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ml6team

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the-stack-smol-python

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ML6 Team 85

""" Django settings for Punyadaan_Website project. Generated by 'django-admin startproject' using Django 2.2.6. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '-=7!#uo@n6l4aboay(tg_!b=mb#r=6=q)xq8@x!(z3jb2!95h_' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'Punyadaan_Website.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'Punyadaan_Website.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

dushyant1singh1/Punyadaan-Website

Punyadaan_Website/Punyadaan_Website/settings.py

Python

# *- coding: utf-8 -* # Created by: ZhaoDongshuang # Created on: 2018/1/27 import unittest from others.survey import AnonymousSurvey class TestAnonymousSurvey(unittest.TestCase): def setUp(self): question = "What language did you first learn to speak?" self.my_survey = AnonymousSurvey(question) self.responses = ['English', 'Spanish', 'Mandarin'] def test_store_single_response(self): self.my_survey.store_response(self.responses[0]) self.assertIn(self.responses[0], self.my_survey.responses) def test_store_three_responses(self): for response in self.responses: self.my_survey.store_response(response) for response in self.responses: self.assertIn(response, self.my_survey.responses) if __name__ == '__main__': unittest.main()

imtoby/LearnPythonRecord

others/test_survey.py

Python

import unittest, sys sys.path.extend(['.','..','py']) import h2o, h2o_cmd, h2o_hosts, h2o_import as h2i # test some random csv data, and some lineend combinations class Basic(unittest.TestCase): def tearDown(self): h2o.check_sandbox_for_errors() @classmethod def setUpClass(cls): global localhost localhost = h2o.decide_if_localhost() if (localhost): h2o.build_cloud(node_count=1) else: h2o_hosts.build_cloud_with_hosts(node_count=1) @classmethod def tearDownClass(cls): h2o.tear_down_cloud() # believe the interesting thing is the NaN in the csv def test_A_parse3(self): parseResult = h2i.import_parse(bucket='smalldata', path='parse3.csv', schema='put') h2o_cmd.runRF(parseResult=parseResult, trees=37, timeoutSecs=10) if __name__ == '__main__': h2o.unit_main()

jhsrcmh/h2o

py/testdir_single_jvm/notest_parse3.py

Python

import torch import time from math import pi import numpy as np from os.path import join from ..utils.tensorboard import Tensorboard from ..utils.output import progress from .convergence import Convergence from ..model.deepmod import DeepMoD from typing import Optional def train(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test = 'mse', split: float = 0.8, log_dir: Optional[str] = None, max_iterations: int = 10000, write_iterations: int = 25, **convergence_kwargs) -> None: """[summary] Args: model (DeepMoD): [description] data (torch.Tensor): [description] target (torch.Tensor): [description] optimizer ([type]): [description] sparsity_scheduler ([type]): [description] log_dir (Optional[str], optional): [description]. Defaults to None. max_iterations (int, optional): [description]. Defaults to 10000. """ start_time = time.time() board = Tensorboard(log_dir) # initializing tb board # Splitting data, assumes data is already randomized n_train = int(split * data.shape[0]) n_test = data.shape[0] - n_train data_train, data_test = torch.split(data, [n_train, n_test], dim=0) target_train, target_test = torch.split(target, [n_train, n_test], dim=0) # Training convergence = Convergence(**convergence_kwargs) print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |') for iteration in np.arange(0, max_iterations + 1): # ================== Training Model ============================ prediction, time_derivs, thetas = model(data_train) MSE = torch.mean((prediction - target_train)**2, dim=0) # loss per output Reg = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))]) loss = torch.sum(MSE + Reg) # Optimizer step optimizer.zero_grad() loss.backward() optimizer.step() if iteration % write_iterations == 0: # ================== Validation costs ================ prediction_test, coordinates = model.func_approx(data_test) time_derivs_test, thetas_test = model.library((prediction_test, coordinates)) with torch.no_grad(): MSE_test = torch.mean((prediction_test - target_test)**2, dim=0) # loss per output Reg_test = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))]) loss_test = torch.sum(MSE_test + Reg_test) # ====================== Logging ======================= _ = model.sparse_estimator(thetas, time_derivs) # calculating l1 adjusted coeffs but not setting mask estimator_coeff_vectors = model.estimator_coeffs() l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0) progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item()) board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test) # ================== Sparsity update ============= # Updating sparsity and or convergence #sparsity_scheduler(iteration, l1_norm) if iteration % write_iterations == 0: if test == 'mse': sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer) else: sparsity_scheduler(iteration, loss_test, model, optimizer) if sparsity_scheduler.apply_sparsity is True: with torch.no_grad(): model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs) sparsity_scheduler.reset() # ================= Checking convergence convergence(iteration, torch.sum(l1_norm)) if convergence.converged is True: print('Algorithm converged. Stopping training.') break board.close() if log_dir is None: path = 'model.pt' else: path = join(log_dir, 'model.pt') torch.save(model.state_dict(), path) def train_multitask(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test = 'mse', split: float = 0.8, log_dir: Optional[str] = None, max_iterations: int = 10000, write_iterations: int = 25, **convergence_kwargs) -> None: """[summary] Args: model (DeepMoD): [description] data (torch.Tensor): [description] target (torch.Tensor): [description] optimizer ([type]): [description] sparsity_scheduler ([type]): [description] log_dir (Optional[str], optional): [description]. Defaults to None. max_iterations (int, optional): [description]. Defaults to 10000. """ start_time = time.time() board = Tensorboard(log_dir) # initializing tb board # Splitting data, assumes data is already randomized n_train = int(split * data.shape[0]) n_test = data.shape[0] - n_train data_train, data_test = torch.split(data, [n_train, n_test], dim=0) target_train, target_test = torch.split(target, [n_train, n_test], dim=0) # Training convergence = Convergence(**convergence_kwargs) print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |') for iteration in np.arange(0, max_iterations + 1): # ================== Training Model ============================ prediction, time_derivs, thetas = model(data_train) MSE = torch.mean((prediction - target_train)**2, dim=0) # loss per output Reg = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))]) loss = torch.sum(torch.exp(-model.s[:, 0]) * MSE + torch.exp(-model.s[:, 1]) * Reg + torch.sum(model.s)) # Optimizer step optimizer.zero_grad() loss.backward() optimizer.step() if iteration % write_iterations == 0: # ================== Validation costs ================ prediction_test, coordinates = model.func_approx(data_test) time_derivs_test, thetas_test = model.library((prediction_test, coordinates)) with torch.no_grad(): MSE_test = torch.mean((prediction_test - target_test)**2, dim=0) # loss per output Reg_test = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))]) loss_test = torch.sum(torch.exp(-model.s[:, 0]) * MSE_test + torch.exp(-model.s[:, 1]) * Reg_test + torch.sum(model.s)) # ====================== Logging ======================= _ = model.sparse_estimator(thetas, time_derivs) # calculating l1 adjusted coeffs but not setting mask estimator_coeff_vectors = model.estimator_coeffs() l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0) progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item()) board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s) # ================== Sparsity update ============= # Updating sparsity and or convergence #sparsity_scheduler(iteration, l1_norm) if iteration % write_iterations == 0: if test == 'mse': sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer) else: sparsity_scheduler(iteration, loss_test, model, optimizer) if sparsity_scheduler.apply_sparsity is True: with torch.no_grad(): model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs) sparsity_scheduler.reset() # ================= Checking convergence convergence(iteration, torch.sum(l1_norm)) if convergence.converged is True: print('Algorithm converged. Stopping training.') break board.close() if log_dir is None: path = 'model.pt' else: path = join(log_dir, 'model.pt') torch.save(model.state_dict(), path) def train_multitask_capped(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, test = 'mse', split: float = 0.8, log_dir: Optional[str] = None, max_iterations: int = 10000, write_iterations: int = 25, **convergence_kwargs) -> None: """[summary] Args: model (DeepMoD): [description] data (torch.Tensor): [description] target (torch.Tensor): [description] optimizer ([type]): [description] sparsity_scheduler ([type]): [description] log_dir (Optional[str], optional): [description]. Defaults to None. max_iterations (int, optional): [description]. Defaults to 10000. """ start_time = time.time() board = Tensorboard(log_dir) # initializing tb board # Splitting data, assumes data is already randomized n_train = int(split * data.shape[0]) n_test = data.shape[0] - n_train data_train, data_test = torch.split(data, [n_train, n_test], dim=0) target_train, target_test = torch.split(target, [n_train, n_test], dim=0) #cutoff = torch.full((model.func_approx.architecture[-1], 1), 1e-5).to(target.device) cutoff = torch.tensor(15.).to(target.device) # Training convergence = Convergence(**convergence_kwargs) print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |') for iteration in np.arange(0, max_iterations + 1): # ================== Training Model ============================ prediction, time_derivs, thetas = model(data_train) MSE = torch.mean((prediction - target_train)**2, dim=0) # loss per output Reg = torch.stack([torch.mean((dt - theta @ coeff_vector)**2, dim=0) for dt, theta, coeff_vector in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))]) s_capped = torch.min(torch.max(model.s, -cutoff), cutoff) loss = torch.sum(torch.exp(-s_capped[:, 0]) * MSE + torch.exp(-s_capped[:, 1]) * Reg + torch.sum(s_capped)) # Optimizer step optimizer.zero_grad() loss.backward() optimizer.step() if iteration % write_iterations == 0: # ================== Validation costs ================ prediction_test, coordinates = model.func_approx(data_test) time_derivs_test, thetas_test = model.library((prediction_test, coordinates)) with torch.no_grad(): MSE_test = torch.mean((prediction_test - target_test)**2, dim=0) # loss per output Reg_test = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))]) loss_test = torch.sum(torch.exp(-s_capped[:, 0]) * MSE_test + torch.exp(-s_capped[:, 1]) * Reg_test + torch.sum(s_capped)) # ====================== Logging ======================= _ = model.sparse_estimator(thetas, time_derivs) # calculating l1 adjusted coeffs but not setting mask estimator_coeff_vectors = model.estimator_coeffs() l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0) progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item()) board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, s=model.s) # ================== Sparsity update ============= # Updating sparsity and or convergence #sparsity_scheduler(iteration, l1_norm) if iteration % write_iterations == 0: if test == 'mse': sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer) else: sparsity_scheduler(iteration, loss_test, model, optimizer) if sparsity_scheduler.apply_sparsity is True: with torch.no_grad(): model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs) sparsity_scheduler.reset() # ================= Checking convergence convergence(iteration, torch.sum(l1_norm)) if convergence.converged is True: print('Algorithm converged. Stopping training.') break board.close() if log_dir is None: path = 'model.pt' else: path = join(log_dir, 'model.pt') torch.save(model.state_dict(), path) def train_gradnorm(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, sparsity_scheduler, alpha, test = 'mse', split: float = 0.8, log_dir: Optional[str] = None, max_iterations: int = 10000, write_iterations: int = 25, **convergence_kwargs) -> None: """[summary] Args: model (DeepMoD): [description] data (torch.Tensor): [description] target (torch.Tensor): [description] optimizer ([type]): [description] sparsity_scheduler ([type]): [description] log_dir (Optional[str], optional): [description]. Defaults to None. max_iterations (int, optional): [description]. Defaults to 10000. """ start_time = time.time() board = Tensorboard(log_dir) # initializing tb board # Splitting data, assumes data is already randomized n_train = int(split * data.shape[0]) n_test = data.shape[0] - n_train data_train, data_test = torch.split(data, [n_train, n_test], dim=0) target_train, target_test = torch.split(target, [n_train, n_test], dim=0) # Training convergence = Convergence(**convergence_kwargs) print('| Iteration | Progress | Time remaining | Loss | MSE | Reg | L1 norm |') for iteration in np.arange(0, max_iterations + 1): # ================== Training Model ============================ prediction, time_derivs, thetas = model(data_train) MSE = torch.mean((prediction - target_train)**2, dim=0) # loss per output Reg = torch.cat([torch.mean((dt - theta @ coeff_vector)**2, dim=0) for dt, theta, coeff_vector in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))]) task_loss = (torch.exp(model.weights) * torch.stack((MSE, Reg), axis=1)).flatten() # weighted losses loss = torch.sum(task_loss) if iteration == 0: # Getting initial loss ini_loss = task_loss.data if torch.any(task_loss.data > ini_loss): ini_loss[task_loss.data > ini_loss] = task_loss.data[task_loss.data > ini_loss] # Getting original grads optimizer.zero_grad() loss.backward(retain_graph=True) model.weights.grad.data = model.weights.grad.data * 0.0 # setting weight grads to zero # Getting Grads to normalize G = torch.tensor([torch.norm(torch.autograd.grad(loss_i, list(model.parameters())[-2], retain_graph=True, create_graph=True)[0], 2) for loss_i in task_loss]).to(data.device) G_mean = torch.mean(G) # Calculating relative losses rel_loss = task_loss / ini_loss inv_train_rate = rel_loss / torch.mean(rel_loss) # Calculating grad norm loss grad_norm_loss = torch.sum(torch.abs(G - G_mean * inv_train_rate ** alpha)) # Setting grads model.weights.grad = torch.autograd.grad(grad_norm_loss, model.weights)[0] # do a step with the optimizer optimizer.step() # renormalize normalize_coeff = task_loss.shape[0] / torch.sum(model.weights) model.weights.data = torch.log(torch.exp(model.weights.data) * normalize_coeff) if iteration % write_iterations == 0: # ================== Validation costs ================ prediction_test, coordinates = model.func_approx(data_test) time_derivs_test, thetas_test = model.library((prediction_test, coordinates)) with torch.no_grad(): MSE_test = torch.mean((prediction_test - target_test)**2, dim=0) # loss per output Reg_test = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs_test, thetas_test, model.constraint_coeffs(scaled=False, sparse=True))]) loss_test = model.weights @ torch.stack((MSE, Reg), axis=0) # ====================== Logging ======================= _ = model.sparse_estimator(thetas, time_derivs) # calculating l1 adjusted coeffs but not setting mask estimator_coeff_vectors = model.estimator_coeffs() l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1)), dim=0) progress(iteration, start_time, max_iterations, loss.item(), torch.sum(MSE).item(), torch.sum(Reg).item(), torch.sum(l1_norm).item()) board.write(iteration, loss, MSE, Reg, l1_norm, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), estimator_coeff_vectors, MSE_test=MSE_test, Reg_test=Reg_test, loss_test=loss_test, w=model.weights) # ================== Sparsity update ============= # Updating sparsity and or convergence #sparsity_scheduler(iteration, l1_norm) if iteration % write_iterations == 0: if test == 'mse': sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer) else: sparsity_scheduler(iteration, loss_test, model, optimizer) if sparsity_scheduler.apply_sparsity is True: with torch.no_grad(): model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs) sparsity_scheduler.reset() # ================= Checking convergence convergence(iteration, torch.sum(l1_norm)) if convergence.converged is True: print('Algorithm converged. Stopping training.') break board.close() if log_dir is None: path = 'model.pt' else: path = join(log_dir, 'model.pt') torch.save(model.state_dict(), path) def train_SBL(model: DeepMoD, data: torch.Tensor, target: torch.Tensor, optimizer, extra_params, sparsity_scheduler, split = 0.8, exp_ID: str = None, log_dir: str = None, max_iterations: int = 10000, write_iterations: int = 25, **convergence_kwargs) -> None: """Trains the DeepMoD model. This function automatically splits the data set in a train and test set. Args: model (DeepMoD): A DeepMoD object. data (torch.Tensor): Tensor of shape (n_samples x (n_spatial + 1)) containing the coordinates, first column should be the time coordinate. target (torch.Tensor): Tensor of shape (n_samples x n_features) containing the target data. optimizer ([type]): Pytorch optimizer. sparsity_scheduler ([type]): Decides when to update the sparsity mask. split (float, optional): Fraction of the train set, by default 0.8. exp_ID (str, optional): Unique ID to identify tensorboard file. Not used if log_dir is given, see pytorch documentation. log_dir (str, optional): Directory where tensorboard file is written, by default None. max_iterations (int, optional): [description]. Max number of epochs , by default 10000. write_iterations (int, optional): [description]. Sets how often data is written to tensorboard and checks train loss , by default 25. """ logger = Logger(exp_ID, log_dir) sparsity_scheduler.path = logger.log_dir # write checkpoint to same folder as tb output. t, a, l = extra_params # Splitting data, assumes data is already randomized n_train = int(split * data.shape[0]) n_test = data.shape[0] - n_train data_train, data_test = torch.split(data, [n_train, n_test], dim=0) target_train, target_test = torch.split(target, [n_train, n_test], dim=0) M = 12 N = data_train.shape[0] threshold = 1e4 # Training convergence = Convergence(**convergence_kwargs) for iteration in torch.arange(0, max_iterations): # ================== Training Model ============================ prediction, time_derivs, thetas = model(data_train) tau_ = torch.exp(t) alpha_ = torch.min(torch.exp(a), torch.tensor(1e8, dtype=torch.float32)) lambda_ = torch.min(torch.exp(l), torch.tensor(2e4, dtype=torch.float32)) y = time_derivs[0] X = thetas[0] / torch.norm(thetas[0], dim=0, keepdim=True) p_MSE = N / 2 * (tau_ * torch.mean((prediction - target_train)**2, dim=0) - t + np.log(2*np.pi)) A = torch.diag(lambda_) + alpha_ * X.T @ X mn = (lambda_ < threshold)[:, None] * (alpha_ * torch.inverse(A) @ X.T @ y) E = alpha_ * torch.sum((y - X @ mn)**2) + mn.T @ torch.diag(lambda_) @ mn p_reg = 1/2 * (E + torch.sum(torch.log(torch.diag(A)[lambda_ < threshold])) - (torch.sum(l[lambda_ < threshold]) + N * a) - N * np.log(2*np.pi)) MSE = torch.mean((prediction - target_train)**2, dim=0) # loss per output Reg = torch.stack([torch.mean((dt - theta @ coeff_vector)**2) for dt, theta, coeff_vector in zip(time_derivs, thetas, model.constraint_coeffs(scaled=False, sparse=True))]) loss = torch.sum(p_MSE + p_reg) # Optimizer step optimizer.zero_grad() loss.backward() optimizer.step() if iteration % write_iterations == 0: # ================== Validation costs ================ with torch.no_grad(): prediction_test = model.func_approx(data_test)[0] MSE_test = torch.mean((prediction_test - target_test)**2, dim=0) # loss per output # ====================== Logging ======================= _ = model.sparse_estimator(thetas, time_derivs) # calculating estimator coeffs but not setting mask logger(iteration, loss, MSE, Reg, model.constraint_coeffs(sparse=True, scaled=True), model.constraint_coeffs(sparse=True, scaled=False), model.estimator_coeffs(), MSE_test=MSE_test, p_MSE = p_MSE, p_reg = p_reg, tau = tau_, alpha=alpha_, lambda_=lambda_, mn=mn) # ================== Sparsity update ============= # Updating sparsity update_sparsity = sparsity_scheduler(iteration, torch.sum(MSE_test), model, optimizer) if update_sparsity: model.constraint.sparsity_masks = model.sparse_estimator(thetas, time_derivs) # ================= Checking convergence l1_norm = torch.sum(torch.abs(torch.cat(model.constraint_coeffs(sparse=True, scaled=True), dim=1))) converged = convergence(iteration, l1_norm) if converged: break logger.close(model)

GJBoth/MultiTaskPINN

src/multitaskpinn/training/training.py

Python

#!/usr/bin/env python3 # Copyright (c) 2015-2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test decoding scripts via decodescript RPC command.""" from test_framework.messages import CTransaction, sha256 from test_framework.test_framework import ErexCoinTestFramework from test_framework.util import assert_equal, bytes_to_hex_str, hex_str_to_bytes from io import BytesIO class DecodeScriptTest(ErexCoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def decodescript_script_sig(self): signature = '304502207fa7a6d1e0ee81132a269ad84e68d695483745cde8b541e3bf630749894e342a022100c1f7ab20e13e22fb95281a870f3dcf38d782e53023ee313d741ad0cfbc0c509001' push_signature = '48' + signature public_key = '03b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb2' push_public_key = '21' + public_key # below are test cases for all of the standard transaction types # 1) P2PK scriptSig # the scriptSig of a public key scriptPubKey simply pushes a signature onto the stack rpc_result = self.nodes[0].decodescript(push_signature) assert_equal(signature, rpc_result['asm']) # 2) P2PKH scriptSig rpc_result = self.nodes[0].decodescript(push_signature + push_public_key) assert_equal(signature + ' ' + public_key, rpc_result['asm']) # 3) multisig scriptSig # this also tests the leading portion of a P2SH multisig scriptSig # OP_0 <A sig> <B sig> rpc_result = self.nodes[0].decodescript('00' + push_signature + push_signature) assert_equal('0 ' + signature + ' ' + signature, rpc_result['asm']) # 4) P2SH scriptSig # an empty P2SH redeemScript is valid and makes for a very simple test case. # thus, such a spending scriptSig would just need to pass the outer redeemScript # hash test and leave true on the top of the stack. rpc_result = self.nodes[0].decodescript('5100') assert_equal('1 0', rpc_result['asm']) # 5) null data scriptSig - no such thing because null data scripts can not be spent. # thus, no test case for that standard transaction type is here. def decodescript_script_pub_key(self): public_key = '03b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb2' push_public_key = '21' + public_key public_key_hash = '5dd1d3a048119c27b28293056724d9522f26d945' push_public_key_hash = '14' + public_key_hash uncompressed_public_key = '04b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb25e01fc8fde47c96c98a4f3a8123e33a38a50cf9025cc8c4494a518f991792bb7' push_uncompressed_public_key = '41' + uncompressed_public_key p2wsh_p2pk_script_hash = 'd8590cf8ea0674cf3d49fd7ca249b85ef7485dea62c138468bddeb20cd6519f7' # below are test cases for all of the standard transaction types # 1) P2PK scriptPubKey # <pubkey> OP_CHECKSIG rpc_result = self.nodes[0].decodescript(push_public_key + 'ac') assert_equal(public_key + ' OP_CHECKSIG', rpc_result['asm']) # P2PK is translated to P2WPKH assert_equal('0 ' + public_key_hash, rpc_result['segwit']['asm']) # 2) P2PKH scriptPubKey # OP_DUP OP_HASH160 <PubKeyHash> OP_EQUALVERIFY OP_CHECKSIG rpc_result = self.nodes[0].decodescript('76a9' + push_public_key_hash + '88ac') assert_equal('OP_DUP OP_HASH160 ' + public_key_hash + ' OP_EQUALVERIFY OP_CHECKSIG', rpc_result['asm']) # P2PKH is translated to P2WPKH assert_equal('0 ' + public_key_hash, rpc_result['segwit']['asm']) # 3) multisig scriptPubKey # <m> <A pubkey> <B pubkey> <C pubkey> <n> OP_CHECKMULTISIG # just imagine that the pub keys used below are different. # for our purposes here it does not matter that they are the same even though it is unrealistic. multisig_script = '52' + push_public_key + push_public_key + push_public_key + '53ae' rpc_result = self.nodes[0].decodescript(multisig_script) assert_equal('2 ' + public_key + ' ' + public_key + ' ' + public_key + ' 3 OP_CHECKMULTISIG', rpc_result['asm']) # multisig in P2WSH multisig_script_hash = bytes_to_hex_str(sha256(hex_str_to_bytes(multisig_script))) assert_equal('0 ' + multisig_script_hash, rpc_result['segwit']['asm']) # 4) P2SH scriptPubKey # OP_HASH160 <Hash160(redeemScript)> OP_EQUAL. # push_public_key_hash here should actually be the hash of a redeem script. # but this works the same for purposes of this test. rpc_result = self.nodes[0].decodescript('a9' + push_public_key_hash + '87') assert_equal('OP_HASH160 ' + public_key_hash + ' OP_EQUAL', rpc_result['asm']) # P2SH does not work in segwit secripts. decodescript should not return a result for it. assert 'segwit' not in rpc_result # 5) null data scriptPubKey # use a signature look-alike here to make sure that we do not decode random data as a signature. # this matters if/when signature sighash decoding comes along. # would want to make sure that no such decoding takes place in this case. signature_imposter = '48304502207fa7a6d1e0ee81132a269ad84e68d695483745cde8b541e3bf630749894e342a022100c1f7ab20e13e22fb95281a870f3dcf38d782e53023ee313d741ad0cfbc0c509001' # OP_RETURN <data> rpc_result = self.nodes[0].decodescript('6a' + signature_imposter) assert_equal('OP_RETURN ' + signature_imposter[2:], rpc_result['asm']) # 6) a CLTV redeem script. redeem scripts are in-effect scriptPubKey scripts, so adding a test here. # OP_NOP2 is also known as OP_CHECKLOCKTIMEVERIFY. # just imagine that the pub keys used below are different. # for our purposes here it does not matter that they are the same even though it is unrealistic. # # OP_IF # <receiver-pubkey> OP_CHECKSIGVERIFY # OP_ELSE # <lock-until> OP_CHECKLOCKTIMEVERIFY OP_DROP # OP_ENDIF # <sender-pubkey> OP_CHECKSIG # # lock until block 500,000 cltv_script = '63' + push_public_key + 'ad670320a107b17568' + push_public_key + 'ac' rpc_result = self.nodes[0].decodescript(cltv_script) assert_equal('OP_IF ' + public_key + ' OP_CHECKSIGVERIFY OP_ELSE 500000 OP_CHECKLOCKTIMEVERIFY OP_DROP OP_ENDIF ' + public_key + ' OP_CHECKSIG', rpc_result['asm']) # CLTV script in P2WSH cltv_script_hash = bytes_to_hex_str(sha256(hex_str_to_bytes(cltv_script))) assert_equal('0 ' + cltv_script_hash, rpc_result['segwit']['asm']) # 7) P2PK scriptPubKey # <pubkey> OP_CHECKSIG rpc_result = self.nodes[0].decodescript(push_uncompressed_public_key + 'ac') assert_equal(uncompressed_public_key + ' OP_CHECKSIG', rpc_result['asm']) # uncompressed pubkeys are invalid for checksigs in segwit scripts. # decodescript should not return a P2WPKH equivalent. assert 'segwit' not in rpc_result # 8) multisig scriptPubKey with an uncompressed pubkey # <m> <A pubkey> <B pubkey> <n> OP_CHECKMULTISIG # just imagine that the pub keys used below are different. # the purpose of this test is to check that a segwit script is not returned for bare multisig scripts # with an uncompressed pubkey in them. rpc_result = self.nodes[0].decodescript('52' + push_public_key + push_uncompressed_public_key +'52ae') assert_equal('2 ' + public_key + ' ' + uncompressed_public_key + ' 2 OP_CHECKMULTISIG', rpc_result['asm']) # uncompressed pubkeys are invalid for checksigs in segwit scripts. # decodescript should not return a P2WPKH equivalent. assert 'segwit' not in rpc_result # 9) P2WPKH scriptpubkey # 0 <PubKeyHash> rpc_result = self.nodes[0].decodescript('00' + push_public_key_hash) assert_equal('0 ' + public_key_hash, rpc_result['asm']) # segwit scripts do not work nested into each other. # a nested segwit script should not be returned in the results. assert 'segwit' not in rpc_result # 10) P2WSH scriptpubkey # 0 <ScriptHash> # even though this hash is of a P2PK script which is better used as bare P2WPKH, it should not matter # for the purpose of this test. rpc_result = self.nodes[0].decodescript('0020' + p2wsh_p2pk_script_hash) assert_equal('0 ' + p2wsh_p2pk_script_hash, rpc_result['asm']) # segwit scripts do not work nested into each other. # a nested segwit script should not be returned in the results. assert 'segwit' not in rpc_result def decoderawtransaction_asm_sighashtype(self): """Test decoding scripts via RPC command "decoderawtransaction". This test is in with the "decodescript" tests because they are testing the same "asm" script decodes. """ # this test case uses a random plain vanilla mainnet transaction with a single P2PKH input and output tx = '0100000001696a20784a2c70143f634e95227dbdfdf0ecd51647052e70854512235f5986ca010000008a47304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb014104d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536ffffffff0100e1f505000000001976a914eb6c6e0cdb2d256a32d97b8df1fc75d1920d9bca88ac00000000' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb[ALL] 04d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536', rpc_result['vin'][0]['scriptSig']['asm']) # this test case uses a mainnet transaction that has a P2SH input and both P2PKH and P2SH outputs. # it's from James D'Angelo's awesome introductory videos about multisig: https://www.youtube.com/watch?v=zIbUSaZBJgU and https://www.youtube.com/watch?v=OSA1pwlaypc # verify that we have not altered scriptPubKey decoding. tx = '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' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('8e3730608c3b0bb5df54f09076e196bc292a8e39a78e73b44b6ba08c78f5cbb0', rpc_result['txid']) assert_equal('0 3045022100ae3b4e589dfc9d48cb82d41008dc5fa6a86f94d5c54f9935531924602730ab8002202f88cf464414c4ed9fa11b773c5ee944f66e9b05cc1e51d97abc22ce098937ea[ALL] 3045022100b44883be035600e9328a01b66c7d8439b74db64187e76b99a68f7893b701d5380220225bf286493e4c4adcf928c40f785422572eb232f84a0b83b0dea823c3a19c75[ALL] 5221020743d44be989540d27b1b4bbbcfd17721c337cb6bc9af20eb8a32520b393532f2102c0120a1dda9e51a938d39ddd9fe0ebc45ea97e1d27a7cbd671d5431416d3dd87210213820eb3d5f509d7438c9eeecb4157b2f595105e7cd564b3cdbb9ead3da41eed53ae', rpc_result['vin'][0]['scriptSig']['asm']) assert_equal('OP_DUP OP_HASH160 dc863734a218bfe83ef770ee9d41a27f824a6e56 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm']) assert_equal('OP_HASH160 2a5edea39971049a540474c6a99edf0aa4074c58 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm']) txSave = CTransaction() txSave.deserialize(BytesIO(hex_str_to_bytes(tx))) # make sure that a specifically crafted op_return value will not pass all the IsDERSignature checks and then get decoded as a sighash type tx = '01000000015ded05872fdbda629c7d3d02b194763ce3b9b1535ea884e3c8e765d42e316724020000006b48304502204c10d4064885c42638cbff3585915b322de33762598321145ba033fc796971e2022100bb153ad3baa8b757e30a2175bd32852d2e1cb9080f84d7e32fcdfd667934ef1b012103163c0ff73511ea1743fb5b98384a2ff09dd06949488028fd819f4d83f56264efffffffff0200000000000000000b6a0930060201000201000180380100000000001976a9141cabd296e753837c086da7a45a6c2fe0d49d7b7b88ac00000000' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('OP_RETURN 300602010002010001', rpc_result['vout'][0]['scriptPubKey']['asm']) # verify that we have not altered scriptPubKey processing even of a specially crafted P2PKH pubkeyhash and P2SH redeem script hash that is made to pass the der signature checks tx = '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' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('OP_DUP OP_HASH160 3011020701010101010101020601010101010101 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm']) assert_equal('OP_HASH160 3011020701010101010101020601010101010101 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm']) # some more full transaction tests of varying specific scriptSigs. used instead of # tests in decodescript_script_sig because the decodescript RPC is specifically # for working on scriptPubKeys (argh!). push_signature = bytes_to_hex_str(txSave.vin[0].scriptSig)[2:(0x48*2+4)] signature = push_signature[2:] der_signature = signature[:-2] signature_sighash_decoded = der_signature + '[ALL]' signature_2 = der_signature + '82' push_signature_2 = '48' + signature_2 signature_2_sighash_decoded = der_signature + '[NONE|ANYONECANPAY]' # 1) P2PK scriptSig txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal(signature_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # make sure that the sighash decodes come out correctly for a more complex / lesser used case. txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature_2) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal(signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # 2) multisig scriptSig txSave.vin[0].scriptSig = hex_str_to_bytes('00' + push_signature + push_signature_2) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal('0 ' + signature_sighash_decoded + ' ' + signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # 3) test a scriptSig that contains more than push operations. # in fact, it contains an OP_RETURN with data specially crafted to cause improper decode if the code does not catch it. txSave.vin[0].scriptSig = hex_str_to_bytes('6a143011020701010101010101020601010101010101') rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal('OP_RETURN 3011020701010101010101020601010101010101', rpc_result['vin'][0]['scriptSig']['asm']) def run_test(self): self.decodescript_script_sig() self.decodescript_script_pub_key() self.decoderawtransaction_asm_sighashtype() if __name__ == '__main__': DecodeScriptTest().main()

Black-NET/erexcoin-source

test/functional/rpc_decodescript.py

Python

# Copyright 2018, OpenCensus Authors # # 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 datetime import mock import unittest from opencensus.common import utils from opencensus.trace.link import Link from opencensus.trace.span import format_span_json from opencensus.trace.time_event import TimeEvent class TestBlankSpan(unittest.TestCase): @staticmethod def _get_target_class(): from opencensus.trace.blank_span import BlankSpan return BlankSpan def _make_one(self, *args, **kw): return self._get_target_class()(*args, **kw) def test_do_not_crash(self): span_id = 'test_span_id' span_name = 'test_span_name' patch = mock.patch( 'opencensus.trace.blank_span.generate_span_id', return_value=span_id) with patch: span = self._make_one(span_name) self.assertEqual(span.name, span_name) self.assertEqual(span.span_id, span_id) self.assertIsNotNone(span.parent_span) self.assertIsNotNone(span.parent_span.span()) self.assertEqual(span.attributes, {}) self.assertIsNone(span.start_time) self.assertIsNone(span.end_time) self.assertEqual(span.children, []) self.assertIsNone(span.context_tracer) span.add_attribute('attribute_key', 'attribute_value') span.add_annotation('This is a test', name='blank-span') link = Link(span_id='1234', trace_id='4567') span.add_link(link) time_event = mock.Mock() with self.assertRaises(TypeError): span.add_time_event(time_event) time_event = TimeEvent(datetime.datetime.utcnow()) span.add_time_event(time_event) span_iter_list = list(iter(span)) self.assertEqual(span_iter_list, [span]) expected_span_json = { 'spanId': 'test_span_id', 'startTime': None, 'endTime': None, 'displayName': { 'truncated_byte_count': 0, 'value': 'test_span_name' }, 'childSpanCount': 0, } span_json = format_span_json(span) self.assertEqual(span_json, expected_span_json) span.start() span.finish() def test_constructor_explicit(self): span_id = 'test_span_id' span_name = 'test_span_name' parent_span = mock.Mock() start_time = utils.to_iso_str() end_time = utils.to_iso_str() attributes = { 'http.status_code': '200', 'component': 'HTTP load balancer', } time_events = mock.Mock() links = mock.Mock() stack_trace = mock.Mock() status = mock.Mock() context_tracer = mock.Mock() span = self._make_one( name=span_name, parent_span=parent_span, attributes=attributes, start_time=start_time, end_time=end_time, span_id=span_id, stack_trace=stack_trace, time_events=time_events, links=links, status=status, context_tracer=context_tracer) self.assertEqual(span.name, span_name) self.assertIsNotNone(span.span_id) self.assertEqual(span.attributes, {}) self.assertEqual(span.start_time, start_time) self.assertEqual(span.end_time, end_time) self.assertEqual(span.time_events, time_events) self.assertEqual(span.stack_trace, stack_trace) self.assertEqual(span.links, []) self.assertEqual(span.status, status) self.assertEqual(span.children, []) self.assertEqual(span.context_tracer, context_tracer) def test_start(self): span_name = 'root_span' span = self._make_one(span_name) self.assertIsNone(span.start_time) span.start() self.assertIsNone(span.start_time) def test_finish_without_context_tracer(self): span_name = 'root_span' span = self._make_one(span_name) self.assertIsNone(span.end_time) span.finish() self.assertIsNone(span.end_time) def test_finish(self): span_name = 'root_span' span = self._make_one(span_name) self.assertIsNone(span.end_time) span.finish() self.assertIsNone(span.end_time) def test_on_create(self): from opencensus.trace.blank_span import BlankSpan self.on_create_called = False self._make_one('span1') self.assertFalse(self.on_create_called) try: @BlankSpan.on_create def callback(span): self.on_create_called = True self._make_one('span2') finally: BlankSpan._on_create_callbacks = [] self.assertFalse(self.on_create_called) def test_context_manager(self): span_name = 'root_span' with self._make_one(span_name) as s: self.assertIsNotNone(s) self.assertEqual(s.name, span_name)

kshithijiyer/opencensus-python

tests/unit/trace/test_blank_span.py

Python

import os import shutil cur_dir= os.getcwd() def make_folder(name): #Checks to see if a folder exists, makes it if it doesn't. if (os.path.exists('.\\'+name)): return else: os.makedirs('.\\'+name) def notepad(name): if os.path.exists(os.path.join(cur_dir,name)) == False: var_create= open(name,'w') var= open(name,'r+') var_data=var.read() var.seek(0) return var_data,var txt_data,txt_file= notepad('clean_folder.txt') skip = notepad('skip.txt')[0] ''' os.path.exists(os.path.join(cur_dir,'clean_folder.txt'))==False: #creates clean_folder.txt if it doesn't exist txt_file_create=open('clean_folder.txt','w') txt_file= open('clean_folder.txt','r+') txt_data= txt_file.read() txt_file.seek(0) ''' make_folder('error_files') if os.path.exists("others"): #takes all files in 'others' folders and copies them to current directory files_others= os.listdir('others') while len(os.listdir('others')) != 0: for file in files_others: if os.path.isdir(file)==False: try: shutil.move(os.path.join(cur_dir,'others',file),os.path.join(cur_dir,file)) print ('File',file' moved from others to ',cur_dir) except OSError: pass shutil.move(os.path.join('others',file),'error_files') else: unpack(file) if len(os.listdir('others'))==0: os.rmdir('others') print ("test dialogue") files= [x for x in os.listdir() if os.path.isdir(x)== False and x != 'clean_folder_v4.py' and x!='clean_folder.txt' and x not in skip and x!= "skip.txt"] #list of files(excluding folders) in current directory, excluding this program and its text file extensions =[os.path.splitext(x)[1] for x in files] #list of extensions of files print (extensions) folders_created=[] #list of folders that will be created for file in files: extension = os.path.splitext(file)[1] #takes extension of file using splitext print ('Extensions of file is : ',extension) if os.path.exists(extension): #sees if that particular file already has a dedicated folder from a previous run of clean_folder print ("File already has folder") try: shutil.move(os.path.join(cur_dir,file),extension) #if yes, it moves it to that folder except OSError: pass shutil.move(os.path.join(cur_dir,file),'error_files') files.remove(file) extensions.remove(extension) else: print ("Folder will have to be created") pass for file in files: extension = os.path.splitext(file)[1] if extensions.count(str(extension))>1: #checks to see how many times files of that extension occurs in list 'extensions' #if more than 1, creates a folder for that extension print ("File type exists more than once. Creating new folder..") make_folder(extension) folders_created.append(extension) #appends to list 'folders_created' try: shutil.move(os.path.join(cur_dir,file),extension) #moves file to folder of that extension print ('File',file,'moved to folder' ,extension) except OSError: pass shutil.move(os.path.join(cur_dir,file),'error_files') else: make_folder("others") # if only 1 file of an extension exists, it is put in others folder folders_created.append("others") try: print ('File',file,'moved to others') shutil.move(os.path.join(cur_dir,file),"others") except OSError: pass shutil.move(os.path.join(cur_dir,file),'error_files') for folder in folders_created: txt_file.write(folder+"\n") txt_file.close() if len(os.listdir('error_files'))== 0 : os.rmdir('error_files') else: print ("The following files could not be moved due to an unknown error. Please move them manually from folder \'error_files\'") print (os.listdir('error_files')) print (files) os.system('pause') ''' Working: When the program is run for the first time, it doesn't harm existing folders. Takes stray files and sorts them extension wise Creates folder for each file type that exists more than once. All the rest are put in 'others' folder When run the next time, it unpacks 'others' folders, in case a file that has been put there now has another file of same extension. Same thing is done again. If a file now placed has a folder already dedicated to its extension, it is put there All the while , it writes the name of the folders created by it in clean_folder.txt . This is so that they can be unpacked using unpack.py, without harming any other folders. Any files in skip.txt will not be moved. '''

Satvik2101/Clean-Folder

clean_folder_v4.py

Python

from datetime import datetime, timedelta from typing import Callable def create_batch_list(n_batches: int) -> list: return list(map(lambda x: x, range(1, n_batches + 1))) def create_batch_dictionary(batch_lst: list, duration_lst: list, expected_finish_lst: list) -> dict: batch_dict = {batch_lst[i]: (duration_lst[i], expected_finish_lst[i]) for i in range(0, len(batch_lst))} return batch_dict def create_result_batch_dictionary(batch_lst: list, start_datetime_lst: list, delta_time_lst: list) -> dict: batch_dict = {batch_lst[i]: (start_datetime_lst[i], delta_time_lst[i]) for i in range(0, len(batch_lst))} return batch_dict def create_dynamic_ordering_constraint(index: int) -> str: """ Creates a valid AMPL constraint of the form: [LaTex]: $start\_time_j+1 >= start\_time_j + duration_j$, $\forall j \in BATCH$ :param index: j index where the current constraint should start :return: single AMPL JIT constraint as a string """ i = str(index) i_next = str(index + 1) constraint_name = f'ordering_{i_next}_{i}' return f's.t. {constraint_name}: start_time[{i_next}] >= start_time[{i}] + duration[{i}];' def create_multiple_ordering_constraints(start_index: int, last_index: int) -> str: constraints = '' for i in range(start_index, last_index): constraints += f'{create_dynamic_ordering_constraint(i)}\n' return constraints def create_multiple_constraints(start_index: int, last_index: int, create_constraints: Callable[[int, int], str]): return create_constraints(start_index, last_index) def dict_to_list(obj: dict) -> list: """ Converts a dictionary to a list, extracting the values of the dictionary. The list is sorted according to the dict's keys ascendant order. The given dictionary should always have the same numeric keys as the result of create_batch_dictionary(). :param obj: the dictionary to convert which should have numeric keys :return: the list of values in the dictionary """ return list(obj.values()) def strings_to_datetimes(str_date_lst: list, datetime_format: str) -> list: """ Converts a list of strings into a list of datetime objects :param str_date_lst: list of string objects compatible with the ISO8601 format :param datetime_format: format of the datetime :return: list of datetime objects equivalent to the given str_date_lst """ return [datetime.strptime(d, datetime_format) for d in str_date_lst] def minute_timedelta(first: datetime, second: datetime) -> int: """ Returns the difference expressed in minutes between 2 datetime objects :param first: datetime object that comes before second :param second: datetime object that comes after first :return: difference in minutes between second and first """ delta: timedelta = second - first return divmod(delta.total_seconds(), 60)[0] def minute_timedeltas_wrt_first(datetime_lst: list) -> list: """ Converts a list of datetime objects into a list of minute time deltas with respect to the first item. For example, given the input datetime_lst: [ '2019-08-22 14:32', '2019-08-22 14:38', '2019-08-22 14:42', '2019-08-22 14:52', '2019-08-22 14:57' ], the result would be: [32, 38, 42, 52, 57] :param datetime_lst: list of datetime objects :return: minute time deltas with respect to the first item of datetime_lst """ first_datetime: datetime = datetime_lst[0] partial_deltas = [minute_timedelta(first=first_datetime, second=v) for v in datetime_lst[1:]] first_minutes = first_datetime.minute return [first_minutes] + list(map(lambda x: x + first_minutes, partial_deltas)) def set_minutes_to_datetimes(datetime_lst: list, minutes_lst: list) -> list: """ Given a list of minutes and datetime objects, sets each amount of minutes to each datetime object with respect to the list index. The two lists must have the same size. :param datetime_lst: list of datetime objects :param minutes_lst: list of minutes to set to a list of datetime objects :return: list of datetime objects similar to datetime_lst but shifted according to minutes_lst """ return [d.replace(minute=0) + timedelta(minutes=m) for d, m in zip(datetime_lst, minutes_lst)] def datetimes_to_strings(datetime_lst: list, datetime_format: str) -> list: """ Converts a list of datetime objects to strings, according to a certain datetime format. :param datetime_lst: list of datetime objects to convert to string :param datetime_format: format of the datetime :return: the list of datetime objects converted to strings in the given datetime format """ return [d.strftime(datetime_format) for d in datetime_lst]

jkomyno/amplrestapi

ampljit/utils.py

Python

from tkinter import messagebox, Tk, Menu, ttk news = ['Mid Day News', 'Evening News'] features = ['Calling Farmers', 'Round About Ja', 'You and the Law', 'Get the Facts', 'Career Talk', 'Economy and you', 'Arts Page', 'Tourism Roundup', 'Jeep','Jamaica Promise', 'House Matters', 'Jamaica House Weekly'] features.sort() class CustomMenu(object): def __init__(self, root, values=[], combo_placement=(0, 0), button_placement=(0, 0), label_placement=(0, 0)): self.frame = root self.combobox = ttk.Combobox(self.frame, values=values) self.combobox.bind("<<>ComboboxSelected>") self.combobox.grid(row=combo_placement[0], column=combo_placement[1]) self.label = ttk.Label(self.frame, textvariable=self.combobox.get()) self.label.grid(row=label_placement[0], column=label_placement[1]) self.button = ttk.Button(self.frame, text="Add", command=self.update_popup) self.button.grid(row=button_placement[0], column=button_placement[1]) def update_popup(self): messagebox.showinfo( title="File update", message="{} has been added".format(self.combobox.get()) ) root = Tk() root.title('Feature Tracking') root.geometry('255x425') update_frame = ttk.Frame(root, padding=(5,10)) def show_update_frame(): update_frame.grid(row=0, column=0) #Update Menu Frame features_frame = CustomMenu(update_frame, features, (1, 0), (3, 0), (0, 0)) news_frame = CustomMenu(update_frame, news, (4, 0), (5, 0), (6, 0)) #Menu bar with menu options menubar = Menu(root) #Update Menu filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label='New', command=show_update_frame) menubar.add_cascade(label='Update', menu=filemenu) root.config(menu = menubar) root.mainloop()

UncleEngineer/TkinterTrick

000-combobox1.py

Python

from flask import Flask, render_template app = Flask(__name__) @app.route("/") def home(): return render_template("index.html") if __name__ == "__main__": app.run(debug=True)

nurmatthias/100DaysOfCode

day48/server.py

Python

# # Copyright 2018 Expedia Inc. # # 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 unittest import numpy as np from functions import bootstrap test_cases = [ { 'sample': [3, 3, 3], 'predstot': 1, 'predsdim': 1, 'len': 1, 'mean': np.float64(3), 'meanInt64': np.int64(3) }, { 'sample': [2, 2, 2], 'predstot': 1000, 'predsdim': 2, 'len': 1000, 'mean': np.float64(2), 'meanInt64': np.int64(2) }, { 'sample': [1, 1, 1], 'predstot': 9999, 'predsdim': 3, 'len': 9999, 'mean': np.float64(1), 'meanInt64': np.int64(1) }, ] results = [] resultsInt64 = [] class BootstrapTestCase(unittest.TestCase): """ docstring """ def setUp(self): for i in range(len(test_cases)): results.append( bootstrap( test_cases[i]['sample'], test_cases[i]['predstot'], test_cases[i]['predsdim'] ) ) resultsInt64.append( bootstrap( test_cases[i]['sample'], test_cases[i]['predstot'], test_cases[i]['predsdim'], True ) ) def test_len(self): """ test for boostrap helper""" for i in range(len(test_cases)): self.assertEqual(len(results[i]), test_cases[i]['len']) self.assertEqual(len(resultsInt64[i]), test_cases[i]['len']) def test_value(self): """ docstring """ for i in range(len(test_cases)): for j in range(len(results[i])): self.assertEqual(results[i][j], test_cases[i]['mean']) self.assertEqual(resultsInt64[i][j], test_cases[i]['meanInt64']) self.assertIsInstance(results[i][j], np.float64) self.assertIsInstance(resultsInt64[i][j], np.int64) def test_less(self): """ docstring """ for i in range(len(test_cases)): for j in range(len(results[i])): self.assertLessEqual(results[i][j], max(test_cases[i]['sample'])) def test_greater(self): """ docstring """ for i in range(len(test_cases)): for j in range(len(results[i])): self.assertGreaterEqual(results[i][j], min(test_cases[i]['sample'])) if __name__ == '__main__': unittest.main()

ExpediaGroup/neaps

neaps-api/neaps_lib/bootstrap_test.py

Python

# coding: utf-8 # Copyright 2020. ThingsBoard # # # 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 pprint import re # noqa: F401 import six class URL(object): """NOTE: This class is auto generated by the swagger code generator program. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'authority': 'str', 'content': 'object', 'default_port': 'int', 'file': 'str', 'host': 'str', 'path': 'str', 'port': 'int', 'protocol': 'str', 'query': 'str', 'ref': 'str', 'user_info': 'str' } attribute_map = { 'authority': 'authority', 'content': 'content', 'default_port': 'defaultPort', 'file': 'file', 'host': 'host', 'path': 'path', 'port': 'port', 'protocol': 'protocol', 'query': 'query', 'ref': 'ref', 'user_info': 'userInfo' } def __init__(self, authority=None, content=None, default_port=None, file=None, host=None, path=None, port=None, protocol=None, query=None, ref=None, user_info=None): # noqa: E501 """URL - a model defined in Swagger""" # noqa: E501 self._authority = None self._content = None self._default_port = None self._file = None self._host = None self._path = None self._port = None self._protocol = None self._query = None self._ref = None self._user_info = None self.discriminator = None if authority is not None: self.authority = authority if content is not None: self.content = content if default_port is not None: self.default_port = default_port if file is not None: self.file = file if host is not None: self.host = host if path is not None: self.path = path if port is not None: self.port = port if protocol is not None: self.protocol = protocol if query is not None: self.query = query if ref is not None: self.ref = ref if user_info is not None: self.user_info = user_info @property def authority(self): """Gets the authority of this URL. # noqa: E501 :return: The authority of this URL. # noqa: E501 :rtype: str """ return self._authority @authority.setter def authority(self, authority): """Sets the authority of this URL. :param authority: The authority of this URL. # noqa: E501 :type: str """ self._authority = authority @property def content(self): """Gets the content of this URL. # noqa: E501 :return: The content of this URL. # noqa: E501 :rtype: object """ return self._content @content.setter def content(self, content): """Sets the content of this URL. :param content: The content of this URL. # noqa: E501 :type: object """ self._content = content @property def default_port(self): """Gets the default_port of this URL. # noqa: E501 :return: The default_port of this URL. # noqa: E501 :rtype: int """ return self._default_port @default_port.setter def default_port(self, default_port): """Sets the default_port of this URL. :param default_port: The default_port of this URL. # noqa: E501 :type: int """ self._default_port = default_port @property def file(self): """Gets the file of this URL. # noqa: E501 :return: The file of this URL. # noqa: E501 :rtype: str """ return self._file @file.setter def file(self, file): """Sets the file of this URL. :param file: The file of this URL. # noqa: E501 :type: str """ self._file = file @property def host(self): """Gets the host of this URL. # noqa: E501 :return: The host of this URL. # noqa: E501 :rtype: str """ return self._host @host.setter def host(self, host): """Sets the host of this URL. :param host: The host of this URL. # noqa: E501 :type: str """ self._host = host @property def path(self): """Gets the path of this URL. # noqa: E501 :return: The path of this URL. # noqa: E501 :rtype: str """ return self._path @path.setter def path(self, path): """Sets the path of this URL. :param path: The path of this URL. # noqa: E501 :type: str """ self._path = path @property def port(self): """Gets the port of this URL. # noqa: E501 :return: The port of this URL. # noqa: E501 :rtype: int """ return self._port @port.setter def port(self, port): """Sets the port of this URL. :param port: The port of this URL. # noqa: E501 :type: int """ self._port = port @property def protocol(self): """Gets the protocol of this URL. # noqa: E501 :return: The protocol of this URL. # noqa: E501 :rtype: str """ return self._protocol @protocol.setter def protocol(self, protocol): """Sets the protocol of this URL. :param protocol: The protocol of this URL. # noqa: E501 :type: str """ self._protocol = protocol @property def query(self): """Gets the query of this URL. # noqa: E501 :return: The query of this URL. # noqa: E501 :rtype: str """ return self._query @query.setter def query(self, query): """Sets the query of this URL. :param query: The query of this URL. # noqa: E501 :type: str """ self._query = query @property def ref(self): """Gets the ref of this URL. # noqa: E501 :return: The ref of this URL. # noqa: E501 :rtype: str """ return self._ref @ref.setter def ref(self, ref): """Sets the ref of this URL. :param ref: The ref of this URL. # noqa: E501 :type: str """ self._ref = ref @property def user_info(self): """Gets the user_info of this URL. # noqa: E501 :return: The user_info of this URL. # noqa: E501 :rtype: str """ return self._user_info @user_info.setter def user_info(self, user_info): """Sets the user_info of this URL. :param user_info: The user_info of this URL. # noqa: E501 :type: str """ self._user_info = user_info def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(URL, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, URL): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

CSTC-WTCB-BBRI/python_tb_rest_client

tb_rest_client/models/models_pe/url.py

Python

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import json from abc import abstractmethod from argparse import ArgumentParser, Namespace from typing import Any, List, NamedTuple, Optional, Tuple from idb.cli.commands.base import TargetCommand from idb.client.client import IdbClient class NoBundleIdentifierProvidedException(BaseException): pass class BundleWithPath(NamedTuple): bundle_id: Optional[str] path: str @classmethod def parse(cls, argument: str) -> "BundleWithPath": split = argument.split(sep=":", maxsplit=1) if len(split) == 1: return BundleWithPath(bundle_id=None, path=split[0]) return BundleWithPath(bundle_id=split[0], path=split[1]) def _extract_bundle_id(args: Namespace) -> str: if args.bundle_id: return args.bundle_id values = [] for value in vars(args).values(): if isinstance(value, List): values.extend(value) else: values.append(value) for value in values: if not isinstance(value, BundleWithPath): continue bundle_id = value.bundle_id if bundle_id is None: continue args.bundle_id = bundle_id return args.bundle_id raise NoBundleIdentifierProvidedException(f"No Bundle ID Provided in args {args}") def _convert_args(args: Namespace) -> Tuple[Namespace, str]: def convert_value(value: Any) -> Any: # pyre-ignore if isinstance(value, List): return [convert_value(x) for x in value] return value.path if isinstance(value, BundleWithPath) else value bundle_id = _extract_bundle_id(args) args = Namespace( **{ key: convert_value(value) for (key, value) in vars(args).items() if key != "bundle_id" } ) return (args, bundle_id) class FSCommand(TargetCommand): def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "--bundle-id", help="Bundle ID of application. Must be provi", type=str, required=False, default=None, ) super().add_parser_arguments(parser) @abstractmethod async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: pass async def run_with_client(self, args: Namespace, client: IdbClient) -> None: (args, bundle_id) = _convert_args(args) return await self.run_with_bundle(bundle_id=bundle_id, args=args, client=client) class FSListCommand(FSCommand): @property def description(self) -> str: return "List a path inside an application's container" @property def name(self) -> str: return "list" @property def aliases(self) -> List[str]: return ["ls"] def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "path", help="Source path", default="./", type=BundleWithPath.parse ) super().add_parser_arguments(parser) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: paths = await client.ls(bundle_id=bundle_id, path=args.path) if args.json: print(json.dumps([{"path": item.path} for item in paths])) else: for item in paths: print(item.path) class FSMkdirCommand(FSCommand): @property def description(self) -> str: return "Make a directory inside an application's container" @property def name(self) -> str: return "mkdir" def add_parser_arguments(self, parser: ArgumentParser) -> None: super().add_parser_arguments(parser) parser.add_argument( "path", help="Path to directory to create", type=BundleWithPath.parse ) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: await client.mkdir(bundle_id=bundle_id, path=args.path) class FSMoveCommand(FSCommand): @property def description(self) -> str: return "Move a path inside an application's container" @property def name(self) -> str: return "move" @property def aliases(self) -> List[str]: return ["mv"] def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "src", help="Source paths relative to Container", nargs="+", type=BundleWithPath.parse, ) parser.add_argument( "dst", help="Destination path relative to Container", type=BundleWithPath.parse, ) super().add_parser_arguments(parser) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: await client.mv(bundle_id=bundle_id, src_paths=args.src, dest_path=args.dst) class FSRemoveCommand(FSCommand): @property def description(self) -> str: return "Remove an item inside a container" @property def name(self) -> str: return "remove" @property def aliases(self) -> List[str]: return ["rm"] def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "path", help="Path of item to remove (A directory will be recursively deleted)", nargs="+", type=BundleWithPath.parse, ) super().add_parser_arguments(parser) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: await client.rm(bundle_id=bundle_id, paths=args.path) class FSPushCommand(FSCommand): @property def description(self) -> str: return "Copy file(s) from local machine to target" @property def name(self) -> str: return "push" def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "src_paths", help="Path of file(s) to copy to the target", nargs="+" ) parser.add_argument( "dest_path", help=( "Directory relative to the data container of the application\n" "to copy the files into. Will be created if non-existent" ), type=BundleWithPath.parse, ) super().add_parser_arguments(parser) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: return await client.push( bundle_id=bundle_id, src_paths=args.src_paths, dest_path=args.dest_path ) class FSPullCommand(FSCommand): @property def description(self) -> str: return "Copy a file inside an application's container" @property def name(self) -> str: return "pull" def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "src", help="Relative Container source path", type=BundleWithPath.parse ) parser.add_argument("dst", help="Local destination path", type=str) super().add_parser_arguments(parser) async def run_with_bundle( self, bundle_id: str, args: Namespace, client: IdbClient ) -> None: await client.pull(bundle_id=bundle_id, src_path=args.src, dest_path=args.dst) class DeprecatedPushCommand(TargetCommand): @property def description(self) -> str: return "Copy file(s) from local machine to target" @property def name(self) -> str: return "push" def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "src_paths", help="Path of file(s) to copy to the target", nargs="+" ) parser.add_argument( "bundle_id", help="Bundle id of the app to contain these files", type=str ) parser.add_argument( "dest_path", help=( "Directory relative to the data container of the application\n" "to copy the files into. Will be created if non-existent" ), type=str, ) super().add_parser_arguments(parser) async def run_with_client(self, args: Namespace, client: IdbClient) -> None: self.logger.warning(f"'push' is deprecated, please use 'file push' instead") return await FSPushCommand().run_with_bundle( bundle_id=args.bundle_id, args=args, client=client ) class DeprecatedPullCommand(TargetCommand): @property def description(self) -> str: return "Copy a file inside an application's container" @property def name(self) -> str: return "pull" def add_parser_arguments(self, parser: ArgumentParser) -> None: parser.add_argument( "bundle_id", help="Bundle id of the app to contain these files", type=str ) parser.add_argument("src", help="Relativer Container source path", type=str) parser.add_argument("dst", help="Local destination path", type=str) super().add_parser_arguments(parser) async def run_with_client(self, args: Namespace, client: IdbClient) -> None: self.logger.warning(f"'pull' is deprecated, please use 'file pull' instead") return await FSPullCommand().run_with_bundle( bundle_id=args.bundle_id, args=args, client=client )

BalestraPatrick/idb

idb/cli/commands/file.py

Python

from __future__ import print_function import numpy as np import scipy.linalg import torch import torch.nn as nn import torch.nn.functional as F from flow_modules.misc import cpd_sum, cpd_mean def squeeze2d(input, factor=2): #assert factor >= 1 and isinstance(factor, int) if factor == 1: return input size = input.size() B = size[0] C = size[1] H = size[2] W = size[3] assert H % factor == 0 and W % factor == 0, "{}".format((H, W)) x = input.view(B, C, H // factor, factor, W // factor, factor) x = x.permute(0, 1, 3, 5, 2, 4).contiguous() x = x.view(B, C * factor * factor, H // factor, W // factor) return x def unsqueeze2d(input, factor=2): assert factor >= 1 and isinstance(factor, int) factor2 = factor ** 2 if factor == 1: return input size = input.size() B = size[0] C = size[1] H = size[2] W = size[3] assert C % (factor2) == 0, "{}".format(C) x = input.view(B, C // factor2, factor, factor, H, W) x = x.permute(0, 1, 4, 2, 5, 3).contiguous() x = x.view(B, C // (factor2), H * factor, W * factor) return x class SqueezeLayer(nn.Module): def __init__(self, factor): super(SqueezeLayer, self).__init__() self.factor = factor def forward(self, input, logdet=0., reverse=False): if not reverse: output = squeeze2d(input, self.factor) return output, logdet else: output = unsqueeze2d(input, self.factor) return output, logdet class InvertibleConv1x1(nn.Module): def __init__(self, num_channels, LU_decomposed=True): super().__init__() w_shape = [num_channels, num_channels] w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(np.float32) if not LU_decomposed: # Sample a random orthogonal matrix: self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init))) else: np_p, np_l, np_u = scipy.linalg.lu(w_init) np_s = np.diag(np_u) np_sign_s = np.sign(np_s) np_log_s = np.log(np.abs(np_s)) np_u = np.triu(np_u, k=1) l_mask = np.tril(np.ones(w_shape, dtype=np.float32), -1) eye = np.eye(*w_shape, dtype=np.float32) self.register_buffer('p', torch.Tensor(np_p.astype(np.float32))) self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(np.float32))) self.l = nn.Parameter(torch.Tensor(np_l.astype(np.float32))) self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(np.float32))) self.u = nn.Parameter(torch.Tensor(np_u.astype(np.float32))) self.l_mask = torch.Tensor(l_mask) self.eye = torch.Tensor(eye) self.w_shape = w_shape self.LU = LU_decomposed def get_weight(self, input, reverse): w_shape = self.w_shape pixels = list(input.size())[-1] if not self.LU: #thops.pixels(input) dlogdet = (torch.slogdet(self.weight)[1]) * pixels*pixels if not reverse: weight = self.weight.view(w_shape[0], w_shape[1], 1, 1) else: weight = torch.inverse(self.weight.double()).float()\ .view(w_shape[0], w_shape[1], 1, 1) return weight, dlogdet else: self.p = self.p.to(input.device) self.sign_s = self.sign_s.to(input.device) self.l_mask = self.l_mask.to(input.device) self.eye = self.eye.to(input.device) l = self.l * self.l_mask + self.eye u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s)) dlogdet = cpd_sum(self.log_s) * pixels*pixels if not reverse: w = torch.matmul(self.p, torch.matmul(l, u)) else: l = torch.inverse(l.cpu().double()).float() u = torch.inverse(u.cpu().double()).float() w = torch.matmul(u, torch.matmul(l, self.p.cpu().inverse())) if torch.cuda.is_available(): w = w.cuda() return w.view(w_shape[0], w_shape[1], 1, 1), dlogdet def forward(self, input, logdet=None, reverse=False): """ log-det = log|abs(|W|)| * pixels """ weight, dlogdet = self.get_weight(input, reverse) if not reverse: z = F.conv2d(input, weight) if logdet is not None: logdet = logdet + dlogdet return z, logdet else: z = F.conv2d(input, weight) if logdet is not None: logdet = logdet - dlogdet return z, logdet class Actnormlayer(nn.Module): def __init__(self, num_features, scale=1.): super(Actnormlayer, self).__init__() self.register_buffer('is_initialized', torch.zeros(1)) self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1)) self.logs = nn.Parameter(torch.zeros(1, num_features, 1, 1)) self.num_features = num_features self.scale = float(scale) self.eps = 1e-6 def initialize_parameters(self, x): if not self.training: return with torch.no_grad(): bias = -cpd_mean(x.clone(), dim=[0, 2, 3], keepdims=True) v = cpd_mean((x.clone() + bias) ** 2, dim=[0, 2, 3], keepdims=True) logs = (self.scale / (v.sqrt() + self.eps)).log() self.bias.data.copy_(bias.data) self.logs.data.copy_(logs.data) self.is_initialized += 1. def _center(self, x, reverse=False): if reverse: return x - self.bias else: return x + self.bias def _scale(self, x, sldj, reverse=False): logs = self.logs if reverse: x = x * logs.mul(-1).exp() else: x = x * logs.exp() if sldj is not None: ldj = logs.sum() * x.size(2) * x.size(3) if reverse: sldj = sldj - ldj else: sldj = sldj + ldj return x, sldj def forward(self, x, ldj=None, reverse=False): if not self.is_initialized: self.initialize_parameters(x) if reverse: x, ldj = self._scale(x, ldj, reverse) x = self._center(x, reverse) else: x = self._center(x, reverse) x, ldj = self._scale(x, ldj, reverse) return x, ldj class Split2dMsC(nn.Module): def __init__(self, num_channels, level=0): super().__init__() self.level = level def split_feature(self, z): return z[:,:z.size(1)//2,:,:], z[:,z.size(1)//2:,:,:] def split2d_prior(self, z): h = self.conv(z) return h[:,0::2,:,:], h[:,1::2,:,:] def forward(self, input, logdet=0., reverse=False, eps_std=None): if not reverse: z1, z2 = self.split_feature(input) return ( z1, z2), logdet else: z1, z2 = input z = torch.cat((z1, z2), dim=1) return z, logdet class TupleFlip(nn.Module): def __init__(self, ): super().__init__() def forward(self, z, logdet=0., reverse=False): if not reverse: z1, z2 = z.chunk(2, dim=1) return torch.cat([z2,z1], dim=1), logdet else: z2, z1 = z.chunk(2, dim=1) return torch.cat([z1,z2], dim=1), logdet class GaussianDiag: Log2PI = float(np.log(2 * np.pi)) @staticmethod def likelihood(mean, logs, x): return -0.5 * (logs * 2. + ((x - mean) ** 2) / torch.exp(logs * 2.) + GaussianDiag.Log2PI) @staticmethod def logp(mean, logs, x): likelihood = GaussianDiag.likelihood(mean, logs, x) return cpd_sum(likelihood, dim=[1,2,3]) @staticmethod def sample(mean, logs, eps_std=None): eps_std = eps_std or 1 eps = torch.normal(mean=torch.zeros_like(mean), std=torch.ones_like(logs) * eps_std) return mean + torch.exp(logs) * eps

