

# coding: utf-8 # %load jupyter_default.py import pandas as pd import numpy as np import os import re import datetime import time import glob from tqdm import tqdm_notebook from colorama import Fore, Style get_ipython().run_line_magic('matplotlib', 'inline') import matplotlib.pyplot as plt import matplotlib.colors import seaborn as sns get_ipython().run_line_magic('config', "InlineBackend.figure_format='retina'") sns.set() # Revert to matplotlib defaults plt.rcParams['figure.figsize'] = (12, 8) plt.rcParams['axes.labelpad'] = 20 plt.rcParams['legend.fancybox'] = True plt.style.use('ggplot') SMALL_SIZE, MEDIUM_SIZE, BIGGER_SIZE = 14, 16, 20 plt.rc('font', size=SMALL_SIZE) plt.rc('axes', titlesize=SMALL_SIZE) plt.rc('axes', labelsize=MEDIUM_SIZE) plt.rc('xtick', labelsize=SMALL_SIZE) plt.rc('ytick', labelsize=SMALL_SIZE) plt.rc('legend', fontsize=MEDIUM_SIZE) plt.rc('axes', titlesize=BIGGER_SIZE) def savefig(plt, name): plt.savefig(f'../../figures/{name}.png', bbox_inches='tight', dpi=300) get_ipython().run_line_magic('load_ext', 'version_information') get_ipython().run_line_magic('version_information', 'pandas, numpy') # ## Bayesian Modeling Discussion # # We can model the probability of an outcome $y$ as $P_t(y)$ using a discrete **Poisson distribution** i.e. if discretizing the time $t$ in seconds. # # $$ # P_t(\mu) = \frac{\mu^te^{-\mu}}{k!} # $$ # # Instead we could also assume a Gamma posterior, which has the advantage of being continuous and has more parameters than can be optimized. For now we'll stick with using the simpler Poisson distribution. # # Based on a set of goalie pull observations $X$ from 2003-2007 NHL games, we'll solve for the posterior distribution $P_t(y|X)$, the probability of the outcome $y$, given the observations. This is done computationally using markov chain monte carlo and the `pymc3` library. # # The outcomes we're interested in are $y = \big\{\mathrm{goal\;for}, \mathrm{goal\;against}, \mathrm{no\;goal}\big\}$. # # We'll use a **uniform prior** over the domain of times (last 5mins). Note: when gathering the observations, we throw out goalie pulls greater than 5 minutes from the end of the game (due to high likelihood of false positives when parsing goalie pulls from the raw game table). # # Once we find the posteriors discussed above, we can study the risk reward of pulling a goalie. We'll compare posteriors to find the odds of scoring a goal (and the odds of getting scored on) over time $t$ where: # - **t = Time elapsed** e.g. if there's 3 minutes left, what is the chance that pulling the goalie will result in a goal for? # - **t = Time since goalie pull** e.g. after the goalie has been pulled for 1 minute, what is the chance of getting a goal? import pymc3 as pm # ### Load the training data ls ../../data/processed/pkl/ def load_data(): files = glob.glob('../../data/processed/pkl/*.pkl') files = sorted(files) print(files) return pd.concat((pd.read_pickle(f) for f in files)) def clean_df(df): _df = df.copy() len_0 = _df.shape[0] print('Removing goal_for_time < 15 mins') _df = _df[~(_df.goal_for_time < datetime.timedelta(seconds=15*60))] print(f'Removed {len_0 - _df.shape[0]} total rows') if 'game_end_time' in df.columns: len_0 = _df.shape[0] print('Removing game_end_time < 15 mins') _df = _df[~(_df.game_end_time < datetime.timedelta(seconds=60*15))] print(f'Removed {len_0 - _df.shape[0]} total rows') return _df df = load_data() df = clean_df(df) def load_training_samples( df, cols, masks=[], dtype='timedelta64[s]' ) -> np.ndarray: ''' Return buckets of training data. ''' if not masks: masks = [None] * len(cols) out = [] for col, m in zip(cols, masks): if m is None: d = df[col].dropna().astype(dtype).values else: d = df[col][m].dropna().astype(dtype).values out.append(d) print(f'Loaded {len(d)} samples for col {col}') out = np.array(out) print(f'Training data shape = {out.shape}') return out # ### Rough work # #### Data loading def load_training_samples( df, cols, masks=[], dtype='timedelta64[s]' ) -> np.ndarray: ''' Return buckets of training data. ''' if not masks: masks = [None] * len(cols) out = [] for col, m in zip(cols, masks): if m is None: d = df[col].dropna().astype(dtype).values else: d = df[col][m].dropna().astype(dtype).values out.append(d) print(f'Loaded {len(d)} samples for col {col}') out = np.array(out) print(f'Training data shape = {out.shape}') return out # Let's start by modeling the 5 on 6 goal times in 3rd period, where time is a continuous (or rather, discretized by second) and measured in minutes. features = ['goal_for_time', 'goal_against_time'] training_samples = load_training_samples(df, features) training_samples[0].shape training_samples[0][:10] # To get the proper probabilities, we should weight the # #### Modeling # with pm.Model() as model: # prior_goal_for = pm.Uniform('prior_goal_for', 15, 20) # prior_goal_against = pm.Uniform('prior_goal_against', 15, 20) # obs_goal_for = pm.Gamma('obs_goal_for', observed=training_samples[0]) # need to set up priors for all the parameters of the gamma!... # THINK ABOUT IT from scipy.stats import poisson get_ipython().run_line_magic('pinfo', 'poisson') # ``` # pmf(k, mu, loc=0) # Probability mass function. # ``` x = np.arange(0, 20, 1) y = [poisson.pmf(_x, 1, 1) for _x in x] plt.plot(x, y) def bayes_model(training_samples): with pm.Model() as model: # Priors for the mu parameter of the poisson distribution # Note that mu = mean(Poisson) mu_goal_for = pm.Uniform('mu_goal_for', 15*60, 20*60) mu_goal_against = pm.Uniform('mu_goal_against', 15*60, 20*60) # Observations obs_goal_for = pm.Poisson('obs_goal_for', mu_goal_for, observed=training_samples[0]) obs_goal_against = pm.Poisson('obs_goal_against', mu_goal_against, observed=training_samples[1]) # Priors for the goal probabilities p_goal_for = pm.Poisson('p_goal_for', mu_goal_for) p_goal_against = pm.Poisson('p_goal_against', mu_goal_against) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace # N = 10 # test_training_samples = np.array([training_samples[0][:N], # training_samples[1][:N]]) # model, trace, burned_trace = bayes_model(test_training_samples) # model model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] get_ipython().run_line_magic('pinfo', 'pm.plots.traceplot') pm.plots.traceplot(trace=trace, varnames=['p_goal_for', 'p_goal_against']) # What do red and blue represent? pm.plots.plot_posterior(trace=trace['p_goal_for']) pm.plots.plot_posterior(trace=trace['p_goal_against']) # The HDR is really interesting! For the above case (normally distributed data), the HDR is pretty much equivalent to the SD based confience interval. However it generalizes to more complicated distributions # # https://stats.stackexchange.com/questions/148439/what-is-a-highest-density-region-hdr # e.g. # # ![](https://i.stack.imgur.com/Dy89t.png) ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); # Include both those plots in blog ^ from scipy.special import factorial poisson = lambda mu, k: mu**k * np.exp(-mu) / factorial(k) poisson(0.5, np.array([1, 4, 5, 2])) from scipy.stats import poisson get_ipython().run_line_magic('pinfo', 'poisson.pmf') poisson.pmf(3, 1) poisson.pmf(np.array([1, 4, 3]), 1) p = poisson.pmf # poisson = lambda k, mu: mu**k * np.exp(-mu) / factorial(k) x = np.arange(16, 22, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() / 60 y_goal_for = p(x, mu_goal_for) mu_goal_against = burned_trace['mu_goal_against'].mean() / 60 y_goal_against = p(x, mu_goal_against) plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{avg})$', color='green') plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{avg})$', color='red') p = poisson.pmf # poisson = lambda k, mu: mu**k * np.exp(-mu) / factorial(k) x = np.arange(16*60, 22*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() y_goal_for = p(x, mu_goal_for) mu_goal_against = burned_trace['mu_goal_against'].mean() y_goal_against = p(x, mu_goal_against) plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{avg})$', color='green') plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{avg})$', color='red') ALPHA = 0.6 LW = 3 # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 22*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() y_goal_for = p(x, mu_goal_for) y_goal_against = p(x, mu_goal_against) # Convert into minutes and rescale to fit chart x = x / 60 scale_frac = 0.7 y_goal_for = y_goal_for / y_goal_for.max() * scale_frac y_goal_against = y_goal_against / y_goal_against.max() * scale_frac plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); # (Do not include this plot ^ in blog, but re-use source code) # In reality, the probability of an empty net goal should be zero after 20 minutes (since the period is over). We would also need to normalize the probabilities such that # # $ # \sum_t \big{[} P(\mathrm{goal\;for}; \mu, t) + P(\mathrm{goal\;against}; \mu, t) + P(\mathrm{game\;end}) \big{]} = 1 # $ # # Since this was just a toy model to get us warmed up with `pymc`, let's just leave this and move on to a more interesting problem. # --- # #### Re-loead better training samples # I wonder if we can answer the question: **what are the odds of scoring a goal based on when the goalie is pulled?** # # It's probably best to decide that based on the "time since goalie pull" metric and the time remaining in the game. For the chart above, the goal for probability is clearly shifted to the left - however this does not mean that pulling a goalie at the 19 minute mark will have lower odds of a good outcome than pulling at the 18 minute mark. This chart is just a litlihood of scoring given the goalie pull times. # # What we should do is label the goalie pull times with the eventual outcome, then model that. df.columns # Load time of pull for eventual outcomes: feature_names = ['goal_for', 'goal_against'] # Logic for loading the data features = ['pull_time', 'pull_time'] masks = [~(df.goal_for_time.isnull()), ~(df.goal_against_time.isnull())] training_samples = load_training_samples(df, features, masks) def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Priors for the mu parameter of the poisson distribution # Note that mu = mean(Poisson) mu_goal_for = pm.Uniform('mu_goal_for', 15*60, 20*60) mu_goal_against = pm.Uniform('mu_goal_against', 15*60, 20*60) # Observations obs_goal_for = pm.Poisson('obs_goal_for', mu_goal_for, observed=training_samples[0]) obs_goal_against = pm.Poisson('obs_goal_against', mu_goal_against, observed=training_samples[1]) # Priors for the goal probabilities p_goal_for = pm.Poisson('p_goal_for', mu_goal_for) p_goal_against = pm.Poisson('p_goal_against', mu_goal_against) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); ALPHA = 0.6 LW = 3 # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 22*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() y_goal_for = p(x, mu_goal_for) y_goal_against = p(x, mu_goal_against) # Convert into minutes and rescale to fit chart x = x / 60 scale_frac = 0.7 y_goal_for = y_goal_for / y_goal_for.max() * scale_frac y_goal_against = y_goal_against / y_goal_against.max() * scale_frac plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); # Let's test this with a uniform prior def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Priors for the goal probabilties # Last 5 minutes of the game, in seconds # p_goal_for = pm.Uniform('p_goal_for', 15*60, 20*60) # p_goal_against = pm.Uniform('p_goal_against', 15*60, 20*60) # Priors for the mu parameter of the poisson distribution # Note that mu = mean(Poisson) mu_goal_for = pm.Uniform('mu_goal_for', 15*60, 20*60) mu_goal_against = pm.Uniform('mu_goal_against', 15*60, 20*60) # Observations obs_goal_for = pm.Poisson( 'obs_goal_for', mu=mu_goal_for, observed=training_samples[0], ) obs_goal_against = pm.Poisson( 'obs_goal_against', mu=mu_goal_against, observed=training_samples[1], ) p_goal_for = pm.Deterministic( 'p_goal_for', pm.Poisson('posterior_for', mu_goal_for) ) p_goal_against = pm.Deterministic( 'p_goal_against', pm.Poisson('posterior_against', mu_goal_against) ) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] ALPHA = 0.6 LW = 3 # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 22*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() y_goal_for = p(x, mu_goal_for) y_goal_against = p(x, mu_goal_against) # Convert into minutes and rescale to fit chart x = x / 60 scale_frac = 0.7 y_goal_for = y_goal_for / y_goal_for.max() * scale_frac y_goal_against = y_goal_against / y_goal_against.max() * scale_frac plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() trace['mu_goal_for'].mean(), trace['mu_goal_against'].mean() plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); burned_trace.varnames # Here I tried to combine the observations and the posterior, but pymc3 treats these as separate types. The observations are deterministic whereas the posteriors are stochastic. def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Observations to train the model # obs_goal_for = pm.Poisson( # 'obs_goal_for', # mu=training_samples[0].mean(), # observed=training_samples[0], # ) # obs_goal_against = pm.Poisson( # 'obs_goal_against', # mu=training_samples[1].mean(), # observed=training_samples[1], # ) # Priors for the mu parameter of the # Poisson distribution. # Note that mu = mean(Poisson) mu_goal_for = pm.Uniform( 'mu_goal_for', 15*60, 20*60 ) mu_goal_against = pm.Uniform( 'mu_goal_against', 15*60, 20*60 ) # Goal probabilities p_goal_for = pm.Poisson( 'p_goal_for', mu_goal_for, observed=training_samples[0] ) p_goal_against = pm.Poisson( 'p_goal_against', mu_goal_against, observed=training_samples[1] ) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['p_goal_for'], bins=50, color='green', label='p_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'], bins=50, color='red', label='p_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); # # # Adding a contraint: # # ```_equation = pm.math.eq(p_goal_for + p_goal_against, 1) # constraint = pm.Potential( # 'constraint', # pm.math.switch(_equation, 0, -np.inf) # )``` def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Observations to train the model obs_goal_for = pm.Poisson( 'obs_goal_for', mu=training_samples[0].mean(), observed=training_samples[0], ) obs_goal_against = pm.Poisson( 'obs_goal_against', mu=training_samples[1].mean(), observed=training_samples[1], ) # Priors for the mu parameter of the # Poisson distribution. # Note that mu = mean(Poisson) mu_goal_for = pm.Uniform( 'mu_goal_for', 15*60, 20*60 ) mu_goal_against = pm.Uniform( 'mu_goal_against', 15*60, 20*60 ) # Goal probabilities p_goal_for = pm.Poisson( 'p_goal_for', mu_goal_for ) p_goal_against = pm.Poisson( 'p_goal_against', mu_goal_against ) # Constraint on probabilties # Add _equation = pm.math.eq(p_goal_for + p_goal_against, 1) constraint = pm.Potential( 'constraint', pm.math.switch(_equation, 0, -np.inf) ) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['p_goal_for'], bins=50, color='green', label='p_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'], bins=50, color='red', label='p_goal_against', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); # That didnt work too well... # # But we're getting closer to the final model # # --- # # #### Including "no goals" variable # # Lets make them bounded and add in the game end var df.columns # Load time of pull for eventual outcomes: feature_names = ['goal_for', 'goal_against', 'no_goals'] # Logic for loading the data features = ['pull_time', 'pull_time', 'pull_time'] masks = [ ~(df.goal_for_time.isnull()), ~(df.goal_against_time.isnull()), (df.goal_for_time.isnull() & df.goal_against_time.isnull()), ] training_samples = load_training_samples(df, features, masks) (training_samples[0][:10], training_samples[1][:10], training_samples[2][:10],) # Trying constrained model again def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Priors for the mu parameter of the # Poisson distribution P. # Note: mu = mean(P) mu_goal_for = pm.Uniform( 'mu_goal_for', 15*60, 20*60 ) mu_goal_against = pm.Uniform( 'mu_goal_against', 15*60, 20*60 ) mu_no_goal = pm.Uniform( 'mu_no_goal', 15*60, 20*60 ) # Observations to train the model on obs_goal_for = pm.Poisson( 'obs_goal_for', mu=mu_goal_for, observed=training_samples[0], ) obs_goal_against = pm.Poisson( 'obs_goal_against', mu=mu_goal_against, observed=training_samples[1], ) obs_no_goal = pm.Poisson( 'obs_no_goal', mu=mu_no_goal, observed=training_samples[2], ) # Outcome probabilities p_goal_for = pm.Bound(pm.Poisson, upper=20*60)('p_goal_for', mu=mu_goal_for) p_goal_against = pm.Bound(pm.Poisson, upper=20*60)('p_goal_against', mu=mu_goal_against) p_no_goal = pm.Bound(pm.Poisson, upper=20*60)('p_no_goal', mu=mu_no_goal) # Constraint on probabilties _equation = pm.math.eq(p_goal_for + p_goal_against + p_no_goal, 1) constraint = pm.Potential( 'constraint', pm.math.switch(_equation, 0, -np.inf) ) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] ALPHA = 0.6 LW = 3 from scipy.stats import poisson # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_no_goal'] / 60, bins=50, color='orange', label='p_no_goal samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot the poisson distributions ''' # p = poisson.pmf # x = np.arange(16*60, 22*60, 1) # mu_goal_for = burned_trace['mu_goal_for'].mean() # mu_goal_against = burned_trace['mu_goal_against'].mean() # mu_no_goal = burned_trace['mu_no_goal'].mean() # y_goal_for = p(x, mu_goal_for) # y_goal_against = p(x, mu_goal_against) # y_no_goal = p(x, mu_no_goal) # # Convert into minutes and rescale to fit chart # x = x / 60 # scale_frac = 0.7 # y_goal_for = y_goal_for / y_goal_for.max() * scale_frac # y_goal_against = y_goal_against / y_goal_against.max() * scale_frac # y_no_goal = y_no_goal / y_no_goal.max() * scale_frac # plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) # plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) # plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW) plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() # Constraints just don't make sense here... # # Removing them. def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Priors for the mu parameter of the # Poisson distribution P. # Note: mu = mean(P) mu_goal_for = pm.Uniform( 'mu_goal_for', 15*60, 20*60 ) mu_goal_against = pm.Uniform( 'mu_goal_against', 15*60, 20*60 ) mu_no_goal = pm.Uniform( 'mu_no_goal', 15*60, 20*60 ) # Observations to train the model on obs_goal_for = pm.Poisson( 'obs_goal_for', mu=mu_goal_for, observed=training_samples[0], ) obs_goal_against = pm.Poisson( 'obs_goal_against', mu=mu_goal_against, observed=training_samples[1], ) obs_no_goal = pm.Poisson( 'obs_no_goal', mu=mu_no_goal, observed=training_samples[2], ) # Outcome probabilities p_goal_for = pm.Bound(pm.Poisson, upper=20*60)('p_goal_for', mu=mu_goal_for) p_goal_against = pm.Bound(pm.Poisson, upper=20*60)('p_goal_against', mu=mu_goal_against) p_no_goal = pm.Bound(pm.Poisson, upper=20*60)('p_no_goal', mu=mu_no_goal) # Fit model step = pm.Metropolis() trace = pm.sample(18000, step=step) return model, trace model, trace = bayes_model(training_samples) model N_burn = 10000 burned_trace = trace[N_burn:] ALPHA = 0.6 LW = 3 from scipy.stats import poisson # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_no_goal'] / 60, bins=50, color='orange', label='p_no_goal samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 22*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() mu_no_goal = burned_trace['mu_no_goal'].mean() y_goal_for = p(x, mu_goal_for) y_goal_against = p(x, mu_goal_against) y_no_goal = p(x, mu_no_goal) # Convert into minutes and rescale to fit chart x = x / 60 scale_frac = 0.7 y_goal_for = y_goal_for / y_goal_for.max() * scale_frac y_goal_against = y_goal_against / y_goal_against.max() * scale_frac y_no_goal = y_no_goal / y_no_goal.max() * scale_frac plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW) plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() plt.plot(trace['mu_goal_for'], label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against'], label='mu_goal_against', color='red') plt.plot(trace['mu_no_goal'], label='mu_no_goal', color='orange') plt.ylabel('$\mu$ (seconds)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend(); ALPHA = 0.6 plt.hist(burned_trace['mu_goal_for'], bins=50, color='green', label='mu_goal_for', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_goal_against'], bins=50, color='red', label='mu_goal_against', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['mu_no_goal'], bins=50, color='orange', label='mu_no_goal', histtype='stepfilled', alpha=ALPHA) plt.ylabel('MCMC counts') plt.xlabel('$\mu$ (seconds)') plt.legend(); # Now I need to normalize these guys. I looks like they don't have an even number of samples... let's check on that (burned_trace['mu_goal_for'].shape, burned_trace['mu_goal_against'].shape, burned_trace['mu_no_goal'].shape) len(burned_trace) * 4 # Nice! Same number of samlpes. Weird that it's 4x my burned trace - probably due to 4 cores normed_factors = np.array([ training_samples[0].shape, training_samples[1].shape, training_samples[2].shape ]) normed_factors = normed_factors / normed_factors.sum() normed_factors # Those ^ are the normalizing class probabilties ALPHA = 0.6 LW = 3 BINS = 60 # plt.hist(burned_trace['p_goal_for'] / 60, bins=50, # color='green', label=r'$P(\rm{goal\;for}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) # plt.hist(burned_trace['p_goal_against'] / 60, bins=50, # color='red', label=r'$P(\rm{goal\;against}\;|\;\rm{goalie\;pulled})$', # histtype='stepfilled', alpha=ALPHA) ''' Plot the MCMC samples ''' plt.hist(np.random.choice( burned_trace['p_goal_for'] / 60, size=int(burned_trace['p_goal_for'].shape[0] * normed_factors[0]) ), bins=BINS, color='green', label='p_goal_for samples', # density='normed', histtype='stepfilled', alpha=ALPHA, zorder=3) plt.hist(np.random.choice( burned_trace['p_goal_against'] / 60, size=int(burned_trace['p_goal_against'].shape[0] * normed_factors[1]) ), bins=BINS, color='red', label='p_goal_against samples', # density='normed', histtype='stepfilled', alpha=ALPHA, zorder=2) plt.hist(np.random.choice( burned_trace['p_no_goal'] / 60, size=int(burned_trace['p_no_goal'].shape[0] * normed_factors[2]) ), bins=BINS, color='orange', label='p_no_goal samples', # density='normed', histtype='stepfilled', alpha=ALPHA) plt.ylabel('Sampled frequency (normed)') plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() from scipy.stats import poisson ALPHA = 0.6 LW = 3 ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 20*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() mu_no_goal = burned_trace['mu_no_goal'].mean() y_goal_for = p(x, mu_goal_for) * normed_factors[0] y_goal_against = p(x, mu_goal_against) * normed_factors[1] y_no_goal = p(x, mu_no_goal) * normed_factors[2] # Convert into minutes and rescale to fit chart x = x / 60 # scale_frac = 0.7 # y_goal_for = y_goal_for / y_goal_for.max() * normed_factors[0] # y_goal_against = y_goal_against / y_goal_against.max() * normed_factors[1] # y_no_goal = y_no_goal / y_no_goal.max() * normed_factors[2] plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW) # plt.ylabel('Posterior PDF') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum() # This is less than 1 because I cut off the tail.. # # We can easily **correct for this by renormalizing** cutoff_renormed_factor = 2 - (y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum()) cutoff_renormed_factor from scipy.stats import poisson ALPHA = 0.6 LW = 3 ''' Plot the poisson distributions ''' p = poisson.pmf x = np.arange(16*60, 20*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() mu_no_goal = burned_trace['mu_no_goal'].mean() y_goal_for = p(x, mu_goal_for) * normed_factors[0] y_goal_against = p(x, mu_goal_against) * normed_factors[1] y_no_goal = p(x, mu_no_goal) * normed_factors[2] cutoff_renormed_factor = 2 - (y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum()) y_goal_for = y_goal_for * cutoff_renormed_factor y_goal_against = y_goal_against * cutoff_renormed_factor y_no_goal = y_no_goal * cutoff_renormed_factor # Convert into minutes and rescale to fit chart x = x / 60 # scale_frac = 0.7 # y_goal_for = y_goal_for / y_goal_for.max() * normed_factors[0] # y_goal_against = y_goal_against / y_goal_against.max() * normed_factors[1] # y_no_goal = y_no_goal / y_no_goal.max() * normed_factors[2] plt.plot(x, y_goal_for, label=r'$P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW) plt.ylabel('Posterior probability') # plt.yticks([]) plt.xlabel('Game clock (3rd period)') plt.legend(); plt.show() y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum() print(f'Final normalizing factors =\n{normed_factors * cutoff_renormed_factor}') mu_mcmc = [ burned_trace['mu_goal_for'].mean(), burned_trace['mu_goal_against'].mean(), burned_trace['mu_no_goal'].mean(), ] print(f'Final values for mu: {mu_mcmc}') def convert_to_time_remaining(x): _x = 20 - x t = datetime.timedelta(seconds=_x*60) return str(t) convert_to_time_remaining(x[np.argmax(y_goal_for)]) print('Time of max posterior probability =\n' f'{x[np.argmax(y_goal_for)], x[np.argmax(y_goal_against)], x[np.argmax(y_no_goal)]}') print() t_remaining = [convert_to_time_remaining(x[np.argmax(y_goal_for)]), convert_to_time_remaining(x[np.argmax(y_goal_against)]), convert_to_time_remaining(x[np.argmax(y_no_goal)])] print(f'Time of max posterior probability =\n{t_remaining}') # Great, now we have properly normalized probabilties. # # Notes: # - From normalizing factors, we can see ~12% chance of scoring when pulling the goalie on average. # - Probability of scoring peaks at 18.55 mins (1:27 remaining), with other probabilties following close after (01:20 for goal against and 01:07 for no goals) # From now on we'll **try to** work from the distributions as our source of truth. # # Let's plot the cumulative distribution. model_normlizing_factors = (normed_factors * cutoff_renormed_factor).flatten() mu_mcmc = [ burned_trace['mu_goal_for'].mean(), burned_trace['mu_goal_against'].mean(), burned_trace['mu_no_goal'].mean(), ] model_normlizing_factors = [ 0.1292882, 0.26528024, 0.62489297, ] mu_mcmc = [ 1113.8279468130681, 1120.1830172722719, 1133.9420018554083 ] from scipy.stats import poisson p = poisson.pmf x = np.arange(16*60, 20*60, 1) mu_goal_for = burned_trace['mu_goal_for'].mean() mu_goal_against = burned_trace['mu_goal_against'].mean() mu_no_goal = burned_trace['mu_no_goal'].mean() y_goal_for = p(x, mu_goal_for) * normed_factors[0] y_goal_against = p(x, mu_goal_against) * normed_factors[1] y_no_goal = p(x, mu_no_goal) * normed_factors[2] cutoff_renormed_factor = 2 - (y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum()) y_goal_for = y_goal_for * cutoff_renormed_factor y_goal_against = y_goal_against * cutoff_renormed_factor y_no_goal = y_no_goal * cutoff_renormed_factor y_goal_for.sum() + y_goal_against.sum() + y_no_goal.sum() # --- # # Trying to figure out the standard error on the odds estimate # https://stats.stackexchange.com/a/15373/130459 # # $$ # odds = P(goal\;for)\;/\;(P(goal\;against) * P(no\;goal)) # $$ std_err = lambda mu, n: np.sqrt(mu/n) std_err(mu_mcmc[0], 1), std_err(mu_mcmc[0], 10), std_err(mu_mcmc[0], 100) # This is tricky... # # --- # # #### 2018-03-10 # # Let's go back to the drawing board and add some things to the model. # # $$ # \alpha \cdot \big[ P(goal\;for) + (P(goal\;against) + P(no\;goal)\big] = 1 \\ # \vdots \\ # \alpha = \big[ P(goal\;for) + (P(goal\;against) + P(no\;goal)\big]^{-1} # $$ # # This will allow us to re-weight the posteriors later, so we can compare them better and yield a different interpretation. # Adding in # - MAP starting points # - $\alpha$ constraint param def bayes_model(training_samples) -> pm.model.Model: """ Solve for posterior distributions using pymc3 """ with pm.Model() as model: # Priors for the mu parameter of the # Poisson distribution P. # Note: mu = mean(P) mu_goal_for = pm.Uniform( 'mu_goal_for', 15*60, 20*60 ) mu_goal_against = pm.Uniform( 'mu_goal_against', 15*60, 20*60 ) mu_no_goal = pm.Uniform( 'mu_no_goal', 15*60, 20*60 ) # Observations to train the model on obs_goal_for = pm.Poisson( 'obs_goal_for', mu=mu_goal_for, observed=training_samples[0], ) obs_goal_against = pm.Poisson( 'obs_goal_against', mu=mu_goal_against, observed=training_samples[1], ) obs_no_goal = pm.Poisson( 'obs_no_goal', mu=mu_no_goal, observed=training_samples[2], ) # Outcome probabilities BoundPoisson = lambda name, mu: pm.Bound(pm.Poisson, upper=20*60)(name, mu=mu) p_goal_for = BoundPoisson('p_goal_for', mu=mu_goal_for) p_goal_against = BoundPoisson('p_goal_against', mu=mu_goal_against) p_no_goal = BoundPoisson('p_no_goal', mu=mu_no_goal) # Constraint parameter for re-weighting # posterior samples alpha = pm.Deterministic( 'alpha', 1 / (p_goal_for + p_goal_against + p_no_goal) ) # Fit model start = pm.find_MAP() step = pm.Metropolis() trace = pm.sample(18000, step=step, start=start) return model, trace model, trace = bayes_model(training_samples) model # > UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way. # # Let's not use MAP N_burn = 10000 burned_trace = trace[N_burn:] from typing import Tuple from scipy.stats import poisson def poisson_posterior( mu=None, norm_factors=None, ) -> Tuple[np.ndarray]: p = poisson.pmf x = np.arange(15*60, 20*60, 1) if mu is None: return (x / 60,) mu_goal_for = mu[0] mu_goal_against = mu[1] mu_no_goal = mu[2] y_goal_for = p(x, mu_goal_for) y_goal_against = p(x, mu_goal_against) y_no_goal = p(x, mu_no_goal) if norm_factors is not None: y_goal_for = p(x, mu_goal_for) * norm_factors[0] y_goal_against = p(x, mu_goal_against) * norm_factors[1] y_no_goal = p(x, mu_no_goal) * norm_factors[2] # Convert into minutes x = x / 60 return x, y_goal_for, y_goal_against, y_no_goal ALPHA = 0.6 LW = 3 ''' Plot MCMC samples ''' plt.hist(burned_trace['p_goal_for'] / 60, bins=50, color='green', label='p_goal_for samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_goal_against'] / 60, bins=50, color='red', label='p_goal_against samples', density='normed', histtype='stepfilled', alpha=ALPHA) plt.hist(burned_trace['p_no_goal'] / 60, bins=50, color='orange', label='p_no_goal samples', density='normed', histtype='stepfilled', alpha=ALPHA) ''' Plot poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior([ burned_trace['mu_goal_for'].mean(), burned_trace['mu_goal_against'].mean(), burned_trace['mu_no_goal'].mean(), ]) # Rescale scale_frac = 0.7 y_goal_for = y_goal_for / y_goal_for.max() * scale_frac y_goal_against = y_goal_against / y_goal_against.max() * scale_frac y_no_goal = y_no_goal / y_no_goal.max() * scale_frac plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$P(\rm{goal\;against};\mu_{MCMC})$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$P(\rm{no\;goal};\mu_{MCMC})$', color='orange', lw=LW) ''' Clean up the chart ''' plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() savefig(plt, 'time_elapsed_poisson_mcmc_samples') plt.show() plt.plot(trace['mu_goal_for']/60, label='mu_goal_for', color='green') plt.plot(trace['mu_goal_against']/60, label='mu_goal_against', color='red') plt.plot(trace['mu_no_goal']/60, label='mu_no_goal', color='orange') plt.ylabel('$\mu$ (minutes)') plt.xlabel('MCMC step') plt.axvline(N_burn, color='black', lw=2, label='Burn threshold') plt.legend() savefig(plt, 'time_elapsed_mu_steps') plt.show() ALPHA = 0.6 plt.hist(burned_trace['alpha']/60, bins=50, color='b', label=r'$\alpha$', histtype='stepfilled', alpha=ALPHA) # plt.ylabel('MCMC counts') # plt.xlabel('$\mu$ (minutes)') plt.legend() # savefig(plt, 'time_elapsed_mu_samples') plt.show() # THis is not really working out... # --- # # Determine $\alpha$ from the normalized poisson distributions model_normlizing_factors = [ 0.1292882, 0.26528024, 0.62489297, ] mu_mcmc = [ 1113.8279468130681, 1120.1830172722719, 1133.9420018554083 ] x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior( mu_mcmc, norm_factors=model_normalizing_factors ) alpha = np.power( np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0), -1 ) plt.plot(x, alpha, label=r'$\alpha$', lw=LW) plt.ylabel('Alpha re-weighting parameter') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() # savefig(plt, 'time_elapsed_poisson_cdf') plt.show() from scipy.stats import poisson ALPHA = 0.6 LW = 3 ''' Plot the poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior( mu_mcmc, norm_factors=model_normalizing_factors ) # Alpha has same shape as x, y above alpha = np.power( np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0), -1 ) y_goal_for = alpha * y_goal_for y_goal_against = alpha * y_goal_against y_no_goal = alpha * y_no_goal plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW) plt.ylabel('Chance of outcome at time $t$') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() savefig(plt, 'time_elapsed_outcome_chance_timeseries') plt.show() # Note how there are very few samples to draw conclusions from for the low and high times. # # e.g. less than 17 np.sum(training_samples[0] < 17*60) + np.sum(training_samples[1] < 17*60) + np.sum(training_samples[2] < 17*60) # more than 17 np.sum(training_samples[0] > 17*60) + np.sum(training_samples[1] > 17*60) + np.sum(training_samples[2] > 17*60) # Let's bring back $\mu$ plt.hist(burned_trace['mu_goal_for']) plt.hist(burned_trace['mu_goal_against']) plt.hist(burned_trace['mu_no_goal']) # To get some idea of the uncertainty we need to figure out the uncertainty on $P$. We can do this using the knowledge of the uncertainty on $\mu$, as calculated with MCMC. # # $$ # \sigma_P = \big| \frac{\partial P}{\partial \mu} \big|\;\sigma_{\mu} # $$ # # where $\sigma_{\mu}$ is the error on mu. This error can be calculated from the MCMC samples mu_mcmc_std = [ burned_trace['mu_goal_for'].std(), burned_trace['mu_goal_against'].std(), burned_trace['mu_no_goal'].std(), ] mu_mcmc_std # Now we need to evaluate the derivative: # $$ # \frac{\partial P}{\partial \mu} # $$ # Trying the analytic derivative # # # $$ # \frac{\partial p}{\partial \mu} = \frac{e^{-\mu} (t - \mu) \cdot \mu^{t-1} }{t!} # $$ # # we can calcualte $\sigma_p$ as done below mu_mcmc mu_mcmc_std model_normalizing_factors x = poisson_posterior()[0] x[:10] from scipy.special import factorial def poisson_derivative(mu, t): return np.exp(-mu) * (t - mu) * np.power(mu, (t-1)) / factorial(t, exact=True) mu = mu_mcmc[0] poisson_derivative(mu, t=int(mu)) # Ahhh! These factorials are not nice from scipy.special import factorial def poisson_derivative(mu, t): return np.exp(-mu) * (t - mu) * np.power(mu, (t-1)) / factorial(t) def calc_posteror_error(mu, mu_std, norm_fac): x = poisson_posterior()[0] * 60 return mu_std * np.array([ norm_fac * poisson_derivative(mu, int(t)) for t in tqdm_notebook(x) ]) err_p_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0], model_normalizing_factors[0]) err_p_goal_against = calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1], model_normalizing_factors[1]) err_p_no_goal = calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2], model_normalizing_factors[2]) err_p_goal_for # I think the factorial is causing issues # plt.hist(err_p_goal_for, bins=100) # Assuming the error is randonly distributed and calculating 95% confidence intervals ($\pm$1.96$\sigma$)... from scipy.stats import poisson ALPHA = 0.6 ALPHA_LIGHT = 0.3 LW = 3 ERR_BAR_CUTOFF = 0 ''' Plot the poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior( mu_mcmc, norm_factors=model_normalizing_factors ) # Alpha has same shape as x, y above alpha = np.power( np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0), -1 ) y_goal_for = alpha * y_goal_for # y_goal_against = alpha * y_goal_against # y_no_goal = alpha * y_no_goal plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW) # plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW) # plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW) plt.plot(x[ERR_BAR_CUTOFF:], (alpha*(err_p_goal_for + err_p_goal_for*1.96))[ERR_BAR_CUTOFF:], label='goal for 95% CI', color='green', alpha=ALPHA_LIGHT) plt.plot(x[ERR_BAR_CUTOFF:], (alpha*(err_p_goal_for - err_p_foal_for*1.96))[ERR_BAR_CUTOFF:], label='goal for 95% CI', color='green', alpha=ALPHA_LIGHT) plt.ylabel('Chance of outcome at time $t$') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() # savefig(plt, 'time_elapsed_outcome_chance_timeseries') plt.show() # ^ Ignore # Let's take the numerical derivative instead import inspect print(inspect.getsource(poisson_posterior)) from scipy.misc import derivative from tqdm import tqdm_notebook def calc_posteror_error(mu, mu_std, mu_step=1e-6): x = poisson_posterior()[0] * 60 # convert back into seconds (discrete) err = mu_std * np.abs(np.array([ derivative(lambda _mu: poisson.pmf(int(t), _mu), mu, dx=mu_step) for t in tqdm_notebook(x) ])) return err err_p_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0]) err_p_goal_for x = poisson_posterior()[0] * 60 plt.plot(x, err_p_goal_for) ALPHA = 0.6 ALPHA_LIGHT = 0.3 LW = 3 ''' Poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(mu_mcmc, norm_factors=normlizing_factors) ''' Errors ''' err_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0]) * normlizing_factors[0] err_bar_top = y_goal_for + err_goal_for err_bar_bottom = y_goal_for - err_goal_for ''' Plot ''' # plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) # plt.fill_between(err_bar_bottom, err_bar_top, alpha=ALPHA_LIGHT, color='green') plt.plot(x, err_goal_for) plt.plot(x, err_bar_top) plt.plot(x, err_bar_bottom) ''' Clean up the chart ''' plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() # savefig(plt, 'time_elapsed_poisson_mcmc_samples') plt.show() ALPHA = 0.6 ALPHA_LIGHT = 0.3 LW = 3 ''' Poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior(mu_mcmc, norm_factors=normlizing_factors) ''' Errors ''' err_goal_for = calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0]) * normlizing_factors[0] err_bar_top = y_goal_for + err_goal_for err_bar_bottom = y_goal_for - err_goal_for ''' Plot ''' # plt.plot(x, y_goal_for, label=r'$P(\rm{goal\;for};\mu_{MCMC})$', color='green', lw=LW) plt.fill_between(x, err_bar_bottom, err_bar_top, alpha=ALPHA_LIGHT, color='green') # plt.plot(x, err_goal_for) # plt.plot(x, err_bar_top) # plt.plot(x, err_bar_bottom) ''' Clean up the chart ''' plt.ylabel('Counts') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() # savefig(plt, 'time_elapsed_poisson_mcmc_samples') plt.show() # So that's the error estimate as derived from uncertainty in $\mu$! Pretty cool. # # Now we can do $\sigma_\alpha = \alpha \cdot \sigma_P$ from scipy.stats import poisson ALPHA = 0.6 ALPHA_LIGHT = 0.3 LW = 3 ''' Plot the poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior( mu_mcmc, norm_factors=model_normalizing_factors ) # Alpha has same shape as x, y above alpha = np.power( np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0), -1 ) y_goal_for = alpha * y_goal_for y_goal_against = alpha * y_goal_against y_no_goal = alpha * y_no_goal plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW) ''' Plot the errors ''' err_p_goal_for = alpha * calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0]) err_p_goal_against = alpha * calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1]) err_p_no_goal = alpha * calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2]) plt.fill_between(x, y_goal_for-err_p_goal_for, y_goal_for+err_p_goal_for, color='green', alpha=ALPHA_LIGHT) plt.fill_between(x, y_goal_against-err_p_goal_against, y_goal_against+err_p_goal_against, color='red', alpha=ALPHA_LIGHT) plt.fill_between(x, y_no_goal-err_p_no_goal, y_no_goal+err_p_no_goal, color='orange', alpha=ALPHA_LIGHT) plt.ylabel('Chance of outcome at time $t$') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.legend() # savefig(plt, 'time_elapsed_outcome_chance_timeseries') plt.show() # We can't say anything conclusive due to huge errors on low times, but we are much more confident on late game predictions from scipy.stats import poisson ALPHA = 0.6 ALPHA_LIGHT = 0.3 LW = 3 ''' Plot the poisson distributions ''' x, y_goal_for, y_goal_against, y_no_goal = poisson_posterior( mu_mcmc, norm_factors=model_normalizing_factors ) # Alpha has same shape as x, y above alpha = np.power( np.sum([y_goal_for, y_goal_against, y_no_goal], axis=0), -1 ) y_goal_for = alpha * y_goal_for y_goal_against = alpha * y_goal_against y_no_goal = alpha * y_no_goal plt.plot(x, y_goal_for, label=r'$\alpha \cdot P(\mathrm{goal\;for}\;|\;X)$', color='green', lw=LW) plt.plot(x, y_goal_against, label=r'$\alpha \cdot P(\mathrm{goal\;against}\;|\;X)$', color='red', lw=LW) plt.plot(x, y_no_goal, label=r'$\alpha \cdot P(\mathrm{no\;goal}\;|\;X)$', color='orange', lw=LW) ''' Plot the errors ''' err_p_goal_for = alpha * calc_posteror_error(mu_mcmc[0], mu_mcmc_std[0]) err_p_goal_against = alpha * calc_posteror_error(mu_mcmc[1], mu_mcmc_std[1]) err_p_no_goal = alpha * calc_posteror_error(mu_mcmc[2], mu_mcmc_std[2]) plt.fill_between(x, y_goal_for-err_p_goal_for, y_goal_for+err_p_goal_for, color='green', alpha=ALPHA_LIGHT) plt.fill_between(x, y_goal_against-err_p_goal_against, y_goal_against+err_p_goal_against, color='red', alpha=ALPHA_LIGHT) plt.fill_between(x, y_no_goal-err_p_no_goal, y_no_goal+err_p_no_goal, color='orange', alpha=ALPHA_LIGHT) plt.ylabel('Chance of outcome at time $t$') # plt.yticks([]) plt.xlabel('Time elapsed (3rd period)') plt.xlim(17, 20) plt.ylim(0, 1) plt.legend() # savefig(plt, 'time_elapsed_outcome_chance_timeseries') plt.show() from IPython.display import HTML HTML('<style>div.text_cell_render{font-size:130%;padding-top:50px;padding-bottom:50px}</style>')