Catherine0505/mar-scf-flow

flow_modules/common_modules.py

Python

# -*- coding: utf-8 -*- from __future__ import division import os import pytest from ethereum import _solidity from ethereum._solidity import compile_file from ethereum.utils import denoms from pyethapp.rpc_client import JSONRPCClient from pyethapp.jsonrpc import default_gasprice from raiden.network.rpc.client import ( decode_topic, patch_send_transaction, patch_send_message ) from raiden.utils import privatekey_to_address, get_contract_path from raiden.blockchain.abi import CHANNEL_MANAGER_ABI solidity = _solidity.get_solidity() # pylint: disable=invalid-name @pytest.mark.timeout(180) @pytest.mark.parametrize('privatekey_seed', ['blockchain:{}']) @pytest.mark.parametrize('number_of_nodes', [3]) @pytest.mark.parametrize('channels_per_node', [0]) @pytest.mark.parametrize('number_of_assets', [0]) def test_new_netting_contract(raiden_network, asset_amount, settle_timeout): # pylint: disable=line-too-long,too-many-statements,too-many-locals app0, app1, app2 = raiden_network peer0_address = app0.raiden.address peer1_address = app1.raiden.address peer2_address = app2.raiden.address blockchain_service0 = app0.raiden.chain asset_address = blockchain_service0.deploy_and_register_asset( contract_name='HumanStandardToken', contract_file='HumanStandardToken.sol', constructor_parameters=(asset_amount, 'raiden', 2, 'Rd'), ) asset0 = blockchain_service0.asset(asset_address) for transfer_to in raiden_network[1:]: asset0.transfer( privatekey_to_address(transfer_to.raiden.privkey), asset_amount // len(raiden_network), ) manager0 = blockchain_service0.manager_by_asset(asset_address) # sanity assert manager0.channels_addresses() == [] assert manager0.channels_by_participant(peer0_address) == [] assert manager0.channels_by_participant(peer1_address) == [] assert manager0.channels_by_participant(peer2_address) == [] # create one channel netting_address_01 = manager0.new_netting_channel( peer0_address, peer1_address, settle_timeout, ) # check contract state netting_channel_01 = blockchain_service0.netting_channel(netting_address_01) assert netting_channel_01.isopen() is False assert netting_channel_01.partner(peer0_address) == peer1_address assert netting_channel_01.partner(peer1_address) == peer0_address # check channels channel_list = manager0.channels_addresses() assert sorted(channel_list[0]) == sorted([peer0_address, peer1_address]) assert manager0.channels_by_participant(peer0_address) == [netting_address_01] assert manager0.channels_by_participant(peer1_address) == [netting_address_01] assert manager0.channels_by_participant(peer2_address) == [] # create other chanel netting_address_02 = manager0.new_netting_channel( peer0_address, peer2_address, settle_timeout, ) netting_channel_02 = blockchain_service0.netting_channel(netting_address_02) assert netting_channel_02.isopen() is False assert netting_channel_02.partner(peer0_address) == peer2_address assert netting_channel_02.partner(peer2_address) == peer0_address channel_list = manager0.channels_addresses() expected_channels = [ sorted([peer0_address, peer1_address]), sorted([peer0_address, peer2_address]), ] for channel in channel_list: assert sorted(channel) in expected_channels result0 = sorted(manager0.channels_by_participant(peer0_address)) result1 = sorted([netting_address_01, netting_address_02]) assert result0 == result1 assert manager0.channels_by_participant(peer1_address) == [netting_address_01] assert manager0.channels_by_participant(peer2_address) == [netting_address_02] # deposit without approve should fail netting_channel_01.deposit(peer0_address, 100) assert netting_channel_01.isopen() is False assert netting_channel_02.isopen() is False assert netting_channel_01.detail(peer0_address)['our_balance'] == 0 assert netting_channel_01.detail(peer1_address)['our_balance'] == 0 # single-funded channel app0.raiden.chain.asset(asset_address).approve(netting_address_01, 100) netting_channel_01.deposit(peer0_address, 100) assert netting_channel_01.isopen() is True assert netting_channel_02.isopen() is False assert netting_channel_01.detail(peer0_address)['our_balance'] == 100 assert netting_channel_01.detail(peer1_address)['our_balance'] == 0 # double-funded channel app0.raiden.chain.asset(asset_address).approve(netting_address_02, 70) netting_channel_02.deposit(peer0_address, 70) assert netting_channel_01.isopen() is True assert netting_channel_02.isopen() is True assert netting_channel_02.detail(peer0_address)['our_balance'] == 70 assert netting_channel_02.detail(peer2_address)['our_balance'] == 0 app2.raiden.chain.asset(asset_address).approve(netting_address_02, 130) app2.raiden.chain.netting_channel(netting_address_02).deposit(peer2_address, 130) assert netting_channel_01.isopen() is True assert netting_channel_02.isopen() is True assert netting_channel_02.detail(peer0_address)['our_balance'] == 70 assert netting_channel_02.detail(peer2_address)['our_balance'] == 130 @pytest.mark.skipif( 'TRAVIS' in os.environ, reason='Flaky test due to mark.timeout not being scheduled. Issue #319' ) @pytest.mark.timeout(60) @pytest.mark.parametrize('privatekey_seed', ['blockchain:{}']) @pytest.mark.parametrize('number_of_nodes', [3]) def test_blockchain( blockchain_type, blockchain_backend, # required to start the geth backend blockchain_rpc_ports, private_keys, poll_timeout): # pylint: disable=too-many-locals # this test is for interaction with a blockchain using json-rpc, so it # doesnt make sense to execute it against mock or tester if blockchain_type not in ('geth',): return addresses = [ privatekey_to_address(priv) for priv in private_keys ] privatekey = private_keys[0] address = privatekey_to_address(privatekey) total_asset = 100 jsonrpc_client = JSONRPCClient( port=blockchain_rpc_ports[0], privkey=privatekey, print_communication=False, ) patch_send_transaction(jsonrpc_client) patch_send_message(jsonrpc_client) humantoken_path = get_contract_path('HumanStandardToken.sol') humantoken_contracts = compile_file(humantoken_path, libraries=dict()) token_proxy = jsonrpc_client.deploy_solidity_contract( address, 'HumanStandardToken', humantoken_contracts, dict(), (total_asset, 'raiden', 2, 'Rd'), contract_path=humantoken_path, gasprice=default_gasprice, timeout=poll_timeout, ) registry_path = get_contract_path('Registry.sol') registry_contracts = compile_file(registry_path) registry_proxy = jsonrpc_client.deploy_solidity_contract( address, 'Registry', registry_contracts, dict(), tuple(), contract_path=registry_path, gasprice=default_gasprice, timeout=poll_timeout, ) log_list = jsonrpc_client.call( 'eth_getLogs', { 'fromBlock': '0x0', 'toBlock': 'latest', 'topics': [], }, ) assert len(log_list) == 0 # pylint: disable=no-member assert token_proxy.balanceOf(address) == total_asset transaction_hash = registry_proxy.addAsset.transact( token_proxy.address, gasprice=denoms.wei, ) jsonrpc_client.poll(transaction_hash.decode('hex'), timeout=poll_timeout) assert len(registry_proxy.assetAddresses.call()) == 1 log_list = jsonrpc_client.call( 'eth_getLogs', { 'fromBlock': '0x0', 'toBlock': 'latest', 'topics': [], }, ) assert len(log_list) == 1 channel_manager_address_encoded = registry_proxy.channelManagerByAsset.call( token_proxy.address, ) channel_manager_address = channel_manager_address_encoded.decode('hex') log = log_list[0] log_topics = [ decode_topic(topic) for topic in log['topics'] # pylint: disable=invalid-sequence-index ] log_data = log['data'] event = registry_proxy.translator.decode_event( log_topics, log_data[2:].decode('hex'), ) assert channel_manager_address == event['channel_manager_address'].decode('hex') assert token_proxy.address == event['asset_address'].decode('hex') channel_manager_proxy = jsonrpc_client.new_contract_proxy( CHANNEL_MANAGER_ABI, channel_manager_address, ) transaction_hash = channel_manager_proxy.newChannel.transact( addresses[1], 10, gasprice=denoms.wei, ) jsonrpc_client.poll(transaction_hash.decode('hex'), timeout=poll_timeout) log_list = jsonrpc_client.call( 'eth_getLogs', { 'fromBlock': '0x0', 'toBlock': 'latest', 'topics': [], }, ) assert len(log_list) == 2

nicksavers/raiden

raiden/tests/integration/test_blockchainservice.py

Python

# coding: utf-8 from __future__ import absolute_import from __future__ import print_function import warnings from ruamel.yaml.error import MarkedYAMLError, ReusedAnchorWarning from ruamel.yaml.compat import utf8 from ruamel.yaml.events import ( StreamStartEvent, StreamEndEvent, MappingStartEvent, MappingEndEvent, SequenceStartEvent, SequenceEndEvent, AliasEvent, ScalarEvent, ) from ruamel.yaml.nodes import ( MappingNode, ScalarNode, SequenceNode, ) __all__ = ['Composer', 'ComposerError'] class ComposerError(MarkedYAMLError): pass class Composer(object): def __init__(self): self.anchors = {} def check_node(self): # Drop the STREAM-START event. if self.check_event(StreamStartEvent): self.get_event() # If there are more documents available? return not self.check_event(StreamEndEvent) def get_node(self): # Get the root node of the next document. if not self.check_event(StreamEndEvent): return self.compose_document() def get_single_node(self): # Drop the STREAM-START event. self.get_event() # Compose a document if the stream is not empty. document = None if not self.check_event(StreamEndEvent): document = self.compose_document() # Ensure that the stream contains no more documents. if not self.check_event(StreamEndEvent): event = self.get_event() raise ComposerError( "expected a single document in the stream", document.start_mark, "but found another document", event.start_mark) # Drop the STREAM-END event. self.get_event() return document def compose_document(self): # Drop the DOCUMENT-START event. self.get_event() # Compose the root node. node = self.compose_node(None, None) # Drop the DOCUMENT-END event. self.get_event() self.anchors = {} return node def compose_node(self, parent, index): if self.check_event(AliasEvent): event = self.get_event() alias = event.anchor if alias not in self.anchors: raise ComposerError( None, None, "found undefined alias %r" % utf8(alias), event.start_mark) return self.anchors[alias] event = self.peek_event() anchor = event.anchor if anchor is not None: # have an anchor if anchor in self.anchors: # raise ComposerError( # "found duplicate anchor %r; first occurence" # % utf8(anchor), self.anchors[anchor].start_mark, # "second occurence", event.start_mark) ws = "\nfound duplicate anchor {!r}\nfirst occurence {}\nsecond occurence "\ "{}".format( (anchor), self.anchors[anchor].start_mark, event.start_mark) warnings.warn(ws, ReusedAnchorWarning) self.descend_resolver(parent, index) if self.check_event(ScalarEvent): node = self.compose_scalar_node(anchor) elif self.check_event(SequenceStartEvent): node = self.compose_sequence_node(anchor) elif self.check_event(MappingStartEvent): node = self.compose_mapping_node(anchor) self.ascend_resolver() return node def compose_scalar_node(self, anchor): event = self.get_event() tag = event.tag if tag is None or tag == u'!': tag = self.resolve(ScalarNode, event.value, event.implicit) node = ScalarNode(tag, event.value, event.start_mark, event.end_mark, style=event.style, comment=event.comment) if anchor is not None: self.anchors[anchor] = node return node def compose_sequence_node(self, anchor): start_event = self.get_event() tag = start_event.tag if tag is None or tag == u'!': tag = self.resolve(SequenceNode, None, start_event.implicit) node = SequenceNode(tag, [], start_event.start_mark, None, flow_style=start_event.flow_style, comment=start_event.comment, anchor=anchor) if anchor is not None: self.anchors[anchor] = node index = 0 while not self.check_event(SequenceEndEvent): node.value.append(self.compose_node(node, index)) index += 1 end_event = self.get_event() if node.flow_style is True and end_event.comment is not None: if node.comment is not None: print('Warning: unexpected end_event commment in sequence ' 'node {}'.format(node.flow_style)) node.comment = end_event.comment node.end_mark = end_event.end_mark self.check_end_doc_comment(end_event, node) return node def compose_mapping_node(self, anchor): start_event = self.get_event() tag = start_event.tag if tag is None or tag == u'!': tag = self.resolve(MappingNode, None, start_event.implicit) node = MappingNode(tag, [], start_event.start_mark, None, flow_style=start_event.flow_style, comment=start_event.comment, anchor=anchor) if anchor is not None: self.anchors[anchor] = node while not self.check_event(MappingEndEvent): # key_event = self.peek_event() item_key = self.compose_node(node, None) # if item_key in node.value: # raise ComposerError("while composing a mapping", # start_event.start_mark, # "found duplicate key", key_event.start_mark) item_value = self.compose_node(node, item_key) # node.value[item_key] = item_value node.value.append((item_key, item_value)) end_event = self.get_event() if node.flow_style is True and end_event.comment is not None: node.comment = end_event.comment node.end_mark = end_event.end_mark self.check_end_doc_comment(end_event, node) return node def check_end_doc_comment(self, end_event, node): if end_event.comment and end_event.comment[1]: # pre comments on an end_event, no following to move to if node.comment is None: node.comment = [None, None] assert not isinstance(node, ScalarEvent) # this is a post comment on a mapping node, add as third element # in the list node.comment.append(end_event.comment[1]) end_event.comment[1] = None

mpercich/Calendarize

ios/dateparser/lib/python2.7/site-packages/ruamel/yaml/composer.py

Python

import argparse import locale import sys from datetime import datetime from model import * from sql import * from common import * from util import * def parse_arguments(): ''' Parse input arguments. Passing the API key is defined as mandatory. ''' parser = argparse.ArgumentParser(description='Incrementally exports JSON orders data into CSV format and optionally into a SQLite DB.') parser.add_argument('-k', '--key', type=str, required=True, help='API key to be used to perform the REST request to the backend.') parser.add_argument('-l', '--locale', type=str, required=False, help='Specify the locale: it_IT for italian. Otherwise machine default one.') parser.add_argument('-d', '--db', action='store_true', required=False, help='Instruct the tool to load a SQLite database up.') parser.add_argument('-p', '--path', type=str, required=True, help='Define datastore base path to csv/ and db/ folders (csv/ and db/ folders should be already created).') parser.add_argument('-n', '--number', type=int, required=True, help='Define how many records each REST call should pull down.') parser.add_argument('-c', '--customer', type=int, required=False, help='Define whether the customer table should be updated contextually: it requires the number of cycles per page (max 50 records') args = parser.parse_args() return args def main(): args = parse_arguments() if args.locale: locale.setlocale(locale.LC_ALL, args.locale) else: locale.setlocale(locale.LC_ALL, 'en_GB') datastore_path = args.path nr_records = args.number if not is_path_existent('%s/%s' % (datastore_path, 'csv')): sys.exit(1) if not is_path_existent('%s/%s' % (datastore_path, 'db')): sys.exit(1) # load or refresh the customer table for enrichment if args.customer: customers = load_customers_pages(args.key, args.customer) persist_customers_to_sqlite(customers, datastore_path) # looking up the customers for successive enrichment of orders lookup = lookup_customers(datastore_path) orders = load_orders_pages(args.key, nr_records, lookup) print('info: loaded %d order(s)...' % len(orders)) print(orders[0]) print('info: all records between FIRST and LAST\n') print(orders[-1]) export_to_csv(orders, datastore_path) print('info: CSV export successul %d order(s)' % len(orders)) if args.db: export_to_sqlite(orders, datastore_path) if __name__ == "__main__": main()

hailpam/data-crunching

scripts/orders-exporter.py

Python

# -*- coding: utf-8 -*- import QUANTAXIS as QA from QUANTAXIS.QAFetch import QATusharePro as pro import pandas as pd import numpy as np from pyspark.sql.functions import pandas_udf, PandasUDFType from pyspark import SparkContext,SparkConf from pyspark.sql.session import SparkSession from QUANTAXIS.ML import RegUtil from pyspark.sql.types import StructType,DoubleType,StructField,StringType #from pyspark.sql.functions import import copy import talib spark = SparkSession.builder.appName("my app").getOrCreate() #spark.sparkContext.setLogLevel("INFO") spark.conf.set("spark.sql.execution.arrow.enabled", "true") start_3years_bf = '20150101' industry_daily = pro.QA_fetch_get_industry_daily(start=start_3years_bf, end='20181231').sort_values(['industry','trade_date'], ascending = True) industry_daily = spark.createDataFrame(industry_daily) new_struct = ['q_dtprofit_ttm_poly', 'q_gr_poly', 'q_profit_poly', 'q_dtprofit_poly', 'q_opincome_poly', 'industry_roe', 'industry_pe', 'roe_ttm', 'industry_pe_ttm'] p1 = StructType() p1.add(StructField('trade_date', StringType())) p1.add(StructField('industry', StringType())) list(map(lambda x: p1.add(StructField(x, DoubleType())), new_struct)) start = '20180101' end = '20181231' @pandas_udf(p1, PandasUDFType.GROUPED_MAP) def _trend(key,data): dates = [str(int(start[0:4]) - 3) + '0831',str(int(start[0:4]) - 3) + '1031', str(int(start[0:4]) - 2) + '0431', str(int(start[0:4]) - 2) + '0831', str(int(start[0:4]) - 2) + '1031', str(int(start[0:4]) - 1) + '0431', str(int(start[0:4]) - 1) + '0831', str(int(start[0:4]) - 1) + '1031'] _lam_f = lambda x, y: y[y.trade_date <= x].iloc[-1] if y[y.trade_date <= x].shape[0]>0 else None resampledf = pd.DataFrame(list(filter(lambda x:x is not None,map(_lam_f, dates,[data]*8)))) col = ['trade_date', 'industry'] col = col+new_struct indicator = pd.DataFrame(columns=col) df = data[data.trade_date >= start] df.reset_index(drop=True) for index,item in df.iterrows(): if item.trade_date[4:8] <= "0831" and item.trade_date[4:8] > "0431" and item.trade_date[0:4] + '0431' not in dates: dates.append([item.trade_date[0:4] + '0431']) t = list(filter(lambda x:x is not None,map(_lam_f, [item.trade_date[0:4] + '0431'],[data]))) if t is not None: resampledf = resampledf.append(t) if item.trade_date[4:8] <= "1031" and item.trade_date[4:8] > "0831" and item.trade_date[0:4] + '0831' not in dates: dates.append([item.trade_date[0:4] + '0831']) t = list(filter(lambda x: x is not None, map(_lam_f, [item.trade_date[0:4] + '0831'], [data]))) if t is not None: resampledf = resampledf.append(t) if item.trade_date[4:8] > "1031" and item.trade_date[0:4] + '1031' not in dates: dates.append([item.trade_date[0:4] + '1031']) t = list(filter(lambda x: x is not None, map(_lam_f, [item.trade_date[0:4] + '1031'], [data]))) if t is not None: resampledf = resampledf.append(t) resample = resampledf.append(list(map(_lam_f, [item.trade_date], [data]))) resample = resample.dropna(how='all') ind = -8 if resample.shape[0]>8 else -resample.shape[0] fit, p3 = RegUtil.regress_y_polynomial(resample[ind:].q_dtprofit_ttm, poly=3, show=False) # fit, p4 = RegUtil.regress_y_polynomial(resample[-8:].q_opincome_ttm, poly=3, show=False) fit, p5 = RegUtil.regress_y_polynomial(resample[ind:].q_gr, poly=3, show=False) fit, p6 = RegUtil.regress_y_polynomial(resample[ind:].q_profit, poly=3, show=False) fit, p7 = RegUtil.regress_y_polynomial(resample[ind:].q_dtprofit, poly=3, show=False) fit, p8 = RegUtil.regress_y_polynomial(resample[ind:].q_opincome, poly=3, show=False) roe = item.q_dtprofit / item.total_hldr_eqy_exc_min_int pe = item.ind_total_mv*10000/item.q_dtprofit roe_ttm = item.q_dtprofit_ttm / item.total_hldr_eqy_exc_min_int pe_ttm = item.ind_total_mv*10000/item.q_dtprofit_ttm indicator.loc[index] = [item.trade_date,key[0],p3(8),p5(8),p6(8),p7(8),p8(8),roe,pe,roe_ttm,pe_ttm] #print(indicator.loc[index]) return indicator industry_daily = industry_daily.groupby("industry").apply(_trend).cache() stock = pro.QA_SU_stock_info() stock_spark = spark.createDataFrame(stock) basic = pd.read_csv('/usr/local/spark/basic-2018.csv') basic = spark.createDataFrame(basic) #df = basic.join(stock_spark, basic.ts_code==stock_spark.ts_code, "inner") df = basic.join(stock_spark,['ts_code'],"inner") #industry_daily.count() df = df.join(industry_daily,['industry', 'trade_date'],"inner") new2_struct = [ 'cnt', 'mean', 'std', 'min', 'per25', 'per50', 'per75', 'per85', 'per95', 'max'] p2 = StructType() p2.add(StructField('category', StringType())) p2.add(StructField('industry', StringType())) list(map(lambda x: p2.add(StructField(x, DoubleType())), new2_struct)) @pandas_udf(p2, PandasUDFType.GROUPED_MAP) def _dailystat(key,df): d = df.loc[:, ['q_dtprofit_ttm_poly','q_gr_poly','q_profit_poly','q_dtprofit_poly','q_opincome_poly','industry_roe','industry_pe','roe_ttm','industry_pe_ttm']] st = d.describe([.25, .5, .75, .85, .95]).T.reset_index(level=0) col = ['category'] col = col+new2_struct st.columns = col st.loc[:,'industry'] = key[0] median = d.median() mad = abs(d - median).median() d[d - (median - mad * 3 * 1.4826) < 0] = np.array((median - mad * 3 * 1.4826).tolist()*d.shape[0]).reshape((d.shape[0],d.columns.size)) d[d - (median + mad * 3 * 1.4826) > 0] = np.array((median + mad * 3 * 1.4826).tolist()*d.shape[0]).reshape((d.shape[0],d.columns.size)) st2 = d.describe([.25, .5, .85, .90, .95]).T.reset_index(level=0) st2.columns = col st2.loc[:,'industry'] = key[0] st2.category = st2.category+'_mad' return pd.concat([st, st2]) dailymarket = industry_daily.groupby('trade_date').apply(_dailystat).toPandas() # add3_struct = ['industry_roe_buy', 'industry_pe_buy', 'q_dtprofit_poly_buy', 'industry_roe_ttm_buy', 'industry_pe_ttm_buy', 'q_dtprofit_ttm_poly_buy', 'industry_roe_buy_mad', 'industry_pe_buy_mad', 'q_dtprofit_poly_buy_mad', 'industry_roe_ttm_buy_mad', 'industry_pe_ttm_buy_mad', 'q_dtprofit_ttm_poly_buy_mad'] p3 = copy.deepcopy(df.schema) list(map(lambda x: p3.add(StructField(x, DoubleType())), add3_struct)) p3.add(StructField('key_flag', StringType())) k = 0 d = [] ud = [] #print(p3) #print(df.columns) @pandas_udf(p3, PandasUDFType.GROUPED_MAP) def _top10(key,df2): global dailymarket global k df = pd.concat([df2, pd.DataFrame(columns=add3_struct, dtype='float')]) market = dailymarket[dailymarket.industry == key[0]] ud.append(key[0]) #print(market) df.loc[:,'key_flag'] = key[0] if market.shape[0]: df.loc[:, 'industry_roe_buy'] = df.industry_roe - market[market.category == 'industry_roe'].per90[0] df.loc[:, 'industry_pe_buy'] = df.industry_pe - market[market.category == 'industry_pe'].per85[0] df.loc[:, 'q_dtprofit_poly_buy'] = df.q_dtprofit_poly - market[market.category == 'q_dtprofit_poly'].per85[0] df.loc[:, 'industry_roe_ttm_buy'] = df.roe_ttm - market[market.category == 'roe_ttm'].per90[0] df.loc[:, 'industry_pe_ttm_buy'] = df.industry_pe_ttm - market[market.category == 'industry_pe_ttm'].per85[0] df.loc[:, 'q_dtprofit_ttm_poly_buy'] = df.q_dtprofit_ttm_poly - market[market.category == 'q_dtprofit_ttm_poly'].per85[0] df.loc[:, 'industry_roe_buy_mad'] = df.industry_roe - market[market.category == 'industry_roe_mad'].per90[0] df.loc[:, 'industry_pe_buy_mad'] = df.industry_pe - market[market.category == 'industry_pe_mad'].per85[0] df.loc[:, 'q_dtprofit_poly_buy_mad'] = df.q_dtprofit_poly - market[market.category == 'q_dtprofit_poly_mad'].per85[0] df.loc[:, 'industry_roe_ttm_buy_mad'] = df.roe_ttm - market[market.category == 'roe_ttm_mad'].per90[0] df.loc[:, 'industry_pe_ttm_buy_mad'] = df.industry_pe_ttm - market[market.category == 'industry_pe_ttm_mad'].per85[0] df.loc[:, 'q_dtprofit_ttm_poly_buy_mad'] = df.q_dtprofit_ttm_poly - market[market.category == 'q_dtprofit_ttm_poly_mad'].per85[0] else: k = k+1 d.append(key[0]) return df rs = df.groupby('trade_date').apply(_top10).toPandas().set_index(['trade_date', 'ts_code'], drop=False) print('############rs key flag ############') print(rs.key_flag.unique()) print('############rs total count ############') print(len(rs)) print('############ mised key ############') print(k) print('############ first 5 key ############') print(ud[0:5]) #print(rs.head) # # if __name__ == '__main__': # print('wtf') # # finacial = pd.read_csv('/usr/local/spark/finace-2018.csv') # # basic = pd.read_csv('/usr/local/spark/basic-2018.csv') # # #df = spark.createDataFrame(basic.loc[:,['ts_code','trade_date']]) # sv = simpleValued('20180101','20181231') # df = sv.non_finacal_top5_valued() # df1 = sv.industry_trend_top10(df) # df1.toPandas().set_index(['trade_date', 'ts_code'], drop=False)

lkaiser/QUANTAXIS

EXAMPLE/test_backtest/example/indicator/simple_valued_spark2.py

Python

# (C) Datadog, Inc. 2021-present # All rights reserved # Licensed under Simplified BSD License (see LICENSE) import pytest from datadog_checks.postgres.relationsmanager import ALL_SCHEMAS, IDX_METRICS, LOCK_METRICS, RelationsManager from .common import SCHEMA_NAME pytestmark = pytest.mark.unit @pytest.mark.parametrize( 'relations_config,expected_filter', [ ( [ {'relation_regex': 'ix.*', 'schemas': ['public', 's1', 's2']}, {'relation_regex': 'ibx.*', 'schemas': ['public']}, {'relation_regex': 'icx.*', 'schemas': ['public']}, ], "( relname ~ 'ix.*' AND schemaname = ANY(array['public','s1','s2']::text[]) ) " "OR ( relname ~ 'ibx.*' AND schemaname = ANY(array['public']::text[]) ) " "OR ( relname ~ 'icx.*' AND schemaname = ANY(array['public']::text[]) )", ), ( [ {'relation_regex': '.+_archive'}, ], "( relname ~ '.+_archive' )", ), ( [ {'relation_name': 'my_table', 'schemas': ['public', 'app'], 'relkind': ['r']}, # relkind ignored {'relation_name': 'my_table2', 'relkind': ['p', 'r']}, # relkind ignored {'relation_regex': 'table.*'}, ], "( relname = 'my_table' AND schemaname = ANY(array['public','app']::text[]) ) " "OR ( relname = 'my_table2' ) " "OR ( relname ~ 'table.*' )", ), ( ['table1', 'table2'], "( relname = 'table1' ) OR ( relname = 'table2' )", ), ], ) def test_relations_cases(relations_config, expected_filter): query = '{relations}' relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert query_filter == expected_filter def test_relation_filter(): query = "Select foo from bar where {relations}" relations_config = [{'relation_name': 'breed', 'schemas': ['public']}] relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert ( query_filter == "Select foo from bar where ( relname = 'breed' AND schemaname = ANY(array['public']::text[]) )" ) def test_relation_filter_no_schemas(): query = "Select foo from bar where {relations}" relations_config = [{'relation_name': 'persons', 'schemas': [ALL_SCHEMAS]}] relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert query_filter == "Select foo from bar where ( relname = 'persons' )" def test_relation_filter_regex(): query = "Select foo from bar where {relations}" relations_config = [{'relation_regex': 'b.*', 'schemas': [ALL_SCHEMAS]}] relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert query_filter == "Select foo from bar where ( relname ~ 'b.*' )" def test_relation_filter_relkind(): query = LOCK_METRICS['query'].replace('{metrics_columns}', 'foo') relations_config = [{'relation_regex': 'b.*', 'schemas': [ALL_SCHEMAS], 'relkind': ['r', 't']}] relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert "AND relkind = ANY(array['r','t'])" in query_filter def test_relkind_does_not_apply_to_index_metrics(): query = IDX_METRICS['query'].replace('{metrics_columns}', 'foo') relations_config = [{'relation_regex': 'b.*', 'schemas': [ALL_SCHEMAS], 'relkind': ['r']}] relations = RelationsManager(relations_config) query_filter = relations.filter_relation_query(query, SCHEMA_NAME) assert 'relkind' not in query_filter

Kyle-Neale/integrations-core

postgres/tests/test_relationsmanager.py

Python

"""Common classes and elements for Omnilogic Integration.""" from datetime import timedelta import logging from omnilogic import OmniLogicException from homeassistant.const import ATTR_NAME from homeassistant.core import HomeAssistant from homeassistant.helpers.update_coordinator import ( CoordinatorEntity, DataUpdateCoordinator, UpdateFailed, ) from .const import ( ALL_ITEM_KINDS, ATTR_IDENTIFIERS, ATTR_MANUFACTURER, ATTR_MODEL, DOMAIN, ) _LOGGER = logging.getLogger(__name__) class OmniLogicUpdateCoordinator(DataUpdateCoordinator): """Class to manage fetching update data from single endpoint.""" def __init__( self, hass: HomeAssistant, api: str, name: str, polling_interval: int, ): """Initialize the global Omnilogic data updater.""" self.api = api super().__init__( hass=hass, logger=_LOGGER, name=name, update_interval=timedelta(seconds=polling_interval), ) async def _async_update_data(self): """Fetch data from OmniLogic.""" try: data = await self.api.get_telemetry_data() except OmniLogicException as error: raise UpdateFailed(f"Error updating from OmniLogic: {error}") from error parsed_data = {} def get_item_data(item, item_kind, current_id, data): """Get data per kind of Omnilogic API item.""" if isinstance(item, list): for single_item in item: data = get_item_data(single_item, item_kind, current_id, data) if "systemId" in item: system_id = item["systemId"] current_id = current_id + (item_kind, system_id) data[current_id] = item for kind in ALL_ITEM_KINDS: if kind in item: data = get_item_data(item[kind], kind, current_id, data) return data parsed_data = get_item_data(data, "Backyard", (), parsed_data) return parsed_data class OmniLogicEntity(CoordinatorEntity): """Defines the base OmniLogic entity.""" def __init__( self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, item_id: tuple, icon: str, ): """Initialize the OmniLogic Entity.""" super().__init__(coordinator) bow_id = None entity_data = coordinator.data[item_id] backyard_id = item_id[:2] if len(item_id) == 6: bow_id = item_id[:4] msp_system_id = coordinator.data[backyard_id]["systemId"] entity_friendly_name = f"{coordinator.data[backyard_id]['BackyardName']} " unique_id = f"{msp_system_id}" if bow_id is not None: unique_id = f"{unique_id}_{coordinator.data[bow_id]['systemId']}" entity_friendly_name = ( f"{entity_friendly_name}{coordinator.data[bow_id]['Name']} " ) unique_id = f"{unique_id}_{coordinator.data[item_id]['systemId']}_{kind}" if entity_data.get("Name") is not None: entity_friendly_name = f"{entity_friendly_name} {entity_data['Name']}" entity_friendly_name = f"{entity_friendly_name} {name}" unique_id = unique_id.replace(" ", "_") self._kind = kind self._name = entity_friendly_name self._unique_id = unique_id self._item_id = item_id self._icon = icon self._attrs = {} self._msp_system_id = msp_system_id self._backyard_name = coordinator.data[backyard_id]["BackyardName"] @property def unique_id(self) -> str: """Return a unique, Home Assistant friendly identifier for this entity.""" return self._unique_id @property def name(self) -> str: """Return the name of the entity.""" return self._name @property def icon(self): """Return the icon for the entity.""" return self._icon @property def device_state_attributes(self): """Return the attributes.""" return self._attrs @property def device_info(self): """Define the device as back yard/MSP System.""" return { ATTR_IDENTIFIERS: {(DOMAIN, self._msp_system_id)}, ATTR_NAME: self._backyard_name, ATTR_MANUFACTURER: "Hayward", ATTR_MODEL: "OmniLogic", }

123dev/core

homeassistant/components/omnilogic/common.py

Python

# -*- coding: utf-8 -*- # Generated by Django 1.11.20 on 2019-03-20 21:25 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('librarian', '0108_dataset_is_external_missing'), ] operations = [ migrations.AddField( model_name='dataset', name='is_uploaded', field=models.BooleanField(default=True, help_text='True if the file was uploaded, not an output.'), ), ]

cfe-lab/Kive

kive/librarian/migrations/0109_dataset_is_uploaded.py

Python

""" MODULE : code_generation.py Purpose : * Class for parsing the text and launching the basic block algorithm. Also houses the code generation algorithm """ # List of Imports Begin import debug as DEBUG import instr3ac as INSTRUCTION import basic_blocks as BB import mips_assembly as ASM import global_objects as G import library as LIB import shlex import re # List of Imports End def LexerIR(inpString): return re.findall(r'(?:[^\s,"]|"(?:\\.|[^"])*")+', inpString) class CodeGenerator(object): """ This class houses the basic-block generation and code-generation algorithm Member Variables: * instructions : Stores all the program instructions * basicBlocks : Stores all the basic blocks * targets : Which line IDs are goto targets. Used for basic block algorithm """ def __init__(self, text, fileName, symTabManager, funcActRecords): text = text.split('\n') text = [i.lstrip().rstrip() for i in text if i != ''] text = [i.replace('\t', '') for i in text] self.instructions = [] self.basicBlocks = [] self.targets = set([]) self.allLineIDs = set([]) self.fileName = fileName # Build Global Objects G.AsmText = ASM.TextRegion(fileName) G.AsmData = ASM.DataRegion(funcActRecords, symTabManager) LIB.AddEssentialLibraries() # Create an instance of the instruction class for each line for line in text: instrTuple = LexerIR(line) if instrTuple == []: continue instrTuple = [i.lstrip().rstrip() for i in instrTuple] instr = INSTRUCTION.Instr3AC(instrTuple) # Disallow multiple input lines with same lineID DEBUG.Assert(instr.lineID not in self.allLineIDs,"Multiple lines with same line ID.") self.allLineIDs.add(instr.lineID) self.instructions += [instr] # print self.instructions[-1] gotoTarget = self.instructions[-1].GetTarget() if gotoTarget: self.targets.add(gotoTarget) # Identify the branch targets and set their isTarget value to true for instr in self.instructions: if "$LID_" + str(instr.lineID) in self.targets: instr.isTarget = True def GenBasicBlocks(self): """ Generate basic blocks using the algorithm """ if len(self.instructions) == 0: return bbCount = 0 self.basicBlocks = [BB.BasicBlock()] # First statement is a leader self.basicBlocks[-1].AddInstruction(self.instructions[0]) for instr in self.instructions[1:]: # Is the current instruction a branch target? If yes, it's a leader if instr.IsTarget(): bbCount += 1 self.basicBlocks += [BB.BasicBlock(bbCount)] self.basicBlocks[-1].AddInstruction(instr) # Is next statement a leader? if instr.instrType.is_JMP(): bbCount += 1 self.basicBlocks += [BB.BasicBlock(bbCount)] self.basicBlocks = [bb for bb in self.basicBlocks if not bb.IsEmpty()] for i, bb in enumerate(self.basicBlocks): bb.bbNum = i def PrintBasicBlocks(self): for bb in self.basicBlocks: bb.PrettyPrint() def BuildCode(self): filePtr = open(self.fileName.replace('.ir', '.s'), 'w+') for bb in self.basicBlocks: bb.Translate() G.AsmText.WriteHeader(filePtr) G.AsmData.GenerateDataRegion(filePtr) G.AsmText.WriteToFile(filePtr) filePtr.close()

vaishious/comperler

project/src/codegen/code_generation.py

Python

# terrascript/provider/alertmixer/amixr.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:11:40 UTC) import terrascript class amixr(terrascript.Provider): """""" __description__ = "" __namespace__ = "alertmixer" __name__ = "amixr" __source__ = "https://github.com/alertmixer/terraform-provider-amixr" __version__ = "0.2.3" __published__ = "2021-01-11T16:21:02Z" __tier__ = "partner" __all__ = ["amixr"]

mjuenema/python-terrascript

terrascript/provider/alertmixer/amixr.py

Python

import pytest from django.test import RequestFactory from tslhub_accounts_api.users.api.views import UserViewSet from tslhub_accounts_api.users.models import User pytestmark = pytest.mark.django_db class TestUserViewSet: def test_get_queryset(self, user: User, rf: RequestFactory): view = UserViewSet() request = rf.get("/fake-url/") request.user = user view.request = request assert user in view.get_queryset() def test_me(self, user: User, rf: RequestFactory): view = UserViewSet() request = rf.get("/fake-url/") request.user = user view.request = request response = view.me(request) assert response.data == { "username": user.username, "name": user.name, "url": f"http://testserver/api/users/{user.username}/", }

vvvti/tslhub-accounts-api

tslhub_accounts_api/users/tests/test_drf_views.py

Python

#!/usr/bin/env python # not used in this project. import sys sys.path.append('../gen-py') from EyePi.ttypes import EyePiInput from EyePi.ttypes import ConfirmInput from GenericStruct.ttypes import ActionEnum from WeatherPi.ttypes import WeatherInput from ConnectionHelpers.DeviceRegistrator import DeviceRegistrator from ConnectionHelpers.ConnectEyePi import ConnectEyePi from thrift import Thrift import cv2 import os.path import random # test import numpy as np import pickle sys.path.append('../../') import config ### test def read_image(): root, dirs, files=next(os.walk(config.file_path)) imageCollection=list(filter(lambda filename:filename.endswith('.jpg'), files)) imageCollection+=list(filter(lambda filename:filename.endswith('.png'), files)) return random.choice(imageCollection) ### end test try: ## mock! ### # normally a device would properly register itself and keep the token. # But in development case, the cahce is resetted every time. This mock registers the device. device_token = DeviceRegistrator().register_device() ### end mock ### input = EyePiInput() filename = config.file_path +read_image() print('image == '+filename) file = open(filename, 'rb') readfile = file.read() nparr = np.fromstring(readfile, np.uint8) image = cv2.imdecode(nparr, cv2.IMREAD_COLOR) input.image = pickle.dumps(obj=image, protocol=None, fix_imports=False) actions = dict() weather_input = WeatherInput() weather_input.location = 'Amsterdam,nl' actionParameter = pickle.dumps(obj=weather_input, protocol=None, fix_imports=False) actions[ActionEnum.WEATHER] = actionParameter input.action = actions #parameter = GenericObject() #parameter.stringValue = "%s" % 'Amsterdam,nl' input.deviceToken = device_token #input.action = ActionEnum.WEATHER #input.actionParameters = parameter output = ConnectEyePi().handleRequest(input) print(output) if output.ok: for face in output.personCollection: confirm_input = ConfirmInput() confirm_input.image = face.image confirm_input.person = face.person ConnectEyePi().confimFace(confirm_input) except Thrift.TException as tx: print("%s" % (tx.message))

emschimmel/BrainPi

1IntegrationTests/py-impl/PythonEyePiClient.py

Python

import json from copy import deepcopy from time import time from asynctest import TestCase as AsyncTestCase from asynctest import mock as async_mock from .....core.in_memory import InMemoryProfile from .....indy.holder import IndyHolder from .....indy.sdk.holder import IndySdkHolder from .....indy.issuer import IndyIssuer from .....ledger.base import BaseLedger from .....messaging.decorators.attach_decorator import AttachDecorator from .....messaging.request_context import RequestContext from .....messaging.responder import BaseResponder, MockResponder from .....storage.error import StorageNotFoundError from .....indy.verifier import IndyVerifier from .....indy.sdk.verifier import IndySdkVerifier from ....didcomm_prefix import DIDCommPrefix from ...indy.xform import indy_proof_req_preview2indy_requested_creds from .. import manager as test_module from ..manager import V20PresManager, V20PresManagerError from ..message_types import ( ATTACHMENT_FORMAT, PRES_20_PROPOSAL, PRES_20_REQUEST, PRES_20, ) from ..messages.pres import V20Pres from ..messages.pres_ack import V20PresAck from ..messages.pres_format import V20PresFormat from ..messages.pres_proposal import V20PresProposal from ..messages.pres_request import V20PresRequest from ..models.pres_exchange import V20PresExRecord CONN_ID = "connection_id" ISSUER_DID = "NcYxiDXkpYi6ov5FcYDi1e" S_ID = f"{ISSUER_DID}:2:vidya:1.0" CD_ID = f"{ISSUER_DID}:3:CL:{S_ID}:tag1" RR_ID = f"{ISSUER_DID}:4:{CD_ID}:CL_ACCUM:0" PROOF_REQ_NAME = "name" PROOF_REQ_VERSION = "1.0" PROOF_REQ_NONCE = "12345" NOW = int(time()) INDY_PROOF_REQ_NAME = { "name": PROOF_REQ_NAME, "version": PROOF_REQ_VERSION, "nonce": PROOF_REQ_NONCE, "requested_attributes": { "0_player_uuid": { "name": "player", "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, }, "1_screencapture_uuid": { "name": "screenCapture", "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, }, }, "requested_predicates": { "0_highscore_GE_uuid": { "name": "highScore", "p_type": ">=", "p_value": 1000000, "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, } }, } INDY_PROOF_REQ_NAMES = { "name": PROOF_REQ_NAME, "version": PROOF_REQ_VERSION, "nonce": PROOF_REQ_NONCE, "requested_attributes": { "0_player_uuid": { "names": ["player", "screenCapture"], "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, } }, "requested_predicates": { "0_highscore_GE_uuid": { "name": "highScore", "p_type": ">=", "p_value": 1000000, "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, } }, } INDY_PROOF_REQ_SELFIE = { "name": PROOF_REQ_NAME, "version": PROOF_REQ_VERSION, "nonce": PROOF_REQ_NONCE, "requested_attributes": { "self_player_uuid": {"name": "player"}, "self_screencapture_uuid": {"name": "screenCapture"}, }, "requested_predicates": { "0_highscore_GE_uuid": {"name": "highScore", "p_type": ">=", "p_value": 1000000} }, } class TestV20PresManager(AsyncTestCase): async def setUp(self): self.profile = InMemoryProfile.test_profile() injector = self.profile.context.injector Ledger = async_mock.MagicMock(BaseLedger, autospec=True) self.ledger = Ledger() self.ledger.get_schema = async_mock.CoroutineMock( return_value=async_mock.MagicMock() ) self.ledger.get_credential_definition = async_mock.CoroutineMock( return_value={"value": {"revocation": {"...": "..."}}} ) self.ledger.get_revoc_reg_def = async_mock.CoroutineMock( return_value={ "ver": "1.0", "id": RR_ID, "revocDefType": "CL_ACCUM", "tag": RR_ID.split(":")[-1], "credDefId": CD_ID, "value": { "IssuanceType": "ISSUANCE_BY_DEFAULT", "maxCredNum": 1000, "publicKeys": {"accumKey": {"z": "1 ..."}}, "tailsHash": "3MLjUFQz9x9n5u9rFu8Ba9C5bo4HNFjkPNc54jZPSNaZ", "tailsLocation": "http://sample.ca/path", }, } ) self.ledger.get_revoc_reg_delta = async_mock.CoroutineMock( return_value=( { "ver": "1.0", "value": {"prevAccum": "1 ...", "accum": "21 ...", "issued": [1]}, }, NOW, ) ) self.ledger.get_revoc_reg_entry = async_mock.CoroutineMock( return_value=( { "ver": "1.0", "value": {"prevAccum": "1 ...", "accum": "21 ...", "issued": [1]}, }, NOW, ) ) injector.bind_instance(BaseLedger, self.ledger) Holder = async_mock.MagicMock(IndyHolder, autospec=True) self.holder = Holder() get_creds = async_mock.CoroutineMock( return_value=( { "cred_info": { "referent": "dummy_reft", "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "highScore": "1234560", }, } }, # leave this comma: return a tuple ) ) self.holder.get_credentials_for_presentation_request_by_referent = get_creds self.holder.get_credential = async_mock.CoroutineMock( return_value=json.dumps( { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": RR_ID, "cred_rev_id": 1, } ) ) self.holder.create_presentation = async_mock.CoroutineMock(return_value="{}") self.holder.create_revocation_state = async_mock.CoroutineMock( return_value=json.dumps( { "witness": {"omega": "1 ..."}, "rev_reg": {"accum": "21 ..."}, "timestamp": NOW, } ) ) injector.bind_instance(IndyHolder, self.holder) Verifier = async_mock.MagicMock(IndyVerifier, autospec=True) self.verifier = Verifier() self.verifier.verify_presentation = async_mock.CoroutineMock( return_value="true" ) injector.bind_instance(IndyVerifier, self.verifier) self.manager = V20PresManager(self.profile) async def test_record_eq(self): same = [ V20PresExRecord( pres_ex_id="dummy-0", thread_id="thread-0", role=V20PresExRecord.ROLE_PROVER, ) ] * 2 diff = [ V20PresExRecord( pres_ex_id="dummy-1", role=V20PresExRecord.ROLE_PROVER, ), V20PresExRecord( pres_ex_id="dummy-0", thread_id="thread-1", role=V20PresExRecord.ROLE_PROVER, ), V20PresExRecord( pres_ex_id="dummy-1", thread_id="thread-0", role=V20PresExRecord.ROLE_VERIFIER, ), ] for i in range(len(same) - 1): for j in range(i, len(same)): assert same[i] == same[j] for i in range(len(diff) - 1): for j in range(i, len(diff)): assert diff[i] == diff[j] if i == j else diff[i] != diff[j] async def test_create_exchange_for_proposal(self): proposal = V20PresProposal() with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresProposal, "serialize", autospec=True ): px_rec = await self.manager.create_exchange_for_proposal( CONN_ID, proposal, auto_present=None ) save_ex.assert_called_once() assert px_rec.thread_id == proposal._thread_id assert px_rec.initiator == V20PresExRecord.INITIATOR_SELF assert px_rec.role == V20PresExRecord.ROLE_PROVER assert px_rec.state == V20PresExRecord.STATE_PROPOSAL_SENT async def test_receive_proposal(self): connection_record = async_mock.MagicMock(connection_id=CONN_ID) proposal = V20PresProposal() with async_mock.patch.object(V20PresExRecord, "save", autospec=True) as save_ex: px_rec = await self.manager.receive_pres_proposal( proposal, connection_record, ) save_ex.assert_called_once() assert px_rec.state == V20PresExRecord.STATE_PROPOSAL_RECEIVED async def test_create_bound_request(self): comment = "comment" proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) px_rec = V20PresExRecord( pres_proposal=proposal.serialize(), role=V20PresExRecord.ROLE_VERIFIER, ) px_rec.save = async_mock.CoroutineMock() (ret_px_rec, pres_req_msg) = await self.manager.create_bound_request( pres_ex_record=px_rec, name=PROOF_REQ_NAME, version=PROOF_REQ_VERSION, nonce=PROOF_REQ_NONCE, comment=comment, ) assert ret_px_rec is px_rec px_rec.save.assert_called_once() async def test_create_exchange_for_request(self): request = async_mock.MagicMock() request.indy_proof_request = async_mock.MagicMock() request._thread_id = "dummy" with async_mock.patch.object(V20PresExRecord, "save", autospec=True) as save_ex: px_rec = await self.manager.create_exchange_for_request(CONN_ID, request) save_ex.assert_called_once() assert px_rec.thread_id == request._thread_id assert px_rec.initiator == V20PresExRecord.INITIATOR_SELF assert px_rec.role == V20PresExRecord.ROLE_VERIFIER assert px_rec.state == V20PresExRecord.STATE_REQUEST_SENT async def test_receive_pres_request(self): px_rec_in = V20PresExRecord() with async_mock.patch.object(V20PresExRecord, "save", autospec=True) as save_ex: px_rec_out = await self.manager.receive_pres_request(px_rec_in) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_REQUEST_RECEIVED async def test_create_pres(self): pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) more_magic_rr = async_mock.MagicMock( get_or_fetch_local_tails_path=async_mock.CoroutineMock( return_value="/tmp/sample/tails/path" ) ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module, "RevocationRegistry", autospec=True ) as mock_rr: mock_rr.from_definition = async_mock.MagicMock(return_value=more_magic_rr) mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_NAME, preview=None, holder=self.holder ) assert not req_creds["self_attested_attributes"] assert len(req_creds["requested_attributes"]) == 2 assert len(req_creds["requested_predicates"]) == 1 (px_rec_out, pres_msg) = await self.manager.create_pres( px_rec_in, req_creds ) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_PRESENTATION_SENT async def test_create_pres_proof_req_non_revoc_interval_none(self): indy_proof_req_vcx = deepcopy(INDY_PROOF_REQ_NAME) indy_proof_req_vcx["non_revoked"] = None # simulate interop with indy-vcx pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req_vcx, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) more_magic_rr = async_mock.MagicMock( get_or_fetch_local_tails_path=async_mock.CoroutineMock( return_value="/tmp/sample/tails/path" ) ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module, "RevocationRegistry", autospec=True ) as mock_rr: mock_rr.from_definition = async_mock.MagicMock(return_value=more_magic_rr) mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( indy_proof_req_vcx, preview=None, holder=self.holder ) assert not req_creds["self_attested_attributes"] assert len(req_creds["requested_attributes"]) == 2 assert len(req_creds["requested_predicates"]) == 1 (px_rec_out, pres_msg) = await self.manager.create_pres( px_rec_in, req_creds ) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_PRESENTATION_SENT async def test_create_pres_self_asserted(self): pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_SELFIE, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) more_magic_rr = async_mock.MagicMock( get_or_fetch_local_tails_path=async_mock.CoroutineMock( return_value="/tmp/sample/tails/path" ) ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module, "RevocationRegistry", autospec=True ) as mock_rr: mock_rr.from_definition = async_mock.MagicMock(return_value=more_magic_rr) mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_SELFIE, preview=None, holder=self.holder ) assert len(req_creds["self_attested_attributes"]) == 3 assert not req_creds["requested_attributes"] assert not req_creds["requested_predicates"] (px_rec_out, pres_msg) = await self.manager.create_pres( px_rec_in, req_creds ) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_PRESENTATION_SENT async def test_create_pres_no_revocation(self): Ledger = async_mock.MagicMock(BaseLedger, autospec=True) self.ledger = Ledger() self.ledger.get_schema = async_mock.CoroutineMock( return_value=async_mock.MagicMock() ) self.ledger.get_credential_definition = async_mock.CoroutineMock( return_value={"value": {"revocation": None}} ) self.profile.context.injector.bind_instance(BaseLedger, self.ledger) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) Holder = async_mock.MagicMock(IndyHolder, autospec=True) self.holder = Holder() get_creds = async_mock.CoroutineMock( return_value=( { "cred_info": {"referent": "dummy_reft"}, "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "highScore": "1234560", }, }, # leave this comma: return a tuple ) ) self.holder.get_credentials_for_presentation_request_by_referent = get_creds self.holder.get_credential = async_mock.CoroutineMock( return_value=json.dumps( { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "cred_rev_id": None, } ) ) self.holder.create_presentation = async_mock.CoroutineMock(return_value="{}") self.profile.context.injector.bind_instance(IndyHolder, self.holder) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module.LOGGER, "info", async_mock.MagicMock() ) as mock_log_info: mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_NAME, preview=None, holder=self.holder ) (px_rec_out, pres_msg) = await self.manager.create_pres( px_rec_in, req_creds ) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_PRESENTATION_SENT # exercise superfluous timestamp removal for pred_reft_spec in req_creds["requested_predicates"].values(): pred_reft_spec["timestamp"] = 1234567890 await self.manager.create_pres(px_rec_in, req_creds) mock_log_info.assert_called_once() async def test_create_pres_bad_revoc_state(self): pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) Holder = async_mock.MagicMock(IndyHolder, autospec=True) self.holder = Holder() get_creds = async_mock.CoroutineMock( return_value=( { "cred_info": {"referent": "dummy_reft"}, "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "highScore": "1234560", }, }, # leave this comma: return a tuple ) ) self.holder.get_credentials_for_presentation_request_by_referent = get_creds self.holder.get_credential = async_mock.CoroutineMock( return_value=json.dumps( { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": RR_ID, "cred_rev_id": 1, } ) ) self.holder.create_presentation = async_mock.CoroutineMock(return_value="{}") self.holder.create_revocation_state = async_mock.CoroutineMock( side_effect=test_module.IndyHolderError("Problem", {"message": "Nope"}) ) self.profile.context.injector.bind_instance(IndyHolder, self.holder) more_magic_rr = async_mock.MagicMock( get_or_fetch_local_tails_path=async_mock.CoroutineMock( return_value="/tmp/sample/tails/path" ) ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module, "RevocationRegistry", autospec=True ) as mock_rr: mock_rr.from_definition = async_mock.MagicMock(return_value=more_magic_rr) mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_NAME, preview=None, holder=self.holder ) with self.assertRaises(test_module.IndyHolderError): await self.manager.create_pres(px_rec_in, req_creds) async def test_create_pres_multi_matching_proposal_creds_names(self): pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAMES, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) Holder = async_mock.MagicMock(IndyHolder, autospec=True) self.holder = Holder() get_creds = async_mock.CoroutineMock( return_value=( { "cred_info": { "referent": "dummy_reft_0", "cred_def_id": CD_ID, "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "highScore": "1234560", }, } }, { "cred_info": { "referent": "dummy_reft_1", "cred_def_id": CD_ID, "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhbm90aGVyIHNjcmVlbiBjYXB0dXJl", "highScore": "1515880", }, } }, ) ) self.holder.get_credentials_for_presentation_request_by_referent = get_creds self.holder.get_credential = async_mock.CoroutineMock( return_value=json.dumps( { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": RR_ID, "cred_rev_id": 1, } ) ) self.holder.create_presentation = async_mock.CoroutineMock(return_value="{}") self.holder.create_revocation_state = async_mock.CoroutineMock( return_value=json.dumps( { "witness": {"omega": "1 ..."}, "rev_reg": {"accum": "21 ..."}, "timestamp": NOW, } ) ) self.profile.context.injector.bind_instance(IndyHolder, self.holder) more_magic_rr = async_mock.MagicMock( get_or_fetch_local_tails_path=async_mock.CoroutineMock( return_value="/tmp/sample/tails/path" ) ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( test_module, "AttachDecorator", autospec=True ) as mock_attach_decorator, async_mock.patch.object( test_module, "RevocationRegistry", autospec=True ) as mock_rr: mock_rr.from_definition = async_mock.MagicMock(return_value=more_magic_rr) mock_attach_decorator.data_base64 = async_mock.MagicMock( return_value=mock_attach_decorator ) req_creds = await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_NAMES, preview=None, holder=self.holder ) assert not req_creds["self_attested_attributes"] assert len(req_creds["requested_attributes"]) == 1 assert len(req_creds["requested_predicates"]) == 1 (px_rec_out, pres_msg) = await self.manager.create_pres( px_rec_in, req_creds ) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_PRESENTATION_SENT async def test_no_matching_creds_for_proof_req(self): pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAMES, ident="indy") ], ) px_rec_in = V20PresExRecord(pres_request=pres_request.serialize()) get_creds = async_mock.CoroutineMock(return_value=()) self.holder.get_credentials_for_presentation_request_by_referent = get_creds with self.assertRaises(ValueError): await indy_proof_req_preview2indy_requested_creds( INDY_PROOF_REQ_NAMES, preview=None, holder=self.holder ) get_creds = async_mock.CoroutineMock( return_value=( { "cred_info": {"referent": "dummy_reft"}, "attrs": { "player": "Richie Knucklez", "screenCapture": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "highScore": "1234560", }, }, # leave this comma: return a tuple ) ) self.holder.get_credentials_for_presentation_request_by_referent = get_creds async def test_receive_pres(self): connection_record = async_mock.MagicMock(connection_id=CONN_ID) indy_proof = { "proof": { "proofs": [ { "primary_proof": { "eq_proof": "...", "ge_proofs": [ { "...": "...", "predicate": { "attr_name": "highscore", "p_type": "GE", "value": 1000000, }, } ], } } ] }, "requested_proof": { "revealed_attrs": { "0_player_uuid": { "sub_proof_index": 0, "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "1_screencapture_uuid": { "sub_proof_index": 0, "raw": "aW1hZ2luZSBhIHNjcmVlbiBjYXB0dXJl", "encoded": "98765432109876543210", }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "timestamp": None, } ], } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) pres = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), ) # cover by_format property by_format = px_rec_dummy.by_format assert by_format.get("pres_proposal").get("indy") == INDY_PROOF_REQ_NAME assert by_format.get("pres_request").get("indy") == INDY_PROOF_REQ_NAME with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex, async_mock.patch.object( self.profile, "session", async_mock.MagicMock(return_value=self.profile.session()), ) as session: retrieve_ex.side_effect = [ StorageNotFoundError("no such record"), # cover out-of-band px_rec_dummy, ] px_rec_out = await self.manager.receive_pres(pres, connection_record) assert retrieve_ex.call_count == 2 save_ex.assert_called_once() assert px_rec_out.state == (V20PresExRecord.STATE_PRESENTATION_RECEIVED) async def test_receive_pres_bait_and_switch_attr_name(self): connection_record = async_mock.MagicMock(connection_id=CONN_ID) indy_proof_req = deepcopy(INDY_PROOF_REQ_NAME) indy_proof_req["requested_attributes"]["1_screencapture_uuid"]["restrictions"][ 0 ][ "attr::screenCapture::value" ] = "c2NyZWVuIGNhcHR1cmUgc2hvd2luZyBzY29yZSBpbiB0aGUgbWlsbGlvbnM=" indy_proof_x = { "proof": { "proofs": [ { "primary_proof": { "eq_proof": "...", "ge_proofs": [ { "...": "...", "predicate": { "attr_name": "highscore", "p_type": "GE", "value": 1000000, }, } ], } } ] }, "requested_proof": { "revealed_attrs": { "0_player_uuid": { "sub_proof_index": 0, "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "1_screencapture_uuid": { # mismatch vs request "sub_proof_index": 0, "raw": "bm90IHRoZSBzYW1lIHNjcmVlbiBjYXB0dXJl", "encoded": "98765432109876543210", }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "timestamp": None, } ], } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "does not satisfy proof request restrictions" in str( context.exception ) indy_proof_req["requested_attributes"]["shenanigans"] = indy_proof_req[ "requested_attributes" ].pop("1_screencapture_uuid") pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "Presentation referent" in str(context.exception) async def test_receive_pres_bait_and_switch_attr_names(self): connection_record = async_mock.MagicMock(connection_id=CONN_ID) indy_proof_req = deepcopy(INDY_PROOF_REQ_NAMES) indy_proof_req["requested_attributes"]["0_player_uuid"]["restrictions"][0][ "attr::screenCapture::value" ] = "c2NyZWVuIGNhcHR1cmUgc2hvd2luZyBzY29yZSBpbiB0aGUgbWlsbGlvbnM=" indy_proof_x = { "proof": { "proofs": [ { "primary_proof": { "eq_proof": "...", "ge_proofs": [ { "...": "...", "predicate": { "attr_name": "highscore", "p_type": "GE", "value": 1000000, }, } ], } } ] }, "requested_proof": { "revealed_attrs": {}, "revealed_attr_groups": { "0_player_uuid": { "sub_proof_index": 0, "values": { "player": { "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "0_player_uuid": { # mismatch vs request "raw": "bm90IHRoZSBzYW1lIHNjcmVlbiBjYXB0dXJl", "encoded": "98765432109876543210", }, }, }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "timestamp": None, } ], } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "does not satisfy proof request restrictions " in str( context.exception ) indy_proof_req["requested_attributes"]["shenanigans"] = indy_proof_req[ "requested_attributes" ].pop("0_player_uuid") pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "Presentation referent" in str(context.exception) async def test_receive_pres_bait_and_switch_pred(self): connection_record = async_mock.MagicMock(connection_id=CONN_ID) indy_proof_req = deepcopy(INDY_PROOF_REQ_NAME) indy_proof_req["requested_predicates"] = {} indy_proof_x = { "proof": { "proofs": [ { "primary_proof": { "eq_proof": "...", "ge_proofs": [ { "...": "...", "predicate": { "attr_name": "highscore", "p_type": "GE", "value": 1000000, }, } ], } } ] }, "requested_proof": { "revealed_attrs": { "0_player_uuid": { "sub_proof_index": 0, "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "1_screencapture_uuid": { # mismatch vs request "sub_proof_index": 0, "raw": "bm90IHRoZSBzYW1lIHNjcmVlbiBjYXB0dXJl", "encoded": "98765432109876543210", }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "timestamp": None, } ], } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "not in proposal request" in str(context.exception) indy_proof_req["requested_predicates"]["0_highscore_GE_uuid"] = { "name": "shenanigans", "p_type": ">=", "p_value": 1000000, "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "shenanigans not in presentation" in str(context.exception) indy_proof_req["requested_predicates"]["0_highscore_GE_uuid"] = { "name": "highScore", "p_type": ">=", "p_value": 8000000, # propose >= 8 million, prove >= 1 million "restrictions": [{"cred_def_id": CD_ID}], "non_revoked": {"from": NOW, "to": NOW}, } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "highScore mismatches proposal request" in str(context.exception) indy_proof_req["requested_predicates"]["0_highscore_GE_uuid"] = { "name": "highScore", "p_type": ">=", "p_value": 1000000, "restrictions": [{"issuer_did": "FFFFFFFFFFFFFFFFFFFFFF"}], # fake issuer "non_revoked": {"from": NOW, "to": NOW}, } pres_proposal = V20PresProposal( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_PROPOSAL][ V20PresFormat.Format.INDY.api ], ) ], proposals_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], request_presentations_attach=[ AttachDecorator.data_base64(indy_proof_req, ident="indy") ], ) pres_x = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof_x, ident="indy") ], ) px_rec_dummy = V20PresExRecord( pres_proposal=pres_proposal.serialize(), pres_request=pres_request.serialize(), pres=pres_x.serialize(), ) with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy with self.assertRaises(V20PresManagerError) as context: await self.manager.receive_pres(pres_x, connection_record) assert "does not satisfy proof request restrictions " in str( context.exception ) async def test_verify_pres(self): indy_proof = { "proof": {"proofs": []}, "requested_proof": { "revealed_attrs": { "0_player_uuid": { "sub_proof_index": 0, "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "1_screencapture_uuid": { "sub_proof_index": 0, "raw": "cG90YXRv", "encoded": "98765432109876543210", }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": None, "timestamp": None, } ], } pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], will_confirm=True, request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) pres = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof, ident="indy") ], ) px_rec_in = V20PresExRecord( pres_request=pres_request.serialize(), pres=pres.serialize(), ) with async_mock.patch.object(V20PresExRecord, "save", autospec=True) as save_ex: px_rec_out = await self.manager.verify_pres(px_rec_in) save_ex.assert_called_once() assert px_rec_out.state == (V20PresExRecord.STATE_DONE) async def test_verify_pres_with_revocation(self): indy_proof = { "proof": {"proofs": []}, "requested_proof": { "revealed_attrs": { "0_player_uuid": { "sub_proof_index": 0, "raw": "Richie Knucklez", "encoded": "12345678901234567890", }, "1_screencapture_uuid": { "sub_proof_index": 0, "raw": "cG90YXRv", "encoded": "98765432109876543210", }, }, "self_attested_attrs": {}, "unrevealed_attrs": {}, "predicates": {"0_highscore_GE_uuid": {"sub_proof_index": 0}}, }, "identifiers": [ { "schema_id": S_ID, "cred_def_id": CD_ID, "rev_reg_id": RR_ID, "timestamp": NOW, } ], } pres_request = V20PresRequest( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20_REQUEST][ V20PresFormat.Format.INDY.api ], ) ], request_presentations_attach=[ AttachDecorator.data_base64(INDY_PROOF_REQ_NAME, ident="indy") ], ) pres = V20Pres( formats=[ V20PresFormat( attach_id="indy", format_=ATTACHMENT_FORMAT[PRES_20][V20PresFormat.Format.INDY.api], ) ], presentations_attach=[ AttachDecorator.data_base64(indy_proof, ident="indy") ], ) px_rec_in = V20PresExRecord( pres_request=pres_request.serialize(), pres=pres.serialize(), ) with async_mock.patch.object(V20PresExRecord, "save", autospec=True) as save_ex: px_rec_out = await self.manager.verify_pres(px_rec_in) save_ex.assert_called_once() assert px_rec_out.state == (V20PresExRecord.STATE_DONE) async def test_send_pres_ack(self): px_rec = V20PresExRecord() responder = MockResponder() self.profile.context.injector.bind_instance(BaseResponder, responder) await self.manager.send_pres_ack(px_rec) messages = responder.messages assert len(messages) == 1 async def test_send_pres_ack_no_responder(self): px_rec = V20PresExRecord() self.profile.context.injector.clear_binding(BaseResponder) await self.manager.send_pres_ack(px_rec) async def test_receive_pres_ack(self): conn_record = async_mock.MagicMock(connection_id=CONN_ID) px_rec_dummy = V20PresExRecord() message = async_mock.MagicMock() with async_mock.patch.object( V20PresExRecord, "save", autospec=True ) as save_ex, async_mock.patch.object( V20PresExRecord, "retrieve_by_tag_filter", autospec=True ) as retrieve_ex: retrieve_ex.return_value = px_rec_dummy px_rec_out = await self.manager.receive_pres_ack(message, conn_record) save_ex.assert_called_once() assert px_rec_out.state == V20PresExRecord.STATE_DONE

wadeking98/aries-cloudagent-python

aries_cloudagent/protocols/present_proof/v2_0/tests/test_manager.py

Python

import setuptools with open('README.md', 'r') as file: long_description = file.read() setuptools.setup( name='anvil-parser', version='0.5.2', author='mat', description='A Minecraft anvil file format parser', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/matcool/anvil-parser', packages=setuptools.find_packages(), classifiers=[ 'Programming Language :: Python :: 3', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', ], install_requires=[ 'nbt', 'frozendict', ] )

MrMallIronmaker/anvil-parser

setup.py

Python

# Script that cuts the greet string into chunks and prints it out greet = "Hello World!" print(greet) print("Start: ", greet[0:3]) print("Middle: ", greet[3:6]) print("End: ", greet[-3:]) a = greet.find(",") print("Portion before comma", greet[:a])

Memeklos/Programming-Code-Snippets

Python/CTF/greet.py

Python

# -*- coding: utf-8 -*- """ NETWORK This module defines the BlendHunter class which can be used to retrain the network or use predefined weights to make predictions on unseen data. :Author: Samuel Farrens <samuel.farrens@cea.fr> """ import os import numpy as np import matplotlib.pyplot as plt import seaborn as sns from time import time from cv2 import imread from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential, Model from keras.layers import Dropout, Flatten, Dense, Input from keras.applications import VGG16 from keras.optimizers import Adam, SGD from keras.callbacks import ModelCheckpoint from keras.callbacks import EarlyStopping from keras.callbacks import ReduceLROnPlateau class BlendHunter(object): """ BlendHunter Class for identifying blended galaxy images in postage stamps. Parameters ---------- image_shape : tuple, optional Expected shape of input images classes : tuple, optional List of classes, default is ('blended', 'not_blended') weights_path : str, optional Path to weights, default is './weights' top_model_file : str, optional File name for top model weights, default is 'top_model_weights' final_model_file : str, optional File name of the final model weights, default is 'final_model_weights' """ def __init__(self, image_shape=None, classes=('blended', 'not_blended'), weights_path='./weights', top_model_file='top_model_weights', final_model_file='final_model_weights', verbose=0): self._image_shape = image_shape self._classes = classes self._weights_path = weights_path self._top_model_file = self._format(weights_path, top_model_file) self._final_model_file = self._format(weights_path, final_model_file) self._verbose = verbose self.history = None @staticmethod def _format(path, name): """ Format Add path to name. Parameters ---------- path : str Base path name : str Path extension Returns ------- str Formated path """ return '{}/{}'.format(path, name) def getkwarg(self, key, default=None): """ Get keyword agrument Get value from keyword agruments if it exists otherwise return default. Parameters ---------- key : str Dictionary key default : optional Default value """ return self._kwargs[key] if key in self._kwargs else default @staticmethod def _get_image_shape(file): """ Get Image Shape Get the input image shape from an example image. Parameters ---------- file : str File name Returns ------- tuple Image shape """ return imread(file).shape def _get_target_shape(self, image_path=None): """ Get Target Shape Get the network target shape from the image shape. Parameters ---------- image_path : str, optional Path to image file """ if isinstance(self._image_shape, type(None)) and image_path: file = self._format(image_path, os.listdir(image_path)[0]) self._image_shape = self._get_image_shape(file) self._target_size = self._image_shape[:2] def _load_generator(self, input_dir, batch_size=None, class_mode=None, augmentation=False): """ Load Generator Load files from an input directory into a Keras generator. Parameters ---------- input_dir : str Input directory batch_size : int, optional Batch size class_mode : str, optional Generator class mode shuffle : bool, optional Option to shuffle input files Returns ------- keras_preprocessing.image.DirectoryIterator Keras generator """ if augmentation: datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) else: datagen = ImageDataGenerator(rescale=1. / 255) generator = (datagen.flow_from_directory(input_dir, target_size=self._target_size, batch_size=batch_size, class_mode=class_mode, shuffle=False)) generator.steps = generator.n // generator.batch_size return generator def _get_feature(self, input_dir): """ Get Feature Get network feature and labels from VGG16 model. Parameters ---------- input_dir : str Input directory Returns ------- tuple VGG16 bottleneck feature, class labels """ generator = self._load_generator(input_dir, batch_size=self._batch_size_top) labels = generator.classes[:generator.steps * self._batch_size_top] return (self._vgg16_model.predict_generator(generator, generator.steps), labels) @staticmethod def _save_data(data, data_type, file_path): """ Save Data Save data to file. Parameters ---------- data : np.ndarray Output data data_type : str Type of feature to be saved file_path : str File path """ file_name = '{}_{}.npy'.format(file_path, data_type) np.save(file_name, data) @staticmethod def _load_data(data_type, file_path): """ Load Data Load data from file. Parameters ---------- data_type : str Type of feature to be loaded file_path : str File path """ file_name = '{}_{}.npy'.format(file_path, data_type) if os.path.isfile(file_name): return np.load(file_name) else: raise IOError('{} not found'.format(file_name)) @staticmethod def _build_vgg16_model(input_shape=None): """ Build VGG16 Model Build VGG16 CNN model using imagenet weights. Parameters ---------- input_shape : str, optional Input data shape Returns ------- VGG16 model """ return VGG16(include_top=False, weights='imagenet', input_shape=input_shape) def _get_features(self): """ Get Features Get the network (bottleneck) features from the VGG16 model. """ self._vgg16_model = self._build_vgg16_model() for key, value in self._features.items(): bot_feat, labels = self._get_feature(value['dir']) if self._save_bottleneck: self._save_data(bot_feat, key, self._bottleneck_file) if self._save_labels: self._save_data(labels, key, self._labels_file) value['bottleneck'] = bot_feat value['labels'] = labels def _load_features(self): """ Load Bottleneck Features Load VGG16 bottleneck features. """ for feature_name in ('bottleneck', 'labels'): if feature_name == 'bottleneck': out_path = self._bottleneck_file else: out_path = self._labels_file for key, value in self._features.items(): if feature_name not in value: value[feature_name] = self._load_data(key, out_path) @staticmethod def _build_top_model(input_shape, dense_output=(256, 1024), dropout=0.1): """ Build Top Model Build the fully connected layers of the network. Parameters ---------- input_shape : tuple Input data shape dense_output : tuple, optional Size of dense output layers, default is (256, 1024) dropout : float, optional Dropout rate, default is 0.1 Returns ------- keras.model Fully connected top model """ model = Sequential() model.add(Flatten(input_shape=input_shape)) model.add(Dense(dense_output[0])) model.add(Dropout(dropout)) model.add(Dense(dense_output[1], activation='relu')) model.add(Dense(1, activation='sigmoid')) return model def _train_top_model(self): """ Train Top Model Train fully connected top model of the network. """ self._load_features() model = (self._build_top_model( input_shape=self._features['train']['bottleneck'].shape[1:])) model.compile(optimizer=self.getkwarg('top_opt', 'adam'), loss=self.getkwarg('top_loss', 'binary_crossentropy'), metrics=self.getkwarg('top_metrics', ['accuracy'])) top_model_file = '{}.h5'.format(self._top_model_file) callbacks = [] callbacks.append(ModelCheckpoint(top_model_file, monitor='val_loss', verbose=self._verbose, save_best_only=True, save_weights_only=True, mode='auto', period=1)) if self.getkwarg('top_early_stop', True): min_delta = self.getkwarg('top_min_delta', 0.001) patience = self.getkwarg('top_patience', 10) callbacks.append(EarlyStopping(monitor='val_loss', min_delta=min_delta, patience=patience, verbose=self._verbose)) callbacks.append(ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_delta=0.001, cooldown=2, verbose=self._verbose)) self.history = (model.fit(self._features['train']['bottleneck'], self._features['train']['labels'], epochs=self._epochs_top, batch_size=self._batch_size_top, callbacks=callbacks, validation_data=(self._features['valid']['bottleneck'], self._features['valid']['labels']), verbose=self._verbose)) model.save_weights(top_model_file) def plot_history(self): """ Plot History Plot the training history metrics. """ sns.set(style="darkgrid") if not isinstance(self.history, type(None)): plt.figure(figsize=(16, 8)) plt.subplot(121) plt.plot(self.history.history['acc']) plt.plot(self.history.history['val_acc']) plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['train', 'valid'], loc='upper left') plt.subplot(122) plt.plot(self.history.history['loss']) plt.plot(self.history.history['val_loss']) plt.title('Model Loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['train', 'valid'], loc='upper left') plt.show() else: print('No history to display. Run training first.') def _freeze_layers(self, model, depth): """ Freeze Network Layers Parameters ---------- model : Keras model depth : int Depth of layers to be frozen """ for layer in model.layers[:depth]: layer.trainable = False def _build_final_model(self, load_top_weights=False, load_final_weights=False): """ Build Final Model Build the final BlendHunter model. Parameters ---------- load_top_weights : bool Option to load the top model weights load_final_weights : bool Option to load the final model weights Returns ------- Final model """ vgg16_model = self._build_vgg16_model(self._image_shape) top_model = self._build_top_model(vgg16_model.output_shape[1:], dropout=0.4) if load_top_weights: top_model.load_weights('{}.h5'.format(self._top_model_file)) model = Model(inputs=vgg16_model.input, outputs=top_model(vgg16_model.output)) if load_final_weights: model.load_weights('{}.h5'.format(self._final_model_file)) return model def _fine_tune(self): """ Fine Tune Fine tune the final model training. """ model = self._build_final_model(load_top_weights=True) self._freeze_layers(model, 18) model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001), metrics=['binary_accuracy']) train_gen = self._load_generator(self._features['train']['dir'], batch_size=self._batch_size_fine, class_mode='binary', augmentation=True) valid_gen = self._load_generator(self._features['valid']['dir'], batch_size=self._batch_size_fine, class_mode='binary') callbacks = [] callbacks.append(ModelCheckpoint('{}.h5'.format(self._fine_tune_file), monitor='val_loss', verbose=self._verbose, save_best_only=True, save_weights_only=True, mode='auto', period=1)) callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0.001, patience=10, verbose=self._verbose)) callbacks.append(ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_delta=0.001, cooldown=2, verbose=self._verbose)) model.fit_generator(train_gen, steps_per_epoch=train_gen.steps, epochs=self._epochs_fine, callbacks=callbacks, validation_data=valid_gen, validation_steps=valid_gen.steps, verbose=self._verbose) self._freeze_layers(model, 19) model.layers[17].trainable = True model.compile(loss='binary_crossentropy', optimizer=SGD(lr=10e-5), metrics=['binary_accuracy']) model.fit_generator(train_gen, steps_per_epoch=train_gen.steps, epochs=self._epochs_fine, callbacks=callbacks, validation_data=valid_gen, validation_steps=valid_gen.steps, verbose=self._verbose) model.save_weights('{}.h5'.format(self._final_model_file)) def train(self, input_path, get_features=True, train_top=True, fine_tune=True, train_dir_name='train', valid_dir_name='validation', epochs_top=500, epochs_fine=50, batch_size_top=250, batch_size_fine=16, save_bottleneck=True, bottleneck_file='bottleneck_features', save_labels=True, labels_file='labels', fine_tune_file='fine_tune_checkpoint', top_model_file='top_model_weights', **kwargs): """ Train Train the BlendHunter network. Parameters ---------- input_path : str Path to input data get_features : bool, optional Option to get bottleneck features, default is True train_top : bool, optional Option to train top model, default is True fine_tune : bool, optional Option to run fine tuning component of training, default is True train_dir_name : str, optional Training data directory name, default is 'train' valid_dir_name : str, optional Validation data directory name, default is 'validation' epochs_top : int, optional Number of training epochs for top model, default is 500 epochs_fine : int, optional Number of training epochs for fine tuning, default is 50 batch_size_top : int, optional Batch size for top model, default is 256 batch_size_fine : int, optional Batch size for fine tuning, default is 16 save_bottleneck : bool, optional Option to save bottleneck features, default is True bottleneck_file : str, optional File name for bottleneck features, default is 'bottleneck_features' fine_tune_file : str, optional Training checkpoint for the fine tuning step, default is 'fine_tune_checkpoint' """ start = time() self._epochs_top = epochs_top self._epochs_fine = epochs_fine self._batch_size_top = batch_size_top self._batch_size_fine = batch_size_fine self._save_bottleneck = save_bottleneck self._save_labels = save_labels self._bottleneck_file = self._format(self._weights_path, bottleneck_file) self._labels_file = self._format(self._weights_path, labels_file) self._fine_tune_file = self._format(self._weights_path, fine_tune_file) self._features = {'train': {}, 'valid': {}} self._features['train']['dir'] = self._format(input_path, train_dir_name) self._features['valid']['dir'] = self._format(input_path, valid_dir_name) self._kwargs = kwargs self._get_target_shape(self._format(self._features['train']['dir'], self._classes[0])) if get_features: self._get_features() if train_top: self._train_top_model() if fine_tune: self._fine_tune() end = time() print('Duration {:0.2f}s'.format(end - start)) def predict(self, input_path=None, input_path_keras=None, input_data=None, weights_type='fine'): """ Predict Predict classes for test data Parameters ---------- input_path : str Path to input data input_path_keras : str Path to input data in Keras format, i.e. path to directory one level above where the data is stored input_data : np.ndarray Array of input images weights_type : str, optional {'fine', 'top'} Type of weights to use for predition, default is 'fine' Returns ------- dict Dictionary of file names and corresponding classes """ if input_path: test_path = '/'.join(input_path.split('/')[:-1]) elif input_path_keras: test_path = input_path_keras else: test_path = None if weights_type not in ('fine', 'top'): raise ValueError('Invalid value for weights_type. Options are ' '"fine" or "top"') if test_path: self._get_target_shape(self._format(test_path, os.listdir(test_path)[0])) if weights_type == 'fine': model = self._build_final_model(load_final_weights=True) elif weights_type == 'top': model = self._build_final_model(load_top_weights=True) test_gen = self._load_generator(test_path, class_mode='categorical', batch_size=1) self.filenames = test_gen.filenames test_gen.reset() res = model.predict_generator(test_gen, verbose=self._verbose, steps=test_gen.steps).flatten() elif not isinstance(input_data, type(None)): self._image_shape = input_data.shape[1:] self._get_target_shape() model = self._build_final_model(load_final_weights=True) res = model.predict(input_data, verbose=self._verbose).flatten() else: raise RuntimeError('No input data provided.') labels = {0: self._classes[0], 1: self._classes[1]} preds = [labels[k] for k in np.around(res)] return preds

CosmoStat/BlendHunter

blendhunter/network.py

Python

""" TimeSeries is a generic time series class from which all other TimeSeries classes inherit from. """ import copy import warnings from collections import OrderedDict import pandas as pd import matplotlib.pyplot as plt import astropy import astropy.units as u from astropy.table import Table, Column from sunpy import config from sunpy.time import TimeRange from sunpy.timeseries import TimeSeriesMetaData from sunpy.util.metadata import MetaDict from sunpy.util.exceptions import SunpyUserWarning # define and register a new unit, needed for RHESSI det = u.def_unit('detector') u.add_enabled_units([det]) TIME_FORMAT = config.get("general", "time_format") class GenericTimeSeries: """ A generic time series object. Parameters ---------- data : `~pandas.DataFrame` A pandas DataFrame representing one or more fields as a function of time. meta : `~sunpy.timeseries.metadata.TimeSeriesMetaData`, optional The metadata giving details about the time series data/instrument. units : dict, optional A mapping from column names in *data* to the physical units of that column. Attributes ---------- data : `~pandas.DataFrame` A pandas DataFrame representing one or more fields as a function of time. meta : `~sunpy.timeseries.metadata.TimeSeriesMetaData` The metadata giving details about the time series data/instrument. units : dict A mapping from column names in *data* to the physical units of that column. Examples -------- >>> from sunpy.timeseries import TimeSeries >>> from sunpy.time import parse_time >>> import datetime >>> from astropy.time import TimeDelta >>> import numpy as np >>> import pandas as pd >>> base = parse_time(datetime.datetime.today()) >>> times = base - TimeDelta(np.arange(24 * 60)*u.minute) >>> intensity = np.sin(np.arange(0, 12 * np.pi, step=(12 * np.pi) / (24 * 60))) >>> df = pd.DataFrame(intensity, index=times, columns=['intensity']) >>> ts = TimeSeries(df) >>> ts.peek() # doctest: +SKIP References ---------- * `Pandas Documentation <https://pandas.pydata.org/pandas-docs/stable/>`_ """ # Class attribute used to specify the source class of the TimeSeries. _source = None _registry = dict() def __init_subclass__(cls, **kwargs): """ An __init_subclass__ hook initializes all of the subclasses of a given class. So for each subclass, it will call this block of code on import. This replicates some metaclass magic without the need to be aware of metaclasses. Here we use this to register each subclass in a dict that has the `is_datasource_for` attribute. This is then passed into the TimeSeries Factory so we can register them. """ super().__init_subclass__(**kwargs) if hasattr(cls, 'is_datasource_for'): cls._registry[cls] = cls.is_datasource_for def __init__(self, data, meta=None, units=None, **kwargs): self.data = data tr = self.time_range # Check metadata input if meta is None: # No meta given, so default self.meta = TimeSeriesMetaData(MetaDict(), tr, list(self.data.columns.values)) elif isinstance(meta, (dict, OrderedDict, MetaDict)): # Given the values for metadata (dict) and infer timerange and colnames from the data self.meta = TimeSeriesMetaData(meta, tr, list(self.data.columns.values)) elif isinstance(meta, tuple): # Given the values all in a tuple self.meta = TimeSeriesMetaData(meta, tr, list(self.data.columns.values)) else: # Should have a list of 3-tuples giving a complex metadata list. self.meta = meta if units is None: self.units = {} else: self.units = units # Validate input data # self._validate_meta() # self._validate_units() # #### Attribute definitions #### # @property def source(self): """ A string/object used to specify the source class of the TimeSeries. """ return self._source @property def columns(self): """A list of all the names of the columns in the data.""" return list(self.data.columns.values) @property def index(self): """The time index of the data.""" return self.data.index @property def time_range(self): """ The start and end times of the TimeSeries as a `~sunpy.time.TimeRange` object """ if len(self.data)>0: return TimeRange(self.data.index.min(), self.data.index.max()) else: return None # #### Data Access, Selection and Organisation Methods #### # def quantity(self, colname, **kwargs): """ Return a `~astropy.units.quantity.Quantity` for the given column. Parameters ---------- colname : `str` The heading of the column you want output. Returns ------- quantity : `~astropy.units.quantity.Quantity` """ values = self.data[colname].values unit = self.units[colname] return u.Quantity(values, unit) def add_column(self, colname, quantity, unit=False, overwrite=True, **kwargs): """ Return an new TimeSeries with the given column added or updated. Parameters ---------- colname : `str` The heading of the column you want output. quantity : `~astropy.units.quantity.Quantity` or `~numpy.ndarray` The values to be placed within the column. If updating values only then a numpy array is permitted. overwrite : `bool`, optional, default:True Set to true to allow the method to overwrite a column already present in the TimeSeries. Returns ------- newts : TimeSeries """ # Get the expected units from the quantity if required if not unit and isinstance(quantity, astropy.units.quantity.Quantity): unit = quantity.unit elif not unit: unit = u.dimensionless_unscaled # Make a copy of all the TimeSeries components. data = copy.copy(self.data) meta = TimeSeriesMetaData(copy.copy(self.meta.metadata)) units = copy.copy(self.units) # Add the unit to the units dictionary if already there. if not (colname in self.data.columns): units[colname] = unit # Convert the given quantity into values for given units if necessary. values = quantity if isinstance(values, astropy.units.quantity.Quantity) and overwrite: values = values.to(units[colname]).value # Update or add the data. if not (colname in self.data.columns) or overwrite: data[colname] = values # Return a new TimeSeries with the given updated/added column. return self.__class__(data, meta, units) def sort_index(self, **kwargs): """Returns a sorted version of the TimeSeries object. Generally this shouldn't be necessary as most TimeSeries operations sort the data anyway to ensure consistent behaviour when truncating. Returns ------- newts : `~sunpy.timeseries.TimeSeries` A new time series in ascending chronological order. """ return GenericTimeSeries(self.data.sort_index(**kwargs), TimeSeriesMetaData(copy.copy(self.meta.metadata)), copy.copy(self.units)) def truncate(self, a, b=None, int=None): """Returns a truncated version of the TimeSeries object. Parameters ---------- a : `sunpy.time.TimeRange`, `str` or `int` Either a time range to truncate to, or a start time in some format recognised by pandas, or a index integer. b : `str` or `int` If specified, the end time of the time range in some format recognised by pandas, or a index integer. int : `int` If specified, the integer indicating the slicing intervals. Returns ------- newts : `~sunpy.timeseries.TimeSeries` A new time series with only the selected times. """ # Evaluate inputs # If given strings, then use to create a sunpy.time.timerange.TimeRange # for the SunPy text date parser. if isinstance(a, str) and isinstance(b, str): a = TimeRange(a, b) if isinstance(a, TimeRange): # If we have a TimeRange, extract the values start = a.start.datetime end = a.end.datetime else: # Otherwise we already have the values start = a end = b # If an interval integer was given then use in truncation. truncated_data = self.data.sort_index()[start:end:int] # Truncate the metadata # Check there is data still truncated_meta = TimeSeriesMetaData([]) if len(truncated_data) > 0: tr = TimeRange(truncated_data.index.min(), truncated_data.index.max()) truncated_meta = TimeSeriesMetaData(copy.deepcopy(self.meta.metadata)) truncated_meta._truncate(tr) # Build similar TimeSeries object and sanatise metadata and units. object = self.__class__(truncated_data.sort_index(), truncated_meta, copy.copy(self.units)) object._sanitize_metadata() object._sanitize_units() return object def extract(self, column_name): """Returns a new time series with the chosen column. Parameters ---------- column_name : `str` A valid column name. Returns ------- newts : `~sunpy.timeseries.TimeSeries` A new time series with only the selected column. """ """ # TODO allow the extract function to pick more than one column if isinstance(self, pandas.Series): return self else: return GenericTimeSeries(self.data[column_name], TimeSeriesMetaData(self.meta.metadata.copy())) """ # Extract column and remove empty rows data = self.data[[column_name]].dropna() # Build generic TimeSeries object and sanatise metadata and units. object = GenericTimeSeries(data.sort_index(), TimeSeriesMetaData(copy.copy(self.meta.metadata)), copy.copy(self.units)) object._sanitize_metadata() object._sanitize_units() return object def concatenate(self, otherts, **kwargs): """Concatenate with another TimeSeries. This function will check and remove any duplicate times. It will keep the column values from the original time series to which the new time series is being added. Parameters ---------- otherts : `~sunpy.timeseries.TimeSeries` Another time series. same_source : `bool` Optional Set to true to check if the sources of the time series match. Returns ------- newts : `~sunpy.timeseries.TimeSeries` A new time series. Debate: decide if we want to be able to concatenate multiple time series at once. """ # check to see if nothing needs to be done if self == otherts: return self # Check the sources match if specified. same_source = kwargs.get('same_source', False) if same_source and not (isinstance(otherts, self.__class__)): raise TypeError("TimeSeries classes must match if specified.") # Concatenate the metadata and data meta = self.meta.concatenate(otherts.meta) data = pd.concat([self.data.copy(), otherts.data], **kwargs) # Add all the new units to the dictionary. units = OrderedDict() units.update(self.units) units.update(otherts.units) # If sources match then build similar TimeSeries. if self.__class__ == otherts.__class__: object = self.__class__(data.sort_index(), meta, units) else: # Build generic time series if the sources don't match. object = GenericTimeSeries(data.sort_index(), meta, units) # Sanatise metadata and units object._sanitize_metadata() object._sanitize_units() return object # #### Plotting Methods #### # def plot(self, axes=None, **plot_args): """Plot a plot of the time series Parameters ---------- axes : `~matplotlib.axes.Axes` or None If provided the image will be plotted on the given axes. Otherwise the current axes will be used. **plot_args : `dict` Any additional plot arguments that should be used when plotting. Returns ------- axes : `~matplotlib.axes.Axes` The plot axes. """ # Get current axes if axes is None: axes = plt.gca() axes = self.data.plot(ax=axes, **plot_args) return axes def peek(self, **kwargs): """Displays the time series in a new figure. Parameters ---------- **kwargs : `dict` Any additional plot arguments that should be used when plotting. """ # Check we have a timeseries valid for plotting self._validate_data_for_ploting() # Now make the plot figure = plt.figure() self.plot(**kwargs) figure.show() def _validate_data_for_ploting(self): """Raises an exception if the timeseries is invalid for plotting. To be added into all the peek methods in all source sup-classes. Currently only checks if we have an empty timeseries, where: len(self.data) == 0 """ # Check we have a valid TS if len(self.data) == 0: raise ValueError("The timeseries can't be plotted as it has no data present. " "(len(self.data) == 0)") # #### Miscellaneous #### # def _validate_meta(self): """ Validates the meta-information associated with a TimeSeries. This method includes very basic validation checks which apply to all of the kinds of files that SunPy can read. Datasource-specific validation should be handled in the relevant file in the sunpy.timeseries.sources package. Allows for default unit assignment for: COL_UNITS """ warnings.simplefilter('always', Warning) for meta_property in ('cunit1', 'cunit2', 'waveunit'): if (self.meta.get(meta_property) and u.Unit(self.meta.get(meta_property), parse_strict='silent').physical_type == 'unknown'): warnings.warn(f"Unknown value for {meta_property.upper()}.", SunpyUserWarning) def _validate_units(self, units, **kwargs): """ Validates the astropy unit-information associated with a TimeSeries. This method includes very basic validation checks which apply to all of the kinds of files that SunPy can read. Datasource-specific validation should be handled in the relevant file in the sunpy.timeseries.sources package. Allows for default unit assignment for: COL_UNITS """ warnings.simplefilter('always', Warning) result = True for key in units: if not isinstance(units[key], astropy.units.UnitBase): # If this is not a unit then this can't be a valid units dict. result = False warnings.warn(f"Invalid unit given for {key}.", SunpyUserWarning) return result def _sanitize_units(self, **kwargs): """ Sanitises the collections.OrderedDict used to store the units. Primarily this method will: Remove entries that don't match up to a column, Add unitless entries for columns with no units defined. Re-arrange the order of the dictionary to match the columns. """ warnings.simplefilter('always', Warning) # Populate unspecified units: for column in set(self.data.columns.tolist()) - set(self.units.keys()): # For all columns not present in the units dictionary. self.units[column] = u.dimensionless_unscaled warnings.warn(f"Unknown units for {column}.", SunpyUserWarning) # Re-arrange so it's in the same order as the columns and removed unused. units = OrderedDict() for column in self.data.columns.tolist(): units.update({column:self.units[column]}) # Now use the amended units Ordered Dictionary self.units = units def _sanitize_metadata(self, **kwargs): """ Sanitises the TimeSeriesMetaData object used to store the metadata. Primarily this method will: Remove entries outside of the datas TimeRange or truncate TimeRanges if the metadata overflows past the data, Remove column references in the metadata that don't match to a column in the data. Remove metadata entries that have no columns matching the data. """ warnings.simplefilter('always', Warning) # Truncate the metadata self.meta._truncate(self.time_range) # Remove non-existant columns redundant_cols = list(set(self.meta.columns) - set(self.columns)) self.meta._remove_columns(redundant_cols) # #### Export/Output Methods #### # def to_table(self, **kwargs): """ Return an Astropy Table of the give TimeSeries object. Returns ------- newtable : `~astrpy.table` A new astropy table containing the data from the time series. The table will include units where relevant. """ # ToDo: Table.from_pandas(df) doesn't include the index column. Add request? # Get data columns table = Table.from_pandas(self.data) # Get index column and add to table. index_col = Column(self.data.index.values, name='date') table.add_column(index_col, index=0) # Add in units. for key in self.units: table[key].unit = self.units[key] # Output the table return table def to_dataframe(self, **kwargs): """ Return a Pandas DataFrame of the give TimeSeries object. Returns ------- newdf : `~pandas.core.frame.DataFrame` A Pandas Dataframe containing the data. """ return self.data def to_array(self, columns=None): """ Return a numpy array of the give TimeSeries object. Parameters ---------- columns: `list`, optional, default:None If None, return all columns minus the index, otherwise, returns specified columns. Returns ------- values : `~numpy.ndarray` If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes. """ if columns: return self.data.values[columns] else: return self.data.values def __eq__(self, other): """ Check two TimeSeries objects are the same, they have matching type, data, metadata and units entries. Parameters ---------- other : `~sunpy.timeseries.GenericTimeSeries` The second TimeSeries object to compare with. Returns ------- result : `bool` """ match = True if isinstance(other, type(self)): if ((not self.data.equals(other.data)) or (self.meta != other.meta) or (self.units != other.units)): match = False else: match = False return match def __ne__(self, other): """ Check two TimeSeries objects are not the same, they don't have matching type, data, metadata and/or units entries. Parameters ---------- other : `~sunpy.timeseries.GenericTimeSeries` The second TimeSeries object to compare with. Returns ------- result : `bool` """ return not self == other @classmethod def _parse_file(cls, filepath): """Parses a file - to be implemented in any subclass that may use files""" return NotImplemented

yashrsharma44/sunpy

sunpy/timeseries/timeseriesbase.py

Python

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for Keras' base preprocessing layer.""" import os import keras from keras import keras_parameterized from keras import testing_utils from keras.engine import base_preprocessing_layer import numpy as np import tensorflow.compat.v2 as tf # Define a test-only implementation of BasePreprocessingLayer to validate # its correctness directly. class AddingPreprocessingLayer(base_preprocessing_layer.PreprocessingLayer): def build(self, input_shape): super(AddingPreprocessingLayer, self).build(input_shape) self.sum = tf.Variable(0., dtype=tf.float32) def update_state(self, data): self.sum.assign_add(tf.reduce_sum(tf.cast(data, tf.float32))) def reset_state(self): # pylint: disable=method-hidden self.sum.assign(0.) def set_total(self, sum_value): """This is an example of how a subclass would implement a direct setter. Args: sum_value: The total to set. """ self.sum.assign(sum_value) def call(self, inputs): return inputs + self.sum @keras_parameterized.run_all_keras_modes(always_skip_v1=True) class PreprocessingLayerTest(keras_parameterized.TestCase): def test_adapt_bad_input_fails(self): """Test that non-Dataset/Numpy inputs cause a reasonable error.""" input_dataset = {"foo": 0} layer = AddingPreprocessingLayer() if tf.executing_eagerly(): with self.assertRaisesRegex(ValueError, "Failed to find data adapter"): layer.adapt(input_dataset) else: with self.assertRaisesRegex(ValueError, "requires a"): layer.adapt(input_dataset) def test_adapt_infinite_dataset_fails(self): """Test that preproc layers fail if an infinite dataset is passed.""" input_dataset = tf.data.Dataset.from_tensor_slices( np.array([[1], [2], [3], [4], [5], [0]])).repeat() layer = AddingPreprocessingLayer() if tf.executing_eagerly(): with self.assertRaisesRegex(ValueError, "infinite dataset"): layer.adapt(input_dataset) else: with self.assertRaisesRegex(ValueError, ".*infinite number of elements.*"): layer.adapt(input_dataset) def test_setter_update(self): """Test the prototyped setter method.""" input_data = keras.Input(shape=(1,)) layer = AddingPreprocessingLayer() output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() layer.set_total(15) self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) def test_pre_build_adapt_update_numpy(self): """Test that preproc layers can adapt() before build() is called.""" input_dataset = np.array([1, 2, 3, 4, 5]) layer = AddingPreprocessingLayer() layer.adapt(input_dataset) input_data = keras.Input(shape=(1,)) output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) def test_post_build_adapt_update_numpy(self): """Test that preproc layers can adapt() after build() is called.""" input_dataset = np.array([1, 2, 3, 4, 5]) input_data = keras.Input(shape=(1,)) layer = AddingPreprocessingLayer() output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() layer.adapt(input_dataset) self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) def test_pre_build_adapt_update_dataset(self): """Test that preproc layers can adapt() before build() is called.""" input_dataset = tf.data.Dataset.from_tensor_slices( np.array([[1], [2], [3], [4], [5], [0]])) layer = AddingPreprocessingLayer() layer.adapt(input_dataset) input_data = keras.Input(shape=(1,)) output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) def test_post_build_adapt_update_dataset(self): """Test that preproc layers can adapt() after build() is called.""" input_dataset = tf.data.Dataset.from_tensor_slices( np.array([[1], [2], [3], [4], [5], [0]])) input_data = keras.Input(shape=(1,)) layer = AddingPreprocessingLayer() output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() layer.adapt(input_dataset) self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) def test_weight_based_state_transfer(self): """Test that preproc layers can transfer state via get/set weights..""" def get_model(): input_data = keras.Input(shape=(1,)) layer = AddingPreprocessingLayer() output = layer(input_data) model = keras.Model(input_data, output) model._run_eagerly = testing_utils.should_run_eagerly() return (model, layer) input_dataset = np.array([1, 2, 3, 4, 5]) model, layer = get_model() layer.adapt(input_dataset) self.assertAllEqual([[16], [17], [18]], model.predict([1., 2., 3.])) # Create a new model and verify it has no state carryover. weights = model.get_weights() model_2, _ = get_model() self.assertAllEqual([[1], [2], [3]], model_2.predict([1., 2., 3.])) # Transfer state from model to model_2 via get/set weights. model_2.set_weights(weights) self.assertAllEqual([[16], [17], [18]], model_2.predict([1., 2., 3.])) def test_loading_without_providing_class_fails(self): input_data = keras.Input(shape=(1,)) layer = AddingPreprocessingLayer() output = layer(input_data) model = keras.Model(input_data, output) if not tf.executing_eagerly(): self.evaluate(tf.compat.v1.variables_initializer(model.variables)) output_path = os.path.join(self.get_temp_dir(), "tf_keras_saved_model") model.save(output_path, save_format="tf") with self.assertRaisesRegex(RuntimeError, "Unable to restore a layer of"): _ = keras.models.load_model(output_path) def test_adapt_sets_input_shape_rank(self): """Check that `.adapt()` sets the `input_shape`'s rank.""" # Shape: (3,1,2) adapt_dataset = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]], dtype=np.float32) layer = AddingPreprocessingLayer() layer.adapt(adapt_dataset) input_dataset = np.array([[[1., 2.], [3., 4.]], [[3., 4.], [5., 6.]]], dtype=np.float32) layer(input_dataset) model = keras.Sequential([layer]) self.assertTrue(model.built) self.assertEqual(model.input_shape, (None, None, None)) def test_adapt_doesnt_overwrite_input_shape(self): """Check that `.adapt()` doesn't change the `input_shape`.""" # Shape: (3, 1, 2) adapt_dataset = np.array([[[1., 2.]], [[3., 4.]], [[5., 6.]]], dtype=np.float32) layer = AddingPreprocessingLayer(input_shape=[1, 2]) layer.adapt(adapt_dataset) model = keras.Sequential([layer]) self.assertTrue(model.built) self.assertEqual(model.input_shape, (None, 1, 2)) class PreprocessingLayerV1Test(keras_parameterized.TestCase): def test_adapt_fails(self): """Test that calling adapt leads to a runtime error.""" input_dataset = {"foo": 0} with tf.Graph().as_default(): layer = AddingPreprocessingLayer() with self.assertRaisesRegex(RuntimeError, "`adapt` is only supported in tensorflow v2"): layer.adapt(input_dataset) if __name__ == "__main__": tf.test.main()

01-vyom/keras

keras/engine/base_preprocessing_layer_test.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """tracking_url Revision ID: ca69c70ec99b Revises: a65458420354 Create Date: 2017-07-26 20:09:52.606416 """ # revision identifiers, used by Alembic. revision = "ca69c70ec99b" down_revision = "a65458420354" from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql def upgrade(): op.add_column("query", sa.Column("tracking_url", sa.Text(), nullable=True)) def downgrade(): op.drop_column("query", "tracking_url")

1AB9502/incubator-superset

superset/migrations/versions/ca69c70ec99b_tracking_url.py

Python

"""An abstract class for entities.""" from __future__ import annotations from abc import ABC import asyncio from collections.abc import Awaitable, Iterable, Mapping, MutableMapping from dataclasses import dataclass from datetime import datetime, timedelta from enum import Enum, auto import functools as ft import logging import math import sys from timeit import default_timer as timer from typing import Any, Final, Literal, TypedDict, final import voluptuous as vol from homeassistant.backports.enum import StrEnum from homeassistant.config import DATA_CUSTOMIZE from homeassistant.const import ( ATTR_ASSUMED_STATE, ATTR_ATTRIBUTION, ATTR_DEVICE_CLASS, ATTR_ENTITY_PICTURE, ATTR_FRIENDLY_NAME, ATTR_ICON, ATTR_SUPPORTED_FEATURES, ATTR_UNIT_OF_MEASUREMENT, DEVICE_DEFAULT_NAME, STATE_OFF, STATE_ON, STATE_UNAVAILABLE, STATE_UNKNOWN, TEMP_CELSIUS, TEMP_FAHRENHEIT, ) from homeassistant.core import ( CALLBACK_TYPE, Context, Event, HomeAssistant, callback, split_entity_id, ) from homeassistant.exceptions import HomeAssistantError, NoEntitySpecifiedError from homeassistant.loader import bind_hass from homeassistant.util import dt as dt_util, ensure_unique_string, slugify from . import entity_registry as er from .device_registry import DeviceEntryType from .entity_platform import EntityPlatform from .event import async_track_entity_registry_updated_event from .typing import StateType _LOGGER = logging.getLogger(__name__) SLOW_UPDATE_WARNING = 10 DATA_ENTITY_SOURCE = "entity_info" SOURCE_CONFIG_ENTRY = "config_entry" SOURCE_PLATFORM_CONFIG = "platform_config" # Used when converting float states to string: limit precision according to machine # epsilon to make the string representation readable FLOAT_PRECISION = abs(int(math.floor(math.log10(abs(sys.float_info.epsilon))))) - 1 @callback @bind_hass def entity_sources(hass: HomeAssistant) -> dict[str, dict[str, str]]: """Get the entity sources.""" return hass.data.get(DATA_ENTITY_SOURCE, {}) def generate_entity_id( entity_id_format: str, name: str | None, current_ids: list[str] | None = None, hass: HomeAssistant | None = None, ) -> str: """Generate a unique entity ID based on given entity IDs or used IDs.""" return async_generate_entity_id(entity_id_format, name, current_ids, hass) @callback def async_generate_entity_id( entity_id_format: str, name: str | None, current_ids: Iterable[str] | None = None, hass: HomeAssistant | None = None, ) -> str: """Generate a unique entity ID based on given entity IDs or used IDs.""" name = (name or DEVICE_DEFAULT_NAME).lower() preferred_string = entity_id_format.format(slugify(name)) if current_ids is not None: return ensure_unique_string(preferred_string, current_ids) if hass is None: raise ValueError("Missing required parameter current_ids or hass") test_string = preferred_string tries = 1 while not hass.states.async_available(test_string): tries += 1 test_string = f"{preferred_string}_{tries}" return test_string def get_capability(hass: HomeAssistant, entity_id: str, capability: str) -> Any | None: """Get a capability attribute of an entity. First try the statemachine, then entity registry. """ if state := hass.states.get(entity_id): return state.attributes.get(capability) entity_registry = er.async_get(hass) if not (entry := entity_registry.async_get(entity_id)): raise HomeAssistantError(f"Unknown entity {entity_id}") return entry.capabilities.get(capability) if entry.capabilities else None def get_device_class(hass: HomeAssistant, entity_id: str) -> str | None: """Get device class of an entity. First try the statemachine, then entity registry. """ if state := hass.states.get(entity_id): return state.attributes.get(ATTR_DEVICE_CLASS) entity_registry = er.async_get(hass) if not (entry := entity_registry.async_get(entity_id)): raise HomeAssistantError(f"Unknown entity {entity_id}") return entry.device_class or entry.original_device_class def get_supported_features(hass: HomeAssistant, entity_id: str) -> int: """Get supported features for an entity. First try the statemachine, then entity registry. """ if state := hass.states.get(entity_id): return state.attributes.get(ATTR_SUPPORTED_FEATURES, 0) entity_registry = er.async_get(hass) if not (entry := entity_registry.async_get(entity_id)): raise HomeAssistantError(f"Unknown entity {entity_id}") return entry.supported_features or 0 def get_unit_of_measurement(hass: HomeAssistant, entity_id: str) -> str | None: """Get unit of measurement class of an entity. First try the statemachine, then entity registry. """ if state := hass.states.get(entity_id): return state.attributes.get(ATTR_UNIT_OF_MEASUREMENT) entity_registry = er.async_get(hass) if not (entry := entity_registry.async_get(entity_id)): raise HomeAssistantError(f"Unknown entity {entity_id}") return entry.unit_of_measurement class DeviceInfo(TypedDict, total=False): """Entity device information for device registry.""" configuration_url: str | None connections: set[tuple[str, str]] default_manufacturer: str default_model: str default_name: str entry_type: DeviceEntryType | None identifiers: set[tuple[str, str]] manufacturer: str | None model: str | None name: str | None suggested_area: str | None sw_version: str | None hw_version: str | None via_device: tuple[str, str] class EntityCategory(StrEnum): """Category of an entity. An entity with a category will: - Not be exposed to cloud, Alexa, or Google Assistant components - Not be included in indirect service calls to devices or areas """ # Config: An entity which allows changing the configuration of a device CONFIG = "config" # Diagnostic: An entity exposing some configuration parameter or diagnostics of a device DIAGNOSTIC = "diagnostic" # System: An entity which is not useful for the user to interact with SYSTEM = "system" ENTITY_CATEGORIES_SCHEMA: Final = vol.Coerce(EntityCategory) class EntityPlatformState(Enum): """The platform state of an entity.""" # Not Added: Not yet added to a platform, polling updates are written to the state machine NOT_ADDED = auto() # Added: Added to a platform, polling updates are written to the state machine ADDED = auto() # Removed: Removed from a platform, polling updates are not written to the state machine REMOVED = auto() @dataclass class EntityDescription: """A class that describes Home Assistant entities.""" # This is the key identifier for this entity key: str device_class: str | None = None entity_category: EntityCategory | None = None entity_registry_enabled_default: bool = True entity_registry_visible_default: bool = True force_update: bool = False icon: str | None = None name: str | None = None unit_of_measurement: str | None = None class Entity(ABC): """An abstract class for Home Assistant entities.""" # SAFE TO OVERWRITE # The properties and methods here are safe to overwrite when inheriting # this class. These may be used to customize the behavior of the entity. entity_id: str = None # type: ignore[assignment] # Owning hass instance. Will be set by EntityPlatform # While not purely typed, it makes typehinting more useful for us # and removes the need for constant None checks or asserts. hass: HomeAssistant = None # type: ignore[assignment] # Owning platform instance. Will be set by EntityPlatform platform: EntityPlatform | None = None # Entity description instance for this Entity entity_description: EntityDescription # If we reported if this entity was slow _slow_reported = False # If we reported this entity is updated while disabled _disabled_reported = False # If we reported this entity is relying on deprecated temperature conversion _temperature_reported = False # Protect for multiple updates _update_staged = False # Process updates in parallel parallel_updates: asyncio.Semaphore | None = None # Entry in the entity registry registry_entry: er.RegistryEntry | None = None # Hold list for functions to call on remove. _on_remove: list[CALLBACK_TYPE] | None = None # Context _context: Context | None = None _context_set: datetime | None = None # If entity is added to an entity platform _platform_state = EntityPlatformState.NOT_ADDED # Entity Properties _attr_assumed_state: bool = False _attr_attribution: str | None = None _attr_available: bool = True _attr_context_recent_time: timedelta = timedelta(seconds=5) _attr_device_class: str | None _attr_device_info: DeviceInfo | None = None _attr_entity_category: EntityCategory | None _attr_entity_picture: str | None = None _attr_entity_registry_enabled_default: bool _attr_entity_registry_visible_default: bool _attr_extra_state_attributes: MutableMapping[str, Any] _attr_force_update: bool _attr_icon: str | None _attr_name: str | None _attr_should_poll: bool = True _attr_state: StateType = STATE_UNKNOWN _attr_supported_features: int | None = None _attr_unique_id: str | None = None _attr_unit_of_measurement: str | None @property def should_poll(self) -> bool: """Return True if entity has to be polled for state. False if entity pushes its state to HA. """ return self._attr_should_poll @property def unique_id(self) -> str | None: """Return a unique ID.""" return self._attr_unique_id @property def name(self) -> str | None: """Return the name of the entity.""" if hasattr(self, "_attr_name"): return self._attr_name if hasattr(self, "entity_description"): return self.entity_description.name return None @property def state(self) -> StateType: """Return the state of the entity.""" return self._attr_state @property def capability_attributes(self) -> Mapping[str, Any] | None: """Return the capability attributes. Attributes that explain the capabilities of an entity. Implemented by component base class. Convention for attribute names is lowercase snake_case. """ return None @property def state_attributes(self) -> dict[str, Any] | None: """Return the state attributes. Implemented by component base class, should not be extended by integrations. Convention for attribute names is lowercase snake_case. """ return None @property def device_state_attributes(self) -> Mapping[str, Any] | None: """Return entity specific state attributes. This method is deprecated, platform classes should implement extra_state_attributes instead. """ return None @property def extra_state_attributes(self) -> Mapping[str, Any] | None: """Return entity specific state attributes. Implemented by platform classes. Convention for attribute names is lowercase snake_case. """ if hasattr(self, "_attr_extra_state_attributes"): return self._attr_extra_state_attributes return None @property def device_info(self) -> DeviceInfo | None: """Return device specific attributes. Implemented by platform classes. """ return self._attr_device_info @property def device_class(self) -> str | None: """Return the class of this device, from component DEVICE_CLASSES.""" if hasattr(self, "_attr_device_class"): return self._attr_device_class if hasattr(self, "entity_description"): return self.entity_description.device_class return None @property def unit_of_measurement(self) -> str | None: """Return the unit of measurement of this entity, if any.""" if hasattr(self, "_attr_unit_of_measurement"): return self._attr_unit_of_measurement if hasattr(self, "entity_description"): return self.entity_description.unit_of_measurement return None @property def icon(self) -> str | None: """Return the icon to use in the frontend, if any.""" if hasattr(self, "_attr_icon"): return self._attr_icon if hasattr(self, "entity_description"): return self.entity_description.icon return None @property def entity_picture(self) -> str | None: """Return the entity picture to use in the frontend, if any.""" return self._attr_entity_picture @property def available(self) -> bool: """Return True if entity is available.""" return self._attr_available @property def assumed_state(self) -> bool: """Return True if unable to access real state of the entity.""" return self._attr_assumed_state @property def force_update(self) -> bool: """Return True if state updates should be forced. If True, a state change will be triggered anytime the state property is updated, not just when the value changes. """ if hasattr(self, "_attr_force_update"): return self._attr_force_update if hasattr(self, "entity_description"): return self.entity_description.force_update return False @property def supported_features(self) -> int | None: """Flag supported features.""" return self._attr_supported_features @property def context_recent_time(self) -> timedelta: """Time that a context is considered recent.""" return self._attr_context_recent_time @property def entity_registry_enabled_default(self) -> bool: """Return if the entity should be enabled when first added to the entity registry.""" if hasattr(self, "_attr_entity_registry_enabled_default"): return self._attr_entity_registry_enabled_default if hasattr(self, "entity_description"): return self.entity_description.entity_registry_enabled_default return True @property def entity_registry_visible_default(self) -> bool: """Return if the entity should be visible when first added to the entity registry.""" if hasattr(self, "_attr_entity_registry_visible_default"): return self._attr_entity_registry_visible_default if hasattr(self, "entity_description"): return self.entity_description.entity_registry_visible_default return True @property def attribution(self) -> str | None: """Return the attribution.""" return self._attr_attribution @property def entity_category(self) -> EntityCategory | None: """Return the category of the entity, if any.""" if hasattr(self, "_attr_entity_category"): return self._attr_entity_category if hasattr(self, "entity_description"): return self.entity_description.entity_category return None # DO NOT OVERWRITE # These properties and methods are either managed by Home Assistant or they # are used to perform a very specific function. Overwriting these may # produce undesirable effects in the entity's operation. @property def enabled(self) -> bool: """Return if the entity is enabled in the entity registry. If an entity is not part of the registry, it cannot be disabled and will therefore always be enabled. """ return self.registry_entry is None or not self.registry_entry.disabled @callback def async_set_context(self, context: Context) -> None: """Set the context the entity currently operates under.""" self._context = context self._context_set = dt_util.utcnow() async def async_update_ha_state(self, force_refresh: bool = False) -> None: """Update Home Assistant with current state of entity. If force_refresh == True will update entity before setting state. This method must be run in the event loop. """ if self.hass is None: raise RuntimeError(f"Attribute hass is None for {self}") if self.entity_id is None: raise NoEntitySpecifiedError( f"No entity id specified for entity {self.name}" ) # update entity data if force_refresh: try: await self.async_device_update() except Exception: # pylint: disable=broad-except _LOGGER.exception("Update for %s fails", self.entity_id) return self._async_write_ha_state() @callback def async_write_ha_state(self) -> None: """Write the state to the state machine.""" if self.hass is None: raise RuntimeError(f"Attribute hass is None for {self}") if self.entity_id is None: raise NoEntitySpecifiedError( f"No entity id specified for entity {self.name}" ) self._async_write_ha_state() def _stringify_state(self, available: bool) -> str: """Convert state to string.""" if not available: return STATE_UNAVAILABLE if (state := self.state) is None: return STATE_UNKNOWN if isinstance(state, float): # If the entity's state is a float, limit precision according to machine # epsilon to make the string representation readable return f"{state:.{FLOAT_PRECISION}}" return str(state) @callback def _async_write_ha_state(self) -> None: """Write the state to the state machine.""" if self._platform_state == EntityPlatformState.REMOVED: # Polling returned after the entity has already been removed return if self.registry_entry and self.registry_entry.disabled_by: if not self._disabled_reported: self._disabled_reported = True assert self.platform is not None _LOGGER.warning( "Entity %s is incorrectly being triggered for updates while it is disabled. This is a bug in the %s integration", self.entity_id, self.platform.platform_name, ) return start = timer() attr = self.capability_attributes attr = dict(attr) if attr else {} available = self.available # only call self.available once per update cycle state = self._stringify_state(available) if available: attr.update(self.state_attributes or {}) attr.update(self.extra_state_attributes or {}) if (unit_of_measurement := self.unit_of_measurement) is not None: attr[ATTR_UNIT_OF_MEASUREMENT] = unit_of_measurement entry = self.registry_entry if assumed_state := self.assumed_state: attr[ATTR_ASSUMED_STATE] = assumed_state if (attribution := self.attribution) is not None: attr[ATTR_ATTRIBUTION] = attribution if ( device_class := (entry and entry.device_class) or self.device_class ) is not None: attr[ATTR_DEVICE_CLASS] = str(device_class) if (entity_picture := self.entity_picture) is not None: attr[ATTR_ENTITY_PICTURE] = entity_picture if (icon := (entry and entry.icon) or self.icon) is not None: attr[ATTR_ICON] = icon if (name := (entry and entry.name) or self.name) is not None: attr[ATTR_FRIENDLY_NAME] = name if (supported_features := self.supported_features) is not None: attr[ATTR_SUPPORTED_FEATURES] = supported_features end = timer() if end - start > 0.4 and not self._slow_reported: self._slow_reported = True report_issue = self._suggest_report_issue() _LOGGER.warning( "Updating state for %s (%s) took %.3f seconds. Please %s", self.entity_id, type(self), end - start, report_issue, ) # Overwrite properties that have been set in the config file. if DATA_CUSTOMIZE in self.hass.data: attr.update(self.hass.data[DATA_CUSTOMIZE].get(self.entity_id)) def _convert_temperature(state: str, attr: dict) -> str: # Convert temperature if we detect one # pylint: disable-next=import-outside-toplevel from homeassistant.components.sensor import SensorEntity unit_of_measure = attr.get(ATTR_UNIT_OF_MEASUREMENT) units = self.hass.config.units if unit_of_measure == units.temperature_unit or unit_of_measure not in ( TEMP_CELSIUS, TEMP_FAHRENHEIT, ): return state domain = split_entity_id(self.entity_id)[0] if domain != "sensor": if not self._temperature_reported: self._temperature_reported = True report_issue = self._suggest_report_issue() _LOGGER.warning( "Entity %s (%s) relies on automatic temperature conversion, this will " "be unsupported in Home Assistant Core 2022.7. Please %s", self.entity_id, type(self), report_issue, ) elif not isinstance(self, SensorEntity): if not self._temperature_reported: self._temperature_reported = True report_issue = self._suggest_report_issue() _LOGGER.warning( "Temperature sensor %s (%s) does not inherit SensorEntity, " "this will be unsupported in Home Assistant Core 2022.7." "Please %s", self.entity_id, type(self), report_issue, ) else: return state try: prec = len(state) - state.index(".") - 1 if "." in state else 0 temp = units.temperature(float(state), unit_of_measure) state = str(round(temp) if prec == 0 else round(temp, prec)) attr[ATTR_UNIT_OF_MEASUREMENT] = units.temperature_unit except ValueError: # Could not convert state to float pass return state state = _convert_temperature(state, attr) if ( self._context_set is not None and dt_util.utcnow() - self._context_set > self.context_recent_time ): self._context = None self._context_set = None self.hass.states.async_set( self.entity_id, state, attr, self.force_update, self._context ) def schedule_update_ha_state(self, force_refresh: bool = False) -> None: """Schedule an update ha state change task. Scheduling the update avoids executor deadlocks. Entity state and attributes are read when the update ha state change task is executed. If state is changed more than once before the ha state change task has been executed, the intermediate state transitions will be missed. """ self.hass.add_job(self.async_update_ha_state(force_refresh)) @callback def async_schedule_update_ha_state(self, force_refresh: bool = False) -> None: """Schedule an update ha state change task. This method must be run in the event loop. Scheduling the update avoids executor deadlocks. Entity state and attributes are read when the update ha state change task is executed. If state is changed more than once before the ha state change task has been executed, the intermediate state transitions will be missed. """ if force_refresh: self.hass.async_create_task(self.async_update_ha_state(force_refresh)) else: self.async_write_ha_state() async def async_device_update(self, warning: bool = True) -> None: """Process 'update' or 'async_update' from entity. This method is a coroutine. """ if self._update_staged: return self._update_staged = True # Process update sequential if self.parallel_updates: await self.parallel_updates.acquire() try: task: asyncio.Future[None] if hasattr(self, "async_update"): task = self.hass.async_create_task(self.async_update()) # type: ignore[attr-defined] elif hasattr(self, "update"): task = self.hass.async_add_executor_job(self.update) # type: ignore[attr-defined] else: return if not warning: await task return finished, _ = await asyncio.wait([task], timeout=SLOW_UPDATE_WARNING) for done in finished: if exc := done.exception(): raise exc return _LOGGER.warning( "Update of %s is taking over %s seconds", self.entity_id, SLOW_UPDATE_WARNING, ) await task finally: self._update_staged = False if self.parallel_updates: self.parallel_updates.release() @callback def async_on_remove(self, func: CALLBACK_TYPE) -> None: """Add a function to call when entity is removed or not added.""" if self._on_remove is None: self._on_remove = [] self._on_remove.append(func) async def async_removed_from_registry(self) -> None: """Run when entity has been removed from entity registry. To be extended by integrations. """ @callback def add_to_platform_start( self, hass: HomeAssistant, platform: EntityPlatform, parallel_updates: asyncio.Semaphore | None, ) -> None: """Start adding an entity to a platform.""" if self._platform_state == EntityPlatformState.ADDED: raise HomeAssistantError( f"Entity {self.entity_id} cannot be added a second time to an entity platform" ) self.hass = hass self.platform = platform self.parallel_updates = parallel_updates self._platform_state = EntityPlatformState.ADDED def _call_on_remove_callbacks(self) -> None: """Call callbacks registered by async_on_remove.""" if self._on_remove is None: return while self._on_remove: self._on_remove.pop()() @callback def add_to_platform_abort(self) -> None: """Abort adding an entity to a platform.""" self._platform_state = EntityPlatformState.NOT_ADDED self._call_on_remove_callbacks() self.hass = None # type: ignore[assignment] self.platform = None self.parallel_updates = None async def add_to_platform_finish(self) -> None: """Finish adding an entity to a platform.""" await self.async_internal_added_to_hass() await self.async_added_to_hass() self.async_write_ha_state() async def async_remove(self, *, force_remove: bool = False) -> None: """Remove entity from Home Assistant. If the entity has a non disabled entry in the entity registry, the entity's state will be set to unavailable, in the same way as when the entity registry is loaded. If the entity doesn't have a non disabled entry in the entity registry, or if force_remove=True, its state will be removed. """ if self.platform and self._platform_state != EntityPlatformState.ADDED: raise HomeAssistantError( f"Entity {self.entity_id} async_remove called twice" ) self._platform_state = EntityPlatformState.REMOVED self._call_on_remove_callbacks() await self.async_internal_will_remove_from_hass() await self.async_will_remove_from_hass() # Check if entry still exists in entity registry (e.g. unloading config entry) if ( not force_remove and self.registry_entry and not self.registry_entry.disabled ): # Set the entity's state will to unavailable + ATTR_RESTORED: True self.registry_entry.write_unavailable_state(self.hass) else: self.hass.states.async_remove(self.entity_id, context=self._context) async def async_added_to_hass(self) -> None: """Run when entity about to be added to hass. To be extended by integrations. """ async def async_will_remove_from_hass(self) -> None: """Run when entity will be removed from hass. To be extended by integrations. """ @callback def async_registry_entry_updated(self) -> None: """Run when the entity registry entry has been updated. To be extended by integrations. """ async def async_internal_added_to_hass(self) -> None: """Run when entity about to be added to hass. Not to be extended by integrations. """ if self.platform: info = { "domain": self.platform.platform_name, "custom_component": "custom_components" in type(self).__module__, } if self.platform.config_entry: info["source"] = SOURCE_CONFIG_ENTRY info["config_entry"] = self.platform.config_entry.entry_id else: info["source"] = SOURCE_PLATFORM_CONFIG self.hass.data.setdefault(DATA_ENTITY_SOURCE, {})[self.entity_id] = info if self.registry_entry is not None: # This is an assert as it should never happen, but helps in tests assert ( not self.registry_entry.disabled_by ), f"Entity {self.entity_id} is being added while it's disabled" self.async_on_remove( async_track_entity_registry_updated_event( self.hass, self.entity_id, self._async_registry_updated ) ) async def async_internal_will_remove_from_hass(self) -> None: """Run when entity will be removed from hass. Not to be extended by integrations. """ if self.platform: self.hass.data[DATA_ENTITY_SOURCE].pop(self.entity_id) async def _async_registry_updated(self, event: Event) -> None: """Handle entity registry update.""" data = event.data if data["action"] == "remove": await self.async_removed_from_registry() self.registry_entry = None await self.async_remove() if data["action"] != "update": return ent_reg = er.async_get(self.hass) old = self.registry_entry self.registry_entry = ent_reg.async_get(data["entity_id"]) assert self.registry_entry is not None if self.registry_entry.disabled: await self.async_remove() return assert old is not None if self.registry_entry.entity_id == old.entity_id: self.async_registry_entry_updated() self.async_write_ha_state() return await self.async_remove(force_remove=True) assert self.platform is not None self.entity_id = self.registry_entry.entity_id await self.platform.async_add_entities([self]) def __eq__(self, other: Any) -> bool: """Return the comparison.""" if not isinstance(other, self.__class__): return False # Can only decide equality if both have a unique id if self.unique_id is None or other.unique_id is None: return False # Ensure they belong to the same platform if self.platform is not None or other.platform is not None: if self.platform is None or other.platform is None: return False if self.platform.platform != other.platform.platform: return False return self.unique_id == other.unique_id def __repr__(self) -> str: """Return the representation.""" return f"<Entity {self.name}: {self.state}>" async def async_request_call(self, coro: Awaitable) -> None: """Process request batched.""" if self.parallel_updates: await self.parallel_updates.acquire() try: await coro finally: if self.parallel_updates: self.parallel_updates.release() def _suggest_report_issue(self) -> str: """Suggest to report an issue.""" report_issue = "" if "custom_components" in type(self).__module__: report_issue = "report it to the custom component author." else: report_issue = ( "create a bug report at " "https://github.com/home-assistant/core/issues?q=is%3Aopen+is%3Aissue" ) if self.platform: report_issue += ( f"+label%3A%22integration%3A+{self.platform.platform_name}%22" ) return report_issue @dataclass class ToggleEntityDescription(EntityDescription): """A class that describes toggle entities.""" class ToggleEntity(Entity): """An abstract class for entities that can be turned on and off.""" entity_description: ToggleEntityDescription _attr_is_on: bool | None = None _attr_state: None = None @property @final def state(self) -> Literal["on", "off"] | None: """Return the state.""" if (is_on := self.is_on) is None: return None return STATE_ON if is_on else STATE_OFF @property def is_on(self) -> bool | None: """Return True if entity is on.""" return self._attr_is_on def turn_on(self, **kwargs: Any) -> None: """Turn the entity on.""" raise NotImplementedError() async def async_turn_on(self, **kwargs: Any) -> None: """Turn the entity on.""" await self.hass.async_add_executor_job(ft.partial(self.turn_on, **kwargs)) def turn_off(self, **kwargs: Any) -> None: """Turn the entity off.""" raise NotImplementedError() async def async_turn_off(self, **kwargs: Any) -> None: """Turn the entity off.""" await self.hass.async_add_executor_job(ft.partial(self.turn_off, **kwargs)) def toggle(self, **kwargs: Any) -> None: """Toggle the entity.""" if self.is_on: self.turn_off(**kwargs) else: self.turn_on(**kwargs) async def async_toggle(self, **kwargs: Any) -> None: """Toggle the entity.""" if self.is_on: await self.async_turn_off(**kwargs) else: await self.async_turn_on(**kwargs)

algra4/core

homeassistant/helpers/entity.py

Python

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class direction_out(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-common-def - based on the path /routing-system/router/router-bgp/address-family/ipv4/ipv4-unicast/default-vrf/neighbor/af-ipv4-neighbor-peergroup-holder/af-ipv4-neighbor-peergroup/prefix-list/direction-out. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__prefix_list_direction_out_prefix_name','__prefix_list_direction_out',) _yang_name = 'direction-out' _rest_name = '' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__prefix_list_direction_out = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="prefix-list-direction-out", rest_name="out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Filter outgoing routes', u'alt-name': u'out'}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True) self.__prefix_list_direction_out_prefix_name = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1..63']}), is_leaf=True, yang_name="prefix-list-direction-out-prefix-name", rest_name="ip-access-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'alt-name': u'ip-access-number', u'cli-incomplete-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='nei-prefix-list-filter', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'routing-system', u'router', u'router-bgp', u'address-family', u'ipv4', u'ipv4-unicast', u'default-vrf', u'neighbor', u'af-ipv4-neighbor-peergroup-holder', u'af-ipv4-neighbor-peergroup', u'prefix-list', u'direction-out'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'router', u'bgp', u'address-family', u'ipv4', u'unicast', u'neighbor', u'af-ipv4-neighbor-peergroup', u'prefix-list'] def _get_prefix_list_direction_out_prefix_name(self): """ Getter method for prefix_list_direction_out_prefix_name, mapped from YANG variable /routing_system/router/router_bgp/address_family/ipv4/ipv4_unicast/default_vrf/neighbor/af_ipv4_neighbor_peergroup_holder/af_ipv4_neighbor_peergroup/prefix_list/direction_out/prefix_list_direction_out_prefix_name (nei-prefix-list-filter) """ return self.__prefix_list_direction_out_prefix_name def _set_prefix_list_direction_out_prefix_name(self, v, load=False): """ Setter method for prefix_list_direction_out_prefix_name, mapped from YANG variable /routing_system/router/router_bgp/address_family/ipv4/ipv4_unicast/default_vrf/neighbor/af_ipv4_neighbor_peergroup_holder/af_ipv4_neighbor_peergroup/prefix_list/direction_out/prefix_list_direction_out_prefix_name (nei-prefix-list-filter) If this variable is read-only (config: false) in the source YANG file, then _set_prefix_list_direction_out_prefix_name is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_prefix_list_direction_out_prefix_name() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1..63']}), is_leaf=True, yang_name="prefix-list-direction-out-prefix-name", rest_name="ip-access-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'alt-name': u'ip-access-number', u'cli-incomplete-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='nei-prefix-list-filter', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """prefix_list_direction_out_prefix_name must be of a type compatible with nei-prefix-list-filter""", 'defined-type': "brocade-bgp:nei-prefix-list-filter", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1..63']}), is_leaf=True, yang_name="prefix-list-direction-out-prefix-name", rest_name="ip-access-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'alt-name': u'ip-access-number', u'cli-incomplete-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='nei-prefix-list-filter', is_config=True)""", }) self.__prefix_list_direction_out_prefix_name = t if hasattr(self, '_set'): self._set() def _unset_prefix_list_direction_out_prefix_name(self): self.__prefix_list_direction_out_prefix_name = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'length': [u'1..63']}), is_leaf=True, yang_name="prefix-list-direction-out-prefix-name", rest_name="ip-access-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'alt-name': u'ip-access-number', u'cli-incomplete-no': None, u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='nei-prefix-list-filter', is_config=True) def _get_prefix_list_direction_out(self): """ Getter method for prefix_list_direction_out, mapped from YANG variable /routing_system/router/router_bgp/address_family/ipv4/ipv4_unicast/default_vrf/neighbor/af_ipv4_neighbor_peergroup_holder/af_ipv4_neighbor_peergroup/prefix_list/direction_out/prefix_list_direction_out (empty) """ return self.__prefix_list_direction_out def _set_prefix_list_direction_out(self, v, load=False): """ Setter method for prefix_list_direction_out, mapped from YANG variable /routing_system/router/router_bgp/address_family/ipv4/ipv4_unicast/default_vrf/neighbor/af_ipv4_neighbor_peergroup_holder/af_ipv4_neighbor_peergroup/prefix_list/direction_out/prefix_list_direction_out (empty) If this variable is read-only (config: false) in the source YANG file, then _set_prefix_list_direction_out is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_prefix_list_direction_out() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="prefix-list-direction-out", rest_name="out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Filter outgoing routes', u'alt-name': u'out'}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """prefix_list_direction_out must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="prefix-list-direction-out", rest_name="out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Filter outgoing routes', u'alt-name': u'out'}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True)""", }) self.__prefix_list_direction_out = t if hasattr(self, '_set'): self._set() def _unset_prefix_list_direction_out(self): self.__prefix_list_direction_out = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="prefix-list-direction-out", rest_name="out", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Filter outgoing routes', u'alt-name': u'out'}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True) prefix_list_direction_out_prefix_name = __builtin__.property(_get_prefix_list_direction_out_prefix_name, _set_prefix_list_direction_out_prefix_name) prefix_list_direction_out = __builtin__.property(_get_prefix_list_direction_out, _set_prefix_list_direction_out) _pyangbind_elements = {'prefix_list_direction_out_prefix_name': prefix_list_direction_out_prefix_name, 'prefix_list_direction_out': prefix_list_direction_out, }

extremenetworks/pybind

pybind/slxos/v17s_1_02/routing_system/router/router_bgp/address_family/ipv4/ipv4_unicast/default_vrf/neighbor/af_ipv4_neighbor_peergroup_holder/af_ipv4_neighbor_peergroup/prefix_list/direction_out/__init__.py

Python

import os import pytest import radical.utils as ru import radical.pilot as rp import radical.pilot.constants as rpc from radical.pilot.agent.scheduler.hombre import Hombre try: import mock except ImportError: from tasktest import mock # ------------------------------------------------------------------------------ # User Input for test resource_name = 'local.localhost' access_schema = 'ssh' # Sample data to be staged -- available in cwd cur_dir = os.path.dirname(os.path.abspath(__file__)) # ------------------------------------------------------------------------------ # Setup for every test def setUp(): session = rp.Session() config = {'lrms_info' : {'lm_info' : 'INFO', 'n_nodes' : 2, 'cores_per_node' : 4, 'gpus_per_node' : 2, 'node_list' : [['0', 0], ['1', 1]]}} return config, session # ------------------------------------------------------------------------------ # def cud_nonmpi(): return {'cpu_process_type' : None, 'cpu_thread_type' : None, 'cpu_processes' : 1, 'cpu_threads' : 2, 'gpu_process_type' : None, 'gpu_thread_type' : None, 'gpu_processes' : 1, 'gpu_threads' : 1} # ------------------------------------------------------------------------------ # def cud_mpi(): return {'cpu_process_type' : rpc.MPI, 'cpu_thread_type' : None, 'cpu_processes' : 3, 'cpu_threads' : 1, 'gpu_process_type' : rpc.MPI, 'gpu_thread_type' : None, 'gpu_processes' : 1, 'gpu_threads' : 1} # ------------------------------------------------------------------------------ # Cleanup any folders and files to leave the system state # as prior to the test def tearDown(session): session.close() # ------------------------------------------------------------------------------ # Test non mpi tasks @mock.patch.object(Hombre, '__init__', return_value=None) @mock.patch.object(Hombre, 'advance') @mock.patch.object(ru.Profiler, 'prof') @mock.patch('radical.utils.raise_on') def test_nonmpi_task_withhombre_scheduler(mocked_init, mocked_method, mocked_profiler, mocked_raise_on): cfg, session = setUp() component = Hombre(cfg=dict(), session=session) component._log = ru.Logger('radical.pilot.test') component._configured = False component._cfg = cfg component._lrms_info = cfg['lrms_info'] component._lrms_lm_info = cfg['lrms_info']['lm_info'] component._lrms_n_nodes = cfg['lrms_info']['n_nodes'] component._lrms_node_list = cfg['lrms_info']['node_list'] component._lrms_cores_per_node = cfg['lrms_info']['cores_per_node'] component._lrms_gpus_per_node = cfg['lrms_info']['gpus_per_node'] component.nodes = list() for node in component._lrms_node_list: component.nodes.append({'uid' : node[0], 'name' : node[1]}) # populate component attributes component._configure() component._oversubscribe = False # we expect these slots to be available all_slots = list() for n in range(component._lrms_n_nodes): all_slots.append({'lm_info' : 'INFO', 'cores_per_node' : 4, 'gpus_per_node' : 2, 'ncblocks' : 1, 'ngblocks' : 1, 'nodes' : [{'name': n, 'uid' : str(n), 'core_map' : [[0, 1]], 'gpu_map' : []}, {'name': n, 'uid' : str(n), 'core_map' : [[0]], 'gpu_map' : [[0]]} ] }) all_slots.append({'lm_info' : 'INFO', 'cores_per_node' : 4, 'gpus_per_node' : 2, 'ncblocks' : 1, 'ngblocks' : 1, 'nodes' : [{'name': n, 'uid' : str(n), 'core_map' : [[2, 3]], 'gpu_map' : []}, {'name': n, 'uid' : str(n), 'core_map' : [[0]], 'gpu_map' : [[1]]} ] }) # Allocate first TD -- should land on second node td = cud_nonmpi() slot = component._allocate_slot(td) chk = all_slots[-1] assert(slot == chk) # Allocate second TD -- should also land on second node td = cud_nonmpi() slot = component._allocate_slot(td) chk = all_slots[-2] assert(slot == chk) # Allocate third TD -- should land on first node td = cud_nonmpi() slot = component._allocate_slot(td) chk = all_slots[-3] assert(slot == chk) # Allocate fourth TD -- should also land on tecond node td = cud_nonmpi() slot = component._allocate_slot(td) assert slot == all_slots[-4] # Fail with ValueError if heterogeneous CUs are scheduled with pytest.raises(ValueError): td = cud_nonmpi() td['gpu_processes'] = 2 slot = component._allocate_slot(td) # expext no slots now, as all resources are used td = cud_nonmpi() noslot = component._allocate_slot(td) assert(noslot is None) # Deallocate last filled slot component._release_slot(slot) # we should get a new slot now, which is the same as the one just freed td = cud_nonmpi() newslot = component._allocate_slot(td) assert(newslot == slot) tearDown(session) # ------------------------------------------------------------------------------ # Test mpi tasks @mock.patch.object(Hombre, '__init__', return_value=None) @mock.patch.object(Hombre, 'advance') @mock.patch.object(ru.Profiler, 'prof') @mock.patch('radical.utils.raise_on') def test_mpi_task_withhombre_scheduler(mocked_init, mocked_method, mocked_profiler, mocked_raise_on): cfg, session = setUp() component = Hombre(cfg=dict(), session=session) component._log = ru.Logger('radical.pilot.test') component._configured = False component._cfg = cfg component._lrms_info = cfg['lrms_info'] component._lrms_lm_info = cfg['lrms_info']['lm_info'] component._lrms_n_nodes = cfg['lrms_info']['n_nodes'] component._lrms_node_list = cfg['lrms_info']['node_list'] component._lrms_cores_per_node = cfg['lrms_info']['cores_per_node'] component._lrms_gpus_per_node = cfg['lrms_info']['gpus_per_node'] component.nodes = list() for node in component._lrms_node_list: component.nodes.append({'uid' : node[0], 'name' : node[1]}) # populate component attributes component._configure() component._oversubscribe = True # we expect these slots to be available all_slots = [{ 'lm_info' : 'INFO', 'cores_per_node' : 4, 'gpus_per_node' : 2, 'nodes' : [[0, '0', [[0], [1], [2]], [[0]]]] }, { 'lm_info' : 'INFO', 'cores_per_node' : 4, 'gpus_per_node' : 2, 'nodes' : [[1, '1', [[0], [1], [2]], [[0]]]] }] # Allocate first TD -- should land on second node td = cud_mpi() slot = component._allocate_slot(td) chk = all_slots[-1] assert(slot == chk) # Allocate second TD -- should land on first node td = cud_mpi() slot = component._allocate_slot(td) assert slot == all_slots[-2] # Fail with ValueError if heterogeneous CUs are scheduled with pytest.raises(ValueError): td = cud_mpi() td['gpu_processes'] = 2 slot = component._allocate_slot(td) # expext no slots now, as all resources are used td = cud_mpi() noslot = component._allocate_slot(td) assert(noslot is None) # Deallocate last filled slot component._release_slot(slot) # we should get a new slot now, which is the same as the one just freed td = cud_mpi() newslot = component._allocate_slot(td) assert(newslot == slot) tearDown(session) # ------------------------------------------------------------------------------

eirrgang/radical.pilot

old_tests/test_hombre_scheduler.py

Python

import pytest from app.models.user import User from datetime import date @pytest.fixture(scope='module') def new_user(): user = User('test', date(day=1, month=12, year=1989)) return user def test_new_user(new_user): """ GIVEN a User model WHEN a new User is created THEN check the username and birthday fields are defined correctly """ assert new_user.name == 'test' assert new_user.birthday == date(day=1, month=12, year=1989)