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0.66356

8,990

57,621

4.018354

0.07564

0.062394

0.023917

0.776111

0.749398

0.734478

0.721522

0.713661

0.704526

0

0.025408

0.187189

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null

0

1

0

7

56f4bfc4cc61d78dd2c0a590d62107f1f27e2d54

33

py

Python

src/error_analysis.py

exue026/spam-detection-model

1a8afc4b4403ef49d8ebbdb5fbcb56c643996b1d

[ "MIT" ]

null

src/error_analysis.py

exue026/spam-detection-model

1a8afc4b4403ef49d8ebbdb5fbcb56c643996b1d

[ "MIT" ]

null

src/error_analysis.py

exue026/spam-detection-model

1a8afc4b4403ef49d8ebbdb5fbcb56c643996b1d

[ "MIT" ]

null

def analyze_error(): return 0

16.5

20

0.69697

5

33

4.4

1

0

0.038462

0.212121

33

2

21

16.5

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true

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null

0

1

0

null

0

1

0

1

0

7

712c62ad55677b24a65bfa64e5786ad4b35a9859

2,070

py

Python

tests/test_spending_rules.py

davidjohnoliver/IncomeForecast

f638a16a3bccb576f7977f9ea3fc08047c96ecce

[ "MIT" ]

null

tests/test_spending_rules.py

davidjohnoliver/IncomeForecast

f638a16a3bccb576f7977f9ea3fc08047c96ecce

[ "MIT" ]

null

tests/test_spending_rules.py

davidjohnoliver/IncomeForecast

f638a16a3bccb576f7977f9ea3fc08047c96ecce

[ "MIT" ]

null

import spending_rules import model import math def standard_previous_deltas(): return model.deltas_state.from_year(1999) def standard_previous_funds(): return model.funds_state(0, 0, 1999) def test_get_luxury_over_basic(): rule = spending_rules.get_luxury_over_basic(20000, 0.05) new_delta = model.get_updated_deltas_from_rules(standard_previous_funds(), standard_previous_deltas().update_spending(50000), [rule]) assert 51500 == new_delta.spending def test_get_luxury_over_basic_capped_below(): rule = spending_rules.get_luxury_over_basic_capped(20000, 0.05, 0.9) def set_salary(deltas: model.deltas_state, previous_funds: model.funds_state, previous_deltas: model.deltas_state): return deltas.update_gross_salary(100000) new_delta = model.get_updated_deltas_from_rules(standard_previous_funds(), standard_previous_deltas().update_spending(50000), [set_salary, rule]) assert 51500 == new_delta.spending def test_get_luxury_over_basic_capped_above(): rule = spending_rules.get_luxury_over_basic_capped(20000, 0.05, 0.9) def set_salary(deltas: model.deltas_state, previous_funds: model.funds_state, previous_deltas: model.deltas_state): return deltas.update_gross_salary(55800) new_delta = model.get_updated_deltas_from_rules(standard_previous_funds(), standard_previous_deltas().update_spending(50000), [set_salary, rule]) assert 50220 == new_delta.spending def test_get_maxed_or_zeroed_out(): end_region = 0.1 c_m = 0.3 assert c_m == spending_rules.get_maxed_or_zeroed_out(0.5, c_m, end_region) assert c_m == spending_rules.get_maxed_or_zeroed_out(0.1, c_m, end_region) assert math.isclose(c_m, spending_rules.get_maxed_or_zeroed_out(0.9, c_m, end_region)) assert 0 == spending_rules.get_maxed_or_zeroed_out(0, c_m, end_region) assert 1 == spending_rules.get_maxed_or_zeroed_out(1, c_m, end_region) assert c_m / 2 == spending_rules.get_maxed_or_zeroed_out(0.05, c_m, end_region) assert c_m / 2 == spending_rules.get_maxed_or_zeroed_out(0.05, c_m, end_region)

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false

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1

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null

0

1

0

1

0

7

71388b3a756ae644ef76b02ab16b59d8cb0ce9bb

40,172

py

Python

observers.py

Data-Science-in-Mechanical-Engineering/joint_state_dynamics_estimation_HGOs_GPs

5980ad4ec1e94c4a2eeb5829b27effee5e069370

[ "MIT" ]

null

observers.py

Data-Science-in-Mechanical-Engineering/joint_state_dynamics_estimation_HGOs_GPs

5980ad4ec1e94c4a2eeb5829b27effee5e069370

[ "MIT" ]

null

observers.py

Data-Science-in-Mechanical-Engineering/joint_state_dynamics_estimation_HGOs_GPs

5980ad4ec1e94c4a2eeb5829b27effee5e069370

[ "MIT" ]

4

2021-02-17T05:14:11.000Z

2021-03-08T14:00:18.000Z

import logging import numpy as np from scipy.integrate import solve_ivp from utils import reshape_pt1, reshape_pt1_tonormal, reshape_dim1 # Possible observers (dynamics functions f(x_estim, u)) and functions to # produce measured data from true data # Input x, u, version and parameters, output x at the next step (dt # later) with scipy ODE solver def dynamics_traj_observer(x0, u, y, t0, dt, init_control, discrete=False, version=None, method='RK45', t_span=[0, 1], t_eval=[0.1], GP=None, **kwargs): if discrete: xtraj = np.zeros((len(t_eval), x0.shape[1])) xtraj[0] = reshape_pt1(x0) t = t0 i = 0 while (i < len(t_eval) - 1) and (t < t_eval[-1]): i += 1 xnext = reshape_pt1( version(t, xtraj[-1], u, y, t0, init_control, GP, **kwargs)) xtraj[i] = xnext t += dt else: sol = solve_ivp( lambda t, x: version(t, x, u, y, t0, init_control, GP, **kwargs), t_span=t_span, y0=reshape_pt1_tonormal(x0), method=method, t_eval=t_eval) xtraj = reshape_pt1(sol.y.T) return reshape_pt1(xtraj) # Observer for the continuous time Duffing equation # Source: Observer design for the duffing equation using Gersgorin’s theorem, # by Alberto Delgado def duffing_observer_Delgado(t, xhat, u, y, t0, init_control, GP, kwargs): alpha = kwargs.get('alpha') beta = kwargs.get('beta') delta = kwargs.get('delta') xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # My gains l1 = delta - 5 l2 = alpha - delta ** 2 + 3 * beta * xhat[:, 0] ** 2 # # Delgado gains # l1 = - 5 # l2 = - (alpha + 3 * xhat[:, 0] ** 2) A = reshape_pt1([[0, 1], [-alpha, -delta]]) F1 = reshape_dim1(np.zeros_like(xhat[:, 0])) F2 = reshape_dim1(- beta * xhat[:, 0] ** 3) F = reshape_pt1(np.concatenate((F1, F2), axis=1)) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatdot = reshape_pt1(np.dot(A, reshape_pt1_tonormal(xhat)) + F + LC + u) return xhatdot # Observer for the discrete time Duffing map # Source: Observer design for the duffing equation using Gersgorin’s theorem, # by Alberto Delgado def duffing_observer_Delgado_discrete(t, xhat, u, y, t0, init_control, GP, kwargs): alpha = kwargs.get('alpha') beta = kwargs.get('beta') delta = kwargs.get('delta') xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # My gains l1 = -0.1 l2 = -0.1 # # Delgado gains # l1 = - 5 # l2 = - (alpha + 3 * xhat[:, 0] ** 2) A = reshape_pt1([[0, 1], [-alpha, -delta]]) F1 = reshape_dim1(np.zeros_like(xhat[:, 0])) F2 = reshape_dim1(- beta * xhat[:, 0] ** 3) F = reshape_pt1(np.concatenate((F1, F2), axis=1)) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatnext = reshape_pt1(np.dot(A, reshape_pt1_tonormal(xhat)) + F + LC + u) return xhatnext # Observer for the continuous time Duffing equation # Using current GP estimation of dynamics for xhat_t+1, + the Delgado gains ( # artificially well chosen for now since form chosen by knowing the # dynamics...) * (xhat_t - y_t) def duffing_observer_Delgado_GP(t, xhat, u, y, t0, init_control, GP, kwargs): alpha = kwargs.get('alpha') beta = kwargs.get('beta') delta = kwargs.get('delta') xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # My gains l1 = delta - 5 l2 = alpha - delta ** 2 + 3 * beta * xhat[:, 0] ** 2 # # Delgado gains # l1 = - 5 # l2 = - (alpha + 3 * xhat[:, 0] ** 2) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # In this case we have continuous observer dynamics, but GP is # discrete # TODO better than Euler? mean = (mean - xhat) / GP.prior_kwargs.get('dt') GP.prior_kwargs['observer_gains'] = {'l1': l1, 'l2': l2} else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean = np.zeros_like(u) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatdot = reshape_pt1(mean + LC) return xhatdot # Observer for the discrete time Duffing map # Using current GP estimation of dynamics for xhat_t+1, + the Delgado gains ( # artificially well chosen for now since form chosen by knowing the # dynamics...) * (xhat_t - y_t) def duffing_observer_Delgado_GP_discrete(t, xhat, u, y, t0, init_control, GP, kwargs): xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # My gains l1 = -0.8 l2 = -0.8 # # Delgado gains # l1 = - 5 # l2 = - (alpha + 3 * xhat[:, 0] ** 2) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean = np.zeros_like(u) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatnext = reshape_pt1(mean + LC) return xhatnext # High gain extended observer for the continuous time Duffing equation # Using current GP estimation of dynamics for xi_dot, high gain observer # from Michelangelo's paper, extended with extra state variable xi def duffing_observer_Michelangelo_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) xi = reshape_pt1(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) Gamma2 = reshape_pt1([k3 * g ** 3]) if GP: if 'GP' in GP.__class__.__name__: mean_deriv, var_deriv, lowconf_deriv, uppconf_deriv = \ GP.predict_deriv(reshape_pt1(xhat), reshape_pt1(u), only_x=True) GP.prior_kwargs['observer_gains'].update({'g': g, 'Gamma1': Gamma1, 'Gamma2': Gamma2, 'k1': k1, 'k2': k2, 'k3': k3}) else: mean_deriv = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean_deriv = np.zeros_like(xhat) if np.any(kwargs.get('saturation')): # Saturate the derivative of the nonlinearity estimate to guarantee # contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean_deriv = np.clip(mean_deriv, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(xi)) DfA = reshape_pt1(np.dot(reshape_pt1_tonormal(mean_deriv), reshape_pt1_tonormal(ABmult + u))) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) LC2 = reshape_pt1(Gamma2 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) xidot = reshape_pt1(DfA + LC2) # Also check eigenvalues of M for stability without high gain AB = np.concatenate((A, B), axis=1) ABO = np.concatenate((AB, np.zeros_like(reshape_pt1(AB[0]))), axis=0) K = np.array([[k1, k2, k3]]) C = np.zeros_like(x) C[0, 0] = 1 M = ABO - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return np.concatenate((xhatdot, xidot), axis=1) # High gain extended observer for the continuous time Duffing equation # Using current LS estimationfor xi_dot, high gain observer # from Michelangelo's paper, extended with extra state variable xi def duffing_observer_Michelangelo_LS(t, xhat, u, y, t0, init_control, LS_deriv, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) xi = reshape_pt1(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) Gamma2 = reshape_pt1([k3 * g ** 3]) if LS_deriv: mean_deriv = LS_deriv(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean_deriv = np.zeros_like(xhat) if np.any(kwargs.get('saturation')): # Saturate the derivative of the nonlinearity estimate to guarantee # contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean_deriv = np.clip(mean_deriv, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(xi)) DfA = reshape_pt1(np.dot(reshape_pt1_tonormal(mean_deriv), reshape_pt1_tonormal(ABmult + u))) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) LC2 = reshape_pt1(Gamma2 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) xidot = reshape_pt1(DfA + LC2) # Also check eigenvalues of M for stability without high gain AB = np.concatenate((A, B), axis=1) ABO = np.concatenate((AB, np.zeros_like(reshape_pt1(AB[0]))), axis=0) K = np.array([[k1, k2, k3]]) C = np.zeros_like(x) C[0, 0] = 1 M = ABO - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return np.concatenate((xhatdot, xidot), axis=1) # Observer for the continuous time Van der Pol equation # Source: Inspired by Observer design for the duffing equation using # Gersgorin’s theorem, by Alberto Delgado, using the approximation x**2v - # xhat**2vhat approximately 2xhatvhat (x-xhat) def VanderPol_observer_simplified(t, xhat, u, y, t0, init_control, GP, kwargs): mu = kwargs.get('mu') xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # My gains l1 = mu - 5 # l2 = 1 - mu ** 2 - 2 * mu * xhat[:, 0] * xhat[:, 1] l2 = 1 - mu ** 2 - 2 * mu * xhat[:, 0] A = reshape_pt1([[0, 1], [-1, 0]]) F = reshape_pt1([0, mu * (1 - xhat[:, 0] ** 2) * xhat[:, 1]]) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatdot = reshape_pt1(np.dot(A, reshape_pt1_tonormal(xhat)) + F + LC + u) return xhatdot # Linear Luenberger observer for harmonic oscillator, with control law u(t), # continuous time def harmonic_oscillator_observer_GP(t, xhat, u, y, t0, init_control, GP, kwargs): k = kwargs.get('k') m = kwargs.get('m') xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gains l1 = - 1 l2 = k / m - 1 if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # In this case we have continuous observer dynamics, but GP is # discrete # TODO better than Euler? mean = (mean - xhat) / GP.prior_kwargs.get('dt') GP.prior_kwargs['observer_gains'] = {'l1': l1, 'l2': l2} else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean = np.zeros_like(u) LC = reshape_pt1([[l1 * (xhat[:, 0] - y), l2 * (xhat[:, 0] - y)]]) xhatdot = reshape_pt1(mean + LC) return xhatdot # High gain extended observer from Michelangelo for the WDC data # Using current GP estimation of dynamics for xi_dot, high gain observer # from Michelangelo's paper, extended with extra state variable xi def WDC_observer_Michelangelo_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) xi = reshape_pt1(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) Gamma2 = reshape_pt1([k3 * g ** 3]) if GP: if 'GP' in GP.__class__.__name__: mean_deriv, var_deriv, lowconf_deriv, uppconf_deriv = \ GP.predict_deriv(reshape_pt1(xhat), reshape_pt1(u), only_x=True) GP.prior_kwargs['observer_gains'].update({'g': g, 'Gamma1': Gamma1, 'Gamma2': Gamma2, 'k1': k1, 'k2': k2, 'k3': k3}) else: mean_deriv = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean_deriv = np.zeros_like(xhat) if np.any(kwargs.get('saturation')): # Saturate the derivative of the nonlinearity estimate to guarantee # contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean_deriv = np.clip(mean_deriv, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(xi)) DfA = reshape_pt1(np.dot(reshape_pt1_tonormal(mean_deriv), reshape_pt1_tonormal(ABmult + u))) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) LC2 = reshape_pt1(Gamma2 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) xidot = reshape_pt1(DfA + LC2) # Also check eigenvalues of M for stability without high gain AB = np.concatenate((A, B), axis=1) ABO = np.concatenate((AB, np.zeros_like(reshape_pt1(AB[0]))), axis=0) K = np.array([[k1, k2, k3]]) C = np.zeros_like(x) C[0, 0] = 1 M = ABO - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return np.concatenate((xhatdot, xidot), axis=1) # High gain observer (simple, not extended like Michelangelo) for the WDC data # Using current GP estimation of dynamics for xdot, regular high gain observer def WDC_observer_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - xhat) / GP.prior_kwargs.get( 'dt') # TODO better than Euler? GP.prior_kwargs['observer_gains'].update({'g': g, 'Gamma1': Gamma1, 'k1': k1, 'k2': k2}) else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get( 'prior_kwargs')) else: mean = np.zeros_like(xhat) if np.any(kwargs.get('saturation')): # Saturate the derivative of the nonlinearity estimate to guarantee # contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean = np.clip(mean, a_min=a_min, a_max=a_max) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(mean + LC1 + u) return reshape_pt1(xhatdot) # High gain observer (simple, not extended like Michelangelo) for the WDC data # Using current GP estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity def WDC_justvelocity_observer_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - reshape_pt1(xhat[:, 1])) / GP.prior_kwargs.get( 'dt') # TODO better than Euler? GP.prior_kwargs['observer_gains'].update({'g': g, 'Gamma1': Gamma1, 'k1': k1, 'k2': k2}) else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get( 'prior_kwargs')) else: mean = np.zeros_like(reshape_pt1(xhat[:, 1])) if np.any(kwargs.get('saturation')): # Saturate the estimate of the nonlinearity to guarantee contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean = np.clip(mean, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(mean)) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) # Also check eigenvalues of M for stability without high gain K = np.array([[k1, k2]]) C = np.zeros_like(xhat) C[0, 0] = 1 M = A - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return reshape_pt1(xhatdot) # High gain observer (simple, not extended like Michelangelo) for the WDC data # Using current LS estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity def WDC_justvelocity_observer_highgain_LS(t, xhat, u, y, t0, init_control, LS, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) dt = kwargs.get('dt') if LS: mean = LS(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - reshape_pt1(xhat[:, 1])) / kwargs.get('dt') # TODO better than Euler? else: mean = np.zeros_like(reshape_pt1(xhat[:, 1])) if np.any(kwargs.get('saturation')): # Saturate the estimate of the nonlinearity to guarantee contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean = np.clip(mean, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(mean)) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) # Also check eigenvalues of M for stability without high gain K = np.array([[k1, k2]]) C = np.zeros_like(xhat) C[0, 0] = 1 M = A - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return reshape_pt1(xhatdot) # High gain observer (simple, not extended like Michelangelo) for the WDC data # Using current GP estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity, but returning xnext using # Euler discretization instead of xdot def WDC_justvelocity_discrete_observer_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): xhatdot = WDC_justvelocity_observer_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs) xnext = reshape_pt1(xhat + kwargs.get('dt_before_subsampling') * xhatdot) # TODO better than Euler? return xnext # High gain observer (simple, not extended like Michelangelo) for the WDC data # Using current GP estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity and with gain following a # dynamical adaptation law def WDC_justvelocity_observer_adaptive_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) g = float(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) adaptation_law = \ kwargs.get('prior_kwargs').get('observer_gains').get('adaptation_law') # Gain (needs to be large enough) k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2]) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - reshape_pt1(xhat[:, 1])) / GP.prior_kwargs.get( 'dt') # TODO better than Euler? else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get( 'prior_kwargs')) else: mean = np.zeros_like(reshape_pt1(xhat[:, 1])) if np.any(kwargs.get('saturation')): # Saturate the estimate of the nonlinearity to guarantee contraction a_min = np.min([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) a_max = np.max([-kwargs.get('saturation'), kwargs.get('saturation')], axis=0) mean = np.clip(mean, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1], [0, 0]]) B = reshape_pt1([[0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(mean)) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1 + u) gdot = reshape_pt1(adaptation_law(g=g, y=y, yhat=reshape_pt1(xhat[:, 0]), kwargs=kwargs.get('prior_kwargs').get( 'observer_gains'))) # Also check eigenvalues of M for stability without high gain K = np.array([[k1, k2]]) C = np.zeros_like(xhat) C[0, 0] = 1 M = A - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return reshape_pt1(np.concatenate((xhatdot, gdot), axis=1)) # High gain extended observer from Michelangelo for the mass-spring-mass system # Using current GP estimation of dynamics for xi_dot, high gain observer # from Michelangelo's paper, extended with extra state variable xi def MSM_observer_Michelangelo_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) xi = reshape_pt1(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') k4 = kwargs.get('prior_kwargs').get('observer_gains').get('k4') k5 = kwargs.get('prior_kwargs').get('observer_gains').get('k5') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2, k3 * g ** 3, k4 * g ** 4]) Gamma2 = reshape_pt1([k5 * g ** 5]) if GP: if 'GP' in GP.__class__.__name__: mean_deriv, var_deriv, lowconf_deriv, uppconf_deriv = \ GP.predict_deriv(reshape_pt1(xhat), reshape_pt1(u), only_x=True) else: mean_deriv = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get('prior_kwargs')) else: mean_deriv = np.zeros_like(xhat) if np.any(kwargs.get('saturation')): # Saturate the derivative of the nonlinearity estimate to guarantee # contraction a_min = np.min([-kwargs.get('saturation')]) a_max = np.max([kwargs.get('saturation')]) mean_deriv = np.clip(mean_deriv, a_min=a_min, a_max=a_max) A = np.eye(xhat.shape[1], k=1) B = np.zeros((xhat.shape[1], 1)) B[-1] = 1 ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(xi)) DfA = reshape_pt1(np.dot(reshape_pt1_tonormal(mean_deriv), reshape_pt1_tonormal(ABmult))) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) LC2 = reshape_pt1(Gamma2 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1) xidot = reshape_pt1(DfA + LC2) # Also check eigenvalues of M for stability without high gain AB = np.concatenate((A, B), axis=1) ABO = np.concatenate((AB, np.zeros_like(reshape_pt1(AB[0]))), axis=0) K = np.array([[k1, k2, k3, k4, k5]]) C = np.zeros_like(x) C[0, 0] = 1 M = ABO - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return np.concatenate((xhatdot, xidot), axis=1) # High gain observer for the mass-spring-mass system # Using current GP estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity def MSM_justvelocity_observer_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(xhat) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) # Gain (needs to be large enough) g = kwargs.get('prior_kwargs').get('observer_gains').get('g') k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') k4 = kwargs.get('prior_kwargs').get('observer_gains').get('k4') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2, k3 * g ** 3, k4 * g ** 4]) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - reshape_pt1(xhat[:, -1])) / GP.prior_kwargs.get( 'dt') # TODO better than Euler? else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get( 'prior_kwargs')) else: mean = np.zeros_like(reshape_pt1(xhat[:, -1])) if np.any(kwargs.get('saturation')): # Saturate the estimate of the nonlinearity to guarantee contraction a_min = np.min([kwargs.get('saturation')]) a_max = np.max([kwargs.get('saturation')]) mean = np.clip(mean, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]) B = reshape_pt1([[0], [0], [0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(mean)) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1) # Also check eigenvalues of M for stability without high gain K = np.array([[k1, k2, k3, k4]]) C = np.zeros_like(xhat) C[0, 0] = 1 M = A - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return reshape_pt1(xhatdot) # High gain observer for the mass-spring-mass system # Using current GP estimation of velocity for xdot, regular high gain # observer but with GP only predicting velocity and with gain following a # dynamical adaptation law def MSM_justvelocity_observer_adaptive_highgain_GP(t, xhat, u, y, t0, init_control, GP, kwargs): x = reshape_pt1(xhat) assert np.any(kwargs.get('saturation')), 'Need to define a saturation ' \ 'value to use the combined ' \ 'observer-identifier framework.' xhat = reshape_pt1(x[:, :-1]) g = float(x[:, -1]) y = reshape_pt1(y(t, kwargs)) u = reshape_pt1(u(t, kwargs, t0, init_control)) adaptation_law = \ kwargs.get('prior_kwargs').get('observer_gains').get('adaptation_law') # Gain (needs to be large enough) k1 = kwargs.get('prior_kwargs').get('observer_gains').get('k1') k2 = kwargs.get('prior_kwargs').get('observer_gains').get('k2') k3 = kwargs.get('prior_kwargs').get('observer_gains').get('k3') k4 = kwargs.get('prior_kwargs').get('observer_gains').get('k4') Gamma1 = reshape_pt1([k1 * g, k2 * g ** 2, k3 * g ** 3, k4 * g ** 4]) if GP: if 'GP' in GP.__class__.__name__: mean, var, lowconf, uppconf = GP.predict(reshape_pt1(xhat), reshape_pt1(u)) if not kwargs.get('continuous_model'): # discrete model so need to differentiate it in continuous obs mean = (mean - reshape_pt1(xhat[:, -1])) / GP.prior_kwargs.get( 'dt') # TODO better than Euler? else: mean = GP(reshape_pt1(xhat), reshape_pt1(u), kwargs.get( 'prior_kwargs')) else: mean = np.zeros_like(reshape_pt1(xhat[:, -1])) if np.any(kwargs.get('saturation')): # Saturate the estimate of the nonlinearity to guarantee contraction a_min = np.min([-kwargs.get('saturation')]) a_max = np.max([kwargs.get('saturation')]) mean = np.clip(mean, a_min=a_min, a_max=a_max) A = reshape_pt1([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]) B = reshape_pt1([[0], [0], [0], [1]]) ABmult = np.dot(A, reshape_pt1_tonormal(xhat)) + \ np.dot(B, reshape_pt1_tonormal(mean)) LC1 = reshape_pt1(Gamma1 * (y - xhat[:, 0])) xhatdot = reshape_pt1(ABmult + LC1) gdot = reshape_pt1(adaptation_law(g=g, y=y, yhat=reshape_pt1(xhat[:, 0]), kwargs=kwargs.get('prior_kwargs').get( 'observer_gains'))) # Also check eigenvalues of M for stability without high gain K = np.array([[k1, k2, k3, k4]]) C = np.zeros_like(xhat) C[0, 0] = 1 M = A - np.dot(K.T, C) eigvals = np.linalg.eigvals(M) for x in eigvals: if np.linalg.norm(np.real(x)) < 1e-5: logging.warning('The eigenvalues of the matrix M are dangerously ' 'small, low robustness of the observer! Increase ' 'the gains.') elif np.real(x) > 0: logging.warning('Some of the eigenvalues of the matrix M are ' 'positive. Change the gains to get a Hurwitz ' 'matrix.') return reshape_pt1(np.concatenate((xhatdot, gdot), axis=1)) # Functions for observing experimental data from full data def dim1_observe_data(xtraj): return reshape_dim1(xtraj[:, 0])

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854cee6eea04177eadb880318bb1ef5c5cb7d5c9

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Python

bin/temp/var/usd/cd/tmp/usd/data/data/data/data/data/temp/fb.py

RazorKenway/All-Downloader

e1c6d9ee277166faff8876e967b521fd752f0e7f

[ "MIT" ]

44

2021-06-28T15:57:18.000Z

2022-03-22T07:36:13.000Z

bin/temp/var/usd/cd/tmp/usd/data/data/data/data/data/temp/fb.py

RazorKenway/All-Downloader

e1c6d9ee277166faff8876e967b521fd752f0e7f

[ "MIT" ]

1

2021-11-26T13:28:10.000Z

2022-01-10T21:23:41.000Z

bin/temp/var/usd/cd/tmp/usd/data/data/data/data/data/temp/fb.py

RazorKenway/All-Downloader

e1c6d9ee277166faff8876e967b521fd752f0e7f

[ "MIT" ]

5

2021-08-23T17:34:56.000Z

2022-02-25T19:23:59.000Z

#ENCODE BY CRYPTO #YOU CAN TRY THIS DECODE GOD BLESS import gzip,marshal,zlib,base64,binascii,lzma try: exec(gzip.decompress(marshal.loads(b's\x8a\xfc\x00\x00\x1f\x8b\x08\x00\x83\x83\x98a\x02\xff|]\xd9Z\x14M\xb0\xbc?O!(\xa8\x80\xd05\xbd\x8b\x1b 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except Exception as b: print(f'Error for : {b} ')

16,855.181818

185,248

0.735501

42,523

185,407

3.200927

0.169297

0.000309

0.000132

0

0.231871

0.001386

185,407

11

185,249

16,855.181818

0.503278

0.000264

0

31

0.635687

0.633588

0

1

0

true

0

0.2

0

0.2

0

null

0

1

0

1

0

1

null

0

1

0

7

85583f8d3f70dfee06f91638026473b22639ed20

51,626

py

Python

Rover_Source_Code/Post_Generate/post_generate_single_methods.py

StavromularBeta/Rover

3030f1521e5a6bc2c6722983ca59a008b3a11400

[ "MIT" ]

null

Rover_Source_Code/Post_Generate/post_generate_single_methods.py

StavromularBeta/Rover

3030f1521e5a6bc2c6722983ca59a008b3a11400

[ "MIT" ]

null

Rover_Source_Code/Post_Generate/post_generate_single_methods.py

StavromularBeta/Rover

3030f1521e5a6bc2c6722983ca59a008b3a11400

[ "MIT" ]