atsikham/flask-test-app

tests/unit/test_app.py

Python

class BitVector: """ This class uses an int called dec_rep as a vector of self.len many bits, where self.len <= self.max_len. The class wraps some common bitwise operations, and some less common ones too (like Gray coding that is needed by Qubiter). In some cases, the bitwise manipulation might be more succinct than the corresponding function in this wrapper, but the wrapper function's name spells out in words what is wanted. Attributes ---------- dec_rep : int decimal representation, the int whose binary representation carries a bit vector of length self.len len : int the length of the bit vector max_len : int maximum self.len allowed """ def __init__(self, length, dec_rep): """ Constructor Parameters ---------- length : int dec_rep : int Returns ------- """ self.len = length self.dec_rep = dec_rep self.max_len = 16 assert length <= self.max_len, "bit vector is too long" assert length > 0, "bit vector len must be >=1" @staticmethod def copy(bvec): """ Copy constructor, returns a new BitVector which is a copy of the BitVector bvec. Parameters ---------- bvec : BitVector Returns ------- BitVector """ return BitVector(bvec.len, bvec.dec_rep) def bit_is_T(self, bpos): """ Returns True iff bit at position bpos is 1 (True) Parameters ---------- bpos : int bit position Returns ------- bool """ assert bpos < self.len, "bit position is too large" mask = (1 << bpos) return (self.dec_rep & mask) == mask def set_bit_T(self, bpos): """ Sets to 1 (True) the bit of self at position bpos. Parameters ---------- bpos : int bit position Returns ------- None """ assert bpos < self.len, "bit position is too large" self.dec_rep |= (1 << bpos) def set_bit_F(self, bpos): """ Sets to 0 (False) the bit of self at position bpos. Parameters ---------- bpos : int bit position Returns ------- None """ assert bpos < self.len, "bit position is too large" self.dec_rep &= ~(1 << bpos) def set_all_bits_T(self): """ Sets to 1 (True) the bits of self at position bpos from 0 to len-1 inclusive. Returns ------- None """ # example: len = 3, dec_rep becomes 15 = b0111 self.dec_rep = (1 << self.len + 1) - 1 def set_all_bits_F(self): """ Sets to 0 (False) the bits of self at positions bpos from 0 to len-1 inclusive. Returns ------- None """ self.dec_rep = 0 def get_num_T_bits(self): """ Returns the number of 1 (True) bits at positions bpos from 0 to len-1 inclusive. Returns ------- int """ count = 0 for bpos in range(self.len): if self.bit_is_T(bpos): count += 1 return count def find_T_bit_to_right_of(self, bpos): """ Returns position of 1 (True) bit immediately to the right of position bpos. Returns -1 if there is no such bit. Parameters ---------- bpos : int bit position Returns ------- int """ if bpos <= 0: return -1 right_T_bit = bpos mask = (1 << right_T_bit) found_it = False while True: right_T_bit -= 1 mask >>= 1 found_it = ((self.dec_rep & mask) == mask) if right_T_bit == 0 or found_it: break if found_it: return right_T_bit else: return -1 def find_T_bit_to_left_of(self, bpos): """ Returns position of 1 (True) bit immediately to the left of position bpos. Returns -1 if there is no such bit. Parameters ---------- bpos : int bit position Returns ------- int """ if bpos >= self.len-1: return -1 left_T_bit = bpos mask = (1 << left_T_bit) found_it = False while True: left_T_bit += 1 mask <<= 1 found_it = ((self.dec_rep & mask) == mask) if left_T_bit == self.len-1 or found_it: break if found_it: return left_T_bit else: return -1 def find_leftmost_T_bit(self): """ Out of all 1 (True) bits, returns position of the leftmost one of those to the the left of position bpos. Returns -1 if there is no such bit. Returns ------- int """ if self.bit_is_T(self.len-1): return self.len-1 else: return self.find_T_bit_to_right_of(self.len - 1) def find_rightmost_T_bit(self): """ Out of all 1 (True) bits, returns position of the rightmost one of those to the the right of position bpos. Returns -1 if there is no such bit. Returns ------- int """ if self.bit_is_T(0): return 0 else: return self.find_T_bit_to_left_of(0) def get_bit_string(self): """ Returns self represented as string of length self.len of ones and zeros. If bit_str is the output, [int(x) for x in bit_str] will turn result to list of ints. Returns ------- str """ bit_str = '' for beta in range(self.len-1, -1, -1): if self.bit_is_T(beta): bit_str += '1' else: bit_str += '0' return bit_str def __str__(self): """ Readable representation of self Returns ------- str """ return self.get_bit_string() + '=' + str(self.dec_rep) @staticmethod def new_with_T_on_diff(bvec1, bvec2): """ Given two BitVectors bevc1 and bvec2, this return a BitVector which is a bitwise xor (mod 2 sum) of the bits of bvec1 and bvec2. Parameters ---------- bvec1 : BitVector bvec2 : BitVector Returns ------- BitVector """ assert bvec1.len == bvec2.len return BitVector(bvec1.len, bvec1.dec_rep ^ bvec2.dec_rep) @staticmethod def get_lazy_from_normal(bit_len, normal): """ Throughout Qubiter, we will often refer to "Gray Code" as "lazy ordering". In lazy ordering with bit_len many bits, one gives a sequence of bit vectors of length bit_len, so that two adjacent items of the sequence differ by just one bit. For example 000=0, 100=4, 110=6, 010=2, 011=3, 111=7, 101=5, 001=1. Each element of this sequence represented as an int will be called lazy, and each int in the sequence 0, 1, 2, 3, 4, 5, 6, 7 will be called normal. Normal ordering is usually called dictionary ordering. Normal and lazy sequences both start at 0. Suppose bit_len = 3. The lazy sequence 000, 100, 110, 010, 011, 111, 101, 001 is easily obtained from the "standard" lazy sequence 000, 001, 011, 010, 110, 111, 101, 100 by "reflecting" each sequence term. We will use the second sequence because it is more common in the literature. References ---------- 1. Martin Gardener, "Knotted DoughNuts and Other Mathematical Entertainments", chapt. 2, "The Binary Gray Code" 2. "Numerical Recipies in C" 3. Many books on Discrete Mathematics for CompSci types 4. On the web, in Eric's Treasure Trove/Math/Gray Codes Parameters ---------- bit_len : int normal : int Function returns the lazy int that corresponds to this normal int. Returns ------- int """ lazy_bvec = BitVector(bit_len, normal) lazy = lazy_bvec.dec_rep if bit_len > 1: for m in range(bit_len-2, -1, -1): # Look at bpos = m+1, if it's ON, then flip bpos=m. # Remember that ^ is same as a mod2 sum. lazy ^= (((normal >> m+1) & 1) << m) return lazy @staticmethod def lazy_advance(old_normal, old_lazy): """ This method takes int "old_lazy" (which corresponds to bit vector "old_normal"), and changes it to the next lazy int, "new_lazy" ( which corresponds to "new_normal"). example: lazy sequence: 000, 001, 011, 010, 110, 111, 101, 100 old_lazy = 011 old_normal = 2 = 010 new_normal = 3 = 011 mask = (new_normal & ~old_normal) = 011 & 101 = 001 new_lazy = new_normal ^ mask = 011 ^ 001 = 010 Parameters ---------- old_normal : int old_lazy : int Returns ------- int, int """ new_normal = old_normal + 1 new_lazy = old_lazy ^ (new_normal & ~(new_normal-1)) return new_normal, new_lazy if __name__ == "__main__": def main(): for length in [3, 4]: print('\nlength=', length) print('normal, lazy, lazy in binary:') max_normal = (1 << length) - 1 normal = 0 lazy = 0 while normal <= max_normal: lazy_bvec = BitVector(length, lazy) print(normal, lazy, BitVector.get_bit_string(lazy_bvec)) normal, lazy = BitVector.lazy_advance(normal, lazy) main()

yourball/qubiter

qubiter/BitVector.py

Python

import os import os.path as osp import pickle import random from collections import deque from datetime import datetime import gym import numpy as np import scipy.stats as stats import torch import torch.optim as optim from mpi4py import MPI import dr from dr.ppo.models import Policy, ValueNet from dr.ppo.train import one_train_iter from dr.ppo.utils import set_torch_num_threads, RunningMeanStd, traj_seg_gen COMM = MPI.COMM_WORLD import tensorboardX def set_global_seeds(i): torch.manual_seed(i) np.random.seed(i) random.seed(i) if torch.cuda.is_available(): torch.cuda.manual_seed_all(i) class CEMOptimizer(object): def __init__(self, sol_dim, max_iters, popsize, num_elites, cost_function, upper_bound=None, lower_bound=None, epsilon=0.001, alpha=0.25, viz_dir=None): """Creates an instance of this class. Arguments: sol_dim (int): The dimensionality of the problem space max_iters (int): The maximum number of iterations to perform during optimization popsize (int): The number of candidate solutions to be sampled at every iteration num_elites (int): The number of top solutions that will be used to obtain the distribution at the next iteration. upper_bound (np.array): An array of upper bounds lower_bound (np.array): An array of lower bounds epsilon (float): A minimum variance. If the maximum variance drops below epsilon, optimization is stopped. alpha (float): Controls how much of the previous mean and variance is used for the next iteration. next_mean = alpha * old_mean + (1 - alpha) * elite_mean, and similarly for variance. """ super().__init__() self.sol_dim, self.max_iters, self.popsize, self.num_elites = sol_dim, max_iters, popsize, num_elites self.ub, self.lb = upper_bound, lower_bound self.epsilon, self.alpha = epsilon, alpha self.cost_function = cost_function if viz_dir is not None: self.writer = tensorboardX.SummaryWriter(viz_dir) else: self.writer = tensorboardX.SummaryWriter() if num_elites > popsize: raise ValueError("Number of elites must be at most the population size.") def reset(self): pass def obtain_solution(self, init_mean, init_var): """Optimizes the cost function using the provided initial candidate distribution Arguments: init_mean (np.ndarray): The mean of the initial candidate distribution. init_var (np.ndarray): The variance of the initial candidate distribution. """ mean, var, t = init_mean, init_var, 0 X = stats.truncnorm(-2, 2, loc=np.zeros_like(mean), scale=np.ones_like(var)) costs_hist = [] mean_hist = [] var_hist = [] while (t < self.max_iters) and np.max(var) > self.epsilon: lb_dist, ub_dist = mean - self.lb, self.ub - mean constrained_var = np.minimum(np.minimum(np.square(lb_dist / 2), np.square(ub_dist / 2)), var) samples = X.rvs(size=[self.popsize, self.sol_dim]) * np.sqrt(constrained_var) + mean samples = samples.astype(np.float32) costs = self.cost_function(samples, t) elites = samples[np.argsort(costs)][:self.num_elites] new_mean = np.mean(elites, axis=0) new_var = np.var(elites, axis=0) mean = self.alpha * mean + (1 - self.alpha) * new_mean var = self.alpha * var + (1 - self.alpha) * new_var for i, m in enumerate(mean): self.writer.add_scalar(f'mean/{i}', m, t) for i, m in enumerate(var): self.writer.add_scalar(f'var/{i}', m, t) self.writer.add_scalar('costs', np.min(costs), t) t += 1 costs_hist.append(costs) mean_hist.append(mean) var_hist.append(var) self.writer.close() return dict( mean_hist=mean_hist, costs_hist=costs_hist, var_hist=var_hist ) class PPO_Pytorch(object): def __init__(self, experiment_name, env_params, train_params, **kwargs): self.experiment_name = experiment_name self.env_params = env_params self.train_params = train_params self.log_dir = osp.join('runs', f'seed_{str(train_params["seed"])}_{datetime.now().strftime("%b%d_%H-%M-%S")}') os.makedirs(self.log_dir, exist_ok=True) with open(osp.join(self.log_dir, 'env_params.pkl'), 'wb+') as f: pickle.dump(env_params, f) with open(osp.join(self.log_dir, 'train_params.pkl'), 'wb+') as f: pickle.dump(train_params, f) super().__init__() def train(self, env_id, backend, train_params, env_params, means, stdevs): # Unpack params hid_size = train_params['hid_size'] pol_init_std = train_params['pol_init_std'] adam_epsilon = train_params['adam_epsilon'] optim_stepsize = train_params['optim_stepsize'] # Convert means and stdevs to dict format assert len(means) == len(stdevs), (len(means), len(stdevs)) mean_dict, stdev_dict = PPO_Pytorch._vec_to_dict(env_id, means, stdevs) # Set parameter of env self.env_dist.default_parameters = mean_dict self.env_dist.stdev_dict = stdev_dict env = self.env_dist.root_env set_torch_num_threads() # Construct policy and value network pol = Policy(env.observation_space, env.action_space, hid_size, pol_init_std) pol_optim = optim.Adam(pol.parameters(), lr=optim_stepsize, eps=adam_epsilon) val = ValueNet(env.observation_space, hid_size) val_optim = optim.Adam(val.parameters(), lr=optim_stepsize, eps=adam_epsilon) optims = {'pol_optim': pol_optim, 'val_optim': val_optim} num_train_iter = int(train_params['num_timesteps'] / train_params['ts_per_batch']) # Buffer for running statistics eps_rets_buff = deque(maxlen=100) eps_rets_mean_buff = [] state_running_m_std = RunningMeanStd(shape=env.observation_space.shape) # seg_gen is a generator that yields the training data points seg_gen = traj_seg_gen(self.env_dist, pol, val, state_running_m_std, env_params, train_params) eval_perfs = [] for iter_i in range(num_train_iter): one_train_iter(pol, val, optims, iter_i, eps_rets_buff, eps_rets_mean_buff, seg_gen, state_running_m_std, train_params, self.eval_envs, eval_perfs) return eval_perfs def run(self): set_global_seeds(self.train_params['seed']) # Unpack params env_name = self.env_params['env_name'] backend = self.env_params['backend'] stdev = self.train_params['env_dist_stdev'] mean_scale = self.train_params['mean_scale'] seed = self.train_params['seed'] num_eval_env = self.train_params['num_eval_env'] collision_detector = self.env_params['collision_detector'] # Obtain the initial value for the simulation parameters env_dist = dr.dist.Normal(env_name, backend, mean_scale=mean_scale) init_mean_param = PPO_Pytorch._dict_to_vec(env_name, env_dist.default_parameters) init_stdev_param = np.array([stdev] * len(init_mean_param), dtype=np.float32) cem_init_mean = np.concatenate((init_mean_param, init_stdev_param)) cem_init_stdev = np.array([1.0] * len(cem_init_mean), dtype=np.float32) # Make envs that will be reused for training and eval self.env_dist = dr.dist.Normal(env_name, backend) self.env_dist.backend.set_collision_detector(env_dist.root_env, collision_detector) self.env_dist.seed(seed) if env_name == 'Walker': self.eval_envs = [gym.make('Walker2d-v2') for _ in range(num_eval_env)] elif env_name == 'Hopper': self.eval_envs = [gym.make('Hopper-v2') for _ in range(num_eval_env)] else: exit('Unrecognized environment') if COMM.Get_rank() == 0: self.optimizer = CEMOptimizer( sol_dim=30, max_iters=300, popsize=self.train_params['pop_size'], num_elites=self.train_params['num_elites'], cost_function=self._cost_function, lower_bound=0.0, # TODO: setting the upper bound this way, means that # if the initial dimension value is 0, then the upper bound is 0 upper_bound=cem_init_mean * 5.0, alpha=0.75, viz_dir=self.log_dir ) # This is buggy # https://github.com/lanpa/tensorboardX/issues/345 self.optimizer.writer.add_text('env_params', str(self.env_params), 0) self.optimizer.writer.add_text('train_params', str(self.train_params), 0) res = self.optimizer.obtain_solution(cem_init_mean, cem_init_stdev) path = osp.join(self.log_dir, 'res.pkl') with open(path, 'wb') as f: pickle.dump(res, f) COMM.Abort(0) else: while True: args = COMM.recv(source=0) r = self.train(*args) COMM.send(r, dest=0) def _cost_function(self, samples, cem_timestep): print(f'cem_timestep: {cem_timestep}') env_name = self.env_params['env_name'] backend = self.env_params['backend'] pop_size = self.train_params['pop_size'] argss = [(env_name, backend, self.train_params, self.env_params, samples[rank][:len(samples[rank]) // 2], samples[rank][len(samples[rank]) // 2:]) for rank in range(len(samples))] # Send args to other MPI processes for rank in range(1, COMM.size): COMM.send(argss[rank], dest=rank) # Obtain results for all args r = self.train(*argss[0]) reses = [(0, r)] # 0 is the rank of this process # Receive results from the other processes: for rank in range(1, COMM.size): r = COMM.recv(source=rank) reses.append((rank, r)) reses = sorted(reses, key=lambda k: k[0]) print(reses) # Get the index of the highest performing model in population # and write result to tensorboard max_idx = 0 max_perf = max(reses[0][1]) # 0 is the result of process rank 0. 1 brings us the eval perf list for i, item in enumerate(reses): perf = max(item[1]) if perf > max_perf: max_perf = perf max_idx = i # Obtain the "costs" that the CEM cost function should return costs = [- max(i[1]) for i in reses] print(costs) print(min(costs)) print() return costs @classmethod def _dict_to_vec(cls, env_id, d): return np.concatenate(( d['mass'], d['damping'], [d['gravity']] )).flatten().copy() @classmethod def _vec_to_dict(cls, env_id, means, stdevs): if env_id == 'Walker': return dict( mass=means[:7], damping=means[7:-1], gravity=means[-1] ), dict( mass=stdevs[:7], damping=stdevs[7:-1], gravity=stdevs[-1] ) elif env_id == 'Hopper': return dict( mass=means[:4], damping=means[4:-1], gravity=means[-1] ), dict( mass=stdevs[:4], damping=stdevs[4:-1], gravity=stdevs[-1] ) else: exit('Unrecognized environment')

quanvuong/domain_randomization

dr/experiment/ppo_pytorch.py

Python

import os import sys sys.path.insert(0, os.path.abspath('../../')) project = 'Domain Trails' copyright = '2021, iAbdullahMughal' author = 'iAbdullahMughal' release = '1.0.0' extensions = [ 'sphinx.ext.duration', 'sphinx.ext.doctest', 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', ] intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), } intersphinx_disabled_domains = ['std'] autosummary_generate = True templates_path = ['_templates'] exclude_patterns = [] html_theme = 'sphinx_rtd_theme' html_static_path = ['_static'] source_suffix = ['.rst', '.md']

iAbdullahMughal/domain-trails

docs/source/conf.py

Python

"""Api calls for sync.""" import asyncio import logging from aiohttp import ClientResponseError from august.api_common import ( API_LOCK_URL, API_RETRY_ATTEMPTS, API_RETRY_TIME, API_UNLOCK_URL, HEADER_AUGUST_ACCESS_TOKEN, ApiCommon, _api_headers, _convert_lock_result_to_activities, _process_activity_json, _process_doorbells_json, _process_locks_json, ) from august.doorbell import DoorbellDetail from august.exceptions import AugustApiAIOHTTPError from august.lock import LockDetail, determine_door_state, determine_lock_status from august.pin import Pin _LOGGER = logging.getLogger(__name__) class ApiAsync(ApiCommon): def __init__(self, aiohttp_session, timeout=10, command_timeout=60): self._timeout = timeout self._command_timeout = command_timeout self._aiohttp_session = aiohttp_session async def async_get_session(self, install_id, identifier, password): return await self._async_dict_to_api( self._build_get_session_request(install_id, identifier, password) ) async def async_send_verification_code(self, access_token, login_method, username): return await self._async_dict_to_api( self._build_send_verification_code_request( access_token, login_method, username ) ) async def async_validate_verification_code( self, access_token, login_method, username, verification_code ): return await self._async_dict_to_api( self._build_validate_verification_code_request( access_token, login_method, username, verification_code ) ) async def async_get_doorbells(self, access_token): response = await self._async_dict_to_api( self._build_get_doorbells_request(access_token) ) return _process_doorbells_json(await response.json()) async def async_get_doorbell_detail(self, access_token, doorbell_id): response = await self._async_dict_to_api( self._build_get_doorbell_detail_request(access_token, doorbell_id) ) return DoorbellDetail(await response.json()) async def async_wakeup_doorbell(self, access_token, doorbell_id): await self._async_dict_to_api( self._build_wakeup_doorbell_request(access_token, doorbell_id) ) return True async def async_get_houses(self, access_token): return await self._async_dict_to_api( self._build_get_houses_request(access_token) ) async def async_get_house(self, access_token, house_id): response = await self._async_dict_to_api( self._build_get_house_request(access_token, house_id) ) return await response.json() async def async_get_house_activities(self, access_token, house_id, limit=8): response = await self._async_dict_to_api( self._build_get_house_activities_request( access_token, house_id, limit=limit ) ) return _process_activity_json(await response.json()) async def async_get_locks(self, access_token): response = await self._async_dict_to_api( self._build_get_locks_request(access_token) ) return _process_locks_json(await response.json()) async def async_get_operable_locks(self, access_token): locks = await self.async_get_locks(access_token) return [lock for lock in locks if lock.is_operable] async def async_get_lock_detail(self, access_token, lock_id): response = await self._async_dict_to_api( self._build_get_lock_detail_request(access_token, lock_id) ) return LockDetail(await response.json()) async def async_get_lock_status(self, access_token, lock_id, door_status=False): response = await self._async_dict_to_api( self._build_get_lock_status_request(access_token, lock_id) ) json_dict = await response.json() if door_status: return ( determine_lock_status(json_dict.get("status")), determine_door_state(json_dict.get("doorState")), ) return determine_lock_status(json_dict.get("status")) async def async_get_lock_door_status( self, access_token, lock_id, lock_status=False ): response = await self._async_dict_to_api( self._build_get_lock_status_request(access_token, lock_id) ) json_dict = await response.json() if lock_status: return ( determine_door_state(json_dict.get("doorState")), determine_lock_status(json_dict.get("status")), ) return determine_door_state(json_dict.get("doorState")) async def async_get_pins(self, access_token, lock_id): response = await self._async_dict_to_api( self._build_get_pins_request(access_token, lock_id) ) json_dict = await response.json() return [Pin(pin_json) for pin_json in json_dict.get("loaded", [])] async def _async_call_lock_operation(self, url_str, access_token, lock_id): response = await self._async_dict_to_api( self._build_call_lock_operation_request( url_str, access_token, lock_id, self._command_timeout ) ) return await response.json() async def _async_lock(self, access_token, lock_id): return await self._async_call_lock_operation( API_LOCK_URL, access_token, lock_id ) async def async_lock(self, access_token, lock_id): """Execute a remote lock operation. Returns a LockStatus state. """ return determine_lock_status( (await self._async_lock(access_token, lock_id)).get("status") ) async def async_lock_return_activities(self, access_token, lock_id): """Execute a remote lock operation. Returns an array of one or more august.activity.Activity objects If the lock supports door sense one of the activities will include the current door state. """ return _convert_lock_result_to_activities( await self._async_lock(access_token, lock_id) ) async def _async_unlock(self, access_token, lock_id): return await self._async_call_lock_operation( API_UNLOCK_URL, access_token, lock_id ) async def async_unlock(self, access_token, lock_id): """Execute a remote unlock operation. Returns a LockStatus state. """ return determine_lock_status( (await self._async_unlock(access_token, lock_id)).get("status") ) async def async_unlock_return_activities(self, access_token, lock_id): """Execute a remote lock operation. Returns an array of one or more august.activity.Activity objects If the lock supports door sense one of the activities will include the current door state. """ return _convert_lock_result_to_activities( await self._async_unlock(access_token, lock_id) ) async def async_refresh_access_token(self, access_token): """Obtain a new api token.""" return ( await self._async_dict_to_api( self._build_refresh_access_token_request(access_token) ) ).headers[HEADER_AUGUST_ACCESS_TOKEN] async def _async_dict_to_api(self, api_dict): url = api_dict["url"] method = api_dict["method"] access_token = api_dict.get("access_token", None) del api_dict["url"] del api_dict["method"] if access_token: del api_dict["access_token"] payload = api_dict.get("params") or api_dict.get("json") if "headers" not in api_dict: api_dict["headers"] = _api_headers(access_token=access_token) if "timeout" not in api_dict: api_dict["timeout"] = self._timeout _LOGGER.debug( "About to call %s with header=%s and payload=%s", url, api_dict["headers"], payload, ) attempts = 0 while attempts < API_RETRY_ATTEMPTS: attempts += 1 response = await self._aiohttp_session.request(method, url, **api_dict) _LOGGER.debug( "Received API response: %s, %s", response.status, await response.read() ) if response.status == 429: _LOGGER.debug( "August sent a 429 (attempt: %d), sleeping and trying again", attempts, ) asyncio.sleep(API_RETRY_TIME) continue break _raise_response_exceptions(response) return response def _raise_response_exceptions(response): try: response.raise_for_status() except ClientResponseError as err: if err.status == 422: raise AugustApiAIOHTTPError( "The operation failed because the bridge (connect) is offline.", ) from err if err.status == 423: raise AugustApiAIOHTTPError( "The operation failed because the bridge (connect) is in use.", ) from err if err.status == 408: raise AugustApiAIOHTTPError( "The operation timed out because the bridge (connect) failed to respond.", ) from err raise err

THATDONFC/py-august

august/api_async.py

Python

#!/usr/bin/python # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from BeautifulSoup import BeautifulSoup import urllib2 import xmltodict import json import Queue from threading import Thread from collections import OrderedDict import itertools from ascii_graph import Pyasciigraph import sys import argparse import os # default build that is used against apache hive precommit test report REPORTS_DIR = "/tmp/slow-test-reports" BUILD_NUMBER = 830 TOP_K = 25 json_dumps = [] # parallel xml report downloader class ReportDownloader(Thread): def __init__(self, q): Thread.__init__(self) self.q = q def run(self): while True: # Get the work from the queue and expand the tuple link = self.q.get() xmlFile = urllib2.urlopen(link) xmlData = xmlFile.read() xmlSoup = BeautifulSoup(xmlData) d = xmltodict.parse(xmlData, xml_attribs=True) d['testsuite'].pop('properties', None) json_dumps.append(d) self.q.task_done() def get_links(rootUrl): html_page = urllib2.urlopen(rootUrl) soup = BeautifulSoup(html_page) result = [] for link in soup.findAll('a'): hrefs = link.get('href') if hrefs.endswith('.xml'): result.append(rootUrl + "/" + hrefs) return result def take(iterable, n=TOP_K): return list(itertools.islice(iterable, 0, n)) def plot_testsuite_time(json_data, top_k=TOP_K, ascii_graph=False, report_file=None): suite_time = {} overall_time = 0.0 for suite in json_data: name = suite['testsuite']['@name'].rsplit(".",1)[-1] time = float(suite['testsuite']['@time'].replace(',','')) overall_time += time if name in suite_time: total_time = suite_time[name] suite_time[name] = total_time + time else: suite_time[name] = time d_descending = OrderedDict(sorted(suite_time.items(), key=lambda kv: kv[1], reverse=True)) gdata = [] for k,v in take(d_descending.iteritems(), top_k): gdata.append((k, v)) print '\nTop ' + str(top_k) + ' testsuite in terms of execution time (in seconds).. [Total time: ' + str(overall_time) + ' seconds]' if ascii_graph: graph = Pyasciigraph() for line in graph.graph('', gdata): print line else: for line in gdata: print line[0] + "\t" + str(line[1]) if report_file != None: with open(report_file, "w") as f: f.write('Top ' + str(top_k) + ' testsuite in terms of execution time (in seconds).. [Total time: ' + str(overall_time) + ' seconds]\n') for line in gdata: f.write(line[0] + "\t" + str(line[1]) + "\n") def plot_testcase_time(json_data, top_k=TOP_K, ascii_graph=False, report_file=None): testcase_time = {} overall_time = 0.0 for suite in json_data: if int(suite['testsuite']['@tests']) > 0: for t in suite['testsuite']['testcase']: if isinstance(t, dict): name = t['@classname'].rsplit(".",1)[-1] + "_" + t['@name'] time = float(t['@time'].replace(',','')) overall_time += time if name in testcase_time: total_time = testcase_time[name] testcase_time[name] = total_time + time else: testcase_time[name] = time if int(suite['testsuite']['@tests']) == 0: print "Empty batch detected for testsuite: " + suite['testsuite']['@name'] + " which took " + suite['testsuite']['@time'] + "s" d_descending = OrderedDict(sorted(testcase_time.items(), key=lambda kv: kv[1], reverse=True)) gdata = [] for k,v in take(d_descending.iteritems(), top_k): gdata.append((k, v)) print '\nTop ' + str(top_k) + ' testcases in terms of execution time (in seconds).. [Total time: ' + str(overall_time) + ' seconds]' if ascii_graph: graph = Pyasciigraph() for line in graph.graph('', gdata): print line else: for line in gdata: print line[0] + "\t" + str(line[1]) if report_file != None: with open(report_file, "a") as f: f.write('\nTop ' + str(top_k) + ' testcases in terms of execution time (in seconds).. [Total time: ' + str(overall_time) + ' seconds]\n') for line in gdata: f.write(line[0] + "\t" + str(line[1]) + "\n") def get_latest_build_with_report(build_number): latest_report = BUILD_NUMBER if not os.path.exists(REPORTS_DIR): os.makedirs(REPORTS_DIR) for i in os.listdir(REPORTS_DIR): if i.endswith(".txt"): current_report = int(i.split(".txt")[0]) if current_report > latest_report: latest_report = current_report return latest_report def get_pending_report_list(last_report, precommit_url): next_report = last_report pending_reports = [] done = False while done == False: try: urllib2.urlopen(precommit_url % next_report) pending_reports.append(next_report) next_report += 1 except urllib2.HTTPError, e: done = True return pending_reports def print_report(reportUrl, json_dump, top_k, ascii_graph, report_file=None): get_links(reportUrl) links = get_links(reportUrl) # Create a queue to communicate with the worker threads q = Queue.Queue() print "\nProcessing " + str(len(links)) + " test xml reports from " + reportUrl + ".." # Create 8 worker threads for x in range(8): worker = ReportDownloader(q) # Setting daemon to True will let the main thread exit even though the workers are blocking worker.daemon = True worker.start() # Put the tasks into the queue as a tuple for link in links: q.put(link) # Causes the main thread to wait for the queue to finish processing all the tasks q.join() # dump test reports in json format if json_dump: with open('data.json', 'w') as outfile: json.dump(json_dumps, outfile, indent = 2) # print or plot top-k tests on console plot_testsuite_time(json_dumps, top_k, ascii_graph, report_file) plot_testcase_time(json_dumps, top_k, ascii_graph, report_file) del json_dumps[:] def main(): parser = argparse.ArgumentParser(description='Program to print top-k test report for Apache Hive precommit tests') parser.add_argument('-b', action='store', dest='build_number', help='build number of the test run. default uses test reports from apache hive precommit test run.') parser.add_argument('-u', action='store', dest='report_url', help='url for the test report') parser.add_argument('-j', action='store_true', default=False, dest='json_dump', help='json dump of test reports') parser.add_argument('-k', action='store', dest='top_k', type=int, help='print top k testsuite and testcases to console') parser.add_argument('-a', action='store_true', default=False, dest='ascii_graph', help='ascii output of the report') parser.add_argument('-l', action='store_true', default=False, dest='latest_report', help='will generate all missing reports up until latest build number') args = parser.parse_args() precommit_url = "http://104.198.109.242/logs/PreCommit-HIVE-Build-%s/test-results/" last_report = get_latest_build_with_report(BUILD_NUMBER) pending_reports = get_pending_report_list(last_report, precommit_url) build = last_report if args.build_number != None: build = args.build_number reportUrl = precommit_url % build if args.report_url != None: reportUrl = args.report_url json_dump = args.json_dump top_k = TOP_K if args.top_k != None: top_k = args.top_k ascii_graph = args.ascii_graph print_report(reportUrl, json_dump, top_k, ascii_graph, REPORTS_DIR + str(build) + ".txt") if args.latest_report: for l in pending_reports: reportUrl = precommit_url % l print_report(reportUrl, json_dump, top_k, ascii_graph, REPORTS_DIR + str(l) + ".txt") main()

10088/hive

testutils/gen-report.py

Python

from terminusdb_client.woqlclient.api_endpoint_const import APIEndpointConst from .connectCapabilitiesResponse import ConnectResponse from .getSchemaTurtleResponse import RESPONSE def mocked_requests(*args, **kwargs): class MockResponse: def json(self): if self._json_data is None: raise ValueError("EXCEPTION NO JSON OBJECT") return self._json_data @property def status_code(self): return self._status_code @property def url(self): return self._url @property def text(self): return self._text def __init__(self, url, status, action_type): # set status code and content self._json_data = None self._text = None self._status_code = status self._content = "cont" self._url = url # add json data if provided if action_type == APIEndpointConst.CONNECT: self._json_data = ConnectResponse elif action_type == APIEndpointConst.GET_TRIPLES: self._text = RESPONSE # elif action_type == APIEndpointConst.WOQL_SELECT: # with open("tests/getAllClassQueryResponse.json") as json_file: # json_data = json.load(json_file) # self._json_data = json_data # json_file.close() elif ( action_type == APIEndpointConst.CREATE_DATABASE or action_type == APIEndpointConst.DELETE_DATABASE or action_type == APIEndpointConst.UPDATE_TRIPLES or action_type == APIEndpointConst.BRANCH or action_type == APIEndpointConst.CREATE_GRAPH ): self._json_data = {"terminus:status": "terminus:success"} if ( args[0] == "http://localhost:6363/branch/admin/myDBName/local/branch/my_new_branch" ): return MockResponse(args[0], 200, APIEndpointConst.BRANCH) elif ( args[0] == "http://localhost:6363/graph/admin/myDBName/local/branch/master/instance/mygraph" ): return MockResponse(args[0], 200, APIEndpointConst.CREATE_GRAPH) elif ( args[0] == "http://localhost:6363/triples/admin/myDBName/local/branch/master/instance/mygraph" ): return MockResponse(args[0], 200, APIEndpointConst.GET_TRIPLES) elif args[0] == "http://localhost:6363/db/admin/myFirstTerminusDB": return MockResponse(args[0], 200, APIEndpointConst.DELETE_DATABASE) return MockResponse(args[0], 200, APIEndpointConst.CONNECT)

Morijarti/terminusdb-client-python

terminusdb_client/tests/mockResponse.py

Python

def main(): with open('inputs/01.in') as f: data = [int(line) for line in f] print(sum(data)) result = 0 seen = {0} while True: for item in data: result += item if result in seen: print(result) return seen.add(result) if __name__ == '__main__': main()

Wattleninja/Advent-of-Code

2018/day_01.py

Python

################################################################################ # # Author: Diego Montufar # Date: Apr/2015 # Name: __init__.py # Description: Here we define the available web services which can be accessed by # following the app.route path as URL. The indexer module will perform the hard work # as it communicates directly with the elasticsearch and couchdb instances through RESTful calls. # Although Flask apps allow us to perform more complex tasks, we only are using basic calls # to the corresponding inexer module method and return to the wenb interface a json response. # # Dependencies: Flask -> Provides you with tools, libraries and technologies that allow you to build a web application # indexer -> For communicating with elasticsearch and couchdb # ################################################################################ from services import indexer #indexer module from datetime import timedelta #datetime tools from flask import make_response, request, current_app, jsonify, Flask , render_template #flask crossdomain tools from functools import update_wrapper #python wrappers app = Flask(__name__) #Python version workaround try: unicode = unicode except NameError: # 'unicode' is undefined, must be Python 3 str = str unicode = str bytes = bytes basestring = (str,bytes) else: # 'unicode' exists, must be Python 2 str = str unicode = unicode bytes = str basestring = basestring #Define a wrapper for supporting crossdomain calls def crossdomain(origin=None, methods=None, headers=None, max_age=21600, attach_to_all=True, automatic_options=True): if methods is not None: methods = ', '.join(sorted(x.upper() for x in methods)) if headers is not None and not isinstance(headers, basestring): headers = ', '.join(x.upper() for x in headers) if not isinstance(origin, basestring): origin = ', '.join(origin) if isinstance(max_age, timedelta): max_age = max_age.total_seconds() def get_methods(): if methods is not None: return methods options_resp = current_app.make_default_options_response() return options_resp.headers['allow'] def decorator(f): def wrapped_function(*args, **kwargs): if automatic_options and request.method == 'OPTIONS': resp = current_app.make_default_options_response() else: resp = make_response(f(*args, **kwargs)) if not attach_to_all and request.method != 'OPTIONS': return resp h = resp.headers h['Access-Control-Allow-Origin'] = origin h['Access-Control-Allow-Methods'] = get_methods() h['Access-Control-Max-Age'] = str(max_age) h['Access-Control-Allow-Credentials'] = 'true' h['Access-Control-Allow-Headers'] = \ "Origin, X-Requested-With, Content-Type, Accept, Authorization" if headers is not None: h['Access-Control-Allow-Headers'] = headers return resp f.provide_automatic_options = False return update_wrapper(wrapped_function, f) return decorator #Service: / #Description: Main web page index.html is called here. i.e http://{localhost}/ or http://{localhost}/index #Parameters: none #output: index.html @app.route('/') @app.route('/index') def index(): return render_template('index.html') #Service: genericSearch #Description: Perform custom searches based on elasticsearch queries in json format #Parameters: <jsonQuery> (String) is a json based tring, which must follow the elasticsearch query structure #output: a json object containing the matched results @app.route('/genericSearch/<jsonQuery>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getGenericSearch(jsonQuery): return jsonify(indexer.genericSearch(jsonQuery)) #Service: genericGeoSearch #Description: Perform custom geo searches based on elasticsearch queries in json format #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <suburbCode> (String) -> Suburb code. i.e. 206041122 # <startP> (Int) -> Pagination support. i.e. 0 if you want all the results, or 15 if you want to skip the first 15 results # <sizeP> (Int) -> Pagination support. i.e 50 if you want up to 50 resutls by taking account of startP parameter # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/genericGeoSearch/<term>/<suburbCode>/<startP>/<sizeP>/<startDate>/<endDate>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getGenericGeoSearch(term,suburbCode,startP,sizeP,startDate,endDate): return jsonify(indexer.getTweetsBySuburb(term,suburbCode,startP,sizeP,startDate,endDate)) #Service: sentimentTotals #Description: Search for sentiment totals by terms, suburb and date range #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <suburbCode> (String) -> Suburb code. i.e. 206041122 # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/sentimentTotals/<term>/<suburbCode>/<startDate>/<endDate>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getSentimentTotals(term,suburbCode,startDate,endDate): return jsonify(indexer.statisticsByTerm(term,suburbCode,startDate,endDate)) #Service: suburbsByCountry #Description: Get a list of the suburbs of main cities of AU as defined on the ABS 2011 census database #Parameters: <countryCode> (Int) -> Country code. In this case we only care about Autralia, that is countryCode = 1 #output: a json object containing the matched results @app.route('/suburbsByCountry/<countryCode>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getSuburbsList(countryCode): return jsonify(indexer.getSuburbsList(countryCode)) #Service: culturesByState #Description: Get GeoJson File containing information relatd to countries of birth by suburb of AU as defined on the ABS 2011 census database #Parameters: <stateCode> (String) -> the code of the state: i.e: VIC, TAS, etc #return: GeoJson object @app.route('/culturesByState/<stateCode>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getCulturesByState(stateCode): return jsonify(indexer.getCulturesByState(stateCode)) #Service: languagesByCountry #Description: Get a list of languages related to its corresponding country where they are spoken #Parameters: <countryCode> (String) -> Country code. In this case we only care about Autralia, that is countryCode = 1 #return: a json object containing the matched results @app.route('/languagesByCountry/<countryCode>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getLanguagesByCountry(countryCode): return jsonify(indexer.getLanguages(countryCode)) #Service: tweetsByCountryOfBirth #Description: Get the count of tweets grouped by language as defined on the languages database #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <stateCode> (String) -> the code of the state: i.e: VIC, TAS, etc # <suburbCode> (String) -> Suburb code. i.e. 206041122 # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/tweetsByCountryOfBirth/<term>/<stateCode>/<suburbCode>/<startDate>/<endDate>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getTweetsByCountryOfBirth(term,stateCode,suburbCode,startDate,endDate): return jsonify(indexer.getTweetsByCountryOfBirth(term,stateCode,suburbCode,startDate,endDate)) #Service: topListBySuburb #Description: List the top N list count of a particular field within a date range by suburb #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <suburbCode> (String) -> Suburb code. i.e. 206041122 # <field> (String) -> Twitter field to aggreagate i.e. user.screen_name # <size> (Int) -> N i.e 5 for a Top five list # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/topListBySuburb/<term>/<suburbCode>/<field>/<size>/<startDate>/<endDate>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getTopList(term,suburbCode,field,size,startDate,endDate): return jsonify(indexer.getTopListBySuburb(term,suburbCode,field,size,startDate,endDate)) #Service: sentimentTotalsByCity #Description: Search for the totals of sentiment analysis in all the cities of AU within a date range #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/sentimentTotalsByCity/<term>/<startDate>/<endDate>',methods=['GET', 'OPTIONS']) @crossdomain(origin='*') def getSentimentTotalsByCity(term,startDate,endDate): return jsonify(indexer.getAllSentimentTotalsByCity(term,startDate,endDate)) #Service: topListByCity #Description: Get the top N list by city within a date range #Parameters: <field> (String) -> Twitter field to aggreagate i.e. user.screen_name # <size> (Int) -> N i.e 5 for a Top five list # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/topListByCity/<field>/<size>/<startDate>/<endDate>',methods=['GET','OPTIONS']) @crossdomain(origin='*') def getAllTopListByCity(field,size,startDate, endDate): return jsonify(indexer.getAllTopListsByCity(field,size,startDate, endDate)) #Service: cultureTotalsByCity #Description: Get the count of languages found on the tweets by terms within a date range #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <stateCode> (String) -> the code of the state: i.e: VIC, TAS, etc # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/cultureTotalsByCity/<term>/<stateCode>/<startDate>/<endDate>',methods=['GET','OPTIONS']) @crossdomain(origin='*') def getAllLanguagesTotalsByCity(term, stateCode, startDate, endDate): return jsonify(indexer.getAllLanguagesTotalsByCity(term, stateCode, startDate, endDate)) #Service: sentimentTotalsByCity #Description: Get the total sentiment by City, by term within a date range. #Disclaimer: This search takes a long time. Must be reviewed. #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <stateCode> (String) -> the code of the state: i.e: VIC, TAS, etc # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/sentimentTotalsByCity/<term>/<stateCode>/<startDate>/<endDate>',methods=['GET','OPTIONS']) @crossdomain(origin='*') def getAllSentimentByCity(term,stateCode, startDate, endDate): return jsonify(indexer.getAllSentimentByCity(term,stateCode, startDate, endDate)) #Service: sentimentAnalysis #Description: Perform sentiment Analysis using the tweet_classifier open source library #Parameters: <text> (String) -> i.e. I'm happy to be here :) #output: json results containing the sentiment analysis performed on the provided text @app.route('/sentimentAnalysis/<text>',methods=['GET','OPTIONS']) @crossdomain(origin='*') def getSentimentAnalysis(text): return jsonify(indexer.getSentimentAnalysis(text)) #Service: cultureSentimentBySuburb #Description: Get the count of sentiment of languages found on the tweets by terms within a date range #Parameters: <term> (String) -> Text you want to search for. i.e. AFL or Tony Abbott or * # <suburbCode> (String) -> Suburb code. i.e. 206041122 # <startDate> (Int) -> Timestamp start date. i.e 1428069500339 # <endDate> (Int) -> Timestamp end date i.e 1430578700339 #output: a json object containing the matched results @app.route('/cultureSentimentBySuburb/<term>/<suburbCode>/<startDate>/<endDate>',methods=['GET','OPTIONS']) @crossdomain(origin='*') def getLanguagesSentimentBySuburb(term, suburbCode, startDate, endDate): return jsonify(indexer.getLanguagesSentimentBySuburb(term, suburbCode, startDate, endDate)) #main if __name__ == "__main__": app.run(debug=True)