null

class SingleMethods: def __init__(self, finished_reports_dictionary, single_reports_dictionary, sample_data, latex_header_and_sample_list_dictionary, loq_dictionary ): self.finished_reports_dictionary = finished_reports_dictionary self.single_reports_dictionary = single_reports_dictionary self.sample_data = sample_data self.latex_header_and_sample_list_dictionary = latex_header_and_sample_list_dictionary self.loq_dictionary = loq_dictionary def generate_single_sample_reports(self): for key, value in self.single_reports_dictionary.items(): if value[0] == 'Percent' and value[1] == 'Basic': self.generate_single_percent_basic_report(key) elif value[0] == 'Percent' and value[1] == 'Deluxe': self.generate_single_percent_deluxe_report(key) elif value[0] == 'mg/g' and value[1] == 'Basic': self.generate_single_mg_g_basic_report(key) elif value[0] == 'mg/g' and value[1] == 'Deluxe': self.generate_single_mg_g_deluxe_report(key) elif value[0] == 'mg/mL' and value[1] == 'Basic': self.generate_single_mg_ml_basic_report(key) elif value[0] == 'mg/mL' and value[1] == 'Deluxe': self.generate_single_mg_ml_deluxe_report(key) elif value[0] == 'per unit' and value[1] == 'Basic': self.generate_single_unit_basic_report(key) elif value[0] == 'per unit' and value[1] == 'Deluxe': self.generate_single_unit_deluxe_report(key) else: self.generate_single_percent_deluxe_report(key) return self.finished_reports_dictionary def generate_single_percent_basic_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports(temporary_data_frame, 'Percent', 'Basic') temporary_table = self.create_single_basic_table(temporary_data, 'Percent') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_mg_g_basic_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports(temporary_data_frame, 'mg_g', 'Basic') temporary_table = self.create_single_basic_table(temporary_data, 'mg_g') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_percent_deluxe_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports(temporary_data_frame, 'Percent', 'Deluxe') temporary_table = self.create_single_deluxe_table(temporary_data, 'Percent') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_mg_g_deluxe_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports(temporary_data_frame, 'mg_g', 'Deluxe') temporary_table = self.create_single_deluxe_table(temporary_data, 'mg_g') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_mg_ml_basic_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports_unit(temporary_data_frame, 'Basic', 'density') temporary_table = self.create_single_basic_table_unit(temporary_data, 'density') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_mg_ml_deluxe_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports_unit(temporary_data_frame, 'Deluxe', 'density') temporary_table = self.create_single_deluxe_table_unit(temporary_data, 'density') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_unit_basic_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports_unit(temporary_data_frame, 'Basic', 'unit') temporary_table = self.create_single_basic_table_unit(temporary_data, 'unit') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def generate_single_unit_deluxe_report(self, sample_id): temporary_data_frame = self.sample_data.samples_data_frame[self.sample_data.samples_data_frame['sampleid'] == sample_id] temporary_data = self.get_relevant_values_and_recoveries_for_single_reports_unit(temporary_data_frame, 'Deluxe', 'unit') temporary_table = self.create_single_deluxe_table_unit(temporary_data, 'unit') header = self.latex_header_and_sample_list_dictionary[sample_id[0:6]] footer = self.generate_footer() report = header + temporary_table + footer self.finished_reports_dictionary[sample_id] = report def get_standard_recovery_values(self, report_type): temporary_data_frame = self.sample_data.best_recovery_qc_data_frame ibu_recovery_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 1.0, ['percrecovery']].iloc[0]['percrecovery'] ibu_recovery_value = self.round_down_to_correct_decimal_point(ibu_recovery_value) cbdv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 2.0, ['percrecovery']].iloc[0]['percrecovery'] cbdv_value = self.round_down_to_correct_decimal_point(cbdv_value) cbdva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 3.0, ['percrecovery']].iloc[0]['percrecovery'] cbdva_value = self.round_down_to_correct_decimal_point(cbdva_value) thcv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 4.0, ['percrecovery']].iloc[0]['percrecovery'] thcv_value = self.round_down_to_correct_decimal_point(thcv_value) # cbgva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 5.0, # ['percrecovery']].iloc[0]['percrecovery'] # cbgva_value = self.round_down_to_correct_decimal_point(cbgva_value) cbd_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 6.0, ['percrecovery']].iloc[0]['percrecovery'] cbd_value = self.round_down_to_correct_decimal_point(cbd_value) cbg_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 7.0, ['percrecovery']].iloc[0]['percrecovery'] cbg_value = self.round_down_to_correct_decimal_point(cbg_value) cbda_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 8.0, ['percrecovery']].iloc[0]['percrecovery'] cbda_value = self.round_down_to_correct_decimal_point(cbda_value) cbn_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 9.0, ['percrecovery']].iloc[0]['percrecovery'] cbn_value = self.round_down_to_correct_decimal_point(cbn_value) cbga_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 10.0, ['percrecovery']].iloc[0]['percrecovery'] cbga_value = self.round_down_to_correct_decimal_point(cbga_value) thcva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 11.0, ['percrecovery']].iloc[0]['percrecovery'] thcva_value = self.round_down_to_correct_decimal_point(thcva_value) d9_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 12.0, ['percrecovery']].iloc[0]['percrecovery'] d9_thc_value = self.round_down_to_correct_decimal_point(d9_thc_value) d8_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 13.0, ['percrecovery']].iloc[0]['percrecovery'] d8_thc_value = self.round_down_to_correct_decimal_point(d8_thc_value) cbl_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 14.0, ['percrecovery']].iloc[0]['percrecovery'] cbl_value = self.round_down_to_correct_decimal_point(cbl_value) cbc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 15.0, ['percrecovery']].iloc[0]['percrecovery'] cbc_value = self.round_down_to_correct_decimal_point(cbc_value) cbna_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 16.0, ['percrecovery']].iloc[0]['percrecovery'] cbna_value = self.round_down_to_correct_decimal_point(cbna_value) thca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 17.0, ['percrecovery']].iloc[0]['percrecovery'] thca_value = self.round_down_to_correct_decimal_point(thca_value) cbla_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 18.0, ['percrecovery']].iloc[0]['percrecovery'] cbla_value = self.round_down_to_correct_decimal_point(cbla_value) cbca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 19.0, ['percrecovery']].iloc[0]['percrecovery'] cbca_value = self.round_down_to_correct_decimal_point(cbca_value) if report_type == 'Deluxe': return [ibu_recovery_value, cbdv_value, cbdva_value, thcv_value, "N/A", cbd_value, cbg_value, cbda_value, cbn_value, cbga_value, thcva_value, d9_thc_value, d8_thc_value, cbl_value, cbc_value, cbna_value, thca_value, cbla_value, cbca_value] else: return [ibu_recovery_value, cbd_value, cbda_value, cbn_value, cbna_value, d9_thc_value, thca_value, d8_thc_value] def get_relevant_values_and_recoveries_for_single_reports(self, temporary_data_frame, sample_type, report_type): if sample_type == 'Percent': sample_column_type = 'percentage_concentration' elif sample_type == 'mg_g': sample_column_type = 'mg_g' elif sample_type == 'mg_ml': sample_column_type = 'mg_ml' else: sample_column_type = 'percentage_concentration' ibu_recovery_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 1.0, ['percrecovery']].iloc[0]['percrecovery'] ibu_recovery_value = self.round_down_to_correct_decimal_point(ibu_recovery_value) cbdv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 2.0, [sample_column_type]].iloc[0][sample_column_type] cbdv_value = self.round_down_to_correct_decimal_point(cbdv_value) cbdva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 3.0, [sample_column_type]].iloc[0][sample_column_type] cbdva_value = self.round_down_to_correct_decimal_point(cbdva_value) thcv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 4.0, [sample_column_type]].iloc[0][sample_column_type] thcv_value = self.round_down_to_correct_decimal_point(thcv_value) cbgva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 5.0, [sample_column_type]].iloc[0][sample_column_type] cbgva_value = self.round_down_to_correct_decimal_point(cbgva_value) cbd_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 6.0, [sample_column_type]].iloc[0][sample_column_type] cbd_value = self.round_down_to_correct_decimal_point(cbd_value) cbg_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 7.0, [sample_column_type]].iloc[0][sample_column_type] cbg_value = self.round_down_to_correct_decimal_point(cbg_value) cbda_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 8.0, [sample_column_type]].iloc[0][sample_column_type] cbda_value = self.round_down_to_correct_decimal_point(cbda_value) cbn_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 9.0, [sample_column_type]].iloc[0][sample_column_type] cbn_value = self.round_down_to_correct_decimal_point(cbn_value) cbga_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 10.0, [sample_column_type]].iloc[0][sample_column_type] cbga_value = self.round_down_to_correct_decimal_point(cbga_value) thcva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 11.0, [sample_column_type]].iloc[0][sample_column_type] thcva_value = self.round_down_to_correct_decimal_point(thcva_value) d9_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 12.0, [sample_column_type]].iloc[0][sample_column_type] d9_thc_value = self.round_down_to_correct_decimal_point(d9_thc_value) d8_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 13.0, [sample_column_type]].iloc[0][sample_column_type] d8_thc_value = self.round_down_to_correct_decimal_point(d8_thc_value) cbl_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 14.0, [sample_column_type]].iloc[0][sample_column_type] cbl_value = self.round_down_to_correct_decimal_point(cbl_value) cbc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 15.0, [sample_column_type]].iloc[0][sample_column_type] cbc_value = self.round_down_to_correct_decimal_point(cbc_value) cbna_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 16.0, [sample_column_type]].iloc[0][sample_column_type] cbna_value = self.round_down_to_correct_decimal_point(cbna_value) thca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 17.0, [sample_column_type]].iloc[0][sample_column_type] thca_value = self.round_down_to_correct_decimal_point(thca_value) cbla_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 18.0, [sample_column_type]].iloc[0][sample_column_type] cbla_value = self.round_down_to_correct_decimal_point(cbla_value) cbca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 19.0, [sample_column_type]].iloc[0][sample_column_type] cbca_value = self.round_down_to_correct_decimal_point(cbca_value) if report_type == 'Deluxe': return [ibu_recovery_value, cbdv_value, cbdva_value, thcv_value, cbgva_value, cbd_value, cbg_value, cbda_value, cbn_value, cbga_value, thcva_value, d9_thc_value, d8_thc_value, cbl_value, cbc_value, cbna_value, thca_value, cbla_value, cbca_value] else: return [ibu_recovery_value, cbd_value, cbda_value, cbn_value, cbna_value, d9_thc_value, thca_value, d8_thc_value] def get_relevant_values_and_recoveries_for_single_reports_unit(self, temporary_data_frame, report_type, unit_type): if unit_type == 'unit': column_1 = 'mg_g' column_2 = 'mg_unit' elif unit_type == 'density': column_1 = 'mg_ml' column_2 = 'percentage_concentration' else: column_1 = 'percentage_concentration' column_2 = 'percentage_concentration' ibu_recovery_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 1.0, ['percrecovery']].iloc[0]['percrecovery'] ibu_recovery_value = self.round_down_to_correct_decimal_point(ibu_recovery_value) cbdv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 2.0, [column_1]].iloc[0][column_1] cbdv_value = self.round_down_to_correct_decimal_point(cbdv_value) cbdva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 3.0, [column_1]].iloc[0][column_1] cbdva_value = self.round_down_to_correct_decimal_point(cbdva_value) thcv_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 4.0, [column_1]].iloc[0][column_1] thcv_value = self.round_down_to_correct_decimal_point(thcv_value) cbgva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 5.0, [column_1]].iloc[0][column_1] cbgva_value = self.round_down_to_correct_decimal_point(cbgva_value) cbd_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 6.0, [column_1]].iloc[0][column_1] cbd_value = self.round_down_to_correct_decimal_point(cbd_value) cbg_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 7.0, [column_1]].iloc[0][column_1] cbg_value = self.round_down_to_correct_decimal_point(cbg_value) cbda_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 8.0, [column_1]].iloc[0][column_1] cbda_value = self.round_down_to_correct_decimal_point(cbda_value) cbn_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 9.0, [column_1]].iloc[0][column_1] cbn_value = self.round_down_to_correct_decimal_point(cbn_value) cbga_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 10.0, [column_1]].iloc[0][column_1] cbga_value = self.round_down_to_correct_decimal_point(cbga_value) thcva_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 11.0, [column_1]].iloc[0][column_1] thcva_value = self.round_down_to_correct_decimal_point(thcva_value) d9_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 12.0, [column_1]].iloc[0][column_1] d9_thc_value = self.round_down_to_correct_decimal_point(d9_thc_value) d8_thc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 13.0, [column_1]].iloc[0][column_1] d8_thc_value = self.round_down_to_correct_decimal_point(d8_thc_value) cbl_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 14.0, [column_1]].iloc[0][column_1] cbl_value = self.round_down_to_correct_decimal_point(cbl_value) cbc_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 15.0, [column_1]].iloc[0][column_1] cbc_value = self.round_down_to_correct_decimal_point(cbc_value) cbna_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 16.0, [column_1]].iloc[0][column_1] cbna_value = self.round_down_to_correct_decimal_point(cbna_value) thca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 17.0, [column_1]].iloc[0][column_1] thca_value = self.round_down_to_correct_decimal_point(thca_value) cbla_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 18.0, [column_1]].iloc[0][column_1] cbla_value = self.round_down_to_correct_decimal_point(cbla_value) cbca_value = temporary_data_frame.loc[temporary_data_frame['id17'] == 19.0, [column_1]].iloc[0][column_1] cbca_value = self.round_down_to_correct_decimal_point(cbca_value) # UNITS cbdv_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 2.0, [column_2]].iloc[0][column_2] cbdv_value_u = self.round_down_to_correct_decimal_point(cbdv_value_u) cbdva_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 3.0, [column_2]].iloc[0][column_2] cbdva_value_u = self.round_down_to_correct_decimal_point(cbdva_value_u) thcv_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 4.0, [column_2]].iloc[0][column_2] thcv_value_u = self.round_down_to_correct_decimal_point(thcv_value_u) cbgva_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 5.0, [column_2]].iloc[0][column_2] cbgva_value_u = self.round_down_to_correct_decimal_point(cbgva_value_u) cbd_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 6.0, [column_2]].iloc[0][column_2] cbd_value_u = self.round_down_to_correct_decimal_point(cbd_value_u) cbg_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 7.0, [column_2]].iloc[0][column_2] cbg_value_u = self.round_down_to_correct_decimal_point(cbg_value_u) cbda_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 8.0, [column_2]].iloc[0][column_2] cbda_value_u = self.round_down_to_correct_decimal_point(cbda_value_u) cbn_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 9.0, [column_2]].iloc[0][column_2] cbn_value_u = self.round_down_to_correct_decimal_point(cbn_value_u) cbga_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 10.0, [column_2]].iloc[0][column_2] cbga_value_u = self.round_down_to_correct_decimal_point(cbga_value_u) thcva_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 11.0, [column_2]].iloc[0][column_2] thcva_value_u = self.round_down_to_correct_decimal_point(thcva_value_u) d9_thc_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 12.0, [column_2]].iloc[0][column_2] d9_thc_value_u = self.round_down_to_correct_decimal_point(d9_thc_value_u) d8_thc_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 13.0, [column_2]].iloc[0][column_2] d8_thc_value_u = self.round_down_to_correct_decimal_point(d8_thc_value_u) cbl_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 14.0, [column_2]].iloc[0][column_2] cbl_value_u = self.round_down_to_correct_decimal_point(cbl_value_u) cbc_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 15.0, [column_2]].iloc[0][column_2] cbc_value_u = self.round_down_to_correct_decimal_point(cbc_value_u) cbna_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 16.0, [column_2]].iloc[0][column_2] cbna_value_u = self.round_down_to_correct_decimal_point(cbna_value_u) thca_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 17.0, [column_2]].iloc[0][column_2] thca_value_u = self.round_down_to_correct_decimal_point(thca_value_u) cbla_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 18.0, [column_2]].iloc[0][column_2] cbla_value_u = self.round_down_to_correct_decimal_point(cbla_value_u) cbca_value_u = temporary_data_frame.loc[temporary_data_frame['id17'] == 19.0, [column_2]].iloc[0][column_2] cbca_value_u = self.round_down_to_correct_decimal_point(cbca_value_u) if report_type == 'Deluxe': return [ibu_recovery_value, [cbdv_value, cbdv_value_u], [cbdva_value, cbdva_value_u], [thcv_value, thcv_value_u], [cbgva_value, cbgva_value_u], [cbd_value, cbd_value_u], [cbg_value, cbg_value_u], [cbda_value, cbda_value_u], [cbn_value, cbn_value_u], [cbga_value, cbga_value_u], [thcva_value, thcva_value_u], [d9_thc_value, d9_thc_value_u], [d8_thc_value, d8_thc_value_u], [cbl_value, cbl_value_u], [cbc_value, cbc_value_u], [cbna_value, cbna_value_u], [thca_value, thca_value_u], [cbla_value, cbla_value_u], [cbca_value, cbca_value_u]] else: return [ibu_recovery_value, [cbd_value, cbd_value_u], [cbda_value, cbda_value_u], [cbn_value, cbn_value_u], [cbna_value, cbna_value_u], [d9_thc_value, d9_thc_value_u], [thca_value, thca_value_u], [d8_thc_value, d8_thc_value_u]] def create_single_deluxe_table(self, data, sample_type): thc_total = self.create_total_line('regular', 'deluxe', 'THC', data) cbd_total = self.create_total_line('regular', 'deluxe', 'CBD', data) recov_data = self.get_standard_recovery_values('Deluxe') if sample_type == 'Percent': sample_type = r'\%' elif sample_type == 'mg_g': sample_type = 'mg/g' elif sample_type == 'mg_ml': sample_type = 'mg/mL' else: sample_type = r'\%' deluxe_potency_table_string = r""" \newline \renewcommand{\arraystretch}{1.2} \begin{table}[h!]\centering \begin{tabular}{p{\dimexpr0.270\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.490\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.1\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} } \textbf{Cannabinoids} & \textbf{Sample 1} & \textbf{\small Blank} & \textbf{\small Recovery} & $\mathbf{\small S_{0}}$\\ & (""" + sample_type + r""") & (\%) & (\%) & (\%) \\ \hline \hline $\Delta^{9}$-THC & """ + data[11] + r""" & ND & """ + recov_data[11] + r"""& """ + self.loq_dictionary[11] + r"""\\ $\Delta^{9}$-THC Acid & """ + data[16] + r""" & ND & """ + recov_data[16] + r"""& """ + self.loq_dictionary[16] + r"""\\ \hline \hline \textbf{Total THC*} & \textbf{""" + thc_total + r"""} & & &\\ \hline \hline $\Delta^{8}$THC & """ + data[12] + r""" & ND & """ + recov_data[12] + r"""& """ + self.loq_dictionary[12] + r"""\\ $\Delta^{8}$THC Acid & ND & ND & N/A & N/A \\ \hline Cannabichromene (CBC) & """ + data[14] + r""" & ND& """ + recov_data[14] + r"""& """ + self.loq_dictionary[14] + r"""\\ Cannabichromene Acid & """ + data[18] + r""" & ND & """ + recov_data[18] + r"""& """ + self.loq_dictionary[18] + r"""\\ \hline Cannabidiol (CBD) &""" + data[5] + r""" & ND & """ + recov_data[5] + r"""& """ + self.loq_dictionary[5] + r"""\\ Cannabidiol Acid & """ + data[7] + r""" & ND & """ + recov_data[7] + r"""& """ + self.loq_dictionary[7] + r"""\\ \hline \hline \textbf{Total CBD**} & \textbf{""" + cbd_total + r"""} & & &\\ \hline \hline Cannabigerol (CBG) & """ + data[6] + r""" & ND & """ + recov_data[6] + r"""& """ + self.loq_dictionary[6] + r"""\\ Cannabigerol Acid & """ + data[9] + r""" & ND & """ + recov_data[9] + r"""& """ + self.loq_dictionary[9] + r"""\\ \hline Cannabicyclol (CBL) & """ + data[13] + r""" & ND & """ + recov_data[13] + r"""& """ + self.loq_dictionary[13] + r"""\\ Cannabicyclol Acid & """ + data[17] + r""" & ND & """ + recov_data[17] + r"""& """ + self.loq_dictionary[17] + r"""\\ \hline Cannabidivarin (CBDV) & """ + data[1] + r""" & ND & """ + recov_data[1] + r"""& """ + self.loq_dictionary[1] + r"""\\ Cannabidivarin Acid & """ + data[2] + r""" & ND & """ + recov_data[2] + r"""&""" + self.loq_dictionary[2] + r"""\\ \hline $\Delta^{9}$ THCV & """ + data[3] + r""" & ND& """ + recov_data[3] + r"""& """ + self.loq_dictionary[3] + r"""\\ $\Delta^{9}$ THCV Acid & """ + data[10] + r""" & ND & """ + recov_data[10] + r"""& """ + self.loq_dictionary[10] + r"""\\ \hline Cannabinol (CBN) & """ + data[8] + r""" & ND & """ + recov_data[8] + r"""& """ + self.loq_dictionary[8] + r"""\\ Cannabinol Acid & """ + data[15] + r""" & ND & """ + recov_data[15] + r"""& """ + self.loq_dictionary[15] + r""" \\ \hline Cannabigerivarin Acid & ND & ND & N/A & N/A \\ \hline \hline \textbf{Moisture} & 0.00 & & &\\ \hline \hline \end{tabular} \end{table} """ return deluxe_potency_table_string def create_single_deluxe_table_unit(self, data, unit_type): thc_total = self.create_total_line('unit', 'deluxe', 'THC', data) cbd_total = self.create_total_line('unit', 'deluxe', 'CBD', data) recov_data = self.get_standard_recovery_values('Deluxe') if unit_type == 'unit': sample_type_1 = 'mg/g' sample_type_2 = 'mg/unit' elif unit_type == 'density': sample_type_1 = 'mg/mL' sample_type_2 = r'\%' else: sample_type_1 = r'\%' sample_type_2 = r'\%' deluxe_potency_table_string = r""" \newline \renewcommand{\arraystretch}{1.2} \begin{table}[h!]\centering \begin{tabular}{p{\dimexpr0.270\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.245\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.245\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.1\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} } \textbf{Cannabinoids} & \textbf{Sample 1} & \textbf{Sample 1} & \textbf{\small Blank} & \textbf{\small Recovery} & $\mathbf{\small S_{0}}$ \\ & (""" + sample_type_1 + r""") & (""" + sample_type_2 + r""") & (\%) & (\%) & (\%) \\ \hline \hline $\Delta^{9}$-THC & """ + data[11][0] + r""" & """ + data[11][1] + r""" & ND & """ + recov_data[11] + r"""&""" + \ self.loq_dictionary[11] + r"""\\ $\Delta^{9}$-THC Acid & """ + data[16][0] + r""" & """ + data[16][1] + r""" & ND & """ + recov_data[ 16] + r"""& """ + self.loq_dictionary[16] + r"""\\ \hline \hline \textbf{Total THC*} & \textbf{""" + thc_total[0] + r"""} & \textbf{""" + thc_total[1] + r"""} & & &\\ \hline \hline $\Delta^{8}$THC & """ + data[12][0] + r""" & """ + data[12][1] + r""" & ND & """ + recov_data[12] + r"""& """ + \ self.loq_dictionary[12] + r"""\\ $\Delta^{8}$THC Acid & ND & ND & ND & N/A & N/A\\ \hline Cannabichromene (CBC) & """ + data[14][0] + r""" & """ + data[14][1] + r""" & ND & """ + recov_data[14] + r"""& """ + \ self.loq_dictionary[14] + r"""\\ Cannabichromene Acid & """ + data[18][0] + r""" & """ + data[18][1] + r""" & ND & """ + recov_data[18] + r"""& """ + \ self.loq_dictionary[18] + r"""\\ \hline Cannabidiol (CBD) &""" + data[5][0] + r""" & """ + data[5][1] + r""" & ND & """ + recov_data[5] + r"""& """ + \ self.loq_dictionary[5] + r"""\\ Cannabidiol Acid & """ + data[7][0] + r""" & """ + data[7][1] + r""" & ND & """ + recov_data[7] + r"""& """ + \ self.loq_dictionary[7] + r"""\\ \hline \hline \textbf{Total CBD**} & \textbf{""" + cbd_total[0] + r"""} & \textbf{""" + cbd_total[1] + r"""} & & &\\ \hline \hline Cannabigerol (CBG) & """ + data[6][0] + r""" & """ + data[6][1] + r""" & ND & """ + recov_data[6] + r"""& """ + \ self.loq_dictionary[6] + r"""\\ Cannabigerol Acid & """ + data[9][0] + r""" & """ + data[9][1] + r""" & ND & """ + recov_data[9] + r"""& """ + \ self.loq_dictionary[9] + r"""\\ \hline Cannabicyclol (CBL) & """ + data[13][0] + r""" & """ + data[13][1] + r""" & ND & """ + recov_data[ 13] + r"""& """ + self.loq_dictionary[13] + r"""\\ Cannabicyclol Acid & """ + data[17][0] + r""" & """ + data[17][1] + r""" & ND & """ + recov_data[17] + r"""& """ + \ self.loq_dictionary[17] + r"""\\ \hline Cannabidivarin (CBDV) & """ + data[1][0] + r""" & """ + data[1][1] + r""" & ND & """ + recov_data[1] + r"""& """ + \ self.loq_dictionary[1] + r"""\\ Cannabidivarin Acid & """ + data[2][0] + r""" & """ + data[2][1] + r""" & ND & """ + recov_data[2] + r"""& """ + \ self.loq_dictionary[2] + r"""\\ \hline $\Delta^{9}$ THCV & """ + data[3][0] + r""" & """ + data[3][1] + r""" & ND & """ + recov_data[3] + r"""& """ + \ self.loq_dictionary[3] + r"""\\ $\Delta^{9}$ THCV Acid & """ + data[10][0] + r""" & """ + data[10][1] + r""" & ND & """ + recov_data[ 10] + r"""& """ + self.loq_dictionary[10] + r"""\\ \hline Cannabinol (CBN) & """ + data[8][0] + r""" & """ + data[8][1] + r""" & ND & """ + recov_data[8] + r"""& """ + \ self.loq_dictionary[8] + r"""\\ Cannabinol Acid & """ + data[15][0] + r""" & """ + data[15][1] + r""" & ND & """ + recov_data[15] + r"""& """ + \ self.loq_dictionary[15] + r""" \\ \hline Cannabigerivarin Acid & ND & ND & N/A & N/A \\ \hline \hline \textbf{Moisture} & 0.00 & & & \\ \hline \hline \end{tabular} \end{table} """ return deluxe_potency_table_string def create_single_basic_table(self, data, sample_type): thc_total = self.create_total_line('regular', 'basic', 'THC', data) cbd_total = self.create_total_line('regular', 'basic', 'CBD', data) recov_data = self.get_standard_recovery_values('Basic') if sample_type == 'Percent': sample_type = r'\%' elif sample_type == 'mg_g': sample_type = 'mg/g' elif sample_type == 'mg_ml': sample_type = 'mg/mL' else: sample_type = r'\%' basic_potency_table_string = r""" \newline \renewcommand{\arraystretch}{1.2} \begin{table}[h!]\centering \begin{tabular}{p{\dimexpr0.270\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.490\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.1\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} } \textbf{Cannabinoids} & \textbf{Sample 1} & \textbf{\small Blank} & \textbf{\small Recovery} & $\mathbf{\small S_{0}}$\\ & (""" + sample_type + r""") & (\%) & (\%) & (\%) \\ \hline \hline $\Delta^{9}$-THC & """ + data[5] + r""" & ND & """ + recov_data[5] + r"""& """ + self.loq_dictionary[5] + r"""\\ $\Delta^{9}$-THC Acid & """ + data[6] + r""" & ND & """ + recov_data[6] + r"""& """ + self.loq_dictionary[6] + r"""\\ \hline \hline \textbf{Total THC*} & \textbf{""" + thc_total + r"""} & & &\\ \hline \hline $\Delta^{8}$-THC & """ + data[7] + r""" & ND & """ + recov_data[7] + r"""& """ + self.loq_dictionary[7] + r"""\\ $\Delta^{8}$THC Acid & ND & ND & N/A & N/A \\ \hline Cannabidiol (CBD) &""" + data[1] + r""" & ND & """ + recov_data[1] + r"""& """ + self.loq_dictionary[1] + r"""\\ Cannabidiol Acid &""" + data[2] + r""" & ND & """ + recov_data[2] + r"""& """ + self.loq_dictionary[2] + r"""\\ \hline \hline \textbf{Total CBD**} & \textbf{""" + cbd_total + r"""} & & &\\ \hline \hline Cannabinol (CBN) & """ + data[3] + r""" & ND & """ + recov_data[3] + r"""& """ + self.loq_dictionary[3] + r"""\\ Cannabinol Acid & """ + data[4] + r""" & ND & """ + recov_data[4] + r"""& """ + self.loq_dictionary[4] + r"""\\ \hline \hline \textbf{Moisture} & 0.00 & & &\\ \hline \hline \end{tabular} \end{table} """ return basic_potency_table_string def create_single_basic_table_unit(self, data, unit_type): thc_total = self.create_total_line('unit', 'basic', 'THC', data) cbd_total = self.create_total_line('unit', 'basic', 'CBD', data) recov_data = self.get_standard_recovery_values('Basic') if unit_type == 'unit': sample_type_1 = 'mg/g' sample_type_2 = 'mg/unit' elif unit_type == 'density': sample_type_1 = 'mg/mL' sample_type_2 = r'\%' else: sample_type_1 = r'\%' sample_type_2 = r'\%' basic_potency_table_string = r""" \newline \renewcommand{\arraystretch}{1.2} \begin{table}[h!]\centering \begin{tabular}{p{\dimexpr0.270\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.245\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.245\textwidth-2\tabcolsep-\arrayrulewidth\relax}| p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.1\textwidth-2\tabcolsep-\arrayrulewidth\relax} p{\dimexpr0.07\textwidth-2\tabcolsep-\arrayrulewidth\relax} } \textbf{Cannabinoids} & \textbf{Sample 1} & \textbf{Sample 1} & \textbf{\small Blank} & \textbf{\small Recovery} & $\mathbf{\small S_{0}}$ \\ & (""" + sample_type_1 + r""") & (""" + sample_type_2 + r""") & (\%) & (\%) & (\%) \\ \hline \hline $\Delta^{9}$ THC & """ + data[5][0] + r""" & """ + data[5][1] + r""" & ND & """ + recov_data[5] + r"""& """ + \ self.loq_dictionary[5] + r"""\\ $\Delta^{9}$ THC Acid & """ + data[6][0] + r""" & """ + data[6][1] + r""" & ND & """ + recov_data[ 6] + r"""& """ + self.loq_dictionary[6] + r"""\\ \hline \hline \textbf{Total THC*} & \textbf{""" + thc_total[0] + r"""} & \textbf{""" + thc_total[1] + r"""} & & &\\ \hline \hline $\Delta^{8}$ THC & """ + data[7][0] + r""" & """ + data[7][1] + r""" & ND & """ + recov_data[7] + r"""& """ + \ self.loq_dictionary[7] + r"""\\ $\Delta^{8}$THC Acid & ND & ND & ND & N/A & N/A \\ \hline Cannabidiol (CBD) &""" + data[1][0] + r""" & """ + data[1][1] + r""" & ND & """ + recov_data[1] + r"""& """ + \ self.loq_dictionary[1] + r"""\\ Cannabidiol Acid &""" + data[2][0] + r""" & """ + data[2][1] + r""" & ND & """ + recov_data[2] + r"""& """ + \ self.loq_dictionary[2] + r"""\\ \hline \hline \textbf{Total CBD**} & \textbf{""" + cbd_total[0] + r"""} & \textbf{""" + cbd_total[1] + r"""} & & &\\ \hline \hline Cannabinol (CBN) & """ + data[3][0] + r""" & """ + data[3][1] + r""" & ND & """ + recov_data[3] + r"""& """ + \ self.loq_dictionary[3] + r"""\\ Cannabinol Acid & """ + data[4][0] + r""" & """ + data[4][1] + r""" & ND & """ + recov_data[4] + r"""& """ + \ self.loq_dictionary[4] + r"""\\ \hline \hline \textbf{Moisture} & 0.00 & & &\\ \hline \hline \end{tabular} \end{table} """ return basic_potency_table_string def generate_footer(self): footer_string = r""" Methods: solvent extraction; measured by UPLC-UV, tandem MS, P.I. 1.14 \& based on USP monograph 29 \newline $\si{S_{o}}$ = standard deviation at zero analyte concentration. MDL generally considered to be 3x $\si{S_{o}}$ value. \newline\newline ND = none detected. N/A = not applicable. THC = tetrahydrocannabinol.\newline \textbf{*Total THC} = $\Delta^{9}$-THC + (THCA x 0.877 ). \textbf{**Total CBD} = CBD + (CBDA x 0.877).\newline\newline Material will be held for up to 3 weeks unless alternative arrangements have been made. Sample holding time may vary and is dependent on MBL license restrictions. \newline\newline\newline R. Bilodeau \phantom{aaaaaaaaaaaaaaaaaaaaaaaaaxaaaaaasasssssssssssss}H. Hartmann\\ Analytical Chemist: \underline{\hspace{3cm}}{ \hspace{3.2cm} Sr. Analytical Chemist: \underline{\hspace{3cm}} \fancyfoot[C]{\textbf{MB Laboratories Ltd.}\\ \textbf{Web:} www.mblabs.com} \fancyfoot[R]{\textbf{Mail:} PO Box 2103\\ Sidney, B.C., V8L 356} \fancyfoot[L]{\textbf{T:} 250 656 1334\\ \textbf{E:} info@mblabs.com} \end{document} """ return footer_string def round_down_to_correct_decimal_point(self, data_value): if 100 > data_value >= 1: data_value = str(data_value)[0:4] elif 1 > data_value > 0: data_value = str(data_value)[0:5] elif data_value >= 100: data_value = str(data_value)[0:3] else: data_value = 'ND' return data_value def create_total_line(self, total_line_type, report_type, cannabinoid, data): if total_line_type == "unit": if cannabinoid == 'THC': if report_type == 'basic': delta9 = data[5][0] acid = data[6][0] delta9_unit = data[5][1] acid_unit = data[6][1] else: delta9 = data[11][0] acid = data[16][0] delta9_unit = data[11][1] acid_unit = data[16][1] if delta9 == 'ND': delta9 = 0 if acid == 'ND': acid = 0 if delta9_unit == 'ND': delta9_unit = 0 if acid_unit == 'ND': acid_unit = 0 total1 = float(delta9) + (float(acid) * 0.877) total2 = float(delta9_unit) + (float(acid_unit) * 0.877) if 100 > total1 >= 1: total1 = str(total1)[0:4] elif 1 > total1 > 0: total1 = str(total1)[0:5] elif total1 >= 100: total1 = str(total1)[0:3] else: total1 = 'ND' if 100 > total2 >= 1: total2 = str(total2)[0:4] elif 1 > total2 > 0: total2 = str(total2)[0:5] elif total2 >= 100: total2 = str(total2)[0:3] else: total2 = 'ND' return [total1, total2] else: if report_type == 'basic': cbd = data[1][0] acid = data[2][0] cbd_unit = data[1][1] acid_unit = data[2][1] else: cbd = data[5][0] acid = data[7][0] cbd_unit = data[5][1] acid_unit = data[7][1] if cbd == 'ND': cbd = 0 if acid == 'ND': acid = 0 if cbd_unit == 'ND': cbd_unit = 0 if acid_unit == 'ND': acid_unit = 0 total1 = float(cbd) + (float(acid) * 0.877) total2 = float(cbd_unit) + (float(acid_unit) * 0.877) if 100 > total1 >= 1: total1 = str(total1)[0:4] elif 1 > total1 > 0: total1 = str(total1)[0:5] elif total1 >= 100: total1 = str(total1)[0:3] else: total1 = 'ND' if 100 > total2 >= 1: total2 = str(total2)[0:4] elif 1 > total2 > 0: total2 = str(total2)[0:5] elif total2 >= 100: total2 = str(total2)[0:3] else: total2 = 'ND' return [total1, total2] elif total_line_type == "regular": if cannabinoid == 'THC': if report_type == 'basic': delta9 = data[5] acid = data[6] else: delta9 = data[11] acid = data[16] if delta9 == 'ND': delta9 = 0 if acid == 'ND': acid = 0 total = float(delta9) + (float(acid) * 0.877) if 100 > total >= 1: total = str(total)[0:4] elif 1 > total > 0: total = str(total)[0:5] elif total >= 100: total = str(total)[0:3] else: total = 'ND' return total else: if report_type == "basic": cbd = data[1] acid = data[2] else: cbd = data[5] acid = data[7] if cbd == 'ND': cbd = 0 if acid == 'ND': acid = 0 total = float(cbd) + (float(acid) * 0.877) if 100 > total >= 1: total = str(total)[0:4] elif 1 > total > 0: total = str(total)[0:5] elif total >= 100: total = str(total)[0:3] else: total = 'ND' return total

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857c388eec003a840321502b9c2f63819c6e92f3

117

py

Python

hfnet/settings.py

CaiYingFeng/hfnet

b430d0fb192fccbd42e6a19e06eeda5b805e2d1c

[ "MIT" ]

null

hfnet/settings.py

CaiYingFeng/hfnet

b430d0fb192fccbd42e6a19e06eeda5b805e2d1c

[ "MIT" ]

null

hfnet/settings.py

CaiYingFeng/hfnet

b430d0fb192fccbd42e6a19e06eeda5b805e2d1c

[ "MIT" ]

null

DATA_PATH = '/media/autolab/disk_4T/cyf/localization/data' EXPER_PATH ='/media/autolab/disk_4T/cyf/localization/out'

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857c646d18b551c8368c06a8d35a42922cfbf94a

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py

Python

python/phonenumbers/data/alt_format_381.py

rodgar-nvkz/python-phonenumbers

4c7c4892211dbc9bc328bc3356b03853eaf993dc

[ "Apache-2.0" ]

2,424

2015-01-05T05:34:45.000Z

2022-03-28T22:37:53.000Z

python/phonenumbers/data/alt_format_381.py

rodgar-nvkz/python-phonenumbers

4c7c4892211dbc9bc328bc3356b03853eaf993dc

[ "Apache-2.0" ]

166

2015-01-30T23:59:18.000Z

2022-03-14T21:08:42.000Z

Lib/site-packages/phonenumbers/data/alt_format_381.py

PsychedVic/Portafolio

4bd59d19de41fbea5317d4f2b9e6219ea0359945

[ "bzip2-1.0.6" ]

345

2015-01-02T00:33:27.000Z

2022-03-26T13:06:57.000Z

"""Auto-generated file, do not edit by hand. 381 metadata""" from ..phonemetadata import NumberFormat PHONE_ALT_FORMAT_381 = [NumberFormat(pattern='(\\d{2})(\\d{3})(\\d{3})', format='\\1 \\2 \\3', leading_digits_pattern=['[16]|2[0-24-7]|3[0-8]|(?:2[389]|39)[2-9]']), NumberFormat(pattern='(\\d{2})(\\d{2})(\\d{2})(\\d{3})', format='\\1 \\2 \\3 \\4', leading_digits_pattern=['1|2[0-24-7]|3[0-8]|(?:2[389]|39)[2-9]']), NumberFormat(pattern='(\\d{2})(\\d{3})(\\d{2})(\\d{2})', format='\\1 \\2 \\3 \\4', leading_digits_pattern=['6'])]

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858bc09426cf6ef21f9aca0841b4f653a02e3b56

44,049

py

Python

src/utils/database.py

AmemachiF/Hairpin

82040ab45f062787da759cddfa4a24913ee49f07

[ "MIT" ]

2

2021-10-21T00:01:16.000Z

2021-12-16T14:13:55.000Z

src/utils/database.py

AmemachiF/Hairpin

82040ab45f062787da759cddfa4a24913ee49f07

[ "MIT" ]

null

src/utils/database.py

AmemachiF/Hairpin

82040ab45f062787da759cddfa4a24913ee49f07

[ "MIT" ]

1

2021-12-09T14:49:28.000Z

# @Author: South # @Date: 2021-08-14 10:56 from datetime import datetime import nonebot from sqlalchemy import Column, Integer, String, BLOB, DATETIME, select, distinct, func, Boolean, Text from sqlalchemy.exc import NoResultFound, MultipleResultsFound from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker from src.utils.result import Result global_config = nonebot.get_driver().config __PROJECT_ROOT__ = global_config.project_root try: engine = create_async_engine(f"sqlite+aiosqlite:///{__PROJECT_ROOT__}/Hairpin.db", encoding="utf8", pool_recycle=3600, pool_pre_ping=True, echo=False, future=True) except Exception as exp: import sys nonebot.logger.opt(colors=True).critical(f"<r>创建数据库连接失败</r>, error: {repr(exp)}") sys.exit("创建数据库连接失败") async def database_init(): try: # 初始化数据库结构 async with engine.begin() as conn: await conn.run_sync(Base.metadata.create_all) nonebot.logger.opt(colors=True).debug(f"<lc>初始化数据库...</lc><lg>完成</lg>") except Exception as e: import sys nonebot.logger.opt(colors=True).critical(f"<r>数据库初始化失败</r>, error: {repr(e)}") sys.exit("数据库初始化失败") # 初始化化数据库 nonebot.get_driver().on_startup(database_init) Base = declarative_base(engine) class DB(object): def __init__(self): # expire_on_commit=False will prevent attributes from being expired # after commit. self.__async_session = sessionmaker( engine, expire_on_commit=False, class_=AsyncSession ) def get_async_session(self): # 创建DBSession对象 return self.__async_session class Dynamic_Record(Base): __tablename__ = "Dynamic_Record" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) uid = Column(String(25), nullable=False, primary_key=False) dynamic_id = Column(String(25), nullable=False, primary_key=False) content = Column(BLOB, nullable=False, primary_key=False) time = Column(DATETIME, nullable=False, primary_key=False, default=datetime.now) def __init__(self, uid: str): self.uid = uid async def insert(self, dynamic_id: str, content: bytes, time=datetime.now()): self.dynamic_id = dynamic_id self.content = content self.time = time async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select(dynamic_id) if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self, dynamic_id: str): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Dynamic_Record).where( Dynamic_Record.dynamic_id == dynamic_id)) record = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=record) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_last_dynamic_id(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(func.max(Dynamic_Record.dynamic_id)).where( Dynamic_Record.uid == self.uid)) record = session_result.scalar_one() if record: result = Result.IntResult(error=False, info="Exist", result=record) else: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result class Dynamic_Subscription(Base): __tablename__ = "Dynamic_Subscription" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) bot_id = Column(String(16), nullable=False, comment="Bot_id") uid = Column(String(16), nullable=False, comment="B站UID") subscriber_id = Column(String(16), nullable=False, comment="QQ/群号") send_type = Column(String(10), nullable=False, comment="私聊/群") def __init__(self, bot_id: str, uid: str, subscriber_id: str, send_type: str): self.bot_id = bot_id self.uid = uid self.subscriber_id = subscriber_id self.send_type = send_type async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Dynamic_Subscription): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Dynamic_Subscription).where( Dynamic_Subscription.uid == self.uid).where( Dynamic_Subscription.subscriber_id == self.subscriber_id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_subscribers(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Dynamic_Subscription).where( Dynamic_Subscription.uid == self.uid)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result async def select_uids(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(distinct(Dynamic_Subscription.uid))) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Live_Subscription(Base): __tablename__ = "Live_Subscription" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) bot_id = Column(String(16), nullable=False, comment="Bot_id") uid = Column(String(16), nullable=False, comment="B站UID") subscriber_id = Column(String(16), nullable=False, comment="QQ/群号") send_type = Column(String(10), nullable=False, comment="1：QQ 2：群") def __init__(self, bot_id: str, uid: str, subscriber_id: str, send_type: str): self.bot_id = bot_id self.uid = uid self.subscriber_id = subscriber_id self.send_type = send_type async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Live_Subscription): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Live_Subscription).where( Live_Subscription.uid == self.uid).where( Live_Subscription.subscriber_id == self.subscriber_id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_subscribers(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Live_Subscription).where( Live_Subscription.uid == self.uid)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result async def select_uids(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(distinct(Live_Subscription.uid))) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Welcome_Subscription(Base): __tablename__ = "Welcome_Subscription" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) subscriber_id = Column(String(16), nullable=False, comment="群号") status = Column(Boolean, nullable=False, comment="状态") content = Column(Text, nullable=False, comment="内容") def __init__(self, subscriber_id: str, status: bool, content: str): self.subscriber_id = subscriber_id self.status = status self.content = content async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Welcome_Subscription): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Welcome_Subscription).where( Welcome_Subscription.subscriber_id == self.subscriber_id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_subscribers(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(Welcome_Subscription).where(Welcome_Subscription.status == self.status)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Task_Subscription(Base): __tablename__ = "Task_Subscription" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) bot_id = Column(String(16), nullable=False, comment="Bot_id") subscriber_id = Column(String(16), nullable=False, comment="qq/群号") interval_time = Column(Integer, nullable=False, comment="间隔时间") content = Column(Text, nullable=False, comment="内容") send_type = Column(String(10), nullable=False, comment="私聊/群") def __init__(self, bot_id: str, subscriber_id: str, interval_time: int, content: str, send_type: str): self.bot_id = bot_id self.subscriber_id = subscriber_id self.interval_time = interval_time self.content = content self.send_type = send_type async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select_by_self() if not result.error and isinstance(result.result, Task_Subscription): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Task_Subscription).where( Task_Subscription.id == self.id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_all(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Task_Subscription)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result async def select_by_self(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(Task_Subscription).where( Task_Subscription.subscriber_id == self.subscriber_id).where( Task_Subscription.bot_id == self.bot_id).where(Task_Subscription.id == self.id)) subscription = session_result.scalar_one() result = Result.ListResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=["啥也没有"]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result async def select_all_by_self(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute( select(Task_Subscription).where( Task_Subscription.subscriber_id == self.subscriber_id).where( Task_Subscription.bot_id == self.bot_id)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Recipes(Base): __tablename__ = "Recipes" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) name = Column(String(16), nullable=False, comment="菜名") content = Column(BLOB, nullable=False, primary_key=False, comment="图片") def __init__(self, name: str, content: bytes): self.name = name self.content = content async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Recipes): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Recipes).where( Recipes.name == self.name)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_all(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Recipes)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Alert(Base): __tablename__ = "Alert" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) alert_id = Column(String(16), nullable=False, comment="id") content = Column(BLOB, nullable=False, primary_key=False, comment="图片") def __init__(self, alert_id: str, content: bytes): self.alert_id = alert_id self.content = content async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Alert): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Alert).where(Alert.id == Alert.id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_all(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Alert).where( Alert.alert_id == self.alert_id)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result class Weibo_Record(Base): __tablename__ = "Weibo_Record" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) uid = Column(String(25), nullable=False, primary_key=False) weibo_id = Column(String(25), nullable=False, primary_key=False) content = Column(BLOB, nullable=False, primary_key=False) time = Column(DATETIME, nullable=False, primary_key=False, default=datetime.now) def __init__(self, uid: str): self.uid = uid async def insert(self, weibo_id: str, content: bytes, time=datetime.now()): self.weibo_id = weibo_id self.content = content self.time = time async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select(weibo_id) if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self, weibo_id: str): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Weibo_Record).where( Weibo_Record.weibo_id == weibo_id)) record = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=record) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_last_weibo_id(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(func.max(Weibo_Record.weibo_id)).where( Weibo_Record.uid == self.uid)) record = session_result.scalar_one() if record: result = Result.IntResult(error=False, info="Exist", result=record) else: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result class Weibo_Subscription(Base): __tablename__ = "Weibo_Subscription" id = Column(Integer, nullable=False, primary_key=True, index=True, autoincrement=True) bot_id = Column(String(16), nullable=False, comment="Bot_id") uid = Column(String(16), nullable=False, comment="微博UID") subscriber_id = Column(String(16), nullable=False, comment="QQ/群号") send_type = Column(String(10), nullable=False, comment="私聊/群") def __init__(self, bot_id: str, uid: str, subscriber_id: str, send_type: str): self.bot_id = bot_id self.uid = uid self.subscriber_id = subscriber_id self.send_type = send_type async def insert(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and result.result == 1: session.add(self) result = Result.IntResult(error=False, info="Insert_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def delete(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: result = await self.select() if not result.error and isinstance(result.result, Weibo_Subscription): await session.delete(result.result) result = Result.IntResult(error=False, info="Delete_Success", result=1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) await session.commit() except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Weibo_Subscription).where( Weibo_Subscription.uid == self.uid).where( Weibo_Subscription.subscriber_id == self.subscriber_id)) subscription = session_result.scalar_one() result = Result.IntResult(error=False, info="Exist", result=subscription) except NoResultFound: result = Result.IntResult(error=False, info="Select_No_Result", result=1) except MultipleResultsFound: result = Result.IntResult(error=True, info="Multiple_Results_Found", result=-1) except Exception as e: result = Result.IntResult(error=True, info=repr(e), result=-1) return result async def select_subscribers(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(Weibo_Subscription).where( Weibo_Subscription.uid == self.uid)) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result async def select_uids(self): async_session = DB().get_async_session() async with async_session() as session: try: async with session.begin(): try: session_result = await session.execute(select(distinct(Weibo_Subscription.uid))) result = Result.ListResult(error=False, info="Exist", result=session_result.scalars().all()) except NoResultFound: result = Result.ListResult(error=False, info="Select_No_Result", result=[]) except MultipleResultsFound: result = Result.ListResult(error=True, info="Multiple_Results_Found", result=[]) except Exception as e: result = Result.ListResult(error=True, info=repr(e), result=[]) return result

49.326988

116

0.573543

4,584

44,049

5.363656

0.040794

0.103469

0.093952

0.116322

0.916013

0.914345

0.9104

0.905763

0.89889

0

0.005765

0.330564

44,049

892

117

49.382287

0.828032

0.003383

0

0.84901

0

0.045521

0.021325

0

1

0.013614

false

0

0.012376

0.001238

0.149752

0

null

0

1

0

1

0

null

0

7

a4225d0e5cb735bfbe5bad23ad24224dd047f552

119

py

Python

darkopt/optimize/__init__.py

iwiwi/darkopt

071ecaa422441909d7f077b8097320bb304178d4

[ "MIT" ]

2

2018-08-02T04:52:53.000Z

2019-09-11T10:02:24.000Z

darkopt/optimize/__init__.py

iwiwi/darkopt

071ecaa422441909d7f077b8097320bb304178d4

[ "MIT" ]

null

darkopt/optimize/__init__.py

iwiwi/darkopt

071ecaa422441909d7f077b8097320bb304178d4

[ "MIT" ]

1

2019-02-12T05:57:17.000Z

from darkopt.optimize.optimizer import Optimizer # NOQA from darkopt.optimize.trial_result import TrialResult # NOQA

39.666667

61

0.831933

15

119

6.533333

0.6

0.22449

0.387755

0

0.117647

119

2

62

59.5

0.933333

0.07563

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

a4580444880f100c51d1ed1435d4b78ea5e0caf9

20,224

py

Python

src/tests/test_database.py

chris2013/pydupe

be8fd2dd2a905899e376be4e0b3d2fc8482e89e6

[ "MIT" ]

null

src/tests/test_database.py

chris2013/pydupe

be8fd2dd2a905899e376be4e0b3d2fc8482e89e6

[ "MIT" ]

3

2022-01-28T15:54:54.000Z

2022-01-28T15:58:23.000Z

src/tests/test_database.py

chris2013/pydupe

be8fd2dd2a905899e376be4e0b3d2fc8482e89e6

[ "MIT" ]

null

import os import tempfile import pytest from pydupe.data import fparms from pydupe.db import PydupeDB import pathlib as pl import typing as tp @pytest.fixture def setup_database() -> tp.Generator[None,None,None]: """ Fixture to se-t up PydupeDB in tmporary Directory""" with tempfile.TemporaryDirectory() as newpath: old_cwd = os.getcwd() os.chdir(newpath) dbname = pl.Path.cwd() / ".dbtest.sqlite" data = [fparms(filename='/tests/tdata/file_exists', hash='be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', size=1, inode=25303464, mtime=1629356592, ctime=1630424506), fparms(filename='/tests/tdata/somedir/file_is_dupe', hash='be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', size=1, inode=25303464, mtime=1629356592, ctime=1630424506), fparms(filename='/tests/tdata/somedir/dupe_in_dir', hash='3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', size=1, inode=25303464, mtime=1629356592, ctime=1630424506), fparms(filename='/tests/tdata/somedir/dupe2_in_dir', hash='3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', size=1, inode=25303464, mtime=1629356592, ctime=1630424506)] with PydupeDB(dbname) as db: db.parms_insert(data) db.commit() yield os.chdir(old_cwd) @pytest.mark.usefixtures("setup_database") class TestDatabase: def test_insert_get(self) -> None: """check data inserted in fixture 'setup_database' works.""" dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] def test_update_hash(self) -> None: """check hash update works.""" dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] # before: assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] # update hash: with PydupeDB(dbname) as db: filename = '/tests/tdata/somedir/dupe2_in_dir' hash = '3aa2ed13ee40ba651e87a0fd60bbbbbb3aa2ed13ee40ba651e87a0fd60bbbbbb' db.update_hash([(hash, filename)]) db.commit() # after: with PydupeDB(dbname) as db: data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60bbbbbb3aa2ed13ee40ba651e87a0fd60bbbbbb', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] def test_rollback(self) -> None: """check autorolback after e.g. hash update works.""" dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] # before: assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] # update hash: with PydupeDB(dbname) as db: filename = '/tests/tdata/somedir/dupe2_in_dir' hash = '3aa2ed13ee40ba651e87a0fd60bbbbbb3aa2ed13ee40ba651e87a0fd60bbbbbb' db.update_hash([(hash, filename)]) # no commit -> auto rollback by context manager with PydupeDB(dbname) as db: data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] def test_get_list_of_files_where_hash_is_NULL(self) -> None: dbname = pl.Path.cwd() / ".dbtest.sqlite" data = [fparms( filename='/tests/tdata/file_exists', hash=None, size=1, inode=25303464, mtime=1629356592, ctime=1630424506)] with PydupeDB(dbname) as db: db.execute( "DELETE from lookup WHERE hash = 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6'") db.parms_insert(data) db.commit() data_get = db.get_list_of_equal_sized_files_where_hash_is_NULL() assert data_get == [ '/tests/tdata/file_exists'] def test_get_list_of_files_in_dir(self) -> None: dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: data_get = db.get_list_of_files_in_dir( '/tests/tdata/somedir') assert data_get == ['/tests/tdata/somedir/dupe2_in_dir', '/tests/tdata/somedir/dupe_in_dir', '/tests/tdata/somedir/file_is_dupe' ] def test_get_file_hash(self) -> None: dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: data_get = db.get_file_hash() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6'}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6'}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0'}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0'} ] def test_delete_dir(self) -> None: dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: db.delete_dir(pl.Path('/tests/tdata/somedir')) data_get = db.get().fetchall() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] def test_delete_file(self) -> None: dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: db.delete_file(filename=pl.Path('/tests/tdata/file_exists')) data_get = db.get_file_hash().fetchall() data_dict = [dict(row) for row in data_get] assert data_dict == [ {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6'}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0'}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0'} ] def test_copy_dir_to_table_permanent(self) -> None: """check data inserted in fixture 'setup_database' works.""" dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: db.copy_dir_to_table_permanent( pl.Path('/tests/tdata/somedir')) db.commit() data_get_lookup = db.execute('SELECT * FROM lookup').fetchall() data_get_permanent = db.execute('SELECT * FROM permanent').fetchall() data_dict_lookup = [dict(row) for row in data_get_lookup] data_dict_permanent = [dict(row) for row in data_get_permanent] assert data_dict_lookup == [ {'filename': '/tests/tdata/file_exists', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] assert data_dict_permanent == [ {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] def test_copy_hash_to_table_lookup_and_clear_permanent(self) -> None: """check data inserted in fixture 'setup_database' works.""" dbname = pl.Path.cwd() / ".dbtest.sqlite" with PydupeDB(dbname) as db: db.execute("INSERT INTO permanent SELECT * FROM lookup WHERE filename like '/tests/tdata/somedir%'") db.update_hash([(None, '/tests/tdata/file_exists')]) db.update_hash([(None, '/tests/tdata/somedir/file_is_dupe')]) db.commit() data_get_lookup = db.execute('SELECT * FROM lookup').fetchall() data_get_permanent = db.execute('SELECT * FROM permanent').fetchall() data_dict_lookup = [dict(row) for row in data_get_lookup] data_dict_permanent = [dict(row) for row in data_get_permanent] assert data_dict_lookup == [ {'filename': '/tests/tdata/file_exists', 'hash': None, 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': None, 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] assert data_dict_permanent == [ {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] with PydupeDB(dbname) as db: db.copy_hash_to_table_lookup(check_filename=True) db.commit() data_get_lookup = db.execute('SELECT * FROM lookup').fetchall() data_get_permanent = db.execute('SELECT * FROM permanent').fetchall() data_dict_lookup = [dict(row) for row in data_get_lookup] data_dict_permanent = [dict(row) for row in data_get_permanent] assert data_dict_lookup == [ {'filename': '/tests/tdata/file_exists', 'hash': None, 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}] assert data_dict_permanent == [ {'filename': '/tests/tdata/somedir/file_is_dupe', 'hash': 'be1c1a22b4055523a0d736f4174ef1d6be1c1a22b4055523a0d736f4174ef1d6', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}, {'filename': '/tests/tdata/somedir/dupe2_in_dir', 'hash': '3aa2ed13ee40ba651e87a0fd60b753d03aa2ed13ee40ba651e87a0fd60b753d0', 'size': 1, 'inode': 25303464, 'mtime': 1629356592, 'ctime': 1630424506}]

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f17b39bd411ab6e9d97543967fdaddd5fecacc2d