diogonal/SentimentAnalyser

web/__init__.py

Python

# reference_metric.py: Define all needed quantities # for a reference metric. # Given uniform (reference metric) coordinate # (xx[0],xx[1],xx[2]), you must define: # 1) xxmin[3],xxmax[3]: Valid ranges for each # uniform coordinate xx0,xx1,xx2 # 2) xxSph[3]: Spherical coordinate (r,theta,phi), # in terms of uniform coordinate xx0,xx1,xx2 # 3) xx_to_Cart[3]: Cartesian coordinate (x,y,z), # in terms of uniform coordinate xx0,xx1,xx2 # 4) scalefactor_orthog: # orthogonal coordinate scale factor # (positive root of diagonal reference metric # components) # 5) Cart_to_xx[3]: Inverse of xx_to_Cart: # xx0,xx1,xx2 as functions of (x,y,z). # In the case that there exists no closed-form # expression, then a root finder might be needed # 6) UnitVectors[3][3]: Unit vectors of reference # metric. # Author: Zachariah B. Etienne # zachetie **at** gmail **dot* com import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends from outputC import outputC, superfast_uniq, add_to_Cfunction_dict, indent_Ccode # NRPy+: Core C code output module # VVVVVVVVVVVVVVVVV ## TO BE DEPRECATED from outputC import outC_function_dict # ^^^^^^^^^^^^^^^^^ from outputC import outC_NRPy_basic_defines_h_dict import NRPy_param_funcs as par # NRPy+: Parameter interface import grid as gri # NRPy+: Functions having to do with numerical grids import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support import os, sys # Standard Python modules for multiplatform OS-level functions # Step 0a: Initialize parameters thismodule = __name__ par.initialize_param(par.glb_param("char", thismodule, "CoordSystem", "Spherical")) par.initialize_param(par.glb_param("char", thismodule, "enable_rfm_precompute", "False")) par.initialize_param(par.glb_param("char", thismodule, "rfm_precompute_to_Cfunctions_and_NRPy_basic_defines", "False")) par.initialize_param(par.glb_param("char", thismodule, "rfm_precompute_Ccode_outdir", "Ccode")) # Step 0b: Declare global variables xx = gri.xx xx_to_Cart = ixp.zerorank1(DIM=4) # Must be set as a function of (xx[0],xx[1],xx[2]) Cart_to_xx = ixp.zerorank1(DIM=4) # Must be set as a function of (xx[0],xx[1],xx[2]) xxSph = ixp.zerorank1(DIM=4) # Must be set as a function of (xx[0],xx[1],xx[2]) scalefactor_orthog = ixp.zerorank1(DIM=4) # Must be set as a function of (xx[0],xx[1],xx[2]) Cartx, Carty, Cartz = sp.symbols("Cartx Carty Cartz", real=True) Cart = [Cartx, Carty, Cartz] scalefactor_orthog_funcform = ixp.zerorank1(DIM=4) # Must be set in terms of generic functions of xx[]s # The following are necessary since SymPy has trouble with its native sinh and cosh functions. def nrpysinh(x): return (sp.exp(x) - sp.exp(-x)) * sp.Rational(1, 2) def nrpycosh(x): return (sp.exp(x) + sp.exp(-x)) * sp.Rational(1, 2) have_already_called_reference_metric_function = False def reference_metric(SymPySimplifyExpressions=True): #, enable_compute_hatted_quantities=True): global f0_of_xx0_funcform, f1_of_xx1_funcform, f2_of_xx0_xx1_funcform, f3_of_xx0_funcform, f4_of_xx2_funcform global f0_of_xx0, f1_of_xx1, f2_of_xx1, f2_of_xx0_xx1, f3_of_xx0, f4_of_xx2 f0_of_xx0_funcform = sp.Function('f0_of_xx0_funcform')(xx[0]) f1_of_xx1_funcform = sp.Function('f1_of_xx1_funcform')(xx[1]) # f2_of_xx1_funcform = sp.Function('f2_of_xx1_funcform')(xx[1]) f2_of_xx0_xx1_funcform = sp.Function('f2_of_xx0_xx1_funcform')(xx[0], xx[1]) f3_of_xx0_funcform = sp.Function('f3_of_xx0_funcform')(xx[0]) f4_of_xx2_funcform = sp.Function('f4_of_xx2_funcform')(xx[2]) f0_of_xx0, f1_of_xx1, f2_of_xx1, f2_of_xx0_xx1, f3_of_xx0, f4_of_xx2 = par.Cparameters("REAL", thismodule, ["f0_of_xx0", "f1_of_xx1", "f2_of_xx1", "f2_of_xx0_xx1", "f3_of_xx0", "f4_of_xx2"], 1e300) # FIXME: Hack # return values of par.Cparameters() in the following code block are unused, so we ignore them. par.Cparameters("REAL", thismodule, ["f0_of_xx0__D0", "f0_of_xx0__DD00","f0_of_xx0__DDD000"], 1e300) par.Cparameters("REAL", thismodule, ["f1_of_xx1__D1", "f1_of_xx1__DD11","f1_of_xx1__DDD111"], 1e300) par.Cparameters("REAL", thismodule, ["f2_of_xx1__D1", "f2_of_xx1__DD11","f2_of_xx1__DDD111"], 1e300) par.Cparameters("REAL", thismodule, ["f2_of_xx0_xx1__D0", "f2_of_xx0_xx1__D1", "f2_of_xx0_xx1__DD00", "f2_of_xx0_xx1__DD11"], 1e300) par.Cparameters("REAL", thismodule, ["f3_of_xx0__D0", "f3_of_xx0__DD00"], 1e300) par.Cparameters("REAL", thismodule, ["f4_of_xx2__D2", "f4_of_xx2__DD22"], 1e300) global have_already_called_reference_metric_function # setting to global enables other modules to see updated value. have_already_called_reference_metric_function = True CoordSystem = par.parval_from_str("reference_metric::CoordSystem") M_PI, M_SQRT1_2 = par.Cparameters("#define", thismodule, ["M_PI", "M_SQRT1_2"], "") global xxmin global xxmax global UnitVectors UnitVectors = ixp.zerorank2(DIM=3) # Set up hatted metric tensor, rescaling matrix, and rescaling vector ##################################################################### # SPHERICAL-LIKE COORDINATE SYSTEMS WITH & WITHOUT RADIAL RESCALING # ##################################################################### if CoordSystem in ('Spherical', 'SinhSpherical', 'SinhSphericalv2'): # Adding assumption real=True can help simplify expressions involving xx[0] & xx[1] below. xx[0] = sp.symbols("xx0", real=True) xx[1] = sp.symbols("xx1", real=True) if CoordSystem == "Spherical": RMAX = par.Cparameters("REAL", thismodule, ["RMAX"],10.0) xxmin = [sp.sympify(0), sp.sympify(0), -M_PI] xxmax = [ RMAX, M_PI, M_PI] r = xx[0] th = xx[1] ph = xx[2] Cart_to_xx[0] = sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2) Cart_to_xx[1] = sp.acos(Cartz / Cart_to_xx[0]) Cart_to_xx[2] = sp.atan2(Carty, Cartx) elif CoordSystem == "SinhSpherical": xxmin = [sp.sympify(0), sp.sympify(0), -M_PI] xxmax = [sp.sympify(1), M_PI, M_PI] AMPL, SINHW = par.Cparameters("REAL",thismodule,["AMPL","SINHW"],[10.0,0.2]) # Set SinhSpherical radial coordinate by default; overwrite later if CoordSystem == "SinhSphericalv2". r = AMPL * (sp.exp(xx[0] / SINHW) - sp.exp(-xx[0] / SINHW)) / \ (sp.exp(1 / SINHW) - sp.exp(-1 / SINHW)) th = xx[1] ph = xx[2] Cart_to_xx[0] = SINHW*sp.asinh(sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2)*sp.sinh(1/SINHW)/AMPL) Cart_to_xx[1] = sp.acos(Cartz / sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2)) Cart_to_xx[2] = sp.atan2(Carty, Cartx) # SinhSphericalv2 adds the parameter "const_dr", which allows for a region near xx[0]=0 to have # constant radial resolution of const_dr, provided the sinh() term does not dominate near xx[0]=0. elif CoordSystem == "SinhSphericalv2": xxmin = [sp.sympify(0), sp.sympify(0), -M_PI] xxmax = [sp.sympify(1), M_PI, M_PI] AMPL, SINHW = par.Cparameters("REAL",thismodule,["AMPL","SINHW"],[10.0,0.2]) const_dr = par.Cparameters("REAL",thismodule,["const_dr"],0.0625) r = AMPL*( const_dr*xx[0] + (sp.exp(xx[0] / SINHW) - sp.exp(-xx[0] / SINHW)) / (sp.exp(1 / SINHW) - sp.exp(-1 / SINHW)) ) th = xx[1] ph = xx[2] # NO CLOSED-FORM EXPRESSION FOR RADIAL INVERSION. # Cart_to_xx[0] = "NewtonRaphson" # Cart_to_xx[1] = sp.acos(Cartz / sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2)) # Cart_to_xx[2] = sp.atan2(Carty, Cartx) xxSph[0] = r xxSph[1] = th xxSph[2] = ph # Now define xCart, yCart, and zCart in terms of x0,xx[1],xx[2]. # Note that the relation between r and x0 is not necessarily trivial in SinhSpherical coordinates. See above. xx_to_Cart[0] = xxSph[0]*sp.sin(xxSph[1])*sp.cos(xxSph[2]) xx_to_Cart[1] = xxSph[0]*sp.sin(xxSph[1])*sp.sin(xxSph[2]) xx_to_Cart[2] = xxSph[0]*sp.cos(xxSph[1]) scalefactor_orthog[0] = sp.diff(xxSph[0],xx[0]) scalefactor_orthog[1] = xxSph[0] scalefactor_orthog[2] = xxSph[0]*sp.sin(xxSph[1]) f0_of_xx0 = xxSph[0] f1_of_xx1 = sp.sin(xxSph[1]) scalefactor_orthog_funcform[0] = sp.diff(f0_of_xx0_funcform,xx[0]) scalefactor_orthog_funcform[1] = f0_of_xx0_funcform scalefactor_orthog_funcform[2] = f0_of_xx0_funcform*f1_of_xx1_funcform # Set the unit vectors UnitVectors = [[sp.sin(xxSph[1])*sp.cos(xxSph[2]), sp.sin(xxSph[1])*sp.sin(xxSph[2]), sp.cos(xxSph[1])], [sp.cos(xxSph[1])*sp.cos(xxSph[2]), sp.cos(xxSph[1])*sp.sin(xxSph[2]), -sp.sin(xxSph[1])], [ -sp.sin(xxSph[2]), sp.cos(xxSph[2]), sp.sympify(0) ]] ###################################################################### # SPHERICAL-LIKE COORDINATE SYSTEMS WITH RADIAL AND THETA RESCALINGS # ###################################################################### elif CoordSystem in ('NobleSphericalThetaOptionOne', 'NobleSphericalThetaOptionTwo'): # WARNING: CANNOT BE USED FOR SENR RUNS; # THESE DO NOT DEFINE xxmin, xxmax, Cart_to_xx # ALSO THE RADIAL RESCALINGS ARE NOT ODD FUNCTIONS OF xx0, # MEANING THAT CURVI. BOUNDARY CONDITIONS WILL NOT WORK. Rin,R0 = par.Cparameters("REAL", thismodule, ["Rin","R0"],[1.08986052555408,0.0]) x0beg = sp.log(Rin-R0) xx[0] = sp.symbols("xx0", real=True) r = R0 + sp.exp(x0beg + xx[0]) # 0.053407075111026485 == 0.017*pi th_c,xi,x1beg = par.Cparameters("REAL", thismodule, ["th_c","xi","x1beg"],[0.053407075111026485,0.25,0.0]) xx[1] = sp.symbols("xx1", real=True) x1j = x1beg + xx[1] if CoordSystem == "NobleSphericalThetaOptionOne": th = th_c + (M_PI - 2*th_c)*x1j + xi*sp.sin(2*M_PI*x1j) elif CoordSystem == "NobleSphericalThetaOptionTwo": x1_n_exponent = par.Cparameters("REAL", thismodule, ["x1_n_exponent"],9.0) th = M_PI/2 * ( 1 + (1 - xi)*(2*x1j - 1) + (xi - 2*th_c/M_PI)*(2*x1j - 1)**x1_n_exponent ) xx[2] = sp.symbols("xx2", real=True) ph = xx[2] xxSph[0] = r xxSph[1] = th xxSph[2] = ph # Now define xCart, yCart, and zCart in terms of x0,xx[1],xx[2]. # Note that the relation between r and x0 is not necessarily trivial in SinhSpherical coordinates. See above. xx_to_Cart[0] = xxSph[0]*sp.sin(xxSph[1])*sp.cos(xxSph[2]) xx_to_Cart[1] = xxSph[0]*sp.sin(xxSph[1])*sp.sin(xxSph[2]) xx_to_Cart[2] = xxSph[0]*sp.cos(xxSph[1]) scalefactor_orthog[0] = sp.diff(xxSph[0],xx[0]) scalefactor_orthog[1] = xxSph[0] scalefactor_orthog[2] = xxSph[0]*sp.sin(xxSph[1]) # Set the unit vectors UnitVectors = [[ sp.sin(xxSph[1])*sp.cos(xxSph[2]), sp.sin(xxSph[1])*sp.sin(xxSph[2]), sp.cos(xxSph[1])], [ sp.cos(xxSph[1])*sp.cos(xxSph[2]), sp.cos(xxSph[1])*sp.sin(xxSph[2]), -sp.sin(xxSph[1])], [ -sp.sin(xxSph[2]), sp.cos(xxSph[2]), sp.sympify(0) ]] ########################################################################## # CYLINDRICAL-LIKE COORDINATE SYSTEMS WITH & WITHOUT RADIAL/Z RESCALINGS # ########################################################################## elif CoordSystem in ('Cylindrical', 'SinhCylindrical', 'SinhCylindricalv2'): # Assuming the cylindrical radial coordinate # is positive makes nice simplifications of # unit vectors possible. xx[0] = sp.symbols("xx0", real=True) if CoordSystem == "Cylindrical": RHOMAX,ZMIN,ZMAX = par.Cparameters("REAL",thismodule,["RHOMAX","ZMIN","ZMAX"],[10.0,-10.0,10.0]) xxmin = [sp.sympify(0), -M_PI, ZMIN] xxmax = [ RHOMAX, M_PI, ZMAX] RHOCYL = xx[0] PHICYL = xx[1] ZCYL = xx[2] Cart_to_xx[0] = sp.sqrt(Cartx ** 2 + Carty ** 2) Cart_to_xx[1] = sp.atan2(Carty, Cartx) Cart_to_xx[2] = Cartz elif CoordSystem == "SinhCylindrical": xxmin = [sp.sympify(0), -M_PI, sp.sympify(-1)] xxmax = [sp.sympify(1), M_PI, sp.sympify(+1)] AMPLRHO, SINHWRHO, AMPLZ, SINHWZ = par.Cparameters("REAL",thismodule, ["AMPLRHO","SINHWRHO","AMPLZ","SINHWZ"], [ 10.0, 0.2, 10.0, 0.2]) # Set SinhCylindrical radial & z coordinates by default; overwrite later if CoordSystem == "SinhCylindricalv2". RHOCYL = AMPLRHO * (sp.exp(xx[0] / SINHWRHO) - sp.exp(-xx[0] / SINHWRHO)) / (sp.exp(1 / SINHWRHO) - sp.exp(-1 / SINHWRHO)) # phi coordinate remains unchanged. PHICYL = xx[1] ZCYL = AMPLZ * (sp.exp(xx[2] / SINHWZ) - sp.exp(-xx[2] / SINHWZ)) / (sp.exp(1 / SINHWZ) - sp.exp(-1 / SINHWZ)) Cart_to_xx[0] = SINHWRHO*sp.asinh(sp.sqrt(Cartx ** 2 + Carty ** 2)*sp.sinh(1/SINHWRHO)/AMPLRHO) Cart_to_xx[1] = sp.atan2(Carty, Cartx) Cart_to_xx[2] = SINHWZ*sp.asinh(Cartz*sp.sinh(1/SINHWZ)/AMPLZ) # SinhCylindricalv2 adds the parameters "const_drho", "const_dz", which allows for regions near xx[0]=0 # and xx[2]=0 to have constant rho and z resolution of const_drho and const_dz, provided the sinh() terms # do not dominate near xx[0]=0 and xx[2]=0. elif CoordSystem == "SinhCylindricalv2": xxmin = [sp.sympify(0), -M_PI, sp.sympify(-1)] xxmax = [sp.sympify(1), M_PI, sp.sympify(+1)] AMPLRHO, SINHWRHO, AMPLZ, SINHWZ = par.Cparameters("REAL", thismodule, ["AMPLRHO", "SINHWRHO", "AMPLZ", "SINHWZ"], [ 10.0, 0.2, 10.0, 0.2]) const_drho, const_dz = par.Cparameters("REAL", thismodule, ["const_drho", "const_dz"], [0.0625, 0.0625]) RHOCYL = AMPLRHO * ( const_drho*xx[0] + (sp.exp(xx[0] / SINHWRHO) - sp.exp(-xx[0] / SINHWRHO)) / (sp.exp(1 / SINHWRHO) - sp.exp(-1 / SINHWRHO)) ) PHICYL = xx[1] ZCYL = AMPLZ * ( const_dz *xx[2] + (sp.exp(xx[2] / SINHWZ ) - sp.exp(-xx[2] / SINHWZ )) / (sp.exp(1 / SINHWZ ) - sp.exp(-1 / SINHWZ )) ) # NO CLOSED-FORM EXPRESSION FOR RADIAL OR Z INVERSION. # Cart_to_xx[0] = "NewtonRaphson" # Cart_to_xx[1] = sp.atan2(Carty, Cartx) # Cart_to_xx[2] = "NewtonRaphson" xx_to_Cart[0] = RHOCYL*sp.cos(PHICYL) xx_to_Cart[1] = RHOCYL*sp.sin(PHICYL) xx_to_Cart[2] = ZCYL xxSph[0] = sp.sqrt(RHOCYL**2 + ZCYL**2) xxSph[1] = sp.acos(ZCYL / xxSph[0]) xxSph[2] = PHICYL scalefactor_orthog[0] = sp.diff(RHOCYL,xx[0]) scalefactor_orthog[1] = RHOCYL scalefactor_orthog[2] = sp.diff(ZCYL,xx[2]) f0_of_xx0 = RHOCYL f4_of_xx2 = sp.diff(ZCYL,xx[2]) scalefactor_orthog_funcform[0] = sp.diff(f0_of_xx0_funcform,xx[0]) scalefactor_orthog_funcform[1] = f0_of_xx0_funcform scalefactor_orthog_funcform[2] = f4_of_xx2_funcform # Set the unit vectors UnitVectors = [[ sp.cos(PHICYL), sp.sin(PHICYL), sp.sympify(0)], [-sp.sin(PHICYL), sp.cos(PHICYL), sp.sympify(0)], [ sp.sympify(0), sp.sympify(0), sp.sympify(1)]] elif CoordSystem in ('SymTP', 'SinhSymTP'): # var1, var2= sp.symbols('var1 var2',real=True) bScale, SINHWAA, AMAX = par.Cparameters("REAL", thismodule, ["bScale", "SINHWAA", "AMAX"], [0.5, 0.2, 10.0 ]) # Assuming xx0, xx1, and bScale # are positive makes nice simplifications of # unit vectors possible. xx[0],xx[1] = sp.symbols("xx0 xx1", real=True) xxmin = [sp.sympify(0), sp.sympify(0), -M_PI] xxmax = [ AMAX, M_PI, M_PI] AA = xx[0] if CoordSystem == "SinhSymTP": xxmax[0] = sp.sympify(1) # With xxmax[0] = 1, sinh(xx0/SINHWAA) / sinh(1/SINHWAA) will evaluate to a number between 0 and 1. # Then AA = AMAX * sinh(xx0/SINHWAA) / sinh(1/SINHWAA) will evaluate to a number between 0 and AMAX. AA = AMAX * (sp.exp(xx[0] / SINHWAA) - sp.exp(-xx[0] / SINHWAA)) / (sp.exp(1 / SINHWAA) - sp.exp(-1 / SINHWAA)) var1 = sp.sqrt(AA**2 + (bScale * sp.sin(xx[1]))**2) var2 = sp.sqrt(AA**2 + bScale**2) RHOSYMTP = AA*sp.sin(xx[1]) PHSYMTP = xx[2] ZSYMTP = var2*sp.cos(xx[1]) xx_to_Cart[0] = AA *sp.sin(xx[1])*sp.cos(xx[2]) xx_to_Cart[1] = AA *sp.sin(xx[1])*sp.sin(xx[2]) xx_to_Cart[2] = ZSYMTP xxSph[0] = sp.sqrt(RHOSYMTP**2 + ZSYMTP**2) xxSph[1] = sp.acos(ZSYMTP / xxSph[0]) xxSph[2] = PHSYMTP if CoordSystem == "SymTP": rSph = sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2) thSph = sp.acos(Cartz / rSph) phSph = sp.atan2(Carty, Cartx) # Mathematica script to compute Cart_to_xx[] # AA = x1; # var2 = Sqrt[AA^2 + bScale^2]; # RHOSYMTP = AA*Sin[x2]; # ZSYMTP = var2*Cos[x2]; # Solve[{rSph == Sqrt[RHOSYMTP^2 + ZSYMTP^2], # thSph == ArcCos[ZSYMTP/Sqrt[RHOSYMTP^2 + ZSYMTP^2]], # phSph == x3}, # {x1, x2, x3}] Cart_to_xx[0] = sp.sqrt(-bScale**2 + rSph**2 + sp.sqrt(bScale**4 + 2*bScale**2*rSph**2 + rSph**4 - 4*bScale**2*rSph**2*sp.cos(thSph)**2))*M_SQRT1_2 # M_SQRT1_2 = 1/sqrt(2); define this way for UnitTesting # The sign() function in the following expression ensures the correct root is taken. Cart_to_xx[1] = sp.acos(sp.sign(Cartz)*( sp.sqrt(1 + rSph**2/bScale**2 - sp.sqrt(bScale**4 + 2*bScale**2*rSph**2 + rSph**4 - 4*bScale**2*rSph**2*sp.cos(thSph)**2)/bScale**2)*M_SQRT1_2)) # M_SQRT1_2 = 1/sqrt(2); define this way for UnitTesting Cart_to_xx[2] = phSph elif CoordSystem == "SinhSymTP": rSph = sp.sqrt(Cartx ** 2 + Carty ** 2 + Cartz ** 2) thSph = sp.acos(Cartz / rSph) phSph = sp.atan2(Carty, Cartx) # Mathematica script to compute Cart_to_xx[] # AA = x1; # var2 = Sqrt[AA^2 + bScale^2]; # RHOSYMTP = AA*Sin[x2]; # ZSYMTP = var2*Cos[x2]; # Solve[{rSph == Sqrt[RHOSYMTP^2 + ZSYMTP^2], # thSph == ArcCos[ZSYMTP/Sqrt[RHOSYMTP^2 + ZSYMTP^2]], # phSph == x3}, # {x1, x2, x3}] Cart_to_xx[0] = sp.sqrt(-bScale**2 + rSph**2 + sp.sqrt(bScale**4 + 2*bScale**2*rSph**2 + rSph**4 - 4*bScale**2*rSph**2*sp.cos(thSph)**2))*M_SQRT1_2 # M_SQRT1_2 = 1/sqrt(2); define this way for UnitTesting # The sign() function in the following expression ensures the correct root is taken. Cart_to_xx[1] = sp.acos(sp.sign(Cartz)*( sp.sqrt(1 + rSph**2/bScale**2 - sp.sqrt(bScale**4 + 2*bScale**2*rSph**2 + rSph**4 - 4*bScale**2*rSph**2*sp.cos(thSph)**2)/bScale**2)*M_SQRT1_2)) # M_SQRT1_2 = 1/sqrt(2); define this way for UnitTesting Cart_to_xx[2] = phSph scalefactor_orthog[0] = sp.diff(AA,xx[0]) * var1 / var2 scalefactor_orthog[1] = var1 scalefactor_orthog[2] = AA * sp.sin(xx[1]) f0_of_xx0 = AA f1_of_xx1 = sp.sin(xx[1]) f2_of_xx0_xx1 = var1 f3_of_xx0 = var2 scalefactor_orthog_funcform[0] = sp.diff(f0_of_xx0_funcform,xx[0]) * f2_of_xx0_xx1_funcform/f3_of_xx0_funcform scalefactor_orthog_funcform[1] = f2_of_xx0_xx1_funcform scalefactor_orthog_funcform[2] = f0_of_xx0_funcform*f1_of_xx1_funcform # Set the transpose of the matrix of unit vectors UnitVectors = [[sp.sin(xx[1]) * sp.cos(xx[2]) * var2 / var1, sp.sin(xx[1]) * sp.sin(xx[2]) * var2 / var1, AA * sp.cos(xx[1]) / var1], [AA * sp.cos(xx[1]) * sp.cos(xx[2]) / var1, AA * sp.cos(xx[1]) * sp.sin(xx[2]) / var1, -sp.sin(xx[1]) * var2 / var1], [-sp.sin(xx[2]), sp.cos(xx[2]), sp.sympify(0)]] ##################################### # CARTESIAN-LIKE COORDINATE SYSTEMS # ##################################### elif CoordSystem == "Cartesian": # return values of par.Cparameters() in the following line of code are unused, so we ignore them. par.Cparameters("REAL",thismodule, ["xmin","xmax","ymin","ymax","zmin","zmax"], [ -10.0, 10.0, -10.0, 10.0, -10.0, 10.0]) xxmin = ["xmin", "ymin", "zmin"] xxmax = ["xmax", "ymax", "zmax"] xx_to_Cart[0] = xx[0] xx_to_Cart[1] = xx[1] xx_to_Cart[2] = xx[2] xxSph[0] = sp.sqrt(xx[0] ** 2 + xx[1] ** 2 + xx[2] ** 2) xxSph[1] = sp.acos(xx[2] / xxSph[0]) xxSph[2] = sp.atan2(xx[1], xx[0]) Cart_to_xx[0] = Cartx Cart_to_xx[1] = Carty Cart_to_xx[2] = Cartz scalefactor_orthog[0] = sp.sympify(1) scalefactor_orthog[1] = sp.sympify(1) scalefactor_orthog[2] = sp.sympify(1) scalefactor_orthog_funcform[0] = sp.sympify(1) scalefactor_orthog_funcform[1] = sp.sympify(1) scalefactor_orthog_funcform[2] = sp.sympify(1) # Set the transpose of the matrix of unit vectors UnitVectors = [[sp.sympify(1), sp.sympify(0), sp.sympify(0)], [sp.sympify(0), sp.sympify(1), sp.sympify(0)], [sp.sympify(0), sp.sympify(0), sp.sympify(1)]] elif CoordSystem == "SinhCartesian": # SinhCartesian coordinates allows us to push the outer boundary of the # computational domain a lot further away, while keeping reasonably high # resolution towards the center of the computational grid. # Set default values for min and max (x,y,z) xxmin = [sp.sympify(-1), sp.sympify(-1), sp.sympify(-1)] xxmax = [sp.sympify(+1), sp.sympify(+1), sp.sympify(+1)] # Declare basic parameters of the coordinate system and their default values AMPLXYZ, SINHWXYZ = par.Cparameters("REAL", thismodule, ["AMPLXYZ", "SINHWXYZ"], [ 10.0, 0.2]) # Compute (xx_to_Cart0,xx_to_Cart1,xx_to_Cart2) from (xx0,xx1,xx2) for ii in [0, 1, 2]: xx_to_Cart[ii] = AMPLXYZ*(sp.exp(xx[ii]/SINHWXYZ) - sp.exp(-xx[ii]/SINHWXYZ))/(sp.exp(1/SINHWXYZ) - sp.exp(-1/SINHWXYZ)) # Compute (r,th,ph) from (xx_to_Cart2,xx_to_Cart1,xx_to_Cart2) xxSph[0] = sp.sqrt(xx_to_Cart[0] ** 2 + xx_to_Cart[1] ** 2 + xx_to_Cart[2] ** 2) xxSph[1] = sp.acos(xx_to_Cart[2] / xxSph[0]) xxSph[2] = sp.atan2(xx_to_Cart[1], xx_to_Cart[0]) # Compute (xx0,xx1,xx2) from (Cartx,Carty,Cartz) Cart_to_xx[0] = SINHWXYZ*sp.asinh(Cartx*sp.sinh(1/SINHWXYZ)/AMPLXYZ) Cart_to_xx[1] = SINHWXYZ*sp.asinh(Carty*sp.sinh(1/SINHWXYZ)/AMPLXYZ) Cart_to_xx[2] = SINHWXYZ*sp.asinh(Cartz*sp.sinh(1/SINHWXYZ)/AMPLXYZ) # Compute scale factors scalefactor_orthog[0] = sp.diff(xx_to_Cart[0], xx[0]) scalefactor_orthog[1] = sp.diff(xx_to_Cart[1], xx[1]) scalefactor_orthog[2] = sp.diff(xx_to_Cart[2], xx[2]) f0_of_xx0 = sp.diff(xx_to_Cart[0], xx[0]) f1_of_xx1 = sp.diff(xx_to_Cart[1], xx[1]) f4_of_xx2 = sp.diff(xx_to_Cart[2], xx[2]) scalefactor_orthog_funcform[0] = f0_of_xx0_funcform scalefactor_orthog_funcform[1] = f1_of_xx1_funcform scalefactor_orthog_funcform[2] = f4_of_xx2_funcform # Set the transpose of the matrix of unit vectors UnitVectors = [[sp.sympify(1), sp.sympify(0), sp.sympify(0)], [sp.sympify(0), sp.sympify(1), sp.sympify(0)], [sp.sympify(0), sp.sympify(0), sp.sympify(1)]] else: print("CoordSystem == " + CoordSystem + " is not supported.") sys.exit(1) # Finally, call ref_metric__hatted_quantities() # to construct hatted metric, derivs of hatted # metric, and Christoffel symbols ref_metric__hatted_quantities(SymPySimplifyExpressions) # ref_metric__hatted_quantities(scalefactor_orthog_funcform,SymPySimplifyExpressions) # ref_metric__hatted_quantities(scalefactor_orthog,SymPySimplifyExpressions) def ref_metric__hatted_quantities(SymPySimplifyExpressions=True): enable_rfm_precompute = False if par.parval_from_str(thismodule+"::enable_rfm_precompute") == "True": enable_rfm_precompute = True # Step 0: Set dimension DIM DIM = par.parval_from_str("grid::DIM") global ReU, ReDD, ghatDD ReU = ixp.zerorank1() ReDD = ixp.zerorank2() ghatDD = ixp.zerorank2() # Step 1: Compute ghatDD (reference metric), ghatUU # (inverse reference metric), as well as # rescaling vector ReU & rescaling matrix ReDD if not enable_rfm_precompute: for i in range(DIM): scalefactor_orthog[i] = sp.sympify(scalefactor_orthog[i]) ghatDD[i][i] = scalefactor_orthog[i]**2 ReU[i] = 1/scalefactor_orthog[i] for j in range(DIM): ReDD[i][j] = scalefactor_orthog[i]*scalefactor_orthog[j] else: for i in range(DIM): scalefactor_orthog_funcform[i] = sp.sympify(scalefactor_orthog_funcform[i]) ghatDD[i][i] = scalefactor_orthog_funcform[i]**2 ReU[i] = 1/scalefactor_orthog_funcform[i] for j in range(DIM): ReDD[i][j] = scalefactor_orthog_funcform[i]*scalefactor_orthog_funcform[j] # Step 1b: Compute ghatUU and detgammahat global ghatUU global detgammahat ghatUU = ixp.zerorank2() ghatUU, detgammahat = ixp.symm_matrix_inverter3x3(ghatDD) # Step 1c: Sanity check: verify that ReDD, ghatDD, # and ghatUU are all symmetric rank-2: for i in range(DIM): for j in range(DIM): if ReDD[i][j] != ReDD[j][i]: print("Error: ReDD["+ str(i) + "][" + str(j) + "] != ReDD["+ str(j) + "][" + str(i) + ": " + str(ReDD[i][j]) + "!=" + str(ReDD[j][i])) sys.exit(1) if ghatDD[i][j] != ghatDD[j][i]: print("Error: ghatDD["+ str(i) + "][" + str(j) + "] != ghatDD["+ str(j) + "][" + str(i) + ": " + str(ghatDD[i][j]) + "!=" + str(ghatDD[j][i])) sys.exit(1) if ghatUU[i][j] != ghatUU[j][i]: print("Error: ghatUU["+ str(i) + "][" + str(j) + "] != ghatUU["+ str(j) + "][" + str(i) + ": " + str(ghatUU[i][j]) + "!=" + str(ghatUU[j][i])) sys.exit(1) # Step 2: Compute det(ghat) and its 1st & 2nd derivatives global detgammahatdD, detgammahatdDD detgammahatdD = ixp.zerorank1(DIM) detgammahatdDD = ixp.zerorank2(DIM) for i in range(DIM): detgammahatdD[i] = (sp.diff(detgammahat, xx[i])) for j in range(DIM): detgammahatdDD[i][j] = sp.diff(detgammahatdD[i], xx[j]) # Step 3a: Compute 1st & 2nd derivatives of rescaling vector. # (E.g., needed in BSSN for betaUdDD computation) global ReUdD, ReUdDD ReUdD = ixp.zerorank2(DIM) ReUdDD = ixp.zerorank3(DIM) for i in range(DIM): for j in range(DIM): ReUdD[i][j] = sp.diff(ReU[i], xx[j]) for k in range(DIM): ReUdDD[i][j][k] = sp.diff(ReUdD[i][j], xx[k]) # Step 3b: Compute 1st & 2nd derivatives of rescaling matrix. global ReDDdD, ReDDdDD ReDDdD = ixp.zerorank3(DIM) ReDDdDD = ixp.zerorank4(DIM) for i in range(DIM): for j in range(DIM): for k in range(DIM): ReDDdD[i][j][k] = (sp.diff(ReDD[i][j], xx[k])) for l in range(DIM): # Simplifying this doesn't appear to help overall NRPy run time. ReDDdDD[i][j][k][l] = sp.diff(ReDDdD[i][j][k], xx[l]) # Step 3c: Compute 1st & 2nd derivatives of reference metric. global ghatDDdD, ghatDDdDD ghatDDdD = ixp.zerorank3(DIM) ghatDDdDD = ixp.zerorank4(DIM) for i in range(DIM): for j in range(DIM): for k in range(DIM): if SymPySimplifyExpressions==True: # ghatDDdD[i][j][k] = sp.trigsimp(sp.diff(ghatDD[i][j], xx[k])) # FIXME: BAD: MUST BE SIMPLIFIED OR ANSWER IS INCORRECT! Must be some bug in sympy... ghatDDdD[i][j][k] = sp.simplify(sp.diff(ghatDD[i][j], xx[k])) # FIXME: BAD: MUST BE SIMPLIFIED OR ANSWER IS INCORRECT! Must be some bug in sympy... else: ghatDDdD[i][j][k] = (sp.diff(ghatDD[i][j], xx[k])) # FIXME: BAD: MUST BE SIMPLIFIED OR ANSWER IS INCORRECT! Must be some bug in sympy... for l in range(DIM): ghatDDdDD[i][j][k][l] = (sp.diff(ghatDDdD[i][j][k], xx[l])) # Step 4a: Compute Christoffel symbols of reference metric. global GammahatUDD GammahatUDD = ixp.zerorank3(DIM) for i in range(DIM): for k in range(DIM): for l in range(DIM): for m in range(DIM): # GammahatUDD[i][k][l] += sp.trigsimp((sp.Rational(1,2))*ghatUU[i][m]*\ GammahatUDD[i][k][l] += (sp.Rational(1, 2))*ghatUU[i][m]*\ (ghatDDdD[m][k][l] + ghatDDdD[m][l][k] - ghatDDdD[k][l][m]) # Step 4b: Compute derivs of Christoffel symbols of reference metric. global GammahatUDDdD GammahatUDDdD = ixp.zerorank4(DIM) for i in range(DIM): for j in range(DIM): for k in range(DIM): for l in range(DIM): GammahatUDDdD[i][j][k][l] = (sp.diff(GammahatUDD[i][j][k], xx[l])) # Step 4c: If rfm_precompute is disabled, then we are finished with this function. # Otherwise continue to Step 5. if not enable_rfm_precompute: return # Step 5: Now that all hatted quantities are written in terms of generic SymPy functions, # we will now replace SymPy functions with simple variables using rigid NRPy+ syntax, # and store these variables to globals defined above. def make_replacements(expr): sympy_version = sp.__version__.replace("rc", "...").replace("b", "...") # Ignore the rc's and b's # (release candidates & betas). sympy_version_decimal = float(int(sympy_version.split(".")[0]) + int(sympy_version.split(".")[1]) / 10.0) is_old_sympy_version = sympy_version_decimal < 1.2 # The derivative representation changed with SymPy 1.2, forcing version-dependent behavior. # Example: Derivative(f0_of_xx0_funcform(xx0)(xx0), (xx0, 2)) >> f0_of_xx0__DD00 rule = {} # replacement dictionary for item in sp.preorder_traversal(expr): if item.func == sp.Derivative: # extract function name before '_funcform' strfunc = str(item.args[0]).split('_funcform(', 1)[0] if is_old_sympy_version: # extract differentiation variable and derivative order (SymPy <= 1.1) var, order = str(item.args[1])[2:], len(item.args) - 1 else: # extract differentiation variable and derivative order (SymPy >= 1.2) var, order = str(item.args[1][0])[2:], item.args[1][1] # build derivative operator with format: __DD...D(var)(var)...(var) where # D and (var) are repeated for every derivative order oper = '__D' + 'D' * (order - 1) + var * order # add replacement rule to dictionary rule[item] = sp.sympify(strfunc + oper) expr = expr.xreplace(rule) rule = {} # Example: f0_of_xx0_funcform(xx0)(xx0) >> f0_of_xx0 for item in sp.preorder_traversal(expr): if "_funcform" in str(item.func): # extract function name before '_funcform' strfunc = str(item.func).split("_funcform", 1)[0] # add replacement rule to dictionary rule[item] = sp.sympify(strfunc) return expr.xreplace(rule) # Step 6: enable_rfm_precompute: precompute and store in memory # expressions related to the reference metric (a.k.a., # "hatted quantities"). These expressions may involve # transcendental functions, which are expensive to compute # within nested loops in C. Hence we precompute them and # store the result. # The precomputed "hatted quantity" expressions will be stored in # a C struct called rfmstruct. As these expressions generally # involve computationally expensive transcendental functions # of xx0,xx1,or xx2, and xx0,xx1, and xx2 remain fixed across # most (if not all) of a given simulation, setting up the # rfmstruct can greatly improve performance. # The core challenge in setting up the rfmstruct is collecting # all the information needed to automatically generate it. # Step 5 and onwards implements this algorithm, using the # *generic functional form* of the hatted quantities (as # opposed to the exact closed-form expressions of the # hatted quantities) computed above. detgammahat = make_replacements(detgammahat) for i in range(DIM): ReU[i] = make_replacements(ReU[i]) detgammahatdD[i] = make_replacements(detgammahatdD[i]) for j in range(DIM): ReDD[i][j] = make_replacements(ReDD[i][j]) ReUdD[i][j] = make_replacements(ReUdD[i][j]) ghatDD[i][j] = make_replacements(ghatDD[i][j]) ghatUU[i][j] = make_replacements(ghatUU[i][j]) detgammahatdDD[i][j] = make_replacements(detgammahatdDD[i][j]) for k in range(DIM): ReDDdD[i][j][k] = make_replacements(ReDDdD[i][j][k]) ReUdDD[i][j][k] = make_replacements(ReUdDD[i][j][k]) ghatDDdD[i][j][k] = make_replacements(ghatDDdD[i][j][k]) GammahatUDD[i][j][k] = make_replacements(GammahatUDD[i][j][k]) for l in range(DIM): ReDDdDD[i][j][k][l] = make_replacements(ReDDdDD[i][j][k][l]) ghatDDdDD[i][j][k][l] = make_replacements(ghatDDdDD[i][j][k][l]) GammahatUDDdD[i][j][k][l] = make_replacements(GammahatUDDdD[i][j][k][l]) # Step 6: At this point, each expression is written in terms of the generic functions # of xx0, xx1, and/or xx2 and their derivatives. Depending on the functions, some # of these derivatives may be zero. In Step 5 we'll evaluate the function # derivatives exactly and set the expressions to zero. Otherwise in the C code # we'd be storing performing arithmetic with zeros -- wasteful! # Step 6.a: Construct the full list of *unique* NRPy+ variables representing the # SymPy functions and derivatives, so that all zero derivatives can be # computed. freevars = [] freevars.extend(detgammahat.free_symbols) for i in range(DIM): freevars.extend(ReU[i].free_symbols) freevars.extend(detgammahatdD[i].free_symbols) for j in range(DIM): freevars.extend(ReDD[i][j].free_symbols) freevars.extend(ReUdD[i][j].free_symbols) freevars.extend(ghatDD[i][j].free_symbols) freevars.extend(ghatUU[i][j].free_symbols) freevars.extend(detgammahatdDD[i][j].free_symbols) for k in range(DIM): freevars.extend(ReDDdD[i][j][k].free_symbols) freevars.extend(ReUdDD[i][j][k].free_symbols) freevars.extend(ghatDDdD[i][j][k].free_symbols) freevars.extend(GammahatUDD[i][j][k].free_symbols) for l in range(DIM): freevars.extend(ReDDdDD[i][j][k][l].free_symbols) freevars.extend(ghatDDdDD[i][j][k][l].free_symbols) freevars.extend(GammahatUDDdD[i][j][k][l].free_symbols) freevars_uniq = superfast_uniq(freevars) freevars_uniq_xx_indep = [] for i in range(len(freevars_uniq)): freevars_uniq_xx_indep.append(freevars_uniq[i]) # Step 6.b: Using the expressions f?_of_xx? set in reference_metric(), # evaluate each needed derivative and, in the case it is zero, # set the corresponding "freevar" variable to zero. freevars_uniq_vals = [] for i, var in enumerate(freevars_uniq): basename = str(var).split("__")[0].replace("_funcform", "") derivatv = "" if "__" in str(var): derivatv = str(var).split("__")[1].replace("_funcform", "") if basename == "f0_of_xx0": basefunc = f0_of_xx0 elif basename == "f1_of_xx1": basefunc = f1_of_xx1 elif basename == "f2_of_xx0_xx1": basefunc = f2_of_xx0_xx1 elif basename == "f3_of_xx0": basefunc = f3_of_xx0 elif basename == "f4_of_xx2": basefunc = f4_of_xx2 else: print("Error: function inside " + str(var) + " undefined.") sys.exit(1) diff_result = basefunc if derivatv == "": pass else: derivorder = derivatv.replace("d", "").replace("D", "").replace("0", "0 ").replace("1", "1 ").replace( "2", "2 ").split(" ") for derivdirn in derivorder: if derivdirn != "": derivwrt = xx[int(derivdirn)] diff_result = sp.diff(diff_result, derivwrt) freevars_uniq_vals.append(diff_result) frees_uniq = superfast_uniq(diff_result.free_symbols) has_xx_dependence = False for dirn in range(3): if gri.xx[dirn] in frees_uniq: has_xx_dependence = True if not has_xx_dependence: freevars_uniq_xx_indep[i] = diff_result # Step 6.c: Finally, substitute integers for all functions & derivatives that evaluate to integers for varidx, freevar in enumerate(freevars_uniq): detgammahat = detgammahat.subs(freevar, freevars_uniq_xx_indep[varidx]) for i in range(DIM): ReU[i] = ReU[i].subs(freevar, freevars_uniq_xx_indep[varidx]) detgammahatdD[i] = detgammahatdD[i].subs(freevar, freevars_uniq_xx_indep[varidx]) for j in range(DIM): ReDD[i][j] = ReDD[i][j].subs(freevar, freevars_uniq_xx_indep[varidx]) ReUdD[i][j] = ReUdD[i][j].subs(freevar, freevars_uniq_xx_indep[varidx]) ghatDD[i][j] = ghatDD[i][j].subs(freevar, freevars_uniq_xx_indep[varidx]) ghatUU[i][j] = ghatUU[i][j].subs(freevar, freevars_uniq_xx_indep[varidx]) detgammahatdDD[i][j] = detgammahatdDD[i][j].subs(freevar, freevars_uniq_xx_indep[varidx]) for k in range(DIM): ReDDdD[i][j][k] = ReDDdD[i][j][k].subs(freevar, freevars_uniq_xx_indep[varidx]) ReUdDD[i][j][k] = ReUdDD[i][j][k].subs(freevar, freevars_uniq_xx_indep[varidx]) ghatDDdD[i][j][k] = ghatDDdD[i][j][k].subs(freevar, freevars_uniq_xx_indep[varidx]) GammahatUDD[i][j][k] = GammahatUDD[i][j][k].subs(freevar, freevars_uniq_xx_indep[varidx]) for l in range(DIM): ReDDdDD[i][j][k][l] = ReDDdDD[i][j][k][l].subs(freevar, freevars_uniq_xx_indep[varidx]) ghatDDdDD[i][j][k][l] = ghatDDdDD[i][j][k][l].subs(freevar, freevars_uniq_xx_indep[varidx]) GammahatUDDdD[i][j][k][l] = GammahatUDDdD[i][j][k][l].subs(freevar, freevars_uniq_xx_indep[varidx]) # Step 7: Construct needed C code for declaring rfmstruct, allocating storage for # rfmstruct arrays, defining each element in each array, reading the # rfmstruct data from memory (both with and without SIMD enabled), and # freeing allocated memory for the rfmstruct arrays. # struct_str: String that declares the rfmstruct struct. struct_str = "typedef struct __rfmstruct__ {\n" define_str = "" # rfmstruct stores pointers to (so far) 1D arrays. The malloc_str string allocates space for the arrays. malloc_str = "" if par.parval_from_str("reference_metric::rfm_precompute_to_Cfunctions_and_NRPy_basic_defines") == "False": malloc_str = "rfm_struct rfmstruct;\n" freemm_str = "" # readvr_str reads the arrays from memory as needed readvr_str = ["", "", ""] readvr_SIMD_outer_str = ["", "", ""] readvr_SIMD_inner_str = ["", "", ""] # Sort freevars_uniq_vals and freevars_uniq_xx_indep, according to alphabetized freevars_uniq_xx_indep. # Without this step, the ordering of elements in rfmstruct would be random, and would change each time # this function was called. if len(freevars_uniq_xx_indep) > 0: freevars_uniq_xx_indep, freevars_uniq_vals = (list(x) for x in zip(*sorted(zip(freevars_uniq_xx_indep, freevars_uniq_vals),key=str))) # Tease out how many variables each function in freevars_uniq_vals which_freevar = 0 for expr in freevars_uniq_vals: if "_of_xx" in str(freevars_uniq_xx_indep[which_freevar]): frees = expr.free_symbols frees_uniq = superfast_uniq(frees) xx_list = [] malloc_size = 1 for i in range(3): if gri.xx[i] in frees_uniq: xx_list.append(gri.xx[i]) malloc_size *= gri.Nxx_plus_2NGHOSTS[i] struct_str += "\tREAL *restrict " + str(freevars_uniq_xx_indep[which_freevar]) + ";\n" malloc_str += "rfmstruct." + str( freevars_uniq_xx_indep[which_freevar]) + " = (REAL *)malloc(sizeof(REAL)*" + str(malloc_size) + ");\n" freemm_str += "free(rfmstruct." + str(freevars_uniq_xx_indep[which_freevar]) + ");\n" output_define_and_readvr = False for dirn in range(3): if (gri.xx[dirn] in frees_uniq) and not (gri.xx[(dirn+1)%3] in frees_uniq) and not (gri.xx[(dirn+2)%3] in frees_uniq): define_str += "for(int i"+str(dirn)+"=0;i"+str(dirn)+"<Nxx_plus_2NGHOSTS"+str(dirn)+";i"+str(dirn)+"++) {\n" define_str += " const REAL xx"+str(dirn)+" = xx["+str(dirn)+"][i"+str(dirn)+"];\n" define_str += " rfmstruct." + str(freevars_uniq_xx_indep[which_freevar]) + "[i"+str(dirn)+"] = " + str(sp.ccode(freevars_uniq_vals[which_freevar])) + ";\n" define_str += "}\n\n" readvr_str[dirn] += "const REAL " + str(freevars_uniq_xx_indep[which_freevar]) + " = rfmstruct->" + \ str(freevars_uniq_xx_indep[which_freevar]) + "[i"+str(dirn)+"];\n" readvr_SIMD_outer_str[dirn] += "const double NOSIMD" + str( freevars_uniq_xx_indep[which_freevar]) + " = rfmstruct->" + str(freevars_uniq_xx_indep[which_freevar]) + "[i"+str(dirn)+"]; " readvr_SIMD_outer_str[dirn] += "const REAL_SIMD_ARRAY " + str(freevars_uniq_xx_indep[which_freevar]) + \ " = ConstSIMD(NOSIMD" + str(freevars_uniq_xx_indep[which_freevar]) + ");\n" readvr_SIMD_inner_str[dirn] += "const REAL_SIMD_ARRAY " + str(freevars_uniq_xx_indep[which_freevar]) + \ " = ReadSIMD(&rfmstruct->" + str(freevars_uniq_xx_indep[which_freevar]) + "[i"+str(dirn)+"]);\n" output_define_and_readvr = True if (not output_define_and_readvr) and (gri.xx[0] in frees_uniq) and (gri.xx[1] in frees_uniq): define_str += """ for(int i1=0;i1<Nxx_plus_2NGHOSTS1;i1++) for(int i0=0;i0<Nxx_plus_2NGHOSTS0;i0++) { const REAL xx0 = xx[0][i0]; const REAL xx1 = xx[1][i1]; rfmstruct.""" + str(freevars_uniq_xx_indep[which_freevar]) + """[i0 + Nxx_plus_2NGHOSTS0*i1] = """ + str(sp.ccode(freevars_uniq_vals[which_freevar])) + """; }\n\n""" readvr_str[0] += "const REAL " + str(freevars_uniq_xx_indep[which_freevar]) + " = rfmstruct->" + \ str(freevars_uniq_xx_indep[which_freevar]) + "[i0 + Nxx_plus_2NGHOSTS0*i1];\n" readvr_SIMD_outer_str[0] += "const double NOSIMD" + str(freevars_uniq_xx_indep[which_freevar]) + \ " = rfmstruct->" + str(freevars_uniq_xx_indep[which_freevar]) + "[i0 + Nxx_plus_2NGHOSTS0*i1]; " readvr_SIMD_outer_str[0] += "const REAL_SIMD_ARRAY " + str(freevars_uniq_xx_indep[which_freevar]) + \ " = ConstSIMD(NOSIMD" + str(freevars_uniq_xx_indep[which_freevar]) + ");\n" readvr_SIMD_inner_str[0] += "const REAL_SIMD_ARRAY " + str(freevars_uniq_xx_indep[which_freevar]) + \ " = ReadSIMD(&rfmstruct->" + str(freevars_uniq_xx_indep[which_freevar]) + "[i0 + Nxx_plus_2NGHOSTS0*i1]);\n" output_define_and_readvr = True if not output_define_and_readvr: print("ERROR: Could not figure out the (xx0,xx1,xx2) dependency within the expression for "+str(freevars_uniq_xx_indep[which_freevar])+":") print(str(freevars_uniq_vals[which_freevar])) sys.exit(1) which_freevar += 1 struct_str += "} rfm_struct;\n" # Step 8: Output needed C code to files outdir = par.parval_from_str(thismodule+"::rfm_precompute_Ccode_outdir") if par.parval_from_str(thismodule+"::rfm_precompute_to_Cfunctions_and_NRPy_basic_defines") == "False": with open(os.path.join(outdir, "rfm_struct__declare.h"), "w") as file: file.write(struct_str) with open(os.path.join(outdir, "rfm_struct__malloc.h"), "w") as file: file.write(malloc_str) with open(os.path.join(outdir, "rfm_struct__define.h"), "w") as file: file.write(define_str) with open(os.path.join(outdir, "rfm_struct__freemem.h"), "w") as file: file.write(freemm_str) else: global NRPy_basic_defines_str NRPy_basic_defines_str = struct_str global rfm_struct__malloc, rfm_struct__define, rfm_struct__freemem rfm_struct__malloc = malloc_str rfm_struct__define = define_str rfm_struct__freemem = freemm_str for i in range(3): with open(os.path.join(outdir, "rfm_struct__read" + str(i) + ".h"), "w") as file: file.write(readvr_str[i]) with open(os.path.join(outdir, "rfm_struct__SIMD_outer_read" + str(i) + ".h"), "w") as file: file.write(readvr_SIMD_outer_str[i]) with open(os.path.join(outdir, "rfm_struct__SIMD_inner_read" + str(i) + ".h"), "w") as file: file.write(readvr_SIMD_inner_str[i]) #################################################### # Core Jacobian (basis) transformation functions, # for reference metric basis to/from the # Cartesian basis. # We define Jacobians relative to the reference metric # basis at a point x^j_rfm=(xx0,xx1,xx2)_rfm on the source grid: # # Jac_dUCart_dDrfmUD[i][j] = dx^i_Cart / dx^j_rfm # # via exact differentiation (courtesy SymPy), and the inverse Jacobian # # Jac_dUrfm_dDCartUD[i][j] = dx^i_rfm / dx^j_Cart # # using NRPy+'s generic_matrix_inverter3x3() function def compute_Jacobian_and_inverseJacobian_tofrom_Cartesian(): # Step 2.a: First construct Jacobian matrix: Jac_dUCart_dDrfmUD = ixp.zerorank2() for i in range(3): for j in range(3): Jac_dUCart_dDrfmUD[i][j] = sp.diff(xx_to_Cart[i], xx[j]) Jac_dUrfm_dDCartUD, dummyDET = ixp.generic_matrix_inverter3x3(Jac_dUCart_dDrfmUD) return Jac_dUCart_dDrfmUD, Jac_dUrfm_dDCartUD def basis_transform_vectorU_from_rfmbasis_to_Cartesian(Jac_dUCart_dDrfmUD, src_vectorU): Cart_dst_vectorU = ixp.zerorank1() for i in range(3): for l in range(3): Cart_dst_vectorU[i] += Jac_dUCart_dDrfmUD[i][l] * src_vectorU[l] return Cart_dst_vectorU def basis_transform_vectorD_from_rfmbasis_to_Cartesian(Jac_dUrfm_dDCartUD, src_vectorD): Cart_dst_vectorD = ixp.zerorank1() for i in range(3): for l in range(3): Cart_dst_vectorD[i] += Jac_dUrfm_dDCartUD[l][i] * src_vectorD[l] return Cart_dst_vectorD def basis_transform_tensorDD_from_rfmbasis_to_Cartesian(Jac_dUrfm_dDCartUD, src_tensorDD): Cart_dst_tensorDD = ixp.zerorank2() for i in range(3): for j in range(3): for l in range(3): for m in range(3): Cart_dst_tensorDD[i][j] += Jac_dUrfm_dDCartUD[l][i]*Jac_dUrfm_dDCartUD[m][j]*src_tensorDD[l][m] return Cart_dst_tensorDD def basis_transform_vectorU_from_Cartesian_to_rfmbasis(Jac_dUrfm_dDCartUD, Cart_src_vectorU): rfm_dst_vectorU = ixp.zerorank1() for i in range(3): for l in range(3): rfm_dst_vectorU[i] += Jac_dUrfm_dDCartUD[i][l] * Cart_src_vectorU[l] return rfm_dst_vectorU def basis_transform_vectorD_from_Cartesian_to_rfmbasis(Jac_dUCart_dDrfmUD, Cart_src_vectorD): rfm_dst_vectorD = ixp.zerorank1() for i in range(3): for l in range(3): rfm_dst_vectorD[i] += Jac_dUCart_dDrfmUD[l][i] * Cart_src_vectorD[l] return rfm_dst_vectorD def basis_transform_tensorDD_from_Cartesian_to_rfmbasis(Jac_dUCart_dDrfmUD, Cart_src_tensorDD): rfm_dst_tensorDD = ixp.zerorank2() for i in range(3): for j in range(3): for l in range(3): for m in range(3): rfm_dst_tensorDD[i][j] += Jac_dUCart_dDrfmUD[l][i]*Jac_dUCart_dDrfmUD[m][j]*Cart_src_tensorDD[l][m] return rfm_dst_tensorDD ################################################## def get_EigenCoord(): CoordSystem_orig = par.parval_from_str("reference_metric::CoordSystem") for EigenCoordstr in ["Spherical", "Cylindrical", "SymTP", "Cartesian"]: if EigenCoordstr in CoordSystem_orig: return EigenCoordstr print("Error: Could not find EigenCoord for reference_metric::CoordSystem == "+CoordSystem_orig) sys.exit(1) # Compute proper distance in all 3 directions. Used to find the appropriate timestep for the CFL condition. def ds_dirn(delxx, append_gridsuffix_to_xx=False): gridsuffix = "" # Disable for now scalefactor_orthog_inj = [] for i in range(3): if append_gridsuffix_to_xx: scalefactor_orthog_inj.append(scalefactor_orthog[i]. subs(xx[0], sp.sympify(str(xx[0]) + gridsuffix)). subs(xx[1], sp.sympify(str(xx[1]) + gridsuffix)). subs(xx[2], sp.sympify(str(xx[2]) + gridsuffix))) else: scalefactor_orthog_inj.append(scalefactor_orthog[i]) ds_dirn = ixp.zerorank1(3) for i in range(3): ds_dirn[i] = delxx[i]*scalefactor_orthog_inj[i] return ds_dirn # Find the appropriate timestep for the CFL condition. def add_to_Cfunction_dict__find_timestep(rel_path_to_Cparams=os.path.join("./"), enable_mask=False, output_dt_local_h_only=False, use_unit_wavespeed=False): gridsuffix = "" # Disable for now ############################## # Step 1: Function description desc = "Find the CFL-constrained timestep" ############################## # Step 2: Function return type c_type = "REAL" ############################## # Step 3: Function name name = "find_timestep" + gridsuffix ############################## # Step 4: Prior to the main loop preloop = " REAL dsmin = 1e38; // Start with a crazy high value... close to the largest number in single precision." ############################## # Step 5: Loop options loopopts = "InteriorPoints,Read_xxs,DisableOpenMP" if gridsuffix != "": loopopts += ","+gridsuffix ############################## # Step 6: function input parameters params = "const paramstruct *restrict params, REAL *restrict xx[3], const REAL CFL_FACTOR" if enable_mask: params += ", int8_t *restrict mask" ############################## # Step 7: function body # Compute proper distance in all 3 directions. if output_dt_local_h_only: ds_drn = ds_dirn(gri.dxx, append_gridsuffix_to_xx=True) else: ds_drn = ds_dirn(gri.dxx, append_gridsuffix_to_xx=False) ds_dirn_h = outputC([ds_drn[0], ds_drn[1], ds_drn[2]], ["ds_dirn0", "ds_dirn1", "ds_dirn2"], "returnstring").\ replace("dxx0", "dxx0"+gridsuffix).\ replace("dxx1", "dxx1"+gridsuffix).\ replace("dxx2", "dxx2"+gridsuffix) body = "REAL ds_dirn0, ds_dirn1, ds_dirn2;\n" + ds_dirn_h + """ #ifndef MIN #define MIN(A, B) ( ((A) < (B)) ? (A) : (B) ) #endif\n""" indent = "" if enable_mask: body += "if(mask[IDX3S" + gridsuffix + "(i0,i1,i2)] >= 0) {\n" indent = " " if not output_dt_local_h_only: # not output_dt_local_h_only -> seeking dsmin over the entire grid, over all directions body += indent + "// Set dsmin = MIN(dsmin, ds_dirn0, ds_dirn1, ds_dirn2):\n" body += indent + "dsmin = MIN(dsmin, MIN(ds_dirn0, MIN(ds_dirn1, ds_dirn2)));\n" else: # output_dt_local_h_only means we seek a minimum over all directions at given gridpoint only body += indent + "// Set dt_local["+gridsuffix.replace("_grid", "")+"] = MIN(ds_dirn0, ds_dirn1, ds_dirn2) * CFL_FACTOR/wavespeed :\n" body += indent + "dt_local["+gridsuffix.replace("_grid", "")+"] = MIN(ds_dirn0, MIN(ds_dirn1, ds_dirn2)) * CFL_FACTOR/wavespeed;\n" if use_unit_wavespeed: body = body.replace("wavespeed", "1.0") if enable_mask: body += "}\n" if output_dt_local_h_only: return body.\ replace("ds_dirn0", "ds_dirn0"+gridsuffix).\ replace("ds_dirn1", "ds_dirn1"+gridsuffix).\ replace("ds_dirn2", "ds_dirn2"+gridsuffix) ############################## # Step 8: after the loop postloop = " return dsmin*CFL_FACTOR/wavespeed;\n" if use_unit_wavespeed: postloop = postloop.replace("wavespeed", "1.0") ############################## # Step 9: add to Cfunction dictionary add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc =desc, c_type =c_type, name =name, params =params, preloop =preloop, body =body, loopopts=loopopts, postloop=postloop, rel_path_to_Cparams=rel_path_to_Cparams) # Find the appropriate timestep for the CFL condition. def add_to_Cfunc_dict__find_dsmin(rel_path_to_Cparams=os.path.join("./")): gridsuffix = "" # Disable for now desc = "Find dsmin = min_i sqrt(ghat_{ii} dx^i dx^i)" c_type = "REAL" name = "find_dsmin" + gridsuffix params = "const paramstruct *restrict params, const int i0i1i2[3], const REAL *restrict xx[3]" body = """ REAL dsmin = 1e38; // Start with a crazy high value... close to the largest number in single precision. const REAL xx0 = xx[0][i0i1i2[0]]; const REAL xx1 = xx[1][i0i1i2[1]]; const REAL xx2 = xx[2][i0i1i2[2]]; """ # Compute proper distance in all 3 directions. ds_drn = ds_dirn(gri.dxx, append_gridsuffix_to_xx=False) body += " REAL ds_dirn0, ds_dirn1, ds_dirn2;\n" body += outputC([ds_drn[0], ds_drn[1], ds_drn[2]], ["ds_dirn0", "ds_dirn1", "ds_dirn2"], "returnstring", params="outCverbose=false,includebraces=false").\ replace("dxx0", "dxx0"+gridsuffix).\ replace("dxx1", "dxx1"+gridsuffix).\ replace("dxx2", "dxx2"+gridsuffix) body += """ #ifndef MIN #define MIN(A, B) ( ((A) < (B)) ? (A) : (B) ) #endif\n""" indent = "" body += indent + " // Set dsmin = MIN(dsmin, ds_dirn0, ds_dirn1, ds_dirn2):\n" body += indent + " return MIN(dsmin, MIN(ds_dirn0, MIN(ds_dirn1, ds_dirn2)));\n" add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc =desc, c_type =c_type, name =name, params =params, body =body, rel_path_to_Cparams=rel_path_to_Cparams) def out_default_free_parameters_for_rfm(free_parameters_file, domain_size=1.0,sinh_width=0.4,sinhv2_const_dr=0.05,SymTP_bScale=0.5): CoordSystem = par.parval_from_str("reference_metric::CoordSystem") with open(free_parameters_file, "a") as file: file.write(""" // Set free-parameter values. const REAL domain_size = """ + str(domain_size) + """; const REAL sinh_width = """ + str(sinh_width) + """; const REAL sinhv2_const_dr= """ + str(sinhv2_const_dr) + """; const REAL SymTP_bScale = """ + str(SymTP_bScale) + ";\n") coordparams = "" if CoordSystem == "Spherical": coordparams += """ params.RMAX = domain_size;\n""" elif "SinhSpherical" in CoordSystem: coordparams += """ params.AMPL = domain_size; params.SINHW= sinh_width;\n""" if CoordSystem == "SinhSphericalv2": coordparams += " params.const_dr = sinhv2_const_dr;\n" elif "SymTP" in CoordSystem: coordparams += """ params.bScale = SymTP_bScale; params.AMAX = domain_size;\n""" if CoordSystem == "SinhSymTP": coordparams += " params.SINHWAA = sinh_width;\n" elif CoordSystem == "Cartesian": coordparams += """ params.xmin = -domain_size, params.xmax = domain_size; params.ymin = -domain_size, params.ymax = domain_size; params.zmin = -domain_size, params.zmax = domain_size;\n""" elif CoordSystem =="SinhCartesian": coordparams += """ params.AMPLX = domain_size; params.SINHWX = sinh_width; params.AMPLY = domain_size; params.SINHWY = sinh_width; params.AMPLZ = domain_size; params.SINHWZ = sinh_width;\n""" elif CoordSystem == "Cylindrical": coordparams += """ params.ZMIN = -domain_size; params.ZMAX = domain_size; params.RHOMAX = domain_size;\n""" elif "SinhCylindrical" in CoordSystem: coordparams += """ params.AMPLRHO = domain_size; params.SINHWRHO= sinh_width; params.AMPLZ = domain_size; params.SINHWZ = sinh_width;\n""" if CoordSystem == "SinhCylindricalv2": coordparams += """ params.const_drho = sinhv2_const_dr; params.const_dz = sinhv2_const_dr;\n""" file.write(coordparams + "\n") ############################ ## TO BE DEPRECATED: def set_Nxx_dxx_invdx_params__and__xx_h(outdir=".",grid_centering="cell"): if grid_centering not in ('cell', 'vertex'): print("rfm.set_Nxx_dxx_invdx_params__and__xx_h(): grid_centering = \""+grid_centering+"\" not supported!") sys.exit(1) with open(os.path.join(outdir,"set_Nxx_dxx_invdx_params__and__xx.h"),"w") as file: file.write(r""" void set_Nxx_dxx_invdx_params__and__xx(const int EigenCoord, const int Nxx[3], paramstruct *restrict params, REAL *restrict xx[3]) { // Override parameter defaults with values based on command line arguments and NGHOSTS. params->Nxx0 = Nxx[0]; params->Nxx1 = Nxx[1]; params->Nxx2 = Nxx[2]; params->Nxx_plus_2NGHOSTS0 = Nxx[0] + 2*NGHOSTS; params->Nxx_plus_2NGHOSTS1 = Nxx[1] + 2*NGHOSTS; params->Nxx_plus_2NGHOSTS2 = Nxx[2] + 2*NGHOSTS; // Step 0d: Set up space and time coordinates // Step 0d.i: Declare \Delta x^i=dxx{0,1,2} and invdxx{0,1,2}, as well as xxmin[3] and xxmax[3]: #include "set_Cparameters.h" REAL xxmin[3],xxmax[3]; if(EigenCoord == 0) { """) for i in range(3): file.write(" xxmin["+str(i)+"] = "+str(xxmin[i])+";\n") file.write(" xxmax["+str(i)+"] = "+str(xxmax[i])+";\n") file.write(""" } else { // if (EigenCoord == 1) """) CoordSystem_orig = par.parval_from_str("reference_metric::CoordSystem") par.set_parval_from_str("reference_metric::CoordSystem",get_EigenCoord()) reference_metric() for i in range(3): file.write(" xxmin["+str(i)+"] = "+str(xxmin[i])+";\n") file.write(" xxmax["+str(i)+"] = "+str(xxmax[i])+";\n") par.set_parval_from_str("reference_metric::CoordSystem",CoordSystem_orig) reference_metric() file.write(""" } params->dxx0 = (xxmax[0] - xxmin[0]) / ((REAL)Nxx[0]); params->dxx1 = (xxmax[1] - xxmin[1]) / ((REAL)Nxx[1]); params->dxx2 = (xxmax[2] - xxmin[2]) / ((REAL)Nxx[2]); params->invdx0 = 1.0/params->dxx0; params->invdx1 = 1.0/params->dxx1; params->invdx2 = 1.0/params->dxx2;\n""") # The following capability was suggested by Terrence Pierre Jacques (Thanks!) cell_offset = "(1.0/2.0)" # default cell-centered offset cell_comment = "Cell-centered grid." if grid_centering == "vertex": cell_offset = "0.0" cell_comment = "Vertex-centered grid." file.write(""" // Now that params.dxx{0,1,2} and params.invdxx{0,1,2} have been set, // Step 0d.iii: Set up uniform coordinate grids xx[0] = (REAL *)malloc(sizeof(REAL)*Nxx_plus_2NGHOSTS0); for(int j=0;j<Nxx_plus_2NGHOSTS0;j++) xx[0][j] = xxmin[0] + ((REAL)(j-NGHOSTS) + """+cell_offset+""")*params->dxx0; // """+cell_comment+""" xx[1] = (REAL *)malloc(sizeof(REAL)*Nxx_plus_2NGHOSTS1); for(int j=0;j<Nxx_plus_2NGHOSTS1;j++) xx[1][j] = xxmin[1] + ((REAL)(j-NGHOSTS) + """+cell_offset+""")*params->dxx1; // """+cell_comment+""" xx[2] = (REAL *)malloc(sizeof(REAL)*Nxx_plus_2NGHOSTS2); for(int j=0;j<Nxx_plus_2NGHOSTS2;j++) xx[2][j] = xxmin[2] + ((REAL)(j-NGHOSTS) + """+cell_offset+""")*params->dxx2; // """+cell_comment+""" //fprintf(stderr,"hey inside setxx: %e %e %e | %e %e\\n",xxmin[0],xxmin[1],xxmin[2],xx[0][0],params->dxx0); } """) def xx_to_Cart_h(funcname,cparamsloc,outfile): # Arbitrary-coordinate NRPy+ file output, Part 1: output the conversion from (x0,x1,x2) to Cartesian (x,y,z) Cout = outputC([xx_to_Cart[0],xx_to_Cart[1],xx_to_Cart[2]], ["xCart[0]","xCart[1]","xCart[2]"], "returnstring",params="preindent=1") with open(outfile, "w") as file: file.write(""" static inline void """+funcname+"""(const paramstruct *restrict params, REAL *restrict xx[3],const int i0,const int i1,const int i2, REAL xCart[3]) { #include """+"\""+cparamsloc+"\""+""" REAL xx0 = xx[0][i0]; REAL xx1 = xx[1][i1]; REAL xx2 = xx[2][i2];\n"""+Cout+"}\n") ####################### ## C FUNCTIONS RELATED TO REFERENCE METRIC # Construct Cart_to_xx_and_nearest_i0i1i2() C function for # mapping from Cartesian->xx for the chosen CoordSystem. def add_to_Cfunc_dict__Cart_to_xx_and_nearest_i0i1i2(rel_path_to_Cparams=os.path.join("./"), relative_to="local_grid_center"): gridsuffix = "" # Disable for now CoordSystem = par.parval_from_str("reference_metric::CoordSystem") prefunc = "" desc = """Given Cartesian point (x,y,z), this function outputs the corresponding (xx0,xx1,xx2) and the "closest" (i0,i1,i2) for the given grid""" namesuffix = "" if relative_to == "global_grid_center": namesuffix = "_" + relative_to name = "Cart_to_xx_and_nearest_i0i1i2" + namesuffix + gridsuffix params = "const paramstruct *restrict params, const REAL xCart[3], REAL xx[3], int Cart_to_i0i1i2[3]" preloop = "" if relative_to == "local_grid_center": preloop = """ // First compute the closest (xx0,xx1,xx2) to the given Cartesian gridpoint (x,y,z), // *relative* to the center of the local grid. // So for example, // 1) if global xCart[012] = (1,1,1), and the // origin of the grid is at global xCart (x,y,z) = (1,1,1), then // (Cartx,Carty,Cartz) = (0,0,0) // 2) if global xCart[012] = (0,0,0), and the // origin of the grid is at global xCart (x,y,z) = (1,1,1), then // (Cartx,Carty,Cartz) = (-1,-1,-1) // Therefore, (Cartx,Carty,Cartz) = (xCart[0]-originx, xCart[1]-originy, xCart[2]-originz) const REAL Cartx = xCart[0] - Cart_originx_GRIDSFX_; const REAL Carty = xCart[1] - Cart_originy_GRIDSFX_; const REAL Cartz = xCart[2] - Cart_originz_GRIDSFX_; """.replace("_GRIDSFX_", gridsuffix) elif relative_to == "global_grid_center": preloop = """ const REAL Cartx = xCart[0]; const REAL Carty = xCart[1]; const REAL Cartz = xCart[2]; """ else: print("Error: relative_to must be set to either local_grid_center or global_grid_center. " + relative_to + " was chosen.") sys.exit(1) if "theta_adj" in CoordSystem: body = outputC([Cart_to_xx[0], Cart_to_xx[1], Cart_to_xx[2]], ["xx[0]", "const REAL target_th", "xx[2]"], "returnstring", params="includebraces=False,preindent=1") body += " xx[1] = NewtonRaphson_get_xx1_from_th(params, target_th);\n" else: body = outputC([Cart_to_xx[0], Cart_to_xx[1], Cart_to_xx[2]], ["xx[0]", "xx[1]", "xx[2]"], "returnstring", params="includebraces=False,preindent=1") body += """ // Then find the nearest index (i0,i1,i2) on underlying grid to (x,y,z) // Recall that: // xx[0][j] = xxmin[0] + ((REAL)(j-NGHOSTS) + (1.0/2.0))*params->dxx0"""+gridsuffix+"""; // Cell-centered grid. // --> j = (int) ( (xx[0][j] - xxmin[0]) / params->dxx0"""+gridsuffix+""" + (1.0/2.0) + NGHOSTS ) Cart_to_i0i1i2[0] = (int)( ( xx[0] - ("""+str(xxmin[0])+""") ) / params->dxx0"""+gridsuffix+""" + (1.0/2.0) + NGHOSTS - 0.5 ); // Account for (int) typecast rounding down Cart_to_i0i1i2[1] = (int)( ( xx[1] - ("""+str(xxmin[1])+""") ) / params->dxx1"""+gridsuffix+""" + (1.0/2.0) + NGHOSTS - 0.5 ); // Account for (int) typecast rounding down Cart_to_i0i1i2[2] = (int)( ( xx[2] - ("""+str(xxmin[2])+""") ) / params->dxx2"""+gridsuffix+""" + (1.0/2.0) + NGHOSTS - 0.5 ); // Account for (int) typecast rounding down """ add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], prefunc=prefunc, desc =desc, c_type ="void", name =name, params =params, preloop=preloop, body =body, rel_path_to_Cparams=rel_path_to_Cparams) def add_to_Cfunc_dict_set_Nxx_dxx_invdx_params__and__xx(rel_path_to_Cparams=os.path.join("./"), NGHOSTS_is_a_param=False): gridsuffix = "" # Disable for now. def set_xxmin_xxmax(): outstr = "" for dirn in range(3): outstr += " xxmin[" + str(dirn) + "] = " + str(xxmin[dirn]) + ";\n" outstr += " xxmax[" + str(dirn) + "] = " + str(xxmax[dirn]) + ";\n" return outstr body = """ // Override parameter defaults with values based on command line arguments and NGHOSTS. params->Nxx0""" + gridsuffix + r""" = Nxx[0]; params->Nxx1""" + gridsuffix + r""" = Nxx[1]; params->Nxx2""" + gridsuffix + r""" = Nxx[2]; """ NGHOSTS_prefix="" if NGHOSTS_is_a_param: NGHOSTS_prefix="params->" body += """ params->Nxx_plus_2NGHOSTS0""" + gridsuffix + """ = Nxx[0] + 2*"""+NGHOSTS_prefix+"""NGHOSTS; params->Nxx_plus_2NGHOSTS1""" + gridsuffix + """ = Nxx[1] + 2*"""+NGHOSTS_prefix+"""NGHOSTS; params->Nxx_plus_2NGHOSTS2""" + gridsuffix + """ = Nxx[2] + 2*"""+NGHOSTS_prefix+"""NGHOSTS; // Now that params->Nxx_plus_2NGHOSTS* has been set, and below we need e.g., Nxx_plus_2NGHOSTS*, we include set_Cparameters.h here: #include \"""" + os.path.join(rel_path_to_Cparams, "set_Cparameters.h") + """\" // Step 0d: Set up space and time coordinates // Step 0d.i: Declare Delta x^i=dxx{0,1,2} and invdxx{0,1,2}, as well as xxmin[3] and xxmax[3]: REAL xxmin[3],xxmax[3]; if(EigenCoord == 0) { """ + set_xxmin_xxmax() + """ } else { // if (EigenCoord == 1) """ CoordSystem_orig = par.parval_from_str("reference_metric::CoordSystem") # If we are using a "holey" Spherical-like coordinate, for certain grids xx0min>0 is # such that xx[0][0] is negative, which causes "Cartesian disagreement" errors. if "Spherical" not in CoordSystem_orig: par.set_parval_from_str("reference_metric::CoordSystem", get_EigenCoord()) reference_metric() body += set_xxmin_xxmax() par.set_parval_from_str("reference_metric::CoordSystem", CoordSystem_orig) reference_metric() else: body += set_xxmin_xxmax() # Now set grid spacing dxx, invdx = 1/dxx, and xx[] body += """ } // Step 0d.iii: Set params.dxx{0,1,2}, params.invdx{0,1,2}, and uniform coordinate grids xx[3][] """ for dirn in ["0", "1", "2"]: body += " params->dxx"+dirn+gridsuffix+" = (xxmax["+dirn+"] - xxmin["+dirn+"]) / ((REAL)Nxx["+dirn+"]);\n" body += " params->invdx"+dirn+gridsuffix+" = 1.0/params->dxx"+dirn+gridsuffix+";\n" body += """ xx["""+dirn+"""] = (REAL *)malloc(sizeof(REAL)*Nxx_plus_2NGHOSTS"""+dirn+gridsuffix + """); for(int j=0;j<Nxx_plus_2NGHOSTS"""+dirn+gridsuffix+""";j++) xx["""+dirn+"""][j] = xxmin["""+dirn+"""] + ((REAL)(j-NGHOSTS) + (1.0/2.0))*params->dxx"""+dirn+gridsuffix+"""; // Cell-centered grid.\n""" if dirn != "2": body += "\n" add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc ="Override default values for Nxx{0,1,2}, Nxx_plus_2NGHOSTS{0,1,2}, dxx{0,1,2}, and invdx{0,1,2}; and set xx[3][]", c_type="void", name ="set_Nxx_dxx_invdx_params__and__xx"+gridsuffix, params="const int EigenCoord, const int Nxx[3],paramstruct *restrict params, REAL *restrict xx[3]", body =body, enableCparameters=False, uses_rfm=True) # Cparameters here must be #include'd in body, not at top of function as usual. def add_to_Cfunc_dict_xx_to_Cart(rel_path_to_Cparams=os.path.join("./")): gridsuffix = "" # Disable for now # Arbitrary-coordinate NRPy+ file output, Part 1: output the conversion from (x0,x1,x2) to Cartesian (x,y,z) # Suppose grid origin is at 1,1,1. Then the Cartesian gridpoint at 1,2,3 will be 2,3,4; hence # the xx_to_Cart[i]+gri.Cart_origin[i] below: body = """ REAL xx0 = xx[0][i0]; REAL xx1 = xx[1][i1]; REAL xx2 = xx[2][i2]; """ + outputC([xx_to_Cart[0]+gri.Cart_origin[0], xx_to_Cart[1]+gri.Cart_origin[1], xx_to_Cart[2]+gri.Cart_origin[2]], ["xCart[0]", "xCart[1]", "xCart[2]"], "returnstring", params="preindent=1"). \ replace("Cart_originx", "Cart_originx" + gridsuffix).\ replace("Cart_originy", "Cart_originy" + gridsuffix).\ replace("Cart_originz", "Cart_originz" + gridsuffix) add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc ="Compute Cartesian coordinates given local grid coordinate (xx0,xx1,xx2), " " accounting for the origin of this grid being possibly offcenter.", c_type ="void", name ="xx_to_Cart"+gridsuffix, params ="const paramstruct *restrict params, REAL *restrict xx[3],const int i0,const int i1,const int i2, REAL xCart[3]", body =body, rel_path_to_Cparams=rel_path_to_Cparams) # Find the appropriate timestep for the CFL condition. def add_to_Cfunction_dict_find_timestep(): # Compute proper distance in all 3 directions. delxx = ixp.declarerank1("dxx", DIM=3) ds_drn = ds_dirn(delxx) ds_dirn_h = outputC([ds_drn[0], ds_drn[1], ds_drn[2]], ["ds_dirn0", "ds_dirn1", "ds_dirn2"],"returnstring") desc="Find the CFL-constrained timestep" add_to_Cfunction_dict( desc =desc, c_type ="REAL", name ="find_timestep", params ="const paramstruct *restrict params, REAL *restrict xx[3]", preloop ="REAL dsmin = 1e38; // Start with a crazy high value... close to the largest number in single precision.", body ="REAL ds_dirn0, ds_dirn1, ds_dirn2;\n"+ds_dirn_h+""" #ifndef MIN #define MIN(A, B) ( ((A) < (B)) ? (A) : (B) ) #endif // Set dsmin = MIN(dsmin, ds_dirn0, ds_dirn1, ds_dirn2); dsmin = MIN(dsmin,MIN(ds_dirn0,MIN(ds_dirn1,ds_dirn2))); """, loopopts ="InteriorPoints,Read_xxs,DisableOpenMP", postloop ="return dsmin*CFL_FACTOR/wavespeed;\n") def out_timestep_func_to_file(outfile): add_to_Cfunction_dict_find_timestep() with open(outfile, "w") as file: file.write(outC_function_dict["find_timestep"]) def register_C_functions_and_NRPy_basic_defines(rel_path_to_Cparams=os.path.join("./"), enable_rfm_precompute=False, use_unit_wavespeed_for_find_timestep=False): add_to_Cfunction_dict__find_timestep(rel_path_to_Cparams=rel_path_to_Cparams, enable_mask=False, output_dt_local_h_only=False, use_unit_wavespeed=use_unit_wavespeed_for_find_timestep) add_to_Cfunc_dict_xx_to_Cart(rel_path_to_Cparams=rel_path_to_Cparams) add_to_Cfunc_dict_set_Nxx_dxx_invdx_params__and__xx(rel_path_to_Cparams=rel_path_to_Cparams) for frame in "local", "global": add_to_Cfunc_dict__Cart_to_xx_and_nearest_i0i1i2(rel_path_to_Cparams=rel_path_to_Cparams, relative_to=frame + "_grid_center") if enable_rfm_precompute: if par.parval_from_str(thismodule+"::rfm_precompute_to_Cfunctions_and_NRPy_basic_defines") == "True": # global rfm_struct__malloc, rfm_struct__define, rfm_struct__freemem add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc="Reference Metric Precomputation infrastructure: Allocate memory for rfmstruct", c_type="void", name="rfm_precompute_rfmstruct_malloc", params="const paramstruct *restrict params, rfm_struct *restrict rfmstruct", body=indent_Ccode(rfm_struct__malloc.replace("rfmstruct.", "rfmstruct->")), rel_path_to_Cparams=rel_path_to_Cparams) add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc="Reference Metric Precomputation infrastructure: Define rfmstruct", c_type="void", name="rfm_precompute_rfmstruct_define", params="const paramstruct *restrict params, REAL *restrict xx[3], rfm_struct *restrict rfmstruct", body=indent_Ccode(rfm_struct__define.replace("rfmstruct.", "rfmstruct->")), rel_path_to_Cparams=rel_path_to_Cparams) add_to_Cfunction_dict( includes=[os.path.join(rel_path_to_Cparams, "NRPy_basic_defines.h")], desc="Reference Metric Precomputation infrastructure: Free rfmstruct memory", c_type="void", name="rfm_precompute_rfmstruct_freemem", params="const paramstruct *restrict params, rfm_struct *restrict rfmstruct", body=indent_Ccode(rfm_struct__freemem.replace("rfmstruct.", "rfmstruct->")), rel_path_to_Cparams=rel_path_to_Cparams) outC_NRPy_basic_defines_h_dict["reference_metric"] = NRPy_basic_defines_str else: outC_NRPy_basic_defines_h_dict["reference_metric"] = """#include "rfm_files/rfm_struct__declare.h"\n"""

Harmohit-Singh/nrpytutorial

reference_metric.py

Python

if __name__ == "__main__": assert currentProgram is None assert state.getProject() is not None print("programless_script executed successfully")

Defense-Cyber-Crime-Center/pyhidra

tests/programless_script.py

Python

from ..config import config import logging import os import shutil import subprocess from subprocess import CalledProcessError, Popen from time import sleep logging.basicConfig( level=config.log_level, format='%(asctime)s | %(levelname)s | %(message)s') PORT = config.port def rm_gen_dir(): try: shutil.rmtree('generated-tests') except(FileNotFoundError): pass def deps_installed(): return shutil.which('oatts') is not None and shutil.which('mocha') is not None def run_oatts(): logging.info('Running oatts tests...') if not deps_installed(): logging.error('oatts is not installed! See the README.') exit(0) rm_gen_dir() try: subprocess.run(['oatts', 'generate', '-w', 'generated-tests', '-s', 'swagger/api.spec.yaml', '--host', 'localhost:{}'.format(PORT), '--customValuesFile', 'test/values.json'], check=True) subprocess.run( ['mocha', '--recursive', 'generated-tests'], check=True) except(CalledProcessError): logging.error('oatts tests failed!') finally: rm_gen_dir() server_process = None def start_server(): global server_process env = os.environ.copy() env['INSIGHTS_CONNEXION_ENV'] = 'test' server_process = Popen(['pipenv', 'run', 'server'], env=env) sleep(5) def test(): try: logging.info('Testing...') start_server() run_oatts() logging.info('Testing is done') except() as err: logging.error(err) finally: server_process.terminate()

fijshion/insights_connexion

insights_connexion/test/oatts.py

Python

import os import numpy as np import random from nn_activations import sigmoid, sigmoid_prime class NeuralNetwork(object): def __init__(self, sizes=list(), learning_rate=1.0, mini_batch_size=16, epochs=10): """Initialize a Neural Network model. Parameters ---------- sizes : list, optional A list of integers specifying number of neurns in each layer. Not required if a pretrained model is used. learning_rate : float, optional Learning rate for gradient descent optimization. Defaults to 1.0 mini_batch_size : int, optional Size of each mini batch of training examples as used by Stochastic Gradient Descent. Denotes after how many examples the weights and biases would be updated. Default size is 16. """ # Input layer is layer 0, followed by hidden layers layer 1, 2, 3... self.sizes = sizes self.num_layers = len(sizes) # First term corresponds to layer 0 (input layer). No weights enter the # input layer and hence self.weights[0] is redundant. self.weights = [np.array([0])] + [np.random.randn(y, x) for y, x in zip(sizes[1:], sizes[:-1])] # Input layer does not have any biases. self.biases[0] is redundant. self.biases = [np.random.randn(y, 1) for y in sizes] # Input layer has no weights, biases associated. Hence z = wx + b is not # defined for input layer. self.zs[0] is redundant. self._zs = [np.zeros(bias.shape) for bias in self.biases] # Training examples can be treated as activations coming out of input # layer. Hence self.activations[0] = (training_example). self._activations = [np.zeros(bias.shape) for bias in self.biases] self.mini_batch_size = mini_batch_size self.epochs = epochs self.eta = learning_rate def fit(self, training_data, validation_data=None): """Fit (train) the Neural Network on provided training data. Fitting is carried out using Stochastic Gradient Descent Algorithm. Parameters ---------- training_data : list of tuple A list of tuples of numpy arrays, ordered as (image, label). validation_data : list of tuple, optional Same as `training_data`, if provided, the network will display validation accuracy after each epoch. """ for epoch in range(self.epochs): random.shuffle(training_data) mini_batches = [ training_data[k:k + self.mini_batch_size] for k in range(0, len(training_data), self.mini_batch_size)] for mini_batch in mini_batches: nabla_b = [np.zeros(bias.shape) for bias in self.biases] nabla_w = [np.zeros(weight.shape) for weight in self.weights] for x, y in mini_batch: self._forward_prop(x) delta_nabla_b, delta_nabla_w = self._back_prop(x, y) nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)] self.weights = [ w - (self.eta / self.mini_batch_size) * dw for w, dw in zip(self.weights, nabla_w)] self.biases = [ b - (self.eta / self.mini_batch_size) * db for b, db in zip(self.biases, nabla_b)] if validation_data: accuracy = self.validate(validation_data) / 100.0 print("Epoch {0}, accuracy {1} %.".format(epoch + 1, accuracy)) else: print("Processed epoch {0}.".format(epoch)) def validate(self, validation_data): """Validate the Neural Network on provided validation data. It uses the number of correctly predicted examples as validation accuracy metric. Parameters ---------- validation_data : list of tuple Returns ------- int Number of correctly predicted images. """ validation_results = [(self.predict(x) == y) for x, y in validation_data] return sum(result for result in validation_results) def predict(self, x): """Predict the label of a single test example (image). Parameters ---------- x : numpy.array Returns ------- int Predicted label of example (image). """ self._forward_prop(x) return np.argmax(self._activations[-1]) def _forward_prop(self, x): self._activations[0] = x for i in range(1, self.num_layers): self._zs[i] = ( self.weights[i].dot(self._activations[i - 1]) + self.biases[i] ) self._activations[i] = sigmoid(self._zs[i]) def _back_prop(self, x, y): nabla_b = [np.zeros(bias.shape) for bias in self.biases] nabla_w = [np.zeros(weight.shape) for weight in self.weights] error = (self._activations[-1] - y) * sigmoid_prime(self._zs[-1]) nabla_b[-1] = error nabla_w[-1] = error.dot(self._activations[-2].transpose()) for l in range(self.num_layers - 2, 0, -1): error = np.multiply( self.weights[l + 1].transpose().dot(error), sigmoid_prime(self._zs[l]) ) nabla_b[l] = error nabla_w[l] = error.dot(self._activations[l - 1].transpose()) return nabla_b, nabla_w def load(self, filename='model.npz'): """Prepare a neural network from a compressed binary containing weights and biases arrays. Size of layers are derived from dimensions of numpy arrays. Parameters ---------- filename : str, optional Name of the ``.npz`` compressed binary in models directory. """ npz_members = np.load(os.path.join(os.curdir, 'models', filename)) self.weights = list(npz_members['weights']) self.biases = list(npz_members['biases']) # Bias vectors of each layer has same length as the number of neurons # in that layer. So we can build `sizes` through biases vectors. self.sizes = [b.shape[0] for b in self.biases] self.num_layers = len(self.sizes) # These are declared as per desired shape. self._zs = [np.zeros(bias.shape) for bias in self.biases] self._activations = [np.zeros(bias.shape) for bias in self.biases] # Other hyperparameters are set as specified in model. These were cast # to numpy arrays for saving in the compressed binary. self.mini_batch_size = int(npz_members['mini_batch_size']) self.epochs = int(npz_members['epochs']) self.eta = float(npz_members['eta']) def save(self, filename='model.npz'): """Save weights, biases and hyperparameters of neural network to a compressed binary. This ``.npz`` binary is saved in 'models' directory. Parameters ---------- filename : str, optional Name of the ``.npz`` compressed binary in to be saved. """ np.savez_compressed( file=os.path.join(os.curdir, 'models', filename), weights=self.weights, biases=self.biases, mini_batch_size=self.mini_batch_size, epochs=self.epochs, eta=self.eta )

fredwangwang/webcam-sudoku-solver

src/nn_model.py

Python

import numpy as np import logging import os, errno from datetime import datetime from ..abstract import Environment from maddux.environment import Environment from maddux.objects import Ball from maddux.robots import simple_human_arm class RobotArm(Environment): def __init__(self, env, training_directory, config): self.discrete = False self.training_directory = training_directory self.config = config self.env = env self.arm = self.env.robot self.current = self.arm.end_effector_position() self.ball = self.env.dynamic_objects[0] self.target = self.ball.position self.static_objects = self.env.static_objects # self.recording_queue = [] logging.info("Robot Arm: End effector starts at {}".format(self.current)) logging.info("Target: Ball at {}".format(self.target)) def reset(self): """ Reset current position to beginning. """ self.arm.reset() self.current = self.arm.end_effector_position() def act(self, location, population, params, master): """ Move end effector to the given location """ valid = True past = self.current self.current = location if population % self.config['record_iterations'] == 0 and master: print("Recording") try: self.arm.ikine(location) timestamp = datetime.now().strftime("%m-%d-%Y_%H-%M-%S") training_path = self.training_directory + "/records/" try: os.makedirs(training_path) except OSError as e: if e.errno != errno.EEXIST: raise record_path = training_path + "pop_" + str(population) + ".npy" video_path = training_path + "pop_" + str(population) + ".mp4" self.arm.save_path(record_path) self.env.animate(duration=5.0, save_path=video_path) np.save(training_path + "net_" + str(population), params) # self.recording_queue.append(record_path) except ValueError as e: valid = False logging.warn("Could not solve IK for position: {}". format(location[0])) # logging.info("Current Position: {}".format(self.current)) return valid def inputs(self, t): """ Return the inputs for the neural network """ inputs = [self.current[0], self.current[1], self.current[2], self.target[0], self.target[1], self.target[2], t+1] return inputs def reward_params(self, valid): """ Return the parameters for the proposed reward function """ # params = [(self.current, self.target), (self.current, self.static_objects)] params = (self.current, self.target) return params def pre_processing(self): """ Complete any pending post processing tasks """ pass def post_processing(self): """ Complete any pending post processing tasks """ # logging.debug("Recording Videos") # for path in self.recording_queue: # self.env.animate(duration=5.0, save_path=path) # logging.debug("Completed recording all videos") pass def reached_target(self): return np.linalg.norm(self.current - self.target) < 3

callaunchpad/MOR

environments/robot_arm/robot_arm.py

Python

""" This module enables the clustering of DataFrame headers into like clusters based on correlations between columns """ from typing import List import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy.cluster.hierarchy as sch from gretel_synthetics.utils import stats LEFT = 0 RIGHT = 1 def _get_correlation_matrix(df, numeric_cat: List[str] = None): if numeric_cat is None: numeric_cat = [] nominal_columns = list(df.select_dtypes(include=["object", "category"]).columns) nominal_columns.extend(x for x in numeric_cat if x not in nominal_columns) corr_matrix = stats.calculate_correlation(df, nominal_columns=nominal_columns) return corr_matrix def _get_leaves(tree, node, totcolcnt): return_list = [] stack = [] curr = node def _walk(node: int, side: int, save=False): # If it's a leaf, return a list with this leaf if node < totcolcnt: if save: return_list.append(node) return None # else perculate else: node = int(node - totcolcnt) child = int(tree[node][side]) return child while True: if curr is not None: stack.append(curr) curr = _walk(curr, LEFT) elif stack: curr = stack.pop() curr = _walk(curr, RIGHT, save=True) else: break return return_list def _traverse_node(tree, node, maxsize, totcolcnt): stack = [] node_list = [] curr = node def _walk(node: int, side: int): child = int(tree[node][side]) child_size = 1 idx = 0 if child > totcolcnt: idx = child - totcolcnt child_size = tree[idx][3] if child_size > maxsize: return idx else: node_list.append(_get_leaves(tree, child, totcolcnt)) return None while True: if curr is not None: stack.append(curr) curr = _walk(curr, LEFT) elif stack: curr = stack.pop() curr = _walk(curr, RIGHT) else: break return node_list def _merge_clusters( clusters: List[List[int]], maxlen: int, columns: List[str], Lopt, plot=False ) -> List[List[str]]: out = [] tmp = [] cluster_map = {} # maps a column name => cluster number cluster_number = 0 for cluster in clusters: # if the size of adding the next cluster # exceeds the max size, flush if len(tmp) + len(cluster) > maxlen: for column_name in tmp: cluster_map[column_name] = cluster_number out.append(tmp) tmp = [] cluster_number += 1 tmp.extend( [columns[idx] for idx in cluster] ) # build the tmp with the actual column names # attach the final cluster if tmp: cluster_number += 1 out.append(tmp) for column_name in tmp: cluster_map[column_name] = cluster_number if plot: labels = [x + "(" + str(cluster_map[x]) + ")" for x in columns] plt.figure(figsize=(25, 8)) plt.title("Field Header Correlation Cluster Hierarchy") sch.dendrogram(Lopt, labels=labels, leaf_rotation=90.0) return out def cluster( df: pd.DataFrame, header_prefix: List[str] = None, maxsize: int = 20, method: str = "single", numeric_cat: List[str] = None, plot=False, ) -> List[List[str]]: """ Given an input dataframe, extract clusters of similar headers based on a set of heuristics. Args: df: The dataframe to cluster headers from. header_prefix: List of columns to remove before cluster generation. maxsize: The max number of header clusters to generate from the input dataframe. method: Linkage method used to compute header cluster distances. For more information please refer to the scipy docs, https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy-cluster-hierarchy-linkage. numeric_cat: A list of fields to define as categorical. The header clustering code will automatically define pandas "object" and "category" columns as categorical. The ``numeric_cat`` parameter may be used to define additional categorical fields that may not automatically get identified as such. plot: Plot header list as a dendogram. """ def prepare_response( col_list: List[List[str]], prefix: List[str] = None ) -> List[List[str]]: if prefix is not None: col_list[0] = prefix + col_list[0] return col_list if numeric_cat is None: numeric_cat = [] if header_prefix is not None: try: df = df.drop(header_prefix, axis=1) except KeyError as err: raise ValueError("Header prefixes do not all exist in source DF") from err # Bug(jm): if the number of columns left in the DF is just one # we just return that single column if df.shape[1] == 1: return prepare_response([list(df.columns)], header_prefix) # Start by getting the correlation matrix corr_matrix = _get_correlation_matrix(df, numeric_cat) # Change it into a distance matrix X = 1 - np.array(1 - abs(corr_matrix)) # Cluster the columns L = sch.linkage(X, method=method) # Optimize the leaf ordering to minimize the distance between adjacent leaves Lopt = sch.optimal_leaf_ordering(L, X) columns = df.columns start = len(Lopt) - 1 # Start at the top of the cluster hierachy with the final two clusters that were merged together # We will recursively work our way down, fetching the subclusters of a cluster if the current # cluster size > maxsize clusters = _traverse_node(Lopt, start, maxsize, len(columns)) # At this point we have one list of column ids, where groups are seperated by -1, translate it into a list of # header lists, and if plot=True, plot the dendogram col_list = _merge_clusters(clusters, maxsize, columns, Lopt, plot) return prepare_response(col_list, header_prefix)

andrewnc/gretel-synthetics

src/gretel_synthetics/utils/header_clusters.py

Python

""" Plugin architecture, based on decorators References: 1. https://play.pixelblaster.ro/blog/2017/12/18/a-quick-and-dirty-mini-plugin-system-for-python/ """

huuhoa/colusa

src/colusa/plugins/__init__.py

Python

# Generated by Django 2.2.10 on 2020-03-20 15:01 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import src.auth.models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=254, unique=True, verbose_name='email address')), ('first_name', models.CharField(blank=True, max_length=40, null=True, verbose_name='first name')), ('last_name', models.CharField(blank=True, max_length=40, null=True, verbose_name='last name')), ('display_name', models.CharField(blank=True, max_length=14, null=True, verbose_name='display name')), ('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')), ('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')), ('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': 'user', 'verbose_name_plural': 'users', 'db_table': 'auth_user', 'abstract': False, }, managers=[ ('objects', src.auth.models.MyUserManager()), ], ), migrations.CreateModel( name='UserProfile', fields=[ ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, primary_key=True, related_name='profile', serialize=False, to=settings.AUTH_USER_MODEL, verbose_name='user')), ('avatar_url', models.CharField(blank=True, max_length=256, null=True)), ('dob', models.DateField(blank=True, null=True, verbose_name='dob')), ], options={ 'db_table': 'user_profile', }, ), ]

SerhatTeker/django-bank-allauth-rest

src/auth/migrations/0001_initial.py

Python

import os # system parameters GPUS = [0] DATALOADER_WORKERS = 8 # optimization parameters BATCH_SIZE = 1 EPOCHS = 50 LR = 0.0001 WEIGHT_DECAY = 0.0005 MOMENTUM = 0.9 # image pre-processing parameters GAUSSIAN_VALUE = 0 # directory locations HOME_DIR = "/home/mbc2004" DATASET_DIR = "/home/mbc2004/datasets" MODEL_SRC_DIR = "/home/mbc2004/models" BASE_MODEL_DIR = "base_models" MODEL_SAVE_DIR = "saved_models" # input parameters INPUT_FRAMES = 64 # 16 application_list = ['block_construction_timed', 'block_construction', 'ikea', 'crepe_action', 'crepe_recipe'] def default_model_params(): class Params: def __init__(self, gpus=GPUS, dataloader_workers=DATALOADER_WORKERS, batch_size=BATCH_SIZE, epochs=EPOCHS, lr=LR, weight_decay=WEIGHT_DECAY, momentum=MOMENTUM, gaussian_value=GAUSSIAN_VALUE, home_dir=HOME_DIR, model_save_dir=MODEL_SAVE_DIR, base_model_dir=BASE_MODEL_DIR, input_frames=INPUT_FRAMES ): self.gpus = gpus self.dataloader_workers = dataloader_workers self.batch_size = batch_size self.epochs = epochs # number of epochs to run experiments for self.lr = lr self.weight_decay = weight_decay # ? self.momentum = momentum # ? self.gaussian_value = gaussian_value self.home_dir = home_dir self.base_model_dir = base_model_dir self.model_save_dir = model_save_dir self.input_frames = input_frames self.model = "unassigned" self.application = "unassigned" class ApplicationDef: def __init__(self, app): self.app = app self.masking = True if app == "block_construction": self.file_directory = os.path.join(DATASET_DIR, "BlockConstruction") self.trace_file = os.path.join(self.file_directory, "traces6.npy") self.obs_label_list = {"n": 0, "r": 1, "rr": 2, "rrr": 3, "g": 4, "gb": 5, "bg": 6, "b": 7} self.act_label_list = {"N": 0, "R": 1, "G": 2, "B": 3} # models self.tsm = {"filename": "c_backbone_tsm_1_bn16", "bottleneck": 16} self.wrn = {"filename": "c_backbone_wrn_2_bn16", "bottleneck": 16} self.i3d = {"filename": "c_backbone_i3d_1_bn8", "bottleneck": 8} self.vgg = {"filename": "c_backbone_vgg_2_bn32", "bottleneck": 32} elif app == "block_construction_timed": self.file_directory = os.path.join(DATASET_DIR, "BlockConstructionTimed") self.trace_file = os.path.join(self.file_directory, "traces6.npy") self.obs_label_list = {"n": 0, "r": 1, "rr": 2, "rrr": 3, "g": 4, "gb": 5, "bg": 6, "b": 7} self.act_label_list = {"N": 0, "R": 1, "G": 2, "B": 3} # models self.tsm = {"filename": "c_backbone_tsm_1_bn16", "bottleneck": 16} self.wrn = {"filename": "c_backbone_wrn_0_bn16", "bottleneck": 16} self.i3d = {"filename": "c_backbone_i3d_1_bn16", "bottleneck": 16} self.vgg = {"filename": "c_backbone_vgg_0_bn32", "bottleneck": 32} elif app == "ikea": self.file_directory = os.path.join(DATASET_DIR, "IKEA_fa") label_path = os.path.join(*[self.file_directory, "frames", "train"]) self.obs_label_list = {k: v for v, k in enumerate(os.listdir(label_path))} self.act_label_list = None # Activity Recognition Dataset self.masking = False # models self.tsm = {"filename": "c_backbone_tsm_0", "bottleneck": 64} self.wrn = {"filename": "c_backbone_wrn_0", "bottleneck": 64} self.i3d = {"filename": "c_backbone_i3d_0", "bottleneck": 64} self.vgg = {"filename": "c_backbone_vgg_0", "bottleneck": 64} elif app == "crepe_action": self.file_directory = os.path.join(DATASET_DIR, "CrepeAction") label_path = os.path.join(*[self.file_directory, "frames", "train"]) self.obs_label_list = {k: v for v, k in enumerate(sorted(os.listdir(label_path)))} self.act_label_list = None # Activity Recognition Dataset self.masking = True # models self.tsm = {"filename": "c_backbone_tsm_1", "bottleneck": 64} self.wrn = {"filename": "c_backbone_wrn_2", "bottleneck": 64} self.i3d = {"filename": "c_backbone_i3d_1", "bottleneck": 64} self.vgg = {"filename": "c_backbone_vgg_0", "bottleneck": 64} elif app == "crepe_recipe": self.file_directory = os.path.join(DATASET_DIR, "CrepeRecipe") label_path = os.path.join(*[self.file_directory, "frames", "train"]) self.obs_label_list = {k: v for v, k in enumerate(sorted(os.listdir(label_path)))} self.act_label_list = None # Activity Recognition Dataset self.masking = True # models self.tsm = {"filename": "c_backbone_tsm_1", "bottleneck": 64} self.wrn = {"filename": "c_backbone_wrn_2", "bottleneck": 64} self.i3d = {"filename": "c_backbone_i3d_1", "bottleneck": 64} self.vgg = {"filename": "c_backbone_vgg_0", "bottleneck": 64} self.num_labels = len(self.obs_label_list) def set_application(self, app): self.application = self.ApplicationDef(app) self.base_model_dir += '_' + app self.model_save_dir += '_' + app class ModelDef: def __init__(self, model_id, bottleneck_size, original_size, iad_frames, spatial_size, backbone_class, pretrain_model_name=None, save_id=None, end_point=-1): self.end_point = end_point self.model_id = model_id self.bottleneck_size = bottleneck_size self.original_size = original_size[self.end_point] self.iad_frames = iad_frames[self.end_point] self.spatial_size = spatial_size self.backbone_class = backbone_class self.pretrain_model_name = pretrain_model_name self.save_id = save_id def set_model_params(self, model_id, end_point=-1): from enums import Backbone assert self.application != "unassigned", "ERROR: call the set_application function before the set_model_params function" if model_id == Backbone.TSM: from model.backbone_model.backbone_tsm import BackboneTSM as backbone_class pretrain_model_name = os.path.join(MODEL_SRC_DIR, "TSM_somethingv2_RGB_resnet101_shift8_blockres_avg_segment8_e45.pth") save_id = self.application.tsm["filename"] bottleneck = self.application.tsm["bottleneck"] self.model = self.ModelDef("tsm", bottleneck, [2048], [64], 7, backbone_class, pretrain_model_name=pretrain_model_name, save_id=save_id) elif model_id == Backbone.WRN: from model.backbone_model.backbone_wrn import BackboneWideResNet as backbone_class save_id = self.application.wrn["filename"] bottleneck = self.application.wrn["bottleneck"] self.model = self.ModelDef("wrn", bottleneck, [2048], [64], 7, backbone_class, save_id=save_id) elif model_id == Backbone.VGG: from model.backbone_model.backbone_vgg import BackboneVGG as backbone_class save_id = self.application.vgg["filename"] bottleneck = self.application.vgg["bottleneck"] self.model = self.ModelDef("vgg", bottleneck, [512], [64], 7, backbone_class, save_id=save_id) elif model_id == Backbone.I3D: original_size = [64, 192, 256, 832, 1024, 128]#1024 iad_frames = [32, 32, 32, 16, 8, 8] from model.backbone_model.backbone_i3d import BackboneI3D as backbone_class pretrain_model_name = os.path.join(MODEL_SRC_DIR, "rgb_imagenet.pt") save_id = self.application.i3d["filename"] bottleneck = self.application.i3d["bottleneck"] self.model = self.ModelDef("i3d", bottleneck, original_size, iad_frames, 7, backbone_class, pretrain_model_name=pretrain_model_name, save_id=save_id, end_point=end_point) return Params()

AssistiveRoboticsUNH/temporal_feature_lfd

parameter_parser.py

Python

# coding: utf-8 """ FlashArray REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.5 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re import six import typing from ....properties import Property if typing.TYPE_CHECKING: from pypureclient.flasharray.FA_2_5 import models class PolicyRuleSmbClientGetResponse(object): """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'more_items_remaining': 'bool', 'total_item_count': 'int', 'continuation_token': 'str', 'items': 'list[PolicyRuleSmbClient]' } attribute_map = { 'more_items_remaining': 'more_items_remaining', 'total_item_count': 'total_item_count', 'continuation_token': 'continuation_token', 'items': 'items' } required_args = { } def __init__( self, more_items_remaining=None, # type: bool total_item_count=None, # type: int continuation_token=None, # type: str items=None, # type: List[models.PolicyRuleSmbClient] ): """ Keyword args: more_items_remaining (bool): Returns a value of `true` if subsequent items can be retrieved. total_item_count (int): The total number of records after applying all filter query parameters. The `total_item_count` will be calculated if and only if the corresponding query parameter `total_item_count` is set to `true`. If this query parameter is not set or set to `false`, a value of `null` will be returned. continuation_token (str): Continuation token that can be provided in the `continuation_token` query param to get the next page of data. If you use the continuation token to page through data you are guaranteed to get all items exactly once regardless of how items are modified. If an item is added or deleted during the pagination then it may or may not be returned. The continuation token is generated if the limit is less than the remaining number of items, and the default sort is used (no sort is specified). items (list[PolicyRuleSmbClient]): Returns a list of all items after filtering. The values are displayed for each name where meaningful. """ if more_items_remaining is not None: self.more_items_remaining = more_items_remaining if total_item_count is not None: self.total_item_count = total_item_count if continuation_token is not None: self.continuation_token = continuation_token if items is not None: self.items = items def __setattr__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `PolicyRuleSmbClientGetResponse`".format(key)) self.__dict__[key] = value def __getattribute__(self, item): value = object.__getattribute__(self, item) if isinstance(value, Property): raise AttributeError else: return value def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): if hasattr(self, attr): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(PolicyRuleSmbClientGetResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, PolicyRuleSmbClientGetResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

Flav-STOR-WL/py-pure-client

pypureclient/flasharray/FA_2_5/models/policy_rule_smb_client_get_response.py

Python

from util.dc_verilog_parser import * def main(): folder = "../dc/sub/adder8/" # folder = "../dc/boom/implementation/" total_nodes = 0 total_edges = 0 ntype = set() for v in os.listdir(folder): if v.startswith("hier"): continue vf = os.path.join(folder, v) print("parsing {}...".format(vf)) # parser = DcParser("BoomCore", ["alu_DP_OP", "add_x"]) parser = DcParser("test1", [ "add_x"], "hadd_s") nodes, edges = parser.parse(vf, label_region=True) print("nodes {}, edges {}".format(len(nodes), len(edges))) # nodes, edges = parser.clip(nodes, edges) nodes, edges = parser.remove_div(nodes, edges) adder_out_type = collections.defaultdict(int) node_type = collections.defaultdict(int) for n in nodes: if n[1]["is_output"]: adder_out_type[n[1]["type"]] += 1 node_type[n[1]["type"]] += 1 print(node_type) print(adder_out_type) print("clipped: nodes {}, edges {}".format(len(nodes), len(edges))) for n in nodes: ntype.add(n[1]["type"]) total_nodes += len(nodes) total_edges += len(edges) # return print(ntype) print(total_nodes, total_edges) if __name__ == "__main__": # dc_parser("../dc/simple_alu/implementation/alu_d0.20_r2_bounded_fanout_adder.v") main() # cProfile.run("main()")

ZeayW/graph-contrastive-learning

util1/find_central.py

Python

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tablo', '0003_auto_20160106_1509'), ] operations = [ migrations.CreateModel( name='FeatureServiceLayerRelations', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False)), ('related_index', models.PositiveIntegerField(default=0)), ('related_title', models.CharField(max_length=255)), ('source_column', models.CharField(max_length=255)), ('target_column', models.CharField(max_length=255)), ('layer', models.ForeignKey(to='tablo.FeatureServiceLayer')), ], ), ]

nrdsdata/tablo

tablo/migrations/0004_featureservicelayerrelations.py

Python

# Library of fault injection functions called at runtime for common operations in TensorFlow # NOTE: These are called by the corresponding functions inserted in the TensorFlow graph at RUNTIME import tensorflow as tf import numpy as np import logging from fiConfig import * from fiLog import * from threading import current_thread # FIXME: Add this to the list of dependencies for this module from sklearn.neighbors import KNeighborsClassifier from sklearn.utils.extmath import softmax # global variable to determine fine grained levels of logging # WARNING: Setting these to True may generate a lot of log data logReturn = False # log return values of functions logArgs = False # log arguments of operators logInjection = True # log fault injection and checking # This is the initialization function for the config file # and is called from TensorFI.py's constructor # NOTE: This has to be in this module or else fiConf won't be accessible def initFIConfig(fiParams): "Initialize the global variable fiConf with the params" global fiConf global count # instance of the current op (e.g., 3 ADD op means 3 instances of ADD op) global visitedOp # random instance of the op to be injected global randInstanceMap # order of the current op (e.g., the sequence of the current op in all of the op in the dataflow graph) global totalVistedOp # which op to be injected in the whole run global injectedOp fiConf = FIConfig(fiParams) logging.debug("Initialized config file : " + str(fiConf)) # Setup the random seed for the fault injector if one is specified if fiConf.faultSeed: np.random.seed( fiConf.faultSeed ) # Initialize the count of the selected operations to 0 (for skipCount) count = 0 visitedOp = {} randInstanceMap = {} totalVistedOp = 0 injectedOp = 0 return fiConf # End of fiConfing def getFIConfig(): "Return the fiConfig that was initialized" global fiConf return fiConf # End of getFIConfig # These functions have to do with the faultLog and are called from TensorFI.py faultLogs = { } # Global map of Threads to their fault logs def initFILog(name): "Initialize the fault injection log - optionally specify a thread number" global faultLogs global logName logName = name faultLog = FILog(logName) # Add the fault log to the log for the current thread current = current_thread() faultLogs[ current ] = faultLog # logging.debug("Initialized faultLog for thread " + str(current) + " as " + logName) # End of initFILog def getCurrentFaultLog(): "Return the fault log for the current thread (if it exists), add it otherwise" # Precondition: faultLogs != None global faultLogs global logName current = current_thread() faultLog = None # If we cannot find the faultLog for the current thread, add it to the faultLogs # FIXME: This doesn't work because TensorFlow uses its own threading infrastructure # and ThreadIDs are not the same during log creation time and log access time # So we always end up writing to the first entry of the faultLogs dictionary if not faultLogs.has_key(current): # logging.debug("Cannot find fault log for " + str(current) ) faultLog = FILog(logName + "-" + current.name) faultLogs[ current ] = faultLog # faultLog = faultLogs.values()[0] else: # Otherwise, return the fault log for the current thread faultLog = faultLogs[current] # logging.debug("Returning fault log " + str(faultLog) + " for thread " + str(current) ) return faultLog # End of getCurrentFaultLog def logRun(runCount): "Update the run count in the log file" global count # Reset the count on a new run count = 0 faultLog = getCurrentFaultLog() # Get the fault log for the current thread # Log the runCount and start a new section of the logFile faultLog.updateRunCount( runCount ) faultLog.dashedLine() # End of logRun # These are the basic fault injection functions that're called at runtime # NOTE: We need to first call initFIConfig before these are called def perturb(val): "Inject a single fault in res - fault type depends on config param" # Precoditions: injectScalar != None && injectTensor != None faultLog = getCurrentFaultLog() # Get the fault log for the current thread isScalar = np.isscalar(val) vType = val.dtype if logInjection: logging.debug("\tPerturbing " + str(val) + " of type: " + str(vType) + " isScalar: " + str(isScalar) ) # Check if the object is a scalar or a tensor, and call the corresponding injection function if isScalar: res = fiConf.injectScalar( vType, val.copy()) else: res = fiConf.injectTensor( vType, val.copy()) # Enter an entry in the fault log that we injected a fault here faultLog.updateOriginal( val ) faultLog.updateInjected( res ) #if logInjection: logging.debug("\t TESTING ... res = " + str(res)) return res # End of perturb def condPerturb(op, res): "Calls the perturb function if and only if the op Operation is included for injection" # Pre-condition: injectMap != None && skipCount != None global count # Keeps track of how many times the selected operation(s) are executed global visitedOp faultLog = getCurrentFaultLog() # Get the fault log for the current thread if logInjection: logging.debug("\tChecking if operation " + str(op) + " is chosen for injection") # Check if the operation is chosen for injection and if so, inject a fault if fiConf.isSelected(op): count = count + 1 # If it's selected, then update the execution count if logInjection: logging.debug("\t TESTING ... Operation " + str(op) + " is chosen for injection") # Enter the op and count in the faultLog - as we won't have access to it later # NOTE: This is not actually written to the logFIle till faultLog.commit is called # so we won't write to the log if a fault is not injected into it faultLog.updateOp( op ) faultLog.updateCount( count ) # If the operation exceeds the number of times it is to be skipped (default=0) if (count > fiConf.skipCount): "(1) inject faults based on the error rate" if(fiConf.injectMode == "errorRate" ): # Retreive the probability of perturbing this instruction # and generate a random number in the interval [0, 1] # and only perturb it only if the random no. <= the probability prob = fiConf.getProbability(op) #if logInjection: logging.debug("\t TESTING ... PROB = " + str(prob)) rn = np.random.random() # random.random returns a number in [0, 1] #if logInjection: logging.debug("\t TESTING ... np = " + str(rn)) if (rn <= prob): res = perturb(res) # Perturb is called to inject the fault faultLog.commit() # Write the log entry to the fault log "(2) inject faults based on the dynamic instance of op, i.e., inject one instance for each op" if(fiConf.injectMode == "dynamicInstance"): # Retreive the total instances of this instruction # each operation will be injected once only # and generate a random number to select a random instance of the operation # and only perturb it only if the current instance has been selected instance = fiConf.getInstance(op) if logInjection: logging.debug("\t TESTING ... instance = " + instance) # You can manually specify the instance here rather than using the random instances # So that you can inject fault into a target operator # E.g., randInstanceMap[op] = instance of op to be injected if (not randInstanceMap.has_key(op)): # random instance of the selected op to be injected randInstanceMap[op] = np.random.randint(low=1, high=instance+1) # first instance of the op if(not visitedOp.has_key(op)): visitedOp[op] = 1 # not the first instance of op else: visitedOp[op] += 1 # determine if the current instance is selected for injection if(visitedOp[op] == randInstanceMap[op]): res = perturb(res) faultLog.updateInjectedInstance(randInstanceMap[op], instance) faultLog.commit() # current run has finished, re-initialize the visit table for the next run # used when you need to do injection on the same op in the next run if(visitedOp[op] == instance): visitedOp[op] = 0 "(3) inject one fault per run" if(fiConf.injectMode == "oneFaultPerRun"): # refer the global variable for memorizing the order of the current op global totalVistedOp global injectedOp # get the amount of total op totalInstance = fiConf.totalInstance totalVistedOp += 1 # select one random op to be injected in the whole run if(injectedOp == 0): injectedOp = np.random.randint(low=1, high=totalInstance+1) # inject fault at the output of the operation if(totalVistedOp == injectedOp): res = perturb(res) faultLog.updateInjectedInstance(injectedOp, totalInstance) faultLog.commit() # current run has finished, re-initialize the visit table for the next run (optional) if(totalVistedOp == totalInstance): totalVistedOp = 0 injectedOp = 0 # Done with if count # Done with if isSelected #if logInjection: logging.debug("\t TESTING ... Operation " + str(op) + " is NOT chosen for injection") return res # End of condPerturb # This is a specialized function to cast into values of different types def castType(type): "Returns the appropriate injection function based on the type" # Create specialized functions for each type # FIXME: Only 4 types are supported now. Support more types later. def castFloat32(value): logging.debug("Casting to " + str(type)) return np.float32(value) def castInt32(value): logging.debug("Casting to " + str(type)) return np.int32(value) def castInt64(value): logging.debug("Casting to " + str(type)) return np.int64(value) def castFloat64(value): logging.debug("Casting to " + str(type)) return np.float64(value) # Check the type parameter and return the appropriate function if (type==np.float32): return castFloat32 elif (type==np.int32): return castInt32 elif (type==np.int64): return castInt64 elif (type==np.float64): return castFloat64 else: raise TypeError("Unknown type " + type) return None # End of castType # Debugging function to log the values of the arguments # if and only if logArgs is set to True def getArgs(*args): "Return a string of the args if logArgs is True; Empty String otherwise" res = " " if logArgs: res +="( " for arg in args: res = res + " , " + str(arg) res += " )" return res # Start the implementation of the injectFault functions for each op type # This is a special case for the Cast function which needs to remember the type # We use closures to remember the type and cast it appropriately at "runtime" def createInjectFaultCast(type): "Returns a Function to call injectFault on cast nodes" castInto = castType(type) # get the appropriate casting function for the type def injectFaultCast(a, b = None): "Inject a fault into a Cast instruction" logging.debug("Calling Operator Cast " + getArgs(a, b)) # If we're given 2 parameters, treat it as the default case if b != None: res = np.cast(a, b) else: # Call the function for this type with 'a' res = castInto(a) res = condPerturb(Ops.CAST, res) if logReturn: logging.debug("\tReturning " + str(res) ) return res # Return the injectFaultCast function return injectFaultCast def injectFaultNoop(): "Inject a fault in the Noop operaton - does nothing" logging.debug("Calling Operator Noop") # No need to call Perturb as there's nothing to return return def injectFaultAssign(a, b): "Inject a fault in the assignement operation" logging.debug("Calling Operator Assigment " + getArgs(a, b)) res = b # FIXME: Check semantics of assignment operator res = condPerturb(Ops.ASSIGN, res) if logReturn: logging.debug("\tReturning from Assignment " + str(res) ) return res def injectFaultIdentity(a): "Inject a fault in the identitiy operation" logging.debug("Calling Operator Identity" + getArgs(a)) res = a res = condPerturb(Ops.IDENTITY, res) if logReturn: logging.debug("\tReturning from Identity " + str(res) ) return res def injectFaultAdd(a, b): "Function to call injectFault on Add nodes" logging.debug("Calling Operator Add " + getArgs(a, b)) #if logInjection: logging.debug("\t TESTING ... Calling Operator Add " + str(a) + "," + str(b)+")") resOp = tf.add(a, b) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.ADD, res) #if logInjection: logging.debug("\t TESTING ... Returning from Add = " + str(res)) if logReturn: logging.debug("\tReturning from Add " + str(res) ) return res def injectFaultSub(a, b): "Function to call injectFault on Sub nodes" logging.debug("Calling Operator Sub " + getArgs(a, b)) res = a - b res = condPerturb(Ops.SUB, res) if logReturn: logging.debug("\tReturning from Sub " + str(res) ) return res def injectFaultMul(a, b): "Function to call injectFault on Mul nodes" logging.debug("Calling Operator Mul " + getArgs(a, b)) res = a * b res = condPerturb(Ops.MUL,res) if logReturn: logging.debug("\tReturning from Mul " + str(res) ) return res def injectFaultSquare(a): "Function to call injectFault on Square nodes" logging.debug("Calling Operator Square " + getArgs(a)) res = a * a res = condPerturb(Ops.SQUARE,res) if logReturn: logging.debug("\tReturning from Square " + str(res) ) return res def injectFaultShape(a): "Function to call injectFault on Shape nodes" logging.debug("Calling Operator Shape " + getArgs(a)) # If it's a tensor, call shape on it directly # Otherwise, use numpy to get its shape if isinstance(a, tf.Tensor): res = a.shape() else: # res = tf.convert_to_tensor( np.shape(a) , dtype = np.int32 ) res = np.int32( np.shape(a) ) # res should be either a scalar or tensor here res = condPerturb(Ops.SHAPE,res) if logReturn: logging.debug("\tReturning from Shape " + str(res) ) return res def injectFaultSize(a): "Function to call injectFault on Size nodes" logging.debug("Calling Operator Size " + getArgs(a)) res = a.size() res = condPerturb(Ops.SIZE, res) if logReturn: logging.debug("\tReturning from Size " + str(res) ) return res def injectFaultFill(a, b): "Function to call injectFault on Shape nodes" logging.debug("Calling Operator Fill " + getArgs(a, b)) res = np.full(a, b) res = condPerturb(Ops.FILL, res) if logReturn: logging.debug("\tReturning from Fill" + str(res) ) return res def injectFaultFloorMod(a, b): "Function to call injectFault on FloorMod nodes" logging.debug("Calling Operator FloorMod " + getArgs(a, b)) # FIXME: Need to check if mod is the equivalent of floorMod in NumPy res = np.mod(a, b) res = condPerturb(Ops.FLOORMOD, res) if logReturn: logging.debug("\tReturning from FloorMod " + str(res) ) return res def injectFaultRange(start, stop, step, dtype = None): "Function to call injectFault on Range nodes" logging.debug("Calling Operator Range " + getArgs(start, stop, step)) res = np.int32(np.arange(start, stop, step, dtype)) res = condPerturb(Ops.RANGE, res) if logReturn: logging.debug("\tReturning from Range " + str(res) ) return res def injectFaultRank(a): "Function to call injectFault on Rank nodes" logging.debug("Calling Operator Rank " + getArgs(a)) res = np.int32( np.ndim(a) ) res = condPerturb(Ops.RANK, res) if logReturn: logging.debug("\tReturning from Rank " + str(res) ) return res def injectFaultSum(a, b): "Function to call injectFault on Sum nodes" logging.debug("Calling Operator Sum " + getArgs(a, b)) # Check if b is an integer scalar array # and if so, pass it to np.sum # Otherwise, ignore it (FIXME: is this the correct behavior ?) if np.isscalar(b): res = np.sum(a, b) else: res = np.sum(a) res = condPerturb(Ops.SUM, res) if logReturn: logging.debug("\tReturning from Sum " + str(res) ) return res def injectFaultReshape(a, b): "Function to call injectFault on Reshape" logging.debug("Calling Operator Reshape " + getArgs(a, b)) res = np.reshape(a, b) res = condPerturb(Ops.RESHAPE, res) if logReturn: logging.debug("\tReturning from Reshape " + str(res) ) return res def injectFaultOneHot(a, b, c, d): "Function to call injectFault on OneHot" logging.debug("Calling Operator One Hot " + getArgs(a, b, c, d)) # TF adds two default arguments, so we need to pass them as well resOp = tf.one_hot(a, b, c, d) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.ONE_HOT, res) if logReturn: logging.debug("\tReturning from One Hot " + str(res) ) return res def injectFaultMatMul(a, b): "Function to call injectFault on matrix multiplication" logging.debug("Calling Operator MatMul " + getArgs(a, b)) matmul = tf.matmul(a,b) with tf.Session() as sess: res = matmul.eval() # res = np.matmul(a, b) res = condPerturb(Ops.MATMUL, res) if logReturn: logging.debug("\tReturning from MatMul " + str(res) ) return res def injectFaultArgMax(a, b): "Function to call injectFault on ArgMax" logging.debug("Calling Operator ArgMax " + getArgs(a, b)) resOp = tf.argmax(a, b) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.ARGMAX, res) if logReturn: logging.debug("\tReturning from ArgMax " + str(res) ) return res def injectFaultArgMin(a, b): "Function to call injectFault on ArgMin" logging.debug("Calling Operator ArgMin " + getArgs(a, b)) res = np.argmin(a, b) res = condPerturb(Ops.ARGMIN, res) if logReturn: logging.debug("\tReturning from ArgMin " + str(res) ) return res def injectFaultEqual(a, b): "Function to call injectFault on equal" logging.debug("Calling Operator Equal " + getArgs(a, b)) res = np.equal(a, b) res = condPerturb(Ops.EQUAL, res) if logReturn: logging.debug("\tReturning from Equal " + str(res) ) return res def injectFaultNotEqual(a, b): "Function to call injectFault on not equal" logging.debug("Calling Operator Not Equal " + getArgs(a, b)) res = np.not_equal(a, b) res = condPerturb(Ops.NOT_EQUAL, res) if logReturn: logging.debug("\tReturning from Not Equal " + str(res) ) return res def injectFaultLessEqual(a, b): "Function to call injectFault on less equal" logging.debug("Calling Operator Less Equal " + getArgs(a, b)) res = np.less_equal(a, b) res = condPerturb(Ops.LESS_EQUAL, res) if logReturn: logging.debug("\tReturning from Less Equal " + str(res) ) return res def injectFaultGreaterEqual(a, b): "Function to call injectFault on greater equal" logging.debug("Calling Operator Greater Equal " + getArgs(a, b)) res = np.greater_equal(a, b) res = condPerturb(Ops.GREATER_EQUAL, res) if logReturn: logging.debug("\tReturning from Greater Equal " + str(res) ) return res def injectFaultMean(a, b): "Function to call injectFault on mean" logging.debug("Calling Operator mean " + getArgs(a, b)) # FIXME: This only works if we call np.mean on b[0]. Need to figure out why. res = np.mean(a, b[0]) res = condPerturb(Ops.MEAN, res) if logReturn: logging.debug("\tReturning from Mean " + str(res) ) return res def injectFaultCountNonZero(a): "Function to call injectFault on countNonZero" logging.debug("Calling Operator CountNonZero " + getArgs(a)) res = np.count_nonzero(a) res = condPerturb(Ops.COUNT_NONZERO, res) if logReturn: logging.debug("\tReturning on CountNonZero " + str(res) ) return res def injectFaultConv2D(a, b, strides, padding): "Function to call injectFault on Conv2D" logging.debug("Calling Operator conv2D " + getArgs(a, b)) conv = tf.nn.conv2d(a , b, strides=strides.tolist(), padding=padding) with tf.Session() as sess: res = conv.eval() res = condPerturb(Ops.CONV2D, res) if logReturn: logging.debug("\tReturning from Conv2D " + str(res) ) return res def injectFaultRelu(a): "Function to call injectFault on RelU" logging.debug("Calling Operator RelU " + getArgs(a)) relu = tf.nn.relu(a) with tf.Session() as sess: res = relu.eval() res = condPerturb(Ops.RELU, res) if logReturn: logging.debug("\tReturning from RelU " + str(res) ) return res def injectFaultMaxPool(a, ksize, strides, padding): "Function to call injectFault on MaxPool" maxpool = tf.nn.max_pool(a, ksize=ksize.tolist(), strides=strides.tolist(), padding=padding) with tf.Session() as sess: res = maxpool.eval() res = condPerturb(Ops.MAXPOOL, res) if logReturn: logging.debug("\tReturningfrom MaxPool " + str(res) ) return res def injectFaultUnpack(a): "Function to call injectFault on unpack" logging.debug("Calling Operator Unpack " + getArgs(a)) # This operation is deprecated in TF 1.0 and above res = np.array_split(a, a.shape[1]) # FIXME: Can't inject faults into unpack as it's not a tensor or scalar # res = condPerturb(Ops.UNPACK, res) if logReturn: logging.debug("\tReturning from Unpack " + str(res) ) return res def injectFaultUnstack(a): "Function to call injectFault on unstack" # This is the same as Unpack in newer versions of TF logging.debug("Calling Operator Unstack " + getArgs(a, b, c)) resOp = tf.unstack(a, b, c) with tf.Session() as sess: res = resOp.eval() if logReturn: logging.debug("\tReturning from Unstack " + str(res) ) return res def injectFaultStridedSlice(a, b, c, d): "Function to call injectFault on StridedSlice" logging.debug("Calling Operator StridedSlice " + getArgs(a, b, c, d)) # FIXME: Implement this functionality resOp = tf.strided_slice(a, b, c, d) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.STRIDEDSLICE, res) if logReturn: logging.debug("\tReturning from StridedSlice " + str(res) ) return res def injectFaultExpandDims(a, b): "Function to call injectFault on ExpandDims" logging.debug("Calling Operator ExpandDims " + getArgs(a, b)) res = np.expand_dims(a, b) res = condPerturb(Ops.EXPANDDIMS, res) if logReturn: logging.debug("\tReturning from ExpandDims " + str(res) ) return res def injectFaultPack(a, b): "Function to call injectFault on Pack" # FIXME: Implement this functionality logging.debug("Calling Operator Pack" + getArgs(a, b)) # res = np.stack(a, b) # FIXME: This throws an exception, so we dummied it out res = a res = condPerturb(Ops.PACK, res) if logReturn: logging.debug("\tReturning " + str(res) ) return res def injectFaultConcatV2(a, b, c): "Function to call injectFault on ConcatV2" logging.debug("Calling Operator ConcatV2" + getArgs(a, b, c)) res = np.concatenate((a, b), c) res = condPerturb(Ops.PACK, res) if logReturn: logging.debug("\tReturning from Concat " + str(res) ) return res def injectFaultSoftmax(a): "Function to call injectFault on Softmax" logging.debug("Calling Operator Softmax " + getArgs(a)) resOp = tf.nn.softmax(a) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.SOFTMAX, res) if logReturn: logging.debug("\tReturning from Softmax " + str(res) ) return res def injectFaultMaximum(a, b): "Function to call injectFault on Maximum" logging.debug("Calling Operator Maximum " + getArgs(a, b)) res = np.maximum(a, b) res = condPerturb(Ops.MAXIMUM, res) if logReturn: logging.debug("\tReturning from Maximum " + str(res) ) return res def injectFaultMinimum(a, b): "Function to call injectFault on Maximum" logging.debug("Calling Operator Minimum " + getArgs(a, b)) res = np.minimum(a, b) res = condPerturb(Ops.MINIMUM, res) if logReturn: logging.debug("\tReturning from Minimum " + str(res) ) return res def injectFaultSwitch(a, b): "Function to call injectFault on Switch" logging.debug("Calling Operator Switch " + getArgs(a, b)) # FIXME: Actually implement the Switch operation # Only there's no TensorFlow documentation for it !!! # res = np.select(a, b) res = a, a # res = condPerturb(Ops.SWITCH, res) if logReturn: logging.debug("\tReturning from Switch " + str(res) ) return res def injectFaultGreater(a, b): "Function to call injectFault on Greater" logging.debug("Calling Operator Greater " + getArgs(a, b)) res = np.greater(a, b) res = condPerturb(Ops.GREATER, res) if logReturn: logging.debug("\tReturning from Greater " + str(res) ) return res def injectFaultNeg(a): "Function to call injectFault on negative" logging.debug("Calling Operator Neg " + getArgs(a)) res = np.negative(a) res = condPerturb(Ops.NEGATIVE, res) if logReturn: logging.debug("\tReturning from Neg " + str(res) ) return res def injectFaultPow(a, b): "Function to call injectFault on pow" logging.debug("Calling Operator Pow " + getArgs(a, b)) res = np.power(a, b) res = condPerturb(Ops.POWER, res) if logReturn: logging.debug("\tReturning from Pow " + str(res) ) return res def injectFaultAbs(a): "Function to call injectFault on absolute" logging.debug("Calling Operator Abs " + getArgs(a)) res = np.absolute(a) res = condPerturb(Ops.ABSOLUTE, res) if logReturn: logging.debug("\tReturning from Abs " + str(res) ) return res def injectFaultRsqrt(a): "Function to call injectFault on Rsqrt" logging.debug("Calling Operator Rsqrt " + getArgs(a)) res = np.reciprocal( np.sqrt(a) ) res = condPerturb(Ops.RSQRT, res) if logReturn: logging.debug("\tReturning from Rsqrt " + str(res) ) return res def injectFaultNN(a, b, c): "Function to call injectFault on Nearest Neighbors" # FIXME: According to the TF docs, this operation doesn't exist ! # Not sure what the third parameter is supposed to be. logging.debug("Calling Operator Nearest Neighbors " + getArgs(a, b, c)) res = KNeighborsClassifier(a) if logReturn: logging.debug("\tReturning from Nearest Neighbors " + str(res) ) return res def injectFaultLog(a): "Function to call injectFault on Log" logging.debug("Calling Operator Log " + getArgs(a)) res = np.reciprocal( np.log(a) ) res = condPerturb(Ops.LOG, res) if logReturn: logging.debug("\tReturning from Log " + str(res) ) return res def injectFaultRealDiv(a, b): "Function to call injectFault on RealDiv" # FIXME: Implement this functionality logging.debug("Calling Operator Log " + getArgs(a, b)) res = np.divide( a, b ) res = condPerturb(Ops.REALDIV, res) if logReturn: logging.debug("\tReturning from RealDiv " + str(res) ) return res def injectFaultBiasAdd(a, b): "Function to call injectFault on BiasAdd" logging.debug("Calling Operator BiasAdd " + getArgs(a, b)) res = a + b res = condPerturb(Ops.BIASADD, res) if logReturn: logging.debug("\tReturning from BiasAdd " + str(res) ) return res def injectFaultSigmoid(a): "Function to call injectFault on Sigmoid" logging.debug("Calling Operator Sigmoid " + getArgs(a)) res = np.reciprocal( 1 + np.exp(-a) ) res = condPerturb(Ops.SIGMOID, res) if logReturn: logging.debug("\tReturning from Sigmoid " + str(res) ) return res def injectFaultTanh(a): "Function to call injectFault on Tanh" logging.debug("Calling Operator Tanh " + getArgs(a)) res = np.tanh( a ) res = condPerturb(Ops.TANH, res) if logReturn: logging.debug("\tReturning from Tanh " + str(res) ) return res def injectFaultLRN(a, bias, alpha, beta): "Function to call injectFault on LRN" logging.debug("Calling Operator LRN" + getArgs(a, bias, alpha, beta)) # FIXME: How to derive the depth_radius from LRN # Currently we manually use the value from the main program. # depth_radius = 2 resOp = tf.nn.lrn( a , 2, bias=bias, alpha=alpha, beta=beta) with tf.Session() as sess: res = resOp.eval() res = condPerturb(Ops.LRN, res) if logReturn: logging.debug("\tReturning from LRN " + str(res) ) return res def injectFaultELU(a): "Function to call injectFault on ELU" logging.debug("Calling Operator ELU " + getArgs(a)) relu = tf.nn.elu(a) with tf.Session() as sess: res = relu.eval() res = condPerturb(Ops.ELU, res) if logReturn: logging.debug("\tReturning from ELU " + str(res) ) return res def injectFaultRandomUniform(a): "Function to call injectFault on Random Uniform" logging.debug("Calling Operator RandomUniform" + getArgs(a)) ru = tf.random_uniform(a) with tf.Session() as sess: res = ru.eval() res = condPerturb(Ops.RANDOM_UNIFORM, res) if logReturn: logging.debug("\tReturning from Random Uniform " + str(res) ) return res def injectFaultFloor(a): "Function to call injectFault on Floor" logging.debug("Calling Operator injectFaultFloor" + getArgs(a)) floor = tf.math.floor(a) with tf.Session() as sess: res = floor.eval() res = condPerturb(Ops.FLOOR, res) if logReturn: logging.debug("\tReturning from Floor operation " + str(res) ) return res # End of implemented operators ##### None of the functions below have been implemented yet as they're not used ##### #### If you implement any of them, please move them above the line #### ##### Otherwise, they will all raise NotImplementedError(OpName) ####3 def injectFaultDynamicStitch(inputs): "Function to call injectFault on Dynamic stitch" # FIXME: Implement this functionality logging.debug("Calling Operator Dynamic stitch ") raise NotImplementedError("DynamicStitch") def injectFaultFloorDiv(inputs): "Function to call injectFault on FloorDiv" # FIXME: Implement this functionality logging.debug("Calling Operator FloorDiv ") raise NotImplementedError("FloorDiv") def injectFaultTile(inputs): "Function to call injectFault on Tile" # FIXME: Implement this functionality logging.debug("Calling Operator Tile") raise NotImplementedError("Tile") def injectFaultConcatOffset(inputs): "Function to call injectFault on ConcatOffset" # FIXME: Implement this functionality logging.debug("Calling Operator ConcatOffset") raise NotImplementedError("ConcatOffset") def injectFaultSplit(inputs): "Function to call injectFault on Split" # FIXME: Implement this functionality logging.debug("Calling Operator Split") raise NotImplementedError("Split") def injectFaultSoftmaxCEWL(inputs): "Function to call injectFault on Softmax CEWL" # FIXME: Implement this functionality logging.debug("Calling Operator SoftmaxCEWL") raise NotImplementedError("SoftmaCEWL") def injectFaultSlice(inputs): "Function to call injectFault on Slice" # FIXME: Implement this functionality logging.debug("Calling Operator Slice") raise NotImplementedError("Slice") def injectFaultBroadcastGA(inputs): "Function to call injectFault on Broadcast gradient args" # FIXME: Implement this functionality logging.debug("Calling Operator BroadcastGA") raise NotImplementedError("BroadcastGA") def injectFaultTruncatedNormal(a): "Function to call injectFault on TruncatedNormal" # FIXME: Implement this functionality logging.debug("Calling Operator TruncatedNormal") # + str(a)) raise NotImplementedError("TruncatedNormal") def injectFaultRandomUniformInt(a): "Function to call injectFault on Random Uniform Int" # FIXME: Implement this functionality logging.debug("Calling Operator RandomUniformInt") raise NotImplementedError("RandomUniformInt") def injectFaultRandomStandardNormal(a): "Function to call injectFault on Random Standard Normal" # FIXME: Implement this functionality logging.debug("Calling Operator RandomStandardNormal") raise NotImplementedError("RandomStandardNormal") def injectFaultRefSwitch(a): "Function to call injectFault on RefSwitch" # FIXME: Implement this functionality logging.debug("Calling Operator RefSwitch") raise NotImplementedError("RefSwitch") def injectFaultProd(a): "Function to call injectFault on Prod" # FIXME: Implement this functionality logging.debug("Calling Operator Prod") raise NotImplementedError("Prod") def injectFaultUnique(a): "Function to call injectFault on Unique" # FIXME: Implement this functionality logging.debug("Calling Operator Unique") raise NotImplementedError("Unique") def injectFaultReciprocal(a): "Function to call injectFault on Reciprocal" # FIXME: Implement this functionality logging.debug("Calling Operator Reciprocal") raise NotImplementedError("Reciprocal") def injectFaultScatterAdd(a): "Function to call injectFault on ScatterAdd" # FIXME: Implement this functionality logging.debug("Calling Operator ScatterAdd") raise NotImplementedError("ScatterAdd") def injectFaultReluGrad(a): "Function to call injectFault on ReluGrad" # FIXME: Implement this functionality logging.debug("Calling Operator ReluGrad") raise NotImplementedError("ReluGrad") def injectFaultMaxPoolGrad(a): "Function to call injectFault on MaxPoolGrad" # FIXME: Implement this functionality logging.debug("Calling Operator MaxPoolGrad") raise NotImplementedError("MaxPoolGrad") def injectFaultTanhGrad(a): "Function to call injectFault on TanhGrad" # FIXME: Implement this functionality logging.debug("Calling Operator TanhGrad") raise NotImplementedError("TanhGrad") def injectFaultSigmoidGrad(a): "Function to call injectFault on SigmoidGrad" # FIXME: Implement this functionality logging.debug("Calling Operator SigmoidGrad") raise NotImplementedError("SigmoidGrad") def injectFaultBiasAddGrad(a): "Function to call injectFault on BiasAddGrad" # FIXME: Implement this functionality logging.debug("Calling Operator BiasAddGrad") raise NotImplementedError("BiasAddGrad") def injectFaultShapeN(inputs): "Function to call injectFault on ShapeN" # FIXME: Implement this functionality logging.debug("Calling Operator ShapeN") raise NotImplementedError("ShapeN") def injectFaultAddN(inputs): "Function to call injectFault on AddN" # FIXME: Implement this functionality logging.debug("Calling Operator AddN") raise NotImplementedError("AddN") def injectFaultConv2DBackprop(inputs): "Function to call injectFault on Conv2DBackprop" # FIXME: Implement this functionality logging.debug("Calling Operator Conv2DBackProp") raise NotImplementedError("Conv2DBackProp") def injectFaultApplyAdam(inputs): "Function to call injectFault on ApplyAdam" # FIXME: Implement this functionality logging.debug("Calling Operator ApplyAdam") raise NotImplementedError("ApplyAdam") def injectFaultSelect(inputs): "Function to call injectFault on Select" # FIXME: Implement this functionality logging.debug("Calling Operator Select") raise NotImplementedError("Select") def injectFaultMerge(inputs): "Function to call injectFault on Merge" # FIXME: Implement this functionality logging.debug("Calling Operator Merge") raise NotImplementedError("Merge") def injectFaultTranspose(inputs): "Function to call injectFault on Transpose" # FIXME: Implement this functionality logging.debug("Calling Operator Transpose") raise NotImplementedError("Transpose") def injectFaultTranspose(inputs): "Function to call injectFault on Transpose" # FIXME: Implement this functionality logging.debug("Calling Operator Transpose") raise NotImplementedError("Transpose") def injectFaultGather(inputs): "Function to call injectFault on Gather" # FIXME: Implement this functionality logging.debug("Calling Operator Gather") raise NotImplementedError("Gather") def injectFaultUnsortedSegmentSum(inputs): "Function to call injectFault on UnsortedSegmentSum" # FIXME: Implement this functionality logging.debug("Calling Operator UnsortedSegmentSum") raise NotImplementedError("UnsortedSegmentSum") def injectFaultInvertPermutation(inputs): "Function to call injectFault on InvertPermutation" # FIXME: Implement this functionality logging.debug("Calling Operator InvertPermuation") raise NotImplementedError("InvertPermutation") def injectFaultApplyGradientDescent(inputs): "Function to call injectFault on applying gradient descent" # FIXME: Implement this functionality logging.debug("Calling Operator ApplyGradientDescent") raise NotImplementedError("ApplyGradientDescent") def injectFaultZerosLike(inputs): "Function to call injectFault on ZerosLike" # FIXME: Implement this functionality logging.debug("Calling Operator ZerosLike") raise NotImplementedError("ZerosLike") def injectFaultPreventGradient(inputs): "Function to call injectFault on PreventGradient" # FIXME: Implement this functionality logging.debug("Calling Operator PreventGradient") raise NotImplementedError("PreventGradient") def injectFaultSSSmcEWL(inputs): "Function to call injectFault on SoftSparseMax.." # FIXME: Implement this functionality logging.debug("Calling Operator SoftSparseMax") raise NotImplementedError("SoftSparseMax") def injectFaultAll(a): "Function to call injectFault on All operation" # FIXME: Implement this functionality # Not clear what this does - TF doc is silent about this logging.debug("Calling Operator All") raise NotImplementedError("All") def injectFaultAssert(a): "Function to call injectFault on Assert operation" # FIXME: Implement this functionality logging.debug("Calling Operator Assert") raise NotImplementedError("Assert") def injectFaultLess(a): "Function to call injectFault on Less operation" # FIXME: Implement this functionality logging.debug("Calling Operator Less") raise NotImplementedError("Less") def injectFaultFSRHOP(a): "Function to call Inject fault on FertileResource Op" # FIXME: Implement this functionality logging.debug("Calling Operator FSRHOP") raise NotImplementedError("FSRHOP") def injectFaultL2Loss(a): "Function to call Inject fault on L2Loss operation" # FIXME: Implement this functionality logging.debug("Calling Operator L2Loss") raise NotImplementedError("L2Loss") def injectFaultApplyMomentum(a): "Function to call Inject fault on ApplyMomentum operation" # FIXME: Implement this functionality logging.debug("Calling Operator ApplyMomentum") raise NotImplementedError("ApplyMomentum") def injectFaultAssignAdd(a): "Function to call Inject fault on AssignAdd operation" # FIXME: Implement this functionality logging.debug("Calling Operator AssignAdd") raise NotImplementedError("AssignAdd") def injectFaultSqueeze(a): "Function to call injectFault on Squeeze" # FIXME: Implement this functionality logging.debug("Calling Operator Squeeze") raise NotImplementedError("Squeeze") ##### End of unimplemented functions ################### # This is the generic "Catch-all" function - it should be last # It takes a variable number of arguments in the inputs array def injectFaultGeneric(*inputs): "Generic Function to call fault injection on each input and zero it out" outputs = [] #logging.debug("Calling generic fiFunc on " + str(inputs)) # Perturb the input and add it to the outpus # FIXME: Should we NOT actually do the operation as well ?? # For now, we don't do any injection at all at this function for input in inputs: outputs.append( input ) #if logReturn: logging.debug("\tReturning " + str(outputs)) return outputs # End of injectFault operations # The functions in this table are the ones defined above # FIXME: These are fairly repetitive, so perhaps generate them automatically # Also, maybe these should be sorted alphabetically - this is getting quite big opTable = { "NoOp" : injectFaultNoop, # First operation "Add": injectFaultAdd, "Sub": injectFaultSub, "Mul": injectFaultMul, "Square" : injectFaultSquare, "Assign" : injectFaultAssign, "Identity": injectFaultIdentity, "Range": injectFaultRange, "Rank": injectFaultRank, "Sum" : injectFaultSum, "Shape": injectFaultShape, "Fill": injectFaultFill, "Size": injectFaultSize, "FloorMod" : injectFaultFloorMod, "DynamicStitch" : injectFaultDynamicStitch, "Maximum" : injectFaultMaximum, "Max" : injectFaultMaximum, # FIXME: Not sure if Max is a synonymn of Maximum or a new operation "Minimum" : injectFaultMinimum, "Min" : injectFaultMinimum, # FIXME: Not sure if Min is a synonymn of Minimum or a new operation "FloorDiv" : injectFaultFloorDiv, "Reshape" : injectFaultReshape, "OneHot": injectFaultOneHot, "Tile" : injectFaultTile, "ConcatV2" : injectFaultConcatV2, "ConcatOffset" : injectFaultConcatOffset, "BiasAdd" : injectFaultBiasAdd, "Split" : injectFaultSplit, "Sigmoid" : injectFaultSigmoid, "Tanh" : injectFaultTanh, "Softmax" : injectFaultSoftmax, "SoftmaxCrossEntropyWithLogits" : injectFaultSoftmaxCEWL, "Pack" : injectFaultPack, "Slice" : injectFaultSlice, "StridedSlice" : injectFaultStridedSlice, "BroadcastGradientArgs" : injectFaultBroadcastGA, "Neg" : injectFaultNeg, "Pow" : injectFaultPow, "Abs" : injectFaultAbs, "Unpack": injectFaultUnpack, "Unstack": injectFaultUnstack, "MatMul" : injectFaultMatMul, "ArgMax" : injectFaultArgMax, "ArgMin" : injectFaultArgMin, "Equal" : injectFaultEqual, "NotEqual" : injectFaultNotEqual, "LessEqual" : injectFaultLessEqual, "GreaterEqual" : injectFaultGreaterEqual, "TruncatedNormal" : injectFaultTruncatedNormal, "Conv2D" : injectFaultConv2D, "Relu" : injectFaultRelu, "MaxPool" : injectFaultMaxPool, "RandomUniform" : injectFaultRandomUniform, "RandomUniformInt" : injectFaultRandomUniformInt, "RandomStandardNormal" : injectFaultRandomStandardNormal, "Floor" : injectFaultFloor, "Rsqrt" : injectFaultRsqrt, "Log" : injectFaultLog, "RefSwitch" : injectFaultRefSwitch, "NearestNeighbors" : injectFaultNN, "Prod" : injectFaultProd, "Squeeze" : injectFaultSqueeze, "Unique" : injectFaultUnique, "Reciprocal" : injectFaultReciprocal, "ScatterAdd" : injectFaultScatterAdd, "ReluGrad" : injectFaultReluGrad, "MaxPoolGrad" : injectFaultMaxPoolGrad, "TanhGrad" : injectFaultTanhGrad, "SigmoidGrad" : injectFaultSigmoidGrad, "BiasAddGrad" : injectFaultBiasAddGrad, "ShapeN" : injectFaultShapeN, "AddN" : injectFaultAddN, "Conv2DBackpropInput" : injectFaultConv2DBackprop, "Conv2DBackpropFilter" : injectFaultConv2DBackprop, "ApplyAdam" : injectFaultApplyAdam, "Select" : injectFaultSelect, "Switch" : injectFaultSwitch, "Merge" : injectFaultMerge, "Transpose" : injectFaultTranspose, "Gather" : injectFaultGather, "UnsortedSegmentSum" : injectFaultUnsortedSegmentSum, "InvertPermutation" : injectFaultInvertPermutation, # Casts are treated differently, so don't add them to this table ! See createInjectFaultCast # "Cast" : injectFaultCast, "Mean" : injectFaultMean, "Count_nonzero" : injectFaultCountNonZero, "RealDiv" : injectFaultRealDiv, "Greater" : injectFaultGreater, "ApplyGradientDescent" : injectFaultApplyGradientDescent, "ZerosLike" : injectFaultZerosLike, "PreventGradient" : injectFaultPreventGradient, "ExpandDims" : injectFaultExpandDims, "SparseSoftmaxCrossEntropyWithLogits" : injectFaultSSSmcEWL, "All" : injectFaultAll, "Assert" : injectFaultAssert, "Less" : injectFaultLess, "FertileStatsResourceHandleOp" : injectFaultFSRHOP, "L2Loss" : injectFaultL2Loss, "ApplyMomentum" : injectFaultApplyMomentum, "AssignAdd" : injectFaultAssignAdd, "LRN" : injectFaultLRN, "Elu" : injectFaultELU, "Unknown": injectFaultGeneric # Last operation # "Unknown": None # For debugging purposes }

PhuongLe/PilotNet

TensorFI/injectFault.py

Python

import glob import yaml import os from generators import intermediate_files from schema import cleaner # This script takes all ECS and custom fields already loaded, and lets users # filter out the ones they don't need. def filter(fields, subset_file_globs, out_dir): subsets = load_subset_definitions(subset_file_globs) for subset in subsets: subfields = extract_matching_fields(fields, subset['fields']) intermediate_files.generate(subfields, os.path.join(out_dir, 'ecs', 'subset', subset['name']), False) merged_subset = combine_all_subsets(subsets) if merged_subset: fields = extract_matching_fields(fields, merged_subset) return fields def combine_all_subsets(subsets): '''Merges N subsets into one. Strips top level 'name' and 'fields' keys as well as non-ECS field options since we can't know how to merge those.''' merged_subset = {} for subset in subsets: strip_non_ecs_options(subset['fields']) merge_subsets(merged_subset, subset['fields']) return merged_subset def load_subset_definitions(file_globs): if not file_globs: return [] subsets = [] for f in eval_globs(file_globs): raw = load_yaml_file(f) subsets.append(raw) if not subsets: raise ValueError('--subset specified, but no subsets found in {}'.format(file_globs)) return subsets def load_yaml_file(file_name): with open(file_name) as f: return yaml.safe_load(f.read()) def eval_globs(globs): '''Accepts an array of glob patterns or file names, returns the array of actual files''' all_files = [] for g in globs: new_files = glob.glob(g) if len(new_files) == 0: warn("{} did not match any files".format(g)) else: all_files.extend(new_files) return all_files # You know, for silent tests def warn(message): print(message) ecs_options = ['fields', 'enabled', 'index'] def strip_non_ecs_options(subset): for key in subset: subset[key] = {x: subset[key][x] for x in subset[key] if x in ecs_options} if 'fields' in subset[key] and isinstance(subset[key]['fields'], dict): strip_non_ecs_options(subset[key]['fields']) def merge_subsets(a, b): '''Merges field subset definitions together. The b subset is merged into the a subset. Assumes that subsets have been stripped of non-ecs options.''' for key in b: if key not in a: a[key] = b[key] elif 'fields' in a[key] and 'fields' in b[key]: if b[key]['fields'] == '*': a[key]['fields'] = '*' elif isinstance(a[key]['fields'], dict) and isinstance(b[key]['fields'], dict): merge_subsets(a[key]['fields'], b[key]['fields']) elif 'fields' in a[key] or 'fields' in b[key]: raise ValueError("Subsets unmergeable: 'fields' found in key '{}' in only one subset".format(key)) # If both subsets have enabled set to False, this will leave enabled: False in the merged subset # Otherwise, enabled is removed and is implicitly true if a[key].get('enabled', True) or b[key].get('enabled', True): a[key].pop('enabled', None) # Same logic from 'enabled' applies to 'index' if a[key].get('index', True) or b[key].get('index', True): a[key].pop('index', None) def extract_matching_fields(fields, subset_definitions): '''Removes fields that are not in the subset definition. Returns a copy without modifying the input fields dict.''' retained_fields = {x: fields[x].copy() for x in subset_definitions} for key, val in subset_definitions.items(): retained_fields[key]['field_details'] = fields[key]['field_details'].copy() for option in val: if option != 'fields': if 'intermediate' in retained_fields[key]['field_details']: retained_fields[key]['field_details']['intermediate'] = False retained_fields[key]['field_details'].setdefault( 'description', 'Intermediate field included by adding option with subset') retained_fields[key]['field_details']['level'] = 'custom' cleaner.field_cleanup(retained_fields[key]) retained_fields[key]['field_details'][option] = val[option] # If the field in the schema has a 'fields' key, we expect a 'fields' key in the subset if 'fields' in fields[key]: if 'fields' not in val: raise ValueError("'fields' key expected, not found in subset for {}".format(key)) elif isinstance(val['fields'], dict): retained_fields[key]['fields'] = extract_matching_fields(fields[key]['fields'], val['fields']) elif val['fields'] != "*": raise ValueError("Unexpected value '{}' found in 'fields' key".format(val['fields'])) # If the field in the schema does not have a 'fields' key, there should not be a 'fields' key in the subset elif 'fields' in val: raise ValueError("'fields' key not expected, found in subset for {}".format(key)) return retained_fields

6un9-h0-Dan/ecs

scripts/schema/subset_filter.py

Python

# Copyright 2020 The Kubric Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import pathlib import functools from typing import Tuple, Dict, Union import bidict import bpy import munch from kubric import core logger = logging.getLogger(__name__) class Blender: def __init__(self, scene: core.Scene): self.objects_to_blend = bidict.bidict() self.ambient_node = None self.ambient_hdri_node = None self.illum_mapping_node = None self.bg_node = None self.bg_hdri_node = None self.bg_mapping_node = None self.clear_and_reset() # as blender has a default scene on load # the ray-tracing engine is set here because it affects the availability of some features bpy.context.scene.render.engine = "CYCLES" self.add(scene) self.set_up_scene_shading() bpy.context.scene.cycles.use_adaptive_sampling = True # speeds up rendering bpy.context.scene.view_layers[0].cycles.use_denoising = True # improves the output quality def add(self, obj: core.Asset): if obj in self.objects_to_blend: return self.objects_to_blend[obj] blender_obj, setters = add_object(obj) # set the name of the object to the UID blender_obj.name = obj.uid # if it has a rotation mode, then make sure it is set to quaternions if hasattr(blender_obj, "rotation_mode"): blender_obj.rotation_mode = "QUATERNION" # remember object association self.objects_to_blend[obj] = blender_obj # if object is an actual Object (eg. not a Scene, or a Material) # then ensure that it is linked into (used by) the current scene collection if isinstance(blender_obj, bpy.types.Object): collection = bpy.context.scene.collection.objects if blender_obj not in collection.values(): collection.link(blender_obj) for name, setter in setters.items(): setter.mapping = self.objects_to_blend # recursively add sub-assets value = getattr(obj, name) if isinstance(value, core.Asset): value = self.add(value) # Initialize values setter(munch.Munch(owner=obj, new=value, type="init")) # Link values obj.observe(setter, names=[name]) obj.destruction_callbacks.append(Destructor([blender_obj])) obj.keyframe_callbacks.append(Keyframer(setters)) return blender_obj def get_blender_object(self, obj: core.Object3D) -> bpy.types.Object: if isinstance(obj, bpy.types.Object): return obj elif isinstance(obj, core.Object3D): return self.objects_to_blend[obj] else: raise ValueError("Not a valid object {}".format(obj)) def clear_and_reset(self): bpy.ops.wm.read_factory_settings(use_empty=True) bpy.context.scene.world = bpy.data.worlds.new("World") def set_up_exr_output(self, path): bpy.context.scene.use_nodes = True tree = bpy.context.scene.node_tree links = tree.links # clear existing nodes for node in tree.nodes: tree.nodes.remove(node) # the render node has outputs for all the rendered layers render_node = tree.nodes.new(type="CompositorNodeRLayers") # create a new FileOutput node out_node = tree.nodes.new(type="CompositorNodeOutputFile") # set the format to EXR (multilayer) out_node.format.file_format = "OPEN_EXR_MULTILAYER" out_node.base_path = str(path) # output directory layers = ["Image", "Depth", "Vector", "UV", "Normal", "CryptoObject00"] out_node.file_slots.clear() for l in layers: out_node.file_slots.new(l) links.new(render_node.outputs.get(l), out_node.inputs.get(l)) def set_up_scene_shading(self): bpy.context.scene.world.use_nodes = True tree = bpy.context.scene.world.node_tree links = tree.links # clear the tree for node in tree.nodes.values(): tree.nodes.remove(node) # create nodes out_node = tree.nodes.new(type="ShaderNodeOutputWorld") out_node.location = 1100, 0 mix_node = tree.nodes.new(type="ShaderNodeMixShader") mix_node.location = 900, 0 lightpath_node = tree.nodes.new(type="ShaderNodeLightPath") lightpath_node.location = 700, 350 self.ambient_node = tree.nodes.new(type="ShaderNodeBackground") self.ambient_node.inputs["Color"].default_value = (0., 0., 0., 1.) self.ambient_node.location = 700, 0 self.bg_node = tree.nodes.new(type="ShaderNodeBackground") self.bg_node.inputs["Color"].default_value = (0., 0., 0., 1.) self.bg_node.location = 700, -120 links.new(lightpath_node.outputs.get("Is Camera Ray"), mix_node.inputs.get("Fac")) links.new(self.ambient_node.outputs.get("Background"), mix_node.inputs[1]) links.new(self.bg_node.outputs.get("Background"), mix_node.inputs[2]) links.new(mix_node.outputs.get("Shader"), out_node.inputs.get("Surface")) # create nodes for HDRI images, but leave them disconnected until set_ambient_illumination or set_background coord_node = tree.nodes.new(type="ShaderNodeTexCoord") self.bg_mapping_node = tree.nodes.new(type="ShaderNodeMapping") self.bg_mapping_node.location = 200, 200 self.bg_hdri_node = tree.nodes.new(type="ShaderNodeTexEnvironment") self.bg_hdri_node.location = 400, 200 links.new(coord_node.outputs.get("Generated"), self.bg_mapping_node.inputs.get("Vector")) links.new(self.bg_mapping_node.outputs.get("Vector"), self.bg_hdri_node.inputs.get("Vector")) #links.new(bg_hdri_node.outputs.get("Color"), self.bg_node.inputs.get("Color")) self.illum_mapping_node = tree.nodes.new(type="ShaderNodeMapping") self.illum_mapping_node.location = 200, -200 self.ambient_hdri_node = tree.nodes.new(type="ShaderNodeTexEnvironment") self.ambient_hdri_node.location = 400, -200 links.new(coord_node.outputs.get("Generated"), self.illum_mapping_node.inputs.get("Vector")) links.new(self.illum_mapping_node.outputs.get("Vector"), self.ambient_hdri_node.inputs.get("Vector")) # links.new(illum_hdri_node.outputs.get("Color"), self.illum_node.inputs.get("Color")) def set_ambient_light(self, hdri_filepath=None, color=(0., 0., 0., 1.0), hdri_rotation=(0., 0., 0.)): tree = bpy.context.scene.world.node_tree links = tree.links if hdri_filepath is None: # disconnect incoming links from hdri node (if any) for link in self.ambient_node.inputs["Color"].links: links.remove(link) self.ambient_node.inputs["Color"].default_value = color else: # ensure hdri_node is connected links.new(self.ambient_hdri_node.outputs.get("Color"), self.ambient_node.inputs.get("Color")) self.ambient_hdri_node.image = bpy.data.images.load(hdri_filepath, check_existing=True) self.illum_mapping_node.inputs.get("Rotation").default_value = hdri_rotation def set_background(self, hdri_filepath=None, color=(0., 0., 0., 1.0), hdri_rotation=(0., 0., 0.)): tree = bpy.context.scene.world.node_tree links = tree.links if hdri_filepath is None: # disconnect incoming links from hdri node (if any) for link in self.bg_node.inputs["Color"].links: links.remove(link) self.bg_node.inputs["Color"].default_value = color else: # ensure hdri_node is connected links.new(self.bg_hdri_node.outputs.get("Color"), self.bg_node.inputs.get("Color")) self.bg_hdri_node.image = bpy.data.images.load(hdri_filepath, check_existing=True) self.bg_mapping_node.inputs.get("Rotation").default_value = hdri_rotation def activate_render_passes(self): view_layer = bpy.context.scene.view_layers[0] view_layer.use_pass_vector = True # flow view_layer.use_pass_uv = True # UV view_layer.use_pass_normal = True # surface normals view_layer.cycles.use_pass_crypto_object = True # segmentation view_layer.cycles.pass_crypto_depth = 2 def set_size(self, width: int, height: int): bpy.context.scene.render.resolution_x = width bpy.context.scene.render.resolution_y = height def save_state(self, path: Union[pathlib.Path, str], filename: str = "scene.blend", pack_textures: bool = True): path = pathlib.Path(path) path.mkdir(parents=True, exist_ok=True) if pack_textures: bpy.ops.file.pack_all() # embed all textures into the blend file bpy.ops.wm.save_mainfile(filepath=str(path / filename)) def render(self, path: Union[pathlib.Path, str]): self.activate_render_passes() path = pathlib.Path(path) bpy.context.scene.render.filepath = str(path / "images" / "frame_") self.set_up_exr_output(path / "exr" / "frame_") bpy.ops.render.render(animation=True, write_still=True) # ########## Functions to import kubric objects into blender ########### @functools.singledispatch def add_object(obj: core.Asset) -> Tuple[bpy.types.Object, Dict[str, core.AttributeSetter]]: raise NotImplementedError() @add_object.register(core.Cube) def _add_object(obj: core.Cube): bpy.ops.mesh.primitive_cube_add() cube = bpy.context.active_object return cube, { 'position': core.AttributeSetter(cube, 'location'), 'quaternion': core.AttributeSetter(cube, 'rotation_quaternion'), 'scale': core.AttributeSetter(cube, 'scale'), 'material': core.AttributeSetter(cube, 'active_material') } @add_object.register(core.Sphere) def _add_object(obj: core.Sphere): bpy.ops.mesh.primitive_ico_sphere_add(subdivisions=5) bpy.ops.object.shade_smooth() cube = bpy.context.active_object return cube, { 'position': core.AttributeSetter(cube, 'location'), 'quaternion': core.AttributeSetter(cube, 'rotation_quaternion'), 'scale': core.AttributeSetter(cube, 'scale'), 'material': core.AttributeSetter(cube, 'active_material') } @add_object.register(core.FileBasedObject) def _add_object(obj: core.FileBasedObject): # TODO: support other file-formats bpy.ops.import_scene.obj(filepath=str(obj.render_filename), axis_forward=obj.front, axis_up=obj.up) assert len(bpy.context.selected_objects) == 1 blender_obj = bpy.context.selected_objects[0] setters = { "position": core.AttributeSetter(blender_obj, "location"), "quaternion": core.AttributeSetter(blender_obj, "rotation_quaternion"), "scale": core.AttributeSetter(blender_obj, "scale"), "material": core.AttributeSetter(blender_obj, "active_material") } return blender_obj, setters @add_object.register(core.DirectionalLight) def _add_object(obj: core.DirectionalLight): sun = bpy.data.lights.new(obj.uid, "SUN") sun_obj = bpy.data.objects.new(obj.uid, sun) setters = { "position": core.AttributeSetter(sun_obj, "location"), "quaternion": core.AttributeSetter(sun_obj, "rotation_quaternion"), "scale": core.AttributeSetter(sun_obj, "scale"), "color": core.AttributeSetter(sun, "color"), "intensity": core.AttributeSetter(sun, "energy")} return sun_obj, setters @add_object.register(core.RectAreaLight) def _add_object(obj: core.RectAreaLight): area = bpy.data.lights.new(obj.uid, "AREA") area_obj = bpy.data.objects.new(obj.uid, area) setters = { "position": core.AttributeSetter(area_obj, "location"), "quaternion": core.AttributeSetter(area_obj, "rotation_quaternion"), "scale": core.AttributeSetter(area_obj, "scale"), "color": core.AttributeSetter(area, "color"), "intensity": core.AttributeSetter(area, "energy"), "width": core.AttributeSetter(area, "size"), "height": core.AttributeSetter(area, "size_y")} return area_obj, setters @add_object.register(core.PointLight) def _add_object(obj: core.PointLight): area = bpy.data.lights.new(obj.uid, "POINT") area_obj = bpy.data.objects.new(obj.uid, area) setters = { "position": core.AttributeSetter(area_obj, "location"), "quaternion": core.AttributeSetter(area_obj, "rotation_quaternion"), "scale": core.AttributeSetter(area_obj, "scale"), "color": core.AttributeSetter(area, "color"), "intensity": core.AttributeSetter(area, "energy")} return area_obj, setters @add_object.register(core.PerspectiveCamera) def _add_object(obj: core.PerspectiveCamera): camera = bpy.data.cameras.new(obj.uid) camera.type = "PERSP" camera_obj = bpy.data.objects.new(obj.uid, camera) setters = { "position": core.AttributeSetter(camera_obj, "location"), "quaternion": core.AttributeSetter(camera_obj, "rotation_quaternion"), "scale": core.AttributeSetter(camera_obj, "scale"), "focal_length": core.AttributeSetter(camera, "lens"), "sensor_width": core.AttributeSetter(camera, "sensor_width")} return camera_obj, setters @add_object.register(core.OrthographicCamera) def _add_object(obj: core.OrthographicCamera): camera = bpy.data.cameras.new(obj.uid) camera.type = 'ORTHO' camera_obj = bpy.data.objects.new(obj.uid, camera) setters = { 'position': core.AttributeSetter(camera_obj, 'location'), 'quaternion': core.AttributeSetter(camera_obj, 'rotation_quaternion'), 'scale': core.AttributeSetter(camera_obj, 'scale'), 'orthographic_scale': core.AttributeSetter(camera, 'ortho_scale')} return camera_obj, setters @add_object.register(core.PrincipledBSDFMaterial) def _add_object(obj: core.PrincipledBSDFMaterial): mat = bpy.data.materials.new(obj.uid) mat.use_nodes = True bsdf_node = mat.node_tree.nodes["Principled BSDF"] setters = { "color": core.AttributeSetter(bsdf_node.inputs["Base Color"], "default_value"), "roughness": core.AttributeSetter(bsdf_node.inputs["Roughness"], "default_value"), "metallic": core.AttributeSetter(bsdf_node.inputs["Metallic"], "default_value"), "specular": core.AttributeSetter(bsdf_node.inputs["Specular"], "default_value"), "specular_tint": core.AttributeSetter(bsdf_node.inputs["Specular Tint"], "default_value"), "ior": core.AttributeSetter(bsdf_node.inputs["IOR"], "default_value"), "transmission": core.AttributeSetter(bsdf_node.inputs["Transmission"], "default_value"), "transmission_roughness": core.AttributeSetter(bsdf_node.inputs["Transmission Roughness"], "default_value"), "emission": core.AttributeSetter(bsdf_node.inputs["Emission"], "default_value"), } return mat, setters @add_object.register(core.MeshChromeMaterial) def _add_object(obj: core.MeshChromeMaterial): # --- Create node-based material mat = bpy.data.materials.new("Chrome") mat.use_nodes = True tree = mat.node_tree tree.remove(tree.nodes["Principled BSDF"]) # remove the default shader # --- Specify nodes LW = tree.nodes.new("ShaderNodeLayerWeight") LW.inputs[0].default_value = 0.7 CR = tree.nodes.new("ShaderNodeValToRGB") CR.color_ramp.elements[0].position = 0.9 CR.color_ramp.elements[1].position = 1 CR.color_ramp.elements[1].color = (0, 0, 0, 1) GLO = tree.nodes.new("ShaderNodeBsdfGlossy") # --- link nodes tree.links.new(LW.outputs[1], CR.inputs["Fac"]) tree.links.new(CR.outputs["Color"], GLO.inputs["Color"]) tree.links.new(GLO.outputs[0], tree.nodes["Material Output"].inputs["Surface"]) setters = { "color": core.AttributeSetter(CR.color_ramp.elements[0], "color"), "roughness": core.AttributeSetter(GLO.inputs[1], "default_value") } return mat, setters @add_object.register(core.FlatMaterial) def _add_object(obj: core.FlatMaterial): # --- Create node-based material mat = bpy.data.materials.new('Holdout') mat.use_nodes = True tree = mat.node_tree tree.nodes.remove(tree.nodes['Principled BSDF']) # remove the default shader output_node = tree.nodes['Material Output'] # This material is constructed from three different shaders: # 1. if holdout=False then emission_node is responsible for giving the object a uniform color # 2. if holdout=True, then the holdout_node is responsible for making the object transparent # 3. if indirect_visibility=False then transparent_node makes the node invisible for indirect # effects such as shadows or reflections light_path_node = tree.nodes.new(type="ShaderNodeLightPath") holdout_node = tree.nodes.new(type="ShaderNodeHoldout") transparent_node = tree.nodes.new(type="ShaderNodeBsdfTransparent") holdout_mix_node = tree.nodes.new(type="ShaderNodeMixShader") indirect_mix_node = tree.nodes.new(type="ShaderNodeMixShader") overall_mix_node = tree.nodes.new(type="ShaderNodeMixShader") emission_node = tree.nodes.new(type="ShaderNodeEmission") tree.links.new(transparent_node.outputs['BSDF'], indirect_mix_node.inputs[1]) tree.links.new(emission_node.outputs['Emission'], indirect_mix_node.inputs[2]) tree.links.new(emission_node.outputs['Emission'], holdout_mix_node.inputs[1]) tree.links.new(holdout_node.outputs['Holdout'], holdout_mix_node.inputs[2]) tree.links.new(light_path_node.outputs['Is Camera Ray'], overall_mix_node.inputs['Fac']) tree.links.new(indirect_mix_node.outputs['Shader'], overall_mix_node.inputs[1]) tree.links.new(holdout_mix_node.outputs['Shader'], overall_mix_node.inputs[2]) tree.links.new(overall_mix_node.outputs['Shader'], output_node.inputs['Surface']) return mat, { 'color': core.AttributeSetter(emission_node.inputs['Color'], 'default_value'), 'holdout': core.AttributeSetter(holdout_mix_node.inputs['Fac'], 'default_value'), 'indirect_visibility': core.AttributeSetter(indirect_mix_node.inputs['Fac'], 'default_value'), } @add_object.register(core.Scene) def _add_scene(obj: core.Scene): blender_scene = bpy.context.scene setters = { "frame_start": core.AttributeSetter(blender_scene, "frame_start"), "frame_end": core.AttributeSetter(blender_scene, "frame_end"), "frame_rate": core.AttributeSetter(blender_scene.render, "fps"), "resolution": core.AttributeSetter(blender_scene.render, ["resolution_x", "resolution_y"]), "camera": core.AttributeSetter(blender_scene, "camera") } return blender_scene, setters # ########### ########### ########### ########### ########### ########### ########### ########## class Destructor: def __init__(self, blender_objects): self.blender_objects = blender_objects def __call__(self, owner=None): for obj in self.blender_objects: try: if isinstance(obj, bpy.types.Object): bpy.data.objects.remove(obj, do_unlink=True) elif isinstance(obj, bpy.types.Material): bpy.data.materials.remove(obj, do_unlink=True) except ReferenceError: pass # In this case the object is already gone class Keyframer: def __init__(self, setters): self.setters = setters def __call__(self, owner, member, frame): setter = self.setters[member] setter.target_obj.keyframe_insert(setter.target_name, frame=frame)