20,041

py

Python

word2vec.py

houshengyuan/Word2vec

8861dce1e9f7043f366ad94146a535d726f72d7c

[ "MIT" ]

null

word2vec.py

houshengyuan/Word2vec

8861dce1e9f7043f366ad94146a535d726f72d7c

[ "MIT" ]

null

word2vec.py

houshengyuan/Word2vec

8861dce1e9f7043f366ad94146a535d726f72d7c

[ "MIT" ]

null

import os import pickle import time from os.path import join from typing import List import numpy as np from utils.dataset import Dataset from utils.vocab import Vocab from utils.hierarchical_softmax import Huffman_tree from utils.negative_sampling import NEG def one_hot(dim: int, idx: int): """ Get one-hot vector """ v = np.zeros(dim) v[idx] = 1 return v def softmax(x, dim: [int]=-1): e_x = np.exp(x - np.max(x, dim)) return e_x / np.sum(e_x, dim) def sigmoid(x): mask = (x>0) pos = (x+np.fabs(x))/2 neg = (x-np.fabs(x))/2 return mask*(1/(1+np.exp(-pos)))+(1-mask)*(np.exp(neg)/(1+np.exp(neg))) class CBOW: def __init__(self, vocab: Vocab, vector_dim: int, hierarchical_softmax: bool=False, negative_sampling: bool=False, size: int=10, subsampling: bool=False, subsample_thr: float=1e-3): self.vocab = vocab self.vector_dim = vector_dim self.hierarchical_softmax = hierarchical_softmax self.negative_sampling = negative_sampling self.subsampling = subsampling self.subsample_thr = subsample_thr os.makedirs("log", exist_ok=True) self.log = open("log/cbow" + ("_hierarchical" if self.hierarchical_softmax else "") + ("_neg" if self.negative_sampling else "") + ("_sub" if self.subsampling else "") + ".txt", "a+") self.W1 = np.random.uniform(-1, 1, (len(self.vocab), self.vector_dim)) # V x N if self.hierarchical_softmax: self.tree = Huffman_tree(self.vocab, dim=self.vector_dim) else: self.W2 = np.random.uniform(-1, 1, (len(self.vocab), self.vector_dim)) # N x V if negative_sampling: self.sampler=NEG(vocab=self.vocab, alpha=0.75, size=size, subsampling = self.subsampling, subsample_thr = self.subsample_thr) def train(self, corpus: str, window_size: int, train_epoch: int, learning_rate: float, save_path: str = None): dataset = Dataset(corpus, window_size, "CBOW") for epoch in range(1, train_epoch + 1): start_time = time.time() avg_loss = self.train_one_epoch(dataset, learning_rate) end_time = time.time() print(f"Epoch {epoch}, loss: {avg_loss:.2f}. Cost {(end_time - start_time) / 60:.1f} min",file=self.log,flush=True) if save_path is not None: self.save_model(save_path) def train_one_epoch(self, dataset: Dataset, learning_rate: float): steps, total_loss = 0, 0.0 for steps, sample in enumerate(iter(dataset), start=1): context_tokens, target_token = sample loss = self.train_one_step(steps,context_tokens, target_token, learning_rate) total_loss += loss if steps % 10000 == 0: print(f"Step: {steps}. Avg. loss: {total_loss / steps: .2f}",file=self.log,flush=True) return total_loss / steps def train_one_step(self, step:int ,context_tokens: List[str], target_token: str, learning_rate: float) -> float: """ Predict the probability of the target token given context tokens. :param context_tokens: List of tokens around the target token :param target_token: Target (center) token :param learning_rate: Learning rate of each step :param hierarchical_softmax: whether use the hierarchical softmax technique :param negative_sampling: whether use the negative sampling technique :return: loss of the target token """ if self.hierarchical_softmax: """CBOW with hierarchical softmax""" # ==== Construct one-hot vectors ==== context_ids = [self.vocab.token_to_idx(i) for i in context_tokens] input_onehot = np.array(list(map(lambda x: one_hot(len(self.vocab), x), context_ids))) # ==== Forward step ==== assert input_onehot.shape[-1] == self.W1.shape[0] word_vector = input_onehot @ self.W1 word_vector = np.average(word_vector, axis=0) target_id = self.vocab.token_to_idx(target_token) target_path = self.tree.get_nodepath(target_id) context_vector = np.array([target_path[i].vector for i in range(len(target_path)-1)]) context_one_hot = np.array([0 if target_path[i + 1].direction == 0 else 1 for i in range(len(target_path) - 1)]) logit = context_vector @ word_vector output = sigmoid(logit) # ==== Calculate loss ==== loss = -np.sum(np.log((-2 * context_one_hot + 1) * output + context_one_hot + 1e-8)) # ==== Update parameters ==== self.W1[context_ids] -= learning_rate * ((output - 1 + context_one_hot) @ context_vector) / len(context_tokens) for j, node in enumerate(target_path): if target_path[j].vector is not None: target_path[j].vector -= learning_rate * (output[j] - 1 + context_one_hot[j]) * word_vector elif self.negative_sampling: """CBOW with nagative sampling""" # ==== Construct one-hot vectors ==== context_ids = [self.vocab.token_to_idx(i) for i in context_tokens] input_onehot = np.array(list(map(lambda x: one_hot(len(self.vocab), x), context_ids))) # ==== Forward step ==== assert input_onehot.shape[-1] == self.W1.shape[0] word_vector = input_onehot @ self.W1 word_vector = np.average(word_vector, axis=0) target_id = self.vocab.token_to_idx(target_token) positive_logit = word_vector @ self.W2[target_id] negative_ids = self.sampler.sample(target_id) negative_logit = -self.W2[negative_ids] @ word_vector positive_output = sigmoid(positive_logit) negative_output = sigmoid(negative_logit) # ==== Calculate loss ==== loss = -np.log(positive_output+1e-8)-np.sum(np.log(negative_output+1e-8)) # ==== Update parameters ==== self.W1[context_ids] -= learning_rate * ((positive_output-1) * self.W2[target_id]+(1-negative_output)@self.W2[negative_ids]) / len(context_ids) self.W2[target_id] -= learning_rate * (positive_output-1) * word_vector self.W2[negative_ids] -= learning_rate * np.outer(1-negative_output,word_vector) else: """naive CBOW""" # ==== Construct one-hot vectors ==== context_ids = [self.vocab.token_to_idx(i) for i in context_tokens] input_onehot = np.array(list(map(lambda x: one_hot(len(self.vocab), x), context_ids))) # ==== Forward step ==== assert input_onehot.shape[-1] == self.W1.shape[0] word_vector = input_onehot @ self.W1 word_vector = np.average(word_vector, axis=0) logit = self.W2 @word_vector output = softmax(logit) # ==== Calculate loss ==== target_id = self.vocab.token_to_idx(target_token) loss = -np.log(output[target_id]+1e-8) # ==== Update parameters ==== target_one_hot = one_hot(len(self.vocab), target_id) self.W1[context_ids] -= learning_rate * (output @ self.W2 - self.W2[target_id]) / len(context_ids) self.W2[:] -= learning_rate * np.outer(output - target_one_hot, word_vector) return loss def similarity(self, token1: str, token2: str): """ Calculate cosine similarity of token1 and token2 """ v1 = self.W1[self.vocab.token_to_idx(token1)] v2 = self.W1[self.vocab.token_to_idx(token2)] v1 = v1 / np.linalg.norm(v1) v2 = v2 / np.linalg.norm(v2) return np.dot(v1, v2) def most_similar_tokens(self, token: str, n: int): """ Find the n words most similar to the given token """ norm_W1 = self.W1 / np.linalg.norm(self.W1, axis=1, keepdims=True) idx = self.vocab.token_to_idx(token, warn=True) v = norm_W1[idx] cosine_similarity = np.dot(norm_W1, v) nbest_idx = np.argsort(cosine_similarity)[-n:][::-1] results = [] for idx in nbest_idx: _token = self.vocab.idx_to_token(idx) results.append((_token, cosine_similarity[idx])) return results def save_model(self, path: str): """ Save model and vocabulary to `path` """ os.makedirs(path, exist_ok=True) self.vocab.save_vocab(path) with open(join(path, "cbow"+("_hierarchical" if self.hierarchical_softmax else "") +("_neg" if self.negative_sampling else "")+ ("_sub" if self.subsampling else "") + ".pkl"), "wb") as f: if self.hierarchical_softmax: param = {"W1": self.W1, "tree": self.tree} else: param = {"W1": self.W1, "W2": self.W2} pickle.dump(param, f) print(f"Save model to {path}",file=self.log,flush=True) @classmethod def load_model(cls, path: str, hierarchical_softmax:bool= False, negative_sampling:bool= False, size:int=10, subsampling:bool= False, subsample_thr:float= 1e-3): """ Load model and vocabulary from `path` """ vocab = Vocab.load_vocab(path) with open(join(path, "cbow" + ("_hierarchical" if hierarchical_softmax else "") + ("_neg" if negative_sampling else "") + ("_sub" if subsampling else "") + ".pkl"), "rb") as f: param = pickle.load(f) if hierarchical_softmax: W1, tree = param["W1"], param["tree"] model = cls(vocab, W1.shape[1], hierarchical_softmax=hierarchical_softmax, negative_sampling=negative_sampling, size=size, subsampling=subsampling,subsample_thr=subsample_thr) model.W1, model.tree = W1, tree else: W1, W2 = param["W1"], param["W2"] model = cls(vocab, W1.shape[1], hierarchical_softmax=hierarchical_softmax, negative_sampling=negative_sampling, size=size, subsampling=subsampling,subsample_thr=subsample_thr) model.W1, model.W2 = W1, W2 print(f"Load model from {path}") return model class Skipgram: def __init__(self, vocab: Vocab, vector_dim: int,hierarchical_softmax: bool=False, negative_sampling: bool=False, size: int=10, subsampling: bool=False, subsample_thr: float=1e-3): self.vocab = vocab self.vector_dim = vector_dim self.hierarchical_softmax = hierarchical_softmax self.negative_sampling = negative_sampling self.subsampling = subsampling self.subsample_thr = subsample_thr os.makedirs("log", exist_ok=True) self.log = open("log/skip-gram"+("_hierarchical" if self.hierarchical_softmax else "") + ("_neg" if self.negative_sampling else "") + ("_sub" if self.subsampling else "") + ".txt","a+") self.W1 = np.random.uniform(-1, 1, (len(self.vocab), self.vector_dim)) # V x N if self.hierarchical_softmax: self.tree = Huffman_tree(self.vocab, dim=vector_dim) else: self.W2 = np.random.uniform(-1, 1, (len(self.vocab), self.vector_dim)) # N x V if negative_sampling: self.sampler = NEG(vocab=self.vocab, alpha=0.75, size=size, subsampling=self.subsampling, subsample_thr=self.subsample_thr) def train(self, corpus: str, window_size: int, train_epoch: int, learning_rate: float, save_path: str = None): dataset = Dataset(corpus, window_size, "skip-gram") for epoch in range(1, train_epoch + 1): start_time = time.time() avg_loss = self.train_one_epoch(dataset, learning_rate) end_time = time.time() print(f"Epoch {epoch}, loss: {avg_loss:.2f}. Cost {(end_time - start_time) / 60:.1f} min",file=self.log,flush=True) if save_path is not None: self.save_model(save_path) def train_one_epoch(self, dataset: Dataset, learning_rate: float): steps, total_loss = 0, 0.0 for steps, sample in enumerate(iter(dataset), start=1): context_tokens, target_token = sample loss = self.train_one_step(context_tokens, target_token, learning_rate) total_loss += loss if steps % 10000 == 0: print(f"Step: {steps}. Avg. loss: {total_loss / steps: .2f}",file=self.log,flush=True) return total_loss / steps def train_one_step(self, context_token: str, target_tokens: List[str], learning_rate: float) -> float: """ Predict the probability of the target token given context tokens. :param context_token: Context (center) token :param target_tokens: List of target tokens around the context token :param learning_rate: Learning rate of each step :return: loss of the target token """ if self.hierarchical_softmax: """skip-gram with hierarchical softmax""" # ==== Construct one-hot vectors ==== context_id = self.vocab.token_to_idx(context_token) input_onehot = one_hot(len(self.vocab),context_id) # ==== Forward step ==== assert input_onehot.shape[0] == self.W1.shape[0] word_vector = input_onehot @ self.W1 target_ids = [self.vocab.token_to_idx(i) for i in target_tokens] target_path = [self.tree.get_nodepath(i) for i in target_ids] loss = 0.0 target_gradient = [] for i,path in enumerate(target_path): gradient = [] context_vector = np.array([path[i].vector for i in range(len(path)-1)]) context_one_hot = np.array([0 if path[i+1].direction==0 else 1 for i in range(len(path)-1)]) logit = context_vector @ word_vector output = sigmoid(logit) # ==== Calculate loss ==== loss += -np.sum(np.log((-2*context_one_hot + 1) * output + context_one_hot + 1e-8))/len(target_tokens) # ==== Update parameters ==== self.W1[target_ids[i]] -= learning_rate * ((output - 1 + context_one_hot) @ context_vector)/len(target_tokens) for j, node in enumerate(path): if path[j].vector is not None: gradient.append((output[j] - 1 + context_one_hot[j]) * word_vector/len(target_tokens)) target_gradient.append(gradient) for i, path in enumerate(target_path): for j, node in enumerate(path): if path[j].vector is not None: path[j].vector -= learning_rate * target_gradient[i][j] elif self.negative_sampling: # ==== Construct one-hot vectors ==== context_id = self.vocab.token_to_idx(context_token) input_onehot = one_hot(len(self.vocab), context_id) # ==== Forward step ==== assert input_onehot.shape[0] == self.W1.shape[0] word_vector = input_onehot @ self.W1 target_ids = [self.vocab.token_to_idx(i) for i in target_tokens] loss = 0.0 for i in target_ids: negative_ids = self.sampler.sample(i) positive_logit = word_vector @ self.W2[i] negative_logit = -self.W2[negative_ids] @ word_vector positive_output = sigmoid(positive_logit) negative_output = sigmoid(negative_logit) # ==== Calculate loss ==== loss += (-np.log(positive_output+1e-8)-np.sum(np.log(negative_output+1e-8)))/len(target_ids) # ==== Update parameters ==== self.W1[context_id] -= learning_rate*(self.W2[i]*(positive_output-1)+(1-negative_output)@self.W2[negative_ids])/len(target_tokens) self.W2[i] -= learning_rate*word_vector*(positive_output-1)/len(target_tokens) self.W2[negative_ids] -= learning_rate*np.outer(1-negative_output,word_vector)/len(target_tokens) else: # ==== Construct one-hot vectors ==== context_id = self.vocab.token_to_idx(context_token) input_onehot = one_hot(len(self.vocab), context_id) # ==== Forward step ==== assert input_onehot.shape[0] == self.W1.shape[0] word_vector = input_onehot @ self.W1 logit = self.W2 @ word_vector output = softmax(logit) target_ids = [self.vocab.token_to_idx(i) for i in target_tokens] # ==== Calculate loss ==== loss = np.average(-np.log(output[target_ids]+1e-8)) # ==== Update parameters ==== W1_grad = np.zeros(self.W1.shape) W1_grad[context_id] += -np.average(self.W2[target_ids], axis=0) + output @ self.W2 W2_grad = np.outer(output, word_vector) W2_grad[target_ids] -= np.expand_dims(word_vector, axis=0) / len(target_ids) assert list(W1_grad.shape) == list(self.W1.shape) assert list(W2_grad.shape) == list(self.W2.shape) self.W1[:] -= learning_rate * W1_grad self.W2[:] -= learning_rate * W2_grad return loss def similarity(self, token1: str, token2: str): """ Calculate cosine similarity of token1 and token2 """ v1 = self.W1[self.vocab.token_to_idx(token1)] v2 = self.W1[self.vocab.token_to_idx(token2)] v1 = v1 / np.linalg.norm(v1) v2 = v2 / np.linalg.norm(v2) return np.dot(v1, v2) def most_similar_tokens(self, token: str, n: int): """ Find the n words most similar to the given token """ norm_W1 = self.W1 / np.linalg.norm(self.W1, axis=1, keepdims=True) idx = self.vocab.token_to_idx(token, warn=True) v = norm_W1[idx] cosine_similarity = np.dot(norm_W1, v) nbest_idx = np.argsort(cosine_similarity)[-n:][::-1] results = [] for idx in nbest_idx: _token = self.vocab.idx_to_token(idx) results.append((_token, cosine_similarity[idx])) return results def save_model(self, path: str): """ Save model and vocabulary to `path` """ os.makedirs(path, exist_ok=True) self.vocab.save_vocab(path) with open(join(path, "skip-gram" + ("_hierarchical" if self.hierarchical_softmax else "") + ("_neg" if self.negative_sampling else "") + ("_sub" if self.subsampling else "") + ".pkl"), "wb") as f: if self.hierarchical_softmax: param = {"W1": self.W1, "tree": self.tree} else: param = {"W1": self.W1, "W2": self.W2} pickle.dump(param, f) print(f"Save model to {path}",file=self.log,flush=True) @classmethod def load_model(cls, path: str, hierarchical_softmax:bool= False, negative_sampling:bool= False, size:int= 10, subsampling:bool= False, subsample_thr:float= 1e-3): """ Load model and vocabulary from `path` """ vocab = Vocab.load_vocab(path) with open(join(path, "skip-gram" + ("_hierarchical" if hierarchical_softmax else "") + ("_neg" if negative_sampling else "") + ("_sub" if subsampling else "") + ".pkl"), "rb") as f: param = pickle.load(f) if hierarchical_softmax: W1, tree = param["W1"], param["tree"] model = cls(vocab, W1.shape[1], hierarchical_softmax=hierarchical_softmax, negative_sampling=negative_sampling, size=size, subsampling=subsampling, subsample_thr=subsample_thr) model.W1, model.tree = W1, tree else: W1, W2 = param["W1"], param["W2"] model = cls(vocab, W1.shape[1], hierarchical_softmax=hierarchical_softmax, negative_sampling=negative_sampling, size=size, subsampling=subsampling, subsample_thr=subsample_thr) model.W1, model.W2 = W1, W2 print(f"Load model from {path}") return model

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7

74b9d8ce2df4d8b8b944d3e7197504e0339b9aec

292

py

Python

concepts/_example.py

plpedrofeitosa/concepts

df1c4e139fef0f7ccff466f9c27024f6b7b4d1ef

[ "MIT" ]

90

2015-03-24T20:09:14.000Z

2022-03-18T07:37:01.000Z

concepts/_example.py

plpedrofeitosa/concepts

df1c4e139fef0f7ccff466f9c27024f6b7b4d1ef

[ "MIT" ]

18

2017-11-03T18:08:13.000Z

2022-02-05T10:10:24.000Z

concepts/_example.py

plpedrofeitosa/concepts

df1c4e139fef0f7ccff466f9c27024f6b7b4d1ef

[ "MIT" ]

27

2015-01-23T13:00:57.000Z

2022-03-08T15:48:41.000Z

EXAMPLE = '''\ |+1|-1|+2|-2|+3|-3|+sg|+pl|-sg|-pl| 1sg| X| | | X| | X| X| | | X| 1pl| X| | | X| | X| | X| X| | 2sg| | X| X| | | X| X| | | X| 2pl| | X| X| | | X| | X| X| | 3sg| | X| | X| X| | X| | | X| 3pl| | X| | X| X| | | X| X| | '''

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29,655

py

Python

src/275. H-Index II.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

src/275. H-Index II.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

src/275. H-Index II.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

class Solution(object): def hIndex(self, citations): """ :type citations: List[int] :rtype: int """ L = len(citations) if not L or citations[-1] == 0: return 0 i, j = 1, L while i < j: m = (i + j) // 2 if citations[L-m] == m or (citations[L-m] > m and citations[L-m-1] <= m): return m elif citations[L-m] < m: j = m - 1 else: # citations[L-m] > m i = m + 1 return i # print Solution().hIndex([4, 4, 0, 0]) cits = 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cits = [0] cits = [1,2] cits = [0,1,1,2] print Solution().hIndex(cits)

1,022.586207

29,130

0.654527

10,094

29,655

1.922925

0.012582

0.025554

0.038022

0.050283

0.985162

0

0.648562

0.004788

29,655

28

29,131

1,059.107143

0.009115

0.001888

0

1

0

null

0

null

0.052632

0

1

null

0

1

0

1

0

1

0

null

1

0

1

0

13

7421084799af7776dd51f8d48d87f131b42144ae

31

py

Python

__init__.py

oursky/forgot_password

9afde8b9d39a2837676628f12c9b6f2c45da592a

[ "Apache-2.0" ]

1

2017-02-09T10:17:50.000Z

__init__.py

oursky/forgot_password

9afde8b9d39a2837676628f12c9b6f2c45da592a

[ "Apache-2.0" ]

54

2016-09-07T11:01:32.000Z

2020-02-12T06:15:43.000Z

__init__.py

oursky/forgot_password

9afde8b9d39a2837676628f12c9b6f2c45da592a

[ "Apache-2.0" ]

14

2016-09-20T05:36:49.000Z

2019-04-02T15:42:37.000Z

from .forgot_password import *

15.5

30

0.806452

4

31

6

1

0

0.129032

31

1

31

0.888889

0

1

0

true

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

747c20c5bce0bc557449aa2375e4bb15c178cdc8

3,849

py

Python

tests/api/api_v1/test_user_roles.py

tsatsujnr139/fastapi-role-based-access-control-auth-service

6c6addb04edad80e167424e39574697008eb0e64

[ "MIT" ]

47

2021-03-06T14:49:43.000Z

2022-03-05T12:18:59.000Z

tests/api/api_v1/test_user_roles.py

tsatsujnr139/fastapi-role-based-access-control-auth-service

6c6addb04edad80e167424e39574697008eb0e64

[ "MIT" ]

5

2021-09-19T15:16:49.000Z

2022-01-26T15:47:48.000Z

tests/api/api_v1/test_user_roles.py

tsatsujnr139/fastapi-role-based-access-control-auth-service

6c6addb04edad80e167424e39574697008eb0e64

[ "MIT" ]

15

2021-03-08T07:54:32.000Z

2022-03-09T13:57:23.000Z

from app import crud from app.constants.role import Role from app.core.config import settings from app.schemas.user import UserCreate from app.schemas.user_role import UserRoleCreate from fastapi.testclient import TestClient from sqlalchemy.orm import Session from tests.utils.utils import random_email, random_lower_string def test_assign_user_role_by_superadmin( client: TestClient, superadmin_token_headers: dict, db: Session ) -> None: username = random_email() password = random_lower_string() full_name = random_lower_string() user_in = UserCreate( email=username, password=password, full_name=full_name ) user = crud.user.create(db, obj_in=user_in) role = crud.role.get_by_name(db, name=Role.ACCOUNT_MANAGER["name"]) data = {"user_id": str(user.id), "role_id": str(role.id)} r = client.post( f"{settings.API_V1_STR}/user-roles", headers=superadmin_token_headers, json=data, ) assert 200 <= r.status_code < 300 created_user_role = r.json() user_role = crud.user_role.get_by_user_id(db, user_id=user.id) assert user_role assert str(user_role.role_id) == created_user_role["role_id"] def test_assign_user_role_by_normal_user( client: TestClient, superadmin_token_headers: dict, db: Session ) -> None: username = random_email() password = random_lower_string() full_name = random_lower_string() user_in = UserCreate( email=username, password=password, full_name=full_name ) user = crud.user.create(db, obj_in=user_in) role = crud.role.get_by_name(db, name=Role.ACCOUNT_MANAGER["name"]) data = {"user_id": str(user.id), "role_id": str(role.id)} r = client.post( f"{settings.API_V1_STR}/user-roles", headers=superadmin_token_headers, json=data, ) assert 200 <= r.status_code < 300 created_user_role = r.json() user_role = crud.user_role.get_by_user_id(db, user_id=user.id) assert user_role assert str(user_role.role_id) == created_user_role["role_id"] def test_update_user_role( client: TestClient, superadmin_token_headers: dict, db: Session ) -> None: username = random_email() password = random_lower_string() full_name = random_lower_string() user_in = UserCreate( email=username, password=password, full_name=full_name ) user = crud.user.create(db, obj_in=user_in) role = crud.role.get_by_name(db, name=Role.ACCOUNT_MANAGER["name"]) user_role_in = UserRoleCreate(user_id=user.id, role_id=role.id) crud.user_role.create(db, obj_in=user_role_in) new_role = crud.role.get_by_name(db, name=Role.ACCOUNT_ADMIN["name"]) data = {"role_id": str(new_role.id)} r = client.put( f"{settings.API_V1_STR}/user-roles/{user.id}", headers=superadmin_token_headers, json=data, ) updated_user_role = r.json() assert 200 <= r.status_code < 300 assert updated_user_role["role_id"] == str(new_role.id) def test_update_user_role_by_unauthorized_user_fails( client: TestClient, normal_user_token_headers: dict, db: Session ) -> None: username = random_email() password = random_lower_string() full_name = random_lower_string() user_in = UserCreate( email=username, password=password, full_name=full_name ) user = crud.user.create(db, obj_in=user_in) role = crud.role.get_by_name(db, name=Role.ACCOUNT_MANAGER["name"]) user_role_in = UserRoleCreate(user_id=user.id, role_id=role.id) crud.user_role.create(db, obj_in=user_role_in) new_role = crud.role.get_by_name(db, name=Role.ACCOUNT_ADMIN["name"]) data = {"role_id": str(new_role.id)} r = client.put( f"{settings.API_V1_STR}/user-roles/{user.id}", headers=normal_user_token_headers, json=data, ) assert r.status_code == 401

36.657143

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null

0

1

0

1

0

null

0

1

0

8

748cab60059c9ef8d41b20df40b1703a9ee9ccde

760

py

Python

psynlp/helpers/builtins.py

Demfier/PsyNLP

e16952ce1bbbe9724071a009743654d11ae386d5

[ "MIT" ]

5

2019-11-23T05:33:01.000Z

2021-12-30T22:37:43.000Z

psynlp/helpers/builtins.py

Demfier/PsyNLP

e16952ce1bbbe9724071a009743654d11ae386d5

[ "MIT" ]

5

2018-04-10T10:31:22.000Z

2018-04-14T06:31:03.000Z

psynlp/helpers/builtins.py

Demfier/PsyNLP

e16952ce1bbbe9724071a009743654d11ae386d5

[ "MIT" ]

1

2021-08-14T17:46:42.000Z

import builtins as __builtin__ def init_verbose(verbose=False): if not verbose: __builtin__.verbose_print_1 = lambda *a, **k: None __builtin__.verbose_print_2 = lambda *a, **k: None __builtin__.verbose_print_3 = lambda *a, **k: None elif verbose == 1: __builtin__.verbose_print_1 = print __builtin__.verbose_print_2 = lambda *a, **k: None __builtin__.verbose_print_3 = lambda *a, **k: None elif verbose == 2: __builtin__.verbose_print_1 = print __builtin__.verbose_print_2 = print __builtin__.verbose_print_3 = lambda *a, **k: None else: __builtin__.verbose_print_1 = print __builtin__.verbose_print_2 = print __builtin__.verbose_print_3 = print

36.190476

58

0.668421

97

760

4.443299

0.206186

0.389791

0.529002

0.167053

0.786543

0.758701

0.730858

0.672854

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0.02439

0.244737

760

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0.111111

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null

1

0

1

0

null

0

1

0

9

77774ed700d7b2fcedafb2264a485bfb746a552c

91

py

Python

polls/views.py

r00m/flask-blueprint-quickstart

7c9dbeae20e4882eb887e1c88854c5f83e890f52

[ "MIT" ]

null

polls/views.py

r00m/flask-blueprint-quickstart

7c9dbeae20e4882eb887e1c88854c5f83e890f52

[ "MIT" ]

null

polls/views.py

r00m/flask-blueprint-quickstart

7c9dbeae20e4882eb887e1c88854c5f83e890f52

[ "MIT" ]

null

from polls import blueprint @blueprint.route('/') def index(): return "Hello World!"

13

27

0.681319

11

91

5.636364

0.909091

0

0.175824

91

6

28

15.166667

0.826667

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0.142857

0

1

0.25

true

0

0.25

0.75

0.5

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null

0

1

0

null

0

1

0

1

0

7

777cd98be4af74d61907b5f29ca27ee92b1266f4

221

py

Python

africanus/rime/cuda/__init__.py

JoshVStaden/codex-africanus

4a38994431d51510b1749fa0e4b8b6190b8b530f

[ "BSD-3-Clause" ]

13

2018-04-06T09:36:13.000Z

2021-04-13T13:11:00.000Z

africanus/rime/cuda/__init__.py

JoshVStaden/codex-africanus

4a38994431d51510b1749fa0e4b8b6190b8b530f

[ "BSD-3-Clause" ]

153

2018-03-28T14:13:48.000Z

2022-02-03T07:49:17.000Z

africanus/rime/cuda/__init__.py

JoshVStaden/codex-africanus

4a38994431d51510b1749fa0e4b8b6190b8b530f

[ "BSD-3-Clause" ]

14

2018-03-29T13:30:52.000Z

2021-06-12T02:56:55.000Z

# flake8: noqa from africanus.rime.cuda.beam import beam_cube_dde from africanus.rime.cuda.feeds import feed_rotation from africanus.rime.cuda.phase import phase_delay from africanus.rime.cuda.predict import predict_vis

31.571429

51

0.846154

35

221

5.2

0.485714

0.285714

0.373626

0.461538

0

0.004975

0.090498

221

6

52

36.833333

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true

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null

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1

0

null

0

1

0

1

0

1

0

7

777d1729b6a670fc439712aae7df957bda909ea3

3,773

py

Python

climdex/temperature.py

weilin2018/pyclimdex

ccaf1ad3eda2d52b586dd4a3755be5eb9ba9a15b

[ "MIT" ]

6

2021-01-11T14:52:23.000Z

2022-03-05T12:53:54.000Z

climdex/temperature.py

dawran/pyclimdex

c526e876016f61ee2fc1a659e68b6ef0f2e9cb24

[ "MIT" ]

null

climdex/temperature.py

dawran/pyclimdex

c526e876016f61ee2fc1a659e68b6ef0f2e9cb24

[ "MIT" ]

2

2020-03-14T00:44:45.000Z

2020-04-09T09:03:32.000Z

import xarray as xr import numpy as np import climdex.utils as utils from typing import Union def indices(time_dim='time', convert_units_fn=lambda x: x): return TemperatureIndices(time_dim=time_dim, convert_units_fn=convert_units_fn) class TemperatureIndices: def __init__(self, time_dim='time', convert_units_fn=lambda x: x): self.time_dim = time_dim self.convert_units_fn = convert_units_fn def annual_frost_days(self, X: Union[xr.DataArray, xr.Dataset], varname='MINT'): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.min(), time_dim=self.time_dim) return (X_arr < self.convert_units_fn(0.0)).astype(X_arr.dtype).groupby(f'{self.time_dim}.year').sum() def annual_tropical_nights(self, X: Union[xr.DataArray, xr.Dataset], varname='MINT'): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.min(), time_dim=self.time_dim) return (X_arr > self.convert_units_fn(20.0)).astype(X_arr.dtype).groupby(f'{self.time_dim}.year').sum() def annual_icing_days(self, X: Union[xr.DataArray, xr.Dataset], varname='MAXT'): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.max(), time_dim=self.time_dim) return (X_arr < self.convert_units_fn(0.0)).astype(X_arr.dtype).groupby(f'{self.time_dim}.year').sum() def annual_summer_days(self, X: Union[xr.DataArray, xr.Dataset], varname='MAXT'): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.max(), time_dim=self.time_dim) return (X_arr > self.convert_units_fn(25.0)).astype(X_arr.dtype).groupby(f'{self.time_dim}.year').sum() def annual_growing_season_length(self, X: Union[xr.DataArray, xr.Dataset], varname='MEANT'): raise NotImplementedError() def monthly_txx(self, X: Union[xr.DataArray, xr.Dataset], varname=None): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.max(), time_dim=self.time_dim) return X.resample({self.time_dim: '1M'}).max() def monthly_txn(self, X: Union[xr.DataArray, xr.Dataset], varname=None): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.max(), time_dim=self.time_dim) return X.resample({self.time_dim: '1M'}).min() def monthly_tnx(self, X: Union[xr.DataArray, xr.Dataset], varname=None): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.min(), time_dim=self.time_dim) return X.resample({self.time_dim: '1M'}).max() def monthly_tnn(self, X: Union[xr.DataArray, xr.Dataset], varname=None): X_arr = utils.data_array_or_dataset_var(X, var=varname) X_arr = utils.resample_daily(X_arr, lambda x: x.min(), time_dim=self.time_dim) return X.resample({self.time_dim: '1M'}).min() def daily_temperature_range(self, X1: Union[xr.DataArray, xr.Dataset], X2: Union[xr.DataArray, xr.Dataset]=None, min_varname='MINT', max_varname='MAXT'): X1_arr = utils.data_array_or_dataset_var(X1, var=min_varname) X2_arr = utils.data_array_or_dataset_var(X2, var=max_varname) X_min_arr = utils.resample_daily(X1_arr, lambda x: x.min(), time_dim=self.time_dim) X_max_arr = utils.resample_daily(X2_arr, lambda x: x.max(), time_dim=self.time_dim) return X_max_arr - X_min_arr

56.313433

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null

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1

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null

0

1

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7

77906d2463d7c579d380ef329f49798605124508

11,091

py

Python

packages/divprop/tests/test_sboxes.py

CryptoExperts/AC21-divprop-convexity

ed4fb715c484b12413a5c59e4f189bcc37889449

[ "MIT" ]

4

2021-11-13T03:31:00.000Z

2022-02-25T02:02:58.000Z

packages/divprop/tests/test_sboxes.py

CryptoExperts/AC21-divprop-convexity

ed4fb715c484b12413a5c59e4f189bcc37889449

[ "MIT" ]

null

packages/divprop/tests/test_sboxes.py

CryptoExperts/AC21-divprop-convexity

ed4fb715c484b12413a5c59e4f189bcc37889449

[ "MIT" ]

null

from random import shuffle, randrange def get_sboxes(): ret = [] name = 'ASCON' n = m = 5 sbox = [4, 11, 31, 20, 26, 21, 9, 2, 27, 5, 8, 18, 29, 3, 6, 28, 30, 19, 7, 14, 0, 13, 17, 24, 16, 12, 1, 25, 22, 10, 15, 23] dppt = ([0], [1, 2, 4, 8, 16], [1, 2, 4, 8, 16], [3, 4, 9, 10, 17, 18, 24], [2, 4, 8, 16], [3, 5, 6, 9, 10, 12, 17, 18, 20, 24], [3, 5, 6, 8, 17, 18, 20], [6, 9, 10, 12, 19, 20, 24], [1, 2, 4, 8, 16], [1, 6, 10, 12, 16], [3, 5, 6, 8, 17, 18, 20], [3, 5, 6, 9, 10, 12, 18, 20, 24], [3, 5, 8, 16], [3, 5, 9, 10, 12, 17, 18, 20, 24], [7, 9, 10, 12, 17, 18, 20, 24], [7, 9, 12, 20, 24], [1, 2, 8, 16], [2, 5, 9, 12, 17, 20, 24], [2, 5, 9, 12, 17, 20, 24], [3, 5, 10, 12, 18, 20, 25], [3, 5, 6, 9, 10, 12, 18, 20, 24], [6, 10, 13, 18, 21, 25, 28], [6, 9, 10, 18, 21, 24], [10, 23, 25], [1, 6, 10, 12, 16], [3, 5, 6, 10, 18, 20, 25], [3, 5, 6, 9, 10, 18, 20, 24], [3, 5, 10, 18, 20, 25], [3, 5, 9, 10, 18, 20, 24], [10, 18, 29], [7, 9, 10, 18, 24], [31]) ret.append((name, sbox, n, m, dppt)) name = 'RECTANGLE' n = m = 4 sbox = [6, 5, 12, 10, 1, 14, 7, 9, 11, 0, 3, 13, 8, 15, 4, 2] dppt = ([0], [1, 2, 4, 8], [1, 2, 4, 8], [1, 4, 10], [1, 2, 4, 8], [3, 4, 8], [3, 4, 8], [3, 4, 9], [1, 2, 4, 8], [3, 5, 6, 8], [2, 5, 8], [6, 11, 13], [3, 4, 8], [6, 10, 13], [3, 5, 8], [15]) ret.append((name, sbox, n, m, dppt)) name = 'AES' n = m = 8 sbox = [99, 124, 119, 123, 242, 107, 111, 197, 48, 1, 103, 43, 254, 215, 171, 118, 202, 130, 201, 125, 250, 89, 71, 240, 173, 212, 162, 175, 156, 164, 114, 192, 183, 253, 147, 38, 54, 63, 247, 204, 52, 165, 229, 241, 113, 216, 49, 21, 4, 199, 35, 195, 24, 150, 5, 154, 7, 18, 128, 226, 235, 39, 178, 117, 9, 131, 44, 26, 27, 110, 90, 160, 82, 59, 214, 179, 41, 227, 47, 132, 83, 209, 0, 237, 32, 252, 177, 91, 106, 203, 190, 57, 74, 76, 88, 207, 208, 239, 170, 251, 67, 77, 51, 133, 69, 249, 2, 127, 80, 60, 159, 168, 81, 163, 64, 143, 146, 157, 56, 245, 188, 182, 218, 33, 16, 255, 243, 210, 205, 12, 19, 236, 95, 151, 68, 23, 196, 167, 126, 61, 100, 93, 25, 115, 96, 129, 79, 220, 34, 42, 144, 136, 70, 238, 184, 20, 222, 94, 11, 219, 224, 50, 58, 10, 73, 6, 36, 92, 194, 211, 172, 98, 145, 149, 228, 121, 231, 200, 55, 109, 141, 213, 78, 169, 108, 86, 244, 234, 101, 122, 174, 8, 186, 120, 37, 46, 28, 166, 180, 198, 232, 221, 116, 31, 75, 189, 139, 138, 112, 62, 181, 102, 72, 3, 246, 14, 97, 53, 87, 185, 134, 193, 29, 158, 225, 248, 152, 17, 105, 217, 142, 148, 155, 30, 135, 233, 206, 85, 40, 223, 140, 161, 137, 13, 191, 230, 66, 104, 65, 153, 45, 15, 176, 84, 187, 22] dppt = ([0], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 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[1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 16, 32, 64, 128], [1, 2, 4, 8, 32, 64, 144], [1, 2, 24, 32, 64, 132], [1, 2, 4, 8, 16, 64, 128], [1, 2, 4, 8, 16, 64, 128], [1, 2, 4, 8, 48, 64, 128], [2, 4, 8, 17, 64, 128], [1, 4, 8, 16, 98, 128], [1, 4, 8, 16, 98, 128], [4, 10, 18, 24, 33, 40, 48, 65, 80, 98, 129, 144], [255]) ret.append((name, sbox, n, m, dppt)) name = '1-bit carry adder' n, m = (3, 2) sbox = [int(x + y + c >= 2) * 2 + (x ^ y ^ c) for x in range(2) for y in range(2) for c in range(2)] dppt = ([0], [1, 2], [1, 2], [2], [1, 2], [2], [2], [3]) ret.append((name, sbox, n, m, dppt)) # bij for n in range(5, 9): for t in range(5): n = m = n sbox = list(range(2**n)) shuffle(sbox) ret.append(("rand-bij", sbox, n, m, None)) # non-bij for n in range(5, 9): for m in range(n - 3, n + 3): sbox = [randrange(2**m) for i in range(2**n)] ret.append(("rand-nbij", sbox, n, m, None)) return ret

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py

Python

python/productDataApi.py

tunchunairarko/mern-btzapp

d8526525cfd69e2884464c30706a43977bafb22b

[ "MIT" ]

null

python/productDataApi.py

tunchunairarko/mern-btzapp

d8526525cfd69e2884464c30706a43977bafb22b

[ "MIT" ]

null

python/productDataApi.py

tunchunairarko/mern-btzapp

d8526525cfd69e2884464c30706a43977bafb22b

[ "MIT" ]

null

import requests import dpath # https://github.com/akesterson/dpath-python from pprint import pprint # Needed for printing responses, can be deleted. import re import AzProductInformation class ProductDataUPC(object): def __init__(self,query): self.keys={ 'primary':'aed3a12f05464b8abb54c24d2750e377' } self.headers={ 'ApiGenius_API_Key': "aed3a12f05464b8abb54c24d2750e377" } self.url="https://api.apigenius.io/products/" for i in range(len(query)): if(query[0]==' '): query=query[1:] else: break for i in range(len(query)-1,0,-1): if(query[len(query)-1]==' '): query=query[:-1] else: break self.query = query self.upc='' def get_upc_from_mpn(self,query=''): if not(query==''): for i in range(len(query)): if(query[0]==' '): query=query[1:] else: break for i in range(len(query)-1,0,-1): if(query[len(query)-1]==' '): query=query[:-1] else: break self.query = query query_url=self.url+'/identifiers?mpn='+self.query r=requests.get(query_url,headers=self.headers) data=r.json() if(data['status']==404): return '' return data['items']['upc'] class ProductDataAPIWithKeyword(object): def __init__(self,query): self.keys={ 'primary':'aed3a12f05464b8abb54c24d2750e377' } self.headers={ 'ApiGenius_API_Key': "aed3a12f05464b8abb54c24d2750e377" } self.url="https://api.apigenius.io/products/" for i in range(len(query)): if(query[0]==' '): query=query[1:] else: break for i in range(len(query)-1,0,-1): if(query[len(query)-1]==' '): query=query[:-1] else: break self.query = query self.image=[] self.description='' self.product_list=[] def get_query_details(self): endpoints=['identifiers','lookup','product-data','search'] query_url=self.url+endpoints[3]+'?keyword='+self.query+'&api_key='+self.keys['primary'] # print(query_url) r=requests.get(query_url,headers=self.headers) data=r.json() #print(data) if(data['status']==404): self.product_list.append({}) return return data['items']['upc'] class ProductDataAPIWithMPN(object): def __init__(self,query): self.keys={ 'primary':'aed3a12f05464b8abb54c24d2750e377' } self.headers={ 'ApiGenius_API_Key': "aed3a12f05464b8abb54c24d2750e377" } self.url="https://api.apigenius.io/products/" for i in range(len(query)): if(query[0]==' '): query=query[1:] else: break for i in range(len(query)-1,0,-1): if(query[len(query)-1]==' '): query=query[:-1] else: break self.query = query self.image=[] self.description='' self.product_list=[] self.product = self.get_query_details() def get_query_details(self): endpoints=['identifiers','lookup','product-data','search'] query_url=self.url+endpoints[3]+'?keyword='+self.query+'&mpn='+self.query+'&api_key='+self.keys['primary'] # print(query_url) r=requests.get(query_url,headers=self.headers) data=r.json() #print(data) if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'', 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) class ProductDataAPI(object): def __init__(self,query): self.keys={ 'primary':'aed3a12f05464b8abb54c24d2750e377' } self.headers={ 'ApiGenius_API_Key': "aed3a12f05464b8abb54c24d2750e377" } self.url="https://api.apigenius.io/products/" for i in range(len(query)): if(query[0]==' '): query=query[1:] else: break for i in range(len(query)-1,0,-1): if(query[len(query)-1]==' '): query=query[:-1] else: break self.query = query self.image=[] self.description='' self.product_list=[] self.product = self.get_query_details() def get_query_details(self): endpoints=['identifiers','lookup','product-data','search'] ql=len(self.query) if(self.query.isdigit()==True): #1st case: check if it is a upc # ql=len(self.query) if(ql>=11 and ql<13):#UPC CONFIRMED query_url=self.url+endpoints[1]+'?upc='+self.query+'&api_key='+self.keys['primary'] r=requests.get(query_url,headers=self.headers) data=r.json() print(r.status_code) if(r.status_code==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'', 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) else: query_url=self.url+endpoints[3]+'?keyword='+self.query+'&mpn='+self.query+'&api_key='+self.keys['primary'] # print(query_url) r=requests.get(query_url,headers=self.headers) data=r.json() print(r.status_code) if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'', 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) else: regex=r'sky[0-9]{4,6}$' match=re.match(regex,self.query,flags=re.IGNORECASE) if(match): #BEST CHOICE PRODUCTS query_url=self.url+endpoints[3]+'?keyword='+self.query+'&mpn='+self.query+'&api_key='+self.keys['primary'] # print(query_url) r=requests.get(query_url,headers=self.headers) data=r.json() print(r.status_code) if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'', 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: # print('lsadf') print(e) pass self.product_list.append(item) return if(ql==10) and (any(char.isdigit() for char in self.query)==True):#there is a chance it is an ASIN regex=r'([A-Z0-9]{10})' match=re.match(regex,self.query) if(match): api=AzProductInformation.AzProductInformation(self.query) prodSearch=api.product_list[0] if not(prodSearch['model_no']==None): mpn=prodSearch['model_no'] query_url=self.url+endpoints[3]+'?keyword='+mpn+'&mpn='+mpn+'&api_key='+self.keys['primary'] r=requests.get(query_url,headers=self.headers) data=r.json() print(r.status_code) if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'', 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) else: query_url=self.url+endpoints[3]+'?keyword='+self.query+'&api_key='+self.keys['primary'] r=requests.get(query_url,headers=self.headers) print(r.status_code) data=r.json() if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'' , 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) else: if(self.query.find(' ')==-1): query_url=self.url+endpoints[3]+'?keyword='+self.query+'&mpn='+self.query+'&api_key='+self.keys['primary'] # print(query_url) r=requests.get(query_url,headers=self.headers) data=r.json() if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'' , 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) return query_url=self.url+endpoints[3]+'?keyword='+self.query+'&api_key='+self.keys['primary'] r=requests.get(query_url,headers=self.headers) data=r.json() if(data['status']==404): self.product_list.append({}) return item = {'asinid': data['items']['upc'], 'title': data['items']['title'], 'rank': '', 'package_quantity': '1', 'retailer': data['items']['brand'], 'image': '', 'description':data['items']['description'], 'price': data['items']['lowest_pricing'], 'url': '', 'height': data['items']['dimension'], 'width':data['items']['dimension'], 'length':data['items']['dimension'], 'weight':data['items']['weight'], 'model_no':data['items']['mpn'], 'source':'' , 'product_url':'' } try: item['source']=data['items']['pricing'][0]['seller'] except Exception as e: print(e) pass try: item['product_url']=data['items']['pricing'][0]['link'] except Exception as e: print(e) pass try: item['image']=data['items']['images'][0] except Exception as e: print(e) pass self.product_list.append(item) # try:https://api.apigenius.io/products/search?keyword=0003093730273&mpn=0003093730273&api_key=5db34e7105f6491e99b02f4d5fca37c0 # query=int(self.query) # # querystring={'upc':query,'api_key':self.keys['primary']} # # r = requests.request("GET", self.url, headers=self.headers, params=querystring) # r=requests.get(query_url,headers=self.headers) # data=r.json() # if(data['status']==404): # return # #print(data) # except Exception as e: # #2nd case: Check if the query is a keyword or not # ql=len(self.query) # if(ql==10) and (any(char.isdigit() for char in self.query)==True):#there is a chance it is an ASIN # #3rd case: check if it is an ASIN # regex=r'([A-Z0-9]{10})' # match=re.match(regex,self.query) # if(match): # api=AzProductInformation.AzProductInformation(self.query) # prodSearch=api.product_list[0] # if not(prodSearch['model_no']==None): # upc=prodSearch['model_no'] # query_url=self.url+endpoints[1]+'?upc='+upc+'&api_key='+self.keys['primary'] # r=requests.get(query_url,headers=self.headers) # data=r.json() # querystring={"upc":self.query} # r = requests.request("GET", self.url, headers=self.headers, params=querystring) # data=r.json() # if(data['status']==404): # return # self.image=data['items']['images'] # self.description=data['items']['description'] # headers = { # 'x-rapidapi-host': "product-data1.p.rapidapi.com", # 'x-rapidapi-key': "3edae6ad4emsh2286662ae9bcb68p1bb68djsn63c5cf6f2cc6" # } # r = requests.request("GET", url, headers=headers, params=querystring) # print(r.json()) def main(): # p=ProductDataUPC('SKY1263') p=ProductDataAPIWithKeyword('x00192KM3T') print(p.product_list) if __name__=='__main__': main()

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0

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null

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1

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8

77f93961df00cd63e5db98ca11469933e2c1b639

124

py

Python

py/src/datacentric/types/time/__init__.py

datacentricorg/datacentric

b9e2dedfac35759ea09bb5653095daba5861512e

[ "Apache-2.0" ]

1

2019-08-08T01:27:47.000Z

py/src/datacentric/types/time/__init__.py

datacentricorg/datacentric

b9e2dedfac35759ea09bb5653095daba5861512e

[ "Apache-2.0" ]

null

py/src/datacentric/types/time/__init__.py

datacentricorg/datacentric

b9e2dedfac35759ea09bb5653095daba5861512e

[ "Apache-2.0" ]

null

from datacentric.types.time.iso_day_of_week import IsoDayOfWeek from datacentric.types.time.local_minute import LocalMinute

41.333333

63

0.887097

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124

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0.722222

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0.45283

0

0.064516

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null

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7

7af14fd96cfb76681c30796807edc66ed015682c

46

py

Python

frarch/utils/logging/__init__.py

victorbadenas/frarch

e75e2a63aaf14cf797ffffc901ca382b3d88b7b0

[ "Apache-2.0" ]

1

2021-12-21T11:00:28.000Z

frarch/utils/logging/__init__.py

vbadenas/frarch

3ce8cfad90b09153cbd22dee975731cae13e3ba7

[ "Apache-2.0" ]

5

2021-11-23T11:08:28.000Z

2021-12-21T14:02:14.000Z

frarch/utils/logging/__init__.py

vbadenas/frarch

3ce8cfad90b09153cbd22dee975731cae13e3ba7

[ "Apache-2.0" ]

1

2022-03-20T23:47:16.000Z

from .create_logger import create_logger_file

23

45

0.891304

7

46

5.428571

0.714286

0.631579

0

0.086957

46

1

46

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0

1

0

true

0

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1

0

null

0

1

0

1

0

1

0

7

bb2aaa2685641d5ed14dc44f506d92e20ddfe00b

865

py

Python

nurbsvisualizer/__init__.py

FernandezErbes/nurbsvisualizer

d69e214448fa274ac6cd30b614e161e8bc22bbe6

[ "MIT" ]

null

nurbsvisualizer/__init__.py

FernandezErbes/nurbsvisualizer

d69e214448fa274ac6cd30b614e161e8bc22bbe6

[ "MIT" ]

null

nurbsvisualizer/__init__.py

FernandezErbes/nurbsvisualizer

d69e214448fa274ac6cd30b614e161e8bc22bbe6

[ "MIT" ]

null

import nurbsvisualizer.bsplinegeometry import nurbsvisualizer.nurbsgeometry import nurbsvisualizer.utilities import nurbsvisualizer.visualizer import sys sys.path.append('..') print(" \n") print(" _ __ __ _ ___ ___ ") print(" / | / /_ _______/ /_ ____| | / (_)______ ______ _/ (_)___ ___ _____") print(" / |/ / / / / ___/ __ \/ ___/ | / / / ___/ / / / __ `/ / /_ / / _ \/ ___/") print(" / /| / /_/ / / / /_/ (__ )| |/ / (__ ) /_/ / /_/ / / / / /_/ __/ / ") print("/_/ |_/\__,_/_/ /_.___/____/ |___/_/____/\__,_/\__,_/_/_/ /___/\___/_/ ") print("\n Created by Federico Fernández Erbes ") print(" All results without warranty \n")