Xtuden-com/kubric

kubric/renderer/blender.py

Python

from bs4 import BeautifulSoup from spotipy.oauth2 import SpotifyOAuth import requests import spotipy SPOTIFY_CLIENT_ID = "YOUR_SPOTIFY_CLIENT_ID" SPOTIFY_CLIENT_SECRET = "YOUR_SPOTIFY_CLIENT_SECRET" sp = spotipy.Spotify( auth_manager=SpotifyOAuth( client_id=SPOTIFY_CLIENT_ID, client_secret=SPOTIFY_CLIENT_SECRET, redirect_uri="https://www.example.com", scope="playlist-modify-private", show_dialog=True, cache_path="token.txt" ) ) user_id = sp.current_user()["id"] travel_date = input("Which year do you want to travel to? Type the date in this format YYYY-MM-DD:") travel_year = travel_date[:4] billboard_url = f"https://www.billboard.com/charts/hot-100/{travel_date}" response = requests.get(billboard_url) soup = BeautifulSoup(response.text, "html.parser") song_names = [name.getText() for name in soup.select(".chart-element__information__song")] song_artists = [name.getText() for name in soup.select(".chart-element__information__artist")] songs = [{ "artist": song_artists[i], "name": song_names[i] } for i in range(len(song_artists))] print(songs) song_urls = [] for song in songs: sp_song = sp.search(f"track:{song['name']} year:{travel_year}", type="track") try: url = sp_song["tracks"]["items"][0]["uri"] song_urls.append(url) except IndexError: print(f"{song['name']} doesn't exist in Spotify. Skipped.") playlist = sp.user_playlist_create(user=user_id, name=f"{travel_date} Billboard 100", public=False) sp.playlist_add_items(playlist_id=playlist["id"], items=song_urls)

gabrielmdr/100-days-of-code

projects/day046/music-time-machine.py

Python

""" config_objects.py By: John-Michael O'Brien Date: 7/25/2020 Data structures that define and load configuration information for the wallpaper watcher. """ from typing import List, Dict, Optional from dataclasses import dataclass, field import jsons import yaml @dataclass class SubredditConfig(): """ Holds any per-subreddit configuration. That's nothing right now. """ @dataclass class MultiredditConfig(): """ Holds information necessary to access a multireddit """ user: str multi: str @dataclass class SourcesConfig(): """ Holds information about image sources """ subreddits: Optional[Dict[str, Optional[SubredditConfig]]] multis: Optional[Dict[str, MultiredditConfig]] @dataclass class Size(): """ Holds a size """ width: int height: int aspect_ratio: float = field(init=False, repr=False) def __post_init__(self): self.aspect_ratio = float(self.width) / float(self.height) @dataclass class TargetConfig(): """ Holds information about a save target """ path: str size: Size sources: List[str] allow_nsfw: bool = True @dataclass class WallpaperConfig(): """ Loads and holds the configuration for wallpaperwatcher. """ aspect_ratio_tolerance: float max_downloads: int update_interval: int sources: SourcesConfig targets: Dict[str, TargetConfig] @staticmethod def from_file(filename: str) -> "WallpaperConfig": """ Creates a WallpaperConfig from a YAML file """ with open(filename, "r") as input_file: return jsons.load(yaml.load(input_file, Loader=yaml.SafeLoader), WallpaperConfig) @dataclass class RedditAuthInfo(): """ Holds Reddit Authentication Values """ client_id: str client_secret: str @staticmethod def from_file(filename: str) -> "RedditAuthInfo": """ Creates a RedditAuthInfo from a YAML file """ with open(filename, "r") as input_file: auth = jsons.load(yaml.load(input_file, Loader=yaml.SafeLoader), RedditAuthInfo) return auth

JimTheCactus/RedditWallpaperWatcher

config_objects.py

Python

# Copyright 2021 Research Institute of Systems Planning, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.from caret_analyze import Application, Lttng from caret_analyze import Application, Lttng from caret_analyze.plot import message_flow from ros2caret.verb import VerbExtension class MessageFlowVerb(VerbExtension): def add_arguments(self, parser, cli_name): parser.add_argument( '-t', '--trace_dir', dest='trace_dir', help='trace dir', required=True) parser.add_argument( '-a', '--architecture_path', dest='architecture_path', help='architecture', required=True) parser.add_argument( '-o', '--output_path', dest='output_path', help='output path to the message flow file', required=True) parser.add_argument( '-p', '--path_name', dest='path_name', help='path name of trace points to be visualized', required=True) parser.add_argument( '-g', '--granularity', dest='granularity', default=None, help='granularity of trace points to be visualized') def main(self, *, args): lttng = Lttng(args.trace_dir, force_conversion=True) app = Application(args.architecture_path, 'yaml', lttng) path = app.path[args.path_name] message_flow(path, export_path=args.output_path, granularity=args.granularity)

tier4/ros2caret

ros2caret/verb/message_flow.py

Python

# -*- coding: utf-8 -*- """ unifonicnextgen This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ). """ class GetScheduledMessageResponse(object): """Implementation of the 'Get Scheduled Message response' model. GetsDetails of specified scheduled message, If MessageID is specified, only one message is returned,Otherwise all messages(paginated) are queried. Attributes: success (bool): The request sent successfully message (string): The Error message if its false, Null if its true error_code (string): the error code if there is any data (object): Message id, Time created, correlation id., status, number of units, cost, balance, Recipient """ # Create a mapping from Model property names to API property names _names = { "success":'success', "message":'message', "error_code":'errorCode', "data":'data' } def __init__(self, success=None, message=None, error_code=None, data=None): """Constructor for the GetScheduledMessageResponse class""" # Initialize members of the class self.success = success self.message = message self.error_code = error_code self.data = data @classmethod def from_dictionary(cls, dictionary): """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class. """ if dictionary is None: return None # Extract variables from the dictionary success = dictionary.get('success') message = dictionary.get('message') error_code = dictionary.get('errorCode') data = dictionary.get('data') # Return an object of this model return cls(success, message, error_code, data)

masaar/unifonic_python_sdk

unifonicnextgen/models/get_scheduled_message_response.py

Python

# Copyright 2019, OpenCensus Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from setuptools import find_packages, setup from version import __version__ setup( name='opencensus-ext-pymongo', version=__version__, # noqa author='OpenCensus Authors', author_email='census-developers@googlegroups.com', classifiers=[ 'Intended Audience :: Developers', 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', ], description='OpenCensus pymongo Integration', include_package_data=True, long_description=open('README.rst').read(), install_requires=[ 'opencensus >= 0.7.13, < 1.0.0', 'pymongo >= 3.1.0', ], extras_require={}, license='Apache-2.0', packages=find_packages(exclude=('tests',)), namespace_packages=[], url='https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-pymongo', # noqa: E501 zip_safe=False, )

census-instrumentation/opencensus-python

contrib/opencensus-ext-pymongo/setup.py

Python

# encoding: utf-8 import json from .. import ( DatabaseTest, sample_data ) from lxml import etree from core.coverage import CoverageFailure from core.model import Contributor, Identifier, Measurement from core.metadata_layer import * from oclc.classify import ( IdentifierLookupCoverageProvider, OCLCClassifyXMLParser, MockOCLCClassifyAPI, ) class MockParser(OCLCClassifyXMLParser): def __init__(self): self.call_count = 0 self.called_with = [] def parse(self, tree, metadata): self.call_count += 1 self.called_with = metadata return self.called_with class MockParserSingle(MockParser): def initial_look_up(self, tree): return 2, [] class MockParserMulti(MockParser): def initial_look_up(self, tree): results = [] owi_numbers = ["48446512", "48525129"] for number in owi_numbers: data = IdentifierData(Identifier.OCLC_WORK, number) results.append(data) return 4, results class MockProvider(IdentifierLookupCoverageProvider): def __init__(self, collection): self.apply_called_with = [] self.apply_call_count = 0 super(MockProvider, self).__init__(collection) def _single(self, tree, metadata): self.called_with = dict(tree=tree, metadata=metadata) metadata.data_source = DataSource.OCLC return metadata def _multiple(self, owi_data, metadata): self.called_with = dict(owi_data=owi_data, metadata=metadata) return metadata def _apply(self, metadata): self.apply_called_with = metadata.primary_identifier self.apply_call_count += 1 class MockProviderSingle(MockProvider): def _get_tree(self, **kwargs): xml = sample_data("single_work_with_isbn.xml", "oclc_classify") return etree.fromstring(xml, parser=etree.XMLParser(recover=True)) class MockProviderMulti(MockProvider): def _get_tree(self, **kwargs): xml = sample_data("multi_work_with_owis.xml", "oclc_classify") return etree.fromstring(xml, parser=etree.XMLParser(recover=True)) class TestIdentifierLookupCoverageProvider(DatabaseTest): SINGLE_ISBN = "9781620281932" MULTI_ISBN = "0345391837" def _tree(self, type): if type == "single": return MockProviderSingle(self._default_collection)._get_tree(isbn=self.SINGLE_ISBN) else: return MockProviderMulti(self._default_collection)._get_tree(isbn=self.MULTI_ISBN) def _id(self, type): if type == "single": return self._identifier(Identifier.ISBN, self.SINGLE_ISBN) else: return self._identifier(Identifier.ISBN, self.MULTI_ISBN) def test_process_item_single(self): # Testing that, when process_item finds out that a document's status code is 2, # it calls _single, passes in the correct tree and blank metadata object as arguments, # and returns the original ISBN. Uses mocked versions of _get_tree, # initial_look_up, and _single. provider = MockProviderSingle(self._default_collection) provider.parser = MockParserSingle() id = self._id("single") result = provider.process_item(id) assert etree.tostring(provider.called_with["tree"]) == etree.tostring(provider._get_tree(isbn=self.SINGLE_ISBN)) assert isinstance(provider.called_with["metadata"], Metadata) assert provider.called_with["metadata"].primary_identifier == id assert result == id def test_process_item_multi(self): # Testing that, when process_item finds out that a document's status code is 4, # it calls _multiple, passes in the correct OWIs, and # returns the original ISBN. Uses mocked versions of _get_tree, initial_look_up, # and _multiple. provider = MockProviderMulti(self._default_collection) provider.parser = MockParserMulti() id = self._id("multi") result = provider.process_item(id) assert [x.identifier for x in provider.called_with["owi_data"]] == ["48446512", "48525129"] assert isinstance(provider.called_with["metadata"], Metadata) assert provider.called_with["metadata"].primary_identifier == id assert result == id def test_process_item_failure(self): # If the ISBN is not found--i.e. the status code is 102--the provider should throw an error. api = MockOCLCClassifyAPI(self._db) api.queue_response(sample_data("isbn_not_found.xml", "oclc_classify")) provider = IdentifierLookupCoverageProvider(self._default_collection, api=api) bad_id = self._identifier(Identifier.ISBN, "9781429984171") failure = provider.process_item(bad_id) # We asked OCLC about the ISBN... assert ['http://classify.oclc.org/classify2/Classify?isbn=9781429984171'] == api.requests # ...but we didn't get anything useful. assert isinstance(failure, CoverageFailure) assert failure.exception == "The work with ISBN 9781429984171 was not found." def test__apply_propagates_replacement_policy(self): # When IdentifierLookupCoverageProvider applies metadata # to the database, it uses the replacement policy associated with # the coverage provider. class MockMetadata(Metadata): def apply(self, *args, **kwargs): self.called_with = (args, kwargs) metadata = MockMetadata(data_source=DataSource.OCLC, primary_identifier=self._identifier()) provider = IdentifierLookupCoverageProvider( self._default_collection, replacement_policy=object() ) provider._apply(metadata) args, kwargs = metadata.called_with assert kwargs['replace'] == provider.replacement_policy def test__apply_single(self): # Testing that, in the case of a single-work response, _apply is called with the return value of _single. provider = MockProviderSingle(self._default_collection) provider.parser = MockParserSingle() id = self._id("single") provider.process_item(id) result = provider.apply_called_with assert result.identifier == id.identifier assert provider.apply_call_count == 1 def test__apply_multiple(self): # Testing that, even in the case of a multi-work response, _apply is only called once; # we only want to end up with one Metadata object (and one corresponding edition). provider = MockProviderMulti(self._default_collection) provider.parser = MockParserMulti() id = self._id("multi") provider.process_item(id) result = provider.apply_called_with assert result == id assert provider.apply_call_count == 1 def test__single(self): # Testing that _single calls parse, passes in the correct tree and # identifier as arguments, and returns the resulting value. Uses a mocked # version of parse. provider = IdentifierLookupCoverageProvider(self._default_collection) provider.parser = MockParserSingle() tree, identifier = self._tree("single"), self._id("single") metadata = self._blank_metadata(identifier) provider._single(tree, metadata) result = provider.parser.called_with assert (result.primary_identifier.type, result.primary_identifier.identifier) == (Identifier.ISBN, self.SINGLE_ISBN) def test__multiple(self): # Testing that _multiple calls parse, passes in the correct OWIs, and # returns the resulting value. Uses mocked versions of # initial_look_up (to get the list of OWIs) and parse. api = MockOCLCClassifyAPI(self._db) for filename in ( 'single_work_48446512.xml', 'single_work_48525129.xml', ): api.queue_response(sample_data(filename, "oclc_classify")) provider = IdentifierLookupCoverageProvider(self._default_collection, api=api) provider.parser = MockParserMulti() tree, identifier = self._tree("multi"), self._id("multi") metadata = self._blank_metadata(identifier) code, owi_data = provider.parser.initial_look_up(tree) provider._multiple(owi_data, metadata) result = provider.parser.called_with # Make sure parse was called twice--once for each of the two OWIs. assert provider.parser.call_count == 2 assert result.primary_identifier.identifier == self.MULTI_ISBN assert isinstance(result.primary_identifier, Identifier) def test__single_with_real_parser(self): # Testing that calling _single actually returns the correct metadata object. provider = IdentifierLookupCoverageProvider(self._default_collection) tree, identifier = self._tree("single"), self._id("single") metadata = self._blank_metadata(identifier) result = provider._single(tree, metadata) assert isinstance(result, Metadata) assert result._data_source == "OCLC Classify" assert result.primary_identifier == identifier self._check_measurements(result.measurements, "single") [author] = result.contributors assert isinstance(author, ContributorData) assert self._get_contributor_info(author) == ("Melville, Herman", "n79006936", "27068555", ["Author"], {"deathDate": "1891", "birthDate": "1819"}) def test__multiple_with_real_parser(self): # Testing that calling _multiple actually returns the correct metadata object. api = MockOCLCClassifyAPI(self._db) for filename in ( 'single_work_48446512.xml', 'single_work_48525129.xml', ): api.queue_response(sample_data(filename, "oclc_classify")) provider = IdentifierLookupCoverageProvider( self._default_collection, api=api ) tree, identifier = self._tree("multi"), self._id("multi") metadata = self._blank_metadata(identifier) code, owi_data = provider.parser.initial_look_up(tree) result = provider._multiple(owi_data, metadata) # Two requests were made to the mock API -- one for each of the OWIs we had to look up # while parsing the multi-OWI document. assert ( [ 'http://classify.oclc.org/classify2/Classify?owi=48446512', 'http://classify.oclc.org/classify2/Classify?owi=48525129', ] == api.requests ) # We ended up with a single Metadata object, which contains # information derived from looking up both OWIs. assert isinstance(result, Metadata) assert result._data_source == "OCLC Classify" assert result.primary_identifier == identifier # The author info just comes from the first work. expected_author_info = ("Adams, Douglas", "n80076765", "113230702", ["Author"], {"deathDate": "2001", "birthDate": "1952"}) [author] = result.contributors author_info = self._get_contributor_info(author) assert author_info == expected_author_info # Measurement info is also just from the first work. self._check_measurements(result.measurements, "multi") # The subject data is collected from both works. We prove this by making sure # that the list of Fast identifiers consists of the unique union of the Fast identifiers # obtained by looking up each of the two <work> tags by its OWI. [ddc], [lcc], fast = self._get_subjects(result.subjects) assert ddc.identifier == "823.914" assert lcc.identifier == "PR6051.D3352" # We got 5 Fast subject classifications from the first <work> tag: fast_work_1 = set([ "Dent, Arthur (Fictitious character)", "Prefect, Ford", "Interplanetary voyages", "Interstellar travel", "Fiction", ]) # And 6 from the second <work> tag, 4 of which overlap with the ones from the first <work> tag: fast_work_2 = set([ "Dent, Arthur (Fictitious character)", "Prefect, Ford", "Interplanetary voyages", "Interstellar travel", "Science fiction, English", "Humorous stories, English", ]) # So, our Metadata object should end up with 7 Fast subject classifications--the 4 shared ones, # plus 1 unique one from work #1 and 2 unique ones from work #2. assert len(fast) == 7 fast_subject_names = set([x.name for x in fast]) assert fast_subject_names == fast_work_1.union(fast_work_2) def _get_subjects(self, subjects): # Everything in the list of subjects should be a SubjectData object. assert len([x for x in subjects if isinstance(x, SubjectData)]), len(subjects) # Extract a sublist for each type of classifier. sublists = [[x for x in subjects if x.type == type] for type in ["DDC", "LCC", "FAST"]] # There should always be 1 DDC classification and 1 LCC classification. assert (len(sublists[0]), len(sublists[1])) == (1, 1) return sublists def _check_measurements(self, measurements, type): values = { "single": { Measurement.HOLDINGS: 41932, Measurement.PUBLISHED_EDITIONS: 1 }, "multi": { Measurement.HOLDINGS: 5976, Measurement.PUBLISHED_EDITIONS: 160 } } assert len(measurements) == 2 [holdings], [editions] = [[x for x in measurements if y in x.quantity_measured] for y in ["holdings", "editions"]] for m in [holdings, editions]: assert isinstance(m, MeasurementData) expected_value = values[type][m.quantity_measured] assert m.weight == 1 assert m.value == expected_value def _get_contributor_info(self, contributor): return ( contributor.sort_name, contributor.lc, contributor.viaf, contributor.roles, contributor.extra ) def _blank_metadata(self, identifier): metadata = Metadata( data_source=DataSource.OCLC, primary_identifier=identifier ) return metadata

NYPL-Simplified/metadata_wrangler

tests/oclc_/test_identifier_lookup_coverage_provider.py

Python

from django.shortcuts import redirect, render from django.views.generic import TemplateView from ..models import Student class SignUpView(TemplateView): template_name = 'registration/signup.html' def home(request): if request.user.is_authenticated: if request.user.is_teacher: return redirect('teachers:quiz_change_list') else: return redirect('students:quiz_list') return render(request, 'classroom/home.html') def save_github_user(backend, user, response, *args, **kwargs): if backend.name == 'github': if not user.is_student: user.is_student = True user.save() student = Student.objects.create(user=user) # avatar_url = response.get('avatar_url') # print(user, response)

netomo/django-schools

django_school/classroom/views/classroom.py

Python

import pytorchresearch as ptr import torch import torchvision if __name__ == "__main__": # transform for data transform = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) ) ]) # dataloaders trainset = torchvision.datasets.CIFAR100(root='./data/datasets', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR100(root='./data/datasets', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=2) # model specific stuff model = torchvision.models.MobileNetV2(num_classes=100) if torch.cuda.is_available(): model = model.cuda() optimizer = torch.optim.SGD( params=model.parameters(), lr=0.001, momentum=0.9 ) criterion = torch.nn.CrossEntropyLoss() # MAGIC GOES HERE research = ptr.ModelResearch( research_path='.temp', research_scheme=[ ptr.ModelConfigurationItem(), ptr.CurrentIterationItem(print_end=' ', iteration_modulo=10), ptr.LossPrintItem(iteration_modulo=10), ptr.LossVisualizationItem(iteration_modulo=10) ], model=model, optimizer=optimizer, criterion=criterion, watch_test=False ) research.start_research_session( trainloader, testloader, epochs=1, iteration_modulo=20)

VoIlAlex/pytorchresearch

docs/example_1/back.py

Python

from django.db.models import Q from .base import EntityType TYPE_VIDEO = "video" class VideoEntity(EntityType): name = TYPE_VIDEO @classmethod def filter_date_lte(cls, qs, dt): return qs.filter(publication_date__lte=dt) @classmethod def filter_date_gte(cls, qs, dt): return qs.filter(publication_date__gte=dt) @classmethod def filter_search(cls, qs, query): from tournesol.models import Entity # Filtering in a nested queryset is necessary here, to be able to annotate # each entity without duplicated scores, due to the m2m field 'tags'. return qs.filter(pk__in=Entity.objects.filter( Q(name__icontains=query) | Q(description__icontains=query) | Q(tags__name__icontains=query) ))

iamnkc/tournesol

backend/tournesol/entities/video.py

Python

import logging import random import time from jobcontrol.exceptions import SkipBuild def job_simple_echo(*args, **kwargs): return (args, kwargs) _cached_words = None def _get_words(): global _cached_words if _cached_words is not None: return _cached_words try: with open('/usr/share/dict/words') as fp: _cached_words = [x.strip() for x in fp] except: _cached_words = [] return _cached_words def _capfirst(s): return s[0].upper() + s[1:] def _random_paragraph(size=10): return _capfirst(' '.join(random.sample(_get_words(), size))) def _log_random(logger): classes = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL) for num in xrange(random.randint(0, 30)): logger.log(random.choice(classes), _random_paragraph(random.randint(10, 20))) def testing_job(progress_steps=None, retval=None, fail=False, skip=False, log_messages=None, step_duration=0): """ Job used for testing purposes. :param progress_steps: A list of tuples: ``(<group_name>, <steps>)``, where "group_name" is a tuple of name "levels", "steps" an integer representing how many steps should that level have. Progress reports will be sent in randomized order. :param retval: The return value for the job. :param fail: Whether this job should fail. :param skip: Whether this job should be skipped. :param log_messages: A list of tuples: ``(level, message)`` :param step_duration: The time to sleep between steps, in milliseconds. """ from jobcontrol.globals import execution_context logger = logging.getLogger('jobcontrol.utils.testing_job') log_messages = list(log_messages or []) if progress_steps is None: progress_steps = [(None, 10)] totals = {} counters = {} progress_report_items = [] for name, steps in progress_steps: if isinstance(name, list): # Safe YAML doesn't have tuples, but names must be tuples name = tuple(name) if not (name is None or isinstance(name, tuple)): raise TypeError("Name must be a tuple or None") for i in xrange(steps): progress_report_items.append(name) totals[name] = steps counters[name] = 0 random.shuffle(progress_report_items) sleep_time = step_duration * 1.0 / 1000 def report_progress(name, cur, tot, status=None): app = execution_context.current_app app.report_progress( group_name=name, current=cur, total=tot, status_line=status) def _should_fail(): return random.randint(0, len(progress_report_items)) == 0 for item in progress_report_items: counters[item] += 1 report_progress(item, counters[item], totals[item], 'Doing action {0} [{1}/{2}]' .format(item, counters[item], totals[item])) if len(log_messages): lev, msg = log_messages.pop(0) logger.log(lev, msg) if fail and _should_fail(): raise RuntimeError( 'This is a simulated exception in the middle of the loop') if skip and _should_fail(): raise SkipBuild( 'This is a simulated skip in the middle of the loop') if sleep_time: time.sleep(sleep_time) if skip: # Make sure the job gets skipped raise SkipBuild('This build should be skipped!') if fail: # Make sure the job fails raise RuntimeError('This is a simulated exception') return retval def job_with_logging(): logger = logging.getLogger('jobcontrol.utils.testing.job_with_logging') logger.setLevel(logging.DEBUG) logger.debug('This is a debug message') logger.info('This is an info message') logger.warning('This is a warning message') logger.error('This is an error message') logger.critical('This is a critical message') try: raise ValueError('Foobar') except: logger.exception('This is an exception message') def job_with_tracer_log(): from jobcontrol.globals import execution_context logger = logging.getLogger(__name__) logger.info('Message from job={0}, build={1}' .format(execution_context.job_id, execution_context.build_id)) pass def job_failing_once(): """ This job will fail exactly once; retry will be successful """ from jobcontrol.globals import current_job exec_count = len(list(current_job.iter_runs())) if exec_count <= 1: # This is the first run raise RuntimeError("Simulating failure") return exec_count def job_echo_config(*args, **kwargs): """ Simple job, "echoing" back the current configuration. """ from jobcontrol.globals import current_job, current_build return { 'args': args, 'kwargs': kwargs, 'build_id': current_build.id, 'job_id': current_job.id, 'dependencies': current_build.config['dependencies'], 'config': current_build.config, } class RecordingLogHandler(logging.Handler): """Log handler that records messages""" def __init__(self): super(RecordingLogHandler, self).__init__() self._messages = [] def flush(self): pass # Nothing to flush! def emit(self, record): self._messages.append(record) def print_messages(self): from nicelog.formatters import ColorLineFormatter formatter = ColorLineFormatter( show_date=False, show_function=False, show_filename=False, message_inline=True) for msg in self._messages: print(formatter.format(msg)) def clear_messages(self): self._messages = [] class NonSerializableObject(object): __slots__ = ['foo', 'bar'] def __init__(self): self.foo = 'foo' self.bar = 'bar' class NonSerializableException(Exception): def __init__(self): super(NonSerializableException, self).__init__() self.nso = NonSerializableObject() def job_returning_nonserializable(): return NonSerializableObject() def job_raising_nonserializable(): raise NonSerializableException() def job_creating_temp_file(): import os import tempfile fd, name = tempfile.mkstemp(text=True) with os.fdopen(fd, 'w') as fp: fp.write('Hello, world!\n') return name def cleanup_temp_file(build): import os os.unlink(build.retval)

rshk/jobcontrol

jobcontrol/utils/testing.py

Python

""" Django settings for HedgeFund project. Generated by 'django-admin startproject' using Django 3.2.8. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent STATIC_ROOT = BASE_DIR / 'staticfiles' # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-v)1&-+#yg2xdd$jddc#$z-c!1gu%v*%_^*_&ppw@pm^x%w-^*8' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'sscapital', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'HedgeFund.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [BASE_DIR / 'templates'] , 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'HedgeFund.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'America/Los_Angeles' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/media/' MEDIA_ROOT = BASE_DIR / 'static' STATICFILES_DIRS = ( os.path.join(BASE_DIR, 'static'), ) LOGIN_REDIRECT_URL = '/administrator' STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' STATICFILES_STORAGE = 'whitenoise.storage.CompressedStaticFilesStorage' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

atreyasinha/Heptra-Capital

HedgeFund/base.py

Python

""" SleekXMPP: The Sleek XMPP Library Copyright (C) 2013 Nathanael C. Fritz, Lance J.T. Stout This file is part of SleekXMPP. See the file LICENSE for copying permission. """ from sleekxmpp.jid import JID from sleekxmpp.xmlstream import ElementBase, register_stanza_plugin class NoSave(ElementBase): name = 'x' namespace = 'google:nosave' plugin_attrib = 'google_nosave' interfaces = set(['value']) def get_value(self): return self._get_attr('value', '') == 'enabled' def set_value(self, value): self._set_attr('value', 'enabled' if value else 'disabled') class NoSaveQuery(ElementBase): name = 'query' namespace = 'google:nosave' plugin_attrib = 'google_nosave' interfaces = set() class Item(ElementBase): name = 'item' namespace = 'google:nosave' plugin_attrib = 'item' plugin_multi_attrib = 'items' interfaces = set(['jid', 'source', 'value']) def get_value(self): return self._get_attr('value', '') == 'enabled' def set_value(self, value): self._set_attr('value', 'enabled' if value else 'disabled') def get_jid(self): return JID(self._get_attr('jid', '')) def set_jid(self, value): self._set_attr('jid', str(value)) def get_source(self): return JID(self._get_attr('source', '')) def set_source(self, value): self._set_attr('source', str(value)) register_stanza_plugin(NoSaveQuery, Item)

Dunedan/SleekXMPP

sleekxmpp/plugins/google/nosave/stanza.py

Python

from sklearn.model_selection import cross_val_score import pandas as pd import matplotlib.pyplot as plt def CrossValidationFolds_Traversal(estimator, vdataset): """ Arguments: - estimator = classifer of model - vdataset = vehicld dataset This function computes acccuracy score with the Cross Validation Score for each KFold with K from 2 to 10 Output: returns matrix conatining value of K with it's corresponding performance score """ X = vdataset.drop(["Class", "Class_code"], axis=1) y = vdataset["Class_code"] scores = [] matrix = pd.DataFrame(columns=["KFold", "Accuracy"]) for i in range(2, 11): ##Kfold 2 to 10 score = cross_val_score(estimator, X, y, cv=i, scoring="accuracy") scores.append(score.mean()) matrix = matrix.append( {"KFold": i, "Accuracy": (score.mean() * 100),}, ignore_index=True, ) return matrix def Visulaize_CrossValidationFolds_Traversal(matrix): """ Argument: - matrix: Dataframe named matrix Line Plot is drawn for each KFold value with it's respective performance score. Output: - plot the line graph """ ax = plt.gca() matrix.plot( kind="line", x="KFold", y="Accuracy", color="red", marker="o", markerfacecolor="blue", markersize=12, ax=ax, ) plt.title("Line plot of No of Kfold with it's corresponding performance score\n") plt.ylabel("Accuracy\n") plt.xlabel("\nNo of KFolds") plt.show()

Bolaji61/PRESC

dev/shiza16/Calibration plot/CrossValidationFold_Traversal.py

Python

#!/usr/bin/env python import os import sys def addPath(rel_path, prepend=False): """ Adds a directory to the system python path, either by append (doesn't override default or globally installed package names) or by prepend (overrides default/global package names). """ path = lambda *paths: os.path.abspath( os.path.join(os.path.dirname(__file__), *paths)) + '/' if prepend: return sys.path.insert(0, path(rel_path)) return sys.path.append(path(rel_path)) # Allow us to not include `djoauth2example` when importing subapps. addPath('djoauth2example', prepend=True) # Use the local version of the `djoauth2` library; very useful for manually # testing the full series of client-server interactions while developing. addPath('..', prepend=True) if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "djoauth2example.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)

Locu/djoauth2

example/manage.py

Python

# TODO: your agent here! import numpy as np from agents.actor import Actor from agents.critic import Critic from agents.buffer import ReplayBuffer from agents.ou_noise import OUNoise class DDPG(): """Reinforcement Learning agent that learns using DDPG.""" def __init__(self, task): self.task = task self.state_size = task.state_size self.action_size = task.action_size self.action_low = task.action_low self.action_high = task.action_high # Actor (Policy) Model self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high) self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high) # Critic (Value) Model self.critic_local = Critic(self.state_size, self.action_size) self.critic_target = Critic(self.state_size, self.action_size) # Initialize target model parameters with local model parameters self.critic_target.model.set_weights(self.critic_local.model.get_weights()) self.actor_target.model.set_weights(self.actor_local.model.get_weights()) # Noise process self.exploration_mu = 0 self.exploration_theta = 0.15 self.exploration_sigma = 0.2 self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma) # Replay memory self.buffer_size = 100000 self.batch_size = 64 self.memory = ReplayBuffer(self.buffer_size, self.batch_size) # Algorithm parameters self.gamma = 0.99 # discount factor self.tau = 0.001 # for soft update of target parameters # Reward tracking self.reward_total = 0 self.best_reward = -np.Inf self.score = 0 self.count = 0 def reset_episode_vars(self): self.noise.reset() state = self.task.reset() self.last_state = state # Reward tracking self.reward_total = 0 self.score = 0 self.count = 0 self.max_z = 0 return state def step(self, action, reward, next_state, done): # Save experience / reward self.memory.add(self.last_state, action, reward, next_state, done) # Track the reward: Compute the total reward for this episode self.reward_total += reward self.count += 1 if done: self.score = self.reward_total / self.count if self.count > 0 else 0 self.score_best = max(self.best_reward, self.reward_total) # Learn, if enough samples are available in memory if len(self.memory) > self.batch_size: experiences = self.memory.sample() self.learn(experiences) # Roll over last state and action self.last_state = next_state def act(self, state): """Returns actions for given state(s) as per current policy.""" state = np.reshape(state, [-1, self.state_size]) action = self.actor_local.model.predict(state)[0] return list(action + self.noise.sample()) # add some noise for exploration def learn(self, experiences): """Update policy and value parameters using given batch of experience tuples.""" # Convert experience tuples to separate arrays for each element (states, actions, rewards, etc.) states = np.vstack([e.state for e in experiences if e is not None]) actions = np.array([e.action for e in experiences if e is not None]).astype(np.float32).reshape(-1, self.action_size) rewards = np.array([e.reward for e in experiences if e is not None]).astype(np.float32).reshape(-1, 1) dones = np.array([e.done for e in experiences if e is not None]).astype(np.uint8).reshape(-1, 1) next_states = np.vstack([e.next_state for e in experiences if e is not None]) # Get predicted next-state actions and Q values from target models # Q_targets_next = critic_target(next_state, actor_target(next_state)) actions_next = self.actor_target.model.predict_on_batch(next_states) Q_targets_next = self.critic_target.model.predict_on_batch([next_states, actions_next]) # Compute Q targets for current states and train critic model (local) Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones) self.critic_local.model.train_on_batch(x=[states, actions], y=Q_targets) # Train actor model (local) action_gradients = np.reshape(self.critic_local.get_action_gradients([states, actions, 0]), (-1, self.action_size)) self.actor_local.train_fn([states, action_gradients, 1]) # custom training function # Soft-update target models self.soft_update(self.critic_local.model, self.critic_target.model) self.soft_update(self.actor_local.model, self.actor_target.model) def soft_update(self, local_model, target_model): """Soft update model parameters.""" local_weights = np.array(local_model.get_weights()) target_weights = np.array(target_model.get_weights()) assert len(local_weights) == len(target_weights), "Local and target model parameters must have the same size" new_weights = self.tau * local_weights + (1 - self.tau) * target_weights target_model.set_weights(new_weights)

GabrielTourinho/dlnd-teach-a-quadcopter-how-to-fly

home/agents/agent.py

Python

# -*- coding: utf-8 -*- """PageParser tests.""" import httpx # noqa: F401 import pytest from core.database import ProductGinoModel from core.services import get_product_name pytestmark = [pytest.mark.asyncio, pytest.mark.api_full] API_URL_PREFIX = "/api/v1" @pytest.fixture def no_css_response() -> bytes: minified_html = b'<!DOCTYPE html><html lang="en"><head> <meta charset="UTF-8"> <title>Title</title></head><body><h1>TestProduct1</h1></body></html>' # noqa: E501 return minified_html @pytest.fixture def css_response() -> bytes: minified_html = '<!DOCTYPE html><html lang="en"><head> <meta charset="UTF-8"> <title>Title</title></head><body><h1 class="b3a8">adidas Gazelle</h1></body></html>' # noqa: E501 return minified_html @pytest.fixture def no_h1_response() -> bytes: minified_html = '<!DOCTYPE html><html lang="en"><head> <meta charset="UTF-8"> <title>Title</title></head><body><h2 class="b3a8">bad</h2></body></html>' # noqa: E501 return minified_html @pytest.fixture def h3_before_h1_response() -> bytes: minified_html = b'<!DOCTYPE html><html lang="en"><head> <meta charset="UTF-8"> <title>Title</title></head><body><h3>Fake</h3><h1>TestProduct3</h1></body></html>' # noqa: E501 return minified_html @pytest.fixture def h11_before_h1_response() -> bytes: minified_html = b'<!DOCTYPE html><html lang="en"><head> <meta charset="UTF-8"> <title>Title</title></head><body><h11>Fake</h11><h1>TestProduct4</h1></body></html>' # noqa: E501 return minified_html @pytest.fixture async def products_1(single_admin): return await ProductGinoModel.create( user_id=single_admin.id, name="test", url="https://devyatkin.dev/1" ) class TestPageParser: """Page Parser core tests.""" async def test_no_css_h1(self, httpx_mock, no_css_response): test_url = "https://no_css_h1" httpx_mock.add_response(data=no_css_response, url=test_url) response = await get_product_name(url=test_url) assert response == "TestProduct1" async def test_css_h1_one_chunk(self, httpx_mock, css_response): test_url = "https://css_h1" httpx_mock.add_response(data=css_response, url=test_url) response = await get_product_name(url=test_url, chunk_size=500) assert response == "adidas Gazelle" @pytest.mark.asyncio async def test_css_h1_several_chunks(self, httpx_mock, css_response): test_url = "https://css_h1" httpx_mock.add_response(data=css_response, url=test_url) response = await get_product_name(url=test_url, chunk_size=100) assert response == "adidas Gazelle" @pytest.mark.asyncio async def test_no_h1(self, httpx_mock, no_h1_response): test_url = "https://no_h1" httpx_mock.add_response(data=no_h1_response, url=test_url) response = await get_product_name(url=test_url) assert response is None @pytest.mark.asyncio async def test_h3_before_h1(self, httpx_mock, h3_before_h1_response): test_url = "https://h3_before_h1" httpx_mock.add_response(data=h3_before_h1_response, url=test_url) response = await get_product_name(url=test_url) assert response == "TestProduct3" @pytest.mark.skip(reason="valid spec indicates that headers can only be h1-h6.") @pytest.mark.asyncio async def test_h11_before_h1(self, httpx_mock, h11_before_h1_response): test_url = "https://h11_before_h1" httpx_mock.add_response(data=h11_before_h1_response, url=test_url) response = await get_product_name(url=test_url) assert response == "TestProduct4" @pytest.mark.asyncio async def test_bad_response(self, httpx_mock): test_url = "https://bad" httpx_mock.add_response(status_code=500) response = await get_product_name(url=test_url) assert response is None class TestApi: """PageParser api response tests.""" API_URL = f"{API_URL_PREFIX}/extract-product-title" async def test_unauthenticated_input(self, snapshot, backend_app): resp = await backend_app.post( f"{self.API_URL}", json={"data": {"attributes": {"url": "https://css_h1.io"}}}, ) assert resp.status_code == 401 async def test_bad_input(self, snapshot, backend_app, single_admin_auth_headers): resp = await backend_app.post( f"{self.API_URL}", json={"data": {"attributes": {"url": "bad-url"}}}, headers=single_admin_auth_headers, ) assert resp.status_code == 422 snapshot.assert_match(resp.json()) async def test_good_input( self, httpx_mock, snapshot, backend_app, css_response, single_admin_auth_headers ): test_url = "https://css_h1.io" httpx_mock.add_response(data=css_response, url=test_url) response = await backend_app.post( f"{self.API_URL}", json={"data": {"attributes": {"url": test_url}}}, headers=single_admin_auth_headers, ) assert response.status_code == 200 snapshot.assert_match(response.json()) async def test_bad_url( self, httpx_mock, snapshot, backend_app, single_admin_auth_headers ): test_url = "https://bad.io" httpx_mock.add_response(status_code=500) response = await backend_app.post( f"{self.API_URL}", json={"data": {"attributes": {"url": test_url}}}, headers=single_admin_auth_headers, ) assert response.status_code == 200 snapshot.assert_match(response.json()) async def test_existing_product( self, snapshot, backend_app, products_1, css_response, single_admin_auth_headers ): test_url = products_1.url response = await backend_app.post( f"{self.API_URL}", json={"data": {"attributes": {"url": test_url}}}, headers=single_admin_auth_headers, ) assert response.status_code == 200 snapshot.assert_match(response.json())

devalv/yawm

backend/tests/test_page_parser.py

Python

print('running module1.py...') a = 100

AadityaGupta/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

python-tuts/0-beginner/8-Modules_Packages_Namespaces/module1.py

Python

# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from urllib import parse from kazoo.client import KazooClient from kazoo.security import make_digest_acl from arch.api.utils import file_utils from arch.api.utils.core_utils import get_lan_ip class CenterConfig(object): SERVERS = None USE_ACL = False ZK_USERNAME = 'fate' ZK_PASSWORD = 'fate' @staticmethod def get_settings(path, servings_zk_path=None, use_zk=False, hosts=None, server_conf_path=''): if servings_zk_path and use_zk: return CenterConfig.get_servings_from_zookeeper(servings_zk_path, hosts) return CenterConfig.get_settings_from_file(path, server_conf_path) @staticmethod def get_settings_from_file(path, server_conf_path): server_conf = file_utils.load_json_conf(server_conf_path) data = server_conf for k in path.split('/')[1:]: data = data.get(k, None) return data @staticmethod def get_zk(hosts): if CenterConfig.USE_ACL: default_acl = make_digest_acl(CenterConfig.ZK_USERNAME, CenterConfig.ZK_PASSWORD, all=True) zk = KazooClient(hosts=hosts, default_acl=[default_acl], auth_data=[("digest", "{}:{}".format( CenterConfig.ZK_USERNAME, CenterConfig.ZK_PASSWORD))]) else: zk = KazooClient(hosts=hosts) return zk @staticmethod def get_servings_from_zookeeper(path, hosts): try: zk = CenterConfig.get_zk(hosts) zk.start() nodes = zk.get_children(path) CenterConfig.SERVERS = nodes_unquote(nodes) zk.stop() return CenterConfig.SERVERS except Exception as e: raise Exception('loading servings node failed from zookeeper: {}'.format(e)) @staticmethod def init(hosts, use_configuation_center, fate_flow_zk_path, fate_flow_port, model_transfer_path): if use_configuation_center: zk = CenterConfig.get_zk(hosts) zk.start() model_host = 'http://{}:{}{}'.format(get_lan_ip(), fate_flow_port, model_transfer_path) fate_flow_zk_path = '{}/{}'.format(fate_flow_zk_path, parse.quote(model_host, safe=' ')) try: zk.create(fate_flow_zk_path, makepath=True) except: pass zk.stop() def nodes_unquote(nodes): urls = [parse.unquote(node) for node in nodes] servings = [] for url in urls: try: servings.append(url.split('/')[2]) except: pass return servings

AustinNeverPee/FedRec

fate_flow/utils/setting_utils.py

Python

import os from easydict import EasyDict import torch # architecture from basicts.archs.DCRNN_arch import DCRNN # runner from basicts.runners.DCRNN_runner import DCRNNRunner from basicts.data.base_dataset import BaseDataset from basicts.metrics.mae import masked_mae from basicts.metrics.mape import masked_mape from basicts.metrics.rmse import masked_rmse from basicts.losses.losses import masked_l1_loss from basicts.utils.serialization import load_adj CFG = EasyDict() resume = False # DCRNN does not allow to load parameters since it creates parameters in the first iteration if not resume: import random _ = random.randint(-1e6, 1e6) # ================= general ================= # CFG.DESCRIPTION = 'DCRNN model configuration' CFG.RUNNER = DCRNNRunner CFG.DATASET_CLS = BaseDataset CFG.DATASET_NAME = "PEMS07" CFG.DATASET_TYPE = 'Traffic speed' CFG._ = _ CFG.GPU_NUM = 1 CFG.METRICS = { "MAE": masked_mae, "RMSE": masked_rmse, "MAPE": masked_mape } # ================= environment ================= # CFG.ENV = EasyDict() CFG.ENV.SEED = 1 CFG.ENV.CUDNN = EasyDict() CFG.ENV.CUDNN.ENABLED = True # ================= model ================= # CFG.MODEL = EasyDict() CFG.MODEL.NAME = 'DCRNN' CFG.MODEL.ARCH = DCRNN adj_mx, _ = load_adj("datasets/" + CFG.DATASET_NAME + "/adj_mx.pkl", "doubletransition") CFG.MODEL.PARAM = { "cl_decay_steps" : 2000, "horizon" : 12, "input_dim" : 2, "max_diffusion_step": 2, "num_nodes" : 883, "num_rnn_layers" : 2, "output_dim" : 1, "rnn_units" : 64, "seq_len" : 12, "adj_mx" : [torch.tensor(i).cuda() for i in adj_mx], "use_curriculum_learning": True } CFG.MODEL.FROWARD_FEATURES = [0, 1] # traffic speed, time in day CFG.MODEL.TARGET_FEATURES = [0] # traffic speed # ================= optim ================= # CFG.TRAIN = EasyDict() CFG.TRAIN.LOSS = masked_l1_loss CFG.TRAIN.OPTIM = EasyDict() CFG.TRAIN.OPTIM.TYPE = "Adam" CFG.TRAIN.OPTIM.PARAM= { "lr":0.003, "eps":1e-3 } CFG.TRAIN.LR_SCHEDULER = EasyDict() CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR" CFG.TRAIN.LR_SCHEDULER.PARAM= { "milestones":[80], "gamma":0.3 } # ================= train ================= # # CFG.TRAIN.CLIP = 5 CFG.TRAIN.NUM_EPOCHS = 200 CFG.TRAIN.CKPT_SAVE_DIR = os.path.join( 'checkpoints', '_'.join([CFG.MODEL.NAME, str(CFG.TRAIN.NUM_EPOCHS)]) ) CFG.TRAIN.SETUP_GRAPH = True # train data CFG.TRAIN.DATA = EasyDict() CFG.TRAIN.NULL_VAL = 0.0 ## read data CFG.TRAIN.DATA.DIR = 'datasets/' + CFG.DATASET_NAME ## dataloader args, optional CFG.TRAIN.DATA.BATCH_SIZE = 64 CFG.TRAIN.DATA.PREFETCH = False CFG.TRAIN.DATA.SHUFFLE = True CFG.TRAIN.DATA.NUM_WORKERS = 2 CFG.TRAIN.DATA.PIN_MEMORY = False # ================= validate ================= # CFG.VAL = EasyDict() CFG.VAL.INTERVAL = 1 # validating data CFG.VAL.DATA = EasyDict() ## read data CFG.VAL.DATA.DIR = 'datasets/' + CFG.DATASET_NAME ## dataloader args, optional CFG.VAL.DATA.BATCH_SIZE = 64 CFG.VAL.DATA.PREFETCH = False CFG.VAL.DATA.SHUFFLE = False CFG.VAL.DATA.NUM_WORKERS = 2 CFG.VAL.DATA.PIN_MEMORY = False # ================= test ================= # CFG.TEST = EasyDict() CFG.TEST.INTERVAL = 1 # validating data CFG.TEST.DATA = EasyDict() ## read data CFG.TEST.DATA.DIR = 'datasets/' + CFG.DATASET_NAME ## dataloader args, optional CFG.TEST.DATA.BATCH_SIZE = 64 CFG.TEST.DATA.PREFETCH = False CFG.TEST.DATA.SHUFFLE = False CFG.TEST.DATA.NUM_WORKERS = 2 CFG.TEST.DATA.PIN_MEMORY = False

zezhishao/BasicTS

basicts/options/DCRNN/DCRNN_PEMS07.py

Python

import pylab as pl import numpy as np from os import path from numpy import abs, linspace, sin, pi, int16 import pandas def plotfft(s, fmax, doplot=False): """ This functions computes the fft of a signal, returning the frequency and their magnitude values. Parameters ---------- s: array-like the input signal. fmax: int the sampling frequency. doplot: boolean a variable to indicate whether the plot is done or not. Returns ------- f: array-like the frequency values (xx axis) fs: array-like the amplitude of the frequency values (yy axis) """ fs = abs(np.fft.fft(s)) f = linspace(0, fmax / 2, len(s) / 2) if doplot: pl.plot(f[1:len(s) / 2], fs[1:len(s) / 2]) return (f[1:len(s) / 2].copy(), fs[1:len(s) / 2].copy()) def synthbeats2(duration, meanhr=60, stdhr=1, samplingfreq=250): #Minimaly based on the parameters from: #http://physionet.cps.unizar.es/physiotools/ecgsyn/Matlab/ecgsyn.m #Inputs: duration in seconds #Returns: signal, peaks ibi = 60 / float(meanhr) * samplingfreq sibi = ibi - 60 / (float(meanhr) - stdhr) * samplingfreq peaks = np.arange(0, duration * samplingfreq, ibi) peaks[1:] = peaks[1:] + np.random.randn(len(peaks) - 1) * sibi if peaks[-1] >= duration * samplingfreq: peaks = peaks[:-1] peaks = peaks.astype('int') signal = np.zeros(duration * samplingfreq) signal[peaks] = 1.0 return signal, peaks def synthbeats(duration, meanhr=60, stdhr=1, samplingfreq=250, sinfreq=None): #Minimaly based on the parameters from: #http://physionet.cps.unizar.es/physiotools/ecgsyn/Matlab/ecgsyn.m #If freq exist it will be used to generate a sin instead of using rand #Inputs: duration in seconds #Returns: signal, peaks t = np.arange(duration * samplingfreq) / float(samplingfreq) signal = np.zeros(len(t)) print(len(t)) print(len(signal)) if sinfreq == None: npeaks = 1.2 * (duration * meanhr / 60) # add 20% more beats for some cummulative error hr = pl.randn(npeaks) * stdhr + meanhr peaks = pl.cumsum(60. / hr) * samplingfreq peaks = peaks.astype('int') peaks = peaks[peaks < t[-1] * samplingfreq] else: hr = meanhr + sin(2 * pi * t * sinfreq) * float(stdhr) index = int(60. / hr[0] * samplingfreq) peaks = [] while index < len(t): peaks += [index] index += int(60. / hr[index] * samplingfreq) signal[peaks] = 1.0 return t, signal, peaks def load_with_cache(file_, recache=False, sampling=1, columns=None, temp_dir='.', data_type='int16'): """@brief This function loads a file from the current directory and saves the cached file to later executions. It's also possible to make a recache or a subsampling of the signal and choose only a few columns of the signal, to accelerate the opening process. @param file String: the name of the file to open. @param recache Boolean: indication whether it's done recache or not (default = false). @param sampling Integer: the sampling step. if 1, the signal isn't sampled (default = 1). @param columns Array-Like: the columns to read from the file. if None, all columns are considered (default = None). @return data Array-Like: the data from the file. TODO: Should save cache in a different directory TODO: Create test function and check size of generated files TODO: receive a file handle """ cfile = '%s.npy' % file_ if (not path.exists(cfile)) or recache: if columns == None: data = np.loadtxt(file_)[::sampling, :] else: data = np.loadtxt(file_)[::sampling, columns] np.save(cfile, data.astype(data_type)) else: data = np.load(cfile) return data def load_data(filename): """ :rtype : numpy matrix """ data = pandas.read_csv(filename, header=None, delimiter='\t', skiprows=9) return data.as_matrix()

novabiosignals/novainstrumentation

novainstrumentation/tools.py

Python

# -*- coding: utf-8 -*- # Generated by Django 1.9 on 2018-09-26 01:53 from __future__ import unicode_literals import ckeditor.fields from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('posts', '0001_initial'), ] operations = [ migrations.AlterField( model_name='post', name='content', field=ckeditor.fields.RichTextField(verbose_name='Content'), ), ]

COAStatistics/aprp

src/apps/posts/migrations/0002_auto_20180926_0953.py

Python

import os import copy import pytest from jikken.api import Experiment import git @pytest.fixture(autouse=True, scope='module') def experiment_setup(tmpdir_factory): expected_variables = { "training_parameters": {"batch_size": 100, "algorithm": "Seq2Seq", "attention": "multiplicative" }, "input_parameters": {'batch_size': 4, "filepath": "/data", "preprocessing": True, "transformations": ["stopwords", "tokenize", "remove_punct"] } } tags = ['test', 'simple'] tmpdir = tmpdir_factory.mktemp('mydir') return expected_variables, tags, tmpdir def test_experiment_equality(experiment_setup): # Given some variables and tags expected_variables, tags, tmpdir = experiment_setup # When I create an experiment exp1 = Experiment("exp1", variables=expected_variables, code_dir=str(tmpdir), tags=tags) # And another one with teh same inputs exp2 = Experiment("exp1", variables=expected_variables, code_dir=str(tmpdir), tags=tags) # Then they are equal assert exp1 == exp2 # And when i create a third one with an extra tag tags3 = tags + ["third tag"] exp3 = Experiment("exp1", variables=expected_variables, code_dir=str(tmpdir), tags=tags3) # Then that is also equal assert exp1 == exp3 def test_experiment_not_equality(experiment_setup): # Given some variables and tags expected_variables, tags, tmpdir = experiment_setup # When I create an experiment exp1 = Experiment("exp1", variables=expected_variables, code_dir=str(tmpdir), tags=tags) # And whe I create one with a different name exp5 = Experiment("exp2", variables=expected_variables, code_dir=str(tmpdir), tags=tags) # Then it is not equal assert exp1 != exp5 # And when I create one with different variables new_variables = copy.deepcopy(expected_variables) new_variables["training_parameters"]["batch_size"] = 5 exp4 = Experiment("exp1", variables=new_variables, code_dir=str(tmpdir), tags=tags) # Then it is not equal assert exp1 != exp4 @pytest.fixture(autouse=True) def jikken_experiment(experiment_setup): expected_variables, tags, tmpdir = experiment_setup repo_dir = str(tmpdir) file_name = os.path.join(repo_dir, 'new-file') r = git.Repo.init(repo_dir) # This function just creates an empty file ... open(file_name, 'wb').close() r.index.add([file_name]) r.index.commit("initial commit") exp = Experiment(name="exp", variables=expected_variables, code_dir=repo_dir, tags=tags) return exp, expected_variables, tags, tmpdir def test_experiment_variables(jikken_experiment): "test variables are initialized properly and are not settable" exp, expected_variables, *_ = jikken_experiment assert exp.variables == expected_variables with pytest.raises(AttributeError): exp.variables = expected_variables def test_experiment_tags(jikken_experiment): "test tags are initialized properly and are not settable" exp, _, expected_tags, _ = jikken_experiment assert exp.tags == expected_tags with pytest.raises(AttributeError): exp.tags = expected_tags def test_experiment_schema(jikken_experiment): "test schema is constructed properly" exp, expected_variables, _, tmpdir = jikken_experiment expected_hash = '40a3f5106cf9426bd4b13b168717e7bf' assert exp.schema_hash == expected_hash exp_2 = Experiment(name="exp1", variables=expected_variables, code_dir=tmpdir.strpath) assert exp_2.schema_hash == exp.schema_hash def test_experiment_parameters_schema(jikken_experiment): "test schema with parameters is constructed properly" exp, expected_variables, _, tmpdir = jikken_experiment expected_hash = '77c861c501833128e1cfb5b398588a7e' assert exp.parameters_hash == expected_hash def test_experiment_parameters_schema_comparison(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment diff_variables = copy.deepcopy(expected_variables) diff_variables['training_parameters']['batch_size'] = 200 exp_2 = Experiment(name="exp2", variables=diff_variables, code_dir=tmpdir.strpath) assert exp_2.schema_hash == exp.schema_hash assert exp.parameters_hash != exp_2.parameters_hash def text_same_experiment_hash(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment exp_same = Experiment(variables=expected_variables, code_dir=tmpdir.strpath) assert exp.hash == exp_same.hash assert exp.parameters_hash == exp_same.parameters_hash assert exp.schema_hash == exp_same.schema_hash def text_experiment_different_hash(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment exp_diff_dir = Experiment(variables=expected_variables, code_dir=os.getcwd()) assert exp.parameters_hash == exp_diff_dir.parameters_hash assert exp.schema_hash == exp_diff_dir.schema_hash assert exp_diff_dir.hash != exp.hash diff_variables = copy.deepcopy(expected_variables) diff_variables['training_parameters']['batch_size'] = 200 exp_diff_variables = Experiment(name="exp1", variables=diff_variables, code_dir=tmpdir.strpath) assert exp.parameters_hash != exp_diff_dir.parameters_hash assert exp.schema_hash == exp_diff_dir.schema_hash assert exp_diff_variables.hash != exp.hash def test_experiment_different_tags_hash(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment exp_diff_tags = Experiment(name="exp", variables=expected_variables, code_dir=tmpdir.strpath, tags='test2') assert exp.schema_hash == exp_diff_tags.schema_hash assert exp.parameters_hash == exp_diff_tags.parameters_hash assert exp.hash == exp_diff_tags.hash def test_experiment_different_names(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment exp_diff_tags = Experiment(name="exp1", variables=expected_variables, code_dir=tmpdir.strpath, tags='test2') assert exp.schema_hash == exp_diff_tags.schema_hash assert exp.parameters_hash == exp_diff_tags.parameters_hash assert exp.hash != exp_diff_tags.hash def test_experiment_from_dict_is_same(jikken_experiment): exp, expected_variables, _, tmpdir = jikken_experiment new_exp = Experiment.from_dict(exp.to_dict()) assert new_exp == exp

outcastofmusic/jikken

tests/unit/test_experiment.py

Python

# Copyright © 2020 Hashmap, Inc # # 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 os import uuid from hdm.core.error.hdm_error import HDMError from hdm.core.orchestrator.orchestrator import Orchestrator from hdm.core.sink.sink import Sink from hdm.core.source.source import Source from hdm.core.utils.parse_config import ParseConfig from hdm.data_link import DataLink from hdm.data_link_builder import DataLinkBuilder class DeclaredOrchestrator(Orchestrator): """This is an orchestrator which build DataLinks and will run them as defined - they must be fully defined.""" def run_pipelines(self): _ = [hdm.run() for hdm in self._data_links] def _build_data_links(self): config = ParseConfig.parse(config_path=os.getenv('HDM_MANIFEST')) state_manager_config = config['state_manager'] manifest_name = os.getenv('HDM_MANIFEST')[os.getenv('HDM_MANIFEST').rindex("/")+1:] run_id = uuid.uuid4().hex for link_config in config['declared_data_links']['stages']: # Create New State Manager link_state = self._generate_state_manager(state_manager_config=state_manager_config, data_link_config=link_config, manifest_name=manifest_name, run_id=run_id) # Add the state manager to the sink and source link_config['source']['conf']['state_manager'] = link_state link_config['sink']['conf']['state_manager'] = link_state source = DataLinkBuilder.build_source(link_config['source']) if not isinstance(source, Source): error = f'Source {type(source)} is not a Source.' self._logger.error(error) raise HDMError(error) sink = DataLinkBuilder.build_sink(link_config['sink']) if not isinstance(sink, Sink): error = f'Sink {type(sink)} is not a Sink.' self._logger.error(error) raise HDMError(error) self._data_links.append( DataLink( source=source, sink=sink ) )

hashmapinc/hdm

hdm/core/orchestrator/declared_orchestrator.py

Python

# Copyright 2020 The StackStorm Authors. # Copyright 2019 Extreme Networks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import import unittest import os import sys import json import uuid import tempfile import time import logging as logbase import mock from oslo_config import cfg from st2common import log as logging from st2common.logging.formatters import ConsoleLogFormatter from st2common.logging.formatters import GelfLogFormatter from st2common.constants.secrets import MASKED_ATTRIBUTE_VALUE from st2common.models.db.action import ActionDB from st2common.models.db.rule import RuleDB from st2common.models.db.execution import ActionExecutionDB import st2tests.config as tests_config CURRENT_DIR = os.path.dirname(os.path.realpath(__file__)) RESOURCES_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "../resources")) CONFIG_FILE_PATH = os.path.join(RESOURCES_DIR, "logging.conf") MOCK_MASKED_ATTRIBUTES_BLACKLIST = [ "blacklisted_1", "blacklisted_2", "blacklisted_3", ] class MockRecord(object): levelno = 40 msg = None exc_info = None exc_text = None stack_info = None created = time.time() def getMessage(self): return self.msg class LoggerTestCase(unittest.TestCase): @classmethod def setUpClass(cls): tests_config.parse_args() def setUp(self): super(LoggerTestCase, self).setUp() self.config_text = open(CONFIG_FILE_PATH).read() self.cfg_fd, self.cfg_path = tempfile.mkstemp() self.info_log_fd, self.info_log_path = tempfile.mkstemp() self.audit_log_fd, self.audit_log_path = tempfile.mkstemp() with open(self.cfg_path, "a") as f: f.write(self.config_text.format(self.info_log_path, self.audit_log_path)) def tearDown(self): self._remove_tempfile(self.cfg_fd, self.cfg_path) self._remove_tempfile(self.info_log_fd, self.info_log_path) self._remove_tempfile(self.audit_log_fd, self.audit_log_path) super(LoggerTestCase, self).tearDown() def _remove_tempfile(self, fd, path): os.close(fd) os.unlink(path) def test_logger_setup_failure(self): config_file = "/tmp/abc123" self.assertFalse(os.path.exists(config_file)) self.assertRaises(Exception, logging.setup, config_file) def test_logger_set_level(self): logging.setup(self.cfg_path) log = logging.getLogger(__name__) self.assertEqual(log.getEffectiveLevel(), logbase.DEBUG) log.setLevel(logbase.INFO) self.assertEqual(log.getEffectiveLevel(), logbase.INFO) log.setLevel(logbase.WARN) self.assertEqual(log.getEffectiveLevel(), logbase.WARN) log.setLevel(logbase.ERROR) self.assertEqual(log.getEffectiveLevel(), logbase.ERROR) log.setLevel(logbase.CRITICAL) self.assertEqual(log.getEffectiveLevel(), logbase.CRITICAL) log.setLevel(logbase.AUDIT) self.assertEqual(log.getEffectiveLevel(), logbase.AUDIT) def test_log_info(self): """Test that INFO log entry does not go to the audit log.""" logging.setup(self.cfg_path) log = logging.getLogger(__name__) msg = uuid.uuid4().hex log.info(msg) info_log_entries = open(self.info_log_path).read() self.assertIn(msg, info_log_entries) audit_log_entries = open(self.audit_log_path).read() self.assertNotIn(msg, audit_log_entries) def test_log_critical(self): """Test that CRITICAL log entry does not go to the audit log.""" logging.setup(self.cfg_path) log = logging.getLogger(__name__) msg = uuid.uuid4().hex log.critical(msg) info_log_entries = open(self.info_log_path).read() self.assertIn(msg, info_log_entries) audit_log_entries = open(self.audit_log_path).read() self.assertNotIn(msg, audit_log_entries) def test_log_audit(self): """Test that AUDIT log entry goes to the audit log.""" logging.setup(self.cfg_path) log = logging.getLogger(__name__) msg = uuid.uuid4().hex log.audit(msg) info_log_entries = open(self.info_log_path).read() self.assertIn(msg, info_log_entries) audit_log_entries = open(self.audit_log_path).read() self.assertIn(msg, audit_log_entries) class ConsoleLogFormatterTestCase(unittest.TestCase): @classmethod def setUpClass(cls): tests_config.parse_args() def test_format(self): formatter = ConsoleLogFormatter() # No extra attributes mock_message = "test message 1" record = MockRecord() record.msg = mock_message message = formatter.format(record=record) self.assertEqual(message, mock_message) # Some extra attributes mock_message = "test message 2" record = MockRecord() record.msg = mock_message # Add "extra" attributes record._user_id = 1 record._value = "bar" record.ignored = "foo" # this one is ignored since it doesnt have a prefix message = formatter.format(record=record) expected = "test message 2 (value='bar',user_id=1)" self.assertEqual(sorted(message), sorted(expected)) @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) def test_format_blacklisted_attributes_are_masked(self): formatter = ConsoleLogFormatter() mock_message = "test message 1" record = MockRecord() record.msg = mock_message # Add "extra" attributes record._blacklisted_1 = "test value 1" record._blacklisted_2 = "test value 2" record._blacklisted_3 = { "key1": "val1", "blacklisted_1": "val2", "key3": "val3", } record._foo1 = "bar" message = formatter.format(record=record) expected = ( "test message 1 (blacklisted_1='********',blacklisted_2='********'," "blacklisted_3={'key3': 'val3', 'key1': 'val1', 'blacklisted_1': '********'}," "foo1='bar')" ) self.assertEqual(sorted(message), sorted(expected)) @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) def test_format_custom_blacklist_attributes_are_masked(self): cfg.CONF.set_override( group="log", name="mask_secrets_blacklist", override=["blacklisted_4", "blacklisted_5"], ) formatter = ConsoleLogFormatter() mock_message = "test message 1" record = MockRecord() record.msg = mock_message # Add "extra" attributes record._blacklisted_1 = "test value 1" record._blacklisted_2 = "test value 2" record._blacklisted_3 = { "key1": "val1", "blacklisted_1": "val2", "key3": "val3", } record._blacklisted_4 = "fowa" record._blacklisted_5 = "fiva" record._foo1 = "bar" message = formatter.format(record=record) expected = ( "test message 1 (foo1='bar',blacklisted_1='********',blacklisted_2='********'," "blacklisted_3={'key3': 'val3', 'key1': 'val1', 'blacklisted_1': '********'}," "blacklisted_4='********',blacklisted_5='********')" ) self.assertEqual(sorted(message), sorted(expected)) @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) def test_format_secret_action_parameters_are_masked(self): formatter = ConsoleLogFormatter() mock_message = "test message 1" parameters = { "parameter1": {"type": "string", "required": False}, "parameter2": {"type": "string", "required": False, "secret": True}, } mock_action_db = ActionDB( pack="testpack", name="test.action", parameters=parameters ) action = mock_action_db.to_serializable_dict() parameters = {"parameter1": "value1", "parameter2": "value2"} mock_action_execution_db = ActionExecutionDB( action=action, parameters=parameters ) record = MockRecord() record.msg = mock_message # Add "extra" attributes record._action_execution_db = mock_action_execution_db expected_msg_part = ( r"'parameters': {u?'parameter1': u?'value1', " r"u?'parameter2': u?'\*\*\*\*\*\*\*\*'}" ) message = formatter.format(record=record) self.assertIn("test message 1", message) self.assertRegexpMatches(message, expected_msg_part) @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) def test_format_rule(self): expected_result = { "description": "Test description", "tags": [], "type": {"ref": "standard", "parameters": {}}, "enabled": True, "trigger": "test tigger", "metadata_file": None, "context": {}, "criteria": {}, "action": {"ref": "1234", "parameters": {"b": 2}}, "uid": "rule:testpack:test.action", "pack": "testpack", "ref": "testpack.test.action", "id": None, "name": "test.action", } mock_rule_db = RuleDB( pack="testpack", name="test.action", description="Test description", trigger="test tigger", action={"ref": "1234", "parameters": {"b": 2}}, ) result = mock_rule_db.to_serializable_dict() self.assertEqual(expected_result, result) @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) @mock.patch("st2common.models.db.rule.RuleDB._get_referenced_action_model") def test_format_secret_rule_parameters_are_masked( self, mock__get_referenced_action_model ): expected_result = { "description": "Test description", "tags": [], "type": {"ref": "standard", "parameters": {}}, "enabled": True, "trigger": "test tigger", "metadata_file": None, "context": {}, "criteria": {}, "action": { "ref": "1234", "parameters": {"parameter1": "value1", "parameter2": "********"}, }, "uid": "rule:testpack:test.action", "pack": "testpack", "ref": "testpack.test.action", "id": None, "name": "test.action", } parameters = { "parameter1": {"type": "string", "required": False}, "parameter2": {"type": "string", "required": False, "secret": True}, } mock_action_db = ActionDB( pack="testpack", name="test.action", parameters=parameters ) mock__get_referenced_action_model.return_value = mock_action_db cfg.CONF.set_override(group="log", name="mask_secrets", override=True) mock_rule_db = RuleDB( pack="testpack", name="test.action", description="Test description", trigger="test tigger", action={ "ref": "1234", "parameters": {"parameter1": "value1", "parameter2": "value2"}, }, ) result = mock_rule_db.to_serializable_dict(True) self.assertEqual(expected_result, result) class GelfLogFormatterTestCase(unittest.TestCase): @classmethod def setUpClass(cls): tests_config.parse_args() def test_format(self): formatter = GelfLogFormatter() expected_keys = [ "version", "host", "short_message", "full_message", "timestamp", "timestamp_f", "level", ] # No extra attributes mock_message = "test message 1" record = MockRecord() record.msg = mock_message message = formatter.format(record=record) parsed = json.loads(message) for key in expected_keys: self.assertIn(key, parsed) self.assertEqual(parsed["short_message"], mock_message) self.assertEqual(parsed["full_message"], mock_message) # Some extra attributes mock_message = "test message 2" record = MockRecord() record.msg = mock_message # Add "extra" attributes record._user_id = 1 record._value = "bar" record.ignored = "foo" # this one is ignored since it doesnt have a prefix record.created = 1234.5678 message = formatter.format(record=record) parsed = json.loads(message) for key in expected_keys: self.assertIn(key, parsed) self.assertEqual(parsed["short_message"], mock_message) self.assertEqual(parsed["full_message"], mock_message) self.assertEqual(parsed["_user_id"], 1) self.assertEqual(parsed["_value"], "bar") self.assertEqual(parsed["timestamp"], 1234) self.assertEqual(parsed["timestamp_f"], 1234.5678) self.assertNotIn("ignored", parsed) # Record with an exception mock_exception = Exception("mock exception bar") try: raise mock_exception except Exception: mock_exc_info = sys.exc_info() # Some extra attributes mock_message = "test message 3" record = MockRecord() record.msg = mock_message record.exc_info = mock_exc_info message = formatter.format(record=record) parsed = json.loads(message) for key in expected_keys: self.assertIn(key, parsed) self.assertEqual(parsed["short_message"], mock_message) self.assertIn(mock_message, parsed["full_message"]) self.assertIn("Traceback", parsed["full_message"]) self.assertIn("_exception", parsed) self.assertIn("_traceback", parsed) def test_extra_object_serialization(self): class MyClass1(object): def __repr__(self): return "repr" class MyClass2(object): def to_dict(self): return "to_dict" class MyClass3(object): def to_serializable_dict(self, mask_secrets=False): return "to_serializable_dict" formatter = GelfLogFormatter() record = MockRecord() record.msg = "message" record._obj1 = MyClass1() record._obj2 = MyClass2() record._obj3 = MyClass3() message = formatter.format(record=record) parsed = json.loads(message) self.assertEqual(parsed["_obj1"], "repr") self.assertEqual(parsed["_obj2"], "to_dict") self.assertEqual(parsed["_obj3"], "to_serializable_dict") @mock.patch( "st2common.logging.formatters.MASKED_ATTRIBUTES_BLACKLIST", MOCK_MASKED_ATTRIBUTES_BLACKLIST, ) def test_format_blacklisted_attributes_are_masked(self): formatter = GelfLogFormatter() # Some extra attributes mock_message = "test message 1" record = MockRecord() record.msg = mock_message # Add "extra" attributes record._blacklisted_1 = "test value 1" record._blacklisted_2 = "test value 2" record._blacklisted_3 = { "key1": "val1", "blacklisted_1": "val2", "key3": "val3", } record._foo1 = "bar" message = formatter.format(record=record) parsed = json.loads(message) self.assertEqual(parsed["_blacklisted_1"], MASKED_ATTRIBUTE_VALUE) self.assertEqual(parsed["_blacklisted_2"], MASKED_ATTRIBUTE_VALUE) self.assertEqual(parsed["_blacklisted_3"]["key1"], "val1") self.assertEqual( parsed["_blacklisted_3"]["blacklisted_1"], MASKED_ATTRIBUTE_VALUE ) self.assertEqual(parsed["_blacklisted_3"]["key3"], "val3") self.assertEqual(parsed["_foo1"], "bar") # Assert that the original dict is left unmodified self.assertEqual(record._blacklisted_1, "test value 1") self.assertEqual(record._blacklisted_2, "test value 2") self.assertEqual(record._blacklisted_3["key1"], "val1") self.assertEqual(record._blacklisted_3["blacklisted_1"], "val2") self.assertEqual(record._blacklisted_3["key3"], "val3")