54.0625

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0

0.4

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86

57.666667

0.485549

0

0.285714

0.705202

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true

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0

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1

0

1

0

8

248b72f8b6d7d19ce9c4cd65f7163713f784ccee

92,017

py

Python

src/ebay_rest/api/sell_marketing/api/ad_api.py

craiga/ebay_rest

a0be2677c65a787e9566df848ffa3ad0c309a9d9

[ "MIT" ]

null

src/ebay_rest/api/sell_marketing/api/ad_api.py

craiga/ebay_rest

a0be2677c65a787e9566df848ffa3ad0c309a9d9

[ "MIT" ]

null

src/ebay_rest/api/sell_marketing/api/ad_api.py

craiga/ebay_rest

a0be2677c65a787e9566df848ffa3ad0c309a9d9

[ "MIT" ]

null

# coding: utf-8 """ Marketing API The Marketing API offers two platforms that sellers can use to promote and advertise their products: <ul><li>Promoted Listings is an eBay ad service that lets sellers set up ad campaigns for the products they want to promote. eBay displays the ads in search results and in other marketing modules as SPONSORED listings. If an item in a Promoted Listings campaign sells, the seller is assessed a Promoted Listings fee, which is a seller-specified percentage applied to the sales price. For complete details, see <a href=\"/api-docs/sell/static/marketing/promoted-listings.html\">Promoted Listings</a>.</li> <li>Promotions Manager gives sellers a way to offer discounts on specific items as a way to attract buyers to their inventory. Sellers can set up discounts (such as \"20% off\" and other types of offers) on specific items or on an entire customer order. To further attract buyers, eBay prominently displays promotion teasers throughout buyer flows. For complete details, see <a href=\"/api-docs/sell/static/marketing/promotions-manager.html\">Promotions Manager</a>.</li></ul> Marketing reports, on both the Promoted Listings and Promotions Manager platforms, give sellers information that shows the effectiveness of their marketing strategies. The data gives sellers the ability to review and fine tune their marketing efforts. Important! Sellers must have an active eBay Store subscription, and they must accept the Terms and Conditions before they can make requests to these APIs in the Production environment. There are also site-specific listings requirements and restrictions associated with these marketing tools, as listed in the \"requirements and restrictions\" sections for <a href=\"/api-docs/sell/marketing/static/overview.html#PL-requirements\">Promoted Listings</a> and <a href=\"/api-docs/sell/marketing/static/overview.html#PM-requirements\">Promotions Manager</a>. The table below lists all the Marketing API calls grouped by resource. # noqa: E501 OpenAPI spec version: v1.10.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from ...sell_marketing.api_client import ApiClient class AdApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def bulk_create_ads_by_inventory_reference(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_create_ads_by_inventory_reference # noqa: E501 This method adds multiple listings that are managed with the Inventory API to an existing Promoted Listings campaign. For each listing specified in the request, this method: Creates an ad for the listing. Sets the bid percentage (also known as the ad rate) for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its inventoryReferenceId and inventoryReferenceType, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ads to with using the campaign_id path parameter. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). You can specify a maximum of 500 items per call and each campaign can have ads for a maximum of 50,000 items. Be aware when using this call that each variation in a multiple-variation listing creates an individual ad. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_ads_by_inventory_reference(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdsByInventoryReferenceRequest body: The container for the bulk request to create ads for eBay inventory reference IDs. eBay inventory reference IDs are seller-defined IDs used by theInventory API. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkCreateAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_create_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_create_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_create_ads_by_inventory_reference_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_create_ads_by_inventory_reference # noqa: E501 This method adds multiple listings that are managed with the Inventory API to an existing Promoted Listings campaign. For each listing specified in the request, this method: Creates an ad for the listing. Sets the bid percentage (also known as the ad rate) for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its inventoryReferenceId and inventoryReferenceType, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ads to with using the campaign_id path parameter. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). You can specify a maximum of 500 items per call and each campaign can have ads for a maximum of 50,000 items. Be aware when using this call that each variation in a multiple-variation listing creates an individual ad. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_ads_by_inventory_reference_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdsByInventoryReferenceRequest body: The container for the bulk request to create ads for eBay inventory reference IDs. eBay inventory reference IDs are seller-defined IDs used by theInventory API. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkCreateAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_create_ads_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_create_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_create_ads_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_create_ads_by_inventory_reference', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkCreateAdsByInventoryReferenceResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def bulk_create_ads_by_listing_id(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_create_ads_by_listing_id # noqa: E501 This method adds multiple listings to an existing Promoted Listings campaign using listingId values generated by either the Trading API or Inventory API. For each listing ID specified in the request, this method: Creates an ad for the listing. Sets the bid percentage (also known as the ad rate) for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its listingId, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ads with using the campaign_id path parameter. Listing IDs are generated by eBay when a seller creates listings with the Trading API. You can specify a maximum of 500 listings per call and each campaign can have ads for a maximum of 50,000 items. Be aware when using this call that each variation in a multiple-variation listing creates an individual ad. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_ads_by_listing_id(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdRequest body: The container for the bulk request to create ads for eBay listing IDs. eBay listing IDs are generated by the Trading API and Inventory API when the listing is created on eBay. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkAdResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_create_ads_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_create_ads_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_create_ads_by_listing_id_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_create_ads_by_listing_id # noqa: E501 This method adds multiple listings to an existing Promoted Listings campaign using listingId values generated by either the Trading API or Inventory API. For each listing ID specified in the request, this method: Creates an ad for the listing. Sets the bid percentage (also known as the ad rate) for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its listingId, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ads with using the campaign_id path parameter. Listing IDs are generated by eBay when a seller creates listings with the Trading API. You can specify a maximum of 500 listings per call and each campaign can have ads for a maximum of 50,000 items. Be aware when using this call that each variation in a multiple-variation listing creates an individual ad. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_create_ads_by_listing_id_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdRequest body: The container for the bulk request to create ads for eBay listing IDs. eBay listing IDs are generated by the Trading API and Inventory API when the listing is created on eBay. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkAdResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_create_ads_by_listing_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_create_ads_by_listing_id`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_create_ads_by_listing_id`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_create_ads_by_listing_id', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkAdResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def bulk_delete_ads_by_inventory_reference(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_delete_ads_by_inventory_reference # noqa: E501 This method works with listings created with the Inventory API. The method deletes a set of ads, as specified by a list of inventory reference IDs, from the specified campaign. Inventory reference IDs are seller-defined IDs that are used with the Inventory API. Pass the campaign_id as a path parameter and populate the payload with a list of inventoryReferenceId and inventoryReferenceType pairs that you want to delete. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_ads_by_inventory_reference(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkDeleteAdsByInventoryReferenceRequest body: This request object defines the fields for a bulkDeleteAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkDeleteAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_delete_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_delete_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_delete_ads_by_inventory_reference_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_delete_ads_by_inventory_reference # noqa: E501 This method works with listings created with the Inventory API. The method deletes a set of ads, as specified by a list of inventory reference IDs, from the specified campaign. Inventory reference IDs are seller-defined IDs that are used with the Inventory API. Pass the campaign_id as a path parameter and populate the payload with a list of inventoryReferenceId and inventoryReferenceType pairs that you want to delete. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_ads_by_inventory_reference_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkDeleteAdsByInventoryReferenceRequest body: This request object defines the fields for a bulkDeleteAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkDeleteAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_delete_ads_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_delete_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_delete_ads_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_delete_ads_by_inventory_reference', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkDeleteAdsByInventoryReferenceResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def bulk_delete_ads_by_listing_id(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_delete_ads_by_listing_id # noqa: E501 This method works with listing IDs created with either the Trading API or the Inventory API. The method deletes a set of ads, as specified by a list of listingID values from a Promoted Listings campaign. A listing ID value is generated by eBay when a seller creates a listing with either the Trading API and Inventory API. Pass the campaign_id as a path parameter and populate the payload with the set of listing IDs that you want to delete. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_ads_by_listing_id(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkDeleteAdRequest body: This request object defines the fields for the bulkDeleteAdsByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkDeleteAdResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_delete_ads_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_delete_ads_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_delete_ads_by_listing_id_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_delete_ads_by_listing_id # noqa: E501 This method works with listing IDs created with either the Trading API or the Inventory API. The method deletes a set of ads, as specified by a list of listingID values from a Promoted Listings campaign. A listing ID value is generated by eBay when a seller creates a listing with either the Trading API and Inventory API. Pass the campaign_id as a path parameter and populate the payload with the set of listing IDs that you want to delete. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_delete_ads_by_listing_id_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkDeleteAdRequest body: This request object defines the fields for the bulkDeleteAdsByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkDeleteAdResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_delete_ads_by_listing_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_delete_ads_by_listing_id`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_delete_ads_by_listing_id`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_delete_ads_by_listing_id', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkDeleteAdResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def bulk_update_ads_bid_by_inventory_reference(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_update_ads_bid_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method updates the bidPercentage values for a set of ads associated with the specified campaign. Specify the campaign_id as a path parameter and supply a list of inventoryReferenceId and inventoryReferenceType pairs with the updated bidPercentage values in the request body. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_update_ads_bid_by_inventory_reference(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdsByInventoryReferenceRequest body: This request object defines the fields for the BulkCreateAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkCreateAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_update_ads_bid_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_update_ads_bid_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_update_ads_bid_by_inventory_reference_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_update_ads_bid_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method updates the bidPercentage values for a set of ads associated with the specified campaign. Specify the campaign_id as a path parameter and supply a list of inventoryReferenceId and inventoryReferenceType pairs with the updated bidPercentage values in the request body. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_update_ads_bid_by_inventory_reference_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdsByInventoryReferenceRequest body: This request object defines the fields for the BulkCreateAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkCreateAdsByInventoryReferenceResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_update_ads_bid_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_update_ads_bid_by_inventory_reference`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_update_ads_bid_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_update_ads_bid_by_inventory_reference', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkCreateAdsByInventoryReferenceResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def bulk_update_ads_bid_by_listing_id(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_update_ads_bid_by_listing_id # noqa: E501 This method works with listings created with either the Trading API or the Inventory API. The method updates the bidPercentage values for a set of ads associated with the specified campaign. Specify the campaign_id as a path parameter and supply a set of listing IDs with their associated updated bidPercentage values in the request body. An eBay listing ID is generated when a listing is created with the Trading API. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_update_ads_bid_by_listing_id(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdRequest body: This request object defines the fields for the BulkCreateAdsByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkAdResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.bulk_update_ads_bid_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.bulk_update_ads_bid_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def bulk_update_ads_bid_by_listing_id_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """bulk_update_ads_bid_by_listing_id # noqa: E501 This method works with listings created with either the Trading API or the Inventory API. The method updates the bidPercentage values for a set of ads associated with the specified campaign. Specify the campaign_id as a path parameter and supply a set of listing IDs with their associated updated bidPercentage values in the request body. An eBay listing ID is generated when a listing is created with the Trading API. Get the campaign IDs for a seller by calling getCampaigns and call getAds to get a list of the seller's inventory reference IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_update_ads_bid_by_listing_id_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param BulkCreateAdRequest body: This request object defines the fields for the BulkCreateAdsByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: BulkAdResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method bulk_update_ads_bid_by_listing_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `bulk_update_ads_bid_by_listing_id`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `bulk_update_ads_bid_by_listing_id`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/bulk_update_ads_bid_by_listing_id', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BulkAdResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def create_ad_by_listing_id(self, body, campaign_id, **kwargs): # noqa: E501 """create_ad_by_listing_id # noqa: E501 This method works with listings created with either the Trading API or the Inventory API. The method: Creates an ad for the specified listing ID. Sets the bid percentage (also known as the "ad rate") for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its listingId, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ad with using the campaign_id path parameter. Listing IDs are generated by eBay when a seller creates listings with the Trading API or Inventory API. Each campaign can have ads for a maximum of 50,000 items, and each item in a multiple-variation listing is considered as an single item. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_ad_by_listing_id(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param CreateAdRequest body: This request object defines the fields used in the createAdByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: object If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_ad_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.create_ad_by_listing_id_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def create_ad_by_listing_id_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """create_ad_by_listing_id # noqa: E501 This method works with listings created with either the Trading API or the Inventory API. The method: Creates an ad for the specified listing ID. Sets the bid percentage (also known as the "ad rate") for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its listingId, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ad with using the campaign_id path parameter. Listing IDs are generated by eBay when a seller creates listings with the Trading API or Inventory API. Each campaign can have ads for a maximum of 50,000 items, and each item in a multiple-variation listing is considered as an single item. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_ad_by_listing_id_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param CreateAdRequest body: This request object defines the fields used in the createAdByListingId request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: object If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_ad_by_listing_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `create_ad_by_listing_id`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `create_ad_by_listing_id`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/ad', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='object', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def create_ads_by_inventory_reference(self, body, campaign_id, **kwargs): # noqa: E501 """create_ads_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method: Creates an ad for the specified listing. Sets the bid percentage (also known as the "ad rate") for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its inventoryReferenceId and inventoryReferenceType, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ad with using the campaign_id path parameter. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). Each campaign can have ads for a maximum of 50,000 items, and each item in a multiple-variation listing is considered as an individual item. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_ads_by_inventory_reference(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param CreateAdsByInventoryReferenceRequest body: This request object defines the fields used in the createAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: AdReferences If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.create_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def create_ads_by_inventory_reference_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """create_ads_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method: Creates an ad for the specified listing. Sets the bid percentage (also known as the "ad rate") for the ad. Associates the ad with the specified campaign. To create an ad for a listing, specify its inventoryReferenceId and inventoryReferenceType, plus the bidPercentage for the ad in the payload of the request. Specify the campaign to associate the ad with using the campaign_id path parameter. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for a multiple-variation listing). Each campaign can have ads for a maximum of 50,000 items, and each item in a multiple-variation listing is considered as an individual item. Use createCampaign to create a new campaign and use getCampaigns to get a list of existing campaigns. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_ads_by_inventory_reference_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param CreateAdsByInventoryReferenceRequest body: This request object defines the fields used in the createAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: AdReferences If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_ads_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `create_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `create_ads_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/create_ads_by_inventory_reference', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AdReferences', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_ad(self, ad_id, campaign_id, **kwargs): # noqa: E501 """delete_ad # noqa: E501 This method removes the specified ad from the specified campaign. Pass the ID of the ad to delete with the ID of the campaign associated with the ad as path parameters to the call. Call getCampaigns to get the current list of the seller's campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_ad(ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str ad_id: Identifier of an ad. This ID was generated when the ad was created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_ad_with_http_info(ad_id, campaign_id, **kwargs) # noqa: E501 else: (data) = self.delete_ad_with_http_info(ad_id, campaign_id, **kwargs) # noqa: E501 return data def delete_ad_with_http_info(self, ad_id, campaign_id, **kwargs): # noqa: E501 """delete_ad # noqa: E501 This method removes the specified ad from the specified campaign. Pass the ID of the ad to delete with the ID of the campaign associated with the ad as path parameters to the call. Call getCampaigns to get the current list of the seller's campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_ad_with_http_info(ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str ad_id: Identifier of an ad. This ID was generated when the ad was created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['ad_id', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_ad" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'ad_id' is set if ('ad_id' not in params or params['ad_id'] is None): raise ValueError("Missing the required parameter `ad_id` when calling `delete_ad`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `delete_ad`") # noqa: E501 collection_formats = {} path_params = {} if 'ad_id' in params: path_params['ad_id'] = params['ad_id'] # noqa: E501 if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/ad/{ad_id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_ads_by_inventory_reference(self, body, campaign_id, **kwargs): # noqa: E501 """delete_ads_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method deletes ads using a list of seller-defined inventory reference IDs, used with the Inventory API, that are associated with the specified campaign ID. Specify the campaign ID (as a path parameter) and a list of inventoryReferenceId and inventoryReferenceType pairs to be deleted. Call getCampaigns to get a list of the seller's current campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_ads_by_inventory_reference(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param DeleteAdsByInventoryReferenceRequest body: This request object defines the fields for the deleteAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: AdIds If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 else: (data) = self.delete_ads_by_inventory_reference_with_http_info(body, campaign_id, **kwargs) # noqa: E501 return data def delete_ads_by_inventory_reference_with_http_info(self, body, campaign_id, **kwargs): # noqa: E501 """delete_ads_by_inventory_reference # noqa: E501 This method works with listings that are managed with the Inventory API. The method deletes ads using a list of seller-defined inventory reference IDs, used with the Inventory API, that are associated with the specified campaign ID. Specify the campaign ID (as a path parameter) and a list of inventoryReferenceId and inventoryReferenceType pairs to be deleted. Call getCampaigns to get a list of the seller's current campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_ads_by_inventory_reference_with_http_info(body, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param DeleteAdsByInventoryReferenceRequest body: This request object defines the fields for the deleteAdsByInventoryReference request. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: AdIds If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_ads_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `delete_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `delete_ads_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/delete_ads_by_inventory_reference', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AdIds', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_ad(self, ad_id, campaign_id, **kwargs): # noqa: E501 """get_ad # noqa: E501 This method retrieves the specified ad from the specified campaign. In the request, supply the campaign_id and ad_id as path parameters. Call getCampaigns to retrieve a list of the seller's current campaign IDs and call getAds to retrieve their current ad IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ad(ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str ad_id: Identifier of an ad. This ID was generated when the ad was created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: Ad If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_ad_with_http_info(ad_id, campaign_id, **kwargs) # noqa: E501 else: (data) = self.get_ad_with_http_info(ad_id, campaign_id, **kwargs) # noqa: E501 return data def get_ad_with_http_info(self, ad_id, campaign_id, **kwargs): # noqa: E501 """get_ad # noqa: E501 This method retrieves the specified ad from the specified campaign. In the request, supply the campaign_id and ad_id as path parameters. Call getCampaigns to retrieve a list of the seller's current campaign IDs and call getAds to retrieve their current ad IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ad_with_http_info(ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str ad_id: Identifier of an ad. This ID was generated when the ad was created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: Ad If the method is called asynchronously, returns the request thread. """ all_params = ['ad_id', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_ad" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'ad_id' is set if ('ad_id' not in params or params['ad_id'] is None): raise ValueError("Missing the required parameter `ad_id` when calling `get_ad`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `get_ad`") # noqa: E501 collection_formats = {} path_params = {} if 'ad_id' in params: path_params['ad_id'] = params['ad_id'] # noqa: E501 if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/ad/{ad_id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Ad', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_ads(self, campaign_id, **kwargs): # noqa: E501 """get_ads # noqa: E501 This method retrieves Promoted Listings ads that are associated with listings created with either the Trading API or the Inventory API. The method retrieves ads related to the specified campaign. Specify the Promoted Listings campaign to target with the campaign_id path parameter. Because of the large number of possible results, you can use query parameters to paginate the result set by specifying a limit, which dictates how many ads to return on each page of the response. You can also specify how many ads to skip in the result set before returning the first result using the offset path parameter. Call getCampaigns to retrieve the current campaign IDs for the seller. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ads(campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :param str limit: Specifies the maximum number of ads to return on a page in the paginated response. Default: 10 Maximum: 500 :param str listing_ids: A comma separated list of listing IDs. The response includes only active ads (ads associated with a RUNNING campaign). The results do not include listing IDs that are excluded by other conditions. :param str offset: Specifies the number of ads to skip in the result set before returning the first ad in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 items from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0 :return: AdPagedCollection If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_ads_with_http_info(campaign_id, **kwargs) # noqa: E501 else: (data) = self.get_ads_with_http_info(campaign_id, **kwargs) # noqa: E501 return data def get_ads_with_http_info(self, campaign_id, **kwargs): # noqa: E501 """get_ads # noqa: E501 This method retrieves Promoted Listings ads that are associated with listings created with either the Trading API or the Inventory API. The method retrieves ads related to the specified campaign. Specify the Promoted Listings campaign to target with the campaign_id path parameter. Because of the large number of possible results, you can use query parameters to paginate the result set by specifying a limit, which dictates how many ads to return on each page of the response. You can also specify how many ads to skip in the result set before returning the first result using the offset path parameter. Call getCampaigns to retrieve the current campaign IDs for the seller. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ads_with_http_info(campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :param str limit: Specifies the maximum number of ads to return on a page in the paginated response. Default: 10 Maximum: 500 :param str listing_ids: A comma separated list of listing IDs. The response includes only active ads (ads associated with a RUNNING campaign). The results do not include listing IDs that are excluded by other conditions. :param str offset: Specifies the number of ads to skip in the result set before returning the first ad in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 items from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0 :return: AdPagedCollection If the method is called asynchronously, returns the request thread. """ all_params = ['campaign_id', 'limit', 'listing_ids', 'offset'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_ads" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `get_ads`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 if 'listing_ids' in params: query_params.append(('listing_ids', params['listing_ids'])) # noqa: E501 if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/ad', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AdPagedCollection', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_ads_by_inventory_reference(self, campaign_id, inventory_reference_id, inventory_reference_type, **kwargs): # noqa: E501 """get_ads_by_inventory_reference # noqa: E501 This method retrieves Promoted Listings ads associated with listings that are managed with the Inventory API from the specified campaign. Supply the campaign_id as a path parameter and use query parameters to specify the inventory_reference_id and inventory_reference_type pairs. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for an inventory item group, which is an entity that's used in the Inventory API to create a multiple-variation listing). To indicate a listing managed by the Inventory API, you must always specify both an inventory_reference_id and the associated inventory_reference_type. Call getCampaigns to retrieve all of the seller's the current campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ads_by_inventory_reference(campaign_id, inventory_reference_id, inventory_reference_type, async_req=True) >>> result = thread.get() :param async_req bool :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :param str inventory_reference_id: The inventory reference ID associated with the ad you want returned. A seller's inventory reference ID is the ID of either a listing or the ID of an inventory item group (the parent of a multi-variation listing, such as a shirt that is available in multiple sizes and colors). You must always supply in both an inventory_reference_id and an inventory_reference_type. (required) :param str inventory_reference_type: The type of the inventory reference ID. Set this value to either INVENTORY_ITEM (a single listing) or INVENTORY_ITEM_GROUP (a multi-variation listing). You must always pass in both an inventory_reference_id and an inventory_reference_type. (required) :return: Ads If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_ads_by_inventory_reference_with_http_info(campaign_id, inventory_reference_id, inventory_reference_type, **kwargs) # noqa: E501 else: (data) = self.get_ads_by_inventory_reference_with_http_info(campaign_id, inventory_reference_id, inventory_reference_type, **kwargs) # noqa: E501 return data def get_ads_by_inventory_reference_with_http_info(self, campaign_id, inventory_reference_id, inventory_reference_type, **kwargs): # noqa: E501 """get_ads_by_inventory_reference # noqa: E501 This method retrieves Promoted Listings ads associated with listings that are managed with the Inventory API from the specified campaign. Supply the campaign_id as a path parameter and use query parameters to specify the inventory_reference_id and inventory_reference_type pairs. In the Inventory API, an inventory reference ID is either a seller-defined SKU value or an inventoryItemGroupKey (a seller-defined ID for an inventory item group, which is an entity that's used in the Inventory API to create a multiple-variation listing). To indicate a listing managed by the Inventory API, you must always specify both an inventory_reference_id and the associated inventory_reference_type. Call getCampaigns to retrieve all of the seller's the current campaign IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_ads_by_inventory_reference_with_http_info(campaign_id, inventory_reference_id, inventory_reference_type, async_req=True) >>> result = thread.get() :param async_req bool :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :param str inventory_reference_id: The inventory reference ID associated with the ad you want returned. A seller's inventory reference ID is the ID of either a listing or the ID of an inventory item group (the parent of a multi-variation listing, such as a shirt that is available in multiple sizes and colors). You must always supply in both an inventory_reference_id and an inventory_reference_type. (required) :param str inventory_reference_type: The type of the inventory reference ID. Set this value to either INVENTORY_ITEM (a single listing) or INVENTORY_ITEM_GROUP (a multi-variation listing). You must always pass in both an inventory_reference_id and an inventory_reference_type. (required) :return: Ads If the method is called asynchronously, returns the request thread. """ all_params = ['campaign_id', 'inventory_reference_id', 'inventory_reference_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_ads_by_inventory_reference" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `get_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'inventory_reference_id' is set if ('inventory_reference_id' not in params or params['inventory_reference_id'] is None): raise ValueError("Missing the required parameter `inventory_reference_id` when calling `get_ads_by_inventory_reference`") # noqa: E501 # verify the required parameter 'inventory_reference_type' is set if ('inventory_reference_type' not in params or params['inventory_reference_type'] is None): raise ValueError("Missing the required parameter `inventory_reference_type` when calling `get_ads_by_inventory_reference`") # noqa: E501 collection_formats = {} path_params = {} if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] if 'inventory_reference_id' in params: query_params.append(('inventory_reference_id', params['inventory_reference_id'])) # noqa: E501 if 'inventory_reference_type' in params: query_params.append(('inventory_reference_type', params['inventory_reference_type'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/get_ads_by_inventory_reference', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Ads', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_bid(self, body, ad_id, campaign_id, **kwargs): # noqa: E501 """update_bid # noqa: E501 This method updates the bid percentage (also known as the "ad rate") for the specified ad in the specified campaign. In the request, supply the campaign_id and ad_id as path parameters, and supply the new bidPercentage value in the payload of the call. Call getCampaigns to retrieve a seller's current campaign IDs and call getAds to get their ad IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_bid(body, ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param UpdateBidPercentageRequest body: This type defines the fields for the updateBid request. (required) :param str ad_id: A unique eBay-assigned ID for an ad that's generated when an ad is created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_bid_with_http_info(body, ad_id, campaign_id, **kwargs) # noqa: E501 else: (data) = self.update_bid_with_http_info(body, ad_id, campaign_id, **kwargs) # noqa: E501 return data def update_bid_with_http_info(self, body, ad_id, campaign_id, **kwargs): # noqa: E501 """update_bid # noqa: E501 This method updates the bid percentage (also known as the "ad rate") for the specified ad in the specified campaign. In the request, supply the campaign_id and ad_id as path parameters, and supply the new bidPercentage value in the payload of the call. Call getCampaigns to retrieve a seller's current campaign IDs and call getAds to get their ad IDs. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_bid_with_http_info(body, ad_id, campaign_id, async_req=True) >>> result = thread.get() :param async_req bool :param UpdateBidPercentageRequest body: This type defines the fields for the updateBid request. (required) :param str ad_id: A unique eBay-assigned ID for an ad that's generated when an ad is created. (required) :param str campaign_id: A unique eBay-assigned ID for an ad campaign that's generated when a campaign is created. Get a seller's campaign IDs by calling getCampaigns. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body', 'ad_id', 'campaign_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_bid" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `update_bid`") # noqa: E501 # verify the required parameter 'ad_id' is set if ('ad_id' not in params or params['ad_id'] is None): raise ValueError("Missing the required parameter `ad_id` when calling `update_bid`") # noqa: E501 # verify the required parameter 'campaign_id' is set if ('campaign_id' not in params or params['campaign_id'] is None): raise ValueError("Missing the required parameter `campaign_id` when calling `update_bid`") # noqa: E501 collection_formats = {} path_params = {} if 'ad_id' in params: path_params['ad_id'] = params['ad_id'] # noqa: E501 if 'campaign_id' in params: path_params['campaign_id'] = params['campaign_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_auth'] # noqa: E501 return self.api_client.call_api( '/ad_campaign/{campaign_id}/ad/{ad_id}/update_bid', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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