Anshika-Gautam/st2

st2common/tests/unit/test_logger.py

Python

import os from enum import IntEnum from typing import Dict, Union, Callable, List, Optional from cereal import log, car import cereal.messaging as messaging from common.conversions import Conversions as CV from common.realtime import DT_CTRL from selfdrive.locationd.calibrationd import MIN_SPEED_FILTER from selfdrive.version import get_short_branch AlertSize = log.ControlsState.AlertSize AlertStatus = log.ControlsState.AlertStatus VisualAlert = car.CarControl.HUDControl.VisualAlert AudibleAlert = car.CarControl.HUDControl.AudibleAlert EventName = car.CarEvent.EventName # Alert priorities class Priority(IntEnum): LOWEST = 0 LOWER = 1 LOW = 2 MID = 3 HIGH = 4 HIGHEST = 5 # Event types class ET: ENABLE = 'enable' PRE_ENABLE = 'preEnable' OVERRIDE = 'override' NO_ENTRY = 'noEntry' WARNING = 'warning' USER_DISABLE = 'userDisable' SOFT_DISABLE = 'softDisable' IMMEDIATE_DISABLE = 'immediateDisable' PERMANENT = 'permanent' # get event name from enum EVENT_NAME = {v: k for k, v in EventName.schema.enumerants.items()} class Events: def __init__(self): self.events: List[int] = [] self.static_events: List[int] = [] self.events_prev = dict.fromkeys(EVENTS.keys(), 0) @property def names(self) -> List[int]: return self.events def __len__(self) -> int: return len(self.events) def add(self, event_name: int, static: bool=False) -> None: if static: self.static_events.append(event_name) self.events.append(event_name) def clear(self) -> None: self.events_prev = {k: (v + 1 if k in self.events else 0) for k, v in self.events_prev.items()} self.events = self.static_events.copy() def any(self, event_type: str) -> bool: return any(event_type in EVENTS.get(e, {}) for e in self.events) def create_alerts(self, event_types: List[str], callback_args=None): if callback_args is None: callback_args = [] ret = [] for e in self.events: types = EVENTS[e].keys() for et in event_types: if et in types: alert = EVENTS[e][et] if not isinstance(alert, Alert): alert = alert(*callback_args) if DT_CTRL * (self.events_prev[e] + 1) >= alert.creation_delay: alert.alert_type = f"{EVENT_NAME[e]}/{et}" alert.event_type = et ret.append(alert) return ret def add_from_msg(self, events): for e in events: self.events.append(e.name.raw) def to_msg(self): ret = [] for event_name in self.events: event = car.CarEvent.new_message() event.name = event_name for event_type in EVENTS.get(event_name, {}): setattr(event, event_type, True) ret.append(event) return ret class Alert: def __init__(self, alert_text_1: str, alert_text_2: str, alert_status: log.ControlsState.AlertStatus, alert_size: log.ControlsState.AlertSize, priority: Priority, visual_alert: car.CarControl.HUDControl.VisualAlert, audible_alert: car.CarControl.HUDControl.AudibleAlert, duration: float, alert_rate: float = 0., creation_delay: float = 0.): self.alert_text_1 = alert_text_1 self.alert_text_2 = alert_text_2 self.alert_status = alert_status self.alert_size = alert_size self.priority = priority self.visual_alert = visual_alert self.audible_alert = audible_alert self.duration = int(duration / DT_CTRL) self.alert_rate = alert_rate self.creation_delay = creation_delay self.alert_type = "" self.event_type: Optional[str] = None def __str__(self) -> str: return f"{self.alert_text_1}/{self.alert_text_2} {self.priority} {self.visual_alert} {self.audible_alert}" def __gt__(self, alert2) -> bool: return self.priority > alert2.priority class NoEntryAlert(Alert): def __init__(self, alert_text_2: str, visual_alert: car.CarControl.HUDControl.VisualAlert=VisualAlert.none): super().__init__("openpilot Unavailable", alert_text_2, AlertStatus.normal, AlertSize.mid, Priority.LOW, visual_alert, AudibleAlert.refuse, 3.) class SoftDisableAlert(Alert): def __init__(self, alert_text_2: str): super().__init__("TAKE CONTROL IMMEDIATELY", alert_text_2, AlertStatus.userPrompt, AlertSize.full, Priority.MID, VisualAlert.steerRequired, AudibleAlert.warningSoft, 2.), # less harsh version of SoftDisable, where the condition is user-triggered class UserSoftDisableAlert(SoftDisableAlert): def __init__(self, alert_text_2: str): super().__init__(alert_text_2), self.alert_text_1 = "openpilot will disengage" class ImmediateDisableAlert(Alert): def __init__(self, alert_text_2: str): super().__init__("TAKE CONTROL IMMEDIATELY", alert_text_2, AlertStatus.critical, AlertSize.full, Priority.HIGHEST, VisualAlert.steerRequired, AudibleAlert.warningImmediate, 4.), class EngagementAlert(Alert): def __init__(self, audible_alert: car.CarControl.HUDControl.AudibleAlert): super().__init__("", "", AlertStatus.normal, AlertSize.none, Priority.MID, VisualAlert.none, audible_alert, .2), class NormalPermanentAlert(Alert): def __init__(self, alert_text_1: str, alert_text_2: str = "", duration: float = 0.2, priority: Priority = Priority.LOWER, creation_delay: float = 0.): super().__init__(alert_text_1, alert_text_2, AlertStatus.normal, AlertSize.mid if len(alert_text_2) else AlertSize.small, priority, VisualAlert.none, AudibleAlert.none, duration, creation_delay=creation_delay), class StartupAlert(Alert): def __init__(self, alert_text_1: str, alert_text_2: str = "Always keep hands on wheel and eyes on road", alert_status=AlertStatus.normal): super().__init__(alert_text_1, alert_text_2, alert_status, AlertSize.mid, Priority.LOWER, VisualAlert.none, AudibleAlert.none, 10.), # ********** helper functions ********** def get_display_speed(speed_ms: float, metric: bool) -> str: speed = int(round(speed_ms * (CV.MS_TO_KPH if metric else CV.MS_TO_MPH))) unit = 'km/h' if metric else 'mph' return f"{speed} {unit}" # ********** alert callback functions ********** AlertCallbackType = Callable[[car.CarParams, messaging.SubMaster, bool, int], Alert] def soft_disable_alert(alert_text_2: str) -> AlertCallbackType: def func(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: if soft_disable_time < int(0.5 / DT_CTRL): return ImmediateDisableAlert(alert_text_2) return SoftDisableAlert(alert_text_2) return func def user_soft_disable_alert(alert_text_2: str) -> AlertCallbackType: def func(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: if soft_disable_time < int(0.5 / DT_CTRL): return ImmediateDisableAlert(alert_text_2) return UserSoftDisableAlert(alert_text_2) return func def startup_master_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: branch = get_short_branch("") if "REPLAY" in os.environ: branch = "replay" return StartupAlert("WARNING: This branch is not tested", branch, alert_status=AlertStatus.userPrompt) def below_engage_speed_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: return NoEntryAlert(f"Speed Below {get_display_speed(CP.minEnableSpeed, metric)}") def below_steer_speed_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: return Alert( f"Steer Unavailable Below {get_display_speed(CP.minSteerSpeed, metric)}", "", AlertStatus.userPrompt, AlertSize.small, Priority.MID, VisualAlert.steerRequired, AudibleAlert.prompt, 0.4) def calibration_incomplete_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: return Alert( "Calibration in Progress: %d%%" % sm['liveCalibration'].calPerc, f"Drive Above {get_display_speed(MIN_SPEED_FILTER, metric)}", AlertStatus.normal, AlertSize.mid, Priority.LOWEST, VisualAlert.none, AudibleAlert.none, .2) def no_gps_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: gps_integrated = sm['peripheralState'].pandaType in (log.PandaState.PandaType.uno, log.PandaState.PandaType.dos) return Alert( "Poor GPS reception", "Hardware malfunctioning if sky is visible" if gps_integrated else "Check GPS antenna placement", AlertStatus.normal, AlertSize.mid, Priority.LOWER, VisualAlert.none, AudibleAlert.none, .2, creation_delay=300.) def wrong_car_mode_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: text = "Cruise Mode Disabled" if CP.carName == "honda": text = "Main Switch Off" return NoEntryAlert(text) def joystick_alert(CP: car.CarParams, sm: messaging.SubMaster, metric: bool, soft_disable_time: int) -> Alert: axes = sm['testJoystick'].axes gb, steer = list(axes)[:2] if len(axes) else (0., 0.) vals = f"Gas: {round(gb * 100.)}%, Steer: {round(steer * 100.)}%" return NormalPermanentAlert("Joystick Mode", vals) EVENTS: Dict[int, Dict[str, Union[Alert, AlertCallbackType]]] = { # ********** events with no alerts ********** EventName.stockFcw: {}, # ********** events only containing alerts displayed in all states ********** EventName.joystickDebug: { ET.WARNING: joystick_alert, ET.PERMANENT: NormalPermanentAlert("Joystick Mode"), }, EventName.controlsInitializing: { ET.NO_ENTRY: NoEntryAlert("System Initializing"), }, EventName.startup: { ET.PERMANENT: StartupAlert("Be ready to take over at any time") }, EventName.startupMaster: { ET.PERMANENT: startup_master_alert, }, # Car is recognized, but marked as dashcam only EventName.startupNoControl: { ET.PERMANENT: StartupAlert("Dashcam mode"), }, # Car is not recognized EventName.startupNoCar: { ET.PERMANENT: StartupAlert("Dashcam mode for unsupported car"), }, EventName.startupNoFw: { ET.PERMANENT: StartupAlert("Car Unrecognized", "Check comma power connections", alert_status=AlertStatus.userPrompt), }, EventName.dashcamMode: { ET.PERMANENT: NormalPermanentAlert("Dashcam Mode", priority=Priority.LOWEST), }, EventName.invalidLkasSetting: { ET.PERMANENT: NormalPermanentAlert("Stock LKAS is on", "Turn off stock LKAS to engage"), }, EventName.cruiseMismatch: { #ET.PERMANENT: ImmediateDisableAlert("openpilot failed to cancel cruise"), }, # openpilot doesn't recognize the car. This switches openpilot into a # read-only mode. This can be solved by adding your fingerprint. # See https://github.com/commaai/openpilot/wiki/Fingerprinting for more information EventName.carUnrecognized: { ET.PERMANENT: NormalPermanentAlert("Dashcam Mode", "Car Unrecognized", priority=Priority.LOWEST), }, EventName.stockAeb: { ET.PERMANENT: Alert( "BRAKE!", "Stock AEB: Risk of Collision", AlertStatus.critical, AlertSize.full, Priority.HIGHEST, VisualAlert.fcw, AudibleAlert.none, 2.), ET.NO_ENTRY: NoEntryAlert("Stock AEB: Risk of Collision"), }, EventName.fcw: { ET.PERMANENT: Alert( "BRAKE!", "Risk of Collision", AlertStatus.critical, AlertSize.full, Priority.HIGHEST, VisualAlert.fcw, AudibleAlert.warningSoft, 2.), }, EventName.ldw: { ET.PERMANENT: Alert( "Lane Departure Detected", "", AlertStatus.userPrompt, AlertSize.small, Priority.LOW, VisualAlert.ldw, AudibleAlert.prompt, 3.), }, # ********** events only containing alerts that display while engaged ********** # openpilot tries to learn certain parameters about your car by observing # how the car behaves to steering inputs from both human and openpilot driving. # This includes: # - steer ratio: gear ratio of the steering rack. Steering angle divided by tire angle # - tire stiffness: how much grip your tires have # - angle offset: most steering angle sensors are offset and measure a non zero angle when driving straight # This alert is thrown when any of these values exceed a sanity check. This can be caused by # bad alignment or bad sensor data. If this happens consistently consider creating an issue on GitHub EventName.vehicleModelInvalid: { ET.NO_ENTRY: NoEntryAlert("Vehicle Parameter Identification Failed"), ET.SOFT_DISABLE: soft_disable_alert("Vehicle Parameter Identification Failed"), }, EventName.steerTempUnavailableSilent: { ET.WARNING: Alert( "Steering Temporarily Unavailable", "", AlertStatus.userPrompt, AlertSize.small, Priority.LOW, VisualAlert.steerRequired, AudibleAlert.prompt, 1.), }, EventName.preDriverDistracted: { ET.WARNING: Alert( "Pay Attention", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.none, .1), }, EventName.promptDriverDistracted: { ET.WARNING: Alert( "Pay Attention", "Driver Distracted", AlertStatus.userPrompt, AlertSize.mid, Priority.MID, VisualAlert.steerRequired, AudibleAlert.promptDistracted, .1), }, EventName.driverDistracted: { ET.WARNING: Alert( "DISENGAGE IMMEDIATELY", "Driver Distracted", AlertStatus.critical, AlertSize.full, Priority.HIGH, VisualAlert.steerRequired, AudibleAlert.warningImmediate, .1), }, EventName.preDriverUnresponsive: { ET.WARNING: Alert( "Touch Steering Wheel: No Face Detected", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.steerRequired, AudibleAlert.none, .1, alert_rate=0.75), }, EventName.promptDriverUnresponsive: { ET.WARNING: Alert( "Touch Steering Wheel", "Driver Unresponsive", AlertStatus.userPrompt, AlertSize.mid, Priority.MID, VisualAlert.steerRequired, AudibleAlert.promptDistracted, .1), }, EventName.driverUnresponsive: { ET.WARNING: Alert( "DISENGAGE IMMEDIATELY", "Driver Unresponsive", AlertStatus.critical, AlertSize.full, Priority.HIGH, VisualAlert.steerRequired, AudibleAlert.warningImmediate, .1), }, EventName.manualRestart: { ET.WARNING: Alert( "TAKE CONTROL", "Resume Driving Manually", AlertStatus.userPrompt, AlertSize.mid, Priority.LOW, VisualAlert.none, AudibleAlert.none, .2), }, EventName.resumeRequired: { ET.WARNING: Alert( "STOPPED", "Press Resume to Go", AlertStatus.userPrompt, AlertSize.mid, Priority.LOW, VisualAlert.none, AudibleAlert.none, .2), }, EventName.belowSteerSpeed: { ET.WARNING: below_steer_speed_alert, }, EventName.preLaneChangeLeft: { ET.WARNING: Alert( "Steer Left to Start Lane Change Once Safe", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.none, .1, alert_rate=0.75), }, EventName.preLaneChangeRight: { ET.WARNING: Alert( "Steer Right to Start Lane Change Once Safe", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.none, .1, alert_rate=0.75), }, EventName.laneChangeBlocked: { ET.WARNING: Alert( "Car Detected in Blindspot", "", AlertStatus.userPrompt, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.prompt, .1), }, EventName.laneChange: { ET.WARNING: Alert( "Changing Lanes", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.none, .1), }, EventName.steerSaturated: { ET.WARNING: Alert( "Take Control", "Turn Exceeds Steering Limit", AlertStatus.userPrompt, AlertSize.mid, Priority.LOW, VisualAlert.steerRequired, AudibleAlert.promptRepeat, 1.), }, # Thrown when the fan is driven at >50% but is not rotating EventName.fanMalfunction: { ET.PERMANENT: NormalPermanentAlert("Fan Malfunction", "Likely Hardware Issue"), }, # Camera is not outputting frames at a constant framerate EventName.cameraMalfunction: { ET.PERMANENT: NormalPermanentAlert("Camera Malfunction", "Likely Hardware Issue"), }, # Unused EventName.gpsMalfunction: { ET.PERMANENT: NormalPermanentAlert("GPS Malfunction", "Likely Hardware Issue"), }, # When the GPS position and localizer diverge the localizer is reset to the # current GPS position. This alert is thrown when the localizer is reset # more often than expected. EventName.localizerMalfunction: { # ET.PERMANENT: NormalPermanentAlert("Sensor Malfunction", "Hardware Malfunction"), }, # ********** events that affect controls state transitions ********** EventName.pcmEnable: { ET.ENABLE: EngagementAlert(AudibleAlert.engage), }, EventName.buttonEnable: { ET.ENABLE: EngagementAlert(AudibleAlert.engage), }, EventName.pcmDisable: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), }, EventName.buttonCancel: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), }, EventName.brakeHold: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), ET.NO_ENTRY: NoEntryAlert("Brake Hold Active"), }, EventName.parkBrake: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), ET.NO_ENTRY: NoEntryAlert("Parking Brake Engaged"), }, EventName.pedalPressed: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), ET.NO_ENTRY: NoEntryAlert("Pedal Pressed", visual_alert=VisualAlert.brakePressed), }, EventName.pedalPressedPreEnable: { ET.PRE_ENABLE: Alert( "Release Pedal to Engage", "", AlertStatus.normal, AlertSize.small, Priority.LOWEST, VisualAlert.none, AudibleAlert.none, .1, creation_delay=1.), }, EventName.gasPressedOverride: { ET.OVERRIDE: Alert( "", "", AlertStatus.normal, AlertSize.none, Priority.LOWEST, VisualAlert.none, AudibleAlert.none, .1), }, EventName.wrongCarMode: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), ET.NO_ENTRY: wrong_car_mode_alert, }, EventName.wrongCruiseMode: { ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage), ET.NO_ENTRY: NoEntryAlert("Adaptive Cruise Disabled"), }, EventName.steerTempUnavailable: { ET.SOFT_DISABLE: soft_disable_alert("Steering Temporarily Unavailable"), ET.NO_ENTRY: NoEntryAlert("Steering Temporarily Unavailable"), }, EventName.outOfSpace: { ET.PERMANENT: NormalPermanentAlert("Out of Storage"), ET.NO_ENTRY: NoEntryAlert("Out of Storage"), }, EventName.belowEngageSpeed: { ET.NO_ENTRY: below_engage_speed_alert, }, EventName.sensorDataInvalid: { ET.PERMANENT: Alert( "No Data from Device Sensors", "Reboot your Device", AlertStatus.normal, AlertSize.mid, Priority.LOWER, VisualAlert.none, AudibleAlert.none, .2, creation_delay=1.), ET.NO_ENTRY: NoEntryAlert("No Data from Device Sensors"), }, EventName.noGps: { ET.PERMANENT: no_gps_alert, }, EventName.soundsUnavailable: { ET.PERMANENT: NormalPermanentAlert("Speaker not found", "Reboot your Device"), ET.NO_ENTRY: NoEntryAlert("Speaker not found"), }, EventName.tooDistracted: { ET.NO_ENTRY: NoEntryAlert("Distraction Level Too High"), }, EventName.overheat: { ET.PERMANENT: NormalPermanentAlert("System Overheated"), ET.SOFT_DISABLE: soft_disable_alert("System Overheated"), ET.NO_ENTRY: NoEntryAlert("System Overheated"), }, EventName.wrongGear: { ET.SOFT_DISABLE: user_soft_disable_alert("Gear not D"), ET.NO_ENTRY: NoEntryAlert("Gear not D"), }, # This alert is thrown when the calibration angles are outside of the acceptable range. # For example if the device is pointed too much to the left or the right. # Usually this can only be solved by removing the mount from the windshield completely, # and attaching while making sure the device is pointed straight forward and is level. # See https://comma.ai/setup for more information EventName.calibrationInvalid: { ET.PERMANENT: NormalPermanentAlert("Calibration Invalid", "Remount Device and Recalibrate"), ET.SOFT_DISABLE: soft_disable_alert("Calibration Invalid: Remount Device & Recalibrate"), ET.NO_ENTRY: NoEntryAlert("Calibration Invalid: Remount Device & Recalibrate"), }, EventName.calibrationIncomplete: { ET.PERMANENT: calibration_incomplete_alert, ET.SOFT_DISABLE: soft_disable_alert("Calibration in Progress"), ET.NO_ENTRY: NoEntryAlert("Calibration in Progress"), }, EventName.doorOpen: { ET.SOFT_DISABLE: user_soft_disable_alert("Door Open"), ET.NO_ENTRY: NoEntryAlert("Door Open"), }, EventName.seatbeltNotLatched: { ET.SOFT_DISABLE: user_soft_disable_alert("Seatbelt Unlatched"), ET.NO_ENTRY: NoEntryAlert("Seatbelt Unlatched"), }, EventName.espDisabled: { ET.SOFT_DISABLE: soft_disable_alert("ESP Off"), ET.NO_ENTRY: NoEntryAlert("ESP Off"), }, EventName.lowBattery: { ET.SOFT_DISABLE: soft_disable_alert("Low Battery"), ET.NO_ENTRY: NoEntryAlert("Low Battery"), }, # Different openpilot services communicate between each other at a certain # interval. If communication does not follow the regular schedule this alert # is thrown. This can mean a service crashed, did not broadcast a message for # ten times the regular interval, or the average interval is more than 10% too high. EventName.commIssue: { ET.SOFT_DISABLE: soft_disable_alert("Communication Issue between Processes"), ET.NO_ENTRY: NoEntryAlert("Communication Issue between Processes"), }, # Thrown when manager detects a service exited unexpectedly while driving EventName.processNotRunning: { ET.NO_ENTRY: NoEntryAlert("System Malfunction: Reboot Your Device"), }, EventName.radarFault: { ET.SOFT_DISABLE: soft_disable_alert("Radar Error: Restart the Car"), ET.NO_ENTRY: NoEntryAlert("Radar Error: Restart the Car"), }, # Every frame from the camera should be processed by the model. If modeld # is not processing frames fast enough they have to be dropped. This alert is # thrown when over 20% of frames are dropped. EventName.modeldLagging: { ET.SOFT_DISABLE: soft_disable_alert("Driving model lagging"), ET.NO_ENTRY: NoEntryAlert("Driving model lagging"), }, # Besides predicting the path, lane lines and lead car data the model also # predicts the current velocity and rotation speed of the car. If the model is # very uncertain about the current velocity while the car is moving, this # usually means the model has trouble understanding the scene. This is used # as a heuristic to warn the driver. EventName.posenetInvalid: { ET.SOFT_DISABLE: soft_disable_alert("Model Output Uncertain"), ET.NO_ENTRY: NoEntryAlert("Model Output Uncertain"), }, # When the localizer detects an acceleration of more than 40 m/s^2 (~4G) we # alert the driver the device might have fallen from the windshield. EventName.deviceFalling: { ET.SOFT_DISABLE: soft_disable_alert("Device Fell Off Mount"), ET.NO_ENTRY: NoEntryAlert("Device Fell Off Mount"), }, EventName.lowMemory: { ET.SOFT_DISABLE: soft_disable_alert("Low Memory: Reboot Your Device"), ET.PERMANENT: NormalPermanentAlert("Low Memory", "Reboot your Device"), ET.NO_ENTRY: NoEntryAlert("Low Memory: Reboot Your Device"), }, EventName.highCpuUsage: { #ET.SOFT_DISABLE: soft_disable_alert("System Malfunction: Reboot Your Device"), #ET.PERMANENT: NormalPermanentAlert("System Malfunction", "Reboot your Device"), ET.NO_ENTRY: NoEntryAlert("System Malfunction: Reboot Your Device"), }, EventName.accFaulted: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Cruise Faulted"), ET.PERMANENT: NormalPermanentAlert("Cruise Faulted", ""), ET.NO_ENTRY: NoEntryAlert("Cruise Faulted"), }, EventName.controlsMismatch: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Controls Mismatch"), }, EventName.roadCameraError: { ET.PERMANENT: NormalPermanentAlert("Camera Error", duration=1., creation_delay=30.), }, EventName.driverCameraError: { ET.PERMANENT: NormalPermanentAlert("Camera Error", duration=1., creation_delay=30.), }, EventName.wideRoadCameraError: { ET.PERMANENT: NormalPermanentAlert("Camera Error", duration=1., creation_delay=30.), }, # Sometimes the USB stack on the device can get into a bad state # causing the connection to the panda to be lost EventName.usbError: { ET.SOFT_DISABLE: soft_disable_alert("USB Error: Reboot Your Device"), ET.PERMANENT: NormalPermanentAlert("USB Error: Reboot Your Device", ""), ET.NO_ENTRY: NoEntryAlert("USB Error: Reboot Your Device"), }, # This alert can be thrown for the following reasons: # - No CAN data received at all # - CAN data is received, but some message are not received at the right frequency # If you're not writing a new car port, this is usually cause by faulty wiring EventName.canError: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("CAN Error: Check Connections"), ET.PERMANENT: Alert( "CAN Error: Check Connections", "", AlertStatus.normal, AlertSize.small, Priority.LOW, VisualAlert.none, AudibleAlert.none, 1., creation_delay=1.), ET.NO_ENTRY: NoEntryAlert("CAN Error: Check Connections"), }, EventName.steerUnavailable: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("LKAS Fault: Restart the Car"), ET.PERMANENT: NormalPermanentAlert("LKAS Fault: Restart the car to engage"), ET.NO_ENTRY: NoEntryAlert("LKAS Fault: Restart the Car"), }, EventName.brakeUnavailable: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Cruise Fault: Restart the Car"), ET.PERMANENT: NormalPermanentAlert("Cruise Fault: Restart the car to engage"), ET.NO_ENTRY: NoEntryAlert("Cruise Fault: Restart the Car"), }, EventName.reverseGear: { ET.PERMANENT: Alert( "Reverse\nGear", "", AlertStatus.normal, AlertSize.full, Priority.LOWEST, VisualAlert.none, AudibleAlert.none, .2, creation_delay=0.5), ET.USER_DISABLE: ImmediateDisableAlert("Reverse Gear"), ET.NO_ENTRY: NoEntryAlert("Reverse Gear"), }, # On cars that use stock ACC the car can decide to cancel ACC for various reasons. # When this happens we can no long control the car so the user needs to be warned immediately. EventName.cruiseDisabled: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Cruise Is Off"), }, # For planning the trajectory Model Predictive Control (MPC) is used. This is # an optimization algorithm that is not guaranteed to find a feasible solution. # If no solution is found or the solution has a very high cost this alert is thrown. EventName.plannerError: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Planner Solution Error"), ET.NO_ENTRY: NoEntryAlert("Planner Solution Error"), }, # When the relay in the harness box opens the CAN bus between the LKAS camera # and the rest of the car is separated. When messages from the LKAS camera # are received on the car side this usually means the relay hasn't opened correctly # and this alert is thrown. EventName.relayMalfunction: { ET.IMMEDIATE_DISABLE: ImmediateDisableAlert("Harness Malfunction"), ET.PERMANENT: NormalPermanentAlert("Harness Malfunction", "Check Hardware"), ET.NO_ENTRY: NoEntryAlert("Harness Malfunction"), }, EventName.noTarget: { ET.IMMEDIATE_DISABLE: Alert( "openpilot Canceled", "No close lead car", AlertStatus.normal, AlertSize.mid, Priority.HIGH, VisualAlert.none, AudibleAlert.disengage, 3.), ET.NO_ENTRY: NoEntryAlert("No Close Lead Car"), }, EventName.speedTooLow: { ET.IMMEDIATE_DISABLE: Alert( "openpilot Canceled", "Speed too low", AlertStatus.normal, AlertSize.mid, Priority.HIGH, VisualAlert.none, AudibleAlert.disengage, 3.), }, # When the car is driving faster than most cars in the training data, the model outputs can be unpredictable. EventName.speedTooHigh: { ET.WARNING: Alert( "Speed Too High", "Model uncertain at this speed", AlertStatus.userPrompt, AlertSize.mid, Priority.HIGH, VisualAlert.steerRequired, AudibleAlert.promptRepeat, 4.), ET.NO_ENTRY: NoEntryAlert("Slow down to engage"), }, EventName.lowSpeedLockout: { ET.PERMANENT: NormalPermanentAlert("Cruise Fault: Restart the car to engage"), ET.NO_ENTRY: NoEntryAlert("Cruise Fault: Restart the Car"), }, EventName.lkasDisabled: { ET.PERMANENT: NormalPermanentAlert("LKAS Disabled: Enable LKAS to engage"), ET.NO_ENTRY: NoEntryAlert("LKAS Disabled"), }, }

GregorKikelj/openpilot

selfdrive/controls/lib/events.py

Python

from rest_framework import views from rest_framework.response import Response from sponsors.models import Sponsor, OpenRole class SponsorAPIView(views.APIView): def get(self, request): sponsor_data = Sponsor.objects.order_by('level') level_dict = {} for sponsor in sponsor_data: if sponsor.level_en_name not in level_dict: level_dict[sponsor.level_en_name] = [] level_dict[sponsor.level_en_name].append({ "name": sponsor.name, "subtitle_en_us": sponsor.subtitle_en_us, "subtitle_zh_hant": sponsor.subtitle_zh_hant, "intro_en_us": sponsor.intro_en_us, "intro_zh_hant": sponsor.intro_zh_hant, "website_url": sponsor.website_url, "logo_url": sponsor.logo.url if sponsor.logo else '' }) response_data = {"data": []} for level_name, sponsors in level_dict.items(): response_data["data"].append({ "level_name": level_name, "sponsors": sponsors }) return Response(response_data) class JobAPIView(views.APIView): def get(self, request): sponsor_has_open_role = set(OpenRole.objects.values_list('sponsor', flat=True)) sponsor_set = Sponsor.objects.filter(id__in=sponsor_has_open_role).order_by('level') open_roles = OpenRole.objects.filter(sponsor__in=sponsor_has_open_role).order_by('sponsor__level') response_data = {"data": []} for sponsor in sponsor_set: jobs = [] for open_role in open_roles: jobs.append({ "job_url": open_role.url, "job_name": open_role.name, "job_description_en_us": open_role.description_en_us, "job_description_zh_hant": open_role.description_zh_hant, }) response_data["data"].append({ "sponsor_logo_url": sponsor.logo.url if sponsor.logo else '', "sponsor_name": sponsor.name, "jobs": jobs }) return Response(response_data)

DoubleTakoMeat/pycon.tw

src/sponsors/api/views.py

Python

import copy import rdtest import renderdoc as rd class D3D11_Vertex_Attr_Zoo(rdtest.TestCase): demos_test_name = 'D3D11_Vertex_Attr_Zoo' def check_capture(self): draw = self.find_draw("Draw") self.check(draw is not None) self.controller.SetFrameEvent(draw.eventId, False) # Make an output so we can pick pixels out: rd.ReplayOutput = self.controller.CreateOutput(rd.CreateHeadlessWindowingData(100, 100), rd.ReplayOutputType.Texture) self.check(out is not None) ref = { 0: { 'SNORM': [1.0, -1.0, 1.0, -1.0], 'UNORM': [12345.0/65535.0, 6789.0/65535.0, 1234.0/65535.0, 567.0/65535.0], 'UINT': [12345, 6789, 1234, 567], 'ARRAY0': [1.0, 2.0], 'ARRAY1': [3.0, 4.0], 'ARRAY2': [5.0, 6.0], 'MATRIX0': [7.0, 8.0], 'MATRIX1': [9.0, 10.0], }, 1: { 'SNORM': [32766.0/32767.0, -32766.0/32767.0, 16000.0/32767.0, -16000.0/32767.0], 'UNORM': [56.0/65535.0, 7890.0/65535.0, 123.0/65535.0, 4567.0/65535.0], 'UINT': [56, 7890, 123, 4567], 'ARRAY0': [11.0, 12.0], 'ARRAY1': [13.0, 14.0], 'ARRAY2': [15.0, 16.0], 'MATRIX0': [17.0, 18.0], 'MATRIX1': [19.0, 20.0], }, 2: { 'SNORM': [5.0/32767.0, -5.0/32767.0, 0.0, 0.0], 'UNORM': [8765.0/65535.0, 43210.0/65535.0, 987.0/65535.0, 65432.0/65535.0], 'UINT': [8765, 43210, 987, 65432], 'ARRAY0': [21.0, 22.0], 'ARRAY1': [23.0, 24.0], 'ARRAY2': [25.0, 26.0], 'MATRIX0': [27.0, 28.0], 'MATRIX1': [29.0, 30.0], }, } in_ref = copy.deepcopy(ref) vsout_ref = copy.deepcopy(ref) gsout_ref = ref vsout_ref[0]['SV_Position'] = [-0.5, 0.5, 0.0, 1.0] gsout_ref[0]['SV_Position'] = [0.5, -0.5, 0.4, 1.2] vsout_ref[1]['SV_Position'] = [0.0, -0.5, 0.0, 1.0] gsout_ref[1]['SV_Position'] = [-0.5, 0.0, 0.4, 1.2] vsout_ref[2]['SV_Position'] = [0.5, 0.5, 0.0, 1.0] gsout_ref[2]['SV_Position'] = [0.5, 0.5, 0.4, 1.2] self.check_mesh_data(in_ref, self.get_vsin(draw)) rdtest.log.success("Vertex input data is as expected") self.check_mesh_data(vsout_ref, self.get_postvs(rd.MeshDataStage.VSOut)) rdtest.log.success("Vertex output data is as expected") self.check_mesh_data(gsout_ref, self.get_postvs(rd.MeshDataStage.GSOut)) rdtest.log.success("Geometry output data is as expected") pipe: rd.PipeState = self.controller.GetPipelineState() tex = rd.TextureDisplay() tex.resourceId = pipe.GetOutputTargets()[0].resourceId out.SetTextureDisplay(tex) texdetails = self.get_texture(tex.resourceId) picked: rd.PixelValue = out.PickPixel(tex.resourceId, False, int(texdetails.width / 2), int(texdetails.height / 2), 0, 0, 0) if not rdtest.value_compare(picked.floatValue, [0.0, 1.0, 0.0, 1.0]): raise rdtest.TestFailureException("Picked value {} doesn't match expectation".format(picked.floatValue)) rdtest.log.success("Triangle picked value is as expected") out.Shutdown()

DmitrySoshnikov/renderdoc

util/test/tests/D3D11/D3D11_Vertex_Attr_Zoo.py

Python

""" Django settings for orders project. Generated by 'django-admin startproject' using Django 3.0. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'mrmul5#_g^qb*mue%y$oz@gs$1t#sz_4oj#ea#hv!-s!xu$y4h' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'app.apps.AppConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'orders.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'orders.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = False # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' REST_FRAMEWORK = { 'DATETIME_FORMAT': '%Y-%m-%d %H:%M:%S' }

rafaeltardivo/micro-order

services/orders/orders/settings.py

Python

# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*- # ex: set sts=4 ts=4 sw=4 et: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the datalad package for the # copyright and license terms. # # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Tests for customremotes archives providing dl+archive URLs handling""" import glob import logging import os import os.path as op import sys from time import sleep from unittest.mock import patch from datalad.api import Dataset from datalad.cmd import ( GitWitlessRunner, KillOutput, StdOutErrCapture, WitlessRunner, ) from datalad.support.exceptions import CommandError from ...consts import ARCHIVES_SPECIAL_REMOTE from ...support.annexrepo import AnnexRepo from ...tests.test_archives import ( fn_archive_obscure, fn_archive_obscure_ext, fn_in_archive_obscure, ) from ...tests.utils_pytest import ( abspath, assert_equal, assert_false, assert_not_equal, assert_not_in, assert_raises, assert_true, chpwd, eq_, in_, known_failure_githubci_win, ok_, serve_path_via_http, swallow_logs, with_tempfile, with_tree, ) from ...utils import unlink from ..archives import ( ArchiveAnnexCustomRemote, link_file_load, ) # TODO: with_tree ATM for archives creates this nested top directory # matching archive name, so it will be a/d/test.dat ... we don't want that probably @known_failure_githubci_win @with_tree( tree=(('a.tar.gz', {'d': {fn_in_archive_obscure: '123'}}), ('simple.txt', '123'), (fn_archive_obscure_ext, (('d', ((fn_in_archive_obscure, '123'),)),)), (fn_archive_obscure, '123'))) @with_tempfile() def test_basic_scenario(d=None, d2=None): fn_archive, fn_extracted = fn_archive_obscure_ext, fn_archive_obscure annex = AnnexRepo(d, backend='MD5E') annex.init_remote( ARCHIVES_SPECIAL_REMOTE, ['encryption=none', 'type=external', 'externaltype=%s' % ARCHIVES_SPECIAL_REMOTE, 'autoenable=true' ]) assert annex.is_special_annex_remote(ARCHIVES_SPECIAL_REMOTE) # We want two maximally obscure names, which are also different assert(fn_extracted != fn_in_archive_obscure) annex.add(fn_archive) annex.commit(msg="Added tarball") annex.add(fn_extracted) annex.commit(msg="Added the load file") # Operations with archive remote URL # this is not using this class for its actual purpose # being a special remote implementation # likely all this functionality should be elsewhere annexcr = ArchiveAnnexCustomRemote(annex=None, path=d) # few quick tests for get_file_url eq_(annexcr.get_file_url(archive_key="xyz", file="a.dat"), "dl+archive:xyz#path=a.dat") eq_(annexcr.get_file_url(archive_key="xyz", file="a.dat", size=999), "dl+archive:xyz#path=a.dat&size=999") # see https://github.com/datalad/datalad/issues/441#issuecomment-223376906 # old style eq_(annexcr._parse_url("dl+archive:xyz/a.dat#size=999"), ("xyz", "a.dat", {'size': 999})) eq_(annexcr._parse_url("dl+archive:xyz/a.dat"), ("xyz", "a.dat", {})) # old format without size # new style eq_(annexcr._parse_url("dl+archive:xyz#path=a.dat&size=999"), ("xyz", "a.dat", {'size': 999})) eq_(annexcr._parse_url("dl+archive:xyz#path=a.dat"), ("xyz", "a.dat", {})) # old format without size file_url = annexcr.get_file_url( archive_file=fn_archive, file=fn_archive.replace('.tar.gz', '') + '/d/' + fn_in_archive_obscure) annex.add_url_to_file(fn_extracted, file_url, ['--relaxed']) annex.drop(fn_extracted) list_of_remotes = annex.whereis(fn_extracted, output='descriptions') in_('[%s]' % ARCHIVES_SPECIAL_REMOTE, list_of_remotes) assert_false(annex.file_has_content(fn_extracted)) with swallow_logs(new_level=logging.INFO) as cml: annex.get(fn_extracted) # Hint users to the extraction cache (and to datalad clean) cml.assert_logged(msg="datalad-archives special remote is using an " "extraction", level="INFO", regex=False) assert_true(annex.file_has_content(fn_extracted)) annex.rm_url(fn_extracted, file_url) assert_raises(CommandError, annex.drop, fn_extracted) annex.add_url_to_file(fn_extracted, file_url) annex.drop(fn_extracted) annex.get(fn_extracted) annex.drop(fn_extracted) # so we don't get from this one next # Let's create a clone and verify chain of getting file through the tarball cloned_annex = AnnexRepo.clone(d, d2) # we still need to enable manually atm that special remote for archives # cloned_annex.enable_remote('annexed-archives') assert_false(cloned_annex.file_has_content(fn_archive)) assert_false(cloned_annex.file_has_content(fn_extracted)) cloned_annex.get(fn_extracted) assert_true(cloned_annex.file_has_content(fn_extracted)) # as a result it would also fetch tarball assert_true(cloned_annex.file_has_content(fn_archive)) # verify that we can drop if original archive gets dropped but available online: # -- done as part of the test_add_archive_content.py # verify that we can't drop a file if archive key was dropped and online archive was removed or changed size! ;) @known_failure_githubci_win @with_tree( tree={'a.tar.gz': {'d': {fn_in_archive_obscure: '123'}}} ) def test_annex_get_from_subdir(topdir=None): ds = Dataset(topdir) ds.create(force=True) ds.save('a.tar.gz') ds.add_archive_content('a.tar.gz', delete=True) fpath = op.join(topdir, 'a', 'd', fn_in_archive_obscure) with chpwd(op.join(topdir, 'a', 'd')): runner = WitlessRunner() runner.run( ['git', 'annex', 'drop', '--', fn_in_archive_obscure], protocol=KillOutput) # run git annex drop assert_false(ds.repo.file_has_content(fpath)) # and verify if file deleted from directory runner.run( ['git', 'annex', 'get', '--', fn_in_archive_obscure], protocol=KillOutput) # run git annex get assert_true(ds.repo.file_has_content(fpath)) # and verify if file got into directory def test_get_git_environ_adjusted(): gitrunner = GitWitlessRunner() env = {"GIT_DIR": "../../.git", "GIT_WORK_TREE": "../../", "TEST_VAR": "Exists"} # test conversion of relevant env vars from relative_path to correct absolute_path adj_env = gitrunner.get_git_environ_adjusted(env) assert_equal(adj_env["GIT_DIR"], abspath(env["GIT_DIR"])) assert_equal(adj_env["GIT_WORK_TREE"], abspath(env["GIT_WORK_TREE"])) # test if other environment variables passed to function returned unaltered assert_equal(adj_env["TEST_VAR"], env["TEST_VAR"]) # test import of sys_env if no environment passed to function with patch.dict('os.environ', {'BOGUS': '123'}): sys_env = gitrunner.get_git_environ_adjusted() assert_equal(sys_env["BOGUS"], "123") def test_no_rdflib_loaded(): # rely on rdflib polluting stdout to see that it is not loaded whenever we load this remote # since that adds 300ms delay for no immediate use runner = WitlessRunner() out = runner.run( [sys.executable, '-c', 'import datalad.customremotes.archives, sys; ' 'print([k for k in sys.modules if k.startswith("rdflib")])'], protocol=StdOutErrCapture) # print cmo.out assert_not_in("rdflib", out['stdout']) assert_not_in("rdflib", out['stderr']) @with_tree(tree= {'1.tar.gz': { 'bu.dat': '52055957098986598349795121365535' * 10000, 'bu3.dat': '8236397048205454767887168342849275422' * 10000 }, '2.tar.gz': { 'bu2.dat': '17470674346319559612580175475351973007892815102' * 10000 }, } ) @serve_path_via_http() @with_tempfile def check_observe_tqdm(topdir=None, topurl=None, outdir=None): # just a helper to enable/use when want quickly to get some # repository with archives and observe tqdm from datalad.api import ( add_archive_content, create, ) ds = create(outdir) for f in '1.tar.gz', '2.tar.gz': with chpwd(outdir): ds.repo.add_url_to_file(f, topurl + f) ds.save(f) add_archive_content(f, delete=True, drop_after=True) files = glob.glob(op.join(outdir, '*')) ds.drop(files) # will not drop tarballs ds.repo.drop([], options=['--all', '--fast']) ds.get(files) ds.repo.drop([], options=['--all', '--fast']) # now loop so we could play with it outside print(outdir) # import pdb; pdb.set_trace() while True: sleep(0.1) @known_failure_githubci_win @with_tempfile def test_link_file_load(tempfile=None): tempfile2 = tempfile + '_' with open(tempfile, 'w') as f: f.write("LOAD") link_file_load(tempfile, tempfile2) # this should work in general ok_(os.path.exists(tempfile2)) with open(tempfile2, 'r') as f: assert_equal(f.read(), "LOAD") def inode(fname): with open(fname) as fd: return os.fstat(fd.fileno()).st_ino def stats(fname, times=True): """Return stats on the file which should have been preserved""" with open(fname) as fd: st = os.fstat(fd.fileno()) stats = (st.st_mode, st.st_uid, st.st_gid, st.st_size) if times: return stats + (st.st_atime, st.st_mtime) else: return stats # despite copystat mtime is not copied. TODO # st.st_mtime) # TODO: fix up the test to not rely on OS assumptions but rather # first sense filesystem about linking support. # For Yarik's Windows 10 VM test was failing under assumption that # linking is not supported at all, but I guess it does. if True: # on_linux or on_osx: # above call should result in the hardlink assert_equal(inode(tempfile), inode(tempfile2)) assert_equal(stats(tempfile), stats(tempfile2)) # and if we mock absence of .link def raise_AttributeError(*args): raise AttributeError("TEST") with patch('os.link', raise_AttributeError): with swallow_logs(logging.WARNING) as cm: link_file_load(tempfile, tempfile2) # should still work ok_("failed (TEST), copying file" in cm.out) # should be a copy (after mocked call) assert_not_equal(inode(tempfile), inode(tempfile2)) with open(tempfile2, 'r') as f: assert_equal(f.read(), "LOAD") assert_equal(stats(tempfile, times=False), stats(tempfile2, times=False)) unlink(tempfile2) # TODO: next two with_tempfile

soichih/datalad

datalad/customremotes/tests/test_archives.py

Python

# Copyright 2015 Hewlett-Packard Development Company, L.P. # # Author: Endre Karlson <endre.karlson@hp.com> # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from sqlalchemy.schema import Table, MetaData from migrate.changeset.constraint import UniqueConstraint meta = MetaData() CONSTRAINT_NAME = "unique_domain_name" def upgrade(migrate_engine): meta.bind = migrate_engine # Load the database tables domains_table = Table('domains', meta, autoload=True) constraint = UniqueConstraint('name', 'deleted', name=CONSTRAINT_NAME, table=domains_table) constraint.drop() constraint = UniqueConstraint('name', 'deleted', 'pool_id', name=CONSTRAINT_NAME, table=domains_table) constraint.create() def downgrade(migrate_engine): domains_table = Table('domains', meta, autoload=True) constraint = UniqueConstraint('name', 'deleted', 'pool_id', name=CONSTRAINT_NAME, table=domains_table) constraint.drop() constraint = UniqueConstraint('name', 'deleted', name=CONSTRAINT_NAME, table=domains_table) constraint.create()

infobloxopen/designate

designate/storage/impl_sqlalchemy/migrate_repo/versions/054_allow_duplicate_domains.py

Python

from flask import Flask, jsonify, request, render_template, redirect, url_for import datetime from alarm import AlarmService app = Flask(__name__, static_url_path='/static') app.config.from_pyfile('config.py') # status file for motion detection motionfile = "motion.txt" # enable temperature sensor (if library is available) try: import w1thermsensor temperature = w1thermsensor.W1ThermSensor() except ImportError: temperature = None except: temperature = None @app.route('/') def index(): return render_template('/index.html', webcam = app.config["WEBCAM"], sensor = app.config["SENSOR"], temperature = app.config["TEMPERATURE"], alarm = app.config["ALARM"], # alarmOn = True, switches = {'A': app.config["SWITCH_A"], 'B': app.config["SWITCH_B"], 'C': app.config["SWITCH_C"]}) @app.route("/status", methods=["GET"]) def status(): import os, time now = datetime.datetime.now() timeString = now.strftime("%Y-%m-%d %H:%M") details = {'time': timeString} # update time # read temperature (if sensor is available) if temperature and app.config["TEMPERATURE"]: tempcelsius = temperature.get_temperature() details.update({'temperature': tempcelsius}) # read status of PIR sensor if os.path.exists(motionfile): details['motion'] = time.ctime(os.path.getmtime(motionfile)) # take snapshot of webcam (max 1 per minute) if app.config["WEBCAM"]: import sh, os timeString = now.strftime("%Y%m%d_%H%M") snapshot = "snapshot-%s.jpg" % timeString snapshot = os.path.join("static", "webcam", snapshot) details['webcam'] = '/'+snapshot snapshot = os.path.abspath(snapshot) if not os.path.exists(snapshot): sh.fswebcam("--title", "Home", "--save", snapshot) return jsonify(**details) @app.route("/alarm/<on>", methods=["POST"]) def alarm(on): details = {} if turnAlarm(on=="on"): now = datetime.datetime.now() timeString = now.strftime("%Y-%m-%d %H:%M") details.update({'time': timeString}) return jsonify(**details) def turnAlarm(on): """write command into file - pass enable/disable command""" print ("turn alarm on? %s" % on) enable = on disable = not on alarm = AlarmService() alarm.load() alarm.save(enable, disable) return True @app.route("/turn/<switch>", methods=["POST"]) def turn(switch): on = request.form['cmd']=="on" details = {} if turnSwitch(switch, on): now = datetime.datetime.now() timeString = now.strftime("%Y-%m-%d %H:%M") details.update({'time': timeString}) return jsonify(**details) def turnSwitch(switch, on=False): from elro import RemoteSwitch devices = { 'A': 1, 'B': 2, 'C': 4, 'D': 8, 'E':16 } device=devices.get(switch, 0) if not device: return False # print "turn switch '%s' to '%s' - device#%d" % (switch, "on" if on else "off", device) device = RemoteSwitch(device, key=app.config["ELRO_KEY"], pin=app.config["ELRO_PIN"]) if on: device.switchOn() else: device.switchOff() return True if __name__ == "__main__": app.run(host='0.0.0.0', port=app.config["HTTP_PORT"], debug=app.config["DEBUG"])

tommykoch/pyhome

web/home.py

Python

"""Support for RFXtrx lights.""" import logging import RFXtrx as rfxtrxmod from homeassistant.components.light import ( ATTR_BRIGHTNESS, SUPPORT_BRIGHTNESS, LightEntity, ) from homeassistant.const import CONF_DEVICES, STATE_ON from homeassistant.core import callback from . import ( CONF_AUTOMATIC_ADD, CONF_DATA_BITS, CONF_SIGNAL_REPETITIONS, DEFAULT_SIGNAL_REPETITIONS, SIGNAL_EVENT, RfxtrxCommandEntity, get_device_id, get_rfx_object, ) from .const import COMMAND_OFF_LIST, COMMAND_ON_LIST _LOGGER = logging.getLogger(__name__) SUPPORT_RFXTRX = SUPPORT_BRIGHTNESS async def async_setup_entry( hass, config_entry, async_add_entities, ): """Set up config entry.""" discovery_info = config_entry.data device_ids = set() def supported(event): return ( isinstance(event.device, rfxtrxmod.LightingDevice) and event.device.known_to_be_dimmable ) # Add switch from config file entities = [] for packet_id, entity_info in discovery_info[CONF_DEVICES].items(): event = get_rfx_object(packet_id) if event is None: _LOGGER.error("Invalid device: %s", packet_id) continue if not supported(event): continue device_id = get_device_id( event.device, data_bits=entity_info.get(CONF_DATA_BITS) ) if device_id in device_ids: continue device_ids.add(device_id) entity = RfxtrxLight( event.device, device_id, entity_info[CONF_SIGNAL_REPETITIONS] ) entities.append(entity) async_add_entities(entities) @callback def light_update(event, device_id): """Handle light updates from the RFXtrx gateway.""" if not supported(event): return if device_id in device_ids: return device_ids.add(device_id) _LOGGER.info( "Added light (Device ID: %s Class: %s Sub: %s, Event: %s)", event.device.id_string.lower(), event.device.__class__.__name__, event.device.subtype, "".join(f"{x:02x}" for x in event.data), ) entity = RfxtrxLight( event.device, device_id, DEFAULT_SIGNAL_REPETITIONS, event=event ) async_add_entities([entity]) # Subscribe to main RFXtrx events if discovery_info[CONF_AUTOMATIC_ADD]: hass.helpers.dispatcher.async_dispatcher_connect(SIGNAL_EVENT, light_update) class RfxtrxLight(RfxtrxCommandEntity, LightEntity): """Representation of a RFXtrx light.""" _brightness = 0 async def async_added_to_hass(self): """Restore RFXtrx device state (ON/OFF).""" await super().async_added_to_hass() if self._event is None: old_state = await self.async_get_last_state() if old_state is not None: self._state = old_state.state == STATE_ON self._brightness = old_state.attributes.get(ATTR_BRIGHTNESS) @property def brightness(self): """Return the brightness of this light between 0..255.""" return self._brightness @property def supported_features(self): """Flag supported features.""" return SUPPORT_RFXTRX @property def is_on(self): """Return true if device is on.""" return self._state async def async_turn_on(self, **kwargs): """Turn the device on.""" brightness = kwargs.get(ATTR_BRIGHTNESS) self._state = True if brightness is None: await self._async_send(self._device.send_on) self._brightness = 255 else: await self._async_send(self._device.send_dim, brightness * 100 // 255) self._brightness = brightness self.async_write_ha_state() async def async_turn_off(self, **kwargs): """Turn the device off.""" await self._async_send(self._device.send_off) self._state = False self._brightness = 0 self.async_write_ha_state() def _apply_event(self, event): """Apply command from rfxtrx.""" super()._apply_event(event) if event.values["Command"] in COMMAND_ON_LIST: self._state = True elif event.values["Command"] in COMMAND_OFF_LIST: self._state = False elif event.values["Command"] == "Set level": self._brightness = event.values["Dim level"] * 255 // 100 self._state = self._brightness > 0 @callback def _handle_event(self, event, device_id): """Check if event applies to me and update.""" if device_id != self._device_id: return self._apply_event(event) self.async_write_ha_state()

1e1/core-1

homeassistant/components/rfxtrx/light.py

Python

# TODO: # # A handler/formatter that can replace django.utils.log.AdminEmailHandler # # Needed: # * A dump of locals() on each affected line in the stacktrace

Uninett/python-logging-humio

src/humiologging/handlers/django.py

Python

import unittest import parameterized import numpy as np from rlutil.envs.tabular_cy import q_iteration, tabular_env from rlutil.envs.tabular_cy import q_iteration_py class QIterationTest(unittest.TestCase): def setUp(self): self.num_states = 128 self.env = tabular_env.RandomTabularEnv(num_states=self.num_states, num_actions=3, transitions_per_action=2) self.env_selfloop = tabular_env.RandomTabularEnv(num_states=self.num_states, num_actions=3, transitions_per_action=2, self_loop=True) def test_num_states(self): self.assertEqual(self.env.num_states, self.num_states) def test_selfloop(self): transitions = self.env_selfloop.transitions(2, 0) self.assertEqual(len(transitions), 1) self.assertEqual(transitions[2], 1.0) transitions = self.env_selfloop.transitions(2, 1) self.assertEqual(len(transitions), 2) def test_num_transitions(self): transitions = self.env.transitions(0, 0) self.assertEqual(len(transitions), 2) for ns in transitions: self.assertAlmostEqual(transitions[ns], 0.5) def test_random_rollout(self): self.env.reset() for _ in range(30): #self.env.render() self.env.step(np.random.randint(0, self.env.num_actions)) def test_q_iteration(self): params = { 'num_itrs': 1000, 'ent_wt': 0.0, 'discount': 0.95, } qvals = q_iteration.softq_iteration(self.env, **params) self.env.reset() rews = 0 for _ in range(200): #self.env_small.render() a_qvals = qvals[self.env.get_state()] _, rew, _, _ = self.env.step(np.argmax(a_qvals)) rews += rew self.assertGreater(rews, 0.0) if __name__ == '__main__': unittest.main()

alexlioralexli/diagnosing_qlearning

rlutil/envs/tabular_cy/test_random_env.py

Python

import json from pathlib import Path from typing import List import criticus.py.edit_settings as es def get_file(filename): with open(filename, 'r', encoding='utf-8') as f: text = f.readlines() return text def get_info_from_filename(filename): filename = filename.split('/')[-1] f = filename.split('_') siglum = f[0] reference_prefix = f[1].replace('.txt', '') return siglum, reference_prefix def format_reference(line: List[str], reference_prefix: str): if reference_prefix[-1].isdigit(): seperator = '.' else: seperator = '' verse = line.pop(0) reference = verse.replace(':', '.') reference = f'{reference_prefix}{seperator}{reference}' return line, reference, verse def convert_this_line(verse: str, verse_from, verse_to) -> bool: if verse.replace(':', '.') in [verse_from.replace(':', '.'), verse_to.replace(':', '.')]: return True else: return False def build_token(word: str, index: str, siglum: str) -> dict: return { 'index': index, 'siglum': siglum, 'reading': siglum, 'original': word, 'rule_match': [word], 't': word } def build_witneses(siglum: str, tokens: List[dict]): return [{ 'id': siglum, 'tokens': tokens }] def build_json(siglum, reference, witnesses: List[dict], line: list): return { 'id': siglum, '_id': siglum, 'transcription': siglum, 'transcription_siglum': siglum, 'siglum': siglum, 'context': reference, 'n': reference, 'text': ' '.join(line), 'witnesses': witnesses } def make_tokens(line, siglum) -> List[dict]: tokens = [] for i, word in enumerate(line, 1): index = f'{i*2}' token = build_token(word, index, siglum) tokens.append(token) return tokens def save_json(to_save: dict, reference: str, output_dir: str): with open(f'{output_dir}/{reference}.json', 'w', encoding='utf-8') as f: json.dump(to_save, f, ensure_ascii=False, indent=4) def save_metadata(siglum, output_dir): metadata = {'_id': siglum, 'siglum': siglum, 'id': siglum} with open(f'{output_dir}/metadata.json', 'w', encoding='utf-8') as f: json.dump(metadata, f, ensure_ascii=False) def construct_json_transcription(line: List[str], reference: str, siglum: str, output_dir): tokens = make_tokens(line, siglum) witnesses = build_witneses(siglum, tokens) complete_json = build_json(siglum, reference, witnesses, line) save_json(complete_json, reference, output_dir) def check_and_save_dirs(output_dir, siglum): output_dir = Path(f'{output_dir}/{siglum}') if not output_dir.exists(): Path.mkdir(output_dir, parents=True) es.edit_settings('output_dir', output_dir.parent.absolute().as_posix()) return output_dir.absolute().as_posix() def convert_text_to_json( filename, output_dir, convert_all: bool, reference_prefix: str, auto: bool, verse_from: str=None, verse_to: str=None, siglum: str=None ): filename = Path(filename).as_posix() if auto: siglum, reference_prefix = get_info_from_filename(filename) output_dir = check_and_save_dirs(output_dir, siglum) save_metadata(siglum, output_dir) text = get_file(filename) capture = False for line in text: line = line.split() if len(line) > 1 and line[0][0].isdigit() and line[0][-1].isdigit(): # check that line contains a reference and a text unit and is not a heading line, reference, verse = format_reference(line, reference_prefix) if not convert_all and verse_to != verse_from: # handle a range of text units to convert if convert_this_line(verse, verse_from, verse_to): capture = not capture construct_json_transcription(line, reference, siglum, output_dir) elif capture: construct_json_transcription(line, reference, siglum, output_dir) elif not convert_all and verse_from == verse_to: # handle a single text unit if convert_this_line(verse, verse_from, verse_to): construct_json_transcription(line, reference, siglum, output_dir) else: construct_json_transcription(line, reference, siglum, output_dir) # handle all text units

d-flood/Criticus

criticus/py/txt2json/convert_text_to_json.py

Python

""" Settings file, which is populated from the environment while enforcing common use-case defaults. """ import os from os.path import join, dirname from dotenv import load_dotenv dotenv_path = join(dirname(__file__), '.env') load_dotenv(dotenv_path) # OR, the same with increased verbosity: load_dotenv(dotenv_path, verbose=True) DEBUG = True if os.getenv('DEBUG', '').lower() in ['0', 'no', 'false']: DEBUG = False API_BIND_HOST = os.getenv('SERVICE_API_HOST', '0.0.0.0') API_BIND_PORT = int(os.getenv('SERVICE_API_PORT', 8080)) SERVICE_NAME = os.getenv('SERVICE_NAME', 'app')

theodesp/flask-golang-grpc-example

src/client/settings.py

Python

# -*- coding: utf8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from pylero.base_polarion import BasePolarion class Change(BasePolarion): """Object to handle the Polarion WSDL tns3:Change class Attributes: creation (boolean) date (dateTime) diffs (ArrayOf_tns3_FieldDiff) empty (boolean) invalid (boolean) revision (string) user (string) """ _cls_suds_map = {"creation": "creation", "date": "date", "diffs": "diffs", "empty": "empty", "invalid": "invalid", "revision": "revision", "user": "user", "uri": "_uri", "_unresolved": "_unresolved"} _obj_client = "tracker_client" _obj_struct = "tns3:Change"

RedHatQE/pylero

src/pylero/change.py

Python

import tabulate import matplotlib.pyplot as plt import math import numpy as np Data=[] data=[] x_axis=[] y_axis=[] def falci(func,a,b,error_accept): def f(x): f = eval(func) return f error=b-a if(f(a)*f(b)>=0 and a>=b): print("wrong a,b") return x=a n = 0 while error>=error_accept: data.append(n) n=n+1 data.append(str(a)) data.append(str(b)) x = (x*f(b)-b*f(x))/(f(b)-f(x)) data.append(str(x)) x_axis.append(x) data.append(str(f(x))) y_axis.append(f(x)) if f(x) == 0: break elif f(x) * f(b) < 0: a = x elif f(a) * f(x) < 0: b = x c=data.copy() Data.append(c) error=abs(b-a) data.clear() print("The root is %0.4f"%x) falci('3*x-math.cos(x)-1',0,1,0.001) print(tabulate.tabulate(Data,headers=['n','a','b','x','f(x)'],tablefmt='fancy_grid')) x_axis=np.array(x_axis) y_axis=np.array(y_axis) plt.style.use('seaborn') plt.plot(x_axis,y_axis,marker='*',color='deepskyblue',label=('f(x)'),linewidth='0.5') plt.legend() plt.title('Regular Falsi Method') plt.xlabel('possible root values x',color='r') plt.ylabel('function output (f(x))',color='r') plt.show()

Shakil-Mahmud-Programmer/Regular-Falsi-Method-or-False-Position-Method

1st_formula.py

Python

from .LocalDispatcher import LocalDispatcher, LocalDispatcherExecutionError

chilopodaHQ/chilopoda

src/lib/kombi/TaskHolder/Dispatcher/Local/__init__.py

Python

import json from django.core.urlresolvers import reverse from rest_framework.compat import patterns, url from rest_framework.decorators import api_view from rest_framework.response import Response from rest_framework import status from rest_framework.test import APITestCase from fluent_contents.models import Placeholder from bluebottle.test.factory_models.pages import PageFactory from bluebottle.test.factory_models.accounts import BlueBottleUserFactory class PageTestCase(APITestCase): """ Base class for test cases for ``page`` module. The testing classes for ``page`` module related to the API must subclass this. """ def setUp(self): self.user = BlueBottleUserFactory.create() self.page1 = PageFactory.create(author=self.user, language = 'nl') placeholder1 = Placeholder.objects.create_for_object(self.page1, 'blog_contents') placeholder1.save() self.page2 = PageFactory.create(author=self.user, language = 'en') placeholder2 = Placeholder.objects.create_for_object(self.page2, 'blog_contents') class PageListTestCase(PageTestCase): """ Test case for ``PageList`` API view. Endpoint: /api/pages/<language>/pages """ def test_api_pages_list_success(self): """ Ensure get request returns 200. """ response = self.client.get(reverse('page_list', kwargs={'language': 'nl'})) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data['count'], 1) def test_api_pages_list_content(self): """ Ensure get request returns record with correct data. """ response = self.client.get(reverse('page_list', kwargs={'language': 'nl'})) page = response.data['results'][0] self.assertEqual(page['title'], self.page1.title) self.assertEqual(page['language'], self.page1.language) self.assertEqual(page['body'], '<!-- no items in placeholder \'blog_contents\' -->') self.assertEqual(page['full_page'], self.page1.full_page) class PageDetailTestCase(PageTestCase): """ Test case for ``PageDetail`` API view. Endpoint: /api/pages/<language>/pages/<slug> """ def test_api_pages_detail_content(self): """ Ensure get request returns record with correct data. """ response = self.client.get(reverse('page_detail', kwargs={'language': 'en', 'slug': self.page2.slug})) results = response.data self.assertEqual(results['title'], self.page2.title) self.assertEqual(results['language'], self.page2.language) self.assertEqual(results['body'], '<!-- no items in placeholder \'blog_contents\' -->') self.assertEqual(results['full_page'], self.page2.full_page)

maykinmedia/bluebottle

bluebottle/pages/tests/test_api.py

Python

import wx from . import UIManager from . import UIControllerObject from . import UIViewObject from . import MainWindowController class ToolBarController(UIControllerObject): tid = 'toolbar_controller' _singleton_per_parent = True _ATTRIBUTES = { 'id': {'default_value': wx.ID_ANY, 'type': int }, 'pos': {'default_value': wx.DefaultPosition, 'type': wx.Point }, 'size': {'default_value': wx.DefaultSize, 'type': wx.Size }, 'style': {'default_value': wx.TB_FLAT|wx.TB_NODIVIDER, 'type': int } } def __init__(self, **state): super().__init__(**state) class ToolBar(UIViewObject, wx.ToolBar): tid = 'toolbar' paneinfo = wx.aui.AuiPaneInfo().Name(tid).ToolbarPane().Top() def __init__(self, controller_uid): UIViewObject.__init__(self, controller_uid) _UIM = UIManager() controller = _UIM.get(self._controller_uid) parent_controller_uid = _UIM._getparentuid(self._controller_uid) parent_controller = _UIM.get(parent_controller_uid) #wx.SystemOptions.SetOption("msw.remap", '0') wx.ToolBar.__init__(self, parent_controller.view, controller.id, controller.pos, controller.size, controller.style ) self.Realize() if isinstance(parent_controller, MainWindowController): mgr = wx.aui.AuiManager.GetManager(parent_controller.view) mgr.AddPane(self, self.paneinfo) mgr.Update()

adrianopls/UIManager

tool_bar.py

Python